An Investigation of Potential Health Risks from Zoonotic Bacterial Pathogens Associated with Farm Rats

Abstract: Background: The 21st century has seen a wide range of diseases resulting from zoonotic infections, of which bacterial infections have led to outbreaks of food-borne diseases. Aim: The study looks at bacterial pathogen carriage by farm rats and their antimicrobial susceptibility, with the view of providing insights for antimicrobial surveillance. Method: Farm rats of Rattus rattus species where randomly collected alive from farms in Al-Ahsa using food baits. They were anaesthetize with urethane within 4 h of collection and were unconscious for the collection of samples. Basic bacteriological culturing methods were used for culturing of bacterial isolates on selective media while the Vitek 2 compact automated system (BioMerieux, Marcy L’Etoile, France) was used for bacteria identification and antimicrobial susceptibility test. Obtained data were analysed using chi-square and paired t -test with significant difference between sensitive and resistance to antimicrobial susceptibility taken at P  < .05. Results: Isolated Gramme-negative pathogenic bacteria included strains of Escherichia coli, Pseudomonas oryzihabitans , strains of Pseudomonas aeruginosa , and Salmonella. For the Gramme-positive bacteria, 4 strains of Staphylococcus aureus were encountered. Other Gramme-positive bacteria were coagulase-negative Staphylococcal species (CoNS) as well as Staphylococcus lugdunensis . There was a 100% resistance to the penicillins and a high resistance to imipenem (71%) by the Staphylococcal isolates. Resistance was also high against the β-lactams by the Gramme-positive bacteria isolates. For the Gramme-negative bacteria, there was a higher than 50% resistance by the isolates against the following antibiotics: ampicillin (78%), amoxicillin/clavulanic acid (67%), cefotaxime (77%), ceftazidime (67%), cefepime (78%), norfloxacin (67%), nitrofurantoin (67%), and trimethoprim/sulfamethoxazole (78%). Conclusion: The results showed high antimicrobial resistance that will need monitoring for control of spread from farm rats to humans.

Lorina Badger-Emeka
Yasmeen Al-Mulhim
Fatimah Al-Muyidi
Maram Busuhail
Salma Alkhalifah
Norah AlEid

Environmental sciences
Public aspects of medicine


Aberrant  gene expression in the diagnosis of childhood leukemias not characterized by -involved cytogenetic abnormalities

Abstract: The AML1 ( acute myeloid leukemia 1 ) gene, a necessary prerequisite of embryonic hematopoiesis and a critical regulator of normal hematopoietic development, is one of the most frequently mutated genes in human leukemia, involving over 50 chromosome translocations and over 20 partner genes. In the few existing studies investigating AML1 gene expression in childhood leukemias, aberrant upregulation seems to specifically associate with AML1 translocations and amplifications. The aim of this study was to determine whether overexpression also extends to other leukemic subtypes than the ones karyotypically involving AML1 . We use quantitative real-time polymerase chain reaction methodology to investigate gene expression in 100 children with acute leukemias and compare them to those of healthy controls. We show that in childhood acute lymphoblastic leukemia, AML1 gene overexpression is associated with a variety of leukemic subtypes, both immunophenotypically and cytogenetically. Statistically significantly higher transcripts of the gene were detected in the acute lymphoblastic leukemia group as compared to the acute myeloid leukemia group, where AML1 overexpression appeared to associate with cytogenetic abnormalities additional to those that engage the AML1 gene, or that are reported as showing a “normal” karyotype. Collectively, our study shows that AML1 gene overexpression characterizes a broader range of leukemic subtypes than previously thought, including various maturation stages of B-cell acute lymphoblastic leukemia and cytogenetic types additional to those involving the AML1 gene.

Maria Adamaki
Spiros Vlahopoulos
George I Lambrou
Athanasios G Papavassiliou
Maria Moschovi

Neoplasms. Tumors. Oncology. Including cancer and carcinogens


Recurrent Episodes of Diffuse Alveolar Hemorrhage in Systemic Sclerosis 30 Days Apart

Abstract: Diffuse alveolar hemorrhage (DAH) is a life-threatening clinicopathologic condition caused by accumulation of intra-alveolar red blood cells (RBCs) after disruption of the alveolar-capillary basement membrane that is often seen as a complication of various diseases, but is rare in systemic sclerosis. A 46-year-old female with systemic sclerosis presented to the emergency department complaining of right-sided chest pain. Initially, her electrocardiogram and chest X-ray (CXR) were unremarkable; however, she progressively decompensated into acute respiratory failure resulting in intubation. Repeat CXR and computed tomography scan showed diffuse bilateral alveolar infiltrates and pleural effusions. Video bronchoscopy with bronchoalveolar lavage showed numerous RBCs, neutrophils, macrophages, and respiratory epithelial cells consistent with acute DAH. She was started on intravenous pulse-dosing Solu-Medrol 1 g daily for 5 days. One month later, the patient returned with intractable nausea and vomiting. Again, she went into acute respiratory distress with a PaO 2 of 59 while on a 10-L non-rebreather mask. CXR revealed development of alveolar infiltrates in the right lung. A bronchoscopy with bronchoalveolar lavage again showed numerous RBCs and neutrophils along with staining positive for hemosiderin-laden macrophages. Systemic sclerosis with alveolar hemorrhage is a rare occurrence; however, most cases are single episodes of hemorrhage, whereas we present a case with 2 confirmed episodes within 30 days. Its life-threatening nature makes a systemic approach and aggressive treatment crucial to decreasing morbidity and mortality—making it a diagnosis that should not be overlooked, especially in patients with nonspecific symptoms.

Hussien Saab MD
Tushar Bajaj MD
Kirandeep Bains BS
Ralph Garcia-Pacheco MD

Journal of Investigative Medicine High Impact Case Reports


Screw Fixation versus Arthroplasty versus Plate Fixation for 3-Part Radial Head Fractures

Abstract: Purpose. To compare the outcome following headless compression screw fixation versus radial head arthroplasty versus plate fixation for 3-part Mason types III or IV radial head fracture. Methods. Records of 25 men and 16 women aged 21 to 80 (mean, 43.3) years who underwent fixation using 2 to 3 2-mm cannulated headless compression screws (n=16), radial head arthroplasty (n=13), or fixation with a 2-mm Synthes plate (n=12) for 3-part Mason types III or IV radial head and neck fracture were reviewed. Treatment option was decided by the surgeon based on the presence of associated injury, neurovascular deficit, and the Mason classification. Bone union, callus formation, and complications (such as heterotopic ossification, malunion, and nonunion) were assessed by an independent registrar or consultant using radiographs. The Mayo Elbow Performance Score and range of motion were assessed by an independent physiotherapist. Results. The median age of the 3 groups were comparable. Associated injuries were most common in patients with arthroplasty, followed by screw fixation and plate fixation (61.5% vs. 50% vs. 33%, p=0.54). The median time to bone union was shorter after screw fixation than plate fixation (55 vs. 86 days, p=0.05). No patient with screw fixation had nonunion, but 4 patients with plate fixation had nonunion. The 3 groups were comparable in terms of the mean Mayo Elbow Performance Score (p=0.56) and the mean range of motion (p=0.45). The complication rate was highest after plate fixation, followed by screw fixation and arthroplasty (50% vs. 18.8% vs. 15.4%, p=0.048). Excluding 20 patients with associated injuries (8 in screw fixation, 8 in arthroplasty, and 4 in plate fixation), the 3 groups were comparable in terms of the median time to bone union (p=0.109), mean Mayo Elbow Performance Score (p=0.260), mean range of motion (p=0.162), and complication rate (p=0.096). Conclusion. Headless compression screw fixation is a viable option for 3-part radial head fracture. It achieves earlier bone union with fewer complications.


Nursing students and teachers experiences and meanings of curricular integration of humanisation:
A qualitative design

Abstract: The dominance of the biomedical model in health education has impeded the integration of humanisation as a cross-cutting competency in nursing curricula. The present study aimed to explore the experiences and perceptions of faculty and students regarding a curricular innovation intended to incorporate humanisation competencies from the early stages of nursing education. A qualitative design was employed, using semi-structured interviews with six faculty members and eleven first- and fifth-year nursing students at a Chilean university. Data were collected between September and November 2024 and analysed using content analysis. The study is reported in accordance with the COREQ guidelines. Students who participated in the curricular intervention associated humanisation with specific tools and clinical experiences, whereas those who had not been exposed to the intervention described it mainly in terms of general attitudes, such as empathy. Faculty members identified institutional barriers and highlighted the importance of mentorship and supportive policies. The early and transversal integration of humanisation into the curriculum facilitated a more meaningful understanding and practice of person-centred care.

Karen Peña-Jara
Graciela Sánchez-Zárate
Claudio Aránguiz-Bravo


How WEIRD Are We? How the Adoption of Certain Social Norms Altered Both Our Biology and Social Behaviour and, Ultimately, the Wealth of Nations

Management. Industrial management
Business


Human Umbilical Cord Blood Mononuclear Cell-Conditioned Media Inhibits Hypoxic-Induced Apoptosis in Human Coronary Artery Endothelial Cells and Cardiac Myocytes by Activation of the Survival Protein Akt

Abstract: We have previously demonstrated in acute myocardial infarctions that human umbilical cord blood mononuclear cells (HUCBCs), which contain hematopoietic, endothelial, and mesenchymal stem cells, reduce acute myocardial infarction size by ≥50% and preserve LV contractility. We hypothesize that the beneficial effects of HUCBCs are due to secretion of biologically active factors that activate in cardiac endothelial cells and myocytes the cell survival protein Akt. We determined by protein microarrays the growth factors and anti-inflammatory cytokines secreted by HUCBCs into culture media during 12 h of hypoxia (1% O 2 ). We then determined by Western blots the effects of cell-free media from hypoxic-conditioned HUCBCs (HUCM) on activation of the cell survival protein Akt in human coronary artery endothelial cells and cardiac myocytes in culture during 24 h of 1% O 2 . We also determined in separate experiments endothelial cell and myocyte apoptosis by caspase-3 and Annexin V. In the present experiments, HUCBCs secreted multiple growth factors, anti-inflammatory cytokines, and inhibitors of metalloproteinase during normoxia and hypoxia. Human cord blood cells increased the concentration in culture media of angiopoietin, hepatocyte growth factor, interleukin-4, insulin-like growth factor, placental growth factor, vascular endothelial cell growth factor, angiogenin, and stem cell factor by 100 to >10,000% during 12 h of 1% O 2 ( p < 0.001). HUCM, which contained these biological factors, significantly increased Akt phosphorylation/activation in coronary artery endothelial cells and cardiac myocytes subjected to 24 h of 1% O 2 by more than 60% ( p < 0.05) and increased the antiapoptotic protein Bcl-2 expression by 34–50% in comparison with endothelial cells and myocytes treated without HUCM in 1% O 2 ( p < 0.05). HUCM also significantly decreased caspase-3 activity and decreased hypoxic endothelial cell and cardiac myocyte apoptosis by more than 40% in comparison with cells cultured without HUCM ( p < 0.05). Inhibition of Akt activation in endothelial cells and myocytes by the sensitive and specific antagonist API-1 during 24 h of hypoxia nearly completely prevented the beneficial effects of HUCM on inhibiting caspase-3 activity and apoptosis. We conclude that HUCBCs secrete biologically active factors during hypoxia that activate survival proteins in endothelial cells and myocytes that significantly limit apoptosis.

Hua Jin
Paul R. Sanberg
Robert J. Henning


Structure and Spatial Distribution of Ge Nanocrystals Subjected to Fast Neutron Irradiation

Abstract: The influence of fast neutron irradiation on the structure and spatial distribution of Ge nanocrystals (NC) embedded in an amorphous SiO2 matrix has been studied. The investigation was conducted by means of laser Raman Scattering (RS), High Resolution Transmission Electron Microscopy (HR-TEM) and X-ray photoelectron spectroscopy (XPS). The irradiation of Ge- NC samples by a high dose of fast neutrons lead to a partial destruction of the nanocrystals. Full reconstruction of crystallinity was achieved after annealing the radiation damage at 8000C, which resulted in full restoration of the RS spectrum. HR-TEM images show, however, that the spatial distributions of Ge-NC changed as a result of irradiation and annealing. A sharp decrease in NC distribution towards the SiO2 surface has been observed. This was accompanied by XPS detection of Ge oxides and elemental Ge within both the surface and subsurface region.

Alexander N. Ionov
Tiecheng Lu
Yossi Gofer
Judith Grinblat
David H. Dressler
Issai Shlimak
Shai Levy

Materials of engineering and construction. Mechanics of materials


Automatic visual inspection system for quality control of the sandwich panel and detecting the dipping and buckling of the surfaces

Abstract: In this paper, an automated inspection system is proposed for detecting the location and measuring the size of existing dipping or buckling on the sandwich panel surface using an RGB camera and an inexpensive linear laser. The proposed method, by observing the radiated laser beams on the sandwich panel surface, can localize and calculate the level of dipping and buckling with acceptable accuracy while being robust to vibrations of moving sandwich panel on the production line conveyor. After a complete processing of the panel by the system, a three-dimensional (surface plot) or two-dimensional (heat map) output is produced to assist the production line supervisor to easily inspect the surface quality of the sandwich panels. Our experimental results show that the proposed system can detect and measure the surface defects including dipping and buckling with a high accuracy and performance.

Control engineering systems. Automatic machinery (General)


Toll-like receptor 4 signaling pathway in sensory neurons mediates remifentanil-induced postoperative hyperalgesia via transient receptor potential ankyrin 1

Abstract: Background: Remifentanil-induced postoperative hyperalgesia (RIH) refers to a state of hyperalgesia or aggravated pre-existing pain after remifentanil exposure. There has been considerable interest in understanding and preventing RIH. However, the mechanisms responsible for RIH are still not completely understood. Toll-like receptor 4 (TLR4), a classic innate immune receptor, has been detected in sensory neurons and participates in various nociceptive conditions, whereas its role in RIH remains unclear. Transient receptor potential ankyrin 1 (TRPA1) always serves as a nociceptive channel, whereas its role in RIH has not yet been investigated. This study aimed to determine whether the TLR4 signaling pathway in sensory neurons engaged in the development of RIH and the possible involvement of TRPA1 during this process. Methods: A rat model of remifentanil-induced postoperative hyperalgesia (RIH) was established, which presented decreased paw withdrawal mechanical threshold (PWMT) and paw withdrawal thermal latency (PWTL). The mRNA and protein expression levels of TLR4, phosphorylated NF-κB, and TRPA1 in the dorsal root ganglion (DRG) from RIH model were analyzed by real-time PCR, western blot, and immunofluorescence. The TLR4 antagonist TAK-242 and the TRPA1 antagonist HC-030031 were applied to determine the role of sensory neuron TLR4 signaling and TRPA1 in RIH. Results: Compared with control, PWMT and PWTL were significantly decreased in RIH model. Moreover, the mRNA and protein expression of TLR4 and TRPA1 in DRG were upregulated after remifentanil exposure together with increased NF-κB phosphorylation. TLR4 antagonist TAK-242 mitigated mechanical pain in RIH together with downregulated expression of TLR4, phosphorylated NF-κB, and TRPA1 in DRG neurons. In addition, TRPA1 antagonist HC-030031 also alleviated mechanical pain and decreased TRPA1 expression in RIH without affecting TLR4 signaling in DRG. Conclusions: Taken together, these results suggested that activation of TLR4 signaling pathway engaged in the development of RIH by regulating TRPA1 in DRG neurons. Blocking TLR4 and TRPA1 might serve as a promising therapeutic strategy for RIH.

Xiaowen Liu
Ruisong Gong
Liang Peng
Jing Zhao


Elderly Age as a Risk Factor for 30-Day Postoperative Outcomes Following Elective Anterior Cervical Discectomy and Fusion

Abstract: Study Design: Retrospective analysis of prospectively collected data. Objective: Anterior cervical discectomy and fusion (ACDF) is one of the most commonly performed spinal procedures. Considering the high success and low complications rate of ACDF and high prevalence of age-related degeneration of the cervical spine, the rates of ACDF are expected to continually rise. The objective is to identify the association between patient age and 30-day postoperative outcomes following elective ACDF. Methods: The 2010-2014 ACS-NSQIP database was queried using Current Procedural Terminology (CPT) codes 22551 or 22554. Patients were divided into age quartiles (18-45, 46-52, 53-60, and ≥61 years). Bivariate and multivariate logistic regression analyses were employed to quantify the increased risk of 30-day postoperative complications in the elderly patient population. Results: A total of 20 563 patients met the inclusion criteria for the study. The analyses found quartile 4 had an increased odds of length of stay (LOS) ≥5 days (odds ratio [OR] = 2.05, confidence interval [CI ] = 1.62-2.60), pulmonary complications (OR = 3.25, CI = 1.81-5.84), urinary tract infections (UTI) (OR = 2.25, 1.04-4.87, P = .038), cardiac complication (OR = 6.01, CI = 1.36-26.62, P = .018), and sepsis (OR = 4.38, CI = 1.30-14.70, P = .017). Quartiles 2 and 4 had an increased odds of venous thromboembolism (OR = 3.13, CI = 1.14-8.56, P = .026; OR = 3.83, CI = 1.44-10.20, P = .007). Quartiles 3 and 4 experienced an increased odds of unplanned readmission (OR = 1.44, CI = 1.01-2.05, P = .045; OR = 1.88, CI = 1.33-2.66). All P values are <.001 unless otherwise noted. Conclusion: Elderly patients experienced an increased odds of LOS ≥5 days, pulmonary complications, cardiac compilations, venous thromboembolism, UTI, sepsis, and unplanned readmission. Identification of these factors can improve the selection of appropriate surgical candidates and postoperative safety.

John Di Capua MHS
Sulaiman Somani BS
Jun S. Kim MD
Kevin Phan BS
Nathan J. Lee BS
Parth Kothari BS
Samuel K. Cho MD

Surgery
Diseases of the musculoskeletal system


GFAP-Dependent Transcriptional Dynamics and Cellular Heterogeneity in Primary, Recurrent, and Grade III Gliomas

Abstract: Objectives Glioma tumors, especially glioblastoma (GBM), are among the most heterogeneous brain cancers, and this characteristic is considered the main cause of treatment resistance and disease recurrence. The aim of this study was to investigate the cellular dynamics and transcriptional pathways dependent on the GFAP gene in three clinical conditions, including grade III glioma, primary glioblastoma, and recurrent glioma. Methods Single-cell RNA sequencing (scRNA-seq) data from the GSE103224 collection were merged and clustered using the Seurat and Harmony software packages. Then, the Monocle3 tool was used to model cellular developmental pathways, and the Metascape database was used for functional enrichment of genes. Results The results showed that malignant cells, microglia, immune cells, and glial progenitors formed distinct clusters in all conditions, and the transition of radial glial cells to cancer cells was observed in all three conditions. Differential gene expression analysis identified six genes, APOE, AQP4, CLU, GFAP, ID3, and ID4 as stable and common genes between all tumor stages, whose expression was directly related to tumor persistence and recurrence. Functional enrichment also identified steroid hormone response pathways, NGF signaling, and the VEGF pathway as common processes in glioma progression. Conclusion Overall, these results indicate that GFAP and its associated co-expression network play an important role in tumor persistence and cellular rearrangement in glioma and can be used as molecular markers and novel therapeutic targets for precise and personalized treatment of patients.

Morteza Hadizadeh
Seyedeh Zahra Mousavi
Sorayya Ghasemi


Residential Satisfaction of Rural Older Adults Aging in Place

Abstract: This study explored residential (housing and town) satisfaction of rural older adults aging in place, relating to demographic and housing characteristics. We conducted a structured, face-to-face survey with 149 rural older adults. Regression results revealed that housing satisfaction for rural older adults were significantly related to demographic and housing characteristics, including personal health status, healthy home status, and structure size. Bivariate analysis revealed that satisfaction with a town feature, shopping location, was the most frequently related to demographic and housing variables, followed by cost of living in town; and that health status was most frequently associated with town feature satisfaction. The study results can enhance programs and/or services for older adults aging in rural communities.

Sung-Jin Lee PhD
Kathleen Rose Parrott PhD
Minyong Lee PhD
Sheryl Renee Robinson BS
Ricky Nimako Owusu MS


Evolution, Psychology, and a Conflict Theory of Culture

Abstract: This article develops an evolutionary theory of conflict over the construction of culture that is informed by current knowledge of psychological mechanisms. Psychological mechanisms important for the production of culture include (1) general intelligence (including the ability to engender hypothetical scenarios and means-end reasoning necessary for constructing tools and other exemplars of technology); (2) explicit processing mechanisms (e.g., symbolic representations of the world). Explicit processing allows humans to regulate modular mechanisms in accordance with culturally constructed norms and culturally constructed cost/benefit payoff schedules. It also enables active attempts to construct culture in accordance with explicit perceptions of possible costs and benefits. Because people have different construals of the costs and benefits of particular forms of culture, there is conflict over the construction of culture. Social controls and ideologies are introduced as general cultural categories that are enabled by explicit processing and which are able to regulate and motivate behavior within particular historical contexts, at times in ways that conflict with evolved predispositions. Ideologies are often intimately intertwined with various social controls but are logically and psychologically independent from social controls. Ideologies typically rationalize extant social controls but they also benefit from the power of social controls to enforce ideological conformity in schools or in religious institutions. Because of the control of explicit processing over behavior, this theory predicts that conflicts over culture will often be intense. Discussion deals with the implications of this model for group selection, cultural transmission, gene-culture co-evolution, and the various types of conflicts of interest apparent in conflicts over the construction of culture.


Return to Sports and Sports Activities after Treatment of Osteochondral Lesions of the Ankle in Elite Athletes

Abstract: Category: Ankle Introduction/Purpose: The aim of this study is to assess the return to sports rate and times after treatment of osteochondral lesions of the ankle in elite level athletes. Methods: All elite level athletes treated for an OLT(P) were selected from a cross-sectional database consisting of consecutive patients with cartilage injuries of the ankle and subsequently underwent prospective follow-up. Outcomes included return to sports outcomes, patient reported outcomes and a qualitative assessment regarding mental health Results: 26 patients with a median age of 22 years (IQR: 19.0 – 24.5) were included with a median follow-up time of 48 months (IQR: 34.5 – 71.0). Seven patients (27%) underwent conservative treatment and 19 patient (73%) operative treatment. Conservative treatment yielded a return to sport rate at any level of 86%, return to pre-injury and return to performance level of both 57%. For operative treatment these rates were 100%, 74% and 63% respectively. Mean time to return to pre-injury level overall was 8.3 months. At latest follow-up only 11 patients (44%) were still active at an elite level of sport. Conclusion: Both conservative and surgical treatment for osteochondral lesions of the ankle yielded return to sport rates above 86% in elite level athletes. The results of this study can be used in the decision-making process between the physician and the elite athlete to inform them about the expected return to sport rates and times for the different treatment options for OLT(P)s. As most athletes at the time of injury and treatment are still highly motivated and disciplined to return to their sport at the same level and be able to compete at the highest level available in their sport.

Jari Dahmen MD
Julian Hollander Bsc
Kishan Ramsodit
Quinten Rikken MD
Sjoerd Stufkens MD, PhD
Gino M M.M.J. Kerkhoffs MD, PhD


RETRACTED: Cyclin-dependent kinase 7 (CDK7) is an emerging prognostic biomarker and therapeutic target in osteosarcoma

Hangzhan Ma
Dylan C. Dean
Ran Wei
Francis J. Hornicek
Zhenfeng Duan

Therapeutic Advances in Musculoskeletal Disease


Illuminating the future of diabetes treatment: Autologous CiPSC-derived islets take center stage

Abstract: The recent clinical success of chemically induced pluripotent stem cell (CiPSC)–derived islet transplantation for type 1 diabetes represents a landmark achievement in regenerative medicine. This article delves into the groundbreaking work presented by Dr. Hongkui Deng and Dr. Candice S.Y. Liew at International Society for Stem Cell Research (ISSCR) 2025, focusing on their innovative chemical reprogramming technology and its clinical application. The discussion highlights the scientific rationale behind chemical reprogramming, the development of functional islets, the novel transplantation strategy, and the remarkable clinical outcomes observed in their first patient. The implications for future stem cell therapies and the challenges of scalability are also explored.


The effect of trepanning parameters on wear of tool and surface quality of titanium alloy

Abstract: There is a growing interest in developing an advanced technique to manufacture titanium alloy for larger holes in the aviation and marine industries. While the application of larger holes is limited due to the low machinability and high cost. The deep-hole trepanning is investigated to manufacture the titanium alloy in a high-efficient low-cost manner, and the technical challenges during the deep-hole trepanning process are studied in this article. Designs of the deep-hole trepanning tool and experiments under different cutting speeds and feed are carried out. Chip morphology, axial cutting force, and tool wear are analyzed. The optimal process parameters are achieved. This study provides a practical solution to process titanium alloy for potential industrial applications.

Yazhou Feng
Huan Zheng
Xiaolan Han
Zhanfeng Liu


Determinative Variables Toward Promoting Use of Active Modes of Transportation: Enhancing Level of Sustainable Mobility in Communities

Abstract: Most current transportation systems around the world are cause for pressing concern and a menace to sustainability of social justice, and economic and environmental vitality. From a universal agreement that transportation needs an urgent alteration in travel modes, policies, planning, and behaviors through to the emergence of sustainability in the transportation sector, a significant and central vision has emerged to guide urban policymakers, transportation planners, and public health officials. During the last four decades, a shift in transportation modes toward use of active modes of transportation has been encouraged, and it is one of the main sustainability movements appearing in many developing and developed countries. This research provides a narrative review and parsing of existing literature, which focuses on promoting active modes of transportation. The aim is to clarify key determinative variables that must be considered by planners and policymakers toward promoting use of active modes of transportation for daily mobility.

Abolfazl Dehghanmongabadi
Şebnem Hoşkara

History of scholarship and learning. The humanities


K-RAS Mutation Profile in Puerto Rican Patients with Colorectal Cancer: Trends from April 2009 to January 2011

Abstract: The frequency of K-RAS mutations ranges between 30% and 48% among the Caucasian, Asian, and European populations and these mutations are predictors of response to EGFR therapies. We sought to determine the expression of K-RAS gene mutations among colorectal cancer patients in PuertoRico. A retrospective study was conducted to determine the expression of mutant K-RAS among colorectal cancer patients in Puerto Rico between April 2009 and January 2011. The mutant expression of K-RAS was found in 39% (n=195) of the Puerto Rican population, and was more common in the age group of 51-69 years (53.8%) and in males (55.4%, p>0.05). Moreover, mutant K-RAS was more commonly found in tumors of the proximal area (43.8%; p=0.03), with distant metastasis (43.3%, p=0.018), with a mucinous histotype (31.7% p>0.05), and in ulcerated tumors (38.8%, p>0.05). K-RAS mutations were observed on codon 12 (87.7%) and codon 13 (12.3%). The most frequent mutation on codon 12 was 12 ASP (39.5%), followed by 12 VAL (25.4%) that is associated with a significant decrease in overall cancer survival. The mutant expression of K-RAS in cases of rectum carcinoma was 39.5%, where the most common mutation was 12 VAL (37.5%). The frequency of K-RAS mutations in the Puerto Rican population here studied was 39% and mutant K-RAS was associated with advanced colorectal cancer stage, mucinous histotype, and ulcerated tumors.

Yelitza Ruiz-Candelaria
Christine Miranda-Diaz
Robert F. Hunter-Mellado

The International Journal of Biological Markers


The Effects of Posterior Rotator Cuff Cable Tears on Glenohumeral Biomechanics in a Cadaveric Model of the Throwing Shoulder

Abstract: Objectives: The rotator cuff cable has been postulated to be the primary load bearing substructure of the superior part of the rotator cuff. Tears of the posterior rotator cable are frequently seen in overhead throwing athletes. Although the biomechanical significance of the anterior rotator cable has been well described, our current understanding of the relevance of the posterior cable is limited. The purpose of this study was to examine how partial-thickness tears and full-thickness tears of the posterior rotator cable would alter glenohumeral biomechanics and kinematics in cadaveric shoulder models. Methods: Eight fresh-frozen cadaveric shoulder specimens were prepared and tested. To simulate the sequence of glenohumeral positions during the throwing motion, specimens were mounted on a custom shoulder testing system with the humerus positioned at 90° of abduction (30° scapular upward rotation, 60° glenohumeral abduction) and tested at 30, 60, 90, and 120 degrees of external rotation. After a circumferential capsulotomy was performed, rotator cuff muscles were loaded based on physiologic cross-sectional area ratios, and testing for each specimen was performed on each the following three conditions: intact posterior cable, partial-thickness (50%) articular-sided posterior cable tear, and full-thickness posterior cable tear. Primary outcome measures tested for each condition under the various degrees of glenohumeral rotation were: 1) anterior and total glenohumeral translation after application of a 30N anterior force; 2) path of glenohumeral articulation; 3) glenohumeral joint force. Results: With a 30N anterior force at 120° of external rotation, there was a significant increase in anterior glenohumeral translation between intact and full-thickness tear specimens (7.28±2.00mm and 17.49±3.75mm, respectively; p<0.05). Similarly, total joint translation at 120° of external rotation significantly increased between intact and full-thickness tear specimens (10.37±3.18mm and 23.37±5.05mm, respectively; p<0.05). No significant differences were apparent at other degrees of rotation (30, 60, 90 degrees), or with partial-thickness tears. Changes in the path of glenohumeral articulation were likewise most evident at 120° of external rotation, with a progressively anterior, inferior, and lateral shift in articulation with sequential sectioning of the posterior cable. Lastly, with regards to alterations in glenohumeral joint force, no significant changes were seen in any of the conditions. Conclusion: In this cadaveric shoulder model of the throwing shoulder, tears of the posterior rotator cuff cable lead to altered glenohumeral biomechanics and kinematics. These changes were most profound at 120° of external rotation, suggesting the importance of an intact posterior cable as a potential stabilizer during the late-cocking phase of throwing.

Christos Demetris Photopoulos MD
Neal S. ElAttrache MD
Alex Doermann
Masaki Akeda MD
Michelle H. McGarry MS
Thay Q. Lee PhD


LatentColorization: Latent Diffusion-Based Speaker Video Colorization

Abstract: While current research predominantly focuses on image-based colorization, the domain of video-based colorization remains relatively unexplored. Many existing video colorization techniques operate frame-by-frame, often overlooking the critical aspect of temporal coherence between successive frames. This approach can result in inconsistencies across frames, leading to undesirable effects like flickering or abrupt color transitions between frames. To address these challenges, we combine the generative capabilities of a fine-tuned latent diffusion model with an autoregressive conditioning mechanism to ensure temporal consistency in automatic speaker video colorization. We demonstrate strong improvements on established quality metrics compared to existing methods, namely, PSNR, SSIM, FID, FVD, NIQE and BRISQUE. Specifically, we achieve an 18% improvement in performance when FVD is employed as the evaluation metric. Furthermore, we performed a subjective study, where users preferred LatentColorization to the existing state-of-the-art DeOldify 80% of the time. Our dataset combines conventional datasets and videos from television/movies. A short demonstration of our results can be seen in some example videos available at <uri>https://youtu.be/vDbzsZdFuxM</uri>.

Rory Ward
Dan Bigioi
Shubhajit Basak
John G. Breslin
Peter Corcoran

Electrical engineering. Electronics. Nuclear engineering


Exploring the Impact of Alternatives of Object-Oriented Cohesion Measures on Machine Learning-Based Predictions of Inheritance Reusability

Abstract: The cohesion of an object-oriented class refers to the relatedness of its methods and attributes. Constructors, destructors, and access methods are special types of methods featuring unique characteristics that can artificially affect class cohesion quantification. Methods within a class can also directly or transitively invoke each other, representing another cohesion aspect not considered by most existing cohesion measures. The impact of considering special methods (SPs) and transitive relations (TRs) in cohesion measurement on the abilities of the measures to predict inheritance reusability has yet to be investigated. In this paper, we empirically explored this effect. We applied a statistical technique to test the significance of the cohesion value changes across seven scenarios of ignoring or considering SPs and TRs. In addition, we applied a machine learning-based technique to build inheritance reusability prediction models using each of the considered measures and scenarios, evaluated the classification performance of the prediction models, and statistically compared the inheritance reusability prediction results. The results show that for most of the considered measures, the ignorance/consideration of SPs and TRs changed the cohesion values and the corresponding prediction significantly. Based on the study findings, when building inheritance reusability prediction models, software engineers are advised to 1) combine cohesion with other quality factors; 2) exclude the TRs from cohesion quantification; and 3) decide whether to consider or ignore SPs in cohesion quantification based on the selected measure(s) to be used in the prediction model, as this decision differs from one measure to another.


InsDD-YOLO: Detection of Transmission Line Insulator Damage Based on the Improved YOLOv13 Model

Abstract: Amidst the rapid global expansion of smart grids, ensuring the safety and reliability of power transmission systems has become paramount. Insulators are critical components of high-voltage transmission lines, providing both electrical insulation and mechanical support. However, their exposure to electrical, mechanical, and environmental stressors renders them vulnerable point within the system. Defective insulators are a major cause of failures in power transmission systems. Consequently, the early and accurate detection of these defects is pivotal for maintaining the integrity and reliability of the power grid. To address this challenge, this study proposes InsDD-YOLO, a novel object detection architecture enhanced from the YOLOv13 framework. The model incorporates a suite of strategic enhancements, including an improved DSConv (IDSConv) module for robust feature extraction, a streamlined Neck architecture augmented with a feature stream from a shallower layer (B2) to improve small-target detection, and a direct Head connection mechanism to maximize the preservation of fine-grained details. Experimental results demonstrate that InsDD-YOLO achieves superior performance, reaching an mAP0.5 of 90.1% and an mAP<inline-formula> <tex-math notation="LaTeX">${}_{0.5:0.95}$ </tex-math></inline-formula> of 46.4%, outperforming the baseline YOLOv13 model by a significant 5.0% in mAP0.5. With an inference time of just 5.4 ms, the proposed model not only establishes a new benchmark for accuracy but also demonstrates an effective trade-off between performance and speed, underscoring its significant potential for deployment in real-time, automated power grid monitoring systems.

Phat T. Nguyen
Duy C. Huynh
Loc D. Ho
Ha T. Tran
Matthew W. Dunnigan


Small Object Detection Algorithm Based on Improved YOLOv8 for Remote Sensing

Abstract: Due to the limitations of small targets in remote sensing images, such as background noise, poor information, and so on, the results of commonly used detection algorithms in small target detection is not satisfactory. To improve the accuracy of detection results, we develop an improved algorithm based on YOLOv8, called LAR-YOLOv8. First, in the feature extraction network, the local module is enhanced by using the dual-branch architecture attention mechanism, while the vision transformer block is used to maximize the representation of the feature map. Second, an attention-guided bidirectional feature pyramid network is designed to generate more discriminative information by efficiently extracting feature from the shallow network through a dynamic sparse attention mechanism, and adding top–down paths to guide the subsequent network modules for feature fusion. Finally, the RIOU loss function is proposed to avoid the failure of the loss function and improve the shape consistency between the predicted and ground-truth box. Experimental results on NWPU VHR-10, RSOD, and CARPK datasets verify that LAR-YOLOv8 achieves satisfactory results in terms of mAP (small), mAP, model parameters, and FPS, and can prove that our modifications made to the original YOLOv8 model are effective.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Ocean engineering
Geophysics. Cosmic physics


Dynamic Behavior Improvement of Normally-Off p-GaN High-Electron-Mobility Transistor Through a Low-Temperature Microwave Annealing Process

Abstract: The surface morphology optimization of ohmic contacts and the Mg out-diffusion suppression of normally off p-GaN gate high-electron-mobility transistors (HEMTs) continue to be challenges in the power electronics industry in terms of the high-frequency switching efficiency. In this study, better current density and reliable dynamic behaviors of p-GaN gate HEMTs were obtained simultaneously by adopting low-temperature microwave annealing (MWA) for the first time. Moreover, HEMTs fabricated using MWA have a higher ION/IOF ratio and lower gate leakage current than the HEMTs fabricated using rapid thermal annealing. Due to the local heating effect, a direct path for electron flow can be formed between the two-dimensional electron gas and the ohmic metals with low bulges surface. Moreover, the Mg out-diffusion of p-GaN gate layer was also suppressed to maintain good current density and low interface traps.

Hsien-Chin Chiu
Chia-Hao Liu
Yi-Sheng Chang
Hsuan-Ling Kao
Rong Xuan
Chih-Wei Hu
Feng-Tso Chien

IEEE Journal of the Electron Devices Society


Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A Survey

Abstract: Automatic vision-based inspection systems have played a key role in product quality assessment for decades through the segmentation, detection, and classification of defects. Historically, machine learning frameworks, based on hand-crafted feature extraction, selection, and validation, counted on a combined approach of parameterized image processing algorithms and explicated human knowledge. The outstanding performance of deep learning (DL) for vision systems, in automatically discovering a feature representation suitable for the corresponding task, has exponentially increased the number of scientific articles and commercial products aiming at industrial quality assessment. In such a context, this article reviews more than 220 relevant articles from the related literature published until February 2023, covering the recent consolidation and advances in the field of fully-automatic DL-based surface defects inspection systems, deployed in various industrial applications. The analyzed papers have been classified according to a bi-dimensional taxonomy, that considers both the specific defect recognition task and the employed learning paradigm. The dependency on large and high-quality labeled datasets and the different neural architectures employed to achieve an overall perception of both well-visible and subtle defects, through the supervision of fine or/and coarse data annotations have been assessed. The results of our analysis highlight a growing research interest in defect representation power enrichment, especially by transferring pre-trained layers to an optimized network and by explaining the network decisions to suggest trustworthy retention or rejection of the products being evaluated.

Michela Prunella
Roberto Maria Scardigno
Domenico Buongiorno
Antonio Brunetti
Nicola Longo
Raffaele Carli
Mariagrazia Dotoli
Vitoantonio Bevilacqua


A Dynamic Discriminative Canonical Correlation Analysis via Adaptive Weight Scheme

Abstract: In multi-view learning, massive literature is devoted to exploring the intrinsic structure between cross-views. It is well known that canonical correlation analysis (CCA) is a conventional multi-view learning method, which considers the correlation between two views. However, it fails to utilize class information and is difficult to suit different issues to extract discriminative features. In this paper, we propose a novel cross-view discriminative feature learning method called dynamic discriminative canonical correlation analysis, which captures class information to yield discriminative features. More specifically, we develop an adaptive weight scheme of cross-view within-class and between-class scatters to make full use of distribution class information. In addition, an iterative algorithm with Cauchy inequalities and the Lagrange multiplier is proposed to handle the non-smooth objective function. Our method is applied to face recognition and multi-linguistic text classification tasks. Extensive experimental results reveal that the adaptive weight scheme plays a beneficial role and our method is an effective feature learning.

Shuzhi Su
Penglian Gao
Yanmin Zhu
Xingzhu Liang
Jun Xie


Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation

Abstract: Accurate Evapotranspiration for saline soils (ETs) is important as well as challenging for the reclamation of saline soils through an effective leaching process. Evapotranspiration (ET) by FAO-56 Penman-Monteith standard method is complex, especially for saline soils. Moreover, existing studies focus on the use of the Internet of Things (IoT) and machine learning-enabled smart and precision irrigation water recommendation systems along with the ET estimation by limited parameters. The ETs for saline soils are also equally important for the reclamation of saline soils, which is ignored by the existing literature. The study proposed IoT and machine leaching-based architecture of context-aware monthly ETs estimations for saline soil reclamation with the effective leaching process. The IoT-enabled crop field contexts in terms of crop field temperature, soil salinity, and irrigation water salinity are used as input features to the Long Short-Term Memory (LSTM) and ensembled LSTM models for monthly ETs predictions. The performance of the proposed solution is observed in terms of the accuracy of the machine learning models along with the comparison against the FAO-56 PM-based standard method. The implementation of the proposed solution reveals that the ensembled LSTM-based approach for ETs is more accurate as compared to the LSTM model with accuracies of 92 and 90% for the training and validation datasets, respectively. The predictions made by the ensembled LSTM are more in line with the FAO-56 PM-based method with a Pearson correlation of 0.916 as compared to LSTM models. The implementation of the proposed solution in real-time environments reveals that the proposed solution is more effective in reducing the soil salinity as compared to the traditional method.

Arfat Ahmad Khan
Muhammad Asif Nauman
Rab Nawaz Bashir
Rashid Jahangir
Roobaea Alroobaea
Ahmed Binmahfoudh
Majed Alsafyani
Chitapong Wechtaisong


Clustering TV Viewing Behavior for Digital Twin Construction Using Television Viewing History Data

Abstract: This study presents the construction of the first digital twin utilizing non-identifiable television viewing history data. As the media landscape continues to evolve, understanding viewer behavior has become increasingly crucial. By simulating viewing behaviors based on real-time data, our approach enables the virtual reproduction of viewer preferences and behavior patterns, facilitating optimized advertising, content production, and marketing strategies. We propose a method for classifying user viewing tendencies using large-scale, non-identifiable data and develop a simulator based on these classifications. A detailed analysis of the data led to the extraction of tailored features for television viewing and the development of a highly accurate classification model. The weekday and weekend models achieved F1 scores of approximately 0.95, demonstrating their strong predictive capabilities. This study provides valuable insights into digital twin construction for television viewing and opens new avenues for data-driven media strategies.

Daiki Mayumi
Hiroki Matsuda
Tetsuya Yokota
Taichi Sakakibara
Yuki Matsuda
Keiichi Yasumoto


Optimization of Communication Schemes for DMA-Controlled Accelerators

Abstract: The hardware accelerator controlled by direct memory access (DMA) is greatly influenced by the communication bandwidth from/to DRAM through on-chip buses. This paper proposes a novel performance estimation algorithm to optimize the communication schemes (CSs), which are defined by the number of direct memory access controllers (DMACs) and the bank allocation of DRAM. In order to facilitate the optimization of CSs, a communication primitive (CP) is defined by the bank allocation and the set of activated DMACs. By using the communication bandwidths of CPs obtained from prior full-system simulations, the proposed performance estimation algorithm can predict the communication performance of CSs more accurately, compared with the conventional performance estimation algorithms. When it is applied to convolutional neural networks (CNNs) and wireless communications (LDPC-coded MIMO-OFDM), the estimation error is measured to be no more than 6.4% and 5%, respectively. In addition, compared with the conventional simulation-based approaches, the proposed estimation algorithm provides a speedup of two orders of magnitudes. The proposed performance estimation algorithm is used to optimize the CS of the CNNs and explore a design space characterized by bank interleaving, outstanding transactions, layer shape, tile size, and hardware frequency. It is shown that the optimized CS improves communication performance by up to 68% for the third convolutional layers of AlexNet and 60% for the MIMO of LDPC-coded MIMO-OFDM. In addition, the DRAM latency is minimized by setting the bank interleaving to the number of outstanding transactions. Moreover, the simulation results show that the optimum CS depends on the application. It is also shown that the use of an extra DMAC does not necessarily improve the communication performance.

Jooho Wang
Sungkyung Park
Chester Sungchung Park


Learning-Aided Deep Path Prediction for Sphere Decoding in Large MIMO Systems

Abstract: In this paper, we propose a novel learning-aided sphere decoding (SD) scheme for large multiple-input-multiple-output systems, namely, deep path prediction-based sphere decoding (DPP-SD). In this scheme, we employ a neural network (NN) to predict the minimum metrics of the “deep” paths in sub-trees before commencing the tree search in SD. To reduce the complexity of the NN, we employ the input vector with a reduced dimension rather than using the original received signals and full channel matrix. The outputs of the NN, i.e., the predicted minimum path metrics, are exploited to determine the search order between the sub-trees, as well as to optimize the initial search radius, which may reduce the computational complexity of SD. For further complexity reduction, an early termination scheme based on the predicted minimum path metrics is also proposed. Our simulation results show that the proposed DPP-SD scheme provides a significant reduction in computational complexity compared with the conventional SD algorithm, despite achieving near-optimal performance.


LBT-YOLO: A Lightweight Road Targeting Algorithm Based on Task Aligned Dynamic Detection Heads

Abstract: Autonomous driving technology plays a key role in addressing traffic safety issues and relieving traffic congestion by virtue of its capabilities of enabling accurate environmental perception and real-time response. Aiming at the problem of limited computing power of mobile driving platform, an improved algorithm based on YOLOv8n: LBT-YOLO was proposed. The algorithm is improved in three aspects: firstly, replacing part of the traditional convolutional layers by linear deformable convolution, and designing a new C2L module by optimizing the C2F module, so as to reduce the number of model parameters and maintain the detection accuracy at the same time. Secondly, a new neck network structure BCFPN (Bidirectional Collocated Feature Pyramid Network) is designed based on the weighted bidirectional feature pyramid network, which enhances the feature fusion and the interaction of contextual information, and improves the detection accuracy of the model. Finally, a new detection head TADDH (Task Aligned Dynamic Detection Head) is proposed. This detection head reduces the number of parameters by sharing the neck network features, and performs task decomposition alignment to achieve high accuracy target detection using dynamic convolution and dynamic feature selection. After a series of improvements, LBT-YOLO outperforms YOLOv8n and other detection algorithms on the Autonomous Driving BDD100K dataset, with an average accuracy improvement of 2.4% while reducing the number of model parameters by 48.2%.

Pei Tang
Zhenyu Ding
Minnan Jiang
Weikai Xu
Mao Lv


Resource Allocation for Cognitive Satellite Communications Downlink

Abstract: Cognitive satellite communications (SatCom) is considered to be able to alleviate the bottleneck of spectrum resource shortage due to traditional spectrum allocation. This paper focuses on a special scenario where the frequency band of SatCom is recommended by the terrestrial terminal according to its spectrum sensing results. Further speaking, frequency bands preferred by terminals in each coverage beam of the satellite may be random, which on the whole forms diverse recommended channels problem that poses a great challenge to traditional multi-beam satellites. To make reasonable use of available resources in this scenario, this paper targetedly proposes a beam hopping (BH) scheme, which is capable of providing services on each frequency band. Based on the BH scheme, two 4-D [i.e., time, frequency, power, and dedicated spot (DS) beam] resource allocation (RA) schemes are presented, which adopt maximizing throughput (MT) and minimizing demand-supply variance (MDSV) as objectives, respectively, corresponding to the fact that satellite resources may be relatively rich or scarce. Both of the RA problems belong to mixed-integer nonlinear programming. By decomposing them, three levels of problems, namely, frequency band selection (FBS) problem, dedicated beam allocation (DBA) problem, and time-power allocation (TPA) problem are successively formed. For the FBS and DBA problems, we correspondingly propose heuristic algorithms to quickly distribute frequency bands and dedicated beams. Whereas for the TPA problem, Lagrangian dual algorithm and water-filling-assisted Lagrangian dual algorithm are respectively adopted to solve the convex problem for MT and the nonconvex problem for MDSV. Taking also spectrum sensing errors into account, numerical simulations show that the proposed schemes and algorithms perform well, and a significant gain can be achieved in cognitive SatCom.

Peiliang Zuo
Tao Peng
Wangdan Linghu
Wenbo Wang


Egocentric Hand Activity Video Dataset and Bidirectional Motion-Priors for Hand Action Recognition

Abstract: Recognizing tool-based hand activities from a first-person view is a critical yet challenging task in computer vision, due to the complexity of hand-object interactions and often subtle, ambiguous motion patterns. In real-world manufacturing scenarios, these challenges are exacerbated by bidirectional action pairs whose visual cues are almost identical, with differences revealed only through subtle motion dynamics. However, existing datasets rarely capture these direction-sensitive interactions at scale, particularly in realistic tool-use contexts, limiting the ability of current models to learn fine-grained motion dynamics essential for accurate recognition. We introduce Ego-Bi (Egocentric-Bidirectional dataset), a large-scale, real-world egocentric RGB video dataset comprising 1,223 video sequences and 622,737 frames that cover diverse tool-use activities in unconstrained environments. Ego-Bi provides an extended 38-category hand type taxonomy, detailed object–tool labels, and challenging bidirectional action pairs, offering rich semantic and temporal cues for modeling complex hand–object interactions. In addition, to address the ambiguity in motion dynamics, we propose a BMP (Bidirectional Motion Prior module) that derives rotation and directional cues from predicted 3D hand poses to improve class separability of visually similar actions. Experimental results on Ego-Bi demonstrate that our approach improves bidirectional action recognition accuracy by + 8.96% over the baseline, while also yielding consistent gains across general action classes without requiring costly 3D pose annotations. Furthermore, the proposed motion priors generalize effectively to other egocentric benchmarks, underscoring their robustness in handling visually similar, direction-sensitive actions.

Jiyoung Seo
Dong In Lee
Pilhyeon Lee
Jiwoo Lee
Younhee Gil
Karthik Ramani
Sangpil Kim


Fast Fourier Transform Networks for Object Tracking Based on Correlation Filter

Abstract: In this paper, we propose fast Fourier transform networks for object tracking, called FFTNet. FFTNet is a correlation filter (CF)-based tracker that integrates two main components of CF, i.e., auto correlation and cross correlation between the features of two images. Thus, FFTNet takes full advantage of CF: 1) Auto correlation and cross correlation of CF are efficiently computed by FFT so that FFTNet achieves high computational efficiency and 2) FFTNet successfully performs displacement detection even in similar signals so that it achieves good tracking performance. Moreover, FFTNet combines the advantage of CF with convolutional neural networks so that it has strong capabilities of learning feature representation and matching function. FFTNet is trained end-to-end in an off-line manner. First, we input two-input patches of target object and search region into shared convolutional layers to get their features. Then, we calculate auto correlation and cross correlation from two features. Next, we concatenate the results of auto correlation and cross correlation, and put them into another subnetwork to learn a matching function. Finally, we get a response map whose values represent the probability of each pixel belonging to the target. Experimental results demonstrate that FFTNet outperforms state-of-the-arts in both tracking accuracy and computational efficiency.

Zhangping He
Zhendong Zhang
Cheolkon Jung


Comparative Evaluation of Radio Network Planning for Different 5G-NR Channel Models on Urban Macro Environments in Quito City

Abstract: The 5th-Generation New Radio (5G-NR) network have been widely deployed around the world in the frequency range 1/range 2 bands. Once specific frequency bands within these ranges can vary across different countries and regions due to regulatory differences, it should be carried out radio network planning to evaluate the 5G coverage considering the particularities of different locations. In this regard, this paper performs a throughly analysis of the following methods for modeling wireless channel propagation in Quito, Ecuador: 3rd Generation Partnership Project, Knife Edge Diffraction (KED), ASTER and Dominant Path model (DPM). Specifically, we focus on KED, ASTER, and DPM for <inline-formula> <tex-math notation="LaTeX">$3.5/28$ </tex-math></inline-formula>-GHz bands to determine the propagation models in three-Dimensional urban macro scenarios. In the radio network planning, the multiple-input multiple-output array antennas, <inline-formula> <tex-math notation="LaTeX">$2\times 2/ 4\times 4$ </tex-math></inline-formula> configuration radiation patterns are deployed using WINPROP tool and <inline-formula> <tex-math notation="LaTeX">$64\times 64$ </tex-math></inline-formula> array configuration with the ATOLL tool. 5G frequency specifications, path-loss, influence of diffraction, reflection, blocking, and fading between transmitter and receiver have been considered for scenarios of interest, such as dense urban and urban in Quito, by using fixed wireless access applications and Vehicular-to-Everything (V2X) communications. In addition, data rates, throughput, and the quality metrics of the received reference signal, i.e., the signal-to-noise plus interference ratio, the reference signal received quality, the reference signal received power, and the received signal strength indicator, are also assessed for each propagation model. Finally, we provide useful insights into propagation models and design usage rules for the bands mentioned in 5G networks for Quito city.

Valdemar Ramon Farre Guijarro
Jose David Vega Sanchez
Martha Cecilia Paredes Paredes
Felipe Grijalva Arevalo
Diana Pamela Moya Osorio


Dual-Channel Dynamic Gated Spatio-Temporal Graph for Traffic Flow Forecasting

Abstract: Traffic flow forecasting is a critical and essential technology in the field of Intelligent Transportation Systems (ITS), as it plays a pivotal role in optimizing traffic management, improving road safety, and enhancing the overall efficiency of transportation networks. However, current research neglects the relationships between the local and global traffic flow data. Additionally, the predefined static graph structure fails to adequately capture the dynamic spatial features of traffic flow. To address the these challenges, this paper proposes a Dual-Channel Dynamic Gated Spatio-Temporal graph network (DC-DGST) for traffic flow prediction. We consider hourly slices as the local feature and daily slices to be the global feature of traffic flow. The DC-DGST framework employs a dual-channel structure to capture spatiotemporal dependencies between global and local features. It transforms the predefined static graph into a dynamic graph, enabling the establishment of connections between input data and historical information. Furthermore, we design gated spatio-temporal blocks based on residual structures within the spatio-temporal module. Specifically, we utilize Graph Gated Neural Networks (GGNNs) to learn and integrate both static and dynamic graphs, while Transformer encoders are used to capture long-range dependencies in the temporal sequence. We conducted a series of experiments on four publicly available benchmark datasets: PEMS03, PEMS04, PEMS07, and PEMS08. The results demonstrate that our model significantly outperforms baseline models. Moreover, the dual-channel structure effectively captures the correlation between local and global traffic flow features, while the dynamic graph enhances the model’s overall performance.

Chao Wang
Jun-Feng Hao
He Huang
Wang Zou
Xia Sun
Ting Peng


Combined Effect of the Parameters of Vacuum Interrupter and L-C Circuit Upon Arc Re-Ignition in HVDC Circuit Breakers

Abstract: The new horizons for HVDC systems and their applications has intensified the research in HVDC protection systems. The HVDC circuit breakers (CB) has been the center of the protection systems with vacuum interrupters (VIs) showcasing a great potential for application as a part of HVDC-CBs. However, VIs still face the problem of arc-reignition with limited studies in this area. This paper tends to address this problem by investigating the impact of the parameters of VI and the shunt L-C branch upon the re-ignition occurrence during switching process. An HVDC testing system and VI-based HVDC-CB were modeled using ATP software. The testing included investigation of the impact of the rate of rise of dielectric strength (RRDS) of VI, the type of LC circuit either active or passive and its timing of injection. The results showed that active L-C circuits demanded a longer delay time before insertion to avoid re-ignitions and the increase in RRDS reduced the reignitions. The main contributions of this paper include; investigating the arc re-ignition in VI-based HVDC-CB; analyzing the combined effect of the VI and L-C parameters upon those reignitions and providing a co-relation between the re-ignition occurrence and these parameters. That co-relation is used to define the most suitable delay time for LC circuit to avoid re-ignitions. The co-relation shows that the delay time is inversely co-related to the RRDS and directly-correlated to active LC types with lower values for passive L-C. This was applied to the simulation results and showed agreement with that co-relation.

Tamer Eliyan
Ali Saeed Almuflih
Z. M. S. Elbarbary
Fady Wadie


Design of an In-Pipe Robot Coupled With Multiple Cams

Abstract: The inchworm in-pipe robot has the advantages of stable support, low walking resistance, and high flexibility. However, the gait motion of in-pipe robots relies on the precise coordination of three motors, which greatly increases the complexity of control. To solve this problem, an inchworm in-pipe robot based on a multi-cam combination is proposed. The robot needs only one motor to achieve active support and bidirectional crawling for the pipe wall, mainly used for detecting straight pipelines, such as the main drainage pipeline. In order to obtain the periodic motion law and characteristics of the inchworm in-pipe robot, structure design, constraint analysis, and dynamic simulation were carried out on the robot. Finally, the principle prototype was tested in the transparent pipe, the test results indicate that the robot can achieve bidirectional creep under a single motor drive, which can simplify the control of gait motion for inchworm in-pipe robots. The average displacement errors for horizontal walking and vertical walking are 2.0% and 11.3%, respectively, due to factors such as gravity. Therefore, the robot can achieve a more accurate step distance in the horizontal pipe.

Qizhi Xie
Song Cui
Peilin Cheng
Qiang Liu


NeuroDiag: Software for Automated Diagnosis of Parkinson’s Disease Using Handwriting

Abstract: Objective: A change in handwriting is an early sign of Parkinson’s disease (PD). However, significant inter-person differences in handwriting make it difficult to identify pathological handwriting, especially in the early stages. This paper reports the testing of NeuroDiag, a software-based medical device, for the automated detection of PD using handwriting patterns. NeuroDiag is designed to direct the user to perform six drawing and writing tasks, and the recordings are then uploaded onto a server for analysis. Kinematic information and pen pressure of handwriting are extracted and used as baseline parameters. NeuroDiag was trained based on 26 PD patients in the early stage of the disease and 26 matching controls. Methods: Twenty-three people with PD (PPD) in their early stage of the disease, 25 age-matched healthy controls (AMC), and 7 young healthy controls were recruited for this study. Under the supervision of a consultant neurologist or their nurse, the participants used NeuroDiag. The reports were generated in real-time and tabulated by an independent observer. Results: The participants were able to use NeuroDiag without assistance. The handwriting data was successfully uploaded to the server where the report was automatically generated in real-time. There were significant differences in the writing speed between PPD and AMC (P<0.001). NeuroDiag showed 86.96% sensitivity and 76.92% specificity in differentiating PPD from those without PD. Conclusion: In this work, we tested the reliability of NeuroDiag in differentiating between PPD and AMC for real-time applications. The results show that NeuroDiag has the potential to be used to assist neurologists and for telehealth applications. Clinical and Translational Impact Statement — This pre-clinical study shows the feasibility of developing a community-wide screening program for Parkinson’s disease using automated handwriting analysis software, NeuroDiag.

Quoc Cuong Ngo
Nicole McConnell
Mohammod Abdul Motin
Barbara Polus
Arup Bhattacharya
Sanjay Raghav
Dinesh Kant Kumar

IEEE Journal of Translational Engineering in Health and Medicine

Computer applications to medicine. Medical informatics


Feature Selection and Rule Generation Integrated Learning for Takagi-Sugeno-Kang Fuzzy System and its Application in Medical Data Classification

Abstract: The rule-based fuzzy systems have successfully applied for numerous medical data classification problems. However, structuring the concise and interpretable fuzzy rules with good classification performance is still a big challenge. To address this issue, a novel feature selection and rule generation integrated learning for Takagi-Sugeno-Kang fuzzy system (called FSRG-IL-TSK) in this paper. FSRG-IL-TSK represents feature selection, structure identification and parameter learning into a Bayesian model, and uses the sequential importance resampling (SIR) algorithm to obtain the optimal parameters simultaneously, including the optimal features for each fuzzy rule, number of rules, and antecedent/consequent parameter of rules. Due to an integrated learning mechanism, it can select a small set of useful features and obtain a small number of rules. The effectiveness and advantages of FSRG-IL-TSK are validated experimentally on real-world medical data classification tasks.


Delay-Dependent H∞ Control for Singular Markovian Jump Systems With Generally Uncertain Transition Rates

Abstract: This article is devoted to the problem of H control for a class of singular Markovian jump systems with time-varying delay and generally uncertain transition rates, which means each transition rate is completely unknown or only its estimated value is known. By using Lyapunov stability theory, a new delay-dependent H admissible criterion in terms of strict linear matrix inequalities is obtained, which guarantees that the singular Markovian jump system with known transitions rates is regular, impulse-free and stochastically stable with a prescribed H disturbance attenuation level γ. Based on this obtained criterion, some suitable state feedback controllers are designed such that the closed-loop delayed singular Markovian jump system with generally uncertain transition rates is H stochastically admissible. Finally, numerical examples are included to illustrate the effectiveness and the less conservativeness of the proposed method.


Reducing Torque Ripple Through Innovative Configuration of Permanent Magnet Based on Air Gap Field Modulation Theory in a Novel Axial Flux Reversal Permanent Magnet Machine

Abstract: In this paper, a novel axial flux reversal permanent magnet (PM) (AFRPM) machine, which combines the merit of the axial flux PM (AFPM) machine and flux reversal PM machine, is proposed. Meanwhile, according to the characteristics of the machine, a method to reduce the torque ripple is presented and researched. Firstly, the topology and operation principle of the machine are analyzed. The power-size equation is derived and the design scheme is confirmed to obtain the structure parameters of the machine. Then, the general electromagnetic performances are analyzed. Secondly, the magnetomotive force (MMF)-permeance model is established. The air gap flux density harmonic distribution principle is researched from the air gap field modulation perspective. The torque generation mechanism is studied by analyzing the influences of the armature reaction air gap flux density harmonics and no-load air gap flux density harmonics on the electromagnetic torque. Furthermore, in order to reduce the torque ripple, an optimization method using the cos-type PMs, which suppress the harmonic pole pairs that do not contribute to the torque output, is investigated. To simplify the manufacturing process, the segmented sector PMs with different heights and arcs are employed instead of the cos-type PM. The effects of the different numbers of segmented sector PMs on the torque ripple are studied. Finally, the results indicate that the proposed machine exhibits large torque capability and high power/torque density. Meanwhile, the torque ripple is significantly reduced by the optimization method.


A Novel Dual-Domain Filtering Method to Improve GNSS Performance Based on a Dynamic Model Constructed by TDCP

Abstract: To overcome the strikes from the tremendous noise of pseudorange and the integer ambiguity of carrier phase observation, we developed a novel dual-domain filtering method that integrates the traditional observation- and an innovative position- domain filtering method. The novel method employed the conventional Hatch's smoother to improve the precision of pseudorange at the observation-domain filtering part. At the position- domain filtering part, the proposed method constructs an accurate dynamical model for KF (Kalman filter) or ARKF (adaptive-robust KF) that relies on the TDCP (time differential carrier phase) velocity measurement technology. To assess the effectiveness of the novel method, we compared it with other methods under various system integration by measured data from IGS (International GNSS Service) MGEX (Multi-GNSS Experiment) and dynamic data collected at CUMT (China University of Mining and Technology). The static numerical experiments obtained from IGS datasets show that the ARKF improves the precision of 60/84 and 55/84 situations' directions compared with the LS method based on OP (original pseudorange) and SP (smoothed pseudorange), respectively, and Hatch's smother improves the precision of all situations' directions. The experiment results prove that both types of observation- and position- domain filtering methods can improve positioning precision. Also, the novel dual-domain filtering improved the precision of 80/84 situations' directions compared with the LS method. Moreover, the dynamic experiment shows that the novel method can improve all of the situations' directions except the N (North) direction provided by KF base on SP under GC and U (Upon) direction provided by LS base on SP under GR. The average of improved accuracy is 0.212m, 0.346m, and 0.588m at the E (East), N, U direction and its corresponding average percentage of improved accuracy is 30.2%, 60.2%, and 26.7%, respectively, which proves the novel dual-domain filtering method provided the best performance among the tested algorithms.

Fangchao Li
Jingxiang Gao
Nanshan Zheng
Cheng Pan
Long Zhao


R-Local Unlabeled Sensing: A Novel Graph Matching Approach for Multiview Unlabeled Sensing Under Local Permutations

Abstract: Unlabeled sensing is a linear inverse problem where the measurements are scrambled under an unknown permutation leading to loss of correspondence between the measurements and the rows of the sensing matrix. Motivated by practical tasks such as mobile sensor networks, target tracking and the pose and correspondence estimation between point clouds, we study a special case of this problem restricting the class of permutations to be local and allowing for multiple views. In this setting, namely multi-view unlabeled sensing under local permutations, previous results and algorithms are not directly applicable. In this paper, we propose a computationally efficient algorithm, R-local unlabeled sensing (RLUS), that creatively exploits the machinery of indefinite relaxations of the graph matching problem to estimate the local permutations. Simulation results on synthetic data sets indicate that the proposed algorithm is scalable and applicable to the challenging regimes of low to moderate SNR.


Graph Theory-Based Approach to Accomplish Complete Coverage Path Planning Tasks for Reconfigurable Robots

Abstract: Extensive studies regarding complete coverage problems have been conducted, but a few tackle scenarios where the mobile robot is equipped with reconfigurable modules. The reconfigurability of these robots creates opportunities to develop new navigation strategies with higher dexterity; however, it also simultaneously adds in constraints to the direction of movements. This paper aims to develop a valid navigation strategy that allows tetromino-based self-reconfigurable robots to perform complete coverage tasks. To this end, a novel graph theory-based model to simulate the workspace coverage and make use of dynamic programming technique for optimal path searching and adaptive robot morphology shifting algorithms is proposed. Moreover, the influence of algorithms starting variables on workspace coverage outcome is analyzed thoughtfully in this paper. The simulation results showed that the proposed method is capable of generating navigation paths throughout the workspace, which ensures complete workspace coverage while minimizing the total number of actions performed by the robot.

Ku Ping Cheng
Rajesh Elara Mohan
Nguyen Huu Khanh Nhan
Anh Vu Le


Dynamic Elliptical Shaping Control for Swarm Robots

Abstract: Solving the robotic swarm coverage problem for an elliptical area has various applications for exploring novel environments. Solutions for this problem should cover a specified ellipse and seamlessly adapt to changing numbers of robots. Previous solutions used techniques such as formation control, vector fields, and neural networks. While these techniques were successful, they all lacked one or more of the three key tenants of swarm elliptical attraction: complete coverage of an ellipse with commandable parameters, simplicity for scaling in the number of robots, and adaptive sizing. Additionally, no previous work presented guidelines for ensuring that the swarms could successfully and safely converge to the commanded ellipse without collisions. In contrast, this work presents a novel swarm elliptical attraction behavior with all three key tenants with guidelines for ellipse and swarm parameter selection. First, a new Lyapunov stable elliptical attraction behavior for Reactive Particle Swarms is presented. The behavior commands robots to cover the entire ellipse area for a specific semimajor axis, eccentricity, and orientation. Additionally, dynamic interagent spacing naturally ensures coverage for different numbers of robots. Second, the work presents a novel adaptive sizing algorithm that varies the ellipse’s semimajor axis based on the swarm state. The adaptive sizing algorithm specifies the eccentricity and orientation using time-varying functions. Third, guidelines for selecting the number of robots, commanded ellipse area, obstacle avoidance distance, and robot communication range that allow for successful aggregation to the commanded ellipse are presented. All three of the results are verified using simulation and hardware-in-the-loop trials.

Shae T. Hart
Jake Kamenetsky
Christopher A. Kitts


ATIB: Design and Evaluation of an Architecture for Brokered Self-Sovereign Identity Integration and Trust-Enhancing Attribute Aggregation for Service Provider

Abstract: Identity management is a principle component of securing online services. In the advancement of traditional identity management patterns, the identity provider remained a Trusted Third Party (TTP). The service provider and the user need to trust a particular identity provider for correct attributes amongst other demands. This paradigm changed with the invention of blockchain-based Self-Sovereign Identity (SSI) solutions that primarily focus on the users. SSI reduces the functional scope of the identity provider to an attribute provider while enabling attribute aggregation. Besides that, the development of new protocols, disregarding established protocols and a significantly fragmented landscape of SSI solutions pose considerable challenges for an adoption by service providers. We propose an Attribute Trust-enhancing Identity Broker (ATIB) to leverage the potential of SSI for trust-enhancing attribute aggregation. Furthermore, ATIB abstracts from a dedicated SSI solution and offers standard protocols. Therefore, it facilitates the adoption by service providers. Despite the brokered integration approach, we show that ATIB provides a high security posture. Additionally, ATIB does not compromise the ten foundational SSI principles for the users.

Andreas Gruner
Alexander Muhle
Christoph Meinel


Deep Learning-Based Channel Estimation for Doubly Selective Fading Channels

Abstract: In this paper, online deep learning (DL)-based channel estimation algorithm for doubly selective fading channels is proposed by employing the deep neural network (DNN). With properly selected inputs, the DNN can not only exploit the features of channel variation from previous channel estimates but also extract additional features from pilots and received signals. Moreover, the DNN can take the advantages of the least squares estimation to further improve the performance of channel estimation. The DNN is first trained with simulated data in an off-line manner and then it could track the dynamic channel in an online manner. To reduce the performance degradation from random initialization, a pre-training approach is designed to refine the initial parameters of the DNN with several epochs of training. The proposed algorithm benefits from the excellent learning and generalization capability of DL and requires no prior knowledge about the channel statistics. Hence, it is more suitable for communication systems with modeling errors or non-stationary channels, such as high-mobility vehicular systems, underwater acoustic systems, and molecular communication systems. The numerical results show that the proposed DL-based algorithm outperforms the existing estimator in terms of both efficiency and robustness, especially when the channel statistics are time-varying.

Yuwen Yang
Feifei Gao
Xiaoli Ma
Shun Zhang


DC-Sync: A Doppler-Compensation Time-Synchronization Scheme for Complex Mobile Underwater Sensor Networks

Abstract: Time synchronization is crucial for effective collaboration among underwater sensors. However, existing synchronization protocols primarily cater to low-speed or simple motion scenarios, neglecting variations in radial velocity during message propagation. A novel Doppler compensation time synchronization scheme, called DC-Sync, was developed in this study to address this issue by targeting complex moving underwater sensors. DC-Sync includes a practical Doppler compensation and estimation method. Simulation results demonstrate that when the target motion follows a specific pattern, DC-Sync outperforms existing similar schemes in terms of time skew and time offset accuracy. Furthermore, the scheme maintains high estimation accuracy even with incomplete Doppler measurement values. Its performance was also validated through physical experiments.


6G Beyond Radio: From Connecting Devices to Sensing the World

Abstract: Future sixth-generation (6G) wireless networks are expected to evolve beyond traditional communication infrastructures and incorporate native sensing and environmental awareness capabilities. While previous generations primarily focused on connectivity metrics such as data rate, latency, and radio-frequency (RF) coverage, emerging applications increasingly require networks that can perceive physical environments and support context-aware decision making. This paper introduces the concept of sensing coverage as a complementary performance dimension to conventional RF coverage in sensing-enabled wireless systems. Within this perspective, a unified 6G vision is presented in which wireless infrastructure operates as a distributed sensing platform enabled by integrated sensing and communication (ISAC). The complementary paradigms of Network for Sensing, Sensing for Network, and Sensing-as-a-Service are systematically analyzed to clarify their roles in sensing-centric wireless architectures. The paper further reviews heterogeneous sensing technologies including cellular sensing, Wi-Fi sensing, visible-light communications (VLC), non-terrestrial networks (NTN), terahertz (THz) communications, and reconfigurable intelligent surface (RIS)-assisted sensing, illustrating how these technologies jointly enable multi-layer sensing coverage. In addition, quantitative foundations and performance metrics for sensing coverage are presented, and an illustrative evaluation is provided to highlight the fundamental differences between RF communication coverage and sensing coverage. Finally, the roles of AI-native intelligence, digital twins, and sensing-oriented service models are discussed in the context of sensing-enabled 6G networks. The presented framework provides a structured view of the emerging sensing-centric 6G ecosystem and highlights key research directions for future wireless systems that jointly integrate communication, sensing, computation, and intelligent services.

Recep Evrim Ozgen
Ahmet Yazar
Mustafa Serdar Osmanca


Correction to “5.8 GHz 4-Channel Beamforming Tx IC for Microwave Power Transfer”

Abstract: In the above article <xref ref-type="bibr" rid="ref1">[1]</xref>, the received power in dc referred from the reference <xref ref-type="bibr" rid="ref2">[2]</xref>, which is the reference [35] in the original article <xref ref-type="bibr" rid="ref1">[1]</xref>, was incorrectly introduced. It was written as 0.2 W (dc) in the introduction of <xref ref-type="bibr" rid="ref1">[1]</xref>, but the correct received power in the specified condition is 2 W (dc).

Jaekyung Shin
Jongseok Bae
Hyungmo Koo
Sooncheol Bae
Jongyun Na
Hansik Oh
Hyungjin Jeon
Hoseok Jung
Young Chan Choi
Seungmin Woo
Chan Mi Song
Keum Cheol Hwang
Kang-Yoon Lee
Youngoo Yang


Blind Dual Watermarking Scheme Using Stucki Kernel and SPIHT for Image Self-Recovery

Abstract: In this paper we propose a blind dual watermarking scheme using Set Partitioning in Hierarchical Trees (SPIHT) and Stucki Kernel halftone technique for the tamper detection and image self-recovery. The watermark consists of authentication bits for tampering area location and recovery bits for image restoration. We generate two recovery bits to ensure the high-quality recovery of the tampered image. The primary recovery bit is generated by the SPIHT encoding, and the secondary recovery bit is generated by the Stucki Kernel halftone technique. Then the authentication bit is generated based on the recovery bits. Before embedding the watermark, we shuffle the watermark bits through Arnold cat mapping and diagonal mapping to improve the security and quality of the restored image. LSB-based watermarking technique is used to embed the watermark into the original image to ensure the invisibility of the watermarked image. Experiments have been conducted on two datasets, BOW2 and USC-SIPI, and results show that the proposed scheme can achieve high restoration quality. Comparison with the existing works demonstrate the good performance and superiority of the proposed scheme.


A Novel End-to-End Corporate Credit Rating Model Based on Self-Attention Mechanism

Abstract: In traditional financial analysis discriminant analyses with statistic methods are considered as primary and widely applied corporate credit rating bases in which human impact plays an essential role. Machine learning approaches enormously assist credit rating in reducing human interference and improving rating efficiency. However, existing methods did not efficiently leverage the time series features of financial data that is beneficial for the classification tasks. We propose a novel end-to-end architecture, the SMAGRU, based on self multi-head attention with gated recurrent neural networks, which is capable of evaluating a corporate credit rating by capturing the time series features assigned the weights of market benchmarks. Experiments compared with statistic and machine learning baseline models on different datasets show that our model contains superiority in multi-class credit rating classification. The result of ablation experiments suggests that time series features are crucial in classification performance, and self multi-head attention can significantly enhance these features and thereby improve the accuracy and convergence speed. We show that the SMAGRU generalizes well, even on sparse data during K-fold cross validation experiments.


Synthetic Data Pretraining for Hyperspectral Image Super-Resolution

Abstract: Large-scale self-supervised pretraining of deep learning models is known to be critical in several fields, such as language processing, where its has led to significant breakthroughs. Indeed, it is often more impactful than architectural designs. However, the use of self-supervised pretraining lags behind in several domains, such as hyperspectral images, due to data scarcity. This paper addresses the challenge of data scarcity in the development of methods for spatial super-resolution of hyperspectral images (HSI-SR). We show that state-of-the-art HSI-SR methods are severely bottlenecked by the small paired datasets that are publicly available, also leading to unreliable assessment of the architectural merits of the models. We propose to capitalize on the abundance of high resolution (HR) RGB images to develop a self-supervised pretraining approach that significantly improves the quality of HSI-SR models. In particular, we leverage advances in spectral reconstruction methods to create a vast dataset with high spatial resolution and plausible spectra from RGB images, to be used for pretraining HSI-SR methods. Experimental results, conducted across multiple datasets, report large gains for state-of-the-art HSI-SR methods when pretrained according to the proposed procedure, and also highlight the unreliability of ranking methods when training on small datasets.

Emanuele Aiello
Mirko Agarla
Diego Valsesia
Paolo Napoletano
Tiziano Bianchi
Enrico Magli
Raimondo Schettini


High-Performance Tuning for Model Predictive Control for a Renewable Energy Grid-Interface Converter With LCL Filter

Abstract: Model predictive control (MPC) has emerged as a highly regarded control strategy in power electronics for renewable energy applications. While it minimizes tracking errors and control effort, a significant challenge is the lack of systematic tuning strategies to meet these systems’ energy quality performance requirements. This paper proposes a comprehensive MPC tuning methodology for grid-integrating converters with LCL filters, incorporating modulation and delay compensation. We conduct a stability analysis to define precise constraints for cost function weights. The fine-tuning strategy systematically maps a Figure of Merit (FoM) for system performance using an advanced computational model, revealing that optimal tunings reside in narrow parameter regions. Experimental validation on a 2 kW workbench confirmed that all proposed MPC tunings met IEEE Std. 519-2014 power quality criteria and consistently outperformed a two-sample deadbeat controller, exhibiting enhanced dynamic response and power quality.

Jefferson S. Costa
Angelo Lunardi
Alessio Iovine
Darlan Alexandria Fernandes
Daniel Albiero
Alfeu J. Sguarezi Filho


On Security and Throughput for Energy Harvesting Untrusted Relays in IoT Systems Using NOMA

Abstract: In this paper, we analyze the secrecy and throughput of multiple-input single-output (MISO) energy harvesting (EH) Internet of Things (IoT) systems, in which a multi-antenna base station (BS) transmits signals to IoT devices (IoTDs) with the help of relays. Specifically, the communication process is separated into two phases. In the first phase, the BS applies transmit antenna selection (TAS) to broadcast the signal to the relays and IoTDs by using non-orthogonal multiple access (NOMA). Here, the relays use power-splitting-based relaying (PSR) for EH and information processing. In the second phase, the selected relay employs the amplify-and-forward (AF) technique to forward the received signal to the IoTDs using NOMA. The information transmitted from the BS to the IoTD risks leakage by the relay, which is able to act as an eavesdropper (EAV) (i.e., an untrusted relay). To analyze the secrecy performance, we investigate three schemes: random-BS-best-relay (RBBR), best-BS-random-relay (BBRR), and best-BS-best-relay (BBBR). The physical layer secrecy (PLS) performance is characterized by deriving closed-form expressions of secrecy outage probability (SOP) for the IoTDs. A BS transmit power optimization algorithm is also proposed to achieve the best secrecy performance. Based on this, we then evaluate the system performance of the considered system, i.e., the outage probability and throughput. In addition, the impacts of the EH time, the power-splitting ratio, the numbers of BS antennas, and the numbers of untrusted relays on the SOP and throughput are investigated. The Monte Carlo approach is applied to verify our analytical results. Finally, the numerical examples indicate that the system performance of BBBR is greater than that of RBBR and BBRR.

Van Nhan Vo
Chakchai So-In
Hung Tran
Duc-Dung Tran
Sovannarith Heng
Phet Aimtongkham
Anh-Nhat Nguyen


A Novel Partitioning Approach in Active Distribution Networks for Voltage Sag Mitigation

Abstract: The growing emphasis on power quality has posed significant challenges for distribution system operators (DSOs). Among these challenges, short-term voltage fluctuations, specifically voltage sag, have drawn considerable attention. In this study, three concepts of average edge (AE), lower average edge (LAE), and upper average edge (UAE) based on the electrical connection matrix and voltage-magnitude sensitivity matrix are defined and used as the partitioning first level. At the second level, a kernel smoothing function is employed to refine the zoning process. Subsequently, strategic locations within each zone are identified: the vertex and middle buses. These carefully selected buses serve as installation points for dynamic voltage restorers (DVRs). In response, this study proposes a novel solution by partitioning the distribution network into distinct zones. The focus lies in developing a two-level offline partitioning approach for active distribution networks (ADNs) that incorporate photovoltaic (PV) systems. To evaluate the effectiveness of the proposed method, numerical studies were conducted on modified IEEE 33-bus, IEEE 69-bus, and Iranian 95-bus systems, with simulations performed using MATLAB/Simulink. The proposed method provides good performance and fast calculation speed for distribution network partitioning, as confirmed by the results. Test results show improved bus voltage with PV unit integration. Additionally, power loss in the IEEE 33-bus, IEEE 69-bus, and Iranian 95-bus networks decreased by 47.73 kW, 56.87 kW, and 69.63 kW, respectively. Furthermore, the voltage profile improved from 0.75 p.u. to 0.928 p.u. during a voltage sag in the IEEE 33-bus system, and in steady state, the voltage increased from 0.933 to 0.959 p.u.


Piezo1 knockdown activates PI3K/AKT and enhances SPP1 to drive M2 macrophage polarization and reduce cardiac inflammation

Abstract: Abstract Piezo1 plays a key role in the immune response during sepsis. To date, our understanding of the role of Piezo1 in inflammatory diseases has mostly been limited to influencing vasomotor function and regulating inflammatory infiltration. Whether and how Piezo1 in macrophages is involved in developing septic cardiac dysfunction has never been explored. Here, we have successfully established a mouse model with myeloid cell-specific knockdown of Piezo1. The intraperitoneal injection of lipopolysaccharide (LPS) resulted in a significant increase in cardiac macrophage infiltration, as well as an increase in the expression of inflammatory factors and the inflammatory response. However, myeloid cell-specific knockdown of Piezo1 impaired this response, leading to an increase in macrophage polarization towards the M2 type and the decreased inflammatory response. As a result, myocardial injury caused by sepsis was attenuated. We have also demonstrated that the PI3K/AKT pathway is significantly activated after Piezo1 knockdown, resulting in reduced myocardial dysfunction. Our data indicate that myeloid cell-specific knockdown of Piezo1 can influence macrophage polarization and thus exert cardioprotective effects in a murine model of sepsis, providing potential ideas and targets for the treatment of infectious cardiac dysfunction.

Yunhan Zhang
Ying Zhang
Jiaoyan Song
Ziwen Zhao
Wenhao Ju
Hao Zhang
Shuang Li
Hanlu Li
Qiuzhe Guo
Yinbo Ma
Zhangwei Nong
Tianyuan Shen
Yuanzheng Wang
Boheng Pang
Min Hao
Chunyuan Luo
Zhiling Luo
Yinjiang Tang
Donglin Zhuang
Jianqiu Pei


Increased remission with fewer corticosteroids and more biologics in rheumatoid arthritis at 7-year follow-up in real-life conditions

Abstract: Abstract Remission in rheumatoid arthritis (RA) is an important therapeutic target that is not easy to achieve in real-life conditions. Some prognostic factors have been identified but the literature is variable. The objectives of this study were to evaluate the remission rate and the maintenance of remission in patients with RA over 7 years of follow-up in real-life conditions and to identify prognostic factors of long-term remission. Patients with RA seen at the Poitiers University Hospital were identified and clinical and biological data were collected. Data were analysed after 1 year and 7 years. Twice as many patients were in remission at 7 years than at 1 year of follow-up. 48.6% of patients who were not in remission at 1 year obtained remission at 7 years of follow-up. Patients achieving remission were more often receiving coprescription of csDMARDs and bDMARDs. Patients not in remission at 7 years were given more corticosteroids at higher doses. After 7 years of follow-up, low initial disease activity and use of csDMARDs and bDMARDs appeared to be independent positive predictive factors. Once obtained at one year, remission was maintained for 76% of our patients. As a conclusion, modern management of RA, whatever disease duration, leads to remission rates similar to those of early RA after 7 years of follow-up.

Guillaume Larid
Justine Vix
Ronan Garlantezec
Elodie Loppin
Elisabeth Gervais


Non-state actor perceptions of legitimacy and meaningful participation in international climate governance

Abstract: Abstract There is a lively debate about the legitimacy of the international climate regime, as represented by the United Nations Framework Convention on Climate Change, and the quality of non-state actor participation in the regime. This commentary examines perceptions of involved non-state actors from 2021–2022 regarding their participation and regime legitimacy. The findings reveal no legitimacy crisis for the adaptation and mitigation regimes, but the surveyed NSAs are divided in their legitimacy beliefs. NSAs also express significant disappointment about their opportunities for participation.

Lisa Dellmuth
Maria-Therese Gustafsson
Suanne Mistel Segovia-Tzompa

Meteorology. Climatology
Environmental sciences