source stringlengths 16 523 | text stringlengths 0 9.39k | date dict | category stringclasses 11
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|---|---|---|---|
Relative Value Encoding in Large Language Models: A Multi-Task, Multi-Model Investigation. | In-context learning enables large language models (LLMs) to perform a variety of tasks, including solving reinforcement learning (RL) problems. Given their potential use as (autonomous) decision-making agents, it is important to understand how these models behave in RL tasks and the extent to which they are susceptible... | {
"Day": null,
"MedlineDate": null,
"Month": null,
"Season": null,
"Year": "2025"
} | reinforcement learning |
Efficient joint resource allocation using self organized map based Deep Reinforcement Learning for cybertwin enabled 6G networks. | Sixth-generation wireless communication has emerged, stimulating the rapid growth of numerous types of real-time applications that are characterized by their high data computing demands and formation of massive data traffic. Cybertwin-enabled edge computing has become a logical way to satisfy the enormous user demands.... | {
"Day": "05",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Stimulus-specific and adaptive value representations in the basolateral amygdala in male mice. | Animals make decisions based on the value of potential outcomes. This perceived value is not fixed; it changes depending on internal needs, such as hunger or thirst, and past experiences. The basolateral amygdala (BLA) is known to be crucial for updating predicted reward values. However, it has been unclear how the BLA... | {
"Day": "05",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Transformative protein scaffold designs for dual-modality cancer applications: Advances in therapeutic delivery and molecular imaging of tumor microenvironments. | Despite significant advances in cancer immunotherapy, current approaches face critical limitations, including systemic toxicity, inadequate tumor penetration, and insufficient therapeutic efficacy against immunosuppressive tumor microenvironments. A significant research gap exists in developing platforms that can simul... | {
"Day": "03",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Divergent effects of win-paired cues on learning from timeout penalties in female and male rats. | In both males and females, linking rewards with salient audiovisual cues in simulated gambling games increases risky choice in humans and rats. However, the prevalence and severity of gambling problems differs in men and women. In previous work, reinforcement learning (RL) models were applied to data from male rats per... | {
"Day": "03",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Designing a Resilient Controller for Cancer Immunotherapy: Application to a Fractional-Order Tumour-Immune Model. | In this paper, we propose a robust control method for the automatic treatment of targeted anti-angiogenic molecular therapy based on multi-input multi-output (MIMO) nonlinear fractional and non-fractional models using the backstepping (BS) approach. This protocol aims to eradicate tumour cells while preserving high lev... | {
"Day": null,
"MedlineDate": null,
"Month": null,
"Season": "Jan-Dec",
"Year": "2025"
} | reinforcement learning |
Neural evidence that humans reuse strategies to solve new tasks. | Generalization from past experience is an important feature of intelligent systems. When faced with a new task, one efficient computational approach is to evaluate solutions to earlier tasks as candidates for reuse. Consistent with this idea, we found that human participants (n = 38) learned optimal solutions to a set ... | {
"Day": null,
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Improving Covalent and Noncovalent Molecule Generation via Reinforcement Learning with Functional Fragments. | Small-molecule drugs play a critical role in cancer therapy by selectively targeting key signaling pathways that drive tumor growth. While deep learning models have advanced drug discovery, there remains a lack of generative frameworks for <i>de novo</i> covalent molecule design using a fragment-based approach. To addr... | {
"Day": "05",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
<i>Radiology: Cardiothoracic Imaging</i> Highlights 2024. | <i>Radiology: Cardiothoracic Imaging</i> publishes research, technical developments, and reviews related to cardiac, vascular, and thoracic imaging. The current review article, led by the <i>Radiology: Cardiothoracic Imaging</i> trainee editorial board, highlights the most impactful articles published in the journal be... | {
"Day": null,
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Learning to suppress tremors: a deep reinforcement learning-enabled soft exoskeleton for Parkinson's patients. | Neurological tremors, prevalent among a large population, are one of the most rampant movement disorders. Biomechanical loading and exoskeletons show promise in enhancing patient well-being, but traditional control algorithms limit their efficacy in dynamic movements and personalized interventions. Furthermore, a press... | {
"Day": null,
"MedlineDate": null,
"Month": null,
"Season": null,
"Year": "2025"
} | reinforcement learning |
Medical students' perspectives on effective and ineffective teaching behaviors in lectures. | Lecture-based teaching is widely used in preclinical medical education, offering a systematic way to deliver complex information efficiently. However, its effectiveness heavily relies on the instructional behaviors of lecturers. Despite its importance, limited research has explored the specific differences between effe... | {
"Day": null,
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
A 3D generation framework using diffusion model and reinforcement learning to generate multi-target compounds with desired properties. | Deep generative models provide a powerful solution for the de novo design of molecules. However, the majority of existing methods only generate molecules for a single target. Generating molecules with biological activities against multiple specific targets and desired properties remains an extremely difficult challenge... | {
"Day": "04",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Study protocol for a multimethod investigation of the development of social and nonsocial reward responsivity and depression in autistic adolescents: Reward and Depression in Autism (RDA). | Autistic adolescents are more likely to experience depression than their non-autistic peers, yet risk factors for depression in autistic adolescents are not well understood. Better mechanistic knowledge of depression in autistic adolescents is critical to understanding higher prevalence rates and developing targeted in... | {
"Day": "04",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
A multidimensional distributional map of future reward in dopamine neurons. | Midbrain dopamine neurons (DANs) signal reward-prediction errors that teach recipient circuits about expected rewards<sup>1</sup>. However, DANs are thought to provide a substrate for temporal difference (TD) reinforcement learning (RL), an algorithm that learns the mean of temporally discounted expected future rewards... | {
"Day": "04",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Multi-timescale reinforcement learning in the brain. | To thrive in complex environments, animals and artificial agents must learn to act adaptively to maximize fitness and rewards. Such adaptive behaviour can be learned through reinforcement learning<sup>1</sup>, a class of algorithms that has been successful at training artificial agents<sup>2-5</sup> and at characterizi... | {
"Day": "04",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Investigating the association between groundwater contaminants and hypertension risk in India: a machine learning-based analysis. | One-fourth of Indians are hypertensive, and the majority relies on groundwater for drinking. But the role of groundwater physicochemical properties and contamination in hypertension remains understudied. The study investigates the association between physicochemical groundwater characteristics andcontaminants and hyper... | {
"Day": "04",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Prediction of compressive strength of fiber-reinforced concrete containing silica (SiO<sub>2</sub>) based on metaheuristic optimization algorithms and machine learning techniques. | Concrete compressive strength (CS) is crucial for ensuring the safety, durability, and performance of structures. So, its precise simulation helps anticipate material behavior under various conditions. Despite a comprehensive experimental investigation of the impact of silica (SiO<sub>2</sub>) on the CS of the fiber-re... | {
"Day": "04",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Resource allocation of distributed MIMO radar based on the hybrid action space reinforcement learning. | The distributed multiple-input multiple-output (MIMO) radar system exhibits superior target localization capability by jointly processing target information from multiple radars under different observation angles. To improve the resource utilization of the distributed MIMO radar system, this paper proposes a hybrid act... | {
"Day": "04",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Adaptive crayfish optimization algorithm for multi-objective scheduling optimization in distributed production workshops. | The increasing demand for wind turbines and cost pressures in the wind energy industry have made the Wind Turbine Pultruded Panels Production Scheduling Problem (WTPP-PSP) a critical challenge. To address the production scheduling requirements of WTPP-PSP, an intelligent platform is proposed for wind turbine pultruded ... | {
"Day": "04",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Distinct spatially organized striatum-wide acetylcholine dynamics for the learning and extinction of Pavlovian associations. | Striatal acetylcholine (ACh) signaling is thought to counteract reinforcement signals, promoting extinction and behavioral flexibility. Changes in striatal ACh signals have been reported during learning, but how ACh signals for learning and extinction are spatially organized to enable region-specific plasticity is uncl... | {
"Day": "04",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Common neural patterns of substance use disorder: a seed-based resting-state functional connectivity meta-analysis. | Substance use disorder (SUD) shares common clinical features, including impulsive and compulsive behaviors, which are associated with dysfunctions in the brain's reward circuit. Resting-state functional magnetic resonance imaging (rs-fMRI) studies have shown inconsistent results due to variability in the substances and... | {
"Day": "04",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
MetaSeeker: sketching an open invisible space with self-play reinforcement learning. | Controlling electromagnetic (EM) waves at will is fundamentally important for diverse applications, ranging from optical microcavities, super-resolution imaging, to quantum information processing. Decades ago, the forays into metamaterials and transformation optics have ignited unprecedented interest to create an invis... | {
"Day": "04",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Impact of Iterative Development and Beta-Testing on the Usability and Acceptability of a Novel Just-in-Time Adaptive Digital Physical Activity Intervention. | The search for cost-effective population-based physical activity interventions continues. Therefore, we developed a novel just-in-time adaptive digital assistant supported by machine learning (ie, MoveMentor). Beta-testing is essential to evaluate both technical performance and user acceptance. The aim of this study wa... | {
"Day": "04",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Web-Based Education Program for Care Partners of People Living With Dementia (iGeriCare): Protocol for a Pilot Randomized Controlled Trial. | The prevalence of dementia is increasing in Canada and in many countries internationally. People living with dementia are highly dependent on family and friend care partners, who may have little knowledge of the disorder. Web-based interventions in dementia have been shown to improve care partner mental health and redu... | {
"Day": "04",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Integrating Nurse Preferences Into AI-Based Scheduling Systems: Qualitative Study. | Nurse scheduling is a complex challenge in health care, impacting both patient care quality and nurse well-being. Traditional scheduling methods often fail to consider individual preferences, leading to dissatisfaction, burnout, and high turnover. Inadequate scheduling practices, including restricted autonomy and lack ... | {
"Day": "04",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Reinforcement-Learning-Based Fuzzy Bipartite Consensus for Multiagent Systems: A Novel Scaling Off-Policy Learning Scheme. | The bipartite consensus (BC) issue for nonlinear multiagent systems (NMASs) with unknown system dynamics information is investigated in this article. Initially, the dynamics of NMASs are represented using the Takagi-Sugeno (T-S) fuzzy model. Subsequently, to achieve distributed control, a minmax game policy is introduc... | {
"Day": "04",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Reflections of Novice Veterinary Clinical Educators on Feedback Training: Insights from a UK Training Programme. | Within veterinary education, there is an increasing shift toward a distributed teaching model, requiring clinicians to assume roles as novice educators. To support their development, the University of Surrey pioneered a training program focused on promoting educational theory and feedback delivery skills. This study in... | {
"Day": "04",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
A review on learning-based algorithms for tractography and human brain white matter tracts recognition. | Human brain fiber tractography using diffusion magnetic resonance imaging is a crucial stage in mapping brain white matter structures, pre-surgical planning, and extracting connectivity patterns. Accurate and reliable tractography, by providing detailed geometric information about the position of neural pathways, minim... | {
"Day": "04",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Oscillatory and Aperiodic Contributions to EEG Event-Related Time-Frequency Metrics During Cognitive Control and Reinforcement Processing: A Registered Report. | Brain oscillations, or rhythms, coordinate communication across distributed brain networks. These rhythms provide a foundation for the brain network interactions required for cognition. Oscillations coexist with non-rhythmic background aperiodic activity that forms a characteristic 1/f pattern in power spectra. Aperiod... | {
"Day": null,
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Supervised optimal control in complex continuous systems with trajectory imitation and reinforcement learning. | Supervisory control theory (SCT) is widely used as safeguard mechanism with control of discrete event systems (DESs). In complex continuous systems, in order to avoid system's behavior violating specifications, the supervised control problem of these systems is quite different. Continuous state and action spaces of hig... | {
"Day": "03",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Data-intelligence driven methods for durability, damage diagnosis and performance prediction of concrete structures. | A large number of in-service reinforced concrete structures are now entering the mid-to-late stages of their service life. Efficient detection of damage characteristics and accurate prediction of material performance degradation have become essential for ensuring the safety of these structures. Traditional damage detec... | {
"Day": "03",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Importance of sample size on the quality and utility of AI-based prediction models for healthcare. | Rigorous study design and analytical standards are required to generate reliable findings in healthcare from artificial intelligence (AI) research. One crucial but often overlooked aspect is the determination of appropriate sample sizes for studies developing AI-based prediction models for individual diagnosis or progn... | {
"Day": "02",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Workshop on professionalism and professional identity formation for newly recruited faculty at a healthcare university: lessons learnt. | Healthcare is inherently human-centered, and professionalism is crucial for improving healthcare systems. Traditionally developed through role modeling, professionalism now necessitates explicit teaching. Despite the inclusion of professionalism-related competencies by Indian regulatory bodies in medicine and nursing, ... | {
"Day": null,
"MedlineDate": null,
"Month": null,
"Season": null,
"Year": "2025"
} | reinforcement learning |
RLSuccSite: succinylation sites prediction based on reinforcement learning dynamic with balanced reward mechanism and three-peaks enhanced method for physicochemical property scores. | Recent progress in computational biology has driven the development of machine learning models for predicting protein post-translational modification sites. However, challenges such as data imbalance and limited sequence-context representation continue to hinder prediction accuracy, particularly for less frequent modif... | {
"Day": "02",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Egocentric value maps of the near-body environment. | Body-part-centered response fields are pervasive in single neurons, functional magnetic resonance imaging, electroencephalography and behavior, but there is no unifying formal explanation of their origins and role. In the present study, we used reinforcement learning and artificial neural networks to demonstrate that b... | {
"Day": "02",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Data-driven diabetes mellitus prediction and management: a comparative evaluation of decision tree classifier and artificial neural network models along with statistical analysis. | Diabetes Mellitus is a chronic metabolic disorder affecting a substantial global population leading to complications such as retinopathy, nephropathy, neuropathy, foot problems, heart attacks, and strokes if left unchecked. Prompt detection and diagnosis are crucial in managing and averting these complications. This st... | {
"Day": "02",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
A deep learning and IoT-driven framework for real-time adaptive resource allocation and grid optimization in smart energy systems. | The rapid evolution of smart grids, driven by rising global energy demand and renewable energy integration, calls for intelligent, adaptive, and energy-efficient resource allocation strategies. Traditional energy management methods, based on static models or heuristic algorithms, often fail to handle real-time grid dyn... | {
"Day": "02",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Early detection of occupational stress: Enhancing workplace safety with machine learning and large language models. | Occupational stress is a major concern for employers and organizations as it compromises decision-making and overall safety of workers. Studies indicate that work-stress contributes to severe mental strain, increased accident rates, and in extreme cases, even suicides. This study aims to enhance early detection of occu... | {
"Day": null,
"MedlineDate": null,
"Month": null,
"Season": null,
"Year": "2025"
} | reinforcement learning |
Indirect punishment can outperform direct punishment in promoting cooperation in structured populations. | Indirect punishment traditionally sustains cooperation in social systems through reputation or norms, often by reducing defectors' payoffs indirectly. In this study, we redefine indirect punishment for structured populations as a spatially explicit mechanism, where individuals on a square lattice target second-order de... | {
"Day": null,
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Scope and Role of Integrating Yoga in Homeopathic Medical Education: An Explorative Review. | Homeopathy and Yoga are rooted in holistic paradigms emphasizing the unity of body, mind, and spirit. Over the recent years, Yoga has been increasingly integrated into the medical education system. Integrating yoga with homeopathic education presents a unique opportunity to reinforce shared healing philosophies while e... | {
"Day": "03",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
EXPRESS: Intentional binding decreases during learning: implications for sense of agency. | The sense of agency refers to the subjective experience of controlling one's own actions and their outcomes. While agency is often thought to increase with better performance, it remains unclear how it evolves during learning. In this study, we investigated how the sense of agency changes as individuals learn when to a... | {
"Day": "02",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Do Human Reinforcement Learning Models Account for Key Experimental Choice Patterns in the Iowa Gambling Task? | The Iowa gambling task (IGT) is widely used to study risky decision-making and learning from rewards and punishments. Although numerous cognitive models have been developed using reinforcement learning frameworks to investigate the processes underlying the IGT, no single model has consistently been identified as superi... | {
"Day": null,
"MedlineDate": null,
"Month": null,
"Season": null,
"Year": "2025"
} | reinforcement learning |
A Deployed Online Reinforcement Learning Algorithm In An Oral Health Clinical Trial. | Dental disease is a prevalent chronic condition associated with substantial financial burden, personal suffering, and increased risk of systemic diseases. Despite widespread recommendations for twice-daily tooth brushing, adherence to recommended oral self-care behaviors remains sub-optimal due to factors such as forge... | {
"Day": null,
"MedlineDate": null,
"Month": null,
"Season": null,
"Year": "2025"
} | reinforcement learning |
High ovarian hormones present during fear extinction reduce fear relapse through a nigrostriatal dopamine pathway. | Elevated ovarian hormones during fear extinction can enhance fear extinction memory retention and reduce fear renewal, but the mechanisms remain unknown. High levels of ovarian hormones are associated with heightened dopamine (DA) transmission, a key player in fear extinction. In males, stimulation of substantia nigra ... | {
"Day": "01",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
[Educational Effects of Introducing a Dental Seminar for Pharmacy Students: Questionnaire on Changes in Learning Attitudes and Knowledge Improvement]. | This study evaluated the educational impact of dental seminars involving practicing pharmacists and dentists on pharmacy students. Its primary objective was to assess changes in learning attitudes and improvements in oral care knowledge through lectures and group discussions. The dental seminars were conducted during O... | {
"Day": null,
"MedlineDate": null,
"Month": null,
"Season": null,
"Year": "2025"
} | reinforcement learning |
Unpleasant mood reverses satiety's effect on tobacco reinforcement. | Despite empirical support of goal-directed behavior models of dependence, the role of mood on substance use is unclear. The Reinforcer Pathology (RP) model may be useful to describe it specific effects in substance-related variables. This study aims to test mood induction's effect on tobacco demand and integrate result... | {
"Day": "26",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Actor critic with experience replay-based automatic treatment planning for prostate cancer intensity modulated radiotherapy. | Achieving highly efficient treatment planning in intensity-modulated radiotherapy (IMRT) is challenging due to the complex interactions between radiation beams and the human body. The introduction of artificial intelligence (AI) has automated treatment planning, significantly improving efficiency. However, existing aut... | {
"Day": "31",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Psychological, economic, and ethical factors in human feedback for a chatbot-based smoking cessation intervention. | Integrating human support with chatbot-based behavior change interventions raises three challenges: (1) attuning the support to an individual's state (e.g., motivation) for enhanced engagement, (2) limiting the use of the concerning human resources for enhanced efficiency, and (3) optimizing outcomes on ethical aspects... | {
"Day": "31",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Incorporating the STOP-BANG questionnaire improves prediction of cardiovascular events during hospitalization after myocardial infarction. | Obstructive sleep apnea (OSA) may impact outcomes in acute coronary syndrome (ACS) patients. The Global Registry of Acute Coronary Events (GRACE) score assesses cardiovascular risk post-ACS. This study evaluated whether incorporating the STOP-BANG score (a surrogate for OSA) enhances GRACE's predictive ability. A total... | {
"Day": "31",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Dynamics of sensorimotor-related brain oscillations: EEG insights from healthy individuals in varied upper limb movement conditions. | Event-related desynchronization (ERD) and event-related synchronization (ERS) are critical neurophysiological phenomena associated with motor execution and inhibitory processes. Their utility spans neurophysiological biomarker research and Brain-Computer Interface (BCI) development. However, standardized frameworks for... | {
"Day": "31",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Wait for It: Defensive Reactivity and Individual Differences in Contrast Avoidance in the NPU-Reward Task. | Defensive reactivity (startle) is increased during anticipation of temporally unpredictable (> predictable) threat. Startle also seems to be potentiated during reward anticipation, yet how this is affected by temporal unpredictability had not previously been examined. In addition to unpredictability, between-subject di... | {
"Day": null,
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Deep Learning Differentiates Papilledema, NAION, and Healthy Eyes with Unsegmented 3D OCT Volumes. | Deep learning (DL) has been used to differentiate papilledema from healthy eyes and optic disc elevation on fundus photos. As we described optic nerve head (ONH) and peripapillary retina (PPR) optical coherence tomography (OCT) features that distinguish non-arteritic anterior ischemic optic neuropathy (NAION) from papi... | {
"Day": "28",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Computationally-informed insights into anhedonia and treatment by k-opioid receptor antagonism. | Anhedonia, the loss of pleasure, is prevalent and impairing. Parsing its computational basis promises to explain its transdiagnostic character. One manifestation of anhedonia-reward insensitivity-may be linked to limited memory. Further, the need to economize on limited memory engenders a perseverative bias towards fre... | {
"Day": "28",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Electrophysiological evidence for flexible adjustments in cognitive control depending on feedback's contingency. | Cognitive control is a fundamental ability that enables to detect and resolve conflict. However, this ability is not encapsulated but liable to learning and motivational factors. Among them, previous studies have shown that the contingency created between conflict and performance by means of feedback, as well as its ac... | {
"Day": "28",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Economic implications of artificial intelligence-driven recommended systems in healthcare: a focus on neurological disorders. | The rapid advancement of Artificial Intelligence (AI)-driven recommendation systems in healthcare presents significant economic implications, particularly in the context of neurological disorders. These systems offer opportunities to enhance diagnostic accuracy, optimize resource allocation, and improve patient outcome... | {
"Day": null,
"MedlineDate": null,
"Month": null,
"Season": null,
"Year": "2025"
} | reinforcement learning |
Co-Learning: code learning for multi-agent reinforcement collaborative framework with conversational natural language interfaces. | Online question-and-answer (Q&A) systems based on the Large Language Model (LLM) have progressively diverged from recreational to professional use. However, beginners in programming often struggle to correct code errors independently, limiting their learning efficiency. This paper proposed a Multi-Agent framework with ... | {
"Day": null,
"MedlineDate": null,
"Month": null,
"Season": null,
"Year": "2025"
} | reinforcement learning |
Artificial intelligence and public health: prospects, hype and challenges. | Objectives and importance of the study Applications of artificial intelligence (AI) platforms and technologies to healthcare have been widely promoted as offering revolutionary improvements and efficiencies in clinical practice and health services organisation. Practical applications of AI in public health are now emer... | {
"Day": null,
"MedlineDate": null,
"Month": "Mar",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Preparing the Nurses of the Future to Address Health Disparities. | Nurses need competence and confidence to assess for Social Determinants of Health (SDOH) and meaningfully mitigate the barriers they present to health. While acute care nurses are in an ideal position to address SDOH and optimize the continuum of care, evidence suggests they lack the necessary knowledge and confidence ... | {
"Day": null,
"MedlineDate": "2025 Jul-Sep 01",
"Month": null,
"Season": null,
"Year": null
} | reinforcement learning |
A bidirectional reasoning approach for blood glucose control via invertible neural networks. | Despite the profound advancements that deep learning models have achieved across a multitude of domains, their propensity to learn spurious correlations significantly impedes their applicability to tasks necessitating causal and counterfactual reasoning. In this paper, we propose a Bidirectional Neural Network, which i... | {
"Day": "27",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Leveling Up: Harnessing Cutting-Edge Technology to Enhance Oncology Education and Learning. | The integration and utilization of digital media, gamified learning strategies, and artificial intelligence (AI) are fundamentally transforming the landscape of oncology education and learning. These technologies collectively enhance knowledge dissemination, facilitate professional networking and mentoring, and enrich ... | {
"Day": null,
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Suitability of just-in-time adaptive intervention in post-COVID-19-related symptoms: A systematic scoping review. | Patients with post-COVID-19-related symptoms require active and timely support in self-management. Just-in-time adaptive interventions (JITAI) seem promising in meeting these needs, as they aim to provide tailored interventions based on patient-centred measures. This systematic scoping review explores the suitability a... | {
"Day": null,
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Knowledge Transfer and Reinforcement Based on Biunbiased Neural Network: A Novel Solution for Open-Set Fault Transfer Diagnosis. | Fault transfer diagnosis is a key technology to ensure the reliability and safety of industrial systems, the core of which is to identify the health status of the equipment among different working conditions with multiclassification methods. However, most of them are based on a closed-set assumption that the label spac... | {
"Day": "29",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Comparative Analysis of Feature Extraction Methods and Machine Learning Models for Predicting Osteoporosis Prevalence. | This study systematically examined the impact of three feature selection techniques (Boruta, Extreme gradient boosting (XGBoost), and Lasso) for optimizing four machine learning models (Random forest (RF), XGBoost, Logistic regression (LR), and Support vector machine (SVM)) in predicting bone density prevalence. Our fi... | {
"Day": "29",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
An examination of how reward associations facilitate and impair Stroop performance. | Rewarded stimuli are prioritized by the attentional system. Behavioral performance is improved when the task-relevant dimension is tied to a potential reward but is impaired when the irrelevant dimension is reward related. Within the rewarded Stroop task, the facilitation (reward responsiveness) and impairment (modulat... | {
"Day": "29",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Simulation-based infection prevention and control training for medical and healthcare students: a systematic review. | Infection prevention and control education has traditionally been conducted in a lecture-based manner, and simulation-based educational strategies have become increasingly prevalent in the field of medical education in recent years. This systematic review aimed to compare the effectiveness of the simulation-based and t... | {
"Day": null,
"MedlineDate": null,
"Month": null,
"Season": null,
"Year": "2025"
} | reinforcement learning |
The Role of the Dietitian Within a Day Programme for Adolescent Anorexia Nervosa: A Reflexive Thematic Analysis of Child and Adolescent Eating Disorder Clinician Perspectives. | Family therapy for anorexia nervosa (FT-AN) is the first-line outpatient treatment for young people with anorexia nervosa (AN) in the UK. However, some require more intensive interventions, such as day programmes (DPs), which provide structured multidisciplinary care, including nutritional rehabilitation. Despite the i... | {
"Day": null,
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Impaired reinforcement learning and coding of prediction errors in patients with cerebellar degeneration - a study with EEG and voxel-based morphometry. | This study investigated cerebellar involvement in reinforcement learning and prediction error (RL-PE) processing. Participants with pure cerebellar degeneration and demographically matched healthy controls performed a probabilistic feedback-based learning task while brain activity was recorded using electroencephalogra... | {
"Day": "28",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Scalable and robust machine learning framework for HIV classification using clinical and laboratory data. | Human Immunodeficiency Virus (HIV) is a retrovirus that weakens the immune system, increasing vulnerability to infections and cancers. HIV spreads primarily via sharing needles, from mother to child during childbirth or breastfeeding, or unprotected sexual intercourse. Therefore, early diagnosis and treatment are cruci... | {
"Day": "28",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Basal ganglia deep brain stimulation restores cognitive flexibility and exploration-exploitation balance disrupted by NMDA-R antagonism. | Learning thrives on cognitive flexibility and exploration. Subjects with schizophrenia have impaired cognitive flexibility and maladaptive exploration patterns. The basal ganglia-dorsolateral prefrontal cortex (BG-DLPFC) network plays a significant role in learning processes. However, how this network maintains cogniti... | {
"Day": "28",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Diffusion based multi-domain neuroimaging harmonization method with preservation of anatomical details. | In multi-center neuroimaging studies, the technical variability caused by the batch differences could hinder the ability to aggregate data across sites, and negatively impact the reliability of study-level results. Recent efforts in neuroimaging harmonization have aimed to minimize these technical gaps and reduce techn... | {
"Day": "26",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Motivational and Self-Regulatory Factors Associated With Yearning and Prolonged Grief Symptoms. | Enhanced motivational sensitivity to reward is associated with several psychiatric conditions, including prolonged grief disorder (PGD). Although reasons for this association remain unclear, it is possible that individuals higher in reward sensitivity are more prone to yearning for a lost loved one, especially if they ... | {
"Day": "01",
"MedlineDate": null,
"Month": "Jun",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Direct and indirect striatal projecting neurons exert strategy-dependent effects on decision-making. | The striatum plays a key role in decision-making, with its effects varying with anatomical location and direct and indirect pathway striatal projecting neuron (d- and iSPN) populations. Using a mouse gambling task with a reinforcement-learning model, we described individual decision-making profiles as a combination of ... | {
"Day": "30",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Learning coordinated badminton skills for legged manipulators. | Coordinating the motion between lower and upper limbs and aligning limb control with perception are substantial challenges in robotics, particularly in dynamic environments. To this end, we introduce an approach for enabling legged mobile manipulators to play badminton, a task that requires precise coordination of perc... | {
"Day": "28",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Intelligent penetration testing method for power internet of things systems combining ontology knowledge and reinforcement learning. | With the application of new-generation information technologies such as big data, artificial intelligence, and the energy Internet in Power Internet of Things (IoT) systems, a large number of IoT terminals, acquisition terminals, and transmission devices have achieved integrated interconnection and comprehensive inform... | {
"Day": null,
"MedlineDate": null,
"Month": null,
"Season": null,
"Year": "2025"
} | reinforcement learning |
A Reinforcement Learning Control Framework Based on Scalable Graph Transformer for Large-Scale Fuzzy Job Shop Scheduling Problems. | The job shop scheduling problem (JSSP) is a classic NP-hard problem. This article focuses on a realistic variant of the JSSP incorporating fuzzy processing times, with the objective of minimizing the maximum completion time. We propose a proximal policy optimization with graph transformer (GT-PPO) algorithm, which leve... | {
"Day": "28",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Even with exposure to errors, motor imagery cannot update internal models. | Recent evidence suggests that motor imagery is insufficient for updating internal models, essential for predicting and refining overt movement outcomes. The covert nature of motor imagery limits exposure to errors, perhaps preventing the updating of internal models. To explore this, 90 participants were exposed to a pr... | {
"Day": "28",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Multimodal Machine Learning Analysis of GaSe Molecular Beam Epitaxy Growth Conditions. | Autonomous synthesis platforms integrating machine learning with in situ diagnostics have the potential to revolutionize thin-film growth by enabling real-time process optimization and reducing the need for manual tuning. However, their application to molecular beam epitaxy (MBE) remains underdeveloped. Here, we presen... | {
"Day": "28",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Lessons Learned in the Management of Eclampsia: A Retrospective Observational Study in Pregnant Women. | Background Eclampsia is a critical obstetric emergency associated with significant maternal and fetal mortality and morbidity. This retrospective observational study assesses the clinical characteristics, management strategies, and findings in eclamptic patients, emphasizing lessons learnt from treatment delays and the... | {
"Day": null,
"MedlineDate": null,
"Month": "Apr",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Empathy and cultural humility: Caribbean medical students' experience in Taiwan's Silent Teacher family interviews. | International medical students at I-Shou University's School of Medicine for International Students (SMIS) receive Taiwan government-funded scholarships to cultivate skilled and compassionate medical professionals from the Caribbean, Central America, and the Pacific Islands. This study examines the meaningful impact of... | {
"Day": "27",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
A Hybrid Security Framework for Train-to-Ground (T2G) Communication Using DOA-Optimized BPNN Detection, Bayesian Risk Scoring, and RL-Based Response. | With the widespread adoption of wireless communication technologies in modern high-speed rail systems, the Train-to-Ground (T2G) communication system for Electric/Diesel Multiple Units (EMU/DMU) has become essential for train operation monitoring and fault diagnosis. However, this system is increasingly vulnerable to v... | {
"Day": "20",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
The Role of Salivary Diagnostic Techniques in Screening for Active Pulmonary Tuberculosis: A Systematic Review and Meta-Analysis. | Since the World Health Organization (WHO) issued guidelines for developing a non-sputum test for active tuberculosis (TB) diagnosis that exhibits similar performance characteristics to sputum-based diagnosis, salivary diagnostic techniques have gained prominence as potential screening tools or adjuncts to existing diag... | {
"Day": "24",
"MedlineDate": null,
"Month": "Apr",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Modeling the Structure-Property Linkages Between the Microstructure and Thermodynamic Properties of Ceramic Particle-Reinforced Metal Matrix Composites Using a Materials Informatics Approach. | The application of ceramic particle-reinforced metal matrix composites (CPRMMCs) in the nuclear power sector is primarily dependent on their mechanical and thermal properties. A comprehensive understanding of the structure-property (SP) linkages between microstructures and macroscopic properties is critical for optimiz... | {
"Day": "15",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Synaptic Plasticity and Memory Retention in ZnO-CNT Nanocomposite Optoelectronic Synaptic Devices. | This study presents the fabrication and characterization of ZnO-CNT composite-based optoelectronic synaptic devices via a sol-gel process. By incorporating various concentrations of CNTs (0-2.0 wt%) into ZnO thin films, we investigated their effects on synaptic behaviors under ultraviolet (UV) stimulation. The CNT addi... | {
"Day": "15",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Tensile Strength Estimation of UHPFRC Based on Predicted Cracking Location Using Deep Learning. | Ultra-high-performance fiber-reinforced concrete (UHPFRC) exhibits exceptional tensile properties, but its tensile strength is highly dependent on fiber distribution, orientation, and count, making accurate strength estimation challenging. This study introduces a novel approach in which tensile strength estimation is a... | {
"Day": "12",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Deep Reinforcement Learning for CT-Based Non-Invasive Prediction of SOX9 Expression in Hepatocellular Carcinoma. | <b>Background:</b> The transcription factor SOX9 plays a critical role in various diseases, including hepatocellular carcinoma (HCC), and has been implicated in resistance to sorafenib treatment. Accurate assessment of SOX9 expression is important for guiding personalized therapy in HCC patients; however, a reliable no... | {
"Day": "15",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Defective Intracortical Inhibition as a Marker of Impaired Neural Compensation in Amputees Undergoing Rehabilitation. | <b>Background/Objectives</b>: Lower-limb amputation (LLA) leads to disability, impaired mobility, and reduced quality of life, affecting 1.6 million people in the USA. Post-amputation, motor cortex reorganization occurs, contributing to phantom limb pain (PLP). Transcranial magnetic stimulation (TMS) assesses changes i... | {
"Day": "22",
"MedlineDate": null,
"Month": "Apr",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Antimicrobial Susceptibility Profiles of <i>Escherichia coli</i> Isolates from Clinical Cases of Ducks in Hungary Between 2022 and 2023. | <b>Background</b>: Antimicrobial resistance (AMR) poses a growing threat to veterinary medicine and food safety. This study examines <i>Escherichia coli</i> antibiotic resistance patterns in ducks, focusing on multidrug-resistant (MDR) strains. Understanding resistance patterns and predicting MDR occurrence are critica... | {
"Day": "10",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Immunogenic cell death biomarkers for sepsis diagnosis and mechanism via integrated bioinformatics. | Immunogenic cell death (ICD) has been implicated in sepsis, a condition with high mortality, through mechanisms involving endoplasmic reticulum stress and other pathophysiological pathways. This study aimed to identify and validate ICD-related biomarkers for sepsis diagnosis and to elucidate their underlying mechanisms... | {
"Day": "27",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
A distributional reinforcement learning model for optimal glucose control after cardiac surgery. | This study introduces Glucose Level Understanding and Control Optimized for Safety and Efficacy (GLUCOSE), a distributional offline reinforcement learning algorithm for optimizing insulin dosing after cardiac surgery. Trained on 5228 patients, tested on 920, and externally validated on 649, GLUCOSE achieved a mean esti... | {
"Day": "27",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Integrating machine learning and symbolic regression for predicting damage initiation in hybrid FRP bolted connections. | The increasing adoption of machine learning (ML) in fiber-reinforced polymer (FRP) composite design has led to a reliance on black-box models, which achieve high predictive accuracy but lack interpretability. Python symbolic regression (PySR) offers a solution by deriving explicit equations that reveal the governing me... | {
"Day": "27",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
The Difficulty, and Power, of Slowing Down. | Primary care physicians often feel pressure to rush through the seemingly endless patient care and administrative work we are faced with daily. In residency, I learned how to be efficient, how to juggle multiple things at once, and how to think quickly: all valuable skills. I received positive reinforcement for taking ... | {
"Day": "27",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Nucleus Accumbens Dopamine Encodes the Trace Period during Appetitive Pavlovian Conditioning. | Pavlovian conditioning tasks have been used to identify the neural systems involved with learning cue-outcome relationships. In delay conditioning, the conditioned stimulus (CS) overlaps or co-terminates with the unconditioned stimulus (US). Prior studies demonstrate that dopamine in the nucleus accumbens (NAc) regulat... | {
"Day": null,
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Decision Threshold Learning in the Basal Ganglia for Multiple Alternatives. | In recent years, researchers have integrated the historically separate, reinforcement learning (RL), and evidence-accumulation-to-bound approaches to decision modeling. A particular outcome of these efforts has been the RL-DDM, a model that combines value learning through reinforcement with a diffusion decision model (... | {
"Day": "23",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Beyond Accuracy: Evaluating certainty of AI models for brain tumour detection. | Brain tumors pose a severe health risk, often leading to fatal outcomes if not detected early. While most studies focus on improving classification accuracy, this research emphasizes prediction certainty, quantified through loss values. Traditional metrics like accuracy and precision do not capture confidence in predic... | {
"Day": "26",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
Advancing Understanding of Treatment Response in Schizophrenia With Psychosis Using a Novel Dynamic Reward Task. | Schizophrenia presents significant treatment challenges, particularly due to medication resistance observed in some patients receiving antipsychotics. Emerging research suggests a potential link between impaired reinforcement learning, the severity of psychotic symptoms, and dopamine system abnormalities. Exploring rei... | {
"Day": "27",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
A measure of event-related potentials (ERP) indices of motivation during cycling. | Although motivation is a central aspect of the practice of a physical activity, it is a challenging endeavour to predict an individual's level of motivation during the activity. The objective of this study was to assess the feasibility of measuring motivation through brain recording methods during physical activity, wi... | {
"Day": null,
"MedlineDate": null,
"Month": null,
"Season": null,
"Year": "2025"
} | reinforcement learning |
Cyber security Enhancements with reinforcement learning: A zero-day vulnerabilityu identification perspective. | A zero-day vulnerability is a critical security weakness of software or hardware that has not yet been found and, for that reason, neither the vendor nor the users are informed about it. These vulnerabilities may be taken advantage of by malicious people to execute cyber-attacks leading to severe effects on organizatio... | {
"Day": null,
"MedlineDate": null,
"Month": null,
"Season": null,
"Year": "2025"
} | reinforcement learning |
Adapting to loss: A computational model of grief. | Grief is a reaction to loss that is observed across human cultures and even in other species. While the particular expressions of grief vary significantly, universal aspects include experiences of emotional pain and frequent remembering of what was lost. Despite its prevalence, and its obvious nature, considering grief... | {
"Day": "26",
"MedlineDate": null,
"Month": "May",
"Season": null,
"Year": "2025"
} | reinforcement learning |
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