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--- |
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license: cc-by-4.0 |
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language: |
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- en |
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- es |
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- fr |
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- de |
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metrics: |
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- accuracy |
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- f1 |
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tags: |
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- misinformation |
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- engineering |
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- robustness |
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- adversarial |
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datasets: |
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- Stevebankz/Emc |
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--- |
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# Model Card for Engineering Misinformation Detection Models |
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This repository provides a suite of models trained on the **Engineering Misinformation Corpus (EMC)**, introduced in our paper *"The Brittleness of Transformer Feature Fusion: A Comparative Study of Model Robustness in Engineering Misinformation Detection (2025)"*. |
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The models are designed to detect **AI-generated misinformation** in **safety-critical engineering documents**. Each represents a different paradigm: traditional feature-based learning, end-to-end Transformers, and hybrid fusion models. |
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--- |
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## Model Details |
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### Models Included |
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1. **XLM-R (Text Only)** |
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- Transformer-based classifier (XLM-RoBERTa) trained end-to-end on raw text. |
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- Files: `config.json`, `model.safetensors`, `sentencepiece.bpe.model`, `tokenizer_config.json`, `tokenizer.json`. |
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2. **Simple Fusion (Text + Features, Concatenation)** |
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- Combines XLM-R embeddings with a 12-dimensional engineered feature set via naive concatenation. |
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- Files: `fusion_simple.pt`, `scaler.pkl`. |
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3. **Gated Fusion (Text + Features, Dynamic Arbitration)** |
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- Combines XLM-R embeddings with engineered features using a gating mechanism to dynamically arbitrate signals. |
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- Files: `fusion_gated.pt`, `scaler.pkl`. |
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4. **XGBoost + Features (Feature-Only Baseline)** |
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- Lightweight tree-based model trained solely on 12D engineered features. |
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- Files: `xgb_model.json`, `scaler.pkl`. |
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- **Developed by:** Steve Nwaiwu and collaborators |
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- **Model type:** Comparative benchmark (feature-based, Transformer-based, and hybrid fusion approaches) |
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- **Languages:** Primarily English, with experimental subsets in ES/FR/DE |
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- **License:** CC-BY 4.0 |
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- **Finetuned from:** `xlm-roberta-base` (for text-based components) |
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--- |
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## Uses |
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### Direct Use |
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- **Detect AI-generated misinformation** in engineering and technical documentation. |
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- Benchmark adversarial robustness across modeling paradigms. |
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- Educational use for comparing model interpretability and brittleness. |
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### Downstream Use |
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- Adaptation to domain-specific technical corpora (e.g., biomedical, aerospace, safety reports). |
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- Fusion-based approaches can be extended with domain-specific structured features. |
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### Out-of-Scope Use |
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- Not suitable for **general misinformation detection** (e.g., politics, social media). |
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- Not validated for **non-technical or informal text domains**. |
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- Should not be used as a standalone safety system without human oversight. |
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--- |
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## Bias, Risks, and Limitations |
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- **Bias in training corpus**: While curated for engineering, domain coverage may be uneven across subfields. |
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- **Fusion brittleness**: Naive feature fusion models fail under semantic adversarial attacks (e.g., synonym swaps). |
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- **Language limits**: Non-English results were confounded by dataset artifacts; multilingual robustness is not yet validated. |
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### Recommendations |
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- Use **XLM-R** when robustness to adversarial perturbations is critical. |
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- Use **XGBoost** in resource-constrained environments, but pair with human-in-the-loop oversight. |
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- Avoid **naive fusion** in safety-critical deployment. |
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### Training Details |
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- Training Data |
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- **Dataset**: Engineering Misinformation Corpus (EMC) |
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- **Splits**: Train / Validation / Test |
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Includes both real engineering documents and AI-generated misinformation. |
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- Training Procedure |
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- **XLM-R models**: finetuned with AdamW optimizer, learning rate warmup, max length 256. |
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- **Fusion models**: trained with joint optimization of Transformer encoder + fusion classifier. |
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- **XGBoost**: trained with grid-searched hyperparameters (depth, learning rate, estimators). |
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### Technical Specifications |
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- Architectures |
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- XLM-RoBERTa: Transformer encoder (base size) |
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- Simple Fusion: Concatenation of Transformer pooled embedding + 12D features |
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- Gated Fusion: Transformer pooled embedding + gated feature projection (64D) |
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- XGBoost: Gradient-boosted decision trees |
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### Software |
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- PyTorch, Hugging Face Transformers, scikit-learn, XGBoost |
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### Evaluation |
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- Metrics |
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- Macro F1, Micro F1, and AP (Average Precision). |
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- Adversarial Robustness tested under: |
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- Structural perturbations (typos, casing) |
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- Semantic/Cue perturbations (synonym swap, term masking) |
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- Feature-targeted perturbations (numeric & unit corruption) |
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--- |
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## How to Get Started with the Models |
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### Example: Load XGBoost + Features |
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```python |
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import joblib, xgboost as xgb |
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import numpy as np |
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# Load scaler + model |
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scaler = joblib.load("scaler.pkl") |
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model = xgb.XGBClassifier() |
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model.load_model("xgb_model.json") |
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# Example features (12D vector extracted with your pipeline) |
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x = np.array([[120, 30, 5, 0.0, 0.0, 0.05, 10, 3, 1, 0, 12.5, 0.33]]) |
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x_scaled = scaler.transform(x) |
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pred = model.predict(x_scaled) |
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print("Predicted class:", pred[0]) |
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### Example: Load XLM-R (Text Only) |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("your-username/emc-models", subfolder="xlmr_text_only") |
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model = AutoModelForSequenceClassification.from_pretrained("your-username/emc-models", subfolder="xlmr_text_only") |
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text = "For safety, follow the shutdown procedure." |
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inputs = tokenizer(text, return_tensors="pt") |
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outputs = model(**inputs) |
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print(outputs.logits.softmax(-1)) |
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### Example: Load GATED FUSION |
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import torch |
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import joblib |
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import numpy as np |
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import pandas as pd |
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from transformers import AutoTokenizer, XLMRobertaModel |
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# ---- Load tokenizer and scaler ---- |
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tokenizer = AutoTokenizer.from_pretrained("your-username/emc-models/xlmr_text_only") |
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scaler = joblib.load("scaler.pkl") |
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# ---- Define Gated Fusion model (must match training definition) ---- |
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import torch.nn as nn |
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class GatedFusion(nn.Module): |
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def __init__(self, model_name: str, n_feats: int = 12, n_labels: int = 2, feat_proj: int = 64): |
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super().__init__() |
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self.encoder = XLMRobertaModel.from_pretrained(model_name) |
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H = self.encoder.config.hidden_size |
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self.fe_proj = nn.Sequential(nn.Linear(n_feats, feat_proj), nn.ReLU()) |
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self.gate = nn.Sequential( |
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nn.Linear(H + n_feats, 64), nn.ReLU(), |
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nn.Linear(64, 1), nn.Sigmoid() |
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) |
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self.dropout = nn.Dropout(0.1) |
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self.classifier = nn.Linear(H + feat_proj, n_labels) |
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def forward(self, input_ids, attention_mask, feats): |
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out = self.encoder(input_ids=input_ids, attention_mask=attention_mask, return_dict=True) |
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mask = attention_mask.unsqueeze(-1).float() |
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pooled = (out.last_hidden_state * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1e-6) |
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alpha = self.gate(torch.cat([pooled, feats], dim=1)) |
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ef = self.fe_proj(feats) |
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fused = torch.cat([pooled, alpha * ef], dim=1) |
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return self.classifier(self.dropout(fused)) |
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# ---- Load trained weights ---- |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model = GatedFusion("xlm-roberta-base", n_feats=12, n_labels=2).to(device) |
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model.load_state_dict(torch.load("fusion_gated.pt", map_location=device), strict=False) |
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model.eval() |
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# ---- Example input ---- |
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text = "For safety, follow the shutdown procedure." |
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features = np.array([[120, 30, 5, 0.0, 0.0, 0.05, 10, 3, 1, 0, 12.5, 0.33]]) # replace with your feature extractor |
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features_scaled = scaler.transform(features) |
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# ---- Tokenize text ---- |
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enc = tokenizer(text, truncation=True, padding="max_length", max_length=256, return_tensors="pt") |
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input_ids = enc["input_ids"].to(device) |
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attn_mask = enc["attention_mask"].to(device) |
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feats = torch.tensor(features_scaled, dtype=torch.float32).to(device) |
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# ---- Run model ---- |
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with torch.no_grad(): |
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logits = model(input_ids, attn_mask, feats) |
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probs = torch.softmax(logits, dim=-1).cpu().numpy()[0] |
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print("Prediction probs:", probs) |
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print("Predicted class:", probs.argmax()) # 0 = Real, 1 = Misinformation |
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# ---- Citation ----- |
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@article{NWAIWU2025107783, |
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title = {The brittleness of transformer feature fusion: A comparative study of model robustness in engineering misinformation detection}, |
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journal = {Results in Engineering}, |
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volume = {28}, |
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pages = {107783}, |
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year = {2025}, |
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issn = {2590-1230}, |
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doi = {https://doi.org/10.1016/j.rineng.2025.107783}, |
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url = {https://www.sciencedirect.com/science/article/pii/S2590123025038368}, |
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author = {Steve Nwaiwu and Nipat Jongsawat and Anucha Tungkasthan}} |
