Search Results (11,133)

Diabetes mellitus is primarily caused by the loss or malfunction of insulin-producing β-cells, and although current therapies improve glycemic control, they do not restore physiologic insulin secretion. Advances in stem cell biology and organoid engineering have led to the development of pancreatic organoids and induced pluripotent stem cell (iPSC)-derived β-cells as promising platforms for disease modeling, drug testing, and regenerative medicine. Pancreatic organoids generated from ductal, acinar, or progenitor populations can recapitulate key anatomical and functional features of native pancreatic tissue, enabling studies of development, injury, and regeneration. In parallel, improvements in iPSC differentiation protocols have produced β-like cells capable of insulin secretion in response to glucose, although achieving full functional maturity remains a challenge. Bioengineering strategies, including biomaterial scaffolds, microfluidic platforms, endothelial co-culture systems, three-dimensional bioprinting, and CRISPR-based genome editing, have enhanced the stability, vascular compatibility, and functional performance of both organoid and iPSC-derived systems. Despite these advances, variability in differentiation efficiency, limited β-cell maturity, and poor long-term survival continue to hinder clinical translation. Together, pancreatic organoids and iPSC-derived β-cells represent complementary platforms that advance fundamental research and support the development of β-cell replacement therapies, with ongoing integration of bioengineering approaches expected to accelerate progress toward reproducible, scalable, and clinically relevant β-cell regeneration. Full article

Nonobstructive coronary artery disease (NOCAD) is increasingly recognized as a heterogeneous condition characterized by diverse pathophysiological mechanisms despite the absence of flow-limiting stenosis. We sought to establish a rule-based dominant imaging phenotype framework integrating functional, structural, and inflammatory dimensions derived from multiparametric coronary computed tomography angiography (CCTA). In this retrospective cohort of 485 patients with NOCAD, CT-derived fractional flow reserve (CT-FFR), quantitative plaque burden and high-risk plaque features, and perivascular fat attenuation index (FAI) were assessed. Using predefined percentile thresholds and hierarchical rules, patients were categorized into function-, structure-, inflammation-dominant, or low-risk phenotypes. During a median follow-up of 36 months, 56 patients (11.5%) experienced major adverse cardiovascular events (MACE). After multivariable adjustment, function dominance was associated with the highest risk (hazard ratio [HR] 4.054, 95% confidence interval [CI] 1.984–8.281; p < 0.001), followed by structure dominance (HR 3.129, 95% CI 1.410–6.944; p = 0.005), whereas isolated inflammation dominance did not show a statistically significant independent association with events, with wide confidence intervals indicating limited precision. These findings suggest a graded pattern of prognostic associations across functional and structural abnormalities in NOCAD and support a phenotype-oriented interpretation of CCTA metrics reflecting distinct biological axes of coronary pathology. Full article

The rapid growth of the elderly population worldwide demands reliable activity recognition technologies to support independent living and continuous health supervision. However, conventional wearable sensor-based human activity recognition (HAR) techniques often fail to capture the complex temporal behaviour and subtle motion patterns characteristic of the elderly. To address these limitations, this study introduces a hybrid deep residual architecture—CNN-CBAM-BiGRU—that integrates convolutional neural networks (CNNs), the convolutional block attention module (CBAM), and bidirectional gated recurrent units (BiGRUs) to improve activity recognition using inertial measurement unit (IMU) data. In the proposed CNN-CBAM-BiGRU framework, CNN layers automatically derive representative features from raw sensor signals, CBAM applies adaptive channel and spatial attention to highlight informative patterns, and BiGRU captures long-range temporal relationships within activity sequences. The approach was evaluated on three benchmark datasets designed for elderly populations—HAR70+, HARTH, and SisFall—covering daily activities and fall events. The proposed model consistently outperforms existing methods across all datasets, achieving accuracies exceeding 96%, F1-scores above 93%, and a fall detection recall of 93.74%, confirming its robustness and suitability for safety-critical monitoring applications. Class-level evaluation indicates excellent recognition of static postures and consistent performance for dynamic actions. Convergence analysis further confirms efficient learning with limited overfitting across datasets. The proposed framework thus provides a robust and accurate solution for wearable-based elderly activity recognition, with strong potential for deployment in fall detection, health monitoring, and ambient assisted living systems. Full article

Cognitive impairment is a prevalent non-motor manifestation of Parkinson’s disease (PD), yet early detection remains limited by the sensitivity of conventional cognitive assessments. Emerging evidence suggests that motor dysfunction, particularly gait and balance abnormalities, reflects underlying cognitive vulnerability. This study examined motor–cognitive associations and evaluated whether motor-derived features can be used to classify low-MoCA status in PD without direct cognitive testing. Data from 102 individuals with PD were analyzed, incorporating clinical assessments, physical function measures, lifestyle factors, and gait-derived biomarkers. Multiple regression identified Unified Parkinson’s Disease Rating Scale Part III, stride length of the more affected side during 360° turning at preferred speed, and maximum ankle jerk on the less affected side during forward walking as independent predictors of Montreal Cognitive Assessment scores, collectively explaining 34.7% of the variance. Network analysis revealed integrative relationships among global motor severity, gait smoothness, and cognitive performance. Using a compact motor-based feature set, logistic regression achieved a mean accuracy of 65.8% and an AUC of 0.737 in classifying low-MoCA status under cross-validation. These findings demonstrate that motor-derived digital biomarkers capture clinically meaningful information about cognitive status in PD and may serve as adjunctive tools for identifying cognitive vulnerability in clinical settings. Full article

Vascular endothelial growth factor (VEGF) is a key regulator of angiogenesis and an established therapeutic target in diseases such as cancer and ocular disorders. However, long-term use of most current anti-VEGF agents is often limited by their associated side effects, including hypertension, bleeding, and gastrointestinal complications. These limitations have stimulated interest in naturally occurring VEGF inhibitors derived from dietary sources, which may offer safer alternatives due to their favorable safety profiles. In this study, we investigated shared structural features of potent VEGFR2 inhibitors, focusing on naturally derived polyphenols. Polyphenols representing multiple structural subclasses were evaluated for their ability to inhibit VEGFR2 kinase activity using an in vitro kinase assay, to suppress VEGF-induced phosphorylation of VEGFR2 and downstream MAPK signaling in endothelial cells by Western blot, and to reduce VEGF-stimulated endothelial cell proliferation. Across all assays, flavonoids with strong VEGFR2 inhibitory activity displayed consistent structural characteristics, including the number and specific positioning of hydroxyl groups on the A- and B-rings, as well as specific structural elements of the C-ring. Our findings provide a strong foundation for further structure–activity relationship (SAR) studies and facilitate identification of key molecular determinants required for VEGFR2 inhibition. Elucidation of these structural patterns may contribute to the development of more effective and safer angiogenesis inhibitors with reduced adverse effects. Full article

Managing blood glucose in type 1 diabetes (T1D) remains a daily clinical challenge, and accurate short-term prediction of glucose levels can meaningfully improve insulin dosing decisions while reducing the risk of dangerous hypoglycaemic episodes. Although numerous machine learning approaches have been proposed for this task, comparing their relative merits is difficult because published studies differ widely in datasets, preprocessing choices, and evaluation criteria. In this work, we address this research gap by benchmarking ten machine learning methods—from a naïve persistence baseline through classical linear regressors, gradient-boosted ensembles, and recurrent neural networks to a novel hybrid that couples LightGBM with stochastic differential equation (SDE)-based glucose–insulin simulation—on two multi-patient datasets comprising 34 T1D subjects, across prediction horizons of 15, 30, 60, and 120 min. Every method is trained and tested under identical preprocessing and temporal splitting conditions to ensure a fair comparison. The proposed Hybrid LightGBM-SDE model consistently outperforms all alternatives, recording RMSE values of 22.42 mg/dL at 15 min, 28.74 mg/dL at 30 min, 33.89 mg/dL at 60 min, and 37.22 mg/dL at 120 min—an improvement of between 13.6% and 27.0% relative to standalone LightGBM. At the clinically important 30 min horizon, 99.7% of predictions lie within the acceptable A and B zones of the Clarke Error Grid. Wilcoxon signed-rank tests confirm that performance differences are statistically significant (p < 10⁻¹⁰), and SHAP-based analysis shows that the SDE-derived simulation features are among the most influential predictors, especially at longer horizons. All source code and evaluation scripts are publicly released to support reproducibility. Due to temporary data access constraints, all experiments reported here use physics-based synthetic datasets generated from the Bergman minimal model, replicating the structural properties of the D1NAMO and HUPA-UCM collections; validation on the original clinical recordings is planned. Among the two synthetic datasets, the D1NAMO-equivalent cohort (nine patients) proves more challenging, with systematically higher per-patient RMSE variance. The clinically acceptable prediction accuracy at the 30 min horizon (99.7% in Clarke zones A + B) suggests potential for integration into insulin dosing decision-support systems. Full article

Activating PIK3CA mutations occur in approximately 40% of hormone receptor-positive (HR+)/HER2-negative breast cancers and represent a major driver of endocrine resistance. The PI3Kα-selective inhibitor alpelisib, in combination with fulvestrant, significantly improves progression-free survival in patients with PIK3CA-mutant disease, as demonstrated in the SOLAR-1 trial. However, this therapeutic strategy is frequently complicated by treatment-induced hyperglycemia, a metabolic disturbance that promotes oxidative stress, mitochondrial dysfunction, and inflammatory signaling, thereby increasing cardiovascular vulnerability. Sodium–glucose cotransporter-2 (SGLT2) inhibitors have emerged as cardiometabolic modulators with benefits extending beyond glucose lowering. In this study, we used a human cardiomyocyte in vitro model designed to recapitulate the hyperglycemic metabolic milieu observed in breast cancer patients receiving PI3Kα-targeted therapy, to investigate whether the SGLT2 inhibitor dapagliflozin directly protects cardiomyocytes from alpelisib- and fulvestrant-induced injury. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) were cultured under hyperglycemic conditions (25 mM glucose) to mimic the metabolic environment associated with PI3Kα inhibitor-induced dysglycemia. Cells were exposed to alpelisib (100 nM) and fulvestrant (100 nM), alone or in combination, in the absence or presence of dapagliflozin (1 μM). Cardiomyocyte viability was assessed using the MTS assay, mitochondrial function by TMRM-based mitochondrial membrane potential (ΔΨm) measurements, and apoptosis by caspase-3 quantification. Cardiomyocyte injury was evaluated by release of cardiac troponin I and heart-type fatty acid binding protein (H-FABP). Lipid peroxidation markers (MDA and 4-HNE) were measured to assess oxidative membrane damage. Intracellular inflammasome-related signaling (NLRP3 and MyD88) and secreted inflammatory mediators (IL-1β, IL-18, IL-6, TNF-α, and CCL2) were quantified by ELISA. Exposure to alpelisib, particularly in combination with fulvestrant, significantly reduced cardiomyocyte viability, induced mitochondrial depolarization, and increased caspase-3-mediated apoptotic signaling. These alterations were accompanied by elevated lipid peroxidation (MDA and 4-HNE) and increased release of cardiac injury biomarkers (troponin I and H-FABP). Alpelisib-based treatments also activated inflammasome-related signaling, as indicated by increased intracellular NLRP3 and MyD88 levels and enhanced secretion of pro-inflammatory mediators (IL-1β, IL-18, IL-6, TNF-α, and CCL2). Co-treatment with dapagliflozin significantly attenuated these alterations, preserving mitochondrial membrane potential, reducing apoptotic signaling, limiting oxidative membrane damage, and suppressing inflammatory cytokine release. This study provides evidence that alpelisib-based therapy under hyperglycemic conditions is associated with oxidative, mitochondrial, and inflammatory stress responses in human cardiomyocytes, recapitulating key features of cardiometabolic stress relevant to PI3Kα-targeted therapy. Importantly, dapagliflozin markedly attenuated these alterations, supporting a potential cardioprotective role that may extend beyond glycemic control. These findings provide a mechanistic rationale for further investigation of SGLT2 inhibition as a cardiometabolic protective strategy in patients receiving PI3Kα inhibitor-based cancer therapy. Full article

The transformation of waste plastics into hydrogen and functional carbon (C) materials represents a promising pathway for achieving both resource recycling and the production of value-added products. Owing to their tunable physicochemical properties, plastic-derived carbons have attracted significant attention in electrochemical energy storage applications. Various C nanostructures, including graphene, porous C, hard C, and C nanotubes (CNTs), can be generated from discarded plastics through thermochemical processes. The electrochemical performance of these materials is closely governed by their structural characteristics, such as pore architecture, specific surface area, heteroatom doping, surface functionalities, and dimensional morphology. This review aims to provide a comprehensive and systematic overview of the conversion of waste plastics into functional C nanomaterials via thermochemical routes, particularly catalytic pyrolysis and carbonization. The resulting C nanostructures are systematically categorized based on their dimensional architectures (0D, 1D, 2D, and 3D) and comparatively analyzed in terms of their structural features and electrochemical performance. Emphasis is placed on the transformation of diverse plastic feedstocks into high-value C materials with tailored dimensional architectures, including graphene, CNTs, C nanospheres, C nanosheets, porous carbons, and their composites. Furthermore, recent progress and critical challenges in utilizing these materials for electrochemical energy storage systems, such as supercapacitors and rechargeable batteries (Li-ion, Na-ion, K-ion, Li-S, and Zn-air), are discussed. Distinct from previous reports, this review highlights the correlation between thermochemical processing strategies, resulting structural features, and electrochemical performance, providing new insights into the rational design of high-performance C materials. These findings are expected to facilitate the advancement of sustainable energy storage technologies while contributing to effective plastic waste valorization. Full article

Background/Objectives: Cancer remains one of the leading global health burdens, mainly because of the lack of specificity and off-target toxicity associated with conventional therapeutic approaches. To move toward more efficient anticancer drug discovery, we have developed an advanced machine-learning-based architecture that allows for predictive modeling of anticancer small molecules. Methods: A total of 3600 compounds with experimentally validated IC₅₀ values were systematically processed to derive a comprehensive suite of molecular representations comprising 2D physicochemical descriptors, structural fingerprints, and hybrid descriptor sets generated via the Mordred and PaDEL frameworks. A total of six machine learning algorithms—Random Forest (RF), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), Extra-Trees classifier (ET), Adaptive Boosting (AdaBoost), and Light Gradient Boosting Machine (LightGBM)—were trained and benchmarked via a rigorous model evaluation protocol incorporating 10-fold cross-validation along with multiple performance metrics. Ensemble voting strategies were also examined to assess potential performance. Result: Of all configurations, the XGB-Hybrid architecture emerged as the most robust and generalizable classifier with an AUC of 0.88 and accuracy of 79.11% on the independent test set. To ensure interpretability and mechanistic insight, SHAP-based feature analysis was conducted, by which feature contributions could be quantified and the molecular determinants most influential for anticancer activity discrimination were revealed. Altogether, the current study establishes an XGB-Hybrid framework as technically rigorous, interpretable, and high-performance predictive modeling with the ability to accelerate early-stage anticancer small molecule identification. Conclusions: The study has brought into focus the transformational effect of machine learning in modern computational oncology and rational drug design pipelines. Full article

Background/Objectives: This study proposes CMT-BUSNet, a hybrid architecture integrating CNN, Mamba, and Transformer branches for breast ultrasound tumor segmentation with built-in explainability. Methods: CMT-BUSNet employs a CNN-anchored hierarchical parallel encoder where Mamba and Transformer branches process CNN-derived features in parallel, fused through an Adaptive Feature Fusion Module (AFFM) with Dense Nested Decoder and Boundary-Aware Composite Loss. Five-fold cross-validation on BUS-BRA (N = 1875) compared nine architectures under identical protocols, plus nnU-Net v2 trained with its default self-configuring protocol as a benchmark. External evaluation used the BUSI dataset (N = 647). Results: CMT-BUSNet achieved DSC = 0.9037 ± 0.0047 on BUS-BRA with higher boundary delineation metrics than nnU-Net v2, which was trained under a different self-configuring protocol (B-IoU: 0.611 vs. 0.557; HD95: 10.07 vs. 13.54 pixels), despite nnU-Net’s marginally higher DSC (0.9108). On BUSI, CMT-BUSNet (DSC = 0.6709) yielded higher scores than nnU-Net (0.5579) across all metrics under zero-shot transfer, though the two methods were trained under different protocols. Training-based ablation confirmed each component’s contribution, and quantitative XAI validation demonstrated attribution faithfulness (nEAR = 2.82×) and uncertainty–error correlation (r = 0.39). Conclusions: CMT-BUSNet achieves competitive accuracy with higher boundary metrics, preliminary cross-dataset transferability, and built-in interpretability relative to nnU-Net (noting different training protocols). Internal validation folds are image-disjoint but not guaranteed to be patient-disjoint, which should be considered when interpreting the reported metrics. Multicenter validation is required before clinical deployment. Full article

Against the backdrop of global coral reef degradation, benthic resource structure is shifting from coral dominance to turf algae and detritus-dominated epilithic algal matrix (EAM). As a typical detritivorous reef fish, Ctenochaetus striatus (Quoy & Gaimard, 1825) plays an important ecological role in regulating the functioning of degraded coral reef ecosystems. Using stable isotope analysis (δ¹³C and δ¹⁵N), this study systematically compared the trophic niche characteristics of different size classes of C. striatus across four reef habitats in the Xisha Islands, South China Sea, representing a gradient of disturbance (Qilianyu Island > Lingyang Reef > North Reef > Langhua Reef), in order to elucidate habitat-specific ontogenetic shifts and their adaptive features. The results showed that C. striatus from Qilianyu Island and Lingyang Reef exhibited overall higher δ¹⁵N values, suggesting an overall pattern consistent with stronger nitrogen enrichment at the more disturbed reefs, whereas individuals from Langhua Reef had significantly lower δ¹³C values, indicating a stronger reliance on offshore-derived carbon pathways. Across size classes, the trophic niche area (SEAc) and intraspecific trophic heterogeneity, measured as mean nearest neighbor distance and standard deviation of nearest neighbor distance, of populations from Qilianyu Island, Lingyang Reef, and North Reef generally decreased with increasing body size, revealing a pattern of trophic convergence toward core resources. In contrast, the Langhua Reef population exhibited a distinct expansion–contraction pattern, suggesting flexible resource use across developmental stages under conditions of low human disturbance and high resource heterogeneity. Although smaller size classes generally showed high probabilities of niche overlap among reefs, overlap declined markedly in the largest size class, with most values falling below 50%, indicating that resource assimilation strategies increasingly reflected reef-specific resource backgrounds. These findings demonstrate that ontogenetic trophic niche shifts in C. striatus are not fixed, but are highly dependent on local resource context and habitat conditions. In degraded reefs with simplified resource structure, individuals tend to converge on core resource spectra to maintain survival, whereas in healthier reefs with greater habitat heterogeneity, they tend to show greater variation in major food sources and resource use. This study provides a theoretical basis for coral reef ecological restoration. Full article

The European Middle Pleistocene represents a critical spatiotemporal interval in human evolution, marked by increasing morphological variability and ongoing debate regarding the evolutionary processes leading to the emergence of Neandertals. In particular, it remains unclear whether this variability reflects the coexistence of multiple evolutionary lineages within Europe or structured variation within a single, evolving lineage. Within this context, the site of Bilzingsleben (Thuringia, Germany) provides a key contribution to discussions of European Middle Pleistocene population structure. This study presents a detailed morphological assessment of the Bilzingsleben E7 mandibular fragment, integrating qualitative anatomical observations with quantitative analyses of discrete characters. The Bilzingsleben mandible was examined directly and evaluated within a broad comparative framework including European Middle Pleistocene hominins, Neandertals, and selected African and Asian specimens. Multivariate analyses, including Principal Coordinates Analysis (PCoA) and neighbor-joining cluster analysis based on Gower distances, were used to explore patterns of morphological affinity. Qualitative analysis indicates that the Bilzingsleben mandible exhibits a mosaic combination of predominantly primitive features—such as multiple mental foramina, marked lateral relief of the corpus, and a weakly developed submandibular fossa—together with a limited number of incipiently derived traits, including posterior extension of the corpus and a downward orientation of the digastric fossae. Quantitative results consistently place Bilzingsleben within the morphological variability of European Middle Pleistocene hominins but outside the compact Neandertal cluster. In the PCoA, Bilzingsleben occupies an intermediate (PCo1) and peripheral position (PCo2), contrasting with more centrally positioned specimens such as Mauer. Taken together, these results support an interpretation of Bilzingsleben as part of a European Middle Pleistocene set of populations exhibiting mosaic morphology, rather than considering Bilzingsleben as evidence for a distinct evolutionary lineage. When integrated with evidence from other anatomical elements from Bilzingsleben, the mandibular morphology supports interpreting this population within the broader evolutionary context of the Neandertal lineage. Full article

Lithium is a rare metal widely used in the renewable energy industry. The Altyn region in Xinjiang, China, contains abundant granitic pegmatite-type lithium resources; however, the deeply incised and complex terrain limits the accuracy of conventional two-dimensional remote sensing approaches for dike identification and segmentation. To address this limitation, a remote sensing segmentation method incorporating terrain information was proposed. A digital elevation model (DEM) derived from LiDAR data, together with its associated topographic factors, was integrated into the Spatial–Spectral Mamba framework to enable the joint utilization of spectral and terrain features. Rather than performing explicit three-dimensional geometric modeling, the proposed approach enhances a two-dimensional segmentation framework by introducing elevation-derived information, allowing the model to capture terrain-related spatial variations of pegmatite dikes. This design enables improved representation of both the planar distribution and terrain-influenced morphological characteristics of dikes under deeply incised conditions. The Xichanggou lithium deposit in the Altyn region is a large-scale, economically valuable pegmatite-type lithium deposit, and was therefore selected as the study area for pegmatite dike segmentation. The results demonstrated that, compared with conventional two-dimensional approaches and representative machine learning methods, the proposed method achieved higher segmentation accuracy in complex terrain. Improvements were also observed in the continuity and spatial consistency of the extracted dike patterns. Field verification indicated that the major pegmatite dikes delineated by the model were highly consistent with their actual surface exposures. Sampling analyses further confirmed the validity and reliability of the identification results. Overall, the terrain-integrated remote sensing segmentation approach exhibited good applicability and robustness under deeply incised and complex geomorphological conditions. Full article

Reliable multi-sensor bearing fault diagnosis is challenged by temporal leakage caused by window-level random splitting, limited modeling of cross-sensor dependencies, and inadequate integration of raw temporal dynamics with time-frequency representations. To address these issues, this study proposes a leakage-resistant multi-sensor diagnosis framework that combines a partition-before-windowing evaluation protocol with adaptive time-frequency graph learning and reliability-aware fusion. Continuous vibration records are first divided into disjoint temporal regions with guard intervals and overlap auditing to suppress time-neighbor leakage. The model then extracts complementary features from a raw-signal branch and a dual-resolution log-STFT branch, while adaptive graph learning captures sample-dependent inter-sensor couplings and sensor reliability weighting highlights informative channels. A cross-gated fusion module further integrates temporal and graph-domain representations in a sample-adaptive manner for final classification. Experiments on a reconstructed nine-class benchmark derived from the HUSTbearing dataset show that the proposed method achieves a Macro-Accuracy of 0.973, a Macro-Recall of 0.964, and a Macro-F1 of 0.954, outperforming representative raw-signal and STFT-based baselines under the same leakage-resistant protocol. These results demonstrate that jointly modeling multi-scale time-frequency structure, dynamic sensor relationships, and reliable evaluation yields an effective and interpretable solution for intelligent bearing fault diagnosis under complex operating conditions. Full article

This study introduces a physics-informed machine learning framework for predicting transient emissions and energy variables in a retrofitted heavy-duty diesel vehicle. It merges data-driven modeling with physically derived features for reliable real-world analysis. A Random Forest regressor is trained on a public dataset (26 trips from one instrumented vehicle) to predict CO₂ and NO_x mass rates, exhaust temperature, exhaust mass flow rate, and fuel flow rate from synchronized multi-sensor inputs using past-only, time-lagged features. On held-out trips, exhaust temperature prediction achieves $R^2$ = 0.9997 and $\mathrm{RMSE}$ = 0.53 g/s; for CO₂, with $R^2$ = 0.9985 and $\mathrm{RMSE}$= 0.38 g/s, comparable performance is reported for NO_x, exhaust flow, and fuel rate. The trained model is integrated into a simulation framework to enable the evaluation of alternative operating conditions and powertrain configurations. First, the impact of cold-start versus hot-start operation is assessed, showing cumulative emission penalties of up to +28% for CO₂ and +30% for NO_x. Second, the effect of hybridization is investigated by comparing the baseline thermal configuration with a hybrid electric architecture, resulting in estimated reductions of −12.2% in CO₂ and −10.5% in NO_x emissions. This tool excels in high-fidelity emission prediction and system-level energy analysis, aiding advanced powertrain assessments under realistic driving conditions. Full article