Search Results (13,523)

Lung cancer remains a leading cause of cancer-related mortality. Although reliable multiclass classification of lung lesions from CT imaging is essential for early diagnosis, it remains challenging due to subtle inter-class differences, limited sample sizes, and class imbalance. We propose an Adaptive Attention-Augmented Convolutional Neural Network with Vision Transformer (AACNN-ViT), a hybrid framework that integrates local convolutional representations with global transformer embeddings through an adaptive attention-based fusion module. The CNN branch captures fine-grained spatial patterns, the ViT branch encodes long-range contextual dependencies, and the adaptive fusion mechanism learns to weight cross-representation interactions to improve discriminability. To reduce the impact of imbalance, a hybrid objective that combines focal loss with categorical cross-entropy is incorporated during training. Experiments on the IQ-OTH/NCCD dataset (benign, malignant, and normal) show consistent performance progression in an ablation-style evaluation: CNN-only, ViT-only, CNN-ViT concatenation, and AACNN-ViT. The proposed AACNN-ViT achieved 96.97% accuracy on the validation set with macro-averaged precision/recall/F1 of 0.9588/0.9352/0.9458 and weighted F1 of 0.9693, substantially improving minority-class recognition (Benign recall 0.8333) compared with CNN-ViT (accuracy 89.09%, macro-F1 0.7680). One-vs.-rest ROC analysis further indicates strong separability across all classes (micro-average AUC 0.992). These results suggest that adaptive attention-based fusion offers a robust and clinically relevant approach for computer-aided lung cancer screening and decision support. Full article

In recent years, the green financial market has been exhibiting heightened volatility daily, largely due to policy changes and economic shifts. To explore the broader potential of predictive modeling in the context of short-term volatility time series, this study analyzes how fine-tuning hyperparameters in predictive models is essential for improving short-term forecasts of market volatility, particularly within the rapidly evolving domain of green financial markets. While traditional econometric models have long been employed to model market volatility, their application to green markets remains limited, especially when contrasted with the emerging potential of machine-learning and deep-learning approaches for capturing complex dynamics in this context. This study evaluates the performance of several data-driven forecasting models starting with machine-learning models: regression tree (RT) and support vector regression (SVR), and with deep-learning ones: long short-term memory (LSTM), convolutional neural network (CNN), and gated recurrent unit (GRU) applied to over a decade of daily estimated volatility data coming from three distinct green markets. Predictive accuracy is compared both with and without hyperparameter optimization methods. In addition, this study introduces the quantile loss metric to better capture the skewness and heavy tails inherent in these financial series, alongside two widely used evaluation metrics. This comparative analysis yields significant numerical and graphical insights, enhancing the understanding of short-term volatility predictability in green markets and advancing a relatively underexplored research domain. The study demonstrates that deep-learning predictors outperform machine-learning ones, and that including a hyperparameter tuning algorithm shows consistent improvements across all deep-learning models and for all volatility time series. Full article

Reliable Internet connectivity is essential for latency-sensitive services such as video conferencing, media streaming, and online gaming. Round-trip time (RTT) is a key indicator of network performance and is central to setting retransmission timeout (RTO); inaccurate RTT estimates may trigger unnecessary retransmissions or slow loss recovery. This paper proposes an Enhanced Regularized Extreme Learning Machine (RELM) for RTT estimation that improves generalization and efficiency by interleaving a bidirectional log-step heuristic to select the regularization constant C. Unlike manual tuning or fixed-range grid search, the proposed heuristic explores C on a logarithmic scale in both directions ($\times 10$ and /10) within a single loop and terminates using a tolerance–patience criterion, reducing redundant evaluations without requiring predefined bounds. A custom RTT dataset is generated using Mininet with a dumbbell topology under controlled delay injections (1–1000 ms), yielding 1000 supervised samples derived from 100,000 raw RTT measurements. Experiments follow a strict train/validation/test split (6:1:3) with training-only standardization/normalization and validation-only hyperparameter selection. On the controlled Mininet dataset, the best configuration (ReLU, 150 hidden neurons, $C={10}^2$) achieves $R^2=0.9999$, $MAPE=0.0018$, $MAE=966.04$, and $RMSE=1589.64$ on the test set, while maintaining millisecond-level runtime. Under the same evaluation pipeline, the proposed method demonstrates competitive performance compared to common regression baselines (SVR, GAM, Decision Tree, KNN, Random Forest, GBDT, and ELM), while maintaining lower computational overhead within the controlled simulation setting. To assess practical robustness, an additional evaluation on a public real-world WiFi RSS–RTT dataset shows near-meter accuracy in LOS and mixed LOS/NLOS scenarios, while performance degrades markedly under dominant NLOS conditions, reflecting physical-channel limitations rather than model instability. These results demonstrate the feasibility of the Enhanced RELM and motivate further validation on operational networks with packet loss, jitter, and path variability. Full article

Global warming leads to premature dormancy release and untimely flowering in southern highbush blueberry during winter, resulting in chilling injury and yield losses. However, effective strategies to delay flowering by modulating dormancy progression without compromising fruit quality remain lacking. This study demonstrated through field trials that spraying 1 mg/mL ethephon (ETH) during the early endodormancy stage effectively delayed dormancy release and reduced the bud break rate of spring shoots by approximately 33.92% relative to the control, with no adverse effects on fruit quality. The treatment also reduces sucrose content in floral buds, a change potentially associated with dormancy maintenance. To explore the molecular basis of this process, we examined two ethylene-responsive transcription factors, VcERF112 and VcERF115, previously identified in our laboratory. Their expression was rapidly upregulated following ETH treatment. Heterologous expression of either gene in Arabidopsis delayed both seed germination and flowering, suggesting a conserved growth-suppressive function. Dual-luciferase reporter assays confirmed that VcERF112 and VcERF115 bind to the T2 region (−2310 to −1595 bp) of the VcBRC1 (VcBRANCHED1) promoter and enhance its expression. In contrast, sucrose treatment suppressed VcBRC1 expression. Collectively, these results propose that ethylene may sustain bud dormancy through a coordinated mechanism that operates independently of the classic abscisic acid (ABA)/gibberellins (GA) balance, a relationship not addressed in this study. This mechanism involves the induction of VcERF112/115 to activate VcBRC1, coupled with the reduction in sucrose levels to alleviate its repressive effect on VcBRC1. These findings provide new molecular insights into the ethylene-mediated regulatory network underlying bud dormancy in blueberry. Full article

Network operators increasingly rely on abstracted telemetry (e.g., flow records and time-aggregated statistics) to achieve scalable monitoring of high-speed networks, but this abstraction fundamentally constrains the forensic and security inferences that can be supported from network data. We present a design-time audit framework that evaluates which threat hypotheses become non-supportable as network evidence is transformed from packet-level traces to flow records and time-aggregated statistics. Our methodology examines three evidence layers (L0: packet headers, L1: IP Flow Information Export (IPFIX) flow records, L2: time-aggregated flows), computes a catalog of 13 network-forensic artifacts (e.g., destination fan-out, inter-arrival time burstiness, SYN-dominant connection patterns) at each layer, and maps artifact availability to tactic support using literature-grounded associations with MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK). Applied to backbone traffic from the MAWI Day-In-The-Life (DITL) archive, the audit reveals selectiveinference loss: Execution becomes non-supportable at L1 (due to loss of packet-level timing artifacts), while Lateral Movement and Persistence become non-supportable at L2 (due to loss of entity-linked structural artifacts). Inference coverage decreases from 9 to 7 out of 9 evaluated ATT&CK tactics, while coverage of defensive countermeasures (MITRE D3FEND) increases at L1 (7 → 8 technique categories) then decreases at L2 (8 → 7), reflecting a shift from behavioral monitoring to flow-based controls. The framework provides network architects with a practical tool for configuring telemetry systems (e.g., IPFIX exporters, P4 pipelines) to reason about and provision the minimum forensic coverage. Full article

Gender incongruence significantly impacts the family system, yet the subjective experiences of caregivers remain relatively underexplored. This narrative review synthesizes contemporary evidence regarding psychological distress, emotional burden, and quality of life among caregivers of transgender and gender-diverse individuals. A targeted literature search of PubMed, Scopus, PsycInfo, and Google Scholar (2015–2025) was conducted, identifying 16 studies for thematic synthesis. Results indicate that caregivers consistently report elevated emotional distress, characterized by chronic anxiety, hypervigilance, and ambiguous loss. This burden is primarily driven by prolonged exposure to uncertainty, the weight of complex medical decision-making—particularly regarding fertility and hormone therapy—and vicarious minority stress stemming from social stigma and systemic barriers. Notably, distress is often intensified by sociopolitical climates rather than the transition process itself. Conversely, access to peer support networks, healthcare relationships, and engagement in advocacy emerged as vital protective factors facilitating resilience and adaptive meaning-making. We can conclude that caregiver well-being is a multifaceted process deeply embedded in social and institutional contexts. These findings underscore the necessity of integrated, family-centered medical-psychological models that explicitly support caregivers to ensure more equitable and effective gender-affirming care pathways. Full article

The integration of a user-side energy storage system (ESS) faces notable economic challenges, including high upfront investment, uncertainty in quantifying battery degradation, and fragmented ancillary service revenue streams, which hinder large-scale deployment. Conventional configuration studies often handle capacity planning and operational scheduling at different stages, complicating consistent life-cycle valuation under degradation and multi-service participation. This paper proposes a life-cycle multi-service co-optimization model (LC-MSCOM) to jointly determine ESS power–energy ratings and operating strategies. A unified revenue framework quantifies stacked revenues from time-of-use arbitrage, demand charge management, demand response, and renewable energy accommodation, while depth of discharge (DoD)-related lifetime loss is converted into an equivalent degradation cost and embedded in the optimization. The model is validated on a modified IEEE benchmark system using real generation and load data. Results show that LC-MSCOM increases net present value (NPV) by 26.8% and reduces discounted payback period (DPP) by 12.7% relative to conventional benchmarks, and sensitivity analyses confirm robustness under discount-rate, inflation-rate, and tariff uncertainties. By coordinating ESS dispatch with distribution network operating limits (nodal power balance, voltage bounds, and branch ampacity constraints), the framework provides practical, investment-oriented decision support for user-side ESS deployment. Full article

Given the increasing demand for rapid emergency management decision-making, which must be both timely and reliable, even slight delays can result in substantial human and economic losses. However, current systems and recent state-of-the-art work often use inflexible rule-based logic that cannot adapt to rapidly changing emergency conditions or dynamically optimise response allocation. As a result, our study presents the Calculation Logic Optimisation Framework (CLOF), a novel data-driven approach that enhances decision-making intelligently and strategically through learning-based predictive and multi-objective optimisation, utilising the 911 Emergency Calls data set, comprising more than half a million records from Montgomery County, Pennsylvania, USA. The CLOF examines patterns over space and time and uses optimised calculation logic to reduce response latency and increase decision reliability. The suggested framework outperforms the standard Decision Tree, Random Forest, Gradient Boosting, and XGBoost baselines, achieving 94.68% accuracy, a log-loss of 0.081, and a reliability score (R²) of 0.955. The mean response time error is reported to have been reduced by 19%, illustrating robustness to real-world uncertainty. The CLOF aims to deliver results that confirm the scalability, interpretability, and efficiency of modern EM frameworks, thereby improving safety, risk awareness, and operational quality in large-scale emergency networks. Full article

Accurate anatomical landmark localization is essential to automate chest X-ray analysis and improve diagnostic reliability. While global context recognition is essential in medical imaging, the inherently high-resolution nature of these images has long made this task particularly difficult. While the U-Net-based heatmap regression methods show strong performance, they still lack explicit modeling of the global spatial relationships among landmarks. To address this limitation, we propose an integrated structural learning framework that captures anatomical correlations across landmarks. The model generates probabilistic heatmaps with U-Net and derives continuous coordinates via soft-argmax. Subsequently, these coordinates, along with their corresponding local feature vectors, are fed into a Graph Neural Network (GNN) to refine the final positions by learning inter-landmark dependencies. Anatomical priors, such as bilateral symmetry and vertical hierarchy, are incorporated into the loss function to enhance spatial consistency. The experimental results show that our method consistently outperforms state-of-the-art models across all metrics, achieving significant improvements in MRE and SDR at 3, 6, and 9 pxl thresholds. This high precision demonstrates the framework’s strong potential to enhance the accuracy and robustness of clinical diagnostic systems. Full article

Background: Several studies have attempted to identify the initiating drivers of small intestinal neuroendocrine tumor (SI-NET) development and the molecular mechanisms underlying their progression and metastatic spread. Previous gene expression studies have used bulk microarrays or RNA sequencing to compare tumor tissue with normal intestinal mucosa. However, the intestine comprises multiple distinct cell types, and bulk analyses are limited by this cellular heterogeneity, which can confound tumor-specific signals. Methods: We performed single-cell RNA sequencing on primary SI-NETs and paired normal mucosa from two patients to directly compare tumor cells with their cells of origin, the enterochromaffin (EC) cells. To minimize type I errors, we applied a two-step validation strategy by overlapping differentially expressed genes with an external single-cell dataset and cross-referencing candidate genes for enteroendocrine expression in the Human Protein Atlas. Results: For further distinction and characterization, ECs were subdivided into serotonergic and non-serotonergic clusters. This analysis revealed that the SI-NET cells are transcriptionally more similar to serotonergic ECs, consistent with serum metabolite profiles derived from clinical parameters. Our analyses uncovered a loss-of-expression program characterized by regulators of epithelial differentiation and in parallel, a gain-of-expression program displayed neuronal signaling gene induction, implicating functional reprogramming toward neuronal-like properties. Together, these specific losses and gains suggest that our patient-derived SI-NETs undergo adaptation through both loss of enteroendocrine functions and acquisition of neurobiological-promoting signaling pathways. Conclusions: These findings nominate candidate drivers for further functional validation and highlight potential therapeutic strategies in our patient cohort, including restoring suppressed Notch signaling and targeting aberrant neuronal signaling networks. However, even with a two-step validation procedure, the modest cohort size limits statistical power and generalizability, particularly for the proposed association to a serotonergic phenotype. Larger, multi-patient single-cell studies are required to confirm these mechanisms and establish their clinical relevance. Full article

In this paper, a microwave metasurface sensor integrated with artificial intelligence (AI) for breast tumor detection is presented. The sensor’s sensitivity is estimated by analyzing shifts in magnitude and the phase of the reflection coefficient ($S_{11}$) obtained from normal and abnormal breast phantoms. The ($S_{11}$) responses of 137 anatomically realistic 3D numerical breast phantoms in standard classes, C1—mostly fatty, C2—scattered fibroglandular, C3—heterogeneously dense, and C4—extremely dense, incorporating different tumor sizes are used as input features. A custom neural network is developed to detect tumor presence using the recorded ($S_{11}$) responses. The model is trained with cross-entropy loss and the AdamW optimizer. The dataset is split into training (70%), validation (15%), and test (15%) sets. The model achieves 99% accuracy, with perfect precision, recall, and F1-score across individual classes. For paired class combinations, accuracies of 71% (C1 with C2) and 65% (C2 with C3) are obtained, while performance degrades to approximately 50% when all four classes are combined. The sensor is fabricated and experimentally validated using two physical breast phantoms, demonstrating reliable detection of a 10 mm tumor. These results highlight the effectiveness of combining microwave metasurface sensing and AI for breast tumor detection. Full article

Wind turbine blades (WTBs) are inevitably exposed to harsh environmental conditions, leading to surface damages such as cracks and corrosion that compromise power generation efficiency. While UAV-based inspection offers significant potential, it frequently encounters challenges in handling irregular defect shapes and preserving fine edge details. To address these limitations, this paper proposes DMR-YOLO, an Improved Wind Turbine Blade Surface Damage Detection Method Based on YOLOv8. The proposed framework incorporates three key innovations: First, a C2f-DCNv2-MPCA module is designed to dynamically adjust feature weights, enabling the model to more effectively focus on the geometric structural details of irregular defects. Secondly, a Multi-Scale Edge Perception Enhancement (MEPE) module is introduced to extract edge textures directly within the network. This approach prevents the decoupling of edge features from global context information, effectively resolving the issue of edge information loss and enhancing the recognition of small targets. Finally, the detection head is optimized using a Re-parameterized Shared Convolution Detection Head (RSCD) strategy. By employing weight sharing combined with Diverse Branch Blocks (DBB), this design significantly reduces computational redundancy while maintaining high localization accuracy. Experimental results demonstrate that DMR-YOLO outperforms the baseline YOLOv8n, achieving a 1.8% increase in mAP@0.5 to 82.2%, with a notable 3.2% improvement in the “damage” category. Furthermore, the computational load is reduced by 9.9% to 7.3 GFLOPs, while maintaining an inference speed of 92.6 FPS, providing an effective solution for real-time wind farm defect detection. Full article

This paper presents a mathematical approach for analyzing the thermal behavior of a dual-permanent-magnet consequent-pole magnetically geared machine. The analytical method, referred to as harmonic modeling, employs a complex Fourier series and the Cauchy product to obtain solutions to the partial differential equations governing the temperature distribution in electrical machines. Unlike lumped-parameter thermal networks that provide only average quantities, the proposed approach enables the prediction of spatial temperature distributions. The machine is further investigated under various operating conditions, including different convection coefficients and loss levels. An 11-pole, 18-slot prototype was evaluated by comparison with finite element method (FEM) simulations. The results demonstrate that the proposed method agreed well with the FEM results, with errors below 10%, while requiring less than 2 s per calculation compared with approximately 20 s for FEM simulations. Full article

Unmanned Aerial Vehicle Communication Networks (UAVCNs) have emerged as a transformative solution to enable resilient, scalable, and infrastructure-independent wireless communication in urban and remote environments. A key challenge in UAVCNs is the optimal placement of Unmanned Aerial Vehicle (UAV) nodes to maximize coverage, connectivity, and overall network performance while minimizing latency, energy consumption, and packet loss. As this node placement problem is NP-hard, numerous meta-heuristic algorithms (MHAs) have been proposed to find near-optimal solutions efficiently. Although research in this area has produced a wide range of meta-heuristic algorithmic solutions, most existing review articles focus on MANETs with terrestrial nodes, while comprehensive reviews dedicated to node placement in UAV communication networks are relatively scarce. This article presents a critical and comprehensive review of meta-heuristic algorithms for UAVCN node placement. Beyond surveying existing methods, it systematically analyzes algorithmic strengths, vulnerabilities, and future research directions, offering actionable insights for selecting effective strategies in diverse UAVCN deployment scenarios. To demonstrate practical applicability, selected hybrid algorithms are evaluated in a reproducible Python framework using computational time and coverage metrics, highlighting their ability to optimize multiple objectives and providing guidance for future UAVCN optimization studies. Full article

To ensure that laser chips meet stringent reliability standards in practical applications, comprehensive limit testing and reliability verification must be performed before deployment. This paper proposes an electroluminescence (EL) imaging-based detection method for Catastrophic Optical Mirror Damage (COMD) and Catastrophic Optical Bulk Damage (COBD). A novel model, PCMBA-YOLO, is developed on the YOLOv12 framework, integrating a Multi-Branch Aided Feature Pyramid Network (MBAFPN) and a Pinwheel Convolution (PConv) structure to enhance weak-signal feature extraction and expand the receptive field with minimal parameters. Furthermore, a Shape-IoU-based regression loss is introduced to model bounding-box shape and scale, improving localization precision and convergence. Experimental results show that PCMBA-YOLO achieves 99.4% mAP@0.5, 97.6% Precision, and 98.7% Recall, with a 14% reduction in parameters compared to the baseline. The proposed method demonstrates superior accuracy, efficiency, and robust generalization, providing a high-performance solution for automated visual inspection in semiconductor manufacturing. Full article