Search Results (928)

Recent advancements in artificial intelligence have driven significant progress in intelligent anti-jamming communications. However, existing methods still face two major limitations: reinforcement learning-based models often suffer from slow convergence, while knowledge graph-based approaches lack dynamic interaction capabilities in complex, time-varying electromagnetic environments. To address these challenges, this paper proposes a novel two-stage intelligent decision-making framework. In the first stage, an anti-jamming knowledge graph repository is constructed to enable rapid decision-making through efficient reasoning, thereby ensuring real-time responsiveness. The second stage introduces a hierarchical reinforcement learning architecture that facilitates environmental interaction for continuous model evolution and self-adaptation. By simplifying multidimensional parameter spaces into two-dimensional decision scenarios, the proposed method effectively reduces computational complexity and accelerates convergence. Experimental results demonstrate that the proposed method achieves a 4.2% increase in the anti-jamming decision success rate and a 104.8% improvement in the transmission rate compared to state-of-the-art methods. Simulation results demonstrate the superiority of the framework in both anti-jamming performance and learning efficiency, validating its practical effectiveness in dynamic electromagnetic environments. Full article

Considering the trade-off between profit maximization and adaptability to supply chain disruptions, we examine herein the decision-making for configuration and distribution plans in a supply chain. Supply chain disruptions are caused by facility accidents and disasters. In this work, we investigate an optimal configuration and distribution plan in the supply chain with disruptions, including the opening of additional facilities while maintaining the optimum supply amounts to customers in the profit maximization plan when no such disruptions occur. Assuming the existence of uncertainties in demands and supplies, we formulate a two-stage model with a simple recourse, in which decisions on the supply chain configuration are made at the first stage. Decisions on the distribution are made at the second stage after the demands and supplies are realized. For such a configuration and distribution in the supply chain, we propose TSA-SCD (Two-Stage Analysis for Supply Chain Disruptions), a novel decision-making framework considering the trade-off between profit maximization and adaptability to supply chain disruptions. Accordingly, we perform numerical experiments with different degrees of disruptions to verify the effectiveness of the proposed decision method. Full article

To ensure healthcare is environmentally sustainable for future generations, it is crucial to analyze the environmental impact of medical activities. With the rise of single-use medical devices, there is a growing need to compare their environmental footprint with that of conventional multiple-use solutions. This study aimed to review existing literature on the life cycle assessment (LCA) of single-use and multiple-use endoscopes, focusing on how system boundaries, goals, and scopes are defined, as well as identifying environmental impacts and hotspots. A literature review was conducted using the PRISMA framework, with searches performed on the Web of Science, Scopus, and PubMed. A multi-stage screening process resulted in the selection of 12 studies for detailed review. The analysis revealed a significant lack of comprehensive, comparative LCA studies that evaluate the environmental trade-offs between these two endoscope types across their entire lifecycles. Many existing studies focus only on specific life cycle stages, making comparison between results impossible. This review highlights the need for more holistic, cradle-to-grave analyses to inform more sustainable healthcare decisions. Full article

Accurate 3D medical image segmentation is crucial for knowledge-driven clinical decision-making and computer-aided diagnosis. However, current deep learning methods often fail to effectively integrate local structural details from Convolutional Neural Networks (CNNs) with global semantic context from Transformers due to semantic inconsistency and poor cross-scale feature alignment. To address this, Progressive Query Aggregation Network (PQAN), a novel framework that incorporates knowledge-guided feature interaction mechanisms, is proposed. PQAN employs two complementary query modules: Structural Feature Query, which uses anatomical morphology for boundary-aware representation, and Content Feature Query, which enhances semantic alignment between encoding and decoding stages. To enhance texture perception, a Texture Attention (TA) module based on Sobel operators adds directional edge awareness and fine-detail enhancement. Moreover, a Progressive Aggregation Strategy with Forward and Backward Cross-Stage Attention gradually aligns and refines multi-scale features, thereby reducing semantic deviations during CNN-Transformer fusion. Experiments on public benchmarks demonstrate that PQAN outperforms state-of-the-art models in both global accuracy and boundary segmentation. On the BTCV and FLARE datasets, PQAN had average Dice scores of 0.926 and 0.816, respectively. These results demonstrate PQAN’s ability to capture complex anatomical structures, small targets, and ambiguous organ boundaries, resulting in an interpretable and scalable solution for real-world clinical deployment. Full article

This study introduces an innovative Honey Badger Optimization (HBO) designed to address the Optimal Power Flow (OPF) challenge in electrical power systems. HBO is a unique population-based searching method inspired by the resourceful foraging behavior of honey badgers when hunting for food. In this algorithm, the dynamic search process of honey badgers, characterized by digging and honey-seeking tactics, is divided into two distinct stages, exploration and exploitation. The OPF problem is formulated with objectives including fuel cost minimization and voltage deviation reduction, alongside operational constraints such as generator limits, transformer settings, and line power flows. HBO is applied to the IEEE 30-bus test system, outperforming existing methods such as Particle Swarm Optimization (PSO) and Gray Wolf Optimization (GWO) in both fuel cost reduction and voltage profile enhancement. Results indicate significant improvements in system performance, achieving 38.5% and 22.78% better voltage deviations compared to GWO and PSO, respectively. This demonstrates HBO’s efficacy as a robust optimization tool for modern power systems. In addition to the single-objective studies, a multi-objective OPF formulation was investigated to produce the complete Pareto front between fuel cost and voltage deviation objectives. The proposed HBO successfully generated a well-distributed set of trade-off solutions, revealing a clear conflict between economic efficiency and voltage quality. The Pareto analysis demonstrated HBO’s strong capability to balance these competing objectives, identify knee-point operating conditions, and provide flexible decision-making options for system operators. Full article

There is a growing demand for digital innovation to facilitate authentic communication during the learning experience at Australian Universities. Student’s communication is considered ‘authentic’ in various ways, from using discipline-specific professional language to expressing personal values through honest self-reflection. Enhancing authentic rational decision-making during social learning online is one priority area now available for students developing clinical reasoning skills. Using a Design-based Research (DBR) methodological framework, 34 students, 26 educators, and 5 learning designers from Torrens University Australia provided iterative feedback on the development and implementation of a simple digital decision wheel tool, aimed at supporting independent and collaborative decision-making. Three DBR phases were implemented, encompassing an initial pilot and development stage with 3 subjects, and two subsequent phases with an additional 17 subjects that were incorporated using a decision wheel tool for independent and problem-based learning. Data were generated through 44 semi-structured interviews and 20 focus groups across twenty undergraduate subjects delivered in various learning modes across five 12-week DBR action cycles. Reflexive thematic analysis and bounded rationality theory guided analysis. Outputs reveal that a simple digital tool contributed positively to making authentic learning moments visible and promoted inclusive and formative dialogue. Benefits included development of psychological authenticity when preparing to make authentic industry decisions. The initiative aligns with broader educational goals for resourcing and developing tools to scaffold a ‘critical pause’ before articulating authentic thinking when engaging with humans and machines. Full article

Smart farming relies heavily on IoT automation and data-driven decision making, but this growing connectivity also increases exposure to cyberattacks. Flow-based unsupervised intrusion detection is a privacy-preserving alternative to signature and payload inspection, yet it still faces three challenges: loss of subtle anomaly cues during Autoencoder (AE) compression, instability of fixed reconstruction-error thresholds, and performance degradation of clustering in noisy high-dimensional spaces. To address these issues, we propose a fog-aware two-stage hierarchical AE with latent-space gating, followed by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for attack categorization. A shallow AE compresses the input into a compact 21-dimensional latent space, reducing computational demand for fog-node deployment. A deep AE then computes reconstruction-error scores to isolate malicious behavior while denoising latent features. Only high-error latent vectors are forwarded to DBSCAN, which improves cluster separability, reduces noise sensitivity, and avoids predefined cluster counts or labels. The framework is evaluated on two benchmark datasets. On CIC IoT-DIAD 2024, it achieves 98.99% accuracy, 0.9897 F1-score, 0.895 Adjusted Rand Index (ARI), and 0.019 Davies–Bouldin Index (DBI). To examine generalizability beyond smart farming traffic, we also evaluate the framework on the CSE-CIC-IDS2018 benchmark, where it achieves 99.33% accuracy, 0.9928 F1-score, 0.9013 ARI, and 0.0174 DBI. These results confirm that the proposed model can reliably detect and categorize major cyberattack families across distinct IoT threat landscapes while remaining compatible with resource-constrained fog computing environments. Full article

Background/Objectives: Charcot-Marie-Tooth (CMT) disease, the most common hereditary peripheral neuropathy, often causes cavovarus foot deformity in children. Surgical interventions to correct deformity or improve function can involve either primary fusion or reconstruction. However, the optimal surgical approach remains contested. This systematic review aims to present and evaluate existing data on both fusion and reconstruction surgical interventions in treating pediatric CMT cavus foot. Methods: A PRISMA-guided search of five electronic databases was conducted (from inception to 17 February 2025). Studies were eligible if they reported surgical outcomes for CMT pediatric patients ($\le$18 years) with cavovarus foot treated by primary fusion or reconstruction. Titles, abstracts and full texts were screened by four independent reviewers, and data were extracted on patient demographics, procedures, follow-up, functional scores, radiographic correction and complications. Results: Fourteen studies met inclusion criteria, encompassing 169 patients and 276 feet, with a mean age at surgery of ~13.5 years. Nine studies evaluated joint-sparing reconstruction, three assessed primary fusion, and two combined both reconstruction and fusion. Both interventions yielded improved outcomes post-operatively. Reconstruction generally produced high patient satisfaction and near-normal radiographic parameters but carried recurrence or reoperation rates of 10–40%. Fusion provided durable correction of rigid deformities but was associated with nonunion, adjacent joint arthritis and higher revision rates. Conclusions: Joint-sparing reconstruction is an effective first-line approach for flexible cavovarus deformities in pediatric CMT patients, while fusion should be reserved for severe, rigid or recurrent cases. A patient-specific staged approach is recommended, and higher-quality comparative studies are needed to refine surgical decision-making. Full article

“GenAI” assistants are gradually being integrated into daily tasks and learning, but their uptake is no less contingent on perceptions of credibility or safety than on their capabilities per se. The current study hypothesizes and tests its proposed two-road construct consisting of two interface-level constructs, namely perceived transparency (PT) and perceived safety/guardrails (PSG), influencing “behavioral intention” (BI) both directly and indirectly, via the two socio-cognitive mediators trust in automation (TR) and psychological reactance (RE). Furthermore, we also provide formulations for the evaluative lenses, namely perceived usefulness (PU) and “perceived risk” (PR). Employing survey data with a sample of 365 responses and partial least squares structural equation modeling (PLS-SEM) with bootstrap techniques in SMART-PLS 4, we discovered that PT is the most influential factor in BI, supported by TR, with some contributions from PSG/PU, but none from PR/RE. Mediation testing revealed significant partial mediations, with PT only exhibiting indirect-only mediated relationships via TR, while the other variables are nonsignificant via reactance-driven paths. To uncover non-linearity and non-compensation, a Stage 2 multilayer perceptron was implemented, confirming the SEM ranking, complimented by an importance of variables and sensitivity analysis. In practical terms, the study’s findings support the primacy of explanatory clarity and the importance of clear rules that are rigorously obligatory, with usefulness subordinated to credibility once the latter is achieved. The integration of SEM and ANN improves explanation and prediction, providing valuable insights for policy, managerial, or educational decision-makers about the implementation of GenAI. Full article

Large vision–language models (VLMs) can describe images fluently, yet their anomaly decisions often rely on opaque heuristics and manual thresholds. We present ThinkAnomaly, a rationale-first vision–language framework for industrial anomaly detection. The model generates a concise structured rationale and then issues a calibrated yes/no decision, eliminating per-class thresholds. To supervise reasoning, we construct chain-of-thought annotations for MVTec-AD and VisA via synthesis, automatic filtering, and human validation. We fine-tune Llama-3.2-Vision with a two-stage objective and a rationale–label consistency loss, yielding state-of-the-art classification accuracy while maintaining a competitive detection AUC: MVTec-AD—93.9% accuracy and 93.8 Image-AUC; VisA—90.3% accuracy and 85.0 Image-AUC. This improves classification accuracy over AnomalyGPT by +7.8 (MVTec-AD) and +12.9 (VisA) percentage points. The explicit reasoning and calibrated decisions make ThinkAnomaly transparent and deployment-ready for industrial inspection. Full article

Accurate segmentation and classification of kidney pathologies from medical images remain a major challenge in computer-aided diagnosis due to complex morphological variations, small lesion sizes, and severe class imbalance. This study introduces DiagNeXt, a novel two-stage deep learning framework designed to overcome these challenges through an integrated use of attention-enhanced ConvNeXt architectures for both segmentation and classification. In the first stage, DiagNeXt-Seg employs a U-Net-based design incorporating Enhanced Convolutional Blocks (ECBs) with spatial attention gates and Atrous Spatial Pyramid Pooling (ASPP) to achieve precise multi-class kidney segmentation. In the second stage, DiagNeXt-Cls utilizes the segmented regions of interest (ROIs) for pathology classification through a hierarchical multi-resolution strategy enhanced by Context-Aware Feature Fusion (CAFF) and Evidential Deep Learning (EDL) for uncertainty estimation. The main contributions of this work include: (1) enhanced ConvNeXt blocks with large-kernel depthwise convolutions optimized for 3D medical imaging, (2) a boundary-aware compound loss combining Dice, cross-entropy, focal, and distance transform terms to improve segmentation precision, (3) attention-guided skip connections preserving fine-grained spatial details, (4) hierarchical multi-scale feature modeling for robust pathology recognition, and (5) a confidence-modulated classification approach integrating segmentation quality metrics for reliable decision-making. Extensive experiments on a large kidney CT dataset comprising 3847 patients demonstrate that DiagNeXt achieves 98.9% classification accuracy, outperforming state-of-the-art approaches by 6.8%. The framework attains near-perfect AUC scores across all pathology classes (Normal: 1.000, Tumor: 1.000, Cyst: 0.999, Stone: 0.994) while offering clinically interpretable uncertainty maps and attention visualizations. The superior diagnostic accuracy, computational efficiency (6.2× faster inference), and interpretability of DiagNeXt make it a strong candidate for real-world integration into clinical kidney disease diagnosis and treatment planning systems. Full article

Background: Disasters generate abrupt surges in humanitarian demand, requiring response strategies that balance operational performance with vulnerability considerations. This study examines how temporary Points of Distribution (PODs) can be planned and activated to support timely and equitable resource distribution after a high-magnitude earthquake. Methods: A two-stage framework is proposed. First, a modular p-median model identifies POD locations and allocates modular capacity to minimize population-weighted distance under capacity constraints; travel-distance percentiles guide the selection of p. Second, a SMART-based multi-criteria model ranks facilities using operational metrics and vulnerability indicators, including seismic and economic conditions and the presence of at-risk groups. Results: Evaluation of p values from 3 to 30 shows substantial reductions in travel distances as PODs increase, with an elbow at $p=12$, where 50% of the residents are within 500 m, 75% within 675 m, and 95% within 1200 m. The SMART analysis forms three priority clusters: facilities 24 and 9 as highest priority; 23, 4, 12, and 22 as medium priority; and the remaining sites as lower priority. Sensitivity analysis shows that rankings are responsive to vulnerability weights, although clusters remain stable. Conclusions: The framework integrates optimization and multi-criteria decision analysis without increasing model complexity, enabling meaningful decision-maker involvement throughout the modeling process. Full article

Exosome-derived microRNAs (miRNAs) have been proposed as minimally invasive biomarkers for laryngeal squamous- cell carcinoma (LSCC). Because oral and maxillofacial surgeons are integral to head-and-neck oncologic and reconstructive pathways, such liquid-biopsy signals could support perioperative decision-making (selection for organ-preserving surgery), margin surveillance, and reconstructive planning. We conducted a preregistered, protocol-driven search of PubMed/MEDLINE, Web of Science, and Scopus from inception to 1 June 2025. Given the very small number of clinically comparable diagnostic studies, discordant index tests/thresholds, and high heterogeneity, we did not perform quantitative pooling or publication-bias testing. Instead, we undertook a narrative synthesis and constructed an evidence map; risk of bias tools (QUADAS-2; ROBINS-I) were applied descriptively to inform qualitative confidence. Nine studies were formally analysed based on eligibility to the study topic. Two serum-based case–control investigations (111 LSCC, 80 controls) reported areas under the ROC curve of 0.876 (miR-21 + HOTAIR) and 0.797 (miR-941), with corresponding sensitivities of 94% and 82%. Seven mechanistic papers showed that vesicular cargos—including miR-1246, circPVT1, and LINC02191—drive STAT3-dependent M2 polarisation, NOTCH1-mediated stemness, Rap1b-VEGFR2 angiogenesis, and glycolytic re-programming, producing 1.6–2.6-fold increases in invasion, tube formation, or xenograft growth. Only three studies fulfilled MISEV-2018 characterisation criteria, and none incorporated external validation. This narrative review and evidence map identifies promising but preliminary diagnostic signals and biologically plausible mechanisms for exosomal miRNAs in LSCC; however, the evidence is sparse, single-region, methodologically inconsistent, and at high risk of bias. Findings do not support clinical implementation at this stage. Priorities include harmonised EV workflows, prespecified thresholds, and prospective, multi-centre validation. Full article

Objective: The current study aimed to develop predictive models based on noninvasive clinical parameters to facilitate the early identification and stratification of patients with suspected bladder outlet dysfunction (BOD), thereby reducing the need for invasive diagnostic procedures. Materials and Methods: This retrospective study included 307 male patients with lower urinary tract symptoms (LUTS) refractory to medical therapy who were enrolled between January 2001 and May 2022. To assess the predictive performance of the model in an independent cohort, the dataset was randomly divided into the training set (70%) for model development and the test set (30%) for external validation. A two-stage modeling approach was adopted: Stage 1 involved detecting BOD, and stage 2 focused on identifying specific BOD subtypes. Backward stepwise logistic regression was conducted for model derivation, with internal validation performed using 5-fold cross-validation repeated 20 times. Clinical nomograms and a clinical decision-making framework were constructed based on the final modeling results. Results: In stage 1, the derived BOD model for detecting suspected BOD incorporated maximum flow rate, voided volume, intravesical prostatic protrusion (IPP), and prostatic urethral angle (PUA) as predictors. In stage 2, the derived benign prostatic obstruction (BPO) model included post-void residual (PVR), total prostate volume (TPV), and IPP as predictors. We also constructed nomogram to broadly screening BOD by the combination of maximum flow rate, voided volume, IPP, and PUA, a total score of ≥107 yielded the probability of 0.78 to identify BOD of 0.78. Subsequently, by combining PVR, TPV, and IPP, a total score of ≥39 yielded the probability of 0.35 to discriminate BPO. However, the BOD model (0.47) had a relatively low specificity, and the BPO model (0.58) had a lower sensitivity. Thus, these findings should be considered when applying the models in clinical practice. Conclusions: The results of this study revealed that using the clinical non-invasive parameters to create models can only yield a low sensitivity and low specificity for identifying BPO and the other BOD subtype. In patients with LUTS and small to moderate prostate volume, invasive video urodynamic study is still necessary when invasive treatment modality is recommended. Full article

This paper proposes a profit optimization method for the natural gas industry chain driven by demand forecasting. The method mainly consists of two core components: the construction of a natural gas demand forecasting model and the solution of an industry chain profit optimization model. In the forecasting stage, three models are trained using historical natural gas demand data, and the optimal model is selected based on performance evaluation indicators to predict natural gas demand for the coming month. In the optimization stage, the physical and operational characteristics of key components in the natural gas pipeline network are fully considered, and a nonlinear programming model is formulated with the objective of maximizing the overall profit of the industry chain. The model is validated using historical data. Finally, the demand forecast results are incorporated into the optimization model to calculate the expected industry chain profit for the next month. The findings of this study can provide theoretical foundations and quantitative decision-making support for natural gas suppliers to develop more economically efficient gas supply strategies. Full article