Search Results (26,623)

Rolling-horizon energy management systems (EMSs) and model predictive control (MPC) for microgrids in smart cities face a fundamental trade-off: finer temporal discretization improves operational performance but rapidly increases the size of the optimization problem and execution time, jeopardizing real-time feasibility. Furthermore, in short-horizon operation, only the first control actions are implemented, while long-horizon decisions primarily guide feasibility and constraints. This paper proposes a computation-aware temporal mesh design layer that jointly selects a variable granularity of blocks and an optimization horizon, explicitly bounded by market-aligned settlement steps and per-cycle computation budgets. Candidate configurations are represented as pairs ⟨B, H⟩, where B is a constant-step block programme, and H is the optimization horizon, and they are uniquely tracked through an auditable mesh signature. The method first evaluates a predefined, market-consistent set of solutions ⟨B, H⟩ to establish reproducible cost and execution-time benchmarks, then applies a greedy value-of-information (Greedy-VoI) search that generates valid neighbouring meshes through local refinement, thickening, and resolution reallocation without violating the basic requirements that every solution must meet. All candidates are evaluated using the same microgrid use case and the same comparative KPIs, enabling the systematic identification of near-optimal mesh–horizon designs for practical EMS implementation. Full article

In Shenzhen, a high-density city facing severe land scarcity, the proliferation of compact primary school campuses poses significant challenges to the outdoor wind environment, which is crucial for outdoor thermal comfort in a hot–humid climate. This study employs Computational Fluid Dynamics (CFD) to systematically evaluate wind comfort across a range of high-density primary school layouts. Typical design proposals are classified and analyzed based on three key planning aspects: education building forms, courtyard openness, and sports field configuration. Wind comfort area ratio and static wind zone area ratio are adopted as key performance indicators to evaluate outdoor wind performance. The findings demonstrate that decentralized teaching building forms, multi-courtyard layouts with openings oriented towards the prevailing summer wind, and juxtaposed sports field placement significantly enhance outdoor ventilation and comfort. Additionally, positioning the main entrance on the windward side and incorporating elevated voids or terraces to form coherent ventilation corridors are effective design strategies. This research provides theoretical guidance for designing high-density school campuses in hot–humid southern China. Full article

This study presents a hybrid digital twin system designed for real-time indoor air quality (IAQ) monitoring and filtration optimization within a residential environment. Using a network of low-cost sensors, physics-based simulations, and machine learning models, the system dynamically replicates the indoor environment to enable continuous assessment and optimization of key pollutants, including particulate matter, volatile organic compounds, and carbon dioxide. The system architecture integrates mass balance and decay models, computational fluid dynamics simulations, regression models, and neural network algorithms, all evaluated under both filtering and non-filtering conditions. A graphical user interface allows users to interact with the system, test air purifier placements, and visualize air quality dynamics in real time. The results demonstrate that, within this system, simpler models, such as linear regression, outperform more complex architectures under data-limited conditions, achieving test-set coefficients of determination ranging from 0.97 to 0.99 across multiple IAQ parameters. At the same time, the hybrid modelling approach enhances interpretability and robustness. Overall, this digital twin system contributes to smart building management by offering a scalable, interpretable, and cost-effective solution for proactive IAQ control and personalized decision-making. Full article

In the context of increasingly complex offshore drilling operations and stricter environmental regulations, the efficient handling and volume reduction of drilling cuttings has emerged as a crucial focus in the advancement of solids control equipment. “Airflow-assisted screening” is a technique that uses directed air currents to enhance the separation of solid cuttings from drilling fluid on a shaker screen, thereby improving dewatering efficiency and reducing waste volume during drilling. This study proposes and designs novel negative-pressure suction-type cuttings reduction equipment by integrating this technology with screw conveying principles. The system features a compact, vacuum-generator-centered design that integrates suction and screening. Key components were optimized, and a monitoring scheme was implemented for real-time performance evaluation. In the mechanism analysis, the relationship between inlet pressure, geometric parameters, and suction performance was explored based on Bernoulli’s principle and Laval nozzle characteristics, and internal flow field characteristics were revealed through computational fluid dynamics (CFDs) simulations. In the experimental section, a prototype system and testing platform were constructed to evaluate the effects of inlet pressure and screen mesh configurations on suction and screening performance. The results indicate that the system achieved optimal performance at an inlet pressure of 400 kPa with a 100-mesh screen, reaching a cuttings reduction efficiency of 9.225%. This study effectively validates the theoretical and simulation findings, providing technical support for the application of this equipment in complex drilling environments and demonstrating strong potential for practical implementation. Full article

Background/Objectives: The global rise in multidrug-resistant Staphylococcus aureus (MDR-SA) threatens the efficacy of existing antibiotics and necessitates alternative antibacterial strategies. Plant-derived isoflavones represent a promising but underexplored source of novel antimicrobials. Biochanin A, isolated from Cajanus cajan seeds, exhibits antibacterial activity and may act via noncanonical mechanisms. This study elucidates the mechanism of action and safety profile of Biochanin A against MDR-SA using integrated experimental and computational approaches. Methods: Antibacterial activity was assessed by minimum inhibitory concentration (MIC) testing. Membrane integrity and morphological alterations were evaluated using flow cytometry and scanning electron microscopy (SEM), respectively. Target gene modulation was analyzed by qRT-PCR, while molecular interactions were examined through in silico docking. Cytotoxicity was evaluated in normal mammalian kidney, liver, and cardiac cells. Results: Biochanin A inhibited MDR-SA with an MIC80 of 64 µg/mL. Flow cytometry showed membrane disruption in 74.46 ± 13.19% of treated cells, and SEM revealed a 20% reduction in cell size (561.95 ± 21.99 nm). Biochanin A markedly downregulated femA (94%) and femB (67%), with minimal effect on femX (10%). Docking analyses supported preferential binding to FemA (−7.7 kcal/mol) and FemB (−7.5 kcal/mol) proteins. No cytotoxic effects were observed in normal mammalian cells. Conclusions: Biochanin A is a promising plant-derived antibacterial candidate against MDR-SA, targeting key cell wall biosynthesis genes while maintaining mammalian safety. These findings position Biochanin A as a viable lead for further biochemical, structural, and in vivo pharmacological validation, highlighting the translational potential of plant-derived isoflavones in combating antibiotic resistance. Full article

This research presents a large-scale thematic analysis of 66,002 Slovak university thesis abstracts, aimed at identifying, categorizing, and visualizing research trends across multiple academic disciplines. Using BERTopic for unsupervised topic modeling with K-Means clustering, 3000 distinct thematic clusters were extracted through rigorous coherence optimization, with each topic characterized by representative keywords derived from class-based TF-IDF weighting. Text embeddings were generated using SlovakBERT-STS, a domain-adapted Slovak BERT model fine-tuned for semantic textual similarity, producing 768-dimensional vectors that enable precise computation of cosine similarity between topics, resulting in a 3000 × 3000 topic similarity matrix. The optimal topic count was determined through systematic evaluation of K values ranging from 1000 to 10,000, with K = 3000 identified as the optimal configuration based on coherence elbow analysis, yielding a mean coherence score of 0.433. Thematic relationships were visualized through Multidimensional Scaling (MDS) projection to 3-D space, where convex hull geometries reveal semantic boundaries and topic separability. The methodology incorporates dynamic stopword filtering, Stanza-based lemmatization for Slovak morphology, and UMAP dimensionality reduction, achieving a balanced distribution of approximately 22 abstracts per topic. Results demonstrate that fine-grained topic models with 3000 clusters can extract meaningful semantic structure from multi-domain, morphologically complex Slovak academic corpora, despite inherent coherence constraints. The reproducible pipeline provides a framework for large-scale topic discovery, coherence-driven optimization, and geometric visualization of thematic relationships in academic text collections. Full article

Metaheuristic optimization algorithms are widely employed to address complex nonlinear and multimodal optimization problems due to their flexibility and strong global search capability. However, the original Connected Banking System Optimizer (CBSO) still exhibits several inherent limitations when handling high-dimensional and highly complex search spaces, including excessive dependence on single global-best guidance, rapid loss of population diversity, weak exploitation ability in later iterations, and inefficient boundary handling. These deficiencies often lead to premature convergence and unstable optimization performance. To overcome these drawbacks, this paper proposes a Multi-Strategy Enhanced Connected Banking System Optimizer (MSECBSO) by systematically enhancing the CBSO framework through multiple complementary mechanisms. First, a multi-elite cooperative guidance strategy is introduced to aggregate information from several high-quality individuals, thereby mitigating search-direction bias and improving population diversity. Second, an embedded differential evolution search strategy is incorporated to strengthen local exploitation accuracy and enhance the ability to escape from local optima. Third, a soft boundary rebound mechanism is designed to replace rigid boundary truncation, improving search stability and preventing boundary aggregation. The proposed MSECBSO is extensively evaluated on the CEC2017 and CEC2022 benchmark suites under different dimensional settings and is statistically compared with nine state-of-the-art metaheuristic algorithms. Experimental results demonstrate that MSECBSO achieves superior convergence accuracy, robustness, and stability across unimodal, multimodal, hybrid, and composition functions. In terms of computational complexity, MSECBSO retains the same order of time complexity as the original CBSO, namely O(N×D×T), while introducing only a marginal increase in constant computational overhead. The space complexity remains O(N×D), indicating good scalability for high-dimensional optimization problems. Furthermore, MSECBSO is applied to corporate bankruptcy forecasting by optimizing the hyperparameters of a K-nearest neighbors (KNN) classifier. The resulting MSECBSO-KNN model achieves higher prediction accuracy and stronger stability than competing optimization-based KNN models, confirming the effectiveness and practical applicability of the proposed algorithm in real-world classification tasks. Full article

This study aims to provide an empirical, decision-oriented comparison of four widely used sustainability assessment frameworks applied to agri-food systems (LCA, MESMIS, SAFA, and RISE). Using a Scopus-based search (2000–2025), we compiled a purposively balanced corpus of 89 empirical applications and coded each study with a standard rubric spanning normative, systemic, and procedural dimensions. Dimension indices were constructed using polychoric PCA (first component) and rescaled to 0–1, and a global index was computed as the mean of the three dimensions. Within the constructed corpus, MESMIS shows the highest mean global index (0.54), followed by LCA (0.46), SAFA (0.45), and RISE (0.32). LCA leads the normative dimension (0.56), while MESMIS leads the systemic (0.64) and procedural (0.53) dimensions; SAFA presents a balanced profile and explicitly incorporates governance considerations. Findings are interpreted as descriptive patterns within the constructed corpus rather than population-level estimates of inherent method superiority. We conclude that framework choice should be driven by evaluation purpose and information conditions and that hybrid approaches can combine complementary strengths across methods. Full article

Background: Visceral artery aneurysms (VAAs) are rare but potentially life-threatening vascular lesions often clinically silent until rupture. The widespread use of advanced imaging has increased incidental detection, highlighting the need for accurate, noninvasive diagnostic strategies. Dual-Energy Computed Tomography Angiography (DECTA) offers potential advantages over conventional CT across diagnostic and post-treatment settings; however, its role in VAAs remains incompletely defined. This narrative review summarizes current evidence on DECTA applications in VAAs, focusing on diagnosis, emergency evaluation, and post-treatment follow-up. Methods: A non-systematic literature search of PubMed and Embase focusing on English-language articles up to June 2025 was performed. The search included peer-reviewed original research articles, systematic reviews, and meta-analyses addressing dual-energy CT and spectral CT in vascular and aneurysmal imaging. Case reports without technical data and non-English articles were excluded. Results: In the diagnostic phase, DECTA enhances tissue differentiation through virtual monoenergetic images, iodine maps, and material decomposition reconstructions. In the post-treatment setting, DECTA supports assessment after endovascular procedures, including coil embolization or stent graft placement. In VAAs, these techniques may improve aneurysm delineation, reduce metal artifacts after endovascular treatment, enable accurate detection of endoleaks or residual perfusion, and support volumetric follow-up. Virtual Non-Contrast images may reduce radiation exposure without compromising diagnostic confidence. Conclusions: DECTA represents a versatile imaging modality with potential benefits across the diagnostic, emergency, and post-treatment phases of VAA management. Although many applications are extrapolated from aortic and peripheral vascular disease, emerging evidence supports its growing clinical relevance. Further dedicated studies are needed to define its role in VAA-specific decision-making and follow-up. Full article

The advancement of Industry 5.0 hinges on the deep integration of artificial intelligence (AI) with domain expertise to foster sustainable industrial development. This study proposes a deep learning-based surrogate modeling framework that integrates reservoir production requirements with AI technologies, providing intelligent decision support for production optimization and enhanced efficiency. To evaluate AI’s effectiveness in complex industrial scenarios, we conduct an integrated analysis encompassing model construction, dynamic prediction, and production optimization using a real-world oilfield case. This oilfield features a dynamically increasing number of wells and requires dynamic adjustments to injection–production relationships. To address this challenge, we enhance the Embed-to-Control model by improving the nonlinear representation capability within its decoder structure. Subsequently, we construct a high-fidelity dataset containing 300 samples for model training and testing. The experimental results demonstrate that the proposed improved model achieves a high accuracy in predicting key state variables (pressure and saturation) and oil production. Regarding computational efficiency, a single model run requires only approximately 17.3 s, achieving an over 200× speedup relative to traditional numerical simulators. Finally, we coupled the trained surrogate model with the particle swarm optimization algorithm to optimize the injection well control strategy. The optimized scheme increases daily oil production by 13.84%, boosting economic benefits. This study demonstrates a practical technological pathway to accelerate the oil and gas industry’s transition toward Industry 5.0. Full article

Objectives: Revascularization in thromboangiitis obliterans (TAO) is limited by distal small-vessel involvement and poor blood flow; no curative treatment exists. This study aimed to evaluate the effect of intravenous iloprost (IVI) on distal perfusion using computed tomography (CT) perfusion imaging and to correlate perfusion changes with clinical outcomes, with a focus on treatment duration. Methods: This retrospective cohort study was conducted at a single tertiary cardiovascular surgery center. Thirty-three patients (32 men and 1 woman) with confirmed TAO treated with IVI were screened. Clinical data, including ankle–brachial index (ABI), claudication distance, and pre- and post-treatment CT perfusion parameters, were obtained from outpatient records. Patients were grouped according to IVI duration: 0 days (n = 8), 7 days (n = 7), 14 days (n = 10), and 21 days (n = 8). One patient was excluded due to incomplete data, leaving 32 patients for analysis. Results: IV iloprost therapy resulted in significant improvements in ABI, claudication distance, and CT perfusion parameters, particularly in the 14- and 21-day treatment groups. No statistically significant differences were observed between the 14- and 21-day regimens; however, both were superior to shorter or no treatment. The 21-day group demonstrated the most consistent overall improvement. Treatment efficacy was independent of active smoking status, and patients with baseline ABI > 0.8 showed a more favorable response. Conclusions: Intravenous iloprost is clinically effective in TAO patients. Improvements in CT perfusion strongly correlate with ABI and claudication distance, suggesting that CT perfusion may serve as an early marker of treatment response and a useful adjunctive tool in TAO assessment. Full article

Accurate staging of rectal cancer is crucial for treatment planning; however, computed tomography (CT) interpretation remains challenging and highly dependent on radiologist expertise. This study aimed to develop and evaluate an AI-assisted system for rectal cancer detection and staging using CT images. The proposed framework integrates three components—a convolutional neural network (RCD-CNN) for lesion detection, a U-Net model for rectal contour delineation and tumor localization, and a 3D convolutional network (RCS-3DCNN) for staging prediction. CT scans from 223 rectal cancer patients at Kaohsiung Medical University Chung-Ho Memorial Hospital were retrospectively analyzed, including both non-contrast and contrast-enhanced studies. RCD-CNN achieved an accuracy of 0.976, recall of 0.975, and precision of 0.976. U-Net yielded Dice scores of 0.897 (rectal contours) and 0.856 (tumor localization). Radiologist-based clinical staging had 82.6% concordance with pathology, while AI-based staging achieved 80.4%. McNemar’s test showed no significant difference between the AI and radiologist staging results (p = 1.0). The proposed AI-assisted system achieved staging accuracy comparable to that of radiologists and demonstrated feasibility as a decision-support tool in rectal cancer management. This study introduces a novel three-stage, dual-phase CT-based AI framework that integrates lesion detection, segmentation, and staging within a unified workflow. Full article

Since the deployment of the Internet of Things (IoT), it has transformed everyday life by enabling intelligent environments that improve efficiency and automate services in domains such as agriculture, healthcare, smart cities, and industry. However, the rapid proliferation of IoT devices has introduced significant security challenges, largely driven by the heterogeneity of devices, resource constraints, and the increasing exposure of network communications. This work proposes a lightweight security protection mechanism for IoT networks based on trust modelling. The proposed approach integrates machine learning techniques to evaluate IoT node behavior using network-layer (Layer 3) traffic features under different labeling granularities, including binary, categorical, and subcategorical classifications. By focusing on network-layer observations, the model remains applicable across heterogeneous IoT devices while preserving a low computational footprint. In addition, the Common Vulnerability Scoring System (CVSS) is incorporated as a standardized vulnerability severity metric, enabling the integration of probabilistic security evidence with contextual information about potential impact. This combination allows the estimation of trust to reflect not only the likelihood of anomalous behavior but also its associated severity. Experimental evaluation was conducted using a representative IoT traffic dataset, multiple preprocessing strategies, and several classical machine learning models. The results demonstrate that aggregating traffic-based intrusion detection outputs with vulnerability severity metrics enables a more robust, flexible, and interpretable trust estimation process. This approach supports the early identification of potentially compromised nodes while maintaining scalability and efficiency, making it suitable for deployment in heterogeneous IoT environments. Full article

This paper develops a reliability-based Decision Support System (DSS) for logistics networks, grounded in the Eurocode EN 1990 and Recommendations for Maritime Works ROM 0.0 framework. The DSS defines logistics-specific limit states (i.e., operational failure thresholds for the overall network) and computes annual exceedance probabilities through a multi-hazard fault-tree model. Its contribution is conceptual and regulatory: it transfers structural reliability principles to system-level assessment, generating auditable, norm-referenced indicators aligned with the EU Critical Entities Resilience Directive (CER) and the Network and Information Security Directive (NIS2). A central result is the Criticality Flip: Systemic vulnerability does not decline monotonically with hub density. Instead, risk shifts non-linearly between gateways and inland integrators, yielding a narrow operating range where the reliability margin (β) is maximized and annual limit-state exceedance is minimized. Beyond this range, additional hubs may provide limited—or even adverse—reliability improvement. The system operates as a compliance audit tool rather than a simulation engine: it evaluates whether a given network configuration meets declared reliability thresholds under multi-hazard scenarios, using standardized input formats and static topology. To support strategic decision-making, the DSS provides normalized and reproducible compliance indicators—such as annual limit-state exceedance probabilities and the associated reliability margin ($\mathsf{\beta }$) referenced to declared thresholds—supporting cross-network benchmarking under CER and NIS2 constraints within an engineering reliability framework. Full article

Genomic Language Models (GLMs), leveraging their vast parameter scales and the similarities between DNA sequences and natural languages, demonstrate immense potential in processing large-scale genomic data and elucidating gene regulation and evolutionary relationships. However, the cross-species generalization capability of large genomic models has not yet been systematically evaluated. This study addresses this critical gap by benchmarking five GLMs (DNABERT-2, GROVER, HyenaDNA, NT-V2, and AgroNT) and a CNN baseline model using human (Homo sapiens) and rice (Oryza sativa) genomes across four downstream tasks: promoter detection, transcription start site (TSS) scanning, species classification, and gene region identification, through both zero-shot testing and fine-tuning. During testing, factors such as hyperparameters, early stopping protocols, and computational resources were fixed to ensure fairness, enabling us to systematically evaluate their performance and cross-species generalization capabilities. The results were further analyzed from multiple mathematical and representational perspectives to provide a more rigorous and objective assessment of each model’s performance. The results show that AgroNT consistently leads on rice tasks, while NT-V2 and DNABERT-2 achieved the best overall performance in fine-tuning and zero-shot experiments, respectively. Although their pretraining data did not include plants, they demonstrate excellent performance on rice-related tasks thanks to cross-species pretraining that enhances their generalization ability across human–rice domains. This benchmark study offers guidance on selecting appropriate genomic language models based on task characteristics and provides insights for future development in this field. Full article