Search Results (40,798)

Metaheuristic optimization algorithms have demonstrated their effectiveness in solving complex optimization tasks, such as those related to Intrusion Detection Systems (IDSs). It was widely used to enhance the detection rate of various types of cyber attacks by reducing the feature space or tuning the model’s hyperparameters. The Artificial Rabbit Optimizer (ARO) mimics rabbits’ intelligent foraging and hiding behavior. The ARO algorithm has seen widespread adoption in the optimization field. The widespread use of the ARO algorithm occurs due to its simple design and ease of implementation. However, ARO can get trapped in local optima due to its limited diversity in population dynamics. Although the transition between phases is managed via an energy shrink factor, fine-tuning this balance remains challenging and unexplored. These limitations could limit the ARO algorithm’s effectiveness in high-dimensional space, as with IDS systems. This paper introduces a novel enhancement of the original ARO by integrating Fuzzy Rule Interpolation (FRI) to compute the energy factor during the optimization process dynamically. In this work, we integrate the FRI along with the ARO algorithm to improve solution accuracy, maintain population diversity, and accelerate convergence, particularly in high-dimensional and complex problems such as IDS. The integration of the FRI and ARO aimed to control the exploration-exploitation balance in the IDS application area. To validate our proposed hybrid approach, we tested it on a diverse set of intrusion datasets, covering eight different benchmark intrusion detection datasets. The suggested hybrid approach has been demonstrated to be effective in handling various intrusion classification tasks. For binary intrusion classification tasks, it achieved accuracy rates ranging from 96% to 99.9%. In the case of multiclass intrusion classification tasks, the accuracy was slightly more consistent, falling between 91.6% and 98.9%. The suggested approach effectively reduced the number of feature spaces, achieving reduction rates from 56% up to 96%. Furthermore, the proposed approach outperformed other state-of-the-art methods in terms of detection rate. Full article

The light environment is a key factor in regulating crop growth and quality in plant factories, where both light intensity and photoperiod strongly influence photosynthetic productivity and energy consumption. This study aimed to elucidate the interactive effects of light intensity and photoperiod on the growth, photosynthetic performance, and energy-use efficiency of Pakchoi in a controlled environment, thereby optimizing lighting strategies. Here, three levels of light intensity (PPFD: 100, 175, and 250 μmol·m⁻²·s⁻¹) and four photoperiods (8, 12, 16, and 20 h·d⁻¹) were combined, resulting in twelve treatments. Plant growth parameters, chlorophyll content, gas exchange indices, CO₂ response curves, and chlorophyll fluorescence characteristics were measured, along with analyses of light-use efficiency (LUE) and electrical energy-use efficiency (EUE). The highest biomass accumulation was observed under a 20 h·d⁻¹–250 μmol·m⁻²·s⁻¹ treatment. In contrast, the optimal LUE (9.69%) and EUE (4.98%) were observed under a 20 h·d⁻¹–175 μmol·m⁻²·s⁻¹ treatment. The best photosynthetic performance (A_max 32.61 μmol·m⁻²·s⁻¹) occurred under a 16 h·d⁻¹–250 μmol·m⁻²·s⁻¹ treatment. This study integrates growth, photosynthetic physiology, and energy-use efficiency, revealing a trade-off between biomass production and energy utilization in Pakchoi cultivation. It clarifies that “moderate light intensity + long photoperiod” is the optimal strategy to balance yield and energy consumption in plant factories. Full article

Background/Objectives: Although body mass index (BMI) is a conventional screening tool for type 2 diabetes mellitus (T2D), its reliability as a sole indicator of metabolic health is controversial, and the metabolic profile of a subset of individuals with normal BMI is indicative of obesity-related complications. This study aimed to estimate the prevalence and predictors of undiagnosed diabetes among Metabolically Unhealthy Normal Weight (MUNW) adults. Methods: Data from the National Health and Nutrition Examination Survey (NHANES) 2017–March 2020 were analyzed. Normal weight adults (BMI 18.5–24.9 kg/m²) were categorized into Metabolically Healthy (MHNW) and Unhealthy (MUNW) phenotypes based on the presence of ≥2 metabolic risk factors, including elevated blood pressure, triglycerides, waist circumference, or low HDL cholesterol. The primary outcome was undiagnosed diabetes, defined as HbA1c ≥ 6.5% or Fasting Plasma Glucose ≥ 126 mg/dL. Results: The study population represented approximately 60 million US adults. The prevalence of undiagnosed diabetes was nearly four times higher in the MUNW group (4.84%) compared to the MHNW group (1.28%). In multivariable logistic regression analysis, age and race emerged as significant predictors. Notably, Asian adults exhibited a significantly higher risk of undiagnosed diabetes (OR 6.10; 95% CI: 1.32–28.2) compared to White adults, independent of metabolic phenotype. Conclusions: Reliance solely on BMI may overlook undiagnosed diabetes in normal-weight adults, particularly those with metabolic clustering or of Asian descent. These findings underscore the importance of multidimensional risk assessment integration into preventive care, optimizing clinical management. Full article

Near-critical and supercritical carbon dioxide (SC-CO₂) is extensively utilized in high-pressure separation, extraction, polymer processing, and carbon capture and utilization (CCU) technologies owing to its tunable density, low viscosity, high diffusivity, and environmentally benign nature. Reliable phase equilibrium data are indispensable for process design and optimization, especially in the near-critical region characterized by pronounced non-idealities, high compressibility, and density fluctuations. This review synthesizes experimental phase behaviour studies for binary mixtures of CO₂ with diverse components, including hydrocarbons, alcohols, ethers, esters, ketones, water, monomers/polymers, ionic liquids (ILs), and deep eutectic solvents (DESs), compiling extensive vapour–liquid equilibrium (VLE), liquid–liquid equilibrium (LLE), and critical data across industrially relevant pressure (up to 40 MPa) and temperature (up to 400 K) ranges. It critically evaluates analytical (sampling and non-sampling) and synthetic methodologies, addressing challenges in CO₂-rich phase handling, depressurization artefacts, and near-critical phenomena, while assessing data consistency against established reliability criteria. Key trends emerge, such as enhanced solubility with increasing pressure and CO₂ density, chain-length dependencies in hydrocarbons and alcohols, and Lewis acid–base interactions driving solvation in polar systems. The review highlights gaps in multicomponent data and proposes integrating high-quality experiments with advanced thermodynamic modelling to enhance predictive accuracy. Future directions emphasize high-precision in situ techniques, expanded datasets for complex mixtures, and novel CO₂-philic solvents to advance sustainable SC-CO₂ applications. Full article

Vision transformers (ViTs) excel at global context modeling with self-attention. However, standard self-attention leads to quadratic computational complexity, which restricts its practical use in high-resolution or latency-sensitive tasks. Existing methods achieve linear complexity via local window constraints or additive approximations. However, they often compromise long-range dependency modeling. To address this issue, we propose the channel-guided additive attention vision transformer (CGA-ViT), which achieves synergistic optimization of multi-scale feature extraction and efficient global context modeling. First, we propose multi-scale dilated feature embedding (MDFE). By designing multi-scale sampling and spatial feature embedding, we can expand the receptive field and capture fine-grained features simply by adjusting the dilation rate in the early stages; second, we design channel-guided additive attention (CGA), dynamically modulating key vectors using query-derived descriptors, enabling long-range semantic interactions while maintaining linear complexity growth. We adopt a hierarchical structure, and in the shallow layers, we use CGA to carry out local-global interactions and use efficient additive attention in deep layers for global integration. Evaluations on ImageNet-1K show that CGA-ViT achieves 84.0% Top-1 accuracy with 4.7 GFLOPs, outperforming Swin-T (81.3%) and ConvNeXt-T (82.1%) by 2.7 and 1.9 percentage points under comparable computational costs. Ablation experiments verify MDFE and CGA, which together contribute to 65.0% of performance gains, with the rest from token-level supervision. Overall, CGA-ViT effectively balances the intrinsic tradeoff between efficiency and global modeling capability, significantly boosts visual recognition performance without extra computational overhead, and provides an efficient solution for lightweight ViT design. Full article

The escalating global surge in Waste Electrical and Electronic Equipment (WEEE) necessitates the strategic deployment of recycling facilities within resilient, multi-period networks. However, existing planning methodologies falter due to the non-stationary spatiotemporal volatility of e-waste generation, the high reconfiguration costs associated with path-dependent infrastructure, and the “curse of dimensionality” inherent in large-scale dynamic optimization. To address these challenges, we propose LAtt-PR, an innovative hybrid reinforced adaptive optimization framework. The methodology integrates a spatiotemporal attention-based neural network, combining Multi-Head Attention (MHA) for spatial correlation with Long Short-Term Memory (LSTM) units for temporal dependencies to accurately capture and predict fluctuating demand patterns. At its core, the framework employs Deep Reinforcement Learning (DRL) as a high-level action proposer to prune the expansive search space, followed by a Particle Swarm Optimization (PSO) module to perform intensive local refinement, ensuring both global strategic foresight and numerical precision. Experimental results on large-scale instances with 150 nodes demonstrate that LAtt-PR significantly outperforms state-of-the-art benchmarks. Specifically, the proposed framework achieves a solution quality improvement of 76% over traditional metaheuristics Genetic Algorithm (GA)/PSO and 55% over pure DRL baselines Deep Q-Network(DQN)/Proximal Policy Optimization (PPO). Furthermore, while maintaining a negligible optimality gap of less than 4% relative to the exact solver Gurobi, LAtt-PR reduces computational time to just 16% of the solver’s requirement. These findings confirm that LAtt-PR provides a robust, scalable, and efficient decision-making tool for optimizing resource circularity and environmental resilience in volatile, real-world recycling logistics. Full article

Hybrid renewable energy systems are increasingly important for enabling sustainable and resilient energy supply in rural smart communities, yet existing tools often lack the ability to integrate environmental variability, multi-technology interactions, and economic–environmental assessment in a unified framework. This study presents Hybrid Smart Micro Energy Community (HySMEC), a novel modeling approach that combines high-resolution meteorological data, technology-specific generation models, detailed demand characterization, and financial analysis to evaluate hybrid configurations of hydropower, solar PV, wind, battery storage, and grid interaction. Hourly simulations capture seasonal dynamics and system behavior under realistic technical efficiencies, investment costs, and emission factors, enabling a transparent assessment of energy flows, self-consumption, and grid dependence. The results show that hybrid systems can achieve competitive economic performance, low Levelized Costs of Energy, and significant CO₂ emission reductions across diverse rural community profiles, even when space or demand constraints are present. The analysis confirms the technical feasibility and environmental benefits of integrating multiple renewable sources with storage, highlighting the importance of self-consumption ratios in improving system profitability. Overall, HySMEC provides a robust and scalable tool to support data-driven design and optimization of distributed energy systems, offering valuable insights for researchers, planners, and decision-makers involved in sustainable rural energy development. Full article

The Beijing–Tianjin–Hebei (BTH) region is one of the most complex and heterogeneous regions in China, where differences in marginal abatement costs across cities jointly shape the effectiveness of regional air quality management. This study develops an integrated analytical framework that combines pollutant-specific abatement cost accounting, optimization of territorial and coordinated emission reduction scenarios, and cooperative game-based cost allocation. Using this framework, we quantify both the total cost-saving potential and the distributional effects of coordinated air pollution control in the BTH region. The results are as follows: (1) Under the territorial governance model, the total cost reaches CNY 22.627 billion, whereas coordinated governance lowers this to CNY 21.647 billion. Joint prevention and control is economically more efficient and produces a globally optimal cost outcome for the region. (2) After redistribution, the final burdens of Beijing (CNY 572 million), Tianjin (CNY 1.154 billion), and Hebei (CNY 19.921 billion) are all lower than their territorial governance costs, ensuring that each region gains from cooperation. (3) Significant differences in marginal removal costs among the three regions, with Hebei exhibiting the highest marginal emission cost and lowest marginal removal cost. Optimal coordinated governance requires Hebei to undertake a greater share of emission reduction, while Beijing and Tianjin reduce less compared with their independent plans. The coordinated model enhances regional resource efficiency, reduces redundant investment, and achieves the same total emission reduction at a lower aggregated cost. Full article

Amid the exponential growth of streaming data and rising demands for real-time decision-making, Online Convex Optimization (OCO) has emerged as a foundational framework for sequential data processing in dynamic environments. This survey presents a systematic review of recent evolutionary and adaptive OCO strategies, offering a detailed taxonomy that classifies algorithms according to their constraint-handling mechanisms and environmental feedback. The analysis first examines Constrained OCO, elucidating the trade-offs between computational efficiency and theoretical guarantees across projection-based methods, projection-free Frank–Wolfe variants, and general convex optimization approaches. It then explores the Unconstrained OCO landscape, emphasizing the shift from parameter-dependent methods to fully adaptive, parameter-free algorithms capable of handling unknown comparator norms and gradient scales. Furthermore, the study synthesizes state-of-the-art applications in power systems, network communication, and quantitative finance, bridging theoretical OCO models with robust engineering solutions. The paper concludes by outlining critical open challenges and future research directions, such as the integration of OCO with deep learning, non-convex optimization, and robustness against adversarial corruptions in data-intensive scenarios. Full article

Background: Exercise oncology research supports multicomponent interventions as complementary therapies to improve quality of life in breast cancer (BC) survivors. Nonetheless, evidence on sport-specific, engaging approaches, such as boxing-based concurrent training, remains scarce. Method: This case study aimed to evaluate the feasibility and safety, and to explore the effects of a 16-week adapted boxing protocol. Two BC survivors with a history of triple-negative BC in treatment were enrolled. The protocol integrated aerobic, strength/power, coordination, balance and boxing-specific exercises through individually adapted, progressive sessions performed twice a week. Outcomes were assessed pre- and post-intervention and included: (I) compliance and adverse event related to the protocol, (II) functional tests (handgrip, single leg stance, 30 s sit-to-stand, trunk/shoulder mobility tests, VO₂max); (III) body composition parameters (fat mass, fat-free mass,); and (IV) validated questionnaires (EORTC QLQ-C30, FA12, PSQI, BIS, HADS, IPAQ). Results: Compliance was high and no serious adverse events were detected. Sit-to-stand performance, as well as VO₂max and mobility/balance, improved in both patients after the intervention. Participant A showed a favorable body modulation. Participant B, on the other hand, reported a stable weight. Participant A reported large improvements across QLQ-C30 domains, while participant B exhibited mixed results, with improved emotional functioning and pain but declines in cognitive/social functioning. Conclusions: The boxing-based concurrent training protocol was feasible, safe, and well-tolerated. Despite the limitation of the case study, the observed functional and psychosocial positive changes highlight the need for adequately larger controlled trials to clarify the training protocol efficacy in order to optimize this exercise approach in BC survivors. Full article

Pancreatic ductal adenocarcinoma (PDAC) remains a highly lethal malignancy due to late presentation, limited resectability, therapeutic resistance, and a dense desmoplastic immunosuppressive tumor microenvironment that impairs drug penetration and antitumor immunity. Focused ultrasound (FUS) is an emerging non-invasive, image-guided therapeutic platform capable of delivering spatially confined acoustic energy to induce tumor ablation, disrupt stromal barriers, and enhance delivery of drugs, nanoparticles, and nucleic acids. Depending on acoustic parameters, FUS can produce thermal effects resulting in coagulative necrosis or non-thermal mechanical effects, including cavitation, sonoporation, and histotripsy which remodel extracellular matrix architecture, increase vascular and cellular permeability, and facilitate tumor debulking. In addition, FUS-induced cell injury can promote immunogenic cell death and release tumor-associated antigens and danger signals, providing a rationale for combination strategies with chemotherapy, radiation, and immunotherapy. This review synthesizes the mechanistic foundations, preclinical modeling advances, and emerging clinical applications of FUS in PDAC, with emphasis on treatment integration, patient selection, real-time monitoring, and acoustic parameter optimization, while acknowledging current safety considerations and limited clinical toxicity data. Key limitations, translational challenges, and priority knowledge gaps are also discussed to define the role of FUS in multimodal PDAC care. Full article

This paper presents a novel integrated gear drive unit (IGDU), which integrates a high torque density flux-focusing magnetic gear with a V-shaped interior permanent magnet (IPM) motor into a compact structure. The proposed configuration enables direct torque amplification and efficient low-speed, high-torque operation, addressing the inherent torque limitations of conventional electric motors. Critical design parameters, including pole-pair selection, modulation ring dimensions, and stator slot openings, are optimized to enhance torque performance and minimize cogging torque. Finite element analysis (FEA) verifies a maximum torque output of 43.7 Nm. A prototype of the proposed IGDU was fabricated, and experimental validation confirms the effectiveness of the design, with a good match between the measured back-EMF and the simulated one. The results highlight the potential of the proposed machine for compact, high-performance applications such as electric vehicles and industrial drives. Full article

Orchids depend on mycorrhizal fungi for seed germination, a critical process especially for endangered species such as Cattleya purpurata. This study elucidates the ultrastructural ontogeny of the symbiosis between C. purpurata and the fungus Tulasnella sp. We demonstrate a defined spatiotemporal colonization pattern: hyphae penetrate exclusively via suspensor cells, migrate through the basal region of the embryo, and only then colonize the apical region. Upon colonization, the fungus triggers changes in the embryonic cells, including nuclear hypertrophy and peloton formation. Ultrastructural analysis revealed a sequence of fungal degradation, from intact hyphae to senescent hyphae containing myelin-like bodies and an electron-dense cytoplasm, suggesting that programmed senescence precedes peloton digestion. This supports the novel hypothesis of active fungal participation in modulating its own digestion, challenging classical models. Simultaneously, embryonic cells exhibited rapid metabolic conversion, with the transition from proplastids to amyloplasts, and then to chloroplasts in less than 20 days, marking the onset of autotrophy. This integrated morphological study not only expands fundamental knowledge about symbiotic development in orchids but also provides an optimized protocol for producing symbiotic seedlings, offering a direct tool for the reintroduction and conservation of this species. Full article

Modern software development fundamentally relies on agile methodologies and DevOps practices to facilitate accelerated software delivery. Continuous integration and continuous deployment CI/CD are among the most critical DevOps practices that require considerable attention to execute successfully. Therefore, this study proposes a multi-class XGBoost-based model to improve the performance of CI/CD failure prediction. The proposed model was trained and tested using the comprehensive TravisTorrent dataset, which contains extensive build information from several projects developed in various programming languages. The experimental results demonstrate that the proposed model achieves a statistically significant performance improvement of nearly 18% over SVM and the Random Forest models. Beyond the performance improvement, SHAP (SHapley Additive exPlanations) analysis was employed to explain the model’s decision-making process, revealing that the most influential features, ranked in descending order of importance, are build log status, build duration, build start time, the number of commits in the repository, and repository age. This interpretability enhances both the reliability and transparency of the proposed model. Full article

The multi-jet polishing process (MJP) demonstrates high shape accuracy and surface quality in the machining of nonlinear and complex surfaces, and it achieves precise and adjustable material removal rates through computer control. However, there are still challenges in terms of machining efficiency, system complexity, and stability. In particular, maintaining the polishing quality presents a greater challenge when working conditions change. To overcome these issues, this paper conceptually proposes a digital twin (DT)-driven, human-centric design framework that integrates key factors of MJP, such as jet kinetic energy, nozzle structure, abrasive type, and machining path. Within this framework, a feature-encoded transfer learning-based model is introduced to enhance surface roughness prediction accuracy and robustness under varying working conditions. The effectiveness of the proposed model was verified by conducting experiments on 3D printed workpieces under two different MJP working conditions. The results show that our proposed method yields better predictive performance and cross-condition adaptability. Overall, this work provides a predictive modeling component that supports DT-driven process design, offering a practical and extensible perspective for optimizing complex ultra-precision manufacturing processes under data-scarce and uncertainty-dominated conditions. Full article