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Steel–concrete composite tubular columns have become increasingly attractive for sustainable structural applications due to their high load-carrying capacity and efficient material utilization. However, optimizing their structural performance while simultaneously minimizing embodied carbon emissions and material cost remains a challenging multi-objective problem. This study presents a comprehensive optimization framework for circular, rectangular, and square composite tubular columns composed of high-strength materials, and using the Particle Swarm Optimization (PSO) and Multi-Objective Particle Swarm Optimization (MOPSO) algorithms. The multi-objective optimization simultaneously maximizes axial load capacity while minimizing embodied CO₂ emissions, considering both sections with (WR) and without (WoR) additional longitudinal reinforcement, enabling the identification of optimal trade-offs between structural performance and environmental impact. Design variables include the cross-sectional dimensions, concrete compressive strength, steel yield strength, and reinforcement configuration. The resulting Pareto-optimal solutions are further evaluated using a multi-criteria decision-making approach based on Minkowski Metrics combined with Entropy Theory to identify the best overall compromise solution. The numerical results demonstrate that the use of high-strength materials can reduce embodied CO₂ emissions by up to 16% in the analyzed cases. The implemented load-bearing capacity formulation was also validated against experimental results, yielding a mean N_exp/N_NBR16239 ratio of 1.01 for the six specimens analyzed. The multi-objective formulation associated with Minkowski metrics produces a robust tool for determining the best solutions in general, as well as the best solutions related to the maximum load that the columns can support. Finally, for both the single-objective and multi-objective problem analyses, increasing the slenderness of the columns resulted in more costly solutions, both economically and environmentally. Full article

In multi-mission air operations, area-patrol missions and long-range missions typically share a limited tanker fleet while imposing different constraint structures: patrol refueling is bounded by hard time windows, whereas long-range missions are governed by restricted-zone avoidance, detour tolerances, and multi-segment fuel verification. Conventional scenario-wise independent planning splits resources and wastes cross-region ferry mileage. This paper adapts the established paradigms of the location–routing problem (LRP) and the vehicle routing problem with time windows (VRPTW) to this joint refueling scenario: a joint planning model prioritizes the number of tanker sorties over total system flight distance, and a decoder-coupled adaptive large neighborhood search (ALNS) integrates airspace selection, task assignment, tanker routing, and dual-timeline rendezvous decoding, with all mission hard constraints embedded in a deterministic, reproducible evaluator that adjudicates feasibility at every search iteration. Experiments at three scales (17, 42, and 100 tasks) show 100% mission coverage and 100% patrol time-window satisfaction: relative to scenario-wise independent planning, tanker sorties decrease by 16.2–19.7% and tanker flight distance by 14.6–15.5% (significant after Bonferroni correction on 90 paired replicates per scale); against genetic algorithm (GA) and ant colony optimization (ACO) baselines—and against a route-encoding GA under an equal solution-space representation—the method is superior in solution quality and runtime ($p<0.001$), and the separation persists when the baselines receive a 25-fold evaluation budget. Monte Carlo simulations characterize how plan feasibility degrades under execution-time disturbances. Within the studied instance families, the framework yields executable joint refueling plans within operational runtimes. Full article

Data centers consumed 415 TWh of electricity in 2024, about 1.5% of global demand, and foundation model training and inference are a growing part of this load. As training and inference continue to grow, energy-efficient foundation models are becoming essential. An efficiency gain may come from the model, the accelerator, or the facility, and existing reviews and primary studies usually address one of these aspects. However, energy is obtained by a different method at each of these stages, so unified and systematic optimization is more difficult than results at any single stage may suggest. Reported metrics range from floating-point operations (FLOPs) and tera-operations per second per watt (TOPS/W) to throughput, power usage effectiveness (PUE), carbon, and water. This review follows energy through each stage and treats each reported value together with its measurement boundary and evidence class. This review covers the chain from how models are designed, compressed, and served through how accelerators execute them at reduced precision to how facilities cool and power them. This review identifies open research problems in wall-plug measurement, cross-layer co-design, lifecycle accounting, and the deployment maturity of emerging accelerators. It aims to serve as a reference for researchers and practitioners seeking a unified view of energy, carbon, and water across foundation model systems. Full article

In large-scale stochastic many-objective time–cost–environment trade-off problems, influence is unevenly distributed: each objective is governed by a reduced subset of the decision variables that drive most of the controllable variation, while the remaining variables enlarge the Pareto search space—and the set of levers managers must deliberate over—without materially affecting performance. This study integrates Monte Carlo simulation, surrogate modeling, Sobol global sensitivity analysis, and many-objective evolutionary optimization to identify that influential subspace and confine the search to it, demonstrated on a 30-task construction project with 68 input variables, five conflicting objectives, and 150,000 training scenarios. Multi-criteria benchmarking of eleven surrogate architectures and eight evolutionary algorithms selected a Multilayer Perceptron (R² = 0.993, MAE = 3.962 × 10⁻³) and AGE-MOEA (HV = 0.741, IGD = 0.064, SP = 0.036). Sobol screening reduced the decision space from 60 to 40 task-interpretable variables; at equal budget, the reduced space retains 91.6–95.5% of the full-space hypervolume, a front-level cost that exceeds the discarded sensitivity mass (0.3–2.2%) and quantifies, for this instance, the behaviour of the Factor Fixing criterion at the front level. A space budget analysis showed that both spaces converge well before the full budget (2.0–2.2× speedup at no more than 1.7% additional hypervolume loss): the efficiency gain stems from the budget, while the screening contribution is structural: it identifies which scheduling decisions can be fixed at nominal values, at the cost measured above. An a posteriori re-evaluation with the exact model bounded the optimistic surrogate error (MAPE 1.26–4.38%; 118 of 126 solutions feasible), and the CRITIC-weighted selection yielded a 36.46-day schedule at USD 283,189 with a sustainability index of 0.924. Full article

Spray disinfection is a necessary measure to reduce virus infection in modern large-scale pig farms. At present, spray disinfection in pig barns in China mainly relies on manual backpack-type, semi-automated and fixed equipment, which have associated problems such as high labor intensity, short spraying range, disinfectant waste and unstable distribution of droplet deposition. To solve these problems, an embedded conical air-assisted spray device for disinfection was designed in this paper. The nozzle is centrally embedded in the conical air duct outlet to form a coaxial gas−liquid-coupled atomization structure. High-speed airflow from the axial fan extends the spraying range. Using the method of computational fluid mechanics (CFD), simulation models of the conical duct and the cylindrical duct are established. The results show that when the fan’s rotation rate is the same, the outlet airflow speed of the former is 39.57% higher than that of the latter, and the area-weighted average speed uniformity index rises by 8.33%. Tests on device obstacle avoidance and spray performance were carried out. The results show that the overall success rate for obstacle avoidance was 93.3%. Under enclosed pig-barn conditions, the average spraying range of the device was 5.45 m, which was 64.16% higher than that of the spraying operation without airflow assistance. When the device sprayed under the three-zone comprehensive optimal parameters, the average coefficient of variation of the lateral droplet deposition mass was 10.23%, demonstrating that the overall spray deposition coverage is relatively uniform. The research carried out can provide a theoretical and engineering reference for the design and parameter optimization of similar sprayers for disinfection in large-scale pig farms. Full article

Constructing and optimizing fishways is essential for ecosystem conservation. In conventional weir–orifice combined fishways, strong jets form local high-velocity zones and hinder continuous fish migration. To solve this problem, cylinder regulating structures are arranged inside fishway pool chambers. Five schemes with various cylinder types and layouts are proposed for hydraulic comparison. Physical experiments and RNG k–ε simulations are adopted to systematically explore how these structures adjust weir–orifice flow distribution and reconstruct pool flow fields, using indicators including weir overflow ratio, velocity distribution, turbulent kinetic energy, dissipation rate and vortex characteristics. The results reveal that cylinder structures block bottom-orifice jets, strengthen lateral flow diffusion and accelerate momentum dissipation, converting jet-dominated flow into a dispersed field with moderate velocities. When the layout changes from a single cylinder to an array, the rising weir overflow ratio mitigates bottom jets and cuts the maximum velocity by around 24.1%. The three-semi-cylinder array achieves lower peak turbulent kinetic energy and smaller vortex areas, effectively restraining turbulence and vortex evolution. This scheme performs well under the maximum discharge of 1.4Q. These findings provide a potential hydraulic regulation strategy for fishway retrofitting and optimization. Full article

Reliable wireless connectivity in underground public transportation can vary with train position because rolling stock modifies propagation paths and local interference conditions. This study proposes a transport-state-aware radio-planning framework for a three-node distributed antenna system (DAS) in a reference underground metro station. A three-dimensional 3.5 GHz reference station model, three operating states (empty station, train at platform, and train entering), a discrete set of 69 candidate antenna positions, and multi-objective placement/power optimization are combined to evaluate received power, SNIR, joint service coverage, and serving-area balance. The optimized configuration increased worst-state joint service coverage from 66.12% to 68.26%, improved worst-state P5 received power from −71.92 to −71.49 dBm, and reduced aggregate transmit power from 753.57 to 682.49 mW. The mean serving-area coefficient of variation decreased from 0.202 to 0.114. Threshold analysis showed that the optimized layout was advantageous at moderate and high SNIR requirements but not at a relaxed 5 dB criterion. The results support treating underground DAS deployment as a transport-state-aware infrastructure-planning problem rather than a static geometric-spacing problem. Full article

The design of bounding box loss functions directly impacts the localization performance and overall accuracy of object detection models. Bounding box loss functions constructed based on Intersection over Union (IoU) tend to induce anchor box expansion during the optimization process, and the design of certain penalty factors can impede anchor box regression. To address these issues, this study first conducts an in-depth analysis of the causes of anchor box expansion and the flaws in the design of some penalty factors. It then proposes a method to construct the loss function using diagonal lines as an equivalent substitute for anchor boxes, converting the anchor box regression problem into diagonal regression. Second, a new metric termed “$L_1+L_2$” is introduced, where $L_1$ and $L_2$ denote the Euclidean distances between the corresponding top-left and bottom-right vertices of the predicted and ground-truth boxes, respectively. Based on this metric, the Two-Point Loss (TPL) is constructed. This loss function can accurately measure the geometric differences between anchor boxes while effectively guiding anchor boxes to achieve fast convergent regression. Finally, an attention factor $\beta$ is introduced to balance the optimization contributions of high- and low-quality anchor boxes, thereby helping to reduce the impact of harmful gradients and improve regression accuracy. Experiments conducted on advanced object detection models (RT-DETR, YOLOv11, and YOLOv12) demonstrate that the proposed diagonal loss functions (TPL and TPLv2) exhibit excellent performance on small-object datasets. This verifies the feasibility and applicability of the proposed methods, which can meet the requirements of small-object detection and provide new ideas and implementation paths for the design and optimization of similar loss functions. Full article

The Long-Range Wide-Area Network (LoRaWAN) is one of the leading low-power wide-area network specifications owing to its capabilities for long-range communication and energy savings. For large-scale sensing applications by a large number of LoRa nodes, it is important to improve communication performance and energy saving. However, redundant sensing and transmissions consume node energy, while simultaneous transmissions, particularly from hidden nodes, cause packet collisions. Centralized optimization of these problems requires the collection of network-wide information and may impose substantial communication overhead due to its narrow communication bandwidth. In this paper, we propose a distributed method for jointly controlling sensing coverage, node energy consumption, and transmission timing using locally exchanged information. Our main idea is to learn from the swarm intelligence of organisms that perform efficient reproductive behavior. The proposed method extends a previously developed mathematical model that reproduced the chorus and satellite behavior observed in three Japanese tree frogs. Whereas the original model describes the satellite behavior of a frog relative to a nearby caller, the proposed method generalizes this interaction to multiple wireless nodes associated with the same sensing target. By embedding target-point and node-state information in transmitted packets, each node identifies the kth-ranked node associated with the target and autonomously determines whether to remain active or enter a low-power satellite state. This mechanism regulates the time- and target-averaged number of active sensing nodes toward k without collecting global node-distribution information. We further introduce an in-phase-flag mechanism that modifies node-specific phase interactions to suppress persistent packet collisions between hidden nodes located two hops apart. Simulation results show that the proposed method reduces transmission energy consumption by 65% for average 1-coverage and by 46% for average 2-coverage compared with the method without satellite-state control. In the collision evaluation, the two-hop packet collision rate was 9.36% without phase control and 4.49% with the basic phase-control mechanism. By additionally applying the in-phase-flag-based hidden-node collision-control mechanism, the two-hop collision rate was further reduced to 3.51%, while maintaining a low one-hop collision rate. These results demonstrate that the proposed extension of the frog-behavior model can jointly regulate sensing redundancy and suppress data collisions through distributed local interactions. Full article

Background: Accurate detection and classification of segmental left ventricular hypertrophy (LVH) are challenging due to rib and reverberation artifacts in two-dimensional short-axis (2D-SAX) echocardiography view. These artifacts lead to ambiguity in the boundaries of the myocardial region. Existing artificial intelligence techniques focus on global LVH detection compared to segmental LVH classification. Methodology: To solve the abovementioned problem, a curriculum-based left ventricular hypertrophy deep learning (LVH-DL) framework is proposed for segmental LVH classification. The proposed framework was tested with 2000 images of segmental LVH, which consists of (a) apical-423, (b) basal-382, (c) mid-ventricular-349, (d) diffuse-453, and (e) normal-393. The curriculum-based deep learning model consists of proposed algorithms such as (i) preprocessing of Mountaineering-Team-Based Optimization Deep Denoised Convolutional Neural Network (MTBO-DnCNN) for removing artifacts and enhancing the boundaries, (ii) segmentation of modified active contour segmentation (MACS) algorithm accurately extracting the myocardium region from the background, and (iii) classification of curriculum-based ConvNeXt-V2 model classifying segmental LVH using inter-channel features such as valvular structures and chamber size from the segmented myocardial region. Results: The proposed LVH-DL framework achieves a classification accuracy of about (a) 96.5% for apical, (b) 95% for basal, (c) 95.8% for mid-ventricular, (d) 97.2% for diffuse, and (e) 95% for normal. Conclusion: Thus, Thus, the proposed LVH-DL framework acts as a decision support tool for early identification of segmental LVH. Full article

Integrating battery energy storage systems is crucial for increasing the flexibility of power systems with a high proportion of variable renewable energy sources. Although lithium-ion batteries dominate applications due to their high energy density and falling costs, their use in large-scale stationary settings is limited by rapid capacity fade, performance degradation, safety concerns and reliance on critical raw materials. Vanadium redox flow batteries (VRFBs) offer a promising alternative due to their scalability, long lifetime, inherent safety, and the recoverability of capacity fade associated with electrolyte imbalance through periodic rebalancing. The optimization problem determines the energy capacity and installed power that minimize the total annualized cost within a predefined storage design range, including investment, operational, and market interaction costs. This is achieved using 2024 real power data at a resolution of 15 min from photovoltaic and wind plants, combined heat and power, and grid exchanges. The annual optimization assumes perfect foresight of electricity demand, generation, and prices over the investigated horizon. Within the investigated design range, the model selects the upper-bound energy capacity of $2000\mathrm{kWh}$ together with an interior power solution of $625\mathrm{kW}$; this configuration reduces annualized system costs by about 8% compared to the no-storage scenario, achieving a levelized cost of electricity supply (LCES) of 0.0847 €/kWh. However, when degradation and end-of-life replacement are accounted for, the lithium-ion LCES increases to 0.09217 €/kWh under the baseline zero-residual-value convention. Full article

To address the limitations of the original Golf Optimization Algorithm (GOA) in high-dimensional complex optimization problems, including insufficient population diversity, limited adaptability of search parameters, and late-stage stagnation, this study proposes a Paddleboard-Inspired Adaptive Memory and Elite Refinement Golf Optimization Algorithm (PAMER-GOA). The proposed algorithm incorporates three complementary mechanisms. First, the Paddleboard Care and Industrial Development-Inspired Strategy (PCIDIS) improves population distribution and enhances global exploration through stratified mirrored initialization, differential search, and elite guidance. Second, the Dual-Memory Adaptive Industrial Investment Strategy (DMAIIS) dynamically adjusts the scaling factor and crossover probability using successful historical information, thereby improving the correspondence between search parameters and different evolutionary stages. Third, the Elite Subspace Principal-Axis Refinement and Stagnation Reallocation Strategy (ESPRS) integrates elite-region refinement, differential perturbation, and sparse Cauchy mutation to strengthen local exploitation and assist stagnant individuals in escaping local optima. Ablation experiments and parameter sensitivity analyses verify the complementary contributions of these strategies and the rationality of the key parameter settings. On the CEC2017 benchmark suite, PAMER-GOA achieves the lowest mean fitness values on 19 and 14 of the 29 functions in the 30- and 100-dimensional settings, respectively. It also ranks first in the Friedman tests, with mean ranks of 1.79 and 2.24, respectively. On the CEC2020 benchmark suite, PAMER-GOA again ranks first, attaining Friedman mean ranks of 1.20 and 1.40 in the 10- and 20-dimensional settings, respectively. The Wilcoxon rank-sum test results further indicate that PAMER-GOA significantly outperforms the comparison algorithms on most test functions. The runtime experiment shows that PAMER-GOA requires an average of 0.5333 s on the 30-dimensional CEC2017 benchmark suite, representing an increase of only 0.0648 s over the 0.4685 s required by the original GOA and indicating that the performance improvements incur only limited additional computational overhead. PAMER-GOA is further applied to the wireless sensor network node deployment problem, achieving the best mean objective values of 0.1553 and 0.1402 in the 30- and 40-node scenarios, respectively, thereby demonstrating its ability to address practical engineering optimization problems. Overall, the results show that PAMER-GOA substantially improves the solution accuracy, convergence behavior, and overall statistical ranking of the original GOA while maintaining strong overall competitiveness across different benchmark suites, dimensional settings, and node deployment scenarios. Full article

Remotely operated vehicles (ROVs) suffer from severe navigation trajectory optimization problems in variable water-depth environments, as near-wall hydrodynamic effects cause synchronous scaling drift of added mass and damping coefficients. This parameter variation leads to obvious model mismatch in traditional fixed-gain controllers and seriously deteriorates ROV trajectory tracking accuracy. To address the scale-type parameter mismatch issue, this paper proposes an adaptive trajectory tracking control strategy combining forgetting-factor recursive least squares (RLS) online identification and periodic linear quadratic regulator (LQR) gain scheduling. A closed-loop coupling framework is established to estimate the discrete state-space matrices of ROVs via the RLS algorithm, and the optimal feedback gains are updated every 50 sampling steps to adapt to time-varying hydrodynamic characteristics. Three typical water-depth scenarios with different parameter mismatch degrees are set up for sinusoidal trajectory tracking simulations, adopting PID and fixed-parameter MPC as comparison methods. The results indicate that the proposed method maintains comparable steady-state performance with fixed-parameter MPC under nominal conditions, and reduces the two-dimensional trajectory RMSE by 8.4% and 57.4% under moderate and severe parameter mismatch conditions, respectively. A critical mismatch threshold of fixed-parameter MPC compensation capability is also determined. This study provides a feasible technical reference for high-precision adaptive motion control of ROVs in variable-depth water environments. Full article

Most Urban Air Mobility (UAM) siting studies treat vertiport location as an isolated spatial-optimization problem, calibrated to cities with mature transport infrastructure and regulatory systems. This study instead develops an integrated planning framework linking spatial and regulatory feasibility, travel demand, vertiport network configuration, multimodal integration, environmental and operational performance, infrastructure and regulatory readiness, and phased deployment within a single process. The framework’s architecture is transferable across cities, while its calibration parameters are city-specific. It is applied to Baghdad, Iraq, as an infrastructure-constrained, rapidly urbanizing case, combining GIS-based suitability mapping, an Analytic Hierarchy Process (AHP), simulation (AnyLogic, BlueSky, VISSIM), and noise/emissions modeling (AEDT/INM) across five candidate sites. A distributed configuration achieves the highest accessibility, travel-time savings of 40–50%, and the largest emission reduction (28%), although the highest-demand sites require noise mitigation before deployment, showing that demand alone does not indicate deployment readiness. Consultation with 28 stakeholders and a four-stage phased-deployment model link these results to an implementable rollout strategy. The findings demonstrate a transferable planning methodology, rather than Baghdad-specific results, for integrating UAM into multimodal transport systems under spatial, infrastructural, environmental, and regulatory constraints. Full article

UAV pursuit–evasion in multi-obstacle environments constitutes a safety-critical decision-making problem in which the pursuer must intercept a maneuvering evader while satisfying obstacle-avoidance constraints. Existing safety-constrained methods typically optimize expected safety costs or apply immediate interventions, but they do not explicitly determine when recovery should begin once finite-horizon risk emerges. To address this issue, we propose the ReSwitch framework, which separates task-oriented pursuit from safety-oriented recovery. When flight risk emerges, a value-preserving switch-time planner evaluates candidate switching times through opponent-conditioned hybrid rollouts. The feasible switch time with the largest pursuit value after recovery is selected for execution, allowing the controller to prioritize safety while preserving pursuit effectiveness whenever possible. Experiments demonstrate that ReSwitch achieves a mean success rate of 89.02% and a physical safety rate of 91.67%, showing favorable performance compared with the baselines. These results indicate that ReSwitch provides a favorable pursuit–safety trade-off under the tested conditions. Full article