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Water scheduling is a complex optimization problem that requires efficient and adaptive solution approaches. Metaheuristic techniques, particularly swarm intelligence models, have increasingly been applied to address these challenges. This study presents a scoping review that maps and synthesizes the existing literature on the application of swarm intelligence in water scheduling. Guided by the PRISMA-ScR framework and the JBI Population–Concept–Context (PCC) model, relevant studies published between 2015 and 2025 were identified across multiple databases. From an initial pool of 1357 studies, only 23 met the inclusion criteria and were subjected to detailed analysis. The findings reveal a strong concentration of research on water distribution networks, coupled with limited methodological diversity across the reviewed studies. There is an absence of explicit focus on resource-constrained or arid environments contexts where water-scheduling challenges are often most acute. Geographically, the literature is heavily skewed toward Asia, with the majority of studies conducted in China (n = 7) and Iran (n = 6). In contrast, only one study originated from Africa and one from Australia despite the disproportionate severity of water scarcity challenges across the African continent. The review exposes a critical gap in the literature and underscores the need for more context-aware, hybrid swarm intelligence models that explicitly account for the socio-economic and environmental constraints of water-stressed regions. Full article

Metaheuristic algorithms remain a widely used class of solvers for solving complex, non-convex optimization problems where gradient information is unavailable, yet two failure modes continue to limit their practical reach: premature convergence caused by inadequate exploration diversity in late iterations and population stagnation that persists even when individual agents are nominally assigned to the exploration phase. This paper proposes the Stagnation-Aware Aquila Optimizer (SAAO), a hybrid algorithm that addresses both failure modes by embedding three targeted mechanisms into the Aquila Optimizer (AO) framework: (i) an adaptive exploration probability that responds to global fitness-improvement history; (ii) individual-level stagnation counters that force exploration re-entry for any agent that fails to improve for more than 30 consecutive iterations, regardless of the global phase schedule; and (iii) a diversity-maintenance module that reinitializes completely stagnant agents via random sampling or opposition-based learning. The biological repertoire of search operators is simultaneously enriched by incorporating four physics-grounded operators from the Animated Oat Optimization (AOO) algorithm centroid-guided dispersal, elite-guided dispersal, hygroscopic rolling, and spring ejection, alongside the original AO operators, yielding six complementary update rules partitioned equally between exploration and exploitation. The SAAO was evaluated against nine state-of-the-art algorithms on the CEC2015 benchmark and CEC2022 under identical experimental settings. The SAAO achieved the best Friedman mean rank on both suites and delivered competitive or superior performance against the nine baselines, with Wilcoxon rank-sum tests confirming statistically significant advantages over most competitors. On three classical engineering design problems, the SAAO achieved competitive outcomes. In a real-world equipment anomaly prediction task, an SAAO-optimized ensemble classifier attained 98.23% accuracy, surpassing the compared baseline models. These results establish SAAO as a robust and computationally tractable optimizer for both benchmark and applied settings. Full article

Purpose: Most children with autism live in resource-limited settings with limited access to timely interventions. To address this gap, the World Health Organization developed Caregiver Skills Training (CST) to support caregivers and expand intervention access globally. This study examined the feasibility and preliminary outcomes of a modified CST in mainland China. Methods: Using the ecological validity model and qualitative interviews, the CST materials were culturally adapted and modified for the Chinese context. A pre- and post-test controlled trial was conducted with caregivers of children with autism aged 2–9 years, who were assigned to either the CST intervention group (N = 15) or a caregiver education control group (N = 15). Clinical outcomes for caregivers and children were evaluated at baseline and after a 10-week intervention period. Results: Cultural adaptation and modifications focused on language adjustments, localization of case examples and demonstrations, and optimization of teaching methods and training schedules. Supplementary within-group analyses indicated pre–post changes in caregiver knowledge and skills, parenting stress, and selected child outcomes, including speech/language/communication, sensory/cognitive awareness, and overall autism symptoms. However, most between-group differences were not statistically significant after baseline adjustment. Conclusions: The findings provide preliminary evidence for the feasibility of culturally adapted and modified CST in mainland China. Given the pilot nature of the study and the absence of statistically significant between-group effects for most outcomes, the outcome findings should be interpreted as exploratory and hypothesis-generating rather than evidence of efficacy. Further large-scale studies with greater statistical power and objective outcome measures are needed to evaluate effectiveness and implementation feasibility. Full article

Imaging techniques used for the care of cutaneous melanoma patients have greatly changed over the past century, from symptom-driven radiography toward a multimodality framework integrated for staging, directing surgery, and systemic therapy, and surveillance. Historically, clinical evaluation and skin exams have been the tenets of melanoma diagnosis and staging. In recent years, noninvasive imaging, such as dermoscopy, total-body photography and reflectance confocal microscopy, has expanded the diagnostic toolset for primary melanoma detection. Concurrently, several imaging techniques have been developed to detect metastases and follow disease progression, including computed tomography (CT), magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT), lymphoscintigraphy, and single-photon emission computed tomography/computed tomography (SPECT/CT). The use of immune checkpoint inhibitors has also altered imaging interpretation by introducing atypical response patterns, including pseudoprogression, requiring immune-adapted assessment frameworks such as Immune Response Evaluation Criteria in Solid Tumors (iRECIST). While there is a strong consensus for high-risk patients, imaging techniques and surveillance schedules for low-risk patients (stage I/II) remain controversial due to limited supporting evidence and conflicting data on costs and patient benefit. The development of new technologies, including image-guided surgery, non-FDG PET tracers, phone apps, artificial intelligence-assisted image analysis, and radiomics, may further change melanoma imaging. The aim of this review is to detail the historical evolution of melanoma imaging, the development of new imaging techniques, and their role and future in clinical practice. Full article

Wireless sensor networks underpin the Internet of Medical Things (IoMT), where connected medical devices and wearable body-area sensors stream patient telemetry across hospital networks, and securing this traffic is safety-critical. Machine learning intrusion-detection systems for the IoMT are usually evaluated within a single public dataset, where they report near-perfect detection scores; whether that accuracy predicts performance in a different hospital has not been measured systematically. We frame the deployed detector as a lightweight human-in-the-loop edge AI agent that observes local traffic, asks an analyst to label a small set of representative flows, trains a tiny model on-premises, and monitors the traffic distribution for drift. Using cross-corpus shift across four public datasets, comprising three sensor-network corpora (WUSTL-EHMS-2020, CIC-IoMT-2024, TON-IoT) and an unrelated enterprise-network corpus, as a reproducible stand-in for cross-hospital deployment, we find the shift is near its theoretical maximum on all twelve transfer directions, that unlabeled domain-adaptation methods collapse on the hardest medical-telemetry targets, and that external pretraining adds no measurable benefit once training schedules are matched, consistent with a domain-adaptation error bound that the measured divergence renders vacuous. The same 1206-parameter network trained from scratch on ten to fifty locally labeled flows matches or exceeds every transfer alternative across all four corpora; on the six sensor-network transfer directions it also outperforms larger and classical local models. A closed-loop evaluation on simulated drifting streams shows the calibrated trigger detects drift within two 500-flow windows at under one false alarm per hundred stationary windows, retraining restores accuracy, and the agent tolerates ten percent analyst error for about five F1 points; we also map the detector’s exposure to white-box evasion and targeted label poisoning. Full article

The proliferation of Internet of Things (IoT) devices operating at the network edge has created unprecedented demand for distributed machine learning capable of functioning under severe resource constraints. Federated learning (FL) has emerged as a promising paradigm for privacy-preserving collaborative model training across distributed nodes; however, its application to energy-constrained edge environments remains insufficiently characterized at the system level, particularly with respect to reproducible evaluation of resource consumption and communication efficiency. In this paper, we present EcoFL (Energy-Conscious Federated Learning), a modular, energy-aware benchmarking and orchestration framework for systematic evaluation of lightweight machine learning models under emulated edge hardware constraints. Rather than proposing a new federated optimization algorithm, EcoFL extends a standard FedAvg-based training pipeline with three principal components: (i) an energy-aware communication scheduler that dynamically adapts aggregation rounds and client participation based on per-node resource availability; (ii) a comprehensive system-level profiling pipeline capturing CPU utilization, RAM consumption, inference latency, communication overhead, and estimated computational energy consumption per training round; and (iii) a reproducible benchmarking methodology enabling fair comparison of centralized, standard federated (FedAvg), and energy-aware federated configurations. We evaluate five lightweight model families—Logistic Regression, Random Forest, XGBoost, Multilayer Perceptron, and Isolation Forest—under emulated Raspberry Pi 4 hardware constraints using an anomaly detection task on synthetic IoT sensor telemetry (50,000 samples, 12 features, Dirichlet non-IID partitioning). Experimental results across five independent seeds show that, within the evaluated benchmark setting, EcoFL reduces estimated federated training energy by 79.9–92.9% (mean 84.4%) relative to standard FedAvg through adaptive round termination (4–7 rounds versus 20 fixed rounds), while showing no statistically significant F1-score degradation for four of the five evaluated model families under the tested seed regime. Notably, EcoFL achieves a higher F1-score than FedAvg for Random Forest (+0.052), which we attribute to reduced overfitting resulting from earlier convergence under non-IID data distributions. The full EcoFL framework is released as open-source software to promote reproducibility in energy-aware federated learning research and to facilitate systematic investigation of the trade-offs between predictive performance, resource utilization, and communication overhead in resource-constrained edge environments. Full article

Resource-constrained dynamic heterogeneous redundancy (DHR) systems use executor diversity and runtime reconfiguration to reduce stable attack surfaces. However, effective scheduling cannot rely only on heterogeneity or movement frequency, because repeated exposure, shared vulnerability sources, service disturbance, and switching overhead jointly shape executor-subset selection. This paper proposes RACS, a risk-aware cost-constrained scheduling method for resource-constrained DHR systems. RACS evaluates candidate subsets by jointly considering heterogeneity, historical confidence, readiness, common-vulnerability risk, exposure memory, and switching cost. We evaluate RACS using a controlled simulation protocol covering multiple scheduling principles and attacker behaviors, including common-vulnerability pressure, burst-adaptive exploitation, and adaptive target selection based on observed scheduling patterns. The results show that RACS does not optimize a single metric in isolation, but maintains a consistent security–cost trade-off. It reduces common-vulnerability risk and switching cost in common-vulnerability settings, reduces burst-triggering high-risk states under adaptive pressure, and maintains competitive risk–cost behavior when attackers adapt to historical scheduling behavior. Robustness and scalability analyses clarify the effects of vulnerability-family estimation errors, parameter choices, and executor-pool size. These findings provide controlled simulation evidence for joint risk–cost modeling in DHR executor-subset scheduling, while testbed validation remains future work. Full article

The rainfed paddy farming system faces profound vulnerabilities due to daily climate non-stationarity. This mixed-methods study in Central Java analyses daily climate signals, total rice production, and household adaptation over 25 years (2001–2025). Moving beyond simple correlation, a Principal Component Regression model integrating five climate variables and three agronomic confounders reveals a profound climate–production decoupling. The composite climate index explains only 7.9% of total production variation, while non-climate factors account for 92.1%. Physical stability is maintained through asymmetric temporal scheduling and a distinct hierarchy of responses, employing active, planned adaptations alongside passive, reactive coping. However, quantitative household evaluation reveals this tonnage stability incurs severe hidden costs; the titip gabah post-harvest system maintains a high Yield Stability Index (0.93) but yields a negative Return on Storage (−7.15%), functioning as a risk-mitigation buffer rather than a profit-maximising tool. Furthermore, climate anomalies drive the progressive alienation of traditional ethnoclimatological knowledge, forcing a cognitive shift toward hybridised decision-making. To prevent passive coping from evolving into systemic maladaptation, we propose a stratified policy framework ranging from village-level knowledge integration and Subdistrict daily risk warnings to regency-level subsidies targeted at smallholders (<0.5 ha). Full article

Glioblastoma (GBM) remains a highly lethal malignancy characterized by profound treatment resistance and metabolic plasticity. While photodynamic therapy (PDT) and the mitochondrial complex I inhibitor carboxyamidotriazole orotate (CTO) have individually shown promise, their combined potential requires further exploration and optimization. This study systematically investigated the interaction between 5-aminolevulinic acid (5-ALA)-mediated PDT and CTO in U87 GBM models. Intriguingly, we discovered a sequence-dependent interaction under the tested treatment schedules: CTO pre-incubation before PDT resulted in attenuated PDT-induced cytotoxicity, possibly due to CTO-mediated suppression of reactive oxygen species (ROS) accumulation. In contrast, a sequential “PDT→CTO” regimen enhanced anti-tumor efficacy both in vitro and in vivo. Mechanistically, the sequential approach was associated with enhanced mitochondrial depolarization and reduced expression of glycolysis-related genes, suggesting a potential metabolic “dual-hit” involving disturbance of mitochondrial function and compensatory glycolytic adaptation. These results highlight treatment sequence as a critical determinant of PDT-CTO interaction and provide a basis for further preclinical investigation of PDT followed by metabolic intervention as a combination strategy with potential translational relevance. Full article

Aiming at the problems of the traditional Statistical Priority-Based Multiple Access (SPMA) protocol in multi-hop Flying Ad Hoc Networks (FANETs), such as single-dimensional queueing only according to priority, unreasonable First-In-First-Out (FIFO) scheduling, high timeout dropping probability of multi-hop forwarding packets, and insufficient utilization of channel opportunities, this paper proposes a multi-hop adaptive SPMA protocol (MA-SPMA) suitable for dynamic multi-hop scenarios. The protocol adopts the Neighbor-Priority Two-Dimensional Queueing (NPTQ) mechanism to store packets jointly according to the next-hop neighbor and priority. A Priority-Utility Dual-round Decision (PUDD) mechanism is designed: in the first round, candidate queues that meet channel load conditions are selected in parallel; in the second round, a utility function constructed by normalized delay, priority, and the end-to-end transmission success rate is used to select the optimal packet for transmission. Theoretical analysis shows that the time and space complexity of MA-SPMA are linearly related to the number of neighbor nodes, with controllable overhead, which is suitable for resource-constrained Unmanned Aerial Vehicle (UAV) platforms. In the MATLAB simulation environment, the Reference Point Group Mobility (RPGM) model is used to construct a multi-hop topology, and comparisons are conducted with two typical improved protocols for multi-hop networks: DCLS-SPMA and BiLSTM-SPMA. The results show that the proposed protocol can significantly improve the end-to-end transmission success rate and network throughput, with more obvious advantages in scenarios with a high proportion of multi-hop services. This paper provides an effective solution for Medium Access Control (MAC) protocol design in FANETs. Full article

Cloud task scheduling is a critical optimization problem in cloud computing environments, aiming to allocate computational tasks to appropriate virtual machines while reducing execution time, balancing resource load, and minimizing scheduling cost. However, due to the high dimensionality, nonlinear characteristics, and complex constraints of cloud scheduling scenarios, traditional optimization methods often struggle to obtain high-quality solutions efficiently. To address these challenges, this paper proposes a Multi-strategy Improved Golden Sine Optimization Algorithm (MIGoldSA) for global optimization and cloud task scheduling problems. First, an adaptive chaotic opposition initialization strategy is incorporated to improve the distribution quality and diversity of the initial population. Second, a dynamic elite-guided sine evolution strategy is designed to reduce the dependence on a single best individual and improve the coordination between global exploration and local exploitation. Third, an Economic Investment Strategy is introduced to adaptively allocate search efforts according to the optimization potential of individuals. To verify the effectiveness of MIGoldSA, extensive experiments are conducted on the IEEE CEC2017 and CEC2022 benchmark suites and compared with nine advanced optimization algorithms. The results show that MIGoldSA obtains the best or tied-best mean fitness values on 60 out of 84 benchmark cases, accounting for 71.43% of all test cases. In the Wilcoxon signed-rank test, MIGoldSA achieves 662 wins, 57 ties, and 37 losses among 756 pairwise comparisons, corresponding to an overall win rate of 87.57% and a non-inferiority rate of 95.11%. In addition, the Friedman mean ranks of MIGoldSA are 1.47, 2.00, 3.98, and 4.17 under the four benchmark settings, which are reduced by 85.26%, 79.94%, 45.25%, and 42.32%, respectively, compared with the original GoldSA. Furthermore, the proposed algorithm is applied to cloud task scheduling problems under different task scales. The experimental results show that MIGoldSA maintains competitive time-cost performance and achieves clear reductions in load cost, price cost, and comprehensive scheduling cost. Compared with the original GoldSA, the normalized comprehensive scheduling cost is reduced by approximately 9–14% in small-scale scenarios and approximately 18–21% in large-scale scenarios. Meanwhile, the normalized load cost and price cost are reduced by about 18–25% and 10–18%, respectively, and the time cost shows an approximately 8–12% reduction in large-scale scheduling scenarios. These quantitative results demonstrate that MIGoldSA can improve the optimization accuracy, statistical robustness, and overall scheduling cost efficiency of the original GoldSA on most tested problems. Full article

Complex cyber-physical systems in the automotive and avionics domains increasingly consolidate safety-critical and non-critical functions onto shared multicore platforms. A central design challenge in these environments is ensuring strict timing guarantees while allowing graceful degradation under resource contention. In partitioned multiprocessor mixed-criticality systems, a mode switch on one processor forces all low-criticality (LC) tasks on that processor to degrade to their mandatory execution budgets, even though neighboring processors may have spare capacity. Existing approaches either focus on uniprocessor systems or address only the offline partitioning problem without considering the runtime survivability of LC tasks. This paper proposes IMC-PALM (Imprecise Mixed-Criticality via Partitioning and Adaptive Lightweight Migration), a two-phase framework for imprecise mixed-criticality (IMC) multiprocessor systems. The offline phase combines a tightened EDF-VD-IMC schedulability test with Mode-Balanced Partitioning (MBP). The tightened test identifies high-criticality tasks whose HI-mode utilization yields a tighter bound, while MBP allocates tasks based on mode-specific residual capacity. The runtime phase migrates LC tasks from a mode-switched processor to other processors that remain in LO mode, exploiting the per-processor isolation property of partitioned scheduling. Simulation results with 2, 4, and 8 processors show that MBP with the tightened test improves the acceptance ratio by 12.3 %p over existing algorithms under standard conditions. Furthermore, runtime migration reduces the degraded job ratio by 28.1 %p compared to the no-migration baseline under these standard settings, of which 4.6 %p is attributable to home-processor recovery. The benefit grows with the number of processors and remains robust under realistic migration overheads. Full article

The post-quantum signature scheme SQIsign achieves extremely compact public keys and signatures, making it attractive for bandwidth-constrained environments. However, its signing efficiency is limited by the high random failure rate of the ideal-to-isogeny mapping procedure and the substantial cost of each retry. Existing optimizations mainly reduce the number or cost of isogeny computations, while overlooking how to schedule commitment retries when multiple candidate ideals are available. We formulate commitment-stage scheduling as a lightweight learning-to-rank problem and provide an instrumented scheduling framework for SQIsign signing only. The pipeline uses two features, trains a weighted logistic regression scorer offline by maximum likelihood with class weighting, and deploys the same scorer online in Rank-ML mode. Live instrumentation on Apple M2 (n = 20,000 candidate attempts at NIST-I) quantifies the commitment bottleneck (86.4% failure; 7.36 mean attempts per session) and shows constant features at a fixed commitment degree (live AUC $=0.50$). Synthetic training supports the scorer when feature variance is present (test AUC $\approx 0.634$). A remeasured four-way ablation with Batch-only control ($n=100$, seed 42) separates batch overhead from learned ordering: Rank-ML is indistinguishable from Batch-only at deployment, while Baseline remains fastest for its wall-clock signing time at batch size 10. These results clarify when lightweight ML scheduling applies in SQIsign and provide a reproducible evaluation template separating live, synthetic, remeasured, and proxy evidence. Full article

The two-speed transmission system significantly enhances the powertrain matching performance of heavy-duty electric military armored vehicles by optimizing high-torque output at low speed and energy efficiency at high speed. However, most existing electric vehicles do not incorporate driving styles or real-time driving condition prediction into their shift control strategies, resulting in suboptimal gear shift timing and smoothness that fail to align with driver expectations and operational requirements. To address these limitations, this study focuses on the two-speed automated manual transmission (AMT) system in heavy-duty electric military armored vehicles. Firstly, a comprehensive shift control model is established, integrating key components such as the drive motor and power battery. Furthermore, a shift control strategy based on vehicle speed prediction and driving style recognition is proposed. The operational logic of this strategy is systematically analyzed under various driving cycles. Simulation and hardware-in-the-loop (HIL) results confirm the performance gains. Simulation and hardware-in-the-loop (HIL) results indicate that the proposed approach improves vehicle power performance by 21.36%, increases energy efficiency by 3.94%, and reduces powertrain shock by 31.81% compared to the conventional vehicle-speed-based gear shifting method. Compared to the adaptive shift schedule design method, the proposed approach reduces shifting frequency by 21.43% and improves ride comfort by at least 19.17% while maintaining comparable dynamic performance and energy efficiency. Full article

Scythris sinensis (Felder & Rogenhofer) is a diurnal moth characterized by its distinct spring and autumn forms. In our study, we analyzed the compound eye morphology of both forms utilizing scanning electron microscopy. The eyes are ellipsoidal and symmetrical, consisting of hexagonal ommatidia characterized by convoluted folds but lacking corneal nipples. We found that female moths have slightly larger compound eyes than males. Our counts of ommatidia indicate that males emerging in autumn (589–675) typically possess a higher number than males emerging in spring (492–698). This finding suggests an adaptation specific to the autumn period, potentially enhancing visual search efficiency in response to seasonal light variations and a constrained reproductive schedule. This research provides a foundational understanding of the visual behavior of S. sinensis concerning photoreception and phototaxis. Full article