Search Results (170)

To address the rapid population diversity loss and premature convergence of the Artificial Lemming Algorithm (ALA) in complex optimization problems, this paper proposes an Improved Artificial Lemming Algorithm (IALA) with multi-strategy enhancements inspired by lemming behavior. First, a non-uniform mutation operator and a nonlinear step-size strategy are introduced to strengthen local optima escape capability and optimization precision. Second, inspired by the foraging and positioning behavior of lemmings, a relative advantage learning strategy is designed to reduce dependence on the global best individual, further enhancing the algorithm’s exploration ability. Finally, a Q-learning-based adaptive mechanism is integrated to intelligently orchestrate five lemming-inspired behavioral modes through a nonlinear reward function, enabling adaptive switching among search patterns. Comparative experiments on the CEC2022 benchmark suite demonstrate that IALA achieves a Friedman mean rank of 1.25, ranking first with a significant margin. Compared with the original ALA and other six classical and state-of-the-art metaheuristic algorithms, and four recently proposed improved ALA variants (EALA, IALA_Tan, DMSALAs, and MsIALA), the Wilcoxon rank-sum test confirms that IALA is significantly outperformed in only 2 out of 120 pairwise comparisons, exhibiting remarkable advantages in optimization accuracy, convergence speed, and robustness. Ablation experiments validate the synergistic necessity of all three strategies, with the Q-learning adaptive mechanism identified as the most critical contributor. Exploration–exploitation balance analysis and search history visualization further confirm that IALA achieves a smooth adaptive transition from global exploration to local exploitation. Space complexity analysis reveals that the Q-table introduces only approximately 19.5 KB of fixed additional overhead, which becomes negligible for high-dimensional problems. Furthermore, IALA is successfully applied to the parameter tuning of underwater manipulator controllers, verifying its efficiency and reliability in real-world engineering applications. Full article

Background: Ocular candidiasis is a serious metastatic complication of candidemia that may lead to irreversible visual impairment. Although recent meta-analyses suggest an overall prevalence of approximately 10%, real-world data from Southeast Asia remain limited. Regional differences in Candida species distribution, antifungal resistance patterns, and health-care resources may influence both the incidence of ocular candidiasis and the utilization of ophthalmic evaluation in routine practice. Methods: We conducted a retrospective cohort study of patients aged ≥15 years with candidemia at a 900-bed tertiary-care university hospital in southern Thailand between January 2014 and August 2025. Only the first episode of candidemia per patient was included. Ophthalmic evaluation was defined as a dilated funduscopic examination performed by an ophthalmologist within 4 weeks of candidemia onset. Ocular candidiasis was classified as Candida chorioretinitis or Candida endophthalmitis according to established definitions. Multivariable logistic regression was used to identify factors independently associated with receipt of ophthalmic evaluation. Results: Among 337 patients with candidemia, 67 (19.9%) underwent ophthalmic evaluation. Ocular candidiasis was diagnosed in 9 of 67 evaluated patients (13.4%), corresponding to an overall incidence of 2.7% in the entire cohort. Five patients (7.5%) had Candida chorioretinitis and four (6.0%) had Candida endophthalmitis, including two concordant and two discordant cases. Visual symptoms were assessable in 35 patients, among whom only 4 (11.4%) reported visual complaints; most patients with ocular candidiasis were asymptomatic at diagnosis. Candida albicans and Candida tropicalis accounted for 55.6% and 44.4% of ocular candidiasis cases, respectively, and bilateral ocular involvement was observed in 77.8%. Ophthalmic findings led to modification of antifungal therapy in 7 of 9 patients with ocular candidiasis (77.8%), most commonly through addition or switching to an azole-based regimen and/or prolongation of treatment duration. In multivariable analysis, vasopressor use at candidemia onset was independently associated with a lower likelihood of ophthalmic evaluation, whereas early infectious diseases consultation was independently associated with increased odds of receiving ophthalmic evaluation. Conclusions: In this Southeast Asian cohort, ophthalmic evaluation was infrequently performed but yielded clinically actionable findings and frequently altered antifungal management. The observed incidence of ocular candidiasis among examined patients was higher than that reported in Western countries. Underutilization of an ophthalmic evaluation appears to reflect illness severity and care pathway factors rather than low disease burden, suggesting that the true incidence of ocular candidiasis may be underestimated. Integrating ophthalmic evaluation into standardized candidemia care pathways may improve detection of ocular involvement, particularly in resource-limited settings. Full article

In recent years, dietary changes towards reducing animal-based proteins was recognized as a nitrogen pollution-mitigating strategy. This is because producing animal protein generates higher nitrogen emissions compared to its plant-based alternatives. In Japan, there is a switch towards an animal-based diet, potentially leading to degraded water quality. While national-scale studies are common, watershed-level scale dietary changes are not researched, even though nitrogen pollution is often localized. This study aims to evaluate whether dietary and feed self-sufficiency changes can reduce nitrogen load and improve water quality in the Kasumigaura watershed. Firstly, nitrogen load was quantified and spatially distributed. Then, the estimated nitrogen concentration was compared with observed data. Finally, the impact of dietary and feed self-sufficiency changes on nitrogen load and water quality was assessed. Results estimated that nitrogen loading for year 2020 was 4403 tons/N/year, correlating with previous research. Results further showed that switch from livestock to legume protein would significantly improve water quality, up to 0.27 mg N/L. On the other hand, increasing feed self-sufficiency would negatively affect the water quality, up to 0.32 mg N/L. The results emphasize the importance of dietary patterns in mitigating nitrogen pollution. This method can be generalized on other watersheds. Full article

Integrating Software-Defined Networking (SDN) to enhance mobility management in Vehicular Ad Hoc Networks (VANETs) comes with an additional critical risk. Because centralized controllers are single points of failure, they create the risk that the network will be subject to denial-of-service (DoS) attacks during handovers. Most Intrusion Detection and Prevention systems (IDPSs) do not adequately address these risks because they are topology-blind and have excessive processing layers. This article presents a novel Location-Aware SDN-IDPS Framework that employs a hierarchical defense approach to protect vehicular networks against volumetric attacks. This two-plane system operates with the first tier, which uses dynamic host-location mappings to drop spoofed traffic at the switch level (data plane). In contrast, the second tier analyzes confirmed traffic through a Suricata-based engine to identify and respond to complex flood attack patterns. The experimental results from the Mininet-WiFi testbed show that the system provides a significant improvement over the unprotected state, with controller CPU utilization reduced by up to 18 times (from 9.0% to below 0.5%). In addition, the system provides a 2.3 s guaranteed recovery time, service continuity, successful microsecond-level mitigation time, and a packet delivery ratio (PDR) of 99.73% for legitimate safety messages. In control-plane stress testing, the proposed location-aware logic improved throughput stability by approximately 76.26% compared to the baseline. These findings confirm that offloading anti-spoofing logic to the network edge significantly enhances resilience without compromising performance in safety-critical vehicular environments. Full article

Growing pressure on electricity grids and the increasing availability of smart meter data have intensified the need for accurate, interpretable, and scalable methods to analyze and forecast household electricity consumption. In this context, this study presents a general, data-agnostic methodology for predicting and classifying household energy consumption. The proposed workflow unifies data preparation, feature engineering, and machine learning techniques (including clustering, classification, regression, and time series forecasting) within a single interpretable pipeline that supports actionable insights. Rather than proposing new prediction algorithms, this work contributes a fully reproducible, end-to-end methodological pipeline that enables the controlled evaluation of the impact of contextual variables, customer segmentation, and cold-start conditions on household energy forecasting. A distinctive aspect of the pipeline is the explicit use of household- and dwelling-level contextual variables to derive customer typologies via clustering and to enrich forecasting models. The models are evaluated for predictive accuracy, reliability under varying conditions, and suitability for operational use. The results show that incorporating contextual variables and clustering significantly improves forecasting accuracy, particularly in cold-start scenarios where no historical consumption data are available. Although numerous public datasets of residential electricity consumption exist, they rarely provide, in an openly accessible form, both detailed load histories and rich contextual attributes, while many are subject to privacy or licensing restrictions. To ensure full reproducibility and to enable controlled experiments where contextual variables can be switched on and off, the experiments are conducted on a synthetically generated dataset that reproduces realistic behavior and seasonal usage patterns. However, the proposed methodology is independent of the specific data source and can be directly applied to any real or synthetic dataset with similar structure. The approach enables applications such as short- and long-term demand forecasting, estimation of household energy costs, and forecasting demand for new customers. These findings demonstrate that the proposed pipeline provides a transparent and effective framework for end-to-end analysis of household electricity consumption. Full article

Lung cancer is considered to be a significant cause of death in the world, and the timely identification of nodules in the lungs in CT scans is very important to enhance the prognosis of patients. Although the state of the art of nodule delineation using deep learning-based segmentation models was achieved, major problems, including high feature diversity, low spatial discrimination, and overfitting of the models, require stronger feature-processing approaches. This research explores an enhanced symmetric encoder–decoder segmentation network known as the Improved Group–Shuffle Module (IGSM) to overcome these shortcomings. The most important feature of the proposed method is the IGSM, which hierarchically divides feature maps into a few groups, then transforms them independently, and then randomly switches channels between groups to increase inter-group interaction of features and diversity. This IGSM method is inspired by human brain functions, which are processed in specialized cortex areas, which are mimicked in this work through small-group feature processing. Channel shuffling is designed based on inter-modular communication in the human brain through coherent information sharing among the small groups of cortices. Through this mechanism, the model is much better at capturing discriminative spatial and contextual patterns, especially on complex and subtle nodule structures. The IGSM configurations have been optimized, specifically, the placement of the modules, grouping size, and shuffle permutation strategies. The proposed model’s performance is then compared with the benchmarked models, like U-Net and DeepLab, with various performance indicators such as mean Intersection over Union (mIoU), Dice Score, Accuracy, Sensitivity, and Specificity. The simulation results proved the superiority of the IGSM-enhanced model with the mIoU of 0.7735, the Dice Score of 0.9665, and the Accuracy of 0.9873. The addition of the group and shuffle module not only enhances the discrimination between the nodules and their background, but it also improves the ability to generalize over a variety of nodules’ morphology, thus producing a reliable tool for automated detection of lung cancer. Full article

Intelligent Tutoring Systems are increasingly used in higher education to support personalized learning and academic monitoring in large-scale digital environments. However, existing systems are predominantly based on static architecture and rigid rule-based mechanisms, which limit scalability and hinder effective adaptation to heterogeneous learners, evolving learning behaviors, and real-world educational contexts. This paper presents a self-adaptive multi-agent architecture based on Reinforcement Learning for autonomous decision-making in intelligent systems deployed in real environments. The proposal integrates an RL Meta-Agent that dynamically optimizes the selection of specialized agents through an intelligent switching mechanism, considering the user’s state, behavior, and interaction patterns. The architecture was implemented in Moodle using flows orchestrated in n8n, LLMs, databases, APIs developed in Django, and real academic data. For the empirical evaluation, a real and a simulated case study were designed. A questionnaire was administered to university students, considering dimensions of usability, satisfaction and usefulness, and accessibility and interaction, to understand the perception of the system and improvements. The quantitative data were analyzed using descriptive statistics and nonparametric tests (Mann–Whitney U and Kruskal–Wallis), while the qualitative data were examined using thematic categorization. A simulated case study was conducted to analyze the behavior of the system. The results show that the RL Meta-Agent significantly improves system efficiency, response relevance, and adaptive agent selection, demonstrating that self-adaptive RL-based MAS architectures are a viable solution for intelligent systems applied in real-world contexts, providing empirical evidence of their performance and adaptability in complex scenarios such as higher education. Full article

Cauliflower (Brassica oleracea var. botrytis) curd formation is a highly complex developmental process governed by tightly coordinated genetic and physiological regulation. Here, we performed transcriptome sequencing of curd and peduncle tissues across multiple developmental stages, generating 171.52 Gb of high-quality data. Genes associated with photosynthesis and glucosinolate biosynthesis were strongly upregulated in the shoot apical meristem (SAM), highlighting substantial metabolic investment during the pre-initiation phase of curd morphogenesis. Key floral transition regulators, particularly AP2 and MADS-box transcription factors, were activated to drive the vegetative-to-reproductive switch and initiate curd primordia, ultimately giving rise to the arrested inflorescence architecture characteristic of cauliflower. Furthermore, hormone signaling pathways—including auxin (AUX), jasmonic acid (JA), and brassinosteroid (BR)—showed marked activation during SAM proliferation and peduncle elongation, underscoring their crucial roles in structural patterning. Collectively, our findings delineate an integrated regulatory network that links metabolic activity, hormone signaling, and developmental programs, providing novel molecular insights into curd formation and identifying potential breeding targets for the genetic improvement of Brassicaceae crops. Full article

Conventional acoustic time-of-arrival (TOA) estimation in complex indoor environments is highly susceptible to multipath reflections and occlusions, resulting in unstable measurements and limited physical interpretability. This paper presents a smartphone-based indoor localization method built on vision-assisted acoustic channel modeling, and develops a fusion anchor integrating a pan–tilt–zoom (PTZ) camera and a near-ultrasonic signal transmitter to explicitly perceive indoor geometry, surface materials, and occlusion patterns. First, vision-derived priors are constructed on the anchor side based on line-of-sight reachability, orientation consistency, and directional risk, and are converted into soft anchor weights to suppress the impact of occlusion and pointing mismatch. Second, planar geometry and material cues reconstructed from camera images are used to generate probabilistic room impulse response (RIR) priors that cover the direct path and first-order reflections, where environmental uncertainty is mapped into path-dependent arrival-time variances and prior probabilities. Finally, under the RIR prior constraints, a path-wise posterior distribution is built from matched-filter outputs, and an adaptive fusion strategy is applied to switch between maximum a posteriori (MAP) and minimum mean square error (MMSE) estimators, yielding debiased TOA measurements with calibratable variances for downstream localization filters. Experiments in representative complex indoor scenarios demonstrate mean localization errors of 0.096 m and 0.115 m in static and dynamic tests, respectively, indicating improved accuracy and robustness over conventional TOA estimation. Full article

The integration of solar energy into smart grids requires high-efficiency power conversion to support grid stability. However, Partial Shading Conditions (PSCs) remain a primary obstacle by inducing multiple local maxima on P–V characteristic curves. This paper presents a hardware-aware and memory-enhanced Maximum Power Point Tracking (MPPT) approach based on a modified Dragonfly Algorithm (DA) for grid-connected microinverter-based photovoltaic (PV) systems. The proposed method utilizes a quasi-switched Boost-Switched Capacitor (qSB-SC) topology, where the DA is specifically tailored by combining Lévy-flight exploration with a dynamic damping factor to suppress steady-state oscillations within the qSB-SC ripple constraints. Coupling the MPPT stage to a seven-level Packed-U-Cell (PUC) microinverter ensures that each PV module operates at its independent Global Maximum Power Point (GMPP). A ZigBee-based Wireless Sensor Network (WSN) facilitates rapid data exchange and supports ‘swarm-memory’ initialization, matching current shading patterns with historical data to seed the population near the most probable GMPP region. This integration reduces the overall response time to 0.026 s. Hardware-in-the-loop experiments validated the approach, attaining a tracking accuracy of 99.32%. Compared to current state-of-the-art benchmarks, the proposed model demonstrated a significant improvement in tracking speed, outperforming the most recent 2025 GWO implementation (0.0603 s) by approximately 56% and conventional metaheuristic variants such as GWO-Beta (0.46 s) by over 94%.These results confirmed that the modified DA-based MPPT substantially enhanced the microinverter efficiency under PSC through cross-layer parameter adaptation. Full article

This paper examines the contrasting yet complementary approaches of the Constrained Disorder Principle (CDP) and Stefan Hell’s deterministic optical nanoscopy for managing noise in complex systems. The CDP suggests that controlled disorder within dynamic boundaries is crucial for optimal system function, particularly in biological contexts, where variability acts as an adaptive mechanism rather than being merely a measurement error. In contrast, Hell’s recent breakthrough in nanoscopy demonstrates that engineered diffraction minima can achieve sub-nanometer resolution without relying on stochastic (random) molecular switching, thereby replacing randomness with deterministic measurement precision. Philosophically, these two approaches are distinct: the CDP views noise as functionally necessary, while Hell’s method seeks to overcome noise limitations. However, both frameworks address complementary aspects of information extraction. The primary goal of microscopy is to provide information about structures, thereby facilitating a better understanding of their functionality. Noise is inherent to biological structures and functions and is part of the information in complex systems. This manuscript achieves integration through three specific contributions: (1) a mathematical framework combining CDP variability bounds with Hell’s precision measurements, validated through Monte Carlo simulations showing 15–30% precision improvements; (2) computational demonstrations with N = 10,000 trials quantifying performance under varying biological noise regimes; and (3) practical protocols for experimental implementation, including calibration procedures and real-time parameter optimization. The CDP provides a theoretical understanding of variability patterns at the system level, while Hell’s technique offers precision tools at the molecular level for validation. Integrating these approaches enables multi-scale analysis, allowing for deterministic measurements to accurately quantify the functional variability that the CDP theory predicts is vital for system health. This synthesis opens up new possibilities for adaptive imaging systems that maintain biologically meaningful noise while achieving unprecedented measurement precision. Specific applications include cancer diagnostics through chromosomal organization variability, neurodegenerative disease monitoring via protein aggregation disorder patterns, and drug screening by assessing cellular response heterogeneity. The framework comprises machine learning integration pathways for automated recognition of variability patterns and adaptive acquisition strategies. Full article

Background: Emotional dysregulation and impulsivity represent key risk factors for adverse trajectories in adults with ADHD and are frequently observed among patients with opioid use disorder (OUD). Levomethadone, the R-enantiomer of methadone, provides more stable dopaminergic modulation than the racemic formulation and may improve emotional control. The primary objective was to examine emotional, clinical, and substance use changes after the switch to levomethadone and to determine whether these trajectories differed according to ADHD screening status. This study evaluated emotional, clinical, and behavioral outcomes—including substance use—after transitioning from racemic methadone to levomethadone maintenance therapy, focusing on the moderating role of ADHD symptoms and dose escalation. Methods: Eighty-three OUD patients in methadone maintenance were assessed at baseline, T1 (mean = 2.13 months, SD = 0.65), and T2 (mean = 6.20 months, SD = 0.91). Emotional dysregulation (RIPOST), clinical severity (Clinical Global Impression), and days of substance use were analyzed using Linear Mixed Models (participants with ≥1 valid follow-up). ADHD symptoms (Adult ADHD Self-Report Scale DSM-5) were evaluated with Wilcoxon signed-rank tests. Dose escalation (↑levomethadone) was defined as ≥1 increase during follow-up and was only included in the mixed models. Substance use analyses were restricted to baseline active users. Results: Emotional impulsivity significantly decreased over time only in participants screening positive for ADHD symptoms (ASRS ≥ 14), independent of dose escalation. Emotional instability also declined but across the full cohort. CGI scores improved in all participants. Substance use patterns showed a modest overall improvement, with reductions most evident for sedatives and alcohol. The findings indicate a specific effect of levomethadone on affective regulation and clinical stabilization, particularly in individuals with impulsivity traits. Conclusions: Levomethadone maintenance appears to improve emotional regulation and global functioning beyond dose-related effects, supporting its potential value in complex OUD patients with clinically relevant ADHD symptomatology. Combined treatment with levomethadone and methylphenidate may further enhance executive control and craving regulation in this population. Full article

Modern deep neural network (DNN) accelerators must sustain high throughput while avoiding performance degradation from supply voltage (VDD) droop, which occurs when large arrays of multiply–accumulate (MAC) units switch concurrently and induce high peak current ($IC{C}_{max}$) transients on the power delivery network (PDN). In this work, we focus on ASIC-class DNN accelerators with tightly synchronized MAC arrays rather than FPGA-based implementations, where such cycle-aligned switching is most pronounced. Conventional guardbanding and reactive countermeasures (e.g., throttling, clock stretching, or emergency DVFS) either waste energy or incur non-trivial throughput penalties. We propose SparseDroop, a unified hardware-conscious framework that proactively shapes instantaneous current demand to mitigate droop without reducing sustained computing rate. SparseDroop comprises two complementary techniques. (1) SparseStagger, a lightweight hardware-friendly droop scheduler that exploits the inherent unstructured sparsity already present in the weights and activations—it does not introduce any additional sparsification. SparseStagger dynamically inspects the zero patterns mapped to each processing element (PE) column and staggers MAC start times within a column so that high-activity bursts are temporally interleaved. This fine-grain reordering smooths $ICC$ trajectories, lowers the probability and depth of transient VDD dips, and preserves cycle-level alignment at tile/row boundaries—thereby maintaining no throughput loss and negligible control overhead. (2) SparseBlock, an architecture-aware, block-wise-structured sparsity induction method that intentionally introduces additional sparsity aligned with the accelerator’s dataflow. By co-designing block layout with the dataflow, SparseBlock reduces the likelihood that all PEs in a column become simultaneously active, directly constraining $IC{C}_{max}$ and peak dynamic power on the PDN. Together, SparseStagger’s opportunistic staggering (from existing unstructured weight zeros) and SparseBlock’s structured, layout-aware sparsity induction (added to prevent peak-power excursions) deliver a scalable, low-overhead solution that improves voltage stability, energy efficiency, and robustness, integrates cleanly with the accelerator dataflow, and preserves model accuracy with modest retraining or fine-tuning. Full article

Rising residential land prices push up housing prices and worsen credit misallocation. These patterns emerge amid cyclical real estate fluctuations and heavy land-based public finance. Such pressures undermine macroeconomic stability and sustainable land-use. The land price circuit breaker is widely applied with a price cap and state dependence, yet its trigger mechanism and interaction with inflation targeting remain underexplored. This study addresses three core questions. First, how does the circuit breaker’s discrete trigger and rule-switching logic differ from traditional static price ceilings? Second, can the mechanism, via the collateral channel, restrain excessive land price hikes, improve credit allocation, and, thereby, stabilize land price dynamics and long-run macroeconomic performance? Third, how does the circuit breaker interact with inflation targeting, and through which endogenous channels does a strict target dampen housing prices and raise activation probability? This study develops a multi-sector DSGE model with an embedded land price circuit breaker. The price cap is modeled as an occasionally binding constraint. A dynamic price band and trigger indicator capture the policy’s switch between slack and binding states. The framework incorporates interactions among local governments, the central bank, developers, and households. It also links firms and the secondary housing market. Under different inflation-targeting rules, this study uses impulse responses, an event study, and welfare analysis to assess trigger conditions and macroeconomic effects. The findings are threefold. First, a strict inflation target increases the probability of a circuit breaker being triggered. It channels housing-demand shocks toward land prices and creates a “nominal anchor–relative price constraint” linkage. Second, once activated, the circuit breaker narrows the gap between land price and house-price growth. It weakens the procyclicality of collateral values. It also restrains credit expansion by impatient households. These effects redirect credit toward firms, improve corporate financing, reduce the decline in investment, and accelerate output recovery. Third, the circuit breaker limits new land supply and shifts demand toward the secondary housing market. This generates a supply-side effect that releases existing stock and stabilizes prices, thereby weakening the amplification mechanism of housing cycles. This study identifies the endogenous trigger logic and cross-market transmission of the land price circuit breaker under a strict inflation target. It shows that the mechanism is not merely a price-management tool in the land market but a systemic policy variable that links the real estate, finance, and fiscal sectors. By dampening real estate procyclicality, improving credit allocation, and stabilizing macroeconomic fluctuations, the mechanism offers new insights for sustainable land-use policy and macroeconomic stabilization. Full article

Industrial electric motors powered by variable frequency drives (VFDs) offer better controllability as compared to the conventional sinusoid-fed motors. However, the switching transients of VFDs induce shaft voltage in electric motors, which can lead to bearing failure. This may cause the machine to shut down and pose a serious threat to the system’s reliability. Several shaft voltage mitigation strategies are suggested in the literature, including insulated bearings, grounding brushes, copper shields, and filters. Although mitigation strategies have been extensively studied, shaft voltage signal processing remains relatively underexplored. This review introduces diffusion models (DMs), a new generative learning technique, as an effective solution for processing shaft voltage signals. These models are good at reducing noise, handling uncertainty, and capturing complex patterns over time. DMs offer robust performance under dynamic conditions as compared to traditional machine learning (ML) and deep learning (DL) techniques. In summary, the review outlines the sources and causes of shaft voltage, its existing mitigation strategies, and the theory behind DMs for shaft voltage analysis. Thus, by combining insights from electrical engineering and artificial intelligence (AI), this work addresses an important gap in the existing literature and provides a strong path forward for improving the reliability of industrial motor systems. Full article