Search Results (5,695)

Objectives: Acute exposure to sustained high cognitive load rapidly depletes executive functions, yet rapid, field-compatible countermeasures remain scarce. This study examined whether transcutaneous auricular vagus nerve stimulation (taVNS) improves cognitive functions across multiple domains—including the attention networks, cognitive flexibility, and working memory—following sustained high cognitive load. Methods: Healthy young men completed a 1 h dual task combining spatial 1-back and color–word Stroop judgments (the BS task) to induce a high-cognitive-load state; 65 participants were randomized to receive 30 min of active taVNS (left cymba conchae; 25 Hz; 500-μs pulse width; 30 s on/30 s off) or sham stimulation under a single-blind design, and 62 provided complete data and were analyzed. Cognitive performance was assessed with the Attention Network Test, Cued Switching Task, and spatial 2-back task immediately after the intervention and after 1, 2, 4, and 8 h; baseline-corrected change scores were analyzed with linear mixed-effects models. Results: The induction task reliably increased subjective workload and degraded task accuracy. Immediately after the intervention, taVNS significantly reduced the conflict effect relative to sham (b = −16.43 ms, 95% CI [−32.03, −0.82], F(1, 127.5) = 4.34, p = 0.039, d = 0.64) and the switch cost (b = −59.79 ms, 95% CI [−109.66, −9.92], F(1, 81.8) = 5.69, p = 0.019, d = 0.53); both immediate effects remained significant after Holm correction. Group × time interactions were nonsignificant, and the temporal course of the between-group differences should therefore be interpreted cautiously. No significant effects were observed for alerting, orienting, or working memory. Conclusions: These findings indicate that taVNS selectively and immediately improves executive control and cognitive flexibility following sustained high cognitive load, supporting its potential as a portable, rapid-acting cognitive countermeasure for high-demand operational settings. Full article

Constantine, an intermediate Algerian city facing complex urban dynamics and growing socio-ecological pressures, provides a relevant context for exploring integrated approaches to urban regeneration. This study introduces the Integrated Operational Tools Framework (IOTF), developed as a methodological and operational framework to overcome the fragmentation that continues to characterize contemporary urban planning practice. Grounded in an action-research perspective, the framework brings together multi-scale territorial diagnosis, participatory governance, adaptive planning, multi-scenario analysis, and dynamic urban performance assessment through the Regenerative Resilience Index (R²I). Its application to the Bardo district of Constantine illustrates the IOTF’s potential to support progressive, measurable, and adaptive urban transformation while enhancing ecological resilience, strengthening social cohesion, and improving the overall quality of the urban environment. The proposed framework provides a structured approach for reconfiguring urban intervention logics, supporting a shift from reactive corrective approaches toward a more systemic regeneration paradigm. Its modular architecture ensures high adaptability and replicability in other intermediate urban contexts, particularly within North African and Mediterranean regions, provided that socio-institutional and environmental parameters are contextualized. Finally, the article highlights the potential of the IOTF as a synergistic system of five interconnected modular tools, organized according to a circular regenerative process. This contributes to the emergence of a hybrid, transferable, and flexible urban governance capable of guiding the transition to planning models that are more integrated, participatory, and oriented toward territorial resilience. Full article

Dermatological pathologies are multifactorial conditions that often show limited response to single-target therapies. Natural compounds may offer advantages through their pleiotropic effects on inflammation, oxidative stress, and tissue repair. This study employed an integrated in silico approach to evaluate selected phytoconstituents from Melaleuca alternifolia, Lavandula angustifolia, Tamarix ramosissima, and Curcuma longa. Drug-likeness, pharmacokinetic properties, dermal permeability, and toxicity were assessed using SwissADME and admetSAR 3.0. SwissTargetPrediction and the Similarity Ensemble Approach (SEA) identified carbonic anhydrases (CAs) among the predicted molecular targets, supporting the selection of CA IV and CA IX for structure-based analysis. Molecular docking identified favorable catalytic-site poses for several compounds, with curcuminoid and flavonoid scaffolds generally showing more favorable predicted affinities. Cyclocurcumin, tamarixetin, and scopoletin were subsequently investigated by molecular dynamics simulations. The simulations revealed scaffold- and isoform-dependent differences in interaction persistence: cyclocurcumin showed pronounced conformational flexibility, tamarixetin displayed more progressive displacement, and scopoletin showed the lowest persistence in the catalytic region. Overall, the results generate testable hypotheses regarding natural compound interactions with CA IV and CA IX, but experimental binding and enzymatic assays are required to establish CA modulation and therapeutic relevance. Full article

Sustainable smart factories require energy management that conserves industrial demand and respects receiver capacity across production campaigns. This retrospective study assessed whether a scenario framework could reduce energy above a fixed daily threshold, exceedance days, maximum daily grid energy, and purchased grid energy without deleting demand. The dataset contained 435 daily records from eight campaigns, including 423 finite positive observations used for scenario evaluation, three products, six metered energy sources, and 10,440 hourly weather records. Weather-derived photovoltaic availability was coupled with daily battery-grid accounting. Data from 2019–2024 supported development, data from 2025 supported temporal validation, and data from 2026 supported chronological testing. Five scenarios compared the baseline (S1) with deterministic tuning (S2), demand-side management (S3), a genetic algorithm (GA; S4), and particle swarm optimization (PSO; S5) under identical objectives and constraints. Across all data, S2, S4, and S5 reduced above-threshold energy by 6.9%, left exceedance days and maximum daily grid energy unchanged, and increased purchased grid energy by 0.1%. Their 2026 reduction was 1.0%. Accepted transfers represented 0.50–0.68% of demand, retained unplaced requests at source, met the three-day limit, and caused no receiver violations. GA and PSO returned identical objectives and outcomes across 30 seeded runs each, providing scenario-based evidence under aggregate daily assumptions. Full article

Monocular RGB imaging offers a low-cost, flexible approach to morphological measurement, quality assessment, growth monitoring, and production automation for edible mushroom fruiting bodies. However, the relationships among visible phenotypes, visual methods, measurement reliability, and production requirements remain insufficiently integrated. This problem-oriented review synthesizes 76 core studies published between 2016 and the final search date in 2026 through a framework linking visible phenotypes, visual tasks, key bottlenecks, and production applications. It covers individual localization and separation, structural measurement, spatial estimation, quality and species recognition, temporal analysis, and production deployment. The reviewed studies reveal a transition from static two-dimensional detection and counting toward instance-level morphological measurement, three-dimensional parameter estimation, and spatiotemporal growth modeling, extending phenotyping from visible appearance description to spatial trait estimation and growth prediction. Quantitative results also highlight the importance of evaluation conditions: for example, MSH-YOLOv8 achieved an ${\mathrm{AP}}_{50}$ of 98.49% on the Fungi dataset, whereas ${\mathrm{AP}}_{50:95}$ and small-object AP were 75.29% and 39.73%, respectively, indicating that high ${\mathrm{AP}}_{50}$ alone does not adequately characterize detection performance under stricter localization criteria or for small targets. These study-specific results cannot be directly extrapolated to commercial production environments. Severe occlusion, projection errors, inconsistent phenotype definitions, and temporal instability continue to constrain measurement reliability. Moreover, reliable performance without extensive retraining following changes in strains, substrates, or lighting systems remains insufficiently demonstrated. Future research should prioritize standardized multi-task and temporal datasets, unified phenotype definitions, uncertainty evaluation, occlusion-robust spatial and temporal modeling, and closed-loop production validation. Commercial scaling of low-cost monocular RGB imaging in protected mushroom cultivation depends on translating its affordability into reliable performance under severe occlusion and across production conditions, thereby enabling accessible automation for small and medium-scale producers. Full article

Life goals are described as a general psychological resource for health, but whether goals of different content relate equally to mental health and to health behaviour is unknown. Participants were 408 students at a Turkish state university (71.1% women; mean age 21.76 years, SD = 4.20) who completed the Life Goals Scale, distinguishing career, relationship, and body-related goals, together with measures of psychological well-being, psychological distress, cognitive flexibility, physical activity, and nutritional risk. Regression models adjusted for gender, age, grade level, body mass index, and faculty used heteroscedasticity-consistent standard errors, and the two domains were compared using case bootstrapping. Goal setting was associated with all five indicators (β = 0.110 to 0.384), and the association was stronger for the mental health block than the behavioural block (Δβ = 0.129, 95% CI [0.049, 0.205]). However, this difference was carried by cognitive flexibility. Specifically, with that indicator excluded, the contrast fell to 0.077 and was no longer reliable. The two behavioural coefficients did not differ (Δβ = 0.086), and the association with nutritional risk was small and weakly supported. With the three subscales entered simultaneously, physical activity was related to body-related goals alone (β = 0.227, p < 0.001) and not to career (β = 0.032) or relationship goals (β = 0.012). Among the three goal contents assessed, what a goal is about, rather than goal setting as such, appears to distinguish which health behaviour it accompanies. Full article

Introduction: Animal models of cardiac dysfunction and heart failure play important roles in preclinical study and drug discovery. Cardiovascular disease and metabolic dysfunction-associated steatohepatitis (MASH) are closely associated metabolic disorders that frequently coexist in individuals with obesity and metabolic syndrome. However, the mechanisms linking diet-induced metabolic stress to cardiac dysfunction and hepatic pathology remain understudied. Method: In this study, six-week-old C57Bl/6J male mice were fed standard normal chow or a Gubra Amylin NASH (GAN) diet for six months to investigate the concurrent cardiac and hepatic manifestations. Results: Our results indicated that GAN diet-fed mice developed early cardiac dysfunction characterized by reduced ejection fraction (EF) and impaired diastolic function, as indicated by increased E/A and E/E′ ratios. These cardiac changes were accompanied by worsening hepatic pathology, including elevated total cholesterol (TC) and liver injury markers such as ALT, AST and ALP. By 6 months, mice developed pronounced fibrotic remodeling and inflammation in both the heart and liver, as confirmed by histopathological and gene expression analyses. Metabolic assessments further demonstrated a decreased respiratory exchange ratio (RER) in GAN diet-fed mice, particularly during the dark cycle, suggesting enhanced lipid utilization and decreased metabolic flexibility. In addition, reduced oxygen consumption, carbon dioxide production, and locomotor activity suggested impaired energy expenditure and reduced physical activity levels. Conclusion: Collectively, our findings demonstrate that chronic GAN diet feeding induces early cardiac dysfunction accompanied by progressive MASH-like hepatic pathology, with inflammation and fibrotic remodeling developing in both the heart and liver. These findings establish GAN diet feeding as a useful model for investigating the concurrent cardiac and hepatic consequences of chronic metabolic stress. Full article

The transition of the USB Power Delivery (USB-PD) standard from fixed 5–20 V profiles to the dynamic extended power range (EPR) of up to 48 V and 240 W has redefined the constraints of isolated AC–DC conversion for high-density adapters and chargers. Delivering high efficiency simultaneously at low (5 V) and high (28–48 V) outputs, while maximising volumetric power density and meeting stringent no-load and average-efficiency regulations, has become the central design challenge. This review provides a structured, comparative analysis of the isolated topologies competing in the sub-240 W adapter space: the quasi-resonant and active-clamp flyback converters, the two-transistor flyback, the resonant hybrid (asymmetrical half-bridge) flyback, and conventional and reconfigurable LLC resonant converters. Each is assessed against a common set of metrics (soft-switching capability, output-voltage-range flexibility, control complexity, component count, and achievable power density), supported by a quantitative meta-analysis of representative published prototypes. The enabling roles of wide-bandgap gallium nitride (GaN) devices, planar magnetics, and advanced digital control are examined. The analysis delineates the region of the power and output-voltage space in which each topology is optimal and proposes a common figure of merit for benchmarking future high-density adapters. Full article

The increasing deployment of Renewable Energy Sources (RESs), particularly wind and solar power, plays a critical role in reducing carbon emissions and supporting sustainable energy transitions. However, the large-scale integration of RESs into smart grids introduces significant technical challenges related to frequency stability, voltage regulation, rotor angle stability, power quality, inertia reduction, harmonic distortion, reverse power flow, Sub-Synchronous Interactions (SSIs), and protection coordination. Although numerous review studies have examined renewable energy integration, most focus on high-level frameworks, bibliometric analyses, optimization techniques, or isolated applications of artificial intelligence (AI) while lacking a comprehensive synthesis that bridges AI-driven solutions with the physical dynamics, control mechanisms, and protection requirements of highly renewable power systems. To address this gap, this review provides a comprehensive technical assessment of wind generator topologies, solar inverter architectures, grid-forming and grid-following control strategies, virtual inertia and virtual Synchronous Generator (SG) technologies, adaptive load-frequency control, energy storage integration, protection coordination, and real-time stability enhancement techniques for high-RES smart grids. Furthermore, the review systematically examines the role of AI in frequency regulation, voltage control, harmonic mitigation, predictive operation, parameter optimization, and system resilience. Unlike previous reviews, this study integrates physical-layer perspectives by connecting AI-driven decision-making with practical grid control mechanisms, inverter dynamics, wide-area monitoring, microgrid operation, High Voltage Direct Current (HVDC) interconnections, EV/Vehicle-to-Grid (V2G) integration, and multi-resource energy management. The review identifies key research priorities, including the development of real-time AI-assisted frequency control, adaptive protection schemes for low-inertia systems, coordinated grid-forming inverter control, resilient autonomous grid operation, and scalable multi-energy management frameworks. The findings provide actionable guidance for researchers, utilities, policymakers, and industry stakeholders seeking to enhance stability, reliability, and operational flexibility in future smart grids with very highly renewable energy penetration. Full article

Modeling bounded data presents a fundamental challenge in reliability and risk analysis, particularly when the underlying observations exhibit heterogeneous distributional shapes and complex failure-rate patterns. To address this challenge, we introduce a novel Unit Hjorth (UHj) distribution, obtained through an exponential transformation of the classical Hjorth model, which transfers its flexible reliability structure to the unit interval while retaining analytical tractability. A comprehensive theoretical investigation of the proposed model is developed, including its boundary behavior, limiting submodels, quantile function, ordinary moments, cumulants, mode characterization, order statistics, stress–strength reliability, and other important reliability measures. The UHj density can be strictly increasing or non-monotone, including unimodal forms, while its hazard rate can accommodate increasing, bathtub-shaped, upside-down-bathtub, and modified-bathtub patterns. This broad hazard-rate flexibility makes the model particularly suitable for representing heterogeneous reliability and risk profiles that cannot be adequately captured by conventional bounded distributions. For statistical inference, a comprehensive estimation framework is established based on maximum likelihood, maximum product of spacings, and six additional classical estimation methods. Their finite-sample performance is systematically assessed through extensive Monte Carlo simulations using multiple measures of bias, efficiency, accuracy, and numerical stability. The simulation results indicate that the likelihood- and spacing-based procedures generally provide the most accurate and stable parameter estimates. The practical relevance of the proposed model is further demonstrated through three real-world datasets from computer science, engineering, and environmental applications. In these applications, the UHj distribution provides competitive, and in several cases superior, goodness-of-fit performance compared with a range of established unit distributions. Overall, the proposed model provides a unified and flexible framework for bounded-data modeling, reliability assessment, risk characterization, and statistical inference, offering a useful addition to the class of bounded probability models for complex applied-data scenarios. Full article

Poor adherence to pharmacotherapy remains a major challenge in the long-term management of severe psychiatric disorders. Although long-acting injectable (LAI) antipsychotics can provide effective and sustained drug exposure while supporting treatment continuity, their pharmaceutical and clinical characteristics vary considerably according to the active pharmaceutical ingredient and formulation technology. Specific formulations may be associated with limitations such as complex release kinetics, injection-site reactions, or the need for oral supplementation during treatment initiation. Hydrogel-based delivery systems have emerged as a versatile class of biomaterials that may address these limitations through their tunable physicochemical properties, high water content, biocompatibility, and capacity for controlled, sustained drug release. Their adaptable architecture further enables diverse drug-loading strategies and multiple routes of administration, expanding the design space for next-generation long-acting formulations. In this narrative review, we provide a comprehensive overview of hydrogel-based depot systems for psychiatric pharmacotherapy, focusing on hydrogel composition (natural, synthetic, and hybrid polymers), crosslinking mechanisms, stimuli-responsive and injectable formulations, drug loading and release strategies, and their translational potential. Current research has primarily focused on antipsychotic delivery, where hydrogel formulations of risperidone, paliperidone, aripiprazole, olanzapine, quetiapine and the investigational peptide PAOPA show possible sustained drug release and prolonged therapeutic activity in preclinical models. Compared with conventional depot technologies, hydrogel systems may offer greater formulation flexibility and the potential for more controlled pharmacokinetic profiles and prolonged drug exposure. These characteristics could ultimately support reduced dosing frequency and treatment continuity. We also discuss the key challenges limiting clinical translation, including optimization of release kinetics, scalable manufacturing, sterilization, long-term stability, and regulatory considerations. Collectively, current evidence supports the technological potential of hydrogel-based systems to provide tunable drug loading, depot formation, and sustained release. However, their development as clinically meaningful next-generation LAI therapies will require long-term in vivo evaluation, robust pharmacokinetic–pharmacodynamic characterization, injection-site safety assessment, reproducible and scalable manufacturing, and well-designed clinical trials. Full article

Background: Artificial intelligence (AI) is increasingly being used in interventions among individuals with neurodevelopmental and cognitive disorders, offering personalized and adaptive approaches that advance traditional therapeutic practices. Although previous reviews have focused on symptom detection or alleviation, less attention has been paid to the impact of AI in fostering the acquisition of higher-order skills essential for being functional and independent. This review uniquely addresses this gap by synthesizing evidence from randomized controlled trials on AI-driven skill acquisition across multiple domains. Objectives: The objective of this systematic review is to synthesize evidence from randomized controlled trials evaluating the effectiveness of AI-driven interventions in promoting skillfulness. More specifically, it investigates the acquisition of neurocognitive, self-regulation, and adaptive and related skills among individuals with neurodevelopmental and cognitive disorders, including attention deficit and hyperactivity disorder, autism spectrum disorder, dyslexia, dyscalculia, and cognitive impairment. Methods: A systematic search, according to the PRISMA 2020 guidelines, was conducted, selecting randomized controlled trials published between 2019 and 2026. Eligible technologies included conversational agents, intelligent tutoring systems, adaptive training platforms, and machine learning-based interventions. Risk of bias was assessed using the Cochrane Risk of Bias 2 tool. Results: Twenty-four randomized controlled trials met the inclusion criteria. The findings demonstrated improvements in a wide range of skills, such as attention, working memory, mental flexibility, metacognitive control, emotional regulation, inhibition control, and social and communication skills. Generative AI showed efficacy for language and communication skills, while machine learning-based systems demonstrated positive effects on attention regulation and self-regulation. Conclusions: This review concludes that artificial intelligence can effectively assist conventional interventions for individuals with neurodevelopmental and cognitive disorders. However, the heterogeneity in intervention designs, outcome measures, and participant populations limits generalizability and highlights the need for standardized assessment frameworks, larger-scale longitudinal trials, and mechanistic investigations to translate these preliminary gains into long-term functional improvements across diverse clinical and cultural contexts. Full article

In cold-region industrial parks, prolonged heating seasons and intensive hot water demands trigger severe temporal mismatches between stochastic renewable generation and rigid thermal requirements. To address this, a multidimensional synergistic optimization framework for industrial thermal energy systems is proposed. The physical architecture integrates wind, solar, and shallow geothermal energy with hybrid storage, establishing thermodynamic boundaries. Concurrently, a customized solver is developed for the coupled electro-thermal scheduling problem. At its core, the active load restructuring strategy (ALRS) exploits the thermal inertia of thermal storage tanks and leverages the high coefficient of performance of ground source heat pumps. ALRS restructures the all-day hot water supply load to nighttime windows characterized by abundant wind power and off-peak tariffs, achieving profound source–load temporal decoupling and transforming rigid thermal demands into cross-period virtual flexible assets. Assessments demonstrate that the proposed strategy reduces typical-day electricity costs by 82.51%. Compared to a grid-dependent rigid baseline, comprehensive daily operational and carbon emission costs decrease by 71.9% and 90.2%, respectively. This study demonstrates the potential of translating thermal flexibility into coordinated energy management and provides a system-level reference for the low-carbon operation of industrial thermal energy systems. Full article

The efficient estimation of a finite-population mean can be materially improved when reliable auxiliary information is available and appropriately incorporated into the estimator. This study develops two flexible families of estimators under simple random sampling without replacement. Each family combines ratio- and exponential-type structures with optimized linear correction terms and one of seven parameterized transformations of the auxiliary mean. Unlike conventional approaches that introduce individual transformations separately, the proposed framework unifies the influence of different auxiliary transformations through a common transformation-error factor $g_m$, allowing the effect of each transformation on auxiliary variability and covariance structure to be systematically evaluated. First-order Taylor linearization is used to derive the bias and mean squared error (MSE) of every family member, closed-form expressions for the MSE-minimizing constants, and general dominance conditions relative to the sample mean, ratio, product, regression, exponential, and recent hybrid estimators. The numerical assessment has two components. First, a Monte Carlo experiment evaluates empirical MSE and percent relative efficiency (PRE), defined from the average squared difference between each estimate and the true finite-population mean, for small, medium, and large samples under low, moderate, and high correlations. A feasible plug-in implementation is used when an optimal constant contains the unknown population mean. The simulation analysis further demonstrates that estimator performance is not universally determined by a single transformation; rather, efficiency depends jointly on the correlation structure, sample size, and appropriate selection of the transformation factor. Second, five engineering-domain populations are examined using the population summaries and PRE values reported in the source study; corresponding MSE values are recovered from the variance of the sample mean. The simulations show that efficiency gains depend jointly on the correlation strength, sample size, and transformation choice. Proposed estimators are competitive across all scenarios and are particularly effective under moderate-to-high correlation, although the regression estimator remains difficult to improve upon in some settings. The engineering applications indicate larger gains for selected transformed estimators, but these results should be interpreted as first-order, population-summary-based comparisons. The proposed methodology therefore provides a unified and analytically tractable extension of transformed ratio-exponential estimation, while highlighting the importance of data-informed transformation selection in practical applications. Full article

Driven by global carbon neutrality targets, coal-fired power generation is undergoing substantial operational changes. Modern boilers must operate more flexibly while maintaining low emissions and reliable performance. This transition has increased the need for intelligent monitoring, operator-supervised optimization, and safety-constrained decision support. Using a reproducible search and screening procedure, this review examines the development of boiler intelligence across four interconnected technological stages. At the sensing layer, data-driven soft sensors support rapid prediction of flue gas emissions, while graph-structured spatiotemporal models are used to characterize flame and combustion states. At the modeling layer, physics-informed neural networks (PINNs) and proper orthogonal decomposition reduced-order models (POD-ROMs) are reviewed as routes for accelerating physical-field reconstruction. Surrogate models coupling computational fluid dynamics (CFD) with artificial intelligence (AI) provide another route to rapid prediction and can incorporate physical constraints. These fast field models can also serve as components of boiler digital twins for online assessment and operational guidance. They may also support early warning when abnormal conditions emerge. At the decision layer, reinforcement learning, model predictive control, and multi-objective optimization are reviewed for combustion and selective catalytic reduction (SCR) control. Because these applications are safety-critical, autonomous control must remain within actuator limits and established operating margins. Emission requirements and ammonia-slip constraints must also be satisfied. Safe deployment further requires fallback mechanisms, cybersecurity protection, and human supervision. Industrial application is still limited by data scarcity and lifecycle concept drift, while limited interpretability and simulator-to-real transfer create additional challenges. Edge latency and insufficient validation under abnormal conditions remain important barriers. Finally, industrial foundation models and large language models are discussed mainly as knowledge interfaces and operator-assistance tools rather than direct safety-critical controllers. Full article