Search Results (2,938)

Photovoltaic (PV) pumping systems offer a sustainable alternative to diesel solutions, yet their nonlinearity, intermittent irradiation, and complex motor-pump dynamics challenge energy extraction and reliability. Currently, these systems predominantly rely on classical Maximum Power Point Tracking (MPPT) algorithms such as Perturb and Observe (P&O) and Incremental Conductance (INC), which suffer from slow convergence, steady-state oscillations, and an inability to track Global MPP (GMPP) under uniform irradiance variation conditions. Furthermore, existing studies typically address MPPT optimization and motor control in isolation, without considering their coupled interaction, and rarely incorporate economic viability assessments. To address these limitations, this paper proposes an innovative control architecture integrating four advanced metaheuristic MPPT techniques, namely the Genetic Algorithm (GA), Gray Wolf Optimizer (GWO), Cuckoo Search (CS) algorithm, and Horse Herd Optimization Algorithm (HOA), with Model Predictive Control (MPC) for a Brushless DC (BLDC) motor-driven pumping system, supplemented by battery storage. Comprehensive simulations were conducted under both constant and variable irradiance profiles (1000 to 500 to 1000 W/m²) to evaluate dynamic performance, tracking accuracy, and system robustness. The results demonstrate that HOA and GWO significantly outperform GA and CS, achieving superior DC bus voltage stability with ripple values below 2.4 V, faster convergence times, reduced electromagnetic torque oscillations, and enhanced MPPT efficiency exceeding 99%. Under variable irradiance, HOA exhibits the fastest stabilization with minimal overshoot and superior disturbance rejection, while GA suffers from severe oscillations and CS displays sawtooth ripple patterns. A techno-economic analysis further confirms the economic viability of the proposed system, with HOA and GWO strategies yielding lower lifecycle costs, extended converter lifespans from 5 to over 12 years, and improved return on investment compared to conventional approaches. This integrated framework offers a robust, efficient, and economically sustainable solution for autonomous PV water pumping applications. Full article

Nanotechnology is rapidly transforming the perspective on pediatric neurology, enabling diagnostic, therapeutic, and monitoring strategies tailored to the unique features of neurological illnesses in children. This review acknowledges the problems caused by delays in diagnosis, the limitations of conventional procedures, and the need for new, focused approaches, highlighting recent advances in nanoscale materials and smart nanocarriers. Specifically, this paper summarizes advances in nanomaterials that can overcome physiological barriers, such as the developing blood–brain barrier (BBB) and age-dependent pharmacokinetics. We discuss innovations in stimuli-responsive delivery systems, theranostic platforms, and multimodal nanohybrids designed for precise targeting and real-time treatment monitoring. Special emphasis is placed on pediatric-specific considerations, including developmental differences in immune and metabolic responses, the necessity for age-adjusted dosing, and the potential long-term safety implications of nanoparticle exposure. Transformative applications are explored in various pediatric neurological conditions, including brain tumors, epilepsy, neurodevelopmental disorders, and rare degenerative diseases, emphasizing both achievements and challenges in translation. This paper evaluates various regulatory, ethical, and societal factors, alongside the integration of converging technologies such as AI-driven nanoparticle optimization, brain organoids, and digital twins to accelerate personalized therapy development. Conclusively, this paper emphasizes the importance of interdisciplinary collaboration, pediatric-focused clinical trial designs, and sustained investment to fully realize the potential of nanotechnology in improving neurological outcomes for children. Full article

Background: Clinical competence is a fundamental outcome of prosthetics and orthotics (P&O) education and is expected to facilitate graduates’ transition into professional practice. However, limited evidence exists regarding the relationship between clinical competence and job market readiness among P&O graduates, particularly in low- and middle-income countries. Objective: To examine the relationship between perceived clinical competence and job market readiness among prosthetics and orthotics graduates in Jordan and to investigate whether openness moderates this relationship. Methods: A cross-sectional survey was conducted among graduates of the Bachelor of Science in Prosthetics and Orthotics program at the University of Jordan who had graduated within the previous five years. Data were collected between 1 July and 31 August 2025 using a structured online questionnaire comprising measures of clinical competence, job market readiness, and openness. Confirmatory factor analysis (CFA) was performed to assess the reliability and validity of the measurement model, followed by structural equation modeling (SEM) to examine the proposed relationships. The moderating effect of openness was further evaluated using Hayes’ PROCESS macro. Results: A total of 231 questionnaires were included in the final analysis. The measurement model demonstrated satisfactory reliability, convergent validity, discriminant validity, and overall model fit. Clinical competence was a significant positive predictor of job market readiness (β = 0.427, p = 0.013), explaining 37% of the variance in job market readiness. Openness did not have a significant direct effect on job market readiness (p = 0.072); however, it significantly moderated the relationship between clinical competence and job market readiness (B = 0.295, p = 0.021), indicating that the positive association between clinical competence and job market readiness was stronger among graduates with higher levels of openness. Conclusions: Clinical competence plays a central role in enhancing graduates’ readiness for professional employment in prosthetics and orthotics, while openness strengthens this relationship. These findings suggest that prosthetics and orthotics educational programs should integrate strategies that foster both clinical competence and adaptive personal attributes, and they may inform curriculum development and educational policies aimed at improving graduate employability and workforce readiness. Full article

The economic scheduling of grid-connected microgrids requires the coordinated dispatch of renewable energy sources, controllable distributed generators, battery energy storage systems, and power exchange with the utility grid while satisfying various operational constraints. Owing to the time-varying nature of renewable generation and load demand, this problem often exhibits strong nonlinearity, temporal coupling, and complex constraint characteristics. To enhance the optimization capability of the original Coati Optimization Algorithm (COA) for such constrained scheduling tasks, this paper proposes an Improved Coati Optimization Algorithm, termed ICOA. Different from the original COA, which mainly depends on random initialization, single-best individual guidance, and simple local perturbation, the proposed ICOA redesigns the search process through several urban-traffic-inspired mechanisms. First, a road-network stratified initialization strategy is employed to improve the spatial coverage and diversity of the initial population. Second, a traffic-signal-guided exploration strategy adaptively adjusts the search direction by considering population congestion and elite information. Third, a lane-changing local exploitation operator is introduced to refine promising solutions with the aid of neighborhood information. Finally, a traffic-rule-based repair mechanism is incorporated to enhance the feasibility of candidate scheduling solutions under operational constraints. The performance of ICOA is first assessed on the CEC2017 benchmark suite with 10-, 30-, 50-, and 100-dimensional test settings. The results obtained from convergence curves, boxplots, Wilcoxon signed-rank tests, and Friedman mean rank tests demonstrate that ICOA achieves competitive performance in terms of convergence accuracy, robustness, and scalability when compared with 11 advanced algorithms. In addition, ICOA is applied to a 24 h grid-connected microgrid economic scheduling case. The simulation results show that ICOA obtains the lowest mean operating cost of 1393.58, which is 13.10% lower than that of the best competing algorithm in terms of mean cost. These results suggest that ICOA is an effective and reliable optimization method for both benchmark function optimization and constrained microgrid scheduling problems. Full article

Background: Deep learning models, particularly convolutional neural networks (CNNs), have shown promising performance for pneumonia detection using chest X-ray images. However, the impact of preprocessing, architecture selection, data augmentation, and ensemble strategies has not been systematically evaluated. This study investigated how these factors affect model robustness and diagnostic performance. Methods: A public pediatric chest X-ray dataset was used to systematically evaluate pixel normalization methods, six CNN architectures, progressive data augmentation strategies for class imbalance, and both feature-level and decision-level ensemble approaches. Model performance was assessed by considering not only overall classification accuracy but also clinically relevant risk metrics, particularly false-negative rates. Results: Pixel normalization to the 0–1 range improved model convergence, while Xception and InceptionV3 achieved the best overall performance. Model-specific augmentation strategies were more effective than a fixed 1:1 class ratio for reducing false negatives. Feature-level ensembles tended to overfit, whereas decision-level ensembles provided more stable but only modest performance improvements. Conclusions: These findings demonstrate that reliable medical AI systems require systematic optimization of preprocessing techniques, model architecture, data augmentation strategies, and clinically meaningful evaluation metrics rather than maximizing a single performance indicator. The proposed framework provides practical guidelines for developing robust deep learning models for pneumonia diagnosis in clinical settings. Full article

With the rapid development of edge intelligence and autonomous unmanned systems, multimodal learning has been increasingly applied to resource-constrained distributed scenarios. However, dynamic environments often introduce modality missingness, quality degradation, and reliability fluctuations, which impair learning robustness. Existing methods mainly rely on complex fusion architectures or reactive compensation strategies based on current modality states, making it difficult to satisfy the real-time and proactive requirements of edge systems. To address this issue, this paper proposes a Prediction-Driven Collaborative Compensation method for Dynamic Multimodal Quality Evolution (PDCC). Instead of reconstructing missing modality features, PDCC introduces a lightweight prediction mechanism based on historical modality confidence sequences to estimate future modality reliability trends. Based on the predicted degradation risks, a prediction-aware confidence collaboration strategy is designed to adaptively adjust reliability estimation, collaborative decisions among neighboring nodes, and replay buffer management by estimating future compensation utility from predicted modality reliability under limited storage resources. Extensive experiments on the CREMA-D and AVE datasets demonstrate that PDCC achieves strong robustness under gradual degradation, abrupt degradation, and heterogeneous multi-node degradation scenarios. Compared with existing state-driven collaborative compensation methods, PDCC improves model convergence efficiency while maintaining low communication overhead through lightweight confidence-level interactions. The results validate that the proposed method improves learning robustness in resource-constrained edge intelligence scenarios under dynamic multimodal quality evolution. Full article

Accurate detection of surface defects in wind turbine blades is critical for condition monitoring and preventive maintenance of wind energy systems. Defects such as cracks, burns, deformation, and peeling are characterized by small dimensions, irregular morphologies, and low contrast, limiting the effectiveness of conventional feature extraction methods. Although YOLOv13 enhances high-order feature correlation and information flow, its fixed-grid spatial sampling and content-agnostic upsampling operations remain limited in adapting to irregular defect geometries and preserving fine-grained boundary information. This study proposes YOLOv13-ADR, an enhanced detection framework integrating Adaptive Deformable Convolution (ADConv) and a Nearest Neighbor Content Perception Recombination (NNCPR) module. ADConv applies a geometry-driven kernel permutation strategy to strengthen multi-scale feature representation, while NNCPR improves neighborhood-aware perception for modeling geometric deformations. A Focus-IoU loss function incorporating an anchor-quality perception mechanism is introduced to accelerate training convergence and improve bounding box regression precision. Additional optimizations include modifications to the DS-C3k2 module and upsampling strategy. Experiments on a wind turbine blade defect dataset demonstrate that YOLOv13-ADR achieves a 7.26-percentage-point improvement in mean average precision over YOLOv8n, with enhanced small-defect recognition and reduced localization errors, demonstrating improved detection precision and localization performance relevant to early fault detection and structural health monitoring of wind turbine blades. Full article

Marine macroalgae represent abundant, renewable reservoirs of structurally unique bioactive compounds, such as sulfated polysaccharides, phlorotannins, and carotenoids, with immense potential for sustainable functional foods. However, their industrial exploitation is severely bottlenecked by complex, repeat-rich genomes, recalcitrant genetic transformation tools, and environmental cultivation variability. Synthesizing evidence from 180 high-quality studies spanning from 1961 to 2026, this review provides a comprehensive synthesis of how artificial intelligence (AI) and synthetic biology may contribute to overcoming these challenges. We highlight key advances across the bioengineering pipeline, including the application of metabolic engineering strategies for enhancing valuable compound production in engineered algal systems. For example, a CrtYB-based metabolic engineering approach achieved β-carotene accumulation of 22.8 mg/g in the microalga Chlamydomonas reinhardtii, providing important insights for future metabolic engineering of marine macroalgae. In addition, AI-assisted approaches show promising potential for enzyme discovery, metabolic pathway prediction, and multi-omics-guided optimization of bioactive compound production. We further discuss critical downstream challenges, including the low gastrointestinal absorption (~14%) and extensive metabolic transformation of seaweed-derived phenolic compounds, as well as the potential application of AI-integrated physiological modeling for improving bioavailability prediction and safety assessment. This review provides a pioneering, data-driven synthesis of how the convergence of artificial intelligence (AI) and synthetic biology is overcoming these roadblocks. Moving beyond generic descriptions, we highlight key empirical milestones across the bioengineering pipeline, including multi-fold yield enhancements in target pigments (up to 22.8 mg/g) and the AI-driven discovery of novel polysaccharide-degrading enzymes. Furthermore, we confront critical downstream challenges, specifically addressing the characteristically low (~14%) gastrointestinal absorption bottleneck and extensive metabolic biotransformation of seaweed phenolics. We demonstrate that integrating digital twins with reinforcement learning-driven physiologically based pharmacokinetic (PB-PK) modeling can compress the R&D cycles of these seaweed functional ingredients by over 60%. Unlike previous reviews that treat these technologies as independent entities, this article proposes a macroalgae-focused approach that delivers a unique, macroalgae-specific computational and experimental framework, providing a future roadmap toward intelligent smart marine biotechnology and sustainable development to drive the global blue bioeconomy. Full article

Background/Objectives: Neuroinflammation is increasingly recognized as a key mechanism linking infectious diseases with psychiatric disorders through interactions between peripheral immune activation, metabolic pathways, and brain network alterations. This review aimed to synthesize current evidence on the neuroimmune mechanisms and biomarkers underlying infection-associated psychiatric disorders. Methods: A narrative literature review structured according to the SANRA (Scale for the Assessment of Narrative Review Articles) criteria was conducted using the Web of Science Core Collection, PubMed/MEDLINE, Scopus and PsycINFO databases. Boolean search strategies identified studies investigating neuroinflammatory biomarkers, neuroimmune mechanisms, and psychiatric outcomes associated with infectious diseases. The search (January 2022–30 June 2026) included 76 studies in the final qualitative analyses. Results: The reviewed evidence consistently identified inflammatory cytokines and chemokines, complement proteins, blood–brain barrier markers, glial activation biomarkers, neuroaxonal injury markers, kynurenine pathway metabolites, neurotrophic factors, and neuroimaging markers as complementary indicators of infection-induced neuroimmune dysfunction. Across diverse bacterial, viral, parasitic, and systemic infections, these mechanisms converged on peripheral immune activation, blood–brain barrier disruption, microglial activation, kynurenine pathway dysregulation, impaired neurotrophic signaling, synaptic dysfunction, and altered brain network connectivity, contributing to depression, anxiety, psychosis, cognitive impairment, and fatigue. Based on these findings, a unified neuroimmune model integrating peripheral and central mechanisms is proposed. Conclusions: Neuroinflammation emerges as a shared biological pathway linking infections with transdiagnostic psychiatric phenotypes. Although no single biomarker currently demonstrates sufficient diagnostic specificity, integrated multimodal biomarker panels may improve biological stratification, facilitate earlier identification of high-risk patients, and support the development of mechanism-based precision approaches—including candidate anti-inflammatory pharmacological strategies currently under clinical investigation—for infection-associated psychiatric disorders. Full article

To further explore the energy-saving performance of liquid desiccant dehumidification air conditioning systems, this paper constructs a performance optimization model based on the established air temperature and humidity prediction model and the actual physical constraints of the system’s operation. The total system energy consumption and cooling/dehumidification performance are adopted as indicators in the optimization model. Furthermore, to address the nonlinearity, multivariate and constraint in the optimization model, a multi-strategy improved parrot optimizer algorithm (MSPOA) is proposed, incorporating Cauchy inverse mapping initialization, adaptive t-distribution mutation, and random walk strategies into the standard parrot optimizer algorithm. To evaluate the performance of the proposed MSPOA, it is compared with other four algorithms using six CEC benchmark test functions. The results show that MSPOA has higher optimization accuracy and faster convergence speed. In addition, a MSPOA-based optimization control strategy is implemented on a two-stage cooling and dehumidification system experimental platform, and its energy consumption is compared with that of the traditional control strategy. The results show that, on the premise of meeting the requirements of cooling and dehumidification performance, the total system energy consumption under the optimized control strategy is reduced by 6.57 kWh compared with the traditional control strategy, and the energy-saving rate reaches 27.91%, verifying the effectiveness and engineering application value of the proposed method. Full article

This study proposes an adaptive Support Vector Machine (SVM) classification method based on an enhanced Starfish Optimization Algorithm (SFOAE-SVM) for hyperspectral image (HSI) classification. HSI classification remains a critical challenge in remote sensing due to the high dimensionality of spectral features, spectral mixing, scarcity of labeled samples, and complex land-cover distributions. The SFOAE algorithm is used for global hyperparameter optimization of SVMs, accounting for the distributional characteristics of the target HSI data. The approach aims to improve search capability and reduce the likelihood of convergence to local optima by combining multi-dimensional topology-oriented expansion with global exploration. Experimental results demonstrate that SFOAE-SVM achieves competitive classification accuracy and stable performance compared with conventional SVM parameter selection strategies and other optimization-based methods across three benchmark hyperspectral remote-sensing datasets. These results indicate that the proposed method offers a promising optimization-assisted SVM framework for hyperspectral remote-sensing image classification. Full article

Reliable electric load forecasting is an important engineering problem for power-system planning, grid stability, and mission-critical energy management. This paper presents a symmetry-aware simulation and modeling framework for medium-range electric load forecasting under noisy and uncertain operating conditions. The proposed approach combines a Multi-Model Partitioning Filter (MMPF) with Nonlinear Autoregressive Exogenous (NARX) submodels, while two adaptive optimization strategies, genetic algorithm-based resource allocation (GARA) and Particle Swarm Optimization (PSO), are used to optimize the contribution weights of the parallel predictors. The modeling process uses real commercial power-system data and evaluates the forecasting framework over April–September 2025. To simulate realistic engineering disturbances, correlated symmetric Gaussian noise is injected into the testing phase under moderate and heavy noise scenarios. The cyclic symmetry of temporal variables, such as hours and months, is preserved through unit-circle encoding, while the symmetry and asymmetry of residual error symmetric distributions are examined through scatter plot analysis. As for the context of forecasting residuals as diagnostic signals, it is important to transfer symmetry properties that can be used to evaluate the behavior of optimized predictors, along with the cyclic encoding of inputs. This means that by implementing residual-symmetry analysis, the conclusion that GARA and PSO produce concentrated, balanced, and biased errors under moderate noise and heavily correlated noise conditions can be achieved. Finally, our results show that both GARA and PSO improve the robustness of the hybrid MMPF-NARX model, but PSO consistently achieves lower MAPE values, smoother convergence, and lower computational burden. The optimal configuration is obtained with nine NARX submodels, beyond which additional model complexity offers no meaningful performance gain. Overall, the study shows that symmetry-aware modeling, adaptive optimization, and noise-based simulation can support more reliable forecasting in modern power-system engineering applications. Full article

Background: Idiopathic syringomyelia (IS) is defined by exclusion: an intramedullary fluid-filled cavity without Chiari malformation, spinal trauma, tumor, infection, or other identifiable cause. Growing evidence suggests IS is a progressively shrinking category as advanced imaging and intraoperative exploration uncover occult arachnoid or hydrodynamic substrates. To the best of our knowledge, no systematic reviews of the literature have been published on this topic. The authors aim to map operational definitions of IS, summarize proposed pathogenetic mechanisms, characterize diagnostic strategies and their yield, describe management approaches and outcomes, and identify pediatric–adult differences. Methods: A scoping review was conducted in accordance with PRISMA-ScR. PubMed and Scopus were searched from inception to May 2026. Eligible sources were original studies of any design addressing IS in pediatric or adult patients, in English. Studies on syringomyelia secondary to Chiari malformation, trauma, tumor, or infection were excluded. Data were extracted across five domains and synthesized narratively. Results: Eighteen studies (365 patients; five case reports, nine retrospective cohorts, two morphometric studies, one cine-MRI case–control, one technical note) were included. Operational definitions were markedly heterogeneous. Four pathogenetic mechanisms emerged: subarachnoid CSF obstruction with abnormal intramedullary pulse pressure, occult arachnoid pathology, posterior fossa morphometric variants overlapping with the Chiari spectrum, and persistent central canal as a developmental variant. In the four largest pediatric series (n = 214), 91–95% of children remained stable or improved on conservative management at up to 7-year follow-up, with no concordance between syrinx size change and clinical course. In symptomatic adults, targeted arachnoid lysis or web excision yielded clinical improvement in 87% of IS-occult arachnoid web patients; syringo-subarachnoid shunting was occasionally effective but risked neurological deterioration without prior substrate identification. Conclusions: The idiopathic label reflects the current limits of diagnostic investigation rather than a fixed nosological entity, since occult arachnoid or hydrodynamic substrates are identified in most adult IS cases when advanced imaging and intraoperative exploration are systematically deployed. Pathogenesis converges on a unified model of subarachnoid CSF obstruction generating abnormal intramedullary pulse pressure. Management should be driven by clinical, not radiological, evolution: arachnolysis or web excision is the primary strategy when an operable substrate is identified, while conservative management remains best supported in clinically stable patients, particularly children. Full article

Optimization plays a critical role in training deep neural networks, directly impacting convergence speed, model generalization, and stability. While existing methods such as stochastic gradient descent (SGD) and adaptive optimizers like Adam and AdamW have achieved significant success, they exhibit limitations in handling extreme gradients and noisy updates, and maintaining stable convergence across diverse architectures and datasets. In this study, we propose Quartz, a novel optimizer that combines momentum accumulation with bounded adaptive scaling to improve convergence efficiency and robustness. Quartz introduces a gradient-saturation mechanism that prevents excessively large updates and enforces adaptive step size bounds, thereby addressing the key limitations observed with conventional optimizers. We evaluate Quartz across three benchmark datasets (MNIST, Fashion-MNIST, and Arabic Character Classification) using multiple convolutional neural network (CNN) architectures, including GoogLeNet, VGGNet, and ResNet-18. The experimental results demonstrate that Quartz achieves up to 99.54% test accuracy on MNIST, 91.6% test accuracy on Fashion-MNIST, and 98.27% test accuracy on Arabic Character Classification. It consistently outperformed or matched the results achieved using state-of-the-art adaptive optimizers under identical training conditions. In terms of efficiency, Quartz reduces training time in several settings while maintaining higher accuracy. Across all the experiments, Quartz also shows statistically significant improvements ($p<$ 0.001 in most comparisons) and smoother convergence behavior, indicating improved optimization stability. These findings highlight Quartz’s potential as a reliable and efficient tool for use in optimization strategies for a broad range of deep learning tasks. Full article

This study introduces Fuzzy Adaptive Adam (FA-Adam), a novel optimization method that enhances the Adam algorithm through a hierarchical, two-level fuzzy inference framework. Although Adam is widely used due to its adaptive moment estimation and strong empirical performance, its fixed update dynamics limit its ability to adapt to the non-stationary nature of deep learning training. Optimization typically evolves through distinct phases, where early training benefits from aggressive and exploratory updates under high gradient noise, while later stages require more stable and conservative behavior. FA-Adam addresses this limitation by enabling dynamic, phase-aware adaptation of optimization behavior throughout training. The proposed method integrates fuzzy inference directly into the optimizer’s internal update process rather than using it solely for external hyperparameter tuning. An upper-level fuzzy system analyzes epoch-level indicators, including loss trends, gradient variance, oscillatory behavior, and convergence rate, to classify the training state as stable, balanced, or fast. Based on this classification, a lower-level fuzzy system adaptively modulates the first- and second-moment estimates of Adam through specialized aggregation operators that blend conservative and aggressive update strategies. This hierarchical design enables smooth transitions between exploration and exploitation while preserving the stability of Adam. We evaluate FA-Adam across 35 experiments involving five neural architectures and seven benchmark datasets. FA-Adam outperforms standard Adam in 29 settings, with two ties and four losses, achieving an average test accuracy improvement of +3.69%. Notably, it delivers substantial gains in challenging training regimes, including cases where Adam fails to converge, and consistent improvements on difficult classification benchmarks. A comparison with modern adaptive optimizers on a representative subset of configurations shows that FA-Adam clearly outperforms Adam and AdamW and is competitive with RAdam and AdaBelief, while a sensitivity analysis indicates robustness to its main hand-tuned constants. Full article