Search Results (5,216)

Neuropathic pain (NP) arises from maladaptive changes in peripheral and central nociceptive circuits, yet molecular alterations spanning the entire pain neuraxis remain poorly understood. Neuroinflammation is increasingly recognized as a central mechanism in NP chronification, yet the region-specific molecular events linking immune activation to affective pain processing remain inadequately defined. In this study, we employed high-resolution LC-HRMS-based quantitative proteomics to investigate chronic constriction injury (CCI)-induced molecular alterations in the sciatic nerve (SN), spinal cord (SC), and orbitofrontal cortex (OFC) of male Wistar rats, a region critical for affective and cognitive pain modulation. Behavioral assessments confirmed the development of NP phenotypes and motor deficits. Proteomic profiling revealed exclusive and differentially expressed proteins enriched in neuroinflammatory pathways across all regions. S100 proteins (S100A8 and S100B) were significantly elevated in SN, SC, and OFC, as confirmed by immunofluorescence. Their up-regulation coincided with increased astrocyte (GFAP) and microglial (Iba-1) activation, highlighting a pervasive inflammatory milieu. Intriguingly, the OFC proteome demonstrated marked up-regulation of dopamine-regulating proteins and positive regulation of dopaminergic neurotransmission, suggesting involvement of reward-related analgesic circuits. Together, our findings delineate a “nociceptive neuraxis” driven by neuroimmune activation and neuromodulatory adaptations that interfaces with dopaminergic signaling to influence sensory and affective components of pain. This integrative molecular map highlights potential therapeutic targets, including glial-derived S100 proteins and dopamine modulators for the comprehensive management of NP. Full article

This paper presents a compact mixed-mode first-order universal filter based on a single voltage differencing current conveyor (VDCC), which can function in all four possible operation modes, i.e., voltage mode (VM), trans-admittance mode (TAM), current mode (CM), and trans-impedance mode (TIM). The proposed configuration requires only two grounded resistors and one floating capacitor, which contributes to a low component count, facilitates integration, and allows for the electronic tunability of the pole frequency through the transconductance gain of the VDCC. This work also demonstrates two practical biomedical applications: an electrocardiogram (ECG) acquisition system utilizing the VM low-pass filter for noise suppression and a bioimpedance (BioZ) measurement system employing the proposed configuration-based CM oscillator circuit as a sinusoidal excitation source. The performance validation confirms the accuracy of impedance extraction and the preservation of waveforms using tissue-equivalent models. The results demonstrate that the proposed VDCC-based filter offers a compact, power-efficient, and versatile analog signal-processing solution suitable for modern biomedical instrumentation. Full article

High-quality spectral filters with versatile tuning mechanisms are essential for applications in photonic integrated circuits, including sensing, laser stabilization, and spectral signal processing. We report the implementation of thermo-optic (TO) and electro-optic (EO) spectral tuning in silicon nitride Mach–Zehnder interferometers (MZIs) and micro-ring resonators (MRRs) by functionalizing the devices with a PMMA:JRD1 polymer cladding and integrating titanium tracks as heaters and electrodes. The fabricated MZIs and MRRs exhibit narrow linewidths of 25–30 pm and achieved TO tuning efficiencies of 1.7 and 13 pm/mW and EO tuning efficiencies of 0.33 and 1.6 pm/V, respectively. Closed-loop regulation using TO and EO effects enables stable half-fringe locking under environmental perturbations. This simple, broadly compatible hybrid platform demonstrates a practical approach to dual-mode spectral tuning and modulation in integrated photonic filters, providing a flexible route toward compact, reconfigurable, and environmentally robust photonic circuits. Full article

This narrative review integrates longitudinal cohort studies, neuroimaging and biomarker research, and major clinical trials to examine how depression and cognitive decline interact across the dementia continuum. Depression and cognitive impairment frequently co-occur in late life and exhibit substantial clinical and biological overlap. Meta-analytic and large population-based cohort studies consistently show that late-life depression increases the risk of mild cognitive impairment and dementia, with stronger associations observed for vascular dementia than for Alzheimer’s disease. Neurobiological studies implicate cerebrovascular pathology, neuroinflammation, hypothalamic–pituitary–adrenal axis dysregulation, and fronto-subcortical circuit dysfunction as key mechanisms linking depressive symptoms to later cognitive decline. In a subset of older adults, new-onset depression—particularly when accompanied by executive dysfunction, subjective cognitive decline, or high white-matter hyperintensity burden—are associated with an increased likelihood of near-term cognitive decline and dementia, although evidence for a definitive prodromal state remains limited. Depression is also highly prevalent as part of the behavioral and psychological symptoms of dementia, occurring in 30–50% of individuals with Alzheimer’s disease and even higher proportions in dementia with Lewy bodies or frontotemporal dementia. Comorbid depression in dementia accelerates cognitive and functional decline, increases neuropsychiatric burden, and worsens quality of life for patients and caregivers. Therapeutically, antidepressant treatment may confer modest benefits on mood and selected cognitive domains (e.g., processing speed and executive function) in non-demented older adults, whereas in established dementia, antidepressant efficacy is limited. In contrast, cholinesterase inhibitors, memantine, and multimodal non-pharmacological interventions yield small but measurable improvements in depressive or apathy-related symptoms. Emerging disease-modifying therapies for Alzheimer’s disease have demonstrated cognitive benefits, but current trial data provide insufficient evidence regarding effects on depressive symptoms, highlighting an important gap for future research. These findings underscore the need for stage-specific, integrative strategies to address the intertwined trajectories of mood and cognition in aging. Full article

The detection of defects in Printed Circuit Boards (PCBs) is a critical yet challenging task in industrial quality control, characterized by the prevalence of small targets and complex backgrounds. While deep learning models like YOLOv5 have shown promise, they often lack the ability to quantify predictive uncertainty, leading to overconfident errors in challenging scenarios—a major source of false alarms and reduced reliability in automated manufacturing inspection lines. From a Bayesian perspective, this overconfidence signifies a failure in probabilistic calibration, which is crucial for trustworthy automated inspection. To address this, we propose MFE-YOLO, a Bayesian-enhanced detection framework built upon YOLOv5 that systematically integrates uncertainty-aware mechanisms to improve both accuracy and operational reliability in real-world settings. First, we construct a multi-background PCB defect dataset with diverse substrate colors and shapes, enhancing the model’s ability to generalize beyond the single-background bias of existing data. Second, we integrate the Convolutional Block Attention Module (CBAM), reinterpreted through a Bayesian lens as a feature-wise uncertainty weighting mechanism, to suppress background interference and amplify salient defect features. Third, we propose a novel FIoU loss function, redesigned within a probabilistic framework to improve bounding box regression accuracy and implicitly capture localization uncertainty, particularly for small defects. Extensive experiments demonstrate that MFE-YOLO achieves state-of-the-art performance, with mAP@0.5 and mAP@0.5:0.95 values of 93.9% and 59.6%, respectively, outperforming existing detectors, including YOLOv8 and EfficientDet. More importantly, the proposed framework yields better-calibrated confidence scores, significantly reducing false alarms and enabling more reliable human-in-the-loop verification. This work provides a deployable, uncertainty-aware solution for high-throughput PCB inspection, advancing toward trustworthy and efficient quality control in modern manufacturing environments. Full article

This paper presents an open-architecture mechatronic drive platform for operating a three-phase BLDC servomotor in an industrial robotic axis. A sequential and iterative mechatronic design methodology is adopted, integrating electronic design, digital control, mechanical development, and experimental prototyping, with emphasis on open-loop operation. The electronic circuit was designed using schematics and a PCB and validated in Proteus Design Suite 8.15 (Labcenter Electronics Ltd., London, UK) to verify switching sequences and inverter behavior. The power stage is based on a six-switch insulated-gate bipolar transistor (IGBT) inverter module, complemented by an independent snubber protection board and a dedicated digital gate-drive control board. The mechanical enclosure was designed using computer-aided design (CAD), CAD software tools (Shapr3D, version 5.911.0 (9224), Shapr3D Zrt., Budapest, Hungary), and fabricated via 3D printing. Switching behavior was simulated in Octave using parameters from a real industrial BLDC servomotor (Yaskawa SGMAH series) extracted from a Motoman robotic axis. The contribution is design-oriented in a mechatronic engineering sense, emphasizing accessibility, openness, and experimental enablement of industrial drive hardware rather than control-performance optimization. An industrial Yaskawa BLDC servomotor from the Motoman robot is used to determine switching sequences and safe operating parameters. Experimental open-loop tests were conducted by directly commanding the six inverter switching sectors, resulting in the stable synchronous rotation of the motor on the developed electromechanical platform. Full article

Background: Obsessive–compulsive disorder (OCD) is a heterogeneous psychiatric condition with substantial heritability. Early genetic studies were often underpowered and produced limited reproducibility, but recent large-scale genomic and multi-omic approaches are beginning to elucidate the genetic architecture of OCD. Objectives: This review aims to synthesise current evidence from recent genomic and epigenomic studies on OCD and their implications for molecular pathways of pathogenesis, including endophenotypes. Methods: We reviewed peer-reviewed literature and preprints published in recent years, focusing on multiple genetic approaches, including genome-wide association studies (GWAS), whole exome sequencing (WES), whole genome sequencing (WGS), and methylome-wide association studies (MWAS). We then integrated the results with endophenotypic evidence at the biochemical, physiological, structural, functional, and executive/cognitive levels. Results: Recent large-scale genomic studies provide strong evidence of a highly polygenic contribution from common variants, while rare coding and structural variants also contribute measurably, with enriched signals in pathways relevant to neurodevelopment and, in some cohorts, early-onset presentations. Epigenomic studies have moved from scattered findings to more replicable methylation patterns, including loci influenced by nearby genetic variation and indications of sex-dependent effects. Although convergence at the single-gene level remains limited, cross-study and cross-omics signals increasingly point to biological domains involving synaptic organisation and plasticity, neurological development and chromatin regulation, immune/stress pathways, and cellular homeostasis. Conclusions: The biology of OCD risk is best represented by an integrative model combining polygenic load, contributions from rare variants, and regulatory (epigenetic) mechanisms that influence intermediate phenotypes at the circuit and cognitive levels. The current findings are not yet clinically applicable for individual diagnosis; however, they may inform future multidisciplinary research frameworks and, in the longer term, contribute to the development of more personalised approaches in OCD. Full article

A zero-dimensional, non-isothermal analytical framework was developed to assess solid oxide fuel cell (SOFC) performance across a broad range of operating conditions. The model integrates the anode, electrolyte, interlayers, and cathode, while resolving the distinct physicochemical processes within each layer. Electrochemical impedance spectroscopy (EIS), followed by distribution of relaxation times (DRT) analysis, was implemented to probe relevant cell polarization resistances under open-circuit and load conditions. The modeling framework couples mass and charge transport, electrochemical reactions, and non-isothermal heat transfer, with multilayer discretization applied to capture localized material properties and operating conditions. It enables the estimation of electrolyte ionic conductivity and total ohmic resistance by accounting for microstructural and geometric parameters, while also quantifying activation energies, exchange current densities, and gas-diffusion-related polarization resistances. Simulations were conducted for an SOFC operating on pure hydrogen with varying oxygen concentrations at 700 °C, 660 °C, 620 °C, and 580 °C. The results were validated against experimental data. The analysis revealed that ohmic overpotential dominates total cell losses, even at high current densities, underscoring the importance of minimizing ionic resistance to improve overall SOFC performance. Full article

This research develops a decision support system for prioritizing Battery Energy Storage System (BESS) installations at existing closed-circuit television (CCTV) camera locations experiencing power interruptions in Latkrabang subdistrict. The methodology integrates nine validated features: outage frequency, downtime duration, maximum outage duration, Net Present Value (NPV), combined ROI, outage impact score, annual BESS cost, combined risk score, and UPS installation cost, derived from historical power outage records (2020–2023) and engineering economics calculations. An unsupervised K-means clustering algorithm, validated through silhouette analysis and the elbow method, categorizes installations into five risk levels, namely critical, very high, high, medium, and low, addressing the absence of predefined ground truth labels. Subsequently, Support Vector Machine (SVM) with hyperparameter optimization classifies priority installations using stratified train-test splitting (80:20). The model was initially developed and validated using 82 CCTV cameras from Lamphla Tiew subdistrict (the pilot area). The validated model was then successfully applied to 101 CCTV cameras in Latkrabang subdistrict (the target area), identifying 27 critical installation points requiring immediate BESS deployment. The weighted recommendation system balances data-driven clustering with scoring: NPV (35%), outage impact (25%), combined ROI (20%), maximum outage duration (10%), and BESS cost efficiency (10%). Full article

In high-frequency circuit design, parameters such as the characteristic impedance and propagation constant of transmission lines directly affect key performance metrics, including signal integrity and power transmission efficiency. To address the challenge of optimizing impedance matching for high-frequency PCB transmission lines, this study applies a hybrid deep Q-network—genetic algorithm (DQN-GA) that integrates deep reinforcement learning with a genetic algorithm (GA). Unlike existing methods that primarily focus on predictive modeling or single-algorithm optimization, the proposed approach introduces a bidirectional interaction mechanism for algorithm fusion: transmission line structures learned by the deep Q-network (DQN) are encoded as chromosomes to enhance the diversity of the genetic algorithm population; simultaneously, high-fitness individuals from the genetic algorithm are decoded and stored in the experience replay pool of the DQN to accelerate its convergence. Simulation results demonstrate that the DQN-GA algorithm significantly outperforms both unoptimized structures and standalone GA methods, achieving substantial improvements in fitness scores and S11 transmission coefficients. This algorithm effectively overcomes the limitations of conventional approaches in addressing complex reflected wave compensation problems in high-frequency applications, providing a robust solution for signal integrity optimization in high-speed circuit design. This study not only advances the field of intelligent circuit optimization but also establishes a valuable framework for the application of hybrid algorithms to complex engineering challenges. Full article

The increasing complexity of modern Very Large-Scale Integration (VLSI) circuits, combined with unavoidable variations in physical and manufacturing parameters, poses significant challenges for accurate and efficient circuit simulation. Parametric model order reduction (PMOR) provides a viable solution by enabling the construction of compact reduced-order models that remain valid across a prescribed parameter space. However, the computational cost of generating such models can become prohibitive for large-scale circuits, particularly when high-fidelity projection subspaces are required. In this work, we present an efficient PMOR framework based on the Asymmetric Extended Krylov Subspace (AEKS). The proposed approach exploits structural sparsity imbalances between system matrices to guide the subspace expansion toward computationally favorable directions, thereby significantly reducing the cost of repeated linear system solves. By integrating AEKS within a concatenation-of-basis PMOR strategy, this method enables the rapid construction of accurate parametric reduced-order models for large-scale circuit systems. The proposed AEKS-PMOR framework is evaluated on industrial power distribution network benchmarks, where it demonstrates substantial reductions in model construction time compared to conventional EKS-based PMOR, while maintaining high approximation accuracy over the entire parameter space. Full article

Peripartum depression (PPD) represents one of the most prevalent and disabling psychiatric conditions among women, yet its underlying biology remains poorly integrated across medical disciplines. Emerging evidence highlights PPD as a prototypical disorder of the heart–brain axis, where neuroendocrine changes, immune activation, and cardiovascular dysregulation converge to shape maternal vulnerability. During pregnancy and the postpartum period, abrupt fluctuations in estrogen, progesterone (P4), and placental corticotropin-releasing hormone (CRH) interact with a sensitized hypothalamic–pituitary–adrenal (HPA) axis, altering neural circuits involved in mood regulation, stress reactivity, and maternal behavior. Parallel cardiovascular adaptations, including endothelial dysfunction, altered blood pressure variability, and reduced heart rate variability (HRV), suggest a profound perturbation of autonomic balance with potential long-term implications for maternal cardiovascular health. Neuroinflammation, microglial activation, and systemic cytokine release further mediate the bidirectional communication between the heart and the brain, linking emotional dysregulation with vascular and autonomic instability. Evidence also indicates that conditions such as preeclampsia and peripartum cardiomyopathy share biological pathways with PPD, reinforcing the concept of a unified pathophysiological axis. This review synthesizes current knowledge on the neurobiological, cardiovascular, endocrine, and inflammatory mechanisms connecting PPD to maternal heart–brain health, while discussing emerging biomarkers and therapeutic strategies aimed at restoring integrative physiology. Understanding PPD as a multisystem heart–brain disorder offers a transformative perspective for early detection, risk stratification, and personalized intervention during one of the most biologically vulnerable periods of a woman’s life. Full article

In modern power distribution networks, robust and intelligent fault management techniques are increasingly important as system complexity grows with the integration of distributed energy resources. This article reviews the use of deep learning methods for short-circuit fault detection, classification, and localization in power distribution systems, including symmetrical, asymmetrical, and high-impedance faults. The approaches examined include convolutional neural networks, recurrent neural networks, deep reinforcement learning, graph neural networks, and hybrid architectures. A comprehensive taxonomy of these models is presented, followed by an analysis of their application across the stages of fault diagnostics. Major contributions to the field are highlighted, and research gaps are identified in relation to data scarcity, model interpretability, real-time responsiveness, and deployment scalability. The paper provides an in-depth technical and performance comparison of deep learning approaches based on current research trends, and it also outlines the limitations of previous review studies. The objective of this work is to support researchers in selecting and implementing appropriate deep learning techniques for fault analytics in complex smart electricity grids with significant penetration of distributed energy resources. The review is intended to serve as an initial foundation for continued research and development in intelligent fault analytics for reliable and sustainable power distribution systems. Full article

Time-varying or temporal metamaterials and metasurfaces, in which electromagnetic parameters are deliberately modulated in time, have emerged as a powerful route to engineer wave–matter interaction beyond what is possible in static media. By enabling the controlled exchange of energy and momentum with the fields, they underpin magnet-free nonreciprocity, low-loss frequency conversion, temporal impedance matching beyond Bode-Fano limit, and unconventional parametric gain and noise control. This survey provides a coherent framework that unifies the main theoretical and experimental developments in the area, from early analyses of velocity-modulated dielectrics to recent demonstrations of temporal photonic crystals, non-Foster temporal boundaries, and spatiotemporally driven metasurfaces relevant to nanophotonic platforms. We systematically compare time-varying permittivity, joint $\epsilon$-$\mu$ modulation, time-varying conductivity, plasmas, and circuit-equivalent implementations, including stochastic and rapidly sign-switching regimes, and relate them to acoustic and quantum analogs using common figures of merit, such as conversion efficiency, isolation versus insertion loss, modulation depth and speed, dynamic range, and stability. Our work concludes by outlining key challenges, loss and pump efficiency, high-speed modulation at the nanoscale, dispersion engineering for broadband operation, and fair benchmarking, which must be addressed for robust, integrable temporal metasurfaces. Full article

Parkinson’s disease (PD) is a multifactorial neurodegenerative disorder characterized by complex interactions across molecular, cellular, circuit, and behavioral scales. While experimental and clinical studies have provided critical insights into PD pathology, integrating these heterogeneous data into coherent mechanistic frameworks and translational strategies remains a major challenge. Computational modeling offers a powerful approach to bridge these scales, enabling the systematic investigation of disease mechanisms, candidate biomarkers, and therapeutic strategies. In this review, we survey state-of-the-art computational approaches applied to PD, spanning molecular dynamics and biophysical models, cellular- and circuit-level network models, systems and abstract-level simulations of basal ganglia function, and whole-brain and data-driven models linked to clinical phenotypes. We highlight how multiscale and hybrid modeling strategies connect α-synuclein pathology, mitochondrial dysfunction, oxidative stress, and dopaminergic degeneration to alterations in neural dynamics and motor and non-motor symptoms. We further discuss the role of computational models in biomarker discovery, including imaging, electrophysiological, and digital biomarkers. In particular, eye-movement-based measures are highlighted as quantitative, reproducible behavioral signals that provide principled constraints for individualized computational modeling. We also review the emerging impact of computational approaches on drug discovery, target prioritization, and in silico clinical trials. Finally, we examine future directions toward personalized and precision medicine in PD, emphasizing digital twin frameworks, longitudinal validation, and the integration of patient-specific data with mechanistic and data-driven models. Together, these advances underscore the growing role of computational modeling as an integrative and hypothesis-generating framework, with the long-term goal of supporting data-constrained predictive approaches for biomarker development and translational applications. Full article