Search Results (33,567)

Background: Type 2 inflammatory diseases are among the most common chronic inflammatory conditions in childhood and represent a growing global health burden. Increasing evidence suggests that early-life nutritional exposures may influence immune programming and allergic disease development. This Position Paper aims to summarize the current evidence regarding the immunomodulatory role of polyunsaturated fatty acids (PUFAs), particularly omega-3 long-chain fatty acids, in the prevention of allergic diseases during early life. Methods: A scoping literature review and consensus process were conducted to map biological mechanisms and clinical evidence linking omega-3 PUFAs with allergic disease prevention. This document analyzed experimental, observational, and randomized controlled studies evaluating maternal prenatal/lactational omega-3 exposure. The clinical evidence was qualitatively appraised using study-design-specific Joanna Briggs Institute (JBI) Critical Appraisal Tools. Particular attention was given to immune modulation, inflammatory pathways, epithelial barrier function, gut microbiota interactions, and the ferroptosis–immune–metabolic axis. Results: Omega-3 PUFAs, including eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), exert immunomodulatory and anti-inflammatory effects through multiple mechanisms, including specialized pro-resolving mediator production, regulation of T-helper cell responses, cytokine modulation, maintenance of epithelial barrier integrity, and microbiota interaction. Emerging evidence also supports their involvement in oxidative stress and ferroptosis regulation. Current clinical evidence, particularly from higher-quality prenatal randomized trials and evidence syntheses, suggests that adequate maternal omega-3 intake during pregnancy and lactation may reduce the risk of respiratory allergic outcomes, especially wheezing and asthma, in selected offspring. Conclusions: Adequate omega-3 PUFA intake, such as 2 g/die, during critical windows of immune maturation may represent a valuable strategy for the primary prevention of allergic diseases. Current evidence most strongly supports supplementation during pregnancy and lactation, particularly in populations with low dietary omega-3 intake or increased allergic risk. Omega-3 supplementation should be considered within a broader multifactorial preventive approach aimed at promoting immune tolerance and reducing the future burden of allergic diseases. Full article

Background: Electroencephalographic (EEG) functional connectivity analysis requires multiple signal-processing, source-modelling, and statistical steps that can limit its adoption in clinician-led randomised controlled trials (RCTs). NeuroStat was developed as a prototype research tool to integrate this workflow; formal usability validation with clinician end-users has not yet been conducted. Methods: NeuroStat is an open-source Python/PyQt6 desktop application that integrates automated artefact removal (a Generalised Eigenvalue Decomposition for Artefact Identification [GEDAI] pathway and a traditional Artefact Subspace Reconstruction (ASR)/Independent Component Analysis (ICA)/ICLabel pathway), boundary element model (BEM) source localisation using the Desikan–Killiany atlas (68 cortical regions), Phase Lag Index (PLI) connectivity estimation across five canonical frequency bands, and RCT-oriented statistical analysis. Evaluation separated sensor-space and source-space claims: a sensor-level simulation (repeated across five independent random seeds) tested preprocessing robustness, a repeated source-space simulation tested recovery of a known cortical parcel-pair contrast after forward projection and inverse reconstruction, a PhysioNet benchmark tested posterior Desikan–Killiany alpha PLI in 20 healthy adults, and an illustrative application to 20 sessions from a published chiropractic RCT demonstrated real-world workflow applicability. Results: In the sensor-level simulation benchmark, the Traditional pathway achieved a mean absolute error of $0.168\pm 0.017$ PLI units and root mean squared error of $0.219\pm 0.045$ (mean ± SD across five independent random seeds) across all artefact conditions. In the source-space simulation, reconstructed alpha PLI for the known bilateral lateral-occipital parcel pair exceeded anterior control edges across 60 repeated condition runs (mean known-control difference = 0.105 PLI units, 95% CI 0.096–0.114; $t\left(59\right)=22.61$, $p<0.001$). In the PhysioNet source-space benchmark, posterior Desikan–Killiany alpha PLI was higher during eyes-closed than eyes-open rest (Cohen’s $d=0.85$, $p=0.001$; 16/20 subjects showing the expected direction) after ICLabel-enabled preprocessing. In the pilot RCT application, all 20 sessions completed processing without manual intervention, with default-mode network alpha PLI showing a pre-to-post change of +0.071 in the intervention group versus +0.015 in the active control group. Conclusions: NeuroStat integrates preprocessing, source-space construction, connectivity estimation, and statistical reporting within a parameter-logged desktop workflow for EEG functional connectivity studies. Current evidence supports initial technical feasibility, sensor-level preprocessing robustness for one pathway in controlled simulations, source-space recovery of a known parcel-level contrast, source-space sensitivity to an expected posterior alpha resting-state contrast, and error-free processing across 20 real RCT sessions in a pilot workflow demonstration. Formal usability testing, test–retest reliability analysis, participant-specific source-model validation, and clinical-population validation remain necessary before clinician-facing or trial-deployment claims can be made. Full article

This paper presents proprietary, multi-platform software developed in Python for analyzing the electrical and microstructural properties of silicon solar cell metallization. Utilizing a sample set of 20 commercial solar cells, electrical resistivity and contact resistance measurements obtained via the potential difference method were correlated with high-resolution topographic data from AFM, SEM, and CLSM. This process enabled the quantification of how specific features, such as surface roughness and finger height, directly influence electrical performance. The developed algorithms offer high-fidelity predictive capabilities, with relative errors below 4%. This “virtual laboratory” serves as a transformative research and educational tool, allowing for complex materials analysis while avoiding the necessity for destructive testing. Full article

Artificial-source frequency-domain electromagnetic methods are important tools for deep mineral exploration and concealed geological structure detection. Apparent resistivity is a key parameter linking measured electromagnetic fields to the interpretation of subsurface electrical structures, and its calculation method directly affects geological interpretation and engineering applicability. Although substantial efforts have been devoted to the theoretical development, data processing, and practical application of different apparent resistivity formulations, most previous studies have focused on the analysis and improvement of a single method. Systematic comparisons of the main apparent resistivity formulations under unified conditions remain limited, particularly in terms of deep basement characterization, anti-interference performance, and engineering applicability. To fill this gap, this study systematically compares the $E--E_x$ wide-field apparent resistivity, the $E--E_x$ far-zone apparent resistivity, and the $E--Z_{xy}$ Cagniard apparent resistivity. Through theoretical derivation, forward modeling of typical one-dimensional models, and field verification, the differences among these three formulations in geological characterization, anti-interference capability, and engineering applicability are analyzed, with the aim of clarifying their applicable boundaries and selection principles for artificial-source frequency-domain electromagnetic exploration. Full article

Software quality assessment in agile projects remains fragmented. Technical, process-related, and team-related indicators are often evaluated through separate models, tools, and reports. This fragmentation limits cross-project comparability and weakens evidence-based decisions for software quality improvement. To address this problem, this study proposes the Overall Software Quality Index (OSQI), a multidimensional decision-support framework for software quality assessment in agile projects. OSQI integrates code quality, process quality, and team quality into a single project-level assessment model. The framework was initially grounded in ISO/IEC 25010:2011 and is discussed in relation to the ISO/IEC 25010:2023 revision, particularly its explicit inclusion of Safety as a product quality characteristic. Since the industrial datasets used in this study were not collected from safety-critical systems, Safety was not modeled as a separate OSQI dimension in the current version; instead, it is addressed as a scope limitation and future extension. The measurement structure was defined using the Goal–Question–Metric (GQM) approach. An initial set of 49 candidate metrics was reduced to 15 core indicators. This reduction was performed using dimension-specific strategies: Random Forest-based feature importance for code quality, Delphi and Analytic Hierarchy Process (AHP) for process quality, and thematic consolidation for team quality. The selected indicators were normalized and integrated through entropy-based weighting. This process generates an interpretable composite quality score. The main contribution of OSQI is not the isolated use of these methods, but their integration into a reproducible and tool-supported framework. The framework converts heterogeneous software engineering signals into a unified decision-support index. OSQI was evaluated using industrial agile project data. The data included static code analysis outputs, issue-tracking records, team assessment results, and product outcome indicators. In an exploratory validation across five industrial projects, OSQI showed a strong positive association with Net Promoter Score ($r=0.97, p=0.0076$) and a strong negative association with churn rate ($r=-0.97, p=0.0061$). A supporting software tool was also developed to automate data integration, score calculation, visualization, and project-level comparison. The findings suggest that OSQI can support quality monitoring, project benchmarking, and evidence-based improvement decisions in agile software engineering contexts. Full article

Global agriculture faces a dual imperative: increase food production to meet rising demand while simultaneously reducing environmental impacts and resource inefficiencies. Addressing this challenge requires repositioning ruminant nutrition as the intelligent nexus linking crop and livestock production within Integrated Crop–Livestock Systems (ICLS). In this role, nutrition becomes central to restoring ecological, nutritional, and economic synergies that have been fragmented by decades of agricultural specialization. While ICLS provides the ecological foundation, Precision Livestock Farming delivers the technological and analytical infrastructure necessary to operationalize integration at the individual-animal level. Real-time sensing, Internet of Things platforms, and Artificial Intelligence (AI) enable dynamic monitoring of animal physiology, behavior, and environmental interactions across scales. A key advancement in this evolution is the development of Hybrid Intelligent Mechanistic Models (HIMM), which integrate biologically grounded mechanistic models with data-driven AI approaches. By combining interpretability with adaptive learning, HIMM enhances predictive accuracy, extrapolative capacity, and decision transparency, enabling the creation of digital twins that simulate biological responses before management interventions are implemented. Such architectures extend precision nutrition beyond feed efficiency and methane mitigation to include nutrient density and product quality, thereby linking different ecosystem processes directly to human dietary needs. Integrating nutrition with advanced modeling and monitoring tools can help livestock systems move beyond static “net-zero” benchmarks toward sustainable strategies that are responsive to local production contexts. In this reframed paradigm, nutrition is not merely a production input but the central analytical framework that computationally links biological mechanisms, environmental stewardship, technological innovation, and human health within sustainable ruminant systems. Full article

Suspended ceiling systems are among the most seismically vulnerable non-structural components in buildings, posing significant life-safety risks and economic losses, yet understanding their full-field kinematic behavior under seismic loading remains a major experimental challenge. Conventional contact sensors offer limited spatial coverage and can alter the dynamic properties of lightweight panels due to mass loading. In contrast, non-contact optical alternatives are rarely feasible in shake-table environments due to restricted viewing angles, extensive areal coverage requirements, and the risk of equipment damage from falling panels. This study proposes an end-to-end three-dimensional displacement measurement framework for large-scale shake-table testing of suspended ceiling systems, employing consumer-grade cameras with purpose-built tools that cover the complete experimental workflow, including motion-based video trimming, semi-automated calibration, a robust multi-stage image-tracking pipeline that maintains trajectory continuity under extreme inter-frame displacements, and a ceiling system motion visualization and analysis tool. The framework was validated through a full-scale shake-table experiment continuously tracking 324 spatial nodes across 81 ceiling panels, achieving an RMSE below 3 mm in all spatial directions and exact peak-frequency agreement in 9 out of 10 test cases. A parallel processing architecture reduced total processing time from over 27 h to under 10 min without GPU acceleration, and six-degree-of-freedom rigid-body analysis resolved the complete panel failure sequence from constrained oscillation through multi-axis rotation to gravitational free fall, a level of kinematic detail unattainable with conventional instrumentation. This framework establishes a practical, scalable foundation for full-field seismic performance assessment of non-structural systems where conventional instrumentation is physically or logistically infeasible. Full article

Equine sports medicine is increasingly oriented toward objective, field-based monitoring systems that support both performance optimization and welfare assessment. In this context, smartphone-based technologies have emerged as accessible tools capable of integrating data acquisition, processing, and interpretation within a single platform. This narrative review aims to examine the role of smartphones in equine sports medicine, focusing on their function as standalone sensing devices and as gateways for wearable and external sensor systems. The analysis is based on a structured synthesis of current literature addressing technological foundations, including embedded sensors, connectivity architectures, and artificial intelligence-driven data processing, as well as their clinical applications across locomotor, cardiovascular, respiratory, behavioural, and thermoregulatory domains. Evidence indicates that smartphone-based systems improve the feasibility of longitudinal monitoring and facilitate real-time decision-making in field conditions, while enhancing communication between veterinarians, trainers, and owners. However, their performance remains influenced by acquisition conditions, system variability, and algorithmic constraints, requiring careful validation and contextual interpretation. In addition, challenges related to data governance, privacy, and ethical use remain insufficiently addressed. Overall, smartphone-based technologies represent enabling tools that support a transition toward more integrated, data-driven, and welfare-oriented management of the equine athlete, while highlighting the need for standardisation and regulatory development. Full article

Glaucoma, the leading cause of irreversible vision loss, often goes undetected in early stages due to its asymptomatic behaviour. Early diagnosis typically involves visual analysis of the optic disc (OD) in eye fundus images. Machine and deep learning techniques have emerged as valuable tools for automating this process; however, their integration into clinical practice still faces limitations. These challenges include the presence of image regions that are not directly related to glaucoma assessment, such as retinal vasculature, the macula, and background structures, which may introduce irrelevant information and negatively affect classification performance, as well as a general lack of transparency in the decision-making process. This article proposes a methodology that enhances both the accuracy and interpretability of glaucoma detection by focusing solely on the OD region. First, a metaheuristic-based strategy is employed for precise OD detection and cropping, generating an OD-centric dataset with glaucoma-labeled images, which is composed of different public datasets. Four convolutional neural networks (CNNs), namely VGG-19, MobileNet-V2, ResNet-50, and DenseNet-161, are trained on this dataset using transfer learning. To address the need for model explainability, Grad-CAM, Score-CAM, and Eigen-CAM are applied to the trained models to generate post hoc visual explanations of their predictions. The experimental results showed that DenseNet-161 achieved the best overall performance on the assembled public dataset, using an 80%-10%-10% training, validation, and testing split, with a test accuracy of 0.9369 and an AUC of 0.9831. By isolating the OD region and incorporating explainability techniques, the methodology provides a robust and interpretable second opinion, supporting more accurate and efficient glaucoma screening. Full article

The most persistent biomedical challenges of the 21st century are neurodegenerative disorders (NDs), where molecular alterations lead to devastating clinical consequences and progressive neuronal loss. The prevalence of neurodegeneration is continuously rising and becoming the main contributor to chronic disability and mortality. Despite their clinical differences, many conditions share pathogenic processes, including oxidative stress, protein misfolding and aggregation, mitochondrial dysfunction, and neuroinflammation. Instead of functioning independently, these processes cooperate to form a self-reinforcing network that gradually weakens synapses and ultimately leads to neuronal death. This study redefines neurodegeneration as a disorder of system-level failure by emphasizing poor cellular stress integration. In addition to demonstrating how gut microbiome gene networks impact inflammation and amyloid production, new research highlights the relationships between mitochondrial–lysosomal interactions, endoplasmic reticulum stress responses, and transcriptionally driven synaptic vulnerability. A key molecular topic is the interaction and pathogenic convergence of the JAK/STAT, HIF-1α, and Notch signaling pathways. Under ongoing metabolic stress, prolonged stimulation of this triad increases inflammation, hinders the regenerative processes, and maintains pseudo-hypoxic conditions, explaining why single-target treatments have mostly been unsuccessful. This review also explores progress in fluid, digital, and imaging biomarkers that facilitate early diagnosis and patient stratification, and assesses new disease-modifying approaches such as antisense oligonucleotides, immunomodulators, gene therapies, and small-molecular agents. Artificial intelligence is emphasized as an essential tool for integrating multimodal data, drug discovery and predictive modeling. Full article

The present contribution provides a systematic review of the international scientific literature on the relationship between contaminated sites and real estate market dynamics. The objective is to investigate whether and to what extent the presence of environmental risk sources—both active or decommissioned—affects the value of surrounding residential properties. In particular, the review is focused on an examination of the methods commonly used in relevant studies to measure, interpret, and represent this impact across different geographical contexts, identifying the main magnitude ranges found in the selected contributions. Several studies consistently confirm a statistically significant negative relationship between proximity to polluting sites and real estate values, although the relevance of this effect varies considerably across case studies. Other records highlight non-notable impacts or even positive effects following remediation and redevelopment interventions. The evidence suggests that this relationship is complex and influenced by factors such as site type, contamination severity, specificities of the local urban context and community perception. Moreover, the findings underscore regional variations in the extent and nature of price impacts, reflecting diverse regulatory frameworks and remediation efforts. The outcomes of the literature review provide a robust foundation for developing more effective evaluation tools able to support decision-making processes, enabling policymakers, planners, and investors to promote sustainable urban regeneration, improve environmental justice, and reduce spatial inequalities. Ultimately, this study highlights the critical need for integrating environmental, social, and economic dimensions to fully capture the multifaceted effects of contaminated sites on property markets, thereby orienting more informed and equitable urban development strategies worldwide. Full article

Background: Agricultural logistics in Kazakhstan is critical for export-oriented supply chains, but its resilience is limited by infrastructure constraints, fluctuating export demand, and insufficient coordination between market and logistics processes. Methods: This study develops a conceptual multi-level model of the agricultural logistics system and a hybrid simulation model combining system dynamics and discrete-event simulation to analyze intermodal transportation under demand and capacity constraints. The model integrates demand formation, storage, transport, and export operations, as well as feedback mechanisms between fulfilled demand, repeat orders, and logistics performance. The model is implemented in AnyLogic 8.9. Results: The conceptual model structures the interaction of key participants, logistics facilities, and infrastructure levels within Kazakhstan’s agricultural logistics system. Simulation experiments reproduce cyclic logistics behavior and show that reduced logistics capacity increases the demand gap and system pressure, while stronger market signals intensify demand and infrastructure load. The results confirm that resilience depends on the balance between demand activation, logistics capacity, and replenishment policy. Conclusions: The proposed approach provides a tool for analyzing the resilience of agricultural intermodal logistics in Kazakhstan and supports scenario-based evaluation of infrastructure and market factors. The novelty lies in combining a conceptual multi-level logistics model with hybrid simulation of demand and logistics flows. Full article

Floodplain wetlands are globally important ecosystems, yet altered hydrological regimes increasingly disrupt the balance between woody and non-woody vegetation. In Australia’s regulated Murray–Darling Basin, it remains unclear whether woody plant encroachment represents a persistent shift toward terrestrialisation or a dynamic process that can be periodically reversed by flooding. This study quantified long-term patterns of woody-vegetation encroachment and retreat across 32,000 ha of mapped wetlands in the mid-Murrumbidgee River floodplain from 1988 to 2023, and assessed how hydrological variability and floodplain connectivity mediate these dynamics. Using open, analysis-ready Earth observation data from Digital Earth Australia (DEA) within the Open Data Cube (ODC) framework, we combined DEA Land Cover for transition mapping, Water Observations for hydrological masking, Landsat surface reflectance for Enhanced Vegetation Index (EVI)-based spectral plausibility testing, and the Wetlands Insight Tool for qualitative temporal context. Woody-vegetation dynamics were strongly non-linear and closely linked to alternating drought and flood phases. During the Millennium Drought (2001–2009), mapped woody-cover decline exceeded 50% of wetland area in some sub-regions, whereas the post-drought recovery interval (2008–2013) produced encroachment exceeding 40% in the most affected areas. Across the full 35-year record, mean encroachment rates ranged from 85 to 250 ha yr⁻¹ among sub-regions, summing to approximately 865 ha yr⁻¹ of woody expansion across the floodplain, while retreat rates were lower overall (approximately 634 ha yr⁻¹), resulting in a net expansion of woody cover. Local hydrological connectivity strongly mediated these responses: infrequently inundated wetlands showed persistent terrestrialisation, whereas more frequently inundated, better-connected wetlands experienced periodic flood-driven retreat. Landsat-derived EVI broadly supported the mapped transitions, indicating general consistency with canopy greening and canopy decline, supporting the ecological plausibility of the detected changes. This open DEA–ODC workflow provides a transparent, transferable framework for operational wetland monitoring and demonstrates that maintaining natural flood frequency, duration, and connectivity is essential for sustaining the resilience of regulated floodplain systems. Full article

This study examines user responses to an AI travel assistant in a cross-border tourism service system. Moving beyond adoption-centered technology acceptance research, it conceptualizes these responses as a staged appraisal process in which social and experiential cues shape performance expectancy and effort expectancy, which then influence attitude and two behavioral outcomes: users’ willingness to accept AI and objections to AI. Cosmopolitanism is introduced as an individual-level boundary condition. Survey data were collected from 499 Chinese tourists holding valid South Korean tourist visas after they evaluated Visit Seoul AI, an official AI-based travel-planning tool. The hypotheses were tested using partial least squares structural equation modeling. The results show that social influence, hedonic motivation, and perceived anthropomorphism significantly affect performance expectancy and effort expectancy, which in turn shape attitude. Attitude increases usersf’ willingness to accept AI and reduces objections to AI, with a stronger effect on users’ willingness to accept AI. Cosmopolitanism strengthens the negative effect of hedonic motivation on effort expectancy. This study extends AIDUA to cross-border AI service systems and shows that users may both accept and object to AI travel assistants. Full article

The pathological mechanism of autism spectrum disorder (ASD) exhibits dual heterogeneity: abnormal local energy metabolism and brain-wide high-order topological failure. To synergistically characterize these complex signals captured by advanced neuroimaging sensors, we propose the Quantum-Enhanced Graph Convolutional Wide Residual Network (QG-WRN), a modality-specific, decoupled parallel dual-stream architecture. In the classical branch, to accurately capture the spatial distribution of local metabolic abnormalities, we employ a wide residual network (WRN) to extract amplitude of low-frequency fluctuation (ALFF) features, leveraging its expanded feature channels to effectively mine regional neurodynamic properties. Furthermore, to overcome the representational bottlenecks of classical linear operators in parsing hidden, long-range network connections, we introduce quantum computing, exploiting its exponentially expansive state space and intrinsic low-parameter regularization mechanism. Guided by these properties, the quantum branch utilizes a variational quantum graph convolutional (QGCN) module—featuring a trainable circular encoding strategy and a hardware-efficient 4-qubit configuration—with a 2-layer nested message passing structure to process the functional connectivity (FC) matrix, harnessing quantum interference in Hilbert space to parse complex topology while effectively mitigating overfitting on small-sample medical data. A unified training scheme achieves full-dimensional fusion of node activity and topology. Achieving 68.49% accuracy, our method outperforms 10 classic and recent new baselines, providing a powerful computational intelligence tool for sensor-based ASD clinical diagnosis. Furthermore, interpretability analysis successfully maps core disease hubs to standard AAL116 atlas coordinates, providing a powerful tool for computationally aided ASD diagnosis. Full article