Search Results (21,645)

Recent studies have witnessed significant advances in unified multi-weather image restoration, which aims to handle diverse weather degradations within a single model. In this work, we observe that rain, haze, and snow restoration exhibit substantial differences in both degradation characteristics and learning dynamics, making straightforward joint optimization prone to performance imbalance and ineffective knowledge transfer. To this end, we propose UMWIR-Net, a unified multi-weather image restoration network equipped with an Asymmetric Task Collaborative Learning strategy. ATCL consists of Intra-Task Difficulty Optimization and Inter-Task Contribution Scheduling. Specifically, Intra-Task Difficulty Optimization jointly models the remaining restoration error and recent learning progress to dynamically estimate the optimization difficulty of each weather task, thereby assigning larger weights to slowly converging and under-optimized tasks. Inter-Task Contribution Scheduling measures the directional influence of a source-task update on the validation objective of a target task, constructs an asymmetric task-contribution matrix, and accordingly promotes tasks that provide stronger transferable knowledge while compensating those that benefit less from collaborative learning. In this manner, different weather restoration tasks collaborate selectively and asymmetrically, allowing the model to exploit complementary knowledge across tasks and improve overall restoration performance. Furthermore, UMWIR-Net adopts a wavelet-based Transformer backbone to capture low- and high-frequency information, enabling effective modeling of both global structures and local details for diverse weather restoration. Extensive experiments on multi-weather image restoration datasets show that UMWIR-Net achieves state-of-the-art performance and delivers more balanced restoration quality across rain, haze, and snow removal. Full article

Despite increased investment in information and communication technologies (ICT), many micro, small, and medium-sized enterprises (MSMEs) struggle to translate digital adoption into improved business performance, particularly in post-pandemic contexts. This study addresses this gap by examining the mediating role of knowledge management (KM) in the relationship between ICT integration and business performance in MSMEs in Michoacán, Mexico. A quantitative, construct associate research framework was employed using survey data from 200 randomly selected MSMEs. Structural equation modeling was applied to test the hypothesized relationships. The results reveal that ICT integration does not have a direct significant effect on business performance. However, it significantly enhances KM processes, which in turn positively influence performance outcomes. Furthermore, KM fully mediates the relationship between ICT integration and business performance, indicating that the value of ICT is realized through effective knowledge processes rather than technology alone. These findings underscore the importance of aligning ICT investments with KM strategies to achieve superior performance. The study contributes to the integration of the resource-based view (RBV) and knowledge-based view (KBV), offering empirical evidence from an emerging economy context. Full article

The present study provides the physicochemical and genomic characterization of three Escherichia bacteriophages (6phi8, 6phi10, and 6phi13) isolated from the commercial phage preparation “Sextaphag^®”. For each bacteriophage, lytic activity against E. coli MG1655, as well as pH and thermal stability, were determined. Whole-genome sequencing was performed, followed by bioinformatic annotation and comparative genomic analysis. Bacteriophages 6phi8 and 6phi13 belong to the T4-like myoviruses with large genomes (~169 and ~171 kb, respectively), whereas 6phi10 is a T7-like podovirus with a genome size of 40.1 kb. Phage 6phi8 was assigned to the genus Mosigvirus of the family Straboviridae, 6phi13 was classified within the genus Tequatrovirus of the same family, and 6phi10 was identified as a putative novel species of the genus Berlinvirus within the family Autotranscriptaviridae. The genomes of the studied phages lack genes associated with the lysogenic cycle, as well as virulence and antibiotic resistance determinants. The results expand current knowledge of the genomic properties of phages included in therapeutic cocktails and may contribute to the development of phage preparations against infections caused by E. coli and other members of the family Enterobacteriaceae. Full article

Regenerative vibrations are a major hindrance to flexible cantilever structures during milling, resulting in a reduced tool life and diminished surface finish. In this research, two actively controlled methods are directly compared: a genetic algorithm (GA)-optimized classical proportional-integral-derivative (PID) controller and a GA-optimized fractional-order PID (FOPID) controller for a milling-dependent regenerative force on a flexible cantilever beam via numerical modeling, using piezoelectric actuator/sensor patches. The original aspect lies in synergistically combining fractional-order control with genetic algorithm-based optimization to actively reduce chatter and increase the machining stability of flexible milling systems. The simulation results from the GA-FOPID controller exhibited a reduction in vibration of approximately 92.70% compared with the open-loop system by reducing the RMS value from 1.5058 × 10⁻⁴ m to 1.0996 × 10⁻⁵ m. By reducing the vibration level and enlarging the predicted stable machining region, these improvements could potentially contribute to longer tool life, improved surface finish, and reduced post-processing requirements, although these technological benefits were not directly modeled in the present study. The main innovation of this work involves a unique combination of fractional-order control, PZT actuation, and genetic algorithm optimization in a regenerative milling delay architecture. To the best of the authors’ knowledge, based on the literature surveyed in this work, this combination of techniques has not previously been reported for active chatter suppression. The stability lobe diagram (SLD) analysis, conducted under the single-mode approximation that serves as the reference framework for the like-for-like comparison of the five investigated configurations, shows that the critical axial depth of cut at the representative spindle speed increases from a_p,crit(1500) = 0.061 mm for the uncontrolled system to 0.52 mm under GA-FOPID control. This enlargement of the predicted stable machining region was further confirmed, at a comparable order of magnitude, when the structural model was extended to include the two next bending modes, indicating that the trend is not an artifact of the single-mode simplification. Therefore, although the results were obtained exclusively from numerical simulation and have not yet been experimentally validated, they support the use of optimization-based methods to implement FOPID strategies as a means to increase both reliability and performance of flexible milling configurations. Full article

Pistachio (Pistacia vera L.) is among the most economically important nut crops worldwide. They are increasingly exposed to the environmental constraints associated with climate change, including drought, salinity, nutritional imbalances, and heightened disease pressure. These stressors compromise plant growth, physiological performance, nutrient acquisition, and orchard productivity, highlighting the need for sustainable strategies to enhance crop resilience. This review critically examines the current knowledge on the functional roles of arbuscular mycorrhizal fungi (AMF) and plant growth-promoting rhizobacteria (PGPR) in pistachio production. Evidence indicates that AMF and PGPR contribute to plant performance through multiple complementary mechanisms, including improved nutrient mobilization and uptake, maintenance of ionic homeostasis, enhancement of water-use efficiency, stimulation of antioxidant defenses, modulation of stress-related signaling pathways, and suppression of phytopathogens. AMF primarily enhance phosphorus acquisition, water relations, and soil structural stability, whereas PGPR contribute to nutrient solubilization, biological control, and induction of plant defense responses. Despite promising experimental results, most studies have been conducted under controlled conditions, limiting the translation of microbial inoculation strategies to commercial orchards in the field. We identified the key knowledge gaps and research priorities required to improve the consistency, scalability, and field validation of microbiome-based approaches for sustainable pistachio production under increasingly challenging environmental conditions. Full article

In this article, general operating principles for modern photocatalytic solar water-splitting systems are reviewed from a physics perspective, and their fundamental limitations are identified. Several potential approaches to overcome the identified limitations are proposed, and a new solar water-splitting system unifying those approaches based on plasmonic metal nanoparticles linked by a metal-insulator junction is described. Based on already existing scientific knowledge, some probable features of the proposed system are briefly discussed, and an initial theoretical analysis of electromagnetic wave-propagation modeling was performed with COMSOL Multiphysics software. In addition, some rectification capabilities for the metal insulator–metal junction embedded in the system are calculated using a simplified Simmons model for tunneling currents. A probable approach for initial system synthesis with existing nanotechnology techniques is proposed, and its limitations and probable bottlenecks are marked. Full article

Background: The immune landscape in head and neck squamous cell carcinoma (HNSCC) has been widely investigated. However, the crucial role played by the lymph node microenvironment in metastatic and non-metastatic HNSCC remains unknown. This systematic review aims to discuss the immunological crosstalk between the tumor and the nodal microenvironment and to describe the distribution of immune cells in metastatic and non-metastatic lymph nodes. Methods: A systematic review was conducted according to the PRISMA guidelines. PubMed, Scopus, and the Cochrane Library were searched for studies published between 1990 and 2025, with the final search performed in December 2025. Prospective and retrospective studies evaluating immune cell infiltration in metastatic and non-metastatic cervical lymph nodes were included, whereas studies focusing exclusively on non-cellular biomarkers and non-English publications were excluded. The risk of bias was assessed using the Newcastle–Ottawa Scale. The results were synthesized narratively, and no meta-analysis was performed. Results: The screening process identified 608 articles, of which 27 met our predefined inclusion criteria. These studies focused on macrophages, dendritic cells, neutrophils, natural killer cells, T helper cells, cytotoxic T cells, regulatory T cells, B cells, total lymphocytes, and surface markers. This systematic review provides a well-structured analysis of current knowledge on the impact of the innate and adaptive immune systems on the response against cancer cells and the recruitment of immune cells in lymph nodes. The most significant findings highlight the crucial role of antigen presentation in the antitumor response, particularly through the recruitment and activation of dendritic cells and subcapsular sinus macrophages in tumor-draining lymph nodes. It also presents in a fairly comprehensible manner that the density of mature dendritic cells, cytotoxic T cells, and B cells is higher in non-metastatic lymph nodes. Discussion: The lymph node microenvironment is highly enriched with immune cell infiltration, and their distribution between metastatic and non-metastatic lymph nodes can contribute to a better understanding of the underlying pathological processes. Particular attention should be given to the innate immune system cells and their implication in antigen presentation. Our findings suggest that identifying immunological profiles of lymph nodes may provide a rationale for treatment de-escalation protocols and raise the question of lymph node preservation in antitumor immune responses. However, the heterogeneity and bias assessment of the included studies warrant a cautious interpretation of these findings. Another important limitation is the limited number of studies comparing the immune microenvironment of primary tumors and lymph nodes. Full article

Knowledge tracing (KT) is a core technology in intelligent tutoring systems that predicts students’ future responses by analyzing their historical interaction sequences. Although existing deep learning-based KT models achieve high predictive accuracy, their “black-box” nature severely hinders practical deployment in educational scenarios. To balance predictive performance and interpretability, this paper proposes AMDKT, an interpretable dual-stream Transformer model grounded in the Student Proficiency–Item Competency Matching (SPIM) mechanism. The model employs two parallel Transformer branches to separately model the temporal evolution of student proficiency and the competency demands of each item and defines the discrepancy between their outputs as “proficiency surplus.” A non-negative regularization loss is further introduced to constrain the proficiency surplus to be non-negative for correctly answered samples, thereby embedding pedagogical rules into the optimization objective and ensuring that the model outputs conform to educational cognitive priors. Experiments on five public datasets show that AMDKT achieves AUC performance comparable to the state-of-the-art AKT model, and obtains statistically superior results against DKT, DKVMN, DEEP-IRT, and DIMKT on most datasets, with relatively weaker performance observed on the KDD Cup 2010 dataset. Ablation studies verify the effectiveness of each component, and visualization results demonstrate that AMDKT produces smooth, coherent, and interpretable student proficiency trajectories, providing a fine-grained tool for quantifying individual learning progress. Therefore, AMDKT offers a feasible solution for applications such as weak knowledge point localization, adaptive exercise recommendation, and learning risk warning. Full article

Background: Cervical cancer remains a major public health concern worldwide, particularly in low- and middle-income countries. Limited knowledge regarding cervical cancer prevention and elevated anxiety levels may hinder women’s participation in preventive measures. Mindfulness-Based Social–Emotional Learning integrates mindfulness practices with social–emotional learning strategies and may enhance health knowledge while reducing anxiety. Materials and Methods: A quasi-experimental pretest–posttest non-equivalent control group design was conducted among 80 women attending gynecological outpatient clinics in Egypt. Participants were assigned to a study group (n = 40) and a control group (n = 40). Data were collected using a structured interview questionnaire, a cervical cancer knowledge questionnaire, and the Beck Anxiety Inventory (BAI). The study group received an eight-week MBSEL intervention incorporating mindfulness exercises, social–emotional learning activities, and cervical cancer education, while the control group received routine health education. Pre- and post-intervention assessments were performed. Results: Baseline characteristics were comparable between groups. Following the intervention, the study group had significantly higher mean knowledge scores regarding cervical cancer prevention, with mean knowledge scores increasing from 38.71 ± 10.41 to 66.25 ± 8.95 compared with 42.09 ± 15.95 in the control group (t = 8.355, p < 0.001). Good knowledge levels increased from 2.5% to 35.0%, while poor knowledge declined from 62.5% to 0.0%. Anxiety levels significantly improved in the study group, with mean BAI scores decreasing from 38.68 ± 6.32 to 22.20 ± 3.91 compared with 34.28 ± 9.53 in the control group (t = 7.414, p < 0.001). Low anxiety increased from 2.5% to 62.5% in the study group compared with 7.5% to 12.5% in the control group, and severe anxiety was eliminated from 27.5% to 0.0% in the study group compared with 32.5% to 22.5% in the control group after the intervention. Conclusions: The MBSEL approach effectively enhanced women’s knowledge regarding cervical cancer prevention and significantly reduced anxiety levels. Integrating mindfulness practices with social–emotional learning and health education appears to strengthen cognitive engagement and emotional regulation. Clinical Trial Registration: Registered in accordance with WHO and ICMJE standards (Trial number: PACTR202605512267205). Registration date: 29 May 2026. Full article

To optimize expensive black-box functions over networks, one of the most dominant frameworks is distributed Bayesian optimization (DBO), where local information can be exchanged among agents. However, DBO suffers from low evaluation efficiency due to limited data in the initial stage and the high cost of evaluations among multiple objectives. To tackle these obstacles, we propose a prior-informed multi-task collaborative Bayesian optimization (PMCBO) algorithm over networks. Concretely, PMCBO integrates expert prior knowledge about the location of optimum into the distributed multi-task Bayesian optimization framework to reduce the cost of evaluations. Meanwhile, PMCBO combines multi-task Bayesian optimization with a collaborative mechanism to improve the evaluation efficiency. Furthermore, we rigorously prove that the cumulative regret bound of PMCBO can achieve sub-linearly with high probability, where the acquisition functions employ expected improvement (EI) and upper-confidence bound (UCB) based on a Gaussian process surrogate. Finally, we implement various experiments to evaluate the effectiveness of PMCBO. The experimental results demonstrate that PMCBO can achieve state-of-the-art performance and benefit all clients based on diverse benchmarks and prior characteristics. Full article

Organic dairy farming has expanded rapidly over the past three decades, driven by regulatory reforms, consumer demand, and growing recognition of its environmental, animal welfare, and potential human health benefits. Despite this growth, evidence comparing organic and conventional dairy systems remains fragmented across genetics, phenomics, animal health, and human health outcomes. This review synthesizes current knowledge through the lens of genotype × environment interactions, integrating evidence from four complementary domains: (1) genomic architecture and breeding strategies; (2) phenotypic performance, including milk production and composition, meat quality, nutrition, and reproductive traits; (3) animal health, disease resistance, antimicrobial use, and welfare; and (4) implications for human health. Holstein–Friesian cattle remain the predominant breed in both systems; however, organic production favors animals with greater robustness, longevity, grazing efficiency, and disease resilience. Genetic studies further demonstrate that highly heritable production traits share similar genetic architecture across production systems, whereas health, fertility, longevity, and other low-heritability functional traits exhibit stronger genotype × environment interactions and more system-specific genomic signatures. These findings suggest that breeding strategies developed for high-input conventional systems are unlikely to maximize performance under organic management. Collectively, the evidence supports a shift from selection focused primarily on milk yield toward genomic improvement of robustness, disease resistance, reproductive resilience, grazing adaptation, and lifetime productivity. We propose Genomically Optimized Organic Dairy (GOOD) as an emerging framework that integrates genomic selection, precision phenotyping, health monitoring, and environmental adaptation to develop dairy cattle better suited to organic production. Full article

Class imbalance in clinical electrocardiogram (ECG) datasets limits the diagnostic sensitivity of automated arrhythmia classifiers, particularly for rare but clinically significant beat types. We propose a three-stage hybrid generative pipeline that combines a spectral-guided conditional variational autoencoder (cVAE), a class-conditional latent denoising diffusion probabilistic model (DDPM), and a Quantum Latent Refinement (QLR) module built on parameterized quantum circuits, implemented and evaluated using a classical quantum-circuit simulator, to augment minority arrhythmia classes, and present results based on the MIT-BIH Arrhythmia Database. The QLR module applies a bounded residual correction guided by Maximum Mean Discrepancy minimization to align synthetic latent distributions with real class-specific latent banks. A lightweight 1D MobileNetV2 classifier evaluated over ten independent random seeds and four augmentation ratios serves as the downstream benchmark. Our findings establish latent diffusion augmentation as an effective strategy for imbalanced ECG classification. To our knowledge, the proposed QLR module is the first use of a parameterized quantum circuit as a distributional refiner within a generative augmentation pipeline. While its performance is comparable to that of the classical latent diffusion framework under the present experimental conditions, the proposed approach demonstrates the feasibility of integrating quantum latent operators into generative medical AI pipelines and provides a foundation for future investigations on quantum-enhanced representation learning and data augmentation. Full article

Background/Objectives: Digital transformation is a strategic priority for healthcare systems, requiring nurses to develop competencies that enable the safe, effective, and critical use of digital technologies in clinical practice. Understanding nurses’ digital competence profile is essential to inform leadership, education, and workforce development strategies. This study aimed to assess digital competence among Portuguese nurses, examine its relationship with soft skills, and identify priority areas for professional development. Methods: A quantitative, descriptive-correlational, cross-sectional study was conducted with a nationally recruited convenience sample of Portuguese nurses. Data were collected using a Digital Competence Assessment Questionnaire based on the European Digital Competence Framework for Citizens (DigComp) and Soft Skills Inventory. Associations between digital competence and soft skills were analysed using descriptive, inferential, and multivariable statistical methods with IBM SPSS Statistics version 30.0. Results: Based on the exploratory classification of the knowledge/performance score, 52.0% of participants fell within the Intermediate and 27.2% within the Advanced proficiency intervals. Adapting and Coping, Analyzing and Interpreting, and Interacting and Presenting were positively associated with self-reflected digital competence. Multivariable analysis showed that soft skills accounted for a larger proportion of variance in self-reflected digital competence than in the knowledge/performance test score; however, the explanatory value of the latter model was limited. Conclusions: Participants were predominantly classified within the Intermediate and Advanced proficiency intervals, although comparatively lower descriptive performance was observed in Safety. The findings highlight the potential complementary role of technical competencies and soft skills in digital capability and suggest the value of further investigating targeted educational approaches. These findings may inform nursing leadership, education, and workforce-development strategies aimed at supporting digital competence and sustainable digital transformation. Full article

Wheat is one of the world’s most important cereal crops, and its technological quality is essential for the production of flour, dough and bakery products. Fusarium infection and the associated accumulation of mycotoxins may adversely affect grain composition, processing performance and food safety. This review summarizes current knowledge on the influence of Fusarium infection and associated mycotoxin contamination on the chemical composition and technological quality of wheat. A literature search was conducted in the Web of Science Core Collection, and eligible studies were included in the qualitative synthesis. The reviewed studies demonstrate that Fusarium infection generally reduces grain quality, gluten functionality, dough rheological properties and baking performance, although the magnitude and direction of changes depend on the Fusarium species, wheat cultivar, infection model and mycotoxin concentration. Considerable heterogeneity among experimental designs limits direct comparison of individual studies. By integrating evidence across grain, flour, dough and bread quality parameters, this review provides a comprehensive and comparative synthesis of the effects of different Fusarium species and associated mycotoxins on wheat technological quality and identifies major areas requiring further investigation. Full article

Hydrogenation reactors are critical equipment in the petrochemical industry, yet their material degradation is governed by coupled multi-mechanism damage. Current assessment practices largely neglect this complexity, remaining single-factor oriented and overlooking synergistic interactions and temporal evolution. This paper proposes a regionally differentiated, multi-level framework integrating 5 primary and 17 secondary indicators with a hybrid AHP-EWM weighting strategy that synthesizes expert knowledge and measured data. A multi-factor coupling correction coefficient is introduced to provide a preliminary estimate of the synergistic acceleration effect among damage mechanisms, while a GM(1,1) gray model enables dynamic trend prediction. Applied to a 25-year 2.25Cr-1Mo steel reactor, the method produces regional degradation values of 0.378, 0.607, and 0.533 for the base metal, welds, and cladding layer, respectively, with an overall baseline of 0.453 rising by 11% to 0.503 after coupling correction. Compared with exponential regression, ARIMA, and BP neural networks, GM(1,1) is selected for its balanced performance in small-sample fitting, extrapolation stability, and physical interpretability. Sensitivity analysis confirms stable degradation grading even with ±50% coupling coefficient variations. The proposed approach mitigates the underestimation inherent in conventional single-mechanism assessments and offers a quantitative tool for full-lifecycle risk management and predictive maintenance of hydrogenation reactors. Full article