Search Results (64,849)

Gibberellin (GA)-based seedless cultivation is widely used in the skin-edible interspecific table grape (Vitis labruscana × Vitis vinifera) ‘Shine Muscat’, yet when and how GA treatment reshapes fracture-type texture during berry development remains unclear. This study aimed to identify developmental stages and tissue/cell-wall features associated with GA-dependent differences in berry fracture behavior. We integrated intact-berry fracture testing at harvest (DAFB105), quantitative histology of pericarp/mesocarp tissues just before veraison (DAFB39) and at harvest, sequential cell-wall fractionation assays targeting pectin-rich (uronic acid) and hemicellulose/cellulose-related pools at cell division period, cell expansion period and harvest, and stage-resolved RNA-Seq across the same three developmental stages. GA-treated berries had a larger diameter and showed a higher fracture load and a lower fracture strain than non-treated berries at harvest, while toughness did not differ significantly. Histology revealed thicker pericarp tissues and lower mesocarp cell density in GA-treated berries, together with increased cell-size heterogeneity and enhanced radial cell expansion. Cell wall analyses showed stage-dependent decreases in uronic acid contents in water-, EDTA-, and Na₂CO₃-soluble fractions in GA-treated berries. Transcriptome profiling indicated GA-responsive expression of putative cell expansion/primary-wall remodeling genes, EXORDIUM and xyloglucan endotransglucosylase/hydrolases, at DAFB24 and suggested relatively enhanced ethylene-/senescence-associated transcriptional programs together with pectin-modifying related genes, Polygaracturonase/pectate lyase and pectin methylesterase, in non-treated mature berries. Collectively, GA treatment modifies mesocarp cellular architecture and pectin-centered wall status in a stage-dependent manner, providing a tissue- and cell wall–based framework for interpreting fracture-related texture differences under GA-based seedless cultivation in ‘Shine Muscat’. Full article

Background/Objective: The rising prevalence of Type 2 Diabetes Mellitus (T2DM), coupled with sedentary behavior and an increase in obesity rates in South Asian countries, calls for effective management strategies. We aimed to assess the efficacy of lifestyle interventions on glycemic control among adults with T2DM in South Asian countries. Methods: A systematic review and meta-analysis of randomized controlled trials (RCTs) were conducted to assess the effectiveness of lifestyle interventions on glycemic control in adults diagnosed with T2DM in South Asia. We conducted a comprehensive search in CINAHL, Embase, PubMed, Cochrane Library, Web of Science (WoS), and Scopus to identify related studies published from 2000 to 13 June 2024. We assessed the risk of bias using the ROB 2.0 tool and calculated the pooled mean differences in HbA1c and FBG levels under a random-effects model. We conducted subgroup and leave-one-out sensitivity analyses to assess and explore sources of heterogeneity. PROSPERO Registration: CRD42024552286. Results: We included 16 RCTs with a total of 1499 participants. Lifestyle interventions reduced HbA1c levels by 0.86% (95% CI: −1.30 to −0.42, p < 0.01) and FBG levels by 22.49 mg/dL (95% CI: −32.88 to −12.10, p < 0.01). We observed substantial heterogeneity (I² = 98% for HbA1c and I² = 87% for FBG). Subgroup analyses indicated larger HbA1c reductions in long-term (−1.44%) than short-term trials (−0.62%), and greater FBG decreases in long-term (−23.7 mg/dL) versus short-term studies (−22.5 mg/dL). Physical activity interventions had the largest improvements (HbA1c −0.99%; FBG −26.1 mg/dL), followed by dietary (HbA1c −0.59%; FBG −15.8 mg/dL) and combined programs (HbA1c −0.55%). Participants aged >50 years achieved greater glycemic improvements (HbA1c −0.92%; FBG −24.0 mg/dL) compared to younger adults (HbA1c −0.60%; FBG −21.3 mg/dL). Despite high heterogeneity, sensitivity analyses confirmed the robustness of the overall findings. Conclusions: Lifestyle modifications yielded a clinically significant reduction in HbA1c and FBG in adults with T2DM in South Asia. Although heterogeneity of the included studies was substantial, the direction of the effects was uniformly consistent across subgroups. To further validate these findings and assess their long-term effects, large-scale and standardized RCTs conducted for longer durations are necessary. Full article

Practical training and recruitment strategies are critical for the sustainable implementation of psychosocial interventions. However, few studies have examined how to prepare community partners and doctoral students to support culturally adapted psycho-oncology interventions. This pre-pilot study aims first to evaluate two distinct training programs and recruitment procedures, and second to explore preliminary pre-post outcomes of the Caregiver-Patients Support to Cope with Advanced Cancer (CASA) intervention, using the Consolidated Framework for Implementation Research (CFIR). Three clinical psychology graduate students received CASA training, and two community partners completed Recruitment training. We present descriptive pre- and post-assessments, along with qualitative feedback, for both training and institutional (Puerto Rico Biobank) and community-based recruitment outcomes. A related-samples nonparametric analysis examined pre- and post-CASA intervention signals. Results indicated knowledge gains among doctoral students (pre-test M = 3.3; post-test M = 9.3) and community partners (pre-test M = 4.5; post-test M = 9.5). Preliminary outcomes revealed significant improvements in spiritual well-being (Z = −2.618, p = 0.009) and quality of life (Z = −2.957, p = 0.003) and a reduction in depressive (Z = −2.764, p = 0.006), anxiety (Z = −2.667, p = 0.008), and distress (Z = −2.195, p = 0.028) symptoms following CASA. Of 26 recruited dyads, institutional referrals enrolled 16 dyads (61.5%), while community partners referred 10 dyads with a 90.9% success rate. Findings support the feasibility of both training and CASA exploratory outcomes suggest meaningful psychosocial benefits for Latino dyads coping with advanced cancer. Combining institutional infrastructure with community engagement may enhance sustainability and equitable access to psycho-oncology care. Full article

With the expansion of photovoltaic (PV) systems, failures of bypass diodes (BPDs) embedded in PV modules can degrade the power-generation performance and pose safety risks. When a BPD fails, current circulates within the module, leading to overheating and eventual burnout of the failed BPD. The heating characteristics of a BPD depend on its fault resistance, and although many modules are connected in series in actual PV systems, the heating risk at the module-string level has not been sufficiently evaluated to date. In this study, a numerical simulation model is constructed to reproduce the operation of PV modules and module strings containing failed BPDs, and its validity is verified through experiments. The validated numerical simulation results quantitatively illustrate how series-connected PV modules modify the fault-resistance dependence of BPD heating under maximum power-point operation. The results show that, under maximum power-point operation, the fault resistance at which BPD heating becomes critical shifts depending on the number of series-connected modules examined, while the magnitude of the maximum heating decreases as the string length increases. The heat generated in a BPD at the maximum power point decreases as the number of series-connected modules increases for the representative string configurations analyzed. However, under open-circuit conditions due to power-conditioner abnormalities, the power dissipated in the failed BPD increases significantly, posing a very high risk of burnout. Considering that lightning strikes are one of the major causes of BPD failure, adopting diodes with higher voltage and current ratings and improving the thermal design of junction boxes are effective measures to reduce BPD failures. The simulation model constructed in this study, which was experimentally validated for short PV strings, can reproduce the electrical characteristics and heating behaviors of PV modules and strings with BPD failures with accuracy sufficient for comparative and parametric trend analysis, and serves as a practical tool for system-level safety assessment, design considerations, and maintenance planning within the representative configurations analyzed. Full article

Fault diagnosis in the power systems of Hybrid Electric Vehicles (HEVs) is crucial for ensuring vehicle safety and energy efficiency. This study proposes an innovative CNN-LSTM fusion model for diagnosing common faults in HEV power systems, such as battery degradation, inverter anomalies, and motor failures. The model integrates the feature extraction capabilities of Convolutional Neural Networks (CNN) with the temporal dependency handling of Long Short-Term Memory (LSTM) networks. Through data preprocessing, model training, and validation, the approach achieves high-precision fault identification. Experimental results demonstrate an accuracy rate exceeding 95% on simulated datasets, outperforming traditional machine learning methods. This research provides a practical framework for HEV fault diagnosis and explores its potential in real-world applications. Full article

The increasing complexity of modern electrical substations—driven by renewable integration, advanced automation, and asset aging—necessitates a transition from reactive maintenance toward intelligent, data-driven strategies. Predictive maintenance (PdM), supported by artificial intelligence, enables early fault detection and remaining useful life (RUL) estimation, while Digital Twin (DT) technology provides synchronized cyber–physical representations for situational awareness and risk-free validation of maintenance decisions. This study proposes a five-layer DT-enabled PdM architecture integrating standards-based data acquisition, semantic interoperability (IEC 61850, CIM, and OPC UA Part 17), hybrid AI analytics, and cyber-secure decision support aligned with IEC 62443. The framework is validated using utility-grade operational data from the SS1 substation of the Badra Oil Field, comprising approximately one million multivariate time-stamped measurements and 139 confirmed fault events across transformer, feeder, and environmental monitoring systems. Fault detection is formulated as a binary classification task using event-window alignment to the 1 min SCADA timeline, preserving realistic operational class imbalance. Five supervised learning models—a Random Forest, Gradient Boosting, a Support Vector Machine, a Deep Neural Network, and a stacked ensemble—were benchmarked, with the ensemble embedded within the DT core representing the operational predictive model. Experimental results demonstrate strong performance, achieving an F1-score of 0.98 and an AUC of 0.995. The results confirm that the proposed DT–AI framework provides a scalable, interoperable, and cyber-resilient foundation for deployment-ready predictive maintenance in modern substation automation systems. Full article

Background/Objectives. Lactiplantibacillus plantarum CRL681 has previously demonstrated a strong antagonistic effect against Escherichia coli O157:H7 in food matrices; however, the molecular mechanisms underlying this activity remain poorly understood. Since initial interactions between beneficial bacteria and pathogens occur mainly at the cell surface and in the extracellular environment, the characterization of the bacterial secretome is essential for elucidating these mechanisms. In this study, the secretome of L. plantarum CRL681 was comprehensively characterized using an integrated in silico and in vitro approach. Methods. The exoproteome and surfaceome were analyzed by LC-MS/MS under pure culture conditions and during co-culture with E. coli O157:H7. Identified proteins were functionally annotated, classified according to subcellular localization and secretion pathways, and evaluated through protein–protein interaction network analysis. Results. A total of 275 proteins were proposed as components of the CRL681 secretome, including proteins involved in cell surface remodeling, metabolism and nutrient transport, stress response, adhesion, and genetic information processing. Co-culture with EHEC induced significant changes in the expression of proteins associated with energy metabolism, transport systems, and redox homeostasis, indicating a metabolic and physiological adaptation of L. plantarum CRL681 under competitive conditions. Notably, several peptidoglycan hydrolases, ribosomal proteins with reported antimicrobial activity, and moonlighting proteins related to adhesion were identified. Conclusions. Overall, these findings suggest that the antagonistic activity of L. plantarum CRL681 against E. coli O157:H7 would be mediated by synergistic mechanisms involving metabolic adaptation, stress resistance, surface adhesion, and the production of non-bacteriocin antimicrobial proteins, supporting its potential application as a bioprotective and functional probiotic strain. Full article

Socioscientific issues (SSIs) provide meaningful contexts for developing students’ competencies in scientific evaluation and decision-making. This study developed an SSI-based instructional unit to support primary school students in making decisions about genome-edited fish by considering risks and benefits and proposing risk mitigation. The study aimed to examine the unit’s effectiveness in improving students’ risk-related decision-making and their attitudes toward critical thinking and risk. Sixty-three fifth-grade students participated in an 18-lesson unit comprising two phases: information gathering and risk management practice. Students completed three decision-making tasks and a post-unit questionnaire on related attitudes. Written arguments were analysed using a rubric based on claims, risk knowledge, benefit knowledge, and risk mitigation. The results indicated that the unit improved the quality of students’ socioscientific arguments. By the final task, about 60% of arguments reached the highest level, demonstrating integration of risk knowledge and corresponding mitigation. However, students’ risk–benefit emphasis ratings showed that their decisions remained predominantly risk-focused, and questionnaire data revealed a persistent zero-risk mindset. These findings provide empirical evidence that an SSI-based unit incorporating risk management practice can foster primary students’ risk-related socioscientific decision-making. Further refinement is needed to shift students’ risk attitudes and support more balanced risk–benefit reasoning. Full article

The growing global reliance on solar photovoltaic (PV) systems requires robust, automated inspection techniques to ensure reliability and efficiency. Thermal infrared (IR) imaging is widely used for detecting PV faults; however, accurate classification remains challenging due to severe class imbalance, low thermal contrast, and high inter-class visual similarity among fault types. This study proposes a hierarchical deep learning framework for thermal PV fault classification, integrating a multi-class dataset-balancing strategy to enhance representational efficiency. The proposed framework consists of two major components: (i) a hierarchical two-stage classification scheme that mitigates data imbalance and leverages limited labeled data for improved fault discrimination; and (ii) a contrast-preserving MixUp augmentation technique designed explicitly for low-contrast thermal imagery, improving minority fault class recognition and overall robustness. Comprehensive experiments were conducted on benchmark 8-class thermal PV datasets using nine deep network architectures. Dataset refactoring decisions are validated through quantitative inter-class distance analysis using multiple complementary metrics. Results demonstrate that the proposed hierarchical SlantNet model achieves the best trade-off between accuracy and computational efficiency, achieving an F1-Efficiency Index of 337.6 and processing 42,072 images per second on a GPU, over twice the efficiency of conventional approaches. Comparatively, the Swin-T Transformer attained the highest classification accuracy of 89.48% and F1 score of 80.50%, while SlantNet achieved 86.15% accuracy and 73.03% F1 score with substantially higher inference speed, highlighting its real-time potential. Ablation studies on augmentation and regularization strategies confirm that the proposed techniques significantly improve minority class detection without compromising overall performance, with detailed per-class precision, recall, and F1 analysis. The proposed framework delivers a high-accuracy, low-latency, and edge-deployable solution for automated PV inspection, facilitating seamless integration into operational PV plants for real-time fault diagnosis. Full article

In recent years, the application of augmented reality digital technology in brands has transformed the way consumers interact with brands. This study focuses on the impact of augmented reality (AR) technology on consumption behavior and brand communication related to intangible cultural heritage products, integrating the TAM and UTAUT2 theories to construct a research model. This study employed a time–location sampling method, utilizing SPSS and AMOS software for data analysis based on valid questionnaires completed by 305 AR-experiencing consumers in Changsha City, Hunan Province. Results indicate that the presence and novelty of AR technology significantly and positively influence consumers’ attitudes toward using AR technology, which in turn affects their purchase intent, social media sharing behavior, and brand attitudes. The study confirms that emotional factors and consumer perceptions play a guiding and decisive role in the new consumption reality enabled by AR technology. These research findings have practical significance and value for ICH brand building and AR marketing, demonstrating that AR is an effective means to enhance the visibility and influence of the ICH brand. They inject new vitality into promoting more sustainable ICH protection and popularization, as well as the development of the digital creative industry. Full article

Background: Gastrointestinal discomfort affects up to 70% of individuals with spinal cord injury (SCI), largely due to gut dysbiosis caused by altered transit time and reduced gastrointestinal motility from autonomic disruption. Emerging evidence links prebiotics and probiotics to improved microbiome balance and reduced inflammation, yet data in SCI remain limited. Methods: Individuals aged ≥ 18 years, with a chronic SCI (≥1 year) experiencing significant gastrointestinal symptoms, will be invited to participate in this single-center randomized controlled crossover trial. Persons currently taking antibiotics, who have relevant eating or digestive disorders, or who have undergone a recent diet change will be excluded from the study. Participants will be randomized (1:1) into two groups. The first group will take a probiotic (Biotics-G, Burgerstein AG, Rapperswil-Jona, Switzerland) supplement for eight weeks, then after a four-week washout period, they will take a prebiotic (Oat Bran, Naturaplan, manufactured by Swissmill, Zurich, Switzerland) supplement for another eight weeks. The second group will receive the supplements in reverse order. The primary outcome is the Gastrointestinal Quality of Life Index, a questionnaire to assess quality of life related to gastrointestinal disorders. Secondary outcomes consist of gastrointestinal transit time, inflammatory blood markers, and gut microbiome composition. Ethics: The study will be conducted in accordance with the Declaration of Helsinki. The study was approved by the Ethics Committee for Northwest/Central Switzerland (EKNZ, ID: 2025-00238, 24.02.2025, Version 2.0). The study is registered at ClinicalTrials.gov (ID: NCT06870331, 02.04.2025). Written informed consent will be obtained from all participants involved in the study. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by dopaminergic neuronal loss and abnormal α-synuclein aggregation. Circulating microRNAs (miRNAs) have emerged as promising biomarkers and potential modulators of PD-related molecular pathways. In this study, we investigated the expression levels of four candidate miRNAs—miR-15a-5p, miR-16-5p, miR-139-5p, and miR-34a-3p—in patients with PD compared with healthy controls. A total of 47 PD patients and 45 age- and sex-matched controls were enrolled. Plasma miRNA levels were quantified using standardized RNA extraction, cDNA synthesis, and qPCR protocols. We observed marked upregulation of miR-15a-5p and robust downregulation of both miR-139-5p and miR-34a-3p in PD patients, whereas miR-16-5p showed no significant difference between groups. Target gene prediction and functional enrichment analysis identified 432 unique genes, with enrichment in biological processes related to protein ubiquitination and catabolic pathways, and signaling cascades such as mTOR, PI3K-Akt, MAPK, and Hippo pathways, all of which are implicated in neurodegeneration. Elevated miR-15a-5p may contribute to pro-apoptotic mechanisms, while reduced miR-139-5p and miR-34a-3p expression may reflect impaired mitochondrial function, diminished neuroprotection, or compensatory regulatory responses. Together, these dysregulated circulating miRNAs provide novel insight into PD pathophysiology and highlight their potential as accessible, non-invasive biomarkers. Further longitudinal studies in larger and more diverse cohorts are warranted to validate their diagnostic and prognostic value and to explore their utility as therapeutic targets. Full article

Long bone formation in vertebrates proceeds via endochondral ossification, a sequential process that begins with mesenchymal condensation, advances through cartilage anlage formation, and culminates in its replacement by mineralized bone. Recent advances in inducible lineage tracing and single-cell genomics have revealed that, rather than being a uniform event, mesenchymal condensation rapidly segregates into progenitor pools with distinct fates. Centrally located Sox9⁺/Fgfr3⁺ chondroprogenitors expand into the growth plate and metaphyseal stroma, peripheral Hes1⁺ boundary cells refine condensation via asymmetric division, and outer-layer Dlx5⁺ perichondrial cells generate the bone collar and cortical bone. Concurrently, dorsoventral polarity established by Wnt7a–Lmx1b and En1 ensures that dorsal progenitors retain positional identity throughout development. These lineage divergences integrate with signaling networks, including the Ihh–PTHrP, FGF, BMPs, and WNT/β-catenin networks, which impose temporal control over chondrocyte proliferation, hypertrophy, and vascular invasion. Perturbations in these programs, exemplified by mutations in Fgfr3, Sox9, and Dlx5, underlie region-specific skeletal dysplasias, such as achondroplasia, campomelic dysplasia, and split-hand/foot malformation, demonstrating the lasting impacts of embryonic patterning errors. Based on these insights, regenerative strategies are increasingly drawing upon developmental principles, with organoid cultures recapitulating ossification centers, biomimetic hydrogels engineered for spatiotemporal morphogen delivery, and stem cell- or exosome-based therapies harnessing developmental microRNA networks. By bridging developmental biology with biomaterials science, these approaches provide both a roadmap to unravel skeletal disorders and a blueprint for next-generation therapies to reconstruct functional bones with the precision of the embryonic blueprint. Full article

Tutoring has played a significant role in pandemic-related learning recovery, supporting student learning and engagement. This paper follows up on a recent randomized controlled trial (RCT) estimating that one-on-one virtual early literacy tutoring was nearly twice as effective as two-on-one tutoring for improving student learning. To better understand this gap, we analyze transcripts from 16,629 tutoring sessions from this RCT—which included over 3.7 million tutor utterances—using natural language processing and machine learning techniques. We explore how tutors allocate attention across content instruction, relationship building, and classroom management between one-on-one and two-on-one formats. While tutors dedicate similar time to content instruction and relationship building across both formats, students receiving one-on-one tutoring receive more attention and personalized support. To improve the effectiveness of two-on-one tutoring, it may be beneficial to equip tutors with strategies that engage multiple students simultaneously, thereby reducing downtime and minimizing the potential for disengagement. Full article

This paper analyzes the impact of COVID-19 on the supply chain and production, investigating countermeasures for industrial recovery. In particular, the study examines how COVID-19 has affected the raw material supply chain, production, and outages on a real case study, that is, Turkey’s Glass-Reinforced Plastic (GRP) pipe industry. Using two- and three-way analysis of variance (ANOVA), significant negative impacts on the raw material supply chain are identified with 95% confidence. To enhance decision-making, the fuzzy q-rung orthopair set (FQROPS) and entropy-based multi-criteria decision-making (MCDM) methods are integrated in the baseline method. Specifically, ANOVA-identified factors, such as cost, supply continuity, production capacity, and risk level, are used as criteria in the MCDM analysis. Entropy determined criteria weights and FQROPS evaluate alternatives based on their proximity to the ideal solution. Findings show that significant disruptions occurred due to the pandemic. In addition, the MCDM analysis reveals that pre-pandemic conditions for key materials, such as fiberglass and resin, were significantly more favorable in terms of cost, supply continuity, production capacity, and risk levels. This integrated approach provides strategic insights for managing supply chains and production in the GRP pipe industry during and after pandemic events. Full article