Search Results (59,658)

Growing pressure on water resources and mineral fertilizer use calls for innovative and resource-efficient agri-food systems. Aquaponics, integrating aquaculture and hydroponics, represents a promising approach for sustainable greenhouse production. This study, aiming to explore alternative water and nutrient sources for greenhouse tomato production without compromising plant adaptability or yield, evaluated the co-cultivation of grape tomato and rainbow trout in a vertical decoupled aquaponic system under controlled greenhouse conditions. Two aquaponic nutrient strategies were tested: unmodified aquaponic water (AP) and complemented aquaponic water (CAP), with conventional hydroponics (HP) as a control, in a Deep Water Culture hydroponic system. Plant performance was assessed through marketable yield and physiological parameters, while system performance was evaluated using combined-biomass Energy Use Efficiency (EUE), Freshwater Use Efficiency (fWUE) and Nitrogen Use Efficiency (NUE), accounting for both plant and fish production. CAP significantly improved tomato yield (9.86 kg m⁻²) compared to AP (2.40 kg m⁻²), although it remained lower than HP (12.14 kg m⁻²). Fresh WUE was comparable between CAP and HP (9.22 vs. 9.24 g L⁻¹), demonstrating effective water reuse. In contrast, EUE and NUE were lower in CAP, reflecting the additional energy demand of the recirculating aquaculture system and nutrient limitations of fish wastewater. These results highlight aquaponics as a water-efficient production system while emphasizing that optimized nutrient management and energy strategies are critical for improving its overall sustainability and performance. Full article

Freeze–thaw cycles (FTCs) are a prevalent weathering process that threatens the stability of canal slopes in seasonally frozen regions. This study combines direct shear tests under multiple F-T cycles with coupled thermo-hydro-mechanical numerical modeling to investigate the failure mechanisms of slopes with different moisture contents (18%, 22%, 26%). The test results quantify a marked strength degradation, where the cohesion decreases to approximately 50% of its initial value and the internal friction angle is weakened by about 10% after 10 freeze–thaw cycles. The simulation reveals that temperature gradient-driven moisture migration is the core process, leading to a dynamic stress–strain concentration zone that propagates from the upper slope to the toe. The safety factors of the three soil specimens with different moisture contents fell below the critical threshold of 1.3. They registered values of 1.02, 0.99, and 0.78 within 44, 44, and 46 days, which subsequently induced shallow failure. The failure mechanism elucidated in this study enhances the understanding of freeze–thaw-induced slope instability in seasonally frozen regions. Full article

TIGIT and its ligand CD155 (PVR) are emerging immune checkpoints in colorectal cancer (CRC), but their associations with mutational subtypes and the tumor immune milieu remain unclear. We quantified TIGIT and CD155 proteins by ELISA in paired CRC tumors and matched surgical margins (n = 131) and evaluated associations with clinicopathological features, MSI status, and KRAS/NRAS/BRAF/PIK3CA/AKT1 mutations (n = 104). Both TIGIT and CD155 were significantly elevated in tumor tissue versus margins (p < 0.0001) and showed no association with TNM stage, clinical stage, grade, or tumor location. TIGIT levels were higher in MSI than MSS tumors and in BRAF-mutant compared to BRAF wild-type tumors, while CD155 expression showed no consistent MSI- or mutation-dependent differences. Cytokine profiling identified IFN-g as the only shared positive associate of TIGIT and CD155; CD155 additionally associated with TRAIL, IL-1Ra, M-CSF, and PDGF-bb. In external transcriptomic validation (TCGA-CRC), GSEA indicated enrichment of interferon/inflammatory programs in TIGIT-high tumors, while CD155-high tumors preferentially showed proliferation-related MYC/E2F/G2M signatures. Together, these findings support tumor-wide upregulation of the TIGIT/CD155 axis in CRC and suggest that TIGIT, more than CD155, tracks with MSI/BRAF-associated immune activation, providing a rationale for patient stratification in checkpoint-directed immunotherapy. Full article

Background/Objectives: Vedolizumab (VDZ), a monoclonal antibody targeting α4β7 integrin, is used in Crohn’s disease (CD) management, yet patients’ responses vary, underscoring the need for pharmacogenomic (PGx) markers. This study aimed to identify PGx pathways associated with suboptimal VDZ response using a rare-variant analytical framework. Methods: DNA from 63 CD patients treated with VDZ as first-line advanced therapy underwent whole-exome sequencing. Clinical response at week 14 classified patients as optimal responders (ORs) or suboptimal responders (SRs). Sequencing data were processed using GATK Best Practices, annotated with variant effect predictors, and filtered for rare damaging variants (damaging missense and high-confidence loss-of-function; minor allele frequency < 0.05). Variants were mapped to genes specific for SRs and ORs, and analyzed for pathway enrichment using the Reactome database. Rare-variant burden and composition differences were assessed with Fisher’s exact test and SKAT-O gene-set association analysis. Results: Suboptimal VDZ response was associated with pathways related to membrane transport (ABC-family proteins, ion channels), L1–ankyrin interactions, and bile acid recycling, while optimal response was associated with pathways involving MET signaling. SKAT-O identified lipid metabolism-related pathways as significantly different—SRs harbored variants in pro-inflammatory lipid signaling and immune cell trafficking genes (e.g., PIK3CG, CYP4F2, PLA2R1), whereas ORs carried variants in fatty acid oxidation and detoxification genes (e.g., ACADM, CYP1A1, ALDH3A2, DECR1, MMUT). Conclusions: This study underscores the potential of exome-based rare-variant analysis to stratify CD patients and guide precision medicine approaches. The identified genes and pathways are potential PGx markers for CD patients treated with VDZ. Full article

Reticuloendotheliosis virus (REV) is an immunosuppressive virus in poultry that can cause acute reticular neoplasms, chronic lymphoid tumors, stunting syndrome, and secondary infections. In many countries, the lack of effective vaccines has resulted in a high prevalence of REV infections and substantial economic losses. Enzyme-linked immunosorbent assay (ELISA)-based antibody detection is an important tool for monitoring the REV prevalence in poultry farms. ELISA coating antigens generally consist of either whole virus or viral protein; however, most commercially available REV antibody ELISA detection kits use whole virus as the coating antigen, which limits their applicability in certain diagnostic and research settings. In this study, the gp90 protein from a dominant REV strain was expressed and purified using 293F suspension cell eukaryotic expression system. Using recombinant gp90 protein as the coating antigen, an indirect ELISA for detecting gp90 antibodies (gp90-ELISA) was developed. After optimization, the optimal conditions were as follows: coating antigen concentration of 4 µg/mL with overnight incubation at 4 °C; blocking with 5% skim milk at 37 °C for 1.5 h; serum dilution of 1:200 with incubation at 37 °C for 45 min; secondary antibody dilution of 1:1000 with incubation at 37 °C for 30 min; and color development using TMB substrate at room temperature in the dark for 10 min. The cut-off value was defined as an OD₄₅₀ ≥ 0.22 for positive samples and < 0.22 for negative samples. The developed gp90-ELISA specifically detected REV-positive sera at a maximum serum dilution ratio of 1:3200. Intra- and inter-assay variation coefficients were ≤10%, indicating that the gp90-ELISA had good specificity, sensitivity, and reproducibility. Laboratory serum testing showed that the gp90-ELISA successfully detected sera from chickens immunized with the gp90 protein or infected with REV. Furthermore, analysis of clinical serum samples demonstrated 100% concordance between the gp90-ELISA results and a commercial whole-virus-coated ELISA kit. These results indicate that the gp90-ELISA is a reliable supplementary method to whole-virus-coated ELISA and has potential utility in disease surveillance and evaluation of immune responses. 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

Reliable detection of safety helmets is essential for ensuring personnel protection in large-scale outdoor operations. However, recognition becomes difficult when monitoring relies on low-resolution or compressed video streams captured by fixed or mobile platforms such as UAVs—conditions commonly encountered in intelligent transportation and urban surveillance. This study proposes a super-resolution-enhanced detection framework that integrates video super-resolution with ROI-guided inference to improve the visibility of small targets while reducing computational cost. Focusing on a single, carefully selected VSR module (BasicVSR++), the framework achieves an F1-score of 0.904 in helmet detection across multiple low-quality surveillance scenarios. This demonstrates the framework’s effectiveness for robust helmet monitoring in low-resolution and compressed surveillance scenarios. Full article

Background: Pseudomonas aeruginosa is one of the leading agents causing healthcare-associated infections (HAIs) due to its intrinsic resistance, its capacity to acquire resistance mechanisms, and its persistence in hospital environments. In Mexico, it ranks among the most frequently reported pathogens in national surveillance systems. The aim of this study was to characterize antimicrobial resistance profiles and the genetic determinants associated with MDR/XDR phenotypes in P. aeruginosa strains from HAIs at Hospital Juárez de México (HJM). Methods: Sixty-three strains from patients with HAIs were analyzed. Identification was confirmed by 16S rRNA gene sequencing. Antimicrobial susceptibility testing followed CLSI guidelines. MDR/XDR phenotypes were classified according to the Latin American consensus for categorizing MDR, XDR, and PDR pathogens. Screening for resistance mechanisms was carried out by PCR for the main β-lactamases circulating at HJM. Finally, mutations in the oprD gene were detected in imipenem-resistant isolates through amino acid sequence alignment. Results: Resistant phenotypes allowed the identification of MDR and XDR profiles. Only the metallo-β-lactamase bla_VIM was detected. Analysis of oprD porin sequences revealed recurrent mutations (S103T, T115K, L170F, G186P, and T189V) associated with imipenem resistance. Conclusions: In P. aeruginosa, the presence of bla_VIM and structural alterations in OprD confirms the multifactorial nature of carbapenem resistance. These findings underscore the need to strengthen microbiological surveillance programs and antimicrobial stewardship strategies to mitigate the impact and spread of MDR/XDR isolates. 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

Background: Repetitive peripheral magnetic stimulation (rPMS) has emerged as a promising non-invasive treatment modality for reducing muscle hypertonus and spasticity. However, standardized protocols regarding stimulation parameters, particularly the number of stimuli required to achieve therapeutic effects, remain largely undefined. Methods: In an exploratory study, seventeen healthy participants (15 male, 2 female) underwent progressive rPMS treatments at 5 Hz frequency with incrementally increasing stimulus counts (105, 210, 315, 420, and 840 stimuli). Seventeen participants served as controls (11 male, 6 female) receiving sham stimulation. Achilles tendon reflexes were elicited using a computer-controlled reflex hammer, and compound muscle action potential (CMAP) peak-to-peak amplitudes were recorded via surface electromyography before and immediately after each stimulation session. Results: The overall repeated-measures ANOVA indicated a significant main effect (F(5, 80) = 4.98, p = 0.001, η²_p = 0.237). All rPMS treatments produced significant reductions in CMAP amplitudes compared to baseline (p < 0.05). No progressive dose-dependent relationship was observed between stimulus count and response magnitude, suggesting a threshold effect rather than progressive inhibition. Control group showed no significant changes (p ≤ 0.56). Conclusions: Low-frequency (5 Hz) rPMS produces rapid inhibitory effects on spinal reflex circuits with onset after as few as 105 stimuli. These findings indicate that treatment effects can be achieved with substantially fewer stimuli than previously assumed. Further research is needed to identify parameters capable of achieving greater reflex suppression. Full article

Remote sensing object detection is a critical task in Earth observation. Despite the remarkable progress made in general object detection, existing detectors struggle with remote sensing scenarios due to the prevalence of numerous small objects with limited discriminative cues. Cutting-edge studies have shown that incorporating contextual information effectively enhances the detection performance for small objects. Meanwhile, recent research has revealed that convolution in the frequency domain is capable of capturing long-range spatial dependencies with high efficiency. Inspired by this, we propose a Frequency-aware Feature Pyramid Framework (FFPF) for remote sensing object detection, which consists of a novel Frequency-aware ResNet (F-ResNet) and a Bilateral Spectral-aware Feature Pyramid Network (BS-FPN). Specifically, the F-ResNet is proposed to extract the spectral context information by plugging the frequency domain convolution into each stage of the backbone, thereby enriching features of small objects. In addition, the BS-FPN employs a bilateral sampling strategy and skipping connection to model the association of object features at different scales, enabling the contextual information extracted by the F-ResNet to be fully leveraged. Extensive experiments are conducted for object detection in the public remote sensing image dataset and natural image dataset. The experimental results demonstrate the excellent performance of the FFPF, achieving 73.8% mAP on the DIOR dataset without using any additional training tricks. Full article

This work examines the impact of integrating recycled rubber particles on the dynamic mechanical properties of polyester/fiberglass (P/F) composites. Rubber particles of several mesh sizes (M20 and M40) and volume fractions (10%, 20%, and 30%) were included in the P/F composite. The findings indicate that increasing rubber content reduces density and affects the tensile strength and fracture characteristics of the composites. Rubber often decreases stiffness while potentially enhancing damping, contingent on its interaction with the polyester matrix. The P/F/M40_20% composite demonstrates significant stiffness and moderate damping, indicating a distinctive reinforcing mechanism. The relationship between rubber tensile strength and fractured behavior is complex. M40 composites weaken at 30% owing to debonding, but M20 composites only slightly decrease in strength at 20% rubber. Interestingly, M20_30% has increased strength due to rubber–fracture interactions. Fiberglass reinforcement stiffens the material but reduces vibration absorption. Rubber enhances flexibility and may attenuate vibrations. A weighted scoring method determines that the P/F/M20_20% rubber composite is the most advantageous for attaining equilibrium of toughness, strength, and damping characteristics. This work elucidates how to optimize the performance of P/F composites by modifying the properties of rubber particles for targeted applications. Full article

In the context of bearing anomaly detection, challenges such as imbalanced sample distribution and complex operational conditions present significant difficulties for data-driven deep learning models. These issues often result in overfitting and high false positive rates in complex real-world scenarios. This paper proposes a strategy that leverages multimodal fusion and Self-Adversarial Training (SAT) to construct and train a deep learning model. First, the one-dimensional bearing vibration time-series data are converted into Gramian Angular Difference Field (GADF) images, and multimodal feature fusion is performed with the original time-series data to capture richer spatiotemporal correlation features. Second, a composite data augmentation strategy combining time-domain and image-domain transformations is employed to effectively expand the anomaly samples, mitigating data scarcity and class imbalance. Finally, the SAT mechanism is introduced, where adversarial samples are generated within the fused feature space to compel the model to learn more generalized and robust feature representations, thereby significantly enhancing its performance in realistic and noisy environments. Experimental results demonstrate that the proposed method outperforms traditional baseline models across key metrics such as accuracy, precision, recall, and F1-score in abnormal bearing anomaly detection. It exhibits exceptional robustness against rail-specific interferences, offering a specialized solution strictly tailored for the unique, high-noise operational environments of intelligent railway maintenance. Full article

Recycling water offers a powerful way to lower the environmental water impact of mining activities. Pressure-retarded osmosis (PRO) represents a promising pathway for simultaneous water reuse and clean energy generation from salinity gradients. In this study, the performance of a thin-film nanocomposite (TFN) membrane containing functionalized multi-walled carbon nanotubes (fMWCNTs) within a polyacrylonitrile (PAN) support layer, followed by polydopamine (PDA) surface modification, was investigated under a PRO operation using pretreated gold mining wastewater as the feed solution. Unlike most previous studies that rely on synthetic feeds, this work evaluates the membrane performance under a PRO operation using a real mining wastewater stream. The membrane with fMWCNTs and PDA exhibited a maximum power density of 25.22 W/m² at 12 bar, representing performance improvements of 23% and 68% compared with the pristine thin-film composite (TFC) and commercial cellulose triacetate (CTA) membranes, respectively. A high water flux of 75.6 L·m⁻²·h⁻¹ was also obtained, attributed to enhanced membrane hydrophilicity and reduced internal concentration polarization. The optimized membrane, containing 0.3 wt% fMWCNTs in the support layer and a PDA coating on the active layer, produced a synergistic enhancement in the PRO performance, resulting in a lower reverse salt flux and an improved flux–selectivity trade-off. Furthermore, the ultrafiltration (UF) and nanofiltration (NF) pretreatment effectively reduced the hardness and ionic content, enabling a stable PRO operation with real mining wastewater over a longer period of time. Overall, this study demonstrates the feasibility of achieving both reusable water and enhanced osmotic power generation using modified TFN membranes under realistic mining wastewater conditions. Full article

Background: Rowing performance in youth athletes is strongly influenced by anthropometric characteristics, body composition, and limb proportions; however, the combined contribution of these factors across developmental stages remains insufficiently understood. This study investigated the relationships between key anthropometric variables and ergometer performance in a multinational cohort of young rowers. Methods: A total of 194 athletes (48 females, 146 males) from ten countries participated. Based on age and sex, participants were categorized into junior female (JF), junior male (JM), adult female (AF), and adult male (AM) groups. Body height, body mass, body fat (F%), relative muscle mass (M%), limb lengths, and body surface area (BSA) were measured. Rowing performance was assessed via maximal 2000 m ergometer trials. Results: Males outperformed females across all age groups (p < 0.001). Performance showed strong positive correlations with body height (r = 0.673, p = 0.003), body mass (r = 0.724, p = 0.005), arm span (r = 0.681, p = 0.002), lower-limb length (r = 0.394, p = 0.004), relative muscle mass (39.9 ± 5.2%; r = 0.531, p < 0.001), and especially BSA (1.94 ± 0.19 m²; r = 0.739, p < 0.001). Relative body fat was negatively associated with performance (17.6 ± 6.9%; r = −0.465, p < 0.001). Conclusions: Findings indicate that rowing performance in youth athletes reflects multidimensional anthropometric configurations rather than isolated traits, characterized primarily by the combined contribution of body surface area, relative muscle mass, and segmental body dimensions. From a practical perspective, higher-performing athletes typically exhibited body surface area values approaching or exceeding ~1.90 m² and relative muscle mass above ~40%, suggesting these ranges as indicative reference benchmarks rather than fixed selection thresholds. Integrating anthropometric profiling with physiological assessment may enhance early talent identification and support individualized training strategies in competitive youth rowing. Full article