Search Results (18,864)

Base running performance in baseball depends on the ability to efficiently transition between linear and curvilinear sprinting; however, current assessment approaches provide limited insight into how speed is developed, maintained, or lost across these phases. This perspective presents a methodological framework for using GPS technology to enhance the analysis and interpretation of base running performance through segment-specific velocity and time diagnostics. GPS data were collected during 54.7 m linear sprints and home-to-second-base curvilinear sprints in three high-school baseball players with differing performance profiles. Sprint paths were divided into standardized linear (L1–L4) and curvilinear (C1–C4) segments, allowing examination of speed changes between successive phases to identify acceleration, maintenance, and deceleration patterns. Comparative case analyses illustrate how athletes differ in their ability to negotiate the curve around first base, reaccelerate toward second base, and maintain speed under increasing curvilinear demands. In addition, a base running efficiency ratio (BREr) is introduced to quantify how effectively linear sprint capacity is preserved during curvilinear base running, both globally and across early and late phases of the sprint. The three players’ data illustrated that GPS-derived velocity–time profiles may provide useful insights into individual running strategies, path selection, and segment-specific performance limitations that are not captured by traditional timing methods. Rather than establishing normative benchmarks, this paper emphasizes the applied value of GPS technology as a diagnostic tool to potentially inform individualized assessment and monitoring in applied settings related to linear and curvilinear sprint performance in baseball. Full article

Sonar is a fundamental tool for underwater target detection. However, conventional detection systems often suffer from poor sensor consistency and high fabrication costs. More critically, for low-frequency operation, the required array aperture becomes prohibitively large, limiting their deployment on small, mobile underwater platforms. To address the demand for compact, high-performance sensing solutions, this paper presents a miniaturized Micro-electromechanical Systems (MEMS) hydrophone array designed for underwater target detection. The array consists of six elements with a spacing of 0.25 m. Each element is approximately 22 mm in diameter and encapsulated in polyurethane via a casting and curing process. The core sensing element, a MEMS acoustic pressure hydrophone, exhibits a sensitivity of −177.2 ± 1.5 dB (re: 1 V/µPa) across the 20 Hz to 4 kHz frequency range and a noise resolution of approximately 59.5 dB (re: 1 µPa/√Hz) at 1 kHz. A key challenge in array-based detection is the phase mismatch among acquisition channels, which degrades algorithm performance. To mitigate this, we propose a phase self-correction method based on interleaved ADC acquisition control, enabling synchronous multi-channel sampling and effectively eliminating system-level phase errors. Furthermore, to overcome the inherent aperture limitations of conventional beamforming (CBF) applied to a miniaturized array, a differential beamforming (DBF) algorithm is adopted. This approach is less frequency-dependent and can approximate a frequency-invariant beam pattern, making it well-suited for miniaturized arrays. Simulation results confirm the theoretical validity of the DBF algorithm for the proposed MEMS hydrophone array. Sea trial data further demonstrate that this method achieves higher target detection accuracy compared to CBF techniques. Full article

This paper presents a systematic methodology for time-domain modeling of three-phase power transformers aimed at electromagnetic transient analysis in shipboard and embedded electrical systems. Accurate modeling of transformers in such environments is critical, as naval power systems are subject to strict electromagnetic compatibility (EMC) requirements and are particularly susceptible to fast transients caused by switching operations, fault events, and nonlinear loads operating in confined and isolated grids. The proposed approach combines the Vector Fitting (VF) algorithm with Clarke modal decomposition to obtain stable, passive, and causal rational approximations of the frequency-dependent admittance matrix over a wide frequency range. The admittance matrix is first identified from frequency-domain measurements or simulations, capturing the transformer’s terminal behavior across multiple frequency sub-bands. Clarke’s transformation is then applied to decouple the three-phase system into independent modal components—namely the zero-sequence and positive-sequence modes, reducing the original multi-phase problem to a set of independent single-phase systems. This modal decoupling significantly improves computational efficiency without sacrificing accuracy, as each mode can be fitted and simulated independently. Full article

Background/Objectives: Spondylarthritis (SA) is characterized by high clinical heterogeneity, often resulting in a discrepancy between systemic inflammation and patient-reported symptoms. While hematologic indices are emerging as cost-effective biomarkers, their role in phenotypic differentiation remains unclear. We investigated the utility of traditional inflammatory markers, including the novel fibrinogen-to-platelet ratio (FPR), in differentiating SA subtypes and predicting patient-reported disease activity. Methods: This cross-sectional study included 64 patients with spondylarthritis: axial SA (n = 32), peripheral SA (n = 8), and psoriatic SA (n = 24). Clinical assessments included the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) and Functional Index (BASFI). Systemic inflammation was evaluated via C-reactive protein (CRP), fibrinogen, and calculated ratios (NLR, PLR, MLR, and FPR). Principal Component Analysis (PCA) was employed to map the inflammatory architecture, while Receiver Operating Characteristic (ROC) curves evaluated the predictive power for high disease activity (BASDAI ≥ 4). Results: Significant phenotypic differences were observed with the FPR demonstrating superior discriminative capacity (p = 0.003). Patients with peripheral SA exhibited significantly higher FPR (median 1.88) compared to axial (1.33) and psoriatic (1.32) subtypes, and the dedicated ROC analysis for phenotypic discrimination yielded an AUC of 0.866 (95% CI: 0.745–0.987) (1.36, p = 0.039). HLA-B27 prevalence was low overall (31.3%) and in psoriatic SA (4.2%, p = 0.012). Correlation analysis revealed strong associations between BASDAI and BASFI (ρ = 0.79), NLR and MLR (ρ = 0.78), and CRP and fibrinogen (ρ = 0.66). PCA identified two independent inflammatory dimensions explaining 74.8% of variance: neutrophil-hypercoagulable axis (41.4%, driven by NLR, PLR, and MLR), and an acute-phase/fibrinogen axis (33.4%, driven by CRP, fibrinogen, and FPR). Notably, FPR clustered with acute-phase reactants rather than leukocyte-derived ratios, supporting its role as a marker of systemic inflammatory burden. Although fibrinogen is involved in the coagulation cascade, the absence of direct coagulation markers precludes definitive characterization of this component as hypercoagulable. ROC analysis revealed that fibrinogen showed the highest discriminative ability for disease activity (BASDAI ≥ 4), with an AUC of 0.690 (95% CI: 0.519–0.861), followed by NLR (0.621), MLR (0.592), and FPR (0.583). However, overall discriminative performance remained modest, with most 95% confidence intervals crossing 0.5. Conclusions: FPR emerges as a robust phenotypic biomarker capable of discriminating against SA subtypes, particularly identifying peripheral SA with high accuracy and excellent negative predictive value. In contrast, its ability to predict patient-reported disease activity remains limited, reinforcing the distinction between trait and state biomarkers. Exploratory PCA revealed that FPR clusters with acute-phase reactants rather than leukocyte-derived ratios, supporting its biological link to systemic inflammatory burden. These findings advocate for a dual-purpose biomarker approach in SA: FPR for phenotypic stratification and fibrinogen for activity assessment, while clinical indices remain indispensable for symptom monitoring. Validation in larger, prospective cohorts is warranted. Full article

Passiflora edulis (passion fruit) seed waste, an abundant by-product of the juice industry, is a promising source of piceatannol (PIC), a hydroxystilbene with superior antioxidant activity compared to resveratrol. However, its translation into a skin-targeted ingredient remains hindered by a lack of standardization and clinical validation. This review synthesizes current evidence on the dermatological potential of PIC and proposes a translational roadmap within a circular bioeconomy framework. Preclinical studies demonstrate that PIC exerts multi-target effects relevant to skin aging and acne, including ROS scavenging, anti-inflammatory activity via NF-κB/MAPK inhibition, suppression of melanogenesis, enhancement of hyaluronic acid and collagen synthesis, and antibacterial action against Cutibacterium acnes. However, clinical data are limited and methodologically inconsistent. To bridge this translational gap, we propose a development strategy focused on: (i) extract standardization with a proposed minimum PIC content (e.g., ≥0.3% w/w); (ii) an integrated biorefinery approach for the co-production of seed oil and phenolic fractions; and (iii) a phase-gate pipeline encompassing dermal safety assessment, advanced delivery optimization, and biomarker-correlated clinical trials. Full article

Accurate crop yield prediction is fundamental to sustainable agricultural management, enabling optimized resource allocation and informed decision-making. However, a critical gap exists in current prediction models: existing approaches overlook the temporal alignment between meteorological conditions and production management phases—defined as the intervals between consecutive agronomic operations (e.g., sowing, fertilization, thinning). This oversight results in suboptimal predictive performance, as conventional whole-season weather aggregation fails to capture phase-sensitive crop–weather interactions. While machine learning (e.g., XGBoost) and deep learning approaches (e.g., CNN, LSTM) have been applied to yield prediction, these models typically treat weather variables as temporally homogeneous inputs, inadequately modeling the correlation between historical yields and phase-specific meteorological patterns. To address this gap, this study proposes CNN-LSTM-AM, an innovative hybrid deep learning model that integrates convolutional neural networks (CNNs), long short-term memory (LSTM), and attention mechanisms (AMs), utilizing weather data explicitly aligned with production management phases as input. The CNN component extracts cross-phase weather patterns, the LSTM captures sequential dependencies across growth stages, and the attention mechanism dynamically weights phase importance based on meteorological conditions. The proposed model is validated using a real-world case study of Bok choy production from an agricultural cooperative in Yunlin County, Taiwan, encompassing 1714 production cycles over eight years (2011–2019). Experimental results demonstrate that CNN-LSTM-AM achieves an RMSE of 1448.24 kg/ha, MAPE of 3.60%, and R² of 0.98, outperforming five baseline models—CNN (RMSE = 2919.18), LSTM (RMSE = 2529.74), CNN-LSTM (RMSE = 1516.44), LSTM-AM (RMSE = 2284.64), and XGBoost (RMSE = 3452.47)—representing a notable reduction in prediction error (58% lower RMSE) compared to XGBoost. Furthermore, prediction accuracy improves progressively as harvest time approaches, and phase-specific weather encoding enhances accuracy by 16.5% compared to whole-season averaging. These findings underscore the critical importance of integrating agronomic domain knowledge into data-driven prediction frameworks. Full article

Understanding and predicting how tourists move through a city is a challenging task, as it involves a complex interplay of spatial, temporal, and social factors. Traditional recommender systems often rely on structured data, trying to capture the nature of the problem. However, recent advances in Large Language Models (LLMs) open new possibilities for reasoning over richer, text-based representations of user context, even without a dedicated pre-training phase. In this study, we investigate the potential of LLMs to interpret and predict tourist movements in a real-world application scenario involving tourist visits to Verona, a municipality in Northern Italy, between 2014 and 2023. We propose an incremental prompt engineering approach that gradually enriches the model input, from spatial features alone to richer behavioral information, including visit histories, time information, and user cluster patterns. The approach is evaluated using six open-source models, enabling us to compare their accuracy and efficiency across various levels of contextual enrichment. The results provide a first insight about the abilities of LLMs to incorporate spatio-temporal contextual factors, thus improving predictions, while maintaining computational efficiency. The analysis of the model-generated explanations completes the picture by adding an interpretability dimension that most existing next-PoI prediction solutions lack. Overall, the study demonstrates the potential of LLMs to integrate multiple contextual dimensions in tourism mobility, highlighting the possibility of a more text-oriented, adaptive, and explainable T-RS. Full article

Background: The antibiotic ibezapolstat and the live biotherapeutic product live-JSLM are promising future approaches for treating Clostridioides difficile infection. Ibezapostat is a highly specific antibiotic for Clostridioides difficile, with minimal impact on the intestinal flora. Live-JSLM is designed to restore healthy intestinal microbiota, thus preventing recurrence of Clostridioides difficile infection. In this narrative review, we reviewed available data on ibezapostat and live-JSLM, considering that they are prototypes of two distinct, unique mechanisms of action against Clostridioides difficile. Methods: Data sources: PubMed and SCOPUS databases were searched from 1 January 2012 to 15 November 2025. Original articles reporting data on ibezapolstat and live-JSLM were included. Results: 31 studies were included. When compared to conventional anti-Clostridioides difficile antibiotics, ibezapolstat had a similar level of effectiveness and minimal impact on the gut microbiota. The available data confirm live-JSLM safety and efficacy in restoring the gut microbiota following the conclusion of the standard anti-Clostridioides difficile antibiotic regimen. Conclusions: The results on ibezapolstat efficacy are promising, but require confirmation in larger patient populations through double-blind, randomised phase III trials. In the near future, an integrated approach may enhance the management of Clostridioides difficile infection: starting with highly specific antibiotics, i.e., ibezapolstat, followed by microbiome-based therapies such as live-JSLM. Full article

Population aging has markedly increased the burden of cancer in older adults, in whom frailty, sarcopenia, and reduced physiological reserve limit tolerance to treatment and worsen clinical outcomes. Aging is accompanied by progressive functional decline and by biological processes such as cellular senescence, characterized by irreversible cell cycle arrest, chronic low-grade inflammation, and impaired immune surveillance. The accumulation of senescent cells and the persistence of a senescence-associated secretory phenotype contribute to tissue dysfunction and generate a microenvironment that favors tumor initiation and progression. Physical exercise has been associated with attenuation of inflammation, improvements in metabolic and immune function, and with lower levels of senescence-related biomarkers. Although aerobic exercise has been extensively studied in this setting, resistance training holds relevance for older adults due to its capacity to counteract sarcopenia, preserve muscle strength and power, and sustain functional independence. Structured and periodized approaches to resistance exercise may further enhance these benefits by delivering targeted stimuli aligned with age-related physiological deficits. Block strength training (BST), a periodized model that concentrates training adaptations into sequential phases of maximal strength, power, and muscular endurance, has demonstrated consistent improvements in functional performance and reductions in frailty risk in community-dwelling older adults. BST improves physical function. It may also influence biological processes related to aging and cancer; however, mechanistic evidence specific to BST remains to be established. We hypothesized that the exercise in block as a targeted, a structured and physiologically grounded resistance training intervention highlights the potential of BST to promote functional aging and healthy. In the case of cancer biology, and the environment near to tumour, the relationship between aging mechanisms in older adults and controlled exercise effects are currently in advance, but mechanistic trials are still lacking. Finally, we propose a novel training method, structured and personalized, that could impact different clinical outcomes in older patients with cancer. Full article

This article introduces an intelligent framework using deep learning to recognize and classify different faults through the real-time detection of multiple faults in power distribution systems. A collection of data representing normal operating conditions, alongside various fault scenarios including line-to-ground (LG), line-to-line (LL), double line-to-ground (LLG), and three-phase line (LLL) faults, was created using three phase current signals obtained from the Real-Time Digital Simulator (RTDS) microgrid test system. To properly model the system dynamics, a feature extraction method that integrates phase currents, differential currents, summation currents and magnitude results was developed. The temporal features of the fault signals were identified by using a sliding window approach to fit the data. A one-dimensional convolutional neural network (CNN) was developed to identify different types of faults. This model performed well, obtaining nearly 96.15% accuracy while testing. In order to evaluate the feasibility of the approach, the trained model was loaded on Raspberry Pi 5, NodeMCU, ESP32 and existing sensing devices. The fault classification performed in real-time was time-sensitive. The proposed intelligent framework is applicable to low-scale operation for smart grid fault monitoring and protection and it is an economically viable solution. Full article

Cotton, a globally significant crop grown in over 100 countries, sustains a $40 billion market and provides employment for over 350 million people worldwide. However, plastic contamination remains a persistent challenge within the industry, degrading cotton fiber quality and disrupting ginning. Manual inspection and optical machine-vision systems struggle when plastic fragments are concealed by fibers or lack sufficient color contrast. To address these challenges, we developed an ultrasonic phased-array imaging system operating at 40 kHz under field-programmable gate array (FPGA) control. Transmitter elements emit pulsed ultrasound along radial paths, separate reflection receivers record echo amplitudes to form acoustic images, and a set of transmission receivers captures signal attenuation, which is overlaid onto the reflection-based image to highlight potential contaminants. In preliminary laboratory-based tests on both seed cotton and lint samples, the system successfully detected visually obscured plastic fragments as small as $2\mathrm{cm}\times 2\mathrm{cm}$ with an angular resolution limit of $\pm 3^{\circ }$. Distinct reflection peaks and corresponding attenuation overlays were produced across the field of view, validating the system’s detection capabilities. These results demonstrate the feasibility of using ultrasonic imaging to reveal concealed plastics in cotton processing. Integrating this approach with existing optical methods could enhance contaminant-removal workflows and improve overall fiber quality and processing efficiency. Full article

Colorectal cancer (CRC) remains a leading cause of cancer mortality, yet no systematic effort has linked druggable CRC driver genes to downstream ion channel effectors. We integrated differential expression analysis, weighted gene co-expression network analysis (WGCNA), and protein–protein interaction (PPI) network pharmacology to identify CRC hub genes and their ion channel connections, validated by dual single-cell perturbation approaches: variational graph autoencoder-based virtual knockout (VGAE-KO) and experimental HCT116 CRISPRi Perturb-seq (6 genes, 8445 cells). WGCNA identified 100 hub genes spanning three functional programs. Ribosomal proteins link to K${}^{+}$ channels (RPS21→KCNQ2, targetable by EMA-approved ataluren, passed dual validation at 97.8th–98.7th percentile). RNA processing genes connect to Cl${}^{-}$ channels (LSM7→CLIC1, strongest signal at 99.8th–99.4th percentile). Immune checkpoint receptors (LAG3, CD27) connect via PPI intermediates to Ca${}^{2+}$ and K${}^{+}$ channels, targetable by relatlimab (FDA-approved) and varlilumab (Phase 2). This work maps previously unknown links between CRC driver genes and ion channel regulation, with the ataluren-RPS21-KCNQ2 axis ready for pharmacological testing. Full article

Due to the ultra-trace concentrations of perfluoroalkyl compounds (PFCs) existing in environmental aqueous matrices, it is imperative to develop sensitive and high-enrichment-efficiency approaches for the determination of these emerging pollutants. In this study, a nitrogen-doped carbon dot-functionalized hollow fiber membrane (NCDs@HFM) was fabricated and employed in solid-phase microextraction (SPME) mode for the simultaneous identification of eight perfluoroalkyl carboxylates (PFCAs). The NCDs@HFM offers several advantages, including multiple active binding sites, chemical durability, a large specific surface area and environmental compatibility. Owing to these properties, the NCDs@HFM-based SPME demonstrated high extraction efficiency for PFCAs, where enrichment factors for target molecules could reach 35–61 fold under the optimum conditions. This established method was then integrated with liquid chromatography–tandem mass spectrometry (LC-MS/MS) for the qualitative and quantitative analysis of eight representative PFCAs in drinking and environmental water samples. The limits of detection (LODs, S/N = 3) and quantitation (LOQs, S/N = 10) of the method were at the scale of 0.0018–0.015 μg/L and 0.006–0.050 μg/L, respectively. This proposed method exhibited good precision, with RSDs below 13.2% and satisfactory accuracy, with recoveries ranging from 70.6% to 122.5%. The developed method was successfully applied in the identification of eight typical PFCAs in drinking and environmental water samples. This method exhibits several merits, including low cost, high sensitivity, good reliability and reusability, representing a promising alternative for measuring trace levels of PFCAs in aqueous matrices. Full article

Extreme natural disasters may simultaneously disrupt power distribution infrastructures and their supporting communication systems, significantly degrading post-disaster recovery performance. To enhance coordinated restoration under such coupled failure conditions, this study proposes a unified optimization framework for pre-disaster deployment and post-disaster repair and service restoration in interdependent distribution–communication networks. First, an interdependency model is developed to characterize the physical and operational couplings between the distribution and communication networks. The impacts of communication outages on remotely controlled switches and repair crew dispatching are quantitatively analyzed, revealing how communication failures influence the restoration process. Based on this interdependency representation, a coordinated optimization model is established to jointly determine repair crew routing, mobile power allocation, and critical load restoration sequencing. The objective is to minimize cumulative outage losses over the recovery horizon, thereby achieving coordinated allocation and routing of multiple types of emergency repair resources. Furthermore, by jointly considering pre-disaster deployment planning and post-disaster restoration strategies, a two-stage emergency recovery framework is designed to integrate pre-event preparedness with post-event response for distribution networks. Case studies on a modified IEEE 33-bus cyber–physical distribution system demonstrate that the proposed coordinated restoration strategy restores approximately 50% of critical loads within the first 3 h, which is of direct significance for maintaining essential services such as hospitals and emergency shelters during the acute phase of a disaster. The proposed approach reduces the total load loss by 49.5% and shortens the restoration time by 120 min. In terms of pre-disaster deployment, the proposed strategy reduces average load shedding by 33.4% and 46.5% relative to the heuristic and random deployment strategies, respectively, demonstrating the effectiveness of proposed method for grid resilience enhancement. Full article

Fast charging of electric vehicles can introduce phase-dependent harmonic distortion and voltage unbalance in low-voltage feeders, which may reduce admissible charging capacity even when voltage magnitudes remain within conventional limits. This paper proposes a phase-aware predictive scheduling framework for harmonic hosting management in feeders with a high penetration of electric vehicle charging. The proposed method formulates feeder operation as a predictive decision problem that jointly determines charging power levels, phase allocation, and the selective activation of multifunctional compensation resources under harmonic distortion, voltage unbalance, and neutral-current constraints. Unlike previous studies centered on harmonic characterization, static hosting assessment, or local converter-level mitigation, the proposed approach treats harmonic hosting as an active feeder-level network management problem. The framework is evaluated through time-series harmonic power-flow simulations using charger harmonic emission profiles and realistic feeder parameters. The numerical results indicate that coordinated phase-aware scheduling can increase admissible charging capacity, improve compliance margins for power-quality indices, and reduce mitigation efforts with respect to uncontrolled charging and non-coordinated compensation strategies. Overall, the results support the use of phase-aware scheduling as a feeder-level strategy to improve electric vehicle charging integration under harmonic and unbalanced constraints. Full article