Search Results (117)

Background: Soccer performance is largely dependent on high-intensity, unilateral actions such as sprints, jumps, and changes of direction. These demands can lead to strength and power differences between limbs, highlighting the importance of individualised assessment in professional players. Rotational inertial devices offer a valuable method to evaluate and train these mechanical variables separately for each leg. The aim of this study was twofold: (a) to characterise the mechanical variables derived from several lower-body strength exercises performed on rotational inertial devices, all targeting the same muscle group; and (b) to compare the mechanical variables between the dominant and non-dominant leg for each exercise. Methods: Twenty-six male professional soccer players (age = 26.3 ± 5.1 years; height = 182.3 ± 0.6 cm; weight = 75.9 ± 5.9 kg; body mass index = 22.8 ± 1.1 kg/m²; fat mass percentage = 9.1 ± 0.6%; fat-free mass = 68.8 ± 5.3 kg), all belonging to the same professional Belgian team, voluntarily participated in this study. The players completed a single assessment session consisting of six unilateral exercises (i.e., quadriceps hip, hamstring knee, adductor, quadriceps knee, hamstring hip, and abductor). For each exercise, they performed two sets of eight repetitions with each leg (i.e., dominant and non-dominant) in a randomised order. Results: The quadriceps hip exercise resulted in higher mechanical values compared to the quadriceps knee exercise in both limbs (p < 0.004). Similarly, the hamstring hip exercise produced greater values across all variables and limbs (p < 0.004), except for peak force, where the hamstring knee exercise exhibited higher values (p < 0.004). The adductor exercise showed higher peak force values for the dominant limb (p < 0.004). The between-limb comparison revealed differences only in the abductor exercise (p < 0.004). Conclusions: These findings suggest the necessity of prioritising movement selection based on targeted outcomes, although it should be considered that the differences between limb differences are very limited. Full article

Background: Gastrointestinal tuberculosis (GI TB) is a rare form of extrapulmonary TB that often mimics other conditions, such as Crohn’s disease (CD) or GI malignancies. Conventional diagnostics, like direct microscopy and culture, are often inconclusive or slow, delaying treatment. In Germany, a low-incidence country, GI TB is underrecognized. Rising migration has led to a resurgence of TB cases, increasing the likelihood of encountering extrapulmonary presentations. This study evaluates the performance and utility of various diagnostic tools and proposes a diagnostic approach to reduce delays and avoid unnecessary interventions. Methods: We retrospectively analyzed eight patients suspected of GI TB based on clinical presentation and testing. Two recent cases are described in detail to highlight diagnostic and therapeutic challenges. Results: GI TB was confirmed in five cases (62.5%), and all the patients presented with abdominal complaints, with the majority experiencing systemic symptoms such as weight loss or fever. Histopathology supported the diagnosis in all GI TB cases, while PCR testing was positive in four. Direct microscopy detected acid-fast bacilli in only one case. The remaining patients were diagnosed with latent genital TB, disseminated TB without GI involvement, or were ruled out clinically. Conclusions: GI TB remains a diagnostic challenge that often mimics other conditions, such as CD or malignancy. Early use of histopathology and PCR in patients with a high risk of GI TB is critical for timely diagnosis. In low-incidence settings like Germany, clinicians should maintain high suspicion in at-risk populations (e.g., migrants from areas or immunocompromised patients), especially when symptoms mimic CD or malignancy, to improve outcomes and avoid unnecessary procedures. Full article

In this study, thirty female dogs, aged one to five years and varying in weight, in the last week of gestation were evaluated. The animals were divided into two groups: GC, which comprised twenty-two bitches undergoing elective cesarean section, and GD, which consisted of eight dogs requiring therapeutic cesarean section as a treatment to dystocia. We found that cortisol levels in the amniotic fluid were significantly higher in pups delivered via elective cesareans (mean: 9.86 ng/mL) compared to those from therapeutic c-sections (mean: 4.11 ng/mL). This observation contrasted with previous studies that reported lower cortisol levels in elective procedures, suggesting complexities in the physiological responses to different delivery methods that warrant further investigation. Notably, our study observed no significant association between amniotic fluid meconium presence and other distress markers, indicating that meconium may be more closely associated with fetal maturation rather than distress (p > 0.05). Neonatal viability (Apgar score) revealed that 92.86% of the neonates from elective procedures demonstrated no distress shortly after delivery, contrasting with 56.25% in therapeutic c-section. Fetal distress can be a direct consequence of dystocia caused by various stressors, such as pain and hypoxia. These factors can impair the fetus’ ability to adapt to extrauterine life, often leading to lower Apgar scores. Notably, neonatal weight was directly related to fetal cortisol levels, while no significant associations were noted between the litter size or birth order and cortisol concentrations, irrespective of the delivery type. These findings underscore the need for ongoing investigation into the relationships between cesarean delivery types, maternal and neonatal stress markers, and resultant health outcomes, aiming to enhance care strategies for expectant canine mothers and their puppies. Full article

Securing food for an expanding population in the face of climate change necessitates a transformation of global food systems towards sustainability, emphasizing nutritional quality and environmental consequences. This research assessed eight cytoplasmic male sterility-based cabbage hybrids and two controls across nine environments from 2020 to 2022 to improve cabbage output and sustainability. Essential characteristics, including head weight, compactness, and yield, were examined, revealing considerable heterogeneity and elevated heritability for features such as ascorbic acid content (98.41%) and net head weight (86.12%). Yield had a favorable correlation with characteristics such as net head weight and harvest index. Path coefficient research revealed that gross and net head weight have the most significant direct effects on yield. Heterosis research indicated UHF-CAB-HYB-1 had the highest significant positive heterosis in yield compared to the standard checks, Pusa Hybrid-81 and Pusa Cabbage-1, across all nine conditions. The results underscore the need to identify essential characteristics for the creation of high-yield, hardy cabbage hybrids, in accordance with sustainable agriculture and food security objectives. Full article

Hybrid composite pile foundations face critical challenges in terms of optimizing load transfer mechanisms across variable soil densities, particularly in regions like Kano, Nigeria, characterized by loose to dense sandy deposits and fluctuating groundwater levels. This study addresses the need for sustainable, high-performance foundation systems that are adaptable to diverse geotechnical conditions. The research evaluates the mechanical behavior of steel–concrete and timber–concrete hybrid piles, quantifying skin friction dynamics, combining eight (8) classical ultimate bearing capacity (UBC) methods (Vesic, Hansen, Coyle and Castello, etc.) with numerical simulations, and assessing load distribution across sand relative densities (10%, 35%, 50%, 75%, 95%). Laboratory investigations included the geotechnical characterization of Wudil River well-graded sand (SW), direct shear tests, and interface shear tests on composite materials. Relative densities were calibrated using electro-pneumatic compaction. Increasing Dr from 10% to 95% reduced void ratios (0.886–0.476) and permeability (0.01–0.0001 cm/s) while elevating dry unit weight (14.1–18.0 kN/m³). Skin friction angles rose from 12.8° (steel–concrete) to 37.4° (timber–concrete) at Dr = 95%, with timber interfaces outperforming steel by 7.4° at Dr = 10%. UBC for steel–concrete piles spanned from 353.1 kN (Vesic, Dr = 10%) to 14,379 kN (Vesic, Dr = 95%), while timber–concrete systems achieved 9537.5 kN (Hansen, Dr = 95%). PLAXIS simulations aligned closely with Vesic’s predictions (14,202 vs. 14,379 kN). The study underscores the significance of soil density, material interfaces, and method selection in foundation design. Full article

Resistance training (RT) has been shown to produce beneficial effects, including on quality of life, renal function, physical fitness, and survival rates in kidney transplant for 24 recipients. However, the optimal periodization of load components for this population remains unclear, as no consensus has been established. This study aimed to characterize the load components of RT programs in kidney transplant recipients. A scoping review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). The literature search was performed up to October 2024 in MEDLINE/PubMed, the Web of Science Core Collection, SCOPUS, ScienceDirect, and SPORTDiscus. Only studies that included RT as part of the intervention were considered. The RT variables analyzed included intervention duration, weekly frequency, session duration, number and types of exercises, intensity, number of sets, rest time between sets, progressive overload, and execution velocity. A total of 645 studies were identified, of which 15 met the eligibility criteria and were selected for analysis. The primary strategy for intensity control was based on the percentage of one-repetition maximum (%1RM), with training zones ranging from 30% to 80%. The number of sets varied from two to eight, while repetitions ranged from 10 to 20. The rest intervals between sets lasted between one and five minutes. The most highly implemented type of resistance involved the use of dumbbells, body weight, and elastic bands. A high degree of heterogeneity was identified in load periodization parameters, highlighting a lack of consensus in exercise prescription for this population. However, this review established general criteria that can serve as a reference for exercise professionals to develop more structured and effective training programs. Full article

Virtual reality (VR) devices that integrate eye-tracking and hand-tracking technologies can capture users’ natural eye–hand data in real time within a three-dimensional virtual space, providing new opportunities to explore users’ attentional states during natural 3D interactions. This study aims to develop an attention-state prediction model based on the multimodal fusion of eye and hand features, which distinguishes whether users primarily employ goal-directed attention or stimulus-driven attention during the execution of their intentions. In our experiment, we collected three types of data—eye movements, hand movements, and pupil changes—and instructed participants to complete a virtual button selection task. This setup allowed us to establish a binary ground truth label for attentional state during the execution of selection intentions for model training. To investigate the impact of different time windows on prediction performance, we designed eight time windows ranging from 0 to 4.0 s (in increments of 0.5 s) and compared the performance of eleven algorithms, including logistic regression, support vector machine, naïve Bayes, k-nearest neighbors, decision tree, linear discriminant analysis, random forest, AdaBoost, gradient boosting, XGBoost, and neural networks. The results indicate that, within the 3 s window, the gradient boosting model performed best, achieving a weighted F1-score of 0.8835 and an Accuracy of 0.8860. Furthermore, the analysis of feature importance demonstrated that the multimodal eye–hand features play a critical role in the prediction. Overall, this study introduces an innovative approach that integrates three types of multimodal eye–hand behavioral and physiological data within a virtual reality interaction context. This framework provides both theoretical and methodological support for predicting users’ attentional states within short time windows and contributes practical guidance for the design of attention-adaptive 3D interfaces. In addition, the proposed multimodal eye–hand data fusion framework also demonstrates potential applicability in other three-dimensional interaction domains, such as game experience optimization, rehabilitation training, and driver attention monitoring. Full article

Squid skin decellularized dermal matrix (SADM) is gaining attention in tissue engineering and regenerative medicine due to its mimicking of the extracellular matrix property. Hence, SADM was used to investigate mimicking the microenvironment of cellular growth, inducing cellular infiltration and angiogenesis, and facilitating the repair of acute craniofacial wounds. For this, tissue regeneration membranes from squid skin were prepared by decolorization, degreasing and decellularisation methods. The effect of SADM in guiding bone tissue regeneration was evaluated using the rabbit skull bone defect model. SEM images of SADM had a bilayer membrane architecture characterized by a reticulated porous structure on one side and a dense, non-porous surface on the opposite side. Notably, the water absorption capacity of SADM was approximately eight times higher than its weight, exhibiting a porosity of 58% and a peak average tensile stress of 10.43 MPa. Additionally, simulations of tissue fluid degradation indicated a degradation rate of 70.42% and 88.33% on days 8 and 12, respectively. Following 4 and 8 weeks of animal studies focused on repairing cranial bone defects in rabbits, the findings demonstrated that SADM served as an effective barrier against fibrous connective tissue, promoted the proliferation of osteoblasts, and supported bone regeneration. This was confirmed through micro-CT imaging, and sections were stained with senna solid green. In summary, SADM is capable of directing cell infiltration and bone tissue formation, modulating the expression and secretion of inflammatory and skin repair-related factors, thereby enhancing tissue healing. Full article

Anomaly detection plays a vital role in the processing of hyperspectral images and has garnered significant attention recently. Hyperspectral images are characterized by their “integration of spatial and spectral information” as well as their rich spectral content. Therefore, effectively combining the spatial and spectral information of images and thoroughly mining the latent structural features of the data to achieve high-precision detection are significant challenges in hyperspectral anomaly detection. Traditional detection methods, which rely solely on raw spectral features, often face limitations in enhancing target signals and suppressing background noise. To address these issues, we propose an innovative hyperspectral anomaly detection approach based on the fractional optimal-order Fourier transform combined with a multi-directional dual-window detector. First, a new criterion for determining the optimal order of the fractional Fourier transform is introduced. By applying the optimal fractional Fourier transform, prominent features are extracted from the hyperspectral data. Subsequently, band selection is applied to the transformed data to remove redundant information and retain critical features. Additionally, a multi-directional sliding dual-window RAD detector is designed. This detector fully utilizes the spectral information of the pixel under test along with its neighboring information in eight directions to enhance detection accuracy. Furthermore, a spatial–spectral combined saliency-weighted strategy is developed to fuse the detection results from various directions using weighted contributions, further improving the distinction between anomalies and the background. The proposed method’s experimental results on six classic datasets demonstrate that it outperforms existing detectors, achieving superior detection performance. Full article

This study addresses the critical challenge of identifying key nodes in complex networks, an essential task for optimizing network stability, efficiency, and resilience. Traditional approaches often rely on single-dimensional metrics, which may fail to fully capture the multifaceted roles that nodes play in maintaining network functionality. To overcome this limitation, a novel framework was proposed, integrating the C³ dimensions (Cohesion, Connectivity, Conciseness) with the TOPSIS method and Pareto dominated set (PDS) to enable a comprehensive, multi-dimensional evaluation of node importance. The method introduces an optimized parameter α for relative conciseness, validated through area-under-curve (AUC) minimization, ensuring adaptability across diverse networks. Scalability analysis demonstrates its feasibility for large-scale systems, with computational complexity managed through approximation algorithms. The method was applied to eight real-world networks including transportation systems, power grids, and social networks. Results demonstrated that the PDS-based STS method outperformed traditional centrality measures, such as degree centrality and betweenness centrality, particularly during the early stages of network degradation. The framework effectively identified critical nodes in highly connected systems, with the conciseness metric proving instrumental in highlighting irreplaceable nodes whose removal would severely disrupt network functionality. This study concludes that the “C³-TOPSIS-Pareto” based model provides a more accurate and robust approach for critical node identification, offering a reliable tool for enhancing network design and resilience, particularly in systems with complex interdependencies. Limitations in fragmented networks are discussed, with future directions proposed for dynamic weight adaptation and functional dynamics integration. Full article

Surface quality control of hot-dip galvanized sheets is a critical research topic in the metallurgical industry. Zinc dross, the most common surface defect in the hot-dip galvanizing process, significantly affects the sheet’s service performance. In this manuscript, a novel magnetic flux leakage (MFL) detection method was proposed to detect zinc dross defects on the surface of hot-dip galvanized steel sheets. Instead of using exciting coils in traditional methods, a tiny permanent magnet with a millimeter magnitude was employed to reduce the size and weight of the equipment. Additionally, a high-precision tunnel magnetoresistance (TMR) sensor with a sensitivity of 300 mV/V/Oe was selected to achieve higher detection accuracy. The experimental setup was established, and the x-axis direction (sample movement direction) was determined as the best measurement axis by vector analysis through experiments and numerical simulation. The detection results indicate that this novel MFL detection method could detect industrial zinc dross with an equivalent size of 400 μm, with high signal repeatability and signal-to-noise ratio. In the range of 0–1200 mm/s, the detection speed has almost no effect on the measurement signal, which indicates that this novel method has higher adaptability to various conditions. The multi-path scanning method with a single probe was used to simulate the array measurement to detect a rectangular area of 30 × 60 mm. Ten zinc dross defects were detected across eight measurement paths with 4 mm intervals, and the positions of these zinc dross defects were successfully reconstructed. The research results indicate that this novel MFL detection method is simple and feasible. Furthermore, the implementation of array measurements provides valuable guidance for subsequent in-depth research and potential industrial applications in the future. Full article

Objective: To investigate the effects of licorice functional ingredient intake on blood pressure, explore its potential mechanisms of action, and provide safety information for personalized nutritional interventions in special populations and for the application of licorice-derived functional foods. Methods: PubMed, Cochrane Library, Medline, Embase, EBSCO, ScienceDirect, and Web of Science databases were searched from inception to 31 August 2024. Randomized controlled trials (RCTs) investigating the intake of licorice or its functional components were included. The range of continuous variables was assessed using the weighted mean difference (WMD) with 95% confidence intervals. Genes associated with hypertension were screened using an online database. Machine learning, receiver operating characteristic(ROC) curve analysis, molecular docking, and gene set enrichment analysis (GSEA) were employed to explore the potential mechanisms underlying licorice-induced blood pressure fluctuations. Results: Eight RCTs (541 participants) were included in the meta-analysis, which indicated interventions containing glycyrrhizic acid (GA) as the main component increased systolic blood pressure (SBP) and diastolic blood pressure (DBP) (SBP: WMD [95% CI] = 3.48 [2.74, 4.21], p < 0.001; DBP: WMD [95% CI] = 1.27 [0.76, 1.78], p < 0.001). However, interventions dominated by licorice flavonoids(LF) had no significant effect on SBP or DBP (SBP: WMD [95% CI] = 0.58 [−1.15, 2.31], p = 0.511; DBP: WMD [95% CI] = 0.17 [−1.53, 1.88], p = 0.843). Three machine learning algorithms identified five biomarkers associated with hypertension: calmodulin 3 (CALM3), cluster of differentiation 9 (CD9), growth factor independence 1B transcriptional repressor (GFI1B), myosin light chain kinase (MYLK), and Ras suppressor-1 (RSU1). After removing biomarkers with lower validity and reliability, GFI1B, MYLK, and RSU1 were selected for subsequent analysis. The network toxicology results suggested that GA and its metabolite glycyrrhetinic acid may act on GFI1B, MYLK, and RSU1, influencing blood pressure fluctuations by modulating nitrogen metabolism signaling pathways. Conclusions: There were distinct differences in the effects of licorice functional components on blood pressure. Functional constituents dominated by GA were shown to increase both SBP and DBP, whereas those dominated by LF did not exhibit significant effects on blood pressure. The hypertensive mechanism of GA may involve the modulation of GFI1B, MYLK, and RSU1 to regulate nitrogen metabolic pathways. Full article

Dual-mode non-invasive brain stimulation using repetitive transcranial magnetic stimulation and transcranial direct current stimulation is known to help neurorehabilitation in patients with stroke. However, this neuromodulation effect may vary depending on the lesion location of patients with stroke, and the basis in lesion location for this is insufficient. This study aims to investigate the difference in neuromodulation effectiveness according to the lesion location after dual-mode brain stimulation using electroencephalography signals. Eight patients with ischemic subacute stroke and 11 healthy controls participated in this study. Brain stimulation was conducted in one session per day for a total of 10 days over the motor cortex, electroencephalography was measured for 5 min with eyes closed, and motor function was evaluated before and after dual-mode stimulation. The lesion location was divided into an infratentorial stroke (ITS) and a supratentorial stroke (STS) based on tentorium cerebelli. In addition, we focused on the mu and beta bands related to motor function. In terms of intrahemispheric connectivity, the mu weighted phase lag index over the contralesional primary motor cortex was significantly higher in only ITS before stimulation compared to healthy controls, and mu Granger causality over the ipsilesional primary motor cortex was significantly higher in both ITS and STS after stimulation compared to healthy controls. In contrast, from the perspective of interhemispheric connectivity, the laterality of beta Granger causality before stimulation in ITS was lower than that of healthy controls and significantly increased after stimulation. The effect of brain stimulation may vary depending on the lesion location of patients with stroke, and these findings provide indicative insights into effective dual-mode stimulation interventions for neurorehabilitation. Full article

With the rapid growth of Internet of Things (IoT) systems, ensuring robust security measures has become paramount. Microservices Architecture (MSA) has emerged as a promising approach for enhancing IoT systems security, yet its adoption in this context lacks comprehensive analysis. This systematic review addresses this research gap by examining the incorporation of MSA in IoT systems from 2010 to 2024. From an initial pool of 4388 studies, selected articles underwent thorough quality assessment with weighted critical appraisal questions and a defined inclusion threshold. This study represents the first comprehensive systematic review to investigate the potential of microservices in IoT, with a particular focus on security aspects. The review explores the merits of MSA, highlighting twelve benefits, eight key challenges, and eight security risks. Additionally, the eight best practices for implementing MSA in IoT systems are extracted. The findings underscore MSA’s utility in fortifying IoT security while also acknowledging complexities and potential vulnerabilities. Moreover, the study calls attention to the importance of incorporating complementary technologies including blockchain and machine learning to address identified gaps effectively. Finally, we propose a taxonomic classification for Microservice-based IoT security patterns, facilitating the categorization and organization of security measures in this context. Such a review can help researchers and practitioners identify existing gaps, highlight potential research directions, and provide guidelines for designing secure and efficient microservice-based IoT systems. Full article

Selecting the most preferred software for teaching power systems engineering at the undergraduate level is a complex problem in developing countries, and it requires making an informed decision by compromising on various criteria. This study proposes a multi-criteria framework to determine the most preferred software solution for instructing undergraduate power system modules using the Fuzzy-ARAS (additive ratio assessment) method and expert opinions. Twelve evaluation criteria were used to evaluate eight widely used software packages. A questionnaire was designed to capture views from professionals in academia and industry on the criteria weights and ranking of software options. Linguistic terms were used to represent the experts’ judgment, and weights were assigned to each criterion. The Fuzzy-ARAS multi-criteria decision approach was applied to obtain ratings for each software alternative. Based on the result, MATLAB emerged as the most preferred software for instructing power systems analysis, whereas MATPOWER (V 8.0) was rated as the least preferred choice. In addition, the Fuzzy-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach was used, producing a separate ranking; the most preferred software was MATPOWER, while the least preferred software was NEPLAN (V 360 10.5.1). A new coefficient that combines the findings of the two approaches was suggested to reconcile the ranks. The combined ranking aligns with the result of the Fuzzy-TOPSIS method by returning MATLAB as the most preferred, while the least preferred software was NEPLAN. This study significantly contributes to the choice of software for undergraduate power systems analysis instruction by providing direction to educators and institutions looking for software solutions to improve undergraduate power systems analysis education. Full article