Search Results (680)

Background and Objectives: COVID-19 has been associated with prolonged inactivity and reduced physical performance, even in mild and moderate cases. This study aimed to evaluate changes in functional mobility and gait speed, assessed with the Timed Up and Go (TUG) and 10-Meter Walk Test (10MWT), in patients with mild to moderate post-COVID-19 conditions undergoing a structured rehabilitation program. Materials and Methods: A controlled observational study was conducted on 193 patients (115 women, 78 men) who had recovered from mild to moderate COVID-19. Participants were divided into a rehabilitation group (n = 160) and a control group (n = 33) who did not undergo structured physical therapy. Functional performance was assessed with TUG and 10MWT at admission and at one-year follow-up. Results: Both tests showed significant improvements following rehabilitation. In the rehabilitation group, the proportion of patients classified as functionally independent increased significantly for both the TUG (Cramér’s V = 0.468, p < 0.001) and 10MWT (Cramér’s V = 0.500, p < 0.001). The McNemar test confirmed a moderate within-group improvement for 10MWT (p = 0.001). Older adults (≥60 years) exhibited functional gains comparable to younger participants. A strong association between final TUG and 10MWT categories (Cramér’s V = 0.40, p < 0.001) confirmed the consistency of outcomes. Conclusions: Structured rehabilitation significantly improves balance, gait speed, and functional independence in mild-to-moderate post-COVID-19 patients. These findings highlight that rehabilitation should be integrated into the continuum of post-COVID care, as meaningful recovery is achievable even outside severe cases. Full article

The phylum Nematoda is host to a vast and diverse virosphere, yet severe viral diseases are rarely observed. This paradox between pervasive infection and limited pathology suggests the existence of a highly effective host–virus “truce”. In this review, we argue that this truce is not a result of viral attenuation but is actively arbitrated by a multi-tiered host immune network, whose primary characteristic is not destructive power but exquisite cost–benefit management. We deconstruct this network into two functional tiers. The first, the “effector layer”, comprises a diverse arsenal of antiviral pathways, including RNA interference (RNAi), the Intracellular Pathogen Response (IPR), and other direct-acting mechanisms. The second, the “regulatory layer”, acts as a command hub, integrating internal physiological states—such as metabolism and aging—with external threat signals to orchestrate a proportional defense, thereby mitigating the high fitness costs of immunity. Understanding this intricate network is critical, as it not only explains the dynamics of infection within nematodes but also has profound implications for a broader medical landscape, particularly through the “Trojan Horse” effect, where nematode-borne viruses might elicit immune responses in their final vertebrate hosts. Together, these insights provide a unified framework for studying nematode–virus interactions and for comparing antiviral strategies across metazoans. Full article

Presently, numerous non-conventional power resources have been applied in power system networks. However, these resources are very effective in islanded microgrid (IMG) scenarios for addressing numerous operational challenges. Additionally, it is observed that the power output of most of these resources is environment-dependent and intermittent in nature. This intermittency causes a power imbalance between the overall generated power and the load demand, which results in an undesired frequency oscillation. In order to address this unwanted frequency fluctuation, this research work proposes power–frequency synchronisation considering an islanded microgrid scenario under numerous non-conventional power resources. The major contribution of this work includes implementing a suitable and optimised control scheme that effectively controls diverse power system disturbances and various uncertainties. A Fick’s law optimisation-based proportional–integral–derivative–acceleration controller (PIDA) is implemented under this proposed power scenario. Additionally, an extensive performance assessment is conducted considering different simulation test cases in order to verify the performance of the proposed control topology. Further, the effectiveness of the suggested power network is tested on a 33-bus radial distribution network. Finally, simulation results are shown to show the effectiveness of the proposed control scheme for the efficient operation of the microgrid in achieving the desired performance under the diverse operating conditions. Full article

In precast concrete shear wall structures, the joints formed during the vertical connection of precast units are referred to as the “horizontal joint”. Serving as vertical connection nodes in this structure system, the construction quality of theses horizontal joints significantly influences the structural integrity. To investigate the influence of horizontal joint quality defects on the mechanical behaviour of precast concrete shear walls, three precast concrete shear wall specimens with quality defects in different regions and three control specimens were designed. Quasi-static tests under a constant axial load were conducted to investigate the effects of defect area, location and other factors on the mechanical behaviour of the walls. Results demonstrate that the quality defects in horizontal joints significantly affect the mechanical behaviour of precast concrete shear walls. When the ratio of the quality defect area to the cross-sectional area of the boundary member reaches 100%, the yield load and peak load of the precast concrete shear wall decrease by 13% and 20%, respectively. Additionally, the structural stiffness exhibited a 13% degradation at a drift angle of 1/1000. Although the failure mode remains largely unchanged, yielding of longitudinal reinforcement in the boundary members is observed. Moreover, as the proportion of the quality defect area to the cross-sectional area decreases, its adverse effects on the mechanical behaviour of the precast concrete shear wall gradually diminish. The established numerical analysis model is shown to be reasonable and reliable. When the defective area of the horizontal joints is less than 25% of the total cross-sectional area, the quality defects essentially have no influence on the mechanical behaviour of the precast concrete shear walls. Full article

Background/Objectives: Antimicrobial resistance (AMR) is a major global health threat that undermines treatment in humans and animals. In Zambia, where livestock production underpins food security and livelihoods, AMR challenges are aggravated by limited surveillance, weak diagnostics, and poor regulatory enforcement, facilitating the spread of resistant pathogens across the human–animal–environment interface. This study aims to analyse AMR patterns of bacterial isolates collected from Zambia’s animal health sector between 2020 and 2024, to generate evidence that informs national AMR surveillance, supports antimicrobial stewardship (AMS) interventions, and strengthens One Health strategies to mitigate the spread of resistant pathogens. Methods: We conducted a retrospective descriptive analysis of previously collected routine laboratory data from five well-established animal health AMR surveillance sentinel sites between January 2020 and December 2024. Data were analysed by year, sample type, and antimicrobial susceptibility testing (AST) profiles using WHONET. Results: A total of 1688 samples were processed, with faecal samples accounting for 87.6%. Animal environmental samples (feed, manure, litter, abattoir/meat processing floor, wall, and equipment surface swabs) (collected from abattoirs, water, and farms) increased significantly over time (p = 0.027). Overall, Escherichia coli (E. coli) (50.4%) and Enterococcus spp. (30%) were the most frequently isolated bacteria. E. coli exhibited high resistance to tetracycline (74%) and ampicillin (72%) but remained susceptible to aztreonam (98%), nitrofurantoin (95%), and imipenem (93%). Enterococcus spp. were susceptible to penicillin (84%) and ampicillin (89%) but showed borderline resistance to vancomycin (53%) and linezolid (50%). Klebsiella spp. demonstrated resistance to ciprofloxacin (52%) and gentamicin (40%), whereas Salmonella spp. remained highly susceptible. Notably, resistance to amoxicillin/clavulanic acid rose sharply from 22.2% to 81.8% (p = 0.027). Across 1416 isolates, high levels of multidrug resistance (MDR) were observed, particularly in E. coli (48.4%) and K. pneumoniae (18.6%), with notable proportions progressing toward possible Extensively Drug-Resistant (XDR) and Pan-Drug-Resistant (PDR) states. Conclusions: The findings of this study reveal rising resistance to commonly used antibiotics in the animal health sector. Despite the lack of molecular analysis, our findings underscore the urgent need for AMS programs and integrated AMR surveillance under Zambia’s One Health strategy. Full article

A two-year (2023–2024) multifactorial field study was conducted under the agro-climatic conditions of Northern Kazakhstan, with the objective of refining cultivation practices for hayfields of perennial legumes and grasses, including alfalfa (Medicago sativa L.), smooth brome (Bromus inermis Leyss.), and sainfoin (Onobrychis arenaria Kit). The elements targeted for optimization included the species composition and component ratios in the mixtures, as well as the regimes of pre-sowing and foliar applications of growth regulators (AminoMax, Black Jak, Miller Start, Lider-S). The integrated experimental design accounted for laboratory and field germination, biometric parameters (plant height, leafiness), phenophase dynamics, autumn survival and overwintering, indicators of photosynthetic activity, as well as yields of green biomass and dry matter, and chemical composition (crude protein, fiber, ash, fat, and nitrogen-free extract). Grass–legume mixtures ensured more stable progression of phenophases, improved overwintering, and enhanced protein value compared to monocultures; the inclusion of sainfoin contributed to improved forage quality without compromising yield. Growth regulators promoted accelerated initial plant development and enhanced the intensity of net photosynthetic productivity. The greatest effect of application was observed in the grass component with Miller Start, whereas in the legume species it was most pronounced with AminoMax. The results of the study revealed that the optimal proportion of legumes in the forage mixtures is 30–40%. Under contrasting hydrothermal conditions, the yield of fresh and dry matter ranged from 4.19 to 4.81 t ha⁻¹ and 1.27–1.51 t ha⁻¹ (2023) to 10.43–14.46 t ha⁻¹ and 3.05–4.63 t ha⁻¹ (2024). The greatest effect was observed with Miller Start and AminoMax treatments (p < 0.05), whereas the action of Black Jak and Lider-S was moderate, confirming differences in their mechanisms of action under contrasting weather conditions. Full article

A sliding mode predictive control (SMPC) scheme integrated with an extreme learning machine (ELM) disturbance observer is proposed for the trajectory tracking of a flexible air-breathing hypersonic vehicle (FAHV). To streamline the controller design, the longitudinal model is decoupled into a velocity subsystem and an altitude subsystem. For the velocity subsystem, a proportional-integral sliding mode surface is designed, and the control law is derived by minimizing a cost function that weights the predicted sliding mode surface and the control input. For the altitude subsystem, a backstepping control framework is adopted, with the SMPC strategy embedded in each step. Multi-source disturbances are modeled as composite additive disturbances, and an ELM-based neural network observer is constructed for their real-time estimation and compensation, thereby enhancing system robustness. The semi-globally uniformly ultimately bounded (SGUUB) stability of the closed-loop system is rigorously proven using Lyapunov stability theory. Simulation results demonstrate the comprehensive superiority of the proposed method: it achieves reductions in Root Mean Square Error (RMSE) of 99.60% and 99.22% for velocity and altitude tracking, respectively, compared to Prescribed Performance Control with Backstepping Control (PPCBSC), and reductions of 98.48% and 97.12% relative to Terminal Sliding Mode Control (TSMC). Under parameter uncertainties, the developed ELM observer outperforms RBF-based observer and Extended State Observer (ESO) by significantly reducing tracking errors. These findings validate the high precision and strong robustness of the proposed approach. Full article

Accurately quantifying grazing intensity (GI) is crucial for assessing grassland utilization and supporting sustainable management. Traditional livestock-based approaches cannot capture the spatial heterogeneity of grazing or its dynamic response to climate variability. The objective of this study was to develop a remote sensing-based quantitative framework for estimating GI across the Inner Mongolian grasslands. The framework integrates MODIS vegetation indices, ERA5-Land climate variables, topographic factors, and field-measured data and GI was quantified as the proportional difference between potential and satellite-derived aboveground biomass (AGB), providing a spatially explicit measure of forage utilization. In this framework, potential AGB (AGBp) represents the climate-driven growth capacity under ungrazed conditions reconstructed using machine learning models, whereas satellite-derived AGB (AGBs) denotes the standing AGB remaining under current grazing pressure. Validation using 324 paired grazed–ungrazed plots demonstrated strong agreement between modeled and observed GI (R² = 0.65, RMSE = 0.18). This AGB-difference-based approach provides an effective and scalable tool for large-scale rangeland monitoring, offering quantitative insights into grass–livestock balance, ecological restoration, and adaptive management in arid and semi-arid regions. Full article

With the comprehensive digitalization and electrification of aircraft, electromechanical actuation systems (EAS) have been increasingly applied. However, EAS are affected by various nonlinear factors, such as friction and mechanical backlash, which can compromise system stability and control accuracy, thereby reducing the operational lifespan of the EAS. This study focuses on these two nonlinear factors and proposes a hybrid control approach to mitigate their effects. In the speed loop of the EAS, a Super-Twisting sliding mode controller combined with a generalized proportional–integral observer (GPIO) is designed, while in the position loop, a hybrid controller integrating a radial basis function (RBF) neural network with sliding mode control is implemented. Leveraging the advantages of numerical analysis in SIMULINK and dynamic simulation in ADAMS, a co-simulation framework is established to evaluate the hybrid control algorithm under nonlinear effects. Furthermore, a control test bench for the control surface transmission system is constructed to analyze the dynamic and static performance of the system under different control strategies and input commands. The experimental results show that, compared with the PID control, the hybrid control method reduces the steady-state error and vibration amplitude of the step response displacement by 51% and 75%, respectively, and decreases the amplitude of speed fluctuations by 75%. For the sinusoidal response, the displacement lag is reduced by 76%, and the amplitude of speed fluctuations is reduced by 50%. Full article

Understanding causal structures in data center cooling systems is essential for energy optimization and fault detection. Conventional methods based on physical connectivity ensure interpretability but often miss latent interactions, while Granger causality captures predictive dependencies yet suffers from sensitivity to data quality and ambiguous directionality. To overcome these limitations, we propose a hybrid causal discovery framework that integrates physics-informed priors with Granger-inspired predictive modeling. A key innovation is the use of a relative increment formulation, which focuses on the proportional change in observables immediately after control actions. This design filters out long-term seasonal trends and emphasizes short-term, actionable effects. Applied to a large-scale dataset from a real data center, the framework successfully recovers known control–feedback links, identifies consistent control–temperature relationships, and reveals cross-unit influences overlooked by traditional approaches. By combining physical priors for directionality with predictive causality for flexibility, the method yields a causal network that is both interpretable and robust, offering a principled basis for decision-making in energy-critical infrastructures. Full article

Background: The use of next-generation sequencing (NGS) in liquid biopsy allows for a comprehensive molecular assessment of circulating tumor DNA (ctDNA) in patients with advanced malignancies. This approach facilitates the detection of clinically relevant mutations linked to prognosis and enables a personalized therapeutic strategy. The objective of this study was to assess the mutational landscape of ctDNA using NGS in patients with advanced non-small cell lung cancer (NSCLC), examine its relationship with clinical and molecular variables, and explore its association with overall survival (OS). Methods: We performed a retrospective observational study including 78 individuals with metastatic NSCLC treated at Arnau de Vilanova Hospital between 2019 and 2021. Plasma samples were analyzed using the AVENIO NGS platform, which targets the exons of 77 genes. Statistical analyses were conducted using SPSS version 25, applying a 95% confidence level. Results: A total of 143 genomic alterations were identified in the study population. NGS-directed therapies were initiated in eight patients (10.25%), including EGFR (n = 5), KRAS (n = 2), and BRCA1 (n = 1). The concordance rate between tissue and plasma NGS for EGFR alterations was 57.02%, with mutation frequencies of 11.4% in tissue and 6.5% in plasma. No BRAF mutations were detected by plasma analysis, despite being present in 4.3% of tissue samples. Patients receiving NGS-informed targeted therapy showed a numerically improved OS compared to those who did not, although this difference did not reach statistical significance (p = 0.34). Conclusions: Liquid biopsy based on NGS represents a reliable and minimally invasive approach for the genomic characterization of advanced NSCLC. In this real-world cohort, ctDNA profiling enabled the identification of actionable alterations in a relevant proportion of patients, supporting its integration into routine clinical practice for therapeutic decision-making. Full article

Arid inland basins represent critical hotspots of intensified conflict among water resources, ecological integrity, and economic development on a global scale. The coevolution of groundwater systems and land use patterns plays a pivotal role in shaping regional sustainability trajectories. This study synthesizes multi-source data spanning 2000 to 2020 from the Wuwei Basin, located within the Shiyang River watershed in China, to elucidate the synergistic dynamics between hydrological and land use transformations. Key findings reveal: (1) Around 2010, a significant structural shift in land use occurred, transitioning from production-oriented expansion to ecologically driven priorities. This shift was characterized by a reduction in cultivated land, increased utilization of artificial surfaces, and accelerated ecological restoration efforts. These changes were jointly influenced by enhanced water governance frameworks and spatial planning policies. (2) Groundwater levels exhibit marked spatial variability. While stability is maintained in piedmont and discharge zones, persistent overdraft has led to pronounced declines in transitional and distal recharge areas. This heterogeneity is primarily governed by the interplay of hydrogeological factors—such as recharge capacity and aquifer permeability—and anthropogenic pressures, including the extent of cultivated land and intensity of groundwater extraction. Notably, these patterns cannot be explained solely by the proportion of cultivated land or total extraction volumes. (3) A positive feedback mechanism—termed the “gain-loss regime shift”—has been identified in the discharge zone, where simultaneous increases in groundwater extraction and water-level recovery are observed. However, human activities have disrupted the natural coupling between precipitation and groundwater recharge, resulting in a significant attenuation of recharge rates (exceeding 80%). These findings offer a robust scientific basis for implementing spatially differentiated water resource management strategies and optimizing land use in arid basin environments. The implications extend beyond regional contexts, contributing to broader efforts in harmonizing human–environment interactions globally. Full article

Cloud liquid water content (CLWC) based on microwave radiometer data was investigated in this study. First, its consistency with radiosonde-based CLWC was established. Integrated CLWC was also checked against the liquid water path. CLWC performance in four weather types was considered: dense fog, clouds in spring, rainstorms, and typhoons. CLWC provides new insights into weather events. In particular, it could be useful for nowcasting low visibility associated with sea fog. It was also found to be inversely proportional to visibility in two cases of low visibility in Hong Kong. In springtime, low-level clouds and liquid water were found to exist extensively inside clouds. In rainstorm cases, supercooled cloud liquid water was absent during heavy rain but may exist within clouds when rain stops or light rain occurs. Similar observations were made in typhoon cases, namely during the direct impact of Typhoon Wipha on Hong Kong. Supercooled cloud liquid was present when outer rainbands of the typhoon affected Hong Kong with a smaller amount of rainfall. However, when Hong Kong was hit by a typhoon’s eyewall, rain was heavier, and supercooled liquid water was absent. These features are consistent with the radiosonde-based CLWC profiles. Radiometer-based CLWC is pseudocontinuous and provides additional insight into liquid water distribution in clouds under various weather conditions. Full article

This paper proposes a novel methodology based on two intelligent algorithms for regulating the power output of a multi-rotor turbine system. A proportional-integral plus second-order integral regulator is utilized to regulate the energy output of an induction generator. The designed controller is characterized by its ease of configuration, cost-effectiveness, high robustness, and ease of implementation. The controller’s parameters are tuned using a genetic algorithm (GA) and a rooted tree optimization (RTO) algorithm, with the objective of maximizing operational performance and power quality. In accordance with the proposed design methodology, the optimal values for the parameters of the designed strategy are attained through the implementation of integral time-weighted absolute error (ITAE). The present controller has been designed to deviate from conventional controllers, and a comparison will be made between the two using MATLAB under various operating conditions. The operational performance was evaluated in comparison to the conventional algorithm in terms of current quality, torque ripples, threshold overshoot, system parameter changes, and so forth. The experimental results, as measured by the tests conducted, demonstrated that the proposed RTO-based regulator exhibited enhancements of up to 89.88% (traditional control) and 51.92% (GA) in active power ripples, 68.19% (compared to traditional control) in ITAE, 51.91% (traditional control) in reactive power overshoot, and 0.5% (compared to GA) in active power response time. Conversely, the proposed GA-based regulator yielded a steady-state error value that was 96.55% superior to the traditional approach and 86.48% more accurate than the RTO algorithm. Moreover, the efficacy of the RTO-based control system was found to be considerably augmented under variable system parameters. Total harmonic distortion improvements of 69% were observed compared to traditional control methods, and 1% compared to the GA technique. The findings of this study offer significant insights into enhancing the robustness of multi-rotor turbine systems and improving power quality. Full article

Fermentative processes are considered one of the most important technological developments in the modern transforming industry, due to this, the applied research to reach high performance standards with a crucial focus on system intensification, which is the the analysis, optimization, and control issues, are a cornerstone. The goal of this proposal is to show a novel nonlinear feedback control structure to assure a stable closed-loop operation in a continuous flow enzymatic–fermentative bioreactor with chaotic dynamic behavior. The proposed structure contains an adaptive-type gain, which, coupled with a proportional term of the named control error, can lead the feedback control trajectories of the bioreactor to the required reference point or trajectory. The Lyapunov method is used to present the stability analysis of the system within a closed loop, where an adequate choice of the controller gains assures asymptotic stability. Moreover, analyzing the dynamic equation of the control error, under some properties of boundedness of the system, shows that the control error can be diminished to close to zero. Numerical experiments are carried out, where a well-tuned standard proportional–integral (PI) controller is also implemented for comparison purposes, the satisfactory performance of the proposed control scheme is observed, including the diminishing offsets, overshoots, and settling times in comparison with the PI controller. Full article