Search Results (45,110)

Introduction: The emergence of SARS-CoV-2 Omicron subvariants characterized by increased transmissibility and immune escape has raised concerns about the efficacy of current treatments. This systematic review and meta-analysis evaluated pharmacological and non-pharmacological interventions in Omicron-infected non-hospitalized patients, focusing on key clinical outcomes such as hospitalization, respiratory failure, ICU admission, and 30-day mortality. Methods: Searches were performed in MEDLINE, EMBASE, Web of Science, Cochrane, and ClinicalTrials.gov (last update: 13 July 2025). Eligible studies reported outcomes on antiviral agents, monoclonal antibodies, adjunctive therapies, or telemedicine. Random-effects meta-analyses were conducted when appropriate, with heterogeneity assessed by I². Publication bias was evaluated via funnel plots and Egger’s test. Subgroup analyses explored sources of heterogeneity. Results: Eighty-eight studies were included. Meta-analyses, comparing treatment vs. no treatment, revealed that nirmatrelvir/ritonavir reduced hospitalization by 52% (RR 0.48, 95% CI 0.36–0.63) and all-cause mortality by 84% (RR 0.16, 95% CI 0.11–0.24). Remdesivir reduced hospitalization by 70% (RR 0.30, 95% CI 0.19–0.47) and respiratory failure by 89% (RR 0.11, 95% CI 0.03–0.44). Sotrovimab decreased hospitalization (RR 0.71, 95% CI 0.54–0.93) and mortality (RR 0.34, 95% CI 0.19–0.61). Molnupiravir modestly reduced hospitalization (RR 0.80, 95% CI 0.70–0.91) and respiratory failure (RR 0.45, 95% CI 0.27–0.77). Conclusions: Nirmatrelvir/ritonavir and remdesivir remain important for reducing severe outcomes, while sotrovimab retains partial efficacy. Rapid access to antivirals remains an important factor in mitigating SARS-CoV-2’s burden. Full article

As the principal dietary source of lipids, edible oils (notably vegetable oils) exist in crude form predominantly as triacylglycerols (about 95%), with the remainder comprising impurities and diverse minor components. Therefore, the refining processes of vegetable oil are particularly important. The application potential of safflower seed oil (SSO) in both nutraceutical and pharmaceutical domains is attributed to its exceptionally high linoleic acid concentration and abundant polyphenolic constituents. However, a systematic analysis of SSO during physical refining has yet to be conducted. This study aims to investigate the effects of refining processes on the fatty acids of SSO compared with flaxseed oil (FSO). In this study, chemical analysis, gas chromatography and ultra-high-performance liquid chromatography were used to analyze and compare the physicochemical indexes, fatty acid composition, and the lipidomics of SSO and FSO. Results indicated that optimized refining significantly enhances quality parameters in both SSO and FSO. A total of 40 and 43 fatty acids were identified in SSO and FSO, respectively. Deacidification significantly altered their fatty acid profiles, particularly polyunsaturated fatty acids, with C18:2 and C18:3 being the most affected. A total of 20 significantly different lipids were screened (variable importance in projection > 1.5, p < 0.05) and were mainly classified as glycerophospholipids and glycerolipids, of which two lipids (C18:2 and C18:3 (9, 12, 15)) demonstrated particularly marked differences, suggesting that these lipid species represent significant discriminators between SSO and FSO groups; these two lipids exhibited significant alterations during the refining processes of SSO and FSO, respectively. Full article

The integration of 5G technology with cloud and edge computing is redefining the digital landscape by enabling ultra-fast connectivity, low-latency communication, and scalable solutions across diverse application domains. This paper investigates the synergistic impact of 5G on cloud-to-edge architectures, emphasizing its transformative role in revolutionizing sectors such as healthcare, smart cities, industrial automation, and autonomous systems. Key advancements in 5G—including Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and Massive Machine-Type Communications (mMTC)—are examined for their role in enabling real-time data processing, edge intelligence, and IoT scalability. In addition to conceptual analysis, the paper presents simulation-based evaluations comparing 5G cloud-to-edge systems with traditional 4G cloud models. Quantitative results demonstrate significant improvements in latency, energy efficiency, reliability, and AI prediction accuracy. The study also explores challenges in infrastructure deployment, cybersecurity, and latency management while highlighting the growing opportunities for innovation in AI-driven automation and immersive consumer technologies. Future research directions are outlined, focusing on energy-efficient designs, advanced security mechanisms, and equitable access to 5G infrastructure. Overall, this study offers comprehensive insights and performance benchmarks that will serve as a valuable resource for researchers and practitioners working to advance next-generation digital ecosystems. Full article

As a novel type of ocean monitoring tool, unmanned sailboats exhibit significant application potential. In this study, a novel wing sail structure for offshore unmanned sailboats is proposed and its performance compared with that of the conventional NACA 0021 wing sail. The Reynolds-averaged Navier–Stokes (RANS) equations are employed for numerical analysis, and the aerodynamic performance is evaluated using ANSYS Fluent. The results indicate that the lift coefficient and lift-to-drag ratio of the HF-14-CE-01 wing sail are significantly superior to those of the NACA 0021 wing sail. Compared to the NACA 0021 wing sail, the HF-14-CE-01 wing sail has undergone structural optimization. The HF-14-CE-01 wing sail demonstrates improved wind direction efficiency, uniform force distribution, ease of adjustment, and extends the service life of the sail. Subsequent research examined the influence of aspect ratio on both the aerodynamic performance of the wing sail and the thrust generated by the unmanned sailboat, identifying an optimal aspect ratio of 4 for the HF-14-CE-01 wing sail. Analysis of the velocity and static pressure contour maps for the HF-14-CE-01 wing sail identified a critical angle of attack of 28°, providing a clear visual representation of its aerodynamic performance. Furthermore, compared with other rigid sail designs, the HF-14-CE-01 wing sail achieved a 30.9% increase in peak lift coefficient, indicating superior propulsion capability. Full article

The MA₂Z₄ family represents a class of two-dimensional materials renowned for their outstanding mechanical properties and excellent environmental stability. By means of elemental substitution, we designed two novel phases of ScSi₂N₄, namely β₁ and β₂. Their dynamical, thermal, and mechanical stabilities were thoroughly verified through phonon dispersion analysis, ab initio molecular dynamics (AIMD) simulations, and calculations of mechanical parameters such as Young’s modulus and Poisson’s ratio. Electronic structure analysis using both PBE and HSE06 methods further revealed that both the β₁ and β₂ phases exhibit metallic behavior, highlighting their potential for battery-related applications. Based on these outstanding properties, the climbing image nudged elastic band (CI-NEB) method was employed to investigate the diffusion behavior of Li, Na, and K ions on the material surfaces. Both structures demonstrate extremely low diffusion energy barriers (Li: 0.38 eV, Na: 0.22 eV, K: 0.12 eV), indicating rapid ion migration—especially for K—and excellent rate performance. The lowest barrier for K ions (0.12 eV) suggests the fastest diffusion kinetics, making it particularly suitable for high-power potassium-ion batteries. The significantly lower barrier for Na ions (0.22 eV) compared with Li (0.38 eV) implies that both β₁ and β₂ phases may be more favorable for fast-charging/discharging sodium-ion battery applications. First-principles calculations were applied to determine the open-circuit voltage (OCV) of the battery materials. The β₂ phase exhibits a higher OCV in Li/Na systems, while the β₁ phase shows more prominent voltage for K. The results demonstrate that both phases possess high theoretical capacities and suitable OCVs. Full article

Under the global decarbonization trend, combined cooling, heating, and power (CCHP) systems are critical for improving regional energy efficiency. However, the integration of high-penetration variable renewable energy (RE) sources introduces significant volatility and multi-dimensional uncertainties, challenging conventional operation strategies designed for stable energy inputs. This review systematically examines recent advances in CCHP optimization under high-RE scenarios, with a focus on flexibility-enabled operation mechanisms and uncertainty-aware optimization strategies. It first analyzes the evolving architecture of variable RE-driven CCHP systems and core challenges arising from RE intermittency, demand volatility, and multi-energy coupling. Subsequently, it categorizes key flexibility resources and clarifies their roles in mitigating uncertainties. The review further elaborates on optimization methodologies tailored to high-RE contexts, along with their comparative analysis and selection criteria. Additionally, it details the formulation of optimization models, model formulation, and solution techniques. Key findings include the following: Generalized energy storage, which integrates physical and virtual storage, increases renewable energy utilization by 12–18% and reduces costs by 45%. Hybrid optimization strategies that combine robust optimization and deep reinforcement learning lower operational costs by 15–20% while strengthening system robustness against renewable energy volatility by 30–40%. Multi-energy synergy and exergy-efficient flexibility resources collectively improve system efficiency by 8–15% and reduce carbon emissions by 12–18%. Overall, this work provides a comprehensive technical pathway for enhancing the efficiency, stability, and low-carbon performance of CCHP systems in high-RE environments, supporting their scalable contribution to global decarbonization efforts. Full article

Background/Objectives: Human leukocyte antigen B27 (HLA-B27) is a genetic marker strongly associated with various inflammatory rheumatic diseases, particularly those within the spondyloarthritis spectrum. Its presence influences disease onset, clinical severity, and therapeutic strategies. However, comparative data between HLA-B*27-positive and -negative patients, especially in Eastern European populations, remain limited. The study aimed to investigate the clinical, paraclinical, and psychosocial differences between HLA-B*27-positive and -negative individuals diagnosed with rheumatic diseases, in order to better understand the implications of HLA-B27 status on disease expression and patient quality of life. Methods: A cross-sectional, observational study was conducted between June 2023 and December 2024 at the Emergency Clinical Hospital for Children “Sf Ioan” in Galati, Romania, in collaboration with “Dunarea de Jos” University. Fifty adult patients with various rheumatic conditions were enrolled and stratified into HLA-B*27-positive (n = 22) and -negative (n = 28) groups. Data collection included clinical evaluations, laboratory biomarkers (CRP = C-reactive protein; ESR = erythrocyte sedimentation rate), and a structured quality-of-life questionnaire. Statistical analysis was performed using SPSS v27. Results: HLA-B*27-positive patients were significantly younger (mean age 46.00 vs. 55.07 years, p = 0.018) and had higher CRP levels (>1 mg/dL in 53.33% vs. 0%, p = 0.001). Ankylosing spondylitis was more prevalent in this group (22.73% vs. 3.57%, p = 0.039). Magnetic resonance imaging (MRI) was more frequently used (68.18% vs. 39.29%, p = 0.042), indicating greater suspicion of axial involvement. HLA-B27-positive patients also reported higher perceived stress (mean score 2.41 vs. 1.21, p< 0.001). Conclusions: HLA-B*27 positivity is associated with earlier disease onset, increased systemic inflammation, greater axial involvement, and higher psychological stress. These findings emphasise the need for personalised, multidisciplinary care that integrates both medical and psychological support for HLA-B*27-positive patients. Full article

Traditional crosslinked-polyethylene (XLPE) insulation suffers from high recycling costs and low efficiency due to its thermosetting properties. In contrast, thermoplastic polypropylene (PP), with advantages of melt recyclability, low energy consumption, and excellent comprehensive performance, has emerged as an ideal alternative to XLPE. This study conducts a comparative analysis of low-voltage cables insulated with PP, silane-crosslinked XLPE (XLPE-S), and UV-crosslinked XLPE (XLPE-U), focusing on production processes, mechanical properties, thermal stability, and electrical performance. Tensile test results show that PP exhibits the highest elongation at break (>600%) before aging, and its tensile strength (>20 MPa) after aging outperforms that of XLPE, indicating superior flexibility and anti-aging capability. PP exhibits a lower thermal elongation (<50%) at 140 °C compared to XLPE, and its high-crystallinity molecular structure endows better heat-resistant deformation performance. The volume resistivity of PP reaches 9.2 × 10¹⁵ Ω·m, comparable to that of XLPE-U (3.9 × 10¹⁵ Ω·m) and significantly higher than XLPE-S (3.0 × 10¹⁴ Ω·m). All three materials pass the 4-h voltage withstand test, confirming their satisfied insulation reliability. PP-insulated low-voltage cables demonstrate balanced performance in production efficiency, energy consumption cost, mechanical toughness, and electrical insulation. Notably, their recyclability significantly surpasses traditional XLPE, showing potential to promote green upgrading of the cable industry and providing a sustainable insulation solution for low-voltage power distribution systems. Full article

Cellular senescence can occur with similar phenotypes in normal cells, during aging, and in tumor cells, spontaneously or after cytostasis. The fall or increase in proliferative activity are key aspects of the respective conditions, in which the levels of reactive oxygen species can vary, affecting the cellular redox homeostasis. This work aimed to study the relationships between senescence and transformation by comparing cells with different proliferative activities and phenotypes attributable to transformation (NIHs cultures) or senescence (NIHv cultures), before and after incubation with hydrogen peroxide. Both cultures were derived from the NIH/3T3 cell line, which was used here as a reference (NIHb), after the serum starvation. Our experimental model can be representative of the heterogeneity of cell subpopulations, with different degrees of transformation and senescence, found in some tumors. The characterization of the functional properties of NIHb, NIHs, and NIHv cells was performed by a morphocytometric analysis of the cell cycle progression, mitochondrial and lysosomal content/activity, and superoxide anion production. The efficiency of the lysosomal compartment was also assessed by estimating the autophagic activity and measuring lipofuscin autofluorescence. Comparisons of nuclear and cytoplasmic parameters before and after the incubation with hydrogen peroxide revealed differences in the expression and modulation of cellular senescence patterns. The treatment effects were very limited in the NIHb culture; the senescence condition was essentially maintained in the NIHv cells, while the most relevant changes were found in the NIHs cells. In the latter, the acquisition of the senescent phenotype, also demonstrated by the positivity of SA-β-galactosidase, was correlated with a decrease in proliferative activity and a change in the content/activity of the mitochondria and lysosomes, which showed similarities with the basal senescence conditions of NIHv cells. In NIHs cells, increased autophagy events and lipofuscin accumulation also indicate the establishment of cytoplasmic dynamics typical of senescence. The variable responses to hydrogen peroxide, besides depending on the different basal cytokinetic activity of the cultures examined, appeared to be related to the specific cell redox state resulting from the balance between endogenous ROS and those produced after treatment. Especially in NIHs cells, the slowing down of the cell cycle was linked to dynamic interconnections between the mitochondrial and lysosomal compartments. This would indicate that transformed cells, such as NIHs, may express morpho-functional aspects and markers typical of cellular senescence, as a consequence of the modulation of their redox state. Full article

Accurate maize yield forecasting under climate uncertainty remains a critical challenge for global food security, yet existing studies predominantly rely on single-model frameworks, limiting generalizability and actionable insights. This study selected three regions, specifically Dali, Lijiang, and Zhaotong, and collected data on 12 agronomic traits of 114 varieties, along with eight sets of meteorological data, covering the period from 2019 to 2023. We employed three machine learning models: Random Forest (RF), Support Vector Machine (SVM), and XGBoost. The results revealed a strong correlation between yield and multiple agronomic traits, particularly grain weight per spike (GWPS) and hundred-kernel weight (HKW). Notably, the XGBoost model emerged as the top performer across all three regions. The model achieved the lowest RMSE (0.22–191.13) and a good R² (0.98–0.99), demonstrating exceptional predictive accuracy for yield-related traits. The comparative analysis revealed that XGBoost exhibited superior accuracy and stability compared to RF and SVM. Through feature importance analysis, four critical determinants of yield were identified: GWPS, shelling percentage (SP), growth period (GP), and plant height (PH). Furthermore, partial dependence plots (PDPs) provided deeper insights into the nonlinear interactive effects between GWPS, SP, GP, PH, and yield, offering a more comprehensive understanding of their complex relationships. This study presents an innovative, data-driven methodology designed to accurately forecast corn yield across diverse locations. This approach offers valuable scientific insights that can significantly enhance precision agricultural practices by enabling the precise tailoring of fertilizer usage and irrigation strategies. The results highlight the importance of integrating agronomic and meteorological data in yield forecasting, paving the way for development of agricultural decision-support systems in the context of future climate change scenarios. This study presents an innovative, data-driven methodology designed to accurately forecast corn yield across diverse locations. This approach offers valuable scientific insights that can significantly enhance precision agricultural practices by enabling the precise tailoring of fertilizer usage and irrigation strategies. Full article

The dynamic behavior of quartz flexure accelerometers remains a subject of ongoing investigation, particularly in areas such as theoretical modeling, standardization, calibration methodology, and performance evaluation. To address the limitation of conventional static calibration models in accurately representing accelerometer responses under dynamic acceleration excitation, a dynamic calibration model is proposed. A mathematical model is first developed based on the physical mechanism of the accelerometer, characterizing its intrinsic dynamic response. Simulation-based analysis demonstrates that the proposed dynamic model offers significantly improved accuracy compared to traditional static approaches. Furthermore, a dynamic calibration method leveraging a dual-axis precision centrifuge is designed and validated. The results confirm that the proposed approach enables the precise calibration of quartz flexure accelerometers in accordance with the dynamic model. The calibration of the dynamic parameter yields a relative standard deviation of −0.048%. Full article

Accurate soil moisture estimation is critical for precision agriculture and water resource management, yet traditional sampling methods are time-consuming, destructive, and provide limited spatial coverage. Ground Penetrating Radar (GPR) offers a promising non-destructive alternative, but optimal machine learning approaches for GPR-based soil moisture prediction remain unclear. This study presents a comparative analysis of regression tree and boosted tree algorithms for predicting soil moisture content from Ground Penetrating Radar (GPR) histogram features across 21 sites in Eastern Thailand. Soil moisture content was measured at multiple depths (0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 m) using samples collected during Standard Penetration Test procedures. Feature extraction was performed using 16-bin histograms from processed GPR radargrams. A single regression tree achieved a cross-validation RMSE of 5.082 and an R² of 0.761, demonstrating superior training accuracy and interpretability. In contrast, the boosted tree ensemble achieved significantly better generalization performance, with a cross-validation RMSE of 4.7915 and an R² of 0.708, representing a 5.7% improvement in predictive performance. Feature importance analysis revealed that specific histogram bins effectively captured moisture-related variations in GPR signal amplitude distributions. A comparative evaluation demonstrates that while single regression trees offer superior interpretability for research applications, boosted tree ensembles provide enhanced predictive performance that is essential for operational deployment in precision agriculture and hydrological monitoring systems. Full article

The gas reservoir of the Sinian Dengying Formation (Member 4) in Sichuan Basin exhibits extensive development of inter-clast dissolution pores and vugs within its carbonate reservoirs, characterized by low porosity (average 3.21%) and low permeability (average 2.19 mD). With the progressive development of the Moxi (MX)structure, the existing stimulation techniques require further optimization based on the specific geological characteristics of these reservoirs. Through large-scale true tri-axial physical simulation experiments, this study systematically evaluated the performance of three principal acid systems in reservoir stimulation: (1) Self-generating acid systems, which enhance etching through the thermal decomposition of ester precursors to provide sustained reactive capabilities. (2) Gelled acid systems, characterized by high viscosity and effectiveness in reducing breakdown pressure (18%~35% lower than conventional systems), are ideal for generating complex fracture networks. (3) Diverting acid systems, designed to improve fracture branching density by managing fluid flow heterogeneity. This study emphasizes hybrid acid combinations, particularly self-generating acid prepad coupled with gelled acid systems, to leverage their synergistic advantages. Field trials implementing these optimized systems revealed that conventional guar-based fracturing fluids demonstrated 40% higher breakdown pressures compared to acid systems, rendering hydraulic fracturing unsuitable for MX reservoirs. Comparative analysis confirmed gelled acid’s superiority over diverting acid in tensile strength reduction and fracture network complexity. Field implementations using reservoir-quality-adaptive strategies—gelled acid fracturing for main reservoir sections and integrated self-generating acid prepad + gelled acid systems for marginal zones—demonstrated the technical superiority of the hybrid system under MX reservoir conditions. This optimized protocol enhanced fracture length by 28% and stimulated reservoir volume by 36%, achieving a 36% single-well production increase. The technical framework provides an engineered solution for productivity enhancement in deep carbonate gas reservoirs within the G-M structural domain, with particular efficacy for reservoirs featuring dual low-porosity and low-permeability characteristics. Full article

Background: Several treatment strategies are available for patients with mitral valve regurgitation (MR). However, evidence regarding their comparative effectiveness remains limited. We sought to compare the performance of different treatment strategies for personalized treatment of patients with MR. Methods: We performed a pairwise and network meta-analyses of randomized trials comparing treatment strategies for patients with MR. Patients were divided in two groups: transcatheter mitral valve repair (TMVR, including edge-to-edge repair and indirect percutaneous annuloplasty) and control (surgery or optimal medical therapy). The primary outcome of this analysis was all-cause death. Main secondary outcomes were re-hospitalization for heart failure and re-intervention. Results: A total of seven trials with 2324 participants, with mainly functional MR (TMVR, n = 1373-control, n = 951) were available for the quantitative synthesis. The median follow-up duration was 14 months. Compared to control therapy, TMVR significantly reduced all-cause death (RR 0.77, 95% CI 0.65–0.91, p = 0.002) and re-hospitalization for heart failure (RR 0.67, 95% CI 0.49–0.91, p = 0.01). Among TMVR strategies, the edge-to-edge repair with MitraClip ranked as possibly the best option to reduce all-cause death. Conclusions: In symptomatic patients with significant MR, TMVR is associated with a significant reduction of all-cause death, and re-hospitalization for heart failure, mainly in patients with functional MR. Additional comparative studies are needed to investigate the best TMVR treatment option, for patients with degenerative MR. Full article

Accurate parameter identification in dynamic models of robotic arms is essential for performing high-performance control and energy-efficient procedures. However, classic methods often encounter difficulties when modeling nonlinear, high-dimensional systems, particularly in the presence of real-world uncertainties. To address these challenges, this study focuses on identifying mass center positions and inertia matrix elements in a six-jointed industrial robotic arm and comparing the influence of optimized algorithms: the classical Particle Swarm Optimization (PSO) and the Quantum-behaved Particle Swarm Optimization (QPSO). The robot’s kinematic model was validated by comparing it with actual motion data, utilizing a high-precision neural network to ensure accuracy before conducting a dynamic analysis. A comprehensive dynamic model was created using Computer-Aided Optimization (CAO) in SolidWorks Premium 2023 to simulate realistic mass parameters, thereby validating the model’s reliability in a practical setting. The real (Referenced) and optimized dynamic models of the robot arm were validated using trajectory tracking simulations under sliding mode control (SMC) to assess the impact of the optimized model on the robot’s performance metrics. Results indicate that QPSO estimates inertia and mass center parameters with Mean Absolute Percentage Errors (MAPE) of 0.76% and 0.43%, outperforming PSO significantly and delivering smoother torque profiles and greater resilience to external disturbances. Full article