Browse Articles
Discover research articles across all indexed journals
Survival and prognostic factors among different types of liposarcomas based on SEER database
Functional resting state connectivity is differentially associated with IL-6 and TNF-α in depression and in healthy controls
A constitutive model for coal gangue coarse-grained subgrade filler incorporating particle breakage
Effects of in situ experimental warming on metabolic expression in a soft sediment bivalve
A pilot study comparing three-dimensional models of tumor histopathology and magnetic resonance imaging
Production scheduling with multi-robot task allocation in a real industry 4.0 setting
Exploring the influence of age on the causes of death in advanced nasopharyngeal carcinoma patients undergoing chemoradiotherapy using machine learning methods
Enhancing proteasome activity by NMDAR antagonists explains their therapeutic effect in neurodegenerative and mental diseases
Water hyacinth conversion to biochar for soil nutrient enhancement in improving agricultural product
CCN1 promotes APRIL/BAFF signaling in esophageal squamous cell carcinoma but attenuates it in esophageal adenocarcinoma
Using a citizen science approach to assess nanoplastics pollution in remote high-altitude glaciers
Abstract Nanoplastics are suspected to pollute every environment on Earth, including very remote areas reached via atmospheric transport. We approached the challenge of measuring environmental nanoplastics by combining high-sensitivity TD-PTR-MS (thermal desorption-proton transfer reaction-mass spectrometry) with trained mountaineers sampling high-altitude glaciers (“citizen science”). Particles < 1 μm were analysed for common polymers (polyethylene, polyethylene terephthalate, polypropylene, polyvinyl chloride, polystyrene and tire wear particles), revealing nanoplastic concentrations ranging 2–80 ng mL − 1 at five of 14 sites. The dominant polymer types found in this study were tire wear, polystyrene and polyethylene particles (41%, 28% and 12%, respectively). Lagrangian dispersion modelling was used to reconstruct possible sources of micro- and nanoplastic emissions for those observations, which appear to lie largely to the west of the Alps. France, Spain and Switzerland have the highest contributions to the modelled emissions. The citizen science approach was found to be feasible providing strict quality control measures are in place, and is an effective way to be able to collect data from remote and inaccessible regions across the world.
Psychometric study of the Maslach Burnout Inventory-Student Survey on Thai university students
Abstract The Maslach Burnout Inventory-Student Survey (MBI-SS) is a widely used instrument to assess burnout levels, which provides valuable insight into their psychological well-being. Accurate measurement of burnout is crucial for developing interventions aimed at reducing stress and promoting mental health among students. This study aims to validate the MBI-SS when applied among Thai university students and to examine whether the psychometric properties of the scale are consistent with the original conceptual framework. A total of 413 undergraduate students from Thailand participated in the study, with 57.63% females and 42.37% males, and a mean age 21.75 years (SD = 2.40). The MBI-SS was translated into Thai by following rigorous procedures to maintain accuracy and cultural relevance. The factorial structure of the MBI-SS Thai version was evaluated using confirmatory factor analysis (CFA) for both a three-factor model and second-order factor model. The Thai version of the MBI-SS demonstrated a three-dimensional structure consistent with the original inventory, with excellent model fit indices. All item factor loadings exceeded the recommended threshold, and the instrument showed high internal consistency, establishing it a valuable tool for future research and practical application in educational settings aimed at addressing and reducing student burnout.
Numerical analysis of the hydraulic fracture propagation behavior encountering gravel in conglomerate reservoirs
A hydrothermal coupling model for permafrost subgrade considering temperature gradient and its application
Quantitative efficiency of optoacoustic ultrasonic treatment in SLM, DED, and LBW applications
Integrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders
The burden of prostate cancer in the North Africa and Middle East Region from 1990 to 2021
Willingness to pay a premium for eco-label products in China: a mediation model based on quality value
A kurtosis-ESPRIT algorithm for RealTime stability assessment in droop controlled microgrids
Abstract Although detailed analytical models for droop-controlled microgrids are available, they are computationally complex and do not consider real-time variations in microgrid parameters and operating conditions. This paper proposes Kurtosis-Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) to identify the dominant modes in droop-controlled inverter-based microgrids (IBMGs) using local real-time measurements. In the proposed approach, a short-duration small disturbance is applied to the selected DG’s active power droop gain, and then, the system’s dominant modes are estimated from its local measurements. Additionally, a kurtosis measure is proposed as a quick measure to assess the estimation signal’s characteristics and evaluate the presence and prominence of significant modes within the signal. The effectiveness of the developed approach is validated via MATLAB/SIMULINK simulations. Four case studies were conducted to verify the robustness of the proposed algorithm as follows: under different values of active power droop gains, several variations of lines’ X/R ratios, various levels of noise, and under large load changes and topological disturbances. Besides, a controller-in-the-loop (CIL) experiment was conducted using OPAL-RT to provide a real-time validation of the results. The modes obtained from the proposed algorithm are validated against the analytically derived modes and the estimation accuracy is compared to the recent methods: Prony, Matrix Pencil, and Subspace Identification techniques. Results show higher estimation accuracy for the proposed approach with a robust performance in noisy environments, across varying load conditions, and under different network configurations.