Search Results (12,977)

Enterobiasis, caused by the nematode Enterobius vermicularis, remains a widespread public health issue, yet data regarding its genetic structure in Southeast Europe are scarce. This study presents the first molecular and phylogenetic characterization of E. vermicularis isolates from Bulgaria. Between 2022 and 2025, perianal tape test samples were collected from 128 individuals (92.2% of whom were children) with enterobiasis from 17 regions of the country. Molecular identification was performed via nested PCR targeting a 324 bp fragment of the mitochondrial cytochrome c oxidase subunit 1 (cox1) gene, followed by Sanger sequencing. Phylogenetic relationships were analyzed using Maximum Likelihood (IQ-TREE), and population genetic indices were calculated using DnaSP v6. Phylogenetic analysis revealed that all 128 Bulgarian isolates belong to genotype B, clustering closely with sequences from other European and Asian countries. Genetic diversity analysis showed remarkably low variation, with a haplotype diversity (Hd) of 0.1507 ± 0.0416 and a nucleotide diversity (π) of 0.00082 ± 0.00015. Among the 11 identified haplotypes, a single dominant haplotype (Hap_1) accounted for 92.2% of all samples and was distributed across all sampled geographic regions. Tajima’s D was significantly negative (−2.314, < 0.05), suggesting a recent population expansion or purifying selection. The dominance of genotype B and the extremely low genetic diversity suggest a recent introduction or clonal expansion of E. vermicularis in Bulgaria. These findings provide essential baseline data for monitoring transmission dynamics and implementing effective control strategies in the Balkan region. Full article

Research on muscle fatigue during dynamic movement using surface electromyography (sEMG) constitutes a significant challenge within biomechanics. Despite a degree of standardization, measurements and their resultant findings continue to attract considerable debate, attributable to factors such as skin impedance, perspiration, and electrode displacement, as well as subjective fatigue perception. Further questions remain regarding signal normalization and the selection of appropriate analytical methodologies. Recent years have witnessed notable progress in dynamic fatigue research, highlighting the limitations of classical metrics (e.g., EMG Median Frequency) and introducing time–frequency methods, such as the wavelet transform (WT), which are better equipped to handle signal non-stationarity. Interest has also expanded to include non-linear metrics (e.g., entropy) and the analysis of multiple signals (EMG, accelerometers, fNIRS, EEG). The inherent complexity of conducting studies under conditions that approximate real-world sporting disciplines requires the consideration of the influence of various confounding factors. The judicious selection of relevant physical activities and the rigorous validation of the measurement apparatus are paramount for the accurate execution of the calculations. Current research is substantially predicated on artificial intelligence (AI) algorithms. The synergistic application of AI with wavelet transform, particularly in the decomposition and extraction of EMG signals, demonstrates efficacy in fatigue detection. Nevertheless, the full realization of these potential mandates requires further investigation into system generalization, the integration of data from multiple sensors, and the standardization of protocols, coupled with the establishment of publicly accessible datasets. This article delineates selected guidelines and challenges pertinent to the planning and execution of research on muscle fatigue in dynamic movement, focusing on activity selection, equipment validation, EMG signal analysis, and AI utilization. Full article

With the core advantages of high energy efficiency, high power density, and reliable operation, high-voltage permanent magnet motors have become the mainstream development direction of modern motor technology. However, the risk of demagnetization caused by excessive temperature increases in permanent magnets has become a key bottleneck restricting motor performance and operational reliability, which makes research on the flow and heat transfer characteristics of motor cooling systems of great engineering value. Taking the 710 kW high-voltage permanent magnet motors as the research object, this study established a global flow field mathematical model covering the internal and external air duct cooling systems of the motor based on the theories of computational fluid dynamics and numerical heat transfer, and systematically analyzed the flow characteristics and distribution laws of cooling air. The thermal–fluid coupling numerical method was employed to simulate the temperature field of the motor, and the overall temperature distribution of the motor, temperature gradient of key components, and maximum temperature value were accurately obtained. To verify the validity of the established model, a test platform for the cooling system performance was designed and built. Measuring points for wind speed, air temperature, and component temperature were arranged at key positions, such as the stator radial ventilation ducts, and experimental tests were conducted under the rated operating conditions. The results show that the flow field distribution of the internal and external air ducts of the motor is reasonable and that the cooling air flows uniformly, with the external and internal circulating air volumes reaching 1.2 m³/s and 0.6 m³/s, respectively, which meets the heat dissipation requirements. The maximum temperature of 95 °C occurs in the stator winding area, and the maximum temperature of the permanent magnets is controlled within the safe range of 65 °C. The simulation results were in good agreement with the experimental data, with an average relative error of only 4%, which fell within the engineering allowable range, thus verifying the accuracy and reliability of the established global model and thermal–fluid coupling calculation method. This study reveals the thermal–fluid coupling transfer mechanism of high-voltage permanent magnet motors and provides a theoretical basis and engineering reference for the optimal design, precise temperature rise control, and reliability improvement of motor cooling systems. Full article

The majority of breast malignancies arise from breast ducts at the small-scale level. Understanding the wave characteristics of breast ducts may assist in developing new technologies to detect very early changes that precede breast cancer. In this study, a two-phase nonlocal integral model is developed to analyse the biomechanical behavior of breast ducts under flexural wave propagation. The influence of surface stiffness, surface residual stress, stress nonlocality, and stromal matrix is taken into consideration. The breast duct consists of different biological layers, including the basement membrane, myoepithelial cells, and luminal epithelial cells. Surface properties are calculated for the outer basement membrane and inner luminal epithelial cell layer. The results of the two-phase nonlocal integral model are validated using available molecular dynamics simulations. In addition, various machine learning algorithms, such as a neural network model, gradient boosting, random forest, logistic regression, and Ridge regression, are developed and integrated with the two-phase nonlocal model to better understand the flexural wave characteristics of breast ducts. Incorporation of two-phase nonlocal integral stress effects, surface energy, and residual stress reduces the root mean square error from 4.16 to 0.24 when compared against molecular dynamics simulation data. Full article

The MERS-CoV (Middle East respiratory syndrome coronavirus) is a zoonotic virus with a high mortality rate and a lack of antiviral drugs, underscoring the need for effective therapeutic methods. Viral entry depends on interactions between viral surface proteins and human receptors, with Dipeptidyl Peptidase-4 (DPP4), a transmembrane glycoprotein, acting as the receptor for MERS-CoV. We employed Molecular Dynamics (MD) Simulations to identify critical interface residues under a high-performance computing (HPC) workflow for accelerated results. Target residue pairs were identified through analysis of salt bridge and hydrogen bond occupancy. The stability of these residues was confirmed through three independent MD Simulations at human body temperature and constant pressure. Additionally, binding affinity predictions were calculated to determine the interaction strength between the virus and human receptors. Applying the scientific threshold criteria, we narrowed our results to seven key interaction pairs; two of the identified pairs (Asp510-Arg317, and Arg511-Asp393) are consistent with findings published in previous research studies, and five novel interactions are proposed for future experimental studies with our active collaborators in Pharmacology. The results provide a molecular basis for targeted mutation-based experiments and support the rational design of structure-based inhibitors aimed at disrupting the MERS-CoV-DPP4 complex, thereby facilitating the translation of computational findings into antiviral drug discovery. Full article

One of the ways to decarbonize cities and to enhance grid stability is to convert existing buildings into prosumers equipped with power plants able to supply electrical and thermal energy. The simulation of such multi-energy systems permits the analysis of their performance in steady-state and dynamic conditions, with the aim of defining effective operating strategies able to reduce emissions and costs. The present paper describes a dynamic simulation model, implemented in the Matlab/Simulink R2025a environment, developed to simulate the daily and weekly operation of a prosumer building equipped with a small-sized cogeneration unit, a Photovoltaic (PV) plant, a back-up boiler, a thermal storage system and some charging points for Electric Vehicles (EVs). The mathematical model is reported in detail, and the main results of the study are described, referring to operating days characterized by different weather conditions. Then, energy, economic and environmental performance indicators are defined and calculated for the different simulated scenarios. Over the considered time horizons, the simulation results highlight a significant increase in the electrical self-sufficiency of the facility up to 91.1% and an important reduction in total net operating costs up to 59.8%, compared to the AS-IS case (i.e., without the newly installed technologies). Full article

Background: Reliable and readily accessible prognostic biomarkers for extensive-stage small-cell lung cancer (ES-SCLC) are still lacking. The neutrophil-to-lymphocyte ratio (NLR), a marker of systemic inflammation, has shown prognostic relevance in several malignancies; however, its dynamic changes during treatment have not been well characterized in SCLC. Methods: We retrospectively analyzed patients with ES-SCLC who received systemic chemotherapy between January 2010 and December 2024. Baseline NLR was calculated within 7 days before first-line treatment, and on-treatment NLR was assessed at 6 weeks. A predefined NLR cutoff value of 5 was applied, and changes in NLR (ΔNLR) were defined as the difference between 6-week and baseline values. Associations with time to treatment failure (TTF) and overall survival (OS) were evaluated. Results: A total of 176 patients were enrolled. High baseline NLR (≥5) was significantly associated with shorter TTF and OS (both p < 0.01). An increase in NLR during treatment (ΔNLR ≥ 0) was significantly associated with poorer OS. Combined assessment of baseline NLR and ΔNLR identified distinct prognostic groups, with patients exhibiting both high baseline NLR and ΔNLR ≥ 0 demonstrating markedly poor survival. In multivariate analyses, baseline NLR, ΔNLR, performance status, and immune checkpoint inhibitor combination therapy were independent predictors of survival. Baseline NLR analyzed as a continuous variable showed a significant inverse correlation with TTF and OS. Conclusions: Combined evaluation of baseline NLR and its on-treatment change provides improved prognostic stratification in patients with ES-SCLC. Full article

The harvesting of Camellia oleifera fruit in hilly areas faces core problems such as low manual efficiency, poor terrain adaptability of existing machinery, and severe emissions and noise from traditional equipment. This study designed a crawler chassis utilizing a permanent magnet synchronous motor-driven hydraulic system. The research integrated kinematic modeling and resistance calculations for parameter matching, followed by AMESim dynamic simulations and motor calibration experiments. Finally, comprehensive field tests were conducted to evaluate the prototype. The results indicate the chassis achieves a maximum travel speed >1.5 m∙s⁻¹, a climbing angle of 41.4°, and a turning radius of 0.72 m, with noise levels strictly below 80 dB(A). Significantly, dynamic power characteristic tests under actual vibration harvesting conditions revealed that the 45 kW motor maintains a rapid response with ample power reserve. The input power exhibited a distinct square-wave pattern synchronized with hydraulic valve commands, peaking at 18.1 kW during vibration bursts. These findings confirm the system’s stability under coupled driving and harvesting loads. This design offers a viable, low-noise solution for electrifying and intelligently upgrading Camellia oleifera harvesting equipment in complex terrains. Full article

Accurate identification of the rotational moment of inertia of a top drive system is essential for dynamic modeling, control design, and performance optimization in drilling operations. However, the strong coupling between the drive motor, transmission components, and drill string makes direct inertia measurement challenging under field conditions. To address this issue, this study proposes a moment of inertia identification method based on dynamic response analysis of the top drive system. A simplified torsional dynamic model is established by representing the top drive and drill string assembly as an equivalent lumped inertia system. By applying controlled torque excitation under no-load conditions, the system’s angular velocity response is measured and analyzed in both time and frequency domains. The relationship between applied torque and angular acceleration is utilized to identify the equivalent rotational inertia through parameter estimation. Experimental results indicate that low-frequency excitation provides more favorable conditions for reliable and accurate inertia identification, yielding improved stability and reduced estimation error compared with higher-frequency inputs. In addition, frequency response characteristics are investigated to validate the consistency and robustness of the identified inertia across different excitation frequencies. Experimental results obtained from a top drive test rig demonstrate that the proposed method can reliably estimate the equivalent moment of inertia with good repeatability under controlled experimental conditions. The identified inertia shows good agreement with theoretical calculations and exhibits stable behavior over a wide frequency range. The proposed approach avoids the need for additional sensors or structural modifications and is well suited for practical engineering applications. This study provides an effective and experimentally validated method for inertia identification of top drive systems, offering valuable support for dynamic modeling, control parameter tuning, and vibration analysis in drilling engineering. Full article

In view of the highly coupled, time-varying, and susceptible to differences in aircraft configuration of the Boeing 787 Electric Environmental Control System (E-ECS), a simplified mechanism model based on effectiveness-number of transfer units is proposed. Firstly, considering the influence of differences in aircraft configuration, part number, and optional components, a heat conduction correction coefficient is introduced to adjust the calculation process of heat exchange efficiency. Secondly, the steady-state characteristic equation of the electric compressor/turbine is established by utilizing the principle of isentropic work. Then, the outlet temperature value of the water removal component is calculated by using secondary heat recovery technology. Finally, to solve the problem of easily getting stuck in local optima during high-dimensional parameter identification, an adaptive hybrid optimization algorithm combining Dung Beetle Optimization (DBO) with mutation operator and Particle Swarm Optimization (PSO) is proposed. The experimental results show that the proposed mechanism model can achieve dynamic representation of the outlet temperature of each component of E-ECS under different aircraft stages. The DBO-PSO algorithm has a fast convergence speed and a low probability of falling into local optima. The temperature values calculated by the model have high computational accuracy, which can provide reliable data support for component level E-ECS health monitoring and early fault warning. Full article

Accurate extraction of the centroid axes of beams with variable cross-sections is critical for infrastructure health monitoring. While 3D laser scanning provides dense point clouds, existing methods face challenges due to fixed slicing directions, sparse or incomplete boundaries, and inaccurate centroid calculations for concave sections. This study proposes a robust framework to overcome these issues. An improved k-d tree ordering algorithm enhances boundary extraction through starting point constraint strategy and dynamic isolated noise point removal mechanism. A ray casting-based boundary-constrained Delaunay triangulation centroid calculation algorithm accurately computes centroids for arbitrary shapes, including concave profiles. An innovative convex hull centroid-driven adaptive normal iterative slicing method dynamically adjusts orientation using historical centroid data, aligning with the local member axis to minimize errors in variable or deformed regions. Experimental validation shows the method outperforms traditional fixed-direction slicing in effectiveness, parameter sensitivity, and deformation robustness, achieving sub-millimeter accuracy. Applied to monitor ultra-high-performance concrete cantilever beams at the Shanghai Grand Opera House, it produced centroid axis data consistent with total station measurements (differences within ±1.2 mm), supporting phased deformation warnings and safety assessments. This work provides a systematic, high-precision solution for extracting geometric axes from complex structural point clouds. Full article

To address the challenges of real-time control in power systems with high renewable penetration, identifying historical transmission sections similar to future scenarios enables efficient reuse of mature control strategies. However, existing data-driven identification methods exhibit two primary limitations: they typically rely on static Total Transfer Capacity (TTC), ignoring the rapid regulation capability of Energy Storage Systems (ESS) in alleviating congestion; and they employ fixed weights for similarity measurement, failing to distinguish the varying importance of different features (e.g., critical line flows vs. ordinary voltages). To overcome these issues, this paper proposes a similarity identification method for transmission sections considering ESS regulation capabilities and adaptive feature weights. First, a hierarchical decision model is utilized to screen basic grid features. An optimization model incorporating ESS charge/discharge constraints and emergency power support potential is established to calculate the Dynamic TTC, constructing a multi-scale feature set that reflects the real-time safety margin of the grid. Second, a Dispersion-Weighted Fuzzy C-Means (DW-FCM) clustering algorithm is proposed. By introducing a dispersion-weighting mechanism, the algorithm utilizes data distribution characteristics to automatically learn and assign higher weights to key features with high distinguishability during the iteration process, overcoming the subjectivity of manual weighting. Furthermore, fuzzy validity indices (XB, PC, FS) are introduced to adaptively determine the optimal number of clusters. Finally, case studies on the IEEE 39-bus system verify that the proposed method significantly improves identification accuracy compared to traditional methods and provides more reliable references for dispatching decisions. Full article

The design of thermal strategies applied in buildings based on the use of renewable energies can play an important role in the development of a built environment that is better adapted to the climate. This paper is focused on the application of a renewable solar energy system coupled with a Heating, Ventilation and Air-Conditioned (HVAC) system to promote occupants’ thermal comfort (TC) and indoor air quality (IAQ) in buildings during heating season. In the building thermal design, a building thermal dynamic model is used to calculate the temperatures of the opaque and transparent building surfaces, the temperature of the water supply ducts, the TC level and the IAQ level, among other variables. The TC conditions of the occupants were evaluated using the Predicted Mean Vote index, commonly used in the literature in similar studies. IAQ was assessed by the usual carbon dioxide concentration in environments where most of the pollution is of human origin. The numerical study was carried out in a virtual residential building consisting of two floors and seven compartments. The building is occupied at night and at midday. Two cases were studied, considering, respectively, the non-use and use of the solar HVAC system. The solar HVAC system consists of solar water collectors, installed above the roof area, and thermo-convector heat exchangers, installed inside each occupied space. The results show that the application of this solar HVAC system in a Mediterranean-type climate is able to guarantee, during occupancy, acceptable TC levels in three compartments and near acceptable TC levels in one compartment. Regarding IAQ, acceptable level can be achieved throughout the day. Full article

The integration of Foamed Concrete (FC) into 3D Concrete Printing (3DCP) processes facilitates the design of energy-efficient building envelopes. However, strategies for optimizing material porosity and printing topology to balance winter and summer performance remain underexplored. This study presents a 2D numerical thermal analysis of an innovative 3D-printed building envelope block characterized by sinusoidal internal partitions. Through a parametric variation in porosity (ranging from 10% to 50%) and internal geometry (amplitude and period of the partitions), 45 distinct configurations were simulated. Performance was evaluated by calculating the steady-state thermal transmittance (U) and the periodic thermal transmittance (Y_ie) under dynamic climatic conditions. The results demonstrate that porosity is the governing parameter; increasing porosity from 10% to 50% reduces U by 31% and, contrary to traditional assumptions for massive structures, also improves Y_ie by 12.3%. These outcomes are physically driven by the drastic reduction in thermal conductivity, which overcompensates for the loss of thermal mass, leading to a net reduction in overall thermal diffusivity. While internal topology plays a secondary role, its optimization allows for fine-tuning dynamic damping without compromising insulation. The study confirms that 3D printing with foamed concrete enables the overcoming of the traditional trade-off between insulation and thermal inertia. High-porosity configurations (50%) with optimized internal topology emerge as the most effective solution, simultaneously guaranteeing beneficial steady-state and dynamic thermal performance for sustainable buildings. Full article

In the design of shaking table tests concerning saturated sand–pile interactions, quantitatively achieving similarity in liquefaction responses between the model and the prototype has long been a challenging task. In addition, the dynamic shear modulus of the prepared model soil often fails to satisfy the ideal similarity conditions, which further exacerbates the difficulty of realizing liquefaction response similarity. To address the above issues, the authors have proposed a liquefaction similarity law for saturated sand–pile shaking table tests under horizontal excitation, considering the dynamic shear modulus error of the model soil. To further verify the accuracy of the proposed liquefaction similarity law, investigate its simulation capability, and evaluate its applicability under different conditions, this paper establishes and validates numerical models of saturated sand–pile dynamic interaction systems based on shaking table test results and conducts a series of parametric analyses via numerical simulation. The results indicate that when the proposed similarity law is applied, the acceleration similarity ratio should be set to 1, which can satisfy both gravity similarity and elastic force similarity simultaneously. A comparison with the artificial mass similarity law demonstrates the distinct advantages of the proposed similarity law. Finally, the applicability of the proposed similarity law under different parametric conditions is verified, and the influence of various parameters on the accuracy of the back-calculated results using the similarity law is investigated. Full article