Search Results (21,533)

Objectives: The aim of this study was (i) to design and develop a portable BCA device for measuring body composition parameters such as body weight, body fat (BF) %, total body water (TBW), fat-free mass (FFM), muscle mass (MM), and bone mass (BM); (ii) to validate the developed portable BCA with the Tanita MC 980 MA BCA device. Methods: For this current study, two hundred healthy and obese subjects, whose ages ranged from 8 to 12 years (8.4 ± 1.7), were considered. Results: The highest percentage difference between the two study groups was found to be in BFat (50.39%), followed by body mass index (BMI) (41.73 kg), FFM (38.32 kg), and MM (37.89 kg), and this was found to be statistically significant. The results obtained from the designed prototype of the body composition analyzer were validated using Tanita MC 980MA BCA. The overall error% was calculated as ±3% for measuring the different body composition parameters. Conclusions: Due to its low standard error and high overall accuracy, the BCA prototype demonstrates the potential to be a dependable instrument for evaluating and tracking the body composition of children. Full article

In the context of urban space constraints, subway and underground projects have become crucial strategies to alleviate urban congestion and enhance residents’ quality of life. However, pit engineering, a frequent accident area in geotechnical engineering, urgently requires innovative safety monitoring technologies. Traditional monitoring methods face challenges such as high labor costs, lengthy monitoring cycles, high-risk working environments, and over-reliance on human judgment. To address these issues, this paper introduces an innovative monitoring system integrating Fiber Bragg Grating (FBG) sensing technology based on a subway pit project in Guangzhou. This system not only achieves fully automated data acquisition but also includes an intelligent monitoring cloud platform, providing unprecedented automated and intelligent monitoring solutions for support structures and the surrounding environment during mechanical shaft construction. The key findings of this paper include the following: (1) The breakthrough application of distributed optical fiber monitoring technology, including successfully deploying this advanced technology in complex pit engineering environments, enabling the precise and continuous monitoring of support structures and surrounding changes, and demonstrating its high effectiveness and intelligence in practical engineering. (2) The innovative design of an intelligent safety monitoring system. By integrating sensors and wireless communication technology, an efficient data networking architecture is constructed, supporting remote configuration and flexible adjustment of monitoring equipment, significantly enhancing data collection‘s real-time performance and continuity while greatly reducing safety risks for field staff, achieving an intelligent upgrade of monitoring work. (3) Comprehensive and accurate empirical analysis. During shaft excavation, the monitoring data collected by the system were stable and reliable, with all indicators maintained within reasonable ranges and closely matching expected changes caused by construction activities, validating the system’s practical application effectiveness in complex construction environments and providing a scientific basis for pit engineering safety management. Full article

This research investigated the properties of photocatalytic cementitious composites, including their air-purification efficiency. A method of characterizing the removal of airborne pollutants (nitrogen oxides), simulating the actual NO_x concentration and irradiation conditions in Warsaw, Poland, in the autumn/winter season was established. The study analyzed the impact of changes in the composition of cement mortars—partial substitution of the binder with mineral fillers—on the properties of the external photoactive surface of the composite. The designed experimental plan included both quantitative and qualitative variables (type and amount of fillers used). It was found that the photocatalytic performance of the composite was correlated with its pore total content and pore size distribution—the higher the content of mineral fillers, the lower the porosity and the less effective its photocatalytic properties. The selectivity of the photocatalytic NO_x reactions also deteriorated as the content of the mineral fillers increased. The study confirmed the validity of increasing the binder content in cementitious composites to enhance their photocatalytic performance. Full article

The removal of plastic contamination from cotton lint is a critical issue for the U.S. cotton industry. One primary source of this contamination is the plastic wrap used on cotton modules by John Deere round module harvesters. Despite rigorous efforts by cotton ginning personnel to eliminate plastic during module unwrapping, fragments still enter the gin’s processing system. To address this, we developed a machine-vision detection and removal system using low-cost color cameras to identify and expel plastic from the gin-stand feeder apron, preventing contamination. However, the system, comprising 30–50 ARM computers running Linux, poses significant challenges in terms of calibration and tuning, requiring extensive technical knowledge. This research aims to transform the system into a plug-and-play appliance by incorporating an auto-calibration algorithm that dynamically tracks cotton colors and excludes plastic images to maintain calibration integrity. We present the image dataset that was used to validate the design, consisting of several key AI Vision-Transformer image classifiers that form the heart of the auto-calibration algorithm, which is expected to reduce setup and operational overhead significantly. The auto-calibration feature will minimize the need for skilled personnel, facilitating the broader adoption of the plastic removal system in the cotton ginning industry. Full article

In recent years, anchor-free object detectors have become predominant in deep learning, the YOLOv8 model as a real-time object detector based on anchor-free frames is universal and influential, it efficiently detects objects across multiple scales. However, the generalization performance of the model is lacking, and the feature fusion within the neck module overly relies on its structural design and dataset size, and it is particularly difficult to localize and detect small objects. To address these issues, we propose the FL-YOLOv8 object detector, which is improved based on YOLOv8s. Firstly, we introduce the FSDI module in the neck, enhancing semantic information across all layers and incorporating rich detailed features through straightforward layer-hopping connections. This module integrates both high-level and low-level information to enhance the accuracy and efficiency of image detection. Meanwhile, the structure of the model was optimized and designed, and the LSCD module is constructed in the detection head; adopting a lightweight shared convolutional detection head reduces the number of parameters and computation of the model by 19% and 10%, respectively. Our model achieves a comprehensive performance of 45.5% on the COCO generalized dataset, surpassing the benchmark by 0.8 percentage points. To further validate the effectiveness of the method, experiments were also performed on specific domain urine sediment data (FCUS22), and the results on category detection also better justify the FL-YOLOv8 object detection algorithm. Full article

This paper presents a trajectory control model using finite state machines for a single-stage soft-switching grid-tied inverter designed with a fast dynamic response. The targeted application is a module-integrated inverter for a single photovoltaic (PV) panel which interfaces distributed energy sources with the grid. To minimize switching lossd provide advanced grid-connected functionality, the soft-switching operation is achieved through a resonant filter using a trajectory control scheme. In recent years, controllers based on digital signal processing platforms have been able to handle complex and high-speed control algorithms with precision for real-time control. In real-time control applications, the finite state machine (FSM) approach enhances responsiveness by minimizing latency with limited memory resources by executing rapid state transitions. The proposed model effectively manages the switching states of the single-stage soft-switching inverters during complex DC/AC bidirectional operations. By directly controlling the energy within the series resonant circuit, the model delivers a fast transient response while minimizing switching actions across all quadrants of operation. The control scheme has been digitally implemented on a Texas Instruments (TI) digital signal processor and validated through Hardware-In-the-Loop (HIL) testing. Full article

The present study aims to carry out a comparative Multi-Objective Optimization (MOO) of a 10 MW FOWT semi-submersible using three different metaheuristic optimization techniques and a sophisticated approach for optimizing a floating platform. This novel framework enables highly efficient 3D plots, an optimization loop, and the automatic and comparative output of solutions. Python, the main interface, integrated PyMAPDL and Pymoo for intricate modeling and simulation tasks. For this case study, the ZJUS10 Floating Offshore Wind Turbine (FOWT) platform, developed by the state key laboratory of mechatronics and fluid power at Zhejiang University, was employed as the basis. Key criteria such as platform stability, overall structural mass, and stress were pivotal in formulating the objective functions. Based on a preliminary study, the three metaheuristic optimization algorithms chosen for optimization were Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Ant Colony Optimization (ACO). Then, the solutions were evaluated based on Pareto dominance, leading to a Pareto front, a curve that represents the best possible trade-offs among the objectives. Each algorithm’s convergence was meticulously evaluated, leading to the selection of the optimal design solution. The results evaluated in simulations elucidate the strengths and limitations of each optimization method, providing valuable insights into their efficacy for complex engineering design challenges. In the post-processing phase, the performances of the optimized FOWT platforms were thoroughly compared both among themselves and with the original model, resulting in validation. Finally, the ACO algorithm delivered a highly effective solution within the framework, achieving reductions of 19.8% in weight, 40.1% in pitch, and 12.7% in stress relative to the original model. Full article

Traditional engineering education cannot effectively respond to the demand for talents in the construction of smart cities. The application of digital twin technology in education is mostly based on case studies and lacks empirical tests. This study takes the practical teaching of a project-based course on smart city parks as an example to explore the action intention of graduate students to use digital twin technology consistently, and to provide a theoretical basis and teaching practice guidance to promote the rational application of digital twin technology in engineering education. This study set up a quasi-experimental design through the digital twin learning system, grouping 24 graduate students with 4 faculty members. The experimental group is digital twin-assisted practical teaching, and the control group is traditional teaching method, the experimental cycle is 12 weeks, and the total lesson time is 24 h. Secondly, combined with UTAUT2 model and TTF theory, the variable factor hypothesis was adopted as the scale design means, and the experimental validity was improved through questionnaire data analysis. Meanwhile, the influencing factors in the use of digital twin platform were recorded in detail through the process of data collection, data processing and modeling, as well as the application practice of digital twin platform. Finally, the results of the comprehensive survey data show that the graduate students in the experimental group are significantly better than the control group in terms of self-confidence, skill enhancement, learning outcomes, and learning experience. All these results provide information for course teaching practice, training professional teaching teams, optimizing innovative teaching paths, and promoting the cultivation and delivery of smart city technology talents. Full article

With the increasing integration of distributed energy resources into modern power systems, virtual synchronous generators (VSGs) have been a promising approach to imitate the inertial response of synchronous generators, thereby enhancing microgrid stability in a dynamic state. When many VSGs are integrated into microgrids, the dynamic characteristics of the system become increasingly complex. Current studies typically assume that different VSGs are connected to a common coupling point, focusing on analyzing the interaction characteristics, which may overlook the widely distributed line impedances in microgrids with distance between different facilities. This may lead to incomplete understanding of the interaction dynamics when VSGs are distributed over long feeder lines. Therefore, this paper proposes and investigates a multi-node, multi-VSG model incorporating line impedances among different nodes, establishing transfer function models for multi-node load disturbances and the frequency responses of individual VSGs. The study explores the dynamic response characteristics of VSGs under varying parameter influences and proposes principles for designing VSG port impedance and inertia parameters to optimize system dynamic frequency characteristics. The findings, validated through simulations in PSCAD v46, provide insights for enhancing the flexibility and reliability of grids incorporating VSGs. Full article

In this article, an innovative online adaptive high-efficiency control strategy is proposed to improve the power generation efficiency of a marine micro gas turbine under partial load. Firstly, a mathematical model of the micro-gas turbine is established, and a control strategy consisting of an on-board prediction model and an online update model is proposed. To evaluate the performance changes of the gas turbine, we applied deep learning techniques to enhance the extreme learning machine (ELM) algorithm, resulting in the development of a high-precision, high-real-time deep extreme learning machine (DL_ELM) prediction model. This model effectively monitors changes in the gas turbine’s performance. Furthermore, an online time-series deep extreme learning machine with a dynamic forgetting factor (DFF_DL_OSELM) model is designed to achieve the real-time tracking of performance variations. When the DL_ELM model detects a gas turbine’s performance change, a particle swarm optimization (PSO) algorithm is employed to iteratively calculate the DFF_DL_OSELM model, determining the optimal speed control scheme to ensure the gas turbine operates at maximum efficiency. To validate the superiority of the proposed control strategy, a comparison is made with traditional high-efficiency control strategies based on polynomial fitting and BP neural networks. The results demonstrate that although all three strategies can achieve efficient operation under constant conditions, traditional strategies fail to identify and adjust to performance changes in real time, leading to decreased control performance and potential engine damage as engine characteristics degrade. In contrast, the proposed online adaptive control strategy dynamically adjusts the speed control plan based on performance degradation, ensuring that the gas turbine operates efficiently while keeping the turbine inlet and exhaust temperatures within safe limits. Full article

Chinese cities are increasingly developing exclusive bicycle paths to improve the safety and efficiency of bicycle transit. The width of bikeways is a critical factor influencing cyclists’ safety and comfort, with insufficient width identified as a major contributor to bicycle accidents. Therefore, determining the minimum operational width for human-powered bicycles is essential for bikeway design. While some countries’ design manuals consider speed as a factor in determining width, there is a lack of field experiments to validate these specifications from the perspective of cyclists’ safety and comfort. This study addresses this gap by conducting a field experiment to measure cycling workload, which reflects safety and comfort under different widths and cycling speeds. The experiment involved 12 cyclists on a test road, where cycling workload was measured at various preset widths and cycling speeds for a single human-powered cyclist. The results were further validated using conventional lateral distance measurement techniques, which are used in the existing literature to determine the cycling width. The results show that wider bikeway widths lead to a lower cycling workload, enhancing comfort and safety. However, both very high (over 20 km/h) and very low (under 5 km/h) speeds significantly increase cyclists’ workload, which in turn requires a wider path to maintain a safe and comfortable cycling experience. The study found that a minimum width of 0.90 m may be adequate for cyclists traveling at speeds between 10 and 15 km/h, while a width of 1.0 m is sufficient for speeds ranging from 5 km/h to 25 km/h, provided the bicycle width does not exceed 0.62 m. Given that cyclists typically progress from slower to faster speeds, a minimum operational width of 1.0 m is recommended for most cases. This study highlights the importance of considering cyclists’ workload in determining appropriate bikeway widths. It provides valuable insights for designing safer, more comfortable bike paths and reducing bicycle accidents, contributing to the sustainable development of urban cycling infrastructure. Full article

Smart agriculture development mainly depends on the intelligence and reliability of autonomous agricultural machinery. Automatic navigation systems (ANSs) play a key role in intelligent agricultural machinery design, as they not only reduce farmers’ workloads but also improve their land utilization rates. In this paper, a tractor ANS based on dynamic path search and a fuzzy Stanley model (FSM) was designed, and its capability for whole-field path tracking was tested. First, the tracking performance of the steering control module was validated after the automatic reconstruction of the tractor platform. Then, a navigation decision system was established based on a unified reference waypoint search framework, where the path generation for whole-field coverage was presented. Finally, the gain coefficient of the Stanley model (SM) was adjusted adaptively according to the tracking error by utilizing the fuzzy logic controller. Subsequently, the developed tractor ANS was tested in the field. The experiment’s results indicate that the FSM outperformed the SM in straight path tracking and whole-field path tracking. When the tractor traveled at a speed of 1 m/s, the maximum lateral tracking error for the straight path was 10 cm, and the average lateral tracking error was 5.2 cm, showing improvements of 16.7% and 10.3% compared to the SM. Whole-field autonomous navigation showed that the maximum lateral tracking error was improved from 34 cm for the SM to 27 cm for the FSM, a reduction of approximately 20.6%, illustrating the superiority of the FSM in the application of whole-field path tracking. As the maximum tracking error of whole-field autonomous navigation appears in the turning stage, where tractors often stop working, the designed ANS satisfies the requirements of a self-driving system for unmanned tractors. Full article

Cardiovascular diseases (CVD) pose a significant global health challenge, requiring innovative therapeutic strategies. Vasodilators, which are central to vasodilation and blood pressure reduction, play a crucial role in cardiovascular treatment. This study integrates quantitative structure– (QSAR) modeling and molecular dynamics (MD) simulations to predict the biological activity and interactions of vasodilatory compounds with the aim to repurpose drugs already known and estimateing their potential use as vasodilators. By exploring molecular descriptors, such as electronegativity, softness, and highest occupied molecular orbital (HOMO) energy, this study identifies key structural features influencing vasodilatory effects, as it seems molecules with the same mechanism of actions present similar frontier orbitals pattern. The QSAR model was built using fifty-four Food Drugs Administration-approved (FDA-approved) compounds used in cardiovascular treatment and their activities in rat thoracic aortic rings; several molecular descriptors, such as electronic, thermodynamics, and topographic were used. The best QSAR model was validated through robust training and test dataset split, demonstrating high predictive accuracy in drug design. The validated model was applied on the FDA dataset and molecules in the application domain with high predicted activity were retrieved and filtered. Thirty molecules with the best-predicted pKI50 were further analyzed employing molecular orbital frontiers and classified as angiotensin-I or β1-adrenergic inhibitors; then, the best scoring values obtained from molecular docking were used to perform a molecular dynamics simulation, providing insight into the dynamic interactions between vasodilatory compounds and their targets, elucidating the strength and stability of these interactions over time. According to the binding energies results, this study identifies novel vasodilatory candidates where Dasabuvir and Sertindole seem to have potent and selective activity, offering promising avenues for the development of next-generation cardiovascular therapies. Finally, this research bridges computational modelling with experimental validation, providing valuable insight for the design of optimized vasodilatory agents to address critical unmet needs in cardiovascular medicine. Full article

Subgrade stabilization is crucial for forest road construction, especially in Northeast China and the Russian Far East, with great economic growth potential. This study explored a novel and green solution of integrating biochar (BC), polyacrylamide (PAM), and straw fiber (SF) in the form of a ternary composite for stabilizing forest subgrade soil in cold regions. Using central composite design-based response surface methodology, the optimal mix ratio design was obtained, and the composite stabilizer was designated as BPS. Afterward, the stabilizing performance of BPS was studied by conducting an unconfined compression strength (UCS) test. The results showed that the optimum composition of BC:PAM:SF stood at 81:9:10. The UCS and deformation modulus with 3% BPS at 28 days reached 565.42 kPa and 17.24 MPa, respectively, which were 3.36 and 6.05 times higher than those of the untreated samples. The BPS-treated soil also possessed better resistance to freeze–thaw cycles. The freezing–thawing-induced loss ratio of strength was 49.3% lower than that of natural soil. Moreover, empirical models for the UCS of BPS-stabilized soil, as well as its relationships with the modulus, were established and validated by data in the literature. Finally, the “filling, cementing, and reinforcing” stabilization mechanism of BPS was elucidated by scanning electron microscopy analysis. Full article

This paper introduces a novel piezoelectric actuator designed for precise linear and rotational movements of a cylindrical slider-rotor. The actuator’s design features two elliptical frames interconnected by two plates, with a cylindrical contact situated on the top of the upper plate to facilitate the motion or rotation of the slider. Two piezoelectric multilayer transducers are housed within each elliptical frame and are used to excite vibrations of the elliptical frames using two harmonic signals with a phase difference of π/2 and varying excitation schemes. This excitation pattern generates elliptical motion trajectories of the contact in two orthogonal planes, enabling both linear and rotational displacements of the slider-rotor. Numerical and experimental investigations were conducted to validate the performance and accuracy of the actuator. Additionally, harmonic response and transient analysis were performed to investigate elliptical motion trajectories of the contact in perpendicular planes under various excitation schemes and frequencies. The results confirm that the rotational and linear motions of the slider-rotor can be independently controlled. The actuator achieved a maximum rotational speed of 163.1 RPM and a maximum linear speed of 41.4 mm/s, with a corresponding peak output torque and force of 236.1 mN·mm and 368.1 mN, respectively. A resolution measurements showed that the actuator can achieve an angular resolution of 1.02 mrad and a linear resolution of 53.8 µm. Full article