Search Results (94,550)

The characteristics of soil ditching and backfilling are crucial for orchard ditching operations. However, experimentally investigating the dynamic ditching and backfilling process is currently not feasible. To address this issue, the 3DGZ-50A self-propelled orchard ditching machine (SPODM) was designed using a modular concept, incorporating three types of ditching cutter discs (01#, 02#, and 03#). These discs were designed, trial-manufactured, and tested in orchard ditching experiments. A corresponding simulation model was also constructed using EDEM 2022 software. This study evaluated the ditching and backfilling process, analyzing the performance of the three cutter discs through experimental and simulation methods. Results indicated that the 01# and 02# cutter discs created V-shaped furrows, whereas the 03# cutter disc formed an arc-shaped furrow. The relative errors in the final furrow depth (D_f) and width (W_f) between experimental and simulated results were 30.70% and 8.61%, respectively, while those in the maximum furrow depth (D_m) and width (W_m) were 9.44% and 3.00%. These minor relative errors confirmed the accuracy of the simulation model. Regarding maximum power consumption, the 01# cutter disc used 86.3% of the power consumed by the 02# cutter disc and 85.1% of that used by the 03# cutter disc. During the ditching process, the blades penetrated the soil to create the maximum furrow cross-section, which then gradually decreased due to backfilling. Both simulation and test results demonstrated that the 01# cutter disc performed best, achieving a maximum furrow cross-sectional area (46.70%), minimum final surface furrow cross-sectional area (6.04%), and lower power consumption (31.03 kW). This study provides equipment for ditching operations in low-height close-planting orchards in northern China. Full article

To evaluate the potential of an upcoming large-swath satellite for estimating surface methane (CH₄) fluxes at a weekly scale, we report the results from a series of observing system simulation experiments (OSSEs) that use an established modeling framework that includes the GEOS-Chem 3D atmospheric transport model and an ensemble Kalman filter. These experiments focus on the sensitivity of CH₄ flux estimates to systematic errors (μ) and random errors (σ) in the column average methane (XCH₄) measurements. Our control test (INV_CTL) demonstrates that with median errors (μ = 1.0 ± 0.9 ppb and σ = 6.9 ± 1.6 ppb) in XCH₄ measurements over a 1000 km swath, global CH₄ fluxes can be estimated with an accuracy of 5.1 ± 1.7%, with regional accuracies ranging from 3.8% to 21.6% across TransCom sub-continental regions. The northern hemisphere mid-latitudes show greater reliability and consistency across varying μ and σ levels, while tropical and boreal regions exhibit higher sensitivity due to limited high-quality observations. In σ-sensitive regions, such as the North American boreal zone, expanding the swath width from 1000 km to 3000 km significantly reduces discrepancies, while such adjustments provide limited improvements for μ-sensitive regions like North Africa. For TanSat-2 mission, with its elliptical medium Earth orbit and 1500 km swath width, the global total estimates achieved an accuracy of 3.1 ± 2.2%. Enhancing the swath width or implementing a dual-satellite configuration is proposed to further improve TanSat-2 inversion performance. Full article

A novel magnetorheological semi-active hydro-pneumatic suspension system was proposed to overcome the shortcoming of the traditional hydro-pneumatic suspension without adaptive vibration damping function. It is based on the magnetorheological semi-active vibration reduction technology to effectively improve the ride performance of the vehicle. Firstly, a nonlinear model was established with the Bouc–Wen model based on the mechanical property test results of magnetorheological hydro-pneumatic spring. Secondly, the dynamic model of the single-wheel magnetorheological hydro-pneumatic suspension system was established. Subsequently, the ON-OFF and PID-Fuzzy semi-active control strategies of the single-wheel magnetorheological hydro-pneumatic suspension were proposed based on the ON-OFF and PID-Fuzzy control methods. The simulation results demonstrate that the magnetorheological hydro-pneumatic suspension under PID-Fuzzy control has the best vibration reduction effect in comparison with the passive hydro-pneumatic suspension. The sprung mass acceleration, suspension working space, and dynamic tire deformation are reduced by 24.50%, 21.62%, and 21.01%, respectively. The bench test results verify that magnetorheological hydro-pneumatic suspension and its control methods can effectively improve the ride performance of the system. Full article

In cold regions, fire pipes are highly susceptible to freezing, which can obstruct water flow and lead to pipe ruptures. Accurately predicting the freeze thickness is crucial to maintaining the functionality of fire protection systems. Traditional methods for predicting fire pipe freezing often rely on simplified models or time-consuming simulations, which limits their accuracy in complex environments. A model for predicting the freeze thickness of fire pipes under low-temperature conditions was developed by integrating Computational Fluid Dynamics with an Artificial Neural Network (ANN). The CFD model was validated to generate data for training and optimizing an ANN based on collected experimental data. The CFD results demonstrate a nonlinear relationship between the freeze thickness of the fire pipe, ambient temperature, and time. Afterwards, the optimal ANN topology, determined through hyperparameter tuning, was found to consist of 12 neurons, the trainlm training function, and tansig–purelin activation functions. Eventually, the ANN model achieved high prediction accuracy with a mean squared error (MSE) of 6.62 × 10⁻⁴ on the test set and a regression coefficient R greater than 0.98 across all datasets. Furthermore, the ANN model agrees closely with the simulated data, not only for trained temperature conditions (−5 °C to −50 °C) but also for unseen temperature conditions (−55 °C and −60 °C), indicating excellent generalization performance. Full article

The increasing demand for sustainable water treatment technologies has driven the development of advanced ceramic membranes with tailored properties. This study explores the fabrication of ceramic membranes using phosphate tailings and clay lithologies as alternative raw materials, offering a sustainable and cost-effective approach to membrane production. The focus is on tailoring membrane porosity through the deposition of multilayered alkali-activated coatings, leveraging geopolymerization chemistry to enhance structural and functional performance. The manufactured ceramic membranes were investigated using X-ray fluorescence spectrometry, X-ray diffraction, thermogravimetric analysis, Fourier transform infrared spectroscopy, scanning electron microscopy, and a filtration test pilot. Results revealed the suitability of both phosphate tailing and the clay for membrane processing, while alkali activation effectively modulates the membrane’s porosity (from 1–10 μm to 0.1–1 μm) and mechanical strength (up to 20 MPa). Both tailored and untailored membranes demonstrated favorable performance. Key findings include the formation of a well-interconnected pore network and improved compressive strength, which resulted in sustained filtration performance under challenging operational conditions. The membranes demonstrated their suitability for environmental and industrial applications by achieving high efficiency in industrial effluent filtration tests. Full article

The increasing use of large glass surfaces in modern architecture requires robust adhesive solutions that balance aesthetic appeal with structural resilience, particularly in timber–glass applications. This study examines the influence of primer treatments on the shear performance of timber–glass adhesive joints, employing a combination of experimental testing and simulation techniques. Double-lap shear tests with epoxy adhesives assess the impact of various surface treatments on joint stiffness, shear stress distribution, and deformation. Additionally, a finite element model is developed to simulate joint behavior, evaluate failure modes, and analyze displacement patterns. Results indicate that primer applications notably enhance structural integrity by reducing displacement and increasing joint stability, thereby supporting more durable timber–glass assemblies. These findings offer valuable insights for advancing adhesive technologies in architectural components, enabling a closer alignment between structural performance and design innovation in timber–glass systems. Full article

The hippopotamus optimization algorithm (HO) is a novel metaheuristic algorithm that solves optimization problems by simulating the behavior of hippopotamuses. However, the traditional HO algorithm may encounter performance degradation and fall into local optima when dealing with complex global optimization and engineering design problems. In order to solve these problems, this paper proposes a modified hippopotamus optimization algorithm (MHO) to enhance the convergence speed and solution accuracy of the HO algorithm by introducing a sine chaotic map to initialize the population, changing the convergence factor in the growth mechanism, and incorporating the small-hole imaging reverse learning strategy. The MHO algorithm is tested on 23 benchmark functions and successfully solves three engineering design problems. According to the experimental data, the MHO algorithm obtains optimal performance on 13 of these functions and three design problems, exits the local optimum faster, and has better ordering and stability than the other nine metaheuristics. This study proposes the MHO algorithm, which offers fresh insights into practical engineering problems and parameter optimization. Full article

The objective of this study is to utilize a simulation employing advanced oxidation processes (AOPs) from photodegradation to examine the treatment of textile effluents. The selection of textile effluents as the material to be degraded is justified by the significant volume of water containing dyes, such as methylene blue (MB), generated daily by the textile industry. Often, this water is discarded without undergoing effective treatment. The purification of textile effluents would enable the reuse of water within the textile production cycle, thereby promoting sustainability. This study focuses on AOPs, which are extensively utilized in photocatalytic processes. The catalytic precursor material consists of two types of clay: a commercial clay and a natural clay. The natural clay is pillared with Al and impregnated with Ce, while the commercial clay is also pillared with Al and impregnated with Ce. Both clays are also pillared with a mixed pillar of Al and Ce. This results in three comparable materials. These clays are characterized by the presence of montmorillonite as their predominant mineral component. The selected clays were commercial bentonite and natural clay (FCN). Photocatalytic performance validation tests were conducted using UV-Vis spectroscopy. Material characterization methods included crystallographic analysis (by X-ray diffraction (XRD)), chemical composition (by X-ray fluorescence (XRF)), morphological studies (by scanning electron microscopy (SEM)) and textural property analysis (by N₂ adsorption). The outcomes of these investigations offer signification insights into the potential applications of these materials in the treatment of textile effluents and the development of more sustainable processes within the textile industry. Furthermore, the results contribute to the advancement of photocatalytic material design. Full article

Maximal oxygen uptake (V̇O₂max) assesses athletic performance; however, its values are inconsistent in post-COVID-19 athletes. This study aimed to analyze the dynamics of V̇O₂max in post-COVID-19 athletes. Observational studies were identified by screening the PubMed database published up to 17 July 2023. The initial electronic search found 320 studies. Of these, 26 employing the cardiopulmonary exercise test (CPET) to assess aerobic fitness were selected. Of the 2625 pooled athletes, 1464 were infected and considered as the post-COVID-19 group, either asymptomatic or symptomatic, while the remaining 1161, who were uninfected or had V̇O₂max results prior to infection, were defined as the infection-free group. Age and V̇O₂max were differently distributed between post-COVID-19 athletes and those without infection (p = 0.03 in both). Persistent symptoms athletes had 8 mL/Kg/min lower V̇O₂max than those without infection (p = 0.04). In addition, post-infected athletes who underwent CPET after 12 weeks showed a significant reduction of 2.9 mL/Kg/min in V̇O₂max according to the increase in body mass index (BMI). The pooled analysis showed that aerobic fitness was reduced in athletes post-COVID-19. V̇O₂max was negatively correlated with BMI in those who underwent CPET at 12 weeks, suggesting that symptoms persist beyond 12 weeks, affecting return-to-play. Full article

Medicine is struggling with the constantly rising incidence of breast cancer. The key to this fight is to be able to speed up diagnosis, as rapid diagnosis reduces the number of aggressive or advanced cases. For this process to be effective, it is necessary to have the right attitude toward diagnosis as a research practice. Our critical analysis of diagnosis, as a methodology of medical science, reflects on it as a research practice that is regulated in a socio-subjective way by a methodological culture. This position allows us to contrast critical methodological culture with the habitual–practical, or methodical, culture of practicing diagnosis. We point to the interpretative status of medical analyses performed by medical historians by referring to Italian Renaissance paintings and historical–artistic interpretations. In this field, analyzing disputes between researchers as a clash of methodologies in the ways interpretation transforms signs into meaning is a critical methodological reflection. Medicine is a diverse scientific discourse with a paradigmatic structure in which new ways of conducting diagnostic tests may appear. It is only possible to see this from the methodological level. In addition, passive respect for existing patterns of conduct hinders an exchange of views between researchers, which limits the possibility of correcting research procedures. The ultimate consequence of such passivity is an inability to improve diagnosis, which, in turn, harms the interests of patients. In this regard, it is worth remembering that the paramount objective of diagnosis is not the disease, but the patient. Full article

The performance of Large Language Models, such as ChatGPT, generally increases with every new model release. In this study, we investigated to what degree different GPT models were able to solve the exams of three different undergraduate courses on warehousing. We contribute to the discussion of ChatGPT’s existing logistics knowledge, particularly in the field of warehousing. Both the free version (GPT-4o mini) and the premium version (GPT-4o) completed three different warehousing exams using three different prompting techniques (with and without role assignments as logistics experts or students). The o1-preview model was also used (without a role assignment) for six runs. The tests were repeated three times. A total of 60 tests were conducted and compared with the in-class results of logistics students. The results show that the GPT models passed a total of 46 tests. The best run solved 93% of the exam correctly. Compared with the students from the respective semester, ChatGPT outperformed the students in one exam. In the other two exams, the students performed better on average than ChatGPT. Full article

This study aims to investigate the flexural performance of ultra-high-performance concrete (UHPC) wet joints subjected to vibration load during the early curing period. The parameters investigated included vibration amplitude (1 mm, 3 mm, and 5 mm) and vibration stage (pouring—final setting, pouring—initial setting, and initial setting—final setting). A novel simulated vibration test set-up was developed to reproduce the actual vibration conditions of the joints. The actuator’s reaction force time-history curves for the UHPC joint indicate that the reaction force is stable during the initial setting stage, and it increases linearly with time from the initial setting to the final setting, trending toward stability after 16 h of casting. Under the vibration of 3 Hz-5 mm, cracks measuring 14 cm × 0.2 mm emerge in the UHPC joint. It occurs during the stage from the initial setting to the final setting. The flexural performance of wet joint specimens after vibration was evaluated by the four-point flexural test, focusing on failure modes, load-deflection curves, and the interface opening. The results show that all specimens with joints exhibited bending failure, with cracks predominantly concentrated at the interfaces and the sides of the NC precast segment. The interfacial bond strength was reduced by vibrations of higher amplitude and frequency. Compared with the specimens without vibration, the flexural strength of specimens subjected to the vibration at 3 Hz-3 mm and 3 Hz-5 mm were decreased by 8% and 19%, respectively. However, as the amplitude and frequency decreased, the flexural strength of the specimens showed an increasing trend, as this type of vibration enhanced the compactness of the concrete. Additionally, the calculation model for the flexural strength of UHPC joints has been established, taking into account the impact of live-load vibration. The average ratio of theoretical calculation values to experimental values is 1.01, and the standard deviation is 0.04, the theoretical calculation value is relatively precise. Full article

This study provides a comparative evaluation of face recognition databases and Convolutional Neural Network (CNN) architectures used in training and testing face recognition systems. The databases span from early datasets like Olivetti Research Laboratory (ORL) and Facial Recognition Technology (FERET) to more recent collections such as MegaFace and Ms-Celeb-1M, offering a range of sizes, subject diversity, and image quality. Older databases, such as ORL and FERET, are smaller and cleaner, while newer datasets enable large-scale training with millions of images but pose challenges like inconsistent data quality and high computational costs. The study also examines CNN architectures, including FaceNet and Visual Geometry Group 16 (VGG16), which show strong performance on large datasets like Labeled Faces in the Wild (LFW) and VGGFace, achieving accuracy rates above 98%. In contrast, earlier models like Support Vector Machine (SVM) and Gabor Wavelets perform well on smaller datasets but lack scalability for larger, more complex datasets. The analysis highlights the growing importance of multi-task learning and ensemble methods, as seen in Multi-Task Cascaded Convolutional Networks (MTCNNs). Overall, the findings emphasize the need for advanced algorithms capable of handling large-scale, real-world challenges while optimizing accuracy and computational efficiency in face recognition systems. Full article

The traditional approach to curating and disseminating information about agricultural injuries relies heavily on manual input and review, resulting in a labor-intensive process. While the unstructured nature of the material traditionally requires human reviewers, the recent proliferation of Large Language Models (LLMs) has introduced the potential for automation. This study investigates the feasibility and implications of filling the role of a human reviewer with an LLM in analyzing information about agricultural injuries from news articles and investigation reports. Multiple language models were tested for accuracy in extracting relevant incident and victim information, and these models include OpenAI’s ChatGPT 3.5 and 4 and an open-source fine-tuned version of Llama 2. To measure accuracy, each LLM was given prompts to gather relevant data from a set of randomly selected online news articles already cataloged by human reviewers, such as the use of drugs or alcohol, time of day, or other information about the victim(s). Results showed that the fine-tuned Llama2 was the most proficient model with an average accuracy of 93% and some categories reaching 100%. ChatGPT-4 also performed well with around 90% accuracy. Additionally, we found that the fine-tuned Llama2 model was somewhat proficient in coding injuries using the OIICS classification scheme, achieving 48% accuracy when predicting the first digit. Though none of the models are perfectly accurate, the methodology and results prove that LLMs are promising in streamlining workflows in order to reduce human and financial resources and increase the efficiency of data analysis. Full article

Computer vision technology is widely used in smart agriculture, primarily because of its non-invasive nature, which avoids causing damage to delicate crops. Nevertheless, the deployment of computer vision algorithms on agricultural machinery with limited computing resources represents a significant challenge. Algorithm optimization with the aim of achieving an equilibrium between accuracy and computational power represents a pivotal research topic and is the core focus of our work. In this paper, we put forward a lightweight hybrid network, named VM-YOLO, for the purpose of detecting strawberry flowers. Firstly, a multi-branch architecture-based fast convolutional sampling module, designated as Light C2f, is proposed to replace the C2f module in the backbone of YOLOv8, in order to enhance the network’s capacity to perceive multi-scale features. Secondly, a state space model-based lightweight neck with a global sensitivity field, designated as VMambaNeck, is proposed to replace the original neck of YOLOv8. After the training and testing of the improved algorithm on a self-constructed strawberry flower dataset, a series of experiments is conducted to evaluate the performance of the model, including ablation experiments, multi-dataset comparative experiments, and comparative experiments against state-of-the-art algorithms. The results show that the VM-YOLO network exhibits superior performance in object detection tasks across diverse datasets compared to the baseline. Furthermore, the results also demonstrate that VM-YOLO has better performances in the mAP, inference speed, and the number of parameters compared to the YOLOv6, Faster R-CNN, FCOS, and RetinaNet. Full article