bluuebunny/crossref_metadata_2025 huggingface dataset:
Saponins of Tomato Extract Improve Non-Alcoholic Fatty Liver Disease by Regulating Oxidative Stress and Lipid Homeostasis
Authors: Yang Ziming , Zhang Li , Liu Jinlei , Chan Albert S. C. , Li Dianpeng
Published: October 2023

The present study investigated the impact of saponins of tomato extract (STE) on non-alcoholic fatty liver disease (NAFLD). The findings demonstrated that introducing STE in NAFLD mice revealed promising results in ameliorating symptoms of oxidative stress, lipid metabolism disorders, visceral fat deposition and fatty liver disease. Moreover, the mechanistic studies have demonstrated that STE delivers its effects by activating adenosine 5′-monophosphate (AMP)-activated protein kinase (AMPK), thereby suppressing downstream protein expression associated with fatty acid synthesis. In such conditions, lipid metabolism can be improved. Simultaneously, STE enhanced nuclear factor erythroid 2-related factor 2 (Nrf2) and entry into the nucleus and initiated the transcription of downstream antioxidant factors, thereby relieving oxidative stress induced by a high-fat diet and lowering oxidative damage to the liver. Such results imply that the administration of STE can be regarded as a viable treatment option for NAFLD, providing a mechanism that can regulate the AMPK and Nrf2 signaling pathways.
A Comparative Analysis of Radical Scavenging, Antifungal and Enzyme Inhibition Activity of 3′-8″-Biflavones and Their Monomeric Subunits
Authors: Jurčević Šangut Iva , Šarkanj Bojan , Karalija Erna , Šamec Dunja
Published: October 2023

Biflavonoids are dimeric forms of flavonoids that have recently gained importance as an effective new scaffold for drug discovery. In particular, 3′-8″-biflavones exhibit antiviral and antimicrobial activity and are promising molecules for the treatment of neurodegenerative and metabolic diseases as well as cancer therapies. In the present study, we directly compared 3′-8″-biflavones (amentoflavone, bilobetin, ginkgetin, isoginkgetin, and sciadopitysin) and their monomeric subunits (apigenin, genkwanin, and acacetin) and evaluated their radical scavenging activity (with DPPH), antifungal activity against mycotoxigenic fungi (Alternaria alternata, Aspergillus flavus, Aspergillus ochraceus, Fusarium graminearum, and Fusarium verticillioides), and inhibitory activity on enzymes (acetylcholinesterase, tyrosinase, α-amylase, and α-glucosidase). All the tested compounds showed weak radical scavenging activity, while antifungal activity strongly depended on the tested concentration and fungal species. Biflavonoids, especially ginkgetin and isoginkgetin, proved to be potent acetylcholinesterase inhibitors, whereas monomeric flavonoids showed higher tyrosinase inhibitory activity than the tested 3′-8″-biflavones. Amentoflavone proved to be a potent α-amylase and α-glucosidase inhibitor, and in general, 3′-8″-biflavones showed a stronger inhibitory potential on these enzymes than their monomeric subunits. Thus, we can conclude that 3′-8″-dimerization enhanced acetylcholinesterase, α-amylase, and α-glucosidase activities, but the activity also depends on the number of hydroxyl and methoxy groups in the structure of the compound.
A New Intelligent Estimation Method Based on the Cascade-Forward Neural Network for the Electric and Magnetic Fields in the Vicinity of the High Voltage Overhead Transmission Lines
Authors: Alipour Bonab Shahin , Song Wenjuan , Yazdani-Asrami Mohammad
Published: October 2023

The evaluation and estimation of the electric and magnetic field (EMF) intensity in the vicinity of overhead transmission lines (OHTL) is of paramount importance for residents’ healthcare and industrial monitoring purposes. Using artificial intelligence (AI) techniques makes researchers able to estimate EMF with extremely high accuracy in a significantly short time. In this paper, two models based on the Artificial Neural Network (ANN) have been developed for estimating electric and magnetic fields, i.e., feed-forward neural network (FFNN) and cascade-forward neural network (CFNN). By performing the sensitivity analysis on controlling/hyper-parameters of these two ANN models, the best setup resulting in the highest possible accuracy considering their response time has been chosen. Overall, the CFNN achieved a significant 56% reduction in Root Mean Squared Error (RMSE) for the electric field and a 5% reduction for the magnetic field, compared to the FFNN. This indicates that the CFNN model provided more accurate predictions, particularly for the electric field than the proposed methods in other recent works, making it a promising choice for this application. When the model is trained, it will be tested by a different dataset. Then, the accuracy and response time of the model for new data points of that layout will be evaluated through this process. The model can predict the fields with an accuracy near 99.999% of the actual values in times under 10 ms. Also, the results of sensitivity analysis indicated that the CFNN models with triple and double hidden layers are the best options for the electric and magnetic field estimation, respectively.
Performance Analysis of an Improved Gravity Anchor Bolt Expanded Foundation
Authors: Zhang Zhenli , Liu Qingyang , Chu Hongmin , Lacidogna Giuseppe , Xu Jie , Cheng Haiyang , Liu Zhitao , Jiang Weitao
Published: October 2023

With the continuous utilization of renewable energy, the number of onshore wind turbines is increasing. Small design improvements can save costs and facilitate the maintenance and repair of the wind turbine foundation. In this paper, an existing gravity expansion foundation with an anchor cage is improved. Our improvements further expand the space inside the foundation and reduce the length of the anchor bolt, which could reduce the costs and facilitate construction. To study the performance of the new foundation, a three-dimensional finite element model of the foundation–soil–anchor bolt was established via a finite element simulation. The damage evolution of the foundation was simulated with the concrete damage plasticity model (CDP). The separation ratio, foundation settlement, inclination ratio, reinforcement stress, foundation stress, and foundation damage of the new foundation under ultimate load conditions were analyzed. The influence of parameters h1 and b3 on the performance of the foundation was further studied. The finite element analysis results show that the tensile stress of concrete can be effectively reduced by appropriately increasing the corbel height and ring beam width of the foundation. The results also show that the improved wind turbine foundation force is reasonable and can meet the use of the actual project requirements on the level of finite element analysis.
Path Optimization Using Metaheuristic Techniques for a Surveillance Robot
Authors: Peñacoba Mario , Sierra-García Jesús Enrique , Santos Matilde , Mariolis Ioannis
Published: October 2023

This paper presents an innovative approach to optimize the trajectories of a robotic surveillance system, employing three different optimization methods: genetic algorithm (GA), particle swarm optimization (PSO), and pattern search (PS). The research addresses the challenge of efficiently planning routes for a LiDAR-equipped mobile robot to effectively cover target areas taking into account the capabilities and limitations of sensors and robots. The findings demonstrate the effectiveness of these trajectory optimization approaches, significantly improving detection efficiency and coverage of critical areas. Furthermore, it is observed that, among the three techniques, pattern search quickly obtains feasible solutions in environments with good initial trajectories. On the contrary, in cases where the initial trajectory is suboptimal or the environment is complex, PSO works better. For example, in the high complexity map evaluated, PSO achieves 86.7% spatial coverage, compared to 85% and 84% for PS and GA, respectively. On low- and medium-complexity maps, PS is 15.7 and 18 s faster in trajectory optimization than the second fastest algorithm, which is PSO in both cases. Furthermore, the fitness function of this proposal has been compared with that of previous works, obtaining better results.
Real-Time Path Planning for Obstacle Avoidance in Intelligent Driving Sightseeing Cars Using Spatial Perception
Authors: Yang Xu , Wu Feiyang , Li Ruchuan , Yao Dong , Meng Lei , He Ankai
Published: October 2023

The increasing prevalence of intelligent driving sightseeing vehicles in the tourism industry underscores the critical importance of real-time planning for effective local obstacle avoidance paths when these vehicles encounter obstacles during operation. To fulfill this requirement, it is imperative to establish real-time dynamic perception as the foundational element. Thus, this paper introduces a novel local path planning algorithm founded on the principles of spatial perception. In the diverse array of road environments characterized by varying spatial features, sightseeing vehicles can effectively achieve safe and comfortable obstacle avoidance maneuvers. The proposed approach employs a high-precision positioning module and a real-time dynamic perception module to acquire real-time spatial information pertaining to the sightseeing vehicle and the road environment. It comprehensively integrates spatiotemporal safety constraints and obstacle avoidance curvature constraints to derive control points for the obstacle avoidance path. Specific control points undergo optimization and adjustment, ultimately resulting in the generation of the obstacle avoidance spatiotemporal path through discrete interpolation using B-spline curves. These locally tailored paths are subsequently compared with local obstacle avoidance paths generated using Bezier curves. The empirical validation of the proposed local obstacle avoidance path algorithm is conducted through a combination of simulation analysis and real vehicle verification. The research outcomes affirm that the algorithm can indeed produce smoother local obstacle avoidance paths, resulting in reduced front-wheel steering angles and yaw angle variations. This enhancement substantially contributes to the overall stability of sightseeing vehicles during obstacle avoidance maneuvers.
Oryctolagus Cuniculus Algorithm and Its Application in the Inversion Method of Asteroid Spectra Reflectance Template
Authors: Jin Dian , Liu Jin , Kang Zhiwei , Ma Xin , Zhang Zijun
Published: October 2023

To improve the global optimization ability and convergence speed of the swarm intelligence algorithm, we proposed a new swarm intelligence optimization algorithm, namely the Oryctolagus cuniculus algorithm. This includes five mechanisms: the determination of safety zones, the cave escape, the agglomeration of Oryctolagus cuniculi, the maintenance of the Oryctolagus cuniculus king, and the zone competition. Each solution is represented by each Oryctolagus cuniculus’s position (including zone number and specific location number). The grass density and safety index at the location of the Oryctolagus cuniculus represents its fitness value. The determination of safety zones implies that predators such as eagles hunt Oryctolagus cuniculi in dangerous zones, and the zone without predators is considered a safety zone. The cave escape refers to the act of Oryctolagus cuniculi using a connected cave system to flee from a dangerous zone and reach a secure zone, thereby evading potential predators. We select the Oryctolagus cuniculus with higher fitness values as the king of each zone, and the Oryctolagus cuniculi gather towards the Oryctolagus cuniculus king. This mechanism ensures that Oryctolagus cuniculus mainly searches in zones with abundant grass and quickly finds the optimal solution. In the maintenance of the Oryctolagus cuniculus king, we choose the one with higher fitness values as the Oryctolagus cuniculus king. Zone competition is induced by an increase in the number of Oryctolagus cuniculi in zones with abundant grass by ordering the fitness values of each zone, and vice versa. We apply the Oryctolagus cuniculus algorithm to the inversion method of the asteroid spectra reflectance template. The experimental results show that compared with artificial rabbit optimization, this algorithm has a faster rate of convergence and better solution, effectively screens the reflectance template, and improves the Doppler difference velocimetry accuracy. In addition, the application of the Oryctolagus cuniculus algorithm to the knapsack problem also performs effectively.
A Petri Net Approach for Business Process Modeling and Simulation
Authors: Medina-Garcia Samuel , Medina-Marin Joselito , Montaño-Arango Oscar , Gonzalez-Hernandez Manuel , Hernandez-Gress Eva Selene
Published: October 2023

A business process is a set of activities executed in a specific sequence involving various actors. This relationship between actors, activities, and sequence is crucial for achieving the goals and objectives of organizations. Business process modeling involves determining the actors, activities, and execution sequence necessary to accomplish the organization’s objectives, permitting analysis of the overall process in order to identify and rectify potential failures, improve operations, or eliminate activities that do not generate value. In this study, Petri nets have been selected as the modeling tool due to their ability to provide graphical and mathematical representations of business processes, which offers a significant advantage over other tools. The objective of this research is to develop a BP modeling algorithm using Petri nets. It aims to simulate the behavior of each activity within a case study in which the mathematical representation of Petri nets is utilized to measure process performance. Additionally, a software artifact is created to assess the algorithm’s functionality and compare the performance of two different business process that share the same goal.
Study on the Rheological Performance and Microscopic Mechanism of PPA/SBS/SBR Composite-Modified Asphalt Cold Replenishment Liquid
Authors: Yu Zongjie , Ban Xiaoyi , Xu Yafeng , Liu Xiaosong , Ma Lei , Liu Shujun , Zhang Zengping , Zhou Yueming
Published: October 2023

To prepare high-performance asphalt cold replacement liquids, a composite modification preparation process was used to synthesize a styrene–butadiene–styrene (SBS) block copolymer, styrene–butadiene–rubber (SBR) copolymer, and polyphosphate acid (PPA), erucamide/diesel/acrylic ester as surfactants/diluents/reinforcing agent respectively. These six were blended to prepare an asphalt cold replenishment liquid (ACRL), and its modification effect on base asphalt was studied using base asphalt as the control group. A comparative study was conducted on the high- and low-temperature rheological properties and microstructure. The modification mechanisms of high-temperature asphalt cold replenishment liquid (H-ACRL) and low-temperature asphalt cold replenishment liquid (L-ACRL) were studied through dynamic shear rheometry, bending beam rheometry, Fourier-transform infrared spectroscopy, fluorescence microscopy, and atomic force microscopy. The results showed that the optimal dosages of PPA/SBS/SBR in H-ACRL and L-ACRL were (1.2%/5%/3%) and (0.9%/4%/4%), respectively. Within the optimal dosage range, the particles in the cold replenishment fluid were uniformly dispersed in the asphalt to form a dense and continuous network. No new functional groups were generated during the preparation of H-ACRL and L-ACRL, showing that the modifiers (surfactants/diluents/reinforcing agent) were only physically blended with the asphalt.
Three-Dimensional Limited-Memory BFGS Inversion of Magnetic Data Based on a Multiplicative Objective Function
Authors: Liu Shuaishuai , Tan Handong , Peng Miao , Li Yanxing
Published: October 2023

At present, the traditional magnetic three-dimensional inversion method has been fully developed and is widely used. Magnetic exploration is a kind of geophysical exploration method that uses the magnetic field changes (magnetic anomalies) caused by the magnetic differences between various rocks in the crust to find useful mineral resources and study the underground structure. Traditional magnetic three-dimensional inversion is relatively inefficient. Moreover, the traditional additive objective function (data fitting difference term plus regularization term and logarithmic obstacle term), which causes the regularization factor selection to be more complicated, is implemented in this method. Therefore, it is necessary to establish a new efficient three-dimensional magnetic inversion algorithm and optimize the selection of regularization factors. In this paper, based on the limited-memory BFGS (L-BFGS) method, the three-dimensional magnetic inversion of a multiplicative objective function is realized. The inversion test is conducted in this paper using both theoretical synthesis data and measured data. The results demonstrate that the limited-memory BFGS method significantly enhances the inversion efficiency and yields superior inversion outcomes compared to traditional magnetic three-dimensional inversion methods. Additionally, the multiplicative objective function-based three-dimensional magnetic inversion method simplifies the process of selecting weight factors for regularization terms.
Reliability and Validity of the Portable KForce Plates for Measuring Countermovement Jump (CMJ)
Authors: Plakoutsis George , Zapantis Dimitrios , Panagiotopoulou Eirini-Maria , Paraskevopoulos Eleftherios , Moutzouri Maria , Koumantakis George A. , Papandreou Maria
Published: October 2023

Physical fitness is of great significance to athletes in both single-player and team sports. The countermovement jump (CMJ) is one of the most commonly applied jump tests for assessing the mechanical capacities of the lower extremities. The KForce Plates system is a portable force platform that sends action-time audio and visual biofeedback to a smartphone or tablet through the KForce application, making it a suitable instrument for assessing the CMJ. The aim of the present study was to evaluate the test–retest reliability and validity of the portable force platform (KForce Plates) in the evaluation of the CMJ in collegiate athletes compared to a validated application, My Jump 2. Thirty-four collegiate professional athletes, deriving from various sports backgrounds, participated in the present study. The CMJs were reported with the portable KForce Plates and the simultaneous use of the ‘My Jump 2’ application using an iPhone 13 between days 1 and 7. Our findings revealed high test–retest reliability (ICC = 1.00 and ICC = 0.99) in-between measurements. High correlations were monitored amongst the portable KForce plates and the My Jump 2 application for measuring the CMJ (r = 0.999, p = 0.001). The Bland–Altman plot exhibits the limits of agreement amongst the portable KForce plates and the My Jump 2 application, where the bulk of the data are within the 95% CIs with an agreement of ≈1 cm. Our findings suggest that the portable KForce Plates system is a reliable and valid instrument and, therefore, can be used by experts in the sports field.
A Heterogeneous Parallel Algorithm for Euler-Lagrange Simulations of Liquid in Supersonic Flow
Authors: Liu Xu , Sun Mingbo , Wang Hongbo , Li Peibo , Wang Chao , Zhao Guoyan , Yang Yixin , Xiong Dapeng
Published: October 2023

In spite of its prevalent usage for simulating the full-field process of the two-phase flow, the Euler–Lagrange method suffers from a heavy computing burden. Graphics processing units (GPUs), with their massively parallel architecture and high floating-point performance, provide new possibilities for high-efficiency simulation of liquid-jet-related systems. In this paper, a central processing unit/graphics processing unit (CPU/GPU) parallel algorithm based on the Euler–Lagrange scheme is established, in which both the gas and liquid phase are executed on the GPUs. To realize parallel tracking of the Lagrange droplets, a droplet dynamic management method is proposed, and a droplet-locating method is developed to address the cell. Liquid-jet-related simulations are performed on one core of the CPU with a GPU. The numerical results are consistent with the experiment. Compared with a setup using 32 cores of CPUs, considerable speedup is obtained, which is as high as 32.7 though it decreases to 20.2 with increasing droplets.
Architecture Differences and Hydrocarbon Accumulation in Passive Continental Margin Basins in East Africa
Authors: Geng Ke , Wen Zhixin , Wang Xueling , Wang Zhaoming , He Zhengjun , Song Chengpeng , Ji Tianyu , Li Hengxuan
Published: October 2023

In this paper, we study the structural characteristics and sedimentary filling differences of the passive continental margin basins of East Africa and establish hydrocarbon accumulation models in different basins. In the research process, the research methods of prototype basin and lithofacies paleogeography restoration, oil and gas reservoir anatomy, two-dimensional seismic data interpretation, etc. The passive continental margin basins in East Africa are divided into four types of passive continental margin basins, namely “rifted type,” “depressed type,” “faulted depression type,” and “reformed delta type,” based on the three prototype stages of intracontinental aborted rift, intracontinental-intercontinental rift, and passive continental margin basin, and the difference in sedimentary filling thickness between rift and depression periods. Finally, the accumulation models of different types of basins are established, and the favorable accumulation combinations and future exploration directions are discussed.
A Scientific Document Retrieval and Reordering Method by Incorporating HFS and LSD
Authors: Feng Ziyang , Tian Xuedong
Published: October 2023

Achieving scientific document retrieval by considering the wealth of mathematical expressions and the semantic text they contain has become an inescapable trend. Current scientific document matching models focus solely on the textual features of expressions and frequently encounter hurdles like proliferative parameters and sluggish reasoning speeds in the pursuit of improved performance. To solve this problem, this paper proposes a scientific document retrieval method founded upon hesitant fuzzy sets (HFS) and local semantic distillation (LSD). Concretely, in order to extract both spatial and semantic features for each symbol within a mathematical expression, this paper introduces an expression analysis module that leverages HFS to establish feature indices. Secondly, to enhance contextual semantic alignment, the method of knowledge distillation is employed to refine the pretrained language model and establish a twin network for semantic matching. Lastly, by amalgamating mathematical expressions with contextual semantic features, the retrieval results can be made more efficient and rational. Experiments were implemented on the NTCIR dataset and the expanded Chinese dataset. The average MAP for mathematical expression retrieval results was 83.0%, and the average nDCG for sorting scientific documents was 85.8%.
Decay Characteristics of Mechanical Properties of Asphalt Mixtures under Sizeable Wet Temperature Cycle
Authors: Li Yi-Liang , Wei Jian-Guo , Fu Qi-Lin , Zhang Li-Duan , Liu Fan
Published: October 2023

Asphalt mixtures will inevitably be affected by rainwater and the effect of the wet temperature cycle during a pavement’s life span. Especially in coastal areas such as Guangdong and Hainan in China, asphalt pavement is particularly susceptible to the sizeable wet temperature cycle formed by the high temperature and sudden temperature drop of rainstorms in summer. For this study, we used a homemade sizeable wet temperature cycle environment simulation device to analyze the decay characteristics and the mechanical properties of asphalt pavements in this environment; modified bending and tensile strength and shear strength tests were used to study the decay patterns of shear strength, bending, and tensile strength, and the stiffness modulus of asphalt mixtures with different air voids and different pavement depths under the action of a sizeable wet temperature cycle. In addition, the Grey correlation method was used to analyze the significance of each influencing factor on the decay of mechanical properties, and mathematical fitting was used to establish the prediction equation of the mechanical properties of asphalt mixtures. The results show that with the increase in the number of sizeable wet temperature cycles, the asphalt mixture’s shear strength, flexural tensile strength, and flexural tensile modulus decrease, the degree of decay increases, and the rate of decay gradually slows down. In the case of the same number of sizeable wet temperature cycles, the degree of decay of the asphalt mixtures gradually decreases with increasing depth or decreasing void ratio. After 100 sizeable wet temperature cycles, the maximum values of the decay of shear strength, modulus of strength, and flexural tensile strength were 22.30%, 23.29%, and 32.01%. The importance of the influence of each factor on the decay of the mechanical properties is as follows: the number of sizeable wet temperature cycles > void rate > depth. The prediction equations of the established mechanical properties have a good prediction effect, and the correlation between predicted values and actual values can be up to 0.925. The prediction equations can effectively predict the mechanical properties of asphalt mixtures with different air voids and depths under the action of sizeable wet temperature cycles.
Multi-Point Deformation Prediction Model for Concrete Dams Based on Spatial Feature Vector
Authors: Chen Zhuoxun , Liu Xiaosheng
Published: October 2023

Deformation can effectively reflect the structural state of concrete dams and, thus, establishing na accurate concrete dam deformation prediction model is important for dam health monitoring and early warning strategies. To address the problem that the spatial coordinates introduced in the traditional multi-point deformation prediction model of dams not being able to accurately and efficiently reflect the spatial correlation of multiple-measuring points, a 2D-1D-CNN model is proposed which expresses the spatial correlation between each measuring point through spatial feature vectors, replacing the spatial coordinates in the traditional multi-point model. First, the spatial feature vector is extracted from the historical spatio-temporal panel series of deformation values of measuring points via a Two-Dimensional Convolutional Neural Network (2D-CNN); second, the vector is combined with the environmental impact factor of dam deformation to form the final input factor of fused spatial features; and, thirdly, this vector is combined with the environmental impact factors of dam deformation to form the final input factor of fused spatial features, and the non-linear linkage between the factors and the measured displacement values is constructed by the efficient feature processing capability of a One-Dimensional Convolutional Neural Network (1D-CNN) to obtain the prediction results. Finally, the actual monitoring data of a concrete dam in China are used as an example to verify the validity of the model. The results show that the proposed model outperforms the other models in most cases, respectively, which verifies the effectiveness of the proposed model in this paper.
Study on Nonlinear Vibration of Vertical Lifting Section of Bulk Grain Entrainment Ship Unloader
Authors: Yan Li , Li Yongxiang , Cheng Min , Wang Mingxu , Liu Peng
Published: October 2023

In view of the prominent problem that nonlinear vibration of a belt conveyor can easily occur during the vertical grain-conveyance process due to the coupling effect of airflow clamping and traction of the conveyor belt, which seriously affects the efficiency and stability of conveying materials by the belt conveyor, a method of solving the vibration analysis of the vertical lifting section of the bulk grain belt unloader by using nonlinear vibration is proposed. Firstly, based on the laminated plate theory, the vertical lifting belt and the grain material clamped by the belt are laminated. The nonlinear vibration differential equation of the vertical lifting section of the bulk grain-carrying ship unloader is established by elastic–plastic mechanics, and solved by perturbation theory and Galerkin discrete analysis. The vibration response curve and structural natural frequency of the vertical lifting section of the bulk grain-carrying machine are obtained by numerical solution, and the influence of the volume content of the clamped material on the vibration response and structural natural frequency of the lifting section is analyzed. This study provides theoretical support for the design of pressure-supply parameters, overall structure and operation parameters of the subsequent entrainment ship unloader, promotes the rapid development of the entrainment ship unloader, provides theoretical support for the design, manufacture, later operation, and maintenance of the entrainment ship unloader, and thus provides equipment and technical support for building an efficient and intelligent port.
Variable Admittance Control in Sliding Mode for Robust Physical Human–Robot Interaction
Authors: Chen Jingdong , Ro Paul I.
Published: October 2023

Intuitive and comfortable physical human–robot interaction (pHRI) can be realized by changing impedance/admittance parameters corresponding to human interaction. However, this dynamic adjustment may result in drastically changed system dynamics, which usually give rise to system instability. We introduce a power envelope regulation strategy designed to constrain the variability of admittance parameters, thereby ensuring system passivity and mitigating the risk of instability. Then, sliding mode control (SMC) is employed to yield stable and robust performance. A new sliding surface is proposed based on feedback linearization, which shows improved tracking performance and stability compared to a conventional sliding surface. The effectiveness of the proposed sliding surface and associated control is theoretically validated. Notably, our modified sliding surface works universally, regardless of the order of the desired admittance equation. The trade-off between system chattering and robustness is effectively managed using a variable–boundary approach, which dynamically adjusts system constraints to optimize performance. In addition, a control algorithm combining acceleration feedback and sliding mode is proposed, showing improved robustness and tracking accuracy performance compared with applying the proposed SMC algorithm exclusively. The efficacy of these methodologies is substantiated through numerical simulations and empirical experiments.
U-Net-Embedded Gabor Kernel and Coaxial Correction Methods to Dorsal Hand Vein Image Projection System
Authors: Chen Liukui , Lv Monan , Cai Junfeng , Guo Zhongyuan , Li Zuojin
Published: October 2023

Vein segmentation and projection correction constitute the core algorithms of an auxiliary venipuncture device, responding to accurate venous positioning to assist puncture and reduce the number of punctures and pain of patients. This paper proposes an improved U-Net for segmenting veins and a coaxial correction for image alignment in the self-built vein projection system. The proposed U-Net is embedded by Gabor convolution kernels in the shallow layers to enhance segmentation accuracy. Additionally, to mitigate the semantic information loss caused by channel reduction, the network model is lightweighted by means of replacing conventional convolutions with inverted residual blocks. During the visualization process, a method that combines coaxial correction and a homography matrix is proposed to address the non-planarity of the dorsal hand in this paper. First, we used a hot mirror to adjust the light paths of both the projector and the camera to be coaxial, and then aligned the projected image with the dorsal hand using a homography matrix. Using this approach, the device requires only a single calibration before use. With the implementation of the improved segmentation method, an accuracy rate of 95.12% is achieved by the dataset. The intersection-over-union ratio between the segmented and original images is reached at 90.07%. The entire segmentation process is completed in 0.09 s, and the largest distance error of vein projection onto the dorsal hand is 0.53 mm. The experiments show that the device has reached practical accuracy and has values of research and application.
A Comparative Assessment of High-Throughput Quantitative Polymerase Chain Reaction versus Shotgun Metagenomic Sequencing in Sediment Resistome Profiling
Authors: Habibi Nazima , Uddin Saif , Behbehani Montaha , Al-Sarawi Hanan A. , Kishk Mohamed , Al-Zakri Waleed , AbdulRazzack Nasreem , Shajan Anisha , Zakir Farhana
Published: October 2023

Prolonged and excessive use of antibiotics has resulted in the development of antimicrobial resistance (AMR), which is considered an emerging global challenge that warrants a deeper understanding of the antibiotic-resistant gene elements (ARGEs/resistomes) involved in its rapid dissemination. Currently, advanced molecular methods such as high-throughput quantitative polymerase chain reaction (HT-qPCR) and shotgun metagenomic sequencing (SMS) are commonly applied for the surveillance and monitoring of AMR in the environment. Although both methods are considered complementary to each other, there are some appreciable differences that we wish to highlight in this communication. We compared both these approaches to map the ARGEs in the coastal sediments of Kuwait. The study area represents an excellent model as it receives recurrent emergency waste and other anthropogenic contaminants. The HT-qPCR identified about 100 ARGs, 5 integrons, and 18 MGEs (total—122). These ARGs coded for resistance against the drug classes of beta-lactams > aminoglycoside > tetracycline, macrolide lincosamide streptogramin B (MLSB) > phenicol > trimethoprim, quinolone, and sulfonamide. The SMS picked a greater number of ARGs (402), plasmid sequences (1567), and integrons (168). Based on the evidence, we feel the SMS is a better method to undertake ARG assessment to fulfil the WHO mandate of “One Health Approach.” This manuscript is a useful resource for environmental scientists involved in AMR monitoring.
Processability of High-Speed Steel by Coaxial Laser Wire Deposition Technology for Additive Remanufacturing of Cutting Tool
Authors: Koruba Piotr , Kędzia Jakub , Dziedzic Robert , Reiner Jacek
Published: October 2023

The recently introduced Coaxial Laser Wire Deposition technology can become a new promising method for remanufacturing high-complexity and expensive cutting tools (e.g., flat broach), which will have a significant impact on their service life. In addition, it is an innovative approach to tool management. An analysis of the feasibility of processing cobalt-added HSS powder steels was carried out for single clads and multilayer structures. The effect of process parameters (laser, beam power, travel speed, wire feed rate) on geometric properties, hardness and microstructure was discussed. In order to avoid cracking during multilayer deposition, an additional preheating to 320 °C was applied. Two sets of process parameters with high and low heat input were obtained. Both sets lead to crack-free structures that fulfill geometric (≥2.5 mm in height) and hardness (≥700 HV) requirements.
Lightweight Solid Wood Panels Made of Paulownia Plantation Wood
Authors: Barbu Marius Cătălin , Radauer Helmut , Petutschnigg Alexander , Tudor Eugenia Mariana , Kathriner Markus
Published: October 2023

Light Paulownia seamless-edged glued solid wood panels (SWPs), single-layered and three-layered, were analyzed in this study. Both panel types were calibrated at a thickness of 19 mm, a dimension very often in demand on the SWP market, but produced with other wood species (for example, spruce, pine, larch and fir). The panels were bonded with melamine-urea formaldehyde, polyurethane and polyvinyl acetate resins. The panels were tested for their physical (density) and mechanical (modulus of rupture, modulus of elasticity, compressive shear strength and wood breakage rate) properties. For the single-layered panels, the mechanical and physical properties did not differ significantly and were similar to massive Paulownia wood. For the three-layered panels, the adhesive application of polyurethane influenced positively all SWP properties. Considering the differences in density, these composites failed to achieve the performance of one- and single-layered panels made of spruce. The results of these findings recommend Paulownia SWPs to be used as lightweight and sustainable core materials in sandwich structures for the furniture and packaging industry, sport articles or non-load-bearing constructions.
SAFP-YOLO: Enhanced Object Detection Speed Using Spatial Attention-Based Filter Pruning
Authors: Ahn Hanse , Son Seungwook , Roh Jaehyeon , Baek Hwapyeong , Lee Sungju , Chung Yongwha , Park Daihee
Published: October 2023

Because object detection accuracy has significantly improved advancements in deep learning techniques, many real-time applications have applied one-stage detectors, such as You Only Look Once (YOLO), owing to their fast execution speed and accuracy. However, for a practical deployment, the deployment cost should be considered. In this paper, a method for pruning the unimportant filters of YOLO is proposed to satisfy the real-time requirements of a low-cost embedded board. Attention mechanisms have been widely used to improve the accuracy of deep learning models. However, the proposed method uses spatial attention to improve the execution speed of YOLO by evaluating the importance of each YOLO filter. The feature maps before and after spatial attention are compared, and then the unimportant filters of YOLO can be pruned based on this comparison. To the best of our knowledge, this is the first report considering both accuracy and speed with Spatial Attention-based Filter Pruning (SAFP) for lightweight object detectors. To demonstrate the effectiveness of the proposed method, it was applied to the YOLOv4 and YOLOv7 baseline models. With the pig (baseline YOLOv4 84.4%@3.9FPS vs. proposed SAFP-YOLO 78.6%@20.9FPS) and vehicle (baseline YOLOv7 81.8%@3.8FPS vs. proposed SAFP-YOLO 75.7%@20.0FPS) datasets, the proposed method significantly improved the execution speed of YOLOv4 and YOLOv7 (i.e., by a factor of five) on a low-cost embedded board, TX-2, with acceptable accuracy.
A Simplified Pointing Model for Alt-Az Telescopes
Authors: Zhao Yong , Wang Yingqiang , He Fei , Yao Zhonghua , Rong Zhaojin , Wei Yong
Published: October 2023

The telescope pointing model plays a crucial role in characterizing and explaining the inaccuracies in the telescope’s pointing. It uses mathematical models to express these errors and suggests methods to calibrate and rectify them. By employing the pointing model, one can gain a deeper understanding of the discrepancies in the telescope’s pointing accuracy at various positions and orientations. This article introduces the establishment and validation of a simplified pointing model for correcting the pointing errors of an alt-az telescope. The traditional telescope pointing model, with its complex formulas, makes it difficult to quickly apply for the calibration of the telescope’s pointing. This method utilizes two-dimensional surface fitting to obtain the telescope’s pointing error function, enabling a rapid determination of the telescope’s pointing model, which is then validated on the Planetary Atmospheric Spectroscopic Telescope (PAST). Using this method, the azimuth axis and altitude axis errors (root mean square error—RMSE) of the PAST telescope are 6.8 arcs and 3.8 arcs, respectively.
Insight into Municipal Reactive Nitrogen Emissions and Their Influencing Factors: A Case Study of Xiamen City, China
Authors: Li Yanmin , Yang Xu , Wang Shihang , Cui Shenghui
Published: October 2023

Reactive nitrogen (Nr) has been confirmed as an indispensable nutrient for the city ecosystem, but high-intensity human activities have led to nitrogen pollution in cities, especially in coastal cities, jeopardizing ecosystem services and human health. Despite this, the characteristics and influencing factors of Nr remain unclear in coastal cities, particularly in the context of rapid urbanization. This study used the material flow analysis method to estimate Nr emissions in Xiamen from 1995 to 2018 and evaluated the characteristics of excessive Nr emissions. The STIRPAT model was used to identify and explore factors contributing to observed Nr levels in coastal cities. As indicated by the results, (1) the quantity of Nr generated by human activities increased 3.5 times from 1995 to 2018. Specifically, the total Nr entering the water environment showed a general increase with fluctuations, exhibiting an average annual growth rate of 3.1%, increasing from 17.2 Gg to 35.1 Gg. (2) Nr loads in the nearby sea increased notably from 8.1 Gg in 1995 to 25.4 Gg in 2018, suggesting a threefold augmentation compared with surface waters and groundwater. (3) NOx was the gaseous Nr with the greatest effect on the atmosphere in Xiamen, which was primarily due to fossil fuel consumption. (4) Population and per capita GDP were major factors contributing to Nr load in the water environment, while Nr emission to the atmosphere was influenced by population and energy consumption. These findings provide valuable insights for tailored approaches to sustainable nitrogen management in coastal cities.
Significant Reduction in Precipitation Seasonality and the Association with Extreme Precipitation in the Hai River Basin of China from 1960 to 2018
Authors: Zhang Xin , Mao Yuna
Published: October 2023

The Hai River Basin (HRB) serves as a vital center for the population, economy and politics in northern China. Natural hazards, particularly floods, pose significant risks to the region, often attributed to extreme precipitation events. Changes in precipitation seasonalitycc play a pivotal role in influencing precipitation extreme events. Therefore, this study presents a comprehensive analysis of precipitation seasonality and its impact on precipitation extremes in HRB. By implementing a novel relative entropy method, we calculated the precipitation seasonality indicators using daily precipitation observations from 1960 to 2018 in HRB. We found a significant decreasing trend in precipitation seasonality (−0.03 decade−1, p = 0.04), accompanied by an earlier onset date (4.0 days decade−1, p = 0.01) and longer duration (4.3 days decade−1, p = 0.03) of the wet season. Notably, these trends are notably concentrated in the Beijing-Tianjin administrative regions. Additionally, a lower precipitation seasonality value indicated a more evenly distributed precipitation throughout the year, resulting in reduced occurrences of precipitation extremes. Consistently, we observed two precipitation extremes, extreme wet day precipitation R99T and maximum 1-day precipitation RX1Day, which exhibited significant decreasing trends at the rate of −0.5 mm decade−1 (p = 0.02) and −1.4 mm decade−1 (p = 0.05), respectively. Furthermore, we detected significant positive correlations of 0.31 (p = 0.02) and 0.35 (p = 0.01) between precipitation seasonality and precipitation extremes (R95T and R99T), suggesting that a more evenly distributed precipitation across seasons corresponds to fewer precipitation extremes over the past sixty years. Metropolitan areas, in particular, experienced a noteworthy reduction in precipitation seasonality and a decreased frequency of precipitation extreme events. The findings of this study shed new light on the intricate relationship between precipitation seasonality and extreme events, further helping policy making develop effective risk regulations for agriculture, floods, and urban waterlogging, ensuring sustainable development within the HRB.
Assessing the Impact of Combined Heat and Power Plants (CHPPs) in Central Asia: A Case Study in Almaty for PM2.5 Simulations Using WRF-AERMOD and Ground Level Verification
Authors: Ogbuabia Theophilus Bright , Guney Mert , Baimatova Nassiba , Ulusoy Ismail , Karaca Ferhat
Published: October 2023

According to the World Health Organization, Kazakhstan is one of the most polluted countries in the world. PM2.5, a major air pollutant, is six times higher than the recommended value of 5 mg/m3. The government has implemented measures to reduce air pollution, such as introducing green energy-powered buses for public transportation, but the results have not been sufficient. Therefore, it is necessary to investigate the sources of PM2.5. This study involved simulating the Combined Heat and Power Plants (CHPPs) emissions in Almaty using AERMOD and WRF for two weeks in January 2021. Two scenarios were performed: controlled and uncontrolled. The results showed that if the control mechanism of the CHPP functions at maximum efficiency, the impact of the CHPP emissions on the total emission concentration will be negligible, which is about 6% on average. However, for uncontrolled CHPPs, the emissions will contribute from 30% to 39% on average to the total PM2.5 concentration when compared with data from US Embassy monitoring stations and public air quality monitoring network, which use Pms5003 PM2.5 sensors.
Lattice Boltzmann Model for a Class of Time Fractional Partial Differential Equation
Authors: Wu Fangfang , Lu Chuangui , Wang Yingying , Hu Na
Published: October 2023

This paper is concerned with the lattice Boltzmann (LB) method for a class of time fractional partial differential equations (FPDEs) in the Caputo sense. By utilizing the properties of the Caputo derivative and discretization in time, FPDEs can be approximately transformed into standard partial differential equations with integer orders. Through incorporating an auxiliary distribution function into the evolution equation, which assists in recovering the macroscopic quantity u, the LB model with spatial second-order accuracy is constructed. The numerical experiments verify that the numerical results are in good agreement with analytical solutions and that the accuracy of the present model is better than the previous solutions.