bluuebunny/crossref_metadata_2025 huggingface dataset:
Solar Filament Detection Based on an Improved Deep Learning Model
Authors: Shang Zhenhong , Song Mingzhao , Li Runxin
Published: April 2024

Solar filaments are good tracers of space weather and magnetic flux ropes in the corona. Identifying and detecting filaments helps to forecast space weather and explore the solar magnetic field. Many automatic detection methods have been proposed to process the large number of observed images. Current methods face issues of unreliable dataset annotations and poor anti-interference capability. First, to address the issue of unreliable dataset annotations, we built a solar filament dataset using a manual annotation method. Second, we introduced Transformer into Convolutional Neural Networks. Transformer, with the ability to extract more global features, can help counter interference. In addition, there is large disparity in the size of solar filaments. Therefore, a multi-scale residual block is designed to extract features across various scales. Deformable large kernel attention and a res path are used to better integrate encoder and decoder information. Results show that this method outperforms the existing solar filament detection methods (improved U-Net and improved V-Net), achieving an F1 score of 91.19%. In particular, our results show lower interference by sunspots and background noise than existing methods. The ability to counter interference is improved.
Two-Way Power Flow Balancing in Three-Phase Three-Wire Networks by Unbalanced Capacitive Shunt Compensation
Authors: Pană Adrian , Băloi Alexandru , Molnar-Matei Florin , Stănese Cristian , Jorza Andrei , Stoica David
Published: April 2024

Developed to achieve the balancing of three-phase loads and improve their power factor, the BCC (Balancing Capacitive Compensator) is presented in this paper as having a broader capability, namely that of balancing the two-way power flow. BCCs are becoming useful in today’s distribution networks with a high content of DERs (Distributed Energy Resources), where unbalanced power transfers to the higher voltage network occur more and more frequently as a result of the excess power generated. The article contains a case study in which, by means of Matlab-Simulink 2021 modelling, such a network is studied by considering two regimes corresponding to the two-way power flow. The numerical analysis of phase components and sequence components confirms the validity of the mathematical model concerning the BCC and also for the case of changing the way of power flow in the section controlled by the compensator. This demonstrates the possibility of extending the load balancing function of the BCC to that of balancing the two-way power flow and is an additional argument in support of replacing, in the more or less near future, conventional shunt capacitive compensators with capacitive balancing compensators.
Exploring the Therapeutic Potential of Spilanthol from Acmella paniculata (Wall ex DC.) R. K. Jansen in Attenuating Neurodegenerative Diseases: A Multi-Faceted Approach Integrating In Silico and In Vitro Methodologies
Authors: Jayashan Sanith Sri , Darai Nitchakan , Rungrotmongkol Thanyada , Dasuni Wasana Peththa Wadu , Nwe San Yoon , Thongphichai Wisuwat , Suriyakala Gunasekaran , Towiwat Pasarapa , Sukrong Suchada
Published: April 2024

Neurodegenerative diseases (NDDs) are marked by progressive degeneration of neurons within the central nervous system. A notable rise in the prevalence of NDDs has been noticed in the recent past. There is an undeniable requirement for the discovery of innovative therapies aimed at treating NDDs, as current medications predominantly address symptoms rather than provide cures. Approved therapies often experience a decline in therapeutic efficacy over time and are associated with significant side effects. The current investigation explores the potential of spilanthol, the major bioactive compound isolated from Acmella paniculata, in attenuating NDDs through a multi-faceted approach combining in silico, and in vitro methodologies. In silico pharmacokinetic and toxicity screening of spilanthol indicated favorable characteristics for oral delivery, blood–brain barrier permeability, and minimal toxicity. Network pharmacology predicts that spilanthol attenuates neuroinflammation in NDDs by suppressing the toll-like receptor signaling pathway. Molecular docking and dynamics simulations demonstrate robust binding affinities between spilanthol and key proteins in the TLR4 pathway. In vitro experiments conducted using BV-2 microglial cells demonstrate the potential of spilanthol to reduce the production of proinflammatory cytokines and mediators such as NO, TNF-α, and IL-6 induced by lipopolysaccharide. The cumulative findings of the present study indicate that spilanthol mitigates neurodegeneration by alleviating neuroinflammation.
Integrating a Physical Model with Multi-Objective Optimization for the Design of Optical Angle Nano-Positioning Mechanism
Authors: Jiang Bo , Dong Yiming , Zhang Zijie , Li Xiangyu , Wei Yuxuan , Guo Yifan , Liu Haixia
Published: April 2024

In light of recent advancements in synchrotron radiation technology and nano-technology, there has been a marked increase in the need for ultra-precision nano-positioning mechanisms. This paper presents a method that integrates physical models with multi-objective optimization for developing an optical angle nano-positioning mechanism. We begin by examining the actual motion law of the mechanism, based on kinematic principles. The outcomes from this kinematic analysis facilitate a static analysis of the flexible hinge, identified as a critical component of the mechanism. Subsequently, we establish a dynamic model for the entire mechanism. By employing the physical model as a base and combining it with the optimization algorithm, we identify the optimal design parameters for the mechanism. The design achieves a resolution of 50 nrad and meets the specified requirements. The first-order inherent frequency of the mechanism is approximately 43.75 Hz. There is a discrepancy of 2.63% from the finite element modal analysis results and a 3.33% difference from the theoretical analysis results, validating the reliability of the design method proposed in this study.
Performance Evaluation of Grounding Systems of Medium-Voltage Concrete Poles: A Comprehensive Analysis
Authors: Ellinas Emmanouil D. , Lianos Georgios , Kontargyri Vassiliki T. , Christodoulou Christos A. , Gonos Ioannis F.
Published: April 2024

Designing and installing efficient grounding systems in power distribution networks is considered a complex and crucial task to ensure the reliable operation of power-protective schemes while mitigating hazardous potentials arising from faults, thereby safeguarding both personnel and equipment. This paper aims to offer guidance on designing effective grounding systems in distribution networks by assessing the influence of parameters such as soil structure, fault current magnitude, and fault clearing time. This involves proposing a more precise methodology for calculating hazardous potentials, leveraging software tools like PowerFactory, to accurately determine short-circuit (SC) currents and fault clearing times at specific locations where grounding grids are to be installed. Consequently, Distribution System Operators (DSOs) can design tailored grounding systems that optimize techno-economic considerations without unnecessary over-dimensioning, accounting for the unique characteristics of the Medium-Voltage (MV) Line and soil structure.
High-Sensitivity Detection of Carbon Fiber-Reinforced Polymer Delamination Using a Novel Eddy Current Probe
Authors: Zhou Yingni , Ye Bo , Cao Honggui , Zou Yangkun , Zhu Zhizhen , Xing Hongbin
Published: April 2024

The demand for non-destructive testing of carbon fiber-reinforced polymer (CFRP) is becoming increasingly pressing to ensure its safety and reliability across different fields of use. However, the complex structural characteristics and anisotropic bulk conductivity of CFRP make achieving high sensitivity in detecting internal defects such as delamination extremely challenging. To address this issue, a novel triple rectangular coil probe with high sensitivity developed for detecting delamination in CFRP is presented in this paper. A finite element model using COMSOL Multiphysics was developed for CFRP delamination eddy current testing with the designed probe. Based on this model, the probe parameters were determined through orthogonal experiments. By analyzing the eddy current distribution in CFRP samples, the scanning mode was defined. Following this, the detection voltage was evaluated for various delamination parameters, and the sensitivity of different probes was compared. Results indicate that, under the same excitation coil parameters, for a 5 mm delamination lateral dimension change, the single pancake and single rectangular coil probes exhibit sensitivities of 88.24% and 72.55%, respectively, compared with the designed probe. For a 0.5 mm delamination thickness change, their sensitivities are 49.04% and 56.69% of those of the designed probe. The designed probe meets the demand for high-sensitivity detection.
Investigation of Car following and Lane Changing Behavior in Diverging Areas of Tunnel–Interchange Connecting Sections Based on Driving Simulation
Authors: Sun Zhenhua , Xu Jinliang , Gu Chenwei , Xin Tian , Zhang Wei
Published: April 2024

Tunnel–interchange connecting sections pose significant safety challenges on mountainous expressways due to their high incidence of accidents. Improving road safety necessitates a comprehensive understanding of driver behavior in such areas. This study explores the influences of road characteristics, signage information volume, and traffic conditions on drivers’ car-following and lane-changing behavior in tunnel–interchange diverging areas. Utilizing driving data from 25 subjects of 72 simulated road models, driving performance is assessed using the Friedman rank test and multivariate variance analysis. The results highlight the significant influence of both connection distance and signage information load on driving behavior. In tunnel–interchange scenarios, the reduction in velocity increased by 62.61%, and speed variability surged by 61.11%, indicating potential adverse effects on driving stability due to the environmental transitions. Decreased connection distances are associated with reduced lane-changing durations, larger steering angles, and increased failure rates. Furthermore, every two units of increase in signage information leads to a 13.16% rise in maximum deceleration and a 5% increase in time headway. Notably, the signage information volume shows a significant interaction with connection distance (F > 1.60, p < 0.045) for most car-following indicators. Hence, the study recommends a maximum connection distance of 700 m and signage information not exceeding nine units for optimal safety and stability.
Lateral Heat Distribution Characteristics of CLP S275 Using Gaussian FFT Algorithm in Optical Thermographic Testing
Authors: Lee Seungju , Chung Yoonjae , Kim Wontae , Suh Hyunkyu
Published: April 2024

In general, when using infrared thermography (IRT) techniques to excite a heat source on the surface of an inspection object, the heat source is focused on the center of the image of the infrared (IR) camera. If the object to be inspected is small, uniform excitation of the heat source is possible, but if the area is large, the heat source is concentrated locally, resulting in uneven heat distribution. Therefore, in this study, heat distribution was analyzed after inducing a non-uniform heat source by exciting the heat source at different locations. Additionally, the fast Fourier transform (FFT) algorithm with Gaussian filtering was applied to resolve the non-uniform distribution of the heat sources. Excellent results were obtained from the amplitude image, and the effectiveness of the FFT algorithm was verified using the Otsu algorithm. Finally, the signal-to-noise ratio (SNR) was calculated, and the detection ability according to each thinning rate was analyzed.
Neuromuscular Control during the Bench Press Exercise Performed with Free Weights and Pneumatic Loading
Authors: Gołaś Artur , Pietraszewski Przemysław , Roczniok Robert , Królikowska Paulina , Ornowski Kajetan , Jabłoński Tomasz , Kuliś Szymon , Zając Adam
Published: April 2024

The main objective of the research was to determine neuromuscular control for different external loads, from 75% to 100% 1 RM (One Rep Max), during the flat bench press (BP) exercise performed with free weights and pneumatic loading. Despite extensive research on the internal structure of the BP exercise, few studies have examined the differences between muscular activity during the flat bench press movement between Free Weights and Pneumatic Loading. For this purpose, 10 male, trained subjects performed the BP exercise under two conditions with three different external loads (70%, 85%, and 100% 1RM), alternately with free weights and pneumatic loading. Pneumatic loading was performed on the Keiser Power Rack, where the pneumatic load was transferred as the resistance of the cables attached to the ground. EMG activity was recorded during the lifts for the following muscles: PM (Pectoralis Major), AD (Anterior Deltoid), Tblat, and TBlong (Triceps Brachii). The EMG signals were sampled at a rate of 1000 Hz. Signals were band-pass filtered with a cutoff frequency of 8 Hz and 450 Hz, after which the root-mean-square (RMS) was calculated. After completion of all the tests in a single day, 2–3 s evaluations of Maximal Voluntary Isometric Contraction (MVIC) of the prime movers in the bench press movement (AD, PM, and TBlong) were performed according to SENIAM procedures. The results of the present study indicate that pneumatic loading provides a significantly different muscle activation pattern compared to a standard bar during a heavy-loaded BP exercise. The pneumatic load was superior in activating the AD and TB muscles compared to the standard bar during the BP exercise.
Therapeutic Effects of 30 nm Cyclosporin A-Loaded Nanoparticles Using PLGA-PEG-PLGA Triblock Copolymer for Transdermal Delivery in Mouse Models of Psoriasis
Authors: Kagawa Akira , Sato Akira , Makino Kimiko , Takeuchi Issei
Published: April 2024

This study aimed to evaluate the effectiveness of poly(DL-lactide-co-glycolide)-block-poly(ethylene glycol)-block-poly(DL-lactide-co-glycolide) triblock copolymers (PLGA-PEG-PLGA) as a drug carrier in the treatment of psoriasis. Nanoparticles containing cyclosporin A (CsA) were prepared, and their cytotoxicity and skin irritation properties were investigated. These results revealed that the nanoparticles themselves had no obvious cytotoxicity or skin irritation effects. Furthermore, it was shown that loading CsA into nanoparticles promoted its cellular uptake. The therapeutic effect of CsA-loaded PLGA-PEG-PLGA nanoparticles on psoriasis was evaluated using a mouse model of psoriasis induced by imiquimod. In psoriatic skin, we confirmed that nanoparticles penetrate deep into the skin. Furthermore, it was suggested that by using PLGA-PEG-PLGA, drug carriers could reach the dermal layer, which is the target site for psoriasis treatment. The observation of skin sections after the treatment experiment showed that excessively proliferated keratinocytes were restored to an almost normal state by using PLGA-PEG-PLGA nanoparticles as drug carriers. Additionally, the quantitative measurement results for cytokines revealed that the levels of TNF-α, IL-17A, and IL-22 were significantly decreased compared with those of the group to which CsA suspended in a 20% ethanol solution was administered. These results indicate that PLGA-PEG-PLGA nanoparticles are promising drug carriers for the transdermal administration of CsA.
Multi-Step Multidimensional Statistical Arbitrage Prediction Using PSO Deep-ConvLSTM: An Enhanced Approach for Forecasting Price Spreads
Authors: Tu Sensen , Qin Panke , Zhu Mingfu , Zeng Zeliang , Cheng Shenjie , Ye Bo
Published: April 2024

Due to its effectiveness as a risk-hedging trading strategy in financial markets, futures arbitrage is highly sought after by investors in turbulent market conditions. The essence of futures arbitrage lies in formulating strategies based on predictions of future futures price differentials. However, contemporary research predominantly focuses on projections of single indicators for the subsequent temporal juncture, and devising efficacious arbitrage strategies often necessitates the examination of multiple indicators across timeframes. To tackle the aforementioned challenge, our methodology leverages a PSO Deep-ConvLSTM network, which, through particle swarm optimization (PSO), refines hyperparameters, including layer architectures and learning rates, culminating in superior predictive performance. By analyzing temporal-spatial data within financial markets through ConvLSTM, the model captures intricate market patterns, performing better in forecasting than traditional models. Multistep forward simulation experiments and extensive ablation studies using future data from the Shanghai Futures Exchange in China validate the effectiveness of the integrated model. Compared with the gate recurrent unit (GRU), long short-term memory (LSTM), Transformer, and FEDformer, this model exhibits an average reduction of 39.8% in root mean squared error (RMSE), 42.5% in mean absolute error (MAE), 45.6% in mean absolute percentage error (MAPE), and an average increase of 1.96% in coefficient of determination (R2) values.
A Study Comparing Waiting Times in Global and Local Queuing Systems with Heterogeneous Workers
Authors: Ainbinder Inessa , Temnikov Evgeni , Allalouf Miriam
Published: April 2024

A virtual marketplace or service-providing system must ensure minimal task response times. Varying working rates among the human workers in the system can lead to longer delays for certain tasks. The waiting time in the queue is crucially affected by the queueing architecture used in the system, whether global or local. Studies generally favor global queue systems over local ones, assuming similar processing rates. However, system behavior changes when workers are heterogeneous. In this research, we used simulation to compare the waiting times of tasks assigned to three categories of processing rates in both architectures and with various routing policies in local queues. We found that when using random tie-breaking, there was a correlation between waiting time duration and the proportion of tie-breaking events. Performance is improved when controlling these events using scheduling awareness of the workers’ processing rates. The global queue outperforms local queues when the workers are homogeneous. However, the push mechanisms that control the assignment processes and heterogeneity-aware algorithms improve local queue system waiting times and load balance. It is better than global queues when tasks are assigned to medium and fast workers, but it also enables specific slow workers’ assignments.
Fatigue Crack and Residual Life Prediction Based on an Adaptive Dynamic Bayesian Network
Authors: Chen Shuai , Ma Yinwei , Wang Zhongshu , Liu Minjing , Wu Zhanjun
Published: April 2024

Monitoring the health status of aerospace structures during their service lives is a critical endeavor, aimed at precisely evaluating their operational condition through observation data and physical modeling. This study proposes a probabilistic assessment approach utilizing Dynamic Bayesian Networks (DBNs), enhanced by an improved adaptive particle filtering technique. This approach combines physical modeling with various predictive sources, encompassing cognitive uncertainties inherent in stochastic predictions and crack propagation forecasts. By employing crack observation data, it facilitates predictions of crack growth and the residual life of metal structure. To demonstrate the efficacy of this method, the research leverages data from three-point bending and single-edge tension fatigue tests. It gathers data on crack length during the fatigue crack progression, integrating these findings with digital twin theory to forecast the residual fatigue life of the specimens. The outcomes show that the adaptive DBN model can precisely predict fatigue crack propagation in test specimens, offering a potential tool for the online health assessment and life evaluation for aerospace structures.
Water Pipeline Leakage Detection Based on Coherent φ-OTDR and Deep Learning Technology
Authors: Zhang Shuo , Xiong Zijian , Ji Boyuan , Li Nan , Yu Zhangwei , Wu Shengnan , He Sailing
Published: April 2024

Leakage in water supply pipelines remains a significant challenge. It leads to resource and economic waste. Researchers have developed several leak detection methods, including the use of embedded sensors and pressure prediction. The former approach involves pre-installing detectors inside pipelines to detect leaks. This method allows for the precise localization of leak points. The stability is compromised because of the wireless signal strength. The latter approach, which relies on pressure measurements to predict leak events, does not achieve precise leak point localization. To address these challenges, in this paper, a coherent optical time-domain reflectometry (φ-OTDR) system is employed to capture vibration signal phase information. Subsequently, two pre-trained neural network models based on CNN and Resnet18 are responsible for processing this information to accurately identify vibration events. In an experimental setup simulating water pipelines, phase information from both leaking and non-leaking pipe segments is collected. Using this dataset, classical CNN and ResNet18 models are trained, achieving accuracy rates of 99.7% and 99.5%, respectively. The multi-leakage point experiment results indicate that the Resnet18 model has better generalization compared to the CNN model. The proposed solution enables long-distance water-pipeline precise leak point localization and accurate vibration event identification.
Application of Digital Holographic Imaging to Monitor Real-Time Cardiomyocyte Hypertrophy Dynamics in Response to Norepinephrine Stimulation
Authors: Akter Wahida , Huang Herman , Simmons Jacquelyn , Payumo Alexander Y.
Published: April 2024

Cardiomyocyte hypertrophy, characterized by an increase in cell size, is associated with various cardiovascular diseases driven by factors including hypertension, myocardial infarction, and valve dysfunction. In vitro primary cardiomyocyte culture models have yielded numerous insights into the intrinsic and extrinsic mechanisms driving hypertrophic growth. However, due to limitations in current approaches, the dynamics of cardiomyocyte hypertrophic responses remain poorly characterized. In this study, we evaluate the application of the Holomonitor M4 digital holographic imaging microscope to track dynamic changes in cardiomyocyte surface area and volume in response to norepinephrine treatment, a model hypertrophic stimulus. The Holomonitor M4 permits non-invasive, label-free imaging of three-dimensional changes in cell morphology with minimal phototoxicity, thus enabling long-term imaging studies. Untreated and norepinephrine-stimulated primary neonatal rat cardiomyocytes were live-imaged on the Holomonitor M4, which was followed by image segmentation and single-cell tracking using the HOLOMONITOR App Suite software version 4.0.1.546. The 24 h treatment of cultured cardiomyocytes with norepinephrine increased cardiomyocyte spreading and optical volume as expected, validating the reliability of the approach. Single-cell tracking of both cardiomyocyte surface area and three-dimensional optical volume revealed dynamic increases in these parameters throughout the 24 h imaging period, demonstrating the potential of this technology to explore cardiomyocyte hypertrophic responses with greater temporal resolution; however, technological limitations were also observed and should be considered in the experimental design and interpretation of results. Overall, leveraging the unique advantages of the Holomonitor M4 digital holographic imaging system has the potential to empower future work towards understanding the molecular and cellular mechanisms underlying cardiomyocyte hypertrophy with enhanced temporal clarity.
Genesis of Cu-Sn Mineralization in the Shuangjianzishan Super-Large Silver Deposit, Inner Mongolia: Trace Element Constraints from Chalcopyrite and Cassiterite
Authors: Liu Yu , Jiang Biao , Chen Yuchuan , Wu Liwen , Zuo Yushan , Liu Zhao
Published: April 2024

The Shuangjianzishan silver polymetallic deposit is located in the copper–tin–lead–zinc–silver polymetallic metallogenic belt in the Southern Great Xing’an Range, with silver resources of more than 18,000 t, which is the largest silver polymetallic deposit in Asia. Early studies concluded that the Shuangjianzishan deposit is typically an epithermal Ag-Pb-Zn deposit that lacks a high-temperature mineralization stage. In recent years, with the deepening of research, a large amount of Cu-Sn mineralization has been found in the deep part of the Shuangjianzishan deposit, but it is less studied. The laser-ablation inductively coupled mass spectroscopy (LA-ICP-MS) technique is used to investigate the distribution and substitution of trace elements in chalcopyrite and cassiterite. In this paper, the trace element study of chalcopyrite and cassiterite from the Shuangjianzishan deposit reveals that Sn, In, As, Se, Sb, and Tl mainly exist in chalcopyrite in isomorphic form, while Pb, Bi, and Ni mainly exist in chalcopyrite in the form of mineral inclusions. The enrichment of the high-temperature elements Sn and Se in chalcopyrite, and the deficit of the middle- and low-temperature elements Ga, Sb, etc., reflect that the chalcopyrite in the Shuangjianzishan deposit was formed in a middle- and high-temperature environment, and it also indicates that the early ore-forming hydrothermal solution may be rich in Sn. Fe, In, Co, and Ni mainly exist in cassiterite in isomorphic form, and the content of W in cassiterite is high. There are two main forms, one is isomorphic and the other is wolframite inclusion. Cassiterite has Fe-rich and W-U-poor characteristics, indicating that cassiterite from the Shuangjianzishan deposit was formed under relatively oxidized conditions, and the relative enrichment of elements such as Fe, W, Zr, and Hf indicates that the temperature of cassiterite formation was high. The elemental content and inter-ionic coupling relationships suggest that the cassiterite from the Shuangjianzishan deposit may have an elemental replacement mechanism of W6+ + Fe2+ ↔ 2Sn4+ or Fe3+ + OH− ↔ Sn4+ + O2−. The trace elements in cassiterite of the Shuangjianzishan deposit are rich in Fe and Mn and depleted in Nb and Ta, according to the Fe-W diagram, and the tin mineralization of the Shuangjianzishan deposit belongs to cassiterite–sulfide-type tin mineralization. Chalcopyrite Co/Ni ratios >1 are consistent with the characteristics of chalcopyrite genesis in hydrothermal deposits.
A Method of Reducing Errors Due to Sampling in the Measurement of Electric Power
Authors: Oancea Constantin-Daniel
Published: April 2024

Although data acquisition is a very usual technique, several aspects are not always considered, such as the synchronization of the acquired measures and the evaluation of the resulting errors. This paper aims to highlight this fact by the mathematical determination of the necessary correction and the implementation of software meant to evaluate the performances of acquisition systems. As an example, a three-phased acquisition system was developed in order to monitor the currents and voltages on the three phases. Also, other measures were performed, such as of power and phase. The components on each phase did not have to be fully identified because a whole system calibration could be performed in the first stage. The calibration consisted in finding the weighting coefficients for each measured quantity. The implemented solution for three-phased measure acquisition started from the hypothesis of a sampling frequency that respected the Shannon theorem. The distance between two samples was small enough to consider a linear evolution between two moments for the same measure. Errors that affected the above-mentioned measures, due to the fact that the samples were examined in different moments, were analyzed and brought to the minimum value. Finding a solution to reduce the sampling errors is closely related to reducing the costs.
Enhancing Network Attack Detection Accuracy through the Integration of Large Language Models and Synchronized Attention Mechanism
Authors: Bai Yuzhe , Sun Min , Zhang Liman , Wang Yinong , Liu Sihan , Liu Yanqiu , Tan Jingling , Yang Yingqiu , Lv Chunli
Published: April 2024

In this study, we propose a novel method for detecting cyberattack behaviors by leveraging the combined strengths of large language models and a synchronized attention mechanism. Extensive experiments conducted on diverse datasets, including server logs, financial behaviors, and comment data, demonstrate the significant advantages of this method over existing models such as Transformer, BERT, OPT-175B, LLaMa, and ChatGLM3-6B in key performance metrics such as precision, recall, and accuracy. For instance, on the server log dataset, the method achieved a precision of 93%, a recall of 91%, and an accuracy of 92%; on the financial behavior dataset, it reached a precision of 90%, a recall of 87%, and an accuracy of 89%; and on the comment data dataset, it excelled with a precision of 95%, a recall of 93%, and an accuracy of 94%. The introduction of a synchronized attention mechanism and a newly designed synchronized loss function proved especially effective, enhancing the method’s ability to process multi-source data and providing superior performance in identifying complex cyberattack patterns. Ablation experiments further validated the crucial roles of these innovations in boosting model performance: the synchronous attention mechanism substantially improved the model’s precision, recall, and accuracy to 93%, 89%, and 91% respectively, far exceeding other attention mechanisms. Similarly, the synchronized loss showcased a significant advantage, achieving the best performance across all tested metrics compared to traditional cross-entropy loss, focal loss, and MSE. These results underscore the method’s ability to deeply mine and analyze semantic information and contextual relationships within text data as well as to effectively integrate and process multimodal data, thereby offering strong technical support for the accurate and efficient detection of cyberattack behaviors.
Seismic Resilience in Critical Infrastructures: A Power Station Preparedness Case Study
Authors: Lifshitz Sherzer Gili , Urlainis Alon , Moyal Shani , Shohet Igal M.
Published: April 2024

The role of critical infrastructures in maintaining the functioning of the economy and society and ensuring national security, particularly their durability in delivering essential services during crises, including natural disasters such as earthquakes, is critical. This work introduces an analytical methodology to quantify potential earthquake damage to power stations and evaluate the cost-effectiveness of measures to enhance their seismic resistance. By employing fragility curves and probabilistic risk analyses, this approach provides a structured framework for the comprehensive assessment of risks and the identification of economically practical mitigation strategies. A detailed examination of strategies to protect critical power station components against seismic activity is presented, revealing that a minor investment relative to the overall project budget for earthquake-proofing measures is economically effective. This investment, representing a marginal fraction of 0.5% of the total project expenditure significantly reduces the seismic risk of power station failure by 36%. Reinforcing essential elements, including switching stations, water treatment facilities, and water tanks, is emphasized to ensure their continued operation during and after an earthquake. This research highlights the critical significance of integrating risk assessment with benefit-to-cost analysis in strategic decision-making processes, supporting the prioritization of investments in infrastructure enhancements. These enhancements promise substantial reductions of risks at minimal costs, thus protecting essential services against the impacts of natural disasters. This research contributes to state-of-the-art research in critical infrastructures resilience.
Structural Design of Pressurized Tube Based on the Discrete Element Method–Computational Fluid Dynamics Coupled Simulation
Authors: Zhao Jinhui , Li Yanjun , Liu Lijing , Liu Zhongjun
Published: April 2024

In order to elucidate the impact of pressurized tubes’ structures on the sowing performance of pneumatic seed delivery systems, the EDEM–CFD coupled simulation method was employed to analyze the influence of pressurized tube parameters, including length (L), corrugation depth (S), corrugation width (K), and the number of corrugations, on seed movement characteristics, distribution uniformity, and airflow patterns. Simulation-validated experiments were conducted to study the impact of the optimal pressurized tube structure on seeding performance. The results indicate that pressurized tubes significantly enhance the uniformity coefficient of seed distribution, reduce seed velocity, and decrease the coefficient of variation in distribution uniformity. When pressurized tube parameters, specifically length (L), corrugation depth (S), corrugation width (K), and the number of corrugations, are set at 800 mm, 8 mm, 50 mm, and 6, respectively, the uniformity coefficient of seed distribution exceeds 95%, and the coefficient of variation in seed discharge consistency for each row is less than 3.2%. Moreover, the seed velocity at the outlet of the pressurized tube, the relative velocity of two-phase flow, and pressure loss are all minimal, indicating superior seeding performance. This research provides valuable insights into the analysis of seed movement characteristics within pressurized tubes and the optimization of their structural parameters.
Three-Dimensional Printed Attachments: Analysis of Reproduction Accuracy Compared to Traditional Attachments
Authors: Bellocchio Angela Mirea , Ciancio Elia , Ciraolo Ludovica , Barbera Serena , Nucera Riccardo
Published: April 2024

Background: The aim of this study was to propose a new 3D printing method for attachment production and compare the reproduction accuracy of traditional attachments with the proposed 3D-printed attachments. Methods: A standardized 3D model attachment was created with the dimensions of 3, 2, and 2 mm for the apico-coronal, mesio-distal, and vestibulo-lingual dimensions, respectively. A 3D ideal model of the maxillary arch was used to apply four standardized attachments on the vestibular surface of selected teeth. The obtained model with placed attachments was used to reproduce composite attachments via the conventional method. A transfer template was used to bond with the flow composite resin 3D-printed attachment on a new arch model without attachments. The models with traditional attachments and 3D-printed attachments were scanned and overlapped with the original CAD model with attachments. To assess the attachment precision, vertical and horizontal cutting planes were used on the overlapped models. The outcome selection focused on puff analysis (excess composite material evaluation) and shape analysis (attachment accuracy evaluation). Results: The results indicated that the 3D-printed attachments showed significant differences (p < 0.05) compared to the traditional attachments. The descriptive statistics showed the higher discrepancies compared to the CAD model of the traditionally created attachments in the shape (0.85 mm) and puff dimension (1.02 mm). Conclusion: Custom 3D-printed attachment production is an effective method for achieving greater attachment precision.
Vibration Control with Reinforcement Learning Based on Multi-Reward Lightweight Networks
Authors: Shu Yucheng , He Chaogang , Qiao Lihong , Xiao Bin , Li Weisheng
Published: April 2024

This paper proposes a reinforcement learning method using a deep residual shrinkage network based on multi-reward priority experience playback for high-frequency and high-dimensional continuous vibration control. Firstly, we keep the underlying equipment unchanged and construct a vibration system simulator using FIR filters to ensure the complete fidelity of the physical model. Then, by interacting with the simulator using our proposed algorithm, we identify the optimal control strategy, which is directly applied to real-world scenarios in the form of a neural network. A multi-reward mechanism is proposed to assist the lightweight network to find a near-optimal control strategy, and a priority experience playback mechanism is used to prioritize the data to accelerate the convergence speed of the neural network and improve the data utilization efficiency. At the same time, the deep residual shrinkage network is introduced to realize adaptive denoising and lightweightness of the neural network. The experimental results indicate that under narrowband white-noise excitation ranging from 0 to 100 Hz, the DDPG algorithm achieved a vibration reduction effect of 12.728 dB, while our algorithm achieved a vibration reduction effect of 20.240 dB. Meanwhile, the network parameters were reduced by more than 7.5 times.
Research on Fatigue Driving Detection Technology Based on CA-ACGAN
Authors: Ye Han , Chen Ming , Feng Guofu
Published: April 2024

Driver fatigue represents a significant peril to global traffic safety, necessitating the advancement of potent fatigue monitoring methodologies to bolster road safety. This research introduces a conditional generative adversarial network with a classification head that integrates convolutional and attention mechanisms (CA-ACGAN) designed for the precise identification of fatigue driving states through the analysis of electroencephalography (EEG) signals. First, this study constructed a 4D feature data model capable of mirroring drivers’ fatigue state, meticulously analyzing the EEG signals’ frequency, spatial, and temporal dimensions. Following this, we present the CA-ACGAN framework, a novel integration of attention schemes, the bottleneck residual block, and the Transformer element. This integration was designed to refine the processing of EEG signals significantly. In utilizing a conditional generative adversarial network equipped with a classification header, the framework aims to distinguish fatigue states effectively. Moreover, it addresses the scarcity of authentic data through the generation of superior-quality synthetic data. Empirical outcomes illustrate that the CA-ACGAN model surpasses various extant methods in the fatigue detection endeavor on the SEED-VIG public dataset. Moreover, juxtaposed with leading-edge GAN models, our model exhibits an efficacy in in producing high-quality data that is clearly superior. This investigation confirms the CA-ACGAN model’s utility in fatigue driving identification and suggests fresh perspectives for deep learning applications in time series data generation and processing.
Exploring the Association between Elevated Anxiety Symptoms and Low Skeletal Muscle Mass among Asymptomatic Adults: A Population-Based Study in Republic of Korea
Authors: Kim Eunsoo , Jung Sra , Lee Mi Yeon , Park Chul-Hyun , Cho Sung Joon
Published: April 2024

Individuals with mental health problems are at higher risk of musculoskeletal diseases. However, the association between low muscle mass (LMM) and anxiety symptoms remains uninvestigated. This cross-sectional study enrolled 174,262 adults (73,833 women, 100,429 men), aged 18 to 89, who completed the anxiety scale and body composition analyses. Using bio-electrical impedance analysis, skeletal muscle mass index (SMI) was calculated based on appendicular skeletal muscle mass (ASM) (kg)/height (m2). LMM was defined as SMI < 7.0 kg/m2 in men and <5.4 kg/m2 in women. Anxiety symptoms were screened using the Clinical Useful Anxiety Outcome Scale (CUXOS) with cut-off scores of 20, 30, and 40. Multivariable logistic regression analyses were performed. LMM prevalence was 20.17% in women, 3.86% in men (p < 0.001). The prevalence of anxiety symptoms in LMM group decreased from mild (CUXOS > 20: women, 32.74%, men, 21.17%) to moderate (CUXOS > 30: 13.34%, 7.32%), to severe anxiety symptoms (CUXOS > 40: 4.00%, 1.73%). In multivariable-adjusted models, LMM was associated with mild (aOR (95% confidence interval)), women, 1.13 (1.08–1.17); men, 1.17 (1.08–1.27)), moderate (1.17 (1.11–1.24); 1.35 (1.19–1.53) and severe anxiety symptoms (1.18 (1.07–1.3), 1.36 (1.06–1.74)), demonstrating an increased risk of ORs with escalating anxiety severity. LMM was independently associated with a higher prevalence of anxiety symptoms.
Hematocrit Predicts Poor Prognosis in Patients with Acute Ischemic Stroke or Transient Ischemic Attack
Authors: Cui Lingyun , Feng Yefang , Lu Ping , Wang Anxin , Li Zixiao , Wang Yongjun
Published: April 2024

This study aims to investigate the association between HCT (Hematocrit) levels and adverse outcomes in patients with acute ischemic stroke (AIS) or transient ischemic attack (TIA); 14,832 participants from the China National Stroke Registry-III with AIS or TIA were analyzed. Participants were categorized into quartiles based on baseline HCT levels. The primary outcome was poor functional outcomes (modified Rankin Scale ≥ 3) during three months, with secondary outcomes including all-cause death, stroke recurrence, and combined vascular events. Logistic regression or Cox regression models were used to assess the relationship between HCT and clinical outcomes. Compared to the third quartile, patients in the lowest quartile group showed increased risk of poor functional outcome (adjusted OR: 1.35, 95% CI: 1.15–1.58, p < 0.001), patients in the lowest quartile had a higher risk of all-cause death (adjusted HR: 1.68, 95% CI: 1.06–2.68, p = 0.028), as did those in the highest quartile (adjusted HR: 2.02, 95% CI: 1.26–3.25, p = 0.004). Sensitivity analysis shows that the association of HCT with all-cause death weakened, while the association with poor functional outcome was strengthened after excluding patients with recurrent stroke. Our results indicated that HCT level could be used as a short-term predictor for poor functional outcomes and all-cause death in patients with AIS or TIA.
CNS Germ Cell Tumors: Molecular Advances, Significance in Risk Stratification and Future Directions
Authors: Zhou Jiajun , Wu Chenxing , Li Shouwei
Published: April 2024

Central Nervous System Germ Cell Tumors (CNS GCTs) represent a subtype of intracranial malignant tumors characterized by highly heterogeneous histology. Current diagnostic methods in clinical practice have notable limitations, and treatment strategies struggle to achieve personalized therapy based on patient risk stratification. Advances in molecular genetics, biology, epigenetics, and understanding of the tumor microenvironment suggest the diagnostic potential of associated molecular alterations, aiding risk subgroup identification at diagnosis. Furthermore, they suggest the existence of novel therapeutic approaches targeting chromosomal alterations, mutated genes and altered signaling pathways, methylation changes, microRNAs, and immune checkpoints. Moving forward, further research is imperative to explore the pathogenesis of CNS GCTs and unravel the intricate interactions among various molecular alterations. Additionally, these findings require validation in clinical cohorts to assess their role in the diagnosis, risk stratification, and treatment of patients.
Research on Brain Networks of Human Balance Based on Phase Estimation Synchronization
Authors: Qiu Yifei , Luo Zhizeng
Published: April 2024

Phase synchronization serves as an effective method for analyzing the synchronization of electroencephalogram (EEG) signals among brain regions and the dynamic changes of the brain. The purpose of this paper is to study the construction of the functional brain network (FBN) based on phase synchronization, with a special focus on neural processes related to human balance regulation. This paper designed four balance paradigms of different difficulty by blocking vision or proprioception and collected 19-channel EEG signals. Firstly, the EEG sequences are segmented by sliding windows. The phase-locking value (PLV) of core node pairs serves as the phase-screening index to extract the valid data segments, which are recombined into new EEG sequences. Subsequently, the multichannel weighted phase lag index (wPLI) is calculated based on the new EEG sequences to construct the FBN. The experimental results show that due to the randomness of the time points of body balance adjustment, the degree of phase synchronization of the datasets screened by PLV is more obvious, improving the effective information expression of the subsequent EEG data segments. The FBN topological structures of the wPLI show that the connectivity of various brain regions changes structurally as the difficulty of human balance tasks increases. The frontal lobe area is the core brain region for information integration. When vision or proprioception is obstructed, the EEG synchronization level of the corresponding occipital lobe area or central area decreases. The synchronization level of the frontal lobe area increases, which strengthens the synergistic effect among the brain regions and compensates for the imbalanced response caused by the lack of sensory information. These results show the brain regional characteristics of the process of human balance regulation under different balance paradigms, providing new insights into endogenous neural mechanisms of standing balance and methods of constructing brain networks.
Analysis of the Spatial Distribution Characteristics and Influencing Factors of Traditional Mosque Architecture in the Hehuang Area (China)
Authors: Huang Yuehao , Xue Qianming
Published: April 2024

Clarifying the spatiotemporal distribution and influencing factors of mosque architecture in China’s Hehuang region has significant positive implications for the overall protection and development of the region’s architectural cultural heritage. This study utilizes field surveys and acquires POI data of traditional mosques built before 1993 in the region to analyze the distribution characteristics of mosques, aiming to explore future development trends of these religious structures. It also investigates the influencing factors, with the goal of emphasizing the primary and secondary factors affecting mosque distribution. The study finds the following: (1) Mosques are generally centered around the Huangshui Valley, displaying a “central clustering, peripheral dispersal” distribution pattern, forming a spatial structure of “two cores, one belt, multiple points”, with distinct differentiation and overall uneven distribution. (2) Mosques are primarily situated at elevations between 2147 and 2764 m; on slopes less than 15°, in sunny and gentle slopes; within 20 km from rivers; within 14 km from roads; in areas receiving 400–500 mm annual rainfall; and within temperature ranges of 5.54–10.22 °C. (3) The study also finds that the spatial distribution of mosques is profoundly influenced by both natural geographical factors and human environmental factors. The better the natural location, the larger and denser the population, the richer the cultural resources, the higher the level of economic development, and the greater the concentration of Hui people, the more numerous and concentrated the mosques. (4) Population factors are the dominant factors for the clustered distribution of traditional mosques in the Hehuang area. Since the construction of mosques in the region is closely related to the number of Hui people and the proportion of Muslim adherents, areas with a high concentration of mosques also have relatively larger populations of Hui people. Temperature, precipitation, altitude, rivers, and roads are foundational factors for traditional mosques in the Hehuang area, influencing mosque distribution as external factors.