bluuebunny/crossref_metadata_2025 huggingface dataset:
Flexural Behavior of an RC Beam Externally Strengthened with a Steel- and CFRP-Based Method
Authors: Lin Yu , Wei Yang , Gong Zihan , Xu Wenping , Zhao Kang
Published: October 2024

Reinforced concrete beam bridges are usually retrofitted by a steel plate or FRP. However, these two methods tend to result in disadvantages, e.g., construction complexity and debonding failure, owing to the corresponding material properties. In this study, a steel- and CFRP-based method is proposed to achieve the merits of typical retrofitting methods by combining a CFRP plate, a steel plate, and angle steel. To investigate the effect of the cooperative strengthening, six full-scale beam specimens were designed and are evaluated through a monotonic four-point bending test. The failure mode, load–deflection relationship, critical parameters, and crack development are systematically and sequentially analyzed. Finally, a predicting method is proposed to calculate the flexural capacity. The retrofitted beam is characterized by an acceptable load-bearing capacity and deformation capacity. With continuous retrofitting, the crack load and ultimate load can be improved up to 84.9% and 4.41 times, respectively. The steel plate and angle steel function in both the load bearing and the anchorage to the CFRP plate contributes more to the ultimate bearing capacity after the steel components yield. Finally, a calculating model is shown to accurately predict the ultimate bearing capacity after retrofitting, with an average error of 4.03%.
Functional Connectivity Differences in the Perception of Abstract and Figurative Paintings
Authors: Suhaili Iffah Syafiqah , Nagy Zoltan , Juhasz Zoltan
Published: October 2024

The goal of neuroaesthetic research is to understand the neural mechanisms underpinning the perception and appreciation of art. The human brain has the remarkable ability to rapidly recognize different artistic styles. Using functional connectivity, this study investigates whether there are differences in connectivity networks formed during the processing of abstract and figurative paintings. Eighty paintings (forty abstract and forty figurative) were presented in a random order for eight seconds to each of the 29 participants. High-density EEG recordings were taken, from which functional connectivity networks were extracted at several time points (−300, 100, 300 and 500 ms). The debiased weighted phase lag index (dwPLI) was used to extract the connectivity networks for the abstract and figurative conditions across multiple frequency bands. Significant connectivity differences were detected for both conditions at each time point and in each frequency band: delta (p < 0.0273), theta (p < 0.0292), alpha (p < 0.0299), beta (p < 0.0275) and gamma (p < 0.0266). The topology of the connectivity networks also varied over time and frequency, indicating the multi-scale dynamics of art style perception. The method used in this study has the ability to identify not only brain regions but their interaction (communication) patterns and their dynamics at distinct time points, in contrast to average ERP waveforms and potential distributions. Our findings suggest that the early perception stage of visual art involves complex, distributed networks that vary with the style of the artwork. The difference between the abstract and figurative connectivity network patterns indicates the difference between the underlying style-related perceptual and cognitive processes.
RSTSRN: Recursive Swin Transformer Super-Resolution Network for Mars Images
Authors: Wu Fanlu , Jiang Xiaonan , Fu Tianjiao , Fu Yao , Xu Dongdong , Zhao Chunlei
Published: October 2024

High-resolution optical images will provide planetary geology researchers with finer and more microscopic image data information. In order to maximize scientific output, it is necessary to further increase the resolution of acquired images, so image super-resolution (SR) reconstruction techniques have become the best choice. Aiming at the problems of large parameter quantity and high computational complexity in current deep learning-based image SR reconstruction methods, we propose a novel Recursive Swin Transformer Super-Resolution Network (RSTSRN) for SR applied to images. The RSTSRN improves upon the LapSRN, which we use as our backbone architecture. A Residual Swin Transformer Block (RSTB) is used for more efficient residual learning, which consists of stacked Swin Transformer Blocks (STBs) with a residual connection. Moreover, the idea of parameter sharing was introduced to reduce the number of parameters, and a multi-scale training strategy was designed to accelerate convergence speed. Experimental results show that the proposed RSTSRN achieves superior performance on 2×, 4× and 8×SR tasks to state-of-the-art methods with similar parameters. Especially on high-magnification SR tasks, the RSTSRN has great performance superiority. Compared to the LapSRN network, for 2×, 4× and 8× Mars image SR tasks, the RSTSRN network has increased PSNR values by 0.35 dB, 0.88 dB and 1.22 dB, and SSIM values by 0.0048, 0.0114 and 0.0311, respectively.
Spatiotemporal Prediction of Landslide Displacement Using Graph Convolutional Network-Based Models: A Case Study of the Tangjiao 1# Landslide in Chongqing, China
Authors: Sun Yingjie , Liu Ting , Zhang Chao , Xi Ning , Wang Honglei
Published: October 2024

Landslide displacement monitoring can directly reflect the deformation process of a landslide. Predicting landslide displacements using monitored time series data through deep learning is a useful method for landslide early warning. Currently, existing prediction models mainly focus on single-point time series displacement prediction and do not consider the spatial relationship between monitoring points. To fully take into account the temporal and spatial correlation of displacement monitoring data, this paper proposes two models based on the graph convolutional network (GCN) to perform spatiotemporal prediction of the displacement of the Tangjiao 1# landslide. Firstly, the landslide monitoring system is transformed into a fully connected graph (FCG) to depict the spatial relationship among monitoring points on the landslide. Secondly, a temporal graph convolutional network (T-GCN) model and an attention temporal graph convolutional network (A3T-GCN) model of landslide displacement based on the GCN and GRU models are established respectively. Thirdly, the two models are used to predict the displacement of the Tangjiao 1# landslide. The results show that the established spatiotemporal prediction models are effective in predicting the displacement of the Tangjiao 1# landslide, and the proposed A3T-GCN model achieves the highest prediction accuracy. Our conclusion validates the effectiveness of the attention mechanism in predicting landslide displacement.
Progressive Collapse Performance of Prefabricated Step-Terrace Frame Structures in Mountain Cities
Authors: Yang Youfa , Huang Zhenyu , Chen Anxu
Published: October 2024

Because they have foundations with two or more different elevations, the mechanical performance of structures in mountain cities is significantly different from that of common structures. Our simulation method and the parameters of a typical bolt steel plate-based dry connection joint were verified based on the static load test of a fully assembled substructure against progressive collapse. The load–displacement curves and the displacement–time history curves of the failure point of six kinds of column removal conditions were obtained by using static nonlinear and dynamic nonlinear analysis methods, establishing a different number of dropped stories and spans for the prefabricated step-terrace frame structure models. Based on the ultimate load amplification factor and the maximum dynamic displacement at the failure point, the progressive collapse performance of prefabricated frame structures in mountain cities, including the number of dropped stories and spans, was obtained. The results show that the progressive collapse performance of a prefabricated step-terrace frame structure is inferior to that of a common prefabricated frame structure. The progressive collapse performance is stronger when the upper ground columns fail than that when the dropped ground columns fail. The progressive collapse performance of the prefabricated step-terrace frame structure decreases gradually with the increase in the number of dropped stories and increases gradually with the increase in the number of dropped spans. With the increase in the number of dropped stories, the redundancy of the structure is reduced, and the progressive collapse performance of the dropped stories is reduced by more than that of the upper stories. The increase in the number of dropped spans increases the redundancy of the structure, and the improvement in the progressive collapse performance of the dropped stories is greater than that of the upper stories. This paper provides a reference for the progressive-collapse-resistant design of step-terrace frame structures in mountain cities.
Muscle Synergy of the Periarticularis Shoulder Muscles during a Wheelchair Propulsion Motion for Wheelchair Basketball
Authors: Tamura Yuki , Maeda Noriaki , Komiya Makoto , Iwamoto Yoshitaka , Tashiro Tsubasa , Arima Satoshi , Tsutsumi Shogo , Mizuta Rami , Urabe Yukio
Published: October 2024

Wheelchair basketball players often develop shoulder pain due to repetitive wheelchair propulsion motion. Wheelchair propulsion involves two phases, push and recovery, with several different muscles simultaneously active in each phase. Although differences in the coordinated activity of multiple muscles may influence the mechanism of injury occurrence, there have been no studies investigating muscle synergy in wheelchair propulsion motion. Twelve healthy adult males with no previous wheelchair driving experience were included. The surface electromyography data of 10 muscles involved in shoulder joint movements were measured during a 20 m wheelchair propulsion motion. Muscle synergies were extracted using non-negative matrix factorization analysis of the electromyography data. Four muscle synergies were identified during wheelchair propulsion. Synergy 1 reflects propulsion through shoulder flexion and elbow flexion, while Synergy 2 involves shoulder flexion and elbow extension. Synergy 3 describes shoulder extension returning the upper limb, which has moved forward during the push, back to its original position, and Synergy 4 relates to stabilize the shoulder girdle during the recovery phase. This study is the first to explore muscle synergy during wheelchair propulsion, and the data from healthy participants without disabilities or pain will provide a baseline for future comparisons with data from wheelchair basketball players.
Research on a Strategy for Prediction Methods of Submarine Self-Propulsion Hydrodynamic Performance
Authors: Xu Pei , Guo Yingchun , Zhang Yin
Published: October 2024

To investigate the impact of different computational strategies on the self-propulsion hydrodynamic performance of a submarine model, a study was conducted using the RANS method, employing both the steady-state Moving Reference Frame (MRF) method and the unsteady Rigid Body Motion (RBM) method. Numerical simulations of the self-propulsion hydrodynamic performance of the submarine model were performed under different computational strategies, including submarine with propeller, submarine first and propeller second, and propeller rotational speed iteration. The differences between these strategies were analyzed from various perspectives, such as submarine resistance, propeller hydrodynamic performance, total solver actual runtime, propeller unsteady bearing forces, and induced fluctuating pressure. The results indicated that when conducting numerical simulations of the self-propulsion hydrodynamic performance of the submarine with a propeller, the fluctuation ranges of the submarine resistance, propeller thrust, and torque obtained by the steady MRF method were within 0.1% to 0.8%, compared with the three strategies in the unsteady state. In terms of computational efficiency, strategy 3 (RBM—submarine with a propeller) had the lowest computational efficiency, and the time to obtain a self-propulsion curve was 11.9 times, 4.4 times, and 3.1 times that of strategy 1 (MRF + RBM—submarine with a propeller), strategy 2 (RBM—submarine first, then propeller), and strategy 4 (RBM—propeller rotational speed iteration). When analyzing the propeller excitation forces under the submarine’s self-propulsion state using the steady-state MRF method combined with the unsteady RBM method, the frequency domain peak fluctuations were within 5% compared with the three strategies in the unsteady state, making this method suitable for numerical simulations of propeller excitation forces. These findings provide methodological support for evaluating the performance of the submarine’s hydrodynamic propulsion system.
Deep Learning as a New Framework for Passive Vehicle Safety Design Using Finite Elements Models Data
Authors: Lahoz Navarro Mar , Jehle Jonas Siegfried , Apellániz Patricia A. , Parras Juan , Zazo Santiago , Gerdts Matthias
Published: October 2024

In recent years, passive vehicle safety has become one of the major concerns for the automotive industry due to the considerable increase in the use of cars as a means of daily transport. Since real crash testing has a high financial cost, finite element simulations are generally used, which entail high computational cost and long simulation times. In this paper, we make use of the recent advances in the deep learning field to propose an affordable method to provide reliable approximations of the finite element simulator model that significantly reduce the computational load and time required. We compare the prediction performance in crash tests of different models, namely feed-forward neural networks and bayesian neural networks, as well as two multi-output regression methods. Our results show promising results, as deep learning models are able to drastically reduce the engineering costs while providing a feasible first approximation to the passenger’s injuries in a crash event, thus being a potential game changer in the vehicle safety design process.
Differential Relapse of Proximal and Distal Segments after Mandibular Setback Surgery
Authors: Kim Jin-Deok , Lim Sung-Hoon , Park Jae Hyun
Published: October 2024

This study aimed to evaluate the differential positional changes of the proximal and distal segments during mandibular setback surgery relapse. Thirty patients with mandibular prognathism who underwent bilateral sagittal split ramus osteotomy were included. Skull models from pretreatment, postsurgery, and post-treatment cone-beam computed tomography were superimposed to evaluate condylar displacement and rotational changes, and to determine the center of rotation (CRot) in the proximal and distal segments. During postsurgical relapse, the proximal segment rotated 2.4 ± 2.1° counterclockwise, with the CRot located near the mandibular condyle. The distal segment rotated 2.0 ± 2.3° counterclockwise, resulting in forward and upward movement of the chin. The displaced condyle partially returned to its original position during postsurgical orthodontic treatment. A vertical bony step (VBS) between the proximal and distal segments commonly occurs during mandibular setback surgery. As the VBS increases, the CRot of the distal segment shifts posterosuperiorly, following a predictable pattern outlined by regression equations. These findings suggest that relapse after mandibular setback surgery consistently occurs in an anterosuperior direction, with translatory movement becoming more prominent as the VBS increases. Including these regression equations in presurgical planning can enhance the precision of relapse simulations and help clinicians more accurately anticipate postsurgical relapse.
The Influence of Rail Unevenness Profile on the Generation of Ground Vibrations by Railway Traffic
Authors: Colaço Aires , Alves Costa Pedro , Hussein Mohammed F. M.
Published: October 2024

This paper presents a sensitivity analysis to assess the impact of random rail unevenness profiles on the generation of ground-borne vibrations. The random profiles are generated from a power spectral density function, assuming that the uneven rail surface is governed by a stationary Gaussian random process in space. Different proposals are employed to generate these profiles. A previously validated PiP model is used to perform a set of parametric studies, involving the dynamic simulation of a train–track–tunnel–ground system. It was found that the number of harmonics in generating ground-borne vibrations has a residual impact on the evaluated vibration levels. The main differences observed, although not significant, are attributed to the stochastic nature of the rail unevenness profile. More critically, it is important to highlight that different existing approaches for generating the PSD function have a pronounced impact on the evaluated vibration levels.
Multilayer Perception-Based Hybrid Spectral Band Selection Algorithm for Aflatoxin B1 Detection Using Hyperspectral Imaging
Authors: Kabir Md. Ahasan , Lee Ivan , Singh Chandra B. , Mishra Gayatri , Panda Brajesh Kumar , Lee Sang-Heon
Published: October 2024

Aflatoxin B1 is a toxic substance in almonds, other nuts, and grains that poses potential serious health risks to humans and animals, particularly in warm, humid climates. Therefore, it is necessary to remove aflatoxin B1 before almonds enter the supply chain to ensure food safety. Hyperspectral imaging (HSI) is a rapid, non-destructive method for detecting aflatoxin B1 by analyzing specific spectral data. However, HSI increases data dimensionality and often includes irrelevant information, complicating the analysis process. These challenges make classification models for detecting aflatoxin B1 complex and less reliable, especially for real-time, in-line applications. This study proposed a novel hybrid spectral band selection algorithm to detect aflatoxin B1 in almonds based on multilayer perceptron (MLP) network weights and spectral refinement (W-SR). In the proposed process, the hyperspectral imaging (HSI) spectral rank was firstly generated based on MLP network weights. The rank was further updated using a spectral confidence matrix. Then, a spectral refinement process identified more important spectra from the lower-ranked ones through iterative processes. An exhaustive search was performed to select an optimal spectral subset, consisting of only the most significant spectral bands, to make the entire process suitable for real-time, in-line aflatoxin B1 detection in industrial environments. The experimental results using the artificially contaminated almonds dataset achieved a cross-validation accuracy of 98.67% with an F1-score of 0.982 for the standard normal variate (SNV) processed data with only four spectral bands. Comparative experiment results showed that the proposed MLPW-SR spectral band selection algorithm outperforms baseline methods.
Investigating Lane Departure Warning Utility with Survival Analysis Considering Driver Characteristics
Authors: Zhang Mingfang , Zhao Xiaofan , Wang Zixi , Zhang Tong
Published: October 2024

Previous studies have focused on the impact of individual factors on lane departure warning (LDW) utility during driving. However, comprehensive analysis has not been considered based on multiple variables, such as driver characteristics. This paper aims to propose a methodology in exploring the utility of LDW under varied warning timing situations, focusing on changes in driving style and distraction level to obtain the optimal warning timing matching relationship. A driving simulator experiment with a mixed 4 × 3 factor design was conducted. The design matrix includes four level of secondary task (ST) conditions and three warning timings situations for drivers with various driving styles. To estimate the utility of the LDW system, lane departure duration (LDD) was selected as a time-based measure of utility. Both the Kaplan-Meier method and COX model were applied and compared. Combined with questionnaire results, the results indicate that both driving style and distraction state are significant influence factors. Generally, the results suggest that the more aggressive drivers lead to the more severe lane departure behavior and they preferred late warning. In terms of distraction state, the LDD increases with the level of ST remarkably. This implies that the earlier warning timing should be given for the higher-level distraction state condition. It was also observed that adaptive warning timing is needed based on the analysis of the interactive effect among multiple variables. The results provide empirical data for the optimization of LDW system design.
Development of Integrated Driving Evaluation Index by Proportion of Autonomous Vehicles for Future Intelligent Transportation Systems
Authors: Kim Minkyung , Kim Hoseon , Oh Cheol
Published: October 2024

As the market penetration rate (MPR) of autonomous vehicles increases, it is expected that the safety of mixed traffic situations will change due to interactions between vehicles. A proactive safety analysis of mixed traffic situations is needed for future intelligent transportation systems; thus, it is necessary to determine the driving safety evaluation indicators that have a significant impact on identifying hazardous sections of actual roads by each MPR. The purpose of this study is to simulate autonomous vehicle behavior by analyzing real-world autonomous vehicle data and to derive a promising integrated driving safety evaluation index for mixed traffic. Autonomous vehicle driving data from an autonomous mobility testbed in Seoul were collected and analyzed to assess autonomous vehicle behavior in VISSIM. The simulation environment was established to match the real road environment. Decision tree (DT) analysis was adopted to derive the indicators influencing the classification of hazardous sections of real roads by MPR. The vehicle–vehicle interaction indicators used to evaluate driving safety were applied as the input variables of the DT, and the classification of real-world hazardous road sections was the output variable. An integrated evaluation index was developed using the promising evaluation indicators and information gains derived for each MPR. The most hazardous section and the factors affecting the driving safety of the section based on the integrated evaluation index for each MPR were then presented. The results of this study can be utilized to proactively identify hazardous road sections in the real world through simulations of mixed traffic conditions.
A Crowd-Intelligence-Driven, Multi-Attribute Decision-Making Approach for Product Form Design in the Cloud Environment
Authors: Chen Jian , He Zhaoxuan , Wang Weiwei , Wang Yi , Li Zhihan , Yang Xiaoyan
Published: October 2024

In the traditional decision-making process for product form design, designers and experts often prioritize schemes based on their own knowledge and experience. This approach can lead to an oversight of user preferences, ultimately affecting decision outcomes. In contrast, crowd-intelligence-driven, multi-attribute decision-making for product form design in the cloud environment builds upon traditional approaches by leveraging the vast and diverse expertise of individuals on cloud platforms, engaging participants from various fields and roles in the decision-making process to enhance comprehensiveness and accuracy. To address the issue of a single decision-maker and limited user participation in the decision-making process for product form design schemes in the cloud environment, a multi-attribute decision-making method integrating expert knowledge and user preferences is proposed. This method aims to select a product form design scheme that optimally balances expert and user satisfaction. Initially, the Pythagorean Hesitant Fuzzy Set (PHFS) is used to quantify qualitative product attributes and to establish a comprehensive multi-attribute evaluation system. In the aspect of expert decision-making, a gray correlation coefficient decision matrix based on expert knowledge is established and the overall score of the base alternative is calculated by the ViseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method and the Improved Osculating Value method. In terms of user decision-making, weights are determined by calculating the similarity between user evaluation matrices, and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is used to calculate scores for product form designs based on user preferences. Ultimately, optimal selection is achieved by aggregating the aforementioned expert evaluation values and user preference values. The method’s effectiveness and feasibility are confirmed through a case study of coffee machine product form design schemes.
Preadolescents’ Smile Outcomes after Two Different Orthodontic Treatments: Traditional Interceptive Therapy with Rapid Maxillary Expansion (RME)/Schwarz Plate and Clear Aligners
Authors: Rozzi Matteo , Mucedero Manuela , Fanelli Silvia , Bollero Patrizio , Cozza Paola
Published: October 2024

To compare preadolescents’ smile outcomes after two different orthodontic treatments: RME/Schwarz plate (RS) and clear aligners (CAs). A sample of 31 patients (13 M, 18 F, mean age 8.3 ± 1.2 y.m) treated with RS and a sample of 28 patients (12 M, 16 F, mean age 7.9 ± 1.1 y.m) treated with CAS were included. Pre-treatment and post-treatment records were taken before the treatment (T1) and at the end of treatment (T2). Seventeen variables were evaluated. Both therapies were effective in smile width increase (RS = p < 0.02; CAs = p < 0.04) and buccal corridor reduction (RS = p < 0.02; CAs = p < 0.04). The intragroup analysis showed in the RS group an increase of incisor gingival display (p < 0.02), overbite (OVB) (p < 0.04) alongside a reduction of overjet (OVJ) (p < 0.01). Conversely, the CAs group evidenced a reduction in incisor gingival display (p < 0.04) and OVB (p < 0.01) with an increase in C angulation (p < 0.02) and maxillary incisor inclination (p < 0.04). An intergroup comparison evidenced a greater improvement in smile width, smile index and buccal corridor in the RS group with respect to the CAs group (p < 0.02; p < 0.02; p < 0.03). CAs were more effective in the management of gingival display, incisor position, midline correction (incisor gingival display p < 0.01; maxillary incisor position p < 0.04; maxillary dental midline p < 0.02), with a better control of OVJ (p < 0.01) and OVB (p < 0.02). The RS approach resulted in a greater smile width and a reduction in buccal corridor with respect to the CAs. The CAs provided a better management of both gingival display and smile aesthetics.
Changing the Position of the Vehicle’s Center of Gravity as a Result of Different Load Distribution
Authors: Synák František , Nedeliaková Eva
Published: October 2024

Dynamical vehicle performances depend on its center of gravity’s position. It is necessary to determine this position as closely as possible. The current methods of determining the center of gravity’s position are either not sufficiently accurate or are very demanding in relation to finances, time and special equipment. At the same time, there is a lack of accurate data on the center of gravity’s position of current vehicles for further research. Therefore, the purpose of this study is to propose such a methodology of measuring the center of gravity’s position, including its height, which is accurate enough and has as low-demand measuring equipment as possible. Another purpose of this study is to obtain data on the change in the centers of gravity’s positions in different vehicles due to various load distributions. Extensive experimental measurements, according to the innovative methodology, were performed with five vehicles. The positions of the center of gravity were determined for various load distributions, and the results were analyzed in detail.
Numerical Analysis of the Dynamic Response of a High-Speed Railway Foundation across a Ground Fissure Zone—A Case Study of the Datong–Xi’an High-Speed Railway Crossing a Ground Fissure in the Taiyuan Basin, China
Authors: Xie Qingyu , Huang Qiangbing , Miao Chenyang , Gao Linfeng , He Guohui
Published: October 2024

The interaction between train vibration load and ground fissure disasters affects the safe operation of trains. However, the interaction between the high-speed railway foundation and the train vibration in the cross-ground fissure zone is not clear. To reveal the dynamic behavior characteristics of train vibration load crossing the ground fissure zone, the Da’xi high-speed railway passing through the ground fissure zone in the Taiyuan Basin is taken as the research object; the dynamic response of the high-speed railway foundation crossing the ground fissure zone at different angles was analyzed through dynamic finite element numerical simulation and orthogonal tests. The results show that when the high-speed railway crosses the ground fissure, the dynamic response fluctuates greatly at the ground fissure, which is manifested in the displacement and acceleration increase in the hanging wall and decrease in the footwall. The composite foundation reduces the fluctuation range and influences the scope of displacement, acceleration, and stress in the foundation of the ground fissure zone. The smaller the intersection angle between the high-speed railway and the ground fissure, the larger the influence range of displacement and stress, and the stability of acceleration at the hanging wall and footwall is poor. It is suggested that the high-speed railway pass through the ground fissure at a large angle. Additionally, the displacement fluctuation of the hanging wall and footwall can be controlled by increasing the pile length in the active area of the ground fissure.
Effective Prevention and Rehabilitation Strategies to Mitigate Non-Contact Anterior Cruciate Ligament Injuries: A Narrative Review
Authors: Franco Domenico , Ambrosio Luca , Za Pierangelo , Maltese Girolamo , Russo Fabrizio , Vadalà Gianluca , Papalia Rocco , Denaro Vincenzo
Published: October 2024

Non-contact anterior cruciate ligament injuries (NC-ACLs) represent a significant concern in sports medicine, particularly among athletes and physically active individuals. These injuries not only result in immediate functional impairment but also predispose individuals to long-term issues such as recurrent instability and early-onset osteoarthritis. This narrative review examines the biomechanical, neuromuscular, and environmental factors that contribute to the high incidence of NC-ACLs and evaluates the effectiveness of current prevention and rehabilitation strategies. The review identifies key risk factors, including improper landing mechanics, deficits in neuromuscular control, and muscle imbalances, which are pivotal in the etiology of NC-ACLs. Prevention programs that incorporate plyometric exercises, strength training, and neuromuscular education have shown efficacy in reducing injury rates. Rehabilitation protocols that emphasize a gradual return to sport, focusing on pain management, restoration of range of motion, and progressive strengthening, are critical for successful recovery and prevention of re-injury. The evidence suggests that an integrated approach, combining prevention and rehabilitation strategies tailored to the individual, is essential for minimizing NC-ACL risk and improving outcomes in affected populations.
Automated Vulnerability Exploitation Using Deep Reinforcement Learning
Authors: AlMajali Anas , Al-Abed Loiy , Ahmad Yousef Khalil M. , Mohd Bassam J. , Samamah Zaid , Abu Shhadeh Anas
Published: October 2024

The main objective of this paper is to develop a reinforcement agent capable of effectively exploiting a specific vulnerability. Automating pentesting can reduce the cost and time of the operation. While there are existing tools like Metasploit Pro that offer automated exploitation capabilities, they often require significant execution times and resources due to their reliance on exhaustive payload testing. In this paper, we have created a deep reinforcement agent specifically configured to exploit a targeted vulnerability. Through a training phase, the agent learns and stores payloads along with their corresponding reward values in a neural network. When encountering a specific combination of a target operating system and vulnerability, the agent utilizes its neural network to determine the optimal exploitation options. The novelty of this work lies in employing Deep Reinforcement Learning in vulnerability exploitation analysis. To evaluate our proposed methodology, we conducted training and testing on the Metasploitable platform. The training phase of the reinforcement agent was conducted on two use cases: the first one has one vulnerability, and the second one has four vulnerabilities. Our approach successfully achieved the attacker’s primary objective of establishing a reverse shell with a maximum accuracy of 96.6% and 73.6% for use cases one and two, respectively.
Deep Spatio-Temporal Graph Attention Network for Street-Level 110 Call Incident Prediction
Authors: Sui Jinguang , Chen Peng , Gu Haishuo
Published: October 2024

Recent advancements in crime prediction have increasingly focused on street networks, which offer finer granularity and a closer reflection of real-world urban dynamics. However, existing studies on street-level graph representation learning often overlook the variability in node features when aggregating information from neighboring nodes. This limitation reduces the model’s capacity to fully capture the diverse street attributes and their influence on crime patterns. To address this issue, we introduce an end-to-end deep spatio-temporal learning model that employs a graph attention mechanism (GAT) to analyze the spatio-temporal features of 110 call incidents. Experimental results show that our proposed model outperforms existing methods across multiple prediction metrics. Additionally, ablation studies confirm that the GAT’s capacity to capture spatial dependencies within the street network significantly enhances the model’s overall predictive performance.
Detection of Arc Faults in Transformer Windings via Transient Signal Analysis
Authors: Alpsalaz Feyyaz , Mamiş Mehmet Salih
Published: October 2024

In power transformers, lightning strikes, switching operations, and short circuit faults can deform the winding insulation, resulting in an electric arc between the windings. If the arc is not detected in its initial phase, it may lead to a solid short circuit and damage the transformer, potentially causing an explosion due to overheating and high pressure. In this study, winding arcs in the transformer are identified from the terminal current and voltage signals. A 3D magnetic model of a 15 MVA power transformer is constructed in Ansys@Maxwell, and the nonlinear arc model is simulated in Matlab@Simulink. The transient voltage and current signals related to the arcing conditions at five different points in the high-voltage side winding are obtained by running Ansys and Matlab simultaneously using ANSYS@Simplorer (Twin Builder). These signals are transformed into the frequency domain using Fast Fourier Transform (FFT). The arcs are detected from the transient-generated frequency components of the transformer voltage and current signals.
Decreases in Sympathetic Activity Due to Low-Intensity Extremely Low-Frequency Electric Field Treatment Revealed by Measurement of Spontaneous Fluctuations in Skin Conductance in Healthy Subjects
Authors: Shinba Toshikazu , Nedachi Takaki , Harakawa Shinji
Published: October 2024

(1) Background: Our previous studies indicated that low-intensity extremely low-frequency electric field (ELF-EF) treatment alters autonomic activities, as revealed through heart rate variability (HRV) analysis. However, the high-frequency (HF) component of HRV that reflects parasympathetic activity showed no changes either during or after the end of the treatment, suggesting the involvement of sympathetic nerves. (2) Methods: To examine this issue in the present study, the effect of ELF-EF on skin conductance (SC), which is controlled solely by sympathetic nerves, was analyzed. Twelve healthy subjects underwent a 20 min ELF-EF treatment (applied voltage: 9 kV, induced current density: below 6 mA/m2) and a sham treatment in a random order with an interval of more than 2 weeks. SC and HRV were recorded under the eyes-open condition during a 2 min period both before and after the treatment. (3) Results: The number of spontaneous fluctuations in skin conductance (SC-SFs) significantly decreased after the ELF-EF treatment, suggesting psychological changes, including relaxation. The skin conductance level, heart rate, and HRV indices did not change after the ELF-EF treatment. (4) Conclusion: The results support the idea that low-intensity ELF-EF affects autonomic nerves by reducing sympathetic activity, as reflected by SC-SFs.
Optimizing Daylighting Typology in Religious Buildings: A Case Study of Electrochromic Glazing Integration in the Masjid Al-Shagroud
Authors: Mesloub Abdelhakim , Hafnaoui Rim , Ibrahim Ahmed Osman , Doulos Lambros T.
Published: October 2024

Mosques, revered as sacred spaces, are distinguished by their sustainable architectural designs. A critical aspect of these designs is the implementation of daylighting strategies, essential for ensuring adequate light levels, particularly for reading the Holy Quran. Traditional daylighting typologies in mosques, such as multi-lateral lighting, clerestories, skylights, and shading devices, are widely recognized. However, the potential application of electrochromic (EC) switchable glazing within mosque architecture remains underexplored. This study delves into the effectiveness of various daylighting typologies, assessing their performance before and after the integration of EC switchable glazing in the Al-Shagroud Mosque located in Saudi Arabia. Employing comprehensive radiance simulations in accordance with the LEEDv4.1 standards, the study evaluates additional metrics including useful daylight illuminance (UDI300–3000lux), workplane illuminance (WPI), uniformity index (Ui), and daylight glare probability (DGP) under multiple scenarios. The results demonstrate that clerestory lighting effectively meets LEEDv4.1 benchmarks but exhibits limitations in achieving uniform light distribution, particularly in the men’s prayer hall. In contrast, the women’s prayer hall benefits from a combination of clerestory lighting and shading devices, which improves light quality and mitigates glare issues. However, the use of skylights without adequate shading leads to excessive WPI and high DGP levels, compromising visual comfort. Moreover, managing daylight quantity and quality through the integration of different typologies, including hybrid models, presents substantial challenges. The integration of EC switchable glazing, while maintaining optimal WPI and reducing DGP, offers enhanced visual comfort in prayer areas. Nevertheless, this technology faces difficulties in ensuring even light distribution, as evidenced by lower uniformity index values. This research highlights the critical need for optimizing daylighting in mosques to balance sustainability and comfort, without detracting from their sacred ambiance.
Antimicrobial Photodynamic Therapy Combined with Photobiomodulation Therapy in Teeth with Asymptomatic Apical Periodontitis: A Case Series
Authors: Rubio Francisco , Arnabat-Domínguez Josep , Sans-Serramitjana Eulàlia , Saa Camila , Grzech-Leśniak Kinga , Betancourt Pablo
Published: October 2024

Introduction: Apical periodontitis (AP) is an inflammatory disease of the periapical tissues that is often asymptomatic and diagnosed through radiographic examination. A challenge in traditional endodontics is disinfection of the root canal system (RCS), which anatomically presents numerous variations, often leading to persistent infections. Antimicrobial photodynamic therapy (aPDT) and photobiomodulation therapy (PBMT) offer promising adjuncts, due to their antimicrobial and tissue-healing properties. Objective: The aim of this article was to report five cases of teeth with pulp necrosis and asymptomatic apical periodontitis (AAP) treated with aPDT and PBMT protocols. Materials and Methods: Five cases of pulp necrosis and AAP were treated with conventional endodontic therapy supplemented with aPDT and PBMT. The treatment protocol included chemomechanical preparation (CMP), aPDT using a 660 nm diode laser (DL) with methylene blue (MB) as a photosensitizer (5 min pre-irradiation time), and PBMT using a 940 nm DL. Treatment results were evaluated through cone-beam computed tomography (CBCT)-based evaluation over 1 year of clinical follow-up. Results: All cases showed significant bone regeneration and tissue healing, demonstrating the efficacy of the combination of aPDT and PBMT. Post-operative pain did not occur in any of the patients, suggesting a possible analgesic effect of PBMT. Conclusions: The combination of aPDT and PBMT in endodontic therapy promoted tissue recovery and improved the prognosis of AAP. Further research and randomized control trials are needed to optimize treatment protocols and evaluate the long-term effects.
The Use of Language Models to Support the Development of Cartographic Descriptions of a Building’s Interior
Authors: Lipka Krzysztof , Gotlib Dariusz , Choromański Kamil
Published: October 2024

The development and popularization of navigation applications are increasing expectations for their quality and functionality. Users need continuous navigation not only outdoors, but also indoors. In this case, however, the perception of space and movement is somewhat different than it is outside. One potential method of meeting this need may be the use of so-called geo-descriptions—multi-level textual descriptions relating to a point, line or area in a building. Currently, geo-descriptions are created manually. However, this is a rather time-consuming and complex process. Therefore, this study undertook to automate this process as much as possible. The study uses classical methods of spatial analysis from GIS systems and text generation methods based on artificial intelligence (AI) techniques, i.e., large language models (LLM). In this article, special attention will be paid to the second group of methods. As part of the first stage of the research, which was aimed at testing the proposed concept, the possibility of LLMs creating a natural description of space based on a list of features of a given place obtained by other methods (input parameters for AI), such as coordinates and categories of rooms around a given point, etc., was tested. The focus is on interior spaces and a few selected features of a particular place. In the next stages, it is planned to extend the research to spaces outside buildings. In addition, artificial intelligence can be used to provide the input parameters mentioned above.
Perturbation Theory Machine Learning Model for Phenotypic Early Antineoplastic Drug Discovery: Design of Virtual Anti-Lung-Cancer Agents
Authors: Kleandrova Valeria V. , Cordeiro M. Natália D. S. , Speck-Planche Alejandro
Published: October 2024

Lung cancer is the most diagnosed malignant neoplasm worldwide and it is associated with great mortality. Currently, developing antineoplastic agents is a challenging, time-consuming, and costly process. Computational methods can speed up the early discovery of anti-lung-cancer chemicals. Here, we report a perturbation theory machine learning model based on a multilayer perceptron (PTML-MLP) model for phenotypic early antineoplastic drug discovery, enabling the rational design and prediction of new molecules as virtual versatile inhibitors of multiple lung cancer cell lines. The PTML-MLP model achieved an accuracy above 80%. We applied the fragment-based topological design (FBTD) approach to physicochemically and structurally interpret the PTML-MLP model. This enabled the extraction of suitable fragments with a positive influence on anti-lung-cancer activity against the different lung cancer cell lines. By following the aforementioned interpretations, we could assemble several suitable fragments to design four novel molecules, which were predicted by the PTML-MLP model as versatile anti-lung-cancer agents. Such predictions of potent multi-cellular anticancer activity against diverse lung cancer cell lines were rigorously confirmed by a well-established virtual screening tool reported in the literature. The present work envisages new opportunities for the application of PTML models to accelerate early antineoplastic discovery from phenotypic assays.
Using Educational Chatbots with Metacognitive Feedback to Improve Science Learning
Authors: Yin Jiaqi , Zhu Yi , Goh Tiong-Thye , Wu Wen , Hu Yi
Published: October 2024

Educational chatbots (ECs) can offer instructional feedback to enhance learning. However, the effect of metacognitive feedback on science education has not been fully explored. This study focuses on the effect of the EC with metacognitive feedback on students’ knowledge retention, transfer, and intrinsic motivation in the field of biology science. A between-group experimental design with 62 college students was conducted. Students in the experiment group received metacognitive feedback, whereas students in the control group received no feedback. The results of the ANCOVA test showed that students in the experiment group demonstrated better knowledge retention and transfer than those in the control group (F = 13.11, p = 0.001; F = 14.39, p < 0.001). Further, students in the experiment group reported more learning interest and higher perceived competence and value than those in the control group (F = 3.72, p = 0.001; F = 1.91, p = 0.009; F = 2.70, p = 0.004). In addition, correlation analysis revealed that perceived competence in the metacognitive feedback group was positively related to knowledge transfer (r = 0.39, p = 0.032). However, there was no significant difference in perceived pressure between both groups (p = 0.203). This study highlights the potential of the EC with metacognitive feedback for science learning.
Bioreactors: Applications and Innovations for a Sustainable and Healthy Future—A Critical Review
Authors: Palladino Fernanda , Marcelino Paulo Ricardo Franco , Schlogl Andersen Escobar , José Álvaro Henrique Mello , Rodrigues Rita de Cássia Lacerda Brambilla , Fabrino Daniela Leite , Santos Igor José Boggione , Rosa Carlos Augusto
Published: October 2024

Biotechnological processes are essential for developing economies that aim to stand out in future markets. The use of bioreactors is one of the most important unit operations of biotechnological processes, and real-time monitoring of bioreactors is essential to ensure precise bioprocess control. This review presents different types of bioreactors, sensors, and applications in other sectors. Bioreactors, controlled systems for cultivating microorganisms and cells, are essential tools in various fields, from scientific research to industrial production. The use of a variety of sensors is critical for accurate, real-time monitoring, early problem detection, reproducibility, cost reduction, and increased efficiency. These benefits are being realized in numerous applications, including biofuel production, bioremediation and leaching processes, tissue engineering, and drug manufacturing. Innovations in bioreactor technology are expanding opportunities for a more sustainable and healthier future. By developing new types of bioreactors, integrating advanced sensors, and exploring promising applications, bioreactors are playing a key role in addressing global challenges and sustainably advancing science and technology.