Damage detection and evaluation are concerns in structural health monitoring. Traditional damage detection techniques are inefficient because of the need for damage detection before evaluation. To address these problems, a novel crack location and degree detector based on YOLOX is proposed, which directly realizes damage detection and evaluation. Moreover, the detector presents a superior detection effect and speed to other advanced deep learning models. Additionally, rather than at the pixel level, the detection results are determined in actual scales according to resolution. The results demonstrate that the proposed model can detect and evaluate damage accurately and automatically. Full article

Elevating the accuracy of streamflow forecasting has always been a challenge. This paper proposes a three-step artificial intelligence model improvement for streamflow forecasting. Step 1 uses long short-term memory (LSTM), an improvement on the conventional artificial neural network (ANN). Step 2 performs multi-step ahead forecasting while establishing the rates of change as a new approach. Step 3 further improves the accuracy through three different kinds of optimization algorithms. The Stormwater and Road Tunnel project in Kuala Lumpur is the study area. Historical rainfall data of 14 years at 11 telemetry stations are obtained to forecast the flow at the confluence located next to the control center. Step 1 reveals that LSTM is a better model than ANN with R 0.9055, MSE 17,8532, MAE 1.4365, NSE 0.8190 and RMSE 5.3695. Step 2 unveils the rates of change model that outperforms the rest with R = 0.9545, MSE = 8.9746, MAE = 0.5434, NSE = 0.9090 and RMSE = 2.9958. Finally, Stage 3 is a further improvement with R = 0.9757, MSE = 4.7187, MAE = 0.4672, NSE = 0.9514 and RMSE = 2.1723 for the bat-LSTM hybrid algorithm. This study shows that the $\delta Q$ model has consistently yielded promising results while the metaheuristic algorithms are able to yield additional improvement to the model’s results. Full article

The importance of fat mass estimation in multiple areas related to health and sports has led to the emergence of a large number of methods and formulas for its estimation. The aim of the present study was to compare the agreement and differences between different formulas for estimating fat mass by anthropometry. Eighty-seven subjects underwent an anthropometric assessment following the protocol from the International Society for the Advancement of Kinanthropometry (ISAK). The fat percentage was calculated with 14 different formulas for men and with 12 different formulas for women. In the case of men, they were proposed by Durnin-Womersley, Yuhasz, Faulkner, Carter, Peterson, Katch-McArdle, Sloan, Wilmore, Evans, Lean, Reilly, Civar, Hastuti, and Kerr. In the case of women, the equations used were those proposed by Durnin-Womersley, Yuhasz, Faulkner, Carter, Peterson, Katch-McArdle, Sloan, Wilmore, Evans, Lean, Thorland, and Kerr. Significant differences were found between the formulas in both men (8.90 ± 2.17% to 17.91 ± 2.84%; p < 0.001–0.016) and women (15.33 ± 2.94% to 28.79 ± 3.30%; p < 0.001–0.004). It was observed that in the case of men, the Carter and Yuhasz formulas and the Civar and Faulkner formulas showed moderate agreement with each other (CCC = 0.910–0.915). In the case of women, it was observed that the Carter and Yuhasz formulas showed moderate agreement with each other (CCC = 0.974). In conclusion, the formulas used for the estimation of lipid mass in anthropometry reported significantly different results between them and were therefore not comparable. Full article

Under the influence of various forces, the conflict between Russia and Ukraine is violent and changeable. The obtaining of battlefield data by conventional means is difficult but necessary in order to ensure security, reliability, and comprehensiveness. The use of remote sensing technology can make up for the deficiencies of conventional methods. By using night-time light data, the total number of night-time lights in the built-up areas of Ukrainian cities within 36 days of the outbreak of the Russian–Ukrainian conflict is compiled in this paper. Furthermore, the dynamic changes in night-time light at the national, regional, and urban scales are analyzed by using the night-time light ratio index and the dynamic degree model combined with the time-series night-time light data. The results show that (1) after the outbreak of the war, more than 60% of the night-time lights in Ukrainian cities were lost. In terms of the night-time light recovery speed, the night-time lights in the pro-Russian areas recovered significantly faster, followed by Russian-controlled areas, and the recovery speed in areas of conflict was the lowest. (2) Decision-making by belligerents affects non-combatant activities and thus corresponds to light at night. The loss of night-time light will be reduced if military operations are reduced and mitigated if humanitarian operations are increased. (3) The changes in night-time light reflect the changes in the conflict situation well. When the conflict between Russia and Ukraine intensifies, the overall change of night-time light shows a downward trend. In this context, night-time light data can be used as an effective source to deduce and predict battlefield situations. Full article

This study aimed to describe the acceleration–speed (AS) profile of soccer players during competition and to analyse their seasonal changes and inter-player differences. The AS profile values (theoretical maximum acceleration (A₀) and speed (S₀)) of 14 elite soccer players were studied in 18 matches, which were divided into five season periods. The main findings showed the A₀ (6.20 ± 0.51 m/s²) and S₀ (9.18 ± 0.58 m/s) average team season values. Significant individual changes (p < 0.05 and effect size (Eta-squared, η²) > 0.5) were confirmed for A₀ (Players 4 and 8) and S₀ (Players 6, 8 and 11). Additionally, standard deviations (SD±) confirmed small (±0.20–0.60) to moderate (±0.60–1.20) seasonal variations for most players in A₀ (SD range: ±0.22 to ±0.69 m/s²) and S₀ (SD range: ±0.27 to ±0.90 m/s). SD showed small to moderate inter-player differences for each period for A₀ (SD range: ±0.39 to ±0.61 m/s²) and S₀ (SD range: ±0.53 to ±0.61 m/s). In summary, coaches are recommended to assess the AS profile to diagnose potential player seasonal changes in sprinting performance, especially for A₀, which seems to be more sensitive to variations than S₀. Full article

Road surfaces are affected by rain, snow, and ice, which influence traffic flow. In this paper, a microscopic traffic flow model based on weather conditions is proposed. This model characterizes traffic based on the weather severity index. The Intelligent Driver (ID) model characterizes traffic behavior based on a constant acceleration exponent resulting in similar traffic behavior regardless of the conditions, which is unrealistic. The ID and proposed models are evaluated over a circular road of length $800$ m. The results obtained indicate that the proposed model characterizes the velocity and density better than the ID model. Further, variations in the traffic flow with the proposed model are smaller during adverse weather, as expected. It is also shown that traffic is stable with the proposed model, even during adverse weather. Full article

With the popularity of online education, multiple technology-based educational tools are gradually being introduced into online learning. The role of gamification in online education has been of interest to researchers. Based on learners’ visual, auditory, and kinesthetic (VAK) learning styles, this study uses an empirical research method to investigate the behavioral intention of students to participate in online gamified classrooms in selected universities located in Guangdong province and Macao. The main contributions of this study are to focus on the impact that differences in learning styles may have on the behavioral intentions of learners and to include the “perceived learning task” as an external variable in the theoretical framework. The main research findings are: perceived usefulness and enjoyment are partially mediated between VAK learning styles and the intention to participate in online gamified classrooms; and perceived learning tasks are partially mediated between perceived usefulness and the intention to participate in online gamified classrooms. According to the findings and the Technology Acceptance Model (TAM), this study constructs the Technology-Learning Behavior Acceptance Model (T-LBAM) to explore the intrinsic influencing factors of students’ intention to participate in gamified online classes and makes suggestions for future online gamification teaching. Full article

The aim of this work is to achieve reliable nondestructive evaluation (NDE) of adhesively bonded aerospace components by developing novel multidimensional data fusion techniques, which would combine the information obtained by ultrasonic and X-ray NDE methods. Separately, both NDE techniques have their advantages and limitations. The integration of data obtained from pulse echo immersion ultrasound testing and radiography holds immense potential to help improve the reliability of non-destructive evaluation. In this study, distinctive features obtained from single techniques, traditional ultrasonic pulse echo testing, and radiography, as well as fused images, were investigated and the suitability of these distinctive features and fusion techniques for improving the probability of defect detection was evaluated. For this purpose, aluminum single lap joints with brass inclusions were analyzed using ultrasound pulse echo and radiography techniques. The distinctive features were extracted from the data obtained, and images of features obtained by both techniques were fused together. Different combinations of features and fusion algorithms were investigated, considering the desire to automate data evaluation in the future. Full article

Tapping panel dryness (TPD), a complex physiological syndrome associated with the rubber tree (Hevea brasiliensis Muell. Arg.), causes cessation of latex drainage upon tapping and thus threatens rubber production. Rubber tree virus 1 (RTV1) is a novel positive-sense single-stranded RNA virus from the Betaflexiviridae (genus Capillovirus), which has been established to cause TPD. MicroRNAs (miRNAs) play an important role in the interplay between viruses and host cells. In this study, we identified the rubber tree genome-encoded miRNAs and their therapeutic targets against RTV1. We applied computational algorithms to predict target binding sites of rubber tree miRNAs potentially targeting RTV1 RNA genome. Mature rubber-tree miRNAs are retrieved from the miRBase database and are used for hybridization of the RTV1 genome. A total of eleven common rubber-tree miRNAs were identified based on consensus genomic positions. The consensus of four algorithms predicted the hybridization sites of the hbr-miR396a and hbr-miR398 at common genomic loci (6676 and 1840), respectively. A miRNA-regulatory network of rubber tree was constructed with the RTV1— ORFs using Circos, is illustrated to analyze therapeutic targets. Overall, this study provides the first computational evidence of the reliable miRNA–mRNA interaction between specific rubber tree miRNAs and RTV1 genomic RNA transcript. Therefore, the predicted data offer valuable evidence for the development of RTV1-resistant rubber tree in the future. Our work suggests that similar computational host miRNA prediction strategies are warranted for identification of the miRNA targets in the other viral genomes. Full article

Pests and diseases are an inevitable problem in agricultural production, causing substantial economic losses yearly. The application of convolutional neural networks to the intelligent recognition of crop pest images has become increasingly popular due to advances in deep learning methods and the rise of large-scale datasets. However, the diversity and complexity of pest samples, the size of sample images, and the number of examples all directly affect the performance of convolutional neural networks. Therefore, we designed a new target-detection framework based on Cascade RCNN (Regions with CNN features), aiming to solve the problems of large image size, many pest types, and small and unbalanced numbers of samples in pest sample datasets. Specifically, this study performed data enhancement on the original samples to solve the problem of a small and unbalanced number of examples in the dataset and developed a sliding window cropping method, which could increase the perceptual field to learn sample features more accurately and in more detail without changing the original image size. Secondly, combining the attention mechanism with the FPN (Feature Pyramid Networks) layer enabled the model to learn sample features that were more important for the current task from both channel and space aspects. Compared with the current popular target-detection frameworks, the average precision value of our model (mAP@0.5) was 84.16%, the value of (mAP@0.5:0.95) was 65.23%, the precision was 67.79%, and the F1 score was 82.34%. The experiments showed that our model solved the problem of convolutional neural networks being challenging to use because of the wide variety of pest types, the large size of sample images, and the difficulty of identifying tiny pests. Full article

Almost all experimental studies of the characteristics of nuclear-excited plasma formed by excitation of gaseous media with nuclear reactions products are conducted at pulsed nuclear reactors that differ in the composition and design of the core, the duration, flux and fluence of the neutron pulse, the impulse repetition frequency, the volume and configuration of the irradiation space. This paper presents a description of the experimental (methodical and hardware) base of the National Nuclear Center of RK (Kurchatov) to conduct experiments on studying the threshold characteristics of laser action at the p-s-transition of noble gas atoms upon ⁶Li(n,α)³H nuclear reaction products excitation in conditions of a pulsed nuclear IGR reactor. To conduct in-pile reactor experiments, a special experimental facility was developed and constructed. The experimental facility functionally includes: in-pile experimental device, a gas–vacuum system, information and measurement system consisting of a system for registering and controlling the temperature of the device housing, and a system for registering optical radiation. The paper also briefly describes the methodology of in-pile reactor experiments on the pulsed nuclear reactor. Full article

To improve the seismic capacity of prestressed concrete (PC) continuous girder bridges with corrugated steel webs (CSWs), suitable damping control measures can be used to effectively reduce the seismic response of the bridge. Based on the semi-active control theory, the semi-active control system of a three-span PC continuous girder bridge with CSWs is designed, and the semi-active control system program of the three-span PC continuous girder bridge with CSWs is compiled by MATLAB. The time–history curves of damper energy consumption of active optimal control algorithm and three different semi-active control algorithms are compared and analyzed, as are the time–history curves of main girder displacement, acceleration, and pier internal force with or without semi-active control. The study shows that the rational determination of the weight matrix coefficient can make the active control achieve a better vibration absorption effect and economy. The semi-active control algorithm of Hrovat has the best vibration absorption effect, which is closest to that of the active optimal control algorithm. Under the state of semi-active control, the average vibration absorption rate of displacement and acceleration of the main girder with CSWs are 71% and 20%, respectively. The time–history curve of bending moment and shear force in the pier bottom is similar, and the average vibration absorption rate of bending moment and shear force at the bottom of pier #2 is 70%. At the same time, the average vibration absorption rate of bending moment and shear force at the bottom of pier #3 is between 42% and 48%. The semi-active control measure has a good vibration absorption effect on the overall seismic response of the PC continuous girder bridges with CSWs. This study provides a certain reference for the seismic reduction and isolation design of composite structure bridge with CSWs. Full article

Fires in university laboratories often lead to serious casualties and property damage, and traditional sensor-based fire detection techniques suffer from fire warning delays. Current deep learning algorithms based on convolutional neural networks have the advantages of high accuracy, low cost, and high speeds in processing image-based data, but their ability to process the relationship between visual elements and objects is inferior to Transformer. Therefore, this paper proposes an improved YOLOX target detection algorithm combining Swin Transformer architecture, the CBAM attention mechanism, and a Slim Neck structure applied to flame smoke detection in laboratory fires. The experimental results verify that the improved YOLOX algorithm has higher detection accuracy and more accurate position recognition for flame smoke in complex situations, with APs of 92.78% and 92.46% for flame and smoke, respectively, and an mAP value of 92.26%, compared with the original YOLOX algorithm, SSD, Faster R-CNN, YOLOv4, and YOLOv5. The detection accuracy is improved, which proves the effectiveness and superiority of this improved YOLOX target detection algorithm in fire detection. Full article

This paper presents an autonomous grasping approach for complex-shaped objects using an anthropomorphic robotic hand. Although human-like robotic hands have a number of distinctive advantages, most of the current autonomous robotic pickup systems still use relatively simple gripper setups such as a two-finger gripper or even a suction gripper. The main difficulty of utilizing human-like robotic hands lies in the sheer complexity of the system; it is inherently tough to plan and control the motions of the high degree of freedom (DOF) system. Although data-driven approaches have been successfully used for motion planning of various robotic systems recently, it is hard to directly apply them to high-DOF systems due to the difficulty of acquiring training data. In this paper, we propose a novel approach for grasping complex-shaped objects using a high-DOF robotic manipulation system consisting of a seven-DOF manipulator and a four-fingered robotic hand with 16 DOFs. Human demonstration data are first acquired using a virtual reality controller with 6D pose tracking and individual capacitive finger sensors. Then, the 3D shape of the manipulation target object is reconstructed from multiple depth images recorded using the wrist-mounted RGBD camera. The grasping pose for the object is estimated using a residual neural network (ResNet), K-means clustering (KNN), and a point-set registration algorithm. Then, the manipulator moves to the grasping pose following the trajectory created by dynamic movement primitives (DMPs). Finally, the robot performs one of the object-specific grasping motions learned from human demonstration. The suggested system is evaluated by an official tester using five objects with promising results. Full article

Warm-Mix-Asphalt (WMA) allows aggregates to be coated with asphalt binder at a temperature lower than the Hot-Mix-Asphalt (HMA) temperatures by using additives that make the asphalt mixtures more workable and the asphalt binder viscosity reduced. Due to the cost and environmental advantages of WMA, it is now more frequently employed in the mixtures of asphalt pavement. WMA with two percentages of zeolite (0.3% and 0.5%) is used in this research. The optimum asphalt content of WMA mixtures has a lower value compared to the HMA mixture. According to Marshall Stability’s findings, the HMA has a higher value of stability, whereas the WMA mixtures for both percentages of zeolite have a lower value of stability. WMA mixture with 0.5% zeolite has a higher Marshall Flow value, which is followed by zeolite at 0.3% and the lower value of flow was for HMA. The resilient modulus for both HMA and WMA was determined. The findings indicate that, for the two percentages of zeolite, the resilience modulus of the WMA mixtures was lower than that of the HMA. Additionally, the findings indicate that the WMA has a high potential resistance against rutting that competes with the HMA mixture. Full article