Applied Sciences

Using the temperature and seepage field-coupling module within COMSOL Multiphysics software, we examined freezing behavior and its evolving patterns in curved underground freezing pipes. This study employed transient states, with the Darcy’s law and porous-media heat-transfer options activated in the Physical Field Interface of the Physical Field and Variable Selection column. The models were created to establish numerical models of freezing reinforcement for both single and multiple pipes with various curvatures. These models were designed to simulate the evolving temperature and seepage fields of soil under diverse freezing conditions. Subsequently, this research utilized the models to simulate the freezing and consolidation conditions of a shallowly buried tunnel within the context of shallow tunnel conditions. The study reveals that after freezing a single pipe using water flow, the change in thickness of the frozen wall in curved pipes is notably smaller than that in straight pipes. This difference is particularly pronounced in the upstream section. Specifically, at a distance of −2000 mm from the main surface, the change in thickness of the frozen wall in straight pipes exceeds that in s = 7 curved pipes by approximately 350 mm. The smaller the long arc ratio s, the greater the arc of the freezing tube and the better the water-blocking effect. In the multi-pipe freezing model, the s = 7 curved pipes exhibit a frozen-wall thickness approximately 120 mm greater than that of straight pipes at a distance of −2000 mm from the main surface. Under the condition of a shallow buried concealed excavation with surging water, a pipe with a long arc ratio s = 7 arc freezing at 46 d attains a permafrost curtain thickness that is equivalent to that achieved by the straight pipe freezing at 58 d. This reduction in thickness shortens the working period by 12 days, resulting in a more efficient process. The successful application of the freezing method in the water-rich aquifer is expected to be a valuable reference for similar projects in the future. Full article

In recent years, human activity monitoring and recognition have gained importance in providing valuable information to improve the quality of life. A lack of activity can cause health problems including falling, depression, and decreased mobility. Continuous activity monitoring can be useful to prevent progressive health problems. With this purpose, this study presents a wireless smart insole with four force-sensitive resistors (FSRs) that monitor foot contact states during activities for both indoor and outdoor use. The designed insole is a compact solution and provides walking comfort with a slim and flexible structure. Moreover, the inertial measurement unit (IMU) sensors designed in our previous study were used to collect 3-axis accelerometer and 3-axis gyroscope outputs. Smart insoles were located in the shoe sole for both right and left feet, and two IMU sensors were attached to the thigh area of each leg. The sensor outputs were collected and recorded from forty healthy volunteers for eight different gait-based activities including walking uphill and descending stairs. The obtained datasets were separated into three categories; foot contact states, the combination of acceleration and gyroscope outputs, and a set of all sensor outputs. The dataset for each category was separately fed into deep learning algorithms, namely, convolutional long–short-term memory neural networks. The performance of each neural network for each category type was examined. The results show that the neural network using only foot contact states presents 90.1% accuracy and provides better performance than the combination of acceleration and gyroscope datasets for activity recognition. Moreover, the neural network presents the best results with 93.4% accuracy using a combination of all the data compared with the other two categories. Full article

People are forced to use all types of soil, especially bad soils, as infrastructure demands grow. Different procedures must be used to ameliorate these poor soils, which are fragile during building. Natural resource depletion and the rising costs of available materials force us to consider alternative supplies. For several years, researchers have investigated the use of by-products from industry and associated approaches to improve the qualities of various soils. Coarse Aggregate Crushing Waste (CACW) is a waste product that results from the primary crushing of aggregates. Massive amounts of CACW are produced in the business, posing serious issues from handling to disposal. As a result, the widespread use of CACW for diverse purposes has been recommended in the civil engineering profession to address these concerns. Because some natural resources, such as gravel, are nonrenewable, it is vital to decrease their consumption and replace them with recycled, cost-effective, and ecologically acceptable alternatives. This research aimed to investigate the possibility of reusing CACW to improve the geotechnical properties of silty sand (SM) soil available in the Najran region. In this research, soil samples were collected from Najran city and subjected to a variety of lab experiments to determine their characterization. Mixes were designed for a parent soil with a range of percentages of CACW with/without 2% cement. The designed mixes were examined through a set of lab tests to obtain the optimum design for use in road construction. The findings of the tests showed that the optimum dosage is 10% CACW with 2% cement, raising the undrained shear strength of silty sand soil by 323%, CBR by 286%, and P-wave by 180%. The durability tests show that soil mixed with 10% CACW and 2% cement fulfills the requirements and stays within the 14% weight loss limit imposed by the Portland Cement Association (PCA). The microscopic investigation results confirmed the outcomes obtained by macro tests. As a result, the carbon footprint values decrease when CAWA is added, making this treatment approach almost carbon neutral. This study clarifies the long-term effects of CACW on improving the geotechnical characteristics of silty sand soil in the Najran Region of the Kingdom of Saudi Arabia and other comparable soils globally. Full article

The production of commercially used cement-based bricks has significant environmental implications, necessitating the development of robust, environmentally friendly alternatives. This study assesses the strength and durability of soil bricks by utilizing an eco-friendly stabilizer, which includes lime and natural-fiber-derived staple fibers. Soil bricks, each sized 50 mm × 100 mm and featuring varying proportions of stabilizer and wood chips, were subjected to unconfined compression and bending strength tests, permeability assessments, steel ball/golf ball (SB/GB) evaluations, and wetting–drying tests. The results demonstrated that higher stabilizer ratios and lower wood chip ratios led to enhanced unconfined compressive strength. Additionally, repeated wetting–drying cycles reduced the strength by up to 63%, while the relative dynamic modulus of elasticity decreased by as much as 45% with increasing wetting–drying cycles. Notably, the eco-friendly stabilizer significantly improved soil shear strength, ultimately enhancing the durability of the soil bricks. Full article

In recent years, the vehicle-based indirect Structural Health Monitoring (iSHM) method has been increasingly used to identify the dynamic characteristics of railway bridges during train crossings, and it has been shown that this method has several advantages compared to traditional SHM methods. A major advantage is that sensors are just mounted on the vehicle, and no sensors or data acquisition systems need to be installed on the railway bridge. In this paper, the application of the vehicle-based iSHM method is demonstrated numerically and experimentally for determining the natural frequencies of railway steel bridges during train crossing. The coupled linear equations of motion of the train-bridge multi-body system are derived, and train crossing simulations are conducted numerically, considering different train speeds. Three different railway bridges are considered, and the train-induced vibration responses are calculated for both the train multi-body system and the railway bridge models. Different representative evaluation points are chosen for the wheelsets, bogies, and car bodies of the considered vehicle. To calibrate the numerical model, the resonance frequencies of an existing single-span steel bridge are measured in situ by the application of forced vibration tests. Besides the executed in situ measurements of the bridge, the considered crossing vehicle is also instrumented with several accelerometers at the wheelsets, bogies, and car bodies, and the vibration responses of both the bridge and the crossing vehicle are measured simultaneously during the duration of several train crossings with different train speeds. The recorded vibration responses are analyzed in the frequency domain and compared with numerical simulation results. It is shown that the first bending frequency of the considered railway bridge can be clearly identified from the computed frequency response spectra and that the vehicle-based iSHM method provides a promising tool for identifying the dynamic characteristics of railway bridges. Full article

This paper aims to determine the effects of local corrosion at three different corrosion areas, the (1) entire area, (2) the constant moment area, and (3) the constant shear area, on the flexural performance of RC beams. To analyze this, an experimental study was carried out to prepare two series of RC beams (200 × 300 × 2800 mm) created with three different degrees of corrosion, inducing local rebar corrosion. Furthermore, two series of experimental tests were conducted under different loading types: monotonic and cyclic loading. It was observed that the strength capacity reduction grew in the RC specimens with induced corrosion in the order of the (1) entire area > (2) the constant moment area > (3) the constant shear area, as the average corrosion rate increased. Our test results further showed that the yield and ultimate strength were kept nearly equivalent to the uncorroded RC specimen, with average corrosion rates of 10% and 15%, respectively. Over these corrosion rates, the yield strength and ultimate strength dropped significantly. Compared to the test results under a monotonic loading condition, the structural capacity under a cyclic loading condition decreased, with a more pronounced tendency for each corrosion case as the corrosion rate increased. Longitudinal cracks developed throughout and adjacent to the corrosion areas as the corrosion rate increased. Thus, we can infer that strength reductions may be strongly influenced by these longitudinal cracks. Full article

In this study, innovative enhancements of rectangular tube-type buckling-restrained braces are proposed to prevent bulging failure on the surface of the outer restrainer and validated experimentally. First, an inner restrainer composed of a bent plate, which increases the stiffness and strength to resist outward force exerted by the steel core subjected to higher-mode buckling, is installed inside the outer restrainer. Second, the unbonding material surrounding the steel core is partially thickened to create additional space to prevent the outward force from being transferred directly along the centerline of the cross-section. Buckling-restrained braces with and without the enhancements are tested via cycling loading to validate the efficiency of the proposed enhancements. Improvements in strength and deformation capacity are evaluated quantitatively. The proposed enhancements increased the compressive strength and cumulative inelastic deformation capacity of the buckling-restrained braces. However, the increased outward force owing to the compression-hardening phenomenon led to bulging failure, where the added inner restrainer terminated. An analytical formula is proposed to estimate the outward-force-resisting capacity of the inner restrainer, which predicted bulging failure adequately. Full article

(1) Background: The distribution of tumor volumes is important for various aspects of cancer research. Unfortunately, tumor volume is rarely documented in tumor registries; usually only maximum tumor diameter is. This paper presents a method to derive tumor volume distributions from tumor diameter distributions. (2) Methods: The hypothesis is made that tumor maximum diameters d are Weibull distributed, and tumor volume is proportional to d^k, where k is a parameter from the Weibull distribution of d. The assumption is tested by using a test dataset of 176 segmented tumor volumes and comparing the k obtained by fitting the Weibull distribution of d and from a direct fit of the volumes. Finally, tumor volume distributions are calculated from the maximum diameters of the SEER database for breast, NSCLC and liver. (3) Results: For the test dataset, the k values obtained from the two separate methods were found to be k = 2.14 ± 0.36 (from Weibull distribution of d) and 2.21 ± 0.25 (from tumor volume). The tumor diameter data from the SEER database were fitted to a Weibull distribution, and the resulting parameters were used to calculate the corresponding exponential tumor volume distributions with an average volume obtained from the diameter fit. (4) Conclusions: The agreement of the fitted k using independent data supports the presented methodology to obtain tumor volume distributions. The method can be used to obtain tumor volume distributions when only maximum tumor diameters are available. Full article

Coherent multi-transducer ultrasound (CoMTUS) imaging creates an extended effective aperture through the coherent combination of multiple arrays, which results in images with enhanced resolution, extended field-of-view, and higher sensitivity. However, this also creates a large discontinuous effective aperture that presents additional challenges for current beamforming methods. The discontinuities may increase the level of grating and side lobes and degrade contrast. Also, direct transmissions between multiple arrays, happening at certain transducer relative positions, produce undesirable cross-talk artifacts. Hence, the position of the transducers and the scan sequence play key roles in the beamforming algorithm and imaging performance of CoMTUS. This work investigates the role of the distribution of the individual arrays and the scan sequence in the imaging performance of a coherent dual-array system. First, the imaging performance for different configurations was assessed numerically using the point-spread-function, and then optimized settings were tested on a tissue mimicking phantom. Finally, a subset of the proposed optimum imaging schemes was experimentally validated on two synchronized ULA OP-256 systems equipped with identical linear arrays. Results show that CoMTUS imaging performance can be enhanced by optimizing the relative position of the arrays and the scan sequence together, and that the use of apodization can reduce cross-talk artifacts without degrading spatial resolution. Adding weighted compounding further decreases artifacts and helps to compensate for the differences in the brightness across the image. Setting the maximum steering angle according to the spatial configuration of the arrays reduces the sidelobe energy up to 10 dB plus an extra 4 dB reduction is possible when increasing the number of PWs compounded. Full article

Due to their effective and affordable joining capabilities, resistance spot-welded (RSW) structures are widely used in many industries, including the automotive, aerospace, and manufacturing sectors. Because spot-welded structures are frequently subjected to cyclic stress conditions while in service, fatigue failure is a serious concern. It is essential to comprehend and predict their fatigue behavior in order to guarantee the dependability and durability of the relevant engineering products. The analysis of fatigue failure in spot-welded structures is the main topic of this paper, along with the prediction of fatigue life (N_f) and identification of failure mechanisms. Also, the effects of parameters such as the amount of cyclic load applied, the load ratio, and size of the spot-welding on the N_f were investigated. To achieve this, the fatigue performance of spot-welded joints was simulated using the extended finite element method (XFEM). The XFEM method is particularly suited for capturing intricate crack patterns in spot-welded structures because it allows for the modeling of crack propagation without the need for remeshing. It was observed that when the cycling load was decreased by 20%, N_f increased by around 250%. On the other hand, the fatigue life of the structure, and, hence, the crack propagation rate, was significantly affected by the load ratio and diameter of the spot-welding. This paper presents the details of the novel approach to studying spot-weld fatigue characterization using XFEMs to simulate crack propagation. Full article

Community outdoor public spaces are indispensable to urban residents’ daily lives. Analyzing community outdoor public spaces from a behavioral perspective is crucial and an effective way to support human-centered development in urban areas. Traditional behavioral analysis often relies on manually collected behavioral data, which is time-consuming, labor-intensive, and lacks data breadth. With the use of sensors, the breadth of behavioral data has greatly increased, but its accuracy is still insufficient, especially in the fine-grained differentiation of populations and behaviors. Computer vision is more efficient in distinguishing populations and recognizing behaviors. However, most existing computer vision applications face some challenges. For example, behavior recognition is limited to pedestrian trajectory recognition, and there are few that recognize the diverse behaviors of crowds. In view of these gaps, this paper proposes a more efficient approach that employs computer vision tools to examine different populations and different behaviors, obtain important statistical measures of spatial behavior, taking the Bajiao Cultural Square in Beijing as a test bed. This population and behavior recognition model presents several improvement strategies: Firstly, by leveraging an attention mechanism, which emulates the human selective cognitive mechanism, it is capable of accentuating pertinent information while disregarding extraneous data, and the ResNet backbone network can be refined by integrating channel attention. This enables the amplification of critical feature channels or the suppression of irrelevant feature channels, thereby enhancing the efficacy of population and behavior recognition. Secondly, it uses public datasets and self-made data to construct the dataset required by this model to improve the robustness of the detection model in specific scenarios. This model can distinguish five types of people and six kinds of behaviors, with an identification accuracy of 83%, achieving fine-grained behavior detection for different populations. To a certain extent, it solves the problem that traditional data face of large-scale behavioral data being difficult to refine. The population and behavior recognition model was adapted and applied in conjunction with spatial typology analysis, and we can conclude that different crowds have different behavioral preferences. There is inconsistency in the use of space by different crowds, there is inconsistency between behavioral and spatial function, and behavior is concentrated over time. This provides more comprehensive and reliable decision support for fine-grained planning and design. Full article

The gradually increasing presence of arsenic, a highly toxic heavy metal, poses a significant threat to both soil environmental safety and human health. Pteris vittata has long been recognized as an efficient hyperaccumulator plant for arsenic pollution. However, the pattern of arsenic accumulation in soil impacts its bioavailability and restricts the extraction efficiency of Pteris vittata. To address this issue, microorganisms have the potential to improve the arsenic accumulation efficiency of Pteris vittata. In this work, we employed anthropogenic enrichment methods to extract functional iron–sulfur-reducing bacteria from soil as a raw material. These bacteria were then utilized to assist Pteris vittata in the phytoremediation of arsenic-contaminated soil. Furthermore, the utilization of organic fertilizer produced from fermented crop straw significantly boosted the remediation effect. This led to an increase in the accumulation efficiency of arsenic by Pteris vittata by 87.56%, while simultaneously reducing the content of available arsenic in the soil by 98.36%. Finally, the experimental phenomena were studied through a soil-microbial batch leaching test and plant potting test. And the mechanism of the microorganism-catalyzed soil iron–sulfur geochemical cycle on arsenic release and transformation in soil as well as the extraction effect of Pteris vittata were systematically investigated using ICP, BCR sequential extraction and XPS analysis. The results demonstrated that using iron–sulfur-reducing microorganisms to enhance the phytoremediation effect is an effective strategy in the field of ecological restoration. Full article

This study addresses dynamic task allocation challenges in coordinated surveillance involving multiple unmanned aerial vehicles (UAVs). A significant concern is the increased UAV flight distance resulting from the assignment of new missions, leading to decreased reconnaissance efficiency. To tackle this issue, we introduce a collaborative multi-target and multi-UAV reconnaissance scheme. Initially, the multitasking constrained multi-objective optimization framework (MTCOM) is employed to optimize task allocation and reconnaissance time in static scenarios. Subsequently, in case of emergency, we iteratively refine the outcomes of static task allocation through an enhanced auction-based distributed algorithm, effectively reducing UAV flight costs in response to new missions, UAV withdrawal, or damage. Simulation results demonstrate the efficacy of our proposed multi-UAV and multi-target cooperative reconnaissance scheme in resolving dynamic task allocation issues. Additionally, our approach achieves a 5.4% reduction in UAV flight distance compared to traditional allocation methods. The main contribution of this paper is to consider a dynamic scenario model involving UAV damage and the emergence of new reconnaissance areas. Then we propose an innovative collaborative multi-target and multi-UAV reconnaissance scheme to address this issue and, finally, conduct experimental simulations to verify the effectiveness of the algorithm. Full article

Cancer diagnosis and treatment depend on accurate cancer-type prediction. A prediction model can infer significant cancer features (genes). Gene expression is among the most frequently used features in cancer detection. Deep Learning (DL) architectures, which demonstrate cutting-edge performance in many disciplines, are not appropriate for the gene expression data since it contains a few samples with thousands of features. This study presents an approach that applies three feature selection techniques (Lasso, Random Forest, and Chi-Square) on gene expression data obtained from Pan-Cancer Atlas through the TCGA Firehose Data using R statistical software version 4.2.2. We calculated the feature importance of each selection method. Then we calculated the mean of the feature importance to determine the threshold for selecting the most relevant features. We constructed five models with a simple convolutional neural networks (CNNs) architecture, which are trained using the selected features and then selected the winning model. The winning model achieved a precision of 94.11%, a recall of 94.26%, an F1-score of 94.14%, and an accuracy of 96.16% on a test set. Full article

This study focuses on the extraction and semantic analysis of data from tables, emphasizing the importance of understanding the semantics of tables to obtain useful information. The main goal was to develop a technology using the ontology for the semantic analysis of tables. An iterative algorithm has been proposed that can parse the contents of a table and determine cell types based on the ontology. The study presents an automated method for extracting data in various languages in various fields, subject to the availability of an appropriate ontology. Advanced techniques such as cosine distance search and table subject classification based on a neural network have been integrated to increase efficiency. The result is a software application capable of semantically classifying tabular data, facilitating the rapid transition of information from tables to ontologies. Rigorous testing, including 30 tables in the field of water resources and socio-economic indicators of Kazakhstan, confirmed the reliability of the algorithm. The results demonstrate high accuracy with a notable triple extraction recall of 99.4%. The use of Levenshtein distance for matching entities and ontology as a source of information was key to achieving these metrics. The study offers a promising tool for efficiently extracting data from tables. Full article

A personal-computer-based and a Raspberry Pi single-board computer-based virtual force sensor with EtherCAT communication for a six-axis robotic arm are proposed in this paper. Both traditional mathematical modeling and machine learning techniques are used in the establishment of the dynamic model of the robotic arm. Thanks to the high updating rate of EtherCAT, the machine learning-based dynamic model on a personal computer achieved an average correlation coefficient between the estimated torque and the actual torque feedback from the motor driver of about 0.99. The dynamic model created using traditional mathematical modeling and the Raspberry Pi single-board computer demonstrates an approximate correlation coefficient of 0.988 between the estimated torque and the actual torque. The external torque observer is established by calculating the difference between the actual torque and the estimated torque, and the virtual force sensor converts the externally applied torques calculated for each axis to the end effector of the robotic arm. When detecting external forces applied to the end effector, the virtual force sensor demonstrates a correlation coefficient of 0.75 and a Root Mean Square Error of 12.93 N, proving its fundamental competence for force measurement. In this paper, both the external torque observer and the virtual force control are applied to applications related to sensing external forces of the robotic arm. The external torque observer is utilized in the safety collision detection mechanism. Based on experimental results, the system can halt the motion of the robotic arm using the minimum external force that the human body can endure, thereby ensuring the operator’s safety. The virtual force control is utilized to implement a position and force hybrid controller. The experimental results demonstrate that, under identical control conditions, the position and force hybrid controller established by the Raspberry Pi single-board computer achieves superior control outcomes in a constant force control scenario with a pressure of 40 N. The average absolute error is 9.62 N, and the root mean square error is 11.16 N when compared to the target pressure. From the analysis of the results, it can be concluded that the Raspberry Pi system implemented in this paper can achieve a higher control command update rate compared to personal computers. As a result, it can provide greater control benefits in position and force hybrid control. Full article

The diagnosis and prognosis of aeronautical-bearing health conditions are essential to proactively ensuring efficient power transmission, safety, and reduced downtime. The rarity of failures in such safety-critical systems drives this process towards data-driven analytics of fault injection and aging experiments, rather than complex physics-based modeling. Nonetheless, data-based condition monitoring is very challenging due to data complexity, unavailability, and drift resulting from distortions generated by harsh operating conditions, scarcity of failure patterns, and rapid data change, respectively. Accordingly, the objective of this work is three-fold. First, to reduce data complexity and improve feature space representation, a robust data engineering scheme, including feature extraction, denoising, outlier removal, filtering, smoothing, scaling, and balancing, is introduced in this work. Second, collaborative selection-based incremental deep transfer learning (CSIDTL) is introduced to overcome the problem of the lack of patterns, incrementing the number of source domains in different training rounds. Third, long short-term memory (LSTM) adaptive learning rules are fully taken into account to combat further data complexity and data change problems. The well-structured methodology is applied on a huge dataset of aeronautical bearings dedicated to both diagnostic and prognosis studies, which perfectly addresses the above challenges in a form of a classification problem with 13 different conditions, 7 operating modes, and 3 stages of damage severity. Conducting CSIDTL following a three-fold cross-validation process allows us to improve classification performance by about 12.15% and 10.87% compared with state-of-the-art methods, reaching classification accuracy rates of 93.63% and 95.65% in diagnosis and prognosis, respectively. Full article

Virtual reality (VR) environments provide a high level of immersion that expands the possibilities for perspective-taking so that people can be in the shoes of others. In that regard, VR storytelling environments are good for situating people in a real migration story. Previous research has investigated how users engage in narrative VR experiences. However, there is a lack of research on the predictors of engagement in VR storytelling environments. To fill this gap in the literature, this study aims to identify the predictors of engagement when VR is used as a medium to tell a migration story. A structural model based on hypotheses was validated using partial least squares structural equation modeling (PLS-SEM) with data from the interaction of 212 university students with a tailor-made VR experience developed in Unity to engage people in two migration stories. The results show that our model explains 55.2% of the variance in engagement because of the positive influence of immersion, presence, agency, usability, and user experience (UX). Full article