bluuebunny/crossref_metadata_2025 huggingface dataset:
Damage Assessment of Shear Wall Structures in an Earthquake–Blast Disaster Chain
Authors: Xue Zhaoyu , Huo Linsheng , Ying Peiyu , Li Hongnan
Published: November 2022

Shear wall structures are widely used in civil engineering, and their seismic design has been the focus of much attention. Explosions can result from the rupture and ignition of gas pipelines under seismic action, and there are currently no methods to appropriately assess the damage levels of shear wall structures under such hazard chains. Thus, this study provides a two-stage analysis method for the damage assessment of shear wall structures in an earthquake–blast disaster chain. A damage index of the structure is derived to evaluate the damage state. A 12-story shear wall structure is analyzed numerically to demonstrate the proposed assessment method. The results show that the damage state (DS) of the shear wall structure in an earthquake–blast disaster chain can be increased by one or even two levels when explosive loads are introduced following exposure to seismic actions. Therefore, the earthquake–blast disaster chain has an important impact on the response of shear wall structures and warrants further study to find ways that can better protect such structures against similar disasters.
Phytochemical Composition of the Fruits and Leaves of Raspberries (Rubus idaeus L.)—Conventional vs. Organic and Those Wild Grown
Authors: Kotuła Marta , Kapusta-Duch Joanna , Smoleń Sylwester , Doskočil Ivo
Published: November 2022

Nutrition is an influential determinant of the risk of present-day metabolic diseases. Raspberries (Rubus idaeus L.) are extraordinary berries with a high nutritional and bioactive component complex. They have a number of major essential minerals and trace elements as well as dietary fibre and other important constituents. This study aimed to analyse and compare raspberry fruits and leaves originated from organic versus conventional agricultural practices and wild grown for the contents of basic composition (water, crude fat, total protein, ash, digestible carbohydrates and dietary fibre) and selected minerals (calcium, iron, potassium, magnesium, sodium, phosphorus, sulphur, selenium, barium, lithium, and beryl) as well as selected antioxidant properties (total polyphenols, total carotenoids, anthocyanin content, and antioxidant activity by methods of ABTS and FRAP). This study was carried out regardless of climatic and agro-technical factors and was of a more consumer-oriented nature, in order to recognize the diversity of raspberry fruits and leaves from more or less monitored crops. The basic composition, mineral content and selected antioxidative properties of raspberry fruits and leaves are fundamentally different. Raspberry fruits have a lower content of protein and ash, and higher levels of dietary fibre and carbohydrates in comparison to fruits. The biggest difference is the amount of protein, whose content in leaves is two to three times higher versus fruits. Raspberry leaves have been found to have a higher mineral content than raspberry fruits and were characterized by up to five times the amount of total polyphenols, with respect to raspberry fruit, regardless of source. The content of total carotenoids was found in some cases to be 100 times higher in raspberry leaves, in comparison to fruit, regardless of origin. It has not been definitely identified, both for raspberry fruits and leaves, which method of growing is the most advantageous in terms of levels of basic nutrients, selected minerals and antioxidant properties.
A Prototype Passive Solar Drying System: Exploitation of the Solar Chimney Effect for the Drying of Potato and Banana
Authors: Firfiris Vasileios K. , Kaffe Zoi D. , Kalamaras Sotirios D. , Lithourgidis Antonios A. , Martzopoulou Anastasia G. , Kotsopoulos Thomas A.
Published: November 2022

Agricultural product drying is of great importance as it is a reliable method for fruit and vegetable preservation. Tackling the high energy consumption of the process will reduce the final product cost and mitigate greenhouse gas emissions. In this work, a passive drying method was experimentally evaluated. The method was based on the principle of the stack effect taking place in the solar chimney structure. Different types of solar chimneys in terms of dimensions and materials were evaluated for the drying of banana and potato slices. The results of the experiments showed that the drying rate was close to solar drying systems. Parameters such as height and material characteristics of drying tubes, as also weather conditions, influenced the drying rate. It was found that the banana and potato slices were dried at a satisfactory rate for almost 48 h during the summer period in Greece. From the parameters of the drying tubes that were varied, it was found that both the height and material played a major role, as did the air flow rate. With the increase in the drying tube by 1 m and with the choice of proper manufacturing material, an increase in the flow rate between 40% and 100% can be achieved. When only the color of two 3 m-high tubes changed, the flow rate varied between 4% and 15%. The proposed method has almost zero energy consumption, and it could be used as a standalone or as a part of a hybrid drying system. It can also be adjusted in existing greenhouse-type agricultural structures as a parallel operation system.
SIMONE: A Dynamic Monitoring Simulator for the Evacuation of Navy Ships
Authors: Martinez-Grueira Heitor , Asorey-Cacheda Rafael , Garcia-Sanchez Antonio-Javier , Garcia-Haro Joan
Published: November 2022

In this paper, the automation of the evacuation process of a military ship is studied in real time. For this purpose, a scenario is reconfigured to produce a failure or damage. Then, an optimal network of alternative escape routes is computed. The resulting escape route map can be indicated by lighting the appropriate corridors on the ship. Through these corridors, the members of the embarked population and the entire process is monitored so that the crew can reach their lifeboats in the shortest possible time. To undertake this automated process, the dynamic ship evacuation monitoring system (SIMONE, from its acronym in Spanish: Sistema de Monitorización Dinámica de Evacuación de Buques) has been developed. This system integrates a communication gateway with the integrated platform control system (IPCS) and integrated lighting system that will be installed in new Spanish naval constructions.
Binary Ebola Optimization Search Algorithm for Feature Selection and Classification Problems
Authors: Akinola Olatunji , Oyelade Olaide N. , Ezugwu Absalom E.
Published: November 2022

In the past decade, the extraction of valuable information from online biomedical datasets has exponentially increased due to the evolution of data processing devices and the utilization of machine learning capabilities to find useful information in these datasets. However, these datasets present a variety of features, dimensionalities, shapes, noise, and heterogeneity. As a result, deriving relevant information remains a problem, since multiple features bottleneck the classification process. Despite their adaptability, current state-of-the-art classifiers have failed to address the problem, giving rise to the exploration of binary optimization algorithms. This study proposes a novel approach to binarizing the Ebola optimization search algorithm. The binary Ebola search optimization algorithm (BEOSA) uses two newly formulated S-shape and V-shape transfer functions to investigate mutations of the infected population in the exploitation and exploration phases, respectively. A model is designed to show a representation of the binary search space and the mapping of the algorithm from the continuous space to the discrete space. Mathematical models are formulated to demonstrate the fitness and cost functions used for evaluating the algorithm. Using 22 benchmark datasets consisting of low, medium and high dimensional data, we exhaustively experimented with the proposed BEOSA method and six other recent similar feature selection methods. The experimental results show that the BEOSA and its variant BIEOSA were highly competitive with different state-of-the-art binary optimization algorithms. A comparative analysis of the classification accuracy obtained for eight binary optimizers showed that BEOSA performed competitively compared to other methods on nine datasets. Evaluation reports on all methods revealed that BEOSA was the top performer, obtaining the best values on eight datasets and eight fitness and cost functions. Computation for the average number of features selected showed that BEOSA outperformed other methods on 11 datasets when population sizes of 75 and 100 were used. Findings from the study revealed that BEOSA is effective in handling the challenge of feature selection in high-dimensional datasets.
Tensile Characteristics and Fracture Mode of Frozen Fractured Rock Mass Based on Brazilian Splitting Test
Authors: Wang Tingting , Li Pingfeng , Tang Chun’an , Zhang Bingbing , Yu Jiang , Geng Tao
Published: November 2022

Frozen fractured rock mass is often encountered during the implementation of geotechnical engineering in cold regions. The tensile strength parameters of frozen rock play an important role in the construction of rock slopes involving tensile failure. In order to study the tensile characteristics of a frozen fractured rock mass in a cold region, original rock specimens were mined and processed in the Yulong Copper Mine, and artificial, frozen fractured marble specimens were made. The effects of different ice-filled crack angles, lengths, and widths on the force–displacement curve and the tensile strength of frozen rock were studied by laboratory Brazilian splitting experiments and RFPA3D, and the evolution law of the tensile strength of frozen rock was revealed. At the same time, wing crack initiation and cracking mode after tensile failure were analyzed by high-speed camera; the whole process of the Brazilian splitting of frozen rock was reconstructed, and the development of microcrack initiation in frozen rock was analyzed. The following conclusions were drawn from the test results: the frozen rock specimens have typical brittle-failure characteristics. The tensile strength of frozen rock decreases gradually with the increase in the width and length of ice-filled cracks, and decreases first and then increases with the increase in the angle of the ice-filled crack. The ice-filled crack incurs damage first, and then the wing cracks start from the tip of the ice-filled crack and extend continuously. The tensile strength of frozen rock is significantly affected by the angle and length of ice-filled cracks.
Seismic Assessment and Retrofitting of an Historical Masonry Building Damaged during the 2016 Centro Italia Seismic Event
Authors: Zucca Marco , Reccia Emanuele , Longarini Nicola , Cazzani Antonio
Published: November 2022

The preservation and definition of the correct retrofitting interventions of historic masonry buildings represents a relevant topic nowadays, especially in a country characterized by high seismicity zones. Considering the Italian Cultural Heritage, most of these buildings are constructed in ancient unreinforced masonry (URM) and showed a high level of vulnerability during the recent 2009 (L’Aquila), 2012 (Emilia Romagna) and 2016 (Centro Italia) earthquakes. In this paper, the seismic assessment of an historic masonry building damaged during 2016 Centro Italia seismic event is presented considering different types of retrofitting interventions. Starting from the results obtained by the post-earthquake survey, different finite element models have been implemented to perform linear and non-linear analyses useful to understand the seismic behaviour of the building and to define the appropriate retrofitting interventions. In particular, reinforced plaster layer and cement-based grout injections have been applied in each masonry wall of the building in order to improve their horizontal load-bearing capacity, and an additional wall made with Poroton blocks and M10 cement mortar has been built adjacent to the central stairwell. In addition, in view of the need to replace the roof seriously damaged during the seismic event, a cross-laminated roof solution characterized by a thickness equal to 14 cm (composed by seven layers, each 2 cm thick) has been proposed. The results show that the proposed retrofitting interventions have led to a significant improvement in the seismic behaviour of the building.
Detecting Anomalies in Network Communities Based on Structural and Attribute Deviation
Authors: Zardi Hedia , Karamti Hanen , Karamti Walid , Alghamdi Norah Saleh
Published: November 2022

Anomaly detection in online social networks (OSNs) is an important data mining task that aims to detect unexpected and suspicious users. To enhance anomaly exploration, anomaly ranking is used to assess the degree of user anomaly rather than applying binary detection methods, which depend on identifying users as either anomalous users or normal users. In this paper, we propose a community-based anomaly detection approach called Community ANOMaly detection (CAnom). Our approach aims to detect anomalous users in an OSN community and rank them based on their degree of deviation from normal users. Our approach measures the level of deviation in both the network structure and a subset of the attributes, which is defined by the context selection. The approach consists of two phases. First, we partition the network into communities. Then, we compute the anomaly ranking score, which is composed of a community-structure-based score and an attribute-based score. Experiments on real-world benchmark datasets show that CAnom detects ground-truth groups and outperforms baseline algorithms on accuracy. On synthetic datasets, the results show that CAnom has high AUC and ROC curves even when the attribute number increases; therefore, our model is suitable for today’s applications, where the number of attributes is rising.
Chaos
Authors: Fortuna Luigi , Buscarino Arturo
Published: November 2022

The fascinating subject of chaos has been the focus of researchers and scientists from many fields over the last 40 years [...]
Wearable Light-and-Motion Dataloggers for Sleep/Wake Research: A Review
Authors: Danilenko Konstantin V. , Stefani Oliver , Voronin Kirill A. , Mezhakova Marina S. , Petrov Ivan M. , Borisenkov Mikhail F. , Markov Aleksandr A. , Gubin Denis G.
Published: November 2022

Long-term recording of a person’s activity (actimetry or actigraphy) using devices typically worn on the wrist is increasingly applied in sleep/wake, chronobiological, and clinical research to estimate parameters of sleep and sleep-wake cycles. With the recognition of the importance of light in influencing these parameters and with the development of technological capabilities, light sensors have been introduced into devices to correlate physiological and environmental changes. Over the past two decades, many such new devices have appeared from different manufacturers. One of the aims of this review is to help researchers and clinicians choose the data logger that best fits their research goals. Seventeen currently available light-and-motion recorders entered the analysis. They were reviewed for appearance, dimensions, weight, mounting, battery, sensors, features, communication interface, and software. We found that all devices differed from each other in several features. In particular, six devices are equipped with a light sensor that can measure blue light. It is noteworthy that blue light most profoundly influences the physiology and behavior of mammals. As the wearables market is growing rapidly, this review helps guide future developments and needs to be updated every few years.
Hybrid Inductive Model of Differentially and Co-Expressed Gene Expression Profile Extraction Based on the Joint Use of Clustering Technique and Convolutional Neural Network
Authors: Babichev Sergii , Yasinska-Damri Lyudmyla , Liakh Igor , Škvor Jiří
Published: November 2022

The development of hybrid models focused on gene expression data processing for the allocation of differentially expressed and mutually correlated genes is one of the current directions in modern bioinformatics. The solution to this problem can allow us to improve the effectiveness of existing systems for complex diseases diagnosis based on gene expression data analysis on the one hand and increase the efficiency of gene regulatory network reconstruction procedures by more careful selection of genes by considering the type of disease on the other hand. In this research, we propose a stepwise procedure to form the subsets of mutually correlated and differentially expressed gene expression profiles (GEP). Firstly, we allocate an informative GEP in terms of statistical and entropy criteria using the Harrington desirability function. Then, we performed cluster analysis using SOTA and spectral clustering algorithms implemented within the framework of objective clustering inductive technology. The result of this step’s implementation is a set of clusters containing co- and differentially expressed GEPs. Validation of the model was performed using a one-dimensional two-layer convolutional neural network (CNN). The analysis of the simulation results has shown the high efficiency of the proposed model. The clusters of GEPs formed based on the clustering quality criteria values allowed us to identify the investigated objects with high accuracy. Moreover, the simulation results have also shown that the hybrid inductive model based on the spectral clustering algorithm is more effective in comparison with the use of the SOTA clustering algorithm in terms of both the complexity of the formed optimal cluster structure and the classification accuracy of the objects that contain the allocated gene expression data as attributes. The proposed hybrid inductive model contributes to increasing objectivity during the formation of the subsets of differentially and co-expressed gene expression profiles for further their application in various disease diagnosis systems and for gene regulatory network reconstruction.
Emotion Detection Using Facial Expression Involving Occlusions and Tilt
Authors: Qazi Awais Salman , Farooq Muhammad Shoaib , Rustam Furqan , Villar Mónica Gracia , Rodríguez Carmen Lili , Ashraf Imran
Published: November 2022

Facial emotion recognition (FER) is an important and developing topic of research in the field of pattern recognition. The effective application of facial emotion analysis is gaining popularity in surveillance footage, expression analysis, activity recognition, home automation, computer games, stress treatment, patient observation, depression, psychoanalysis, and robotics. Robot interfaces, emotion-aware smart agent systems, and efficient human–computer interaction all benefit greatly from facial expression recognition. This has garnered attention as a key prospect in recent years. However, due to shortcomings in the presence of occlusions, fluctuations in lighting, and changes in physical appearance, research on emotion recognition has to be improved. This paper proposes a new architecture design of a convolutional neural network (CNN) for the FER system and contains five convolution layers, one fully connected layer with rectified linear unit activation function, and a SoftMax layer. Additionally, the feature map enhancement is applied to accomplish a higher detection rate and higher precision. Lastly, an application is developed that mitigates the effects of the aforementioned problems and can identify the basic expressions of human emotions, such as joy, grief, surprise, fear, contempt, anger, etc. Results indicate that the proposed CNN achieves 92.66% accuracy with mixed datasets, while the accuracy for the cross dataset is 94.94%.
Study on the Removal of Iron and Manganese from Groundwater Using Modified Manganese Sand Based on Response Surface Methodology
Authors: Kang Han , Liu Yan , Li Dan , Xu Li
Published: November 2022

This study used modified manganese sand as an adsorbent to explore its adsorption effect on iron and manganese ions from groundwater. The effects of pH, manganese sand dosage, and the initial concentration of Fe/Mn on the removal rate of iron and manganese ions were studied through single-factor experiments. Based on the above three factors, a quadratic polynomial model between the adsorption rate and the above factors was established to determine the optimal adsorption conditions. The response surface analysis showed that pH had the most significant effect on the adsorption process. The optimum conditions for the adsorption of iron and manganese ions by modified manganese sand were pH = 7.20, the dosage of manganese sand = 3.54 g/L, and the initial concentration ratio of Fe/Mn = 3.80. The analysis of variance showed that the RSM model could accurately reflect the adsorption process of manganese sand. In addition, we confirmed that the relative error between model predictions and experimental values was close to 1%, proving that the response surface model was reliable. The kinetic data of the manganese sand were described well with the pseudo-second-order model. The isothermal adsorption of iron and manganese ions by modified manganese sand was fitted well using the Langmuir equation.
Hybrid-Supervised-Learning-Based Automatic Image Segmentation for Water Leakage in Subway Tunnels
Authors: Qiu Dongwei , Liang Haorong , Wang Zhilin , Tong Yuci , Wan Shanshan
Published: November 2022

Quickly and accurately identifying water leakage is one of the important components of the health monitoring of subway tunnels. A mobile vision measurement system consisting of several high-resolution, industrial, charge-coupled device (CCD) cameras is placed on trains to implement structural health monitoring in tunnels. Through the image processing technology proposed in this paper, water leakage areas in subway tunnels can be found and repaired in real time. A lightweight automatic segmentation approach to water leakage using hybrid-supervised-deep-learning technology is proposed. This approach consists of the weakly supervised learning Water Leakage-CAM and fully supervised learning WRDeepLabV3+. The Water Leakage-CAM is used for the automatic labeling of data. The WRDeepLabV3+ is used for the accurate identification of water leakage areas in subway tunnels. Compared with other end-to-end semantic segmentation networks, the hybrid-supervised learning approach can more completely segment the water leakage region when dealing with water leakage in complex environments. The hybrid-supervised-deep-learning approach proposed in this paper achieves the highest MIoU of 82.8% on the experimental dataset, which is 6.4% higher than the second. The efficiency is also 25% higher than the second and significantly outperforms other end-to-end deep learning approaches.
Improved Acoustic Emission Tomography Algorithm Based on Lasso Regression
Authors: Qiao Xin , Kobayashi Yoshikazu , Oda Kenichi , Nakamura Katsuya
Published: November 2022

This study developed a novel acoustic emission (AE) tomography algorithm for non-destructive testing (NDT) based on Lasso regression (LASSO). The conventional AE tomography method takes considerable measurement data to obtain the elastic velocity distribution for structure evaluation. However, the new algorithm in which the LASSO algorithm is applied to AE tomography eliminates these deficiencies and reconstructs equivalent velocity distribution with fewer event data to describe the defected range. Three numerical simulation models were studied to reveal the capacity of the proposed method, and the functional performance was verified by three different types of classical concrete damage numerical simulation models and compared to that of the conventional SIRT algorithm in the experiment. Finally, this study demonstrates that the LASSO algorithm can be applied in AE tomography, and the shadow parts are eliminated in resultant elastic velocity distributions with fewer measurement paths.
A Multiscale Multi-Feature Deep Learning Model for Airborne Point-Cloud Semantic Segmentation
Authors: He Peipei , Ma Zheng , Fei Meiqi , Liu Wenkai , Guo Guihai , Wang Mingwei
Published: November 2022

In point-cloud scenes, semantic segmentation is the basis for achieving an understanding of a 3D scene. The disorderly and irregular nature of 3D point clouds makes it impossible for traditional convolutional neural networks to be applied directly, and most deep learning point-cloud models often suffer from an inadequate utilization of spatial information and of other related point-cloud features. Therefore, to facilitate the capture of spatial point neighborhood information and obtain better performance in point-cloud analysis for point-cloud semantic segmentation, a multiscale, multi-feature PointNet (MSMF-PointNet) deep learning point-cloud model is proposed in this paper. MSMF-PointNet is based on the classical point-cloud model PointNet, and two small feature-extraction networks called Mini-PointNets are added to operate in parallel with the modified PointNet; these additional networks extract multiscale, multi-neighborhood features for classification. In this paper, we use the spherical neighborhood method to obtain the local neighborhood features of the point cloud, and then we adjust the radius of the spherical neighborhood to obtain the multiscale point-cloud features. The obtained multiscale neighborhood feature point set is used as the input of the network. In this paper, a cross-sectional comparison analysis is conducted on the Vaihingen urban test dataset from the single-scale and single-feature perspectives.
Study of Social Presence While Interacting in Metaverse with an Augmented Avatar during Autonomous Driving
Authors: Voinea Gheorghe Daniel , Gîrbacia Florin , Postelnicu Cristian Cezar , Duguleana Mihai , Antonya Csaba , Soica Adrian , Stănescu Ruxandra-Cristina
Published: November 2022

In this paper, we studied the effects of using Microsoft HoloLens 2 in a Metaverse-based collaborative mixed reality environment on the driver’s social presence while using an autonomous driving system. In (semi-) autonomous vehicles the driver is the system’s monitor, and the driving process becomes a secondary task. Our approach is motivated by the advent of Microsoft Mesh XR technology that enables immersion in multi-person, shared mixed reality environments. We conducted a user study comparing the effects on social presence in two scenarios: baseline and mixed reality collaboration. During the baseline condition, participants communicated and interacted with another person using Skype/Meet which was installed on a mobile tablet. In the second scenario the participants used the Microsoft Mesh application installed on HoloLens 2 to collaborate in a mixed reality environment where each user is represented by an augmented 3D avatar. During the experiment, the participant had to perform a social interaction tell-a-lie task and a remote collaborative tic-tac-toe game, while also monitoring the vehicle’s behavior. The social presence was measured using the Harms and Biocca questionnaire, one of the most widely used tools for evaluating the user’s experience. We found that there are significant statistical differences for Co-presence, Perceived Emotional Interdependence, and Perceived Behavioral Interdependence, and participants were able to easily interact with the avatar in the mixed reality scenario. The proposed study procedure could be taken further to assess the driver’s performance during handover procedures, especially when the autonomous driving system encounters a critical situation.
Special Issue on Biomechanical and Biomedical Factors of Knee Osteoarthritis
Authors: Nagano Hanatsu
Published: November 2022

Knee osteoarthritis (OA) is a prevalent condition with up to 30–40% lifetime risk [...]
Models of HF Interference over Cyprus
Authors: Constantinides Antonios , Haralambous Haris , Papadopoulos Harris , Makrominas Michalis
Published: November 2022

Interference from other users, due to the long-distance propagation of HF signals, causes spectral congestion, which is considered a primary detrimental effect on HF (3–30 MHz) communication systems. More than a year of HF electric field measurements from a calibrated monopole HF antenna collected by a dedicated measurement system installed in Cyprus have been used to compare neural networks and non-linear regression for the development of single-allocation congestion models in the HF spectrum. These models can be used to advise operators on typical interference spectral congestion levels and assist in the planning and management of frequency usage by assessing the interference level to alleviate spectral congestion in the HF band over Cyprus. The performance of these alternative modeling approaches is discussed in the context of their ability to successfully capture the diurnal variability of HF interference at a certain incident field strength threshold.
Imaging Ultrasound Propagation Using the Westervelt Equation by the Generalized Kudryashov and Modified Kudryashov Methods
Authors: Ghazanfar Sidra , Ahmed Nauman , Iqbal Muhammad Sajid , Akgül Ali , Bayram Mustafa , De la Sen Manuel
Published: November 2022

This article deals with the study of ultrasound propagation, which propagates the mechanical vibration of the molecules or of the particles of a material. It measures the speed of sound in air. For this reason, the third-order non-linear model of the Westervelt equation was chosen to be studied, as the solutions to such problems have much importance for physical purposes. In this article, we discuss the exact solitary wave solutions of the third-order non-linear model of the Westervelt equation for an acoustic pressure p representing the equation of ultrasound with high intensity, as used in acoustic tomography. Moreover, the non-linear coefficient B/A (being a part of space-dependent coefficient K), has also been investigated in this literature. This problem is solved using the Generalized Kudryashov method along with a comparison of the Modified Kudryashov method. All of the solutions have been discussed with both surface and contour plots, which shows the behavior of the solution. The images are prepared in a well-established way, showing the production of tissues inside the human body.
An Improvement of a Mapping Method Based on Ant Colony Algorithm Applied to Smart Cities
Authors: Xu Kaiming , Wu Jianjun , Huang Tengchao , Liang Lei
Published: November 2022

The ant colony algorithm has been widely used in the field of data analysis of smart cities. However, the research of the traditional ant colony algorithm is more focused on one-to-one scenarios and there is insufficient research on many-to-one scenarios. Therefore, for the many-to-one topology mapping problem, this paper proposes a mapping method based on the ant colony algorithm. The design purpose of the mapping algorithm is to study the optimal mapping scheme, which can effectively reduce the cost of solving the problem. The core of the mapping algorithm is to design the objective function of the algorithm optimization. The commonly used optimization objective function and evaluation index is the average hop count; the average hop count is the most important indicator to measure the entire system. The smaller the average hop count, the less the pulse data needs to be forwarded, which can reduce the communication pressure of the system, reduce congestion, reduce the energy consumption caused by communication, and reduce the delay from the generation of pulse data to the response, etc. Therefore, this paper chooses the average hop count as the optimization objective and reduces the average hop count by designing a mapping algorithm. Through the simulation and verification of the improved ant colony algorithm in the scenario of many-to-one topology mapping, it is concluded that the final convergence result and convergence speed of the improved ant colony algorithm are significantly better than those of the traditional ant colony algorithm.
Band-Gap Engineering: Lithium Effect on the Electronic Properties of Hydrogenated 3C-SiC (1 1 0) Surfaces
Authors: Cuevas Jose Luis , Martinez Miguel Ojeda , Thirumuruganandham Saravana Prakash
Published: November 2022

Silicon carbide has structural strength, high electronic conductivity, low diffusion barrier and high storage capacity, which are suitable for engineering applications such as lithium-ion batteries, electric vehicles, uninterruptible power supplies and SiC diodes. In particular, 3C-SiC monolayers oriented along the (1 1 0) crystallographic direction that could have symmetric surfaces have been poorly studied, as have the effects of surface passivation on their physical and electronic properties. In this work, we investigate the influence of lithium on the electronic properties of hydrogenated surfaces in 3C-SiC monolayers using density functional theory. We examine the electronic properties of surfaces fully passivated with hydrogen with those of surfaces fully passivated with lithium and those with mixed passivation. Our results show that only fully hydrogenated surfaces exhibit a direct band-gap, while the full Li, CH+SiLi, and H+Lic passivations exhibit metallic behavior. The CLi+SiH, H+1LiC, and H+1LiSi passivation systems decrease the band-gap compared to the hydrogenated case and show an indirect band-gap. The formation energy of the system shows that the most stable arrangement is full-H, followed by H+1LiC, and the most unstable system is full-Li, which has a positive formation energy.
Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images
Authors: Ukwuoma Chiagoziem C. , Qin Zhiguang , Heyat Md Belal Bin , Akhtar Faijan , Smahi Abla , Jackson Jehoiada K. , Furqan Qadri Syed , Muaad Abdullah Y. , Monday Happy N. , Nneji Grace U.
Published: November 2022

According to research, classifiers and detectors are less accurate when images are blurry, have low contrast, or have other flaws which raise questions about the machine learning model’s ability to recognize items effectively. The chest X-ray image has proven to be the preferred image modality for medical imaging as it contains more information about a patient. Its interpretation is quite difficult, nevertheless. The goal of this research is to construct a reliable deep-learning model capable of producing high classification accuracy on chest x-ray images for lung diseases. To enable a thorough study of the chest X-ray image, the suggested framework first derived richer features using an ensemble technique, then a global second-order pooling is applied to further derive higher global features of the images. Furthermore, the images are then separated into patches and position embedding before analyzing the patches individually via a vision transformer approach. The proposed model yielded 96.01% sensitivity, 96.20% precision, and 98.00% accuracy for the COVID-19 Radiography Dataset while achieving 97.84% accuracy, 96.76% sensitivity and 96.80% precision, for the Covid-ChestX-ray-15k dataset. The experimental findings reveal that the presented models outperform traditional deep learning models and other state-of-the-art approaches provided in the literature.
Empirical Study of Autism Spectrum Disorder Diagnosis Using Facial Images by Improved Transfer Learning Approach
Authors: Alam Md Shafiul , Rashid Muhammad Mahbubur , Roy Rupal , Faizabadi Ahmed Rimaz , Gupta Kishor Datta , Ahsan Md Manjurul
Published: November 2022

Autism spectrum disorder (ASD) is a neurological illness characterized by deficits in cognition, physical activities, and social skills. There is no specific medication to treat this illness; only early intervention can improve brain functionality. Since there is no medical test to identify ASD, a diagnosis might be challenging. In order to determine a diagnosis, doctors consider the child’s behavior and developmental history. The human face can be used as a biomarker as it is one of the potential reflections of the brain and thus can be used as a simple and handy tool for early diagnosis. This study uses several deep convolutional neural network (CNN)-based transfer learning approaches to detect autistic children using the facial image. An empirical study is conducted to select the best optimizer and set of hyperparameters to achieve better prediction accuracy using the CNN model. After training and validating with the optimized setting, the modified Xception model demonstrates the best performance by achieving an accuracy of 95% on the test set, whereas the VGG19, ResNet50V2, MobileNetV2, and EfficientNetB0 achieved 86.5%, 94%, 92%, and 85.8%, accuracy, respectively. Our preliminary computational results demonstrate that our transfer learning approaches outperformed existing methods. Our modified model can be employed to assist doctors and practitioners in validating their initial screening to detect children with ASD disease.
Walking Speed Classification from Marker-Free Video Images in Two-Dimension Using Optimum Data and a Deep Learning Method
Authors: Sikandar Tasriva , Rahman Sam Matiur , Islam Dilshad , Ali Md. Asraf , Mamun Md. Abdullah Al , Rabbi Mohammad Fazle , Ghazali Kamarul H. , Altwijri Omar , Almijalli Mohammed , Ahamed Nizam U.
Published: November 2022

Walking speed is considered a reliable assessment tool for any movement-related functional activities of an individual (i.e., patients and healthy controls) by caregivers and clinicians. Traditional video surveillance gait monitoring in clinics and aged care homes may employ modern artificial intelligence techniques to utilize walking speed as a screening indicator of various physical outcomes or accidents in individuals. Specifically, ratio-based body measurements of walking individuals are extracted from marker-free and two-dimensional video images to create a walk pattern suitable for walking speed classification using deep learning based artificial intelligence techniques. However, the development of successful and highly predictive deep learning architecture depends on the optimal use of extracted data because redundant data may overburden the deep learning architecture and hinder the classification performance. The aim of this study was to investigate the optimal combination of ratio-based body measurements needed for presenting potential information to define and predict a walk pattern in terms of speed with high classification accuracy using a deep learning-based walking speed classification model. To this end, the performance of different combinations of five ratio-based body measurements was evaluated through a correlation analysis and a deep learning-based walking speed classification test. The results show that a combination of three ratio-based body measurements can potentially define and predict a walk pattern in terms of speed with classification accuracies greater than 92% using a bidirectional long short-term memory deep learning method.
Identification of Kinetic Abnormalities in Male Patients after Anterior Cruciate Ligament Deficiency Combined with Meniscal Injury: A Musculoskeletal Model Study of Lower Limbs during Jogging
Authors: Ren Shuang , Liu Xiaode , Li Haoran , Guo Yufei , Zhang Yuhan , Liang Zixuan , Zhang Si , Huang Hongshi , Huang Xuhui , Ma Zhe , Rong Qiguo , Ao Yingfang
Published: November 2022

There is little known about kinetic changes in anterior cruciate ligament deficiency (ACLD) combined with meniscal tears during jogging. Therefore, 29 male patients with injured ACLs and 15 healthy male volunteers were recruited for this study to investigate kinetic abnormalities in male patients after ACL deficiency combined with a meniscal injury during jogging. Based on experimental data measured by an optical tracking system, a subject-specific musculoskeletal model was employed to estimate the tibiofemoral joint kinetics during jogging. Between-limb and interpatient differences were compared by the analysis of variance. The results showed that decreased knee joint forces and moments of both legs in ACLD patients were detected during the stance phase compared to the control group. Meanwhile, compared with ACLD knees, significantly fewer contact forces and flexion moments in ACLD combined with lateral and medial meniscal injury groups were found at the mid-stance, and ACLD with medial meniscal injury group showed a lower axial moment in the loading response (p < 0.05). In conclusion, ACLD knees exhibit reduced tibiofemoral joint forces and moments during jogging when compared with control knees. A combination of meniscus injuries in the ACLD-affected side exhibited abnormal kinetic alterations at the loading response and mid-stance phase.
Stem Cell- and Cell-Based Therapies for Ischemic Stroke
Authors: Nistor-Cseppentö Delia Carmen , Jurcău Maria Carolina , Jurcău Anamaria , Andronie-Cioară Felicia Liana , Marcu Florin
Published: November 2022

Stroke is the second cause of disability worldwide as it is expected to increase its incidence and prevalence. Despite efforts to increase the number of patients eligible for recanalization therapies, a significant proportion of stroke survivors remain permanently disabled. This outcome boosted the search for efficient neurorestorative methods. Stem cells act through multiple pathways: cell replacement, the secretion of growth factors, promoting endogenous reparative pathways, angiogenesis, and the modulation of neuroinflammation. Although neural stem cells are difficult to obtain, pose a series of ethical issues, and require intracerebral delivery, mesenchymal stem cells are less immunogenic, are easy to obtain, and can be transplanted via intravenous, intra-arterial, or intranasal routes. Extracellular vesicles and exosomes have similar actions and are easier to obtain, also allowing for engineering to deliver specific molecules or RNAs and to promote the desired effects. Appropriate timing, dosing, and delivery protocols must be established, and the possibility of tumorigenesis must be settled. Nonetheless, stem cell- and cell-based therapies for stroke have already entered clinical trials. Although safe, the evidence for efficacy is less impressive so far. Hopefully, the STEP guidelines and the SPAN program will improve the success rate. As such, stem cell- and cell-based therapy for ischemic stroke holds great promise.
Striated Muscle Evaluation Based on Velocity and Amortization Ratio of Mechanical Impulse Propagation in Simulated Microgravity Environment
Authors: Nistorescu Alexandru , Busnatu Stefan Sebastian , Dinculescu Adrian , Olteanu Gabriel , Marin Mihaela , Jercalau Cosmina Elena , Vizitiu Cristian , Papacocea Ioana Raluca
Published: November 2022

Long-duration space flight missions impose extreme physiological stress and/or changes, such as musculoskeletal function degradation, on the crew due to the microgravity exposure. A great deal of research studies have been conducted in order to understand these physiological stress influences and to provide countermeasures to minimize the observed negative effects of weightlessness exposure on musculoskeletal function. Among others, studies and experiments have been conducted in DI analogue Earth-based facilities in order to reproduce the weightlessness negative effects on the human body. This paper presents a complex muscular analysis of mechanical wave propagation in striated muscle, using MusTone, a device developed in-house at the Institute of Space Science, Romania. The data were collected during a 21-day DI campaign in order to investigate muscle fibers’ behavior in longitudinal direction, after applying a mechanical impulse, taking into account two particular parameters, namely propagation velocity and amortization ratio. The parameters were determined based on the wave-propagation data collected from five points (one impact point, two distal direction points, and two proximal direction points) along the muscle fiber. By statistically analyzing propagation velocity and amortization ratio parameters, the study revealed that muscle deconditioning is time dependent, the amortization ratio is more significant in the distal direction, and the lower fibers are affected the most.
UPLC-QTOF/MS Metabolomics and Biochemical Assays Reveal Changes in Hepatic Nutrition and Energy Metabolism during Sexual Maturation in Female Rainbow Trout (Oncorhynchus mykiss)
Authors: Ding Lu , Liu Yingjie , Kang Meng , Wei Xiaofeng , Geng Chuanye , Liu Wenzhi , Han Lin , Yuan Fangying , Wang Peng , Wang Bingqian , Sun Yanchun
Published: November 2022

Mobilization and repartition of nutrients and energy are prerequisites for the normal sexual maturity of broodstock. However, there are few studies on the mechanisms of hepatic nutrients and energy metabolism during sexual maturation in female rainbow trout (Oncorhynchus mykiss). This study investigated hepatic metabolite changes and explored the potential nutritional regulation mechanism between mature and immature female rainbow trout by combining UPLC-QTOF/MS metabolomics and biochemical assays. It was observed that hepatic biochemical assays differed considerably between the two groups, such as glucose, triglycerides, hexokinase, lipase, and aspartate aminotransferase. Liver metabolomics showed that various differential metabolites involved in amino acid and lipid metabolism markedly increased, suggesting the enhancement of lipid metabolism and amino acid anabolism in the liver provides the necessary material basis for ovarian development. Meanwhile, glycogen catabolism and glycolysis hold the key to maintaining organismal energy homeostasis with normal sexual maturation of female rainbow trout. Overall, the results from this study suggested that the liver undergoes drastic reprogramming of the metabolic profile in response to mobilization and repartition of nutrients and energy during the sexual maturation of female rainbow trout. This study further deepened the understanding of the reproductive biology of rainbow trout, and provided the theoretical basis and practical ramifications for nutritional requirements of breeding high-quality broodstock in the artificial propagation of rainbow trout.
Proteases—From Basic Structure to Function to Drug Design as Targeted Therapy
Authors: Kwok Hang Fai , Walker Brian , Shaw Chris
Published: November 2022

In the last two decades, proteases have become a primary and vital target in drug discovery [...]
The Effects of TRPC6 Knockout in Animal Models of Kidney Disease
Authors: Dryer Stuart E. , Kim Eun Young
Published: November 2022

Diseases that induce a loss of renal function affect a substantial portion of the world’s population and can range from a slight decline in the glomerular filtration rate or microalbuminuria to complete kidney failure. Kidney disorders can be acute or chronic, but any significant reduction in renal function is associated with increased all-cause morbidity and mortality, especially when the conditions become chronic. There is an urgent need for new therapeutic approaches to slow or halt the progression of kidney disease. One potential target of considerable interest is the canonical transient receptor potential-6 (TRPC6) channel. TRCP6 is a cationic channel with a significant permeability to Ca2+. It is expressed in several tissues, including in multiple cell types of the kidney in glomeruli, microvasculature, and tubules. Here, we will describe TRPC6 channels and their roles in signal transduction, with an emphasis on renal cells, and the studies implicating TRPC6 channels in the progression of inherited and acquired kidney diseases. We then describe studies using TRPC6 knockout mice and rats subjected to treatments that model human diseases, including nephrotic syndromes, diabetic nephropathy, autoimmune glomerulonephritis, and acute kidney injuries induced by renal ischemia and by obstruction of the urinary tract. TRPC6 knockout has been shown to reduce glomerular manifestations of disease in several of these models and reduces renal fibrosis caused by urinary tract obstruction. TRPC6 knockout has proven to be less effective at reducing diabetic nephropathy in mouse and rat models. We also summarize the implications of these studies for drug development.