bluuebunny/crossref_metadata_2025 huggingface dataset:
Separating Curing and Temperature Effects on the Temperature Coefficient of Resistance for a Single-Walled Carbon Nanotube Nanocomposite
Authors: Jafarypouria Milad , Mahato Biltu , Abaimov Sergey G.
Published: January 2023

The temperature coefficient of resistance (TCR) determines the electrical performance of materials in electronics. For a carbon nanotube (CNT) nanocomposite, change of resistivity with temperature depends on changes in CNT intrinsic conductivity, tunnelling thresholds and distances, matrix’ coefficient of thermal expansion, and other factors. In our study, we add one more influencing factor–the degree of cure. Complexities of the curing process cause difficulties to predict, or even measure, the curing state of the polymer matrix while uncertainty in the degree of cure influences TCR measurements leading to biased values. Here we study the influence of the cure state on the TCR of a single-walled CNT/epoxy polymer nanocomposite. For the given degree of cure, TCR measurements are conducted in the temperature range 25–100 °C, followed by the next 24 h of post-curing and a new cycle of measurements, 8 cycles in total. We find that contrary to industry practice to expect a high degree of cure after 3 h at 130 °C, the curing process is far from reaching the steady state of the material and continues at least for the next 72 h at 120 °C, as we observe by changes in the material electrical resistivity. If TCR measurements are conducted in this period, we find them significantly influenced by the post-curing process continuing in parallel, leading in particular to non-monotonic temperature dependence and the appearance of negative values. The unbiased TCR values we observe only when the material reaches the steady state are no longer influenced by the heat input. The dependence becomes steady, monotonically increasing from near zero value at room temperature to 0.001 1/°C at 100 °C.
Characterizing Transitions between Successional Stages in a Tropical Dry Forest Using LiDAR Techniques
Authors: Duan Menglei , Bax Connor , Laakso Kati , Mashhadi Nooshin , Mattie Nelson , Sanchez-Azofeifa Arturo
Published: January 2023

Secondary succession is defined as natural regeneration following complete forest clearance from anthropogenic or natural disturbances. Traditional strategies aimed to map and characterize secondary succession using remote sensing are usually based on deterministic approaches, where transitions between successional stages are not considered. These transitions represent rich environments between successional stages and play a key role in ecosystem regeneration. Here, we evaluate the use of the Full-waveform Airborne LiDAR to characterize changes in forest structure between the transition of early-to-intermediate and intermediate-to-late forest succession at the Santa Rosa National Park Environmental Monitoring Super Site (SRNP-EMSS), Guanacaste, Costa Rica. The vertical forest structure was analyzed on twenty cross-sections selected between forest transitions previously mapped using machine learning; leaf area density (LAD) and waveform metrics were studied based on the waveform profile derived from twenty-seven plots distributed in different successional forest patches. Results suggest that LiDAR techniques can identify forest structure differences between successional stages and their transitions. The significance proves that transitions exist, highlights the unique transitional characteristics between intermediate and late successional stages and contributes to understanding the significance of inter-successional stages (transitions) in secondary dry forests.
Improved Estimation of the Open Boundary Conditions in Tidal Models Using Trigonometric Polynomials Fitting Scheme
Authors: Jiao Shengyi , Zhang Yibo , Pan Haidong , Lv Xianqing
Published: January 2023

Tidal open boundary conditions (OBCs) of the M2 tidal constituent in the Bohai and Yellow Seas (BYS) are estimated via the assimilation of multi-satellite altimeter data to optimize regional tidal numerical simulation. A two-dimensional adjoint assimilation model is used for tidal numerical simulation and, as an improvement, trigonometric polynomials fitting (TPF) is applied in the inversion of OBCs. It is assumed that the linearized amplitudes/phases in the open boundary are spatially varying and can be represented by nonlinear functions. Based on the discrete Fourier series, taking the trigonometric function as the basis function, the spatially varying OBCs are constructed by selecting the maximum truncation period. The independent points scheme used in previous studies was also compared in the experiments. Twin experiments show that the errors of simulations with TPF are the smallest in different schemes, and their results show the highest correlation with observations while maintaining the best performance in terms of observation errors. The mean absolute errors (MAEs) in amplitude/phase between the simulated results using estimated OBCs and the satellite altimeter records are 2.82 cm and 2.26°, respectively.
Optical Correlators for Cryptosystems and Image Recognition: A Review
Authors: Drăgulinescu Andrei
Published: January 2023

Optical correlators are efficient optical systems that have gained a wide range of applications both in image recognition and encryption, due to their special properties that benefit from the optoelectronic setup instead of an all-electronic one. This paper presents, to the best of our knowledge, the most extensive review of optical correlators to date. The main types are overviewed, together with their most frequent applications in the newest contributions, ranging from security uses in cryptosystems, to medical and space applications, femtosecond pulse detection and various other image recognition proposals. The paper also includes a comparison between various optical correlators developed recently, highlighting their advantages and weaknesses, to gain a better perspective towards finding the best solutions in any specific domain where these devices might prove highly efficient and useful.
Design Compact Absorptive Common-Mode Noise Suppression Filter with Series Unified Circuit
Authors: Zhuang Cheng-Yi , Lin Ding-Bing
Published: January 2023

In the PCB process, overcoming common-mode noise radiation is critical. In past years, most studies have focused on a common-mode noise filter (CMNF) that can solve electromagnetic interference in high-speed digital systems by blocking and absorbing common-mode noise radiation. Unfortunately, connecting with any reflective common-mode noise filter (R-CMNF) and reducing the area of an absorptive common-mode noise filter (A-CMNF) are the most troublesome tasks in the PCB process. A novel equivalent circuit is proposed in this research to minimize the complexity of the design and improve accuracy. Detailed analyses of this proposed approach are entirely depicted in this article. The experiment result shows that 9% of fractional bandwidth centered at 2.25 Hz can achieve at least 90% absorption efficiency. With our proposed method, the area of A-CMNF is smaller than in state-of-the-art research.
Improving Odometric Model Performance Based on LSTM Networks
Authors: Fariña Bibiana , Acosta Daniel , Toledo Jonay , Acosta Leopoldo
Published: January 2023

This paper presents a localization system for an autonomous wheelchair that includes several sensors, such as odometers, LIDARs, and an IMU. It focuses on improving the odometric localization accuracy using an LSTM neural network. Improved odometry will improve the result of the localization algorithm, obtaining a more accurate pose. The localization system is composed by a neural network designed to estimate the current pose using the odometric encoder information as input. The training is carried out by analyzing multiple random paths and defining the velodyne sensor data as training ground truth. During wheelchair navigation, the localization system retrains the network in real time to adjust any change or systematic error that occurs with respect to the initial conditions. Furthermore, another network manages to avoid certain random errors by using the relationship between the power consumed by the motors and the actual wheel speeds. The experimental results show several examples that demonstrate the ability to self-correct against variations over time, and to detect non-systematic errors in different situations using this relation. The final robot localization is improved with the designed odometric model compared to the classic robot localization based on sensor fusion using a static covariance.
Heart Rate Variability’s Association with Positive and Negative Affect in Daily Life: An Experience Sampling Study with Continuous Daytime Electrocardiography over Seven Days
Authors: Hachenberger Justin , Li Yu-Mei , Siniatchkin Michael , Hermenau Katharin , Ludyga Sebastian , Lemola Sakari
Published: January 2023

Heart rate variability has been found to be related to emotional processing and emotional responses. Studies that investigated these relationships were mostly lab-based or cross-sectional. Only limited research used intensive longitudinal data, in particular investigating within-individual processes in real-life settings. This study addresses the applicability of ambulatory-assessed electrocardiograms in combination with the experience sampling methodology by investigating the associations of various HRV measures with affective states on within- and between-individual levels. A total of 26 participants aged 18–29 years (23 females) wore electrocardiograms continuously for seven days. The participants received seven prompts per day and answered questions about their affective wellbeing. The heart rate and heart rate variability measures differed between body positions and activity classes. The heart rate and ratio of low-to-high-frequency heart rate variability were consistently associated with positive affect on a within-individual (state-like) level. These associations were mainly driven by the items of feeling “enthusiastic” and “happy”. No associations were found with negative affect. Overall, we found evidence that the dominance of the sympathetic nervous system over the parasympathetic nervous system was associated with higher levels of positive affect on a within-individual (state-like) level. Suggestions for the application of ambulatory electrocardiogram assessment in the study of the association between autonomous nervous system activity and ecological momentary assessment-based variables are discussed.
Multi-Threaded Sound Propagation Algorithm to Improve Performance on Mobile Devices
Authors: Kim Eunjae , Choi Sukwon , Kim Cheong Ghil , Park Woo-Chan
Published: January 2023

We propose a multi-threaded algorithm that can improve the performance of geometric acoustic (GA)-based sound propagation algorithms in mobile devices. In general, sound propagation algorithms require high computational cost because they perform based on ray tracing algorithms. For this reason, it is difficult to operate sound propagation algorithms in mobile environments. To solve this problem, we processed the early reflection and late reverberation steps in parallel and verified the performance in four scenes based on eight sound sources. The experimental results showed that the performance of the proposed method was on average 1.77 times better than that of the single-threaded method, demonstrating that our algorithm can improve the performance of mobile devices.
Towards Sustainable Distributed Sensor Networks: An Approach for Addressing Power Limitation Issues in WSNs
Authors: Alaerjan Alaa
Published: January 2023

Distributed wireless sensor networks (WSNs) have been implemented in multiple applications. Those networks are intended to support the quality of operations and enhance applications’ productivity and safety. WSNs are constructed of a large amount of sensor nodes that are battery powered. Typically, wireless sensors are deployed in complex terrain which makes battery replacement extremely difficult. Therefore, it is critical to adopt an energy sustainability approach to enhance the lifetime of each sensor node since each node contributes to the lifetime of the entire WSN. In this work, we propose an approach to reduce power consumption in wireless sensors. The approach addresses power reduction in a sensor node at the sensing level, as well as the communication level. First, we propose configuring the microcontroller of the sensor to conserve energy based on the performed tasks. Then, we implement an interface to reduce consumed power by the radio module. Based on the approach, we carried out field experiments and we measure the improvement of power-consumption reduction. The results show that the approach contributes to saving up to 50% of the wasted energy at the sensor node and it improves communication reliability especially when the number of sensors in a network scales.
A THD-Based Fault Protection Method Using MSOGI-FLL Grid Voltage Estimator
Authors: Al Hanaineh Wael , Matas Jose , El Mariachet Jorge , Xie Peilin , Bakkar Mostafa , Guerrero Josep. M.
Published: January 2023

The rapid growth of the distributed generators (DGs) integration into the distribution systems (DSs) creates new technical issues; conventional relay settings need to be updated depending on the network topology and operational mode as fault protection a major challenge. This emphasizes the need for new fault protection methods to ensure secure protection and prevent undesirable tripping. Total harmonic distortion (THD) is an important indicator for assessing the quality of the grid. Here, a new protection system based on the THD of the grid voltages is proposed to address fault events in the electrical distribution network. The proposed protection system combines the THD with the estimates of the amplitude voltages and the zero-sequence component for defining an algorithm based on a finite state machine (FSM) for the detection, identification, and isolation of faults in the grid. The algorithm employs communication lines between all the protective devices (PDs) of the system to transmit tripping signals, allowing PDs to be coordinated. A second order generalized integrator (SOGI) and multiple SOGI (MSOGI) are used to obtain the THDs, estimated amplitude voltages, and zero-sequence component, which allows for fast detection with a low computational burden. The protection algorithm performance is evaluated through simulations in MATLAB/Simulink and a comparative study is developed between the proposed protection method and a differential relay (DR) protection system. The proposed method shows its capability to detect and isolate faults during different fault types with different fault resistances in different locations in the proposed network. In all the tested scenarios, the detection time of the faults has been between 7–10 ms. Moreover, this method gave the best solution as it has a higher accuracy and faster response than the conventional DR protection system.
Study on Rapid Detection of Pesticide Residues in Shanghaiqing Based on Analyzing Near-Infrared Microscopic Images
Authors: Sun Haoran , Zhang Liguo , Ni Lijun , Zhu Zijun , Luan Shaorong , Hu Ping
Published: January 2023

Aiming at guiding agricultural producers to harvest crops at an appropriate time and ensuring the pesticide residue does not exceed the maximum limit, the present work proposed a method of detecting pesticide residue rapidly by analyzing near-infrared microscopic images of the leaves of Shanghaiqing (Brassica rapa), a type of Chinese cabbage with computer vision technology. After image pre-processing and feature extraction, the pattern recognition methods of K nearest neighbors (KNN), naïve Bayes, support vector machine (SVM), and back propagation artificial neural network (BP-ANN) were applied to assess whether Shanghaiqing is sprayed with pesticides. The SVM method with linear or RBF kernel provides the highest recognition accuracy of 96.96% for the samples sprayed with trichlorfon at a concentration of 1 g/L. The SVM method with RBF kernel has the highest recognition accuracy of 79.16~84.37% for the samples sprayed with cypermethrin at a concentration of 0.1 g/L. The investigation on the SVM classification models built on the samples sprayed with cypermethrin at different concentrations shows that the accuracy of the models increases with the pesticide concentrations. In addition, the relationship between the concentration of the cypermethrin sprayed and the image features was established by multiple regression to estimate the initial pesticide concentration on the Shanghaiqing leaves. A pesticide degradation equation was established on the basis of the first-order kinetic equation. The time for pesticides concentration to decrease to an acceptable level can be calculated on the basis of the degradation equation and the initial pesticide concentration. The present work provides a feasible way to rapidly detect pesticide residue on Shanghaiqing by means of NIR microscopic image technique. The methodology laid out in this research can be used as a reference for the pesticide detection of other types of vegetables.
Hierarchical Image Transformation and Multi-Level Features for Anomaly Defect Detection
Authors: Farady Isack , Kuo Chia-Chen , Ng Hui-Fuang , Lin Chih-Yang
Published: January 2023

Anomalies are a set of samples that do not follow the normal behavior of the majority of data. In an industrial dataset, anomalies appear in a very small number of samples. Currently, deep learning-based models have achieved important advances in image anomaly detection. However, with general models, real-world application data consisting of non-ideal images, also known as poison images, become a challenge. When the work environment is not conducive to consistently acquiring a good or ideal sample, an additional adaptive learning model is needed. In this work, we design a potential methodology to tackle poison or non-ideal images that commonly appear in industrial production lines by enhancing the existing training data. We propose Hierarchical Image Transformation and Multi-level Features (HIT-MiLF) modules for an anomaly detection network to adapt to perturbances from novelties in testing images. This approach provides a hierarchical process for image transformation during pre-processing and explores the most efficient layer of extracted features from a CNN backbone. The model generates new transformations of training samples that simulate the non-ideal condition and learn the normality in high-dimensional features before applying a Gaussian mixture model to detect the anomalies from new data that it has never seen before. Our experimental results show that hierarchical transformation and multi-level feature exploration improve the baseline performance on industrial metal datasets.
Comparative Study of Cooperative Platoon Merging Control Based on Reinforcement Learning
Authors: Irshayyid Ali , Chen Jun
Published: January 2023

The time that a vehicle merges in a lane reduction can significantly affect passengers’ safety, comfort, and energy consumption, which can, in turn, affect the global adoption of autonomous electric vehicles. In this regard, this paper analyzes how connected and automated vehicles should cooperatively drive to reduce energy consumption and improve traffic flow. Specifically, a model-free deep reinforcement learning approach is used to find the optimal driving behavior in the scenario in which two platoons are merging into one. Several metrics are analyzed, including the time of the merge, energy consumption, and jerk, etc. Numerical simulation results show that the proposed framework can reduce the energy consumed by up to 76.7%, and the average jerk can be decreased by up to 50%, all by only changing the cooperative merge behavior. The present findings are essential since reducing the jerk can decrease the longitudinal acceleration oscillations, enhance comfort and drivability, and improve the general acceptance of autonomous vehicle platooning as a new technology.
Cluster-Based Multi-User Multi-Server Caching Mechanism in Beyond 5G/6G MEC
Authors: Samir Rasha , El-Hennawy Hadia , Elbadawy Hesham
Published: January 2023

The work on perfecting the rapid proliferation of wireless technologies resulted in the development of wireless modeling standards, protocols, and control of wireless manipulators. Several mobile communication technology applications in different fields are dramatically revolutionized to deliver more value at less cost. Multiple-access Edge Computing (MEC) offers excellent advantages for Beyond 5G (B5G) and Sixth-Generation (6G) networks, reducing latency and bandwidth usage while increasing the capability of the edge to deliver multiple services to end users in real time. We propose a Cluster-based Multi-User Multi-Server (CMUMS) caching algorithm to optimize the MEC content caching mechanism and control the distribution of high-popular tasks. As part of our work, we address the problem of integer optimization of the content that will be cached and the list of hosting servers. Therefore, a higher direct hit rate will be achieved, a lower indirect hit rate will be achieved, and the overall time delay will be reduced. As a result of the implementation of this system model, maximum utilization of resources and development of a completely new level of services and innovative approaches will be possible.
Communication and Computing Task Allocation for Energy-Efficient Fog Networks
Authors: Kopras Bartosz , Idzikowski Filip , Bossy Bartosz , Kryszkiewicz Paweł , Bogucka Hanna
Published: January 2023

The well known cloud computing is being extended by the idea of fog with the computing nodes placed closer to end users to allow for task processing with tighter latency requirements. However, offloading of tasks (from end devices to either the cloud or to the fog nodes) should be designed taking energy consumption for both transmission and computation into account. The task allocation procedure can be challenging considering the high number of arriving tasks with various computational, communication and delay requirements, and the high number of computing nodes with various communication and computing capabilities. In this paper, we propose an optimal task allocation procedure, minimizing consumed energy for a set of users connected wirelessly to a network composed of FN located at AP and CN. We optimize the assignment of AP and computing nodes to offloaded tasks as well as the operating frequencies of FN. The considered problem is formulated as a Mixed-Integer Nonlinear Programming problem. The utilized energy consumption and delay models as well as their parameters, related to both the computation and communication costs, reflect the characteristics of real devices. The obtained results show that it is profitable to split the processing of tasks between multiple FNs and the cloud, often choosing different nodes for transmission and computation. The proposed algorithm manages to find the optimal allocations and outperforms all the considered alternative allocation strategies resulting in the lowest energy consumption and task rejection rate. Moreover, a heuristic algorithm that decouples the optimization of wireless transmission from implemented computations and wired transmission is proposed. It finds the optimal or close-to-optimal solutions for all of the studied scenarios.
Cascaded Segmentation U-Net for Quality Evaluation of Scraping Workpiece
Authors: Yin Hsin-Chung , Lien Jenn-Jier James
Published: January 2023

In the terms of industry, the hand-scraping method is a key technology for achieving high precision in machine tools, and the quality of scraping workpieces directly affects the accuracy and service life of the machine tool. However, most of the quality evaluation of the scraping workpieces is carried out by the scraping worker’s subjective judgment, which results in differences in the quality of the scraping workpieces and is time-consuming. Hence, in this research, an edge-cloud computing system was developed to obtain the relevant parameters, which are the percentage of point (POP) and the peak point per square inch (PPI), for evaluating the quality of scraping workpieces. On the cloud computing server-side, a novel network called cascaded segmentation U-Net is proposed to high-quality segment the height of points (HOP) (around 40 μm height) in favor of small datasets training and then carries out a post-processing algorithm that automatically calculates POP and PPI. This research emphasizes the architecture of the network itself instead. The design of the components of our network is based on the basic idea of identity function, which not only solves the problem of the misjudgment of the oil ditch and the residual pigment but also allows the network to be end-to-end trained effectively. At the head of the network, a cascaded multi-stage pixel-wise classification is designed for obtaining more accurate HOP borders. Furthermore, the “Cross-dimension Compression” stage is used to fuse high-dimensional semantic feature maps across the depth of the feature maps into low-dimensional feature maps, producing decipherable content for final pixel-wise classification. Our system can achieve an error rate of 3.7% and 0.9 points for POP and PPI. The novel network achieves an Intersection over Union (IoU) of 90.2%.
Diagnosis of Multiple Faults in Rotating Machinery Using Ensemble Learning
Authors: Inyang Udeme Ibanga , Petrunin Ivan , Jennions Ian
Published: January 2023

Fault diagnosis of rotating machines is an important task to prevent machinery downtime, and provide verifiable support for condition-based maintenance (CBM) decision-making. Deep learning-enabled fault diagnosis operations have become increasingly popular because features are extracted and selected automatically. However, it is challenging for these models to give superior results with rotating machine components of different scales, single and multiple faults across different rotating components, diverse operating speeds, and diverse load conditions. To address these challenges, this paper proposes a comprehensive learning approach with optimized signal processing transforms for single as well as multiple faults diagnosis across dissimilar rotating machine components: gearbox, bearing, and shaft. The optimized bicoherence, spectral kurtosis and cyclic spectral coherence feature spaces, and deep blending ensemble learning are explored for multiple faults diagnosis of these components. The performance analysis of the proposed approach has been demonstrated through a single joint training of the entire framework on a compound dataset containing multiple faults derived from three public repositories. A comparison with the state-of-the-art approaches that used these datasets, shows that our method gives improved results with different components and faults with nominal retraining.
Estimating the Depth of Anesthesia from EEG Signals Based on a Deep Residual Shrinkage Network
Authors: Shi Meng , Huang Ziyu , Xiao Guowen , Xu Bowen , Ren Quansheng , Zhao Hong
Published: January 2023

The reliable monitoring of the depth of anesthesia (DoA) is essential to control the anesthesia procedure. Electroencephalography (EEG) has been widely used to estimate DoA since EEG could reflect the effect of anesthetic drugs on the central nervous system (CNS). In this study, we propose that a deep learning model consisting mainly of a deep residual shrinkage network (DRSN) and a 1 × 1 convolution network could estimate DoA in terms of patient state index (PSI) values. First, we preprocessed the four raw channels of EEG signals to remove electrical noise and other physiological signals. The proposed model then takes the preprocessed EEG signals as inputs to predict PSI values. Then we extracted 14 features from the preprocessed EEG signals and implemented three conventional feature-based models as comparisons. A dataset of 18 patients was used to evaluate the models’ performances. The results of the five-fold cross-validation show that there is a relatively high similarity between the ground-truth PSI values and the predicted PSI values of our proposed model, which outperforms the conventional models, and further, that the Spearman’s rank correlation coefficient is 0.9344. In addition, an ablation experiment was conducted to demonstrate the effectiveness of the soft-thresholding module for EEG-signal processing, and a cross-subject validation was implemented to illustrate the robustness of the proposed method. In summary, the procedure is not merely feasible for estimating DoA by mimicking PSI values but also inspired us to develop a precise DoA-estimation system with more convincing assessments of anesthetization levels.
An Image-Based Quantized Compressive Sensing Scheme Using Zadoff–Chu Measurement Matrix
Authors: Xue Linlin , Qiu Weiwei , Wang Yue , Wang Zhongpeng
Published: January 2023

In this paper, a complex-valued Zadoff–Chu measurement matrix is proposed and used in an image-based quantized compressive sensing (CS) scheme. The results of theoretical analysis and simulations show that the reconstruction performance generated by the proposed Zadoff–Chu measurement matrix is better than that obtained by commonly used real-valued measurement matrices. We also applied block compressive sensing (BCS) to reduce the computational complexity of CS and analyzed the effect of block size on the reconstruction performance of the method. The results of simulations revealed that an appropriate choice of block size can not only reduce the computational complexity but also improve the accuracy of reconstruction. Moreover, we studied the effect of quantization on the reconstruction performance of image-based BCS through simulations, and the results showed that analog-to-digital converters with medium resolutions are sufficient to implement quantization and achieve comparable reconstruction performance to that obtained at high resolutions, based on which an image-based BCS framework with low power consumption can thus be developed.
Bearing Fault Diagnosis Using a Hybrid Fuzzy V-Structure Fault Estimator Scheme
Authors: Piltan Farzin , Kim Jong-Myon
Published: January 2023

Bearings are critical components of motors. However, they can cause several issues. Proper and timely detection of faults in the bearings can play a decisive role in reducing damage to the entire system, thereby reducing economic losses. In this study, a hybrid fuzzy V-structure fuzzy fault estimator was used for fault diagnosis and crack size identification in the bearing using vibration signals. The estimator was designed based on the combination of a fuzzy algorithm and a V-structure approach to reduce the oscillation and improve the unknown condition’s estimation and prediction in using the V-structure method. The V-structure surface is developed by the proposed fuzzy algorithm, which reduces the vibrations and improves the stability. In addition, the parallel fuzzy method is used to improve the robustness and stability of the V-structure algorithm. For data modeling, the proposed combination of an external autoregression error, a Laguerre filter, and a support vector regression algorithm was employed. Finally, the support vector machine algorithm was used for data classification and crack size detection. The effectiveness of the proposed approach was evaluated by leveraging the vibration signals provided in the Case Western Reserve University bearing dataset. The dataset consists of four conditions: normal, ball failure, inner fault, and outer fault. The results showed that the average accuracy of fault classification and crack size identification using the hybrid fuzzy V-structure fuzzy fault estimation algorithm was 98.75% and 98%, respectively.
DCP-SLAM: Distributed Collaborative Partial Swarm SLAM for Efficient Navigation of Autonomous Robots
Authors: Mahboob Huma , Yasin Jawad N. , Jokinen Suvi , Haghbayan Mohammad-Hashem , Plosila Juha , Yasin Muhammad Mehboob
Published: January 2023

Collaborative robots represent an evolution in the field of swarm robotics that is pervasive in modern industrial undertakings from manufacturing to exploration. Though there has been much work on path planning for autonomous robots employing floor plans, energy-efficient navigation of autonomous robots in unknown environments is gaining traction. This work presents a novel methodology of low-overhead collaborative sensing, run-time mapping and localization, and navigation for robot swarms. The aim is to optimize energy consumption for the swarm as a whole rather than individual robots. An energy- and information-aware management algorithm is proposed to optimize the time and energy required for a swarm of autonomous robots to move from a launch area to the predefined destination. This is achieved by modifying the classical Partial Swarm SLAM technique, whereby sections of objects discovered by different members of the swarm are stitched together and broadcast to members of the swarm. Thus, a follower can find the shortest path to the destination while avoiding even far away obstacles in an efficient manner. The proposed algorithm reduces the energy consumption of the swarm as a whole due to the fact that the leading robots sense and discover respective optimal paths and share their discoveries with the followers. The simulation results show that the robots effectively re-optimized the previous solution while sharing necessary information within the swarm. Furthermore, the efficiency of the proposed scheme is shown via comparative results, i.e., reducing traveling distance by 13% for individual robots and up to 11% for the swarm as a whole in the performed experiments.
Research on Educational Robot System Based on Vision Processing
Authors: Zhao Jianwei , Gu Yutian , Hou Qifeng , Zhang Zhiwei
Published: January 2023

Aimed at the poor recognition effect of current educational robots on objects with complex shapes and colors and the single design of related experiments, this paper proposes a robot teaching instrument. The robot adopts a servo motor with an encoder, a drive, and a variety of sensors to realize a motor current loop, speed loop, position loop, and closed-loop control functions. Three experimental schemes were designed: a PID adjustment experiment, a robot obstacle avoidance and object-grasping program writing experiment, and a complex object recognition experiment based on cascade classifiers. The robot is conducive to improving students’ self-initiative ability, deepening their understanding of PID closed-loop control, multi-sensor fusion, and deep learning knowledge. It can improve students’ programming ability, enabling them to effectively combine theory and practice, as well as to comprehensively apply professional knowledge.
Drawing Ageing: Using Participant-Generated Drawing to Explore Older Australians Expectations and Experiences of Ageing in a Retirement Village
Authors: Miller Evonne
Published: January 2023

This article discusses the use of an arts-based visual methodology, drawing, to explore older people’s experiences and expectations of ageing in a retirement village. Tactile, generative, and visual, drawing is a quick, inexpensive, and extremely participatory process, which, compared to traditional text-based data, provides rare and compelling insight into conscious and unconscious feelings, emotions, sentiments, and experiences. As part of a broader project exploring life in retirement villages, 12 older adults in their 70s and 80s were asked to sketch their experiences of ageing, as well as their expectations and experiences of retirement village life. Sketches were analysed using reflexive thematic analysis, which revealed that participants drew ageing as both a time for opportunity and enjoyment and decline, while retirement village life very much met their expectations in terms of being a place of fun, friendship, and leisure. While drawing as a method is rarely used in gerontological research, the outcomes of this project demonstrate how it enables the powerful production of evocative, interactive, and memorable imagery, and it should be a greater part of the methodological toolbox.
Proboscis Extension Response of Three Apis mellifera Subspecies toward Water and Sugars in Subtropical Ecosystem
Authors: Alqarni Abdulaziz S. , Ali Hussain , Iqbal Javaid , Raweh Hael S. A.
Published: January 2023

The proboscis extension response (PER) assay revealed the responsiveness of three subspecies of the honeybee Apis mellifera [A. m. jemenitica (AMJ), A. m. carnica (AMC), and A. m. ligustica (AML)] to water and different concentrations (0.00001, 0.0001, 0.001, 0.01, 0.1, 0.5, 1.0, and 1.5 M) of three sugars (fructose, glucose, and sucrose) during the summer and fall seasons. The tested bee subspecies showed significantly different PERs to sugar types across the seasons. The water responsiveness of AMJ, a native bee subspecies, was significantly lower than that of AMC and AML, which showed an equally higher water response in both seasons. During the summer season, AMJ and AMC were equally responsive to each sugar type at all tested concentrations. AML was relatively less responsive to glucose at 0.001, 0.001, 0.01, 0.1, 0.5, and 1.0 M than to fructose and sucrose during the summer season. During the fall season, AMJ was equally responsive to glucose and sucrose at all tested concentrations but showed a significantly different response between fructose and sucrose at 0.001, 0.01, 0.1, 0.5, and 1.0 M concentrations. The PER of AMJ to fructose was lower than that of glucose and sucrose. AMC was equally responsive to all tested sugars at all concentrations, and AML showed a differential response between glucose and sucrose at different concentrations during the fall season. The inter-specific species comparisons revealed that all tested subspecies were equally responsive to fructose at all tested concentrations, and AMJ was more responsive to glucose and sucrose than AMC and AML during both seasons. AMC and AML showed no differences in PER to glucose and sucrose in either season. The AMJ, AMC, and AML nectar and pollen foragers showed no significant differences in PER to glucose and sucrose. The AMC nectar foragers were highly responsive to sucrose than pollen foragers at higher sucrose concentrations (0.1, 0.5, 1.0, and 1.5 M). The AML (nectar forager vs. pollen forgers) showed identical PER to sucrose and glucose but a higher response of nectar foragers to high glucose concentrations (0.5, 1.0, and 1.5 M) than pollen foragers. For water responsiveness, AMJ nectar and pollen foragers showed similar PER to water, whereas AMC and AML pollen foragers were significantly more responsive to water than nectar foragers.
Rainfall Projections from Coupled Model Intercomparison Project Phase 6 in the Volta River Basin: Implications on Achieving Sustainable Development
Authors: Dotse Sam-Quarcoo , Larbi Isaac , Limantol Andrew Manoba , Asare-Nuamah Peter , Frimpong Louis Kusi , Alhassan Abdul-Rauf Malimanga , Sarpong Solomon , Angmor Emmanuel , Ayisi-Addo Angela Kyerewaa
Published: January 2023

Climate change has become a global issue, not only because it affects the intensity and frequency of rainfall but also because it impacts the economic development of regions whose economies heavily rely on rainfall, such as the West African region. Hence, the need for this study, which is aimed at understanding how rainfall may change in the future over the Sahel, Savannah, and coastal zones of the Volta River Basin (VRB). The trends and changes in rainfall between 2021–2050 and 1985–2014 under the Shared Socioeconomic Pathway (SSP2-4.5 and SSP5-8.5) scenarios were analyzed after evaluating the performance of three climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) using Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) as observation. The results show, in general, a relatively high correlation and low spatial biases for rainfall (r > 0.91, −20% < Pbias < 20%) over the entire Volta Basin for the models’ ensemble mean. An increasing trend and projected increase in annual rainfall under the SSP2-4.5 scenario is 6.0% (Sahel), 7.3% (Savannah), and 2.6% (VRB), but a decrease of 1.1% in the coastal zone. Similarly, under SSP5-8.5, the annual rainfall is projected to increase by 32.5% (Sahel), +22.8% (Savannah), 23.0% (coastal), and 24.9% (VRB), with the increase being more pronounced under SSP5-8.5 compared to the SSP2-4.5 scenario. The findings of the study would be useful for planning and designing climate change adaptation measures to achieve sustainable development at the VRB.
Factors Indicating Media Dependency and Online Misinformation Sharing in Jordan
Authors: Habes Mohammed , Elareshi Mokhtar , Mansoori Ahmed , Pasha Saadia , Salloum Said A. , Al-Rahmi Waleed Mugahed
Published: January 2023

Although social media is a vital platform in our life, it is blamed for poor efforts to moderate content included mis/disinformation and fake news. This could have an impact on its legacy and on sustainability in society in the long term. This research examined the role of social media in spreading misinformation during the COVID-19 outbreak in Jordan. A cross-sectional design questionnaire (350 responses) was used. The results revealed that social media played a key role in updating users with COVID-19 information. However, the availability of misinformation remained highly prevalent. Respondents revealed that they relied heavily on social media for information gathering and knowledge sharing about COVID-19 updates. The role of behavioural intention remained prominent and highly significant for these two reasons. Their behavioural intention was linked to the sharing of unchecked information, suggesting that online information in Jordan needs greater regulation to reduce the spread of misinformation.
Effects of Double-Diffusive Convection on Calculation Time and Accuracy Results of a Salt Gradient Solar Pond: Numerical Investigation and Experimental Validation
Authors: Rghif Yassmine , Colarossi Daniele , Principi Paolo
Published: January 2023

The main aim of this study is to investigate numerically and experimentally the effects of double-diffusive convection on calculation time and accuracy results of a Salt Gradient Solar Pond (SGSP). To this end, two-numerical models are developed based on the Fortran programming language. The first one is based on energy balance neglecting the development of double-diffusive convection, while the second is two-dimensional and is based on Navier-Stokes, heat, and mass transfer equations considering the development of double-diffusive convection. The heat losses via the upper part, bottom, and vertical walls, as well as the internal heating of saltwater, are considered. In order to validate and compare both numerical models, a laboratory-scale SGSP is designed, built, and tested indoors for 82 h. Results indicate that the two numerical models developed can predict the SGSP thermal behavior with good accuracy. Furthermore, the average relative error between experimental and numerical results is around 9.39% for Upper Convective Zone (UCZ) and 2.92% for Lower Convective Zone (LCZ) based on the first model. This error reduces to about 5.98% for UCZ and 3.74% for LCZ by using the second model. Consequently, the neglect of double-diffusive convection in the SGSP modeling tends to overestimate the thermal energy stored in the storage zone by about 4.3%. Based on the calculation time analysis, results show that the second model returns a calculation time hundreds of times larger than the first one and, accordingly, an increase in computational cost.
Compatibility of Sustainable Facility Management and Building Information Modeling Applications: The Role of Naming Conventions
Authors: Foster John D. , Everett Jess W. , Riddell William T.
Published: January 2023

This research focuses on data transfer from Sustainable Facility Management (SFM) to Building Information Modeling (BIM) applications—specifically BUILDER SMS to Autodesk Revit—where the BIM models are created for existing buildings. BIM models were created for ten existing buildings to support initial data transfer based on different object parameters: (a) alphanumeric identifier (five buildings); and (b) name (five buildings). The identifier is randomly assigned by BUILDER SMS. The name is derived from the cardinal direction for exterior components and room names for interior components. The comparison of the methods was accomplished by observing times to ensure data compatibility, complete Revit models, and transfer data from BUILDER SMS to Revit. The name method was faster regarding data compatibility, but no statistical evaluation could be made. The identifier method resulted in shorter model completion times, but the difference was not statistically significant. It was also faster for first-time data transfer, but, again, no statistical evaluation could be made. The difference in total effort required by the methods was not large. Furthermore, the first data transfer for either method can transfer the other parameter, i.e., a name-based data transfer can add the identifier to the Revit model and an identifier-based transfer can add the name. The name method may be slightly better than the identifier method because object names follow an intuitive and logical standard naming convention (specified in the SFM application). The process of ensuring data compatibility by manually entering object names may be less error-prone compared to manually entering alphanumeric identifiers; however, that was not demonstrated here as all of the resulting models were error-free regarding data transfer. The methods described provide useful insights for other SFM/BIM transfer scenarios, such as other applications and models generated during the design phase.
Application of Electrocoagulation for the Removal of Transition Metals in Water
Authors: Aguiar Tales , Baumann Luis , Albuquerque Antonio , Teixeira Luiza , de Souza Gil Eric , Scalize Paulo
Published: January 2023

Urban and industrial effluents, stormwater, road runoff, agricultural runoff, urban or mine waste deposits and fuel storage sites can lead to the contamination of water sources with compounds that are hard to biodegrade, such as heavy metals, whose removal requires advanced and expensive technologies. The Sustainable Development Goals (SDGs) established by the UN and the current requirements in terms of energy efficiency, reduction of carbon emissions, water reuse, waste valorization and preservation of public health, have led to a rethink concerning the typology of technologies for the treatment of water and the production of drinking water. Electrocoagulation (EC) stands out in this scenario due to its high efficiency in the removal of several pollutants, production of low sludge volumes and adaptability to the use of renewable energies. This is in addition to the ease with which it can be combined with other water treatment technologies. This work presents a literature review to systematize the use of EC for the removal of transition metals in water to produce drinking water, since these elements are present in several natural water sources and are parameters used in the legislation of many countries for the quality control of drinking water. The works found were analyzed in detail, and relationships between pre-set variables and categories were determined through regression analysis. Generally, it was found that EC is a highly efficient technology for the removal of transition metals in water (above 75% for most metals), specifically through parallel plates technologies with iron and aluminum electrodes with a minimum spacing of 1 cm and density minimum applied current of 10 A/m².