bluuebunny/crossref_metadata_2025 huggingface dataset:
Damage Detection Using Ultrasonic Techniques in Concrete-Filled Steel Tubes (CFSTs) Columns
Authors: Callejas Antonio , Palma Roberto , Hernández-Figueirido David , Rus Guillermo
Published: June 2022

Concrete-filled steel tubes (CFSTs) are structural elements that, as a consequence of an incorrect elaboration, can exhibit internal defects that cannot be visualized, being usually air voids. In this work, the detection of internal damage in CFST samples elaborated with a percentage of contained air voids in concrete, was carried out by performing a complete ultrasound scan using an immersion tank. The analysis of the ultrasound signals shows the differences presented in the amplitude of the fundamental frequency of the signal, and in the Broadband Ultrasound Attenuation (BUA), in comparison with a sample without defects. The main contribution of this study is the application of the BUA technique in CFST samples for the location of air voids. The results present a linear relationship between BUA averages over the window of the CFSTs and the percentage of air voids contained (Pearson’s correlation coefficient r = 0.9873), the higher percentage of air voids, the higher values of BUA. The BUA algorithm could be applied effectively to distinguish areas with defects inside the CFSTs. Similar to the BUA results, the analysis in the frequency domain using the FFT and the STFT was sensitive in the detection of internal damage (Pearson’s correlation coefficient r = −0.9799, and r = −0.9672, respectively). The results establish an improvement in the evaluation of CFST elements for the detection of internal defects.
DOC-IDS: A Deep Learning-Based Method for Feature Extraction and Anomaly Detection in Network Traffic
Authors: Yoshimura Naoto , Kuzuno Hiroki , Shiraishi Yoshiaki , Morii Masakatu
Published: June 2022

With the growing diversity of cyberattacks in recent years, anomaly-based intrusion detection systems that can detect unknown attacks have attracted significant attention. Furthermore, a wide range of studies on anomaly detection using machine learning and deep learning methods have been conducted. However, many machine learning and deep learning-based methods require significant effort to design the detection feature values, extract the feature values from network packets, and acquire the labeled data used for model training. To solve the aforementioned problems, this paper proposes a new model called DOC-IDS, which is an intrusion detection system based on Perera’s deep one-class classification. The DOC-IDS, which comprises a pair of one-dimensional convolutional neural networks and an autoencoder, uses three different loss functions for training. Although, in general, only regular traffic from the computer network subject to detection is used for anomaly detection training, the DOC-IDS also uses multi-class labeled traffic from open datasets for feature extraction. Therefore, by streamlining the classification task on multi-class labeled traffic, we can obtain a feature representation with highly enhanced data discrimination abilities. Simultaneously, we perform variance minimization in the feature space, even on regular traffic, to further improve the model’s ability to discriminate between normal and abnormal traffic. The DOC-IDS is a single deep learning model that can automatically perform feature extraction and anomaly detection. This paper also reports experiments for evaluating the anomaly detection performance of the DOC-IDS. The results suggest that the DOC-IDS offers higher anomaly detection performance while reducing the load resulting from the design and extraction of feature values.
FPGA-Based Processor for Continual Capacitive-Coupling Impedance Spectroscopy and Circuit Parameter Estimation
Authors: Tsukahara Akihiko , Yamaguchi Tomiharu , Tanaka Yuho , Ueno Akinori
Published: June 2022

In principle, the recently proposed capacitive-coupling impedance spectroscopy (CIS) has the capability to acquire frequency spectra of complex electrical impedance sequentially on a millisecond timescale. Even when the measured object with time-varying unknown resistance Rx is capacitively coupled with the measurement electrodes with time-varying unknown capacitance Cx, CIS can be measured. As a proof of concept, this study aimed to develop a prototype that implemented the novel algorithm of CIS and circuit parameter estimation to verify whether the frequency spectra and circuit parameters could be obtained in milliseconds and whether time-varying impedance could be measured. This study proposes a dedicated processor that was implemented as field-programmable gate arrays to perform CIS, estimate Rx and Cx, and their digital-to-analog conversions at a certain time, and to repeat them continually. The proposed processor executed the entire sequence in the order of milliseconds. Combined with a front-end nonsinusoidal oscillator and interfacing circuits, the processor estimated the fixed Rx and fixed Cx with reasonable accuracy. Additionally, the combined system with the processor succeeded in detecting a quick optical response in the resistance of the cadmium sulfide (CdS) photocell connected in series with a capacitor, and in reading out their resistance and capacitance independently as voltages in real-time.
An Unsupervised Transfer Learning Framework for Visible-Thermal Pedestrian Detection
Authors: Lyu Chengjin , Heyer Patrick , Goossens Bart , Philips Wilfried
Published: June 2022

Dual cameras with visible-thermal multispectral pairs provide both visual and thermal appearance, thereby enabling detecting pedestrians around the clock in various conditions and applications, including autonomous driving and intelligent transportation systems. However, due to the greatly varying real-world scenarios, the performance of a detector trained on a source dataset might change dramatically when evaluated on another dataset. A large amount of training data is often necessary to guarantee the detection performance in a new scenario. Typically, human annotators need to conduct the data labeling work, which is time-consuming, labor-intensive and unscalable. To overcome the problem, we propose a novel unsupervised transfer learning framework for multispectral pedestrian detection, which adapts a multispectral pedestrian detector to the target domain based on pseudo training labels. In particular, auxiliary detectors are utilized and different label fusion strategies are introduced according to the estimated environmental illumination level. Intermediate domain images are generated by translating the source images to mimic the target ones, acting as a better starting point for the parameter update of the pedestrian detector. The experimental results on the KAIST and FLIR ADAS datasets demonstrate that the proposed method achieves new state-of-the-art performance without any manual training annotations on the target data.
Enhancing PM2.5 Prediction Using NARX-Based Combined CNN and LSTM Hybrid Model
Authors: Moursi Ahmed Samy AbdElAziz , El-Fishawy Nawal , Djahel Soufiene , Shouman Marwa A.
Published: June 2022

In a world where humanity’s interests come first, the environment is flooded with pollutants produced by humans’ urgent need for expansion. Air pollution and climate change are side effects of humans’ inconsiderate intervention. Particulate matter of 2.5 µm diameter (PM2.5) infiltrates lungs and hearts, causing many respiratory system diseases. Innovation in air pollution prediction is a must to protect the environment and its habitants, including those of humans. For that purpose, an enhanced method for PM2.5 prediction within the next hour is introduced in this research work using nonlinear autoregression with exogenous input (NARX) model hosting a convolutional neural network (CNN) followed by long short-term memory (LSTM) neural networks. The proposed enhancement was evaluated by several metrics such as index of agreement (IA) and normalized root mean square error (NRMSE). The results indicated that the CNN–LSTM/NARX hybrid model has the lowest NRMSE and the best IA, surpassing the state-of-the-art proposed hybrid deep-learning algorithms.
FPGA-Based Pedestrian Detection for Collision Prediction System
Authors: Cambuim Lucas , Barros Edna
Published: June 2022

Pedestrian detection (PD) systems capable of locating pedestrians over large distances and locating them faster are needed in Pedestrian Collision Prediction (PCP) systems to increase the decision-making distance. This paper proposes a performance-optimized FPGA implementation of a HOG-SVM-based PD system with support for image pyramids and detection windows of different sizes to locate near and far pedestrians. This work proposes a hardware architecture that can process one pixel per clock cycle by exploring data and temporal parallelism using techniques such as pipeline and spatial division of data between parallel processing units. The proposed architecture for the PD module was validated in FPGA and integrated with the stereo semi-global matching (SGM) module, also prototyped in FPGA. Processing two windows of different dimensions permitted a reduction in miss rate of at least 6% compared to a uniquely sized window detector. The performances achieved by the PD system and the PCP system in HD resolution were 100 and 66.2 frames per second (FPS), respectively. The performance improvement achieved by the PCP system with the addition of our PD module permitted an increase in decision-making distance of 3.3 m compared to a PCP system that processes at 30 FPS.
A Survey of Robot Swarms’ Relative Localization Method
Authors: Chen Siyuan , Yin Dong , Niu Yifeng
Published: June 2022

For robot swarm applications, accurate positioning is one of the most important requirements for avoiding collisions and keeping formations and cooperation between individuals. However, in some worst cases, the GNSS (Global Navigation Satellite System) signals are weak due to the crowd being in a swarm or blocked by a forest, mountains, and high buildings in the environment. Thus, relative localization is an indispensable way to provide position information for the swarm. In this paper, we review the status and development of relative localization. It is first assessed that relative localization to obtain spatio-temporal relationships between individuals is necessary to achieve the stable operation of the group. After analyzing typical relative localization systems and algorithms from the perspective of functionality and practicality, this paper concludes that the UWB-based (ultra wideband) system is suitable for the relative localization of robots in large-scale applications. Finally, after analyzing the current challenges in the field of fully distributed localization for robotic swarms, a complete mechanism encompassing the relative localization process and the relationship between local and global localization that can be a possible direction for future research is proposed.
Micromagnetic and Microstructural Characterization of Ferromagnetic Steels in Different Heat Treatment Conditions
Authors: Ankener Werner , Böttger David , Smaga Marek , Gabi Yasmine , Strass Benjamin , Wolter Bernd , Beck Tilmann
Published: June 2022

The paper addresses the investigation of microstructures from AISI 52100 and AISI 4140 in hardened as well as in quenched and tempered conditions. The specimens are compared in terms of their magnetic hysteresis and their microstructural and mechanical properties. Material properties were determined by hardness, microhardness, and X-ray diffraction measurements. Two different approaches were used to characterize magnetic properties via a hysteresis frame device, aiming, on the one hand, to record the magnetic hysteresis with established proceedings by setting a constant magnetic flux and, on the other hand, by offsetting a constant field strength to facilitate reproducibility of the results with other micromagnetic measurement systems. Comparable differences in both the micromagnetic and the mechanical material properties could be determined and quantified for the specifically manufactured specimens. The sensitivity of the magnetic hysteresis and, determined from that, the relationship between magnetic flux and magnetic field strength were confirmed. It was shown that a consistent change in hysteresis shape from hardened to high temperature tempered material states develops and that this change allows the characterization of different materials without the need to adjust magnetization parameters. Repeatedly, an increase in remanence with decreasing hardness was found for both test approaches. Likewise, a decreasing coercivity and increasing maximum magnetic flux could be detected with decreasing retained austenite content. The investigated correlations should thus contribute to the calibration of comparable measurement systems through the holistic characterized specimens.
Interference-Aware Two-Level Differentiated Transmission for Improving Downlink Spatial Reuse in Dense WLANs
Authors: Kwon Lam , Park Eun-Chan
Published: June 2022

In this study, we address the problem of downlink throughput degradation in dense wireless local area networks (WLANs) based on the IEEE 802.11ax standard. We demonstrate that this problem essentially results from the asymmetric characteristic of carrier sense multiple access between downlink and uplink transmissions in infrastructure WLANs, and it is exacerbated by a dynamic sensitivity control algorithm that aims to improve spatial reuse (SR) in IEEE 802.11ax. To solve this problem, we propose the interference-aware two-level differentiation mechanism consisting of the dual channel access (DCA) and supplemental power control (SPC) schemes. The proposed mechanism introduces a new measure called a spatial reusability indicator, which roughly estimates the signal-to-interference ratio from the received signal strength of beacon frames. Based on this measure, stations (STAs) are classified into the following two categories: spatial reusable STAs (SR-STAs) and non-spatial reusable STAs (NSR-STAs). Because SR-STAs are more robust to interference than NSR-STAs, the DCA scheme prioritizes transmissions to SR-STAs over those to NSR-STAs by using differentiated carrier sensing thresholds. Moreover, the SPC scheme selectively increases the transmission power to NSR-STAs to compensate for transmission failure due to interference. By combining the SPC and DCA schemes, the proposed mechanism effectively differentiates the downlink transmissions to SR-STAs and NSR-STAs in terms of channel access and transmission power, and it can boost the possibility of successful SR. The proposed mechanism can be easily implemented in IEEE 802.11ax without any complex calculation or significant signaling overhead. Moreover, we provide a practical guideline to determine appropriate parameter values for use in the proposed mechanism. The extensive simulation results obtained in this study confirm that the proposed mechanism increases the downlink throughput by more than several times without decreasing the overall throughput, compared to the existing mechanisms, and it maintains fairness between SR-STAs and NSR-STAs in terms of the ratio of successful transmission.
A Comparison of Inertial Measurement Unit and Motion Capture Measurements of Tibiofemoral Kinematics during Simulated Pivot Landings
Authors: Baek So Young , Ajdaroski Mirel , Shahshahani Payam Mirshams , Beaulieu Mélanie L. , Esquivel Amanda O. , Ashton-Miller James A.
Published: June 2022

Injuries are often associated with rapid body segment movements. We compared Certus motion capture and APDM inertial measurement unit (IMU) measurements of tibiofemoral angle and angular velocity changes during simulated pivot landings (i.e., ~70 ms peak) of nine cadaver knees dissected free of skin, subcutaneous fat, and muscle. Data from a total of 852 trials were compared using the Bland–Altman limits of agreement (LoAs): the Certus system was considered the gold standard measure for the angle change measurements, whereas the IMU was considered the gold standard for angular velocity changes. The results show that, although the mean peak IMU knee joint angle changes were slightly underestimated (2.1° for flexion, 0.2° for internal rotation, and 3.0° for valgus), the LoAs were large, ranging from 35.9% to 49.8%. In the case of the angular velocity changes, Certus had acceptable accuracy in the sagittal plane, with LoAs of ±54.9°/s and ±32.5°/s for the tibia and femur. For these rapid motions, we conclude that, even in the absence of soft tissues, the IMUs could not reliably measure these peak 3D knee angle changes; Certus measurements of peak tibiofemoral angular velocity changes depended on both the magnitude of the velocity and the plane of measurement.
Applying Different Strategies of Task Constraint Manipulation in Small-Sided and Conditioned Games: How Do They Impact Physical and Tactical Demands?
Authors: Machado João Cláudio , Góes Alberto , Aquino Rodrigo , Bedo Bruno L. S. , Viana Ronélia , Rossato Mateus , Scaglia Alcides , Ibáñez Sérgio J.
Published: June 2022

This study aimed to investigate how different strategies of task constraint manipulation impact physical and tactical demands in small-sided and conditioned games (SSCG). Ten recreational U-17 soccer players participated in this study (16.89 ± 0.11 years). We used different strategies of task manipulation to design two 4 vs. 4 SSCG: Structural SSCG and Functional SSCG. In Structural SSCG, pitch format and goal sizes were manipulated, while in Functional SSCG, players were allowed to kick the ball twice and at least 5 passes to shoot at the opponent’s goal. Players participated in four Structural and Functional SSCG, of five minutes duration with a two-minute interval in between. Players’ physical performance and tactical behavior were assessed using the WIMU PROTM inertial device. Structural SSCG stimulated players to cover more distance in sprinting (p = 0.003) and high-speed running (p < 0.001). Regarding tactical behavior, Structural SSCG stimulated players to explore game space better (p < 0.001). Moreover, Functional SSCG stimulated players to be closer to the ball, decreasing the effective playing space (p = 0.008). We conclude that these strategies of task constraint manipulation impact physical and tactical demands of the game.
TA-Unet: Integrating Triplet Attention Module for Drivable Road Region Segmentation
Authors: Li Sijia , Sultonov Furkat , Ye Qingshan , Bai Yong , Park Jun-Hyun , Yang Chilsig , Song Minseok , Koo Sungwoo , Kang Jae-Mo
Published: June 2022

Road segmentation has been one of the leading research areas in the realm of autonomous driving cars due to the possible benefits autonomous vehicles can offer. Significant reduction of crashes, greater independence for the people with disabilities, and reduced traffic congestion on the roads are some of the vivid examples of them. Considering the importance of self-driving cars, it is vital to develop models that can accurately segment drivable regions of roads. The recent advances in the area of deep learning have presented effective methods and techniques to tackle road segmentation tasks effectively. However, the results of most of them are not satisfactory for implementing them into practice. To tackle this issue, in this paper, we propose a novel model, dubbed as TA-Unet, that is able to produce quality drivable road region segmentation maps. The proposed model incorporates a triplet attention module into the encoding stage of the U-Net network to compute attention weights through the triplet branch structure. Additionally, to overcome the class-imbalance problem, we experiment on different loss functions, and confirm that using a mixed loss function leads to a boost in performance. To validate the performance and efficiency of the proposed method, we adopt the publicly available UAS dataset, and compare its results to the framework of the dataset and also to four state-of-the-art segmentation models. Extensive experiments demonstrate that the proposed TA-Unet outperforms baseline methods both in terms of pixel accuracy and mIoU, with 98.74% and 97.41%, respectively. Finally, the proposed method yields clearer segmentation maps on different sample sets compared to other baseline methods.
Implementation of Aging Mechanism Analysis and Prediction for XILINX 7-Series FPGAs with a 28-nm Process
Authors: Li Zeyu , Huang Zhao , Wang Quan , Wang Junjie , Luo Nan
Published: June 2022

Commercial off-the-shelf (COTS) field-programmable gate arrays (FPGAs) with a 28-nm process have become popular devices for computing systems. Although current generation FPGAs have advantages over previous models, the phenomenon of circuit aging has become more significant with the sharp reduction in the process size of FPGAs. Aging results in FPGA performance degradation over time and, ultimately, hard faults. However, few studies have focused on understanding aging mechanisms or estimating the aging trend of 28-nm FPGAs. For this, we used a ring oscillator (RO)-based test structure to extract data and build a dataset that could be used to predict aging trends and determine the primary aging mechanisms of 28-nm FPGAs. Moreover, we proposed a correction method to correct temperature-induced measurement errors in accelerated tests. Furthermore, we employed four machine learning (ML) technologies that were based on accurate measurement datasets to predict FPGA aging trends. In the experiment, 24 XILINX 7-series FPGAs (28 nm) were evaluated for 10+ years of circuit operation using accelerated tests. The results showed that the aging effects of negative-bias temperature instability (NBTI) was the primary aging mechanism. The correction method proposed in this paper could effectively eliminate measurement errors. In addition, the minimum prediction error rate of the ML model was only 0.292%.
A Novel Detection Refinement Technique for Accurate Identification of Nephrops norvegicus Burrows in Underwater Imagery
Authors: Naseer Atif , Baro Enrique Nava , Khan Sultan Daud , Vila Yolanda
Published: June 2022

With the evolution of the convolutional neural network (CNN), object detection in the underwater environment has gained a lot of attention. However, due to the complex nature of the underwater environment, generic CNN-based object detectors still face challenges in underwater object detection. These challenges include image blurring, texture distortion, color shift, and scale variation, which result in low precision and recall rates. To tackle this challenge, we propose a detection refinement algorithm based on spatial–temporal analysis to improve the performance of generic detectors by suppressing the false positives and recovering the missed detections in underwater videos. In the proposed work, we use state-of-the-art deep neural networks such as Inception, ResNet50, and ResNet101 to automatically classify and detect the Norway lobster Nephrops norvegicus burrows from underwater videos. Nephrops is one of the most important commercial species in Northeast Atlantic waters, and it lives in burrow systems that it builds itself on muddy bottoms. To evaluate the performance of proposed framework, we collected the data from the Gulf of Cadiz. From experiment results, we demonstrate that the proposed framework effectively suppresses false positives and recovers missed detections obtained from generic detectors. The mean average precision (mAP) gained a 10% increase with the proposed refinement technique.
DE-PNN: Differential Evolution-Based Feature Optimization with Probabilistic Neural Network for Imbalanced Arrhythmia Classification
Authors: Nasim Amnah , Kim Yoon Sang
Published: June 2022

In this research, a heartbeat classification method is presented based on evolutionary feature optimization using differential evolution (DE) and classification using a probabilistic neural network (PNN) to discriminate between normal and arrhythmic heartbeats. The proposed method follows four steps: (1) preprocessing, (2) heartbeat segmentation, (3) DE feature optimization, and (4) PNN classification. In this method, we have employed direct signal amplitude points constituting the heartbeat acquired from the ECG holter device with no secondary feature extraction step usually used in case of hand-crafted, frequency transformation or other features. The heartbeat types include normal, left bundle branch block, right bundle branch block, premature ventricular contraction, atrial premature, ventricular escape, ventricular flutter and paced beat. Using ECG records from the MIT-BIH, heartbeats are identified to start at 250 ms before and end at 450 ms after the respective R-peak positions. In the next step, the DE method is applied to reduce and optimize the direct heartbeat features. Although complex and highly computational ECG heartbeat classification algorithms have been proposed in the literature, they failed to achieve high performance in detecting some minority heartbeat categories, especially for imbalanced datasets. To overcome this challenge, we propose an optimization step for the deep CNN model using a novel classification metric called the Matthews correlation coefficient (MCC). This function focuses on arrhythmia (minority) heartbeat classes by increasing their importance. Maximum MCC is used as a fitness function to identify the optimum combination of features for the uncorrelated and non-uniformly distributed eight beat class samples. The proposed DE-PNN scheme can provide better classification accuracy considering 8 classes with only 36 features optimized from a 253 element feature set implying an 85.77% reduction in direct amplitude features. Our proposed method achieved overall 99.33% accuracy, 94.56% F1, 93.84% sensitivity, and 99.21% specificity.
Gravity-Matching Algorithm Based on K-Nearest Neighbor
Authors: Gao Shuaipeng , Cai Tijing , Fang Ke
Published: June 2022

The gravity-aided inertial navigation system is a technique using geophysical information, which has broad application prospects, and the gravity-map-matching algorithm is one of its key technologies. A novel gravity-matching algorithm based on the K-Nearest neighbor is proposed in this paper to enhance the anti-noise capability of the gravity-matching algorithm, improve the accuracy of gravity-aided navigation, and reduce the application threshold of the matching algorithm. This algorithm selects K sample labels by the Euclidean distance between sample datum and measurement, and then creatively determines the weight of each label from its spatial position using the weighted average of labels and the constraint conditions of sailing speed to obtain the continuous navigation results by gravity matching. The simulation experiments of post processing are designed to demonstrate the efficiency. The experimental results show that the algorithm reduces the INS positioning error effectively, and the position error in both longitude and latitude directions is less than 800 m. The computing time can meet the requirements of real-time navigation, and the average running time of the KNN algorithm at each matching point is 5.87s. This algorithm shows better stability and anti-noise capability in the continuously matching process.
A Novel Deep Supervised Learning-Based Approach for Intrusion Detection in IoT Systems
Authors: Baniasadi Sahba , Rostami Omid , Martín Diego , Kaveh Mehrdad
Published: June 2022

The Internet of Things (IoT) has become one of the most important concepts in various aspects of our modern life in recent years. However, the most critical challenge for the world-wide use of the IoT is to address its security issues. One of the most important tasks to address the security challenges in the IoT is to detect intrusion in the network. Although the machine/deep learning-based solutions have been repeatedly used to detect network intrusion through recent years, there is still considerable potential to improve the accuracy and performance of the classifier (intrusion detector). In this paper, we develop a novel training algorithm to better tune the parameters of the used deep architecture. To specifically do so, we first introduce a novel neighborhood search-based particle swarm optimization (NSBPSO) algorithm to improve the exploitation/exploration of the PSO algorithm. Next, we use the advantage of NSBPSO to optimally train the deep architecture as our network intrusion detector in order to obtain better accuracy and performance. For evaluating the performance of the proposed classifier, we use two network intrusion detection datasets named UNSW-NB15 and Bot-IoT to rate the accuracy and performance of the proposed classifier.
Public Pressure, Environmental Policy Uncertainty, and Enterprises’ Environmental Information Disclosure
Authors: Wu Die , Memon Hafeezullah
Published: June 2022

Under the Chinese strategy of “carbon peaking and carbon neutrality”, Enterprises’ Environmental Information Disclosure (EEID), as one of the important ways for enterprises to achieve low-carbon development, has gained increased attention from the government, media, investors, and other stakeholders. the EEID is not only an important tool for companies to communicate environmental performance to the outside world, but also an effective way for the government to monitor corporate pollution behavior. Its importance is self-evident. However, relevant research shows that 70% of Chinese listed companies had not implemented the EEID in 2020. Also, there are common problems in the disclosure content and the polarization of the disclosure level among the companies that do disclose. These problems weaken the objectivity and practicability of the EEID and have a negative impact on the government’s environmental supervision, the environmental protection demands of the public, and investors’ decision making. This paper takes listed companies in China’s A-share heavily polluting industries as the research sample to solve the optimization problem of the EEID. By adopting a fixed effects model (FEM), this paper empirically studies the impact of three public pressures on the EEID: government environmental regulation, media attention, and institutional investment preference. Based on China’s unique socialist market economic system, this paper innovatively uses environmental policy uncertainty as a moderator variable. This paper examines the limitations of theoretical research on public pressure and environmental information disclosure by studying the impact of local environmental leadership change on the relationship between public pressure and the EEID. The conclusions of this paper reveal the driving mechanism of how stakeholders such as government, media, and institutional investors influence the EEID. At the same time, it expands the application of public pressure theory in environmental information disclosure research by introducing the perspective of environmental policy uncertainty.
Multivariate Statistical Analysis and Structural Sovereignty for Geochemical Assessment and Groundwater Prevalence in Bahariya Oasis, Western Desert, Egypt
Authors: Abd El-Wahed Mohamed , El-Horiny Mohamed M. , Ashmawy Mahmoud , El Kereem Samar Abd
Published: June 2022

The Bahariya Oasis is an example of an extremely hyperarid environment and it is characterized by an extensive nonrenewable Nubian Sandstone Aquifer System (NSAS), which is deemed the crucial provenance for agrarian and national development ventures. The present work aimed to assess the groundwater occurrences in the NSAS, and to document the main factors that control the geochemistry of the groundwater in the Bahariya Oasis. Groundwater samples were collected from 52 locations in April 2019 and were analyzed for a total of 13 water-quality physicochemical parameters. A diverse geological and structural setup has greatly impacted the groundwater flow pattern and has diverted it towards the NE by the great Bahariya anticline structure, the ENE-oriented Bahariya mid dextral strike-slip fault, and NE-striking normal faults, while NW-oriented normal faults cause the groundwater to diverge perpendicular to the groundwater flow lines. The groundwater is highly contaminated by trace metals (Fe2+ and Mn2+), which exceed the permissible limit for different purposes. Conventional graphical plots and geochemical modeling integrated with multivariate factor analysis (FA) revealed that the chemical composition of the groundwater is strongly affected by its interaction with the lithologies of the NSAS. The dissolution of aquifer host rocks (carbonates and iron oxides) and chloride salts through the infiltration of groundwater, and the incorporation of cations by the ionic exchange of Na+ by Ca2+ in clay minerals, emerged as worthy mechanisms for the groundwater development. Furthermore, the region’s rapidly increasing population, agricultural expansion, and the associated anthropogenic practices have generated a need for groundwater-quality assurance as a prime source of the water supply. Consequently, reducing the effects of the NSAS’s unsustainable extraction requires long-term monitoring and the ongoing evaluation of the groundwater.
A Price Premium for the District Heating System: An Empirical Investigation on South Korean Residents
Authors: Kim Ju-Hee , Lim Seul-Ye , Yoo Seung-Hoon
Published: June 2022

Representative heating methods in residential buildings in South Korea are the boiler-based individual heating system (IHS) and the district heating system (DHS). When constructing a large-scale new city or redeveloping an old one, the heating method must be determined in advance by reflecting consumers’ preferences. This article intends to explore the price premium that South Korean residents are willing to pay for DHS over IHS. The price premium means the consumer’s additional willingness to pay (AWTP). To obtain this, contingent valuation was employed and the data were gathered by conducting a nationwide survey of 1000 people. The one-and-one-half-bounded model was adopted as the method of inducing the AWTP. Comparison of the results from estimating the model with those from estimating other models revealed that there was no significant difference between the two. Moreover, the former held statistical significance. The price premium or AWTP was estimated as KRW 4353 (USD 3.88) per Gcal. This value corresponds to about 5.9% of the residential heat price, which was KRW 73,587 (USD 65.59) per Gcal in 2020. Heating prices are almost the same, with little difference between DHS and IHS. The results suggest that a large number of residents place a price premium on DHS over IHS.
Towards Safe and Sustainable Autonomous Vehicles Using Environmentally-Friendly Criticality Metrics
Authors: Jurj Sorin , Werner Tino , Grundt Dominik , Hagemann Willem , Möhlmann Eike
Published: June 2022

This paper presents an analysis of several criticality metrics used for evaluating the safety of Autonomous Vehicles (AVs) and also proposes environmentally friendly metrics with the scope of facilitating their selection by future researchers who want to evaluate both the safety and environmental impact of AVs. Regarding this, first, we investigate whether existing criticality metrics are applicable as a reward component in Reinforcement Learning (RL), which is a popular learning framework for training autonomous systems. Second, we propose environmentally friendly metrics that take into consideration the environmental impact by measuring the CO2 emissions of traditional vehicles as well as measuring the motor power used by electric vehicles. Third, we discuss the usefulness of using criticality metrics for Artificial Intelligence (AI) training. Finally, we apply a selected number of criticality metrics as RL reward component in a simple simulated car-following scenario. More exactly, we applied them together in an RL task, with the objective of learning a policy for following a lead vehicle that suddenly stops at two different opportunities. As demonstrated by our experimental results, this work serves as an example for the research community of applying metrics both as reward components in RL and as measures of the safety and environmental impact of AVs.
A Model for a Process Approach in the Governance System for Sustainable Development
Authors: Popescu Maria , Mandru Lidia
Published: June 2022

The paper refers to the governance of “sustainable development (SD)” and aims to describe a new way of addressing it consisting of a process-based approach. In accordance with this objective, the paper presents three distinct sections: basic concepts on SD and public administration governance; an overview of publications on the SD governance framework; schematic presentation and description of the key SD governance processes, with reference to the “United Nations (UN)” governance system for SD. Applying the process approach to the UN governance for SD is a novelty, and results in a model that reflects its global picture and links with other governance levels. The proposed model is important for decision makers in multilevel governance for SD, helping to better coordinate the changes needed to build inclusive and effective governance systems. It also addresses academics and researchers, creating the framework for the future studies on SD governance processes and ways to improve them.
Influence of Freeze–Thaw Cycles on Physical and Mechanical Properties of Cement-Treated Silty Sand
Authors: Sagidullina Nazerke , Abdialim Shynggys , Kim Jong , Satyanaga Alfrendo , Moon Sung-Woo
Published: June 2022

The problem of weak ground conditions is currently of great interest, as with the rapid development of infrastructure, researchers are trying to cope with the improvement of problematic soil properties to build structures on it. In cold regions, the problem of weak soils is further exacerbated by freeze–thaw cycling. For the improvement of soil properties, the soil stabilization method using ordinary Portland Cement (OPC) is commonly applied, but it produces a significant amount of carbon dioxide emissions. Therefore, the purpose of this research study is to present laboratory testing results for the evaluation of soil treatment using Calcium Sulfoaluminate (CSA) cement that has a lesser carbon footprint. On stabilized soil specimens cured for 3, 7, and 14 days and subjected to freeze–thaw cycles, unconfined compressive strength (UCS) and ultrasonic pulse velocity (UPV) testing were performed. Samples were prepared at optimum moisture content using different cement content, 3%, 5%, and 7%. Applying the results from the UCS test, the strength loss/gain and resilient modulus of treated soil were obtained. The test results show that the strength and pulse velocity values decreased with the increase of freeze–thaw cycles. However, improvement in soil performance can be observed with the increase in cement content. Overall, the use of CSA as a stabilizer for silty sand would be useful to achieve sufficient strength of subgrade.
Impacts of Work–Family Culture on Employee Job Performance in Achieving Sustainable Development Goals
Authors: A Rahim Nurul Aisyah Awanis binti , Omar Khatijah , Haque Adnan ul , Muhammad Hamizah , Saputra Jumadil , Hulu Fasaaro
Published: June 2022

The harmonized balance between work and family is tough to reach in modern industrial societies because of increasing demands at work and in family settings. Previous studies analyzed work–family culture and its relationship with employees’ job performance without focusing on specific industries. Paralleled with the Sustainable Developments Goals (SDGs), this study aims to analyze the relationship between work–family culture and employees’ job performance in selected industries in Malaysia. This study surveyed 336 employees working in four industries in Peninsular Malaysia, and data were collected using a self-reported questionnaire. The collected data were analyzed using two statistical analyses, i.e., descriptive and inferential statistic multiple linear regression by assisting statistical software, namely SPSS version 26. This study found that organizational time demands, and managerial support have a significant positive relationship with employees’ job performance. Moreover, the career consequences variable does not have a significant effect on employees’ job performance. Therefore, the results provided valuable insights into the work–family culture among employees with family responsibilities. Applied implications for managers are suggested to give full support to the employees while juggling their family matters. This study also contributes to the existing work–family culture literature. After a thorough analysis of this field, the researcher believed that other factors might significantly affect employees’ job performance, such as religiosity, working environment, and integrity in the organizations.
Population, Wars, and the Grand Canal in Chinese History
Authors: Lee Harry F. , Jia Xin , Ji Baoxiang
Published: June 2022

Throughout Chinese history, the Grand Canal served as a regional infrastructure in facilitating socio-economic and political development. The core regions of each dynasty were located in China’s eastern plain, and the Grand Canal ran from south to north through the plain, connecting northern and southern China. In unison, the areas along the Grand Canal also suffered from frequent wars. The role of the Grand Canal in influencing regional stability has yet to be sufficiently explored in the literature. This study seeks to (1) figure out the spatial distribution of population and wars, and (2) quantitatively measure the relationship between wars, population, and the distance from waterways in the Grand Canal Area in AD752–1910 by using their high-resolution geo-referenced data. Kernel density analysis was employed to serve the first purpose, while Pearson correlation and curve estimation analyses were applied to serve the second. Our results show that the areas surrounding the Grand Canal were densely populated. There was a war hot zone in the Beijing–Luoyang–Nanjing region near the Grand Canal, which shifted gradually over time. The correlation between war and population densities was positive, whereas the correlation between war density and distance from the waterway was negative. Finally, the cubic model captures the non-linear relationship between population, wars, and waterways. The above findings may shed more light on the Grand Canal’s role in influencing regional population and war patterns in historical China, a topic that has received little academic attention. More importantly, they may help advance empirical understanding of the impact of large-scale infrastructure on regional sustainability.
Observations of Strategies Used by Secondary School Teachers in Physical Classrooms to Promote Positive Behaviour
Authors: Awang Mohd Mahzan , Hashim Abdul Talib Mohamed , Hua Tan Kim , Ahmad Abdul Razaq , Mamat Nordin
Published: June 2022

Difficult behaviour in pupils poses a challenge to teachers. Although teachers have been trained to implement various pedagogical strategies, teachers’ spontaneous actions in classrooms may affect pupils’ behaviour. Indeed, teachers may face difficulties in making decisions regarding pupils’ behaviour in the classroom. Therefore, we carried out 12 observations in two schools in Malaysia to analyse a pattern of teachers’ initiatives to promote positive behaviour and discourage negative behaviour in classrooms. The study was carried in two urban national secondary schools located in the capital city of Malaysia, Kuala Lumpur. We observed pupils’ behaviour in response to strategies used by teachers for carrying out teaching and learning in the first ten minutes of the lessons. All 12 lessons were video-recorded, then analysed by three coders by looking at the frequency of the following targeted behaviours (using a checklist): teachers’ action: encouragement, praise, and guidance; pupils’ behaviour: attentive, uninvolved, initiative, and disruptive; teachers’ response: positive and ignore. The data gathered were analysed using an event-based analysis technique. The results indicate that teachers were more likely to guide pupils in lessons. There was less encouragement and praise used in the classroom. A clear pattern from this study is that pupils were more likely to pay attention when a teacher responded to their action positively. The data also show that ignoring pupils’ negative behaviour increases such behaviour (uninvolved and disruptive). The implications of the study suggest that there should be intensive training to increase teachers’ skills in managing pupils’ behaviour in the classroom. A specific module is suggested to be designed and implemented.
Compositional Analysis of Street Market Food Waste in Brazil
Authors: Brancoli Pedro , Makishi Fausto , Lima Paula Garcia , Rousta Kamran
Published: June 2022

Current understanding of food waste quantities in the Brazilian retail sector is limited. In order to develop efficient measures for food waste prevention and valorisation, reliable data on waste generation and composition are necessary. In this study, a compositional analysis of street market waste was conducted in São Paulo, Brazil. In total, 4.1 tonnes of waste were sorted into 27 waste fractions, categorised using a three-level approach. The average waste generation in the studied street markets was 23.7 kg per stall, of which 12.8 kg was classified as unavoidable food waste, 3.6 kg as packaging waste, and 7.4 kg as avoidable waste. The results show large amounts of unavoidable food waste, comprised of coconut, sugarcane bagasse, and peels. A large share of the avoidable food waste is comprised of single leaves, tomatoes, oranges, and bananas. Large variations were observed among the street markets analysed, both in terms of the food waste generation rate, and composition. The results from scaling up the data at the city level indicated a total wastage of 59,300 tonnes per year, of which 18,400 tonnes are classified as avoidable food waste.
Assessment of Indoor Air Quality in Academic Buildings Using IoT and Deep Learning
Authors: Marzouk Mohamed , Atef Mohamed
Published: June 2022

Humans spend most of their lifetime indoors; thus, it is important to keep indoor air quality within acceptable levels. As a result, many initiatives have been developed by multiple research centers or through academic studies to address the harmful effects of increased indoor pollutants on public health. This research introduces a system for monitoring different air parameters to evaluate the indoor air quality (IAQ) and to provide real-time readings. The proposed system aims to enhance planning and controlling measures and increase both safety and occupants’ comfort. The system combines microcontrollers and electronic sensors to form an Internet of Things (IoT) solution that collects different indoor readings. The readings are then compared with outdoor readings for the same experiment period and prepared for further processing using artificial intelligence (AI) models. The results showed the high effectiveness of the IoT device in transferring data via Wi-Fi with minimum disruptions and missing data. The average readings for temperature, humidity, air pressure, CO2, CO, and PM2.5 in the presented case study are 30 °C, 42%, 100,422 pa, 460 ppm, 2.2 ppm, and 15.3 µ/m3, respectively. The developed model was able to predict multiple air parameters with acceptable accuracy. It can be concluded that the proposed system proved itself as a powerful forecasting and management tool for monitoring and controlling IAQ.