bluuebunny/crossref_metadata_2025 huggingface dataset:
Effective Video Scene Analysis for a Nanosatellite Based on an Onboard Deep Learning Method
Authors: Tamire Natnael Alemayehu , Kim Hae-Dong
Published: April 2023

The latest advancements in satellite technology have allowed us to obtain video imagery from satellites. Nanosatellites are becoming widely used for earth-observing missions as they require a low budget and short development time. Thus, there is a real interest in using nanosatellites with a video payload camera, especially for disaster monitoring and fleet tracking. However, as video data requires much storage and high communication costs, it is challenging to use nanosatellites for such missions. This paper proposes an effective onboard deep-learning-based video scene analysis method to reduce the high communication cost. The proposed method will train a CNN+LSTM-based model to identify mission-related sceneries such as flood-disaster-related scenery from satellite videos on the ground and then load the model onboard the nanosatellite to perform the scene analysis before sending the video data to the ground. We experimented with the proposed method using Nvidia Jetson TX2 as OBC and achieved an 89% test accuracy. Additionally, by implementing our approach, we can minimize the nanosatellite video data download cost by 30% which allows us to send the important mission video payload data to the ground using S-band communication. Therefore, we believe that our new approach can be effectively applied to obtain large video data from a nanosatellite.
An Adaptive Multi-Mode Navigation Method with Intelligent Virtual Sensor Based on Long Short-Term Memory in GNSS Restricted Environment
Authors: Wang Rong , Rui Yu , Zhao Jingxin , Xiong Zhi , Liu Jianye
Published: April 2023

Aiming at the problem of fast divergence of pure inertial navigation system without correction under the condition of GNSS restricted environment, this paper proposes a multi-mode navigation method with an intelligent virtual sensor based on long short-term memory (LSTM). The training mode, predicting mode, and validation mode for the intelligent virtual sensor are designed. The modes are switching flexibly according to GNSS rejecting situation and the status of the LSTM network of the intelligent virtual sensor. Then the inertial navigation system (INS) is corrected, and the availability of the LSTM network is also maintained. Meanwhile, the fireworks algorithm is adopted to optimize the learning rate and the number of hidden layers of LSTM hyperparameters to improve the estimation performance. The simulation results show that the proposed method can maintain the prediction accuracy of the intelligent virtual sensor online and shorten the training time according to the performance requirements adaptively. Under small sample conditions, the training efficiency and availability ratio of the proposed intelligent virtual sensor are improved significantly more than the neural network (BP) as well as the conventional LSTM network, improving the navigation performance in GNSS restricted environment effectively and efficiently.
Regularization for Unsupervised Learning of Optical Flow
Authors: Long Libo , Lang Jochen
Published: April 2023

Regularization is an important technique for training deep neural networks. In this paper, we propose a novel shared-weight teacher–student strategy and a content-aware regularization (CAR) module. Based on a tiny, learnable, content-aware mask, CAR is randomly applied to some channels in the convolutional layers during training to be able to guide predictions in a shared-weight teacher–student strategy. CAR prevents motion estimation methods in unsupervised learning from co-adaptation. Extensive experiments on optical flow and scene flow estimation show that our method significantly improves on the performance of the original networks and surpasses other popular regularization methods. The method also surpasses all variants with similar architectures and the supervised PWC-Net on MPI-Sintel and on KITTI. Our method shows strong cross-dataset generalization, i.e., our method solely trained on MPI-Sintel outperforms a similarly trained supervised PWC-Net by 27.9% and 32.9% on KITTI, respectively. Our method uses fewer parameters and less computation, and has faster inference times than the original PWC-Net.
Implementing Model Predictive Control and Steady-State Dynamics for Lane Detection for Automated Vehicles in a Variety of Occlusion in Clothoid-Form Roads
Authors: Waykole Swapnil , Shiwakoti Nirajan , Stasinopoulos Peter
Published: April 2023

Lane detection in driving situations is a critical module for advanced driver assistance systems (ADASs) and automated cars. Many advanced lane detection algorithms have been presented in recent years. However, most approaches rely on recognising the lane from a single or several images, which often results in poor performance when dealing with extreme scenarios such as intense shadow, severe mark degradation, severe vehicle occlusion, and so on. This paper proposes an integration of steady-state dynamic equations and Model Predictive Control-Preview Capability (MPC-PC) strategy to find key parameters of the lane detection algorithm for automated cars while driving on clothoid-form roads (structured and unstructured roads) to tackle issues such as the poor detection accuracy of lane identification and tracking in occlusion (e.g., rain) and different light conditions (e.g., night vs. daytime). First, the MPC preview capability plan is designed and applied in order to maintain the vehicle on the target lane. Second, as an input to the lane detection method, the key parameters such as yaw angle, sideslip, and steering angle are calculated using a steady-state dynamic and motion equations. The developed algorithm is tested with a primary (own dataset) and a secondary dataset (publicly available dataset) in a simulation environment. With our proposed approach, the mean detection accuracy varies from 98.7% to 99%, and the detection time ranges from 20 to 22 ms under various driving circumstances. Comparison of our proposed algorithm’s performance with other existing approaches shows that the proposed algorithm has good comprehensive recognition performance in the different dataset, thus indicating desirable accuracy and adaptability. The suggested approach will help advance intelligent-vehicle lane identification and tracking and help to increase intelligent-vehicle driving safety.
Hierarchical Trajectory Planning for Narrow-Space Automated Parking with Deep Reinforcement Learning: A Federated Learning Scheme
Authors: Yuan Zheng , Wang Zhe , Li Xinhang , Li Lei , Zhang Lin
Published: April 2023

Collision-free trajectory planning in narrow spaces has become one of the most challenging tasks in automated parking scenarios. Previous optimization-based approaches can generate accurate parking trajectories, but these methods cannot compute feasible solutions with extremely complex constraints in a limited time. Recent research uses neural-network-based approaches that can generate time-optimized parking trajectories in linear time. However, the generalization of these neural network models in different parking scenarios has not been considered thoroughly and the risk of privacy compromise exists in the case of centralized training. To address the above issues, this paper proposes a hierarchical trajectory planning method with deep reinforcement learning in the federated learning scheme (HALOES) to rapidly and accurately generate collision-free automated parking trajectories in multiple narrow spaces. HALOES is a federated learning based hierarchical trajectory planning method to fully exert high-level deep reinforcement learning and the low-level optimization-based approach. HALOES further fuse the deep reinforcement learning model parameters to improve the generalization capabilities with a decentralized training scheme. The federated learning scheme in HALOES aims to protect the privacy of the vehicle’s data during model parameter aggregation. Simulation results show that the proposed method can achieve efficient automatic parking in multiple narrow spaces, improve planning time from 12.15% to 66.02% compared to other state-of-the-art methods (e.g., hybrid A*, OBCA) and maintain the same level of trajectory accuracy while having great model generalization.
Crowdsourced Indoor Positioning with Scalable WiFi Augmentation
Authors: Dong Yinhuan , He Guoxiong , Arslan Tughrul , Yang Yunjie , Ma Yingda
Published: April 2023

In recent years, crowdsourcing approaches have been proposed to record the WiFi signals annotated with the location of the reference points (RPs) extracted from the trajectories of common users to reduce the burden of constructing a fingerprint (FP) database for indoor positioning. However, crowdsourced data is usually sensitive to crowd density. The positioning accuracy degrades in some areas due to a lack of FPs or visitors. To improve the positioning performance, this paper proposes a scalable WiFi FP augmentation method with two major modules: virtual reference point generation (VRPG) and spatial WiFi signal modeling (SWSM). A globally self-adaptive (GS) and a locally self-adaptive (LS) approach are proposed in VRPG to determine the potential unsurveyed RPs. A multivariate Gaussian process regression (MGPR) model is designed to estimate the joint distribution of all WiFi signals and predicts the signals on unsurveyed RPs to generate more FPs. Evaluations are conducted on an open-source crowdsourced WiFi FP dataset based on a multi-floor building. The results show that combining GS and MGPR can improve the positioning accuracy by 5% to 20% from the benchmark, but with halved computation complexity compared to the conventional augmentation approach. Moreover, combining LS and MGPR can sharply reduce 90% of the computation complexity against the conventional approach while still providing moderate improvement in positioning accuracy from the benchmark.
Camera-Based Smart Parking System Using Perspective Transformation
Authors: Liu Bowie , Lai Hawking , Kan Stanley , Chan Calana
Published: April 2023

The concept of the “smart city” has emerged with the advancement of technology, but some facilities are not sufficiently intelligent, such as parking lots. Hence, this paper proposes an inexpensive and plug-to-play camera-based smart parking system for airports. The system utilizes inverse perspective mapping (IPM) to provide an aerial view image of the parking lot, which is then processed to extract parking space information. The system also includes a guidance system to assist drivers in finding available parking spaces. The system is simulated on a 3D scene based on the parking lot of Macao International Airport. In the experiment, our system achieved an accuracy rate of 97.03% and a mean distance error of 8.59 pixels. This research study shows the potential of enhancing parking lots using only cameras as data collectors, and the results show that the system is capable of providing accurate and useful information. It performs well in parking lots with open space, in particular. Moreover, it is an economical solution for implementing a smart parking lot.
Refugee Population and Environmental Quality in Sweden and Lebanon: Is Fertility Rate Changing the Dynamics?
Authors: Alola Andrew Adewale , Aliyev Anar , Obekpa Hephzibah Onyeje , Olagunju Ishaaqa
Published: April 2023

Arising from the arrays of man-made factors (including political- and socioeconomic-related factors) and natural disasters across the globe, population displacement, as one of the consequences of the above-mentioned factors, has continued to trigger refugee populations. This is the reason Lebanon (with the highest number of refugees per total population in the world) and Sweden (with the highest number of refugees per total population in Europe) are considered in a comparative investigation. Importantly, this study examines the environmental effect of greenhouse gas (GHG) emissions via the refugee population and fertility rate, alongside the roles of income and natural resource abundance, from 1990 to 2021. The results reveal that fertility rates spur GHG emissions, thereby hampering environmental sustainability in both countries. While the refugee population mitigates GHG emissions in Sweden, the effect is contrary and environmentally damaging in Lebanon. The disparity in the countries’ environmental proactiveness, which has supposedly permeated societal values, especially in Sweden, could have influenced the above result. Meanwhile, in both countries, the refugee populations moderate the effect of the fertility rate, increasing the environmental demerit of the increase in the fertility rate. However, the Swedish income level improvement helps mitigate GHG emissions, thus validating the environmental Kuznets curve (EKC) hypothesis in the long run. In Sweden, the natural resource abundance also promotes environmental benefit. Contrarily, in Lebanon, increases in both the natural resource abundance and income per person increase GHG emissions, thus worsening the environmental quality. Finally, this study offers policy recommendations for improving refugee programs.
Understanding Narratives in Governance: Naming and Framing Regional Inequality in the United Kingdom
Authors: Kaiser Tamás
Published: April 2023

Narratives play a pivotal role in solving complex problems, as they provide an interpretive framework for facilitating the solution to a given challenge. We presume that if the basis of a narrative applied to a complex problem is incorrect, the interpretation of the problem will also be distorted. Therefore, solutions that are primarily low-efficiency in nature demand new or “rframed” narratives. We examine this premise through the case of the United Kingdom in the light of changes in narratives created to solve regional inequalities, particularly regarding the interpretative framework of the “Levelling Up” policy agenda and narrative, which was introduced by the government of Boris Johnson. Additionally, we conducted a literature review on the Levelling Up policy to provide a supplementary theoretical background beyond the concept of narratives. Conclusions on narratives and Levelling Up are also outlined.
Environmentally Friendly Water-Based Electrolyte for Dye-Sensitized Solar Cells: Future Prospective and Outlook
Authors: Spadaro Donatella , Barichello Jessica , Citro Ilaria , Calogero Giuseppe
Published: April 2023

The use of traditional Dye-sensitized solar cells (DSSCs) is limited due to the use of toxic and non-environmentally safe solvents. In this review, water is proposed as a viable alternative to developing green chemistry and sustainable materials for DSSCs. However, water-based DSSCs (WBDSSCs) require improvement in their photovoltaic parameters. The detachment of dye molecules from the semiconductor and the poor solubility of iodine in water are the primary reasons for their low efficiency. This review analyzes the best results achieved by 100% aqueous electrolytes containing synthetic, organic, and natural dyes to identify the best conditions to overcome these limitations. Developing a suitable photoanode/electrolyte interface and finding a compromise in the choice of dye are the main research goals in the coming years. WBDSSCs can contribute significantly to producing clean energy using sustainable and environmentally friendly materials. Furthermore, here we report the state of the art of the emerging technology of underwater dye-sensitized solar cells, which are a promising technology for generating renewable energy in aquatic environments. Recent advancements in material science and device engineering have shown promising results in enhancing their efficiency and durability. Further research and development can make these devices a viable alternative for sustainable energy generation in a wide range of underwater applications.
Relationship between Football-Specific Training Characteristics and Tibial Bone Adaptation in Male Academy Football Players
Authors: Varley Ian , Sale Craig , Greeves Julie P. , Morris John G. , Sunderland Caroline , Saward Chris
Published: April 2023

We examined the relationship between football-specific training and changes in bone structural properties across a 12-week period in 15 male football players aged 16 years (Mean ± 1 SD = 16.6 ± 0.3 years) that belonged to a professional football academy. Tibial scans were performed at 4%, 14% and 38% sites using peripheral quantitative computed tomography immediately before and 12 weeks after increased football-specific training. Training was analysed using GPS to quantify peak speed, average speed, total distance and high-speed distance. Analyses were conducted with bias-corrected and accelerated bootstrapped 95% confidence intervals (BCa 95% CI). There were increases in bone mass at the 4% (mean ∆ = 0.15 g, BCa 95% CI = 0.07, 0.26 g, g = 0.72), 14% (mean ∆ = 0.04 g, BCa 95% CI = 0.02, 0.06 g, g = 1.20), and 38% sites (mean ∆ = 0.03 g, BCa 95% CI = 0.01, 0.05 g, g = 0.61). There were increases in trabecular density (4%), (mean ∆ = 3.57 mg·cm−3, BCa 95% CI = 0.38, 7.05 mg·cm−3, g = 0.53), cortical dentsity (14%) (mean ∆ = 5.08 mg·cm−3, BCa 95% CI = 0.19, 9.92 mg·cm−3, g = 0.49), and cortical density (38%) (mean ∆ = 6.32 mg·cm−3, BCa 95% CI = 4.31, 8.90 mg·cm−3, g = 1.22). Polar stress strain index (mean ∆ = 50.56 mm3, BCa 95% CI = 10.52, 109.95 mm3, g = 0.41), cortical area (mean ∆ = 2.12 mm2, BCa 95% CI = 0.09, 4.37 mm2, g = 0.48) and thickness (mean ∆ = 0.06 mm, BCa 95% CI = 0.01, 0.13 mm, g = 0.45) increased at the 38% site. Correlations revealed positive relationships between total distance and increased cortical density (38%) (r = 0.39, BCa 95% CI = 0.02, 0.66), and between peak speed and increased trabecular density (4%) (r = 0.43, BCa 95% CI = 0.03, 0.73). There were negative correlations between total (r = −0.21, BCa 95% CI = −0.65, −0.12) and high-speed distance (r = −0.29, BCa 95% CI = −0.57, −0.24) with increased polar stress strain index (38%). Results suggest that despite football training relating to increases in bone characteristics in male academy footballers, the specific training variables promoting adaptation over a 12-week period may vary. Further studies conducted over a longer period are required to fully elucidate the time-course of how certain football-specific training characteristics influence bone structural properties.
Stability Analysis of Cavern Collapse in Fractured-Cavity Oil Reservoirs
Authors: Ding Yanzhi , Zhang Qiangyong , Xiang Wen , Wang Bin , Lyu Xinrui , Zhang Longyun
Published: April 2023

Fractured-vuggy oil reservoirs, with the decrease of formation pressure during the exploitation process, lead to the collapse of caverns or the closure of sizeable fractured oil channels, which seriously affects oil well production and the recovery rate of oil reservoirs. True three-dimensional geomechanical model tests were carried out to avoid the impact of cave collapse and fracture closure on oil well production. Taking the Tahe Oilfield in Xinjiang area of China as the engineering background, we researched the collapse failure mechanism of the karstic caves in fractured-cavity oil reservoirs and the evolution of fracture closure through a true three-dimensional geomechanical model test and the numerical simulation software RFPA. The collapse failure modes of caverns with and without prefabricated cracks were revealed, along with the displacement and stress changes during cave collapse and the mechanism of cave collapse failure. Our study revealed the mechanism of the interaction between cracks and the cave. The research results show that prefabricated cracks reduce the roof of the cave’s bearing capacity, making the karst cave collapse with incomplete cracks. The impact of the collapse is much smaller than the cavern without prefabricated cracks. The crack closure extends from the near end to the far end. The research results will provide necessary theoretical support for the large-scale safe extraction of deep petroleum resources, increase oil production in China, and have important theoretical significance and engineering application value.
Preventive Measures against Ebola and COVID-19 and Their Impact on Human Dirty-Hand Disease Mitigation in Côte d’Ivoire
Authors: Traoré Sylvain Gnamien , Fokou Gilbert , Wognin Affou Seraphin , Heitz-Tokpa Kathrin , Tetchi Sopi Mathilde , Ouattara Foungoye Allassane , Orou Seko Malik , Akoua-Koffi Chantal , Tiembre Issaka , Bonfoh Bassirou
Published: April 2023

Hygiene is the most useful public health measure for preventing infections of important endemic and emerging diseases of global significance. This study aimed to assess the impact of these public health preventive measures on dirty hand diseases. A retrospective survey was conducted in the rural general hospitals of Taabo (south-central Côte d’Ivoire) and Marcory (urban Abidjan) to collect clinical data on dirty hand diseases in the patients’ records from 2013 to 2020. In addition, focus group discussions (N = 8) were conducted in the communities in both settings to identify the sociocultural and economic hindering or fostering factors that affected the adoption of and the compliance with handwashing and disinfection practices. A total of 3245 and 8154 patients’ records were examined in the general hospitals of Taabo and Marcory, respectively. Compared with women, men were more affected by typhoid fever (OR, 0.68 [95%CI, 0.53–0.88]) and influenza (OR, 0.87 [95%CI, 0.75–1]). Hygiene measures promoted during Ebola and COVID-19 outbreaks had no impact on the prevalence of typhoid fever in both settings; however, a positive impact was observed regarding influenza infections. Populations were aware of the importance of handwashing for public health but had difficulties adhering due to financial constraints, access to drinking water, and the absence or scarcity of handwashing facilities.
Effects of Romanian Student’s Awareness and Needs Regarding Plastic Waste Management
Authors: Boca Gratiela Dana , Saraçli Sinan
Published: April 2023

The purpose of this study is to examine the effects of needs and awareness of university students on their environmental behaviour. With this purpose the data was collected from 537 students from the University of Cluj Napoca, Romania, from the engineering and management specializations respectively via an online questionnaire. The questionnaire was structured in four parts including 29 questions in total. The first part is meant to identify the students’ characteristics (gender, field of study, participation and attendance in field-specific activities, and if he/she is an environmentalist). The second part is meant to determine the students’ awareness regarding plastic and plastic pollution. Another part is meant to determine the needs of students and the manner in which they learn and gather information. The last part allows the determination of the students’ behavior in their daily life (use of bio plastic bags, environmental protection). The results show that students have enough information about biodegradable plastic but they act depending on the situation, respecting or not the rules for selecting plastic waste. The female student’ pay a lot of attention to selecting and choosing bioplastic products. The male students are directly involved in cleaning nature. Management students pay attention to small details as compared to engineering students who choose bioplastic even though the costs are higher. Related with their thoughts the factors effecting the opinion of either they are environmentalist or not are also examined. Being aware of the plastic waste show significant effect from the sides of awareness and behaviour. Finally, the structural model show that strongest connection is between students’ awareness about the plastic problem and the need to adapt to new regulations. Using the model universities can promote the importance of bioplastic through study programs or by involving students in volunteering activities, through their active involvement in environmental protection, and selective waste recycling.
Challenge of Supplying Power with Renewable Energy Due to the Impact of COVID-19 on Power Demands in the Lao PDR: Analysis Using Metaheuristic Optimization
Authors: Keokhoungning Thongsavanh , Wongsinlatam Wullapa , Remsungnen Tawun , Namvong Ariya , Khunkitti Sirote , Inthakesone Bounmy , Siritaratiwat Apirat , Premrudeepreechacharn Suttichai , Surawanitkun Chayada
Published: April 2023

Human activities have been limited by coronavirus disease 2019 (COVID-19), and the normal conditions of our lifestyles have changed, particularly in terms of electricity usage. The aim of this study was to investigate the impact of COVID-19 on the power sector in the Lao PDR in 2020, as well as the challenge of using solar energy to supply power to the network using an optimal approach. The returns on investment of network extension and the purchase of solar energy were also evaluated. Furthermore, load conditions caused by the country’s lockdown policy were analyzed. We analyzed the optimal sizing and location of solar energy using a particle swarm optimization method based on the main objective functions, with the system’s power loss decreasing and its reliability improved. The results demonstrated that the suddenly reduced load from industry and commercial business did not have a large impact on its operations; however, revenue was reduced. The optimal method for connecting solar energy to a network can reduce power loss and improve system reliability. In addition, we discovered that the location and capacity of solar generation can reduce the investment costs of extensions for new lines, with the surplus power being exported.
Small Pests Detection in Field Crops Using Deep Learning Object Detection
Authors: Khalid Saim , Oqaibi Hadi Mohsen , Aqib Muhammad , Hafeez Yaser
Published: April 2023

Deep learning algorithms, such as convolutional neural networks (CNNs), have been widely studied and applied in various fields including agriculture. Agriculture is the most important source of food and income in human life. In most countries, the backbone of the economy is based on agriculture. Pests are one of the major challenges in crop production worldwide. To reduce the overall production and economic loss from pests, advancement in computer vision and artificial intelligence may lead to early and small pest detection with greater accuracy and speed. In this paper, an approach for early pest detection using deep learning and convolutional neural networks has been presented. Object detection is applied on a dataset with images of thistle caterpillars, red beetles, and citrus psylla. The input dataset contains 9875 images of all the pests under different illumination conditions. State-of-the-art Yolo v3, Yolov3-Tiny, Yolov4, Yolov4-Tiny, Yolov6, and Yolov8 have been adopted in this study for detection. All of these models were selected based on their performance in object detection. The images were annotated in the Yolo format. Yolov8 achieved the highest mAP of 84.7% with an average loss of 0.7939, which is better than the results reported in other works when compared to small pest detection. The Yolov8 model was further integrated in an Android application for real time pest detection. This paper contributes the implementation of novel deep learning models, analytical methodology, and a workflow to detect pests in crops for effective pest management.
Analysis of Eco-Environmental Geological Problems and Their Driving Forces in the Henan Section of the Yellow River Basin, China
Authors: Wen Guangchao , Wu Bingjie , Wang Lin , Wang Xiaohe , Xie Hongbo
Published: April 2023

(1) Background: The Henan section of the Yellow River Basin plays an important role in the economic zone of the middle reaches of the Yellow River. However, ecological environmental geological problems such as soil erosion have seriously affected the lives of residents and economic development, resulting in increasingly prominent conflicts between humans and the environment. Therefore, this paper made use of remote sensing images and other reference data, integrated image classification, remote sensing inversion and statistical analysis methods to explore the ecological environmental geological problems and their causes in the Henan section of the Yellow River Basin. (2) Results: the main eco-environmental geological problems in the Sanmenxia–Zhengzhou section are serious soil erosion, degradation of water conservation function and being prone to geological disasters. The main eco-environmental geological problems in the Zhengzhou–Puyang section are poor water and soil conservation function, degradation of water conservation function and poor biodiversity maintenance function. In the last 19 years, the eco-environmental geological problems in the Henan section of the Yellow River Basin have shown a significant improvement trend as a whole. Along the main stream of the Yellow River in Sanmenxia, Luoyang, Jiyuan, Jiaozuo, Zhengzhou and other areas, the eco-environmental geological problems are still prominent; altitude, vegetation and rainfall are the key driving factors of eco-environmental geological problems in the Sanmenxia–Zhengzhou section and rainfall, vegetation and land-use type are the key driving factors in the Zhengzhou–Puyang section. (3) Conclusions: This study comprehensively considered the three aspects of ecology, environment and geology in a total of five research topics. The temporal and spatial distribution characteristics and driving factors of ecological environmental geological problems in the Yellow River Basin were analyzed, which could provide technical support for ecological environmental protection and high-quality development in the Yellow River Basin.
Maturity Analysis of Stock Exchanges in Africa: Prepandemic Sustainability Perspective
Authors: Próchniak Joanna , Płoska Renata , Zamojska Anna , Lepczyński Błażej , Cirella Giuseppe T.
Published: April 2023

This paper focuses on the economic dimension of sustainability by examining the stock exchange interface of financial markets, the influence of capital market stakeholders, and the instruments that contribute to a supportive fiscal framework. Only mature stock exchanges are present in sustainability indices; hence, comparative assessment of stock exchanges is limited and contributes to the complexity of conducting such a study. Utilizing multivariate analysis, this study investigates the potential for African stock exchanges to support sustainability. An empirical study was conducted on a selected sample of 15 African stock exchanges at the end of 2020 using collected 5-year interval data from Q1 of 2021. A total of 22 variables were selected based on their legitimacy to support sustainability. Using exploratory factor analysis, two key sustainability drivers of differentiation and classified exchanges were identified, i.e., hard and soft. K-means classification method verified the results and found that of the four identified homogeneous groups, one—the Johannesburg Stock Exchange, Nigerian Stock Exchange, and the Egyptian Exchange—emerged on top. Two smaller groups had the potential to be strengthened, and the majority group lagged behind. The research demonstrated the importance of identifying key sustainability drivers and examined the materiality of the drivers within an African context.
Modeling and Simulation of a Transient Process and an Analysis of Transient Characteristics in the Switching Operation of Disconnectors
Authors: Tong Yue , Wang Qi , Wang Yuqing , Liu Xiang
Published: April 2023

High-voltage switchgears can generate high-frequency and high-amplitude electromagnetic interference during their operation, which may lead to abnormal outputs of the measurement devices, thus giving rise to measurement errors or protective relay malfunctions and impacts on the stable running of the power grid. Considering the air breakdown delay, a dynamic reignition model consisting of the hyperbolic model, the static model and the improved Mayr model is proposed to analyze the transient process during the on/off operation of open-type disconnectors. Meanwhile, the influence of arc resistance, line length and breakdown delay on the model were analyzed, and an experimental circuit was built to verify the simulation results through a comparison. It was found that the proposed model could reflect the high-frequency voltage and current during the on/off operation more accurately and thus provide an effective method for the precise modeling of the switching operation of the disconnector.
Comprehensive Treatment for River Pollution in a Coastal City with a Complex River Network: A Case Study in Sanya, China
Authors: Song Wenqing , Wang Shizhuo , Zhao Jiang , Xu Shiliang , Zhou Xuefei , Zhang Yalei
Published: April 2023

The contamination of urban rivers substantially threatens urban ecology, public health, and general progress. Addressing this matter is complex and challenging, particularly in tidal rivers. After investigating the climate conditions, population, and distribution of rivers and pollution in Sanya, China, a hydraulic and water quality model was built for Sanya utilizing InfoWorks ICM, and a comprehensive remediation approach was proposed. The implementation of this scheme led to a substantial decrease in point and non-point sources of pollution. In particular, COD, NH3-N, and TP were reduced by 87.00%, 84.01%, and 74.24%, respectively, from point source pollution. Point source pollution was well-managed north of the bridge, with a reduction rate close to 40% for non-point source pollution. This research confirmed that pollutants are only temporarily retained in the river at high tide and are discharged as the tide recedes, thus having a limited impact on long-term pollution discharge. This research provides valuable references for applying water environment models and the comprehensive treatment of tidal river pollution. The final plan scheme is to significantly improve the water quality of Sanya’s rivers by effectively controlling the discharge of pollutants while achieving considerable cost savings of approximately CNY 1.7 billion.
Enhancement of System and Environmental Performance of High Solids Anaerobic Digestion of Lignocellulosic Banana Waste by Biochar Addition
Authors: Yang Xia , Zhang Qiong , Ergas Sarina J.
Published: April 2023

Banana waste, a lignocellulosic waste material, is generated in large quantities around the world. High Solids Anaerobic Digestion (HS-AD) of lignocellulosic waste can recover energy and reduce its environmental impacts. However, high carbon/nitrogen ratios and low water content in HS-AD can potentially cause system acidification and/or failure. This study investigated the addition of biochar to enhance the performance of HS-AD of mixed banana waste (peel, stem, and leaf). Biochemical methane potential assays with varying biochar dosages (2.5–30%) showed that 10% biochar addition increased methane yields by 7% compared with unamended controls. Semi-continuous HS-AD studies, without and with 10% biochar addition, were conducted at varying solids retention times (42, 35, and 28 days) for long-term performance evaluation. Biochar addition reduced volatile fatty acid accumulation, improved system stability, and increased methane production by 20–47%. The nutrient content of digestate from HS-AD of banana waste indicated its potential use as a bio-fertilizer. Life cycle assessment results showed that biochar addition to HS-AD resulted in greater environmental benefits in most categories compared with the unamended control, including eutrophication, ecotoxicity, and fossil fuel depletion when biochar was available within a radius of 8830 km.
Location of Railway Emergency Rescue Spots Based on a Near-Full Covering Problem: From a Perspective of Diverse Scenarios
Authors: Wang Huizhu , Zhou Jianqin
Published: April 2023

The location of railway emergency rescue spots is facing diverse scenarios including the location of new facilities and optimization of existing layouts with limited or non-limited conditions. Generally there will be heavily redundant covering ability if all the edge demands on a network are fully covered. Here, we first proposed a near-full covering model to balance investment in the facility and the actual coverage rate, and successfully applied this model in the optimal location of railway emergency rescue spots under diverse scenarios. We also developed a feasible solution that can select an effective algorithm or a greedy algorithm based on the total consumed time. With the constraint of a fixed coverage rate threshold, a larger coverage radius may lead to fewer facilities and higher relative redundancy. Flexible designs of the important node set where all the elements must be selected and the exclusive node set where all the elements cannot be selected are carried out to construct several scenarios. The comparative analysis shows that the optimal solution is an obvious improvement on the existing emergency rescue spot layout in the real railway network. This study provides an alternative version of the edge covering problem, and shows a successful application in the location problem of railway rescue spots.
Fiscal Decentralization, Enterprise Digital Transformation and Enterprise Green Innovation—The Case of 11 Years A-Share Listed Companies in China
Authors: Wang Anqi , Zhu Lianmei , Sun Huanan , Wang Shali , Ma Haijing
Published: April 2023

In the context of accelerating economic transformation and upgrading, and comprehensively promoting the construction of digital China and ecological civilization in China, this paper uses text mining and OLS to quantitatively study the relationship between fiscal decentralization, enterprise digital transformation and green innovation in 31 provinces from 2011 to 2021. This study finds that fiscal decentralization will promote enterprise green innovation and digital transformation, and that digital transformation has a partial mediating effect between fiscal decentralization and enterprise green innovation. Furthermore, this expansive study finds that fiscal decentralization has a positive role in promoting the green innovation of heterogeneous enterprises and enterprises in different regions, among which its promotion effect on state-owned enterprises is higher than that of private enterprises, and its promotion effect on enterprises in the central and western regions is higher than that of enterprises in the eastern region. Moreover, an inverted U-shaped relationship exists between fiscal decentralization and the green innovation of enterprise. In addition, financing constraints have a masking effect between fiscal decentralization and green innovation in enterprise and green innovation significantly promotes enterprise environmental, social and governance (ESG) development.
The Association between Perceived Teacher Support, Students’ ICT Self-Efficacy, and Online English Academic Engagement in the Blended Learning Context
Authors: Feng Lei , He Li , Ding Jie
Published: April 2023

The present study intended to examine the relationship between perceived teacher support, students’ ICT self-efficacy, and online English academic engagement in the blended learning setting, especially in mobile-assisted foreign language instruction contexts. A sample of 960 Chinese undergraduate and postgraduate students was recruited to participate in the online questionnaire. SPSS version 24.0 was used for descriptive, correlation, independent samples t-test, and mediation analysis of the three variables. The results showed that: (1) there is a significant correlation between perceived teacher support, students’ ICT self-efficacy, and online English academic engagement; (2) students’ ICT self-efficacy partially mediates the relationship between perceived teacher support and student online English academic engagement; (3) students’ ICT self-efficacies differed by sex and level of education, but not by major; (4) students’ sense of self-competence in ICT self-efficacy has a significant positive influence on engagement with online English learning. The findings reveal that students’ ICT self-efficacy positively impacts students’ online English learning, and perceived teacher support also affects students’ learning engagement. School administrators should encourage teachers to focus on students’ online self-efficacy, especially the sense of environmental control. Implications and further directions for future research are presented at the end.
The Transient Unloading Response of a Deep-Buried Single Fracture Tunnel Based on the Particle Flow Method
Authors: Liu Xiqi , Wang Gang , Wen Zhijie , Wang Dongxing , Song Leibo , Lin Manqing , Chen Hao
Published: April 2023

Particle flow numerical simulation was used to reproduce the transient unloading process of a deep-buried single fracture tunnel. The influence of fracture characteristics on the transient unloading effect was analyzed from the aspects of stress state, deformation characteristics, fracture propagation, and energy conversion. The results shows that the surrounding rock stress field of the deep-buried tunnel is divided into four areas: weak stress area I, strong stress area II, stress adjustment area III, and initial stress area IV. The fracture has an important impact on the stress adjustment process of transient unloading of the deep-buried tunnel, and the stress concentration area will be transferred from the bottom corner of the chamber and the vault to the fracture tip. With the increase in the fracture length, the distance from the stress concentration area at the fracture tip to the free surface gradually increases, and the damage area of the surrounding rock gradually migrates to the deep area of the rock mass. At this time, the release amount of strain energy gradually decreases and tends to be stable, while the dissipation energy shows a near ‘U’ shape change trend of decreasing first and then increasing. Under different fracture angles, the number of mesocracks is significantly different. Among them, the number of mesocracks in the 60° and 30° fractured surrounding rocks is greater followed by the 0° fractured surrounding rock, and the number of mesocracks in the 45° and 90° fractured surrounding rocks is relatively less. In addition, the proportion of compression-shear cracks shows a change trend of increasing first and then decreasing with the increase in the fracture angle, and it reaches the maximum value in the 45° fractured surrounding rock.
The Eligibility of Green Bonds as Safe Haven Assets: A Systematic Review
Authors: Khamis Munir , Aassouli Dalal
Published: April 2023

This study follows Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to examine the existing literature on the connectedness of green bonds with other markets as an attempt to highlight the effectiveness of green bonds in risk management and the benefits associated with incorporating green bonds in investment portfolios. An extensive search of relevant research papers to the scope of the review led to the identification of 31 articles published by February 2022. Our analysis traces the evolution of studies on green bonds’ interactions with other markets, the methodologies and data frequencies used for cross-market relations analysis, and the role of green bonds in portfolio risk management (diversifier, hedge, and safe-haven) in normal and extreme market conditions. The study reports several interesting findings. First, green bonds can be a strategic safe-haven avenue for investors in stocks, dirty energy stocks, and the foreign exchange market in the US and China in extreme market downturns. Second, green bonds demonstrated hedging properties against spillovers from Bitcoin, forex, soft commodities, and CO2 emission allowance. Third, the role of green bonds in the markets of natural gas, industrial metals, and crude oil is limited to a portfolio diversifier in different investment horizons. Fourth, green bonds had no diversification or hedge benefits for investors in conventional bonds. Fifth, the interrelationships between green bonds and most markets’ understudy were influenced by macroeconomic and global factors such as the COVID-19 pandemic, economic policy uncertainty, OVX, and VIX. Our review of the literature also facilitated identification of future research topics. The outcome of the review offers insightful information to investors in green bonds in risk management and assets allocation. Policy makers can benefit from this review in effective policy legislation for the advancement of the green bonds market and acceleration of a smooth transition to a net zero emission economy.
Performance of a Full-Scale Vermifilter for Sewage Treatment in Removing Organic Matter, Nutrients, and Antibiotic-Resistant Bacteria
Authors: Gutiérrez Victor , Monsalves Naomi , Gómez Gloria , Vidal Gladys
Published: April 2023

The vermifilter (VF) is regarded as a sustainable solution for treating rural sewage. However, few studies have investigated the performance of a full-scale vermifilter. The objective of this study is to evaluate the performance of a full-scale vermifilter in reducing organic matter, nutrients, and antibiotic-resistant bacteria contained in sewage. Influent and effluents were obtained from a rural sewage treatment plant using a VF and UV disinfection system. The results show a significant removal (p < 0.05) of chemical organic demand (COD) (77%), biochemical oxygen demand (BOD5) (84%), total nitrogen (TN) (53%), and total phosphorus (36%). Seasonality is an influential variable for COD, BOD5, and TN removal. In addition, the molecular weight distribution shows that the VF does not generate a considerable change in the distribution of organic matter (COD and total organic carbon (TOC)) and NH4+-N. The UV disinfection system eliminated 99% of coliform bacteria; however, they are not eliminated to safe concentrations. Therefore, it is possible to detect bacteria resistant to the antibiotics ciprofloxacin, amoxicillin, and ceftriaxone at 63.5%, 87.3%, and 63.5%, respectively, which were detected in the effluents. This study shows the potential of a system for the removal of pollution and the need to optimize the VF to be a safe treatment.
Evidence-Based Methods of Communicating Science to the Public through Data Visualization
Authors: Jensen Eric A. , Borkiewicz Kalina , Naiman Jill P. , Levy Stuart , Carpenter Jeff
Published: April 2023

This essay presents a real-world demonstration of the evidence-based science communication process, showing how it can be used to create scientific data visualizations for public audiences. Visualizing research data can be an important science communication tool. Maximizing its effectiveness has the potential to benefit millions of viewers. As with many forms of science communication, creators of such data visualizations typically rely on their own judgments and the views of the scientists providing the data to inform their science communication decision-making. But that leaves out a critical stakeholder in the communications pipeline: the intended audience. Here, we show the practical steps that our team, the Advanced Visualization Lab at the University of Illinois at Urbana-Champaign, has taken to shift toward more evidence-based practice to enhance our science communication impact. We do this by using concrete examples from our work on two scientific documentary films, one on the theme of “solar superstorms” and the other focusing on the black hole at the center of the Milky Way galaxy. We used audience research with each of these films to inform our strategies and designs. Findings revealed specific techniques that were effective in information labels. For example, audiences appreciated the use of an outline of the Earth to demonstrate scale in scientific visualizations relating to the Sun. We describe how such research evidence informed our understanding of “what works and why” with cinematic-style data visualizations for the public. We close the essay with our key take-home messages from this evidence-based science communication process.