bluuebunny/crossref_metadata_2025 huggingface dataset:
Nationwide, Operational Sentinel-1 Based InSAR Monitoring System in the Cloud for Strategic Water Facilities in Hungary
Authors: Ronczyk Levente , Zelenka-Hegyi András , Török Gábor , Orbán Zoltán , Defilippi Marco , Kovács István Péter , Kovács Dániel Márton , Burai Péter , Pasquali Paolo
Published: July 2022

The intensive development of both interferometric technology and sensors in recent years allows Interferometric Synthetic Aperture Radar (InSAR)-based applications to be accessible to a growing number of users. InSAR-based services now cover entire countries and soon even the whole of Europe. These InSAR systems require massive amounts of computer processing power and significant time to generate a final product. Most, if not all, of these projects have a limited “monitoring component”, aimed at historical analysis but are rarely, if ever, updated. Consequently, the results do not necessarily meet every purpose or specific user requirement. It is now clear that the increasing computing capacity and big data provided by the sensors have initiated the development of new InSAR services. However, these systems are only useful when linked to specific real-world operational problems. Continuous monitoring of a country’s ageing water management infrastructure has become an increasingly critical issue in recent years, in addition to the threats posed by climate change. Our article provides a comprehensive overview of a nationwide, dedicated, operational InSAR application, which was developed to support the operational work of the Hungarian Disaster Management Service (HDMS). The objective was to provide monthly monitoring of 63 water facilities, including 83 individual objects, distributed throughout Hungary, in combination with the development of a near real-time warning system. Our work involved the compilation of a completely new InSAR System as a Service (SaaS) which incorporates user requirements, preparatory work, the compilation of the Sentinel-1 automatic processing pipeline, the installation of corner reflectors, a special early warning system, and a dedicated user interface. The developed system can automatically start to evaluate the S1 measurements within 24 h of downloading the data into the system storage forward the results toward the warning system before the next image arrives. Users are provided with detailed information on the stability of 70% of the 83 water facility objects monitored through the dedicated user interface. The additional early warning system currently operates as a preliminary “spatial decision support system”, but the HDMS is willing to make it fully operational over the next few years.
Middle- and Long-Term UT1-UTC Prediction Based on Constrained Polynomial Curve Fitting, Weighted Least Squares and Autoregressive Combination Model
Authors: Yang Yuguo , Xu Tianhe , Sun Zhangzhen , Nie Wenfeng , Fang Zhenlong
Published: July 2022

Universal time (UT1-UTC) is a key component of Earth orientation parameters (EOP), which is important for the study of monitoring the changes in the Earth’s rotation rate, climatic variation, and the characteristics of the Earth. Many existing UT1-UTC prediction models are based on the combination of least squares (LS) and stochastic models such as the Autoregressive (AR) model. However, due to the complex periodic characteristics in the UT1-UTC series, LS fitting produces large residuals and edge distortion, affecting extrapolation accuracy and thus prediction accuracy. In this study, we propose a combined prediction model based on polynomial curve fitting (PCF), weighted least squares (WLS), and AR, namely, the PCF+WLS+AR model. The PCF algorithm is used to obtain accurate extrapolation values, and then the residuals of PCF are predicted by the WLS+AR model. To obtain more accurate extrapolation results, annual and interval constraints are introduced in this work to determine the optimal degree of PCF. Finally, the multiple sets prediction experiments based on the International Earth Rotation and Reference Systems Service (IERS) EOP 14C04 series are carried out. The comparison results indicate that the constrained PCF+WLS+AR model can efficiently and precisely predict the UT1-UTC in the mid and long term. Compared to Bulletin A, the proposed model can improve accuracy by up to 33.2% in mid- and long-term UT1-UTC prediction.
A Novel and Effective Cooperative RANSAC Image Matching Method Using Geometry Histogram-Based Constructed Reduced Correspondence Set
Authors: Chung Kuo-Liang , Tseng Ya-Chi , Chen Hsuan-Ying
Published: July 2022

The success of many computer vision and pattern recognition applications depends on matching local features on two or more images. Because the initial correspondence set—i.e., the set of the initial feature pairs—is often contaminated by mismatches, removing mismatches is a necessary task prior to image matching. In this paper, we first propose a fast geometry histogram-based (GH-based) mismatch removal strategy to construct a reduced correspondence set Creduced,GH from the initial correspondence set Cini. Next, we propose an effective cooperative random sample consensus (COOSAC) method for remote sensing image matching. COOSAC consists of a RANSAC, called RANSACini working on Cini, and a tiny RANSAC, called RANSACtiny,GH working on a randomly selected subset of Creduced,GH. In RANSACtiny,GH, an iterative area constraint-based sampling strategy is proposed to estimate the model solution of Ctiny,GH until the specified confidence level is reached, and then RANSACini utilizes the estimated model solution of Ctiny,GH to calculate the inlier rate of Cini. COOSAC repeats the above cooperation between RANSACtiny,GH and RANSACini until the specified confidence level is reached, reporting the resultant model solution of Cini. For convenience, our image matching method is called the GH-COOSAC method. Based on several testing datasets, thorough experimental results demonstrate that the proposed GH-COOSAC method achieves lower computational cost and higher matching accuracy benefits when compared with the state-of-the-art image matching methods.
Phytoplankton Blooms Expanding Further Than Previously Thought in the Ross Sea: A Remote Sensing Perspective
Authors: Chen Shuangling , Meng Yu
Published: July 2022

Accurate and robust measurements from ocean color satellites are critical to studying spatial and temporal changes of surface ocean properties. Satellite-derived Chlorophyll-a (Chl) is an important parameter to monitor phytoplankton blooms on synoptical scales, particularly in remote seas. However, the present NASA standard Chl algorithm tends to strongly underestimate the Chl in the Ross Sea. Based on a locally-tuned Chl algorithm in the Ross Sea and using the data record from MODIS between 2002 and 2020, here we investigated the spatial expansion of phytoplankton blooms in the Ross Sea. Our results show the geometric areas of the phytoplankton blooms could reach (7.20 ± 2.8) × 104 km2 on average, which was ~3.1 times that of those identified using the NASA default Chl algorithm. Spatially, blooms were frequently identified on the shelf of the Ross Sea polynya with a typical chance of ≥80%. In the context of climate change and global warming, the general decrease and interannual dynamics of sea ice cover tends to affect solar light penetration and surface seawater temperature, which were found to regulate the spatial expansion of the phytoplankton blooms over the years. Statistical analyses showed that the spatial coverages of the phytoplankton blooms were significantly correlated with sea surface temperature (Spearman correlation coefficient R = 0.55, at p < 0.05), sea surface wind speed (R = 0.42, at p < 0.05), and sea ice concentration (R = −0.84, at p < 0.05), yet without significant long-term (>10 years) trends over the study period. The stronger phytoplankton blooms than those previously observed may indicate larger carbon sequestration, which needs to be investigated in the future. More valid satellite observations under cloud covers will further constrain the estimates.
Mapping Shrub Coverage in Xilin Gol Grassland with Multi-Temporal Sentinel-2 Imagery
Authors: Gan Liqin , Cao Xin , Chen Xuehong , He Qian , Cui Xihong , Zhao Chenchen
Published: July 2022

In recent decades, shrubs dominated by the genus Caragana have expanded in a large area in Xilin Gol grassland, Inner Mongolia, China. This study comprehensively evaluated the performances of multiple factors for mapping shrub coverage across the Xilin Gol grassland based on the spectral and temporal signatures of Sentinel-2 imagery, and for the first time produced a large-scale shrub coverage mapping result in this region. Considering the regional differences and gradients in the types and sizes of shrub in the study area, the study area was divided into three subregions based on precipitation data, i.e., west, middle and east regions. The shrub coverage estimation accuracy from dry- and wet-year data, different types of vegetation indices (VIs) and multiple regression methods were compared in each subregion, and the key phenological periods were selected. We also compared the accuracy of four mapping strategies, which were pairwise combinations of zoning (i.e., subregions divided by precipitation) and non-zoning, and full time series of VIs and key phenological period. Results show that the mapping accuracy in a dry year (2017) is higher than that in a wet year (2018). The optimal VIs and key phenological periods show high spatial variability. In terms of mapping strategies, the accuracy of zoning is higher than that of non-zoning. The root mean square error (RMSE), overall accuracy (OA) and recall for ‘zoning + full time series (or + key phenological period)’ strategy were 0.052 (0.055), 76.4% (79.7%) and 91.7% (94.6%), respectively, while for ‘non-zoning + full time series (or + key phenological period)’ strategy were 0.057 (0.060), 75.5% (74.6%) and 91.7% (88.6%), respectively. The mapping using VIs in key phenological periods is better than that of using full time series in the low-value prediction of shrub cover. Based on the strategy of ‘zoning + key phenological period’, the shrub coverage map of the whole region was generated with a RMSE of 0.055, OA of 80% and recall of 95%. This study not only provides the first large-scale mapping data of shrub coverage, but also provides reference for shrub dynamic monitoring in this area.
Hybrid Electrostatic–Atomic Accelerometer for Future Space Gravity Missions
Authors: Zahzam Nassim , Christophe Bruno , Lebat Vincent , Hardy Emilie , Huynh Phuong-Anh , Marquet Noémie , Blanchard Cédric , Bidel Yannick , Bresson Alexandre , Abrykosov Petro , Gruber Thomas , Pail Roland , Daras Ilias , Carraz Olivier
Published: July 2022

Long-term observation of Earth’s temporal gravity field with enhanced temporal and spatial resolution is a major objective for future satellite gravity missions. Improving the performance of the accelerometers present in such missions is one of the main paths to explore. In this context, we propose to study an original concept of a hybrid accelerometer associating a state-of-the-art electrostatic accelerometer (EA) and a promising quantum sensor based on cold atom interferometry. To assess the performance potential of such an instrument, numerical simulations were performed to determine its impact in terms of gravity field retrieval. Taking advantage of the long-term stability of the cold atom interferometer (CAI), it is shown that the reduced drift of the hybrid sensor could lead to improved gravity field retrieval. Nevertheless, this gain vanishes once temporal variations of the gravity field and related aliasing effects are taken into account. Improved de-aliasing models or some specific satellite constellations are then required to maximize the impact of the accelerometer performance gain. To evaluate the achievable acceleration performance in-orbit, a numerical simulator of the hybrid accelerometer was developed and preliminary results are given. The instrument simulator was in part validated by reproducing the performance achieved with a hybrid lab prototype operating on the ground. The problem of satellite rotation impact on the CAI was also investigated both with instrument performance simulations and experimental demonstrations. It is shown that the proposed configuration, where the EA’s proof-mass acts as the reference mirror for the CAI, seems a promising approach to allow the mitigation of satellite rotation. To evaluate the feasibility of such an instrument for space applications, a preliminary design is elaborated along with a preliminary error, mass, volume, and electrical power consumption budget.
A Recurrent Adaptive Network: Balanced Learning for Road Crack Segmentation with High-Resolution Images
Authors: Zhang Yi , Fan Junfu , Zhang Mengzhen , Shi Zongwen , Liu Rufei , Guo Bing
Published: July 2022

Road crack segmentation based on high-resolution images is an important task in road service maintenance. The undamaged road surface area is much larger than the damaged area on a highway. This imbalanced situation yields poor road crack segmentation performance for convolutional neural networks. In this paper, we first evaluate the mainstream convolutional neural network structure in the road crack segmentation task. Second, inspired by the second law of thermodynamics, an improved method called a recurrent adaptive network for a pixelwise road crack segmentation task is proposed to solve the extreme imbalance between positive and negative samples. We achieved a flow between precision and recall, similar to the conduction of temperature repetition. During the training process, the recurrent adaptive network (1) dynamically evaluates the degree of imbalance, (2) determines the positive and negative sampling rates, and (3) adjusts the loss weights of positive and negative features. By following these steps, we established a channel between precision and recall and kept them balanced as they flow to each other. A dataset of high-resolution road crack images with annotations (named HRRC) was built from a real road inspection scene. The images in HRRC were collected on a mobile vehicle measurement platform by high-resolution industrial cameras and were carefully labeled at the pixel level. Therefore, this dataset has sufficient data complexity to objectively evaluate the real performance of convolutional neural networks in highway patrol scenes. Our main contribution is a new method of solving the data imbalance problem, and the method of guiding model training by analyzing precision and recall is experimentally demonstrated to be effective. The recurrent adaptive network achieves state-of-the-art performance on this dataset.
Wi-Fi Fingerprint-Based Indoor Localization Method via Standard Particle Swarm Optimization
Authors: Zheng Jin , Li Kailong , Zhang Xing
Published: July 2022

With the continuous development and improvement in Internet-of-Things (IoT) technology, indoor localization has received considerable attention. Particularly, owing to its unique advantages, the Wi-Fi fingerprint-based indoor-localization method has been widely investigated. However, achieving high-accuracy localization remains a challenge. This study proposes an application of the standard particle swarm optimization algorithm to Wi-Fi fingerprint-based indoor localization, wherein a new two-panel fingerprint homogeneity model is adopted to characterize fingerprint similarity to achieve better performance. In addition, the performance of the localization method is experimentally verified. The proposed localization method outperforms conventional algorithms, with improvements in the localization accuracy of 15.32%, 15.91%, 32.38%, and 36.64%, compared to those of KNN, SVM, LR, and RF, respectively.
Pose Estimation for Visible Light Systems Using a Quadrature Angular Diversity Aperture Receiver
Authors: Shen Shengqiang , Menéndez Sánchez Jose Miguel , Li Shiyin , Steendam Heidi
Published: July 2022

The quadrature angular diversity aperture (QADA) receiver, consisting of a quadrant photodiode (QPD) and an aperture placed above the QPD, has been investigated for pose estimation for visible light systems. Current work on pose estimation for the QADA receiver uses classical camera sensor algorithms well known in computer vision. To this end, however, the light spot center first has to be obtained based on the RSS. However, this is less straightforward than for camera sensors, as in contrast to such sensors where the relationships are linear, the RSS output from the QADA is a non-linear function of the light spot position. When applying closed form solutions or iterative methods for cameras on a QADA, the non-linearity will degrade their performance. Furthermore, since in practice the aperture is not always perfectly aligned with the QPD, a procedure to calibrate the receiver is needed. Current work on calibration requires additional sophisticated equipment to measure the pose during calibration, which increases the difficulty of implementation. In this paper, we target the above problems for pose estimation and calibration of the QADA receiver. To this end, we first study the effect of the strategy of differencing and normalization on the probability density function (PDF), a commonly applied strategy for the QPD’s robustness against RSS variation, and it is shown that the applied strategy results in a complex PDF, which makes an effective and efficient estimation hard to achieve. Therefore, we derive an approximated PDF in a simple closed-form, based on which the calibration and the pose estimation algorithms using the least squares principle are proposed. The proposed calibration does not require any information about the pose of the receiver and is robust to variation of the received power and imperfect knowledge of the radiation pattern of the LED, making it easy to implement. We also derive the corresponding Cramér-Rao lower bound on the misalignment to benchmark the performance of the misalignment and to serve as an indicator to determine the required signal-to-noise ratio (SNR) or number of LEDs to obtain a desired accuracy. The calibration and pose estimation are evaluated by means of a Monte Carlo simulation. Computer simulations show that this theoretical bound is close to the RMSE of the proposed estimator and that the proposed pose estimator outperforms the PnP algorithm.
Integrating Deep Learning-Based IoT and Fog Computing with Software-Defined Networking for Detecting Weapons in Video Surveillance Systems
Authors: Fathy Cherine , Saleh Sherine Nagy
Published: July 2022

Due to the widespread proliferation of multimedia traffic resulting from Internet of Things (IoT) applications and the increased use of remote multimedia-based applications, as a consequence of COVID-19, there is an urgent need to develop intelligent adaptive techniques that improve the Quality of Service (QoS) perceived by end-users. In this work, we investigate the integration of deep learning techniques with Software-Defined Network (SDN) architecture to support delay-sensitive applications in IoT environments. Weapon detection in real-time video surveillance applications is deployed as our case study upon which multiple deep learning-based models are trained and evaluated for detection using precision, recall, and mean absolute precision. The deep learning model with the highest performance is then deployed within a proposed artificial intelligence model at the edge to extract the first detected video frames containing weapons for quick transmission to authorities, thus helping in the early detection and prevention of different kinds of crimes, and at the same time decreasing the bandwidth requirements by offloading the communication network from massive traffic transmission. Performance improvement is achieved in terms of delay, throughput, and bandwidth requirements by dynamically programming the network to provide different QoS based on the type of offered traffic and current traffic load, and based on the destination of the traffic. Performance evaluation of the proposed model was carried out using the mininet emulator, which revealed improvement of up to 75.0% in terms of average throughput, up to 14.7% in terms of mean jitter, and up to 32.5% in terms of packet loss.
Deployment Optimization Algorithms in Wireless Sensor Networks for Smart Cities: A Systematic Mapping Study
Authors: Abdulwahid Huda M. , Mishra Alok
Published: July 2022

In recent years, different types of monitoring systems have been designed for various applications, in order to turn the urban environments into smart cities. Most of these systems consist of wireless sensor networks (WSN)s, and the designing of these systems has faced many problems. The first and most important problem is sensor node deployment. The main function of WSNs is to gather the required information, process it, and send it to remote places. A large number of sensor nodes were deployed in the monitored area, so finding the best deployment algorithm that achieves maximum coverage and connectivity with the minimum number of sensor nodes is the significant point of the research. This paper provides a systematic mapping study that includes the latest recent studies, which are focused on solving the deployment problem using optimization algorithms, especially heuristic and meta-heuristic algorithms in the period (2015–2022). It was found that 35% of these studies updated the swarm optimization algorithms to solve the deployment problem. This paper will be helpful for the practitioners and researchers, in order to work out new algorithms and seek objectives for the sensor deployment. A comparison table is provided, and the basic concepts of a smart city and WSNs are presented. Finally, an overview of the challenges and open issues are illustrated.
Analysis of Shear Resistance and Mechanism of Construction and Demolition Waste Improved by Polyurethane
Authors: Ma Qiang , Li Junhui , Huang Shaoping , Xiao Henglin
Published: July 2022

A large amount of construction and demolition waste (CDW) is generated during the construction of projects. In this paper, polyurethane foam adhesive (PFA) was used to improve the mechanical properties of CDW. The large-scale direct shear tests, California bearing ratio (CBR) tests and Scanning electron microscope (SEM) tests were carried out to study the variation regularities of mechanical properties of treated CDW during the laboratory tests. The test results show that the shear strength of CDW increases with the increase of PFA content, vertical pressure and the shear rate. However, the increase of vertical pressure on the shear strength of CDW is smaller than that of PFA, and the improvement of the shear rate is relatively small. The California bearing ratio (CBR) test also proves that PFA can effectively improve the bearing capacity of CDW and reduce the loss of CBR caused by the 4-day soaking. Scanning Electron Microscope (SEM) finds that polyurethane wraps multiple particles and enhances the internal connection, which results in the cohesion between the particles being greatly increased. The study presented in this paper will better assess the shear resistance of improved CDW with PFA as a substitute for pavement base materials in practical engineering applications.
Improving Access to Export Market for Fresh Vegetables through Reduction of Phytosanitary and Pesticide Residue Constraints
Authors: Lengai Geraldin M. W. , Fulano Alex M. , Muthomi James W.
Published: July 2022

The horticultural sector is a key contributor to Kenya’s gross domestic product through the export of fresh-cut flowers, fruits and vegetables to various niche markets. It employs over 350,000 people, while about six million depend on it. However, the sector is constrained by the strict technical and phytosanitary quality requirements set by the export markets. The phytosanitary concerns include the presence of pests and microbial contaminants, while the presence of chemical residues constitute the major technical challenge. These constraints cause the interception and rejection of produce at the export destinations. The fresh produce should be free of quarantine and regulated non-quarantine pests, pest damage, pesticides above the stipulated maximum residue levels (MRLs) and phytosanitary certification. This review discusses the following four-tiered approach to compliance with phytosanitary and pesticide residue requirements: (i) use of alternative pest management approaches, including biocontrol options, cultural and physical practices; (ii) collaboration among regulatory agencies, institutions, producers and regional countries in the enforcement of standards; (iii) investment in research and the adoption of innovative technologies; (iv) awareness creation and training of actors along the fresh vegetable value chain. It is envisaged that this approach will contribute to sustainable fresh vegetable value chains, leading to improved access to export markets, and increased export volumes and income to smallholder farmers and other actors in the fresh vegetable value chain.
Green Innovation Sustainability: How Green Market Orientation and Absorptive Capacity Matter?
Authors: Du Yueping , Wang Huanhuan
Published: July 2022

Although there has been much research into green innovation, limited studies focus on the influence of internal factors on green innovation. This paper investigates whether and how two internal factors, such as green market orientation and absorptive capacity, facilitate green innovation. By dividing green innovation into green product innovation and process innovation, we also test the differential effects of the two internal factors on green product innovation and process innovation. Using a survey of 262 manufacturing firms in China, our results show that green market orientation improves only green product innovation, not green process innovation, whereas green absorptive capacity improves green product innovation and process innovation simultaneously. Green market orientation (green absorptive capacity) has a stronger impact on green product (process) innovation than on green process (product) innovation. Further, we find that a positive interaction effect of green market orientation and absorptive capacity on green product innovation and process innovation is significant. The findings contribute to the green innovation literature by providing an integrated framework for exploring whether the pursuit of green market intelligence and technological knowledge improves green product innovation and process innovation.
The Effect of the Knowledge, Attitude, and Behavior of Workers Regarding COVID-19 Precautionary Measures on Food Safety at Foodservice Establishments in Jordan
Authors: Olaimat Amin N. , Al-Nabulsi Anas A. , Nour Mohammad O. , Osaili Tareq M. , Alkhalidy Hana , Al-Holy Murad , Ayyash Mutamed , Holley Richard A.
Published: July 2022

The novel coronavirus (COVID-19) pandemic has caused sequential ripples of public health concern worldwide. Restaurant owners and workers have been significantly affected by safety regulations which have governed the activities of both employees and consumers. The objective of this study was to investigate the knowledge, attitude, and practices (KAP) of restaurant owners and workers in the context of COVID-19 and assess the effect of COVID-19 precautions on the implementation of food safety measures at foodservice establishments in Jordan. A cross-sectional survey was conducted that involved 605 participants from 91 restaurants and catering establishments in Jordan. The questionnaire was filled out during a face-to-face interview or via online platforms. Most (77%) of the respondents were male and under 35 years old (79%), with 42% of them having a high educational level (bachelor’s degree or postgraduate studies) and 46% having 1–5 years of work experience. It was found that only 20% of workers possessed good knowledge (scores above 75%), 56% had positive attitudes, and 55% had good practices, with a mean of 47% being compliant with the KAP levels expected. In total, 19 to 34% of participants observed that the precautions and preventive measures put in place during the pandemic improved the application of key food safety regulations within their workplaces. It is evident that more training is required for both employees and employers to ensure the effective implementation of the regulations required to prevent the spread of COVID-19 and food-borne pathogens via the application of good hygienic practices that improve food safety, reducing illnesses and food waste while maintaining food security and economic sustainability.
The Role of Community-Engaged Learning in Engineering Education for Sustainable Development
Authors: Goggins Jamie , Hajdukiewicz Magdalena
Published: July 2022

This paper presents the positive experience of facilitating over 300 community-engaged engineering projects at an Irish higher-education institution. The projects are framed by a research orientation, a commitment to civic engagement, and building university–community partnerships, city–university partnerships, and partnerships with other official agencies, so that community users can provide real learning problems and contexts for students and researchers and benefit from the results. The paper highlights how well the outlined approach fits with the ideas of engaged scholarship and civic professionalism, and facilitates sustainable development. Students recognise the long-term value of engaging with community partners, understanding their future role in the community as engineers, reinforcing the idea that their work can respond directly to real needs in the community, while promoting the sustainability agenda at the same time. The approach presented in this study will not only enable the development of future models for embedding sustainability in engineering programs, but will also equip future engineers with transferable skills to ensure that sustainable development goes beyond university courses and is practiced every day.
Relationship between Teachers’ Awareness of Disaster Prevention and Concerns about Disaster Preparedness
Authors: Kawasaki Hiromi , Yamasaki Satoko , Kurokawa Mina , Tamura Hiroshi , Sonai Kei
Published: July 2022

Disaster preparedness in schools is critical for the health and well-being of students and staff. To enhance disaster preparedness, awareness of disaster prevention should be raised among teachers. The aim of the current study was to assess disaster prevention awareness among teachers and to clarify the relationship between disaster prevention awareness and concern regarding disaster preparedness. Teachers from special needs schools, for which disaster preparedness is particularly important, were selected. Study questionnaires were sent to 1310 schools for children with special health care needs across 47 Japanese prefectures. Five-hundred-and-thirty-one complete responses were obtained. The disaster preparedness in the schools was insufficient. Teachers were concerned about child management, life saving, life maintenance, evacuation shelter management, and school management. There was a positive correlation between disaster prevention awareness and concern about disaster (r = 0.217, p-value = 0.000). The disaster awareness scale items “A sense of crisis about disaster” and “Anxiety” were positively correlated with concern about disaster, whereas “Imagining the disaster situation” was negatively correlated with concern about disaster. The current study analyzed the relationship between disaster prevention awareness and concern regarding disaster preparedness, which is relevant for the practice and indicates the importance of imagination strengthening.
Research on the Development and Influence on the Real Economy of Digital Finance: The Case of China
Authors: Zhao Yuexu , Feng Yujing
Published: July 2022

Based on the panel data of 31 provinces in China from 2011 to 2020, this paper analyzes the development status and distribution characteristics of digital finance, and studies the impact of digital finance on the growth of the real economy. First, whether China’s digital finance development can be classified by region through quartile images is investigated, and whether there are differences in the development of digital finance between regions and within regions is explored. Then, the dynamic characteristics of regional digital finance development distribution are analyzed by kernel density estimation, and the regression model is constructed to analyze the effect of digital finance development on promoting the growth of the real economy. The numerical result shows that the development characteristics of digital finance are different between regions and within regions, and the development of digital finance can significantly promote the growth of the real economy.
Uncovering the Effect of Forest Certification on the Dynamic Evolution of the Global Log Trade Network: A Stochastic Actor-Oriented Model Approach
Authors: Zhou Yingying , Cheng Baodong , Chen Jianbin
Published: July 2022

Clarifying the dynamic evolution characteristics and driving mechanism of the global log trade network (GLTN) can provide references for the trade decision-making of various countries. Based on the stochastic actor-oriented model, this paper analyzed the GLTN’s dynamic evolution from 2010 to 2019 and paid more attention to the effect of forest certification on the dynamic evolution of the GLTN. Results indicate that from 2010 to 2019, many changes have occurred in the network. The change rate in the 2010–2015 period is faster than that in the 2015–2019 period. Countries tend to form reciprocity, three-cycles, and transitive trade ties over time. A country with more certified forest area tends to form new log export ties with the other countries. The trade imbalance ratio (TII) has a significant negative mediating effect on the evolutionary relationships between the certified forest area and the log trade network dynamic. Net exporters of log tend to avoid establishing export ties with countries with more certified forest areas over time. Countries with similar cultural backgrounds are easier to establish log trade ties with, while countries with farther geographical distances tend to avoid establishing trade ties over time. A free trade agreement (FTA) has a significant positive impact on the formation of log trade ties among nations. Our findings shed new light on the dynamic evolution mechanism of the global log trade network and offer implications for future trade and forest conservation policy design.
COVID-19 Burnout Subject to the Dynamic Zero-COVID Policy in Hong Kong: Development and Psychometric Evaluation of the COVID-19 Burnout Frequency Scale
Authors: Lau Sam S. S. , Ho Cherry C. Y. , Pang Rebecca C. K. , Su Susan , Kwok Heather , Fung Sai-fu , Ho Roger C.
Published: July 2022

We sought to develop and validate a self-assessment burnout scale of the Chinese general population during the COVID-19 pandemic in the context of a dynamic zero-COVID policy. Factors relevant to individuals’ burnout during the prolonged COVID-19 pandemic were identified in the literature and through the reviews of an expert panel. A convenience sample of 1087 was randomly divided into two subsamples and the scale’s psychometric properties were assessed. Findings suggested that the COVID-19 BFS has adequate reliability (α = 0.90) along with factorial, concurrent, and convergent validity. Results of confirmatory factor analysis (CFA) supported the one-factor structure of the scale. Concurrent validity results indicate a significant positive correlation between COVID-19 BFS and the Fear of COVID-19 Scale (r = 0.131, p < 0.001), suggesting that individuals with higher levels of burnout may also have higher levels of fear of COVID, or vice versa. The scale was also correlated positively with being against the dynamic zero-COVID strategy (r = 0.340, p < 0.001), indicating that a higher level of burnout may be associated with individuals who are against the dynamic zero-COVID strategy. The results suggest the five-item COVID-19 BFS is a valid and reliable scale for the measurement of burnout frequency of the Chinese general population in relation to the prolonged COVID-19 pandemic in a dynamic zero-COVID policy context.
A Lifecycle-Based Smart Sustainable City Strategic Framework for Realizing Smart and Sustainability Initiatives in Riyadh City
Authors: Alshuwaikhat Habib M. , Adenle Yusuf A. , Almuhaidib Thamer
Published: July 2022

Smart cities rely on innovative technologies, guidelines, and mechanisms to advance city dwellers’ quality of life (QoL). Notwithstanding the global attention the smart city concept has received within the last decade, studies establishing pragmatic approaches for implementing smart sustainable city strategies in the Gulf region are rare. This study modelled a practical framework for implementing smart sustainable city strategies and megaprojects in one of the Gulf cities. A qualitative research methodology was used to assess smart city strategies from four cities to identify the optimum implementation strategies. This study design framework adopted a case study methodology, the identification of knowledge gaps, data collection and analysis, and interpretations of key findings. Best practices, paramount/relevant stakeholders, main issues/relevant considerations, 15 key performance indicators (KPI), and outputs/deliverables involved in diverse smart sustainable city strategies and project lifecycle phases were identified. The lifecycle phases adopted in the study were (i) conceptualization, (ii) planning/design, and (iii) installation/closure. A pragmatic understanding of how to effectively appraise, monitor, and implement smart sustainable city strategies and megaprojects is provided for policy/decision-makers and built environment experts in Saudi Arabia and globally. The proposed implementation strategic framework can perform the function of an appraisal tool for assessing each phase of the smart city project’s life cycle progress, informing preventions of delays or implementation challenges. This study’s contribution to research knowledge is the development of a model that reveals and illustrates the connections between different phases of smart sustainable city strategies and projects.
Evaluation and Optimization of Sustainable Development Level of Construction Industrialization: Case Beijing-Tianjin-Hebei Region
Authors: Jin Zhanyong , Xia Shuang , Cao Huanhuan , Geng Xiaohan , Cheng Zimeng , Sun Hongbo , Jia Menglin , Liu Qingyue , Sun Jie
Published: July 2022

In order to promote the sustainable development of architectural industrialization, it is necessary to evaluate its development level, identify the development status and key restricting factors, and achieve the effect of “promoting the development by evaluation”. However, the existing studies are mostly limited to the scope of provinces and cities, and there are few studies on the construction industrialization of an economic circle as a whole. Therefore, this paper locates the research within the scope of the region, constructs the evaluation model of the sustainable development level of the regional construction industrialization, and selects the Beijing-Tianjin-Hebei region as a case study. The research shows that the sustainable development level of construction industrialization in the Beijing-Tianjin-Hebei region is in the middle level, which needs to be improved from the aspects of economic support, technological innovation, and management. This paper provides a reasonable reference for how to evaluate and better promote the sustainable development of regional construction industrialization.
Nano-Iron and Nano-Zinc Induced Growth and Metabolic Changes in Vigna radiata
Authors: Rani Neelam , Kumari Kusum , Sangwan Parul , Barala Poonam , Yadav Jyoti , Vijeta  , Rahul  , Hooda Vinita
Published: July 2022

The widespread industrial use and consequent release of nanosized iron (nFe3O4) and zinc oxide (nZnO) particles into the environment have raised concerns over their effects on living organisms, including plants. These nanoparticles are the source of their respective metal ions and although plants require both Fe and Zn ions for proper growth, excessive levels of these metals are toxic to them. A better understanding of the effects of these nanoparticles on plants also offers an opportunity for their useful applications in agriculture. The present work evaluates the changes in seed germination, plant growth, photosynthetic capacity, levels of biomolecules and antioxidant enzymes in Vigna radiata (L.) Wilczek when grown in the presence of nFe3O4 (size 1–4 nm) and nZnO (size 10–20 nm) and compared to the control plants. The plants were raised hydroponically for up to 14 days at two different concentrations of nanoparticles, viz. 10 and 100 mg/L. Inductively coupled plasma mass spectrometry (ICP-MS) results established that V. radiata can accumulate Fe and Zn in shoots with high efficiency. The results indicated that nFe3O4 had a favourable effect on V. radiata, whereas no apparent benefit or toxicity of nZnO was observed at the tested concentrations.
Attractiveness of Collaborative Platforms for Sustainable E-Learning in Business Studies
Authors: Sternad Zabukovšek Simona , Deželak Zdenko , Parusheva Silvia , Bobek Samo
Published: July 2022

E-learning platforms have become more and more complex. Their functionality included in learning management systems is extended with collaborative platforms, which allow better communication, group collaboration, and face-to-face lectures. Universities are facing the challenge of advanced use of these platforms to fulfil sustainable learning goals. Better usability and attractiveness became essential in successful e-learning platforms, especially due to the more intensive interactivity expected from students. In the study, we researched the user experience of students who have used Moodle, Microsoft Teams, and Google Meet. User experience is, in most cases, connected with a person’s perception, person’s feelings, and satisfaction with the platform used. Data were collected using a standard UEQ questionnaire. With this research, we examined whether user experience factors: perceived efficiency, perceived perspicuity, perceived dependability, perceived stimulation, and perceived novelty affect perceived attractiveness, which is an important factor in the sustainability of e-learning tools. The collected data were processed using SmartPLS. The research study showed that all studied factors have a statistically significant impact on perceived attractiveness. Factor perceived stimulation has the strongest statistically significant impact on the perceived attractiveness of e-learning platforms, followed by perceived efficiency, perceived perspicuity, perceived novelty, and perceived dependability.
Overview of the Special Issue “New Frontiers in Design and Planning for Healthy Built Environments”
Authors: Fikfak Alenka , Kosanović Saja , Nikšič Matej , Mady Christine
Published: July 2022

Public health issues are closely related to the design and planning of the built environment [...]
Examining the Effect of Tax Reform Determinants, Firms’ Characteristics and Demographic Factors on the Financial Performance of Small and Micro Enterprises
Authors: Bhalla Neba , Kaur Inderjit , Sharma Rakesh Kumar
Published: July 2022

Taxation is a powerful tool to achieve sustainable development goals (SDG) as tax policies help strengthen economic growth and improve revenue capacity. So, after tax reform, it is vital to analyze their impact on the performance of enterprises. Keeping in mind the SDG, the present study was conducted in India after the major tax overhaul—Goods and Service Tax (GST) on 1 July 2017, to measure the impact on Return on Equity (ROE) and Return on Investment (ROI) as they are the barometers to measure performance (DuPont Analysis). We opted for tax reform determinants, the characteristics of firms, demographic variables, and drivers from DuPont analysis to conduct the research, as all these variables can help identify the different causes of factors impacting ROI and ROE among different types of firms and demographics across countries. An econometric analysis of 546 registered SMEs (small and micro enterprises) was conducted using the regression model, structured equation modeling, and exploratory and confirmatory factor analysis to achieve the objectives. The empirical findings highlighted that a firm’s size, turnover, and DuPont analysis drivers (earnings × asset to sales × asset turnover) positively enhanced the ROI and ROE. Further, the change in the tax system after the tax reforms has enabled the detection of tax fraud and wrong invoices, reducing the missing insolvent traders and increasing the working capital flow of the firms, which in turn has augmented financial performance.
Spatial-Temporal Driving Factors of Urban Landscape Changes in the Karst Mountainous Regions of Southwest China: A Case Study in Central Urban Area of Guiyang City
Authors: Luo Yuanhong , Wang Zhijie , Zhou Xuexia , Hu Changyue , Li Jing
Published: July 2022

Rapid urban expansion has significantly altered the regional landscape pattern, posing a serious threat to the sustainable development of natural and social ecosystems. By using landscape patterns indices and an area transfer matrix, this study analyzed the spatial-temporal changes of landscape patterns in the karst mountainous cities of southwest China from 2000 to 2020, by taking the central urban area of Guiyang City (CUAG) as the study area. This study explored the spatial and temporal driving factors of landscape pattern changes by using stepwise multiple linear regression and geographic detector methods. The results show: (1) CUAG’s landscape types altered changed drastically, with the area of forestland and construction land rapid increment and cultivated land decrement significantly. (2) The patches of construction land and forestland tended to be aggregated, the degree of fragmentation was reduced, and the shape was complex; cultivated land fragmentation was intensified. The connectivity of the landscape was improved, while the level of landscape diversity declined, the trend of landscape homogenization was obvious. (3) Socioeconomic and geographical endowment drivers have determined landscape pattern changes. The findings of this study may be used to interpret other similar landscapes worldwide and may imply the protection of urban ecosystem and sustainable development.
Thread-Based Modeling and Analysis in Multi-Core-Based V2X Communication Device
Authors: Choi Won-Seok , Choi Seong-Gon
Published: July 2022

We propose a thread-based modeling and analysis method for a vehicle-to-everything (V2X) communication device, according to the performance requirements of the V2X application. For a developer to develop a V2X device or application, its performance requirements must first be determined. Furthermore, the developer selects the hardware system to enable the service to function while satisfying the performance requirements. Through this process, the developer designs and creates the program in a manner that considers thread configuration and core mapping with the given hardware resources. Then, the performance evaluation of a system is tested via instruction-level program analysis with executable programs. This process is an essential procedure for developing a program that satisfies the performance requirements. However, the debugging procedure repeated through program development, performance analysis, and program modification requires significant time and is costly. Hence, to reduce the time and cost of unnecessary work, we propose a thread-level modeling and analysis method using a queueing theory for a V2X communication device that can be applied at the design level. First, we propose a thread-level performance modeling and analysis method based on queuing theory in a multi-core-based vehicle service system. Furthermore, we analyze the performance of a multi-core-based vehicle service system utilizing the proposed method.