bluuebunny/crossref_metadata_2025 huggingface dataset:
Mitigating Satellite-Induced Code Pseudorange Variations at GLONASS G3 Frequency Using Periodical Model
Authors: Li Linyang , Shen Yang , Li Xin
Published: January 2023

With the modernization of GLONASS, four M+ and two K satellites are able to broadcast code-division multiple-access signals at a G3 frequency. The evaluation of the G3 frequency is necessary, among which the satellite-induced code pseudorange variation is one of the most important indicators. Using the code-minus-carrier (CMC) combination, it was found that the magnitude of the code pseudorange variations at the G3 frequency is about 1 m, which is primarily caused by the fact that G3 is transmitted from a different antenna, the same as G1 and G2. However, different from BDS-2 medium Earth orbit and inclined geo-synchronous orbit satellites, the code pseudorange variations at the GLONASS G3 frequency have a very weak relationship with the elevation angle, while a strong correlation exists with the time series, by using wavelet transformation and correlation analysis. Validation is carried out using a single-site model and a continuous multi-site model over 24 h, and the correction performance of these two models is comparable. The systematic deviation of the CMC and Melbourne–Wübbena combinations are significantly corrected, so only random errors remain. With a more concentrated distribution of the pseudorange residuals of single point positioning, the standard deviation of the pseudorange residuals is reduced.
Image Registration Algorithm for Remote Sensing Images Based on Pixel Location Information
Authors: Zhang Xuming , Zhou Yao , Qiao Peng , Lv Xiaoning , Li Jimin , Du Tianyu , Cai Yiming
Published: January 2023

Registration between remote sensing images has been a research focus in the field of remote sensing image processing. Most of the existing image registration algorithms applied to feature point matching are derived from image feature extraction methods, such as scale-invariant feature transform (SIFT), speed-up robust features (SURF) and Siamese neural network. Such methods encounter difficulties in achieving accurate image registration where there is a large bias in the image features or no significant feature points. Aiming to solve this problem, this paper proposes an algorithm for multi-source image registration based on geographical location information (GLI). By calculating the geographic location information that corresponds to the pixel in the image, the ideal projected pixel position of the corresponding image is obtained using spatial coordinate transformation. Additionally, the corresponding relationship between the two images is calculated by combining multiple sets of registration points. The simulation experiment illustrates that, under selected common simulation parameters, the average value of the relative registration-point error between the two images is 12.64 pixels, and the registration accuracy of the corresponding ground registration point is higher than 6.5 m. In the registration experiment involving remote sensing images from different sources, the average registration pixel error of this algorithm is 20.92 pixels, and the registration error of the image center is 21.24 pixels. In comparison, the image center registration error given by the convolutional neural network (CNN) is 142.35 pixels after the registration error is manually eliminated. For the registration of homologous and featureless remote sensing images, the SIFT algorithm can only offer one set of registration points for the correct region, and the neural network cannot achieve accurate registration results. The registration accuracy of the presented algorithm is 7.2 pixels, corresponding to a ground registration accuracy of 4.32 m and achieving more accurate registration between featureless images.
Natural Nitrogen-Doped Carbon Dots Obtained from Hydrothermal Carbonization of Chebulic Myrobalan and Their Sensing Ability toward Heavy Metal Ions
Authors: Atchudan Raji , Perumal Suguna , Edison Thomas Nesakumar Jebakumar Immanuel , Sundramoorthy Ashok K. , Vinodh Rajangam , Sangaraju Sambasivam , Kishore Somasundaram Chandra , Lee Yong Rok
Published: January 2023

Chebulic Myrobalan is the main ingredient in the Ayurvedic formulation Triphala, which is used for kidney and liver dysfunctions. Herein, natural nitrogen-doped carbon dots (NN-CDs) were prepared from the hydrothermal carbonization of Chebulic Myrobalan and were demonstrated to sense heavy metal ions in an aqueous medium. Briefly, the NN-CDs were developed from Chebulic Myrobalan by a single-step hydrothermal carbonization approach under a mild temperature (200 °C) without any capping and passivation agents. They were then thoroughly characterized to confirm their structural and optical properties. The resulting NN-CDs had small particles (average diameter: 2.5 ± 0.5 nm) with a narrow size distribution (1–4 nm) and a relatable degree of graphitization. They possessed bright and durable fluorescence with excitation-dependent emission behaviors. Further, the as-synthesized NN-CDs were a good fluorometric sensor for the detection of heavy metal ions in an aqueous medium. The NN-CDs showed sensitive and selective sensing platforms for Fe3+ ions; the detection limit was calculated to be 0.86 μM in the dynamic range of 5–25 μM of the ferric (Fe3+) ion concentration. Moreover, these NN-CDs could expand their application as a potential candidate for biomedical applications and offer a new method of hydrothermally carbonizing waste biomass.
A Two-Stage Network for Zero-Shot Low-Illumination Image Restoration
Authors: Tang Hao , Fei Linfeng , Zhu Hongyu , Tao Huanjie , Xie Chao
Published: January 2023

Due to the influence of poor lighting conditions and the limitations of existing imaging equipment, captured low-illumination images produce noise, artifacts, darkening, and other unpleasant visual problems. Such problems will have an adverse impact on the following high-level image understanding tasks. To overcome this, a two-stage network is proposed in this paper for better restoring low-illumination images. Specifically, instead of manipulating the raw input directly, our network first decomposes the low-illumination image into three different maps (i.e., reflectance, illumination, and feature) via a Decom-Net. During the decomposition process, only reflectance and illumination are further denoised to suppress the effect of noise, while the feature is preserved to reduce the loss of image details. Subsequently, the illumination is deeply adjusted via another well-designed subnetwork called Enhance-Net. Finally, the three restored maps are fused together to generate the final enhanced output. The entire proposed network is optimized in a zero-shot fashion using a newly introduced loss function. Experimental results demonstrate that the proposed network achieves better performance in terms of both objective evaluation and visual quality.
Force Identification Based on Response Signals Captured with High-Speed Three-Dimensional Digital Image Correlation
Authors: Mendrok Krzysztof , Molina-Viedma Ángel J. , López-Alba Elias , Díaz Garrido Francisco A. , Pieczonka Lukasz
Published: January 2023

Structural Health Monitoring (SHM) systems allow three types of diagnostic tasks to be performed, namely damage identification, loads monitoring, and damage prognosis. Only if all three tasks are correctly fulfilled can the useful remaining life of a structure be estimated credibly. This paper deals with the second task and aimed to extend state-of-the-art in load identification, by demonstrating that it is feasible to achieve it through the analysis of response signals captured with high-speed three-dimensional Digital Image Correlation (HS 3D-DIC). The efficacy of the proposed procedure is demonstrated experimentally on a frame structure under broadband vibration excitation. Full-field vibration displacement signals are captured with the use of two high-speed cameras and processed with 3D-DIC. Loads are identified with two different algorithms based on inverting the Frequency Response Function (FRF) matrix and modal filtration (MF). The paper discusses both methods providing their theoretical background and experimental performance.
Few-Shot User-Adaptable Radar-Based Breath Signal Sensing
Authors: Mauro Gianfranco , De Carlos Diez Maria , Ott Julius , Servadei Lorenzo , Cuellar Manuel P. , Morales-Santos Diego P.
Published: January 2023

Vital signs estimation provides valuable information about an individual’s overall health status. Gathering such information usually requires wearable devices or privacy-invasive settings. In this work, we propose a radar-based user-adaptable solution for respiratory signal prediction while sitting at an office desk. Such an approach leads to a contact-free, privacy-friendly, and easily adaptable system with little reference training data. Data from 24 subjects are preprocessed to extract respiration information using a 60 GHz frequency-modulated continuous wave radar. With few training examples, episodic optimization-based learning allows for generalization to new individuals. Episodically, a convolutional variational autoencoder learns how to map the processed radar data to a reference signal, generating a constrained latent space to the central respiration frequency. Moreover, autocorrelation over recorded radar data time assesses the information corruption due to subject motions. The model learning procedure and breathing prediction are adjusted by exploiting the motion corruption level. Thanks to the episodic acquired knowledge, the model requires an adaptation time of less than one and two seconds for one to five training examples, respectively. The suggested approach represents a novel, quickly adaptable, non-contact alternative for office settings with little user motion.
Using Whale Optimization Algorithm and Haze Level Information in a Model-Based Image Dehazing Algorithm
Authors: Hsieh Cheng-Hsiung , Chen Ze-Yu , Chang Yi-Hung
Published: January 2023

Single image dehazing has been a challenge in the field of image restoration and computer vision. Many model-based and non-model-based dehazing methods have been reported. This study focuses on a model-based algorithm. A popular model-based method is dark channel prior (DCP) which has attracted a lot of attention because of its simplicity and effectiveness. In DCP-based methods, the model parameters should be appropriately estimated for better performance. Previously, we found that appropriate scaling factors of model parameters helped dehazing performance and proposed an improved DCP (IDCP) method that uses heuristic scaling factors for the model parameters (atmospheric light and initial transmittance). With the IDCP, this paper presents an approach to find optimal scaling factors using the whale optimization algorithm (WOA) and haze level information. The WOA uses ground truth images as a reference in a fitness function to search the optimal scaling factors in the IDCP. The IDCP with the WOA was termed IDCP/WOA. It was observed that the performance of IDCP/WOA was significantly affected by hazy ground truth images. Thus, according to the haze level information, a hazy image discriminator was developed to exclude hazy ground truth images from the dataset used in the IDCP/WOA. To avoid using ground truth images in the application stage, hazy image clustering was presented to group hazy images and their corresponding optimal scaling factors obtained by the IDCP/WOA. Then, the average scaling factors for each haze level were found. The resulting dehazing algorithm was called optimized IDCP (OIDCP). Three datasets commonly used in the image dehazing field, the RESIDE, O-HAZE, and KeDeMa datasets, were used to justify the proposed OIDCP. Then a comparison was made between the OIDCP and five recent haze removal methods. On the RESIDE dataset, the OIDCP achieved a PSNR of 26.23 dB, which was better than IDCP by 0.81 dB, DCP by 8.03 dB, RRO by 5.28, AOD by 5.6 dB, and GCAN by 1.27 dB. On the O-HAZE dataset, the OIDCP had a PSNR of 19.53 dB, which was better than IDCP by 0.06 dB, DCP by 4.39 dB, RRO by 0.97 dB, AOD by 1.41 dB, and GCAN by 0.34 dB. On the KeDeMa dataset, the OIDCP obtained the best overall performance and gave dehazed images with stable visual quality. This suggests that the results of this study may benefit model-based dehazing algorithms.
Cyber-Physical-Social Awareness Platform for Comprehensive Situation Awareness
Authors: Mirza Irfan Baig , Georgakopoulos Dimitrios , Yavari Ali
Published: January 2023

Cyber-physical-social computing system integrates the interactions between cyber, physical, and social spaces by fusing information from these spaces. The result of this fusion can be used to drive many applications in areas such as intelligent transportation, smart cities, and healthcare. Situation Awareness was initially used in military services to provide knowledge of what is happening in a combat zone but has been used in many other areas such as disaster mitigation. Various applications have been developed to provide situation awareness using either IoT sensors or social media information spaces and, more recently, using both IoT sensors and social media information spaces. The information from these spaces is heterogeneous and, at their intersection, is sparse. In this paper, we propose a highly scalable, novel Cyber-physical-social Awareness (CPSA) platform that provides situation awareness by using and intersecting information from both IoT sensors and social media. By combining and fusing information from both social media and IoT sensors, the CPSA platform provides more comprehensive and accurate situation awareness than any other existing solutions that rely only on data from social media and IoT sensors. The CPSA platform achieves that by semantically describing and integrating the information extracted from sensors and social media spaces and intersects this information for enriching situation awareness. The CPSA platform uses user-provided situation models to refine and intersect cyber, physical, and social information. The CPSA platform analyses social media and IoT data using pretrained machine learning models deployed in the cloud, and provides coordination between information sources and fault tolerance. The paper describes the implementation and evaluation of the CPSA platform. The evaluation of the CPSA platform is measured in terms of capabilities such as the ability to semantically describe and integrate heterogenous information, fault tolerance, and time constraints such as processing time and throughput when performing real-world experiments. The evaluation shows that the CPSA platform can reliably process and intersect with large volumes of IoT sensor and social media data to provide enhanced situation awareness.
A Hardware-Friendly High-Precision CNN Pruning Method and Its FPGA Implementation
Authors: Sui Xuefu , Lv Qunbo , Zhi Liangjie , Zhu Baoyu , Yang Yuanbo , Zhang Yu , Tan Zheng
Published: January 2023

To address the problems of large storage requirements, computational pressure, untimely data supply of off-chip memory, and low computational efficiency during hardware deployment due to the large number of convolutional neural network (CNN) parameters, we developed an innovative hardware-friendly CNN pruning method called KRP, which prunes the convolutional kernel on a row scale. A new retraining method based on LR tracking was used to obtain a CNN model with both a high pruning rate and accuracy. Furthermore, we designed a high-performance convolutional computation module on the FPGA platform to help deploy KRP pruning models. The results of comparative experiments on CNNs such as VGG and ResNet showed that KRP has higher accuracy than most pruning methods. At the same time, the KRP method, together with the GSNQ quantization method developed in our previous study, forms a high-precision hardware-friendly network compression framework that can achieve “lossless” CNN compression with a 27× reduction in network model storage. The results of the comparative experiments on the FPGA showed that the KRP pruning method not only requires much less storage space, but also helps to reduce the on-chip hardware resource consumption by more than half and effectively improves the parallelism of the model in FPGAs with a strong hardware-friendly feature. This study provides more ideas for the application of CNNs in the field of edge computing.
A Method of Setting the LiDAR Field of View in NDT Relocation Based on ROI
Authors: Gu Jian , Lan Yubin , Kong Fanxia , Liu Lei , Sun Haozheng , Liu Jie , Yi Lili
Published: January 2023

LiDAR placement and field of view selection play a role in detecting the relative position and pose of vehicles in relocation maps based on high-precision map automatic navigation. When the LiDAR field of view is obscured or the LiDAR position is misplaced, this can easily lead to loss of repositioning or low repositioning accuracy. In this paper, a method of LiDAR layout and field of view selection based on high-precision map normal distribution transformation (NDT) relocation is proposed to solve the problem of large NDT relocation error and position loss when the occlusion field of view is too large. To simulate the real placement environment and the LiDAR obstructed by obstacles, the ROI algorithm is used to cut LiDAR point clouds and to obtain LiDAR point cloud data of different sizes. The cut point cloud data is first downsampled and then relocated. The downsampling points for NDT relocation are recorded as valid matching points. The direction and angle settings of the LiDAR point cloud data are optimized using RMSE values and valid matching points. The results show that in the urban scene with complex road conditions, there are more front and rear matching points than left and right matching points within the unit angle. The more matching points of the NDT relocation algorithm there are, the higher the relocation accuracy. Increasing the front and rear LiDAR field of view prevents the loss of repositioning. The relocation accuracy can be improved by increasing the left and right LiDAR field of view.
Deep Learning-Based Defect Detection Framework for Ultra High Resolution Images of Tunnels
Authors: Lee Kisu , Lee Sanghyo , Kim Ha Young
Published: January 2023

This study proposes a defect detection framework to improve the performance of deep learning-based detection models for ultra-high resolution (UHR) images generated by tunnel inspection systems. Most of the scanning technologies used in tunnel inspection systems generate UHR images. Defects in real-world images, on the other hand, are noticeably smaller than the image. These characteristics make simple preprocessing applications, such as downscaling, difficult due to information loss. Additionally, when a deep learning model is trained by the UHR images under the limited computational resource for training, problems may occur, including a reduction in object detection rate, unstable training, etc. To address these problems, we propose a framework that includes preprocessing and postprocessing of UHR images related to image patches rather than focusing on deep learning models. Furthermore, it includes a method for supplementing problems according to the format of the data annotation in the preprocessing process. When the proposed framework was applied to the UHR images of a tunnel, the performance of the deep learning-based defect detection model was improved by approximately 77.19 percentage points (pp). Because the proposed framework is for general UHR images, it can effectively recognize damage to general structures other than tunnels. Thus, it is necessary to verify the applicability of the defect detection framework under various conditions in future works.
Embedded Coexistence: Social Adaptation of Chinese Female White-Collar Workers in Japan
Authors: Liu Jing , Chen Shaojun
Published: January 2023

In Japan, Chinese female white-collar workers have emerged as a rapidly growing social group. Unlike traditional female migrants, high-skilled women exhibit more autonomy and strategy in their interactions with mainstream society. Traditional immigrant theories do not apply to their patterns of social adaptation. The paper draws on qualitative research with 38 Chinese female white-collar workers working in Tokyo after graduating from Japanese colleges. It illustrates their performance and strategies in adapting to Japanese society and explores how their decision-making process is shaped. The findings show that they exhibit a selective adaptation: They self-identify as “permanent sojourners”—they are eclectic, but inclined to maintain a cultural cognition ordered around their homeland culture, and they have multiple contacts across ethnic groups and reserve cultural differences in social interactions. Furthermore, this mode of adaptation results from the interaction of three factors: individual rational choice, the mutual pressure of the in-group and the out-group, and the national policies and historical issues between China and Japan. This paper argues that the migration patterns of different migrant groups should be interpreted in light of the subjectivity of migrants, taking into account their initiative, human capital, gender, and ethnicity. This study enriches the study of international female migration and adds to the practical research on social adaptation patterns among immigrants.
Using Transport to Quantify the Impact of Vertical Integration on the Construction Supply Chain: A New Zealand Assessment
Authors: Dhawan Kamal , Tookey John E. , GhaffarianHoseini Ali , Poshdar Mani
Published: January 2023

New Zealand (NZ) construction is highly fragmented, comprises primarily micro small and medium enterprises (MSMEs); 32.5% of approximately 67,000 operating businesses are ‘small’ (up to 19 employees), while 65% are sole traders. The construction supply chain (CSC) is extensive, prone to inefficiencies at segmental boundaries accentuated by project-centric delivery. Conversely, it presents significant opportunity for consolidation and improved efficiency. Vertical integration and CSC management from the supplier-end rather than the project-end enable component elements to be individually independent in terms of ownership, while integrating their management above the tactical CSC level. This leads to improved operational philosophy and employment. Quantifying impacts, however, is a challenge due to lack of tangibility. This can be effectively overcome using quantifiable parameters associated with the CSC’s transport component. The paper investigates transport operations in a narrow NZ CSC segment over a three-month period to quantify improved performance using operational data and further potential for resource optimisation using operations research-based planning. Research outcomes point towards: (i) Fleet management strategy; (ii) Integrated planning and operational delivery; (iii) Non-price attributes in tendering/contracting; (iv) Change in the delivery model of manufactured construction products; (v) Information and communication technology-based solutions; and (vi) Integration of reverse logistics.
Construction of Enterprise Marketing Management System in Digital Economic Environment from the Perspective of Green Ecology
Authors: Jia Dian , Zhang Honghou , Han Xiaoyang
Published: January 2023

In the context of the digital economy, the speed of enterprise development is becoming faster and faster. To understand and establish the environment of network marketing, we must first understand the obstacles. Successful establishment depends on its promotion in society and the active participation of the government and enterprises. Only with the active cooperation of all parties can network marketing be rapidly established. At present, the marketing methods adopted by many enterprises are very traditional. These management methods are not only inefficient, but also present problems such as poor real-time information, a low degree of informatization, information asymmetry and closed marketing data. In order to address these problems, this paper proposes to use digital economic means to scientifically manage enterprise marketing. Here we examine the marketing effect of enterprises and determine the needs of consumers by constructing an enterprise marketing management system, so that enterprises can observe trends in consumer needs and consumer markets at any time, and make timely responses to markets and technological changes. We use a B/S model to build an enterprise marketing management system that can develop steadily in a green ecology. This paper mainly uses the mean clustering algorithm to analyze the performance of the enterprise marketing management system. Through the experiment, it is found that a good marketing management system can help enterprises improve economic efficiency and data processing ability. During the operation, it was found that the transaction success rate of the system reached 100%. Most enterprises responded within 1.5 s. The functional changes of the enterprise before and after the B/S architecture enterprise marketing management system were analyzed and it was found that the real-time information acquisition by enterprise employees, the sharing of marketing data, the degree of informatization of the system, the security of system information and the data processing capabilities had all been improved. Among these, the degree of informatization of enterprises improved the fastest and went from 48% to 94%. Under the enterprise marketing management system, the annual economic income of the enterprise increased by 1.35 million yuan, which was a year-on-year increase of 7.2%. The experimental data showed that under the green ecological perspective, the enterprise marketing management system based on the digital economic environment has certain practicality, which can promote the healthy development of enterprise marketing management.
The Impact of the Dual-Credit Policy on Production and Cooperative R&D in the Automotive Supply Chain
Authors: Ding Lian , Zhu Xiaodong
Published: January 2023

The dual-credit policy has increased the complexity of the automotive supply chain. This study constructs a game model and uses reverse induction to explore the impact of the dual-credit policy on production and cooperative R&D. The results show that compared with no dual-credit policy, the dual-credit policy may not be able to promote improvement of the fuel economy, the development of the new energy vehicle (NEV) industry, and cooperative research and development (R&D). The policy effects are related to other parameters. Meanwhile, increasing control of the dual-credit policy can suppress the scale of fuel vehicles (FVs), but has little effect on the level of fuel economy improvement. Additionally, the impact on the intensity of NEV production and cooperative R&D needs to be discussed within a specific threshold. Manufacturers can obtain policy dividends by adjusting their operational strategies. The government can quantify the effect of policies through production supervision and flexibly adjust the intensity of regulation, to build a green and win–win automotive supply chain.
The Impact of Road Investments on the Forest Environment—Case Study: The Impact of Asphalt Roads on the Health Condition and Growth of Trees
Authors: Turski Mieczysław , Beker Cezary , Czerniak Andrzej
Published: January 2023

The study was conducted within the framework of the R&D project entitled “Environmental aspects of reconstruction and construction of forest roads using selected asphalt binders”. The aim of this study was to determine the impact of an asphalt road segment located in the Przymuszewo Forest District, the Regional Directorate of the State Forests in Toruń, Poland (53°57′24.14″ N, 17°34′38.30″ E), on growth increments and health condition of trees in a 60-year-old pine stand. The first stage of the investigation required laboratory analyses and field observations to assess the impact of mineral filler and asphalt mixtures on roadside habitats. Chemical analyses were performed on samples of wood and soil collected from transects located at three distances from the asphalt road, as well as dendrological and dendrometric analyses in circular sample plots of 0.02 ha in analogous transects parallel to the selected asphalt pavement. Analyses of soils and wood showed no negative environmental impact associated with the road. Contents of all the assayed elements in wood were lower compared to their contents in soil, which indicates their markedly reduced phytoextraction. Statistical analyses showed that the asphalt road has an effect on the adjacent forest mass, but that this effect is statistically insignificant with respect to the mean annual increments diameter at breast height and the defoliation of trees growing at various distances from the road.
A Comparison between Supervised Classification Methods: Study Case on Land Cover Change Detection Caused by a Hydroelectric Complex Installation in the Brazilian Amazon
Authors: Affonso Alynne Almeida , Mandai Silvia Sayuri , Portella Tatiana Pineda , Quintanilha José Alberto , Conti Luis Américo , Grohmann Carlos Henrique
Published: January 2023

The Volta Grande do Xingu (VGX) in the Amazon Forest of Brazil was chosen to analyze the land use and land cover changes (LULCC) from 2000 to 2017, with the aim of assessing the most suitable classification method for the area. Three parametric (Mahalanobis distance, maximum likelihood and minimum distance) and three non-parametric (neural net, random forest and support vector machine) classification algorithms were tested in two Landsat scenes. The accuracy assessment was evaluated through a confusion matrix. Change detection of the landscape was analyzed through the post-classification comparison method. While maximum likelihood was more capable of highlighting errors in individual classes, support vector machine was slightly superior when compared with the other non-parametric options, these being the most suitable classifiers within the scope of this study. The main changes detected in the landscape were from forest to agro-pasture, from forest/agro-pasture to river, and from river to non-river, resulting in rock exposure. The methodology outlined in this research highlights the usefulness of remote sensing tools in follow-up observations of LULCC in the study area (with the possibility of application to the entire Amazon rainforest). Thus, it is possible to carry out adaptive management that aims to minimize unforeseen or underestimated impacts in previous stages of environmental licensing.
Environmental Hazards of the Railway Infrastructure of Kazakhstan
Authors: Ashimova Balgyn , Beisenova Raikhan , Menéndez-Pidal Ignacio , Jumabayev Serik , Zhupysheva Aktoty , Tazitdinova Rumiya
Published: January 2023

The railway sector is the largest branch of the economy in Kazakhstan. Nevertheless, environmental safety issues and the reduction and prevention of harmful impacts are not given sufficient attention. There is very little research on the impact of the railway sector of Kazakhstan on the natural environment, which hinders the further development of the railway sector and thus necessitates the addressal of environment issues. This study aimed to show possible environmentally hazardous sections of the country’s railway infrastructure. The criteria chosen for the analysis were soil cover, water resources, rainfall, protected natural areas, and population. A map of environmentally sensitive areas was created to determine which areas require priority protection from the environmental hazards posed by the country’s railway infrastructure. The map was developed in a GIS environment using the weighted overlay, expert assessment, and snowball methods. Additionally, a model for the Kazakhstan segment of the International Northern Railway Corridor was constructed in this article to identify integral indices that assess the susceptibility of the territory to environmental hazards. The data and results presented in the article can be used to solve current and future environmental issues concerning the country’s railway communications and can be implemented in many practical applications.
EU Citizens’ Twitter Discussions of the 2022–23 Energy Crisis: A Content and Sentiment Analysis on the Verge of a Daunting Winter
Authors: Vrana Vasiliki , Kydros Dimitrios , Kotzaivazoglou Iordanis , Pechlivanaki Ioanna
Published: January 2023

The 2022–2023 winter period is alleged to be one of the toughest since World War II with respect to energy, especially electricity, natural gas and oil. The paper investigates the public discussions on Twitter in five widely spoken European languages and English. Networks of users are formed in order to locate possible important nodes that control the distribution of information. The networks are rather sparse and do not belong to the general class of ‘small worlds’. The communities of users seem to gather around one user; however, users also interact with others within the groups. Regarding the users’ sentiments, the negatives are definitely higher than the positive ones. Sentiments appear to be stable in their scores during the examined period and for each language; fear and sadness are dominant among them. Energy prices are frequently discussed in all languages, along with major political events. Findings may help governments to better understand public views and develop an effective strategy to communicate with and protect EU citizens.
The COVID-19 Pandemic and Tourists’ Risk Perceptions: Tourism Policies’ Mediating Role in Sustainable and Resilient Recovery in the New Normal
Authors: Zhang Shiqi , Sun Tianwei , Lu Yuan
Published: January 2023

The COVID-19 health crisis has had unprecedented impacts on the global tourism industry, creating a sense of insecurity among tourists about destinations. Thus, rebuilding tourists’ confidence in the tourism industry is the biggest challenge faced by policymakers in the new normal. The tourism industry needs innovative solutions for sustainable recovery, but limited literature is available on the tourism policies necessary for sustainable and resilient recovery in the new normal. This study investigated the impact of COVID-19 and risk perception on the recovery of tourism. Moreover, this study also explored the mediating role of attitudes toward tourism policies between COVID-19, risk perceptions, and tourism recovery. Data collected from 1437 tourists through an online survey were analyzed using PLS-SEM and descriptive statistics. The results showed that a large majority of the tourists still felt unsafe and insecure about tourism destinations. COVID-19 risk perceptions were found to be negatively associated with tourism recovery in the new normal. Risk perceptions had a significant positive impact on transportation selection behavior (β = 0.725, p < 0.01), as did avoiding overcrowded places (β = 0.692, p < 0.01). Transportation selection behaviors also had a statistically significant negative impact on the recovery of tourism (β = −0.220, p < 0.01). The findings showed that attitudes toward tourism policies mediated the effect between COVID-19 and tourism intentions. This study has important policy implications for the sustainable recovery of the tourism industry and for making it resilient against future crises.
Evaluating the Economic Feasibility of Plant Factory Scenarios That Produce Biomass for Biorefining Processes
Authors: Pesch Heino , Louw Louis
Published: January 2023

The use of a plant factory is typically associated with the cultivation of edible biomass for local markets within the urban environment and leads to economic feasibility being evaluated in this context. This paper explored the use of plant factories to produce biomass and value-added compounds for the biorefining industry to help frame the debate regarding the expansion of plant factory applicability to the greater biorefining value chain. Information regarding plant factory technology, crop selection for biorefining markets, and the industrial integration potential of plant factories was used to evaluate the economic feasibility of theoretical plant factory scenarios. From these scenarios, it was shown that plant factories showed economic feasibility while serving the food market and had significant potential in the biopharmaceutical market when accumulating adequate levels of biopharmaceutical products within the plants grown in the plant factories. These results suggested economic feasibility beyond the food market by selecting appropriate crops, based on plant factory and end-user market demands, and value-added compounds which could be accumulated in economically viable quantities.
Accumulation of Heavy Metals, Yield and Nutritional Composition of Potato (Solanum tuberosum L.) Cultivars Irrigated with Fly-Ash-Treated Acid Mine Drainage
Authors: Raletsena Maropeng Vellry , Munyai Rabelani , Mongalo Nkoana Ishmael
Published: January 2023

This study compared the responses of two potato cultivars, Fianna and Lady rosetta (both determinate and indeterminate), to irrigation with various fly ash: acid mine drainage (AMD) amelioration ratios in two separate seasons. In order to calculate crude protein (CP), the nutritional composition (NC) of potato tuber samples was examined using the combustion method. The results showed that the NC of plants irrigated with AMD mixed with fly ash was significantly affected differently at harvest. In plants exposed to 75% AMD irrigation, the mean moisture content of Lady rosetta was reduced by twice more compared to Fianna. Concisely, our study has demonstrated that irrigation with AMD mixed with fly ash can promote plant growth of potato cultivars and reduce their crude protein (CP) by approximately 4% when irrigated with both 50% FA: AMD and untreated AMD (100% AMD) ratio being applied; nonetheless, it was decreased by 1% when watered with 75% FA: AMD treatment, lipids content, carbohydrates, fresh tuber weight and dry tuber weight. On average, plants from both cultivars irrigated with the 75% AMD had higher fresh tuber weight (FTW), higher dry tuber weight (DTW) and carbohydrates compared to the controls. Furthermore, Lady rosetta cultivar exhibited higher carbohydrate and crude fiber compared to the control at 75 and 50% AMD treatments, respectively. Use of AMD in agriculture is likely to reduce its devastating environmental conditions and assist in irrigation of food crops, thereby alleviating both water and food shortages. Last, all the FA-AMD-treated potatoes had Pb concentrations that were below the minimum standard limits, and this proves that FA was able to adsorb the Pb ions in the tuber samples. Importantly, fly ash reduces a metal’s concentration in AMD. However, presence of heavy metals in such potatoes needs to be explored. It is also important to relate the possible metal intake relative to the standards by World Health Organization (WHO).
Measuring the Bilateral Energy Security Cooperation Sustainability between China and Its Neighboring Countries Based on the National Energy Security Level
Authors: Yang Zijie , Huang Dong , Wang Yanzhen
Published: January 2023

Strengthening bilateral energy security cooperation is crucial in the process of fostering the sustainable growth of China and neighboring Asian nations. Using data chosen from China and 25 adjacent nations between 2010 and 2019, this article first assesses the level of energy security using the TOPSIS (Technique of Ranking Preference with Similarity to Ideal Solution) and GRA (Grey Relational Analysis) methodologies. Additionally, based on the level of energy security, this article analyzes the coupling degree of bilateral capacity structure and, in the end, suggests a theoretical model to assess the stability of bilateral energy security cooperation. The findings demonstrate that China and its neighboring countries share the energy trilemma, which is the inability of these nations to simultaneously achieve the needs of energy supply security, energy justice, and environmental sustainability. The report also makes the case that Thailand, Kazakhstan, and the Philippines all have theoretically stronger sustainability of bilateral energy security cooperation with China. This study also offers some recommendations for improving bilateral collaboration between China and its neighbors on energy security.
Optimal Return Policy of Competitive Retailers’ Pre-Sale Products Based on Strategic Consumer Behavior
Authors: Wu Dan , Lu Xinxin
Published: January 2023

Because of incomplete information on pre-sold products, consumers face uncertainty about the value of what they have purchased, which leads to a mismatch between supply and demand and a large number of returns. By developing appropriate return policies and effectively managing and handling consumer returns, retailers can not only reduce waste but also ensure better resource utilization, which is essential for sustainable development. In this paper, we analyze the full-refund and full-and-freight-refund policies of retailers and develop a game model based on binary competition for selecting the optimal return policy for the pre-sold products of two retailers. The study shows that when both retailers have low capacity, there is no pre-sale stage. However, when their combined capacity is high and exceeds the demand of non-strategic consumers, equilibrium depends on their combined capacity and the proportion of strategic consumers who choose to keep the pre-purchased product under both return policies. When the number of strategic consumers who retain pre-order products is low under the full-refund policy and both retailers have moderate capacity, equilibrium is achieved when an asymmetric return policy is followed rather than a symmetric return policy. Specifically, when the percentage of strategic consumers who keep their reserved products under the product return strategy is small and the capacity of the two retailers is moderate, the maximum benefit is achieved if one of the retailers adopts the policy of a full refund and the other adopts the policy of a full-and-freight refund. Otherwise, when one retailer adopts the policy of a full refund or a full-and-freight refund, its competitor should adopt the same strategy to gain maximum revenue. The research on retailers’ pre-sale and return strategies in this paper helps to optimize the operational strategies and operational processes of e-retailers, further improve the management and decision making of their joint pre-sale and return strategies, and help optimize retailers’ profits.
The Mediating Effect of Organizational Justice Perception on the Relationship between Ethical Leadership and Workplace Deviant Behaviors
Authors: Tufan Cenk , Namal Mete Kaan , Arpat Bulent , Yesil Yeliz , Mert Ibrahim Sani
Published: January 2023

The present study examines whether there is a relationship between the ethical leadership (EL), organizational justice perception (OJP), and deviant behaviors (WDB) of customs officers working at international airports in Turkey, and aims to determine whether OJP has a mediating effect on the relationship between EL and WDB. For this purpose, the study was conducted on the 487 customs officers working at 28 airports open to international flights in Turkey. In this study, which was carried out with the quantitative method, the obtained data were analyzed with structural equation modeling (SEM). The results showed that EL had a direct and significant effect on OJP. On the other hand, OJP had a negative direct, and significant effect on deviant workplace behaviors. Moreover, there was no direct and consequential effect of EL on WDB. In addition, EL indirectly affected WDB through OJP, and the OJP had a full mediation effect between these two variables. The findings were interpreted by using social exchange theory. This study develops our understanding of the organization-level antecedents of WDB and mediating role of OJP as the social mechanism of EL. In the Conclusion section of the study, some theoretical conclusions are discussed, and recommendations are offered for customs managers.
Solid and Liquid Phases of Anaerobic Digestate for Sustainable Use of Agricultural Soil
Authors: Slepetiene Alvyra , Ceseviciene Jurgita , Amaleviciute-Volunge Kristina , Mankeviciene Audrone , Parasotas Irmantas , Skersiene Aida , Jurgutis Linas , Volungevicius Jonas , Veteikis Darijus , Mockeviciene Ieva
Published: January 2023

Given the growth in the number of biogas power plants and the increase in the generation of waste from energy production, it is relevant to study the sustainable nature of this waste. Digestate is a product of the anaerobic digestion process, and is a valuable bio-fertilizer containing organic matter and nutrients necessary for agricultural plants’ growth. The study showed that different rates of liquid and solid phases of anaerobic digestate influenced the contents of carbon and nitrogen in genetically young soil in alluvial deposits—Fluvisol. The application of solid digestate (SD) considerably increased soil organic carbon content (SOC) in the 0–10 cm soil layer; however, SOC did not reach the 20–30 cm layer. Liquid digestate (LD) significantly increased SOC in the deeper layers. The levels of mineral nitrogen (Nmin) and water extractable organic carbon (WEOC) increased in the 0–10 cm soil layer soon after fertilization with LD and SD. The mobile components of the soil (Nmin and WEOC) were characterized by high variability during the growing season. Within the 2-month period, their concentrations decreased drastically and were close to those of unfertilized soil. The research indicates that anaerobic digestate had a greater effect on mobile forms of carbon and nitrogen in the soil than on their total amounts.
Examining Challenges in Complying with the Principles of Sustainability for the Design of Urban Bridges in Ethiopia
Authors: Hailemariam Leule M. , Nuramo Denamo A.
Published: January 2023

Existential issues obstruct the practice of incorporating sustainability concepts, which is the holistic consideration of urban bridge design factors. Bridge infrastructure is considered a connecting structure for separated highways and railways. The case for ensuring the safe mobility of people and goods across obstacles from one urban corner to another is viewed as an essential component of transportation infrastructure. The design and provision of urban bridges to attain sustainability are associated with tremendous challenges because of a lack of awareness and existential issues and obstacles. The problem in the practice of urban bridge design in Ethiopia is indicated as being “traditional” in delivery, with a lack of accommodation for many essential components of sustainable design. Therefore, a change in thinking is needed to address sustainability. The question of how designers could make design practice sustainable is complicated by multiple challenges. In this research, we used a survey questionnaire to collect the opinions of design professionals. Principal component analysis was employed to explore the major gaps in sustainable urban bridge design practice. A lack of sustainable design impact; sustainability awareness; design codes, practices, and standards that consider sustainability criteria; working guiding protocols and frameworks; and support for sustainability practice were identified as major challenges. Addressing the design problem requires a mechanism to consider the challenges through the defined participation of the designer, client, and public during rule setting, monitoring, and evaluation. Sustainability rating tools must also be deployed to evaluate and quantify the performance of urban bridges.