bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing the Global Drivers of Sustained Economic Development: The Role of Trade Openness, Financial Development, and FDI
Authors: Radmehr Riza , Ali Ernest Baba , Shayanmehr Samira , Saghaian Sayed , Darbandi Elham , Agbozo Ebenezer , Sarkodie Samuel Asumadu
Published: October 2022

Achieving economic development is one of the most important economic goals of every country. Identifying the determinants of economic growth, is a useful tool for adopting appropriate economic policies. This study, therefore, empirically examines the impact of trade openness, foreign direct investment, and financial development on economic growth, across 62 countries over the period 1995–2016. These countries are divided into two groups: low-income and high-income countries. We employ the pooled mean group (PMG), mean group (MG), and dynamic fixed effect (DFE) estimation techniques on the cross-country panel data. The findings show a positive long run association between trade openness, foreign direct investment (FDI), financial development, labor, government expenditure, and economic growth in low-income countries, with a positive and negative short run effect from capital and government expenditures, respectively. For high-income countries, a positive long run association between trade openness, FDI, capital, and economic growth exist. The short run estimates indicate a positive effect on trade openness and capital as well as a negative effect on government expenditure. Our study shows that the adoption of policies that improves access to skilled labor and international trade, affect the attainment of a sustainable economic development.
The Relationship between Perceived Organizational Support and the Intentions of International Students in Hungary to Stay in Hungary: The Role of Conflict Climate and Intercultural Adjustment
Authors: Rudnák Ildikó , Alshaabani Ayman , Wu Jingjing
Published: October 2022

In recent years, Hungary has seen a dramatic increase in the number of international students, particularly since the government launched a scholarship program to encourage more talented overseas students to study at Hungarian universities. The mobilities of these students have notably increased their participation in part-time jobs or internships in the host country. These work experiences could broaden the perspectives of international students regarding the organizational operation modes in the host country’s firms; moreover, they may impact their ability to interculturally adjust to the host country’s culture, clarifying their intentions to stay or not. This article attempts to find out how perceived organizational support (POS) affects international students’ plans to stay and work abroad (IS), as well as what roles conflict management climate (CMC) and intercultural adjustment (IA) play in this relationship. A linear snowball sampling method was used to collect the data. The study’s sample was taken from international students in Hungary with part-time jobs or internships. A self-administrated questionnaire was distributed to the participants through online channels. The PLS-SEM method was used to empirically test the proposed hypothesis. Later, a total of 433 valid answers were used for the analysis. The results reveal that POS positively and significantly affects CMC and IA but has no significant effect on IS. Moreover, IA is the only factor that has a direct impact on IS and it shows a significant mediating impact on the relationship between POS and IS. The findings contribute to the success of Hungarian organizations in retaining talented young (or soon-to-graduate) employees in their companies by encouraging their intentions to stay in the country.
When Do Climate Services Achieve Societal Impact? Evaluations of Actionable Climate Adaptation Science
Authors: Bamzai-Dodson Aparna , McPherson Renee
Published: October 2022

To cope with complex environmental impacts in a changing climate, researchers are increasingly being asked to produce science that can directly support policy and decision making. To achieve such societal impact, scientists are using climate services to engage directly with stakeholders to better understand their needs and inform knowledge production. However, the wide variety of climate-services outcomes—ranging from establishing collegial relationships with stakeholders to obtaining specific information for inclusion into a pre-existing decision process—do not directly connect to traditional methods of measuring scientific impact (e.g., publication citations, journal impact factor). In this paper, we describe how concepts from the discipline of evaluation can be used to examine the societal impacts of climate services. We also present a case study from climate impacts and adaptation research to test a scalable evaluation approach. Those who conduct research for the purposes of climate services and those who fund applied climate research would benefit from evaluation from the beginning of project development. Doing so will help ensure that the approach, data collection, and data analysis are appropriately conceived and executed.
Extensible Metaverse Implication for a Smart Tourism City
Authors: Suanpang Pannee , Niamsorn Chawalin , Pothipassa Pattanaphong , Chunhapataragul Thinnagorn , Netwong Titiya , Jermsittiparsert Kittisak
Published: October 2022

The metaverse is an innovation that has created the recent phenomenon of new tourism experiences from a virtual reality of a smart tourism destination. However, the existing metaverse platform demonstrated that the technology is still difficult to develop, as the service provider did not disclose the internal mechanisms to developers, and it was a closed system, which could not use or share the user’s data across platforms. The aim of this paper was to design and develop an open metaverse platform called the “extensible metaverse”, which would allow new developers to independently develop the capabilities of the metaverse system. The acquisition of this new technology was conducted through requirements analysis, then the analysis and design of the new system architecture, followed by the implementation, and the evaluation of the system by the users. The results found that the extended metaverse was divided into three tiers that created labels, characters, and virtual objects. Furthermore, the linking tier combined the 3D elements, and the deployment tier compiled the results of the link to use all three parts by using the Blender program, Godot Engine, and PHP + WebGL as their respective key mechanisms. This system was tested in Suphan Buri province, Thailand, which was evaluated by 428 users. The results of the metaverse satisfaction, created tourism experience, and overall satisfaction of the variation of the satisfaction of using the metaverse were 86.0%, 79.7%, and 92.9%, respectively. The relative Chi-square (χ2/df) of 1.253 indicated that the model was suitable. The comparative fit index (CFI) was 0.984, the goodness-of-fit index (GFI) was 0.998, and the model based on the research hypothesis was consistent with the empirical data. The root mean square error of approximation (RMSEA) was 0.024. In conclusion, the extended metaverse is more flexible than other platforms and also creates the user’s satisfaction and tourism experience in the smart destination to support sustainable tourism.
Experimental Investigations of Flow Boiling Heat Transfer under Near-Critical Pressure for Selected Working Fluids
Authors: Butrymowicz Dariusz , Śmierciew Kamil , Karwacki Jarosław , Borsukiewicz Aleksandra , Gagan Jerzy
Published: October 2022

This paper deals with experimental investigations of flow boiling in tubular ducts of selected refrigerants—R134a, R507A, and R600a—under near-critical pressures. Near-critical boiling is characterised by low specific enthalpy of evaporation. The positive effect of this feature is the fact that only a small amount of heat consumed by Organic Rankine Cycles is at a constant temperature. This allows a lower terminal temperature of the heating fluid and more effective utilisation of heat sources, especially of low-grade heat sources. The experimental investigations covered a heat flux density of 0.4 to 10 kW/m2 and a mass velocity of 60 to 200 kg/(m2·s). The results of the experimental data were compared to the modified heat transfer correlation of Gungor and Winterton, which provices the best fit for the obtained experimental data. The maximum heat transfer coefficient occurred at the two-phase quality—approximately 0.4 for all the tested fluids under high pressure conditions—which may be thought of as a characteristic feature of the boiling process under near-critical conditions. A modified Gungor–Winterton correlation improves prediction accuracy, especially under the lowest (up to 3 kW/m2) and highest (over 7 kW/m2) heat flux densities for all the tested fluids.
Integrating Indigenous Knowledge with MCDA in the GIS Environment to Determine Site Potential for Water Harvesting in Wadi Hammad Basin in Jordan
Authors: Al-Shabeeb Abdel Rahman , Hamdan Ibraheem , Al Amoush Hani , Al-Adamat Rida , Al-Fugara A’kif
Published: October 2022

The significance of water harvesting in Wadi Hammad basin lies in the fact that the Jordanian government encourages the cultivation of vegetables, wheat, and barley in the country in an effort to improve food security in Jordan and create job opportunities for young people in the agricultural sector. Water harvesting in this basin will augment the water resources used for plant production and livestock watering by flash floods that involve large quantities of runoff. This study aimed to identify the best locations for water harvesting in the Wadi Hammad basin in Jordan via a Multi-Criterion Decision Analysis (MCDA) and indigenous knowledge. This study focused on consulting with indigenous knowledge where they provided information on the study area for water-collecting sites that have been used for years to provide water. In this study, site selection was based on six criteria that had been determined through a review of related literature (drainage density, rainfall depth, lineament density, soil clay content, geology, and slope). Following MCDA analysis, a water-harvesting suitability map was created. The final water-harvesting map uncovered that a large part of the basin (66.53%) has high to very high potential for water harvesting. The technique of water harvesting was subdued to statistical analysis, sensitivity analysis, and the map removal test. This study demonstrates that the selection of relevant water harvesting locations is a lengthy method that needs consultation with indigenous knowledge and the use of MCDA in the GIS environment. The study results, in general, and the final map, in particular, show the good relationship between the sites defined by the use of MCDA and the site suitability for water harvesting that was specified based on indigenous knowledge. Finally, the results of this study, which integrated indigenous knowledge with MCDA, may be employed to help in effective planning for water resource management to warrant the sustainable development of water in Jordan.
A Novel Multimodal Species Distribution Model Fusing Remote Sensing Images and Environmental Features
Authors: Zhang Xiaojuan , Zhou Yongxiu , Peng Peihao , Wang Guoyan
Published: October 2022

Species distribution models (SDMs) are critical in conservation decision-making and ecological or biogeographical inference. Accurately predicting species distribution can facilitate resource monitoring and management for sustainable regional development. Currently, species distribution models usually use a single source of information as input for the model. To determine a solution to the lack of accuracy of the species distribution model with a single information source, we propose a multimodal species distribution model that can input multiple information sources simultaneously. We used ResNet50 and Transformer network structures as the backbone for multimodal data modeling. The model’s accuracy was tested using the GEOLIFE2020 dataset, and our model’s accuracy is state-of-the-art (SOTA). We found that the prediction accuracy of the multimodal species distribution model with multiple data sources of remote sensing images, environmental variables, and latitude and longitude information as inputs (29.56%) was higher than that of the model with only remote sensing images or environmental variables as inputs (25.72% and 21.68%, respectively). We also found that using a Transformer network structure to fuse data from multiple sources can significantly improve the accuracy of multimodal models. We present a novel multimodal model that fuses multiple sources of information as input for species distribution prediction to advance the research progress of multimodal models in the field of ecology.
Performance Assessment of Sewer Networks under Different Blockage Situations Using Internet-of-Things-Based Technologies
Authors: Alshami Ahmad , Elsayed Moustafa , Mohandes Saeed Reza , Kineber Ahmed Farouk , Zayed Tarek , Alyanbaawi Ashraf , Hamed Mohammed Magdy
Published: October 2022

This study aims to model the performance of sewage networks under diverse blockage situations in terms of overflow occurrence using internet-of-things-based technologies in Hong Kong. To this end, a multi-stage methodological approach is employed, starting from collecting required data using smart sensors, utilizing novel data mining techniques, and using a case study simulation. From the results obtained, the following conclusions are drawn: (1) several sites under investigation are imbued with partial blockages, (2) the overall performance of the sewer network has a nonlinear relationship with the blockages in terms of the remaining time to overflow, (3) in cases of complete blockages, the sewer only takes few minutes to reach the manhole cover level that causes the system to experience overflow, and (4) cleaning work significantly improve the performance of the sewage network by 86%. The outcomes of this study provide a solid foundation for the concerned environmental engineers and decision-makers towards reducing the magnitude of sewer overflow and improving different aspects of our environment.
Socially Sustainable Accessibility to Goods and Services in the Metropolitan Area of Concepción, Chile, Post-COVID-19
Authors: Núñez Francisco , Albornoz Elías , Gutiérrez Mariella , Zumelzu Antonio
Published: October 2022

The COVID-19 pandemic affected people’s mobility and access to urban activities. When the contagion was at a community level, quarantine measures were taken, causing population immobility. The lack of alternatives significantly altered the satisfaction of people’s basic needs. The objective of this article was to explore and generate real accessibility indicators for goods and services, in addition to the levels of spatial satisfaction of the population, at a regional level in the metropolitan area of Concepción, Chile. To focus on citizens’ social welfare, social geomarketing was applied as the method, obtaining the delimitation of accessibility areas for goods and services through population surveys and the delimited spatial decelerated satisfaction. Pre-pandemic and during-pandemic situations were evaluated. The results showed an improvement in the delimitation of accessibility areas of goods and services, as the citizens’ preferences as consumers were included, revealing an increment during the pandemic, especially in the food typology. In the same way, the existence of geospatial satisfaction and its increment under the pandemic context when accessing the diverse facilities that offer these kinds of goods was confirmed. In conclusion, the satisfaction areas were useful for analyzing urban form designs and focusing them to promote revitalization, as well as for inclusive and sustainable urbanization and proactive measures to make urban areas more resilient to natural or human risks, incorporating the role of geospatial tools for promoting sustainable urban development.
The Relationships between Government Subsidies, Innovation Input, and Innovation Output: Evidence from the New Generation of Information Technology Industry in China
Authors: Wang Haiyan , Sawur Yasinjan
Published: October 2022

Strategic emerging industries are gradually becoming the main driving force promoting the development of the national economy. As one of the strategic emerging industries, the New Generation of Information Technology Companies (NGITCs) play a crucial role in accelerating the integration of information and industrialization and in promoting the information processing of the whole society, with the support of various policy tools, such as government subsidy policy. Based on the panel data on the Chinese A-Stock Market (Shanghai and Shenzhen Stock Markets) from 2011 to 2020, this article empirically studies the correlations between the government subsidies, innovation input, and innovation output of the NGITCs. In the first step, we check the immediate effect and delayed effect of the government subsidies on the innovation output of the NGITCs and further test whether the ownership and geographical locations of the NGITCs have moderating effects between the government subsidies and innovation output of the NGITCs. In the second step, we investigate the government subsidies’ immediate impact and the delayed effect on the innovation input of the NGITCs. In the third step, we examine the innovation input’s immediate effect and the delayed effect on the innovation output of the NGITCs. In the last step, we analyze the mediating role of innovation input between government subsidies and the innovation output of the NGITCs. Our findings indicate that government subsidies positively promote the innovation output of the NGITCs and have a two-year-delayed effect. However, the government subsidies can most significantly increase the innovation output of the non-state-owned enterprises and those in the coastal areas. The government subsidies enhance the innovation input and have a three-year positive delayed effect. Innovation input positively impacts innovation output and also has a two-year-delayed effect. Our results also show that innovation input presents a partial mediating effect between government subsidies and the innovation output of the NGITCs.
Environmental Taxation Assessment on Clean Technologies Reducing Carbon Emissions Cost-Effectively
Authors: Koval Viktor , Laktionova Olga , Udovychenko Iryna , Olczak Piotr , Palii Svitlana , Prystupa Liudmyla
Published: October 2022

Anthropogenic emissions increase the concentration of greenhouse gases, including carbon dioxide, which necessitates the promotion of environmental protection as one of the most urgent tasks of European environmental policy. The reduction of greenhouse gas emissions and the development of clean technologies in production also depends on the impact of environmental taxation; in this regard, a methodology for analyzing its impact and assessment on the development of eco-friendly technologies is proposed. An analysis of environmental tax revenues to the budgets of the EU countries revealed the insufficiency of environmental revenues to cover the costs of environmental protection from the damage caused by greenhouse gas emissions, which requires the transformation of the system of fiscal mechanisms. The total receipts of all environmental taxes in the EU budget for the period 2000–2020 increased by 53%, and the receipts from taxes on greenhouse gas emissions into the atmosphere increased by 71% in the EU budget, with a tax rate increase of 1.5-fold over this period. The application of the proposed methodology made it possible to determine, on the basis of the correlation coefficient, a high connection strength of +0.971 for the period 2000–2020 between the receipts of the environmental tax for greenhouse gas emissions into the atmosphere and the total values of all environmental taxes, as well as a fairly strong feedback of +0.913 from the receipts of the environmental tax to the EU budget with gross domestic product. Therefore, it is proposed to use differentiated environmental tax rates for different stages of the development of clean technologies.
The Potential Role of Hybrid Renewable Energy System for Grid Intermittency Problem: A Techno-Economic Optimisation and Comparative Analysis
Authors: Bakht Muhammad Paend , Salam Zainal , Gul Mehr , Anjum Waqas , Kamaruddin Mohamad Anuar , Khan Nuzhat , Bukar Abba Lawan
Published: October 2022

The renewed interest for power generation using renewables due to global trends provides an opportunity to rethink the approach to address the old yet existing load shedding problem. In the literature, limited studies are available that address the load shedding problem using a hybrid renewable energy system. This paper aims to fill this gap by proposing a techno-economic optimisation of a hybrid renewable energy system to mitigate the effect of load shedding at the distribution level. The proposed system in this work is configured using a photovoltaic array, wind turbines, an energy storage unit (of batteries), and a diesel generator system. The proposed system is equipped with a rule-based energy management scheme to ensure efficient utilisation and scheduling of the sources. The sizes of the photovoltaic array, wind turbine unit, and the batteries are optimised via the grasshopper optimisation algorithm based on the multi-criterion decision that includes loss of power supply probability, levelised cost of electricity, and payback period. The results for the actual case study in Quetta, Pakistan, show that the optimum sizes of the photovoltaic array, wind turbines, and the batteries are 35.75 kW, 10 kW, and 28.8 kWh, respectively. The sizes are based on the minimum values of levelised cost of electricity (6.64 cents/kWh), loss of power supply probability (0.0092), and payback period (7.4 years). These results are compared with conventional methods (generators, uninterruptible power supply, and a combined system of generator and uninterruptible power supply system) commonly used to deal with the load shedding problem. The results show that the renewable based hybrid system is a reliable and cost-effective option to address grid intermittency problem.
Design and Practice of Deep Foundation Pits for Large Storage Ponds in Complex Environments
Authors: Lin Zhihang , Xie Senhua , Xia Chang , Dou Hongqiang
Published: October 2022

Storage ponds (storm detention ponds) are an important part of sponge city construction, and the construction process involves deep and large pit excavation in a complex and sensitive environment. Combined with a large storage pond pit engineering design and practice process, in this study, the complex environment of the design of the large storage pond pit method has been explored, using the field measurement data to test the support design of the deformation control effect and to summarize the practice of the key, difficult points of treatment measures. The results show that the procedure adopted in this project is a successful practice for using the pit characteristics, and the special treatment carried out for addressing the difficult points can effectively control the deformation, thus achieving waterproofing and deformation requirements of large-capacity water storage ponds. The design of the support is very effective in controlling the duration and deformation. The cover-cut excavation method, in combination with the support system, is highly effective in deformation control, whereas the open-cut excavation method is more likely to shorten the duration. However, there is an uncertainty in the deformation control during the removal of temporary structures in this method. The deformation characteristics of the pit enclosure and the surrounding ground surface are within the design requirements. The horizontal displacement of the wall and the surrounding settlement are mainly developed during the stage of dismantling the temporary support structure after excavating the first basement level and during the construction of the first and second slabs. The other stages benefit from the strong integrity of the support system, which can efficiently control the deformation. The treatment measures, support types, and component connection methods summarized in this article can provide a reference for the construction of similar underground storage ponds.
Cellular Automata Model for Analysis and Optimization of Traffic Emission at Signalized Intersection
Authors: Marzoug Rachid , Lakouari Noureddine , Pérez Cruz José Roberto , Vega Gómez Carlos Jesahel
Published: October 2022

Traffic emission is one of the most severe issues in our modern societies. A large part of emissions occurs in cities and especially at intersections due to the high interactions between vehicles. In this paper, we proposed a cellular automata model to investigate the different traffic emissions (CO2, PM, VOC, and NOx) and speeds at a two-lane signalized intersection. The model is designed to analyze the effects of signalization by isolating the parameters involved in vehicle-vehicle interactions (lane changing, speed, density, and traffic heterogeneity). It was found that the traffic emission increases (decreases) with the increasing of green lights duration (Tg) at low (high) values of vehicles injection rate (α). Moreover, by taking CO2 as the order parameter, the phase diagram shows that the system can be in four different phases (I, II, III, and IV) depending on α and Tg. The transition from phase II (I) to phase III (II) is second order, while the transition from phase II to phase IV is first order. To reduce the traffic emission and enhance the speed, two strategies were proposed. Simulation results show a maximum reduction of 13.6% in vehicles’ emissions and an increase of 9.5% in the mean speed when adopting self-organizing intersection (second strategy) at low and intermediate α. However, the first strategy enhances the mean speed up to 28.8% and reduces the traffic emissions by 3.6% at high α. Therefore, the combination of both strategies is recommended to promote the traffic efficiency in all traffic states. Finally, the model results illustrate that the system shows low traffic emission adopting symmetric lane-changing rules than asymmetric rules.
A Hybrid DNN Model for Travel Time Estimation from Spatio-Temporal Features
Authors: Rajagopal Balaji Ganesh , Kumar Manish , Samui Pijush , Kaloop Mosbeh R. , Shahdah Usama Elrawy
Published: October 2022

Due to recent advances in the Vehicular Internet of Things (VIoT), a large volume of traffic trajectory data has been generated. The trajectory data is highly unstructured and pre-processing it is a very cumbersome task, due to the complexity of the traffic data. However, the accuracy of traffic flow learning models depends on the quantity and quality of preprocessed data. Hence, there is a significant gap between the size and quality of benchmarked traffic datasets and the respective learning models. Additionally, generating a custom traffic dataset with required feature points in a constrained environment is very difficult. This research aims to harness the power of the deep learning hybrid model with datasets that have fewer feature points. Therefore, a hybrid deep learning model that extracts the optimal feature points from the existing dataset using a stacked autoencoder is presented. Handcrafted feature points are fed into the hybrid deep neural network to predict the travel path and travel time between two geographic points. The chengdu1 and chengdu2 standard reference datasets are used to realize our hypothesis of the evolution of a hybrid deep neural network with minimal feature points. The hybrid model includes the graph neural networks (GNN) and the residual networks (ResNet) preceded by the stacked autoencoder (SAE). This hybrid model simultaneously learns the temporal and spatial characteristics of the traffic data. Temporal feature points are optimally reduced using Stacked Autoencoder to improve the accuracy of the deep neural network. The proposed GNN + Resnet model performance was compared to models in the literature using root mean square error (RMSE) loss, mean absolute error (MAE) and mean absolute percentile error (MAPE). The proposed model was found to perform better by improving the travel time prediction loss on chengdu1 and chengdu2 datasets. An in-depth comprehension of the proposed GNN + Resnet model for predicting travel time during peak and off-peak periods is also presented. The model’s RMSE loss was improved up to 22.59% for peak hours traffic data and up to 11.05% for off-peak hours traffic data in the chengdu1 dataset.
Subsistence Farmers’ Understanding of the Effects of Indirect Impacts of Human Wildlife Conflict on Their Psychosocial Well-Being in Bhutan
Authors: Yeshey  , Ford Rebecca M. , Keenan Rodney J. , Nitschke Craig R.
Published: October 2022

Indirect impacts of Human Wildlife Conflict (HWC) are largely ignored, poorly understood, and scantly reported in the literature on HWC. Subsistence farmers in the Himalayan kingdom of Bhutan experience an increasing intensification of HWC impacts. Working across four districts representing different geographic regions of the country, we explored the perceived indirect impacts of HWC and how they affect the well-being and happiness of subsistence farmers using qualitative interviews (n = 48) and focus group discussions (n = 8). We conducted a qualitative thematic analysis. Based on respondent’s explanations, we coded the data according to effect of indirect impacts on human, social, financial, physical, natural, and psychological capitals. Mental distress, constant worries about food insecurity, fears for physical safety, frustration of movement restriction due to fear of being attack by wildlife, feelings of economic insecurity and anger over loss of crop and livestock due to wild predators affect the psychological health and well-being of research participants. Vulnerabilities related to gender and wealth status further deepen the effect of indirect impacts. Policies designed to address HWC should incorporate an understanding of the effects of indirect impacts of HWC and should focus on female-headed and poor households to reduce the negative effects of wildlife impacts.
The Relationship between Practitioners’ Trust in the City and Their Adoption of the Virtual Convention Platform—A Case Study of Virtual SEOUL 2.0
Authors: Ha Myunghee , Lee Woojin , Park Jihye
Published: October 2022

Many conferences, exhibitions, and trade shows planned in the early days of COVID-19 were canceled or postponed, and only some events were able to convert quickly to virtual events by using existing virtual conference and exhibition platforms. This study examined whether the virtual convention platform, combined with advanced technologies, can enhance the trust in Seoul city and further influence the attitude and intention to use Virtual SEOUL 2.0. Furthermore, this study applies the Extended Technology Acceptance Model (ETAM) framework to identify external variables that affect perceived usefulness and ease of use and describes how practitioners embrace the new convention technology of Virtual SEOUL 2.0. The 300 usable survey data were collected and used to identify the proposed hypothesized relationship of seven latent constructs. The hypothesized relationships were tested by structural equation modeling (SEM). The analytic results confirmed the proposed hypothesized relationship among these seven constructs (Personal innovativeness, Perceived ease of use, Perceived usefulness, Perceived trust of Seoul, Perceived enjoyment, Attitude toward using Virtual SEOUL 2.0, and Intention to use Virtual SEOUL 2.0). More interestingly, the study’s findings provide that perceived trust in a smart city is an essential factor that directly or indirectly enhances users’ intention to use technology.
Research on Archaeology and Digital Restoration of Costumes in DaoLian Painting
Authors: Zhu Chun , Liu Kaixuan , Li Xiaoning , Zeng Qingwei , Wang Ruolin , Zhang Bin , Lü Zhao , Chen Chen , Xin Xiaoyu , Wu Yunlong , Zhang Junjie , Zeng Xianyi
Published: October 2022

Costume restoration is one of the important ways to study costume history and culture. The purpose of this paper is to show the characteristics of Chinese costumes more than 1000 years ago, through the research on the costume in the famous ancient Chinese painting DaoLian painting, and provide strong technical support for the research of Chinese ancient costume culture. DaoLian painting is the work of Xuan Zhang, a famous painter in Tang dynasty (618–907), China. From the perspective of clothing engineering, we analyzed the characteristics of costume style, color, and pattern and used the virtual fitting technology to realize digital restoration of the costume of 12 characters in the painting. The results show that it is a practical method to study costume from paintings. The colors, patterns, and character gestures in the paintings provide sufficient information for the archaeology and restoration of ancient costumes. The research results of this paper can provide a new idea for costume archaeology and a reference for modern fashion design and materials for the VR Museum of Ancient Costumes.
Quarterly Instability Analysis of Injury Severities in Truck Crashes
Authors: Wei Fulu , Dong Danping , Liu Pan , Guo Yongqing , Wang Zhenyu , Li Qingyin
Published: October 2022

The impact of trucks on road traffic safety has been extensively studied, but the factors influencing truck crash injury severity have not yet been examined from the quarterly perspective. Crash data for Shandong Province in China for 10 years (2012–2021) were reviewed to investigate the transferability of the determinants of the severity of truck crash injuries in four quarters. Three injury severity levels were considered and a random parameters logit model (RPL) considering the heterogeneity of means and variances was constructed to assess the factors affecting the severity of crash injury. The significant variables were explored from the influencing factors of driver, vehicle, crash type, road, environment, and temporal characteristics. A likelihood ratio test was employed to assess the transferability of the crash model over four quarters, and we used marginal effects to analyze the stability of the influencing factors. The results indicated that there was instability among the four quarterly variables that had to be modeled separately. There were also some variables, such as heavy vehicle and multiple-vehicle crashes, that simultaneously affected the severity of truck crash injuries across the four quarters, but the degree of impact was different. The results could enable engineers and policy makers to better formulate management rules and propose appropriate measures according to quarterly changes.
Core Content, Experience and Enlightenment of CSR in the J-League
Authors: Zhang Shaojie , Yang Xiannan , Cai Junren
Published: October 2022

In comparison with European and American countries, the CSR of professional sports in Asian countries is affected by many factors due to differences in management system, social environment, etc. At present, the research on CSR of professional sports in Asian countries is very limited, and the practice form of CSR under this special management system has great research space and value. For booming professional sports of China, neighboring Japanese professional sports system is similar to it, and has a more advanced level of professional sports CSR development, which is worthy of further learning and reference for Chinese Super League clubs. Therefore, this study combs the historical background, core content and governance experience of CSR of J League in Japan, and puts forward valuable enlightenment for Chinese Super League clubs. The results show that the CSR of J League in Japan has distinctive characteristics and times, and the Chinese Super League clubs need to combine their own development stages, Chinese national conditions and other factors for reference.
Environmental Impact Modeling for a Small-Scale Field Test of Methane Removal by Iron Salt Aerosols
Authors: Sturtz Timothy M. , Jenkins Peter T. , de Richter Renaud
Published: October 2022

Various authors have highlighted the possible removal of methane from the atmosphere via oxidation by broad releases of iron salt aerosols in order to serve climate protection goals. This technique is known as enhanced atmospheric methane oxidation (EAMO). This study proposes and employs a modeling approach for the potential environmental impacts associated with a hypothetical small-scale field test of EAMO consisting of seeding cargo-ship exhaust plumes with iron salt aerosols. Using a sample region in the Southern Caribbean Sea as a hypothetical testing site, it provides assessments of potential impacts to air quality, human health, and the marine environment. The modeling focuses on the incremental difference between conducting the hypothetical field test and a no-action scenario. The model results are compared to ambient air standards and pertinent screening thresholds, including those associated with pertinent health risk metrics. The overall loading to the marine environment is contrasted against background rates of iron deposition to the marine surface. No significant impacts were identified in this assessment. The hypothetical atmospheric emissions of both FeCl3 and HCl that the ship’s crew may be exposed to remained below governmental guidance levels. The potential deposition of FeCl3 to the marine environment was found to be very minor in relation to the natural contributions experienced within the Southern Caribbean. Similarly, HCl deposition was assessed for potential impacts to the marine environment but was found to have no significant impact.
Spatial Distribution Characteristics and Influencing Factors of Key Rural Tourism Villages in China
Authors: Zhang Yunxing , Li Weizhen , Li Ziyang , Yang Meiyu , Zhai Feifei , Li Zhigang , Yao Heng , Li Haidong
Published: October 2022

Key villages of rural tourism have become an important carrier for the high-quality development of rural tourism. The precise identification of the spatial distribution characteristics and influencing factors of rural tourism key villages is of great value in promoting the quality upgrading of rural tourism in China and realizing the goal of rural revitalization strategy. The aim is to realize the complementary coordination and integration of urban and rural areas, promote rural transformation and increase farmers’ incomes. Taking three batches of 1199 key rural tourism villages in China as research samples, the nearest neighbor index, disequilibrium index and kernel density methods were used to reveal the spatial differentiation characteristics of key rural tourism villages and the influencing factors were further analyzed using the geographical connection rate and buffer analysis. The results showed that (1) the key villages of rural tourism demonstrated a typical agglomeration in the spatial distribution, with the Hu Huanyong line as the obvious boundary. (2) The very high-density agglomeration centers were mainly located in the Beijing–Tianjin–Hebei region and the Yangtze River Delta region, while the high-density agglomeration zones were mainly located in the North China Plain on the east side of the Taihang Mountains and the middle and lower reaches of the Yangtze River Basin. (3) The spatial distribution characteristics of hot regions were in the central and eastern regions; however, the cold regions were in the northwest and northeast regions. (4) The spatial distribution characteristics of key rural tourism villages were the result of the interaction and coupling of multiple factors. The key villages of rural tourism were mainly distributed in plain and hilly areas with dense river networks, dense populations, high levels of economic development, developed transportation and suburban areas.
PID-Based Design of Automatic Control System for a Travel Speed of the 4UM-120D Electric Leafy Vegetable Harvester
Authors: Chen Wenming , Wang Gongpu , Hu Lianglong , Yuan Jianning , Wu Wen , Bao Guocheng , Yin Zicheng
Published: October 2022

The study developed a PID-based automatic control system for travel speed based on the analysis of the entire machine structure of the 4UM-120D electric leafy vegetable harvester to enhance the operation quality of the harvester and lessen the workload of operators. Taking the system adjustment time during the process of starting and maintaining the travel speed of the harvester within ±2% of the set value as the main evaluation index, the Box–Behnken test method was applied based on the single-factor test, and the proportional coefficient Kp, the integral coefficient Ki and the initial value U of the travel motor drive voltage of the PID control algorithm were used as the test factors, and the PID-based 4UM-120D electric leafy vegetable harvester travel speed automatic control system operating parameters based on PID was studied in a three-factor, three-level experiment, and a multiple regression model of evaluation indexes on each factor was established to analyze the influence of each factor on system rapidity and to obtain the optimal operating parameters. The test findings demonstrated that the beginning value of the driving voltage of the travel motor U, the integration factor Ki, and the proportionality factor Kp had the largest to smallest influences on the system regulation time. When the initial value of the driving voltage of the travel motor was 1.81 V, the integration factor was 0.020, the proportionality factor was 0.127, the system regulation time was 2.14 s, and the relative error was 1.8% compared with the optimized value. The results of the study could provide a way to improve the operation quality of leafy vegetable harvesters and reduce the work intensity of operators.
Car-Following Model Optimization and Simulation Based on Cooperative Adaptive Cruise Control
Authors: Song Cheng-Ju , Jia Hong-Fei
Published: October 2022

This study aims to improve the desired distance adaptability of the cooperative adaptive cruise control (CACC) during car-following. In this study, the characteristics of the desired distance in different traffic flow states were analyzed. The general functional form of the desired distance in the car-following process was formulated. Then, a car-following platoon was constructed to compare the car-following effect of the platoon under different conditions, using the following speed and the lead vehicle disturbance, as the observed variable and the simulation condition, respectively. The car-following effect of the platoon under different parameters was also compared, based on the improved CACC model. The results show that the improved CACC model exhibited more advantages in car-following efficiency, it can better describe the state of the car-following queue under different traffic flow parameters and car-following behavior conditions, it has a strong anti-interference ability for the fluctuation of the car-following queue and is conducive to further improving the intelligent operation of car-following queue.
Gender Disparity in Science, Technology, Engineering, and Mathematics (STEM) Programs at Jordanian Universities
Authors: Bataineh Omar , Qablan Ahmad , Belbase Shashidhar , Takriti Rachel , Tairab Hassan
Published: October 2022

Science, Technology, Engineering, and Mathematics (STEM) has been one of the top educational priorities of several countries to promote 21st-century skills with creativity, critical thinking, collaboration, communication together with problem-solving. This study aimed to identify university students’ preference for STEM subjects and the percentage of those who earn a university degree in any STEM field. The sample consisted of 16,134 male and female students who had graduated with a degree in any of the STEM fields between 2008 and 2018. Data analysis revealed a disparity in preferences and percentages of males and females who enroll in STEM education, whilst male students prefer to pursue a degree in almost every field of engineering, female students prefer to study fields related to medical sciences, basic sciences, and mathematics. The study attributes that disparity to the job market’s hiring preferences. The study suggested certain multisectoral suggestions to address gender disparity and spark Jordanian women’s interest in pursuing STEM education.
Fuzzy Artificial Intelligence—Based Model Proposal to Forecast Student Performance and Retention Risk in Engineering Education: An Alternative for Handling with Small Data
Authors: Bressane Adriano , Spalding Marianne , Zwirn Daniel , Loureiro Anna Isabel Silva , Bankole Abayomi Oluwatobiloba , Negri Rogério Galante , de Brito Junior Irineu , Formiga Jorge Kennety Silva , Medeiros Liliam César de Castro , Pampuch Bortolozo Luana Albertani , Moruzzi Rodrigo
Published: October 2022

Understanding the key factors that play an important role in students’ performance can assist improvements in the teaching-learning process. As an alternative, artificial intelligence (AI) methods have enormous potential, facilitating a new trend in education. Despite the advances, there is an open debate on the most suitable model for machine learning applied to forecast student performance patterns. This paper addresses this gap, where a comparative analysis between AI methods was performed. As a research hypothesis, a fuzzy inference system (FIS) should provide the best accuracy in this forecast task, due to its ability to deal with uncertainties. To do so, this paper introduces a model proposal based on AI using a FIS. An online survey was carried to collect data. Filling out a self-report, respondents declare how often they use some learning strategies. In addition, we also used historical records of students’ grades and retention from the last 5 years before the COVID pandemic. Firstly, two experimental groups were composed of students with failing and passing grades, compared by the Mann-Whitney test. Secondly, an association between the ‘frequency of using learning strategies’ and ‘occurrence of failing grades’ was quantified using a logistic regression model. Then, a discriminant analysis was performed to build an Index of Student Performance Expectation (SPE). Considering the learning strategies with greater discriminating power, the fuzzy AI-based model was built using the database of historical records. The learning strategies with the most significant effect on students’ performance were lesson review (34.6%), bibliography reading (25.6%), class attendance (23.5%), and emotion control (16.3%). The fuzzy AI-based model proposal outperformed other AI methods, achieving 94.0% accuracy during training and a generalization capacity of 91.9% over the testing dataset. As a practical implication, the SPE index can be applied as a tool to support students’ planning in relation to the use of learning strategies. In turn, the AI model based on fuzzy can assist professors in identifying students at higher risk of retention, enabling preventive interventions.