bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of the Uncertainty of Interpersonal Communications on Behavioral Responses of the Participants in an Immersive Virtual Reality Experience: A Usability Study
Authors: Hajahmadi Shirin , Marfia Gustavo
Published: February 2023

Two common difficulties which people face in their daily lives are managing effective communication with others and dealing with what makes them feel uncertain. Past research highlights that the result of not being able to handle these difficulties influences people’s performance in the task at hand substantially, especially in the context of a social environment such as a workplace. Perceived uncertainty of information is a key influential factor in this regard, with effects on the quality of the information transfer between sender and receiver. Uncertainty of information can be induced into the communication system in three ways: when there is any kind of information deficit that makes the target message unclear for the receiver, when there are some requested changes that could not be predicted by the receiver, and when the content of the message is so interconnected and complex that it limits understanding. Since uncertainty is an inseparable feature of our lives, studying the effects that different levels of it have on individuals and how individuals nevertheless accomplish the tasks of daily living is of high importance. Modern technologies such as immersive virtual reality (VR) have been successful in providing effective platforms to support human behavioral and social well-being studies. In this paper, we suggest the design, development, and evaluation of an immersive VR serious game platform to study behavioral responses to the uncertain features of interpersonal communications. In addition, we report the result of a within-subject user study with 17 participants aged between 20 and 35 and their behavioral responses to two levels of uncertainty with subjective and objective measures. The results convey that the application successfully and meaningfully measured some behavioral responses related to exposure to different levels of uncertainty and overall, the participants were satisfied with the experience.
Machine Learning Model Validated to Predict Outcomes of Liver Transplantation Recipients with Hepatitis C: The Romanian National Transplant Agency Cohort Experience
Authors: Zabara Mihai Lucian , Popescu Irinel , Burlacu Alexandru , Geman Oana , Dabija Radu Adrian Crisan , Popa Iolanda Valentina , Lupascu Cristian
Published: February 2023

Background and Objectives: In the early period after liver transplantation, patients are exposed to a high rate of complications and several scores are currently available to predict adverse postoperative outcomes. However, an ideal, universally accepted and validated score to predict adverse events in liver transplant recipients with hepatitis C is lacking. Therefore, we aimed to establish and validate a machine learning (ML) model to predict short-term outcomes of hepatitis C patients who underwent liver transplantation. Materials and Methods: We conducted a retrospective observational two-center cohort study involving hepatitis C patients who underwent liver transplantation. Based on clinical and laboratory parameters, the dataset was used to train a deep-learning model for predicting short-term postoperative complications (within one month following liver transplantation). Adverse events prediction in the postoperative setting was the primary study outcome. Results: A total of 90 liver transplant recipients with hepatitis C were enrolled in the present study, 80 patients in the training cohort and ten in the validation cohort, respectively. The age range of the participants was 12–68 years, 51 (56,7%) were male, and 39 (43.3%) were female. Throughout the 85 training epochs, the model achieved a very good performance, with the accuracy ranging between 99.76% and 100%. After testing the model on the validation set, the deep-learning classifier confirmed the performance in predicting postoperative complications, achieving an accuracy of 100% on unseen data. Conclusions: We successfully developed a ML model to predict postoperative complications following liver transplantation in hepatitis C patients. The model demonstrated an excellent performance for accurate adverse event prediction. Consequently, the present study constitutes the foundation for careful and non-invasive identification of high-risk patients who might benefit from a more intensive postoperative monitoring strategy.
Hybrid-AI-Based iBeacon Indoor Positioning Cybersecurity: Attacks and Defenses
Authors: Huang Chi-Jan , Chi Cheng-Jan , Hung Wei-Tzu
Published: February 2023

iBeacon systems have been increasingly established in public areas to assist users in terms of indoor location navigation and positioning. People receive the services through the Bluetooth Low Energy (BLE) installed on their mobile phones. However, the positioning and navigation functions of an iBeacon system may be compromised when faced with cyberattacks issued by hackers. In other words, its security needs to be further considered and enhanced. This study took the iBeacon system of Taipei Main Station, the major transportation hub with daily traffic of at least three hundred thousand passengers, as an example for exploring its potential attacks and further studying the defense technologies, with the assistance of AI techniques and human participation. Our experiments demonstrate that in the early stage of iBeacon system information security planning, information security technology and a rolling coding encryption should be included, representing the best defense methods at present. In addition, we believe that the adoption of rolling coding is the most cost-effective defense. However, if the security of critical infrastructure is involved, the most secure defense method should be adopted, namely a predictable and encrypted rolling coding method.
INAM-Based Image-Adaptive 3D LUTs for Underwater Image Enhancement
Authors: Xiao Xiao , Gao Xingzhi , Hui Yilong , Jin Zhiling , Zhao Hongyu
Published: February 2023

To the best of our knowledge, applying adaptive three-dimensional lookup tables (3D LUTs) to underwater image enhancement is an unprecedented attempt. It can achieve excellent enhancement results compared to some other methods. However, in the image weight prediction process, the model uses the normalization method of Instance Normalization, which will significantly reduce the standard deviation of the features, thus degrading the performance of the network. To address this issue, we propose an Instance Normalization Adaptive Modulator (INAM) that amplifies the pixel bias by adaptively predicting modulation factors and introduce the INAM into the learning image-adaptive 3D LUTs for underwater image enhancement. The bias amplification strategy in INAM makes the edge information in the features more distinguishable. Therefore, the adaptive 3D LUTs with INAM can substantially improve the performance on underwater image enhancement. Extensive experiments are undertaken to demonstrate the effectiveness of the proposed method.
High-Quality 3D Visualization System for Light-Field Microscopy with Fine-Scale Shape Measurement through Accurate 3D Surface Data
Authors: Kwon Ki Hoon , Erdenebat Munkh-Uchral , Kim Nam , Khuderchuluun Anar , Imtiaz Shariar Md , Kim Min Young , Kwon Ki-Chul
Published: February 2023

We propose a light-field microscopy display system that provides improved image quality and realistic three-dimensional (3D) measurement information. Our approach acquires both high-resolution two-dimensional (2D) and light-field images of the specimen sequentially. We put forward a matting Laplacian-based depth estimation algorithm to obtain nearly realistic 3D surface data, allowing the calculation of depth data, which is relatively close to the actual surface, and measurement information from the light-field images of specimens. High-reliability area data of the focus measure map and spatial affinity information of the matting Laplacian are used to estimate nearly realistic depths. This process represents a reference value for the light-field microscopy depth range that was not previously available. A 3D model is regenerated by combining the depth data and the high-resolution 2D image. The element image array is rendered through a simplified direction-reversal calculation method, which depends on user interaction from the 3D model and is displayed on the 3D display device. We confirm that the proposed system increases the accuracy of depth estimation and measurement and improves the quality of visualization and 3D display images.
Positioning with GNSS and 5G: Analysis of Geometric Accuracy in Urban Scenarios
Authors: Alghisi Marianna , Biagi Ludovico
Published: February 2023

GNSS positioning in urban scenarios suffers for the scarce visibility of satellites. Integration with 5G services for positioning could improve this situation. In this paper, the digital surface models (DSMs) relevant to different urban scenarios, namely residential streets and urban canyons, are simulated around one observer in northern Italy (Milano) for one day of the year chosen as an example. The time series of the number of in-view GNSS satellites, their geometry and the derived quality indexes (position dilution of precision (PDOP)) are computed and analyzed. As expected, in urban canyons, a significant number of epochs does not provide four satellites within view, and many more epochs present really mediocre PDOPs. In residential streets, the situation is always quite fair. Different geometric configurations of 5G base stations are simulated around the observer. The availability of 5G times of arrival (ToAs) and their differences (TDoAs) is hypothesized, and the integration of these observations with GNSS pseudoranges is analyzed, again in terms of the PDOPs. In residential streets, 5G availability improves the positioning. In urban canyons, the optimal configuration of 5G base stations (five base stations around the observer) completely solves the positioning problem for all the epochs of the day. Less favorable configurations (four and three base stations) improve epochs with poor PDOPs in a GNSS-only configuration. They allow the positioning of epochs with few satellites but cannot completely replace the GNSS.
Wave Propagation Characteristics and Compaction Status of Subgrade during Vibratory Compaction
Authors: Yao Junkai , Yue Mao , Ma Hongsheng , Yang Changwei
Published: February 2023

Vibratory compaction status has a significant influence on the construction quality of subgrade engineering. This study carried out field experiments to study the propagation characteristics of the vertical vibration wave in the soil field along the traveling direction of the vibratory roller. The propagation coefficients of the peak acceleration at different positions and compacting rounds are compared in both the time and frequency domains. The compaction status is estimated in the form of dynamic modulus of deformation (Evd) obtained by plate load tests. The experiment results show that the propagation coefficient of peak acceleration is affected by the traveling speed, excitation amplitude, and frequency of the vibratory roller, as well as the compacting rounds. An exponential relationship between the wave amplitudes of the fundamental mode and higher-order modes is revealed. The amplitude of the fundamental wave is maximum at the speed of 3 km/h, whereas the amplitudes of higher-order waves have a maximum of 1.5 km/h. The influences of compaction rounds on the average value of Evd are also investigated to provide a practical reference for engineering construction.
Design of Low-Complexity Convolutional Neural Network Accelerator for Finger Vein Identification System
Authors: Chang Robert Chen-Hao , Wang Chia-Yu , Li Yen-Hsing , Chiu Cheng-Di
Published: February 2023

In the biometric field, vein identification is a vital process that is constrained by the invisibility of veins as well as other unique features. Moreover, users generally do not wish to have their personal information uploaded to the cloud, so edge computing has become popular for the sake of protecting user privacy. In this paper, we propose a low-complexity and lightweight convolutional neural network (CNN) and we design intellectual property (IP) for shortening the inference time in finger vein recognition. This neural network system can operate independently in client mode. After fetching the user’s finger vein image via a near-infrared (NIR) camera mounted on an embedded system, vein features can be efficiently extracted by vein curving algorithms and user identification can be completed quickly. Better image quality and higher recognition accuracy can be obtained by combining several preprocessing techniques and the modified CNN. Experimental data were collected by the finger vein image capture equipment developed in our laboratory based on the specifications of similar products currently on the market. Extensive experiments demonstrated the practicality and robustness of the proposed finger vein identification system.
Review of Seawater Fiber Optic Salinity Sensors Based on the Refractive Index Detection Principle
Authors: Li Gaochao , Wang Yongjie , Shi Ancun , Liu Yuanhui , Li Fang
Published: February 2023

This paper presents a systematic review of the research available on salinity optic fiber sensors (OFSs) for seawater based on the refractive index (RI) measurement principle for the actual measurement demand of seawater salinity in marine environmental monitoring, the definition of seawater salinity and the correspondence between the seawater RI and salinity. To further investigate the progress of in situ measurements of absolute salinity by OFSs, the sensing mechanisms, research progress and measurement performance indices of various existing fiber optic salinity sensors are summarized. According to the Thermodynamic Equation of Seawater-2010 (TEOS-10), absolute salinity is recommended for sensor calibration and measurement. Comprehensive domestic and international research progress shows that fiber-optic RI sensors are ideal for real-time, in situ measurement of the absolute salinity of seawater and have excellent potential for application in long-term in situ measurements in the deep ocean. Finally, based on marine environmental monitoring applications, a development plan and the technical requirements of salinity OFSs are proposed to provide references for researchers engaged in related industries.
Detection of Wheel Polygonization Based on Wayside Monitoring and Artificial Intelligence
Authors: Guedes António , Silva Ruben , Ribeiro Diogo , Vale Cecília , Mosleh Araliya , Montenegro Pedro , Meixedo Andreia
Published: February 2023

This research presents an approach based on artificial intelligence techniques for wheel polygonization detection. The proposed methodology is tested with dynamic responses induced on the track by passing a Laagrss-type rail vehicle. The dynamic response is attained considering the application of a train-track interaction model that simulates the passage of the train over a set of accelerometers installed on the rail and sleepers. This study, which considers an unsupervised methodology, aims to compare the performance of two feature extraction techniques, namely the Autoregressive Exogenous (ARX) model and Continuous Wavelets Transform (CWT). The extracted features are then submitted to data normalization considering the Principal Component Analysis (PCA) applied to suppress environmental and operational effects. Next to data normalization, data fusion using Mahalanobis distance is performed to enhance the sensitivity to the recognition of defective wheels. Finally, an outlier analysis is employed to distinguish a healthy wheel from a defective one. Moreover, sensitivity analysis is performed to analyze the influence of the number of sensors and their location on the accuracy of the wheel defect detection system.
Knock, Knock! The Next Wave of Populism Has Arrived! An Analysis of Confirmations, Denials, and New Developments in a Phenomenon That Is Taking Center Stage
Authors: Battista Daniele
Published: February 2023

Giorgia Meloni has long been one of the most important figures on the political scene. Her strength has been, from the very beginning, clear and effective communication, combined with a populist style based on the credibility of her path. Our contribution will attempt to highlight the interweaving that links this discipline to the complex and varied representation of populism. The analyses are based on extensive documentation of data for a period from July 2020 to September 2022, the month in which the vote was held in Italy. This periodisation allowed us to have a broad view of the phenomenon and a consequent linear picture of the state of the art. Moreover, in this way, we were able to cover three years of particularly intense political debate, allowing us to outline the underlying motivations that led to electoral success. Based on existing research, the phenomena are described and then framed in relation to the social and political context in which they were experienced, taking into account the interconnection with communication. In this sense, this contribution aims to introduce a perspective that corrects most analyses in this field, which assume that populist parties have lost ground in Europe with the advent of the pandemic. In fact, this study argues that the communication strategies linked to some of Giorgia Meloni’s political choices, including all forms of populism, have generally succeeded in attracting greater public interest while maintaining the characteristics of a phenomenon with pop traits that favoured its rise.
An Analysis of Responses to Sexual Assault against Women in Public Space: Practical Gender Needs or Strategic Gender Interests?
Authors: Silvestre Cabrera María , Aristegui Fradua Iratxe , Royo Prieto Raquel
Published: February 2023

This article focuses on sexual violence and the learned fear of rape experienced by women in their use of public space, understood as social constructions of a system of domination. We analyze a series of data, drawn from secondary sources, on the prevalence and perception of sexual assault in public space. This data confirms that sexual assault in public spaces is a real risk and that, as such, it is perceived and experienced by the majority of women. We have also selected and presented a series of institutional initiatives aimed at preventing sexual assaults on women in public spaces at night-time. Finally, we have constructed an index to study whether the selected institutional responses respond to practical gender needs or to strategic gender interests. The article concludes that all of these initiatives have a greater impact in the area of gender needs, but they are not able to reverse the structural causes of sexual assault or to contribute to true social change.
Work Adjustment in an Employment Program for Colombian People Involved in Armed Conflict: A Multilevel Mixed-Methods Case Study
Authors: Antón Rubio Concha , Grueso Hinestroza Merlin Patricia , López-Santamaría Mónica
Published: February 2023

Work plays a significant role in societies; however, currently, work is a particularly difficult area for those in the most vulnerable social categories who have been heavily affected by the COVID-19 pandemic. Because there is scarce research on labor integration of people affected by armed conflicts from work adjustment (WA) perspectives, we have developed research to understand the relationship between people and their work setting in an employment program inspired in supported employment models (SEMs). To achieve this, a nonexperimental mixed-methods research design with a convergent parallel approach and multilevel design was developed. The results show that the work-adjustment approach is a useful framework with which to understand the relationship between the work setting and individuals. The conclusions of the research are as follows: (1) people who have been involved in armed conflicts can develop attitudes and abilities to properly develop their work; (2) the configuration of a labor-inclusion model is related to the results in the medium and long term, both in the program itself and in its participants and society; (3) the SEMs contribute to the effective inclusion of people affected by armed conflicts, which have low skills, scarce social contacts, and low educational levels.
The ‘Arab Clans’ Discourse: Narrating Racialization, Kinship, and Crime in the German Media
Authors: Özvatan Özgür , Neuhauser Bastian , Yurdakul Gökçe
Published: February 2023

In the last decade’s media discourse, particular Arab immigrant groups received the name ‘Arab clans’ and have been portrayed as criminal kinship networks irrespective of actual involvement in crime. We question how ‘Arab clans’ are categorized, criminalized, and racialized in the German media. To answer this question, we collected clan-related mainstream media articles published between 2010 and 2020. Our first-step quantitative topic modeling of ‘clan’ coverage (n = 23,893) shows that the discourse about ‘Arab clans’ is situated as the most racialized and criminalized vis-à-vis other ‘clan’ discourses and is channeled through three macro topics: law and order, family and kinship, and criminal groupness. Second, to explore the deeper meaning of the discourse about ‘Arab clans’ by juxtaposing corpus linguistics and novel narrative approaches to the discourse-historical approach, we qualitatively analyzed 97 text passages extracted with the keywords in context search (KWIC). Our analysis reveals three prevalent argumentative strategies (Arab clan immigration out of control, Arab clans as enclaves, policing Arab clans) embedded in a media narrative of ethnonational rebirth: a story of Germany’s present-day need (‘moral panic’) to police and repel the threats associated with ‘the Arab clan Other’ in order for a celebratory return to a nostalgically idealized pre-Arab-immigration social/moral order.
Maturity Models for Testing and Calibration Laboratories: A Systematic Literature Review
Authors: Gerônimo Bruna Maria , Lenzi Giane Gonçalves
Published: February 2023

Currently, testing and calibration laboratories are undergoing organizational restructuring in view of technical and regulatory requirements. To assist these laboratories, maturity models (MMs) can be used for the implementation and maintenance of management systems. The use of fuzzy logic is often found in association with the construction of MMs. Fuzzy logic helps in the construction of these models, removing subjective elements from the maturity assessment. Therefore, the objective of this study was to perform a systematic literature review (SLR) using the Methodi Ordinatio focused on MMs built with fuzzy logic that aim to evaluate the degree of maturity of testing and calibration laboratories that have implemented ISO/IEC 17025 for their quality management systems (QMSs). This analysis was performed with articles published between 2012 and 2022 in several databases using keywords such as “maturity model”, “fuzzy” and “ISO 17025” and resulted in 18 articles, which made up the bibliographic portfolio. After analyzing the content of these studies, it was possible to conclude that, although no study specifically discussed this MM, the discovered articles were important for presenting ideas and suggestions for future research.
Contradictions about Sustainability: A Case Study of College Students from Saudi Arabia
Authors: Al-Zohbi Gaydaa , Pilotti Maura A. E.
Published: February 2023

The present study offers a window into college students’ views of environmental sustainability in a country that is one of the major oil producers in the world. College students are the main constituents of the country’s programmatic plan (i.e., the 2030 Vision) for restructuring its infrastructure and human capital away from oil and gas production and toward a knowledge and service economy. Thus, their views can influence the success of the plan. In the present study, 430 college students at a university located at the heart of the oil and gas region were surveyed via an online questionnaire. Participants were of Middle Eastern descent. It was found that their beliefs and reported actions were generally supportive of a green economy. However, their beliefs in climate change and specifically in renewable energy did not predict much of their sustainability-based behaviors (e.g., recycling). The latter findings were interpreted as reflecting the internal conflict between embracing change (i.e., a desirable but uncertain future), and resisting change (i.e., a choice that would preserve, at least for some time, the benefits of the fossil fuel economy).
A Patent Bibliometric Analysis of Carbon Capture, Utilization, and Storage (CCUS) Technology
Authors: Zhu Yaozong , Wang Yezhu , Zhou Baohuan , Hu Xiaoli , Xie Yundong
Published: February 2023

Large amounts of CO2 from human socioeconomic activities threaten environmental sustainability. Moreover, uncontrolled resource use and lack of relevant technology exacerbate this issue. For this reason, carbon capture, utilization, and storage (CCUS) technology has gained worldwide attention. Many scholars have researched CCUS, but few have used CCUS patent bibliometric analysis from a unified perspective. This article aims to provide a conclusive analysis for CCUS researchers and policymakers, as well as summarize the innovation trends, technological distribution, and topic evolution. Based on 11,915 pieces of patent data from the Derwent Innovations Index, we used bibliometric analysis and data mining methods to conduct research on four dimensions: overall trend, geographical distribution, patentees, and patent content. The results of this article are as follows. CCUS has entered a rapid development stage since 2013. Patents are mainly distributed geographically in China, the US, and Japan, especially in heavy industries such as energy and electricity. Large enterprises hold patents with a relatively stable network of cooperators and attach great importance to international patent protection. A total of 12 topics were identified through clustering, and these topics gradually shifted from technicalities to commercialization, and from industrial production to all aspects of people’s daily lives.
Is China’s Urbanization Inclusive?—Comparative Research Based on Machine Learning Algorithms
Authors: Gao Zhipeng , Wang Zhenyu , Zhou Mi
Published: February 2023

Improving the inclusiveness of urban development is crucial to improving the wages of low- and middle-income workers. In this study, we used machine learning to cluster urban labor into low, middle, and high socioeconomic groups in order to analyze the effects of economic agglomeration and compare them with the results, which were classified according to income. The results showed that economic agglomeration has improved the wages of the low and middle socioeconomic groups; the estimated wage spillover effect was 3.9%. By contrast, the estimated result based on the groups classified by a single index of income was 20.3%, which represents an overestimation of the wage spillover effect of economic agglomeration. This method is often used to explain the inclusiveness of China’s urbanization, leading to overestimation. Further mechanism analysis found that the characteristics of the industrial structure affect the change in wage elasticity caused by economic agglomeration, which has a moderating effect on the wages of workers. The differing wage elasticity associated with economic agglomeration is responsible for wage disparities in China.
A Two-Stage Bilateral Matching Study of Teams-Technology Talents in New R&D Institutions Based on Prospect Theory
Authors: Jiang Lin , Chen Biyun
Published: February 2023

This study considers two-stage bilateral matching of teams and scientific and technological talents in new R&D organizations and proposes a two-stage dual-objective bilateral matching method based on prospect theory. The matching of teams and scientific and technological talent in new R&D institutions is divided into two stages: elimination matching in the first stage and selection matching in the second stage. In the first stage, the evaluation index of the team to talent and the cost index of talent are constructed, the dual reference points of peer and expectation are set for evaluating talent, and the bottom-line reference points are set for talent cost. The comprehensive prospect value in the first stage is calculated based on prospect theory, and the matching in the first stage is completed based on the dual-objective optimization model with the highest evaluation value and the lowest cost value. In the second stage, using the matching results of the first stage, the team evaluates the talent again, while the talent ranks the team to obtain the satisfaction value, and completes the second stage of bilateral matching based on prospect theory and the dual-objective optimization model with the highest evaluation value and the highest satisfaction value. Finally, a case study and method comparison show that the proposed method is feasible and effective.
Risk Assessment and Spatial Distribution of Heavy Metals with an Emphasis on Antimony (Sb) in Urban Soil in Bojnourd, Iran
Authors: Rouhani Abdulmannan , Makki Mohsen , Hejcman Michal , Shirzad Razieh , Gusiatin Mariusz Z.
Published: February 2023

Recently, one of the major environmental issues is the pollution of soil with Antimony (Sb), which is ecologically detrimental and potentially carcinogenic to humans. In developing countries such as Iran, Sb concentrations in soils have not yet been accurately determined. Therefore, the purpose of this study was to examine the levels of Sb and the other HMs (Pb, Cd, As, Ni, Hg and Cr) in the surface soils of Bojnourd, Iran, as well as their distribution and potential risks to the environment and human health. A total of 37 soil samples (0–20 cm) were taken from different sampling stations: 900 × 900 m regular grid for traffic areas (TA), residential areas (RA) and suburb areas (SA). The contamination factor (CF) and geo-accumulation index (Igeo) are two indices that were used to reflect the potential ecological risk from HMs. Geographic information system (GIS), Spearman correlation matrix and health risk indexes were utilized to investigate the source and potential hazard of HMs. The results showed that the average concentration of HMs in TA was highly enriched compared to other areas. Most soil samples were identified to have low levels of Sb and Ni pollution, while having moderate to high levels of Pb, Cr, As, Hg and Cd pollution, as determined by the pollution indices (Igeo and CF). Geostatistical analysis and GIS mapping of the spatial distribution of HM concentrations showed that there have been similar patterns of spatial distribution for Cd, Cr, Ni and Sb and their hot spots were in the southeast, west and center of the city. Neither the hazard quotient (HQ) nor the hazard index (HI) of the examined HMs indicated any non-carcinogenic risk to adults or children. However, carcinogenic risk assessment revealed that cancer risk was raised from Cr and Cd contents for children, while these elements showed an acceptable risk for adults. Furthermore, children’s carcinogenic and non-carcinogenic values were greater than adults’, indicating more potential health hazards associated with these HMs. Therefore, assessing the risk posed by HM pollution in urban surface soil is vital and urgent for children. A more detailed investigation is also required to identify the spatial distribution of soil pollution in areas recognized as enriched in Sb. A proper assessment of the environmental risk and the corresponding risk to humans from HM in a study area can be critical to developing an appropriate remediation method.
You Can Handle, You Can Teach It: Systematic Review on the Use of Extended Reality and Artificial Intelligence Technologies for Online Higher Education
Authors: Rangel-de Lázaro Gizéh , Duart Josep M.
Published: February 2023

Over the past year, defined by the COVID-19 pandemic, we have witnessed a boom in applying key emerging technologies in education. In such challenging situations, technology and education expanded their work together to strengthen and interactively impact the learning process in the online higher education context. From a pedagogical perspective, extended reality (XR) and artificial intelligence (AI) were accessible toolboxes to amplify an active and learner-centered teaching method. Whether and how such activities will continue in a post-COVID-19 situation remains unclear. In this systematic literature review, we document the application of XR and AI in online higher education settings and build up an accurate depiction of their influence after the COVID-19 pandemic outbreak. A significant contribution of the thorough analysis conducted was the corroboration of the growing interest of these fast-emerging technologies and their impact on learner agency and outcomes, making online education more accessible, effective, engaging, collaborative, self-paced, and adapted to the diverse academic trajectories. The momentum brought about by the pandemic has served as an impulse for educators and universities to expand the use of these technologies progressively, meet new challenges, and shape the future of online higher education.
A Nuanced Analysis on Livelihood Resilience of Vietnamese Upland Households: An Intersectional Lens of Ethnicity and Gender
Authors: Tran Phuong Thi , Nguyen Tan Quang , Huynh Chuong Van , Pham Ty Huu , Schinkel Ulrike
Published: February 2023

“How to gauge the resilience of a household’s livelihood?” and “who is resilience for?”; as the world’s volatility increases, especially with unprecedented changes in climate, interest in these questions continues to rise. While many previous conventional attempts to measure resilience at the household level have largely employed the “objective” top-down framework relying mostly on a macro observed socioeconomic dataset, this present work seeks to estimate household resilience through an alternative bottom-up method, called the “subjective” resilience approach. With specific reference to the context of two ethnic Pa Cô and Tà Ôi minorities living in upland areas of Central Vietnam, this study aims to (1) measure household resilience to climate change by scoring five livelihood capitals, financial, human, social, physical, and natural, by applying the Household Livelihood Resilience (HLR) framework; and (2) provide an intersectional lens of ethnicity and gender in relation to the household’s livelihood resilience. To achieve these goals, in addition to two focus group discussions and eleven in-depth interviews, an empirical survey of 236 households was conducted between September to December 2021. Our findings indicated that there are differences in observed livelihood resilience among the ethnic minority groups and, within that, between gender (sex, status) factors. Ethnic women and poor households have a lower resilience than other groups in society due to their lower human capital and limited access to public and financial resources, which are to some degree linked to the difference in the gender division of labor and the cultural norms of patriarch traditions. This study highlights the importance of considering gender and poverty in resilience-building efforts and offers insights for future programs in multi-ethnic developing countries such as Vietnam.
Eating Sturgeon: An Endangered Delicacy
Authors: Raposo António , Alturki Hmidan A. , Alkutbe Rabab , Raheem Dele
Published: February 2023

Since ancient times, sturgeon species have been valued for their rich nutritional qualities, which are crucial for human health today. They are linked with gastronomic delicacy and offer economic benefits, especially for the caviar industry. Today aquaculture produces more farmed and hybrid species due to rapidly declining wild sturgeon populations. Sturgeon diversification through processing can yield fingerlings, stocking material, meat or caviar. Because of its variety, sturgeon flesh includes highly digestible proteins, lipids, vitamins and minerals. Consuming sturgeon provides essential fatty acids that play important oxidative and anti-inflammatory roles in human cells. The purpose of this study is to examine the sustainability and economic value of eating sturgeon worldwide, the technology applied in food processing, and the challenges that food quality and authenticity, nutritional content and health effects pose. The issue of counterfeiting high-quality sturgeon products by dishonest means has to be adequately addressed. Digital tools to guarantee authenticity and transparency in the sturgeon value chain should be considered in the future.
Investigating Preceding Determinants Affecting Primary School Students Online Learning Experience Utilizing Deep Learning Neural Network
Authors: Ong Ardvin Kester S. , Cuales Jelline C. , Custodio Jose Pablo F. , Gumasing Eisley Yuanne J. , Pascual Paula Norlene A. , Gumasing Ma. Janice J.
Published: February 2023

The pandemic has caused all of the programs that are offered in primary schools to be interrupted. Evaluating the student’s learning at this level is essential because education development throughout the epidemic is critical, as there was no other educational alternative available during the pandemic. This study examines the use of deep learning neural network (DLNN) to evaluate the parameters influencing primary school students’ online learning experiences during the COVID-19 pandemic. The researchers considered this issue since primary students’ online learning experiences needed more attention. To carefully analyze the relationships between the parameters of primary students’ learning experience, an online questionnaire was utilized, subject to parents’ participation. A total of 385 Filipino elementary school students were selected and surveyed using a purposive sampling method. Participants in this research ranged in age from seven to thirteen and were supervised by their parents or legal guardians. The result of the study showed that open communication, social presence, design and organization, and facilitation had the most impact on predicting students’ experiences with online education, having a high accuracy from DLNN of 96.12%. This demonstrates the significance of open communication, draws attention to the importance of helping students feel welcomed and appreciated, and demonstrates the influence that instructors have on the overall positive learning experiences of their students. Finally, the findings of this study gave a strong framework and clear conclusions that both schools and the government’s education department could use to improve the way primary education is taught online across the country. Finally, the results and findings of this study could be applied and extended to other related education studies worldwide.
How Does Public Opinion Influence Production Safety within Small and Medium Enterprises in the Sustainability Context?
Authors: Sun Hui , Xu Dan , Wang Lu , Wang Kai
Published: February 2023

Safety accidents are a major hazard to the community and impact sustainable development. Production safety, as the basic corporate social responsibility (CSR), is mostly overlooked by small and medium-sized enterprises (SMEs) in China. The traditional safety management model of SMEs has some loopholes. Therefore, it is critical to explore other non-traditional and effective approaches. With the rapid development of the internet and the wide use of social media, the influence of public opinion on the production safety of SMEs has now become more and more powerful. However, the influence mechanism hasn’t been fully studied and understood. Based on grounded theory, this paper conducted an in-depth study on how SME behavior in production safety is influenced by public opinion. The study found that public opinion influences SME production safety through three channels: public awareness, media response, and government guidance. Public opinion influences the production safety of SMEs through intermediaries such as government supervision, the willingness of SMEs to produce safely, the behavior of employees to participate in safety management, and the self-disciplinary behavior of the industry. The impact of the willingness of SMEs to produce safely is affected by the resource guarantee ability of SMEs, therefore, the limited public opinion resources should be combined with the reality of SMEs through information exchange to promote the optimum game relationship between bodies, in order to make up for the lack of production safety resources capacity of SMEs. The conclusion of this study is crucial in understanding how to improve the production safety of SMEs by the means of public opinion to promote SMEs to engage in sustainability practices and reduce the occurrence of safety problems.
Bus Fleet Accident Prediction Based on Violation Data: Considering the Binding Nature of Safety Violations and Service Violations
Authors: Ding Tongqiang , Zhang Lianxin , Xi Jianfeng , Li Yingjuan , Zheng Lili , Zhang Kexin
Published: February 2023

The number and severity of bus traffic accidents are increasing annually. Therefore, this paper uses the historical data of Chongqing Liangjiang Public Transportation Co., Ltd. bus driver safety violations, service violations, and road traffic accidents from January to June 2022 and constructs road traffic accident prediction models using Extra Trees, BP Neural Network, Support Vector Machine, Gradient Boosting Tree, and XGBoost. The effects of safety and service violations on vehicular accidents are investigated. The quality of the prediction models is measured by five indicators: goodness of fit, mean square error, root mean square error, mean absolute error, and mean absolute percentage error. The results indicate that the XGBoost model provides the most accurate predictions. Additionally, simultaneously considering safety and service violations can improve the accuracy of the model’s predictions compared to a model that only considers safety violations. Bus safety violations, bus service violations, and bus safety operation violations significantly influence traffic accidents, which account for 27.9%, 20%, and 16.5%, respectively. In addition to safety violations, the service violation systems established by bus companies, such as bus service codes, can be an effective method of regulating the behavior of bus drivers and reducing accidents. They are improving both the safety and quality of public transportation.
Evaluation of Railway Station Infrastructure to Facilitate Bike–Train Intermodality
Authors: Pazzini Margherita , Lantieri Claudio , Zoli Annalisa , Simone Andrea , Imine Hocine
Published: February 2023

In recent years, emissions into the atmosphere have been brought to the attention of the authorities and some action has been taken to try to solve the problem. One is the application of EU legislation 2008/50/EC, which requires states adhering to this law to constantly monitor air quality and subsequently find solutions to reduce the impact of emissions. The data show that 20% of emissions come from transport, 70% of which come from private vehicles. Sustainable mobility can be a possible solution to reduce pollution and traffic congestion. The promotion of cycling, as part of sustainable mobility, is a required action to achieve the objectives pursued. This research aims to define the quality of infrastructure and accessibility of railway stations to the use of bicycles. The approach used was to define a technical checklist to estimate the criticalities of the structure in a quantitative way. An example is the case study developed in the Emilia-Romagna region (Italy) within the PREPAIR project where 33 railway stations were classified and analyzed. In the end, the checklist was effective in defining the necessary interventions and the required activities and can be used in similar cases during the decision-making processes.
Transboundary River Water Availability to Ravi Riverfront under Changing Climate: A Step towards Sustainable Development
Authors: Ahmed Naveed , Lü Haishen , Ahmed Shakeel , Adeyeri Oluwafemi E. , Ali Shahid , Hussain Riaz , Shah Suraj
Published: February 2023

The Indus Water Treaty allocated the water of the Ravi River to India, and India constructed the Thein Dam on the Ravi River. This study investigates the water availability of the Ravi Riverfront for both pre-dam and post-dam scenarios augmented with pre-flood, flood, and post-flood sub-scenarios. The study also investigates river water availability for low and high magnitudes (Flow Duration Curves) and its linkages with climate change. The modified Mann–Kendall, Sen’s slope estimator, and Pearson correlation were used to investigate the river flows. It was found that there is a remarkable decrease in the river water by −36% of annual mean flows as compared to the pre-dam scenario. However, during the flood season, it was −32% at the riverfront upstream (Ravi Syphon Gauge). The reduction in water volume was found as 2.13 Million Acre Feet (MAF) and 1.03 MAF for maximum and mean, respectively, in the Rabi (Winter) season, and 4.07 MAF and 2.76 MAF for max and mean, respectively, in the Kharif (Summer) season. It was also revealed that 180–750 cusecs of water would be available or exceeded for 90% to 99% of the time at Ravi Riverfront during the flood season. The high flows were mainly controlled by temperature in the pre-dam scenario; presently, this water is stored in the Thein Dam reservoir. In contrast, the precipitation role is significant in the post-dam scenario, which means that the flows in the Ravi River are mainly due to base flow contributions and precipitation. This study is the first step in analyzing the river water availability of the Ravi Riverfront, which will ultimately address the associated problems and their solutions to decision-makers. Additionally, implementing an eco-friendly riverfront promotes urban sustainability in developed urban areas, such as Lahore City, and will lead to a comfortable and healthy lifestyle; this will only be possible with water availability in the Ravi Riverfront reach.
Application of Deep Learning to Enforce Environmental Noise Regulation in an Urban Setting
Authors: Carrasco Vicente , Arenas Jorge P. , Huijse Pablo , Espejo Diego , Vargas Victor , Viveros-Muñoz Rhoddy , Poblete Victor , Vernier Matthieu , Suárez Enrique
Published: February 2023

Reducing environmental noise in urban settings, i.e., unwanted or harmful outdoor sounds produced by human activity, has become an important issue in recent years. Most countries have established regulations that set maximum permitted noise levels. However, enforcing these regulations effectively remains challenging as it requires active monitoring networks and audio analysis performed by trained specialists. The manual evaluation of the audio recordings is laborious, time-consuming, and inefficient since many audios exceeding the noise level threshold do not correspond to a sound event considered by the regulation. To address this challenge, this work proposes a computational pipeline to assist specialists in detecting noise sources in the built environment that do not comply with the Chilean noise regulation. The system incorporates a deep neural model following a pre-trained audio neural network architecture transferred to a dataset compiled from public sources and recordings in Valdivia, Chile. The target dataset follows a customized taxonomy of urban sound events. The system also uses a public API so potential users can post audio files to obtain a prediction matrix reporting the presence of noise sources contributing to environmental noise pollution. Experiments using recordings from two continuous noise monitoring stations showed that the amount of data to be inspected by the specialist is decreased by 97% when the deep-learning tools are used. Therefore, this system efficiently assists trained experts in enforcing noise legislation through machine-assisted environmental noise monitoring.