bluuebunny/crossref_metadata_2025 huggingface dataset:
A Concept of 2U Spaceborne Multichannel Heterodyne Spectroradiometer for Greenhouse Gases Remote Sensing
Authors: Zenevich Sergei , Gazizov Iskander , Churbanov Dmitry , Plyashkov Yegor , Spiridonov Maxim , Talipov Ravil , Rodin Alexander
Published: June 2021

We present the project of a 2U CubeSat format spaceborne multichannel laser heterodyne spectroradiometer (MLHS) for studies of the Earth’s atmosphere upper layers in the near-infrared (NIR) spectral range (1258, 1528, and 1640 nm). A spaceborne MLHS operating in the solar occultation mode onboard CubeSat platform, is capable of simultaneous vertical profiling of CO2, H2O, CH4, and O2, as well as Doppler wind measurements, in the tangent heights range of 5–50 km. We considered the low Earth orbit for the MLHS deployment and analyzed the expected surface coverage and spatial resolution during one year of operations. A ground-based prototype of the MLHS for CO2 and CH4 molecular absorption measurements with an ultra-high spectral resolution of 0.0013 cm−1 is presented along with the detailed description of its analytical characteristics and capabilities. Implementation of a multichannel configuration of the heterodyne receiver (four receivers per one spectral channel) provides a significant improvement of the signal-to-noise ratio with the reasonable exposure time typical for observations in the solar occultation mode. Finally, the capability of building up a tomographic picture of sounded gas concentration distributions provided by high spectral resolution is discussed.
Comparison and Evaluation of Different Pit-Filling Methods for Generating High Resolution Canopy Height Model Using UAV Laser Scanning Data
Authors: Quan Ying , Li Mingze , Hao Yuanshuo , Wang Bin
Published: June 2021

As a common form of light detection and ranging (LiDAR) in forestry applications, the canopy height model (CHM) provides the elevation distribution of aboveground vegetation. A CHM is traditionally generated by interpolating all the first LiDAR echoes. However, the first echo cannot accurately represent the canopy surface, and the resulting large amount of noise (data pits) also reduce the CHM quality. Although previous studies concentrate on many pit-filling methods, the applicability of these methods in high-resolution unmanned aerial vehicle laser scanning (UAVLS)-derived CHMs has not been revealed. This study selected eight widely used, recently developed, representative pit-filling methods, namely first-echo interpolation, smooth filtering (mean, medium and Gaussian), highest point interpolation, pit-free algorithm, spike-free algorithm and graph-based progressive morphological filtering (GPMF). A comprehensive evaluation framework was implemented, including a quantitative evaluation using simulation data and an additional application evaluation using UAVLS data. The results indicated that the spike-free algorithm and GPMF had excellent visual performances and were closest to the real canopy surface (root mean square error (RMSE) of simulated data were 0.1578 m and 0.1093 m, respectively; RMSE of UAVLS data were 0.3179 m and 0.4379 m, respectively). Compared with the first-echo method, the accuracies of the spike-free algorithm and GPMF improved by approximately 23% and 22%, respectively. The pit-free algorithm and highest point interpolation method also have advantages in high-resolution CHM generation. The global smooth filter method based on the first-echo CHM reduced the average canopy height by approximately 7.73%. Coniferous forests require more pit-filling than broad-leaved forests and mixed forests. Although the results of individual tree applications indicated that there was no significant difference between these methods except the median filter method, pit-filling is still of great significance for generating high-resolution CHMs. This study provides guidance for using high-resolution UAVLS in forestry applications.
Spatiotemporal Patterns of Urbanization in the Three Most Developed Urban Agglomerations in China Based on Continuous Nighttime Light Data (2000–2018)
Authors: Li Yu , Ye Haipeng , Gao Xing , Sun Dongqi , Li Zehong , Zhang Ninghui , Leng Xuejing , Meng Dan , Zheng Ji
Published: June 2021

Urban agglomeration is an advanced spatial form of integrating cities, resulting from the global urbanization of recent decades. Understanding spatiotemporal patterns and evolution is of great importance for improving urban agglomeration management. This study used continuous time-series NTL data from 2000 to 2018 combined with land-use images to investigate the spatiotemporal patterns of urbanization in the three most developed urban agglomerations in China over the past two decades: the Beijing–Tianjin–Hebei urban agglomeration (BTH), the Yangtze River Delta urban agglomeration (YRD), and the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). The NTL intensity indexes, dynamic thresholds, extracted urban areas, and landscape metrics were synthetically used to facilitate the analysis. This study found that the urbanization process in the study areas could be divided into three stages: rapid urbanization in core cities from 2000 to 2010, a fluctuating urbanization process in both core cities and surrounding cities from 2010 to 2015, and stable urbanization, mainly in surrounding cities with a medium size after 2015. Meanwhile, the urbanization level of GBA was higher than that of YRD and BTH. However, with the acceleration of urban development in YRD, the gap in the urbanization level between GBA and YRD narrowed significantly in the third stage. In addition, this study confirmed that the scattered, medium-sized cities in YRD and GBA were more developed than those in BTH. This study showed that continuous NTL data could be effectively applied to monitor the urbanization patterns of urban agglomerations.
The Bistatic Radar as an Effective Tool for Detecting and Monitoring the Presence of Phytoplankton on the Ocean Surface
Authors: Rodriguez-Alvarez Nereida , Oudrhiri Kamal
Published: June 2021

A massive dust storm formed over the Sahara Desert in June 2020. The African dust cloud, which traveled over the tropical Atlantic’s main development region for hurricanes, resulted in the highest aerosol optical thickness (AOT) for the past two decades. Dust particles contained in dust clouds are at some point deposited on the ocean surface, impacting the ocean biogeochemistry through the supply of nutrients. Although there are remote sensing systems that can map the AOT, the locations of the aerosol particles deposited on the ocean surface remain unknown quantities with remote sensing measurements. In addition, the supplied nutrients are not static and are displaced by ocean currents. Nutrients trigger the phytoplankton (algae) blooms, which form a film on the ocean surface and affect the ocean surface tension. The change in ocean surface tension causes a local decrease of ocean surface roughness over the areas covered with phytoplankton. Bistatic radar data from the CYclone Global Navigation Satellite System (CYGNSS) mission can detect changes in the ocean surface roughness, expressed as an increase in reflectivity when the surface becomes smoother. Therefore, decreased ocean surface roughness correlated with a recent dust storm represents a key indicator of the presence of phytoplankton. In this paper, we present for the first time the capability of bistatic radar measurements to provide an effective tool to map information of areas covered with phytoplankton, establishing bistatic radar as the most reliable remote sensing tool for detecting phytoplankton blooms and monitoring their presence across the ocean surface. We present the analysis of low ocean roughness signatures in the bistatic radar measurements from the CYGNSS mission observed in the Gulf of Mexico after the Sahara’s dust storm circulation from Africa to the American continent from May to July 2020. CYGNSS data offer an unprecedented spatial and temporal coverage that allows for the analysis of those signatures at time scales of 1-day, robust to the presence of clouds and dust clouds. The described capability benefits the improvement of models, promoting a better constraint of the supply of dust into the ocean surface and a better understanding of the excess of nutrients that triggers the phytoplankton blooms. This new bistatic radar application enhances our understanding on the role of dust storms on ocean biogeochemistry and the global carbon cycle.
Tropical Forest Monitoring: Challenges and Recent Progress in Research
Authors: Murrins Misiukas Jennifer , Carter Sarah , Herold Martin
Published: June 2021

Forest monitoring is the recurrent measurement of forest parameters to identify changes over time. There is currently a rising demand for monitoring, as well as growing capacities for it. This study identifies recent research on tropical forest monitoring using a systematic literature review. The research explores whether the location of these studies is in the countries where monitoring is most needed. Three characteristics, biophysical conditions, anthropogenic influences, and forest monitoring capacities were used to identify the need for tropical forest monitoring advances. This provided an understanding as to where research should be targeted in the future. The findings revealed that research appears to be concentrated in countries with strong forest monitoring capabilities that face challenges due to biophysical and anthropogenic influences (e.g., logistically difficult ground sampling and rapid pace of forest change, respectively). Consequently, future research could be targeted in countries with lower capacities and higher needs, in order to improve forest monitoring and conservation.
Retrieval and Spatio-Temporal Variations Analysis of Yangtze River Water Clarity from 2017 to 2020 Based on Sentinel-2 Images
Authors: Zhao Yelong , Wang Shenglei , Zhang Fangfang , Shen Qian , Li Junsheng
Published: June 2021

The Yangtze River is the third longest river in the world. Monitoring and protecting its water quality are important for economic and social development. Water clarity (Secchi disk depth, SDD) is an important reference index for evaluating water quality. In this study, Sentinel-2 multispectral instrument (MSI) remote sensing images were utilized together with the Forel-Ule index (FUI) and hue angle α to construct an SDD retrieval model, which was applied to the Yangtze River from 2017 to 2020, which was used to describe color in the International Commission on Illumination (CIE) color space to construct an SDD retrieval model that was applied to the Yangtze River for the period 2017–2020. Further, the spatial distribution, seasonal variation, inter-annual variation, and driving factors of the observed SDD variations were analyzed. The spatial distribution pattern of the Yangtze River was high in the west and low in the east. The main driving factors affecting the Yangtze River SDD was sediment runoff, water level, and precipitation. The upstream and downstream Yangtze River SDD were negatively correlated with the change in water level and sediment runoff, whereas the midstream Yangtze River SDD was positively correlated with the change in water level and sediment runoff. The upper and lower reaches of the Yangtze River and overall SDD showed a weak downward trend, and the middle reaches of the Yangtze River remained almost unchanged.
A New Approach for Cylindrical Steel Structure Deformation Monitoring by Dense Point Clouds
Authors: Jia Dongfeng , Zhang Weiping , Wang Yuhao , Liu Yanping
Published: June 2021

As fundamental load-bearing parts, the cylindrical steel structures of transmission towers relate to the stability of the main structures in terms of topological relation and performance. Therefore, the periodic monitoring of a cylindrical steel structure is necessary to maintain the safety and stability of existing structures in energy transmission. Most studies on deformation analysis are still focused on the process of identifying discrepancies in the state of a structure by observing it at different times, yet relative deformation analysis based on the data acquired in single time has not been investigated effectively. In this study, the piecewise cylinder fitting method is presented to fit the point clouds collected at a single time to compute the relative inclination of a cylindrical steel structure. The standard deviation is adopted as a measure to evaluate the degree of structure deformation. Meanwhile, the inclination rate of each section is compared with the conventional method on the basis of the piecewise cylinder fitting parameters. The validity and accuracy of the algorithm are verified by real transmission tower point cloud data. Experimental results show that the piecewise cylinder fitting algorithm proposed in this research can meet the accuracy requirements of cylindrical steel structure deformation analysis and has high application value in the field of structure deformation monitoring.
On the Use of NB-IoT over GEO Satellite Systems with Time-Packed Optical Feeder Links for Over-the-Air Firmware/Software Updates of Machine-Type Terminals
Authors: Bas Joan , Dowhuszko Alexis A.
Published: June 2021

The verticals of 5G, such as the automotive, smart grid, and smart cities sectors, will bring new sensors and IoT devices requiring Internet connectivity. Most of these machine-type terminals will be sparsely distributed, covering a very large geographical area and, from time to time, will have to update their software, firmware, and/or other relevant data. Given this situation, one viable solution to implement the “Over-the-Air” update of these IoT terminals can be done with the aid of GEO satellite systems. However, due to the ultra-dense radio frequency reuse factor that contemporary High-Throughput Satellite (HTS) systems implement in the access link to serve the IoT terminals, the use of a time-packed Free Space Optical (FSO) link represents a practical solution to avoid the bottleneck that the satellite gateway experiences in the feeder link. The performance of both Detect-and-Forward and Decode-and-Forward relaying strategies are studied, assuming that the single-carrier M-PAM symbols that are transmitted on the optical feeder link are mapped into M-QAM symbols that modulate the multiple sub-carriers of the OFDM-based radio access link. In addition, the benefits of encapsulating the NB-IoT frames into DVB-S2(X) satellite frames is also analyzed in detail. The effects of the impairments introduced in both the optical feeder and radio access links are characterized in detail, and the end-to-end error correction capabilities of the Modulation and Coding Schemes (MCS) defined in the contemporary releases of the NB-IoT and DVB-S2(X) standards are studied for different working regimes.
A New Approach for Estimating Dissolved Oxygen Based on a High-Accuracy Surface Modeling Method
Authors: Zhao Na , Fan Zemeng , Zhao Miaomiao
Published: June 2021

Dissolved oxygen (DO) is a direct indicator of water pollution and an important water quality parameter that affects aquatic life. Based on the fundamental theorem of surfaces in differential geometry, the present study proposes a new modeling approach to estimate DO concentrations with high accuracy by assessing the spatial correlation and heterogeneity of DO with respect to explanatory variables. Specifically, a regularization penalty term is integrated into the high-accuracy surface modeling (HASM) method by applying geographically weighted regression (GWR) with some covariates. A modified version of HASM, namely HASM_MOD, is illustrated through a case study of Poyang Lake, China, by comparing the results of HASM, a support vector machine (SVM), and cokriging. The results indicate that HASM_MOD yields the best performance, with a mean absolute error (MAE) that is 38%, 45%, and 42% lower than those of HASM, the SVM, and cokriging, respectively, by using the cross-validation method. The introduction of a regularization penalty term by using GWR with respect to covariates can effectively improve the quality of the DO estimates. The results also suggest that HASM_MOD is able to effectively estimate nonlinear and nonstationary time series and outperforms three other methods using cross-validation, with a root-mean-square error (RMSE) of 0.20 mg/L and R2 of 0.93 for the two study sites (Sanshan and Outlet_A stations). The proposed method, HASM_MOD, provides a new way to estimate the DO concentration with high accuracy.
Real-Time High-Level Acute Pain Detection Using a Smartphone and a Wrist-Worn Electrodermal Activity Sensor
Authors: Kong Youngsun , Posada-Quintero Hugo F. , Chon Ki H.
Published: June 2021

The subjectiveness of pain can lead to inaccurate prescribing of pain medication, which can exacerbate drug addiction and overdose. Given that pain is often experienced in patients’ homes, there is an urgent need for ambulatory devices that can quantify pain in real-time. We implemented three time- and frequency-domain electrodermal activity (EDA) indices in our smartphone application that collects EDA signals using a wrist-worn device. We then evaluated our computational algorithms using thermal grill data from ten subjects. The thermal grill delivered a level of pain that was calibrated for each subject to be 8 out of 10 on a visual analog scale (VAS). Furthermore, we simulated the real-time processing of the smartphone application using a dataset pre-collected from another group of fifteen subjects who underwent pain stimulation using electrical pulses, which elicited a VAS pain score level 7 out of 10. All EDA features showed significant difference between painless and pain segments, termed for the 5-s segments before and after each pain stimulus. Random forest showed the highest accuracy in detecting pain, 81.5%, with 78.9% sensitivity and 84.2% specificity with leave-one-subject-out cross-validation approach. Our results show the potential of a smartphone application to provide near real-time objective pain detection.
Multivariable Optimisation for Waiting-Time Minimisation at Roundabout Intersections in a Cyber-Physical Framework
Authors: Pauca Ovidiu , Maxim Anca , Caruntu Constantin-Florin
Published: June 2021

The evolution of communication networks offers new possibilities for development in the automotive industry. Smart vehicles will benefit from the possibility of connecting with the infrastructure and from an extensive exchange of data between them. Furthermore, new control strategies can be developed that benefit the advantages of these communication networks. In this endeavour, the main purposes considered by the automotive industry and researchers from academia are defined by: (i) ensuring people’s safety; (ii) reducing the overall costs, and (iii) improving the traffic by maximising the fluidity. In this paper, a cyber-physical framework (CPF) to control the access of vehicles in roundabout intersections composed of two levels is proposed. Both levels correspond to the cyber part of the CPF, while the physical part is composed of the vehicles crossing the roundabout. The first level, i.e., the edge-computing layer, is based on an analytical solution that uses multivariable optimisation to minimise the waiting times of the vehicles entering a roundabout intersection and to ensure a safe crossing. The second level, i.e., the cloud-computing layer, stores information about the waiting times and trajectories of all the vehicles that cross the roundabout and uses them for long-term analysis and prediction. The simulated results show the efficacy of the proposed method, which can be easily implemented on an embedded device for real-time operation.
Patch-Type Vibration Visualization (PVV) Sensor System Based on Triboelectric Effect
Authors: Kim Sun-Jin , Seol Myeong-Lok , Chung Byun-Young , Jang Dae-Sic , Kim Jong-Hwan , Choi Young-Chul
Published: June 2021

Self-powered wireless sensor systems have emerged as an important topic for condition monitoring in nuclear power plants. However, commercial wireless sensor systems still cannot be fully self-sustainable due to the high power consumption caused by excessive signal processing in a mini-electronic computing system. In this sense, it is essential not only to integrate the sensor system with energy-harvesting devices but also to develop simple data processing methods for low power schemes. In this paper, we report a patch-type vibration visualization (PVV) sensor system based on the triboelectric effect and a visualization technique for self-sustainable operation. The PVV sensor system composed of a polyethylene terephthalate (PET)/Al/LCD screen directly converts the triboelectric signal into an informative black pattern on the LCD screen without excessive signal processing, enabling extremely low power operation. In addition, a proposed image processing method reconverts the black patterns to frequency and acceleration values through a remote-control camera. With these simple signal-to-pattern conversion and pattern-to-data reconversion techniques, a vibration visualization sensor network has successfully been demonstrated.
Optimal Sensor Placement of the Artificial Lateral Line for Flow Parametric Identification
Authors: Xu Dong , Zhang Yuanlin , Tian Jian , Fan Hongjie , Xie Yifan , Dai Wei
Published: June 2021

The multi-sensor artificial lateral line system (ALLS) can identify the flow-field’s parameters to realize the closed-loop control of the underwater robotic fish. An inappropriate sensor placement of ALLS may result in inaccurate flow-field parametric identification. Therefore, this paper proposes a method to optimize the sensor placement configuration of the ALLS, which mainly included three algorithms, the feature importance algorithm based on mean and variance (MVF), the feature importance algorithm based on distance evaluation (DF), and the information redundancy (IR) algorithm. The optimal sensor placement performance selected by this method is verified by simulation. In addition, further experimental verification was conducted using the ALLS. Compared with the uniform sensor placement configuration mentioned in recent studies, the experimental results suggest that the optimal sensor placement method can achieve a more effective prediction of the flow-field parameters, therefore strengthening the underwater robotic fish’s perception and control function.
Different Discussion Partners and Their Effect on Depression among Older Adults
Authors: Lee Keunbok
Published: June 2021

Although the multidimensionality of core discussion networks has been well established and widely studied, studies of the effects of social support on depression rarely consider the multifaceted aspects of dyadic discussion partner ties. This article proposes defining dyadic social relationships as a construct comprising several tie-level attributes and differentiating multiple forms of support relationships by assessing the configuration pattern of multiple attributes. The current study examines various forms of older adults’ discussion partners and identifies which form of discussion partner relationship is effective at buffering the negative effects of adverse life events on depression symptoms. Results from the University of California Social Network Survey show that older adults’ discussion partners can be classified into five distinct types of dyadic ties: spouse/romantic partners, close neighbors, remote type, social companions, and acquaintances. The discussion network with more close neighbor confidants is more effective at buffering the negative effects of adverse life events. These results offer an alternative way of investigating the differential significance of various social support relationships in mental well-being.
Meeting in the Middle: TVET Programs’ Education–Employment Linkage at Different Stages of Development
Authors: Caves Katherine M. , Ghisletta Andrea , Kemper Johanna Mirka , McDonald Patrick , Renold Ursula
Published: June 2021

Technical and vocational education and training (TVET) programs are most successful at supporting youth labor markets when they combine education and employment. Education–employment linkage theory describes this combination in terms of power-sharing between actors from the education system and their counterparts in the employment system over key processes in the curriculum value chain of curriculum design, curriculum application (program delivery), and curriculum updating. The Education–Employment Linkage Index measures linkage for every function in a TVET program where actors from the two systems interact, aggregating those into processes and phases and eventually an index score. We apply this index to the largest upper-secondary TVET programs in Benin, Chile, Costa Rica, and Nepal. We find that Benin has relatively high education–employment linkage, while the other three countries score very low. Benin’s situation is unique because its TVET program is moving from employer-led to linked, rather than the typical employer integration into an education-based program. Other countries with large informal economies, low formal education and training rates, and existing non-formal employer-led training may be able to implement similar approaches.
The Role of Project-Based Language Learning in Developing Students’ Life Skills
Authors: Ghosheh Wahbeh Dua’ , Najjar Eman A. , Sartawi Adel F. , Abuzant Maysa , Daher Wajeeh
Published: June 2021

Project-based learning is suggested for independent and collaborative learning that could positively impact students’ learning. This study aimed to identify the role of project-based language learning in developing life skills of students through studying a case of a language class that included 80 students in two grade 6 classes in a private school. The study attempted to answer the following question: What is the role of project-based learning in developing students’ life skills in an Arabic language class? To answer this question, we adopted a theoretical framework that included the following categories of life skills: personal and collaborative skills, self-orientation skills, and collective responsibility. The following three data collecting tools were used during the study: observation, semi-structured interviews, and the teacher and students’ documents. Data collection lasted for two months from late September 2019 to December 2019. Data analysis followed the thematic analyses framework, by categorizing data into themes. The research results indicated that project-based learning helps to improve language students’ personal and cooperative skills through developing their communication skills between themselves or with their teacher. In addition, project-based learning developed language students’ mutual respect, their confidence, and their self-regulation of learning the Arabic language.
Enhancing the Positive Impact Rating: A New Business School Rating in Support of a Sustainable Future
Authors: Rodenburg Kathleen , Rizwan Taimoor , Liu Ruifeng , Christensen Hughes Julia
Published: June 2021

Business school rankings are “big business”, influencing donors and potential students alike, holding much sway over decanal and faculty priorities, particularly with respect to the curriculum as well as the focus and destination of research publications (i.e., in so-called “top” journals). Over the past several years, the perverse effects of these priorities have begun to be acknowledged, and new ratings and ranking systems have emerged. One promising newcomer is the Positive Impact Rating (PIR), which uniquely and exclusively focuses on student perceptions of their business school’s priorities and the learning experience. In addition, it organizes schools by tier, in an effort to foster collaboration and continuous improvement, as opposed to ranked competition. If this new approach is to achieve its stated objective and help shift the focus of business schools to developing future business leaders and research output in alignment with a more sustainable world (and the United Nations Sustainable Development Goals), it is essential that the metrics used be and be perceived as both valid and reliable. The current research aims to make a contribution in this regard, analyzing the results at one business school in detail and making recommendations for strengthening these aims. Results show that the parametric properties of the survey are highly interrelated, suggesting that the predictive utility of the separate elements within the scale could be improved. Additionally, biases in scores may exist depending on where the responses are collected and who solicited them, as well as the students’ perception of their overall academic experience and on socio-cultural factors.
Climate Analogues for Temperate European Forests to Raise Silvicultural Evidence Using Twin Regions
Authors: Mette Tobias , Brandl Susanne , Kölling Christian
Published: June 2021

Climate analogues provide forestry practice with empirical evidence of how forests are managed in “twin” regions, i.e., regions where the current climate is comparable to the expected future climate at a site of interest. As the twin regions and their silvicultural evidence change with each climate scenario and model, we focus our investigation on how the uncertainty in future climate affects tree species prevalence. We calculate the future climate from 2000 to 2100 for three ensemble variants of the mild (representative concentration pathway (RCP) 4.5) and hard (RCP 8.5) climate scenarios. We determine climatic distances between the future climate of our site of interest ‘Roth’ and the current climate in Europe, generating maps with twin regions from 2000 to 2100. From forest inventories in these twin regions we trace how the prevalence of 23 major tree species changes. We realize that it is not the ‘how’ but the ‘how fast’ species’ prevalence changes that differs between the scenario variants. We use this finding to develop a categorization of species groups that integrates the uncertainty in future climate. Twin regions provide further information on silvicultural practices, pest management, product chains etc.
Identifying the Source of Heavy Metal Pollution and Apportionment in Agricultural Soils Impacted by Different Smelters in China by the Positive Matrix Factorization Model and the Pb Isotope Ratio Method
Authors: Yu Danyang , Wang Jingran , Wang Yanhong , Du Xueli , Li Guochen , Li Bo
Published: June 2021

In this study, the agricultural soil around Zhuzhou Smelter in Zhuzhou district, Hunan, China and Huludao Zinc Plant in Huludao district, Liaoning, China was selected as the research area to discuss the current situation of heavy metal pollution in the surrounding agricultural soil caused by different smelting plants for soil environmental management and sustainable development of soil resources. Eight elements’ (Cd, Pb, As, Hg, Cr, Ni, Cu, and Zn) contents were measured to assess their pollution risk level and spatial distribution distinction. Correlation analysis, the positive matrix factorization (PMF), and Pb isotope ratio method were employed to analyze the sources of soil heavy metal pollution in the research area. The contents of Cd, Pb, Hg, and Zn in the soil of the two research areas were seriously polluted, and the changes of their spatial content were related to the migration and sedimentation of the smelter waste gas. Four types of pollution sources, including the smelting source, agricultural sources, natural sources, and mixed sources of industrial activity and traffic were identified in both areas by PMF, and the contribution rates of the four pollution sources in both areas were similar. Taking the agricultural soil around Huludao Zinc Plant as an example, the contribution rates of the different pollution sources analyzed by Pb isotope ratio method were the lead smelting source (43.7%), followed by the agricultural source (34.6%), traffic source (14.2%), and natural source (7.5%), which were basically consistent with that of PMF analysis, verifying the reliability of the two methods. The results above showed that the smelters were the main cause of heavy metal pollution in agricultural soils around the two research areas, and the analysis results of element content ratio and smelting source characteristic element contribution rate ratio could provide reference for the analysis of heavy metal pollution in agricultural soil around smelters for soil pollution control decision making.
Applying Talent Quality-Management System (TTQS) to Enhance Information Literacy, Learning Motivation, and Computational Thinking Competency of Nursing Undergraduates
Authors: Wu Pei-Jung , Hou Hsing-Yu , Huang Cheng-Chih
Published: June 2021

In Taiwan, the Ministry of Education started promoting computational thinking (CT) and design skills in the programming curriculum in 2018 at all universities. CT, which has been widely discussed in the field of education, is the ability to solve problems that use computer science concepts. For students in non-information technology fields, such as nursing students, a curriculum that integrates nursing education and information literacy can bolster evidence-based practice, enhance professional development, and encourage lifelong learning. However, the programming courses were difficult for non-information learners to apprehend, given their lack of knowledge in programming, and had a low attrition rate. Therefore, it is necessary to establish a sustainable management system in the learning process. It is also important to support the interest and confidence of the nursing learner in the programming curriculum. In this study, a programming course for nursing students was completed. A talent quality-management system (TTQS) for this programming course was built and implemented. A technology acceptance model and learning attitude questionnaires were administered to investigate learners’ learning motivation and information literacy. Two nursing classes with 74 freshmen participated in the curriculum in the second semester of the 2020 academic year. The results showed that TTQS, which supports teachers in adjusting pedagogy in a timely manner, can enhance learners’ motivation and performance. In this programming course, nursing students can learn CT and information concepts and improve their learning motivation through the design of learner-centred and collaborative learning.
Examining Effect of Green Transformational Leadership and Environmental Regulation through Emission Reduction Policy on Energy-Intensive Industry’s Employee Turnover Intention in China
Authors: Li Liang , Zhu Bangzhu , Che Xiahui , Sun Huaping , Tan Meixuen
Published: June 2021

This study explored a theoretical model of factors affecting employee turnover intentions in the energy-intensive industry from the perspective of environmental regulation through emission reduction policy. Moreover, we examined whether green transformational leadership has a negative effect on the perception of turnover risk of energy-intensive corporate employees and their turnover intentions; we collected data on 531 employees in the energy-intensive industries in China. Data analysis was conducted using exploratory factor analysis, reliability and validity analysis, stepwise regression model analysis, and a structural equation model to test the research hypothesis. The results revealed that environmental regulation through emission reduction policy has a significant impact on employee perception of turnover risk and turnover intention in energy-intensive industries in China. The perception of turnover risk has a greater effect on the turnover intention among employees than the emission reduction policy. Moreover, we found that the perceived risk of turnover has a mediating effect in the relationship between environmental regulation through carbon emission reduction policy and turnover intention. However, green transformational leadership has an inhibiting effect on the perception of turnover risk and turnover intention among employees. This research has crucial theoretical significance for the transformation of energy-intensive enterprises and promoting the sustainable development of energy-intensive enterprises in China.
Sustainability of Recycling Waste Picker Sustainopreneurs for Prevention and Mitigation of Municipal Solid Waste in Swat
Authors: Nawaz Muhammad , Yousafzai Muhammad Tariq , Shah Tariq , Xin Chunlin , Ahmad Wisal
Published: June 2021

This study assessed the role of waste picker sustainopreneurs (WPS) by examining their recycling contributions in a special case of District Swat, Pakistan. Using a transformative worldview, this study acknowledges their contributions to recycling. The study envisages how entrepreneurial agency works in unanticipated ways where the poorest of the poor enterprise without resources demonstrate unexpected agency to improve the environment. An inductive research design in conjunction with grounded theory was used to analyze data from 37 interviews in three tehsils of District Swat. The recycling sector uses primitive measures, which are completely informal, self-organized, and self-controlled. Surprisingly, we discovered that informal recycling contributes unfavorably to waste, energy, and food (WEF) security due to intermixing and adulteration, as well as acts as a gray channel for illicit practices that have taken advantage of tax amnesty in the area until 2023. The uncontrolled welding of half cut and nose cut car parts has skyrocketed the motorization index and CO2 emissions; however, it has also resulted in alternative sources of livelihoods, as these accidental environmentalists had found modern sources of income. This is similar to low-tech innovation and business creation that takes advantage of tax holidays due to the special status of District Swat. The study highlights the most and least valuable recyclables and identifies the gray channel markets of spare parts, metal recycling, counterfeit products, fuel intermixing, and adulteration. The study contributes by untangling the understanding of a legal gambit of tax amnesty as a critical pre-policy input as well as advocates for rights of invisible stakeholders of waste management in Pakistan.
Environmental Risk Assessment of a Diesel Fuel Tank: A Case Study
Authors: Oulehlova Alena , Tušer Irena , Rehak David
Published: June 2021

The article deals with the application of the environmental damage assessment procedure and H&V index II method to the diesel fuel tank storage facility in a sand mining company using a case study. The procedures enabled the researchers to semi-quantitatively assess the operated diesel fuel tank’s impact on the selected environmental components and the possible damage risk by the leakage of stored fuel. It was discovered, by assessing the operating conditions, the state of the environment at the mining facility, and the risk of a diesel fuel leakage accident, that it is not necessary for the company to implement further steps in the field of environmental damage minimization. The H&V index II method examined both the impacts of diesel fuel leakage on soil, biotic component, groundwater, surface water, and the impact of flammable substances on the biotic environmental component in six steps. Slight or significant impacts were identified depending on the environmental component during the determination of the accident severity. The accident severity, together with the estimated probability, was plotted in the risk matrix which resulted in acceptable risks for all affected environmental components. The results of both approaches showed that the diesel fuel leakage in the mining company represents an acceptable environmental risk in relation to the countermeasures implemented so far.
Minimum Wage and Household Consumption in Canada: Evidence from High and Low Wage Provinces
Authors: Sabokkhiz Leila , Guven Lisaniler Fatma , Nwaka Ikechukwu D.
Published: June 2021

The minimum wage is a major factor for the successful implementation of much of the sustainable development goals (SDGs). The present research will investigate whether minimum wage (MW) as a sustainable wage policy improves household consumption. Thus, a panel-based analysis comparing high wage (Alberta, British Columbia, Ontario, and Saskatchewan) and low wage provinces (Manitoba, New Brunswick, Newfound land/Lab, Nova Scotia, Prince Edward Island, and Quebec) is employed for the Canadian case within the study period from 1981 to 2019. We analyze the long-term and short-term effects of MW on household consumption using the Dynamic Autoregressive Distributed Lag techniques of the Pooled Mean Group, Dynamic Fixed Effects, and Mean Group estimators. Results show that the long-term impact of MW on household consumption is positive in both the low- and high-wage provinces. The short-term effect is negative in both wage groups, but not significant for the low-wage group. This offers significant debate on the relevance of the MW towards economic stabilization and consumption-led growth.
Could Unsustainable Electronics Support Sustainability?
Authors: Moreau Nicolas , Pirson Thibault , Le Brun Grégoire , Delhaye Thibault , Sandu Georgiana , Paris Antoine , Bol David , Raskin Jean-Pierre
Published: June 2021

Information and communication technologies are often considered by policymakers, industrial stakeholders and scientists as a key lever in the run towards sustainability, since they should ease energy efficiency and dematerialization. In this opinion article, nurtured by the inputs of a broad panel of experts, we challenge this widely spread view by highlighting the detrimental social and environmental footprints caused by digital technologies. We further take a critical look on the ways innovation is conducted nowadays, i.e., with an almost exclusive focus on performance and few considerations for externalities. This leads us to call for academic teaching programs advocating for a holistic approach, for new business models, and for ambitious political decisions able to drive a paradigm shift in the mainstream agenda of electronics innovation and digital transition that shall significantly contribute to the well-being of everyone, everywhere, without compromising future generations. We conclude that digital technologies cannot support long-term sustainability if their only purpose remains the optimization of the current system.
Game Theory-Based Minimization of the Ostracism Risk in Construction Companies
Authors: Kifokeris Dimosthenis , Xenidis Yiannis
Published: June 2021

Strategic and managemerial decision-making in an organization can have a crucial effect for the whole entity; however, it rarely involves the organization’s employees evenly at the different organizational levels. The result is—what is addressed in this paper as—the ostracism risk, namely the risk accruing from the lack of satisfaction of underprivileged employees’ groups during the decision-making process. The ostracism risk could jeopardize the organization’s integrity and therefore requires effective treatment. This paper aims at verifying a conceptual approach, which is proposed as a methodology for assessing the probability of organizational cooperation when deciding under risk, thus minimizing ostracism risk. The proposed approach is based on organizational and human resources management (HRM) theories and is contextualized for construction through the understanding of systems theory. The proposed methodology presents a potential modelling via game theory of a medium-sized construction company that is organized according to Mintzberg’s organizational model. The utilization of the bounded Pareto distribution is presented as an approach of the model’s probabilistic processing, and the potential for estimating the probabilities to adopt a favorable cooperational decision is verified. The paper concludes with the reference to the next steps required for the methodology’s validation and further improvement.
Nonintrusive Residential Electricity Load Decomposition Based on Transfer Learning
Authors: Yang Mingzhi , Liu Yue , Liu Quanlong
Published: June 2021

Monitoring electricity consumption in residential buildings is an important way to help reduce energy usage. Nonintrusive load monitoring is a technique to separate the total electrical load of a single household into specific appliance loads. This problem is difficult because we aim to extract the energy consumption of each appliance by only using the total electrical load. Deep transfer learning is expected to solve this problem. This paper proposes a deep neural network model based on an attention mechanism. This model improves the traditional sequence-to-sequence model with a time-embedding layer and an attention layer so that it can be better applied in nonintrusive load monitoring. In particular, the improved model abandons the recurrent neural network structure and shortens the training time, which means it is more appropriate for use in model pretraining with large datasets. To verify the validity of the model, we selected three open datasets and compared them with the current leading model. The results show that transfer learning can effectively improve the prediction ability of the model, and the model proposed in this study has a better performance than the most advanced available model.
The Determinants of Tourist Expenditure Per Capita in Thailand: Potential Implications for Sustainable Tourism
Authors: Chulaphan Wanvilai , Barahona Jorge Fidel
Published: June 2021

Tourism authorities in Thailand have consistently pursued profit-seeking mass tourism, resulting in the detriment of the natural resources in major tourist destinations. In response, sustainable tourism projects centered on preserving the environment have been established but neglect the financial needs of tour operators. The objective of this study was to investigate the determinants of tourist expenditure per capita in Thailand using a dataset consisting of 31 countries from 2010 to 2017. The analysis was based on an autoregressive distributed lag model (ARDL) and used a panel estimated generalized least square (ELGS). Generating such knowledge is essential for tourist authorities to develop profitable and sustainable tourism projects in tourist destinations whose natural resources have been affected by profit-seeking tourism. The tourism expenditure per capita is positively affected by word of mouth, income, and the rising prices in other major tourist destinations in Asia. However, it was negatively affected by relative levels of price and corruption. Sustainable tourism projects can be used to develop activities that will help distinguish Thailand from other tourism destinations in Asia. However, in implementing these sustainable tourism initiatives, the mark-up should be minimized to keep tourist prices in Thailand competitive.
Corporate Social Responsibility in the COVID-19 Pandemic Period: A Traditional Way to Address New Social Issues
Authors: Raimo Nicola , Rella Angela , Vitolla Filippo , Sánchez-Vicente María-Inés , García-Sánchez Isabel-María
Published: June 2021

The COVID-19 pandemic has had serious health, social and economic consequences. In this difficult context, companies are called upon to implement corporate social responsibility (CSR) activities to support society and the weakest individuals. This study examines how Spanish companies have supported society and vulnerable individuals through partnerships with non-governmental organizations (NGOs). In this regard, a multiple case study analysis based on 14 companies listed on the Madrid Stock Exchange that have created collaborations with different NGOs was conducted. The results show that these partnerships are mainly aimed at providing food, health, social and technological support to society and vulnerable individuals. To the best of our knowledge, this is the first study that examines the support provided by companies to society during the COVID-19 pandemic in the European context.