bluuebunny/crossref_metadata_2025 huggingface dataset:
Detection of Surface Crevasses over Antarctic Ice Shelves Using SAR Imagery and Deep Learning Method
Authors: Zhao Jingjing , Liang Shuang , Li Xinwu , Duan Yiru , Liang Lei
Published: January 2022

Crevasses are formed by glacier movement and the stresses within glacier ice. Knowledge of the crevasses’ distribution is critical for understanding the glacier and ice shelf stability. In this study, we propose an automated crevasse extraction framework based on Sentinel-1 SAR imagery and an improved U-Net network. The spatial distribution of crevasses on Antarctic ice shelves in 2020 was mapped with a spatial resolution of ~40 m, and the characteristics of crevasses on the Nickerson Ice Shelf, Jelbart Ice Shelf, Amery Ice Shelf, Thwaites Glacier, and Shackleton Ice Shelf were analyzed. The results indicated the extraction accuracy of our method was 84.2% and the F1 score was 72.5%. Compared with previous published studies, the identification of the crevasse areas had good visual consistency. However, in some scenes, the recall rate was relatively lower due to the quality of the SAR image, terrain surrounding the crevasses, and observation geometry. The crevasses on different ice shelves had different characteristics in terms of length, density, type, and spatial pattern, implying the different stress structures of ice shelves. The Thwaites Glacier and the Nickerson Ice Shelf in the West Antarctica Ice Sheet (WAIS) had shorter ice crevasses, whereas the lengths of ice crevasses on the Jelbart Ice Shelf and the Amery Ice Shelf in the East Antarctica Ice Sheet (EAIS) were relatively long. Nevertheless, there are more closely spaced crevasses on the ice shelf in WAIS compared to that in the EAIS. For the distribution of crevasse types, the Nickerson Ice Shelf and the Shackleton Ice Shelf had various forms of crevasses. There were mainly transverse crevasses developed on the Jelbart Ice Shelf and the Amery Ice Shelf. This study provides a helpful reference and guidance for automated crevasse extraction. The method proposed by this study manifests great application potential and the efficacy of producing a time-series crevasse data set with higher spatial resolution and larger coverage. In the future, more Sentinel-1 SAR imagery will be applied and the effect of temporal and spatial variations in crevasses on the stability of ice shelves will be investigated, which will contribute to project the ice shelf stability and explore the sea level rise implications of recent and future cryosphere changes.
Multi-Coil FD-EMI in Tidal Flat Areas: Prospection and Ground Truthing at a 17th Century Wooden Ship Wreckage
Authors: Wilken Dennis , Zwick Daniel , Majchczack Bente Sven , Blankenfeldt Ruth , Erkul Ercan , Fischer Simon , Bienen-Scholt Dirk
Published: January 2022

We present a case study of multi-coil frequency-domain electromagnetic (FD-EMI) prospection of a wooden ship wreckage from the 17th century. The wreckage is buried in a sandbar in the German part of the tidal flat area of the North Sea. Furthermore, the wreckage was excavated in advance and covered again after investigation. This ground truthing background and the position of the wreckage makes it a unique investigation object to test the feasibility of FD-EMI for prospecting wooden archaeological objects in the high conductive sediments of tidal flat areas. Our results reveal the shape and position of the wreckage in terms of conductivity maps. The resulting signal change caused by the wreckage in conductivity is only 10% of the value of the water-saturated sandy background, respectively, making a cautious process necessary, including a precise height correction. The data, furthermore, reveals a sensitivity to the vertical shape of the wreckage and thus sufficient depth sensitivity, but with reduced sensing depth. The study highlights the great potential of EMI for both in situ heritage management and archaeological research in the Wadden Sea.
Direct Georeferencing UAV-SfM in High-Relief Topography: Accuracy Assessment and Alternative Ground Control Strategies along Steep Inaccessible Rock Slopes
Authors: Nesbit Paul Ryan , Hubbard Stephen M. , Hugenholtz Chris H.
Published: January 2022

Steep rock slopes present key opportunities and challenges within Earth science applications. Due to partial or complete inaccessibility, high-precision surveys of these high-relief landscapes remain a challenge. Direct georeferencing (DG) of unoccupied aerial vehicles (UAVs) with advanced onboard GNSS receivers presents opportunities to generate high-resolution 3D datasets without ground-based access to the study area. However, recent research has revealed large vertical errors using DG that may prove problematic in near-vertical terrain. To address these concerns, we examined more than 75 photogrammetric UAV-datasets with various imaging angles (nadir, oblique, and combinations) and ground control scenarios, including DG, along a steep slope exposure. Results demonstrate that mean errors in DG scenarios are up to 0.12 m higher than datasets using integrated georeferencing with well-distributed GCPs. Inclusion of GCPs greatly reduced mean error values but had limited influence on precision (<0.01 m) for any given imaging strategy. Use of multiple image angles resulted in the highest precisions, regardless of georeferencing strategy. These findings have implications for applications requiring the highest precision and accuracy (e.g., geotechnical engineering, hazard mitigation and mapping, and geomorphic change detection), which should consider using ground control whenever possible. However, for applications less concerned with absolute accuracy, our results show that DG datasets provide strong internal consistency and relative accuracy that may be suitable for high precision measurements within a model, without use of ground control.
Detection and Tracking of Weak Exoatmospheric Target with Elliptical Hough Transform
Authors: Rao Bin , Zhou Yongkun , Nie Yuanping
Published: January 2022

An elliptical Hough transform (EHT) algorithm is proposed in the framework of track-before-detect (TBD) for joint detection and tracking of weak exoatmospheric targets. The new approach exploits the fact that when restricted to a two-body problem, the exoatmospheric target often follows an elliptical orbit, and thus the Hough transform integrated with orbital geometry information would have better detection performance. The relationship between the original radar measurements in data space and the elliptical parameters in parameter space is explicitly derived with multiple steps of coordinate transformation. It is found that the data points mapping into the parameter space essentially represent a quartic curve. An EHT-based algorithm is then designed, and orbit planarity is also taken into account to reduce the effect of noise accumulation. The influences of primary and secondary thresholds and the signal-to-noise ratio (SNR) on the detection performance are compared by simulations. Additionally, a real radar tracking dataset from a scientific satellite on 28 May 2017 is used to investigate the efficiency of the method. By adding some imaginary clutter to the raw orbit, the results indicate that it is very effective in detecting the real satellite trajectory in a low signal-to-noise ratio (SNR) environment. The advantage of the new method lies in it can not only simultaneously detect and track weak exoatmospheric targets but also can predict the trajectory by using these available detected parameters.
Mapping Plant Diversity Based on Combined SENTINEL-1/2 Data—Opportunities for Subtropical Mountainous Forests
Authors: Yang Qichi , Wang Lihui , Huang Jinliang , Lu Lijie , Li Yang , Du Yun , Ling Feng
Published: January 2022

Plant diversity is an important parameter in maintaining forest ecosystem services, functions and stability. Timely and accurate monitoring and evaluation of large-area wall-to-wall maps on plant diversity and its spatial heterogeneity are crucial for the conservation and management of forest resources. However, traditional botanical field surveys designed to estimate plant diversity are usually limited in their spatiotemporal resolutions. Using Sentinel-1 (S-1) and Sentinel-2 (S-2) data at high spatiotemporal scales, combined with and referenced to botanical field surveys, may be the best choice to provide accurate plant diversity distribution information over a large area. In this paper, we predicted and mapped plant diversity in a subtropical forest using 24 months of freely and openly available S-1 and S-2 images (10 m × 10 m) data over a large study area (15,290 km2). A total of 448 quadrats (10 m × 10 m) of forestry field surveys were captured in a subtropical evergreen-deciduous broad-leaved mixed forest to validate a machine learning algorithm. The objective was to link the fine Sentinel spectral and radar data to several ground-truthing plant diversity indices in the forests. The results showed that: (1) The Simpson and Shannon-Wiener diversity indices were the best predicted indices using random forest regression, with ȓ2 of around 0.65; (2) The use of S-1 radar data can enhance the accuracy of the predicted heterogeneity indices in the forests by approximately 0.2; (3) As for the mapping of Simpson and Shannon-Wiener, the overall accuracy was 67.4% and 64.2% respectively, while the texture diversity’s overall accuracy was merely 56.8%; (4) From the evaluation and prediction map information, the Simpson, Shannon-Wiener and texture diversity values (and its confidence interval values) indicate spatial heterogeneity in pixel level. The large-area forest plant diversity indices maps add spatially explicit information to the ground-truthing data. Based on the results, we conclude that using the time-series of S-1 and S-2 radar and spectral characteristics, when coupled with limited ground-truthing data, can provide reasonable assessments of plant spatial heterogeneity and diversity across wide areas. It could also help promote forest ecosystem and resource conservation activities in the forestry sector.
Maize Characteristics Estimation and Classification by Spectral Data under Two Soil Phosphorus Levels
Authors: Qiao Baiyu , He Xiongkui , Liu Yajia , Zhang Hao , Zhang Lanting , Liu Limin , Reineke Alice-Jacqueline , Liu Wenxin , Müller Joachim
Published: January 2022

As an essential element, the effect of Phosphorus (P) on plant growth is very significant. In the early growth stage of maize, it has a high sensitivity to the deficiency of phosphorus. The main purpose of this paper is to monitor the maize status under two phosphorus levels in soil by a nondestructive testing method and identify different phosphorus treatments by spectral data. Here, the Analytical Spectral Devices (ASD) spectrometer was used to obtain canopy spectral data of 30 maize inbred lines in two P-level fields, whose reflectance differences were compared and the sensitive bands of P were discovered. Leaf Area Index (LAI) and yield under two P levels were quantitatively analyzed, and the responses of different varieties to P content in soil were observed. In addition, the correlations between 13 vegetation indexes and eight phenotypic parameters were compared under two P levels so as to find out the best vegetation index for maize characteristics estimation. A Back Propagation (BP) neural network was used to evaluate leaf area index and yield, and the corresponding prediction model was established. In order to classify different P levels of soil, the method of support vector machine (SVM) was applied. The results showed that the sensitive bands of P for maize canopy included 763 nm, 815 nm, and 900–1000 nm. P-stress had a significant effect on LAI and yield of most varieties, whose reduction rate reached 41% as a whole. In addition, it was found that the correlations between vegetation indexes and phenotypic parameters were weakened under low-P level. The regression coefficients of 0.75 and 0.5 for the prediction models of LAI and yield were found by combining the spectral data under two P levels. For the P-level identification in soil, the classification accuracy could reach above 86%. These abilities potentially allow for phenotypic parameters prediction of maize plants by spectral data and different phosphorus contents identification with unknown phosphorus fertilizer status.
Characteristics and Impacts of Conflict-Related Sexual Violence against Men in the DRC: A Phenomenological Research Design
Authors: Yagi Ines , Malette Judith , Mwindo Timothee , Maisha Buuma
Published: January 2022

There is increased evidence of the existence of sexual violence against men and boys in many war-stricken areas. Yet, there are still discrepancies in understanding male victims’ experience in depth. Furthermore, limited research on sexual violence against men in the context of the war in the Eastern Region of the Congo has been undertaken to date. As part of addressing this knowledge gap, a phenomenological study was conducted to shed light and understand the experience of male survivors of sexual violence. The participants were males who experienced sexual violence in the war. Individual semi-structured interviews were conducted. The findings show that participants experienced a wide range of psychological and physical wounds other than rape. Their experience during the event (s) falls under the umbrella term gender-based violence (GBV) which encompasses other forms of harmful acts against one’s will including sexual assault, genital mutilation, acts of penetration with different objects, and cultural inappropriate actions with intention to sexually harass and humiliate. The results show a wide and complex range of short and long-term impact on multiple levels. The findings add clarification and understanding to the controversial and taboo subject around conflict-related sexual violence against men in the Congo. They shed light on how the understanding of gender impacts participants’ masculine identity, their sexual victimization experience, and healing journey.
Analysis of Child Traffic Safety near Primary School Areas Using UAV Technology
Authors: Renard Anton , Novačko Luka , Babojelić Karlo , Kožul Nikola
Published: January 2022

Road safety in primary school areas is a delicate issue due to the vulnerability of children as road users. They are inexperienced traffic participants and sometimes their behavior in traffic situations is unpredictable. This paper reports a safety analysis conducted using video processing of conflict trajectories of vehicles and active transportation users (AT users). The videos were collected using unmanned aerial vehicles (UAVs) as this technology does not affect the actual behavior of traffic participants. Due to their airborne position, UAVs can conveniently gather information about driving behaviors and the exact positions of various participants. The safety analysis was conducted using surrogate indicators suitable for evaluating the risk of potential collisions as they incorporate both spatial proximity and speed. Three conflict indicators were used in the safety analysis: (i) time-to-collision, (ii) post-encroachment time, and (iii) heavy braking. The methodology was tested in a primary school area in the city of Zagreb. With the applied methodology, a total of 43 potential conflicts were identified in the school area (14 time-to-collision, 23 post-encroachment time, and 6 heavy braking). Based on the determined potential conflicts, safety measures were proposed to decrease the number and severity of potential conflicts and to increase traffic safety near school areas.
Accountability in the Social Economy: The Case of Private Social Solidarity Institutions
Authors: Ferreira Augusta , Santos Carlos , Inácio Helena , Costa Alberto J. , Bandeira Ana Maria , Tomé Brízida , Joaquim Carla , Góis Cristina , Curi Denise , Meira Deolinda , Azevedo Graça , Jesus Mafalda , Teixeira Maria Goreti , Monteiro Patrícia , Duarte Rúben , Marques Rui Pedro
Published: January 2022

Given the extreme importance of improving the accountability of private social solidarity institutions (IPSS), both for reasons of legal compliance and for reasons of improving legitimacy and notoriety among their stakeholders, in order to be accountable to them and in order to maintain their sustainability, this article aims to present a framework designed under a more comprehensive research project for the assessment of IPSS accountability, as well as the preliminary results of a pilot test of Portuguese IPSS. The framework was developed from a combination of methodologies that included a literature review, field work and a focus group, resulting in six dimensions with 76 indicators. For the pilot test, the data were collected by questionnaire for the years 2018, 2019 and 2020. The results of the pilot test, despite the limited number of entities, allowed the identification of some trends and indicators where entities show lower results and where they will have to focus to improve their accountability. Some possible effects of the COVID-19 pandemic were also identified. Therefore, we believe that the framework designed answers the research question: how can we promote accountability (social, financial and economic) in the social economy sector, in particular in the case of IPSS?
Recovery after Demerger: Evidence from Romanian Companies
Authors: Duguleana Constantin , Duguleana Liliana , Baba Camelia Mirela , Drumea Cristina
Published: January 2022

The paper follows the demerger phenomenon in Romania in order to find out whether companies regain their economic performance after reorganization. The research is based on four samples of companies, divided into 2012, 2013, 2014, and 2015, that recorded their financial indicators in the period from 2005–2019. Using the financial indicators of companies that demerged in the same year, we analyzed the economic performances before and after the demergers, using statistical and econometric methods. The model with the fixed effects of the cross sections proved to be the most suitable for each panel, both for the entire analyzed period and for the two subperiods: ante and post demerger. The subperiod models are better than the panel econometric models for the entire period. The results show that all of the Romanian companies recovered after the demergers, and also to what extents. The validities of the econometric models confirm the sustainability of the economic activities after the demergers. This paper provides a study methodology and econometric models to investigate the demerger phenomenon among Romanian companies.
Sustainability in Geotechnics through the Use of Environmentally Friendly Materials
Authors: Vieira Castorina S.
Published: January 2022

The reduction in the exploitation of non-renewable natural resources is nowadays widely recognized as a pressing need for a more sustainable society [...]
An Evolutionary Game Model of the Supply Decisions between GNPOs and Hospitals during a Public Health Emergency
Authors: Yuan Youwei , Du Lanying , Li Xiumei , Chen Fan
Published: January 2022

The distribution of medical supplies tied to the government-owned nonprofit organizations (GNPOs) is crucial to the sustainable and high-quality development of emergency response to public health emergencies. This paper constructs a two-sided GNPO–hospital game model in a Chinese context, and explores the strategies and influencing factors of medical supply distribution in public health emergencies based on evolutionary game theory. The results show that: (1) GNPOs, as the distributor of medical supplies, should choose strategies that balance efficiency and equity as much as possible. (2) Hospitals, as the recipient of medical supplies, should actively choose strategies that maximize the total benefit to society and strengthen trust in GNPOs. Meanwhile, hospital managers need to pay attention to reducing the impact of communication and coordination costs and strive for the reduction of conflicts between different values. (3) The government should strengthen supervision to avoid conflicts between medical distributors and receivers during a public health emergency and ensure the rescue efficiency. This study provides some reference for the sustainable development of emergency relief in public health emergencies.
A Hawaiian Tropical Dry Forest Regenerates: Natural Regeneration of Endangered Species under Biocultural Restoration
Authors: Libby Reko , Sato Aimee Y. , Alapai Lehua , Brawner Wilds Pihanui , Carter Yvonne Yarber , Carter Keoki Apokolani , Tomich Kekaulike , Ticktin Tamara
Published: January 2022

Tropical dry forests (TDFs) are among the most at-risk ecosystems globally. In Hawai‘i, more than 45% of TDF species are threatened or endangered. Despite decades of active TDF restoration, there remains limited information on the potential for long-term success, since there are few studies of natural regeneration. We assess natural regeneration of endangered plants at Ka‘ūpūlehu dryland forest, a Hawaiian biocultural restoration initiative. Drawing on 6 annual censuses we (1) assess rates and patterns of natural regeneration across species and as a function of rainfall and (2) identify bottlenecks. Our surveys document natural recruits of 11 of the 12 endangered species first outplanted 15–20 years ago. Higher annual rainfall increased the number of new recruits per year and growth, but decreased survival of larger recruits. The total number of natural recruits increased three-fold over the study period and varied across species. For nearly half of the species, we documented a second generation of recruits. Successes appear to be a function of time, including a changing microclimate and adaptive management practices. Remaining bottlenecks include lack of seed dispersal, and seed predation and herbivory by introduced species. The success at Ka‘ūpūlehu highlights the potential for TDF restoration and the value of a biocultural approach.
Afforestation of Transformed Savanna and Resulting Land Cover Change: A Case Study of Zaria (Nigeria)
Authors: Kurowska Ewa E. , Czerniak Andrzej , Garba Muhammad Lawal
Published: January 2022

This paper presents the effects of actions aiming at tree planting, and thus protection of the land belonging to Ahmadu Bello University (ABU) in Zaria (Nigeria) from erosion. This area is located within the northern limits of the Guinea savanna belt, which has been considerably transformed primarily for agricultural purposes. For years this area has been subjected to the destructive surface and gully erosion processes, which were documented both by field work and the analysis of high-resolution images presented by Google Earth. Land erosion leads to the silting of an impounding reservoir (Kubanni)—the primary water source for the university campus and the basis for its existence. Since 2000, the university has been actively involved in tree-planting efforts in the campus vicinity, executed mainly through the establishment of small plantations ranging in area from approximately 1 ha to 56 ha. Today, these measures have brought tangible effects such as increment of tree-planted expanses, as evidenced in the multitemporal analysis of the Landsat 7 ETM+ and Landsat 8 OLI satellite images. The increasing planted area protects both the soil and the reservoir. A marked stand increment (almost doubled) has been recorded, which is evident from the satellite imageries, and is presented on the land-use map produced with GIS technique for this project.
Assessing the Environmental Footprint of Distiller-Dried Grains with Soluble Diet as a Substitute for Standard Corn–Soybean for Swine Production in the United States of America
Authors: Haque Md Ariful , Liu Zifei , Demilade Akinbile , Kumar Nallapaneni Manoj
Published: January 2022

The swine diet formulation in the United States of America (U.S.A.) is entering a new era of decision making to promote low-carbon pork production systems. As a part of the decision-making process, the precision nutrition approaches to customize diet and alternative feeding options that are economically viable and environmentally sustainable are given priority. Hence, the objective of this study is to identify an alternative diet over a standard corn–soybean meal diet. The byproducts from the supply chain of human food and biofuels, i.e., distiller-dried grain with solubles (DDGS), are chosen as an alternative option to formulate a swine diet. First, two alternative byproduct diets with low and high DDGS inclusion (10.1% and 28.8%, respectively) were formulated using the least-cost technique. Second, a life cycle inventory was created, followed by data collection from the key sources, including DATA SMART-2017, USDA, RIA-GREET 2018, and the relevant literature. Third, in SimaPro 8.5.2.0 (PRé Sustainability: LE Amersfoort, The Netherlands), the ReCiPe 2016, the midpoint method by economic allocation was used to investigate the environmental footprint of the formulated diets to inform sustainability decisions of swine-farm managers. The considered functional unit is the ‘lb diet’, and the system boundary is the farm gate that considers only the feed production stage. The observed results include global warming potential, land use, water consumption, fossil resources scarcity, and terrestrial ecotoxicity. The comparative results of a 28.8% DDGS diet over the standard corn–soybean meal diet for the displacement ratio of 0.69 show an approximate global warming potential saving of 0.04 kg CO2 eq. per lb DDGS feed at the feed production stage. Moreover, the DDGS displacement ratio of 0.69 does not significantly impact water consumption and fossil resources; however, it can reduce land use by 26% and terrestrial ecotoxicity by 8% compared to the standard diet. Overall, the quantified environmental footprint results of the byproduct DDGS diets indicate that the footprints of DDGS diets were lower than the standard diet.
Influence of Tourism Disturbance on Soil Microbial Community Structure in Dawei Mountain National Forest Park
Authors: Li Qunjun , Dai Meiqi , Luo Fen
Published: January 2022

This research aimed to reveal the response characteristics of soil microbial community structure to different degrees of tourism disturbance. To explore the soil microbial community structure’s response mechanism, we set up continuous plots with different interference intensities: high disturbance, middle disturbance, and the control area. We collected 0–10 cm topsoil in all plots and used Illumina MiSeq high-throughput sequencing method to obtain and analyze the response characteristics of soil microbial community composition and structure under different tourism disturbances. These results were then combined with alpha diversity and environmental factors to explore the microbial response mechanism. In the tested soil, Acidobacteria, Chlorocurve, and Proteobacteria were the main bacterial phyla, while Basidiomycota and Ascomycota were the main fungal phyla. Based on the phylum, the relative abundance of the microbial community between the interference groups was compared using a significance test, with significant differences found between the interference groups in the phyla Chloroflexus, GAL15, Rokubacteria, and Blastomonas (p < 0.05). The relative abundance of the dominant phyla in the fungal community was significantly different among the groups (p < 0.05). A principal component analysis of the soil microbial community structure suggested that the soil microbial community structure was significantly different for different interference levels.
Energy Management Strategy of a Novel Electric Dual-Motor Transmission for Heavy Commercial Vehicles Based on APSO Algorithm
Authors: Liang Jiajia , Xu Xiangyang , Dong Peng , Feng Tao , Guo Wei , Wang Shuhan
Published: January 2022

With the development of electric vehicles, dual-motor transmission has become a potential alternative for automated manual transmission (AMT) due to the solution of power interruption and the improvement of energy efficiency. In this paper, a novel electric dual-motor transmission (eDMTP) for heavy commercial vehicles is proposed. Then, a 4-layer energy management strategy is developed to optimize dynamics performance and energy efficiency. Subsequently, a real vehicle operation is performed to validate the control strategy and the performance of eDMTP. The results demonstrate that the operating points of the two motors are both in and around the high-efficiency area under normal mode. This research lays the foundation for the development of a pure electric vehicle transmission system.
Belt and Road Initiative in Times of ‘Synchronized Downturn’: Issues, Challenges, and Opportunities
Authors: Visvizi Anna , Lytras Miltiadis D.
Published: January 2022

In 2013, the launch of the Belt and Road Initiative (BRI) triggered questions among experts and academicians [...]
Assessment of Clean Energy Transition Potential in Major Power-Producing States of India Using Multi-Criteria Decision Analysis
Authors: Indrajayanthan Venkatraman , Mohanty Nalin Kant
Published: January 2022

India has an ambitious target to promote clean energy penetration, but as of 2021, the electricity mix of India is dominated by coal to about 71%. Therefore, analyzing the clean energy potential and the ability of the individual states to entrench energy transition in the upcoming years will be supportive for policymakers. This study is propounded to assess the clean energy transition potential with a focused analysis on seven major power-producing states of India. These states include Maharashtra, Gujarat, Tamil Nadu, Uttar Pradesh, Karnataka, Madhya Pradesh, and Andhra Pradesh. The clean energy transition potential assessment is performed by utilizing multi-criteria decision analysis methodologies such as the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Multi-Objective Optimization Method by Ratio Analysis (MOORA). Further, the analysis is performed against four major criteria that include high carbon energy resource dependency, low carbon energy resource dependency, clean energy potential, and policy support. Altogether, the assessment criteria include four primary level criteria and fourteen secondary level parameters. In order to reflect the significance of each parameter and criterion to the characteristics of clean energy transition potential, appropriate weightage is provided using the Fuzzy Analytic Hierarchy Process (AHP). The results indicate that Gujarat has the highest clean energy transition potential in both the multi-criteria decision analysis methods. On the other hand, Uttar Pradesh exhibited the least performance, and a complete energy transition to clean energy resources is less likely in this state. The rest of the states obtained intermediate ranking, and a comparative analysis between the two methods was also accomplished. This study suggests that India should focus on the clean energy policy with vigorous efforts on top-performing states which will effectively accelerate the power sector decarbonization.
Analysis of Influencing Factors and Trend Forecast of CO2 Emission in Chengdu-Chongqing Urban Agglomeration
Authors: Zeng Huibin , Shao Bilin , Bian Genqing , Dai Hongbin , Zhou Fangyu
Published: January 2022

Urban agglomeration is a primary source of global energy consumption and CO2 emissions. It is employed as a major means of modern economic and social activities. Analysis of the temporal and spatial characteristics of CO2 emissions in urban agglomerations and prediction of the future trends of CO2 emissions in urban agglomerations will help in the implementation of CO2 reduction policies within region-wide areas. So, based on that, this study contains four aspects. Firstly, it calculates the energy CO2 emissions of China’s Chengdu-Chongqing urban agglomeration. Secondly, it analyzes the time and space changes in the area by using ArcGIS. Then, the STIRPAT model is used to investigate the factors influencing CO2 emissions, and the elasticity coefficient of the influencing factors is estimated using the ridge regression method, and the important influencing factors are screened on the basis of the estimated results, which are then used as input features for prediction. Finally, a combined prediction model based on the improved GM (1, N) and SVR models is constructed, and then the optimal solution is found through the particle swarm optimization algorithm. It sets up different CO2 emission scenarios to predict the energy CO2 emission of the region and its cities. The results show that, first, the CO2 emissions of the Chengdu-Chongqing urban agglomeration have accumulated year by year, but by 2030, as predicted, it will not reach its peak. The spatial layout of CO2 emissions in this region is not expected to undergo major changes by 2030. Second, population, GDP, gas and electricity consumption, and industrial structure have served as important factors affecting energy CO2 emissions in the region. Third, on the basis of the prediction results for different scenarios, the CO2 emissions in the baseline scenario are low in the short term, but the CO2 emissions in the low-carbon scenario are low in the long run. This study also puts forward some policy recommendations on how to reduce CO2 emissions.
Day-Ahead DSM-Integrated Hybrid-Power-Management-Incorporated CEED of Solar Thermal/Wind/Wave/BESS System Using HFPSO
Authors: Kumar Kothalanka Kameswara Pavan , Soren Nirmala , Latif Abdul , Das Dulal Chandra , Hussain S. M. Suhail , Al-Durra Ahmed , Ustun Taha Selim
Published: January 2022

This paper presents a day-ahead demand-side management (DSM)-integrated hybrid power management algorithm (PMA) with an objective of combined economic and emission load dispatch (CEED) considering losses. The algorithm was tested on an IEEE 30-bus six-generator system consisting of solar thermal/wind/wave/battery energy storage systems (BESSs) considering real-time data of the Gujarat (19°07′ N, 72°51′ E) coastal region and diverse renewable energy (RES) and storage sources. A maiden attempt of utilizing hybrid firefly particle swarm optimization (HFPSO) to reduce thermal energy consumption and carbon emission was presented. Further, a novel attempt for a versatile renewable power management system was proposed based on a day-ahead pricing scheme to manage load demand and generation effectively. The PMA permits the users to bring down the general load demand and adjust the load curve during the peak time frame. The comparative performance of particle swarm optimization (PSO), firefly algorithm (FA), and HFPSO algorithms in solving the objective was presented. The HFPSO algorithm was found to be the best in terms of a fuel cost of 544.160 (USD/h), emission 20.301 (kg/h), and peak-load reduction of 31.292%, 24.210%, and 51.197% for residential, commercial, and industrial loads, respectively, when contrasted with the other two algorithms PSO and FA.
Applying Game-Based Experiential Learning to Comprehensive Sustainable Development-Based Education
Authors: Ho Shin-Jia , Hsu Yu-Shan , Lai Chien-Hung , Chen Fong-Han , Yang Ming-Hour
Published: January 2022

To promote the United Nations’ Sustainable Development Goals (SDGs), public awareness of the importance and urgency of sustainable development should be raised by providing relevant education programs. Although game-based learning has been confirmed to be one of the most effective routes to deepen public understanding of the SDGs and sustainable development in general, games for comprehensive sustainable development-based courses have yet to be popularized. Thus, we developed a game-based learning approach that delivers comprehensive conceptual information on SDGs. Based on Kolb’s theory of experiential learning, students understood the relevance of the SDGs by playing a board game designed to simulate the real world, including national and international policies. Furthermore, considering the suspension of in-person learning and shifts to digital instruction caused by the ongoing COVID-19 pandemic, a digital version of the board game was created to compare the effects of digital and non-digital game-based learning. A comprehensive sustainable development evaluation questionnaire was developed and optimized using the fuzzy Delphi method to assess the participants’ knowledge of and attitudes toward the SDGs. Our results reveal that the digital and non-digital board game both improve students’ knowledge and attitude toward sustainable development. However, the digital board game was more effective than the non-digital board game.
The Relationships among Microelement Composition of Reindeer Meat (Rangifer tarandus) and Adaptation: A Systematic Review and Meta-Analysis
Authors: Andronov Sergei , Lobanov Andrey , Bogdanova Elena , Popov Andrei , Yuzhakov Alexander , Shaduyko Olga , Raheem Dele , Kobelkova Irina
Published: January 2022

This systematic review and meta-analysis based on PRISMA statements aimed to summarise the data on the chemical composition of reindeer meat depending on the region of the Rangifer tarandus. We searched SCOPUS, PubMed, Embase, CrossRef, Medline, Cochrane library, eLibrary, and CyberLeninka. A total of 3310 records published between January 1980 and December 2021 were screened. We identified 34 relevant studies conducted in Russia, Norway, the USA, Canada, and Finland for the synthesis. Overall, the consumption of reindeer meat reduces arterial hypertension and atherosclerosis due to many polyunsaturated fatty acids (linoleic, linolenic, arachidonic) and vitamin C, which balances lipid fractions. Venison is an effective means of preventing obesity and adapting to cold due to the content of a complete set of essential trace elements, amino acids, and even L-carnitine. The high content of vitamin C and microelements (iron, zinc, copper) in reindeer meat is likely to increase the body’s antioxidant defence against free radicals and help prevent chronic non-infectious diseases. Thus, venison is an essential component of the adaptation mechanism for the Arctic population.
The Effect of Stress on Individuals’ Wasting Behavior: The Mediating Role of Impaired Self-Control
Authors: Zhang Ke , Cai Yuanyuan
Published: January 2022

Wasting behavior has become a serious issue in modern society, especially when individuals face economic recessions and environmental problems. Despite the literature exploring cultural and sociological antecedents of wasting behavior, limited attention has been given to the role of individuals’ associated psychological states. The present research fills this gap by examining how and why stress, a psychological state pervasive among people in the modern world, can influence individuals’ wasting behavior through three studies. Pilot study and Study 1 provide evidence of the positive relationship between stress and wasting behavior. Then, Study 2 sheds light on the mechanism underlying the proposed effect by taking impaired self-control as a mediator. Lastly, the theoretical contributions and practical implications of this research are discussed.
A Thorough Economic Evaluation by Implementing Solar/Wind Energies for Hydrogen Production: A Case Study
Authors: Ahmadi Mohammad Hossein , Hosseini Dehshiri Seyyed Shahabaddin , Hosseini Dehshiri Seyyed Jalaladdin , Mostafaeipour Ali , Almutairi Khalid , Ao Hoa Xuan , Rezaei Mohammadhossein , Techato Kuaanan
Published: January 2022

A technical–economic assessment was carried out in this study to determine the possibilities for wind and solar power generation in Afghanistan’s Helmand province. The results showed that most of the province has a solar irradiance of over 400 W/m2, and also showed that wind and solar power generated in the province can be up to twice as cheap as the official price of renewable power in Afghanistan. The most suitable site for solar and hydrogen production was found to be Laškar Gāh, where solar and hydrogen can be produced at a cost of 0.066 $/kWh and 2.1496 $/kg-H2, respectively. In terms of wind power production and hydrogen production from wind, the most suitable site was Sangīn, where wind power and hydrogen could be produced at costs of 0.057 $/kWh and 1.4527 $/kg-H2, respectively. Despite the high potential of wind and solar energy in the Helmand province, the most suitable place in this region to produce hydrogen from wind/solar energy was evaluated from technical, economic, and environmental perspectives with the Multi-Criteria Decision-Making (MCDM) method. The Stepwise Weight Assessment Ratio Analysis (SWARA) method was used for weighting criteria and the Weighted Aggregated Sum Product Assessment (WASPAS) method was used to prioritize locations. The results show that Sangīn is the most suitable place for the construction of a wind hydrogen power plant and Laškar Gāh is the most suitable place for the construction of a solar hydrogen power plant.
The Use of Social Media to Promote Sustainable Fashion and Benefit Communications: A Data-Mining Approach
Authors: Zhao Li , Lee Stacy H. , Li Muzhen , Sun Peng
Published: January 2022

Numerous brands utilize social media to capture consumers’ interests while promoting their sustainability goals. To understand how sustainable fashion brands communicate with their consumers, this study explored the visual and textual information sustainable fashion brands post on social media. Data were collected from sustainable fashion brands’ social media pages, and a total of 1525 images and captions and 140,735 comments were analyzed. By employing color theory and the theory of speech acts, HSV color analysis and the SVM classification model were used to extract information. The results showed that the images and captions posted by all three brands were consistent with their brand identities and sustainability goals. We also found that there were significant differences among the three brands when comparing posts employing expressive and assertive acts with posts using directive and assertive acts. These results indicate that social media users are more likely to leave comments when they read posts containing expressive and directive acts. These findings will allow fashion social media marketers to select appealing images and colors to engage consumers as well as to choose appropriate speech acts to deliver information to achieve their sustainability goals.
Comparative Assessment of Individual and Ensemble Machine Learning Models for Efficient Analysis of River Water Quality
Authors: Alqahtani Abdulaziz , Shah Muhammad Izhar , Aldrees Ali , Javed Muhammad Faisal
Published: January 2022

The prediction accuracies of machine learning (ML) models may not only be dependent on the input parameters and training dataset, but also on whether an ensemble or individual learning model is selected. The present study is based on the comparison of individual supervised ML models, such as gene expression programming (GEP) and artificial neural network (ANN), with that of an ensemble learning model, i.e., random forest (RF), for predicting river water salinity in terms of electrical conductivity (EC) and dissolved solids (TDS) in the Upper Indus River basin, Pakistan. The projected models were trained and tested by using a dataset of seven input parameters chosen on the basis of significant correlation. Optimization of the ensemble RF model was achieved by producing 20 sub-models in order to choose the accurate one. The goodness-of-fit of the models was assessed through well-known statistical indicators, such as the coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), and Nash–Sutcliffe efficiency (NSE). The results demonstrated a strong association between inputs and modeling outputs, where R2 value was found to be 0.96, 0.98, and 0.92 for the GEP, RF, and ANN models, respectively. The comparative performance of the proposed methods showed the relative superiority of the RF compared to GEP and ANN. Among the 20 RF sub-models, the most accurate model yielded the R2 equal to 0.941 and 0.938, with 70 and 160 numbers of corresponding estimators. The lowest RMSE values of 1.37 and 3.1 were yielded by the ensemble RF model on training and testing data, respectively. The results of the sensitivity analysis demonstrated that HCO3− is the most effective variable followed by Cl− and SO42− for both the EC and TDS. The assessment of the models on external criteria ensured the generalized results of all the aforementioned techniques. Conclusively, the outcome of the present research indicated that the RF model with selected key parameters could be prioritized for water quality assessment and management.
Supply Chain Social Sustainability: Unveiling Focal Firm’s Archetypes under the Lens of Stakeholder and Contingency Theory
Authors: Morais Dafne Oliveira Carlos , Barbieri José Carlos
Published: January 2022

Sustainable supply chain management (SSCM) has received increasing attention from scholars and practitioners. Despite its importance, we know little about critical aspects of it, as some important gaps are highlighted in the SSCM literature: deepening social dimension, exploring governance mechanisms, and strengthening theoretical development. This research aims to analyze how stakeholder salience and contingency factors influence the extent to which focal firms implement governance mechanisms to address social issues in supply chains. We conduct a multiple case study in six focal firms operating in Brazil. We intend to reduce the shortage of empirical evidence on emerging economies by focusing on a leading emerging country. This study contributes to the literature in three main aspects: (1) We classify social issues into central, peripheral, and remote, based on their priority within SSCM practices from emerging economy cases; (2) We outline three archetypes of social-SCM, namely elementary, selective, and extensive, to reflect the extent that focal firms incorporate social issues within their practices; (3) We also provide a typology to assess the extent to which focal firms address social issues, therefore contributing to reducing the gap regarding the social dimension within SSCM scholarship, combining the stakeholder theory and contingency theory.
Identification of Retained Austenite in 9Cr-1.4W-0.06Ta-0.12C Reduced Activation Ferritic Martensitic Steel
Authors: Mythili Rengachari , Kirana Ravi , Singh Loushambam Herojit , Govindaraj Ramanujam , Sinha Anil K. , Singh Manvendra N. , Saroja Saibaba , Vijayalakshmi Muraleedharan , Deb Sudip K.
Published: January 2022

Reduced activation ferritic martensitic (RAFM) 9Cr steels, which are candidate materials for the test blanket module (TBM) of nuclear fusion reactors, are considered to be air hardenable. However, alloy composition and the processing conditions play a significant role during the transformation of austenite to martensite/ferrite on cooling. The presence of retained austenite is known to influence the mechanical properties of the steel. Identifying very low amounts of retained austenite is very challenging though conventional microscopy. This paper aims at identifying a low amount of retained austenite in normalized 9Cr-1.4W-0.06Ta-0.12C RAFM steel using synchrotron X-ray diffraction and Mossbauer spectroscopy and confirmed by advanced automated crystal orientation mapping in transmission electron microscopy. Homogeneity of austenite has been understood to influence the microstructure of the normalized steel, which is discussed in detail.
The Optimal Control Problems for Generalized Elliptic Quasivariational Inequalities
Authors: Chang Shih-Sen , Ahmadini Abdullah Ali H. , Salahuddin  , Liu Min , Tang Jinfang
Published: January 2022

In this article, we propose an optimal control problem for generalized elliptic quasi-variational inequality with unilateral constraints. Then, we discuss the sufficient assumptions that ensure the convergence of the solutions to the optimal control problem. The proofs depend on convergence results for generalized elliptic quasi-variational inequalities, obtained by the arguments of compactness, lower semi-continuity, monotonicity, penalty and different estimates. As an application, we addressed the abstract convergence results in the analysis of optimal control associated with boundary value problems.