bluuebunny/crossref_metadata_2025 huggingface dataset:
An Unsupervised Feature Extraction Using Endmember Extraction and Clustering Algorithms for Dimension Reduction of Hyperspectral Images
Authors: Alizadeh Moghaddam Sayyed Hamed , Gazor Saeed , Karami Fahime , Amani Meisam , Jin Shuanggen
Published: August 2023

Hyperspectral images (HSIs) provide rich spectral information, facilitating many applications, including landcover classification. However, due to the high dimensionality of HSIs, landcover mapping applications usually suffer from the curse of dimensionality, which degrades the efficiency of supervised classifiers due to insufficient training samples. Feature extraction (FE) is a popular dimension reduction strategy for this issue. This paper proposes an unsupervised FE algorithm that involves extracting endmembers and clustering spectral bands. The proposed method first extracts existing endmembers from the HSI data via a vertex component analysis method. Using these endmembers, it subsequently constructs a prototype space (PS) in which each spectral band is represented by a point. Similar/correlated bands in the PS remain near one another, forming several clusters. Therefore, our method, in the next step, clusters spectral bands into multiple clusters via K-means and fuzzy C-means algorithms. Finally, it combines all the spectral bands in the same cluster using a weighted average operator to decrease the high dimensionality. The extracted features were evaluated by applying an SVM classifier. The experimental results confirmed the superior performance of the proposed method compared with five state-of-the-art dimension reduction algorithms. It outperformed these algorithms in terms of classification accuracy on three widely used hyperspectral images (Indian Pines, KSC, and Pavia Centre). The suggested technique also showed comparable or even stronger performance (up to 9% improvement) compared with its supervised competitor. Notably, the proposed method exhibited higher accuracy even when only a limited number of training samples were available for supervised classification. Using only five training samples per class for the KSC and Pavia Centre datasets, our method’s classification accuracy was higher than that of its best-performing unsupervised competitors by about 7% and 1%, respectively, in our experiments.
Application of Deep Learning in Multitemporal Remote Sensing Image Classification
Authors: Cheng Xinglu , Sun Yonghua , Zhang Wangkuan , Wang Yihan , Cao Xuyue , Wang Yanzhao
Published: August 2023

The rapid advancement of remote sensing technology has significantly enhanced the temporal resolution of remote sensing data. Multitemporal remote sensing image classification can extract richer spatiotemporal features. However, this also presents the challenge of mining massive data features. In response to this challenge, deep learning methods have become prevalent in machine learning and have been widely applied in remote sensing due to their ability to handle large datasets. The combination of remote sensing classification and deep learning has become a trend and has developed rapidly in recent years. However, there is a lack of summary and discussion on the research status and trends in multitemporal images. This review retrieved and screened 170 papers and proposed a research framework for this field. It includes retrieval statistics from existing research, preparation of multitemporal datasets, sample acquisition, an overview of typical models, and a discussion of application status. Finally, this paper discusses current problems and puts forward prospects for the future from three directions: adaptability between deep learning models and multitemporal classification, prospects for high-resolution image applications, and large-scale monitoring and model generalization. The aim is to help readers quickly understand the research process and application status of this field.
YOLO-RS: A More Accurate and Faster Object Detection Method for Remote Sensing Images
Authors: Xie Tianyi , Han Wen , Xu Sheng
Published: August 2023

In recent years, object detection based on deep learning has been widely applied and developed. When using object detection methods to process remote sensing images, the trade-off between the speed and accuracy of models is necessary, because remote sensing images pose additional difficulties such as complex backgrounds, small objects, and dense distribution to the detection task. This paper proposes YOLO-RS, an optimized object detection algorithm based on YOLOv4 to address the challenges. The Adaptively Spatial Feature Fusion (ASFF) structure is introduced after the feature enhancement network of YOLOv4. It assigns adaptive weight parameters to fuse multi-scale feature information, improving detection accuracy. Furthermore, optimizations are applied to the Spatial Pyramid Pooling (SPP) structure in YOLOv4. By incorporating residual connections and employing 1 × 1 convolutions after maximum pooling, both computation complexity and detection accuracy are improved. To enhance detection speed, Lightnet is introduced, inspired by Depthwise Separable Convolution for reducing model complexity. Additionally, the loss function in YOLOv4 is optimized by introducing the Intersection over Union loss function. This change replaces the aspect ratio loss term with the edge length loss, enhancing sensitivity to width and height, accelerating model convergence, and improving regression accuracy for detected frames. The mean Average Precision (mAP) values of the YOLO-RS model are 87.73% and 92.81% under the TGRS-HRRSD dataset and RSOD dataset, respectively, which are experimentally verified to be 2.15% and 1.66% higher compared to the original YOLOv4 algorithm. The detection speed reached 43.45 FPS and 43.68 FPS, respectively, with 5.29 Frames Per Second (FPS) and 5.30 FPS improvement.
Linguistic Explanations of Black Box Deep Learning Detectors on Simulated Aerial Drone Imagery
Authors: Alvey Brendan , Anderson Derek , Keller James , Buck Andrew
Published: August 2023

Deep learning has become increasingly common in aerial imagery analysis. As its use continues to grow, it is crucial that we understand and can explain its behavior. One eXplainable AI (XAI) approach is to generate linguistic summarizations of data and/or models. However, the number of summaries can increase significantly with the number of data attributes, posing a challenge. Herein, we proposed a hierarchical approach for generating and evaluating linguistic statements of black box deep learning models. Our approach scores and ranks statements according to user-specified criteria. A systematic process was outlined for the evaluation of an object detector on a low altitude aerial drone. A deep learning model trained on real imagery was evaluated on a photorealistic simulated dataset with known ground truth across different contexts. The effectiveness and versatility of our approach was demonstrated by showing tailored linguistic summaries for different user types. Ultimately, this process is an efficient human-centric way of identifying successes, shortcomings, and biases in data and deep learning models.
Mapping Pharmacologically Evoked Neurovascular Activation and Its Suppression in a Rat Model of Tremor Using Functional Ultrasound: A Feasibility Study
Authors: Nayak Rohit , Lee Jeyeon , Sotoudehnia Setayesh , Chang Su-Youne , Fatemi Mostafa , Alizad Azra
Published: August 2023

Functional ultrasound (fUS), an emerging hemodynamic-based functional neuroimaging technique, is especially suited to probe brain activity and primarily used in animal models. Increasing use of pharmacological models for essential tremor extends new research to the utilization of fUS imaging in such models. Harmaline-induced tremor is an easily provoked model for the development of new therapies for essential tremor (ET). Furthermore, harmaline-induced tremor can be suppressed by the same classic medications used for essential tremor, which leads to the utilization of this model for preclinical testing. However, changes in local cerebral activities under the effect of tremorgenic doses of harmaline have not been completely investigated. In this study, we explored the feasibility of fUS imaging for visualization of cerebral activation and deactivation associated with harmaline-induced tremor and tremor-suppressing effects of propranolol. The spatial resolution of fUS using a high frame rate imaging enabled us to visualize time-locked and site-specific changes in cerebral blood flow associated with harmaline-evoked tremor. Intraperitoneal administration of harmaline generated significant neural activity changes in the primary motor cortex and ventrolateral thalamus (VL Thal) regions during tremor and then gradually returned to baseline level as tremor subsided with time. To the best of our knowledge, this is the first functional ultrasound study to show the neurovascular activation of harmaline-induced tremor and the therapeutic suppression in a rat model. Thus, fUS can be considered a noninvasive imaging method for studying neuronal activities involved in the ET model and its treatment.
Verification of Data from Supersensitive Detector of Hydrosphere Pressure Variations
Authors: Dolgikh Grigory , Dolgikh Stanislav , Ivanov Mikhail
Published: August 2023

The paper describes experimental research and the results of these studies carried out in various bays of the Primorsky Territory of Russia using a supersensitive detector of hydrosphere pressure variations and a sound velocity profiler with pressure and temperature sensors. In all experiments, instruments, rigidly fixed to each other, were placed on the bottom at a depth of up to 10 m. Comparison of in-situ data from these instruments allowed us to experimentally calculate the coefficient of data conversion of the supersensitive detector of hydrosphere pressure variations when registering sea waves with periods ranging from several seconds to tens of minutes.
Recovery of Tellurium from Waste Anode Slime Containing High Copper and High Tellurium of Copper Refineries
Authors: Sarangi Chinmaya Kumar , Sheik Abdul Rauf , Marandi Barsha , Ponnam Vijetha , Ghosh Malay Kumar , Sanjay Kali , Minakshi Manickam , Subbaiah Tondepu
Published: August 2023

Tellurium is used in cadmium tellurium-based solar cells. Mercury cadmium telluride is used as a sensing material for thermal imaging devices. High-purity tellurium is used in alloys for electronic applications. It is one of the important raw materials for solar energy applications. It is used as an alloying element in the production of low-carbon steel and copper alloys. Tellurium catalysts are used chiefly for the oxidation of organic compounds and as vulcanizing/accelerating agents in the processing of rubber compounds. Even though several researchers tried to recover tellurium from different raw materials, there is no attempt to develop a process flow sheet to recover tellurium from waste anode slime having a high tellurium concentration. In this study, optimum conditions were developed to recover Te and Cu from anode slime with the composition Cu: 31.8%, Te: 24.7%, and As: 0.96%. The unit operations involved are leaching, purification, and electro winning. The optimum conditions for producing Te at a recovery of 90% are found to be roasting of anode slime at 450 °C without the addition of soda ash followed by leaching in 1 M NaOH at 10% pulp density for 2 h. The purity of Te metal achieved was up to 99.99%, which could provide a sustainable energy future. The major impurities of the tellurium are observed to be in the order: Se > Sb > As > Cu.
Predicting Changes in Population Exposure to Precipitation Extremes over Beijing–Tianjin–Hebei Urban Agglomeration with Regional Climate Model RegCM4 on a Convection-Permitting Scale
Authors: Qin Peihua , Xie Zhenghui , Jia Binghao , Han Rui , Liu Buchun
Published: August 2023

In this study, we have investigated changes in precipitation extremes and the population’s exposure to these extremes during 2091–2099 in China’s Beijing–Tianjin–Hebei (JJJ) region relative to the historical period of 1991–1999. First, the regional climate model RegCM4, with a hydrostatic dynamic core, was run for east Asia, including China, at a 12 km resolution for 1990–1999 and 2090–2099. This model is forced by global climate model (GCM) MPI-ESM1.2-HR under the middle shared socioeconomic pathways (SSP245). The first year was used as a model spinup. Then, the 12 km results were used to force RegCM4 with a non-hydrostatic dynamic core (RegcM4-NH) at a 3 km convection-permitting scale over the JJJ region during the historical and future periods. Future precipitation extremes were predicted to increase over the whole of China and its four subregions, while decreases were predicted over the JJJ region. This may partly be caused by lower increases in specific humidity over the JJJ region. The percentage contributions of the three components of total population exposure, i.e., changes in exposure due to changes in the population, precipitation extremes and the joint impact of the population and extremes, were then analyzed. Changes in the population and wet extremes were closely related to changes in the total exposure over the JJJ region. The population is the dominant factor that most impacts the total exposure to dry extremes. Finally, changes in future population exposure to precipitation extremes per degree of warming were quantified for the JJJ region.
Evaluation of the Productive Potential of a World Collection of Chickpeas (Cicer arietinum L.) for the Initiation of Breeding Programs for Adaptation to Conservation Agriculture
Authors: Abderemane Bacar Abdallah , Fakiri Malika , Idrissi Omar , Baidani Aziz , Zeroual Abdelmonim , Mazzucotelli Elisabetta , Özkan Hakan , Marcotuli Ilaria , Gadaleta Agata , Houasli Chafika
Published: August 2023

Transitioning to conservation agriculture is proving to be a better alternative and could become the norm in the future. Morocco, geographically located in a hot spot, is much more vulnerable to the hazards of climate change and the advantages of conservation agriculture remain a good compromise to ensure sustainable agricultural production. However, the lack of sufficient knowledge about this agricultural technology could be a hindrance and thus create mistrust among farmers. Therefore, the objective of our study is to evaluate the performance of a collection of chickpeas in each tillage system to identify genotypes that can be integrated into breeding programs for adaptation to conservation agriculture. Our study shows no significant effect of tillage on grain yield. Chlorophyll content and pod number made the strongest direct and positive contributions to yield for conventional and no-till, respectively. Nine genotypes including two checks (C1 and C2) were selected in both systems through MGIDI (multi-trait genotype–ideotype distance index) analysis. These genotypes would be potential candidates for breeding programs for adaptation to no-till because of their plasticity to reproduce acceptable yields in both till systems.
Public Transport Modeling for Commuting in Cities with Different Development Levels Using Extended Theory of Planned Behavior
Authors: Arslannur Bircan , Tortum Ahmet
Published: August 2023

Reducing the use of private vehicles and promoting public transportation (PT) have always been the primary policy objectives of transport authorities. This study aims to model the factors affecting intentions and behaviors of employees to use PT for their commutes by creating an extended theory of planned behavior (ETPB). The ETPB model’s applicability was evaluated using the Partial Least Squares Structural Equation Model (PLS-SEM) technique on a total of 2048 employees in three distinct cities. Then, the Multigroup analysis (MGA) method was used to compare various cities, and demographic variables such as age, education, gender, household income, and walking time to nearest PT stop. The analysis revealed that attitude, perceived norm, and personal agency have a statistically positive influence on employees’ intention to use PT. Moreover, behavioral capability, intention, and habit have a positive effect on PT use, whereas environmental constraints have a negative effect. The results of the MGA analysis revealed significant differences between regions, particularly in terms of environmental factors, intention, and habit. Similarly, the article describes disparities that have emerged according to other demographic variables. The findings imply that interventions by decision makers have the potential to alter the mode of transportation chosen for commuting.
Wood Production and Its Correlation with Socioeconomic and Environmental Indicators in the Amazon Region
Authors: Silva Edwin Francisco Ferreira , Brasil Augusto César de Mendonça , Blanco Claudio Jose Cavalcante
Published: August 2023

In the Amazon region, land-use cover change has been identified as the primary factor responsible for deforestation and CO2 emissions. Concurrently, the average sawn yield of wood industries in the Amazon serves as a vital sustainability indicator due to its linkage with forest degradation and CO2 emissions. The first part of this study reviews published works and proposes a unified value of 46.54% with a standard deviation of 7.07% for the sawn yield in the State of Pará—Amazon Region. In the second part of this study, principal component analysis is applied to a ten-year historical series, from 2008 to 2018, encompassing 14 indicators across all 144 municipalities of the State of Pará. The results suggest that the socioeconomic dimension, with component loadings between 0.86 and 0.96, is predominantly influenced by population, value-added tax, and the number of households with electric consumption. The number of medical doctors per 1000 people (DOC) is also tied to the socioeconomic dimension, presenting component loadings between 0.72 and 0.80. Indicators such as cattle, deforestation, and CO2 emissions correlate with the environmental dimension, registering component loadings above 0.7. The factors examined embody the Amazon’s socioeconomic and environmental problems, including cattle rearing, deforestation, agricultural areas, and consequent CO2 emissions. Indeed, deforestation and CO2 emissions arise primarily from agriculture and animal husbandry, particularly cattle rearing. Wood production indicators yield low values of component loadings, suggesting a weak correlation with the environmental dimension. However, they simultaneously imply that wood production activity has been associated with CO2 emissions since 2014 and is therefore connected to land-use change. In summary, this work’s analysis suggests that wood products and wood residues are linked not only to socioeconomic factors but also to forest degradation and CO2 emissions.
Prediction of Annual Daylighting Performance Using Inverse Models
Authors: Li Qinbo , Haberl Jeff
Published: August 2023

This paper presents the results of a study that developed improved inverse models to accurately predict the annual daylighting performance (sDA and lighting energy use) of various window configurations. This inverse model is an improvement over previous inverse models because it can be applied to variable room geometries at different weather locations in the US. The room geometries can be varied from 3 m × 3 m × 2.5 m to 15 m × 15 m × 10 m (length × width × height). The other variables used in the model include orientation (N, E, S, W), window-to-floor ratio, window location in the exterior wall, glazing visible transmittance, ceiling visible reflectance, wall visible reflectance, shade type (overhangs, fins), shade visible reflectance, lighting power density (LPD) (W/m2), and lighting dimming setpoint (lux). Such models can quickly advise architects during the preliminary design phase about which daylighting design options provide useful daylighting, while minimizing the annual auxiliary lighting energy use. The inverse models tested and developed were multi-linear regression (MLR) models, which were trained and tested against Radiance-based annual daylighting simulation results. In the analysis, 482 cases with different model conditions were simulated, to develop and validate the inverse models. This study used 75% of the data to train the model and 25% of the data to validate the model. The results showed that the new inverse models had a high accuracy in the annual daylighting performance predictions, with an R2 of 0.99 and an CV(RMSE) of 15.19% (RMSE of 58.91) for the lighting energy (LE) prediction, and an R2 of 0.95 and an CV(RMSE) of 14.38% (RMSE of 8.02) for the sDA prediction. In addition, the validation results showed that the LE MLR model and sDA MLR model had an R2 of 0.96 and 0.85, and RASE of 121.89 and 8.54, respectively, which indicate that the inverse models could accurately predict daylighting results for sDA and lighting energy use.
The Impact of Urban Sprawl on Carbon Emissions from the Perspective of Nighttime Light Remote Sensing: A Case Study in Eastern China
Authors: Zhang Ling , Zhang Jiawei , Li Xiaohui , Zhou Kaidi , Ye Jiang
Published: August 2023

This paper collects the nighttime light data and six control variables of 77 cities in the eastern China region from 2000 to 2020 and analyzes the spatio-temporal changes of urban sprawl and carbon emissions in the eastern China region. Using the baseline regression model, the correlation and influencing factors between urban sprawl and carbon emissions are explored. The results show that although the impact of urban sprawl on carbon emissions in the eastern China region is positive, it is the result of a trade-off of various factors. Different factors have different impacts on carbon emissions, and urban expansion of different city sizes also has different impacts on carbon emissions in the eastern China region. This paper uses nighttime light data to measure the urban sprawl index more finely, directly explores its impact on carbon emissions from the perspective of urban sprawl and conducts an in-depth analysis of multiple external factors and different city types, providing references for decision-makers to construct sustainable low-carbon city development models and low-carbon city planning systems from different dimensions.
Water Management and Environmental Engineering: Current Practices and Opportunities
Authors: Aivazidou Eirini , Tsolakis Naoum
Published: August 2023

Water is a vital resource for both agricultural and industrial operations, which cumulatively account for more than 90% of the worldwide freshwater use [...]
Acceptance Factors for the Social Norms Promoted by the Community-Led Total Sanitation (CLTS) Approach in the Rural Areas: Case Study of the Central-Western Region of Burkina Faso
Authors: Kouassi Hemez Ange Aurélien , Andrianisa Harinaivo Anderson , Traoré Maïmouna Bologo , Sossou Seyram Kossi , Nguematio Rikyelle Momo , Djambou Maeva Dominique
Published: August 2023

Although access to sanitation has been recognized as a fundamental human right, 3.6 billion people do not enjoy this right globally. In this group, the practice of unhealthy sanitation behaviors, such as open defecation (OD), is very common. To alleviate this problem, several governments in low-income countries have adopted Community-led Total Sanitation (CLTS). This is a participatory approach focused on ending OD and promoting good hygiene behaviors in target communities. This approach is centered around and highly depends upon a given community’s willingness to adopt the practices it advocates. However, the determinant factors in a community’s acceptance or refusal of these practices during and after CLTS implementation remain unclear. The aim of this paper is to highlight and categorize these factors to increase the sustainability of the approach. To achieve this, a study was conducted in the central-western region of Burkina Faso, where CLTS has successfully been deployed. We began this study by drawing a list of possible determinants through a literature review and grouping them into six categories. This enabled us to set up an evaluation matrix with scores for each factor and data collection tools. Scoring was based on the number of positive respondents for that factor. Data were then collected at the institutional, regional, local, and household levels from CLTS stakeholders to identify context-specific factors that underpinned behavior change in the surveyed villages. The literature review revealed six categories of acceptance factors. In our study, the importance of these factors according to our respondents were ranked in descending order as follows: environmental (C1 = 592), social (C2 = 390), governance (C4 = 247), territorial (C6 = 189), economic (C3 = 15), and technological (C5 = 0). The most frequently discussed factors obtained included the understanding of the health and economic consequences of OD (C1, score = 550); the popularity and reputation of Open Defecation-Free (ODF)-certified villages (C6, score = 179); men’s desire to protect their wives’ privacy (C2, score = 138); and women’s understanding of the adverse effects of OD on their children’s health (C2, score = 119). Incorporating the acceptance factors found in this study into future CLTS interventions will improve the effectiveness of the approach and increase the sustainability of ODF status in similar contexts.
Assessing Refugee Preferences for SDG 2 (Zero Hunger) Solutions in Irbid Camp and Sakhra Region: Cultivated Roofs and Refrigerators as Food Banks Interventions
Authors: Alkharouf Reem , Shehadeh Ali , Khazaleh Khaled , Al-Azzam Azzam , Khalayleh Muneer
Published: August 2023

Addressing hunger, particularly within impoverished communities in Jordan and globally, demands innovative, practical solutions. The research focused on refugee populations and their preferences for interventions aligned with Sustainable Development Goal (SDG) 2: Zero Hunger remains limited. This study explores the preferences of refugees in the Irbid Camp and Sakhra region, Jordan, for two potential interventions—cultivated roofs (CRs) and refrigerators as food banks (RaFB). Surveys conducted among 402 households serve to determine refugee preferences in hunger reduction, the influence of demographic attributes on these choices, and the feasibility of each proposed intervention. Chi-square tests were utilized to establish correlations between refugee intervention preferences and demographic variables, such as age, gender, education level, and family size. The results reveal a strong preference (90%) for RaFB over CRs (10%). While no significant demographic influence was identified on the acceptance of CRs, a strong correlation was discovered between the education level and the acceptance of the RaFB intervention. RaFB was predominantly favored due to its lower implementation costs, reduced effort, lower risk, cultural compatibility, and demonstrated success in similar contexts. Conversely, highly educated refugees were more likely to reject RaFB, indicating potential influences from diverse cultural perspectives or access to alternate solutions. This study provides valuable insight into the potential advantages and challenges of implementing CRs and RaFB projects. It further underscores the need for policymakers to consider demographic factors and cultural nuances in future intervention designs to achieve SDG 2 more effectively.
The Nexus between Employment and Economic Growth: A Cross-Country Analysis
Authors: Haider Azad , Jabeen Sunila , Rankaduwa Wimal , Shaheen Farzana
Published: August 2023

The main objective of this paper is to examine the relationship between employment and economic growth in developed and developing countries over the period of 1970–2019. As documented in the literature in the past, economic growth in most developing countries has been less job-generating than in developed countries, even though high economic growth is observed in most of the developing world, indicating jobless growth. Based on the Cobb–Douglas production function, we developed an employment demand model to find the employment elasticity with respect to economic growth using working hours and population as explanatory variables. The main findings of the present study reveal that the employment elasticities with respect to GDP are positive and significant in developing and developed countries. But in the developing countries, the employment elasticity is relatively very low (0.11 to 0.15) compared to the developed countries (0.43 to 0.48), which led to the conclusion that a possibility of jobless growth exists in these countries. The findings of the study imply that policymakers should focus more on employment-led growth policies instead of growth-led employment policies, especially in developing countries.
Computational Fluid Dynamics Simulation to Investigate Diffuser Outlet Factors in Anaerobic Membrane Bioreactors Treating Wastewater
Authors: Wang Haoran , Ohta Makoto , Anzai Hitomi , Ji Jiayuan
Published: August 2023

Anaerobic membrane bioreactors (AnMBRs) require biogas recycling to stir the mixed liquid and flush particles away from the membrane surfaces for stable operation. With the fixed gas cycling rate, gas diffuser configuration is an important factor that affects stirring and flushing performance. This study investigated the effect of different outlet diameters on biogas diffusers in AnMBR by using computational fluid dynamics (CFD) to analyze gas–liquid flow in a numerical model constructed based on an experimental AnMBR. According to the CFD results, as the outlet diameter increased from 2.5 to 5.0 mm, the average velocity increased from 0.15 to 0.31 m/s and the average wall shear stress (WSS) increased from 0.21 to 1.10 Pa on the membrane surface. The increase in gas velocity enhances the stirring effect, and the increase in WSS improves the flushing performance. However, when it was further increased to 10.0 mm, the average velocity and average WSS was 0.27 m/s and 0.22 Pa, respectively, indicating that too large an outlet diameter leads to a concentrated gas distribution, which reduces the performance of stirring and flushing. Furthermore, these results provide a basis for optimizing diffuser configuration, which is significant for promoting the practical application of AnMBR in wastewater treatment.
Analysis of Financial Outsourcing Management in Regional Environmental Systems
Authors: Koval Viktor , Olczak Piotr , Hakova Mira , Bilyi Mykhailo , Kretov Dmitry , Laktionova Olga
Published: August 2023

This study contributes to the creation of a model of effective economic relations by the organizer—the outsourcer between the participants of the regional ecological financial system—financial services clients, creditors, investors, insurers, and municipalities. This study proposes to reduce the financial limitations of business entities that are environmentally active by forming a regional ecological financial system, organized by an outsourcer for the provision of financial services. The outsourcer attracts financial instruments (FIs), including special environmental financial instruments (GFIs) and financial management mechanisms (FMs), to accelerate capital turnover. When calculating the financing models, the well-known concept of the innovation life cycle was used. Research on the relationship between variables “revenues from environmental taxation by country” and “the sum of all environmental revenues” in the EU27 budget for the period 2012–2021 made it possible to determine the Pearson coefficient, according to which countries were distributed according to the degree of connection strength according to the Chaddock scale: 32% of countries had a very high connection (0.9–1.0) during the study period, and 53% of EU countries had a noticeable and moderate connection (0.7–0.9). The proposed method for studying the relationship can be useful in managing financial flows attracted to the ecological financial system by an outsourcer.
Workplace-Related Negative Career Shocks on Perceived Employability: The Role of Networking Behaviors and Perceived Career Opportunities
Authors: Zhou Wenxia , Jiang Xinling , Khapova Svetlana N. , Qu Jinzhao
Published: August 2023

While there is a growing interest in career shocks in the careers literature, little is known about how and when they trigger employees’ proactive behavioral responses leading to sustainable career outcomes. The research aims to extend previous knowledge about the effects of such career shocks and uncover their potential benefits. Drawing on the conservation of resources theory, the study proposes that workplace-related negative career shocks indirectly impact employees’ perceived employability by mobilizing their networking behaviors, especially when perceived career opportunities are present. To investigate this, a three-wave survey was conducted among 269 employees over four months in three companies in China. The results show that workplace-related negative career shocks indirectly contribute to employees’ perceived employability by motivating them to engage in networking behaviors. Furthermore, this positive indirect relationship is stronger for employees with high perceived career opportunities. This study is the first to provide a positive perspective on the outcomes of workplace-related negative career shocks and sheds light on employees’ proactive behavioral responses as a mechanism to explain the impact of such shocks on career sustainability.
Analysis of the Spatiotemporal Evolution and Driving Factors of China’s Digital Economy Development Based on ESDA and GM-GWR Model
Authors: Shang Xiaoting , Niu Huayong
Published: August 2023

Research on the geographical aspects of the digital economy is valuable. We base our study on 10 consecutive years of panel data from 2011–2020 for 31 Chinese provinces. First, we measure the Digital Economy Index using the entropy weight method and analyze its spatiotemporal heterogeneity characteristics using the Exploratory Spatial Data Analysis (ESDA) method. Next, the Grey Model (GM) is utilized to conduct time series predictions of each geographical unit. Finally, we use the GM predicted values and Geographic Weighted Regression (GWR) model to explore the spatial heterogeneity effects of external factors. This study finds that: (1) The overall development shows a trend of vigorous growth, with significant spatial heterogeneity. The gradient difference shows a decreasing trend from the eastern coastal areas to the western inland areas. (2) There is an obvious “digital divide” and a “Matthew effect” in regional development, with agglomeration and spillover effects gradually increasing. (3) Considering the influencing factors, technological progress has a positive impact, and the technology-oriented spatial spillover is obvious, showing a pattern of high in the south and low in the north. The industrial structure is significantly positive, and increases year by year, showing a distribution characteristic of high in the north and low in the south in general, with a clear effect of reducing the “bipolar” distribution. The marginal effects of government support and foreign investment are reduced and there is spatial non-stationarity. This study provides a scientific basis for further research on the spatial development of the digital economy.
Assessment of Groundwater Trends in Bhopal, Madhya Pradesh: A Statistical Approach
Authors: Mishra Satyam , Chauhan Mrityunjay , Sundaramurthy Suresh
Published: August 2023

Groundwater is a critical resource for the development of any region. In Bhopal, groundwater is also used for various purposes, including domestic use and irrigation. Overexploitation due to increasing demand coupled with pollution raises concern about the sustainability of groundwater supplies in the future. This study aimed to analyze trends in groundwater parameters in Bhopal over 22 years using the Mann-Kendall (MK) and Modified Mann-Kendall (MMK) trend tests in MATLAB. The parameters SO42−, Ca2+, HCO3−, NO3−, and F− showed a decreasing trend, while the trends for pH were mixed. In contrast, the Na+ and Cl− levels significantly increased in both trend tests, suggesting the effects of various existing aquifer conditions and human activities whose long-term exposure may lead to harmful effects. The results showed that nitrate and electrical conductivity levels exceeded the permissible limits. The MMK method provided more accurate results than the traditional Mann-Kendall method. The findings of this study are valuable for policymakers and water resource managers.
Optimal Management of Seasonal Pumped Hydro Storage System for Peak Shaving
Authors: Abdelfattah Asmaa I. , Shaaban Mostafa F. , Osman Ahmed H. , Ali Abdelfatah
Published: August 2023

Power demand varies on a daily and seasonal basis. Responding to changing demands over time is challenging for energy suppliers as it causes expensive power plants to operate in high-demand seasons, usually summer, increasing the cost of electricity. Peak load shaving makes the load curve flatten by reducing the peak load and shifting it to times of lower demand, hence reducing the operation of expensive power plants. Hence, there is a need for large-scale and long-term ESS to store energy in the time of low-demand seasons for future utilization in the highest-demand ones. In this work, an energy management system (EMS) is developed to optimally manage a grid-connected pumped hydro storage (PHS) for peak shaving. The proposed model incorporates a dynamic economic dispatch (DED) over a study period of one year; hence, a DC power flow analysis considering transmission constraints is utilized to ensure a fast load flow estimation and a manageable simulation time. The framework can be adopted to assess the long-term profitability of PHS-utilizing GAMS as an optimization tool. Further, to draw conclusions that would suit the characteristics of the demand pattern. This analysis is essential to motivate the construction of new seasonal PHS plants due to the high construction costs they are identified with, especially in geographical areas where this technology is not yet considered or is hard to construct. The simulation results demonstrate that integrating 1500 MWh PHS reduced the operation of expensive thermal units by 1224 MWh annually. Further, a reduction in operation costs was recorded after integrating a PHS unit that ranged from 2.6 M to 22 M USD/year, depending on the storage capacity.
Young People and Nature: What Drives Underlying Behavioural Intentions towards Protected Areas Conservation?
Authors: Aprile Maria Carmela , Punzo Gennaro
Published: August 2023

This paper investigates university students’ motivations in nature park conservation, an area that has received limited research attention compared to other pro-environmental behaviours. By formulating a set of research hypotheses, an extended version of the Theory of Planned Behaviour is employed to examine the determinants of university students’ intentions to engage in nature park conservation. Structural equation modelling is conducted using survey data collected from university students in the Metropolitan City of Naples, Italy. The results show that attitudes, perceived behavioural control, and personal moral norms directly and positively influence university students’ intentions to undertake nature park preservation actions. Subjective norms indirectly influence behavioural intentions through attitudes, perceived behavioural control, and personal moral norms. Moreover, the findings reveal that university students with nature-oriented altruistic values exhibit a higher propensity for nature park conservation compared to their more egoistic peers. These findings have important implications for park management institutions, suggesting the need to increase awareness among young people about their potential role in preserving the environmental quality of nature parks. Collaboration between universities and nature park institutions is also crucial in providing young individuals with the necessary skills to participate in decision-making processes aligned with the principles of sustainable development.
The Eclectic Approach in English Language Teaching Applications: A Qualitative Synthesis of the Literature
Authors: Valledor Adrián , Olmedo Alvaro , Hellín Carlos J. , Tayebi Abdelhamid , Otón-Tortosa Salvador , Gómez Josefa
Published: August 2023

This paper proposes a study of the methodologies used to teach English as a second language through computer applications. It reviews the different methodologies used in language teaching in order to contrast them with different fundamental approaches. It examines the applications used for teaching English online, especially those in line with personalised learning environments and learner-centred autonomy. The use of a mixed methodology is reported, allowing the selection of papers from both a traditional systematic review and the search engine elicit.org. Finally, it is concluded that Blended Learning is the most appropriate method, highlighting that current applications are mainly based on the Audio-Lingual method, which limits their practical use in wider contexts. Further research is needed on software applications that prioritise personalised learning environments with learner-centred autonomy, incorporating technologies based on Blended Learning and artificial intelligence (AI). To this end, it is proposed in the future to develop an application using Automatic Speech Recognition (ASR), Text To Speech (TTS), Natural Language Understanding (NLU) and Dialog Management (DM) algorithms, focusing on the above and simulating the capabilities of a teacher. This may lead to innovations in the development of software applications capable of integrating different methods, teaching modalities and learning methods.
Transforming Supply Chains: Powering Circular Economy with Analytics, Integration and Flexibility Using Dual Theory and Deep Learning with PLS-SEM-ANN Analysis
Authors: Shafique Muhammad Noman , Rashid Ammar , Yeo Sook Fern , Adeel Umar
Published: August 2023

The Sustainable Development Goals and circular economy are two critical aspects of the 2030 Agenda for Sustainable Development. They both seek to reduce the waste of natural resources and enhance society’s social, economic, and environmental goals. This study aims to identify, develop, test, and verify the significant antecedents that affect the adoption of supply chain analytics and its consequences for achieving the circular economy. We have divided the conceptual framework into two parts. In the first part, the relationship among data integration and scalability, organizational readiness, and policies and regulations as Technological–Organizational–Environmental factors as antecedents in adopting supply chain analytics. In the second part, the dynamic capabilities view grounded the relationship among supply chain analytics, supply chain integration, and sustainable supply chain flexibility effect directly and indirectly on the circular economy. Data have been collected using the survey method from 231 respondents from the manufacturing industry in Pakistan. Data have been analyzed using (i) partial least square structure equation modeling (ii) and artificial neural network approaches. The empirical findings proved that antecedents (data integrity and scalability, organizational readiness, and policy and regulation) and consequences (supply chain integration and sustainable supply chain flexibility) of supply chain analytics adoption would improve the circular economy performance. Additionally, artificial neural networks have supported these relationships. The adoption of supply chain analytics will enable organizations to supply chain integration. Additionally, organizations with more integration and analytics in their operations tend to have more flexibility and a circular economy. Moreover, organizations and society will obtain social, economic, and environmental benefits and reduce wastage and negative environmental impacts.
Challenges Facing Andean Communities in the Protection of the Páramo in the Central Highlands of Ecuador
Authors: Torres María Cristina , Naranjo Efraín , Fierro Vanessa
Published: August 2023

In the rural parish of Salinas, located in the central highlands of Ecuador, most of the communities are settled in a páramo ecosystem, whose deterioration has become a concern since the beginning of the century. The scarcity of water that some communities have experienced has led to a reconsideration of economic activities, as part of a community process that began 50 years ago, which has currently allowed them to overcome extreme poverty. This research aims to analyze the socio-technical reconfiguration process these communities have undergone in their new role as conservationists that has simultaneously ensured their livelihoods. The methodology applied is a qualitative approach based on a discourse analysis. The results indicate a process of applying social technology to protect the páramo. The availability of economic resources, a new mentality and the exercise of authority are key starting elements for the latter, and through strengthened organizations, permanent reflection, creativity, and the search for sustainability, the primary objective can be reached: the protection of the páramo.
Solutions of Fractional Differential Inclusions and Stationary Points of Intuitionistic Fuzzy-Set-Valued Maps
Authors: Alansari Monairah , Shehu Shagari Mohammed
Published: August 2023

One of the tools for building new fixed-point results is the use of symmetry in the distance functions. The symmetric property of metrics is particularly useful in constructing contractive inequalities for analyzing different models of practical consequences. A lot of important invariant point results of crisp mappings have been improved by using the symmetry of metrics. However, more than a handful of fixed-point theorems in symmetric spaces are yet to be investigated in fuzzy versions. In accordance with the aforementioned orientation, the idea of Presic-type intuitionistic fuzzy stationary point results is introduced in this study within a space endowed with a symmetrical structure. The stability of intuitionistic fuzzy fixed-point problems and the associated new concepts are proposed herein to complement their corresponding concepts related to multi-valued and single-valued mappings. In the instance where the intuitionistic fuzzy-set-valued map is reduced to its crisp counterparts, our results complement and generalize a few well-known fixed-point theorems with symmetric structure, including the main results of Banach, Ciric, Presic, Rhoades, and some others in the comparable literature. A significant number of consequences of our results in the set-up of fuzzy-set- and crisp-set-valued as well as point-to-point-valued mappings are emphasized and discussed. One of our findings is utilized to assess situations from the perspective of an application for the existence of solutions to non-convex fractional differential inclusions involving Caputo fractional derivatives with nonlocal boundary conditions. Some nontrivial examples are constructed to support the assertions and usability of our main ideas.