This work concerns the assessment of soil reclamation and its impact on biological life in areas destroyed by the sulfur industry in Jeziórko. Sulfur extraction using the borehole method causes enormous destruction to the soil environment. Among the many forms of degradation, the most pronounced are the chemical transformations of the environment and the disturbances in water relations in large areas, which could theoretically impact areas not within the direct range of the mining plant. This work aimed to assess the condition of biological life in soil reclaimed with waste in areas devastated by the sulfur industry in Jeziórko. The reclamation of these soils was difficult but necessary due to the complete disappearance of biological life. Appropriate actions were taken to restore and improve the properties of the soil, which resulted in an improvement in their production capacity. Reclamation was carried out, among other techniques, by deacidifying the soil using post-flotation lime and fertilizing the soil with municipal sewage sludge and post-use mineral wool. Studies have shown an improvement in many soil properties, such as its physical, water, chemical, and biological properties. The implemented reclamation methods significantly influenced, among other things, the density and water properties of the degraded soil. The soil reclaimed with mineral wool and sewage sludge recorded the highest density and water capacity. Applying mineral wool to the degraded soil influenced the changes in the analyzed physical and water properties. The obtained research results also show the beneficial effect of mineral wool and sewage sludge on the increase in organic carbon content. In the soil treated with these substances, the organic carbon content ranged from 13.60 g·kg−1 to 14.30 g·kg−1. It is shown that reclamation has had a considerable impact on and is essential for biological life in Jeziórko.
{"title":"The Impact of Waste Application on the Reclamation and Biological Life of Degraded Soils","authors":"Marta Bik-Małodzińska","doi":"10.3390/su16188126","DOIUrl":"https://doi.org/10.3390/su16188126","url":null,"abstract":"This work concerns the assessment of soil reclamation and its impact on biological life in areas destroyed by the sulfur industry in Jeziórko. Sulfur extraction using the borehole method causes enormous destruction to the soil environment. Among the many forms of degradation, the most pronounced are the chemical transformations of the environment and the disturbances in water relations in large areas, which could theoretically impact areas not within the direct range of the mining plant. This work aimed to assess the condition of biological life in soil reclaimed with waste in areas devastated by the sulfur industry in Jeziórko. The reclamation of these soils was difficult but necessary due to the complete disappearance of biological life. Appropriate actions were taken to restore and improve the properties of the soil, which resulted in an improvement in their production capacity. Reclamation was carried out, among other techniques, by deacidifying the soil using post-flotation lime and fertilizing the soil with municipal sewage sludge and post-use mineral wool. Studies have shown an improvement in many soil properties, such as its physical, water, chemical, and biological properties. The implemented reclamation methods significantly influenced, among other things, the density and water properties of the degraded soil. The soil reclaimed with mineral wool and sewage sludge recorded the highest density and water capacity. Applying mineral wool to the degraded soil influenced the changes in the analyzed physical and water properties. The obtained research results also show the beneficial effect of mineral wool and sewage sludge on the increase in organic carbon content. In the soil treated with these substances, the organic carbon content ranged from 13.60 g·kg−1 to 14.30 g·kg−1. It is shown that reclamation has had a considerable impact on and is essential for biological life in Jeziórko.","PeriodicalId":22183,"journal":{"name":"Sustainability","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reem Alshagri, Talal H. Alsabhan, Jawaher Binsuwadan
This paper aims to investigate the relationship between financial development and renewable energy consumption using a fractional response model. The study examines a sample of 34 advanced economies and 64 emerging markets and developing economies from 2008 to 2020. The findings from the fractional response model indicate that financial development has a positive impact on renewable energy consumption in advanced economies. However, in emerging and developing economies, financial development negatively affects the consumption of renewable energy. Additionally, the findings illustrate that financial development has a more pronounced positive impact in advanced economies. This effect is especially strong in countries with higher levels of financial development. On the other hand, in emerging and developing economies, the consumption of renewable energy is more strongly affected by the negative impact of financial development on countries with lower financial development.
{"title":"Investigating the Role of Financial Development in Encouraging the Transition to Renewable Energy: A Fractional Response Model Approach","authors":"Reem Alshagri, Talal H. Alsabhan, Jawaher Binsuwadan","doi":"10.3390/su16188153","DOIUrl":"https://doi.org/10.3390/su16188153","url":null,"abstract":"This paper aims to investigate the relationship between financial development and renewable energy consumption using a fractional response model. The study examines a sample of 34 advanced economies and 64 emerging markets and developing economies from 2008 to 2020. The findings from the fractional response model indicate that financial development has a positive impact on renewable energy consumption in advanced economies. However, in emerging and developing economies, financial development negatively affects the consumption of renewable energy. Additionally, the findings illustrate that financial development has a more pronounced positive impact in advanced economies. This effect is especially strong in countries with higher levels of financial development. On the other hand, in emerging and developing economies, the consumption of renewable energy is more strongly affected by the negative impact of financial development on countries with lower financial development.","PeriodicalId":22183,"journal":{"name":"Sustainability","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kübra Yılmaz, İnayet Özge Aksu, Mustafa Göçken, Tuğçe Demirdelen
The textile industry, a substantial component of the global economy, holds significant importance due to its environmental impacts. Particularly, the use of water and chemicals during dyeing processes raises concerns in the context of climate change and environmental sustainability. Hence, it is crucial from both environmental and economic standpoints for textile factories to adopt green industry standards, particularly in their dyeing operations. Adapting to the green industry aims to reduce water and energy consumption in textile dyeing processes, minimize waste, and decrease the carbon footprint. This approach has become crucial in achieving sustainability in textiles following the signing of the Paris Climate Agreement. Important elements of this transformation include the reuse of washing waters used in the dyeing process, the recycling of wastewater, and the enhancement of energy efficiency through necessary methodological and equipment changes. This study analyzes the energy, labor, production, and consumption data since 2011 for a textile factories with four branches located in the Adana Organized Industrial Zone. Among these factories, the one designated as UT1, which has the highest average energy and water consumption compared to the other three branches, is selected. In recent years, the use of artificial intelligence and machine learning technologies in predicting industrial processes has been increasingly observed. The data are analyzed using LSTM (Long Short-Term Memory) and ANN (Artificial Neural Networks) forecasting methods. Particularly, the LSTM algorithms, which provided the most accurate results, have enabled advanced forecasting of electricity consumption in dyeing processes for future years. In 2020, electricity consumption was recorded as 3,717,224 kWh and this consumption was reflected in the total energy cost as TRY 1,916,032. Electricity consumption accounts for 22.34% of total energy consumption, while the share of this energy type in the cost is 43.25%. In the light of these data, the MAPE value for energy consumption forecasts using the LSTM model was 0.45%, which shows that the model is able to forecast with high accuracy. As a result, a solar power plant was installed to optimize energy consumption, and in 2023 60% energy savings were achieved in summer and 25% in winter. The electricity consumption forecasting results have been an essential guide in planning strategic initiatives to enhance factory efficiency. Following improvement efforts aimed at reducing energy consumption and lowering the carbon footprint, significant optimizations in processes and layouts have been made at specific bottleneck points within the facility. These improvements have led to savings in labor, time, and space, and have reduced unit production costs.
{"title":"Sustainable Textile Manufacturing with Revolutionizing Textile Dyeing: Deep Learning-Based, for Energy Efficiency and Environmental-Impact Reduction, Pioneering Green Practices for a Sustainable Future","authors":"Kübra Yılmaz, İnayet Özge Aksu, Mustafa Göçken, Tuğçe Demirdelen","doi":"10.3390/su16188152","DOIUrl":"https://doi.org/10.3390/su16188152","url":null,"abstract":"The textile industry, a substantial component of the global economy, holds significant importance due to its environmental impacts. Particularly, the use of water and chemicals during dyeing processes raises concerns in the context of climate change and environmental sustainability. Hence, it is crucial from both environmental and economic standpoints for textile factories to adopt green industry standards, particularly in their dyeing operations. Adapting to the green industry aims to reduce water and energy consumption in textile dyeing processes, minimize waste, and decrease the carbon footprint. This approach has become crucial in achieving sustainability in textiles following the signing of the Paris Climate Agreement. Important elements of this transformation include the reuse of washing waters used in the dyeing process, the recycling of wastewater, and the enhancement of energy efficiency through necessary methodological and equipment changes. This study analyzes the energy, labor, production, and consumption data since 2011 for a textile factories with four branches located in the Adana Organized Industrial Zone. Among these factories, the one designated as UT1, which has the highest average energy and water consumption compared to the other three branches, is selected. In recent years, the use of artificial intelligence and machine learning technologies in predicting industrial processes has been increasingly observed. The data are analyzed using LSTM (Long Short-Term Memory) and ANN (Artificial Neural Networks) forecasting methods. Particularly, the LSTM algorithms, which provided the most accurate results, have enabled advanced forecasting of electricity consumption in dyeing processes for future years. In 2020, electricity consumption was recorded as 3,717,224 kWh and this consumption was reflected in the total energy cost as TRY 1,916,032. Electricity consumption accounts for 22.34% of total energy consumption, while the share of this energy type in the cost is 43.25%. In the light of these data, the MAPE value for energy consumption forecasts using the LSTM model was 0.45%, which shows that the model is able to forecast with high accuracy. As a result, a solar power plant was installed to optimize energy consumption, and in 2023 60% energy savings were achieved in summer and 25% in winter. The electricity consumption forecasting results have been an essential guide in planning strategic initiatives to enhance factory efficiency. Following improvement efforts aimed at reducing energy consumption and lowering the carbon footprint, significant optimizations in processes and layouts have been made at specific bottleneck points within the facility. These improvements have led to savings in labor, time, and space, and have reduced unit production costs.","PeriodicalId":22183,"journal":{"name":"Sustainability","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. S. Praveena Krishna, Jayalakshmi N. Sabhahit, Vidya S. Rao, Amit Saraswat, Hannah Chaplin Laugaland, Pramod Bhat Nempu
The global transportation sector is rapidly shifting towards electrification, aiming to create more sustainable environments. As a result, there is a significant focus on optimizing performance and increasing the lifespan of batteries in electric vehicles (EVs). To achieve this, the battery pack must operate with constant current charging and discharging modes of operation. Further, in an EV powertrain, maintaining a constant DC link voltage at the input stage of the inverter is crucial for driving the motor load. To satisfy these two conditions simultaneously during the energy transfer, a hybrid energy storage system (HESS) consisting of a lithium–ion battery and a supercapacitor (SC) connected to the semi-active topology of the bidirectional DC–DC converter (SAT-BDC) in this research work. However, generating the duty cycle for the switches to regulate the operation of SAT-BDC is complex due to the simultaneous interaction of the two mentioned constraints: regulating the DC link voltage by tracking the reference and maintaining the battery current at a constant value. Therefore, this research aims to efficiently resolve the issue by incorporating a highly flexible nonlinear model predictive control (NMPC) to control the switches of SAT-BDC. Furthermore, the converter system design is tested for operational performance using MATLAB 2022B with the battery current and the DC link voltage with different priorities. In the NMPC approach, these constraints are carefully evaluated with varying prioritizations, representing a crucial trade-off in optimizing EV powertrain operation. The results demonstrate that battery current prioritization yields better performance than DC link voltage prioritization, extending the lifespan and efficiency of batteries. Thus, this research work further aligns with the conceptual realization of the sustainability goals by minimizing the environmental impact associated with battery production and disposal.
{"title":"Optimizing EV Powertrain Performance and Sustainability through Constraint Prioritization in Nonlinear Model Predictive Control of Semi-Active Bidirectional DC-DC Converter with HESS","authors":"P. S. Praveena Krishna, Jayalakshmi N. Sabhahit, Vidya S. Rao, Amit Saraswat, Hannah Chaplin Laugaland, Pramod Bhat Nempu","doi":"10.3390/su16188123","DOIUrl":"https://doi.org/10.3390/su16188123","url":null,"abstract":"The global transportation sector is rapidly shifting towards electrification, aiming to create more sustainable environments. As a result, there is a significant focus on optimizing performance and increasing the lifespan of batteries in electric vehicles (EVs). To achieve this, the battery pack must operate with constant current charging and discharging modes of operation. Further, in an EV powertrain, maintaining a constant DC link voltage at the input stage of the inverter is crucial for driving the motor load. To satisfy these two conditions simultaneously during the energy transfer, a hybrid energy storage system (HESS) consisting of a lithium–ion battery and a supercapacitor (SC) connected to the semi-active topology of the bidirectional DC–DC converter (SAT-BDC) in this research work. However, generating the duty cycle for the switches to regulate the operation of SAT-BDC is complex due to the simultaneous interaction of the two mentioned constraints: regulating the DC link voltage by tracking the reference and maintaining the battery current at a constant value. Therefore, this research aims to efficiently resolve the issue by incorporating a highly flexible nonlinear model predictive control (NMPC) to control the switches of SAT-BDC. Furthermore, the converter system design is tested for operational performance using MATLAB 2022B with the battery current and the DC link voltage with different priorities. In the NMPC approach, these constraints are carefully evaluated with varying prioritizations, representing a crucial trade-off in optimizing EV powertrain operation. The results demonstrate that battery current prioritization yields better performance than DC link voltage prioritization, extending the lifespan and efficiency of batteries. Thus, this research work further aligns with the conceptual realization of the sustainability goals by minimizing the environmental impact associated with battery production and disposal.","PeriodicalId":22183,"journal":{"name":"Sustainability","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Utilizing calcium-based adsorbents for CO2 adsorption through cyclic calcination/carbonization is one of the most cost-effective methods for carbon emission reduction. In order to improve the cycle stability of the adsorbents and the capture efficiency of CO2, this study used industrial solid waste coal fly ash for the hydration treatment of calcium-based adsorbent to explore the variations in the cyclic adsorption performance of the adsorbent under different doping ratios and hydration conditions. By means of various characterization techniques, the microscopic mechanism for improving the performance of the modified adsorbent was analyzed from the perspectives of chemical composition, physical structure, and surface functional groups of the adsorbents. The results demonstrated that the modification of coal fly ash could significantly enhance the carbonation performance and cycle stability of the adsorbent in multiple CO2 capture processes. The modified material doped with 5% coal fly ash had the highest total CO2 adsorption capacity, which increased by 13.7% compared to before modification. Additionally, the modified material doped with 10% coal fly ash exhibited the strongest cyclic adsorption capacity, which was 14.0% higher than that before modification, and the adsorption attenuation rate decreased by 32.2%. The characterization results showed that the reaction between calcium oxide and coal fly ash formed CaSiO3 and Ca12Al14O33 during the modification process, which was the primary reason for the improvement in the CO2 capture performance of the modified materials. This study provided a new perspective on the resource utilization of solid waste fly ash and efficient CO2 capture.
{"title":"A Study on the Mechanisms of Coal Fly Ash to Improve the CO2 Capture Efficiency of Calcium-Based Adsorbents","authors":"Ziyu Zhao, Kefan Zhang, Jianfeng Luo, Meixuan Wu, Xiyue Wang, Keke Wang, Shengyu Liu","doi":"10.3390/su16188139","DOIUrl":"https://doi.org/10.3390/su16188139","url":null,"abstract":"Utilizing calcium-based adsorbents for CO2 adsorption through cyclic calcination/carbonization is one of the most cost-effective methods for carbon emission reduction. In order to improve the cycle stability of the adsorbents and the capture efficiency of CO2, this study used industrial solid waste coal fly ash for the hydration treatment of calcium-based adsorbent to explore the variations in the cyclic adsorption performance of the adsorbent under different doping ratios and hydration conditions. By means of various characterization techniques, the microscopic mechanism for improving the performance of the modified adsorbent was analyzed from the perspectives of chemical composition, physical structure, and surface functional groups of the adsorbents. The results demonstrated that the modification of coal fly ash could significantly enhance the carbonation performance and cycle stability of the adsorbent in multiple CO2 capture processes. The modified material doped with 5% coal fly ash had the highest total CO2 adsorption capacity, which increased by 13.7% compared to before modification. Additionally, the modified material doped with 10% coal fly ash exhibited the strongest cyclic adsorption capacity, which was 14.0% higher than that before modification, and the adsorption attenuation rate decreased by 32.2%. The characterization results showed that the reaction between calcium oxide and coal fly ash formed CaSiO3 and Ca12Al14O33 during the modification process, which was the primary reason for the improvement in the CO2 capture performance of the modified materials. This study provided a new perspective on the resource utilization of solid waste fly ash and efficient CO2 capture.","PeriodicalId":22183,"journal":{"name":"Sustainability","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elena Lo Piccolo, Gerardo Petruzziello, Rita Chiesa, Luca Pietrantoni, Marco Giovanni Mariani
Corporate websites are crucial in recruitment, as the prospective applicants’ experiences in digital recruitment may influence their intentions. Therefore, understanding how opportunity-to-perform perceptions (OPP) integral to procedural justice experienced by potential applicants while visiting a corporate recruitment website impact their reactions towards the company can be key. This study aims to elucidate the influence of OPP on applicants’ intentions to apply (ITA) via corporate websites. Specifically, it explores the indirect relationship between OPP during recruitment and ITA one month after visiting a company website, mediated by organizational attractiveness and ITA measured immediately after and one week after the website visit. This multi-wave study collected 260 cases from master’s students in psychology who completed a questionnaire across waves. Hypotheses were tested using the PROCESS macro in SPSS. The findings highlighted a serial mediation pathway, wherein the indirect connection between OPP and ITA after one month was mediated through organizational attractiveness, ITA immediately after, and one week after website visits. The results highlight the importance of enhancing procedural justice corporate websites to influence applicants’ perceptions and intentions positively and improve recruitment outcomes. Future research should explore the effect of long-term justice perceptions as a basis for a sustainable employee–employer relationship.
企业网站在招聘中至关重要，因为潜在应聘者在数字招聘中的体验可能会影响他们的意向。因此，了解潜在申请人在访问企业招聘网站时所体验到的与程序公正不可分割的 "表现机会感知"（OPP）如何影响他们对企业的反应至关重要。本研究旨在阐明 OPP 对申请人通过企业网站申请（ITA）的意向的影响。具体来说，它探讨了招聘期间的 OPP 与访问公司网站一个月后的 ITA 之间的间接关系，并以组织吸引力和访问网站后立即及一周后测量的 ITA 为中介。这项多波研究从心理学硕士研究生中收集了 260 个案例，他们在各波研究中都填写了问卷。使用 SPSS 中的 PROCESS 宏对假设进行了检验。研究结果凸显了一个序列中介途径，即一个月后，OPP 和 ITA 之间的间接联系通过组织吸引力、网站访问后立即和一周后的 ITA 进行中介。研究结果突出表明，加强企业网站的程序公正性对于积极影响应聘者的认知和意向、改善招聘结果非常重要。未来的研究应探索长期公正感的影响，以此作为员工与雇主关系可持续发展的基础。
{"title":"Fairness in E-Recruitment: Examining Procedural Justice Perceptions and Job Seekers’ Intentions","authors":"Elena Lo Piccolo, Gerardo Petruzziello, Rita Chiesa, Luca Pietrantoni, Marco Giovanni Mariani","doi":"10.3390/su16188124","DOIUrl":"https://doi.org/10.3390/su16188124","url":null,"abstract":"Corporate websites are crucial in recruitment, as the prospective applicants’ experiences in digital recruitment may influence their intentions. Therefore, understanding how opportunity-to-perform perceptions (OPP) integral to procedural justice experienced by potential applicants while visiting a corporate recruitment website impact their reactions towards the company can be key. This study aims to elucidate the influence of OPP on applicants’ intentions to apply (ITA) via corporate websites. Specifically, it explores the indirect relationship between OPP during recruitment and ITA one month after visiting a company website, mediated by organizational attractiveness and ITA measured immediately after and one week after the website visit. This multi-wave study collected 260 cases from master’s students in psychology who completed a questionnaire across waves. Hypotheses were tested using the PROCESS macro in SPSS. The findings highlighted a serial mediation pathway, wherein the indirect connection between OPP and ITA after one month was mediated through organizational attractiveness, ITA immediately after, and one week after website visits. The results highlight the importance of enhancing procedural justice corporate websites to influence applicants’ perceptions and intentions positively and improve recruitment outcomes. Future research should explore the effect of long-term justice perceptions as a basis for a sustainable employee–employer relationship.","PeriodicalId":22183,"journal":{"name":"Sustainability","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the light of the worsening of, and the adverse effects produced by, global warming, a study of Shanghai’s transport carbon emissions can provide an advanced model that can be replicated throughout other cities, thus assisting in the management and reduction of carbon emissions. Considering the volatility and nonlinearity of the carbon emission data series of the transport industry, a prediction model combining complementary ensemble empirical modal decomposition (CEEMD), the improved whale optimization algorithm (IWOA), and the Kernel Extreme Learning Machine (KELM) is proposed for a more accurate prediction of the forecasting of carbon emissions from Shanghai’s transport sector. First, nine indicators were screened as the influencing factors of Shanghai’s transport carbon emissions through the STIRPAT model, and the corresponding carbon emissions were calculated with data related to Shanghai’s transport carbon emissions from 1995 to 2019; Secondly, CEEMD was used to decompose the original data into multiple smooth series and one residual term, and KELM was applied to build a prediction model for each decomposition result, and IWOA was used to optimize the model parameters. The experimental results also demonstrate that CEEMD can effectively reduce model errors. Comparative experiments show that the IWOA algorithm can significantly enhance the stability of machine learning models. The outcomes of various experiments indicate that the CEEMD-IWOA-KELM model produces optimal results with the highest accuracy. Additionally, this model exhibits high stability, as it provides a wider range of methods for predicting carbon emissions and contributing to carbon reduction targets.
{"title":"Shanghai Transport Carbon Emission Forecasting Study Based on CEEMD-IWOA-KELM Model","authors":"Yueyang Gu, Cheng Li","doi":"10.3390/su16188140","DOIUrl":"https://doi.org/10.3390/su16188140","url":null,"abstract":"In the light of the worsening of, and the adverse effects produced by, global warming, a study of Shanghai’s transport carbon emissions can provide an advanced model that can be replicated throughout other cities, thus assisting in the management and reduction of carbon emissions. Considering the volatility and nonlinearity of the carbon emission data series of the transport industry, a prediction model combining complementary ensemble empirical modal decomposition (CEEMD), the improved whale optimization algorithm (IWOA), and the Kernel Extreme Learning Machine (KELM) is proposed for a more accurate prediction of the forecasting of carbon emissions from Shanghai’s transport sector. First, nine indicators were screened as the influencing factors of Shanghai’s transport carbon emissions through the STIRPAT model, and the corresponding carbon emissions were calculated with data related to Shanghai’s transport carbon emissions from 1995 to 2019; Secondly, CEEMD was used to decompose the original data into multiple smooth series and one residual term, and KELM was applied to build a prediction model for each decomposition result, and IWOA was used to optimize the model parameters. The experimental results also demonstrate that CEEMD can effectively reduce model errors. Comparative experiments show that the IWOA algorithm can significantly enhance the stability of machine learning models. The outcomes of various experiments indicate that the CEEMD-IWOA-KELM model produces optimal results with the highest accuracy. Additionally, this model exhibits high stability, as it provides a wider range of methods for predicting carbon emissions and contributing to carbon reduction targets.","PeriodicalId":22183,"journal":{"name":"Sustainability","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wenhao Wan, Yongzhong Tian, Jinglian Tian, Chengxi Yuan, Yan Cao, Kangning Liu
Land Use and Land Cover Change (LUCC) represents the interaction between human societies and the natural environment. Studies of LUCC simulation allow for the analysis of Land Use and Land Cover (LULC) patterns in a given region. Moreover, these studies enable the simulation of complex future LUCC scenarios by integrating multiple factors. Such studies can provide effective means for optimizing and making decisions about the future patterns of a region. This review conducted a literature search on geographic models and simulations in the Web of Science database. From the literature, we summarized the basic steps of spatiotemporal dynamic simulation of LUCC. The focus was on the current major models, analyzing their characteristics and limitations, and discussing their expanded applications in land use. This review reveals that current research still faces challenges such as data uncertainty, necessitating the advancement of more diverse data and new technologies. Future research can enhance the precision and applicability of studies by improving models and methods, integrating big data and multi-scale data, and employing multi-model coupling and various algorithmic experiments for comparison. This would support the advancement of land use spatiotemporal dynamic simulation research to higher levels.
土地利用和土地覆被变化 (LUCC) 代表了人类社会与自然环境之间的相互作用。通过对 LUCC 模拟的研究，可以分析特定区域的土地利用和土地覆被 (LULC) 模式。此外，这些研究还可以通过整合多种因素来模拟复杂的未来 LUCC 情景。此类研究可为优化和决策一个地区的未来模式提供有效手段。本综述在 Web of Science 数据库中对地理模型和模拟进行了文献检索。我们从文献中总结了 LUCC 时空动态模拟的基本步骤。重点是当前的主要模型，分析了它们的特点和局限性，并讨论了它们在土地利用方面的扩展应用。综述显示，目前的研究仍面临数据不确定性等挑战，因此需要更多样化的数据和新技术。未来的研究可以通过改进模型和方法、整合大数据和多尺度数据、采用多模型耦合和各种算法实验进行比较，来提高研究的精度和适用性。这将支持土地利用时空动态模拟研究向更高水平迈进。
{"title":"Research Progress in Spatiotemporal Dynamic Simulation of LUCC","authors":"Wenhao Wan, Yongzhong Tian, Jinglian Tian, Chengxi Yuan, Yan Cao, Kangning Liu","doi":"10.3390/su16188135","DOIUrl":"https://doi.org/10.3390/su16188135","url":null,"abstract":"Land Use and Land Cover Change (LUCC) represents the interaction between human societies and the natural environment. Studies of LUCC simulation allow for the analysis of Land Use and Land Cover (LULC) patterns in a given region. Moreover, these studies enable the simulation of complex future LUCC scenarios by integrating multiple factors. Such studies can provide effective means for optimizing and making decisions about the future patterns of a region. This review conducted a literature search on geographic models and simulations in the Web of Science database. From the literature, we summarized the basic steps of spatiotemporal dynamic simulation of LUCC. The focus was on the current major models, analyzing their characteristics and limitations, and discussing their expanded applications in land use. This review reveals that current research still faces challenges such as data uncertainty, necessitating the advancement of more diverse data and new technologies. Future research can enhance the precision and applicability of studies by improving models and methods, integrating big data and multi-scale data, and employing multi-model coupling and various algorithmic experiments for comparison. This would support the advancement of land use spatiotemporal dynamic simulation research to higher levels.","PeriodicalId":22183,"journal":{"name":"Sustainability","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The energy sector is a critical contributor to the growth and development of any country’s economy. However, ensuring robust cybersecurity within the context of smart energy services presents persistent usability challenges in an increasingly digital environment. This study explores the intersection of human-computer interaction (HCI), cybersecurity, and usability to identify and address issues that impact the overall security of smart energy management systems. By analyzing the complex relationships between users and security protocols, this research aims to enhance the security framework, promote better user adherence, and improve system usability. The study focuses on three primary objectives: (1) identifying the most prevalent usability issues in current cybersecurity practices; (2) examining the relationship between HCI and user compliance with security measures; and (3) proposing strategies to improve cybersecurity usability by leveraging HCI principles. Hybrid approaches utilizing artificial intelligence facilitate empirical analysis and framework evaluation. Additionally, a comparative study with six existing models has been conducted. By envisioning a future where security measures not only ensure enhanced protection but also integrate seamlessly into user experiences, this approach seeks to provide valuable insights into ongoing cybersecurity discussions and contribute to a more resilient security landscape against evolving digital threats.
{"title":"Integration of Cybersecurity, Usability, and Human-Computer Interaction for Securing Energy Management Systems","authors":"Abdullah M. Albarrak","doi":"10.3390/su16188144","DOIUrl":"https://doi.org/10.3390/su16188144","url":null,"abstract":"The energy sector is a critical contributor to the growth and development of any country’s economy. However, ensuring robust cybersecurity within the context of smart energy services presents persistent usability challenges in an increasingly digital environment. This study explores the intersection of human-computer interaction (HCI), cybersecurity, and usability to identify and address issues that impact the overall security of smart energy management systems. By analyzing the complex relationships between users and security protocols, this research aims to enhance the security framework, promote better user adherence, and improve system usability. The study focuses on three primary objectives: (1) identifying the most prevalent usability issues in current cybersecurity practices; (2) examining the relationship between HCI and user compliance with security measures; and (3) proposing strategies to improve cybersecurity usability by leveraging HCI principles. Hybrid approaches utilizing artificial intelligence facilitate empirical analysis and framework evaluation. Additionally, a comparative study with six existing models has been conducted. By envisioning a future where security measures not only ensure enhanced protection but also integrate seamlessly into user experiences, this approach seeks to provide valuable insights into ongoing cybersecurity discussions and contribute to a more resilient security landscape against evolving digital threats.","PeriodicalId":22183,"journal":{"name":"Sustainability","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jiangang Xu, Ning Huang, Jie Zhang, Xiaoan Zhang, Guangtian Shi, Xuanmin Li
Aeolian sand hazards are often a threat to culverts, which are important channels and pieces of infrastructure of the desert railway. In addition to wind speed, wind direction, and culvert structure, terrain may also be an important reason for the formation of culvert sand hazards. However, there are few studies on the effect of terrain on the sediment accumulation characteristics of culverts. This paper established computational fluid dynamics (CFD) models of railway culverts (flat and concave culverts) based on Euler’s two-fluid theory. An analysis of the influence of terrain on the distribution law of the flow fields and sand accumulation around railway culverts was carried out. The results show that the horizontal wind speed curves changes in a “W” shape along the centre axis surface from the forecourt to the rearcourt within a range of 30 m~66.8 m. Low-speed backflow is formed at the inlet and outlet of the culvert, and the minimum wind speed reaches −3.6 m/s and −4.2 m/s, respectively, when the height from the bottom of the culvert is 1.0 m and 1.5 m, resulting in intensified sand sedimentation. In concave culverts, the lower the roadbed height, the easier it is for sand to accumulate at the culvert outlet, the rearcourt, and the track; the sand volume fraction is close to 0.63, affecting the normal operation of the trains. On the contrary, the higher the roadbed, the easier it is for sand to accumulate at the culvert inlet, hindering the passage of engineering vehicles and reducing the function of the culverts. These results reveal that terrain plays a pivotal role in the sand accumulation around culverts and that it should be one of the key considerations for the design of new railway culverts. This work can provide a theoretical basis for preventing and managing sand hazards in railway culverts.
作为沙漠铁路重要通道和基础设施的涵洞经常受到风沙的威胁。除了风速、风向和涵洞结构外，地形也可能是形成涵洞风沙危害的重要原因。然而，有关地形对涵洞泥沙淤积特性影响的研究却很少。本文基于欧拉双流体理论，建立了铁路涵洞（平涵和凹涵）的计算流体动力学（CFD）模型。分析了地形对铁路涵洞周围流场分布规律和积沙的影响。结果表明，在 30 m~66.8 m 范围内，水平风速曲线沿中轴面从前庭到后庭呈 "W "形变化，在涵洞进口和出口处形成低速逆流，当涵洞底部高度为 1.0 m 和 1.5 m 时，最小风速分别达到-3.6 m/s 和-4.2 m/s，导致泥沙沉积加剧。在凹形涵洞中，路基高度越低，涵洞出口、后道和轨道处越容易积沙，沙体积分数接近 0.63，影响列车的正常运行。相反，路基越高，涵洞进口处越容易积沙，阻碍工程车辆通行，降低涵洞的功能。这些结果表明，地形对涵洞周围的积沙起着举足轻重的作用，应成为新建铁路涵洞设计的重要考虑因素之一。这项工作可为预防和管理铁路涵洞的风沙危害提供理论依据。
{"title":"Influence of Terrain on Windblown Sand Flow Field Characteristics around Railway Culverts","authors":"Jiangang Xu, Ning Huang, Jie Zhang, Xiaoan Zhang, Guangtian Shi, Xuanmin Li","doi":"10.3390/su16188128","DOIUrl":"https://doi.org/10.3390/su16188128","url":null,"abstract":"Aeolian sand hazards are often a threat to culverts, which are important channels and pieces of infrastructure of the desert railway. In addition to wind speed, wind direction, and culvert structure, terrain may also be an important reason for the formation of culvert sand hazards. However, there are few studies on the effect of terrain on the sediment accumulation characteristics of culverts. This paper established computational fluid dynamics (CFD) models of railway culverts (flat and concave culverts) based on Euler’s two-fluid theory. An analysis of the influence of terrain on the distribution law of the flow fields and sand accumulation around railway culverts was carried out. The results show that the horizontal wind speed curves changes in a “W” shape along the centre axis surface from the forecourt to the rearcourt within a range of 30 m~66.8 m. Low-speed backflow is formed at the inlet and outlet of the culvert, and the minimum wind speed reaches −3.6 m/s and −4.2 m/s, respectively, when the height from the bottom of the culvert is 1.0 m and 1.5 m, resulting in intensified sand sedimentation. In concave culverts, the lower the roadbed height, the easier it is for sand to accumulate at the culvert outlet, the rearcourt, and the track; the sand volume fraction is close to 0.63, affecting the normal operation of the trains. On the contrary, the higher the roadbed, the easier it is for sand to accumulate at the culvert inlet, hindering the passage of engineering vehicles and reducing the function of the culverts. These results reveal that terrain plays a pivotal role in the sand accumulation around culverts and that it should be one of the key considerations for the design of new railway culverts. This work can provide a theoretical basis for preventing and managing sand hazards in railway culverts.","PeriodicalId":22183,"journal":{"name":"Sustainability","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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