Abdulrahman Bin Mahmoud, Abdullah Alrashdi, Salman Akhtar, Ayman Altuwaim, Abdulmohsen Almohsen
The construction industry plays a substantial role in shaping the economies of many countries. Construction management faces various challenges that can lead to project failures, particularly in infrastructure projects struggling to meet cost and time requirements. Inadequate project planning and the intricate nature of construction projects can cause participants’ project goals to not align. It is crucial to address these challenges early in the planning stages to ensure project success. This research involved investigating previous studies to understand current practices for improving infrastructure project planning and selecting the best pre-project planning tool. Infrastructure projects in the Saudi construction industry are used as a case study. A questionnaire was prepared based on essential alignment issues affecting team alignment during pre-project planning. Participants rated the level of agreement with alignment issues and the overall success of a project they worked on. The study utilized descriptive and inferential analysis techniques to assess infrastructure project success rates and develop a predictive model driven by the alignment tool. Multiple linear regression techniques were used during the model’s development, and validation and reliability outputs were obtained. By evaluating all relevant stakeholders, the model generates a score to facilitate the pre-project planning process, increasing the likelihood of project success. The study found that the model’s predictive accuracy was 94%. This research is significant in creating a predictive model applicable to infrastructure projects, enhancing project management practices by enabling project teams to evaluate project progress, identify projects in need of corrective action, and ultimately improve project performance, leading to cost and time savings.
{"title":"Development of a Predictive Model Based on the Alignment Tool in the Early Stages of Projects: The Case of Saudi Arabia Infrastructure Projects","authors":"Abdulrahman Bin Mahmoud, Abdullah Alrashdi, Salman Akhtar, Ayman Altuwaim, Abdulmohsen Almohsen","doi":"10.3390/su16188122","DOIUrl":"https://doi.org/10.3390/su16188122","url":null,"abstract":"The construction industry plays a substantial role in shaping the economies of many countries. Construction management faces various challenges that can lead to project failures, particularly in infrastructure projects struggling to meet cost and time requirements. Inadequate project planning and the intricate nature of construction projects can cause participants’ project goals to not align. It is crucial to address these challenges early in the planning stages to ensure project success. This research involved investigating previous studies to understand current practices for improving infrastructure project planning and selecting the best pre-project planning tool. Infrastructure projects in the Saudi construction industry are used as a case study. A questionnaire was prepared based on essential alignment issues affecting team alignment during pre-project planning. Participants rated the level of agreement with alignment issues and the overall success of a project they worked on. The study utilized descriptive and inferential analysis techniques to assess infrastructure project success rates and develop a predictive model driven by the alignment tool. Multiple linear regression techniques were used during the model’s development, and validation and reliability outputs were obtained. By evaluating all relevant stakeholders, the model generates a score to facilitate the pre-project planning process, increasing the likelihood of project success. The study found that the model’s predictive accuracy was 94%. This research is significant in creating a predictive model applicable to infrastructure projects, enhancing project management practices by enabling project teams to evaluate project progress, identify projects in need of corrective action, and ultimately improve project performance, leading to cost and time savings.","PeriodicalId":22183,"journal":{"name":"Sustainability","volume":"101 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265048","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 traditional villages along the Wanli Tea Road carry rich historical and cultural heritage, holding significant humanistic and scientific value. However, with the rapid pace of urbanization and modernization, the inheritance and protection of the cultural landscapes in these traditional villages face numerous challenges. Based on this, this study focuses on Xiamen Village, a traditional village along the Jinzhong section of the Wanli Tea Road, utilizing the perspective of the landscape gene information chain to reveal the performance and genetic characteristics of its unique and regionally distinctive cultural landscape genes. The study provides theoretical support for the protection and inheritance of cultural landscapes in traditional villages along the Wanli Tea Road. The results show that: (1) The overall cultural landscape of Xiamen Village has been well preserved, with notable characteristics in environment, layout, architecture, and culture, demonstrating its rich historical and cultural accumulation, and offering high research and conservation value; (2) The landscape gene information chain of Xiamen Village plays a critical role in integrating the village’s cultural landscape. The landscape gene information elements and points express the village’s unique historical inheritance through regional culture and material forms. The “branch-like” structure of the landscape gene information corridors effectively connects the various landscape gene information points, while the landscape gene information network reflects the interaction between tradition and modernity; (3) The landscape gene information chain of Xiamen Village shows a relationship of coexistence between inheritance and change in its genetic characteristics. Although some landscape genes face challenges from modernization, their core traits have not been lost. The inheritance of the landscape genes is not static but adjusts and reconstructs within an evolving social and cultural context, reflecting adaptability and flexibility in response to modern demands.
{"title":"Analysis of Performance and Genetic Characteristics of Cultural Landscapes in Traditional Villages along the Jinzhong Section of the Wanli Tea Road from a Landscape Gene Information Chain Perspective: A Case Study of Xiamen Village","authors":"Wei Wang, Qianfei Shi, Guoyu Wang","doi":"10.3390/su16188131","DOIUrl":"https://doi.org/10.3390/su16188131","url":null,"abstract":"The traditional villages along the Wanli Tea Road carry rich historical and cultural heritage, holding significant humanistic and scientific value. However, with the rapid pace of urbanization and modernization, the inheritance and protection of the cultural landscapes in these traditional villages face numerous challenges. Based on this, this study focuses on Xiamen Village, a traditional village along the Jinzhong section of the Wanli Tea Road, utilizing the perspective of the landscape gene information chain to reveal the performance and genetic characteristics of its unique and regionally distinctive cultural landscape genes. The study provides theoretical support for the protection and inheritance of cultural landscapes in traditional villages along the Wanli Tea Road. The results show that: (1) The overall cultural landscape of Xiamen Village has been well preserved, with notable characteristics in environment, layout, architecture, and culture, demonstrating its rich historical and cultural accumulation, and offering high research and conservation value; (2) The landscape gene information chain of Xiamen Village plays a critical role in integrating the village’s cultural landscape. The landscape gene information elements and points express the village’s unique historical inheritance through regional culture and material forms. The “branch-like” structure of the landscape gene information corridors effectively connects the various landscape gene information points, while the landscape gene information network reflects the interaction between tradition and modernity; (3) The landscape gene information chain of Xiamen Village shows a relationship of coexistence between inheritance and change in its genetic characteristics. Although some landscape genes face challenges from modernization, their core traits have not been lost. The inheritance of the landscape genes is not static but adjusts and reconstructs within an evolving social and cultural context, reflecting adaptability and flexibility in response to modern demands.","PeriodicalId":22183,"journal":{"name":"Sustainability","volume":"101 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264759","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 presented review concerns the cross-disciplinary approaches to the subject of blue food and blue colourants, the socio-cultural aspects of blue food and beverage consumption, human health effects, environmental impact, and economic aspects. Blue colour in relation to food is not only about improving visual appeal, to which the addition of food colouring is usually limited when the food is coloured in some way that does not encourage eating. It is also the rich and complex sociological side related to food, that is, not only the food itself but also the background, dishware, and light, depending on whether we want to encourage—to increase consumption—or discourage—to, for example, reduce the amount of food eaten for dietary purposes. The negative side of consuming and disposing of synthetic dyes and the health-promoting aspects of natural dyes are also mentioned, with the economic and environmental aspects of sourcing natural dyes being discussed. The food industry uses blue dyes not only for consumption, but also for food quality control, taking advantage of the pH-dependent colour change properties of the compound.
{"title":"Blue in Food and Beverages—A Review of Socio-Cultural, Economic, and Environmental Implications","authors":"Agnieszka Szmagara","doi":"10.3390/su16188142","DOIUrl":"https://doi.org/10.3390/su16188142","url":null,"abstract":"The presented review concerns the cross-disciplinary approaches to the subject of blue food and blue colourants, the socio-cultural aspects of blue food and beverage consumption, human health effects, environmental impact, and economic aspects. Blue colour in relation to food is not only about improving visual appeal, to which the addition of food colouring is usually limited when the food is coloured in some way that does not encourage eating. It is also the rich and complex sociological side related to food, that is, not only the food itself but also the background, dishware, and light, depending on whether we want to encourage—to increase consumption—or discourage—to, for example, reduce the amount of food eaten for dietary purposes. The negative side of consuming and disposing of synthetic dyes and the health-promoting aspects of natural dyes are also mentioned, with the economic and environmental aspects of sourcing natural dyes being discussed. The food industry uses blue dyes not only for consumption, but also for food quality control, taking advantage of the pH-dependent colour change properties of the compound.","PeriodicalId":22183,"journal":{"name":"Sustainability","volume":"31 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265017","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}
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":"13 1","pages":""},"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}
In recent years, time series land cover products have been developed rapidly. However, the traditional classification strategy rarely considers time continuity and spatial consistency, which leads to the existence of unreasonable changes among the multi-period products. In order to solve the existing problems, this paper proposes a matrix decomposition model and an optimized hidden Markov model (HMM) to improve the consistency of the time series land cover maps. It also compares the results with the spatio-temporal window filtering model. The spatial weight information is introduced into the singular value decomposition (SVD) model, and the regression model is constructed by combining the eigenvalues and eigenvectors of the image to predict the unreasonable variable pixels and complete the construction of the matrix decomposition model. To solve the two problems of reliance on expert experience and lack of spatial relationships, this paper optimizes the model and proposes the HMM Land Cover Transition (HMM_LCT) model. The overall accuracy of the matrix decomposition model and the HMM_LCT model is 90.74% and 89.87%, respectively. It is found that the matrix decomposition model has a better effect on consistency adjustment than the HMM_LCT model. The matrix decomposition model can also adjust the land cover trajectory to better express the changing trend of surface objects. After consistent adjustment by the matrix decomposition model, the cumulative proportion of the first 15 types of land cover trajectories reached 99.47%, of which 83.01% were stable land classes that had not changed for three years.
{"title":"Improvement of Spatio-Temporal Inconsistency of Time Series Land Cover Products","authors":"Ling Zhu, Jun Liu, Shuyuan Jiang, Jingyi Zhang","doi":"10.3390/su16188127","DOIUrl":"https://doi.org/10.3390/su16188127","url":null,"abstract":"In recent years, time series land cover products have been developed rapidly. However, the traditional classification strategy rarely considers time continuity and spatial consistency, which leads to the existence of unreasonable changes among the multi-period products. In order to solve the existing problems, this paper proposes a matrix decomposition model and an optimized hidden Markov model (HMM) to improve the consistency of the time series land cover maps. It also compares the results with the spatio-temporal window filtering model. The spatial weight information is introduced into the singular value decomposition (SVD) model, and the regression model is constructed by combining the eigenvalues and eigenvectors of the image to predict the unreasonable variable pixels and complete the construction of the matrix decomposition model. To solve the two problems of reliance on expert experience and lack of spatial relationships, this paper optimizes the model and proposes the HMM Land Cover Transition (HMM_LCT) model. The overall accuracy of the matrix decomposition model and the HMM_LCT model is 90.74% and 89.87%, respectively. It is found that the matrix decomposition model has a better effect on consistency adjustment than the HMM_LCT model. The matrix decomposition model can also adjust the land cover trajectory to better express the changing trend of surface objects. After consistent adjustment by the matrix decomposition model, the cumulative proportion of the first 15 types of land cover trajectories reached 99.47%, of which 83.01% were stable land classes that had not changed for three years.","PeriodicalId":22183,"journal":{"name":"Sustainability","volume":"9 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264756","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}
Agricultural droughts in South Africa, particularly in the Amahlathi Local Municipality (ALM), significantly impact socioeconomic activities, sustainable livelihoods, and ecosystem services, necessitating urgent attention to improved resilience and food security. The study assessed the interdecadal drought severity and duration in Amahlathi’s agricultural potential zone from 1989 to 2019 using various vegetation indicators. Landsat time series data were used to analyse the land surface temperature (LST), soil-adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), and standardized precipitation index (SPI). The study utilised GIS-based weighted overlay, multiple linear regression models, and Pearson’s correlation analysis to assess the correlations between LST, NDVI, SAVI, and SPI in response to the agricultural drought extent. The results reveal a consistent negative correlation between LST and NDVI in the ALM, with an increase in vegetation (R2 = 0.9889) and surface temperature. LST accuracy in dry areas increased to 55.8% in 2019, despite dense vegetation and a high average temperature of 40.12 °C, impacting water availability, agricultural land, and local ecosystems. The regression analysis shows a consistent negative correlation between LST and NDVI in the ALM from 1989 to 2019, with the correlation between vegetation and surface temperature increasing since 2019. The SAVI indicates a slight improvement in overall average vegetation health from 0.18 in 1989 to 0.25 in 2009, but a slight decrease to 0.21 in 2019. The SPI at 12 and 24 months indicates that drought severely impacted vegetation cover from 2014 to 2019, with notable recovery during improved wet periods in 1993, 2000, 2003, 2006, 2008, and 2013, possibly due to temporary drought relief. The findings can guide provincial drought monitoring and early warning programs, enhancing drought resilience, productivity, and sustainable livelihoods, especially in farming communities.
{"title":"Interdecadal Variations in Agricultural Drought Monitoring Using Land Surface Temperature and Vegetation Indices: A Case of the Amahlathi Local Municipality in South Africa","authors":"Phumelelani Mbuqwa, Hezekiel Bheki Magagula, Ahmed Mukalazi Kalumba, Gbenga Abayomi Afuye","doi":"10.3390/su16188125","DOIUrl":"https://doi.org/10.3390/su16188125","url":null,"abstract":"Agricultural droughts in South Africa, particularly in the Amahlathi Local Municipality (ALM), significantly impact socioeconomic activities, sustainable livelihoods, and ecosystem services, necessitating urgent attention to improved resilience and food security. The study assessed the interdecadal drought severity and duration in Amahlathi’s agricultural potential zone from 1989 to 2019 using various vegetation indicators. Landsat time series data were used to analyse the land surface temperature (LST), soil-adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), and standardized precipitation index (SPI). The study utilised GIS-based weighted overlay, multiple linear regression models, and Pearson’s correlation analysis to assess the correlations between LST, NDVI, SAVI, and SPI in response to the agricultural drought extent. The results reveal a consistent negative correlation between LST and NDVI in the ALM, with an increase in vegetation (R2 = 0.9889) and surface temperature. LST accuracy in dry areas increased to 55.8% in 2019, despite dense vegetation and a high average temperature of 40.12 °C, impacting water availability, agricultural land, and local ecosystems. The regression analysis shows a consistent negative correlation between LST and NDVI in the ALM from 1989 to 2019, with the correlation between vegetation and surface temperature increasing since 2019. The SAVI indicates a slight improvement in overall average vegetation health from 0.18 in 1989 to 0.25 in 2009, but a slight decrease to 0.21 in 2019. The SPI at 12 and 24 months indicates that drought severely impacted vegetation cover from 2014 to 2019, with notable recovery during improved wet periods in 1993, 2000, 2003, 2006, 2008, and 2013, possibly due to temporary drought relief. The findings can guide provincial drought monitoring and early warning programs, enhancing drought resilience, productivity, and sustainable livelihoods, especially in farming communities.","PeriodicalId":22183,"journal":{"name":"Sustainability","volume":"11 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264762","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":"74 1","pages":""},"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}
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":"31 1","pages":""},"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}
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":"101 1","pages":""},"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":"20 1","pages":""},"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}
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