Search Results (155)

Hyperketonemia (HYK) is a common disorder in high-producing dairy cows, resulting in significant economic losses. Defined by elevated beta-hydroxybutyrate (BHB; ≥1.2 mmol/L) without clinical signs, HYK is often considered a gateway disease, predisposing cows to other metabolic and infectious problems. Our objective was to investigate the association between previous lactation risk factors and both BHB concentration and HYK status during the first week postpartum in the subsequent lactation. A retrospective study was conducted using previously collected blood samples from 2336 Holstein multiparous dairy cows from 7 dairy herds, where BHB concentration was measured during the first week postpartum. Data from the previous lactation were extracted from electronic farm records. Log-transformed BHB concentrations and HYK status were each modeled using separate linear mixed models. Both models included the same set of risk factors—lactation, previous lactation total times bred, dry length period, previous lactation days in milk, previous lactation days open, previous lactation days carried calf, previous lactation peak milk production, previous lactation total milk production, previous lactation total milk fat, and previous lactation total milk protein—to investigate their association with these outcomes. Potential confounding variables were offered to the models, and stepwise backward elimination was used to determine which covariates to retain. Significant associations were detected between BHB concentration and dry period length (DDRY), lactation number (LACT), previous lactation total milk protein (TOTP), and previous lactation days open (PDOPN). Inclusive, significant associations were detected between HYK status and previous lactation total milk production (PTOTM), DDRY, LACT, TOTP, and PDOPN. Our results suggest that a dry period longer than 60 days, days open exceeding 130 days, being in their third or greater lactation, and each additional 1000 kg of milk produced in the previous lactation are associated with an increased risk of having high BHB and HYK in the first week postpartum in the subsequent lactation. Full article

Brownfields are abundant, widely dispersed, and subject to complex contamination, resulting in waste land, ecological degradation, and barriers to economic growth. The accurate identification of brownfield sites is key to formulating effective remediation and reuse strategies. However, the heterogeneity of surface features poses significant challenges for identifying various types of brownfields across entire urban areas. To address these challenges, this study proposes a “Target–Classification–Modification” (TCM) method for brownfield identification, which was applied to Tangshan City, China. This method consists of a three-stage process: target area localization, visual interpretation and classification, and site-level modification. It leverages integrated multi-source open-access data and clear rules for subtype classification and the determination of spatial boundaries and abandonment status. The results for Tangshan show that (1) the overall accuracy of the TCM method reached 84.9%; (2) a total of 1706 brownfield sites were identified, including 422 raw-material mining sites, 576 raw-material manufacturing sites, and 708 non-raw-material manufacturing sites; (3) subtype analysis revealed distinct spatial distribution and morphological patterns, driven by resource endowments, transportation networks, and industrial space organization. The TCM method improved the identification efficiency by 34.7% through precise target-area localization. It offers well-defined criteria to distinguish different brownfield subtypes. In addition, it employs a multi-approach strategy to determine the abandonment status, further enhancing accuracy. This method is scalable and widely applicable, providing support for urban-scale brownfield research and practice. Full article

Sustained green innovation in firms is a crucial driver of sustainable economic development. Chief executive officer (CEO) openness, as a key personality trait related to leadership effectiveness, has an important but largely overlooked impact on sustained green innovation. This study aims to explore the impact of CEO openness on sustained green innovation and its boundary conditions. Using data from Chinese A-share-listed firms between 2011 and 2023, we find that CEO openness has a significant positive impact on sustained green innovation in firms. The moderating effects reveal that both digitalization level and CEO shareholding strengthen the positive effect of CEO openness on sustained green innovation. Heterogeneity analysis indicates that this positive effect is more pronounced in state-owned enterprises, firms in non-heavily polluting industries, and those with high analyst coverage. These findings provide theoretical support for understanding the determinants of sustained green innovation through the lens of CEO personality. They also enrich the growing literature on the impact of CEO openness on corporate decision-making. Furthermore, this study recommends that firms prioritize CEO openness in selection, enhance digital infrastructure, and improve equity incentive measures to ultimately foster sustained green innovation. Full article

Slope failures in open-pit mining pose significant operational and safety issues, underscoring the importance of implementing effective stability monitoring frameworks for early hazard detection to allow for timely intervention and risk mitigation. This systematic review presents a comprehensive synthesis of existing and emerging methods and technologies used for slope stability monitoring in open-pit mining, including both remote sensing and in situ methods, as well as advanced technologies, such as Artificial Intelligence (AI), the Internet of Things (IoT), and Wireless Sensor Networks (WSNs). Using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020 guidelines, a total of 49 studies were selected from a collection of four engineering databases, and a comparative analysis was conducted to determine the underlying differences between the various methods for open-pit slope stability monitoring in terms of their performance across key attributes, such as monitoring accuracy, spatial and temporal coverage, operational complexity, and economic viability. Their juxtaposition highlighted the notion that no universally optimal slope stability monitoring system exists, due to a series of compromises that arise as a result of inherent technological limitations and site-specific constraints. Notably, remote sensing methods offer large-scale, non-intrusive monitoring, but are often limited by environmental factors and data acquisition infrequency, whereas in situ methods provide high precision, but suffer from limited spatial coverage and scalability. This review further highlights the capacity of emerging methods and technologies to address these limitations, providing suggestions for future research directions involving the integration of multiple sensing technologies for the enhancement of monitoring capabilities. This study provides a consolidated knowledge base on open-pit slope stability monitoring methods, technologies, and techniques, to guide the development of integrated, cost-effective, and scalable slope monitoring solutions that enhance mine safety and efficiency. Full article

The development of students’ skills, abilities, competences and knowledge is the basis for sustainable socio-economic development. Today we live in a world that is in continuous change, both economically and socially, which also determines a change in the requirements on the labor market and therefore graduates and higher education institutions must continuously adapt to these changes. Thus, higher education institutions must adapt their teaching strategies and educational offer, while students must develop new skills and competences. The main purpose of this article is to analyze the extent to which the information, skills, attitudes and competences acquired by graduates of physical education and sports faculties during their years of study influence their standard of living, job satisfaction and confidence. To achieve this objective, we asked the following research questions: 1. To what extent do the information, skills, abilities and competences acquired by students during their years of study influence their income level, standard of living, job satisfaction and level of confidence in the workplace? 2. What is the self-perception of students regarding the information, skills, abilities and knowledge that students possess? 3. What is the perception of employers regarding the information, skills, abilities and knowledge that students possess? 4. To what extent are there differences between students’ self-perception and employers’ perception regarding the information, skills, abilities and knowledge that students possess? Thus, data were collected through two questionnaires, one distributed among 333 graduates from physical education and sports faculties in Romania and one to 11 employers working in the sports industry in Romania. The data obtained from the students were analyzed using SPSS 24, and it was found that there is a small correlation between the information, skills, competences and knowledge acquired during the years of study and the standard of living, job satisfaction and the confidence in one’s own ability to successfully perform tasks at work. Among the skills, abilities and aptitudes that students consider themselves to excel in are a passion for sports, the continuous desire for improvement, conscientiousness, teamwork, openness to new things and respect for hierarchies and regulations. At the opposite end, graduates consider that they need to improve their public speaking skills, management skills, their ability to communicate in a foreign language, their ability to sell themselves and their ability to manage a project. Full article

Health disparities among populations across geographic regions, demographic and socio-economic groups are well documented; however, ecological studies which visually demonstrate health disparities associated with structural racism among racialized populations are limited. The purpose of this study was to examine low birthweight (LBW) as a measurable indicator of disproportionate health impacts across three race/ethnicity groups—non-Hispanic Black, Hispanic and non-Hispanic White–in the United States (US) for visualizing ecological manifestation of this disparity attributed to structural racism. We begin by providing the contextual background of structural racism through a literature review, and then more specifically, we examine LBW as a selected health indicator characterized with a socio-biological pathway of structural racism via socio-economic and politico–legal determinants and associated mediating factors to health disparities, from which we synthesized a visualization model with the indicators of structural racism reported in the literature reviewed. To further visualize these impacts, publicly available US County Health Ranking data for LBW, at the county level in two US states, Tennessee and Ohio, were analyzed to uncover area-based ecological health outcome—LBW. Significant correlation and scatter plots provided evidence of LBW as a racially sensitive health indicator associated with impacts of structural racism. These findings were further notable through examination of socio-economic determinants (e.g., race/ethnicity, income, education, and employment) and environmental factors such as housing issues as well as other underlying health conditions. Our case study has opened a window for visualizing disparity across non-Hispanic Black, Hispanic, non-Hispanic White populations as demonstrated by the prevalence of LBW disparity through its determinants and mediating factors at the county level. Potentially important policy implications for reparative change are drawn through our study findings that are salutary and/or reductive for addressing impacts of structural racism. Further studies are needed to fully understand the comprehensive web of area-based ecological factors impacting various health outcomes through the impacts of structural racism. Full article

This paper presents two innovative procedures developed for rural electrification planning. To address the challenges of processing vast geospatial data, handling complex and computationally intensive network design, and making detailed yet accessible economic assessments, this work introduces a Buffering plugin for community identification and a Grid Routing and Cost Allocation plugin for network design and economic assessment, both integrated into the open-source QGIS platform. The first enables the identification of potential electrification zones through dual methodologies, while the second introduces three key processes: hierarchical clustering, a modified minimum spanning tree, and a novel cost allocation methodology that provides village-specific LCOE calculations. Testing in Zambia has proven that this approach is not only effective but also—compared to existing tools—offers significant advantages in terms of computational efficiency and accessibility, while providing practical solutions to large-scale challenges. This synergistic approach enables planners to move from granular geospatial data to actionable electrification decisions through a streamlined process. The analysis covered over 3 million buildings, grouped into 162,142 settlement clusters, and subsequently determined optimal electrification strategies for 3025 villages—40.4% connected to grid extensions and 59.6% to mini-grids—serving a total population of 18 million people. Full article

Disaggregated data on electricity generation from bioenergy are very helpful for investigating the economic and technical effects of this form of renewable energy on the German power sector with a high temporal and spatial resolution. But the lack of high-resolution feed-in data for Germany makes it necessary to apply numerical simulations to determine the electricity generation from biomass power plants for a time period and geographic region of interest. This article presents how such a simulation model can be developed using public power plant data as well as open information from German TSOs as input data. The physical model is applied to an ensemble of 20,863 biomass power plants, most of which are in continuous operation, to simulate their electricity generation in Germany for the year 2020. For this period, the spatially aggregated simulation results correlate well with the official electricity feed-in from bioenergy. The disaggregated time series can be used to analyze the electricity generation at any spatial scale, as each power plant is simulated with its technical parameters and geographical location. Furthermore, this article introduces the electricity generation landscape of bioenergy as a high-resolution map and at the federal state level with meaningful energy figures, enabling comprehensive assessments of this form of renewable energy for different regions of Germany. Full article

This research determines the contribution of Corporate Social Responsibility (CSR) to reducing financial risks and, consequently, to the sustainable development of companies in different sectors of the economy and at different phases of the economic cycle (using Russia as an example). The informational and empirical base comprises data on the dynamics of stock prices of sectoral indices of the Moscow Exchange’s total return “gross” (in Russian rubles): oil and gas, electricity, telecommunications, metals and mining, finance, consumer sector (retail trade), chemicals and petrochemicals, and transportation, as well as the “Responsibility and Openness” index in 2019 (before the crises), in 2020 (COVID-19 crisis), 2022 (sanction crisis), and 2024 (Russia’s economic growth). Economic–mathematical models, compiled through regression analysis, showed that the contribution of CSR to reducing the financial risks of companies is highly differentiated among economic sectors and phases of the economic cycle. The research presents a new sectoral perspective on counter-cyclical management of the financial risks of companies through CSR, enabling a deeper study of the cause-and-effect relationships of such management for the sustainable development of companies from different economic sectors. This is the theoretical significance of this research, its novelty, and its contribution to the literature. The research has practical significance, revealing previously unknown best practices for the sustainable development of companies from different economic sectors of Russia across different phases of the economic cycle. The systematized experience will be useful for forecasting the financial risks of companies during future economic crises in Russia and improving the practice of planning and organizing the financial risk management of Russian companies through CSR. The authors’ conclusions have managerial significance because they will help enhance the flexibility and efficiency of corporate financial risk management by considering the sectoral specifics and cyclical nature of the economy when implementing CSR. Full article

Building information modeling (BIM) has proven to be a valuable technology in the fields of architecture, construction management, and maintenance management. However, its full implementation in structural engineering remains unfulfilled due to the persistent use of outdated design methods. Insufficient automation in the design process could lead to structural defects, construction rework, and structural clashes, each of which can have significant financial implications. Given the inherent complexity of large-scale construction projects, manual structural design and detailing are challenging tasks and are prone to human errors. This paper presents a novel BIM framework that leverages BIM, Industry Foundation Classes (IFC), Python scripting, the IfcOpenShell library, and Octave programming to automate the design of reinforced concrete (RC) slabs, benefiting design professionals and contractors by integrating automated processes into project workflows. The framework achieved a 40% reduction in design time and a 25% decrease in human errors, as demonstrated through case studies. In this study, a 3D structural model in BIM software is firstly created, extracting slab geometrical data that are linked to Microsoft (MS) Excel/.csv and Octave spreadsheets via Python and IfcOpenShell. Midspan and end span moment coefficients and floor perimeter data following Indian standards are then gathered in Octave, and this information is further processed with Python scripts. Octave programming is used to determine the most accurate, reliable, and economical design for the slab and its detailing. This design information is then pushed back to BIM software via FreeCAD using Python coding, which can be used to develop bar bending scheduling and 2D drawings of the reinforcement details. The proposed framework is validated through case studies, demonstrating its effectiveness in reducing design time, minimizing human errors, and improving overall project efficiency. The core finding of this research is an automated approach that offers a cost-effective and accurate solution to the limitations of traditional RC slab design, addressing structural errors and reducing rework through seamless BIM integration. This research presents a novel contribution to the integration of structural design, construction processes, and operational aspects within BIM. The findings highlight the potential for further advancements in BIM adoption, particularly in addressing the lag in structural engineering applications compared to architecture. Full article

Entrepreneurial endeavors are essential for stimulating economic growth and rendering them is a primary concern for policymakers. In recent years, smart city ecosystems have garnered attention for enhancing urban living and tackling contemporary difficulties. The contribution of smart cities in promoting entrepreneurship and improving well-being has received little attention. This study aims at examining the potential of smart city as an ecosystem to promote entrepreneurship and enhance well-being and quality of life (QoL). This study uses a Fuzzy evaluation model and the Analytic Hierarchy Process (AHP) to evaluate essential determinants of smart cities and their significance. Data from sources such as the Smart City Index, Ease of Doing Business Ranking, Global Innovation Index, Sustainable Development Report, and Technological Readiness Ranking are utilized with normalization, guaranteeing a dependable evaluation. The findings underscore the significance of open data efforts and transparent governance in recruiting innovative enterprises and promoting entrepreneurship. The study highlights the necessity of cooperative urban planning and public participation in decision-making. Moreover, the authors propose a new definition of smart cities from citizens’ well-being perspective. This research enhances the comprehension of smart cities’ influence on entrepreneurial endeavors, pinpointing problems and prospects for future investigations focused on improving well-being through smart city advancement. Full article

In the energy-saving retrofit of existing buildings, investors are particularly concerned about the energy-saving performance of exterior windows and the payback period of additional costs. This study evaluates representative cities in four different climate zones in China to simulate the energy consumption of large office buildings after replacing different glass windows and conducting energy-saving and economic feasibility assessments. The research method includes the following steps: First, a baseline model of large office buildings in four cities was established using AutoBPS and OpenStudio. Then, the baseline and retrofit models of replacing glass windows were simulated using the EnergyPlus V9.3.0 to obtain multiple hourly energy consumption results. The commercial electricity and gas prices in the four cities were adjusted to calculate the total cost within 20 years after replacing different types of windows. Using the discounted payback period (DPP), net present value (NPV), and profitability index (PI) as evaluation indicators, a feasibility analysis was conducted in the four regions to evaluate the economic feasibility of replacing building windows. The simulation results show that considering economic feasibility and meeting energy-saving standards, it is more economical to choose windows with moderate U-value and SHGC value in the four regions than to choose windows with the smallest U-value and SHGC value, and that both energy savings and economic benefits are closely related to building age, with older buildings (especially those in Changsha and Shenzhen) showing greater benefits. Furthermore, the optimal window types in the four cities determined in this study can recover the investment cost within the window life, with Harbin (SC), Beijing (C), Changsha (HC), and Shenzhen (HW) with the payback period of 6.60, 15.66, 10.16, and 11.42 years, respectively. The research model established in this study provides a useful evaluation path for selecting windows for the energy-saving retrofit of large office buildings in cities in different climate zones and provides data support for the decision making of energy-saving retrofit investors. Full article

The Sustainable Development Goals (SDGs) represent a fundamental global commitment to addressing a wide range of socio-economic and environmental challenges. A key component of these goals is the commitment to ocean sustainability, encapsulated in the concept of the blue economy. The blue economy, emerging in an era characterized by intricate dynamics and openness to transformation, is influenced by various determinants. This study utilizes panel data analysis and the pooled least squares method to investigate the factors influencing the share of the blue economy in the archipelagic provinces of Indonesia from 2012 to 2021. With its vast maritime territory and numerous islands, Indonesia provides a highly relevant context for examining these dynamics. The empirical results indicate that information and communication technology (ICT), fisheries capture, and aquaculture production positively impact the blue economy’s share. Conversely, trade openness and electricity consumption exhibit a negative relationship with the blue economy’s share. Moreover, the analysis reveals that investment does not have a significant effect on the blue economy’s share. These findings underscore the critical importance of developing robust infrastructure and implementing stringent regulatory oversight on fishery product trade to enhance sustainable growth within the blue economy framework. Full article

Sustainable development can only be achieved when jointly considering social, economic, and environmental dimensions. Social capital and local wisdom offer important contributions to the development process and the capabilities of individuals and groups as development actors. This study analyzes the role of social capital and local wisdom in managing business partnerships between farmers and palm oil plantation companies in North Konawe, Indonesia. This research was conducted in a palm oil plantation area by involving landowner farmers, palm oil companies, and other stakeholders such as the local government, NGOs, and academics. Data were collected through in-depth interviews, field observations, and focus group discussions (FGDs), totaling 320 respondents, and analyzed descriptively and qualitatively. The selection of informants for the in-depth interviews was determined by considering their involvement in and understanding of the partnership between farmers and companies in oil palm plantations; field observations were carried out to determine the field conditions of these plantations, while FGDs were held to obtain stakeholder information regarding problems and solutions in implementing farmer and company partnerships with the aim of having a positive impact on economic, social, and environmental welfare. The results underscore the importance of social capital and local wisdom in organizing institutional programs for strengthening palm oil business partnerships. Trust, social networks, and participation negatively affected the sustainability of these partnerships, whilst local wisdom and social solidarity positively influenced institutional strengthening. Company inconsistency and lack of openness can lead to a trust crisis that can threaten the sustainable operations of palm oil companies, while building good cooperative commitment and maintaining collaboration play key roles in enhancing community welfare and increasing company profits. The social capital and local wisdom of farmer institutions in villages are expected to significantly contribute to the establishment of sustainable palm oil business partnerships. Full article

The development of high-tech firms is a vital driver for the economic growth of a city but their distribution and location determinants at the intra-urban level are still unclear. We aim to deepen the understanding of location determinants of high-tech firms, so we construct an analytical framework and use GeoDetector to investigate high-tech firms in Shenzhen based on firms and POI open data in 2023. We find that high-tech firms are distributed in a spatial pattern of ‘one core and six clusters’ with high density in the western area despite industrial heterogeneity. Agglomeration economies and amenity-based factors play a significant role in the distribution of high-tech firms. Institutional factors and classical locational factors have more significant effects on the location of high-tech service and manufacturing firms, respectively. This study contributes to the literature on study spatial units, the influence of amenities, and industrial specificities. These findings highlight public policies on industrial park planning, transportation systems, and public services. Full article