Côte d’Ivoire is recognized as one of the principal gold-producing countries in West Africa, where artisanal and small-scale gold mining (ASGM) constitutes the second most prevalent livelihood activity after agriculture, particularly within rural communities. As a result, high concentrations of ASGM activity have been recorded in 78% of the country’s regions. In this context, the ecological impacts of ASGM on biodiversity in the Daoukro region were examined. A diachronic geospatial analysis was conducted using satellite imagery from 2010 to 2020, in conjunction with field-based spatial data collection and semi-structured interviews. The findings reveal that extensive environmental degradation has been driven by the unregulated techniques and substances employed in gold extraction processes, including the widespread use of mercury and cyanide. These practices have resulted in severe soil contamination, structural weakening due to erosion, and inhibited vegetative regeneration. Over the decade-long period, the proportion of bare soil increased at an annual growth rate of +7.90%, while forested areas declined markedly from 31,258 hectares to 24,750 hectares—representing a cumulative reduction of 20.34%. This deforestation has contributed to the disruption and loss of native biodiversity that relies on forest ecosystems for survival. Additionally, land fragmentation and habitat degradation have reduced ecological resilience, further intensifying species vulnerability in the region. These findings underscore the urgent need for sustainable land management policies and biodiversity conservation strategies tailored to mitigate the ecological footprint of ASGM in Côte d’Ivoire.
Organic rice farming in Kulonprogo Regency, recognized by the Indonesian government as a pioneering region for organic agriculture, has been increasingly adopted as a sustainable agricultural practice. However, the implementation of organic rice farming systems continues to face significant challenges that hinder their full potential. This study evaluates the sustainability of organic rice farming in Kulonprogo Regency using the Multidimensional Scaling (MDS) technique, facilitated by the Rapfish application, across three critical dimensions: ecological, economic, and social. Data were collected from 70 respondents across three farmer groups—Srijati, Tegal Mulyo, and Jatingarang Lor—selected through a census method, all of which have fully transitioned to organic rice farming practices. The sustainability indices derived from the analysis revealed that the ecological dimension scored 87.79%, indicating a "sustainable" status, while the economic and social dimensions scored 52.35% and 71.61%, respectively, both categorized as "quite sustainable." These findings underscore the ecological robustness of organic rice farming in the region while highlighting the need for targeted interventions to enhance economic viability and social acceptance. Overall, the sustainability level of organic rice farming in Kulonprogo Regency was classified as "moderately sustainable." The validity of the analysis was confirmed through a Standardized Residual Sum of Squares (STRESS) value of 0.25%, which is categorized as "excellent," ensuring the reliability and accuracy of the results. This study provides critical insights into the sustainability dynamics of organic rice farming, offering a foundation for policymakers and stakeholders to develop strategies that address existing challenges and promote long-term sustainability in the region. These findings indicate the urgency of implementing sustainable agricultural practices in organic rice cultivation, as well as the strategic role of government support in price stabilization policies, waste management, and equitable program distribution to strengthen the economic, ecological, and social aspects of the sustainable agricultural system.
Solid waste management in rural areas remains an underexplored domain, despite its growing significance in the context of environmental sustainability and the circular economy. Key challenges include inadequate municipal infrastructure, a shortage of waste collection containers, and the absence of suitable vehicle fleets capable of navigating narrow and steep rural pathways. Moreover, the lack of a strategic framework for waste management, the insufficient application of the 3R (Reduce, Reuse, Recycle) principles, and the absence of circular economy practices further exacerbate these issues. In rural areas, approximately 40% of the waste produced is organic and could be used as a resource for compost production, a valuable input for organic agricultural practices. Projections suggest that by 2027, biowaste will account for 8% of the total waste generated in rural communities. The transition to a circular economy offers significant potential for transforming waste management practices in these areas. Emphasis on innovative collection methods, such as localised and adaptive waste separation techniques, can facilitate this transition. The adoption of circular economy principles in waste management strategies is critical, not only for reducing environmental impact but also for promoting resource efficiency, enhancing soil fertility, and supporting sustainable local economies. Raising public awareness, engaging local communities, and introducing more effective waste management systems will be vital in overcoming existing barriers and ensuring the success of these initiatives.
Tourism has emerged as a pivotal economic driver in Pakistan’s Swat Valley, yet its long-term viability is contingent upon sustained support from the host community. In this study, the multifaceted perceptions and attitudes of residents toward tourism impacts were examined through a quantitative survey of 400 participants. Data were analyzed using Exploratory Factor Analysis (EFA) to identify latent perceptual dimensions and correlation analysis to explore interrelationships among these dimensions. The findings revealed a pronounced dichotomy: strong positive attitudes, primarily driven by perceived economic benefits (r = 0.804), were significantly counterbalanced by a robust negative association with environmental concerns (r = -0.684), particularly those related to pollution and ecological degradation. Socio-cultural impacts were perceived with ambivalence, reflecting both appreciation for cultural exchange and apprehension regarding cultural erosion. A pivotal factor, termed “development and governance”, was identified, linking economic growth trajectories to the quality and effectiveness of policy implementation. The results indicate that community support for tourism is conditional, reflecting a calculated trade-off between economic opportunity and environmental preservation. The evidence further suggests that a transition from unregulated expansion to a sustainable tourism paradigm is imperative, integrating economic aspirations with rigorous environmental governance and ensuring active community participation in decision-making processes. Such an approach is posited to enhance tourism’s resilience, safeguard the Swat Valley’s ecological integrity, and align local development trajectories with long-term sustainability goals. The study provides actionable insights for policymakers, development agencies, and tourism planners, offering a comprehensive framework for fostering a balanced and mutually beneficial relationship between tourism development and host community welfare.
The rapid advancement of artificial intelligence (AI) technology has significantly impacted the higher education sector, creating an urgent demand for composite talents equipped with interdisciplinary knowledge. The cultivation of “AI + X” talents, combining AI expertise with various domain-specific skills, has been increasingly recognized as a critical educational goal. This study explores the development and implementation of teaching practices aimed at fostering such composite talents in higher education institutions. The growing integration of AI into diverse fields necessitates the construction of a robust curriculum that combines AI technology with other disciplines, thereby enhancing students’ interdisciplinary capabilities. A comprehensive literature review and experimental research methods were employed to analyze both domestic and international trends in AI-related talent development. Additionally, a predictive model of student learning performance was developed through exploratory data analysis (EDA) and machine learning (ML), with results validating the efficacy of linear regression models in performance prediction. The research identifies key strategies for enhancing teaching practices, including reinforcing theoretical and technological knowledge, promoting personalized and practical teaching approaches, strengthening foundational disciplinary learning, and encouraging cross-disciplinary synergies. These strategies were designed to enhance students’ critical thinking and practical competencies, with the aim of preparing them for the complex challenges of a rapidly evolving workforce. Furthermore, the paper discusses how AI-driven educational reforms can support the development of key industries, such as smart cities, smart finance, and the broader digital economy. The findings suggest that integrating AI technology into educational practices is essential for the effective cultivation of “AI + X” talents. However, significant challenges remain, including the scarcity of educational resources and the need for more contemporary teaching methodologies. Further research is required to refine talent training systems and to optimize institutional mechanisms, ensuring that higher education institutions can meet the demands of future technological and economic transformations. Through sustained educational innovation, it is envisaged that a new generation of innovative and versatile professionals will be equipped to contribute to societal advancement.
Heart disease remains a leading cause of mortality worldwide, necessitating early and accurate detection to improve clinical outcomes. Traditional diagnostic approaches relying on conventional clinical data analysis often encounter limitations in precision and efficiency. Machine learning (ML) techniques offer a promising solution by enhancing predictive accuracy and decision-making capabilities. This study evaluates the performance of a clinical support system (CSS) for heart disease prediction using a hybrid classification approach that integrates support vector machine (SVM) and k-nearest neighbor (KNN). Patient data were stratified by age group and gender to assess the model’s performance across diverse demographic profiles. Key performance metrics, including accuracy, recall, precision, F1-score, and area under the curve (AUC), were employed to quantify predictive efficacy. Experimental results demonstrated that the combined SVM-KNN model achieved superior classification performance, yielding an accuracy of 97.2%, recall of 97.6%, precision of 96.8%, AUC of 97.1%, and an F1-score of 98.2%. These findings indicate that the integration of SVM and KNN enhances heart disease prediction accuracy, thereby reinforcing the potential of CSS in improving early diagnosis and patient management.
The environmental conditions in large-scale, intensive poultry farming systems require high precision, and accurate prediction of environmental factors is critical for effective control. Existing control methods generally focus on the prediction and control of individual environmental factors without considering the interdependencies among these factors, leading to low prediction and control accuracy. To address the complex nature of the environmental system in poultry houses, characterised by multi-factor dependencies, an adaptive environmental control system based on Multi-feature Long Short-Term Memory (Multif-LSTM) was proposed. The Multif-LSTM model within the system calculates the dependencies between environmental factors using correlation coefficients and establishes a multi-input, multi-output neural network architecture. External climate factors are also incorporated during the input phase. Experimental comparisons conducted in a duck house environment, with Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models, show that the Multif-LSTM model outperforms others in terms of prediction accuracy. For NH3 concentration, the Root Mean Square Error (RMSE), the Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2) values are 1.34, 8.3, and 0.55, respectively; for temperature, they are 0.29, 2.83, and 0.98; and for relative humidity, they are 1.73, 2.46, and 0.95, respectively. Compared to the average performance of the RNN and LSTM models, the RMSE is reduced by 2.5, MAPE by 4.6, and R2 increased by 0.32. The results demonstrate that the Multif-LSTM model achieves higher prediction accuracy and is suitable for high-precision adaptive environmental control in poultry houses.
In recent decades, the number of scientific publications on natural food consumption has increased significantly, and part of this work addressed the phenomenon of customer resistance to natural foods. Despite these studies having broad implications for understanding the mechanisms of barriers to natural food consumption, they have produced fragmented streams of knowledge. Therefore, this paper seeks to conduct a comprehensive review by using a bibliometric analysis approach to assess the historical development and design future agenda for upcoming research in this field. Consequently, 155 Scopus publications from 1989 to 2023 were included based on the inclusion criteria. Furthermore, the analysis tools (e.g., VOSviewer and Harzing’s Publish or Perish apps) are used in analysis phase to visualize the conceptual framework of the study. The findings unveil the publications’ production related to the impact of consumption barriers in the natural food context is still in its early stages. In addition, the main gaps (i.e., number of publications, research design, and contextual gaps) in the published literature are identified. The findings offer several meaningful insights for scholars and marketers in the natural food setting.
Urban competitiveness is an essential determinant of the long-term sustainability and economic development of cities, influencing not only local prosperity but also national growth. The accurate measurement of urban competitiveness is critical for policymakers, as it provides insights into the strengths and weaknesses of cities, informing strategic development. This study evaluates the competitiveness of 17 European cities through an integrated Multi-Criteria Decision-Making (MCDM) framework, combining the Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) method for criteria weighting with the Ranking of Alternatives with Weights of Criterion (RAWEC) method for city ranking. The dataset utilised in this analysis was derived from the 2024 Global Power City Index (GPCI), a comprehensive report assessing various urban performance dimensions. The LOPCOW methodology revealed that the livability (L) criterion holds the highest weight in determining urban competitiveness, whereas research and development (R&D) emerged as the least influential factor. Using the RAWEC method, cities were ranked based on their overall competitiveness, with London identified as the most competitive urban centre, while Istanbul was ranked lowest. The findings highlight the importance of livability in enhancing urban competitiveness and suggest that cities should prioritise improvements in R&D to foster more balanced and sustainable competitiveness. This research contributes to the growing body of literature on urban performance measurement, offering a novel methodological approach that integrates both objective weighting and ranking techniques, which can be applied to further studies on global urban competitiveness.
This study aims to develop a model of sustainable business performance within small and medium-sized enterprises (SMEs) in the manufacturing sector, with a particular focus on Human Resource Management (HRM) factors, and to compare this model across different sectors, namely manufacturing, trade, and services. The empirical research was conducted in February 2024 across the Visegrad Four (V4) countries—Czech Republic, Slovakia, Poland, and Hungary. Data collection was managed by the European Centre for Economic and Social Research through a Computer-Assisted Web Interviewing (CAWI) method, using a survey questionnaire designed by the research team. Linear regression modeling (LRM) was employed to test the proposed hypotheses. The findings reveal that a participative management style and a high-quality employee appraisal system were identified as significant factors influencing business sustainability within the manufacturing sector. Additionally, career growth planning, employee satisfaction, and low turnover rates were found to have positive effects on sustainability. When comparing the models across the sectors, the research highlighted significant sectoral differences. In the trade sector, all HRM factors were found to be influential, whereas, in the manufacturing sector, only the factors related to participative management style (x1), employee appraisal quality (x2), and career growth planning (x4) showed significant effects. The least significant impact of HRM factors on business sustainability was observed in the services sector, where only two factors (x2 and x4) were significant. Notably, differences were observed in the significance of certain factors across the sectors: while factor x2 (employee appraisal quality) was crucial in the manufacturing sector, it was insignificant in the services sector. Conversely, factor x3 (employee satisfaction) showed no significant effect in the manufacturing sector but was significant in the trade sector. These findings underscore the importance of adapting HRM practices to the specific characteristics of each sector in order to enhance sustainability. The study highlights the necessity for tailored HRM strategies that align with the sector-specific dynamics of SMEs to promote long-term business sustainability.
The free vibration characteristics of functionally graded porous (FGP) beams were investigated through the application of hyperbolic shear deformation theory (HSDT). The material properties were described using a modified rule of mixtures, incorporating the porosity volume fraction to account for various porosity distribution types, enabling the continuous variation of properties across the beam thickness. The kinematic relations for FGP beams were formulated within the framework of HSDT, and the governing equations of motion were derived using Hamilton’s principle. Analytical solutions for free vibration under simply supported boundary conditions were obtained using Navier’s method. Validation was conducted through comparisons with existing data, demonstrating the accuracy and reliability of the proposed approach. The effects of porosity distribution patterns, power-law indices, span-to-depth ratios, and vibrational mode numbers on the natural frequency values of FGP beams were comprehensively examined. The findings provide critical insights into the influence of porosity and geometric parameters on the dynamic behavior of functionally graded (FG) beams, offering a robust theoretical foundation for their design and optimization in advanced engineering applications.
Laser additive manufacturing, a pivotal technology in advanced manufacturing, is extensively applied in the restoration industry. However, its development has been hindered by challenges such as residual stress and excessive grain size during the manufacturing process. The integration of ultrasonic enhancement technology with laser cladding has emerged as a prominent research direction, offering significant improvements in the quality and performance of the cladding layer. This review focuses on two primary approaches: ultrasonic-enhanced synchronous laser cladding and ultrasonic strengthening as a post-processing method. The ultrasonic processes discussed include ultrasonic vibration, ultrasonic rolling, ultrasonic impact, and their composite variants. Each method is evaluated for its ability to modify the microstructure, alleviate defects, and enhance the mechanical properties of the cladding layer. While ultrasonic enhancement during synchronous laser cladding primarily facilitates greater molten pool agitation, post-processing techniques induce severe plastic deformation on the surface of the cladding layer. Both approaches have been shown to reduce residual stress, refine grain structure, and improve surface hardness. The underlying mechanisms governing these improvements, particularly microstructural evolution and grain refinement, are examined in detail. Additionally, the potential advantages and limitations of each ultrasonic introduction method are discussed. Finally, the application prospects and future development trends of ultrasonic-enhanced laser cladding are explored, with particular attention to the role of ultrasonic technology in enhancing the durability, wear resistance, and corrosion resistance of cladding layers. The synergy between ultrasonic techniques and laser cladding promises to expand the potential of additive manufacturing in both industrial and repair applications.
Open Access
