Search Results (11,544)

Contamination of food with heavy metals is an important factor leading to serious health concerns. Rapid identification of these heavy metals is of utmost priority. There are several methods to identify traces of heavy metals in food. Conventional methods for the detection of heavy metal residues have their limitations in terms of cost, analysis time, and complexity. In the last decade, voltammetric analysis has emerged as the most prominent electrochemical determination method for heavy metals. Voltammetry is a reliable, cost-effective, and rapid determination method. This review provides a detailed primer on recent advances in the development and application of graphene-based electrochemical sensors for heavy metal monitoring over the last decade. We critically examine aspects of graphene modification (fabrication process, stability, cost, reproducibility) and analytical properties (sensitivity, selectivity, rapid detection, lower detection, and matrix effects) of these sensors. Furthermore, to our knowledge, meta-analyses were performed for the first time for all investigated parameters, categorized based on graphene materials and heavy metal types. We also examined the pass–fail criteria according to the WHO drinking water guidelines. In addition, the effects of heavy metal toxicity on human health and the environment are discussed. Finally, the contribution of heavy metal contamination to the seventeen Sustainable Development Goals (SDGs) stated by the United Nations in 2015 is discussed in detail. The results confirm the significant impact of heavy metal contamination across twelve SDGs. This review critically examines the existing knowledge in this field and highlights significant research gaps and future opportunities. It is intended as a resource for researchers working on graphene-based electrochemical sensors for the detection of heavy metals in food safety, with the ultimate goal of improving consumer health protection. Full article

With the rapid pace of urbanization, cities are increasingly facing severe challenges related to environmental pollution, ecological degradation, and climate change. Extreme climate events—such as heatwaves, droughts, heavy rainfall, and wildfires—have intensified public concern about sustainability, environmental protection, and low-carbon development. Ensuring environmental preservation while maintaining residents’ quality of life has become a central focus of urban governance. In this context, evaluating green indicators and predicting urban greenness is both necessary and urgent. This study incorporates international frameworks such as the EU Green City Index, the European Green Capital Award, and the United Nations Sustainable Development Goals to assess urban sustainability. The Extreme Gradient Boosting (XGBoost) algorithm is employed to predict the green level of cities and to develop multiple optimized models. Comparative analysis with traditional models demonstrates that XGBoost achieves superior performance, with an accuracy of 0.84 and an F1-score of 0.81. Case study findings identify “Greenhouse Gas Emissions per Person” and “Per Capita Emissions from Transport” as the most critical indicators. These results provide practical guidance for policymakers, suggesting that targeted regulations based on these key factors can effectively support emission reduction and urban sustainability goals. Full article

Today, ‘social sustainability’ is a key feature of many organisations’ environmental, social, and governance strategies, as well as underpinning sustainable development goals. The term refers to the implementation of targets such as reduced societal inequalities, the promotion of social well-being, and the practice of positive community relations. Building a meaningful, accountable, and quantifiable evidence-base from which to translate these high-level concepts into tangible and achievable goals is, however, challenging. The complexities of measuring social capital—often described as a building block of social sustainability—have been documented. The challenge lies in measuring the person, group, or collective in interaction with the context under investigation, whether that be a climate goal, an institution, or a national policy. Social identity theory is a social psychological approach that articulates the processes through which an individual internalises the values, norms, and behaviours of their contexts. Levels of social identification—a concept capturing the state of internalisation—have been shown to be predictive of outcomes as diverse as communication and cognition, trust and citizenship, leadership and compliance, and health and well-being. Applying this perspective to the articulation and measurement of social sustainability provides an opportunity to build an empirical approach with which to reliably translate this high-level concept into achievable outcomes. Full article

This study investigates the critical capabilities and integration mechanisms that enable organizations to achieve substantive sustainability transformations. Using a mixed-methods approach combining survey data (n = 234), in-depth interviews (n = 42), and comparative case studies (n = 6), the research identifies how organizations effectively navigate sustainability paradoxes while developing integration practices that embed sustainability throughout organizational systems. Our research is primarily grounded in paradox theory, complemented by insights from organizational learning theory, institutional logics, and power dynamics perspectives to develop a comprehensive theoretical framework. Statistical analysis reveals strong relationships between paradox navigation capabilities and transformation outcomes (β = 0.31, p < 0.01), with integration practices emerging as the strongest predictor of sustainability success (β = 0.42, p < 0.01). Qualitative findings illuminate four essential integration mechanisms—governance integration, strategic integration, operational integration, and performance integration—and their temporal development. The significant interaction between power mobilization and integration practices (β = 0.19, p < 0.01) demonstrates that structural interventions are insufficient without attention to power relationships. The research contributes to sustainability science by advancing theory on paradoxical tensions in transformation processes, demonstrating how organizations can transcend the gap between sustainability rhetoric and substantive action through both structural integration and power-conscious approaches. By identifying contextual contingencies across sectors and organizational types, the study challenges universal prescriptions for sustainability transformation, offering instead a nuanced framework for creating organizational conditions conducive to context-specific transformation toward more sustainable futures. Our findings offer practical guidance for organizations navigating the complex landscape of sustainability transformation and contribute to the implementation of UN Sustainable Development Goals, particularly SDG 12 (Responsible Consumption and Production) and SDG 17 (Partnerships for the Goals). Full article

The increasing demand for sustainable practices in road construction has prompted the search for environmentally friendly and cost-effective materials. This study explores the incorporation of reclaimed asphalt pavement (RAP) and Panasqueira mine waste (greywacke aggregates) as full replacements for virgin aggregates in hot mix asphalt (HMA), aligning with the objectives of UN Sustainable Development Goal 9. Three asphalt mixtures were prepared: a reference mixture (MR) with granite aggregates, and two modified mixtures (M15 and M20) with 15% and 20% RAP, respectively. All mixtures were evaluated through Marshall stability, stiffness modulus, water sensitivity, and wheel tracking tests. The results demonstrated that mixtures containing RAP and mine waste met Portuguese specifications for surface courses. Specifically, the M20 mixture showed the highest stiffness modulus, improved moisture resistance, and the best performance against permanent deformation. These improvements are attributed to the presence of stiff aged binder in RAP and the mechanical characteristics of the greywacke aggregates. Overall, the findings confirm that the combined use of RAP and mining waste provides a technically viable and sustainable alternative for asphalt pavement construction, contributing to resource efficiency and circular economy goals. Full article

This article examines training itineraries for early childhood education professionals in Portugal, focusing on promoting social and human development through music and the arts for infants. The training models discussed are categorized as short-term and long-term, encompassing both theory and practice through a transdisciplinary approach. Based on initiatives promoted by the Companhia de Música Teatral (CMT) and the Education and Human Development Group of the Centre for the Study of Sociology and Musical Aesthetics (CESEM) at NOVA University Lisbon, the article highlights projects such as: (i) Opus Tutti and GermInArte, developed between 2011 and 2018; (ii) the Postgraduate Course Music in Childhood: Intervention and Research, offered at the University since 2020/21, which integrates art, health, and education, promoting collaborative work between professionals; and (iii) Mil Pássaros (Thousand Birds), developed since 2020, which exemplifies the integration of environmental education and artistic practices. The theoretical basis of these training programs combines neuroscientific and educational evidence, emphasizing the importance of the first years of life for integral development. Studies, such as those by Heckman, reinforce the impact of early investment in children’s development. Edwin Gordon’s Music Learning Theory and Malloch and Trevarthen’s concept of ‘communicative musicality’ structure the design of these courses, recognizing music as a catalyst for cognitive, emotional, and social skills. The transformative role of music and the arts in educational and social contexts is emphasized, in line with the Sustainable Development Goals of the 2030 Agenda, by proposing approaches that articulate creation, intervention, and research to promote human development from childhood onwards. Full article

Environmental monitoring plays a key role in understanding and mitigating the effects of climate change, pollution, and resource mismanagement. The growth of printed sensor technologies offers an innovative approach to addressing these challenges due to their low cost, flexibility, and scalability. Printed sensors enable the real-time monitoring of air, water, soil, and climate, providing significant data for data-driven decision-making technologies and policy development to improve the quality of the environment. The development of new materials, such as graphene, conductive polymers, and biodegradable substrates, has significantly enhanced the environmental applications of printed sensors by improving sensitivity, enabling flexible designs, and supporting eco-friendly and disposable solutions. The development of inkjet, screen, and roll-to-roll printing technologies has also contributed to the achievement of mass production without sacrificing quality or performance. This review presents the current progress in printed sensors for environmental applications, with a focus on technological advances, challenges, applications, and future directions. Moreover, the paper also discusses the challenges that still exist due to several issues, e.g., sensitivity, stability, power supply, and environmental sustainability. Printed sensors have the potential to revolutionize ecological monitoring, as evidenced by recent innovations such as Internet of Things (IoT) integration, self-powered designs, and AI-enhanced data analytics. By addressing these issues, printed sensors can develop a better understanding of environmental systems and help promote the UN sustainable development goals. Full article

Multi-objective evolutionary algorithms (MOEAs) have emerged as powerful optimization tools for addressing the complex, often conflicting goals present in modern waste disposal systems. This review explores recent advances and practical applications of MOEAs in key areas, including waste collection routing, waste-to-energy (WTE) systems, and facility location and allocation. Real-world case studies from cities like Braga, Lisbon, Uppsala, and Cyprus demonstrate how MOEAs can enhance operational efficiency, boost energy recovery, and reduce environmental impacts. While these algorithms offer significant advantages, challenges remain in computational complexity, adapting to dynamic environments, and integrating with emerging technologies. Future research directions highlight the potential of combining MOEAs with machine learning and real-time data to create more flexible and responsive waste management strategies. By leveraging these advancements, MOEAs can play a pivotal role in developing sustainable, efficient, and adaptive waste disposal systems capable of meeting the growing demands of urbanization and stricter environmental regulations. Full article

Tuberculosis (TB) remains a leading cause of death in Bangladesh, disproportionately affecting low socio-economic status (SES) populations. This review, guided by the WHO Social Determinants of Health framework and Rockefeller-Lancet Planetary Health Report, examined how social, economic, and ecological factors link SES to TB burden. The first literature search identified 28 articles focused on SES-TB relationships in Bangladesh. A second search through snowballing and conceptual mapping yielded 55 more papers of diverse source types and disciplines. Low-SES groups face elevated TB risk due to smoking, biomass fuel use, malnutrition, limited education, stigma, financial barriers, and hazardous housing or workplaces. These factors delay care-seeking, worsen outcomes, and fuel transmission, especially among women. High-SES groups more often face comorbidities like diabetes, which increase TB risk. Broader contextual drivers include urbanisation, weak labour protections, cultural norms, and poor governance. Recommendations include housing and labour reform, gender parity in education, and integrating private providers into TB programmes. These align with the WHO End TB Strategy, UN SDGs and Planetary Health Quadruple Aims, which expand the traditional Triple Aim for health system design by integrating environmental sustainability alongside improved patient outcomes, population health, and cost efficiency. Future research should explore trust in frontline workers, reasons for consulting informal carers, links between makeshift housing and TB, and integrating ecological determinants into existing frameworks. Full article

The purpose of this paper is to analyze how China’s outward foreign direct investment (OFDI), driven by different motivations, affects the bilateral global value chain (GVC) relationship between the home country (China) and host countries, evaluating both bilateral GVC trade value and relative GVC positions. Employing the OECD Trade in Value Added (TiVA) database combined with Chinese listed firm data, we found the following results: (1) Strategic asset-seeking OFDI strengthens the GVC relationship between China and host countries while enhancing China’s GVC position relative to host countries. (2) Efficiency-seeking OFDI increases the domestic value-added exported from host countries to China but does not improve China’s relative GVC position. (3) Natural resource-seeking OFDI enhances bilateral GVC trade volumes but has no significant impact on the relative GVC positions of China and host countries. (4) China’s OFDI, not driven by these motivations, generates a trade substitution effect between home and host countries. We also examined the heterogeneity of these effects. Our findings suggest that China’s OFDI fosters equitable and sustainable international cooperation, supports mutually beneficial GVC trade and host-country economic growth, and therefore, progresses toward Sustainable Development Goal (SDG) 8. Full article

This paper investigates how governance frameworks shape sustainable tourism outcomes in protected areas by comparing Tara National Park (Serbia) and Triglav National Park (Slovenia). Both parks, established in 1981 and classified under IUCN Category II, exhibit rich biodiversity and mountainous terrain but differ markedly in governance structures, institutional integration, and local community engagement. Using a qualitative, indicator-based methodology, this research evaluates ecological, economic, and social dimensions of sustainability. The findings reveal that Triglav NP demonstrates higher levels of participatory governance, tourism integration, and educational outreach, while Tara NP maintains stricter ecological protection with less inclusive management. Triglav’s zoning model, community council, and economic alignment with regional development policies contribute to stronger sustainability outcomes. Conversely, Tara NP’s centralized governance and infrastructural gaps constrain its potential despite its significant conservation value. This study highlights the importance of adaptive, inclusive governance in achieving the Sustainable Development Goals (SDGs) within protected areas. It concludes that hybrid approaches, combining legal rigor with participatory flexibility, can foster resilience and sustainability in ecologically sensitive regions. Full article

In the globalizing world, raising individuals equipped with 21st-century skills is very important for the economic development of countries. Educational practices that support 21st-century skills are also gaining importance. In this context, STEM education, an interdisciplinary educational practice that develops 21st-century skills, emerges. STEM education aims to contribute to sustainable development by training individuals equipped with 21st-century skills and competencies. In a globalizing world, countries must set sustainable development goals to gain a foothold in the global market. In today’s world, where artificial intelligence also shows itself in every area of human life, it is possible to discuss the importance of artificial intelligence-supported STEM education. This study aims to reveal the educational needs of primary school teachers regarding artificial intelligence-supported STEM education. The study was conducted according to the phenomenological design, and the data were collected using a semi-structured interview form and literature review techniques. The thematic analysis method was used in the analysis of the data. According to the research results obtained from the findings of the study, teachers need training on 21st-century skills, interdisciplinary thinking, technology integration into courses, and artificial intelligence practices in courses to develop their knowledge and skills in the context of artificial intelligence-supported STEM education. Full article

Urban heat islands (UHIs) are a growing sustainability challenge impacting public health, energy use, and climate resilience, especially in hot, arid cities like San Antonio, Texas, where land surface temperatures reach up to 47.63 °C. This study advances a data-driven, interdisciplinary approach to UHI mitigation by integrating Machine Learning (ML) with physical and socio-demographic data for sustainable urban planning. Using high-resolution spatial data across five functional zones (residential, commercial, industrial, official, and downtown), we apply three ML models, Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM), to predict land surface temperature (LST). The models incorporate both environmental variables, such as imperviousness, Normalized Difference Vegetation Index (NDVI), building area, and solar influx, and social determinants, such as population density, income, education, and age distribution. SVM achieved the highest R² (0.870), while RF yielded the lowest RMSE (0.488 °C), confirming robust predictive performance. Key predictors of elevated LST included imperviousness, building area, solar influx, and NDVI. Our results underscore the need for zone-specific strategies like more greenery, less impervious cover, and improved building design. These findings offer actionable insights for urban planners and policymakers seeking to develop equitable and sustainable UHI mitigation strategies aligned with climate adaptation and environmental justice goals. Full article

Traditional Pavement Condition Index (PCI) assessments are highly resource-intensive, demanding substantial time and labor while generating significant carbon emissions through extensive field operations. To address these sustainability challenges, this research presents an innovative methodology utilizing Gene Expression Programming (GEP) to determine PCI values based on International Roughness Index (IRI) measurements from Iraqi road networks, offering an environmentally conscious and resource-efficient approach to pavement management. The study incorporated 401 samples of IRI and PCI data through comprehensive visual inspection procedures. The developed GEP model exhibited exceptional predictive performance, with coefficient of determination (R²) values achieving 0.821 for training, 0.858 for validation, and 0.8233 overall, successfully accounting for approximately 82–85% of PCI variance. Prediction accuracy remained robust with Mean Absolute Error (MAE) values of 12–13 units and Root Mean Square Error (RMSE) values of 11.209 and 11.00 for training and validation sets, respectively. The lower validation RMSE suggests effective generalization without overfitting. Strong correlations between predicted and measured values exceeded 0.90, with acceptable relative absolute error values ranging from 0.403 to 0.387, confirming model effectiveness. Comparative analysis reveals GEP outperforms alternative regression methods in generalization capacity, particularly in real-world applications. This sustainable methodology represents a cost-effective alternative to conventional PCI evaluation, significantly reducing environmental impact through decreased field operations, lower fuel consumption, and minimized traffic disruption. By streamlining pavement management while maintaining assessment reliability and accuracy, this approach supports environmentally responsible transportation systems and aligns contemporary sustainability goals in infrastructure management. Full article

Pork is a major global source of animal protein, and improving both its production efficiency and meat quality is a central goal in modern animal agriculture and food systems. This study investigated post-inflection-point growth patterns in two genetically distinct pig breeds—the lean-type Yorkshire pig (YP) and the fatty-type Qingyu pig (QYP)—with the aim of elucidating breed-specific characteristics that influence pork quality and yield. Comprehensive evaluations of carcass traits, meat quality attributes, nutritional composition, and gene expression profiles were conducted. After the growth inflection point, carcass traits exhibited greater variability than meat quality traits in both breeds, though with distinct patterns. YPs displayed superior muscle development, with the longissimus muscle area (LMA) increasing rapidly before plateauing at ~130 kg, whereas QYPs maintained more gradual but sustained muscle growth. In contrast, intramuscular fat (IMF)—a key determinant of meat flavor and texture—accumulated faster in YPs post inflection but plateaued earlier in QYPs. Correlation and clustering analyses revealed more synchronized regulation of meat quality traits in QYPs, while YPs showed greater trait variability. Gene expression patterns aligned with these phenotypic trends, highlighting distinct regulatory mechanisms for muscle and fat development in each breed. In addition, based on the growth curves, we calculated the peak age at which the growth rate declined in lean-type and fat-type pigs, which was approximately 200 days for YPs and around 270 days for QYPs. This suggests that these ages may represent the optimal slaughter times for the respective breeds, balancing both economic efficiency and meat quality. These findings provide valuable insights for enhancing pork quality through precision management and offer theoretical guidance for developing breed-specific feeding strategies, slaughter timing, and value-added pork production tailored to consumer preferences in the modern food market. Full article