Search Results (6,380)

Reducing the negative effects associated with floods in cities constitutes one of the highest-priority contemporary social challenges on the global sustainability agenda. In general, most historical studies focus on the consequences, but not on the causes of the phenomenon, which is essential for moving towards sustainable and resilient territories. The aim of this research is to quantify the effect that urban expansion has exerted on floods, taking the city of Belo Horizonte as a critical and representative case study. To this end, an integrative, qualitative, and quantitative approach has been developed, based on previous studies and on distributed hydrological modelling for the period 1940–2024. The results show that urban growth has contributed to a 7%, 14%, and 21% increase in the first three quartiles of annual floods. Likewise, the increase in the magnitude and frequency of the floods is also attributable, since it is more noticeable in the events of higher frequency than in those of lower frequency, in a range from 15% to 7%. The above results show the way in which the application of quantitative knowledge derived from the environmental history is highly useful for decision-making regarding the measures required to increase resilience, considering the possible effects of climate change. Thus, the recovery of the infiltration capacity of the soil constitutes a priority measure to reverse the effect that urban growth has exerted on the hydrological cycle. Full article

Educational tourism serves as a vital avenue for enhancing students’ practical skills and knowledge acquisition. As key components of educational tourism, the spatial distribution and accessibility of educational tourism bases significantly influence the effectiveness of study tour programs. Here, we employed stepwise regression analysis and geographically weighted regression (GWR) to analyze the spatial distribution characteristics, accessibility, and driving factors of educational tourism bases along the Yellow River region. The results indicate that museums accounted for the greatest proportion at 25.14% of the nine types of educational tourism bases. The educational tourism bases show a pattern of “dense in the southeast and sparse in the northwest.” Furthermore, they demonstrate clustered distributions centered around provincial capitals. The key factors influencing the distribution of educational tourism bases, ranked from highest to lowest based on the absolute value of the GWR coefficient, are as follows: 4A and above scenic spots > urbanization rate > internet broadband users > length of classified roads > education expenditure. The accessibility of educational tourism bases is basically consistent in terms of time and spatial distance, both showing better accessibility in the southeast than in the northwest. However, where Sichuan Province and Gansu Province meet, time accessibility is better, while distance accessibility is worse. These findings provide valuable insights for promoting the sustainable development of educational tourism in the Yellow River region. Full article

Science remains a foundational component of STEM education; however, its impact is constrained by a persistent shortage of qualified science teachers in U.S. high schools, particularly in the discipline of physics. This shortage has led to the widespread placement of teachers without subject-specific degrees or formal teaching credentials in physics classrooms. To mitigate this long-standing challenge, there is a critical need for sustained professional learning opportunities that enhance the content knowledge and pedagogical skills of out-of-field physics teachers. This case study examines the impact of teacher disposition on learning outcomes among participants in a three-year, targeted, and intensive physics professional development (PD) program. Using a qualitative data collection process, this study found that teachers who demonstrated a positive disposition toward the PD program exhibited notable gains in physics content knowledge, adopted student-centered instructional approaches, and reported increased self-efficacy in teaching physics compared to their less positively disposed counterparts. The findings underscore the need to consider and address teacher disposition as a mediating factor in the effectiveness of professional development for educators. The implications highlight the critical role of teacher disposition in shaping their learning outcomes, emphasizing the need to engage educators’ beliefs, motivations, and attitudes within supportive, reflective learning environments. By embedding dispositional awareness into PD frameworks, program designers can enhance and foster more meaningful and sustained teacher benefits. Full article

Oxidative stress is a factor implicated in chronic diseases and aging, motivating the search for natural antioxidants. Over the past ten years, food-derived peptides have been recognized as potent antioxidants. Carp, a globally farmed fish, is a protein-rich raw material for producing antioxidant peptides and hydrolysates. This review summarizes the current knowledge on these antioxidant peptides and hydrolysates, including their production, bioactivity, and applications. We discuss how enzymatic hydrolysis of carp by-products (e.g., skin, scales, and swim bladders) represents a strategy for waste valorization. Cellular and in vivo findings demonstrate the effectiveness of carp peptides and hydrolysates in tackling oxidative stress by reducing reactive oxygen species and enhancing cellular antioxidant enzymes. In addition to their antioxidant properties, these peptides and hydrolysates also possess anti-inflammatory, anti-melanogenic, and wound-healing properties. Potential applications of carp peptides and hydrolysates include their use as natural food preservatives and as active ingredients for skincare, nutraceuticals, and sports nutrition. Future research should focus on validating the in vivo bioavailability and assessing the long-term safety of carp peptides and hydrolysates to support their potential application in health. Carp-derived peptides are a valuable resource for developing functional foods and health products, which can contribute to a more sustainable food industry. Full article

In response to the ongoing challenges associated with natural enzymes, their high production costs, low stability and limited functionality; nanozymes have rapidly emerged as versatile alternative. Such nanocatalysts, based on nanomaterials and nanostructures, offer remarkable tunability of physicochemical properties and excellent durability, and adapt themselves effectively to the requirements of modern biotechnological applications. This review article aims to provide a comprehensive overview of recent advances in the use of naturally occurring iron oxide nanoparticles, produced by magnetotactic bacteria, and to highlight their emerging role as key elements in the development of the new generation of nano-guided biosensors. It provides a comprehensive and systematic analysis of publications in the Web of Science database between 2022 and August 2025, conducted in accordance with PRISMA guidelines. The aim was to assess the current state of the art and identify knowledge gaps in the exploration and application of magnetotactic bacteria as natural and sustainable sources in the design of next-generation biosensors. The natural nanoparticles, formed through biological processes, represent a unique and sustainable alternative to synthetic nanoparticles, offering naturally mimetic enzymatic activity, high biocompatibility, and exceptional stability. This approach opens up revolutionary perspectives in the field of biosensors, proposing a new class of functional materials, iron nanoparticles of biological origin, capable of fundamentally changing the performance, sustainability and reliability of future nanoenzymatic sensing platforms. Full article

Urban safety is a critical concern for sustainable city development, with crime patterns often linked to localized environmental factors. Understanding the spatial dynamics of safety is critical for informed design and planning of urban environments. This study employs a Geographically Weighted Regression (GWR) approach to investigate how crime in Austin, Texas, correlates with Points of Interest (POIs) such as bars, transit stations, financial businesses, and public spaces, while accounting for localized socio-economic factors. Building on theoretical frameworks like Routine Activity Theory and Crime Pattern Theory, the analysis integrates crime data from the Austin Police Department (APD), POI datasets, and census variables to explore spatially varying relationships often overlooked by traditional global models (e.g., OLS). A novel adaptive geo-grid method refines spatial units by clustering high-density downtown areas into smaller zones and retaining larger grids in suburban regions, ensuring precision without over-fragmentation. Analysis of crime incidents and POI data reveals significant spatial non-stationarity in crime–environment associations. Transportation-related facilities demonstrate strong spatial correlation with crime citywide, particularly forming persistent crime hotspots around transit hubs in areas like Rundberg Lane, South Congress, and East Riverside. Alcohol-related establishments show a strong positive correlation with crime in entertainment districts (coefficient up to 13.5, p < 0.001) but a negligible association in suburban residential areas (coefficient close to 0, p > 0.05). The GWR model significantly outperforms traditional OLS regression, capturing critical local variations obscured by global models. Downtown Austin emerges as a complex hotspot for urban safety where multiple high-risk POI types overlap. This research advances urban design and planning knowledge by providing empirical evidence that environmental factors’ influence on safety is spatially conditional rather than universally consistent, aligning with Crime Pattern Theory and Routine Activity Theory. The findings support place-specific crime prevention strategies, offering policymakers data-driven insights for developing targeted design strategies for urban zones. Full article

The pressing global transition to sustainable energy has intensified interest in overcoming the efficiency bottlenecks of conventional solar technologies. Hybrid photovoltaic–thermoelectric generator (PV–TEG) systems have recently emerged as a compelling solution, synergistically harvesting both electrical and thermal energy from solar radiation. By converting both sunlight and otherwise wasted heat, these integrated systems can substantially enhance total energy yield and overall conversion efficiency—mitigating the performance limitations of standalone PV panels. This review delivers a comprehensive, systematic assessment of maximum-power-point tracking (MPPT) methodologies specifically tailored for hybrid PV–TEG architectures. MPPT techniques are meticulously categorized and critically analyzed within the following six distinct groups: conventional algorithms, metaheuristic approaches, artificial intelligence (AI)-driven methods, mathematical models, hybrid strategies, and novel emerging solutions. For each category, we examine operational principles, implementation complexity, and adaptability to real-world phenomena such as partial shading and non-uniform temperature distribution. Through thorough comparative evaluation, the review uncovers existing research gaps, highlights ongoing challenges, and identifies promising directions for technological advancement. This work equips researchers and practitioners with an integrated knowledge base, fostering informed development and deployment of next-generation MPPT solutions for high-performance hybrid solar–thermal energy systems. Full article

The urban heat island (UHI) effect, intensified by urbanisation and climate change, leads to increased urban temperatures and poses a serious environmental challenge. Understanding its causes, impacts, and mitigation strategies is essential for sustainable urban planning. The aim of this study is to systematically analyse how the Urban Heat Island (UHI) effect has been addressed in the scientific literature, to identify key research themes and their temporal evolution, and to critically highlight knowledge gaps in order to provide guidance for future research and urban planning policies. Using BERTopic, an advanced natural language processing (NLP) tool, the study extracts dominant themes from a large corpus of academic literature and tracks their evolution over time. A total of 9061 research articles from the Web of Science database were collected, pre-processed, and analysed. BERTopic clustered semantically related topics and revealed their temporal dynamics, offering insights into emerging and declining research areas. The results show that pavement materials and urban vegetation are among the most studied themes, highlighting the importance of surface materials and green infrastructure in mitigating UHI. In line with this aim, the study identifies a rising interest in urban cooling strategies, particularly reflective surfaces and ventilation corridors. Consistent with its aim, the study provides a comprehensive overview of UHI literature, critically identifies existing gaps, and proposes clear directions for future research. It provides supports for urban planners, policymakers, and researchers in developing data-driven strategies to mitigate UHI impacts and strengthen enhance urban climate resilience. Full article

The greatest influence on the economic and ecological sustainability of a product can be exerted in the early stages of product development. However, limited knowledge about the product makes it difficult to make decisions based on a solid data base. For this purpose, it is required to provide the necessary information for an economic and ecological evaluation as well as decision support during the design in the CAD system. This raises the question of how a sustainability assessment for the raw material and production phase can be carried out based on a 3D CAD model and the requirements for a part. For this purpose, this paper presents a methodology to generate feasible and assessable material and manufacturing process chain combinations. Furthermore, a methodology for automatic economic and ecological evaluation of the 3D CAD model is implemented. The result is a CAD assistant that enables decisions to be made directly within the CAD system based on economic and ecological criteria. A suitable database structure is developed to provide the necessary information. The CAD assistant is then demonstrated with two case studies, which show the possibilities of the developed tool. Full article

The main objective of this paper is to examine the factors influencing investor intention to adopt robo-advisory services in Saudi Arabia, with a particular focus on sustainability and platform interface quality (PIQ) within a socio-technical framework. Drawing on the Diffusion of Innovation (DOI), Technology Acceptance Model (TAM), Value-Based Adoption Model (VAM), and Trust theory, the research integrates constructs such as Knowledge about Robo-Advisors (KRA), PIQ, Green Perceived Value (GPV), and Perceived Trust (PT). Data were collected through a structured questionnaire targeting financially active individuals, with 387 valid responses analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings reveal that KRA significantly influences Intention to Use Robo-Advisors (IURA) both directly and indirectly, through GPV and Relative Advantage (RA), with only marginal support observed for Perceived Usefulness (PU). PIQ strongly influences perceived ease of use (PEOU) and PU, contributing to IURA, while PT significantly moderates the effects of KRA and PIQ. Multi-group analysis (MGA) further highlights heterogeneity across age, education, and investment groups, underscoring the contextual nature of adoption. The study highlights the critical role of PT, PIQ, and GPV alignment in investor decision-making when engaging with robo-advisory platforms. It offers theoretical contributions by extending traditional adoption models through the inclusion of green value and interface quality, and practical implications for FinTech developers and policymakers aiming to build inclusive, trustworthy, and environmentally aligned robo-advisory platforms. Full article

Plants are crucial for sustaining community livelihood and should be thoroughly integrated into education; however, students often suffer from Plant Awareness Disparity (PAD). This phenomenon causes students to fail to appreciate the value of plants, often because they fail to notice or value them in their surroundings. Although numerous interventions have been suggested to address PAD, we still lack a comprehensive instrument with which to measure the interconnectedness of plant awareness knowledge and the effectiveness of such interventions. To address this gap, this study developed and validated a new scale to measure plant awareness knowledge in elementary school students. We used the Nipa palm (Nypa fruticans Wurmb) as a specific case study within the Pak Phanang Basin of Nakhon Si Thammarat Province, Thailand. This study was conducted in two phases, following the standards for education and psychology testing. In the first phase, a systematic literature review based on the Plants, People, and Planet (PPP) concept was used to identify the dimensions and components of the scale. In the second phase, the scale was developed, and its construct validity was analyzed through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The EFA and CFA provided evidence of a three-factor structure, confirming three distinct yet correlated dimensions of plant knowledge. The three subscales are as follows: Nature of Life, which focuses on students’ knowledge of the physical and biological characteristics of the plant; Interconnectedness of All Things, which measures knowledge of the plant’s relationship with its ecosystem and the community’s way of life; and Greatest Public Benefit, which assesses knowledge of the plant’s economic and cultural value to the community. The scale, comprising 13 items, demonstrated satisfactory internal consistency, with Cronbach’s alpha values above 0.75 across the three subscales. These findings provide educators with a valuable tool for assessing plant awareness and implementing interventions that foster ecological literacy and community sustainability. Full article

This research investigates the structural drivers of Poland’s energy transition to decarbonization and wider sustainable development goals. With a focus on the period 2010–2023, we use longitudinal regression analysis and cluster-based segmentation to examine the dynamic interactions between investment expenditure, deployed renewable capacity, and innovation expenditure in driving renewable electricity production. Our findings suggest that although installed capacity continues to be the nearest cause of renewable energy output, innovation expenditure has an extraordinarily large marginal effect, acknowledging the system-transformational role of technology innovation in low-carbon systems. Regression specifications suggested that the establishment of Poland’s transformation process is not only guided by the growth in capital, but also by the systemic embedment of knowledge-driven innovation. Cluster analysis reveals three successive stages of sectoral development—initial growth (2010–2013), consistent expansion (2014–2019), and rapid transformation (2020–2023)—with blended policy actions and structural effects. Despite the long shadow of Poland’s coal-linked past and post-2015 stagnation in innovation, the results signal a major move towards a more low-emitting, resilient power system. The report offers empirical facts and prescriptive evidence to guide policy formulation supporting collective, innovation-driven approaches essential for driving energy change in coal-dominated economies. Full article

Sewer systems are essential for sustainable infrastructure management, influencing environmental, social, and economic aspects. However, sewer network capacity is under significant pressure, with many systems overwhelmed by challenges such as climate change, ageing infrastructure, and increasing inflow and infiltration, particularly through groundwater infiltration (GWI). Current research in this area has primarily focused on general sewer performance, with limited attention to high-resolution, spatially explicit assessments of sewer exposure to GWI, highlighting a critical knowledge gap. This study responds to this gap by developing a high-resolution GWI assessment. This is achieved by integrating fuzzy-analytical hierarchy process (AHP) with geographic information systems (GISs) and machine learning (ML) to generate GWI probability maps across the Dawlish region, southwest United Kingdom, complemented by sensitivity analysis to identify the key drivers of sewer network vulnerability. To this end, 16 hydrological–hydrogeological thematic layers were incorporated: elevation, slope, topographic wetness index, rock, alluvium, soil, land cover, made ground, fault proximity, fault length, mass movement, river proximity, flood potential, drainage order, groundwater depth (GWD), and precipitation. A GWI probability index, ranging from 0 to 1, was developed for each 1 m × 1 m area per season. The model domain was then classified into high-, intermediate-, and low-GWI-risk zones using K-means clustering. A consistency ratio of 0.02 validated the AHP approach for pairwise comparisons, while locations of storm overflow (SO) discharges and model comparisons verified the final outputs. SOs predominantly coincided with areas of high GWI probability and high-risk zones. Comparison of AHP-weighted GIS output clustered via K-means with direct K-means clustering of AHP-weighted layers yielded a Kappa value of 0.70, with an 81.44% classification match. Sensitivity analysis identified five key factors influencing GWI scores: GWD, river proximity, flood potential, rock, and alluvium. The findings underscore that proxy-based geospatial and machine learning approaches offer an effective and scalable method for mapping sewer network exposure to GWI. By enabling high-resolution risk assessment, the proposed framework contributes a novel proxy and machine-learning-based screening tool for the management of smart cities. This supports predictive maintenance, optimised infrastructure investment, and proactive management of GWI in sewer networks, thereby reducing costs, mitigating environmental impacts, and protecting public health. In this way, the method contributes not only to improved sewer system performance but also to advancing the sustainability and resilience goals of smart cities. Full article

Background: Individuals who are victimized and exploited by the heinous crimes of human trafficking (HT) access healthcare during their exploitation, yet gaps in education on HT content exist in prelicensure nursing programs. This study explored the impact of an HT simulation on nursing students’ preparedness in the identification of victims as well as their perceptions of the impact of this educational intervention on future practices. Methods: A quasi-experimental design with a qualitative component was used. A convenience sample of 120 nursing students were recruited. The participants completed a pretest survey, viewed a preparatory education video, and participated in the simulation followed by a debriefing, a 20-min video, and posttest survey. Results: More than 3/4 of the participants reported no previous exposure to this content. A paired sample t-test showed efficacy (p < 0.001) with a Cohen’s d > 0.8, illustrating an increase in knowledge gained. The qualitative data yielded four themes: eye-opening, educational and informative, increased awareness, and preparedness. Conclusions: Nurses are well-positioned to identify, treat, and respond to victims of HT. The findings underscore the critical need to incorporate comprehensive HT content into prelicensure nursing curricula. Through integration of an HT simulation, future nurses can be better prepared to address this pervasive issue, ultimately improving victim outcomes and ensuring progress towards UN Sustainable Development Goal 5 of Gender Equality and Goal 16 of Peace, Justice, and Strong Institutions. In addition, addressing this topic in prelicensure nursing education ensures that future nurses are not only clinically competent but also morally and emotionally prepared to handle the complexities of HT in their professional roles. Full article

Proper management of water resources in agriculture is of utmost importance for sustainable productivity, especially under the current context of climate change. However, many smart agriculture systems, including for managing irrigation, involve costly, complex tools for most farmers, especially small/medium-scale producers, despite the availability of user-friendly and community-accessible tools supported by well-established providers (e.g., Google). Hence, this paper proposes an irrigation management system integrating low-cost Internet of Things (IoT) sensors with community-accessible cloud-based data management tools. Specifically, it resorts to sensors managed by an ESP32 development board to monitor several agroclimatic parameters and employs Google Sheets for data handling, visualization, and decision support, assisting operators in carrying out proper irrigation procedures. To ensure reproducibility for both digital experts but mainly non-technical professionals, a comprehensive set of guidelines is provided for the assembly and configuration of the proposed irrigation management system, aiming to promote a democratized dissemination of key technical knowledge within a do-it-yourself (DIY) paradigm. As part of this contribution, a market survey identified numerous e-commerce platforms that offer the required components at competitive prices, enabling the system to be affordably replicated. Furthermore, an irrigation management prototype was tested in a real production environment, consisting of a 2.4-hectare yellow kiwi orchard managed by an association of producers from July to September 2021. Significant resource reductions were achieved by using low-cost IoT devices for data acquisition and the capabilities of accessible online tools like Google Sheets. Specifically, for this study, irrigation periods were reduced by 62.50% without causing water deficits detrimental to the crops’ development. Full article