Search Results (2,437)

Payments for Ecosystem Services (PESs) have become a central instrument in global biodiversity finance, yet endangered species-specific PESs remain rare and poorly understood in implementation terms. Taiwan provides a revealing case: a three-year program paying farmers to conserve four threatened species—Prionailurus bengalensis, Lutra lutra, Tyto longimembris, and Hydrophasianus chirurgus—in working farmland across Taiwan and Kinmen island. Through semi-structured interviews with farmers, residents, and local conservation actors, we examine how payments are interpreted, rationalized, enacted, and emotionally experienced at the ground level. This study adopts Colaizzi’s data analysis method, the primary advantage of which lies in its ability to systematically transform fragmented and emotive interview narratives into a logically structured essential description. This is achieved through the rigorous extraction of significant statements and the subsequent synthesis of thematic clusters. Participants reported willingness to continue not only because subsidies offset losses, but because rarity, community pride, and the visible arc of “we helped this creature survive” became internalized rewards. NGOs amplified this shift by translating science into farm practice and “normalizing” coexistence. In practice, conservation work became a social project—identifying threats, altering routines, and defending habitat as a shared civic act. This study does not estimate treatment-effect size; instead, it delivers mechanistic insight at a live policy moment, as Taiwan expands PESs and the OECD pushes incentive reform. The finding is simple and strategically important: endangered-species PESs work best where payments trigger meaning—not where payments replace it. Full article

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), known as Artificial Intelligence of Things (AIoT), has emerged as a transformative paradigm for enabling intelligent, data-driven, and context-aware decision-making in urban environments to reduce the carbon footprint of mobility and industry. This review examines the conceptual foundations, and state-of-the-art developments of AIoT, with a particular emphasis on its applications in smart cities and its relevance to climate change management. AIoT integrates sensing, connectivity, and intelligent analytics to provide optimized solutions in transportation systems, energy management, waste collection, and environmental monitoring, directly influencing urban sustainability. Beyond urban efficiency, AIoT can play a critical role in addressing the global challenges and management of climate change by (a) precise measurements and autonomously remote monitoring; (b) real-time optimization in renewable energy distribution; and (c) developing prediction models for early warning of climate disasters. This paper performs a literature review and bibliometric analysis to identify the current landscape of AIoT research in smart city contexts. Over 1885 articles from Web of Sciences and over 1854 from Scopus databases, published between 1993 and January 2026, were analyzed. The results reveal a strong and accelerating growth in research activity, with publication output doubling in the most recent two years compared to 2023. Waste management and air quality monitoring have emerged as leading application domains, where AIoT-based optimization and predictive models demonstrate measurable improvements in operational efficiency and environmental impact. Altogether, these support faster and more effective decisions for reducing greenhouse gas emissions and ensuring the sustainable use of resources. The reviewed studies reveal rapid advancements in edge intelligence, federated learning, and secure data sharing through the integration of AIoT with blockchain technologies. However, significant challenges remain regarding scalability, interoperability, privacy, ethical governance, and the effective translation of research outcomes into policy and citizen-oriented tools such as climate applications, insurance models, and disaster alert systems. By synthesizing current research trends, this article highlights the potential of AIoT to support sustainable, resilient, and citizen-centric smart city ecosystems while identifying both critical gaps and promising directions for future investigations. Full article

This article provides a systematic literature review of the scientific literature on forms of communitization that feature alternative nutritional practices to reveal their organizational structures, opportunities, challenges, and transformative potential. The forms studied are alternative food networks and are characterized by their sustainable commitment in food production, distribution, and consumption practices. This review focused solely on articles investigating these forms of communitization in Germany. A systematic literature search was conducted using the databases Web of Science and Business Source Premier in accordance with the PRISMA statement guidelines. Forty-two articles were included in the final analysis, with the oldest article published in 2006 and the newest in 2025. The systematic literature review identifies five forms of communitization with alternative nutritional practices: community, urban and self-harvest gardens; food cooperatives or cooperative initiatives; food sharing and redistribution initiatives; community-supported agriculture and networks; and ecovillages, commune, food initiatives, and other partnerships. The review highlights key forms of communitization that feature alternative nutritional practices, the methods used, and the geographical areas involved. Using content analysis, the organizational structures, opportunities, and challenges of various forms of communitization that feature alternative nutritional practices are identified and their transformative potential discussed. Full article

Early-age shrinkage in 3D-printed concrete constitutes a critical applied challenge due to the rapid development of deformations and the absence of conventional reinforcement systems. From a scientific standpoint, a clear knowledge gap exists in materials science concerning the reliable quantification of very small, rapidly evolving strains in fresh and early-age cementitious materials produced by additive manufacturing. This study investigates practical and low-cost alternatives to commercial optical systems for monitoring early-age shrinkage in 3D-printed concrete, a key challenge given the rapid deformation of printed elements and their typical lack of reinforcement. The work focuses on identifying both the most precise method for capturing minor, fast-developing strains and affordable tools suitable for laboratories without access to advanced equipment. Three mixtures with different aggregate types were examined to broaden the applicability of the findings and to evaluate how aggregate selection affects fresh properties, hardened performance, and shrinkage behavior. Shrinkage measurements were carried out using a commercial digital image correlation system, which served as the reference method, along with simplified optical setups based on a smartphone camera and a GoPro device. Additional measurements were performed with laser displacement sensors and Linear Variable Differential Transformer LVDT transducers mounted in a dedicated fixture. Results were compared with the standardized linear shrinkage test to assess precision, stability, and the influence of curing conditions. The findings show that early-age shrinkage must be monitored immediately after printing and under controlled environmental conditions. When the results obtained after 12 h of measurement were compared with the values recorded using the commercial reference system, differences of 19%, 13%, 16%, and 14% were observed for the smartphone-based method, the GoPro system, the laser sensors, and the LVDT transducers, respectively. Full article

In the context of environmental challenges and digital transformation, artificial intelligence (AI) plays a key role in promoting sustainable development within Industry 4.0 and the emerging paradigm of Industry 5.0. This study systematically reviewed the literature (2015–2025) from Scopus and Web of Science on the connections between AI, circular economy, industrial paradigms, and the Sustainable Development Goals (SDGs), with a particular focus on supply chains and SDG 12—responsible consumption and production. The majority of research emphasizes managerial aspects, the application of machine learning and robotics, as well as waste reduction, resource optimization, and circular economy practices within supply chain and production–consumption systems. Geographical analysis shows that larger economies serve as central research hubs, while some countries that are not among the most populous often achieve the highest average citations per document. Temporal keyword trends indicate a shift in research focus from operational efficiency in traditional supply chains (optimization) toward supply chain digitalization (artificial intelligence) and sustainability (circular economy). Keyword trends reveal four thematic clusters: supply chain digitalization, agritech, smart industry, and sustainability. The study highlights future research directions, including integrating circular economy with managerial and technical approaches, linking Industry 5.0 with SDG 12, and applying advanced AI in sustainable industrial practices. The increasing attention to ethical and social dimensions underscores the need for AI solutions that are both technologically advanced and sustainability oriented. Full article

Background: The global increase in orthopedic implant use—both for trauma fixation and arthroplasty—has profoundly transformed musculoskeletal surgery. As a consequence, fractures occurring in the presence of implants have become more frequent and clinically relevant. Yet, these injuries are currently described using highly heterogeneous terminology, including periprosthetic (fracture occurring in the presence of a prosthetic joint replacement) peri-implant (fracture occurring around an osteosynthesis or fixation device), implant-related, and hardware-related fractures (umbrella terms encompassing both prosthetic and fixation devices, used descriptively rather than classificatorily). This coexistence of multiple, context-specific terminologies hinders clinical communication, complicates registry documentation, and limits research comparability across orthopedic subspecialties. Because fractures occurring in the presence of orthopedic implants significantly alter load transfer, muscle force distribution, and musculoskeletal biomechanics, a clear and unified terminology is also relevant for muscle-focused research addressing implant–tissue interaction and functional recovery. Objective: This systematic review aimed to critically analyze the terminology used to describe fractures influenced by orthopedic implants, quantify the heterogeneity of current usage across anatomical regions and publication periods, and explore the rationale for adopting a unified umbrella term—“artificial fracture.” Methods: A systematic search was performed in PubMed, Scopus, and Web of Science from January 2000 to December 2024, following PRISMA guidelines. Eligible studies included clinical investigations, reviews, registry analyses, and consensus statements explicitly employing or discussing terminology related to implant-associated fractures. Data were extracted on publication characteristics, anatomical site, terminology employed, and classification systems used. Quantitative bibliometric and qualitative thematic analyses were conducted to assess frequency patterns and conceptual trends. Results: Of 1142 records identified, 184 studies met the inclusion criteria. The most frequent descriptor in the literature was periprosthetic fracture (68%), reflecting its predominance in arthroplasty-focused studies, whereas broader and more practical terms such as implant-related and peri-implant fracture were more commonly used in musculoskeletal and fixation-related research. Terminological preferences varied according to anatomical site and implant type, and no universally accepted, cross-anatomical terminology was identified despite multiple consensus efforts. Discussion and Conclusions: The findings highlight persistent heterogeneity in terminology describing fractures influenced by orthopedic implants. A transversal, descriptive framework may facilitate communication across subspecialties and support registry-level harmonization. Beyond orthopedic traumatology, this approach may also benefit muscle and musculoskeletal research by enabling more consistent interpretation of data related to muscle–bone–implant interactions, rehabilitation strategies, and biomechanical adaptation. Full article

Urban resilience has emerged as a critical paradigm for addressing the intertwined challenges of climate change, rapid urbanization, and social inequality, positioning green public spaces as catalysts for social, ecological, and institutional transformation. This article presents a systematic review conducted under the PRISMA 2020 guidelines, examining how collaborative and community participation influenced transformative urban resilience in green public spaces between 2021 and 2025. A total of 6179 records were initially identified across ScienceDirect and MDPI (last search: July 2025), of which 26 empirical studies met the inclusion criteria (peer-reviewed, empirical, published 2021–2025). Methodological rigor was strengthened through the application of the Mixed Methods Appraisal Tool (MMAT, 2018) and confidence in qualitative evidence was assessed using the GRADE-CERQual approach, enhancing transparency and reliability. Data extraction and synthesis followed a theoretical-methodological coding framework, allowing for the comparison of participatory strategies, typologies of green spaces, resilience dimensions, and applied instruments. The results show that multi-actor co-management, co-design, and community self-organization are the most frequent participatory strategies, while urban green infrastructure, pocket parks, and urban gardens constitute the predominant spatial contexts. Socio-ecological and social-participatory resilience emerged as dominant theoretical perspectives, with qualitative and mixed-methods designs prevailing across studies. Evidence synthesis through GRADE-CERQual identified seven key pathways—multi-actor co-management, Nature-based Solutions, community-based actions, social equity, cultural identity, institutional innovation, and planned densification—each contributing differently to resilience dimensions. Overall, the findings highlight that transformative resilience depends on deep, inclusive participatory processes, multi-level governance, and the integration of social, ecological, and cultural dimensions. Despite the heterogeneity of designs and unequal data adequacy, this review confirms that transformative urban resilience is a co-produced process grounded in community action, ecological sustainability, and collaborative governance. Strengthening underexplored areas—technological innovation, cultural resilience, and standardized methodological instruments—is essential for advancing comparative research and practice. Full article

Against the backdrop of rapid urbanization, megacities have become crucial drivers of development. As the country with the largest number of megacities (seven in total), China is confronted with significant challenges such as population–resource–environment conflicts, which render high-quality development an imperative pursuit. This study employs a system dynamics approach to assess high-quality development in China’s megacities. It analyzes interactions among economic growth, technological innovation, environmental quality, and livelihood security under policy regulation, clarifying their evolutionary mechanisms and constructing a model to project the high-quality development index (HQDI) and coupling coordination degree (CCD) among subsystems. Findings reveal an upward trend in both HQDI and CCD across the seven megacities, with notable stratification. Beijing, Shanghai, and Shenzhen form the top echelon, leveraging financial and technological resources, driven by science and green development. Guangzhou and Chongqing constitute the second tier, supported by regional integration and industrial clusters, while Chengdu and Tianjin form the third echelon via regional strategic transformations. In coordinated development, Shanghai, Beijing, Shenzhen, and Guangzhou lead with multi-link synergy, whereas Chengdu, Chongqing, and Tianjin advance industry–ecology–livelihood coordination through regional strategies. This study offers insights for overcoming development bottlenecks, optimizing policies, and enhancing urban governance to foster a coordinated, high-quality development pattern. Full article

The discovery and development of new drugs is a lengthy, complex, and costly process, often requiring 10–20 years to progress from initial concept to market approval, with clinical trials representing the most resource-intensive stage. In recent years, Artificial Intelligence (AI) has emerged as a transformative technology capable of reshaping the entire pharmaceutical research and development (R&D) pipeline. The purpose of this narrative review is to examine the role of AI in drug discovery and development, highlighting its contributions, challenges, and future implications for pharmaceutical sciences and global public health. A comprehensive review of the scientific literature was conducted, focusing on published studies, reviews, and reports addressing the application of AI across the stages of drug discovery, preclinical development, clinical trials, and post-marketing surveillance. Key themes were identified, including AI-driven target identification, molecular screening, de novo drug design, predictive toxicity modelling, and clinical monitoring. The reviewed evidence indicates that AI has significantly accelerated drug discovery and development by reducing timeframes, costs, and failure rates. AI-based approaches have enhanced the efficiency of target identification, optimized lead compound selection, improved safety predictions, and supported adaptive clinical trial designs. Collectively, these advances position AI as a catalyst for innovation, particularly in promoting accessible, efficient, and sustainable healthcare solutions. However, substantial challenges remain, including reliance on high-quality and representative biomedical data, limited algorithmic transparency, high implementation costs, regulatory uncertainty, and ethical and legal concerns related to data privacy, bias, and equitable access. In conclusion, AI represents a paradigm shift in pharmaceutical research and drug development, offering unprecedented opportunities to improve efficiency and innovation. Addressing its technical, ethical, and regulatory limitations will be essential to fully realize its potential as a sustainable and globally impactful tool for therapeutic innovation. Full article

Over the last decades, a significant improvement in cancer patient outcomes has occurred due to advances in cancer cell biology, systemic immunity, tumor-immune microenvironment (TIME) and precision cancer therapy. Despite this explosion of knowledge, its usefulness in clinical practice has been limited by the ability to translate multidimensional data into clinical care. Progress in artificial intelligence (AI) opens up a new frontier, with the promise of achieving synergistic and comprehensive integration. The classification of cancer biology and immunobiology into hallmarks of cancer by Hanahan and Weinberg provides a framework for organizing this information. This systematic classification has enabled the understanding of the interplay and cross-talk between its parts. Targeted cancer therapies and immunotherapies have achieved considerable success, yet their combinatorial potential is still being uncovered. Molecular diagnostics has worked hand-in-hand with precision oncology in deploying new therapies in a cancer-informed and patient-specific way. Harnessing the full power of the advances in these three fields with the aid of AI promises a transformation of molecular diagnostics. This review conceptualizes molecular diagnostics in the context of cancer hallmarks using nonsmall cell lung cancer (NSCLC) as a template, highlighting the potential of a new diagnostic science through the integrative power of AI. Full article

This paper presents a modulation signal classification and recognition algorithm based on data augmentation and time–frequency joint attention (DA-TFJA) for underwater acoustic (UWA) communication systems. UWA communication, as an important means of marine information transmission, plays a key role in fields such as marine engineering, military reconnaissance, and marine science research. Accurate recognition of modulated signals is a core technology for ensuring the reliability of UWA communication systems. Traditional classification and recognition methods, mostly based on pure neural network algorithms, suffer from insufficient feature representation and limited generalization performance in complex and changing UWA channel environments. They also struggle to address complex factors such as multipath, Doppler shift, and noise interference, often resulting in scarce effective training samples and inadequate classification accuracy. To overcome these limitations, the proposed DA-TFJA algorithm simulates the characteristics of real UWA channels through two novel data augmentation strategies: the adaptive time–frequency transform enhancement algorithm (ATFT) and dynamic path superposition enhancement algorithm (DPSE). An end-to-end recognition network is developed that integrates a multiscale time–frequency feature extractor (MTFE), two-layer long short-term memory (LSTM) temporal modeling, and a time–frequency joint attention mechanism (TFAM). This comprehensive architecture achieves high-precision recognition of six modulation types, including 2FSK, 4FSK, BPSK, QPSK, DSSS, and OFDM. Experimental results demonstrate that compared with existing advanced methods, DA-TFJA achieves a classification accuracy of 98.36% on the measured reservoir dataset, representing an improvement of 3.09 percentage points, which fully verifies the effectiveness and practical value of the proposed approach. Full article

In this study, an analysis of fractional-order Lü systems is performed through a framework approach consisting of analytical solution strategies in combination with numerical methods. On the analytical methodology front, the recently developed form of the new generalized differential transform method (NGDTM) is adopted for its efficiency in providing an approximate solution with high capability in tracking the behavior of these systems. On the other hand, the Grünwald–Letnikov via Riemann–Liouville scheme (GLNS) is adopted within this study as one of its tools in confirming whether chaos exists within these systems. The performance and accuracy of the proposed method are also rigorously tested, and comparisons are made numerically with the Adams–Bashforth–Moulton method, which is used here as a standard method for validation purposes. It is clear from the results that the combination of analytical and numerical methods can greatly enhance both the speed of computation and the accuracy of results. Additionally, the proposed method or approach is found to be quite robust and accurate and can thus be employed for analyzing various fractional dynamical systems that display chaotic attractors. The proposed method can also be expanded upon in the future for analyzing complex models in science and engineering. Full article

Scientific literacy is a key element in today’s society, shaping everyday life and fostering informed decision-making and critical thinking. However, the traditional transmission of science, among other factors, has fostered a simplistic and negative view of this field of knowledge, leading to a detachment of the population from it. In this context, teachers need to assume a transformative role. To this end, it must be recognised that didactic change cannot be limited to cognitive aspects, given the relevance of attitudes as a key component of professional knowledge and as a driver of a consolidated shift. Concern about this reality leads us to describe the structure and content of scientific knowledge related to the study of Primary Education teachers’ attitudes towards the teaching of the Nature of Science and Technology. A mixed-methodological design was employed, comprising a documentary-bibliometric study with a science-mapping approach and documentary analysis. The results showed that studies often focus on the cognitive component of attitudes, mainly on beliefs about knowledge or self-efficacy. However, studies on affective or conative components remain scarce, and none have been found that comprehensively address all three components of attitudes, despite their potential to provide a deeper understanding of their role in educational change. The need to address teachers’ attitudes holistically is highlighted to better understand the evaluative and motivational factors that guide teaching practices. Likewise, the importance of moving towards studies based on educational interventions that promote the development of science as useful for life is emphasised. Full article

The growing global environmental crisis calls for fundamental transformations in production and consumption systems, but the understanding of how circular economy strategies translate into quantifiable environmental benefits remains fragmented across sectors and geographies. The objective of this study is to synthesize current scientific knowledge on the circular economy as an environmental mitigation strategy, identifying conceptual convergences, methodological patterns, geographic distributions, and critical knowledge gaps. A systematic review combined with a bibliometric analysis of 62 peer-reviewed articles published between 2018 and 2024, retrieved from Scopus, Web of Science, ScienceDirect, Springer Link and Wiley Online Library, was conducted following the PRISMA 2020 guidelines. The results reveal a marked methodological convergence around life cycle assessment, with Europe dominating the scientific output (58% of the corpus). Four complementary conceptual frameworks emerged, emphasizing closed-loop material flows, environmental performance, integration of economic sustainability and business model innovation. The thematic analysis identified bioenergy and waste valorization as the most mature implementation pathways, constituting 23% of the research emphasis. However, critical gaps remain: geographic concentration limits the transferability of knowledge to diverse socioeconomic contexts; social, cultural and behavioral dimensions remain underexplored (12% of publications); and environmental justice considerations receive negligible attention. Crucially, the evidence reveals nonlinear relationships between circularity metrics and environmental outcomes, calling into question automatic benefits assumptions. This review contributes to an integrative synthesis that advances theoretical understanding of circularity-environment relationships while providing evidence-based guidance for researchers, practitioners, and policy makers involved in transitions to the circular economy. Full article