Search Results (168)

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

A central challenge in artificial intelligence and cognitive science is identifying a unifying principle that governs inference, learning, and action. Active inference proposes such a principle: the minimization of variational free energy. Advocates of active inference argue that the framework subsumes classical models of optimal behavior—including Bayesian decision theory, resource rationality, optimal control, and reinforcement learning—while also instantiating information-theoretic principles such as rate-distortion theory and maximum entropy. However, the literature outlining these conceptual links remains fragmented, limiting integration across fields. This review develops these connections systematically. We show how these major frameworks admit formal correspondences with expected free energy minimization when expressed in variational form, exposing a shared optimization principle that underlies theories of optimal decision-making and information processing. This synthesis is intended both to orient researchers from other fields who are new to active inference and to clarify foundational assumptions for those already working within the framework. Full article

This article explores the pedagogical potential of soundscapes as creative learning tools for advancing environmental education in Chilean primary schools. Drawing on the Soundlapse project, we designed and implemented a school workshop that combined activity sheets, an online bird-sound repository, structured soundwalks, and immersive audio concerts with teachers and students in Valdivia. The study employed a qualitative, participatory design, analyzing teacher interviews through reflexive thematic analysis. Four themes emerged: (1) listening as pedagogical practice, (2) learning through place and the senses, (3) creativity and cross-disciplinarity, and (4) implementation challenges and opportunities. Teachers emphasized the transformative role of attentive listening, which reconfigured classroom dynamics through shared silence and cultivated students’ capacity for self-regulation. Soundwalks and sensory encounters with local wetlands positioned the environment as a ‘living laboratory,’ fostering ecological awareness, attachment to place, and intergenerational knowledge. Creative activities such as sound mapping legitimized symbolic and artistic modes of representation, while interdisciplinary collaborations between science and music expanded curricular possibilities. At the same time, institutional rigidity and lack of resources highlighted the importance of teacher agency, co-designed materials, and flexible frameworks to sustain these practices. We argue that soundscape-based education offers a timely opportunity to integrate sensory, creative, and ecological dimensions into school curricula, aligning with national and international calls for interdisciplinary sustainability education. By treating listening and creativity as core rather than peripheral, such approaches may open new pathways for cultivating ecological awareness, cultural belonging, and pedagogical innovation. Full article

Regenerative agroecology promotes a suite of methods that diversify farmers’ techniques, crops, and income-generating opportunities. Many low- and middle-income countries struggle with food insecurity, malnutrition, and poverty, relying on natural resources for their livelihoods. In Madagascar, we led agroecology interventions, sharing locally grounded technologies to increase productivity and regenerate biodiversity. We evaluated the short- and medium-term outcomes using a mixed-methods social science approach. We monitored a cohort of over 500 participants in 27 rural communities who trained in market vegetable farming and poultry husbandry between 2019 and 2025. For participants in market vegetable and poultry husbandry interventions, over half adopted new technologies, reporting positive experiences, though outcome achievement varied. Participants in the market vegetable interventions reported they had the knowledge and skills to practice sustainable agriculture, remarking that the hands-on demonstrations and practice facilitated learning, as well as the accessibility of resources for implementation. Women were 1.68× more likely to adopt vegetable farming than men (multinomial regressions, gender log odds = 0.53, p < 0.01), while there was no difference in genders in adoption of poultry husbandry (gender log odds = 0.28, p > 0.05). Most (95–98%, n = 1012) responded they were satisfied with the results of the interventions and would continue to use the skills they learned. Insights generated by this program evaluation led to the following recommendations and improvements: (1) more hands-on demonstrations compared to classroom presentations; (2) more frequent medium-term consultations with participants; (3) introducing microcredit mechanisms to combat cost-related barriers; (4) diversifying outreach approaches. Implementing these recommendations continues to improve outcomes as we scale our interventions. Full article

This systematic review focuses on intelligent navigation as a core enabler of autonomy in smart warehouses, where mobile robots must dynamically perceive, reason, and act in complex, human-shared environments. By synthesizing advancements in AI-driven decision-making, SLAM, and multi-sensor fusion, the study highlights how intelligent navigation architectures reduce operational uncertainty and enhance task efficiency in logistics automation. Smart warehouses, powered by mobile robots and AGVs and integrated with AI and algorithms, are enabling more efficient storage with less human labour. This systematic review followed PRISMA 2020 guidelines to systematically identify, screen, and synthesize evidence from 106 peer-reviewed scientific articles (including pri-mary studies, technical papers, and reviews) published between 2020–2025, sourced from Web of Science. Thematic synthesis was conducted across 8 domains: AI, SLAM, sensor fusion, safety, network, path planning, implementation, and design. The transition to smart warehouses requires modern technologies to automate tasks and optimize resources. This article examines how intelligent systems can be integrated with mathematical models to improve navigation accuracy, reduce costs and prioritize human safety. Real-time data management with precise information for AMRs and AGVs is crucial for low-risk operation. This article studies AI, the IoT, LiDAR, machine learning (ML), SLAM and other new technologies for the successful implementation of mobile robots in smart warehouses. Modern technologies such as reinforcement learning optimize the routes and tasks of mobile robots. Data and sensor fusion methods integrate information from various sources to provide a more precise understanding of the indoor environment and inventory. Semantic mapping enables mobile robots to navigate and interact with complex warehouse environments with high accuracy in real time. The article also analyses how virtual reality (VR) can improve the spatial orientation of mobile robots by developing sophisticated navigation solutions that reduce time and financial costs. Full article

While the future of college student mental health is leaning towards systemic-level integrated behavioral health care models, existing mental health support for science, technology, engineering, and mathematics (STEM) students remains highly individual. The REDFLAGS Model is a mental health resource comprising an acronym of warning signs that suggest a college student might be struggling with mental distress. The aim of this study was to test the utility of The REDFLAGS Model, with a large sample of STEM students (N = 358). Results revealed support for the latent dimensionality of The REDFLAGS Model among a large sample of STEM students. Results also demonstrated that higher recognition of the items on The REDFLAGS Model as warning signs for mental distress was a significant predictor of peer-to-peer referrals to counseling among STEM students. Additionally, STEM students with help-seeking histories and those who identified as female were more likely to recognize the items on The REDFLAGS Model as warning signs of mental distress than those without help-seeking histories and men, respectively. Collectively, results indicated that The REDFLAGS Model has potential to provide college counselors with an empirically supported framework for supporting STEM student mental health. It is available at no cost and can be shared in print or digital formats. Full article

This review provides a comprehensive analysis of the technological, environmental, economic, regulatory, and social dimensions shaping the development and operation of agricultural biogas plants. The paper adopts a primarily European perspective, reflecting the comparatively high share of agricultural inputs in anaerobic digestion (AD) across EU Member States, while drawing selective comparisons with global contexts to indicate where socio-geographical conditions may lead to different outcomes. It outlines core principles of the AD process and recent innovations—such as enzyme supplementation, microbial carriers, and multistage digestion systems—that enhance process efficiency and cost-effectiveness. The study emphasises substrate optimisation involving both crop- and livestock-derived materials, together with the critical management of water resources and digestate within a circular-economy framework to promote sustainability and minimise environmental risks. Economic viability, regulatory frameworks, and social dynamics are examined as key factors underpinning successful biogas implementation. The paper synthesises evidence on cost–benefit performance, investment drivers, regulatory challenges, and support mechanisms, alongside the importance of community engagement and participatory governance to mitigate land-use conflicts and ensure equitable rural development. Finally, it addresses persistent technical, institutional, environmental, and social barriers that constrain biogas deployment, underscoring the need for integrated solutions that combine technological advances with policy support and stakeholder cooperation. This analysis offers practical insights for advancing sustainable biogas use in agriculture, balancing energy production with environmental stewardship, food security, and rural equity. The review is based on literature identified in Scopus and Web of Science for 2007 to 2025 using predefined keyword sets and supplemented by EU policy and guidance documents and backward- and forward-citation searches. Full article

Background and Objectives: Public health needs collaborative, privacy-preserving analytics, but centralized AI is constrained by data sharing and governance. Federated learning (FL) enables training without moving sensitive data. This review assessed how FL is used for disease prevention in population and public health, and mapped benefits, challenges, and policy implications. Methods: Following PRISMA 2020, we searched PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar for peer reviewed English-language studies from January 2020–30 June 2025, applying FL to surveillance, outbreak detection, risk prediction, or policy support. Two reviewers screened and extracted data with third-reviewer arbitration. Quality was appraised with a tool adapted from MMAT and AI reporting frameworks. No meta-analysis was performed. Results: Of 5230 records identified (4720 after deduplication), 200 full texts were assessed and 19 were included. Most used horizontal FL across multiple institutions for communicable diseases, COVID-19, tuberculosis and some chronic conditions. Reported gains included privacy preservation across sites, better generalizability from diverse data, near real-time intelligence, localized risk stratification, and support for resource planning. Common barriers were non-IID data, interoperability gaps, compute and network limits in low-resource settings, unclear legal pathways, and concerns about fairness and transparency. Few studies linked directly to formal public-health policy or low-resource deployments. Conclusions: FL is promising for equitable, secure, and scalable disease-prevention analytics that respect data sovereignty. Priorities include robust methods for heterogeneity, interoperable standards, secure aggregation, routine fairness auditing, clearer legal and regulatory guidance, and capacity building in underrepresented regions. Full article

Polycentric governance enables decentralized yet coherent multilevel decision-making by fostering alignment across governance, policy, and strategic goals. In the United States (U.S.), a prominent global climate actor, this polycentric structure is being tested. An Executive Order issued on 8 April 2025, opens the possibility to stop the enforcement of state-level laws that might condition the exploitation of energy resources based on considerations concerning climate change and the environment. This federal action might disrupt subnational efforts to reduce greenhouse gas emissions and mitigate climate impacts, exposing a misalignment between federal and state climate governance—a dynamic that remains underexplored in the existing literature. This critical mini-review article proposes a novel conceptual framework that presents this misalignment between federal and state climate perspectives as an emerging meta-system pathology in U.S. climate governance, introducing the concept of perspective desalignment. Drawing on the analysis of 73 Web of Science papers and a review of 16 journal articles published in 2018–2025, this study highlights the breakdown of shared understanding and strategic coherence among key stakeholders, including federal and state governments, industry, and academia. The findings underscore that any effective climate governance will require federal–state realignment. The paper concludes with implications and recommendations for restoring alignment and enabling more effective, collaborative climate governance. Full article

Higher Education Institutions (HEI) face increasing expectations to engage in sustainability reporting despite limited resources and heterogeneous practices. This study explores how Joint Sustainability Reports (JSR), built on the EU Voluntary Sustainability Reporting Standard for non-listed SMEs (VSME), can serve as a cooperative and digitally supported framework to enhance transparency, comparability, and efficiency while strengthening universities’ third mission of societal engagement and knowledge transfer. Qualitative interviews with six sustainability experts from German and Austrian universities of applied sciences (UAS) highlight persistent challenges such as data gaps, staffing shortages, and weak strategic anchoring. The findings show that VSME-based JSRs, through shared data collection, centralised coordination, and modular reporting, enable resource and data pooling, standardised indicators, and cross-university synergies. By making social contributions more visible and credible, JSRs reinforce accountability and advance universities’ third mission in fostering community outreach and sustainable development. Full article

Quantum teleportation is a fundamental protocol in quantum information science. It represents a critical resource for quantum communication and distributed quantum computing. We derive an analytical expression of the fidelity of teleportation of an input squeezed thermal state using for teleportation a bipartite Gaussian resource state shared between Alice and Bob. Each mode of the resource state is susceptible to the influence of the environment. We employ the characteristic function approach in conjunction with the covariance matrix formalism. The fidelity of teleportation is expressed in terms of input and resource state covariance matrices. We investigate, as an example, the feasibility of secure quantum teleportation of a squeezed thermal state using a two-mode resource state whose modes are placed in separate thermal baths. A successful quantum teleportation requires meeting two criteria: the presence of two-way quantum steering and a teleportation fidelity exceeding the classical threshold. The quantum steering is by nature asymmetric and has found applications in quantum cryptography and secure quantum teleportation. Weak squeezing and a high number of average thermal photons in the input states lead to an increase in the fidelity of teleportation. Generally, steering disappears much faster than the fidelity of teleportation decreases below its classical limit. Full article

The term Environmental Biotechnology is widely used, but lacks a universally accepted definition, with varying interpretations across disciplines and sectors leading to challenges in funding, policy formulation, and interdisciplinary collaboration. Through a literature review and engagement activities, this study examines existing definitions, identifies key areas of divergence, and explores pathways toward a more cohesive understanding. Findings reveal a spectrum of valid interpretations, often shaped by specific contexts, with researchers generally recognising a shared conceptual framework within their own subfields but encountering ambiguities across subject boundaries. Common points of difference include whether Environmental Biotechnology is restricted to microorganisms or encompasses other biological systems. Some understandings reflect sector-specific needs, contributing to fragmentation, though a broader approach could strengthen the field’s identity by providing a unifying framework, mapping overlaps with related fields such as Industrial Biotechnology. A working definition is proposed for Environmental Biotechnology as the use of biologically mediated systems for environmental protection and bioremediation, incorporating resource recovery and bioenergy production where these enhance system sustainability. Importantly, it was recognised that any definition must remain adaptable, reflecting the evolving nature of both the science and its applications. Full article

Because they account for realistic effects in opportunistic maintenance modeling, dependency hypotheses are extremely diverse in the literature. Despite recent reviews, a clear view of the dependency hypotheses is currently missing in the literature, especially regarding component interactions, resource constraints and human factors. In this paper, we provide a conceptual background on dependence modeling and the notion of maintenance opportunity. Then, a critical systematic literature review, following the PRISMA guidelines, is carried out, focusing on the current hypotheses in opportunistic maintenance, including component interactions, workers’ skills and resource constraints, economic dependence and optimization objectives. The different dependence types are identified and defined, and their presence in the literature is quantified. The included papers in this review ($n=91$) were selected on the basis of relevance to the research questions from the Web of Science, Scopus and Google Scholar databases. Exclusion criteria were set, related to the year of publication (from 2000) and language (limited to French or English), and inclusion criteria required the paper to cover modeling, simulating or reviewing literature related to opportunistic maintenance with dependencies. The results show that economic dependence is mostly modeled by sharing downtime or set-up costs. The objective function for optimization is mostly found to be the economic cost of maintenance, with concerningly little consideration for environmental indicators. These results are finally discussed in light of advances in predictive analytics and current challenges in the sustainability of industrial processes. Further developments should consider including the social and environmental aspects of sustainability in the dependencies, but also look into the benefits that predictive analytics can bring to opportunistic maintenance. The variety of modeling assumptions and dependences presented in the literature does not always allow comparing the results of the models. Full article

Innovation projects with sustainable characteristics are increasingly seen as strategic drivers for organizations to expand market share and retain customers. Yet, firms face limited resources while dealing with many potential projects. To address this challenge, an integrated framework for evaluating and ranking innovation projects using sustainability-related factors can support more consistent decision-making. Although several models for project selection exist in the literature, few provide a comprehensive approach that incorporates sustainability criteria. This study proposes a model for selecting innovation projects by explicitly considering sustainability aspects, supported by multi-criteria decision support methods. The methodological approach followed the Design Cycle method, grounded in Design Science Research. The main result is a novel, customizable model for evaluating, ranking, and managing innovation projects within a sustainability-oriented context. The model was validated through application in two high-performance organizations recognized for their innovation and sustainability practices. Additionally, this research offered reflections on how sustainability-driven innovation can be implemented in practice. Overall, the findings demonstrated that the proposed model is adaptable to different organizational realities, sectors, and sizes, enhancing the capacity to assess and understand the role of sustainability in innovation projects more effectively. Full article