Search Results (8,178)

The maritime industry is under increased challenges of balancing operational effectiveness and environmental responsibility. This study examines the application of intelligent agents as a technology that can align these two goals in the triple-bottom-line model that involves social responsibility, environmental footprint, and economic sustainability. An agent architecture taxonomy is outlined and adapted to the maritime industry, distinguishing between reactive, deliberative, hybrid, and multi-agent systems (MAS). The application of these architectures is analysed throughout the maritime domain. In the ship-centric environment, the analysis highlights the role of agents in autonomous navigation, energy-efficient meteorological routing, and predictive maintenance. The analysis in the port and supply-chain domain demonstrates a shift towards decentralized asset orchestration and logistic coordination rather than centralized control. The paper outlines certain barriers to widespread adoption, namely the reality gap of simulation-based training and the lack of transparency in deep-learning models (“black box” problem). The paper concludes by outlining a future research agenda proposing a use of explainable artificial intelligence (XAI), high-fidelity simulation-to-real transfer, and communication protocol standardization to continue the trend of developing strong autonomous capabilities in sustainable maritime logistics. Full article

It is difficult to capture the realities of the illicit antiquities market because of the lack of accessible, unsiloed data from underground trade networks. Despite existing literature on social network analyses and machine-learning experiments with antiquities data, there is a gap in simple open-source methodologies accessible to the non-academic public. By using a provenance-based analysis, we present a case study of the Italian antiquities trafficking networks that more fully captures their complexity. This study culls provenance data from repatriated antiquities gathered in the Museum of Looted Antiquities’ dataset to create a network visualization for analysis. Using open-source provenance and repatriation data from 1950 to July 2025, we built a dataset of 233 repatriation events with 15.858 objects to produce a network that reveals central actors, roles, and locations while staying within ethical privacy limits. This study captures large portions of the trafficking network by using accessible data and produces a reproducible, ethically framed workflow. Full article

Massive Open Online Courses (MOOCs) serve as catalysts for sustainable education by democratizing access to lifelong learning. While this potentially positions them as a key driver of the United Nations Sustainable Development Goal 4 (SDG 4), their long-term impact depends heavily on the implementation of inclusive design and ethical governance. This study evaluates the social sustainability of the AKADEMA platform—defined through equity of access, institutional trust, and long-term learner retention—using Badrul Khan’s e-learning framework. Employing a multi-layered mixed-methods design, the study triangulates subjective user perceptions—gathered via quantitative surveys (N = 209; a convenience sample of 6140 contacted users) and qualitative insights (n = 122)—with objective structural evidence from a technical accessibility audit. Although the results indicate high satisfaction with pedagogical quality, the findings reveal specific structural nuances regarding platform inclusivity and user diversity. Specifically, data triangulation highlights a notable ‘privacy awareness gap’—where working professionals demonstrate higher sensitivity regarding data governance than learners—alongside structural barriers hindering ‘Universal Design’ for learners with disabilities. Consequently, to strengthen the sustainability of open education models, future strategies should emphasize digital equity and institutional trust, ensuring that technical environments align with the promise of inclusive quality education. Full article

School-based social and emotional learning (SEL) programs have the potential to meaningfully foster healthy development. NaliniKIDS is a school-based program designed to focus on promoting SEL and school connectedness by bridging physical and mental health through exercises, a book series, and schoolwide activities. This mixed-method study explored the effects of NaliniKIDS on students’ mental health and school climate and the teachers’ perspectives regarding the implementation in a racially diverse, urban, Title I public elementary school. Quantitative data comprised of student (N = 253) and parent (N = 29) self-reports at pre- and post-test; qualitative data were collected via two focus groups with teachers (N = 10 participants). Survey results showed small increase in prosocial behavior among students after NaliniKIDS implementation. Focus groups highlighted the importance of training, protecting time and resources, and adapting the program to students’ background to maximize its implementation and relevance. NaliniKIDS may be a promising SEL program that facilitates the integration of physical and emotional health. The findings emphasize the health benefits of investments and policies focused on school-based programs that are integrated in the school. The successful implementation of SEL programs such as NaliniKIDS may significantly promote elementary student physical and mental health. Full article

Background/Objectives: Children and young people (CYP) with a learning disability are at higher risk of living with overweight and obesity and may consume fewer fruits and vegetables compared to the general paediatric population. They are more likely to experience eating and drinking difficulties, restrictive eating, and mealtime behavioural challenges. The school environment is considered an ideal setting to improve CYP’s dietary intakes. The primary objective was to identify existing interventions to support healthier food choices for CYP attending specialist schools. Secondary objectives considered intervention development, fidelity and outcomes. Methods: A scoping review and narrative synthesis. Eligible studies were identified from bibliographic databases (e.g., Medline, Embase, PsychInfo) and grey literature (e.g., Clinicaltrials.gov, the Cochrane Library). A two-stage screening process was used. Intervention components were mapped according to the TIDieR-PHP and AACTT frameworks. Results: Seven studies, reported in ten records, were included. Interventions included modifications to the dining environment, sensory exploration, health promotion and social reinforcement. Interventions were implemented across the school day: lunchtime (n = 2), breaktime (n = 3) and other times (n = 2). Studies mainly focused on adolescents. There was some mixed evidence of increased consumption of fruits and vegetables, whole grains and water. Due to small sample sizes and heterogeneity, definitive conclusions are limited. A key finding is the lack of interventions to improve CYP’s food choices in specialist schools. Conclusions: This review highlights a crucial need for the development of multi-component interventions co-produced with stakeholders to promote healthy food choices and improve the dietary intakes of CYP attending specialist schools. Full article

In this paper, by means of a comprehensive analysis of the statutory and non-statutory documents that govern its preschool provision, we examine how early childhood education and care (ECEC), particularly in relation to mathematics, is conceptualised by the English educational authorities. Situated within international debates about economic (school-readiness, accountability-driven) versus social (holistic, play-based, rights-oriented) models of ECEC, the study explores how curriculum expectations, assessment practices and didactical guidance collectively frame young children’s learning opportunities. Drawing on a document-based analytic approach, and guided by six literature-derived questions, the analysis reveals significant inconsistencies both within and between documents, including conflicting messages about the purpose of preschool, an uneven emphasis on school readiness, and ambivalent statements regarding the role of play, instruction and practitioner agency, as well as contradictory and shifting expectations surrounding the scope, status and pedagogical treatment of early mathematics. While statutory materials frequently privilege school readiness and narrowly defined number outcomes, non-statutory guidance promotes broader mathematical thinking, exploratory play and child-initiated reasoning. Overall, the findings demonstrate limited coherence across the English authorities’ ECEC expectations and highlight the interpretive and professional challenges faced by practitioners expected to implement this fragmented early years mathematics policy landscape. Full article

Autism Spectrum Disorder (ASD) is defined by ongoing difficulties in social communication, flexibility in behavior, and adaptive learning skills. Interventions that utilize robots have demonstrated potential in providing organized training for children with ASD; however, there is a lack of controlled studies that specifically examine the effects of reinforcement strategies. This research introduces a systematic interaction policy based on reinforcement, founded on the principles of Applied Behavior Analysis (ABA), and assesses its effectiveness through a randomized controlled experimental design with observation. The humanoid robot NAO was used in two different interaction scenarios, one involving a reinforcement condition (RC) and the other a non-reinforcement condition (RC), ensuring that the instructional material and environment were maintained, while only the availability of contingent positive feedback was altered. A total of 50 participants diagnosed with ASD Level 2 engaged in structured word-learning sessions. Learning outcomes were assessed using institutional performance criteria, average response time, and emotion analysis derived from a CNN-based facial expression model. Independent samples t-tests revealed statistically significant improvements in both performance scores (t(48) = 3.779, p < 0.05) and response times (t(48) = 3.758, p < 0.05) in the reinforcement condition compared to the non-reinforcement condition. The findings demonstrate that structured ABA-based reinforcement within robotic interaction significantly enhances learning efficiency and task engagement, contributing methodologically rigorous evidence to robot-assisted ASD intervention research. Full article

Building on Gutman’s (1987) argument that architectural practice should reflect the nature of the problem, this article explores four eras of architectural practice: the Patronage Model, the Clientage Model, the Transitional Models, and Future Models. Each era is examined in relation to six “Questions of Praxis”: (1) What is the nature of the problem?, (2) What is the nature of the intervention?, (3) What knowledge is valued?, (4) What is the stance toward the problem?, (5) What is the continuity in the relationship?, and (6) What is the prioritization of professional obligations? Through a comparative analysis of questions 2–5—the analytic core of action-taking—alongside four drivers of change in today’s volatile, uncertain, complex, ambiguous world, yields 16 possible futures for architects. Further synthesis identifies five primary roles for architects of the future: systems-thinking designer (embracing complexity), steward (building trust amid volatility), facilitator (reducing ambiguity through shared meaning), curator (making sense of uncertainty), and strategic forecaster (transforming volatility into preparedness). These roles embody a care-based approach—prioritizing ongoing relationships over episodic interventions, collective capacity-building over expert prescriptions, and adaptive readiness over static solutions. This reflects the positioning of architecture as a public good, focused on strengthening social, ecological, and systemic foundations so communities not only withstand disruption but also adapt, learn, and thrive through it. Full article

In conditions of liquid modernity, marked by accelerated technological change, the virtualization of essential services, and the erosion of stable institutional support, digital participation in later life is less a matter of initial access than of continuously renegotiating engagement within unstable socio-technical environments. While established technology adoption models such as TAM, UTAUT, and STAM have provided robust explanations of cognitive and age-related determinants of adoption, they remain limited in accounting for the relational processes through which technological engagement is learned, stabilized, and sustained over time. This article advances a relational perspective on technology appropriation by foregrounding the role of warm experts—trusted informal supporters who mediate learning, interpretation, and adaptation in everyday contexts. Moving beyond dyadic understandings of assistance, the paper conceptualizes mediation as a distributed ecology of roles embedded within relational networks that both enable and constrain digital inclusion. Building on this perspective, the study proposes the Relational Technology Appropriation Model (RELTAM) as a general multi-level architecture integrating individual determinants, relational mediation processes, and network-level support configurations within a dynamic framework of appropriation. The Relational (Older Adult) Technology Appropriation Model (REL(OA)TAM) is introduced as a context-specific instantiation of this broader framework, calibrated to the distinctive conditions of later life. By incorporating temporal instability and mediation ecologies as structural components, REL(OA)TAM offers a socially grounded account of digital inclusion as an ongoing process of adaptive negotiation within the fluid and uncertain conditions of liquid modernity. Full article

The integration of digital tools into mathematics education has the potential to transform teaching and learning for students with special needs by fostering inclusion, accessibility, and engagement. Guided by the principles of Universal Design for Learning (UDL) and Vygotsky’s social constructivist theory, this study investigates how digital technologies can enhance learning experiences and promote inclusive education in mathematics classrooms. Using a mixed-method design, data were collected from 110 mathematics teachers and 210 Grade 11 students in special schools across Nigeria to assess the availability, utilization, and impact of digital tools on students’ engagement, motivation, collaboration, and problem-solving. Findings indicate that while access to digital tools remains limited, their effective use significantly improves students’ learning experiences and supports inclusive pedagogical practices. The study underscores the importance of policy alignment to ensure equitable access to digital resources for all students. Findings contribute to global discussions on inclusive digital pedagogies by providing empirical insights into how technology can mediate participation, interaction, and achievement in mathematics for students with special needs. Full article

As modern power markets integrate more renewable generation, day-ahead energy clearing remains the central procurement step, while flexibility products are procured to ensure that the cleared energy schedule can be operated securely. This paper proposes a closed-loop framework linking the Power Exchange (PX) and the Independent System Operator (ISO) to bridge energy-market settlement and network-feasible operation. The PX performs staged day-ahead clearing with energy settled first, followed by aAutomatic generation control (AGC) and spinning reserve (SR) procured from the residual headroom of committed (energy-awarded) units. The ISO then validates the cleared schedule using an equivalent current injection (ECI)-based screening. This paper uses a single-period (single-hour) IEEE 30-bus case setting; multi-period scheduling and intertemporal constraints are not modeled. When congestion is detected, power-flow tracing identifies the main contributors and guides a minimal-change redispatch. The ISO-feasible dispatch is then sent back to the PX for re-clearing, aligning prices and welfare with an executable operating point. The resulting nonconvex clearing problems with valve-point effects and prohibited operating zones are solved by Artificial Protozoa Optimizer with Social Learning (APO–SL) and evaluated against representative metaheuristic baselines. IEEE 30-bus studies show that off-peak and average-load cases pass ISO screening directly, whereas the peak case tightens reserve headroom (SR capped at 39.08 MW) and triggers congestion. After ISO feedback and energy re-clearing, line loadings return within limits. The ISO-feasible dispatch changes the marginal accepted offer and lifts the MCP (3.73 → 4.38 $/MWh). The welfare value reported here follows the paper’s settlement-based definition (purchase total minus accepted offer cost), and it increases accordingly (113.77 → 190.17 $/h). Full article

In rapidly urbanising developing economies, construction activity frequently relies on informal and semi-skilled labour. This coincides with limited opportunities for systematic skill development, leading to persistent labour deskilling. While existing research has predominantly addressed these challenges through policy reform, industrialisation, or efficiency-driven technological models, less emphasis has been placed on the role of architectural design in shaping labour–technology relations on-site. This article adopts a constructivist perspective on technology to investigate how architectural design can serve as a socio-technical framework for non-formal labour upskilling within construction practice. Drawing upon qualitative case studies of two architectural projects in Sri Lanka—a suburban residential retrofit and a low-income rural housing prototype—this study analyses how design strategies such as systemisation, construction sequencing, material hybridity, and craft-based component detailing embed tacit learning within production processes. The findings demonstrate that craft, understood as a mode of tacit knowledge and on-the-job learning rather than as a stylistic or nostalgic response, can facilitate skill acquisition across diverse economic and technical contexts. By repositioning architectural design as an active mediator between technology and labour, this article contributes to debates within construction studies, social sciences, and architectural theory and proposes design-led construction strategies as a context-sensitive alternative to purely policy- or efficiency-driven approaches to labour development. Full article

Social media platforms have been established as relevant sources of real-time information for urban traffic analysis. This study proposes an intelligent framework for the classification and spatiotemporal analysis of traffic incidents based on semi-synthetic data streams constructed from historical geolocated seeds for controlled validation, utilizing real reports from platforms such as X and Telegram. The approach integrates adaptive machine learning and incremental density-based clustering. An Adaptive Random Forest (ARF) incremental classifier is used to identify the type of incident, allowing for continuous updating of the model in response to changes in traffic flow and concept drift. The classified events are then processed using DenStream, a clustering algorithm that incorporates a temporal decay mechanism designed to identify dynamic spatial patterns and discard older information. The evaluation is performed in a controlled streaming simulation environment that replicates the dynamics of cities such as Panama and Guayaquil. The proposed framework demonstrated robust quantitative performance, achieving a prequential accuracy of up to 86.4% and a weighted F1-score of 0.864 in the Panama scenario, maintaining high stability against semantic noise. The results suggest that this hybrid architecture is a highly viable approach for urban traffic monitoring, providing useful information for Intelligent Transportation Systems (ITS) by processing authentic social signals. Full article

As artificial intelligence (AI) drives significant challenges in education, understanding and addressing the training needs of in-service teachers has become a critical issue for ensuring a responsible and long-term technological transition. Framed within Sustainable Development Goal 4 (SDG 4) and the principles of Education for Sustainable Development (ESD), teacher preparation in AI is increasingly recognized as a key mechanism for promoting ethical, equitable, and inclusive educational transformation. This study explores the influence of several key variables on intention and learning behaviors in relation to AI among a sample of 704 Spanish in-service teachers (71% women) from all compulsory educational levels. Using a validated questionnaire, this study assessed teachers’ anxiety towards AI, basic AI knowledge, personal relevance of AI, AI for social good, perceived self-efficacy, social pressure, and perceived usefulness of AI. Structural equation modeling (SEM) was employed to analyze the direct and indirect relationships among these variables. The results indicate that the perceived usefulness of AI and self-efficacy directly and positively influence the behavioral intention to learn about AI. Furthermore, social pressure and basic AI knowledge indirectly influence this intention. In turn, both behavioral intention and social pressure significantly predicted AI learning behaviors. The model demonstrates strong explanatory power, accounting for 91% of the variance in the behavioral intention to learn about AI. These findings provide evidence to inform the design of teacher training initiatives and policies that promote responsible, ethical, and inclusive integration of AI in educational settings, contributing to sustainable development through education. Full article

Cities are increasingly expected to address digital transformation and sustainability challenges at the same time. However, existing urban indices generally approach smart city and sustainable city perspectives separately, which limits their ability to capture the integrated nature of contemporary urban development. In addition, many index-based studies rely on similar methodological choices. This study develops a composite Sustainable Smart City (SSC) index supported by a systematic scoring framework that brings smartness and sustainability together. The proposed framework follows a step-by-step procedure covering data preparation, normalization, weighting, aggregation, and final scoring. To address information overlap among indicators, a Redundancy-Penalized Entropy Weighting (RPEW) approach is applied. Then, overall SSC scores are calculated using a soft non-compensatory aggregation to emphasize balanced performance across dimensions. The framework is empirically illustrated through a cross-country case study including 38 OECD (Organization for Economic Co-Operation and Development) countries. A machine-learning-based polynomial forecasting approach is used for a limited number of indicators to deal with data gaps allowing the assessment to reflect more up-to-date conditions. The results highlight clear differences in SSC performance and show that strong outcomes in a single dimension are not sufficient to achieve high overall SSC scores. Instead, balanced progress across economic, digital, environmental, governance, mobility, and social dimensions plays an important role. In addition, the proposed framework provides a practical basis for comparative analysis, benchmarking, and policy-oriented evaluation of smart and sustainable urban development. Full article