Search Results (1,117)

The transition from soft to hard law is reshaping global agri-food governance, particularly in relation to sustainability and corporate responsibility. This article analyzes this shift by examining two regulatory approaches: voluntary instruments such as the OECD-FAO Guidance for Responsible Agricultural Supply Chains and binding EU directives like the Corporate Sustainability Due Diligence Directive (CSDDD) and the Corporate Sustainability Reporting Directive (CSRD). Using a qualitative and interpretive methodology, the study combines a literature review and two case studies (Nicoverde and Lavazza) to explore the evolution from soft law to hard law and the synergies and analyze how these tools are applied in the Italian agri-food sector and how they can contribute to improving corporate sustainability performance. Findings show that soft law has paved the way for more rigorous regulation, but the increasing compliance burden poses challenges, especially for small and medium-sized enterprises (SMEs). These cases serve as virtuous examples to illustrate how soft and hard law interact in practice, offering concrete insights into the translation of general sustainability principles into corporate strategies. A hybrid governance framework—combining voluntary and binding tools—can foster sustainability if supported by coherent policies, stakeholder collaboration and adequate support mechanisms. The study offers practical insights for both companies and policymakers navigating the evolving legal scenario. Full article

The evolution of Industrial Internet of Things (IIoT) systems demands flexible and intelligent architectures capable of addressing low-latency requirements, real-time analytics, and adaptive resource management. In this context, softwarized edge computing emerges as a key enabler, supporting advanced IoT deployments through programmable infrastructures, distributed intelligence, and seamless integration with cloud environments. This paper presents an extended and publicly available proof of concept (PoC) for a softwarized, data-driven architecture designed to operate across the cloud/edge/IoT continuum. The proposed architecture incorporates containerized microservices, open standards, and ML-based inference services to enable runtime decision-making and on-the-fly network reconfiguration based on real-time telemetry from IIoT nodes. Unlike traditional solutions, our approach leverages a modular control plane capable of triggering dynamic adaptations in the system through RESTful communication with a cloud-hosted inference engine, thus enhancing responsiveness and autonomy. We evaluate the system in representative IIoT scenarios involving multi-agent collaboration, showcasing its ability to process data at the edge, minimize latency, and support real-time decision-making. This work contributes to the ongoing efforts toward building advanced IoT ecosystems by bridging conceptual designs and practical implementations, offering a robust foundation for future research and deployment in intelligent, software-defined industrial environments. Full article

Severe snowstorms pose multiple threats to high-speed rail systems, including sudden drops in track friction coefficients, icing of overhead contact lines, and reduced visibility. These conditions can trigger dynamic risks such as train speed restrictions, cascading delays, and operational disruptions. Addressing the limitations of traditional scheduling methods in spatio-temporal modeling during blizzards, real-time multi-objective trade-offs, and high-dimensional constraint solving efficiency, this paper proposes a collaborative optimization approach integrating temporal forecasting with deep reinforcement learning. A dual-module LSTM-PPO model is constructed using LSTM (Long Short-Term Memory) and PPO (Proximal Policy Optimization) algorithms, coupled with a composite reward function. This design collaboratively optimizes punctuality and scheduling stability, enabling efficient schedule adjustments. To validate the proposed method’s effectiveness, a simulation environment based on the Lanzhou-Xinjiang High-Speed Railway line was constructed. Experiments employing a three-stage blizzard evolution mechanism demonstrated that this approach effectively achieves a dynamic equilibrium among safety, punctuality, and scheduling stability during severe snowstorms. This provides crucial decision support for intelligent scheduling of high-speed rail systems under extreme weather conditions. Full article

This study investigates the utilisation of AI tools, including Grammarly Free, QuillBot Free, Canva Free Individual, and others, to enhance learning outcomes for 180 s-year telecommunications engineering students at Universidad Politécnica de Madrid. This research incorporates teaching methods like problem-based learning, experiential learning, task-based learning, and content–language integrated learning, with English as the medium of instruction. These tools were strategically used to enhance language skills, foster computational thinking, and promote critical problem-solving. A control group comprising 120 students who did not receive AI support was included in the study for comparative analysis. The control group’s role was essential in evaluating the impact of AI tools on learning outcomes by providing a baseline for comparison. The results indicated that the pilot group, utilising AI tools, demonstrated superior performance compared to the control group in listening comprehension (98.79% vs. 90.22%) and conceptual understanding (95.82% vs. 84.23%). These findings underscore the significance of these skills in enhancing communication and problem-solving abilities within the field of engineering. The assessment of the pilot course’s forum revealed a progression from initially error-prone and brief responses to refined, evidence-based reflections in participants. This evolution in responses significantly contributed to the high success rate of 87% in conducting complex contextual analyses by pilot course participants. Subsequent to these results, a project for educational innovation aims to implement the AI-PBL-CLIL model at Universidad Politécnica de Madrid from 2025 to 2026. Future research should look into adaptive AI systems for personalised learning and study the long-term effects of AI integration in higher education. Furthermore, collaborating with industry partners can significantly enhance the practical application of AI-based methods in engineering education. These strategies facilitate benchmarking against international standards, provide structured support for skill development, and ensure the sustained retention of professional competencies, ultimately elevating the international recognition of Spain’s engineering education. Full article

Background: Evolutionary game theory (EGT), originating from Darwinian competition studies, offers a powerful framework for understanding complex healthcare interactions where multiple stakeholders with conflicting interests evolve strategies over time. Unlike traditional game theory, EGT accounts for bounded rationality and strategic evolution through imitation and selection. Aims and objectives: In our study, we use Synthetic Knowledge Synthesis (SKS) that integrates descriptive bibliometrics and bibliometric mapping to systematically analyze the application of EGT in healthcare. The SKS aimed to identify prolific research topics, suitable publishing venues, and productive institutions/countries for collaboration and funding. Data was harvested from the Scopus bibliographic database, encompassing 539 publications from 2000 to June 2025, Results: Production dynamics is revealing an exponential growth in scholarly output since 2019, with peak productivity in 2024. Descriptive bibliometrics showed China as the most prolific country (376 publications), followed by the United States and the United Kingdom. Key institutions are predominantly Chinese, and top journals include PLoS One and Frontiers in Public Health. Funding is primarily from Chinese entities like the National Natural Science Foundation of China. Bibliometric mapping identified five key research themes: game theory in cancer research, evolution game-based simulation of supply management, evolutionary game theory in epidemics, evolutionary games in trustworthy connected public health, and evolutionary games in collaborative governance. Conclusions: Despite EGT’s utility, significant research gaps exist in methodological robustness, data availability, contextual modelling, and interdisciplinary translation. Future research should focus on integrating machine learning, longitudinal data, and explicit ethical frameworks to enhance EGT’s practical application in adaptive, patient-centred healthcare systems. Full article

Microwave pretreatment (MWP) has emerged as a promising strategy to enhance the pyrolysis of lignocellulosic biomass due to its rapid, volumetric, and selective heating. By disrupting the recalcitrant structure of cellulose, hemicellulose, and lignin, MWP improves biomass deconstruction, increases carbohydrate accessibility, and enhances yields of bio-oil, syngas, and biochar. When combined with complementary pretreatments—such as alkali, acid, hydrothermal, ultrasonic, or ionic-liquid methods—MWP further reduces activation energies, facilitating more efficient saccharification and thermal conversion. This review systematically evaluates scientific progress in this field through bibliometric analysis, mapping research trends, evolution, and collaborative networks. Key research questions are addressed regarding the technical advantages of MWP, the physicochemical transformations induced in biomass, and associated environmental benefits. Findings indicate that microwave irradiation promotes hemicellulose depolymerization, reduces cellulose crystallinity, and weakens lignin–carbohydrate linkages, which facilitates subsequent thermal decomposition and contributes to improved pyrolysis efficiency and product quality. From an environmental perspective, MWP contributes to energy savings, mitigates greenhouse gas emissions, and supports the integration of renewable electricity in biomass conversion. Full article

Background/Objectives: Precision medicine has transformed how we manage cancer patients. As treatments and drug targets become more complex, the associated diagnostic technologies must also evolve to actualize the benefit of these therapeutic innovations. Digital and computational pathology (DP/CP) play a pivotal role in this evolution, offering enhanced analytical techniques and addressing workflow constraints in pathology labs. This study aims to understand clinicians’ awareness, utilization, and willingness to adopt DP/CP-based tools, as well as the role they perceive themselves playing in the adoption of CP-based tests. Methods: A double-blinded, online quantitative survey was conducted among 101 U.S.-based medical oncologists. Results: Awareness of DP/CP varied among clinicians, with only 17% identifying as very aware. Subsequently, the current utilization of CP-based tests is also low. Despite this, clinicians are optimistic about the potential benefits of DP/CP, including reduced turnaround times, improved therapy selection, and more consistent slide review. To achieve full adoption, clinicians recognize that barriers must be addressed, including cost, regulatory guidance and, to a lesser extent, concerns with the “black box” nature of CP algorithms. While the focus for the adoption of DP has centered on pathologists, clinicians anticipate playing a more significant role in the adoption of CP-based tests. Finally, clinicians demonstrated clear willingness to utilize a CP-based CDx, with 90% of respondents identifying as potential adopters. Conclusions: This study highlights a positive outlook for the adoption of DP/CP among clinicians, despite varied awareness and low current utilization. Clinicians recognize the potential benefits of DP/CP but also acknowledge barriers to adoption. Addressing these barriers through education, regulatory approval, and collaboration with pathologists and biopharma is essential for successfully integrating DP/CP technologies into clinical practice. Full article

This autoethnographic study examines my transformation as an educator teaching gender and sexuality to future helping professionals in South African higher education. Through systematic analysis of personal journals, teaching reflections, and pedagogical materials collected over 180 contact hours, I explore how innovative approaches can create collaborative learning environments in traditionally sensitive subject areas. Drawing on critical pedagogy, queer theory, and decolonizing methodologies, the research reveals three interconnected pedagogical innovations: structured vulnerability protocols that transcend traditional “safe space” models, progressive exposure pedagogy that challenges heteronormative assumptions by introducing diverse content early, and indigenous knowledge integration that positions students as knowledge co-creators. The findings demonstrate how my professional evolution from knowledge authority to learning facilitator enabled authentic engagement with diverse epistemologies while maintaining academic rigor. Students consistently contributed concepts absent from academic literature—from social media discourse about sexual identity hierarchies to traditional cultural practices—enriching collective understanding. This study addresses significant gaps in South African literature on tertiary-level sexuality education pedagogy, offering concrete strategies for implementing transformative approaches. The research contributes to autoethnographic scholarship by demonstrating how systematic reflection can generate theoretical insights about collaborative knowledge construction while acknowledging the ongoing challenges of teaching sensitive subjects within complex cultural contexts. Full article

Municipal solid waste (MSW) refers to solid and semi-solid waste generated during human production and daily activities. The process of incinerating such waste, known as municipal solid waste incineration (MSWI), serves as a critical method for reducing waste volume and recovering resources. Automatic online recognition of flame combustion status during MSWI is a key technical approach to ensuring system stability, addressing issues such as high pollution emissions, severe equipment wear, and low operational efficiency. However, when manually selecting optimized features and hyperparameters based on empirical experience, the MSWI flame combustion state recognition model suffers from high time consumption, strong dependency on expertise, and difficulty in adaptively obtaining optimal solutions. To address these challenges, this article proposes a method for constructing a flame combustion state recognition model optimized based on reinforcement learning (RL), long short-term memory (LSTM), and parallel differential evolution (PDE) algorithms, achieving collaborative optimization of deep features and model hyperparameters. First, the feature selection and hyperparameter optimization problem of the ViT-IDFC combustion state recognition model is transformed into an encoding design and optimization problem for the PDE algorithm. Then, the mutation and selection factors of the PDE algorithm are used as modeling inputs for LSTM, which predicts the optimal hyperparameters based on PDE outputs. Next, during the PDE-based optimization of the ViT-IDFC model, a policy gradient reinforcement learning method is applied to determine the parameters of the LSTM model. Finally, the optimized combustion state recognition model is obtained by identifying the feature selection parameters and hyperparameters of the ViT-IDFC model. Test results based on an industrial image dataset demonstrate that the proposed optimization algorithm improves the recognition performance of both left and right grate recognition models, with the left grate achieving a 0.51% increase in recognition accuracy and the right grate a 0.74% increase. Full article

Multi-Robot Task Assignment (MRTA) is a critical and inherently multi-objective problem in diverse real-world applications, demanding the simultaneous optimization of conflicting objectives such as minimizing total travel distance and balancing robot workload. Existing multi-objective evolutionary algorithms (MOEAs) often struggle with slow convergence and insufficient diversity when tackling the combinatorial complexity of large-scale MRTA instances. This paper introduces the Collaborative Swarm-Differential Evolution (CSDE) algorithm, a novel MOEA designed to overcome these limitations. CSDE’s core innovation lies in its deep, operator-level fusion of Differential Evolution’s (DE) robust global exploration capabilities with Particle Swarm Optimization’s (PSO) swift local exploitation prowess. This is achieved through a unique fused velocity update mechanism, enabling particles to dynamically benefit from their personal experience, collective swarm intelligence, and population diversity-driven knowledge transfer. Comprehensive experiments on various MRTA scenarios demonstrate that CSDE consistently achieves superior performance in terms of convergence, solution diversity, and Pareto front quality, significantly outperforming standard multi-objective algorithms like Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Differential Evolution (MODE), and Multi-Objective Genetic Algorithm (MOGA). This study highlights CSDE’s substantial contribution to the MRTA field and its potential for more effective and efficient multi-robot system deployment. Full article

Smart tourism destinations, shaped by the integration of tourism and information technology, have become a central theme in international academic research. This study employs bibliometric methods using CiteSpace to conduct co-authorship, co-citation, keyword co-occurrence, and burst analyses, with the aim of mapping the knowledge structure and research evolution of the field. Drawing on 232 articles from the Web of Science Core Collection (2013–2025), the results reveal a shift from technology-centered approaches toward themes of visitor experience, collaborative governance, and sustainable development. The Universitat d’Alacant (Spain) and The Hong Kong Polytechnic University (China) have emerged as leading research hubs, with Ivars-Baidal and colleagues as major contributors. Foundational studies by Buhalis and Gretzel continue to shape the domain. Keyword trends highlight increasing attention to technological efficiency and sustainable ethics. Overall, the study traces the developmental trajectory of smart tourism destinations, proposes a systematic knowledge framework, and identifies future directions for theoretical integration and methodological innovation. The findings provide both conceptual insights for academic research and strategic guidance for destination governance and policy. Full article

In the context of team collaborative tasks, continuous operational capability represents a crucial indicator of operational efficiency and a pivotal area of current research. A reduction in the continuous operational capability of team members will inevitably result in an increase in the error rate, which will prevent the completion of the task. In certain circumstances, this may even result in more severe consequences. To guarantee that team members possess optimal operational capabilities, it is imperative to conduct research on continuous operational capability prediction. This paper presents a Bayesian network-based continuous operational capability prediction model for team collaborative tasks. The model is developed based on the causal relationship of the continuous operational capability evolution, and through the improvement on the Bayesian network so that it can be suitable for individual personnel. The experimental verification demonstrates that the model produces accurate results and can be employed to predict the continuous operational capability and its changing trend. Full article

Artificial Intelligence (AI) has become deeply embedded in the advertising industry, presenting both opportunities and challenges. Understanding this transformation requires mapping the dyad’s structure and identifying the conditions that shape its evolution. This study applies a scientometric framework, integrating bibliometric network analysis with statistical modeling, to examine AI advertising as a knowledge ecosystem. By analyzing patterns of collaboration, thematic convergence, and structural centrality, we interpret how scholarly networks generate, connect, and diffuse ideas in ways that influence both academic and industry practices. The findings reveal that the field’s growth is underpinned by interconnected clusters of expertise with strategic opportunities emerging from interdisciplinary integration and global collaboration. Simultaneously, consolidating influence among a few dominant actors raises questions about diversity, access, and the balance between innovation and ethical responsibility. Statistical analyses conducted in SPSS Statistics version 29.0.2.0 further identify the bibliometric and structural factors that most predict citation impact, strengthening the study’s contribution to understanding how influence is built and sustained in AI-driven advertising research. Full article

Integrating human–robot collaboration (HRC) into construction sites has significantly enhanced efficiency and quality. However, it also introduces new or intensifies existing risks as it brings in new entities, relationships, and construction activities. Safety remains the top priority and a persistent concern in HRC systems. However, the current literature on human–robot collaboration safety (HRCS) is vast yet fragmented, and a systematic exploration of its status and research trends in the construction context is still lacking. This paper explores advances in HRCS over the past two decades through a mixed quantitative and qualitative analysis method. Initially, 287 related articles were identified by keyword-searching in Scopus, followed by bibliometric analysis using CiteSpace to uncover the knowledge structure and track emerging research trends. Subsequently, a qualitative discussion highlights achievements in HRCS across five dimensions: (1) optimization of remote intelligent machinery; (2) hazard analysis and risk assessment in HRCS; (3) digital twin for safety monitoring; (4) cognitive and psychological impacts; (5) organizational management perspective. This study quantitatively maps the scientific landscape of HRCS at a macro level and qualitatively identifies key research areas. It provides a comprehensive foundation for understanding the evolution of HRCS and exploring future research directions and applications. Full article

Critical energy infrastructures (CEIs) are fundamental pillars for economic and social development. However, their accelerated digitalization and the convergence between operational technologies (OTs) and information technologies (ITs) have increased their exposure to advanced cyber threats. This study examines the evolution of OT cybersecurity models with artificial intelligence in the energy sector between 2015 and 2024, through a systematic literature review following a four-phase method (planning, development, results, and analysis). To this end, we answer the following questions about the aspects of CEI cybersecurity models: What models exist? What energy services, technical means, and facilities do they encompass? And what algorithms do they include? From an initial set of 1195 articles, 52 studies were selected, which allowed us to identify 49 cybersecurity models classified into seven functional categories: detection, prediction and explanation; risk management; regulatory compliance; collaboration; response and recovery; architecture-based protection; and simulation. These models are related to 10 energy services, 6 technical means, 10 types of critical facilities, and 15 AI algorithms applied transversally. Furthermore, the integrated and systemic relationship of these study aspects has been identified in an IT-OT cybersecurity model for CEIs. The results show a transition from conventional approaches to solutions based on machine learning, deep learning, federated learning, and blockchain. Algorithms such as CNN, RNN, DRL, XAI, and FL are highlighted, which enhance proactive detection and operational resilience. A broader coverage is also observed, ranging from power plants to smart grids. Finally, five key challenges are identified: legacy OT environments, lack of interoperability, advanced threats, emerging IIoT and quantum computing risks, and low adoption of emerging technologies. Full article