Search Results (179)

Small and medium-sized enterprises (SMEs) continue to face significant cybersecurity challenges due to limited financial resources, technical capacity, and awareness. This study addresses these issues by pursuing four key objectives: (1) conducting a comprehensive assessment of cybersecurity knowledge and awareness within the SME sector through a systematic literature review, (2) evaluating the impact and effectiveness of cybersecurity awareness programs on SME behaviors and risk mitigation, (3) identifying core barriers—financial, technical, and organizational—that hinder effective cybersecurity adoption, and (4) introducing and validating the enhanced ROHAN model in conjunction with the Cyber Guardian Framework (CGF) to offer a scalable roadmap for cybersecurity resilience. Drawing on secondary data from Rawindaran (2023), the research highlights critical deficiencies in SME cybersecurity practices and emphasizes the need for tailored role-specific awareness initiatives. The enhanced ROHAN model addresses this need by delivering customized cybersecurity education based on industry sector, professional role, and educational background. Integrated with the CGF, the framework promotes structured, ongoing improvements across organizational, technological, and human domains. A mixed-methods approach was used, combining quantitative survey data from Welsh SMEs with qualitative interviews involving SME stakeholders. Advanced analytical techniques, including regression testing, Principal Component Analysis (PCA), and data visualization, were employed to uncover key insights and patterns. A distinctive feature of the ROHAN model is its integration of AI-powered tools for real-time risk assessment and decision-making, reflecting the principles of Industry 5.0. By aligning technological innovation with targeted education, this study presents a practical and adaptable cybersecurity framework for SMEs. The findings aim to bridge critical knowledge gaps and provide a foundation for a more resilient, cyber-aware SME sector in Wales and comparable regions. Full article

Adopting educational technology remains a critical challenge in developing countries, particularly given limited resources and the urgency of achieving the United Nations’ Sustainable Development Goal 4 by 2030. This paper aims to create and validate a model for educational technology adoption for developing countries (ETADC) that addresses the gaps in existing models by incorporating education-specific factors and local contexts. The ETADC model integrates foundational theories with local and educational elements within the technological pedagogical content knowledge (TPACK) framework, empowering educators to enhance teaching–learning experiences for a tech-driven world. The ETADC framework includes six components—four sourced from established theories and two based on research into the experiences of in-service and pre-service teachers in developing countries regarding educational technology adoption. These components formulate an appropriate model for evaluating, identifying, and implementing educational technologies within developing countries’ educational contexts. Validation through meta-analysis and two-stage structural equation modeling in R Studio version 4.4.0 with data from 30 high-impact studies (sample size N = 8934) confirmed the model’s effectiveness, showcasing a strong fit and significant path coefficient. This model has been used to evaluate certain educational technologies for further adoption. ETADC offers a practical and scalable roadmap for sustainable EdTech adoption, potentially supporting educational transformation and development worldwide, particularly in under-resourced contexts. Full article

Additive manufacturing (AM) has made rapid progress in most industries; however, the construction sector lags behind, despite substantial potential for growth. This study aims to evaluate recent innovations in AM, with a focus on multi-material additive manufacturing (MMAM), to identify transferable knowledge and technologies for the construction industry. A systematic Boolean search reviewing the Scopus and Web of Science databases identified 33 relevant articles out of 368 papers published in English over the last five years. Material properties, manufacturing processes, and design approaches were collectively identified as key interdisciplinary factors; these included thermal and mechanical property gradation techniques from materials science, multi-scale optimization approaches from engineering, and real-time monitoring systems from manufacturing, which are each transferable to architectural applications. Bibliometric analysis demonstrated growing research trajectories in AI-driven optimization methods and functionally graded materials that could bridge the implementation gap in construction. This article identifies significant knowledge gaps in scaling laboratory-proven MMAM techniques to architectural applications, including material interface challenges, environmental durability concerns, and the absence of design tools specific to building-scale components. We provide a critical roadmap for researchers that prioritizes the development of integrated optimization frameworks; multiscale modeling techniques; novel material combinations suitable for construction environments; and standardized protocol bases for Equipment Design, Process Control, Design Integration, Digital Tools, and Materials Research for evaluating the long-term performance and safety of MMAM building components. Full article

Hydrogen is a promising candidate for addressing environmental challenges in aviation, yet its use in structural validation tests for Wing Structure-Integrated high-pressure Hydrogen Tanks (SWITHs) remains underexplored. To the best of the authors’ knowledge, this study represents the first attempt to assess the feasibility of conducting such tests with hydrogen at aircraft scales. It first introduces hydrogen’s general properties, followed by a detailed exploration of the potential hazards associated with its use, substantiated by experimental and simulation results. Key factors triggering risks, such as ignition and detonation, are identified, and methods to mitigate these risks are presented. While the findings affirm that hydrogen can be used safely in aviation if responsibly managed, they caution against immediate large-scale experimental testing of SWITHs due to current knowledge and technology limitations. To address this, a roadmap with two long-term objectives is outlined as follows: first, enabling structural validation tests at scales equivalent to large aircraft for certification; second, advancing simulation techniques to complement and eventually reduce reliance on costly experiments while ensuring sufficient accuracy for SWITH certification. This roadmap begins with smaller-scale experimental and numerical studies as an initial step. Full article

This review synthesizes current knowledge on the transformative impacts of artificial intelligence (AI)—computational systems capable of performing tasks requiring human-like reasoning—on business innovation. It addresses the potential of AI to reshape strategies, operations, and value creation across various industries. Key themes include AI-driven business model innovation, human–AI collaboration, ethical governance, operational efficiency, customer experience personalization, organizational capability development, and adoption disparities. AI enables scalable product development, personalized service delivery, and data-driven strategic decisions. Successful implementations hinge on overcoming technical, cultural, and ethical barriers, with ethical AI adoption enhancing consumer trust and competitiveness, positioning responsible innovation as a strategic imperative. For practitioners, this review offers evidence-based frameworks for aligning AI with business objectives. For academics, it identifies research frontiers, including longitudinal impacts, context-specific roadmaps for small- and medium-sized enterprises, and sustainable innovation pathways. This review conceptualizes AI as a driver of systemic organizational transformation, requiring continuous learning, ethical foresight, and strategic ability for competitive advantage. Full article

Kidney disease poses a significant global health challenge, affecting millions and straining healthcare systems due to limited nephrology resources. This paper examines the transformative potential of Generative AI (GenAI), Large Language Models (LLMs), and Large Vision Models (LVMs) in addressing critical challenges in kidney care. GenAI supports research and early interventions through the generation of synthetic medical data. LLMs enhance clinical decision-making by analyzing medical texts and electronic health records, while LVMs improve diagnostic accuracy through advanced medical image analysis. Together, these technologies show promise for advancing patient education, risk stratification, disease diagnosis, and personalized treatment strategies. This paper highlights key advancements in GenAI, LLMs, and LVMs from 2018 to 2024, focusing on their applications in kidney care and presenting common use cases. It also discusses their limitations, including knowledge cutoffs, hallucinations, contextual understanding challenges, data representation biases, computational demands, and ethical concerns. By providing a comprehensive analysis, this paper outlines a roadmap for integrating these AI advancements into nephrology, emphasizing the need for further research and real-world validation to fully realize their transformative potential. Full article

Advances in single-cell multi-omics technologies have deepened our understanding of cancer biology by integrating genomic, transcriptomic, epigenomic, and proteomic data at single-cell resolution. These single-cell multi-omics technologies provide unprecedented insights into tumour heterogeneity, tumour microenvironment, and mechanisms of therapeutic resistance, enabling the development of precision medicine strategies. The emerging field of single-cell multi-omics in genomic medicine has improved patient outcomes. However, most clinical applications still depend on bulk genomic approaches, which fail to directly capture the genomic variations driving cellular heterogeneity. In this review, we explore the common single-cell multi-omics platforms and discuss key analytical steps for data integration. Furthermore, we highlight emerging knowledge in therapeutic resistance and immune evasion, and the potential of new therapeutic innovations informed by single-cell multi-omics. Finally, we discuss the future directions of the application of single-cell multi-omics technologies. By bridging the gap between technological advancements and clinical implementation, this review provides a roadmap for leveraging single-cell multi-omics to improve cancer treatment and patient outcomes. Full article

Historically, innovation has been a milestone in achieving sustainable agriculture for small-scale producers. For several centuries, innovation has improved agricultural activity. However, there is still the challenge of introducing technologies pertinent to the knowledge and practices of small producers to achieve sustainability. Therefore, the present study explores the traditional knowledge embedded in the activities of Planting–Harvest and First Disposal circuit (PHFDc) of beans (Phaseolus vulgaris L.) for its innovation involving the social, economic, and environmental context. Applying the methodology of roadmapping technology to 73 small-scale producers in Guanajuato, Mexico, combining the SDGs catalogue, in addition to statistical analysis, the results show access to government financial support; improving sales price, production, area, and profitability; having accessible tools; creating their inputs; in addition to having more excellent knowledge for plant care and advice as strategies to develop within economic sustainability. In this sense, based on the assertion that social and productive conditions are directly related to innovation, the proposal for reorientation is towards the creation of word credit, improving bean varieties, sustainable practices, mechanical seeders, bean corridors, and the connection with associations and institutes as the most pertinent ones that are developing in similar contexts. This research can be significant for small producers and the general population regarding food security, zero hunger, and the fight against climate change, as well as for researchers and politicians who support continuing new studies. Full article

Morinda citrifolia L. (M. citrifolia), commonly referred to as noni, a Polynesian medicinal plant with over 2000 years of traditional use, has garnered global interest for its rich repertoire of antioxidant phytochemicals, including flavonoids (kaempferol, rutin), iridoids (aucubin, asperulosidic acid, deacetylasperulosidic acid, asperuloside), polysaccharides (nonioside A), and coumarins (scopoletin). This comprehensive review synthesizes recent advances (2018–2023) on noni’s bioactive constituents, pharmacological properties, and molecular mechanisms, with a focus on its antioxidant potential. Systematic analyses reveal that noni-derived compounds exhibit potent free radical scavenging capacity (e.g., 2,2-Diphenyl-1-picrylhydrazyl/2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonicacid) (DPPH/ABTS) inhibition), upregulate endogenous antioxidant enzymes (Superoxide Dismutase (SOD), Catalase (CAT), Glutathione Peroxidase (GPx)), and modulate key pathways such as Nuclear factor erythroid 2-related factor 2/Kelch-like ECH-associated protein 1 (Nrf2/Keap1) and Nuclear Factor kappa-B (NF-κB). Notably, polysaccharides and iridoids demonstrate dual antioxidant and anti-inflammatory effects via gut microbiota regulation. This highlights the plant’s potential for innovation in the medical and pharmaceutical fields. However, it is also recognized that further research is needed to clarify its mechanisms of action and ensure its safety for widespread application. We emphasize the need for mechanistic studies to bridge traditional knowledge with modern applications, particularly in developing antioxidant-rich nutraceuticals and sustainable livestock feed additives. This review underscores noni’s role as a multi-target antioxidant agent and provides a roadmap for future research to optimize its health benefits. Full article

Shape-memory polymer nanocomposites (SMPNCs) have emerged as a transformative class of smart materials, combining the versatility of shape-memory polymers (SMPs) with the enhanced properties imparted by nanostructures. Integrating these nanofillers, this review explores the pivotal role of SMPNCs in addressing critical limitations of traditional SMPs, including low tensile strength, restricted actuation modes, and limited recovery stress. It comprehensively examines the integration of nanofillers, such as nanoparticles, nanotubes, and nanofibers, which augment mechanical robustness, thermal conductivity, and shape-recovery performance. It also consolidates foundational knowledge of SMPNCs, covering the principles of the shape-memory phenomenon, fabrication techniques, shape-recovery mechanisms, modeling approaches, and actuation methods, with an emphasis on the structural parameters of nanofillers and their interactions with polymer matrices. Additionally, the transformative real-world applications of SMPNCs are also highlighted, including their roles in minimally invasive medical devices, adaptive automotive systems, 4D printing, wearable electronics, and soft robotics. By providing a systematic overview of SMPNC development and applications, this review aims to serve as a comprehensive resource for scientists, engineers, and practitioners, offering a detailed roadmap for advancing smart materials and unlocking the vast potential of SMPNCs across various industries in the future. Full article

The relentless consumption of fossil fuels and soaring CO₂ emissions have plunged the world into an energy and environmental crisis. As society grapples with these challenges, the demand for clean, renewable, and sustainable energy solutions has never been more urgent. However, even though many efforts have been made in this field, there is still room for improvement concerning efficiency, material stability, and catalytic enhancement regarding kinetics and selectivity of photoelectrochemical (PEC) processes. Herein, we provide the experimental proof for the enhancement of the photocurrent efficiency by the critical focus on semiconductor–electrolyte interface (SEI) properties. By tailoring electrolyte composition, researchers can unlock significant improvements in catalytic efficiency and stability, paving the way for advanced PEC technologies. In this study, we investigate the influence of electrolyte composition on SEI properties and its impact on PEC performance. By employing electrolytes enriched with carbonates, borates, sulphates, and alkali cations, we demonstrate their profound role in optimising photoelectrochemical CO₂ reduction reaction (CO₂RR) efficiency. Central to this work is Cu₂O—an affordable, highly promising photocatalyst. While its potential is undeniable, Cu₂O’s inherent instability and diverse reduction products, ranging from CH₃OH to CO, HCOOH, CH₃COOH, and CH₃CH₂OH, have hindered its widespread adoption in PEC CO₂ reduction (CO₂RR). Our approach leverages a straightforward yet powerful electrodeposition method, enabling a deeper exploration of SEI dynamics during photocatalysis. Key parameters, such as carbonate concentration, local pH, alkali cation presence, anionic geometry, CO₂ solubility, and electrolyte conductivity, are systematically investigated. The findings reveal the formation of a unique “rigid layer” at the photocatalyst surface, driven by specific cation–anion interactions. This rigid layer plays a pivotal role in boosting PEC performance, offering a new perspective on optimising, among other PEC processes, CO₂RR catalytic efficiency. This profound study bridges a critical knowledge gap, shedding light on the dual influence of cations and anions on SEI properties and PEC CO₂RR. By unravelling these intricate interactions, we provide a roadmap for designing next-generation PEC systems. These insights pave the way for sustainable energy advancements, inspiring innovative strategies to tackle one of the most pressing challenges of our time. Full article

Fuzzy Cognitive Maps (FCMs) have emerged as powerful tools for addressing diverse engineering challenges, leveraging their cognitive nature and ability to encapsulate causal relationships. This paper provides a comprehensive review of FCM applications across 15 engineering sub-domains, categorizing 80 studies by their learning family, task type, and case-specific application. We analyze the methodological advancements and practical implementations of FCMs, showcasing their strengths in areas such as decision-making, classification, time-series, diagnosis, and optimization. Qualitative criteria are systematically applied to classify FCM-based methodologies, highlighting trends, practical implications of varying complexity, and human intervention across task types and learning families. However, this study also identifies key limitations, including scalability challenges, reliance on expert knowledge, and sensitivity to data distribution shifts in real-world settings. To address these issues, we outline key areas and directions for future research focusing on adaptive learning mechanisms, hybrid methodologies, and scalable computational frameworks to enhance FCM performance in dynamic and evolving contexts. The findings of this review offer a structured roadmap for advancing FCM methodologies and broadening their application scope in both contemporary and emerging engineering domains. Full article

As the global economy moves toward net-zero carbon emissions, large-scale energy storage becomes essential to tackle the seasonal nature of renewable sources. Underground hydrogen storage (UHS) offers a feasible solution by allowing surplus renewable energy to be transformed into hydrogen and stored in deep geological formations such as aquifers, salt caverns, or depleted reservoirs, making it available for use on demand. This study thoroughly evaluates UHS concepts, procedures, and challenges. This paper analyzes the most recent breakthroughs in UHS technology and identifies special conditions needed for its successful application, including site selection guidelines, technical and geological factors, and the significance of storage characteristics. The integrity of wells and caprock, which is important for safe and efficient storage, can be affected by the operating dynamics of the hydrogen cycle, notably the fluctuations in pressure and stress within storage formations. To evaluate its potential for broader adoption, we also examined economic elements such as cost-effectiveness and the technical practicality of large-scale storage. We also reviewed current UHS efforts and identified key knowledge gaps, primarily in the areas of hydrogen–rock interactions, geochemistry, gas migration control, microbial activities, and geomechanical stability. Resolving these technological challenges, regulatory frameworks, and environmental sustainability are essential to UHS’s long-term and extensive integration into the energy industry. This article provides a roadmap for UHS research and development, emphasizing the need for further research to fully realize the technology’s promise as a pillar of the hydrogen economy. Full article

This study delves into interdisciplinary research directions in human posture recognition, covering vision-based and non-vision-based methods. Visually analyzing 3066 core research papers published from 2011 to 2024 with CiteSpace software reveals knowledge structures, research topics, key documents, trends, and institutional contributions. In-depth citation analysis identified 1200 articles and five significant research clusters. Findings show that in recent years, deep learning and sensor-based methods have dominated, significantly improving recognition accuracy, like the deep learning-based posture recognition method achieving 99.7% verification set accuracy with a 20-ms delay in a controlled environment. Logarithmic growth analysis of annual publications, supported by logistic model fitting, indicates the field’s maturation since 2011, with a shift from early simple applications of traditional and deep learning algorithms to integrating interdisciplinary approaches for problem-solving as the field matures and a predicted decline in future breakthroughs. By integrating indicators like citation bursts, degree centrality, and sigma, the research identifies interdisciplinary trends and key innovation directions, showing a transition from traditional to deep learning and multi-sensor data fusion methods. The integration of biomechanics principles with engineering technologies highlights new research paths. Overall, this study offers a systematic overview to identify gaps, trends, and innovation directions, facilitating future research and providing a roadmap for innovation in human posture recognition. Full article

Digital twin (DT) is an emerging technology gaining traction across various industries. However, its development and application in the architecture, engineering, and construction (AEC) industry remain in their early stage, lagging considerably behind other sectors. This is primarily attributed to the challenges facing the AEC industry, including digital transformation and the lack of formal standards for DT implementation. This study aims to contribute to the conceptualization of DT planning—the early stage of the DT lifecycle—focusing on the road transportation sector, particularly road physical twin planning within the AEC industry. To achieve this, we reviewed the relevant literature defining DT planning. We also examined stakeholders’ relevant guidelines and documents from national bodies that roadmap the road DT planning process to understand the scope and identify knowledge gaps. Based on the findings, mapping the existing road planning process to the constituents of road DT planning was performed for the applicable planning steps. Finally, we proposed a five-layered road DT planning framework that will roadmap future implementations comprising data acquisition, data processing, data modeling and algorithms, data analysis and control, and a service layer plus users. In addition, a case study is incorporated to validate the feasibility of the framework toward applying it further in practice. Full article