Search Results (361)

The accurate prediction of significant wave height presents a complex yet vital challenge in the fields of ocean engineering. This capability is essential for disaster prevention, fostering sustainable development and deepening our understanding of various scientific phenomena. We explore the development of a comprehensive predictive methodology for wave height prediction by integrating novel Takagi–Sugeno–Kang fuzzy models within a multiresolution analysis framework. The multiresolution analysis emerges via wavelets, since they are prominent models characterised by their inherent multiresolution nature. The maximal overlap discrete wavelet transform is utilised to generate the detail and resolution components of the time series, resulting from this multiresolution analysis. The novelty of the proposed model lies on its hybrid training approach, which combines least squares with AdaBound, a gradient-based algorithm derived from the deep learning literature. Significant wave height prediction is studied as a time series problem, hence, the appropriate inputs to the model are selected by developing a surrogate-based wrapped algorithm. The developed wrapper-based algorithm, employs Bayesian optimisation to deliver a fast and accurate method for feature selection. In addition, we introduce a projection step, to further refine the approximation capabilities of the resulting predictive system. The proposed methodology is applied to a real-world time series pertaining to spectral wave height and obtained from the Poseidon operational oceanography system at the Institute of Oceanography, part of the Hellenic Center for Marine Research. Numerical studies showcase a high degree of approximation performance. The predictive scheme with the projection step yields a coefficient of determination of $0.9991$, indicating a high level of accuracy. Furthermore, it outperforms the second-best comparative model by approximately $\mathrm{49\%}$ in terms of root mean squared error. Comparative evaluations against powerful artificial intelligence models, using regression metrics and hypothesis test, underscore the effectiveness of the proposed methodology. Full article

Cutaneous and oral mucosal adverse events (AEs) are among the most common non-hematologic toxicities observed during breast cancer treatment. These complications arise across various therapeutic modalities including chemotherapy, targeted therapy, hormonal therapy, radiotherapy, and immunotherapy. Although often underrecognized compared with systemic side effects, dermatologic and mucosal toxicities can severely impact the patients’ quality of life, leading to psychosocial distress, pain, and reduced treatment adherence. In severe cases, these toxicities may necessitate dose reductions, treatment delays, or discontinuation, thereby compromising oncologic outcomes. The growing use of precision medicine and novel targeted agents has broadened the spectrum of AEs, with some therapies linked to distinct dermatologic syndromes and mucosal complications such as mucositis, xerostomia, and lichenoid reactions. Early detection, accurate classification, and timely multidisciplinary management are essential for mitigating these effects. This review provides a comprehensive synthesis of current knowledge on cutaneous and oral mucosal toxicities associated with modern breast cancer therapies. Particular attention is given to clinical presentation, underlying pathophysiology, incidence, and evidence-based prevention and management strategies. We also explore emerging approaches, including nanoparticle-based delivery systems and personalized interventions, which may reduce toxicity without compromising therapeutic efficacy. By emphasizing the integration of dermatologic and mucosal care, this review aims to support clinicians in preserving treatment adherence and enhancing the overall therapeutic experience in breast cancer patients. The novelty of this review lies in its dual focus on cutaneous and oral complications across all major therapeutic classes, including recent biologic and immunotherapeutic agents, and its emphasis on multidisciplinary, patient-centered strategies. Full article

This theoretical essay aims to analyze some of the socio-economic innovations introduced by Platform Capitalism Specifically, it focuses on two main aspects: first, the digital platform as a radical organizational innovation. Digital platforms represent a structural novelty in the market economy, signaling a new organization of production and labor. Second, the essay examines the role of platforms in directly generating value through the concept of “network value”. To this end, it explores the function of “business intelligence” as a strategic and competitive tool. Finally, the paper discusses the key issues associated with platform capitalism, which could threaten its social sustainability and contribute to economic and financial instability. These issues include the increasing commodification of everyday activities, the devaluation of paid labor in favor of free production driven by platform users (the so-called prosumers), and the emergence of proprietary and financial monopolies. Hence, digital platforms do not inherently ensure comprehensive social and environmental sustainability unless supported by targeted economic policy interventions. Conclusively, it is emphasized that defining robust social welfare frameworks—which account for emerging value creation processes—is imperative. Simultaneously, policymakers must incentivize the proliferation of cooperative platforms capable of fostering experimental circular economy models aligned with ecological sustainability. Full article

The transition to clean and renewable energy sources is critically dependent on efficient hydrogen production technologies. This review surveys recent advances in photocatalytic water splitting, focusing on MNb₂O₆ nanomaterials, which have emerged as promising photocatalysts due to their tunable band structures, chemical robustness, and tailored morphologies. The objectives of this work are to (i) encompass the current synthesis strategies for MNb₂O₆ compounds; (ii) assess their structural, electronic, and optical properties in relation to photocatalytic performance; and (iii) elucidate the mechanisms underpinning enhanced hydrogen evolution. Main data collection methods include a literature review of experimental studies reporting bandgap measurements, structural analyses, and hydrogen production metrics for various MNb₂O₆ compositions—especially those incorporating transition metals such as Mn, Cu, Ni, and Co. Novelty stems from systematically detailing the relationships between synthesis routes (hydrothermal, solvothermal, electrospinning, etc.), crystallographic features, conductivity type, and bandgap tuning in these materials, as well as by benchmarking their performance against more conventional photocatalyst systems. Key findings indicate that MnNb₂O₆, CuNb₂O₆, and certain engineered heterostructures (e.g., with g-C₃N₄ or TiO₂) display significant visible-light-driven hydrogen evolution, achieving hydrogen production rates up to 146 mmol h⁻¹ g⁻¹ in composite systems. The review spotlights trends in heterojunction design, defect engineering, co-catalyst integration, and the extension of light absorption into the visible range, all contributing to improved charge separation and catalytic longevity. However, significant challenges remain in realizing the full potential of the broader MNb₂O₆ family, particularly regarding efficiency, scalability, and long-term stability. The insights synthesized here serve as a guide for future experimental investigations and materials design, advancing the deployment of MNb₂O₆-based photocatalysts for large-scale, sustainable hydrogen production. Full article

An altermagnet, characterized by its distinctive magnetic properties, may hold potential applications in diverse fields such as magnetic materials, spintronics, data storage, and quantum computing. As a prototypical altermagnet, RuO₂ exhibits spin polarization and demonstrates the advantageous characteristics of high electrical conductivity and low thermal conductivity. These exceptional properties endow it with considerable promise in the emerging field of thermal spintronics. We studied the electronic structure and thermoelectric properties of RuO₂; the constructed RuO₂/TiO₂/RuO₂ all-antiferromagnetic tunnel junction (AFMTJ) exhibited thermally induced magnetoresistance (TIMR), reaching a maximum TIMR of 1756% at a temperature gradient of 5 K. Compared with prior studies on RuO₂-based antiferromagnetic tunnel junctions, the novelty of this work lies in the thermally induced magnetoresistance based on its superior thermoelectric properties. In parallel structures, the spin-down current dominates the transmission spectrum, whereas in antiparallel structures, the spin-up current governs the transmission spectrum, underscoring the spin-polarized thermal transport. In addition, thermoelectric efficiency emphasizes the potential of RuO₂ to link antiferromagnetic robustness with ferromagnetic spin functionality. These findings promote the development of efficient spintronic devices and spin-based storage technology for waste heat recovery and emphasize the role of spin splitting in zero-magnetization systems. Full article

Generalising deep-learning models to perform well on unseen data domains with minimal retraining remains a significant challenge in computer vision. Even when the target task—such as quantifying the number of elements in an image—stays the same, data quality, shape, or form variations can deviate from the training conditions, often necessitating manual intervention. As a real-world industry problem, we aim to automate stock level estimation in retail cabinets. As technology advances, new cabinet models with varying shapes emerge alongside new camera types. This evolving scenario poses a substantial obstacle to deploying long-term, scalable solutions. To surmount the challenge of generalising to new cabinet models and cameras with minimal amounts of sample images, this research introduces a new solution. This paper proposes a novel ensemble model that combines DenseNet-201 and Vision Transformer (ViT-B/8) architectures to achieve generalisation in stock-level classification. The novelty aspect of our solution comes from the fact that we combine a transformer with a DenseNet model in order to capture both the local, hierarchical details and the long-range dependencies within the images, improving generalisation accuracy with less data. Key contributions include (i) a novel DenseNet-201 + ViT-B/8 feature-level fusion, (ii) an adaptation workflow that needs only two images per class, (iii) a balanced layer-unfreezing schedule, (iv) a publicly described domain-shift benchmark, and (v) a 47 pp accuracy gain over four standard few-shot baselines. Our approach leverages fine-tuning techniques to adapt two pre-trained models to the new retail cabinets (i.e., standing or horizontal) and camera types using only two images per class. Experimental results demonstrate that our method achieves high accuracy rates of 91% on new cabinets with the same camera and 89% on new cabinets with different cameras, significantly outperforming standard few-shot learning methods. Full article

Technology roadmapping is conducted by systematic mapping of technological evolution through patent analytics to inform innovation strategies. This study proposes an integrated framework combining hierarchical Latent Dirichlet Allocation (LDA) modeling with multiphase technology lifecycle theory, analyzing 113,449 Derwent patent abstracts (2008–2022) across three dimensions: technological novelty, functional applications, and competitive advantages. By segmenting innovation stages via logistic growth curve modeling and optimizing topic extraction through perplexity validation, we constructed dynamic technology roadmaps to decode latent evolutionary patterns in AI-powered programmable manipulators (B25J classification) within an innovation trajectory. Key findings revealed: (1) a progressive transition from electromechanical actuation to sensor-integrated architectures, evidenced by 58% compound annual growth in embedded sensing patents; (2) application expansion from industrial automation (72% early stage patents) to precision medical operations, with surgical robotics growing 34% annually since 2018; and (3) continuous advancements in adaptive control algorithms, showing 2.7× growth in reinforcement learning implementations. The methodology integrates quantitative topic modeling (via pyLDAvis visualization and cosine similarity analysis) with qualitative lifecycle theory, addressing the limitations of conventional technology analysis methods by reconciling semantic granularity with temporal dynamics. The results identify core innovation trajectories—precision control, intelligent detection, and medical robotics—while highlighting emerging opportunities in autonomous navigation and human–robot collaboration. This framework provides empirically grounded strategic intelligence for R&D prioritization, cross-industry investment, and policy formulation in Industry 4.0. Full article

Sustainable Supply Chain Management (SSCM) emerges as a vital catalyst for inclusive growth and sustainable development, particularly in emerging economies where the manufacturing sector is central to economic progress. This study offers an in-depth analysis of the current research landscape on SSCM in the context of developing nations, outlining key theoretical frameworks and advocating for a solid conceptual foundation alongside a structured agenda for future research initiatives. This study employs a structured literature review technique to analyze 92 published articles indexed by Scopus from 2013 to 2024, revealing a burgeoning trend in the subject of global supply chains in developing nations. The analysis identifies key keywords such as “sustainable supply chain management,” “manufacturing industries,” “inclusive growth,” and “supply chain and sustainability,” and develops a conceptual model that elucidates how SSCM practices can be effectively integrated into manufacturing sectors to facilitate equitable growth and enhance business competitiveness. This work’s novelty lies in employing a systematic literature review to develop a holistic SSCM conceptual framework constructed upon six primary drivers: business model innovation, inclusive SSCM, corporate governance and leadership, technological and innovation capabilities, policy and regulatory environment, and circular feedback. This model addresses the ambiguity surrounding SSCM and inclusive growth, providing a robust foundation for future research and performance measurement. This study contributes to the field by providing a practical and theoretically grounded framework for researchers, policymakers, and practitioners seeking to implement impactful and effective SSCM initiatives in developing nations’ manufacturing sectors to promote inclusive growth and sustainable development. Full article

Prior research has focused on the technical and institutional challenges of blockchain adoption. However, little is known about how blockchain ventures claim categorical space in the market and how such domain positioning influences their visibility and evaluation. This study investigates the relationship between strategic domain positioning and market recognition among blockchain-based ventures, with a particular focus on applications relevant to e-commerce, such as non-fungible tokens (NFTs) and decentralized finance (DeFi). Drawing on research on categorization, legitimacy, and the technology lifecycle, we propose a domain lifecycle perspective that accounts for the evolving expectations and legitimacy criteria across blockchain domains. Using BERTopic, a transformer-based topic modeling method, we classify 9665 blockchain ventures based on their textual business descriptions. We then test the impact of domain positioning on market recognition—proxied by Crunchbase rank—while examining the moderating effects of external validation signals such as funding events, media attention, and organizational age. Our findings reveal that clear domain positioning significantly enhances market recognition, but the strength and direction of this effect vary by domain. Specifically, NFT ventures experience stronger recognition when young and less institutionally validated, suggesting a novelty premium, while DeFi ventures benefit more from conventional legitimacy signals. These results advance our understanding of how categorical dynamics operate in emerging digital ecosystems and offer practical insights for e-commerce platforms, investors, and entrepreneurs navigating blockchain-enabled innovation. Full article

The rapid advancement of materials science is driving the development of emerging advanced materials, such as nanomaterials, composites, biomaterials, and high-performance metals. These materials possess unique properties and offer significant potential for innovative applications across industries. Standardization plays a crucial role in ensuring the reliability, consistency, and comparability of material quality assessments. Although typical material specification standards, which rigidly define allowable characteristic ranges, are well-suited for established materials like steel, they may not be directly applicable to emerging advanced materials due to their novelty and evolving nature. To address this challenge, a distinct approach is required—flexible yet robust testing standards for assessing material quality. This paper introduces scenario-based methodologies, a structured approach to developing such standards, with a particular focus on metrological aspects of measurement methods and procedures. Additionally, self-assessment processes aimed at verifying measurement reliability are integrated into the methodology. These methodologies involve defining target materials and their applications, identifying critical material characteristics, specifying appropriate measurement methods and procedures, and promoting adaptable yet reliable guidelines. To maintain relevance with metrological advancements and evolving market demands, these quality testing standards should undergo periodic review and updates. This approach enhances industrial confidence and facilitates market integration. Full article

The construction of a fence in the sea made of bamboo sticks along the coastal areas of Tangerang, Indonesia, caused controversy and many public debates in most Indonesian media. The case is, however, not unique. It provides a means to pose three questions related to the following topics: (1) which controversies and contradictions between formal procedures and informal practices related to land and sea rights exist; (2) which values and perceptions of the involved stakeholders play a role in these controversies and contradictions; and (3) which kinds of boundary work or boundary objects could resolve these controversies and contradictions. The theoretical embedding for the subject lies in the theories of territory and space on the one hand and formal institutional models of land and sea on the other. The analytical model used to evaluate the controversies and contradictions is McKinsey’s 7S model, while the data used are extracted from journalistic public media reports and social media. The results show a significant discrepancy between the values connected to formal and informal territorial claims, as well as a lack of enforcement capacity to address this discrepancy. Instead, the policy response exhibits an excessive and uncontrolled discretionary space for all stakeholders to pursue their own interests. The theoretical novelty is that institutional models governing territorial sea and land rights, restrictions and responsibilities need to be aligned and connected based on detecting where and how the values of affected stakeholders can be harmonized, rather than enforcing a unilateral system of values of disconnected systems (of either land or sea). The policy implementation implications are to create stricter procedural steps when providing building permits in coastal areas, with better enforcement and stricter control. Soft governance campaigns should raise awareness of what is allowed and required for coastal building permits and reclamations. Additionally, there could be quicker, more thorough inspections of emerging or hidden practices of non-approved fencing and non-approved occupation of coastal land and sea. Full article

This paper introduces a meta-logical framework—based on the theory of institutions (a categorical version of abstract model theory)—to be used as a tool for the formalization of the two main views regarding the structure of scientific theories, namely the syntactic and the semantic views, as they have emerged from the relevant contemporary discussion. The formalization leads to a proof of the equivalence of the two views, which supports the claim that the two approaches are not really in tension. The proof is based on the Galois connection between classes of sentences and classes of models defined over some institution. First, the history of the syntactic–semantic debate is recalled and the theory of institutions formally introduced. Secondly, the notions of syntactic and semantic theories are formalized within the institution and their equivalence proved. Finally, the novelty of the proposed framework is highlighted with respect to existing formalizations. Full article

The valorization of agri-food waste has emerged as a global priority. In this context, fruit seed waste is being investigated for oil extraction due to its richness in bioactive compounds with remarkable health benefits. This review (2020–2025) focuses on the current state of eco-friendly extraction techniques for obtaining high-yield oils enriched with compounds such as tocopherols, polyphenols, fatty acids, phytosterols, and carotenoids. A comparison of the present method with conventional extraction techniques reveals several notable distinctions. Conventional methods are generally characterized by prolonged extraction times, elevated temperatures, and high amounts of solvents and/or energy. The findings of this review suggest that the extraction methodologies employed exerts a substantial influence on the yield and bioactive composition of the oil, which in turn affects its health-promoting properties. Furthermore, the results have demonstrated that alternative methodologies (microwave-assisted extraction, ultrasound-assisted extraction, pressurized liquid extraction, electric pulse extraction, enzyme-assisted extraction, subcritical extraction, and combinations thereof) have analogous oil yields in comparison with conventional methods. In addition, these oils present a superior bioactive profile with feasible potential in industrial and health applications. The novelty of this work lies in its emphasis on the valorization of fruit seed waste, as well as its sustainable approach. This sustainable approach utilizes experimental design strategies, the implementation of developments that employ comprehensive ecological metrics, and the latest trends in the application of artificial intelligence. Full article

Accurately controlling the mechanical properties of silicon is essential for developing high-performance micro-devices and systems. In this study, we investigate the influence of phosphorus doping on the elastic constants of silicon across a wide temperature range using a combination of tight-binding simulations and deformation potential theory. The mechanical properties were derived using Keyes’s framework integrated with Fermi–Dirac statistics. The Goodwin–Skinner–Pettifor functional form was applied to estimate dopant-induced stress potentials and their effect on lattice stiffness. In particular, we investigated the change in elastic constants and their temperature dependence under ultra-high doping concentrations. The results show a monotonic decrease in c₁₁ and a non-monotonic increase in c₁₂ with both temperature and doping, while c₄₄ remains relatively unaffected, consistent with experimental and theoretical studies. These changes are attributed to anisotropic carrier redistribution among conduction band valleys and strain-modulated interactions between valleys. The novelty of this work lies in the explicit, atomistically informed calculation of deformation potential constants using tight-binding parameters specific to phosphorus doping in silicon, enabling the accurate prediction of temperature-dependent elastic constants and anisotropic mechanical behaviour in emerging microsystem applications. Full article

This study proposes a forecast-aided energy management strategy tailored for industrial microgrids operating in Vietnam’s tropical climate. The core novelty lies in the implementation of a converter-based EMS that enables bidirectional DC power exchange between multiple subsystems. To improve forecast accuracy, an artificial neural network (ANN) is used to model the relationship between electric load and localized meteorological features, including temperature, dew point, humidity, and wind speed. The forecasted load data is then used to optimize charge/discharge schedules for energy storage systems (ESS) using a Particle Swarm Optimization (PSO) algorithm. The strategy is validated using real-site data from a Vietnamese industrial complex, where the proposed method demonstrates enhanced load prediction accuracy, cost-effective ESS operation, and multi-microgrid flexibility under weather variability. This integrated forecasting and control approach offers a scalable and climate-adaptive solution for EMS in emerging industrial regions. Full article