Search Results (7,648)

Blockchain technology (BCT) is often discussed as digital support for timber traceability in the context of the EU Deforestation-Free Products Regulation (EUDR), which requires operators and traders to demonstrate legality, deforestation-free production, geolocation at the forest parcel level, and verifiable supply chain documentation from 30 December 2026, with an extended timeline for micro, small, and medium-sized enterprises (SMEs) until 30 June 2027. This study assesses where BCT can realistically add value in timber supply chains under operational forestry conditions and identifies the necessary technical, organizational, and legal prerequisites. A combination of a targeted literature review and empirical input from experts in the forestry and timber industry, including guided expert web-conferencing interviews (n = 41), an online survey (n = 69 completed responses), and a transdisciplinary workshop (n = 18) was utilized to obtain a comprehensive overview of industry perspectives. Qualitative data from interviews and workshop sessions were analyzed using structured qualitative content analysis, while survey data were evaluated using descriptive statistics. The expected benefits are associated with the introduction of tamper-proof timber harvesting practices and cross-organizational verification mechanisms. To address the discrepancy between biological uncertainty and digital rigidity, the study proposes a dynamic allocation model adapted from the energy sector that distinguishes between fixed and variable wood capacities to automate logistical planning via smart contracts. However, respondents emphasize that practical obstacles, such as limited digital maturity in forestry, fragmented data infrastructures across the supply chain, and unresolved issues of data sovereignty hinder the implementation of BCT. BCT alone is unable to resolve the problem of weak physical-digital identity continuity, a phenomenon widely known as the oracle problem; however, coupling the ledger with physical or biological anchors (e.g., photo-optical, automated inkjet marking identification) can re-establish this physical–digital continuity and thereby resolve the oracle problem. The results demonstrate that blockchain acts most plausibly as a supporting component within hybrid traceability architectures that prioritize event-based authentication, off-chain data processing where appropriate, and integration with existing certification systems. The study highlights the necessity of defining distinct organizational roles and responsibilities while integrating user-centric digital solutions tailored specifically to small and medium-sized enterprises (SMEs). Full article

In the Anthropocene, dryland ecosystems—natural and semi-natural ecosystems—are highly sensitive to anthropogenic warming, and their carbon cycling dynamics are easily disrupted by this trend. In this perspective, we propose a novel yet long-overlooked conceptual framework. It reveals that the oxygen footprint, defined as the atmospheric oxygen consumption-to-production ratio, could serve as an important diagnostic indicator for assessing the impacts of anthropogenic warming on dryland carbon cycling. This framework can be divided into three parts, including the increasing oxygen footprint, warming effects on dryland carbon cycling, and direct or indirect links between oxygen footprint and carbon-cycle responses. In short, it centers on the core logical chain: oxygen footprint-anthropogenic warming-dryland carbon cycling. This work strives to enhance dryland sustainability by filling essential knowledge gaps, combining separate research findings, and providing actionable field guidance for ecosystem management. Full article

The viscosity peak phenomenon at the phase inversion point in crude oil emulsions can be understood through the lens of physical gelation. This study employs coarse-grained molecular dynamics (CG-MD) simulations to investigate the gel-like network structures formed at oil–water interfaces across varying water-to-oil particle-number ratios. We reveal that pure water forms a fully connected hydrogen bond network (500 molecules, 313.15 K, 2.68 H-bonds per molecule) behaving as a flexible physical gel scaffold, while pure oil exhibits a dispersed sol-like structure (35.9 clusters average). At the phase inversion point (50% water cut), the water network fragments into 44 gel-like clusters (193 network bonds) while oil forms 76 small clusters acting as physical crosslinking nodes embedded within the water network voids. This creates an interlocked gel structure with a maximum Interlocking Index (LI_CG = 36.67), directly corresponding to the viscosity peak. At 30% water cut, a W/O morphology with (LI_CG = 22.17) represents a weaker gel state. We demonstrate that gel rigidity rather than network existence determines macroscopic viscosity, with LI serving as an effective crosslinking density metric. Model parameters calibrated via differential evolution optimization against experimental data from three oil wells yield R² = 0.94. This work provides a molecular mechanism revealing the flexible-network-to-rigid-gel transition as the origin of emulsion viscosity peaks, offering a gel-science perspective on emulsion rheology control in petroleum engineering. Full article

Functional near-infrared spectroscopy (fNIRS) has become an important signal source in motor imagery (MI) brain–computer interface research due to its non-invasive nature and high application flexibility. However, fNIRS signals exhibit significant inter-subject variability, complementary information from oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR), and complex spatiotemporal dynamics, making their efficient and robust classification challenging. To address these issues, this paper proposes a Dual-Stream Gated Fusion Network (DSGF-Net). This model employs a dual-branch architecture to perform complementary feature modeling of fNIRS signals: one branch focuses on extracting multi-scale temporal dynamic features, while the other learns the spatial distribution of hemodynamic features across channels, thereby effectively characterizing the signals from different perspectives. Upon this foundation, a gated fusion mechanism was designed to adaptively adjust the importance of different feature dimensions after the fusion of the two feature streams, thereby enhancing the discriminative power of the fused representation. On two public datasets, MI and UFFT, experimental results based on leave-one-subject-out (LOSO) cross-validation show that the proposed method achieves competitive performance across metrics such as classification accuracy, F1-score, and Kappa coefficient. Furthermore, a comparative analysis of performance under different network component configurations validates the contributions of the dual-branch structure and the gated fusion mechanism to performance improvements. Furthermore, complexity analysis results show that DSGF-Net achieves superior classification performance while maintaining a relatively small parameter size, striking a good balance between performance and computational complexity. DSGF-Net provides an effective, lightweight deep learning framework for offline fNIRS-based motor task classification, with potential applications in cross-subject BCI systems and brain signal decoding. Full article

Immunonutrition continues to generate heterogeneous and often contradictory clinical outcomes, suggesting that nutrients do not exert fixed immunological effects but interact with the biological context in which they operate. Sarcopenic obesity (SO) represents a paradigmatic clinical model of this complexity, where chronic low-grade inflammation, mitochondrial dysfunction, anabolic resistance, metabolic inflexibility, and microbiota remodeling converge to compromise the adaptive capacity of integrated immunometabolic networks. We propose that this condition may be interpreted as a state of impaired immunometabolic plasticity, which may help explain the context-dependent variability of nutritional responses. Within this perspective, micronutrients are viewed not simply as cofactors supporting immune competence but as dynamic regulators of interconnected immunometabolic pathways. Particular attention is devoted to vitamin D and resveratrol, presented as complementary regulators of immunometabolic plasticity. Within the proposed framework, vitamin D may contribute to immunometabolic competence, whereas resveratrol may act as a broader signaling modulator through the SIRT1/AMPK–PGC-1α axis, influencing mitochondrial function, inflammatory tone, metabolic flexibility, and epigenetic adaptation. Beyond isolated compounds, bioactive-rich food matrices, exemplified by Opuntia ficus-indica, are discussed as examples of systems-level modulators capable of coordinating inflammatory, metabolic, redox, and microbiota-dependent biological circuitry. This review introduces immunometabolic plasticity as a conceptual framework linking nutritional signals to the coordinated regulation of immune and metabolic adaptation across diverse biological contexts. Finally, we discuss how biomarker-guided phenotyping, multi-omics integration, and context-aware nutritional interventions may provide a foundation for precision immunonutrition, shifting the field from generalized supplementation strategies toward restoration of adaptive immunometabolic resilience. Full article

The availability of environmental indicators at the NUTS-2 level remains limited in European statistical sources, often resulting in regional sustainability analyses that reflect short-term policy dynamics rather than long-term conditions. To address this gap, this paper proposes a parsimonious framework based on a spatial stock indicator measuring the share of artificial land within each region (ENV_ARTIFICIAL_LAND), derived from CORINE Land Cover data and aggregated at the NUTS-2 level. Rather than serving as a short-term metric of policy performance, the indicator describes an inherited territorial stock reflecting historical land-use trajectories, consistent with path-dependent development processes. Using the Italian NUTS-2 regions as a case study, the indicator is analysed alongside key socio-economic variables covering economic capacity, social vulnerability, and human capital formation through a non-aggregative, quadrant-based trade-off framework. The results suggest pronounced regional asymmetries and structural mismatches, demonstrating that territorial rigidities and socio-economic outcomes follow differentiated, non-linear alignments. From a policy perspective, the analysis highlights the limits of uniform benchmarking and underscores the necessity of place-based strategies tailored to inherited spatial constraints. Future developments will include integration into territorialised lifecycle frameworks, to account for the cumulative effects of land occupation and environmental debt. While this framework offers a transparent screening tool for regional spatial rigidity, its convergent validity against high-resolution spatial datasets (such as HRL Imperviousness) and disaggregated land-use subclasses remains to be formally tested in future empirical research. Full article

Offshore wind power has expanded rapidly in recent decades and is expected to continue growing through the deployment of larger turbines in deeper waters. The safe and stable operation of offshore wind turbines (OWTs) depends critically on foundation performance, which is severely threatened by seabed scour. To better understand these threats, this paper reviews recent studies on the effects of seabed scour on the structural performance and safety of fixed-bottom offshore wind turbine foundations (OWTFs), focusing on four aspects: (1) the changes induced by scour in both the seabed morphology and the soil mechanical state, which form the basis for the subsequent analyses; (2) the effects of scour on the bearing capacity and natural frequency of the foundation, covering both its static and dynamic performance; (3) the behavior of scoured foundations under long-term cyclic loading during normal operation and under transient seismic action during extreme events; and (4) the role of scour protection in enhancing structural safety. Several future research directions are also highlighted. This review offers helpful insights for assessing scour-induced risk and for designing scour protection from a structural safety perspective, thereby supporting the safe operation of OWTs. Full article

Continual learning has made significant progress toward enabling adaptive machine learning under evolving environments, yet real-world deployment increasingly exposes systems to persistent harsh conditions, including distributional shifts, feature evolution, delayed or scarce supervision, imbalance, noise, and recurring or novel classes. While prior research has largely addressed these challenges in isolation, growing environmental complexity motivates a broader rethinking of continual adaptation as a self-regulating process rather than solely a parameter update problem. Building upon the emerging framework of Self-Adaptive Learning (SAL), this perspective explores how learning systems may progress beyond reactive adaptation toward autonomous recognition, policy selection, and context-sensitive regulation of learning behavior under persistent uncertainty. Rather than proposing a specific algorithmic solution, we position SAL as a conceptual systems framework for organizing future research on resilient, long-lived machine learning systems. We discuss key implications for deployment robustness, evaluation, safety, and adaptive governance, while outlining major open challenges in developing practical self-regulating learners. By strengthening SAL as a forward-looking framework, this work aims to advance the broader conversation on machine learning systems capable of sustained autonomy in dynamic real-world environments. Full article

Acute pancreatitis is a common and heterogeneous inflammatory disorder whose clinical course ranges from a self-limited illness to persistent organ failure, infected pancreatic necrosis, and prolonged critical illness. Contemporary management has moved away from protocolised aggressive fluid loading, prolonged fasting, prophylactic antibiotics, and early open necrosectomy. Instead, current care emphasises repeated physiological assessment, moderate goal-directed resuscitation, early enteral or oral nutrition, organ-specific support, antimicrobial stewardship, and delayed minimally invasive intervention within a multidisciplinary step-up strategy. This narrative review examines acute pancreatitis from an intensive care perspective. Particular attention is given to early risk stratification, intensive care unit triage, haemodynamic and respiratory support, acute kidney injury, intra-abdominal hypertension, nutrition, biliary source control, diagnosis and treatment of infected necrosis, and the timing and selection of endoscopic, radiological, and surgical interventions. The implications of obesity, pregnancy, advanced age, and multimorbidity are also discussed. Recent randomised trials have clarified several clinically important questions: aggressive hydration increases fluid overload without improving outcomes; routine urgent endoscopic retrograde cholangiopancreatography is not beneficial in predicted severe biliary pancreatitis without cholangitis; postponed drainage may avoid invasive intervention in a substantial proportion of patients with infected necrosis; and endoscopic or minimally invasive approaches reduce treatment burden compared with primary open surgery. Persistent organ failure remains the principal determinant of mortality, while infected necrosis further increases risk and complexity. Future progress will depend on dynamic prediction models, biomarker-guided antimicrobial decisions, personalised haemodynamic strategies, phenotype-directed immunomodulation, and regionalised multidisciplinary care. Full article

Congenital diaphragmatic hernia (CDH) is increasingly recognized as a dynamic cardiopulmonary disease in which pulmonary hypoplasia, pulmonary hypertension, and cardiac dysfunction interact to shape clinical instability, therapeutic response, and outcomes. Hemodynamic phenotyping has emerged as a strategy to move beyond binary classification of pulmonary hypertension and toward physiology-directed care. Early single-center experiences suggest potential clinical utility of echocardiography-guided, phenotype-directed management; however, external validation remains limited. Further important challenges remain, including technical and institutional barriers to timely echocardiography, limitations of static single-time-point assessments, uncertainty regarding what exactly defines each phenotype, and incomplete understanding of the impact of time and therapies on phenotype presentations. In this perspective, we summarize the evolution of heart-focused care in CDH, describe current hemodynamic phenotyping approaches, examine unresolved questions in phenotype classification and implementation, and outline future research priorities needed to advance dynamic, mechanism-based precision cardiopulmonary care for infants with CDH. Full article

Carbon inclusivity (CI) mechanisms face weak low-carbon adoption despite strong consumer intention in China. Prior studies explain low-carbon behavior mainly from individual psychological perspectives yet rarely investigate the intention–behavior gap in the digital–physical scenarios of CI mechanisms. Furthermore, single theoretical lenses such as the Motivation–Opportunity–Ability (MOA) framework or Social Practice Theory (SPT) have clear limitations in interpreting this gap. This study integrates the MOA framework with SPT to construct a novel “Motivation–Intention–Context–Practice–Behavior” (MICPB) model, framing behavioral change as a dynamic interplay of internal motivation, external opportunities, and evolving social practices. Different from studies adopting MOA or SPT in isolation, the MICPB model bridges individual psychological processes and routine social practices to unpack the intention–behavior gap. Adopting grounded theory to analyze 42 interviews from China’s CI pilot regions, we identify three core dimensions of the gap: limited internal competencies and emotional conflicts, fragmented external incentives and platform defects, and misalignment between low-carbon values and daily routines. The MICPB model reveals that the gap stems from misalignment across motivation, context and practice, and enriches MOA theory by incorporating practice-based dynamics. This integrated framework deepens understandings of barriers to consumer low-carbon participation under CI schemes. This study suggests coordinated policy and corporate efforts to restructure social practices, bridge the intention–behavior gap, and align digital governance with low-carbon transitions. Full article

From the perspective of coordinating development and security for high-quality development in border regions, this paper focuses on the adaptation relationship between development and security functions in the Yunnan–Guangxi border region. The study aims to characterize the spatiotemporal evolution and spatial differentiation of these two functions and identify distinct types of functional adaptation and their spatial characteristics. Based on the development–security linkage perspective, a regional functional evaluation system incorporating specialized indicators, such as border trade development and national territorial security, is constructed. The CRITIC method, a functional adaptation model, and spatial statistical methods are then employed to examine the spatiotemporal evolution of the development and security functions and their adaptation in the Yunnan–Guangxi border region from 2002 to 2022, followed by a classification of regional functional adaptation types. By integrating development and security functions into a unified analytical framework, this study extends the research perspective on regional functions and the development–security relationship in border areas and provides a reference for coordinating development and security and implementing differentiated spatial governance. The results indicate the following: ① The overall regional functional level of the Yunnan–Guangxi border region increased steadily, although the growth was characterized by distinct stages, with rapid growth in the early period followed by stabilization in the later period. Regional disparities first narrowed and then widened. Both the development and security functions increased from low to medium or high levels. Specifically, high-value areas of the development function were concentrated in inland hub cities, whereas those of the security function were distributed along the border. ② The overall adaptation between the development and security functions was favorable, with the adaptation index increasing slightly. The adaptation level in the Guangxi border region was higher than that in Yunnan, while the spatial distribution of adaptation types shifted from relative clustering to dispersion. ③ The county-level administrative units in the Yunnan–Guangxi border region were classified into five functional adaptation types. During the study period, the transitional upgrading type accounted for the largest proportion, and the overall spatial pattern was characterized by “advancement along the border while maintaining stability in the hinterland.” Therefore, differentiated guidance and governance strategies should be implemented according to the characteristics of each functional adaptation type to promote the dynamic coordination between development and security functions, thereby supporting spatial governance and functional improvement in border regions. Full article

Artificial intelligence (AI)-based digital twins (DTs) are emerging as a groundbreaking paradigm in rehabilitation and physiotherapy, enabling the creation of dynamic virtual representations of patients for continuous monitoring, prognostic assessment and personalised therapeutic interventions. This article presents a global, interdisciplinary review of AI-based DT technologies in rehabilitation settings utilising the Internet of Medical Things (IoMT), with particular emphasis on the integration of wearable and implantable sensor systems in next-generation wireless healthcare applications. The article analyses how multimodal wearable sensors, implantable devices and smart wireless communication networks can support the acquisition of real-time biomechanical and physiological data for adaptive rehabilitation. By combining perspectives from biomedical engineering, physiotherapy, computational intelligence and wireless healthcare systems, this article highlights the emerging opportunities and challenges associated with the creation of scalable digital twin ecosystems for precision rehabilitation. The proposed vision contributes to the development of smart, connected and personalized rehabilitation infrastructures, in line with future paradigms of healthcare and wireless communication. Full article

The maneuvering flight of future aircraft which employ morphing variable camber wings (VCWs) requires rapid and accurate aerodynamic regulation. However, the dynamic lift responses during fast morphing deflection exhibit unsteady hysteresis effects, hindering shape and flight control performance. This study proposes a novel modeling and feedforward compensation algorithm based on the Prandtl–Ishlinskii (PI) model to identify and mitigate such unsteady hysteresis effects from a control perspective. First, unsteady lift responses of a two-dimensional trailing-edge VCW under periodic and non-periodic morphing motions are analyzed, and the influences of morphing trajectories on lift characteristics are investigated. The results reveal that the maximum lift decreases significantly as the morphing frequency increases. Under point-to-point non-periodic morphing conditions, pronounced hysteretic lift responses are observed and are strongly influenced by the morphing trajectories. A forward model mapping “morphing trajectory-lift response” is developed using PI hysteresis operators and log(t)-creep operators, identified using time-domain data from two-dimensional computational fluid dynamics (CFD) calculations. From this, an inverse model of the “expected lift response-compensated morphing trajectory” is derived using a hysteresis compensation function. Simulations indicate that periodic lift hysteresis is effectively compensated, yielding a quasi-steady linear relationship. For fast terminal morphing, compensated trajectories enable lift to reach targets rapidly, smoothly, and stably without lag. Robustness is validated for varying lift targets and terminal times. This work offers new insights into fast morphing-wing and high-maneuverability control of future smart aircraft. Full article

In this paper, we investigate a simulation-based optimization framework that implements discrete-event simulation with evolutionary search methods to optimize stochastic manufacturing systems efficiently. The proposed methodology couples a Tecnomatix Plant Simulation model with a MATLAB R2025b-based optimization environment using a data exchange interface, allowing for the iterative assessment of complex manufacturing systems. The study examines an adaptive replication strategy designed to manage stochastic variability in simulation outcomes. In the proposed method, the required number of simulation runs are determined dynamically based on confidence interval estimation. The stopping criterion is specified using a 95% confidence interval, ensuring adequate statistical accuracy while decreasing excess computational effort. The framework allows multiple performance indicators, such as throughput, congestion levels, and machine failures, which are built into an objective function. The optimization is driven by a (1, λ)-evolution strategy with Gaussian mutation and adaptive step-size control, allowing robust search in noisy objective function. However, thanks to the framework presented, it is also possible to apply other optimization algorithms. A case study of a manufacturing system was built and modeled in Tecnomatix Plant Simulation to validate the proposed methodology. In comparison with the baseline production configuration in one of the simulation runs, the suggested framework reduced the objective function by 43.36%. Benchmark experiments demonstrated that the adaptive replication strategy achieved a solution quality comparable to fixed replication schemes while requiring fewer simulation evaluations on average, thereby reducing the computational effort without compromising statistical reliability. The benchmark comparison showed that the adaptive replication strategy improved the objective value by up to 17.20% compared with fixed-replication strategies while requiring substantially less computational time than the fixed-20 and fixed-30 strategies. The robustness analysis further demonstrates that the adaptive replication strategy produces consistent optimization results across independent runs despite the stochastic nature of both the simulation model and the optimization process. From an industrial perspective, the proposed framework provides a practical decision-support tool for the optimization of manufacturing systems under uncertainty, enabling more reliable parameter tuning with reduced computational effort and facilitating the implementation of digital twin technologies. Full article