Search Results (2,657)

Material degradation is a critical factor in assessing the vulnerability of industrial infrastructure, particularly in the presence of extreme natural events. This study shows that the relation between material degradation and NaTech risk is both bidirectional and systemic, with important implications for industrial safety. Through the analysis of an emblematic case study, it was demonstrated that latent defects, originating during the construction phase, can remain silent for decades and manifest critically under the action of extreme natural events. The objective is to provide a useful methodological tool for the early diagnosis of systemic risk conditions and for planning preventive and resilient strategies. The proposed approach overcomes the traditional separation between degradation analysis and environmental risk assessment, promoting a holistic and adaptive view of vulnerability. Specifically, integrating the concept of structural obsolescence into NaTech risk models allows for a more realistic representation of systemic exposure and supports the planning of more effective prevention strategies. The case study analysis highlights the interaction between latent structural defects and environmental stresses, offering insights for interpreting vulnerability in complex and multifactorial scenarios. The outcome provides perspectives for the integration of quantitative indicators into NaTech risk models. Full article

Flavonoids, a ubiquitous class of plant secondary metabolites, are increasingly pivotal as chemical markers for ensuring the quality, safety, and efficacy of herbal medicines (HMs). Their broad distribution, biological activities, and detectability make them ideal for this role. This comprehensive review critically examines current trends and analytical perspectives regarding flavonoids in HM quality control. We first explore advanced quality control strategies that move beyond single-compound quantification, including chemical fingerprinting, metabolomics, network pharmacology, and the innovative concept of Q-markers. The review then provides an in-depth analysis of the analytical techniques central to flavonoid analysis, from the routine use of HPTLC and HPLC-UV to advanced hyphenated systems like UHPLC-QTOF-MS, highlighting their applications in authentication, standardization, and adulteration detection. Furthermore, we emphasize the growing importance of modern data analysis workflows, particularly the integration of chemometrics and molecular networking, for interpreting complex datasets and identifying robust, bioactivity-relevant markers. By synthesizing recent research (2017–2024), this work underscores a paradigm shift towards holistic, multi-marker approaches and data-driven methodologies. It concludes that the synergistic application of advanced analytical techniques with sophisticated data modeling is essential for the future of HM quality control, ensuring reliable and standardized herbal products for global consumers. Full article

Futures Literacy, as defined and promoted by UNESCO, is the capability to imagine, question, and use the future as a resource for better understanding the present and acting with intention. When applied to Cultural Heritage Education, it reframes heritage from a static object of preservation into a dynamic anticipatory system that evolves through dialogue between past, present, and future. This integrative approach enables learners and communities to strengthen what can be called cultural adaptive capacity, understood as the ability to ensure continuity of identity and traditions, to promote responsive innovation in the face of change, and to transmit heritage knowledge across generations. This entry situates Futures Literacy within a wider theoretical framework that includes complexity theory, anticipatory systems, and sustainability education. It emphasizes that heritage education must increasingly address uncertainty, diversity of perspectives, and interconnected challenges such as globalization, climate change, and cultural transformations. UNESCO Futures Literacy Laboratories conducted in different regions of the world, as well as ICCROM’s foresight initiatives, provide concrete examples of how anticipatory competences can be fostered in varied cultural contexts, demonstrating both universal patterns and context-specific adaptations. By embedding Futures Literacy into heritage education, cultural heritage becomes a living resource for nurturing resilience, global citizenship, and creativity. It allows communities not only to preserve their legacy but also to reimagine it as a driver of innovation and inclusion. Ultimately, this perspective highlights the potential of education to enhance cultural sustainability, foster intergenerational solidarity, and cultivate temporal justice, preparing societies to face the uncertainties of the future with confidence and responsibility. Full article

This paper develops an information-theoretic reliability inference framework for consecutive r-out-of-n:G systems by employing the concept of residual extropy, a dual measure to entropy. Explicit analytical representations are established in tractable cases, while novel bounds are derived for more complex lifetime models, providing effective tools when closed-form expressions are unavailable. Preservation properties under classical stochastic orders and aging notions are examined, together with monotonicity and characterization results that offer deeper insights into system uncertainty. A conditional formulation, in which all components are assumed operational at a given time, is also investigated, yielding new theoretical findings. From an inferential perspective, we propose a maximum likelihood estimator of residual extropy under exponential lifetimes, supported by simulation studies and real-world reliability data. These contributions highlight residual extropy as a powerful information-theoretic tool for modeling, estimation, and decision-making in multicomponent reliability systems, thereby aligning with the objectives of statistical inference through entropy-like measures. Full article

The transition to 5G networks brings unprecedented speed, ultra-low latency, and massive connectivity. Nevertheless, it introduces complex traffic patterns and broader attack surfaces that render traditional intrusion detection systems (IDSs) ineffective. Existing rule-based methods and classical machine learning approaches struggle to capture the temporal and dynamic characteristics of 5G traffic, while many deep learning models lack interpretability, making them unsuitable for high-stakes security environments. To address these challenges, we propose Bidirectional Temporal Anomaly Detector (BiTAD), a deep temporal learning architecture for anomaly detection in 5G networks. BiTAD leverages dual-direction temporal sequence modelling with attention to encode both past and future dependencies while focusing on critical segments within network sequences. Like many deep models, BiTAD’s faces interpretability challenges. To resolve its “black-box” nature, a dual-perspective explainability module, coined TwinLens, is proposed. This module integrates SHAP and TimeSHAP to provide global feature attribution and temporal relevance, delivering dual-perspective interpretability. Evaluated on the public 5G-NIDD dataset, BiTAD demonstrates superior detection performance compared to existing models. TwinLens enables transparent insights by identifying which features and when they were most influential to anomaly predictions. By jointly addressing the limitations in temporal modelling and interpretability, our work contributes a practical IDS framework tailored to the demands of next-generation mobile networks. Full article

This study introduces a novel approach for analyzing theoretical Raman spectra, designed to facilitate spectral interpretation, particularly for complex systems such as functional mesoporous silica-based thin films. The proposed methodology relies on spectral decomposition supported by theoretical calculations, representing a step toward the development of autonomous research laboratories. The method assigns vibrational shifts to individual atoms within a molecular model and uses this information to generate partial spectra corresponding to specific atomic groupings. Unlike separate calculations for isolated components, this approach preserves the mutual interactions within the entire molecular structure, providing a more accurate representation of the vibrational environment. Decomposing the theoretical spectrum into contributions from atomic groups significantly simplifies the assignment of Raman bands to specific structural units, thereby enhancing the interpretative power of theoretical spectra and their correlation with experimental data. The method was demonstrated using real Raman spectroscopic data obtained from mesoporous SBA-15 silica thin films containing copper phosphonate groups. This work also highlights the critical role of molecular modeling and DFT calculations in Raman spectral analysis and outlines future perspectives for the use of artificial intelligence to automate and optimize the spectral interpretation process. Full article

Large-scale production systems (LSPS) operate under growing complexity driven by digital transformation, tighter environmental regulations, and the demand for resilient and resource-efficient operation. Conventional control strategies, particularly PID and isodromic regulators, remain dominant in industrial automation due to their simplicity and robustness; however, their capability to achieve near-optimal performance is limited under constraints on control amplitude, rate, and energy consumption. This study develops an analytical–computational approach for the approximate realization of optimal nonlinear control using standard regulator architectures. The method determines switching moments analytically and incorporates practical feasibility conditions that account for nonlinearities, measurement noise, and actuator limitations. A comprehensive robustness analysis and simulation-based validation were conducted across four representative industrial scenarios—energy, chemical, logistics, and metallurgy. The results show that the proposed control strategy reduces transient duration by up to 20%, decreases overshoot by a factor of three, and lowers transient energy losses by 5–8% compared with baseline configurations, while maintaining bounded-input–bounded-output (BIBO) stability under parameter uncertainty and external disturbances. The framework provides a clear implementation pathway combining analytical tuning with observer-based derivative estimation, ensuring applicability in real industrial environments without requiring complex computational infrastructure. From a broader sustainability perspective, the proposed method contributes to the reliability, energy efficiency, and longevity of industrial systems. By reducing transient energy demand and mechanical wear, it supports sustainable production practices consistent with the following United Nations Sustainable Development Goals—SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production). The presented results confirm both the theoretical soundness and practical feasibility of the approach, while experimental validation on physical setups is identified as a promising direction for future research. Full article

Since the earliest investigations into the impact of sleep-related breathing disorders on cardiovascular risk, the association between sleep–wake disorders and major cerebrovascular events has been increasingly and robustly established. Recent international joint statements—endorsed by leading scientific societies (e.g., American Heart Association, American Stroke Association, European Academy of Neurology, European Stroke Organization, European Sleep Research Society, and European Respiratory Society)—represent a milestone in stroke prevention and rehabilitation by formally recognizing sleep disorders as both risk factors for ischemic stroke and determinants of poor short- and long-term outcomes. Nevertheless, despite these strong epidemiological and mechanistic associations, the therapeutic evidence supporting sleep–wake interventions (e.g., positive airway pressure therapy, GABA-receptor agonists, melatonin) for stroke prevention remains limited and requires further validation through well-designed clinical trials. In this perspective article, we review recent advances in understanding the bidirectional relationship between sleep disorders and stroke, discuss the proposed pathophysiological mechanisms underpinning this complex interplay—with particular emphasis on arousal-related activation of the autonomic nervous system—and provide a critical appraisal of current research directions and future perspectives. Finally, we underscore the need for closer collaboration between sleep and stroke specialists to bridge existing knowledge gaps and optimize patient care. Full article

Improving building energy efficiency has become essential for reducing global greenhouse gas emissions. (1) Background: We aim to strengthen early-stage collaboration among stakeholders based on integrated design principles, rather than relying solely on individual designers’ subjective decisions. The goal is to propose an objective method for optimizing apartment building layouts. (2) Methods: Accordingly, key design elements for energy optimization were identified, and corresponding energy usage prediction data were collected to build a database. Generative Design (GD) techniques were applied to generate and evaluate alternative layout configurations. (3) Results: The conventional apartment block layout process, which heavily depends on the expertise and intuition of experienced designers, was automated using Revit-Dynamo. An energy optimization method from the integrated design perspective was subsequently proposed. (4) Conclusions: GD enabled the identification of comprehensively optimized layout alternatives. We demonstrate the applicability of Revit-Dynamo-GBS in apartment complex design from an integrated design perspective and suggest improvements to existing certification systems and procedures in light of domestic policy considerations. Full article

The Science and Technology Finance ecosystem plays an increasingly important role in shaping the sustainable development of new quality productive forces (NQPF). This study, based on the perspective of complex systems and using a multi-period fsQCA approach, takes 31 provinces, municipalities, and autonomous regions in mainland China as cases to analyze the relationship between the Science and Technology Finance ecosystem and NQPF from 2017 to 2022. The findings are as follows: first, the antecedent configurations of NQPF are multiple, with the variables of the Science and Technology Finance ecosystem jointly matching and working together to drive its development. Second, in 2017–2018, there were three configurations: the “Bank–Enterprise” collaborative-driven type, the Bank-led type, and the Enterprise-led type; in 2019–2020, there were three configurations: the “Bank–Enterprise” collaborative-driven type, the “Bank–Enterprise–Market” collaborative-driven type, and the “Enterprise–Market” collaborative-driven type; in 2021–2022, there was one configuration, namely the Multi-Actor collaborative-driven type. Third, the development of NQPF across the three stages underwent an evolution from being dominated by core financial resources, to coordinated driving by core finance and the market, and finally to multi-stakeholder collaborative promotion. Fourth, in the configurations where high-level NQPF was not achieved, insufficiency of Enterprise Self-owned Funds (ESOF) was identified as a common problem. These findings provide theoretical references and policy implications for optimizing the Science and Technology Finance ecosystem in line with local conditions. Full article

Despite the devastating consequences of fungal disease, research struggles to catch up to present needs. This study aims to give a broad perspective on the situation, investigating patterns and distribution of fungal pathogens and monitoring trends of resistance to antifungal drugs, over an 8-year timeframe, at the National Institute of Infectious Diseases “Prof. Dr. Matei Balș” in Bucharest, Romania. Samples were inoculated on Sabouraud or Brilliance Candida Agar media; strains were identified using MALDI-TOF MS; and antifungal sensitivity testing was performed using E-Tests strips, VITEK2 Compact and MICRONAUT-AM automatic systems. Candida albicans, accounting for 42% of the positive samples, was the most common pathogen observed, with only 17% of the isolates being susceptible to all antifungals tested, while it was also predominant and deadly in the ICU. The emerging Candidozyma auris, found in 8% of the candidoses, exhibited a fluconazole resistance rate of 96.3%. Of the Aspergillus fumigatus strains, 35.7% showed resistance to azoles, and 25% to amphotericin B. In the ICU, more than half of A. flavus-, A. fumigatus- or A. niger-related cases culminated in death. Antifungal resistance is not to be treated lightly, as it is still a complex and dynamic threat, with devastating consequences. Full article

Polypharmacy is typically seen as an unavoidable consequence of multimorbidity and aging, with clinicians addressing complex medication lists unsystematically. In this perspective, we argue that polypharmacy should be managed as a chronic condition. Like diabetes or hypertension, for example, the medication burden shows persistence, progression in its absence despite active management, predictable complications (such as falls, delirium, renal injury, functional decline), and a need for structured surveillance. We introduce a pragmatic diagnostic framework that moves beyond pill counts to modality-agnostic, regimen-level risk across prescribed and non-prescribed medicines. Diagnosis rests on prolonged exposure, composite burden indices (e.g., anticholinergic/sedative load), medication-related complications or prescribing cascades, and the need for a planned review. As biologics, gene therapies and long-acting formulations can lower tablet numbers while increasing monitoring, administration, and interaction complexity. We treat polypharmacy as cumulative pharmacodynamic and operational burden. We advocate stage matched care with unique, functional aims—decreasing the harmful burden instead of mass deprescribing—and position a structured medication review as the standard for polypharmacy with support from pharmacists, shared decision making, and safety netted taper plans. The framework fosters patient-centred care, embedding continuity and equity, and outlines a concise outcome set that integrates pharmacometric measures with patient-reported function and treatment burden. At the systems level, the framework enables registries, recall systems, decision support, and audit/feedback mechanisms to shift from sporadic medication list clean-up to a structured, measurable long-term program. Redefining polypharmacy in this way aligns clinical practice, education, and policy with real-world evidence, fostering a cohesive pathway to safer, streamlined, and more patient-centred care in community settings. Full article

With the rapid development of unmanned aerial vehicle (UAV) technology, quadrotor UAVs have demonstrated extensive application potential in various fields. However, due to parameter uncertainties and strong coupling, the flight attitude of quadrotors is prone to external disturbances, posing challenges for achieving precise control and stable flight. In this paper, we address the tracking control problem under unknown command rate variations by proposing a Modified Linear Active Disturbance Rejection Control (LADRC) strategy, aiming to enhance flight stability and anti-disturbance capability in complex environments. First, based on the attitude dynamics model of quadrotors, an LADRC technique is adopted to realize three-channel decoupling-free control. By integrating a parameter estimator, the proposed method can compensate unknown disturbances in real time, thereby improving the system’s anti-disturbance ability and dynamic response performance. Second, to further enhance system robustness, a linear extended state observer (LESO) is designed to accurately estimate the tracking error rate and total disturbances. Additionally, a Levant differentiator is introduced to replace the traditional differentiation component for optimizing the response speed of command rate. Finally, a modified LADRC controller incorporating error rate estimation is constructed. Simulation results validate that the proposed scheme maintains good tracking accuracy under high-frequency disturbances, providing an effective solution for stable UAV flight in complex scenarios. Compared with traditional control methods, the modified LADRC strategy exhibits significant advantages in tracking performance, anti-disturbance capability, and dynamic response. This research not only offers a novel perspective and solution for quadrotor control problems but also holds important implications for improving UAV performance and reliability in practical applications. Full article

Photon-counting computed tomography (PCCT) represents a major technological innovation compared to conventional CT, offering improved spatial resolution, reduced electronic noise, and intrinsic spectral capabilities. These advances open new perspectives for synergy with radiomics, a field that extracts quantitative features from medical images. The ability of PCCT to generate multiple types of datasets, including high-resolution conventional images, iodine maps, and virtual monoenergetic reconstructions, increases the richness of extractable features and potentially enhances radiomics performance. This narrative review investigates the current evidence on the interplay between PCCT and radiomics in cardiothoracic imaging. Phantom studies demonstrate reduced reproducibility between PCCT and conventional CT systems, while intra-scanner repeatability remains high. Nonetheless, PCCT introduces additional complexity, as reconstruction parameters and acquisition settings significantly may affect feature stability. In chest imaging, early studies suggest that PCCT-derived features may improve nodule characterization, but existing machine learning models, such as those applied to interstitial lung disease, may require recalibration to accommodate the new imaging paradigm. In cardiac imaging, PCCT has shown particular promise: radiomic features extracted from myocardial and epicardial tissues can provide additional diagnostic insights, while spectral reconstructions improve plaque characterization. Proof-of-concept studies already suggest that PCCT radiomics can capture myocardial aging patterns and discriminate high-risk coronary plaques. In conclusion, evidence supports a growing synergy between PCCT and radiomics, with applications already emerging in both lung and cardiac imaging. By enhancing the reproducibility and richness of quantitative features, PCCT may significantly broaden the clinical potential of radiomics in computed tomography. Full article

As the complexity of convolutional neural networks (CNN) continues to increase, efficient deployment on computationally constrained hardware platforms has become a significant challenge. Against this backdrop, field-programmable gate arrays (FPGA) emerge as an up-and-coming CNN acceleration platform due to their inherent energy efficiency, reconfigurability, and parallel processing capabilities. This paper establishes a systematic analytical framework to explore CNN optimization strategies on FPGA from both algorithmic and hardware perspectives. It emphasizes co-design methodologies between algorithms and hardware, extending these concepts to other embedded system applications. Furthermore, the paper summarizes current performance evaluation frameworks to assess the effectiveness of acceleration schemes comprehensively. Finally, building upon existing work, it identifies key challenges in this field and outlines future research directions. Full article