Search Results (2,530)

An intrusion into the operational network (OT) of a production site can cause serious damage by affecting productivity, reliability, and quality. The presence of embedded neural networks (NNs), such as classifiers, in physical devices opens the door to new attack vectors. Due to the stochastic behavior of the classifier and the difficulty of reproducing results, the Artificial Intelligence (AI) Act requires the NN’s behavior to be explainable. For this purpose, the platform HistoTrust enables tracing NN behavior, thanks to secure hardware components issuing attestations registered in a blockchain ledger. This solution helps to build trust between independent actors whose devices perform tasks in cooperation. This paper proposes going further by integrating a mechanism for detecting tampering of embedded NN, and using smart contracts executed on the blockchain to propagate the alert to the peer devices in a distributed manner. The use case of a bit-flip attack, targeting the weights of the NN model, is considered. This attack can be carried out by repeatedly injecting very small messages that can be missed by the Intrusion Detection System (IDS). Experiments are being conducted on the HistoTrust platform to demonstrate the feasibility of our distributed approach and to qualify the time required to detect intrusion and propagate the alert, in relation to the time it takes for the attack to impact decisions made by the AI. As a result, the blockchain may be a relevant technology to complement traditional IDS in order to face distributed attacks. Full article

Accurately predicting pollutant emission factors (EFs) from woody biomass fuels remains challenging because small-scale combustion tests are fuel-specific, time-consuming, and highly sensitive to operating conditions. This study combines controlled laboratory combustion experiments with a physics-informed artificial neural network (ANN–PINN) to estimate the emission factors of particulate matter (${\mathrm{EF}}_{PM}$), carbon monoxide (${\mathrm{EF}}_{CO}$), and nitrogen oxides (${\mathrm{EF}}_{NOx}$) using only laboratory-scale fuel characterization. Three pelletized woody biomass, Pinus radiata, Acacia dealbata, and Nothofagus obliqua, were analyzed through ultimate and proximate composition, lignin content, and TGA-derived parameters and tested in a residential pellet stove under identical control setpoints, resulting in a narrow and well-defined operating regime. A medium-depth ANN–PINN was constructed by integrating mechanistic constraints, monotonicity based on known emission trends and a weak carbon balance penalty, into a feed-forward neural network trained and evaluated using Leave-One-Out Cross-Validation. The model accurately reproduced the experimental behavior of ${\mathrm{EF}}_{CO}$ and captured structured variability in ${\mathrm{EF}}_{PM}$, while the limited nitrogen variability of the fuels restricted generalization for ${\mathrm{EF}}_{NOx}$. Sensitivities derived via automatic differentiation revealed physically coherent relationships, demonstrating that PM emissions depend jointly on fuel chemistry and aero-thermal conditions, CO emissions are dominated by mixing and temperature, and ${\mathrm{NO}}_x$ formation is primarily governed by fuel-bound nitrogen. When applied to external biomass fuels characterized independently in the literature, the ANN–PINN produced physically plausible predictions, highlighting its potential as a rapid, low-cost screening tool for assessing new biomass feedstocks and supporting cleaner residential heating technologies. The integrated experimental–PINN framework provides a physically consistent and data-efficient alternative to classical empirical correlations and purely data-driven ANN models. Full article

Bridge health monitoring is crucial for ensuring the safety and durability of infrastructure. In traditional methods, physics-based models have high interpretability but are difficult to handle complex nonlinear problems, while purely data-driven machine learning methods are limited by data scarcity and physical inconsistency. Physics-informed machine learning, as an emerging “gray box” paradigm, effectively integrates the advantages of both by embedding physical laws (such as control equations) into machine learning models in the form of constraints, priors, or residuals. This article systematically elaborates on the core fusion mechanism of physics-informed machine learning (PIML) in bridge engineering, innovative applications throughout the entire lifecycle of design, construction, operation, and maintenance, as well as its unique data augmentation strategy. Research has shown that PIML can significantly improve the accuracy and robustness of damage identification, load inversion, and performance prediction, and is the core engine for constructing dynamic and predictive digital twin systems. Despite facing challenges in complex physical modeling, loss function balancing, and engineering interpretability, PIML represents a fundamental shift in bridge health monitoring towards intelligent and predictive maintenance by combining advanced strategies such as active learning and meta learning with IoT technology. Full article

This study examines a seven-week informal familycentric rocketry pilot program designed for Latina girls in grades 5 and 6 and their parents. Grounded in Community Cultural Wealth and Culturally Sustaining Pedagogy, the program integrated Family Problem-Based Learning to position families as co-educators in science learning. Through activities such as designing NASA-style mission patches, constructing egg-drop devices, and launching rockets, the program sought to center family knowledge, bilingual practices, and cultural values within physical science experiences. Data reported here were collected through mid- and post-program surveys with both parents and daughters. Responses indicate strong engagement from families, with parents reporting increased high confidence in supporting their daughters’ science learning and daughters expressing enjoyment and strong interest in science learning. Both groups valued the use of English and Spanish and the program’s emphasis on collaborative, family-centered participation. Responses highlight the potential of culturally sustaining, familycentric approaches to address the underrepresentation of Latina women in Science, Technology, Engineering, and Math (STEM) by fostering a sense of belonging. This study contributes to informal science education by demonstrating how families can be centered in a program focused on physical science. School-based outreach of this kind may also strengthen families and parent–child relationships. Full article

Microplastics (MPs) have emerged as persistent environmental pollutants with adverse effects on ecosystems and human health. Conventional removal methods, such as filtration and sedimentation, primarily rely on physical separation without addressing the degradation of MPs, leading to their accumulation and the risk of secondary pollution. This review explores the potential of advanced oxidation processes (AOPs), including photocatalysis, electrochemical oxidation, Fenton processes, sulfate radical-based oxidation, sonochemical treatment, ozonation, and plasma technologies, which generate reactive oxygen and nitrogen species capable of promoting polymer chain scission, microbial biodegradation, and the oxidative fragmentation and mineralization of MPs into non-toxic byproducts. Hybrid AOP systems combined with biological treatments or membrane-based filtration are also examined for their effectiveness in degrading MPs, as well as for scalability and the environmental impacts of their byproducts when integrated into existing wastewater treatment systems. The review further discusses challenges related to operational parameters, energy consumption, and the formation of secondary pollutants. By identifying current knowledge gaps and future research directions, this review provides insights into optimizing AOPs and integrations of AOPs with biological treatments or membrane-based processes for sustainable MP remediation and water treatment applications. Full article

This study provides a systematic overview of Natural Language Processing (NLP)-based frameworks for Cyber Threat Intelligence (CTI) and the early prediction of cyberattacks in Industry 4.0. As digital transformation accelerates through the integration of IoT, SCADA, and cyber-physical systems, manufacturing environments face an expanding and complex cyber threat landscape. Following the PRISMA 2020 systematic review protocol, 80 peer-reviewed studies published between 2015 and 2025 were analyzed across IEEE Xplore, Scopus, and Web of Science to identify methods that employ NLP for CTI extraction, reasoning, and predictive modelling. The review finds that transformer-based architectures, knowledge graph reasoning, and social media mining are increasingly used to convert unstructured data into actionable intelligence, thereby enabling earlier detection and forecasting of cyber threats. Large Language Models (LLMs) demonstrate strong potential for anticipating attack sequences, while domain-specific models enhance industrial relevance. Persistent challenges include data scarcity, domain adaptation, explainability, and real-time scalability in operational-technology environments. The review concludes that NLP is reshaping Industry 4.0 cybersecurity from reactive defense toward predictive, adaptive, and intelligence-driven protection, and it highlights the need for interpretable, domain-specific, and resource-efficient frameworks to secure Industry 4.0 ecosystems. Full article

The concept of a digital twin has become a key driver of industrial transformation, enabling a seamless connection between physical systems and their virtual counterparts. The growing need for adaptability has accelerated the use of advanced technologies and tools to maintain competitiveness. In this context, the article introduces the concept of a hierarchical digital twin and illustrates its operation through a practical example. Production resource structures and timing data were generated in the KbRS (Knowledge-based Rescheduling System), which will serve as the Level II digital twin in this article. The acquired data is transferred via Excel to the FlexSim simulation environment, which represents the Level I digital twin responsible for modeling the flow of production processes. Because a digital twin must accurately reflect a specific production system, the study begins by formulating a general mathematical model. Algorithms for product ordering and for constructing the digital twin of the production processes were developed. Furthermore, three implementation scenarios for the hierarchical digital twin were proposed using the KbRS and FlexSim tools. The implementation of the hierarchical digital twin concept facilitated the development of the more comprehensive virtual model. At the same time, the integration of data between the two software environments enabled the generation of more detailed and precise results. Traditionally, a digital twin created solely within a single simulation platform is unable to represent all the structural components of a production system—an issue addressed by the hierarchical approach presented in this study. Full article

Amphibians, as sensitive bioindicators, reflect environmental health issues that also impact human communities through shared pathways, including contaminated water and agricultural products. This perspective addresses the need to standardize the reporting of anomalies (defined as significant phenotypic deviations from typical morphology, structure, or coloration) in amphibians in Mexico, where inconsistent terminology and incomplete data limit their utility for environmental monitoring. We propose a framework that includes a classification of anomalies (structural and chromatic) and a field-based physical examination protocol to systematically document these cases. The approach integrates detailed guidelines to ensure comprehensive reporting and data comparability, addressing geographic and taxonomic biases. Recent findings highlight that over 50% of anomaly reports in Mexico are incidental, with predominant cases in Ambystomatidae, Hylidae, and Ranidae, and linked to anthropogenic pressures such as agrochemicals. The framework promotes interdisciplinary collaboration, citizen science, and emerging technologies like artificial intelligence for sustainable monitoring. By standardizing the detection and reporting of anomalies, this proposal strengthens the role of amphibians as sentinels of ecosystem health, with applications in Mexico and other regions facing high environmental degradation. Full article

Male infertility is a prevalent global health issue, with urological disorders representing some of the most common and correctable causes. Key conditions such as varicocele, obstructive azoospermia, erectile dysfunction and Peyronie’s disease impair fertility through distinct pathophysiological mechanisms, including disrupted spermatogenesis, reproductive tract obstruction and failed sperm delivery. The effective management of these conditions hinges on a systematic diagnostic evaluation, which integrates clinical history, physical examination, semen analysis and specialized imaging. Modern management follows a logical progression, beginning with foundational lifestyle modifications, advancing to targeted medical or surgical interventions, and culminating, when necessary, in assisted reproductive technologies. Treatment strategies are therefore highly targeted, ranging from medical management and surgical correction—such as varicocelectomy or microsurgical reconstruction—to sperm retrieval techniques. Furthermore, evidence-based lifestyle modifications and a multidisciplinary clinical approach are fundamental to optimizing reproductive outcomes for affected couples. A comprehensive understanding of these urological etiologies is therefore essential for guiding appropriate intervention and improving the prospects of achieving pregnancy. Full article

Digital Twin technology is transforming how buildings are designed, operated, and optimized, serving as a key enabler of smarter, more energy-efficient, and sustainable built environments. By creating dynamic, data-driven virtual replicas of physical assets, Digital Twins support continuous monitoring, predictive maintenance, and performance optimization across a building’s lifecycle. This paper provides a structured review of current developments and future trends in Digital Twin applications within the building sector, particularly highlighting their contribution to decarbonization, operational efficiency, and performance enhancement. The analysis identifies major challenges, including data accessibility, interoperability among heterogeneous systems, scalability limitations, and cybersecurity concerns. It emphasizes the need for standardized protocols and open data frameworks to ensure seamless integration across Building Management Systems (BMSs), Building Information Models (BIMs), and sensor networks. The paper also discusses policy and regulatory aspects, noting how harmonized standards and targeted incentives can accelerate adoption, particularly in retrofit and renovation projects. Emerging directions include Artificial Intelligence integration for autonomous optimization, alignment with circular economy principles, and coupling with smart grid infrastructures. Overall, realizing the full potential of Digital Twins requires coordinated collaboration among researchers, industry, and policymakers to enhance building performance and advance global decarbonization and urban resilience goals. Full article

Tropical buildings face the coupled effects of four-high environmental factors, which accelerate thermal–humidity degradation, increase operational energy demands, and diminish building health attributes. This paper systematically integrates global research advancements to establish a theoretical framework for Tropical Low-Carbon Healthy Building Enclosures (TLHBEs) by linking materials, structures, and buildings across scales. It identifies three key scientific questions: (1) Establishing a multi-scale parametric design model that couples materials, structures, and architecture. (2) Elucidating experimental and simulated multi-scale equivalent relationships under the coupled effects of temperature, humidity, radiation, and salinity. (3) Design multi-objective optimization strategies balancing energy efficiency, comfort, indoor air quality, and carbon emissions. Based on this, a technical implementation pathway is proposed, integrating multi-scale unified parametric design, multi-physics testing and simulation, machine learning, and intelligent optimization technologies. This aims to achieve multi-scale parametric design, data–model fusion, interpretable decision-making, and robust performance prediction under tropical climatic conditions, providing a systematic technical solution to address the key scientific questions. This framework not only provides scientific guidance and engineering references for designing, retrofitting, and evaluating low-carbon healthy buildings in tropical regions but also aligns with China’s dual carbon goals and healthy building development strategies. Full article

The European Union’s enhanced greenhouse gas (GHG) reduction targets for 2030 make the large-scale deployment of carbon capture and storage (CCS) technologies essential to achieve deep decarbonization goals. Within this context, this study aims to advance CCS research by developing and testing a pilot-scale system that integrates gasification for syngas and power production with CO₂ absorption and solvent regeneration. The work focuses on improving and validating the operability of a pilot plant section designed for CO₂ capture, capable of processing up to 40 kg CO₂ per day through a 6 m absorber and stripper column. Experimental campaigns were carried out using different amine-based absorbents under varied operating conditions and liquid-to-gas (L/G) ratios to evaluate capture efficiency, stability, and regeneration performance. The physical properties of regenerated and CO₂-saturated solvents (density, viscosity, pH, and CO₂ loading) were analyzed as potential indicators for monitoring solvent absorption capacity. In parallel, a process simulation and optimization study was developed in Aspen Plus, implementing a split-flow configuration to enhance energy efficiency. The combined experimental and modeling results provide insights into the optimization of solvent-based CO₂ capture processes at pilot scale, supporting the development of next-generation capture systems for low-carbon energy applications. Full article

In the context of urban stock renewal, coordinating spatial fairness with low-carbon goals remains a critical challenge. Existing planning often leads to spaces that are “nominally compliant but functionally ineffective,” failing to support low-carbon behaviors. To address this, this study adopts a spatial justice framework coupled with space syntax technology to empirically analyze the structural defects of the Beijing Baiwanzhuang Community and their constraints on low-carbon behaviors. We utilized a “Moving Snapshot Observation” method to collect behavioral data and constructed a quantitative regression model to identify the key drivers of elderly gathering (a proxy for low-carbon behavior). The results reveal “significant spatial differentiation and accessibility fractures” within the physical space, where structural imbalances lead to systematic spatial deprivation. Specifically, the multivariate regression analysis (R² = 0.50) indicates that low-carbon behaviors are significantly associated with a “dual-core mechanism”: community-scale spatial integration (NAIN 3600 m) and the density of seating within a short radius (100–200 m). A key finding indicates that the driving role of spatial network accessibility is significantly stronger than facility abundance alone. Based on this, a “Space-Facility-Governance” collaborative reconstruction paradigm is proposed, including using green infrastructure to stitch spatial fractures, precisely configuring low-carbon facilities at high-integration nodes, and establishing inclusive governance mechanisms. This research breaks through the limitation of traditional spatial justice studies that focus on qualitative critique, constructing a “physical spatial structure–low-carbon behavior” quantitative attribution model. It empirically validates that “accessibility justice” is a prerequisite for achieving community low-carbon transitions, providing a quantitative renewal paradigm that balances equity and efficiency for existing communities. Full article

Addressing the challenges of online measurement of oil-gas-water three-phase flow under high gas–liquid ratio (GVF > 90%) conditions (fire-driven mining, gas injection mining, natural gas mining), which rely heavily on radioactive sources, this study proposes an integrated, radiation-source-free three-phase measurement scheme utilizing a “Venturi tube-microwave resonator”. Additionally, a physics-informed neural network (PINN) is introduced to predict the volumetric flow rate of oil-gas-water three-phase flow. Methodologically, the main features are the Venturi differential pressure signal ($\mathrm{\Delta }P$) and microwave resonance amplitude ($V$). A PINN model is constructed by embedding an improved L-M model, a cross-sectional water content model, and physical constraint equations into the loss function, thereby maintaining physical consistency and generalization ability under small sample sizes and across different operating conditions. Through experiments on oil-gas-water three-phase flow, the PINN model is compared with an artificial neural network (ANN) and a support vector machine (SVM). The results showed that under high gas–liquid ratio conditions (GVF > 90%), the relative errors (REL) of PINN in predicting the volumetric flow rates of oil, gas, and water were 0.1865, 0.0397, and 0.0619, respectively, which were better than ANN and SVM, and the output met physical constraints. The results indicate that under current laboratory conditions and working conditions, the PINN model has good performance in predicting the flow rate of oil-gas-water three-phase flow. However, in order to apply it to the field in the future, experiments with a wider range of working conditions and long-term stability testing should be conducted. This study provides a new technological solution for developing three-phase measurement and machine learning models that are radiation-free, real-time, and engineering-feasible. Full article

Chronic diseases disproportionately impact Indigenous peoples in Australia, with type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD) representing leading causes of morbidity and mortality. Despite evidence supporting community-based exercise interventions for T2DM management, no culturally adapted programs utilizing wearable technology have been co-designed specifically with Indigenous Australian communities. This study protocol aims to determine if wearable-based exercise interventions can effectively prevent CVD development and manage T2DM progression in Indigenous Australians through culturally safe, community-led approaches. The PrIDE study protocol describes a mixed-methods translational research design incorporating Indigenous and Western methodologies across three phases: (1) co-designing culturally adapted exercise programs and assessment tools, (2) implementing interventions with wearable monitoring, and (3) conducting evaluation and scale-up assessment. Sixty-four Indigenous Australian adults with T2DM will be recruited across remote, rural/regional sites to self-select into either individual or group exercise programs using the Withings ScanWatch 2. Primary outcomes include cardiovascular risk factors, physical fitness, and health self-efficacy measured using culturally adapted tools. Indigenous governance structures will ensure cultural safety and community ownership throughout. The PrIDE protocol presents a novel approach to improving health equity while advancing understanding of wearable technology integration in Indigenous healthcare, informing future larger-scale trials and policy development. Full article