Search Results (7,267)

Urban industrial heritage is increasingly embedded in urban regeneration, public space provision, and community governance, yet existing studies have insufficiently examined how heterogeneous publics perceive its value through everyday digital discourse. Taking the Guangzhou Iron and Steel Plant industrial heritage site (hereafter, the Guanggang industrial heritage site) as a case study, this study used user-generated content from Rednote posts and local WeChat public-account comments to identify platform-mediated expressions of public value perception. A corpus of 745 valid samples comprising 51,459 Chinese characters was constructed after data collection, screening, and text preprocessing. Word-frequency analysis, semantic network analysis, and sentiment analysis were conducted using ROST CM 6.0. The results show that the two retrieved platform-contextual corpora foregrounded different concerns. Rednote discourse foregrounded ruin landscapes, industrial aesthetics, photography-based check-ins, and exploratory experiences, whereas WeChat comments emphasized park construction, public facilities, governance responsiveness, safety, and the residential environment. At the corpus level, lexicon-based sentiment classification indicated that Rednote texts were dominated by positive and neutral categories, while WeChat comments contained a higher proportion of texts classified as negative. This study conceptualizes dual foregrounding as a bounded selection process through which platform affordances, user self-selection, and users’ relationships with the site influence which concerns become visible in each corpus; it does not treat the observed differences as a causal platform effect. It argues that industrial heritage regeneration must translate historical, technological, and aesthetic values into public values that are interpretable, accessible, usable, and trusted by local communities. Full article

The increasing interconnectivity and digital transformation of Communication, Navigation, and Surveillance (CNS) systems have expanded their attack surface, rendering traditional perimeter-based security models inadequate for protecting these critical infrastructures. Zero Trust Architecture (ZTA), founded on the principle of “never trust, always verify,” offers a paradigm shift towards continuous, context-aware security. This paper presents a literature review investigating the application of ZTA principles to secure modern CNS ecosystems, following the guidelines of the International Civil Aviation Organization (ICAO) through its Cybersecurity Strategy and Plan. We analyze the alignment of ZTA core tenets—such as least-privilege access, micro-segmentation, and continuous authentication—with the unique operational requirements of CNS systems. This paper also presents a cybersecurity framework, under development within the Future Communications Digital Infrastructure (FCDI) project of the SESAR JU program, which aims to assist CNS stakeholders in collaboratively identifying cybersecurity threats within their scope of responsibility. The review critically examines implementation challenges for specific CNS subsystems: secure aeronautical communications (e.g., LDACS), resilient PNT (Positioning, Navigation, and Timing) services, and integrated surveillance networks (e.g., ADS-B, multilateration). Furthermore, we identify and evaluate domain-specific challenges, including integration with legacy avionics and ground systems, managing stringent latency and reliability constraints, and protecting against sophisticated threats targeting supply chains and data fusion processes. By synthesizing current research and practical deployment insights, this review aims to provide a foundational reference for aerospace engineers, cybersecurity specialists, and policymakers, offering a roadmap to enhance the cyber-resilience of vital CNS infrastructure in an era of evolving digital threats. Full article

The application of Internet of Things (IoT) technology to Digital Twin (DT) in smart manufacturing has opened significant opportunities for real-time monitoring, predictive maintenance, and closed-loop control; however, the inherent latency that exists in these architectures (the temporal gap between a physical event and its reflection in a digital model) remains one of the most significant and least systematically understood barriers to fulfill its full potential. This paper aims to propose a formal four-layer taxonomy of latency sources in IoT-based Digital Twin systems for smart manufacturing and to review the current approaches and tools that are available for their measurement. The PRISMA protocol has been used to perform a systematic literature review, where 58 primary survey studies published between 2020 and 2026 were extracted from IEEE Xplore, Elsevier Scopus, Google Scholar and arXiv, with all the studies being coded along six dimensions (architectural layer, application domain, latency metrics reported, evaluation methodology, quantitative impact, and enabling technologies). The proposed taxonomy presents 28 different types of latencies under four layers: (L1) network, (L2) compute, (L3) data, and (L4) end-to-end (E2E), whose magnitudes vary from 0.1 ms for local network propagation to tail latencies above 500 ms in production (P99). Three categories and three cross-layer interaction patterns are formalized here and are absent from prior partial taxonomies. Among the most promising results is the finding that several high-impact interventions require no infrastructure investment: a protocol migration from Modbus to WebSocket reduces telemetry latency by 32%, while Age of Information-aware synchronization and clock drift correction deliver substantial data layer gains through software updates alone, yet remain underutilized. The review identifies a systematic under-reporting of tail-latency percentiles across the corpus, the lack of a cross-protocol jitter benchmark, and a predominance of simulation-based evaluation over real-hardware measurement. The systematic review contributions of this paper (the formal four-layer taxonomy, the proportional metric audit across the 58 papers, and the formalization of three cross-layer interaction patterns) are derived from cross-corpus analysis. The investigation also identifies three open research directions (a standardized manufacturing IoT-DT benchmark, cross-layer joint optimization frameworks, and wireless TSN validation on real manufacturing testing grounds) that together form a well-organized and practical basis to advance both the science and the application of ultra-low-latency Digital Twin technology in the industrial field. Full article

The integrity of halal supply chains is increasingly threatened by fragmented paper-based records, certificate fraud, and the absence of real-time traceability. This paper presents HalalChain, a blockchain-based halal product traceability system that enforces role-based access control (RBAC) through three Solidity smart contracts deployed on an Ethereum-compatible blockchain. HalalChain is designed for production deployment on an EVM-compatible Layer-2 or sidechain such as Polygon or BNB Chain, on which the contracts run without code changes. A dual-storage architecture synchronises every supply chain event to both a PostgreSQL relational database and the blockchain, balancing on-chain immutability with off-chain query performance. The system supports five stakeholder roles, namely administrator, supplier, manufacturer, logistics, and retailer, each restricted to specific supply chain event types enforced at the smart contract level. Consumers can verify product halal status and full supply chain history by scanning a QR code linked to a public verification endpoint that cross-checks database records against on-chain event counts, producing a chain-integrity indicator. As the current chain-integrity check is count-base, it can detect missing or extra database rows, but it cannot detect content-level modification if the row count remains unchanged. A total of 107 automated test cases were executed covering functional correctness, edge cases, end-to-end integration, and gas performance benchmarks. Core smart contract operations consume between 25,365 and 213,684 gas units, indicating feasible deployability on Ethereum-compatible networks. An exploratory analysis was carried out with a preliminary survey of 40 respondents (mean = 4.10 on a 5-point Likert scale), suggesting that consumer demand for blockchain-verified halal certification is encouraging. The results demonstrate that HalalChain provides a tamper-evident, role-enforced traceability foundation for the halal food industry. The system secures the digital chain of custody cryptographically and the physical–digital binding between the QR code, and the product remains a separate trust assumption requiring complementary anti-tamper mechanisms. Full article

Background: Allergic contact dermatitis (ACD) is a common inflammatory skin disease and patch testing (PT) remains the gold standard for its diagnosis; however, PT interpretation is time-consuming and prone to inter-observer variability. Growing advances in digital imaging and artificial intelligence (AI) have encouraged the development of automated PT evaluation systems. This review aimed to summarize the use of deep learning networks (DNNs) and image-preprocessing techniques for PT classification. Methods: A literature review was conducted to identify original research published between 2020 and 2025 that applied deep learning algorithms to PT image analysis. Included studies were assessed with respect to model architecture, dataset characteristics, preprocessing strategies, and diagnostic performance. Results: Six original studies employing deep learning for PT image classification met the inclusion criteria. They employed a range of architectures, including YOLOv5x, EfficientNetB0, Xception, and custom CNN models. Reported diagnostic performance varied, with accuracy values ranging from 90% to 99.5%, F1-scores from 0.37 to 0.98, and AUROC values up to 0.94. Despite promising results, models remain unreliable for ICDRG grading, especially for severe reactions, and methodological variability in dataset composition, imaging conditions, preprocessing pipelines, and classification tasks limits comparability across studies. Conclusions: Deep learning shows promise for automated PT interpretation, but further standardized and multicenter studies with detailed preprocessing protocols and comprehensive ICDRG grading are required for clinical implementation. Full article

Background: While region-based models have informed pain neuroscience, chronic pain is now increasingly conceptualized as a network disorder. This narrative review aimed to critically examine the conceptual evolution of chronic pain models from region-based representations toward large-scale functional network frameworks across adult and pediatric populations while exploring how emerging artificial intelligence (AI)-driven approaches may support future precision pain medicine. Methods: A structured literature search was performed in PubMed, Scopus, and Web of Science, focusing on the scientific output addressing adult and pediatric chronic pain, pain-related neuroplasticity, functional network alterations, neuromodulation, and AI-based applications in pain medicine. Results: The reviewed literature supports a progressive conceptual shift from region-based representations of pain toward network-oriented models involving dysfunctional interactions among the salience, default mode, central executive, and sensorimotor networks. Although emerging evidence suggests developmental network alterations in pediatric chronic pain, current conclusions remain limited by the relative scarcity of longitudinal neuroimaging studies. Emerging AI applications demonstrate promising potential for objective pain assessment, trajectory prediction, and personalized therapeutic decision-making. Conclusions: The transition from the pain matrix to functional network models represents one of the most important conceptual advances in contemporary pain neuroscience. A network-based perspective may accelerate AI-enabled pain biomarkers and individualized interventions. Full article

The global transition toward renewable energy has intensified interest in dispatchable low-carbon sources that can support reliability-critical infrastructure in smart grid systems. Data centers represent one of the fastest-growing electricity loads globally, yet their compatibility with biomass-based energy systems as a dispatchable renewable source within smart grid architectures remains poorly understood. This study presented a comprehensive review of biomass power generation, data center energy management, and smart grid integration, drawing on a corpus of 347 peer-reviewed sources. A staged analytical design separated demand characterization from supply evaluation, ensuring that data center energy requirements emerged independently of supply-side assumptions. Using Latent Dirichlet Allocation topic modeling validated with BERTopic and VOSviewer network analysis, the study identified four distinct thematic clusters and found no single topic spanning data center reliability requirements, biomass supply dynamics, and smart grid integration simultaneously, a pattern that points to an underexplored cross-domain space in the literature. A demand–supply–grid alignment framework was introduced to illustrate compatibility conditions across temporal resolution, reliability requirements, and grid management dimensions. The alignment framework and illustrative simulation developed here are offered as analytical starting points to guide future engineering and empirical investigation rather than as demonstrations of operational readiness. An illustrative application demonstrated that biomass feedstock logistics constraints create persistent availability gaps at data center operational timescales, suggesting that supply chain resilience and grid-mediated buffering are likely necessary conditions for viable integration, a proposition that warrants empirical validation through full-scale engineering studies. The findings indicate that integration constraints reflect temporal and operational misalignment rather than technological infeasibility, providing a new analytical perspective for evaluating renewable energy integration in reliability-critical digital Full article

Background: Epicardial coronary artery spasm (CAS) is a frequent and important cause of myocardial ischemia. We aimed to develop and validate a noninvasive, artificial intelligence (AI)-driven risk score using routine clinical data to predict CAS in patients without obstructive coronary artery disease (CAD). Methods: This retrospective study analyzed a derivation cohort of 1050 patients and an external validation cohort of 600 patients who underwent intracoronary methylergonovine provocation testing between September 2008 and March 2025. A random forest (RF) model was developed using 15 clinical variables and simplified to a nine-variable model. Additionally, a convolutional neural network-long short-term memory (CNN-LSTM) deep learning model was implemented to predict CAS from raw digital electrocardiogram data (2611 electrocardiogram records). Results: The final nine-variable RF model, including predictors such as diastolic/systolic blood pressure, age, BSA, hemoglobin, smoking, heart rate, sex, and estimated glomerular filtration rate, demonstrated strong discriminatory power. The area under the curve was 85.8% (95% confidence interval [CI]: 85.8–89.9%) in the derivation cohort and 84.1% in the validation cohort (95% CI: 80.6–87.7%). A dose–response relationship was confirmed, with CAS prevalence increasing from 42.1% (0–1 risk factors) to 82.4% (≥5 risk factors). The electrocardiogram-based CNN-LSTM deep learning model achieved high sensitivity (91.4%) but limited specificity (11.9%); therefore, it should be considered a proof of concept rather than a clinical screening tool until further refinement is achieved. Conclusions: The nine-variable RF model provides a practical and accurate tool for early identification and risk stratification of CAS. The electrocardiogram deep learning model complements the RF model to improve clinical decisions and resource allocation in diagnosing CAS. Full article

Background/Objective: The increasing global population of older adults presents significant challenges for oral healthcare, particularly regarding the management of chronic conditions and prosthetic rehabilitation. Teledentistry, combined with intraoral scanning, offers a promising solution to enhance access, interdisciplinary collaboration, and clinical outcomes in geriatric populations. This study aimed to evaluate the utilisation of intraoral digital scanning within teledentistry for interdisciplinary oral assessment of older patients. Specifically, it investigated current clinical practices, collaboration among healthcare professionals, and perceptions regarding the effectiveness, challenges, and future potential of teledentistry in prosthodontic care. Methods: An analytical cross-sectional survey was conducted among 84 healthcare professionals, including dentists, prosthodontists, and postgraduate students, recruited via an international network. Participants completed a 40-item electronic questionnaire covering demographics, clinical practice, digital technology use, interdisciplinary collaboration, and attitudes toward research and innovation. Descriptive statistics summarised responses, and inferential analyses, including chi-square tests and Spearman correlations, examined associations between career stage, technology adoption, and interdisciplinary practices. Results: Early-career professionals demonstrated the highest adoption of intraoral scanning (76.3%), while mid-career adoption was lowest (28.6%). Sustained usage significantly increased after one year of adoption (93.8%). While 91.7% of respondents valued interdisciplinary care, active collaboration remained limited. Cost, technical barriers, and training gaps were identified as primary obstacles. Professionals perceived intraoral scanning as beneficial for prosthodontic outcomes and chronic inflammation management, though adoption was influenced by experience, systemic factors, and financial support. Conclusions: Teledentistry integrated with intraoral scanning has substantial potential to improve geriatric oral healthcare. Successful implementation depends on structured training, financial investment, and promotion of interdisciplinary collaboration. Future longitudinal and multicenter studies are warranted to evaluate clinical, economic, and patient-centred outcomes, supporting sustainable digital transformation in geriatric dental care. Full article

Background/Objectives: Several novel biologics and small-molecule therapies have emerged for the treatment of antihistamine-refractory chronic spontaneous urticaria (CSU), yet no study has directly compared their speed of response. This study aims to provide indirect evidence on the relative time to meaningful clinical response across approved and investigational therapies using a Bayesian network meta-analysis. Methods: Phase 2 and phase 3 randomized controlled trials reporting UAS7 scores in a graphical format for antihistamine-refractory CSU were included. The primary outcome was the mean time in weeks to minimal clinically important difference (MCID), defined as a UAS7 reduction of 10 points. Data were extracted using WebPlotDigitizer (v4.7) and analyzed via Bayesian random-effects network meta-analysis in MetaInsight (v6.4.0), with placebo as the reference node. Results: All drugs except rilzabrutinib 400 mg daily demonstrated faster mean time to MCID than placebo. Fenebrutinib had the fastest mean time to MCID (0.67–0.76 weeks), and tezepelumab the slowest (5.41–5.65 weeks). Only omalizumab 300 mg every 4 weeks, dupilumab 300 mg every 2 weeks, and ligelizumab 72 mg and 120 mg every 4 weeks achieved statistically significant reductions compared with placebo. All treatments had wide credible intervals reflecting limited direct comparisons. Conclusions: This is the first network meta-analysis comparing time to meaningful symptom control across therapies for antihistamine-refractory CSU. Omalizumab, dupilumab, and ligelizumab demonstrated statistically significant reductions in time to MCID compared with placebo. Head-to-head trials with standardized outcome reporting would enable more definitive comparative conclusions. Full article

The increasing computational demands of deep neural network inference drive the need for energy-efficient hardware accelerators that minimize data movement between memory and processing units. Compute-in-memory (CIM) architectures address this bottleneck by embedding computation directly within memory arrays, reducing the overhead of repeated weight transfers in conventional von Neumann systems. Conventional 6T SRAM-based digital CIM bitcells incur significant transistor overhead as arrays scale, motivating exploration of reduced-transistor bitcell alternatives. We propose a compact 4T+2T SRAM-based digital CIM bitcell implemented in 45 nm CMOS, combining a 4T SRAM storage cell with a 2T multiplier for bitwise multiply-and-accumulate (MAC) operations. The proposed design reduces transistor count from 8 to 6 compared to the 6T+2T reference, lowering parasitic capacitance and hardware overhead without compromising memory or computation functionality. Transient simulations confirm correct write, read, and CIM operations. The bitcell achieves a read delay of 26.91 ps, read power of 1.351 nW, and read energy of 0.005403 fJ—reductions of 98.7%, 86.5%, and 73.1% over the 6T+2T reference, respectively. For CIM operation, bitwise multiplication power decreases from 1.772 µW to 0.8014 µW and energy from 10.63 fJ to 4.808 fJ, representing a 54.8% reduction in both metrics, with only a marginal CIM delay increase of 3.13 ps. Monte Carlo analysis across 100 samples confirms robust write behavior under process variation, with write delay ranging from 55.02 to 69.59 ps and write energy from 0.05870 to 0.06557 fJ. Static noise margin analysis yields an SNM of 83.7 mV under nominal conditions, confirming stable data retention. These results demonstrate that the proposed 4T+2T bitcell offers strong transistor efficiency, energy savings, and computational correctness, making it a promising candidate for area-efficient digital CIM architectures targeting edge AI inference. Full article

Aerobiology, the science that studies atmospheric biological particles (including pollen, fungal spores, bacteria, and viruses), has undergone a profound transformation from a descriptive, observational discipline into a predictive, data-driven field, thanks to advances in statistical methods and environmental sensing technologies. Early research, based on classical statistical methods such as descriptive analysis, correlation analysis, and linear regression, established a fundamental understanding of seasonal dynamics and environmental relationships. However, the inherent complexity of aerosol biological systems—characterized by nonlinear interactions, spatiotemporal variability, and multiscale processes—has spurred the adoption of modern statistical techniques. These techniques include time-series analysis, generalized linear and additive models, spatial statistics, Bayesian inference, machine learning, and data assimilation, often combined with high-resolution environmental monitoring and sensor networks. In recent years, hybrid modeling approaches have emerged, combining mechanistic understanding of atmospheric transport and biological emissions processes with data-driven learning to improve the accuracy, robustness, and interpretability of predictions. This review comprehensively compares classical, modern, and hybrid statistical methods in air biology, exploring their theoretical foundations, practical applications, and inherent limitations. Furthermore, this review highlights emerging paradigms such as uncertainty quantification, causal inference, digital twins, and AI-driven real-time prediction systems. It also discusses challenges, including data heterogeneity, model interpretability, and cross-regional portability. By treating aerobiology as a complex adaptive environmental–biological system, this study highlights statistical methods that link observations to mechanisms and advance scalable, reliable, systems-oriented prediction frameworks for future research and applications. Full article

Rapid and non-destructive estimation of maize (Zea mays L.) leaf flavonoid (Flav) content is important for crop stress monitoring and precision agriculture. This study aimed to improve Flav estimation by integrating unmanned aerial vehicle (UAV)-based multispectral data, texture features, and phenological parameters across six key growth stages in the Guanzhong Plain, China. Maize Flav content was measured in situ using a Dualex Scientific+ meter, while canopy reflectance was acquired with a DJI M300 RTK UAV equipped with an MS600 Pro multispectral camera. A comprehensive feature set, including spectral bands, vegetation indices, texture features, texture indices, and logistic curve-derived phenological parameters, was constructed. Three feature selection methods, competitive adaptive reweighted sampling (CARS), the genetic algorithm (GA), and the successive projections algorithm (SPA), together with three regression models, partial least squares regression (PLSR), extreme gradient boosting (XGBoost), and convolutional neural network (CNN), were evaluated for Flav estimation. The results showed that integrating spectral, texture, and phenological information significantly improved model performance compared with spectral variables alone. CNN and XGBoost generally outperformed PLSR. Across the six growth stages, the stage-specific optimal models achieved coefficient of determination (R²) values ranging from 0.7749 to 0.8686 and residual prediction deviation (RPD) values ranging from 2.0046 to 2.6019, indicating high to outstanding predictive ability. The highest accuracy was obtained at R3 using the CARS-X_II-CNN model, with R² = 0.8686, root mean square error of validation (RMSEV) = 0.0382, and RPD = 2.6019. Texture features and phenological metrics, especially the start of season derived from the normalized difference vegetation index (NDVI_SOS) and the rate of senescence derived from the enhanced vegetation index (EVI_ROS), contributed substantially to model accuracy. In addition, maize Flav showed a unimodal response to nitrogen supply, with moderate nitrogen levels associated with higher Flav content. This study demonstrates the potential of UAV-based multisource feature integration and machine learning for accurate maize Flav estimation, and provides a useful framework for digital crop phenotyping and stress diagnosis. Full article

Against the backdrop of global energy transition and the widespread adoption of Hydrogen-Blended Natural Gas (HBNG), the safety of urban gas pipeline networks faces severe challenges. This paper systematically reviews the research progress of numerical simulation in the field of natural gas pipeline safety, focusing on its core supporting roles throughout the “Leakage–Dispersion–Evolution–Consequence” disaster chain. First, it analyzes the kinetic modeling of high-pressure leakage holes and property corrections based on real gas equations of state, elaborating on the numerical characterization of HBNG multi-component transport. Second, it compares the dispersion mechanisms and environmental coupling modeling methods in typical scenarios such as buried porous media, confined spaces in utility tunnels, underwater environments, and urban building clusters. Third, it reviews leakage monitoring technologies based on physical field simulation and data-driven approaches (e.g., Convolutional Neural Network, Long Short-Term Memory), emphasizing the value of numerical simulation in constructing digital twin training sets. Furthermore, it explores the dynamic evolution of explosion flame–shock wave interactions and the evaluation models for secondary disaster consequences. Finally, the current research status of grid-based risk pre-warning and emergency response strategies is summarized. In conclusion, numerical simulation is not only a robust method for precisely quantifying and characterizing complex physical mechanisms but also a critical technological foundation for building smart and resilient energy cities. Future research should focus on the deep coupling of multi-physics fields, physics-informed learning, and the development of system-level integrated defense systems. Full article

Precision Livestock Farming (PLF) requires real-time control systems that connect high-level Decision Support Systems with resource-constrained edge devices. This paper presents a hybrid Multi-Agent System (MAS) architecture for swine precision feeding designed to address the trade-off between semantic interoperability and real-time operational efficiency. The proposed Controlling Module uses a dual-layer communication strategy: a lightweight character-delimited TCP/IP protocol ensures deterministic performance for embedded controllers, while an XML-serialized format that maps to the FIPA Agent Communication Language preserves semantic interoperability. A custom serialization/deserialization algorithm was developed to process this XML structure within LabVIEW while avoiding the overhead typically associated with generic DOM/SAX parsers. The architecture was validated in a 120 h laboratory test that combined a Digital Twin simulation of 50 virtual feeders with Hardware-in-the-Loop testing of key sensing components. Under these test conditions, no communication failures were observed, all simulated network interruptions were recovered from, and the system operated with a modest resource footprint, including an average CPU use of 15% and a peak memory use of 350 MB. The platform also processed 2590 consumption events without reported data loss during the validation period. These results indicate that the proposed hybrid MAS architecture is a feasible solution for integrating interoperable decision support and deterministic edge control in PLF applications. Full article