Search Results (3,669)

Hybrid girder bridges can be likened to plant grafting, where mechanical traits are inherited from both rootstock and scion girders, enabling performance that exceeds that of the individual components. To quantitatively evaluate this inheritance and optimize hybrid girder performance, this study develops a bionic binary grafting model inspired by the genetic principles of quantitative trait inheritance. By analyzing the flexural behavior of hybrid girders through classical beam theory, the research explores two sequential phases: trait inheritance and trait optimization. In the inheritance phase, the bending moment is governed by the hybrid ratio and the positional advantage of scion girders. In the optimization phase, iterative refinements in girder height and internal force further enhance structural performance. The key contributions of this study are as follows: (1) a novel bionic framework is proposed to quantitatively characterize mechanical trait inheritance in hybrid girders, introducing inheritance ratios to describe the distribution of bending moment between rootstock and scion girders as functions of the hybrid ratio, stiffness ratio, and load ratio; (2) a design-oriented framework for mechanical trait optimization is developed, demonstrating that hybrid girders can achieve equivalent stress performance with reduced structural height; and (3) the proposed inheritance and optimization formulations are validated against representative engineering cases, confirming their accuracy in estimating the optimal inheritance ratio and girder height for hybrid girder bridges. This bio-inspired framework enhances our understanding of hybrid girder performance enhancement mechanisms, enabling the efficient optimization of structural systems during conceptual design by leveraging materials with diverse mechanical properties. Full article

With the expanding application of synthetic aperture radar (SAR) in ocean monitoring and port regulation, nearshore ship detection based on SAR image faces notable challenges arising from strong background scattering, dense target occlusion, and large pose variations. Therefore, this paper proposes a two-stage oriented detection network named EARS-Net to improve the accuracy of ship detection in complex nearshore environments. Specifically, a lightweight convolutional block attention module (CBAM) is embedded into the high-level semantic stages of ResNet50 to enhance discriminative ship features while suppressing interference from port infrastructures and shoreline structures. Then, the dynamic angle regression loss (DAL) is proposed, and the angle weight function is designed according to the ship direction distribution characteristics, which allocates higher regression weight to the ship target with larger tilt angle, improving the defect of insufficient positioning accuracy for large angle ships. Moreover, a training strategy that combines focal loss, multi-scale training, and rotated online hard example mining (ROHEM) is employed to alleviate sample imbalance and improve generalization in dense scenes. Experimental results on the nearshore subset of the SSDD show that EARS-Net achieves an average precision (AP) of 0.903 on the test set, demonstrating reliable detection capability under complex backgrounds and dense target distributions. These results validate the effectiveness of our method and highlight its potential as a practical engineering solution for enhancing port situational awareness and coastal security monitoring. Full article

Background: Multimorbidity frequently involves overlapping neuro-psychic, cardiometabolic, and renal disturbances, yet clinical assessment often relies on diagnosis-based comorbidity counts that may not fully capture cumulative physiological stress. We developed the Neuro–Cardio–Renal Stress Index (NCR-SI) as a pragmatic composite framework to describe multisystem burden using routinely available clinical data. Methods: This cross-sectional study analyzed electronic medical record data from adult patients with chronic conditions. NCR-SI integrates three domains: neuro-psychic burden (text-derived indicators and psychotropic medication use), cardiometabolic stress (triglyceride–glucose index and cardiometabolic diagnoses), and renal function (MDRD-estimated eGFR staging). Importantly, this study is not intended to demonstrate incremental predictive value over individual components or established comorbidity indices. Rather, it presents NCR-SI as a transparent, domain-based descriptive framework and reports its internal coherence and distribution across clinically recognizable multimorbidity contexts. Results: A total of 148 patient records were screened; 143 patients met complete-case criteria and were included in the main NCR-SI analyses. NCR-SI ranged from 0 to 10 (median 5). Higher scores were observed in renometabolic profiles. NCR-SI showed expected structural associations with declining renal function (eGFR; ρ ≈ −0.71), moderately with the TyG index (ρ ≈ 0.42), and weakly with medication burden. Correlation with age-adjusted CCI was minimal (ρ ≈ 0.09), indicating limited overlap with diagnosis-based comorbidity counts. Domain-specific correlations were consistent with predefined score construction rules, particularly between the renal domain and eGFR, and between the cardiometabolic domain and TyG. Conclusions: NCR-SI provides a pragmatic, integrative descriptor of neuro-cardio-renal stress using routinely collected clinical data. Rather than replacing established comorbidity indices, NCR-SI may complement them by summarizing multidimensional physiological burden patterns. NCR-SI is proposed as a research-oriented, hypothesis-generating descriptive framework. External validation in independent cohorts and longitudinal evaluation against clinically meaningful outcomes (e.g., hospitalization, mortality, functional status, healthcare utilization) are required before any claims of clinical performance can be made. Full article

Effective port and coastal surveillance require persistent monitoring, flexible deployment, and reliable target detection in dynamic maritime environments. This paper presents a system- and deployment-oriented autonomous tethered drone architecture, integrated with AI-based perception, for persistent maritime surveillance in ports and coastal areas. Mounted on a moving maritime platform and powered through a tether, the drone provides a persistent elevated viewpoint without the endurance limitations of conventional battery-powered Unmanned Aerial Vehicles (UAVs). The system combines maritime platform integration, tethered flight operation, fail-safe and safety mechanisms, and a distributed Artificial Intelligence (AI) pipeline for real-time object detection and tracking. The perception module is based on YOLOv8m for vessel detection and BoT-SORT for multi-object tracking, enabling continuous monitoring of maritime targets in realistic operational scenarios. Field trials conducted from moving vessels in maritime environments demonstrate autonomous take-off and landing, stable surveillance operation under realistic wind and wave conditions, and effective vessel detection and tracking on real image sequences. The results show the potential of AI-enabled tethered drone surveillance as a persistent and operationally relevant tool for maritime monitoring and security. Full article

Background/Objectives: Tablet development requires simultaneous optimization of multiple quality attributes under limited experimental budgets, yet formulation–property relationships are highly nonlinear in mixture systems. To support pre-formulation decision-making prior to extensive tablet prototyping, this study proposes an AI framework that organizes formulation and process data together with raw-material property records into a reusable database, and enriches conventional composition/process features with physically motivated mixture descriptors derived from raw-material properties and formulation/process settings. Methods: Mixture-level scalar descriptors are constructed by composition-weighted aggregation of material properties, and particle size distribution (PSD) is incorporated via a compact set of summary statistics computed from composition-weighted mixture PSDs. Three feature sets are compared: (i) Materials + Processes (MP), (ii) MP with scalar Descriptors (MPD), and (iii) MPD with PSD summaries (MPDD). Five target properties are modeled: hardness, disintegration time, flow function, cohesion, and thickness. We train and evaluate Random Forest, Extra Trees Regressor, Lasso, Partial Least Squares, Support Vector Regression, and a multi-branch neural network that processes the three feature blocks separately and concatenates them for prediction. For interpolation assessment, repeated Train/Dev/Test splitting (5:3:2) across multiple random seeds is used, and the effect of feature augmentation is quantified by paired RMSE improvements with bootstrap confidence intervals and paired Wilcoxon signed-rank tests. To assess robustness under practical formulation updates, rolling-origin time-series splits are employed and Applicability Domain indicators are computed to characterize out-of-distribution coverage. Results: Across interpolation evaluations, mixture-descriptor augmentation (MPD/MPDD) improves hardness and disintegration time in most settings, whereas gains for flow function are smaller and cohesion/thickness show mixed effects under limited sample sizes. Conclusions: Under extrapolation-oriented evaluation, the descriptors can improve hardness but may degrade disintegration-time prediction under covariate shift, emphasizing the need for careful descriptor selection and dimensionality control when deploying pre-formulation predictors. Full article

Exploring public awareness, participation, and emotional inclination toward intangible cultural heritage (ICH) clarifies public attitudes and demands toward traditional culture, providing a crucial basis for targeted ICH protection and inheritance. Based on ICH text big data collected from China’s mainstream social media platform Weibo, this study improves the TF-IDF algorithm, integrates LDA topic analysis for semantic feature mining, and trains a new sentiment analysis model to explore public emotional attitudes and their formation mechanisms. The study is geographically limited to China and covers the entire year of 2023. The results show that: (1) Public ICH perception is multi-dimensional, with close attention to crafts like paper-cutting and traditional Chinese medicine; action-oriented terms reflect dynamic inheritance demands. Public discussions focus on three dimensions: ICH inheritance and development (39%), introduction and promotion (45%), and public experience and participation (16%), with the latter accounting for a low proportion. (2) Public sentiment toward ICH is predominantly positive, with all regions scoring above 0.730 (full score = 1), and Zhejiang (0.751) and Jiangsu (0.750) ranking significantly higher. (3) Spatial econometric analysis reveals marked regional differences in ICH sentiment distribution, mainly affected by three key factors—the number of ICH projects, the number of inheritors, and regional GDP—with regression coefficients of 0.699, 0.632, and 0.458 (p < 0.01). This finding provides a basis for formulating targeted ICH protection strategies. Full article

The security of biometric data is a critical challenge in modern information security due to their uniqueness and non-revocability. Compromise of biometric characteristics leads to irreversible consequences; therefore, storing or transmitting them in plaintext is unacceptable. This paper addresses the confidentiality and integrity of fingerprint data using cryptographic protection methods. Considering the specific nature of biometrics, fingerprint features are used only to generate a cryptographic secret rather than being stored directly. To protect the derived secret, a modified threshold secret-sharing scheme based on non-positional polynomial notation and the Chinese Remainder Theorem is proposed. The method generates a cryptographic secret from fingerprint minutiae described by spatial coordinates and ridge orientation. Concatenating minutiae coordinates and converting them into binary form produces a unique value deterministically linked to a specific user. Compared to the classical Shamir scheme, the modified scheme reduces the computational complexity of secret reconstruction from O($n {log}^{2}n$) to O(k log k), decreases data storage requirements by 30–40% through compact polynomial remainders, and increases successful secret reconstruction by 12–15% in the presence of noise in biometric samples. The results show that the proposed algorithm can be effectively applied in biometric authentication systems to protect personal data in distributed environments. Security analysis confirms resistance to major attack classes and demonstrates practical applicability in real-world systems. Full article

The present study examines the relationship between environmental, economic, and bioenergy impacts of renewable energy in Egypt, spanning from 1990 to 2023. The study utilizes three distinct models, concluding that all variables remain stationary at I(0) and I(1). Therefore, analysis is conducted using the Autoregressive Distributed Lag (ARDL) approach. Model 1 captures the relationship between bioenergy production and GDP per capita, as well as the significant impact of capital formation on economic growth. Models 2 and 3 of the study have CO₂ emissions as the dependent variable, which indicates that renewable energy, urbanization, GDP per capita, and bioenergy production have a significant impact. Moreover, the short-run analysis conducted using the Error Correction Model (ECM) reveals the model’s long-run convergence. The study’s findings also support the environmental Kuznets curve (EKC) hypothesis, and the interaction term between bioenergy and GDP per capita is found to contribute to carbon dioxide emissions. To achieve a low-carbon and growth-oriented economy, policymakers should encourage investment in renewable energy, as it enhances technological efficiency and fosters sustainable agricultural growth. Full article

Additive manufacturing by Selective Laser Melting (SLM) or, precisely, Laser Powder Bed Fusion (L-PBF), offers new opportunities for producing electrically functional metal components with tailored geometric designs and material properties. In this study, the electrical contact resistance and related properties of 3D-printed samples made from Al5086 aluminum alloy are tested. The benefits of Al5086 include flexibility without cracking, welding ability and exceptional resistance to corrosion in saltwater and industrial environments. This makes it an excellent candidate for power electric applications due to its good electrical conductivity and corrosion resistance. In this study, an analysis is performed to assess the impact of internal volumetric properties and surface parameters on general contact resistance performance. This analysis combines advanced testing procedures and parameter identification of the electric contact resistance model. This study investigates how these parameters affect contact resistance, which is a critical factor in the reliability of electrical devices. Electrical contact resistance was measured using a dedicated test setup that applied consistent pressure and maintained directional alignment. The results show that the printing direction of the samples slightly affects resistance values due to the continuity of current paths along the build direction, likely due to homogenous inter-layer boundaries and mechanical stress distribution. These findings suggest that both print orientation and internal structure must be considered when designing 3D-printed contact elements for electrical applications. Overall, this study demonstrates the feasibility of using L-PBF-fabricated aluminum components in electric applications where both electrical and structural performances are essential. Full article

Large-scale urban tree donation campaigns are widely implemented worldwide as nature-based solutions for climate adaptation and mitigation; however, most programs lack individual-level traceability and post-donation monitoring, limiting accountability and evidence-based management. A fundamental prerequisite for longitudinal survival assessment is the existence of a reliable traceability infrastructure capable of linking individual trees to verified planting records over time. This study proposes and empirically evaluates a participatory digital traceability system that establishes this foundational infrastructure, conceptualized as a distributed data validation architecture for donation-based urban afforestation programs. The framework integrates (i) persistent digital identifiers, (ii) geospatial registration, (iii) distributed multi-stage validation, and (iv) structured citizen reporting, and is operationalized through an installation-free progressive web application (ArborizaCL). The approach was deployed in five real-world campaigns conducted in Valdivia, Chile (May–September 2025), registering 642 trees distributed to 240 participants. A total of 190 georeferenced planting reports were submitted, corresponding to an overall reporting rate of 29.6%. Reporting behavior varied substantially by institutional follow-up strategy: campaigns with active follow-up achieved a mean reporting rate of 54.0%, compared with 13.0% under passive strategies, yielding a 41.0 percentage point difference (315.8% relative increase). Spatial analysis of reported plantings showed a predominance of urban (51.1%) and peri-urban (42.1%) locations, enabling differentiated territorial assessment. These results indicate that while digital infrastructure enables traceability and transparent monitoring, sustained citizen engagement is strongly associated with institutional coordination mechanisms. Beyond environmental monitoring, the proposed framework contributes to information governance by demonstrating how participatory digital traceability systems can support distributed public-sector oversight and outcome-oriented evaluation. The framework provides a transferable methodological basis for strengthening monitoring capacity, transparency, and governance design in publicly funded afforestation initiatives and other distributed civic programs. Full article

Poly(lactic acid) (PLA) devices can be functionalized with plant-derived bioactives to introduce antioxidant activity while maintaining manufacturability and cytocompatibility. Here, a polyphenol-rich mango leaf extract (MLE) was obtained by enhanced solvent extraction and incorporated into PLA using supercritical carbon dioxide-assisted impregnation. Two manufacturing sequences were compared: impregnation after three-dimensional (3D) printing of discs and impregnation of filaments prior to printing. Extract yield and radical scavenging capacity were quantified, and impregnation efficiency was assessed as a function of pressure and temperature. Biological performance was evaluated using adipose tissue-derived endothelial colony-forming cells (ECFCs) and adipose tissue-derived mesenchymal stromal cells (MSCs), cultured separately and in co-culture on functionalized substrates. Impregnation after printing provided higher and more reproducible loading while preserving disc geometry, whereas impregnation before printing promoted swelling and printing-associated deformation that compromised structural fidelity. Cell-based analyses supported improved adhesion, spatial distribution, and proliferative status on discs produced by impregnation after printing under low-temperature and high-pressure conditions, without evidence of selective loss of either population in co-culture by flow cytometry. These results support post-print supercritical impregnation as a robust route to generate antioxidant, cell-supportive PLA scaffolds from agricultural by-products with potential relevance for vascular-oriented biomedical applications. Full article

In this study, a generalized and advanced form of the near soft set theory (NST) framework is proposed for information aggregation (IA) processes. The primary motivation of the study is to address the lack of similarity-based uncertainty modeling in the literature by integrating the parametric structure of soft sets with the similarity-oriented structure of nearness approximation spaces. Within this framework, the AND-product and OR-product operations are introduced as the main methodological tools, and their algebraic structures are analyzed in detail. It is mathematically demonstrated that these operations satisfy fundamental properties such as idempotency, absorption, distributivity, and De Morgan identities. The principal original contribution of the study is the development of a novel Uni–Int-based decision-making mechanism that enables the systematic distinction between strong and acceptable alternatives. In addition, the boundary frequency indicator ($b_r$), which quantitatively evaluates the reliability of objects under perceptual uncertainty and is introduced for the first time in the literature, is proposed. The applicability of the proposed model is demonstrated through a real-estate selection problem, and a sensitivity analysis is conducted to reveal the determining effect of the nearness parameter r on decision granularity. The obtained findings indicate that the proposed NST framework provides a more flexible, more discriminative, and structurally robust decision-support model than classical approaches, particularly for similarity-based IA problems. Full article

Background: Lung cancer (LC) remains the leading cause of cancer-related mortality worldwide. Survival outcomes are strongly stage-dependent. Many patients are diagnosed at advanced stages due to pre-clinical and diagnostic delays. While advances in imaging, bronchoscopic techniques, molecular diagnostics, and systemic therapies have improved individualized treatment, system-level delays continue to limit their impact. Aim of the study: The aim of this narrative review is a synthesis with an implementation-oriented framework proposal. Part I synthesizes the peer-reviewed literature, Part II presents an operational framework integrating a Fast Trac Clinic (FTC) and a network of Lung Cancer Units (LCUs) including proposed turnaround-time (TAT) goals. Methods: A narrative review of the literature of selected European policy documents addressing diagnostic delays, rapid-access lung cancer pathways, and coordinated care models was conducted. Results: European models demonstrate that structured referral criteria, centralized coordination, and predefined interval targets can achieve the first specialist assessment within 7–10 days and the completion of diagnostics within 21–28 days in optimized settings. Key determinants of timeliness include: direct primary care referral, parallel diagnostic processes, prioritized pathology and molecular testing, and multidisciplinary team (MDT) assessment. We propose operational TAT targets for chest CT, PET-CT, histopathology, NGS, PFTs, and MDT decision-making. Conclusions: Reducing avoidable diagnostic and therapeutic delays in LC requires a coordinated, system-level approach. A standardized FTC-LCU pathway with explicit TAT benchmarks, multidisciplinary governance, and digital support infrastructure may improve diagnostic efficiency, increase the proportion of patients treated at earlier stages, and enhance patient experience. Prospective evaluation of implementation impact on stage distribution and survival is advised. Full article

To enhance the sustainability of temporary office buildings, energy-saving and emissions-reduction technologies, as well as the optimization of photovoltaic (PV) energy storage systems in such structures, are of great importance. In this study, a distributed energy storage system was developed for a temporary office building in Jincheng, China. Measurements showed climatic factors had the greatest effect on building energy consumption due to the building envelope’s low thermal performance and airtightness. The air conditioning system accounted for the highest proportion (87%) of building energy consumption. The PV system’s peak output occurred in the morning due to illumination conditions and module orientation. On this basis, a time-of-use (TOU)- and state-of-charge (SOC)-aware scheduling strategy was developed for the PV-ESS of the temporary office building to improve renewable-energy utilization and reduce user-end electricity cost. Unlike purely theoretical optimization studies, this work focuses on the practical application and validation of the scheduling framework in a real temporary office building using monitored data. The electricity cost decreased by 0.3 RMB/kWh, and the revenue from electricity sales during the scheduling period increased by 0.03 RMB/kWh after model optimization. The optimized scheduling strategy resulted in significantly fewer charge–discharge cycles of the storage battery, substantially decreasing the battery’s storage capacity and the system’s investment costs. Full article

The Internet of Things (IoT) plays an increasingly central role in healthcare by enabling continuous patient monitoring, remote diagnosis, and data-driven clinical decision-making through interconnected medical devices and sensing infrastructures. Despite these advances, IoT healthcare systems remain constrained by persistent challenges related to data privacy, computational efficiency, scalability, and regulatory compliance. Federated learning (FL) reduces reliance on centralised data aggregation but remains vulnerable to inference-based privacy risks, while edge-oriented approaches are limited by device heterogeneity and restricted computational and energy resources; the deployment of large language models (LLMs) further exacerbates concerns surrounding privacy exposure, communication overhead, and practical feasibility. This study introduces Trustworthy Intelligence (TI) as a guiding framework for privacy-preserving distributed intelligence in IoT healthcare, explicitly integrating predictive performance, privacy protection, and deployment-oriented system design. Within this framework, split learning (SL) is examined as a core architectural mechanism and extended to support split-aware LLM integration across heterogeneous devices, supported by a structured taxonomy spanning architectural configurations, system adaptation strategies, and evaluation considerations. The study establishes a systematic mapping between SL design choices and representative healthcare scenarios, including wearable monitoring, multi-modal data fusion, clinical text analytics, and cross-institutional collaboration, and analyses key technical challenges such as activation-level privacy leakage, early-round vulnerability, reconstruction risks, and communication–computation trade-offs. An energy- and resource-aware adaptive cut layer selection strategy is outlined to support efficient deployment across devices with varying capabilities. A proof-of-concept experimental evaluation compares the proposed SL–LLM framework with centralised learning (CL), federated learning (FL), and conventional SL in terms of training latency, communication overhead, model accuracy, and privacy exposure under realistic IoT constraints, providing system-level evidence for the applicability of the TI framework in distributed healthcare environments and outlining directions for clinically viable and regulation-aligned IoT healthcare intelligence. Full article