Search Results (504)

Additive manufacturing enables lightweight sandwich structures with complex cellular cores, but the selection of material and process settings typically involves trade-offs among mechanical performance, cost, and sustainability. This study proposes an integrated multi-criteria decision-making framework to identify the most suitable configuration for Fused Filament Fabrication (FFF) sandwich structures featuring a gyroid triply periodic minimal surface (TPMS) core. Eight alternatives are evaluated by combining two materials (PLA and PLA–Flax biocomposite) with two extrusion temperatures (200 °C and 220 °C) and two infill densities (20% and 30%). Mechanical performance is represented by flexural strength obtained from three-point bending tests reported in a previously published experimental campaign, while economic and environmental indicators are quantified through material cost and printing energy consumption, respectively. Criteria weights are derived using the Analytic Hierarchy Process (AHP) based on expert judgment and consistency-ratio verification, and the alternatives are ranked using the TOPSIS method. The results highlight a clear dominance of PLA-based configurations under the adopted weighting scheme, with PLA printed at 200 °C and 20% infill emerging as the best compromise solution. PLA–Flax options are penalized by higher material cost, higher printing-process energy demand, and lower flexural strength in the investigated conditions. The proposed AHP–TOPSIS workflow supports transparent, data-driven selection of AM process–material combinations for gyroid sandwich structures, and it can be readily extended by including additional sustainability metrics (e.g., CO₂-equivalent) and application-specific constraints. A sensitivity analysis under alternative weighting scenarios further confirms the robustness of the obtained ranking. Full article

Satellite precipitation products offer great potential for acquiring reliable precipitation data in data-sparse areas, yet they have inherent uncertainties and errors as indirect observations. This study evaluated the accuracy of multi-source satellite precipitation products from daily and precipitation magnitude perspectives and discussed the spatiotemporal variation in their inversion errors. Based on ground rainfall observations, satellite products, and environmental factors, a Transformer-based multi-source precipitation fusion method was proposed, with its effectiveness preliminarily analyzed for daily precipitation in the Jingle River Basin. The main conclusions are as follows: (1) Compared with the observed precipitation data, the GSMaP_Gauge satellite remote sensing precipitation product showed the closest agreement with the observations, ranking first in all indicators except the Probability of Detection (POD). The MSWEP satellite remote sensing precipitation product followed in performance, while the CHIRPS satellite product performed the poorest. Satellite products showed distinct error characteristics across seasons and rainfall intensities, as well as general overestimation of light rain frequency and insufficient heavy rain capture; however, these products also showed better detection capability in flood seasons. Error spatial distribution was consistent with topography, vegetation coverage, and temperature. (2) Verification demonstrated that the Transformer fusion algorithm effectively reduced relative bias and improved correlation with ground data. The scheme which incorporated environmental factors outperformed the other, which only considered precipitation characteristics, achieving higher estimation accuracy and fusion stability. Full article

The development of modern production systems involves numerous interdependent disciplines, heterogeneous data sources, and frequent design iterations, making the conceptual design phase particularly complex and error-prone. Model-Based Systems Engineering (MBSE) provides a promising approach to manage this complexity by enabling consistent and structured system representations. While domain-specific modeling languages (DSMLs) can tailor MBSE methods to specific domains, existing approaches often lack standardized semantics, user guidance, and tool support to ensure consistent model creation and verification. This paper introduces a DSML framework tailored for the conceptual design of production systems, integrating both methodological guidance and standard-based domain knowledge. The approach builds upon the Software Platform Embedded Systems (SPES) framework and extends Systems Modeling Language (SysML) through the Unified Modeling Language (UML) profile mechanism, providing clear modeling constructs, viewpoint-specific diagram types, and automated consistency checks. To enhance comprehensibility and domain alignment, the framework incorporates supplementary DSMLs that capture structures and semantics from established industrial standards. The proposed method is evaluated using an aircraft production case study, demonstrating improved applicability of MBSE for the conceptual design of complex production systems. Full article

TwinArray Sort is a non-comparison integer sorting algorithm designed for non-negative integers with relatively dense key ranges, offering competitive runtime performance and reduced memory usage relative to other counting-based methods. The algorithm introduces a conditional distinct-array verification mechanism that adapts the reconstruction strategy based on data characteristics while maintaining worst-case time and space complexity of O(n + k). Comprehensive experimental evaluations were conducted on datasets containing up to 10⁸ elements across multiple data distributions, including random, reverse-sorted, nearly sorted, and their unique variants. The results demonstrate consistent performance improvements compared with established algorithms such as Counting Sort, Pigeonhole Sort, MSD Radix Sort, Spreadsort, Flash Sort, Bucket Sort, and Quicksort. TwinArray Sort achieved execution times up to 2.7 times faster and reduced memory usage by up to 50%, with particularly strong performance observed for unique and reverse-sorted datasets. The algorithm exhibits good scalability for large datasets and key ranges, with performance degradation occurring primarily in extreme cases where the key range significantly exceeds the input size due to auxiliary array requirements. These findings indicate that TwinArray Sort is a competitive solution for in-memory sorting in high-performance and distributed computing environments. Future work will focus on optimizing performance for wide key ranges and developing parallel implementations for multi-core and GPU architectures. Full article

Salt stress is one of the main abiotic stresses that restrict crop production. Melatonin (MT), a signal molecule widely present in plants, plays an important role in regulating abiotic stress response. In this study, celery seedlings were used as experimental materials, and the control, salt stress, and exogenous MT treatment groups under salt stress were set up. Through phenotypic, physiological index determination, transcriptome sequencing, and expression analysis, the alleviation effects of MT on salt stress were comprehensively investigated. The results showed that exogenous MT treatment significantly reduced seedling growth inhibition caused by salt stress. Physiological measurements showed that MT significantly reduced malondialdehyde content, increased the activities of superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT), promoted the accumulation of free proline and soluble protein, and increased photosynthetic parameters such as chlorophyll, ΦPSII, Fv/Fm, and ETR. Transcriptome analysis showed that MT regulates the expression of several genes associated with carbon metabolism, including β-amylase gene (AgBAM), sucrose-degrading enzyme genes (AgSUS, AgINV), and glucose synthesis-related genes (AgAG, AgEGLC, AgBGLU). The results of qRT-PCR verification were highly consistent with the transcriptome sequencing data, revealing that MT synergistically regulates starch and sucrose metabolic pathways, and effectively alleviates the damage of celery seedlings under salt stress at the molecular level. In summary, exogenous MT significantly improved the salt tolerance of celery by enhancing antioxidant capacity, maintaining photosynthetic function, promoting the accumulation of osmotic adjustment substances, and synergistically regulating carbon metabolism-related pathways. The concentration of 200 μM was identified as optimal, based on its most pronounced alleviating effects across the physiological parameters measured. This study provides an important theoretical basis for utilizing MT to enhance plant salt resistance. Full article

Background: Digital transformation is reshaping healthcare operations, with loyalty programs increasingly used to strengthen patient engagement and streamline administrative workflows. However, fragmented information systems and manual verification routines continue to create bottlenecks, inconsistencies, and extended lead times. Methods: This study applies a mixed-methods approach within the Business Process Management (BPM) lifecycle to redesign the eligibility verification process for a loyalty program at Casa de Saúde São Mateus Hospital. Quantitative time measurements were collected during peak periods, while qualitative insights from staff observations and discussions supported process discovery and bottleneck identification. The proposed solution integrates a centralized SQL database, automated verification routines, and a dedicated administrative interface synchronized with the MedicineOne system. Results: The redesigned process reduced eligibility verification time by approximately 80% and improved Flow Efficiency by around 11.7%. Manual interventions, data fragmentation, and discount-application errors decreased substantially. The centralized database improved data reliability, while automated checks enhanced consistency and reduced staff workload. The system also enabled more accurate beneficiary management and improved coordination across administrative activities. Conclusions: Integrating Healthcare 4.0 principles with BPM enhances internal logistics, reduces lead times, and improves operational reliability. The proposed model offers a replicable framework for modernizing healthcare service delivery. Full article

Operational expenditure in wind farms is heavily influenced by unplanned maintenance, much of which stems from undetected rotor system faults. Although many fault-detection methods have been proposed, most remain confined to laboratory test. Blade-root bending-moment measurements are among the few techniques applied in the field, yet their reliability is limited by strong sensitivity to varying operational and environmental conditions. This study presents a data-driven rotor health-monitoring framework that enhances the diagnostic value of blade bending-moments. Assuming that the wind speed profile remains approximately stationary over short intervals (e.g., 20 s), a machine-learning model is trained on bending-moment data from healthy blades to predict the incident wind-speed profile under a wide range of conditions. During operation, real-time bending-moment signals from each blade are independently processed by the trained model. A healthy rotor yields consistent wind-speed profile predictions across all three blades, whereas deviations for an individual blade indicate rotor asymmetry. In this study, the methodology is verified using high-fidelity OpenFAST simulations with controlled blade pitch misalignment as a representative fault case, providing simulation-based verification of the proposed framework. Results demonstrate that the proposed inverse-modeling and cross-blade consistency framework enables sensitive and robust detection and localization of pitch-related rotor faults. While only pitch misalignment is explicitly investigated here, the approach is inherently applicable to other rotor asymmetry mechanisms such as mass imbalance or aerodynamic degradation, supporting reliable condition monitoring and earlier maintenance interventions. Using OpenFAST simulations, the proposed framework reconstructs height-resolved wind profiles with RMSE below 0.15 m/s (R² > 0.997) under healthy conditions, and achieves up to 100% detection accuracy for moderate-to-severe pitch misalignment faults. Full article

Nowadays, biowaste valorization is a key point in the circular economy. Digestate and compost from organic waste treatment can be used as nutrient-rich fertilizers. In Europe, the use of biowaste-derived fertilizers is promoted by the European Fertilizer Regulation (EU) 2019/1009, which requires verification of their biological stability through regulated indices; however, it is not clear whether the proposed indices and threshold values indicate the same level of stability and what correlations there are between them. This study compared four biological stability indices, namely Oxygen Uptake Rate (OUR), Self-Heating (SH), Residual Biogas Potential (RBP), and Dynamic Respirometric Index (DRI), which were tested on 50 samples of compost and digestate. Overall, the results revealed that most of the compost and digestate samples were quite far from European standards. On the contrary, the RBP test seemed to be less stringent than the other indices, since a much larger number of samples was closer to or in compliance with the established threshold. Data analysis using Pearson’s coefficients showed a strong linear correlation between the indices. Nevertheless, the linear regression predictive model based on experimental data demonstrated that the indices could not represent the same level of stability, providing poor consistency and variability in the predicted values and established threshold. In particular, the DRI test appeared to be more severe than the other aerobic indices. This work could provide valuable support in improving evaluation criteria and promoting a sustainable use of compost and digestate as organic fertilizers from a circular economy perspective. Full article

This study presents the design, implementation, and experimental validation of an integrated fastening monitoring platform for vehicle ground equipment, aimed at supporting structural maintenance and operational safety. Rather than introducing a fundamentally new sensing principle, the work focuses on the system-level integration and verification of existing sensing, communication, and control technologies for reliable bolt loosening detection and torque-controlled pneumatic fastening. The proposed platform consists of a Smart Control Gateway (SCG), a Signal Transducer Socket (STS), and a Smart Washer Set (SWS), incorporating smart nuts and clamping-force sensing washers for M50 and M35 bolts. Sub-GHz wireless RF communication and wired RS-485 transmission are employed to provide scalable and robust connectivity among system components. The SCG hardware and firmware are fully implemented and verified, enabling continuous acquisition and transmission of fastening-state data. Experimental evaluations include functional verification, mechanical integration tests, and durability assessments. The smart washers demonstrate stable sensing performance over 100 assembly and disassembly cycles without observable degradation. The STS is validated through 200,000 impact cycles under intermittent loading conditions (3 s impact, 3 s pause), confirming its suitability for repeated industrial operation. Real-time data transmission tests verify the system’s capability to detect bolt loosening events induced by vibration or external interference. The results indicate that the proposed platform provides a practical and reliable solution for fastening-state monitoring in safety-relevant ground equipment. This work contributes validated engineering evidence for deploying integrated smart fastening systems in industrial maintenance applications and establishes a foundation for future studies on environmental robustness, false-alarm characterization, and real-time performance guarantees. Full article

Mobile crowdsensing (MCS) is an emerging paradigm that enables cost-effective, large-scale, and participatory data collection through mobile devices. However, the open nature of MCS raises significant privacy and trust challenges. Existing reputation models have made progress in assessing the quality of contributions, but they still struggle to manage prolonged inactivity, which can lead to outdated scores that no longer reflect current engagement. To address these issues, this paper presents RBCrowd, a dynamic reputation management system based on a dual blockchain architecture. It consists of the Sensing Chain (SC), a public blockchain recording sensing tasks and results, and the Reputation Chain (RC), a consortium blockchain managing user reputation scores. To guarantee privacy, the framework limits identity verification to the RC, ensuring that data on the SC is stored without direct links to the worker. We paired this privacy mechanism with a reputation model that rewards consistent, high-quality contributions. The system updates reputation scores by first validating the specific task and then adjusting for historical engagement, specifically penalizing prolonged inactivity. We evaluate RBCrowd through simulations in realistic MCS scenarios, and the results show that our framework provides more effective dynamic trust management than existing models. It also achieves increased reliability and fairness while managing prolonged inactivity through adaptive penalties. Full article

As a high-fidelity digital mapping of the physical built environment, the Building Digital Twin (BDT) relies on physical–virtual interaction as a core enabler for lifecycle management. However, existing BDT conceptual models predominantly focus on unidirectional or single-threaded physical–virtual interactions, neglecting the dynamic, concurrent exchanges among multiple digital twins and human users. To overcome this limitation, the Multi-Interactive-Object BDT (MIO-BDT) framework is proposed. The central hypothesis is that explicitly modeling concurrent, multi-party interactions within a formalized conceptual structure can address a key representational gap in current BDT paradigms. The work pursues two testable objectives: (1) to formally define the components, relationships, and rules of the MIO-BDT framework and (2) to validate through a representative use case that the framework can model complex interaction scenarios that are inadequately supported by existing approaches. A systematic analysis of the state of the art is first conducted to ground the framework’s design. The MIO-BDT is then elaborated at both the system level (supporting dynamic interactions among twins, users, and physical entities) and the component level (integrating visual, physical, and interaction sub-models). Formal modeling and verification demonstrate that the framework is logically consistent and deadlock-free and effectively coordinates multi-entity data flows. These findings confirm that the MIO-BDT framework provides enhanced representational capacity, structural clarity for system design, and a unified model for diverse interaction types, thereby establishing a validated conceptual foundation for next-generation, interaction-aware BDT systems. Full article

To address the bottlenecks of traditional jaw crusher liner wear detection—high safety risks, insufficient precision, and limited full-range analysis—this paper proposes a non-contact, high-precision wear analysis method based on binocular vision and deep learning. At its core is the integration of the state-of-the-art FoundationStereo zero-shot stereo matching algorithm, following scenario-specific adaptations, into the 3D reconstruction of industrial liners for wear analysis. A novel wear quantification methodology and corresponding indicator system are also proposed. After calibrating the ZED2 binocular camera and fine-tuning the algorithm, FoundationStereo achieves an Endpoint Error (EPE) of 0.09, significantly outperforming traditional algorithms. To meet on-site efficiency requirements, a “single-view rapid acquisition + CUDA engineering acceleration” strategy is implemented, reducing point cloud generation latency from 165 ms to 120 ms by rewriting kernel functions and optimizing memory access patterns. Geometric accuracy verification shows a Mean Absolute Error (MAE) ≤ 0.128 mm, fully meeting industrial measurement standards. A complete process of “3D reconstruction–model registration–quantitative analysis” is constructed, utilizing three core indicators (maximum wear depth, average wear depth, and wear area ratio) to characterize liner wear. Statistical results—such as an average maximum wear depth of 55.05 mm—are highly consistent with manual inspection data, providing a safe, efficient, and precise digital solution for the predictive maintenance and intelligent operation and maintenance (O&M) of liners. Full article

In order to solve the dynamic analysis and interactive imaging control problems in the deformation process of bionic soft lenses, dielectric elastomer (DE) actuators are separated from a convex lens, and data-driven eye-controlled motion technology is investigated. According to the DE properties, which are consistent with the deformation characteristics of hydrogel electrodes, the motion and deformation effect of eye-controlled lenses under film prestretching, lens size, and driving voltage, is studied. The results show that when the driving voltage increases to 7.8 kV, the focal length of the lens, whose prestretching λ is 4, and the diameter d is 1 cm, varies in the range of 49.7 mm and 112.5 mm. And the maximum focal-length change could reach 58.9%. In the process of eye controlling design and experimental verification, a high DC voltage supply was programmed, and eye movement signals for controlling the lens were analyzed by MATLAB software (R2023b). Eye-controlled interactive real-time motion and tunable imaging of the lens were realized. The response efficiency of soft lenses could reach over 93%. The adaptive lens system developed in this research has the potential to be applied to medical rehabilitation, exploration, augmented reality (AR), and virtual reality (VR) in the future. Full article

Foundational biomedical sciences are commonly taught without routine integration of local population health contexts, limiting students’ ability to connect mechanisms to community disease burden and practice responsibilities. In this method paper, we developed and piloted an AI-enabled “Sacramento County Public Health (SACPH)” AI workflow and app prototype, a structured, faculty-authored prompt sequence designed to guide population-to-practice reasoning using publicly available data. The workflow was implemented during a TBL session with first-year PharmD students in an immunology course. Using splenectomy and risk of overwhelming post-splenectomy infection (OPSI) as an illustrative use case, students executed a standardized prompt sequence addressing data source identification, coding logic (diagnosis vs. procedure codes), population-level estimation with uncertainty framing, and translation to pharmacist-relevant prevention and counseling implications. Feasibility was defined by conceptual convergence. The validated reasoning workflow was subsequently translated into a prototype, app-style interface using generative design prompts. Across student teams, outputs converged on similar categories, consistent recognition of coding frameworks and verification steps, and directionally similar interpretations of local burden and pharmacist responsibilities. The prototype demonstrated successful externalization of the reasoning workflow into a modular, reproducible artifact. SACPH demonstrates a feasible, reproducible method for using generative AI to integrate foundational science instruction with local population health context and pharmacist practice reasoning, while supporting AI literacy competencies. Full article

In reinforced concrete structures, the quality of rebar sleeve connections directly impacts the structure’s safety reserve and durability. However, quality inspection is complicated by the periodic distribution of stirrups, concrete obstruction, and noise interference, presenting challenges for assessing sleeve connection integrity. This paper proposes a training-free, interpretable framework for automated rebar sleeve connection quality inspection, leveraging point cloud semantic filtering and geometric a priori segmentation. The method constructs a polar-cylindrical framework, employing hierarchical semantic filtering to eliminate stirrup layers. Geometric a priori instance segmentation techniques are then applied, integrating θ histograms, Kasa circle fitting, and axial bridging domain constraints to reconstruct each longitudinal rebar. Sleeve detection occurs within the rebar coordinate system via radial profile analysis of length, angular coverage, and stability tests, subsequently stratified into two layers and parameterised. Sleeve projections onto column axes calculate spacing and overlap area percentages. Experiments using 18 BIM-TLS paired datasets demonstrate that this method achieves zero residual error in stirrup detection, with sleeve parameter accuracy reaching 98.9% in TLS data and recall at 57.5%, alongside stable runtime transferability. All TLS datasets meet the quality requirements of rebar sleeve connection spacing ≥35d and percentage of overlap area ≤50%. This framework enhances on-site quality inspection efficiency and consistency, providing a viable pathway for digital verification of rebar sleeve connection quality. Full article