Search Results (842)

Against the backdrop of global economic digital transformation and the rapid flow of creative factors, innovation spaces, as the key carriers of inventive activities, drive high-quality development in urban agglomerations. This study develops a three-dimensional framework of “Spatial Structure–Factor Synergy–Institutional Drivers” to uncover the evolution of innovation spaces and industrial shifts in the Beijing–Tianjin–Hebei urban agglomeration, China. Methodologically, spatial econometric techniques were applied to capture both the overall concentration and spatial disparities of innovation. Spatial Gini and variation coefficients measured innovation clustering, while standard deviation ellipses and location entropy identified spatial linkages among high-tech innovation clusters. Geographically weighted regression models explored spatial heterogeneity in influencing factors, and a policy intensity index was constructed to assess the effectiveness of differentiated policy interventions in optimizing innovation resources. Key findings include the following: (1) Innovation spaces are spatially polarized in a “core–periphery” pattern, yet require cross-regional collaboration. Concurrently, high-tech industries demonstrate a gradient structure: central cities leading in R&D, sub-central cities driving industrial applications, and node cities achieving specialized development through industrial transfer. (2) The driving mechanisms exhibit significant spatial heterogeneity: economic density shows diminishing returns in core areas, whereas R&D investment and ecological quality demonstrate increasingly positive effects, with foreign investment’s role evolving positively post-institutional reforms. (3) Regional innovation synergy has formed a preliminary framework, but strengthening sustainable policy mechanisms remains pivotal to advancing market-driven coordination and dismantling administrative barriers. These findings underscore the importance of integrated policy reforms for achieving balanced and high-quality innovation development in administratively coordinated urban agglomerations like BTH. Full article

Currently, intelligent algorithms are becoming a reliable alternative source of data analysis in many areas of human activity. In materials science, the integration of machine learning methods is effectively applied to predictive modeling of building materials properties. This is particularly interesting and relevant for predicting the strength properties of building materials under aggressive environmental conditions. In this study, machine learning methods (Linear Regression, K-Neighbors, Decision Tree, Random Forest, CatBoost, Support Vector Regression, and Multilayer Perceptron) were used to analyze the relationship between the strength properties of heavy concrete depending on the freeze–thaw cycle, the average area of damaged areas during this cycle, and the number of damaged areas. The Random Forest and CatBoost methods demonstrate the smallest errors: deviations from actual values are 0.27 MPa and 0.25 MPa, respectively, with an average absolute percentage error of less than 1%. The determination coefficient R² for both models is greater than 0.99. High values of this statistical measure indicate that the implemented models adequately describe changes in the observed data. The theoretical and practical development of intelligent algorithms in materials science opens up vast opportunities for the development and production of materials that are more resistant to aggressive influences. Full article

The high cost of traditional structural health monitoring systems limits their application to only a few major bridges, leaving most structures unmonitored between manual inspections. To address this issue, this study proposes a UAV mobile detection device (UMD) system that integrates a Raspberry Pi, data acquisition module, and accelerometer for rapid, contact-based vibration measurement. A vibration transmission model between the UMD and the bridge deck is developed to guide hardware design and quantify the influence of isolator stiffness and damping. The UMD’s performance is validated through both laboratory floor tests and field bridge experiments, demonstrating reliable identification of modal frequencies in the range of 0.00–51.95 Hz with a maximum acceleration error below 0.01 g and a relative modal frequency deviation within 3.4%. The analysis further determines that an accelerometer resolution of $0.02\times {10}^{-1}$ g is required for accurate frequency domain measurement. These findings establish the UMD as a fast, low-cost, and accurate tool for rapid bridge vibration assessment and lay the groundwork for future multi-UAV synchronized monitoring. Full article

Driven by carbon neutrality goals, electric–hydrogen coupled virtual power plants (EHCVPPs) integrate renewable hydrogen production with power system flexibility resources, emerging as a critical technology for large-scale renewable integration. As distributed energy resources (DERs) within EHCVPPs diversify, heterogeneous resources generate diversified market values. However, inadequate benefit allocation mechanisms risk reducing participation incentives, destabilizing cooperation, and impairing operational efficiency. To address this, benefit allocation must balance fairness and efficiency by incorporating DERs’ regulatory capabilities, risk tolerance, and revenue contributions. This study proposes a multi-stage benefit allocation framework incorporating risk–reward tradeoffs and an enhanced optimization model to ensure sustainable EHCVPP operations and scalability. The framework elucidates bidirectional risk–reward relationships between DERs and EHCVPPs. An individualized risk-adjusted allocation method and correction mechanism are introduced to address economic-centric inequities, while a hierarchical scheme reduces computational complexity from diverse DERs. The results demonstrate that the optimized scheme moderately reduces high-risk participants’ shares, increasing operator revenue by 0.69%, demand-side gains by 3.56%, and reducing generation-side losses by 1.32%. Environmental factors show measurable yet statistically insignificant impacts. The framework meets stakeholders’ satisfaction and minimizes deviation from reference allocations. Full article

Accurate object detection and measurement within indoor environments—particularly unfurnished or minimalistic spaces—pose unique challenges for conventional computer vision methods. Previous research has been limited to small objects that can be fully detected by applications such as YOLO, or to outdoor environments where reference elements are more abundant. However, in indoor scenarios with limited detectable references—such as walls that exceed the camera’s field of view—current models exhibit difficulties in producing complete detections and accurate distance estimates. This paper introduces a geometry-driven, redundancy-based framework that leverages proportional laws and architectural heuristics to enhance the measurement accuracy of walls and spatial divisions using standard smartphone cameras. The model was trained on 204 labeled indoor images over 25 training iterations (500 epochs) with augmentation, achieving a mean average precision (mAP@50) of 0.995, precision of 0.995, and recall of 0.992, confirming convergence and generalisation. Applying the redundancy correction method reduced distance deviation errors to approximately 10%, corresponding to a mean absolute error below 2% in the use case. Unlike depth-sensing systems, the proposed solution requires no specialised hardware and operates fully on 2D visual input, allowing on-device and offline use. The framework provides a scalable, low-cost alternative for accurate spatial measurement and demonstrates the feasibility of camera-based geometry correction in real-world indoor settings. Future developments may integrate the proposed redundancy correction with emerging multimodal models such as SpatialLM to extend precision toward full-room spatial reasoning in applications including construction, real estate evaluation, energy auditing, and seismic assessment. Full article

Accurate assessment of aerodynamic resistance in mine ventilation networks is essential for ensuring operational safety and energy efficiency, yet traditional measurement approaches remain time-consuming and prone to uncertainty. This study presents a novel methodology for constructing digital ventilation models of underground mine workings using markerless LiDAR scanning combined with automated data processing. The proposed procedure includes segmentation of point clouds into sections, calculation of geometric parameters, and direct determination of resistance coefficients, which are subsequently exported to VentSim software. The approach was validated through a case study conducted in a Polish coal mine, where a 369 m ventilation siding was scanned and analyzed. The comparison between numerical simulations and in situ measurements demonstrated strong agreement, with differences not exceeding ±5% for airflow velocity, pressure drop, and total flow rate, while larger deviations were observed for cross-sectional area (+5.1%). The method is limited by potential inaccuracies in determining excavation geometry, which can lead to errors in calculating resistance coefficients, particularly at excavation intersections and at the beginning and end of scanning sections. Point cloud analysis, determination of resistance coefficients for individual sections (segments), spatial transformation, and point cloud reduction, along with integration with VentSim, are based on Python scripts. Calculation results can be easily exported to other computational programs. The proposed approach enables integration with various sensors and allows for assigning this value directly to a given section (segment of the excavation). The method can support the construction of digital twins for mines or underground tunnels. The implementation codes of the developed algorithms have also been made available for educational and scientific purposes under the Modified GNU General Public License v3 (GPLv3). Full article

This study presents the development of a portable DOBI meter prototype designed for the rapid, low-cost evaluation of crude red palm oil (RPO) quality. The device employs two narrow-spectrum LEDs (UV at 269 nm and visible at 446 nm) as light sources, paired with a broadband photodiode (PD) detector to measure light absorption in a quartz cuvette containing 95% hexane-diluted oil samples. Dedicated LED driver circuits, a PD receiver module, and microcontroller-based data acquisition and display systems were integrated into a compact enclosure. Calibration procedures involved the measurement of LED emission spectra and PD responses, followed by standard curve generation using known RPO concentrations. The results from the DOBI meter were validated against a commercial spectrophotometer (Merck Prove 600), demonstrating high accuracy with less than 5% deviation. Further analysis of RPO extracted from microwave-treated mesocarps showed consistent DOBI values and carotenoid concentrations across both instruments. The developed device offers a reliable, accessible alternative for assessing palm oil quality, particularly in field or small-scale industrial settings. Full article

Objectives: This study proposes a systematic strategy for optimizing sustained-release formulations using mixture experiments. Methods: Model variables were identified and screened via LASSO regression, Smoothly Clipped Absolute Deviation (SCAD), and Minimax Concave Penalty (MCP), leading to the construction of a quadratic inference function-based objective model. Using this model, three multi-objective optimization algorithms—NSGA-III, MOGWO, and NSWOA—were employed to generate a Pareto-optimal solution set. Solutions were further evaluated through the entropy weight method combined with TOPSIS to reduce subjective bias. Results: The MCP-screened model demonstrated strong fit (AIC = 19.8028, BIC = 45.2951) and suitability for optimization. Among the Pareto-optimal formulations, formulation 45, comprising HPMC K4M (38.42%), HPMC K100LV (13.51%), MgO (6.28%), lactose (17.07%), and anhydrous CaHPO₄ (7.52%), exhibited superior performance, achieving cumulative release rates of 22.75%, 64.98%, and 100.23% at 2, 8, and 24 h, respectively. Compared with the original formulation, drug release was significantly improved across all time points. Conclusions: This integrated workflow effectively accounted for component interactions and repeated measurements, providing a robust and scientifically grounded approach for optimizing multi-component sustained-release formulations. Full article

This study examines the integration of digital fabrication technologies into the design and production of mycelium-based components, addressing the growing demand for sustainable and innovative interior design solutions. Using a parametric design approach, modular and customized suspended ceiling elements were developed for a specific interior setting to explore a material-specific design approach for mycelium-based components. Three-dimensional printing was employed to produce molds, which were subsequently tested with plaster, silicone, and mycelium across three different scales. Experimental observations focused on the overall form, surface details, growth behavior and dimensional accuracy, systematically capturing volumetric deviations arising from the living nature of the material. In parallel, acoustic performance was evaluated through simulations using the Sabine method. The untreated condition demonstrated the longest reverberation times, whereas conventional panels achieved reductions consistent with typical comfort standards. Prototypes produced with mycelium yielded measurable decreases in reverberation time compared to the untreated condition, particularly within the speech frequency range, and approached the performance of standard acoustic panels. These findings suggest that mycelium-based components, when further optimized in terms of density and geometry, hold the potential to contribute both aesthetic and acoustic value within sustainable interior environments. Full article

The application of orchard inspection robots has become increasingly widespread. How-ever, achieving autonomous navigation in unstructured environments continues to pre-sent significant challenges. This study investigates the Simultaneous Localization and Mapping (SLAM) navigation system of an orchard inspection robot and evaluates its performance using Light Detection and Ranging (LiDAR) technology. A mobile robot that integrates tightly coupled multi-sensors is developed and implemented. The integration of LiDAR and Inertial Measurement Units (IMUs) enables the perception of environmental information. Moreover, the robot’s kinematic model is established, and coordinate transformations are performed based on the Unified Robotics Description Format (URDF). The URDF facilitates the visualization of robot features within the Robot Operating System (ROS). ROS navigation nodes are configured for path planning, where an improved A* algorithm, combined with the Dynamic Window Approach (DWA), is introduced to achieve efficient global and local path planning. The comparison of the simulation results with classical algorithms demonstrated the implemented algorithm exhibits superior search efficiency and smoothness. The robot’s navigation performance is rigorously tested, focusing on navigation accuracy and obstacle avoidance capability. Results demonstrated that, during temporary stops at waypoints, the robot exhibits an average lateral deviation of 0.163 m and a longitudinal deviation of 0.282 m from the target point. The average braking time and startup time of the robot at the four waypoints are 0.46 s and 0.64 s, respectively. In obstacle avoidance tests, optimal performance is observed with an expansion radius of 0.4 m across various obstacle sizes. The proposed combined method achieves efficient and stable global and local path planning, serving as a reference for future applications of mobile inspection robots in autonomous navigation. Full article

The air-gap airflow significantly influences the performance of high-speed slotless permanent magnet synchronous motors (HSSPMSM), yet this critical factor is frequently overlooked during the design phase, resulting in performance deviations. This paper presents the design and multi-physical analysis of a 10 kW/40,000 rpm HSSPMSM, explicitly accounting for air-gap airflow effects. A comprehensive coupling model integrating electromagnetic, thermal, mechanical, and airflow fields is established to guide the motor design. Based on this analysis, the motor dimensions and parameters are determined, and a prototype is fabricated. Experimental validation demonstrates that the developed HSSPMSM successfully meets the design specifications. Considering air-gap airflow can obtain more accurate thermal design results with an accuracy improvement of 6.8% compared to not considering air-gap airflow. The close agreement between the simulated and measured performance confirms the effectiveness of the proposed design methodology that incorporates airflow effects. Full article

Lameness remains a significant welfare and productivity challenge in dairy farming, often underdiagnosed due to the limitations of conventional detection methods. Unlike most previous approaches to lameness detection that rely on a single-sensor or gait-based measurement, this study integrates four complementary data domains—behavioural, physiological, biochemical, and milk composition parameters—collected from 272 dairy cows during early lactation to enhance diagnostic accuracy and biological interpretability. The main objective of this study was to evaluate and compare the diagnostic classification performance of multiple machine learning (ML) algorithms trained on multimodal data collected at the time of clinical lameness diagnosis during early lactation, and to identify the most influential physiological and biochemical traits contributing to classification accuracy. Specifically, six algorithms—random forest (RF), neural network (NN), Ensemble, support vector machine (SVM), k-nearest neighbors (KNN), and logistic regression (LR)—were assessed. The input dataset integrated physiological parameters (e.g., water intake, body temperature), behavioural indicators (rumination time, activity), blood biochemical biomarkers (non-esterified fatty acids (NEFA), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), gamma-glutamyl transferase (GGT)), and milk quality traits (fat, protein, lactose, temperature). Among all models, RF achieved the highest validation accuracy (97.04%), perfect validation specificity (100%), and the highest normalized Matthews correlation coefficient (nMCC = 0.94), as determined through Monte Carlo cross-validation on independent validation sets. Lame cows showed significantly elevated NEFA and body temperatures, reflecting enhanced lipid mobilization and inflammatory stress, alongside reduced water intake, milk protein, and lactose content, indicative of systemic energy imbalance and impaired mammary function. These physiological and biochemical deviations emphasize the multifactorial nature of lameness. Linear models like LR underperformed, likely due to their inability to capture the non-linear and interactive relationships among physiological, biochemical, and milk composition features, which were better represented by tree-based and neural models. Overall, the study demonstrates that combining sensor data with blood biomarkers and milk traits using advanced ML models provides a powerful, objective tool for the clinical classification of lameness, offering practical applications for precision livestock management by supporting early, data-driven decision-making to improve welfare and productivity on dairy farms. Full article

India’s aviation sector, crucial for connectivity, economic growth, and national integration, faces sustainability measurement challenges focused solely on carbon emissions. This study proposes the Aviation Sustainability Performance Index (ASPI-India), spanning four pillars: Environmental Stewardship, Social Responsibility, Governance Maturity, and Economic Resilience. Measurable indicators are derived from regulatory filings, commercial flight databases, geospatial tracking, and targeted surveys. Data sources include DGCA safety audits, AAI operational statistics, ADS-B flight path data, and passenger satisfaction surveys from 2010 to 2024. Fixed-effects panel models link ASPI-India to operational and financial outcomes like load factor stability, CASK, and credit rating resilience. Quasi-experimental designs exploit policy shocks through difference-in-differences estimation. Factor analysis validates the four-pillar structure, and robustness checks compare entropy, PCA, and equal weighting. Results show that a one-standard-deviation increase in ASPI-India improves load factor stability, ancillary revenue share, and credit terms, especially for carriers with diversified route networks. The framework provides actionable insights for airlines, regulators, and investors to embed sustainability in aviation management. Full article

Background/Objectives: Optimization of pediatric head computed tomography (CT) protocols is essential to minimize radiation exposure while maintaining diagnostic image quality. Previous studies mainly relied on phantom-based measurements or visual assessments, and validation using clinical images remains limited. This study aimed to establish quantitative thresholds for noise and contrast-to-noise ratio (CNR) in pediatric head CT by integrating multicenter clinical data with phantom evaluations. Methods: A multicenter retrospective study was conducted using CT systems from eight hospitals, combined with Catphan phantom experiments and pediatric head CT data. Scan parameters, automatic exposure control settings, and reconstruction methods were collected. Image quality was quantified by the standard deviation (SD) of noise and CNR obtained from regions of interest in gray and white matter. Radiation dose was represented by CTDIvol. Relationships among CTDIvol, SD, and CNR were analyzed across scanners from three manufacturers (Canon, FUJI, and GE). Results: Consistent dose–response trends were observed across institutions and manufacturers. Image noise decreased as CTDIvol increased, but reached a plateau at higher doses. CNR improved with dose escalation, then stabilized. Both phantom experiments and clinical analyses identified a target SD of 5 and CNR of 2 as optimal indicators for pediatric head CT. Conclusions: Quantitative thresholds were determined as practical indicators for balancing diagnostic image quality with dose reduction. Further reduction may be achieved through advanced reconstruction methods, such as deep learning-based algorithms. These findings may contribute to standardizing pediatric head CT protocols and supporting safer and more effective diagnostic imaging. Full article

Accurate monitoring of settlement in high-speed railway embankments is critical for operational safety and long-term serviceability. This study investigates the applicability of Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) for quantifying millimeter-scale deformations and emphasizes how surrounding environmental factors influence measurement accuracy. Using 29 TerraSAR-X images acquired between 2016 and 2018, PS-InSAR-derived settlements were compared with precise leveling survey data across twelve representative embankment sections of the Honam High-Speed Railway in South Korea. Temporal and spatial discrepancies between the two datasets were harmonized through preprocessing, allowing robust accuracy assessment using mean absolute error (MAE) and standard deviation (SD). Results demonstrate that PS-InSAR reliably captures settlement trends, with MAE ranging from 1.7 to 4.2 mm across different scenes. However, significant variability in accuracy was observed depending on local land-cover composition. Correlation analysis revealed that vegetation-dominated areas, such as agricultural and forest land, reduce persistent scatterer density and increase measurement variability, whereas high-reflectivity surfaces, including transportation facilities and buildings, enhance measurement stability and precision. These findings confirm that environmental conditions are decisive factors in determining the performance of PS-InSAR. The study highlights the necessity of integrating site-specific land-cover information when designing and interpreting satellite-based monitoring strategies for railway infrastructure management. Full article