Search Results (32,741)

To address the limitations of conventional social force models in simulating high-density pedestrian crowds, this study proposes an enhanced model that incorporates visual perception constraints, group-type labeling, and collective avoidance mechanisms. Pedestrian trajectories were extracted from a bidirectional commercial street scenario using OpenCV, with YOLOv8 and DeepSORT employed for multiple object tracking. Analysis of pedestrian grouping patterns revealed that 52% of pedestrians walked in pairs, with distinct avoidance behaviors observed. The improved model integrates three key mechanisms: a restricted 120° forward visual field, group-type classification based on social relationships, and an exponentially formulated inter-group repulsive force. Simulation results in MATLAB R2023b demonstrate that the proposed model outperforms conventional approaches in multiple aspects: speed distribution (error < 8%); spatial density overlap (>85%); trajectory similarity (reduction of 32% in Dynamic Time Warping distance); and avoidance behavior accuracy (82% simulated vs. 85% measured). This model serves as a quantitative simulation tool and decision-making basis for the planning of pedestrian spaces, crowd organization management, and the optimization of emergency evacuation schemes in high-density pedestrian areas such as commercial streets and subway stations. Consequently, it contributes to enhancing pedestrian mobility efficiency and public safety, thereby supporting the development of a sustainable urban slow transportation system. Full article

This paper examines the growing field of AI-assisted urban planning within the context of sustainable urban development, with a particular focus on spatial optimization of urban green spaces under conditions of scarcity, density, and economic pressure. While the economic, ecological, and social values of UGS are widely acknowledged, urban planners lack a cohesive, data-driven framework to quantify and spatially optimize these often-conflicting values for effective land-use optimization. To address this gap, we propose a methodology that combines Geographic Information Systems (GISs), the Analytic Hierarchy Process (AHP), and an Artificial Intelligence-Based Genetic Algorithm (AI-GA). Vračar was chosen as the case study area. Our approach evaluates (1) the economic value of UGS through housing prices; (2) the ecological value through UGS density; and (3) the social value by measuring access to urban green pockets. The integrated method simulates environmental scenarios and optimizes UGS placement for resilient urban areas. Results demonstrate that properties in mixed-use green areas proximate to urban parks have the highest economic and social value. Additionally, higher densities of UGS correlate with higher housing prices, highlighting the economic impact of green space distribution. The methodology enables planners to make decisions based on evidence that integrates statistical modeling, expert judgment, and artificial intelligence into one cohesive platform. Full article

The effective focal length is a critical determinant of optical performance and imaging quality, serving as a fundamental parameter for components ranging from ophthalmic lenses to precision microlens arrays. With the rapid advancement of complex optical systems in microscopy and smart manufacturing, there is an increasing demand for high-precision measurement techniques that can characterize these parameters with low uncertainty. In this paper, a quadri-wave lateral shearing interferometry (QWLSI) measurement system was developed. A novel precision focal length measurement method of optical lenses based on the principle of QWLSI is presented. A theoretical model for solving the focal length of the measured lens from the curvature radius of the wavefront was derived. We also proposed a novel algorithm and subsequently developed a dedicated hardware platform and a corresponding software package for its real-time implementation. Different sets of repeated measurement experiments were carried out for two convex lenses with symmetrical and asymmetrical structures, a large-scale concave lens, and a microlens array, to verify the measurement uncertainty and robustness of the QWLSI measurement system. The expanded uncertainty was also analyzed and determined as 0.31 mm (k = 1.96, normal distribution). The results show that the proposed QWLSI measuring system possesses good performance in measuring the focal lengths of different kinds of lenses and can be widely used in fields such as advanced optics manufacturing. Full article

Non-destructive monitoring of fruit ripening is essential for optimising harvest time, yet its application to tropical viticulture remains largely unexplored. This study evaluated in situ chlorophyll a fluorescence as a non-invasive physiological marker to track berry development and metabolic maturation in two table grape cultivars (Vitis labrusca L. var. Niagara Rosada and var. Romana) under tropical field conditions, characterised by the latitude position, absence of chilling-induced dormancy, and variable rainfall during ripening. Berries’ fluorescence parameters (Fo, Fm, Fv and Fv/Fm) were monitored weekly from the pea-size stage to commercial harvest (67–123 days after pruning) using a portable modulated fluorometer, along with chlorophyll and quality trait measurements. A decline in fluorescence parameters during maturation coincided with chlorophyll degradation and the accumulation of glucose and fructose. The maximum quantum yield of PSII (Fv/Fm) remained stable (≈0.75) throughout development, indicating sustained photochemical efficiency despite chloroplast disassembly. Significant correlations (r > 0.80) were established between fluorescence parameters and key maturity indices, with distinct cultivar-specific patterns evident between the NR and RM cultivars. Therefore, chlorophyll a fluorescence provided a reliable, portable, non-destructive tool for monitoring ripening dynamics and estimating quality parameters in table grapes, offering practical advantages for tropical viticulture where environmental variability demands flexible monitoring. Full article

The pulsed power switch serves as a critical component in pulsed power systems. The electric radiation generated by switching operations threatens the miniaturization of pulsed power systems, causing significant electromagnetic interference (EMI) to nearby signal circuits. The pseudospark switch’s (PSS) exceptionally fast transient response, a key enabler for sophisticated pulsed power systems, is also a major source of severe EMI. This study investigated the electric field radiation from a high voltage PSS within a capacitor discharge unit (CDU), using a near-field scanning system based on an electro-optic probe. The time-frequency distribution of the radiation was characterized, identifying contributions from three sequential stages: the application of the trigger voltage, the main gap breakdown, and the subsequent oscillating high voltage. During the high-frequency oscillation stage, the distribution of the peak electric field radiation aligns with the predictions of the dipole model, with a maximum value of 43.99 kV/m measured near the PSS. The spectral composition extended to 60 MHz, featuring a primary component at 1.24 MHz and distinct harmonics at 20.14 MHz and 32.33 MHz. Additionally, the impacts of circuit parameters and trigger current on the radiated fields were discussed. These results provided essential guidance for the electromagnetic compatibility (EMC) design of highly-integrated pulsed power systems, facilitating more reliable PSS applications. Full article

This study focuses on the calibration and verification of a large-scale coupled numerical model to simulate the complex hydrodynamic–wave–sediment transport processes in the coastal and estuarine regions of the Mekong River Delta (MRD), Vietnam. Using the MIKE 21/3 modeling system, the research integrates Hydrodynamics (HD), Spectral Wave (SW), and Mud Transport (MT) modules across a computational domain of 270 × 300 km. The models were rigorously tested using field measurement data from three distinct periods: May 2004 (dry season calibration), September 2017 (first verification), and June 2024 (second verification). The results from the hydrodynamic model demonstrated high accuracy in predicting water levels, with the average Root Mean Square Error (RMSE) values ranging between 4.4% and 5.8%. The wave spectral model showed reliable performance, with the average RMSE values for wave height ranging from 15.1% to 18.0%. Furthermore, the Mud Transport module successfully captured suspended sediment concentrations (SSC), yielding average RMSE values between 26.0% and 32.1% after the fine-tuning of site-specific parameters such as critical shear stress for erosion and deposition. The study highlights the critical importance of utilizing site-specific sedimentological parameters to accurately predict morphological changes in highly dynamic estuarine environments. This validated model provides a robust tool for assessing coastal erosion and developing protection measures in regions that are increasingly vulnerable to climate change and human activities. Full article

The Wiegand effect is a nonlinear magnetic phenomenon observed in specially processed Wiegand wires, representing a macroscopic manifestation of the Barkhausen effect. It is characterized by a large, sharp Barkhausen jump in the wire’s magnetization curve under an external alternating magnetic field. However, the underlying magnetic structure of these wires and the precise mechanism responsible for the Wiegand effect remain inadequately understood. In this study, we propose a conceptual model for the magnetic structure of Wiegand wires. Experimental samples with varying diameters were prepared through FeCl₃ solution etching. The magnetic properties of individual layers within the wire were systematically investigated using the surface magneto-optic Kerr effect, Wiegand pulse measurements, and minor hysteresis loop analysis. By correlating these experimental results with JMAG simulations based on the proposed magnetic structure model, we elucidate the layer-by-layer magnetization reversal processes under alternating magnetic fields and clarify the fundamental mechanism that triggers the large Barkhausen jump. Full article

Background/Objectives: Considering that the kinematics of the temporomandibular joints (TMJs) is concomitant with head movements and that temporomandibular joint disorders (TMDs) are frequently associated with neck pain in clinics but seldom or never investigated, the aim of this study was to develop a reliable in vivo measurement protocol of the simultaneous amplitudes of the mandible and of the skull. The development of such a protocol is part of a project to build an accurate kinematic assessment tool for clinicians in the orofacial field who treat patients suffering from TMD. Methods: Mouth opening, laterotrusion and protrusion movements for three different positions of the head (neutral, slouched and military) on 12 asymptomatic voluntary subjects (5 men and 7 women, mean 33.6 yo +/− 11.1) were recorded using 20 markers palpated and taped and 14 optoelectronic cameras. The acquisition frequency was set at 150 hertz. The inter- and intra-examiner reliability of marker palpation in mm was calculated using standard deviation (SD), mean difference (MD) and standard error (SE). Amplitudes of movement according to axes defined by the International Society of Biomechanics (ISB) are given for the mandible and skull segments. The propagation of error on the amplitudes was calculated with the root mean square propagation error (RMSPE) in degrees. Repeated-measures ANOVA or Friedman tests were used to assess the influence of the position of the head on the amplitudes of the jaw. Power analysis of the sample size was estimated with Cohen’s f3 size effect test. Steady-state plots (SSPs) and normalized motion graphs between the skull and the mandible motion were performed to study the coordination of their maximum amplitude over time. Results: The protocol demonstrated good intra-examiner reliability (1.5 < MD < 5.8; 2.6 < SD < 7.8; 2.0 < SE < 3.8), good inter-examiner reproducibility (0.2 < MD < 4.0; 3.5 < SD < 4.6; 2.0 < SE < 2.5) and small error propagation (0.0 < RMSPE intra < 2.8; 0.0 < RMSPE inter < 1.0). The amplitudes of the jaw and head found during the three types of movements correspond to the values reported in the literature. Head positions did not appear to significantly influence the amplitudes of jaw movements, which could be explained by the power estimation of our sample (Type II error β = 0.692). The participation of head movements in those of the jaw, for all motions and in all positions, was demonstrated and discussed in detail. Conclusions: The accuracy, test–retest reliability, and intra-individual variability of the TMJ kinematic analysis, including head movements, was ensured. The small sample size and the absence of standardized head positions for the subjects limit the scope of the intra- and inter-group analysis results. Given the natural biological and complex coordination of jaw–head movement, the authors consider its evaluation useful in clinical intervention and would like to further develop the present protocol. The next step should be to test the feasibility of its clinical application with a larger group of asymptomatic subjects compared to patients suffering from TMD. Full article

Whey protein concentrate (WPC-80) was reconstituted to 10% (m/v) and pumped through a pulsed electric field (PEF) system using three treatment conditions. The PEF-treated whey solution was assessed for viscosity, whereas dried whey was resolubilized and tested for protein structure integrity by circular dichroism (CD), fluorescence, and differential scanning calorimetry (DSC), and functionality was assessed by measuring solubility, foamability, emulsification, and particle size. PEF treatment resulted in a reduction in apparent viscosity (from 2.74 cP down to 2.57 cP) and particle size (from 325.9 nm down to 297.6 nm), and increased solubility (from 90.41% up to 92.34%) and emulsification stability (from 1727 min up to 4821 min), while emulsification stability decreased initially (from 1.645 m²/g to 1.283 m²/g) then increased at the high treatment level (1.915 m²/g). The foamability and molecular weight profile did not change with PEF treatment. Exposure to PEF resulted in no statistically significant changes to protein structure based on data obtained from CD, fluorescence, or DSC. This study represents the first instance of a WPC-80 being treated with a commercially available, scalable, continuous flow PEF system at a higher concentration (10% m/v), resulting in favorable changes to the physical and functional properties of the whey solution and dried powder. Full article

This paper presents a study of the tonality of sound emitted by a wind farm into the surrounding environment. The wind turbines installed at the site have a rated power of 3.0 MW. The aim of the study was to analyse the tonality of sounds in the environment at the nearest residential area. The issue of tonal noise near the wind farm was identified during routine periodic noise monitoring. An experienced survey team identified the phenomenon and carried out preliminary field analyses. Detailed studies were then carried out to identify the environmental hazard and failure-free operation of the turbines. The recorded acoustic events are described in detail and an in-depth analysis is carried out. An action plan has been implemented in consultation with the wind farm operator to reduce tonal sound emissions to the surrounding environment. As a result of these interventions, tonal noise from the wind turbines was successfully reduced. It was determined that the detection of the potential tonality of the sounds emitted by wind turbines should take place during the analysis (active listening) of the .wav file, synchronised with Fast Fourier Transform (FFT) analysis. Conducting tonality assessments solely during field measurements may lead to incorrect identification of tonal sources. Full article

This study investigates radio frequency (RF) wave propagation in sandstone and limestone cave environments, emphasizing the use of LiDAR-derived three-dimensional (3D) models to characterize cave geometry and support waveguide-based propagation analysis incorporating skin depth effects. RF transmission and reception measurements were conducted under line-of-sight (LOS) conditions across frequency bands from Low Frequency (LF) to Ultra-High Frequency (UHF). Comparative results reveal distinct attenuation behaviors governed by differences in cave geometry and electrical properties. The sandstone cave, with a more uniform geometry and relatively higher electrical conductivity, exhibits lower attenuation across most frequency bands, whereas the limestone cave shows higher attenuation due to its irregular structure. LiDAR-based 3D models are employed to extract key geometric parameters, including cavity dimensions, wall roughness, and wall inclination, which are incorporated into the proposed analytical framework. The model is further validated using experimental field measurements, demonstrating that the inclusion of LiDAR-derived geometry and skin depth effects enables a more realistic representation of underground RF propagation for communication system analysis. Full article

Reliability and safety of high-voltage transmission lines are essential for stable and continuous operation of a power system. Environmental factors such as pressure, temperature, surface contamination, humidity, etc., significantly affect the dielectric strength of air, often causing unpredictable voltage breakdowns. This research presents a novel machine learning-based predictive framework that integrates Paschen’s Law with simulated and empirical data to estimate the breakdown voltage $\left(V_{bk}\right)$ of transmission lines in various environmental conditions. The main contribution is to demonstrate that data-driven prediction of breakdown voltage $\left(V_{bk}\right)$ using a hybrid machine learning model is in agreement with physical discharge theory. The model achieved root mean square error (RMSE) of 5.2% and mean absolute error (MAE) of 3.5% when validated against field data. Despite the randomness of avalanche breakdown, model predictions strongly match experimental measurements. This approach enables early detection of insulation stress, real-time monitoring, and optimises maintenance scheduling to reduce outages, costs, and safety risks. Its robustness is confirmed experimentally. Overall, this work advances the prediction of avalanche breakdown behaviour using machine learning. Full article

We present E-CAHORS—a compact mid-infrared open-path diode-laser spectrometer designed for the simultaneous measurement of carbon dioxide, methane, and water vapor concentrations in the near-surface atmospheric layer. These measurements, combined with simultaneous data from a three-dimensional anemometer, can be used to determine fluxes using the eddy-covariance method. The instrument utilizes two interband cascade lasers operating at 2.78 µm and 3.24 µm within a novel four-pass M-shaped optical cell, which provides high signal power and long-term field operation without requiring active air sampling. Two detection techniques—tunable diode laser absorption spectroscopy (TDLAS) and a simplified wavelength modulation spectroscopy (sWMS)—were implemented and evaluated. Laboratory calibration demonstrated linear responses for all gases (R² ≈ 0.999) and detection precisions at 10 Hz of 311 ppb for CO₂, 8.87 ppb for CH₄, and 788 ppb for H₂O. Field tests conducted at a grassland site near Moscow showed strong correlations (R = 0.91 for CO₂ and H₂O, R = 0.74 for CH₄) with commercial LI-COR LI-7200 and LI-7700 analyzers. The TDLAS mode demonstrated lower noise and greater stability under outdoor conditions, while sWMS provided baseline-free spectra but was more sensitive to power fluctuations. E-CAHORS combines high precision, multi-species sensing capability with low power consumption (10 W) and a compact design (4.2 kg). Full article

Thermally induced deformation is a major source of dimensional error in end-milling, especially under high-speed or high-load conditions. Direct measurement of workpiece deformation during machining is impractical, while temperature signals can be obtained with good stability using embedded thermocouples. This study proposes an indirect method for predicting milling-induced thermal deformation based on temperature measurements. A three-dimensional thermo-mechanical finite element model is established to simulate the transient temperature field and corresponding deformation of the workpiece during milling. The numerical model is validated using cutting experiments performed under the same boundary conditions and machining parameters. Based on the validated results, the relationship between deformation at critical machining locations and temperature responses at candidate monitoring points is analyzed. To improve applicability to complex workpieces, a statistical prediction model is developed. Temperature monitoring points are optimized, and significant temperature–deformation correlations are identified using multiple linear regression combined with information-criterion-based model selection. The final model is constructed using simulation-derived datasets and provides stable deformation prediction over the entire milling process. Full article

Accurate estimation of ship emissions is essential for the effective enforcement of emission control policies in inland waterways. However, existing “bottom-up” models face significant challenges owing to severe data scarcity for inland ships, particularly regarding ship static parameters. This study proposes a novel data fusion and machine learning framework to address this issue. The methodology integrates real-time SO₂ and CO₂ pollutant concentrations on the Nanjing Dashengguan Yangtze River Bridge, Automatic Identification System (AIS) data, and meteorological information. To address the scarcity of design data for inland ships, web scraping was used to extract basic parameters, which were then used to train five machine learning models. Among them, the XGBoost model demonstrated superior performance in predicting the main engine rated power. A refined activity-based emission model combines these predicted parameters, ship operational profiles, and specific emission factors to calculate real-time emission source strengths. Furthermore, the model was validated against field measurements by comparing the calculated and measured emission source strengths from ships, demonstrating high predictive accuracy with R² values of 0.980 for SO₂ and 0.977 for CO₂, and MAPE below 13%. This framework provides a reliable and scalable approach for real-time emission monitoring and supports regulatory enforcement in inland waterways. Full article