Search Results (33,187)

Penetration rate effects and partial drainage can govern piezocone (CPTu) response in intermediate permeability geomaterials, but field testing at a fixed standard rate limits systematic evaluation. This study presents the development and laboratory validation of a miniature piezocone system and testing framework to investigate rate-dependent penetration response in laboratory-prepared silty sand. Baseline dry and flooded specimens were tested using a triaxial-based configuration at penetration velocities of 9.6, 0.28, 0.10, and 0.03 mm/s, including selected holding periods for dissipation. A dedicated servo-controlled penetration system was then implemented for slurry-prepared specimens, enabling continuous constant-velocity penetration over a wider velocity range (0.004–15 mm/s). Cone resistance was interpreted using normalized net resistance (Q) and normalized velocity (V_h), and pore pressure using normalized excess pore pressure (Δu₂/σ′_v0). The results show a monotonic rate dependency, with Q increasing as V_h decreases, while Δu₂/σ′_v0 progressively decreases toward zero at intermediate-to-low V_h; at the lowest rates, pore-pressure readings were affected by instrument signal limitations. A hyperbolic-cosine backbone fitted to the normalized response provided good agreement for resistance (R² = 0.99, RMSE = 3.41) and more limited agreement for pore pressure (R² = 0.30, RMSE = 0.23). The drainage transition for the tested material occurs in an interval of approximately V_h ≈ 0.3~30. The study provides a reproducible laboratory approach—combining miniature instrumentation, controlled specimen preparation, and variable-rate penetration—to generate normalized drainage-transition trends for rate-effect investigations in intermediate geomaterials. Full article

To improve the efficiency of comparative process-window screening in selective laser melting (SLM), this study developed a finite element simulation-driven machine learning framework for 316L stainless steel. A simulation dataset covering laser power (LP), scanning speed (SS), heat-source diameter (HSD), and substrate preheating temperature (SPH) was generated using ANSYS and used to train nine regression models. In the present work, the primary machine learning target was defined as the simulated average thermal stress, σ_avg, which is used as a simulation-derived comparative thermal stress indicator for ranking process conditions within the investigated parameter window rather than as a direct prediction of the final residual-stress field. Among the evaluated models, the Backpropagation Neural Network (BPNN) showed the best predictive performance and was selected as the representative surrogate model because of its strong predictive accuracy, stable behavior, and direct applicability to the present structured tabular dataset. Shapley additive explanations (SHAP) and partial dependence plots (PDPs) indicated that LP is the dominant variable governing the σ_avg-based response, followed by SPH, whereas SS and HSD mainly affect the response through secondary or coupled effects. Within the investigated parameter window, conditions near 180–200 W corresponded to a relatively lower predicted σ_avg level. Experimental observations provided limited but meaningful trend-level support for the simulation-guided screening results: metallographic examination showed improved forming quality near 200 W, while XRD-derived macroscopic stress estimates exhibited a similar variation trend to the simulated σ_avg values under the tested LP–SS conditions. These results suggest that the proposed framework can serve as an efficient surrogate-based tool for comparative parameter screening in SLM-fabricated 316L stainless steel within the assumptions and parameter range of the present model. Full article

Aerostatic spindles are indispensable in the ultra-precision manufacturing field due to their high accuracy and low friction. However, rotor manufacturing errors will affect the thickness and uniformity of the air film, thereby limiting the improvement and application of the aerostatic spindle. To explore this issue, this paper presents theoretical modelling and experimental work. Rotor cylindricity errors were first evaluated based on manufacturing errors, and a calculation model of the film thickness considering rotor cylindricity errors was established. By solving the dynamic Reynolds equation considering cylindricity errors, the dynamic stiffness and damping of aerostatic spindles were obtained. The influence mechanism of rotor cylindricity errors on the dynamic stiffness and damping coefficients of the rotor–bearing system was revealed. The stiffness coefficients K_xx, K_yy, and K_xy are more sensitive to the saddle-shaped errors, and the stiffness coefficient K_yx and both damping coefficients are more closely related to bucket-shaped errors. Regarding the influence of the cylindricity errors’ extremal position, the main and cross stiffness coefficients are sensitive to saddle-shaped errors and bucket-shaped errors, respectively; the main and cross-damping coefficients are sensitive to bucket-shaped errors. Under the effect of three kinds of error shapes, when the rotor cylindricity errors value is less than 1 μm, the dynamic stiffness and damping coefficients are conducive to improving the dynamic characteristics of the rotor–bearing system. Multiple rotors were manufactured, and their cylindricity errors were measured, and then the dynamic characteristics of the assembled aerostatic spindles with these rotors were tested. It was found that the dynamic stiffness of spindles with saddle-shaped errors is larger than that of spindles with conical-shaped errors, and the greater the error values are, the worse the rotation accuracy. The experimental results are consistent with the theoretical findings, thus verifying the feasibility and validity of the established theoretical model. This study improves the error tolerance design accuracy of rotors and thereby enhances the dynamic performance of aerostatic spindles. Full article

To address the low mechanization level, high labor intensity, and severe substrate disturbance in intertidal shellfish harvesting, a vibratory harvesting method based on local vibration-induced substrate fluidization was proposed, and a vibratory harvesting device for Mactra veneriformis was developed. Bench and intertidal field tests were conducted to systematically investigate the effects of vibration frequency, vibration pressure, and vibration amplitude on substrate fluidization, clam uplift, and harvesting performance. The single-factor results showed that all three parameters significantly affected the pore water pressure ratio, substrate viscosity, uplift distance, and harvesting rate, with better fluidization obtained at 8 Hz, 30 kPa, and 25 mm. A Box–Behnken response surface design was further used to establish quadratic regression models for these responses, and all models were highly significant with a non-significant lack of fit. The optimized parameter combination was 10 Hz, 35 kPa, and 25 mm, under which the predicted pore water pressure ratio and uplift distance were 101.3% and 97.2 mm, respectively, and the substrate viscosity was 1364 Pa·s. Field tests showed that the pore water pressure ratio remained above 85.3%, viscosity decreased to 1331–2639 Pa·s, shear strength decreased by 57.2–64.9%, and the average uplift distance at 100 mm burial depth reached 80–92 mm. The results indicate that vibratory harvesting can effectively promote substrate fluidization and reduce clam uplift resistance, providing a reference for the development of low-disturbance mechanized harvesting equipment for intertidal shellfish. Full article

For low-energy rare-isotope beam experiments, a large-area quadrant silicon pad sensor (5 × 5 cm${}^2$) has been developed for the TexAT active target system. Unlike finely segmented sensors such as small-scale pad or strip sensors, the operational stability of large-area segmented sensors is critically dependent on the electric field distribution at the device termination; thus, optimizing the guard-ring design is essential to prevent premature breakdown. In this study, we systematically investigated three different guard-ring configurations featuring 6, 9, and 14 rings (denoted as G6, G9, and G14, respectively) through TCAD simulations and experimental measurements. The TCAD results demonstrated that the G9 design, which utilizes a graded-spacing strategy, is more effective in mitigating the maximum electric-field concentration at the sensor edge than designs that simply feature a higher number of rings (G14). Accordingly, the G9-based quadrant sensor was fabricated, and its performance was validated through electrical performance evaluations and radioactive source tests, confirming a low leakage current of several tens of nA and an energy resolution of approximately 31 keV (FWHM) (for 3.18 MeV $\alpha$-particles from ${}^{148}$Gd). Furthermore, beam tests performed at the RAON facility verified the operational reliability of the sensor in a practical in-beam environment. In conclusion, these results provide essential design criteria for large-area silicon detectors in rare-isotope beam experiments, and the developed detectors will be equipped to the TexAT array to enhance the precision of nuclear physics measurements. Full article

Astrocytes play a crucial role in maintaining neuronal microenvironment homeostasis and regulating synaptic plasticity within the hippocampus. Magnoflorine (MGN), a naturally occurring isoquinoline alkaloid, has demonstrated biological activity in the central nervous system. However, its effects on astroglial cells remain poorly understood. The present study aimed to evaluate the impact of acute and chronic administration of MGN (10 and 20 mg/kg body weight) on the morphology and morphometric parameters of GFAP-positive astrocytes in the CA1 field of the mouse hippocampus. Immunohistochemical and morphometric analyses were performed in the oriens layer (SO), pyramidal layer (SP), radiate layer (SR), and lacunose-molecular layer (SLM). MGN significantly modulated astrocyte density, cell size, and the number of processes in a dose-, time-, and layer-dependent manner. A heterogeneous and layer-specific astroglial response was particularly evident following chronic administration of the tested compound. Together with the observed lack of significant differences in analysed parameters, decreases were mainly detected after administration of the low MGN dose, whereas the 20 mg/kg dose induced primarily increased structural complexity. Thus, the direction of changes was not uniform across all layers. The most prominent changes were detected in the SLM layer. Overall, MGN modulated astrocyte morphology and reactivity in a context-dependent manner. These findings indicate a modulatory influence of MGN on astroglial structural plasticity rather than a uniform directional effect. Although the observed changes may be associated with alterations in astroglia-mediated mechanisms involved in maintaining neuronal homeostasis and responses to stress, their functional significance requires further investigation. Full article

Turbulent wind conditions pose significant challenges to the blade structural reliability of small wind turbines. Different from the authors’ previous work, which mainly focused on the output characteristics of the same 5 kW prototype under variable inflow conditions, this study combines field-test observations with numerical simulations to further investigate the blade structural dynamic responses of a 5 kW variable-pitch wind turbine under both uniform inflow and extreme wind conditions. Owing to the unique pitch-regulation mechanism of the proposed turbine, two pitch-control modes, namely conventional power-limited pitch control and active stall pitch control, are comparatively analyzed to clarify their effects on blade load, stress, and displacement responses. The results indicate that, under uniform inflow conditions, stresses are concentrated near the leading edge of the blade mid-span, while the maximum displacement occurs at the blade tip. Both stress and displacement decrease with increasing conventional pitch angle. Under extreme wind conditions, increasing gust intensity causes a nonlinear growth in blade loads and aggravates blade structural response. During active stall pitch control, the load distribution pattern is generally consistent with that under conventional pitch control, whereas the blade structural response first decreases and then increases as the pitch angle is adjusted toward negative values. Under uniform inflow at the rated wind speed of 11 m/s, the blade-tip maximum displacement decreased from 56.51 mm under the +6° power-limited/reference pitch condition to 48.42 mm under the −6° active-stall-related pitch condition, corresponding to a reduction of approximately 14.3%. These results provide a useful reference for the blade structural design and control optimization of distributed small wind turbines under complex inflow conditions. Full article

Series arc faults on the DC side of photovoltaic (PV) systems are a critical hazard that can trigger system fires. Conventional contact-based detection methods suffer from cumbersome installation and high retrofit cost, whereas existing non-contact approaches mostly rely on megahertz-level high-frequency sampling and therefore require expensive radio-frequency instrumentation or high-performance computing platforms. As a result, it remains difficult to simultaneously achieve strong interference immunity and real-time performance on low-cost embedded devices with limited resources. To address this engineering paradox between high-frequency sampling and constrained computational capability, this paper proposes a fully embedded, non-contact arc fault detection system based on a 12–80 kHz low-frequency sub-band selection strategy. By exploiting the physical characteristic of broadband energy elevation induced by arc faults, the proposed strategy avoids dependence on high-bandwidth hardware. Guided by this strategy, a Moebius-topology coaxial shielded loop antenna is employed as the near-field sensor, while an ultra-simplified passive analog front end is constructed directly by using the on-chip programmable gain amplifier and analog-to-digital converter of the microcontroller unit, enabling efficient signal acquisition and fast Fourier transform processing within the target sub-band. To cope with complex background noise in the low-frequency range, an environment-adaptive baseline mechanism based on exponential moving average and exponential absolute deviation is developed for dynamic decoupling. In addition, a lightweight INT8-quantized multilayer perceptron is introduced as a nonlinear auxiliary module, thereby forming a robust hybrid decision architecture with complementary rule-based and artificial intelligence components. Experimental results show that, under the tested household, laboratory, and PV-site conditions, the proposed system achieved an overall detection rate of 97%, while the remaining 3% mainly corresponded to failed ignition or non-sustained arc attempts rather than persistent false triggering during normal monitoring. Full article

Cities are artificial ecosystems that suffer most from environmental issues and climate change. Urban Heat Island (UHI) effects represent an increasing challenge, especially for compact Mediterranean cities characterized by high population density and extensive impervious surfaces. This study assessed localized microclimatic conditions within the small Maltese coastal town of Isla through a 15-day summer field monitoring campaign. Air temperature, relative humidity, and wind speed were measured across urban locations characterized by different levels of vegetation coverage and thermal vulnerability. The analysis combined descriptive statistics, Mann–Whitney U testing, and Multiple Linear Regression (MLR) models. In addition, site-specific Nature-based Solutions (NbS) scenarios were proposed as context-sensitive strategies to support urban heat mitigation and climate resilience. The results highlighted distinct microclimatic responses between the sites investigated. In particular, the MLR analysis suggested that non-vegetated areas were more sensitive to short-term atmospheric variability associated with wind speed and relative humidity fluctuations. These findings suggest that urban vegetation may contribute not only to localized cooling, but also to increased microclimatic stability within compact Mediterranean urban environments. Full article

Moldy pear core constitutes a severe internal defect that compromises fruit quality. This study proposes a nondestructive detection method for Korla pear moldy core using Vis/NIR spectral signals, aimed at supporting post-harvest quality control and automated industrial sorting. We collected spectral signals from pears and quantified the moldy pear core area to classify samples into healthy (S = 0%), slightly moldy (0 < S ≤ 10%), and severely moldy (S > 10%) categories. We constructed a three-tier comparative framework to evaluate the progression from conventional machine learning to advanced deep learning: traditional methods using univariate selection (US) and random forest (RF) for feature extraction followed by support vector machine (SVM) classification; 1D-ResNet for direct processing of spectral signals; and two-dimensional approaches transforming signals into improved gramian angular field (IGAF) or Laplacian pyramid Markov transition field (LPMTF) images processed through deep belief network (DBN), MobileNetv3, and Vision Transformer (ViT). The LPMTF-ViT combination delivered the best performance with 98.98% test accuracy and 94.44% external validation accuracy, significantly exceeding traditional approaches and 1D-ResNet. This innovative approach delivers effective technical support for early-stage, nondestructive detection of internal fruit defects. It also establishes a scalable foundation for automated industrial inspection systems, potentially reducing post-harvest losses while ensuring premium quality control in modern fruit supply chains. Full article

Objectives: This study investigated whether visual field abnormalities are present in eyes with suspected nasal optic disc hypoplasia (NOH) by using fundus photography and optical coherence tomography (OCT). Methods: NOH was diagnosed using the following criteria: (1) small optic disc, (2) nasal optic disc pallor or optic disc margin irregularity, (3) wedge-shaped temporal visual field defects extending from Mariotte’s blind spot, and (4) reduced nasal circumpapillary retinal nerve fiber layer (cpRNFL) thickness. Eyes fulfilling criteria 1, 2, and 4 without visual field abnormalities were classified as pseudo-NOH (pNOH), whereas eyes without visual field or cpRNFL abnormalities were considered normal. Nasal cpRNFL thickness was measured using OCT, radial peripapillary capillary (RPC) density was assessed using OCT angiography (OCTA), visual field testing was performed, and optic disc blood flow velocity was evaluated using the mean blur rate (MBR) and laser speckle flowgraphy (LSFG). Results: Seven eyes with NOH, 13 eyes with pNOH, and 24 normal right eyes were included. Nasal cpRNFL thickness and MBR were significantly reduced in both the NOH and pNOH groups compared with the normal group, with no significant difference between the NOH and pNOH groups. Nasal RPC density was significantly lower in the NOH group than in both the pNOH and normal groups, and no significant difference was observed between the pNOH and normal groups. Conclusions: Even when NOH was suspected from fundus, LSFG, and OCT C-scan findings, visual field abnormalities were not consistently present. Differences in RPC density measured using OCTA may have contributed to this variability. This study examined whether suspected nasal optic disc hypoplasia (NOH) is always associated with visual field defects. Using fundus imaging, OCT, OCT angiography, and laser speckle flowgraphy, we compared eyes with NOH, pseudo-NOH, and normal eyes. Although structural changes such as reduced nasal nerve fiber layer thickness and decreased blood flow were observed in both NOH and pseudo-NOH, visual field abnormalities were not consistently present. Notably, reduced radial peripapillary capillary density was specific to NOH, suggesting that vascular differences may explain variability in visual function. These findings highlight the importance of multimodal imaging in NOH evaluation. Full article

Additive manufacturing through a laser cladding has been shown to be an effective technology for the mitigation of wear and rolling contact fatigue (RCF) of railway track. Small-scale tests have consistently shown that creating a thin layer of premium material on the tribo-active surface of the railhead vastly reduces wear and suppresses the onset of RCF due to the ratcheting mechanism being almost eliminated in comparison to standard rail material. Cladding reduces material plastic flow by 60% which is a cause of insulated track joint failure. This paper reports results from the first full-scale trials of additively manufactured laser clad layers on railway rails by studying their mechanical properties and microstructure. This is a vital step in safely progressing this technology from lab scale to network application. Tested full-scale insulated block joint (IBJ) specimens, clad with martensitic stainless steel (MSS) and Stellite 6, were sectioned, polished and etched and the microstructures of the clad, heat-affected zone and parent rail materials were inspected using optical and scanning electron microscopy (SEM) (Hitachi TM3030 plus, Tokyo, Japan). Residual stress was also measured. Cladding with MSS and Stellite 6 showed high wear and RCF resistance after the tests. Material flow was reduced with the clad layer applied. No defects such as porosity or large precipitates were observed in the heat-affected zone (HAZ), particularly close to the rail surface at the clad end which could act as a point of weakness. Residual stress states varied between materials, MSS being compressive (−344 MPa average) and Stellite 6 being tensile (+391 MPa average) which could have an impact on the fatigue life of the clad. This finding matches previous work, indicating that MSS may be preferable in the field, where bending of rails can occur. Overall, the work showed that laser cladding can provide a good solution to lipping issues and wear problems of rail in IBJs. Analysis in this work confirmed that the HAZ where clad meets the bulk rail at the surface has good structural integrity; however, this needs to be a focus of attention in field application of these layers. Full article

Background/Objectives: Obesity represents a growing public health concern worldwide. Chrononutrition, a research field examining the timing and regularity of food intake, has been shown in animal models to influence body weight regulation and obesity-related outcomes. Previous research has also explored the association between chrononutrition information and BMI. Using INRAN-SCAI 2005/2006 adult nutrition data based on 3-day diet diaries (n = 2312), this study aims to assess whether chrononutritional information on the distribution of energy intake during the day is able to improve prediction of overweight status (BMI > 25 kg/m²), compared to information on energy from the whole day alone. Methods: This research investigates it using logistic regression and random forest models. For both types of models, three different specifications were compared: a model trained on the mean and irregularity of calorie intake over 3 days for 6 day-time intervals (MI6); a model trained on repeated measures over 3 days of calorie intake from the same 6 time intervals (RM); and a model trained on mean and irregularity of calorie intake over 3 days for the whole day (MID). The performance of the models was compared using risk prediction metrics and ROC curves. Results: When including additional demographic and behavioural predictors beside the energy variables, the results only showed a statistically significant difference in the performance of the logistic regression models if they were trained and tested on the same data. The models trained using chrononutrition information performed better, but the difference in diagnostic accuracy was very small (AUC = 0.7909 for MI6, p = 0.0086; 0.7923 for RM compared to 0.7850 for MID, p = 0.0072) and possibly attributable to overfitting, as it was no longer significant in the comparison within a testing set (70% training and 30% testing samples). For the random forest models, no significant difference was found. In the same models including only the energy variables, the improved performance of MI6 and RM was significantly better than for MID also in the test set (respectively, p = 0.0001 and p = 0.0002), and the gap in AUCs became substantial (AUC = 0.622 for MI6, 0.618 for RM and 0.507 for MID), indicating that socio-demographic and behavioural variables encapsulate information on energy intake by time of the day. Typical under-reporting bias present in nutritional epidemiology and the cross-sectional nature of the sample based on 3-day diaries may have affected these results, although use of diet diaries should minimize recall bias. Conclusions: In conclusion, the impact on health of timing and regularity of calorie intake in the day may act through other mechanisms than via overweight and may be captured by other demographic and behavioural variables; larger and prospective longitudinal studies are warranted to thoroughly investigate the added value of time-of-day information. Full article

The market for coloured photovoltaic modules offers a key opportunity for building-integrated photovoltaics (BIPV), as it enables more aesthetic and seamless integration into architecture. This study investigates how three common BIPV colours—anthracite, green, and terracotta—affect the performance of a BIPV ventilated façade. It presents a year-long field comparison, including thermal modelling and residual spectral loss estimation, of three screen-printed coloured BIPV strings installed on an east-facing ventilated façade, at the CIEMAT research centre in Madrid, Spain. Although anthracite modules exhibit the highest efficiency under standard test conditions (STC), green modules achieve the best performance ratio (PR) due to their lower spectral and thermal impacts. Results indicate that system design factors—such as façade orientation, module positioning and rear ventilation—significantly influence thermal and electrical performance. In particular, changes in solar spectral irradiance can have a strong impact on the performance of coloured modules, mainly due to their distinct spectral reflectance characteristics. This effect is especially relevant for reddish modules mounted on east- and west-facing façades, which, on clear days, receive sunlight with spectra shifted toward the near-infrared (NIR) region compared with midday conditions, which are closer to the standard AM1.5G solar spectrum. Prior optical characterisation, particularly spectral reflectance measurements, is therefore essential to accurately assess and predict the performance of coloured modules under real operating conditions. Full article

Recent advances in satellite missions, particularly the Landsat, Sentinel, and Gaofen series, have led to the rapid accumulation of high-quality remote sensing data with frequent revisits. As these data have become more widely available, Satellite Image Time Series (SITS) have become an important tool for monitoring Earth surface dynamics. SITS now supports a wide range of applications, including precision agriculture, Land Use/Cover Change (LULCC) monitoring, environmental management, and disaster response. This growth has also promoted the development of advanced SITS classification datasets. However, existing reviews have mainly focused on SITS classification algorithms or specific applications, while systematic comparisons of public SITS benchmark datasets remain limited. This lack of synthesis makes it difficult for researchers to navigate fragmented resources and select datasets that match specific scientific or operational tasks. To address this gap, this paper provides a comprehensive review and analysis of 29 publicly available medium-to-high-resolution SITS classification benchmark datasets released between 2017 and 2025. These datasets are intended for training, testing, and validating land-cover classification algorithms, rather than for direct use as operational map products. We conduct a detailed statistical and comparative analysis of these datasets, focusing on their key characteristics across spectral, temporal, and spatial dimensions, as well as their labeling systems. In addition, this review summarizes the SITS classification algorithms that have been developed and benchmarked using these datasets. Finally, we identify the main challenges in constructing and applying SITS classification datasets and discuss future research directions, particularly in data reconstruction, multimodal fusion, change analysis, and advanced model architectures. This survey provides the research community with a systematic overview of SITS classification benchmark datasets and aims to support continued progress in this rapidly developing field. Full article