Search Results (4,595)

Accurate micro gas flow measurement is critical for medical ventilator calibration, environmental gas monitoring, and semiconductor manufacturing. Laminar flowmeters are widely employed in micro gas flow measurement applications owing to their inherent advantages of high linearity, the absence of moving components, and a broad measurement range. Nevertheless, due to the low measurement accuracy under micro gas flow caused by nonlinear errors and a relatively complex structure, traditional laminar flow measurement devices exhibit limitations in micro gas flow measurement scenarios. This study proposes a novel micro gas flow measurement method based on a single capillary laminar flow element, which simplifies the structure and enhances applicability in the field of micro gas flow. Through structural optimization with precise control of the capillary length–diameter ratios and theoretical error correction based on computational analysis, nonlinear errors were effectively reduced while improving the measurement accuracy in the field of micro gas flow. The proposed methodology was systematically validated through computational fluid dynamics simulations (ANSYS Fluent 2021 R1) and experimental investigations using a dedicated test platform. The experimental results show that the relative error of the measurement system within the full measurement range is less than ±0.6% (1–10 cm³/min; cm³/min means cubic centimeter per minute), and its accuracy is superior to 1% of reading (1% Rd) or 1.5% of reading (1.5% Rd) of conventional laminar flowmeters. The fitting curve of the flow rate versus the pressure difference derived from the measurement results maintains an excellent linear correlation (R² > 0.99), thus confirming that this method has practical application value in the field of micro gas flow measurement. Full article

Many applications in health research involve the analysis of multivariate distributions of random variables. In this paper, we review the basic theory of copulas to illustrate their advantages in deriving a joint distribution from given marginal distributions, with a specific focus on bivariate cases. Particular attention is given to the Archimedean family of copulas, which includes widely used functions such as Clayton and Gumbel–Hougaard, characterized by a single association parameter and a relatively simple structure. This work differs from previous reviews by providing a focused overview of applied studies in biomedical research that have employed Archimedean copulas, due to their flexibility in modeling a wide range of dependence structures. Their ease of use and ability to accommodate rotated forms make them suitable for various biomedical applications, including those involving survival data. We briefly present the most commonly used methods for estimation and model selection of copula’s functions, with the purpose of introducing these tools within the broader framework. Several recent examples in the health literature, and an original example of a pediatric study, demonstrate the applicability of Archimedean copulas and suggest that this approach, although still not widely adopted, can be useful in many biomedical research settings. Full article

Vitamin D is the only vitamin that is conditionally essential, as it is synthesized from precursors after UV light exposure, whilst also being obtained from the diet. It has numerous health benefits, with deficiency becoming a major concern globally, such that dietary supplementation has more recently achieved vital importance to maintain satisfactory levels. In recent years, measurements made from blood have, therefore, become critical to determine the status of vitamin D levels in individuals and the larger population. Tests for vitamin D have routinely relied on laboratory analysis with sophisticated equipment, often being slow and costly, whilst rapid immunoassays have suffered from poor specificity and sensitivity. Here, we have evaluated a new rapid immunoassay test on the market (Rapi-D & IgLoo) to quickly and accurately measure vitamin D levels in small capillary blood specimens and compared this to measurements made using the standard laboratory method of liquid chromatography and mass spectrometry. Our results show that vitamin D can be measured very quickly and over a broad range using the new method, as well as correlate relatively well with standard laboratory testing; however, it cannot be fully relied upon currently to accurately diagnose deficiency or sufficiency in individuals. Our statistical and comparative analyses find that the rapid immunoassay with digital quantification significantly overestimates vitamin D levels, leading to diminished diagnosis of vitamin D deficiency. The speed and simplicity of the rapid method will likely provide advantages in various healthcare settings; however, further calibration of this rapid method and testing parameters for improving quantification of vitamin D from capillary blood specimens is required before integration of it into clinical decision-making pathways. Full article

Despite rapid advances in robot learning, the coordination of closed-loop manipulation in cluttered environments remains a challenging and relatively underexplored problem. We present a novel two-level hierarchical architecture for a depth vision-equipped robotic arm that integrates pushing, grasping, and high-level decision making. Central to our approach is a prioritised action–selection mechanism that facilitates efficient early-stage learning via behaviour cloning (BC), while enabling scalable exploration through reinforcement learning (RL). A high-level decision neural network (DNN) selects between grasping and pushing actions, and two low-level action neural networks (ANNs) execute the selected primitive. The DNN is trained with RL, while the ANNs follow a hybrid learning scheme combining BC and RL. Notably, we introduce an automated demonstration generator based on oriented bounding boxes, eliminating the need for manual data collection and enabling precise, reproducible BC training signals. We evaluate our method on a challenging manipulation task involving five closely packed cubic objects. Our system achieves a completion rate (CR) of 100%, an average grasping success (AGS) of 93.1% per completion, and only 7.8 average decisions taken for completion (DTC). Comparative analysis against three baselines—a grasping-only policy, a fixed grasp-then-push sequence, and a cloned demonstration policy—highlights the necessity of dynamic decision making and the efficiency of our hierarchical design. In particular, the baselines yield lower AGS (86.6%) and higher DTC (10.6 and 11.4) scores, underscoring the advantages of content-aware, closed-loop control. These results demonstrate that our architecture supports robust, adaptive manipulation and scalable learning, offering a promising direction for autonomous skill coordination in complex environments. Full article

Vitamin A deficiency (VAD) remains a significant cause of preventable blindness worldwide, with ocular surface changes representing early manifestations that require prompt recognition and treatment. Conventional examination methods are capable of detecting advanced changes; however, subtle conjunctival abnormalities may be overlooked, potentially delaying the administration of appropriate interventions. We herein present the case of a 5-year-old Japanese boy with severe VAD due to selective eating patterns. This case demonstrates the utility of infrared photography as a novel diagnostic approach for detecting and monitoring conjunctival surface abnormalities. The patient exhibited symptoms including corneal ulcers, night blindness, and reduced visual acuity. Furthermore, blood tests revealed undetectable levels of vitamin A (5 IU/dL), despite relatively normal physical growth parameters. Conventional slit-lamp examination revealed characteristic sandpaper-like conjunctival changes. However, infrared photography (700–900 nm wavelength) revealed distinct abnormal patterns of conjunctival surface folds and keratinization that were not fully appreciated on a routine examination. Following high-dose vitamin A supplementation (4000 IU/day), complete resolution of ocular abnormalities was achieved within 2 months, with infrared imaging objectively documenting treatment response and normalization of conjunctival surface patterns. This case underscores the potential for severe VAD in developed countries, particularly in the context of dietary restrictions, thereby underscoring the significance of a comprehensive dietary history and a meticulous ocular examination. Infrared photography provides a number of advantages, including the capacity for non-invasive assessment, enhanced visualization of subtle changes, objective monitoring of treatment response, and cost-effectiveness due to the use of readily available equipment. This technique represents an underutilized diagnostic modality with particular promise for screening programs and clinical monitoring of VAD-related ocular manifestations, potentially preventing irreversible visual loss through early detection and intervention. Full article

Accurate prediction of landslide-induced displacement is essential for the structural integrity and operational safety of buildings and infrastructure situated in geologically unstable regions. This study introduces a novel hybrid predictive framework that synergistically integrates Gaussian Process Regression (GPR) with Temporal Convolutional Neural Networks (TCNs), herein referred to as the GTCN model, to forecast displacement at building monitoring points subject to landslide activity. The proposed methodology is validated using time-series monitoring data collected from the slope adjacent to the Zhongliang Reservoir in Wuxi County, Chongqing, an area where slope instability poses a significant threat to nearby structural assets. Experimental results demonstrate the GTCN model’s superior predictive performance, particularly under challenging conditions of incomplete or sparsely sampled data. The model proves highly effective in accurately characterizing both abrupt fluctuations within the displacement time series and capturing long-term deformation trends. Furthermore, the GTCN framework outperforms comparative hybrid models based on Gated Recurrent Units (GRUs) and GPR, with its advantage being especially pronounced in data-limited scenarios. It also exhibits enhanced capability for temporal feature extraction relative to conventional imputation-based forecasting strategies like forward-filling. By effectively modeling both nonlinear trends and uncertainty within displacement sequences, the GTCN framework offers a robust and scalable solution for landslide-related risk assessment and early warning applications. Its applicability to building safety monitoring underscores its potential contribution to geotechnical hazard mitigation and resilient infrastructure management. Full article

Inter-turn short-circuit (ITSC) faults in permanent magnet synchronous machines (PMSMs) present a significant reliability challenge in electric vehicle (EV) drivetrains, particularly under non-stationary operating conditions characterized by inverter-driven transients, variable loads, and magnetic saturation. Existing diagnostic approaches, including motor current signature analysis (MCSA) and wavelet-based methods, are primarily designed for steady-state conditions and rely on manual feature selection, limiting their applicability in real-time embedded systems. Furthermore, the lack of publicly available, high-fidelity datasets capturing the transient dynamics and nonlinear flux-linkage behaviors of PMSMs under fault conditions poses an additional barrier to developing data-driven diagnostic solutions. To address these challenges, this study introduces a simulation framework that generates a comprehensive dataset using finite element method (FEM) models, incorporating magnetic saturation effects and inverter-driven transients across diverse EV operating scenarios. Time-frequency features extracted via Discrete Wavelet Transform (DWT) from stator current signals are used to train a Transformer model for automated ITSC fault detection. The Transformer model, leveraging self-attention mechanisms, captures both local transient patterns and long-range dependencies within the time-frequency feature space. This architecture operates without sequential processing, in contrast to recurrent models such as LSTM or RNN models, enabling efficient inference with a relatively low parameter count, which is advantageous for embedded applications. The proposed model achieves 97% validation accuracy on simulated data, demonstrating its potential for real-time PMSM fault detection. Additionally, the provided dataset and methodology contribute to the facilitation of reproducible research in ITSC diagnostics under realistic EV operating conditions. Full article

Laser beam powder bed fusion of metals (PBF-LB/M, or simply L-PBF) has emerged as one of the most competitive additive manufacturing technologies for producing complex metallic components with high precision, design freedom, and minimal material waste. Among the various categories of additive manufacturing processes, L-PBF stands out, paving the way for the execution of part designs with geometries previously considered unfeasible. Despite offering several advantages, parts with overhang features require the use of support structures to provide dimensional stability of the part. Support structures achieve this by resisting residual stresses generated during processing and assisting heat dissipation. Although the scientific community acknowledges the role of support structures in the success of L-PBF manufacturing, they have remained relatively underexplored in the literature. In this context, the present work investigated the impact of laser power and scanning speed on the dimensioning, integrity and tensile strength of single-walled block type support structures manufactured in AISI 316L stainless steel. The method proposed in this work is divided in two stages: processing parameter exploration, and mechanical characterization. The results indicated that support structures become more robust and resistant as laser power increases, and the opposite effect is observed with an increment in scanning speed. In addition, defects were detected at the interfaces between the bulk and support regions, which were crucial for the failure of the tensile test specimens. For a layer thickness corresponding to 0.060 mm, it was verified that the combination of laser power and scanning speed of 150 W and 500 mm/s resulted in the highest tensile resistance while respecting the dimensional deviation requirement. Full article

High-voltage diodes, as key devices in power electronic systems, have important significance for system reliability and preventive maintenance in terms of storage life prediction. In this paper, we propose a hybrid modeling framework that integrates the Long Short-Term Memory Network (LSTM) and Transformer structure, and is hyper-parameter optimized by the Improved Artificial Bee Colony Algorithm (IABC), aiming to realize the high-precision modeling and prediction of high-voltage diode storage life. The framework combines the advantages of LSTM in time-dependent modeling with the global feature extraction capability of Transformer’s self-attention mechanism, and improves the feature learning effect under small-sample conditions through a deep fusion strategy. Meanwhile, the parameter type-aware IABC search mechanism is introduced to efficiently optimize the model hyperparameters. The experimental results show that, compared with the unoptimized model, the average mean square error (MSE) of the proposed model is reduced by 33.7% (from 0.00574 to 0.00402) and the coefficient of determination (R²) is improved by 3.6% (from 0.892 to 0.924) in 10-fold cross-validation. The average predicted lifetime of the sample was 39,403.3 h, and the mean relative uncertainty of prediction was 12.57%. This study provides an efficient tool for power electronics reliability engineering and has important applications for smart grid and new energy system health management. Full article

Polypropylene (EPP) concrete offers advantages such as low density and good thermal insulation properties, but its relatively low strength limits its engineering applications. Waste steel fibers (WSFs) obtained during the sorting and processing of machining residues can be incorporated into EPP concrete (EC) to enhance its strength and toughness. Using the volume fractions of EPP and WSF as variables, specimens of EPP concrete (EC) and waste steel fiber-reinforced EPP concrete (WSFREC) were prepared and subjected to cube compressive strength tests, splitting tensile strength tests, and four-point flexural strength tests. The results indicate that EPP particles significantly improve the toughness of concrete but inevitably lead to a considerable reduction in strength. The incorporation of WSF substantially enhanced the splitting tensile strength and flexural strength of EC, with increases of at least 37.7% and 34.5%, respectively, while the improvement in cube compressive strength was relatively lower at only 23.6%. Scanning electron microscopy (SEM) observations of the interfacial transition zone (ITZ) and WSF surface morphology in WSFREC revealed that the addition of EPP particles introduces more defects in the concrete matrix. However, the inclusion of WSF promotes the formation of abundant hydration products on the fiber surface, mitigating matrix defects, improving the bond between WSF and the concrete matrix, effectively inhibiting crack propagation, and enhancing both the strength and toughness of the concrete. Full article

Histamine sensing that uses enzymatic reactions is the most common form of testing due to its selectivity for histamine. However, enzymes are difficult to store for long periods of time, and the inactivation of enzymes decreases the reliability of the results. In this study, we developed a novel, quick, and easily operated histamine sensing technique that takes advantage of the histamine redox reaction and does not require enzyme-based processes. Because the redox potential of histamine is relatively high, we used a boron-doped diamond (BDD) electrode that has a wide potential window. At pH 8.4, which is between the acidity constant of histamine and the isoelectric point of histidine, it was found that an oxygen-terminated BDD surface successfully detected histamine, both selectively and exclusively. Measurements of the sensor’s responses to extracts from fish meat samples that contained histamine at various concentrations revealed that the sensor responds linearly to the histamine concentration, thus allowing it to be used as a calibration curve. The sensor was used to measure histamine in another fish meat sample treated as an unknown sample, and the response was fitted to the calibration curve to perform an inverse estimation. When estimated in this way, the histamine concentration matched the certified value within the range of error. A more detailed examination showed that the sensor response was little affected by the histidine concentration in the sample. The detection limit was 20.9 ppm, and the linear response range was 0–150 ppm. This confirms that this sensing method can be used to measure standard histamine concentrations. Full article

The combined effect of non-thermal plasma (NTP) and hydropriming on the germination performance and seedling characteristics of specific varieties of cucumber (Cucumis sativus L.) and broccoli (Brassica oleracea var. italica Plenck.) seeds after short-term storage is reported. Seeds were treated with NTP for 10 and 15 min, followed by hydropriming in distilled water for 24 h, and then stored for six months in the dark before evaluation. The treated cucumber seeds demonstrated a statistically significant enhancement in seed germination and seedling vitality indices. In contrast, broccoli seeds showed no significant improvement. The stimulatory effects observed in cucumber may be attributed to reactive oxygen and nitrogen species, which act as signaling molecules to promote stress tolerance and early growth. This study also highlights the potential of combined NTP treatment and hydropriming as a pre-sowing treatment for select crops, underscoring the need for species-specific optimization. The used, portable, and relatively inexpensive NTP device offers practical advantages for agricultural applications. Full article

Range-expanding species, or neonatives, are native plants that spread beyond their original range due to recent climate or human-induced environmental changes. Retama monosperma was initially planted near the Guadalquivir estuary for dune stabilisation. However, changes in the sedimentary regime and animal-mediated dispersal have facilitated its exponential expansion, threatening endemic species and critical dune habitats. The main objective of this study was to identify the key functional traits that may explain the competitive advantage and rapid spread of R. monosperma in coastal dune ecosystems. We compared its seasonal responses with those of three co-occurring woody species, two native (Juniperus phoenicea and J. macrocarpa) and one naturalised (Pinus pinea), at two sites differing in groundwater availability within a coastal dune area (Doñana National Park, Spain). We measured water relations, leaf traits, stomatal conductance, photochemical efficiency, stable isotopes, and shoot elongation in 12 individuals per species. Repeated-measures ANOVA showed significant effects of species and species × season interaction for relative water content, shoot elongation, effective photochemical efficiency, and stable isotopes. R. monosperma showed significantly higher shoot elongation, relative water content, and photochemical efficiency in summer compared with the other species. Stable isotope data confirmed its nitrogen-fixing capacity. This characteristic, along with the higher seasonal plasticity, contributes to its competitive advantage. Given the ecological fragility of coastal dunes, understanding the functional traits favouring the success of neonatives such as R. monosperma is essential for biodiversity conservation and ecosystem management. Full article

Land consolidation (LC) is a sustainability-oriented policy tool designed to address land fragmentation, inefficient spatial organization, and ecological degradation in rural areas. This research proposes a Production–Living–Ecological (PLE) spatial utilization efficiency evaluation system, based on an integrated methodological framework combining Principal Component Analysis (PCA), Entropy Weight Method (EWM), Attribute-Weighting Method (AWM), Linear Weighted Sum Method (LWSM), Threshold-Verification Coefficient Method (TVCM), Jenks Natural Breaks (JNB) classification, and the Obstacle Degree Model (ODM). The framework is applied to Qian County, located in the Guanzhong Plain in Shaanxi Province. The results reveal three key findings: (1) PLE efficiency exhibits significant spatial heterogeneity. Production efficiency shows a spatial pattern characterized by high values in the central region that gradually decrease toward the surrounding areas. In contrast, the living efficiency demonstrates higher values in the eastern and western regions, while remaining relatively low in the central area. Moreover, ecological efficiency shows a marked advantage in the northern region, indicating a distinct south–north gradient. (2) Integrated efficiency consolidation potential zones present distinct spatial distributions. Preliminary consolidation zones are primarily located in the western region; priority zones are concentrated in the south; and intensive consolidation zones are clustered in the central and southeastern areas, with sporadic distributions in the west and north. (3) Five primary obstacle factors hinder land use efficiency: intensive utilization of production land (PC1), agricultural land reutilization intensity (PC2), livability of living spaces (PC4), ecological space security (PC7), and ecological space fragmentation (PC8). These findings provide theoretical insights and practical guidance for formulating tar-gated LC strategies, optimizing rural spatial structures, and advancing sustainable development in similar regions. Full article

(1) Background: Wetlands are ecologically significant ecosystems that support biodiversity and contribute to essential environmental functions such as water purification, carbon storage and flood regulation. However, these ecosystems face increasing pressures from land-use change and degradation, prompting the need for scalable and accurate classification methods to support conservation and policy efforts. In this research, our motivation was to test whether high-spatial-resolution PlanetScope imagery can be used with pixel-based machine learning to support the mapping and monitoring of wetlands at a national scale. (2) Methods: This study compared four machine learning classification models—Random Forest (RF), XGBoost (XGB), Histogram-Based Gradient Boosting (HGB) and a Multi-Layer Perceptron Classifier (MLPC)—to detect and map wetland areas across New Zealand. All models were trained using eight-band SuperDove satellite imagery from PlanetScope, with a spatial resolution of ~3 m, and ancillary geospatial datasets representing topography and soil drainage characteristics, each of which is available globally. (3) Results: All four machine learning models performed well in detecting wetlands from SuperDove imagery and environmental covariates, with varying strengths. The highest accuracy was achieved using all eight image bands alongside features created from supporting geospatial data. For binary wetland classification, the highest F1 scores were recorded by XGB (0.73) and RF/HGB (both 0.72) when including all covariates. MLPC also showed competitive performance (wetland F1 score of 0.71), despite its relatively lower spatial consistency. However, each model over-predicts total wetland area at a national level, an issue which was able to be reduced by increasing the classification probability threshold and spatial filtering. (4) Conclusions: The comparative analysis highlights the strengths and trade-offs of RF, XGB, HGB and MLPC models for wetland classification. While all four methods are viable, RF offers some key advantages, including ease of deployment and transferability, positioning it as a promising candidate for scalable, high-resolution wetland monitoring across diverse ecological settings. Further work is required for verification of small-scale wetlands (<~0.5 ha) and the addition of fine-spatial-scale covariates. Full article