Search Results (1,060)

The implementation of Machine Vision (MV) systems for Tool Condition Monitoring (TCM) plays a critical role in reducing the total cost of operation in manufacturing while expediting tool wear testing in research settings. However, conventional MV-TCM edge detection strategies process each image independently to infer edge positions, rendering them susceptible to inaccuracies when tool edges are compromised by material adhesion or chipping, resulting in imprecise wear measurements. In this study, an MV system is developed alongside an automated, feature-based image registration strategy to spatially align tool wear images, enabling a more consistent and accurate detection of tool edge position. The MV system was shown to be robust to the machining environment, versatile across both turning and milling machining centers and capable of reducing tool wear image capturing time up to 85% in reference to standard approaches. A comparison of feature detector-descriptor algorithms found SIFT, KAZE, and ORB to be the most suitable for MV-TCM registration, with KAZE presenting the highest accuracy and ORB being the most computationally efficient. The automated registration algorithm was shown to be efficient, performing registrations in 1.3 s on average and effective across a wide range of tool geometries and coating variations. The proposed tool reference line detection strategy, based on spatially aligned tool wear images, outperformed standard methods, resulting in average tool wear measurement errors of 2.5% and 4.5% in the turning and milling tests, respectively. Such a system allows machine tool operators to more efficiently capture cutting tool images while ensuring more reliable tool wear measurements. Full article

Traditional villages are an important cultural heritage left by China’s agrarian civilization and serve as a testament to the historical development of the Chinese nation. The study of spatial and cultural differentiation in traditional villages is significant for their future preservation and development. Existing studies predominantly adopt a macro perspective, focusing on large-scale regions, and lack investigations from a micro perspective in medium- and small-scale areas. This study utilizes ArcGIS 10.8 for spatial analysis, multi-factor geographic detectors, and cultural geography spatial zoning methods to explore the spatial structure and cultural differentiation of 305 traditional villages in western Henan. The results indicate that the distribution of traditional villages in this region is significantly clustered and uneven, primarily concentrated in specific districts of Sanmenxia and Luoyang. Per capita GDP and the distance to roads are negatively correlated with the distribution of traditional settlements, reflecting the positive impact of lower economic levels and remote locations on village preservation. The spatial layout of traditional villages in western Henan exhibits clustering patterns, with cultural zoning characterized by distinct residential forms. This study, through the analysis of the spatial structure characteristics and influencing factors of traditional villages in the western Henan region, provides a new perspective on the formation and evolution of traditional villages, revealing the cultural differentiation characteristics of western Henan. The research results offer directional guidance for the conservation strategies of traditional villages in western Henan and provide a decision-making reference for cultural heritage conservation practices in similar regions. Full article

Recently, the passive synthetic aperture (PSA) technique has been used in passive localization to improve the position accuracy of single source by estimating the Doppler parameter of the received signal. However, in the presence of multiple sources, time-frequency aliasing will lead to serious cross-term interference during Doppler signal extraction, resulting in low localization performance. To solve this problem, a spaceborne passive synthetic aperture localization algorithm based on the multiple-stay detector HOUGH transform (MSD-HOUGH) is proposed in this paper. Firstly, an improved convolutional neural network based on the adaptive histogram equalization method (AHE-CNN) is proposed to achieve source number estimation. Then, the PSA Doppler equations are established in the HOUGH domain, which can suppress the cross-term interference of the multiple emitters. Meanwhile, a multiple-stay detector (MSD) is designed to reduce the pseudo-peaks in HOUGH domain. The estimated source number determines when the MSD will be terminated. Finally, a PSA cost function is established based on the estimated Doppler parameter to achieve signal source localization. Experimental results show that compared with other localization methods, the proposed algorithm has a significant improvement for multiple signal source localization. Full article

Grape (Vitis vinifera L.) stems, a by-product of winemaking, possess significant potential value due to their rich polyphenolic composition, which allows their exploitation for cosmetic and pharmaceutical applications. This presents a promising opportunity for valorisation aimed at developing innovative products with potential health-promoting effects. In this study, the polyphenolic profile of extracts from grape stems of seven white grape varieties was determined using spectrophotometric and chromatographic methods, specifically high-performance liquid chromatography coupled with a photodiode array detector and electrospray ionization multi-stage mass spectrometry (HPLC-PDA-ESI-MSn), as well as on their ferric-reducing antioxidant power (FRAP) and radical scavenging capacity, using 2,2-diphenyl-1-picrylhydrazyl (DPPH^●) and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS^●+) radicals. This study also evaluated the anti-aging activity and skin depigmenting activity of these extracts. These findings revealed a diverse polyphenolic profile, encompassing proanthocyanidins and catechin derivatives (PCDs), phenolic acids, and flavonols. Among the varieties studied, ‘Códega do Larinho’ exhibited the highest concentrations of six distinct polyphenols and the highest total phenolic content. It also demonstrated the highest results for antioxidant capacity and elastase and tyrosinase inhibition. Pearson’s correlation analysis showed a significant positive correlation between certain PCDs with both FRAP and DPPH assays, as well as between the identified flavonols and anti-elastase activity. These results underscore the potential health benefits of grape stem extracts and emphasize the importance of their polyphenolic composition in enhancing antioxidant and anti-aging properties, thus supporting their application in different industries. Full article

Background/Objectives: Tinnitus is a debilitating auditory disorder commonly described as a ringing in the ears in the absence of an external sound source. Sound trauma is considered a primary cause. Neuronal hyperactivity is one potential mechanism for the genesis of tinnitus and has been identified in the cochlear nucleus (CN) and the auditory cortex (AC), where there may be an imbalance of excitatory and inhibitory neurotransmissions. However, no study has directly correlated tinnitus with the extracellular levels of amino acids in the CN and the AC using microdialysis, which reflects the functions of these neurochemicals. In the present study, rats were exposed to acoustic trauma and then subjected to behavioural confirmation of tinnitus after one month, followed by microdialysis. Methods: Rats were divided into sham (aged, n = 6; young, n = 6); tinnitus-positive (aged, n = 7; young, n = 7); and tinnitus-negative (aged, n = 3; young, n = 3) groups. In vivo microdialysis was utilized to collect samples from the CN and the AC, simultaneously, in the same rat. Extracellular levels of amino acids were quantified using high-performance liquid chromatography (HPLC) coupled with an electrochemical detector (ECD). The effects of sound stimulation and age on neurochemical changes associated with tinnitus were also examined. Results: There were no significant differences in either the basal levels or the sound stimulation-evoked changes of any of the amino acids examined in the CN and the AC between the sham and tinnitus animals. However, the basal levels of serine and threonine exhibited age-related alterations in the AC, and significant differences in threonine and glycine levels were observed in the responses to 4 kHz and 16 kHz stimuli in the CN. Conclusions: These results demonstrate the lack of a direct link between extracellular levels of amino acids in the CN and the AC and tinnitus perception in a rat model of tinnitus. Full article

Timing and/or position-sensitive MCP detectors, which detect secondary electrons (SEs) emitted from a conversion foil during ion passage, are widely utilized in nuclear physics and nuclear astrophysics experiments. This review covers high-performance timing and/or position-sensitive MCP detectors that use SE emission for mass measurements of exotic nuclei at nuclear physics facilities, along with their applications in new measurement schemes. The design, principles, performance, and applications of these detectors with different arrangements of electromagnetic fields are summarized. To achieve high precision and accuracy in mass measurements of exotic nuclei using time-of-flight (TOF) and/or position (imaging) measurement methods, such as high-resolution beam-line magnetic-rigidity time-of-flight (B$\rho$-TOF) and in-ring isochronous mass spectrometry (IMS), foil-MCP detectors with high position and timing resolution have been introduced and simulated. Beyond TOF mass measurements, these new detector systems are also described for use in heavy ion beam trajectory monitoring and momentum measurements for both beam-line and in-ring applications. Additionally, the use of position-sensitive timing foil-MCP detectors for Penning trap mass spectrometers and multi-reflection time-of-flight (MR-TOF) mass spectrometers is proposed and discussed to improve efficiency and enhance precision. Full article

Photovoltaic panel defect detection presents significant challenges due to the wide range of defect scales, diverse defect types, and severe background interference, often leading to a high rate of false positives and missed detections. To address these challenges, this paper proposes the LEM-Detector, an efficient end-to-end photovoltaic panel defect detector based on the transformer architecture. To address the low detection accuracy for Crack and Star crack defects and the imbalanced dataset, a novel data augmentation method, the Linear Feature Augmentation (LFA) module, specifically designed for linear features, is introduced. LFA effectively improves model training performance and robustness. Furthermore, the Efficient Feature Enhancement Module (EFEM) is presented to enhance the receptive field, suppress redundant information, and emphasize meaningful features. To handle defects of varying scales, complementary semantic information from different feature layers is leveraged for enhanced feature fusion. A Multi-Scale Multi-Feature Pyramid Network (MMFPN) is employed to selectively aggregate boundary and category information, thereby improving the accuracy of multi-scale target recognition. Experimental results on a large-scale photovoltaic panel dataset demonstrate that the LEM-Detector achieves a detection accuracy of 94.7% for multi-scale defects, outperforming several state-of-the-art methods. This approach effectively addresses the challenges of photovoltaic panel defect detection, paving the way for more reliable and accurate defect identification systems. This research will contribute to the automatic detection of surface defects in industrial production, ultimately enhancing production efficiency. Full article

When handling complex remote sensing scenarios, rotational angle information can improve detection accuracy and enhance algorithm robustness, providing support for fine-grained detection. Point set representation is one of the most commonly used methods in arbitrary-oriented object detection tasks, leveraging discrete feature points to represent oriented targets and achieve high accuracy in angle prediction. However, due to the inherent discreteness of point set representation, it is prone to significant impact from isolated points and representational ambiguity in harsh application scenarios, leading to inaccurate detection. To address this issue, an efficient aerial object detector named BE-Det is proposed, which uses the optimal transport (OT) strategy to constrain the positions of isolated points. Additionally, a candidate point set quality evaluation scheme is designed to effectively assess the quality of candidate point sets. Experimental results on two challenging aerial datasets demonstrate that the proposed method outperforms several advanced detection methods. Full article

The paper proposes and verifies a small-angle measurement method based on the defect spot mode of the position-sensitive detector (PSD). With the output characteristics of the PSD in the defect spot mode and the size transformation properties of a focused beam, the measurement sensitivity can be significantly improved. Calibration experiments with the piezoelectric transducer (PZT) indicate that compared with the current PSD-based autocollimation method, the proposed method can improve the sensitivity of small-angle measurement by 57 times, and the measurement sensitivity of the proposed method can be further improved by optimizing the system parameters, while the proposed method has the advantages of a simple system and high real-time performance. Therefore, the proposed method is expected to be used in high-precision motion error detection, as well as in shape and position measurement. Full article

The optimization of a contact-less magnetoelastic sensing setup designed to detect substances/agents accumulating in its environment is presented. The setup is intended as a custom-built, low-cost yet effective magnetoelastic sensor for pest/bug detection in constrained places (small museums, labs, etc.). It involves a short, thin, and flexible polymer slab in a cantilever arrangement, with a short Metglas^® 2826 MB magnetoelastic ribbon attached on part of its surface. A mobile phone both supports and supplies low-amplitude vibration to the slab’s free end. When vibrating, the magnetoelastic ribbon generates variable magnetic flux, thus inducing voltage in a contact-less manner into a pick-up coil suspended above the ribbon. This voltage carries specific characteristic frequencies of the slab’s vibration. If substances/agents accumulate on parts of the (suitably coated) slab surface, its mass distribution and, hence, characteristic frequencies change. Then, simply monitoring shifts of such frequencies in the recorded voltage enables the detection of accumulating substances/agents. The current work uses extensive testing via various vibration profiles and load positions on the slab, for statistically evaluating the sensitivity of the mass detection of the setup. It is shown that, although this custom-built substance/agent detector involves limited (low-cost) hardware and a simplified design, it achieves promising results with respect to its cost. Full article

Net primary production (NPP) serves as a critical proxy for monitoring changes in the global capacity for vegetation carbon sequestration. The assessment of the factors (i.e., human activities and climate changes) influencing NPP is of great value for the study of terrestrial systems. To investigate the influence of factors on grassland NPP, the ecologically vulnerable Qinghai–Tibet Plateau region was considered an appropriate study area for the period from 2000 to 2020. We innovated the use of the RICI index to quantitatively represent human activities and analyzed the effects of RICI and climatic factors on grassland NPP using the geographical detector. In addition, the future NPP was predicted through the integration of two modeling approaches: The Patch-Generating Land Use Simulation (PLUS) model and the Carnegie–Ames–Stanford Approach (CASA) model. The assessment revealed that the expanded grassland contributed 7.55 × 10⁴ Gg C (Gg = 10⁹ g) to the total NPP, whereas the deterioration of grassland resulted in a decline of 1.06 × 10⁵ Gg C. The climatic factor was identified as the dominant factor in grassland restoration, representing 70.85% of the total NPP, as well as the dominant factor in grassland degradation, representing 92.54% of the total NPP. By subdividing the climate change and human activity factors into sub-factors and detecting them with a geographical detector, the results show that climate change and anthropogenic factors have significant ability to explain geographic variation in NPP to a considerable extent, and the effect on NPP is greater when the factors interact. The q-values of the Relative Impact Contribution Index (RICI) and the RICI of the land use change NPP are consistently greater than 0.6, with the RICI of the human management practices NPP and the evapotranspiration remaining at approximately 0.5. The analysis of the interaction between climate and human activity factors reveals an average impact of greater than 0.8. By 2030, the NPP of the natural development scenario, economic development scenario (ED), and ecological protection scenario (EP) show a decreasing trend due to climate change, the dominant factor, causing them to decrease. Human activities play a role in the improvement. The EP indicates a positive expansion in the growth rate of forests, water, and wetlands, while the ED reveals rapid urbanization. It is notable that this is accompanied by a temporary suspension of urban greening. Full article

The silicon carbide (SiC) Schottky diode (SBD) detector in a SiC hybrid photomultiplier tube (HPMT) generates signals by receiving photocathode electrons with an energy of 10 keV. So, the performance of the SiC SBD under electron irradiation with an energy of 10 keV has an important significance for the application of the SiC-HPMT. However, studies on 10 keV radiation effects on the SiC SBDs were rarely reported. In this paper, the performance degradation of the SiC SBDs irradiated by 10 keV electrons at different fluences was investigated. After the irradiation, the forward current of the SiC SBDs increased, and the turn-on voltage decreased with the irradiation fluences until 1.6 × 10¹⁶ cm⁻². According to the capacitance–voltage (C-V) curves, the effective doping concentration increased slightly after the irradiation, and an obvious discrepancy of C-V curves occurred below 5 V. Moreover, as a radiation detector, the peak position of the α-particles’ amplitude spectrum changed slightly, and the energy resolution was also slightly reduced after the irradiation due to the high collection charge efficiency (CCE) still being larger than 99.5%. In addition, the time response of the SiC SBD to the 50 ns pulsed X-ray was almost not affected by the irradiation. The results indicated that the performance degradation of the SiC SBD irradiated at the fluence of 1.5 × 10¹⁷ cm⁻² would not result in a deterioration of the properties of the SiC-HPMT and showed an important significance for the supplement of the radiation resistance of the SiC SBD radiation detector. Full article

The Rurality Index is an important reference for the formulation of rural development strategies and policies, but the evaluation of the rurality of megacities based on the township scale is relatively limited. Based on the perspective of spatial governance, this study constructed the evaluation index system of Shanghai’s rurality and carried out the evaluation of Shanghai’s rurality at the township scale from 2005 to 2020. The article adopts the MGWR model to analyze the driving effects of five key driving factors (the proportion of foreign population, per capita industrial output value, public finance revenue, social fixed asset investment, and rail transit coverage), and adopts the Geo-Detector model to analyze the interactive driving effects of two factors. The results indicate that the rurality index of megacities and townships as a whole shows a weakening trend, and the above factors have a predominantly negative impact on rurality, with differences in the intensity of the impact in different periods. There is an obvious interactive additive effect between the factors. When formulating policies for township development, government departments need to take into account the functional positioning of the region and comprehensively adopt targeted policies on population, industry, transportation, finance and investment to regulate and guide the transformation or sustainable development of the countryside. Full article

Single-stage detectors have drawbacks of insufficient accuracy and poor coverage capability. YOLOF (You Only Look One-level Feature) has achieved better performance in this regard, but there is still room for improvement. To enhance the coverage capability for objects of different scales, we propose an improved single-stage object detector: Dq-YOLOF. We have designed an output encoder that employs a series of modules utilizing deformable convolution and SimAM (Simple Attention Module). This module replaces the dilated convolution in YOLOF. This design significantly improves the ability to express details. Simultaneously, we have redefined the sample selection strategy, which optimizes the quality of positive samples based on SimOTA. It can dynamically allocate positive samples according to their quality, reducing computational load and making it more suitable for small objects. Experiments conducted on the COCO 2017 dataset also verify the effectiveness of our method. Dq-YOLOF achieved 38.7 $AP$, 1.5 $AP$ higher than YOLOF. To confirm performance improvements on small objects, our method was tested on urinary sediment and aerial drone datasets for generalization. Notably, it enhances performance while also lowering computational costs. Full article

With advancements in technology, scientists are delving deeper in their explorations of the universe. Integral field spectrograph (IFS) play a significant role in investigating the physical properties of supermassive black holes at the centers of galaxies, the nuclei of galaxies, and the star formation processes within galaxies, including under extreme conditions such as those present in galaxy mergers, ultra-low-metallicity galaxies, and star-forming galaxies with strong feedback. IFS transform the spatial field into a linear field using an image slicer and obtain the spectra of targets in each spatial resolution element through a grating. Through scientific processing, two-dimensional images for each target band can be obtained. IFS use concave gratings as dispersion systems to decompose the polychromatic light emitted by celestial bodies into monochromatic light, arranged linearly according to wavelength. In this experiment, the working environment of a star was simulated in the laboratory to facilitate the wavelength calibration of the space integral field spectrometer. Tools necessary for the calibration process were also explored. A mercury–argon lamp was employed as the light source to extract characteristic information from each pixel in the detector, facilitating the wavelength calibration of the spatial IFS. The optimal peak-finding method was selected by contrasting the center of weight, polynomial fitting, and Gaussian fitting methods. Ultimately, employing the 4FFT-LMG algorithm to fit Gaussian curves enabled the determination of the spectral peak positions, yielding wavelength calibration coefficients for a spatial IFS within the range of 360 nm to 600 nm. The correlation of the fitting results between the detector pixel positions and corresponding wavelengths was >99.99%. The calibration accuracy during wavelength calibration was 0.0067 nm, reaching a very high level. Full article