Search Results (376)

(1) Background: Fetal chromosomal examination is a critical component of modern prenatal testing. Traditionally, maternal serum biomarkers such as free β-human chorionic gonadotropin (Free β-HCG) and pregnancy-associated plasma protein A (PAPPA) have been employed for screening, achieving a detection rate of approximately 90% for fetuses with Down syndrome, albeit with a false positive rate of 5%. While amniocentesis remains the gold standard for the prenatal diagnosis of chromosomal abnormalities, including Down syndrome and Edwards syndrome, its invasive nature carries a significant risk of complications, such as infection, preterm labor, or miscarriage, occurring at a rate of 7 per 1000 procedures. Beyond Down syndrome and Edwards syndrome, other chromosomal abnormalities, such as trisomy of chromosomes 9, 16, or Barr bodies, pose additional diagnostic challenges. Non-invasive prenatal testing (NIPT) has emerged as a powerful alternative for fetal genetic screening by leveraging maternal blood sampling. However, due to the extremely low abundance of fetal cells in maternal circulation, NIPT based on fetal cells faces substantial technical challenges. (2) Methods: Fetal nucleated red blood cells (FnRBCs) were first identified in maternal circulation in a landmark study published in The Lancet in 1959. Due to their fetal origin and presence in maternal peripheral blood, FnRBCs represent an ideal target for non-invasive prenatal testing (NIPT). In this study, we introduce a novel self-assembled cell array (SACA) chip system, a microfluidic-based platform designed to efficiently settle and align cells into a monolayer at the chip’s base within five minutes using lateral flow dynamics and gravity. This system is integrated with a fully automated, multi-channel fluorescence scanning module, enabling the real-time imaging and molecular profiling of fetal cells through fluorescence-tagged antibodies. By employing a combination of Hoechst+/CD71+/HbF+/CD45− markers, the platform achieves the precise enrichment and isolation of FnRBCs at the single-cell level from maternal peripheral blood. (3) Results: The SACA chip system effectively reduces the displacement of non-target cells by 31.2%, achieving a single-cell capture accuracy of 97.85%. This isolation and enrichment system for single cells is well suited for subsequent genetic analysis. Furthermore, the platform achieves a high purity of isolated cells, overcoming the concentration detection limit of short tandem repeat (STR) analysis, demonstrating its capability for reliable non-invasive prenatal testing. (4) Conclusions: This study demonstrates that the SACA chip, combined with an automated image positioning system, can efficiently isolate single fetal nucleated red blood cells (FnRBCs) from 50 million PBMCs in 2 mL of maternal blood, completing STR analysis within 120 min. With higher purification efficiency compared to existing NIPT methods, this platform shows great promise for prenatal diagnostics and potential applications in other clinical fields. Full article

This paper aims to propose inductive wireless power transfer (IWPT) technology for pinpointing fault locations in LV distribution underground cables following the use of other pre-location methods. The proposed device is portable, hence battery-powered, and operates by scanning for faults above ground via inductive coupling with the de-energized cable. This primarily relies on impedance changes in the cable due to permanent faults as the device scans the length of the cable. A detailed frequency domain mathematical model for the system is deduced and circuit design/parameters affecting the inductive coupling are investigated. An optimal design strategy for the portable device is demonstrated to achieve high fault-locating sensitivity with a minimum device VA rating. The device is tested under multiple fault scenarios (including shunt and open-circuit (cable break) faults) using a MATLAB/Simulink circuit model, and the results are validated against the mathematical model. The device’s performance with single-core and multi-core cables is examined. Finally, a critical comparative evaluation of the IWPT method with existing fault pinpointing techniques is conducted that highlights both the advantages and limitations of the proposed technique. The research shows that the proposed technology provides a promising new solution for LV network operators to minimize excavations for underground cable faults by pinpointing locations where a considerable deflection in induced cable current occurs when passing a fault point. Full article

Aerosol optical and microphysical properties determine their radiative capabilities, climatic impacts, and health effects. Satellite remote sensing is a crucial tool for obtaining aerosol parameters on a global scale. However, traditional physical and statistical retrieval methods face bottlenecks in data mining capacity as the volume of satellite observation information increases rapidly. Artificial intelligence methods are increasingly applied to aerosol parameter retrieval, yet most current approaches focus on end-to-end single-parameter retrieval without considering the inherent relationships among multiple aerosol properties. In this study, we propose a sequence-to-sequence aerosol parameter joint retrieval algorithm based on the transformer model S2STM. Unlike conventional end-to-end single-parameter retrieval methods, this algorithm leverages the encoding–decoding capabilities of the transformer model, coupling multi-source data such as polarized satellite, meteorological, model, and surface characteristics, and incorporates a physically coherent consistency loss function. This approach transforms traditional single-parameter numerical regression into a sequence-to-sequence relationship mapping. We applied this algorithm to global observations from the Chinese polarimetric satellite (the Particulate Observing Scanning Polarimeter, POSP) and simultaneously retrieved multiple key aerosol optical and microphysical parameters. Event analyses, including dust and pollution episodes, demonstrate the method’s responsiveness in hotspot regions and events. The retrieval results show good agreement with ground-based observation products. This method is also adaptable to satellite instruments with various configurations (e.g., multi-wavelength, multi-angle, and multi-dimensional polarization) and can further improve its spatiotemporal generalization performance by enhancing the spatial balance of ground station training datasets. Full article

The combination of UAV-LiDAR and LiDAR-SLAM (Simultaneous Localization and Mapping) technology can overcome the scanning limitations of different platforms and obtain comprehensive 3D structural information of forest stands. To address the challenges of the traditional registration algorithms, such as high initial value requirements and susceptibility to local optima, in this paper, we propose a high-precision, robust, NDT-VGICP registration method that integrates voxel features to register UAV-LiDAR and LiDAR-SLAM point clouds at the forest stand scale. First, the point clouds are voxelized, and their normal vectors and normal distribution models are computed, then the initial transformation matrix is quickly estimated based on the point pair distribution characteristics to achieve preliminary alignment. Second, high-dimensional feature weighting is introduced, and the iterative closest point (ICP) algorithm is used to optimize the distance between the matching point pairs, adjusting the transformation matrix to reduce the registration errors iteratively. Finally, the algorithm converges when the iterative conditions are met, yielding an optimal transformation matrix and achieving precise point cloud registration. The results show that the algorithm performs well in Chinese fir forest stands of different age groups (average RMSE—horizontal: 4.27 cm; vertical: 3.86 cm) and achieves high accuracy in single-tree crown vertex detection and tree height estimation (average F-score: 0.90; R² for tree height estimation: 0.88). This study demonstrates that the NDT-VGICP algorithm can effectively fuse and collaboratively apply multi-platform LiDAR data, providing a methodological reference for accurately quantifying individual tree parameters and efficiently monitoring 3D forest stand structures. Full article

Surface quality control of hot-dip galvanized sheets is a critical research topic in the metallurgical industry. Zinc dross, the most common surface defect in the hot-dip galvanizing process, significantly affects the sheet’s service performance. In this manuscript, a novel magnetic flux leakage (MFL) detection method was proposed to detect zinc dross defects on the surface of hot-dip galvanized steel sheets. Instead of using exciting coils in traditional methods, a tiny permanent magnet with a millimeter magnitude was employed to reduce the size and weight of the equipment. Additionally, a high-precision tunnel magnetoresistance (TMR) sensor with a sensitivity of 300 mV/V/Oe was selected to achieve higher detection accuracy. The experimental setup was established, and the x-axis direction (sample movement direction) was determined as the best measurement axis by vector analysis through experiments and numerical simulation. The detection results indicate that this novel MFL detection method could detect industrial zinc dross with an equivalent size of 400 μm, with high signal repeatability and signal-to-noise ratio. In the range of 0–1200 mm/s, the detection speed has almost no effect on the measurement signal, which indicates that this novel method has higher adaptability to various conditions. The multi-path scanning method with a single probe was used to simulate the array measurement to detect a rectangular area of 30 × 60 mm. Ten zinc dross defects were detected across eight measurement paths with 4 mm intervals, and the positions of these zinc dross defects were successfully reconstructed. The research results indicate that this novel MFL detection method is simple and feasible. Furthermore, the implementation of array measurements provides valuable guidance for subsequent in-depth research and potential industrial applications in the future. Full article

Automatically identifying mine and tunnel infrastructure elements, such as rock bolts, from point cloud data improves deformation and quality control analyses and could ultimately contribute to improved safety on engineering projects. However, we hypothesize that existing methods are sensitive to small changes in object characteristics across datasets if trained insufficiently, and previous studies have only investigated single datasets. In this study, we present a cross-site training (generalization) investigation for a multi-class tunnel infrastructure classification task on terrestrial laser scanning data. In contrast to previous work, the novelty of this work is that the models are trained and tested across multiple datasets collected in different tunnels. We used two random forest (RF) implementations and one neural network (NN), as proposed in recent studies, on four datasets collected in different mines and tunnels in the US and Canada. We labeled points as belonging to one of four classes—rock, bolt, mesh, and other—and performed cross-site training experiments to evaluate accuracy differences between sites. In general, we found that the NN and RF models had similar performance to each other, and that same-site classification was generally successful, but cross-site performance was much lower and judged as not practically useful. Thus, our results indicate that standard geometric features are often insufficient for generalized classification of tunnel infrastructure, and these types of methods are most successful when applied to specific individual sites using interactive software for classification. Possible future research directions to improve generalized performance are discussed, including domain adaptation and deep learning methods. Full article

To address the technical challenges, production quality issues, and inefficiencies caused by the heavy reliance on traditional manual processing of small assembly plates in the shipbuilding industry, this paper presents the design and analysis of a track-based automatic welding device. This equipment provides a solution for achieving batch and continuous welding in the field of automatic welding technology. The design section includes the mechanical design of the equipment’s core mechanisms, the design of the operating systems, the development of visual scanning strategies under working conditions, and the formulation of multi-layer and multi-pass welding processes. The analysis section comprises the static analysis of the equipment’s mechanical structure, kinematic analysis of the robotic arm, and inspection analysis of the device. Compared with manual welding, multi-layer and multi-pass welding experiments conducted using the equipment demonstrated stabilized welding quality for small assembly plates. Under the conditions of single plates with different groove positions and gaps, when the gap was 4 mm, processing efficiency increased by 7.35%, and processing time was reduced by 10.2%; when the gap was 5 mm, processing efficiency increased by 10.7%, and processing time decreased by 7.39%. The welding formation rate for the overall processing of single plate panels and web grooves increased by 11.48%, total material consumption decreased by 13.4%, and unit material consumption decreased by 13.5%. For mass production of small assembly plates of the same specifications, processing time was reduced by 16.7%, and there was a 41.4% reduction in costs. The equipment effectively addresses the low level of automation and heavy dependence on traditional manual processing in the shipbuilding industry, contributing to cost reduction and efficiency improvement. Full article

Visual recognition and localization of underwater optical beacons are critical for AUV docking, but traditional beacons are limited by fixed directionality and light attenuation in water. To extend the range of optical docking, this study designs a novel omnidirectional rotating optical beacon that provides 360-degree light coverage over 45 m, improving beacon detection probability through synchronized scanning. Addressing the challenges of light centroid detection, we introduce a parallel deep learning detection algorithm based on an improved YOLOv8-pose model. Initially, an underwater optical beacon dataset encompassing various light patterns was constructed. Subsequently, the network was optimized by incorporating a small detection head, implementing dynamic convolution and receptive-field attention convolution for single-stage multi-scale localization. A post-processing method based on keypoint joint IoU matching was proposed to filter redundant detections. The algorithm achieved 93.9% AP at 36.5 FPS, with at least a 5.8% increase in detection accuracy over existing methods. Moreover, a light-source-based measurement method was developed to accurately detect the beacon’s orientation. Experimental results indicate that this scheme can achieve high-precision omnidirectional guidance with azimuth and pose estimation errors of -4.54° and 3.09°, respectively, providing a reliable solution for long-range and large-scale optical docking. Full article

In this study, a new species of Alanomyces was isolated as an endophyte from the bark of Azadirachta indica from Mulshi, Maharashtra. The identity of this isolate was confirmed based on the asexual morphological characteristics as well as multi-gene phylogeny based on the internal transcribed spacer (ITS) and large subunit (LSU) nuclear ribosomal RNA (rRNA) regions. As this was the second species to be reported in this genus, we sequenced the genome of this species to increase our knowledge about the possible applicability of this genus to various industries. Its genome length was found to be 35.01 Mb, harboring 7870 protein-coding genes as per Augustus and 8101 genes using GeMoMa. Many genes were annotated using the Clusters of Orthologous Groups (COGs) database, the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), Swiss-Prot, NCBI non-redundant nucleotide sequences (NTs), and NCBI non-redundant protein sequences (NRs). The number of repeating sequences was predicted using Proteinmask and RepeatMasker; tRNA were detected using tRNAscan and snRNA were predicted using rfam_scan. The genome was also annotated using the Pathogen–Host Interactions Database (PHI-base) and AntiSMASH. To confirm the evolutionary history, average nucleotide identity (ANIb), phylogeny based on orthologous proteins, and single nucleotide polymorphisms (SNPs) were carried out. Metabolic profiling of the methanolic extract of dried biomass and ethyl acetate extract of the filtrate revealed a variety of compounds of great importance in the pharmaceutical and cosmetic industry. The characterization and genomic analysis of the newly discovered species Alanomyces manoharacharyi highlights its potential applicability across multiple industries, particularly in pharmaceuticals and cosmetics due to its diverse secondary metabolites and unique genetic features it possesses. Full article

The foundation of robot autonomous movement is to quickly grasp the position and surroundings of the robot, which SLAM technology provides important support for. Due to the complex and dynamic environments, single-sensor SLAM methods often have the problem of degeneracy. In this paper, a multi-sensor fusion SLAM method based on the LVI-SAM framework was proposed. First of all, the state-of-the-art feature detection algorithm SuperPoint is used to extract the feature points from a visual-inertial system, enhancing the detection ability of feature points in complex scenarios. In addition, to improve the performance of loop-closure detection in complex scenarios, scan context is used to optimize the loop-closure detection. Ultimately, the experiment results show that the RMSE of the trajectory under the 05 sequence from the KITTI dataset and the Street07 sequence from the M2DGR dataset are reduced by 12% and 11%, respectively, compared to LVI-SAM. In simulated complex environments of animal farms, the error of this method at the starting and ending points of the trajectory is less than that of LVI-SAM, as well. All these experimental comparison results prove that the method proposed in this paper can achieve higher precision and robustness performance in localization and mapping within complex environments of animal farms. Full article

The interaction of ultra-short laser pulses (USLP) with Nickel/Titanium (Ni/Ti) thin film has been presented. The nano layer thin film (NLTF), composed of ten alternating Ni and Ti layers, was deposited on silicon (Si) substrate by ion-sputtering. A single and multi-pulse irradiation was performed in air with focused and linearly polarized laser pulses. For achieving selective ablation of one or more surface layers, without reaching the Si substrate, single pulse energy was gradually increased from near the ablation threshold value to an energy value that caused the complete removal of the NLTF. In addition to single-pulse selective ablation, the multi-pulse USLP irradiation and production of laser-induced periodic surface structures (LIPSSs) were also studied. In the presented experiment, we found the optimal combination of accumulated pulse number and pulse energy to achieve the LIPSS formation on the thin film. The laser-induced morphology was examined with optical microscopy, scanning electron microscopy, and optical profilometry. To interpret the experimental observations, a theoretical simulation has been performed to explore the thermal response of the NLTFs after irradiation with single laser pulses. Full article

This study examined the decline of traditional villages due to urbanization, focusing on their spatial patterns and architectural characteristics in China, particularly in the Guanzhong region. Using ArcGIS tools, kernel density and nearest-neighbor analyses quantitatively assessed the spatial distribution of these villages at macro- and micro-levels. Additionally, 3D laser scanning was employed to qualitatively analyze architectural features. The study demonstrated that (1) traditional villages are unevenly clustered nationwide, primarily in the southeast and southwest, creating a “three cores and multiple points” spatial pattern. (2) In the Guanzhong region, traditional village distribution also shows clustering with diverse patterns, including regiment, belt, and point formations. Higher densities are found in the eastern and northern regions, while the west and south are sparsely populated. Most villages are located at altitudes of 501–700 m, on slopes of 6–15°, and near water sources. (3) The basic residential structures in Guanzhong included the single, vertical multi-entry, and horizontal coupled courtyards, as well as the vertical and horizontal interleaved layouts; these buildings typically featured the foundations and walls made of earth, stone, and brick, combined with various wooden frames and single- or double-sloped roofs. This study overcomes the limitations of the traditional literature and field surveys by quantitatively and qualitatively analyzing the spatial patterns of traditional villages and the architectural forms of residential buildings from an architectural perspective. It graphically presents the data to provide an efficient and practical theoretical basis for the heritage preservation and development of traditional villages. Full article

With the continuous advancement of autonomous driving technology, an increasing number of high-definition (HD) maps have been generated and stored in geospatial databases. These HD maps can provide strong localization support for mobile robots equipped with light detection and ranging (LiDAR) sensors. However, the global localization of heterogeneous robots under complex environments remains challenging. Most of the existing point cloud global localization methods perform poorly due to the different perspective views of heterogeneous robots. Leveraging existing HD maps, this paper proposes a base-map-guided heterogeneous robots localization solution. A novel co-view context descriptor with rotational invariance is developed to represent the characteristics of heterogeneous point clouds in a unified manner. The pre-set base map is divided into virtual scans, each of which generates a candidate co-view context descriptor. These descriptors are assigned to robots before operations. By matching the query co-view context descriptors of a working robot with the assigned candidate descriptors, the coarse localization is achieved. Finally, the refined localization is done through point cloud registration. The proposed solution can be applied to both single-robot and multi-robot global localization scenarios, especially when communication is impaired. The heterogeneous datasets used for the experiments cover both indoor and outdoor scenarios, utilizing various scanning modes. The average rotation and translation errors are within 1° and 0.30 m, indicating the proposed solution can provide reliable localization support despite communication failures, even across heterogeneous robots. Full article

This study investigates the pyrolytic decomposition of apple and potato peel waste using thermogravimetric analysis (TGA). In addition, using Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS), the influence of pyrolysis temperature on the physicochemical characteristics and structural properties of biochar was studied. The degradation of biomass samples was studied between 25 °C and 800 °C. Although apple and potato peel decomposition present similar thermogravimetric profiles, there are some differences that can be evidenced from DTG curves. Potato peel showed one degradation peak in the range 205–375 °C with 50% weight loss; meanwhile, the apple peel exhibited two stages: one with a maximum at around 220 °C and about 38% weight loss caused by degradation of simple carbohydrates and a second peak between 280 °C and 380 °C with a maximum at 330 °C, having a weight loss of approximately 24%, attributed to cellulose degradation. To gain more insight into the phenomena involved in biomass conversion, the kinetics of the reaction were analyzed using thermal data collected in non-isothermal conditions with a constant heating rate of 5, 10, 20, or 30 °C /min. The kinetic analysis for each decomposed biomass (apple and potato) was carried out based on single-step and multi-step type techniques by combining the Arrhenius form of the decomposition rate constant with the mass action law. The multi-step approaches provided further insight into the degradation mechanisms for the whole range of the decomposition temperatures. The effect of temperature on biomass waste structure showed that the surface morphologies and surface functional groups of both samples are influenced by the pyrolysis temperature. A higher pyrolysis temperature of 800 °C results in the disappearance of the bands characteristic of the hydroxyl, aliphatic, ether, and ester functional groups, characteristic of a porous surface with increased adsorption capacity. Therefore, this study concludes that biomass waste samples (apple and potato) can produce high yields of biochar and are a potential ecological basis for a sustainable approach. The preliminary adsorption tests show a reasonably good nitrate removal capacity for our biochar samples. Full article

Neovascular age-related macular degeneration (nAMD) is one of the major causes of vision impairment that affect millions of people worldwide. Early detection of nAMD is crucial because, if untreated, it can lead to blindness. Software and algorithms that utilize artificial intelligence (AI) have become valuable tools for early detection, assisting doctors in diagnosing and facilitating differential diagnosis. AI is particularly important for remote or isolated communities, as it allows patients to endure tests and receive rapid initial diagnoses without the necessity of extensive travel and long wait times for medical consultations. Similarly, AI is notable also in big hubs because cutting-edge technologies and networking help and speed processes such as detection, diagnosis, and follow-up times. The automatic detection of retinal changes might be optimized by AI, allowing one to choose the most effective treatment for nAMD. The complex retinal tissue is well-suited for scanning and easily accessible by modern AI-assisted multi-imaging techniques. AI enables us to enhance patient management by effectively evaluating extensive data, facilitating timely diagnosis and long-term prognosis. Novel applications of AI to nAMD have focused on image analysis, specifically for the automated segmentation, extraction, and quantification of imaging-based features included within optical coherence tomography (OCT) pictures. To date, we cannot state that AI could accurately forecast the therapy that would be necessary for a single patient to achieve the best visual outcome. A small number of large datasets with high-quality OCT, lack of data about alternative treatment strategies, and absence of OCT standards are the challenges for the development of AI models for nAMD. Full article