Search Results (253)

The resting postures of piglets are crucial indicators for assessing their health status and environmental comfort. This study proposes a resting posture recognition method for piglets during lactation based on the PPR-YOLO model, aiming to enhance the detection accuracy and classification capability for different piglet resting postures. Firstly, to address the issue of numerous sows and piglets in the farrowing house that easily occlude each other, an image edge detection algorithm is employed to precisely locate the sow’s farrowing bed area. By cropping the images, irrelevant background interference is reduced, thereby enhancing the model’s recognition accuracy. Secondly, to overcome the limitations of the YOLOv11 model in fine feature extraction and small object detection, improvements are made, resulting in the proposed PPR-YOLO model. Specific enhancements include the introduction of a multi-branch Conv2 module to enrich feature extraction capabilities and the adoption of an inverted bottleneck IBCNeck module, which expands the number of channels and incorporates a channel attention mechanism. This strengthens the model’s ability to capture and differentiate subtle posture features. Additionally, in the post-processing stage, the relative positions between sows and piglets are utilized to filter out piglets located outside the sow region, eliminating interference from sow nursing behaviors in resting posture recognition, thereby ensuring the accuracy of posture classification. The experimental results show that the proposed method achieves accurate piglet posture recognition, outperforming mainstream object detection algorithms. Ablation experiments validate the effectiveness of image cropping and model enhancements in improving performance. This method provides effective technical support for the automated monitoring of piglet welfare in commercial farms and holds promising application prospects. Full article

This study aims to determine the extent to which coating composition and workpiece properties impact machinability and tool selection when turning Compacted Graphite Iron (CGI) under extreme roughing conditions. Two CGI workpieces, differing in pearlite content and graphite nodularity, were machined at a cutting speed of 180 m/min, feed rate of 0.18 mm/rev, and depth of cut of 3 mm. To assess the impact of tool properties across a wide range of commercially available tools, four diverse multilayered cemented carbide tools were evaluated: Tool A and Tool B with a thin AlTiSiN PVD coating, Tool C with a thick Al₂O₃-TiCN CVD coating, and Tool D with a thin Al₂O₃-TiC PVD coating. The machinability of CGI and wear mechanisms were analyzed using pre-cutting characterization, in-process optical microscopy, and post-test SEM analysis. The results revealed that CGI microstructural variations only affected tool life for Tool A, with a 110% increase in tool life between machining CGI Grade B and Grade A, but that the effects were negligible for all other tools. Tool C had a 250% and 70% longer tool life compared to the next best performance (Tool A) for CGI Grade A and CGI Grade B, respectively. With its thick CVD-coating, Tool C consistently outperformed the others due to its superior protection of the flank face and cutting edge under high-stress conditions. The cutting-induced stresses played a more significant role in the tool wear process than minor differences in workpiece microstructure or tool properties, and a thick CVD coating was most effective in addressing the tool wear effects for the extreme roughing conditions. However, differences in tool life for Tool A showed that tool behavior cannot be predicted based on a single system parameter, even for extreme conditions. Instead, tool properties, workpiece properties, cutting conditions, and their interactions should be considered collectively to evaluate the extent that an individual parameter impacts machinability. This research demonstrates that a comprehensive approach such as this can allow for more effective tool selection and thus lead to significant cost savings and more efficient manufacturing operations. Full article

Lactobacillus is a key genus of probiotics commonly utilized for the treatment of oral infections The primary aim of our research was to investigate the probiotic potential of the newly isolated Levilactobacillus brevis DPL5 strain from human breast milk, focusing on its ability to combat biofilm-forming pathogens such as Staphylococcus aureus. Employing in vitro approaches, we demonstrate L. brevis DPL5′s ability to endure at pH 3 with survival rates above 30%, and withstand the osmotic stress often found during industrial processes like fermentation and freeze drying, retaining over 90% viability. The lyophilized cell-free supernatant of L. brevis DPL5 had a significant antagonistic effect against biofilm-producing nasal strains of Staphylococcus aureus, and it completely eradicated biofilms at subinhibitory concentrations of 20 mg·mL⁻¹. Higher concentrations of 69 mg·mL⁻¹ were found to have a 99% bactericidal effect, based on the conducted probability analysis, indicating the production of bactericidal bioactive extracellular compounds capable of disrupting the biofilm formation of pathogens like S. aureus. Furthermore, genome-wide sequencing and analysis of L. brevis DPL5 with cutting-edge Nanopore technology has uncovered over 50 genes linked to probiotic activity, supporting its ability to adapt and thrive in the harsh gut environment. The genome also contains multiple biosynthetic gene clusters such as lanthipeptide class IV, Type III polyketide synthase (T3PKS), and ribosomally synthesized, and post-translationally modified peptides (RiPP-like compounds), all of which are associated with antibacterial properties. Our study paves the way for the further exploration of DPL5, setting the stage for innovative, nature-inspired solutions to combat stubborn bacterial infections. Full article

Hypersonic flight in the atmosphere is associated with high thermal flux impacting the vehicle surface. The nose, leading edges, and some elements of the engine typically require the implementation of highly refractory materials or an active thermal protection system to maintain structural stability during the vehicle mission. Carbon–carbon (C–C) composites are commonly considered for the application thanks to their unique thermal and mechanical properties. However, C–C composites’ ablation and oxidation under long cruise flights at high speeds (Mach number > 5) are the limiting factors for their application. In this paper, the results of an experimental study of C–C composite thermal ablation and oxidation with test article surface temperatures up to 2000 K are presented. The tests were performed under atmospheric conditions and hypersonic flow in the ND_ArcJet facility at the University of Notre Dame. The test articles were preheated with CW laser radiation and then exposed to M = 6 flow at stagnation pressures up to 14 bar. It was found that C–C composite oxidation and mechanical erosion rates are significantly increased in hypersonic airflow compared to those at ambient conditions and nitrogen M = 6 flow. Compared to atmospheric air, mass loss occurred at a rate of 1.5 orders of magnitude faster for M = 6 airflow. During high-speed flow conditions, rapid chemical oxidation and the mechanical destruction of weakened C-fibers likely cause the accelerated degradation of C–C composite material. In this study, a post-mortem microscopic analysis of the morphology of the C–C surface is used to explain the physical processes of the material destruction. Full article

Accurate and near-real-time flood monitoring is crucial for effective post-disaster relief efforts. Although extensive research has been conducted on flood classification, efficiently and automatically processing multi-source imagery to generate reliable flood inundation maps remains challenging. In this study, a new automatic flood monitoring method, utilizing optical and Synthetic Aperture Radar (SAR) imagery, was developed based on the Google Earth Engine (GEE) cloud platform. The Normalized Difference Flood Vegetation Index (NDFVI) was innovatively combined with the Edge Otsu segmentation method, utilizing SAR imagery, to enhance the initial accuracy of flood area mapping. To more effectively distinguish flood areas from non-seasonal water bodies, such as lakes, rivers, and reservoirs, pre-flood Landsat-8 imagery was analyzed. Non-seasonal water bodies were classified using multi-index methods and water body probability distributions, thereby further enhancing the accuracy of flood mapping. The method was applied to the catastrophic floods in Poyang Lake, Jiangxi Province, in 2020, and East Dongting Lake, Hunan Province, China, in 2024. The results demonstrated classification accuracies of 92.6% and 97.2% for flood inundation mapping during the Poyang Lake and East Dongting Lake events, respectively. This method offers efficient and precise information support to decision-makers and emergency responders, thereby fully demonstrating its substantial potential for practical applications. Full article

Shrinkage, a heat-induced process, reorganizes collagen fibers, thereby reducing wound surface area. This technique, commonly applied in surgeries like periareolar mastopexy and skin grafting, is well-established. Despite its widespread use, modern imaging has recently enabled detailed observation of shrinkage’s effects on tissue temperature and oxygenation. The aim of this study is to investigate the effects of shrinkage on histological level, temperature, and tissue oxygenation. Skin flaps were collected, marked, and subjected to shrinkage in vitro, with wound dimensions recorded before and after shrinkage. Biopsy samples were analyzed histologically. In our clinical set up, Snapshot NIR^® and FLIR thermography were used to assess tissue oxygenation and temperature changes before and after shrinkage. Shrinkage significantly reduced wound area by almost 47% ± 8.5%, with a 16.5% ± 6.0% reduction in length and a 36.5% ± 7.7% reduction in width. Tissue temperature rose by an average of 38.3 °C post-shrinkage, reaching approximately 65 °C. A slight decrease in oxygen saturation was observed following shrinkage. Histological analyses reveal collagen fiber denaturation and structural reorganization. Thermal shrinkage is an effective method for reducing wound size and tension, demonstrating potential for facilitating larger full-thickness skin grafts. Although minor decreases in oxygenation were observed, shrinkage may enhance wound healing by reducing tension at wound edges. Further studies are needed to quantify its impact on functional and cosmetic outcomes. Full article

Ion adsorption rare earths are an important strategic resource, but their leach mining causes post-mining wastelands and tailings to suffer from soil sanding, acidification, and heavy metal contamination. This makes natural vegetation recovery difficult, relying mainly on artificial reclamation; however, the reclaimed vegetation grows poorly due to environmental stress. Hyperspectral remote sensing technology, with its high efficiency, non-destructive nature, and wide-range monitoring capability, can accurately estimate the physiological parameters of reclaimed vegetation. This provides support for environmental regulation in mining areas. In this study, three typical types of reclaimed vegetation in the Lingbei Rare Earth Mining Area, Dingnan County, Ganzhou City, were analyzed. Hyperspectral data and the corresponding chlorophyll content were collected to compare the spectral differences between reclaimed and normal vegetation. The spectral data were processed using mathematical transformation, fractional order differentiation, discrete wavelet transform, and continuous wavelet transform. Sensitive bands were extracted, and multispectral transformed feature bands were integrated. Linear and machine learning regression models were used to estimate chlorophyll content. The effects of different spectral processing methods on chlorophyll estimation were then analyzed. The results showed that reclaimed vegetation had higher spectral reflectance than normal vegetation, with the red valley shifting towards the long-wave direction and a steeper red edge slope. Different spectral transformation methods impact the accuracy of chlorophyll content estimation. Using appropriate methods can improve estimation accuracy. Fusing multi-spectral transformation features can achieve relatively good results. Among the models, the random forest regression model provides the best performance in estimating the chlorophyll content of reclaimed vegetation. This study provides a scientific basis for rapid and accurate monitoring of reclaimed vegetation growth in rare earth mining areas, supporting environmental management and decision-making and contributing to ecological restoration. Full article

Background: Secondary mitral regurgitation (SMR) is characterized by a pathological process impacting the left ventricle (LV) as opposed to the mitral valve (MV). In the absence of structural alterations to the MV, the expansion of the LV or impairment of the papillary muscles (PMs) may ensue. A number of technical procedures are accessible for the purpose of determining the optimal resolution for MR. Nevertheless, there is a dearth of rigorous data to facilitate a comparative analysis of MV replacement, MV repair (including subvalvular repair), and transcatheter mitral valve interventions (TMV-Is). The objective of this investigation is to evaluate and compare the efficacy and clinical outcomes of transcatheter mitral valve repair (TMV-r) utilizing the edge-to-edge mitral valve repair (TEER) procedure in comparison to conventional surgical mitral valve interventions (S-SMVis) in patients with secondary mitral regurgitation. Methods and analysis: A consortium of five cardiac surgery institutions from four European states and Japan have joined forces to establish a multicenter observational registry, designated TEERMISO. Patients who underwent technical procedures for SMR between January 2007 and December 2023 will be enrolled consecutively into the TEERMISO registry. The investigation team evaluated the comparative efficacy of replacement and repair techniques, utilizing both the standard surgical methodology and the transcatheter intervention. The primary clinical outcome will be the degree of left ventricular remodeling, as assessed by the left ventricular end-diastolic volume index, at 10 years. The forthcoming research will assess a variety of secondary endpoints, among which all-cause mortality will be the primary endpoint. Subsequent assessments will be made in the following order: functional status, hospitalization, neurocognition, physiological measures (echocardiographic assessment), occurrence of adverse clinical incidents, and reoperation. Ethics and dissemination: The multicenter design of the database is anticipated to reduce the potential for bias associated with institutional caseload and surgical experience. All participating centers possess an established mitral valve protocol that facilitates comprehensive follow-up and management of any delayed mitral complications following replacement surgery or surgical repair of the secondary mitral regurgitation. The data collected will provide insights into the impact of diverse surgical approaches on standard mitral valve surgery and TEER. This will facilitate the evaluation of LV remodeling over the course of long-term post-procedural follow-up. Trial Registration: ClinicalTrials.gov ID: NCT05090540; IRB ID: 202201143 Full article

In today’s rapidly evolving digital landscape, customer reviews play a crucial role in shaping the reputation and success of hotels. Accurately analyzing and classifying the sentiment of these reviews offers valuable insights into customer satisfaction, enabling businesses to gain a competitive edge. This study undertakes a comparative analysis of traditional natural language processing (NLP) models, such as BERT and advanced large language models (LLMs), specifically GPT-4 omni and GPT-4o mini, both pre- and post-fine-tuning with few-shot learning. By leveraging an extensive dataset of hotel reviews, we evaluate the effectiveness of these models in predicting star ratings based on review content. The findings demonstrate that the GPT-4 omni family significantly outperforms the BERT model, achieving an accuracy of 67%, compared to BERT’s 60.6%. GPT-4o, in particular, excelled in accuracy and contextual understanding, showcasing the superiority of advanced LLMs over traditional NLP methods. This research underscores the potential of using sophisticated review evaluation systems in the hospitality industry and positions GPT-4o as a transformative tool for sentiment analysis. It marks a new era in automating and interpreting customer feedback with unprecedented precision. Full article

In the current landscape dominated by Artificial Intelligence, the integration of Machine Learning and Deep Learning within the realm of Cultural Heritage, particularly within architectural contexts, is paramount for the efficient processing and interpretation of point clouds. These advanced methods facilitate automated segmentation and classification, significantly improving both the clarity and practical use of data acquired from laser scanning and photogrammetry. The present study investigates the Palacio de Sástago—a prominent Renaissance palace in Zaragoza, Spain—and introduces a cutting-edge modus operandi for the automated recognition of architectural elements within the palace’s inner courtyard. Employing the well-established Random Forest algorithm, implemented in a Python environment, the framework begins with a comprehensive evaluation of the geometric features identified in the LiDAR point cloud. This process employs the Mean Decrease in Impurity metric to evaluate the relevance of each variable. To boost the accuracy and efficiency of the final classifications, the features are refined post-assessment, enhancing both the training phase and the algorithm’s later evaluation. The research’s findings demonstrate significant potential, supporting advancements in CAD systems and HBIM that will enable more precise, automated modelling of architectural elements, thereby enhancing the accuracy of digital reconstructions and improving conservation planning for heritage sites. Full article

This paper describes an all-digital clock and data recovery (CDR) circuit for implementing edge processing with a wireless body area network (WBAN). The CDR circuit performs delay-locked loop (DLL)-based and phase-locked loop (PLL)-based operations depending on the use of an external reference clock and is implemented using a digital method that is robust against external noise. The clock generator circuit shared by the two operation methods is described in detail, and the CDR circuit recovers 42 Mb/s input data and a 42 MHz clock, which are the specifications of human body communication (HBC). In DLL-based CDR operation, the clock generator operates as a digitally controlled delay line (DCDL) that delays the reference clock by more than one period. In PLL-based CDR operations, it operates as a digitally controlled oscillator (DCO) that oscillates the 42 MHz clock and adjusts the clock frequency. The proposed all-digital CDR is fabricated in 65 nm CMOS technology with an area of 0.091 mm² and operates with a supply voltage of 1.0 V. Post-layout simulation results show that the lock time for DLL-based CDR operation is 1.6 μs, the clock peak-to-peak jitter is 0.38 ns, and the power consumption is 341.8 μW. For PLL-based CDR operations, the lock time is 6 μs, the clock peak-to-peak jitter is 2.92 ns, and the power consumption is 280.2 μW, respectively. Full article

Since industrialization, global greenhouse gas emissions have gradually increased. Storage tanks, as industrial facilities for storing fossil energy, are one of the main sources of greenhouse gas emissions. Using remote sensing images to detect and locate storage tank targets over a large area can provide data support for regional air pollution prevention, control, and monitoring. Due to the circular terrain on the ground and the circular traces caused by human activities, the target detection model has a high false detection rate when detecting tank targets in large-scale remote sensing images. To address the above problems, a YOLOv7-OT model for tank target detection in large-scale remote sensing images is proposed. This model proposes a data pre-processing method of edge re-stitching for large-scale remote sensing images, which reduces the target loss caused by the edge of the image without losing the target information. In addition, to address the problem of small target detection, the CBAM is added to the YOLOv7 backbone network to improve the target detection accuracy under complex backgrounds. Finally, in response to the model’s misjudgment of targets during detection, a data post-processing method combining the spatial distribution characteristics of tanks is proposed to eliminate the misdetected targets. The model was evaluated on a self-built large-scale remote sensing dataset, the model detection accuracy reached 90%, and the precision rate reached 95.9%. Its precision rate and detection accuracy are better than those of the other three classic target detection models. Full article

This study explores the impact of short-term annealing on the thermal stability and mechanical properties of oxygen-free copper subjected to high-pressure torsion (HPT). Copper samples were deformed through HPT with varying numbers of turns at room temperature and subsequently subjected to short-term annealing at temperatures of 398 K and 423 K. Microstructural analysis revealed that annealing led to grain growth and a reduction in dislocation density, with samples processed with fewer HPT turns exhibiting more significant grain coarsening. The microhardness measurements indicated a reduction in hardness after annealing, particularly at the edges of the discs, suggesting recrystallization. Samples processed with 10 HPT turns demonstrated higher thermal stability and less grain growth compared to 1/2-turn samples. The findings suggest that post-HPT short-term annealing can be used to tailor the balance between strength and ductility in oxygen-free copper, enhancing its suitability for industrial applications. Full article

Workpiece surface quality is a critical metric for assessing machining quality. However, due to the complex coupling characteristics of cutting factors, accurately predicting surface roughness remains challenging. Typically, roughness is measured post-machining using specialized instruments, which delays feedback and hampers timely problem detection, ultimately resulting in cutting resource wastage. To address this issue, this paper introduces a predictive model for workpiece surface roughness based on the finite element (FE) method and advanced image processing techniques. Initially, an orthogonal turning experiment was designed, and an FE cutting model was constructed to assess the distribution of cutting forces and temperatures under varying cutting parameters. Image processing methods (including mesh calibration, edge extraction, and contour fitting) were then applied to extract surface characteristics from the FE simulation outputs, yielding preliminary estimates of surface roughness. By employing range and regression analyses methods, this study quantitatively evaluates the interdependencies among cutting parameters, forces, temperatures, and roughness, subsequently formulating a multivariate regression model to predict surface roughness. Finally, a turning experiment under actual working conditions was conducted, confirming the model’s capacity to predict the Ra trend with an accuracy of 85.07%. Thus, the proposed model provides a precise predictive tool for surface roughness, offering valuable guidance for optimizing machining parameters and supporting proactive control in the turning process, ultimately enhancing machining efficiency and quality. Full article

Unmanned aerial systems (UASs) are increasingly playing a crucial role in earthquake emergency response and disaster assessment due to their ease of operation, mobility, and low cost. However, post-earthquake scenes are complex, with many forms of damaged buildings. UAS imagery has a high spatial resolution, but the resolution is inconsistent between different flight missions. These factors make it challenging for existing methods to accurately identify individual damaged buildings in UAS images from different scenes, resulting in coarse segmentation masks that are insufficient for practical application needs. To address these issues, this paper proposed DB-Transfiner, a building damage instance segmentation method for post-earthquake UAS imagery based on the Mask Transfiner network. This method primarily employed deformable convolution in the backbone network to enhance adaptability to collapsed buildings of arbitrary shapes. Additionally, it used an enhanced bidirectional feature pyramid network (BiFPN) to integrate multi-scale features, improving the representation of targets of various sizes. Furthermore, a lightweight Transformer encoder has been used to process edge pixels, enhancing the efficiency of global feature extraction and the refinement of target edges. We conducted experiments on post-disaster UAS images collected from the 2022 Luding earthquake with a surface wave magnitude (Ms) of 6.8 in the Sichuan Province of China. The results demonstrated that the average precisions (AP) of DB-Transfiner, APbox and APseg, are 56.42% and 54.85%, respectively, outperforming all other comparative methods. Our model improved the original model by 5.00% and 4.07% in APbox and APseg, respectively. Importantly, the APseg of our model was significantly higher than the state-of-the-art instance segmentation model Mask R-CNN, with an increase of 9.07%. In addition, we conducted applicability testing, and the model achieved an average correctness rate of 84.28% for identifying images from different scenes of the same earthquake. We also applied the model to the Yangbi earthquake scene and found that the model maintained good performance, demonstrating a certain level of generalization capability. This method has high accuracy in identifying and assessing damaged buildings after earthquakes and can provide critical data support for disaster loss assessment. Full article