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In industrial robotic arm gripping operations within disordered environments, the loss of physical information on the object's surface is often caused by changes such as varying lighting conditions, weak surface textures, and sensor noise. This leads to inaccurate object detection and pose estimation information. A method for industrial object pose estimation using point cloud data is proposed to improve pose estimation accuracy. During the feature extraction process, both global and local information are captured by integrating the appearance features of RGB images with the geometric features of point clouds. Integrating semantic information with instance features effectively distinguishes instances of similar objects. The fusion of depth information and RGB color channels enriches spatial context and structure. A cross-entropy loss function is employed for multi-class target classification, and a discriminative loss function enables instance segmentation. A novel point cloud registration method is also introduced to address re-projection errors when mapping 3D keypoints to 2D planes. This method utilizes 3D geometric information, extracting edge features using point cloud curvature and normal vectors, and registers them with models to obtain accurate pose information. Experimental results demonstrate that the proposed method is effective and superior on the LineMod and YCB-Video datasets. Finally, objects are grasped by deploying a robotic arm on the grasping platform. [ABSTRACT FROM AUTHOR]

Steam turbine blades may crack, break, or suffer other failures due to high temperatures, high pressures, and high-speed rotation, which seriously threatens the safety and reliability of the equipment. The signal characteristics of different fault types are slightly different, making it difficult to accurately classify the faults of rotating blades directly through vibration signals. This method combines a one-dimensional convolutional neural network (1DCNN) and a channel attention mechanism (CAM). 1DCNN can effectively extract local features of time series data, while CAM assigns different weights to each channel to highlight key features. To further enhance the efficacy of feature extraction and classification accuracy, a projection head is introduced in this paper to systematically map all sample features into a normalized space, thereby improving the model's capacity to distinguish between distinct fault types. Finally, through the optimization of a supervised contrastive learning (SCL) strategy, the model can better capture the subtle differences between different fault types. Experimental results show that the proposed method has an accuracy of 99.61%, 97.48%, and 96.22% in the classification task of multiple crack fault types at three speeds, which is significantly better than Multilayer Perceptron (MLP), Residual Network (ResNet), Momentum Contrast (MoCo), and Transformer methods. [ABSTRACT FROM AUTHOR]

With the continuous development of the global logistics industry, cold chain transportation and joint distribution, as critical strategies in supply chain management, are gradually becoming key means to ensure the safe transportation of perishable goods, pharmaceuticals, and other temperature-sensitive commodities. The present study is dedicated to an in-depth exploration of cold chain logistics and joint distribution, with a particular focus on a review of fresh food logistics modes, aiming to comprehensively understand their operational modes, advantages, challenges, and future development trends. The present study elucidates the basic concepts of fresh food logistics and underscores its significance in supply chain management. Through comparative analysis of different operational modes, it reveals their advantages in enhancing efficiency, reducing costs, and mitigating environmental impacts. The present study focuses on the operational mode of joint distribution, discussing its application in cold chain logistics and its differences from traditional logistics modes. Through case studies and empirical analysis, it evaluates the impact of joint distribution on logistics efficiency and costs, as well as its potential to enhance transportation efficiency and reduce carbon emissions. Lastly, the present study provides an outlook on the future development trends of cold chain logistics and joint distribution, discussing the influences of technological innovation, policy support, and industry collaboration and offering recommendations and prospects to drive the sustained development of the industry. Through a comprehensive summary of fresh food logistics, cold chain logistics operational modes, and joint distribution operational modes, this paper aims to provide in-depth theoretical support and practical guidance for related research and practices. [ABSTRACT FROM AUTHOR]

A sulfur-containing polyphosphonate compound, polysulfonyldiphenylene phenylphosphonate (PSPPP), was prepared based on diphenylphosphine and bisphenol S, and applied to flame retarded polyethylene terephthalate (PET). The flame retardancy and thermal stability of PSPPP/PET composites were investigated by limiting oxygen index (LOI), vertical burning test (UL-94), cone calorimeter and thermogravimetric analysis (TGA). LOI value increased from 20.5% for pure PET to 29.2% for flame retardant PET with 8 wt.% PSPPP, and 8%PSPPP/PET passed UL-94 V-0 rating. The cone calorimeter test displayed that PSPPP not only inhibited the maximum combustion strength of PET composites but also suppressed the smoke release. TGA results indicated that PSPPP significantly enhanced the char yield of composites and the thermal stability of residues at higher temperature. The flame retardant modes of action of PSPPP were confirmed by experiments using TGA-Fourier transform infrared (TGA-FTIR), pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS), Hot-Stage FTIR, and scanning electron microscope (SEM). PSPPP played a role of flame retardant in condensed phase and gas phase. During combustion, PSPPP continuously released phosphorus-oxygen-free radicals with quenching effect, thus playing the gas phase flame retardant effect. On the other hand, the transesterification reactions between PSPPP and PET promoted the formation of dense carbon layer and led to high barrier effect. [ABSTRACT FROM AUTHOR]