Search

With the development of industrial IoT and the arrival of smart manufacturing, the field of edge computing has gained more and more attention. However, traditional industrial computing scenarios relying on industrial clouds make data latency a greater challenge. In this paper, for the contradiction between edge devices and task resource allocation encountered in edge computing scenarios in smart manufacturing, we propose an industrial internet task scheduling model for smart manufacturing and introduce a scheduling node state matrix to realize the state management of each scheduling subtask. Aiming at the problem of multiple tasks seizing resources in a complex, intelligent manufacturing environment, the study combines the caching mechanism to realize the task offloading computational processing of order scheduling, in which the caching mechanism is used to solve the problem of computational resource limitations at the edge. It is found through simulation that when the computational task factor ξk =2 is larger, more offloading power is allowed to be transmitted to the edge ser ver for computation. For computational tasks with smaller task factor ξk, the device tends to allocate more computational rate to that computational task. Eventually the data queue length will be continuously reduced and the data queue is concentrated in the interval of very small values, this result verifies that the task scheduling algorithm is able to perform task scheduling efficiently and reduce the latency.

In this paper, we first mine the interconnections between data in large-scale datasets through association rule models in machine learning and then perform T -time K-Means clustering on the mined datasets to realize large-scale data integration. On this basis, a classification prediction model based on an enhanced ChebNet model is proposed, which combines the efficient feature extraction capability of graph convolutional neural network and the accurate prediction advantage of big data analysis to effectively realize the processing of large-scale data sets. Taking the tobacco production monitoring data as an example, the model performs well in predicting the correlation of cigarette sensory indexes, especially when the sliding window size is 30 and the prediction jump step is 1. The model performance reaches the optimal, which provides strong support for the quality control of cigarette production, and is capable of processing large-scale datasets of tobacco production.

Isoalantolactone (ISA) is a sesquiterpene lactone that could be isolated from Inula helenium as well as many other herbal plants belonging to Asteraceae. Over the past 2 decades, lots of researches have been made on ISA, which owns multiple pharmacological effects, such as antimicrobial, anticancer, anti-inflammatory, neuroprotective, antidepressant-like activity, as well as others. The anticancer effects of ISA involve proliferation inhibition, ROS overproduction, apoptosis induction and cell cycle arrest. Through inhibiting NF-κB signaling, ISA exerts its anti-inflammatory effects which are involved in the neuroprotection of ISA. This review hackled the reported pharmacological effects of ISA and associated mechanisms, providing an update on understanding its potential in drug development. [ABSTRACT FROM AUTHOR]

Purpose: Lichen sclerosus urethral stricture disease (LS USD) is a refractory and progressive disease primarily affecting the anterior urethra in males. Various potential etiological factors, such as genetics, autoimmunity, infection, and exposure to infectious urine, have been suggested. However, the accurate etiology of LS in the male urethra remains unclear. Patients and Methods: In this study, we conducted single-cell RNA sequencing to identify the transcriptional profiles of three patients with LS USD and three patients with non-LS USD. Immunofluorescence was used to confirm the single-cell sequence results. Results: Our study revealed distinct subsets of vein endothelial cells (ECs), smooth muscle cells (SMCs), and fibroblasts (FBs) with high proportions in LS USD, contributing to the tissue microenvironment primarily involved in proinflammatory and immune responses. In particular, FBs displayed a unique subset, Fib7, which is exclusively present in LS USD, and exhibited high expression levels of SAA1 and SAA2. The accumulation of macrophages, along with the dysregulated ratios of M1/M2-like phenotype macrophages, may be engaged in the pathogenesis of LS USD. Through cell–cell communication analysis, we identified significant interactions involving CXCL8/ACKR1 and CCR7/CCL19 in LS USD. Remarkably, Fib7 exhibited exclusive communication with IL-1B macrophages through the SAA1/FPR2 receptor–ligand pair. Conclusion: Our study provides a profound understanding of the tissue microenvironment in LS USD, which may be valuable for understanding the pathogenesis of LS USD. [ABSTRACT FROM AUTHOR]

In the context of the development of the new era, computer network technology has become an indispensable and important technological means in people's daily work and life. Through network technology, information resources can be collected, integrated, processed, and applied, thereby improving information analysis and mining capabilities, constructing big data environments for various industries, providing convenient and fast intelligent information services, and promoting social transformation and development. However, in practical development, network security issues seriously affect information security and social stability, and computer viruses and hackers pose a huge threat to computer systems. The computer network security evaluation is the MAGDM problems. Recently, the Logarithmic TODIM (LogTODIM) and TOPSIS technique has been utilized to cope with MAGDM issues. The interval neutrosophic sets (INSs) are utilized as a technique for characterizing uncertain information during the computer network security evaluation. In this paper, the interval neutrosophic number Logarithmic TODIM-TOPSIS (INN-LogTODIM-TOPSIS) technique is conducted to solve the MAGDM under INSs. Finally, a numerical case study for computer network security evaluation is utilized to validate the proposed technique. The prime contributions of this paper are put forward: (1) The entropy technique based on score values and accuracy value are conducted to obtain weight information under INSs; (2) an integrated INN-LogTODIM-TOPSIS technique is conducted to put forward the MAGDM issue; (3) An illustrative example for computer network security evaluation has been accomplished to put forward the INN-LogTODIM-TOPSIS technique. [ABSTRACT FROM AUTHOR]

Non‐line‐of‐sight (NLOS) imaging enables the reconstruction of targets beyond the direct line of sight, which has extensive applications across various fields. However, it remains unclear how and to what extent the vectorial nature of light, which plays a critical role in optical imaging and object recognition, can enhance the imaging performance of active NLOS imaging. Here, this work proposes a method that utilizes vector optical fields (VOFs) with arbitrarily designed polarization distributions to realize NLOS imaging in challenging conditions with low signal‐to‐noise ratio (SNR). A generalized VOF reflection model is established at the relay surface for active NLOS imaging to determine the optimal illumination angle and polarization as well as the received polarization. Based on this model, the feasibility of multi‐view NLOS imaging is demonstrated with enhanced SNR of echo signals under proper VOF modulation, surpassing the limitations of conventional single‐view NLOS imaging. Simulations and experiments validate the superior performance of the proposed approach to realize accurate reconstruction and recognition of hidden objects. [ABSTRACT FROM AUTHOR]