NBMOD: Find It and Grasp It in Noisy Background

Grasping objects is a fundamental yet important capability of robots, and many tasks such as sorting and picking rely on this skill. The prerequisite for stable grasping is the ability to correctly identify suitable grasping positions. However, finding appropriate grasping points is challenging due to the diverse shapes, varying density distributions, and significant differences between the barycenter of various objects. In the past few years, researchers have proposed many methods to address the above-mentioned issues and achieved very good results on publicly available datasets such as the Cornell dataset and the Jacquard dataset. The problem is that the backgrounds of Cornell and Jacquard datasets are relatively simple - typically just a whiteboard, while in real-world operational environments, the background could be complex and noisy. Moreover, in real-world scenarios, robots usually only need to grasp fixed types of objects. To address the aforementioned issues, we proposed a large-scale grasp detection dataset called NBMOD: Noisy Background Multi-Object Dataset for grasp detection, which consists of 31,500 RGB-D images of 20 different types of fruits. Accurate prediction of angles has always been a challenging problem in the detection task of oriented bounding boxes. This paper presents a Rotation Anchor Mechanism (RAM) to address this issue. Considering the high real-time requirement of robotic systems, we propose a series of lightweight architectures called RA-GraspNet (GraspNet with Rotation Anchor): RARA (network with Rotation Anchor and Region Attention), RAST (network with Rotation Anchor and Semi Transformer), and RAGT (network with Rotation Anchor and Global Transformer) to tackle this problem. Among them, the RAGT-3/3 model achieves an accuracy of 99% on the NBMOD dataset. The NBMOD and our code are available at https://github.com/kmittle/Grasp-Detection-NBMOD

TeViS:Translating Text Synopses to Video Storyboards

A video storyboard is a roadmap for video creation which consists of shot-by-shot images to visualize key plots in a text synopsis. Creating video storyboards, however, remains challenging which not only requires cross-modal association between high-level texts and images but also demands long-term reasoning to make transitions smooth across shots. In this paper, we propose a new task called Text synopsis to Video Storyboard (TeViS) which aims to retrieve an ordered sequence of images as the video storyboard to visualize the text synopsis. We construct a MovieNet-TeViS dataset based on the public MovieNet dataset. It contains 10K text synopses each paired with keyframes manually selected from corresponding movies by considering both relevance and cinematic coherence. To benchmark the task, we present strong CLIP-based baselines and a novel VQ-Trans. VQ-Trans first encodes text synopsis and images into a joint embedding space and uses vector quantization (VQ) to improve the visual representation. Then, it auto-regressively generates a sequence of visual features for retrieval and ordering. Experimental results demonstrate that VQ-Trans significantly outperforms prior methods and the CLIP-based baselines. Nevertheless, there is still a large gap compared to human performance suggesting room for promising future work. The code and data are available at: \url{https://ruc-aimind.github.io/projects/TeViS/}Comment: Accepted to ACM Multimedia 202

Research on Green Comprehensive Evaluation Model of Urban Distribution Network Electric Energy Based on CRITIC-Order Relation Analysis Method Combination Weighting

The green evaluation of urban power distribution network is the main reference basis for power grid transformation and upgrading. Based on this, this article constructs a comprehensive evaluation index system from the four dimensions of power supply side, consumer side, platform area and related policies; To comprehensively consider the difference and relevance of indicators, this paper adopts the order relationship analysis method and the CRITIC weighting method for subjective and objective combination weighting, and uses linear weighting to score indicators; Finally, combining the four evaluation dimensions to analyze the application approach of the comprehensive evaluation model to verify the practicability of the model

Big data cleaning modeling of operation status of coal mine fully—mechanized coal mining equipment

In view of problems of large amount of data and noise and missing values existed in data of operation status of coal mine fully—mechanized coal mining equipment, a big data cleaning model of operation status of coal mine fully—mechanized coal mining equipment based on MapReduce was established. The model is composed of dual MapReduce. Noise points and missing values in data are corrected and multiple cleaned data files are output through the first MapReduce. The multiple cleaned data files are sorted according to collection time and date and combined into a single data file through the second MapReduce. The experimental results show that the model can effectively eliminate noise data and complement missing data with good data cleaning effect

Research on weighted energy consumption and delay optimization algorithm based on dual‐queue model

Abstract This article investigates a mobile edge computing (MEC) network assisted by multiple unmanned aerial vehicles (UAVs) to address the computational and offloading requirements for mobile intelligent terminals (MITs) within crowded venues. The objective is to tackle intricate task processing and diminish MITs' waiting times. Considering the randomness of task arrival at the MITs and the imbalance between the amount of data and computation for complex tasks, a dual‐queue model with data cache queue and computation queue is proposed, with minimizing the weighted system total energy consumption and average delay as the optimization objectives. Lyapunov optimization theory is employed to convert the stochastic optimization problem into a deterministic one, and the initial deployment quantity and hovering position of the UAVs are determined by the density‐based spatial clustering of applications with noise (DBSCAN) method with noise. Then PPO algorithm for MIT task, resource allocation, and UAV trajectory optimization. Numerical results display the proposed scheme can efficaciously diminish energy consumption and delay by 10% and 33% respectively, compared with the baseline scheme. This paper proposes a practical and feasible solution for stochastic computing offloading in UAV‐assisted MEC, which fills the gap in existing research on regarding the consideration of complex task imbalances

Research on whole life cycle service system of shearer

In view of problems that current shearer life cycle management has low real-time performance and low accuracy of information collection, and unable to uniquely identify shearer, a design scheme of RFID-based whole life cycle service system of shearer was proposed. The system encodes a unified electronic product code (EPC) code into an RFID tag, and uses the RFID tag to accurately identify identity information of shearer; it achieves real time collection of status and position information of shearer through cooperation of RFID reader and tag, and uses EPCIS event to describe status information change of device corresponding to EPC, and data integration and data mining were carried out on service platform. The system can realize information exchange and data sharing among enterprises at each node of industrial chain of whole life cycle of shearer, and provide real-time dynamic information and decision support for whole life cycle management of shearer

Epidemiological and molecular survey of a foodborne disease outbreak caused by Enterococcus faecalis

Objective To investigate a foodborne disease outbreak in a small restaurant, analyze its causes and risk factors, and propose the measures for prevention and control. Methods Using field epidemiology survey to describe the features and analyze the risk factors of the incident. The strains homology was evaluated by pulsed field gel electrophoresis (PFGE). Results The major clinic manifestations of 5 cases were diarrhea and abdominal pain. Dietary survey showed that suspicious food was pot-stewed chicken leg at dinner on June 13, 2019. The result of field hygienic survey indicated that the risk factor of the incident was the long placement time of the pot-stewed chicken leg at normal temperature status and insufficient re-heating time before dinner. Enterococcus faecalis was isolated from 3 anal swabs of cases, the pot-stewed chicken leg and the cutting board swab which sampled in the kitchen, and the strains were 100% homological. Conclusion The outbreak was caused by eating the pot-stewed chicken leg which was contaminated by abundant Enterococcus faecalis. It was suggested to strengthen the food safety awareness of small restaurant employees and the proper cooking method should be mastered

Novel Angiotensin-Converting Enzyme-Inhibitory Peptides Obtained from <i>Trichiurus lepturus</i>: Preparation, Identification and Potential Antihypertensive Mechanism

Peptides possessing antihypertensive attributes via inhibiting the angiotensin-converting enzyme (ACE) were derived through the enzymatic degradation of Trichiurus lepturus (ribbonfish) using alkaline protease. The resulting mixture underwent filtration using centrifugation, ultrafiltration tubes, and Sephadex G-25 gels. Peptides exhibiting ACE-inhibitory properties and DPPH free-radical-scavenging abilities were isolated and subsequently purified via LC/MS-MS, leading to the identification of over 100 peptide components. In silico screening yielded five ACE inhibitory peptides: FAGDDAPR, QGPIGPR, IFPRNPP, AGFAGDDAPR, and GPTGPAGPR. Among these, IFPRNPP and AGFAGDDAPR were found to be allergenic, while FAGDDAPRR, QGPIGPR, and GPTGPAGP showed good ACE-inhibitory effects. IC50 values for the latter peptides were obtained from HUVEC cells: FAGDDAPRR (IC50 = 262.98 μM), QGPIGPR (IC50 = 81.09 μM), and GPTGPAGP (IC50 = 168.11 μM). Peptide constituents derived from ribbonfish proteins effectively modulated ACE activity, thus underscoring their therapeutic potential. Molecular docking and modeling corroborated these findings, emphasizing the utility of functional foods as a promising avenue for the treatment and prevention of hypertension, with potential ancillary health benefits and applications as substitutes for synthetic drugs

Transglutaminase 3 regulates cutaneous squamous carcinoma differentiation and inhibits progression via PI3K-AKT signaling pathway-mediated Keratin 14 degradation

Abstract Cutaneous squamous carcinoma is the second most common epithelial malignancy, associated with significant morbidity, mortality, and economic burden. However, the mechanisms underlying cSCC remain poorly understood. In this study, we identified TGM3 as a novel cSCC tumor suppressor that acts via the PI3K-AKT axis. RT-qPCR, IHC and western blotting were employed to assess TGM3 levels. TGM3-overexpression/knockdown cSCC cell lines were utilized to detect TGM3’s impact on epithelial differentiation as well as tumor cell proliferation, migration, and invasion in vitro. Additionally, subcutaneous xenograft tumor models were employed to examine the effect of TGM3 knockdown on tumor growth in vivo. Finally, molecular and biochemical approaches were employed to gain insight into the tumor-suppressing mechanisms of TGM3. TGM3 expression was increased in well-differentiated cSCC tumors, whereas it was decreased in poor-differentiated cSCC tumors. Loss of TGM3 is associated with poor differentiation and a high recurrence rate in patients with cSCC. TGM3 exhibited tumor-suppressing activity by regulating cell proliferation, migration, and invasion both in vitro and in vivo. As a novel cSCC tumor differentiation marker, TGM3 expression was positively correlated with cell differentiation. In addition, our results demonstrated an interaction between TGM3 and KRT14 that aids in the degradation of KRT14. TGM3 deficiency disrupts keratinocytes differentiation, and ultimately leads to tumorigenesis. Furthermore, RNA-sequence analysis revealed that loss of TGM3 enhanced EMT via the PI3K-AKT signaling pathway. Deguelin, a PI3K-AKT inhibitor, blocked cSCC tumor growth induced by TGM3 knockdown in vivo. Taken together, TGM3 inhibits cSCC tumor growth via PI3K-AKT signaling, which could also serve as a tumor differentiation marker and a potential therapeutic target for cSCC. Proposed model depicted the mechanism by which TGM3 suppress cSCC development. TGM3 reduces the phosphorylation level of AKT and degrades KRT14. In the epithelial cell layer, TGM3 exhibits a characteristic pattern of increasing expression from bottom to top, while KRT14 and pAKT are the opposite. Loss of TGM3 leads to reduced degradation of KRT14 and activation of pAKT, disrupting keratinocyte differentiation, and eventually resulting in the occurrence of low-differentiated cSCC