Design and Research of High-throughput Automation System for Food Testing Laboratory

In the process of food testing,human operation is an important variable affecting the experimental results.In order to reasonably avoid the influence of human subjective operation behavior on the accuracy of detection results,the laboratory information management system was used as the information platform to design a high-throughput laboratory automation pre-treatment system based on the deep integration of mechanical principles,visual analysis,high-speed conduction,intelligent storage and other technical systems.The experimental results showed that the system could shorten the sample circulation cycle,effectively improve the laboratory biosafety,and meet the requirements of high-throughput processing of samples.

基于Automation Studio仿真软件的液压与气动技术人才培养实践

通过对《液压与气动技术》课程在教学过程中的问题盘点,总结出教学中存在的实验资源少、设备陈旧、成本高、利用率低等痛点,Automation Studio仿真软件具有强大的系统仿真与动画设计功能,有针对液压与气动及电气系统的功能模块,将Automation Studio仿真软件引入课堂教学可以很好的弥补上述问题,利用液压气动系统与电气系统的仿真功能,设计相应的实践教学课程内容,分别对课前、课中、课后三个环节的具体实施过程及要求进行详细阐述,通过对软件的教、学、用达到提升课程教学效果的目的。

Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

The concept of smart houses has grown in prominence in recent years.Major challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device itself.Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features.This paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in homes.We have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT devices.Our system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing devices.We have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache server.The feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time settings.It is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation systems.Additionally,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber threats.The trial results support the proposed system and demonstrate its potential for use in everyday life.

BLE beacon-based floor detection for mobile robots in amulti-floor automation laboratory

As an important task of multi-floor localization,floor detection has elicited great attention.Wireless infrastructures like Wi-Fi and Bluetooth Low Energy(BLE)play important roles in floor detection.However,most floor detection research tends to focus on data modelling but pays little attention to the data collection system,which is the basis of wireless infrastructure-based floor detection.In fact,the floor detection task can be greatly simplified with proper data collection system design.In this paper,a floor detection solution is developed in a multi-floor life science automation lab.A data collection system consisting of BLE beacons,a receiver node and an Internet of Things(IoT)cloud is provided.The features of the BLE beacon under different settings are evaluated in detail.A mean filter is designed to deal with the fluctuation of the received signal strength indicator data.A simple floor detection method without a training process was implemented and evaluated in more than 100 floor detection tests.The time delay and floor detection accuracy under different settings are discussed.Finally,floor detection is evaluated on the H20 multi-floor transportation robot.Two sensor nodes are installed on the robot at different heights.The floor detection performance with different installation heights is discussed.The experimental results indicate that the proposed floor detection method provides floor detection accuracy of 0.9877 to 1 with a time delay of 5s.

Detection Algorithm of Laboratory Personnel Irregularities Based on Improved YOLOv7

Due to the complex environment of the university laboratory,personnel flow intensive,personnel irregular behavior is easy to cause security risks.Monitoring using mainstream detection algorithms suffers from low detection accuracy and slow speed.Therefore,the current management of personnel behavior mainly relies on institutional constraints,education and training,on-site supervision,etc.,which is time-consuming and ineffective.Given the above situation,this paper proposes an improved You Only Look Once version 7(YOLOv7)to achieve the purpose of quickly detecting irregular behaviors of laboratory personnel while ensuring high detection accuracy.First,to better capture the shape features of the target,deformable convolutional networks(DCN)is used in the backbone part of the model to replace the traditional convolution to improve the detection accuracy and speed.Second,to enhance the extraction of important features and suppress useless features,this paper proposes a new convolutional block attention module_efficient channel attention(CBAM_E)for embedding the neck network to improve the model’s ability to extract features from complex scenes.Finally,to reduce the influence of angle factor and bounding box regression accuracy,this paper proposes a newα-SCYLLA intersection over union(α-SIoU)instead of the complete intersection over union(CIoU),which improves the regression accuracy while increasing the convergence speed.Comparison experiments on public and homemade datasets show that the improved algorithm outperforms the original algorithm in all evaluation indexes,with an increase of 2.92%in the precision rate,4.14%in the recall rate,0.0356 in the weighted harmonic mean,3.60%in the mAP@0.5 value,and a reduction in the number of parameters and complexity.Compared with the mainstream algorithm,the improved algorithm has higher detection accuracy,faster convergence speed,and better actual recognition effect,indicating the effectiveness of the improved algorithm in this paper and its potential for practical application in laboratory scenarios.

基于Automation Studio的液压动力滑台控制系统仿真设计

动力滑台是组合机床实现进给运动的通用部件,其控制系统是实现组合机床自动化或半自动化的关键环节。在分析动力滑台工作过程及液压控制回路的基础上,基于Automation Studio进行了控制系统仿真设计,主要包括液压系统建模、PLC控制系统建模及软件设计,仿真结果满足各种控制要求。

Intelligent Decision-Making in Warehouse Management: How AI Automation Improves Inventory Tracking, Order Fulfillment, and Logistics Efficiency Compared to Drone Technology

This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse functions—inventory tracking, order fulfillment, and logistics efficiency. Our findings indicate AI automation enables real-time inventory visibility, optimized picking routes, and dynamic delivery scheduling, which drones cannot match. AI better leverages data insights for intelligent decision-making across warehouse operations, supporting improved productivity and lower operating costs.

Instrument Automation Control System Faults and Maintenance

This article explores the topic of fault diagnosis and maintenance strategies for instrument automation control systems,analyzing them through specific cases.The aim of this research is to improve the stability and reliability of the system by conducting a thorough investigation of faults and maintenance in instrument automation control systems.By doing so,this research hopes to provide a strong guarantee for the smooth progress of industrial production.

Teaching Mode Reform of the Electrical Automation Control Course

Under the background of the new era,higher requirements are put forward for colleges and universities to carry out teaching reform.Teachers of electrical automation control courses should update their teaching ideas regularly,innovate teaching methods,and take novel and effective measures to carry out related work.Because electrical automation control courses tend to be technology-oriented,teachers can help students consolidate basic knowledge.They should also focus on developing practical skills so students can easily adapt to future job positions.However,there are many problems in the actual teaching process,which hampers the improvement of the teaching quality to a certain extent.In view of this,this paper presents an in-depth exploration based on theories and practical experiences.It starts with an analysis of the current teaching status of electrical automation control courses in colleges and universities,followed by suggestions to improve them based on their characteristics and students’needs.

A360 Bot Framework: Empowering Smart Robotic Process Automation Solutions

This research paper explores the significance of the “A360 Bot Framework” in Automation 360 (A360) platform. A360 is Automation Anywhere’s cloud-based automation platform designed to make business processes more efficient. It’s known for its user-friendly interface, which allows both technical and non-technical users to use it effectively. Automation 360 is versatile, offering a range of tools to automate tasks, manage complex workflows, and integrate various applications. It empowers users to create customized solutions for their specific needs. Being cloud-based it ensures scalability, security, and real-time updates, making it a top choice in the fast-paced digital world. As demand for A360 rises, having a structured way to develop bots becomes crucial. The paper introduces the “A360 Bot Framework” as a guiding approach for bot developments. This framework ensures consistency and scalability, especially when working with both professional developers and non-technical users. It outlines key elements like setting up work folders, managing logs, dealing with errors, and ensuring secure bot execution. Ultimately, the “A360 Bot Framework” is presented as a foundational structure that enhances consistency, resiliency, and development efficiency. By following predefined practices and templates, bot developers can mitigate risks and streamline debugging processes. This framework accelerates the bot development lifecycle, allowing developers to focus on specific functionalities and value-added features. The research paper aims to provide insights into the benefits of adopting the A360 Bot Framework and its potential to revolutionize A360 bot development practices, leading to more efficient and effective automation solutions.

Progress in the Study of Laboratory Indicators Related to Acute Pancreatitis

Acute pancreatitis (AP) is one of the more common gastrointestinal diseases in clinics and is characterized by rapid progression, many complications, and high mortality. When it develops into severe pancreatitis, its prognosis is poor. Therefore, early assessment of the degree of inflammatory response plays a crucial role in the treatment plan and prognosis of patients. More and more studies have shown that the levels of D-dimer (D-D), angiotensin-2 (Ang-2), phosphate, heparin-binding protein (HBP), retinol-binding protein-4 (RBP4), and osteoblastic protein (OPN) are closely related to the severity of acute pan-creatitis and can be used as effective indicators for early assessment of AP. In this paper, the research progress of the above indicators in assessing the severity of AP is summarized.

Impact of numerous larval diets on the biology of pink bollworm Pectinophora gossypiella(Saunders)(Lepidoptera:Gelechiidae)under laboratory conditions

Background Pink bollworm,Pectinophora gossypiella(Saunders)(Lepidoptera:Gelechiidae)has become a poten-tial pest of cotton by causing substantial yield losses around the world including Pakistan.Keeping in view the facts like limited research investigations,unavailability,and high cost of artificial diet’s constituents and their premixes,the present research investigations on the dietary aspect of P.gossypiella were conducted.The larvae of P.gossypiella were reared on different diets that were prepared using indigenous elements.The standard/laboratory diet com-prised of wheat germ meal 34.5 g,casein 30.0 g,agar–agar 20.0 g,sucrose 10.0 g,brewer’s yeast 5.0 g,α-cellulose 1.0 g,potassium-sorbate1.5 g,niplagin 0.5 g,decavitamin 0.01 g,choline-chloride 0.06 g,maize-oil 3.30 g,honey 2.0 g,and water 730.0 mL.Alternatives to cotton bolls and wheat germ meal were okra seed sprouts,okra fruit,cottonseed meal,and okra seed meals,which were included in the study to introduce an efficient and economic mass-rearing system.Results The larval development completed in 19.68d±0.05 d with a weight of 20.18mg±0.20 mg at the fourth instar fed on the cottonseed meal-based diet instead of wheat germ meal based diet.On the same diet,84.00%±4.00%,17.24 mg±0.03 mg,and 7.76d±0.06 d were recorded as pupae formation,pupal weight,and pupal duration,respectively.Adult emergence,76.00%±1.00%was recorded from pupae collected from larvae raised on cottonseed meal-based diet.These male and female moths lived for 40.25d±0.10 d,and 44.34d±0.11 d,respectively.Females deposited 21.28±0.04 eggs per day with the viability of 65.78%±0.14%.The larval mortal-ity at the fourth instar was 37.20%±1.36%and malformed pupation of 12.00%±1.41%was recorded.Replacement of wheat germ meal with that of local meals(cottonseed and okra seed)in the standard laboratory diet has saved 463.80 to 467.10 PKR with 1.62 to 1.63 cost economic returns,respectively.Conclusion This research is of novel nature as it provides a concise and workable system for the economic and suc-cessful rearing of P.gossypiella under laboratory conditions.

Impact of Laboratory Value Flowsheet in Electronic Health Record (EHR) Documentation Time

Research on the use of EHR is contradictory since it presents contradicting results regarding the time spent documenting. There is research that supports the use of electronic records as a tool to speed documentation;and research that found that it is time consuming. The purpose of this quantitative retrospective before-after project was to measure the impact of using the laboratory value flowsheet within the EHR on documentation time. The research question was: “Does the use of a laboratory value flowsheet in the EHR impact documentation time by primary care providers (PCPs)?” The theoretical framework utilized in this project was the Donabedian Model. The population in this research was the two PCPs in a small primary care clinic in the northwest of Puerto Rico. The sample was composed of all the encounters during the months of October 2019 and December 2019. The data was obtained through data mining and analyzed using SPSS 27. The evaluative outcome of this project is that there is a decrease in documentation time after implementation of the use of the laboratory value flowsheet in the EHR. However, patients per day increase therefore having an impact on the number of patients seen per day/week/month. The implications for clinical practice include the use of templates to improve workflow and documentation as well as decreasing documentation time while also increasing the number of patients seen per day. .

Deep learning automation of radiographic patterns for hallux valgus diagnosis

Artificial intelligence(AI)and deep learning are becoming increasingly powerful tools in diagnostic and radiographic medicine.Deep learning has already been utilized for automated detection of pneumonia from chest radiographs,diabetic retinopathy,breast cancer,skin carcinoma classification,and metastatic lymphadenopathy detection,with diagnostic reliability akin to medical experts.In the World Journal of Orthopedics article,the authors apply an automated and AIassisted technique to determine the hallux valgus angle(HVA)for assessing HV foot deformity.With the U-net neural network,the authors constructed an algorithm for pattern recognition of HV foot deformity from anteroposterior highresolution radiographs.The performance of the deep learning algorithm was compared to expert clinician manual performance and assessed alongside clinician-clinician variability.The authors found that the AI tool was sufficient in assessing HVA and proposed the system as an instrument to augment clinical efficiency.Though further sophistication is needed to establish automated algorithms for more complicated foot pathologies,this work adds to the growing evidence supporting AI as a powerful diagnostic tool.

National Key Laboratory of Baiyun Obo Rare Earth Resource Researches and Comprehensive Utilization

National Key Laboratory of Baiyun Obo Rare Earth Resource Researches and Comprehensive Utilization was approved by the Ministry of Science and Technology to be one of the national key laboratories in November 2022.The laboratory was reconstructed based on former State Key Laboratory of Baiyun Obo Rare Earth Resources Researches and Comprehensive Utilization.The key laboratory takes Baotou Research Institute of Rare Earths as the main research body,cooperating with scientific and technological strength from Lanzhou University and Changsha Research Institute of Mining and Metallurgy.

Automation Studio在“变频技术”课程中的仿真应用

改革“变频技术”课堂教学中的常规教学法,将仿真软件Automation Studio引入到课堂教学中,通过对三相异步电动机变频调速的具体仿真实例讲解,采用情景式的教学方式激发学生学习兴趣,从而提高课堂听课效率。

Maximizing the Efficiency of Automation Solutions with Automation 360: Approaches for Developing Subtasks and Retry Framework

I present a solution that explores the use of A360 subtasks as a comparable concept to functions in programming. By leveraging subtasks as reusable and maintainable functions, users can efficiently develop customized high-quality automation solutions. Additionally, the paper introduces the retry framework, which allows for the automatic retrying of subtasks in the event of system or unknown exceptions. This framework enhances efficiency and reduces the manual effort required to retrigger bots. The A360 Subtask and Retry Framework templates provide valuable assistance to both professional and citizen developers, improving code quality, maintainability, and the overall efficiency and resiliency of automation solutions.

The ChatGPT After: Building Knowledge Factories for Knowledge Workers with Knowledge Automation

Briefing: This perspective introduces the concept and framework of knowledge factories with knowledge machines for knowledge workers to achieve knowledge automation for Industry 5.0 and intelligent industries.Introduction The big hit of Chat GPT makes it imperative to contemplate the practical applications of big or foundation models [1]-[5]. However, as compared to conventional models, there is now an increasingly urgent need for foundation intelligence of foundation models for real-world industrial applications.

基于Automation Studio的橡胶开炼机液压调距系统设计与仿真

针对现有机械式橡胶开炼机存在机构工作行程大、可靠性差、安全性低等问题,设计了一种橡胶开炼机液压调距系统。该系统通过液压泵吸油提升油液的压力推动液压杆,采用给定信号控制各阀门开口大小和开口方向实现液体的流向和速度改变,从而控制液压杆的位移和速度以实现调距功能。采用Automation Studio软件搭建液压调距系统仿真模型,得到该系统液压缸侧压力和液压杆位移随时间变化曲线,验证了此系统运行平稳,可代替传统调距装置实现调距功能。

基于Automation Studio的自动化生产线数字孪生系统的设计与研究

以YL-335B自动化生产线供料单元作为物理实体,选择Automation Studio作为数字孪生系统开发平台。首先在Automation Studio软件平台中导入YL-335B自动化生产线供料单元3D模型,完成供料单元气动回路的设计及电气控制设计,然后软件内系统集成进行供料单元数字孪生系统的功能仿真,正确地实现了供料单元数字孪生系统工作装置的动作,最后采用OPC UA作为模块之间的标准接口,通过OPC UA服务器建立Automation Studio与TIA Portal V16之间的OPC UA通信连接,实现供料单元数字孪生虚拟系统与供料单元实体PLC 1200之间的软硬件通信,通过供料单元数字虚体与供料单元物理实体之间的交互试验,验证了该设计的正确性。