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.

BLE beacons for sample position estimation in a life science automation laboratory

Estimation of the sample position is essential for working process monitoring and management in the life science automation laboratory.Bluetooth low-energy(BLE)beacons have the advantages of low price,small size and low energy consumption,which make them a promising solution for sample position estimation in the automated laboratory.Several fingerprinting models have been proposed to achieve indoor localization with the received signal strength(RSS)data.However,most of the research depends on intensive beacon installation.Proximity estimation,which depends entirely on one beacon,is more suitable for sample position estimation in large automated laboratories.The complexity of the life science automation laboratory environment brings challenges to the traditional path loss model(PLM),which is a widely used radio wave propagation model-based proximity estimation method.In this paper,BLE sensing devices for sample position estimation are proposed.The BLE beacon-based proximity estimation is discussed in the framework of machine learning,in which the support vector regression(SVR)is utilized to model the nonlinear relationship between the RSS data and distance,and the Kalman filter is utilized to decrease the RSS data deviation.The experimental results over different environments indicate that the SVR outperforms the PLM significantly,and provides 1 m absolute errors for more than 95%of the testing samples.The Kalman filter brings benefits to stable distance predictions.Apart from proximity-based sample position estimation,the proposed framework turned out to be effective in position estimation between parallel workbenches and position estimation on an automated workstation.

Evaluation of Laboratory Request Forms Completion in a Tertiary Medical Laboratory of the Democratic Republic of Congo

Background: The inadequacy in the completeness of the Laboratory Request Form (LRF) has been reported as one of the major sources of errors during the pre-analytical step of laboratory analysis. To prevent the occurrence of such errors, this study aimed at assessing the level of completeness of LRFs. Methods: A retrospective analysis of laboratory request forms was conducted at the Clinical Biology Laboratory of the Kinshasa University Clinic, DR Congo, between November 2021 to May 2022. The LRFs were evaluated according to the completeness of all sections including administrative data of the patient, data of physician who ordered the test, relevant patient’s clinical data and data of the biological sample. Results: From a total of 2842 LRFs evaluated, none was fully completed with all required information. Particularly, patient’s clinical data including the medical history, provisional diagnosis and current treatment, were the most absent in 99% LRFs. However, two sections related to patient’s ID and prescribed test were informed in 100% LRFs. Conclusion: The results of this preanalytical audit can serve as an improvement opportunity focused on strengthening awareness about complete filling of LRF.

Automation 5.0: The Key to Systems Intelligence and Industry 5.0

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with machinery.The rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:L.Vlacic,H.Huang,M.Dotoli,Y.Wang,P.Ioanno,L.Fan,X.Wang,R.Carli,C.Lv,L.Li,X.Na,Q.-L.Han,and F.-Y.Wang,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA J.Autom.Sinica,vol.11,no.8,pp.1723-1727,Aug.2024.

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.

An ethically guided preclinical device for phenotyping H_(2)production in laboratory rodents

Background:Dihydrogen(H_(2))is produced endogenously by the intestinal microbiota through the fermentation of diet carbohydrates.Over the past few years,numer-ous studies have demonstrated the significant therapeutic potential of H_(2)in various pathophysiological contexts,making the characterization of its production in labora-tory species of major preclinical importance.Methods:This study proposes an innovative solution to accurately monitor H_(2)pro-duction in free-moving rodents while respecting animal welfare standards.The devel-oped device consisted of a wire rodent cage placed inside an airtight chamber in which the air quality was maintained,and the H_(2)concentration was continuously analyzed.After the airtightness and efficiency of the systems used to control and maintain air quality in the chamber were checked,tests were carried out on rats and mice with different metabolic phenotypes,over 12 min to 1-h experiments and repeatedly.H_(2)production rates(HPR)were obtained using an easy calculation algorithm based on a first-order moving average.Results:HPR in hyperphagic Zucker rats was found to be twice as high as in control Wistar rats,respectively,2.64 and 1.27 nmol.s^(−1)per animal.In addition,the ingestion of inulin,a dietary fiber,stimulated H_(2)production in mice.HPRs were 0.46 nmol.s^(−1)for animals under control diet and 1.99 nmol.s^(−1)for animals under inulin diet.Conclusions:The proposed device coupled with our algorithm enables fine analysis of the metabolic phenotype of laboratory rats or mice with regard to their endogenous H_(2)production.

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。

Abnormal Action Detection Based on Parameter-Efficient Transfer Learning in Laboratory Scenarios

Laboratory safety is a critical area of broad societal concern,particularly in the detection of abnormal actions.To enhance the efficiency and accuracy of detecting such actions,this paper introduces a novel method called TubeRAPT(Tubelet Transformer based onAdapter and Prefix TrainingModule).Thismethod primarily comprises three key components:the TubeR network,an adaptive clustering attention mechanism,and a prefix training module.These components work in synergy to address the challenge of knowledge preservation in models pretrained on large datasets while maintaining training efficiency.The TubeR network serves as the backbone for spatio-temporal feature extraction,while the adaptive clustering attention mechanism refines the focus on relevant information.The prefix training module facilitates efficient fine-tuning and knowledge transfer.Experimental results demonstrate the effectiveness of TubeRAPT,achieving a 68.44%mean Average Precision(mAP)on the CLA(Crazy LabActivity)small-scale dataset,marking a significant improvement of 1.53%over the previous TubeR method.This research not only showcases the potential applications of TubeRAPT in the field of abnormal action detection but also offers innovative ideas and technical support for the future development of laboratory safety monitoring technologies.The proposed method has implications for improving safety management systems in various laboratory environments,potentially reducing accidents and enhancing overall workplace safety.

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.

Risk Prediction Model for Hypertensive Disorders of Pregnancy Based on Routine Laboratory Indicators and Risk Factors: A Retrospective Analysis

Background: Hypertensive disorder of pregnancy (HDP) is a group of diseases in which pregnancy and elevated blood pressure coexist. There is still a lack of reliable clinical tools to predict the incidence of HDP. The purpose of this study was to establish and validate a nomogram prediction model for assessing the risk of HDP in pregnant women based on laboratory indicators and HDP risk factors. Method: A total of 307 pregnant women who were hospitalized in the obstetrics and gynecology department of our hospital were included in this study, and were randomly divided into a training cohort and validation cohort at a ratio of 7:3. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for the development of HDP on laboratory indicators as well as risk factors for HDP in the training cohort of patients. The results of the multivariate regression model were visualized by forest plots. A nomogram was constructed based on the results of multivariate logistic regression to predict the risk of HDP in pregnant women. The validity of the risk prediction model was evaluated by the area under the receiver operating characteristic curve (AUC), the consistency index (C-index), the calibration curve and the decision curve analysis (DCA). Results: BMI ≥ 25 Kg/m2, total cholesterol in early pregnancy, uric acid and proteinuria in late pregnancy were independent risk factors for HDP. The AUC and C-index of the nomogram constructed by the above four factors were both 0.848. The calibration curve is closely fitted with the ideal diagonal, showing a good consistency between the nomogram prediction and the actual observation of HDP. The DCA has demonstrated the great clinical utility of nomogram. Internal verification proves the reliability of the predicted nomograms. Conclusion: The BTUP nomogram model based on laboratory indicators and risk factors proposed in this study showed good predictive value for the risk assessment of HDP. It is expected to provide evidence for clinical prediction of the risk of HDP in pregnant women.

Re-evaluating the necessity of routine laboratory monitoring during isotretinoin therapy for acne

In this letter,we discuss the topic of necessity of routine laboratory monitoring during isotretinoin treatment for acne.According to Park and colleagues,it is advisable to monitor the levels of triglycerides,alanine aminotransferase,and aspartate aminotransferase every 5 to 6 months.Additionally,the levels of total cholesterol and low-density lipoprotein should be checked within the first two months of treatment.Isotretinoin is a commonly prescribed agent mainly used to treat acne.Despite its high effectiveness,it necessitates regular monitoring of laboratory parameters due to its side effect profile.Currently,there remains a lack of consensus on the appropriate frequency for monitoring these parameters during treatment with isotretinoin.This letter will provide insight into this complex and controversial topic.Based on existing literature,we concluded that the incidence of changes in lipid and liver aminotransferase levels during isotretinoin treatment for acne was low and likely clinically insignificant.For generally healthy people,we recommend testing lipid and liver profiles once at baseline and a second time at the peak dosage.However,frequent testing might still be beneficial in certain populations of patients.

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. .

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 a multi-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.

Laboratory or Department?Exploration and Creation in Computer Science and Technology

In the very beginning,the Computer Laboratory of the University of Cambridge was founded to provide computing service for different disciplines across the university.As computer science developed as a discipline in its own right,boundaries necessarily arose between it and other disciplines,in a way that is now often detrimental to progress.Therefore,it is necessary to reinvigorate the relationship between computer science and other academic disciplines and celebrate exploration and creativity in research.To do this,the structures of the academic department have to act as supporting scaffolding rather than barriers.Some examples are given that show the efforts being made at the University of Cambridge to approach this problem.

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.

Field and Laboratory Study of Accumulated Debrisflow-Forming Solid Mass Sediment on the Example of River Chokheltkhevi (Mtskheta-Mtianeti Region, Georgia)

The paper presents the results of the field and laboratory research carried out in the Chokheltkhevi river basin, according to which the sediment-forming solid mass accumulated in the bed of the Chokhelthkhevi river was recorded and its granulometric and chemical composition, as well as physical-mechanical characteristics, were studied. Based on the results of the research, it can be said that in the debrisflow channel of the Chokheltkhevi River, coarse and sandy-clay soils are mainly accumulated, which represent an unstable mass for the expected debrisflow in the gorge, which, together with other geological, hydrological and climatic factors, helps to increase the scale of the expected ecological danger. According to the results of the laboratory research, it can be concluded that the soil accumulated in the drainage channel is low in ion concentration, and the humus content in it is minimal, which indicates the possibility of easy displacement of the solid mass accumulated in the drainage channel and, accordingly, the risk of a catastrophic debrisflow.

Direct measurement of the break-out ^(19)F(p,γ)^(20)Ne reaction in the China Jinping underground laboratory(CJPL)

Calcium production and the stellar evolution of first-generation stars remain fascinating mysteries in astrophysics.As one possible nucleosynthesis scenario,break-out from the hot carbon–nitrogen–oxygen(HCNO)cycle was thought to be the source of the calcium observed in these oldest stars.However,according to the stellar modeling,a nearly tenfold increase in the thermonuclear rate ratio of the break-out ^(19)F(p,γ)^(20) Ne reaction with respect to the competing ^(19)F(p,α)^(16) O back-processing reaction is required to reproduce the observed calcium abundance.We performed a direct measurement of this break-out reaction at the China Jinping underground laboratory.The measurement was performed down to the low-energy limit of E_(c.m.)=186 keV in the center-of-mass frame.The key resonance was observed at 225.2 keV for the first time.At a temperature of approximately 0.1 GK,this new resonance enhanced the thermonuclear ^(19)F(p,γ)^(20) Ne rate by up to a factor of≈7.4,compared with the previously recommended NACRE rate.This is of particular interest to the study of the evolution of the first stars and implies a stronger breakdown in their“warm”CNO cycle through the ^(19)F(p,γ)^(20) Ne reaction than previously envisioned.This break-out resulted in the production of the calcium observed in the oldest stars,enhancing our understanding of the evolution of the first stars.

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.