Research on the intelligent internet nursing model based on the child respiratory and asthma control test scale for asthma management of preschool children

BACKGROUND Childhood asthma is a common respiratory ailment that significantly affects preschool children.Effective asthma management in this population is particularly challenging due to limited communication skills in children and the necessity for consistent involvement of a caregiver.With the rise of digital healthcare and the need for innovative interventions,Internet-based models can potentially offer relatively more efficient and patient-tailored care,especially in children.AIM To explore the impact of an intelligent Internet care model based on the child respiratory and asthma control test(TRACK)on asthma management in preschool children.METHODS The study group comprised preschoolers,aged 5 years or younger,that visited the hospital's pediatric outpatient and emergency departments between January 2021 and January 2022.Total of 200 children were evenly and randomly divided into the observation and control groups.The control group received standard treatment in accordance with the 2016 Guidelines for Pediatric Bronchial Asthma and the Global Initiative on Asthma.In addition to above treatment,the observation group was introduced to an intelligent internet nursing model,emphasizing the TRACK scale.Key measures monitored over a six-month period included the frequency of asthma attack,emergency visits,pulmonary function parameters(FEV1,FEV1/FVC,and PEF),monthly TRACK scores,and the SF-12 quality of life assessment.Post-intervention asthma control rates were assessed at six-month follow-up.RESULTS The observation group had fewer asthma attacks and emergency room visits than the control group(P<0.05).After six months of treatment,the children in both groups had higher FEV1,FEV1/FVC,and PEF(P<0.05).Statistically significant differences were observed between the two groups(P<0.05).For six months,children in the observation group had a higher monthly TRACK score than those in the control group(P<0.05).The PCS and MCSSF-12 quality of life scores were relatively higher than those before the nursing period(P<0.05).Furthermore,the groups showed statistically significant differences(P<0.05).The asthma control rate was higher in the observation group than in the control group(P<0.05).CONCLUSION TRACK based Intelligent Internet nursing model may reduce asthma attacks and emergency visits in asthmatic children,improve lung function,quality of life,and the TRACK score and asthma control rate.The effect of nursing was significant,allowing for development of an asthma management model.

Food Additives-Ascorbic Acid Quality Assay Test Intelligent Management

In this paper, based on the analysis and test methods of national standards （GB 14754-2010） and chemical analysis and test items carried out by chemical enterprises, a set of automatic processing of quality analysis test data of ascorbic acid products was developed by using access database technology and Visual Basic programming language system, and its stability was investigated. The results show that the software can manage intelligently all aspects of the quality analysis and test of ascorbic acid products, uploading timely the data and results of the analysis and inspection to the network and saving it for users, enterprises and quality management, which set up a network of information sharing platform to ensure the authenticity and reliability of measurement results, improving greatly the speed of data processing, saving valuable time, reducing production costs with good economic efficiency and social benefit. It has practical value for ascorbic acid quality analysis test data processing automatically the results of the implementation of intelligent management.

Efficient Penetration Testing Path Planning Based on Reinforcement Learning with Episodic Memory

Intelligent penetration testing is of great significance for the improvement of the security of information systems,and the critical issue is the planning of penetration test paths.In view of the difficulty for attackers to obtain complete network information in realistic network scenarios,Reinforcement Learning(RL)is a promising solution to discover the optimal penetration path under incomplete information about the target network.Existing RL-based methods are challenged by the sizeable discrete action space,which leads to difficulties in the convergence.Moreover,most methods still rely on experts’knowledge.To address these issues,this paper proposes a penetration path planning method based on reinforcement learning with episodic memory.First,the penetration testing problem is formally described in terms of reinforcement learning.To speed up the training process without specific prior knowledge,the proposed algorithm introduces episodic memory to store experienced advantageous strategies for the first time.Furthermore,the method offers an exploration strategy based on episodic memory to guide the agents in learning.The design makes full use of historical experience to achieve the purpose of reducing blind exploration and improving planning efficiency.Ultimately,comparison experiments are carried out with the existing RL-based methods.The results reveal that the proposed method has better convergence performance.The running time is reduced by more than 20%.

Intelligent Tester for Geotextiles Products-Determination of Water Flow Capacity in their Plane

Geotextiles and geotextile-related products Determination of water flowcapacity in their plane has just National standard. But has not a formal instrument at present. There are many kinds of geotextile and also lots of factors influential to the penetration coefficient thereof. The intelligent tester may be involved in testing penetration coefficient under different pressures/gradients resulted in fine repeatability controlled intelligently by microcomputer system.

Intelligent System for Parallel Fault-Tolerant Diagnostic Tests Construction

This investigation deals with the intelligent system for parallel fault-tolerant diagnostic tests construction. A modified parallel algorithm for fault-tolerant diagnostic tests construction is proposed. The algorithm is allowed to optimize processing time on tests construction. A matrix model of data and knowledge representation, as well as various kinds of regularities in data and knowledge are presented. Applied intelligent system for diagnostic of mental health of population which is developed with the use of intelligent system for parallel fault-tolerant DTs construction is suggested.

Modeling and Simulation for Complex Repairable System with Built-in Test Equipment

In order to research the effects of built-in test(BIT) on the system and select BITand test strategy,the complex repairable systems with BITequipment are modeled and simulated by using Simulink.Based on the model,the influences of different built-in test equipments,maintenance time and error probabilities on the system usability are evaluated.The simulation results showthat they effect on the system differently.The simulation method of complex system based on Simulink provides a technique approach to research the effects of BITon the system and select BITand test strategy.

Intelligent Management Based on Chemical Reagent Quality Analysis

Access database technology and Visual Basic programming language are used to develop a set of chemical reagents quality analysis and inspection of intelligent management application software, and its stability was investigated. The results show that the sotfware is used to manage intelligently the quality analysis of chemical reagents test each linkage, to upload and save the analysis of test data and results in the network in time for users, to build a network of information sharing platform for enterprises and quality management departments, and its quality of chemical reagents test results is used to manage by the computer with practical application value. The software is simple, convenient, fast, friendly interface, stable operation, accurate and reliable results, with a high value to promote the application.

A Study of Visual Event-Related Potential and Reaction Time in Elderly People: Comparative Analysis of the Scores of Intelligence Test in 30 Subjects

The results of visual event-related potential(ERP)examinations and reactiontime(RT)tests were reported in 30 elders and compared with their performanceintellegence quotient(PIQ)scores.The subjects consisted of 18 males and 12 femalesaged 50-71(mean 61.4)years old.No history of central nervous system disease wasfound.The visual stimuli were randomly presented to the subject,including three sym-bols:E as target stimulus with 0.15 probability,and H and E as nontarget stimuliwith 0.15 and 0.70 probability respectively.The recording electrodes were placed on Fzand Pz.The duration from the subject seeing the target to touching a button immediatelywas considered as reaction time(RT).It was shown that the P3 latency at Pz was longer than that at Fz and the P3amplitude at Pz was larger than that at Fz,and that the RT was longer than P3 latencywith obvious effect of distribution(P【0.05 at Fz and P】0.05 at Pz)as well .The higherthe PIQ scores,the longer the RT and the P3 latency.It is suggested that the ERPmight reflect the differences of PIQ scores,and the P3 is an objective index.We considerthat the research of ERP is of great interest in the neuropsychological and neurological sci-ences.

The Testing Intelligence System Based on Factor Models and Self-Organizing Feature Maps

Presented is a new testing system based on using the factor models and self-organizing feature maps as well as the method of filtering undesirable environment influence. Testing process is described by the factor model with simplex structure, which represents the influences of genetics and environmental factors on the observed parameters - the answers to the questions of the test subjects in one case and for the time, which is spent on responding to each test question to another. The Monte Carlo method is applied to get sufficient samples for training self-organizing feature maps, which are used to estimate model goodness-of-fit measures and, consequently, ability level. A prototype of the system is implemented using the Raven＇s Progressive Matrices （Advanced Progressive Matrices） - an intelligence test of abstract reasoning. Elimination of environment influence results is performed by comparing the observed and predicted answers to the test tasks using the Kalman filter, which is adapted to solve the problem. The testing procedure is optimized by reducing the number of tasks using the distribution of measures to belong to different ability levels after performing each test task provided the required level of conclusion reliability is obtained.

Support-Vector-Machine-Based False Alarm Filter of Mechatronic Built-in Test

Diagnosing intermittent fault is an important approach to reduce built-in test（BIT） false alarms. Aiming at solving the shortcoming of the present diagnostic method of intermittent fault, and according to the merit of support vector machines （ SVM） which can be trained with a small-sample, an SVM-based diagnostic model of 3 states that include OK state, intermittent state and faulty state is presented. With the features based on the reflection coefficients of an alarm rate （ AR ） model extracted from small vibration samples, these models are trained to diagnose intermittent faults. The experimental results show that this method can diagnose multiple intermittent faults accurately with small training samples and BIT false alarms are reduced.

Managing Health Treatment by Optimizing Complex Lab-Developed Test Configurations: A Health Informatics Perspective

A complex Laboratory Developed Test(LDT)is a clinical test developed within a single laboratory.It is typically configured from many fea-ture constraints from clinical repositories,which are part of the existing Lab-oratory Information Management System(LIMS).Although these clinical repositories are automated,support for managing patient information with test results of an LDT is also integrated within the existing LIMS.Still,the support to configure LDTs design needs to be made available even in standard LIMS packages.The manual configuration of LDTs is a complex process and can generate configuration inconsistencies because many constraints between features can remain unsatisfied.It is a risky process and can lead patients to undergo unnecessary treatments.We proposed an optimized solution(opt-LDT)based on Genetic Algorithms to automate the configuration and resolve the inconsistencies in LDTs.Opt-LDT encodes LDT configuration as an optimization problem and generates a consistent configuration that satisfies the constraints of the features.We tested and validated opt-LDT for a local secondary care hospital in a real healthcare environment.Our results,averaged over ten runs,show that opt-LDT resolves 90%of inconsistencies while taking between 6 and 6.5 s for each configuration.Moreover,positive feedback based on a subjective questionnaire from clinicians regarding the performance,acceptability,and efficiency of opt-LDT motivates us to present our results for regulatory approval.

Intelligence Test

1.If you drop a white hat into the Red Sea,what does itbecome?2.What do people do in clock factories?3.Whv do seagulls live near the sea?4.A cowboy rode to an inn on Friday,stayed two nightsand 1eft on Friday.How could that be?5.Where does a bird go when it loses its tail?(Key:1.Wet.2.They make faces all day. 3.Because ifthey live near the bay they will be called bagels.4.Hishorse’s name was Friday.5.The retail store.)

English Intelligence Test

ANow,imagine(设想)there is a large live duckinside a very large bottle.

基于平行测试的认知自动驾驶智能架构研究

在大数据、云计算和机器学习等新一代人工智能技术的推动下,自动驾驶的感知智能在近年来得到显著的提升与发展.然而,与人类驾驶过程中隐含的以自我目的实现为引导的自探索性和自主性相比,现阶段自动驾驶技术主要以辅助驾驶功能为主,还停留在以被动感知、规划与控制为主的初级智能自动驾驶阶段.为实现车辆智能从数据驱动的环境感知、辅助决策、被动规划到知识驱动的场景认知、推理决策、主动规划的提升,亟需增强车辆自身对复杂外界信息归纳提炼、推理决策、评价估计等类人能力.首先回顾自动驾驶关键技术演化及其应用发展历程;随后分析测试对车辆智能评估的效用;然后基于平行测试理论,提出自动驾驶车辆认知智能训练、测试与评估空间的构建方法,并设计基于平行测试的认知自动驾驶智能训练框架.该项研究工作预期能为推动自动驾驶从感知智能向认知智能的升级提供可行的技术支撑与实现路径.

基于Arduino的SMA丝材智能修复损伤钢筋混凝土梁试验研究

为修复钢筋混凝土梁在使用阶段中由于材料老化、裂缝开展等原因造成的损伤,提出一种基于Arduino智能平台和形状记忆合金(Shape Memory Alloy,SMA,具有形状记忆效应和超弹性特性)的智能修复控制系统,根据挠度及裂缝控制原理,设计智能修复控制装置。该装置由输入模块、处理模块和输出模块组成,当检测到钢筋应变超过限值时,输出模块升高SMA丝材温度对梁体进行修复。为验证该智能修复装置的修复性能,设计对比梁A(无修复装置)与试验梁B(加装智能修复控制装置),在加载过程中对试验梁B进行3次修复,对2根梁的跨中挠度、钢筋应变及裂缝宽度等进行对比分析。结果表明:破坏时试验梁B的跨中挠度、裂缝宽度均小于对比梁A,并且在3次修复中试验梁B的跨中挠度、裂缝宽度和钢筋应变最大回复率分别为31.0%、47.0%和71.5%。说明该智能修复控制装置可以监测试验梁受损情况并激发SMA材料对试验梁实施修复,实现钢筋混凝土梁的智能修复。

岩土工程勘察、测试与评价进展

文章全面总结了近5年岩土工程勘察测试与评价领域的技术进展,主要包括土工室内试验技术,原位测试技术、模型试验以及岩土工程勘察测试智能化技术。重点介绍了特殊环境(低温)试验技术、特殊材料(含气土、污染土等)、新型微观分析、技术原位测试新功能与新应用、水陆域超深原位测试技术、土工离心机技术、大型模型试验技术、智能勘察技术、智能监测技术、智能评价技术以及岩土工程智能管理平台建设等方面的进展。分析表明,重大工程建设、低碳绿色理念、智能技术、极端环境催生了一批岩土工程勘察测试新技术与新方法,智能化测试与评价是未来一段时期我国岩土工程勘察评价技术发展的方向。

茶叶生产装备自动化与智能化技术研究进展与展望

茶产业是我国传统特色优势产业,大数据、物联网、云计算等新一代信息技术与农业的融合促进了茶产业向智能化转型与升级,在茶叶全产业链赋能增效中发挥重要作用。本文在概述了茶产业智能化技术体系的基础上,围绕种植、加工、检测和销售等4方面总结了国内外信息技术在茶产业智能化中应用的研究成果,分析了茶产业实现智能化的关键技术。最后对茶产业智能化未来发展方向进行展望,建议增强茶产业信息技术基础设施建设、加强人机协作智能茶机设备研发、重视茶叶种植加工大模型开发、提升大数据分析助力茶叶销售能力,为更好利用信息技术进行茶产业升级奠定基础。

重点工程建设单位中心实验室的作用与实施技术方案

四川岷江龙溪口航电枢纽工程是水运重点工程,2021年获批为交通部“平安百年品质工程”,由此对工程的质量标准提出了更高的要求。如何保障工程的高质量建设,更好的监督管控参建单位的施工质量,建立建设单位的中心实验室是必然举措。阐述了四川大型航电枢纽工程第一个建设单位中心实验室发挥的作用和采用全过程质量管理体系的实施技术方案,展望了向智能试验检测的转变,建议以“中西医结合、优势互补”的手段为工程建设的全过程健康保驾护航。相关理念为后续岷江航电枢纽工程以及其他水运重点工程的中心实验室建设提供借鉴。

人工智能辅助诊断系统在液基细胞学宫颈腺上皮病变筛查中的应用价值

目的:探讨人工智能(AI)辅助系统在宫颈腺上皮病变细胞学诊断中的应用价值。方法:收集诊断为非典型腺细胞(AGC)的液基细胞学薄层宫颈涂片143例,阴性涂片631例,所有涂片均进行AI辅助阅片和中级医师阅片,以主任医师复核的诊断结果为金标准,进行对比分析,并统计特异性、敏感性等指标。结果:宫颈涂片腺上皮细胞病变的检测中,AI辅助阅片系统的阳性率为15.7%、特异性为99.8%,而中级医师阅片的阳性率为18.3%、特异性为99.2%,两组间差异无统计学意义(P>0.05)。AI辅助系统的准确率为97.0%,敏感性为84.6%,中级医师阅片的准确率为99.2%,敏感性为99.3%,两组间差异具有统计学意义(P<0.05)。AI辅助阅片的ROC曲线下面积(AUC)为0.922,低于中级医师阅片的0.993,差异有统计学意义(P<0.05)。此外,AI辅助阅片与中级医师阅片的诊断一致率达到99%,相应的Kappa值为0.888,表明两种阅片方法基本一致。结论:AI辅助系统在宫颈腺上皮病变的诊断中展现出较高的特异性,然而其敏感性相对较低,存在一定的漏诊可能性。尽管AI辅助阅片的准确率尚未达到中级医师的水平,但其仍展现出重要的诊断潜力。未来的研究和发展应着重于提升AI辅助阅片系统的敏感性,并通过临床验证来确保其在实际应用中的可靠性和安全性。

在役螺栓超声三维成像监测数据的智能化分析方法

螺栓是一种常见的机械连接件,一旦失效,可能会导致设备损坏、结构崩塌,甚至危及人身安全。因此,在役螺栓的监测一直受到广泛关注。将相控阵超声全聚焦技术应用于螺栓在役监测,开发了专用系统和阵列探头,实现了三维成像可视化监测,改变了螺栓监测数据单一的现状。针对大量三维图像化数据处理的关键问题,提出了智能化分析方法,对比分析了决策树、支持向量机和神经网络算法的适用性,最终开发了基于决策树算法的智能分析评价软件模块并取得了较好的检测效果。