Physical neural networks with self-learning capabilities

Physical neural networks are artificial neural networks that mimic synapses and neurons using physical systems or materials.These networks harness the distinctive characteristics of physical systems to carry out computations effectively,potentially surpassing the constraints of conventional digital neural networks.A recent advancement known as“physical self-learning”aims to achieve learning through intrinsic physical processes rather than relying on external computations.This article offers a comprehensive review of the progress made in implementing physical self-learning across various physical systems.Prevailing learning strategies that contribute to the realization of physical self-learning are discussed.Despite challenges in understanding the fundamental mechanism of learning,this work highlights the progress towards constructing intelligent hardware from the ground up,incorporating embedded self-organizing and self-adaptive dynamics in physical systems.

Assessment of the Physical Capabilities of Heart Failure Patients before and after Cardiovascular Rehabilitation: A Study of 125 Patients from West Africa, Dakar, Senegal

Background: Cardiac rehabilitation represents a critical therapeutic strategy for patients suffering from chronic heart failure. The physical capacity of patients with heart failure, assessed using the exercise test and the 6-minute walk test, is the measure of the patient’s overall functional ability to perform physical activities and tolerate exercise loads. The objective of this study was to assess the impact of cardiac rehabilitation on patients’ physical capabilities and to conduct a thorough comparison of data obtained via exercise testing and the 6-minute walk test before and after the rehabilitation programme. Methods: This was a descriptive and analytical cross-sectional study, conducted from 1 February 2021 to 31 June 2022. Included were heart failure patients who had participated in an outpatient cardiovascular rehabilitation programme. The collected data included anamnestic, clinical, paraclinical data, and the 6-minute walk test. Informed consent was obtained. Data analysis, word processing, and charting were performed using Microsoft Word 2016, Excel 2013, and Sphinx version 5.1.0.2. Data analysis was performed using SPSS (Statistical Package for Social Sciences) version 24.0. Any difference less than 0.05 was considered statistically significant. Results: In a Senegalese study, heart failure patients undergoing rehabilitation in a cardiac unit represented 45.59% of all cases, with a prevalence rate of 3.21%. The average participant was 57.97 years old, with those aged 61 to 70 forming the largest group (35.5%). The study noted a male predominance (sex ratio of 2.1) and identified dyslipidaemia (80.6%) and sedentarism (71%), as prevalent cardiovascular risk factors. All participants initially suffered from NYHA stage 2 or 3 dyspnoea, yet 80.65% showed no symptoms following rehabilitation. Significant improvements were recorded in resting heart rate (from 79 to 67 bpm), and the 6-minute walk test distance (from 328 m to 470 m). Enhanced exercise tolerance and walking test outcomes were particularly notable in patients with LVEF ≥ 50%, women, non-obese individuals, those initially walking less than 300 m, achieving more than 3 METs, and non-smokers. Conclusion: The findings underscore the effectiveness of cardiovascular rehabilitation in improving symptoms, physical capability, and overall quality of life for heart failure patients in Senegal.

Self-Learning and Its Application to Laminar Cooling Model of Hot Rolled Strip

The mathematical model for online controlling hot rolled steel cooling on run-out table （ROT for abbreviation） was analyzed, and water cooling is found to be the main cooling mode for hot rolled steel. The calculation of the drop in strip temperature by both water cooling and air cooling is summed up to obtain the change of heat transfer coefficient. It is found that the learning coefficient of heat transfer coefficient is the kernel coefficient of coiler temperature control （CTC） model tuning. To decrease the deviation between the calculated steel temperature and the measured one at coiler entrance, a laminar cooling control self-learning strategy is used. Using the data acquired in the field, the results of the self-learning model used in the field were analyzed. The analyzed results show that the self-learning function is effective.

Self-Learning of Multivariate Time Series Using Perceptually Important Points

In machine learning,positive-unlabelled(PU)learning is a special case within semi-supervised learning.In positiveunlabelled learning,the training set contains some positive examples and a set of unlabelled examples from both the positive and negative classes.Positive-unlabelled learning has gained attention in many domains,especially in time-series data,in which the obtainment of labelled data is challenging.Examples which originate from the negative class are especially difficult to acquire.Self-learning is a semi-supervised method capable of PU learning in time-series data.In the self-learning approach,observations are individually added from the unlabelled data into the positive class until a stopping criterion is reached.The model is retrained after each addition with the existent labels.The main problem in self-learning is to know when to stop the learning.There are multiple,different stopping criteria in the literature,but they tend to be inaccurate or challenging to apply.This publication proposes a novel stopping criterion,which is called Peak evaluation using perceptually important points,to address this problem for time-series data.Peak evaluation using perceptually important points is exceptional,as it does not have tunable hyperparameters,which makes it easily applicable to an unsupervised setting.Simultaneously,it is flexible as it does not make any assumptions on the balance of the dataset between the positive and the negative class.

Where Have Network-based Self-learning Classes Gone?——Reflections & Expectations on the Employment of Network-based Self-learning Classes

To respond to the further development of college English reforms,many universities employed network-based selflearning classes to aid the traditional classroom teaching,especially in teaching listening,but as time went by,some universities gradually gave them up.The paper intends to reflect on the employment of network-based self-learning listening classes,analyz ing the learning with and without its aid,and meanwhile introduce the need to re-employ it,and discuss how we can improve the network-based self-learning classes to help with students' listening.

SELF-LEARNING FUZZY CONTROL RULES USING GENETIC ALGORITHMS

This papcr presents a new genetic algorithms(GAs)-based method for self-learniag fuzzy control rules. An improved GA is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the condition portion of each rule, and to automatically generate fuzzy control actions under each condition. The dynamics of the controlled system is unknown to the GA. The only information for evaluating performance is a failure signal indicating that the controlled system is out of control. We compare its performance with that of other learning methods for the same problem. We also examine the ability of the algorithm to adapt to changing conditions. Simulation results show that such an approach for self-learning fuzzy control rules is both effective and robust.

Mathematical model for cooling process and its self-learning applied in hot rolling mill

Control precision of coiling temperature is one of the key factors affecting the profile shape and surface quality during the cooling process of hot rolled steel strip.For this reason,the core of temperature control precision is to establish an effective cooling mathematical model with self-learning function.Starting from this point,a cooling mathematical model with nonlinear structural characteristics is established in this paper for the cooling process of hot rolled steel strip.By the analysis of self-learning ability,key parameters of the mathematical model could be constantly corrected so as to improve temperature control precision and adaptive capability of the model.The site actual application results proved the stable performance and high control precision of the proposed mathematical model,which would lay a solid foundation to improve the steel product qualities.

Study on intelligent digital welding machine with a self-learning function

A design idea was proposed that it was about intelligent digital welding machine with self-learning and self- regulation functions. The overall design scheme of software and hardware was provided. It was introduced that a parameter self-learning algorithm was based on large-step calibration and partial Newton interpolation. Furthermore, experimental verification was carried out with different welding technologies. The results show that weld bead is pegrect. Therefore, good welding quality and stability are obtained, and intelligent regulation is realized by parameters self-learning.

Self-learning Fuzzy Controllers Based On a Real-time Reinforcement Genetic Algorithm

This paper presents a novel method for constructing fuzzy controllers based on a real time reinforcement genetic algorithm. This methodology introduces the real-time learning capability of neural networks into globally searching process of genetic algorithm, aiming to enhance the convergence rate and real-time learning ability of genetic algorithm, which is then used to construct fuzzy controllers for complex dynamic systems without any knowledge about system dynamics and prior control experience. The cart-pole system is employed as a test bed to demonstrate the effectiveness of the proposed control scheme, and the robustness of the acquired fuzzy controller with comparable result.

Neuron self-learning PSD control for backside width of weld pool in pulsed GTAW with wire filler

In this paper, the weld pool shape control by intelligent strategy was studied. A neuron self-learning PSD controller for backside width of weld pool in pulsed GTAW with wire filler was designed. The PSD control arithmetic was analyzed, simulating experiment by MATLAB software was done, and the validating experiments on varied heat sink workpiece and varied gap workpiece were successfully implemented. The study results show that the neuron self-learning PSD control method can attain a perfect control effect under different set values and conditions, and is suitable for the welding process with the varied structure and coefficients of control model.

The Self-Learning Gate for Quantum Computing

Self-learning is one of the most important scientific methods that helps develop sciences, as it derives from the desire and interests of the individual. However, self-learning loses importance if it does not follow the scientific methodology for building and organizing information. The case becomes harder if the science is new and few scientific sources are available. Quantum computing is one of the new sciences in computer science and needs the support of specialists to develop it. Quantum computing overlaps with many sciences such as physics, chemistry, and mathematics, so any student in one of the previous disciplines may lose the correct self-learning path to find themselves learning the details of another discipline that does not achieve their goals. This article motivates students and those interested in computer science to begin studying the science of quantum computing and choose the same specialization that suits their interests. The article also provides a roadmap for self-learning steps to protect the learner from losing the correct learning path. I have categorized the stages of learning quantum computing into four steps through which all the essential basics can be learned, provided the goals mentioned in each stage which should be achieved. The learning strategy proposed in this article corresponds with individuals’ self-learning rules. Through my personal experience, the proposed learning strategy has proven its effectiveness in building information in an enjoyable scientific way.

A Self-Learning Diagnosis Algorithm Based on Data Clustering

The article describes an approach to building a self-learning diagnostic algorithm. The self-learning algorithm creates models of the object under consideration. The models are formed periodically through a certain time period. The model includes a set of functions that can describe whole object, or a part of the object, or a specified functionality of the object. Thus, information about fault location can be obtained. During operation of the object the algorithm collects data received from sensors. Then the algorithm creates samples related to steady state operation. Clustering of those samples is used for the functions definition. Values of the functions in the centers of clusters are stored in the computer’s memory. To illustrate the considered approach, its application to the diagnosis of turbomachines is described.

考虑碳流约束的电力系统能碳安全域模型与计算方法

针对当前电力系统碳减排任务与安全稳定运行需求,研究电力系统低碳安全运行技术。现有的能量流安全域模型使电力系统安全监视与控制更加科学有效,但忽略了系统运行过程中可能存在的“高碳”风险。文中提出了一种考虑碳流约束的电力系统能碳耦合安全域(简称能碳安全域)模型与计算方法,旨在保证系统安全性的同时减排提效。为完整刻画电力系统的低碳运行能力极限,分别从负荷端和源端角度提出了考虑碳流约束的系统最大供电能力曲线及最大消纳能力曲线模型。基于系统最大供电能力工作点,提出了能碳安全边界仿真拟合计算方法,实现了能碳安全域的降维观测。最后,结合算例验证了所提模型的有效性。

企业数字化能力影响因素分析——以广东省制造业为例

制造业是我国国民经济的重要组成部分,随着新一代信息技术和数字技术的迅猛发展,民众日益增长的智能化需求和数字化市场趋势推动着企业不断探索和构建自身的数字化能力来实现数字化转型,进而获取持续的竞争优势。本文基于动态能力理论和TOE分析框架,以广东省制造业为例,设计了企业数字化能力的研究模型,并提出假设,在对模型和假设的不同影响路径进行实证检验和修正后,分析了组织结构、高层管理者和数字技术能力对广东省制造企业数字化能力构建的作用机理。

数字化转型与企业国际化绩效研究——基于供应链能力的中介作用

本文借助Python爬虫技术刻画上市公司数字化转型强度,利用2012—2021年A股上市公司和国泰安数据库相关匹配数据,从理论和实证两个方面探讨数字化转型对企业国际化绩效的影响及其作用机理。研究结果表明,数字化转型对企业国际化绩效具有显著的促进作用且通过一系列内生性和稳健性检验,供应链能力在其中发挥了中介效应。进一步分析发现,数字化转型对企业国际化绩效的提升效果在非国有企业、技术和资本密集型行业、发达国家或地区更明显。这一结论对促进企业实现数字化转型、提升国际化绩效、增强国际竞争力具有重要的现实意义。

计及灾后恢复全过程储能应急响应能力的配电网韧性规划

储能因响应快、灵活性高的特点,已成为可再生能源发展必不可少的支撑技术,不仅具有削峰填谷、改善电能质量的作用,在灾后恢复过程中的应急响应能力同样不可忽视。提出了一种计及灾后恢复全过程储能应急响应能力的配电网韧性规划方法,构建了同时考虑网架增强和储能配置策略的配电网韧性增强双层规划模型,系统地刻画了极端灾害发生后配电网网架重构、设备抢修及复电等灾后恢复全过程的源-网-荷-储互动模式场景。最后,通过IEEE33节点系统的算例分析表明,所提方法能更充分地挖掘储能的应急响应潜力,有效提高增强性规划决策的精准度,获得更佳的投资回报。

制造企业数字创新如何促进企业绩效提升

为探究制造企业数字创新促进企业绩效提升的过程机理,揭示数字创新的协同效应、扩散效应和增值效应,以及数字化投入持续性的情境效应,将数字创新划分为效率型、新颖型和融合型3种类型,并将数字化能力划分为数字协同能力、数字创新扩散能力和数字价值化能力3个维度,以251家中国制造企业为调查对象进行实证检验,结果发现,第一,数字创新能够显著提升企业绩效水平。第二,数字创新通过推动数字化能力重构促进企业绩效水平提升。第三,不同类型数字化能力在数字创新与企业绩效间发挥中介作用。具体表现如下:数字创新扩散能力在新颖型数字创新与企业绩效间发挥中介作用;数字价值化能力在融合型数字创新与企业绩效间发挥中介作用。第四,数字化投入持续性在企业数字创新过程中发挥重要调节作用,正向调节数字创新与企业绩效间关系,强化数字创新与数字化能力间关系,以及效率型数字创新与数字协同能力间关系。结论可为制造企业数字创新提供理论指导与实践启示。

高校图书馆科研数据管理服务能力评价指标体系研究

本文通过对相关文献和主流模型的梳理,从形式、内容、效用三个维度构建高校图书馆科研数据管理服务能力评价指标体系,并通过一系列科学验证,最终得到由机构支撑力、管理服务力、社会效应力3个一级指标,经费资源支撑、数据资源管理、用户素养教育、学术影响力等11个二级指标,成本效益分析、数据使用规范、用户素养教育内容、科研成果产出等35个三级指标构成的指标体系。构建高校图书馆科研数据管理服务能力评价指标体系,在为国内实践研究提供评价工具的同时,也可以促进高校图书馆科研数据管理服务规范化发展。

中小企业数字化转型的路径探析

加快推动中小企业数字化转型是数字经济发展的重要基础。由于缺乏足够的技术与资源,中小企业数字化转型需依附第三方数字平台完成产品体系、组织结构、业务流程的改造,进而降低成本、提升效率。其数字化转型过程为企业层面的数字技术部分采用、行业层面的数字技术整体融合以及生态层面的数字技术共生发展。但中小企业在数字化转型过程中,还面临数字意识薄弱、转型能力不足、转型资源受限、转型数据欠缺、转型成本过高等诸多困境。中小企业要立足推动数字经济发展的战略要求,从培养动态管理能力、运用轻体量数字平台、提升组织使用信息技术的能力、获得政府外部帮扶及支持等方面创新路径,推动数字化转型。

Survey on the Status Quo of Emergency Response Capabilities of Public Health Emergencies in Grassroots Disease Control Institutions in a Certain Area

Objective:To survey and study the emergency response capabilities of grassroots disease control institutions in a certain area for public health emergencies,and to put forward suggestions for rectification.Methods:The study was carried out from March 2022 to March 2023.Field surveys,questionnaire surveys,and interviews were used to investigate and analyze the emergency response capabilities of public health emergencies in 5 county Centers for Disease Control and Prevention(CDCs)in the region.Results:Through the survey,it was found that the professional level of the existing emergency team personnel of the grassroots disease control institutions in this region needs to be improved.There was an overall lack of emergency plans,and the compliance rate of the equipment,inspection,and testing items was low.The health emergency system of the CDCs in the region and the ability of the talent team need to be further improved.Conclusion:The emergency response capacity of grassroots disease control institutions in this region needs to be improved.For this reason,government departments need to increase investment and strengthen the construction of talent teams and hardware settings,and grassroots disease control institutions need to strengthen the construction of the public health emergency system to improve the ability to respond to public health emergencies.