关于微信Health Manager公众号用户情况的数据分析

目的随着Health Manager公众号的开通运营,关注量和浏览量日益增加。人们对于健康有了更多的忧虑,对于健康知识有了更大的需求,我们作为健康信息传播类的公众号,不定期推送健康方面的相关内容,尽可能满足用户的需求体验。本文通过分析Health Manager公众号运营情况,了解大众对于具体推文内容的偏好、推文内容获取途径和用户性别等数据,发现目前存在的问题并且寻求对应的改进策略,从而更好的为用户提供服务,更好地发挥健康传播类媒介的作用,提高国民健康水平。方法平台272名用户的使用情况数据反馈。结果从用户分析来看,新增用户278人,去掉6人,净增272人,累计272人,主要集中于浙江、广东、江西等地区。结论Health manager服务平台微信公众号具有推广价值。

The Role of Impulsivity and Addiction in Gambling Behaviors: How Applied Neuroscience Research Can Help in the Monitoring and Management of Mental Health Issues

One in five Americans experience symptoms associated with at least one mental health disorder every year. These include behavioral addictions, which have long been overlooked despite their similar neural bases with substance addictions. Gambling addiction, a type of behavioral addiction, deserves particular attention given the significant negative effects, this addiction has on financial and interpersonal health. The purpose of this paper is to review the available literature concerning the behavioral and neural processes involved in gambling addiction, including: the anticipation of reward, the role of dopamine, and the neural substrates of the decision-making processes involved in gambling addiction. Market research has also identified solutions that integrate applied neuroscience and self-tracking systems to monitor and manage mental health issues associated with gambling addiction. The authors then propose a gambling treatment-focused mobile app solution that addresses outstanding issues with a special design aimed at reversing plasticity in order to relieve the effects of gambling addiction. .

Observation on the Effect of Health Education in Health Management of Chronic Disease Patients

Objective: By the end of 2021, the aging rate of China’s population is 18.9%, and the prevalence rate of chronic diseases in the elderly population is increasing year by year, and chronic diseases have become the main causes of death and health threats of Chinese residents. Therefore, how to manage this huge group well is crucial. This paper analyzes the value of health education in the process of health management for patients with chronic diseases. Methods: 102 patients with chronic diseases treated from January 2021 to December 2021 were divided into control group and experimental group by random number table method. The control group was given routine health management while the experimental group was given health education based on the control group, and the implementation effect was analyzed. Results: After management, the scores of chronic disease knowledge in the experimental group were significantly higher than those in the control group, and the dimensions of ESCA were higher than those in the control group, and P < 0.05;Conclusion: The implementation of health education in the process of chronic disease health management is helpful to improve patients’ self-care ability and better control disease progression.

Effect of Health Management in Raising Awareness Rate of Disease Prevention and Treatment in Patients with Prehypertension

Objective: To analyze the effect of health management on improving the awareness rate of disease prevention and treatment in patients with prehypertension, so as to provide guidance for clinical management of patients with prehypertension. Methods: 108 patients diagnosed with prehypertension in our hospital were divided into a control group and an experimental group. The control group was not given management measures, while the experimental group was given health management. The incidence of hypertension and cognition level of hypertension knowledge were compared between the two groups after management. Results: The incidence of hypertension in the experimental group was 7.41% lower than that in the control group 29.63%. The cognitive level of hypertension in the patients (66.54 ± 1.25) was significantly higher than that in the patients without health management (41.45 ± 2.45), and P < 0.05;Conclusion: For patients with prehypertension, the implementation of health management is helpful to improve their cognition of hypertension, master related prevention knowledge, and reduce the incidence of hypertension.

Battery prognostics and health management for electric vehicles under industry 4.0

Transportation electrification is essential for decarbonizing transport. Currently, lithium-ion batteries are the primary power source for electric vehicles (EVs). However, there is still a significant journey ahead before EVs can establish themselves as the dominant force in the global automotive market. Concerns such as range anxiety, battery aging, and safety issues remain significant challenges.

Boosting battery state of health estimation based on self-supervised learning

State of health(SoH) estimation plays a key role in smart battery health prognostic and management.However,poor generalization,lack of labeled data,and unused measurements during aging are still major challenges to accurate SoH estimation.Toward this end,this paper proposes a self-supervised learning framework to boost the performance of battery SoH estimation.Different from traditional data-driven methods which rely on a considerable training dataset obtained from numerous battery cells,the proposed method achieves accurate and robust estimations using limited labeled data.A filter-based data preprocessing technique,which enables the extraction of partial capacity-voltage curves under dynamic charging profiles,is applied at first.Unsupervised learning is then used to learn the aging characteristics from the unlabeled data through an auto-encoder-decoder.The learned network parameters are transferred to the downstream SoH estimation task and are fine-tuned with very few sparsely labeled data,which boosts the performance of the estimation framework.The proposed method has been validated under different battery chemistries,formats,operating conditions,and ambient.The estimation accuracy can be guaranteed by using only three labeled data from the initial 20% life cycles,with overall errors less than 1.14% and error distribution of all testing scenarios maintaining less than 4%,and robustness increases with aging.Comparisons with other pure supervised machine learning methods demonstrate the superiority of the proposed method.This simple and data-efficient estimation framework is promising in real-world applications under a variety of scenarios.

An Edge-Fog-Cloud Computing-Based Digital Twin Model for Prognostics Health Management of Process Manufacturing Systems

The prognostics health management(PHM)fromthe systematic viewis critical to the healthy continuous operation of processmanufacturing systems(PMS),with different kinds of dynamic interference events.This paper proposes a three leveled digital twinmodel for the systematic PHMof PMSs.The unit-leveled digital twinmodel of each basic device unit of PMSs is constructed based on edge computing,which can provide real-time monitoring and analysis of the device status.The station-leveled digital twin models in the PMSs are designed to optimize and control the process parameters,which are deployed for the manufacturing execution on the fog server.The shop-leveled digital twin maintenancemodel is designed for production planning,which gives production instructions fromthe private industrial cloud server.To cope with the dynamic disturbances of a PMS,a big data-driven framework is proposed to control the three-level digital twin models,which contains indicator prediction,influence evaluation,and decisionmaking.Finally,a case study with a real chemical fiber system is introduced to illustrate the effectiveness of the digital twin model with edge-fog-cloud computing for the systematic PHM of PMSs.The result demonstrates that the three-leveled digital twin model for the systematic PHM in PMSs works well in the system’s respects.

Didactic Sequences as an Educational Product to Facilitate Teaching-Learning Processes in Lato Sensu Graduate Courses in the Area of Health Management in Primary Care

Teaching strategies can be considered as techniques that are constructed to be used with the objective of promoting teaching and learning in the classroom, so that the teacher is considered a mentor, as he is the one who selects, analyzes, studies, organizes, builds and proposes the most classic tools to facilitate the learning process [1]. This study is an experience report related to the construction of an educational product that consists of the elaboration of pedagogical strategies, characterized by five didactic sequences in the perspective of collaborating with teaching-learning processes in lato sensu graduate courses. The themes that are part of the didactic sequences were built based on the results obtained in scientific research carried out during the development of the strict sensu postgraduate course in Teaching in Health and Technology, which involved: the work of coordinators who work in the field of health management in Primary Care, situations that challenge the management of Primary Care and the potentialities of work in the management of Primary Care. The didactic sequences have fun teaching strategies that provide meaningful learning for a future qualified professional performance. These sequences involve the use of active methodologies and the use of digital tools. The educational product developed seeks to promote benefits that can collaborate with the improvement of Primary Care Management and teaching-learning processes in the training of health professionals. Therefore, the pedagogical strategies, as well as its entire construction process, were developed through the collaboration of professors of the Health and Society discipline at the State University of Health Sciences of Alagoas (UNCISAL), seeking to make it qualify for effective construction of knowledge and that promote its wide use in the academic environment.

Oral Health Management of Three Head and Neck Cancer Patients Performed by Dental Hygienists

Introduction: Oral health management is often difficult in patients after surgical tumor resection and/or radiotherapy due to defects, bulky flaps, limitation of tongue movement, and trismus. In addition, patients who have undergone radiotherapy have serious disabilities such as oral mucositis, mucosal weakening, soft tissue fibrosis, salivary gland disorder, and osteoradionecrosis. Dental hygienists must understand the characteristics of patients after surgical tumor resection and/or radiotherapy. In this report, the oral health management of three patients after maxillectomy, mandibulectomy, and radiotherapy was shown. Case Description: Case 1: A 53-year-old male patient visited our clinic after chemotherapy, radiotherapy, and partial maxillectomy for cancer of the right maxillary gingiva. Case 2: A 65-year-old male patient visited our clinic after radiotherapy for a right-sided tongue cancer, partial mandibulectomy of the right tongue to the oropharynx, a right-sided segmental mandibulectomy and a speech aid and a mandibular prosthesis were provided. Case 3: A 36-year-old female visited our clinic for radiotherapy for left-sided tongue cancer. Dental hygienists provided oral health management to the patients. In oral health management, it was suggested that intervention before cancer treatment, oral hygiene instructions tailored to patients’ symptoms, and frequent professional care are important. Conclusion: Oral health management by dental hygienists is extremely important for patients undergoing maxillofacial cancer treatment to maintain their quality of life in the long term.

Virtual sample generation for model-based prognostics and health management of on-board high-speed train control system

In view of class imbalance in data-driven modeling for Prognostics and Health Management(PHM),existing classification methods may fail in generating effective fault prediction models for the on-board high-speed train control equipment.A virtual sample generation solution based on Generative Adversarial Network(GAN)is proposed to overcome this shortcoming.Aiming at augmenting the sample classes with the imbalanced data problem,the GAN-based virtual sample generation strategy is embedded into the establishment of fault prediction models.Under the PHM framework of the on-board train control system,the virtual sample generation principle and the detailed procedures are presented.With the enhanced class-balancing mechanism and the designed sample augmentation logic,the PHM scheme of the on-board train control equipment has powerful data condition adaptability and can effectively predict the fault probability and life cycle status.Practical data from a specific type of on-board train control system is employed for the validation of the presented solution.The comparative results indicate that GAN-based sample augmentation is capable of achieving a desirable sample balancing level and enhancing the performance of correspondingly derived fault prediction models for the Condition-based Maintenance(CBM)operations.

The Management of Mental Health, and Service Networks in Italy

Madness has attracted and frightened for centuries,and talking about this means discussing how this diversity was built and managed in different social contexts and historical periods.Not all societies have had,and still have,the same relationship with madness.It is only with the affirmation of the Modern State,and of Capitalism,that the idea of“normality”indispensable to be able to conceive diversity as something dangerously distant and different from the norm takes over.In our post-modern society,people with mental illness in Italy can resort to specialists and social-health services.But the heterogeneous answers given after the approval of law 180 appear to be increasingly diversified.In this research,much attention will be paid to how the social and health services,located in different areas of Italy(Messina,Rome,Trento)face the current growing risk of social,housing and economic isolation of these fragile subjects.The aim of the research is to explore the possibility of a new relationship between the social-health service and the local community.On the one hand,research investigates what the contribution of the services could be.On the other what the spaces of protagonism and participation of the community could be in inclusion process account.In order to better understand the differences between these two dimensions,a qualitative research approach was chosen through the conduct of in-depth interviews.In this way it was possible to investigate:(1)the partial representations characteristic of the single individual,family members,operators and stackholders in general;(2)the services around the topic dealt with is articulated.From the first results of the research it emerges that the territory can no longer be considered as an abstract entity,but becomes the social space within which the construction of a new community welfare can and must take place.

Computational Reproducibility Within Prognostics and Health Management

Scientific research frequently involves the use of computational tools and methods.Providing thorough documentation,open-source code,and data–the creation of reproducible computational research(RCR)–helps others understand a researcher’s work.In this study,we investigate the state of reproducible computational research,broadly,and from within the field of prognostics and health management(PHM).In a text mining survey of more than 300 articles,we show that fewer than 1%of PHM researchers make their code and data available to others.To promote the RCR further,our work also highlights several personal benefits for those engaged in the practice.Finally,we introduce an open-source software tool,called PyPHM,to assist PHM researchers in accessing and preprocessing common industrial datasets.

Health management based on fusion prognostics for avionics systems

Health management permits the reliability of a system and plays a increasingly important role for achieving efficient system-level maintenance.It has been used for remaining useful life（RUL） prognostics of electronics-rich system including avionics.Prognostics and health management（PHM） have become highly desirable to provide avionics with system level health management.This paper presents a health management and fusion prognostic model for avionics system,combining three baseline prognostic approaches that are model-based,data-driven and knowledge-based approaches,and integrates merits as well as eliminates some limitations of each single approach to achieve fusion prognostics and improved prognostic performance of RUL estimation.A fusion model built upon an optimal linear combination forecast model is then utilized to fuse single prognostic algorithm representing the three baseline approaches correspondingly,and the presented case study shows that the fusion prognostics can provide RUL estimation more accurate and more robust than either algorithm alone.

Effects of WeChat platform-based health management on health and self-management effectiveness of patients with severe chronic heart failure

BACKGROUND Epidemiological studies have found that the prevalence of chronic heart failure in China is 0.9%,the number of people affected is more than 4 million,and the 5-year survival rate is even lower than that of malignant tumors.AIM To determine the impact of WeChat platform-based health management on severe chronic heart failure patients’health and self-management efficacy.METHODS A total of 120 patients suffering from chronic heart failure with cardiac function grade III-IV,under the classification of the New York Heart Association,were admitted to our hospital in May 2017.In January 2020,they were divided into two groups:A control group(with routine nursing intervention)and an observation group(with WeChat platform-based health management intervention).Changes in cardiac function,6-min walking distance(6MWD),high-sensitivity cardiac troponin(hs-cTnT),and N-terminal pro B-type natriuretic peptide(NT-proBNP)were detected in both groups.The Self-Care Ability Scale(ESCA)score,Minnesota Living with Heart Failure Questionnaire score,and compliance score were used to evaluate self-management ability,quality of life,and compliance of the two groups.During a follow-up period of 12 mo,the occurrence of cardiovascular adverse events in both the groups was counted.RESULTS The left ventricular ejection fraction,stroke output,and 6MWD increased,and the hs-cTnT and NT-proBNP decreased in both the groups,as compared to those before the intervention.Further,cardiac function during the 6MWD,hs-cTnT,and NT-proBNP improved significantly in the observation group after intervention(P<0.05).The scores of self-care responsibility,self-concept,self-care skills,and selfcare health knowledge in the observation group were higher than those of the control group before intervention,and their ESCA scores were significantly improved after intervention(P<0.05).The Minnesota heart failure quality of life(LiHFe)scores of physical restriction,disease symptoms,psychological emotion,social relations,and other items were decreased compared to those of the control group before intervention,and the LiHFe scores of the observation group were significantly improved compared to those of the control group(P<0.05).With intervention,the compliance scores of rational diet,regular medication,healthy behavior,and timely reexamination were increased,thereby leading to the compliance scores of the observation group being significantly improved compared to those of the control group(P<0.05).During the 12 mo follow-up,the incidence rates of acute myocardial infarction and cardiogenic rehospitalization in the observation group were lower than those of the control group,and the hospitalization time in the observation group was shorter than that of the control group,but there was no significant difference between the two groups(P>0.05).CONCLUSION WeChat platform-based health management can improve the self-care ability and compliance of patients with severe chronic heart failure,improve the cardiac function and related indexes,reduce the occurrence of cardiovascular adverse events,and enable the avoidance of rehospitalization.

Optimization of dynamic sequential test strategy for equipment health management

Testing is the premise and foundation of realizing equipment health management （EHM）. To address the problem that the static periodic test strategy may cause deficient test or excessive test, a dynamic sequential test strategy （DSTS） for EHM is presented. Considering the situation that equipment health state is not completely observable in reality, a DSTS optimization method based on partially observable semi-Markov decision pro- cess （POSMDP） is proposed. Firstly, an equipment health state degradation model is constructed by Markov process, and the control limit maintenance policy is also introduced. Secondly, POSMDP is formulated in great detail. And then, POSMDP is converted to completely observable belief semi-Markov decision process （BSMDP） through belief state. The optimal equation and the corresponding optimal DSTS, which minimize the long-run ex- pected average cost per unit time, are obtained with BSMDP. The results of application in complex equipment show that the proposed DSTS is feasible and effective.

Deep neural network based classification of rolling element bearings and health degradation through comprehensive vibration signal analysis

Rolling element bearings are machine components used to allow circular movement and hence deliver forces between components of machines used in diverse areas of industry.The likelihood of failure has the propensity of increasing under prolonged operation and varying working conditions.Hence, the accurate fault severity categorization of bearings is vital in diagnosing faults that arise in rotating machinery.The variability and complexity of the recorded vibration signals pose a great hurdle to distinguishing unique characteristic fault features.In this paper, the efficacy and the leverage of a pre-trained convolutional neural network(CNN) is harnessed in the implementation of a robust fault classification model.In the absence of sufficient data, this method has a high-performance rate.Initially, a modified VGG16 architecture is used to extract discriminating features from new samples and serves as input to a classifier.The raw vibration data are strategically segmented and transformed into two representations which are trained separately and jointly.The proposed approach is carried out on bearing vibration data and shows high-performance results.In addition to successfully implementing a robust fault classification model, a prognostic framework is developed by constructing a health indicator(HI) under varying operating conditions for a given fault condition.

A Certifiable Framework for Health Monitoring and Management

The scope of this paper is to provide an E2 Eperspective of health monitoring and management(HMM)and structural health mornitoring(SHM)as an integrated system element of an integrated system health monitoring and management(ISHM)system.The paper will address two main topics:(1)The importance of a diagnostics and prognostic requirements specification to develop an innovative health monitoring and management system;(2)The certification of a health monitoring and management system aiming at a maintenance credit as an integral part of the maintenance strategies.The development of a maintenance program which is based on combinations of different types of strategies(preventive,condition-based maintenance(CBM)and corrective maintenance…)for different subsystems or components and structures of complex systems like an aircraft to achieve the most optimized solution in terms of availability,cost and safety/certification is a real challenge.The maintenance strategy must satisfy the technical-risk and cost feasibility of the maintenance program.

Satisfaction of Village Doctors with the Township and Village Health Services Integration Policy in the Western Minority-inhabited Areas of China

Township and Village Health Services Integration Management（TVHSIM） is an essential form of China＇s two-tiered health service integration plan at the township and village level. Its main purpose, also one of the target goals in China＇s new healthcare reform, is to gradually integrate rural health services and appropriately allocate rural health resources. This study aims to assess the village doctors＇ satisfaction with the TVHSIM and provide scientific base to further improve TVHSIM. A cross-sectional study was carried out in which 162 village doctors from Qinghai, Inner Mongolia and Xinjiang in western China were interviewed. Descriptive analysis, independent t-test, one-way ANOVA, Spearman rank correlation and multiple linear regression were used to analyze the difference and relevance between village doctors＇ personal characteristics and their satisfaction with TVHSIM and six subscales. Village doctors with different years of practice, social insurance status and essential medical knowledge level showed statistically significant differences in their satisfaction levels（all P〈0.05）. Age（P〈0.05） and years of practice（P〈0.01） were negatively correlated with Drug and Medical Device Management and Financing Management. Essential medical knowledge level（P〈0.05） was negatively correlated with Operations Management as well. However, social insurance status（P〈0.05） was positively correlated with Human Resources Management and Drug and Medical Device management. Gender, age and years of practice respectively had significant influence on village doctors＇ satisfaction with TVHSIM（P〈0.01）. In conclusion, in order to further promote TVHSIM policy in rural China, a well-rounded social insurance model for village doctors is urgently needed. In addition, the development of TVHSIM is regionally imbalanced. Efficient and effective measures aiming at rationalizing gender and age structure and enhancing essential medical training should be carefully considered.

Applying healthcare failure mode and effect analysis to patient pain management in the anesthesia recovery period

Objective： To standardize pain management in the anesthesia recovery period and improve the effects of analgesia on acute postoperative pain. Methods： Using healthcare failure mode and effect analysis （HFMEA）, we analyzed the primary cause of patients＇ pain and subsequently determined the process and risk priority number （RPN）. Results： Actions were taken to improve patients＇ pain. After using HFMEA, the experimental group＇s visual analog scale （VAS） scores were lower than those of the control group at 1 h and at discharge from the post-anesthetic intensive care unit （PAICU）. The differences were statistically significant （P 〈 0.05）. Conclusions： The application of failure mode and effect analysis can relieve pain and improve the quality of nursing.

The Application of Layered Progressive Teaching Method in Nursing Teaching of Health Management Centre

Objective:To explore the clinical effect of the layered progressive teaching method in the process of nursing teaching in the health management centre.Methods:100 nursing students in the health management centre of our hospital were randomly divided into two groups from April 2018 to April 2019,in which students of the control group were treated with routine teaching,while those of the experimental group were treated with layered progressive teaching.Then,the teaching effect of the two groups was compared and analysed.Results:The assessment results of nursing students in the two groups were compared,in which the theoretical knowledge scores and practical operation scores of nursing students in the experimental group were(94.34±2.33)and(90.45±2.20)respectively.By contrast,the score of students in the control group was lower and the difference between the two groups was not significant(P<0.50).The teaching effect of students in the experimental group is more significant.Conclusion:During the process of nursing teaching in the Health Management Centre,the progressive teaching method showed a significant clinical effect and could effectively enhance students’scores.Therefore,it is positively significant for clinical development.