Distribution of licensed acupuncturists and educational institutions in the United States at the start of 2023,during the late stage of the COVID-19 pandemic

This study was conducted to identify the number and density of active licensed acupuncturists(LAcs),as well as the number of accredited schools in acupuncture and Oriental medicine(AOM),as of January 1,2023,in the United States(U.S.).The number of active LAcs as of January 1,2023 was 34,524,potentially33,364 after removing license duplication in multiple states,among which the largest three states were California(with 7317 LAcs[21.19%of the total]),New York(5024[14.55%])and Florida(2644[7.66%]).The total number of LAcs decreased by 8.87%from 2018,and fell short of our projected number of LAcs in 2023 by 9037,or 20.75%.The overall LAc density in the U.S.Dmeasured as the number of LAcs per 100,000 populationDwas 10.36,less than in 2018.There were 56 active,accredited AOM schools which offered a total of 147 programs(121 at the level necessary for licensing[entry-level],12 for an advanced practicing degree[advanced-level],and 14 for certifications).Broken down further,offerings included 50 master's degrees in acupuncture,40 master's degrees in Oriental medicine,31 entry-level doctorate degrees(10 in acupuncture and 21 in acupuncture plus Chinese herbal medicine),and 12advanced-level doctorate degrees in AOM.The certification programs included one in East-Asian Medical Bodywork and 13 in CHM.Among these schools in 2023,institutions in the West and East Coast states comprised 67.86%(decreased from 77.42%in 2018)of the national total.California,Florida and Illinois represented 39.29%.There were 48 jurisdictions with acupuncture practice laws in place.The data suggests that the acupuncture profession in the U.S.has been significantly impacted during the coronavirus disease 2019 pandemic.

Distribution of licensed acupuncturists and educational institutions in the United States in early 2015

In recent decades, acupuncture has been used more widely and extensively in the United States （U.S.）. However, there have been no national surveys or analyses reported in academic journals on the number of practicing or licensed acupuncturists, This study was conducted to identify the approximate number of licensed acupuncturists active in 2015. The Board of Acupuncture or Board of Medicine in each state or U.S. territory was contacted to collect data, Online license information searching was also performed in order to get accurate numbers of licensed acupuncturists for those states in which a board was unable to be contacted. The study found that the number of licensed acupuncturists in 2015 in the U,S, was 34,481. Of this, more than 50% were licensed in three states alone： California （32.39%）, New York （11,89%） and Florida （7.06%）. The number of licensed acupuncturists increased 23.30% and 52.09%, compared to the year 2009 （n = 27,965） and 2004 （n = 22,671）, respectively; increasing about 1,266 per year. There were 62 and 10 accredited acupuncture institutions providing master and doctoral degrees, respectively. The West Coast comprised 51.39% of degree granting programs, while the East Coast comprised 29.17%; together the coastal states housed more than 80% of all programs, with the remainder sprinkled across the southern （9.72%）, northern （8.33%）, and the middle/central states （1.39%）. Forty-four states and the District of Columbia regulated acupuncture practice by law at the time of data collection. Acupuncture continues to be a quickly growing profession in the U.S,

New Approach for Information Security Evaluation and Management of IT Systems in Educational Institutions

Security evaluation and management has become increasingly important for Web-based information technology(IT)systems,especially for educational institutions.For the security evaluation and management of IT systems in educational institutions,determining the security level for a single IT system has been well developed.However,it is still dificult to evaluate the information security level of the entire educational institution consid-ering multiple IT systems,because there might be too many different IT systems in one institution,educational institutions can be very different,and there is no standard model or method to provide a just ifable information security evaluation among different educational inst itutions considering their differences.In light of these difi-culties,a security evaluation model of educational institutions'IT systems(SEMEIS)is proposed in this work to facilitate the information security management for the educat ional institutions.Firstly,a simplified educational industry information system security level protection rating(EIISSLPR)with a new weight redistribution strategy for a single IT systern is proposed by choosing important evaluation questions from EIISSLPR and redistributing the weights of these questions.Then for the entire educational institution,analytic hierarchy process(AHP)is used to redistribute the weights of multiple IT systems at different security levels.considering the risk of pos-sible network security vulnerabilities,a risk index is forulated by weighting different factors,normalized by a utility function,and calculated with the real data collected from the institutions under the evaluation.Finally,the information security performance of educational institutions is obtained as the final score from SEMEIS.The results show that SEMEIS can evaluate the security level of the educat ion institutions practically and provide an efficient and effective management tool for the information security management.

A Sentiment Analysis Based Model for Recruitment by Higher Institutions

The traditional roles of a university are teaching and research with the aim of developing society and contributing positively to the national economic development by producing skilled and well-tutored graduates. However, recruitments by these higher institutions are too reliant on the eligibility provided by Resumes of candidates, while neglecting their suitability drawn from their research activity and publications online. This study identifies insights in recruitment trends in higher institutions of learning and uses Artificial Intelligence to produce a more rounded and balanced decision-making process that caters for both eligibility and suitability. The methodology employs the machine learning process using the Multinomial Naïve Bayes for training the model as well as the Vader sentiment analyzer for accuracy and testing. The datasets used contained Resume instances as well as author publication information. The results show a score of 83.9% for the model as well as a sentiment analysis score of 1, indicating an overall positive score. The results show that sentiment analysis can help educational institutions in improving their recruitment models and attracting more suitable candidates for such roles.

Exploring University Campus Community’s Perceptions and Barriers toward Biking: A Case Study of the University of Tennessee Knoxville

Bicycling constitutes an integral component of sustainable non-motorized means of travel. Despite the immense benefits of bicycling, the modal share of bicycling is quite low, specifically among young and low-income commuters e.g., college and university students. This study explores the perceptions of and barriers to bicycling of the campus community at the University of Tennessee, Knoxville with the aim to identify the factors that could enhance bike use among students at the university campus as well as highlight the barriers that keep young population away from biking. An online survey about bike use at the university campus was administered and responses to the survey were recorded. On the methodological front, a binary logit model was estimated with students’ willingness to use bikes at campus as the dependent variable. The survey results revealed that about 47% of the students considered vehicular traffic as the reason for feeling unsafe while biking and emphasized having separate bike routes. Most of the respondents highlighted the need to change clothes after biking in summer or carry things as the reason for not selecting bikes as a mode of transport at the campus, whereas inadequate bike lanes, paths, or trails, insufficient lighting along bike routes, and the lack of adequate bicycle parking were highlighted as the major barriers deterring bike use within the campus. The study suggests development of a campus bike network with proper bike lanes, bike runnels, installation of safety cameras and better lighting at the campus, and bike-supporting infrastructure such as communal showers to be provided in campus buildings to encourage bike use among university students. The study findings can assist campus transportation planners in devising a sustainable campus transportation plan incorporating the influencing factors and deterrents of bike use in a university campus setting.

Determinants of Satisfaction at Work and Its Reflections on Performance

This study examines the relationship between job satisfaction and performance,investigating personality traits and satisfaction aspects among employees of a Federal Higher Education Institution.A questionnaire was administered to 658 participants,using structural equation modeling for analysis.Results highlighted that challenging work,neuroticism,and self-esteem significantly influenced overall workplace satisfaction,while general satisfaction,self-efficacy,and lack of attention were key determinants of work performance.This emphasizes the importance for managers to prioritize factors enhancing employee satisfaction,as it positively correlates with job performance.

Threefold Optimized Forecasting of Electricity Consumption in Higher Education Institutions

Energy management benefits both consumers and utility companiesalike. Utility companies remain interested in identifying and reducing energywaste and theft, whereas consumers’ interest remain in lowering their energyexpenses. A large supply-demand gap of over 6 GW exists in Pakistan asreported in 2018. Reducing this gap from the supply side is an expensiveand complex task. However, efficient energy management and distributionon demand side has potential to reduce this gap economically. Electricityload forecasting models are increasingly used by energy managers in takingreal-time tactical decisions to ensure efficient use of resources. Advancementin Machine-learning (ML) technology has enabled accurate forecasting ofelectricity consumption. However, the impact of computation cost affordedby these ML models is often ignored in favour of accuracy. This studyconsiders both accuracy and computation cost as concurrently significantfactors because together they shape the technology environment as well ascreate economic impact. Thus, a three-fold optimized load forecasting modelis proposed which includes (1) application specific parameters selection, (2)impact of different dataset granularities and (3) implementation of specificdata preparation. It deploys and compares the widely used back-propagationArtificial Neural Network (ANN) and Random Forest (RF) models for theprediction of electricity consumption of buildings within a university. In addition to the temporal and historical power consumption date as input parameters, the study also embeds weather data as well as university operationalcalendars resulting in improved performance. The outcomes are indicativethat the granularity i.e. the scale of details in data, and set of reduced and fullinput parameters impact performance accuracies differently for ANN and RFmodels. Experimental results show that overall RF model performed betterboth in terms of accuracy as well as computational time for a 1-min, 15-minand 1-h dataset granularities with the mean absolute percentage error (MAPE)of 2.42, 3.70 and 4.62 in 11.1 s, 1.14 s and 0.3 s respectively, thus well suitedfor a real-time energy monitoring application.

Optimizing University Faculty Teams: Addressing the Talent Mobility Challenges in the Context of “Double First-Class” Initiative

With the advancement of the“Double First-Class”initiative,universities are increasingly focusing on attracting top-tier talent to enhance faculty quality and strengthen academic disciplines.The blind competition for high-end talent among universities reflects an overshadowing of academic values by economic considerations,neglecting the growth patterns of top talent and the development laws of higher education.This study investigates and reflects on the real-world challenges of faculty mobility in universities,aiming to provide effective guidance for managing university faculty teams.

Image Instrument Construct Validation of University Institutions

The main purpose of this study was to determine the validation of construct and reliability of the image instrument in educational institutions. A non-random sample selection of 314 high school students from Montemorelos, Nuevo Leon was made. An instrument with 25 statements was created to measure the image of The University of Montemorelos （reliability a = 0.956）. The methodologies that were utilized in the validation were factorial analysis and confirmatory analysis, with the latter made with structural equations. In the factorial analysis, it was observed that the statements were grouped into four dimensions, as it was initially planned. With the exception of one item, all the other statements were grouped in the proposed dimension. In the confirmatory validation utilizing structural equations, all the selected criteria were fulfilled.

A Tailor-made Data Quality Approach for Higher Educational Data

Purpose: This paper relates the definition of data quality procedures for knowledge organizations such as Higher Education Institutions. The main purpose is to present the flexible approach developed for monitoring the data quality of the European Tertiary Education Register(ETER) database, illustrating its functioning and highlighting the main challenges that still have to be faced in this domain.Design/methodology/approach: The proposed data quality methodology is based on two kinds of checks, one to assess the consistency of cross-sectional data and the other to evaluate the stability of multiannual data. This methodology has an operational and empirical orientation. This means that the proposed checks do not assume any theoretical distribution for the determination of the threshold parameters that identify potential outliers, inconsistencies, and errors in the data. Findings: We show that the proposed cross-sectional checks and multiannual checks are helpful to identify outliers, extreme observations and to detect ontological inconsistencies not described in the available meta-data. For this reason, they may be a useful complement to integrate the processing of the available information.Research limitations: The coverage of the study is limited to European Higher Education Institutions. The cross-sectional and multiannual checks are not yet completely integrated.Practical implications: The consideration of the quality of the available data and information is important to enhance data quality-aware empirical investigations, highlighting problems, and areas where to invest for improving the coverage and interoperability of data in future data collection initiatives.Originality/value: The data-driven quality checks proposed in this paper may be useful as a reference for building and monitoring the data quality of new databases or of existing databases available for other countries or systems characterized by high heterogeneity and complexity of the units of analysis without relying on pre-specified theoretical distributions.

Sustainability in Higher Education Institutions(HEI):Merging the Study Systematic Review,Analysis Content and Bibliometrics

This study aims to explore how the topic of sustainability in Higher Education Institutions(HEI)has been addressed in the scientific literature.It presents a systematic literature review of peer-reviewed journal articles published between 1975 and 2019.After that,bibliometric and content analyses were performed in order to identify the behavior and evolution of the literature and identify the frequency with which they appear in the text,making it possible to raise replicable and valid inferences to the data.The results of this study indicate that the theme is emerging since the year 2010 and with a strong focus on qualitative studies,focused on critical theory.The results indicate that although studies involve the tripod environment,economy,and society,there is still a gap in studies that deal with specific issues to solve practical problems and that analyze the impacts from a more holistic perspective.The combination of methods made it possible to identify and interpret the articles in the selected sample,but based on results reported by other authors,with little room for quality control and integrity.

Artificial Intelligence Techniques Based Learner Authentication in Cybersecurity Higher Education Institutions

Education 4.0 is being authorized more and more by the design of artificial intelligence(AI)techniques.Higher education institutions(HEI)have started to utilize Internet technologies to improve the quality of the service and boost knowledge.Due to the unavailability of information technology(IT)infrastructures,HEI is vulnerable to cyberattacks.Biometric authentication can be used to authenticate a person based on biological features such as face,fingerprint,iris,and so on.This study designs a novel search and rescue optimization with deep learning based learning authentication technique for cybersecurity in higher education institutions,named SRODLLAC technique.The proposed SRODL-LAC technique aims to authenticate the learner/student in HEI using fingerprint biometrics.Besides,the SRODLLACtechnique designs a median filtering(MF)based preprocessing approach to improving the quality of the image.In addition,the Densely Connected Networks(DenseNet-77)model is applied for the extraction of features.Moreover,search and rescue optimization(SRO)algorithm with deep neural network(DNN)model is utilized for the classification process.Lastly,template matching process is done for fingerprint identification.A wide range of simulation analyses is carried out and the results are inspected under several aspects.The experimental results reported the effective performance of the SRODL-LAC technique over the other methodologies.

Enhanced Artificial Intelligence-based Cybersecurity Intrusion Detection for Higher Education Institutions

As higher education institutions(HEIs)go online,several benefits are attained,and also it is vulnerable to several kinds of attacks.To accomplish security,this paper presents artificial intelligence based cybersecurity intrusion detection models to accomplish security.The incorporation of the strategies into business is a tendency among several distinct industries,comprising education,have recognized as game changer.Consequently,the HEIs are highly related to the requirement and knowledge of the learner,making the education procedure highly effective.Thus,artificial intelligence(AI)and machine learning(ML)models have shown significant interest in HEIs.This study designs a novel Artificial Intelligence based Cybersecurity Intrusion Detection Model for Higher Education Institutions named AICIDHEI technique.The goal of the AICID-HEI technique is to determine the occurrence of distinct kinds of intrusions in higher education institutes.The AICID-HEI technique encompassesmin-max normalization approach to preprocess the data.Besides,the AICID-HEI technique involves the design of improved differential evolution algorithm based feature selection(IDEA-FS)technique is applied to choose the feature subsets.Moreover,the bidirectional long short-term memory(BiLSTM)model is utilized for the detection and classification of intrusions in the network.Furthermore,the Adam optimizer is applied for hyperparameter tuning to properly adjust the hyperparameters in higher educational institutions.In order to validate the experimental results of the proposed AICID-HEI technique,the simulation results of the AICIDHEI technique take place by the use of benchmark dataset.The experimental results reported the betterment of the AICID-HEI technique over the other methods interms of different measures.

Management of the Higher Education Institution in the Ethical Actions Context

Managing the university in the context of ethics is one of many elements in the management of this specific organization. The task of university is functioning based on universal values, mission, vision, ethical codes, and responsibility and worthy behavior towards internal and external environment. In the era of globalization and constant changes, only a modernly managed higher education institution, acting according to principles of ethics, is able to meet requirements of surroundings. Behaviors of academic community should promote ethical actions in higher education institutions. It should be noted that the reputation, prestige in higher education, should also be based on the proceedings consistent with ethics.

The Comparison of Ten Biggest Distance Education Institutions in the World

The 1995 International Council of Distance Education（ICDE）In Blrmingham，Britain selected ten（llstance education mstltutlons with an enrollment of over 100，000Inthe world． Theyrespectively belong to China，阶ance，India，Indonesia，

A Study on the Management of Career Development of Young Teachers in Higher Education Institutions from the Perspective of Career Anchors

In January 2021,six departments in China,including the Ministry of Education jointly issued the Guiding Opinions on Strengthening Reform in HEI Teacher Capacity Building for the New Era which emphasizes support for career development of young teachers.From the perspective of the career anchor theory,this paper takes human resource management as a tool to analyze organizational environment at the macro and micro levels to find out how to facilitate career development of young teachers in higher education institutions.

Randomized Controlled Trials and Ph.D. Level Training in Educational Research

This invited paper reviews recent initiatives in teaching about randomized field trials at the graduate level in education research. Reports in a book on randomized trials by Mosteller and Boruch are used as a benchmark. The initiatives have been driven heavily by recent governmental emphasis on ＂evidence based policy＂ in education, criminology, welfare and other sectors. This policy has been backed, in the US at least, by substantial investments in mounting randomized controlled trials to evaluate curriculum packages and programs in grades K-12, crime prevention programs, welfare, and other work. Accelerated growth in randomized trials has entailed cross discipline pre-doctoral and post-doctoral institutes and graduate education programs aimed at enhancing the quality in trial design, execution, and analysis of results. Specific illustrations of graduate education in this area of statistics are taken mainly from experience at the University of Pennsylvania＇s Graduate School of Education and selected other institutions.

Coping Strategies of Nigeria Higher Education Institutions during COVID-19 Lockdown

Beginning at the end of the year 2019,the novel coronavirus(COVID-19)has drowned the world into uncertainty and has threatened the continuity of traditional classroom learning that is the backbone and,in many cases,the main method of learning in most countries.This paper seeks to ascertain how Higher Institutions of learning in Nigeria have thrived during the lockdown period,based on the National policy document that spells out the guidelines for school reopening.Official documents such as newspaper reports university bulletins are used to corroborate in order to ascertain the implementation of the provisions of the policy guideline document as well as assess the efficacy of the measures on students’quality of learning.

Equal Education Opportunities Call for Institutional Equality

The report to the 17th Communist Party of China （CPC） National Congress called for efforts to enable all people to have places to learn and put it in the first place of the five major issues concerning the livelihood of the people. To enable everyone to have places to learn is not only the goal of education but also the goal of promoting social equity and justice. It has endowed every member of the society with the opportunities and rights to receive education on an equal footing. It does not only enable all children to have equal opportunities to receive public education but also provide every citizen with the conditions for lifelong learning.

Expedition to the blockchain application potential for higher education institutions

In the education sector,blockchain is currently at the end of the peak of inflated expectations in Gartner’s Hype Cycle.Thus,it is crucial to understand whether this technology meets the expectations of Higher Education Institutions(HEIs).We go on an expedition to identify blockchain application scenarios and its potential for HEI administration-the universities are digitalized to just 23.3%.Current information systems research addresses classifications of blockchain-based projects(application level)rather than their technical realization(protocol level).Thus,when evaluating blockchain application scenarios in HEI administration,we intensively consider the technical side of blockchain-based projects.We perform a threestep approach:(1)systematic literature review,(2)qualitative exploratory semi-structured interviews to sup-plement information on market-ready solutions,and(3)an evaluation of the potential of the blockchain-based projects identified,based on HEI administration requirements.We find that the leading blockchain application scenarios are credential verification and record-sharing.At the protocol level,we obtain equivocal results regarding the technical realization of projects,e.g.,their underlying blockchain types and storage models.At the application level,when discussing the potential of different projects,we find that most of them address adaptability,complexity decomposition,and cost reduction requirements between HEIs;interest diversity and stakeholder collaboration between HEIs and business actors;privacy and trust between HEIs and students.