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.

Plan the Introduction of a TQM System in an Education Establishment

The successful of total quality management(TQM) apply in industry have prompted people to think about the usage of total quality management in education.This is a challenge for education system to accept a new system in able to improve themselves,and also it is a new stage for them going to a new education quality control era.In education system,the productivity of teacher could be enhanced,teachers and students may find enjoyable in academic life,and leaving students positive contribution in society gain reputation for education institutions.

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.

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.

New Features and Trends of Higher Education during the Epidemic Period

The epidemic that ravaged the world has had impact on every element of human endeavors,including education.This devastation was evident in the speed with which several countries'ministries of education shut down school activities and halted academic sessions.In the wake of the epidemic,several trends and features has emerged in the higher education sector as higher education institutions tries to create methods to offset the disruptive impacts of the COVID-19 epidemic on their activities and operations.The trends that will shape the future of the higher education industry are not new.However,the epidemic on the other hand has hastened their progress.It has become evident over the last year what the post-epidemic higher education institutions will look like.The rapid speed of change that has already begun will continue.It is in tandem to this that this paper identifies and discusses some of the emerging trends and new features of higher education in the epidemic period.

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,

A financial perspective:categories of higher education institutions

From a financial perspective,the criteria for category distinction of higher education institutions should be based on the ownership of institution property and income for recurrent expenditure.The development of modern higher education witnessed the period wherein higher education institutions have both private property and private payment for recurrent expenditure.The development also saw the period wherein all the institution properties were owned by the state and all the recurrent expenditure were paid by the government.Accordingly,universities could be divided into two categories of“public”and“private”institutions until the 1970s.However,things have been changing greatly over the past 20 years.Property ownership and payment for recurrent expenditure have been separated.The public institutions appeal to more and more individuals sharing recurrent expenditure,while private institutions ask for more and more public financial support.Therefore,some financially mixed institutions came into being and“three categories with sub-groups”has become a new phenomenon.

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.