Performance and planning model for HEIS: A developmental engine for intellectual capital

BACKGROUND: HEIs implement different techniques for the problem of assessing the progress towards the notified goals or the achievements, which is the only way to ensure that the HEIs are functioning properly along the right way or not. OBJECTIVE: The aim of this paper is to propose a model for assessing the performance, which may be of interest to institutions, partners, society and students. METHODS: In this paper, the technique of performance-related goals of HEIs, and a statistical approach are proposed. The methodology of this paper will manage briefly the issues of the applicability of the technique, how to derive the “goals” for HEI’s, taking into consideration the indicators of the Intellectual Capital (Human, Organizational, and Relational), and how to collect the data from HEIs successfully. The processes of assuring the technique of performance-related goals, deriving and implementing the goals, and the acquisition and testing of data are developed. RESULTS: The findings of this paper are in two directions, i.e. in the proposing of a performance model for HEIs which is based on the goals of HEIs; and in the application side, i.e. in the practical results of the evaluation of HEIs and how to clarify the performance of institutions and how to compare between them. The findings show that the practical results are significant, and reliable. CONCLUSIONS: This paper develops a significance model for performance appraisal of HEIs, how we can compare between the performance of HEIs, and to rank their performance. The practical results of the model are both accurate and significant.

The Effect of Education on Information Systems Success

Research into the relationship between education and information systems (IS) success (use, satisfaction, and impact) has produced mixed results. Such results seem counterintuitive, given the many benefits that education brings to the workplace. However, workplace research from Human Resources (HR) has similarly found that education has little direct effect on job performance. Instead, education has indirect effects on job performance through job expertise, which is what drives behavior and job performance. The present research integrated the Delone & McLean IS Success Model with the Job Performance Model, and found similar results: in a survey of 465 professionals working in business analytics (BA), user education level had no direct effect on IS success (BA tool use, satisfaction, and impact). Instead, education level had a positive effect on expertise with the BA tool, which in turn positively affected BA tool use. These results build upon those from HR, and suggest that education has an indirect effect on IS success, rather than a direct effect.

Reconciling Authenticity and Reenactment

Locating authenticity in artworks that are remade (all or in part) or re-performed over time presents a unique challenge for art conservators, whose activities have traditionally been oriented toward caring for the material aspects of art objects. The paper offers a brief overview of perspectives on authenticity and discusses various theoretical models that have been developed to conceptualize how media, installation, and performance artworks are displayed and cared for over time. These include the score/performance model, the concepts of autographicity and allographicity, the concept of iteration, and authenticity as a practice. The author proposes a theoretical model based on the ritual aspects of presenting artworks, arguing that authenticity, repetition, and community participation can be reconciled within a ritual context.

An Automated Geometric Appraisal Model

The idea of the circular economy is gaining steam in academia through the green economics of human resources policies in Asia. A high performance model delivers superior outcomes but does not deter praiseworthy professors from biased human interference. Using the computerized-based geometric performance base incentive model (CGPBI) is particularly beneficial in encouraging faculty who have achieved superior outcomes in all areas of science, academia, and other contributions. To fill this gap, the author proposed a geometric hybrid reward policy model that includes a number of fictitious variables such as topic tolerance, the effects of subject matter, and the average outcome across all subjects. This model uses Python to construct a standardized framework to gather data on the success of faculty. It provides a robust indication of comparative success and motivates workers to achieve more transparent performance outcomes. The author proposes the use of a multi-source assessment (MSA) to evaluate the faculty's annual results.

Improved Deep Learning Model Based on Self-Paced Learning for Multiscale Short-Term Electricity Load Forecasting

Electricity loads are basic and important information for power generation facilities and traders, especially in terms of production plans, daily operations, unit commitments, and economic dispatches. Short-term load forecasting (STLF), which predicts power loads for a few days, plays a vital role in the reliable, safe, and efficient operation of a power system. Currently, two main challenges are faced by existing STLF prediction models. The first involves how to fuse multiscale electricity load data to obtain a high-performance model and remove data noise after integration. The second involves how to improve the local optimal solution despite the sample quality problem. To address the above issues, this paper proposes a multiscale electricity load data fusion- and STLF-based short time series prediction model built on a sparse deep autoencoder and self-paced learning (SPL). A sparse deep autoencoder was used to solve the multiscale data fusion problem with data noise. Furthermore, SPL was utilized to solve the local optimal solution problem. The experimental results showed that our model was better than the existing STLF prediction models by more than 15.89% in terms of the mean squared error (MSE) indicator.

Numerical analysis of safety distances of reinforced concrete structures subjected to blast loading by Perform-3D

Purpose The purpose of this study is to develop the performance model of buildings designed by the seismic code 2800 against the explosion wave and determination of safety distance. Design/methodology/approach Analytical models of three-, five- and ten story structures that used moment frame system and also a ten-storey building with shaer wall designed based on the seismic code 2800 in term of design and nonlinear analysis were generated for use with Perform-3D software. Extensive parametric analysis is executed on different explosive loads with 100, 500, 1,000 and 5,000 Trinitrotoluene, soil types 2 and 3, models eqs and eqbs, the number of story buildings and the effect of shear wall to determine the safety distance based on collapse threshold performance (CP) level criterion. Findings The results indicate that by increasing the explosives mass from 100 to 5,000 kg and the number of the stories three and five induce increasing the safety distance of CP level in buildings to 4.5 meter and 3 meter times, respectively. Ten-story structures modeled on shear wall show very good performance because of stiffness rising and high energy absorption. In addition, by increasing the stories from five to ten, the amount of the safety distance reduces the CP level to 3.9 meter times. Originality/value The results of this work are meaningful for explosion-resistant design and damage assessments of reinforced concrete moment framed structures subjected to explosive explosion.