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The purpose of this paper is to examine the advantages of developing a simulation model of the activities of a university library, in order to balance the available resources with demand for services. Meeting the demand is an essential part of managing a modern library. Inadequate levels of resources will result in excessive waiting times for the patrons.

A simulation modeling approach is undertaken and the library activities of the University of Tennessee at Chattanooga (UTC) are simulated. Simulation modeling is an invaluable tool to understand the resource usage patterns and making intelligent budgeting choices. In the epoch of severe budget cuts, available financial resources must be carefully allocated where there is a critical need. Such a simulation modeling of the critical operations of an academic library of a campus upward of 11,000 students is presented in this study.

A simulation model of the arrivals, flow, and usage patterns of library resources by the patrons described in this research, determined that there were four critical resources: the number of reference librarians, the number of checkout assistants, the number of computers, and the number of electronic gadgets. This study illustrates the need for careful analysis of arrival of patrons, resource levels, and resource usage with the objective of developing a resource level decision strategy that will result in an optimal level of resource usage by patrons.

With the application of simulation models, it is possible to determine the needs for the limited resources at the library and coordinate the availability of the resources with the needs.

Research in the use of simulation models for the determination of the resource levels that are best suited for an anticipated level of patron arrivals in the libraries is limited at best. This research adds value by providing information to fill this dearth of literature. The results of the simulation provided in this study can be used to develop budget priorities and to validate those priorities.

This study seeks to understand the connection of methodology by finding relevant papers and their full review using the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA).

Concrete-filled steel tubular (CFST) columns have gained popularity in construction in recent decades as they offer the benefit of constituent materials and cost-effectiveness. Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Gene Expression Programming (GEP) and Decision Trees (DTs) are some of the approaches that have been widely used in recent decades in structural engineering to construct predictive models, resulting in effective and accurate decision making. Despite the fact that there are numerous research studies on the various parameters that influence the axial compression capacity (ACC) of CFST columns, there is no systematic review of these Machine Learning methods.

The implications of a variety of structural characteristics on machine learning performance parameters are addressed and reviewed. The comparison analysis of current design codes and machine learning tools to predict the performance of CFST columns is summarized. The discussion results indicate that machine learning tools better understand complex datasets and intricate testing designs.

This study examines machine learning techniques for forecasting the axial bearing capacity of concrete-filled steel tubular (CFST) columns. This paper also highlights the drawbacks of utilizing existing techniques to build CFST columns, and the benefits of Machine Learning approaches over them. This article attempts to introduce beginners and experienced professionals to various research trajectories.

The purpose of this study is to analyze the influence of Islamic spirituality toward organizational citizenship behavior from Islamic perspective (OCBIP), influence Islamic spirituality toward OCBIP in which spirituality at work and organizational commitment become mediators and influence of OCBIP toward working performance.

The study was categorized as an explanatory research. The setting of the study was the branch office of BRI Syariah in Malang. The population was employees of the branch office. The samples were 217 employees of BRI Syariah Malang. With an estimated population of 193 individuals and a 5 per cent confidence level, the total samples were 150 employees, including the supervisors. The sampling technique was proportional random sampling, in which all members of the population have an equal chance to become a sample based on proportion per section (Sekaran, 2003). The data were primary data obtained through questionnaires. The questionnaire consisted of question items on Islamic spirituality, workplace spirituality, organizational commitment and OCBIP. The data analysis technique was partial least squares (PLS).

Islamic spirituality is not directly influencing toward OCBIP, spirituality at work and organizational commitment as moderation variables in the influence of Islamic spirituality toward OCB IP, OCBIP had influence toward working performance. Higher OCBIP would result in better working performance accepted, and at the opposite, lower OCBIP would result in poorer working performance.

There are some limitations of previous studies that examine spirituality relationships with OCB. Nasrudin et al. (2013) and Kazemipour et al. (2012) found significant correlations between spirituality with OCB, but there is an inconsistency of research findings to suggest that spirituality has no direct effect on OCB, but through individual perceptions of organization. As the study of spirituality with OCB is still limited, this study attempts to explain OCB from an Islamic perspective, to propose a framework on Islamic spirituality, spirituality at work as an individual source of OCB and moderation of organizational commitment using Djafri and Noordin’s (2017) and previous empirical studies, with an aim to integrate the spirituality and OCB in a model that can be used to better understand OCB. It is hoped that this model development will reduce the scarcity of literature on spirituality with OCB through organizational commitment. This will help the organization to understand the role of spirituality and organizational commitment to improve OCB of employees that ultimately will improve organizational performance.

The purpose of this paper is to develop an appropriate approach for detecting unbalanced fault in rotating machines using KNN and SVM classifiers.

To fulfil this goal, a fault diagnosis approach based on signal processing, feature extraction and fault classification, was used. Vibration signals were acquired from a designed experimental system with three conditions, namely, no load, balanced load and unbalanced load. FFT technique was applied to transform the vibration signals from time-domain into frequency-domain. In total, 29 feature parameters were extracted from FFT amplitude of the signals. SVM and KNN were employed to classify the three different conditions. The performances of the two classifiers were obtained under different values of their parameter.

The experimental results show the potential application of SVM for machine fault diagnosis.

The results demonstrate that the proposed approach can be used effectively for detecting unbalanced condition in rotating machines.

In this paper, an intelligent approach for unbalanced fault detection was proposed based on supervised learning method. Also, a performance comparison was made between KNN and SVM in fault classification. In addition, this approach gave a high level of classification accuracy. The proposed intelligent approach can be used for other mechanical faults.

This study was intended to investigate a small-scale School-based Motivational Interviewing (SBMI) pilot with first-year college students. This approach honors student autonomy, supports self-determination and has the potential to impact educational outcomes in higher education. Motivational Interviewing (MI) is an evidence based conversational skill set, defined as “a collaborative conversational style for strengthening a person's own motivation and commitment to change” (Miller and Rollnick, 2013, p. 12). Student perceptions of satisfaction with the faculty-student mentoring intervention were sought. Relational aspects of MI (partnership, empathy and alliance) were also explored.

A mixed-method approach was used for the SBMI study, focused on college students with recent academic setbacks (N = 19).

The intervention was deployed with high levels of MI technical fidelity and relational quality. Participants reported high satisfaction with the intervention. The relational aspects and participant perceived alliance with their faculty were highly correlated across the intervention, adding to the discussion of the mechanisms of MI that contribute to its effectiveness.

This work is formative, yet at this point is not generalizable given the scope of the study.

Findings are encouraging for further development of this innovative pedagogical approach. Possible future applications of research are provided.

Discussed herein, SBMI has the potential to meet the needs of traditionally underrepresented student groups.

The reported study is the initial portion of a larger intervention development project.

To amend the efficiency of engineering processes and electronic devices, it is very urgent to assess the irreversibility in the term entropy generation (EG). The efficiency of energy transportation in a system can be improved by minimization of the rate of EG. In this context, the aim of the present study is to estimate irreversible losses of an unsteady magnetohydrodynamic (MHD) flow of a viscous incompressible electrically conducting non-Newtonian molybdenum disulfide-polyethylene glycol Casson nanofluid past a moving vertical plate with slip condition under the influence of Hall current, thermal radiation, internal heat generation/absorption and first-order chemical reaction. Molybdenum disulfide (MoS₂) nanoparticles are dispersed in the base fluid polyethylene glycol (PEG) to make Casson nanofluid. Casson fluid model is considered to characterize the rheology of the non-Newtonian fluid, whereas Rosseland approximation is adopted to simulate the thermal radiative heat flux in the energy equation.

The closed-form solutions are obtained for the model equations by using the Laplace transform method (LTM). Graphs and tables are prepared to examine the impact of pertinent flow parameters on the pertinent flow characteristics. The energy efficiency of the system via the Bejan number is studied extensively.

Analysis reveals that Hall current has diminishing behavior on entropy production of the thermal system. Strengthening of the magnetic field declines the velocity components and prop-ups the rate of EG. Adding nanoparticles into the base fluid reduces the EG, whereas there are an optimum volume fraction of nanoparticles for which the EG is minimized. Further, the rate of decay of EG is prominent in molybdenum disulfide-polyethylene glycol in comparison to PEG.

The results of this study would benefit the industrial sector in achieving the maximum heat transfer at the cost of minimum irreversibilities with an optimal choice of embedded thermophysical parameters. In view of this agenda, this study would be adjuvant in powder technology, polymer dynamics, metallurgical process, manufacturing dynamics of nano-polymers, petroleum industries, chemical industries, magnetic field control of material processing, synthesis of smart polymers, etc.

The novelty of this study is to encompass the analytical solution by using the LTM. Such an exact solution of non-Newtonian fluid flow is rare in the literature. Limited research articles are available in the field of EG analysis during the flow of non-Newtonian nanoliquid subject to a strong magnetic field.

This research sought to propose a theoretical model that analyzes the factors associated with unlearning (individual and organizational) and contributes to generating and realizing ideas among young people at the beginning of their careers based on the predominant type of structure.

The study had a sample (n = 971) and used the multivariate data analysis partial least squares - Structural Equation Modeling (PLS-SEM regular) and multigroup analysis (PLS-MGA) to identify significant differences between the estimates of the specific parameters of each group (a- Organic/b- Mechanistic).

All the direct relationships and formulated mediations were found to be supported, except for H6 (ET→EO) within the group that had a primarily mechanistic organizational structure. Thus, the more turbulent the environmental, the more initiative-taking, innovative and risk-taking a company tends to be. However, it remains to be seen whether the organizational structure plays a role in facilitating or hindering this relationship. H1 (IG→IR) indicates that predominantly organic organizations have a stronger and more consistent relationship with the knowledge developed through individual and organizational unlearning process. This knowledge contributes to the idea-generation process and ultimately leads to realizing those ideas.

The article contributes to literature by proposing an original and integrated theoretical model incorporating individual and organizational approaches to unlearning to understand the effect on idea generation and realization.

Because of the appreciable application of heat recovery systems for the increment of overall efficiency of micro gas turbines, promising evaluation and optimization are crucial. This paper aims to propose a multi-factor theoretical methodology for analysis, optimization and comparison of potential plate-fin recuperators incorporated into micro gas turbines. Energetic, exergetic, economic and environmental factors are covered.

To demonstrate applicability and reliability of the methodology, detailed thermo-hydraulic analysis, sensitivity analysis and optimization are conducted on the recuperators with louver and offset-strip fins using a genetic algorithm. To assess the relationship between investment cost and profit for the recuperated systems, payback period (PBP), which incorporates all the factors is used as the universal objective function. To compare the performance of the recuperated and non-recuperated systems, exergy efficiency, exergy destruction and corresponding cost rate, fuel consumption and environmental damage cost rates, capital and operational cost rates and acquired profit rates are determined.

Based on the results, optimal PBP of the louvered-fin recuperator (147 days) is slightly lower than that with offset-strip fins (153 days). The highest profit rate is acquired by reduction of exergy destruction cost rate and corresponding decrements for louver and offset-strip fins are 2.3 and 3.9 times compared to simple cycle, respectively.

This mathematical study, for the first time, focuses on introducing a reliable methodology, which covers energetic, exergetic, economic and environmental points of view beneficial for design and selection of efficient plate-fin recuperators for micro gas turbine applications.