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The purpose of this paper is to describe a novel method to compromise between planned (regulated) maintenance and outage initiation and unplanned (unregulated) maintenance and to find an economic model using which one can perform maintenance adequately and in the most optimal state.

In this paper, a system consisting of similar components is considered, and the role of each component in the system is explained. Then, the cost pertaining to failure in each asset is determined. Costs such as energy not supplied, penalties, human resources to resolve the defect and replacing assets are taken into account. Finally, a new comprehensive objective is proposed, and optimization is performed for a sample system.

In this paper, some graphs have been plotted from which plenty of information may be extracted. This is mentioned in the Conclusion.

In this paper, some graphs have been plotted from which plenty of information may be extracted. This is mentioned in the Conclusion.

Underreporting of new tuberculosis (TB) cases is one of the main problems in TB control, particularly in countries with high incidence and dominating role of a private sector in TB cases diagnosing. The purpose of this paper was to explore behavioral determinants of underreporting of new TB cases among private sector physicians in Iran.

The authors conducted a population-based, cross-sectional study of physicians working in private clinics. The data collection tool was designed using the theory of planned behavior (TPB). The authors used structural equation models with maximum likelihood estimation to examine attitude toward the notification behavior.

Of 519 physicians, 433 physicians completed the questionnaire. Attitude toward notification had the highest score (mean score = 87.65; sd = 6.79; range: 0–100). The effect of perceived behavioral controls on the notification behavior ((β^) = 0.13; CI: 0.01–0.25) was stronger than the total effect of attitude ((β^) = 0.06; CI: 0.00–0.12) and subjective norms ((β^) = 0.01; CI: −0.00–0.03) on the behavior. However, the attitude was the main predictor of intention and justified 46% of the intention variance. Intention had a significant effect on the behavior ((ß^) = 0.09; CI: 0.1–0.16).

Considering stronger effect of perceived behavioral control on the behavior, interventions aiming at facilitating notification process would be more effective than those aiming at changing the attitude or enhancing intention among physicians.

To the best of our knowledge, no other study previously explored determinants of underreporting from the behavioral and cognitive perspective. Specifically, the authors explored the role of the TPB constructs in predicting intention to notify new TB cases.

As far as the authors know, no research has already been carried out on the multi-floor dynamic facility layout problem (MF-DFLP) in the continuous form regarding the flexible bay structure, the number and the variable location of the elevator. Therefore, the present paper models the given problem and attempts to find a sub-optimal solution for it using a meta-heuristic simulated annealing (SA) algorithm.

The efficient use of resources has always been a prominent matter for decision-makers. Many reasons including land use, construction considerations and proximity of departments have led to the design of multi-floor facilities. On the other hand, their fast-evolving environment calls for dynamic planning. Therefore, in this paper, a model and the SA algorithm for MF-DFLP are presented.

After presenting a mathematical model, the problem was solved precisely in a small size using the GAMS software. Also, a near-optimal solution method using a SA meta-heuristic algorithm is suggested and the proposed algorithm was run in the MATLAB software. To evaluate the presented model and the proposed solution, some test cases were considered in two aspects. The first aspect was the test cases that are newly generated in small, medium and large sizes to compare the exact optimal solution with the results of the meta-heuristic algorithm. Eight test cases with small sizes were solved using the GAMS software, the optimum solutions were obtained in a reasonable time, and the cost of their solutions was equal to that of the SA algorithm. Eight test cases with medium sizes were run in the GAMS software with the time limit of 80,000 s, and the SA algorithm had performed better for these test cases. Two test cases were also considered in large size that GAMS could not solve them, whereas the SA algorithm successfully found a proper solution for each. The second aspect included the test cases from the literature. The result showed that suggested algorithm is more capable of finding best solutions than compared algorithms.

In this paper, an unequal area MF-DFLP was studied in a continuous layout form in which the location and number of the elevators were considered to be variable, and the layouts were considered with flexible bay structure. These conditions were investigated for the first time.

This study aims to use an environomics method to assess the environmental impacts of selected gas turbine power plants in Nigeria.

In this study, exergoenvironomic analysis has been carried out to investigate the environmental impact of selected gas turbine power plants in Nigeria from an exergetic point of view.

The exergy analysis reveals that the combustion chamber is the most exergy destructive component compared to other cycle components. The exergy destruction of this component can be reduced by increasing gas turbine inlet temperature (GTIT). The results of the study show that thermodynamic inefficiency is responsible for the environmental impact associated with gas turbine components. The study further shows that CO₂ emissions and cost of environmental impact decrease with increasing GTIT.

The exergo-environomic parameters computed in this study are CO₂ emission in kg per MWh of electricity generated, depletion number, sustainability index, cost flow rate of environmental impacts (Ċ_env) in $/h and total cost rates of products (Ċ_Tot) in $/hr. For the period considered, the CO₂ emissions for the selected plants vary from 100.18 to 408.78 kgCO₂/MWhm, while cost flow rate of environmental impacts varies from $40.18 /h to $276.97 /h and the total cost rates of products vary from $2935.69/h to $12,232.84/h. The depletion number and sustainability index vary from 0.69 to 0.84 and 1.20 to 1.44, respectively.

The sensitivity of orifice plate metering to poorly conditioned and swirling flows are subjects of concerns to flow meter users and manufacturers. The distortions caused by pipe fittings and pipe installations upstream of the orifice plate are major sources of this type of non‐standard flows. These distortions will alter the accuracy of metering up to an unacceptable degree.Design/methodology/approach – The design of orifice plate meters that are independent of the initial flow conditions of the upstream is a major object of flow metering. Either using a long straight pipe or a flow conditioner upstream of an orifice plate usually achieves this goal. The effect of cone swirler flow conditioner for both standard and non‐standard flow conditions has been carried out in the experimental rig. The measuring of mass flow rate under different conditions and different Reynolds numbers were used to establish a change in discharge coefficient relative to a standard one.Findings – The experimental results using the cone swirler flow conditioner showed that the combination of an orifice plate and cone swirler flow conditioner is broadly insensitive to upstream disturbances. The results clearly show that this flow conditioner can attenuate the effect of both swirling and asymmetric flows on metering to an acceptable level.Originality/value – Previous work on the orifice plate has shown that the concept has promise. The results of using a combination of a cone swirler and orifice plate for non‐standard flow conditions including swirling flow and asymmetric flow show this package can preserve the accuracy of metering up to the level required in the standards, providing that a new discharge coefficient is used for the combined swirler and orifice plate.

This work aims to determine the best linear model using an artificial neural network (ANN) with the imperialist competitive algorithm (ICA-ANN) and ANN to predict the energy consumption for land leveling.

Using ANN, integrating artificial neural network and imperialist competitive algorithm (ICA-ANN) and sensitivity analysis (SA) can lead to a noticeable improvement in the environment. In this research, effects of various soil properties such as embankment volume, soil compressibility factor, specific gravity, moisture content, slope, sand per cent and soil swelling index on energy consumption were investigated.

According to the results, 10-8-3-1, 10-8-2-5-1, 10-5-8-10-1 and 10-6-4-1 multilayer perceptron network structures were chosen as the best arrangements and were trained using the Levenberg–Marquardt method as the network training function. Sensitivity analysis revealed that only three variables, namely, density, soil compressibility factor and cut-fill volume (V), had the highest sensitivity on the output parameters, including labor energy, fuel energy, total machinery cost and total machinery energy. Based on the results, ICA-ANN had a better performance in the prediction of output parameters in comparison with conventional methods such as ANN or particle swarm optimization (PSO)-ANN. Statistical factors of root mean square error (RMSE) and correlation coefficient (R²) illustrate the superiority of ICA-ANN over other methods by values of about 0.02 and 0.99, respectively.

A limited number of research studies related to energy consumption in land leveling have been done on energy as a function of volume of excavation and embankment. However, in this research, energy and cost of land leveling are shown to be functions of all the properties of the land, including the slope, coefficient of swelling, density of the soil, soil moisture and special weight dirt. Therefore, the authors believe that this paper contains new and significant information adequate for justifying publication in an international journal.

This study aims at enhancing the performance of a 16-stage axial compressor and improving the operating stability. The adopted approaches for upgrading the compressor are artificial neural network, optimization algorithms and computational fluid dynamics.

The process starts with developing several data sets for certain 2D sections by means of training several artificial neural networks (ANNs) as surrogate models. Afterward, the trained ANNs are applied to the 3D shape optimization along with parametrization of the blade stacking line. Specifying the significant design parameters, a wide range of geometrical variations are considered by implementation of appropriate number of design variables. The optimized shapes are analyzed by applying computational fluid dynamic to obtain the best geometry.

3D optimal results show improvements, especially in the case of decreasing or elimination of near walls corner separations. In addition, in comparison with the base geometry, numerical optimization shows an increase of 1.15 per cent in total isentropic efficiency in the first four stages, which results in 0.6 per cent improvement for the whole compressor, even while keeping the rest of the stages unchanged. To evaluate the numerical results, experimental data are compared with obtained data from simulation. Based on the results, the highest absolute relative deviation between experimental and numerical static pressure is approximately 7.5 per cent.

The blades geometry of an axial compressor used in a heavy-duty gas turbine is optimized by applying artificial neural network, and the results are compared with the base geometry numerically and experimentally.

The purpose of this paper is to present a vision-based method for the kinematic calibration of a six-degrees-of-freedom parallel robot named Hexa using only one Universal Serial Bus (USB) camera and a chess pattern installed on the robot's mobile platform. Such an approach avoids using any internal sensors or complex three-dimensional measurement systems to obtain the pose (position/orientation) of the robot's end-effector or the joint coordinates.

The setup of the proposed method is very simple; only one USB camera connected to a laptop computer is needed and no contact with the robot is necessary during the calibration procedure. For camera modeling, a pinhole model is used; it is then modified by considering some distortion coefficients. Intrinsic and extrinsic parameters and the distortion coefficients are found by an offline minimization algorithm. The chess pattern makes image corner detection very straightforward; this detection leads to finding the camera and then the kinematic parameters. To carry out the calibration procedure, several trajectories are run (the results of two of them are presented here) and sufficient specifications of the poses (positions/orientations) are calculated to find the kinematic parameters of the robot. Experimental results obtained when applying the calibration procedure on a Hexa parallel robot show that vision-based kinematic calibration yields enhanced and efficient positioning accuracy. After successful calibration and addition of an appropriate control scheme, the robot has been considered as a color-painting prototype robot to serve in relevant industries.

Experimental results obtained when applying the calibration procedure on a Hexa parallel robot show that vision-based kinematic calibration yields enhanced and efficient positioning accuracy.

The enhanced results show the advantages of this method in comparison with the previous calibration methods.

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.