A generalized fuzzy Multiple-Layer NDEA: An application to performance-based budgeting

Network data envelopment analysis (NDEA) is capable of considering operations and interdependence of a system’s component processes to measure efficiencies. There are numerous performance evaluation applications in which some indicators have hierarchical structures with a considerable number of sub-indicators. This problem of ignoring the hierarchical structure of indicators weakens the discrimination power of NDEA models and may result in inaccurate efficiency scores. In this paper we propose a generalized fuzzy Multiple-Layer NDEA (GFML-NDEA) model and GFML-NDEA-based composite indicators (GFML-NDEA-CI) to incorporate the hierarchical structures of indicators in the ambit of the particular two-stage NDEA models. To demonstrate the usefulness of the GFMLNDEA-CI model proposed, its application was tested by evaluating the efficiency of the performance-based budgeting (PBB) system in 14 governmental agencies in Iran. The comparative analysis results obtained from the GFML-NDEA-CI (multi-layer) model with those from the single-layer fuzzy NDEA-CI model indicate that the number of efficient decision-making units (DMUs) in the one-layer model is eight, whereas it is solely one DMU in the multi-layer model. The discrimination power of the multi-layer model proposed is significantly increased by observing that standard deviation of efficiency scores are increased by 41%, 61%, and 84% for possibility levels 0, 0.5, and 1, respectively. This is obtained while reducing information entropy, thus suggesting that the proposed model yields more reliable scores

Designing, Proposing and Comparing the Methods Predicting the Compressive Strength of the Ferro cement Mortar

Ferro cement is a commonly used type of concrete. As the Ferro cement mortar is one of its components, and different parameters affect its compressive strength, prediction of the compressive strength of Ferro cement mortar before construction can seriously affect its properties and costs.In this study, DIN and ACI methods were used to examine the mix design of Ferro cement mortar. To this end, an equation was proposed for prediction of a 28-day strength based on the weight of cement, water, fine aggregate, nano silica, micro silica, and slump value. Results of previous researcher in this regard were evaluated to verify the equation and the designed mix designs.Results showed that the prediction based on the proposed equation had more favorable standard deviation and less error than did other equations and statistical methods besides its applicability for the mix design of all types of cement mortars

An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector

The transportation sector is deemed one of the primary sources of energy consumption and greenhouse gases throughout the world. To realise and design sustainable transport, it is imperative to comprehend relationships and evaluate interactions among a set of variables, which may influence transport energy consumption and CO2 emissions. Unlike recent published papers, this study strives to achieve a balance between machine learning (ML) model accuracy and model interpretability using the Shapley additive explanation (SHAP) method for forecasting the energy consumption and CO2 emissions in the UK's transportation sector. To this end, this paper proposes an interpretable multi-stage forecasting framework to simultaneously maximise the ML model accuracy and determine the relationship between the predictions and the influential variables by revealing the contribution of each variable to the predictions. For the UK's transportation sector, the experimental results indicate that road carbon intensity is found to be the most contributing variable to both energy consumption and CO2 emissions predictions. Unlike other studies, population and GDP per capita are found to be uninfluential variables. The proposed multi-stage forecasting framework may assist policymakers in making more informed energy decisions and establishing more accurate investment

Designing and Solving Location-Routing-Allocation Problems in a Sustainable Blood Supply Chain Network of Blood Transport in Uncertainty Conditions

Purpose: In this paper, a location-routing-allocation problem in a multi-objective blood supply chain network was designed to reduce the total cost of the supply chain network, the maximum unmet demand from distribution of goods, and decline greenhouse gas emissions due to the transport of goods among different levels of the network. The network levels considered for modeling include blood donation clusters, permanent and temporary blood transfusion centers, major laboratory centers and blood supply points. Other objectives included determining the optimal number and location of potential facilities, optimal allocation of the flow of goods between the selected facilities and determining the most suitable transport route to distribute the goods to customer areas in uncertainty conditions. Methodology: Given that the model was NP-hard, the NSGA II and MOPSO algorithms were used to solve the model with a priority-based solution. Findings: The results of the design of the experiments showed the high efficiency of the NSGA II algorithm in comparison with the MOPSO algorithm in finding efficient solutions. Originality/Value: This study addresses the issue of blood perishability from blood sampling to distribution to customer demand areas

Optimizing Compressive Strength of Micro- and Nano-silica Concrete by Statistical Method

In recent years, the use of nano-particles to improve the properties of concrete has created a new perspective on concrete technology. Studies in this field indicate improved concrete properties and higher strength by adding nano and micro silica particles to concrete mixes. In this regard, 12 mixing designs with different amounts of these admixtures with three types of cement strength classes (525,425,325) and 36 cubic samples (10 × 10 × 10) were designed and tested to measure compressive strength, of which we have only used 6 mixing plans in this research. The purpose of this research is to present a new method for concrete mix design by optimizing principles. Therefore, in this paper, the Taguchi statistical methods and the factorial design of the optimal mixing plan for this type of concrete are used to reduce the number of experiments to predict the optimal composition of the materials. The results obtained from the MINITAB software show that the effect of combined micro-silica and nano-silica on the compressive strength is in one direction and the effect of these two factors is more than cement strength grade of the cement and also the optimal value for micro-silica and nano-silica are estimated to have an optimum amount of micro-silica and nano-silica of 95 and 38 grams, respectively

The effect of aerobic exercise after exposure to black carbon particulate matter 2.5 on expression of NF-κB and TNF-α genes in the lung tissue of male rats

Background: The aim of the present study was to investigate the effect of aerobic exercise after exposure to black carbon particulate matter 2.5 (PM 2.5) on expression of NF-κB, and TNF-α genes in the lung tissue of male rats. Materials and Methods: Twenty-four male Wistar rats (mean weight, 279.29±26.97 g) were randomly divided into four equal groups: 1) control (without any intervention), 2) without aerobic exercise and with exposure to PM2.5 black carbon, 3) aerobic exercise without exposure to black carbon, and 4) aerobic exercise after exposure to PM2.5 black carbon. The aerobic exercise protocol was performed at 50 the maximal speed of each group for 4 weeks 15 minutes after exposure to black carbon particles in the chamber. Animals were sacrificed 24 hours after the last training session and exposure to black carbon particles. The expression of NF-κB and TNF-α genes were analyzed in the lung tissue of rats by the real-time PCR. Results: A significant difference was found in the NF-κB expression between the group 1 and other groups and also there was a significant difference in the TNF-α expression between the group 2 and group 3 after exposure to black carbon in the lung tissue of male rats. Conclusion: The findings show that aerobic exercise after exposure to the black carbon particles (PM2.5) is associated with adjustment in lung inflammatory factors. It seems that these changes in part are associated with a lower risk of inflammatory pulmonary disease

Effect of Main Factors on Fracture Mode of Mortar, A Graphical Study

One of the most effective ways to identify the concrete properties is to understand further about the cement mortar, which is a mixture of cement paste and fine aggregate. In order to identify the behavior of cement mortar, all required materials including cement, fine aggregate, water as well as the different ratios of each material should beinvestigated. The main objective of this research is to study the effectiveness of main parameters of mortar on the fracture mode and related factors. Specifically 26 mixing designs of flexural mortar with three cement strength classes (32.5, 42.5 and 52.5 MPa), three water to cement (W/C) ratios (0.25, 0.3 and 0.35) and three sand to cement (S/C) ratios (2.5, 2.75 and 3) were first prepared. The prepared samples were then tested using a stress-strain apparatus. Some pictures were finally taken from the fracture surfaces to investigate the mode and angle of fractures. The results indicated that any change in the main parameters of mortar changes the fracture mode and the fracture angle

Prediction of Mortar Compressive Strengths for Different Cement Grades in the Vicinity of Sodium Chloride Using ANN

AbstractThe compressive strength values of cement mortar usually affect by sodium chloride quantities, chemical admixtures and cement grades so that an artificial neural network model was performed to predict the compressive strength of mortar value for different cement grades and sodium chloride (NaCl) percent. A three layer feed forward artificial neural network (ANN) model having four input neurons such as cement grades, various water to cement ratio, sodium chloride solution content, one output neuron and five hidden neurons was developed to predict of mortar each compressive strength.To this aim, twelve different mixes under three sodium chloride solution of 0%, 5% and 10% submerged after 60 days has been adopted to measure compressive strength.Artificial neural network (ANN) analysis indicated that by using ANN as non-linear statistical data modeling tool, a strong correlation between the sodium chloride percent of cement mortar and compressive strength can be established. Moreover modeling tools has great influence on the different cement grade such as 42.5 and 32.5 MPa

Lifetime Analysis on Centrifugal ID Fan Foundation in Cement Plants

The dynamic behavior of centrifugal ID fan foundation of cement plants to perform the lifetime analysis was investigated in this study. To this end, a three-dimensional (3D) finite-element model (FEM) of this foundation was proposed and the analyses under static and dynamic loads for various modulus of elasticity of concrete (Ec= 20, 25, 28 and 30 GPa) and reinforcement (Es= 200, 250, 300 GPa) and rotor speeds of 400, 800, 1200, and 1800 rounds per minute (rpm) were conducted.The results show that the stress and deflection for a certain ID fan is around 2.5 and 10 times more under dynamic loading compared to static loading. Therefore, the excessive vibrations induced by increased speed of ID fan can result in reduced lifetime and fatigue of the foundation. Comparison of FEM results with laboratory-based evidence verified the accuracy of simulations made by the FEM. The proposed model can thus be applied to determine the critical points in foundation structure in order to obtain optimum results of design parameters

Quantitative evaluation of human sperm viability using MTT assay: A laboratory study

Background: 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyl tetrazolium bromide (MTT) assay which evaluates cellular mitochondrial activity is widely used for the assessment of cell proliferation and viability. Objective: This study was performed to assess human sperm viability using MTT assay. Materials and Methods: In this laboratory study, human-ejaculated semen samples (n = 56 from different donors) were used. The sperm viability was determined using quantitative MTT assay and the sperm motility was assessed according to World Health Organization guidelines. Sperm viability and the correlation between sperm viability and motility were analyzed. Results: Data revealed a marked positive correlation between MTT reduction rate and the percentage of viable spermatozoa. The Pearson’s correlation coefficients also showed a significant correlation between sperm viability and motility. Conclusion: MTT assay which is based on mitochondrial functionality is a reliable method for evaluating human sperm viability and could be used as a diagnostic test for predicting sperm fertilization ability in clinical settings. Key words: Human sperm, Viability, MTT assay