An Entrepreneurial Business Model for Personal Branding: Proposing a Framework

Purpose- Personal branding has become a mature field of research; however, there are many ques-tions to be answered yet. One of the most important questions is that what does a typical entrepre-neurial business model look like? Design/methodology/approach- To answer the above question, the present paper tries to use meta-analysis approach to making an integrated view of the extant literature. Thus, 25 papers, which were mainly focusing on the topic, was selected and critically reviewed. Finally, a framework is proposed based on Osterwalder's (2004) approach. Findings- Findings of this research are mainly focused on the characteristics of each dimension in the entrepreneurial business model for personal branding. Research limitations/implications- Research implications imply that a typical entrepreneurial busi-ness model for personal branding might be helpful for both entrepreneurs as well as policy makers. Research paper Reference to this paper should be made as follows: Raftari, M. and Amiri, B. (2014). ―An Entrepre-neurial Business Model for Personal Branding: Proposing a Framework‖, Journal of Entrepreneur-ship, Business and Economics, Vol. 2, No. 2, pp. 121–139

Characteristics of continuous unidirectional kenaf fiber reinforced epoxy composites

Kenaf fibers generally has some advantages such as eco-friendly, biodegradability, renewable nature and lighter than synthetic fibers. The aims of the study are to characterize and evaluate the physical and mechanical properties of continuous unidirectional kenaf fiber epoxy composites with various fiber volume fractions. The composites materials and sampling were prepared in the laboratory by using the hand lay-up method with a proper fabricating procedure and quality control. Samples were prepared based on ASTM: D3039-08 for tensile test and the scanning electron microscopy (SEM) was employed for microstructure analysis to observe the failure mechanisms in the fracture planes. A total of 40 samples were tested for the study. Results from the study showed that the rule of mixture (ROM) analytical model has a close agreement to predict the physical and tensile properties of unidirectional kenaf fiber reinforced epoxy composites. It was also observed that the tensile strength, tensile modulus, ultimate strain and Poisson's ratio of 40% fiber volume content of unidirectional kenaf fiber epoxy composite were 164. MPa, 18150. MPa, 0.9% and 0.32, respectively. Due to the test results, increasing the fiber volume fraction in the composite caused the increment in the tensile modulus and reduction in the ultimate tensile strain of composite

Evaluation of kaolin slurry properties treated with cement

Kaolin clay has features high compressibility and also very low strength. Stabilization methods are normally used to improve the mechanical and chemical characteristics of cohesive soil. This study has examined the kaolin properties treated with cement using the unconfined compression strength (UCS) test, direct shear test, and constant rate of strain (CRS) consolidation test. The strength characteristics of kaolin mixed with cement have been investigated using the UCS test and direct shear test. Then the consolidation behaviour of this treated soil was evaluated by performing the constant rate of strain (CRS) consolidation test. The selected cement content range was 5%, 7.5%, 10%, 12.5% and 15%. Water content was used at twice the liquid limit of kaolin in order to produce a homogeneous and workable sample to be placed inside a curing mould. All the samples were cured for 12 days. Based on the UCS results, it was found that the increment of the cement content led to an increase in unconfined shear strength and elasticity modulus of the improved soil and it also caused the water content to decrease after curing. Although the internal friction angle is not considered in saturated clay soils, this experimental result shows that it can be improved by raising the amount of cement. The results of the CRS test indicated a decrease in the slope of the void ratio curve with an increase in cement content. In addition, the variations of void ratio are augmented by the increase of cement content in a constant effective stress

A predictive model based on an optimized ANN combined with ICA for predicting the stability of slopes

In this study, we optimized artificial neural network (ANN) with imperialist competition algorithm (ICA) for the problem of slope stability design charts. To prepare training and testing datasets for the ANN and ICA–ANN predictive models, an extensive number of limit equilibrium analysis modelings (e.g., for the lower bound, LB, limit analysis and upper bound, UB, limit analysis) was conducted. The analyses were conducted using OptumG2 computer software and implemented on two-layered cohesive soil layer sets. For each of the LB and UB limit analysis, the database consisted of 320 training datasets and 80 testing datasets. Variables of the ICA algorithm such as the number of countries, the number of initial imperialists and the number of decades were optimized using a series of trial-and-error process. The input parameters that used thorough the OptumG2 finite element modeling (FEM) analysis include depth factor (i.e., the ratio of first soil layer thickness to the slope height), slope angle, undrained shear strength ratio where the output was taken dimensionless stability number. The estimated results for both of datasets (e.g., training and testing) from ANN and ICA–ANN models were assessed based on three known statistical indices namely value account for (VAF), root means squared error (RMSE), and coefficient of determination (R2). To evaluate the performance of proposed models, color intensity rating (CER) and total ranking method (TRM), i.e., based on the result of statistical indices, was used. After 72 trial-and-error processes (e.g., sensitivity analysis on some neurons) the optimal architecture of 3 × 6 × 1 were found for both of the ANN–UB and ANN–LB models. As a result, both models presented excellent performance, however according to the introduced ranking system the ICA–ANN model could slightly perform a better performance compared to ANN. Based on R2, RMSE and VAF values of (0.9999, 0.0107 and 99.9924) and (0.9991, 0.0102 and 99.9913), respectively, were found for training and testing of the optimized ICA–ANN–LB predictive model. Similarly, for the ICA–ANN–UB predictive model, values of (0.9984, 0.0129 and 99.9659) and (0.9984, 0.01047 and 99.9915) were obtained for the R2, RMSE and VAF of training and testing datasets, respectively. However, in the ANN model, the R2 and RMSE for both of the training and testing datasets were (0.9982 and 0.01815) and (0.9972 and 0.01748), respectively. This proves a better performance of the ICA–ANN model in predicting the behaviors of slope stability of cohesive soils and consequently more reliable design solution charts provided herein

Optimization of ANFIS with GA and PSO estimating α ratio in driven piles

This study aimed to optimize Adaptive Neuro-Fuzzy Inferences System (ANFIS) with two optimization algorithms, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for the calculation friction capacity ratio (α) in driven shafts. Various studies are shown that both ANFIS are valuable methods for prediction of engineering problems. However, optimizing ANFIS with GA and PSO has not been used in the area of pile engineering. The training data set was collected from available full-scale results of the driven piles. The input parameters used in this study were pile diameter (m), pile length (m), relative density (Id), embedment ratio (L/D), both of the pile end resistance (qc) and base resistance at relatively 10% base settlement (qb0.1) from CPT result, whereas the output was α. A learning fuzzy-based algorithm was used to train the ANFIS model in the MATLAB software. The system was optimized by changing the number of clusters in the FIS and then the output was used for the GA and PSO optimization algorithm. The prediction was compared with the real-monitoring field data. As a result, good agreement was attained representing reliability of all proposed models. The estimated results for the collected database were assessed based on several statistical indices such as R2, RMSE, and VAF. According to R2, RMSE, and VAF, values of (0.9439, 0.0123 and 99.91), (0.9872, 0.0117 and 99.99), and (0.9605, 0.0119 and 99.97) were obtained for testing data sets of the optimized ANFIS, GA–ANFIS, and PSO–ANFIS predictive models, respectively. This indicates higher reliability of the optimized GA–ANFIS model in estimating α ratio in driven shafts

Development of a new consolidation test for soil cement columns treated ground

In this study, Medium Rapid Consolidation Equipment (MRCE) was developed to determine the settlement behaviour of soft ground treated by soil-cement columns. MRCE operates based on Constant Rate Strain (CRS) consolidation theory, which is a continuous loading method of testing that can accelerate the consolidation process of cohesive soil as compared to the conventional Oedometer and Rowe cell tests. Since the stiffness of soil-cement columns increases with time, the CRS concept is the best method to determine the settlement at a particular time. Several modifications were made on the MRCE including the introduction of a back pressure system to further saturate the soil and measurement of excess pore water pressure. Four tests with varied values of the area of improvement were carried out using the MRCE to investigate settlement behaviour of stabilized soft soil. It was found that by increasing the area of improvements ranging from 15.3% to 30.7%, the strain rates improved from 41% to 53% compared to untreated soil. To conclude, the MRCE was able to quantify the consolidation characteristics of the stabilized soil with varying values of the area of improvements into a comparable settlement reduction factor