Search results for: Supplier Performance Prediction

Authors: Maryam Mirzaei, Sinisa Krajnovic´

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The paper reports a study about the prediction of flows around simplified vehicles using Partially-Averaged Navier-Stokes (PANS). Numerical simulations are performed for two simplified vehicles: A slanted-back Ahmed body at Re=30 000 and a square back Ahmed body at Re=300 000. A comparison of the resolved and modeled physical flow scales is made with corresponding LES and experimental data for a better understanding of the performance of the PANS model. The PANS model is compared for coarse and fine grid resolutions and it is indicated that even a coarse-grid PANS simulation is able to produce fairly close flow predictions to those from a well-resolved LES simulation. The results indicate the possibility of improvement of the predictions by employing a finer grid resolution.

Keywords: partially-averaged Navier-Stokes, large eddy simulation, PANS, LES, Ahmed body

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Authors: I. Alhajri, G. Zahedi, R. Alazmi, A. Akbari

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In this paper, an Artificial Neural Network (ANN) was developed to predict Asphaltene Precipitation (AP) during the injection of carbon dioxide into crude oil reservoirs. In this study, the experimental data from six different oil fields were collected. Seventy percent of the data was used to develop the ANN model, and different ANN architectures were examined. A network with the Trainlm training algorithm was found to be the best network to estimate the AP. To check the validity of the proposed model, the model was used to predict the AP for the thirty percent of the data that was unevaluated. The Mean Square Error (MSE) of the prediction was 0.0018, which confirms the excellent prediction capability of the proposed model. In the second part of this study, the ANN model predictions were compared with modified Hirschberg model predictions. The ANN was found to provide more accurate estimates compared to the modified Hirschberg model. Finally, the proposed model was employed to examine the effect of different operating parameters during gas injection on the AP. It was found that the AP is mostly sensitive to the reservoir temperature. Furthermore, the carbon dioxide concentration in liquid phase increases the AP.

Keywords: artificial neural network, asphaltene, CO2 injection, Hirschberg model, oil reservoirs

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Authors: Arjita Biswas, Sandeep Potnis

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Plastic roads (bituminous roads using waste plastic in the wearing course ) have now become familiar in the Road Construction Sector in India. With the Indian Road Congress Code (IRC SP: 98 -2013), many agencies are coming forward to implement Plastic Roads in India. This paper discuss and compare about the various properties of bituminous mix with 8% waste plastic and normal bituminous mix. This paper also signifies the performance of both the types of roads after 4 months of age under loading conditions. Experiments were carried out to evaluate its performance. The result shows improved performance of plastic roads.

Keywords: bituminous roads, experiments, performance, plastic roads

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Authors: Kazeem K. Adewole

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This paper presents the finite element analysis numerical investigation of the effects of the location of across-the-width lamination on the tensile properties of rectangular wires for civil engineering applications. FE analysis revealed that the presence of the mid-thickness across-the-width lamination changes the cup and cone fracture shape exhibited by the lamination-free wire to a V-shaped fracture shape with an opening at the bottom/pointed end of the V-shape at the location of the mid-thickness across-the-width lamination. FE analysis also revealed that the presence of the mid-width across-the-thickness lamination changes the cup and cone fracture shape of the lamination-free wire without an opening to a cup and cone fracture shape with an opening at the location of the mid-width across-the-thickness lamination. The FE fracture behaviour prediction approach presented in this work serves as a tool for failure analysis of wires with lamination at different orientations which cannot be conducted experimentally.

Keywords: across-the-width lamination, tensile properties, lamination location, wire

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Authors: Kanchan Mondal, Dasharath Koulage, Dattatray Manerikar, Asmita Ghate

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This paper presents an additive reliability model using warranty data and Finite Element Analysis (FEA) data. Warranty data for any product gives insight to its underlying issues. This is often used by Reliability Engineers to build prediction model to forecast failure rate of parts. But there is one major limitation in using warranty data for prediction. Warranty periods constitute only a small fraction of total lifetime of a product, most of the time it covers only the infant mortality and useful life zone of a bathtub curve. Predicting with warranty data alone in these cases is not generally provide results with desired accuracy. Failure rate of a mechanical part is driven by random issues initially and wear-out or usage related issues at later stages of the lifetime. For better predictability of failure rate, one need to explore the failure rate behavior at wear out zone of a bathtub curve. Due to cost and time constraints, it is not always possible to test samples till failure, but FEA-Fatigue analysis can provide the failure rate behavior of a part much beyond warranty period in a quicker time and at lesser cost. In this work, the authors proposed an Additive Weibull Model, which make use of both warranty and FEA fatigue analysis data for predicting failure rates. It involves modeling of two data sets of a part, one with existing warranty claims and other with fatigue life data. Hazard rate base Weibull estimation has been used for the modeling the warranty data whereas S-N curved based Weibull parameter estimation is used for FEA data. Two separate Weibull models’ parameters are estimated and combined to form the proposed Additive Weibull Model for prediction.

Keywords: bathtub curve, fatigue, FEA, reliability, warranty, Weibull

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Authors: Rahul Paliwal, Gulshan Maheshwari, Anant S. Jhaveri, Channamallikarjun S. Mathpati

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Large pumping facility is the necessary requirement of the cooling water systems for power plants, process and manufacturing facilities, flood control and water or waste water treatment plant. With a large capacity of few hundred to 50,000 m3/hr, cares must be taken to ensure the uniform flow to the pump to limit vibration, flow induced cavitation and performance problems due to formation of air entrained vortex and swirl flow. Successful prediction of these phenomena requires numerical method and turbulence model to characterize the dynamics of these flows. In the past years, single phase shear stress transport (SST) Reynolds averaged Navier Stokes Models (like k-ε, k-ω and RSM) were used to predict the behavior of flow. Literature study showed that two phase model will be more accurate over single phase model. In this paper, a 3D geometries simulated using detached eddy simulation (LES) is used to predict the behavior of the fluid and the results are compared with experimental results. Effect of different grid structure and boundary condition is also studied. It is observed that two phase flow model can more accurately predict the mean flow and turbulence statistics compared to the steady SST model. These validate model will be used for further analysis of vortex structure in lab scale model to generate their frequency-plot and intensity at different location in the set-up. This study will help in minimizing the ill effect of vortex on pump performance.

Keywords: grid structure, pump intake, simulation, vibration, vortex

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Digitalisation in production technology is a driver for the application of machine learning methods. Through the application of predictive quality, the great potential for saving necessary quality control can be exploited through the data-based prediction of product quality and states. However, the serial use of machine learning applications is often prevented by various problems. Fluctuations occur in real production data sets, which are reflected in trends and systematic shifts over time. To counteract these problems, data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets to extract stable features. Successful process control of the target variables aims to centre the measured values around a mean and minimise variance. Competitive leaders claim to have mastered their processes. As a result, much of the real data has a relatively low variance. For the training of prediction models, the highest possible generalisability is required, which is at least made more difficult by this data availability. The implementation of a machine learning application can be interpreted as a production process. The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model with six phases that describes the life cycle of data science. As in any process, the costs to eliminate errors increase significantly with each advancing process phase. For the quality prediction of hydraulic test steps of directional control valves, the question arises in the initial phase whether a regression or a classification is more suitable. In the context of this work, the initial phase of the CRISP-DM, the business understanding, is critically compared for the use case at Bosch Rexroth with regard to regression and classification. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. Suitable methods for leakage volume flow regression and classification for inspection decision are applied. Impressively, classification is clearly superior to regression and achieves promising accuracies.

Keywords: classification, CRISP-DM, machine learning, predictive quality, regression

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Authors: Abdullah Karaman, Kursad Sayin

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In this research, which information technologies are used in small hotel businesses, and the information technologies-performance perception of the managers are pointed out. During the research, the questionnaire was prepared and the small scale hotel managers were interviewed face to face and they filled out the questionnaire and the answers acquired were evaluated. As the result of the research, it was obtained that the managers do not care much about the information technologies usage in practice even though they accepted that the information technologies are important in terms of performance.

Keywords: information technologies, managers, performance, small hotels

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Authors: Michael Ayodeji Boyede

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Studies have shown that workers’ job performance is very low in Nigeria, especially in the food and beverage industry. This trend had been partially attributed to low workers’ self-efficacy, poor self-knowledge, lack of empathy and poor psychological well-being. The descriptive survey design was adopted. Four factories were purposively selected from three states in Southwestern, Nigeria (Lagos, Ogun and Oyo States). Proportionate random sampling techniques were used in selecting 1,820 junior and supervisory cadre workers in Nestle Plc (369), Coca-Cola Plc (392), Cadbury Plc (443) and Nigeria Breweries (616). The five research instruments used were: Workers’ self-efficacy (r=0.81), Workers’ self-knowledge (r=0.78), Workers’ empathy (r=0.74), Workers’ psychological well-being (r=0.70) and Workers’ performance rating (r=0.72) scales. Quantitative data were analysed using Pearson product moment correlation, Multiple regression at 0.05 level of significance. Findings show that there were significant relationships between Workers’ job performance and self-efficacy (r=.56), self-knowledge (r=.54), Empathy (r=.55) and Psychological Well-being (r=.69) respectively. Self-efficacy, self-knowledge, empathy and psychological well-being jointly predict workers’ job performance (F (4,1815) = 491.05) accounting for 52.0% of its variance. Psychological well-being (B=.52). Self-efficacy (B=.10), self-knowledge (B=.11), empathy (B=. 09) had predictive relative weights on workers’ job performance. Inadequate knowledge and training of the supervisors led to a mismatch of workers thereby reducing workers’ job performance. High self-efficacy, empathy, psychological well-being and good self-knowledge influence workers job performance in the food and beverage industry. Based on the finding employers of labour should provide work environment that would enhance and promote the development of these factors among the workers.

Keywords: self-efficacy, self-knowledge, empathy, psychological well-being, job performance

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Authors: Ghulam Mustafa, Richard Glavee-Geo

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This study examined the relationship between traditional variables of organizational structure (formalization and centralization), employee work related self-efficacy and employee subjective performance. The study further explored the moderating role of formalization and centralization on the link between employee self-efficacy and job performance. Five hypotheses were tested using a sample of employees from a large public organization in Pakistan. The results indicated a significant positive relationship between employee self-efficacy and job performance. Regarding the direct effects of formalization and centralization on self-efficacy, the results showed that formalization relates positively while centralization has a negative impact on self-efficacy. However, the results revealed no empirical evidence to confirm the hypotheses that formalization and centralization strengthen or weaken the relationship between self-efficacy and job performance.

Keywords: centralization, formalization, job performance, self-efficacy

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Authors: J. Racedo, J. Torres

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Globalization has led to a worldwide competition for participation in markets of goods and productive factors, with significant effects on transports requirements. The port industry has not been an exception to this event, in fact, it has received increasing attention in recent years due to its crucial role on international trade. Because of this, the measurement of port performance has become an important issue in transport policy. Port performance and port efficiency has been widely studied in the last decades, resulting in noteworthy contributions to improving the industry competitiveness. In this paper, we aim to present a review of the literature on port performance and the relation between this concept and transport policies. This study has the objective to describe the approaches that have been developed in recent years, and especially those that include the modeling of public policies. Finally, we highlight existing gaps in this field, as well as possible directions for future research.

Keywords: port performance, port efficiency, transport, policy

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Authors: Uma Maheshwari V., Rajanikanth Aluvalu, Kumar Gautam

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The COVID-19 disease is a highly contagious viral infection with major worldwide health implications. The global economy suffers as a result of COVID. The spread of this pandemic disease can be slowed if positive patients are found early. COVID-19 disease prediction is beneficial for identifying patients' health problems that are at risk for COVID. Deep learning and machine learning algorithms for COVID prediction using X-rays have the potential to be extremely useful in solving the scarcity of doctors and clinicians in remote places. In this paper, a convolutional neural network (CNN) with deep layers is presented for recognizing COVID-19 patients using real-world datasets. We gathered around 6000 X-ray scan images from various sources and split them into two categories: normal and COVID-impacted. Our model examines chest X-ray images to recognize such patients. Because X-rays are commonly available and affordable, our findings show that X-ray analysis is effective in COVID diagnosis. The predictions performed well, with an average accuracy of 99% on training photographs and 88% on X-ray test images.

Keywords: deep CNN, COVID–19 analysis, feature extraction, feature map, accuracy

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Authors: Hiba Naccache

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The present research investigated whether gender differences affect performance in a simple math quiz in statistics course. Participants of this study comprised a sample of 567 statistics students in two different universities in Lebanon. Data were collected through a simple math quiz. Analysis of quantitative data indicated that there wasn’t a significant difference in math performance between males and females. The results suggest that improvements in student performance may depend on improved mastery of basic algebra especially for females. The implications of these findings and further recommendations were discussed.

Keywords: gender, education, math, statistics

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Authors: Novita Verayanti Manalu, Alvin Salim

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Background: Palliative care is caring holistically for patients and families to improve their quality of life. Experts stated that palliative care could be applied not only for terminally ill cases but also for acute illnesses. Therefore, this study wants to find out the level of knowledge about palliative care of the nurses along with the relationship with attitude and performance. Method: This study applies a cross-sectional survey design and allows the respondents to fill two questionnaires to determine the level of knowledge and attitude toward palliative care, while one questionnaire is filled out by the head nurse to evaluate nurses’ performance. The relationship was analyzed by Spearman rho’s correlation in alpha < 0,05 by SPSS. Results: The majority of respondents were females, aged above 25 years old, and married. Most of the nurses are staff nurses and the ratio of education level is not significantly different. The knowledge level is poor, while the attitude and performance are at an adequate level. Knowledge may affect attitude, but it doesn’t happen toward performance. Conclusion: There is a need for increased knowledge about palliative care to improve attitude and work performance. Future researchers might use this finding as a reference to conduct further study in improving knowledge of palliative care.

Keywords: knowledge, attitude, work performance, palliative care

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Authors: Abdulmalek Ahmed

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Formation pressure is the main function that affects drilling operation economically and efficiently. Knowing the pore pressure and the parameters that affect it will help to reduce the cost of drilling process. Many empirical models reported in the literature were used to calculate the formation pressure based on different parameters. Some of these models used only drilling parameters to estimate pore pressure. Other models predicted the formation pressure based on log data. All of these models required different trends such as normal or abnormal to predict the pore pressure. Few researchers applied artificial intelligence (AI) techniques to predict the formation pressure by only one method or a maximum of two methods of AI. The objective of this research is to predict the pore pressure based on both drilling parameters and log data namely; weight on bit, rotary speed, rate of penetration, mud weight, bulk density, porosity and delta sonic time. A real field data is used to predict the formation pressure using five different artificial intelligence (AI) methods such as; artificial neural networks (ANN), radial basis function (RBF), fuzzy logic (FL), support vector machine (SVM) and functional networks (FN). All AI tools were compared with different empirical models. AI methods estimated the formation pressure by a high accuracy (high correlation coefficient and low average absolute percentage error) and outperformed all previous. The advantage of the new technique is its simplicity, which represented from its estimation of pore pressure without the need of different trends as compared to other models which require a two different trend (normal or abnormal pressure). Moreover, by comparing the AI tools with each other, the results indicate that SVM has the advantage of pore pressure prediction by its fast processing speed and high performance (a high correlation coefficient of 0.997 and a low average absolute percentage error of 0.14%). In the end, a new empirical correlation for formation pressure was developed using ANN method that can estimate pore pressure with a high precision (correlation coefficient of 0.998 and average absolute percentage error of 0.17%).

Keywords: Artificial Intelligence (AI), Formation pressure, Artificial Neural Networks (ANN), Fuzzy Logic (FL), Support Vector Machine (SVM), Functional Networks (FN), Radial Basis Function (RBF)

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Authors: C. R. Danielle, S. Erik, T. Patrick, M. Hugh

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In the last decade there has been an increase in demand for fine grinding due to the depletion of coarse-grained orebodies and an increase of processing fine disseminated minerals and complex orebodies. These ores have provided new challenges in concentrator design because fine and ultra-fine grinding is required to achieve acceptable recovery rates. Therefore, the correct design of a grinding circuit is important for minimizing unit costs and increasing product quality. The use of ball mills for grinding in fine size ranges is inefficient and, therefore, vertical stirred grinding mills are becoming increasingly popular in the mineral processing industry due to its already known high energy efficiency. This work presents a hypothesis of a methodology to predict the product size distribution of a vertical stirred mill using a Bond ball mill. The Population Balance Model (PBM) was used to empirically analyze the performance of a vertical mill and a Bond ball mill. The breakage parameters obtained for both grinding mills are compared to determine the possibility of predicting the product size distribution of a vertical mill based on the results obtained from the Bond ball mill. The biggest advantage of this methodology is that most of the minerals processing laboratories already have a Bond ball mill to perform the tests suggested in this study. Preliminary results show the possibility of predicting the performance of a laboratory vertical stirred mill using a Bond ball mill.

Keywords: bond ball mill, population balance model, product size distribution, vertical stirred mill

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Authors: Ali Naghshineh, Ashutosh Bagchi

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Performance-based seismic design concepts are increasingly being adopted in various jurisdictions. While the National Building Code of Canada (NBCC) is not fully performance-based, it provides some features of a performance-based code, such as displacement control and objective-based solutions. Performance evaluation is an important part of a performance-based design. In this paper, the seismic performance of a set of code-designed 4, 8 and 12 story moment resisting concrete frames located in Victoria, BC, in the western part of Canada at different hazard levels namely, SLE (Service Level Event), DLE (Design Level Event) and MCE (Maximum Considered Event) has been studied. The seismic performance of these buildings has been evaluated based on FEMA 356 and ATC 72 procedures, and the nonlinear time history analysis. Pushover analysis has been used to investigate the different performance levels of these buildings and adjust their design based on the corresponding target displacements. Since pushover analysis ignores the higher mode effects, nonlinear dynamic time history using a set of ground motion records has been performed. Different types of ground motion records, such as crustal and subduction earthquake records have been used for the dynamic analysis to determine their effects. Results obtained from push over analysis on inter-story drift, displacement, shear and overturning moment are compared to those from the dynamic analysis.

Keywords: seismic performance., performance-based design, concrete moment resisting frame, crustal earthquakes, subduction earthquakes

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Authors: Shahrzad Zolfagharnassab, Abdul Rashid Mohamed Shariff, Siti Khairunniza Bejo

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Conventional methods for nutrient soil mapping are based on laboratory tests of samples that are obtained from surveys. The time and cost involved in gathering and analyzing soil samples are the reasons that researchers use Predictive Soil Mapping (PSM). PSM can be defined as the development of a numerical or statistical model of the relationship among environmental variables and soil properties, which is then applied to a geographic database to create a predictive map. Kriging is a group of geostatistical techniques to spatially interpolate point values at an unobserved location from observations of values at nearby locations. The main problem with using kriging as an interpolator is that it is excessively data-dependent and requires a large number of closely spaced data points. Hence, there is a need to minimize the number of data points without sacrificing the accuracy of the results. In this paper, an Artificial Neural Networks (ANN) scheme was used to predict macronutrient values at un-sampled points. ANN has become a popular tool for prediction as it eliminates certain difficulties in soil property prediction, such as non-linear relationships and non-normality. Back-propagation multilayer feed-forward network structures were used to predict nitrogen, phosphorous and potassium values in the soil of the study area. A limited number of samples were used in the training, validation and testing phases of ANN (pattern reconstruction structures) to classify soil properties and the trained network was used for prediction. The soil analysis results of samples collected from the soil survey of block C of Sawah Sempadan, Tanjung Karang rice irrigation project at Selangor of Malaysia were used. Soil maps were produced by the Kriging method using 236 samples (or values) that were a combination of actual values (obtained from real samples) and virtual values (neural network predicted values). For each macronutrient element, three types of maps were generated with 118 actual and 118 virtual values, 59 actual and 177 virtual values, and 30 actual and 206 virtual values, respectively. To evaluate the performance of the proposed method, for each macronutrient element, a base map using 236 actual samples and test maps using 118, 59 and 30 actual samples respectively produced by the Kriging method. A set of parameters was defined to measure the similarity of the maps that were generated with the proposed method, termed the sample reduction method. The results show that the maps that were generated through the sample reduction method were more accurate than the corresponding base maps produced through a smaller number of real samples. For example, nitrogen maps that were produced from 118, 59 and 30 real samples have 78%, 62%, 41% similarity, respectively with the base map (236 samples) and the sample reduction method increased similarity to 87%, 77%, 71%, respectively. Hence, this method can reduce the number of real samples and substitute ANN predictive samples to achieve the specified level of accuracy.

Keywords: artificial neural network, kriging, macro nutrient, pattern recognition, precision farming, soil mapping

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Authors: Seung Hwan Park, Cheng-Sool Park, Jun Seok Kim, Youngji Yoo, Daewoong An, Jun-Geol Baek

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Depending on the big data analysis becomes important, yield prediction using data from the semiconductor process is essential. In general, yield prediction and analysis of the causes of the failure are closely related. The purpose of this study is to analyze pattern affects the final test results using a die map based clustering. Many researches have been conducted using die data from the semiconductor test process. However, analysis has limitation as the test data is less directly related to the final test results. Therefore, this study proposes a framework for analysis through clustering using more detailed data than existing die data. This study consists of three phases. In the first phase, die map is created through fail bit data in each sub-area of die. In the second phase, clustering using map data is performed. And the third stage is to find patterns that affect final test result. Finally, the proposed three steps are applied to actual industrial data and experimental results showed the potential field application.

Keywords: die-map clustering, feature extraction, pattern recognition, semiconductor manufacturing process

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Authors: Sonia Arradaza-Pajaron

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English as a second language is widely spoken in the Philippines. In fact, it is used as a medium of instruction in school. However, Filipino students, in general, are still not proficient in the use of the language. Since it plays a very crucial role in the learning and comprehension of some subjects in the school where important key concepts and in English, it is imperative to look into other factors that may affect such concern. This study may post an answer to the said concern because it aimed to investigate the association between a psychological construct, known as emotional intelligence, and the English language performance of the 55 senior high school students. The study utilized a descriptive correlational method to determine the significant relationship of variables with preliminary data, like GPA in English subject as baseline information of their performance. Results revealed that the respondents had an average GPA in the English subject; however, improving from their first-year high school level to the fourth year. Their English performance resulted to an above average level with a notable higher performance in the speaking test than in the written. Further, a strong correlation between English performance and emotional intelligence was manifested. Based on the findings, it can be concluded that students with higher emotional intelligence their English language performance is expected to be the same. It can be said further that when students’ emotional intelligence (EI components) is facilitated well through various classroom activities, a better English performance would just be spontaneous among them.

Keywords: English language performance, emotional intelligence, EI components, emotional literacy, emotional quotient competence, emotional quotient outcomes, values and beliefs

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Authors: Chieh Lee

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Today, supply chain managers face extensive disruptions in raw material pricing, transportation block, and quality issue due to product complexity. While digitalization might help managers to mitigate the disruption risk and increase supply chain resilience by sharing information between sellers and buyers through the supply chain, entities are reluctant to build such a system. The main reason is it is not clear what information should be shared and who has access to the stored information. In this research, we propose a smart contract built by blockchain technology. This contract helps both buyer and seller to identify the type of information, the access to the information, and how to trace the information. This contract helps managers control their orders through the supply chain and address any disruption they see fit. Furthermore, with the same smart contract, the supplier can track the production process of an order and increase production efficiency by eliminating waste.

Keywords: blockchain, production process, smart contract, supply chain resilience

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Authors: Mohamed Aly, Aliaa Rehan Youssef, Emad Sawerees, Mounir Guirgis

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Relevance: Osteoarthritis (OA) is the most common degenerative disease seen in all populations. It causes disability and substantial socioeconomic burden. Evidence supports that exercise are the most effective conservative treatment for patients with OA. Therapists experience and clinical judgment play major role in exercise prescription and scientific evidence for this regard is lacking. The development of clinical prediction rules to identify patients who are most likely benefit from exercise may help solving this dilemma. Purpose: This study investigated whether body mass index and functional ability at baseline can predict patients’ response to a selected exercise program. Approach: Fifty-six patients, aged 35 to 65 years, completed an exercise program consisting of open kinetic chain strengthening and passive stretching exercises. The program was given for 3 sessions per week, 45 minutes per session, for 6 weeks Evaluation: At baseline and post treatment, pain severity was assessed using the numerical pain rating scale, whereas functional ability was being assessed by step test (ST), time up and go test (TUG) and 50 feet time walk test (50 FTW). After completing the program, global rate of change (GROC) score of greater than 4 was used to categorize patients as successful and non-successful. Thirty-eight patients (68%) had successful response to the intervention. Logistic regression showed that BMI and 50 FTW test were the only significant predictors. Based on the results, patients with BMI less than 34.71 kg/m2 and 50 FTW test less than 25.64 sec are 68% to 89% more likely to benefit from the exercise program. Conclusions: Clinicians should consider the described strengthening and flexibility exercise program for patents with BMI less than 34.7 Kg/m2 and 50 FTW faster than 25.6 seconds. The validity of these predictors should be investigated for other exercise.

Keywords: clinical prediction rule, knee osteoarthritis, physical therapy exercises, validity

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Authors: Yassir Mahmood, Nadia Ehsan

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In current research stream, empirical work regarding knowledge and motivation management along their dimensions is sparse. This study partially filled this void by investigating the influence of knowledge management (tacit and explicit) and motivation management (intrinsic and extrinsic) on firm performance with the mediating effects of innovative performance. Based on the quantitative research method, data were collected through questionnaire from 284 employees working in 18 different firms across the citrus industry located in Sargodha region (Pakistan). The proposed relationships were tested through regression analysis while mediation relations were analyzed through Barron and Kenny (1986) technique. The results suggested that knowledge management (KM) and motivation management (MM) have significant positive impacts on innovative performance (IP). In addition, the role of IP as full mediator between KM and firm performance (FP) is confirmed. Also, IP proved to be a partial mediator between MM and FP. From the managerial perspective, the findings of the study are vital as some of the important constituents of FP have been highlighted. The study produced important underpinnings for managers. In last, implications for policymakers along with future research directions are discussed.

Keywords: innovative performance, firm performance, knowledge management, motivation management, Sargodha

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Authors: Intan Waheedah Othman, Maslinawati Mohamad, Azizah Abdullah

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Few discussions were made on cooperative governance reforms despite the fact that cooperative movements operate and compete in an identical business environment as the private as well as the public corporations. Due to the scarcity of research examining the issue of governance among cooperatives, this paper is motivated to examine the extent of governance compliance and disclosure among cooperatives, hence the relationship between cooperative governance and its firm performance. Results from the study provide empirical evidence that disclosure on ownership structure and exercise of control rights was found to have significant negative relationship with cooperative firm performance.

Keywords: cooperative, governance, firm performance, Malaysia

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Authors: Ehsan Mehryaar

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The rate of penetration is (RoP) one of the vital factors in the cost and time of tunnel boring projects; therefore, predicting it can lead to a substantial increase in the efficiency of the project. RoP is heavily dependent geological properties of the project site and TBM properties. In this study, 151-point data from Queen’s water tunnel is collected, which includes unconfined compression strength, peak slope index, angle with weak planes, and distance between planes of weaknesses. Since the size of the data is small, it was observed that it is imbalanced. To solve that problem synthetic minority oversampling technique is utilized. The model based on the model tree is proposed, where each leaf consists of a support vector machine model. Proposed model performance is then compared to existing empirical equations in the literature.

Keywords: Model tree, SMOTE, rate of penetration, TBM(tunnel boring machine), SVM

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Authors: Stephen Pembi, Samuel K. Msheliza, Helen A. Andow

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Organizational culture is very important in the organization because it enhances organizational performance and serves as a sense of making and control mechanism that guides and shapes the attitude and behaviour of employees. However, organizational culture issues are frequently disregarded in lieu of activities that may or may not have a good impact on performance. This study examines the relationship between organizational culture and organizational performance of Adama Beverages Ltd, Adamawa State. The study employed an explanatory survey research design with a questionnaire as a source of data collection. One hundred and thirty-five copies of the questionnaire were administered using the convenience method of sampling, out of which one hundred and twenty were retrieved and well answered. The data collected were subjected to the Pearson product-moment correlation technique to test the hypotheses of the study using SPSS. The overall results signify that organizational culture has a significant positive relationship with organizational performance. The multiple regression results show that mission, adaptability, and involvement have a significant positive influence on organizational performance, while consistency has a significant negative influence on organizational performance. Therefore, this study concluded that organizational culture is a strong determinant of organizational performance in Adama Beverages Ltd, Adamawa State. The study recommends that the level of employee input into decision-making, flexibility in responding to changes in the business environment, consistency with values and traditions, and organizational performance should all be maintained by Adama Beverages Ltd.

Keywords: adaptability, consistency, involvement, mission, organizational performance

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Authors: Won Kyung Ham, Kang Hoon Cho, Sang C. Park

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Presented in this paper is a framework of a software ‘WEMax’. The WEMax is invented for analysis and simulation for manned assembly lines to sustain and improve performance of manufacturing systems. In a manufacturing system, performance, such as productivity, is a key of competitiveness for output products. However, the manned assembly lines are difficult to forecast performance, because human labors are not expectable factors by computer simulation models or mathematical models. Existing approaches to performance forecasting of the manned assembly lines are limited to matters of the human itself, such as ergonomic and workload design, and non-human-factor-relevant simulation. Consequently, an approach for the forecasting and improvement of manned assembly line performance is needed to research. As a solution of the current problem, this study proposes a framework that is for generation and simulation of virtual manned assembly lines, and the framework has been implemented as a software.

Keywords: performance forecasting, simulation, virtual manned assembly line, WEMax

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Authors: Quaid Farooq, Zainab Mujtaba

Abstract:

The main aim of this paper was to conduct a research regarding social media usage, psychological absenteeism and job performance at workplace in Pakistan. This research examined the effects of social media usage and psychological absenteeism at workplace on job performance of employees. It was a popular belief that social media usage and psychological absenteeism at workplace have a negative relation with job performance. However, to date there was no study to support this argument, and this compelled us to choose this topic and find out the results. Secondly, this research also found effect of social media usage on psychological absenteeism. Despite the theoretical appeal of these variables and significance in today’s workplace environment, no previous study has investigated the relationship between them in detail. Data was collected from a sample (N = 100 paired responses) of employees and supervisors from different organizations in Pakistan. Study results indicate that performance is negatively related to psychological absenteeism, and such individuals were rated as exhibiting low performance level by their supervisors. However, it had no significant relationship with social media. Moreover, psychological absenteeism was positively related to social media. Individuals, who used more social media at workplace, were more psychologically absent at work according to our results.

Keywords: employee, job performance, psychological absenteeism, social media

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Authors: Alfonso Unceta, Javier Castro-Spila

Abstract:

This paper offers a conceptual and empirical exploration between the organizational performance and the impact of social innovation. The paper contributes on the social innovation field in three domains: a) It provides analytical and empirical evidence linking organizational performance to the impact of social innovation; b) it provides a first outline of impact assessment of social innovation when it is developed by a diversity of heterogeneous actors (systemic social innovation); c) it provides a first outline for the development of innovation policies to support social innovations according to a typology of organizations and a typology of impact.

Keywords: absorptive capacity, social innovation impact, organizational performance, RESINDEX, Basque Country

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Authors: Jan Henrik Wosnitza

Abstract:

The logistic regression has developed into a standard approach for estimating conditional probabilities in a wide range of applications including credit risk prediction. The article at hand contributes to the current literature on logistic regression fourfold: First, it is demonstrated that the binary logistic regression automatically meets its model assumptions under very general conditions. This result explains, at least in part, the logistic regression's popularity. Second, the requirement of homoscedasticity in the context of binary logistic regression is theoretically substantiated. The variances among the groups of defaulted and non-defaulted obligors have to be the same across the level of the aggregated default indicators in order to achieve linear logits. Third, this article sheds some light on the question why nonlinear logits might be superior to linear logits in case of a small amount of data. Fourth, an innovative methodology for estimating correlations between obligor-specific log-odds is proposed. In order to crystallize the key ideas, this paper focuses on the example of credit risk prediction. However, the results presented in this paper can easily be transferred to any other field of application.

Keywords: correlation, credit risk estimation, default correlation, homoscedasticity, logistic regression, nonlinear logistic regression

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