Reducing the Sodium Chloride Content in Chicken Pate by Using Potassium and Ammonium Chloride

AbstractThe aim of this research was to investigate possibility of chicken pate production with reduced sodium chloride content, as well as to establish changes in sensory characteristics. In the study, six experimental groups of chicken pate were produced with the same basic ingredients, but different amounts of added salts. Sensory evaluation was performed in order to determine general taste acceptability, and of the sodium and potassium levels in the chicken pate. The pate from EI and EII groups in which the amount of added sodium chloride was reduced and/or partially substituted with ammonium chloride had a most acceptable taste

Occupational Safety and Health Management at Alumina Ltd

Occupational safety and health (OSH) is an integral part of the organization of work, and includes measures and means necessary to achieve safe working conditions. In terms of the current Law on Occupational Safety and Health of the Republic of Srpska, among other things, the employer is responsible for drafting the Risk Assessment Act. This paper presents a risk assessment for the position of Technological Equipment Operator. The risk assessment was performed, hazards were identified and measures to reduce them were proposed using the modified AUVA method. Jobs with risk rank I and II are considered jobs with acceptable or low risk, i.e. those including risk levels 1-5, and 6-9 respectively. Jobs with increased risk are jobs with a risk rating of medium, high and unacceptable - III, IV and V, i.e. encompassing risk levels 10-12, 15-16 and 20-25, respectively was estimated that the position of the technological equipment operator is a high-risk (IV) position, i.e. a position with difficult working conditions, and a risk of the loss of working ability or impairment of health

Illness perception in tuberculosis by implementation of the Brief Illness Perception Questionnaire : a TBNET study

How patients relate to the experience of their illness has a direct impact over their behavior. We aimed to assess illness perception in patients with pulmonary tuberculosis (TB) by means of the Brief Illness Perception Questionnaire (BIPQ) in correlation with patients’ demographic features and clinical TB score. Our observational questionnaire based study included series of consecutive TB patients enrolled in several countries from October 2008 to January 2011 with 167 valid questionnaires analyzed. Each BIPQ item assessed one dimension of illness perceptions like the consequences, timeline, personal control, treatment control, identity, coherence, emotional representation and concern. An open question referred to the main causes of TB in each patient’s opinion. The over-all BIPQ score (36.25 ± 11.054) was in concordance with the clinical TB score (p ≤ 0.001). TB patients believed in the treatment (the highest item-related score for treatment control) but were unsure about the illness identity. Illness understanding and the clinical TB score were negatively correlated (p < 0.01). Only 25% of the participants stated bacteria or TB contact as the first ranked cause of the illness. For routine clinical practice implementation of the BIPQ is convenient for obtaining fast and easy assessment of illness perception with potential utility in intervention design. This time saving effective personalized approach may improve communication with TB patients and contribute to better behavioral strategies in disease control

Grundlagen der statistischen Datenanalyse in SPSS (in German)

This book is primarily intended for students who attend lectures on Data Analysis and Statistical Data Analysis at the Faculty of Applied Sciences. The textbook's structure is entirely adapted to the requirements of above mentioned courses

Improved Effort and Cost Estimation Model Using Artificial Neural Networks and Taguchi Method with Different Activation Functions

Software estimation involves meeting a huge number of different requirements, such as resource allocation, cost estimation, effort estimation, time estimation, and the changing demands of software product customers. Numerous estimation models try to solve these problems. In our experiment, a clustering method of input values to mitigate the heterogeneous nature of selected projects was used. Additionally, homogeneity of the data was achieved with the fuzzification method, and we proposed two different activation functions inside a hidden layer, during the construction of artificial neural networks (ANNs). In this research, we present an experiment that uses two different architectures of ANNs, based on Taguchi’s orthogonal vector plans, to satisfy the set conditions, with additional methods and criteria for validation of the proposed model, in this approach. The aim of this paper is the comparative analysis of the obtained results of mean magnitude relative error (MMRE) values. At the same time, our goal is also to find a relatively simple architecture that minimizes the error value while covering a wide range of different software projects. For this purpose, six different datasets are divided into four chosen clusters. The obtained results show that the estimation of diverse projects by dividing them into clusters can contribute to an efficient, reliable, and accurate software product assessment. The contribution of this paper is in the discovered solution that enables the execution of a small number of iterations, which reduces the execution time and achieves the minimum error

Artificial Neural Network Architecture and Orthogonal Arrays in Estimation of Software Projects Efforts

Accurate assessment of software project development using the proper artificial intelligence tools can be a significant challenge for success in the software industry. This paper aims to minimize the relative error in software estimation using the proposed model of an artificial neural network (ANN) based on Taguchi's orthogonal vector plan. By selecting methods of clustering and fuzzification of different project values within several used datasets such as COCOMO2000, NASA60, and Kemerer15, reducing the number and time of iterations minimizes Mean Magnitude Relative Error (MMRE) and include a wide range of observed data. Additional criteria, such as monitoring prediction, correlation, and comparison with RBF (Radial Basis Function) relative error, were used to confirm that the proposed model gives two to three times better results depending on the observed cluster. Based on the obtained results, the accuracy and reliability of the proposed model for estimating software projects were determine

Influence of input values on the prediction model error using Artificial Neural Network based on Taguchi's Orthogonal Array

Rapid and accurate assessment of software project development using artificial intelligence tools can be essential for success in the software industry. This article has two objectives: to reduce the magnitude relative error (MRE) value in estimating the effort and cost of software development using the proposed artificial neural network architecture based on the Taguchi method and examine the influence of input variables on the change in relative error value. Clustering and fuzzification methods further mitigate the heterogeneous structure of the different project values of the datasets used. Taguchi method contributes to the reduction of the number of iterations by 99%, which achieves a significant reduction in estimation and value of MRE. By monitoring additional criteria, such as prediction, correlation, and comparing two activation functions, such as sigmoid and radial basis function, the proposed model's correctness, reliability, and stability are confirmed. Significantly better results are expected using the sigmoid activation function and a decrease in the value of the mean (MRE)

A New Approach to Software Effort Estimation Using Different Artificial Neural Network Architectures and Taguchi Orthogonal Arra

In this article, two different architectures of Artificial Neural Networks (ANN) are proposed as an efficient tool for predicting and estimating software effort. Artificial Neural Networks, as a branch of machine learning, are used in estimation because they tend towards fast learning and giving better and more accurate results. The search/optimization embraced here is motivated by the Taguchi method based on Orthogonal Arrays (an extraordinary set of Latin Squares), which demonstrated to be an effective apparatus in a robust design. This article aims to minimize the magnitude relative error (MRE) in effort estimation by using Taguchi's Orthogonal Arrays, as well as to find the simplest possible architecture of an artificial Neural Network for optimized learning. A descending gradient (GA) criterion has also been introduced to know when to stop performing iterations. Given the importance of estimating software projects, our work aims to cover as many different values of actual efficiency of a wide range of projects as possible by division into clusters and a certain coding method, in addition to the mentioned tools. In this way, the risk of error estimation can be reduced, to increase the rate of completed software projects