Search results for: statistical learning.

Authors: N. P. Singh, Brahmjit Singh

Abstract:

CDMA cellular networks support soft handover, which guarantees the continuity of wireless services and enhanced communication quality. Cellular networks support multimedia services under varied propagation environmental conditions. In this paper, we have shown the effect of characteristic parameters of the cellular environments on the soft handover performance. We consider path loss exponent, standard deviation of shadow fading and correlation coefficient of shadow fading as the characteristic parameters of the radio propagation environment. A very useful statistical measure for characterizing the performance of mobile radio system is the probability of outage. It is shown through numerical results that above parameters have decisive effect on the probability of outage and hence the overall performance of the soft handover algorithm.

Keywords: CDMA, Correlation coefficient, Path loss exponent, Probability of outage, Soft handover.

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Authors: Kasthurirangan Gopalakrishnan, Naga Shashidhar, Xiaoxiong Zhong

Abstract:

During the process of compaction in Hot-Mix Asphalt (HMA) mixtures, the distance between aggregate particles decreases as they come together and eliminate air-voids. By measuring the inter-particle distances in a cut-section of a HMA sample the degree of compaction can be estimated. For this, a calibration curve is generated by computer simulation technique when the gradation and asphalt content of the HMA mixture are known. A two-dimensional cross section of HMA specimen was simulated using the mixture design information (gradation, asphalt content and air-void content). Nearest neighbor distance methods such as Delaunay triangulation were used to study the changes in inter-particle distance and area distribution during the process of compaction in HMA. Such computer simulations would enable making several hundreds of repetitions in a short period of time without the necessity to compact and analyze laboratory specimens in order to obtain good statistics on the parameters defined. The distributions for the statistical parameters based on computer simulations showed similar trends as those of laboratory specimens.

Keywords: Computer simulations, Hot-Mix Asphalt (HMA), inter-particle distance, image analysis, nearest neighbor

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Authors: E. Oliver, A. Cebolla, A. Dominguez, A. Gonzalez-Segura, E. de la Cruz, S. Albertini, L. Ferrini, K. Kronika, T. Nilsen, R. Baños

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The main objective of MEAL is to develop a pedagogical tool aimed to help teachers and nutritionists (students and professionals) to acquire, train, promote and deliver to children basic nutritional education and healthy eating behaviours competencies. MEAL is focused on eating behaviours and not only in nutritional literacy, and will use new technologies like Information and Communication Technologies (ICTs) and serious games (SG) platforms to consolidate the nutritional competences and habits.

Keywords: Nutritional Education, Pedagogical ICT Platform, Serious Games, Teachers and Nutritionists, Training Course.

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Authors: L. J. de Bessa Neto, V. S. Filho, J. V. Ferreira Nunes, G. C. Bergamo

Abstract:

There are many situations in which human activities have significant effects on the environment. Damage to the ozone layer is one of them. The objective of this work is to use the Least Squares Method, considering the linear, exponential, logarithmic, power and polynomial models of the second degree, to analyze through the coefficient of determination (R²), which model best fits the behavior of the chlorodifluoromethane (HCFC-142b) in parts per trillion between 1992 and 2018, as well as estimates of future concentrations between 5 and 10 periods, i.e. the concentration of this pollutant in the years 2023 and 2028 in each of the adjustments. A total of 809 observations of the concentration of HCFC-142b in one of the monitoring stations of gases precursors of the deterioration of the ozone layer during the period of time studied were selected and, using these data, the statistical software Excel was used for make the scatter plots of each of the adjustment models. With the development of the present study, it was observed that the logarithmic fit was the model that best fit the data set, since besides having a significant R² its adjusted curve was compatible with the natural trend curve of the phenomenon.

Keywords: Chlorodifluoromethane (HCFC-142b), ozone (O3), least squares method, regression models.

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Authors: N. Gigauri, V. Kukhalashvili, V. Sesadze, A. Surmava, L. Intskirveli

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Pollution of the Tbilisi atmospheric air with PM2.5 and PM10 in usual and pandemic situations by using the data of 5 stationary observation points is investigated. The values of the statistical characteristic parameters of PM in the atmosphere of Tbilisi are analyzed and trend graphs are constructed. By means of analysis of pollution levels in the quarantine and usual periods the proportion of vehicle traffic in pollution of city is estimated. Experimental measurements of PM2.5, PM10 in the atmosphere have been carried out in different districts of the city and map of the distribution of their concentrations were constructed. It is shown that maximum pollution values are recorded in the city center and along major motorways. It is shown that the average monthly concentrations vary in the range of 0.6-1.6 Maximum Permissible Concentration (MPC). Average daily values of concentration vary at 2-4 days intervals. The distribution of PM10 generated as a result of traffic is numerical modeled. The modeling results are compared with the observation data.

Keywords: Air pollution, numerical modeling, PM2.5, PM10.

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Authors: S. MacDonald, A. Manuel, R. Law, N. Bandruak, A. Dubrowski, V. Curran, J. Smith-Young, K. Simmons, A. Warren

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High fidelity human patient simulation has been used for many years by health sciences education programs to foster critical thinking, engage learners, improve confidence, improve communication, and enhance psychomotor skills. Unfortunately, there is a paucity of research on the use of high fidelity human patient simulation to foster teamwork among nursing, medicine and pharmacy undergraduate students. This study compared the impact of high fidelity and low fidelity simulation education on teamwork among nursing, medicine and pharmacy students. For the purpose of this study, two innovative teaching scenarios were developed based on the care of an adult patient experiencing acute anaphylaxis: one high fidelity using a human patient simulator and one low fidelity using case based discussions. A within subjects, pretest-posttest, repeated measures design was used with two-treatment levels and random assignment of individual subjects to teams of two or more professions. A convenience sample of twenty-four (n=24) undergraduate students participated, including: nursing (n=11), medicine (n=9), and pharmacy (n=4). The Interprofessional Teamwork Questionnaire was used to assess for changes in students’ perception of their functionality within the team, importance of interprofessional collaboration, comprehension of roles, and confidence in communication and collaboration. Student satisfaction was also assessed. Students reported significant improvements in their understanding of the importance of interprofessional teamwork and of the roles of nursing and medicine on the team after participation in both the high fidelity and the low fidelity simulation. However, only participants in the high fidelity simulation reported a significant improvement in their ability to function effectively as a member of the team. All students reported that both simulations were a meaningful learning experience and all students would recommend both experiences to other students. These findings suggest there is merit in both high fidelity and low fidelity simulation as a teaching and learning approach to foster teamwork among undergraduate nursing, medicine and pharmacy students. However, participation in high fidelity simulation may provide a more realistic opportunity to practice and function as an effective member of the interprofessional health care team.

Keywords: Acute anaphylaxis, high fidelity human patient simulation, low fidelity simulation, interprofessional education.

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Authors: M. Sadeghi, H. Pezeshk, R. Tusserkani, A. Sharifi Zarchi, A. Malekpour, M. Foroughmand, S. Goliaei, M. Totonchi, N. Ansari–Pour

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The role and relative importance of intrinsic and extrinsic factors in the development of complex diseases such as cancer still remains a controversial issue. Determining the amount of variation explained by these factors needs experimental data and statistical models. These models are nevertheless based on the occurrence and accumulation of random mutational events during stem cell division, thus rendering cancer development a stochastic outcome. We demonstrate that not only individual genome sequencing is uninformative in determining cancer risk, but also assigning a unique genome sequence to any given individual (healthy or affected) is not meaningful. Current whole-genome sequencing approaches are therefore unlikely to realize the promise of personalized medicine. In conclusion, since genome sequence differs from cell to cell and changes over time, it seems that determining the risk factor of complex diseases based on genome sequence is somewhat unrealistic, and therefore, the resulting data are likely to be inherently uninformative.

Keywords: Cancer risk, extrinsic factors, genome sequencing, intrinsic factors.

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Authors: Chen Bi-Hsiang, Yang Hung-Wen, Lou Jie-Chung, Han Jia-Yun

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This study was investigated on sampling and analyzing water quality in water reservoir & water tower installed in two kind of residential buildings and school facilities. Data of water quality was collected for correlation analysis with frequency of sanitization of water reservoir through questioning managers of building about the inspection charts recorded on equipment for water reservoir. Statistical software packages (SPSS) were applied to the data of two groups (cleaning frequency and water quality) for regression analysis to determine the optimal cleaning frequency of sanitization. The correlation coefficient (R) in this paper represented the degree of correlation, with values of R ranging from +1 to -1.After investigating three categories of drinking water users; this study found that the frequency of sanitization of water reservoir significantly influenced the water quality of drinking water. A higher frequency of sanitization (more than four times per 1 year) implied a higher quality of drinking water. Results indicated that sanitizing water reservoir & water tower should at least twice annually for achieving the aim of safety of drinking water.

Keywords: cleaning frequency of sanitization, parameters ofwater quality, regression analysis, water reservoir & water tower

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Authors: Milena M. Melo, Jane M. Santos, Severine Frere, Valderio A. Reisen, Neyval C. Reis Jr., Maria de Fátima S. Leite

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Although there had been a many studies that shows the impact of air pollution on physical health, comparatively less was known of human behavioral responses and annoyance impacts. Annoyance caused by air pollution is a public health problem because it can be an ambient stressor causing stress and disease and can affect quality of life. The objective of this work is to evaluate the annoyance caused by air pollution in two different industrialized urban areas, Dunkirk (France) and Vitoria (Brazil). The populations of these cities often report feeling annoyed by dust. Surveys were conducted, and the collected data were analyzed using statistical analyses. The results show that sociodemographic variables, importance of air quality, perceived industrial risk, perceived air pollution and occurrence of health problems play important roles in the perceived annoyance. These results show the existence of a common problem in geographically distant areas and allow stakeholders to develop prevention strategies.

Keywords: Air pollution, annoyance, industrial risks, perception of pollution, public health, settled dust.

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Authors: Shin Je Lee, Go Bong Choi, Jeong Cheol Seo, Jong Min Lee, Gibaek Lee

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Water pipe network is installed underground and once equipped, it is difficult to recognize the state of pipes when the leak or burst happens. Accordingly, post management is often delayed after the fault occurs. Therefore, the systematic fault management system of water pipe network is required to prevent the accident and minimize the loss. In this work, we develop online fault detection system of water pipe network using data of pipes such as flow rate or pressure. The transient model describing water flow in pipelines is presented and simulated using MATLAB. The fault situations such as the leak or burst can be also simulated and flow rate or pressure data when the fault happens are collected. Faults are detected using statistical methods of fast Fourier transform and discrete wavelet transform, and they are compared to find which method shows the better fault detection performance.

Keywords: fault detection, water pipeline model, fast Fourier transform, discrete wavelet transform.

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Authors: Insaf Ajili, Malik Mallem, Jean-Yves Didier

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Motion recognition from videos is actually a very complex task due to the high variability of motions. This paper describes the challenges of human motion recognition, especially motion representation step with relevant features. Our descriptor vector is inspired from Laban Movement Analysis method. We propose discriminative features using the Random Forest algorithm in order to remove redundant features and make learning algorithms operate faster and more effectively. We validate our method on MSRC-12 and UTKinect datasets.

Keywords: Human motion recognition, Discriminative LMA features, random forest, features reduction.

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Authors: Anas M. Fares, A. Touqan

Abstract:

A common approach in resisting lateral forces is the use of reinforced concrete shear walls in buildings. These walls represent the main elements to resist the lateral forces due to their large strength and stiffness. However, such walls may contain many openings due to functional requirements, and this may largely affect the overall lateral stiffness of them. It is thus of prime importance to quantify the effect of openings on the dynamic performance of the shear walls. SAP2000 structural analysis program is used as a main source after verifying the results. This study is made by using linear elastic analysis. The results are compared to ASCE7-16 code empirical equations for estimating the fundamental period of shear wall structures. Finally, statistical regression is used to fit an equation for estimating the increase in the fundamental period of shear-walled regular structures due to windows openings in the walls.

Keywords: Concrete, earthquake-resistant design, finite element, fundamental period, lateral stiffness, linear analysis, modal analysis, rayleigh, SAP2000, shear wall, ASCE7-16.

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Authors: B. Rajkumarsingh, S. Goolaup, A. Galleegadoo

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Laboratory classes in Electrical Engineering are often hampered by safety issues, as students have to work on high voltage lines. One solution is to make use of virtual laboratory simulations, to help students understand the concepts taught in their coursework. In this context, we have conceived and implemented virtual lab experiments in connection with the study of earthing arrangements. In this work, software was developed, which aid student in understanding the working of a residual current device (RCD) in a TT earthing system. Various parameters, such as the earthing resistances, leakage currents and harmonics were included for a TT system with RCD connection.

Keywords: TT system, RCD, LabVIEW, Learning aids.

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Authors: Asterios Leonidis, George Margetis, Margherita Antona, Constantine Stephanidis

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Ambient Intelligence (AmI) environments bring significant potential to exploit sophisticated computer technology in everyday life. In particular, the educational domain could be significantly enhanced through AmI, as personalized and adapted learning could be transformed from paper concepts and prototypes to real-life scenarios. In this paper, an integrated framework is presented, named ClassMATE, supporting ubiquitous computing and communication in a school classroom. The main objective of ClassMATE is to enable pervasive interaction and context aware education in the technologically augmented classroom of the future.

Keywords: Ambient intelligence, smart classroom, pervasivecomputing, education.

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Authors: Samson Henry Dogo, Allan Clark, Elena Kulinskaya

Abstract:

The objective of meta-analysis is to combine results from several independent studies in order to create generalization and provide evidence base for decision making. But recent studies show that the magnitude of effect size estimates reported in many areas of research significantly changed over time and this can impair the results and conclusions of meta-analysis. A number of sequential methods have been proposed for monitoring the effect size estimates in meta-analysis. However they are based on statistical theory applicable only to fixed effect model (FEM) of meta-analysis. For random-effects model (REM), the analysis incorporates the heterogeneity variance, τ 2 and its estimation create complications. In this paper we study the use of a truncated CUSUM-type test with asymptotically valid critical values for sequential monitoring in REM. Simulation results show that the test does not control the Type I error well, and is not recommended. Further work required to derive an appropriate test in this important area of applications.

Keywords: Meta-analysis, random-effects model, sequential testing, temporal changes in effect sizes.

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Authors: Nnamdi I. Nwulu, Shola G. Oroja

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It has become crucial over the years for nations to improve their credit scoring methods and techniques in light of the increasing volatility of the global economy. Statistical methods or tools have been the favoured means for this; however artificial intelligence or soft computing based techniques are becoming increasingly preferred due to their proficient and precise nature and relative simplicity. This work presents a comparison between Support Vector Machines and Artificial Neural Networks two popular soft computing models when applied to credit scoring. Amidst the different criteria-s that can be used for comparisons; accuracy, computational complexity and processing times are the selected criteria used to evaluate both models. Furthermore the German credit scoring dataset which is a real world dataset is used to train and test both developed models. Experimental results obtained from our study suggest that although both soft computing models could be used with a high degree of accuracy, Artificial Neural Networks deliver better results than Support Vector Machines.

Keywords: Artificial Neural Networks, Credit Scoring, SoftComputing Models, Support Vector Machines.

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Authors: S. Kurien, S. Satpathy, S. K. Gupta, S. K. Arya, D. K. Sharma

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Advances in the use of health care technology have resulted in increased adverse events (AEs) related to the use of medical devices. The study focused on the existing reporting systems. This study was conducted in a tertiary care public sector hospital. Devices included Syringe infusion pumps, Cardiac monitors, Pulse oximeters, Ventilators and Defibrillators. A total of 211 respondents were recruited. Interviews were held with 30 key informants. Medical records were scrutinized. Relevant statistical tests were used. Resident doctors reported maximum frequency of AEs, followed by nurses; and least by consultants. A significant association was found between the cadre of health care personnel and awareness that the patients and bystanders have a risk of sustaining AE. Awareness regarding reporting of AEs was low, and it was generally done verbally. Other critical findings are discussed in the light of the barriers to reporting, reasons for non-compliance, recording system, and so on.

Keywords: Adverse events, health care technology, public sector hospital, reporting systems.

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Authors: Hyun-Woo Cho

Abstract:

The fault detection and diagnosis of complicated production processes is one of essential tasks needed to run the process safely with good final product quality. Unexpected events occurred in the process may have a serious impact on the process. In this work, triangular representation of process measurement data obtained in an on-line basis is evaluated using simulation process. The effect of using linear and nonlinear reduced spaces is also tested. Their diagnosis performance was demonstrated using multivariate fault data. It has shown that the nonlinear technique based diagnosis method produced more reliable results and outperforms linear method. The use of appropriate reduced space yielded better diagnosis performance. The presented diagnosis framework is different from existing ones in that it attempts to extract the fault pattern in the reduced space, not in the original process variable space. The use of reduced model space helps to mitigate the sensitivity of the fault pattern to noise.

Keywords: Real-time Fault diagnosis, triangular representation of patterns in reduced spaces, Nonlinear kernel technique, multivariate statistical modeling.

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Authors: Thomas Bryan, Veton Kepuska, Ivica Kostanic

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Speaker recognition is performed in high Additive White Gaussian Noise (AWGN) environments using principals of Computational Auditory Scene Analysis (CASA). CASA methods often classify sounds from images in the time-frequency (T-F) plane using spectrograms or cochleargrams as the image. In this paper atomic decomposition implemented by matching pursuit performs a transform from time series speech signals to the T-F plane. The atomic decomposition creates a sparsely populated T-F vector in “weight space” where each populated T-F position contains an amplitude weight. The weight space vector along with the atomic dictionary represents a denoised, compressed version of the original signal. The arraignment or of the atomic indices in the T-F vector are used for classification. Unsupervised feature learning implemented by a sparse autoencoder learns a single dictionary of basis features from a collection of envelope samples from all speakers. The approach is demonstrated using pairs of speakers from the TIMIT data set. Pairs of speakers are selected randomly from a single district. Each speak has 10 sentences. Two are used for training and 8 for testing. Atomic index probabilities are created for each training sentence and also for each test sentence. Classification is performed by finding the lowest Euclidean distance between then probabilities from the training sentences and the test sentences. Training is done at a 30dB Signal-to-Noise Ratio (SNR). Testing is performed at SNR’s of 0 dB, 5 dB, 10 dB and 30dB. The algorithm has a baseline classification accuracy of ~93% averaged over 10 pairs of speakers from the TIMIT data set. The baseline accuracy is attributable to short sequences of training and test data as well as the overall simplicity of the classification algorithm. The accuracy is not affected by AWGN and produces ~93% accuracy at 0dB SNR.

Keywords: Time-frequency plane, atomic decomposition, envelope sampling, Gabor atoms, matching pursuit, sparse dictionary learning, sparse autoencoder.

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Authors: Ivana Tadić, Snježana Pivac

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Although human resources are recognized as the crucial companies’ resources and their positive influence on companies’ performances has been confirmed through different researches, scientists are still debating it. In order to contribute this debate, this paper firstly discusses the most important human resource management elements and practices and its influence on companies’ success. Afterwards it defines human resource “bundles” – interrelated and internally consistent human resource practices, complementary to each other, or the most important human resource practices and elements regarding Croatian companies and its human resource management activities. Finally, the paper provides empirical results; more precisely it reveals the relation of the level of development of human resource management function (“bundles”) and companies’ financial performances (using profitability ratios, liquidity ratios, solvency ratios and a group of additional ratios related to employees’ indicators).

Keywords: Companies’ performances, human resource bundles, multivariate statistical analysis.

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Authors: Vineet Kansal

Abstract:

Enterprise Wide Information Systems (EWIS) implementation involves the entire business and will require changes throughout the firm. Because of the scope, complexity and continuous nature of ERP, the project-based approach to managing the implementation process resulted in failure rates of between 60% and 80%. In recent years ERP systems have received much attention. The organizational relevance and risk of ERP projects make it important for organizations to focus on ways to make ERP implementation successful. Once these systems are in place, however, their performance depends on the identified macro variables viz. 'Business Process', 'Decision Making' and 'Individual / Group working'. The questionnaire was designed and administered. The responses from 92 organizations were compiled. The relationship of these variables with EWIS performance is analyzed using inferential statistical measurements. The study helps to understand the performance of model presented. The study suggested in keeping away from the calamities and thereby giving the necessary competitive edge. Whenever some discrepancy is identified during the process of performance appraisal care has to be taken to draft necessary preventive measures. If all these measures are taken care off then the EWIS performance will definitely deliver the results.

Keywords: Enterprise Systems, performance, technology

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Authors: Babesse Saad, Ameddah Djameleddine

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In this paper, a learning algorithm using neuronal networks to improve the roll stability and prevent the rollover in a single unit heavy vehicle is proposed. First, LQR control to keep balanced normalized rollovers, between front and rear axles, below the unity, then a data collected from this controller is used as a training basis of a neuronal regulator. The ANN controller is thereafter applied for the nonlinear side force model, and gives satisfactory results than the LQR one.

Keywords: Rollover, single unit heavy vehicle, neural networks, nonlinear side force.

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Authors: Şakir Görmüş, Ali Kabasakal

Abstract:

The main purpose of this study is to provide a detailed statistical overview of the time and regional distribution, relative timing occurrence of economic crises and government changes in 51 economies over the 1990–2007 periods. At the same time, the predictive power of the economic crises on set government changes will be examined using “signal approach". The result showed that the percentage of government changes is highest in transition economies (86 percent of observations) and lowest in Latin American economies (39 percent of observations). The percentages of government changes are same in both developed and developing countries (43 percent of observations). However, average crises per year (frequency of crises) are higher (lower) in developing (developed) countries than developed (developing) countries. Also, the predictive power of economic crises about the onset of a government change is highest in Transition economies (81 percent) and lowest in Latin American countries (30 percent). The predictive power of economic crises in developing countries (43 percent) is lower than developed countries (55 percent).

Keywords: Economic crises, Government Changes, PoliticalEconomy, Signal Approach.

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Authors: S. Mat Radzi, N. J. Abd Rahman, H. Mohd Noor, N. Ariffin

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Ferulic acid has widespread industrial potential by virtue of its antioxidant properties. However, it is partially soluble in aqueous media, limiting their usefulness in oil-based processes in food, cosmetic, pharmaceutical, and material industry. Therefore, modification of ferulic acid should be made by producing of more lipophilic derivatives. In this study, a preliminary investigation of lipase-catalyzed trans-esterification reaction of ethyl ferulate and olive oil was investigated. The reaction was catalyzed by immobilized lipase from Candida antarctica (Novozym 435), to produce ferulate ester, a sunscreen agent. A statistical approach of Response surface methodology (RSM) was used to evaluate the interactive effects of reaction temperature (40-80°C), reaction time (4-12 hours), and amount of enzyme (0.1-0.5 g). The optimum conditions derived via RSM were reaction temperature 60°C, reaction time 2.34 hours, and amount of enzyme 0.3 g. The actual experimental yield was 59.6% ferulate ester under optimum condition, which compared well to the maximum predicted value of 58.0%.

Keywords: Ferulic acid, Enzymatic Synthesis, Esters, RSM.

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Authors: Hyunchul Ahn, William X. S. Wong

Abstract:

Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM.

Keywords: Corporate credit rating prediction, feature selection, genetic algorithms, instance selection, multiclass support vector machines.

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Authors: Daniel I. Morariu, Lucian N. Vintan, Volker Tresp

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Text categorization is the problem of classifying text documents into a set of predefined classes. In this paper, we investigated three approaches to build a meta-classifier in order to increase the classification accuracy. The basic idea is to learn a metaclassifier to optimally select the best component classifier for each data point. The experimental results show that combining classifiers can significantly improve the accuracy of classification and that our meta-classification strategy gives better results than each individual classifier. For 7083 Reuters text documents we obtained a classification accuracies up to 92.04%.

Keywords: Meta-classification, Learning with Kernels, Support Vector Machine, and Performance Evaluation.

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Authors: S. Raissi

Abstract:

Response surface methodology (RSM) is a very efficient tool to provide a good practical insight into developing new process and optimizing them. This methodology could help engineers to raise a mathematical model to represent the behavior of system as a convincing function of process parameters. Through this paper the sequential nature of the RSM surveyed for process engineers and its relationship to design of experiments (DOE), regression analysis and robust design reviewed. The proposed four-step procedure in two different phases could help system analyst to resolve the parameter design problem involving responses. In order to check accuracy of the designed model, residual analysis and prediction error sum of squares (PRESS) described. It is believed that the proposed procedure in this study can resolve a complex parameter design problem with one or more responses. It can be applied to those areas where there are large data sets and a number of responses are to be optimized simultaneously. In addition, the proposed procedure is relatively simple and can be implemented easily by using ready-made standard statistical packages.

Keywords: Response Surface Methodology (RSM), Design of Experiments (DOE), Process modeling, Process setting, Process optimization.

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Authors: Mengist Hailemariam, Assegid Ajeme

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The ever increasing amount of solid waste (SW) generated which is exacerbated by lack of proper waste management system is of growing concern worldwide and in major cities in developing countries due to its social, economic and environmental implications. This study attempts to describe the aspects of solid waste management (SWM) in Adama, one of the fast urbanizing cities in Ethiopia, and highlights the challenges thereof. Data were gathered through interview supplemented by field observation and self-administered questionnaire. Then, the data were analyzed using the Statistical Package for Social Science (SPSS) software. In addition, secondary data were gathered from documents. Findings revealed that the current SWM practice couldn’t cope with the fast urbanizing needs and the rapid population growth exhibited by the city. Besides, major factors contributing to the inefficient system were identified. The study would provide practical insights to decision makers in developing a sustainable SWM system leading to minimized risk in the city.

Keywords: Adama, Aspects and challenges, Ethiopia, Solid waste management.

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Authors: Jiabei Zhang, Amanda Rapelje, Christopher Farr, Kristin Colwell, Zezhao Chen

Abstract:

The purpose of this study was to investigate the effectiveness of a recreational workout program for adults with disabilities over two semesters. This investigation was an action study conducted in a naturalistic setting. Participants included equal numbers of adults with severe cognitive impairments (n = 35) and adults without disabilities (n = 35). Adults with disabilities severe cognitive impairments were trained 6 self-initiated workout activities over two semesters by adults without disabilities. The numbers of task-analyzed steps of each activity performed correctly by each participant at the first and last weeks of each semester were used for data analysis. Results of the paired t-tests indicate that across two semesters, significant differences between the first and last weeks were found on 4 out of the 6 task-analyzed workout activities at a statistical level of significance p < .05. The recreational workout program developed in this study was effective.

Keywords: Workout program, exercise performance, adults, sever cognitive impairment.

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Authors: Ayub V. O. Ofulla, Jackson H. O. Onyuka, Samuel Wagai, Douglas Anyona, Gabriel O. Dida, John Gichuki

Abstract:

This study was set to determine the antimicrobial activities of brine salting, chlorinated solution, and oil frying treatments on enteric bacteria and fungi in Rastrineobola argentea fish from fish landing beaches within L. Victoria basin of western Kenya. Statistical differences in effectiveness of the different treatment methods was determined by single factor ANOVA, and paired two-tail t-Test was performed to compare the differences in moisture contents before and after storage. Oil fried fish recorded the lowest microbial loads, sodium chloride at 10% concentration was the second most effective and chlorinated solution even at 150ppm was the least effective against the bacteria and fungi in fish. Moisture contents of the control and treated fish were significantly lower after storage. These results show that oil frying of fish should be adopted for processing and preserving Rastrineobola argentea which is the most abundant and affordable fish species from Lake Victoria.

Keywords: Fish landing beaches, Lake Victoria, oil frying, preservatives.

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