Artificial Neural Networks for Prediction of COVID-19 in Saudi Arabia

In this study, we have proposed an artificial neural network (ANN) model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17, 2020. The proposed model is based on the existing data (training data) published in the Saudi Arabia Coronavirus disease (COVID-19) situation—Demographics. The Prey-Predator algorithm is employed for the training. Multilayer perceptron neural network (MLPNN) is used in this study. To improve the performance of MLPNN, we determined the parameters of MLPNN using the prey-predator algorithm (PPA). The proposed model is called the MLPNN–PPA. The performance of the proposed model has been analyzed by the root mean squared error (RMSE) function, and correlation coefficient (R). Furthermore, we tested the proposed model using other existing data recorded in Saudi Arabia (testing data). It is demonstrated that the MLPNN-PPA model has the highest performance in predicting the number of infected and recovering in Saudi Arabia. The results reveal that the number of infected persons will increase in the coming days and become a minimum of 9789. The number of recoveries will be 2000 to 4000 per day.     

Partial replacement of sodium chloride with potassium chloride in marinated rabbit meat

This study was aimed at evaluating the consequences of sodium chloride reduction by potassium chloride up to 50% on technological, sensorial and microbiological traits of marinated rabbit meat. In total, 226 rabbit loin meat samples were obtained and subjected to vacuum tumbling using solutions with different NaCl/KCl ratios. Replacing of sodium chloride up to 30% by potassium chloride did not change microbiological traits (total aerobic mesophilic and lactic acid bacteria maximum cell loads), sensorial acceptability (perceived saltiness and overall liking) and technological traits (pH, colour, texture, cooking loss and yield). Otherwise, reduction in sodium chloride to 50% significantly decreased perceived saltiness (4.15 vs. 4.73; P < 0.05) and reduced microbial shelf life by 1 day when compared to control, even if there was still no effect on technological traits. In conclusion, it is feasible imparting an added value for processed rabbit meat products by reduction in sodium content that could increase market interest.     

Automatic analysis of human posture equilibrium using empirical mode decomposition

The present study proposes a new approach for the assessment of the human balance control. This approach is based on the decomposition of the center of pressure displacement using empirical mode decomposition (EMD) that provides an effective time-frequency analysis of non-stationary signals. Twenty-eight healthy subjects performed quiet standing in four conditions—feet apart/together with respect to eyes open/closed—while recording the stabilometric signals in the anteroposterior (AP) and mediolateral (ML) directions. The EMD method decomposes each stabilometric signal into several subsignals called intrinsic mode functions (IMFs). Stabilogram-diffusion analysis technique is applied to generate the diffusion curve of each IMF signal. Each diffusion curve is modeled as a second-order system and provides representative features, such as the gain parameter. Analysis of the gain parameter shows the major effect of visual input and feet conditions on the strategy to control/stabilize the balance. Significant differences were found between young and elderly, and between women and men. In addition, the impact of feet position seems to be higher in ML direction than in AP direction.     

Melt spun matrix fibers of toughened polypropylene copolymers modified by high energy electrons

Binary blends of polypropylene (PP) and ethylene‐octene copolymer (EOC) are prepared by continuous electron‐induced reactive processing at various mass ratios of the blend components and various doses without adding of any grafting agents. The influence of mass ratio and dose is investigated in order to get the optimum processing behavior of toughened PP as well as optimum properties of resulting fibers. It is found that toughened PP with a PP/EOC blend ratio of 97.5–2.5  mass % can be used advantageously as a matrix component for the process of online spinning of glass fiber/toughened PP hybrid yarns. Such hybrid yarns belong to one of the most advanced production methods for the manufacturing of fiber reinforced thermoplastic composites with an increased mechanical performance. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 44011.     

Investigations into the correlation of component failure rates to system supply availability in asset management methods

Liberalization has increased economic pressure on network operators, urging them to compromise between cost savings and supply reliability. In this context, the detailed and quantitative evaluation of supply reliability is gaining ever more importance—especially its application in comprehensive asset management methods. However, the large number of components especially in medium voltage distribution networks leads to high computation times for such probabilistic reliability analyses. A recent research project deals with investigations into a forecast of system supply reliability performance following a simplified methodology. Developing different forecasting methods, also several aspects of significant importance for the calculation of supply reliability in MV distribution systems in general were identified. Especially for electrical networks of a certain size, the overlapping of independent single failures, as well as complex failures play an important role. This paper presents the background of the reliability centered asset management approach as well as the general idea and different approaches to the forecasting methods in theory briefly. Following, the application on two different example networks demonstrates the approaches and the achieved results, and reveals also the relevance of overlapping and complex failures in these results.     

Hybrid Time-Cost Optimization of Nonserial Repetitive Construction Projects

Time-cost trade-off analysis represents a challenging task because the activity duration and cost have uncertainty associated with them, which should be considered when performing schedule optimization. This study proposes a hybrid technique that combines genetic algorithms (GAs) with dynamic programming to solve construction projects time-cost trade-off problems under uncertainty. The technique is formulated to apply to project schedules with repetitive nonserial subprojects that are common in the construction industry such as multiunit housing projects and retail network development projects. A generalized mathematical model is derived to account for factors affecting cost and duration relationships at both the activity and project levels. First, a genetic algorithm is utilized to find optimum and near optimum solutions from the complicated hyperplane formed by the coding system. Then, a dynamic programming procedure is utilized to search the vicinity of each of the near optima found by the GA, and converges on the global optima. The entire optimization process is conducted using a custom developed computer code. The validation and implementation of the proposed techniques is done over three axes. Mathematical correctness is validated through function optimization of test functions with known optima. Applicability to scheduling problems is validated through optimization of a 14 activity miniproject found in the literature for results comparison. Finally implementation to a case study is done over a gas station development program to produce optimum schedules and corresponding trade-off curves. Results show that genetic algorithms can be integrated with dynamic programming techniques to provide an effective means of solving for optimal project schedules in an enhanced realistic approach.     

Intergroup helping as status relations: Effects of status stability, identification, and type of help on receptivity to high-status group's help.

Integrating research on social identity processes and helping relations, the authors proposed that low-status group members who are high identifiers will be unwilling to receive help from the high-status group when status relations are perceived as unstable and help is dependency-oriented. The first experiment, a minimal group experiment, found negative reactions to help from a high-status outgroup when status relations were unstable. The 2nd and 3rd experiments, which used real groups of Israeli Arabs and Israeli Jews, replicated this finding and showed that high identifiers were less receptive to help from the high-status outgroup than low identifiers. The 4th experiment, a help-seeking experiment with real groups of competing high schools, found that the least amount of help was sought from a high-status group by high identifiers when status relations were perceived as unstable and help was dependency-oriented. Theoretical and applied implications are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Finite-Element Analysis of Hot Mix Asphalt Microstructure Using Effective Local Material Properties and Strain Gradient Elasticity

This paper presents the development of a methodology for microstructure analysis and modeling of hot mix asphalt (HMA). This methodology relies on using effective local material properties and strain gradient theory in the finite element analysis of HMA microstructure. The effective local properties are calculated using an analytical micromechanical model that captures the influence of percent of particles on the microscopic response of HMA. Strain gradient elasticity is used in order to account for the effect of particle size in the finite element analysis. The autocorrelation function and the moving window technique are used to determine the microstructure characteristic length scales that are used in strain gradient elasticity. A number of asphalt mixes with different aggregate types and size distributions are analyzed in this paper.     

Using duration models to reduce fragmentation in audio segmentation

We investigate explicit segment duration models in addressing the problem of fragmentation in musical audio segmentation. The resulting probabilistic models are optimised using Markov Chain Monte Carlo methods; in particular, we introduce a modification to Wolff’s algorithm to make it applicable to a segment classification model with an arbitrary duration prior. We apply this to a collection of pop songs, and show experimentally that the generated segmentations suffer much less from fragmentation than those produced by segmentation algorithms based on clustering, and are closer to an expert listener’s annotations, as evaluated by two different performance measures. Editor: Gerhard Widmer     

A logistic regression framework for information technology outsourcing lifecycle management

We present a methodology for managing outsourcing projects from the vendor's perspective, designed to maximize the value to both the vendor and its clients. The methodology is applicable across the outsourcing lifecycle, providing the capability to select and target new clients, manage the existing client portfolio and quantify the realized benefits to the client resulting from the outsourcing agreement. Specifically, we develop a statistical analysis framework to model client behavior at each stage of the outsourcing lifecycle, including: (1) a predictive model and tool for white space client targeting and selection—opportunity identification (2) a model and tool for client risk assessment and project portfolio management—client tracking, and (3) a systematic analysis of outsourcing results, impact analysis, to gain insights into potential benefits of IT outsourcing as a part of a successful management strategy. Our analysis is formulated in a logistic regression framework, modified to allow for non-linear input–output relationships, auxiliary variables, and small sample sizes. We provide examples to illustrate how the methodology has been successfully implemented for targeting, tracking, and assessing outsourcing clients within IBM global services division.Scope and purposeThe predominant literature on IT outsourcing often examines various aspects of vendor–client relationship, strategies for successful outsourcing from the client perspective, and key sources of risk to the client, generally ignoring the risk to the vendor. However, in the rapidly changing market, a significant share of risks and responsibilities falls on vendor, as outsourcing contracts are often renegotiated, providers replaced, or services brought back in house. With the transformation of outsourcing engagements, the risk on the vendor's side has increased substantially, driving the vendor's financial and business performance and eventually impacting the value delivery to the client. As a result, only well-ran vendor firms with robust processes and tools that allow identification and active management of risk at all stages of the outsourcing lifecycle are able to deliver value to the client. This paper presents a framework and methodology for managing a portfolio of outsourcing projects from the vendor's perspective, throughout the entire outsourcing lifecycle. We address three key stages of the outsourcing process: (1) opportunity identification and qualification (i.e. selection of the most likely new clients), (2) client portfolio risk management during engagement and delivery, and (3) quantification of benefits to the client throughout the life of the deal.