Scatter search technique for exam timetabling

At universities where students enjoy flexibility in selecting courses, the Registrar’s office aims to generate an appropriate exam timetable for numerous courses and large number of students. An appropriate, real-world exam timetable should show fairness towards all students, respecting the following constraints: (a) eliminating or minimizing the number of simultaneous exams; (b) minimizing the number of consecutive exams; (c) minimizing the number of students with two or three exams per day (d) eliminating the possibility of more than three exams per day (e) exams should fit in rooms with predefined capacity; and (f) the number of exam periods is limited. These constraints are conflicting, which makes exam timetabling intractable. Hence, solving this problem in realistic time requires the use of heuristic approaches. In this work, we develop an evolutionary heuristic technique based on the scatter search approach for finding good suboptimal solutions for exam timetabling. This approach is based on maintaining and evolving a population of solutions. We evaluate our suggested technique on real-world university data and compare our results with the registrar’s manual timetable in addition to the timetables of other heuristic optimization algorithms. The experimental results show that our adapted scatter search technique generates better timetables than those produced by the registrar, manually, and by other meta-heuristics.     

A computer simulation model for studying the performance of coordinate measuring machines

The increasing trend toward computer-integrated manufacturing (CIM) in today's industry created a need for an effective process control. The objective of the inspection process is not only preventing shipment of defective parts but also providing a feedback to keep the manufacturing process in control. Through data processing capability, speed, and flexibility of operation, coordinate measuring machines (CMMs) play an important role for computer-integrated manufacturing (CIM). This paper introduces coordinate measuring machines and studies their performance. A computer simulation method for studying the performance of such machines working in a production line is developed. In this paper, CMM performance is measured by its speed and flexibility in performing measurements. In flexible manufacturing systems (FMS), CMMs serve as the inspection work station where arrival time of parts to be measured vary according to the flow of operations. The developed simulation model provides information about the machine, scheduled time for parts to be measured, and delay time for the measuring process.     

Selection of wild lactic acid bacteria isolated from traditional Egyptian dairy products according to production and technological criteria

A total of 755 wild lactic acid bacteria (LAB), belonging to Lactococcus, Lactobacillus, Enterococcus, Streptcoccus, Leuconostoc and Pediococcus isolated from Egyptian raw milk and its products were screened and selected according to their production and technological properties. Many strains showed high yield of biomass in fermentation and some strains were resistant to lyophilization conditions. Most strains showed a good separation after centrifugation, 2% of Lactococcus, 13% of Lactobacillus and 1% of Enterococcus were fast acidifying strains. Aminopeptidase and autolytic activity were generally higher for most lactobacilli compared to other strains. In milk cultures many strains were able to produce pleasant flavours. Antimicrobial activity was detected in 47% of Lactococcus, 21% of Lactobacillus and 41% of Enterococcus strains and some strains produced exopolysaccharides (slime and capsule). Several strains were able to maintain a high activity of two or three technological characteristics together. Detailed information about the characteristics of each strain is available in the culture collection of Faculty of Agriculture Alexandria University (FAAU). A wide variety of LAB from traditional Egyptian dairy products that showed potentially important properties are not only valuable for practical application but they may also provide an expanded gene pool for designing genetic modified strains with improved traits.     

Metaheuristic Algorithm for State-Based Software Testing

This article presents a metaheuristic algorithm for testing software, especially web applications, which can be modeled as a state transition diagram. We formulate the testing problem as an optimization problem and use a simulated annealing (SA) metaheuristic algorithm to generate test cases as sequences of events while keeping the test suite size reasonable. SA evolves a solution by minimizing an energy function that is based on testing objectives such as coverage, diversity, and continuity of events. The suggested method includes a “significance weight” assigned to events, which leads to important web pages and ensures coverage of relevant features by test cases. The experimental results demonstrate the effectiveness of simulated annealing and show that SA yields good results for testing web applications in comparison with other heuristics.     

Parallel physical optimization algorithms for allocating data to multicomputer nodes

Three parallel physical optimization algorithms for allocating irregular data to multicomputer nodes are presented. They are based on simulated annealing, neural networks and genetic algorithms. All three algorithms deviate from the sequential versions in order to achieve acceptable speedups. The parallel simulated annealing (PSA) and neural network (PNN) algorithms include communication schemes that are adapted to the properties of the allocation problem and of the algorithms themselves for maintaining both good solutions and reasonable execution times. The parallel genetic algorithm (PGA) is based on a natural model of evolution. The performances of these algorithms are evaluated and compared. The three parallel algorithms maintain the good solution qualities of their sequential counterparts. Their comparison shows their suitability for different applications. For example, PGA yields the best solutions, but it is the slowest of the three. PNN is the fastest, but it yields lower quality solutions. PSA's performance lies in the middle.     

Design and Implementation of a Wireless Medical Robot for Communication Within Hazardous Environments

Wireless Personal Communications - The huge spreading of COVID-19 viral outbreak to several countries motivates many of the research institutions everywhere in numerous disciplines to try...     

Allocating data to distributed-memory multiprocessors by genetic algorithms

We present three genetic algorithms (GAs) for allocating irregular data sets to multiprocessors. These are a sequential hybrid GA, a coarse-grain GA and a fine-grain GA. The last two are based on models of natural evolution that are suitable for parallel implementation; they have been implemented on a hypercube and a Connection Machine. Experimental results show that the three GAs evolve good suboptimal solutions which are better than those produced by other methods. The GAs are also robust and do not show a bias towards particular problem configurations. The two parallel GAs have reasonable execution times, with the coarse-grain GA producing better solutions for the allocation of loosely synchronous computations.     

Escherichia coli O157:H7 facilitates the penetration of Staphylococcus aureus into table eggs

Abstract: Fresh eggshells collected from a local farm were subjected to different levels of surface contamination with feces containing different levels (3 to 5 log₁₀) of Escherichia coli O157:H7 or Staphylococcus aureus and incubated at 3 different temperatures (10, 25, and 32 °C). The penetration rates of contaminating bacteria were followed throughout the incubation period by tracing bacterial presence in shell, shell membranes, albumen, and yolk. The study revealed the ability of both E. coli O157:H7 and enterotoxigenic S. aureus to grow on shell in feces, penetrate the shell, and move and multiply within egg contents at different rates and periods depending on bacterial type and incubation conditions. High temperatures (25 and 32 °C) increased penetration rate, whereas storage at 10 °C decreased significantly the rate of penetration. High levels of contamination with E. coli O157:H7 also shortened the time needed for the penetration process. Results showed that when eggshells were contaminated with both organisms simultaneously, the penetration of E. coli O157:H7 preceded that of S. aureus and facilitated the invasion of the latter bacteria.     

Allocating data to multicomputer nodes by physical optimization algorithms for loosely synchronous computations

Three optimization methods derived from natural sciences are considered for allocating data to multicomputer nodes. These are simulated annealing, genetic algorithms and neural networks. A number of design choices and the addition of preprocessing and postprocessing steps lead to versions of the algorithms which differ in solution qualities and execution times. In this paper the performances of these versions are critically evaluated and compared for test cases with different features. The performance criteria are solution quality, execution time, robustness, bias and parallelizability. Experimental results show that the physical algorithms produce better solutions than those of recursive bisection methods and that they have diverse properties. Hence, different algorithms would be suitable for different applications. For example, the annealing and genetic algorithms produce better solutions and do not show a bias towards particular problem structures, but they are slower than the neural network algorithms. Preprocessing graph contraction is one of the additional steps suggested for the physical methods. It produces a significant reduction in execution time, which is necessary for their applicability to large problems.     

Multi-class colour inspection of baked foods featuring support vector machine and Wilk’s λ analysis

An automated, intelligent system for the colour inspection of biscuit products is proposed. In this system, advanced classification techniques featuring Support Vector Machines (SVM) and Wilk’s λ analysis were used to classify biscuits into one of eight distinct groups, corresponding to different degrees of baking. The results of the analyses were compared using standard discriminant analysis employing direct and multi-step classifications. It was discovered that the directed acyclic graph (DAG) and the balanced binary tree (BBT) after Wilk’s λ were more precise in the classification, as compared to other classifiers. In all cases, these methods resulted in the correct classification rate of 87.25% and 86.75% for DAG and BBT, respectively. Since the algorithm was implemented using software, the system could be programmed to inspect other bakery products.