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BackgroundFreon includes a number of gaseous, colorless chlorofluorocarbons. Although freon is generally considered to be a fluorocarbon of relatively low toxicity; significantly detrimental effects may occur upon over exposure. The purpose of the present study is to investigate whether occupational exposure to fluorocarbons can induce arterial hypertension, myocardial ischemia, cardiac arrhythmias, elevated levels of plasma lipids and renal dysfunction.MethodsThis comparative cross-sectional study was conducted at the cardiology clinic of the Suez Canal Authority Hospital (Egypt). The study included 23 apparently healthy male workers at the refrigeration services workshop who were exposed to fluorocarbons (FC 12 and FC 22) and 23 likewise apparently healthy male workers (unexposed), the control group. All the participants were interviewed using a pre-composed questionnaire and were subjected to a clinical examination and relevant laboratory investigations.ResultsThere were no significant statistical differences between the groups studied regarding symptoms suggesting arterial hypertension and renal affection, although a significantly higher percentage of the studied refrigeration services workers had symptoms of arrhythmias. None of the workers had symptoms suggesting coronary artery disease. Clinical examination revealed that the refrigeration services workers had a significantly higher mean pulse rate compared to the controls, though no significant statistical differences were found in arterial blood pressure measurements between the two study groups. Exercise stress testing of the workers studied revealed normal heart reaction to the increased need for oxygen, while sinus tachycardia was detected in all the participants. The results of Holter monitoring revealed significant differences within subject and group regarding the number of abnormal beats detected throughout the day of monitoring (p < 0.001). There were no significant differences detected in the average heart rate during the monitoring period within subject or group. Most laboratory investigations revealed absence of significant statistical differences for lipid profile markers, serum electrolyte levels and glomerular lesion markers between the groups except for cholesterol and urinary β2-microglobulin (tubular lesion markers) levels which were significantly elevated in freon exposed workers.ConclusionsUnprotected occupational exposure to chlorofluorocarbons can induce cardiotoxicity in the form of cardiac arrhythmias. The role of chlorofluorocarbons in inducing arterial hypertension and coronary artery diseases is unclear, although significantly elevated serum cholesterol and urinary β2-microglobulin levels raise a concern.

Branch and Bound technique is commonly used for intelligent search in finding a set of integer solutions within a space of interest. The corresponding binary tree structure provides a natural parallelism allowing concurrent evaluation of subproblems using parallel computing technology. While the master-worker paradigm is successfully used in many parallel applications as a common framework to implement parallel applications, it has drawbacks when a large number of computing resources are connected via WAN. A supervisor-master-sub-master-worker algorithm has been proposed. From the solved benchmark example this algorithm proved to provide a considerable save of time. Results show that a consistently better efficiency can be achieved in solving integer equations, providing reduction of time. The hierarchical supervisor-master-sub-master-worker algorithm sustains good performance revealed from the knapsack problem solved as a benchmark example.

The real-world data analysis and processing using data mining techniques often are facing observations that contain missing values. The main challenge of mining datasets is the existence of missing values. The missing values in a dataset should be imputed using the imputation method to improve the data mining methods’ accuracy and performance. There are existing techniques that use k-nearest neighbors algorithm for imputing the missing values but determining the appropriate k value can be a challenging task. There are other existing imputation techniques that are based on hard clustering algorithms. When records are not well-separated, as in the case of missing data, hard clustering provides a poor description tool in many cases. In general, the imputation depending on similar records is more accurate than the imputation depending on the entire dataset's records. Improving the similarity among records can result in improving the imputation performance. This paper proposes two numerical missing data imputation methods. A hybrid missing data imputation method is initially proposed, called KI, that incorporates k-nearest neighbors and iterative imputation algorithms. The best set of nearest neighbors for each missing record is discovered through the records similarity by using the k-nearest neighbors algorithm (kNN). To improve the similarity, a suitable k value is estimated automatically for the kNN. The iterative imputation method is then used to impute the missing values of the incomplete records by using the global correlation structure among the selected records. An enhanced hybrid missing data imputation method is then proposed, called FCKI, which is an extension of KI. It integrates fuzzy c-means, k-nearest neighbors, and iterative imputation algorithms to impute the missing data in a dataset. The fuzzy c-means algorithm is selected because the records can belong to multiple clusters at the same time. This can lead to further improvement for similarity. FCKI searches a cluster, instead of the whole dataset, to find the best k-nearest neighbors. It applies two levels of similarity to achieve a higher imputation accuracy. The performance of the proposed imputation techniques is assessed by using fifteen datasets with variant missing ratios for three types of missing data; MCAR, MAR, MNAR. These different missing data types are generated in this work. The datasets with different sizes are used in this paper to validate the model. Therefore, proposed imputation techniques are compared with other missing data imputation methods by means of three measures; the root mean square error (RMSE), the normalized root mean square error (NRMSE), and the mean absolute error (MAE). The results show that the proposed methods achieve better imputation accuracy and require significantly less time than other missing data imputation methods.

This paper presents a novel cuckoo search algorithm called elite opposition-cuckoo search algorithm (ECS) for solving integer programming problems. The opposite solution of the elite individual in the population is generated by an opposition-based strategy in the proposed algorithm and form an opposite search space by constructing the opposite population that locates inside the dynamic search boundaries, then, the search space of the algorithm is guided to approximate the space in which the global optimum is included by simultaneously evaluating the current population and the opposite one. The results show that ECS algorithm has faster convergence speed, higher computational precision and is more effective for solving integer programming problems.

Branch and Bound technique (B&B) is commonly used for intelligent search in finding a set of integer solutions within a space of interest. The corresponding binary tree structure provides a natural parallelism allowing concurrent evaluation of subproblems using parallel computing technology. Flower pollination Algorithm is a recently-developed method in the field of computational intelligence. In this paper is presented an improved version of Flower pollination Meta-heuristic Algorithm, (FPPSO), for solving integer programming problems. The proposed algorithm combines the standard flower pollination algorithm (FP) with the particle swarm optimization (PSO) algorithm to improve the searching accuracy. Numerical results show that the FPPSO is able to obtain the optimal results in comparison to traditional methods (branch and bound) and other harmony search algorithms. However, the benefits of this proposed algorithm is in its ability to obtain the optimal solution within less computation, which save time in comparison with the branch and bound algorithm.

Optimization is a more important field of research. With increasing the complexity of real-world problems, the more efficient and reliable optimization algorithms vital. Traditional methods are unable to solve these problems so, the first choice for solving these problems becomes meta-heuristic algorithms. Meta-heuristic algorithms proved their ability to solve more complex problems and giving more satisfying results. In this paper, we introduce the more popular meta-heuristic algorithms and their applications in addition to providing the more recent references for these algorithms.

In this paper, a hybridization of two different swarm intelligent approaches, stochastic diffusion search, and particle swarm optimization techniques is presented  for solving integer programming problems. The hybrid implementation allows us to avoid certain drawbacks and weaknesses of each algorithm, which means that we are able to find an optimal solution in an acceptable computational time. Our hybrid implementation allows the IP algorithm to reach the optimal solution in a considerably shorter time than is needed to solve the model using the entire dataset directly within the model. Our hybrid approach outperforms the results obtained by each technique separately. It is able to find the optimal solution in a shorter time than each technique on its own, and the results are highly competitive with the state-of-the-art in large-scale optimization. Furthermore, according to our results, combining the PSO with SDS approach for solving IP problems appears to be an interesting research area in combinatorial optimization.Â