The Impacts of Human Resource Management Practices on Company Labor Productivity: Empirical Evidence from Iron and Steel Company in Libya

This paper investigates the relationship between human resource management practices  and labor productivity for the iron and steel Libyan company. This paper operationally defined human resource practices as recruitment and selection, training and development, performance appraisal, communication, compensation, teamwork and employment security. Also, productivity measured as labor productivity. The data was collected using the questionnaire that consists of questions with 5-points Likert scales distributed to our samples of 386 employees. By using a stepwise multiple regression analysis, it is found that, all practices had strong positive correlate and effect on each of labor productivity

إسهامات المجتمع الدولي في مكافحة ظاهرة الهجرة غير الشرعية = Contributions of the international community in combating the phenomenon of Illegal Immigration

ملخ ص يهدف هذا البحث إلى التعرف على ظاهرة الهجرة غير الشرعية ومسؤولية المجتمع الدولي تجاه هذه الظاهرة، وكذلك يهدف إلى بيان أسباب ودوافع ومبررات الهجرة غير الشرعية. ولقد اتبع الباحث المنهج الاستقرائي والمنهج المقارن لاستقراء المصادر المتعلقة بماهية الهجرة غير الشرعية. ولقد توصل الباحث إلى العديد من النتائج من بينها: معرفة التطور التاريخي للهجرة غير الشرعية والمراحل التي تعرضت لها الهجرة غير الشرعية منذ بدايتها بعد الحرب العالمية الثانية وصولا إلى مرحلة تشجيع الهجرة من جانب الدول الأوروبية لتحقيق أهداف التنمية في هذه الدول وصولا إلى مرحلة وقف الهجرة ومحاربتها

An Improved LEACH Algorithm Based On Fuzzy C-Means Algorithm And Distributed Cluster Head Selection Mechanism.

The beneficial capability of Wireless Sensor Networks (WSNs) to check numerous types of environmental settings via detection mechanisms on the physical state of matters discovered through researches, has attracted great interest in many quarters. Significant necessities in a majority of applications related to sensor networks are the extensive duration of network lifespan. Towards the fulfillment of such requirements, the clustering sensor nodes is an efficient method for the purpose of achieving these ends. The fundamental goal of WSN routing protocol are in maintaining the equilibrium of network energy usage, and in stretching the total network lifespan. The most famous protocol that utilized clustering technique is Low Energy Adaptive Clustering Hierarchy (LEACH). In LEACH algorithm, the random manner is used to select specific nodes as a cluster heads. In addition, the resulting clusters suffer from an imbalance in cluster size. In this study, the LEACH issues are solved by utilizing Fuzzy C-means algorithm (FCM) to form balanced clusters,which is followed by the selection of the cluster head in each cluster according to the energy of nodes and distance to the cluster centroid. The comparison of therecommended algorithm with LEACH algorithm is conducted, in relations to energy consumption and network lifetime. The result showed that our proposed algorithm has further advantage over LEAC

Clustering Approach In Wireless Sensor Networks Based On K-Means: Limitations And Recommendations

Clustering approach in wireless sensor network is very important, the structure of cluster and how to improve it is a first challenge that faced the developers, because of it represent as a base for design the cluster-based routing protocol. One of most popular cluster algorithms that utilizing into organize sensor nodes is K-means algorithm. This algorithm has beneficial in construct the clusters for various real-world applications of WSN.K-means algorithm suffering from many drawbacks that hampering his work.The lack of adequate studies that investigates in the limitations of this algorithm and seek to propose the solutions motivated us to do this study. In this paper the limitations of K-means and some suggestions are proposed. These suggestions can improve the performance of K-means, which will be reflected on saving the energy forsensor nodes and consequently maximize the lifetime of the wireless sensor networks

A review of coagulation explaining its definition, mechanism, coagulant types, and optimization models; RSM, and ANN

The textile business is one of the most hazardous industries since it produces several chemicals, such as dyes, which are released into water streams with ef-fluents. For the survival of the planet's life and the advancement of humanity, water is a crucial resource. One of the anthropogenic activities that pollute and consume water is the textile industry. Thus, the purpose of the current effort is to Apply coagulation as a Physico-chemical and biological treatment strat-egy with different techniques and mechanisms to treat the effluent streams of textile industries. The discharge of these effluents has a negative impact on the environment, marine life, and human health. Therefore, the treatment of these effluents before discharging is an important matter to reduce their adverse ef-fect. Many physico-chemical and biological treatment strategies for contaminants removal from polluted wastewater have been proposed. Coagulation is thought to be one of the most promising physico-chemical strategies for removing con-taminants and colouring pollutants from contaminated water. Coagulation is accompanied by a floculation process to aid precipitation, as well as the collection of the created sludge following the treatment phase.. Different commercial, and natural coagulants have been applied as a coagulants in the process of coagulation. Additionally, many factors such as; pH, coagulant dose, pollu-tants concentration are optimized to obtain high coagulants removal capacity. This review will discuss the coagulation process, coagulant types and aids in addition to the factors affecting the coagulation process. Additionally, a brief comparison between the coagulation process, and the other processes; princi-ple, advantages, disadvantages, and their efficiency were discussed throgh the review. Furthermore, it discusses the models and optimization techniques used for the coagulation process including response surface methodology (RSM), ar-tificial neural network (ANN), and several metaheuristic algorithms combined with ANN and RSM for optimization in previous work. The ANN model has more accurate results than RSM. The ANN combined with genetic algorithm gives an accurate predicted optimum solution

Potentials of algae-based activated carbon for the treatment of M.orange in wastewater

Activated carbon is a promising material with high efficiency in dye removal from polluted wastewater. However, commercial activated carbon is expensive and generates black color in the medium. Therefore, searching for low-cost, eco-friendly activated carbon sources such as agricultural wastes and algal biomasses is essential. Hence, this study is directed to prepare the physical and the H3PO4 chemical activated carbon from the algae ”Sargassum dent folium” and the raw algae itself and apply it for Methyl Orange (M. orange) removal from contaminated wastewater and compare its performance with the commercial activated carbon. First, adsorbent materials are prepared and involved in the optimization process for M. orange removal using some preliminary experiments, followed by Response Surface Method-ology (RSM) and Artificial Neural Network (ANN). Finally, Isotherm and kinetics are studied to explain the adsorption mechanism. In contrast to other materials, results show that physical algae-activated carbon achieves the maximum removal efficiency of 96.687%. These results are obtained from ANN combined with Moth Search Algorithm (MSA), representing the most effective model for achieving the highest M. orange removal efficiency from Physical algae activated carbon. In the algae case, the best experimental and predicted removal efficiencies are 85.9407 RE%, 88.5 indicated RSM RE%, and 85.9431 predicted ANN RE%. The best observed and predicted removal efficiencies for the H3PO4 chemical activated carbon are 89.6157 RE%, 82.38 predicted RSM RE%, and 89.5442 predicted ANN RE%. The best experimental and predicted removal efficiencies for the physical-activated carbon are 94.7935 RE%, 95.49 indicated RSM RE%, and 95.4298 predicted ANN RE%. The best observed and predicted removal efficiencies for the commercial-activated carbon are 92.2659 RE%, 96.65 predicted RSM RE%, and 92.2658 predicted ANN RE%. In the algae case, the best experimental and predicted removal efficiencies are 85.9407 %RE, 88.5 predicted RSM RE %, and 85.9431 expected ANN RE%. For the H3PO4 chemical activated carbon, the best experimental and predicted removal efficiencies are 89.6157%RE, 82.38 indicated RSM RE%, and 89.5442 predicted ANN RE%. For the physical-activated carbon, the best observed and predicted removal efficiencies are 94.7935 %RE, 95.49 predicted RSM RE%, and 95.4298 indicated ANN RE%. For the commercial-activated carbon, the best experimental and predicted removal efficiencies are 92.2659 %RE, 96.65 predicted RSM RE%, and 92.2658 predicted ANN RE%. This study intends to treat industrial wastewater contaminated with the anionic M. orange dye using raw algae and their generated activated carbon (physical and chemical forms), which are economical. It then compares the results to the effectiveness of commercial activated carbon. In the state of the raw algae, Temkin and Langmuir isotherm models best suit the data, while Temkin agrees well with the data from physical-activated carbon. Temkin and Freundlich's models are fitted with the H3PO4 chemical activated carbon. The model that fits the raw algae physically activated carbon and H3PO4 chemical-activated carbon the best is pseudo-second-order kinetics. Future research could examine the produced activated carbon-based algae's capacity to extract more contaminants from contaminated wastewater. This study intends to treat industrial wastewater contaminated with the anionic M. orange dye using raw algae and their generated activated carbon (physical and chemical forms), which are economical. It next compares the results to the effectiveness of commercial activated carbon