Of all the natural resources, water is unarguably the most essential and precious. Life began in water and life is nurtured by water. Ninety seven percent of the world's water is found in oceans. Only 2.5% of the world's water are non-saline fresh water. Saudi Arabia is a desert country with no permanent rivers or lakes and very little rainfall. Water is scarce and extremely valuable and with the country's rapid growth, the demand for water is increasing. Seven samples of water are collected, six samples from Wells (1-6) and the last sample from Al-Mallah Valley Dam, Mukhwa (7), Al-Mukhwah, in order to find impurities and pollutants and found some suitable solution. Some physical properties of water are measured such as turbidity, conductivity, pH and also, some pollutants such as iron, manganese, nitrate, nitrite fluoride, phosphate as well as calcium, magnesium, sulfate and chloride as well as detection of some microorganisms. The results shown that, the water of Al-Mallah Valley Dam has a high percentage of turbidity as a result of contamination of water with clay, plant residues and also some dead animals. On the other hand, the samples of ground water have high conductivity and high value of fluoride, nitrite, nitrate contents as well as Mn and Fe. Also the result of microorganisms showed the presence of some the water of Al-Mallah Valley Dam can be treated with a very simple method and become suitable for drinking. Also ground water can be treated with a suitable method to reduce the total hardness and some pollutants. But its content of fluoride is higher than that of gulf specifications so it must be treated before used.

This article draws on prior research studies and comparable measures to empirically investigate American views on what constitutes critical success and failure factors in negotiating with the Saudis. The findings from this new venue of research indicate that the Americans consider non-personal factors, such as technical expertise and financing terms, as more important than personal factors in successful negotiations with the Saudis. Moreover, among the failure factors studied, none were rated high in importance. Yet, from a Saudi viewpoint, Americans should not necessarily ignore these factors in their negotiations with the Saudis. Personal relationships may be important in generating pre-negotiation contacts, and cultural factors, linked to patience, language, and social customs, may help to prevent breakdowns in the negotiation process. Based on these conclusions, the study provides implications and draws some parallels with previous research on American perceptions.

Social media has become a major factor in people's lives, which affects their communication and psychological state. The widespread use of social media has formed new types of violence, such as cyberbullying. Manual detection and reporting of violent texts in social media applications are challenging due to the increasing number of social media users and the huge amounts of generated data. Automatic detection of violent texts is language-dependent, and it requires an efficient detection approach, which considers the unique features and structures of a specific language or dialect. Only a few studies have focused on the automatic detection and classification of violent texts in the Arabic Language. This paper aims to build a two-level classifier model for classifying Arabic violent texts. The first level classifies text into violent and non-violent. The second level classifies violent text into either cyberbullying or threatening. The dataset used to build the classifier models is collected from Twitter, using specific keywords and trending hashtags in Saudi Arabia. Supervised machine learning is used to build two classifier models, using two different algorithms, which are Support Vector Machine (SVM), and Naive Bayes (NB). Both models are trained in different experimental settings of varying the feature extraction method and whether stop-word removal is applied or not. The performances of the proposed SVM-based and NB-based models have been compared. The SVM-based model outperforms the NB-based model with F1 scores of 76.06%, and 89.18%, and accuracy scores of 73.35% and 87.79% for the first and second levels of classification, respectively.

The cold and hot water extracts of Cumin (Cuminum cyminum), Thyme (Thymus vulgaris) and Anise (Piminella anisum) were examined for their antimicrobial activity against some food borne pathogens such as Escherichia coli, Salmonella typhimurium and Staphylococcus aureus subsp. aureus and food spoilage bacteria such as Bacillus cereus and B. stearothermophilus. The hot extraction method of the used plant parts gave a pronounced inhibition effect as well as bactericidal activity more than the cold extraction method. It was found that hot water extracts of cumin and thyme have the strongest inhibition effect on E coli, S. typhimurium and Staph. aureus subsp. aureus when experimented by disc diffusion assay method. Moreover, E. coli was also strongly inhibited by the hot water extract of anise. On the other hand, B. cereus and B. stearothermophilus were slightly inhibited by hot extract of cumin and thyme. Whereas, Anise extracts show no inhibition effect on Staph. aureus subsp. aureus, B. cereus and B. stearothermophilus. The extracts which exhibit strong inhibition effect were examined for their bactericidal activity by using of kill curve method. It was found that cumin and thyme extracts have bactericidal effect on E. coli, S. typhimurium and Staph. aureus subsp. aureus. While, Anise extract has bactericidal effect only on E. coli.