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Background In arid regions such as Jennawen, Libya, groundwater is the primary source for drinking and irrigation, yet it is highly vulnerable to geogenic and anthropogenic contamination. This study aimed to assess the chemical and microbiological quality of 26 groundwater samples from rural and remote, including desalinated water. Material and Methods Physico-chemical parameters (e.g. pH, salinity, major ions) and microbiological indicators (total coliforms, E. coli, total bacterial counts) were analyzed according to established standards. Results revealed that most untreated groundwater samples exceeded the permissible limits for salinity, hardness, and sulfate. Furthermore, microbial bacterial contamination was found at various locations. While some RO desalinated water was free from microbiological contaminants, it was severely deficient in essential minerals. Conclusion The groundwater in the investigated area requires proper attention. Research and management strategies are critically needed to address both chemical and microbial contamination while maintaining an optimal mineral balance in potable water.

The potential association between ABO/Rh blood groups and diabetes mellitus (DM) susceptibility remains a topic of global epidemiological interest, with population-specific findings. This systematic review aims to synthesize and analyze all available evidence on the distribution of ABO and Rh blood groups among diabetic patients across all cities in Libya to determine any consistent national pattern or association. A systematic search was conducted for studies published between 2010 and 2024. Electronic databases (PubMed, Google Scholar, Scopus) and Libyan journal archives were searched. Keywords included "ABO blood group," "Rhesus factor," "diabetes mellitus," "Libya," and specific city names. Observational studies reporting ABO/Rh frequencies in Libyan diabetic patients and controls were included. Data on study characteristics, blood group distribution, and odds ratios were extracted. Four studies from four major Libyan cities (Tripoli, Benghazi, Zliten, Zintan) met the inclusion criteria, encompassing 2,819 participants (1,919 diabetic patients and 900 controls). The pooled prevalence of blood group O was highest in both cases and controls. A meta-analysis of the pooled data revealed a significant association between blood group B and DM (OR: 1.52, 95% CI: 1.24-1.86, p < 0.001). Studies from Tripoli and Benghazi individually reported this significant association, while studies from Zliten and Zintan did not. No significant association was found between Rh factor and DM risk (OR: 1.08, 95% CI: 0.82-1.42, p = 0.59). This first systematic review from Libya indicates a potential national-level association between blood group B and an increased risk of diabetes mellitus. The lack of significance in two smaller studies highlights the need for larger, standardized, multi-center national research to confirm this finding and explore underlying genetic and environmental modifiers. The ABO blood group could be considered a modest genetic risk marker in the Libyan population.

Background: Hemophilia is a rare inherited bleeding disorder with significant public health implications. Limited epidemiological data are available on haemophilia in Libya, making evidence-based planning and care. Objective: To characterize hemophilia patients in Libya by examining their epidemiological, challenging clinical, and laboratory features, exploring correlations among demographic and clinical parameters, and providing an overview to improve care and analyze the frequency and patterns of bleeding disorders. Methods: A cross-sectional study was conducted on haemophilia patients in various regions of Libya, focusing on those registered with the Libyan Association for Hemophilia. Data were collected through structured questionnaires and laboratory records, covering demographics, haemophilia types, severity, coagulation deficiencies, antibody presence, bleeding patterns, family history, comorbidities, and treatment practices. Statistical analysis included descriptive statistics and chi-square tests, with significance set at p < 0.05. Results: A total of 230 patients were included. Most cases were concentrated in Tripoli (36.1%), followed by Zliten (8.3%), Benghazi (7%), and Al Khoms (5.2%). Haemophilia A was the most common type (67.8%), followed by haemophilia B (10.9%), von Willebrand disease (9.6%), and haemophilia C (3%). Factor VIII deficiency predominated (68.3%). Moderate severity was most frequent (50%), followed by severe (37%) and mild (13.5%) cases. Most patients were antibody-negative (80%). Joint bleeding was the most frequent site (36.5%). Kinship between parents was reported in 49.1% of cases, and 66.5% of participants reported relatives with haemophilia. Conclusion: This study highlights the predominance of haemophilia A and factor VIII deficiency in Libya, with a high proportion of moderate-to-severe cases. Joint bleeding remains the most frequent clinical complication, and consanguinity plays a considerable role in disease occurrence. Early diagnosis and targeted prevention strategies are needed to improve patient outcomes.

Abstract

The Car manufacturing sector represents a major focus in the development of the automotive industry. In this research paper, a proposed data mining application for the automotive manufacturing sector is explained and tested. The dataset was retrieved from the machine learning repository at the University of California, Irvine. This research paper aims to create a more reliable classifier for future object classification. Classification is an important technique in data mining. It is a supervised learning process that involves classifying an object into one of the predefined classes based on its attributes. In this paper, we use a large database containing 7 attributes and 1,728 instances. We compare the

results of a simple classification technique (using the J48 decision tree inference algorithm and MONK) with results based on different parameters using WEKA (Waikato Environment Knowledge Analysis), a data mining tool. The results of the experiment show a comparison between three algorithms to see which is the best and least error-prone algorithm. The physical characteristics of a car viz . Engine location ,price, how many doors, stroke, city fuel consumption, and other factors determine a vehicle's performance. Therefore, developing such a classification, although a huge undertaking, is absolutely essential in the car industry. Machine learning techniques can help integrate computer-based systems to predict vehicle quality and improve system efficiency. Classification models were trained using 214 datasets. The predicted values of the classifiers were evaluated using 10-fold cross-validation, and the results were compared.

Keywords: Data mining, Machine learning techniques, J48, decision trees, Car market, WEKA classification.