African Journal of Advanced Pure and Applied Sciences

Enterprise Integration Challenges in Libya's Digital Transformation Journey: A Government Service Bus Perspective

2026-07-08T11:52:25+00:00

Government digital transformation initiatives increasingly depend on secure and governed interoperability among public-sector institutions. Yet fragmented information systems, organizational silos, and the absence of common integration standards continue to impede the delivery of integrated government services—challenges particularly pronounced in emerging digital government environments such as Libya. This paper presents a Government Service Bus (GSB) reference architecture grounded in Enterprise Architecture principles and evaluates its practical applicability through the Nawa interoperability pilot, a GSB-based proof-of-concept connecting four Libyan governmental institutions: the Tax Authority, Commercial Registry, Central Bank of Libya, and Customs Authority. Implemented by a five-member team within approximately one month using exclusively open-source technologies, the pilot successfully deployed 23 cross-agency services supporting taxpayer verification, compliance assessment, and tax-clearance workflows. The solution demonstrated that a standards-based interoperability platform can substantially reduce integration complexity. For the four participating institutions, the required integration links were reduced from six point-to-point connections to four governed interfaces while preserving institutional autonomy and existing information systems. The proposed solution was subsequently awarded first place in a national interoperability hackathon organized under the patronage of the General Information Authority and Expertise France in Libya. The findings indicate that successful interoperability initiatives require governance structures, regulatory modernization, and organizational readiness alongside technical integration capabilities. The paper further discusses governance, organizational, and regulatory considerations, together with a phased roadmap for scaling GSB adoption within Libya's emerging digital government ecosystem.

Larvicidal Activity of Some Heterocyclic Thiosemicarbazones and Semicarbazones Derivatives Against Anopheles arabiensis Larvae

2026-07-11T20:30:33+00:00

Malaria remains one of the most significant vector-borne diseases worldwide and is transmitted through the bites of infected female Anopheles mosquitoes. The increasing prevalence of insecticide resistance has substantially compromised the effectiveness of conventional chemical control measures, necessitating the development of novel, potent, and environmentally sustainable larvicides. In this study, the larvicidal activity of three thiosemicarbazone derivatives (TS1–TS3) and three semicarbazone derivatives (SC1–SC3) was evaluated against Anopheles arabiensis larvae following standard World Health Organization (WHO) bioassay procedures. Larvae were exposed to different concentrations of the tested compounds, and mortality rates were recorded after 24 and 48 h of exposure. Lethal concentration (LC₅₀) values were estimated using probit and logit analyses. The thiosemicarbazone derivatives exhibited clear concentration- and time-dependent larvicidal activity, whereas the semicarbazone derivatives exhibited no detectable larvicidal activity under the tested conditions. Among the active compounds, TS3 demonstrated the highest potency, with the LC₅₀ value decreasing markedly from 248 ppm after 24 h to 107 ppm after 48 h of exposure. Statistical analysis revealed strong mortality–concentration relationships, with coefficients of determination (R²) equal to or greater than 0.80. These findings indicate that the larvicidal activity observed in this study was restricted to thiosemicarbazone derivatives, while semicarbazone derivatives were inactive against An. arabiensis larvae under the tested conditions. Thiosemicarbazone derivatives therefore represent promising candidates for mosquito control programs, although further studies on structural optimization, mode of action, and environmental safety are required before practical application.

Morphological Characterization and Spatial Distribution of Dactylopius opuntiae (Hemiptera: Dactylopiidae) in Northwestern Libya

2026-07-12T16:32:15+00:00

This research investigates the cochineal insect Dactylopius opuntiae (Cockerell) (Hemiptera: Dactylopiidae), a newly recorded invasive pest in Libya. This study investigates on mapping the spatial distribution of this insect across municipalities in Al-Jafara region in northwestern Libya, which represents the primary infestation site in the country. In addition, the study aimed to identify the precise morphological and field characteristics of this species. The results confirm that D. opuntiae was the dominant species throughout all surveyed municipalities of Al-Jafara. Morphological identification was confirmed using diagnostic microscopic characters, particularly the distribution of Wide-Rimmed Pores (WRP). Heat maps reveal that insect density and infestation severity were highest in the northwestern municipalities and gradually declined toward the south and southeast. Infestation severity varied markedly among municipalities, ranging from severe to low levels, the highest infestation severity was recorded in Al-Maamoura (86.4%), followed by Al-Maya (80.9%). These findings indicate that the rapid and severe spread of this pest urgently requires an Integrated Pest Management (IPM) strategy combining mechanical, chemical, and biological control methods to reduce population levels, limit further spread, and achieve long-term suppression through sustainable biological control and resistant host varieties.

Selection Indices for The Friesian Heifers' Weights in Libya

2026-07-13T12:00:16+00:00

This study constructed restricted, multi source, and desired gain selection indices for two growth traits – birth weight (BW) and weight at service (WS) – using data from 548 female Friesian heifers that had complete records for both traits at alnasir project in period(1983-1994) . Economic values were derived by the Beta regression method with the goal of improving WS while restricting BW (a_WS = 1, a_BW = 0). Eight indices were constructed: general (I1), partial restriction on BW at 25 % (I2), 50 % (I3), 75 % (I4), complete restriction (I5), multi source (I6), and two desired gain indices (I7), (I8). The general index gave an accuracy (RIh) of 0.464. Partial restriction at 75 % (I4) maintained good accuracy (RIh = 0.417, RE = 89.9 %) and limited BW increase to 0.59 kg/generation. Complete restriction (I5) reduced accuracy to 0.320 (RE = 69.0 %). The multi source index achieved the highest accuracy (RIh = 0.501, RE = 108.0 %). Desired gain indices achieved specified targets in about one generation at i = 1. It is recommended to use partially restricted indices when BW is near its optimum, and multi source or desired gain indices when higher accuracy or specific targets are needed.

Improving Coding Performance Using Numerical Methods and an Applied Study on Elliptical Curves

2026-07-13T17:31:23+00:00

Modern digital infrastructures, including smart cities and the Internet of Things (IoT), increasingly require secure and efficient cryptographic mechanisms to ensure data confidentiality and privacy. Elliptic Curve Cryptography (ECC) has become one of the most widely utilized encryption schemes because it provides strong security with relatively small key sizes compared to traditional algorithms such as RSA. However, the practical deployment of ECC encounters several numerical and computational challenges related to efficiency and precision, especially when operating over large finite fields or within resource‑constrained devices. This research addresses these challenges by integrating advanced numerical methods to improve the performance of ECC algorithms. The study examines the core arithmetic operations in ECC—specifically point addition and point multiplication—and identifies critical computational stages that can benefit from numerical optimization. A set of numerical techniques is reviewed and applied, including the Newton–Raphson method to accelerate modular inverse calculations, numerical integration methods such as the trapezoidal rule and Simpson’s rule to approximate nonlinear elliptic‑curve functions, and fast multiplication algorithms.

To validate the proposed approach, an applied experimental study was conducted using MATLAB. Experimental codes were developed to implement ECC algorithms enhanced with the selected numerical methods. Performance evaluation was based on key metrics such as execution time, computational accuracy, and numerical error magnitude. The study also explores the potential use of artificial intelligence techniques, including machine learning algorithms, to optimize initial parameter selection and accelerate convergence in iterative processes.

The results demonstrate clear improvements in execution time and significant reductions in numerical error, confirming the effectiveness of numerical methods in enhancing ECC performance for modern resource‑limited systems.

The Impact of Geographic Location on the Variation of Cosmic Ray Radiation Dose: An Analytical Study

2026-07-15T11:06:22+00:00

This paper investigates the relationship between geographic location and atmospheric cosmic radiation doses. The study evaluates the individual effects of latitude, longitude, and altitude above sea level on the absorbed dose rate in ambient air. To isolate these geographical variables, the influence of the solar cycle was eliminated by conducting all calculations during a period of solar minimum, specifically spanning the transition between the end of Solar Cycle 24 and the onset of the current Solar Cycle 25. The findings demonstrate a strong correlation between geographic coordinates and atmospheric cosmic ray exposure. Altitude above sea level emerged as the primary determinant governing the absorbed dose. Latitude was found to be the second most significant factor, followed closely by longitude. Furthermore, the cosmic-ray-induced effective dose was calculated for selected Libyan cities to quantify and illustrate the direct impact of geographic variation on the continuous baseline background radiation exposure to the human population.

Effect of adding wild chamomile (Matricaria chamomilla L.) powder to rabbit diets on certain blood biochemical parameters

2026-07-15T12:00:11+00:00

The experiment was conducted on 27 rabbits in the city of Sirte during the spring of 2025 (from April 3 to May 13, 2025). The rabbits were divided into three treatment groups, with 9 rabbits per group distributed across three replicates (3 rabbits per replicate): T1 (control) received a standard diet without additives; T2 received a standard diet supplemented with chamomile powder at a rate of 0.4 g/kg of feed; and T3 received a standard diet supplemented with chamomile powder at a rate of 0.6 g/kg of feed. The experiment lasted 40 days. Three rabbits were randomly selected from each treatment group, and blood samples were collected via cardiac puncture. Levels of glucose, urea, creatinine, cholesterol, triglycerides, potassium, and sodium were measured. The results showed a significant difference (P<0.05) in the levels of glucose, creatinine, cholesterol, triglycerides, sodium, and potassium in the treated groups (T2 and T3) compared to the control group (T1), whereas no significant difference (P<0.05) was observed in urea levels.

Developing an AI-Based Digital Twin for Predictive Quality Control in 3D Printing Processes

2026-07-15T12:14:11+00:00

Additive manufacturing can produce complex parts, yet print quality often changes between runs. Small changes in temperature, speed, material, or layer settings may cause rough surfaces and weak parts. This paper develops an AI-based digital twin for predictive quality control in 3D printing. The twin joins machine data, process settings, quality models, and operator decisions within one synchronized system. It predicts surface roughness, tensile strength, and elongation before final inspection. A public material-extrusion dataset provides the practical test. The dataset contains 70 printing runs from an Ultimaker S5 printer. Nine process settings and three measured quality outcomes are available. One invalid record was removed before model training. Ridge regression, random forest, gradient boosting, and extra trees were evaluated. Repeated five-fold cross-validation reduced dependence on one favorable data split. Gradient boosting achieved the lowest average normalized error across the three outcomes. Its out-of-fold R² reached 0.90 for roughness, 0.53 for tensile strength, and 0.54 for elongation. The mean absolute errors were 21.49 µm, 4.50 MPa, and 0.39%, respectively. A replay test converted these predictions into simple quality alerts. The alert logic reached 79.1% balanced accuracy under illustrative acceptance limits. Results show useful predictive value, but they do not prove industrial readiness. Larger datasets, sensor streams, uncertainty limits, and printer-specific validation remain necessary. The proposed structure offers a practical path from offline machine learning toward safe, traceable, closed-loop quality control.

Enhancing productivity of onion plant using a mixture of organic and chemical fertilizers with charcoal powder

2026-07-15T12:25:37+00:00

This study aimed to evaluate the effect of the interaction between chemical and organic fertilizers and different charcoal levels (0%, 5%, and 10%) on the growth of onion (Allium cepa L.) and its content of essential mineral nutrients. The experiment included three treatments consisting of chemical fertilization (20–20–20 NPK and diammonium phosphate fertilizer, DAP 18–46%) combined with organic fertilization and varying charcoal application rates. Several vegetative and root growth parameters were evaluated, in addition to the plant contents of nitrogen, phosphorus, and potassium.

The results demonstrated that the third treatment (chemical fertilization + organic fertilization + 10% charcoal) significantly outperformed the other treatments in most of the studied traits. It recorded the highest mean plant height (57.76 cm) and the greatest mean leaf length (44.39 cm), along with superior root system development. Chemical analysis also revealed a significant increase in nutrient content in plants treated with 10% charcoal, with the highest values recorded for nitrogen (1.66%), phosphorus (0.31%), and potassium (2.14%).

This improvement may be attributed to the role of charcoal in enhancing the physical and chemical properties of the soil, increasing its capacity to retain nutrients, and improving their availability to plants. The findings indicate that integrating 10% charcoal with chemical and organic fertilization represents an effective strategy for improving onion growth and enhancing the uptake of essential nutrients.

The Contribution of AI Techniques to Enhancing Software Requirements Quality: A Hybrid Explainable RAG Framework for Automated Software Requirements Quality Assessment

2026-07-22T10:47:34+00:00

Software requirements quality is a critical determinant of downstream project success, yet manual review of requirements specifications for ambiguity, incompleteness, inconsistency, and related defects remains labor-intensive and inconsistent across reviewers. This paper proposes a hybrid, explainable, retrieval-augmented framework that combines Sentence-BERT embeddings, FAISS-based retrieval over established requirements-engineering standards (ISO/IEC 29148, IEEE 830, SWEBOK, and a requirements-smells catalogue), a large language model for defect detection, natural-language explanation, and rewriting, and SHAP-based explainability for transparent quality scoring. We construct a Unified Requirements Quality Dataset (URQD) by merging and re-labelling three public sources - the PURE dataset, the PROMISE repository, and a requirements-smells dataset - and benchmark the proposed SBERT+XGBoost hybrid classifier against classical machine-learning baselines (SVM, Random Forest, XGBoost over TF-IDF features) and fine-tuned transformer models (BERT, RoBERTa, DeBERTa-v3). On a held-out test split, the proposed hybrid model achieves 97.31% accuracy and an F1-score of 0.9692, outperforming all TF-IDF-based baselines and improving Matthews Correlation Coefficient by 18.2 percentage points over the strongest baseline. Ten-fold stratified cross-validation confirms the improvement (mean F1 = 0.9773), and a Friedman test indicates the differences among models are statistically significant (chi-square = 25.56, p = 1.18e-05). Fine-tuned transformer models, evaluated separately, achieve the highest raw classification scores (RoBERTa: 99.82% accuracy), at substantially higher computational and data-labelling cost, motivating the retrieval-augmented hybrid design as a lighter-weight, more explainable alternative suited to practical deployment. We discuss the trade-offs among accuracy, explainability, and cost, and outline threats to validity and directions for future work.

Immunohistochemical Expression of Metallothionein as A Biomarker in Salivary Gland Neoplasms

2026-07-23T15:39:40+00:00

Background: Metallothionein is a cysteine-rich protein present in myoepithelial cells of several benign and malignant neoplasms and as a potential myoepithelial cell marker in breast cancer. The function of metallothionein is associated with DNA protection, oxidative stress and anti-apoptosis.

Aims: The present work was done to assess metallothionein (MT) expression with immunohistochemical localization in benign and malignant salivary gland tumors and to compare metallothionein (MT) expression among the different histological patterns of salivary gland tumors.

Materials/Methods: Forty diagnosed cases of benign and malignant salivary gland tumors were selected, and immunohistochemistry was performed for metallothionein detection.

Results: All cases showed positive metallothionein expression with different intensity. The expression of metallothionein protein is higher in malignant salivary gland tumors than in the benign ones.

Conclusion: Nuclear metallothionein may play a critical role in tumor progression. It can be used as a prognostic marker in salivary gland adenocarcinomas.

Cladding Process Design, Implementation & Simulation to Investigate the Quality of the Austenitic Stainless Steel Overlay on carbon steel Obtained by GMAW Process

2026-07-24T00:00:05+00:00

In recent years, Weld cladding has attracted interest as a way to improve the surface property of equipment in corrosive environment by overlays of poor corrosion resistance materials with high corrosion resistance materials like stainless steel or nickel-based alloys. There are various problems effect clad quality such as residual stresses, distortion and bad dilution. In this study, the deposition of successive layer of austenitic stainless steel ( ER 309L) on low carbon steel substrate were carried out using gas metal arc welding process by different deposition strategy. After experimental work, numerical simulation of cladding process based on Simufact welding software was done to estimate thermal cycle, residual stresses and distortion and compare the results with experimental output. The results show that the Relative percentage errors for the distortion derived from the simulations and the actual experimental data can reach 12.5% for one bead on plate strategy and 10.4 % for back strep strategy, for bead width the relative percentage error of one bead on plate strategy and back strep strategy is 11.8% and 10.2 % respectively.

An Analysis of Digital Transformation in Turkish Enterprises: Tracing the Evolution of Digital Strategies and Their Economic Impact

2026-07-24T00:23:17+00:00

This reseach offers a comprehensive historical examination of the digital revolution in Turkish businesses,
with a specific emphasis on strategic advancements and their economic consequences. The research examines the gradual adoption of digital technologies in different sectors of Turkey, focusing on how this has led to changes in business operations, market expansion, and competitiveness. By conducting thorough literature reviews and analyzing case studies, this study traces the development of digital strategies, emphasizing notable enhancements in operational efficiencies and innovations resulting from digitalization. The results indicate that digital transformation has significantly improved the efficiency and effectiveness of corporate processes in Turkey, allowing businesses to streamline operations, decrease costs, and enhance overall productivity. Moreover, the implementation of mobile cloud computing and Internet of Things (IoT) technologies has greatly revolutionized these fields, providing fresh opportunities for enhanced efficiency and innovation. The research has important implications, suggesting that the continuous progress of digital computing could lead to significant societal changes, especially with the growing prevalence of digital applications. The study emphasizes the necessity for policymakers and business leaders to incorporate these advancements into their strategies to guarantee that digital technologies are advantageous for all segments of society.

A descriptive Study to Illustrate the Effect of Salinity on Small Palm Trees in the Al-Jufra Region

2026-07-24T00:50:06+00:00

This study investigated the effect of soil salinity on date palm offshoots in the Al-Jufra region, with a particular focus on the Al-Hamam Agricultural Project in the Sukna area. The study aimed to evaluate the chemical properties of the project's soil, determining salinity (EC), pH, and the concentrations of positive and negative ions, and assessing the suitability of these conditions for the cultivation and growth of date palm offshoots, which are more sensitive to salinity than mature trees.

The study employed a descriptive-analytical approach supported by fieldwork and laboratory analysis. Soil samples collected from nine different locations within the project area, encompassing both cultivated and fallow land. At each location, samples taken at two different depths (surface layer 0-30 cm and subsurface layer 30-60 cm). Laboratory analyses conducted to determine electrical conductivity, exchangeable sodium percentage (ESP), and the concentrations of cations (Ca, Mg, Na, K) and anions (Cl, SO4, HCO3). The results showed that the soil in the study area characterized by mild to moderate alkalinity (pH between 7.13 and 8.45) and significant variation in electrical conductivity (EC) values, ranging from 1.03 to 107.91 dS/m, with 67% of the samples exceeding the permissible critical limits for agriculture. The results also indicated a high concentration of salts in the surface layer (0-30 cm), posing a direct threat to the roots of young seedlings. The analyses further revealed a clear predominance of sodium and chloride ions, classifying most of the soil as "saline-sodium," which leads to a deterioration of the soil's physical properties and hinders seedling growth.

The study concluded that the current conditions in large parts of the project area present a significant challenge to the successful cultivation of seedlings without intervention. Accordingly, the study recommended the need to implement reclamation programs that include soil washing, the use of amendments such as agricultural gypsum, and the transition to modern irrigation systems (drip irrigation) to reduce salt accumulation, and the selection of palm varieties with high tolerance in the early stages of growth.

Artificial Intelligence-Assisted Raman Spectroscopy in Pharmacology and Pharmaceutical Manufacturing

2026-08-02T12:48:59+00:00

Raman spectroscopy integrated with artificial intelligence (AI) provides a non-destructive method for pharmaceutical quality control and process monitoring. This study evaluates a Raman–AI workflow designed for compound identification, spectral classification, and the detection of counterfeit pharmaceuticals. Spectra were collected from representative formulations, including paracetamol, ibuprofen, aspirin, and amoxicillin. The data underwent automated preprocessing and dimensionality reduction before being analyzed using Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN) models. The CNN model achieved a classification accuracy of 99.1%, while the SVM provided comparable performance with lower computational requirements. The framework also demonstrated high sensitivity and specificity in distinguishing authentic from counterfeit formulations. These results indicate that combining Raman spectroscopy with machine learning models offers a reliable approach for automated analysis and process monitoring in pharmaceutical manufacturing.

Application of Adomian Decomposition Method Solving Volterra Integral Equation

2026-08-03T13:43:58+00:00

In this work, definitions and classifications of integral equations are described. Next, Adomian decomposition method is observed. Moreover, Adomian decomposition method is set out with the aim of solving linear and nonlinear Volterra integral equations. Finally, some applications are expressed to demonstrate the effectiveness of Adomian decomposition methods for solving linear and nonlinear Volterra integral equations, the modified decomposition method and the Laplace transform method by using MATLAB.

Using the Binary Logistic Regression Model to Predict Underweight Among Children Under Five "An Analytical Study of Social and Behavioral Determinants in Zawiya City"

2026-08-08T10:11:13+00:00

Background: Childhood malnutrition among children under five years remains one of the most serious global health challenges, contributing to approximately 45% of child deaths. This study aims to identify the social and behavioral determinants associated with malnutrition using binary logistic regression analysis.

Methods: A cross-sectional analytical study was conducted involving 150 children under five years (52% male, 48% female) randomly selected from three primary healthcare centers. Data were collected using a structured questionnaire validated for reliability (Cronbach's α = 0.82). Binary logistic regression was employed to calculate odds ratios (OR) with 95% confidence intervals.

Results: Statistical analysis revealed that the prevalence of underweight reached 33.3%, compared to 13.3% for children with overweight. Three main factors were identified as significantly increasing the likelihood of malnutrition, namely low family income, lack of exclusive breastfeeding, and spending more than two hours daily on electronic screens. It was found that children from low-income families are the most vulnerable, followed by those deprived of exclusive breastfeeding, and then those who spend long hours in front of screens. Moreover, the statistical model demonstrates good explanatory power, accounting for more than 42% of the factors influencing underweight, with an overall classification accuracy of approximately 79%, alongside high sensitivity in detecting affected cases and specificity in excluding healthy ones.

Predictive AI Modeling of Biopsychosocial Determinants of Coagulation Risks Post-AstraZeneca Vaccination: A Retrospective Psychosomatic Cohort Study in Tripoli, Libya

2026-08-11T20:13:30+00:00

The ChAdOx1 nCoV-19 (AstraZeneca) vaccine has been globally linked to rare, severe thromboembolic complications like Vaccine-Induced Immune Thrombotic Thrombocytopenia (VITT). While physiological mechanisms are heavily investigated, a substantial research gap exists regarding how post-vaccination psychological distress (e.g., vaccine-related anxiety) interacts with hematological parameters in North African populations. This study bridges this gap using Machine Learning (ML) to process complex biopsychosocial datasets. Methods: A retrospective cohort study was conducted on 100 vaccinated individuals in Tripoli, Libya. Longitudinal laboratory data (D-Dimer, White Blood Cell count [WBC], and Platelet count [PLT]) were paired with psychometric scores evaluating post-vaccination health anxiety. Artificial Intelligence (AI) architectures, specifically Random Forest classifiers, were deployed to discover non-linear correlations and predict sub-clinical clotting risks. Results: Elevated D-Dimer levels (> 500 ng/mL) were identified in 6% (n=6/100) of the global cohort, showing an incidence rate of 11% among women. An explicit age-dependent escalation in D-Dimer concentration was recorded among the affected female patients. Crucially, all six hypercoagulable individuals presented with completely normal WBC and PLT counts, demonstrating a clear clinical dissociation from typical acute thrombocytopenia. Conclusion: This investigation establishes that AstraZeneca vaccination is associated with a sub-clinical hypercoagulable risk within a 6% subset of the cohort in Tripoli, Libya, driven by clear gender and age factors. Standard complete blood counts are insufficient on their own to rule out localized clotting risks, making quantitative D-Dimer testing a vital independent diagnostic tool.

Assessing the readiness of volunteered geographic information as geospatial information technology infrastructure for urban information systems: an ISO/IEC 25012-based evaluation of OpenStreetMap in Libya (2010–2024)

2026-08-11T21:05:36+00:00

Volunteered geographic information (VGI), exemplified by OpenStreetMap (OSM), is increasingly treated as a pragmatic geospatial data source for urban information systems in resource-constrained settings. Whether such data are formally ready to function as an information technology (IT) infrastructure component—when judged against information systems data quality standards rather than cartographic quality frameworks—remains largely untested, particularly in post-conflict states. This study presents what is, to our knowledge, the first systematic evaluation of VGI readiness grounded in ISO/IEC 25012, applied to Libya, where the collapse of national mapping capacity after 2011 coincided with the growth of one of North Africa's more active OSM contributor communities. Fourteen years of contribution history (2010–2024) extracted through the ohsomeAPI, element-level attributes obtained via the Overpass API, and WorldPop population grids are combined to compute six quality dimensions—Accuracy, Completeness, Currentness, Consistency, Credibility and Operational Attribute Coverage—which are aggregated, using Analytic Hierarchy Process weights, into a composite VGI Readiness Index (VRI) for Libya's 22 administrative districts. Spatial autoregressive regression and structural change-point analysis support four findings. OSM Libya falls below the IT-integration threshold (VRI < 0.50) in 14 of 22 districts (63.6%), and operational attribute coverage (national mean = 0.15) is the binding constraint for service-directory applications. Contributor numbers, urbanization and conflict exposure correlate most strongly with district-level VRI, although these associations are observational rather than causal. Three structural breaks in contribution dynamics coincide with the 2011, 2014 and 2023 political shocks (permutation test, p = 0.018). The 2023 Derna humanitarian activation raised completeness while degrading credibility and leaving attribute coverage essentially unchanged. The VRI offers IT planners a replicable pre-assessment tool for VGI integration in data-scarce environments.

Evaluation of the Insecticidal Efficacy and Toxicity of Ethanolic Leaf Extracts of Ziziphus spina-christi L., Lawsonia inermis L., and Cupressus sempervirens L. Against Adults of the Red Flour Beetle Tribolium castaneum (Herbst) (Coleoptera: enebrionida

2026-08-11T21:56:34+00:00

This study aimed to evaluate the insecticidal efficacy and toxicity of ethanolic leaf extracts of three medicinal plants, namely Ziziphus spina-christi L., Lawsonia inermis L. and Cupressus sempervirens L. against adults of the red flour beetle, Tribolium castaneum (Herbst), under different exposure periods (24 hours, 48 hours, and 7 days). The results demonstrated that all tested ethanolic extracts exhibited significant insecticidal activity by increasing adult mortality throughout all exposure periods. The ethanolic extract of C. sempervirens leaves showed the highest insecticidal efficacy, followed by the extract of Z. spina-christi leaves, whereas the extract of L. inermis leaves exhibited the lowest efficacy among the tested extracts. A strong positive relationship was observed between increasing extract concentration and adult mortality, as well as between exposure time and insecticidal effectiveness. At the highest concentration (2.0%) after seven days of exposure, mortality reached 83.33% for C. sempervirens, 93.33% for Z. spina-christi, and 63.33% for L. inermis. These findings indicate the cumulative and progressive action of the bioactive compounds present in the ethanolic extracts over time. Furthermore, the extracts exhibited pronounced toxic effects against adult T. castaneum, as reflected by the calculated LC₅₀ and LC₉₀ values at different exposure periods. Toxicity varied according to plant species, with the C. sempervirens extract being the most toxic, followed by Z. spina-christi, while L. inermis showed the lowest toxicity. In addition, the toxicity of all extracts increased with prolonged exposure, as evidenced by the gradual reduction in LC₅₀ and LC₉₀ values after 48 h and 7 days of treatment

Weak Derivatives and Their Role in the Construction of Sobolev Spaces

2026-08-12T09:23:39+00:00

This paper studies weak derivatives as a generalization of classical derivatives defined in an integral sense. By introducing test functions and spaces. We prove that weak derivatives coincide with classical derivatives for smooth functions and are unique up to sets of measure zero. Through particular examples, we show that weak derivatives can exist even when classical differentiability fails, and we identify how discontinuity can prevent their existence. We then define Sobolev spaces as functions with weak derivatives in and prove that they are Banach spaces. Finally, we establish that weak derivatives keep the main properties of classical derivatives, such as linearity and Leibniz's formula, under mild assumption.

Parental Consanguinity and Chromosomal Abnormality Yield in a Five-Year Cytogenetic Referral Cohort from Eastern Libya

2026-08-12T16:38:09+00:00

Background/Objective: Parental consanguinity is common in eastern Libya and is an established risk factor for autosomal-recessive disease, but its relationship to chromosomal abnormality is less clear; we tested whether consanguinity was associated with karyotype abnormality, reproductive loss, or previously affected children among patients referred to the First International Laboratory in Benghazi (2021–2025) from a 319-record cytogenetic registry. Methods: Consanguinity was coded as present, absent, or unknown; records with unknown status were excluded. The abnormality rate was compared between consanguineous and non-consanguineous referrals with an interpretable karyotype (Fisher's exact test), and reproductive history and parental age with the Mann–Whitney U and Fisher's exact tests. Results: Consanguinity was documented for 150 of 319 records (47.0%); 42 (28.0%) were consanguineous, mostly first-cousin unions. Among 107 patients with known consanguinity and an interpretable karyotype, a chromosomal abnormality was found in 11 of 26 (42.3%) consanguineous versus 37 of 81 (45.7%) non-consanguineous referrals (odds ratio [OR] 0.87, 95% CI 0.36–2.13; P = .82). Reproductive history did not differ: at least one prior miscarriage in 43.6% versus 39.8% (OR 1.17, 95% CI 0.55–2.46; P = .71), and at least one previously affected child in 26.7% versus 26.9% (OR 0.99, 95% CI 0.38–2.56; P = 1.00). Maternal and paternal age were also similar. Conclusion: Consanguinity was not associated with chromosomal abnormality yield or adverse reproductive history, consistent with consanguinity raising risk chiefly for autosomal-recessive single-gene disorders rather than aneuploidy; these findings support molecular rather than cytogenetic testing for consanguineous families with recurrent pregnancy loss.

Association Between ABO Blood Groups and Diabetes Duration: A Large Cross-Sectional Study from Al-Marj City, Eastern Libya

2026-08-12T16:59:49+00:00

The correlation of ABO and Rhesus (Rh) blood group with diabetes mellitus is still debated. A cross-sectional study was carried out at the Al-Marj Specialized Hospital for Diabetes and Endocrinology in eastern Libya In the period from September 2025 to January 2026..A consecutive sampling method was employed to recruit a total of 4,361 diabetic individuals (2,030 males and 2,331 females). The duration of diabetes, ABO blood group and Rh factor were collected from medical records. Statistical analyses One‑way ANOVA with Tukey's HSD was used to analyze the data, as well as independent t‑test (SPSS v20.0, p<0.05). The frequency distribution of ABO blood type was: type A (45.2%), followed by type O (31.2%), type B (16.9%) and type AB (6.6%). RhPositive patients accounted for 89.7%. The average length of diabetes was 11.80±9.42 years. Significant differences were observed among ABO groups (F=10.262, p<0.001) and ABO/Rh phenotypes (F=5.358, p<0.001). Diabetes duration was significantly shorter in patients with blood group A (11.05±9.13 years) than in patients with blood group B (12.86±9.57 years) and blood group O (12.76±9.92 years) (p<0.001). The longest duration was observed in AB⁻ (15. 25±10.24 years). There was no significant sex difference (p=0.401). This is the first large‑scale report in eastern Libya revealing a strong correlation between ABO blood groups and diabetes duration, This study represents the first large-scale investigation in eastern Libya to demonstrate a significant association between ABO blood groups and diabetes duration, with blood group A showing the shortest duration and AB⁻ the longest. In contrast, Rh factor alone and sex were not identified as significant predictors for diabetes duration.