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.