African Journal of Advanced Pure and Applied Sciences

Evaluation of Shear Bond Strength and Biaxial Flexural Strength of Zirconia Cores Veneered Using CAD-on and Press-on Techniques

2026-10-03T18:01:32+00:00

Objectives: This in vitro study compared the shear bond strength and biaxial flexural strength of CAD/CAM-fabricated zirconia cores veneered using CAD-on and Press-on techniques and evaluated their modes of failure. Materials and Methods: Twenty zirconia core discs were fabricated from IPS e.max Zir CAD using CAD/CAM technology. Ten specimens were prepared for shear bond strength testing and ten for biaxial flexural strength testing, with five specimens allocated to each veneering technique per test. The CAD-on group was veneered using IPS e.max CAD, whereas the Press-on group was veneered using IPS e.max ZirPress. All specimens underwent thermocycling before testing. Failure modes were evaluated using scanning electron microscopy, and data were analyzed using SPSS software with the level of statistical significance set at p≤0.05. Results: The t-test analysis of the shear strength test showed a statistically significant difference (p < 0.05) for the CAD-on group. There were no statistically significant differences (p > 0.05) for the biaxial flexural strength test between the two groups. The CAD-on group showed adhesive/cohesive failure, while the Press-on group showed cohesive failure within the veneering ceramic. Conclusion: Within the limitations of this in vitro study, the veneering technique affected shear bond strength. The CAD-on technique showed significantly higher shear bond strength than the Press-on technique. Biaxial flexural strength did not differ significantly between the techniques.

In Silico Docking and ADMET Profiling of Cyclopentane-Based Enaminones as Candidate PARP1 Inhibitors

2026-10-06T17:44:56+00:00

Poly(ADP-ribose) polymerase 1 (PARP1) represents a significant therapeutic target in cancer treatment focused on DNA repair; yet, cyclopentane-derived enaminones have not been extensively investigated as chemotypes that bind to PARP1. In this research, ten previously created cyclopentane-derived enaminone compounds (1A–5A and 1B–5B) were assessed through structure-based molecular docking against the catalytic domain of human PARP1 (PDB ID: 7KK4), followed by thorough in silico evaluations of pharmacokinetics, drug-likeness, and toxicity. The docking protocol was confirmed through re-docking the co-crystallized inhibitor olaparib, resulting in an RMSD of 1.183 Å. The studied derivatives showed docking S-scores between −6.234 and −7.972 kcal/mol, with compounds 1B (−7.972 kcal/mol) and 1A (−7.748 kcal/mol) presenting the most advantageous scores compared to olaparib (−8.714 kcal/mol). Binding-mode assessment identified significant interactions within the PARP1 catalytic pocket, highlighting connections with TYR889, TYR896, ARG878, ASP766, SER864, and ALA880. Significantly, 4B established a strong hydrogen-bond interaction with TYR896, while 2B paired ARG878 hydrogen bonding with a TYR896 π–π interaction. All derivatives adhered to Lipinski’s rule of five and exhibited a predicted bioavailability score of 0.55, although 1A and 1B showed increased lipophilicity, with 1B surpassing the Veber rotatable-bond criterion. ADMET predictions also suggested that blood–brain barrier penetration varied by compound and highlighted significant potential issues related to CYP2C19, CYP2C8, and certain CYP1A2 and CYP2C9 pathways. Together, these results pinpoint 1A and 1B as the top docking candidates, underscoring the importance of metabolic optimization and experimental confirmation before definitive PARP1 inhibitory effects can be confirmed.

Subject-Independent Automated Detection of Amyotrophic Lateral Sclerosis from Electromyography Signals Using ALSNet

2026-10-06T18:10:40+00:00

Amyotrophic Lateral Sclerosis (ALS) is a progressive disorder of the nervous system that affects motor neurons and causes progressive weakness and loss of function of motor neurons. Clinically relevant information on neuromuscular activity is provided by Electromyography (EMG) and can be used to assist the diagnosis of ALS. The use of EMG interpretation can be difficult due to their complex temporal and morphological characteristics, however. In this study, the novel approach of a deep learning method (ALSNet) to automatically classify ALS and Normal EMG signals is explored. The experiments were performed with processed EMG data extracted from publicly available N2001 EMGLAB database. All 302 EMGs (151 Normal, 151 ALS) were divided into 3,322 one-second EMG segments. A subject-independent partitioning strategy for the dataset was used. ALSNet is a deep network developed based on Keras and TensorFlow frameworks, and trained with Adam optimizer by using binary cross-entropy loss. The accuracy, sensitivity, specificity, precision, F1-score, and balanced accuracy were used for evaluation of the model. ALSNet obtained 97.74% accuracy, 96.77% sensitivity, 98.59% specificity, 98.36% precision, 97.56% F1-score and 97.68% balanced accuracy on the independent test set. The findings show the promise of deep learning analysis of EMG signals for automated ALS detection and offer a foundation for future analysis with more and varied clinical datasets.

Effect of Heat Treatment on the Hardness of Cast EN AC-43000-F (AlSi10Mg) Aluminum Alloy

2026-10-12T17:33:16+00:00

An Heat treatment is an important method for modifying the mechanical properties of heat-treatable cast aluminum alloys. This study experimentally investigates the effects of annealing and T6 heat treatment on the Rockwell hardness of cast EN AC-43000-F (AlSi10Mg) aluminum alloy. The investigated conditions included the as-cast state, annealing at 350 °C for 1.5 h followed by furnace cooling, solution treatment at 540 °C for 2 h followed by water quenching, and artificial aging at 170 °C for 4, 8, and 12 h. The specimens measured 50 × 40 × 10 mm. Hardness was measured using the Rockwell HRF scale in accordance with ASTM E18. For each heat-treatment condition, three independent specimens were evaluated, and the reported hardness values represent their average. The mean hardness of the as-cast alloy was 64.9 HRF. Annealing reduced the hardness to 52.9 HRF, corresponding to an approximately 18.5% decrease relative to the as-cast condition. Following solution treatment and water quenching, the mean hardness increased to 74.4 HRF. Subsequent artificial aging increased the mean hardness to 91.5, 110.4, and 102.5 HRF after 4, 8, and 12 h, respectively. The highest mean hardness was therefore obtained after 8 h at 170 °C. The subsequent decrease after 12 h is consistent with a transition beyond the maximum age-hardening condition; however, without direct microstructural characterization, it cannot be considered conclusive evidence of over-aging. Overall, the results demonstrate that hardness is strongly dependent on thermal history, with T6 treatment producing a substantial increase compared with the as-cast and annealed states.

Design and Implementation of a Multi-Sensor IoT System for Smart Laboratory Monitoring and Access Control Using ESP32

2026-10-12T18:07:43+00:00

This paper presents the design and practical implementation of an IoT-based smart laboratory monitoring and access-control system using an ESP32 microcontroller. The proposed system addresses limitations of conventional laboratory monitoring by integrating environmental monitoring, human presence detection, and access control within a unified platform. The implemented system consists of a DHT22 sensor for temperature and humidity measurement, an MQ-2 sensor for smoke and gas detection, a PIR motion sensor for human presence detection, and an RC522 RFID module for automated access control. The ESP32 serves as the central processing and communication unit, collecting data from the connected sensors, processing the measurements, and supporting wireless communication through its integrated Wi-Fi capability. The study adopts a design-based Cisco PPDIOO framework to structure the implementation process. Experimental results confirm the feasibility and reliability of the deployed system in detecting environmental hazards and managing laboratory safety in real time.

Existence of a Unique Monotone Solutions for a Fractional Differential Equation in L^1 Using a Measure of Noncompactness

2026-10-16T12:02:08+00:00

In this paper, we investigate the existence and uniqueness of monotone solutions for a class of fractional differential equations in the Banach space . The considered problem involves fractional-order terms with multiple non-independent components, which allows us to formulate the problem in an appropriate integral form. By constructing a suitable operator associated with the corresponding integral equation, we establish the existence and uniqueness of solutions under appropriate assumptions on the nonlinear terms and the fractional operators. The proof is mainly based on the theory of measures of noncompactness and an appropriate fixed-point theorem.

Consequently, the existence of a unique monotone and non-decreasing solution in is established.

Assessment of Ash Content Composition in the Muscles of Economically Important Libyan Marine Fish Species from Tripoli, Libya

2026-10-16T12:54:38+00:00

This study aims to evaluate the ash content in the muscles of six economically important marine fish species, namely sardine (Sardinella aurita), barracuda (Sphyraena sphyraena), dusky grouper (Epinephelus costae), red seabream (Pagellus erythrinus), chub mackerel (Scomber japonicus), and saddled seabream (Oblada melanura). A total of 18 specimens of each species were obtained from local fishermen at the Tripoli fish market. The mean lengths and weights were as follows: sardine (19.22 cm, 60.17 g), barracuda (32.35 cm, 122.5 g), dusky grouper (25.02 cm, 190.17 g), red seabream (20.55 cm, 119.5 g), chub mackerel (24.35 cm, 139.17 g), and saddled seabream (20.5 cm, 181.67 g). Ash content was determined using the AOAC 938.08 (2005) method, with the objective of quantifying the concentrations of major minerals (Ca, P, Mg, Na, K) and trace elements (Fe, Zn, Cu) in each species studied. The study further seeks to assess the nutritional value of these fish and to discuss the potential application of the findings in improving aquafeed formulations to ensure food safety.

Using Exploratory Factor Analysis with Principal Component Analysis to Identify Factors Influencing Customer Satisfaction with Private Pharmacy Services in Al-Marj City

2026-10-16T13:19:25+00:00

This study aims to identify the underlying dimensions and latent factors explaining customer satisfaction with private pharmacy services using Exploratory Factor Analysis (EFA) based on Principal Component Analysis (PCA). The study utilized data collected through a questionnaire comprising 20 variables designed to measure customer satisfaction, administered to a sample of 139 respondents. To assess the suitability of the data for factor analysis, the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s Test of Sphericity were employed. The KMO value was 0.924, indicating a high level of sampling adequacy and the suitability of the data for factor analysis. Bartlett’s test also revealed statistically significant correlations among the variables, supporting the appropriateness of conducting factor analysis.

Following the application of Principal Component Analysis (PCA) and Varimax rotation, three principal factors representing the underlying dimensions of customer satisfaction were identified. The first factor, Service Efficiency, comprised variables Y9–Y16. The second factor, Trust in the Pharmacist, included variables Y1–Y8. The third factor, Customer Care and Responsiveness to Customer Needs, comprised variables Y17–Y20. These findings indicate that customer satisfaction with private pharmacy services can be explained by three main dimensions: service efficiency, trust in the pharmacist, and customer care and responsiveness to customer needs. These dimensions provide a statistical basis for developing a more concise and clearly structured scale for assessing customer satisfaction with pharmaceutical services and identifying key areas for service quality improvement.

Design and Implementation of an Automated Spine-and-Leaf Data Center using Infrastructure as Code

2026-10-16T13:35:20+00:00

Modern data centers require fast and reliable networks to handle massive amounts of information; thus, traditional three-tier designs are rendered inefficient for heavy east-to-west traffic. To address this, the Spine-and-Leaf architecture has been adopted to provide faster, and highly predictable connectivity. Because the manual configuration of these network switches is slow and prone to human error, an Infrastructure as Code (IaC) approach was utilized to completely automate a modern data center network. A scalable Spine-and-Leaf network was designed in a virtual lab using Arista vEOS switches. A physical underlay was established using OSPF and IS-IS, and a virtual overlay network was created utilizing MP-BGP EVPN and VXLAN technologies.

The entire network was provisioned using automation tools, specifically Ansible playbooks and Python scripts. Following successful deployment, key network metrics, including latency, throughput, and jitter, were measured. A comparative analysis was performed between a baseline 2-leaf topology and a scaled 4-leaf topology, proving the automated network can scale smoothly to handle increased traffic without losing performance. Finally, a self-healing automation system was successfully developed. A custom dashboard was engineered to continuously monitor network health in real-time, executing remediation scripts to fix failures within sixty seconds without human intervention.

Integrating Renewable Energy into the AL-Hera Distribution Network: A Load Flow and Protection Coordination Assessment

2026-10-16T13:47:47+00:00

The integration of renewable energy sources into existing distribution networks presents both opportunities and challenges for system planners and operators. This paper investigates the technical feasibility of integrating photovoltaic (PV) systems into the AL-Hera distribution network to address load growth challenges over a ten-year planning horizon. Using ETAP software, load flow analysis was conducted with an assumed annual load growth rate of 80%, revealing significant voltage drops and transformer overloading issues beginning in 2022 and recurring throughout the study period. The results demonstrate that strategic deployment of 10 MW PV systems at critical 11 kV busbars effectively resolve both voltage violation and transformer overloading problems. Furthermore, this study examines the protection coordination implications of distributed generation (DG) integration, drawing on established threshold analysis indicating that protection coordination is maintained up to 20% DG penetration under typical distribution feeder conditions. The findings provide practical guidance for utility engineers seeking to integrate renewable energy into aging distribution infrastructure while maintaining system reliability and protection selectivity.

Impact of Image Preprocessing on OCR Performance for English Printed Receipts: A Comparative Study of Tesseract and Easy-OCR

2026-10-16T15:58:00+00:00

Optical Character Recognition (OCR) plays an important role in extracting machine-readable information from scanned documents and receipts; however, recognition performance can be affected by image quality and document characteristics. Although image preprocessing is commonly used to improve OCR input quality, its effectiveness may vary across OCR systems. This study experimentally investigates the effect of image preprocessing on the performance of two open-source OCR engines, Tesseract and EasyOCR, for English printed receipt images. A controlled comparative experiment was conducted using 200 images from the Scanned Receipts OCR and Information Extraction (SROIE) dataset. Each OCR engine was evaluated under four image conditions: the original images as a baseline, Gaussian Blur, Contrast Limited Adaptive Histogram Equalization (CLAHE), and Fast Non-Local Means (Fast NL-Means) denoising. Recognition performance was assessed using Character Error Rate (CER) and Word Error Rate (WER), while OCR processing time was used to evaluate computational efficiency. The results demonstrate that preprocessing does not provide a uniform improvement across OCR systems. CLAHE produced the best recognition performance for EasyOCR, reducing CER from 27.10% to 26.23% and WER from 61.17% to 58.38%. For Tesseract, Gaussian Blur produced the lowest WER, reducing it from 49.46% to 48.54%, whereas the original images yielded the lowest CER of 26.19%. Fast NL-Means did not provide an overall recognition benefit for either system. Tesseract also exhibited substantially lower OCR execution time than EasyOCR under the tested conditions. These findings indicate that image preprocessing should not be regarded as a universally beneficial OCR step; instead, its selection should be considered in relation to the OCR engine, document type, and image characteristics.

Electrical and Environmental Aging of Wind Turbine Generator Insulation: A Critical Review of Stress Mechanisms, Degradation, and Mitigation

2026-10-16T16:14:43+00:00

Wind turbine generators are essential components of modern renewable-energy systems, and their operational reliability is strongly dependent on the performance and durability of their electrical insulation systems. These insulation systems are continuously exposed to interacting electrical, thermal, mechanical, and environmental stresses that may progressively reduce insulation performance, reliability, and service life. The increasing application of power electronic converters introduces additional electrical stresses, particularly rapid voltage transitions, repetitive impulses, and high-frequency components, while thermal cycling, vibration, humidity, water exposure, and salinity may further accelerate insulation degradation. Therefore, understanding the combined effects of these ageing mechanisms is important for improving the reliability and maintenance of wind turbine generators, particularly in demanding offshore environments.

This study aims to critically review and integrate the principal electrical, thermal, mechanical, and environmental ageing mechanisms affecting wind turbine generator insulation. It specifically examines converter-induced electrical stress, partial discharge, thermal ageing, mechanical degradation, humidity, water exposure, salinity, and the interactions among these mechanisms. The study adopts a descriptive and critical review methodology based on the analysis and synthesis of relevant technical and scientific literature. The reviewed evidence is comparatively examined to identify major degradation pathways, condition-monitoring indicators, insulation-protection measures, testing approaches, and maintenance strategies for both onshore and offshore wind turbine applications.

The findings indicate that insulation degradation is not generally attributable to a single stress factor but results from complex interactions among multiple ageing mechanisms. Partial discharge emerges as an important diagnostic indicator, although its interpretation requires consideration of operating and environmental conditions. The findings also demonstrate the practical importance of integrating insulation design, converter control, environmental protection, condition monitoring, and maintenance planning. In real-world applications, an integrated condition-assessment approach can support earlier detection of insulation deterioration, more informed maintenance decisions, reduced unexpected downtime, and improved generator reliability and service life. The study consequently provides a consolidated framework for understanding and managing electrical and environmental ageing in wind turbine generator insulation.

Design Provisions and Structural Behaviour of Reinforced Concrete Deep Beams

2026-10-22T17:12:11+00:00

Reinforced concrete (RC) remains a fundamental material in structural engineering due to its strength, durability, and versatility in diverse applications. Design provisions for RC elements have evolved significantly, particularly in enhancing the reliability of static load-bearing performance. This paper investigates the application of the Strut-and-Tie Model (STM) as outlined in major design codes from Canada, the United States, and Europe for reinforced concrete deep beams. The study analyzes over 300 experimental test results to assess the accuracy of these codes in predicting ultimate strength and failure modes. The results indicate that while the Canadian code generally provides more conservative strength estimates, none of the codes reliably predict the actual failure modes or their locations. In addition, the study examines the influence of parameters such as shear span-to-depth ratio, web reinforcement, and concrete compressive strength on the predicted ca-pacities using STM-based approaches.

Permeability Estimation and Reservoir Characterization Using the Hydraulic Flow Unit (HFU) Approach: A Case Study of the Paleocene Beda C Carbonate Reservoir, Sabah Oil Field, Sirte Basin

2026-10-25T20:10:44+00:00

Permeability determination in carbonate reservoirs represents a major challenge in petroleum engineering due to severe rock heterogeneity resulting from complex depositional histories and diagenetic modifications. Core samples are typically available for only a limited number of wells due to high acquisition costs. Consequently, developing low-cost, accurate, and reliable predictive models for permeability is paramount for efficient reservoir management and development strategies. This paper presents a comprehensive study to establish an optimal permeability predictive model for the Paleocene Beda C formation within the Sabah Oil Field, located in the Sirte Basin, Libya. Data collected from eight cored wells were analyzed using the Hydraulic Flow Unit (HFU) methodology, which is based on the modified Kozeny-Carman equation and the Flow Zone Indicator (FZI) concept. Traditional empirical formulations (Timur, Wyllie-Rose, and Morris-Biggs) as well as classical regression models (linear, power, and exponential) were applied and statistically compared against the HFU method. The results demonstrate that traditional porosity-permeability correlations exhibit low predictive capability due to the highly scattered nature of carbonate petrophysical trends. In contrast, the HFU method effectively classified the heterogeneous reservoir into four distinct flow units, each characterized by unique pore-throat geometrical attributes. The HFU-derived models achieved high accuracy with coefficients of determination (R2 ≥ 0.70). The developed framework was successfully validated through application to well logging data from the un-cored well G-18, offering a robust tool for fields-wide permeability prediction. Keywords: Carbonate Reservoir, Permeability Estimation, Hydraulic Flow Unit (HFU), Flow Zone Indicator (FZI), Sirte Basin, Reservoir Heterogeneity.

Carathéodory Solutions for a Nonlinear Coupled Two-Dimensional Implicit Differential System with Nonlocal Boundary Constraints

2026-10-31T17:27:27+00:00

We study the existence and uniqueness of solutions for a system consisting of two coupled nonlinear first-order differential equations involving implicit derivatives. The study focuses on systems described under multipoint nonlocal boundary conditions in two-dimensional, bounded and convex domains. Using an appropriate transformation, such implicit differential system is reduced to a corresponding integral form to analyze its fixed points in a product Lebesgue space. We follow closely Schauder’s approach employed to establish a rigorous existence result; in particular, we verify the compactness of the operator via the Kolmogorov compactness criterion. Uniqueness of solutions is further investigated by adapting certain conditions due to Banach contraction technique. More notably, we prove that despite the derivatives being defined in the integrable space, the solutions exist in the product space of absolutely continuous functions. The relaxed problem involving nonlocal integral conditions of the coupled system type is also successfully treated by the same framework. Finally, numerical simulations are performed in MATLAB to support our theoretical findings.

Wavelet Transform Analysis of the 1963 Al-Marj Earthquake: Signal Processing and Site Effect

2026-10-31T18:39:58+00:00

The 1963 Al-Marj earthquake (Mw 5.6) caused disproportionate structural damage relative to its moderate magnitude, suggesting significant local site amplification effects. This study applies wavelet transform techniques to analyze the seismic signal characteristics and investigate the role of sedimentary basin resonance in damage distribution. A comprehensive signal processing framework was developed using Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT) multi-resolution decomposition, and wavelet-based denoising to process synthetic seismic signals representative of the 1963 event. The analysis demonstrates that wavelet methods outperform traditional Fourier analysis for non-stationary seismic data, providing simultaneous time-frequency localization of seismic phases. Wavelet denoising improved signal-to-noise ratio by 5–10 dB, enabling recovery of usable signals from noise-corrupted historical analog recordings. Multi-resolution decomposition successfully isolated P-waves (6.25-12.5 Hz), S-waves (3.125-6.25 Hz), and surface waves (0.78-3.125 Hz), revealing distinct time-frequency signatures for each phase. Site response analysis identified a fundamental frequency of 1.0 Hz for the 50-m thick Quaternary sedimentary basin, with theoretical amplification factors reaching 10×. The surface wave dominant frequency (1.5 Hz) lies within the amplification bandwidth, explaining the severe damage to masonry structures with natural frequencies of 0.78–1.56 Hz. These findings support the hypothesis that local geological conditions specifically basin resonance in unconsolidated sediments were the primary cause of destruction, rather than earthquake magnitude alone. The methodology provides a robust framework for analyzing historical seismic data and assessing site-specific seismic hazards in sedimentary basins.

Numerous Chirped and Optical Solitons for the Cubic Nonlinear Schrödinger Equation with Kerr Nonlinearity and Beta Derivatives

2026-10-31T19:00:23+00:00

This work explores new optical and chirped optical solitons for the space-time fractional cubic nonlinear Schrödinger equation (NLFSEs) in the presence of Kerr law nonlinearity, utilizing the new extended auxiliary equation approach. The solutions show a wide range of behaviors within the system and are stated in terms of trigonometric and hyperbolic functions. A broad range of phenomena is displayed by the several varieties of optical and chirped optical solitons that have been found, including dark, bright, kink, and periodic. A better knowledge of solution creation and properties may be attained by representing 2D and 3D graphs with different values of parameters.

Statistical Analysis of Diabetes Patients and Associated Causal Factors in Wadi Alshati Regions

2026-10-31T19:24:57+00:00

This study aimed to estimate the prevalence of type 2 diabetes mellitus and to identify factors associated with it in selected areas of Wadi Alshati. An analytical cross sectional study was conducted in 2025 among 360 adults. Data were collected using a structured questionnaire, together with measurements of body mass index (BMI), blood pressure, fasting blood glucose, and glycated hemoglobin (HbA1c). The prevalence of diabetes was 23.9% (86 of 360). After adjustment in multiple logistic regression, diabetes was significantly associated with age ≥50 years (adjusted OR = 3.84), obesity (OR = 2.71), family history of diabetes (OR = 4.26), and low physical activity (OR = 1.98). These findings highlight the importance of early screening programs and health education in the region, while longitudinal studies are needed to confirm these associations.

Evaluation of Artificial Neural Network Performance in Classifying Anemia Cases Among Pregnant Women in Benghazi, Libya

2026-10-31T20:04:21+00:00

This study aimed to evaluate the ability of artificial neural networks (ANNs) to classify anemia cases among pregnant women, as anemia represents a major health challenge affecting the health of both mothers and fetuses. The study also sought to identify the most important variables associated with the classification process. The significance of this research lies in the potential use of modern classification methods to analyze health data and identify patterns and relationships among different variables. The study was based on secondary data collected from a sample of 600 pregnant women in Benghazi, Libya. The participants were randomly selected from several clinics and hospitals during the period 2020–2021, as reported in Nasr (2023). The dataset included a variety of demographic, health-related, and pregnancy-related variables. Anemia status was determined based on blood hemoglobin levels. Cases with hemoglobin levels below 11 (g/dL) were classified as anemic, whereas cases with levels of 11 (g/dL) or higher were classified as non-anemic, according to the criterion adopted in the study. Artificial neural networks were used to construct a classification model. The data were divided into two sets: a training set and a testing set, in order to evaluate the model's ability to classify cases correctly. The results showed that the classification accuracy reached 90.8% for the training data and 88.8% for the testing data. These findings indicate that the model achieved a good level of classification performance, with relatively similar results across the training and testing sets. The variable importance analysis also showed that bleeding during pregnancy ranked first, followed by nutritional supplements, iron tablet intake, previous miscarriage, weight, age, menstrual cycle duration, educational level, and gestational age in weeks. These findings highlight the potential of artificial neural networks for classifying health data and identifying the variables that contribute most to the classification process. The study recommends conducting further research using independent samples to assess the model's generalizability and utilizing classification results to support health research on anemia among pregnant women. It also emphasizes that the importance of variables within the model does not necessarily imply a causal relationship between those variables and the occurrence
of anemia.

The Effect of Using Mind Maps on Improving Fractions Comprehension among Fifth-Grade Primary Students

2026-10-31T20:31:46+00:00

This study aimed to investigate the effect of using the Mind Mapping Strategy on improving the understanding of fractions among fifth-grade students at Al-Awael Basic Education School in Nalut during the academic year 2025–2026. The study adopted a quasi-experimental design involving two groups. The study sample consisted of 35 female students, who were randomly assigned to two groups: an experimental group of 17 students taught using the Mind Mapping Strategy, and a control group of 18 students taught using the traditional teaching method. The research instrument consisted of an achievement test administered as both a pre-test and a post-test to measure students’ academic achievement in the unit on common fractions. The statistical analysis revealed no statistically significant differences between the mean scores of the experimental and control groups on the post-test, although the experimental group achieved a higher mean score than the control group. The results also showed statistically significant differences between the pre-test and post-test scores of the experimental group in favor of the post-test, indicating an improvement in the students’ academic achievement after being taught using the Mind Mapping Strategy. Based on these findings, the study recommends encouraging mathematics teachers to employ the Mind Mapping Strategy in teaching mathematics because of its potential to enhance students’ academic achievement. It also recommends conducting further studies with larger samples and in different educational settings to examine the effectiveness of this strategy more comprehensively.

Cancer Susceptibility, Prognosis, and Hematological Malignancies Associated with Blood-Group Systems: A Biomolecular Systematic Review and Targeted Meta-Analysis

2026-10-31T20:59:14+00:00

Background: Blood-group antigens are inherited surface structures with roles extending beyond transfusion compatibility. ABO antigens are terminal glycans expressed on erythrocytes and many epithelial tissues, Duffy/ACKR1 regulates chemokine availability and leukocyte trafficking, and OK/BSG-CD147 participates in monocarboxylate-transporter stabilization, extracellular-matrix remodeling and tumour invasion. These biomolecular functions provide plausible links between blood-group biology and cancer susceptibility, prognosis and hematological malignancy, although the strength of epidemiological evidence varies by blood-group system and tumour site.

Methods: PubMed searches were conducted on 1 July 2026 using a broad blood-group/cancer strategy and focused queries for ABO, Rh, Duffy/ACKR1, OK/BSG-CD147, and review/meta-analysis literature. The PubMed phase retrieved 10,718 records, including 4,973 from the broad query and 5,745 from focused queries. After removal of 5,616 duplicate records, 5,102 unique records were available for title/abstract assessment. Evidence synthesis followed PRISMA 2020 and MOOSE reporting principles. A focused secondary random-effects meta-analysis was performed using published extractable estimates for ABO-A comparisons across cancer sites.

Results: The PubMed evidence set contained 94 review/meta-analysis candidates, 2,652 primary-study candidates, 2,028 biomolecular/mechanistic candidates and 764 hematological-malignancy candidates. Published pooled estimates consistently supported a modest risk signal for blood group A in several solid tumours. In the focused secondary random-effects synthesis of five published ABO-A estimates, the summary odds ratio was 1.22 (95% CI 1.13-1.32), indicating approximately 22% higher odds across the included published comparisons. Heterogeneity was high (I²=80.9%), meaning that the summary estimate should be interpreted as an evidence-landscape signal rather than a universal cancer-risk coefficient. Duffy/ACKR1 and OK/BSG-CD147 showed stronger biomolecular plausibility than population-level blood-typing evidence.

Conclusions: ABO has the strongest epidemiological support among blood-group systems, particularly in gastrointestinal and pancreatic cancer literature, whereas Duffy/ACKR1 and OK/BSG-CD147 are more strongly represented in mechanistic, immune-trafficking and tumour-metabolism studies. Blood group should not be used as a stand-alone clinical triage marker; its most defensible role is as a stratification variable in research models that already include age, sex, ancestry, tumour site, stage, infection status and treatment access.

A Predictive Framework for Infrastructure Project Failure in Libya: Integrating Instability Indicators, Temporal Machine Learning, and Explainable Early Warning (Simulation-Informed Proof-of-Concept Study)

2026-10-31T21:27:39+00:00

Projects in fragile settings face political, financial, institutional, procurement, and control disruptions. This article proposes a simulation-informed framework to estimate six-month infrastructure-project failure risk in Libya using instability, financing, approval delays, organizational capacity, and project-performance signals. No real Libyan project data were available; therefore, this is a proof of concept, not a field-validated study. A synthetic longitudinal dataset (seed 20260902) covered 420 projects and 8,631 project-month records; 324 projects were used for training and 96 for temporal testing. Four classifiers were compared. In this simulation only, logistic regression achieved the highest ROC-AUC (0.700), with PR-AUC 0.381, recall 0.567, specificity 0.709, and Brier score 0.204. SHAP highlighted instability, progress gap, procurement and payment delays, financing stability, and management capacity; these are model attributions, not causal effects or evidence of Libyan field performance. The proposed Project Failure Risk Index uses normalized variables and training-derived weights. The framework offers a transparent architecture for future validation with Libyan data. Its real-world accuracy and decision value remain unknown; field validation, calibration, and governance review are prerequisites for deployment.

A Deep Learning-Based Artificial Neural Network for Breast Cancer Classification: A Comprehensive Performance Evaluation

2026-10-31T21:48:39+00:00

Accurate and prompt diagnosis of cancers is critical to improve patient outcomes and to make timely clinical decisions. The present study built a multilayer feedforward artificial neural network (ANN) deep learning model to identify between malignant and healthy instances. The model included 186 inputs, one output neuron and four hidden layers with 64, 32, 16 and 8 neurons each. The network was trained with Scaled Conjugate Gradient (SCG) technique. The cross-entropy loss function was minimized and early stopping was applied on validation performance. This strategy was designed to improve generality and reduce over-fitting. The developed model achieved an F1-score of 93.07%, an Area under the Receiver Operating Characteristic Curve (AUC) of 0.8045, and an overall classification accuracy of 87.07 percent. Together these metrics give a significant discrimination between malignant and normal samples. The model was validated using the Receiver Operating Characteristic (ROC) analysis where it was found that the model was confident in its categorization as the cancer patients always received a higher prediction probability than healthy instances. The training was successful and exhibited steady learning, converging in 69 epochs with a final cross-entropy loss of 0.295. The confusion matrix revealed that the model was highly sensitive to correctly identify most of the cancer cases. The considerably reduced specificity was mostly due to the considerable imbalance in the sample class. The learning curves were smooth and did not indicate any overfitting. Further error analysis indicated that performance was generally age-independent, and there was no appreciable drop in accuracy with age. To further illustrate the robustness of the neural network, a comprehensive performance dashboard was deployed to consolidate many evaluation measures, dataset characteristics, prediction distributions, and optimization behavior into a single assessment. The proposed artificial neural network provides a reliable framework with strong classification performance and feasible computational features for cancer prediction in general. Future studies should aim on enhancing the specificity, e.g. via enhanced feature selection, threshold optimization and class balance. The results to-date highlights the promise of deep learning for medical diagnostics and cancer risk prediction. The study highlights the need to apply the latest machine learning techniques to improve the quality of patient care and accuracy of cancer diagnoses.

Front Propagation in a Fractional Viscoelastic Rod Model: Control of the Minimal Speed by the Rouse Exponent and Its Slow Approach

2026-11-01T17:32:53+00:00

We consider a Caputo-fractional nonlinear telegraph equation with a Rouse memory exponent as a model of strain fronts in a viscoelastic rod. A closed-form hyperbolic-tangent kink of this model, obtained with the -expansion method, predicts a front speed of 451 m/s. We study front propagation in this model by direct numerical solution of the Caputo equation. From localised (step) data the front speed rises towards a minimal speed given in closed form by a leading-edge argument; at it is in units of the elastic wave speed m/s, i.e. 12.5 m/s. This is about 36 times lower than the kink value and 34 times lower than the classical ( ) minimal speed of 427 m/s. The approach to is slow: the speed is 42% of at , 90% at and 97% at , with a shortfall of the order (within 20% of it for ). The front widens to about 90 length units. The speed does not depend on the cubic coefficient and so not on the strain amplitude, and its elasticity with respect to is 7.4 at : a 1% error in the memory exponent changes the speed by about 7%. Measurements over times much shorter than in these units would therefore report a speed far below the asymptotic one.