UHD Journal of Science and Technology

Sustainable Hybrid Modification of Asphalt Binder Using Styrene-Butadiene-Styrene and Waste Polyvinyl Chloride for Enhanced High-Temperature Performance

Tarza Othman Ramadhan — 2026-07-01

Standard asphalt binders are thermally unstable; thus, modification is required to prevent rutting at high pavement temperatures and under heavy truck axle loads. Using a hybrid styrene-butadiene-styrene (SBS) and waste polyvinyl chloride (PVC) technology, a standard asphalt binder (60/70 penetration grade) was sustainably modified to improve high-temperature performance and rheological stability. Virgin control and nine hybrid mixes containing SBS (2%, 3%, and 4%) and waste PVC (1%, 3%, and 5%) by weight were tested. This hybrid change stiffened the matrix, reducing penetration and increasing softening point, according to conventional tests. Rotational viscosity rose with SBS and waste PVC concentrations, improving flow resistance at mixing and compaction temperatures. PVC concentrations elevated rolling thin film oven (RTFO) mass loss and lowered flash point due to waste polymer thermal instability. PVC’s plasticity partially disrupts the SBS’s continuous elastic network, decreasing elastic recovery as waste PVC concentration increased. Using the dynamic shear rheometer, a transition from viscous to elastic behavior was observed, with greater G* and lower δ values. The original and RTFO-aged binders greatly improved in rutting parameter (G*/sinδ), indicating increased ageing resistance. In the base performance grade (PG) 64-16 binder, hybrid SBS/PVC raises the high-temperature PG by two to six levels. These findings enhance asphalt binder performance in extreme temperatures and weather, enabling resilient pavement systems and more sustainable, durable, and cost-effective road infrastructure.

Performance Enhancement of Low-Density Parity-Check Decoder Using Neural Network Optimized Parameters

Amany Sabah Hassan — 2026-07-05

Low-density parity-check (LDPC) codes are of prime importance in achieving near-Shannon capacity in current communication systems. However, the optimal decoding process for LDPC codes is computationally intensive. This paper presents a neural normalized min-sum (NNMS) decoding network that improves error correction capabilities with minimal computational overhead. A weight-sharing approach is adopted in the NNMS model, in which the correction factors (α, β) are shared across nodes within a single layer. This approach decreases the total number of parameters in the model compared to traditional neural decoders, making it easier for hardware implementation. The NNMS model is tested using a (576, 432) LDPC code for an additive white Gaussian noise channel with binary phase shift keying modulation. Simulation results show that the NNMS model outperforms traditional normalized min-sum (NMS) decoders. At a signal-to-noise ratio of 5 dB, the NNMS model has a bit-error rate (BER) of 1.0 × 10−7, whereas traditional NMS models have a less steep slope. In particular, the NNMS model has a coding gain of 1.25 dB compared to the traditional NMS model at a BER threshold of 1.0 × 10−3. This shows that the NNMS model is an efficient solution for high-performance, real-time digital communication systems.

Impact of Humic Acid on Growth, Yield, and Nitrogen, Phosphorus, and Potassium Uptake of Eggplant (Solanum melongena L.) in Coarse-texture Soils

Azad Salih Abdullah Krusk — 2026-07-15

To study how coarse-textured soils affect plant growth, yield, and Nitrogen (N), Phosphorus (P), and Potassium (K) content in eggplant (Solanum melongena L.), this study was carried out in pots using a factorial completely randomized design with three replications, and five different levels of humic acid (HA) (0, 40, 60, 80, and 120 kg/ha) was used during the 2024–2025 growing season. Field validation is recommended before making broad agronomic recommendations. Growth parameters, fruit yield components, N, P, and K concentrations in soil and plant organs were determined based on dry weight. Generally, plant height, leaf number, fruit diameter, and fruit weight increased significantly with HA application; the highest values were recorded at 80–120 kg/ha. Studied parameters were significantly affected by soil texture, loamy sand, and sandy loam, responding more strongly to HA than to loam. Most growth, yield, and nutrient concentrations recorded significant interaction effects between HA and soil texture. The availability of N, P, and K in soil increased with an increase in HA, as well as their accumulation in roots, shoots, and fruits. These results show that HA is effective as a soil improver and bio-stimulant for increasing eggplant productivity in coarse-textured soils; an ideal approach is the application of 80–120 kg/ha, which represents an optimal management strategy.

Child Guard System: A Cross-Platform Mobile Child Monitoring System Using Global Positioning System Tracking, Geofencing, and Firebase Cloud Messaging

Zhwan Namiq Ahmed — 2026-07-19

This study proposes a child guard system (CGS), a mobile-based solution that supports real-time location monitoring and emergency communication between parents and children. The proposed system adopts a client–server architecture that integrates a global positioning system (GPS), Wi-Fi positioning, RESTful communication, and Firebase Backend-as-a-Service to ensure scalable and reliable data synchronization. Location data collected from the child’s smartphone are transmitted to a cloud-based backend, enabling near-real-time monitoring through a mobile application. The CGS provides several key functionalities, including real-time location tracking, geofencing alerts, emergency notifications, location history, and a built-in chat feature for direct parent–child communication. Unlike many existing solutions that require additional hardware devices, the proposed system operates entirely using smartphones and cloud services, reducing system complexity and deployment cost. Experimental evaluation demonstrated an average GPS positioning accuracy of approximately 5–10 m, a location update latency of 1.25–2.8 s, an average notification delivery time of 1.85 s, and an overall user satisfaction score of 4.7/5, highlighting the effectiveness of the proposed system for child monitoring apps.

Efficacy of Pelvic Floor Muscle Exercises in Improving Severe Urinary Incontinence Symptoms in Women of Sulaimani City

Zhino Raouf Ali — 2026-07-29

Urinary incontinence (UI) is a highly prevalent disorder that significantly impacts women’s daily activities and social life. While pelvic floor muscle exercise (PFME) is recommended as the first-line management approach, there is increasing recognition that combining exercise-based interventions with educational support may enhance clinical outcomes. A quasi-experimental study was conducted involving 100 women, who were divided equally into two groups (n = 50). At baseline, both groups were comparable regarding age, medical history, gynecological history, and occupation (P > 0.05). Outcomes were measured and compared pre- and post-intervention. Post-intervention, the experimental group demonstrated significant improvements in body mass index and waist-to-hip ratio (P < 0.001), whereas the control group showed no significant changes. Furthermore, UI symptoms, particularly leakage triggered by coughing, sneezing, or physical activity, were almost entirely resolved in the intervention group (P < 0.001), yielding very large effect sizes. The intervention group also experienced significant reductions in anxiety, avoidance behaviors, and leakage triggers. Neither medication (42%, 34%), rectocele repair (22%, 28%), nor cystocele repair (24%, 32%) in the control and intervention groups, respectively, led to satisfactory or complete improvement. The combination of PFME and educational support effectively improves physical metrics and alleviates the symptoms and psychological burden of severe UI.

Applying Some Mathematical Models to Estimate Leaf Area of Christ’s-thorn Jujube trees (Ziziphus spina-christi L.) Based on Leaf Morphometric Measurements in the Koya District

Ikbal M. Albarzinji — 2026-08-02

The Christ’s-thorn jujube (Ziziphus spina-christi L.) is an important agricultural, medicinal, and ecological plant. Understanding photosynthetic capability, biomass buildup, transpiration, and ecological modeling all depend on accurate estimates of leaf area. In this study, 610 mature leaves of Z. spina-christi were collected in Koya district, Iraqi Kurdistan Region, Erbil, and measured for leaf length (L), width (W), and area (LA) in October 2025. Leaf area was estimated using regression-based models, which were statistically validated using the Statistical Package for the Social Sciences software. Simple linear, multiple linear, and exponential regression models were developed to predict leaf area from their dimensions. Although simple linear regressions based on L or W individually were statistically significant (P < 0.001), they accounted for only 78–85% of the observed variation in leaf area (R2 = 0.783–0.849); greater predictive performance was achieved by models using the product of leaf dimensions (LW). Among all models evaluated, the simple linear regression model LA = 0.082 + 0.69 (LW) emerged as the most effective, providing the highest precision for its type (R2 = 0.972, mean square error = 0.1). The more complex multiple linear regression model, LA = 0.15 + 8.28 L2 + 0.41 W2 (R2 = 0.96), and the exponential model, LA = 0.89e0.31(L + W) (R2 = 0.94), also displayed high accuracy but with increased computational complexity. The model LA = a + b (LW) is suggested as the best method, taking into account both practical simplicity and predicted accuracy. It provides a dependable, effective, and non-destructive instrument for physiological and agronomic studies on Z. spina-christi.

Factors Influencing Early Puberty in School-Age Girls: A Comparative Study between Garmian and Sulaymaniyah Provinces, Iraq

Tara Ahmed Hassan — 2026-08-18

Precocious puberty in females is a growing public health concern due to potential harm to the child’s physical, psychological, and social development. Even though there is literature internationally that provides some possible causative factors, evidence within Iraq is still lacking, especially when it comes to regional variations and multiple environmental, genetic, and behavioral determinants. Hence, the main purpose of conducting this research is to describe factors associated with precocious puberty and compare regional variations among affected girls. In Iraqi girls from the Garmian and Sulaymaniyah provinces, 100 girls between the ages of 6 and 12 who had been diagnosed with early puberty participated in a quantitative descriptive cross-sectional study design. The findings show significant regional variations in precocious puberty causes among Sulaymaniyah and Garmian’s schoolgirls, suggesting potential links between biological, environmental, and lifestyle factors and the issue. The results mentioned above can raise awareness and encourage more research on the problem in Iraq. Therefore, it is critical to identify and implement preventative interventions for Iraqi girls who are at risk of early puberty.

A Comparative Analysis of Spark GraphX and GraphFrames for Healthcare Data Analytics

Soz Raouf Hama — 2026-08-19

Healthcare data analytics is essential for identifying disease patterns, understanding patient relationships, and supporting data-driven decision-making in modern healthcare systems. As healthcare datasets continue to grow in size and complexity, scalable graph-processing frameworks have become increasingly important for analyzing interconnected patient data. This paper compares two Apache Spark graph-processing frameworks, GraphX and GraphFrames, using the Centers for Disease Control and Prevention Diabetes Health Indicators dataset. A patient similarity graph was constructed by representing 70,692 patients as vertices and connecting highly similar patients through cosine–similarity relationships, resulting in 16,009,101 graph edges. The two frameworks were evaluated using the same graph structure and execution environment with respect to graph construction time, execution performance, memory consumption, application programming interface usability, and scalability. In addition to the framework comparison, graph-based features, including in-degree, out-degree, total degree, and PageRank, were extracted to examine the structure of the patient similarity network. The experimental results showed that GraphX completed graph construction, PageRank computation, and degree calculations considerably faster than GraphFrames. However, GraphFrames offered a higher-level programming interface, simpler integration with Spark SQL, and easier implementation of graph analytics workflows. The extracted graph measures also highlighted highly connected and structurally important patients within the network, demonstrating the usefulness of graph-based feature extraction for healthcare data analysis. GraphX completed graph construction approximately 33 times faster (92.5 s vs. 3037 s), PageRank computation approximately 780 times faster (9.1 s vs. 7132 s), and degree computation over 10,000 times faster (1.0 s vs. 10202 s) than GraphFrames, whereas GraphFrames used substantially less memory; these results indicate that GraphX is more suitable for performance-oriented graph workloads, whereas GraphFrames is advantageous when development flexibility and DataFrame integration are primary considerations.

A Robust Heterogeneous Ensemble Framework for Software Cost Estimation with Prediction Uncertainty Quantification

Hawar Othman Sharif — 2026-08-21

Successfully estimating software costs is critical to project management because poor estimates lead to overruns, scheduling issues, and project failure. Machine learning improves estimates, but usually only point estimates are produced without providing valid uncertainty around them. This paper presents a method using an uncertainty-aware heterogeneous ensemble of 100 bootstrap-trained base learners (using gradient boosting, extra trees, and random forests), which jointly produce point predictions and prediction intervals using a robust trimmed mean, with effort modeled on the log scale. Using the NASA93 dataset (with 93 projects, split 80/20 into 74 for the training set and 19 for the test set) and repeated cross-validation (feature selection and scaling were performed only within the training folds to avoid leakage), the model achieved a mean absolute error of 309.1 and a percentage of relative error deviation from the predicted value rate of 51.3% (30 out of 74 projects) outperforming all eight of the Bayesian baselines it was compared to, while being the only model to provide the validity of its prediction intervals through calibration. Given that normality of the ensemble predictions is rejected, empirical and distribution-free percentiles (with 89.5% of the prediction intervals containing the test project true outcomes) are used because they are not biased by the training data. The average interval width covered by the validation of those prediction intervals to cross-validate the interval width over 86.1% of the test set differs significantly from the linear bases using a paired Wilcoxon test (P < 0.001). Thus, the framework described couples competitive levels of accuracy with quantifiable and empirically validated confidence levels.