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

2026-04-23T11:10:05+00:00

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

2026-04-12T23:36:01+00:00

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

2026-05-25T01:33:17+00:00

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

2026-05-20T10:14:21+00:00

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

2026-04-24T01:08:39+00:00

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

2026-06-13T14:23:19+00:00

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.