Current Issue

Sana'a University Journal of Applied Sciences and Technology

Vol. 4 No. 8 (2026)

2026-08-28

15 Articles

The Fourth Volume – The Eighth Issue of the Sana'a University Journal of Applied Sciences and Technology

Article

14 Articles

Website Fingerprinting (WF) attack scenario within the JAP architecture.

A Comparative Analysis of Feature Representation Paradigms for Website Fingerprinting on the Java Anon Proxy Network

Abdulqader Shaawa, Abdoulwase Al Azzani

2292- 2301

2026-08-28

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DOI: 10.59628/jast.v4i8.3272

Abstract

Website fingerprinting (WF) attacks pose a serious threat to online privacy, even when users employ anonymizing networks. The effectiveness of deep learning-based WF critically depends on how raw network telemetry is transformed into input representations. In this paper, we systematically compare five feature encoding paradigms–Direction Only, Inter-arrival Times, Unsigned Packet Sizes, Signed Inter-arrival Times, and Signed Packet Sizes–using two deep learning architectures: a 1D Convolutional Neural Network (CNN) and a CNN-LSTM-Attention (CLA) model. We conduct our experiments on a newly collected Java Anon Proxy (JAP) dataset of 10,000 traces, measuring accuracy, stability (standard deviation and learning curves), training time, and peak RAM usage. Our results show that Direction Only achieves high accuracy (99.14% with CLA) and low variance, confirming its discriminative power. However, Signed Packet Sizes combining packet size with directional sign–deliver the best overall performance: CLA reaches 99.56% accuracy with low variance (0.24%), while the CNN model attains 98.22% with even lower variance (0.18%) and faster training time. A sensitivity analysis with four weighting schemes (accuracy-focused, efficiency-focused, stability-focused, balanced) shows that while Signed Sizes CLA is optimal for absolute peak accuracy, Signed Sizes-CNN provides the best trade-off for practical deployments where computational resources are limited.

Keywords

Website fingerprinting JAP Anonymity WF attack Deep learning

Sana'a University Journal of Applied Sciences and Technology

تأثير مزج المياه الرمادية ومياه المجاري المعالجة مع المياه الجوفية على التربة الكلسية وإنتاجية علف الذرة الرفيعة: دراسة في الاستدامة البيئية

علي محمد عبدالرحمن المساوى

2302-2323

2026-08-28

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DOI: 10.59628/jast.v4i8.3400

Abstract

أُجريت هذه التجربة عام 2022م في بيت زجاجي بكلية الزراعة - جامعة صنعاء خلال الموسم الزراعي للذرة، حيث امتدت الفترة الزمنية للتجربة من الأول من يونيو وحتى الثاني والعشرين من يوليو بواقع 52 يوماً لتقييم تأثير ري الذرة الرفيعة في تربة كلسية طميية بمستويات مختلفة من المياه الرمادية ومياه المجاري المعالجة جزئياً/أولية الممزوجة بالمياه الجوفية. ويُقصد بنسب المزج: النسب الحجمية المعتمدة للمزج بين المياه الجوفية والمياه الرمادية والمجاري المعالجة، والتي شملت خمسة مستويات(4:0، 3:1، 1:1، 1:3، 0:4 . ( أظهرت النتائج أن مياه المجاري غير الممزوجة (4:0) رفعت ملوحة التربة (ECe) إلى 1.36 ديسيسيمنز/متر مقارنة بـ 0.24 ديسيسيمنز/متر قبل التجربة، وزادت تركيز النحاس الميسر إلى 17.00 ملغم/لتر. في المقابل، حققت نسبة المزج 1:3 (جوفية: مجاري) أعلى وزن طازج للمحصول بلغ 18.67 جم/أصيص بزيادة قدرها 17.3% عن المعاملة غير الممزوجة. كما حققت نسبة المزج 1:1 لكل من مياه المجاري والمياه الرمادية أعلى وزن جاف للعلف بلغ 5.05 و 4.68 جم/أصيص على التوالي، مما يثبت دور نسب المزج الكمية في تحسين الإنتاجية والحد من تدهور التربة.

Keywords

المياه الرمادية مياه المجاري المعالجة جزئياً خواص التربة مزج المياه التربة الكلسية علف الذرة الرفيعة

Chromatograms of unspiked and spiked lotion sample with 5–100% Lard

Method Development for the Detection of Lard in Lotion Cosmetics Using Gas Chromatography-Flame Ionization Detection and Chemometrics

Raja’a A. H. Saif, Ranya A. Al-Alie, Mahfoudh M. AL-Hamadi, Anass A. Al-Nedhary, Najwa S. AL-Darwish, Fatima A. Murshed

2324-2333

2026-08-28

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DOI: 10.59628/jast.v4i8.3112

Proposed UAV-assisted 6G IoT network slicing architecture with DRL-based resource allocation.

AI-Driven Energy-Efficient Network Slicing for UAV-Assisted 6G IoT Communications Using Deep Reinforcement Learning

Murad Abdullah Abdo Ahmed Albahri

2334-2343

2026-08-28

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DOI: 10.59628/jast.v4i8.3250

Abstract

Sixth-generation (6G) wireless networks are expected to support massive Internet of Things (IoT) connectivity, ultra-reliable low latency services, high-throughput multimedia traffic, and intelligent and autonomous infrastructures. Conventional terrestrial deployments may be insufficient in rural areas, disaster recovery scenarios, emergency zones, and temporary high-density IoT events, where rapid coverage extension and adaptive resource management are required. Unmanned aerial vehicles (UAVs) can operate as aerial base stations to enhance service availability; however, limited onboard energy, altitude-dependent air-to ground channels, constrained bandwidth and transmit power, and heterogeneous quality-of-service (QoS) requirements make static resource allocation inefficient. This revised paper proposes an AI driven energy-efficient network slicing framework for UAV assisted 6G IoT communication. The network is divided into enhanced Mobile Broadband (eMBB), Ultra- Reliable Low-Latency Communication (URLLC), and massive Machine-Type Communication (mMTC) slices. A DQN-based deep reinforcement learning (DRL) agent dynamically allocates the slice-level bandwidth, transmit power, and altitude-control actions after converting continuous decision variables into a finite feasible action set. The reward function jointly maximizes the throughput and energy efficiency while penalizing the latency, packet loss, and QoS violations. To address the reviewers’ concerns, the revised manuscript adds an LoS/NLoS air- to-ground channel model, a propulsion-aware UAV energy model, detailed DRL hyperparameters, a nine-action discretization table, Monte Carlo validation over 30 independent seeds, Welch significance testing, DRL variant comparison, computational complexity analysis, and three relevant references from Sana’a University Journal
of Applied Sciences and Technology. The proposed method improves the throughput by 15.7%, reduces the latency by 19.8%, improves the energy efficiency by 16.4%, and reduces the packet loss by 24.6% compared with the greedy baseline. The results confirm that slice-aware DRL improves resource utilization and service reliability in UAV-assisted 6G IoT networks.

Keywords

6G UAV communications network slicing deep reinforcement learning energy efficiency.

Monthly RTRWH potential compared with observed and estimated domestic water demand for the sampled residential buildings

Household-Level Assessment of Urban Rooftop Rainwater Harvesting Potential and Its Contribution to Domestic Water Supply in Al-Wahdah District, Sana’a City, Yemen

Adel Maaodhah, Marwan Al-Badani, Sharafaddin Salleh

2344-2351

2026-08-28

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DOI: 10.59628/jast.v4i8.3287

Hematological and Platelet Indices Alterations Associated with Khat Chewing Among University Students in Conflict-Affected Yemen: A Cross-Sectional Study

Naif Taleb Ali, Radfan Saleh Abdullah, Mansour Abdulnabi H. Mehdi, Mohammed Ali Qassem, Amira Tawfiq, Anhar Sufyan, Bashar Omer, Mohammed Fissal, Sondas Amin, Wazira Saleh, Abdulaziz Abdullah, Alawi Abdul Salam, Ahmed Naji Abbas, Emad Mohammed, Fadl kamal Fadl, Fekri Saif Taher, Laila Ahmed Hamid, Nasser Jamal Ahmed, Osama Abdul Qader, Qusay Ashal Saleh, Younes Adel Saleh

2352-2360

2026-08-28

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DOI: 10.59628/jast.v4i8.3106

Abstract

Background: Khat chewing is prevalent among Yemeni youth; however, its impact on hematological parameters in conflict settings remains underexplored.
Objective: To assess the association between khat chewing and complete blood count (CBC) parameters, particularly platelet indices, among university students in Al-Dhale’e, Yemen.
Methods: A cross-sectional study was conducted with 226 undergraduate students. Khat use was assessed using a structured questionnaire, and CBC was analyzed using an automated hematology analyzer. Statistical analyses included independent t-tests, Mann-Whitney U tests, and multiple linear regression adjusted for age, sex, smoking, body mass index (BMI), caffeine intake, and psychological stress score.
Results: The prevalence of khat chewing was 77.9% (176/226). Compared with non-chewers, chewers had significantly higher mean platelet volume (MPV: 9.7 vs. 8.8 fL; p < 0.001; d = 0.78), higher platelet distribution width (PDW: 16.1 vs. 15.0%; p = 0.003; d = 0.48), higher white blood cell count (WBC: 7.6 vs. 6.8 × 109/L; p = 0.004; d = 0.46), and lower hemoglobin (13.7 vs. 14.3 g dL; p = 0.006; d = -0.46). Regression analysis confirmed khat as an independent predictor of elevated MPV (β = 0.85, p < 0.001), elevated WBC (β = 0.78, p = 0.008), and reduced hemoglobin levels (β = -0.52, p = 0.012). Trend analysis suggested graded associations with the frequency and duration of use.
Conclusion: Khat chewing is independently associated with elevated MPV and WBC, suggesting a possible proinflammatory state and reduced hemoglobin levels. Routine CBC monitoring should be considered for habitual khat users in resource-limited settings.

Keywords

Khat Mean Platelet Volume Platelet Indices Hematological Parameters Yemen University Students Inflammation Thrombosis.

Moisture Stability and Sensory Quality of Metallized-Packaged Biscuits During Storage: A Comparison with Non-Metallized Packaging in a Single Product

Khaled Mohammed Assag, Jalal Fadel, Abdulmajid Bajash

2361-2383

2026-08-28

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DOI: 10.59628/jast.v4i8.3050

Abstract

Biscuits are dry food products that are highly susceptible to moisture uptake during storage due to their porous structure and low initial moisture content, making the control of water vapor transfer a critical factor in maintaining product stability. This study aimed to evaluate the effectiveness of metallized biaxially oriented polypropylene (Metallized BOPP) packaging in reducing moisture uptake through a direct comparison with non-metallized biaxially oriented polypropylene (BOPP) packaging in a single biscuit product, while also monitoring the response of three biscuit products stored under metallized packaging. A longitudinal field study was conducted over a twelve-month period at three storage locations in Yemen. Monthly measurements of moisture content, water activity, and sensory properties were collected, and the data were analyzed using linear mixed models. The results showed that metallized packaging was associated with significantly lower moisture content and water activity compared with BOPP packaging in the product used for the direct packaging comparison, accompanied by a clear improvement in crispness. The analysis also indicated that storage time was the factor most strongly associated with changes in both physical and sensory properties. Among the products stored under metallized packaging only, no significant differences were observed in moisture content, whereas differences were detected in water activity and sensory properties. These findings support the effectiveness of metallized packaging within the limits of a direct comparison conducted on a single product, while highlighting differences in product responses under the same packaging system.

Keywords

Biscuits Metallized BOPP Moisture uptake Water activity Sensory properties Longitudinal analysis Mixed-effects model

Comment on “Quantum Fundamental Speed Theory: Mathematical Derivations of Dark Matter and Dark Energy from the Speed Field

Elie Almurr

2384-2386

2026-08-28

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DOI: 10.59628/jast.v4i8.3675

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Commentary Quantum Fundamental Speed Theory: Mathematical Derivations of Dark Matter and Dark Energy from the Speed Field

Reply to the Comment on “Quantum Fundamental Speed Theory: Mathematical Derivations of Dark Matter and Dark Energy from the Speed Field”

Raheb Ali Mohammed Saleh Aoudh

2387-2388

2026-08-28

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DOI: 10.59628/jast.v4i8.3678

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Response Comment on “Quantum Fundamental Speed Theory: Mathematical Derivations of Dark Matter and Dark Energy from the Speed Field

Formal Comment on “Quantum Fundamental Speed Theory: Mathematical Derivations of Dark Matter and Dark Energy from the Speed Field”

Osama Abdullah Hassan Al-Dhuhyani

2389-2394

2026-08-28

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DOI: 10.59628/jast.v4i8.3625

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Response Formal Reply to the Formal Comment on''Quantum Fundamental Speed Theory: Mathematical Derivations of Dark Matter and Dark Energy from the Speed Field''

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Commentary Quantum Fundamental Speed Theory: Mathematical Derivations of Dark Matter and Dark Energy from the Speed Field

Formal Reply to the Formal Comment on''Quantum Fundamental Speed Theory: Mathematical Derivations of Dark Matter and Dark Energy from the Speed Field''

Raheb Ali Mohammed Saleh Aoudh

2395-2399

2026-08-28

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DOI: 10.59628/jast.v4i8.3677

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Comment on The Spatiotemporal Analysis of Rainfall Variability in the Sana’a Basin, Yemen: An Integrated Remote Sensing and GIS Approach

Khaled Ismail M. Al-Shami Ismail M. Al-shami, Abdulla A. Noman

2400-2402

2026-08-28

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DOI: 10.59628/jast.v4i8.3568

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Response Reply To Comments On The Spatiotemporal Analysis of Rainfall Variability in the Sana’a Basin, Yemen: An Integrated Remote Sensing and GIS Approach

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Commentary Spatiotemporal Analysis of Rainfall Variability in the Sana’a Basin, Yemen: An Integrated Remote Sensing and GIS Approach

Reply To Comments On The Spatiotemporal Analysis of Rainfall Variability in the Sana’a Basin, Yemen: An Integrated Remote Sensing and GIS Approach

Hadi A. Quriaa, Bassim S. Al Khirbash, Hisham M. Nagi, Amer M. Al-Sabri

2403-2406

2026-08-28

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DOI: 10.59628/jast.v4i8.3668

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Solar-Driven Photocatalytic Degradation of Pharmaceutical Wastewater using ZnO Nanoparticles

Rehab Sh. AL-Selwi, Anass A. Alnedhary, Mahfoudh M. AL-Hamadi

2407-2415

2026-08-28

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DOI: 10.59628/jast.v4i8.3246

Abstract

The increasing discharge of pharmaceutical residues into aquatic environments poses a serious environmental threat due to their persistence and resistance to conventional wastewater treatment processes. In this study, zinc oxide nanoparticles (ZnO NPs) were synthesized via a simple precipitation method and applied as an efficient photocatalyst for the degradation and mineralization of paracetamol under UV irradiation (365 nm) and natural solar irradiation. The structural, optical, and crystalline properties were confirmed using Ultraviolet–Visible Spectrophotometer, Fourier-Transform Infrared Spectroscopy and X-ray Diffraction analyses, revealing high crystallinity and nanoscale features with an average crystallite size ranging between 32.4 and 36.4 nm, which directly facilitated the surface-driven photocatalytic pathway. The results demonstrated that the synthesized ZnO NPs achieved 100% degradation of 10 mg/L paracetamol within 60 min under optimized solar irradiation conditions (pH 11, 2.5 g/L catalyst dose, 600 rpm stirring speed at room temperature). Furthermore, Chemical Oxygen Demand (COD) analysis confirmed complete mineralization of organic content, reducing the initial COD of real wastewater from 21,780 mg/L to 0.5 mg/L, achieving a 99.99% removal efficiency while High Performance Liquid Chromatography results verified the fully degraded parent compound. A major strength of this study is the utilization of both paracetamol and real pharmaceutical industrial wastewater, which enhances the environmental relevance and practical applicability of the proposed treatment system. The study highlights ZnO NPs as a highly efficient, low-cost, and sustainable photocatalyst for solar-driven wastewater treatment applications, with strong potential for large-scale environmental remediation.

Keywords

ZnO NPs Photocatalytic degradation Pharmaceutical Wastewater treatment Paracetamol

Review

1 Articles

Conceptual flowchart of stability-aware and feasibility-sensitive aggregation in Edge-IoT FRL

Stability-Aware and Feasibility-Sensitive Aggregation in Federated Reinforcement Learning for Edge-IoT Systems: A Review

Majid A. Aslan, Ahmed A. S. AL-SHalabi, Ahmed S. Al-Hegami

2416-2427

2026-08-28

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DOI: 10.59628/jast.v4i8.3275

Abstract

Federated Reinforcement Learning (FRL) provides a useful basis for distributed policy learning in Edge-IoT systems, where clients interact with local environments without transferring raw operational data to a central server. Yet aggregation becomes difficult when clients operate under different transition dynamics, workloads, resource capacities, communication conditions, and operational constraints. In these settings, local policy updates may not differ only in magnitude or direction; they may also differ in stability, reliability, resource support, and operational feasibility. Conventional averaging is therefore limited, since it does not distinguish stable and feasible updates from unstable or constraint-violating ones. This paper examines aggregation stability and feasibility-sensitive aggregation in FRL for Edge-IoT systems. It reviews and synthesizes related literature across four connected streams: heterogeneous Federated Learning, FRL-based edge decision-making, constrained and safe Reinforcement Learning, and adaptive or reliability-aware aggregation. The reviewed studies are analyzed through six dimensions: learning paradigm, type of heterogeneity, role of policy learning, treatment of operational constraints, aggregation strategy, and whether local feasibility signals influence global aggregation weights. The analysis indicates that existing studies provide valuable foundations, but they usually treat heterogeneity, constraint handling, and aggregation adaptation as separate concerns. The paper identifies a need for aggregation mechanisms that jointly account for update stability, update reliability, resource availability, and constraint feasibility. It positions aggregation as adaptive client influence regulation rather than passive averaging in future Edge-IoT FRL systems.

Keywords

Federated Reinforcement Learning Edge-IoT Adaptive Aggregation Aggregation Stability Constraint Feasibility.

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