Current Issue

Sana'a University Journal of Applied Sciences and Technology

Vol. 4 No. 10 (2026)

2026-10-28

12 Articles

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

Article

12 Articles

Alimentary canal of Canthidermis macrolepis col- lected from Sana’a market, Yemen

Feeding Ecology of the Large-Scale Triggerfish, Canthidermis macrolepis (Boulenger, 1888), from Yemeni Coastal Waters

Ebtisam F. Shamsan, Maryam A. Taher

2612-2618

2026-10-28

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DOI: 10.59628/jast.v4i10.3435

Abstract

Information on fish feeding ecology is crucial for understanding trophic interactions, energy flow, and ecosystem dynamics in marine environments. Although the feeding ecology of several balistid species has been well documented, the trophic dynamics of the large-scale triggerfish, Canthidermis macrolepis, remain largely unstudied. To address this gap and define its ecological role, we investigated the diet and feeding intensity of C. macrolepis harvested from the Yemeni Red Sea coast between June and October 2025. Stomach content analysis (N = 57) was performed utilizing qualitative taxonomic identification and quantitative metrics, specifically frequency of occurrence (%F) and volumetric percentage composition (%C). The results characterize C. macrolepis as an active pelagic carnivore. The diet was predominantly composed of small teleosts, with zooplanktonic crustaceans serving as a critical secondary resource. Benthic invertebrates (sipunculids and worms), small mollusks, and macroalgae were present only as minor, opportunistic dietary components. Furthermore, feeding intensity was exceptionally high (Fullness Index: 98.25%; Vacuity Index: 1.75%), indicating continuous foraging activity. These findings establish C. macrolepis as a pelagic predator, differentiating its ecological niche from that of heavily benthic-dependent reef triggerfishes, and provide essential baseline trophic data to support regional ecosystem-based fisheries management.

Keywords

Balistidae Red Sea Diet composition Trophic dynamics Pelagic predator

Sentiment-classification schemes used in the included studies.

A Scoping Review of Sentiment Analysis for Customer Satisfaction in Digital Banking: Mapping the Research Gap Toward Multimodal Integration

Maimona Al-Maamari, Ghaleb Al-Gaphari

2619-2634

2026-10-28

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DOI: 10.59628/jast.v4i10.3463

Abstract

Sentiment analysis has become increasingly important for interpreting customer-generated content across social media and digital technologies. However, research on digital banking continues to rely predominantly on textonly data, with limited attention to multimodal integration. This scoping review mapped the literature on sentiment analysis for customer satisfaction in digital banking, identified research gaps, and outlined future directions. The review followed the PRISMA-ScR guidelines and searched six databases—ScienceDirect, IEEE Xplore, Wiley Online Library, ACM Digital Library, SpringerLink, and Google Scholar—for English-language studies published from 2015 to 2026; the final search was conducted on May 10, 2026. Of 1,631 records identified, 35 studies met the inclusion criteria. Text only data were used in 31 studies (88.6%), multi-source text was used in one study (2.9%), text combined with survey data in two studies (5.7%), and numerical time-series data in one study (2.9%); no included study integrated text, audio, and video within a single sentiment analysis framework. The literature was geographically concentrated and relied mainly on traditional machine-learning and hybrid approaches, whereas transformer/LLM based methods, explainable AI, and real-time approaches remained limited. The absence of banking-specific multimodal datasets is a key barrier to progress. Future research should prioritize privacy-preserving multimodal datasets, multilingual and culturally diverse models, explainable AI, and real-time deployment to support more reliable assessment of customer experience in digital banking.

Keywords

Sentiment Analysis Digital Banking Scoping Review Multimodal Customer Satisfaction Text-Based Data Traditional Machine Learning Hybrid Approach.

شكل (1) يوضح المحتوى الميكروبيولوجي لعينات الدراسة

Quality Evaluation of Locally Produced Puffs and Crisps Made from Corn Grits Using Extrusion Technology

Asma’a Qaid Al-Ghwil, Jalal Ahmad Fadl, Abdulmajeed Bajash Abdullah

2635-2652

2026-10-28

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DOI: 10.59628/jast.v4i10.3597

Abstract

Extruded foods are widely available in the Yemeni market, necessitating an assessment of their quality and food safety. This study aimed to evaluate the quality and safety of selected locally produced extruded foods made from corn grits by determining certain additives and mineral contents and evaluating the physical, sensory, and microbiological characteristics of eight samples, including four samples of puffed corn and four samples of corn crisps. The results showed that all samples were free of preservatives specifically benzoic acid and sorbic acid and artificial sweeteners, namely aspartame, sodium saccharin, and acesulfame potassium. Magnesium concentrations ranged from 116 to 226 mg/kg, zinc from 0.1 to 16.8 mg/kg, manganese from 0.41 to 5.11 mg/kg, and nickel from 0.1 to 1.5 mg/kg, while iron was not detected in most samples, with the exception of three samples ranging from 44.7 to 0.7 mg/kg; copper and cadmium were not detected. Sensory evaluation scores ranged from 2.89 to 3.84 out of 5; sample Pd of puffed corn showed the highest scores for overall appearance, taste, and texture, whereas sample Ch of corn crisps recorded the highest mouthfeel score. Sample Pd also exhibited the lowest bulk density (0.07 g/cm³) and the highest expansion ratio (5.79), while sample Cg of corn crisps recorded the highest product increase percentage (98.33%). Microbiologically, all samples were free of molds, and yeast counts ranged from 1×10² to 1×10³ CFU/g, within permissible limits. However, the total bacterial count in sample Ce of corn crisps reached approximately 1.6×10⁵ CFU/g, exceeding the permissible limit. Overall, most samples showed acceptable quality and microbiological safety, although variations were observed in their mineral, physical, and sensory characteristics. These findings emphasize the importance of periodic monitoring and adherence to good manufacturing practices to ensure product quality and safety.

Keywords

Extrusion Technology Snacks Physicochemical properties Sensory evaluation Food safety

Publication-quality workflow of the proposed incremental tree-based learning framework for brain stroke prediction.

Interpretable Incremental Tree-Based Learning for Brain Stroke Prediction with Rule Evolution

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

2653-2667

2026-10-28

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DOI: 10.59628/jast.v4i10.3271

Abstract

Accurate brain stroke prediction requires models that can detect high-risk cases while adapting to newly arriving clinical data. This study proposes an interpretable incremental tree-based learning approach for brain stroke prediction using Decision Tree, Random Forest, and Gradient Boosting. Experiments were conducted using the Cerebral Stroke Dataset, a public imbalanced stroke prediction dataset with 43,400 records and 12 attributes. Since the dataset does not provide verified patient-level timestamps, the data were divided into three sequential simulated cohorts to evaluate model behavior under progressive data availability. Decision Tree was used as a refitted interpretable baseline, while Random Forest and Gradient Boosting were updated using a warm-start strategy by appending additional estimators at later stages. Rule extraction and rule classification were used to translate tree-based decision logic into readable rules and categorize them as conforming, generalized, specialized, unexpected, or novel, enabling analysis of rule evolution across cohorts. Experimental results showed that the incremental approach improved sensitivity oriented performance and reduced update time compared with traditional retraining. At the final stage, incremental Gradient Boosting achieved 98.044% accuracy, 75.32% F1-score, 91.03% recall, and 94.66% ROC AUC, with only 40 false negatives. Therefore, incremental Gradient Boosting provided the most clinically suitable trade-off between stroke-case detection, update efficiency, and interpretable rule behavior.

Keywords

Brain Stroke Prediction Incremental Learning Tree-Based Models Decision Tree Random Forest Gradient Boosting Streaming Data Rule Evolution.

PhiUSIIL dataset class distribution: 134,850 legitimate URLs and 100,945 phishing URLs [15].

An Explainable Zero-Trust Framework for Detecting Generative-AI-Enhanced Phishing and Credential Theft Using Real-World Threat Data

Mohammed Ameen Saleh Al-Bareda

2668-2678

2026-10-28

PDF

DOI: 10.59628/jast.v4i10.3204

Abstract

Phishing has evolved from simple fraudulent messages into a multi-channel credential-theft ecosystem that combines malicious URLs, QR-code phishing, adversary-in-the-middle proxying, business e-mail compromise, deepfake assisted social engineering, and generative-AI-assisted content generation. This paper proposes an explainable zero-trust framework for detecting and reducing phishing-related credential exposure using real-world threat intelligence and the PhiUSIIL public benchmark dataset. The study integrates APWG observations from 2024 through Q3 2025 with a benchmark containing 235,795 URLs, 54 variables, 134,850 legitimate samples, and 100,945 phishing samples. The framework combines URL and page-content features, brand-mismatch evidence, QR-code detonation, sender-authentication signals, phishing-resistant MFA, conditional access, browser isolation, and human-readable explanations. A supervised benchmark experiment was conducted using a single stratified 80/20 train-test split and three baseline classifiers. Decision Tree achieved 100.00% F1-score, Random Forest achieved 99.9975% F1-score, and Gaussian Naive Bayes achieved 98.1923% F1-score on the held-out test set. These single-split benchmark results support a layered conclusion: URL detection is useful, but stronger risk reduction is achieved when detection, identity, isolation, reporting, and continuous governance operate as one zero-trust system.

Keywords

Phishing Detection Generative AI Zero Trust Credential Theft Passkeys FIDO2/WebAuthn Quishing Explainable AI Threat Intelligence Cybersecurity Framework.

Proposed hybrid motion planning system architecture. The Perception module (left) uses YOLOP to extract scene data encoded as both a structured text prompt and a numerical feature vector. The Planning module (center) processes these in parallel through the fine-tuned LLM and the multi-branch DNN, fuses the outputs, and refines them. The Control module (right) applies MPC optimization to produce the final 15-waypoint trajectory. Arrows denote data flow direction.

LLM-DNN Fusion for Data-Efficient Trajectory Generation in Autonomous Driving: A Hybrid Approach with MPC Optimization

Yusuf Al-jamaly, Khalil Al-Hatab, Adel Rawea

2679-2691

2026-10-28

PDF

DOI: 10.59628/jast.v4i10.3391

Abstract

Autonomous vehicle (AV) trajectory planning in dense urban environments requires models that generalize to rare or previously unseen scenario types. Deep neural networks (DNNs) trained on large datasets perform poorly in such data-scarce conditions, while large language models (LLMs) generalize well but lack the numerical precision required for low-level vehicle control. This paper presents a hybrid trajectory planning framework that fuses a finetuned LLM with a multi-branch DNN at the trajectory level and applies a Model Predictive Control (MPC) controller as a final optimization stage. Gemini 1.5 Flash, fine-tuned on 479 labeled scenario-trajectory pairs, generates 15- aypoint trajectory proposals from structured textual scene descriptions. A modular DNN processes the same scene as a numerical input vector and produces a parallel trajectory proposal. The two proposals are fused by a learned trajectory refinement network, and the output is passed to an MPC controller that enforces vehicle dynamics constraints while minimizing a multi-objective cost function. Training relies on a three-step distortion- based augmentation technique, curve distortion, scale distortion, and Gaussian smoothing, that generates semi-realistic trajectory variants from a base dataset of over 70,000 labeled rows, reducing the data volume required for effective DNN training. Evaluation across 200 runs in the CARLA simulator on unseen urban scenarios shows that the MPC-enhanced hybrid model achieves an Average Displacement Error (ADE) of 0.66 m and a Final Displacement Error (FDE) of 1.94 m, corresponding to improvements of 37.7% and 34.3% over the DNN-only baseline. The collision rate (CR) decreases from 3.71% to 2.90%, a 21.8% reduction. These results establish that trajectory-level fusion of LLM semantic reasoning with DNN-based feature extraction delivers measurable accuracy and safety gains in data-efficient autonomous driving conditions. The ADE/FDE improvements are statistically significant (p < 0.001). The collision-rate reduction is a consistent trend across ablations, but it is not statistically significant at n = 200 runs (see Section 4.4).

Keywords

Autonomous vehicles trajectory planning large language models deep neural networks model predictive control data-efficient learning CARLA simulation

Massive MIMO beamforming pattern showing spatial focusing capability.

AI-Driven Secure Channel Estimation and Energy-Efficient Power Allocation for Cell-Free Massive MIMO

Murad Abdullah Abdo Ahmed Albahri, M. A. Hankel

2692- 2700

2026-10-28

PDF

DOI: 10.59628/jast.v4i10.3389

Abstract

Cell-free massive multiple-input multiple-output (MIMO) improves user fairness by allowing many distributed access points (APs) to cooperatively serve users without fixed cell boundaries. Its practical reliability, however, is limited by pilot contamination, pilot spoofing, channel state-information (CSI) uncertainty, inter-user interference, and inefficient power consumption. This paper proposes an AI-driven secure channel estimation and energy-efficient power allocation (AI-SCE-EEPA) framework for uplink cell-free massive MIMO. The AI component is a lightweight supervised anomaly classifier trained on synthetically generated pilot observations representing clean and spoofed contaminated CSI conditions. Its output is used as a soft reliability gate before minimum mean-square error (MMSE) channel refinement. The refined secure CSI is then used by an adaptive regularized MMSE receiver and an energy-efficient power allocation rule. A reproducible Monte Carlo simulation is conducted under distributed APs, Rayleigh fading, large-scale path loss, log-normal shadowing, QPSK transmission, pilot contamination, and active pilot spoofing/eavesdropping stress. The proposed framework is compared with MR, conventional LMMSE, and adaptive R-MMSE baselines using bit error rate (BER), normalized mean-square error (NMSE), spectral efficiency (SE), energy efficiency (EE), secrecy rate, and power-allocation behavior. At 30 dB, AI-SCE-EEPA improves the reported NMSE, BER, SE, EE, and secrecy-rate indicators over fixed-power LMMSE baselines. The results indicate that combining trainable CSI anomaly detection with interpretable model-based estimation and energy-aware power allocation is a practical direction for secure and green cell-free massive MIMO.

Keywords

Cell-free massive MIMO AI-based channel estimation pilot spoofing detection power allocation physicallayer security energy efficiency.

Distribution of areas covered by the RWH suitability classes

GIS-Based Multi-Criteria Analysis for Identifying Suitable Rainwater Harvesting Sites to Enhance Groundwater Recharge: A Case Study of the Western Wellfield in Thaqban Area, Sana’a, Yemen

Mohammed Modaess, Yahia Alwathaf, Nabil M. Al-Areeq, Alkhateeb Al-Kebsi

2701-2716

2026-10-28

PDF

DOI: 10.59628/jast.v4i10.3437

Abstract

Groundwater depletion in the Sana’a Basin, Yemen, has intensified due to over-abstraction, rapid urbanization, and limited natural recharge. The Western Wellfield, which supplies a significant portion of Sana’a City’s domestic water demand, has experienced substantial groundwater level declines, highlighting the need for sustainable recharge interventions. This study evaluates the potential of rainwater harvesting (RWH) for enhancing groundwater recharge in the Thaqban area using a Geographic Information System (GIS)-based Multi Criteria Decision Analysis (MCDA) integrated with the Analytic Hierarchy Process (AHP). Ten thematic criteria were considered, including rainfall, runoff depth, slope, geological media, soil texture, land use/land cover, drainage density, distance to wadis, distance to groundwater wells, and groundwater depth. The criteria were weighted using AHP and combined through a weighted linear combination (WLC) approach to generate an RWH suitability map. The results show that 1.34% of the study area is highly suitable for RWH implementation, 37.60% is moderately suitable, 58.21% is marginally suitable, and 2.85% is unsuitable. Highly suitable areas are mainly associated with permeable geological formations, favorable topography, and proximity to production wells. This study demonstrates the usefulness of GIS-MCDA techniques for identifying priority recharge zones and supporting sustainable groundwater management in arid environments.

Keywords

Rainwater harvesting Groundwater recharge GIS MCDA Western Wellfield Yemen.

Subsidence curves of Sunah-01 well in Block-14

Geohistory Analysis of Block 14, Say’un–Masilah Basin, Yemen.

Zahrah Abdualaziz Al-Shawafi, Adel Mohammad Al-Matary

2717-2728

2026-10-28

PDF

DOI: 10.59628/jast.v4i10.3197

Abstract

This study presents a comprehensive geohistory analysis of Block 14 (Masila Block), Say’un–Masilah Basin, eastern Yemen, based on one-dimensional (1D) Airy backstripping of stratigraphic data from 22 wells across the Cammal, Sunah, and Tawilah fields. Eleven formations spanning approximately 161 million years (Ma), from the Middle Jurassic Kuhlan Formation (161.2 Ma) to the Paleogene Umm Er Radhuma Formation (56 Ma), were analyzed. For each formation and well, present day and initial porosity, decompacted sediment thicknesses, total and tectonic subsidence and their rates, and sedimentation rates were quantified. Decompacted thickness consistently exceeds present-day values across all formations, with the greatest compaction effect recorded in the Shuqra Formation at Sunah-01 (∼ 60 mlost to compaction). Total subsidence ranges from 28 m (Kuhlan Formation) to 943 m (Harshiyat Formation). Tectonic subsidence was lowest during pre-rift Kuhlan deposition (∼ 25 m average), increased during syn-rift Naifa deposition (∼ 162 m average), and peaked during Harshiyat deposition (∼ 665 m; 5.33 cm/1,000 yr). The Mukalla Formation records the highest sedimentation rate (14.5 cm/1,000 yr average). Sunah-01, located in the northeastern part of the study area, records the highest cumu- lative total subsidence (3779 m) and tectonic subsidence (2970 m), corresponding to the thickest sedimentary succession. Tawilah-99, in the southeastern part of the study area, records the lowest cumulative values (total: 2479 m; tectonic: 1813 m), corresponding to the thinnest succession. These results provide a quantitative geohistory framework for Block 14 and a basis for future petroleum system modeling within the Say’un–Masilah Basin.

Keywords

Yemen Backstripping Geohistory Masilah Basin Block 14 Basin Analysis

Comment on The Identifying the Key Factors Affecting the Success of Occupational Health and Safety Management during the Implementation of Construction Projects for NGOs in Sana’a, Yemen

Khaled Ismail M. Al-Shami

2729-2732

2026-10-28

PDF

DOI: 10.59628/jast.v4i10.3960

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Commentary Identifying the Key Factors Affecting the Success of Occupational Health and Safety Management during the Implementation of Construction Projects for NGOs in Sana’a, Yemen

Reply to Comment on The Identifying the Key Factors Affecting the Success of Occupational Health and Safety Management during the Implementation of Construction Projects for NGOs in Sana’a, Yemen

Ibtisam M. H. Al-Ma’anisi, Fadhl A. S. Al-Nozaily, Tariq A. Barakat

2733-2740

2026-10-28

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DOI: 10.59628/jast.v4i10.4021

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Response Comment on The Identifying the Key Factors Affecting the Success of Occupational Health and Safety Management during the Implementation of Construction Projects for NGOs in Sana’a, Yemen

Allowed region in the (, |λ|) plane for μ = 1, σ = 1/2. Blue: (H2) bound Λgap() = √5/2. Red dashed: the stricter (H4) bound ΛG>0()/4 = √2. Green dotted: the (H5) upper frequency < 3.5116. The allowed region is the intersection of the three.

Logarithmic Deformations of Spectral Systems —Part I: Spectral Analysis and Classification of Logarithmically Perturbed Harmonic Systems

Osama A. H. Al-Dahyani, Abdu A. Alkelly

2026-10-28

DOI: 10.59628/jast.v4i10.3894

Abstract

In this first paper of a series, we develop the complete spectral and classification structure of the half-line Schrödinger operator O0 = − d² /dx² + μ(μ+1) /x² + ω² x² (x > 0, μ > 1/2, ω > 0) deformed by a logarithmic potential γ log x. Building on the established spectral properties of O0, we introduce a spectral-transcendental framework and prove: 1 Spectral Zeta: A closed form for the logarithmic spectral zeta S(s; ω) = ∑n≥0 V^log _nn /A^s_n (Re(s) > 1) in terms of the Hurwitz zeta function. We establish its meromorphic continuation to C, featuring a double pole at s = 1, a simple pole at s = 0, and simple poles at negative integers. 2 Spectral Sum Classification: A complete classification of {S_k(N) _k,N≥1: S_k(N) ∈ Q + Q log 2 for N even; S_k(N) ∈ Q for N ≥ 2k + 1 odd; and S_k(N) ∈ Q + Qπ² for N ≤ 2k − 1 odd. 3 Exact Constants: Closed-form evaluations for key constants, including B0(1, 1) = 23π⁴
/46080 − γE ζ(3) 32 + γ²E π²768 ≈ 0.03122, C(1) = 1, the MZV constant c, and the Borel-2 constant C^Borel₁ . 4 Commutator Trace Identity: The analytical identity T(α) = 1/2 ζ_H (4, (α + 1)/2) for α ∈ {0, 1, 2}, its general conjecture for all α > 0, and the Pochhammer-ratio obstruction at non-integer values.

Keywords

Schrödinger operator logarithmic deformation spectral zeta logarithmic matrix elements digamma classification multiple zeta values PSLQ analysis Pochhammer-twisted Euler sums explicit constants.

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