Sana'a University Journal of Applied Sciences and Technology

The primary objective of this study is to enhance the properties of Sn-In alloys by incorporating varying amounts of zinc (Zn) to develop alternative lead-free alloys suitable for electronic applications. The samples were synthesized using high-purity in (Sn), indium (In), and zinc (Zn). Zn was added at different weight percentages (2, 4, 6, 8, and 10 wt. %) to the Sn-In base alloy. Structural analysis was performed using X-ray diffraction (XRD) to identify the phase formation and crystalline characteristics. Scanning electron microscopy (SEM) was employed to examine the microstructure, and energy-dispersive X-ray spectroscopy (EDX) was used to determine the elemental composition and phase distribution within the samples. The XRD results indicated that the Sn-Zn-In alloys exhibited a single-phase tetragonal structure. Increasing the Zn concentrations in the Sn matrix refined the grain size of the ternary Sn-Zn-In alloy. Additionally, Zn incorporation into the Sn-In binary alloy altered the intensity of the diffraction peaks and induced shifts in the (2θ) position, particularly in the 80Sn-10Zn-10In alloy. The calculated lattice parameters closely matched the standard values reported in JCPDS PDF# 04-0673,  and no new phases were detected in the ternary systems. The SEM analysis revealed the presence of three distinct regions within the alloy microstructure: a gray region rich in β-Sn, a dark region corresponding to the indium-rich phase, and a white region representing the zinc-rich phase. The EDX spectra confirmed that the elemental composition of the Sn-Zn-In alloys closely matched the nominal ratios, indicating successful alloying and homogeneous elemental distribution.

As cyber threats continue to evolve in sophistication, this study investigates critical vulnerabilities within Cisco ASA firewalls, with a particular emphasis on the Adaptive Security Device Manager (ASDM) interface. The research demonstrates how advanced malware can exploit the file-based architecture of ASDM to circumvent traditional firewall protections. A controlled virtual environment was established using VMware, incorporating a native Cisco ASA 5512-X firewall running software version 8.6(1), a Windows 10-based victim machine, and a Kali Linux attacker system. To facilitate the exploitation process, substantial modifications were applied to the open source tool “theway”, enabling it to extract and reassemble the .bin installation package without altering the structure of its internal components. Additionally, 7-Zip was employed to unpack the embedded .msi installer files, allowing for the insertion of malicious payloads. The WiX Toolset, used in its unmodified state, was then utilized to rebuild the .msi packages. These .msi files were selected as the primary attack vector due to their broad compatibility across ASDM versions and reduced likelihood of detection. In contrast, attempts to inject unsigned Java .class files into the pdm.sgz archive were unsuccessful, as recent ASDM versions enforce strict digital signature verification. The attack scenarios simulated in this study included reverse shell injection and credential interception through a man-in-the middle (MITM) attack. Results revealed significant weaknesses in Cisco ASA’s file validation procedures and outbound traffic monitoring mechanisms. To address these issues, a comprehensive multi-layered defense strategy is proposed, incorporating SIEM/EDR integration, robust digital signature enforcement, and internal network segmentation. The findings of this research contribute to both academic and practical efforts to enhance the security posture of enterprise firewall deployments against sophisticated malware threats.

The growing complexity of remote robotic surgery (RRS) has heightened the demand for communication systems that deliver ultralow latency, high bandwidth, and stable signal transmission. Conventional optical and wireless communication architectures often fail to satisfy these stringent requirements, particularly in time critical and high-fidelity data-exchange scenarios. In this study, a simulation-based evaluation of Optical Injection Locking (OIL) was conducted to investigate its potential in enhancing the signal quality and transmission efficiency for telesurgical applications. The results demonstrated that OIL significantly improves the frequency response characteristics, with the bandwidth reaching up to 32 GHz through controlled variation of the injection ratio. Furthermore, phase-noise suppression and spectral narrowing were observed with increasing wavelength detuning, contributing to improved signal coherence and reduced dispersion. The end-to-end latency was maintained below 50 ms, satisfying the safety constraints for real-time surgical interaction. The system also exhibited a 30% reduction in power consumption compared with conventional optical fiber systems. These findings confirmed the technical feasibility of OIL as a high-performance communication approach for latency-sensitive biomedical applications. Future work will focus on the prototype development and real-world integration of OIL-based links for next generation telesurgery platforms.

This study illustrates the low-cost, eco-friendly synthesis of silica nanoparticles using local bamboo leaves collected in August 2024 from the Aqqan Valley in the Al-Masimir District of Lahij Governorate, Yemen. Nanosilica powder synthesis by precipitation method is generated subsequent to thermal treatment of BLA at 750°C followed by characterization using advanced characterization techniques. The absorption peaks close to λmax = 240 nm in the UV spectrum showed silica nanoparticles, which were observed using UV-Vis spectroscopy. The presence of a siloxane group (Si-O-Si) in the experimental FT-IR spectral data indicated the high purity of the nanosilica particles. The prepared silica showed a high purity of 97.82% using XRF. The particle size determined by transmission electron microscopy (TEM) was found to be between 10.79 nm and 22.73 nm with minor agglomerations. Analysis of the nanosilica by X-ray diffraction (XRD) confirmed an amorphous structure with a
peak intensity at 2θ=23°.

Age-Invariant Face Recognition (AIFR) addresses the critical challenge of identifying individuals despite age induced facial changes over time. This paper presents a structured survey of recent advancements in AIFR, emphasizing deep learning-based solutions, particularly Generative Adversarial Networks (GANs). Through a comparative analysis of benchmark datasets MORPH-II, FG-NET, and CACD we highlight the performance trends and limitations of current models. GANs have shown exceptional promise in synthesizing realistic age- progressed or age regressed facial images, thereby improving recognition accuracy across age gaps. Further- more, we analyze key factors affecting model performance, including dataset diversity, illumination, and pose variations. Despite significant progress, challenges remain in generalizing across ethnicities, age ranges, and real-world conditions.

Phishing attacks continue to be a persistent and critical challenge in the cybersecurity landscape, exploiting weaknesses in Internet security through sophisticated tactics, such as social engineering, deceptive domain names, and URL obfuscation. Traditional detection systems often fail to identify these evolving threats because of their limited adaptability and high false positive rates. In this study, a deep learning approach is proposed to enhance the accuracy of phishing detection and minimize erroneous classifications. Leveraging a comprehensive dataset composed of key URL-related features, including URL structure, domain age, presence of HTTPS, and lexical patterns, we implemented and compared the performance of four deep learning models: Convolutional Neural Network with Bidirectional Long Short-Term Memory (CNN-BLSTM), Self-Normalizing Neural Network (SNN), Transformer, and Deep Belief Network (DBN). Among these, the CNN-BLSTM model achieved the highest accuracy of 81\%, demonstrating its superior ability to capture sequential and spatial patterns inherent in phishing URLs. The experimental results confirm that deep-learning methods outperform conventional techniques in detecting phishing attempts. However, challenges such as high computational complexity limit real-time deployment. This study highlights the transformative potential of deep learning for strengthening online threat detection and enhancing cybersecurity defenses.

يُعدّ الحديد عنصرًا أساسيًا في تغذية النبات، حيث يساهم في تحسين نموه وإنتاجيته. وتُعاني الترب القاعدية من انخفاض تيسر عنصر الحديد رغم توفره بكميات كافية، وذلك نتيجة لارتفاع درجة تفاعل التربة (pH) الناجم عن زيادة نسبة كربونات الكالسيوم، مما يؤدي إلى تثبيت الحديد وظهور أعراض نقصه على النبات. وعلى الرغم من استخدام الأسمدة التقليدية المحتوية على الحديد، إلا أنها لم تحقق الكفاءة المطلوبة في تحسين خصوبة التربة. في السنوات الأخيرة، برزت الأسمدة النانوية كبديل واعد لتعزيز امتصاص العناصر الغذائية وتحسين خصوبة التربة. وهدفت هذه الدراسة إلى تقييم تأثير سماد الحديد النانوي المحضّر باستخدام مستخلص ثمار الليمون، مقارنةً بالحديد التجاري، على بعض خصائص التربة ونمو نبات الجزر، بالإضافة إلى تحليل تأثير مستويات مختلفة من السماد النانوي على تركيز العناصر الغذائية في التربة وخصائصها الكيميائية. نُفّذت التجربة في البيت المحمي التابع لكلية الزراعة والأغذية والبيئة بجامعة صنعاء خلال الموسم الخريفي 2022–2023، باستخدام تصميم القطاعات العشوائية الكاملة (RCBD) بثلاث مكررات. تم تطبيق الحديد النانوي بأربعة مستويات (0.5، 1، 1.5، 2 ملغم/لتر) بطريقة الإضافة الأرضية، ومقارنته بسماد الحديد التجاري بتركيز 1.5 غم/لتر، بالإضافة إلى معاملة الشاهد (دون إضافة الحديد). أظهرت النتائج تفوّق السماد النانوي على السماد التجاري في معظم الصفات المدروسة، حيث حققت معاملة الحديد النانوي (2 ملغم/لتر) أعلى القيم في تركيز النيتروجين الكلي (0.21%)، والبوتاسيوم الميسر (103.07 جزء في المليون)، مع انخفاض في التوصيل الكهربائي (0.42 ديسيمنز/متر)، وزيادة في تركيز المنغنيز (81.23 جزء في المليون)، وارتفاع النبات (19.28 سم)، ومحتوى الكلوروفيل (1.31 ملغم/جم وزن طري)، والوزن الجاف للمجموع الخضري (4.98 غرام/نبات). كما تميزت معاملة الحديد النانوي (1 ملغم/لتر) بزيادة تركيز الفسفور الميسر (7.30 جزء في المليون) وخفض درجة حموضة التربة. (7.63) تؤكد نتائج الدراسة أن السماد النانوي المحضّر من ثمار الليمون يعزز خصوبة التربة ويزيد من توافر العناصر الغذائية مقارنةً بالحديد التجاري، مما يساهم في تحسين نمو نبات الجزر. وعليه، تُظهر الأسمدة النانوية إمكانات كبيرة كبديل مستدام وفعّال لتعزيز إنتاجية المحاصيل.

Copper sulfide (CuS) nanoparticles have garnered significant interest due to their unique structural, optical, and biological properties, making them promising candidates for cancer therapy. This work systematically examines how strontium incorporation influences the structural properties, optical behavior, and anticancer activity of copper sulfide nanoparticles for potential lung cancer therapeutics. Using the co-precipitation approach, Sr-doped CuS nanoparticles with different Sr concentrations were produced. Characterization techniques, including XRD, TEM, and UV-VIS-NIR spectroscopy, were employed to analyze their physicochemical properties. The results indicate that Sr doping enhances crystallinity, modifies the bandgap, and influences optical conductivity. Cytotoxicity analysis using MTT assays on A549 lung cancer cells emonstrated that Sr-doped CuS nanoparticles exhibit enhanced anticancer activity compared to pure CuS. The 2.5% Sr-doped sample showed the most significant reduction in cell viability. These findings suggest that Sr-doped CuS nanoparticles are promising candidates for biomedical applications. However, further studies are needed to assess their photothermal and photodynamic effects under controlled light exposure.

The study presents a comprehensive evaluation of optimal locations for solar energy facilities in Yemen by com-
bining Geographic Information System (GIS) technology with Multi-Criteria Decision-Making (MCDM) methodolo-
gies. It identifies twelve crucial criteria, assigning weights through the Analytical Hierarchy Process (AHP) and
subsequently validating them using the Best-Worst Method (BWM). A weighted overlay analysis conducted in
GIS resulted in a national suitability map, indicating that the eastern, southern, and northern regions of Yemen are
particularly well-suited for solar energy development. The findings reveal that 30.84% of the country is classified
as highly suitable, while 69.16% is moderately suitable, and merely 0.0022% is deemed less suitable for solar
installations. The sensitivity analysis highlights the significant influence of weighting the criteria, as even slight modifications to factors such as Global Horizontal Irradiance (GHI) and slope can lead to substantial changes in suitability assessments. Overall, the integration of AHP, BWM, and GIS is shown to create a robust framework for solar site selection, providing practical guidance for policymakers and energy planners in Yemen.

Myrrh, derived from Commiphora myrrha (C. myrrha) has been valued since biblical times for its use in incense, perfumes, and traditional medicines. Scientific investigations of its chemical composition began more than a century ago. This review compiles recent findings on the historical significance, geographical distribution, traditional medicinal uses, phytochemical constituents, biological activities, pharmaceutical effects, toxicity, and extraction techniques of C. myrrha. Particular focus is given to the components of the volatile oil, resin, and gum. Information was sourced from digital databases (PubMed, Google Scholar, Web of Science) and ethnopharmacological literature published between 2000 and December 2024. Traditionally, C. myrrha has been used to treat ulcers, pain, digestive and bone disorders, wounds, arthritis, and circulatory problems, particularly in Ayurvedic and Chinese medicines. Pharmacological studies have confirmed its antioxidant, anti-inflammatory, cytotoxic, antimicrobial, hepatoprotective, antiviral, and antiulcer properties, with emerging interest in its potential role in the treatment of Coronavirus Disease 2019. In addition, it is widely used in cosmetics and aromatherapy. Analytical studies have identified essential oils, terpenoids, and steroids, with the resin being particularly rich in bioactive compounds. Future research on the stem, bark, and leaves may uncover additional therapeutic agents. 

Polycystic ovary syndrome (PCOS) is a serious hormonal disorder that affects women and significantly impacts their quality of life. In modern times, women are increasingly susceptible to this syndrome, which is a major cause of numerous health problems, most notably infertility. Early detection of PCOS significantly reduces complications, making an early and accurate diagnosis system crucial.

Among all diagnostic techniques, machine learning (ML) has demonstrated superior performance due to its ability to extract features and patterns from data. Therefore, this field has received widespread attention from researchers, and numerous studies have been conducted to detect PCOS using machine learning techniques. These methods have included convolutional neural networks (CNN), support vector machines (SVM), k-nearest neighbors (KNN), random forests, logistic regression, decision trees, and the Naive Bayes algorithm, among others.

This paper aims to shed light on all current techniques used in PCOS detection using machine learning algorithms, providing a comprehensive descriptive and contextual review. It also provides a detailed analysis of how various ML techniques have been used in this field over the past decades, with an in-depth discussion of these approaches. A comprehensive review of the various datasets used in PCOS diagnosis is also provided, comparing the performance of algorithms from both quantitative and qualitative perspectives. Finally, the paper discusses the most prominent challenges facing this field, in addition to exploring future research prospects.