The widespread use of the Internet of things (IoT) in different aspects of an individual’s life like banking, wireless intelligent devices and smartphones has led to new security and performance challenges under restricted resources. The Elliptic Curve Digital Signature Algorithm (ECDSA) is the most suitable choice for the environments due to the smaller size of the encryption key and changeable security related parameters. However, major performance metrics such as area, power, latency and throughput are still customisable and based on the design requirements of the device.

The present paper puts forward an enhancement for the throughput performance metric by p

a laser ablation Q-switched Nd: YAG laser with a wave-length of 355 nm at a variety of laser pulse energies (E) and deposited on porous silicon (PS). Optical emission spectrometer was used to diagnosed medium air to study gold plasma characteristics and prepared Au nanoparticles. The laser pulse energy influence has been studied on the plasma characteristics in air. The data showed the emergence of the ionic (Au II) spectral emission lines in the gold plasma emission spectrum. XRD has been utilized to examine structural characteristics. Moreover, AFM results 37.2 nm as the mean value of the diameter that is coordinated in a shape similar to the rod that appears for Au NPs, in addition to that, TEM has been an indication of the fact that syn

Aromatic Schiff-bases are known to have antibacterial activity, but most of these compounds are sparingly soluble in water. The present work describes the synthesis of new Schiff-bases derived from branched aminosugars. Treatment of 3-Amino-3-Cyano-3-Deoxy-1,2:5,6-Di-O-Isopropylene-α-D-Allofuranose (1) with the aldehydes (2) under reflux in methanol afforded the Schiff-bases (3) in good yields. The new Schiff-bases were in accord with their NMR, IR spectral data and elemental analysis.

novel spectrofluorimetric flow injection analysis (FIA) method was developed for the selective quantification of ascorbic acid via fluorescence quenching of serotonin hydrochloride. The system employs a custom-designed photometric array comprising 16 irradiation sources arranged in a dual-axis matrix—eight aligned horizontally and eight orthogonally, enabling multi-angle excitation and enhanced spectral resolution. Fluorescence signals were captured using a twin-pair solar cell detector, offering high sensitivity and minimal optical interference. The method exhibited a linear calibration range of 0.1–30 limit of detection (LOD) of 0.025 μ μ g/mL with a correlation coefficient (r mol /L, equivalent to 4.403 * 10 4 μ 2 ) of 0.9966, a g

With the rapid development of smart devices, people's lives have become easier, especially for visually disabled or special-needs people. The new achievements in the fields of machine learning and deep learning let people identify and recognise the surrounding environment. In this study, the efficiency and high performance of deep learning architecture are used to build an image classification system in both indoor and outdoor environments. The proposed methodology starts with collecting two datasets (indoor and outdoor) from different separate datasets. In the second step, the collected dataset is split into training, validation, and test sets. The pre-trained GoogleNet and MobileNet-V2 models are trained using the indoor and outdoor se

يصب المدخل المستند إلى الموارد (Resources Based Approach) في دراسة المنظمات المتعلمة (Learning Organizations) اهتمامه أساساً على دراسة القابليات البشرية وكيفية تطويرها، وجعلها قدرات جوهرية تنافسية. فالدراسات الحديثة تنظر إلى استراتيجية إدارة الموارد البشرية (Human Resources Management Strategy) على أنها من المكونات المركزية الاستراتيجية منظمة الأعمال (Business Organization Strategy)<

This paper offers a systemic review of the deep learning methods to detect violence on campus, which is a critical issue in intelligent surveillance to improve the student safety and prompt cut off of violent accidents. The review reviews studies published 2018-2025, concentrating on model structure to detect fights, bullying, vandalism, and aggressive behavior on problematic campuses due to occlusion and light variations and complicated human interactions. The research design includes a comparative study of different deep learning networks, such as CNNs, RNNs, 3D CNNs, attention-based networks, transformers, graph neural networks, neuro-fuzzy, and multimodal systems and federated learning methods. The paper also assesses benchmark

Methicillin resistant Staphylococcus aureus (MRSA) is one of the principal nosocomial causative agents. This bacterium has the capability to resist wide range of antibiotics and it is responsible for many diseases like skin, nose and wounds infection. In this study, randomly amplified polymorphic DNA (RAPD)-PCR was applied with ten random primers to examine the molecular diversity among methicillin resistant Staphylococcus aureus (MRSA) isolates in the hospitals and to investigate the genetic distance between them. 90 Isolates were collected from clinical specimens from Iraqi hospitals for a total of 90 isolates. Only 10 strains (11.11%) were found to be MRSA. From these 10 primers, only 9 gave clear amplification products. 91 fragment l

NH₃ gas sensor was fabricated based on deposited of Functionalized Multi-Walled Carbon Nanotubes (MWCNTs-OH) suspension on filter paper substrates using suspension filtration method. The structural, morphological and optical properties of the MWCNTs film were characterized by XRD, AFM and FTIR techniques. XRD measurement confirmed that the structure of MWCNTs is not affected by the preparation method. The AFM images reflected highly ordered network in the form of a mat. The functional groups and types of bonding have appeared in the FTIR spectra. The fingerprint (C-C stretch) of MWCNTs appears in 1365 cm^-1, and the backbone of CNTs observed at 1645 cm^-1. A homemade sensi

<p><span>A Botnet is one of many attacks that can execute malicious tasks and develop continuously. Therefore, current research introduces a comparison framework, called BotDetectorFW, with classification and complexity improvements for the detection of Botnet attack using CICIDS2017 dataset. It is a free online dataset consist of several attacks with high-dimensions features. The process of feature selection is a significant step to obtain the least features by eliminating irrelated features and consequently reduces the detection time. This process implemented inside BotDetectorFW using two steps; data clustering and five distance measure formulas (cosine, dice, driver &amp; kroeber, overlap, and pearson correlation