This paper addresses the critical challenge of human-induced signal attenuation in millimeter-wave (mmWave) communications, a key concern for fifth-generation (5G) network reliability in indoor environments. Our study introduces a simplified model to quantify the impact of human body blockage on indoor communication links at a frequency of 32.5 GHz., a frequency relevant to 5G systems. The influence of nearby scattering objects is investigated through experimental measurements involving a human body. Key wave propagation phenomena, including diffraction, are considered for each scattering object. The Double Knife-Edge Diffraction (DKED) model is used to estimate the attenuation caused by the human body (to estimate blockage losses). Through controlled experiments with human subjects, we systematically analyze how scattering objects and body positioning influence signal propagation. The model's performance is validated by comparing simulation results with experimental data. The findings show that the proposed model effectively predicts signal attenuation in indoor environments, providing valuable insights for future studies on human presence effects in fifth-generation (5G) communication systems. Keywords: 5G, DKED, diffraction, human shadowing, millimeter-wave, blockage.

-This paper examines the effect of human body blockage on signal propagation (millimeter-wave (mmWave) signal propagation) in indoor environments links at 32.5 GHz (a critical frequency for fifth-generation (5G) network), with a particular focus on the diffraction effects caused by the human body, where diffraction is one of the important wave propagation mechanisms. In this study, measurements were taken to assess the effect of the human body as it moves between the transmitter and the receiver. To predict the signal attenuation, the principles of Fresnel diffraction were utilized, particularly emphasizing complex Fresnel integrals. Our results show that the received power varies significantly based on the person’s position, as diffraction loss highly depends on the body’s location. This study enhances our understanding of how human-induced diffraction, is critical for designing more reliable wireless networks. As the findings demonstrate that the proposed model effectively predicts signal attenuation in indoor environments and emphasizes the importance of accounting for human interference when optimizing communication systems, thus supporting the effective deployment of 5G technology. 

This paper presents a comprehensive study of modelling human body blockage (the most critical challenges in fifth-generation (5G)) effects on indoor millimetre wave (mmWave) communication links at 32.5 GHz, a key frequency for 5G networks. Through controlled experiments in a laboratory environment, we analyse signal attenuation as a human subject obstructs the line-of-sight (LOS) path between transmitter and receiver, recording received power at incremental positions. To model the observed phenomena, we propose a hybrid framework integrating deterministic and statistical components: (1) a modified Double Knife-Edge Diffraction (DKED) model with Gaussian-shaped blockage attenuation (20.8 dB peak at full blockage) and reflection-induced signal enhancement (−15.0 dB peak from nearby objects), and (2) a log-normal shadowing component (σ = 11.8 dB) capturing environmental randomness. Our results reveal strong agreement between simulations and measurements, achieving a mean absolute error of 3.2 dB and a correlation coefficient R² = 0.89. The analysis demonstrates that human-induced diffraction dominates near the LOS centre, while multipath reflections significantly alter signal strength at peripheral positions. We further derive practical guidelines for 5G network design, recommending a 44.4 dB link budget safety margin to account for combined blockage and shadowing effects. This work advances indoor mmWaves channel modelling by unifying physics-based diffraction analysis with empirical reflection characterization, the framework achieves strong experimental validation and offers actionable insights for 5G network design. Keywords— mmWaves, blockage, DKED, attenuation, shadowing  

This paper investigates human-induced signal attenuation in indoor mm-wave communications at 32.5 GHz, a critical concern for 5G systems. Two distinct diffraction-based models are applied to the same indoor scenario to assess human blockage effects: one employs the Double Knife-Edge Diffraction (DKED) approach, and the other uses Fresnel diffraction principles with complex Fresnel integrals. Controlled experiments with a human subject moving between a transmitter (TX) and a receiver (RX) reveal that the DKED model consistently underestimates the received power by 2 6 dB, while the Fresnel diffraction approach underestimates it by 2–5 dB Based on the comparative results, the DKED model demonstrates higher accuracy in predicting signal attenuation, offering valuable insights for improving indoor 5G network performance

Diabetic cardiac autonomic neuropathy (DCAN) is a significant condition that affects cardiovascular health worldwide and is associated with increased morbidity and mortality rates. Therefore, early detection and management of DCAN are crucial for reducing the risk of cardiovascular disease among individuals with T2DM. Identifying this disorder can enhance patient outcomes and quality of life by minimizing the chances of serious complications. This cross-sectional study aims to identify diabetic individuals with DCAN and to investigate its relationship with various risk factors, including hyperglycemia, the duration of diabetes, the presence of peripheral somatic neuropathy, and diabetic microvascular complications. The study included 261 patients with T2DM, comprising 61.5% females and 38.5% males. Participants underwent cardiovascular testing and clinical evaluations to identify cases of cardiac autonomic neuropathy. Out of the 261 randomly selected patients, 82 were diagnosed with DCAN, resulting in a prevalence rate of 31.4%. The average age for female patients was 57.5 ± 0.7 years, while for male patients, it was 56.3 ± 1.2 years from the total recruited patients. In addition, there is a strong association between DCAN and clinical and biochemical parameters such as lipid profile, duration of diabetes, poor diabetic control, and presence of microalbuminuria in patients with DCAN and above 60 years old, compared to younger patients. The study highlighted a strong association between DCAN and factors such as poor glycemic control, prolonged diabetes duration, and the presence of chronic microvascular complications, including neuropathy, retinopathy, and nephropathy. These findings emphasize the importance of raising awareness and proactively assessing Libyan patients who are at risk for cardiovascular autonomic neuropathy. This is crucial to reduce the likelihood of recurrent acute cardiac complications, especially in patients undergoing emergency surgery without a prior diagnosis. It is vital to recognize this risk.

Keywords. Type 2 Diabetes Mellitus, Pulse Rate, Metabolic Syndrome, Blood Pressure.

Abstract— Arabic language is characterized by its rich morphological structure, presenting unique challenges in Nat- ural Language Processing (NLP). The categorization of Arabic plurals is the subject of this study, which uses a trans- former-based model—more precisely, the pre-trained Arabic BERT architecture—and has never been studied previously. Given the complexities of Arabic language, particularly in pluralization which includes sound masculine, sound feminine, and irregular (broken) plurals, the research aims to enhance NLP capabilities in this area. By utilizing a dataset of 7,400 instances classified into four distinct categories, the study demonstrates the effectiveness of transfer learning in achieving high classification accuracy, with results indicating an accuracy of 97% across both validation and testing sets. Addition- ally, the model achieves high precision, recall, and F1-score metrics. A confusion matrix provides insights into classifica- tion performance, highlighting areas of misclassification. The findings underscore the potential of transformer models in overcoming the linguistic challenges posed by Arabic plural forms.

Abstract— This survey explores different machine translation methods utilized in various systems and platforms for commercial and research purposes. These methods play a vital role in enabling global communication, enhancing accessibility, supporting business and trade, fostering intercultural understanding, facilitating travel and tourism, aiding education, delivering fast and efficient translations, contributing to humanitarian aid efforts, promoting research and collaboration, and preserving language and culture. The survey aims to equip software developers and researchers interested in machine translation with valuable insights into these methods. Its objective is to help them improve translation quality with great accuracy by providing them with the necessary knowledge and understanding of these approaches. The papers utilized in this survey were obtained from Open Access Journals and online databases. All these methods are essential and can differ based on the specific context, available resources, and the quality of the translation required. To achieve optimal translation results, researchers and practitioners commonly employ a combination of various methods and techniques. 

Abstract—Arabic Named Entity Recognition (NER) serves as a crucial facet within Natural Language Processing, given the intricacies of the Arabic language. This survey consolidates the current landscape of Arabic NER, covering methodologies, challenges, and advance- ments. The review encompasses an in-depth analysis of diverse approaches, from rule-based systems to modern deep learning techniques, highlighting their effectiveness and limitations. It also addresses the specific challenges inherent to Arabic NER, such as dialectal variations and limited annotated data, while exploring recent advancements and their applications in sentiment analysis, information retrieval, and other domains. This survey aims to provide a comprehensive overview, catering to researchers, practitioners, and enthusiasts in the field of Arabic NER and NLP.

Abstract: The exact solution of a nonlinear fractional Volterra-Fredholm integro-differential equation is found in this paper through the successful application of the Abaoub Shkheam decomposition method. These techniques have a wider range of applications due to their dependability and decreased computational effort. 

ABSTRACT: This paper applies the Abaoub – Shkheam Decomposition Method (QDM) to obtaining solutions of linear fractional diffusion equations. The fractional derivative is described in the Caputo sense. Some illustrative examples are given, revealing the effectiveness and convenience of the method.