Nile University

Reconfigurable Intelligent Surfaces (RIS) are emerging technology to enhance the 6G wireless communication systems by intelligently reconfiguring the propagation environment. This paper propose a Modified Prioritized Deep Deterministic Policy Gradient (MP-DDPG) algorithm for jointly optimizing beamforming at the Base Station (BS) and RIS phases in a Multiple Input Single Output (MISO) downlink communication system. The primary objective is to minimize transmitted powers while adhering to critical constraints such as maximum power limits and Quality of Service (QoS) requirements of the User's Equipment (UE). The simulation results show that the MP-DDPG algorithm offers a robust and adaptive framework for addressing the inherent non-convexity and dynamic nature of such an optimization problem. Key findings highlight the remarkable resilience to imperfect Channel State Information (CSI), and the crucial trade-offs between performance, computational complexity, and signaling overhead.

Bone-muscle diseases, such as osteoporosis, rheumatoid arthritis, sarcopenia and cachexia, represent a growing global health concern, particularly among aging populations and older adults. These multifactorial disorders are characterized by progressive decline in bone density and muscle mass, increasing the chances of immobility and eventually disability. Such manifestations are driven by a complex molecular crosstalk between bones and muscles. This review highlights the key role of the irisin-sclerostin-inflammation triad in the pathophysiology of musculoskeletal degeneration. Irisin is a myokine induced by exercise. It is associated with osteogenesis and muscle regeneration. Sclerostin is an osteocyte-derived Wnt antagonist, inhibits bone formation and is linked to impaired muscle regeneration. Inflammatory mediators such as TNF-α and IL-6 drive muscle catabolism and bone resorption through the NF-κB and STAT3 signaling pathways. Dysregulation of this triad accelerates musculoskeletal degeneration, particularly in chronic diseases and aging. We described the correlation between these diseases and mediators with age and gender. Additionally, we discussed current and emerging therapeutic strategies targeting these mediators, including anti-sclerostin antibodies for high-risk osteoporosis, cytokine/JAK-pathway inhibitors for inflammatory disease, and structured resistance/weight-bearing exercise as a cornerstone intervention. We highlighted assay standardization needs, proposed human-focused models, and outlined priorities for precision, combination strategies targeting the triad in bone-muscle wasting disorders.

Abstract This study investigated how the presence of orthography facilitates novel word learning in Arabic-speaking third-grade children, addressing a gap in our understanding of literacy development in non-alphabetic writing systems. The aim of this study was to examine how Arabic-speaking children learn new words and to identify cognitive/linguistic abilities that may facilitate word learning. Novel word learning was examined in 116 third-grade monolingual children. The training comprised a paired-associate learning (PAL) task involving presentation of pictures with spoken novel words, with orthography present or absent. The potential influence of two characteristics of written Arabic that have been suggested to affect printed word learning, connectedness and density of consonant diacritics, was investigated. Three post-training assessments were administered: one assessed spoken production of the verbal labels that had been associated with the pictures in training, another, nonword-picture matching, assessed the phonology-concept associations that had been established during training, and the third, spelling to dictation, tapped recall of orthographic information. The child-related cognitive and linguistic abilities that were assessed were nonverbal ability, vocabulary, and reading of words and nonwords. Results from the PAL task revealed that the presence of orthography facilitated novel word learning as assessed by the three post-tests. In addition, consonant diacritics seemed to be a source of difficulty. A further source of difficulty, diglossic phonemes (phonemes that exist in spoken vernacular), was identified during data analysis. In relation to children’s abilities, orthographic facilitation in the production post-test was significantly associated with nonword reading fluency scores, indicating that more skilled readers are likely to benefit more from the presence of orthography. The findings are discussed in terms of the factors responsible for the documented difficulties in learning to read in Arabic. Findings highlight the importance of orthographic exposure and decoding skills in Arabic literacy development.

Image denoising is a crucial preprocessing step in various computer vision applications, yet remains challenging due to the diverse nature of noise and the trade-off between denoising efficacy and computational cost. This paper introduces MPDenoiseNet, a multi-path, resource-efficient deep learning network for image denoising, designed to handle a range of noise types including Bernoulli, Poisson, Salt-and-Pepper, and Gaussian noise, while being more computationally efficient than the transformer based, noise type agnostic restoration models. MPDenoiseNet architecture integrates parallel paths of Selective Convolutional (SeConv) blocks, adept at denoising Salt-and-Pepper corrupted images, and novel Anisotropic Diffusion blocks, designed to smooth images while preserving structural details. The features extracted from these paths are fused and further refined by Transformer blocks, inspired by Restormer, to capture long-range pixel interactions. Finally, an asymmetric U-Net AutoEncoder with skip connections module serves as a post-processing stage to enhance perceptual quality. MPDenoiseNet manages to reduce dependence on computationally expensive transformer blocks, using significantly fewer of them than the current state-of-the-art models and replacing them with more specialized lightweight blocks. We demonstrate MPDenoiseNet denoising performance is competitive with state-of-the-art methods like Restormer, while exhibiting efficient resource utilization in terms of both runtime and memory consumption.

В условиях глобализации и цифровой трансформации знание английского языка как второго становится неотъемлемой частью профессиональной подготовки студентов. Однако отношение к языку, мотивация и трудности в его изучении могут существенно различаться в зависимости от профиля подготовки. Настоящее исследование направлено на выявление таких различий среди студентов различных специальностей. Цель статьи - провести сравнительный анализ восприятия английского языка как второго среди студентов различных специальностей (гуманитарных, технических, педагогических и экономических) с целью выявления различий в мотивации, отношении к изучению языка и характерных трудностей, а также предложить рекомендации для адаптации языкового обучения в соответствии с профессиональными потребностями обучающихся. В статье представлен сравнительный анализ восприятия английского языка среди студентов 2-3 курсов гуманитарных, технических, педагогических и экономических направлений обучения. В выборку вошло 200 студентов, пропорционально распределённых по профилям: гуманитарные специальности (филология, история, журналистика) - 50 студентов; технические специальности (информатика, инженерное дело, математика) - 60 человек; педагогические специальности (начальное образование, иностранные языки, дефектология) - 45 студентов; экономические специальности (экономика, маркетинг, мировая экономика) - 45 студентов. Методы исследования включали анкетирование, полуструктурированное интервью и контент-анализ. Анализ выявил выраженные различия в отношении к английскому языку. Студенты-гуманитарии демонстрировали высокую интегративную мотивацию и стремление к академическому и культурному освоению языка. Студенты, обучающиеся техническим специальностям, чаще воспринимали английский как инструмент профессиональной самореализации и карьерного роста. Студенты педагогических специальностей фокусировались на методологическом применении языка в будущей преподавательской практике. Будущие экономисты рассматривали язык через призму делового общения и глобальной мобильности. Также были зафиксированы специфические трудности: нехватка специализированной лексики, различия в стилях, сложности адаптации учебных материалов. Полученные результаты позволили сформулировать практические рекомендации для преподавания английского языка с учётом профессиональной направленности обучения и уровня языковой мотивации студентов.

Accurate classification of mitotic figures in glioma histopathology images is critical for reliable tumor grading and informed treatment planning. In this study, we introduce HybridNet, a hybrid deep learning framework that combines the high-level semantic capabilities of a pre-trained Vision Transformer (ViT) with the domain-specific discriminative strength of handcrafted features. Specifically, four handcrafted feature sets, local binary patterns, texture descriptors, morphological attributes, and color statistics, are extracted to complement the ViT’s learned representations. These engineered features are concatenated with the partially fine-tuned ViT features to enhance classification performance and model interpretability. An ablation study was conducted to evaluate the contribution of each handcrafted feature group, revealing consistent performance gains with each module addition. In addition to HybridNet, we benchmark several conventional machine learning classifiers trained solely on handcrafted features, as well as standalone deep learning architectures including ResNet50, DenseNet121, EfficientNet-B0, and ViT, to establish robust performance baselines. The proposed method is evaluated on a curated dataset of glioma cell crops using five-fold cross-validation, achieving a mean classification accuracy of 91.75% with consistent performance across folds. The results underscore the complementary value of integrating handcrafted and deep features and highlight HybridNet’s potential as an interpretable and computationally efficient solution for mitotic figure classification in digital pathology workflows.

Background: Change is inevitable; readiness for change at the individual as well asorganizational level determines the success or failure of change initiatives inhealthcare settings. Exploring the relation between readiness for change and nurses’loyalty offers valuable-knowledge into workforce stability, organizationalperformance, and occupational adjustment, which plays a key role in how nursescope with transitions and organizational reforms. The present research aims toidentify nurses perception of hospital readiness for organizational change and itsrelation to their loyalty and occupational adjustment. Design: A descriptivecorrelational approach was used. Setting: The research was conducted in threespecialized hospitals, Minia governorate, Egypt. Sample: Randomly selectedrepresentative 30-% of the overall on-duty nurses working at each studied hospital atdata collection time (n=517). Tools: Nurses Perception of Hospital Readiness forOrganizational Change Scale, Nurses Loyalty scale, and Occupational Adjustmentscale. Results: (40.6%) of nurses perceived a moderate level of hospital readiness fororganizational change, (71.2%) had a high level of loyalty, and (68.4%) exhibited ahigh level of occupational adjustment. Conclusion: Positive association existedbetween nurses’ perception of hospital readiness for organizational change level andthe level of occupational adjustment, while no statistically significant correlation wasfound between loyalty level and either the perception level of hospital readiness fororganizational change or the level of occupational adjustment. Recommendations:Offer educational programs and workshops for nurse leaders focusing on changemanagement. Involve nurses in planning, evaluating, and executing organizationalchange strategies to strengthen ownership, engagement, and adjustment to transitions.