Cairo Higher Institute

The exponential growth of Internet of Things (IoT) deployments demands fog computing architectures that position computational resources at the network edge to ensure low-latency processing. However, optimal fog node placement in dynamic environments remains a significant challenge due to node mobility, random failures, and fluctuating traffic patterns, which require continuous adaptation to maintain service quality. This paper introduces the Dynamic Pufferfish Optimization Algorithm (D-POA), a bio-inspired metaheuristic that leverages behavioral strategies observed in pufferfish species to adaptively reposition fog nodes in real-time, balancing exploration of the solution space with refinement of promising configurations. D-POA formulates the placement problem as a continuous multi-objective optimization that simultaneously addresses network connectivity, area coverage, and movement costs to minimize service disruption during reconfiguration. Comprehensive experimental evaluation across five dynamic scenarios demonstrates that D-POA achieves 97.8% connectivity and 98.4% coverage while reducing movement costs by 38-57% compared to baseline algorithms, with near-linear scalability maintaining over 96% solution quality across networks ranging from 50 to 1000 nodes.

Background Artificial Intelligence (AI) is transforming service-oriented industries, yet its adoption in developing countries remains underexplored, particularly from the employee perspective. Employees’ perceptions, intentions, and organizational support are pivotal for effective AI integration in hospitality. Objective This study investigates the determinants of AI adoption among employees in Egypt’s hotel sector, drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT). It examines the effects of performance expectancy, effort expectancy, social influence, and facilitating conditions on AI usage, the mediating role of behavioral intention, and the moderating role of gender. Methods A quantitative design was applied using a structured online survey administered to 346 frontline employees in five-star hotels in Greater Cairo. Partial Least Squares Structural Equation Modeling (PLS-SEM) tested direct and indirect effects. Results Findings reveal that effort expectancy, social influence, and facilitating conditions directly and positively influence AI usage, while performance expectancy affects adoption indirectly via behavioral intention. BI mediates the link between UTAUT constructs and AI use, with gender moderating the intention–usage relationship: male employees exhibit stronger adoption tendencies. Conclusions The study underscores the role of user-friendly systems, inclusive training, and gender-sensitive strategies, extending UTAUT by establishing BI as a central mediator and revealing demographic contingencies.

Despite the economic importance of potatoes in Egypt in general, and in Qalyubia Governorate in particular, and their role in achieving food security, producers face several problems related to the costs of production inputs and price fluctuations. Therefore, this research aims to: determine the optimal farm capacity, clarify the safe limits for farm prices that lead to financially viable production decisions, and examine the problems and proposed solutions from the farmers' perspective.Main Results and Recommendation: The need for indicative planning of potato cultivation areas, using standard models to estimate optimal and profit-maximizing farm capacities, to assist investors or farmers, To determine their investments for the study crop, with the importance of producers (investors or farmers) joining together under the umbrella of agricultural cooperatives to achieve the greatest possible profits, (With the importance of utilizing the research results to estimate the optimal farm capacities for potato farms at approximately 47.97 acres), The importance of providing production inputs, Work is underway to find policies to prevent price fluctuations from being random and unplanned, which negatively affect food security and the profitability of farmers and the state from this strategic crop, Work is underway to expand local manufacturing operations for the potato crop to increase farm income, as well as to increase exports to maximize the benefit from the crop at the national level to increase foreign currency income.

Supply chain management (SCM) serves as the backbone of modern business, intricately connecting manufacturers, suppliers, distributors, retailers, and consumers. Its role involves orchestrating a complex interplay of goods, information, and financial flows across global networks to optimize efficiency and reduce operational costs. However, Risks of the supply chain are vulnerable to various risks which can significantly impact operations. These consequences become more inadequate in the case of industries directly dealing with customer health, such as the healthcare and pharmaceutical industries. This research focuses on the pharmaceutical supply chain, offering an in-depth analysis of the specific risks faced in this region, particularly the critical issues related to temperature excursions and supply chain complexity. In this paper, we reviewed the literature to gain a better understanding of the risks associated with the pharmaceutical industry and performed a questionnaire to determine the most important supply chain risks; finally, we constructed a conceptual model that represents how to use technological solutions to mitigate the identified risks. The questionnaire results showed that temperature excursions during storage, transport, and handling of pharmaceutical products were the most concerning for the study participants. The main technological solutions represented in the conceptual model are machine learning, the Internet of Things (IoT), cybersecurity, and blockchains.

Ensuring global food security requires accurate and robust solutions for crop health monitoring, weed detection, and large-scale land-cover classification. To this end, we propose AgroVisionNet, a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) for local feature extraction, Vision Transformers (ViTs) for capturing long-range global dependencies, and Graph Neural Networks (GNNs) for modeling spatial relationships between image regions. The framework was evaluated on five diverse benchmark datasets—PlantVillage (leaf-level disease detection), Agriculture-Vision (field-scale anomaly segmentation), BigEarthNet (satellite-based land-cover classification), UAV Crop and Weed (weed segmentation), and EuroSAT (multi-class land-cover recognition). Across these datasets, AgroVisionNet consistently outperformed strong baselines including ResNet-50, EfficientNet-B0, ViT, and Mask R-CNN. For example, it achieved 97.8% accuracy and 95.6% IoU on PlantVillage, 94.5% accuracy on Agriculture-Vision, 92.3% accuracy on BigEarthNet, 91.5% accuracy on UAV Crop and Weed, and 96.4% accuracy on EuroSAT. These results demonstrate the framework’s robustness across tasks ranging from fine-grained disease detection to large-scale anomaly mapping. The proposed hybrid approach addresses persistent challenges in agricultural imaging, including class imbalance, image quality variability, and the need for multi-scale feature integration. By combining complementary architectural strengths, AgroVisionNet establishes a new benchmark for deep learning applications in precision agriculture.

Accurate determination of volume percentages in three-phase fluids is paramount for the success of various industrial processes, ranging from oil and gas production to chemical engineering. This study presents a comprehensive approach to this challenge by leveraging advanced signal processing techniques and machine learning paradigms. Our methodology integrates the time, frequency, and wavelet transform features extracted from X-ray-based measurement systems whose structure consists of an X-ray tube source, two sodium iodide detectors, and a test pipe, all of which were simulated using the Monte Carlo N Particle code. The amalgamation of these features provides a rich representation of the fluid composition that captures both temporal and spectral characteristics. To enhance the discriminative power of the features, we employ a simulated annealing algorithm to strategically reduce their dimensionality and select pertinent features. The simulated annealing unit systematically evaluates the contribution of each feature to predictive accuracy. Further, through iterative elimination and re-evaluation, the algorithm refines the feature set, retaining only those with the highest relevance to the three-phase fluid composition. This feature selection process optimises the performance of subsequent machine learning models, streamlining the input space for enhanced interpretability and efficiency. Finally, to determine the volume percentages, we employ a support vector regression (SVR) neural network, which is trained on a refined dataset with capability to handle complex relationships and high-dimensional data. The proposed approach demonstrates superior accuracy in determining volume percentages of three-phase fluids compared to traditional methods, thereby making it an effective and integrated technique to analyse fluid composition in a variety of industrial settings and applications.