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    IDENTIFYING THE SUCCESS FACTORS FOR AI-SUPPORTED CRISIS MANAGEMENT IN PUBLIC SECTOR IN PALESTINE: A MULTI-CRITERIA DECISION-MAKING APPROACH
    (An-Najah National University, 2026-08-27) Taymaa AlSaleh
    Integrating artificial intelligence (AI) into public sector crisis management poses a substantial institutional challenge, especially within resource-constrained situations with fragmented data infrastructure, underdeveloped personnel competencies, and largely absent regulatory frameworksIn order to identify and rank the key success factors (CSFs) influencing AI adoption in the Palestinian public sector, this study uses the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) as its primary methodological framework, utilizing Chang's (1996) extent analysis method to calculate criteria weights. 14 domain experts from 12 Palestinian public sector organizations were given structured pairwise comparison tools; 14 of them were kept in the final analysis after the data quality was evaluated. Technical and Organizational Factors, Ethical Factors, and Training and Capacity-Building Factors are the three hierarchical dimensions in which the instrument produced 778 individual judgments among 19 sub-factors. Chang's extension analysis and geometric mean aggregation analysis resulted in a hierarchy of priorities. With 53.28% of the total important weight, Training and Capacity-Building Factors are the dominant dimension. Among the 19 sub-factors in this category, Skill Level of Personnel received the greatest priority within the Palestinian public sector (65.95% local weight; 35.14% global weight), providing clear factual proof of a key basic competency shortage. The Internet of Things infrastructure (22.72% local weight), Deep learning (21.84%), and Large-scale Machine Learning (19.80%), which together account for 64.36% of the technical category's internal weight, lead Technical and Organizational Factors, which came in second (35.15%). Reliability and Safety (18.88%) and Accountability (18.82%) were determined to be the most important governance criteria, while Ethical Factors obtained the lowest category weight (11.56%). The lack of a thorough AI regulatory framework in the Palestinian legal system is reflected in the weight of 0.00% given to regulatory training. These results, which were confirmed by sensitivity analysis in three analytical scenarios with ±20% weight perturbations, provide an evidence-based, sequentially structured framework for adoption: technological investment must be conditioned and preceded by foundational workforce development. The four pairwise comparison matrices all fulfilled the Consistency Ratio (CR) of < 0.10. The methodology is transferable to similar developing-country contexts with comparable institutional and infrastructure problems.
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    Choco Matic
    (Dr. Mahmoud Assad, 2026-06-14) Ahmad Dardook; Sayed Qutob
    The rising demand for high-quality, customized chocolate bars has driven the need for small- scale, automated production solutions. This project presents the design and implementation of a chocolate bar production line that incorporates a fully automated chocolate tempering machine and a topping system for nuts and raisins. The system integrates three microcontrollers an Arduino Mega 2560 for production line control, an ESP32 for managing the tempering machine and wireless app connectivity, and an Arduino Uno for monitoring filling levels and driving additional LCD displays. Key features include a PT100 temperature sensor for precise heating, a 1950W heating element controlled via SSR, ultrasonic sensors for topping level monitoring, and mode-selection keypads for both chocolate type and topping. Chocolate dispensing and topping systems are actuated using NEMA23 stepper motors, while a windshield-wiper motor drives the conveyor belt. Additionally, real-time feedback is provided through LCD displays, and the entire system can be operated manually or remotely via a mobile application. Thermal behavior and energy usage were modeled using fundamental heat transfer formulas, ensuring reliable tempering with realistic time estimates. The project aims to deliver a scalable, energy-efficient, and user- friendly solution suitable for artisan or semi-industrial chocolate production environments.
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    MindQuest
    (Dr. Sufyan Samara, 2026-01-27) Ahmad Dardook; Sayed Qutob
    MindQuest addressed a recognized deficiency in online learning systems, namely limited interactivity, which often leads to reduced student engagement and delayed formative feedback. The project involved the development of a full-stack prototype that integrates authored courses, animated instructional content, embedded quizzes, progress aggregation, and role-based workflows within a unified platform. The system combines a React-based frontend with a Node.js/Express backend, utilizing a relational database alongside document-based storage for event and activity data. JWT-based authentication was implemented to manage user access, and a third-party payment service was integrated to support gated content delivery. The implemented artifact demonstrates the feasibility of assembling these components into coherent learning flows and fulfills the primary engineering objectives of the project, including role-aware enrollment and access control, media-rich lesson delivery, assessment data capture, and derived progress reporting. The resulting system represents a reproducible engineering prototype accompanied by documented design decisions, substantiating a pragmatic approach to integrating interactivity and assessment within an educational platform