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PREVALENCE AND RISK FACTORS OF MULTIDRUG-RESISTANT BACTERIAL INFECTIONS AMONG PALESTINIAN ONCOLOGICAL PATIENTS: AN EPIDEMIOLOGICAL RETROSPECTIVE STUDY
(An-Najah National University, 2026-09-10) Fatima Omar Fuad Suleiman
Abstract Background and Objectives Multidrug-resistant organisms (MDROs) infections pose a major threat to hospitalized immunocompromised patients, frequent healthcare exposures, and heavy broad-spectrum antibiotic usage among cancer patients makes them highly vulnerable. In Palestinian tertiary healthcare settings, rising antimicrobial resistance increasingly compromises empiric therapy and drives adverse outcomes. This study evaluated MDRO epidemiology, antimicrobial susceptibility profiles, empiric treatment concordance, and independent risk factors for mortality and treatment escalation among cancer patients. Methods A retrospective multicentred study analysed 11,357 positive bacterial cultures collected between 2022 and 2023 across two tertiary facilities to establish isolate-level MDRO prevalence. Detailed clinical, microbiological, and therapeutic data were abstracted for 402 cancer patients with laboratory-confirmed MDRO infections. Univariate analysis and multivariable logistic regression identified independent predictors of in-hospital mortality and treatment escalation (p < 0.05). Results MDRO prevalence was significantly elevated in cancer patients compared to non-cancer controls (33.1% vs. 28.0%) ¸ (p < 0.001). Gram-negative pathogens predominated, driven by carbapenem-resistant Enterobacteriaceae (CRE, 37.6%), Pseudomonas aeruginosa (20.6%), and Acinetobacter baumannii (17.7%). Initial empiric coverage demonstrated poor concordance with culture-directed therapy, especially for vancomycin-resistant Enterococci (14.0%) and P. aeruginosa (22.9%). Overall, in-hospital mortality reached 32.3%, septic shock developed in 27.9%, and 62.7% of patients required antimicrobial escalation. Independent predictors of mortality included septic shock (aOR = 8.94), hospital-acquired infection (aOR = 3.49), A. baumannii infection (aOR = 3.09), and central venous catheterization (aOR = 2.80), while recent chemotherapy was protective (aOR = 0.23). Hospital-acquired infection (aOR = 2.92), active neutropenia (aOR = 2.13), and prior antibiotic use (aOR = 1.82) independently predicted treatment escalation. Conclusions Palestinian oncology patients face a heavy burden of MDRO infections characterized by extensive resistance, frequent empiric failure, and high mortality. Enhancing outcomes requires robust infection control, rapid diagnostic technologies, tailored empiric protocols, and active antimicrobial stewardship
CircuitAI: an Interactive Hardware Learning Platform
(Eng. Haya Samanneh, 2026-01-28) Ahmed Taher Amin Gharib; Maen Haseeb Omar Thaher
Engineering students have been facing significant challenges bridging theoretical circuit knowledge with its practical implementation. While current general purpose AI systems exist, they have showcased inadequate accuracy in addressing hardware design questions, frequently providing incorrect instructions, flawed circuit analysis, and misleading debugging guidance, leaving students with no reliable assistance once outside of the classroom. This is where our project comes in: an interactive cross-platform hardware learning application that integrates three foundational components into a single system. The platform includes a Moodle-like learning management layer responsible for course structuring, enrollment, assignments, quizzes, grading, and progress tracking. It also provides interactive circuit simulation, featuring a real-time, client-side digital logic simulator for immediate feedback and conceptual validation, alongside a SPICE-based electrical circuit simulator capable of DC operating point analysis, DC sweep, AC frequency analysis, and transient time-domain simulation. Last but not least, a domain-specific AI engine, enhanced through Retrieval-Augmented Generation (RAG) and grounded in An-Najah University course materials and circuit design principles, which ensures that it delivers accurate, context-specific tutoring, and then we applied the same advanced AI capabilities to quiz generation and AI-assisted grading designed to support instructors while preserving human oversight. The resulting system enables students to learn theory, design circuits, perform simulations, and complete assignments within a unified workflow aligned with academic and laboratory practices. Integrating learning management, accurate circuit simulation, and curriculum-grounded AI ensures coherence between instruction, practical application, and evaluation. The front-end development was done using NextJS and CSS modules, while the back-end development was done in python for the AI inference server and setup, while the RESTful API endpoints were tied closely to our front-end via NextJS API routes. As for database choice, we used a PostgreSQL database for user data and general storage, and ChromaDB vector database for AI embeddings. While general circuit tools such as Multisim and Proteus support heavy simulation, and TinkerCAD and CircuitLab offer lighter educational interfaces, and while general-purpose AI assistants are becoming widely available, no project combines the best of all worlds into a single, easily accessible platform. This work demonstrates the effectiveness of such integration, with An- Najah University as its starting point.
Vision-Assisted Cartesian Robotic System for 3D Block Construction
(Dr. Samer Arandi, 2026-09-09) Ahmed Taher Gharib; Mohie Aldeen Amjad Halawa; Khalil Mahmoud Qanabita
Small-scale automated assembly is one of those problems that looks solved from a distance and stops looking solved the moment a real machine has to do it. A gantry that moves to a coordinate is straightforward. A machine that takes a structure a person designed, feeds itself one part at a time, picks each part up, turns it the right way round, stacks it on top of the parts already placed, and knows at every moment whether that actually happened, is a different problem, and most of the difference is feedback rather than motion. This is where our project comes in: a vision-assisted Cartesian robotic cell that builds three-dimensional structures out of 2.2 x 6.0 x 1.5 cm wooden blocks with no human placing a single block by hand. The system is built around three controllers and one brain. A Raspberry Pi 5 is the sole master: it runs the camera, the web server, the orchestration and every safety rule. An Arduino MEGA 2560 runs the gantry, a CoreXY X/Y stage with an added Z axis, a servo claw and a 28BYJ-48 rotation stepper, and executes a fourteen-phase pick-place-park cycle for every block. An Arduino Uno runs a separate feeder module, a two-stage hopper gate, a belt and an alignment servo, and confirms with two HC-SR04 ultrasonic sensors that exactly one block left the hopper and arrived at the fixed pickup point. The two Arduinos never talk to each other; the Pi is the only thing that couples them, and it does so over two independent USB serial links. The block can be laid either way round, which is why the machine carries two separately calibrated grids and not one: a vertical grid of 7 x 6 addressable cells and a horizontal grid of 3 x 10, sharing one 22.8 x 38.0 cm holder-travel envelope and one feeder cell at [0,0]. An overhead 160-degree fisheye camera watches the build surface through a colour-corrected, lens-corrected pipeline, detects the wooden blocks by warm-colour segmentation, and maps camera pixels to physical centimetres to grid cells through a saved workspace homography. The operator side is a browser application served from the Pi: a click-to-build console over the live camera image, and a 3D Build Studio in which a person designs a structure, gets immediate physics feedback on support, collisions and toppling, compiles the design into an ordered program of B col row level commands separated by grid-mode latches, and runs it while a live digital twin mirrors the real build from the firmware's own phase telemetry. The completed system placed blocks on hardware with a measured cycle time of 19.8 to 32.2 seconds per block, with the fixed phases of the cycle repeating to within 0.01 s across sixteen logged placements, and stacked a five-level tower on a single cell. The software is covered by 492 browser tests and 68 Python protocol tests. The work demonstrates that the useful engineering in a machine like this is not the kinematics: it is the discipline of never letting one part of the system assume what another part has actually done
العلاقة بين استخدام الإنستجرام وأعراض الاكتئاب لدى الطلبة الجامعيين في فلسطين
(An-Najah National University, 2026-09-09) Mohammad Alkhatib
تتزايد الحاجة إلى فهم العلاقة بين الاستخدام الإشكالي لمنصات التواصل الاجتماعي وأعراض الاكتئاب في السياق الفلسطيني. هدفت الدراسة إلى فحص العلاقة بين درجة استخدام الإنستغرام وأعراض الاكتئاب لدى طلبة الجامعات في الضفة الغربية، وتحديد الأعراض الأكثر ارتباطاً بالاستخدام، وفحص الفروق وفق الجنس ومكان السكن والمرحلة الأكاديمية. اتبعت الدراسة تصميماً كمياً مقطعياً ارتباطياً، وشملت 552 طالباً وطالبة جرى الوصول إليهم بأسلوب كرة الثلج، واستخدمت النسخة العربية من مقياس بيك للاكتئاب (BDI؛ α = 0.892) ومقياس إدمان الإنستغرام المكوّن من 15 بنداً (IAS-15؛ α = 0.901). وُجد ارتباط موجب معتدل بين درجة استخدام الإنستغرام وأعراض الاكتئاب الكلية (r = 0.372، p < 0.001). وكانت أقوى الارتباطات على مستوى البنود مع مشاعر الذنب (β = 0.349، R² = 0.122)، والحزن (β = 0.330، R² = 0.109)، وفقدان الطاقة (β = 0.324، R² = 0.105)، وجميعها عند p < 0.001. وأبلغت الإناث عن درجات استخدام أعلى من الذكور (p = 0.017) دون فرق دال في الاكتئاب (p = 0.131)، كما أبلغ طلبة البكالوريوس عن أعراض اكتئابية أعلى من طلبة الدراسات العليا (p = 0.004)، ولم تظهر فروق دالة حسب مكان السكن. وأظهرت المتوسطات ارتفاعاً متدرجاً عبر فئات الاستخدام، إلا أن فئة الاستخدام الشديد كانت صغيرة جداً (n = 5)، لذلك تُفسر هذه المقارنة بحذر. تشير النتائج إلى تلازم إحصائي بين استخدام الإنستغرام وأعراض الاكتئاب، ولا تسمح طبيعة الدراسة المقطعية بالاستدلال على السببية أو اتجاه العلاقة.
ASSESSING THE IMPACT OF ADOPTING AI TOOLS ON ENGINEERING PROJECT MANAGEMENT PERFORMANCE
(An-Najah National University, 2026-08-06) Roaa Khraisha
The construction industry faces continuous and various challenges related to inefficiencies in project planning, scheduling, and resource allocation, which lead to delays, cost overruns, and low productivity. Despite advancements in digital technologies, many construction firms still deal with traditional management methods that lack real-time data integration and predictive capabilities. With the rapid advancement of artificial intelligence, the construction industry is being influenced. Artificial intelligence improves project efficiency and performance. Although artificial intelligence has recently attracted significant global attention across various sectors, convincing construction companies to adopt AI in their projects depends on several factors, including their awareness and readiness to adopt these technologies. This research will explain the impact of using AI tools in construction projects. The methodological approach applied in this research is mixed methods, using questionnaires and interviews to collect data. The proposed research model was analyzed using SMART PLS (PLS-SEM). The measurement model was assessed in terms of reliability and validity, while the structural model was evaluated by using path coefficients, bootstrapping, coefficient of determination (R²), effect size (f²), and multicollinearity diagnostics, as well as conducting a manual analysis of 7 interviews with different backgrounds and years of experience. According to the model proposed in the research, AI awareness has a small effect on AI adoption, and AI adoption has a strong impact on construction project performance. This study helps managers and engineers working on construction projects to gain practical insights by leveraging AI technologies to improve project outcomes.