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اساليب مواجهة الضغوط ومفهوم الذات وعلاقتهما بالقلق الاجتماعي لدى طلبة الجامعات في الداخل الفلسطيني
(جامعة النجاح الوطنية, 2026-01-29) سمية عادل موسى بيادسة
تتناول هذه الدراسة العلاقة بين مفهوم الذات، وأساليب مواجهة الضغوط، ومستويات القلق الاجتماعي لدى الطلبة الجامعيين العرب في الداخل الفلسطيني، في ظل بيئة تعليمية تتسم بتحديات اجتماعية وثقافية واقتصادية مركبة. تنبع أهمية البحث من الحاجة لفهم العوامل النفسية والاجتماعية التي تؤثر على تكيف الطلبة الجامعيين في سياق يعيشون فيه شعورًا بالهامشية والتمييز أحيانًا، مما يجعلهم أكثر عرضة للضغوط والانفعالات السلبية. هدفت الدراسة إلى فحص طبيعة العلاقة بين هذه المتغيرات الثلاثة، والوقوف على الفروق الديموغرافية المرتبطة بها، مع التركيز على دور الدعم الاجتماعي كعامل وسيط يعزز أو يضعف من تأثير مفهوم الذات في القلق الاجتماعي. اعتمدت الدراسة على منهج وصفي–ارتباطي مدعوم بتطبيق أدوات قياس مقننة لقياس مفهوم الذات، أنماط المواجهة، ومستوى القلق الاجتماعي. شملت العينة مجموعة من الطلبة الجامعيين العرب في مؤسسات التعليم العالي، وتم تحليل البيانات باستخدام أساليب إحصائية لاستخراج العلاقات بين المتغيرات وفحص الفروق بحسب متغيرات ديموغرافية مثل الجنس والوضع الاقتصادي. أظهرت النتائج أن هناك علاقة سلبية بين مفهوم الذات والقلق الاجتماعي، وأن تبني أساليب مواجهة إيجابية يسهم في تقليل مستويات القلق، بينما الأساليب السلبية تزيده. كما تبين وجود فروق ديموغرافية مرتبطة بالجنس والوضع الاقتصادي، حيث كانت مستويات القلق الاجتماعي أعلى لدى الطالبات ولدى الطلبة من خلفيات اقتصادية ضعيفة، في حين لم يكن للعمر تأثير جوهري. وأكدت النتائج كذلك على الدور الوسيط الفعّال للدعم الاجتماعي في تعزيز أثر مفهوم الذات على خفض القلق الاجتماعي. تخلص الدراسة إلى أن بناء مفهوم ذات إيجابي، وتبني أساليب مواجهة فعّالة، وتعزيز شبكات الدعم الاجتماعي، جميعها عناصر مترابطة تمثل مفتاحًا أساسيًا لفهم ظاهرة القلق الاجتماعي والتعامل معها لدى هذه الفئة من الطلبة.
EFFECT OF THE BEAM EMITTANCE ON THE PHOTON FLUX AND BRIGHTNESS OF SYNCHROTRON RADIATION EMITTED BY THE UNDULATOR U20
(An-Najah National University, 2026-02-16) Dubeik, Thiab
This thesis investigates the radiation performance of the U20 undulator for a potential future installation at SESAME, and the magnetic design of the sector dipole for the PERLE. The study aims to evaluate how beam parameters affect photon flux and brightness, and to provide a validated magnetic model for PERLE. Using SRW simulations, the U20 undulator was analyzed under realistic SESAME beam conditions, showing that reducing the horizontal emittance significantly enhances light output: the fundamental harmonic increased from 2.46×10^13 to 3.28×10^13 while the brightness improved by nearly an order of magnitude. These performance gains can be directly adopted by SESAME in future upgrades. In the second part, the PERLE dipole magnet was designed and optimized using Opera 3D, achieving uniform field quality across the Good Field Region and confirming an efficient turbulent water-cooling regime (Re = 9685). The resulting magnetic model forms part of the PERLE Technical Design Report (TDR). Overall, the outcomes of this work contribute to improving undulator performance at SESAME and advancing magnet development for PERLE.
FACTORS AFFECTING THE PROFITABILITY OF GCC BANKS IN THE COVID-19 PANDEMIC
(جامعة النجاح الوطنية, 2026-02-12) Qab, Rowa
This study examines the factors influencing the profitability of Islamic and traditional banks operating in the Gulf Cooperation Council (GCC) countries, with a particular emphasis on the impact of the COVID-19 pandemic. The objective of this study is to examine internal and external factors of banks and the impact of the COVID-19 pandemic that significantly influence profitability. The internal factors used in this study are the CAMEL variables (Capital Adequacy, Asset Quality, Management Efficiency, Earnings Ability, and Liquidity), while the external factors include macroeconomic variables such as GDP, inflation rate, and interest rate. The panel data of 59 banks, collected from the annual reports available on the official websites of the banks and the stock exchanges operating in six GCC countries, is used for the period from 2015 to 2023. Employing a quantitative approach and panel data regression analysis, the impact of internal and external explanatory variables is evaluated using fixed-effects and random-effects models. The measures of profitability used as dependent variables are return on assets (ROA) and return on equity (ROE). Fixed effects results are used for interpretation based on the Hausman test. The results show that inflation has a positive and significant impact, while GDP and interest rate have a negative and insignificant effect. Capital adequacy, Asset quality, and liquidity exhibit a positive and significant effect on profitability. Furthermore, the COVID-19 pandemic is found to have a negative and statistically insignificant impact on profitability. During the pandemic, the importance of internal financial indicators, particularly capital strength and liquidity, became more pronounced, while the influence of external macroeconomic variables diminished. The findings underscore the need for regulators across the GCC to harmonize banking regulations, ensuring consistent standards for capital, liquidity, and risk management requirements, thereby strengthening regional financial stability.
THERMALLY REGENERABLE MODIFIED NATURAL CLAY FOR SUSTAINABLE REMOVAL OF TETRACYCLINE AND PHENAZOPYRIDINE FROM WATER
(An-Najah National University, 2026-04-02) Dawood, Iyad Fahmi
Abstract This study introduces a sustainable and cost-effective water treatment method that uses thermally modified natural clay (MC) to remove pharmaceutical contaminants, specifically tetracycline (TC) and phenazopyridine hydrochloride (PHY), from water. The clay underwent thermal and chemical modifications to improve its ability to adsorb, its surface reactivity, and thermal stability. Researchers thoroughly evaluated the performance of MC using batch adsorption and fixed-bed column experiments to understand both equilibrium behavior and continuous-flow use. Batch adsorption studies showed that the best results occurred at pH 3.5 and 25 °C, with initial concentrations of 20 mg/L for PHY and 40 mg/L for TC. Under these conditions, removal efficiencies reached 84% for PHY and 93% for TC. This was achieved with an adsorbent dosage of 4.0 g/L and a contact time of 45 minutes. The adsorption efficiency increased with a higher adsorbent dosage and lower temperature, indicating that the process is exothermic. Additionally, using smaller particle sizes (less than 75 µm) and heating the clay to 550 °C improved adsorption due to increased surface area and active sites. The equilibrium data fit best with the Langmuir isotherm model, showing R² values of 0.9827 for PHY and 0.9713 for TC, which suggests monolayer adsorption. Kinetic analysis indicated that the pseudo-second-order model best represented the data, implying that strong physical adsorption was the main mechanism. Fixed-bed column experiments confirmed that MC works well under dynamic conditions. The results showed that both pH and flow rate significantly affected adsorption performance. Maximum removal efficiencies were found at an acidic pH of around 3.5, supporting the batch results due to improved electrostatic interactions. At a flow rate of 2.5 mL/min, the column had much higher removal efficiency and delayed breakthrough compared to faster flow rates, which improved contact time and mass transfer. Increasing the flow rate led to earlier breakthrough and lower overall removal efficiency for both TC and PHY. The column system maintained stable adsorption performance across multiple runs, showing reliable contaminant removal during continuous operation. Furthermore, the modified clay effectively captured pollutants at initial concentrations of 80 mg/L for PHY and 100 mg/L for TC, proving its strength under higher loading conditions. A key aspect of this work is combining adsorption with thermal regeneration (thermolysis). The used MC was regenerated at 550 °C for 120 minutes, completely breaking down adsorbed contaminants into harmless substances like CO₂ and H₂O. The regenerated adsorbent retained high efficiency across five consecutive adsorption-thermolysis cycles in both batch and column systems, with little drop in performance, emphasizing its excellent reusability and structural stability. Extensive characterization through XRD, SEM, EDS, FT-IR, and TGA confirmed the successful modifications, improved surface properties, and high thermal resistance of MC. Overall, this combined adsorption-thermolysis method provides a strong, eco-friendly, and scalable solution for effectively removing persistent pharmaceutical contaminants from water, showing great potential for continuous treatment in real-world applications.
VERIFYING THE PRESENCE OF AI-GENERATED TEXT IN ARABIC AND ENGLISH WRITINGS
(An-Najah National University, 2026-02-02) Dwikat, Aya
The accelerating progress of generative AI has led to growing concerns about the authenticity and source of text content, particularly in academic and professional contexts. While several detection systems already exist, a significant challenge remains in developing frameworks that achieve high detection accuracy, remain robust across varying text lengths, and support multiple languages, including the Arabic language. This study proposes a detection framework, the Hybrid Fusion System (HFS), for identifying AI-generated text. The proposed framework is an ensemble system that contains two complementary components: the Feature Engineered Classifier (FEC) and the Language Model Classifier (LMC). The first component uses two type of features for AI-generated text classification: the linguistic features extracted from the input text, and probability features generated by combining token probabilities obtained from a series of language models using scalar and vector functions. The second component includes fine-tuned language models adapted for the text classification task. Finally, the outputs of the FEC and LMC are combined using an ensemble mechanism that synthesizes their outputs to produce the final classification result. In the first stage, our proposed system was designed to recognize English-language text written by AI, and then adapted to support the Arabic language through necessary linguistic and structural modifications. The study is also introduces a novel Arabic and English abstract dataset. This dataset is curated specifically for detecting AI-generated abstracts submitted by students at An-Najah National University. The proposed system achieved 99.76% accuracy on the English dataset and 99.55% accuracy on the Arabic dataset. Furthermore, it demonstrated substantially stronger performance than three publicly available AI-text detectors: ZeroGPT, Sapling, and Detecting- AI. On the English dataset, these tools reached only 94–96% accuracy, while on the Arabic dataset their performance dropped sharply to 56–73%.