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THE RELATIONSHIP BETWEEN BIG FIVE PERSONALITY TRAITS AND PSYCHOLOGICAL RESILIENCE AND THEIR EFFECTS ON LEVEL OF PSYCHOLOGICAL ANXIETY AMONG MOTHERS OF PALESTINIAN PRISONERS IN OCCUPATION PRISONS
(An-Najah National University, 2026-02-05) Isa, Fatima
Introduction: Psychological resilience and anxiety are closely linked to various personality traits, especially as conceptualized in Five-Factor Model developed by McCrae and Costa, This model—comprising neuroticism, openness, conscientiousness, extraversion, and agreeableness—offers framework for understanding how personality influences individual responses to stress and adversity, For mothers of Palestinian prisoners in occupation prisons, intersection of personality, resilience, and psychological anxiety becomes particularly salient due to prolonged emotional and social challenges they face. Objective: This study aims to examine relationship between Big Five personality traits and psychological resilience, and how both affect level of psychological anxiety among mothers of Palestinian prisoners, Additionally, study explores influence of several demographic variables, including gender of prisoner, sentence duration, family order, age of prisoner, place of residence, number of children, marital status, and economic level. Method: study employed descriptive-analytical approach, utilizing validated tools including Big Five Inventory by McCrae and Costa, Connor-Davidson Resilience Scale (CD-RISC). and Taylor’s Manifest Anxiety Scale, sample consisted of 108 mothers of Palestinian prisoners. Results: Results revealed significant negative correlations between psychological anxiety and four of Big Five traits: extraversion, agreeableness, openness, and conscientiousness. Neuroticism, by contrast, showed positive correlation with anxiety. Psychological resilience also exhibited strong negative correlation with anxiety. Multiple regression analysis confirmed that extraversion, agreeableness, and resilience significantly predicted lower levels of psychological anxiety. The study also found statistically significant difference in anxiety levels based on gender of prisoner, with mothers of Palestinian mother’s of prisoners reporting higher anxiety. Other demographic factors such as sentence duration, family order, age, and residence showed no significant differences in most psychological measures.
PREDICTION OF TYPE2 DIABETES USING ARTIFICIAL INTELLIGENCE METHODS
(An-Najah National University, 2026-03-29) Barahmeh, Mai Basem
This paper explores the usefulness of incorporating a feature based on fuzzy logic with machine learning models in predicting diabetes. Diabetes is a multifactorial and complex disease where physiological variables interrelate with each other and traditional machine learning methods fail to represent such non-linear interrelations and uncertainty of clinical data. In order to overcome this shortcoming, a fuzzy inference system was created to produce a composite risk feature (denoted as fuzzy_risk) that captures domain knowledge and reflects the overall impact of critical clinical indicators. The three classification models that were trained and assessed were Decision Tree (DT), K-Nearest Neighbors (KNN) and Multi-Layer Perceptron (MLP) neural network in the presence of the fuzzy-derived feature and the absence of this feature. Preprocessing of the dataset was conducted following standard methods, such as stratified data division and normalization of features, in order to compare the models fairly. Accuracy, weighted F1-score and area under the receiver operating characteristic curve (AUC-ROC) were used to measure model performance, particularly the effect of class imbalance. The findings indicate that the fuzzy_risk feature is able to yield similar positive performance in all the models. Nevertheless, the extent of improvement is small, and it is usually between 1 and 2 per cent in the measures of evaluation. The results also indicate that the simpler models like Decision Tree and KNN can take advantage of the fuzzy feature as opposed to the neural network, which can learn non-linear relationships on its own. Also, all the models merge to the same performance range which shows the existence of a performance ceiling imposed by the data. In practical terms, fuzzy feature increases the interpretability by giving us a structured risk representation consistent with clinical reasoning. This enhances transparency and can be adopted in health care environments. The study, in general, shows a significant but gradual effect of incorporating the fuzzy logic with machine learning on diabetes prediction, noting the role of feature engineering and data quality in the development of even greater improvements.
ASSESSMENT OF CYBERSECURITY KNOWLEDGE, ATTITUDES, AND PRACTICES AMONG HEALTHCARE WORKERS AT AN-NAJAH NATIONAL UNIVERSITY HOSPITAL: A CROSS-SECTIONAL STUDY
(An-Najah National University, 2026-06-02) Abu Lehia, Noor Salim Khalil
Background: In the light of the increasing reliance on digital technologies and electronic health records in healthcare institutions, cybersecurity has become an essential safeguard for patients’ safety and the continuity of health services. Healthcare workers play an important role in ensuring information security; nevertheless, a lack of cybersecurity awareness, attitude, or practice may be a potential threat and cause a cyberattack. Consequently, this research attempted to evaluate the cybersecurity knowledge, attitudes, and practices (KAP) level among healthcare workers in a university teaching hospital in Nablus, Palestine. Methods: A cross-sectional survey using a structured questionnaire based on past literature was conducted. A stratified proportional convenience sample of 245 healthcare workers was recruited from a university teaching hospital in Nablus, Palestine. Participants’ sociodemographic information was also obtained. Descriptive analysis was used to provide summary statistics on the sociodemographic details and knowledge, attitude, and practice (KAP) scores. Categorical variables were analyzed using frequency and percentages, whereas KAP scores were reported using the median and interquartile range (IQR). Normality was assessed using the Shapiro-Wilk test, because the distribution of KAP scores was non-normal, nonparametric tests were performed. Mann-Whitney U test and Kruskal-Wallis test were used to determine differences in KAP scores according to various sociodemographic variables. Spearman’s rank correlation coefficient was used to identify correlations between various KAP domains. Internal consistency reliability was assessed separately for each domain using the Kuder-Richardson Formula 20 (KR-20) for the Knowledge section and Cronbach’s alpha for the Attitudes and Practices sections. The level of significance was set at 0.05. Results: The number of participants involved in this study was 245 healthcare workers. There were 124 males (50.6%) and 121 females (49.4%), with a median age of 32 years (IQR = 7) and median work experience of 8 years (IQR = 7). The largest proportion among participants consisted of nurses (44.5%), followed by allied healthcare professionals (21.2%), physicians (18.0%), and administrative staff (16.3%). It is worth noting that 64.5% of all the participants did not have any cybersecurity training, while those who received cybersecurity training showed much greater knowledge, attitude, and practice scores (p ≤ 0.001). Participants were aware of the most common threats concerning cybersecurity, for example, phishing emails (82.4%) and unauthorized devices (87.8%), but they did not show sufficient knowledge about password security (42.4%) and ransomware (51.0%). Overall, median scores revealed that attitudes (77.5) and practices (77.8) were higher than the level of knowledge (70.0). In addition, there was a significant association between attitudes and practices (ρ = 0.537, p < 0.001). Conclusions: Healthcare workers demonstrated higher levels of attitudes and practices toward cybersecurity compared to knowledge, with limitations in essential areas like password security and ransomware awareness. Cybersecurity training was significantly associated with higher KAP scores; however, as this was a cross-sectional study, causal relationships cannot be established, and longitudinal studies are needed to further evaluate this association. Findings underscore the need for targeted training programs and continuous awareness initiatives to enhance information security within healthcare settings.
UNLOCKING THE POWER OF PULMONARY ARTERY ACCELERATION TIME FOR ACCURATE ESTIMATE OF SYSTOLIC PULMONARY ARTERY PRESSURE DURING TRANSTHORACIC ECHOCARDIOGRAPHY
(An-Najah National University, 2026-01-11) Awwad, Raghad Raed
Background: Pulmonary hypertension (PH) is a progressive cardiovascular disorder associated with high morbidity and mortality. Right heart catheterization (RHC) remains the diagnostic gold standard in diagnosing PH but it is invasive, costly, and not widely accessible, especially in low- and middle-income countries. Echocardiographic estimation of estimated peak systolic pulmonary artery pressure (EPSPAP) traditionally relies on the peak tricuspid regurgitant velocity (TRVmax); however, this parameter is absent or technically inadequate in a substantial proportion of patients. Pulmonary artery acceleration time (PAAT) is a simple Doppler-derived measurement independent of TR, has emerged as a potential non-invasive surrogate, yet validation in local populations remains limited. Objectives: This study was aimed to investigate the correlation, diagnostic agreement, and reproducibility of PAAT compared with TRVmax-derived estimated peak systolic pulmonary artery pressure (EPSPAP) among patients underwent routine transthoracic echocardiography (TTE) in Palestine. Methods: A cross-sectional study was conducted at An-Najah National University Hospital (NNUH), enrolling 200 consecutive patients between February and May 2025. PAAT was measured in the right ventricular outflow tract (RVOT) using pulsed-wave (PW) Doppler, while EPSPAP was calculated from TRVmax according to current guidelines. Correlation and Bland–Altman agreement analyses were performed. Results: The median PAAT was 109 ms. Shorter PAAT values were observed among patients with valvular heart disease and right ventricular dysfunction. PAAT showed a moderate inverse correlation with TRVmax-derived EPSPAP (ρ = –0.49, p < 0.001). Bland–Altman analysis demonstrated a systematic overestimation (+14.6 mmHg bias) with wide limits of agreement (–8.1 to +37.3 mmHg), highlighting variability at the individual patient level. According to the sensitivity and specificity across various PAAT values from 80 ms to 130 ms in identifying PH, patients with PAAT< 80 ms predicts PH and PAAT> 130 ms excludes the presence of PH. The best optimal PAAT cutoff in identifying PH was 95 ms. Conclusions: PAAT is a practical, reproducible, and TR-independent echocardiographic parameter. While it tends to overestimate pulmonary pressures compared with TRVmax-derived values, it provides complementary diagnostic value and may help close diagnostic gaps in resource-limited settings where invasive testing or reliable TRVmax signals are lacking, facilitating earlier recognition and improved management of PH.
العوامل المؤثرة في توظيف تطبيقات الذكاء الاصطناعي في العملية التعليمية لدى معلمي المرحلة الأساسية
(جامعة النجاح الوطنية, 2026-06-04) رزان "محمد عاطف" بريك; Razan "Mohamad Atef" Brik
هدفت هذه الدراسة إلى التعرف إلى العوامل المؤثرة في توظيف تطبيقات الذكاء الاصطناعي في العملية التعليمية لدى معلمي المرحلة الأساسية في مديرية نابلس، والكشف عن المعيقات التي تواجههم في دمج هذه التطبيقات، إضافة إلى فحص الفروق ذات الدلالة الإحصائية في كل من العوامل المؤثرة والمعيقات تبعاً لمتغيري العمر والجنس. ولتحقيق أهداف الدراسة، تم استخدام المنهج الوصفي التحليلي، حيث طُوِّرت استبانة استناداً إلى الأدبيات التربوية الحديثة ونموذج القبول والتوظيف التكنولوجي (UTAUT)، وتم تطبيقها على عينة من معلمي المرحلة الأساسية في مديرية نابلس. وقد تم التحقق من صدق الأداة وثباتها باستخدام الأساليب الإحصائية المناسبة. أظهرت نتائج الدراسة أن مستوى العوامل المؤثرة في توظيف تطبيقات الذكاء الاصطناعي في العملية التعليمية جاء مرتفعاً، بمتوسط حسابي بلغ (3.84)، حيث تصدر البعد الأخلاقي المرتبة الأولى، يليه البعد الشخصي والمهني، ثم العوامل المرتبطة بنموذج (UTAUT)، ثم دور المعلم في تحسين العملية التعليمية، في حين جاءت العوامل المؤسسية والتكنولوجية بمستوى متوسط. وتشير هذه النتيجة إلى أن توظيف الذكاء الاصطناعي في التعليم يُعد ممارسة تربوية ومهنية وأخلاقية متكاملة، تتجاوز كونه مجرد استخدام تقني. كما أظهرت النتائج أن مستوى المعيقات التي تواجه المعلمين في دمج تطبيقات الذكاء الاصطناعي في التعليم جاء مرتفعاً، بمتوسط حسابي بلغ (4.19)، وتمثلت أبرز هذه المعيقات في ضعف البنية التحتية التقنية، وارتفاع تكلفة بعض التطبيقات، وعدم مواءمة المناهج الدراسية لاستخدام هذه التقنيات، مما يعكس وجود فجوة بين الاستعداد الإيجابي لدى المعلمين والجاهزية المؤسسية والتقنية في البيئة التعليمية. وأظهرت النتائج وجود فروق ذات دلالة إحصائية في بعض العوامل المؤثرة تعزى إلى متغير العمر، لصالح الفئات العمرية الأصغر نسبياً، في حين لم تظهر فروق ذات دلالة إحصائية تعزى إلى متغير الجنس، سواء في العوامل المؤثرة أو في المعيقات. وفي ضوء هذه النتائج، أوصت الدراسة بضرورة تطوير برامج تدريبية متخصصة لمعلمي المرحلة الأساسية تركز على الاستخدامات التطبيقية لتطبيقات الذكاء الاصطناعي، وتحسين البنية التحتية التقنية في المدارس، وتبني سياسات تعليمية داعمة لدمج هذه التطبيقات في العملية التعليمية، إضافة إلى تعزيز الوعي بالاستخدام الأخلاقي والمسؤول لهذه التقنيات.