The study aimed to assess the influence of corporate governance on social responsibility, applied on the Palestinian Investment Fund. The study utilized a quantitative analytical approach and gathered essential data from the Investment Fund's annual financial reports spanning the period from 2006 to 2020, available on its official website. The content analysis method, employing a checklist for measuring the social responsibility disclosure index, was applied to the annual financial reports. Data processing was carried out using SATAA. The study's findings revealed that corporate governance, particularly in its dimensions of board size, double CEO, independence, and gender diversity, significantly impacted social responsibility across dimensions combined, including society, environment, employees, customers, and the quality of service. However, the educational background dimension did not exhibit a notable effect on social responsibility. As a result of the research, it was recommended that organizations conduct annual assessments of corporate governance performance, making comparisons with previous years. Moreover, institutions were advised to provide comprehensive financial statements along with complementary explanations. This practice is deemed crucial for decision-makers and stakeholders, enabling them to obtain a clear understanding of the organization's status and system concerning various aspects of social responsibility and corporate governance.

Short-Term Load Forecasting (STLF) is needed to efficiently manage the power systems. In this paper, two kinds of models that depend on the Fuzzy based techniques are developed to represent the STLF models in Palestine. Different types of these models have been developed using the available data sets that include the past electric load values and the climatic variables as inputs. It is shown that the climatic variables have a major effect on the predicted load. Various optimization techniques are used to develop the proposed models including hybrid and Backpropagation optimization techniques, Subtractive Clustering, and combining the Subtractive Clustering and Hybrid optimization techniques. The obtained results indicate the efficiency of the proposed models using the time and weather data.

Abstract The date palm (Phoenix dactylifera) is a dioecious plant, and its sexual characteristics remain indiscernible until it reaches the reproductive stage, typically between 4 to 5 years of age. The cultivar C. madjoole is widely grown in Palestine and neighboring regions. Early identification of male and female seedlings could significantly benefit date palm cultivation and breeding efforts. This study aimed to confirm the effectiveness of microsatellite markers in distinguishing between male and female date palms within the cv. Madjoole variety. Out of eight microsatellite primers used, four markers were able to determine the sex of the date palm tree. mPdCIR010 produced a male specific band (325pb) while mPdCIR093 produced a female specific band (335 pb); on the other side, mPdCIR015 and mPdCIR078 produced unique female bands (160 and 128 pb), respectively. A total of 15 loci were identified, averaging 2.1 loci per primer. SSR markers were able to provide a reliable technique for sex determination in date palm (cv. Madjoole) at an early stage. The utilization of SSR markers will not only advance but also facilitate the establishment of commercial date palm cultivation through seed propagation in Palestine and the surrounding region. Consequently, the available date palm genetic resources could be expanded, leading to enhanced national breeding programs. The ability to use mPdCIR093 as a female specific marker is approved for cv. Madjoole but needs to be validated with other date palm cultivars.

Teachers have a critical influence on the success or failure of any educational institution. The effectiveness of teachers is thought to be the most important component in influencing pupils’ academic progress. This paper is intended to find out the effectiveness of Big Five Traits toward classroom management in English language teaching in Palestinian schools. This study used a fully quantitative method with a survey as its data collection method where the participants were 35 elementary school teachers. Out of the total number of the participants, 45.7% were females, 34.3% were aged between 23-30 or 31-40 years old, 85.7% had Bachelor’s degrees, and 37.1% reported having over 10 years of teaching experience. The result of this study showed that there was a strong positive correlation between the teachers’ big five personality traits and their effective classroom management practices.

Despite foreign language classroom anxiety having been studied since the 1980s, there has been little research into this phenomenon in the context of online and blended learning environments. There is a clear need for this study as higher education becomes ever more adaptable, post-pandemic. The portfolio of methods for supporting teaching and learning is widening, with most institutions now offering online and hybrid modules and courses. The purpose of this study was to investigate the levels of anxiety among Palestinian undergraduates learning the English language through online courses. This study also investigated the effects of gender variables (310 males and 1210 females) on foreign language classroom anxiety levels. We surveyed a random sample of 1520 undergraduate students from different universities in Palestine. We used the well-established Foreign Language Classroom Anxiety Scale FLCAS (Horwitz et al., 1986; Horwitz, 2016), which is the most commonly-used measure of anxiety related to language learning. The findings of the study revealed that undergraduate learners experience anxiety when speaking with native speakers. Results indicate no significant differences in the students’ gender and foreign language classroom anxiety at universities. However, female learners were more anxious in language classrooms where the level of classroom anxiety was high. Finally, we offer tentative solutions for how tutors can support students experiencing foreign language anxiety.

The sudden transition from a face-to-face learning environment into a flipped classroom due to the COVID-19 pandemic has affected the education process (Fanounna, 2021). This study aims to examine the impact of adopting innovative pedagogies and interactive learning on developing EFL students’ attitudes toward learning the English language and to investigate how students feel about the effectiveness of interactive learning in developing individualized learning and a good learning process. In addition, the study aims to identify the most common challenges, that students face when using educational technology. To achieve the purposes of this study, the researcher developed a 5 Likert scale questionnaire consisting of three parts. The validity and the reliability of this instrument were tested and assured. The selected sample consisted of 112 freshman students distributed randomly in the two available sections at the Nablus branch of Al-Quds Open University during the second semester of the academic year 2022-2023. These two sections were randomly distributed into two groups, control and experimental. Therefore, the quasi-experimental method was used. The results of the study revealed that the interactive teaching/learning method had positively influenced students’ attitudes towards the English language. In addition, the results showed that the use of educational technologies offers a better opportunity to support individualized learning processes. Moreover, the results of the study revealed the most common difficulties and challenges associated with adopting interactive learning. Finally, the study recommended developing and including more interactive content and adopting new methodologies that focus on boosting language proficiency in HE in Palestine.    

Segmenting brain tumors in 3D magnetic resonance imaging (3D-MRI) accurately is critical for easing the diagnostic and treatment processes. In the field of energy functional theory-based methods for image segmentation and analysis, level set methods have emerged as a potent computational approach that has greatly aided in the advancement of the geometric active contour model. An important factor in reducing segmentation error and the number of required iterations when using the level set technique is the choice of the initial contour points, both of which are important when dealing with the wide range of sizes, shapes, and structures that brain tumors may take. To define the velocity function, conventional methods simply use the image gradient, edge strength, and region intensity. This article suggests a clustering method influenced by the Quantum Inspired Dragonfly Algorithm (QDA), a metaheuristic optimizer inspired by the swarming behaviors of dragonflies, to accurately extract initial contour points. The proposed model employs a quantum-inspired computing paradigm to stabilize the trade-off between exploitation and exploration, thereby compensating for any shortcomings of the conventional DA-based clustering method, such as slow convergence or falling into a local optimum. To begin, the quantum rotation gate concept can be used to relocate a colony of agents to a location where they can better achieve the optimum value. The main technique is then given a robust local search capacity by adopting a mutation procedure to enhance the swarm's mutation and realize its variety. After a preliminary phase in which the cranium is disembodied from the brain, tumor contours (edges) are determined with the help of QDA. An initial contour for the MRI series will be derived from these extracted edges. The final step is to use a level set segmentation technique to isolate the tumor area across all volume segments. When applied to 3D-MRI images from the BraTS' 2019 dataset, the proposed technique outperformed state-of-the-art approaches to brain tumor segmentation, as shown by the obtained results.