Explore the vast range of titles available.

Introduction The prevalence of low back pain among nurses is notably high, which negatively affects their job performance and overall work quality. Aim of study This research aimed to evaluate the prevalence of low back pain among nurses in Nablus city and investigate how personal and work-related factor and the occurrence of low back pain in this population. Method Employing a cross-sectional study design, we employed validated instruments, including the Nordic Musculoskeletal Disorder Questionnaire. The study encompassed 258 nursing employees from both the largest and smallest Ministry of Health (MOH) and private sector hospitals in Nablus city, West Bank, Palestine. Data were collected through a self-administered questionnaire. Result A total of 258 nurses participated in the study, yielding a response rate of 98%. The lifetime prevalence of Low Back Pain (LBP) was 82% ( n  = 212) throughout their life. Additionally, the prevalence of LBP in the 12 months preceding data collection was 78% ( N  = 201), 71% ( N  = 182) in the month leading up to the study, and 61% ( N  = 156) on the day of data collection. Conclusion The study disclosed a high prevalence of low back pain among nurses, underscoring the urgency for effective solutions. These findings highlight the need for more comprehensive research to better understand and address this issue.

Changes in both temperature (25, 40, 60, 80°C) and time (15, 30, 45, 60 min) had no significant influence on TPC and AA% of Pistacia extracts. [...]phenolic compounds could be extracted in a short time and low temperature and could be used as antioxidant agents. A number of methods have been developed in recent years such as microwave, ultrasound-assisted extractions, and techniques based on use of compressed fluids as extracting agents, such as subcritical water extraction (SWE), supercritical fluid extraction (SFE), pressurized fluid extraction (PFE) or accelerated solvent extraction (ASE) were also applied in the extraction of phenolic compounds from plant materials[7A10,11] . According to results shown in Table 1, the moisture content was best reduced by microwave oven drying method for 3 minutes. According to results presented in table 1, the highest antioxidant activity was observed in dried fruits with microwave (3 minutes) and under vacuum with a DPPH scavenging effect of 96.07% and 95.75%, respectively. 3.2 Effect of maturity of fruits on TPC and AA Fruit ripening is a biologically complex process, typically involving changes in chemical composition, pigmentation, texture, color, flavor, and other organoleptic characteristics^.

Human papillomavirus is the most common viral sexually transmitted infection worldwide and affects individuals of all ages and both sexes. It is also the most preventable cause of cervical cancer. The World Health Organization’s Cervical Cancer Elimination Strategy (2030 targets) aims for 90% of girls to receive the Human papillomavirus vaccine by age 15, 70% of women to be screened by ages 35–45, and 90% of cervical-cancer cases to be appropriately treated. To determine cervical-cancer–related risk factors and knowledge levels, and to assess Human papillomavirus -related knowledge and attitudes toward vaccination. The survey also examined general adult vaccination behaviors—including measles, influenza, coronavirus, respiratory syncytial virus, and shingles vaccines—among adults. A cross-sectional study was conducted among 1,174 randomly selected adults from Egypt, Syria, Jordan, and Yemen between March and May 2025, using a validated Arabic self-administered questionnaire. Most participants were female (635, 54.2%), university-educated or higher (699, 59.6%), and living in urban areas (796, 67.9%). Regarding risk factors, 489 (41.7%) were smokers, 94 (8.0%) reported genital laser hair removal, 84 (7.2%) had oral or other herpes lesions, and 81 (6.9%) had a family history of reproductive-system cancer. Overall, 626 (53.4%) had poor Human papillomavirus knowledge and 779 (66.5%) had poor cervical-cancer knowledge. Only 60 (9.5%) had ever undergone cervical-cancer screening, and 23 (2.0%) had received at least one Human papillomavirus -vaccine dose. Reported adverse events included allergic reactions (13, 6.8%) and dizziness or fainting (13, 56.5%). Approximately 578 (49.3%) were unaware of the Human papillomavirus vaccine, while 976 (83.3%) had not received—and did not intend to receive—the vaccine, citing lack of information, perceived low risk, adherence to preventive measures, or vaccine unavailability. Most participants demonstrated poor overall knowledge of Human papillomavirus and cervical cancer. Several demographic determinants significantly influenced knowledge and attitudes toward vaccination. Comprehensive health-education initiatives are urgently needed to enhance awareness and progress toward the World Health Organization 2030 elimination targets.

Spam detection on social networks, considered a short text classification problem, is a challenging task in natural language processing due to the sparsity and ambiguity of the text. One of the key tasks to address this problem is a powerful text representation. Traditional word embedding models solve the data sparsity problem by representing words with dense vectors, but these models have some limitations that prevent them from handling some problems effectively. The most common limitation is the “out of vocabulary” problem, in which the models fail to provide any vector representation for the words that are not present in the model’s dictionary. Another problem these models face is the independence from the context, in which the models output just one vector for each word regardless of the position of the word in the sentence. To overcome these problems, we propose to build a new model based on deep contextualized word representation, consequently, in this study, we develop CBLSTM (Contextualized Bi-directional Long Short Term Memory neural network), a novel deep learning architecture based on bidirectional long short term neural network with embedding from language models, to address the spam texts problem on social networks. The experimental results on three benchmark datasets show that our proposed method achieves high accuracy and outperforms the existing state-of-the-art methods to detect spam on social networks.

The paper is devoted to building up a comprehensive model of the relationship between corporate social responsibility (CSR) and sustainability practices based on the analysis of their main predictors to ease the process of managing CSR and sustainability activities and provide practical recommendations for businesses regarding successful realization of their business, social and sustainable development goals. Currently, businesses integrate corporate social responsibility (CSR) and sustainability practices into their strategies to enable the fulfillment of sustainability goals and gain competitive advantages. Therefore, to achieve the aim of the study, a systematic review methodology was used in six stages: (1) defining the benchmarks; (2) extraction of papers from the two most cited databases: Web of Science and Scopus; (3) Manual content analysis of all extracted papers; (4) Identification of the dominant categories of this research topic; (5) The development of a comprehensive model of the relationship between CSR and sustainability, and(6) Discussion and control of obtained results and provision of recommendations for future studies. The model suggested is seen as a roadmap for organizations in different sectors of the economy and includes a variety of determinants that were divided into two groups depending on their relevance to an organization: the components of human and social capital, the technical characteristics of an organization and financial dimensions, and the outside business environment, which is determined by the political system and the level of corruption.

The use of social networks has become an integral part of our daily lives. Even though social networking sites offer many advantages, they also pose a number of problems for their users. One of the most famous problems is unwanted messages. It is not desirable for social network users to be bothered by annoying and time-wasting messages. These unwanted messages, which include ads, malicious content, and any low-quality content, are called spam. It is challenging to combat spam on social networks, because messages exchanged through social media are short, sparse, and may contain grammatical and spelling errors in addition to complex characters and special patterns. The main task to solve such a problem depends essentially on an appropriate representation of the text to increase the efficiency of the classifier. Therefore, in this study, we introduce a RoBERTa-based bi-directional Recurrent Neural Network model for spam detection on social networks. The RoBERTa model is used to learn contextualized word representations to improve the performance of the stacked BLSTM network. Moreover, a comparative study, in which we apply the most common transformer-based models, has been conducted as well to solve the spam problem. The experimental results on three benchmark data set state that our RoBERTa–BLSTM model outperforms all common models used to detect spam on social networks with an accuracy of 98.15%, 94.41%, and 99.74% on Twitter, YouTube, and SMS data sets, respectively.

Background and Objectives: Glucose Transporter-1 (GLUT1) is the key target gene for hypoxia-inducible factor (HIF), which helps cells uptake glucose during cell division, malignant transformation, and nutrient depletion. Cancer hypoxia is a well-known condition caused by an oxygen imbalance in the cancer microenvironment. During chronic hypoxia, certain cancer cells can survive and adapt. These cellular alterations can make cancer more aggressive, causing invasion and metastasis. The study investigated the presence of GLUT1 in oral epithelial dysplasia (OED) and various histopathological grades of oral squamous cell carcinoma (OSCC) to assess the significance of GLUT1 as a prognostic indicator. Material and Methods: A total of 40 samples of tissue blocks, including 5 cases of normal oral mucosa, 5 cases of epithelial dysplasia, and 30 cases of OSCC with 10 cases each of well-differentiated, moderately differentiated, and poorly differentiated OSCCs, these cases were diagnosed using the Hematoxylin and Eosin (H&E) staining technique. GLUT1 expression was assessed using immunohistochemical staining, which involved evaluating the location of the stain and the percentage of staining. Results: The mean area percent was highest in poorly differentiated cases (47.37) and lowest in well-differentiated cases (13.42). In poorly differentiated cases, diffuse expression was observed in almost all malignant cells, exhibiting membrane, cytoplasmic and nuclear staining. A significant difference (p < 0.001) between all groups in regard to immunostaining was detected. Conclusions: GLUT1 expression increased from oral epithelial dysplasia to oral squamous cell carcinoma histological grades. GLUT1 in actively dividing cells may reflect the tumor’s aggressiveness and treatment response. Hypoxia increases this marker’s expression, indicating division and proliferation.

Social media platforms are getting an important communication medium in our daily life, and their increasing popularity makes them an ideal platform for spammers to spread spam messages, known as spam problems. Moreover, messages on social media are vague and messy, so a good representation of the text may be the first step to address spam problem. While traditional weighting methods suffer from both high dimensionality and high sparsity problems, traditional word embedding methods suffer from context independence and out of vocabulary problems. To overcome these problems, in this study, we propose a novel architecture based on a context-dependent representation of text using the BERT model. The model was tested using the Twitter dataset, and experimental results show that the proposed method outperforms traditional weighting methods, traditional word embedding based methods as well as the existing state of the art methods used to detect spam on the twitter platform.

This study aims to fill the previous research gap by examining the relationship between job stress, work-family conflict (WFC), and job satisfaction. It also investigates the mediating effect of job burnout, through which job demands influence job satisfaction, and examines the moderating effect of emotional intelligence (EI) on these relationships through the lens of the job demands-resources (JD-R) model. The data for this study was collected from 279 respondents who were frontline employees in 14 banks in Palestine. A cross-sectional research approach was performed using a partial least squares path modeling approach. The study finds that job demands (job stress and WFC) increase job burnout. Contrary to expectations, job demands have a negative but not significant direct effect on job satisfaction. Further, job burnout reduces frontline bank employees’ job satisfaction. Regarding the mediating effect, job burnout fully mediates the relationship between job demands and job satisfaction. The findings suggest that the relationship between job stress and job burnout is stronger when EI is comparatively low. The study thus extends prior research by investigating the conditional indirect effect of job stress on job satisfaction when job burnout acts as a mediator and EI is the moderator. It contributes to the JD-R literature by providing support from the Palestinian banking sector.