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This study analyzed physical violence against physicians in Egypt from a medicolegal perspective. 88%, 42%, and 13.2% of participants were exposed to verbal, physical, and sexual violence. Concerning the tools of violence, 75.2% of attackers used their bodies. Blunt objects (29.5%), sharp instruments (7.6%), and firearm weapons (1.9%) were used. The commonest manners of attacks were pushing/pulling (44.8%), throwing objects (38.1%), and fists (30.5%). Stabbing (4.8%) and slashing (2.9%) with sharp instruments were also reported. Traumas were mainly directed towards upper limbs (43.8%), trunks (40%), and heads (28.6%). Considering immediate effects, simple injuries were reported that included contusions (22.9%), abrasions (16.2%), and cut wounds (1.9%). Serious injuries included firearm injuries (4.8%), internal organs injuries (3.8%), fractures (2.9%), and burns (1.9%). Most (90.5%) of injuries healed completely, whereas 7.6% and 1.9% left scars and residual infirmities, respectively. Only 14.3% of physicians proceeded to legal action. The current study reflects high aggression, which is disproportionate to legal actions taken by physicians. This medicolegal analysis could guide protective measures for healthcare providers in Egypt. In addition, a narrative review of studies from 15 countries pointed to violence against physicians as a worldwide problem that deserves future medicolegal analyses.

Ontologies provide a powerful method for representing, reusing, and sharing domain knowledge. They are extensively used in a wide range of disciplines, including artificial intelligence, knowledge engineering, biomedical informatics, and many more. For several reasons, developing domain ontologies is a challenging task. One of these reasons is that it is a complicated and time-consuming process. Multiple ontology development methodologies have already been proposed. However, there is room for improvement in terms of covering more activities during development (such as enrichment) and enhancing others (such as conceptualization). In this research, an enhanced ontology development methodology (ON-ODM) is proposed. Ontology-driven conceptual modeling (ODCM) and natural language processing (NLP) serve as the foundation of the proposed methodology. ODCM is defined as the utilization of ontological ideas from various areas to build engineering artifacts that improve conceptual modeling. NLP refers to the scientific discipline that employs computer techniques to analyze human language. The proposed ON-ODM is applied to build a tourism ontology that will be beneficial for a variety of applications, including e-tourism. The produced ontology is evaluated based on competency questions (CQs) and quality metrics. It is verified that the ontology answers SPARQL queries covering all CQ groups specified by domain experts. Quality metrics are used to compare the produced ontology with four existing tourism ontologies. For instance, according to the metrics related to conciseness, the produced ontology received a first place ranking when compared to the others, whereas it received a second place ranking regarding understandability. These results show that utilizing ODCM and NLP could facilitate and improve the development process, respectively.

Recently, many users prefer online shopping to purchase items from the web. Shopping websites allow customers to submit comments and provide their feedback for the purchased products. Opinion mining and sentiment analysis are used to analyze products’ comments to help sellers and purchasers decide to buy products or not. However, the nature of online comments affects the performance of the opinion mining process because they may contain negation words or unrelated aspects to the product. To address these problems, a semantic-based aspect level opinion mining (SALOM) model is proposed. The SALOM extracts the product aspects based on the semantic similarity and classifies the comments. The proposed model considers the negation words and other types of product aspects such as aspects’ synonyms, hyponyms, and hypernyms to improve the accuracy of classification. Three different datasets are used to evaluate the proposed SALOM. The experimental results are promising in terms of Precision, Recall, and F-measure. The performance reaches 94.8% precision, 93% recall, and 92.6% f-measure.

The roots of Tagetes lucida Cav. (Asteraceae) have antioxidant and antimicrobial properties. This study aimed to examine the hepatoprotective effects of T. lucida roots ethanol extract (TLRE) using carbon tetrachloride (CCl 4 )-induced hepatotoxicity in rats. The active ingredients of TLRE were identified by high-performance liquid chromatography, infra-red spectrum, and mass spectrometric procedures. Ninety rats were distributed into four main groups: positive, therapeutic, protective, and negative group. The therapeutic group was implemented using CCl 4 (a single dose of 2 mL/kg) before TLRE or silymarin administration. Meanwhile, the protective group was implemented by administering CCl 4 (a single dose of 2 mL/kg) after force-feeding TLRE or silymarin. Each therapeutic and protective group was divided into three subgroups: force-fed with saline, TLRE (500 mg/kg), and silymarin (25 mg/kg). The positive group was split into two subgroups that were force-fed TLRE and silymarin. Positive, therapeutic, and protective groups were compared to the negative group (untreated rats). CCl 4, TLRE, and silymarin were orally administrated using a gastric tube. In the therapeutic and protective groups, TLRE significantly reduced liver enzymes, i.e., aspartate aminotransferase (12.47 and 6.29%), alanine aminotransferase (30.48 and 11.39%), alkaline phosphatase (17.28 and 15.90%), and cytochrome P450-2E1 (39.04 and 48.24%), and tumour necrosis factor-α (53.72 and 53.72%) in comparison with CCl 4 -induced hepatotoxicity controls. TLRE has a potent hepatoprotective effect with a good safety margin. After a repeated study on another type of small experimental animal, their offspring, and an experiment with a large animal, this study may lead to clinical trials.

Purpose The setting up of e-university has been slow-going. Much of e-university slow progress has been attributed to poor business models, branding, disruptive technologies, lack of organisational structure that accommodates such challenges, and failure to integrate a blended approach. One of the stumbling blocks, among many, is the handling of evaluation process. E-university models do not provide much automation compared to the original brick-and-mortar classroom model of delivery. The underlining technologies may not have been supportive; however, the conditions are changing, and more evaluation tools are becoming available for academics. The paper aims to discuss these issues. Design/methodology/approach This paper identifies the extent of current online evaluation processes. In this process, the team reviews the case study of a UK E-University using Adobe Connect learning model that mirrors much of the physical processes as well as online exams and evaluation tools. Using the Riva model, the paper compares the physical with the online evaluation processes for e-universities to identify differences in these processes to evaluate the benefits of e-learning. As a result, the models can help us to identify the processes where improvements can take place for automating the process and evaluate the impact of this change. Findings The paper concludes that this process can be significantly shortened and provide a fairer outcome but there remain some challenges for e-university processes to overcome. Originality/value This paper examines the vital quality assurance processes in academia as more universities move towards process automation, blended or e-university business models. Using the case study of Arden University online distance learning, the paper demonstrates, through modelling and analysis that the process of online automation of the evaluation process is achieved with significant efficiency.

INCOM Egypt has undergone automation in some processes where critical aspects of its operations are transformed and automated. This paper presents an overview of INCOM Egypt processes using Ould Riva and analyses the process of ‘handling a product’. It aims to demonstrate effective automation of the production of wires and cables process accompanied to Industry 4.0 while considering environmental and economic sustainability goals that were inhibited by COVID-19 restrictions. Ould’s Riva method is used to analyse the production process of wires and cables to propose improvements for automating the process. Business process modelling is utilised to study the processes for clearer understating. The flow of information within the process is also analysed to integrate the production process with other processes and supply chains, which helps to identify which production activities can be automated and mainstreamed into the information flow to achieve environmental and economic sustainability. The context of INCOM Egypt, as a case study, is presented along with the Riva model of its operations. The paper identifies the before, i.e., As-Is process, and after, i.e., To-Be Process, automation of the ‘handle a product’ process using the Role Activity Diagram (RAD). The process involved redesigning and improving different activities to increase resource-use efficiency to participate in achieving the goals of sustainability. The focus of this paper is to investigate the negative impact of COVID-19 on sustainability and to examine the accomplishments of process automation of wire production towards environmental and economic sustainability. The results of the research reveal a relationship between business process modelling and sustainability. Moreover, automation of processes (Industry 4.0) is found to reduce the negative effect of COVID-19 on production. A triangulation between process modelling, process automation (Industry 4.0), and sustainability was determined. Each one is reinforcing and impacting one another. The RAD model demonstrates that automation of the activities in the process reduces waste, time, cost, and redundant processes as factors of sustainability, which may also help to lessen the unfavorable effects of the pandemic. The results proved generalisation on other organisations in the same line of business.

Liver cancer is one of the main causes of cancer-related deaths worldwide. Due to the extreme heterogeneity of this disease, its prognosis and management are still not yet standardized. Different treatment modalities are available. However, the patient’s response to each of them varies. Therefore, it is critical to establish a model to help physicians individualize the management of this aggressive tumor. This paper presents one of the first investigations into personalizing liver cancer treatment for patients with genotype 4 using their clinical and genetic data. In this study, we analyzed the data of 1427 Egyptian patients with liver cancer who were either treated by one of five different treatment methods or not treated. We proposed and compared between two pipelines, a Single-Model pipeline and a Multi-Model pipeline, for analyzing the patient’s clinical and genetic data to recommend the best liver cancer treatment and, therefore, potentially improve their prognosis. We studied the performance of six regression methods in predicting the outcome of the treatment modalities for liver cancer patients. The best performing method was used in building the models in the proposed pipelines. Our results show a difference in performance among different regression models, which proves the importance of choosing an appropriate one in decision-making, especially when dealing with important issues such as liver cancer treatment recommendations. In our analysis, we also prove the crucial importance of genetic data and their effect on patients’ prognosis and response to treatment. Finally, this study signifies the great potential that data-mining methods could have in improving healthcare especially for serious diseases such as liver cancer.

This study aims to test the effectiveness of AI (Artificial Intelligence), which took a new turn after ChatGPT as a tool for the social sustainability of academics in the Egyptian private higher education sector. Digitalization reflects the intensity of artificial intelligence usage in enhancing the performance of professors and its reflection on their quality of life. Moreover, the degree of facilitation and progress can provide educators with the best educational experience they can provide to students. This study relies on two theories and their backgrounds. The first is the theory of project-based learning as a tool for enhancing the quality of education using AI. The second is Martec's Law, which is a derivation of the law of accelerating returns. Two main assumptions are addressed in this study, the first is: Using artificial intelligence as a tool that can facilitate, enhance, and provide a variety of ways for professors to engage their students online and in class. The second is based on measuring the degree of effectiveness and performance advancement seen by professors in their social sustainability Enhanced experience of the students will be measured by their rates of attendance and engagement. The amount of impact on project-based learning is going to be measured by the degree of reliance of professors on digital learning methods and their reliance on using artificial intelligence in constructing them. Data will be provided by professors through a constructed survey. The professor's social sustainability will be measured by quality time saved and related career advancement. Data collection depends on testing faculty members at 4 private universities in the greater Cairo area. A cross-sectional survey was conducted on a single shot in time. Results showed that we accepted the hypotheses and that there is a strong relevance between the variables.