درخت حوزه‌های تخصصی

The present study aimed to investigate the causal pattern of the relationship between personality variables and coronavirus-related health attitudes mediated by the internal locus of control, and the moderating role of self-esteem in students. The sample participants consisted of 150 students of varying educational levels who completed the Health Behavior, Health Attitude, Internal Locus of Control, risk-taking, self-esteem, and conscientiousness and neuroticism questionnaires, online and through messaging applications (Telegram and WhatsApp). To analyze data, structural equation modeling (SEM) was used to measure fitness of the model and examine direct relationships. Measurement of the mediator relationship was assessed via, Bootstrap, and the moderator relations were examined with hierarchical regression. The results showed that the model's fitness well with the data and all direct relationships, except the relationship between risk-taking, and health attitudes were confirmed. The findings also confirmed the mediating role of the internal locus of control in the relationship between personality traits and health attitudes. The results further indicated the moderating role of self-esteem in the relationship between the internal locus of control and students' health attitudes. In other words, the relationship between the internal locus of control and health attitudes in students with higher self-esteem is stronger than the relationship in which students reported lower self-esteem. The results also confirmed the moderating role of self-esteem in the relationship between neuroticism and students' health attitudes. In other words, the relationship between neuroticism and health attitudes in students with lower self-esteem is stronger than the relationship in which students reported higher self-esteem

At the Le Thor Seminar in 1969, Heidegger characterises his thinking as taking the form of what he calls a ‘topology of being’ (Topologie des Seins) and as thereby giving a key role to place (topos, Ort/Ortschaft). Much of my work over the last 25 years has been devoted to exploring how such a topology is indeed present in Heidegger’s thinking, both early and late, and so to showing how place figures in that thinking – to showing, in effect, how the questioning of being is also the thinking of place. The aim here is to provide a summary introduction to the topology that this exploration has aimed at uncovering, but to do so in a way that is focussed on the early work, especially Being and Time. To this end, the discussion proceeds through an explication of the topological elements that are present in the form of key terms and ideas such as facticity, questionability, being-in, existential spatiality, and there-being or Dasein. There is also a brief exploration of the way the term Dasein figures in German philosophical discourse prior to Heidegger in ways that are not only reflected in Heidegger’s early work, but also draw directly upon that term’s topological connotations.

Cancer is an abnormal cell growth that occurs uncontrollably within the human body and has the potential to spread to other organs. One of the primary causes of mortality and morbidity for people is cancer, particularly lung cancer. Lung cancer is one of the non-communicable diseases (NCDs), causing 71% of all deaths globally, and is the second most common cancer diagnosed worldwide. The effectiveness of treatment and the survival rate of cancer patients can be significantly increased by early and exact cancer detection. An important factor in specifying the type of cancer is the histopathological diagnosis. In this study, we present a Simple Convolutional Neural Network (CNN) and EfficientNetB3 architecture that is both straightforward and efficient for accurately classifying lung cancer from medical images. EfficientnetB3 emerged as the best-performing classifier, acquiring a trustworthy level of precision, recall, and F1 score, with a remarkable accuracy of 100%, and superior performance demonstrates EfficientnetB3’s better capacity for an accurate lung cancer detection system. Nonetheless, the accuracy ratings of 85% obtained by Simple CNN also demonstrated useful categorization. CNN models had significantly lower accuracy scores than the EfficientnetB3 model, but these determinations indicate how acceptable the classifiers are for lung cancer detection. The novelty of our research is that less work is done on histopathological images. However, the accuracy of the previous work is not very high. In this research, our model outperformed the previous result. The results are advantageous for developing systems that effectively detect lung cancer and provide crucial information about the classifier’s efficiency.