مطالب

This study examined the impact of auditory input enhancement on the speaking abilities of Iranian EFL learners. Given the importance of speaking in language acquisition and the challenge of limited exposure to spoken English, the study focused on auditory input enhancement—altering intonation and pitch—to improve speaking proficiency. Fifty intermediate-level female learners, aged 18-28, from an English Language institute were selected based on their Oxford Placement Test (OPT) scores. Participants were divided into an experimental group, which received auditory input enhancement, and a control group, which followed traditional methods. Speaking abilities were evaluated with pretests and posttests using the Preliminary English Test (PET). An independent samples t-test analyzed the scores to test the hypothesis. Results indicated a statistically significant improvement in the experimental group's speaking performance compared to the control group, with Cohen's d showing a large effect size. These findings support previous research on input enhancement, underscoring the role of auditory input in focusing learners' attention on linguistic features and enhancing speaking performance. The study suggests that auditory input enhancement could be a valuable tool for EFL teachers and curriculum designers to improve learners' speaking skills.

The neighborhood is considered as the center of thinking about the City. Neighborhoods have undergone changes in different eras based on different crises. The emergence of the Corona pandemic has caused immediate changes in the way of living and the creation of new demands in the last four years. The daily concerns caused by the pandemic have put the dwellers' physical, mental, and psychological health at risk. Therefore, the research's main goal is to rethink the concept of neighborhood health as required in the post-pandemic era. This research was developed based on the correspondence of experiences and literature of "contemporary neighborhood" and "pandemic concept." Based on theme base method, studies related to the "pandemic" were investigated to base the obtained data on the content matching of two concepts of the contemporary neighborhood and pandemic and to make it possible to realize the research goals. Based on the findings, eighteen criteria related to place, ten related to biopsychology, and seven related to biomedicine have been promoted in contemporary urban planning. The summation of the criteria related to the contemporary neighborhood also confirms eighteen criteria. Examining the content of the basis of livability criteria in the pandemic era and the contemporary neighborhood shows that these two phenomena (chronological) not only do not have a fundamental conflict with each other in content, but the content coherence between the criteria is evident.

Cancer is a complex and dangerous disease in which cells uncontrollably begin to grow. Some cells, with mutated genes, cause abnormalities in the cell. These abnormalities are transferred to other genes through specific interactions between genes, leading to disruptions in the normal function of cells. The result of these cell abnormalities will be the occurrence of cancer. In cancer, modules are considered as clusters of genes and regulatory molecules that play a role in the processes of cancer initiation and progression. These modules usually have a specific gene sequence as a central unit that is important in controlling and regulating cellular processes related to cancer.In this study, a novel network-based method called mdGRN is proposed for identifying modules effective in lung cancer occurrence in the gene regulatory network. In this method, first, using gene expression data and regulatory interactions, a lung cancer regulatory network is constructed. Then, using a greedy modularity optimization approach, communities related to lung cancer are identified. Subsequently, the obtained communities are ranked using influence diffusion metrics in the network. Finally, the top-ranked communities are introduced as effective modules.To assess the efficacy of the proposed method, the standard Cancer Genome Atlas (TCGA) database and four classifiers including a decision tree, k-nearest neighbors, support vector machine, and random forest were utilized. The results obtained demonstrated that the proposed mdGRN method outperforms other methods in identifying cancer modules in terms of the average harmonic mean metric with the support vector machine classifier. Additionally, in terms of the AUC metric, the proposed method achieved a value of 0.997 using the random forest classifier, indicating better performance compared to other previous methods in identifying cancer modules. Furthermore, the number of genes identified by the top module is compared with other previous computational and network methods. The results show that the top-ranked module, besides containing a considerable number of driver genes, contains unique genes that have not been identified by other methods.