مطالب

Wind environment in the populated built area especially in campus is important. This s tudy tes ted 57 crossroad spots over of the whole campus in North China University of Technology (NCUT). The data of wind speed and direction were measured at fixed time and fixed place. The average, maximum values as well as the speed variation scale were calculated. Wind comfort criteria were adopted to examine the spots of s till wind zone, wake flow zone and risky zone. The overall measured data in wind scale map was analyzed to find out the changes of wind speed and direction in different seasons and in different locations of the school. Research and analysis show that The results show that the overall wind environment of the campus is good, the average wind speed is between 0.04-4.8, the average wind speed in spring (1.38m/s) is slightly larger than that in autumn (0.87m/s). However, the overall frequency of campus wind comfort location measurement is not high (52% and 30% in spring and autumn respectively). Finally, with analysis results, some sugges tions were given to improve the wind environment of NCUT campus. Additional CFD simulations are undertaken in order to help identifying the wind risky points.

This paper presents the results of a virtual ethnographic study developed in an online classroom in a public university in Brazil. The online activities aimed to help 70 students to develop English oral skills in a 60-hourcoursetaught along 15 weeks using the Moodle Language Management System (LMS). After the initial weeks, and some dropouts, there were 59 participants in this study. The course was planned and managed by the authors of this article and some modifications were made along the experience in face of unexpected changes in the learning environment. Having as theoretical support complexity, connectivism, and learning ecology, we assessed digital tools for oral communication and verified that they had a positive impact on language learningand increased learners’ opportunities for language practice. The tools not only contributed to the development of the students’ oral skills but also decreased their anxiety when speaking English

Objective: The use of cohesive devices makes discourse comprehend. However, in the discourse of senile dementia of the Alzheimer's type (SDAT) participants, the lack of the use of cohesive devices hinders effective communication. The present study aims to investigate cohesion in the discourse of SDAT participants. Understanding how these people manage their interactions may lead to suitable approaches for them. Method: The methodology of this quantitative research was ex post facto type. The present study has been performed in one of the nursing home in Kermanshah in 2019. And also, the statistic population of this study included 20 participants (10 SDAT participants and 10 normal elderly participants (NE)) who were matched based on age (63-75 years old), gender, illiteracy, and Kurdish language (Kalhori dialect) speaker. To determine the severity of dementia, the Clinical Dementia Rating Scale (CDR) was performed. Then, the collected interviews were transcribed and coded. The data were analyzed based on Halliday and Hasan's theory (1976) and independent t-test was used to obtain the statistic results. Results: The findings indicate significant differences between groups using grammatical cohesive devices, such as reference (p=0.006), conjunction (p=0.004), ellipsis (p=0.007), and substitution (p=0.426) and lexical cohesive devices such as the same word (p=0.006), synonym (p=0.012), superordinate (p=0.001), general word (p=0.002), and collocation (p=0.387). Conclusion: The results show that grammatical and lexical cohesive devices are used less in the discourse of SDAT Kurdish speakers. However, in the discourse of both SDAT and NE groups, grammatical cohesive devices have more frequency than lexical cohesive devices.

Todays, Intrusion Detection Systems (IDS) are considered as key components of security networks. However, high false positive and false negative rates are the important problems of these systems. On the other hand, many of the existing solutions in the articles are restricted to class datasets due to the use of a specific technique, but in real applications they may have multi-variant datasets. With the impetus of the facts, this paper presents a new anomaly based intrusion detection system using J48 Decision Tree, Support Vector Classifier (SVC) and k-means clustering algorithm in order to reduce false alarm rates and enhance the system performance. J48 decision tree algorithm is used to select the best features and optimize the dataset. Also, an SVM classifier and a modified k-means clustering algorithm are used to build a profile of normal and anomalous behaviors of dataset. Simulation results on benchmark NSL-KDD, CICIDS2017 and synthetic datasets confirm that the proposed method has significant performance in comparison with previous approaches.