AI-Based Traffic Forecasting in 5G Network

Forecasting of the telecommunication traffic is the foundation for enabling intelligent management features as cellular technologies evolve toward fifth-generation (5G) technology. Since a significant number of network slices are deployed over a 5G network, it is crucial to evaluate the resource requirements of each network slice and how they evolve over time. Mobile network carriers should investigate strategies for network optimization and resource allocation due to the steadily increasing mobile traffic. Network management and optimization strategies will be improved if mobile operators know the cellular traffic demand at a specific time and location beforehand. The most effective techniques nowadays devote computing resources in a dynamic manner based on mobile traffic prediction by machine learning techniques. However, the accuracy of the predictive models is critically important. In this work, we concentrate on forecasting the cellular traffic for the following 24 hours by employing temporal and spatiotemporal techniques, with the goal of improving the efficiency and accuracy of mobile traffic prediction. In fact, a set of real-world mobile traffic data is used to assess the efficacy of multiple neural network models in predicting cellular traffic in this study. The fully connected sequential network (FCSN), one-dimensional convolutional neural network (1D-CNN), single-shot learning LSTM (SS-LSTM), and autoregressive LSTM (AR-LSTM) are proposed in the temporal analysis. A 2-dimensional convolutional LSTM (2D-ConvLSTM) model is also proposed in the spatiotemporal framework to forecast cellular traffic over the next 24 hours. The 2D-ConvLSTM model, which can capture spatial relations via convolution operations and temporal dynamics through the LSTM network, is used after creating geographic grids. The results reveal that FCSN and 1D-CNN have comparable performance in univariate temporal analysis. However, 1D-CNN is a smaller network with less number of parameters. One of the other benefits of the proposed 1D-CNN is having less complexity and execution time for predicting traffic. Also, 2D-ConvLSTM outperforms temporal models. The 2D-ConvLSTM model can predict the next 24-hour traffic of internet, sms, and call with root mean square error (RMSE) values of 75.73, 26.60, and 15.02 and mean absolute error (MAE) values of 52.73, 14.42, and 8.98, respectively, which shows better performance compared to the state of the art methods due to capturing variables dependencies. It can be argued that this network has the capability to be utilized in network management and resource allocation in practical applications

Anxiety Effect: A Case of Text Modification and the Effect of High and Low Anxiety Levels on Medical Students’ Comprehension Performance

Introduction: The current study sought to investigate the impact of text modifications (lexically and grammatically modified) on reading comprehension ability of medical students with high and low levels of anxiety.Materials and Methods: To pursue the purpose of this study, 150 male and female medical students from Shahid Beheshti University of Medical Sciences participated in the study. The participants did not take a language proficiency test to ensure homogeneity, as pretesting might affect the internal and external validity of the study due to the interaction effect of pretesting [1]. The framework proposed by [2]. Moreover, a questionnaire developed by [3] entitled “Foreign Language Reading Anxiety Scale (FLRAS)” on a five-point Likert scale with 20 items served as an instrument. MANOVA was run to analyze the data. Results: The findings revealed that there was a significant difference between the reading comprehension ability of medical students with high and low levels of anxiety exposed to lexically modified (p<0.01), grammatically modified (p<0.01), lexically and grammatically modified (p<0.01), and unmodified passages (p<0.01).Conclusion: It is hoped that the findings from this study will guide researchers into new directions so that they may go on to discover profound insights about text simplification for medical students in Iran and all over the world.

Psychological Attitude of Medical Students towards Course-book Modification: A Case of Text Simplification in ESP Courses

Introduction: The current study sought to investigate the impact of linguistic modification of medical textbooks on reading comprehension ability of medical students and their attitude towards text simplification.Materials and Methods:  150 male and female medical students coming from Shahid Beheshti University of Medical Sciences participated in this study. The homogeneity of the participants was attained through passing a placement test, followed by completing the pre-requisite and general English courses.  For the purpose of modifying the texts, the framework proposed by Van Den Branden (2000) was adopted. Moreover, the questionnaire developed by Saito, Horwitz, and Garza (1999) called Foreign Language Reading Anxiety Scale (FLRAS) was used.Results: Data analysis was conducted, using one-way analysis of variances (ANOVA). Concerning the major research question, analysis of the data indicated that there were significant differences between the participants’ performance on the four reading tests. Thus, the major null-hypothesis as “lexical modification, grammatical modification, and lexical and grammatical modification in comparison with no modification of input do not significantly affect the reading comprehension ability of Iranian medical students differently” was rejected. The last research question deals with medical students’ perceptions towards different types of input modifications Conclusion: The overwhelming majority of the interviewees chose lexically and grammatically modified texts. However, “not modified text” was regarded as the most boring one. Also, the participants believed that grammatically modified texts were best for improving students’ work knowledge. Moreover, the interviewees mentioned that grammatically modified texts with fewer complicated structures were more straightforward for them.

Senior Managers’ Viewpoints Toward Challenges of Implementing Clinical Governance: A National Study in Iran

Background: Quality improvement should be assigned as the main mission for healthcare providers. Clinical Governance (CG) is used not only as a strategy focusing on responding to public and government’s intolerance of poor healthcare standards, but also it is implemented for quality improvement in a number of countries. This study aims to identify the key contributing factors in the implementation process of CG from the viewpoints of senior managers in curative deputies of Medical Universities in Iran. Methods: A quantitative method was applied via a questionnaire distributed to 43 senior managers in curative deputies of Iran Universities of Medical Sciences. Data were analyzed using SPSS. Results: Analysis revealed that a number of items were important in the successful implementation of CG from the senior managers’ viewpoints. These items included: knowledge and attitude toward CG, supportive culture, effective communication, teamwork, organizational commitment, and the support given by top managers. Medical staff engagement in CG implementation process, presence of an official position for CG officers, adequate resources, and legal challenges were also regarded as important factors in the implementation process. Conclusion: Knowledge about CG, organizational culture, managerial support, ability to communicate goals and strategies, and the presence of effective structures to support CG, were all related to senior managers’ attitude toward CG and ultimately affected the success of quality improvement activities

The XDEM Multi-physics and Multi-scale Simulation Technology: Review on DEM-CFD Coupling, Methodology and Engineering Applications

The XDEM multi-physics and multi-scale simulation platform roots in the Ex- tended Discrete Element Method (XDEM) and is being developed at the In- stitute of Computational Engineering at the University of Luxembourg. The platform is an advanced multi- physics simulation technology that combines flexibility and versatility to establish the next generation of multi-physics and multi-scale simulation tools. For this purpose the simulation framework relies on coupling various predictive tools based on both an Eulerian and Lagrangian approach. Eulerian approaches represent the wide field of continuum models while the Lagrange approach is perfectly suited to characterise discrete phases. Thus, continuum models include classical simulation tools such as Computa- tional Fluid Dynamics (CFD) or Finite Element Analysis (FEA) while an ex- tended configuration of the classical Discrete Element Method (DEM) addresses the discrete e.g. particulate phase. Apart from predicting the trajectories of individual particles, XDEM extends the application to estimating the thermo- dynamic state of each particle by advanced and optimised algorithms. The thermodynamic state may include temperature and species distributions due to chemical reaction and external heat sources. Hence, coupling these extended features with either CFD or FEA opens up a wide range of applications as diverse as pharmaceutical industry e.g. drug production, agriculture food and processing industry, mining, construction and agricultural machinery, metals manufacturing, energy production and systems biology

Exploring Knowledge Strategy Dimensions Using Fuzzy AHP

Living in knowledge-based world and increasing competition between companies- especially in IT-based companies- in determining the best knowledge strategy lead to more attention to this concept. The purpose of this paper is to review previous studies on knowledge strategy and its dimensions. Then, after categorizing these dimensions, a popular multi-criteria decision-making (MCDM) method- AHP- in Fuzzy environment is used to evaluate dimensions of knowledge strategy (KS). The results show that knowledge sourcing, learning sourcing, learning speed are more effective dimensions of knowledge strategy. As every organization has a leading dimension in KS, IT-based companies should focus on these dimensions for better developing knowledge strategy

Lipase Production in Tray-Bioreactor via Solid State Fermentation under Desired Growth Conditions

Lipase was produced under desired growth conditions in a novel tray bioreactor using the fungus strain of Rhizopus oryzae. Several agricultural residues/products including sugarcane bagasse, wheat bran, corn meal, barely bran and equal mixtures of sugarcane bagasse with agricultural residues were applied as solid substrate. Lipase produced from the pure sugarcane bagasse showed higher activities than other substrates; which resulted enzyme activities of 155.76 and 138.37 U/gds for the top and middle trays respectively. Furthermore, the influence of carbon and nitrogen supplements was investigated. Addition of carbon sources as substrate was found to be ineffective, while lipase activity remarkably increased by supplementation of bagasse with adequate amount of nitrogen source. Among the nitrogen supplements, urea as a suitable nitrogen source was considered; as a result the average lipolytic activity of 229.355 U/gds was achieved. In addition, various concentrations of vegetable oils including canola oil, soybean oil, olive oil and castor oil were applied. The inducing effect of vegetable oil on lipase activity was investigated. Among them, olive oil and canola oil increased lipolytic activity of lipase with an average value of 192.26 and 183.57 U/gds, respectively

ALKALINE PRETREATMENT OF SPRUCE AND BIRCH TO IMPROVE BIOETHANOL AND BIOGAS PRODUCTION

Alkaline pretreatment with NaOH under mild operating conditions was used to improve ethanol and biogas production from softwood spruce and hardwood birch. The pretreatments were carried out at different temperatures between minus 15 and 100ºC with 7.0% w/w NaOH solution for 2 h. The pretreated materials were then enzymatically hydrolyzed and subsequently fermented to ethanol or anaerobically digested to biogas. In general, the pretreatment was more successful for both ethanol and biogas production from the hardwood birch than the softwood spruce. The pretreatment resulted in significant reduction of hemicellulose and the crystallinity of cellulose, which might be responsible for improved enzymatic hydrolyses of birch from 6.9% to 82.3% and spruce from 14.1% to 35.7%. These results were obtained with pretreatment at 100°C for birch and 5°C for spruce. Subsequently, the best ethanol yield obtained was 0.08 g/g of the spruce while pretreated at 100°C, and 0.17 g/g of the birch treated at 100°C. On the other hand, digestion of untreated birch and spruce resulted in methane yields of 250 and 30 l/kg VS of the wood species, respectively. The pretreatment of the wood species at the best conditions for enzymatic hydrolysis resulted in 83% and 74% improvement in methane production from birch and spruce