High Number Of Users Research Articles

Enhancing the e-learning system based on a novel tasks' classification load-balancing algorithm.

In the educational field, the system performance, as well as the stakeholders’ satisfaction, are considered a bottleneck in the e-learning system due to the high number of users who are represented in the educational system’s stakeholders including instructors and students. On the other hand, successful resource utilization in cloud systems is one of the key factors for increasing system performance which is strongly related to the ability for the optimal load distribution. In this study, a novel load-balancing algorithm is proposed. The proposed algorithm aims to optimize the educational system’s performance and, consequently, the users’ satisfaction in the educational field represented by the students. The proposed enhancement in the e-learning system has been evaluated by two methods, first, a simulation experiment for confirming the applicability of the proposed algorithm. Then a real-case experiment has been applied to the e-learning system at Helwan University. The results revealed the advantages of the proposed algorithm over other well-known load balancing algorithms. A questionnaire was also developed to measure the users’ satisfaction with the system’s performance. A total of 3,670 thousand out of 5,000 students have responded, and the results have revealed a satisfaction percentage of 95.4% in the e-learning field represented by the students.

Trends in Al-Qur’an Learning Applications for Indonesian Students: Solutions for Learning Qur’an during the Covid-19 Pandemic?

Government policy related to the Covid-19 pandemic conditions requires learning at school and learning the al-Qur’an at TPQ online, so must innovate and adapt to awareness and intelligence disclosure, one of which is the number of applications for learning the al-Qur’an as an alternative in this condition. This study aims to identify trends, determine the distribution and analyze the appropriateness of the al-Qur'an learning application using descriptive qualitative research, with 988 students as respondents from 38 junior high schools and high schools in Indonesia. This research shows that there are 10 application trends based on the highest number of users, including the al-Quran Indonesia, muslim pro, al-Quran, umma, my Quran dan terjemah, al-Quran digital, youtube, al-Quran terjemah, al-Quran bahasa Indonesa, belajar membaca al-Quran. The overall application level is 81% based on the TAM (Technology Acceptance Model) theory, which is determined by the benefits of use, interest and completeness of features, besides the trend of these 10 applications in knowledge or cognition can be a companion solution for learning the al-Qur'an during the covid-19 pandemic

Digital transformation of major scientific meetings induced by the COVID-19 pandemic: insights from the ESC 2020 annual congress.

As a consequence of the COVID-19 pandemic, the European Society of Cardiology (ESC) was forced to pivot the scientific programme of the ESC Congress 2021 into a totally new format for online consumption, The Digital Experience. A variety of new suppliers were involved, including experts in TV studio, cloud infrastructure, online platforms, video management, and online analytics. An information technology platform able to support hundreds of thousands simultaneous connections was built and cloud computing technologies were put in place to help scale up and down the resources needed for the high number of users at peak times. The video management system was characterized by multiple layers of security and redundancy and offered the same fluidity, albeit at a different resolution, to all user independently of the performance of their internet connection. The event, free for all users, was an undisputed success, both from a scientific/educational as well as from a digital technology perspective. The number of registrations increased by almost four-fold when compared with the 2019 record-breaking edition in Paris, with a greater proportion of younger and female participants as well as of participants from low- and middle-income countries. No major technical failures were encountered. For the first time in history, attendees from all around the globe had the same real-time access to the world’s most popular cardiovascular conference.

Framework for Behavioral Analysis of Mobile Networks.

The arrival of the Fifth Generation (5G) entails a significant evolution in the context of mobile communication networks. This new technology will bring heterogeneous scenarios with new types of services and an increasingly high number of users and nodes. The efficient management of such complex networks has become an important challenge. To address this problem, automatic and efficient algorithms must be developed to facilitate operators’ management and optimization of their networks. These algorithms must be able to cope with a very high number of heterogeneous data and different types of scenarios. In this paper, a novel framework for a cellular network behavioral analysis and monitoring is presented. This framework is based on a combination of unsupervised and supervised machine learning techniques. The proposed system can analyze the behavior of cells and monitor them, searching for behavior changes over time. The information extracted by the framework can be used to improve subsequent management and optimization functions.

Improving Mental Health of Adolescents through Self-Presentation

Mental health is currently the problem faced by youth especially during the pandemic of COVID-19.Adolescents use of social media is also increasing due to this coronavirus pandemic. This situation have riskof increasing the risk of lower mental health of Adolescent as the impact of posting their self-presentationthrough a social media such as Instagram. Instagram is a social media platform that has a rapid user growth.Indonesia is one of the countries with highest number of users. Instagram users recorded as of May 2020had reached 9.2 million users. Instagram as social media is a means for users to present themselves towide audience. Self-presentation can be in the form posting photos or videos on their Instagram socialmedia accounts. By posting photos, user hope to get feedback in the form of likes and comments. Thisresearch aims at the improving mental health of adolescent through self-presentation. This research usedis secondary data obtained from journal database. The sample was 35 scientific articles obtained from thebasis of screening journal that has been screened using pre determine inclusion criteria. The result found thatself-presentation on social media Instagram was related to giving or managing impression, popular, selfdisclosure, narcissistic behavior, self-image and self-esteem that in turn will result in lowering adolescentmental health if it is not manage well.

A new predictive model for the description of the growth of Salmonella spp. in Italian fresh ricotta cheese

In this study, cardinal parameter models were developed for the growth of Salmonella spp. in different brands of Italian fresh ricotta cheese. Two models were proposed, including the effect of temperature or the combined effect of temperature, pH, and concentration of lactic, citric and, acetic acid. Validation of the models included an assessment of the ability to predict maximum specific growth rate μmax using two indices: bias-factor (Bf) and accuracy factor (Af), and the acceptable simulation zone (ASZ). The new models for Salmonella spp. showed good performances with Bf of 1.11–1.10 (model with 1 or 5 variables), and an average of 91% and 89% of observations within the ASZ (model with 1 or 5 variables, respectively). Comparing the performances of other existing models when applied to ricotta cheese, a general underprediction of the growth rate was evidenced. The proposed models can be applied by a high number of users with the aim to assess levels of this pathogen in ricotta cheese under both static and dynamic environmental conditions, being useful for the dairy business as the tested conditions cover a wide range of the available brands on the market.

Unmanned Aerial Vehicles in Agriculture: A Survey

The number of tasks that nowadays are accomplished by using unmanned aerial vehicles is rising across many civil applications, including agriculture. Thus, this work aims at providing the reader with an overview of the agronomical use of unmanned aerial vehicles. The work starts with a historical analysis of the use of aircrafts in agriculture, as pioneers of their use in modern precision agriculture techniques, currently applied by a high number of users. This survey has been carried out by providing a classification of the vehicles according to their typology and main sensorial and performance features. An extensive review of the most common applications and the advantages of using unmanned aerial vehicles is the core of the work. Finally, a brief summary of the key points of the legislation applicable to civil drones that could affect to agricultural applications is analyzed.

Time series forecast modeling of vulnerabilities in the android operating system using ARIMA and deep learning methods

Security vulnerability prediction models allow estimation of the number of potential vulnerabilities and evaluation of the risks caused by these vulnerabilities. In particular, for modeling the vulnerabilities that may occur in software versions over time, it is appropriate to take the necessary countermeasures. These models are crucial in areas such as determining the number of resources required to cope with security vulnerabilities. These reported vulnerabilities, we anticipate the actions of OS companies to make strategic and operational decisions such as secure deployment. The operating system includes backup provisioning, disaster recovery. Although many vulnerability predictions models have been constructed, most of these are for operating systems and internet browsers, and non-exist for the Android mobile operating system, which has the highest number of users. In contrast to other studies, the present study investigated Android vulnerabilities that directly depend on time. Time series, multilayer perceptron (MLP), convolutional neural network (CNN), long short term memory (LSTM), Convolutional LSTM (ConvLSTM) and CNN-LSTM based models were generated, and the best model, providing the lowest error rates for the prediction of future security vulnerabilities, was selected. Data for the creation of the models were obtained by filtering security vulnerabilities published in the National Vulnerability Database (NVD) using the keyword Android. It was observed that the LSTM model has an error rate of 26.830 and the ARIMA model has an error rate of 18.449. Finally, it has been determined that LSTM based algorithms reach error rates that can compete with classical time series models despite limited data.

Secure Data Sharing with Efficient Key Update for Industrial Cloud-based Access Control

Sharing data in industrial cloud requires both secure and flexibility management of data shared among users in the industry. Users should have ability to access authorized portion of data from smart edge devices with integrated secure protocol for interacting with cloud storage. Among cryptographic-based access control solutions, ciphertext-policy attribute-based encryption (CP-ABE) is recognized as a suitable solution for supporting secure and fine-grained data access control for outsourced data. However, the attribute revocation is a major drawback of CP-ABE since it introduces subsequent costs such as ciphertext re-encryption, user key re-generation, and key re-distribution. Existing revocable CP-ABE based access control models have focused on minimizing the cost of communication and computation cost for ciphertext re-encryption. Unfortunately, the key update issue is subject to re-key generation as traditional CP-ABE does. In essence, key update becomes crucial when there are a high number of users accessing shared data in the cloud. In this paper, we propose a lightweight access control model featured with the efficient key updating scheme. Finally, the performance evaluation is conducted to demonstrate the efficiency of our proposed scheme.

Incentive-Based D2D Relaying in Cellular Networks

Device-to-device (D2D) relaying is a concept, where some users relay data of cell-edge users (CUEs) experiencing a bad channel quality to a base station. While this research topic has received plenty of attention, a critical aspect of the D2D relaying remains a selfish nature of the users and their limited willingness to relay data for others. Thus, we propose a scheme to identify potential candidates for the relaying and provide a sound incentive to these relaying users (RUEs) to motivate them helping other users. First, we provide a detailed theoretical analysis showing when and if the relaying is beneficial for the CUE(s) and related RUE. Second, to choose among all possible incentive-compliant relaying options, we formulate the optimal CUE-to-RUE matching problem maximizing a network-wide performance. Since the optimal solution is hard to obtain for a high number of users, we propose a low-complexity greedy algorithm and prove its constant worst-case approximation guarantees to the optimum. Finally, we derive a closed-form expression for a fair allocation of the resources among the CUEs and the RUEs. The proposed framework more than doubles the users' capacity and/or reduces the energy consumption by up to 87% comparing to existing incentive-based relaying schemes.

Responding to Global Learning Needs during a Pandemic: An Analysis of the Trends in Platform Use and Incidence of COVID-19

On 11 March 2020, the World Health Organization (WHO) declared the outbreak of coronavirus disease (COVID-19) to be a pandemic. As a result, the OpenWHO.org online platform, which serves as WHO’s learning hub for emergencies, was tested for the first time on its core purpose of scaling up trusted public health information in a global emergency. This descriptive study examines and documents the WHO learning response in the early months of the pandemic by comparing epidemiological information and OpenWHO.org use in the countries with the highest COVID-19 cases. Statistical datasets from OpenWHO.org and WHO’s COVID-19 dashboard were overlaid for the period 11 March–22 May 2020. During this period, for most of the 24 countries with the highest COVID-19 cases, platform use showed a corresponding trend. Courses published in the official languages spoken in these countries were well utilized, indicating a need to produce materials in languages spoken by affected communities. Of the countries with the highest number of users on OpenWHO, only half were top users of the platform before the pandemic. The existence of an established online platform for health emergencies assisted WHO in massively and quickly scaling up the dissemination of essential learning materials for COVID-19.

Eksistensi Media Sosial,Youtube, Instagram dan Whatsapp Ditengah Pandemi Covid-19 Dikalangan Masyarakat Virtual Indonesia

Introduction. This article discusses the existence of social media YouTube, Instagram and Watsapp in the midst of the Covid-19 pandemic among virtual communities in Indonesia. The development of social media is increasing every year, especially in the conditions of the corona virus outbreak. The development of social media in Indonesia has experienced a very significant increase, it can be seen that Indonesia is in the 3rd position of social media users in the world.
 Data Collection Method. The method used in this article is by using the literature method by reading literature related to discussion and it can be analyzed that the existence of social media such as YouTube in the midst of the Covid pandemic has experienced a surge in users of around 88% and is then followed by Whatsapp social media with the number of accesses of 84% and Instagram with 79%.
 Results and Discussions. Based on the data above, we can see the development and level of existence of several social media which is frequently accessed by the world's population during the Covid 19 pandemic. This development has certainly experienced a very clear penetration of internet users among the Indonesian virtual community.
 Conclusions. the development of social media in Indonesia and globally has increased very significantly. The existence of social media which was popular during the Covid-19 pandemic, which placed YouTube as the first media that was very popular with the highest number of users in the world.

Using Instagram to deliver a theory-based wellness intervention designed to promote behavior change

Abstract Background With a high number of users, social networking sites (SNS), such as Instagram, have the potential to serve as a tool to dispense health information and promote health. This pilot study examined the effect of a four-week wellness intervention on Instagram users with targeted posts regarding fitness, nutritious eating, and self-care behaviors. Methods A review of best practices in Instagram posts was conducted to create daily posts relating to Theory of Planned Behavior constructs for the wellness areas of fitness (e.g., daily movement), nutritious eating (e.g., vegetable and fruit consumption, healthy recipes), and self-care (e.g., social time, journaling). The intervention group (N = 22) and control group (N = 11) were assessed pre and post test using a Theory of Planned Behavior survey. Results At post-test, self-care intention for the intervention group was significantly higher compared to the control. However, there was not a significant difference between the intervention group and control for engaging in actual self-care behaviors reported at post-test. There were no significant differences between the groups for other constructs pre to post-test. User engagement or lack of engagement with posts did not relate to any differences in constructs at post-test. Conclusions A wellness intervention delivered through Instagram did not impact health behaviors over a four-week period in the intervention group compared to the control group. Future research should continue examining if SNS interventions, specifically changing what users see and interact with, can influence health behavior outcomes. This pilot study also provides an example of a theory-based intervention that can be tailored to examine effects on behavior change in other populations. Key messages Though there is potential to use social networking sites as health promotion tools, there is a lack of understanding how to best use this technology, engage participants, and promote health. Using health behavior change theories in intervention research using social networking sites is considered the best approach to evoke and understand behavior change among populations.

A feasibility study of the burden of disease of atopic dermatitis using a smartphone research application, myEczema.

BackgroundOur understanding of chronic diseases, such as atopic dermatitis (AD), could benefit from the ability to rapidly collect patient-reported, longitudinal data from a large population.ObjectiveThis study aimed to determine whether a smartphone app can be used to collect demographic and longitudinal symptom data and recognize prescribing patterns and affordability of medications to study the burden of AD.MethodsWe collected data using the myEczema smartphone app between July 2017 and April 2018. The data were de-identified and analyzed.ResultsA total of 519 users (94.2%) completed the initial demographic survey. The majority of users were female (n = 387; 70.2%) and Caucasian (n = 358; 65.0%). A total of 335 users (60.8%) had at least a university degree and were employed (n = 348; 63.1%). A total of 189 users (29.2%) reported difficulty affording their medications, and 363 users (65.9%) took advantage of the itch score recording feature. Finally, 184 users (33.4%) logged their treatments, with the highest number of users (65.2%) listing topical steroids as one of their treatments.LimitationsThe operating platform was limited to iPhones, and the results were subject to reporting bias.ConclusionA smartphone-based research app can be used to rapidly collect patient-reported data to study the burden of AD and to highlight the prescribing patterns and affordability of medications.

Performance analysis of handover delay and buffer capacity in mobile OpenFlow‐based networks

SummarySoftware‐defined networking (SDN) is a network concept that brings significant benefits for the mobile cellular operators. In an SDN‐based core network, the average service time of an OpenFlow switch is highly influenced by the total capacity and type of the output buffer, which is used for temporary storage of the incoming packets. In this work, the main goal is to model the handover delay due to the exchange of OpenFlow‐related messages in mobile SDN networks. The handover delay is defined as the overall delay experienced by the mobile node within the handover procedure, when reestablishing an ongoing session from the switch in the source eNodeB to the switch in the destination eNodeB. We propose a new analytical model, and we compare two systems with different SDN switch designs that model a continuous time Markov process by using quasi‐birth–death processes: (1) single shared buffer without priority (model SFB), used for all output ports for both control and user traffic, and (2) two isolated buffers with priority (model priority finite buffering [PFB]), one for control and the other for user plane traffic, where the control traffic is always prioritized. The two proposed systems are compared in terms of total handover delay and minimal buffer capacity needed to satisfy a certain packet error ratio imposed by the link. The mathematical modeling is verified via extensive simulations. In terms of handover delay, the results show that the model PFB outperforms the model SFB, especially for networks with high number of users and high probability of packet‐in messages. As for the buffer dimensioning analysis, for lower arrival rates, low number of users, and low probability of packet‐in messages, the model SFB has the advantage of requiring a smaller buffer size.

Platform Competition and User Generated Content: Evidence from Game Wikis

Today, many platforms rely on contributions by users to create value. While they play an increasing role in today's digital economy, little is known about how dominant user-driven platforms emerge and what determines the quality and amount of the available content. We aim to extend our understanding of this phenomenon by studying the relationship between the level of competition and user activity. We argue that a platform's competitive position influences user behavior through two mechanisms: First, a more dominant position entails more favorable beliefs, leading to increased activity at the extensive margin, i.e. a higher number of users. Second, the level of competition affects the non-pecuniary benefits users can derive, impacting their activity at the intensive margin, i.e. how much and how frequently each contributes. We study these dynamics in the context of two competing game wiki platforms and exploit content updates as a source of exogenous variation in a quasi-experimental research design. We find that a more dominant position is associated with a higher level of user activity in aggregate, which is primarily driven by the extensive margin of content creation. In addition, this entails higher social benefits, leading to increased activity at the intensive margin. Lastly, we find evidence that a higher competition intensity can act as a motivating factor in itself.

Applied anthropometry for common industrial settings design: Working and ideal manual handling heights

Anthropometry has been used extensively for designing safe and sustainable products and workplaces. However, it is common that designers need straightforward guidelines and dimensions, which they often lack, for specific design situations. Anthropometric data are usually presented in tables that summarize percentile values, separated by gender, of a specific population, which makes it difficult for designers to generate applications for mixed populations, such as industrial settings. Using a recently collected anthropometric database of Chilean workers (male and female), international standards of dimensions for working height, depth, and ideal manual handling height are tested with univariate and bivariate methods. Alternative dimensions are presented for both adjustable and non-adjustable designs. Additionally, procedures to combine samples, and for knowing how many users match with a particular design are explained using the sample data. As expected, adjustable designs proved to match with higher numbers of users, while non-adjustable dimensions recommended by ISO presented low levels of matching. Furthermore, the non-adjustable design achieved 83% of matching, which increased to the desired levels (90%) with the inclusion of a 50 mm increase platform. Finally, the Z-Score equation proved to be a useful tool to know the percentages of the population that are matched with a particular design dimension. Relevance for the industryDimensions for working height, depth, and ideal manual handling heights, which are currently not available, are provided for Chilean workers. A method to determine the matching percentage in a population is explained, in order to assess matching probabilities when having only summarized anthropometric tables and the dimensions for the design itself.

Investigation of Enhanced Double Weight Code in Point to Point Access Networks

In this paper, an investigation and evaluation to enhanced double weight (EDW) code is performed, a new technique for code structuring and building using modified arithmetical model has been given for the code in place of employing previous technique based on Trial Inspections. Innovative design has been employed for the code into P2P networks using diverse weighted EDW code to be fitting into optical CDMA relevance applications. A new developed relation for EDW code is presented, the relation is based on studying and experimenting the effect of input transmission power with code weight, and the relation developed using numerical analysis method. This relation makes the estimation for the system input power needed more efficient. The results of the code has been explained by eye diagram and parametric illustrations from the simulated results. The result shows a magnificent performance of the code during high number of users and weight. On the other hand, the relation developed for power measurement helps to prevent power loss and consumption.

Intelligent IoT Framework for Indoor Healthcare Monitoring of Parkinson’s Disease Patient

Parkinson's disease is associated with high treatment costs, primarily attributed to the needs of hospitalization and frequent care services. A study reveals annual per-person healthcare costs for Parkinson's patients to be $21,482, with an additional $29,695 burden to society. Due to the high stakes and rapidly rising Parkinson's patients' count, it is imperative to introduce intelligent monitoring and analysis systems. In this paper, an Internet of Things (IoT) based framework is proposed to enable remote monitoring, administration, and analysis of patient's conditions in a typical indoor environment. The proposed infrastructure offers both static and dynamic routing, along with delay analysis and priority enabled communications. The scheme also introduces machine learning techniques to detect the progression of Parkinson's over six months using auditory inputs. The proposed IoT infrastructure and machine learning algorithm are thoroughly evaluated and a detailed analysis is performed. The results show that the proposed scheme offers efficient communication scheduling, facilitating a high number of users with low latency. The proposed machine learning scheme also outperforms state-of-the-art techniques in accurately predicting the Parkinson's progression.

Social media exploration for understanding food product attributes perception: the case of coffee and health with Twitter data

PurposeFood companies and consumers are increasingly interested in healthy food and beverages. Coffee is one of the most commonly consumed beverages worldwide. There is increasing consensus that coffee consumption can have beneficial effects on human body. This paper aims at exploring Twitter messages' content and sentiment towards health attributes of coffee.Design/methodology/approachThe research adopted a utilitarian and hedonic consumer behaviour perspective to analyse online community messages. A sample of 13,000 tweets, from around 4,800 users, that mentions keywords coffee and health was collected on a daily basis for a month in mid-2017. The tweets were categorized with a term frequency analysis, keyword-in-context analysis and sentiment analysis.FindingsResults showed that the majority of tweets are neutral or slightly positive towards coffee’s effects on health. Media and consumers are dynamic Twitter users. Findings support that coffee consumption brings favourable emotions, wellness, energy, positive state of mind and an enjoyable and trendy lifestyle. Many tweets have a positive perception of coffee health benefits, especially relating to mental and physical well-being.Research limitations/implicationsThe high number of users and tweets analysed compensates the limited amount of time of data collection, Twitter messages' restricted number of characters and quantitative software analysis limitations.Practical implicationsThe research provides valuable suggestions for food and beverage industry managers.Originality/valueThis work adds value to the literature by expanding scholars' research on food product attributes perception analysis by using social media as a source of information. Moreover, it provides valuable information on marketable coffee attributes.