The impact of mobility network properties on predicted epidemic dynamics in Dhaka and Bangkok

1AbstractProperties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dhaka (Bangladesh), two megacities in Asia with populations of 16 and 21 million people, respectively. We model the dynamics of directly-transmitted infections (such as SARS-CoV2) propagating on these commuting networks, and find that differences in network structure between the two cities drive divergent predicted epidemic trajectories: the commuting network in Bangkok is composed of geographically-contiguous modular communities and epidemic dispersal is correlated with geographic distance between locations, whereas the network in Dhaka has less distinct geographic structure and epidemic dispersal is less constrained by geographic distance. We also find that the predicted dynamics of epidemics vary depending on the local topology of the network around the origin of the outbreak. Measuring commuter mobility, and understanding how commuting networks shape epidemic dynamics at the city level, can support surveillance and preparedness efforts in large cities at risk for emerging or imported epidemics.

Sistem Monitoring Dan Pengkabutan Otomatis Berbasis Internet Of Things (IoT) Pada Budidaya Jamur Tiram Menggunakan NodeMCU dan Blynk

The Mushroom is a wild plant and grow a lot in damp places, at the former rice bran and even grows on rotten trees. In the process of drying oyster mushroom it is very easy, only requires land for nurseries and maintains the temperature inside the oyster mushroom kumbung itself, for maintenance you must maintain the temperature and the humidity so that in the kumbung it remains moist and the oyster mushroom baglog not dry out because the temperature is not as required oyster mushroom baglog. So far, the cultivation that has been carried out is still manual, usually watering the oyster mushroom kumbung in the morning and evening, sometimes when it is hot during the day, watering is done to reduce the temperature inside the oyster mushroom kumbung. In this case the research was carried out on monitoring of temperature and humidity of oyster mushroom kumbung and automatic misting based on IoT which is combined with the blynk application which can be opened on an android smart phone by relying on an internert connection so that you can find out the temperature and humidity in the oyster mushroom kumbung This system uses a DHT11 sensor which functions to read temperature and humidity so that it sends data to NodeMCU V3 and then sends it to the user via the blynk application. If the temperature obtained is >30,200 the it will give a fog epek to the baglog automatically and the led in the blynk application will light up as a notification to the user that in the kumbung the oyster mushrooms are doing mist to lower the temperature of the kumbung and when the temperature is <=30.00 the fog will be stop and the led light in the blynk application will turn off, to be able to see the temperature and humidity of the oyster mushroom through the blynk application, you must first connect to the internet, both from the monitoring tool for oyster mushroom kumbung monitoring and automatic fogging based on iot or the smart phone user.

Verification and Optimization of Metro Fare Clearing Models Based on Travel Route Reconstruction

How to verify and optimize metro fare clearing models efficiently and accurately is a research focus in metro operations. Metro fare clearing models are mostly based on probability distributions. In such models, the normal distribution of travel time corresponding to the section probabilities is used to calculate the route choice probabilities of passengers on a multi-route metro network. By integrating the operating mileage proportions of each metro line operator and the corresponding route choice probabilities, the fare clearing proportions are calculated for all the operators of the metro network. To verify the accuracy of the fare clearing proportions, we propose a travel route reconstruction approach based on cell phone data acquisition technique. With wireless access point (AP) sensors installed at transfer stations, the unique medium access control (MAC) address of the smart phone with Wi-Fi function turned on is recorded and transmitted to a data analysis platform. After matching the MAC address information with time and location, the travel route of the smart phone user is reconstructed. Then, the parameters in the fare clearing model are verified and optimized according to the travel route choice probabilities. The proposed methodology is applied in Hangzhou metro network for experiment, and the metro fare clearing model is verified and modified by reconstructing the actual travel routs of the local passengers.

FACTORS AFFECTING CUSTOMER LOYALTY FOR MOBILE SERVICE PROVIDERS

Intense competition in the mobile services market encourages these service providers to take measures to create, maintain and enhance consumer loyalty. In such a concentrated market, it becomes important for service providers to identify and enhance the factors determining customer loyalty and their expression, whereas maintaining existing users requires significantly less effort and financial, human and time resources than attracting new ones. This article deals with a case study of Lithuania’s mobile operators: UAB “Bitė Lietuva”, UAB “Tele2”, UAB “Telia” in the context of consumer loyalty. The paper raises the question of what factors influence mobile phone user loyalty and how these factors can be evaluated. The article presents theoretical analysis of factors determining consumer loyalty in the service sector. An expert evaluation and multi-criteria study reveals which loyalty factors have the strongest influence on customers of mobile operators when choosing a particular service provider in Lithuania. The relevance of this article has potential for a practical study of consumer loyalty factors in the future. In the future, the research may be continued on a larger scale, involving more companies in the telecommunications sector, choosing research methods that are suitable for a broader sample of research and formulating consumer loyalty enhancement solutions applicable exclusively to organizations in the industry.

Deep Learning applied to the handoff of cellular systems: a survey

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px 'Times New Roman'; min-height: 15.0px} p.p2 {margin: 0.0px 0.0px 0.0px 0.0px; font: 9.0px 'Times New Roman'} span.s1 {font: 12.0px 'Times New Roman'} <p><br></p> <p> <b>Mobility is a key aspect in current cellular networks, allowing users to access the provided services almost anywhere. When a user transitions from a base station’s coverage area to another cell being serviced by another station, a handoff process takes place, where resources are released in the first base station, and allocated in the second for the purpose of servicing the user. Predicting the future location of a cell phone user allows the handoff process to be optimized. This optimization allows for a better utilization of the available resources, regarding bot the transmitted power and the frequency allocation, resulting in less amount of wasted power in unwanted directions and the possibility of reusing frequencies in a single base station. To achieve this goal, Deep Learning techniques are proposed, which have proven to be efficient tools for predicting and detecting patterns. The purpose of this paper is to give an overview of the state of the art in Deep Learning techniques for making spatio-temporal predictions, which could be used to optimize the handoff process in cellular systems. </b></p>

Deep Learning applied to the handoff of cellular systems: a survey

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px 'Times New Roman'; min-height: 15.0px} p.p2 {margin: 0.0px 0.0px 0.0px 0.0px; font: 9.0px 'Times New Roman'} span.s1 {font: 12.0px 'Times New Roman'} <p><br></p> <p> <b>Mobility is a key aspect in current cellular networks, allowing users to access the provided services almost anywhere. When a user transitions from a base station’s coverage area to another cell being serviced by another station, a handoff process takes place, where resources are released in the first base station, and allocated in the second for the purpose of servicing the user. Predicting the future location of a cell phone user allows the handoff process to be optimized. This optimization allows for a better utilization of the available resources, regarding bot the transmitted power and the frequency allocation, resulting in less amount of wasted power in unwanted directions and the possibility of reusing frequencies in a single base station. To achieve this goal, Deep Learning techniques are proposed, which have proven to be efficient tools for predicting and detecting patterns. The purpose of this paper is to give an overview of the state of the art in Deep Learning techniques for making spatio-temporal predictions, which could be used to optimize the handoff process in cellular systems. </b></p>

Consumers’ Preference and Their Buying Choice

Today’s consumers are too smart to buy their needs through various means. But before buying their needs, they go through various online sites and social media review about product performances and price. While surfing this information they can able to evaluate its real value and price advantages, since online establishment need not spend cost for showroom with staff. Consumers need not roam here and there to various shops to evaluate the product performance and its cost. Moving from one place to other is tedious journey and time-consuming part. It is also difficult to ensure their required models are available or not. Moreover, consumers can view forthcoming new models in the manufacture’s site whereas these details may not be shared in showrooms. Earlier accessing internet is complicated and needs a system to view. Now this can be accessed through smart phone. The prices of smart phone were also drastically lowered. After the entry of Jio network, the cost of one GB data were brought down to Rs 15/to Rs 20/- from Rs 250/-. This could be an affordable price for the common people. With the above improvements, the smart phone usage in the country has increased and every smart phone user are in a habit of surfing the internet or interacting in the social media now and then. While so, everybody can be able to see the various products in the online markets and its review. This will provoke the user to buy the products through online. In America ToySaras and BabySaras, retail showrooms of baby products were forced to close during 2018, since most of Americans preferred to shop through online and few other retail shops are in the same stage of closing. The above situation may also be aroused to Indian Market in the near future. To know the objective of the consumers’ preference and their choice, customers’ voice obtained through survey may be helped to us and its findings may help the online marketers to fine tune their strategies accordingly.

Use of Cell Phone in Receiving Agricultural Information by the Farmers

Cell phone is an easy, fast and convenient device for communication. The main purpose of the study was to determine the extent of use of Cell Phone in receiving agricultural information and to explore the relationship between the selected characteristics of the farmers in using Cell Phone for receiving agricultural information. Data were obtained from 97 Cell Phone user farmers in selected village named Chorjamalpur of Boyra union under Singair upazila of Manikganj through face-to-face interview. Appropriate scales were developed in order to measure the concerned variables. Pearson Product Moment Correlation test was used to ascertain the relationship between each of the selected characteristics of the farmers with their use of Cell Phone for receiving agricultural information. The finding shows that 89.7 percent of the respondents had no use to low use of Cell Phone for receiving agricultural information and 10.3 percent of the respondents had medium use to high use of Cell Phone for receiving agricultural information. The finding clearly indicates the ignorance of the respondents about the use of Cell Phone for receiving agricultural information. Among 11 selected characteristics of the farmers, eight characteristics namely, education, land possession, effective farm size, annual family income, agricultural training exposure, organizational participation, innovativeness, cosmopoliteness showed significant and positive relationship with their use of Cell Phone. Problem confrontation of the farmers in using Cell Phone showed significant negative relationship with their use of Cell Phone for receiving agricultural information. But age of the farmers and farming experience of the farmers showed non significant relationship with the use of Cell Phone by the farmers.