Academic literature on the topic 'Smart cards logs'

In a world that is becoming more and more digital, Multi-Factor Authentication (MFA) is essential for making network access safer. The purpose of this study is to suggest a way to adopt MFA that will make network systems more resistant to cyber risks and illegal access. Adding different types of authentication, like passwords, fingerprints, smart cards, and one-time codes, is the first part of the suggested approach. By making users prove their identity in more than one way, the system adds more layers of protection, making it less likely that someone will get in without permission, even if one of the factors is broken. In addition, the application stresses how important it is to have a strong method for verifying identities. This includes making sure users are who they say they are by using personal data, like fingerprints or face recognition, which are hard to fake or copy. The system also has flexible authentication features that change the amount of authentication needed based on things like the user’s position, the device they’re using, and how they usually behave. This flexible method improves the user experience by reducing the hassle of identification while still meeting high security standards. Furthermore, the application includes full logs and tracking tools to quickly find and stop any shady actions. By constantly watching tries at authentication and how users act, the system can spot possible security holes and take steps to reduce risks.

Background Medical referral is the transfer of a patient’s care from one physician to another upon request. This process involves multiple steps that require provider-to-provider and provider-to-patient communication. In Taiwan, the National Health Insurance Administration (NHIA) has implemented a national medical referral (NMR) system, which encourages physicians to refer their patients to different health care facilities to reduce unnecessary hospital visits and the financial stress on the national health insurance. However, the NHIA’s NMR system is a government-based electronic medical referral service, and its referral data access and exchange are limited to authorized clinical professionals using their national health smart cards over the NHIA virtual private network. Therefore, this system lacks scalability and flexibility and cannot establish trusting relationships among patients, family doctors, and specialists. Objective To eliminate the existing restrictions of the NHIA’s NMR system, this study developed a scalable, flexible, and blockchain-enabled framework that leverages the NHIA’s NMR referral data to build an alliance-based medical referral service connecting health care facilities. Methods We developed a blockchain-enabled framework that can integrate patient referral data from the NHIA’s NMR system with electronic medical record (EMR) and electronic health record (EHR) data of hospitals and community-based clinics to establish an alliance-based medical referral service serving patients, clinics, and hospitals and improve the trust in relationships and transaction security. We also developed a blockchain-enabled personal health record decentralized app (DApp) based on our blockchain-enabled framework for patients to acquire their EMR and EHR data; DApp access logs were collected to assess patients’ behavior and investigate the acceptance of our personal authorization-controlled framework. Results The constructed iWellChain Framework was installed in an affiliated teaching hospital and four collaborative clinics. The framework renders all medical referral processes automatic and paperless and facilitates efficient NHIA reimbursements. In addition, the blockchain-enabled iWellChain DApp was distributed for patients to access and control their EMR and EHR data. Analysis of 3 months (September to December 2018) of access logs revealed that patients were highly interested in acquiring health data, especially those of laboratory test reports. Conclusions This study is a pioneer of blockchain applications for medical referral services, and the constructed framework and DApp have been applied practically in clinical settings. The iWellChain Framework has the scalability to deploy a blockchain environment effectively for health care facilities; the iWellChain DApp has potential for use with more patient-centered applications to collaborate with the industry and facilitate its adoption.

Urban mobility is significantly affected by the disorderly growth of cities. Many problems in urban centers today are caused by the increase in the number of cars on the streets and the few incentives for public transport are just some of these problems. The accelerated urban growth registered in recent years has increased the concern regarding urban mobility in the city of Rio de Janeiro. Smart cities are an alternative to the problems found in cities where, based on Information Technology, search for databases, and thus share important information with the effective participation of the population, helping the management itself to do something for the citizen. In the last eight years, there is an excessive number of people and vehicles on the streets, generating congestion, insecurity and loss of time, based on statistics from the Dutch company Tom Tom, Rio de Janeiro loses an average of 47% more time in traffic compared to the time lost around the world. The objective of this study is to present how the concept of smart cities can influence urban mobility, through sustainability, innovation, research through world statistics, intelligences among world cities, in order to bring improvements to congestion and difficulty of circulation in the city of Rio de Janeiro. Therefore, in this study, in addition to statistical data, it was used materials of Smart cities and urban mobility in the current world, how to demonstrate different forms of urban mobility in smart cities through the use of applications of interaction between people and cities.

In dealing with information it often turns out that one has to face a huge amount of data, often not completely homogeneous and often without an immediate grasp of an underlying simple structure. Many records, each one instantiating many variables, are usually collected with the help of various technologies. Given the opportunity to have so many data not easy to correlate by the human reader, but probably hiding interesting properties, one of the typical goals one has in mind is to classify subjects on the basis of a hopefully reduced meaningful subset of the measured variables. The complexity of the problem makes it worthwhile to resort to automatic classification procedures. Then, the question arises of reconstructing a synthetic mathematical model, capturing the most important relations between variables, in order to both discriminate classes of subjects and possibly also infer rules of behaviours that could help identify their habits. Such interrelated aspects will be the focus of the present contribution. The data mining procedures that will be introduced in order to infer properties hidden in the data are in fact so powerful that care should be put in their capability to unveil regularities that the owner of the data would not want to let the processing tool discover, like for instance, in some cases the customer habits investigated via the usual smart card used in commerce with the apparent reward of discounting. Four main general purpose approaches will be briefly discussed in the present article, underlying the cost effectiveness of each one. In order to reduce the dimensionality of the problem, simplifying both the computation and the subsequent understanding of the solution, the critical issues of selecting the most salient variables must be addressed. This step may already be sensitive, pointing to the very core of the information to look at. A very simple approach is to resort to cascading a divisive partitioning of data orthogonal to the principal directions (PDDP) (Boley, 1998) already proven to be successful in the context of analyzing micro-arrays data (Garatti, Bittanti, Liberati, & Maffezzoli, 2007). A more sophisticated possible approach is to resort to a rule induction method, like the one described in Muselli and Liberati (2000). Such a strategy also offers the advantage to extract underlying rules, implying conjunctions or disjunctions between the identified salient variables. Thus, a first idea of their even nonlinear relations is provided as a first step to design a representative model, whose variables will be the selected ones. Such an approach has been shown (Muselli & Liberati, 2002) to be not less powerful over several benchmarks than the popular decision tree developed by Quinlan (1994). An alternative in this sense can be represented by Adaptive Bayesian networks (Yarmus, 2003) whose advantage is also to be available on a commercial wide spread data base tool like Oracle. Dynamics may matter. A possible approach to blindly build a simple linear approximating model is thus to resort to piece-wise affine (PWA) identification (Ferrari-Trecate, Muselli, Liberati, & Morari, 2003). The joint use of (some of) such four approaches briefly described in this article, starting from data without known priors about their relationships, will allow to reduce dimensionality without significant loss in information, then to infer logical relationships, and, finally, to identify a simple input-output model of the involved process that also could be used for controlling purposes, even those potentially sensitive to ethical and security issues.

Abstract Data monitoring in a big oil field without any digital platform is a challenging task but it is critical for ALS monitoring and optimization. In sucker rod pumping wells, real time dyna card collection and analyzation is very important for understanding downhole pump behavior and system health. Manual dyna card collection twice a week for ~47 horizontal wells is very low. Real time dyna card requires atleast 256 data points per minute frequency. Its analysis is very effective to optimize well production and increase the pump life and rod run life. The lack of real time monitoring resulted in well downtime and associated production loss. The combination of IOT, Cloud Computing and Machine learning implementation shifted our approach from reactive to proactive which assisted in ALS Optimization and reduced production loss. The data is transmitted to Se Suite Central, a web based Descisison Support System hosted on cloud. Since thousands of dynacards are generated in a day, therefore the algorithm was made with automated card classification using computer driven pattern recognition techniques. The real time data is used for analysis including basic statistics and machine learning algorithms to classify thousands of dynacards every day. The pump signatures were identified using machine learning libraries and categorized. Several informative dashboards are developed which provided quick analysis of ALS performance, a few of them are - Well Operational Status Dyna cards Interpretation module SRP parameters visualization Machine Learning model calibration module Pump Performance Statistics After collection of enough data and creation of analytical dashboards using domain knowledge, the gained insights were used for ALS optimization. Smart Alarms were generated using statistics and machine learning settings by the system which gave alerts by e-mail if an abnormal behavior or erratic dyna-cards were identified. This helped in reduction of well downtime in some events which were treated instinctively before. The integration of domain knowledge and digitalization enabled to take informed and effective decisions. This project supported in effectively managing a complete asset of more than 47 wells remotely with limited resources. This set up has capably run low PI wells intermittently, which has saved power consumption to run the surface pumping units. This digitalization plan has prevented many pump & rods failures, resulting in saving numerous workover jobs and well downtime.

Face recognition is a way to identify or verify someone’s identity by using their face. Face recognition systems may be used to pick out humans in photographs, video, or in actual-time. The main objective of the project is to authenticate users including students, faculty and staff using their faces to maintain the data of those who are logged in and logged out from the library database. The student or faculty library login and logout information can be maintained in a smart way i.e. the count of the students and faculty as library users can be easily available and data of users such as entry and exit time will be stored. Face Recognition Phase can be done through Live Streaming. In previous days all the library login and logout information is stored in records manually which is a time taking process. There is a chance of data loss by maintaining books and it is difficult to maintain multiple records. Nowadays users are entering their identification details for check in and checkout to use the library. and RFID also uses in many library systems which comes under manually inserting the cards which is time taking procedure So the project mainly focuses on proposing a smart way to maintain faculty/student login and logout library information by using Convolution Neural Network (CNN) and also AWS is used in the project to securely store all the data on the cloud so that Database can access them from anywhere.

In big cities, there are plenty of parking spaces, but we often find nowhere to park. For example, New York has 1.4 million cars and 4.4 million on-street parking spaces, but it is still not easy to find a parking place near our destination, especially during peak hours. The reason is the lack of prediction of parking behavior. If we could provide parking behavior in advance, we can ease this parking problem that affects human well-being. We observe that parking lots have periodic parking patterns, which is an important factor for parking behavior prediction. Unfortunately, existing work ignores such periodic parking patterns in parking behavior prediction, and thus incurs low accuracy. To solve this problem, we propose PewLSTM, a novel periodic weather-aware LSTM model that successfully predicts the parking behavior based on historical records, weather, environments, and weekdays. PewLSTM has been successfully integrated into a real parking space reservation system, ThsParking, which is one of the top smart parking platforms in China. Based on 452,480real parking records in 683 days from 10 parking lots, PewLSTM yields 85.3% parking prediction accuracy, which is about 20% higher than the state-of-the-art parking behavior prediction method. The code and data can be obtained fromhttps://github.com/NingxuanFeng/PewLSTM.

Engine induced swirl improves mixing of fuel and air and at optimal values accelerates burn, improves the combustion stability and can decrease particulate matter (PM). However, swirl increases convective heat loss and cylinder charge loss and could increase nitrogen oxides (NOx) emissions. High intensity of swirl could impede flame development and increases emissions of total hydrocarbons (THC) and carbon monoxide (CO). Therefore, careful and smart selection of optimal swirl values is paramount in order to obtain beneficial impact on combustion and emissions performance. This study is conducted on a 0.5L single cylinder research engine with common rail (CR) diesel injection system, with parameters corresponding to modern engines of passenger cars. The engine has three separate ports in the cylinder head. The change of swirl ratio is defined by closing appropriate ports. There are three levels of swirl ratio under study — 1.7, 2.9 and 4.5, corresponding to low, medium and high swirl levels respectively. This study highlights the influence of intake induced swirl on combustion parameters and emissions. Assessed combustion parameters are, among others, heat release rate, cylinder pressure rise and indicated mean effective pressure. Assessed emissions are standard gaseous emissions and smoke, with emphasis on PM emissions. An engine speed of 1500 rpm was selected, which well represents common driving conditions of this engine size. Various common rail pressures are used at ambient inlet manifold pressure (without boost pressure) and at 1 bar boosted pressure mode. It is found that when the swirl level is increased, the faster heat release during the premixed combustion and during early diffusion-controlled combustion causes a quick increase in both in-cylinder pressure and temperature, thus promoting the formation of NOx. However, since swirl enhances mixing and potentially produces a leaning effect, PM formation is reduced in general. However, maximum peak temperature is lower for high swirl ratio and boosted modes due to the increase of heat transfer into cylinder walls. Furthermore, it is necessary to find optimal values of common rail pressures and swirl ratio. Too much mixing allows increase on PM, THC and CO emissions without decrease on NOx emissions in general. Common rail injection system provides enough energy to achieve good mixing during all the injection time in the cases of supercharged modes and high common rail pressure modes. Positive influence of swirl ratio is found at lower boost pressures, lower revolution levels and at lower engine loads. The results obtained here help providing a better understanding on the swirl effects on diesel engine combustion and exhaust emissions over a range of engine operating conditions, with the ultimate goal of finding optimal values of swirl operation.