Real Analytics Research Articles

Study of Impact of Engine and Vehicle Level Parameters for Reduction in Engine Oil Consumption for Advanced Emission Architecture Commercial Vehicles

Automotive industry has seen implementation of advanced emission regulations like BS-VI in India along with growing market demand for increased product performance and reduction in total cost of ownership. This has made the engine architecture more intricate leading to complex interaction among engine and vehicle level parameters. This poses technical challenge for achieving critical product attributes like increased power density, higher fluid economy and reduced oil consumption (OC). The current paper focusses on reducing engine oil consumption across diverse duty cycles using simulation tools, vehicle data analytics and test cell Design of Experiments (DOE). The contribution of oil consumption mechanisms viz. oil evaporation, oil throw and oil transport have been understood across different loads and duty cycles patterns. The critical parameters at engine and vehicle levels are identified affecting low load and high load oil consumption. Vehicle testing is conducted, and the real time data analytics was used to identify correlation of vehicle duty cycle parameters like percentageof Idling,Thermal Management Operation, Coolant Temperature, etc. with measured oil consumption. Piston ring dynamics simulation has been used to optimize critical ring parameters impacting oil consumption through directional trends. DOE was conducted in engine test cell environment to assess effect of critical parameters like combustion temperature and oil ring tension for high load oil consumption. The new test cycles for verifying oil consumption at various loads are described. Results of interaction and main effects for individual factors are discussed. The parameters having weaker co-relations are also highlighted. The proposed solution is a combination of piston ring pack geometry features, thermal management calibration strategyand vehicle idling controls. The demonstration of final recipe of solution at vehicle level showed substantial improvement in oil consumption over baseline as well as over global industry benchmark. The improvement is demonstrated in the actual vehicle applications for mining tippers and tractors

The continuation method and the real analyticity of the accessory parameters: the general elliptic case

We apply the Le Roy–Poincaré continuation method to prove the real analytic dependence of the accessory parameters on the position of the sources in the Liouville theory in presence of any number of elliptic sources. The treatment is easily extended to the case of the torus with any number of elliptic singularities. A discussion is given of the extension of the method to parabolic singularities and higher genus surfaces.

Worldwide Prediction on COVID-19 Spreading Future Trends using Gaussian Model

Background: COVID-19 is a pandemic, which covered to all over the world and started the end of 2019. COVID-19 spreading rapidly from person to person and from one environment to another environment. In the current situation the entire world is passing through a very critical situation and medical services almost collapse due to the span of COVID-19. The virus is directly threatening to human being's life and attack to their nervous system, collapse lungs, breathing problems and damage other parts of the body system. The researcher build a predictive model using a Gaussian approach to find out the current status of COVID-19 and its future prediction. This predictive model is very helpful for countries and before timely they can manage their health related services, make a change in their decision making policy to stop COVID-19 spreading. Method: in this research paper the researcher builds a predicting model using real time analytics to measure the intensity of spreading COVID-19 in major concern countries. The main objective of this research article to predict the rate of spreading COVID-19 cases, visualize, and represent the future trends of COVID-19 cases. For the predictive analysis the researcher used the Gaussian Prediction model, time series analysis, exploratory data analysis, and K-means clustering. The researcher used the parameters such as rate of spreading, slow down speed, a sudden change in rate, prediction of the number of cases and differences in mortality rate. The results: The researcher discussed the weekly, monthly rate of spreading COVID-19 cases and predicted how it covered the world entire populations. The predictive model is very helpful to the countries where a number of cases are rapidly spreading and showing the future trends whether it is decreasing or increasing ratio. The countries can manage their health related services and other possible resources to stop the COVID-19 cases in their countries. If the prediction is unknown that situation is horrible for the entire world. Conclusion: Finally the researcher concluded that the predictive model of COVID-19 cases has significant impact to all over countries to show the future spreading trends, the accuracy level of this predictive model is 92% which is verified by using Gaussian approach. In some cases prediction might be unfavorable to handle the health care industries that are only %8 chances. The researcher giving the assurance the developed predictive model is more reliable and efficient to predict the COVID-19 case and its future trends, so the countries and their health related unit can manage the health related services in rapid manner.

Assessing the representativeness of trial populations for contemporary real-world cancer patients: A case study of adjuvant chemotherapy for colon cancer.

306 Background: Randomized controlled trials are the gold standard for assessing the efficacy of new cancer therapies and are required for marketing approval. However, participants enrolled into trials are often not representative of the more diverse populations in whom treatment will ultimately be delivered. Real-world data can be used to potentially bridge this gap by evaluating the effectiveness and safety of therapies in more generalizable populations. In this study, we describe differences in demographic and clinical characteristics between participants enrolled in a phase III trial of adjuvant chemotherapy for colon cancer and a contemporary comparator cohort using structured oncology electronic medical records (EMR) data. Methods: We drew upon publications from the Multicenter International Study of Oxaliplatin/5 Fluorouracil/Leucovorin in the Adjuvant Treatment of Colon Cancer (MOSAIC) recruiting patients primarily from Western Europe with stage II or III colon cancer from October 1998-January 2001. This trial established FOLFOX (oxaliplatin + 5-FU) as the standard of care over 5FU alone in treating stage III colon cancer and led to US marketing approval. For comparison, we identified patients diagnosed with stage II or III colon cancer (September 2014-2020) who received FOLFOX in the IQVIA US Oncology EMR database via E360, IQVIA’s Real World Discovery and Analytics platform. In the two populations, we described distributions of prognostic factors including age, sex, stage of disease, body mass index (BMI), and Eastern Cooperative Group (ECOG) performance status. Results: The MOSAIC trial included 2,246 participants; we identified 4,566 patients starting FOLFOX in the EMR comparator. Median age was similar: 61 versus 62 years, but the range differed (19-75 years versus 18-85 years) in the trial and EMR comparator, respectively. Female sex was similar at 44% and 48%, while the proportion of patients with stage III disease differed considerably at 60% and 84% for trial and EMR comparator, respectively. Half (51%) of all EMR patients were missing ECOG status; among those with a reported ECOG status, 96% had a score of 0-1, while 87% of trial participants had an ECOG of 0-1. BMI was missing in 34% of patients in the EMR comparator; among those with a reported BMI, 28% had a BMI < 25, while 54% of trial participants had a BMI < 25. Conclusions: Colon cancer patients receiving FOLFOX in an EMR comparator had a similar age and sex distribution as MOSAIC trial participants, but were more likely to have stage III disease. Missing ECOG and BMI data were common in the EMR comparator, which limited comparisons. Structured oncology EMR data represent an important resource for generating real-world evidence; future data enrichment efforts focused on critical patient-level variables via unstructured EMR extraction may improve data completeness and quality.

Big data analytics, order imbalance and the predictability of stock returns

Financial institutions have adopted big data to a considerable extent to provide better investment decisions. Consequently, high-frequency algorithmic traders use a vast amount of historical data with various statistical models to maximize their trading profits. Until recently, high-frequency algorithmic trading was the domain of institutional traders with access to supercomputers. Nowadays, any investor can potentially make high-frequency trades because of easy access to big data and software to analyze and execute trades. With that in mind, Borsa Istanbul introduced real time big data analytics as a product to its customers. These analytics are derived in real time from order book and trade data and aim to level the playing field between investment firms and retail traders. Using classical benchmark models in the literature, we show that Borsa Istanbul’s order imbalance-based data analytics are useful in predicting both time-series and cross-sectional intraday excess future returns, proving that this product is extremely beneficial to market participants, particularly day traders.

FOG COMPUTING TECHNOLOGIES FOR PATIENT SENSOR NETWORKS – TRENDS, ISSUES AND FUTURE DIRECTIONS

Advances in sensors and internet of things promise broad opportunities in many areas and one of them is health care. There are many solutions to manage health care data based on cloud computing. However, high response latency, large volumes of data transferred and security are the main issues of such approach. Fog computing provides immediate response and ways to process large amounts of data using real time analytics which includes machine learning and AI. Fog computing has not yet fully matured and there are still many challenges when managing health care data. It was chosen to investigate the most relevant e­health fog computing topics by analyzing review articles to explain the fog computing model and present the current trends – fog computing e­health technology application environments, deployment cases, infrastructure technologies, data processing challenges, problems and future directions. 38 scientific review articles published in the last 5 years were selected for analysis, filtering the most significant works with Web of Science article search tool.

Process analytics 4.0: A paradigm shift in rapid analytics for biologics development.

Analytical testing of product quality attributes and process parameters during the biologics development (Process analytics) has been challenging due to the rapid growth of biomolecules with complex modalities to support unmet therapeutic needs. Thus, the expansion of the process analytics tool box for rapid analytics with the deployment of cutting-edge technologies and cyber-physical systems is a necessity. We introduce the term, Process Analytics 4.0; which entails not only technology aspects such as process analytical technology (PAT), assay automation, and high-throughput analytics, but also cyber-physical systems that enable data management, visualization, augmented reality, and internet of things (IoT) infrastructure for real time analytics in process development environment. This review is exclusively focused on dissecting high-level features of PAT, automation, and data management with some insights into the business aspects of implementing during process analytical testing in biologics process development. Significant technological and business advantages can be gained with the implementation of digitalization, automation, and real time testing. A systematic development and employment of PAT in process development workflows enable real time analytics for better process understanding, agility, and sustainability. Robotics and liquid handling workstations allow rapid assay and sample preparation automation to facilitate high-throughput testing of attributes and molecular properties which are otherwise challenging to monitor with PAT tools due to technological and business constraints. Cyber-physical systems for data management, visualization, and repository must be established as part of Process Analytics 4.0 framework. Furthermore, we review some of the challenges in implementing these technologies based on our expertise in process analytics for biopharmaceutical drug substance development.

Integration of Emergency Response Management System with Internet of Things

Disasters can be classified either from mother nature such as landslide, earthquake, tsunami and lightning or cause by human such as leakage in gas production or terrorist attacks. Disaster leads to huge destruction economically and affected human lives. Whilst various technologies available towards disaster analysis occurrence, the Internet of Things (IoT) offers reliable solutions in handling the relevant disasters situation, such as early warning system, real time data analytics, remote monitoring and search and rescue localization. This research developed an integration of emergency response management system with IoT. The system integrates heterogeneous wireless-enabled embedded controller with sensor, smart phones, desktop and wireless communication for end-to-end network connectivity which is monitored by a cloud platform. The system performance was evaluated in terms of the end-to-end link quality estimation and delay.

Low-cost Fabrication of PDMS Microfluidic Chamber using Digital Cutter Machine

Microfluidic chamber or channel is one important tool in the lab-on-a-chip (LoC) technology, which usually serves as a platform to contain liquid analytes. The conventional method to fabricate microfluidic chamber is based on photolithography technique. However, it is a high-cost process and requires special equipment, such as mask aligners and expensive photosensitive resist material. In this work, we fabricated the microfluidic chamber and channel using a much simpler and lower-cost technique that is using PDMS as the main material and polyimide tape as the mold. Using this method, the diameter of the microfluidic pattern (channel/chamber) could be made up to as small as 1 mm and the height of the flow channel was down to tens or hundreds of μm scale-depending on the thickness of the tape. The flow test results showed that low cost and simple microfluidic fabrication method had a good performance and could potentially be further used for real analyte flow test.

WITHDRAWN: Development and deployment of real-time cloud applications on red hat OpenShift and IBM bluemix

Most computing services, hosted by a number of cloud service providers, are available on demand nowadays. There are a range of cloud computing realms and core fields that have been under scrutiny for a long time due to rising digital infrastructure demands and applications. The cloud service providers bill based on the space or service offered to the customer to get access to cloud data. It is not always possible in research and development to provide the actual cloud technology to conduct the experiments. It is not possible for any research scholar, educator or scientist to employ cloud providers at all times and then perform their algorithms or implementations. For research, production and testing purposes, the real time cloud platforms can be taken by the researchers with free tier. In this research manuscript, the inclusion of IBM Bluemix and Red Hat OpenShift clouds are done for the development and effectual deployment of web apps on cloud. Such services can be used by the researchers and academicians for high performance web applications on cloud with real time logs and analytics on assorted parameters.

The ability of personas: An empirical evaluation of altering incorrect preconceptions about users

False preconceptions about users can result in poor design, product development, and marketing decisions, so rectifying these preconceptions is essential for organizations. This research quantitatively evaluates the ability of data-driven personas to alter decision makers’ preconceptions about their online social media users. We conduct a within-participant experiment of 31 professionals carrying out a workplace task scenario. The participants’ conceptions of user attributes are recorded both before and after interacting with personas created from real user analytics data. Using statistical analysis to compare the responses, we find that personas had a significant effect on changing preconceptions about the audience segments. After interacting with personas, 81% of the participants changed their preconceptions of the audience, and 94% of the participants maintained or increased the accuracy of their perceptions of the audience after engaging with the personas. Moreover, the confidence of the participants in their responses increased. However, two participants did not change their preconceptions even when faced with contradictory factual information, which highlights the need for accompanying initiatives to align user preconceptions with user data fully.

Automatic real analyticity and a regal proof of a commutative multivariate Löwner theorem

We adapt the “royal road” method used to simplify automatic analyticity theorems in noncommutative function theory to several complex variables. We show that certain families of functions must be real analytic if they have certain nice properties on one-dimensional slices. Let E ⊂ R d E \subset \mathbb {R}^d be open. A function f : E → R f:E \to \mathbb {R} is matrix monotone lite if f ( φ 1 ( t ) , … , φ d ( t ) ) f(\varphi _1(t), \ldots , \varphi _d(t)) is a matrix monotone function of t t whenever t ∈ ( 0 , 1 ) t \in (0,1) , the φ i \varphi _i are automorphisms of the upper half plane, and the tuple ( φ 1 ( t ) , … , φ d ( t ) ) (\varphi _1(t), \ldots , \varphi _d(t)) maps ( 0 , 1 ) (0,1) into E E . We use the “royal road" to show that a function is matrix monotone lite if and only if it analytically continues to the multivariate upper half plane as a map into the upper half plane. Moreover, matrix monotone lite functions in two variables are locally matrix monotone in the sense of Agler-McCarthy-Young.

Learning analytics for higher education: proposal of big data ingestion architecture

Research background: Higher education institutions are generating multiple formats of data from diverse sources across the globe. The data ingestion layer is responsible for collecting data and transform for analysis. Learning analytics plays a vital role in providing decision-making support and selection of suitable timely intervention. The lack of tailored big-data ingestion architectures for academics led to several implementation challenges. Purpose of the article: The purpose of this article is to propose data ingestion architecture enabled for big data learning analytics. Methods: The study reviews existing literature to examine big-data ingestion tools and frameworks; and identify big-data ingestion challenges. An optimized framework for the real world learning analytics application was not yet in place at global higher educations. Consequently, the big-data ingestion pipeline is experiencing challenges of inefficient and complex data access, slow processing time, and security issues associated with transferring data to the system. The proposed data ingestion architecture is based on review of recent literature and adapts best international practices, guidelines, and techniques to meet the demand of current big-data ingestion issues. Findings & value added: This study identifies the current global challenges in implementing learning analytics projects. Review of recent big data ingestion techniques has been done based on defined metrics tuned for learning analytics purposes. The proposed data ingestion framework would increase the effectiveness of collecting, importing, processing and storing of learning data. Besides, the proposed architecture contributes to the construction of full-fledged big-data learning analytics ecosystem of higher educations.

Pressure metrics and Manhattan curves for Teichmüller spaces of punctured surfaces

In this paper, we extend the construction of pressure metrics to Teichmuller spaces of surfaces with punctures. This construction recovers Thurston’s Riemannian metric on Teichmuller spaces. Moreover, we prove the real analyticity and convexity of Manhattan curves of finite area type-preserving Fuchsian representations, and thus we obtain several related entropy rigidity results. Lastly, relating the two topics mentioned above, we show that one can derive the pressure metric by varying Manhattan curves.

SAP ECC to S/4HANA System Conversion Critical Success Factors

SAP ECC to S/4HANA conversion is a critical part of an enterprise’s Digital transformation strategy which prepares their ERP system to become more agile, robust, intelligent & standardized. The journey to attain improved user experience, in memory capabilities of HANA database, flexible API’s, real time analytics to gain competitive advantage is further accelerated by SAP’s 2027 deadline to phase out support for SAP ECC forcing customers to embrace SAP S/4HANA. SAP install base customers have 2 options - either follow a single step approach by moving from any database to S/4HANA in one step with single downtime or opt for a two-step approach where we first move database to SAP HANA and then convert to S/4HANA. In both the approaches, there is overwhelming complexity of dependencies and activities which create a lot of pressure on organizations to navigate with caution and gain learning curve. To address this need, this article provides a list of critical success factors in SAP ECC to S/4HANA system conversion journey and the available SAP tools which can give a head start and make organizations better prepared to undertake this journey. This article explains what the key critical success factors which the install base customers need to plan for upfront to ensure the conversion is seamless.

Bayesian Inference for Predicting the Monetization Percentage in Free-to-Play Games

Free-to-play has become one of the most popular monetization models, and as a consequence game developers need to get the players to purchase in the game instead of getting players to buy the game. Game analytics and player monetization prediction are important parts in estimating the profitability of a free-to-play game. In this paper, we concentrate on predicting the fraction of monetizing players among all players. Our method is based on a survival analysis mixture cure model, and can be applied to unlabeled data collected from any free-to-play game. We formulate a statistical model and use the Expectation Maximization algorithm to solve the latent monetization percentage and the monetization rate. The original method is modified by using Bayesian inference, and the results of the versions are compared. The method can be applied as a preliminary profitability study in situations where there is no extensive historical game data available, such as game and business development scenarios that need to utilize real time analytics.

Real Time Video Analytics Based on Deep Learning and Big Data for Smart Station

CCTV is a sophisticated tool for monitoring and surveillance. It was chosen because of the low price and ease of use. CCTV creates issues in monitoring because officers must always supervise every CCTV monitor to look for abnormal conditions. This research proposes the implementation of smart CCTV for surveillance in Bandung Station. It applied deep learning algorithm to create smart CCTV for sensing and understanding station environment such as passengers’ flow, crowded area, prohibited location and others. Big data technologies are also carried out to solve the complexity of data. The objective of this research is to improve the quality of services in Bandung Station and make the station safer, more secure and more convenient.

Cuspidal edges with the same first fundamental forms along a knot

Letting [Formula: see text] be a compact [Formula: see text]-curve embedded in the Euclidean [Formula: see text]-space ([Formula: see text] means real analyticity), we consider a [Formula: see text]-cuspidal edge [Formula: see text] along [Formula: see text]. When [Formula: see text] is non-closed, in the authors’ previous works, the local existence of three distinct cuspidal edges along [Formula: see text] whose first fundamental forms coincide with that of [Formula: see text] was shown, under a certain reasonable assumption on [Formula: see text]. In this paper, if [Formula: see text] is closed, that is, [Formula: see text] is a knot, we show that there exist infinitely many cuspidal edges along [Formula: see text] having the same first fundamental form as that of [Formula: see text] such that their images are non-congruent to each other, in general.

Binding constants determination of cyclodextrin inclusion complexes by affinity capillary electrophoresis. How to overcome the limitations induced by the UV-detector?

In most cases, determination of binding constant for analyte-cyclodextrin complexes in capillary electrophoresis is investigated by affinity capillary electrophoresis using a UV detector (ACE-UV). The limitations induced by the UV-detector include : (i) the difficulty of dealing with poor chromogenic analytes and more generally with any analyte presenting strong affinity towards the cyclodextrin (CD), i.e. for which the prerequisite to work with analyte concentration much smaller than those of the CD is difficult to fulfill (ii) the impossibility of studying non-chromogenic analyte. In this paper, two simple methodologies were developed to overcome these limitations. Regarding the analytes which present poor UV-absorbance and/or very high CD-affinity, a methodology using an algorithmic data treatment and taking into account the real analyte concentration in the capillary at the determined migration times allows to correctly estimate the binding constants, even if the experimental prerequisite ([analyte]<<[CD]) is not complied. Moreover, it is proved that classical linearization treatment by picking the migration time of the infinite diluted analytes (at the start of the peak) also provide satisfactory results. Regarding UV-transparent analyte, a competitive methodology combined with algorithmic data treatment allows the determination of their affinity towards cyclodextrins. Last, the applicability of the described competitive method is extended to the study of interaction between two neutral partners, which is another well-known limitation of ACE.

BIM-VR Framework for Building Information Modelling in Engineering Education

With the advancement of information technology, Building Information Modeling (BIM) is evolving fast and play an essential role in Architecture, Engineering, Construction, Owner and Operators (AECOO) industry. Universities play an important role in the current BIM transition in construction projects as well as to embrace Industry 4.0. In this research, immersive Virtual Reality (VR) in BIM offer a unique, supportive environment for the user experience in visualizing and collecting data from the model. With Virtual Reality (VR), engineering education has a major breakthrough. Immersive interactions, stereoscopic 3D, real time multisensory simulations, virtual explorations, synchronous communications, data analytics and visual analysis – all these are impossible without VR. VR offers a student-centred learning environment that promotes active and collaborative learning, situated within simulated real-world contexts.