Analytical Services Research Articles

Leveraging Azure Platform Data Services for Efficient Data Analytics in the Oil & Gas Industry A Case Study of Nature Labs Project at Leading Oil & Gas Company

Leveraging Azure Platform Data Services for Efficient Data Analytics in the Oil & Gas Industry A Case Study of Nature Labs Project at Leading Oil & Gas Company

Assessing the Readiness of Students in Adopting Learning Analytics in Higher Education

Purpose: Getting students involved in developing learning analytics (LA) services is a major challenge in the academic world. Despite calls for more stakeholder involvement, research into students' views and expectations of learning analytics services is scant. Improving end-user buy-in and resource planning is possible by addressing these concerns and learning about student expectations before rolling out LA.Methodology: An extensive literature review is included, with a focus on previous research into the value, existing models, and difficulties of implementing LA. To fill this void, we used the SHIELA approach to survey students' beliefs and expectations concerning LA adoption. Factor loading, construct validity, and discriminant validity tests are performed in SmartPLS 4 to ensure the accuracy and credibility of the data. A structural equation model further verified the relationship between students' anticipations and their actual acceptance of LA.Findings: Our research confirms that the effectiveness of LA adoption is highly influenced by the three highlighted constructs: privacy and ethics and organizational and meaningful expectations. The research also showed that the student considered creating a detailed learning profile for each module to be the best application of LA. They believed LA would give them feedback on their learning and help them make better decisions. They think institutions have a moral and legal responsibility to act, which means they should also include, promote, and empower students. Institutions must take the necessary precautions to reduce risks, even though teaching staff should share the load.Originality and Value: The research supplies university officials with data for improving their LA adoption tactics while also informing students of new ways in which LA might inform their own educational choices. This paradigm shift and the higher education sector's sustained, productive interest in learning analytics will lead to better results for students, universities, and society. Based on the findings, the study also suggests several avenues and topics for future research.

‘Even if the algorithm is a terrible workmate, you just need to learn to live with it’: Perceptions of data analytics among game industry professionals

The digital game industry has actively integrated data-driven methods into its core processes. This interview-based study shows how game industry professionals perceive the role of data as part of their everyday work. Analysing the data-related notions and negotiations helps to explicate how mainstream data imaginaries are both reproduced and challenged in the different phases and contexts of game making. The analysis is divided into the following themes: data is everywhere, data is messy, data is constructed and data redefines creativity. The qualitative inquiry shows how the meaning of game data cannot be reduced to individual metrics or analytics services, or new positions like data analysts. Data-driven development is based on particular values and assumptions, and it creates new practices, working cultures and conflicting forms of agency.

Enhancing OPAC through Geospatial Data: Stackmaps and Text Visualization in Koha

Physical stacks in academic libraries, despite the advent of digital repositories, remain important to users, especially in countries like India where physical resources hold considerable value. This study seeks to develop a system that enables users to locate books physically by integrating stackmaps functionalities with Koha OPAC. In addition, the study showcases how an open-source text analytics server can be incorporated inside an OPAC in Koha to generate various word-level visualizations by analyzing a text corpus, including the identification of geospatial features such as place names. This research aims to contribute to the advancement of information retrieval and visualization techniques in OPACs in academic libraries, and to improve the user experience in locating physical resources. (The video abstract of this paper may be found at: https://youtu.be/q940TUkcTTE ).

Business Modeling Innovation Using Artificial Intelligence Technology

Startups embracing artificial intelligence (AI) as a component of their business models are quickly emerging right now. The application of AI technology in business has been going on for a while, even though recent research reveals that new or alternative business models are being implemented.It might be claimed that AI technology has been used to business models for a very long time, which puts into question the uniqueness of these business models. In order to better understand how AI companies' business models may differ from traditional IT business models, this study compares them to each other. The first step is to create a taxonomy of business models for AI businesses using a sample of 162 Worldwide Stratup, from which four archetypal business models are derived: Deep technology researcher, data analytics supplier, AI product and service provider, and facilitator of AI development The following are three main elements of startup business models for AI firms that are discussed based on this descriptive analysis: (1) new value propositions made possible by AI, (2) Various uses of data to create value and (3) How AI technology affects general business reasoning. By defining their key purposes, common instantiations, and distinguishing features, this research adds to our fundamental understanding of the AI start-up business model. This study suggests intriguing directions for further investigation in the field of entrepreneurship. À It is structured to foster entrepreneurial activity. Taxonomies and models are actually instrument.

Business Modeling Innovation Using Artificial Intelligence Technology

Startups embracing artificial intelligence (AI) as a component of their business models are quickly emerging right now. The application of AI technology in business has been going on for a while, even though recent research reveals that new or alternative business models are being implemented.It might be claimed that AI technology has been used to business models for a very long time, which puts into question the uniqueness of these business models. In order to better understand how AI companies' business models may differ from traditional IT business models, this study compares them to each other. The first step is to create a taxonomy of business models for AI businesses using a sample of 162 Worldwide Stratup, from which four archetypal business models are derived: Deep technology researcher, data analytics supplier, AI product and service provider, and facilitator of AI development The following are three main elements of startup business models for AI firms that are discussed based on this descriptive analysis: (1) new value propositions made possible by AI, (2) Various uses of data to create value and (3) How AI technology affects general business reasoning. By defining their key purposes, common instantiations, and distinguishing features, this research adds to our fundamental understanding of the AI start-up business model. This study suggests intriguing directions for further investigation in the field of entrepreneurship. À It is structured to foster entrepreneurial activity. Taxonomies and models are actually instrument.

How does service robot anthropomorphism affect human co-workers?

PurposeService robots are gradually becoming more anthropomorphic and intelligent. This research aims to investigate how anthropomorphic service robots with different levels of intelligence affect their human counterparts.Design/methodology/approachTwo between-subject experimental studies were used to test whether different levels of service robot anthropomorphism with different levels of intelligence influence employees' morale and resistance to service robots.FindingsStudy 1 shows that the effect of service robot anthropomorphism (low vs. high) on employees' resistance and morale is mediated by perceived job-security threat. Study 2 validates this mediating effect and shows that it is moderated by the type of AI (mechanical vs. analytical). Specifically, when exposed to mechanical AI-powered service robots, employees exhibit a higher perceived job-security threat toward robots with a high (vs. low) degree of anthropomorphism. This moderating effect is not observed when employees are exposed to analytical AI-powered service robots. This moderated mediation effect is also found for the signing of a petition as the behavioral outcome.Practical implicationsService firms considering the adoption of mechanical AI-powered service robots should choose a low (vs. high) anthropomorphic robot to reduce the sense of job-security threat felt by human employees, which subsequently increases their acceptance. However, if analytical AI-powered service robots with are to replace their human employees, the degree of anthropomorphism becomes irrelevant.Originality/valueThis is the first empirical study to explore how anthropomorphic service robots can influence human employees' evaluations and behaviors.

Advanced Critical Care Monitoring with Alert and Auto Intelligence System

Abstract: With great advancements in lifestyle and practices along with pandemic outbreaks such as COVID 19, the number of patients in hospitals is increasing which is a major concern globally. To overcome this situation, an IoT platform likely to provide the scalability required for this challenge is support the continuous monitoring of patients on a global scale. The technical specifications used are Arduino IDE, ThingSpeak Cloud, Max 30100, Temperature sensor, and ESP8266. Arduino kit is an open source platform that has an easy to use software and hardware. It can read inputs from any device. ThingSpeak is an IOT analytics service that allows the collection of data, picturing, and examine live data streams in the cloud. ESP8266 is a WiFi Module that gives the microcontroller access to the Wi-Fi network. LM35 is a series of devices used for measuring temperature with accurate precision. Its output voltage is linearly proportional to the temperature in Centigrade units. Hence, this project paper is focused to analyse the vitals of patients by using wearable, safe, and modern sensors to observe various patients who are affected by cardiovascular disease.

Testing times for dementia: a community survey identifying contemporary barriers to risk reduction and screening

BackgroundAdvances in pharmacological and non-pharmacological dementia interventions may mean future dementia prevention incorporates a combination of targeted screening and lifestyle modifications. Elucidating potential barriers which may prevent community engagement with dementia prevention initiatives is important to maximise the accessibility and feasibility of these initiatives across the lifespan.MethodsSix hundred seven adults aged over 18 years completed a 54-item, multiple-choice survey exploring contemporary attitudes towards, and barriers to, dementia risk reduction and screening relative to other common health conditions. Participants were sourced from Australia’s largest, paid, data analytics service (ORIMA).ResultsFinances (p = .009), poor motivation (p = .043), and time (p ≤ .0001) emerged as significant perceived barriers to dementia risk reduction behaviours. Lack of time was more likely to be reported by younger, relative to older, participants (p ≤ .0001), while females were more likely than males to report financial (p = .019) and motivational (p = .043) factors. Binary logistic regression revealed willingness to undertake dementia testing modalities was significantly influenced by gender (genetic testing, p = .012; saliva, p = .038, modifiable risk factors p = .003), age (cognitive testing, p ≤ .0001; blood, p = .010), and socio-economic group (retinal imaging, p = .042; modifiable risk-factor screening, p = .019). Over 65% of respondents felt adequately informed about risk reduction for at least one non-dementia health condition, compared to 30.5% for dementia.ConclusionsThis study found perceived barriers to dementia risk reduction behaviours, and the willingness to engage in various dementia testing modalities, was significantly associated with socio-demographic factors across the lifespan. These findings provide valuable insight regarding the accessibility and feasibility of potential methods for identifying those most at risk of developing dementia, as well as the need to better promote and support wide-scale engagement in dementia risk reduction behaviours across the lifespan.

Exploratory Time-Series Analysis of Consecutive Case Series Data: A Quality Improvement and Adherence Study of a Behavior Analytic Service Provider.

Program evaluation is an essential practice for providers of behavior analytic services, as it helps providers understand the extent to which they are achieving their intended mission to the community they serve. A proposed method for conducting such evaluations, is through the use of a consecutive case series design, for which cases are sequentially gathered following the onset of a specific occurrence. Given the sequential nature in which data are collected within a consecutive case series, analytic techniques that adopt a time-series framework may be particularly advantageous. Although such methods are commonly used for program evaluation in medicine and economics, their application within the field of applied behavior analysis is largely absent. To serve as a model for providers undertaking evaluation efforts, I conducted a program evaluation of an outpatient severe behavior clinic, in which I employed quasi-experimental methods using an interrupted time-series analysis.

EzDPS: An Efficient and Zero-Knowledge Machine Learning Inference Pipeline

Machine Learning as a service (MLaaS) permits resource-limited clients to access powerful data analytics services ubiquitously. Despite its merits, MLaaS poses significant concerns regarding the integrity of delegated computation and the privacy of the server’s model parameters. To address this issue, Zhang et al. (CCS'20) initiated the study of zero-knowledge Machine Learning (zkML). Few zkML schemes have been proposed afterward; however, they focus on sole ML classification algorithms that may not offer satisfactory accuracy or require large-scale training data and model parameters, which may not be desirable for some applications. We propose ezDPS, a new efficient and zero-knowledge ML inference scheme. Unlike prior works, ezDPS is a zkML pipeline in which the data is processed in multiple stages for high accuracy. Each stage of ezDPS is harnessed with an established ML algorithm that is shown to be effective in various applications, including Discrete Wavelet Transformation, Principal Components Analysis, and Support Vector Machine. We design new gadgets to prove ML operations effectively. We fully implemented ezDPS and assessed its performance on real datasets. Experimental results showed that ezDPS achieves one-to-three orders of magnitude more efficient than the generic circuit-based approach in all metrics while maintaining more desirable accuracy than single ML classification approaches.

Library and Analytical Service in the Conditions of Military and Information Confrontation (case of SIAS NBUV)

Library and Analytical Service in the Conditions of Military and Information Confrontation (case of SIAS NBUV)

Leading with Compassion: A Discussion and Steps Forward for Behavior Analysts

The provision of applied behavior analytic (ABA) services is a highly efficacious intervention approach most often used to improve the lives of individuals with autism spectrum disorder (ASD)/Autistics. Given the advancement of the field, more nuanced skill sets of behavior analysts, such as compassionate care skills, are emerging as the focus of intervention as measurable, observable, and essential. As the field progresses, the identification, refinement, and assessment of more nuanced skills become crucial to the success of our interventions. Leading with a compassionate approach that balances habilitation with client happiness, assent, and engagement is of the utmost importance. This paper discusses the current trend in existing compassionate care literature and how those evaluations may potentially be extended to direct interventionists and Registered Behavior Technicians. In this paper, we argue that a behavioral framework should be used to conceptualize, train, and evaluate the demonstration of these skills in front line ABA practitioners.

An assessment of the qualities and behaviors of exemplary practitioners: Perspectives from international and award-winning behavior analysts

ABSTRACT Identifying potential qualities and corresponding behaviors that contribute to effective behavior analytic service delivery may assist practitioners in strengthening their behavioral repertoire and overall client/family/staff outcomes. Credentialed (Board Certified Behavior Analyst – Doctoral or Board Certified Behavior Analyst) individuals practicing outside of the United States of America, and award-winning behavior analysts in the field, were presented with the Exemplary Behavior Analyst Checklist (EBAC; Zayac et al., 2021) and rated the extent to which they believed exemplary behavior analysts display each quality and engage in the associated behaviors. Respondents also selected a “top 10” list of the qualities they believe are important to being/becoming an exemplary behavior analyst. Comparisons of these rankings are made to prior research on the EBAC along with a discussion about the implications for training and continued applications of the EBAC in the field. Limitations of the current studies are identified as well as useful recommendations for future research.

Sodium nitrite poisoning: A series of 20 fatalities in which post-mortem blood nitrite and nitrate concentrations are reported

Sodium nitrite has several industrial applications however its accidental or intentional ingestion has been associated with severe toxicity and death. We present a series of 20 cases over 2 years in which evidence of sodium nitrite ingestion was found at the scene and supported by biochemical analysis of post-mortem blood nitrite and nitrate levels. Routine toxicological screening was performed on post-mortem blood samples received at University Hospitals of Leicester (UHL) NHS Trust, including ethanol analysis by headspace gas chromatography–flame ionisation detection (HS GC-FID), drug screening by high resolution accurate mass–mass spectrometry (HRAM-MS) and confirmatory drug quantitation by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Cases in which the history indicated the possibility of nitrite salts present at the scene, purchase of a suicide kit or a dusky-ash appearance of skin on post-mortem were referred to a specialist laboratory for nitrite and nitrate analysis. Analysis was based upon the gas-phase chemiluminescent reaction between nitric oxide (NO) and ozone; NO levels were determined using an NOA 280A, Sievers NO analyser. Twenty post-mortem cases in which sodium nitrite ingestion was the most probable cause of death were reported between January 2020 and February 2022; mean age was 31 years (range 14–49) with 9/20 (45%) female. 16/20 (80%) of cases had a history of depression and / or mental health issues. In half of the cases, anti-depressant / anti-psychotic drugs were prescribed; these drugs were detected in 8/20 (40%) cases. Ethanol was detected in 4/20 (20%) cases and anti-emetic drugs in 7/20 (35%) cases; anti-emetic drugs may be used to aid retention of sodium nitrite. Illicit drugs (amphetamine, cannabis and cocaine) were present in 3/20 cases (15%). Nitrite was found to be elevated in all but one case (95%), and nitrate was elevated in 17/20 (85%) cases. This paper highlights a surge in numbers of deaths across England and Wales due to sodium nitrite toxicity. Although, nitrite poisoning remains a rare cause of death, it is worthwhile considering its use in individuals with suicidal ideation given its unregulated availability online. The detection and quantitation of nitrite and nitrate requires specialised, highly reliable methodology currently only available in research laboratories. Implication of sodium nitrite ingestion also relies heavily upon circumstantial evidence combined with quantification. The provision of a quantitative nitrite / nitrate analytical service greatly assists in determining the cause of death in these cases.

Intrinsic factors for decreasing role of libraries in information activities

Digital transformation of document arrays has changed the nature of information infrastructure, as the digital documents are created, distributed, discovered and delivered in principally other way than in the epoch of printed matters as the main information media. The transition to digital information infrastructure has resulted in the loss of role of libraries as an essential intermediate on the document author-to-reader way, reasonably displacing libraries to the periphery of information activities and depreciating the majority of methods and technologies of document array organization developed through the centuries. The author substantiates the rationale for degrading information component of library mission and the necessity to transit to prevalent analytical services. The decreasing role of libraries is determined by the following factors: reduction of overall printed publications; their lower contextual value; capabilities to store, distribute, process digital information array formally and semantically, information deliver to users without the input of librarian. Therefore, not all the manual processing operations have become demanded any more. The print arrays grow scanty quantitively and contextually, and the increasing and dynamical digital flows are processed by ever-improving software. The author sees the solution in expanding analytical function of libraries which envisages inclusion of professional competences related to complex analytical processes, i. e. fact search and verification; acquisition and processing of statistical and experimental research data, preparation of analytical surveys, reviews and reports.

A Brief Survey of Methods for Analytics over RDF Knowledge Graphs

There are several Knowledge Graphs expressed in RDF (Resource Description Framework) that aggregate/integrate data from various sources for providing unified access services and enabling insightful analytics. We observe this trend in almost every domain of our life. However, the provision of effective, efficient, and user-friendly analytic services and systems is quite challenging. In this paper we survey the approaches, systems and tools that enable the formulation of analytic queries over KGs expressed in RDF. We identify the main challenges, we distinguish two main categories of analytic queries (domain specific and quality-related), and five kinds of approaches for analytics over RDF. Then, we describe in brief the works of each category and related aspects, like efficiency and visualization. We hope this collection to be useful for researchers and engineers for advancing the capabilities and user-friendliness of methods for analytics over knowledge graphs.

Introduction of modern technologies as a tool to increase the efficiency of trading on marketplaces

The article considers a scientific problem that is the lack of development of methodology, concepts, and mechanisms for the effective functioning and development of digital platform solutions in the process of implementing SMART=technologies in the activities of such trade market drivers as marketplaces. The results of the analysis of the current state of research on this problem are presented. The analysis showed that the development of effective mechanisms and tools for digital transformation of the economy and its individual sectors and industries (including trade) based on platform solutions is among the priorities of modern science. The study made it possible to identify the future key direction of the development of marketplaces based on modern service technologies, among which are the following: analytics services, services for setting up advertising campaigns, external promotion services, technical services, fulfillment. It is noted that such technologies are tools for improving the efficiency of trading on marketplaces.

Interfacial Engineering of Particulate & Surfactant Systems for Enhanced Performance in Industrial Applications

Particulate and surfactant systems are an integral part either in processing or product lines in essentially every major industry, including Energy and Minerals, Pharmaceutical, Agriculture & Food, Microelectronics, Healthcare, Cosmetics, Consumer Products, and Analytical Instrumentation & Services. In most applications, product and process specifications depend on the synergistic or competitive interactions between the particles and reagent schemes. The primary goal of our research efforts has been to generate the structure-property-performance correlations-based knowledge and technology platforms for industry to develop more sustainable products and processes. Engineering the physicochemical/mechanical properties of surfaces, particles, and self-assembling surfactant systems enables their enhanced performance in industrial applications. Specifically, understanding and control of the nano and atomic-scale forces between particles and synthesis of functionalized particles form the foundation for targeted contributions in biomedical, advanced materials and minerals, sensor, and coating technologies. A synoptic overview of selected projects is presented in this review. Additional details can be found in the topic-specific references listed at the end of this manuscript.

The validation case on invasive crustaceans of the LifeWatch ERIC Internal Joint Initiative: State of the art and next steps forward

LifeWatch ERIC, the e-Science European infrastructure for biodiversity and ecosystem research, launched an Internal Joint Initiative on Non-indigenous Species and Invasive Alien Species (NIS-IAS) as they are considered one of the major drivers of biodiversity and ecosystem change. Here, the case study focused on the trophic biogeography of invasive crustaceans is presented, describing the procedures, resources, and analytical web services implemented to investigate the trophic habits of these taxa by using carbon and nitrogen stable isotope data. The case study offers a number of analytical tools to determine the variability of the trophic position of invasive crustaceans in a spatially-explicit context and to model it as a function of relevant environmental predictors. Literature-based stable isotope data of the Atlantic blue crab Callinectes sapidus and of the Louisiana crayfish Procambarus clarkii have been used to evaluate the functionalities and outcomes of the workflow. The Tesseract Virtual Research Environment integrates all the analytical services offered by LifeWatch ERIC, including the ones developed for this case study, by means of a user-friendly interface. The analytical functions implemented for the crustacean workflow provide a proof of concept for future open e-science platforms focusing on NIS-IAS. The workflow conceptual structure can be adapted to a wide range of species, and can be further improved to support researchers in monitoring and predicting trophic-related impacts of NIS-IAS. In addition, it can support policymakers and stakeholders in the implementation of effective management and control measures to limit the negative effects of bioinvaders in recipient environments.