Analytics Technologies Research Articles

Simplifying the detection and monitoring of protein glycosylation during in vitro glycoengineering

The majority of mammalian proteins are glycosylated, with the glycans serving to modulate a wide range of biological activities. Variations in protein glycosylation can have dramatic effects on protein stability, immunogenicity, antibody effector function, pharmacological safety and potency, as well as serum half-life. The glycosylation of therapeutic biologicals is a critical quality attribute (CQA) that must be carefully monitored to ensure batch-to-batch consistency. Notably, many factors can affect the composition of the glycans during glycoprotein production, and variations in glycosylation are among the leading causes of pharmaceutical batch rejection. Currently, the characterization of protein glycosylation relies heavily on methods that employ chromatography and/or mass spectrometry, which require a high level of expertise, are time-consuming and costly and, because they are challenging to implement during in-process biologics production or during in vitro glycan modification, are generally performed only post-production. Here we report a simplified approach to assist in monitoring glycosylation features during glycoprotein engineering, that employs flow cytometry using fluorescent microspheres chemically coupled to high-specificity glycan binding reagents. In our GlycoSense method, a range of carbohydrate-sensing microspheres with distinct optical properties may be combined into a multiplex suspension array capable of detecting multiple orthogonal glycosylation features simultaneously, using commonplace instrumentation, without the need for glycan release. The GlycoSense method is not intended to replace more detailed post-production glycan profiling, but instead, to complement them by potentially providing a cost-effective, rapid, yet robust method for use at-line as a process analytic technology (PAT) in a biopharmaceutical workflow or at the research bench. The growing interest in using in vitro glycoengineering to generate glycoproteins with well-defined glycosylation, provides motivation to demonstrate the capabilities of the GlycoSense method, which we apply here to monitor changes in the protein glycosylation pattern (GlycoPrint) during the in vitro enzymatic modification of the glycans in model glycoproteins.

Communicating dataism

ABSTRACT The ideology of dataism has been highly influential during the first two decades of the 21st century, impacting emergent Big Data analytic technologies’ practical application and how the public receives them. In this article, I draw upon William R. Brown's Rhetoric of Social Intervention to interpret the dataism ideology as a communication process. I argue dataism comprises constituent discourses of attention, power, and need that combine to create, reify, and maintain an understanding of Big Data technologies rooted in technoliberalism and technoutopianism. Interplay between these systems has helped keep intact a generally positive public view of Big Data, naming it a collection of innovative technologies that use large quantities of previously unused data to progress social and economic decision-making. I conclude by suggesting the ecosystem of rhetorical interventions into dataism's discourses helps explain how Big Data has grown more popular despite repeated scandals and argue interpreting dataism as a communication process offers fruitful ground for research and critical intervention.

CURRENT METHODS OF COMMUNICATION OF PUBLIC POLITICIANS OF MOSCOW WITH CITIZENS IN THE INTERNET SPACE (ON THE EXAMPLE OF THE ELECTIONS TO THE STATE DUMA IN 2021)

The digital transformation of all spheres of society has led to the need to develop Internet communications and increase the importance of social networks as platforms for discussing and broadcasting positions. The purpose of this work is to analyze and evaluate the activity of public politicians in Moscow in social networks to develop practical recommendations for conducting Internet communications. In the course of the research, the authors of the article used structural-functional and systematic approaches to determine the key parameters of building communications in the virtual space; discourse analysis and technologies of social media predictive analytics to study the posted content, its tonality and the dynamics of discussions both in social media and on the Internet in general; as well as classification and generalization methods for highlighting the main types of page maintenance by public politicians in social networks. The set of methods used made it possible to give a comprehensive assessment of the positioning of public politicians in the Internet space. In the course of the study, a qualitative and quantitative analysis of the content posted on social media by both public politicians on the pages of their own social networks and the audience discussing their activities was carried out; the types of maintaining pages in social networks by politicians of different levels are highlighted: federal, regional and municipal; the strengths and weaknesses of each type are identified. The authors of the article analyzed the main indicators that determine the effectiveness of public politicians' pages in social networks, identified the most popular communication platforms, developed recommendations and techniques for Internet communication of public politicians in Moscow with citizens. The results obtained in the course of the study can be used by public politicians in their activities both in the context of the implementation of election campaigns, and for regular interaction with their audience, as well as by research scientists for further in-depth study.

ПРИНЯТИЕ РЕШЕНИЙ В УПРАВЛЕНИИ ЦЕНОВОЙ ПОЛИТИКОЙ НА РЫНКЕ РЕЧЕВОЙ АНАЛИТИКИ

The article is devoted to adapting general mathematical models of the software markets for describing the mar-ket of a specific product. These are the technologies of artificial intellect in speech analytics. The purpose of this study is to create a modeling instrumentation for pricing the technologies of speech analytics in companies which enter this mar-ket. The purpose also includes recommendations provided with the price policy. The object of the study is the Russian market of speech analytics technologies. The subject of the study are the prices of this product in companies which enter the market being explored. In this studying the authors use classical methods of economical and mathematical modeling the markets with different competitive levels (monopoly, duopoly, oligopoly, monopolistic competition). The results of the study are the foundations of prices for the companies which enter the speech analytics market. These prices are based on three kinds of economical mathematical models: regression, rating and marginal indicators. All three kinds of models lead to one recommendation. A company entering the speech analytics market, when setting a price, should focus less on the quality of speech recognition (this factor has little effect on the price in all three models), and more on the availability of additional options: the more of them, the higher the company’s ability to set the price at a level closer to the market leaders.

Fast Detection and Classification of Dangerous Urban Sounds Using Deep Learning

Video analytics is an integral part of surveillance cameras. Compared to video analytics, audio analytics offers several benefits, including less expensive equipment and upkeep expenses. Additionally, the volume of the audio datastream is substantially lower than the video camera datastream, especially concerning real-time operating systems, which makes it less demanding of the data channel’s bandwidth needs. For instance, automatic live video streaming from the site of an explosion and gunshot to the police console using audio analytics technologies would be exceedingly helpful for urban surveillance. Technologies for audio analytics may also be used to analyze video recordings and identify occurrences. This research proposed a deep learning model based on the combination of convolutional neural network (CNN) and recurrent neural network (RNN) known as the CNN-RNN approach. The proposed model focused on automatically identifying pulse sounds that indicate critical situations in audio sources. The algorithm’s accuracy ranged from 95% to 81% when classifying noises from incidents, including gunshots, explosions, shattered glass, sirens, cries, and dog barking. The proposed approach can be applied to provide security for citizens in open and closed locations, like stadiums, underground areas, shopping malls, and other places.

Simulation of the calculation of deviations in the system "standard-cost" on the basis of factor analysis

The subject of the research is methodical approaches used in the framework of management accounting to determine the deviations of actual indicators from the planned ones during the implementation of plan-actual analysis in the "standard-cost(ing)" system. The purpose of the work is to substantiate the expediency of using the universal tool of classical factor analysis in the process of plan-fact analysis - the method of relative differences, which simplifies mastering the methodology of calculating deviations. The methodology of the conducted research is based on the use of general scientific and specialized methodological techniques. A bibliographic review on the topic of the study made it possible to formulate the hypothesis that currently "ready-made solutions" are used to calculate deviations, without interpreting the sources of the methodology. As a result, a universal approach is proposed, which is based on the methods of analysis and synthesis, which were used to decompose the differences between the obtained actual indicators and the planned ones into components that logically reflect economic regularities and dependencies within the framework of the built economic models, therefore, the modeling method was used. Research results, their use and conclusions.This study raises the issue of the methodology of plan-fact analysis within the "standard-cost(ing)" cost management system. "Standard cost(ing) is considered an effective way of managing production costs and controlling expenses, but it requires a thorough understanding of the reasons for deviations in profit-forming factors - sales and expenses. This understanding is provided through variance analysis, which is based on establishing discrepancies between planned and actual indicators. A literature review was conducted and the peculiarities of the calculation of deviations for various components of production costs were determined. A universal approach to the analysis of deviations based on the method of absolute differences is proposed. An additive-multiplicative model of profit determination is proposed, which can be analyzed using the method of absolute differences. This approach allows you to master the principle of calculating the influence of factors regardless of specific indicators in the model, which simplifies the analysis and ensures clarity for all participants. The proposed methodological aspects of the analysis of deviations can find application in modern technologies of business analytics and contribute to the improvement of the processes of cost control and cost control. These results open perspectives for further research and improvement of the "standard-cost(ing)" system in the context of modern requirements of economic analytics.

Оценка возможностей и ограничений внедрения HR-аналитики в практику управления персоналом телекоммуникационной компании: кейс-стади

The article presents a study of the introduction of HR analytics technologies into the practice of a Russian telecommunications company, which examines the problems of developing and embedding HR data analytics methods and tools into the human resource management system. The scientific novelty of the research lies in the author's approach to the interpretation and refinement of the concept of HR analytics.The author's interpretation of HR analytics is proposed based on a review and generalization of scientific and analytical publications of researchers devoted to general and particular issues of the development of Russian human data analytics. The practical significance of the study is due to the results of the analysis of empirical data obtained by the authors during expert interviews and personnel questionnaires, as well as the development of individual elements of the HR analytics subsystem and algorithms for their implementation in the organization.

Descriptive Analytics and Interactive Visualizations for Performance Monitoring of Extension Services Programs, Projects, and Activities

Providing universities with high technology-enabled automation tools to support the administrative decision-making processes will enable them to achieve their objectives. For an institution to succeed in its everyday tasks, it should come up with the emerging and modernized management services constituted, among others, by cloud, mobile, and business analytics technology. With this, the institution’s operations and management efficiency are ensured. This study aims to develop a system with descriptive analytics named as MET Online Services that will automate and optimize the monitoring of extension services key performance indicators (KPIs) in order to help the institution in making better, data-driven decisions. The dashboards and interactive visualizations of the developed system will provide quick access to real-time progress of the extension services programs, projects, and activities. Results interpretation depicted that the developed system is indeed feasible for implementation, proven to be fully-functional and passed the quality software standards of a Certified Software Quality Assurance Specialist. As the developed system satisfied the users’ expectations and requirements, it would be an effective tool for the institution, extension services unit, and community, to make better strategic decisions and continuously deliver quality services to the community.

Effective energy management tools: inventory management and monitoring of energy consumption by personnel

This article introduces an innovative management system for retail businesses, based on modern computer vision and data analytics technologies. The primary focus is on effective energy management: optimizing inventory control and monitoring employee energy consumption. The system is designed to address several key challenges faced by stores and trading companies. It provides a comprehensive solution for employee monitoring, utilizing computer vision for automated attendance tracking and establishing a detailed absence database. This streamlines supervisory responsibilities and ensures fair payroll distribution, eliminating unjustified payouts for absenteeism. Furthermore, the system revolutionizes inventory management by offering accurate sales statistics through automated data entry and in-depth analytics. It identifies popular items and forecasts future needs, optimizing procurement decisions and preventing losses due to stock shortages.

Improving Cut and Fill Operations in Construction using Drone Technology and Aerial Analytics

The construction industry is always looking for new, creative ways to increase the effectiveness and precision of its operations. The use of drones for cut-and-fill operations in construction is one area where great progress has been made. Construction sites are now being surveyed by drones in order to gather data for analysis and decision-making. In this paper, we examine the usage of drones for cut and fill operations in construction and how they might be used in conjunction with aerial analytics technologies to produce better outcomes. We present a case study in which a construction site was surveyed by drones in order to gather topographic and elevational data. To create detailed maps and 3D models of the location, this data was analyzed using a suite of aerial analytics technologies. The article's findings demonstrate that traditional surveying techniques can be greatly sped up and cost-effectively replaced by drones in cut and fill construction operations. Additionally, using aerial analytics technologies enables a more precise and in-depth analysis of the collected data. As a result, we draw the conclusion that the application of drone technology and aerial analytics has the potential to transform the construction sector and boost project productivity.

The Impact of the COVID-19 Pandemic on the Growing Importance of Cybersecurity of Data Transfer on the Internet

Information technologies, ICT and Industry 4.0 are developing particularly fast in the fourth technological revolution. During the COVID-19 pandemic, there has been an increase in the scale of digitisation and internationalisation of remote communication processes and various aspects of economic activity. Digitisation of the economy is currently taking place simultaneously in many areas of economic processes and the functioning of many economic entities and public, financial and other institutions. Many companies, enterprises and public institutions that previously operated mostly or solely offline during the pandemic have switched to remote, electronic operation via the Internet. The development of electronic banking is also increasing, including internet and mobile banking. The share of electronic, cashless payments via the Internet and payments with electronic bank cards is increasing. In addition, in recent years, the range of applications of Data Science, Big Data, and Data Analytics technologies in economics, finance and organisation management, including enterprises, financial and public institutions, has been increasing. Therefore, the importance of implementing analytical instruments for advanced processing of large Data Science data sets in enterprises, financial and public institutions is also growing, including the construction of Big Data Analytics platforms to support organisation management processes in various aspects of operations, including improvement relationship with customers. The scale of cybercrime has also increased during the pandemic, as has the importance of improving cybersecurity techniques and instruments.

PREVALENCE AND MANAGEMENT OF URINARY TRACT INFECTIONS IN TYPE 2 DIABETIC PATIENTS

Diabetes mellitus (DM) is a polygenic illness that affects many other organs. In people with type 2 diabetes, urinary tract infection is the most prevalent, the most severe, and has the worst consequences. They are also more frequently caused by microorganisms that are resistant to antibiotics. Immune system dysfunction, poor metabolic regulation, and inadequate bladder emptying owing to autonomic neuropathy may all contribute to these individuals' increased risk of urinary tract infections. The novel anti-diabetic sodium glucose cotransporter 2 inhibitors were not shown to raise the likelihood of symptomatic urinary tract infections appreciably. Urinary tract infection symptoms are comparable to those seen in people without diabetes, while some patients with diabetic neuropathy may exhibit changed clinical findings. The existence of symptoms, the severity of systemic symptoms, whether the infection is limited in the bladder or also includes the kidney, the presence of urologic abnormalities, concomitant metabolic disturbances, and renal function all influence treatment. There is no evidence that diabetic people with asymptomatic bacteriuria should be treated. Diabetes length, age, gender, method of drug administration, kind of medicines used in diabetes, poor glycemic control, obesity, unsanitary settings, sexual intercourse, and type of DM are all risk factors for UTI in diabetic patients. Antibiotic usage on a daily basis has rendered numerous harmful microorganisms resistant to their effects. In the long run, if UTI in diabetes people is not treated, it might lead to serious problems. Furthermore, the pattern of antibiotic resistance varies by geographical area. As a result, the use of susceptible antibiotics to treat UTI in diabetes patients has become critical. Medline and PubMed public database searches was carried out for papers written all over the world on UTI in diabetic patients. No predictive analytics technology was used. To evaluate the initial results and the methods of conducting the paper, the group members reviewed the data.

The role of an open analytics culture and analytics skills in adopting real-time supply chain analytics for higher performance

PurposeThe study objective was to understand what components of organizational culture and capability combined with analytic skillsets are needed to allow organizations to exploit real-time analytic technologies to create supply chain performance improvements.Design/methodology/approachThe authors relied on information processing theory to support a hypothesized model, which is empirically tested using an ordinary least squares equation model, and survey data from a sample of 208 supply chain executives across multiple industries.FindingsThe authors found strong support for the concept that real-time analytics will require specialized analytical skills for the managers who use them in their daily work, as well as an analytics-focused organizational culture that promotes data visibility and fact-based decision-making.Practical implicationsBased on the study model, the authors found that a cultural bias to embrace analytics and a strong background in statistical fluency can produce decision-makers who can make sense of a sea of data, and derive significant supply chain performance improvements.Originality/valueThe research was initiated through five workshops and presentations with supply chain executives leading real-time analytics initiatives within their organizations, which were then mapped onto survey items and tested. The authors complement our findings with direct observations from managers that lend unique insights into the field.

Fiscal responses to COVID-19 outbreak for healthy economies: Modelling with big data analytics

Fiscal responses to the COVID-19 crisis have varied a lot across countries. Using a panel of 127 countries over two separate subperiods between 2020 and 2021, this paper seeks to determine the extent that fiscal responses contributed to the spread and containment of the disease. The study first documents that rich countries, which had the largest total and health-related fiscal responses, achieved the lowest fatality rates, defined as the ratio of COVID-related deaths to cases, despite having the largest recorded numbers of cases and fatalities. The next most successful were less developed economies, whose smaller total fiscal responses included a larger health-related component than emerging market economies. The study used a promising big data analytics technology, the random forest algorithm, to determine which factors explained a country's fatality rate. The findings indicate that a country's fatality ratio over the next period can be almost entirely predicted by its economic development level, fiscal expenditure (both total and health-related), and initial fatality ratio. Finally, the study conducted a counterfactual exercise to show that, had less developed economies implemented the same fiscal responses as the rich (as a share of GDP), then their fatality ratios would have declined by 20.47% over the first period and 2.59% over the second one.

Bioinformatics Identification of miRNA-mRNA Regulatory Network Contributing Primary Lung Cancer

Introduction: In clinical practice, distinguishing invasive lung tumors from primary tumors remains a challenge. With recent advances in understanding biological alterations of tumorigenesis and molecular analytic technologies, using these molecular alterations can be sensitive and tumor-specific as biomarker for the stratification of patients. In this study, the molecular network of miRNA-mRNA contributing to primary lung cancer has been assessed by bioinformatics approaches.
 Methods: In this analytical-observational study gene expression profiles of patients with primary lung cancer were collected from the RNASeq data of the Cancer Genome Atlas (TCGA) database by TCGAbiolinks package. With edgeR and limma packages in R, non-specific expression genes were filtered and the significant differentially expressed mRNAs and miRNAs between tumor tissues and normal tissues were saved and their targets were predicted by 2 databases; miRWalk, and Targetscan. Subsequently, the interaction regulatory network of miRNA-mRNA was visualized using Cytoscape software.
 Results: By miRNA-mRNA network analysis revealed that, 7 miRNAs included; hsa-miR-373-3p, hsa-let-7a-5p, hsa-miR-23b-3p, hsa-miR-152-3p, hsa-miR -216a-3p, hsa-miR-106-5p, hsa-let-7i-5p and 6 miRNAs including; has-miR-107, has-miR-17-5p, has-185-5p, has-miR-34a- 5p, has-miR-130a-5p and has-96-5p, mediated regulation of up-regulated and down-regulated mRNAs in primary lung cancer patients, respectively.
 Conclusion: This bioinformatics study proposes a miRNA–mRNA network associated with primary lung cancer, which may help to screening and new therapeutic targets for primary lung cancer as prognostic marker.

Evolution of the concept of “Business Performance Management”

The article is dedicated to the study of the evolution of the concept of Business Performance Management (BPM) – an approach to managing business processes that focuses on achieving strategic goals and maximizing efficiency. The article explores the history of the development of the BPM concept, from the initial idea of controlling financial indicators to modern innovative performance management methods, such as the Balanced Scorecard (BSC) and data analytics. The purpose of the article is to analyze the evolution of the concept of “business performance management” (BPM) and to determine its role in ensuring successful management activities of modern companies. The authors discuss the main trends and challenges that have influenced the evolution of BPM, such as the growth of data volume and complexity, the expansion of the global market, and the changing perceptions of the role of information in business management. Specifically, the role of business analytics technologies and their impact on the development of BPM is examined. The article highlights that BPM is a complex approach to management that requires the integration of many factors, such as strategy, business processes, information technologies, and the human factor. The authors provide recommendations for successful BPM implementation and emphasize the need for an integrated approach to performance management. The article provides examples of areas of application of Business Performance Management (BPM) tools that allow metallurgical enterprises to increase production efficiency and optimize costs by collecting and analyzing data. The scientific novelty of this article is that the recommendations and conclusions provided by the authors can be useful for companies looking to improve their performance management. The recommendations contains an overview of the key trends and stages of the evolution of the Business Performance Management concept, as well as guidance on selecting and implementing a performance management system in an organization. The authors emphasize the importance of a balanced approach to measuring performance and the need to consider the individual needs and characteristics of each company. In general, this article is useful for those who wish to gain a more detailed understanding of performance management in business.

ESG Audit Training Perspective in Asia

<p>The paper examines the effectiveness of Environmental, Social, and Governance (ESG) audit training courses offered by the International Computer Auditing Education Association (ICAEA) in Taiwan and the Middle East. The training courses are tailored to meet the growing demand for professionals who possess ESG audit skills. In Taiwan, the course focuses on using artificial intelligence and data analytic technology to audit ESG data. In the Middle East, ESG education and training are gaining momentum, and regulators are introducing ESG standards for all businesses. The paper provides an overview of the current ESG landscape in the region and discusses the challenges and opportunities for companies to demonstrate their commitment to sustainability and ESG principles. Overall, the paper highlights the growing importance of ESG audits and the need for continuous education and training to improve sustainability performance.</p> <p> </p>

Adaptive water governance research in social sciences journals: a bibliometric analysis

Abstract Adaptive water governance plays an increasingly important role in sustainable urban development and water governance response to global climate change. To comprehensively understand the research situation and development trend of adaptive water management, this study conducts a systematic literature review of articles published in International Social Sciences Citation Index (SSCI) journals based on bibliometric analysis. The results show that adaptive water governance as an emerging topic of water governance has an overall growth trend in published articles. The articles on adaptive water governance mainly concentrated on the disciplines of environmental science and environmental studies. Developed countries are a hub for water governance research, and China has the sixth largest number of articles from around the world. The adaptive water governance research has formed a preliminary global collaborative network, but the authors' collaboration needs to be strengthened. The most popular topics of adaptive water governance include South Africa, adaptive management, groundwater, principal component analysis, scenario planning, the analytic hierarchy process, resources, basins, computer experiments, and technology development. This finding suggests that adaptive water governance is a critical driver for sustainable urban development and represents a critical direction in the future research of water governance.

Creating business value with process mining

Information systems research has a long-standing interest in how organizations gain value through information technology. In this article, we investigate a business process intelligence (BPI) technology that is receiving increasing interest in research and practice: process mining. Process mining uses digital trace data to visualize and measure the performance of business processes in order to inform managerial actions. While process mining has received tremendous uptake in practice, it is unknown how organizations use it to generate business value. We present the results of a multiple case study with key stakeholders from eight internationally operating companies. We identify key features of process mining – data & connectivity, process visualization, and process analytics – and show how they translate into a set of affordances that enable value creation. Specifically, process mining affords (1) perceiving end-to-end process visualizations and performance indicators, (2) sense-making of process-related information, (3) data-driven decision making, and (4) implementing interventions. Value is realized, in turn, in the form of process efficiency, monetary gains, and non-monetary gains, such as customer satisfaction. Our findings have implications for the discourse on IT value creation as we show how process mining constitutes a new class of business intelligence & analytics (BI&A) technology, that enables behavioral visibility and allows organizations to make evidence-based decisions about their business processes.

AN UNDERSTANDING THE SOCIAL NETWORKING OF THE PEPPER FARMERS IN BANGKA SELATAN, INDONESIA

Currently, the local government has put extra efforts on the pepper as the eminent commodity in Bangka Belitung Islands Province. The concern of the government is emphasized on how to uplift the welfare of the farmer. However, the efforts have not met the expectancy as the society remains at the unfavourable situation. The study aims to understand the social networking among the farmers and the stakeholders as the endeavour to scrutinize the communication pattern amongst them. The data collection was conducted on September 2021. The quantitative data using purposive sampling was employed as the data collection technique. The Ucinet software was applied as the analytic technology by showing the sociogram. The study found how the close relationship amongst the actors affect the farming activity. The finding was expected to be used by policy maker to determine the best extension program in the future so that the farmers can improve their living condition.