Data Manipulation Research Articles

Python For Data Analytics: A Systematic Literature Review Of Tools, Techniques, And Applications

In the era of big data, the ability to collect, process, and analyze data efficiently has become a vital component for decision-making across various industries. Python, as a versatile programming language, has emerged as a powerful tool for data analytics due to its extensive libraries and user-friendly nature. This systematic literature review explores Python’s role in streamlining data analytics by examining its applications across various stages of the data analysis process, including data collection, cleaning, manipulation, and visualization. Key Python libraries such as NumPy, Pandas, and Matplotlib are discussed, highlighting their functionality in handling large datasets and enabling accurate and efficient analysis. Real-world examples demonstrate how Python can be applied in diverse sectors, from retail to healthcare, enhancing decision-making processes through data-driven insights. Furthermore, the limitations of Python, as well as alternative data analysis tools such as R and RapidMiner, are explored to provide a comprehensive view of Python’s place in modern data analytics. The review concludes that while Python offers significant advantages in data analysis, a combination of tools may often be necessary to meet the complex demands of today’s data-driven industries.

Illusion of stability: An empirical analysis of inflation data manipulation by russia after 2022

This paper explores the perceived resilience of russia’s economy under severe sanctions, investigating the potential falsification of economic data to demonstrate the growth. The hypothesis is that the relationship between the official inflation rate and the FMCG deflator index during 2019–2021 significantly differs from that of 2022–2024. Statistical methods, such as correlation analysis, Granger causality tests, and differences tests (e.g., t-tests and Wilcoxon tests), are used along with vector autoregressive (VAR) models and robust linear regressions. The study covers the pre-invasion period (2019–2021) and the post-invasion period (2022–2024), focusing on indicators like the official inflation rate, inflation expectations, CPI, and the FMCG deflator index. Findings reveal a shift from a direct to an inverse correlation between official inflation and the FMCG deflator post-2022, suggesting data manipulation. Pre-2022 models predict inflation 2-3 times higher than both post-2022 models and official statistics, raising concerns about the reliability of russia’s economic data. Further research should explore indirect metrics, such as electricity production and cargo shipments, for additional evidence of data falsification. AcknowledgmentsAlex Plastun gratefully acknowledges financial support from the New Europe College (NEC), the Center for Advanced Study, and Sumy State University.Anna Vorontsova gratefully acknowledges financial support from Sumy State University.

Optimization of frequent item set mining parallelization algorithm based on spark platform

In this paper, we propose a new method that combines the parallelism of the Spark-based platform with fast frequent mining, called STB_Apriori. Previous research has shown that traditional frequent itemset mining algorithms have high overhead when faced with large datasets and high-dimensional data computation, and generate a large number of candidate itemsets; at the same time, when faced with diverse user requirements, they often generate very sparse and diverse data. In order to solve the problem of fast mining of massive data, our idea originates from the capability of Spark distributed computing and the common optimisation ideas in Apriori mining, by using the efficient operator BitSet to achieve transaction compression, bit storage and data manipulation by Boolean matrices, and at the same time by parallelising the processing and optimising the algorithmic logic to achieve fast and frequent mining. In experiments on real-world datasets, our model consistently outperforms five widely used methods by a significant margin on very large data and maintains its excellence in the remaining cases, proving its effectiveness on real-world tasks, while further analysis shows that increasing the number of distributed nodes also incrementally and continuously improves performance.

De-Med—a tool to identify and classify potentially inappropriate medication according to Beers criteria, STOPP, and FORTA: a cross-sectional study

Abstract Objective A significant number of older adults are affected by the use of potentially inappropriate medications (PIMs), leading to various adverse outcomes, such as increased physical and cognitive impairment, frailty, hospitalization, and mortality. The objective of this study was to develop and test the reliability and feasibility of the De-Med tool. Methods This tool was developed using ReactJS for data extraction and manipulation. Users can upload drug lists for analysis, and the software delineates PIMs adhering to the Beers 2019 version, Screening Tool of Older Persons’ Potentially Inappropriate Prescriptions (STOPP) Version 2, and Fit fOR The Aged (FORTA) 2021 criteria. Reliability was assessed by juxtaposing software-generated outputs with manual assessments conducted by independent research pharmacists using retrospective medication records. The System Usability Scale (SUS) and AttrakDiff Mini questionnaires were used to assess feasibility and usability, respectively. Key findings Manual and software-generated PIM identifications agreed completely, demonstrating high reliability (100% concordance). A total of 1496 PIMs were identified from 231 records, with an average of 0.63 encounters per patient. The overall usability score was 83.75, indicating “Excellent.” According to the AttrakDiff ratings, users attributed the software to both pragmatic (mean PQ:1.84) and hedonic (mean HQ:1.50) qualities, with moderate overall attractiveness (mean ATT:2.13). Conclusions This web-based tool successfully identified PIMs with perfect reliability, high usability, and a moderate level of attractiveness. This tool has the potential to improve medication management in older adults by reducing PIMs.

Achieving Air Pollution Control Targets with Technology‑Aided Monitoring: Better Enforcement or Localized Efforts?

Weak enforcement of environmental regulations remains a global issue due to inadequate monitoring and misaligned incentives. This paper examines the effects of automated monitoring on achieving air pollution control targets amidst China’s war on pollution. Utilizing the staggered rollout process and remote-sensing data, we find local governments respond to the advanced monitoring system by strategically targeting areas near monitors, resulting in a 3.2 percent decrease in pollution adjacent to automated monitors compared to areas farther away. Furthermore, we observe heterogeneity in response across cities with varying degrees of preexisting data manipulation and among officials facing different incentives and public pressure. (JEL O13, O18, P25, P28, Q53, Q55, R11)

Performance Analysis of 8x4 Barrel Shifters in CMOS and FINFET Technology

Shifters are essential components in digital circuits, enabling data manipulation and routing for a wide range of computational tasks. This study presents a comprehensive performance analysis of 8x4 barrel shifters implemented in two distinct semiconductor technologies: Complementary Metal-Oxide-Semiconductor (CMOS) and FinFET. This study compares and evaluates the important parameters for these shifters at both technological nodes, such as power consumption. Additionally, we examine the effects of scaling on CMOS barrel shifters. Conversely, FinFET technology, recognized for overcoming certain limitations of CMOS, is assessed for its potential to enhance performance while preserving energy efficiency. Through detailed simulation and analysis, this study offers valuable insights into the benefits and limitations of CMOS and FinFET implementations for 8x4 barrel shifters. The circuit is implemented using Cadence Virtuoso and optimized to leverage the unique features and address the limitations of 45nm CMOS and 22nm FinFET technologies. Furthermore, the study offers recommendations for selecting the appropriate technology based on specific performance criteria and application needs. The findings aim to guide future design decisions and improvements in shifter technology.

AI security in different industries: A comprehensive review of vulnerabilities and mitigation strategies

Artificial intelligence has quickly and gradually spread across all sectors and fields, achieving exceptional performance in operations and decision-making. Yet, as industries process their tasks with the help of AI systems, new threats appear that require proper solutions. In the following section of this paper, the use of security solutions involving AI in various industries, which are finance, health, manufacturing, and government, as well as a discussion of risks involving artificial intelligence in particular fields and the corresponding measures to be taken against them will be discussed. AI is often applied to detect fraud and credit scoring in the financial sector but has potential risks, such as adversarial attacks and data manipulation. Similarly, the healthcare industry uses AI for predictive healthcare diagnostics and patient information protection; threats are data theft and adversarial examples attacking diagnostic models. In manufacturing, AI individuals contain predictive servicing and excellent control, though they are constantly exposed to industrial espionage and the sabotage of AI models. In retail, however, AI is used to improve marketing and predict consumer behavior. While these benefits exist, questions about using Algorithms that could be biased and privacy infringements continue to raise their ugly head. Defensive and governmental organizations that apply AI for surveillance or automatic operating systems are most vulnerable to adversarial intervention with key control systems and innate security systems. To counter such threats, this paper provides details of industry-specific measures, including adversarial training data sanitization, AI model audit, and privacy-preserving approaches. We illustrate examples linked to every one of these methods to show how they can be deployed in a variety of industries and to guard AI systems. To this end, the following review of the relationship between AI and cybersecurity demonstrates that security measures that rely on artificial intelligence must be regularly checked and updated. This reveals the call for proper, protective, and industry-specific measures to manage the risks and protect AI applications to ensure that the world’s modern, interconnected world is safe for AI.

Curve smoothening for the analysis of fractal dimensions of blast-cleaned steel surface textures for tribological and adhesion interfaces

Abstract Fractal dimensions are increasingly used for the characterization of surface textures for tribological and adhesion applications. The resolution of 2D profiles acquired from various measurement methods can be adjusted via curve smoothing. It is still unclear whether curve smoothing would notably affect the estimated fractal dimensions. In this communication, 2D profiles of twelve blast-cleaned surface settings were derived by means of a data manipulation software, including a curve smoothing procedure. The fractal dimensions were estimated, and Design of Experiments was applied to analyze the relationships. Factors included abrasive type, preparation grade, roughness, and curve smoothing. Analysis of Variance indicated that the contribution of curve smoothing to the regression model was as low as 1.0%. Therefore, for blast-cleaned substrates, curve smoothing effects can be neglected with respect to the contributions of roughness and abrasive type, when estimating the fractal dimensions.

Implementasi Teknologi Blockchain Dalam Pengembangan Aplikasi Manajemen Penyaluran Bantuan Sosial Berbasis Web

The distribution of social assistance is a crucial component of the government's efforts to reducepoverty and social inequality. However, the implementation of aid programs often faces issuesrelated to transparency, data security, and the risk of misuse. Blockchain technology, with its abilityto provide a transparent and immutable record of data, offers a potential solution to address theseissues. Blockchain can increase transparency by providing a digital ledger that can be accessed byall relevant parties, without the possibility of data manipulation. The technology also allows forimmutable data logging, thus addressing data security concerns and the risk of misuse. In addition,the use of smart contracts allows for the automation of the aid distribution process in accordancewith predetermined provisions, thereby reducing errors and potential abuse. As such, blockchainoffers a potential solution to increase effectiveness and accountability in the implementation ofsocial assistance programs.

Analysis of patterns and trends in competition manipulation in sport global data: 2018–2023

ABSTRACT Competition manipulation in sports has evolved from a localized issue to a global concern. Advances in technology and the growth of sports betting markets have attracted criminal networks that manipulate sporting events for financial gain. The aim of this study was to examine patterns and trends in competition manipulation in sport from a global dataset over a 6-year period. Manipulation of sports competition investigations and sanctions were assessed across world regions and sports. The Macolin Convention typology was also employed to categorize manipulation types and instigators. Results showed an increase in investigations and sanctions year-on-year, with Europe consistently having the highest numbers annually. The majority of investigations and sanctions were consistent across the sports of football, cricket, and tennis, and also an emerging trend in sanctions in esports. Investigation types have mostly been due to direct interference of a sports competition or use of external means. The instigator of manipulation trends shifted from opportunistic types to the exploitation of power or influence. The patterns and trends reported can help inform the work of sport management professionals.

Integrating AI in food contaminant analysis: Enhancing quality and environmental protection

This paper marks a groundbreaking step toward ensuring food safety by applying artificial intelligence (AI) in the detection of food contaminants. It argues that AI offers a significant advantage over traditional methods, addressing both food safety and environmental risk issues. We aim to make rapid, precise online analysis of chemical contaminants a reality. While traditional methods work well, they struggle with the demands for simplicity, large datasets and speed. In contrast, AI excels with its data manipulation and predictive analytics. This paper explores AI's applications and future perspectives in detecting, quantifying and reducing food contaminants, showcasing examples like machine learning, neural networks, and data mining techniques for identifying pests, heavy metals and mycotoxins. Additionally, AI-driven sensor technologies and spectroscopic methods are discussed for improving detection accuracy. AI's real-time detection capabilities can help prevent health crises and economic loss, while its predictive power supports sustainable agriculture by reducing the use of harmful chemicals.

A Generic Transformation Approach for Complex Laboratory Data Using the Fast Healthcare Interoperability Resources Mapping Language: Method Development and Implementation.

Reaching meaningful interoperability between proprietary health care systems is a ubiquitous task in medical informatics, where communication servers are traditionally used for referring and transforming data from the source to target systems. The Mirth Connect Server, an open-source communication server, offers, in addition to the exchange functionality, functions for simultaneous manipulation of data. The standard Fast Healthcare Interoperability Resources (FHIR) has recently become increasingly prevalent in national health care systems. FHIR specifies its own standardized mechanisms for transforming data structures using StructureMaps and the FHIR mapping language (FML). In this study, a generic approach is developed, which allows for the application of declarative mapping rules defined using FML in an exchangeable manner. A transformation engine is required to execute the mapping rules. FHIR natively defines resources to support the conversion of instance data, such as an FHIR StructureMap. This resource encodes all information required to transform data from a source system to a target system. In our approach, this information is defined in an implementation-independent manner using FML. Once the mapping has been defined, executable Mirth channels are automatically generated from the resources containing the mapping in JavaScript format. These channels can then be deployed to the Mirth Connect Server. The resulting tool is called FML2Mirth, a Java-based transformer that derives Mirth channels from detailed declarative mapping rules based on the underlying StructureMaps. Implementation of the translate functionality is provided by the integration of a terminology server, and to achieve conformity with existing profiles, validation via the FHIR validator is built in. The system was evaluated for its practical use by transforming Labordatenträger version 2 (LDTv.2) laboratory results into Medical Information Object (Medizinisches Informationsobjekt) laboratory reports in accordance with the National Association of Statutory Health Insurance Physicians' specifications and into the HL7 (Health Level Seven) Europe Laboratory Report. The system could generate complex structures, but LDTv.2 lacks some information to fully comply with the specification. The tool for the auto-generation of Mirth channels was successfully presented. Our tests reveal the feasibility of using the complex structures of the mapping language in combination with a terminology server to transform instance data. Although the Mirth Server and the FHIR are well established in medical informatics, the combination offers space for more research, especially with regard to FML. Simultaneously, it can be stated that the mapping language still has implementation-related shortcomings that can be compensated by Mirth Connect as a base technology.

Securing oil port logistics: A blockchain framework for efficient and trustworthy trade documents.

The oil port logistics involves multiple parties including oil tanker owners, port authorities, customs, oil suppliers, and shipping companies. These parties need to exchange a significant amount of data and documentation related to cargo, such as bills of lading, customs declarations, and cargo manifests. This huge amount of data and documentation provides ample opportunities for data manipulation and corruption. Moreover, physical documentation is slow and prone to errors and manipulation. This data can be securely stored and shared between different parties in a tamper-proof and transparent manner using blockchain. Blockchain is a decentralized technology that employs secure hashing and consensus algorithms that can detect any data modification. Hence, this work proposes a blockchain-enabled immutable, and efficient framework for trade documentation in oil port logistics. The proposed framework provides timely processing of oil trade documents and ensures immutability while increasing trust among the trade entities. In addition, this work implements a private blockchain for the execution of smart contracts, which can ensure that all parties involved in the logistics process comply with pre-agreed rules and regulations. Simulation results validate the effectiveness of the proposed framework in terms of transparency, immutability, network latency, throughput, and resource utilization.

Blockchain Technology: Unlocking New Frontiers in Data Management and Transparency

This article explores the transformative potential of blockchain technology in revolutionizing data management and enhancing transparency across various industries. As a decentralized, immutable ledger system, blockchain offers a secure and efficient way to record, share, and verify information, eliminating the need for intermediaries and reducing the risk of fraud. The article reviews the fundamental principles of blockchain, including distributed consensus, cryptographic security, and smart contracts, to highlight how these features improve data integrity and trust in digital transactions. Through case studies in sectors such as finance, supply chain management, healthcare, and government services, the article demonstrates how blockchain can streamline operations, reduce costs, and promote accountability. Particular attention is given to the role of blockchain in improving transparency by providing an auditable and tamper-proof record of transactions, which is especially valuable in industries prone to corruption and data manipulation. The article also addresses the challenges facing blockchain adoption, including scalability issues, regulatory concerns, and the need for standardization. Additionally, the potential of blockchain in emerging applications, such as decentralized finance (DeFi) and non-fungible tokens (NFTs), is discussed, highlighting the technology’s expanding role in reshaping digital economies. The article concludes by emphasizing that while blockchain is still in its developmental stages, its ability to foster greater transparency and more secure data management systems positions it as a key driver of innovation in the digital age. Future research is recommended to explore further integration strategies and address challenges in widespread blockchain adoption.

Assessment of Cybersecurity Risks in Digital Twin Deployments in Smart Cities

Digital Twin (DT) technology has become a cornerstone of modern smart city infrastructure, providing real-time insights and operational efficiencies by creating a virtual replica of physical systems such as traffic networks, energy grids, and public services. While these advancements enable optimized urban management and improved decision-making, they also present new cybersecurity challenges that can potentially jeopardize the safety and reliability of critical infrastructures. This study addresses the cybersecurity risks associated with Digital Twin deployments in smart cities, focusing on threats such as unauthorized access, data manipulation, and hijacking of the DT models, which could result in service disruptions and compromise public safety. The research employs a comprehensive risk assessment methodology based on the NIST Cybersecurity Framework, where potential risks are identified, evaluated, and prioritized according to their severity and likelihood of occurrence. To mitigate these risks, a multi-layered security framework was developed, incorporating encryption mechanisms, robust access control, and an Artificial Immune System (AIS)-based anomaly detection model. The framework was tested through a simulated case study on a smart transportation system within a smart city environment, demonstrating its effectiveness in preventing data tampering and detecting unauthorized access. The results indicate that the proposed security model reduced data manipulation incidents by 35%, decreased response times for threat detection by 25%, and improved overall system resilience by 40%. These findings underscore the critical need for proactive cybersecurity strategies in ensuring the secure and resilient deployment of Digital Twin technologies in smart cities. The study concludes by emphasizing the importance of continuous security monitoring and adaptive threat management to safeguard smart city ecosystems from evolving cyber threats

Tinjauan Peranan Income Audit Terhadap Pengendalian Internal Kas Pada Swiss-Belinn Hotel Bogor

The tourism industry currently plays an important role in improving the economy, especially for the Indonesian economy. It is proven that part of the tourism industry is experiencing rapid growth and development, which can help the development of other industries, including the hotel industry. Because the city of Bogor is not only a famous cultural city but also a famous tourist destination, the demand is high and the hotel accommodation sector is also growing. There are many different hotels in Bogor, from cheap hotels to four stars. The accommodation sector such as hotels is a promising business with high profits from the income earned. Some income must be managed so that everything runs smoothly, so that the adjustment of the income received is in the form of documentary evidence. Income audit is part of the finance and accounting department. The purpose of this final project is to determine the role of income audit on the internal control of Swiss-Belinn Hotel Bogor. The data analysis technique used includes descriptive analysis. By using descriptive data analysis techniques, the role and workflow of Income Audit are considered from start to finish. From the results of the analysis, it can be seen that the role of Income Audit in hotels including Swiss-Belinn Hotel Bogor is very important. The role of Income Audit Swiss-Belinn Hotel Bogor is running quite well and in accordance with existing procedures. Income Audit implements good control over the processing of business transaction income evidence assessment reports, especially for sales, so that the company's goal of generating income with a balance of evidence in the form of existing documents can be achieved in an effort to avoid data manipulation and income reduction. Keywords: Department of Finance and Accounting, Income Audit, income.

PULPO: A framework for efficient integration of life cycle inventory models into life cycle product optimization

AbstractThis work presents the PULPO (Python‐based user‐defined lifecycle product optimization) framework, developed to efficiently integrate life cycle inventory (LCI) models into life cycle product optimization. Life cycle optimization (LCO), which has found interest in both the process systems engineering and life cycle assessment (LCA) communities, leverages LCA data to go beyond simple assessments of a limited number of alternatives and identify the best possible product systems configuration subject to a manifold of choices, constraints, and objectives. However, typically, aggregated inventories are used to build the optimization problems. Contrary to existing frameworks, PULPO integrates whole LCI databases and user inventories as a backbone for the optimization problem, considering economy‐wide feedback loops between fore‐ and background systems that would otherwise be omitted. The open‐source implementation combines functions from Brightway2 for the manipulation of inventory data and pyomo for the formulation and solution of the optimization problem. The advantages of this approach are demonstrated in a case study focusing on the design of optimal future global green methanol production systems from captured CO2 and electrolytic H2. It is shown that the approach can be used to assess sector‐coupling with multi‐functional processes and prospective background databases that would otherwise be impractical to approach from a standalone LCA perspective. The use of PULPO is particularly appealing when evaluating large‐scale decisions that have a strong impact on socioeconomic systems, resulting in changes in the technosphere on which the background system is based and which is often assumed constant in standard LCO approaches regardless of the decisions taken. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.

A Note on the Dedication of the Churches in Shivta

ABSTRACT This paper aims to refute the widely accepted identification of the North Church of Shivta as the Church of St. George. A close examination of the issue reveals that the dedication relies on an outdated interpretation of the North Church by H. D. Colt, as well as on a misleading statement by F.-M. Abel. In addition, attention is drawn to an inscription from Shivta which indicates that one of the churches at the site was dedicated to St. Stephen the Martyr.

The Duck’s Brain

AbstractAlthough database systems perform well in data access and manipulation, their relational model hinders data scientists from formulating machine learning algorithms in SQL. Nevertheless, we argue that modern database systems perform well for machine learning algorithms expressed in relational algebra. To overcome the barrier of the relational model, this paper shows how to transform data into a coordinate relational representation for training neural networks in SQL: We first describe building blocks for data transformation, model training and inference in SQL-92 and their counterparts using an extended array data type. Then, we compare the implementation for model training and inference using array data types to the one using a coordinate relational representation in SQL-92 only. The evaluation in terms of runtime and memory consumption proves the suitability of modern database systems for matrix algebra, although specialised array data types perform better than matrices in coordinate relational representation.

A conceptual framework proposal for the implementation of Prognostic and Health Management in production systems

AbstractPrognostic and Health Management (PHM) is an emerging maintenance concept that is highly regarded by the scientific community and practitioners, as its adoption can bring economic, technical and environmental benefits to a company. PHM fully reflects the smart maintenance paradigm encompassing data collection, data manipulation, state detection, health assessment, prognostic assessment and advisory generation. Despite the undeniable benefits, there is still a large gap between the scientific and the real world. Several authors have investigated on the barriers to PHM implementation for companies, highlighting among them the lack of systematic approaches to its design and implementation. As a first contribution to this topic, the authors conducted a systematic literature review (SLR) to investigate the use of Decision Support Systems (DSSs) to support the PHM implementation. The SLR highlighted that few DSS had been developed and were limited to critical unit identification, maintenance strategy selection and data acquisition phase of PHM. Therefore, a conceptual framework for PHM implementation was provided as a second contribution. This framework summarises the decisions that should be addressed by a practitioner wishing to implement PHM services; moreover, it could lay the foundations for the development/improvement of the missing/existing DSSs for PHM implementation.