Data Analysis Tools Research Articles

Assessing the Effect of Performance Appraisal on Employee Productivity in Organzations; A Study of Chikankata Town Council, Kafue Gorge Training Centre and Riverside Farm Institute

The study assessed the effects of Performance Appraisal on Employee Productivity in organizations; A study of Chikankata Town Council, Kafue Gorge Training Centre and Riverside Farm Institute. The objectives were to determine the effects of performance appraisal on employee’s productivity in an organization. The results obtained from the study would generate insight that would assist organizations in developing effective performance measurement strategies for it would enable the organizations to identify and develop guidelines that would enhance effective staff appraisal that would improve staff performance and for academicians and scholars, the study findings would be beneficial to forming the basis for future research on the subject, providing a critical examination of the field. The study used qualitative research design. A stratified proportion sampling was employed to obtain a suitable unit representative of analysis with the target population of 310 employees from three (3) different institutions namely; Chikankata Town Council, Riverside Institute and Kafue Gorge Training Centre. The study was anchored on expectancy theory. The study targeted the population of 310 staff based in Chikankata District with the sample size of 45 employees. The researcher used the interview guide and focus group questions as the data collection instruments that was pilot tested using 15 staff based in Chikankata District. The data was analysed by use of Microsoft excel. Specifically, means, averages and percentages were employed by the researcher. The data analysis tools were simple tabulations and presentations of the report using spreadsheets. The data is presented using tables, charts and graphs. The findings indicated that performance appraisal is important because the process offers organizations a valuable opportunity to measure how each staff in relations to previously established standards and expectations. The study recommends that Managers, supervisors and employees should be involved in the planning process of performance appraisal and the Performance appraisal policy should be easily accessible to all members of staff and performance appraisal process in the organization should be more transparent and rational.

The role of digital literacy and knowledge management on process innovation in SMEs

Research on digital literacy, knowledge management and process innovation variables has not been widely carried out in Indonesia, therefore more studies need to be carried out immediately since small and medium enterprises (SMEs) play an important role in economic activities. The purpose of this research is to investigate the effect of digital literacy on knowledge management, digital literacy on process innovation and financial management on process innovation. The research method is quantitative using partial least square structural equation modeling (SEM) analysis with data analysis tools using SmartPLS 3.0 software. The study involved 489 respondents who owned SMEs and it was determined using simple random sampling. The type of variable scale used is the ordinal scale. The rating scale for each statement uses a rating scale technique with a Likert scale type. Online questionnaires are distributed through online media, the data analysis stage is the outer model test, namely the validity and reliability test and the inner model test, namely the hypothesis test or significance test. The independent variable of this research is digital literacy, the mediating variable is knowledge management, and the dependent variable is process innovation variable. Based on the results of research data analysis it was found that digital literacy had a positive and significant relationship on knowledge management, digital literacy had a positive and significant relationship on process innovation, knowledge management had a positive and significant relationship on process innovation. Knowledge management played as full mediators in the relationship between digital leisure variables and process innovation.

Artificial intelligence and financial decisions: Empirical evidence from developing economies

Recent technological advancements are endless and have had a profound influence on everyone in every part of life throughout the preceding decades. Artificial intelligence is one such invention that has the potential to change the world. Now, artificial intelligence is being used in almost all commercial operations. Hence, this research attempted to investigate the impact of artificial intelligence dimensions, including natural language processing, machine learning, expert systems, and computer vision on the financial decisions of pharmaceutical companies in Jordan. A cross-sectional approach was used through a comprehensive survey to collect research data from 148 accountants and financial managers in pharmaceutical companies listed on the Amman Stock Exchange with a response rate of 81.3%. The research hypotheses were examined using structural equation modeling of the collected quantitative data. The results indicated that the dimensions of artificial intelligence positively impact financial decisions. Accordingly, companies should spend on building strong artificial intelligence infrastructure and skills. Access to modern artificial intelligence technology, data analysis tools and cloud computing resources are also essential to rationalizing financial decision-making. Besides, Jordan's pharmaceutical sector can overcome these limitations and realize the full potential of artificial intelligence in financial decision-making by solving data privacy issues, encouraging ethical AI re-search, investing in artificial intelligence expertise, and enhancing collaboration.

Прикладний програмний інтерфейс як метод моніторингу даних соціальних мереж для досліджень у поведінковій економіці

In today’s behavioral economics, research based on social media data is becoming increasingly popular. Interactive platforms such as TikTok, Facebook, Instagram, X (Twitter), LinkedIn, and others generate a wealth of data that can be used to analyze user behavior. Processing large amounts of information requires the development of effective tools for collection and analysis of such data. The application programming interface (API) is one of the key methods that provides real-time access to data. The application programming interface allows scientists to automate the process of collecting information, customize queries according to various criteria, and obtain structured data for further analysis. The article examines the use of application programming interfaces as methods for monitoring social network data for research in behavioral economics. The features of the use of social networks for scientific research are analyzed. A monitoring method has been developed for collecting and processing data using APIs to study the behavior of social media users. The parameters of data collection (keywords, hashtags, time intervals, etc.) are determined, scripts for processing and storing data are created, a mechanism for starting the data collection process and monitoring its effectiveness and stability are designed. The proposed monitoring algorithm allows you to collect a representative database, analyze the content and interactions of social media users. APIs provide the ability to obtain a large amount of up-to-date and relevant information about user behavior, preferences, social interactions, and reactions to economic events, which allows for effective real-time data collection and analysis and increases the accuracy of forecasts in behavioral economics. Prospects for further research include the analysis of social media data, which will identify the impact of social and information factors on investment decision-making in the context of the market of virtual assets.

Impacts of hinterland surface water on the coastline of Al Hoceima Bay (Central Rif, Morocco)

The present work aims to assess the chemical and bacteriological quality of the two Ghiss-Nekôr tributaries of Al-Hoceima Bay. A set of field data collected, either from direct analysis or from satellite data, was used to conduct this study. We relied on GIS tools for data processing and analysis. The nutrient analysis indicates normal surface water quality, with nitrogen and phosphorus concentrations typical of less polluted waters. These nutrients originate from continental inputs due to runoff. Nitrates are the most common mineral nitrogen species, resulting from the leaching of nitrate-based fertilizers from agricultural and domestic sources. Bacterial analysis reveals water contamination by total coliforms (TC), Escherichia coli (EC), and intestinal enterococci (IE). Hinterland inputs from the two rivers are the primary source of liquid and solid pollutants in Al-Hoceima Bay. They are influenced by climatic and anthropogenic factors such as hydraulic development, agriculture and urbanization.

Noisy Tensor Completion via Low-Rank Tensor Ring.

Tensor completion is a fundamental tool for incomplete data analysis, where the goal is to predict missing entries from partial observations. However, existing methods often make the explicit or implicit assumption that the observed entries are noise-free to provide a theoretical guarantee of exact recovery of missing entries, which is quite restrictive in practice. To remedy such drawback, this article proposes a novel noisy tensor completion model, which complements the incompetence of existing works in handling the degeneration of high-order and noisy observations. Specifically, the tensor ring nuclear norm (TRNN) and least-squares estimator are adopted to regularize the underlying tensor and the observed entries, respectively. In addition, a nonasymptotic upper bound of estimation error is provided to depict the statistical performance of the proposed estimator. Two efficient algorithms are developed to solve the optimization problem with convergence guarantee, one of which is specially tailored to handle large-scale tensors by replacing the minimization of TRNN of the original tensor equivalently with that of a much smaller one in a heterogeneous tensor decomposition framework. Experimental results on both synthetic and real-world data demonstrate the effectiveness and efficiency of the proposed model in recovering noisy incomplete tensor data compared with state-of-the-art tensor completion models.

Introducing “time-of-flight single particle investigator” (TOF-SPI): a tool for quantitative spICP-TOFMS data analysis

TOF-SPI is software for accurate, robust, and high-throughput analysis of single-particle ICP-TOFMS data.

Zooming in on Urban Life in Early Modern Amsterdam. Time Machine as a Tool for Researching Everyday Urban Experience Across Space and Time

How do we make sense of urban life in the past? What do we do when we study urban history, and to what extent do our methods fully capture the complexities of historical city living? These are crucial questions for any scholar interested in the historical dimensions of urban experience. Notwithstanding the interest of most urban historians in the relationship between the physical form of urban space and its experience by inhabitants and visitors, very few scholars have written histories that systematically integrate these two areas of inquiry. In this article the authors argue that such research requires a method and an accompanying digital tool that can analyze historical urban life in a more integrated, holistic way. The authors propose a way forward by introducing the Time Machine platform as a scalable data visualization and analysis tool for researching everyday urban experience across space and time. To illustrate the potential, the authors focus on a case study: the area of the Bloemstraat in early modern Amsterdam. Unpacking a section of the Bloemstraat, house by house and room by room, this case shows how the Time Machine forms an instrument to connect spatial layouts to the arrangement of objects and to the practical and social use of the space by the inhabitants and visitors. The authors also sketch how this tool illuminates more dynamic spatial and temporal practices such as how people, goods and activities are connected to locations in the wider city and beyond.

Contribution of environmental factors to genetic variation in the Pacific white-sided dolphin

Environmental changes driven by pollution and climate change have affected many species globally. The Pacific white-sided dolphin, or Lagenorhynchus obliquidens (L. obliquidens), is threatened by water pollution, which is the product of human actions and can impact the genetic structure of species in affected areas. While researchers have studied genetic differences between populations of L. obliquidens in Japan, they failed to explore the potential environmental drivers of these differences. Our research aimed to identify the major environmental factors impacting the genetic variation of L. obliquidens, which will increase our understanding of how this species responds to changes in the environment. We studied several different variables, including salinity, ocean currents, temperature, and microplastic levels. Due to the significant presence and variation of microplastics in the ocean surrounding Japan, we hypothesized that this would have the most notable impact on the genetic variation of L. obliquidens. We began our research by gathering genetic sequencing data from a preexisting study focusing on populations along the Japanese coast. We collected environmental data and analyzed it alongside a genetic distance matrix, leveraging data analysis and machine learning tools in R. We found that the most impactful variable was current stability, which did not match our original prediction of microplastic levels having the most significant impact. We then investigated why ocean currents may have had such a strong impact on L. obliquidens and proposed that it has a connection to nutrient and prey distribution, which can be majorly impacted by changes in ocean currents.

An Intelligent Search & Retrieval System (IRIS) and Clinical and Research Repository for Decision Support Based on Machine Learning and Joint Kernel-based Supervised Hashing.

Large-scale, multi-site collaboration is becoming indispensable for a wide range of research and clinical activities in oncology. To facilitate the next generation of advances in cancer biology, precision oncology and the population sciences it will be necessary to develop and implement data management and analytic tools that empower investigators to reliably and objectively detect, characterize and chronicle the phenotypic and genomic changes that occur during the transformation from the benign to cancerous state and throughout the course of disease progression. To facilitate these efforts it is incumbent upon the informatics community to establish the workflows and architectures that automate the aggregation and organization of a growing range and number of clinical data types and modalities ranging from new molecular and laboratory tests to sophisticated diagnostic imaging studies. In an attempt to meet those challenges, leading health care centers across the country are making steep investments to establish enterprise-wide, data warehouses. A significant limitation of many data warehouses, however, is that they are designed to support only alphanumeric information. In contrast to those traditional designs, the system that we have developed supports automated collection and mining of multimodal data including genomics, digital pathology and radiology images. In this paper, our team describes the design, development and implementation of a multi-modal, Clinical & Research Data Warehouse (CRDW) that is tightly integrated with a suite of computational and machine-learning tools to provide actionable insight into the underlying characteristics of the tumor environment that would not be revealed using standard methods and tools. The System features a flexible Extract, Transform and Load (ETL) interface that enables it to adapt to aggregate data originating from different clinical and research sources depending on the specific EHR and other data sources utilized at a given deployment site.

Utilizing Flipped Classroom and the First Principles of Effective Instruction in Teaching Finite Geometry

The flipped classroom approach has been used as a variation of digital learning even before the covid-19 pandemic. Using this approach and the First Principles of Effective Instruction, this study determined and compared the proving skills of higher education students in finite geometries using true experimental research involving two equivalent classes in Modern Geometry. Data on proving competencies were gathered from the 27 pairs of randomly selected respondents and were subjected to data exploratory analysis to ensure the appropriateness of statistical tools for data analysis. Results reveal that both classes performed equivalently in the pretest. However, the flipped class exhibited a statistically significant improvement in the posttest and in-class activities than the non-flipped class. Additionally, the flipped class was found to have equivalent performances in both individual and group in-class activities. The results showed that necessary competencies in proving theorems can be attained using the flipped classroom approach following the activation, demonstration, application, and integration phases of instruction. The study recommended using the approach to support students learning achievement, performance enhancement, and active learning environment. It can also be incorporated into crafting an adaptive learning continuity plan for the post-pandemic recovery period and beyond. Keywords: active learning, critical thinking and problem-solving skills, finite geometry, first principles of effective instruction, flipped classroom, pandemic, proving theorems

The impact of tax revenue on economic growth in Nigeria

This research aimed to investigate the influence of tax revenue on Nigeria's economic growth over a three-decade period spanning from 1991 to 2021. The tax data was collected from the Federal Internal Revenue Service (FIRS) and the Nigerian Bureau of Statistics (NBS), while economic data for Nigeria was sourced from the 2021 Statistical Bulletin of the Central Bank of Nigeria (CBN). The study utilized personal income tax (PIT), corporate income tax (CIT), and value-added tax (VAT) as proxies for tax revenue, with gross domestic product (GDP) serving as the dependent variable to represent the Nigerian economy. Several diagnostic tests were conducted, including a descriptive statistic to assess data normality and the Augmented Dickey-Fuller unit root test to evaluate data stability. The Autoregressive Distributed Lag (ARDL) technique was employed as the statistical tool for data analysis, utilizing E-View version 9 as the statistical package. The findings from the ARDL test revealed that personal income tax (PIT) and value-added tax (VAT) had a negative impact on GDP, while company income tax (CIT) showed a positive impact. As a result, the study concluded that tax revenue exhibits a positive and significant correlation with the growth of the Nigerian economy. The study suggests that governments and relevant tax authorities should focus on increasing taxes as a revenue source and target sectors that drive economic growth.

Label-free cell classification in holographic flow cytometry through an unbiased learning strategy.

Nowadays, label-free imaging flow cytometry at the single-cell level is considered the stepforward lab-on-a-chip technology to address challenges in clinical diagnostics, biology, life sciences and healthcare. In this framework, digital holography in microscopy promises to be a powerful imaging modality thanks to its multi-refocusing and label-free quantitative phase imaging capabilities, along with the encoding of the highest information content within the imaged samples. Moreover, the recent achievements of new data analysis tools for cell classification based on deep/machine learning, combined with holographic imaging, are urging these systems toward the effective implementation of point of care devices. However, the generalization capabilities of learning-based models may be limited from biases caused by data obtained from other holographic imaging settings and/or different processing approaches. In this paper, we propose a combination of a Mask R-CNN to detect the cells, a convolutional auto-encoder, used to the image feature extraction and operating on unlabelled data, thus overcoming the bias due to data coming from different experimental settings, and a feedforward neural network for single cell classification, that operates on the above extracted features. We demonstrate the proposed approach in the challenging classification task related to the identification of drug-resistant endometrial cancer cells.

Complementarity of two proteomic data analysis tools in the identification of drug-metabolising enzymes and transporters in human liver.

Several software packages are available for the analysis of proteomic LC-MS/MS data, including commercial (e.g. Mascot/Progenesis LC-MS) and open access software (e.g. MaxQuant). In this study, Progenesis and MaxQuant were used to analyse the same data set from human liver microsomes (n = 23). Comparison focussed on the total number of peptides and proteins identified by the two packages. For the peptides exclusively identified by each software package, distribution of peptide length, hydrophobicity, molecular weight, isoelectric point and score were compared. Using standard cut-off peptide scores, we found an average of only 65% overlap in detected peptides, with surprisingly little consistency in the characteristics of peptides exclusively detected by each package. Generally, MaxQuant detected more peptides than Progenesis, and the additional peptides were longer and had relatively lower scores. Progenesis-specific peptides tended to be more hydrophilic and basic relative to peptides detected only by MaxQuant. At the protein level, we focussed on drug-metabolising enzymes (DMEs) and transporters, by comparing the number of unique peptides detected by the two packages for these specific proteins of interest, and their abundance. The abundance of DMEs and SLC transporters showed good correlation between the two software tools, but ABC showed less consistency. In conclusion, in order to maximise the use of MS datasets, we recommend processing with more than one software package. Together, Progenesis and MaxQuant provided excellent coverage, with a core of common peptides identified in a very robust way.

Impacto de la estrategia de marketing en el rendimiento de las ventas: Un estudio de una muestra de propietarios de microempresas y pequeñas empresas

The marketing strategies (7Ps) are the cornerstone of any enterprise. Therefore, the current research was designed to investigate the impact of marketing strategies on the sales performance of small and micro-scale enterprises in Duhok Province. In this regard, the research aims to test the impact of relationship marketing strategies (product, price, place, promotion, process, people, and physical evidence) on sales performance, as well as how they can be implemented and managed. Undoubtedly, the current research relied on descriptive and analytical method to conduct this research. For this purpose, the data collection technique included a central and secondary source in order to provide a theoretical framework. As for data analysis tools consist of validity and reliability tests, descriptive statistics of participants and variables, correlation analysis, and impact factor analysis via using the most recent version of Statistical Package for Social Sciences (SPSS V. 28). Nevertheless, the present research employed a random sampling that consists of business owners of micro and small scale businesses in Iraq-Kurdistan region, which this research just selected Duhok governorate out of 4 cities. In contrast, the questionnaire survey is the key tool of this research, which was distributed to (150) business leaders (130) questionnaires were gathered, (123) surveys were validated and ready for data analysis and research tests with a response rate valued (82%) of the targeted sample, and reliability factor was (0.928). As a result, several conclusions and suggestions were deduced from this research with an enormous majority of respondents of agreement scales valued (74%). Furthermore, the research concluded that there is a positive significant relationship and impact between marketing strategies (MS) and sales performance (SP) it infers this link is critical to expand profitability, productivity, and growth rates of micro and small areas.

Perilous Journeys Across the Sea in Search of Prosperity: Causes of Illegal Migration from Sri Lanka and Present Trends (A Case Study of Illegal Immigration to Australia

Illegal migration is a vexing contemporary issue for the countries from which these illegal travelers originate as well as the destination countries where they hope to settle. Therefore, countries now struggle to curb illegal migration across the world. After the end of the civil war in 2009, the favorite destination of Sri Lankans planning to migrate illegally has turned out to be Australia. Attempts by people from Sri Lanka to travel to Australia illegally have turned out to be a veritable phenomenon in recent years. This is mainly attributed to certain causes and trends that have prompted people to make attempts to reach Australia illegally. The research problem of this study is to figure out why people try to migrate to Australia by illegal means. The main objective of this study is to propose a feasible solution to mitigate the negative impacts of illegal migration to Australia. To answer these questions, this study has used both qualitative and quantitative methodologies. In addition to the secondary data collected from written sources, 10 case studies were undertaken, in which in-depth interviews were carried out using open-ended and unstructured questions. Discourse analysis and phenomenological data analysis tools were used to analyze the data. Findings of the research showed that a variety of causes, such as poor economic prospects, social problems, and political marginalization, were responsible for the increasing trend towards illegal migration to other countries. This study also found a paradigm shift in the illegal migration of Sri Lankans from Australia to New Zealand.

AVALIAÇÃO MULTIVARIADA DA QUALIDADE DAS ÁGUAS SUPERFICIAIS E ÁGUA FINAL DE ABASTECIMENTO PÚBLICO DE BELÉM, PARÁ, BRASIL

MULTIVARIATE ASSESSMENT OF SURFACE WATER QUALITY AND FINAL PUBLIC WATER SUPPLY IN BELÉM, PARÁ, BRAZIL. The present study investigated the sources of variability in the water quality of surface springs that supply the Metropolitan Region of Belém (Pará, Brazil), as well as the treated water produced by the main water treatment station in the region. Data provided by Companhia de Saneamento do Pará were used, referring to physical-chemical and bacteriological analyzes carried out in the period from 2010 to 2020. In addition, precipitation data were considered in this study, in units of mm of rain, obtained by consulting the database of the National Institute of Meteorology. The application of principal component analysis, as an exploratory data analysis tool, allowed the grouping of the most relevant parameters of the results and proved to be efficient in reducing the data set, explaining more than 65% of the total variance, in all objects of study. Thus, it was possible to observe a general profile of the quality of surface water investigated, and, in this way, demonstrate the impact of urban pressure on the environment, as well as the need for maintenance and conservation of surface water resources.

Short Communication: Indian Public Sector Commercial Banks: An Empirical Analysis of Bank-Specific Determinants of Credit Risk

The public sector banks dominate India’s financial sector and play a crucial role. Commercial banks are indispensable in the processes of capital circulation, capital integration, capital resource allocation, and overall societal demand and supply adjustment. But performing these vital roles, the commercial banks are associated with different risks out of which credit risk hampers them a lot. This risk attacks the major pillar of the banking industry i.e., solvency on which the industry survives. In this paper, a modest attempt is made to identify the bank-specific determinants of credit risk in the public sector commercial banks in India. Data was collected from the RBI database for the period 2005 to 2020 for 13 banks. Variables pertaining to credit risk, bank size, capital adequacy ratio, profitability, liquidity and diversification were taken. These variables were analysed with the help of different panel data analytical tools. The size of the bank, represented by the log of total assets, and diversification, measured as the ratio of non-interest income to total income, were found to positively and significantly influence credit risk in public sector commercial banks. The Return on Asset, Loan-deposit ratio, and Capital adequacy ratio were found to be negatively correlated with credit risk.

Student learning of engineering systems through simulation-based design using Onshape and Blender

This study examines how the introductory engineering course can engage students in learning Industry 4.0 concepts and enhance their digital technology skills through simulation-based design activities. The students used cloud-based design and simulation tools to resolve a system operation problem. They examined the motion of a digital model of a satellite under micro-gravity conditions and detected its instability (Dzhanibekov effect). Then, they redesigned the model and verified its motion stability through simulation. The experimental setup combined the cloud-based CAD platform Onshape and the simulation software Blender. The students’ performances, understanding, and perceptions were evaluated using conventional research tools and the data analytics tools of Onshape. Findings showed that the students successfully performed the workshop assignment, correctly answered the questions about the factors that cause motion instability and appreciated the contribution of the workshop. The study demonstrated the potential of online simulation-based design practices for educating novice engineering students.

A Holistic Approach Towards Digitized Audit Procedures in Manufacturing for Data Quality Assessment

Digitized assessment of data quality in smart manufacturing facilitates the transformation towards efficient decision making in production environments. Inference can be drawn from the need of manufacturers, implementing robust and effective data analytics tools which emphasizes the demand for holistic data quality standards. Therefore, an interactive chatbot containing a comprehensive audit procedure incorporating a guideline aligned to ISO standards was developed. Using this tool, an applicable and structured audit process is offered to ensure data quality. This concludes in specific recommendations for manufacturers improving smart applications.