Data Processing Tools Research Articles

The effectiveness of career development in mediating the influence of the working environment and training towards the performance of employee

This research aims to identify and analyze the effectiveness of career development in connecting the influence of the work environment and training on employee achievement. The research method adopts a descriptive quantitative approach through a causal methodology. The objects and population of this study are all employees from the steel industry in the Banten Provinces and DKI Jakarta, with a total number of 609 employees. The sample consists of 242 respondents selected using the snowball sampling technique. The data analysis method uses Structural Equation Modeling (SEM), specifically the SEM-LISREL data processing tool, to assess the hypotheses proposed in this study. The research results signify that the environment of work, training, and career development directly impact employee performance. Both the work environment and training also have a direct effect on career development. Furthermore, career development acts as a partial mediator that affects the relationship between the work environment, training, and employee performance.

The Determinants of a Successful Family Business Succession

Majority of business in Indonesia is family business firm. In fact, according to Daya Qarsa Research Firm in 2022, family businesses have contributed about 80% to Indonesia’s GDP as well as 40% to Indonesia’s market capitalization. However, most of the family businesses start to falter during the second generation and doesn’t last until the third generation. This article aims to determine 1) the influence of succession planning on family business succession, 2) the influence of family relationship on family business succession, 3) the influence of entrepreneurial spirit on family business succession, 4) the influence of intergeneration managerial mindset on family business succession. The method used to collect data in this study is online question form with google-form questionnaire with a total sample of 37 respondents. The data analysis technique used is Structural Equation Modeling (SEM) with SmartPLS as the data processing tool. The samples in this study are family business successors in Gorontalo. The result of this study is 1) succession planning has an influence on family business succession, 2) family relationship has an influence on family business succession, 3) entrepreneurial spirit has an influence on family business succession, 4) intergeneration managerial mindset has no influence on family business succession.

The Association of Biomolecular Resource Facilities Proteome Informatics Research Group Study on Metaproteomics (iPRG-2020).

Metaproteomics research using mass spectrometry data has emerged as a powerful strategy to understand the mechanisms underlying microbiome dynamics and the interaction of microbiomes with their immediate environment. Recent advances in sample preparation, data acquisition, and bioinformatics workflows have greatly contributed to progress in this field. In 2020, the Association of Biomolecular Research Facilities Proteome Informatics Research Group launched a collaborative study to assess the bioinformatics options available for metaproteomics research. The study was conducted in 2 phases. In the first phase, participants were provided with mass spectrometry data files and were asked to identify the taxonomic composition and relative taxa abundances in the samples without supplying any protein sequence databases. The most challenging question asked of the participants was to postulate the nature of any biological phenomena that may have taken place in the samples, such as interactions among taxonomic species. In the second phase, participants were provided a protein sequence database composed of the species present in the sample and were asked to answer the same set of questions as for phase 1. In this report, we summarize the data processing methods and tools used by participants, including database searching and software tools used for taxonomic and functional analysis. This study provides insights into the status of metaproteomics bioinformatics in participating laboratories and core facilities.

FUZZY-TOPSIS method for defining optimal parameters and finding suitable sites for PV power plants

This study aims to assess and locate the potential for solar power plants (SPP) by utilizing TOPSIS decision analysis and based Fuzzy-Boolean logic based on GIS in Tehran province, Iran. GIS was used as a robust tool for data processing to gather practical and necessary information for effective resources and facilities. Through the multicriteria decision-making method (MCDM), alternative sites were identified and prioritized. The fuzzy logic method was employed to evaluate regions and generate thematic maps, revealing that approximately 95% (14,350 km2) of the region's area was unsuitable for constructing a power plant, while 5% (741 km2) was suitable. From the suitable areas, nine locations were selected for photovoltaic plant construction, totaling 22 km2, occupying 0.15% of the province's total area. Fuzzy TOPSIS was then used to prioritize the alternatives, resulting in the selection of Ghiyeh Naserieh in the southern part of the province as the preferred location for the power generation unit.

Strategies for MALDI-MS method development to investigate different pharmaceutical drug modalities

High-throughput screening (HTS) is an ultra-high throughput tool that allows the identification of diverse, pharmaceutically active compounds in the early stages of drug discovery, by enabling chemists to screen reactions at micromole/nanomole scales. To eliminate the bottleneck arising from analysis of large libraries, Matrix Assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS) can be utilized as a compatible, ultra-fast, label-free, analysis tool. Upon integrating this technique with suitable liquid handling robots that can accurately spot matrices and analytes on a MALDI plate, chemical reactions can be screened at a sampling rate of <1 s per sample. While earlier efforts to improve MALDI-HTS applications were geared towards the development of automation and data processing tools, a systematic method development study across different drug modalities was needed to understand the contribution of different parameters such as MALDI matrices, choice of matrices, additives, and solvent composition, on MALDI-MS signal intensities of analytes. The current study will demonstrate the importance of initial MALDI-MS method development on small molecules, peptides, antibodies, and antibody drug conjugates. This study is designed as a starting point for MALDI-MS signal optimization, which can eventually lead to the development of effective MALDI-HTS methods for various modalities, thus paving the way for a wide range of applications, including but not limited to conformational analysis.

Online Grain Size Measurement by Laser Ultrasonics in a Hot Rolling Mill

Laser ultrasonics (LUS) has shown through numerous studies and in-situ laboratory experiments, its suitability to track microstructural features evolution as well as metallurgical transformation in steels during thermal cycles with temperatures reaching 1250°C. This prior work has motivated the use of this technology to be in applied in an industrial hot strip mill (HSM) in Eisenhüttenstadt, Germany. For this purpose, a transportable trolley which embedded LUS components (Generation and Detection Lasers, Two-Wave Mixing Interferometer, Dedicated Fibered Optical Head) was designed. The industrial investigation was carried on moving strips during normal production at several locations of the HSM, resulting in signals for high-strength lowalloy (HSLA) and advanced high-strength steel (AHSS) grades. The development of a specific data processing tool allowed calculating the grain size from the analysis of the online recorded signals. Such results were then compared to predictions from metallurgical models, finding reasonably close values. LUS appears as a suitable solution to monitor microstructural features and metallurgical transformations occurring in steel in a Hot Strip Mill (HSM) line. This approach could enable in the future the development of more reliable models and control loop of the process. This action was supported by the European commission and led by a consortium composed by ArcelorMittal, Imagine Optic and IMI-NRC.

The Role of the Government's Internal Control System and Human Resource Competence on the Quality of Financial Reports With Leadership Commitment as Moderation

Quality financial reports can provide information about an organisation's performance, financial position and changes in a reliable, relevant way that can be understood and compared. This study aims to empirically examine the effect of the government's internal control system and human resource competence on the quality of financial reports with leadership commitment as a moderating variable. This research recruited 680 financial managers at the Ministry of Public Works and Public Housing as respondents. Research data were analysed using the SEM-PLS method by using the SmartPLS version 3.2 application as a data processing tool. The findings show that the government's internal control system and human resource capacity significantly affect the quality of financial reporting. A strong and structured internal control system provides a solid foundation for producing reliable and relevant financial reporting by applicable standards. In addition, high human resource capabilities in accounting and internal control can help improve the quality of financial reporting. This research implies providing a deeper understanding of the factors influencing the quality of financial reports within the government context, urging the government to enhance investments in the development of human resource competencies, particularly in terms of a more profound comprehension of accounting and financial reporting. Furthermore, this study could have a significant impact in enhancing the quality of financial reporting within the governmental environment and potentially in other organizations as well.

Pengaruh Store Atmosphere, Price Discount dan Sales Person Terhadap Implusive Buying

The purpose of this study was to examine the effect of store atmosphere, price discount, and sales person on impulsive buying on Indomaret consumers. This research includes descriptive quantitative research, with the form of a causal relationship because this research aims to determine the causal relationship between variables. The method used is quantitative method. The population in this study were all people who had bought or shopped in the city of Bekasi Indomaret, so the nature of the population was non-probability sampling, and in this study the sample taken by researchers was 100 respondents. Data collection techniques using questionnaires and literature studies. in this study, data processing tools were used in the form of computer software, namely the SmartPLS (Partial Least Square) version 3.0 program. The analysis method used is outer model analysis (convergent validity, discriminant validity, and reliability test (Cronbach's alpha and composite reliability)), inner model analysis (R-Square value test), and hypothesis testing. The results of this study are Variable Store Atmosphere has no effect on Impulsive Buying, Variable Price Discount has a positive and significant effect on Impulsive Buying, Variable Sales Person has a positive and significant effect on Impulsive Buying, and Variable Store Atmosphere, Price Discount, Sales Person has a positive effect on and Impulsive Buying.

Bagaimana Tingkat Pemahaman Financial Technology dan Financial Literacy Berperan Mengubah Perilaku Konsumsi Pada Generasi Millenial?

&lt;p dir="ltr"&gt;&lt;span&gt;Abstract &lt;/span&gt;&lt;/p&gt;&lt;span&gt;&lt;span&gt;&lt;br /&gt;&lt;/span&gt;&lt;/span&gt;&lt;p dir="ltr"&gt;&lt;span&gt;The development of technology and the development of understanding in the community participate in shaping the behavior of the younger generation of Gen Z. This research was carried out at SMA Negeri 1 Madiun which aims to determine the pattern of student consumption behavior amid the rise of technology and various new understandings in society. This study used quantitative research with a sample of 179 students. SPSS v26 was used as a data processing tool in this study. The results showed that financial technology payments on consumption behavior variables showed no reaction. In the financial literacy variable test on consumption behavior variables showed a reaction that led to a significant positive. Then, on the variable of peer conformity to the variable of consumption behavior with positive results. The variables fintech payment financial literacy and peer conformity on consumption behavior have a significant positive effect. This explains that the variables of fintech payment, financial literacy, and peer conformity can simultaneously influence the consumption behavior patterns of SMA Negeri 1 Madiun students.&lt;/span&gt;&lt;/p&gt;&lt;p dir="ltr"&gt;&lt;span&gt;keywords: financial technology, financial literacy, peer conformity, consumption behavior.&lt;/span&gt;&lt;/p&gt;&lt;div&gt;&lt;span&gt;&lt;br /&gt;&lt;/span&gt;&lt;/div&gt;

CONSIDERATIONS FOR OPTIMIZING THE FLIGHT PERFORMANCE OF miniUAVs

Unmanned aerial vehicles benefit from a series of mature technologies that transform them into data acquisition, processing and dissemination tools with high levels of impact on areas of interest and great utility for users. The paper exposes the stages of analysis regarding the upgrade process of UAV (ATM-1) from the variant of equipping a target aircraft to a superior variant that can be used for data collection, from areas of interest, in the form of static or dynamic images. The proposed variant has implications both on the air vector balance and on the performance of use due to the new onboard equipment.

ICSF: An Improved Cloth Simulation Filtering Algorithm for Airborne LiDAR Data Based on Morphological Operations

Ground filtering is an essential step in airborne light detection and ranging (LiDAR) data processing in various applications. The cloth simulation filtering (CSF) algorithm has gained popularity because of its ease of use advantage. However, CSF has limitations in topographically and environmentally complex areas. Therefore, an improved CSF (ICSF) algorithm was developed in this study. ICSF uses morphological closing operations to initialize the cloth, and estimates the cloth rigidness for providing a more accurate reference terrain in various terrain characteristics. Moreover, terrain-adaptive height difference thresholds are developed for better filtering of airborne LiDAR point clouds. The performance of ICSF was assessed using International Society for Photogrammetry and Remote Sensing urban and rural samples and Open Topography forested samples. Results showed that ICSF can improve the filtering accuracy of CSF in the samples with various terrain and non-ground object characteristics, while maintaining the ease of use advantage of CSF. In urban and rural samples, ICSF obtained an average total error of 4.03% and outperformed another eight reference algorithms in terms of accuracy and robustness. In forested samples, ICSF produced more accuracy than the well-known filtering algorithms (including the maximum slope, progressive morphology, and cloth simulation filtering algorithms), and performed better with respect to the preservation of steep slopes and discontinuities and vegetation removal. Thus, the proposed algorithm can be used as an efficient tool for LiDAR data processing.

Prioritas Pengembangan Permukiman Berdasarkan Analisis Kesesuaian Lahan Di Kota Ambon

The development of sustainable settlements requires land suitability analysis. This study aims to analyze land use based on satellite imagery, land suitability classes and criteria for settlements, as well as limiting factors and development priorities. The methods used include qualitative descriptive analysis for spatial analysis and quantitative descriptive analysis to explain the ranking based on land units. The study utilizes Landsat 8 data, administrative maps, soil types, and rock data. Tools used include a compass, camera, and GPS. Data collection is obtained through surveys, satellite imagery, and relevant documents. Data processing tools include ArcGIS 9.3, Er Mapper 7.0, and Global Mapper 15.0 software. Data is collected through field measurements and analyzed based on ranking and overlay techniques. The study results show that land use classification, including geometric and radiometric correction and maximum likelihood classification, demonstrates good accuracy. The classes and criteria for suitable settlement land are predominantly categorized as highly unsuitable, covering an area of 23,592.48 hectares, while the highly suitable category covers an area of 6,033.39 hectares. Settlement development priorities are categorized as Priority I covering an area of 3,181.34 hectares; Priority II, covering an area of 2,852.05 hectares; and Priority III, covering an area of 8,237.89 hectares. The study reveals the existence of settlement development in problematic areas, covering an area of 204.19 hectares. This phenomenon can be explained by the geographical conditions of Kota Ambon, which is primarily hilly, making it the preferred choice for residents in fulfilling their housing needs. Therefore, local governments need to strictly enforce regional regulations to preserve land and prevent potential impacts.

Pengaruh Sosial Ekonomi terhadap Perilaku Pengelolaan Sampah Rumah Tangga di Kota Jambi

The rate of increase in waste generation in Jambi City continues to increase every year in line with the increasing population of Jambi City. The biggest source of waste problems is household waste. The purpose of this study was to analyze and determine the influence of socio-economic factors of the people of Jambi City on their behavior in household waste management and to provide recommendations in the form of suggestions related to household waste management in Jambi City. This study uses data from the National Socioeconomic Survey (Susenas), Health and Housing Modules carried out by the Central Bureau of Statistics in 2019. The analytical method used is descriptive analysis and binary logistic regression, with a sample of 159 respondents, and with the help of the SPSS data processing tool version 20. Descriptive analysis shows that the people of Jambi City do not have sufficient awareness in managing waste. This is reflected in the high number of respondents who do not separate organic waste from inorganic waste and in terms of separating B3 waste. With binary logistic regression analysis, it can be concluded that the socio-economic factor that influences the behavior of how to dispose of garbage is education. Meanwhile, age, working status, per capita income, and residence ownership status did not affect the behavior of how to dispose of household waste. The resulting odds ratio interpretation is: the probability of respondents with higher education (high school graduates and above) to dispose of waste indiscriminately is 4.301 times the chance of respondents with junior high school education and below. This means that respondents with higher education (high school graduates and above) are more likely to dispose of waste indiscriminately than respondents with lower education. This reinforces the existing theory, namely that the higher a person's level of education, the better his behavior in waste management (the more he cares about the environment).

Social Relationship and Content Analyses in an Online Class Community under Covid-19

Clarifying the blog’s background and successfully grasping its main subject are essential components of any examination of the blogging community. There are numerous previous researches on how to evaluate blogs and comments to discover the connections and content in a social group. This work seeks to focus on blog comments based on students’ relationships and conduct in a class community and to complement prior research techniques by emphasizing the importance of blog community analysis under Covid-19. It also provides an evaluation of the participation and performance results. We mainly focus on analyzing comments as the interaction among students and discuss the similarity in some highly performance students. With the help of social network analysis for online course student behavior, some tendency related to student behavior in this group has been shown in the experiment results by bias analysis, Pearson correlation, and Latent Dirichlet allocation, which serve as the data processing tools in this experiment.

BIG DATA PROCESSING WITH APACHE SPARK

With the exponential growth of information, it is no surprise that we are in a period of history as the Information Age. The rapid growth of data has presented challenges regarding storage and processing technology. This article refers to Apache Spark, an ecosystem that provides many integrated technologies in Big Data processing, including machine learning libraries and data storage platforms. Apache Spark provides distributed data processing for open source applications, loading data in-memory and making operations for analyzing data of any size, with efficient support for popular programming languages like Java, Scala, R, and Python. The article aims to compare the superior computing power of Saprk compared to Hadoop and how to connect Spark with today's popular data processing tools such as the R language.

The Role of Transformational Leadership, and Knowledge Sharing on Innovative Work Behavior of Public Organization in the Digital Era

Purpose: This study aims to analyze the role of Transformational Leadership on Innovative Work Behavior, and Transformational Leadership on Knowledge sharing and Knowledge sharing on Innovative Work Behavior of Public Organizations in the digital era. Design/Methodology/Approach: This research uses quantitative research methods. The dimensions of transformational leadership are idealized influence, inspirational motivation, intellectual stimulation, and individualized consideration). &amp; The variable dimension of knowledge sharing is knowledge donating and knowlede collecting. The dimensions of innovative behavior are idea exploration, idea generation, idea championing, and idea implementation. The population in this study are senior employees of public organizations. Respondents in this study were 454 senior employees of public organizations who were determined by simple random sampling method. Data was collected through an online questionnaire using a Likert scale using a scale of 7. Data analysis in this study used Partial Least Square structural equation modeling (SEM) with the SmartPLS 3.0 software data processing tool. The stages of data analysis begin with testing the validity and reliability of the instrument, determination and finally testing the hypothesis. Findings: Transformational leadership has a significant positive effect on behaviour Innovative, transformational leadership has a significant positive effect on knowledge sharing, knowledge sharing has no significant effect on innovative work behavior. Practical implications: The findings of this study can be used by public organizations to attract innovative work behavior. Moreover, for academics, it can support new knowledge as well as supporting theories. Original/Value: A significant contribution to the body of knowledge is achieved through testing and subsequent confirmation of the effects of Transformational Leadership on Innovative Work Behavior, Transformational Leadership on Knowledge sharing and Knowledge sharing on Innovative Work Behavior of Public Organizations in the digital era.

Development of a prototype for high-frequency mental health surveillance in Germany: data infrastructure and statistical methods.

In the course of the COVID-19 pandemic and the implementation of associated non-pharmaceutical containment measures, the need for continuous monitoring of the mental health of populations became apparent. When the pandemic hit Germany, a nationwide Mental Health Surveillance (MHS) was in conceptual development at Germany's governmental public health institute, the Robert Koch Institute. To meet the need for high-frequency reporting on population mental health we developed a prototype that provides monthly estimates of several mental health indicators with smoothing splines. We used data from the telephone surveys German Health Update (GEDA) and COVID-19 vaccination rate monitoring in Germany (COVIMO). This paper provides a description of the highly automated data pipeline that produces time series data for graphical representations, including details on data collection, data preparation, calculation of estimates, and output creation. Furthermore, statistical methods used in the weighting algorithm, model estimations for moving three-month predictions as well as smoothing techniques are described and discussed. Generalized additive modelling with smoothing splines best meets the desired criteria with regard to identifying general time trends. We show that the prototype is suitable for a population-based high-frequency mental health surveillance that is fast, flexible, and able to identify variation in the data over time. The automated and standardized data pipeline can also easily be applied to other health topics or other surveys and survey types. It is highly suitable as a data processing tool for the efficient continuous health surveillance required in fast-moving times of crisis such as the Covid-19 pandemic.

Ultrahigh performance LC/FT-MS non-targeted screening for biomass burning organic aerosol with MZmine2 and MFAssignR

In recent years, ultrahigh performance liquid chromatography Fourier transform mass spectrometry (LC/FT-MS) based non-targeted screening (NTS) methods have become increasingly popular for comprehensive analysis of complex organic mixtures. However, applying these methods for environmental complex mixture analysis is challenging due to the extreme complexity of natural samples and a lack of standard samples or surrogates for environmental complex mixtures. Furthermore, limited molecular markers in the databases and insufficient data processing software workflows make the application of these methods more challenging for environmental complex mixtures. In this work, we implement a new NTS data processing workflow to process data collected from ultrahigh performance liquid chromatography and Fourier transform Orbitrap Elite Mass Spectrometry (LC/FT-MS) by combining MZmine2 and MFAssignR, two opensource data processing tools and commercial Mesquite liquid smoke as a surrogate for biomass burning organic aerosol. MZmine2.53 data extraction followed MFAssignR molecular formula assignment offered noise free and highly accurate 1733 individual molecular formulas presented in liquid smoke with 4906 molecular species, including isomers. The results of this new approach were consistent with the results of direct infusion FT-MS analysis confirming its reliability. Over 90% of the molecular formulas presented in mesquite liquid smoke were matched with the molecular formulas of ambient biomass burning organic aerosol. This suggests the potential use of commercial liquid smoke is an acceptable surrogate for biomass burning organic aerosol research. The presented method significantly improves the identification of the molecular composition of biomass burning organic aerosol by successfully addressing some of the limitations related to the data analysis and giving a semi quantitative insight into the analysis.

Multiple selected ion monitoring mode for sensitive imaging of eicosanoids in tissues using nanospray desorption electrospray ionization (nano-DESI) mass spectrometry

Nanospray desorption electrospray ionization mass spectrometry imaging (nano-DESI MSI) enables sensitive label-free mapping of multiple classes of molecules in biological samples. Previously, we demonstrated that combining nano-DESI MSI with selected ion monitoring (SIM) increases the sensitivity of the technique in a narrow m/z range. In this study, we use a series of adjacent multiple SIM (mSIM) windows to acquire nano-DESI MSI data with high sensitivity over a broader m/z range. We demonstrate that this approach substantially improves the quality of imaging data obtained for low-abundance eicosanoids, important lipid mediators derived from eicosapolyenoic acid products of arachidonic acid oxidation. Despite their importance as mediators of inflammation, asthma, fever, pain, hypertension, and stroke, eicosanoids have not been extensively investigated using MSI. This is mainly attributed to their low ionization efficiency and low, subnanomolar, concentrations in tissue, which make it difficult to detect these species in MSI experiments alongside abundant phospholipids. We demonstrate substantial enhancements in the signal-to-noise ratios (S/N) of eicosanoids using imaging in mSIM mode. We have also developed data processing and visualization tools to support this type of MSI data acquisition. S/N enhancements observed in mSIM mode are independent of the properties of analytes making this approach applicable to other low-abundance species in biological and environmental samples.

Data processing and visualization tool for atomic and molecular data for collisional radiative models

The Monte Carlo kinetic code EIRENE (Sect. 2) is used for simulating the behavior of neutral species in the edge of tokamak plasma and coupled with fluid plasma codes for a self-consistent description to be compared with measured experimental conditions. The data and visualization toolbox HYDKIN (Sect. 3) has been developed as a pre-processing tool for validation of those atomic and molecular (A &M) data used in EIRENE simulations, such as cross sections and reaction rates for plasma-neutral and neutral-neutral collisions. The restructuring that is being performed to increase HYDKIN readability and usability is here presented (Sect. 4).Graphic