Data Analysis Tools Research Articles

Analisis Pengaruh Variabel Makroekonomi Terhadap Cadangan Devisa Negara Indonesia Menurut Perspektif Ekonomi Islam Periode Triwulan I-III 2023

This study provides an explanation of the macroeconomic variables of export variables, the rupiah exchange rate against the US dollar, and foreign debt to Indonesia's foreign exchange reserves during 2023 which began in January and ended in September in accordance with the Islamic economic perspective. This study uses time series data published by the Central Bureau of Statistics and Bank Indonesia as the author's data source. The data Analysis tool using software Eviews 12 with classical assumptions and statistical tests. The results showed that exports, the rupiah exchange rate against the US dollar and foreign debt had no significant effect on the country's foreign exchange reserves in 2023 due to fluctuations .

Comparative analysis of machine learning algorithms for breast cancer prediction

Breast cancer is a global health concern, and early diagnosis is crucial for successful treatment. The objective of this paper is to conduct a comparative analysis of machine-learning algorithms for the prediction of breast cancer. This study used the Wisconsin Diagnostic Breast Cancer Dataset. Data preparation, technique selection, and performance evaluation are included in the study. The inquiry begins by comparing malignant and benign instances according to input factors and diagnostic outcomes. Finding components having an inverse relationship to the diagnosis is prioritized. Next, a careful approach is used to choose attributes to improve the dataset for model construction. The preprocessed data trains and optimizes four well-known machine learning algorithms: Random Forest, Support Vector Machine, K-Nearest Neighbor, and Logistic Regression. The models are evaluated for accuracy, precision, recall, F1-score, and ROC curve. This study aimed to evaluate numerous breast cancer prediction systems to determine their strengths and weaknesses. To provide openness and replicability, the study uses the Jupyter Notebook platform, Python, and data analytic tools. The logistic regression model has a test accuracy percentage of 99.26%, surpassing all other models examined in this study. Furthermore, it has a minimum false positive rate (FPR) of 1 and a false negative rate (FNR) of 4. The model exhibits a higher level of precision in comparison to the studies examined in the literature review. This study is crucial for early diagnosis and therapy development. The effects include lower healthcare expenses, better patient outcomes, and better diagnostics. Machine learning has shown promise in fighting breast cancer, boosting its relevance in healthcare.

A novel comprehensive strategy for high-thoroughly studying released compounds during the combustion process of herbs. A case study for Artemisia argyi Levl. et Vant

The comprehensive study of compound variations in released smoke during the combustion process is a great challenge in many scientific fields related to analytical chemistry like traditional Chinese medicine, environment analysis, food analysis, etc. In this work, we propose a new comprehensive strategy for efficiently and high-thoroughly characterizing compounds in the online released complex smokes: (i) A smoke capture device was designed for efficiently collecting chemical constituents to perform gas chromatography-mass spectrometry (GC-MS) based untargeted analysis. (ii) An advanced data analysis tool, AntDAS-GCMS, was used for automatically extracting compounds in the original acquired GC-MS data files. Additionally, a GC-MS data analysis guided instrumental parameter optimizing strategy was proposed for the optimization of parameters in the smoke capture device. The developed strategy was demonstrated by the study of compound variations in the smoke of traditional Chinese medicine, Artemisia argyi Levl. et Vant. The results indicated that more than 590 components showed significant differences among released smokes of various moxa velvet ratios. Finally, about 88 compounds were identified, of which phenolic compounds were the most abundant, followed by aromatics, alkenes, alcohols and furans. In conclusion, we may provide a novel approach to the studies of compounds in online released smoke.

Efficacy of Novel Bracing for Treating Sciatica and Cadaveric Dissection to Examine Excursion of the Sciatic Nerve

Background/Objective:Sciatica affects nearly half of all Americans and can often become debilitating, leading to severe pain that can limit performing activities of daily living. Brace application has not been tried for alleviation of pain. In this study, we seek to find if a novel brace can decrease pain and decrease bothersome level of symptoms for those suffering from sciatica. In addition, this study utilizes a cadaveric dissection to understand how the sciatic nerve stretches and tensions upon lower limb manipulation.
 Methods:Fourteen patients self-reported pain, functionality, and bothersome levels pre- and post-bracing. Excel’s data analysis tool was utilized to run statistical tests. One cadaver (2 lower limbs) was dissected, revealing the sciatic nerve at the hip and knee, while tibial nerve at the ankle. Excursion was measured utilizing a fixed pin and an initial distance, the leg manipulated, and final distance from pin measured. Ultimately, excursion was deemed final distance minus initial distance from the pin.
 Results:The brace decreases Visual Analogue Scale (VAS) scores, increases Patient Reported Outcomes, and decreases Sciatica Bothersome Indexes. There was a significant difference in VAS pre- versus post-brace values at initial and 7-day post-visit but not at 21- or 42-day postvisit. Sciatic nerve excursion was greatest at the ankle.
 Conclusion and Potential Impact:Brace use decreases pain levels, increases functionality, and decreases bothersome level of symptoms. The distal nerve moves more upon manipulation and therefore is more prone to tensioning than the proximal nerve. Dissection data illustrates how the brace positions the limb in a way that promotes “detensioning” of the nerve, alleviating sciatica. More cadaver data is needed.

Construction and validation of a method for automated time label segmentation of heart sounds.

Heart sound detection technology plays an important role in the prediction of cardiovascular disease, but the most significant heart sounds are fleeting and may be imperceptible. Hence, obtaining heart sound information in an efficient and accurate manner will be helpful for the prediction and diagnosis of heart disease. To obtain heart sound information, we designed an audio data analysis tool to segment the heart sounds from single heart cycle, and validated the heart rate using a finger oxygen meter. The results from our validated technique could be used to realize heart sound segmentation. Our robust algorithmic platform was able to segment the heart sounds, which could then be compared in terms of their difference from the background. A combination of an electronic stethoscope and artificial intelligence technology was used for the digital collection of heart sounds and the intelligent identification of the first (S1) and second (S2) heart sounds. Our approach can provide an objective basis for the auscultation of heart sounds and visual display of heart sounds and murmurs.

Strategi meningkatkan pendapatan asli Daerah Kota Manado melalui pengelolaan pajak

The aim of this research is to 1) Analyze strategies for increasing Original Regional Income in Manado City through the management of Parking Tax; 2) Analyze the factors that hinder the Manado City Regional Revenue Agency in managing Parking Tax. The author uses qualitative research methods with SWOT analysis as a data analysis tool. The research results show that the Manado City Regional Revenue Agency has Strength and Opportunity (SO), so that its strengths and opportunities can be an advantage for the Manado City Regional Revenue Agency in increasing Original Regional Income through parking taxes. There are four strategic recommendations, namely: 1) Be firm in implementing parking tax sanctions and fines, 2) Improve parking tax services, 3) Improve parking tax communication and education, and 4) Increase parking tax supervision. Meanwhile, the inhibiting factors are: (1) Low discipline/compliance of parking taxpayers in paying taxes, 2) Low honesty/awareness of parking taxpayers in manipulating the amount of income turnover so that tax payments are low, (3) Limited number of human resources working and ineffectiveness of the joint task force in carrying out its main duties (4) Ineffective application of administrative sanctions such as fines, closure and sealing for tax objects that are late, in arrears or even do not pay parking tax, 5) Lack of supporting facilities such as operational vehicles, computers, laptops and printers.

Artificial intelligence and the scientific method: How to cope with a complete oxymoron

Artificial intelligence (AI) can be a powerful tool for data analysis, but it can also mislead investigators, due in part to a fundamental difference between classic data analysis and data analysis using AI. A more or less limited data set is analyzed in classic data analysis, and a hypothesis is generated. That hypothesis is then tested using a separate data set, and the data are examined again. The premise is either accepted or rejected with a value p, indicating that any difference observed is due merely to chance. By contrast, a new hypothesis is generated in AI as each datum is added to the data set. We explore this discrepancy and suggest means to overcome it.

Cervical Cerclage: Prolonging Gestational Period for Successful Pregnancy Outcome

Background: Cervical Cerclage may be an effective treatment option in true cervical insufficiency. Cervical function plays a crucial role to protect growing fetus in utero in maintaining successful pregnancy. As the gestational weeks progress, the cervix undergoes dynamic changes, including softening and shortening, to accommodate fetal growth. Objective: The objective of this study is to evaluate the effectiveness and outcome of cervical cerclage placement as a preventive measure against recurrent pregnancy loss and preterm delivery and its complications, to analyze pregnancy outcomes among participants who received cervical cerclage and to investigate the relationship between cervical cerclage and pregnancy outcome based on gestational age. Method and Materials: The study was conducted over three years at CMH, Jashore and Ibn Sina Hospital Jashore, utilizing a retrospective and longitudinal interventional approach. Ethical approval was obtained before commencing the study, which involved 92 cases undergoing mid-trimester cervical cerclage. Cervical length measurements, cervical cerclage using the McDonald technique, and subsequent medical management were performed with outcome analyzed using descriptive statistics and odds ratio. Data were analyzed retrospectively through SPSS data analysis tool. Result: 45.65% of patients experienced term deliveries, while 33.70% had preterm deliveries, with 20.65% facing abortions. Distinct inclusion criteria unveiled diverse outcomes, such as recurrent pregnancy loss cases showing 40.82% preterm and 38.78% term deliveries. Odds ratios highlighted cervical length's significance, with a 0.25 ratio indicating reduced preterm risk. Interestingly, 61.69% of preterm births occurred between 34 and 36 weeks. Maternal outcomes indicated low incidence rates of hemorrhage (4.8%), infections (1.1%) and cervix laceration (2.4%). Conclusion: The study underscores cervical cerclage's potential in enhancing full-term pregnancies, particularly for patients with cervical incompetence. While highlighting its success, the study also acknowledges associated risks and complications. J Bangladesh Coll Phys Surg 2024; 42: 57-62

Versatile and automated workflow for the analysis of oligodendroglial calcium signals.

Although intracellular Ca2+ signals of oligodendroglia, the myelin-forming cells of the central nervous system, regulate vital cellular processes including myelination, few studies on oligodendroglia Ca2+ signal dynamics have been carried out and existing software solutions are not adapted to the analysis of the complex Ca2+ signal characteristics of these cells. Here, we provide a comprehensive solution to analyze oligodendroglia Ca2+ imaging data at the population and single-cell levels. We describe a new analytical pipeline containing two free, open source and cross-platform software programs, Occam and post-prOccam, that enable the fully automated analysis of one- and two-photon Ca2+ imaging datasets from oligodendroglia obtained by either ex vivo or in vivo Ca2+ imaging techniques. Easily configurable, our software solution is optimized to obtain unbiased results from large datasets acquired with different imaging techniques. Compared to other recent software, our solution proved to be fast, low memory demanding and faithful in the analysis of oligodendroglial Ca2+ signals in all tested imaging conditions. Our versatile and accessible Ca2+ imaging data analysis tool will facilitate the elucidation of Ca2+-mediated mechanisms in oligodendroglia. Its configurability should also ensure its suitability with new use cases such as other glial cell types or even cells outside the CNS.

Using machine learning to identify indicators of rare earth element enrichment in sedimentary strata with applications for metal prospectivity

Rare earth elements (REE), classified as critical minerals which are crucial for clean energy technologies, face soaring demand. While economic deposits are found in limited geologic environments including carbonatites and ion-adsorption clays, unconventional, secondary sources such as those from sedimentary basins could hold potential to meet this increased demand. Coal and its associated combustion by-products, phosphorites, oil sands tailings, and formation waters have all garnered interest for REE recovery, yet they remain significantly underexplored. Accordingly, new tools for data analysis and optimization such as machine learning can assist in mineral prospectivity, with these tools being subject to rapid proliferation in the Earth sciences.This work leverages compositional data analysis principles and machine learning to probe geochemical relationships and predict REE abundances in sedimentary lithologies using unsupervised (correlation, principal component, and cluster analysis) and supervised (regression, support vector machine, random forest, and boosting) machine learning models. These three unsupervised models display similar results, with REE typically being associated with incompatible elements (e.g., Th, Nb, and Hf). Gradient boosting, Adaboost, and Random Forest had the highest performance for predicting REE concentrations, with Th and P commonly being the most important predictor variables. Identifying geochemical indicators of REE enrichment that may be used to assist in discovering potentially exploitable REE resources based on existing data, as well as increasing the understanding of metal behaviour in sedimentary systems, is a step forward in understanding novel secondary and unconventional REE sources. Although REE concentrations from these sources are generally lower than primary ore deposits, the amount of available feedstock, potentially simpler, cheaper, and less environmentally taxing extraction processes, and the added benefit of remediating waste streams and contributing to the circular economy make these sources alluring.

ANALYSIS OF THE ROLE OF EMPLOYEE ACHIEVEMENT IN THE FRAMEWORK OF ACHIEVING POSITION PROMOTION AT THE LAMONGAN LIBRARY SERVICE

The aim of this research is to find out whether work performance influences job promotions at the Lamongan Regency Library Service. This research has a population of 100 employees, and a sample of 100 employees. This research uses quantitative research methods. The sampling technique used was a saturated sampling technique. The data analysis tools used in this research are validity test, reliability test, terminal coefficient test, t test and F test. Based on the partial test, it was found that the t count (0.405) was smaller than the t table (1.6607) with a significance of 0.686<0 .1 so it can be concluded that there is an influence but not significant between the independent variables on the dependent variable Position Promotion (Y) in the research model. Meanwhile, the results of the simultaneous test of Work Performance with a calculated F value (0.164) < F table (2.70) with a significance value of 0.686 < 0.1, it can be concluded that H0 is accepted and H1 is rejected. From these results it can be concluded that the independent variable Job Performance (X1), simultaneously or together has an influence but is not significant on Position Promotion (Y) in the Lamongan Regency Library Service.

A fast, scalable and versatile tool for analysis of single-cell omics data.

Single-cell omics technologies have revolutionized the study of gene regulation in complex tissues. A major computational challenge in analyzing these datasets is to project the large-scale and high-dimensional data into low-dimensional space while retaining the relative relationships between cells. This low dimension embedding is necessary to decompose cellular heterogeneity and reconstruct cell-type-specific gene regulatory programs. Traditional dimensionality reduction techniques, however, face challenges in computational efficiency and in comprehensively addressing cellular diversity across varied molecular modalities. Here we introduce a nonlinear dimensionality reduction algorithm, embodied in the Python package SnapATAC2, which not only achieves a more precise capture of single-cell omics data heterogeneities but also ensures efficient runtime and memory usage, scaling linearly with the number of cells. Our algorithm demonstrates exceptional performance, scalability and versatility across diverse single-cell omics datasets, including single-cell assay for transposase-accessible chromatin using sequencing, single-cell RNA sequencing, single-cell Hi-C and single-cell multi-omics datasets, underscoring its utility in advancing single-cell analysis.

How tool combinations in different pipeline versions affect the outcome in RNA-seq analysis.

Data analysis tools are continuously changed and improved over time. In order to test how these changes influence the comparability between analyses, the output of different workflow options of the nf-core/rnaseq pipeline were compared. Five different pipeline settings (STAR+Salmon, STAR+RSEM, STAR+featureCounts, HISAT2+featureCounts, pseudoaligner Salmon) were run on three datasets (human, Arabidopsis, zebrafish) containing spike-ins of the External RNA Control Consortium (ERCC). Fold change ratios and differential expression of genes and spike-ins were used for comparative analyses of the different tools and versions settings of the pipeline. An overlap of 85% for differential gene classification between pipelines could be shown. Genes interpreted with a bias were mostly those present at lower concentration. Also, the number of isoforms and exons per gene were determinants. Previous pipeline versions using featureCounts showed a higher sensitivity to detect one-isoform genes like ERCC. To ensure data comparability in long-term analysis series it would be recommendable to either stay with the pipeline version the series was initialized with or to run both versions during a transition time in order to ensure that the target genes are addressed the same way.

INCAWrapper: a Python wrapper for INCA for seamless data import, -export, and -processing.

INCA is a powerful tool for metabolic flux analysis, however, import and export of data and results can be tedious and limit the use of INCA in automated workflows. The INCAWrapper enables the use of INCA purely through Python, which allows the use of INCA in common data science workflows. The INCAWrapper is implemented in Python and can be found at https://github.com/biosustain/incawrapper. It is freely available under an MIT License. To run INCA, the user needs their own MATLAB and INCA licenses. INCA is freely available for noncommercial use at mfa.vueinnovations.com.

Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA.

Motor-task functional magnetic resonance imaging (fMRI) is crucial in the study of several clinical conditions, including stroke and Parkinson's disease. However, motor-task fMRI is complicated by task-correlated head motion, which can be magnified in clinical populations and confounds motor activation results. One method that may mitigate this issue is multi-echo independent component analysis (ME-ICA), which has been shown to separate the effects of head motion from the desired blood oxygenation level dependent (BOLD) signal but has not been tested in motor-task datasets with high amounts of motion. In this study, we collected an fMRI dataset from a healthy population who performed a hand grasp task with and without task-correlated amplified head motion to simulate a motor-impaired population. We analyzed these data using three models: single-echo (SE), multi-echo optimally combined (ME-OC), and ME-ICA. We compared the models' performance in mitigating the effects of head motion on the subject level and group level. On the subject level, ME-ICA better dissociated the effects of head motion from the BOLD signal and reduced noise. Both ME models led to increased t-statistics in brain motor regions. In scans with high levels of motion, ME-ICA additionally mitigated artifacts and increased stability of beta coefficient estimates, compared to SE. On the group level, all three models produced activation clusters in expected motor areas in scans with both low and high motion, indicating that group-level averaging may also sufficiently resolve motion artifacts that vary by subject. These findings demonstrate that ME-ICA is a useful tool for subject-level analysis of motor-task data with high levels of task-correlated head motion. The improvements afforded by ME-ICA are critical to improve reliability of subject-level activation maps for clinical populations in which group-level analysis may not be feasible or appropriate, for example, in a chronic stroke cohort with varying stroke location and degree of tissue damage.

HTSplotter: An end-to-end data processing, analysis and visualisation tool for chemical and genetic in vitro perturbation screening.

In biomedical research, high-throughput screening is often applied as it comes with automatization, higher-efficiency, and more and faster results. High-throughput screening experiments encompass drug, drug combination, genetic perturbagen or a combination of genetic and chemical perturbagen screens. These experiments are conducted in real-time assays over time or in an endpoint assay. The data analysis consists of data cleaning and structuring, as well as further data processing and visualisation, which, due to the amount of data, can easily become laborious, time-consuming and error-prone. Therefore, several tools have been developed to aid researchers in this process, but these typically focus on specific experimental set-ups and are unable to process data of several time points and genetic-chemical perturbagen screens. To meet these needs, we developed HTSplotter, a web tool and Python module that performs automatic data analysis and visualization of visualization of eitherendpoint or real-time assays from different high-throughput screening experiments: drug, drug combination, genetic perturbagen and genetic-chemical perturbagen screens. HTSplotter implements an algorithm based on conditional statements to identify experiment types and controls. After appropriate data normalization, including growth rate normalization, HTSplotter executes downstream analyses such as dose-response relationship and drug synergism assessment by the Bliss independence (BI), Zero Interaction Potency (ZIP) and Highest Single Agent (HSA) methods. All results are exported as a text file and plots are saved in a PDF file. The main advantage of HTSplotter over other available tools is the automatic analysis of genetic-chemical perturbagen screens and real-time assays where growth rate and perturbagen effect results are plotted over time. In conclusion, HTSplotter allows for the automatic end-to-end data processing, analysis and visualisation of various high-throughput in vitro cell culture screens, offering major improvements in terms of versatility, efficiency and time over existing tools.

Bio-Economic Assessment of Novel Fish Feed Formulation Software (FUTA AQUAFEEDAPP) for African Catfish Clarias gariepinus (Burchell 1822) Raised in Recirculatory Aquaculture System

In the current study, data on nutritional composition and cost of conventional and non-conventional fish feed ingredients were gathered, a spreadsheet was created to bank the data. Four fish feeds were formulated and produced using three conventional fish feed formulation methods namely Pearson’s Square feed formulation method and two other conventional fish feed softwares, WINFEEDTM, ALLIXTM and a developed software (FUTA AQUAFEEDAPP); the software was developed using data analytical tools: simple harmonic equation, linear programming and stochastic programming techniques. The nutrient composition of the four diets were evaluated in the laboratory. The result of the proximate analyses indicated that the four diets met the crude protein requirements for African Catfish fingerlings, the values ranged from 40.68±0.62 (Pearson square) to 40.98±0.86 (ALLIX). There were no significant differences in the moisture content, crude protein, crude fibre, crude lipid and NFE across all treatments, however there was significant difference in the ash content across the four diets. The result of the cost assessment across the four treatments were significantly different, it revealed that AQUAFEED had the lowest investment cost ($1/Kg) when compared with the three other treatments, while Pearson Square had the highest investment cost ($1.5/kg) due to the high inclusion of fishmeal in the diet.

Novel dimensionality reduction method, Taelcore, enhances lung transplantation risk prediction

In this work, we present a new approach to predict the risk of acute cellular rejection (ACR) after lung transplantation by using machine learning algorithms, such as Multilayer Perceptron (MLP) or Autoencoder (AE), and combining them with topological data analysis (TDA) tools. Our proposed method, named topological autoencoder with best linear combination for optimal reduction of embeddings (Taelcore), effectively reduces the dimensionality of high-dimensional datasets and yields better results compared to other models. We validate the effectiveness of Taelcore in reducing the prediction error rate on four datasets. Furthermore, we demonstrate that Taelcore’s topological improvements have a positive effect on the majority of the machine learning algorithms used. By providing a new way to diagnose patients and detect complications early, this work contributes to improved clinical outcomes in lung transplantation.

Analysis of Green Moral Obligation on Intention to Adopting Electric Vehicles (EV) with Openness to Changes and Self Enhancement as Moderating Variables

The purpose of this research is to predict and analyze the influence of Green Moral Obligation on consumers' intention to adopt EV (electric vehicle)s in Indonesia, with Self Enhancement and Openness to Change as moderating variables. The research subjects are driving consumers distributed across the entire territory of the Republic of Indonesia, with a sample size of 250 respondents distributed through online questionnaires using convenience sampling technique. The data analysis tool used SEM-PLS (SEM-Partial Least Squares). The results of this research indicate that the Green Moral Obligation variable has a significant impact on the intention to adopt electric vehicles, and the Self Enhancement and Openness to Change variables have been proven to positively moderate the relationship between Green Moral Obligation and the intention to adopt electric vehicles. This research is expected to be used for policy development in the automotive industry, as a reference for academics and researchers for further research or theory development.

Pengaruh Label Halal, Harga Dan Citra Merek Terhadap Minat Beli Konsumen Kosmetik Skincare Melalui Aplikasi Tiktok Pada Masyarakat Di Kota Mataram

The purpose of the study is to examine the effects of halal labeling, price, and brand image on the purchasing intention of cosmetic or skincare consumers through Tiktok Application among the population of Mataram City. The study utilized a random sampling technique, and the sample size consisted of 100 respondents. It is a quantitative study that employs primary data. The data collection technique used in the study is a questionnaire, and the data analysis tool used is SPSS version 25.0. The quality of the data is tested using validity and reliability tests. The analytical technique used in the study is multiple linear regression, and the study employs partial correlation, simultaneous testing, and determination coefficient testing.
 Based on the data analysis, the results obtained from the t indicate that the partial effects of halal labeling and brand image significantly influence consumer purchasing intention of cosmetics among the residents of Mataram City. Each independent variable (halal labeling, brand image) has a t-value exceeding the t-table, while the price variable shows insignificant influence as its t-value is less than the t-table. From the F-test, it can be explained that halal labeling, price, and brand image simultaneously have a significant effect on consumer purchasing intention of cosmetics or skincare through TikTok application among the residents of Mataram City. The obtained F-value of 12.642 is greater than the F-table value of 2.72. The coefficient of determination analysis indicates that halal labeling, price, and brand image affect consumer purchasing intention of cosmetics/skincare through TikTok application among the residents of Mataram City by 28.3%, while the remaining 71.7% is influenced by other variables outside of this research model