Data Processing Errors Research Articles

One-dimensional wave theory analysis of factors and characteristics influencing errors in two-wave method data processing in SHPB

One-dimensional wave theory analysis of factors and characteristics influencing errors in two-wave method data processing in SHPB

Methodological Planning and Data Collection. Ensuring a Valid and Credible Research of a Questionnaire Survey in Social Work

The validity and credibility of scientific research are determined by the strictness of planning and elaborating each stage of the research, by correlating the purpose of the research with the chosen research method, by avoiding methodological planning errors, as well as by carrying out each research stage in a manner that excludes errors in data collection, processing and interpretation. In the field of social work, par excellence, the value of an empirical research is conferred, among other things, by the possibility of extrapolating the conclusions of that research to similar situations in the practical activity of social workers, in case work with the beneficiaries of social work services. Due to the close connection between research and practice in this field of social sciences, the methodological design must be transparent and convincing, so as to give practitioners a high degree of confidence in the conclusions of the scientific study, as well as the possibility to capitalize on its informative benefits. So, methodological errors of research projects carried out in the field of social work can have a much wider and more tangible negative impact than in the case of other fields of science, at least from a pragmatic point of view, when theory is translated to practical applicability, and the scientific study is implemented in case work performed with the beneficiaries. This study aims to analyze the questionnaire survey in social work, by highlighting the main methodological errors that can affect the validity of such an empirical study, in order to identify the optimal solutions for a correct planning and methodological development of the scientific study.

Information System for Procurement of Goods and Services of the Politeknik Harapan Bersama (SIPHARBER)

The application of information technology in various fields, including higher education, is rapidly evolving and has extended to critical operational activities such as the procurement of goods and services. This research aims to design and implement a Procurement Information System at Politeknik Harapan Bersama (SIPHARBER), which is expected to enhance efficiency, transparency, and accountability in the procurement process. The system is designed to facilitate coordination between the procurement unit and vendors, reduce the time required for tender processes, and minimize errors in data processing. The development method used for this system is the Waterfall model, which involves stages from requirement analysis to system testing. The outcome of this research is a web-based information system accessible by all related units, enabling real-time monitoring of the procurement process by the leadership, and simplifying vendors' processes to offer their products or services. With the implementation of SIPHARBER, the procurement process at Politeknik Harapan Bersama is expected to be conducted more effectively and efficiently.

Artificial Intelligence in Modernization of Pharmaceutical and Healthcare Industry: A Review

: Artificial intelligence (AI) falls under the purview of computer technology, which analyzes complex data and helps solve problems in different segments. Big Data, Machine Learning, and AI are currently being used by the major pharmaceutical industries to minimize time and costs and increase possibilities. Artificial intelligence is used in the pharmaceutical industry in diverse ways, such as drug discovery and development, clinical trials, disease diagnosis, and different stages in pharmaceutical manufacturing, data analysis, and supply management. Most of the cost and time are involved in drug discovery and clinical trials. Artificial intelligence can minimize human error in data processing, documentation, data integrity issues, and data selection throughout the journey. It works in descriptive, diagnostic, predictive, and prescriptive mode. Major pharmaceutical conglomerates like Pfizer, Roche, Novartis, and Johnson & Johnson have already applied Artificial Intelligence in different segments of pharmaceutical and medicinal science. Tech companies like IBM Watson, Catalia Health, Intel, Microsoft, and Google, in collaboration with pharmaceutical companies, are working in the different areas of drug discovery, early diagnosis, and personalized medicine. Further, AI finds application in the health sector for data management, scanning and evaluation of medical history reports, and finding optimum treatment strategies for chronic care patients. Though lots of research and development are being done on the utilization of artificial intelligence in the pharmaceutical industry, it is still in the nascent stage. This article is our endeavor to study, in detail, the present and future opportunities of machine learning and AI in the pharmaceutical industry as a whole.

A Comparative Study on Imputation Techniques: Introducing a Transformer Model for Robust and Efficient Handling of Missing EEG Amplitude Data.

In clinical datasets, missing data often occur due to various reasons including non-response, data corruption, and errors in data collection or processing. Such missing values can lead to biased statistical analyses, reduced statistical power, and potentially misleading findings, making effective imputation critical. Traditional imputation methods, such as Zero Imputation, Mean Imputation, and k-Nearest Neighbors (KNN) Imputation, attempt to address these gaps. However, these methods often fall short of accurately capturing the underlying data complexity, leading to oversimplified assumptions and errors in prediction. This study introduces a novel Imputation model employing transformer-based architectures to address these challenges. Notably, the model distinguishes between complete EEG signal amplitude data and incomplete data in two datasets: PhysioNet and CHB-MIT. By training exclusively on complete amplitude data, the TabTransformer accurately learns and predicts missing values, capturing intricate patterns and relationships inherent in EEG amplitude data. Evaluation using various error metrics and R2 score demonstrates significant enhancements over traditional methods such as Zero, Mean, and KNN imputation. The Proposed Model achieves impressive R2 scores of 0.993 for PhysioNet and 0.97 for CHB-MIT, highlighting its efficacy in handling complex clinical data patterns and improving dataset integrity. This underscores the transformative potential of transformer models in advancing the utility and reliability of clinical datasets.

Iterative Stress Reconstruction Algorithm to Estimate Three-Dimensional Residual Stress Fields in Manufactured Components

Abstract Residual stress (RS) significantly impacts the mechanical performance of components. Measurement of RS often provides incomplete data in terms of components of stress and spatial density. Employing such fields in finite element simulations results in significant modification of the field to achieve equilibrium and compatibility among strains. To overcome this, an iterative stress reconstruction algorithm (ISRA) is developed to estimate 3D RS fields that satisfy equilibrium, are stress component-wise complete, and represent the characterized data sampled. An Al 7075-T651 plate and an additively manufactured (AM) A36 steel wall are considered for RS reconstruction using measurement data from the literature. A maximum variation of ∼2.5 MPa in the Al plate, and ∼10 MPa in the steel wall are observed between the reconstructed and measured stresses. Furthermore, unknown stress components emerge and reach significant magnitudes (upto ∼2.3 MPa in the Al plate and ∼45 MPa in the AM wall) during ISRA. Indeed, it is found that minor errors in measurement or data processing are eliminated through the physical requirements during ISRA. Employing a reconstructed RS field is hence not just more accurate given its compatibility, but it additionally corrects for minor errors in measurement. Furthermore, it is found that spatially dense measurement data result in convergence with fewer iterations. Finally, although ISRA yields a nonunique solution dependent on boundary conditions, measurement errors, fitting errors, and mesh density, it accommodates for uncertainties and inaccuracies in measurement, as opposed to failing to reach a physically realistic converged solution.

Evaluation method for ecology-agriculture-urban spaces based on deep learning

With the increasing global population and escalating ecological and farmland degradation, challenges to the environment and livelihoods have become prominent. Coordinating urban development, food security, and ecological conservation is crucial for fostering sustainable development. This study focuses on assessing the "Ecology-Agriculture-Urban" (E-A-U) space in Yulin City, China, as a representative case. Following the framework proposed by Chinese named "environmental capacity and national space development suitability evaluation" (hereinafter referred to as "Double Evaluation"), we developed a Self-Attention Residual Neural Network (SARes-NET) model to assess the E-U-A space. Spatially, the northwest region is dominated by agriculture, while the southeast is characterized by urban and ecological areas, aligning with regional development patterns. Comparative validations with five other models, including Logistic Regression (LR), Naive Bayes (NB), Gradient Boosting Decision Trees (GBDT), Random Forest (RF) and Artificial Neural Network (ANN), reveal that the SARes-NET model exhibits superior simulation performance, highlighting it’s ability to capture intricate non-linear relationships and reduce human errors in data processing. This study establishes deep learning-guided E-A-U spatial evaluation as an innovative approach for national spatial planning, holding broader implications for national-level territorial assessments.

Pembinaan analisis data dan informasi statistik perencanaan pembangunan daerah [Development of statistical data analysis and information for regional development planning

The purpose of this development activity is to enhance skills in planning, data analysis, and the use of information technology so that the Subulussalam City Government can improve the quality of planning documents produced, respond to the needs of the community, and provide a good understanding to employees related to statistical analysis. The method of implementing this activity is through training or seminars held on October 5, 2023, at the Regional Development Planning Agency (Bappeda) Hall of Subulussalam City for approximately 3 hours. The material presented includes basic concepts of statistical analysis, data analysis techniques, and the application of statistical analysis in regional development planning. Three main challenges in statistical analysis of regional development data include data limitations, data collection errors, and data processing errors. These challenges can be overcome by increasing understanding of methodology, quantity and quality of human resources, and inter-agency coordination. Statistical analysis in regional development planning can result in more targeted regional development planning and can identify challenges and opportunities to achieve sustainable development plans. Statistical analysis in regional development planning is important to be used as the basis for objective policies.

Analyzing crop production statistics of the Philippines using the newcomb-benford law

The agricultural sector plays a crucial role in the Philippines, contributing significantly to its food security and economic growth. However, the sector faces various challenges that hinder its productivity and growth. To address these challenges and promote sustainable agricultural practices, accurate and reliable crop production statistics are essential for informed decision-making and resource allocation. The Newcomb-Benford Law (NBL) provides a statistical distribution pattern for leading digits in numerical datasets, offering insights into data reliability. In this research study, we applied the NBL to analyze crop production statistics for major crops (rice, corn, coconut, sugarcane, banana, cassava, and pineapple) in the Philippines. We assessed the conformity of the datasets to NBL expectations and identified significant deviations, indicating potential data accuracy issues, collection method discrepancies, or reporting irregularities. Although these deviations do not conclusively suggest fraud, they underscore the need for meticulous data validation. The first two-digit test further strengthened the findings, providing a comprehensive understanding of dataset conformity. Transparent data collection and validation processes are crucial for trustworthy agricultural statistics, supporting effective policy-making and resource allocation. Future research should investigate the root causes of the deviations, explore data processing errors, and implement stringent data validation procedures. Additionally, expanding the analysis to include other crops and conducting comparative studies between regions and time periods would enhance data integrity and contribute to sustainable agricultural practices and food security in the Philippines.

Kerr effect based optical switching for the assessment of all-optical sequence generator and decoder circuits

Abstract The sequence generator and decoders are used for noise analysis, signal propagation, data processing, and bit error rate analysis. The semiconductor and modern electronic components are facing challenges in terms of processing speed and data rates. However, by using photons rather than electrons as the information carriers, these difficulties can be reduced. Photonic devices attained the operating speed in the regime of terahertz, but sit back due to the diffraction limit, which can be overcome by employing plasmon-based devices or all-optical devices. This work presents a numerical investigation of metal-insulator-metal (MIM) plasmonic waveguides based two-bit sequence generator (SG) and decoder. The structure of SG and decoder circuits are designed within the footprints of 86 × 9 and 140 × 9 µm, by cascading one power splitter (PS) and four Mach-Zehnder interferometers (MZIs), and two PS and five MZI, respectively. To design the MZI, the optimization of the S-bend waveguide, the coupling length of the power splitter, and the length of the interferometric arm are done by recording the output power. The highest extinction ratio of 22.1 dB is attained at the coupling and interferometric arm lengths of 1.5 µm and 5 µm, respectively. The propagation of the optical signal through the structure of SG is observed by using a two-dimensional finite difference time domain method-based tool.

E Purchasing Application to Improve Company Performance

This research aims to create a web-based purchasing application. With the current purchasing system, there are often errors in data processing and also difficulties in searching for documents because there is no use of databases in the system. Therefore, an application is needed in the management of web-based purchasing data with the warerfall development method. While the analysis and design of the application is done with an object-oriented approach described by UML notation. This system is made using the php programming language and MySql database, the result of this research is the creation of a web-based data processing application that can be used by companies to facilitate the data processing process.

Method to Solve Underwater Laser Weak Waves and Superimposed Waves.

With the rapid development of Lidar technology, the use of Lidar for underwater terrain detection has become feasible. There is still a challenge in the process of signal resolution: the underwater laser echo signal is different to propagating in the air, and it is easy to produce weak waves and superimposed waves. However, existing waveform decomposition methods are not effective in processing these waveform signals, and the underwater waveform signal cannot be correctly decomposed, resulting in subsequent data-processing errors. To address these issues, this study used a drone equipped with a 532 nm laser to detect a pond as the study background. This paper proposes an improved inflection point selection decomposition method to estimate the parameter. By comparing it with other decomposition methods, we found that the RMSE is 2.544 and R2 is 0.995975, which is more stable and accurate. After estimating the parameters, this study used oscillating particle swarm optimization (OPSO) and the Levenberg-Marquardt algorithm (LM) to optimize the estimated parameters; the final results show that the method in this paper is closer to the original waveform. In order to verify the processing effect of the method on complex waveform, this paper decomposes and optimizes the simulated complex waveforms; the final RMSE is 0.0016, R2 is 1, and the Gaussian component after decomposition can fully represent the original waveform. This method is better than other decomposition methods in complex waveform decomposition, especially regarding weak waves and superimposed waves.

Utilization Of UML (Unified Modeling Language) In The Design Of Academic Information Systems Based On The OOAD Method

Junior high school is one of the organizations whose business processes involve teaching and learning activities under the principal's responsibility. SMP ABC is a junior high school that continually strives to improve the quality of education, one of which is its academic services. However, the process of storing and processing data at SMP ABC still uses Microsoft word and Microsoft excel. Hence, the data management process is not good and allows a lot of errors in processing academic data. The academic information system is a system that can help solve the problem of errors in data processing and make the educational service process better and more effective. So SMP ABC also needs to implement an academic information system. The results of this study are to analyze and design educational information systems using the OOAD method using UML (Unified Modeling Language) tools with several diagrams, including use case diagrams, sequence diagrams, and ERD, to facilitate academic data management and improve efficiency and effectiveness so that management data on SPM ABC can be computerized appropriately.

Errors in structural biology are not the exception.

During the COVID-19 pandemic, the structural biology community swung into action quickly and efficiently, and many urgent questions were solved by macromolecular structure determination. The Coronavirus Structural Task Force evaluated all structures from SARS-CoV-1 and SARS-CoV-2, but errors in measurement, data processing and modelling are present beyond these structures and throughout the structures deposited in the Protein Data Bank. Identifying them is only the first step; in order to minimize the impact that errors have in structural biology, error culture needs to change. It should be emphasized that the atomic model which is published is an interpretation of the measurement. Furthermore, risks should be minimized by addressing issues early and by investigating the source of a given problem, so that it may be avoided in the future. If we as a community can do this, it will greatly benefit experimental structural biologists as well as downstream users who are using structural models to deduce new biological and medical answers in the future.

Pemilihan Mahasiswa Berprestasi Menggunakan Algoritma AHP (Studi Kasus: Fakultas Teknik UBB)

The Selection of outstanding students at the Faculty of Engineering is an activity that is routinely carried out every year. It starts with each department selecting student candidates who are considered outstanding and collecting some data such as GPA, certificates of academic or non-academic achievements, and certificates of organizational history. Furthermore, representatives of each department follow the selection at the faculty level. In the process of selecting outstanding students, the faculty forms a team that will assess the outstanding student candidates. However, the process is still done manually and takes a long time to process the data. In addition, the manual processing of high-achieving student selection scores is prone to data processing errors which ultimately lead to inaccuracies in the assessment results. Therefore, based on this problem, a decision support system (DSS) is needed to assist the Faculty in determining one of the outstanding students using the AHP method. The criteria that become input in the selection of outstanding students are GPA, scientific papers, number of academic achievement certificates, non-academics, and English language skills. The results of this study indicate that the AHP method can determine alternative recommendations for outstanding students based on the specified input criteria with a consistency value of 0.56.

Performance and Consistency of Final Global Ionospheric Maps from Different IGS Analysis Centers

Ionospheric delay is one of the most problematic errors in satellite-based positioning data processing. The Global Ionospheric Map (GIM), which is publicly available daily in various analysis centers, is thus vitally important for positioning users. There are variations in the accuracy and consistency of GIMs issued by Ionosphere Associate Analysis Centers (IAACs) due to the differences in ionospheric modeling methods and selected tracking stations. In this study on the International GNSS Service’s (IGS) final GIM, the ionospheric total electron content (TEC) (from 243 global navigation satellite system (GNSS) monitoring stations around the world) and the ionospheric TEC (from the Jason-3 altimeter satellite) are selected as reference. By using these three references, we evaluate the performance and consistency of final GIM products from seven IGS IAACs, including the Chinese Academy of Sciences (CAS), the Center for Orbit Determination in Europe (CODE), Natural Resources Canada (EMR), the European Space Agency (ESA), the Jet Propulsion Laboratory (JPL), Universitat Politècnica de Catalunya (UPC), and Wuhan University (WHU) in the mid-solar activity year (2022) and the low-solar activity year (2020). Firstly, the comparison with the IGS final GIM shows that the consistency of each GIMs is basically the same, with the mean value ranging from −0.3 TECu (total electron content unit) to 1.4 TECu. Secondly, the validation with Jason-3 altimeter satellite shows that the accuracy of several GIMs is almost the same, except for the JPL with the worst accuracy and an overall mean deviation (BIAS) between 2 and 6 TECu. Thirdly, the comparison with VTEC extracted from GNSS monitor stations shows that the CAS has the best accuracy in different latitude bands with a root mean square (RMS) of about 2.2–4.7 TECu. In addition, it is found that the accuracy in areas with more stations for ionospheric modelling is better than those with less stations in different latitude bands; meanwhile, the accuracy is closely related to the modeling methods of different GIMs.

Challenges Encountered and Lessons Learned when Using a Novel Anonymised Linked Dataset of Health and Social Care Records for Public Health Intelligence: The Sussex Integrated Dataset

Background: In the United Kingdom National Health Service (NHS), digital transformation programmes have resulted in the creation of pseudonymised linked datasets of patient-level medical records across all NHS and social care services. In the Southeast England counties of East and West Sussex, public health intelligence analysts based in local authorities (LAs) aimed to use the newly created “Sussex Integrated Dataset” (SID) for identifying cohorts of patients who are at risk of early onset multiple long-term conditions (MLTCs). Analysts from the LAs were among the first to have access to this new dataset. Methods: Data access was assured as the analysts were employed within joint data controller organisations and logged into the data via virtual machines following approval of a data access request. Analysts examined the demographics and medical history of patients against multiple external sources, identifying data quality issues and developing methods to establish true values for cases with multiple conflicting entries. Service use was plotted over timelines for individual patients. Results: Early evaluation of the data revealed multiple conflicting within-patient values for age, sex, ethnicity and date of death. This was partially resolved by creating a “demographic milestones” table, capturing demographic details for each patient for each year of the data available in the SID. Older data (≥5 y) was found to be sparse in events and diagnoses. Open-source code lists for defining long-term conditions were poor at identifying the expected number of patients, and bespoke code lists were developed by hand and validated against other sources of data. At the start, the age and sex distributions of patients submitted by GP practices were substantially different from those published by NHS Digital, and errors in data processing were identified and rectified. Conclusions: While new NHS linked datasets appear a promising resource for tracking multi-service use, MLTCs and health inequalities, substantial investment in data analysis and data architect time is necessary to ensure high enough quality data for meaningful analysis. Our team made conceptual progress in identifying the skills needed for programming analyses and understanding the types of questions which can be asked and answered reliably in these datasets.

IRs-corrections induce potentially misjudging toward electrocatalytic water oxidation

‘Outstanding’ electrocatalysts towards oxygen evolution reaction (OER) are springing up at present, to the extent that one questions if most nanomaterials are inherently highly active electrocatalysts. The answer is negative for certain easy-to-ignore data processing errors could lead to potential misjudgment of electrocatalytic activity. Combing through 100 highly cited papers (ESI 1% in Web of Science) concerning OER published in the last three years, the applications of uncompensated potential (iRs)-correction rates are severely inconsistent. Herein, taking the benchmarks RuO2 and IrO2 as model catalysts, the authors investigated the effects of various iRs-correction rates on OER performance (overpotential, Tafel slope, and turnover frequency). The unanticipated discoveries may provoke a re-examination of those reported as ‘great’ OER electrocatalysts as well as help draw attention to iRs-correction rates in the field of electrocatalysis.

Information System Self Acces Center (SAC) Language Center Muhammadiyah University Bengkulu

Self Access Center (SAC) is an independent learning tool to access various knowledge outside the acquisition of material in the central classroom to access various knowledge independently without teacher guidance. Self Access Center Language Center University of Muhammadiyah Bengkulu (UMB) is one of the training and test institutions in Bengkulu, Self Access Center data processing requires a lot of accuracy so that there are no errors in data processing. The Self Access Center (SAC) of the University of Muhammadiyah Bengkulu said that there was no information system for processing the Self Access Center (SAC) data, all the TOEFL Self Access Center (SAC) test registration processes were still carried out manually. This study examines how to design a Self Access Center Information system for the Language Center of the University of Muhammadiyah Bengkulu. The purpose of this study was to design a Self Access Center Information system for the Language Center of the University of Muhammadiyah Bengkulu. The development model used is the Rapid Application Development (RAD) method. PHP programming language and MySQL database have been well designed so that they can be implemented. The results of this study are the new system replaces the old system by overcoming the weaknesses in the manual system (the old one), then the system provides convenience for UMB Self access center administrative officers in accessing stock and making reports, thus making the work of UMB Self access center administrative officers be effective and efficient.

Feature-Based Characterisation of Turned Surface Topography with Suppression of High-Frequency Measurement Errors.

Errors that occur when surface topography is measured and analysed can be classified depending on the type of surface studied. Many types of surface topographies are considered when frequency-based errors are studied. However, turned surface topography is not comprehensively studied when data processing errors caused by false estimation (definition and suppression) of selected surface features (form or noise) are analysed. In the present work, the effects of the application of various methods (regular Gaussian regression, robust Gaussian regression, and spline and fast Fourier Transform filters) for the suppression of high-frequency measurement noise from the raw measured data of turned surface topography are presented and compared. The influence and usage of commonly used available commercial software, e.g., autocorrelation function, power spectral density, and texture direction, which function on the values of areal surface topography parameters from selected (ISO 25178) standards, are also introduced. Analysed surfaces were measured with a stylus or via non-contact (optical-white light interferometry) methods. It was found that the characterisation of surface topography, based on the analysis of selected features, can be crucial in reducing measurement and data analysis errors when various filters are applied. Moreover, the application of common functions can be advantageous when feature-based studies are proposed for both profile and areal data processing.