Data Processing Research Articles

Hardware and Software Design of Programmable Medium and High-Speed Data Acquisition (DAQ) Board of Fiber Optic Signal for Partial Discharge Acquisition

The anti-electromagnetic interference capability of partial discharge (PD) acoustic signal conversion and collection circuits severely restrict the sensitivity of PD detection. The data acquisition (DAQ) systems available in the current market are costly and have limited functionality, making it difficult to satisfy the acquisition requirements for PD detection. This paper proposes a medium to high-speed fiber optic signal acquisition board with an adjustably controlled sampling rate and filter cutoff frequency. The circuit achieves a higher signal-to-noise (SNR) ratio by distributing the noise in each part of the signal acquisition chain reasonably. The temperature characteristics of the acquisition module are improved by utilizing the programmable T-type structure for transimpedance amplification of photocurrent. The DAQ card performs data acquisition and processing using STM32H743 internal ADC and caches data in bulk with an SRAM and SD card. A data uploading method based on time reference has been proposed, which enables full, effective information signal upload through a low-cost transmission interface. The research ultimately achieves a stable sampling of three channels at 1 MSps, SNR of 63 dB, and programmable gain amplification of the photocurrent with 0–60 dB. Finally, the system is used for PD acoustic signal acquisition in the frequency range of 20 Hz to 100 kHz.

Specifics of Due Diligence procedure in mergers and acquisitions in the Russian Federation

Aim. To study the elements and main features of the Due Diligence procedure, including mandatory and additional stages of due diligence of Russian companies, as well as taking into account the peculiarities of the Russian legislation and regulatory bodies.Objectives. To perform an in-depth theoretical analysis of the main tasks of the Due Diligence procedure in M&A of enterprises; to develop proposals for improving the methodology of the Due Diligence procedure in the Russian Federation (RF).Methods. The authors used general scientific research methods, including data collection and processing, content analysis, generalization, systematization, analysis of legislation and legal practice in the field of mergers and acquisitions, as well as the experience of Russian enterprises in conducting the Due Diligence procedure.Results. The Due Diligence procedure is an important stage of corporate transactions in the M&A process in Russia. The article shows that one of the key features is the necessity to take into account specific legal and tax aspects of the Russian legislation when conducting due diligence. It is necessary to take into account not only the risks, but also the influence of state regulators and regulatory bodies on the results of the transaction. Based on the results of the analysis and systematization of the information obtained, general methods and recommendations in the course of the Due Diligence procedure were determined. The statistical data on M&A transactions in Russia are considered, the attention is emphasized on the importance of Due Diligence at the rapid growth of M&A transactions. It is proved that the Due Diligence procedure increases the transparency of Russian companies and contributes to the improvement of the investment climate in the country.Conclusions. The study of the peculiarities of Due Diligence in M&A in Russia allows us to focus on the importance of legal analysis, financial and economic evaluation, as well as personnel and corporate culture assessment. Legal analysis provides an opportunity to identify and assess legal risks and problems related to the company's operations. Financial and economic analysis and audit help to identify and assess financial risks and problems, current and potential. Personnel and corporate culture assessment helps to understand how well they are aligned with the business objectives and strategy of the transaction. All three areas of assessment must comply with the requirements of domestic legislation and take into account the specifics of the labor market in Russia. A proper Due Diligence assessment of all three areas will help to detect possible risks and problems related to legal, financial and personal aspects of the company's activities, as well as provide a reliable basis for making correct and informed decisions regarding the M&A transaction.

RANCANGAN APLIKASI SPK PENENTUAN WALI KELAS BERDASARKAN PRESTASI GURU DENGAN METODE AHP PADA SMK JAKARTA BARAT 1

A decision support system (DSS) is the definition of a procedural model of data processing and assisting management in making decisions. Computer-based decision support systems can improve decision-making ability to solve problems that appear structured or unstructured. The Analytical Hierarchy Process (AHP) method can be used to solve the DSS problem. This method is one of the most sought-after optimal solutions among several other decision support system method options. The research was conducted at SMKN 1 West Jakarta which aimed to assist the school in selecting homeroom teachers based on the effectiveness of teachers in the learning process to students. The creation of this application is done through data collection, system design, system analysis and database design. The creation of this application program is developed using PHP, the database is MySQL. In running this application, input or alternative information is needed, namely the names of teachers, and the assessment criteria are professional values, pedagogic values, personality values, and social values. The process requirements needed are alternative data entry process, teacher data entry process and also criteria data. The result is a teacher report that is expected to help the principal to determine homeroom based on teacher achievement scores.

Methods for estimating entropy and analyzing sectoral development of the economy

Aim. To substantiate the method of estimating the entropy of the sectoral structure of the economy and on this basis to develop methods of analyzing its sectoral development.Objectives. To define the essence of sectoral entropy of socio-economic systems; to consider the approaches to its assessment and the relationship between the entropy of industries and average per capita gross domestic product (GDP) with the structure of value added of industries of the economy.Methods. To achieve the set goals, the general scientific methods of analysis, synthesis, comparison, generalization, as well as methods of statistical data processing were applied.Results. The expediency of using a probabilistic approach to the estimation of entropy of homogeneous economic structures, which with their development changes according to the exponential law, is substantiated. The position that it is reasonable to estimate the entropy of a homogeneous structure by the averaged sum of entropies of its elements has been argued. The boundary values of the entropy of a homogeneous structure are established. The exponential relationship between the entropy of industries and average GDP per capita and the structure of value added of industries, industry entropy and the share of net taxes on production and imports in the GDP structure has been determined.Conclusions. The identified features of sectoral development of the economy constitute a theoretical and methodological basis, which allows us to establish the significance of influencing factors. This is necessary for sectoral regulation of the economy.

Chemokine CXCL8 and its receptors as markers of colorectal cancer

BACKGROUND: The chemokine CXCL8 and its receptor are involved in the activation and transport of inflammatory mediators and regulate the proliferation and renewal of cancer stem cells in colon cancer. It is believed that CXC signaling may be associated with poor prognosis in colorectal cancer. AIM: To study the possibilities of using CXCL8-CXCR1/2 pathway indicators as markers in colorectal cancer. MATERIAL AND METHODS: Isolation of ribonucleic acid (RNA) from histological sections of tumors obtained intraoperatively from 59 patients diagnosed with colorectal cancer was carried out using magnetic particles, and quantitative polymerase chain reaction in real time was performed. Calculation of the normalized expression of the CXCL8 and CXCR1 genes relative to the referee gene was done using special software. Statistical data processing was performed using Statistica 3.0, BioStat v. 7.1 programs. Comparisons of characteristics were performed using the Mann–Whitney U test. Cox and Kaplan–Meier criteria were used to analyze overall and disease-free survival. RESULTS: The expression of CXCL8 in intestinal adenocarcinoma cells with low differentiation [Me (Q1–Q3) — 8.770 (1.127–1.114)] was significantly higher than in the groups of moderately and well differentiated tumors (p=0.004 and p=0.012, respectively); and in tumor tissue refractory to chemotherapy, it was significantly higher [4.374 (2.052–7.045)] compared to resistant [2.200 (1.388–5.037); p=0.008] and sensitive [1.624 (0.739–2.586); p=0.042]. The level of CXCR1 messenger RNA in tumor tissue was increased in the presence of BRAF mutation [3.645 (0.801–1.090); p=0.009] and low tumor differentiation [6.965 (3.938–12.225); p=0.002], as well as in tumor tissue refractory to FOLFOX/XELOX chemotherapy [46.224 (27.580–83.570); p=0.0009]. CONCLUSION: Expression of components of the CXCL8-CXCR1/2 signaling pathway in tumor tissue may be a marker of sensitivity to FOLFOX/XELOX chemotherapy in colorectal cancer.

A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions

Internet of Things (IoT) devices often operate with limited resources while interacting with users and their environment, generating a wealth of data. Machine learning models interpret such sensor data, enabling accurate predictions and informed decisions. However, the sheer volume of data from billions of devices can overwhelm networks, making traditional cloud data processing inefficient for IoT applications. This paper presents a comprehensive survey of recent advances in models, architectures, hardware, and design requirements for deploying machine learning on low-resource devices at the edge and in cloud networks. Prominent IoT devices tailored to integrate edge intelligence include Raspberry Pi, NVIDIA’s Jetson, Arduino Nano 33 BLE Sense, STM32 Microcontrollers, SparkFun Edge, Google Coral Dev Board, and Beaglebone AI. These devices are boosted with custom AI frameworks, such as TensorFlow Lite, OpenEI, Core ML, Caffe2, and MXNet, to empower ML and DL tasks (e.g., object detection and gesture recognition). Both traditional machine learning (e.g., random forest, logistic regression) and deep learning methods (e.g., ResNet-50, YOLOv4, LSTM) are deployed on devices, distributed edge, and distributed cloud computing. Moreover, we analyzed 1000 recent publications on “ML in IoT” from IEEE Xplore using support vector machine, random forest, and decision tree classifiers to identify emerging topics and application domains. Hot topics included big data, cloud, edge, multimedia, security, privacy, QoS, and activity recognition, while critical domains included industry, healthcare, agriculture, transportation, smart homes and cities, and assisted living. The major challenges hindering the implementation of edge machine learning include encrypting sensitive user data for security and privacy on edge devices, efficiently managing resources of edge nodes through distributed learning architectures, and balancing the energy limitations of edge devices and the energy demands of machine learning.

Multistate, Ultrathin, Back-End-of-Line-Compatible AlScN Ferroelectric Diodes.

The growth in data generation necessitates efficient data processing technologies to address the von Neumann bottleneck in conventional computer architecture. Memory-driven computing, which integrates nonvolatile memory (NVM) devices in a 3D stack, is gaining attention, with CMOS back-end-of-line (BEOL)-compatible ferroelectric (FE) diodes being ideal due to their two-terminal design and inherently selector-free nature, facilitating high-density crossbar arrays. Here, we demonstrate BEOL-compatible, high-performance FE diodes scaled to 5, 10, and 20 nm FE Al0.72Sc0.28N/Al0.64Sc0.36N films. Through interlayer (IL) engineering, we show substantial improvements in the on/off ratios (>166 times) and rectification ratios (>176 times) in these scaled devices. These characteristics also enable 5-bit multistate operation with a stable retention. We also experimentally and theoretically demonstrate the counterintuitive result that the inclusion of an IL can lead to a decrease in the ferroelectric switching voltage of the device. An in-depth analysis into the device transport mechanisms is performed, and our compact model aligns seamlessly with the experimental results. Our results suggest the possibility of using scaled AlxSc1-xN FE diodes for high-performance, low-power, embedded NVM.

Imaging and 3D Analysis Based on Two or More Three-Dimensional CBCT Recordings before and after Orthodontic Treatment and Maxillofacial Therapy

Cone beam computed tomography (CBCT) has emerged as a crucial radiographic technique for orthodontic diagnosis and treatment planning, particularly for cases requiring the assessment of complex anatomical relationships. In the first part of the study, we examined metric characteristics measured on 3D cranial models of patients before and after surgery. In the second part, we conducted more complex data processing, analyzing a set of 12 cranial feature points using Procrustes analysis to quantify and visually represent surgical modifications. The third part involved comparing 3D facial surfaces using Iterative Closest Point (ICP) alignment and nearest point-to-point distances. Additionally, we tested point configurations in the facial soft tissues. The study included a group of orthodontic patients from whom CBCT data and 3D facial scans were obtained during treatment. The results demonstrated that each method could assess preoperative and postoperative changes to varying degrees. They also highlighted potential gender differences in surgical modifications that warrant further investigation and consideration during surgical planning. The aim of our study was to compare 3D visualizations of skull and facial models before and after surgery, to assess the degree of relative agreement or similarity, and to identify any morphological differences.

Hand Sign Interpretation through Virtual Reality Data Processing

The research lays the groundwork for further advancements in VR technology, aiming to develop devices capable of interpreting sign language into speech via intelligent systems. The uniqueness of this study lies in utilizing the Meta Quest 2 VR device to gather primary hand sign data, subsequently classified using Machine Learning techniques to evaluate the device's proficiency in interpreting hand signs. The initial stages emphasized collecting hand sign data from VR devices and processing the data to comprehend sign patterns and characteristics effectively. 1021 data points, comprising ten distinct hand sign gestures, were collected using a simple application developed with Unity Editor. Each data contained 14 parameters from both hands, ensuring alignment with the headset to prevent hand movements from affecting body rotation and accurately reflecting the user's facing direction. The data processing involved padding techniques to standardize varied data lengths resulting from diverse recording periods. The Interpretation Algorithm Development involved Recurrent Neural Networks tailored to data characteristics. Evaluation metrics encompassed Accuracy, Validation Accuracy, Loss, Validation Loss, and Confusion Matrix. Over 15 epochs, validation accuracy notably stabilized at 0.9951, showcasing consistent performance on unseen data. The implications of this research serve as a foundation for further studies in the development of VR devices or other wearable gadgets that can function as sign language interpreters.

Integrating Cosmic Microwave Background Readings with Celestial Navigation to Enhance Deep Space Navigation.

This research unveils a cutting-edge navigation system for deep space missions that utilizes cosmic microwave background (CMB) sensor readings to enhance spacecraft positioning and velocity estimation accuracy significantly. By exploiting the Doppler-shifted CMB spectrum and integrating it with optical measurements for celestial navigation, this approach employs advanced data processing through the Unscented Kalman Filter (UKF), enabling precise navigation amid the complexities of space travel. The simulation results confirm the system's exceptional precision and resilience in deep space missions, marking a significant advancement in astronautics and paving the way for future space exploration endeavors.

Synergistic Solvation as the Enhancement of Local Mixing.

Mixing two solvents can sometimes make a much better solvent than expected from their weighted mean. This phenomenon, called synergistic solvation, has commonly been explained via the Hildebrand and Hansen solubility parameters, yet their inability in other solubilization phenomena, most notably hydrotropy, necessitates an alternative route to elucidating solubilization. While, recently, the universal theory of solubilization was founded on the statistical thermodynamic fluctuation theory (as a generalization of the Kirkwood-Buff theory), its demand for experimental data processing has been a hindrance for its wider application. This can be overcome by the solubility isotherm theory, which is founded on the fluctuation theory yet reduces experimental data processing significantly to the level of isotherm analysis in sorption. The isotherm analysis identifies the driving force of synergistic solvation as the enhancement of solvent mixing around the solute, opposite in behavior to hydrotropy (characterized by the enhancement of demixing or self-association around the solute). Thus, the fluctuation theory, including its solubility isotherms, provides a universal language for solubilization across the historic subcategorization of solubilizers, for which different (and often contradictory) mechanistic models have been proposed.

Predicting problem-solving success in an office simulation applying N-grams and a random forest to behavioral process data

Predicting students' problem-solving success in computer-based simulations at an early stage allows adaptive educational systems to provide learners with personalized support. In this paper, we predict students' problem-solving success by applying a machine-learning model, the random forest, to produce a binary classification (more vs. less successful students). During a business-related problem scenario that lasted 55 min, early behavioral data (during the first 5, 10, and 20 min) such as mouse clicks and keyboard strokes (approx. 29,800 early-window clickstreams and keystrokes during the first 20 min) of 234 trainees were recorded, mirroring the students' problem-solving behavior. We used the n-gram sequence mining technique, which was originally introduced within the emerging disciplines of natural language processing, text mining, and machine learning and has proven to be effective, particularly in the examination of online behavior. We trained the random forest model with training datasets that included all features (bigrams), as well as selected features (the most predictable bigrams explaining inter-group differences). Our results show that early predictions based on the first 10 and 20 min contained sufficient information to accurately predict problem-solving success, while predictions that are too early (based on the first 5 min) do not. As the size of the initial time window expanded, the classification performance improved. Moreover, the selection of the most predictable features improved the models' performance for all three time intervals. The model that was trained with only selected robust features that occurred in the first 20 min achieved the highest ROC AUC score of almost 0.70. This result falls within the range of accuracy scores observed in similar studies. From the instructor's perspective, predictions help in the early identification of weak students and can provide them with personalized learning prompts. For more successful students, tasks can be enriched adaptively.

The relevance of open educational resources for post-soviet higher education in the digital age

ABSTRACT The digital era has seen the rise of innovations such as online training platforms and digital toolkits for teaching and learning whilst at the same time open access initiatives are losing the attention of educators. The problem under consideration in this paper is the replacement of open educational resources (OER) with digital innovations in higher education (HE). The purpose of this study is to identify the relevance of OER to post-Soviet HE in the digital age. The research employed methods of descriptive analytics to process data on users’ activities extracted from OER repositories with web-analytic apps. The survey was conducted among 441 lecturers to elucidate their request for access to and patterns of usage of digital OER. The findings confirmed the relevance of OER in contemporary education through the similarity of lecturers’ experience worldwide. During the urgent transition to remote learning owing to the COVID-19 pandemic, OER helped lecturers access informed digital practices through appropriate methods and content. OER has thus retained its relevance against the backdrop of the emergence of digital innovations in HE. The international comparison of findings demonstrates the existence of a shared awareness of the importance of OER and similar drivers for OER production and usage.

Smart Wireless Transducer Dedicated for Use in Aviation Laboratories.

Reliable testing of aviation components depends on the quality and configuration flexibility of measurement systems. In a typical approach to test instrumentation, there are tens or hundreds of sensors on the test head and test facility, which are connected by wires to measurement cards in control cabinets. The preparation of wiring and the setup of measurement systems are laborious tasks requiring diligence. The use of smart wireless transducers allows for a new approach to test preparation by reducing the number of wires. Moreover, additional functionalities like data processing, alarm-level monitoring, compensation, or self-diagnosis could improve the functionality and accuracy of measurement systems. A combination of low power consumption, wireless communication, and wireless power transfer could speed up the test-rig instrumentation process and bring new test possibilities, e.g., long-term testing of moving or rotating components. This paper presents the design of a wireless smart transducer dedicated for use with sensors typical of aviation laboratories such as thermocouples, RTDs (Resistance Temperature Detectors), strain gauges, and voltage output integrated sensors. The following sections present various design requirements, proposed technical solutions, a study of battery and wireless power supply possibilities, assembly, and test results. All presented tests were carried out in the Components Test Laboratory located at the Łukasiewicz Research Network-Institute of Aviation.

Coffee Shop Business Strategy Analysis Using the Business Model Canvas Method and Quantitative Strategic Planning Matrix (Case Study: Coffee Shop 'Kopi Dul')

Coffee Shop Kopi Dul is a Coffee Shop that has been established since 2018. The development of Coffee Shop which is getting higher from year to year requires Coffee Shop Kopi Dul to continue to innovate in order to survive amidst competition by mapping business models and creating business development strategies based on internal factors and external. The aim of this research is to plan the development of the Kopi Dul Coffee Shop business using the Business Model Canvas and QSPM methods. The data processing process in this research begins by creating a Business Model Canvas, the input stage is the IFE and EFE matrices, the matching stage is the IE, SPACE and SWOT matrices, the decision stage uses the QSPM method, finally a new Business Model Canvas which has been added to alternative strategies. obtained. The results of the research analysis show that the Kopi Dul Coffee Shop is in a hold and maintain position based on the results of the IE matrix, is in the aggressive quadrant based on the results of the SPACE matrix, and has 8 alternative strategy formulations based on the SWOT matrix. At the Decision stage, the alternative priority strategy is "Conducting regular competitor observations" with an STA value of 6.371. Next, a proposed Business Model Canvas is created by adding alternative strategy formulations at the matching stage.

Pioneering Data Processing for Convolutional Neural Networks to Enhance the Diagnostic Accuracy of Traditional Chinese Medicine Pulse Diagnosis for Diabetes.

Traditional Chinese medicine (TCM) has relied on pulse diagnosis as a cornerstone of healthcare assessment for thousands of years. Despite its long history and widespread use, TCM pulse diagnosis has faced challenges in terms of diagnostic accuracy and consistency due to its dependence on subjective interpretation and theoretical analysis. This study introduces an approach to enhance the accuracy of TCM pulse diagnosis for diabetes by leveraging the power of deep learning algorithms, specifically LeNet and ResNet models, for pulse waveform analysis. LeNet and ResNet models were applied to analyze TCM pulse waveforms using a diverse dataset comprising both healthy individuals and patients with diabetes. The integration of these advanced algorithms with modern TCM pulse measurement instruments shows great promise in reducing practitioner-dependent variability and improving the reliability of diagnoses. This research bridges the gap between ancient wisdom and cutting-edge technology in healthcare. LeNet-F, incorporating special feature extraction of a pulse based on TMC, showed improved training and test accuracies (73% and 67%, respectively, compared with LeNet's 70% and 65%). Moreover, ResNet models consistently outperformed LeNet, with ResNet18-F achieving the highest accuracy (82%) in training and 74% in testing. The advanced preprocessing techniques and additional features contribute significantly to ResNet18-F's superior performance, indicating the importance of feature engineering strategies. Furthermore, the study identifies potential avenues for future research, including optimizing preprocessing techniques to handle pulse waveform variations and noise levels, integrating additional time-frequency domain features, developing domain-specific feature selection algorithms, and expanding the scope to other diseases. These advancements aim to refine traditional Chinese medicine pulse diagnosis, enhancing its accuracy and reliability while integrating it into modern technology for more effective healthcare approaches.

End‐Point Prediction of Converter Steelmaking Based on Main Process Data

End‐Point Prediction of Converter Steelmaking Based on Main Process Data

The Thought Construction of Yusuf al-Qaradhawi in Understanding The Hadith: An Analysis of Non-Fixed Variable and Fixed Means of Achieving Fixed Goals

This article discusses the construction of Yusuf al-Qaradhawi in applying changing and fixed means in achieving fixed goals. This article constructs how the process and stages to get to it so that there are no mistakes in achieving fixed goals. This research method is qualitative. While the data processing uses analytical descriptive methods. In this article, the problems studied include Yusuf al-Qaradhawi's anxiety in seeing the phenomenon of people who tend to understand hadith textually, Presentation of several examples of hadith listed by Yusuf al- Qaradhawi in strengthening his thinking regarding fixed and changing means, and the formulation of Yusuf al-Qaradhawi's construction in understanding hadith with changing means and fixed means. The results of the study show that the steps in knowing the right means or changing foxes are as follows; (1) Determine wasīlah and gāyah in a matan hadith. (2) Formulate a gāyah if it is not present in the matan. (3) Determine functionally similar contemporary wasīlah. (4) Consider the wasīlah to the state of social society

The weight of data: an analysis based on the impact on the environment

The carbon footprint generated by the information and communications technology (ICT) sector is increasingly significant, emitting greenhouse gases due to high energy consumption, regardless of the way in which energy is generated, the expansion and growth in data centers, as well as the impact generated by the cryptocurrency sector that in the end represents is reflected in greater consumerism, processing, storage, and transport of information that will be somewhere in the world. Current research addresses the problems and the contrast of figures in energy consumption due to the use of a computer, data processing, the role of the user as an internet consumer, the impact of data centers both in carbon footprint, water footprint and soil footprint, the impact of cryptocurrency mining and its contribution to global energy expenditure as well as the ethical debate of new technologies. And finally, the advances in seeking to optimize energy resources, sustainable and conscious for both consumers and service providers, show the trends focused on energy optimization through software and hardware based on a judicious review of research documents.

Implementing ABCD studyⓇ MRI sequences for multi-site cohort studies: Practical guide to necessary steps, preprocessing methods, and challenges

Large multi-site studies that combine magnetic resonance imaging (MRI) data across research sites present exceptional opportunities to advance neuroscience research. However, scanner or site variability and non-standardised image acquisition protocols, data processing and analysis pipelines can adversely affect the reliability and repeatability of MRI derived brain measures. We implemented a standardised MRI protocol based on that used in the Adolescent Brain Cognition Development (ABCD)Ⓡ study in two sites, and across four MRI scanners.Twice repeated measurements of a single healthy volunteer were obtained in two sites and in four 3T MRI scanners (vendors: Siemens, Philips, and GE). Imaging data included anatomical scans (T1 weighted, T2 weighted), diffusion weighted imaging (DWI) and resting state functional MRI (rs-fMRI). Standardised containerized pipelines were utilised to pre-process the data and different image quality metrics and test-retest variability of different brain metrics were evaluated.The implementation of the MRI protocols was possible with minor adjustments in acquisition (e.g. repetition time (TR), higher b-values) and exporting (DICOM formats) of images due to different technical performance of the scanners. This study provides practical insights into the implementation of standardised sequences and data processing for multisite studies, showcase the benefits of containerised preprocessing tools, and highlights the need for careful optimisation of multisite image acquisition.