Data Processing Research Articles

Integrating transformer-based machine learning with SERS technology for the analysis of hazardous pesticides in spinach

This study introduces an innovative strategy for the rapid and accurate identification of pesticide residues in agricultural products by combining surface-enhanced Raman spectroscopy (SERS) with a state-of-the-art transformer model, termed SERSFormer. Gold-silver core-shell nanoparticles were synthesized and served as high-performance SERS substrates, which possess well-defined structures, uniform dispersion, and a core-shell composition with an average diameter of 21.44 ± 4.02nm, as characterized by TEM-EDS. SERSFormer employs sophisticated, task-specific data processing techniques and CNN embedders, powered by an architecture features weight-shared multi-head self-attention transformer encoder layers. The SERSFormer model demonstrated exceptional proficiency in qualitative analysis, successfully classifying six categories, including five pesticides (coumaphos, oxamyl, carbophenothion, thiabendazole, and phosmet) and a control group of spinach data, with 98.4% accuracy. For quantitative analysis, the model accurately predicted pesticide concentrations with a mean absolute error of 0.966, a mean squared error of 1.826, and an R2 score of 0.849. This novel approach, which combines SERS with machine learning and is supported by robust transformer models, showcases the potential for real-time pesticide detection to improve food safety in the agricultural and food industries.

Monitoring Activities On Student Abilities And Obstacles During English Learning Process

This article was written to help readers understand the results of reading activities and the abilities and obstacles experienced by students during the learning process. understand the results of monitoring achievement activities and abilities and obstacles experienced by students during the learning process. Monitoring is the routine data collection and process of targeting program targets that emphasizes the process and core of change. program target-targeting process that emphasizes process and kernel changes. the quality of the services we purchase while also keeping an eye on our accounts of what is done.

A comparative review of subsea navigation technologies in offshore engineering projects

Subsea navigation technologies play a critical role in enhancing precision and efficiency in offshore engineering projects. This comparative review examines the evolution and application of subsea navigation systems, with a focus on improving precision in offshore construction and surveying. The study evaluates various subsea navigation technologies, including acoustic positioning systems, inertial navigation systems, and optical imaging systems, highlighting their capabilities and limitations. The review begins by discussing the historical development of subsea navigation technologies, tracing their evolution from early acoustic systems to modern integrated solutions. It then explores the key components and operation principles of each technology, providing insights into their functionalities and suitability for different offshore applications. The study also examines the challenges and advancements in subsea navigation, including issues related to signal interference, accuracy, and real-time data processing. It discusses how these challenges have been addressed through technological innovations, such as improved sensor technologies and advanced data fusion algorithms. Furthermore, the review assesses the practical application of subsea navigation technologies in offshore engineering projects, including pipeline installation, subsea infrastructure maintenance, and underwater surveying. It analyzes case studies and industry practices to illustrate the effectiveness of these technologies in improving precision, reducing costs, and mitigating risks in offshore operations. Overall, this comparative review provides a comprehensive overview of subsea navigation technologies in offshore engineering projects. It highlights the evolution, capabilities, and applications of these technologies, emphasizing their role in enhancing precision and efficiency in offshore construction and surveying. The study concludes with recommendations for future research and development to further improve the performance and reliability of subsea navigation systems in offshore operations.

Multi-Purpose Tracker Robot

Abstract: This paper introduces a multi-purpose tracking robot designed with Arduino, ultrasonic, infrared sensors, and motor drivers. The robot is capable of performing diverse tasks such as obstacle avoidance, line following, and object tracking. Its hardware comprises an Arduino Uno microcontroller, ultrasonic sensors for distance measurement, infrared sensors for line detection and obstacle avoidance, and motor drivers for controlling DC motors. The software, developed using Arduino IDE, includes algorithms for sensor data processing, decision-making logic, and motor control. By implementing PID control algorithms, the robot achieves enhanced stability and accuracy in navigation tasks like motor speed control and line following. Experimental results validate its effectiveness in various scenarios, showcasing its potential for advanced robotics and automation applications through intelligent sensor integration and control strategies.

The Next Frontier of Security: Homomorphic Encryption in Action

Abstract: Encryption is essential in preventing unauthorized access to sensitive data in light of the growing concerns about data security in cloud computing. Homomorphic encryption promises to enable secure calculations on encrypted data without the need for decryption, particularly for cloud-based operations. To evaluate the effectiveness and applicability of several homomorphic encryption algorithms for safe cloud computing, we compare and contrast them in this research paper. Partially homomorphic encryption (PHE), somewhat homomorphic encryption (SHE), and fully homomorphic encryption (FHE) are the three basic homomorphic encryption subtypes that we examine. The implications of this study can aid cloud service providers and organizations in selecting the most appropriate homomorphic encryption scheme based on their specific security requirements and performance considerations. The research contributes to the ongoing efforts to enhance data privacy in cloud computing environments, opening new possibilities for secure data processing in an increasingly connected digital world. The exploration of homomorphic encryption schemes in this study opens new avenues for research and development in the field of cryptographic techniques. As technology continues to evolve, so too must our approaches to safeguarding data. This research serves as a catalyst for further innovations in homomorphic encryption algorithms, enabling even more efficient and robust methods for secure data processing in cloud environments and beyond. The insights derived from this research paper not only empower cloud service providers and organizations to make informed decisions about selecting the most appropriate homomorphic encryption scheme but also contribute to the broader mission of fortifying data privacy and security in cloud computing.

Emerging underwater survey technologies: A review and future outlook

Emerging underwater survey technologies are revolutionizing the way we explore and understand the underwater world. This review examines the latest advancements in underwater survey equipment, highlighting their operational benefits and potential areas for future development. Recent developments in underwater survey technologies have led to significant improvements in accuracy, efficiency, and data quality. Advanced sonar systems, such as multibeam and sidescan sonars, provide high-resolution images of the seafloor, allowing for detailed mapping of underwater features. Autonomous underwater vehicles (AUVs) equipped with sophisticated sensors and cameras enable precise data collection in challenging environments, such as deep-sea areas or complex underwater structures. The operational benefits of these technologies are vast. They allow for faster surveying, reduced costs, and improved safety for personnel. Additionally, the high-quality data obtained from these surveys can lead to better decision-making in various industries, including offshore energy, marine research, and underwater archaeology. Looking ahead, the future of underwater survey technologies is promising. There is a growing interest in developing integrated systems that combine multiple sensors and data processing capabilities to provide a more comprehensive view of underwater environments. Artificial intelligence and machine learning algorithms are also being increasingly utilized to analyze large datasets and extract valuable insights. However, several challenges remain, such as the need for better underwater communication systems, improved battery life for autonomous vehicles, and enhanced data processing capabilities. Addressing these challenges will be crucial for the continued advancement of underwater survey technologies. In conclusion, the latest advancements in underwater survey technologies offer exciting opportunities for enhancing our understanding of the underwater world. By leveraging these technologies and addressing key challenges, we can unlock new possibilities for underwater exploration and research.

Effect of eccentric mass on rotor dynamics as a source of harvesting energy vibration

This research focuses on the potential for vibration energy from the use of eccentric masses in dynamic rotors to become an electrical energy source. Prior studies on eccentric mass in dynamic rotors were primarily concerned with examining the rotor's vibrational characteristics; however, little research was done on converting vibrational energy into electrical energy. The purpose of this study is to ascertain the maximum amount of electrical energy that can be produced by dynamic rotor vibrations using an eccentric mass. Utilizing an electromagnetic energy harvester, an experimental study is the methodology used. With a rotor rotation speed of 450 rpm, the eccentric mass variations used are 6.5 grams, 8.5 grams, and 10.5 grams. Matlab is used in this research to process data. The highest energy, using an eccentric mass of 8.5 grams, was found to be 24.15 mV. Nevertheless, this study shows that while the eccentric mass has an impact on the amplitude, it has no effect on the voltage. In order to increase and improve the efficiency of the electrical energy produced, further research on the utilization of vibration energy from dynamic rotors can be guided by the findings of this study

Flextory: Flexible Software Factory of IoT Data Consumers

The success of the Internet of Things (IoT) has driven the development, among others, of many different software architectures for producing, processing, and analyzing heterogeneous data. In many cases, IoT applications share common features, such as the use of a platform or middleware, also known as message broker, that collects and manages data traffic between endpoints. However, in general, data processing is very dependent on the case study (sensors that send temperature data, drones that send images, etc.). Thus, the applications responsible for receiving and processing data, which we call consumers, have to be built ad hoc, since some of their elements have to be specially configured to solve specific needs of the case study. This paper presents Flextory, a software factory tool to make it easier for IoT developers to automatically construct configurable consumer applications, which we call FLEX-consumers. Flextory guides developers through the process of generating Java consumers by selecting some desired features such as, for instance, the particular communication protocol to be used. This way, the developer only has to concentrate on designing the algorithm to process the data. In short, the use of Flextory will result in consumer applications with configurable behavior, namely FLEX-consumers, that can connect to a messaging server (for example RabbitMQ) and process the received messages.

Advancing Drone Operations through Lightweight Blockchain and Fog Computing Integration: A Systematic Review

This paper presents a systematic literature review investigating the integration of lightweight blockchain and fog computing technologies to enhance the security and operational efficiency of drones. With a focus on critical applications such as military surveillance and emergency response, this review examines how the combination of blockchain’s secure, decentralized ledger and fog computing’s low-latency, localized data processing can address the unique challenges of drone operations. By compiling and analyzing current research, this study highlights innovative approaches and solutions that leverage these technologies to improve data integrity, reduce communication latency, and facilitate real-time decision-making in drone missions. Our findings underscore the significant potential of this technological integration to advance the capabilities and reliability of drones in high-stakes scenarios.

Exam Assessor Tool: An Automated System for Efficient Answer Sheet Evaluation

With Education 4.0 and four quadrant approach number of innovations have gone into academics for efficient, experiential, and outcome-based education however assessment schemes are still very much dependent on manual assessment methods which are time-consuming and cumbersome. The grading system can sometimes be irrational, with diversified schemes for the same course and can also be biased. Covid 19 pandemic caused a global economic avalanche like we’ve never experienced in our lifetime. Many countries have implemented control measures such as blockades and curfews. The education system in this chaos saw a silver lining with academics shifting to online mode, with paradigm shift in teaching, assessment techniques too need to evolve. Work done is an effort to ease the process of assessment, a machine learning assisted model is developed that automates subjective answer evaluation in the education sector. Our project involved several crucial steps, including grayscale conversion, Natural Language Processing (NLP) for data cleansing, data splitting, and training an artificial neural network (ANN) to predict scores based on extracted features. ANN-based system grades subjective responses without human intervention, reducing the workload of teachers and professors. Model constructed an ANN architecture with three layers using Rectified Linear Activation Unit (ReLU) and Sigmoid activation functions. Trained model was incorporated into a user-friendly web application using the Streamlit library. Model design gives a major boost in grading efficiency and accuracy while providing valuable feedback to students. Research surveys were conducted, and a dataset was constructed for training and testing the model. study yielded an accuracy of 83.14% after employing techniques such as text cleaning, preprocessing, and feature extraction.

Ray-tracing analytical absorption correction for X-ray crystallography based on tomographic reconstructions

Processing of single-crystal X-ray diffraction data from area detectors can be separated into two steps. First, raw intensities are obtained by integration of the diffraction images, and then data correction and reduction are performed to determine structure-factor amplitudes and their uncertainties. The second step considers the diffraction geometry, sample illumination, decay, absorption and other effects. While absorption is only a minor effect in standard macromolecular crystallography (MX), it can become the largest source of uncertainty for experiments performed at long wavelengths. Current software packages for MX typically employ empirical models to correct for the effects of absorption, with the corrections determined through the procedure of minimizing the differences in intensities between symmetry-equivalent reflections; these models are well suited to capturing smoothly varying experimental effects. However, for very long wavelengths, empirical methods become an unreliable approach to model strong absorption effects with high fidelity. This problem is particularly acute when data multiplicity is low. This paper presents an analytical absorption correction strategy (implemented in new software AnACor) based on a volumetric model of the sample derived from X-ray tomography. Individual path lengths through the different sample materials for all reflections are determined by a ray-tracing method. Several approaches for absorption corrections (spherical harmonics correction, analytical absorption correction and a combination of the two) are compared for two samples, the membrane protein OmpK36 GD, measured at a wavelength of λ = 3.54 Å, and chlorite dismutase, measured at λ = 4.13 Å. Data set statistics, the peak heights in the anomalous difference Fourier maps and the success of experimental phasing are used to compare the results from the different absorption correction approaches. The strategies using the new analytical absorption correction are shown to be superior to the standard spherical harmonics corrections. While the improvements are modest in the 3.54 Å data, the analytical absorption correction outperforms spherical harmonics in the longer-wavelength data (λ = 4.13 Å), which is also reflected in the reduced amount of data being required for successful experimental phasing.

The FDA Reclassification of Cervical Pedicle and Lateral Mass Screws: A Case Study in Regulatory History.

The classification of medical devices by the Food and Drug Administration (FDA) involves rigorous scrutiny from specialized panels that designate devices as Class I, II, or III depending on their levels of relative risk to patient health. Posterior rigid pedicle screw systems were first classified by the FDA in 1984 and have since revolutionized the treatment of many spine pathologies. Despite this early classification by the FDA, posterior cervical pedicle and lateral mass screws were not reclassified from unclassified to Class III and then to Class II until 2019, nearly 35 years after their initial classification. This reclassification process involved a decades-long interplay between the FDA, formal panels, manufacturers, academic leaders, practicing physicians, and patients. It was delayed by lawsuits and a paucity of data demonstrating the ability to improve outcomes for cervical spinal pathologies. The off-label use of thoracolumbar pedicle screw rigid fixation systems by early adopters assisted manufacturers and professional organizations in providing the necessary data for the reclassification process. This case study highlights the collaboration between physicians and professional organizations in facilitating FDA reclassification and underscores changes to the current classification process that could avoid the prolonged dichotomy between common medical practice and FDA guidelines.

Student Result Management System using PHP

Our educational institution maintains digital records of student data to ensure ease of access and management. However, these records are currently dispersed across various formats, posing challenges in maintenance, especially with the growing student population. This fragmentation often results in redundant data entry and inconsistencies, as students are required to repeatedly provide the same information. In response, we propose the development of a comprehensive web application designed to function as an integrated platform for aggregating and categorizing student data. This platform aims to consolidate all student-provided information into a centralized repository, which can then be categorized and selectively accessed by different user types according to their respective needs. Additionally, the web application will empower students to effortlessly update their information while facilitating efficient retrieval of selected student data by college staff. Moreover, it will streamline the online admission process by offering the option of online payment through a secure payment gateway. Furthermore, the application will include features for recording student marks in their respective courses, as well as tracking attendance. It will also inform students of new placement opportunities offered by the college and maintain records of faculty and departments. Additionally, it will log online transactions conducted by students during the admission process. The development of this application will utilize HTML, CSS, and JavaScript for the front-end, ensuring an intuitive and visually appealing user interface. On the backend, PHP and MySQL will be employed to provide robust data management and processing capabilities

The effect of process variability and data quality on performance of a state-space stock assessment model

State-space modeling is an emerging approach to age structured fisheries stock assessment that can accommodate multiple sources of variability in processes like recruitment, abundance, and selectivity. By maximizing the marginal likelihood by treating yearly deviations as random effects and then integrating them from the likelihood, these models can estimate multiple process variances. Several fisheries software packages have been developed that use a state-space framework with marginal likelihood, which has increased their popularity and usage across the U.S. Atlantic coast, Canada, and Europe. However, robust testing is still needed to gauge the applicability of these models and understand how they perform under a range of realistic variability in the process or observation error. Using an assessment model fit to Gulf of Maine Haddock as a baseline, we used a simulation-estimation procedure to test if state-space stock assessment models could produce approximately unbiased and precise estimates over a range of process variances that extended from zero to well above the levels estimated in the Gulf of Maine Haddock assessment, or when observations were noisier (i.e., more variable around their true value) than had been assumed in the assessment. We fit alternative estimation models that differed in which processes errors were included (of recruitment, expected survival, and fishery selectivity). State-space models which specify random effects in all three processes produced approximately unbiased and precise estimates of biomass and exploitation for most operating models and therefore are recommended except when variability in expected survival is absent (or very low), in which case the model is unlikely to converge. A conventional statistical-catch-at-age model with recruitment estimated as a fixed effect for each year, deterministic expected survival, and constant selectivity produced estimates that were comparable to the best state-space model, but do not provide internal estimates of process variances and did not perform well when recruitment was highly variable. This work will facilitate the use of state-space stock assessment models and choosing the parameterization that will produce the most accurate output to inform future predictions and management.

Upgrade of crystallography beamline BL19U1 at the Shanghai Synchrotron Radiation Facility

BL19U1, an energy-tunable protein complex crystallography beamline at the Shanghai Synchrotron Radiation Facility, has emerged as one of the most productive MX beamlines since opening to the public in July 2015. As of October 2023, it has contributed to over 2000 protein structures deposited in the Protein Data Bank (PDB), resulting in the publication of more than 1000 scientific papers. In response to increasing interest in structure-based drug design utilizing X-ray crystallography for fragment library screening, enhancements have been implemented in both hardware and data collection systems on the beamline to optimize efficiency. Hardware upgrades include the transition from MD2 to MD2S for the diffractometer, alongside the installation of a humidity controller featuring a rapid nozzle exchanger. This allows users to opt for either low-temperature or room-temperature data collection modes. The control system has been upgraded from Blu-Ice to MXCuBE3, which supports website-mode data collection, providing enhanced compatibility and easy expansion with new features. An automated data processing pipeline has also been developed to offer users real-time feedback on data quality.

Integration of Cloud Computing and Wearable Technology for Enhanced Interactivity

The emergence of wearable computing has revolutionized the way we interact with technology, blurring the lines between the physical and digital worlds. In this research, we explore the dynamic interaction between cloud technology and wearable computing, a synergy that is reshaping the landscape of personal technology and data management. The study delves into how cloud comput-ing provides a powerful platform for wearable devices, enabling enhanced data processing capabili-ties, storage, and ubiquitous access to information. We investigate the various applications of this interaction, ranging from health monitoring to augmented reality, emphasizing the transformative impact on everyday life and various industries. The research also addresses the challenges inherent in this integration, such as data security, privacy concerns, and the need for robust, low-latency communication networks. Through a comprehensive analysis of current trends and future prospects, this study highlights the potential of cloud-assisted wearable technology in creating more personal-ized, efficient, and interconnected experiences. The findings suggest that the convergence of cloud technology and wearable computing not only offers significant benefits in terms of functionality and user experience but also poses critical considerations for data governance and ethical implications in an increasingly connected world.

A novel fault diagnosis method for imbalanced datasets based on MCNN‐Transformer model in industrial processes

SummaryFault diagnosis methods based on deep learning have been extensively applied to the fault classification of rolling bearings, yielding favorable results. However, many of these methods still have substantial room for improvement in practical industrial scenarios. This article addresses the issue of imbalanced fault data categories commonly encountered in real‐world contexts and discusses the characteristics of long time series data in fault signals. To tackle these challenges, a model based on multi‐scale convolutional neural networks and transformer (MCNNT) is proposed. First, in the data processing stage, a diffusion model is introduced to handle the problem of data imbalance. This model learns the distribution of minority samples and generates new samples. Second, the proposed model incorporates an attention mechanism, enabling it to capture the global information of the data during the feature learning stage and effectively utilize the relationships between preceding and subsequent elements in long sequential data. This allows the model to accurately focus on key features. Experimental results demonstrate the exceptional performance of the proposed method, which is capable of generating high‐quality samples and providing a solution to address challenges in practical industrial scenarios. Consequently, the proposed method exhibits significant potential for further development.

Empirical Green’s function analysis of some induced earthquake pairs from the Groningen gas field

AbstractWe have applied the empirical Green’s function (EGF) method to 53 pairs of earthquakes, with magnitudes ranging from M = 0.4 to M = 3.4, induced by gas production from the Groningen field in the Netherlands. For a subset of the events processed, we find that the relative source time functions obtained by the EGF deconvolution show clear indications of a horizontal component of rupture propagation. The earthquake monitoring network used has dense azimuthal coverage for nearly all events such that wavelet duration times can be picked as a function of source-station azimuth and inverted using the usual Doppler broadening model to estimate rupture propagation strike, distance, and velocity. Average slip velocities have also been estimated and found to be in agreement with typical published values. We have used synthetic data, from both a simple convolutional model of the seismogram and more sophisticated finite difference rupture simulations, to validate our data processing workflow and develop kinematic models which can explain the observed characteristics of the field data. Using a measure based on the L1-norm to discriminate results of differing quality, we find that the highest quality results show very good alignment of the rupture propagation with directions of the detailed fault map, obtained from the full-field 3D seismic data. The dip direction rupture extents were estimated from the horizontal rupture propagation distances and catalogue magnitudes showing that, for all but the largest magnitude event (the M = 3.4 event of 8th January 2018), the dip-direction extent is sufficiently small to be contained wholly within the reservoir.

THE EFFECTIVENESS OF THE PROJECT METHOD IN TEACHING HUMANITARIAN DISCIPLINES

Cutting-edge methodologies within higher education necessitate the cultivation of a proficient and competitive specialist capable of innovative thinking and can perform professionally amidst contemporary volatile circumstances. The purpose of this study was to identify the effectiveness of the project method as an innovative tool for the teaching of humanitarian subjects. The research used such methods as follows: questionnaire, observation, experiment, as well as statistical methods for data processing. In the course of the research, it was determined that the students who were engaged in the project activity, during the project, autonomously researched scientific, reference, educational, and methodological literature. A group of experts found that students’ participation in project activities influenced the development of creative activity and creative systemic thinking, contributed to their self-organization. During the survey, it was determined that students' independent work on creating projects ensures the development of personal and functional components of self-organization.The research's scientific novelty lies in its exclusive focus on students studying humanitarian disciplines. The restricted scope of the experiment facilitated the identifying the project approach as such that can be integrated into the academic system to enhance students' personal and practical proficiencies, thereby promoting their overall growth. Further research can be aimed at determining the effectiveness of using game methods in the course of teaching of humanitarian disciplines.

Docker Container-Based Framework of Apache Kafka Node Ecosystem: Vehicle Tracking System by License Plate Recognition on Surveillance Camera Feeds

AbstractVehicle tracking and license plate recognition (LPR) over video surveillance cameras are essential intelligent traffic monitoring systems. Due to the enormous amount of data collected each day, it would be difficult to track vehicles by license plate in a real-world traffic setting. Large volumes of data processing, real-time request responses, and emergency scenario response may not be possible using conventional approaches. By combining license plate recognition with the docker container-based structure of the Apache Kafka node ecosystem, the suggested solution takes a novel approach to vehicle tracking. The primary components of our suggested framework for reading license plates are the identification of license plates and text data queries. License plate localization is performed with You Only Look Once version 3 (YOLOv3) and character recognition with Optical Character Recognition (OCR). The detected vehicle images with license plate results are published on related topics with Apache Kafka. Apache Kafka is a publish-subscribe (producers-consumers) messaging system and one of the most popular architectures used for streaming data. For each license plate search, a topic will be created in the framework where producers publish and consumers receive data. Thus, the workload of the operators will be reduced and they will be able to pay attention to more important events in traffic.