Data Processing System Research Articles

Improving realty management ability based on big data and artificial intelligence decision-making.

Realty management relies on data from previous successful and failed purchase and utilization outcomes. The cumulative data at different stages are used to improve utilization efficacy. The vital problem is selecting data for analyzing the value incremental sequence and profitable utilization. This article proposes a knowledge-dependent data processing scheme (KDPS) to augment precise data analysis. This scheme operates on two levels. Data selection based on previous stagnant outcomes is performed in the first level. Different data processing is performed in the second level to mend the first level's flaws. Data processing uses knowledge acquired from the sales process, amenities, and market value. Based on the knowledge determined from successful realty sales and incremental features, further processing for new improvements and existing stagnancy mitigation is recommended. The stagnancy and realty values are used as knowledge for training the data processing system. This ensures definite profitable features meeting the amenity requirements under reduced stagnancy time. The proposed scheme improves the processing rate, stagnancy detection, success rate, and training ratio by 8.2%, 10.25%, 10.28%, and 7%, respectively. It reduces the processing time by 8.56% compared to the existing methods.

Evaluation of atmospheric-plasma-source absorption mode Fourier transform Orbitrap mass spectrometry for chlorinated paraffin mixtures

Chlorinated paraffins (CP) are complex molecular mixtures occurring in a wide range of isomers and homologs of environmental hazards, whose analytical complexity demand advanced mass spectrometry (MS) methods for their characterization. The reported formation of chlorinated olefins (COs) and other transformation products during CP biotransformation and degradation can alter the MS analysis, increasing the high resolution required to distinguish CPs from their degradation products. An advanced setup hyphenating a plasma ionization source and an external high-performance data acquisition and processing system to the legacy hybrid LTQ Orbitrap XL mass spectrometer is reported. First, the study demonstrated the versatility of a liquid sampling atmospheric pressure glow discharge, as a soft ionization technique, for CP analysis. Second, enhanced resolution and sensitivity provided by the absorption mode Fourier transform spectral representation on this legacy mass spectrometer are shown. The developed Orbitrap-based platform allowed the detection of new isotopic clusters and CPs and COs to be distinguished at medium resolution (setting 30,000 at m/z 400, ~ 400 ms transients), and even chlorinated di-olefins (CdiOs) at higher resolution (setting 100,000 at m/z 400, ~ 1500 ms transients). Overall, such proof-of-principle instrumental improvements are promising for environmental and analytical research in the field of CP analysis.Graphical

Development of Sentiment Analysis System of Simple Pol Application on Google Play Store Using Naive Bayes Classifier Method and BERT Prediction

Digitalization in public services raises various sentiments that are very dynamic, one example is the Simpel Pol Health Test application made by PT Cipta Sari Arsonia (CSA). The research objective is to obtain useful information from accurate community review sentiments for service improvement and feedback for service providers and application developers. The method used is Naïve Bayes Classifier with Tf-idf weighting, Multinomial Naïve Bayes with review value indicators and review sentences predicted by the BERT method as a determinant of sentiment value whether positive or negative. Sentiment towards the application shows quite encouraging results, from 3000 data analyzed with 1772 positive reviews and 263 negative reviews with 80% training data and 20% test data, the naïve bayes classification model is able to provide a high level of accuracy, which is 88.7% with a precision of 88.5%, recall of 100% and f1-score of 93.9%. The data showed that most people gave a positive response to this application, with the dominant word being 'easy'. This system was developed using the local-based streamlit framework and proved to be quite reliable in developing systems for data processing and web-based data analysis even though the scraping process is slightly longer than the google colab service. Future research is expected to be able to predict data that is positive or negative with several parameters and several sentiment analysis methods at once and their comparison.

Performance Analysis of an Aerial Remote Sensing Platform Based on Real-Time Satellite Communication and Its Application in Natural Disaster Emergency Response

The frequency of natural disasters has increased recently, posing a huge threat to human society. Rapid, accurate, authentic, and comprehensive acquisition and transmission of disaster information are crucial in emergency response. In this paper, we propose a design scheme for an aerial remote sensing platform based on real-time satellite communication. This platform mainly includes a civilian heavy-duty unmanned aerial vehicle, ground observation system with the self-developed orthographic image stabilization device, wireless communication system with an airborne mobile communication device using Ku band, ground satellite information receiving station, and data processing and application analysis system. The image stabilization capability of the ground observation system and the communication capability of the wireless communication system were verified through ground and flight tests respectively. The results showed that the stability accuracy of the platform was better than the theoretical threshold, the system transmission rate was not less than 2 M bandwidth, the data packet loss rate was low, and the time delay was not more than 2 s. The images captured in the experiment were clear, with a resolution of less than 1cm and an overlap rate of more than 70%. These all results meet the emergency observation requirement, which indicates that the aerial remote sensing platform based on real-time satellite communication has great potential for application in natural disaster emergency response.

AI-based automatic identification and processing techniques for agricultural safety information

In the context of globalization, agricultural safety is directly linked to food security and human health. Modern agriculture’s challenge is effectively monitoring and processing vast agricultural safety information in the digital era. This study aims to achieve the automatic identification and intelligent processing of agricultural safety information within a digital media environment by applying artificial intelligence (AI) technologies. Initially, the background of AI applications in agriculture is explored, followed by an analysis of the urgent need for automated processing of agricultural safety information and an overview of current research in this field. It is demonstrated that, despite progress, existing methods still lack in-depth feature extraction and real-time capabilities of the data processing systems. A method based on multi-level feature fusion for identifying agricultural product safety is proposed to address these limitations alongside a decentralized AI Internet of Things (IoT) system for processing agricultural safety information. The multi-level feature fusion method can extract and integrate essential details on agricultural products from different dimensions and levels, thus enhancing the identification accuracy. Meanwhile, the decentralized AIoT system strengthens the efficiency and reliability of data processing, ensuring timely responses to agricultural safety information. Through deep neural networks, efficient recognition and classification of agricultural product images were achieved, enabling the automatic identification of agricultural safety information in a digital media environment. Utilizing deep learning algorithms, the system could learn and understand the characteristics of different agricultural products and accurately identify potential safety issues, such as pests, diseases, or spoilage, providing timely monitoring and management for agricultural production. The innovation of this research lies in the comprehensive utilization of multi-level data features and advanced Internet of Things (IoT) technology, significantly improving the level of intelligence in agricultural safety supervision.

Pengaruh Harga terhadap Keputusan Pembelian pada Ravis Catering di Surakarta

New businesses face their own challenges due to developments in the current business world. Business is currently experiencing a lot of progress due to the emergence of various companies in various fields, both industry and services. The research carried out by the author is a type of quantitative research. 285 Ravis customers were involved in this research. The purposive nonprobability method was used to find samples in this research. Data is provided directly to data collectors by data sources. Data obtained from questionnaires and interviews in this research were processed through the Likert data processing system. Ho is rejected and ha is accepted, according to the t test results, because the price variable is tcount 8.717 and ttable 1.66660. Based on the results of the coefficient of determination table, ho and ha are accepted.

Broadband optical nonreciprocity by emulation of nonlinear non-Hermitian time-asymmetric loop

Broadband and power-stable optical nonreciprocity is highly desirable for practical implementation of optical isolation of coherent sources and receivers in high-speed data-processing systems. Here, we experimentally demonstrate a 2 × 2 waveguide-coupler system that produces broadband optical nonreciprocity based on simple emulation of nonlinear non-Hermitian time-asymmetric loop. The coupler system consists of five lumped optical components including two linear attenuators and two saturable amplifiers. Under moderate operation conditions which are routinely obtainable with commercial erbium-doped fiber amplifiers, we obtain 20 dB isolation ratio persisting over the entire gain bandwidth of the amplifier and over a wide incident-intensity range from 0.1 mW to 10 kW. The isolation ratio can be enhanced up to 70 dB with the forward transmission efficiency adjustable at any desired stable point by tuning amplifier’s internal dissipation in principle. Therefore, our result strongly encourages further experimental development of broadband on-chip optical isolators which have been elusive so far.

A pragmatic data processing system for large resistive sensor arrays.

Large resistive sensor arrays (RSAs) show great potential in tactile perception. However, the large number of sensors can result in great hardware overhead and bring difficulties for acquiring and processing mass data timely in transient measurement applications. This paper implements a field programmable gate array (FPGA)-based data processing system for a large RSA of 96 × 96, which shows good power consumption and high-speed wireless data update. For crosstalk-free measure, the zero potential method is improved with bus switches, leading to fewer operational amplifiers required and less negative power consumption. A real-time embedded data processing system is realized by FPGA for excellent parallel processing ability. A high-speed wireless transfer scheme with automatic regulated transfer size is proposed and realized by a wireless fidelity module, which allows timely data analysis at the remote end. Moreover, fault identification of RSAs fabricated by micro-electromechanical system technology is achieved. Tests carried out on a 32 × 32 RSA show that the total power consumption is 2209 mW, including 1261 mW of processors and 948 mW of readout circuits, corresponding to 2.15 mW/pixel. The total negative power consumption of 549 mW has been reduced by 50% compared with the zero potential method. The scanning speed is 400fps, and the wireless transfer speed is up to 120fps when the transceiver and receiver are 5m apart.

Deep Learning Model Construction of Urban Planning Image Data Processing and Health Intelligence System

Deep Learning Model Construction of Urban Planning Image Data Processing and Health Intelligence System

CMK: Enhancing Resource Usage Monitoring across Diverse Bioinformatics Workflow Management Systems

The increasing use of multiple Workflow Management Systems (WMS) employing various workflow languages and shared workflow repositories enhances the open-source bioinformatics ecosystem. Efficient resource utilization in these systems is crucial for keeping costs low and improving processing times, especially for large-scale bioinformatics workflows running in cloud environments. Recognizing this, our study introduces a novel reference architecture, Cloud Monitoring Kit (CMK), for a multi-platform monitoring system. Our solution is designed to generate uniform, aggregated metrics from containerized workflow tasks scheduled by different WMS. Central to the proposed solution is the use of task labeling methods, which enable convenient grouping and aggregating of metrics independent of the WMS employed. This approach builds upon existing technology, providing additional benefits of modularity and capacity to seamlessly integrate with other data processing or collection systems. We have developed and released an open-source implementation of our system, which we evaluated on Amazon Web Services (AWS) using a transcriptomics data analysis workflow executed on two scientific WMS. The findings of this study indicate that CMK provides valuable insights into resource utilization. In doing so, it paves the way for more efficient management of resources in containerized scientific workflows running in public cloud environments, and it provides a foundation for optimizing task configurations, reducing costs, and enhancing scheduling decisions. Overall, our solution addresses the immediate needs of bioinformatics workflows and offers a scalable and adaptable framework for future advancements in cloud-based scientific computing.

Development of the Online Data Processing System for the BM@N Experiment at NICA

Development of the Online Data Processing System for the BM@N Experiment at NICA

Ultrafast spatiotemporal control of orthogonally directional surface plasmon polariton via a compact nanoantenna

Ultrafast spatiotemporal control over the launch direction of surface plasmon polariton (SPP), which is highly desirable for the realization of integrated nanophotonic devices and ultrafast data processing systems within plasmonic nano-circuitry, remains a significant challenge. In particular, ultrafast spatial-temporal switching of SPP directional emissions in orthogonal directions has not yet been reported. Here, we propose a novel compact plasmonic nanoantenna structure design for spatiotemporal control of SPP launch at the nano−femto scale. This device possesses the capacity to launch the SPP directionally into different orthogonal output channels by changing the polarization direction of incident light. More importantly, ultrafast switching of the SPP directional launch at a femtosecond time scale has been proposed via adjusting the instantaneous polarization state of the excitation light. This study can lay the foundation for the development of miniaturized signal processing systems for high-speed data processing, and highly integrated nanophotonic devices, as well as for enhancing the functionality of next-generation optical nanocircuits.

External Data Systems Enable Enhanced (and Sustainable) Fourier Transform Mass Spectrometry Imaging for Legacy Hybrid Linear Ion Trap-Orbitrap Platforms.

Legacy Fourier transform (FT) mass spectrometers provide robust platforms for bioanalytical mass spectrometry (MS) yet lack the most modern performance capabilities. For many laboratories, the routine investment in next generation instrumentation is cost prohibitive. Field-based upgrades provide a direct path to extend the usable lifespan of MS platforms which may be considered antiquated based on performance specifications at the time of manufacture. Here we demonstrate and evaluate the performance of a hybrid linear ion trap (LTQ)-Orbitrap mass spectrometer that has been enhanced via an external high-performance data acquisition and processing system to provide true absorption mode FT processing during an experimental acquisition. For the application to mass spectrometry imaging, several performance metrics have been improved including mass resolving power, mass accuracy, and dynamic range to provide an FTMS system comparable to current platforms. We also demonstrate, perhaps, the unexpected ability of these legacy platforms to detect usable time-domain signals up to 5 s in duration to achieve a mass resolving power 8× higher than the original platform specification.

Underwater Mapping and Optimization Based on Multibeam Echo Sounders

Multibeam echo sounders (MBESs) enable extensive underwater environment exploration. However, due to weak correlation between adjacent multibeam sonar data and difficulties in inter-frame feature matching, the resulting underwater mapping accuracy frequently falls short of the desired level. To address this issue, this study presents the development of a multibeam data processing system, which includes functionalities for sonar parameter configuration, data storage, and point cloud conversion. Subsequently, an Iterative Extended Kalman Filter (iEKF) algorithm is employed for odometry estimation, facilitating the initial construction of the point cloud map. To further enhance mapping accuracy, we utilize the Generalized Iterative Closest Point (GICP) algorithm for point cloud registration, effectively merging point cloud data collected at different times from the same location. Finally, real-world lake experiments demonstrate that our method achieves an Absolute Trajectory Error (ATE) of 15.10 m and an average local point cloud registration error of 0.97 m. Furthermore, we conduct measurements on various types of artificial targets. The experimental results indicate that the average location error of the targets calculated by our method is 4.62 m, which meets the accuracy requirements for underwater target exploration.

Sistem Informasi Manajemen Pengawasan Dan Pengendalian (WASDAL) Menara Telekomunikasi (Studi Kasus: Dinas Komunikasi Dan Informatika Kota Cimahi)

Telecommunication tower management information system is a crucial data processing system for telecommunication tower fee collection, given the importance of having an efficient and integrated system. However, the Department of Communication and Informatics of Cimahi City still lacks an integrated system, resulting in a slower telecommunication tower fee collection process. To address this issue, a Website named Telecommunication Tower Supervision and Control Management Information System (Wasdal) was developed as an integrated system to facilitate the fee collection process and generate fee collection reports. The methodology employed in this research is the Software Development Life Cycle (SDLC), and the Website is constructed using Laravel framework, Bootstrap, and MySQL database. The implementation results reveal that the overall built management information system Website meets user requirements. The User Acceptance Test (UAT) outcomes indicate average parameter scores of design (78%), features (79.20%), user satisfaction (80.60%), with an overall testing score of 81.3%.

Optimal attempt scheduling and aborting in heterogenous system performing asynchronous multi-attempt mission

Multi-attempt mission aborting systems have recently received significant attention from the reliability community. Existing models mostly assume parallel or sequential execution of multiple attempts, incurring great cost or low mission success probability (MSP), respectively. This paper advances the state of the art by considering a new model, where system components may be activated with certain delay allowing to activate next one before the previous component leaves the operation, balancing the expected cost of lost components (ECC) and MSP. Each component may abort its attempt according to an individual aborting policy defined by two parameters (the number of survived shocks and an operation time threshold) or upon receiving a common abort command. Because components may have different shock resistances and performance rates, their activation order can affect both MSP and ECC. Thus, we formulate and solve the optimal attempt scheduling and aborting policy (SAP) problem, which determines the vector of component activation times and the individual attempt aborting policy for each component to minimize the expected mission losses (EML). The EML, a function of MSP and ECC, is evaluated using a new numerical procedure. A detailed case study of a cloud data processing system is provided to demonstrate the proposed model.

Complexity analysis of polarization components of chaotic fiber laser

We experimentally study the complexity of polarization components of chaotic fiber lasers by permutation entropy, skewness and kurtosis. The experimental setup for complexity analysis is composed of the chaotic fiber laser, polarization beam splitter and data-processing systems. The effects of the different polarization states and pump currents on complexity, skewness and kurtosis are experimentally discussed in detail. The result shows chaotic laser with high-complexity can be obtained by analyzing the polarization component of chaotic laser with different polarization. The random characteristics of polarization component of the chaotic fiber laser are analyzed by the autocorrelation function. The spatial resolution is analyzed by combining characteristics of the autocorrelation curve of chaotic fiber laser. The study of the polarization component dynamic characteristics of the chaotic fiber laser is beneficial to obtain chaotic signal with high-complexity and further effectively improve the detection precision of a sensing system based on the chaotic fiber laser.

Interpersonal violence against people with intellectual disabilities in São Paulo, Brazil: characteristics of victims, perpetrators and referrals

IntroductionInterpersonal violence is a phenomenon that can occur with different people and conditions. However, people with intellectual disabilities have increased vulnerability to this problem, with potential risks to their health and well-being. The aim of this study was to identify the sociodemographic characteristics of people with disabilities who have been victims of interpersonal violence, the profile of the perpetrators and the measures taken after the victims have been cared for.MethodsThis is an exploratory, descriptive, cross-sectional study using the Interpersonal Violence Notification Forms entered into the Brazilian Ministry of Health’s Notifiable Diseases Information System. The city of São Paulo was chosen as the setting because it is the largest city in Latin America and has a faster data processing system than other cities. The period covered notifications made between 2016 and 2022. The information was collected between October and November 2023 and a univariate statistical analysis was carried out. Fisher’s exact test was used, with a significance level of 5% (α = 0.05).ResultsThere were 4,603 notifications against people with intellectual disabilities in the period. The forms of physical violence, neglect/abandonment and psychological/moral violence were more frequent in the 15–19 age group, while sexual violence was more frequent in the 10–14 age group (p < 0.001). The sex most often attacked was female in all the forms investigated (p < 0.001) and the skin colors of the most victimized people were black and/or brown, except in cases of neglect/abandonment (p = 0.058). Most of the victims had little schooling (p = 0.012). The aggressions were committed by one person (p < 0.001), known or related to the victim, such as mother or father, except in cases of sexual violence, where strangers were the main perpetrators (p < 0.001). The sex of the perpetrator was male, except in cases of neglect and/or abandonment (p < 0.001), and the age was between 25 and 29 (p = 0.004). In cases of sexual violence, rape was the most frequent and the procedures carried out were blood collection followed by prophylaxis for Sexually Transmitted Infections (STIs) were the main procedures carried out by health professionals (p = 0.004). The majority of referrals made after receiving care were to the health and social assistance network, with few referrals to bodies such as the human rights reference center, guardianship council and police stations (p < 0.001).ConclusionPeople with intellectual disabilities are highly vulnerable to the forms of violence studied, especially children and adolescents, black or brown, with low levels of education. The perpetrators are usually close people, male and older than the victims. The referrals made by health professionals did not prioritize the victim’s safety and the guarantee of human rights. Lines of care for the health of victims of violence should be implemented, taking into account special aspects, such as people with intellectual disabilities, whose search for help can be difficult.

Real-time data processing method for CO2 dispersion interferometer on EAST

A real-time data processing system is designed for the carbon dioxide dispersion interferometer (CO2-DI) on EAST. The system utilizes the parallel and pipelining capabilities of an field-programmable gate array (FPGA) to digitize and process the intensity of signals from the detector. Finally, the real-time electron density signals are exported through a digital-to-analog converter (DAC) module in the form of analog signals. The system has been successfully applied in the CO2-DI system to provide low-latency electron density input to the plasma control system on EAST. Experimental results of the latest campaign with long-pulse discharges on EAST (2022–2023) demonstrate that the system can respond effectively in the case of rapid density changes, proving its reliability and accuracy for future electron density calculation.

A modified method of self-recovery of distributed software in heterogeneous computer systems

The object of research is the distributed computing process in heterogeneous computer systems. The subject of the research is methods of self-healing for distributed software on heterogeneous computer systems. The goal is to increase the efficiency of distributed data processing systems with support for the functional stability of the computing process by developing a modified method of self-healing of distributed software. Tasks: to investigate the existing methods of restoring the distributed computing process, to draw conclusions about their advantages and disadvantages; on the basis of mathematical models of tasks, computing resources and existing methods of resource allocation, develop a modification of the method of self-recovery of distributed software taking into account management strategies, finding the best solution for the selected criteria, reducing energy consumption during the execution of tasks; conduct a number of experiments comparing the developed method with existing ones. Research methods are based on the use of set theory, general systems theory, and simulation modeling theory. The results of the experiments obtained during the simulation of the allocation of software tasks to computing resources in a simulated simulation environment and the simulation of the computing process during self-recovery in case of resource failures confirm the effectiveness of the proposed method. Conclusion: the application of the method in distributed computing control systems does not increase the time the system spends on performing the task in the absence of failures, at the same time, in the presence of failures, it allows to restore the functionality of the software task faster and reduces the execution time by 8–17%, and energy consumption by 7–12%. There is also an increase in efficiency with an increase in the size of the tasks and the probability of failures. The development of technologies for automated or automatic use of methods of resource allocation and self-recovery can be indicated as areas for future research.