Data Processing Research Articles

Differential absorption laser spectroscopy at 8 kHz using precompensated current modulation

We present a differential laser absorption spectroscopy (DLAS) system operating at 1550 nm for rapid and sensitive gas concentration measurements. A dual-wavelength toggling mechanism is presented, which significantly reduces data processing, hence supporting a high update rate and data robustness against fast-changing environmental conditions. We showcase the ability to toggle between two wavelengths separated by 90 pm in 14 μs and with minimal chirp (∼1 pm), facilitating sensitive DLAS measurements at 8 kHz update rate. This performance is achieved by driving a 1550 nm diode laser with a modified square-wave current pulse, overcoming the thermal time constant limited wavelength-modulation response of the diode laser. A sensitive feedback mechanism ensures excellent long-term wavelength stability better than 1.4 pm peak-to-peak at 8 kHz toggling over 20 h. As a performance test, we measured the volumetric ratio (VMR) of hydrogen cyanide (HCN) gas in a fiber-coupled gas cell with less than 0.2% peak-to-peak variation over 20 h at 40 Hz. A best sensitivity in VMR of 8×10−6 was achieved at 25 ms integration time. The simplicity and high update rate of our system make it well-suited for gas monitoring in dynamic atmospheric and industrial environments. Further, it offers potential utility in applications requiring precise wavelength control, such as injection seeding of pulsed lasers. A simple analytical model is derived, which, in detail, supports the experimental results, hence offering a tool for future design optimization.

Integrated optical switches based on Kerr symmetry breaking in microresonators

With the rapid development of the Internet of Things and big data, integrated optical switches are gaining prominence for applications in on-chip optical computing, optical memories, and optical communications. Here, we propose a novel approach for on-chip optical switches by utilizing the nonlinear optical Kerr effect induced spontaneous symmetry breaking (SSB), which leads to two distinct states of counterpropagating light in ring resonators. This technique is based on our first experimental observation of on-chip symmetry breaking in a high-Q (9.4×106) silicon nitride resonator with a measured SSB threshold power of approximately 3.9 mW. We further explore the influence of varying pump powers and frequency detunings on the performance of SSB-induced optical switches. Our work provides insights into the development of new types of photonic data processing devices and provides an innovative approach for the future implementation of on-chip optical memories.

AI: Apakah Guru Masih Punya Peran di Masa Depan

The advancement of artificial intelligence (AI) has significantly impacted education, particularly in providing personalized learning and administrative efficiency. However, questions arise about whether the teacher's role remains relevant in this technological era. This study aims to examine the role of teachers in the context of AI development through a literature review method, analyzing previous research on AI integration in education. The findings reveal that while AI excels in data processing and adaptive learning, teachers maintain an irreplaceable role in building emotional connections, instilling moral values, and guiding students' character development. Teachers also act as mentors who provide motivation and facilitators who create interactive learning environments. These findings highlight the importance of collaboration between AI and teachers to create more inclusive and effective education. Therefore, AI should be seen not as a replacement but as a tool to enhance the teacher's role in fostering holistic student development.

Представления пожилых людей о благополучии

<p>The study is aimed at identifying the structure and content of elderly people's ideas about well-being. The study involved 264 people aged 55 to 97 years (M = 69.54; SD = 6.98). Data collection method: free association method in the form of simple limited associations. Participants were asked to give associations to the word “well-being”. Nine associations were received from each person, three verbs, adjectives, nouns. Data processing was carried out using frequency analysis, rank calculation, z-criterion calculation to compare the frequencies of the identified associative and semantic groups. 2376 associates were received, of which the most frequent ones were identified; the selection boundary was 1% of the total number of associates (24 units). The most frequent ones were combined into 21 associative groups. Then the associative groups were combined into semantic groups (8) according to the principle of semantic enlargement. The obtained results allow us to say that well-being in the ideas of elderly people is health combined with emotions of calmness and happiness. In elderly people, the semantic group “emotions” belongs to the core ideas, and the associative group “health” belongs to the middle zone together with the groups “employment”, “material wealth”, “family”. At the same time, this does not indicate a lower significance of the associative group “health”, since the semantic group “emotions” collects its high rank only through a set of different emotions. The semantic groups “values", “existence”, “success” belong to the peripheral zone of ideas. In the semantic group “emotions” in the structure of ideas about well-being in elderly people, emotions of greater intensity prevail over emotions of lesser intensity. Markers of negative emotions in the structure of ideas about well-being are absent.</p>

An Embodied Intelligence System for Coal Mine Safety Assessment Based on Multi-Level Large Language Models

Artificial intelligence (AI), particularly through advanced large language model (LLM) technologies, is reshaping coal mine safety assessment methods with its powerful cognitive capabilities. Given the dynamic, multi-source, and heterogeneous characteristics of data in typical mining scenarios, traditional manual assessment methods are limited in their information processing capacity and cost-effectiveness. This study addresses these challenges by proposing an embodied intelligent system for mine safety assessment based on multi-level large language models (LLMs) for multi-source sensor data. The system employs a multi-layer architecture implemented through multiple LLMs, enabling not only rapid and effective processing of multi-source sensor data but also enhanced environmental perception through physical interactions. By leveraging the tool invocation and reasoning capabilities of LLM in conjunction with a coal mine safety knowledge base, the system achieves logical inference, anomalous data detection, and potential safety risk prediction. Furthermore, its memory functionality ensures the learning and utilization of historical experiences, providing a solid foundation for continuous assessment processes. This study established a comprehensive experimental framework integrating numerical simulation, scenario simulation, and real-world testing to evaluate the system through embodied intelligence. Experimental results demonstrate that the system effectively processes sensor data and exhibits rapid, efficient safety assessment capabilities during embodied interactions, offering an innovative solution for coal mine safety.

Zootechnical and Municipal Solid Waste Digestates: Effects on Soil Nitrogen Mineralization and Kinetics

Soil fertilization with fertilizers derived from renewable sources is a topic of great interest in terms of the sustainable management of organic waste. To optimize the management of nitrogen supplied to the soil with digestates, it is necessary to deepen knowledge on the process of mineralization of organic nitrogen over time. In this research, a laboratory incubation system was utilized to study the impact of various digestate sources on nitrogen mineralization processes in soils and nitrogen mineralization kinetics. Six types of digestates of different origins and composition were administered to soil and the soil samples were placed under controlled conditions. The release of N was determined by measuring ammonium-N and nitrate-N concentrations in leachates during a 12-week period of incubation. The nonlinear regression technique was used to fit the cumulative leaching of total N to the Stanford and Smith first-order kinetic model during the incubation period. The results showed that the differences between digestates, nitrogen and organic carbon concentration, and C/N ratio influenced both ammonification and nitrification processes in the soil and the nitrogen mineralization kinetics. The processing of the statistical data highlighted that the potentially mineralizable nitrogen (MPN) followed first-order kinetics.

Hotel digital intelligence capability: dimension exploration and scale development

Purpose This study aims to explore the dimensional structure of hotel digital intelligence capability and develop a measurement scale. Design/methodology/approach This study adopts qualitative and quantitative approaches to conduct an exploratory inquiry into the structural dimensions of hotel digital intelligence capability with the help of grounded theory. Based on this, several questionnaires were developed to test the measurement scale and verify its validity. Findings The results reveal that hotel digital intelligence capability comprises four dimensions: data collection and processing capability, customer service personalization capability, digital intelligence decision support capability and sustainable development capability. The measurement scale consists of four factors and 13 items, with reliability and validity tests demonstrating ideal levels. Originality/value This study not only provides a new perspective to understand hotel digital intelligence capability but also develops a corresponding measurement scale, laying a solid theoretical basis for hotel managers to scientifically evaluate this capability to achieve sustainable competitive advantage.

ОРГАНІЗАЦІЯ СИСТЕМИ ЗАХИСТУ ЦИФРОВОЇ ІНФОРМАЦІЇ У ФІНАНОВОМУ ОБЛІКУ

Abstract. Introduction. The constant development of technologies, changing forms of threats and the need to adapt to new conditions will require organizations to constantly improve their security systems. The purpose of this work is to systematize theoretical provisions and substantiate practical recommendations regarding the organization of protection of digital financial accounting data. Methods. In the process of research, the method of induction was used at the stage of collecting, processing and systematization of the received information, the method of analysis and synthesis - to combine the constituent elements of economic phenomena in a single process. Results. The essential characteristics of the concepts of information security, digital security and data cyber security in the context of economic security of the enterprise are revealed. A set of measures in the system of organizing digital security of financial accounting data has been identified. National and international legal acts, standards and recommendations define mandatory requirements for data storage, processing and transmission, thus providing a legal basis for the implementation of information security policies. The main directions of state policy in the field of information security have been determined. The state in which business entities currently work is accompanied by constant cyber-attacks both on the enterprises themselves and on the totality of their digital data in the field of accounting and taxation. The main measures to ensure cyber security for critical infrastructure enterprises have been determined. They singled out the main problems of digital information protection in accounting, namely: data leakage; software attacks; phishing and social engineering; malware. The main methods of preventing cyber threats are summarized. Recommendations on the organization of the system for ensuring the security of digital data in financial accounting have been formed. The actions of the accounting staff of the enterprise or the enterprise of the partner, which may create a threat of information leakage, are singled out. A set of information sources in need of protection has been identified. Conclusions. The digital information protection system should become an element of the company's accounting policy, and its implementation will contribute to the economic security of the business entity.

SECON: Maintaining Semantic Consistency in Data Augmentation for Code Search

Efficient code search techniques are crucial in accelerating software development by aiding developers in locating specific code snippets and understanding code functionalities. This study investigates code search methodologies, focusing on the emerging significance of semantic consistency in data augmentation techniques. While existing approaches predominantly enhance raw data, often requiring additional preprocessing and incurring higher training costs, this research introduces a pioneering method operating at the code and query representation levels. By bypassing the need for extensive data processing, this novel approach fosters an interactive alignment between code and query, augmenting the semantic coherence crucial for effective code search. An extensive empirical evaluation of a diverse dataset across multiple programming languages substantiates the efficacy of this approach in significantly enhancing code search model performance compared to traditional methodologies. The implementation is publicly available on GitHub, 1 offering an accessible resource for further exploration and application.

Calibration and optimal transport approaches for harmonizing survey weights

Abstract Originally, the construction of weighting systems to estimate parameters of interest in survey data does not depend on the variables of interest. However, recent research raises questions about the practice of using specific weighting systems for each variable of interest, thus deviating from the universal nature of the weights proposed by survey data processing methods. This article examines the challenge of harmonizing weights for variables with different weighting systems in survey data processing, by presenting and comparing two methods: one that creates a common weight for both variables using the calibration method, and one that uses optimal transport to match the variables. A simulation study is carried out to evaluate the performance of these methods in different sampling scenarios and with continuous and categorical variables. The results of the simulation study show that the optimal transport method for weight harmonization can provide accurate parameter estimates in different scenarios, particularly in situations where disparities between sample and population distributions are large. It therefore appears to be a more versatile solution.

Edge Computing-Aided Dynamic Wireless Charging and Trip Planning of UAVs

In today’s era of rapid technological advancement, unmanned aerial vehicles (UAVs) are transforming sectors such as remote delivery, surveillance, and disaster response. However, challenges related to energy consumption and operational efficiency continue to hinder their broader adoption. To address these issues, this study proposes an integrated system design combining dynamic wireless charging (DWC), intelligent trip planning, and intelligent edge computing (IEC). The proposed system leverages IEC for local data processing to reduce latency and optimize energy management, 6G networks for real-time vehicle-to-infrastructure (V2I) communication, and DWC to enable efficient, on-the-go energy replenishment. Additionally, a dynamic arrival management algorithm is introduced to minimize UAV wait times to enhance operational efficiency. Simulations of this system demonstrated significant improvements: larger UAVs achieved an average charging efficiency of 91.2%, while smaller UAVs achieved 92.75%, with dynamic arrival management reducing wait times by an average of 1.5 min for smaller UAVs and 5.0 min for larger UAVs. These findings underscore the system’s effectiveness in optimizing UAV operations and charging efficiency. This integrated approach offers a scalable framework to enhance UAV capabilities and sets a benchmark for future advancements in operational efficiency and charging technology for urban and environmental applications.

ІНФОРМАЦІЙНИЙ ШУМ ТА МАРКЕТИНГОВІ ІНСТРУМЕНТИ ДЛЯ ЙОГО ПОДОЛАННЯ

Introduction. In the modern conditions of the formation of the digital economy and the intensive growth of the volume of information to be processed by analysts, fundamentally new requirements are being formed for consumers and businesses. At the same time, data processing tools do not always provide adequate analytical information regarding changes occurring both in the global and local markets. The reason for this is information noise, which is an excessive flow of data that complicates the perception of important messages and affects the effectiveness of marketing strategies. All this requires the formation of a systematic approach to managing information flows and optimizing communications between the enterprise and the target audience. The purpose of the article. The purpose of the study is to analyze information noise as a barrier to the effective perception of marketing communications, as well as to identify tools and strategies that allow minimizing its impact on consumers and increasing the effectiveness of marketing activities. Methods. The research is based on the use of an interdisciplinary approach, which includes content analysis, abstract modeling methods for studying consumer perception of information, as well as empirical methods for studying the effectiveness of modern marketing technologies, based on the analysis of approaches to managing specialized marketing tools for interacting with consumers. Results. The article considers the conceptual aspects of information noise and its functional impact on consumer behavior. The effectiveness of using various marketing tools based on the automation of business processes, the use of communication platforms and data for analyzing consumer behavior is analyzed, which results in increasing the efficiency of communications and reducing noise effects. Key strategies for combating information noise are identified, which contribute to strengthening the communication interaction of the enterprise in the context of dynamic changes in market conditions. The need for implementing innovative approaches to developing advertising campaigns that take into account the specifics of modern media channels and consumer behavioral models is proven. Conclusions. It has been proven that minimizing information noise is an important factor in ensuring the long-term effectiveness of a company's marketing communications. It has been determined that the use of modern data analysis tools, process automation, and an individual approach to the consumer allows a company to increase the level of trust and loyalty from customers, which results in an increase in sales and profitability of the business entity.

Endpoint carbon content and temperature prediction model in BOF steelmaking based on posterior probability and intra-cluster feature weight online dynamic feature selection

Abstract A posterior probability and intra-cluster feature weight online dynamic feature selection algorithm is proposed to address the issues of high dimensionality and high volatility of data in the basic oxygen furnace (BOF) steelmaking production process. First, a genetic algorithm with fixed feature space dimensions is introduced, which narrows the solution space by predefining the number of selected features, thereby enhancing the stability of feature selection. Second, the posterior probability of samples and intra-cluster feature weights are used to weigh and calculate the feature importance of the current sample, obtaining the optimal features that align with the current operating conditions. Finally, the dynamically selected features are used in a regression model to predict the carbon content and temperature of the BOF steelmaking process data. Simulations of actual BOF steelmaking process data showed that the prediction accuracy was 86% within a carbon content error range of 0.02, and 88% within a temperature error range of 10°C.

Data Processing and Machine Learning for Assistive and Rehabilitation Technologies

This Special Issue (SI), “Data Processing and Machine Learning for Assistive and Rehabilitation Technologies”, aimed to collect cutting-edge research papers that frame how data-driven approaches and machine learning techniques are advancing the field of assistive and rehabilitation technologies [...]

Smart Gas Sensors Toward Signal and Data Processing: A Review

Smart Gas Sensors Toward Signal and Data Processing: A Review

PENGARUH PENERAPAN METODE AIDA (Attention, Interest, Desire, Action) TERHADAP MINAT BELI KONSUMEN DI SHOPEE

This research aims to find out whether there is an influence between AIDA (Attention, Interest, Desire, Action) on consumer buying interest at Shopee. The research method used in this research is a quantitative method. The population in this study are Shopee users, while the sample required in this research is 160 Shopee user respondents, with the sampling method being incidental sampling. Next, data processing uses multiple linear regression analysis using the SPSS version 21 program. The results of the t test for the Attention variable (X1) t count are 2.480 > t table 1.975, this is reinforced by a significance value of 0.00 < 0.05, so Attention has a significant effect on buying interest. Interest (X2) t count is 8.909 > t table 1.975 with a significance value of 0.00 < 0.05, so Interest has a significant effect on buying interest. Desire (X3) has a t count of 1.420 < t table 1.975, with a significance value of 0.00 < 0.05, so Desire has no significant effect on buying interest. Action (X4) has a calculated t value of 6.267 > t table 1.975, with a significance value of 0.00 < 0.05, so Desire has a significant effect on buying interest. Based on the f test calculation, the value obtained is 37.426 > from f table 2.43, reinforced by a significance value of 0.00 < 0.05, meaning it is accepted, so it can be concluded that AIDA (Attention, Interest, Desire, Action) together or Simultaneous influence on buying interest.

Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm

BackgroundThe Department of Rehabilitation Medicine is key to improving patients’ quality of life. Driven by chronic diseases and an aging population, there is a need to enhance the efficiency and resource allocation of outpatient facilities. This study aims to analyze the treatment preferences of outpatient rehabilitation patients by using data and a grading tool to establish predictive models. The goal is to improve patient visit efficiency and optimize resource allocation through these predictive models.MethodsData were collected from 38 Chinese institutions, including 4,244 patients visiting outpatient rehabilitation clinics. Data processing was conducted using Python software. The pandas library was used for data cleaning and preprocessing, involving 68 categorical and 12 continuous variables. The steps included handling missing values, data normalization, and encoding conversion. The data were divided into 80% training and 20% test sets using the Scikit-learn library to ensure model independence and prevent overfitting. Performance comparisons among XGBoost, random forest, and logistic regression were conducted using metrics, including accuracy and receiver operating characteristic (ROC) curves. The imbalanced learning library’s SMOTE technique was used to address the sample imbalance during model training. The model was optimized using a confusion matrix and feature importance analysis, and partial dependence plots (PDP) were used to analyze the key influencing factors.ResultsXGBoost achieved the highest overall accuracy of 80.21% with high precision and recall in Category 1. random forest showed a similar overall accuracy. Logistic Regression had a significantly lower accuracy, indicating difficulties with nonlinear data. The key influencing factors identified include distance to medical institutions, arrival time, length of hospital stay, and specific diseases, such as cardiovascular, pulmonary, oncological, and orthopedic conditions. The tiered diagnosis and treatment tool effectively helped doctors assess patients’ conditions and recommend suitable medical institutions based on rehabilitation grading.ConclusionThis study confirmed that ensemble learning methods, particularly XGBoost, outperform single models in classification tasks involving complex datasets. Addressing class imbalance and enhancing feature engineering can further improve model performance. Understanding patient preferences and the factors influencing medical institution selection can guide healthcare policies to optimize resource allocation, improve service quality, and enhance patient satisfaction. Tiered diagnosis and treatment tools play a crucial role in helping doctors evaluate patient conditions and make informed recommendations for appropriate medical care.

Analisis Pengendalian Persediaan Produk Darah Menggunakan Metode Continuous Review System (Sistem-Q)

Blood is a vital necessity because in the event of a shortage, patients can only obtain blood from fellow humans. Blood available at the Blood Donor Unit, such as packed red cells (PRC) is most often needed for medical treatment, but is also often limited. This is due to the fluctuating supply and demand of blood. Based on the problem, a stochastic model with the continuous review system (CRS) or Q-system method is used. The approach will monitor inventory levels continuously, triggering reordering (r), when the stock reaches a certain threshold. This research applies a stochastic model to forecast demand (q), safety stock (ss), reorder point (r), maximum inventory (S), and total inventory cost (Ot). Before the calculation is done, the demand forecasting method is used, then the calculation with the Q-System. Based on data processing, obtained for each blood type A, B, AB, and O, namely, q values of 47, 48, 40, and 20 blood bags. The value of r is 73, 73, 71, and 17 blood bags. This study applies a stochastic model to estimate demand (q), safety stock (ss), reorder point (r), and maximum inventory (S) 259, 56, 42, 21. And the total inventory cost (Ot) IDR 212,429,215, IDR 238,485,254, IDR 157,422,496, and IDR 59,592,451. Keywords: Blood, Inventory, Demand, Q-System

Tomato Stem and Leaf Segmentation and Phenotype Parameter Extraction Based on Improved Red Billed Blue Magpie Optimization Algorithm

In response to the structural changes of tomato seedlings, traditional image techniques are difficult to accurately quantify key morphological parameters, such as leaf area, internode length, and mutual occlusion between organs. Therefore, this paper proposes a tomato point cloud stem and leaf segmentation framework based on Elite Strategy-based Improved Red-billed Blue Magpie Optimization (ES-RBMO) Algorithm. The framework uses a four-layer Convolutional Neural Network (CNN) for stem and leaf segmentation by incorporating an improved swarm intelligence algorithm with an accuracy of 0.965. Four key phenotypic parameters of the plant were extracted. The phenotypic parameters of plant height, stem thickness, leaf area and leaf inclination were analyzed by comparing the values extracted by manual measurements with the values extracted by the 3D point cloud technique. The results showed that the coefficients of determination (R2) for these parameters were 0.932, 0.741, 0.938 and 0.935, respectively, indicating high correlation. The root mean square error (RMSE) was 0.511, 0.135, 0.989 and 3.628, reflecting the level of error between the measured and extracted values. The absolute percentage errors (APE) were 1.970, 4.299, 4.365 and 5.531, which further quantified the measurement accuracy. In this study, an efficient and adaptive intelligent optimization framework was constructed, which is capable of optimizing data processing strategies to achieve efficient and accurate processing of tomato point cloud data. This study provides a new technical tool for plant phenotyping and helps to improve the intelligent management in agricultural production.

Fault Diagnosis and Optimized Operation and Maintenance of Drop-out Fuses in Alpine Regions Using MKL-SVM and GRU

Extreme climate in alpine regions leads to frequent failures of drop-out fuses, but existing methods have low detection accuracy and response speed. This article combines MKL-SVM (Multiple Kernel Learning-Support Vector Machine) and GRU (Gated Recurrent Unit) model to achieve precise fault diagnosis of drop-out fuses, leveraging their efficient processing of high-dimensional time series data. Sensors are used to collect data such as voltage, current, temperature, and wind speed. Based on GRU, global trends, temporal hidden states, and dynamic nonlinear features of the data are extracted as inputs for subsequent MKL-SVM. In MKL-SVM, a linear kernel function is used to simplify the linear relationship between static features; targeting temporal features, RBF (Radial Basis Function) kernel effectively captures dynamic trends, fluctuations, and periodic changes in temporal features. To further enhance the classification ability of the model, the kernel function parameters, including linear kernel weights and RBF kernel widths, are adjusted through cross-validation. Finally, based on the model diagnosis results and predicted data, an optimized operation and maintenance plan is formulated to reduce maintenance costs and improve equipment operating efficiency. The results show that the fusion model can significantly improve the reliability of fault detection and adapt to the unique environmental challenges of alpine regions. The accuracy of fault diagnosis for drop-out fuses is 95%, with an MSE (mean square error) value of about 0.15 and a maintenance cost reduction of about 21.89%. This method can achieve accurate fault diagnosis and efficient optimization of operation and maintenance under extreme weather conditions.