Traditional Analysis Methods Research Articles

Fostering Cultural Awareness and Academic Engagement through Poetry Analysis in Kazakh Educational Contexts

This study investigated the effects of activity-based and traditional analysis methods on students’ attitudes toward poetry, academic achievement, and cultural identity. A pre-test-post-test control group experimental design—a quantitative research method—was employed. The class using activity-based analysis methods for poetry analysis was designated as the experimental group, while the class following the traditional analysis method served as the control group. Participants were second-year students from the Kazakh Literature Department at a university in Almaty, Kazakhstan. The study spanned eight weeks and involved 18 female and 15 male students in the experimental group, with 17 female and 15 male students in the control group. All students provided informed consent to participate voluntarily. The Scale of Attitude toward Poetry, Poetry Analysis Skills Academic Achievement Test, and Cultural Identity Perception Scale were administered to measure the study variables. The findings revealed that activity-based analysis methods positively impacted students’ attitudes toward poetry, with the experimental group demonstrating a more favorable approach than the control group. Additionally, students in the experimental group achieved higher academic performance in poetry analysis skills than those in the control group. Although both groups showed a general improvement in their cultural identity perception, no significant difference was found between them. These results support the potential of activity-based approaches in poetry education to increase student achievement and attitudes. Consequently, integrating such methods should be actively encouraged in future educational practices.

AI-integrated network for RNA complex structure and dynamic prediction

RNA complexes are essential components in many cellular processes. The functions of these complexes are linked to their tertiary structures, which are shaped by detailed interface information, such as binding sites, interface contact, and dynamic conformational changes. Network-based approaches have been widely used to analyze RNA complex structures. With their roots in the graph theory, these methods have a long history of providing insight into the static and dynamic properties of RNA molecules. These approaches have been effective in identifying functional binding sites and analyzing the dynamic behavior of RNA complexes. Recently, the advent of artificial intelligence (AI) has brought transformative changes to the field. These technologies have been increasingly applied to studying RNA complex structures, providing new avenues for understanding the complex interactions within RNA complexes. By integrating AI with traditional network analysis methods, researchers can build more accurate models of RNA complex structures, predict their dynamic behaviors, and even design RNA-based inhibitors. In this review, we introduce the integration of network-based methodologies with AI techniques to enhance the understanding of RNA complex structures. We examine how these advanced computational tools can be used to model and analyze the detailed interface information and dynamic behaviors of RNA molecules. Additionally, we explore the potential future directions of how AI-integrated networks can aid in the modeling and analyzing RNA complex structures.

Comparative Sensitivity Analysis in Composite Material Selection: Evaluating OAT and COMSAM Methods in Multi-criteria Decision-making

The decision-making process is critical across various fields, influencing essential aspects like performance, safety, and sustainability. In engineering and materials science, selecting suitable materials is a complex process involving multiple interdependent criteria, which impact the reliability and lifecycle of a design. Traditional sensitivity analysis methods, such as the one-at-a-time (OAT) approach, assess the effects of individual parameter changes but often fail to capture the cumulative impact of simultaneous modifications. It can limit the effectiveness of decision tools in real-world scenarios where multiple factors vary concurrently. This study utilizes the Comprehensive Sensitivity Analysis Method (COMSAM) to address limitations in traditional sensitivity analysis by modeling simultaneous parameter adjustments across multiple criteria. Specifically, the study aims to validate COMSAM’s effectiveness in the practical problem of composite material selection, comparing it with the traditional OAT approach within a multi-criteria decision-making (MCDM) framework. By integrating both sensitivity analysis techniques with the Characteristic Objects Method (COMET), this research ensures robust evaluation and mitigates vulnerabilities like the rank reversal paradox. The findings demonstrate that COMSAM provides enhanced insights into the impact of parameter interdependencies, offering a more resilient foundation for decision-making in high-stakes environments where material properties and performance are paramount.

Molecularly Imprinted Ratiometric Fluorescent Sensors for Analysis of Pharmaceuticals and Biomarkers

Currently, analyzing pharmaceuticals and biomarkers is crucial for ensuring medication safety and protecting life and health, and there is an urgent need to develop new and efficient analytical techniques in view of the limitations of traditional analytical methods. Molecularly imprinted ratiometric fluorescent (MI-RFL) sensors have received increasing attention in the field of analytical detection due to their high selectivity, sensitivity and anti-interference ability, short response time, and visualization. This review summarizes the recent advances of MI-RFL sensors in the field of pharmaceuticals and biomarkers detection. Firstly, the fluorescence sources and working mechanisms of MI-RFL sensors are briefly introduced. On this basis, new techniques and strategies for preparing molecularly imprinted polymers, such as dummy template imprinting, nanoimprinting, multi-template imprinting, and stimulus-responsive imprinting strategies, are presented. Then, dual- and triple-emission types of fluorescent sensors are introduced. Subsequently, specific applications of MI-RFL sensors in pharmaceutical analysis and biomarkers detection are highlighted. In addition, innovative applications of MI-RFL sensors in point-of-care testing are discussed in-depth. Finally, the challenges of MI-RFL sensors for analysis of pharmaceuticals and biomarkers are proposed, and the research outlook and development trends of MI-RFL sensors are prospected.

Visualization of particulate matters in air using lensless microscopy

Abstract. Air quality is crucial for health, yet monitoring airborne particulate matter (PM), particularly PM2.5, remains challenging due to pollution and widespread distribution. PM2.5, with diameters less than 2.5 micrometers, are known to have significant health implications. Traditional methods of PMs detection and analysis are often time-consuming or require complex optical systems. This study explores the use of lensless microscopy as an innovative and cost-effective alternative for the visualization and measurement of PMs. Utilizing computational imaging techniques, lensless microscopy captures high-resolution images without the need for traditional lenses, facilitating rapid and accurate analysis of air quality. We describe the implementation of lensless imaging to detect and quantify PM2.5 particles in the air, highlighting the use of the single light wavelength and the computational reconstruction method. Our experiments demonstrated that this approach could effectively capture fine details of PMs, achieving resolutions comparable to traditional microscopy. This validates the potential of lensless microscopy as a practical tool for real-time air quality monitoring. By adopting lensless microscopy, we offer a more efficient and economical method for the continuous monitoring of PMs, which can significantly contribute to environmental health studies and pollution control efforts. According to specific needs, simply replacing the flow rate of the air pump can adapt to the PM measurement speed requirements in different environments. This research introduces a novel approach to air quality assessment, providing a promising solution for the rapid and detailed analysis of airborne particulate matter while at an affordable cost.

Analysis of temperature data in Yili, Xinjiang in July and August of 2023 and 2022

Abstract. Global climate change has a significant impact on ecosystems and human society, and studying its impact on temperature changes in specific regions is crucial to understanding and responding to climate change. Xinjiang is an important agricultural and animal husbandry base in China. Studying climate change and temperature changes in the region is of great significance to ensuring agricultural production and ecological security. The research investigates temperature changes in Yining City, Ili, Xinjiang, during July and August 2022 and 2023, focusing on the rising average temperatures, increased variance, and instability. Despite prior studies, there is still a lack of detailed, localized analysis over consecutive years. The study employs traditional data analysis methods, calculating average temperatures, variance, and standard deviation, and visualizing the results with line charts. The findings reveal significant temperature increases and greater variability, highlighting the potential risks to agriculture and local ecosystems. The discussion suggests measures to mitigate the effects of global warming and underscores the importance of harmonizing human activities with environmental sustainability.

The Enhancement of Machine Learning-Based Engine Models Through the Integration of Analytical Functions

The integration of analytical functions into machine learning-based engine models represents a significant advancement in predictive performance and operational efficiency. This paper focuses on the development of hybrid approaches to model engine combustion and temperature indices and on the synergistic effects of combining traditional analytical methods with modern machine learning techniques (such as artificial neural networks) to enhance the accuracy and robustness of such models. The main innovative contribution of this paper is the integration of analytical functions to improve the extrapolation capabilities of the data-driven models. In this work, it is demonstrated that the integrated models achieve superior predictive accuracy and generalization performance across dynamic engine operating conditions, with respect to purely neural network-based models. Furthermore, the analytical corrective functions force the output of the complete model to follow a physical trend and to assume consistent values also outside the domain of values assumed by the input features during the training procedure of the neural networks. This study highlights the potential of this integrative approach based on the implementation of the effects superposition principle. Such an approach also allows us to solve one of the intrinsic issues of data-driven modeling, without increasing the complexity of the training data’s collection and pre-processing.

Spectral element method for random vibration analysis of the vehicle-track coupling system

In this paper, we propose a new random vibration analysis model for the vertical vehicle-track coupling system, which combines the symplectic method and the spectral element method (SEM) to enhance calculation efficiency and accuracy. The model assumed that the vehicle is composed of a multi-rigid body with 10 DOFs, and its motion equation is derived using the traditional frequency analysis method. Tracks are considered periodic structures, and the part between sleeper support points is considered a substructure. The motion equation of the periodic track-substructure is established by spectral element method (SEM), and the propagation of vibration wave between the substructures is calculated by the symplectic method. The frequency responses of the vehicle-track coupling system are calculated by using the German low-interference irregularity spectrum and short-wave irregularity spectrum as excitation. Then, the rationality of this method is verified by comparing it with the results from the traditional symplectic method and simulations using Universal Mechanism software. The comparison shows that this method surpasses the traditional symplectic method in high-frequency accuracy while maintaining high computational efficiency. Although the finite element method also has the capability for high-frequency analysis, this method can calculate the high-frequency vibration of an infinitely long track using just one element, making it more suitable for the efficient analysis of vehicle-track coupling systems. Finally, the model is applied to analyze the distribution characteristics of the vibration response of the coupled system in the frequency domain.

Personalized Data Insights: How­­ Machine Learning is Revolutionizing Everyday Business Decisions

The rise of machine learning has fundamentally transformed the way businesses approach decision-making, particularly in the realm of data analytics. In today’s data-driven landscape, organizations are not just gathering information—they’re using advanced algorithms to turn raw data into personalized, actionable insights that drive business strategy. Machine learning enables businesses to go beyond surface-level trends and dig deeper into customer behavior, operational inefficiencies, and market dynamics. This technology is revolutionizing everyday business decisions by delivering real-time, data-driven intelligence that is specifically tailored to each business's unique needs. By leveraging machine learning, businesses can analyze massive datasets, uncover hidden patterns, and make predictions that were previously unimaginable with traditional analytics methods. One of the key benefits of machine learning is its ability to personalize data insights, offering businesses a clearer, more detailed understanding of their customers and operations. It empowers companies to make more accurate decisions in marketing, product development, customer service, and beyond, leading to improved efficiency, customer satisfaction, and overall business performance. This article explores the transformative role of machine learning in generating personalized data insights and its profound impact on everyday business decisions. It highlights how this technology can improve decision-making processes, enhance customer experiences, and drive more effective business strategies through real-time analysis and prediction. Through practical examples, we will demonstrate how businesses across various sectors are already using machine learning to revolutionize their operations and improve outcomes.

Enhancing slope stability prediction through integrated PCA-SSA-SVM modeling: a case study of LongLian expressway

China is one of the regions most frequently affected by landslides, which have significant socio-economic impacts. Traditional slope stability analysis methods, such as the limit equilibrium method, limit analysis method, and finite element method, often face limitations due to computational complexity and the need for extensive soil property data. This study proposes a novel approach that combines Principal Component Analysis (PCA), Sparrow Search Algorithm (SSA), and Support Vector Machine (SVM) to improve the accuracy of slope stability prediction. PCA effectively reduces data dimensionality while retaining critical information. SSA optimizes SVM parameters, addressing the limitations of traditional optimization methods. The integrated PCA-SSA-SVM model was applied to a dataset of 257 slope stability samples and validated using five-fold cross-validation to ensure the model’s generalization capability. The results show that the model exhibits superior performance in prediction accuracy, precision, recall, and F1-score, with the test set achieving an accuracy of 84.6%, a recall of 84.7%, a precision of 83.1%, and an F1-score of 84.6%. The model’s robustness was further validated using slope data from the LongLian Expressway, demonstrating high consistency with the actual stability status. These findings indicate that the PCA-SSA-SVM-based slope stability prediction model has significant potential for practical engineering applications, providing a reliable and efficient tool for slope stability forecasting. Classify the training samples through cross-validation, using the accuracy of cross-validation as the fitness of the sparrow individual. Retain the optimal fitness value and position information.

Mission Reliability Modeling and Analysis Methods for Reconfigurable Ship Electronic Information Systems

With the increasing intensity of ship missions, the mission reliability requirements of various ship systems are getting higher. Especially the new reconfigurable ship electronic information systems, which have the characteristics of resource sharing and reuse, function reconfiguration on demand, software and hardware loose coupling, and business integration, etc., is difficult to be taken into account by traditional reliability design and analysis methods. Therefore, it is imperative to design a reliability modeling and analysis method with careful consideration and strong applicability. Taking the new reconfigurable ship electronic information system as the research subject, this paper firstly adopts the architecture analysis and design language (AADL) to establish a reliability model from three perspectives, namely, system structure, system behavior, and failure impact; then, a model of mission reliability allocation under multiple constraints is developed and solved using the hybrid algorithm of Slime Mode Optimization Differential Evolution (SMA-DE) based on the optimal allocation model of reliability; next, a method for analyzing the mission reliability of ship electronic information systems based on the Goal Oriented (GO) method is proposed, and based on the state probability algorithm therein, a correction algorithm oriented to the co-causal failure modes is derived to realize the quantitative calculation of the mission reliability of ship electronic information systems; finally, an application validation was performed in conjunction with a typical ship electronic information system task. The results show that the method proposed in this study can successfully achieve the mission reliability allocation as well as quantitative analysis of reconfigurable ship electronic information systems under multi-mission requirements.

Tornado Occurrence in the United States as Modulated by Multidecadal Oceanic Oscillations Using Empirical Model Decomposition

Studies have analyzed U.S. tornado variability and correlated F1–F5 tornado occurrence with various natural climate oscillations and anthropogenic factors. Using a relatively new empirical mode decommission (EMD) method that extracts time-frequency modes adaptively without priori assumptions like traditional time-series analysis methods, this study decomposes U.S. tornado variability during 1954–2022 into intrinsic modes on specific temporal scales. Correlating the intrinsic mode functions (IMFs) of EMD with climate indices found that 1. the U.S. overall tornado count is negatively (positively) correlated with the Atlantic Multidecadal Oscillation (AMO) index (the Southern Oscillation Index (SOI)); 2. the negative (positive) correlation tends to be more prevalent in the western (eastern) U.S.; 3. the increase in weak (F1–F2) and decrease in strong (F3–F5) tornadoes after around 2000, when both the AMO and the Pacific Decadal Oscillation (PDO) shifted phases, are likely related to their secular trends and low-frequency IMFs; and 4. the emerging Dixie Tornado Alley coincides with an amplifying intrinsic mode of the SOI that correlates positively with the eastern U.S. and Dixie Alley tornadoes. The long-term persistence of these climate indices can offer potential guidance for future planning for tornado hazards.

Contribution of Torsional Vibration Modes and the Influence on Period Ratios in the Seismic Response of Elastic Plate Bent Frame Structures

The structural characteristics of large-span structures inherently differ from those of conventional multistorey structures, making it challenging to accurately describe the contribution of various vibration modes to the overall response using traditional dynamic response analysis methods. Based on the response spectrum method, this paper investigates the influence of the first torsional mode on the overall effects of large-span structures. It proposes a new metric, called the torsional mode contribution factor, to characterize the contribution of torsional modes. Focusing primarily on single-span frames, the study explores the impact of factors such as eccentricity ratio, aspect ratio, and roof stiffness on the torsional mode contribution factor. Additionally, the relationship between the period ratio and the torsional mode contribution factor is examined to assess the necessity of controlling the period ratio. The findings reveal that the contribution of torsional modes to the overall seismic response varies significantly under different conditions, such as eccentricity ratio, aspect ratio, roof stiffness, and torsional stiffness. The torsional mode’s contribution is minimal for small eccentricity ratios, with the response primarily driven by translational modes. As eccentricity increases, translational-torsional coupling becomes more pronounced, amplifying the influence of torsional modes on the overall dynamic response. The study also highlights that increasing roof stiffness and aspect ratios can mitigate torsional effects to a certain extent. Still, excessive eccentricity ratios and stiffness may result in higher torsional contributions. Additionally, it is found that increasing torsional stiffness reduces the influence of torsional modes but does not eliminate the overall torsional deformation. The proposed torsional mode contribution factor offers an effective way to quantify these effects, demonstrating that traditional control methods, such as period ratio control, may not fully capture the torsional contributions.

Modeling of an inductive displacement sensor based on 1DCNN-LSTM-AT

Abstract The input-output relationship of inductive displacement sensors is typically nonlinear, which demands numerous computational resources for precise calculation. Therefore, the traditional analytical methods for the inductive displacement sensors cannot meet the real-time high-precision measurement requirements. In response to the aforementioned issues, this paper proposes an optimized Long Short-Term Memory (LSTM) neural networks based on one-dimensional convolutional neural networks (1DCNN) to modeling the input-output relationship of the inductive displacement sensor. First, the measurement principle of the inductive displacement sensors was analyzed, and the analytical model of the sensor output was derived. And the influences of the key parameters of sensors on the relationship between the induced voltage and the displacement were studied. Then, the 1DCNN-LSTM-AT network for modelling the input-output relationship of the inductive displacement sensor was studied. The spatial features of historical induced electromotive force (EMF) data generated by the induction coil were initially extracted using the 1DCNN network. And these spatial features were then utilized as input to the LSTM neural network to capture the temporal features of the historical induced EMF data. Subsequently, the spatiotemporal features of the induced EMF data were fed into the regression prediction layer to compute the displacement measurement results corresponding to the current input. Moreover, the attention mechanism was used for the 1DCNN-LSTM model to enhance the prediction accuracy and stability of the model. Finally, the experimental results demonstrate that the proposed 1DCNN-LSTM-AT model achieves an average absolute percentage error (MAPE) of 3.1%, significantly outperforming traditional models such as LSTM (29.3%), CNN (4.7%), and ANN (4.3%). This paper provides a new method for modelling the nonlinear relationships of the inductive displacement sensor, and presents a fresh perspective for research in the data processing of nonlinear sensors.

Explainable machine learning models for predicting the ultimate bending capacity of slotted perforated cold-formed steel beams under distortional buckling

This study develops explainable machine learning (ML) models to predict the ultimate bending capacity of cold-formed steel (CFS) beams with staggered slotted perforations, focusing on distortional buckling behavior. Utilizing a dataset from 432 non-linear finite element analysis simulations of CFS Lipped channels, ten ML algorithms, including four basic and six ensemble models, were evaluated. Ensemble models, specifically CatBoost and XGBoost, demonstrated superior accuracy, with test-set performances reaching a coefficient of determination (R2) of 99.9%, outperforming traditional analytical methods such as the Direct Strength Method (DSM). SHapley Additive Explanations (SHAP) were applied to highlight how features like plate thickness and root radius critically influence predictions. The findings underscore the enhanced predictive capabilities of ML models for structural performance, suggesting a significant potential to refine traditional design methodologies and optimize CFS beam designs.

Sentiment analysis using graph-based Quickprop method for product quality enhancement

ABSTRACT The degree to which customers express satisfaction with a product on Twitter and other social media platforms is increasingly used to evaluate product quality. However, the volume and variety of textual data make traditional sentiment analysis methods challenging. The nuanced and context-dependent nature of product-related opinions presents a challenge for existing tools. This research addresses this gap by utilizing complex graph-based modelling strategies to capture the intricacies of real-world data. The Graph-based Quickprop Method constructs a graph model using the Sentiment140 dataset with 1.6 million tweets, where individuals are nodes and interactions are edges. Experimental results show a significant increase in sentiment classification accuracy, demonstrating the method’s efficacy. This contribution underscores the importance of relational structures in sentiment analysis and provides a robust framework for extracting actionable insights from user-generated content, leading to improved product quality evaluations. The GQP-PQE method advances sentiment analysis and offers practical implications for businesses seeking to enhance product quality through a better understanding of consumer feedback on social media.

Prediction of feed force with machine learning algorithms in boring of AISI P20 plastic mold steel

Feed force plays a critical role in machining performance, including efficiency, hole quality, energy consumption, and tool life. While feed force is typically measured using dynamometers, these devices are often impractical in real-world experiments due to their complexity and cost. In particular, boring operations, which are essential for enlarging holes within tight tolerances, depend on precise feed force control to maintain high-quality hole outcomes. Achieving this precision requires reliable pre-process feed force estimation. However, traditional analytical methods for calculating feed force in boring operations often fall short, largely due to factors such as in-hole dynamics, the inclination of the rake surface, and the slenderness of the boring bar. This study aims to apply single and hybrid machine learning methods to accurately model and predict feed force in the boring process. Unlike previous research, which focused on feature selection, we propose the use of all possible combinations of input feature subsets to determine the optimal input features. The results indicate that feed force can be predicted effectively using these optimized feature combinations. Such accurate feed force prediction is crucial for effective process optimization, material selection, and process planning.

Monitoring the Biochemical Activity of Single Anammox Granules with Microbarometers.

Granule-based anaerobic ammonium oxidation (Anammox) is a promising biotechnology for wastewater treatments with extraordinary performance in nitrogen removal. However, traditional analytical methods often delivered an average activity of a bulk sample consisting of millions and even billions of Anammox granules with distinct sizes and components. Here, we developed a novel technique to monitor the biochemical activity of individual Anammox granules in real-time by recording the production rate of nitrogen gas with a microbarometer in a sealed chamber containing only one granule. It was found that the specific activity of a single Anammox granule not only varied by tens of folds among different individuals with similar sizes (activity heterogeneity) but also revealed significant breath-like dynamics over time (temporal fluctuation). Statistical analysis on tens of individuals further revealed two subpopulations with distinct color and specific activity, which were subsequently attributed to the different expression levels of heme c content and hydrazine dehydrogenase activity. This study not only provides a general methodology for various kinds of gas-producing microbial processes but also establishes a bottom-up strategy for exploring the structural-activity relationship at a single sludge granule level, with implications for developing a better Anammox process.

Summary of legal regulation of additional mandatory particulars for specific types or categories of foods according to the regulation FIC

The paper presents a summary of the legal treatment of additional mandatory particulars for specific types or categories of foods under Regulation (EU) 1169/2011 of the European Parliament and of the Council of 25 October 2011 on the provision of food information to consumers, amending Regulations (EC) No 1924/2006 and (EC) No 1925/2006 of the European Parliament and of the Council, and repealing Commission Directive 87/250/EEC, Council Directive 90/496/EEC, Commission Directive 1999/10/EC, Directive 2000/13/EC of the European Parliament and of the Council, Commission Directives 2002/67/EC and 2008/5/EC and Commission Regulation (EC) No 608/2004. The authors of the paper analyse and interpret the relevant legislation using traditional methods of legal analysis and legal-hermeneutical methods, with an emphasis on the linguistic and systematic interpretation of those provisions that are directly related to the indication of additional mandatory particulars for foods packaged in certain gases, foods containing sweeteners, foods containing glycyrrhizinic acid or its ammonium salt, Beverages with high caffeine content or foods with added caffeine, Foods with added phytosterols, phytosterol esters, phytostanols or phytostanol esters, and frozen meat, frozen meat preparations and frozen unprocessed fishery products. The paper's authors aim to provide the reader with a comprehensive summary of the legislation on the indication of additional mandatory particulars for specific types or categories of food.

Pilot study for non-invasive diabetes detection through classification of photoplethysmography signals using convolutional neural networks

Diabetes is a chronic disorder affecting vascular health, often altering pulse wave characteristics. Traditional pulse wave analysis (PWA) methods face challenges such as variability and complexity of signals. This study aims to overcome these limitations by leveraging deep learning models for more accurate and efficient classification. The methodology used in this study involves four key steps: data collection, data preprocessing, Convolutional Neural Network (CNN) model development, and model evaluation. Primary data were collected using a multipara patient monitor, including finger photoplethysmography (PPG) signals, blood pressure, mean arterial pressure, oxygen saturation, and pulse rate. Single pulse wave cycles from 60 healthy individuals and 60 patients with type 2 diabetes underwent preprocessing. The CNN model was trained using 50 PPG images from each group and achieved a training accuracy of 92%. The prediction capability of the model was evaluated using 20 unseen images, comprising 10 healthy and 10 diabetes PPG images. It attained a 90% overall test accuracy in distinguishing between PPG images of individuals with diabetes and those who are healthy. These findings suggest that CNN-based analysis of PPG signals provides a precise, non-invasive tool for diabetes screening. To further enhance accuracy, future studies should focus on increasing the dataset size and performing hyperparameter tuning to optimize the CNN model.