Traditional Analysis Methods Research Articles

Reliability Analysis of High-Pressure Tunnel System Under Multiple Failure Modes Based on Improved Sparrow Search Algorithm–Kriging–Monte Carlo Simulation Method

It is often difficult for a structural safety design method based on deterministic analysis to fully and reasonably reflect the randomness of mechanical parameters, while the traditional reliability analysis method has a large calculation cost and low accuracy. In this paper, based on the seepage–stress coupling numerical model, the random variables affecting the reliability of the collaborative bearing of surrounding rock and lining structures are successfully identified. Then, the improved sparrow search algorithm (ISSA) is used to optimize the hyper-parameters of the Kriging surrogate model, in order to improve the computational efficiency and accuracy of the reliability analysis model. Finally, the ISSA-Kriging-MCS model is used to quantitatively evaluate the reliability of the surrounding rock-reinforced concrete lining structure under multiple failure modes, and the sensitivity of each random variable is discussed in depth. The results show that the high-pressure tunnel structure has high safety and reliability. The reliability indexes of each failure mode decrease with the increase in the coefficient of variation (COV) of random variables. In addition, the same random variable also exhibits varying degrees of influence in different failure modes.

Synchronous decomposition match-reassigning transform and its application in planetary gearbox fault diagnosis

Abstract Under time-varying operating conditions, the instantaneous frequency (IF) of the vibration signal of the planetary gearbox exhibits non-stationary time-varying closely spaced characteristics as well as non-proportional and non-synchronous characteristics, making it difficult for traditional time-frequency analysis (TFA) methods to accurately identify its fault features and obtain accurate time-frequency representations (TFR). To address this challenge, this paper proposes a TFA method based on Synchronous Decomposition Match-Reassigning Transform (SDMRT). Firstly, the successive variational mode decomposition (SVMD) is used to adaptively separate non-synchronous frequency components in the original vibration signal. Then, the chirp rate (CR) describing the signal harmonic structure is used to synchronously match each frequency component, obtaining the TFR corresponding to each component. Next, all TFRs are merged to obtain a complete TFA result. Finally, Finally, the energy spread is re-aggregated to the ridge of the IF using the reassignment principle, resulting in a new frequency estimation operator called the Synchronous Decomposition Match-Reassigning Operator (SDMRO). SDMRT can adaptively separate non-proportional and non-synchronous IFs and achieve precise matching of time-varying closely spaced IFs, thereby completing the TFR of the vibration signal generated by the planetary gearbox. By analyzing simulated signals and measured vibration signals from planetary gearboxes, the method proposed in this paper is significantly superior to other TFA methods, thereby validating the effectiveness and superiority of SDMRT.

AI-Enhanced Predictive Analytics for Proactive Healthcare Management: Leveraging Machine Learning to Improve Patient Care and Operational Efficiency

As healthcare changes quickly, combining artificial intelligence (AI) and machine learning (ML) technologies has become a major force that can change things. This is because it improves predictive analytics for proactive healthcare management. This essay looks at how AI-powered predictive analytics might be able to help healthcare systems provide better care to patients and run more smoothly. Machine learning algorithms can find patterns and trends in huge amounts of patient data that traditional analysis methods might miss. This lets healthcare workers make smart decisions before big problems happen. The study looks into many uses of AI-enhanced prediction analytics, such as figuring out which patients are at the highest risk, finding diseases early, and making treatment plans work better. Using past patient data and real-time tracking, predictive models can figure out how likely it is that a person will develop a chronic condition. This lets doctors act quickly and make personalized care plans for each person. This cautious method not only improves the health of patients, but it also makes healthcare systems less busy by cutting down on hospital readmissions and trips to the emergency room. The paper also looks at how predictive analytics can help healthcare groups streamline their working processes. Healthcare facilities can better arrange staff and tools, making sure that resources are available where and when they are needed, by predicting how many patients will be admitted and how resources will be used. This speed makes patients happier, cuts down on wait times, and raises the standard of care generally. The study also looks at the problems that come with using AI and predictive analytics in healthcare, such as worries about data privacy, integrating new systems with old ones, and the need for strong algorithms that can handle a wide range of patients. It stresses how important it is for lawmakers, healthcare workers, and data scientists to work together to make ethical rules for the use of AI in healthcare situations.

What are microsatellites and how to choose the best tool: a user-friendly review of SSR and 74 SSR mining tools

Microsatellites, also known as SSR or STR, are essential molecular markers in genomic research, playing crucial roles in genetic mapping, population genetics, and evolutionary studies. Their applications range from plant breeding to forensics, highlighting their diverse utility across disciplines. Despite their widespread use, traditional methods for SSR analysis are often laborious and time-consuming, requiring significant resources and expertise. To address these challenges, a variety of computational tools for SSR analysis have been developed, offering faster and more efficient alternatives to traditional methods. However, selecting the most appropriate tool can be daunting due to rapid technological advancements and the sheer number of options available. This study presents a comprehensive review and analysis of 74 SSR tools, aiming to provide researchers with a valuable resource for SSR analysis tool selection. The methodology employed includes thorough literature reviews, detailed tool comparisons, and in-depth analyses of tool functionality. By compiling and analyzing these tools, this study not only advances the field of genomic research but also contributes to the broader scientific community by facilitating informed decision-making in the selection of SSR analysis tools. Researchers seeking to understand SSRs and select the most appropriate tools for their projects will benefit from this comprehensive guide. Overall, this study enhances our understanding of SSR analysis tools, paving the way for more efficient and effective SSR research in various fields of study.

Real-time SERS analysis of programmed cell death patterns based on black Phosphorus–gold nanoparticles

Programmed cell death (PCD) plays a crucial role in the biological processes of living organisms and occurs in various forms, such as apoptosis, necroptosis and ferroptosis. However, traditional methods for PCD analysis are time-consuming and complex. In this paper, we propose a facile surface-enhanced Raman spectroscopy (SERS)-based strategy for the real-time analysis of three PCD patterns utilizing black phosphorus–gold nanoparticles (BP–Au NPs) as the ultrasensitive unlabeled Raman probe. BP–Au NPs, which possess excellent biocompatibility, are capable of detecting dye molecules at concentrations as low as [Formula: see text][Formula: see text]M and remain stable for at least one week in different physiological environments. In view of this, BP–Au NPs-based SERS technique can distinguish the tiny differences in the molecular fingerprints of cancer cells undergoing three PCD patterns (apoptosis, necroptosis and ferroptosis) triggered by doxorubicin, shikonin and erastin, respectively. We also have real-time monitoring of the intracellular molecular events during PCD, which spy the fluctuations of some typical SERS bands assigned to protein, DNA and lipid, revealing the unique phenotypic characteristics of each PCD pattern. This strategy provides a detailed and comprehensive analysis of the mechanisms of drug-induced PCD at the Raman level.

Implemention of Innovative Process Analytical Technologies to Characterize Critical Quality Attributes of Co-Formulated Monoclonal Antibody Products.

Characterizing co-formulated monoclonal antibodies (mAbs) poses significant challenges in the pharmaceutical industry. Due to the high structural similarity of the mAbs, traditional analytical methods, compounded by the lengthy method development process, hinder product development and manufacturing efficiency. There is increasing critical need in the pharmaceutical industry to streamline analytical approaches, minimizing time and resources, ensuring a rapid clinical entry and cost-effective manufacturing. This study investigates the application of process analytical technologies (PAT) to address such challenges. Our investigation introduces two complementary technologies, on-line ultra-performance liquid chromatography (online UPLC) and multimode fluorescence spectroscopy (MMFS), as potential PAT tools tailored for characterizing critical quality attributes (CQA) in co-formulated mAb products. Specifically, the CQAs under evaluation include the total protein concentration of the mAbs within the co-formulation and the ratio of mAb A to mAb B. Online UPLC enables direct and automated measurement of the CQAs through physical separation, while MMFS determines them in a non-destructive and more swift manner based on chemometric modeling. We demonstrate these technologies' comparable performance to conventional methods, alongside substantial benefits such as reduced analytical turnaround time and decreased laboratory efforts. Ultimately, integrating them as innovative PAT tools expedites the delivery of therapeutic solutions to patients and enhances manufacturing efficiency, aligning with the imperative for swift translation of scientific discoveries into clinical benefits.

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.

Heart Sound Classification Using Harmonic and Percussive Spectral Features from Phonocardiograms with a Deep ANN Approach

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, with a particularly high burden in India. Non-invasive methods like Phonocardiogram (PCG) analysis capture the acoustic activity of the heart. This holds significant potential for the early detection and diagnosis of heart conditions. However, the complexity and variability of PCG signals pose considerable challenges for accurate classification. Traditional methods of PCG signal analysis, including time-domain, frequency-domain, and time-frequency domain techniques, often fall short in capturing the intricate details necessary for reliable diagnosis. This study introduces an innovative approach that leverages harmonic–percussive source separation (HPSS) to extract distinct harmonic and percussive spectral features from PCG signals. These features are then utilized to train a deep feed-forward artificial neural network (ANN), classifying heart conditions as normal or abnormal. The methodology involves advanced digital signal processing techniques applied to PCG recordings from the PhysioNet 2016 dataset. The feature set comprises 164 attributes, including the Chroma STFT, Chroma CENS, Mel-frequency cepstral coefficients (MFCCs), and statistical features. These are refined using the ROC-AUC feature selection method to ensure optimal performance. The deep feed-forward ANN model was rigorously trained and validated on a balanced dataset. Techniques such as noise reduction and outlier detection were used to improve model training. The proposed model achieved a validation accuracy of 93.40% with sensitivity and specificity rates of 82.40% and 80.60%, respectively. These results underscore the effectiveness of harmonic-based features and the robustness of the ANN in heart sound classification. This research highlights the potential for deploying such models in non-invasive cardiac diagnostics, particularly in resource-constrained settings. It also lays the groundwork for future advancements in cardiac signal analysis.

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.

Modelling behavior of Crested gecko (Correlophus ciliatus) using classification algorithms

Animal behavior plays a crucial role in evolution of many species. Many studies focused on animal behavior enhance the ability to collect large and detailed data. However, this kind of data is surpassing the capability of traditional statistical methods for analysis. In this study we propose to use artificial intelligence (AI) with machine learning models (ML) as tools to study animal behavior and potentially assumed evolution patterns in their behavior. For the Crested gecko (Correlophus ciliatus), some guidelines have been published regarding the breeding of these reptiles, focusing on their behavior. However, little is known about moderating their behavior using AI and advanced ML algorithms. In this study, based on information collected from twenty individuals, we proposed building a supervised classifier model using simple Decision Tree classifier (DT), Gradient Boosting classifier (GB) and Extreme Gradient Boosting classifier (XGBoost). Our results show that the highest accuracy (above 60 %) was achieved for variables which were not complex in terms of animal behavior. The analysis presented in this study, demonstrates that it is possible to model Crested Gecko behavior using ML models.

All in the Details: A First Assessment for the Viability of Metabarcoding in Diet Composition Analysis of African Wild Dogs (Lycaon pictus).

DNA metabarcoding is a contemporary technique in diet composition studies and stands to fill key knowledge gaps left by traditional diet analysis methods. For endangered species such as the African wild dog (Lycaon pictus), the fulfilment of these knowledge gaps presents an opportunity for improved management practices and vulnerability assessments. There are an estimated ~600 African wild dogs remaining in South Africa. These dogs are generally understood to prey upon impala (Aepyceros melampus) and other medium-sized ungulates. Here, we present the first assessment of DNA metabarcoding as a valuable method for diet composition analysis of this highly social carnivore. DNA from faecal samples collected across seven landscape types in the Kruger National Park (KNP) was extracted and used to determine the presence of seven unique prey taxa, including novel species such as the Cape hare (Lepus capensis). Impala was identified as a prey item in all landscape types, complementing the diet preference prediction made with stable isotope analysis using the same samples and existing understanding of wild dog diet. Given recommended improvements, the application of DNA metabarcoding in wild dog diet analysis shows promising prospects for identifying novel prey species and validating previous records of this endangered canids diet.

Wing optimization for static aeroelastic effect

Abstract An aeroelastic analysis module based on the VLM model and FE model with non-linear static analysis was developed to calculate the lift and deformation of a wing in a cruise case. The traditional static aeroelastic analysis method focuses on compensation of loss of lift by adjusting wing root AoA (angle of attack). To improve the aeroelastic property, integrated static aeroelastic optimization was developed, which compensates for the loss lift by adjusting a series of sectional AoA instead of only wing root AoA. Compared with traditional static aeroelastic analysis, the manufacture wing optimized by integrated static aeroelastic optimization is closer to the initial design wing.

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.

Predicting Total Alkalinity in Saline Water Using Machine Learning: A Case Study with RapidMiner

This study investigates the use of machine learning models to predict total alkalinity (TA) based on chloride concentration (Cl-), pH and temperature. Utilizing RapidMiner's Auto Mode, six machine learning models were applied to a dataset of 111 water samples from the Nhà Bè River. The models' performances were evaluated using Root Mean Square Error (RMSE) and R² metrics, with the Generalized Linear Model (GLM), Support Vector Machine (SVM) and Deep Learning models identified as the top performers. Correlation and coefficient analyses revealed that Cl- had the most significant impact on TA prediction, followed by temperature and pH. These findings underscore the effectiveness of machine learning in water quality monitoring, presenting a cost-effective alternative to traditional chemical analysis methods.

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.