Efficient Analysis Approach Research Articles

An efficient and robust approach for continuation and bifurcation analysis of quasi-periodic solutions by multi-harmonic balance method

An efficient and robust approach for continuation and bifurcation analysis of quasi-periodic solutions by multi-harmonic balance method

Symplectic contact analysis of a horizontally laminated magneto-electro-elastic finite specimen

This work formulates a symplectic framework for contact analysis of a horizontally laminated, magneto-electro-elastic, finite specimen. By incorporating dual variables and deriving the Hamiltonian operator matrix within the symplectic space, the constraints on the state variables are fulfilled through the property of symplectic orthogonality. The state variables between different layers are connected via the transfer matrices, and the Hamiltonian mixed energy variational principle is generalized for the laminated medium. Distinct from traditional methods, the symplectic framework offers a more realistic and efficient analysis approach for a finite-sized specimen, applicable to indenters with arbitrary profiles but predetermined contact region. The interfacial and boundary effects are explored, with remarkably accurate results as compared to the finite element simulations. This research not only enhances our understanding of the underlying phenomena but also lays a foundation for materials characterization using high-throughput testing techniques.

A NURBS-based isogeometric formulation for geometrically nonlinear analysis of shells

Due to their high slenderness, shell structures are vulnerable to collapse caused by loss of stability, hence nonlinear analysis is crucial to ensure a safe design. The advantage of isogeometric analysis (IGA) to exactly describe the geometry of the problem independently of the degree of discretization and allow easy refinements has led to its increasing application in shell analysis. However, IGA does not remove the shear and membrane locking presented by fully integrated shell elements based on Reissner-Mindlin's theory. Thus, several alternatives have been proposed in the literature to solve the locking problem, such as mixed formulation and reduced integration. This work aims to study the effectiveness of different reduced integration schemes to eliminate or alleviate locking in the context of stability and geometrically nonlinear analysis of Reissner-Mindlin shells based on the degenerated solid approach. The proposed formulation is applied to the stability analysis of plates and shells, where critical loads and nonlinear equilibrium paths are evaluated and compared to solutions available in the literature or obtained by the Finite Element Method. Excellent results were obtained, demonstrating that a very accurate and efficient approach for nonlinear analysis of plates and shells can be achieved by using high regularity basis functions obtained by k-refinement and an appropriate reduced integration scheme. This approach not only avoids locking but also reduces the computational cost of nonlinear analysis of shells.

An efficient sequential Kriging model for structure safety lifetime analysis considering uncertain degradation

Safety lifetime analysis performs a crucial role in ensuring structural safety in service and developing effective maintenance strategies, which also places higher demands on calculation. However, existing safety lifetime analysis methods generally suffer from inefficiency, which is more prominent for complex engineering structures. In this paper, a novel sequential single-loop Kriging (SSK) surrogate modeling approach is proposed to calculate the safety lifetime in an efficient and accurate manner. To reduce the computational cost, a single-loop safety lifetime analysis framework is proposed. In this framework, there is no need to accurately calculate the time-dependent failure probability (TDFP) in any sub-time interval. By searching the safety lifetime in the process of time-dependent reliability analysis (TRA) and dynamically adjusting the interest time interval, the safety lifetime can be quickly determined by constructing only one Kriging model. To maximize the utilization of sample information, SSK employs a modified learning function that allows most of the training points to be added before the safety lifetime. For accuracy, a convergence criterion that includes two Kriging models is proposed. Mathematical engineering examples are used to illustrate the accuracy and efficiency of SSK. The proposed method offers a promising approach for efficient safety lifetime analysis of engineering problems.

Research on coal rock parameter calibration based on discrete element method

To ensure that the discrete element model of the coal wall accurately reflects the actual cutting process of coal and rock during virtual prototype simulation of mining equipment, this study aims to expedite the development of intelligent mining machinery. Using coal samples from the 4602 working face of Yangcun Coal Mine, operated by Yanzhou Coal Mining Group, we conducted coal rock packing experiments and uniaxial compression tests to obtain the packing angle and compressive strength of the coal samples. Based on the experimental results, we designed Plackett–Burman experiments (PB experiments), steepest ascent experiments, and Box-Behnken experiments to study the influence of particle contact mechanics parameters and bonding mechanics parameters on the packing angle and compressive strength, using the packing angle and compressive strength as response variables. Our objective is to minimize the relative error between the simulated packing angle and the measured packing angle. We solved and optimized the parameter calibration model and conducted simulation calibration experiments based on the optimization results. A comparative analysis with the actual test results revealed that the maximum relative errors between the simulated and measured values for the packing angle and compressive strength were only 2.9% and 5.0%, respectively. Additionally, a discrete element model of a typical working face coal wall was established based on the parameters obtained from this calibration method. A bidirectional coupling model of the cutting process between the coal and rock was created using EDEM-RecurDyn to simulate the rigid-flexible coupling of the coal cutter. An experimental coal wall model was constructed based on similarity theory, and both simulation and physical experiments were conducted. The evaluation metrics for comparison were the time-domain and frequency-domain characteristics of the drum’s vibration signals. The maximum relative error for the time-domain signal characteristics between the two experimental setups was only 4.19%, while the maximum relative error for the frequency-domain signal characteristics was 3.75%. This validates the feasibility of the proposed calibration method for the discrete element coal wall model and the accuracy of the calibration results. Furthermore, it demonstrates that the constructed discrete element coal wall accurately represents the actual coal and rock properties. The virtual simulation based on this model effectively replicates the interaction process between mining machinery and coal, providing a safe, efficient, and low-cost technological approach for performance analysis of mining equipment and intelligent control of supporting devices.

An Efficient Integral Approach for Kinematic Reliability Sensitivity Analysis of Industrial Robots

Abstract Assessment of the reliability and reliability sensitivity of positioning accuracy for industrial robots subject to aleatoric and epistemic uncertainties registers a challenging task. This study proposes a new optimized moment-based method for kinematic reliability analysis and its sensitivity analysis, which incorporates the sparse grid (SPGR) technique and the saddlepoint approximation (SPA) method. To start with, the positioning accuracy reliability and its sensitivity models of industrial robots are established via computational optimization techniques and kinematic criteria. The kinematic accuracy reliability and its sensitivity are then calculated. Specifically, the sparse grid technique is adopted to approach the positioning error statistical moments and moment sensitivities. On this basis, positioning accuracy reliability bounds and reliability sensitivity bounds are obtained by the saddlepoint approximation method and optimization techniques. Finally, two practical examples are implemented to demonstrate the proficiency of the currently proposed method against Monte Carlo simulation (MCS) results. The results show that the currently proposed method exhibits superior computational accuracy and efficiency in kinematic reliability and its sensitivity analyses for industrial robots.

Sentiment Analysis of Political Parties on social media: A Machine Learning and Lexicon-Based Approach

Social media platforms like Facebook, Twitter, Instagram, and YouTube have become central to communication and entertainment, with users sharing opinions on various topics. These opinions, often categorized as positive, negative, or neutral sentiments, provide valuable data for sentiment analysis. Our research analyzed political YouTube comments related to India’s Bhartiya Janata Party (BJP) and Indian National Congress (INC) using a combination of the AFINN lexicon and machine learning techniques. We applied feature representation methods such as Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF), alongside five machine learning algorithms: Multinomial Naïve Bayes, Logistic Regression, Random Forest, Support Vector Machine (SVM), and K-nearest neighbor (K-NN). We aimed to determine the most efficient sentiment analysis approach by comparing the performance of these models using standard evaluation metrics. For the BJP dataset, Logistic Regression performed best with BoW, while SVM was most effective with TF-IDF. Similarly, for the INC dataset, Random Forest excelled with BoW, and SVM outperformed others with TF-IDF. The AFINN lexicon showed poor performance across both datasets, and K-NN consistently achieved lower accuracy. Our findings suggest that SVM and Random Forest are more suitable for political sentiment analysis.

An improved game cross-efficiency approach with dual-role factors for measuring the efficiency of Chinese “985 project” universities

Evaluating the efficiency of universities is essential to gain sustained competitive advantage for regional/national development, especially from the long-run perspective. In this paper, we develop an improved game cross-efficiency (GCE) approach to assess Chinese “985 Project” universities considering dual-role factors. First, building on the concept of “joint technology” and the GCE approach, we provide a new approach for efficiency analysis with dual-role factors. In addition, we identify a potential inconsistency problem while searching the Nash equilibrium point from a computational perspective. Importantly, we propose an improved searching process that is consistent with the original GCE paradigm. Finally, we illustrate the proposed approach by applying it to “985 Project” universities in China. The findings validate the importance of adopting the proposed improved approach.

Response-Aided Score-Matching Representative Approaches for Big Data Analysis and Model Selection under Generalized Linear Models

In this paper, we propose an efficient method called the response-aided score-matching representative (RASMR) approach to facilitate massive data model selection and data analysis with generalized linear models (GLMs) and a predetermined data partition due to data localization. Similar to the original score-matching representative (SMR) approach, RASMR constructs an artificial data point, called the representative, for each data block. It then fits a GLM on the representative dataset, which provides not only an efficient approach for massive data analysis but also an ideal solution in response to privacy concerns by avoiding the transfer of sensitive data. By further splitting the data blocks according to the values of the response variables, RASMR can obtain more accurate parameter estimates than SMR. Furthermore, by theoretical justifications and simulation studies, we show that RASMR can be more efficiently utilized for model selection and variable selection for a massive dataset by approximating the Akaike information criterion (AIC) and the aggregated prediction errors for cross-validation, which are commonly used for choosing the most appropriate statistical model and drawing reliable conclusions. We also apply the proposed RASMR approach to the airline on-time performance data, which consists of 371 data files labeled by month, and show that RASMR can be successfully used for selecting the most appropriate model for real massive data analysis.

Power flow analysis using quantum and digital annealers: a discrete combinatorial optimization approach

Power flow (PF) analysis is a foundational computational method to study the flow of power in an electrical network. This analysis involves solving a set of non-linear and non-convex differential-algebraic equations. State-of-the-art solvers for PF analysis, therefore, face challenges with scalability and convergence, specifically for large-scale and/or ill-conditioned cases characterized by high penetration of renewable energy sources, among others. The adiabatic quantum computing paradigm has been proven to efficiently find solutions for combinatorial problems in the noisy intermediate-scale quantum (NISQ) era, and it can potentially address the limitations posed by state-of-the-art PF solvers. For the first time, we propose a novel adiabatic quantum computing approach for efficient PF analysis. Our key contributions are (i) a combinatorial PF algorithm and a modified version that aligns with the principles of PF analysis, termed the adiabatic quantum PF algorithm (AQPF), both of which use Quadratic Unconstrained Binary Optimization (QUBO) and Ising model formulations; (ii) a scalability study of the AQPF algorithm; and (iii) an extension of the AQPF algorithm to handle larger problem sizes using a partitioned approach. Numerical experiments are conducted using different test system sizes on D-Wave’s Advantage™ quantum annealer, Fujitsu’s digital annealer V3, D-Wave’s quantum-classical hybrid annealer, and two simulated annealers running on classical computer hardware. The reported results demonstrate the effectiveness and high accuracy of the proposed AQPF algorithm and its potential to speed up the PF analysis process while handling ill-conditioned cases using quantum and quantum-inspired algorithms.

B-057 Evaluating the Use of Unconcentrated Urine for Protein Electrophoresis and Immunofixation: An Alternative Approach for Efficient Analysis

Abstract Background Protein electrophoresis is a widely employed technique for separating and analyzing proteins based on their charge and size. Traditionally, concentrated urine samples have been utilized to enhance protein detection sensitivity. However, this study explores the feasibility of using unconcentrated urine as an alternative sample type for protein electrophoresis (UEL) and immunofixation (UIF) to streamline the analysis process and reduce costs and turnaround time in clinical laboratories. It offers a more efficient and resource-friendly alternative. Methods Both UEL and UIF were performed on routinely ordered 80 urine specimens (random or 24 Hr.) for patient management and performed by both concentrated (X40) by Helena and unconcentrated using Sebia (HYDRAGEL 9IF gels using HYDRAGEL BJ dynamic mask procedure. The electrophoretic patterns obtained from unconcentrated urine were compared with those from concentrated urine. Various electrophoretic parameters, including resolution, sensitivity, and reproducibility, were evaluated to determine the reliability of unconcentrated urine in protein electrophoresis. In addition, retrospective analysis was performed before and after implementation on all urine specimens ordered to analyze if there were any differences in the monoclonal band distributions in the population. Results All the protein concentrations were correlated well with 95-100% agreement between the methods (Figure 1) clear separation of protein fractions. Serial dilution studies of monoclonal band analyses showed that these bands can be detected at 1.25 mg/dL using an unconcentrated method. Retrospective analysis of patient data before and after the implementation of the new method shows no differences in the identification of monoclonal proteins (Helena [69 of 872 total patients tested (8.0%)] vs. Sebia [90/800 (11.0%)]. Conclusions These results indicate that unconcentrated urine can be successfully utilized for UEL and UIF with comparable outcomes to traditional concentrated samples. Utilizing unconcentrated urine simplifies the sample preparation process and presents a promising alternative.

A Crack Detection Method for Civil Engineering Bridges Based on Feature Extraction and Parametric Modeling of Point Cloud Data

Accurate detection and analysis of cracks is critical for ensuring the safety and reliability of concrete bridges. Point cloud data (PCD) obtained from 3D scanning provides a promising avenue for automated crack assessment. However, processing the massive and unstructured PCD poses significant challenges in feature extraction and crack modeling. This paper proposes a novel method for bridge crack analysis by combining PCD feature extraction with a hierarchical neural network and Rodriguez rotation. The method first extracts crack features from PCD using outlier removal, denoising, and 3D coordinate conversion. A crack analysis model is then constructed by integrating multi-scale feature extraction and Rodriguez rotation into a hierarchical neural network, enabling the capture of both local and global crack patterns. Experiments on a benchmark data set demonstrate the effectiveness of the proposed approach, achieving 92.83% feature extraction accuracy, 95.73% parameter analysis accuracy, 93.51% recognition accuracy, and 0.91 F1 score. The method also shows improved efficiency compared to existing techniques. These results highlight the potential of the proposed PCD-based approach for accurate and efficient crack analysis in concrete bridges.

A semi-analytical approach for dynamic responses of monopile-supported offshore wind turbines subjected to accidental loads

Offshore wind energy is leading the way in sustainable energy generation. Offshore wind turbines (OWTs) need to be designed to withstand extreme or accidental loads in Ultimate Limit States (ULS) and Accidental Limit States (ALS), whereby high order eigenmodes may become significant. This paper introduces a semi-analytical approach for efficient and reliable analysis of the dynamic responses of monopile-supported OWTs subjected to accidental loads. A Rayleigh-Ritz solution is initially adopted to determine the high-order natural frequencies and eigenmodes of monopile OWTs, explicitly considering tapered towers and soil-pile interactions. Dynamic responses of OWTs are then calculated depending on the interaction schemes between accidental loads and the structures, e.g. soft contact loads (e.g. slamming, wind) and hard contact loads (e.g. ship collisions, ice impact). For soft contact loading conditions, the loads are considered independent of turbine responses, and the transient dynamic response is computed using the classical modal superposition method. While for hard contact loading conditions, the load actions depend on the contact stiffnesses and responses of the interacting bodies. A numerical contact algorithm is thus developed, and numerical iterations are performed to ensure convergence. The proposed approach is applied to the DTU 10 MW monopile-supported OWT subjected to extreme water slamming and ship collisions, respectively as example applications of soft and hard contact scenarios. The results are verified against nonlinear finite element analysis using USFOS and discussed with respect to the turbine natural frequencies and eigenmodes, contact forces and dynamic responses. A parametric analysis is conducted for ship-OWT collisions, exploring different impact scenarios by varying ship sizes, contact stiffnesses, and initial impact velocities. The proposed approach can serve as a promising tool for accidental load response analysis and the design of monopile OWTs.

Automating Government Report Generation: A Generative AI Approach for Efficient Data Extraction, Analysis, and Visualization

This application paper introduces a transformative solution to address the labour-intensive manual report generation, data searching & report revision process in government entities. Traditional methods of data extraction, analysis, and graph creation for annual reports are not only time-consuming but also prone to human errors. To mitigate these challenges, we propose an innovative system leveraging Generative Artificial Intelligence (GenAI), with a specific focus on large language models (LLMs). Our solution incorporates automated data extraction from diverse sources designated as internal knowledge base, text analysis, summarization using advanced language models, and the generation as well as revisions of informative graphs. Different LLMs like Google's Gemini Pro and OpenAI's GPT 4.0 has been used to read different data visualisation graphs and fetching the information from internal knowledge base, respectively, to update the graphs in automated manner. Solution implementation using Python language on the World Economic Situation and Prospects report by Department of Economic & Social Affairs, United Nation shows that the early result produces almost 0.87% to 17.46% average error rates in the task of factual data visualisation graph. Key benefit of this approach include improved time efficiency, consistency in report format, enhanced insights, and a user-friendly interface. A review and approval workflow facilitate user feedback, contributing to continuous model performance improvement.

A novel efficient plastic hinge approach for direct analysis of steel structures

A robust and efficient plastic analysis method incorporated into beam-column elements is highly demanded for economic design and safety evaluation of structures under earthquakes and extreme events. This paper presents a novel approach for the direct analysis of steel structures allowing for material yielding by introducing an enhanced plastic hinge method to an advanced second-order beam-column element, which is derived based on a rigorous incremental-iterative force recovery procedure. The new element considers various critical factors at the element level, including transverse shear deformation, initial geometrical imperfections, and residual stresses. Thus, the approach enables the analysis and design of a wide range of steel structures using a single element per member, which contrasts with commercial software that requires many elements per member. Several benchmark problems are investigated and the results are in good agreement with those in literature and commercial software. The paper offers a reliable, efficient, and practical solution for direct analysis of steel structures in various engineering applications without the use of the conventional effective length method.

LAMAIS: A library-aided approach for efficient 1D 1H NMR qualitative analysis in plant metabolomics

BackgroundOne-dimensional proton nuclear magnetic resonance (1D 1H NMR) spectroscopy is a non-destructive, non-targeted analytical technique providing both qualitative and quantitative insights, particularly beneficial for mixture analysis. However, the qualitative analysis of 1D 1H NMR spectra for mixture samples is laborious and time-consuming, involving extensive database searches and verification experiments like spiking. This process heavily relies on the analyst's expertise, leading to efficiency discrepancies. There is a pressing need for a reliable method to streamline operations and enhance the efficiency of qualitative analysis in complex mixtures. ResultsWe introduce a library-aided method for spectral profiling, named LAMAIS. This method achieves compound identification through similarity assessment between samples and template data, allowing rapid, automatic compound identification and full-spectrum peak assignment without the need for fitting. LAMAIS correctly identifies over 90 % of components in synthetic mixtures and more than 75 % in experimental mixtures, surpassing other representative methods with a higher F2 score. Our reference library, which currently includes 71 compounds, is tailored to capture the commonality of primary metabolites across diverse plant species. The analysis of real-world samples yielded encouraging results, underscoring LAMAIS's versatility as an auxiliary tool suitable for a variety of botanical sources. For analyst convenience, interactive graphics are utilized as the output format. SignificanceLAMAIS excels, demonstrating competitiveness and reliability. The approach minimizes repetitive tasks and sample wastage, improving the efficiency of 1D 1H NMR qualitative analysis. Constructing a reference library effectively preserves knowledge, mitigates reliance on human experience, and addresses gaps in the analysis of plant source samples.

Ant-observer: A new approach for automatic acquisition and autonomous analyses of individual species abundance and interactions

Traditional methods, such as direct observations or manually photographing bait stations, are limited in both data collection efficiency and accuracy. Consequently, they fail to capture the dynamic changes and complex relationships between ant species. We developed a novel system as a potential solution that combines processed consumer-grade camera equipment and software that acquires data needed for in situ experiments. This software features a user-friendly graphics user interface (GUI) for data collection and post-hoc analysis from videos captured by the camera equipment. Researchers can use this system to (1) count species abundance per frame; (2) calculate species speed; (3) detect ant interactions; and (4) count the abundance of different subcastes within species on baits. To demonstrate the complete workflow, we applied this system on ant videos collected in Hong Kong. Practical guides (supplement materials) detailing suggestions for camera equipment and software operations in both field and laboratory studies are provided. We present the results of ten in situ experiments of ants (each lasting 1–1.5 h) conducted in Hong Kong though processed consumer-grade camera equipment, with videos analyzed using the newly developed software. The analysis captured a range of variables related to ant foraging behavior, including resource discovery time, nestmate recruitment rate, and interspecific interactions. Our results confirm that the developed software provides an integrated and efficient approach for data collection, extraction, and analysis in in situ experiments, enhancing the capabilities of insect ecology research.

Prediction of saccharides concentration in intact and ground Codonopsis root using near-infrared reflectance spectroscopy

Codonopsis root, a traditional Chinese medicine, produces saccharides that serve as key indicators for assessing its quality. In this study, we employ near-infrared reflectance spectroscopy to predict total polysaccharide concentration as well as the major individual saccharides of intact (surface and cross-section) and ground samples of Codonopsis roots. We developed regression relationships between the spectra and the concentration of fructose, glucose and polysaccharides for the ground spectra set (Rp2 > 0.8, RPD>2.0) based on partial least squares regression (PLSR) model. However, for the intact cross-section spectra set, only the dominant component of the fructose, could be quantitatively detected (Rp2 > 0.8, RPD>2.0). All models based on the intact surface spectra set were of unacceptable performance. This discrepancy could be attributed to the low concentration levels of different saccharides and the distinct chemical profiles reflected by the spectra sets from samples with varying homogeneity. The findings provide an approach for efficient and sustainable analysis for the purposes of quality control and standardization of Codonopsis roots.

Fiscal space for the immunisation program in Zambia– an efficiency analysis approach

ObjectiveThe immunisation programme in Zambia remains one of the most effective public health programmes. Its financial sustainability is, however, uncertain. Using administrative data on immunisation coverage rate, vaccine utilisation, the number of health facilities and human resources, expenditure on health promotion, and the provision of outreach services from 24 districts, we used Data Envelopment Analysis to determine the level of technical efficiency in the provision of immunisation services. Based on our calculated levels of technical efficiency, we determined the available fiscal space for immunisation.ResultsOut of the 24 districts in our sample, 9 (38%) were technically inefficient in the provision of immunisation services. The average efficiency score, however, was quite high, at 0.92 (CRS technology) and 0.95 (VRS technology). Based on the calculated level of technical efficiency, we estimated that an improvement in technical efficiency can save enough vaccine doses to supply between 5 and 14 additional districts. The challenge, however, lies in identifying and correcting for the sources of technical inefficiency.

Efficient slope reliability and sensitivity analysis using quantile-based first-order second-moment method

This paper introduces a novel approach for parameter sensitivity evaluation and efficient slope reliability analysis based on quantile-based first-order second-moment method (QFOSM). The core principles of the QFOSM are elucidated geometrically from the perspective of expanding ellipsoids. Based on this geometric interpretation, the QFOSM is further extended to estimate sensitivity indices and assess the significance of various uncertain parameters involved in the slope system. The proposed method has the advantage of computational simplicity, akin to the conventional first-order second-moment method (FOSM), while providing estimation accuracy close to that of the first-order reliability method (FORM). Its performance is demonstrated with a numerical example and three slope examples. The results show that the proposed method can efficiently estimate the slope reliability and simultaneously evaluate the sensitivity of the uncertain parameters. The proposed method does not involve complex optimization or iteration required by the FORM. It can provide a valuable complement to the existing approximate reliability analysis methods, offering rapid sensitivity evaluation and slope reliability analysis.