Evaluation Of Algorithms Research Articles

The NEDC-GTOPSIS Node Influence Evaluation Algorithm Based on Multi-Layer Heterogeneous Classroom Networks

To address the deficiency in the analysis of individual students within existing research on in-classroom social networks and the constraints of traditional centrality metrics in identifying influential nodes, this paper presents the NEDC-GTOPSIS evaluation method for evaluating node influence in multi-layer heterogeneous networks. Initially, students' friendship, interaction, and attribute information are leveraged to compute neighborhood overlap and attribute similarity between nodes, to construct the Composite Relationship Network. Subsequently, the Seat Similarity Network is constructed by applying the Nearest-Neighbor Effective Distance Criterion to compute seat similarity across various class sessions among nodes. Finally, the structure characteristics of two networks serve as influence decision indicators, and the GRA-TOPSIS algorithm, based on the combined weight method, evaluates nodes' influence. Experiments demonstrate that, compared to traditional single-layer relational networks and classical algorithms, this method can more effectively assess influential student nodes.

Digital twinning, prediction and multi-objective optimization of an azeotrope system separation process in pharmaceutical manufacturing process

Digital twin is to make full use of the physical model, sensor update, operation history and other data to integrate multidisciplinary, multi-physical quantity, multi-scale and multi-probability simulation process, and to complete the full life cycle process of the corresponding physical equipment in the virtual space. In this paper, digital twin technology was used to simulate the production process of a pharmaceutical workshop, thus realizing solvent recovery by an azeotrope system separation process. The separation was optimized by multi-objective genetic algorithm. separation process, wherein the polynomial curve fitting and entropy-weighted TOPSIS data evaluation algorithms were combined to optimize the operating parameters, then the solvent purity and yield reach 99.96 % and 77.9 %, respectively, which is much higher than the corporate requirements. In addition, the effectiveness of the digital twin in providing data-driven prediction capability was verified by a CNN-LSTM-based regression prediction model, and the CNN-LSTM model has an accuracy of more than 98.9 % for all prediction results. Finally, a 3D visualization model was built using the Unity engine, enabling researchers to visualize and explore changes in key metrics during the separation process. This study can provide some guidance for digital transformation and intelligent development in pharmaceutical industry.

Marine management and monitoring system based on AHP cost-benefit analysis with integrated evaluation and genetic algorithms for recurrent searches

Marine management and monitoring systems need to develop a safety procedure for submersibles that predicts changes in the position of the submersible over time. This would effectively prevent the submersible from losing communication with the host and would allow it to react to possible deficiencies. In this paper, a random walk model is used to predict the position of the submersible at each moment on the seafloor. The probability of the highest point on each plane is 0.87-0.92. The error in the position of the submersible caused by the uncertainty on the seafloor is corrected by the Kalman filter model. In this paper, we use AHP cost-benefit analysis to comprehensively evaluate and select the best additional search equipment. In this paper, the genetic algorithm based on cyclic search is used to determine the optimal initial deployment point and search pattern. After a large number of iterations, the shortest search path length of 817.273 is obtained, and the shortest search path is taken as the optimal path for search and rescue. In this paper, the model is extended using particle swarm algorithm and environment modelling. The predictive model of the submersible is combined with the environmental model of different sea areas. Therefore, the actual motion model of the submersible is approximated. After simulation, it was found that the accuracy of the submersible probability increased by 23.72%.

GLIM: 3D range-inertial localization and mapping with GPU-accelerated scan matching factors

This article presents GLIM, a 3D range-inertial localization and mapping framework with GPU-accelerated scan matching factors. The odometry estimation module of GLIM employs a combination of fixed-lag smoothing and keyframe-based point cloud matching that makes it possible to deal with a few seconds of completely degenerated range data while efficiently reducing trajectory estimation drift. It also incorporates multi-camera visual feature constraints in a tightly coupled way to further improve the stability and accuracy. The global trajectory optimization module directly minimizes the registration errors between submaps over the entire map. This approach enables us to accurately constrain the relative pose between submaps with a small overlap. Although both the odometry estimation and global trajectory optimization algorithms require much more computation than existing methods, we show that they can be run in real-time due to the careful design of the registration error evaluation algorithm and the entire system to fully leverage GPU parallel processing.

Research on ultrasonic non-destructive evaluation algorithm for ultimate tensile strength of stainless-steel welded pipe welds based on width learning

ABSTRACT The regression prediction between ultrasonic signal and ultimate tensile strength of stainless-steel pipe weld is studied in this paper. First, ultrasonic signals were collected from the weld of AISI304 austenitic stainless steel welded pipe sample, and its ultimate tensile strength was tested. The data sets of ultrasonic signal and ultimate tensile strength of weld were constructed. The random forest algorithm is used to extract the characteristic value set of the ultrasonic signal set of the welding seam and reduce the dimension of the characteristic value set. Then, the set of eigenvalues after dimensionality reduction is input into the Bayesian BLS network for training. The results of the training were compared with those of other regression models. The results show that the model established by Bayes BLS algorithm is feasible and reliable to predict the ultimate tensile strength of stainless-steel pipe weld. The results also show that the model established by the Bayesian BLS algorithm has high accuracy and stability in predicting the ultimate tensile strength of stainless-steel pipe weld. This method can be used to predict the ultimate tensile strength of welds in different types of materials.

A simple and fast measurement algorithm for power system flicker severity evaluation

A simple and fast measurement algorithm for power system flicker severity evaluation

O-323 Sex-dependent discrepancies in automatic scoring: implications for the performance of embryo evaluation and selection methods

Abstract Study question Are artificial intelligence-based embryo evaluation and selection algorithms equally effective for male and female embryos? Summary answer Compared to female embryos, male embryos exhibited higher automatic scores, superior performance in KIDScore D5 v3, and poorer performance in iDAScore v2 in predicting implantation. What is known already It is known that the secondary sex ratio favors males in assisted reproductive techniques. The reason could be an unbalanced selection, as there is evidence that male embryos undergo faster development and have better morphology than female embryos. Based on this premise, artificial intelligence (AI) models currently used for embryo selection may not perform equally well for embryos of both sexes. We aimed to analyze the performance of two models for assessing male and female embryos: KIDScore D5 v3 and iDAScore v2. Study design, size, duration This is a retrospective cohort study involving 481 patients included in the preimplantation genetic testing program over a year and a half. The euploid embryos (n = 1,181) were divided into male (n = 577) and female (n = 604) groups and the automatic scores received by two artificial intelligence-based evaluation models (KIDScore D5 v3 and iDAScore v2) were considered with respect to implantation. Participants/materials, setting, methods All embryos underwent assisted hatching on day 3 of embryo development and trophoectoderm biopsy at the blastocyst stage (day 5 or 6). The embryos’ sex chromosomes were revealed using a highly validated method. Routine embryo assessment and selection were performed using conventional morphology according to ASEBIR criteria. Retrospectively, embryos were automatically scored from 1 to 9.9. Means, standard deviations, odds ratios (OR), and areas under the ROC curve (AUC) were calculated using IBMSPSS Statistics software. Main results and the role of chance In general, male embryos exhibited a significantly higher iDAScore than female embryos (6.9±2.2 vs. 6.4±2.5)**; no differences for KIDScore (6.1±1.7 vs. 5.9±1.8). This indicates that if the selection were based on the iDAScore, more male embryos would be transferred. Male embryos: 323 male embryos were transferred with an implantation rate of 53.8%. Implanted male embryos had higher automatic score compared to non-implanted ones (6.7±1.6 vs. 6±1.7 for KIDScore** and 7.3±2 vs. 6.8±2.1 for iDAScore*). For KIDScore: OR = 1.3, 95% CI [1.1-1.5] and AUC 0.612 (0.545-0.678). For iDAScore: OR = 1.1, 95% CI [1-1.3] and AUC 0.575 (0.511-0.638). This suggests a higher performance of KIDScore in predicting implantation in male embryos. Female embryos: 318 female embryos were transferred with an implantation rate of 47.7%. Implanted female embryos had higher iDAScore compared to non-implanted ones (7±2.3 vs. 6.3±2.5)*; no differences for KIDScore (6.2±1.8 vs. 6±1.8). For KIDScore: OR = 1.1, 95% CI [0.9-1.2] and AUC 0.542 (0.472-0.612). For iDAScore: OR = 1.1 95% CI [1-1.2] and AUC 0.584 (0.520-0.648). This suggests a higher performance of iDAScore in predicting implantation in female embryos. *pvalue<0.05; **pvalue<0.01 Limitations, reasons for caution The primary limitation of our study is the assisted hatching process performed on embryos on day 3 of development, which may impact late-stage development. This could be a possible reason for the considerably lower performance of the models. Wider implications of the findings Our study revealed objective differences between male and female embryos, as well as distinct performance for two AI-based embryo selection models. These findings suggest that the employed embryo selection model may impact the secondary sex ratio. Trial registration number Not applicable

Phased Array Ultrasonic Imaging for Characterizing Steel with an Elevated Temperature Gradient.

In this research work a phased array ultrasonic Testing (PAUT) is used for inspecting steel at elevated temperature. Velocity of ultrasound is affected by temperature of medium, and the study of ultrasound is done when it is propagating through High temperature media of steel. For this purpose, a steel sample is heated from one surface and temperature gradient is generated in it. The methods like Full Matrix Capture (FMC) and Total Focusing Method (TFM) are used to generate real time ultrasonic image at particular instant of time. It is a method to generate the real time two-dimensional ultrasonic image. The FEA model of steel with temperature gradient is created and the ultrasonic inspection methods, FMC and TFM are applied in order to generate the image by taking elementary A-scan data from simulation and plotting the same to generate the image. The simulation results can be validated using high temperature experiments with steel samples and the experimental data can be taken to use TFM algorithm for ultrasonic evaluation. These TFM images provide a visual representation of the steel cross section at elevated temperature at specific instances, facilitating early-stage in-situ monitoring and optimization of the steel solidification process.

Deep Inspiration Breath Hold in Left-Sided Breast Radiotherapy: A Balance Between Side Effects and Costs

BackgroundDeep inspiration breath hold (DIBH) is an effective technique for reducing heart exposure during radiotherapy for left-sided breast cancer. Despite its benefits, cost considerations and its impact on workflow remain significant barriers to widespread adoption. ObjectivesThis study aimed to assess the cost-effectiveness of DIBH and compare its operational, financial, and clinical outcomes with free breathing (FB) in breast cancer treatment. MethodsTreatment plans for 100 patients with left-sided breast cancer were generated using both DIBH and FB techniques. Dosimetric data, including the average mean heart dose, were calculated for each technique and used to estimate the cardiotoxicity of radiotherapy. A state-transition microsimulation model based on SCORE2 (Systematic Coronary Risk Evaluation) algorithms projected the effects of DIBH on cardiovascular outcomes and quality-adjusted life-years (QALYs). Costs were calculated from a provider perspective using time-driven activity-based costing, applying a willingness-to-pay threshold of €40,000 for cost-effectiveness assessment. A discrete event simulation model assessed the impacts of DIBH vs FB on throughput and waiting times in the radiotherapy workflow. ResultsIn the base case scenario, DIBH was associated with an absolute risk reduction of 1.72% (95% CI: 1.67%-1.76%) in total cardiovascular events and 0.69% (95% CI: 0.67%-0.72%) in fatal cardiovascular events over 20 years. Additionally, DIBH was estimated to provide an incremental 0.04 QALYs (95% CI: 0.05-0.05) per left-sided breast cancer patient over the same time period. However, DIBH increased treatment times, reducing maximum achievable throughput by 12.48% (95% CI: 12.36%-12.75%) and increasing costs by €617 per left-sided breast cancer patient (95% CI: €615-€619). The incremental cost-effectiveness ratio was €14,023 per QALY. ConclusionsDespite time investments, DIBH is cost-effective in the Belgian population. The growing adoption of DIBH may benefit long-term cardiovascular health in breast cancer survivors.

Research on three-dimensional dynamic design and decision system of airport

Abstract Due to the lack of elevation information, the traditional airport design system based on the AutoCAD 2D platform is often unable to make a comprehensive and objective analysis and judgment when the designer evaluates the advantages and disadvantages of the design scheme. Therefore, this paper focuses on the development of airport 3D dynamic design and decision systems based on the Cesium framework. Firstly, based on the 3D geographic information platform, a multi-type 3D digital map loading and display module will be developed to realize the combination of 3D geographic information and the airfield model. Then, according to the 3D model data and the characteristics of the platform development, the method of calculating the earth volume of clearance processing, the evaluation algorithm of airport clearance obstacle limit surface, the optimization analysis method of tower position, and the analysis method of navigation station and flight track visibility are constructed.

Seven Years SHM of Offshore Wind Turbine Foundations: Review, Experiences and Outlook

Wölfel Engineering is a leading expert on the design and provision of SHM systems for offshore wind turbines, as well as the assessment of their structural integrity based on the implementation of sophisticated data evaluation algorithms. This contribution aims to provide an insight into the SHM results of offshore wind turbine foundations in numerous wind farms over the last seven years, focusing on presenting an overview of the evolution of SHM concepts, monitoring goals and features monitored for structural assessment – alternatively referred to as key performance indicators (KPIs). Over the years, design approaches have become more accurate, alongside with the continuous increase of the size of the structures. Examples from offshore wind farms are used in order to show the effect of these evolutions to KPIs, such as the natural frequencies and the fatigue life consumption. The challenges met in industrial SHM applications for offshore foundations are discussed. A novel approach to SHM, the agile SHM concept, is Wölfel’s answer to the future requirements on monitoring accuracy and reliability. The proposed concept enables the efficient exchange of information between the results of the SHM system and the design process, so that the design model is at all times an accurate representation of the real structures.

Carbon-Efficient Neural Architecture Search

This work presents a novel approach to neural architecture search (NAS) that aims to reduce energy costs and increase carbon efficiency during the model design process. The proposed framework, called carbon-efficient NAS (CE-NAS), consists of NAS evaluation algorithms with different energy requirements, a multi-objective optimizer, and a heuristic GPU allocation strategy. CE-NAS dynamically balances energy-efficient sampling and energy-consuming evaluation tasks based on current carbon emissions. Using a recent NAS benchmark dataset and two carbon traces, our trace-driven simulations demonstrate that CE-NAS achieves better carbon and search efficiency than the three baselines.

Validation and reliability of the 3D calibration evaluation algorithm for SPM

Abstract Novel marker-based 3D standards, in the form of cascading step-slope pyramids, enable the calibration of three scaling factors and three coupling factors of scanning probe microscopes using only a single sample and a single measurement. They offer better convenience than conventional methods and are therefore increasingly applied. From a quality management perspective, it is of high importance to validate the evaluation algorithm of the 3D calibration strategy and analyse its error contribution. This paper proposes and develops a new software validation method based on both synthetic data and measurement data. Finally, results of our study are discussed, and the impact of the computational error on the measurement uncertainty is examined.

Detection and Analysis of Corrosion on Coated Metal Surfaces Using Enhanced YOLO v5 Algorithm for Anti-Corrosion Performance Evaluation

This study addresses the severe corrosion issues in the coastal regions of southern China by proposing an improved YOLO v5-GOLD-NWD model. Utilizing corrosion data from the National Center for Materials Corrosion and Protection Science of China, a dataset was constructed for metal-surface corrosion under different protective coatings. This dataset was used for model training, testing, and comparison. Model accuracy was validated using precision, recall, F1 score, and prediction probability. The results demonstrate that the proposed improved model exhibits better identification precision in metal corrosion detection, achieving 78%, a 4% improvement compared to traditional YOLO v5 models. Additionally, through identification and statistical analysis of corrosion image datasets from five types of coated metal specimens, it was found that powder epoxy coating, fluorocarbon coating, epoxy coating, and chlorinated rubber coating showed good corrosion resistance after 24 months of exposure. Conversely, Wuxi anti-fouling coating exhibited poor corrosion resistance. After 60 months of natural exposure, the powder epoxy coating specimens had the highest corrosion occurrence probability, followed by chlorinated rubber coating and epoxy coating, with fluorocarbon coating showing relatively lower probability. The fluorocarbon coating demonstrated relatively good corrosion resistance at both 24 and 60 months of exposure. The findings of this study provide a theoretical basis for enhancing the corrosion protection effectiveness of steel structures in coastal areas.

AC–DC security-constrained optimal power flow for the Australian National Electricity Market

This paper presents a new methodology to solve the integrated ac–dc security-constrained optimal power flow (SCOPF) problem for large-scale power systems, with a focus on the Australian National Electricity Market (NEM). The proposed SCOPF is based on a decomposition scheme that significantly reduces the dimensionality of the problem and thus improves the computational performance. It further supports a contingency ranking, evaluation, and non-dominated filtering algorithm, cyclic selection of filtered contingencies, careful choice of slack variables, and a tailored warm start to the nonlinear problem. The methodology is validated against the two-stage mathematical programming model that uses an explicit nonlinear model of both the base case and the contingency cases, at a slightly higher cost but significantly faster execution. Moreover, the NEM power system, is studied addressing different scenarios including load variations, renewable energy zone integration, and additional reinforcements like point-to-point and meshed HVDC. The proposed approaches provides preventive solutions in less than 5 min thus affirming the model’s effectiveness in real-time applications and planning studies.

A Study of Delayed Competitive Influence Propagation Based on Shortest Path Calculation

In social network applications, competitive influence propagation often exhibits a certain degree of time lag. In the scenario of positive and negative competitive propagation studied in this paper, under the premise that negative nodes are activated first, how to find a set of positive seed nodes to participate in competitive propagation is studied, aiming to minimize the spread of negative influence. In the current study, the time complexity of the improved algorithms based on greedy strategies is high, which limits their scope of application in practical scenarios; some heuristic algorithms achieve better scalability, but there is still much room for improvement. Therefore, this paper proposes a new method to solve the influence propagation problem of delayed competition, also known as a heuristic propagation factor evaluation algorithm (HeuPFE). The main process is as follows: (1) We build a shortest path snapshot for the nearest propagation region of negative nodes and reduce the search space of competing nodes. (2) Then, we construct a node propagation factor evaluation method based on this shortest negative-oriented node path snapshot to reduce computational complexity. Comparing results with those of traditional heuristic algorithms, we carry out experiments on real datasets and verify the effectiveness of the proposed method.

Correlation Between Scientific and Technological Innovation and Financial Performance of Seed Enterprises: Application of Principal Component Analysis Algorithm in Performance Evaluation

This study explores the relationship between scientific and technological innovations and the financial performance of startups, focusing on a representative Artificial Intelligence (AI) healthcare startup. Utilizing Principal Component Analysis (PCA), the research aims to dissect the complex interplay between innovation-related metrics—such as R&D spending, patent counts, and technology adoption rates—and financial outcomes like revenue growth, profitability, and market share. The PCA methodology enabled the reduction of high-dimensional data into PCA, which clearly illustrates how various dimensions of innovation impact financial metrics. The approach used in the investigation helps people comprehend more thoroughly how development benefits business viability in several different manners and provides startups with practical advice to use when preparing their revolutionary investments. The objective of the research is to assist the ecosystem of startup consumers (including investors, business owners, and policymakers) in making better decisions that balance technological progress with economic objectives.

Discussion on multi-condition load capacity evaluation algorithm of transmission line based on fusion algorithm

ABSTRACT High temperature, strong wind, ice cover and other problems may lead to transmission line breakage, tripping, tower toppling and so on. In order to reduce the probability of such problems, it is necessary to predict the load capacity of transmission line in multiple working conditions. In order to improve the accuracy of load capacity prediction of transmission lines under multiple working conditions and reduce the occurrence of bad transmission line problems, Particle Swarm Optimisation-Back Propagation neural network algorithm is used to simulate the prediction of transmission lines under different working conditions with different accuracy. The results show that the error of the optimised fusion algorithm is smaller than that of the pre-optimised algorithm. Combined with the prediction error results of the traditional neural network, it is found that the fusion algorithm can improve the prediction effect of transmission lines, has strong robustness, and can be popularised in practice.

Twin-Tool Orientation Synchronous Smoothing Algorithm of Pinch Milling in Nine-Axis Machine Tools.

Pinch milling is a new technique for slender and long blade machining, which can simultaneously improve the machining quality and efficiency. However, two-cutter orientation planning is a major challenge due to the irregular blade surfaces and the structural constraints of nine-axis machine tools. In this paper, a method of twin-tool smoothing orientation determination is proposed for a thin-walled blade with pinch milling. Considering the processing status of the two cutters and workpiece, the feasible domain of the twin-tool axis vector and its characterization method are defined. At the same time, an evaluation algorithm of global and local optimization is proposed, and a smoothing algorithm is explored within the feasible domain along the two tool paths. Finally, a set of smoothly aligned tool orientations are generated, and the overall smoothness is nearly globally optimized. A preliminary simulation verification of the proposed algorithm is conducted on a turbine blade model and the planning tool orientation is found to be stable, smooth, and well formed, which avoids collision interference and ultimately improves the machining accuracy of the blade with difficult-to-machine materials.

Risk assessment of emergency operations of floating storage and regasification unit

As the receiving terminal of liquefied natural gas (LNG), the efficient emergency response of the floating storage and regasification unit (FSRU) is crucial to ensure the safety of LNG transportation at sea. However, few existing literature study the risk issues of FSRUs during emergency operations. In order to improve the emergency response capability of FSRU, this study proposes an innovative assessment method to identify hazards, quantify and rank the risks associated with emergency response and disposal operations of FSRU accidents. Firstly, a comprehensive index hierarchy system applicable to human, equipment, environment, and management aspects of emergency response and disposal operations of FSRU accident is established through an extensive literature review, analysis of accident reports, and expert judgments. Secondly, based on the concept of Intuitionistic Fuzzy Numbers, the Intuitionistic Fuzzy Hybrid Weighted Euclidean Distance (IFHWED) operator is used to enhance the conventional FMEA approach. This method considers the varying levels of expert confidence and integrates subjective and objective weights of risk influential factors (RIFs), and the efficacy is validated through sensitivity analysis. Finally, a comprehensive evaluation model employing the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation algorithms is used to aggregate the risk values of RIFs. The findings of this study offer decision-makers insights into risks during emergency operation, provide valuable guiding strategies for FSRU accident management, and improve the capability for emergencies at sea.