Sequential Algorithm Research Articles

Mutual-Energy Inner Product Optimization Method for Constructing Feature Coordinates and Image Classification in Machine Learning

As a key task in machine learning, data classification is essential to find a suitable coordinate system to represent the data features of different classes of samples. This paper proposes the mutual-energy inner product optimization method for constructing a feature coordinate system. First, by analyzing the solution space and eigenfunctions of the partial differential equations describing a non-uniform membrane, the mutual-energy inner product is defined. Second, by expressing the mutual-energy inner product as a series of eigenfunctions, it shows the significant advantage of enhancing low-frequency features and suppressing high-frequency noise, compared to the Euclidean inner product. And then, a mutual-energy inner product optimization model is built to extract the data features, and the convexity and concavity properties of its objective function are discussed. Next, by combining the finite element method, a stable and efficient sequential linearization algorithm is constructed to solve the optimization model. This algorithm only solves positive definite symmetric matrix equations and linear programming with a few constraints, and its vectorized implementation is discussed. Finally, the mutual-energy inner product optimization method is used to construct feature coordinates, and multi-class Gaussian classifiers are trained on the MINST training set. Good prediction results of the Gaussian classifiers are achieved on the MINST test set.

Post-GWAS Prioritization of Genome–Phenome Association in Sorghum

Genome-wide association studies (GWAS) are widely used to infer the genetic basis of traits in organisms; however, selecting appropriate thresholds for analysis remains a significant challenge. In this study, we introduce the Sequential SNP Prioritization Algorithm (SSPA) to investigate the genetic underpinnings of two key phenotypes in Sorghum bicolor: maximum canopy height and maximum growth rate. Using a subset of the Sorghum Bioenergy Association Panel cultivated at the Maricopa Agricultural Center in Arizona, we performed GWAS with specific permissive-filtered thresholds to identify genetic markers associated with these traits, enabling the identification of a broader range of explanatory candidate genes. Building on this, our proposed method employed a feature engineering approach leveraging statistical correlation coefficients to unravel patterns between phenotypic similarity and genetic proximity across 274 accessions. This approach helps prioritize Single Nucleotide Polymorphisms (SNPs) that are likely to be associated with the studied phenotype. Additionally, we conducted a complementary analysis to evaluate the impact of SSPA by including all variants (SNPs) as inputs, without applying GWAS. Empirical evidence, including ontology-based gene function, spatial and temporal expression, and similarity to known homologs demonstrates that SSPA effectively prioritizes SNPs and genes influencing the phenotype of interest, providing valuable insights for functional genetics research.

Distributed algorithm for parallel computation of the n queens solutions

The n-queen problem represents a classic challenge in artificial intelligence (AI) research. It involves the placement of n queens on an n x n chessboard, with the objective of ensuring that no queen threatens another. This problem has long been a source of fascination for mathematicians and computer scientists alike due to its inherent complexity. It is a well-known fact that as the value of ‘n’ increases, the problem becomes more challenging and falls into the NP problem class. Given the computational demands of the problem, parallel methods are of critical importance. It is noteworthy that scalable and parallel approaches for the n-queen problem remain to be developed. The majority of existing methods working on graphs attempt to parallelize a recursive sequential algorithm. However, the unpredictable nature of these algorithms makes it challenging to parallelize them on modern computer architectures. Consequently, we have selected an iterative algorithm from the literature in order to facilitate parallelization. This paper presents an innovative approach to parallelization that differs from traditional matrix-based strategies. The n-queen graph is distributed among a network of nodes, ensuring effective load balancing through dynamic partitioning and real-time computation. Our bespoke distributed algorithm, designed for the maximum clique problem on the n-queen graph, operates with true concurrency, thus obviating the necessity for resource or data sharing. The results of our assessment demonstrate that the parallel algorithm outperforms a cutting-edge sequential algorithm in terms of task completion time. The findings demonstrate that the speedups are almost perfect and that the workloads are distributed evenly across the network nodes. Furthermore, the results demonstrate high scalability, with task completion times decreasing as the number of nodes increases.

Optimizing of selective catalytic reduction urea injection and NOx conversion analysis of diesel engine based on higher-order physical model and sequential quadratic programming algorithm

In order to maximize the NOx conversion rate and minimize NH3 slip in the diesel selective catalytic reduction (SCR) system, first, the high-order SCR model was simplified and solved using the trapezoidal method and second-order backward difference formula, while the chemical reaction parameters were optimized using the Gauss-Newton method. The results of the engine bench test show that the mean absolute errors of NH3 and NOx concentrations under steady-state operating conditions were 2.67 ppm and 1.72 ppm, respectively, and 5.46 ppm and 42.50 ppm under severe operating conditions. Second, urea injection rate is optimized based on a high-order model combined with the sequential quadratic programming (SQP) algorithm. The change in NOx conversion rate at different temperatures and air speeds is analyzed, and the results show that the NOx conversion is best at 650 K and 29,000 h⁻1. Third, the experiment validation show that the optimal NH3/NOx ratio is 0.69 at 554 K and 54,200 h⁻1; 0.8; and 1.37 at 746 K and 116,700 h⁻1. Finally, this paper simulates and analyzes the SCR emission characteristics at different temperatures and air speeds when the upstream NOx concentration is 1,000 ppm.

VeTraSPM: Novel Vehicle Trajectory Data Sequential Pattern Mining Algorithm for Link Criticality Analysis

VeTraSPM: Novel Vehicle Trajectory Data Sequential Pattern Mining Algorithm for Link Criticality Analysis

Automation of Fiscal Auditing in SMEs through a Sequential Algorithm for Extracting Tax Receipts: A Focus on the Digital Economy

Automation of Fiscal Auditing in SMEs through a Sequential Algorithm for Extracting Tax Receipts: A Focus on the Digital Economy

A nonlinear optimisation considering axle load transfer for enhancing adhesion utilisation of a locomotive with an oblique traction rod

A nonlinear optimisation considering axle load transfer for enhancing adhesion utilisation of a locomotive with an oblique traction rod

An Accurate Construction Method of E-commerce User Profile Based on Artificial Intelligence Algorithm and Big Data Analysis

User’s basic attributes, behavior characteristics, value attributes, social attributes, interest attributes, psychological attributes, and other factors will lead to poor user experience, information overload, interference, and other negative effects. In order to develop more accurate marketing strategies, optimize user experience, and improve the conversion rate and user satisfaction of e-commerce platforms, an accurate construction method of e-commerce user profile based on artificial intelligence algorithm and big data analysis is proposed. Based on big data analysis technology, the basic attributes, behavior characteristics, value attributes, social attributes, interest attributes, and psychological attributes of e-commerce users are collected and integrated from multiple dimensions. The improved sequential pattern mining algorithm (PBWL) is applied to mine the frequent sequential pattern in the e-commerce user behavior, and to reveal the user’s behavior habit. The comprehensive attribute representation of e-commerce users is obtained by combining the LINE network model and the convolutional neural network. The firefly K-means clustering algorithm is used to cluster the e-commerce users, group the users based on the similarity of user attribute information, create different types of user clusters, and achieve the accurate construction of an e-commerce user profile. The experimental results show that this method can build an accurate e-commerce user profile and provide strong support for personalized recommendation and precision marketing of e-commerce platforms. This method can dig deeply into the behavior habits of e-commerce users and accurately reflect their interest preferences and consumption characteristics. This method can quickly and stably cluster e-commerce users, and the clustering effect of user profiles is optimal. This method can also divide the data into meaningful groups according to the user’s consumption behavior, and reveal the characteristics and values of different groups.

Identification and crude protein prediction of porcini mushrooms via deep learning-assisted FTIR fingerprinting

Wild edible mushrooms are natural, green, and sustainable foods, and the key to their post-harvest safety and quality control lies in species identification, geographic traceability, and quality assessment. This study analyses the validity of partial least squares discriminant analysis (PLS-DA) model with residual convolutional neural network (ResNet) model based on Fourier transform infrared (FTIR) spectroscopy and two-dimensional correlation spectroscopy (2DCOS) for identifying species and geographic origin of porcini mushrooms. Exploring the feasibility of the partial least squares regression (PLSR) model and long short-term memory network (LSTM) model for predicting the crude protein content of porcini mushrooms. The results show that the ResNet model outperforms PLS-DA with 1.00 and 0.98 prediction results and 1.00 and 0.92 prediction accuracy for new samples, respectively. The LSTM model was more effective than the PLSR model in estimating crude protein content. Among them, LSTM model with first order derivative-multiple scattering correction-Savitzky-Golay (1D-MSC-SG) preprocessing combination was optimal with a prediction set RPD = 2.7, Sig. = 0.418. Besides, the sequential projection algorithm (SPA) feature extraction improves the prediction ability of the LSTM model. Its residual prediction deviation (RPD) was 2.9, Sig. = 0.627. The study provides a new reference for quality evaluation of edible mushrooms and other food products in the market.

A novel implementation of the cohesive zone model for the fatigue propagation of delamination in composites using a sequential static fatigue algorithm

Composite materials are particularly exposed to delamination under fatigue loading conditions, which can significantly compromise their structural integrity. The ability to accurately and efficiently estimate the progression of delamination under fatigue is crucial for enhancing the safety and reliability of lightweight composite structures. The aim of this paper is to implement the cohesive elements formulation in a Sequential Static Fatigue (SSF) algorithm named C-SSF. The C-SSF algorithm simulates delamination propagation under fatigue loading by conducting a series of sequential static simulations. The accuracy of the C-SSF method is validated by comparing its results with experimental data from two published case studies. The results demonstrate that this approach can effectively simulate delamination growth under fatigue loading. Compared to a similar approach based on the Virtual Crack Closure Technique (VCCT), the C-SSF algorithm provided superior accuracy, especially when large and curved delamination fronts were involved. The C-SSF method proved its capability to simulate propagation of delamination in composite structures, making it a valuable tool for modelling the fatigue behaviour of other similar structures.

Inference for the stochastic FitzHugh-Nagumo model from real action potential data via approximate Bayesian computation

The stochastic FitzHugh-Nagumo (FHN) model is a two-dimensional nonlinear stochastic differential equation with additive degenerate noise, whose first component, the only one observed, describes the membrane voltage evolution of a single neuron. Due to its low-dimensionality, its analytical and numerical tractability and its neuronal interpretation, it has been used as a case study to test the performance of different statistical methods in estimating the underlying model parameters. Existing methods, however, often require complete observations, non-degeneracy of the noise or a complex architecture (e.g., to estimate the transition density of the process, ‘‘recovering’’ the unobserved second component) and they may not (satisfactorily) estimate all model parameters simultaneously. Moreover, these studies lack real data applications for the stochastic FHN model. The proposed method tackles all challenges (non-globally Lipschitz drift, non-explicit solution, lack of available transition density, degeneracy of the noise and partial observations). It is an intuitive and easy-to-implement sequential Monte Carlo approximate Bayesian computation algorithm, which relies on a recent computationally efficient and structure-preserving numerical splitting scheme for synthetic data generation and on summary statistics exploiting the structural properties of the process. All model parameters are successfully estimated from simulated data and, more remarkably, real action potential data of rats. The presented novel real-data fit may broaden the scope and credibility of this classic and widely used neuronal model.

Machine learning-driven prediction of EBNA1 inhibitors against Epstein–Barr virus in nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC), particularly prevalent in regions such as Malaysia, is a significant health concern often linked to Epstein-Barr virus (EBV) infection. The EBV nuclear antigen 1 (EBNA1), crucial for EBV survival and NPC tumorigenicity, has emerged as a potential therapeutic target for EBV-positive NPC. In this study, we utilized quantitative structure-activity relationship (QSAR) models to predict potential inhibitors of EBNA1. These models were developed based on the molecular fingerprints of known EBNA1 inhibitors, using both classification and regression approaches. Our QSAR classification models demonstrated consistently high precision, recall, F1 score, and accuracy scores across the training set. The top-performing models, constructed using logistic regression algorithms, achieved perfect precision scores of 1.000 in the test set evaluation. These models’ recall, F1 score, and accuracy scores were 0.571, 0.727, and 0.667, respectively. On the other hand, the best-performing model among the regression models was built using the sequential minimal optimization regression algorithm, achieving a correlation coefficient of 0.703. The mean absolute error and root mean square error of this QSAR regression model were 0.173 and 0.217, respectively, whereas the relative absolute error was 0.689. We screened the enamine advanced compound library using this regression model to predict compounds with potential EBNA1 inhibitory effects. This led to the identification of the top 10 compounds with the most promising predicted EBNA1 inhibitory properties.

A two‐scale numerical analysis of intra‐yarn hybrid natural/synthetic woven composites

AbstractThis paper investigates how combining natural and synthetic fibers within yarns, altering the degree of hybridisation, and varying weave architectures affect the homogenized elastic properties and stress distributions in 2D woven composite laminae using a two‐scale homogenization scheme. The novelty lies in integrating a micro‐scale representative volume element model with a meso‐scale repeating unit cell model to capture intra‐yarn natural/synthetic fiber hybridisation. The study focuses on 2/2 twill and 5‐harness satin woven laminae with flax/E‐glass, hemp/E‐glass and basalt/E‐glass hybrid yarns, all with a total fiber volume fraction of 0.6. Fiber distributions are created through a random sequential expansion algorithm for hybrid RVEs, while periodic meso‐structures are used to define weave architectures for RUCs. Results show that yarn‐level fiber hybridisation significantly influences matrix stress distributions, with variations of up to 22%. In contrast, weave architecture has a minimal effect, with variations in homogenized properties below 2%. Varying fiber types, degrees of hybridisation and weave architectures allow for the tailoring of yarn and lamina properties and altering the stress distributions at micro‐ and meso‐scales and potentially influencing damage mechanisms. The modeling approach for analyzing the mechanical behavior of intra‐yarn hybrid natural/synthetic woven laminae could potentially be used to tailor their tolerance to damage.Highlights Two‐scale homogenization integrates RVE and RUC for 2D woven hybrid composites. Analyzed flax/E‐glass, hemp/E‐glass, basalt/E‐glass in twill and satin weaves. Intra‐yarn hybridisation significantly affects properties and stress fields. Hybridizing fibers offers tailored properties and may improve damage tolerance.

Quantitative Defect Analysis in CVD‐Grown Monolayer MoS2 via In‐Plane Raman Vibration

ABSTRACTThe synthesis of two‐dimensional transition metal dichalcogenide (2D‐TMD) materials gives rise to inherent defects, specifically chalcogen vacancies, due to thermodynamic equilibrium. Techniques such as chemical vapor deposition (CVD), metal‐organic chemical vapor deposition (MOCVD), atomic layer deposition (ALD), flux growth method, and mechanical exfoliation produce large‐scale, uniform 2D TMD films, either in bulk or monolayers. However, defects on the film surface impact its quality, and it is necessary to measure defect density. The phonon confinement model indicates that the first‐order Raman band frequency shift depends on defect density. Monolayer Molybdenum disulfide (MoS2) exhibits three phonon dispersions at the Brillouin zone edge (M point): out‐of‐plane optical phonon vibration (ZO), in‐plane longitudinal optical phonon vibration (LO), and in‐plane transverse optical phonon vibration (TO). The LO and ZO modes overlap with Raman in‐plane vibration (𝐸12g) and Raman out‐of‐plane vibration (𝐴1g), respectively, causing peak broadening. In the presence of defects, the Raman 𝐸12g vibration energy decreases due to a reduced restoring force constant. The Raman 𝐴1g vibration trend is random, influenced by both restoring force constant and mass. The study introduces a quantitative defect measurement technique for CVD‐grown monolayer MoS2 using Raman 𝐸12g mode, employing sequential data processing algorithms to reveal defect density on the film surface.

MapReduce teaching learning based optimization algorithm for solving CEC-2013 LSGO benchmark Testsuit

Teaching Learning Based Optimization (TLBO) algorithm, introduced in 2011 is widely used in optimization problems across various domains. It is a powerful tool that is capable of solving complex, multidimensional, linear, and nonlinear problems. MapReduce is a distributed programming model developed by Google. It is widely used for processing large datasets in parallel way. This paper proposes the use of the MapReduce programming paradigm for the implementation of the TLBO algorithm on distributed systems, creating a novel approach known as MapReduce Teaching Learning Based Optimization (MRTLBO). The proposed MRTLBO algorithm is tested on Congress of Evolutionary Computations (CEC)-2013 Large-Scale Global Optimization Benchmark Problems dataset, and its performance is compared with sequential TLBO algorithm on the same dataset. The experimental output exhibits that the MRTLBO algorithm is effective in working with high-dimensional problems, and it outperforms the sequential TLBO algorithm with respect to the final result, and speedup. Overall, the proposed MRTLBO algorithm gives a scalable and effective optimization strategy for working on optimization problems in distributed systems.

Trajectory reconstruction method for spacecraft formation considering collision probability constraints

Abstract This paper introduces a trajectory reconstruction algorithm for spacecraft formation, combining collision probability and sequential convex programming algorithms. When reconstructing the trajectory of spacecraft formation, external collision avoidance constraint is added to avoid collision threats from space objects. The constraint accuracy of the external collision avoidance constraints of the BOX method and the collision probability method is analyzed. The simulation results indicate that the collision probability method has higher accuracy compared to the BOX method. It addresses the issue of inaccurate division of collision avoidance zones in the BOX method, which tends to be aggressive in the velocity direction and conservative in other directions. A reference is provided for trajectory reconstruction when formation satellites encounter non-cooperative targets.

Reliability assessment of distribution networks with distributed generation considering different types of user loads

Abstract This paper meticulously evaluates the reliability of distribution networks integrated with distributed generation (DG), considering various user classifications. Initially, we delve into the daily load profiles of residential, commercial, and industrial sectors, establishing a nuanced time-series load model that encapsulates the distinct characteristics of these customer segments. Subsequently, we formulate a model for the harmonious operation of energy storage systems, tailored to the interplay between DG output and load requirements. Utilizing the sequential Monte Carlo simulation algorithm, we compute the reliability metrics for the IEEE RBTSBUS6 feeder system, revealing that DG integration significantly enhances distribution network reliability. Notably, identical DG output yields varied impacts on the reliability of distinct customer sectors, underscoring the need for tailored strategies in DG deployment.

Per Segment Plane Sweep Line Segment Intersection on the GPU

Polygon overlay operations are used for various purposes such as GIS searches and queries, VLSI and basic geometric operations of intersection, union and difference. There have been recent research articles presenting algorithms using the GPU to perform line segment intersection for geometric operations. We present two parallel algorithms implemented on the GPU that focus on the active list portion of the traditional serial plane sweep algorithm. The first algorithm uses a single block of threads to simulate the active list data structure in hardware; this algorithm is slow due to GPU thread block size limitations and synchronization points, but demonstrates favorable time complexity. The second algorithm uses dynamic parallelism to remove synchronization and scales to utilize available GPU hardware (single GPU). We perform experiments on both synthetic and real world data sets. The presented results show improvement in execution time with respect to recent algorithms, and low memory usage compared to recent algorithms. We achieve speedups of up to 38.8 over the serial sweep line algorithm on real world data.

Quantum state reconstruction via disentanglement with sequential optimization algorithm

Abstract In this work, we report a novel quantum state reconstruction process based on the disentanglement algorithm. We propose a sequential disentanglement scheme, which can transform an unknown quantum state into a product of computational zero states. The inverse evolution of the zero states reconstructs the quantum state up to an overall phase. By sequentially disentangling the qubits one by one, we reduce the required measurements with only individual qubit measurement and identify the transformation unitary efficiently. Variational quantum circuit and reinforcement learning methods are used for the quantum circuit design for continuous and discrete quantum gates implementation. Demonstrations with our proposal for the reconstruction of the random states are presented. Our method is universal and imposes no specific ansatz or constraint on the quantum state.

MIRD Pamphlet No. 31: MIRDcell V4-Artificial Intelligence Tools to Formulate Optimized Radiopharmaceutical Cocktails for Therapy.

Radiopharmaceutical cocktails have been developed over the years to treat cancer. Cocktails of agents are attractive because 1 radiopharmaceutical is unlikely to have the desired therapeutic effect because of nonuniform uptake by the targeted cells. Therefore, multiple radiopharmaceuticals targeting different receptors on a cell is warranted. However, past implementations invivo have not met with convincing results because of the absence of optimization strategies. Here we present artificial intelligence (AI) tools housed in a new version of our software platform, MIRDcell V4, that optimize a cocktail of radiopharmaceuticals by minimizing the total disintegrations needed to achieve a given surviving fraction (SF) of tumor cells. Methods: AI tools are developed within MIRDcell V4 using an optimizer based on the sequential least-squares programming algorithm. The algorithm determines the molar activities for each drug in the cocktail that minimize the total disintegrations required to achieve a specified SF. Tools are provided for populations of cells that do not cross-irradiate (e.g., circulating or disseminated tumor cells) and for multicellular clusters (e.g., micrometastases). The tools were tested using model data, flow cytometry data for suspensions of single cells labeled with fluorochrome-labeled antibodies, and 3-dimensional spatiotemporal kinetics in spheroids for fluorochrome-loaded liposomes. Results: Experimental binding distributions of 4 211At-antibodies were considered for treating suspensions of MDA-MB-231 human breast cancer cells. A 2-drug combination reduced the number of 211At decays required by a factor of 1.6 relative to the best single antibody. In another study, 2 radiopharmaceuticals radiolabeled with 195mPt were each distributed lognormally in a hypothetical multicellular cluster. Here, the 2-drug combination required 1.7-fold fewer decays than did either drug alone. Finally, 2 225Ac-labeled drugs that provide different radial distributions within a spheroid require about one half of the disintegrations required by the best single agent. Conclusion: The MIRDcell AI tools determine optimized drug combinations and corresponding molar activities needed to achieve a given SF. This approach could be used to analyze a sample of cells obtained from cell culture, animal, or patient to predict the best combination of drugs for maximum therapeutic effect with the least total disintegrations.