Computational Algorithms Research Articles

Levy flight elitism-enhanced salp swarm algorithms for solving the ORPD problem

The optimal reactive power dispatch (ORPD) challenge is crucial for ensuring the efficient and reliable operation of power systems, hinging on the effective coordination of multiple devices.As a result, solving the ORPD problem involves tackling a complex nonlinear optimization challenge that demands advanced computational algorithms to determine the optimal solution. This article introduces an enhanced version of the modified algorithm, called L-E-SSA, which is inspired by the coordinated movement of a swarm led by a single member (salp swarm algorithm SSA), incorporating Levy Flight elitism, to effectively solve the ORPD problem. This study aims to reduce power losses (Ploss) and total voltage deviation (TVD) by optimizing the control variables. Numerical simulations are carried out on IEEE (30-bus) and IEEE (118-bus) power systems. To evaluate the performance and effectiveness of the modified algorithm (L-E-SSA). The results indicate that the L-E-SSA algorithm delivered strong performance and secured a competitive ranking through statistical analysis. Simulations demonstrate that the proposed method has the potential to be a highly effective solution for the ORPD problem.

The New Method for Ground Insulation Monitoring of the Secondary Circuit in Substations

When two-or-more-point grounding faults occur in the secondary circuit of a substation, they will lead to large data measurement errors in the protection, measurement, and control devices, which can easily cause protection malfunction. At present, there is a lack of an online panoramic monitoring system for the secondary circuits of substations, and it is difficult to accurately identify the faults in these secondary circuits. In order to solve this problem, this paper proposes a cloud–edge collaboration framework for the power Internet of Things (IoT), which can accurately identify faults by monitoring the secondary circuit of power grid substations with panoramic online status, reducing maintenance costs and improving overall operational efficiency. Firstly, the collection requirements of current transformer (CT) and potential transformer (PT) fault characteristics in the secondary circuit of the substation are analyzed. The platform architecture of the secondary loop panoramic monitoring system of the cloud-side cooperative substation is proposed, along with the algorithm and data wireless acquisition scheme of the edge computing, cloud computing, and cloud-side system. Furthermore, the edge computing platform and the current and voltage wireless sensors are developed. The maximum error in the accuracy test of the voltage sensor is 1.9180%, while the maximum error in the accuracy test of the current sensor is 1.3406%.

A preliminary exploration of quantitative models for evaluating binocular visual perception impairments among patients diagnosed with stroke.

The objective of this study is to formulate and implement graded biological models pertaining to binocular visual perception function with the use of computer algorithms. We aim to quantitatively assess the location, severity, and degree of impairment in binocular visual perception among patients who have suffered stroke, thereby providing valuable insights into the repercussions of cerebral tissue damage on the visual system. To overcome the shortcomings of previous instruments used to assess binocular function in terms of stereoscopic effects and the challenges posed by physiological and psychological interference during examinations, this study optimized its approach by integrating polarized stereovision technology with computer graphic modeling techniques. This study employed computer models to assess binocular visual perception function in stroke patients. Computer models refer to psychophysical testing methods used to measure binocular visual perception function, including various assessment tasks such as recognizing inverted letters and assessing stereopsis during high-speed movements. The cross-into-circle test was used as a means to quantify perceptual eye position. Subsequently, the collected data was analyzed to assess the magnitude of impairment in binocular visual perception. The results of the study revealed a spectrum of binocular visual perception impairment among patients diagnosed with stroke, demonstrating discernible variations in the recognition of inverted letters and stereopsis across different movement speeds. Importantly, perceptual eye position measurements offered valuable insights into ocular misalignment. The computational models effectively quantified both the spatial distribution and severity of these identified impairments. Damage to brain tissue resulting from a stroke can give rise to notable impairments in binocular visual perception function. Graded biological models, formulated through computer algorithms, provide a systematic framework for the comprehensive evaluation and quantification of these impairments. The comprehension of the nature and extent of visual impairments observed in patients with stroke establishes a basis for the development of personalized visual perception learning methodologies. Based on such tailored approaches, we aim to facilitate the recovery of impaired visual function, thereby contributing to the broader objective of neural system rehabilitation.

Geometallurgical modeling and reconciliation with production data in a fhosphate mine

The characterization of the ore and its performance prediction in the processing unit are essential for the success of mining projects and should be part of the resource model. The greatest difficulties encountered in the elaboration of these models are the characteristics of the metallurgical variables, such as non-additivity and non-linearity, lack of uniformity of the database and the few samples of process tests. The generalization of geometallurgical models from primary attributes is a valid option when the relationships between geological characteristics and metallurgical responses are well established and will be tested in this work. Bench tests that simulate the mineral concentration process were performed and compared with geological and chemical information. Multivariate statistical analyses and computational algorithms allowed the prediction of metallurgical attributes from the properties of the rocks simulated by Gaussian sequential simulation. The results were reconciled with the plant data over a period of one year and proved to be consistent.

Human–robot interaction: predicting research agenda by long short-term memory

The article addresses the identification and prediction of research topics in human–robot interaction (HRI), fundamental in Industry 4.0 (I4.0) and future Industry 5.0 (I5.0). In the absence of research agendas in the scientific literature, the study proposes a multilayered model to create a precise agenda to guide the scientific community in new developments in collaborative robotics and HRI technologies. The methodology is divided into four stages, which make up the three layers of the model. In the first two stages, scientific articles on HRI for the period 2020–2021 were collected and analyzed using data mining techniques together with VantagePoint and Gephi software to identify keywords and their relationships. These initial stages form layer 1 of the model, where the main scientific themes are recognized. In the third stage, article titles and abstracts are cleaned and processed using natural language processing (NLP) techniques, generating word embeddings models that highlight relevant HRI-related terms, forming layer 2. The fourth and final stage uses Recurrent Neural Networks (RNN) with long short-term memory (LSTM) architecture to predict future topics, consolidating the previously identified terms and forming layer 3 of the model. The results show that in layer 1 HRI has intensive application in various sectors through advanced computational algorithms, with trust as a key feature. In layer 2, terms such as vision, sensors, communication, collaboration and anthropomorphic aspects are fundamental, while layer 3 anticipates future topics such as design, performance, method and controllers, essential to improve robot interaction. The study concludes that the methodology is effective in defining a robust and relevant research agenda. By identifying future trends and needs, this work fills a gap in the scientific literature, providing a valuable tool for the research community in the field of HRI.

Construction of parallel algorithms for the study of complex systems objects with a hierarchical-network structure

The procedure for complexite evaluation of complex systems objects that have a hierarchical-network structure has been formalized.In order to realization this procedure in real time mode, parallel computation algorithms are proposed.Speed up estimates were obtained for the mentioned algorithms, which confirm their high efficiency. The proposed parallel algorithms are oriented for execution on modern computing means with shared and distributed memory: computers with multi-core processors, clusters, hybrid architectures and in high performance distributed environments.

Evaluating model fit for type II censored data: a Bayesian non-parametric approach based on the Kullback-Leibler divergence estimation

Model checking evaluates the appropriateness of a statistical model based on the observed data, and it is essential to make valid statistical analyses. In this paper, a new procedure for model checking type II censored data is proposed. This procedure combines the Kullback-Leibler divergence, the Dirichlet process, and the relative belief ratio. The method is implemented via a computational algorithm and is explained through several examples.

A modified variational approach to noisy cell signaling.

Signaling in cells is full of noise and, hence, described with stochastic biochemical models. Thus, an efficient computation algorithm for these fluctuating reactions is much needed. Apart from the very popular Monte Carlo simulation, methods based on probability distributions are frequently desired due to their analytical tractability and possible numerical advantages in diverse circumstances, among which the variational approach is the most notable. In this paper, new basis functions are proposed to better depict possibly complex distribution profiles, and an extra regularization scheme is supplied to the variational equation to remove occasional degeneracy-induced singularities during the evolution. The new extension is applied to four typical biochemical reaction models and restores the Gillespie results accurately but with greatly reduced simulation time. This modified variational approach is expected to work in a wide range of cell signaling networks.

Improving bioinformatics software quality through teamwork.

Since high-throughput techniques became a staple in biological science laboratories, computational algorithms, and scientific software have boomed. However, the development of bioinformatics software usually lacks software development quality standards. The resulting software code is hard to test, reuse, and maintain. We believe that the root of inefficiency in implementing the best software development practices in academic settings is the individualistic approach, which has traditionally been the norm for recognizing scientific achievements and, by extension, for developing specialized software. Software development is a collective effort in most software-heavy endeavors. Indeed, the literature suggests teamwork directly impacts code quality through knowledge sharing, collective software development, and established coding standards. In our computational biology research groups, we sustainably involve all group members in learning, sharing, and discussing software development while maintaining the personal ownership of research projects and related software products. We found that group members involved in this endeavor improved their coding skills, became more efficient bioinformaticians, and obtained detailed knowledge about their peers' work, triggering new collaborative projects. We strongly advocate for improving software development culture within bioinformatics through collective effort in computational biology groups or institutes with three or more bioinformaticians.

LncPepAtlas: a comprehensive resource for exploring the translational landscape of long non-coding RNAs.

Long non-coding RNAs were commonly viewed as non-coding elements. However, they are increasingly recognized for their ability to be translated into proteins, thereby playing a significant role in various cellular processes and diseases. With developments in biotechnology and computational algorithms, a range of novel approaches are being applied to investigate the translation of long non-coding RNA (lncRNAs). Herein, we developed the LncPepAtlas database (http://www.cnitbiotool.net/LncPepAtlas/), which aims to compile multiple evidences for the translation of lncRNAs and annotations for the upstream regulation of lncRNAs across various species. LncPepAtlas integrated compelling evidence from nine distinct sources for the translation of lncRNAs. These include a dataset comprising 2631 publicly available Ribo-seq samples from nine species, which has been collected and analysed. LncPepAtlas offers extensive annotation for lncRNA upstream regulation and expression profiles across various cancers, tissues or cell lines at transcriptional and translational levels. Importantly, it enables novel antigen predictions for lncRNA-encoded peptides. By identifying numerous peptide candidates that could potentially bind to major histocompatibility complex class I and II molecules, this work may provide new insights into cancer immunotherapy. The function of peptides were inferred by aligning them with experimentally detected proteins. LncPepAtlas aims to become a convenient resource for exploring translatable lncRNAs.

Computational algorithm based on health and lifestyle traits to categorize lifemetabotypes in the NUTRiMDEA cohort

Classifying individuals based on metabotypes and lifestyle phenotypes using exploratory factor analyses, cluster definition, and machine-learning algorithms is promising for precision chronic disease prevention and management. This study analyzed data from the NUTRiMDEA online cohort (baseline: n = 17332 and 62 questions) to develop a clustering tool based on 32 accessible questions using machine-learning strategies. Participants ranged from 18 to over 70 years old, with 64.1% female and 35.5% male. Five clusters were identified, combining metabolic, lifestyle, and personal data: Cluster 1 (“Westernized Millennial”, n = 967) included healthy young individuals with fair lifestyle habits; Cluster 2 (“Healthy”, n = 10616) consisted of healthy adults; Cluster 3 (“Mediterranean Young Adult”, n = 2013) represented healthy young adults with a healthy lifestyle and showed the highest adherence to the Mediterranean diet; Cluster 4 (“Pre-morbid”, n = 600) was characterized by healthy adults with declined mood; Cluster 5 (“Pro-morbid”, n = 312) comprised older individuals (47% >55 years) with poorer lifestyle habits, worse health, and a lower health-related quality of life. A computational algorithm was elicited, which allowed quick cluster assignment based on responses (“lifemetabotypes”). This machine-learning approach facilitates personalized interventions and precision lifestyle recommendations, supporting online methods for targeted health maintenance and chronic disease prevention.

Synergism of Computational Simulation Technique and Machine Learning Algorithm for Prediction of Anticorrosion Properties of Some Antipyrine Derivatives.

This study aimed to predict the selected antipyrine compounds' inhibitory efficiencies and anticorrosion properties in a hydrochloric acid (HCl) environment. Molecular descriptors and input variables were obtained using density functional theory (DFT), and the variance inflation factor (VIF) was employed to reduce redundant variables, leading to the selection of seven quantum chemical descriptors as input variables. Using machine learning techniques such as K-nearest neighbor (KNN) and artificial neural network (ANN), a predictive model was built for 39 antipyrine compounds with known corrosion inhibition efficiencies for carbon and low alloy steel in hydrochloric acid solutions. The models' predictive capability was assessed using cross-validation, with the ANN model showing superior performance, achieving a coefficient of determination (R2) value of 0.715 compared to 0.548 for the KNN model. Performance metrics such as the mean square error (MSE), mean absolute error (MAE), and root-mean-square error (RMSE) further confirmed the superiority of the ANN model over the KNN model. The corrosion inhibition efficiencies (CIEs) of the selected antipyrine compounds ranged from 68.78 to 99.79%, with compound A1 demonstrating the highest CIE of 99.79% and compound A3 the lowest, as evaluated by the ANN model. Analysis of Fukui index parameters obtained from the Mulliken population analysis suggested that the nucleophilic and electrophilic sites play a crucial role in the interactions between the inhibitor and the metal atom through electron donor-acceptor interactions. Moreover, the energy of adsorption (Eads) in kcal·mol-1 decreased in the order of A1 (-187.8) > A2 (-132.0) > A2 (-84.4), with the high negative value of Eads indicating strong and spontaneous adsorption. Further analysis using radial distribution functions and molecular dynamics simulations revealed that inhibitor A1 exhibited predominantly chemisorption, inhibitor A2 showed a mixed type, and inhibitor A3 demonstrated predominantly physisorption, aligning well with the results of the predictive studies.

Evaluation of retinal nerve fiber layer and choroidal structure in obese children and adolescents

Aim: Obesity-related vascular damage and endothelial dysfunction have deleterious effects on the ocular vasculature. It was aimed to examine optical coherence tomography (OCT) parameters in obese and overweight children and to define their relationship with metabolic markers in this study. Methods: The patient group consisted of 26 obese, 24 overweight patients aged between 8 and 18 years. The control group consisted of 25 healthy children with normal body mass index (BMI). This was a cross-sectional observational study. Serum glucose, lipid parameters, and homeostasis model assessment of insulin resistance (HOMA-IR) were investigated. Measurement of choroidal thickness was performed with Cirrus HD-OCT (Carl Zeiss Meditec Inc., Dublin, CA, USA). Retinal nerve fiber layer (RNFL) thickness was determined by an automatic computer algorithm without the need for user measurement. Results: There were no differences in subfoveal, nasal, temporal choroidal thickness, and RNFL between obese, overweight, and control groups (p>0.05). A positive (linear) moderate relationship was found between RNFL and the HOMA-IR of 26 patients in the obese group (r=0.389) (p=0.049). A positively weak correlation was found between height and RNFL in obese patient group (r=0.264, p=0.028). Conclusion: In the study, RNFL thickness increased as HOMA-IR level increased in obese children and adolescents. RNFL decreased as the height increased in obese children and adolescents. We believe that more comprehensive data about the effect of obesity on RNFL and choroidal thickness will be obtained with prospective studies in which the obese patient group with insulin resistance is taken separately and disease durations are defined, and long-term patient follow-up is performed.

Artificial Intelligence and Omics in Malignant Gliomas.

The availability of large scale multi-omics data requires development of computational models to infer valuable biological insights for the implementation of precision medicine. Artificial intelligence (AI) refers to a host of computational algorithms that is becoming a major tool capable of integrating large genomic, transcriptomic, proteomic, and metabolomic data. Machine learning (ML) is the most significant AI algorithm in health sciences have exploded, specifically due to the recent progress made by deep learning. Although the use of AI/ML tools in GBM-omics is still at an early stage, a comprehensive discussion of how AI can be used to unravel various aspects of GBM (intratumor heterogeneity, biomarker discovery, survival prediction, and treatment optimization) would be highly relevant to both researchers and clinicians. Here, we aim to review the different AI-based techniques that have been used to study GBM pathogenesis using multi-omics data over the last decade. We first summarize different types of GBM related omics resources that can be used to develop AI models. We then discuss various AI applications for multi-omics data in order to enhance GBM precision medicine. Finally, we discuss the technical and ethical challenges that limit its application and ways to improve its implementation in clinics.

Using Machine Learning to Link Genotype Data with Phenotypic Traits in Complex Genetic Disorders

Genetic disorders are among the most devastating diseases that affect humans. Yet, several genetic disorders remain incomprehensible to scientists and doctors. Correlating genotype data with phenotypic manifestations has the potential to help scientists better understand complex genetic disorders, such as Fibrodysplasia Ossificans Progressiva (FOP), Wilson’s disease, and Stiff Skin Syndrome. However, if such processes were done manually by humans, they might lead to severe errors or misconceptions and, quite simply, be a waste of time. Computer algorithms and machine learning techniques can be efficiently utilized to analyze genotype data along with phenotypic manifestations, providing scientists with the necessary data to reach accurate conclusions on misunderstood diseases.

COLMAR1d: A Web Server for Automated, Quantitative One-Dimensional Nuclear Magnetic Resonance-Based Metabolomics at Arbitrary Magnetic Fields.

The field of metabolomics, which is quintessential in today's omics research, involves the large-scale detection, identification, and quantification of small-molecule metabolites in a wide range of biological samples. Nuclear magnetic resonance spectroscopy (NMR) has emerged as a powerful tool for metabolomics due to its high resolution, reproducibility, and exceptional quantitative nature. One of the key bottlenecks of metabolomics studies, however, remains the accurate and automated analysis of the resulting NMR spectra with good accuracy and minimal human intervention. Here, we present the COLMAR1d platform, consisting of a public web server and an optimized database, for one-dimensional (1D) NMR-based metabolomics analysis to address these challenges. The COLMAR1d database comprises more than 480 metabolites from GISSMO enabling a database query of spectra measured at arbitrary magnetic field strengths, as is demonstrated for spectra acquired between 1H resonance frequencies of 80 MHz and 1.2 GHz of mouse serum, DMEM cell growth medium, and wine. COLMAR1d combines the GISSMO metabolomics database concept with the latest tools for automated processing, spectral deconvolution, database querying, and globally optimized mixture analysis for improved accuracy and efficiency. By leveraging advanced computational algorithms, COLMAR1d offers a user-friendly, automated platform for quantitative 1D NMR-based metabolomics analysis allowing a wide range of applications, including biomarker discovery, metabolic pathway elucidation, and integration with multiomics strategies.

Applications of Machine Learning Technologies for Feedstock Yield Estimation of Ethanol Production

Biofuel has received worldwide attention as one of the most promising renewable energy sources. Particularly, in many countries such as the U.S. and Brazil, first-generation ethanol from corn and sugar cane has been used as automobile fuel after blending with gasoline. Nevertheless, in order to continuously increase the use of biofuels, efforts are needed to reduce the cost of biofuel production and increase its profitability. This can be achieved by increasing the efficiency of a sequential biofuel production process consisting of multiple operations such as feedstock supply, pretreatment, fermentation, distillation, and biofuel transportation. This study aims at investigating methodologies for predicting feedstock yields, which is the earliest step for stable and sustainable biofuel production. Particularly, this study reviews feedstock yield estimation approaches using machine learning technologies that focus on gradually improving estimation accuracy by using big data and computer algorithms from traditional statistical approaches. Given that it is becoming increasingly difficult to stably produce biofuel feedstocks as climate change worsens, research on developing predictive modeling for raw material supply using the latest ML techniques is very important. As a result, this study will help researchers and engineers predict feedstock yields using various machine learning techniques, and contribute to efficient and stable biofuel production and supply chain design based on accurate predictions of feedstocks.

First Derivative Approximations and Applications

In this paper, we consider constructions of first derivative approximations using the generating function. The weights of the approximations contain the powers of a parameter whose modulus is less than one. The values of the initial weights are determined, and the convergence and order of the approximations are proved. The paper discusses applications of approximations of the first derivative for the numerical solution of ordinary and partial differential equations and proposes an algorithm for fast computation of the numerical solution. Proofs of the convergence and accuracy of the numerical solutions are presented and the performance of the numerical methods considered is compared with the Euler method. The main goal of constructing approximations for integer-order derivatives of this type is their application in deriving high-order approximations for fractional derivatives, whose weights have specific properties. The paper proposes the construction of an approximation for the fractional derivative and its application for numerically solving fractional differential equations. The theoretical results for the accuracy and order of the numerical methods are confirmed by the experimental results presented in the paper.

Computational Algorithm for Evaluating Gravitational and Inertial Loads Acting on an Industrial Robot for Palletizing Operations

Computational Algorithm for Evaluating Gravitational and Inertial Loads Acting on an Industrial Robot for Palletizing Operations

XU-NetFullSharp: The Novel Deep Learning Architecture for Chest X-ray Bone Shadow Suppression

Background and objectivesChest X-ray image (CXR) is vital for screening, preventing, and monitoring various lung diseases. In particular, the early detection of lung cancer can significantly improve patients’ chances of survival and quality of life. Unfortunately, approximately 82–95 % of missed pulmonary nodules are estimated to be obscured by rib shadows, making them difficult to recognize. This study addresses this problem by considering the rib shadows in CXRs as noise that can be reduced using deep learning. The result of the proposed model is a CXR with improved clarity for easier and more accurate analysis by radiologists or computer algorithms. MethodsAn automated deep learning-based model for bone shadow suppression from frontal CXRs, called xU-NetFullSharp, was proposed. This network is inspired by the most modern U-NetSharp architecture and was modified using different approaches to preserve as many details as possible and accurately suppress bone shadows. For comparison, recent state-of-the-art models were implemented and trained. JSRT, VinDr-CXR, and Gusarev DES datasets were utilized for the experiments, where the JSRT dataset was extensively augmented. ResultsThe performance of the proposed xU-NetFullSharp was analyzed using statistical measures and compared with that of other architectures. The proposed model significantly outperformed the others, reaching the best values of the most used metrics (0.9846 SSIM; 0.9870 MS-SSIM). It also achieves a correlation of 96.31 % and an intersection of 10.0285 between the predicted and ground truth histograms, together with the smallest value of the Bhattacharyya distance. The obtained results were validated by experts from the University Hospital Olomouc with positive feedback, thus achieving the best objective and subjective results. The proposed method has the potential to be implemented in hospital environments. ConclusionA comprehensive comparison of the proposed architecture with state-of-the-art methods proves its efficiency in suppressing noise in CXRs and its ability to distinguish the signals of important tissues from noise components. This methodology can potentially improve the performance of the existing CXR processing methods. The source code is released in a GitHub repository that can be accessed from the following link: https://github.com/xKev1n/xU-NetFullSharp.