Computational Algorithms Research Articles

An evaluation of computational methods for aggregate data meta-analyses of diagnostic test accuracy studies

BackgroundA Generalized Linear Mixed Model (GLMM) is recommended to meta-analyze diagnostic test accuracy studies (DTAs) based on aggregate or individual participant data. Since a GLMM does not have a closed-form likelihood function or parameter solutions, computational methods are conventionally used to approximate the likelihoods and obtain parameter estimates. The most commonly used computational methods are the Iteratively Reweighted Least Squares (IRLS), the Laplace approximation (LA), and the Adaptive Gauss-Hermite quadrature (AGHQ). Despite being widely used, it has not been clear how these computational methods compare and perform in the context of an aggregate data meta-analysis (ADMA) of DTAs.MethodsWe compared and evaluated the performance of three commonly used computational methods for GLMM - the IRLS, the LA, and the AGHQ, via a comprehensive simulation study and real-life data examples, in the context of an ADMA of DTAs. By varying several parameters in our simulations, we assessed the performance of the three methods in terms of bias, root mean squared error, confidence interval (CI) width, coverage of the 95% CI, convergence rate, and computational speed.ResultsFor most of the scenarios, especially when the meta-analytic data were not sparse (i.e., there were no or negligible studies with perfect diagnosis), the three computational methods were comparable for the estimation of sensitivity and specificity. However, the LA had the largest bias and root mean squared error for pooled sensitivity and specificity when the meta-analytic data were sparse. Moreover, the AGHQ took a longer computational time to converge relative to the other two methods, although it had the best convergence rate.ConclusionsWe recommend practitioners and researchers carefully choose an appropriate computational algorithm when fitting a GLMM to an ADMA of DTAs. We do not recommend the LA for sparse meta-analytic data sets. However, either the AGHQ or the IRLS can be used regardless of the characteristics of the meta-analytic data.

Bio-inspired foveal super-resolution method for multi-focal-length images based on local gradient constraints

Most existing super-resolution (SR) imaging systems, inspired by the bionic compound eye, utilize image registration and reconstruction algorithms to overcome the angular resolution limitations of individual imaging systems. This article introduces a multi-aperture multi-focal-length imaging system and a multi-focal-length image super-resolution algorithm, mimicking the foveal imaging of the human eye. Experimental results demonstrate that with the proposed imaging system and an SR imaging algorithm inspired by the human visual system, the proposed method can enhance the spatial resolution of the foveal region by up to 4 × compared to the original acquired image. These findings validate the effectiveness of the proposed imaging system and computational imaging algorithm in enhancing image texture and spatial resolution.

A Stepwise Cosimulation Framework for Modeling Critical Elements in Copper Porphyry Deposits

AbstractThe increased attention given to batteries has given rise to apprehensions regarding their availability; they have thus been categorized as essential commodities. Cobalt (Co), copper (Cu), lithium (Li), nickel (Ni), and molybdenum (Mo) are frequently selected as the primary metallic elements in lithium-ion batteries. The principal aim of this study was to develop a computational algorithm that integrates geostatistical methods and machine learning techniques to assess the resources of critical battery elements within a copper porphyry deposit. By employing a hierarchical/stepwise cosimulation methodology, the algorithm detailed in this research paper successfully represents both soft and hard boundaries in the simulation results. The methodology is evaluated using several global and local statistical studies. The findings indicate that the proposed algorithm outperforms the conventional approach in estimating these five elements, specifically when utilizing a stepwise estimation strategy known as cascade modeling. The proposed algorithm is also validated against true values by using a jackknife method, and it is shown that the method is precise and unbiased in the prediction of critical battery elements.

PLEIADES: A numerical framework dedicated to the multiphysics and multiscale nuclear fuel behavior simulation

The aim of this paper is to introduce the PLEIADES framework offering a set of services and numerical tools to model and simulate the behavior of nuclear fuels of different concepts of reactors. The framework provides in particular the following features: interfaces to manipulate meshes and fields, services to deal with different physical solvers, setup of coupling trees to realize multiphysics partitioned (accelerated) fixed point couplings, automated time-marching algorithm, checkpoint/restart strategies, capability to realize on-the-fly multiscale couplings. It is built with a permanent concern for sustainability, scalability and maintainability. PLEIADES framework supports multidimensional simulations, typically 1D, 2D and 3D, possibly multilayered. To date, this framework relies on the generic thermomechanical finite elements solver Cast3M to deal with partial derivative problems (mechanical, thermal or diffusion problems) at the scale of the structure or the (heterogeneous) microstructure. It also makes use of so-called point models (mainly based on ODE — ordinary differential equations) to describe the local (mesoscale) evolution of the material through the physics of irradiation. Several software applications are built on the PLEIADES framework, they can use its parallel features to use multiple processors. The multilayered calculation scheme provides a way to loosen the computations and access to a good parallel performance up to hundred cores. It is also possible to achieve efficient scalable concurrent multiscale simulations through finite element square (FE2) computational homogenization algorithms.

Current Applications of Artificial Intelligence in the Neonatal Intensive Care Unit

Artificial intelligence (AI) refers to computer algorithms that replicate the cognitive function of humans. Machine learning is widely applicable using structured and unstructured data, while deep learning is derived from the neural networks of the human brain that process and interpret information. During the last decades, AI has been introduced in several aspects of healthcare. In this review, we aim to present the current application of AI in the neonatal intensive care unit. AI-based models have been applied to neurocritical care, including automated seizure detection algorithms and electroencephalogram-based hypoxic-ischemic encephalopathy severity grading systems. Moreover, AI models evaluating magnetic resonance imaging contributed to the progress of the evaluation of the neonatal developing brain and the understanding of how prenatal events affect both structural and functional network topologies. Furthermore, AI algorithms have been applied to predict the development of bronchopulmonary dysplasia and assess the extubation readiness of preterm neonates. Automated models have been also used for the detection of retinopathy of prematurity and the need for treatment. Among others, AI algorithms have been utilized for the detection of sepsis, the need for patent ductus arteriosus treatment, the evaluation of jaundice, and the detection of gastrointestinal morbidities. Finally, AI prediction models have been constructed for the evaluation of the neurodevelopmental outcome and the overall mortality of neonates. Although the application of AI in neonatology is encouraging, further research in AI models is warranted in the future including retraining clinical trials, validating the outcomes, and addressing serious ethics issues.

Counting \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal{N}$$\\end{document} = 8 black holes as algebraic varieties

We calculate the helicity trace index B14 for \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal{N}$$\\end{document} = 8 pure D-brane black holes using various techniques of computational algebraic geometry and find perfect agreement with the existing results in the literature. For these black holes, microstate counting is equivalent to finding the number of supersymmetric vacua of a multi-variable supersymmetric quantum mechanics which in turn is equivalent to solving a set of multi-variable polynomial equations modulo gauge symmetries. We explore four different techniques to solve a set of polynomial equations, namely Newton Polytopes, Homotopy continuation, Monodromy and Hilbert series. The first three methods rely on a mixture of symbolic and high precision numerics whereas the Hilbert series is symbolic and admit a gauge invariant analysis. Furthermore, exploiting various exchange symmetries, we show that quartic and higher order terms are absent in the potential, which if present would have spoiled the counting. Incorporating recent developments in algebraic geometry focusing on computational algorithms, we have extended the scope of one of the authors previous works [1, 2] and presented a new perspective for the black hole microstate counting problem. This further establishes the pure D-brane system as a consistent model, bringing us a step closer to \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal{N}$$\\end{document} = 2 black hole microstate counting.

Improved Test Procedure and Sample Size Calculation for Assessing Similarity in Two-Group Comparative Studies with Heterogeneous Variances

AbstractThe two one-sided tests (TOST) method for mean equivalence or average equivalence has been extended to assessing similarity or switchability for individual equivalence in clinical trials. Tolerance interval procedures are available to establish similarity with respect to the proportion of the response differences covered by a prespecified threshold range. However, the extended TOST procedures based on tolerance intervals are potentially susceptible to the control of Type I errors. This article aims to present an exact approach with the specified Type I error probability for appraising similarity between two treatments in comparative studies with heterogeneous variances. Analytic examination and numerical comparison are conducted to clarify the utility of the suggested similarity test and the drawback of the current TOST procedures. To enhance the usefulness of the described exact method, the related power and sample size issues are also considered. Computer algorithms are provided to implement the proposed test procedure, power calculation, and sample size determination in similarity studies.

Pan-cancer analysis for the prognostic and immunological role of CD47: interact with TNFRSF9 inducing CD8 + T cell exhaustion

PurposeThe research endeavors to explore the implications of CD47 in cancer immunotherapy effectiveness. Specifically, there is a gap in comprehending the influence of CD47 on the tumor immune microenvironment, particularly in relation to CD8 + T cells. Our study aims to elucidate the prognostic and immunological relevance of CD47 to enhance insights into its prospective utilities in immunotherapeutic interventions.MethodsDifferential gene expression analysis, prognosis assessment, immunological infiltration evaluation, pathway enrichment analysis, and correlation investigation were performed utilizing a combination of R packages, computational algorithms, diverse datasets, and patient cohorts. Validation of the concept was achieved through the utilization of single-cell sequencing technology.ResultsCD47 demonstrated ubiquitous expression across various cancer types and was notably associated with unfavorable prognostic outcomes in pan-cancer assessments. Immunological investigations unveiled a robust correlation between CD47 expression and T-cell infiltration rather than T-cell exclusion across multiple cancer types. Specifically, the CD47-high group exhibited a poorer prognosis for the cytotoxic CD8 + T cell Top group compared to the CD47-low group, suggesting a potential impairment of CD8 + T cell functionality by CD47. The exploration of mechanism identified enrichment of CD47-associated differentially expressed genes in the CD8 + T cell exhausted pathway in multiple cancer contexts. Further analyses focusing on the CD8 TCR Downstream Pathway and gene correlation patterns underscored the significant involvement of TNFRSF9 in mediating these effects.ConclusionA robust association exists between CD47 and the exhaustion of CD8 + T cells, potentially enabling immune evasion by cancer cells and thereby contributing to adverse prognostic outcomes. Consequently, genes such as CD47 and those linked to T-cell exhaustion, notably TNFRSF9, present as promising dual antigenic targets, providing critical insights into the field of immunotherapy.

SkinSwinViT: A Lightweight Transformer-Based Method for Multiclass Skin Lesion Classification with Enhanced Generalization Capabilities

In recent decades, skin cancer has emerged as a significant global health concern, demanding timely detection and effective therapeutic interventions. Automated image classification via computational algorithms holds substantial promise in significantly improving the efficacy of clinical diagnoses. This study is committed to mitigating the challenge of diagnostic accuracy in the classification of multiclass skin lesions. This endeavor is inherently formidable owing to the resemblances among various lesions and the constraints associated with extracting precise global and local image features within diverse dimensional spaces using conventional convolutional neural network methodologies. Consequently, this study introduces the SkinSwinViT methodology for skin lesion classification, a pioneering model grounded in the Swin Transformer framework featuring a global attention mechanism. Leveraging the inherent cross-window attention mechanism within the Swin Transformer architecture, the model adeptly captures local features and interdependencies within skin lesion images while additionally incorporating a global self-attention mechanism to discern overarching features and contextual information effectively. The evaluation of the model’s performance involved the ISIC2018 challenge dataset. Furthermore, data augmentation techniques augmented training dataset size and enhanced model performance. Experimental results highlight the superiority of the SkinSwinViT method, achieving notable metrics of accuracy, recall, precision, specificity, and F1 score at 97.88%, 97.55%, 97.83%, 99.36%, and 97.79%, respectively.

Reduced-order modeling on a near-term quantum computer

Quantum computing is an advancing area of research in which computer hardware and algorithms are developed to take advantage of quantum mechanical phenomena. In recent studies, quantum algorithms have shown promise in solving linear systems of equations as well as systems of linear ordinary differential equations (ODEs) and partial differential equations (PDEs). Reduced-order modeling (ROM) algorithms for studying fluid dynamics have shown success in identifying linear operators that can describe flowfields, where dynamic mode decomposition (DMD) is a particularly useful method in which a linear operator is identified from data. In this work, DMD is reformulated as an optimization problem to propagate the state of the linearized dynamical system on a quantum computer. This reformulation was chosen as a means of facilitating implementation on a near-term quantum computer. Quadratic unconstrained binary optimization (QUBO), a technique for optimizing quadratic polynomials in binary variables, allows for quantum annealing algorithms to be applied. A quantum circuit model (quantum approximation optimization algorithm, QAOA) is utilized to obtain predictions of the state trajectories. Results are shown for the quantum-ROM predictions for flow over a 2D cylinder at Re = 220 and flow over a NACA0009 airfoil at Re = 500 and α=15∘. The quantum-ROM predictions are found to depend on the number of bits utilized for a fixed point representation and the truncation level of the DMD model. Comparisons with DMD predictions from a classical computer algorithm are made, as well as an analysis of the computational complexity and prospects for future, more fault-tolerant quantum computers.

A multichannel analysis of imbalanced computed tomography data for lung cancer classification

Lung cancer holds the highest fatality rate among cancers, emphasizing the importance of early detection. Computer algorithms have gained prominence across various domains, including lung cancer diagnosis. These algorithms assist specialists, especially in medical imaging, yet current efforts lack comprehensive CT data analysis; especially in handling imbalanced datasets and fully exploiting spatial information. The lack of spatial analysis hinders the ability to identify subtle variations in texture and structure that are crucial for detecting lung cancer early and accurately. Therefore, this study uses a multichannel analysis of computed tomography (CT) images and deep learning-based ensemble learning (MC-ECNN) to find lung cancer even when the data is not balanced. Firstly, the data imbalance issue is tackled through the synthetic minority oversampling technique (SOMTE); afterwards, a multi-channel analysis of the data is performed to explore a distinct set of abstract features. Lastly, a deep ensemble learning method is used to classify the extracted distinct abstract feature set into the appropriate classes. The proposed method uses the discrete Fast Fourier transform (DFFT) and discrete cosine transform (DCT), along with the actual CT scans, for the multi-channel analysis of the data in different domains. The proposed model yielded 99.60% test accuracy on unseen data, which is at least 3% better than the other state-of-the-art studies considered for the comparison. In addition to the classification accuracy, the efficacy of the proposed model has also been justified through precision, recall, F1-score, support value, and misclassification rate.

FACNN: fuzzy-based adaptive convolution neural network for classifying COVID-19 in noisy CXR images.

COVID-19 detection using chest X-rays (CXR) has evolved as a significant method for early diagnosis of the pandemic disease. Clinical trials and methods utilize X-ray images with computer and intelligent algorithms to improve detection and classification precision. This article thus proposes a fuzzy-based adaptive convolution neural network (FACNN) model to improve the detection precision by confining the false rates. The feature extraction process between the successive regions is validated using a fuzzy process that classifies labeled and unknown pixels. The membership functions are derived based on high precision features for detection and false rate suppression process. The convolution neural network process is responsible for increasing detection precision through recurrent training based on feature availability. This availability analysis is verified using fuzzy derivatives under local variances. Based on variance-reduced features, the appropriate regions with labeled and unknown features are used for normal or infected classification. Thus, the proposed FACNN improves accuracy, precision, and feature extraction by 14.36%, 8.74%, and 12.35%, respectively. This model reduces the false rate and extraction time by 10.35% and 10.66%, respectively.

An Image-based method for evaluating changes in particle size and morphology distributions of aggregate materials after vibratory compaction test

Particle breakage and abrasion of aggregate materials can be induced by field compaction during construction, which adversely affects their mechanical properties. However, current compaction quality control testing does not evaluate changes in particle size distribution and particle morphology of aggregate materials due to the lack of fast and inexpensive testing methods. In this study, a modified watershed-based segmentation image analysis method and computational geometry algorithms were used to quickly quantify changes in particle size and morphology distributions of a large amount of contacting aggregate particles. The accuracy and required minimum sample size of the image analysis method were statistically evaluated. Compared to the conventional ASTM C136 aggregate sieve analysis test, the image analysis method conducted on a 40% sample size of the conventional method yielded less than 6% difference between the results, which can meet the ASTM C136 test repeatability criteria. In addition, demonstration tests were conducted to incorporate the image analysis method into a laboratory vibratory compaction test for evaluating the overall particle size and morphology changes of an aggregate material after compaction. Results indicate that the initial gradation and water content of the aggregate material can influence the particle breakage and abrasion characteristics of aggregate materials under compaction.

Surrogate-based aerodynamic shape optimization of high-speed train heads: A review of four key technologies

With the increase in running speed, the aerodynamic characteristics of high-speed trains have a significant impact on running stability, energy consumption and passenger comfort. Since the shape of the high-speed train head can directly influence the surrounding airflow, optimizing the head shape is the primary way to improve the aerodynamic performance of the train. This paper reviews current research studies on the surrogate-based aerodynamic shape optimization of high-speed train heads, aiming to provide a comprehensive reference for designers to enhance design efficiency and optimization performance. The entire optimization process is divided into four essential steps, and the key optimization technologies in each step are discussed, including parametric modeling, computational fluid dynamics (CFD) simulation, surrogate model and optimization algorithm. By introducing the practical applications of these technologies, we summarize their advantages and disadvantages and suggest four potential research directions for the future.

Abstract PO5-14-09: Does transcriptome based DEPTH2 score reflect intratumor heterogeneity in breast cancer patients?

Abstract Background: Intratumor heterogeneity (ITH) refers to the presence of distinct cancer cell populations with different clones within a single tumor mass. Tumors with high ITH are more prone to progression, exhibit resistance to therapies, and are associated with an unfavorable prognosis. Therefore, evaluation of ITH is anticipated to serve as a breakthrough biomarker for prognosis and treatment strategies. Several computational algorithms have been proposed to quantify ITH. However, with the exception of DEPTH2, all of these algorithms require both DNA and RNA-sequence data from the same tumor, which can be challenging. In this study, we investigated whether DEPTH2 which solely analyzes RNA-sequence data, accurately reflects the biological characteristics of ITH. Methods: We analyzed clinicopathological and gene expression data from a total of 6573 breast cancer patients from several large independent cohorts: METABRIC (n = 1903), GSE96058 (n = 3273), GSE21974 (n = 57), GSE28844 (n = 61), GSE87455 (n = 153), GSE180280 (n = 57), and The Cancer Genome Atlas (TCGA, n = 1069). Results: DEPTH2 score was high in Triple Negative Breast Cancer (TNBC) subtype (p < 0.001), but no consistent correlation was observed with lymph node or distal metastasis. Higher DEPTH2 score was associated with cell proliferation, as evidenced by higher Nottingham histological grade and Ki67 gene expression (both p < 0.001). In agreement, high DEPTH2 tumors enriched all cell proliferation-related Hallmark gene sets such as E2F targets, G2M checkpoint, MYC targets v1 and v2, and Mitotic Spindle (FDR < 0.25). Additionally, high DEPTH2 tumors exhibited reduced immune cell infiltrations, such as CD8, CD4, Th1, Th2, B cells, and macrophages, and increased infiltrations of stromal cells, such as adipocytes, fibroblasts, blood and lymphatic endothelial cells (all p < 0.05). In TCGA cohort, DEPTH2 score exhibited a similar trend to intratumoral heterogeneity, homologous recombination deficiency, fraction altered, silent and nonsilent mutations, as well as SNV and Indel neoantigens. However, the Pearson’s correlation coefficient was very weak (p < 0.05). DEPTH2 score decreased in the GSE21974 and GSE87455 cohorts after neoadjuvant chemotherapy (NAC), whereas no change was observed in the GSE28844 and GSE180280 cohorts. DEPTH2 high tumors showed a lower rate of pathologic complete response (pCR) after NAC in estrogen receptor (ER)+HER2- subtype across all cohorts, however, opposite pattern was observed in the HER2+ and TNBC subtypes in all cohorts. Lastly, high DEPTH2 score was associated with lower disease free and overall survival in METABRIC and GSE96058 (p < 0.05), but not in the TCGA cohort. Conclusions: Our findings suggest that DEPTH2 score may not completely capture the biological characteristics associated with intratumor heterogeneity. Citation Format: Kohei Chida, Rongrong Wu, Arya Mariam Roy, Takashi Ishikawa, Kazuaki Takabe. Does transcriptome based DEPTH2 score reflect intratumor heterogeneity in breast cancer patients? [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-14-09.

Correlation Between Prostate Volume and Lower Urinary Tract Symptoms: A Hospital-Based Study

Background: Lower urinary tract symptoms (LUTS) are manifestations of storage and voiding disorders involving the urinary bladder, urethra and the prostate gland. This study aimed to correlate prostate volume and lower urinary tract symptoms.  Materials and methods: This study was a prospective cross-sectional study carried out among 120 males aged 40 years and above who presented with LUTS for prostate ultrasonography at the Radiology department within a period of six months. Informed consent and ethical approval were obtained from participants before commencement of the study. Severity of LUTS was evaluated using the International Prostate Symptom Score (IPSS). Each question was scored between 0 to 5 and the sum recorded as total IPSS score. The total score was graded as mild (0-7), moderate (8-19) or severe (20-35). Quality of life due to urinary symptoms was also evaluated and scored between 0 (delighted) and 6 (terrible). Prostate volume estimation was done by transabdominal ultrasonography using an ultrasound scanner fitted with a 3.5-MHz curvilinear transducer (Logic F6, General Electric, USA, 2017). Measurements were taken and the average values computed; volume calculation was obtained by default using the inbuilt computer algorithm in the ultrasound machine. The data obtained were statistically analyzed using the Statistical Package for Social Sciences (SPSS) software version 24 (International Business Machines, USA, 2016), and the results presented in tables, charts, and percentages. Pearson’s correlation coefficient was used to correlate between variables with P-values < 0.05 considered significant.  Results: Results showed that the cumulative mean age of the participants was 65.1 ± 9.6 years with majority of the participant’s between 60 and 69 years. The most common presenting LUTS was nocturia (98.0%), followed by frequency (92.7%). Overall, the mean IPSS score was 13.2 ± 4.6 while quality of life (QoL) was 4.1 ± 1.2. The mean prostate volume was 69.8 ± 63.5cm3, with 79.2% of the subjects having a prostate volume ≥ 30cm3. Pearson correlation between prostate volume and IPSS was significant (r = 0.304, p = 0.001). Conclusion: The study established a positive significant correlation between prostate volume and International Prostate Symptoms Score (IPSS). This implies that sonographic PV assessment could be useful in objective assessment of LUTS severity and should be considered in management decisions and follow-up of patients with lower urinary tracts symptoms. 

Applications Of Artificial Intelligence Algorithms In Artificial Lift Systems: A Critical Review

In the oil and gas industry, the implementation of an artificial lift system is utilized to enhance the extraction of hydrocarbons from a reservoir that has depleted its natural energy. This can be achieved by employing pumps such as positive-displacement or centrifugal downhole pumps, or by injecting gas into the well tubing to reduce liquid hydrostatic pressure and improve fluid flow towards the surface. Recently, artificial intelligence-based algorithms have emerged as the leading approach for modelling various artificial lift parameters and problems. Consequently, there has been a renewed interest in understanding the role and effectiveness of different computational algorithms used in the artificial lift system domain. While there is an extensive body of literature on this subject, no prior research has effectively consolidated the findings from various artificial lift system domains, underscoring the need for an all-encompassing review. The purpose of this review is to create a guide that serves as a framework that helps scholars identify knowledge gaps and suggest novel ideas that would enhance the workings of artificial lift systems in the oil and gas industry. This article presents the first comprehensive analysis of artificial intelligence applications in five key areas of artificial lift systems in the oil and gas industry namely: prediction of oil and gas flow rates in artificial lift wells, prediction of artificial lift system failures, selection of artificial lift systems, parameter optimization, and the application of soft sensing technologies in artificial lift systems. The research studies pertaining to each of these areas were systematically identified from the existing literature, and their main features were highlighted. To ensure the uniqueness and novelty of this work, a critical evaluation of the artificial lift articles in each broad area was conducted. The review outcomes were then presented in clear tables that highlighted the main features of each study. Findings from the study indicate that artificial intelligence models are prevalent in almost every aspect of artificial lift systems, and the number of articles on this topic continues to grow. Some of these models have even been field tested and proven effective in improving artificial lift system operations and reducing costs. However, there is need to enhance the explicitness and interpretability of some of the predictive models by including vital details, to enhance seamless implementation and easy deployment in the field. This work will be valuable for industry operators, researchers, and students who seek a guide or reference material on artificial intelligence-based models for artificial lift systems.

Forward genetics combined with unsupervised classifications identified zebrafish mutants affecting biliary system formation.

Impaired formation of the biliary network can lead to congenital cholestatic liver diseases; however, the genes responsible for proper biliary system formation and maintenance have not been fully identified. Combining computational network structure analysis algorithms with a zebrafish forward genetic screen, we identified 24 new zebrafish mutants that display impaired intrahepatic biliary network formation. Complementation tests suggested these 24 mutations affect 24 different genes. We applied unsupervised clustering algorithms to unbiasedly classify the recovered mutants into three classes. Further computational analysis revealed that each of the recovered mutations in these three classes has a unique phenotype on node-subtype composition and distribution within the intrahepatic biliary network. In addition, we found most of the recovered mutations are viable. In those mutant fish, which are already good animal models to study chronic cholestatic liver diseases, the biliary network phenotypes persist into adulthood. Altogether, this study provides unique genetic and computational toolsets that advance our understanding of the molecular pathways leading to biliary system malformation and cholestatic liver diseases.

Improving the artificial bee colony algorithm with a proprietary estimation of distribution mechanism for protein-ligand docking

The protein-ligand docking problem plays an essential role in structure-based drug design. The challenge for a protein-ligand docking method is how to execute an efficient conformational search to explore a well-designed scoring function. In this study, we improved the artificial bee colony (ABC) algorithm and proposed an approach called ABC-EDM to solve the protein-ligand docking problem. ABC-EDM employs the scoring function of the classical AutoDock Vina to evaluate a solution during docking simulation. ABC-EDM adopts the search framework of the canonical ABC algorithm to execute conformational search. By further investigating the characteristics of the protein-ligand docking problem, a proprietary search mechanism inspired by estimation of distribution algorithm, i.e., estimation of distribution mechanism (EDM), is designed to enhance the performance of ABC-EDM. To verify the effectiveness of the proposed ABC-EDM, we compare it with three variants of the ABC algorithm, three evolutionary computation algorithms, and AutoDock Vina. The experimental results show that ABC-EDM can effectively solve the protein-ligand docking problem, and it can achieve a success rate 5% higher than AutoDock Vina on the GOLD dataset. This study reveals that taking advantage of problem-specific information about the protein-ligand docking problem to enhance a docking method contributes to solving this problem.

Prediction of clog time of amorphous silica in the pipes of the wells from Los Humeros geothermal field – A new geochemical model along with a computational algorithm

The primary objective of this article is to introduce a physicochemical model along with a computational algorithm that is valuable for quantifying the occurrence of silica scaling and fouling within geothermal production wells. This model utilizes two-phase flow parameters, temperature and pressure profiles, as well as brine composition to forecast the timing of production pipe constriction caused by amorphous silica deposition over time. The model is applicable to systems with temperatures ranging from 270°C to 305°C, vapor pressures ranging from 55 to 93 bars, and enthalpies ranging from 1189 kJ/kg to 1400 kJ/kg. The predictive model was calibrated using data gathered from six distinct wells within the Los Humeros geothermal field in Mexico, and subsequently, it was applied to forecast outcomes in two additional wells. When the conditions of temperature, pressure, and enthalpy remain relatively constant throughout the production process, the model developed in this study accurately predicts the times at which a certain percentage of pipe sealing occurs in wells with high volumetric fractions of liquid (εl>0.5) and temperatures between 270°C and 305°C. The prediction error is less than 3%.