Rigorous Computational Analysis Research Articles

Multi-Directional Wind Turbine with Combined Savonius and Darrieus Rotors: A Comparative Performance Analysis

This study presents a comprehensive investigation into five distinct combinations of wind turbine configurations, each integrating Savonius and Darrieus rotors on a single shaft. The studied configurations include Single Stage Savonius and H-Darrieus, Two-Stage Savonius and D-Darrieus, Two-Stage Savonius and H-Darrieus, Helical Darrieus and Two-Stage Savonius, and Two-Stage Savonius and H-Darrieus in series. Our research uses Computational Fluid Dynamics (CFD) simulations conducted using ANSYS Fluent to meticulously analyze and optimize these turbine designs. Through rigorous computational modeling and flow analysis, we aim to elucidate the performance characteristics, including torque generation, torque nature, flow time, and power output, for each configuration. Following thorough comparison, our findings highlight the double-stage Savonius and D Darrieus turbine as exhibiting superior performance attributes. This research contributes valuable insights into the advancement of multi-directional wind turbine technology, facilitating enhanced energy harnessing from varying wind conditions.

Exploring the Theoretical Foundations of Thermally Activated Delayed Fluorescence (TADF) Emission: A Comprehensive TD-DFT Study on Phenothiazine Systems.

This study conducts a thorough theoretical investigation of Thermally Activated Delayed Fluorescence (TADF) in phenothiazine-based systems, examining ten molecular configurations recognized experimentally as TADF-active. Employing Time-Dependent Density Functional Theory (TD-DFT), our analysis spans the investigation of singlet-triplet energy gaps (ΔEST), spin-orbit coupling, and excitation characteristics using Multiwfn. This approach not only validates the adherence to El Sayed's rule across these systems but also provides a detailed understanding of charge transfer dynamics, as visualized through heat maps. A significant aspect of our study is the exploration of different oxidation states of sulfur and site substitutions on phenothiazine. This systematic variation aims to identify additional TADF-active compounds, drawing parallels with properties characterizing other known TADF emitters. Our investigation into Reverse Intersystem Crossing (rISC) rates and the analysis of dihedral angles in relation to ΔEST values offer nuanced insights into the TADF behaviours of these molecules. By integrating rigorous computational analysis with practical implications, we provide a foundational understanding that enhances the design and optimization of phenothiazine-based materials for optoelectronic applications. This work not only advances our theoretical understanding of TADF in phenothiazine derivatives but also serves as a guide for experimentalists and industry professionals in the strategic design of new TADF materials.

A Theoretical Investigation on Nitrogen Dioxide Adsorption on Self-Assembled Monolayer-Functionalized ZnO Monolayer

This study employs Density Functional Theory (DFT) calculations to elucidate the adsorption mechanism in the context of a ZnO monolayer system integrated with a Self-Assembled Monolayer (SAM) for NO2 detection. Through rigorous computational analysis, we delve into the intricate interplay of geometric transformations, the dissociation of NO2 bonds, and shifts in electronic properties ensuing from the introduction of the SAM. The observed modifications underscore the pronounced influence exerted by the SAM on the system’s behaviour. This investigation not only sheds light on the underlying mechanisms but also paves the way for potential experimental applications involving the functionalization of ZnO with SAM for enhanced gas sensing performance. The findings hold significant promise for the advancement of gas sensor technologies with improved sensitivity and selectivity.

Combined application of ideological and political education and big data Internet technology in the context of education reform

Abstract In the course of today’s socio-political and economic development, along with the advent of the era of big data and the internet, various ideas have emerged and more ideological content and political ideas have continued to emerge. In order to cope with this situation, education reform is constantly innovating, among which the ideological education is particularly important. Various ideological education applications have appeared one after another in the Internet, which allows ideological education to see a new direction of development. The article develops a rigorous computational analysis of the combination of Civic Education and Big Data Internet, applied to the development of a new system. Using decision tree classification algorithm, ID3 algorithm, Bayesian classification algorithm, cohesive hierarchical clustering method for a large number of experimental data processing, the system was completed to build a framework for the combination of big data and Civic Education application. Through data analysis and comparison, it is found that the application of these big data algorithms in the field of Civic Education not only provides more detailed information on Civic Education reform, but also provides objective and comprehensive feedback on the dissemination and learning of Civic Education. In the relevant feedback information, the use of Internet applications for Civic Education reached 100%, Excel was 84%, and all the learning levels of the fixed-point test could reach over 60 points. The above data shows that people have also been able to improve their learning level in Civic Education to a great extent with the Internet technology.

Computational Investigation of c-GaN/GaAs1–x N x /GaAs Heterojunction Solar Cell

GaN-based electronics have witnessed an increase in both research and industrial activities, first spurred by the successful demonstration of GaN LEDs, and are now expanding into transistors and photovoltaic cells. In addition, GaN/GaAs heterojunction devices are currently receiving much interest. In this study, we conduct rigorous optoelectronic computational analysis of cubic phase GaN (c-GaN)/GaAs heterojunction solar cells for a comprehensive understanding of the cell. We utilize a compositionally graded GaAs1–xNx buffer layer to reduce defect states at the heterojunction interface caused by a significant lattice mismatch between c-GaN and GaAs. Furthermore, we enhance the performance of the cell by optimizing GaAs absorber layer thickness and c-GaN buffer layer doping concentration. Moreover, we examine the effects of GaAs1–xNx/GaAs interface recombination velocity (IRV) on the cell. Overall, we achieve ~23% power conversion efficiency within 1.25- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> thin-film GaAs at low GaAs1–xNx/GaAs IRV. The analyses and results presented in this study demonstrate the vast application potential of c-GaN/GaAs heterojunction in high-efficiency solar cells.

A Distributionally Robust Optimization Based Method for Stochastic Model Predictive Control

Two stochastic model predictive control algorithms, which are referred to as distributionally robust model predictive control algorithms, are proposed in this article for a class of discrete linear systems with unbounded noise. Participially, chance constraints are imposed on both of the state and the control, which makes the problem more challenging. Inspired by the ideas from distributionally robust optimization (DRO), two deterministic convex reformulations are proposed for tackling the chance constraints. Rigorous computational complexity analysis is carried out to compare the two proposed algorithms with the existing methods. Recursive feasibility and convergence are proven. Simulation results are provided to show the effectiveness of the proposed algorithms.

A priori error analysis for a mixed VEM discretization of the spectral problem for the Laplacian operator

The aim of the present work is to derive error estimates for the Laplace eigenvalue problem in mixed form, implementing a virtual element method. With the aid of the theory for non-compact operators, we prove that the proposed method is spurious free and convergent. Optimal order of convergence for the eigenvalues and eigenfunctions are derived. Finally, we report numerical tests to confirm the theoretical results together with a rigorous computational analysis of the effects of the stabilization parameter, inherent for the virtual element methods, in the computation of the spectrum.

Qualitative and Quantitative Shotgun Proteomics Data Analysis from Data-Dependent Acquisition Mass Spectrometry.

Shotgun proteomics is the inferential analysis of proteoforms using peptide proxies produced by enzyme-catalyzed hydrolysis of entire proteomes. Such peptides are usually identified by nanoflow liquid chromatography coupled to tandem mass spectrometry analysis (nLC-MS/MS). Traditionally, MS/MS analysis is performed in data-dependent acquisition (DDA) mode, which usually produces a pattern of fragment masses unique to a single peptide's fragmentation. Here, I describe a statistically rigorous qualitative and quantitative computational analysis for shotgun proteomics DDA analysis using free open-source software tools. MS/MS data are used to identify peptides, and the area of peptide mass/charge over chromatographic elution is used to quantify peptides. All peptides that uniquely map to a protein sequence predicted from the genome are combined into a single protein quantity, which can then be compared across experimental conditions. Statistically significant protein changes can be summarized using gene ontology or pathway term enrichment analysis.

The Beneficial Role of Sunitinib in Tumor Immune Surveillance by Regulating Tumor PD-L1.

Immune checkpoints blockades have shown promising clinical effects in various malignancies, but the overall response rate is low. Here, the immune features are comprehensively characterized in >10 000 cancer patients from The Cancer Genome Atlas and significantly positive correlations are observed between targets of Sunitinib and inhibitory immune checkpoints and suppressive immune cells. It is further confirmed that Sunitinib treatment increases the antitumor immunity in a phase III trial. Mechanistically, it is discovered that Sunitinib regulates the stability of tumor PD‐L1 via p62, that p62 can bind to PD‐L1 and specifically promote its translocation into autophagic lysosome for degradation. Preclinically, Sunitinib shows a synergistic antitumor effect with cytotoxic T‐lymphocyte‐associated protein 4 (CTLA‐4) monoclonal antibody (mAb) in melanoma and nonsmall cell lung cancer (NSCLC) immune competent mice by promoting the tumor‐infiltrating lymphocytes activity. Clinically, a higher PD‐L1 level but a lower p62 level in the tumor region of responders as compared to those of nonresponders among anti‐PD‐1‐treated NSCLC patients is observed. Taken together, by utilizing rigorous computational analysis, functional characterization in vitro and in vivo, and neoadjuvent clinical trial, a novel molecular mechanism is revealed regarding the regulation of PD‐L1 via p62, thus providing a novel therapeutic strategy by the combination treatment of CTLA‐4 with Sunitinib.

Text as big data: Develop codes of practice for rigorous computational text analysis in energy social science

Augmenting traditional social science methods with computational analysis is crucial if we are to exploit the vast digital archives of text data that have become available over the past two decades. In this journal, Benites-Lazaro et al. [1] showcase this in an application of topic modeling and other computational methods to an actor-specific examination of changes in policy discourse on ethanol in Brazil and point out methodological promises and challenges. However, their contribution also highlights the need for establishing codes of practice for computational text analysis. In this perspective, we discuss five areas for improvement when treating text as big data in light of guiding principles from computational research – transparency, reproducibility and validation – to facilitate rigorous research practice: (1) full transparency over data collection and corpus construction, (2) comprehensive method descriptions that enable reproducibility by other researchers, (3) application of rigorous model validation procedures, (4) results interpretation based on primary text and clear research design and (5) critical discussion and contextualization of main findings. We conclude that the energy social science community needs to develop codes of practice to build on the promising research within the field of computational text analysis and suggest first steps into this direction.

Computer-aided drug design against spike glycoprotein of SARS-CoV-2 to aid COVID-19 treatment

BackgroundSARS-CoV-2 has the Spike glycoprotein (S) which is crucial in attachment with host receptor and cell entry leading to COVID-19 infection. The current study was conducted to explore drugs against Receptor Binding Domain (RBD) of SARS-CoV-2 using in silico pharmacophore modelling and virtual screening approach to combat COVID-19. MethodsAll the available sequences of RBD in NCBI were retrieved and multiple aligned to get insight into its diversity. The 3D structure of RBD was modelled and the conserved region was used as a template to design pharmacophore using LigandScout. Lead compounds were screened using Cambridge, Drugbank, ZINC and TIMBLE databases and these identified lead compounds were screened for their toxicity and Lipinski's rule of five. Molecular docking of shortlisted lead compounds was performed using AutoDock Vina and interacting residues were visualized. ResultsActive residues of Receptor Binding Motif (RBM) in S, involved in interaction with receptor, were found to be conserved in all 483 sequences. Using this RBM motif as a pharmacophore a total of 1327 lead compounds were predicted initially from all databases, however, only eight molecules fit the criteria for safe oral drugs. Conclusion: The RBM region of S interacts with Angiotensin Converting Enzyme 2 (ACE2) receptor and Glucose Regulated Protein 78 (GRP78) to mediate viral entry. Based on in silico analysis, the lead compounds scrutinized herewith interact with S, hence, can prevent its internalization in cell using ACE2 and GRP78 receptor.The compounds predicted in this study are based on rigorous computational analysis and the evaluation of predicted lead compounds can be promising in experimental studies.

Distributionally Robust Two-Stage Energy Management for Hybrid Energy Powered Cellular Networks

Energy harvesting has been recognized as a good paradigm to decrease traditional grid energy expenditure, which caters for 5G visions on the green evolution of cellular networks. However, the harvested energy at each base station is random and intermittent, which imposes a great challenge to reliably satisfy the time-varying users’ wireless traffic requirements. What's worse, the perfect probability distributions of harvested energy and wireless traffic are hard to obtain in practical systems, making it difficult to apply the conventional optimization approach to cope with the uncertainties in the energy management problems. Inspired by this fact, we propose a distributionally robust two-stage stochastic optimization framework to minimize the expected total energy cost of mobile network operators (MNOs) in the finite time horizon. To circumvent the computational difficulty of this stochastic optimization problem, we define a novel ambiguity set to capture the uncertainties of the harvested energy and wireless traffic based on their first- and second-order statistics information. By employing the distributionally robust optimization (DRO) methodology, we equivalently transform the original two-stage stochastic programming problem into a computationally tractable second-order cone programming problem. Then, a distributionally robust two-stage energy management algorithm (DRTEMA) is proposed, which has good performance of low complexity and only requires partial information of the stochastic parameters. Rigorous computational complexity analysis and extensive numerical simulations are conducted to show the advantages of the proposed algorithm.

Development of an MS Workflow Based on Combining Database Search Engines for Accurate Protein Identification and Its Validation to Identify the Serum Proteomic Profile in Female Stress Urinary Incontinence

A critical stage of shotgun proteomics is database search, a process which attempts to match the experimental spectra to the theoretical one. Given the considerable time and effort spent in analysis, it is self-evident for a researcher to aspire for rigorous computational analysis and a more confident and accurate peptide/protein identification. Mass spectrometry (MS) has been applied across several clinical disciplines. The pathophysiology of Stress Urinary Incontinence (SUI), caused by a damaged pelvic floor, has become a boundless disease altering the quality of life worldwide. Although some studies pointed markers that can be bioindicators for SUI, these findings raise the issue of sensitivity and specificity. Therefore, it is critical to have a sensitive and specific analytical approach to identify markers that have been associated with protective and deleterious associations in disease. Here, we describe our designed and developed workflow for protein identification from tandem mass spectrometry that uses multiple search engines. We apply our workflow to an existing study addressing the pathophysiology of SUI. We demonstrate how using the combined approach together with high-performance computing techniques can surmount the challenges of complex analyses and extended computing time. We also compare the relative performance of each combination. Our results suggest that a combination of MS-GF+ and COMET represents the best sensitivity-specificity trade-off, outperforming all other tested combinations. The approach was also sensitive and accurately identified a set of protein that was shown to be markers for categories of diseases associated with the pathophysiology of SUI. This workflow was developed to encourage proteomic researchers to adopt MS-based techniques for accurate analysis and to promote MS as a routine tool to the clinical cohorts.

A chance-constrained stochastic model predictive control problem with disturbance feedback

In this paper, we develop two algorithms for stochastic model predictive control (SMPC) problems with discrete linear systems. Participially, chance constraints on the state and control are considered. Different from the state-of-the-art robust model predictive control (RMPC) algorithm, the proposed is less conservative. Meanwhile, the proposed algorithms do not assume the full knowledge of the disturbance distribution. It only requires the mean and variance of the disturbance. Rigorous computational analysis is carried out for the proposed algorithms. Numerical results are provided to demonstrate the effectiveness and the superior of the proposed SMPC algorithms.

Functional variants of hepatocyte growth factor identified in patients with adolescent idiopathic scoliosis.

The genetic etiology of adolescent idiopathic scoliosis (AIS) remains obscure. Whole-genome sequencing was performed in four members of one family. Then, we performed a rigorous computational analysis to determine the deleterious effects of the identified variants. Furthermore, the structural differences between the native hepatocyte growth factor (HGF) protein and a protein encoded by an HGF variant containing one mutation (p.T596M) were analyzed using molecular dynamic stimulation. A novel heterozygous mutation (p.T596M) within the HGF gene was identified and found to cosegregate with scoliosis phenotypes in three affected family members. Subsequent modeling and structure-based analyses supported the theory that this mutation is functionally deleterious. Functional analyses demonstrated that the HGF p.T596 M mutation changed the ability of the HGF protein to be secreted and impaired migration and invasion in HEK293T cells. Furthermore, an HGF knockdown zebrafish model exhibited a curly tailed phenotype. Mutation in HGF is associated with an autosomal dominant pattern of inheritance of AIS. This finding increases our understanding of the genetic heterogeneity of AIS.

Molecular insight into the T798M gatekeeper mutation-caused acquired resistance to tyrosine kinase inhibitors in ErbB2-positive breast cancer

Human epidermal growth factor receptor 2 (ErbB2) is an attractive therapeutic target for metastatic breast cancer. The kinase has been clinically observed to harbor a gatekeeper mutation T798M in its active site, which causes acquired resistance to the first-line targeted breast cancer therapy with small-molecule tyrosine kinase inhibitors. Previously, several theories have been proposed to explain the molecular mechanism of gatekeeper mutation-caused drug resistance, such as blocking of inhibitor binding and increasing of ATP affinity. In the current study, the direct binding of three wild type-selective inhibitors (Lapatinib, AEE788 and TAK-285) and two wild type-sparing inhibitors (Staurosporine and Bosutinib) to the wild-type ErbB2 and its T798M mutant are investigated in detail by using rigorous computational analysis and binding affinity assay. Substitution of the polar threonine with a bulky methionine at residue 798 can impair and improve the direct binding affinity of wild type-selective and wild type-sparing inhibitors, respectively. Hindrance effect is responsible for the affinity decrease of wild type-selective inhibitors, while additional nonbonded interactions contribute to the affinity increase of wild type-sparing inhibitors, thus conferring selectivity to the inhibitors for mutant over wild type. The binding affinity of Staurosporine and Bosutinib to ErbB2 kinase domain is improved by 11.9-fold and 2.1-fold upon T798M mutation, respectively. Structural analysis reveals that a nonbonded network of S–π contact interactions (for Staurosporine) or an S-involving halogen bond (for Bosutinib) forms with the sulfide group of mutant Met798 residue.

Crack arrest testing at the micro-scale

Crack arrest testing of micro-sized cantilever beams (≈8 × 4 × 6 μm, length, width and height, respectively) was conducted in order to evaluate the suitability of a new method to quantify local crack arrest properties. Chevron notched cantilevers were milled to match the (1 0 0)[0 1¯ 1] crack system in α-iron, where earlier attempts to obtain brittle or rapidly propagating fracture proved difficult. Brittle crack initiation and propagation was achieved by means of the deposition of a layer of SiOX on the surface, acting as a brittle starter. All tests were performed at −75 °C, using an in-house designed cooling system. The cracks arrested after propagation into the iron cantilever. A finite element model was developed to determine the appropriate dimensionless shape factor and provide a rigorous computer analysis of these complexly shaped cantilevers. KQC and KQa, at initiation and arrest respectively, were determined and evaluated. The cantilevers were later displaced further at 40 K to allow evaluation of crack jump lengths and to obtain a more complete analysis of the fracture surfaces. The average fracture toughness was determined to be 3.89 ± 1.00 MPam, and the average arrest toughness to be 2.6 ± 0.86 MPam. The finite element model highlights the effect of small variations in geometry which was larger than anticipated and strongly affects the shape factor, up to a 25% difference in f(a/W). As small variations in geometry are inevitable when milling with FIB, the need for individual models tailored to every cantilever is discussed.

Structural Analysis of G1691S Variant in the Human Filamin B Gene Responsible for Larsen Syndrome: A Comparative Computational Approach

Larsen syndrome (LRS) is a rare genetic disease associated with variable manifestations including skeletal malformations, dislocations of the large joints, and notable changes in facial and limb features. Genetic variants in the Filamin B (FLNB) gene are associated with the development of LRS. We searched two literature databases (OMIM and PubMed) and three gene variant databases (HGMD, UniProt, & dbSNP) to capture all the possible variants associated with LRS phenotype, which may have an impact on the FLNB function. Our search yielded 77 variants that might impact the FLNB protein function in patients with LRS. We performed rigorous computational analysis such as conservational, biochemical, pathogenicity, and structural computational analyses to understand the deleterious effect of the G1691S variant. Further, the structural changes of the G1691S variant was compared with a null variant (G1691A) and the native protein through a molecular dynamic simulation study of 50 ns. We found that the variant G1691S was highly deleterious and destabilize the protein when compared to the native and variant G1691A. This might be due to the physicochemical changes in the variant G1691S when compared to the native and variant G1691A. The destabilization was further supported by transformation of bend to coil in variant G1691S whereas bend was retained in native and variant G1691A through molecular dynamics analysis. Our study shed light on the importance of computational methods to understand the molecular nature of genetic variants and structural insights on the function of the FLNB protein. J. Cell. Biochem. 118: 1900-1910, 2017. © 2017 Wiley Periodicals, Inc.

Accelerating Stochastic Collocation Methods for Partial Differential Equations with Random Input Data

This work proposes and analyzes a generalized technique for decreasing the computational complexity of stochastic collocation (SC) methods to solve partial differential equations (PDEs) with random input data. Specifically, we predict the solution of the parametrized PDE at each collocation point using a previously assembled lower fidelity interpolant and use this prediction to provide deterministic (linear/nonlinear) iterative solvers with initial approximations which continue to improve as the algorithm progresses through the levels of the interpolant. With nested collocation points, these coarse predictions can be assembled as a substep in the construction of the high-fidelity interpolant. As a concrete example, we develop our approach in the context of SC approaches employing sparse tensor products of globally defined Lagrange polynomials on nested one-dimensional Clenshaw--Curtis abscissas, providing a rigorous computational complexity analysis of the resulting fully discrete sparse grid SC approximation, with and without acceleration, and demonstrating the effectiveness of our proposed algorithm. Numerical examples include linear and nonlinear parametrized PDEs and illustrate the theoretical results and the improved efficiency of this technique compared with several others.

Visualization of High Throughput Genomic Data Using R and Bioconductor

DNA microarrays, technology aims at the measurement of mRNA levels in particular cells or tissues for many genes simultaneously. Microarray in molecular biology results in huge datasets that need rigorous computational analysis to extract biological information that lead to some conclusion. From printing of microarray chip to hybridization and scanning process it results in variability in quality of data due to which actual information is either lost or it is over represented. Computational analysis plays an important part related to the processing of the biological information embedded in microarray results and for comparing gene expression result obtained from different samples in different condition for biological interpretation. A basic, yet challenging task is quality control and visualization of microarray gene expression data. One of the most popular platforms for microarray analysis is Bioconductor, an open source and open development software project for the analysis and comprehension of genomic data, based on the R programming language. This paper describes specific procedures for conducting quality assessment of Affymetrix Gene chip using data from GEO database GSE53890 and describes quality control packages of bioconductor with reference to visualization plots for detailed analysis. This paper can be helpful for any researcher working on microarray analysis for quality control analysis of affymetrix chip along with scientific interpretations.