Computational Procedure Research Articles

CO2 inside sI clathrate-like cages: Automated construction of neural network/machine learned guest-host potential and quantum spectra computations.

We present new results on the underlying guest-host interactions and spectral characterization of a CO2 molecule confined in the cages of the sI clathrate hydrate. Such types of porous solids raise computational challenges, as they are of practical interest as gas storage/capture materials. Accordingly, we have directed our efforts toward addressing their modeling in a proper manner, ensuring the quality of the input data and the efficiency of the computational approaches. The computational procedure for spectral simulations, within the multi-configurational time-dependent Hartree framework, involves the development of a fully coupled Hamiltonian, including an exact kinetic energy operator and a many-body representation of the potential, along with dipole moment surfaces, both obtained through neural network machine learning techniques. The resulting models were automatically trained and tested on extensive datasets generated by PW86PBE-XDM calculations, following the outcome of previous benchmark studies. Our simulations enable us to explore various aspects of the quantized dynamics upon confinement of CO2@D/T, such as constrained rotational-translational quantum motions and the averaged position/orientation of the CO2 guest in comparison to the experimental data available. Particularly notable are the distinct energy patterns observed in the computed spectra for the confined CO2 in the D and T cages, with a considerably high rotational-translational coupling in the CO2@T case. Leveraging reliable computations has proved instrumental, highlighting the sensitivity of the spectral features to the shape and strength of the potential interactions, with the explicit description of many-body contributions being significant.

Кількісне оцінювання функціональної ефективності мережевих структур за аналізом чутливості оптимізаційної моделі до зовнішніх впливів

The real network organizational structure is constantly under the influence of external influences, which can cause unexpected consequences due to the cascading spread of relevant disturbances directed at critically important parameters. Parametric sensitivity analysis is the latest computational procedure, the results of which provide the researcher with an idea and quantitative assessment of the appropriate response of the structure in the form of an analytical platform as a composition and interaction of scenario analysis, cascade process development and optimization modeling technology. The main task is timely adaptation to expected changes in business analytics, oriented to optimal resources distribution. The generalized minimum cost problem is a typical example of widespread problems of optimal distribution and use of limited, in particular, energy resources, of any nature and purpose, using the scientific management and analysis of decisions, in its network model attention is paid to the potentials of nodes. The maximum flow problem is an example of the active and effective application of methods and models of flows optimization, where the processes and objects used a network organization, the energy systems and complexes investigated, where arc parameters are critical in its model. In the article, for these classic optimization problems, mathematical and spreadsheet models were built on concrete examples. Direct and dual mathematical programming problems were solved, the following stages were carried out - the organization of a computer experiment and the construction of an analytical platform for evaluating the behavior of the network structure under the influence of external influences and disturbances as their consequences. To take into account the specific conditions of real objects with a network structure at the stage of modification, the model supplemented with appropriate restrictions and correction of input data sets. The obtained results should be useful for planning and management personnel and top-management for discussion and decision-making regarding, for example, the optimal placement of energy equipment, the weighted distribution of energy flows in the "source-sink" system or the appointment of executors for work, automated project management using project networks, etc. Keywords: optimization modeling, maximum network flow, minimum cost flows, network organizational structures, decision making, spreadsheet modeling and analytics.

A streamlined approach to uncertainty and sensitivity analysis for models with spatial outputs with an example from geodiversity assessment

The paper proposes a joint use of two global sensitivity analysis methods to evaluate the influence of model inputs on spatially explicit outputs. The analysis is conducted on spatial outputs of geodiversity assessment. First, the screening method is used to identify inputs with low importance and interactions, which can be discarded in later analysis. A simplified model is compared with a complete model using the mean and variance calculated on spatial outputs to evaluate outcome uncertainty. Finally, the variance decomposition method computes two sensitivity index maps per input. The results are mapped using bivariate classification, allowing for the simultaneous identification of regions with higher geodiversity scores and their corresponding variability. The research reveals that the streamlined approach preserves the overall patterns of outcome uncertainty and sensitivity observed in an original model while simultaneously reducing the number of outcome maps to interpret. Additionally, bivariate mapping proves efficient in concurrently depicting spatial sensitivity and interaction effects. The analytic efficiencies afforded by the presented approach and bivariate mapping enhance the operational knowledge of spatially explicit uncertainty and sensitivity analysis by simplifying the computational procedure and offering an alternative way of visualizing and interpreting input factors driving the sensitivity of model output.

Wet cooling of air oN plate finned tube heat exchangers

The objective of this paper is to provide the reliable calculation procedure for determining heat and mass flow rates for plate finned tube heat exchangers in dehumidification regimes that can be used easily in engineering practice. For this purpose, the experiments are conducted on two heat exchangers, and datasets for six more heat exchangers are used from literature. Comprehensive database is established with total of 637 sets of data, and it gathers 365 new measurement sets and 272 sets from open literature. The new calculation procedure predicts heat transfer rate and condensate flow rate where new methodology approach (based on the porous velocity, the ratio of characteristic surfaces and hydraulic diameter) is used. Predicted results show 5 to 10 % deviation for heat duty and up to 20 % for mass flow rate from experimental results, which is of importance to industrial practice.

Obtaining an Accurate Estimate of the Covid-19 Mutation Rate via Coronavirus Sequence Analysis Preeminent Themes using Convolutional Neural Networks

It is believed that Wuhan is where the SARS-CoV-2 virus first surfaced in 2019. This virus was the cause of the coronavirus epidemic (COVID-19), which was officially declared a worldwide pandemic. To identify and manage COVID-19 while the disease is progressing, a great deal of research has been done. It is difficult to distinguish between COVID-19 and other coronavirus strains due to their genetic similarities. That being said, it is imperative that we quickly determine whether an epidemic is caused by a newly discovered virus or a long-standing illness. This article discusses the DeepCoV deep-learning (DL) deep learning approach, which uses layered convolutional neural networks (CNNs) to classify viral illnesses, including SARS-CoV-2. Numerous SARS-CoV-2-related motifs may also be found by carefully examining the computational filter procedures. By spotting these important patterns, DeepCoV makes CNN transparent. The research was done using the 2019nCoVR datasets, and the findings show that DeepCoV outperformed other benchmark machine learning models in terms of accuracy. DeepCoV also achieved maximum scores of 98.58% and 98.62%, respectively, on the receiver operating characteristic curve (AUC-ROC) and precision-recall curve (AUCPR). Furthermore linked to the coronavirus genome are notable patterns, filter activities, and particular feature activation actions. When combined, these results provide important insights into how to apply deep learning (DL) algorithms in lieu of SARS-CoV-2 detection and illness patterns in the SARS-CoV-2 genes.

Reliability Analysis of A Combination of [formula omitted] and [formula omitted] Systems

In the paper, two-element (f,k) and <f,k> combined systems are developed, and two-kind combined reliability systems and some related new concepts such as irreducible structure and structure equivalence are introduced, which can be used not only in real practical situations, but also enrich the reliability contents. We proved that four-class combined reliability systems need to be considered actually, which facilitates the handling of these combined reliability systems. Meanwhile, a coordinate system with two axes, the number of total failed components and the number of maximal consecutive failed components, is introduced in the paper for representing the working and failed areas for the combined reliability systems, which brings out a useful tool for understanding the working and failure mechanisms, and system state transition ways, for the combined reliability systems. To summarize the reliability calculation, a procedure for calculations of the combined reliability systems is presented, which is an efficient guidance for these reliability computations. Finally, two numerical examples are given to illustrate the computation procedure in which the detailed steps, special on using Finite Markov Chain Imbedding Approach (FMCIA), are shown. We believe that the present paper can shed light on researches on combined reliability systems.

Numerical study of shock wave boundary layer interaction flows using a novel turbulence model

This study proposes a novel partial average fluctuation velocity (PAFV) turbulence based on the PAFV and the average strain rate tensor, which is used to calculate the shock wave boundary layer interaction flows, including cascade flow and transonic compressor flow. In order to illustrate the adequacy, the calculation results of several other turbulence models were also compared with PAFV, including k-ω, shear stress transport, SA-neg (negative Spalart-Allmaras), and stress-BSL (ω-based Reynolds stress model). The suitability of the PAFV turbulence model was initially tested by calculating the transonic flow in the L030-4 cascade. Subsequently, numerical computations were performed for the 3D (three-dimensional) National Aeronautics and Space Administration Rotor 67 transonic compressor rotor. In the numerical simulation, the flow control equations were transformed in a general curvilinear coordinate system for precision enhancement, and the spatial discretization of control equations was executed using the finite difference method. The convection term was processed using the Steger–Warming flux vector splitting method, the diffusion term was discretized using a second-order central difference scheme, and the time derivative term was discretized using the third-order Runge–Kutta method. A parallel computing strategy with multi-block partitioning was employed to speed up the calculations and facilitate faster convergence alongside a local time step method. All numerical computation procedures were written in Python, revealing the following: (1) The PAFV turbulence model aptly simulates the transonic flow within the cascade channel. The shock wave pattern in the cascade channel aligns well with the experimental schlieren images. (2) During the Rotor 67 transonic compressor rotor calculation, the flow–pressure ratio, flow efficiency, and inlet and outlet total temperatures and pressures align well with the experimental data. Furthermore, the resolution of the flow field structure related to the interaction between the tip leakage vortex and channel shock wave is high. (3) The turbulence model uses PAFV as the turbulence velocity scale, thereby suppressing the fluctuation velocity and turbulence viscosity near shock wave areas, preventing excessive turbulence viscosity. Moreover, PAFV inherently exhibits transport properties and anisotropic characteristics, making it well-suited for handling transonic compressor flows.

Micro-CT image-based computation of effective thermal and mechanical properties of fibrous porous materials

Fibrous porous materials are extensively employed as heat shielding material for space vehicles and capsules. To predict the performance of these materials using computational modeling at the vehicle/capsule scale, the effective material properties are needed. In this work, the effective thermal conductivity and elastic constants of a carbon fibrous porous material called FiberForm (precursor of PICA) were calculated using a direct image-based approach. In this image-based approach, the microstructures obtained using X-ray computed tomography technique are represented as binary voxel data. Nonlocal interactions are introduced between neighboring voxels. Integro-differential equations, with respect to spatial and temporal dimensions respectively, are developed to govern material thermal and mechanical behaviors. Using this approach, energy-based computational procedures were developed to calculate the effective material properties of fibrous and porous materials irrespective of the periodicity of underlying microstructures. The size of representative volume element was determined by convergence of effective properties with respect to microstructure size.

Numerical Calculation of 1P Aerodynamic Loads on Aviation Propellers

To accurately predict the 1P aerodynamic loads of aviation propellers, this paper established a mathematical model of aviation propeller 1P aerodynamic loads based on the coupling method of blade element theory and momentum theory. Correction methods such as the Prandtl tip correction method and the propeller root correction method were implemented to further improve calculation accuracy. A 1P aerodynamic load calculation procedure was developed based on the mathematical model by using the Matlab software. 1P aerodynamic loads of a three-blade propeller were predicted for three different angles including 3 °, 9 °, and 12°. The numerical calculation results show that the calculated aerodynamic characteristic parameters of individual propeller blades obtained based on the propeller 1P aerodynamic load mathematical model deviate less than 6% from the CFD simulation results, and regular periodic pulsations are observed. The numerical calculations in this paper show that the propeller 1P aerodynamic load calculation procedure developed based on this model can accurately predict the propeller 1P aerodynamic load, which can provide some reference for the study of aviation propeller aerodynamic characteristics.

Determining the mechanism for calculating the tension of a working conveyor belt during a change in the transportation length

This paper examines the working process of a belt conveyor with a working drive that can change the length of transportation. The conveyor can be used for tunneling, development of minerals in mines and quarries, transportation of materials in warehouses. The use of such a conveyor makes it possible to reduce the time for operations to increase or decrease the length of transportation, to exclude reloaders between the working equipment and conveyor itself from the transport chain. It was established that when changing the length of transportation of a working conveyor, the static and dynamic load on the belt increases. The change in the static load of the belt on the drum of the mobile station depends on the speed of the mobile station and the speed of the beltgenerated by the conveyor drive. The dynamic loading on the belt depends on the acceleration of the belt, which is related to the acceleration of the mobile station during the change in the length of the conveyor. For a working conveyor that changes the length of transportation, the static tension of the belt on the drum of the mobile station can increase by 1.1–1.4 times from the initial one. The dynamic loading of the belt can have a significant increase if the acceleration of the mobile station is not stretched over time and acquires large values. Based on the dependencesderived in the current work, a calculation procedure is proposed for the design of a belt conveyor with a working drive that can change the length of transportation. The Mathcad software was applied to verify the designcalculation procedure. The results make it possible to employ new design methods in the construction of competitive machines equipped with a belt conveyor with a variable length of transportation

Simulating X-ray Absorption Spectroscopy in Challenging Environments: Methodological Insights from Water-Solvated Ammonia and Ammonium Systems.

Core-electron excitations in solvated systems, influenced by solvent geometry and hydrogen bonding, make X-ray absorption spectroscopy (XAS) a valuable tool for assessing solvent-solute interactions. However, calculating XAS spectra with electronic-structure methods has proven challenging due to a delicate interplay between correlation and solvation effects. This study provides a computational procedure for XAS modeling in solvated systems, with water-solvated ammonia and ammonium systems serving as probes. Exploring methodological challenges, we investigate explicit embedding models, specifically the polarizable embedding family, including polarizable density embedding and extended polarizable density embedding. Our linear-response time-dependent density functional theory (LR-TDDFT) XAS calculations reveal the efficiency of this approach, with extended polarizable density embedding emerging as a robust improvement over polarizable density embedding. Contrary to some recent literature, our study challenges the belief that LR-TDDFT cannot accurately describe XAS spectra of ammonia and ammonium solvated in water.

Wagers for work: Decomposing the costs of cognitive effort.

Some aspects of cognition are more taxing than others. Accordingly, many people will avoid cognitively demanding tasks in favor of simpler alternatives. Which components of these tasks are costly, and how much, remains unknown. Here, we use a novel task design in which subjects request wages for completing cognitive tasks and a computational modeling procedure that decomposes their wages into the costs driving them. Using working memory as a test case, our approach revealed that gating new information into memory and protecting against interference are costly. Critically, other factors, like memory load, appeared less costly. Other key factors which may drive effort costs, such as error avoidance, had minimal influence on wage requests. Our approach is sensitive to individual differences, and could be used in psychiatric populations to understand the true underlying nature of apparent cognitive deficits.

An asymmetric S-curve trajectory planning based on an improved jerk profile

Abstract In this paper, a method of planning the expanded S-curve trajectory of robotic manipulators is proposed to minimize the execution time as well as to achieve the smoother trajectory generation in the deceleration stage for point-to-point motions. An asymmetric parameter is added to the piecewise sigmoid function for an improved jerk profile. This asymmetric profile is continuous and infinitely differentiable. Based on this profile, two analytical algorithms are presented. One is applied to determine the suitable time intervals of trajectory satisfying the time optimality under the kinematic constraints, and the other is to determine the asymmetric parameter generating the minimum execution time. Also, the calculation procedure for the time-scaled synchronization for all joints is given to decrease unnecessary loads onto the actuators. The velocity, acceleration, jerk and snap (the derivative of jerk) of the joints and the end-effector are equal to zero at two end points of motion. The simulation results through 3 DOF and 6 DOF robotic manipulators show that our approach reduces the jerk and snap of the deceleration stage effectively while decreasing the total execution time. Also, the analysis for a single DOF mass-spring-damper system indicates that the residual vibration could be reduced to 10% more than the benchmark techniques in case velocity, acceleration and jerk are limited to 1.24 m/s, 6 m/s2 and 80 m/s3, respectively and displacement is set to 0.8m. These results manifest that the performance of reducing residual vibrations is good and demonstrate an important characteristic of the proposed profile suitable for point-to-point motion.

An Improved Fourier-Based Method for Path Generation of Planar Four-Bar Linkages without Prescribed Timing

Four-bar linkages are critical fundamental elements of many mechanical systems, and their design synthesis is often mathematically complicated with iterative numerical solutions. Analytical methods based on Fourier coefficients can circumvent these difficulties but have issues with time parameters assignment for path generation without prescribed time in previous studies. In this paper, an improved Fourier-based point-to-point combination method is presented, which generates more points by Fourier approximation and assigns the time parameters to the given points while allowing discarding solutions with order defects. This method relies on the Fourier coefficients, improving the accuracy of synthesis solutions, and simplifying the computational procedure. Time parameters are assigned directly to the given points, which avoids the complex calculations to find intersection points in the given path, eliminates combinations that would lead to solutions with order defects, and simplifies the assessment process of synthesis results. The parameters obtained by the point-to-point combination method can be used as the parameters of the input dyad, skipping the first set of design equations for faster calculation. Several examples are presented to demonstrate the advantages of the proposed synthesis method, which is easy-understanding, computationally efficient, and yields more accurate solutions than available synthesis methods.

Multi-physics analysis of SBLOCA by coupled Neutronics/Thermal-Hydraulics full plant model

With the development of computational tools, the accident analysis would be performed by best estimate approaches. The employment of multi-physics codes would be considered as new calculation procedure to obtain high valuable results by improving calculations with specialized codes. The small break loss of coolant accident (SBLOCA) is an accident that has typically been analyzed using system codes with a limited number of Thermal-Hydraulics (TH) channels in the core region, neglecting the use of multi-physics calculations. This approach results in insufficient accuracy and resolution in both neutronics and TH calculations within the core zone. To address this issue, this study proposes the implementation of a Neutronics/Thermal-Hydraulics coupling with one-to-one mapping in the core zone, achieved through the development of a comprehensive plant TH model. This approach aims to enhance the accuracy and resolution of calculations, enabling the identification of local phenomena, parameters, and feedback during accident simulations, and the incorporation of these effects on the macroscopic cross-sections at each time step. The results of SBLOCA simulation reveals the effects of conservative/best estimate approach on the sequence of events, the initiation time of the safety systems, the critical controlled parameters of the power plant, and the precise understanding of the accident. Also, it was found that the radial power distribution becomes disrupted and loses its symmetry immediately after the SCRAM leading to different characteristics of steam generators (SGs) and natural circulation flow rate in each loop of primary side.

An explainable stacking-based approach for accelerating the prediction of antidiabetic peptides

Diabetes is a chronic disease that is characterized by high blood sugar levels and can have several harmful outcomes. Hyperglycemia, which is defined by persistently elevated blood sugar, is one of the primary concerns. People can improve their overall well-being and get optimal health outcomes by prioritizing diabetes control. Although the use of experimental approaches in diabetes treatment is cost-effective, it necessitates the development of many strategies for evaluating the efficacy of therapies. Researchers can quickly create new strategies for managing diabetes and get vital insights by enabling virtual screening with computational tools and procedures. In this study, we suggest a predictor named STADIP (STacking-based predictor for AntiDiabetic Peptides), a new method to predict antidiabetic peptides (ADPs) utilizing a stacked-based ensemble approach. It uses 12 different feature encodings and seven machine-learning techniques to construct 84 baseline models. The impacts of various baseline models on ADP prediction were then thoroughly examined. A two-step feature selection method, eXtreme Gradient Boosting with Sequential Forward Selection (XGB-SFS), was employed to determine the optimal number, out of 84 PFs to enhance predictive performance. Subsequently, utilizing the meta-predictor approach, 45 selected PFs were integrated into an XGB classifier to formulate the final hybrid model. The proposed method demonstrated superior predictive capabilities compared to constituent baseline models, as evidenced by evaluations on both cross-validation and independent tests. During extensive independent testing, STADIP achieved promising performance with accuracy and mathew's correlation coefficient of 0.954 and 0.877, respectively. It is anticipated that it will be useful tool in helping the scientific community to identify new antidiabetic proteins.

The Effect of the Temperature Increase on Heat Demand Calculation for Building in the Polish Climatology

Heat demand calculation procedures for buildings are currently being conducted based on the Polish Standard grounded on the European Standard for heat losses methodology. Poland was divided into several climatic zones with given calculations and annual average external air temperature values. In each zone, the technical committee gives the temperature that must be considered. The data included in the Polish Standard preparation concern several years backwards. However, throughout the previous years, we have observed the tendency of the temperature to increase throughout the year, not only in Poland. The phenomenon provides scientists with the conclusion that the temperatures will continue to increase yearly. The low temperatures in the winter season will no longer occur. Therefore, it is necessary to analyse the possibility of lowering the heat demand calculation, including the actual prevailing temperature values. The study was conducted regarding possible economic benefits for the building users due to the lower temperature difference between the internal and external temperatures. Three types of heating systems were analysed. The calculations were conducted for air to water heat pump, pellet boiler and gas condensing boiler, proving that regarding the actual meteorological data, the average annual cost reduction will be in the range of 858 PLN to 1,035 PLN.

Upaya Meningkatkan Motivasi Belajar Matematika Siswa SD Melalui Optimasi Game “Moving Twins”

Elementary school (SD) students must possess fundamental mathematical abilities. One of them is the capacity to execute numerical calculation procedures. Evidence from the field indicates that the academic performance of primary school kids in mathematics remains at a poor level. This occurs due to the constraints imposed by teachers on pupils' activities at school, leading to a rapid onset of boredom during the learning process. Hence, it is imperative to establish a novel educational environment by leveraging media tools to enhance the enjoyment and dynamism of the learning process. The learning method "MOVING TWINS" will be utilized. The objective of this study is to enhance the learning motivation of primary school children in the subject of arithmetic operations by utilizing the Moving Twins game. The participants of this study consisted of elementary school kids, selected using the purposive sampling technique. This study employs a pre-experimental design known as a one group pretest-posttest design. The research findings indicate that employing the Moving Twins Game can enhance the learning motivation of primary school pupils in the domain of number counting operations.  Keywords: Learning Motivation, Arithmetic Operations, Moving Twins Game.

The Application of Sand Transport with Cohesive Admixtures Model for Predicting Flushing Flows in Channels

The feature of self-cleansing in sewer pipes is a standard requirement in the design of drainage systems, as sediments deposited on the channel bottom cause changes in channel geometric properties and in hydrodynamic parameters, including the friction caused by the cohesive forces of sediment fractions. Here, it is shown that the content of cohesive fractions significantly inhibits the transport of non-cohesive sediments. This paper presents an advanced calculation procedure for estimating flushing flows in channels. This procedure is based on innovative predictive models developed for non-cohesive and granulometrically heterogeneous sediment transport with additional cohesive fraction content to estimate the magnitude of increased flow necessary to ensure self-cleansing of channels. The computations according to the proposed procedure were carried out for a wide range of hydrodynamic conditions, two grain diameters, six cohesive (clay) fraction additive contents and two critical stress values. The trend lines of calculations were composed with the results of experimental studies in hydraulic flumes.

Uncovering uncharacterized binding of transcription factors from ATAC-seq footprinting data

Transcription factors (TFs) are crucial epigenetic regulators, which enable cells to dynamically adjust gene expression in response to environmental signals. Computational procedures like digital genomic footprinting on chromatin accessibility assays such as ATACseq can be used to identify bound TFs in a genome-wide scale. This method utilizes short regions of low accessibility signals due to steric hindrance of DNA bound proteins, called footprints (FPs), which are combined with motif databases for TF identification. However, while over 1600 TFs have been described in the human genome, only ~ 700 of these have a known binding motif. Thus, a substantial number of FPs without overlap to a known DNA motif are normally discarded from FP analysis. In addition, the FP method is restricted to organisms with a substantial number of known TF motifs. Here we present DENIS (DE Novo motIf diScovery), a framework to generate and systematically investigate the potential of de novo TF motif discovery from FPs. DENIS includes functionality (1) to isolate FPs without binding motifs, (2) to perform de novo motif generation and (3) to characterize novel motifs. Here, we show that the framework rediscovers artificially removed TF motifs, quantifies de novo motif usage during an early embryonic development example dataset, and is able to analyze and uncover TF activity in organisms lacking canonical motifs. The latter task is exemplified by an investigation of a scATAC-seq dataset in zebrafish which covers different cell types during hematopoiesis.