Computational Modeling Analysis Research Articles

Computational analysis of expectile and deviation expectile portfolio optimization models

Computational analysis of expectile and deviation expectile portfolio optimization models

Generic modelling to develop thermal yield nomograms for coaxial deep borehole heat exchangers (DBHEs)

Numerical modelling of coaxial deep borehole heat exchangers (DBHEs) can be resource-intensive. Simpler, transparent analytical models and nomograms would be valuable to developers and geologists for evaluating thermal output. In this paper, Beier’s published analytical computational model was used to produce nomograms of geothermal heat yield by systematically varying the DBHE depth and rock thermal conductivity, while assuming two generic simplified DBHE designs, a geothermal gradient of 25°C km −1 and a fluid circulation rate of 5 l s −1 . Continuous 25 year heat yields from a 1000 m DBHE range from 27.3 to 54.8 kW for thermal conductivities of 1.6–3.6 W m −1 K −1 . For a 3000 m DBHE, they range from 165 to 281 kW. Effective borehole thermal resistance ( R b,eff ) increases strongly as DBHE depth increases due to internal heat transfer between the upflow and downflow elements. Simulations correspond well with results from industry-standard Earth Energy Designer software for a shallow 200 m coaxial BHE. They modestly underestimate the OpenGeoSys numerical modelled thermal yields by 2–4% for DBHE in the depth range 1000–3000 m. Modelled temperature evolution closely approximates an analytical ‘line heat source’ approach, implying that simpler analytical approaches are plausible for DBHE simulation. Future research should focus on methods for forward quantification of R b,eff .

Preface

From 22nd to 24th December 2023, the 2023 3rd International Conference on Computational Modeling, Simulation and Data Analysis (CMSDA 2023) was held in Sanya, China via virtual form. The Conference attracted about 150 researchers, engineers, teachers and students worldwide working in or interested in the fields related to computational modeling, simulation and data analysis. The general objectives of the CMSDA 2023 were: To generate an adequate academic and scientific forum to present the work and experiences of researchers/teachers/students, contributing, in this way, to the production of new knowledge in the fields of computational modeling and data analysis. To create a path of establishing certain relationships for the speakers, authors and listeners, and providing opportunities for collaboration among the universities and institutions, in order to promote scientific research activities and develop relevant technologies. The subject areas in which the accepted papers were presented cover but are but not limited to: Perceptual Issues in Visualization and Modeling; Mathematical and Numerical Methods in Simulation and Modeling; Parallel and Distributed Computing Simulation; Big Data Visual Analysis; Data Analysis Algorithms and Systems, etc. We have invited renowned professors at home and abroad in these academic fields who shared with the attendees the latest innovations and research results in the domains of computational modeling, data mining and analysis. The Conference mainly featured keynote speeches by renowned experts and presentations of peer-reviewed papers by the authors. Among them, Prof. Daowen Qiu (Sun Yat-Sen University, China) shared his research on Distributed Quantum Algorithm for Simon’s Problem. Limited by today’s physical devices, quantum circuits are usually noisy and difficult to be designed deeply. The novel computing architecture of distributed quantum computing is expected to reduce the noise and depth of quantum circuits. And in his study, he presented the Simon’s problem in distributed scenarios and designed a distributed quantum algorithm to solve the problem. The algorithm proposed by him has the advantage of not only exponential acceleration compared with the classical distributed computing, but also square acceleration compared with the best distributed quantum algorithm proposed before. The Conference Proceedings was made so fruitful with all the excellent contributions collected from both domestic and international channels including the wonderful presentations in the Conference. It is a pleasure to thank the organizers, sponsors and all the participants and contributors for making the Conference possible and interesting. And our appreciation also to the members of Journal of Physics: Conference Series for providing us help in publishing this paper volume. The Committee of CMSDA 2023 List of Committee Member is available in this pdf.

Computational Modeling Differentiates Learning Rate From Reward Sensitivity Deficits Produced by Early-Life Adversity in a Rodent Touchscreen Probabilistic Reward Task

BackgroundExposure to adversity, including unpredictable environments, during early life is associated with neuropsychiatric illness in adulthood. One common factor in this sequela is anhedonia, the loss of responsivity to previously reinforcing stimuli. To accelerate the development of new treatment strategies for anhedonic disorders induced by early-life adversity, animal models have been developed to capture critical features of early-life stress and the behavioral deficits that such stressors induce. We have previously shown that rats exposed to the limited bedding and nesting protocol exhibited blunted reward responsivity in the probabilistic reward task, a touchscreen-based task reverse translated from human studies. MethodsTo test the quantitative limits of this translational platform, we examined the ability of Bayesian computational modeling and probability analyses identical to those optimized in previous human studies to quantify the putative mechanisms that underlie these deficits with precision. Specifically, 2 parameters that have been shown to independently contribute to probabilistic reward task outcomes in patient populations, reward sensitivity and learning rate, were extracted, as were trial-by-trial probability analyses of choices as a function of the preceding trial. ResultsSignificant deficits in reward sensitivity, but not learning rate, contributed to the anhedonic phenotypes in rats exposed to early-life adversity. ConclusionsThe current findings confirm and extend the translational value of these rodent models by verifying the effectiveness of computational modeling in distinguishing independent features of reward sensitivity and learning rate that complement the probabilistic reward task’s signal detection end points. Together, these metrics serve to objectively quantify reinforcement learning deficits associated with anhedonic phenotypes.

Peculiarities of identification and customs commodity examination of children’s goods

Special attention is paid to the issue of identification and safety of children's products, which is aimed at ensuring the safety and quality of this category of goods. In the scientific article, the issue of classification of goods of group 95 in accordance with the Ukrainian Commodity Nomenclature of Foreign Economic Activity was considered, and a study of the practice of appointing customs examinations was conducted. The main identifying features of the classification of scooters in the category of goods for children are: purpose; design features, their modification; type of movement; number and diameter of wheels; type of ride Attention should also be paid to the following identification features of children’s goods, which are necessary both during customs control and during the examination of toys: the size of the toy, the material of manufacture, design features, application and main functional purpose. The development and improvement of current methods of identification of toys will simplify the procedure of their inspection and increase the objectivity of the results. The work summarizes the ways to classify children’s scooters in the procedure of commodity examination. The diagnostic tasks of the forensic commodity examination to determine safety, compliance with standards, which the expert must solve during the study of a children’s scooter, are defined. State-of-the-art research methods, in particular, visual inspection, functional tests, measurement of geometric parameters, mechanical tests, chemical analysis of materials, technical expertise, safety tests, computer modeling and analysis, electrical and electronic tests, expert evaluation are used to establish indicators. quality and safety of goods and obtaining objective information. The main identification tasks of the commodity examination of children’s scooters, the most typical defects of these products and defects that indicate their low quality are indicated. The mechanism of forensic commodity examination of children's scooters is described.

NADPH oxidase 2 activity disrupts Calmodulin/CaMKIIα complex via redox modifications of CaMKIIα-contained Cys30 and Cys289: Implications in Parkinson's disease

Ca2+/calmodulin-dependent protein kinase II α (CaMKIIα) signaling in the brain plays a critical role in regulating neuronal Ca2+ homeostasis. Its dysfunctional activity is associated with various neurological and neurodegenerative disorders, including Parkinson's disease (PD). Using computational modeling analysis, we predicted that, two essential cysteine residues contained in CaMKIIα, Cys30 and Cys289, may undergo redox modifications impacting the proper functioning of the CaMKIIα docking site for Ca2+/CaM, thus impeding the formation of the CaMKIIα:Ca2+/CaM complex, essential for a proper modulation of CaMKIIα kinase activity. Our subsequent in vitro investigations confirmed the computational predictions, specifically implicating Cys30 and Cys289 residues in impairing CaMKIIα:Ca2+/CaM interaction. We observed CaMKIIα:Ca2+/CaM complex disruption in dopamine (DA) nigrostriatal neurons of post-mortem Parkinson's disease (PD) patients' specimens, addressing the high relevance of this event in the disease. CaMKIIα:Ca2+/CaM complex disruption was also observed in both in vitro and in vivo rotenone models of PD, where this phenomenon was associated with CaMKIIα kinase hyperactivity. Moreover, we observed that, NADPH oxidase 2 (NOX2), a major enzymatic generator of superoxide anion (O2●-) and hydrogen peroxide (H2O2) in the brain with implications in PD pathogenesis, is responsible for CaMKIIα:Ca2+/CaM complex disruption associated to a stable Ca2+CAM-independent CaMKIIα kinase activity and intracellular Ca2+ accumulation. The present study highlights the importance of oxidative stress, in disturbing the delicate balance of CaMKIIα signaling in calcium dysregulation, offering novel insights into PD pathogenesis.

Internally Formed Preferences for Options only Influence Initial Decisions in Gambling Tasks, while the Gambling Outcomes do not Alter these Preferences.

All humans must engage in decision-making. Decision-making processes can be broadly classified into internally guided decision-making (IDM), which is determined by individuals' internal value criteria, such as preference, or externally guided decision-making (EDM), which is determined by environmental external value criteria, such as monetary rewards. However, real-life decisions are never made simply using one kind of decision-making, and the relationship between IDM and EDM remains unclear. This study had individuals perform gambling tasks requiring the EDM using stimuli that formed preferences through the preference judgment task as the IDM. Computational model analysis revealed that strong preferences in the IDM affected initial choice behavior in the EDM. Moreover, through the analysis of the subjective preference evaluation after the gambling tasks, we found that even when stimuli that were preferred in the IDM were perceived as less valuable in the EDM, the preference for IDM was maintained after EDM. These results indicate that although internal criteria, such as preferences, influence EDM, the results show that internal and external criteria differ.

Experimental and computational modeling analysis of double slope solar still with a trapezoidal channel for preheating

In specific locations in India, water scarcity is an important issue, therefore several efforts are made to produce treated water so that rural and urban areas have access to safe and sustainable drinking water. The solar still method is the most affordable water purification option available in these hot areas. This research aims to enhance the performance of passive double-slope solar stills by analyzing the effects of preheated water on evaporation and yield rates. A new method was adopted to maintain the lowest water depth in the basin of a solar still by using a channel attachment to move the feed water. Additionally, the study investigates the impact of a trapezoidal channel attachment within the solar still, a first-time innovation, on its efficiency and yield rate. Computational fluid dynamics and experimental data collected on February 10, 2024, in Kattankulathur was analyzed to assess temperature variations and yield rates. A three-dimensional full-scale simulation was carried out using Ansys Fluent with a two-phase (evaporation and condensation) model, where the flow in the channels of the rate varied from 0.87 × 10-4 to 5.21 × 10-4 Ls−1 and the highest yield is achieved at flow rate of 1.74 × 10-4 Ls−1. The accuracy of the simulation technique was evaluated by comparing it to experimental data, and found a good match. The results demonstrated that solar still with trapezoidal channels significantly increased the overall yield rate. Optimal performance was achieved under specific environmental conditions, with a cumulative yield of 2.45 Lm−2 at a basin water depth of 10 mm, marking a 37.64 % increase over the conventional solar still. Economic analysis indicated a reduction in the payback period to 299 days, affirming the system’s cost-effectiveness. The trapezoidal channel design is limited to a cross-section of 10 mm2, and positioned at a 17° angle on the middle sidewall to minimize the shadow formation on the basin of still and maintain high yield and evaporation–condensation rates. The novelty of this work is to integrate trapezoidal channels on the double slope solar still and to optimize its design and flow rate to maximize the overall production.

Computational fluid dynamic model analysis of multipurpose dryer with bio-waste heat source: An experimental validation using paddy rice

The quest to develop an appropriate drying technology that is sustainable while minimizing energy losses was the key motivation of this work. In this study, COMSOL multiphysics CFD software was used to model and simulate computational behaviour of a fabricated multipurpose crop dryer equipped with a bio-waste heat source. The generated thermal flow was deployed for drying of paddy rice given the rice drying characteristics. Meanwhile, the drying chamber was finitely discretized into little fragments in order to obtain a better distribution, result and efficiency. The temperature, pressure, and velocity distribution were analysed with simulated optimal drying temperature of paddy rice as 43 °C. This was attained for both simulated and experimented. It was observed that there was higher pressure and velocity at the fan orifice but a decrease as the air moves up the chimney surface as the drying chamber’s average temperature variation required for drying paddy rice was established, with relative error of 0.019 %. The simulated and experimental mean drying chamber efficiency were 90.3 % and 89 % respectively. Therefore, the fabricated multipurpose dryer is a good system for grain drying.

A thermodynamic computational model analysis of a newly designed Tri-Rotor (T-R) volume air compressor

A thermodynamic computational modelling (TCM) approach is developed to model and simulate thermodynamic processes of a newly designed rotary type air compressor named ‘tri-rotor (T-R) compressor’ using a Lattice-Boltzmann Method (LBM) based fluid flow solution, which is applied for the first time to a rotary compressor flow. With this method, which is combined with a Wall-Adapting Local Eddy-viscosity sub-grid scale stress (SGS) model (WALE), time-consuming classic fluid-domain meshing/re-meshing process is not required and a mesh deformation problem leading to numerical instability is overcome by generating a fixed Cartesian lattice. The proposed TCM approach here is expected to serve as a basis for transient simulations of rotating turbulent and highly compressible flow for accurate and more complete computational representation of an ideal, adiabatic compression process through pressure − rotational angle (P-Theta degree) and volume − rotational angle (V-Theta degree) diagrams. The designed compressor is comprised of three specifically shaped, simultaneously rotating rotors, which are well-positioned in a cylinder to draw and discharge air uninterruptedly through two-suction and two-discharge ports, respectively and achieves higher mass-flow rates with increasing rotational speed. A comparative study with different rotational speeds predicts that high isentropic efficiency ranging from 0.850 to 0.941 can be attained for the investigated rotational speed range without any compromise on mass flow rates and pressure ratios. A proof-of-concept experiment of a full-scale T-R compressor model is conducted to assess its operational feasibility and performance. It is found that the TCM approach reproduces comparable results to those obtained from the preliminary experimental studies.

The Neurocomputational Mechanism Underlying Decision-Making on Unfairness to Self and Others.

Fairness is a fundamental value in human societies, with individuals concerned about unfairness both to themselves and to others. Nevertheless, an enduring debate focuses on whether self-unfairness and other-unfairness elicit shared or distinct neuropsychological processes. To address this, we combined a three-person ultimatum game with computational modeling and advanced neuroimaging analysis techniques to unravel the behavioral, cognitive, and neural patterns underlying unfairness to self and others. Our behavioral and computational results reveal a heightened concern among participants for self-unfairness over other-unfairness. Moreover, self-unfairness consistently activates brain regions such as the anterior insula, dorsal anterior cingulate cortex, and dorsolateral prefrontal cortex, spanning various spatial scales that encompass univariate activation, local multivariate patterns, and whole-brain multivariate patterns. These regions are well-established in their association with emotional and cognitive processes relevant to fairness-based decision-making. Conversely, other-unfairness primarily engages the middle occipital gyrus. Collectively, our findings robustly support distinct neurocomputational signatures between self-unfairness and other-unfairness.

GP+: A Python library for kernel-based learning via Gaussian processes

In this paper we introduce GP+, an open-source library for kernel-based learning via Gaussian processes (GPs) which are powerful statistical models that are completely characterized by their parametric covariance and mean functions. GP+ is built on PyTorch and provides a user-friendly and object-oriented tool for probabilistic learning and inference. As we demonstrate with a host of examples, GP+ has a few unique advantages over other GP modeling libraries. We achieve these advantages primarily by integrating nonlinear manifold learning techniques with GPs’ covariance and mean functions. As part of introducing GP+, in this paper we also make methodological contributions that (1) enable probabilistic data fusion and inverse parameter estimation, and (2) equip GPs with parsimonious parametric mean functions which span mixed feature spaces that have both categorical and quantitative variables. We demonstrate the impact of these contributions in the context of Bayesian optimization, multi-fidelity modeling, sensitivity analysis, and calibration of computer models.

Hydrogen bonding, halogen bonding and C–H…π interactions governing the supramolecular architecture of 1-(4-(4-bromophenyl)piperazin-1-yl)-2-chloroethan-1-one: Insights from X-ray crystallography, DFT calculations and urease inhibitory assessment

The use of organic ligands for the generation of supramolecular architectures exhibiting interesting structural properties in crystal engineering have garnered significant attention. In this context, the current work highlights the use of 1-(4-(4-bromophenyl)piperazin-1-yl)-2-chloroethan-1-one (3) as a valuable synthon for evaluating the conspicuous role of several types of noncovalent interactions governing the formation of supramolecular assemblies. Compound 3 was successfully synthesized in good yield using a facile two-step approach and characterized through spectroscopic and X-ray crystallographic methods. Noncovalent interactions including C—H…O, C—H…Cl, C—H…π as well as C—Br…Cl halogen bonds were found as the key contacts in the crystal packing of compound 3. DFT calculations were employed to identify and assess the impact of the C—Br…Cl halogen bond, utilizing MEP, QTAIM, and NBO analyses. These studies confirmed that the Br atom acts as the halogen bond donor and the Cl atom as the halogen bond acceptor. Assessment of urease inhibitory efficacy revealed that the tested compound exhibits a 16-folds superior inhibition than thiourea. Computational modeling analysis augmented the in vitro results by exhibiting numerous favorable binding interactions between compound 3 and active site residues such as TYR32, LYS709, PHE712, ASP730, VAL744 and MET746. The pharmacokinetic evaluation classified the investigated piperazine derivative into a druggable compound.

UV activated formaldehyde gas sensing based on gold decorated ZnO@ In2O3 hollow nanospheres at room temperature

Using uniform carbon nanospheres as sacrificial templates, gold nanoparticle-modified ZnO@ In2O3 hollow nanospheres with controlled dimensions and porous architectures were successfully synthesized through water bath, hydrothermal and chemical reduction methods. Gas sensing evaluation revealed that the Au-decorated ZnO@ In2O3 hollow nanostructures exhibited a dramatically amplified response up to 14.8 when exposed to 10 ppm formaldehyde analyte at room temperature. Furthermore, multifaceted performance benefits of Au-decorated ZnO@ In2O3 over single-phase ZnO, Au-ZnO, ZnO@ In2O3 heterojunction-based composites were observed, including excellent selectivity towards formaldehyde even in complex gas mixtures; tremendous sensitivity enhancement stemming from synergies between efficient gas diffusion through hollow heterojunction nanospheres and accelerated surface reactions at catalytically-active Au sites; and rapid, fully-reversible response/recovery time of 32 s and 42 s, respectively. In-depth investigations were conducted into the fundamental sensing mechanisms enabling formaldehyde detection using AC impedance spectroscopy, in situ diffuse reflectance Fourier transform infrared spectroscopy (DRIFTS), and density functional theory (DFT) computational modeling analyses.

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.

Computational modeling of interval distributions in tonal space reveals paradigmatic stylistic changes in Western music history

Diachronic stylistic changes in music are to a large extent affected by composers’ different choices, for example regarding the usage of tones, intervals, and harmonies. Analyzing the tonal content of pieces of music and observing them over time is thus informative about large-scale historical changes. In this study, we employ a computational model that formalizes music-theoretic conceptualizations of tonal space, and use it to infer the most likely interval distributions for pieces in a large corpus of music, represented as so-called ‘bags of tonal pitch classes’. Our results show that tonal interval relations become increasingly complex, that the interval of the perfect fifth dominates compositions for centuries, and that one can observe a stark increase in the usage of major and minor thirds during the 19th century, which coincides with the emergence of extended tonality. In complementing prior research on the historical evolution of tonality, our study thus demonstrates how example-based music theory can be informed by quantitative analyses of large corpora and computational models.

A Protocol for Comprehensive Analysis of Gait in Individuals with Incomplete Spinal Cord Injury.

This is a protocol for comprehensive analysis of gait and affecting factors in individuals with incomplete paraplegia due to spinal cord injury (SCI). A SCI is a devastating event affecting both sensory and motor functions. Due to better care, the SCI population is changing, with a greater proportion retaining impaired ambulatory function. Optimizing ambulatory function after SCI remains challenging. To investigate factors influencing optimal ambulation, a multi-professional research project was grounded with expertise from clinical rehabilitation, neurophysiology, and biomechanical engineering from Karolinska Institutet, the Spinalis Unit at Aleris Rehab Station (Sweden's largest center for specialized neurorehabilitation), and the Promobilia MoveAbility Lab at KTH Royal Institute of Technology. Ambulatory adults with paraplegia will be consecutively invited to participate. Muscle strength, sensitivity, and spasticity will be assessed, and energy expenditure, 3D movements, and muscle function (EMG) during gait and submaximal contractions will be analyzed. Innovative computational modeling and data-driven analyses will be performed, including the identification of clusters of similar movement patterns among the heterogeneous population and analyses that study the link between complex sensorimotor function and movement performance. These results may help optimize ambulatory function for persons with SCI and decrease the risk of secondary conditions during gait with a life-long perspective.

Computational Solution and Analysis of Fuzzy Nonlinear HIV Infection Model via New Multistage Fuzzy Variational Iteration Method

Computational Solution and Analysis of Fuzzy Nonlinear HIV Infection Model via New Multistage Fuzzy Variational Iteration Method

Investigation and perspectives of using Graph Neural Networks to model complex systems: the simulation of the helium II bayonet heat exchanger in the LHC

Large cryogenic systems, like those installed at CERN, are complex systems relying on many diverse physical processes and phenomena that are difficult to simulate and monitor in detail. With only a limited number of properties measured and made available for monitoring and control purposes, several processes contributing to the dynamics of the systems are ignored. This lack of information can reduce the accuracy and the capability of a model to track, predict, and anticipate the behavior of the system. Accurate analytical or numerical computer modeling can be developed to simulate the non-linear dynamics of the processes but they are complex, computationally intensive, and cumbersome to test, validate, and implement with different configurations and limited measurements of the hidden properties. In this work, we present our investigation of using Graph Neural Networks (GNN) to build a model of the helium II bayonet heat exchanger operating in the LHC at CERN. We are proposing to use a hybrid machine learning approach, where the parameters of the GNN model are estimated by a combination of supervised learning algorithms trained on experimental data and bounding physics equations and parameters. The GNN model was initially trained on data from the experiments performed on the LHC prototype magnet strings and validated on data extracted during the operation of the LHC machine. We demonstrate the model’s accuracy, repeatability, and robustness in various configurations. The model is also well inspectable and explainable, providing the time evolution of all variables. We report on the results and expected applications, which include predictive control, diagnostic, and operator training.

Computational modeling and theoretical analysis of polyvinylidene fluoride microarrays for hybrid piezo‐pyroelectric energy harvesting

AbstractPoly(vinylidene fluoride) (PVDF) is a promising piezoelectric and pyroelectric material for energy harvesting applications. This paper investigates the electrical outputs of PVDF microarrays using both simulation studies and theoretical calculations. The finite element method in COMSOL Multiphysics® is utilized to model PVDF microarrays and simulate their responses under mechanical loading and temperature changes. Theoretical calculations are also performed to estimate the piezoelectric and pyroelectric voltage outputs using established mathematical formulas. A series of PVDF microarrays are modeled in two dimensions with defined geometry, materials, boundary conditions, and parametric sweeps of wind speed and temperature. The simulation results for electrical outputs show good agreement with the theoretical calculations, demonstrating the validity of both approaches. The maximum voltage outputs are on the order of 10−3 V for the range of wind speeds from 6.5 to 11 m/s and temperatures from 319.3 to 336.3 K. In addition, the mechanical and thermal sensitivities are quantified. This work provides a framework for design and optimization of PVDF‐based energy harvesters. The combined methodology enables characterization of the relationships between operational conditions, PVDF microarray properties, and electrical outputs.