Computational Modeling Techniques Research Articles

Modulation of dlPFC function and decision-making capacity by repetitive transcranial magnetic stimulation in methamphetamine use disorder

This study explores the impact of repetitive transcranial magnetic stimulation (rTMS) on decision-making capabilities in individuals with methamphetamine use disorder (MUD), alongside potential underlying psychological mechanisms. Employing the Iowa Gambling Task (IGT) and computational modeling techniques, we assessed the decision-making processes of 50 male MUD participants (24 underwent rTMS treatment, 26 received no treatment) and 39 healthy controls (HC). We compared pre- and post-rTMS treatment alterations in the left dorsolateral prefrontal cortex (dlPFC). Results revealed inferior performance in the IGT among the MUD group, characterized by aberrant model parameters in the Value-Plus-Perseverance (VPP) model, including heightened learning rate, outcome sensitivity, and reinforcement learning weight, alongside diminished response consistency and loss aversion. RTMS treatment demonstrated efficacy in reducing craving scores, enhancing decision-making abilities, and partially restoring normalcy to certain model parameters in the MUD cohort. Nonetheless, no linear relationship between changes in model parameters and craving was observed. These findings lend support to the somatic marker hypothesis, implicating the dlPFC in the decision-making deficits observed in MUD, with rTMS potentially ameliorating these deficits by modulating the function of these brain regions. This study not only offers novel insights and methodologies for MUD rehabilitation but also underscores the necessity for further research to corroborate and refine these findings. Trial Registration www.chictr.org.cn Identifier: No. ChiCTR17013610.

A comprehensive city-level final energy consumption dataset including renewable energy for China, 2005–2021

The role of China is increasingly pivotal in climate change mitigation, and the formulation of energy conservation and emission reduction policies requires city-level information. The effectiveness of national policy implementation is contingent upon the support and involvement of local governments. Accurate data on final energy consumption is vital to formulate and implement city-level energy transitions and energy conservation and emission reduction policies. However, there is a dearth of data sources pertaining to China’s city-level final energy consumption. To address these gaps, we developed computational modeling techniques along with top-down and downscaling methods to estimate China’s city-level final energy consumption. In this way, we compiled a final energy consumption inventory for 331 Chinese cities from 2005 to 2021, covering seven economic sectors, 30 fossil fuels, and four clean power sources. Moreover, we discussed the validity of the estimation results from multiple perspectives to enhance estimation accuracy. This dataset can be utilized for analysis in various cutting-edge research fields such as energy transition dynamics, transition risk management strategies, and policy formulation processes.

Analysis of Unsteady Internal Flow Characteristics in Axial Pump with Varying Number of Blades using Computational Modelling and Vibration Techniques

Axial pumps were extensively applied in many varying applications because of their large flow and low head. In this research investigation, the comparative analysis of the findings unveiled that the predominant source of hydraulic-induced vibration in the pump stemmed from pressure pulsations at the impeller inlet. Notably, similar patterns in amplitude were observed as flow rates increased. Furthermore, time-domain analysis confirmed that pressure pulsations and vibrations are highly correlated under high flow rates. pressure pulsation’s Frequency-domain analysis also revealed that it was a multiple of shaft frequency and changed from one multiple to three multiples of vibration. While analyzing the flow rate characteristics pertaining to pressure pulsation and vibration, it was determined that pressure pulsations at the impeller inlet had the potential to generate frequency components across a broad spectrum, encompassing both low and high flow rates, as well as their respective multiples. This phenomenon was particularly pronounced in regions characterized by unstable and high flow rates. Vibration ingredients likely influenced by pressure pulsation at the impeller inlet could be as low as design flow rates and as high as high flow rates. A vibration frequency with a multiple did not seem to be influenced by pulsating pressure at the impeller entrance. The present study focuses on investigation of flow behaviors in an axial pump with varying numbers of blades. It is an important geometric parameter that significantly affects the pump's performance. Therefore, the dynamic flow patterns in a pump, considering changed flow and impeller blade configurations, are investigated by employing the sliding mesh technique in combination with SST (k-ω) turbulence model. The numerical results exhibit a commendable alignment with the existing experimental data, enhancing the predictive accuracy of pump performance. Qualitative analyses encompass static pressure, shear stress, and various velocity components. Concurrently, quantitative investigations delve into pressure fluctuations and average pressure across a spectrum of operating conditions and impeller blade configurations. These comprehensive findings underscore the substantial influence of the impeller blade on pressure, velocity magnitudes radial, axial, tangential shear stress, average pressure within the system.

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.

Computational Modeling of the Prefrontal-Cingulate Cortex to Investigate the Role of Coupling Relationships for Balancing Emotion and Cognition.

Within the prefrontal-cingulate cortex, abnormalities in coupling between neuronal networks can disturb the emotion-cognition interactions, contributing to the development of mental disorders such as depression. Despite this understanding, the neural circuit mechanisms underlying this phenomenon remain elusive. In this study, we present a biophysical computational model encompassing three crucial regions, including the dorsolateral prefrontal cortex, subgenual anterior cingulate cortex, and ventromedial prefrontal cortex. The objective is to investigate the role of coupling relationships within the prefrontal-cingulate cortex networks in balancing emotions and cognitive processes. The numerical results confirm that coupled weights play a crucial role in the balance of emotional cognitive networks. Furthermore, our model predicts the pathogenic mechanism of depression resulting from abnormalities in the subgenual cortex, and network functionality was restored through intervention in the dorsolateral prefrontal cortex. This study utilizes computational modeling techniques to provide an insight explanation for the diagnosis and treatment of depression.

Systematic Review of Computational Fluid Dynamics Modelling and Simulation Techniques Employed in Vertical Axis Hydrokinetic Turbines

At present, Computational Fluid Dynamics (CFD) is being utilized to explore tidal and hydrokinetic turbine systems, advancing comprehension of turbulent flow phenomena and raising the accuracy of performance predictions for these fluid-driven turbines. Consequently, this utilization of CFD contributes to the optimization of efficiency in these devices. Turbines are subject to variations from the best design point due to the influence of fluctuating flow rates, despite the findings of recent research that have identified the ideal design points. In contrast to conventional literature reviews, systematic analysis offers numerous advantages. The methodological strategy applied in this study entailed cross-referencing the findings obtained from Web of Science (WOS) and Scopus databases to establish the comprehensiveness as well as reliability of researcher data. Improving the review process, elevating the prominence of the field of study, and establishing critical priorities to mitigate research bias are all potential strategies for enhancing the quality of these evaluations. This research divides its findings into three core themes: (1) Performance assessment for practical implications, (2) Numerical analysis for theoretical insights, and (3) Design parameter optimization for engineering relevance. These themes collectively provide a well-rounded examination of the research outcomes across practical, theoretical, and engineering dimensions. Finally, the research findings have the potential to act as a significant point of reference for informing the best design considerations pertaining to vertical axis turbines.

Harnessing Computational Modeling for Efficient Drug Design Strategies

Abstract: Computational modeling has become a crucial tool in drug design, offering efficiency and cost-effectiveness. This paper discusses the various computational modeling techniques used in drug design and their role in enabling efficient drug discovery strategies. Molecular docking predicts the binding affinity of a small molecule to a target protein, allowing the researchers to identify potential lead compounds and optimize their interactions. Molecular dynamics simulations provide insights into protein-ligand complexes, enabling the exploration of conformational changes, binding free energies, and fundamental protein-ligand interactions. Integrating computational modeling with machine learning algorithms, such as QSAR modeling and virtual screening, enables the prediction of compound properties and prioritizes potential drug candidates. High-performance computing resources and advanced algorithms are essential for accelerating drug design workflows, with parallel computing, cloud computing, and GPU acceleration reducing computational time. The paper also addresses the challenges and limitations of computational modeling in drug design, such as the accuracy of scoring functions, protein flexibility representation, and validation of predictive models. It emphasizes the need for experimental validation and iterative refinement of computational predictions to ensure the reliability and efficacy of designed drugs.

Computational modelling of tyre behavior on concrete and sandy soil surfaces using solid works software

Tyres are integral components of vehicles, crucial for controlling movement, supporting vehicle weight, and providing traction. Consequently, there's a growing need for accurate tyre modelling to optimize performance under varying conditions. While extensive literature exists on tyre-soil interaction, information is scarce regarding tyre behaviour across different soil types. This study aims to address this gap by employing computational modelling techniques to analyse tyre behaviour on two distinct surfaces: concrete and sandy soil. Utilizing SolidWorks software, a comprehensive investigation was conducted to evaluate tyre performance under varying road conditions. The study focused on analysing shear forces, soil resistances, and partial soil resistant forces exerted on the tyres. The findings indicate that tyres exhibit superior performance on concrete surfaces compared to sandy soil. The computational simulations predicted shear forces of 0.6 kN, soil resistances of 0.2 kN, and partial soil resistant forces of 0.25 kN for the concrete surfaces. These results were notably higher than those observed on sandy soil surfaces. This study sheds light on the intricate dynamics of tyre behaviour under different road conditions and underscores the importance of computational modelling in understanding and optimizing tyre performance. The insights gained from this research have significant implications for vehicle design, road engineering, and the development of more efficient and durable tyres tailored to specific environmental conditions.

Anomalous Aortic Origin of a Coronary Artery in Pediatric Patients

Abstract Purpose of Review We present a contemporary approach to risk assessment and management of patients with anomalous aortic origin of a coronary artery (AAOCA). Recent Findings Anomalous left coronary artery from the right aortic sinus (L-AAOCA) with interarterial course carries a high risk of sudden cardiac death (SCD); therefore, current guidelines recommend exercise restriction and surgical intervention. Recent data in intraseptal and juxtacommissural L-AAOCA showed inducible perfusion abnormalities, leading to consideration of surgical intervention. Anomalous right coronary artery from the left aortic sinus (R-AAOCA) carries a much lower risk and stress perfusion imaging is helpful in identifying patients with inducible ischemia. Perfusion abnormalities resolve following successful surgical intervention of AAOCA. Computational modeling techniques identifying risk features shows promise in the evaluation of AAOCA. Summary Stress perfusion imaging is helpful in assessing AAOCA upon presentation and following surgical intervention. Computational modeling has potential in bridging knowledge gaps in AAOCA.

Computational modeling of animal behavior in T-mazes: Insights from machine learning

This study investigates the intricacies of animal decision-making in T-maze environments through a synergistic approach combining computational modeling and machine learning techniques. Focusing on the binary decision-making process in T-mazes, we examine how animals navigate choices between two paths. Our research employs a mathematical model tailored to the decision-making behavior of fish, offering analytical insights into their complex behavioral patterns. To complement this, we apply advanced machine learning algorithms, specifically Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and a hybrid approach involving Principal Component Analysis (PCA) for dimensionality reduction followed by SVM for classification to analyze behavioral data from zebrafish and rats. The above techniques result in high predictive accuracies, approximately 98.07% for zebrafish and 98.15% for rats, underscoring the efficacy of computational methods in decoding animal behavior in controlled experiments. This study not only deepens our understanding of animal cognitive processes but also showcases the pivotal role of computational modeling and machine learning in elucidating the dynamics of behavioral science.

Multi-spectroscopic and theoretical analysis on the interaction between human serum albumin and Cm-E2O2-Cm gemini surfactants

Human serum albumin (HSA) is among the blood plasma proteins crucial for transporting various natural and pharmaceutical substances throughout the body. This research examined how the structure of HSA changes when exposed to Cm-E2O2-Cm gemini surfactants, which possess various advantageous properties, utilizing a combination of multispectral analysis and computational modeling techniques. The aggregation behavior of gemini surfactants in the presence of HSA was also investigated to demonstrate the HSA-Cm-E2O2-Cm complexation. The intrinsic fluorescence findings showed that gemini surfactants caused HSA fluorescence to diminish due to static quenching. The binding constants (Kb) for HSA-Cm-E2O2-Cm at varying temperatures were around 103 M−1, with approximately one binding site detected. In addition, the effect of gemini surfactants on the conformation of HSA was analyzed by performing UV–vis, extrinsic, synchronous, RRS and 3-D fluorescence, and CD experiments. The interactions’ binding sites and driving forces (also spontaneity) were calculated using molecular docking analysis, which agrees with site marker studies and thermodynamic parameter value results.

Coupled fluid-structure simulation of a flapping wing using free multibody dynamics software

AbstractComputer simulations offer invaluable insights into fluid-structure interaction phenomena, increasing our understanding of complex behaviors within fluid flows and enabling predictions of consequential effects. This paper explores flapping wing simulation using an original toolchain based on free software. The structural domain is modeled using multibody dynamics, interfaced with arbitrary fluid dynamics solvers through a general-purpose multiphysics coupling library. The proposed toolchain is validated against benchmark models, demonstrating its effectiveness in various applications. Our study, inspired by experimental ones, applies this coupling to investigate the hydroelastic behavior of a flexible wing. Wing motion characteristics, structural properties, and convergence criteria are analyzed through numerical simulations. While achieving appreciable agreement with experimental data on wing motion ratios, challenges in dealing with large displacements have been identified. Nonetheless, the present study provides valuable insights into fluid-structure interactions, laying the groundwork for future refinements in computational modeling techniques and advancing the understanding of bio-inspired flight mechanisms.

Relationship between Solvent Polarity and Reaction Rate in Organic Synthesis

Purpose: The aim of the study was to assess the relationship between solvent polarity and reaction rate in organic synthesis. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Polar solvents tend to enhance reaction rates for polar reactions due to their ability to stabilize charged intermediates and transition states. Conversely, nonpolar solvents often accelerate nonpolar reactions by minimizing solvation effects and promoting favorable interactions between reactants. However, there are exceptions to these trends, as solvent effects can also be influenced by factors such as hydrogen bonding, solute-solvent interactions, and solvent viscosity. Additionally, the choice of solvent can impact the selectivity and yield of the desired product. Thus, understanding the interplay between solvent polarity and reaction kinetics is crucial for optimizing organic synthesis processes. Experimental studies and computational modeling techniques play key roles in elucidating these relationships and guiding solvent selection for specific reactions. Implications to Theory, Practice and Policy: Transition state theory, solvent-solute interactions and microscopic solvation models may be used to anchor future studies on assessing the relationship between solvent polarity and reaction rate in organic synthesis. Audit firms should invest in ongoing training and professional development programs to enhance the skills and knowledge of auditors. This includes staying updated on industry-specific issues, emerging risks, and advanced audit methodologies. Regulatory authorities should continue to strengthen their oversight of the audit profession. This includes periodically reviewing and updating auditing standards, enforcing compliance with auditing regulations, and imposing penalties for audit failures.

Computational Approach for Architecture, Tailoring, and Advancements in Perfluorinated Compounds: Synthesis, Characterization, and Future Directions in Fire Suppression Technology

The demand for effective fire extinguishants has spurred investigation into novel perfluorinated molecules due to their exceptional properties, including high thermal stability, low toxicity, and eco-friendliness. This study presents a computational framework for designing and tailoring perfluorinated compounds as potential alternatives to traditional extinguishing agents. The research centers on elucidating the synthesis, molecular architecture, and characterization of these compounds to enhance their fire suppression capabilities while mitigating environmental impact. By employing computational methods, molecular modeling, and advanced spectroscopic techniques, the structural intricacies and potential applications of perfluorinated compounds in fire suppression are investigated. Additionally, future research directions aimed at addressing challenges and advancing the development of environmentally sustainable fire extinguishants are discussed. This manuscript contributes to the ongoing efforts to replace conventional extinguishing agents with safer and more environmentally friendly alternatives.

Unveiling the potential of lead-free Cs2AgBi0.75Sb0.25Br6 double perovskite solar cells with multilayer charge transport for 30% efficiency

The present study delves into the utilization of computer modeling techniques to analyze a photovoltaic (PV) design incorporating a lead (Pb)-free perovskite absorber material. This innovative structure employs a cesium-based double perovskite material, specifically Cs2AgBi0.75Sb0.25Br6, characterized by an energy bandgap of 1.80 eV. The Cs2AgBi0.75Sb0.25Br6 exhibits a range of advantageous properties, including heightened stability in ambient conditions and appropriately aligned bandgaps. Given these considerations, an extensive exploration of the FTO/CdS/Cs2AgBi0.75Sb0.25Br6 /HTL/Au configuration was undertaken to assess the impact of ten distinct hole transport layers (HTLs) on the solar cell's efficiency. The simulation and analysis of all devices were conducted employing the one-dimensional SCAPS-1D (Solar Cell Capacitance Simulator) tool. Furthermore, the efficiency of the FTO/ETL/Cs2AgBi0.75Sb0.25Br6 /MoO3/Au architecture was probed, employing six different electron transport layers (ETLs) (BaSnO3, PC61BM, SnS2, Ti2O3:Nb, CdS, ZnOS), to discern the optimal combination. Post-optimization, adjustments were made to the absorber and ETL thickness, as well as the acceptor doping and defect density of the absorber, and the donor and defect density of the ETL, for the six combinations assessed. These topologies were also examined for their impacts on series and shunt resistance, capacitance, the Mott-Schottky effect, generation and recombination processes, current–voltage density, and quantum efficiency. Eventually, the most efficient cell in this study achieved a power conversion efficiency (PCE) of 30.30 % and featured the FTO/ZnOS/Cs2AgBi0.75Sb0.25Br6/MoO3/Au configuration. The aforementioned findings hold significant potential for advancing lead-free, double perovskite solar cells (PSCs) that are both more efficient and environmentally sustainable, paving the way for their widespread adoption in the future.

МОДЕЛИРОВАНИЕ РЕЖИМОВ ЭЛЕКТРИЧЕСКИХ СЕТЕЙ, ПИТАЮЩИХ ТЯГОВЫЕ ПОДСТАНЦИИ С ДВЕНАДЦАТИПУЛЬСНЫМИ ПРЕОБРАЗОВАТЕЛЯМИ

A technique for computer modeling of the modes of electrical networks feeding traction substations with twelve-pulse converters in phase coordinates is proposed. The developed models can be used when choosing measures to increase the energy efficiency of transportation processes and improve the quality of electricity

Weight Optimization for missing data prediction of Landslide Susceptibility Mapping in Remote sensing Analysis

To keep track of changes to the Earth's surface, extensive time series of data from remote sensing using image processing is required. This research is motivated by the effectiveness of computational modelling techniques; however, the problem of missing data is multifaceted. When data at numerous a-periodic timestamps are absent during multi-temporal analysis, the issue becomes increasingly problematic. To make remote sensing time series analysis easier, weight optimised machine learning is used in this study to rebuild lost data. Keeping the causality restriction in mind, this method makes use of data from previous and subsequent timestamps. The architecture is based on an ensemble of numerous forecasting modules, built on the observed data in the time-series order. Dummy data is used to connect the forecasting modules, which were previously linked by the earlier half of the sequence. After that, iterative improvements are made to the dummy data to make it better fit the next segment of the sequence. On the basis of Landsat-7 TM-5 satellite imagery, the work has been proven to be accurate in forecasting missing images in normalised difference vegetation index time series. In a performance evaluation, the proposed forecasting model was shown to be effective.

SYMULACJA I MODELOWANIE KOMPUTEROWE DYNAMIKI KONSTRUKCJI MOSTÓW Z WYKORZYSTANIEM ANSYS

This study focuses on utilizing computer modeling and simulation techniques, specifically the ANSYS software, to analyze the dynamics of bridge structures. The primary objective was to study the vibrations of a riverbed metal bridge structure and determine their characteristics. The research involved theoretical dynamic calculations considering the design features of the bridge components and the materials used in their construction. The obtained results enabled the determination of resonance frequencies for the vibration modes. By utilizing the ANSYS software, a three-dimensional virtual model of the bridge structure was created, allowing for a detailed analysis of its dynamic behavior. The first three vibration modes of the riverbed metal bridge structure were calculated, and numerical results were obtained for six modes. The findings of this research have practical significance as they provide informed decision-making support during the construction, maintenance, and modernization of bridge structures. The study of bridge dynamics using advanced technologies contributes to enhancing the safety, reliability, and longevity of these vital infrastructure assets.

Numerical simulations and mathematical models in laser welding: a review based on physics and heat source models

The dominant phenomenon in laser welding processes is heat transfer by conduction, making it crucial to gain insights into energy distribution within the heat-affected region, including the melt pool. Thermal analysis enables the description of thermo-mechanical, metallurgical aspects, and also addresses studies related to fluid flow and energy transfer. As research in welding processes has advanced, these models have evolved. This is why it is now efficient to use computational modeling techniques as it allows us to analyze the behavior of laser welding during the process. This underlines the importance of this work which has carried out an exhaustive theoretical literature review with the objective of classifying and describing the numerical simulations of laser welding based on the physics involved. In that sense, the mathematical models and strategies used in laser welding are explored in a general way. Therefore, two types of laser welding by conduction and deep penetration are defined from this point and they are categorized according to the phenomena involved in Model Heat Conduction and Model Integral Multiphysics. This comprehensive review article serves as a valuable resource for higher education students by providing a structured and detailed exploration of laser welding and its mathematical modeling. By classifying and describing numerical simulations based on the physics involved, it offers a framework for students to understand the complexities of this field. Additionally, this innovative approach to organizing and presenting research contributes to educational innovation by facilitating a more efficient and effective learning experience, helping students acquire the knowledge and research skills necessary for advancements in the laser welding domain.

Structural insights into the C-terminus of the histone-lysine N-methyltransferase NSD3 by small-angle X-ray scattering.

NSD3 is a member of six H3K36-specific histone lysine methyltransferases in metazoans. Its overexpression or mutation is implicated in developmental defects and oncogenesis. Aside from the well-characterized catalytic SET domain, NSD3 has multiple clinically relevant potential chromatin-binding motifs, such as the proline-tryptophan-tryptophan-proline (PWWP), the plant homeodomain (PHD), and the adjacent Cys-His-rich domain located at the C-terminus. The crystal structure of the individual domains is available, and this structural knowledge has allowed the designing of potential inhibitors, but the intrinsic flexibility of larger constructs has hindered the characterization of mutual domain conformations. Here, we report the first structural characterization of the NSD3 C-terminal region comprising the PWWP2, SET, and PHD4 domains, which has been achieved at a low resolution in solution by small-angle X-ray scattering (SAXS) data on two multiple-domain NSD3 constructs complemented with size-exclusion chromatography and advanced computational modeling. Structural models predicted by machine learning have been validated in direct space, by comparison with the SAXS-derived molecular envelope, and in reciprocal space, by reproducing the experimental SAXS profile. Selected models have been refined by SAXS-restrained molecular dynamics. This study shows how SAXS data can be used with advanced computational modeling techniques to achieve a detailed structural characterization and sheds light on how NSD3 domains are interconnected in the C-terminus.