Computational Modeling Analysis Research Articles

Computational analysis of the Covid-19 model using the continuous Galerkin–Petrov scheme

Abstract Epidemiological models feature reliable and valuable insights into the prevention and transmission of life-threatening illnesses. In this study, a novel SIR mathematical model for COVID-19 is formulated and examined. The newly developed model has been thoroughly explored through theoretical analysis and computational methods, specifically the continuous Galerkin–Petrov (cGP) scheme. The next-generation matrix approach was used to calculate the reproduction number ( R 0 ) ({R}_{0}) . Both disease-free equilibrium (DFE) ( E 0 ) ({E}^{0}) and the endemic equilibrium ( E ⁎ ) ({E}^{\ast }) points are derived for the proposed model. The stability analysis of the equilibrium points reveals that ( E 0 ) ({E}^{0}) is locally asymptotically stable when R 0 < 1 {R}_{0}\lt 1 , while E ⁎ {E}^{\ast } is globally asymptotically stable when R 0 > 1 {R}_{0}\gt 1 . We have examined the model’s local stability (LS) and global stability (GS) for endemic equilibrium \text{ } and DFE based on the number ( R 0 ) ({R}_{0}) . To ascertain the dominance of the parameters, we examined the sensitivity of the number ( R 0 ) ({R}_{0}) to parameters and computed sensitivity indices. Additionally, using the fourth-order Runge–Kutta (RK4) and Runge–Kutta–Fehlberg (RK45) techniques implemented in MATLAB, we determined the numerical solutions. Furthermore, the model was solved using the continuous cGP time discretization technique. We implemented a variety of schemes like cGP(2), RK4, and RK45 for the COVID-19 model and presented the numerical and graphical solutions of the model. Furthermore, we compared the results obtained using the above-mentioned schemes and observed that all results overlap with each other. The significant properties of several physical parameters under consideration were discussed. In the end, the computational analysis shows a clear image of the rise and fall in the spread of this disease over time in a specific location.

Research on the Impact of Geographical Environment on English Learning Based on Machine Learning

College English course is an important basic course in colleges and universities. The purpose of this study is to better understand the influence mechanism of geographical environment on English learning and provide scientific basis for formulating targeted English education policies. This study uses the method of machine learning to analyze the collected data and explore the influence of geographical environment on college students' English learning. This study constructs a comprehensive data set including students' English learning achievements, academic background and geographical location, and obtains the results through computer model analysis. The results show that geographical environment has an influence on college English learning, which will lead to differences in students' English learning achievements. This study suggests that geographical factors should be considered when formulating English education policies. This study is of great significance to promoting the development of English education and improving students' learning effect.

Stability and computational analysis of Influenza-A epidemic model through double time delay

Delay factors demonstration has a significant role in controlling a strain of infectious disease instead of a pharmaceutical strategy. According to the World Health Organization (WHO), 3–5 million cases are reported annually and approximately 290,000 to 650,000 respiratory deaths annually. So, in the present study, we develop a delayed mathematical model based on delay differential equations (DDEs) for the influenza epidemic using a deterministic approach by introducing double delay parameters. The four distinct sub-populations are considered susceptible, exposed, infected, and recovered. For the rigorous analysis, the fundamental properties of the model like positivity, boundedness, existence, and uniqueness, were studied. The influenza-free equilibrium (IFE) and influenza-existing equilibrium (IEE), are the two nonnegative equilibrium points that the model demonstrates. Both locally and globally, the asymptotic stability of the equilibrium points of the model is established and shown under specific situations of reproduction number. Additionally, investigated the model's parameter sensitivity and determined the relative sensitivity of each parameter. Both standard and nonstandard methods—such as Euler, Runge-Kutta, and nonstandard finite difference with a delayed sense—are presented to make computational analysis support a dynamical analysis and the best visualization of results. The stability of the non-standard finite difference scheme is thoroughly analyzed around the steady states of the model. Additionally, the results show that the nonstandard finite difference approximation is an efficient, cost-effective method, independent of time step size, to solve such highly nonlinear and complex real-world problems.

Raloxifene Prevents Chemically-Induced Ferroptotic Neuronal Death In Vitro and In Vivo.

Ferroptosis, a regulated form of cell death characterized by excessive iron-dependent lipid peroxidation, can be readily induced in cultured cells by chemicals such as erastin and RSL3. Protein disulfide isomerase (PDI) has been identified as an upstream mediator of chemically induced ferroptosis and also a target for ferroptosis protection. In this study, we discovered that raloxifene (RAL), a selective estrogen receptor modulator known for its neuroprotective actions in humans, can effectively inhibit PDI function and provide robust protection against chemically induced ferroptosis in cultured HT22 neuronal cells. Specifically, RAL can bind directly to PDI both in vitro and in intact neuronal cells and inhibit its catalytic activity. Computational modeling analysis reveals that RAL can tightly bind to PDI through forming a hydrogen bond with its His256 residue, and biochemical analysis further shows that when PDI's His256 is mutated to Ala256, RAL loses its inhibition of PDI's catalytic activity. This inhibition of PDI by RAL significantly reduces the dimerization of both the inducible and neuronal nitric oxide synthases and the accumulation of nitric oxide, both of which have recently been shown to play a crucial role in mediating chemically induced ferroptosis through subsequent induction of ROS and lipid-ROS accumulation. In vivo behavioral analysis shows that mice treated with RAL are strongly protected against kainic acid-induced memory deficits and hippocampal neuronal damage. In conclusion, this study demonstrates that RAL is a potent inhibitor of PDI and can effectively prevent chemically induced ferroptosis in hippocampal neurons both in vitro and in vivo. These findings offer a novel estrogen receptor-independent mechanism for RAL's neuroprotective actions in animal models and humans.

The role of training variability for model-based and model-free learning of an arbitrary visuomotor mapping.

A fundamental feature of the human brain is its capacity to learn novel motor skills. This capacity requires the formation of vastly different visuomotor mappings. Using a grid navigation task, we investigated whether training variability would enhance the flexible use of a visuomotor mapping (key-to-direction rule), leading to better generalization performance. Experiments 1 and 2 show that participants trained to move between multiple start-target pairs exhibited greater generalization to both distal and proximal targets compared to participants trained to move between a single pair. This finding suggests that limited variability can impair decisions even in simple tasks without planning. In addition, during the training phase, participants exposed to higher variability were more inclined to choose options that, counterintuitively, moved the cursor away from the target while minimizing its actual distance under the constrained mapping, suggesting a greater engagement in model-based computations. In Experiments 3 and 4, we showed that the limited generalization performance in participants trained with a single pair can be enhanced by a short period of variability introduced early in learning or by incorporating stochasticity into the visuomotor mapping. Our computational modeling analyses revealed that a hybrid model between model-free and model-based computations with different mixing weights for the training and generalization phases, best described participants' data. Importantly, the differences in the model-based weights between our experimental groups, paralleled the behavioral findings during training and generalization. Taken together, our results suggest that training variability enables the flexible use of the visuomotor mapping, potentially by preventing the consolidation of habits due to the continuous demand to change responses.

Co-Active Subspace Methods for the Joint Analysis of Adjacent Computer Models

Active subspace (AS) methods are a valuable tool for understanding the relationship between the inputs and outputs of a Physics simulation. In this paper, an elegant generalization of the traditional ASM is developed to assess the co-activity of two computer models. This generalization, which we refer to as a Co-Active Subspace (Co-AS) Method, allows for the joint analysis of two or more computer models allowing for thorough exploration of the alignment (or non-alignment) of the respective gradient spaces. We define co-active directions, co-sensitivity indices, and a scalar “concordance” metric (and complementary “discordance” pseudo-metric) and we demonstrate that these are powerful tools for understanding the behavior of a class of computer models, especially when used to supplement traditional AS analysis. Details for efficient estimation of the Co-AS and an accompanying R package (concordance) are provided. Practical application is demonstrated through analyzing a set of simulated rate stick experiments for PBX 9501, a high explosive, offering insights into complex model dynamics.

Multiphysics computational modelling and experimental analysis of friction stir welding of aluminium alloy metal matrix composites (AAMMCs)

Friction stir welding is a modern method for fabricating aluminium alloy metal matrix composites (AAMMCs) that significantly enhance the properties of the parent materials. In this study, Al2O3 particles were incorporated into a 6061-T6 aluminium matrix. Experimental and numerical modeling provided in-situ observations of particle-aluminium matrix mixing in the weld stir zone (SZ). Two different pin profiles, threaded and square, were used to fabricate the AAMMC joints. The threaded pin profile resulted in uniform dispersion and a homogeneous distribution of Al2O3 particles within the aluminium matrix. In contrast, the square pin profile led to defects in the SZ and a non-homogeneous particle distribution. The volume fraction mixing of the AA6061-T6 matrix and Al2O3 at the workpiece/tool interface from top to bottom was analyzed using a level set technique. Experimental results showed effective mixing of Al2O3 particles in the AA6061-T6 matrix with the threaded pin profile, resulting in a defect-free joint, uniform particle distribution, and ultra-fine grain refinement, with grain sizes reduced to 4.19 µm. The main shear texture components observed in the threaded pin joints included B/B̅ and C, along with deformed and recrystallized texture components such as Copper {112} 〈111〉, Brass {110} 〈112〉, Goss {011} 〈100〉, Cube {001} 〈101〉, and P {011} 〈112〉 . Additionally, joints fabricated using threaded pin profiles exhibited significantly higher tensile strength (up to 270 MPa) and microhardness (average of 120 HV0.2) compared to those with square-shaped pin profiles, which showed lower values of 240 MPa and 105 HV0.2, respectively. Thus, AAMMC joints made with threaded pins demonstrate ultra-fine grain refinement, increased high-angle grain boundaries, enhanced recrystallized texture components, and improved mechanical properties, resulting in superior overall joint performance.

Computational and intelligence modeling analysis of pharmaceutical freeze drying for prediction of temperature in the process

Accurate temperature prediction is crucial for various scientific and engineering applications, yet it remains challenging due to the complex relationships between spatial coordinates and temperature variations. This study addresses this challenge by exploring advanced machine learning models to improve prediction accuracy, filling the research gap in optimizing predictive models for temperature estimation in freeze drying of pharmaceuticals. We employed Support Vector Regression (SVR), Poisson Regression (POR), and Adaptive Neuro-Fuzzy Inference System (ANFIS) models, each enhanced using the Cheetah Optimizer (CO), to estimate temperature based on spatial coordinates (X, Y, Z) in the domain of process. The methodology involved training and testing these models on a dataset comprising spatial and temperature data, with a focus on improving accuracy through optimization techniques. Validation of the models showed that the CO-SVR model outperformed the others, achieving an R2 score of 0.953, Mean Squared Error (MSE) of 9.893, and Mean Absolute Error (MAE) of 2.549. This represents a significant improvement over the CO-ANFIS model, which obtained an R2 of 0.863, MSE of 23.583, and MAE of 3.912. The CO-POR model showed the lowest predictive capability, with an R2 score of 0.753, MSE of 57.608, and MAE of 6.756. These findings underscore the effectiveness of the Cheetah Optimizer in enhancing the accuracy of SVR models for temperature prediction, suggesting its potential for broader applications in predictive modeling where accuracy is paramount.

Human hippocampus and dorsomedial prefrontal cortex infer and update latent causes during social interaction.

Latent-cause inference is the process of identifying features of the environment that have caused an outcome. This problem is especially important in social settings where individuals may not make equal contributions to the outcomes they achieve together. Here, we designed a novel task in which participants inferred which of two characters was more likely to have been responsible for outcomes achieved by working together. Using computational modeling, univariate and multivariate analysis of human fMRI, and continuous theta-burst stimulation, we identified two brain regions that solved the task. Notably, as each outcome occurred, it was possible to decode the inference of its cause (the responsible character) from hippocampal activity. Activity in dorsomedial prefrontal cortex (dmPFC) updated estimates of association between cause-responsible character-and the outcome. Disruption of dmPFC activity impaired participants' ability to update their estimate as a function of inferred responsibility but spared their ability to infer responsibility.

Learning to Move and Plan like the Knight: Sequential Decision Making with a Novel Motor Mapping.

Many skills that humans acquire throughout their lives, such as playing video games or sports, require substantial motor learning and multi-step planning. While both processes are typically studied separately, they are likely to interact during the acquisition of complex motor skills. In this work, we studied this interaction by assessing human performance in a sequential decision-making task that requires the learning of a non-trivial motor mapping. Participants were tasked to move a cursor from start to target locations in a grid world, using a standard keyboard. Notably, the specific keys were arbitrarily mapped to a movement rule resembling the Knight chess piece. In Experiment 1, we showed the learning of this mapping in the absence of planning, led to significant improvements in the task when presented with sequential decisions at a later stage. Computational modeling analysis revealed that such improvements resulted from an increased learning rate about the state transitions of the motor mapping, which also resulted in more flexible planning from trial to trial (less perseveration or habitual responses). In Experiment 2, we showed that incorporating mapping learning into the planning process, allows us to capture (1) differential task improvements for distinct planning horizons and (2) overall lower performance for longer horizons. Additionally, model analysis suggested that participants may limit their search to three steps ahead. We hypothesize that this limitation in planning horizon arises from capacity constraints in working memory, and may be the reason complex skills are often broken down into individual subroutines or components during learning.

Simulation and calculation of the radiation attenuation parameters of newly developed bismuth boro-tellurite glass system

This paper presents a comprehensive simulation and calculation study of the radiation attenuation parameters for a newly developed bismuth boro tellurite glass system. Through advanced computational modeling and theoretical analysis, linear and mass attenuation coefficients (μ and μ/ρ), and other key radiation attenuation parameters of the studied glass system are thoroughly investigated across a range of photon energies. Utilizing state-of-the-art simulation techniques and theoretical models, the radiation shielding effectiveness of the glass system is evaluated, offering insights into its potential applications in radiation protection and shielding. We found that the ΣR values for fast neutrons are 0.10013, 0.10221, 0.10953, 0.11347 and 0.11383 cm−1 for BTBiBV-1, BTBiBV-2, BTBiBV-3, BTBiBV-4, and BTBiBV-5, respectively. The results of this study contribute to the understanding of the radiation attenuation properties of bismuth boro tellurite glasses and provide valuable information for their utilization in various fields, including medical imaging, nuclear power, and industrial radiography. Additionally, accurate modeling and characterization of attenuation parameters across different photon energies are crucial for designing effective radiation protection strategies and ensuring the safety of personnel and equipment in radiation environments.

Formation of a heterogeneous group of UAVS with a reasonable number of false and real drones

The subject of scientific research is the joint use of false and real unmanned aerial vehicles (UAVs) as part of a heterogeneous group of UAVs to perform military tasks. This article aimsto determine the appropriate number of false and real drones (UAVs) as part of a heterogeneous group of UAVs to ensure that a certain number of real UAVs can fly to targets with the aim of further reliable target destruction. The scientific task is to develop a methodology for determining the appropriate number of false drones in a heterogeneous group of UAVs, considering the diversity of UAVs included in the UAV group. To achieve the goals of scientificresearch, partial scientific tasks were solved. The joint use of false drones as part of a UAV group to defeat targets with a given degree of damage was formalized. The formalization was carried out taking into account two possible cases of use: a) when the enemy has a sufficient number of means to destroy the entire group of UAVs; b) when the enemy has an insufficient number of means to destroy the entire UAV group. A mathematical model for determining the optimal composition of false and real drones (UAVs) as parts of a heterogeneous group of UAVs has been developed, which will allow to fulfill the task of defeating enemy targets with the desired degree of reliability. A program code has been developed that simplifies the mathematical calculations in the presented mathematical model and allows it to be used in the process of making an appropriate military decision. An algorithm to find the numbers of real and false UAVs in a heterogeneous group of UAVs is proposed. The obtained formulas and algorithms were verified by computer simulation using the Monte Carlo method. Methods. The mathematical model is based on combinatorial methods of probability theory. Programming for calculating analytical formulas and computer modeling of the Monte Carlo method was carried out based on the R computer language. The following results were obtained. A multifunctional algorithm is presented: on one hand, its application makes it possible to determine the optimal number of false UAVs in a heterogeneous group of UAVs to ensure that the required number of real UAVs reach the target, and on the other hand, to determine the predicted loss level of real UAVs in a heterogeneous group of UAVs when using a certain number of false drones.Conclusions. The availability of the developed mathematical model, algorithm, and program code makes it possible to predict the possible results of the combat use of heterogeneous groups of UAVs based on the initial parameters and to substantiate recommendations for a possible composition of such groups.

Dopamine transients encode reward prediction errors independent of learning rates.

Biological accounts of reinforcement learning posit that dopamine encodes reward prediction errors (RPEs), which are multiplied by a learning rate to update state or action values. These values are thought to be represented in synaptic weights in the striatum, and updated by dopamine-dependent plasticity, suggesting that dopamine release might reflect the product of the learning rate and RPE. Here, we leveraged the fact that animals learn faster in volatile environments to characterize dopamine encoding of learning rates in the nucleus accumbens core (NAcc). We trained rats on a task with semi-observable states offering different rewards, and rats adjusted how quickly they initiated trials across states using RPEs. Computational modeling and behavioral analyses showed that learning rates were higher following state transitions, and scaled with trial-by-trial changes in beliefs about hidden states, approximating normative Bayesian strategies. Notably, dopamine release in the NAcc encoded RPEs independent of learning rates, suggesting that dopamine-independent mechanisms instantiate dynamic learning rates.

Computational modeling and virtual analysis using a moving heat source to join AZ61A and AA7075 alloys with the application of a titanium alloy interlayer

Laser beam welding (LBW) uses a concentrated laser beam to fuse materials, providing benefits such as a smaller heat-affected zone (HAZ), speed, accuracy, adaptability across multiple materials, and seamless adoption into automation processes. Achieving optimal weld quality with LBW remains difficult due to the need to select the appropriate welding process variables and proper interlayer between dissimilar materials. Due to the high cost and safety risks to operators and equipment in LBW, it is challenging to use the practical experiment. Additionally, welding materials with high thermal conductivity and lower melting temperatures, such as AZ61A and AA7075, is a considerable challenge. Previous research focused on process parameters in laser beam welding of various metals, but not enough consideration has been given to the effect of a titanium interlayer on AA7075 and AZ61A using virtual data. This study aims to fill these gaps by using simulation data to assess the effects of process parameters and the titanium interlayer during LBW of dissimilar materials. A backpropagation neural network with the gradient descent learning rule was used for optimization, and a central composite design was used to predict the optimal process parameter interaction. The result indicates the optimum equivalent strain is obtained at the values of beam radius between 4.5 to 5 mm. The maximum equivalent stress reached during welding speed is between 4 to 4.5 mm/s. The maximum residual stress was obtained at the number of segments of 160. The predicted maximum and average anticipated errors for peak temperature are 0.1655 % and 0.0210 %, while for residual stress it is computed as 0.1766 % and 0.0754 %. The Ti-interlayer in magnesium and aluminum laser welding reduces peak temperatures, allows for uniform energy distribution, minimizes localized heating, and enhances weld quality while lowering residual stress.

Computational modeling and statistical analysis of buckling characteristics of polysilicon reinforced fiber

The purpose of this study is to evaluate the effect of volume fraction of continuous carbon fiber and sample length on buckling characteristics of polysilicon. A statistical design of 12 samples were formulated with constant cross-section area of 2500 mm2 using Design of Experiment software (DOE). The samples were sketched using ABAQUS 2019 software, and the total buckling force each sample was estimated. The estimated buckling forces were statically evaluated as a response using DOE. The estimated forces of 3.48776e07, 4.00652e07 and 5.78142e07 newton for the simulated samples of 100 mm in length and 0,15, and 25% volume fraction respectively, is an indication of positive effect of fiber volume fraction on the necessary force for buckling. In addition, similar tendency was found in other samples (the higher fiber volume fractions the higher buckling force). However, the estimated buckling force for each sample was negatively affected with length of the sample. The result indicated a value of 4.00652E+07, 5.00447E+06 and 1.80390E+06 newton at a constant fiber volume fraction and different length of 100, 300 and 500 mm respectively. The statistical analysis of the simulated buckling force showed a signification design, and the date of one factor effect is highly supported by the simulated buckling forces. The equation of a significant design can be used to estimate the buckling force at any fiber volume fraction and sample length.

Biomimetic Plasmonic Nanophages by Head/Tail Self-Assembling: Gold Nanoparticle/Virus Interactions.

Gold nanoparticles (AuNPs), because of their dual plasmonic and catalytic functionalities, are among the most promising nanomaterials for the development of therapeutic and diagnostic tools for severe diseases such as cancer and neurodegeneration. Bacteriophages, massively present in human biofluids, are emerging as revolutionary biotechnological tools as they can be engineered to display multiple specific binding moieties, providing effective targeting ability, high stability, low cost, and sustainable production. Coupling AuNPs with phages can lead to an advanced generation of nanotools with great potential for biomedical applications. In the present study, we analyzed the interactions between differently sized AuNPs and filamentous M13 phages, establishing an advanced characterization platform that combines analytical techniques and computational models for an in-depth understanding of these hybrid self-assembling systems. A precise and structurally specific interaction of the AuNP-M13 hybrid complexes was observed, leading to a peculiar head/tail "tadpole-like" configuration. In silico simulations allowed explaining the mechanisms underlying the preferential assembly route and providing information about AuNPs' size-dependent interplay with specific M13 capsid proteins. The AuNP-M13 structures were proven to be biomimetic, eluding the formation of biomolecular corona. By keeping the biological identity of the virion, hybrid nanostructures maintained their natural recognition/targeting ability even in the presence of biomolecular crowding. In addition, we were able to tune the hybrid nanostructures' tropism toward E. coli based on the AuNP size. Overall, our results set the fundamental basis and a standard workflow for the development of phage-based targeting nanotools, valuable for a wide spectrum of nanotechnology applications.

Distinct alterations in probabilistic reversal learning across at-risk mental state, first episode psychosis and persistent schizophrenia

We used a probabilistic reversal learning task to examine prediction error-driven belief updating in three clinical groups with psychosis or psychosis-like symptoms. Study 1 compared people with at-risk mental state and first episode psychosis (FEP) to matched controls. Study 2 compared people diagnosed with treatment-resistant schizophrenia (TRS) to matched controls. The design replicated our previous work showing ketamine-related perturbations in how meta-level confidence maintained behavioural policy. We applied the same computational modelling analysis here, in order to compare the pharmacological model to three groups at different stages of psychosis. Accuracy was reduced in FEP, reflecting increased tendencies to shift strategy following probabilistic errors. The TRS group also showed a greater tendency to shift choice strategies though accuracy levels were not significantly reduced. Applying the previously-used computational modelling approach, we observed that only the TRS group showed altered confidence-based modulation of responding, previously observed under ketamine administration. Overall, our behavioural findings demonstrated resemblance between clinical groups (FEP and TRS) and ketamine in terms of a reduction in stabilisation of responding in a noisy environment. The computational analysis suggested that TRS, but not FEP, replicates ketamine effects but we consider the computational findings preliminary given limitations in performance of the model.

Origins of High-Activity Cage-Catalyzed Michael Addition.

Cage catalysis continues to create significant interest, yet catalyst function remains poorly understood. Herein, we report mechanistic insights into coordination-cage-catalyzed Michael addition using kinetic and computational methods. The study has been enabled by the detection of identifiable catalyst intermediates, which allow the evolution of different cage species to be monitored and modeled alongside reactants and products. The investigations show that the overall acceleration results from two distinct effects. First, the cage reaction shows a thousand-fold increase in the rate constant for the turnover-limiting C-C bond-forming step compared to a reference state. Computational modeling and experimental analysis of activation parameters indicate that this stems from a significant reduction in entropy, suggesting substrate coencapsulation. Second, the cage markedly acidifies the bound pronucleophile, shifting this equilibrium by up to 6 orders of magnitude. The combination of these two factors results in accelerations up to 109 relative to bulk-phase reference reactions. We also show that the catalyst can fundamentally alter the reaction mechanism, leading to intermediates and products that are not observable outside of the cage. Collectively, the results show that cage catalysis can proceed with very high activity and unique selectivity by harnessing a series of individually weak noncovalent interactions.

Investigation of cold tube structure on flow characteristics and energy separation in vortex tube based on numerical and thermodynamic analyses

A combined approach of computational fluid dynamics modelling, experimental validation and thermodynamic analysis is used to investigate the effect of cold end tube geometry (namely, straight tube, divergent tube, convergent tube, and convergent-divergent tube) on flow characteristics and energy separation, which to improve the energy efficiency and sustainability of the vortex tube in industrial applications. The analyses indicate that the highest axial velocity (212 m/s) and swirl velocity (193 m/s) at different axial locations in the cold tube are demonstrated in the divergent and convergent-divergent structures, respectively. The divergent tube shows better energy separation at lower cold mass fraction, while the convergent-divergent tube is dominant at higher cold mass fraction. The highest coefficient of performance is found at μc = 0.6, with maximum cooling and heating energy transfer efficiencies of 0.112 and 0.103, respectively. By innovatively incorporating the work-heat transfer theory into structural investigation of cold tubes, it is found that tangential viscous shear effect is the dominant factor in energy separation process. It is noteworthy that the highest net energy transfer of 973 W is achieved by convergent-divergent tube at a cold mass fraction of 0.7.

Computational analysis of expectile and deviation expectile portfolio optimization models

Computational analysis of expectile and deviation expectile portfolio optimization models