MATLAB Code Research Articles

The H222P-Lamin mutation induces heart failure via impaired mitochondrial calcium uptake in human cardiac laminopathy

Abstract Background Mutations in LMNA gene, which encodes lamins A/C, cause a variety of diseases, called laminopathies. Some mutations are particularly associated with the occurrence of dilated cardiomyopathy. The pathological mechanisms are still unclear limiting the development of specific therapies. Purpose Here, we focused on a mutation in the LMNA gene (c.665A>C, p.His222Pro), associated with the Emery-Dreyfuss Muscular dystrophy. We used LMNA H222P mutant human induced pluripotent stem cells (hiPSCs) and CRISPR/Cas9 corrected isogenic control hiPSCs to better characterize the cardiac phenotype associated with the mutation. Methods LMNA H222P mutant and the isogenic control hiPSCs clones were differentiated into cardiomyocytes (CMs). Immunofluorescence staining was performed on hiPSC-CMs to quantify their sarcomere organization (SarcOrgScore) using a Matlab code. Ring-shaped cardiac organoids were generated to compare the contractile properties of the two clones. Calcium transients in mutant and corrected hiPSC-CMs were measured by live confocal imaging. Mitochondrial respiration was measured by Seahorse. Results hiPSC-CMs were generated from the LMNA H222P mutant and the corrected hiPSCs with no difference in the differentiation yield (proportion of troponin-positive cells: 92.74% for LMNA H222P vs. 89.38% for Ctrl-iso1, p=0.1038). The nuclei of LMNA H222P mutant hiPSC-CMs showed morphological abnormalities, typically observed with Lamin A/C mutations. hiPSC-CMs displayed well-formed sarcomeres and their organization was similar between the two cell lines. However, 3D cardiac organoids generated with LMNA H222P hiPSC-CMs showed an impaired contractility compared to control organoids. Calcium transient recordings in LMNA H222P mutant hiPSC-CMs showed a significantly higher calcium transient amplitude with a significantly slower calcium re-uptake. Transcriptomic analyses suggested a global mitochondrial dysfunction and in particular an impaired mitochondrial calcium uptake with a significantly decreased expression of the mitochondrial calcium uniporter (MCU). This decrease in MCU expression was confirmed by Western blot and was accompanied by an increased MICU1 : MCU ratio, as well as an increased PDH Ser232 and Ser300 phosphorylation, indicating a decreased mitochondrial calcium uptake in the LMNA H222P mutant hiPSC-CMs. Finally, measurement of mitochondrial respiration using Seahorse showed lower basal and maximal respiration in LMNA H222P hiPSC-CMs. Conclusions LMNA H222P mutant hiPSC-CMs exhibit contractile dysfunction associated with mitochondrial dysfunction with impaired MCU complex activity, decreased mitochondrial calcium homeostasis, and reduced mitochondrial energy production.

Geometric optimization to improve the performance of a new design of concentration‐based solar distiller using particle swarm optimization algorithm

AbstractSolar distillers are one of the oldest and simplest technologies for desalinating salt water using solar energy. The main problem is the low freshwater production compared to the amount of energy input from the sun. To overcome this problem, a solar distiller was developed using a parabolic trough concentrator to increase the freshwater yield. The geometry optimization of various parameters of the developed system was also studied using the particle swarm optimization (PSO) algorithm to obtain the optimal design and give the improved freshwater yield. The simulation is carried out based on the algorithm using the climatic condition data of Rabat City, Morocco. Numerical resolution of the energy balance equations was performed using MATLAB code. In terms of flexibility, speed, and global convergence, this method's final results were satisfactory. The maximum optimized freshwater yield using the PSO approach is 12.83 kg/m2 and the maximum optimized energy and exergy efficiency were about 204.7% and 34.3%, respectively. Furthermore, the results showed that the absorber basin accounts for the highest exergy destruction, 88.7% of the total optimized exergy destruction was found from exergy analysis.

Modifying the Refuse Chute Design to Prevent Infection Spread: Engineering Analysis and Optimization

Considering the presence of airborne viruses, there is a need for renovation in refuse chutes, regarded as the first step in recycling household waste in buildings. This study aimed to revise the design of existing refuse chutes in light of the challenging experiences in waste management and public health during the coronavirus pandemic. This research primarily focused on the risks posed by various types of coronaviruses, such as the novel coronavirus (COVID-19) and acute respiratory syndrome (SARS and SARS-CoV), on stainless steel surfaces, with evidence of their survival under certain conditions. Refuse chutes are manufactured from stainless steel to resist the corrosive effects of waste. In examining the existing studies, it was observed that Casanova et al. and Chowdhury et al. found that the survival time of coronaviruses on stainless steel surfaces decreases as the temperature increases. Based on these studies, mechanical revisions have been made to the sanitation system of the refuse chute, thus increasing the washing water temperature. Additionally, through mechanical improvements, an automatic solution spray entry is provided before the intake doors are opened. Furthermore, to understand airflow and clarify flow parameters related to airborne infection transmission on residential floors in buildings equipped with refuse chutes, a computational fluid dynamics (CFD) analysis was conducted using a sample three-story refuse chute system. Based on the simulation results, a fan motor was integrated into the system to prevent pathogens from affecting users on other floors through airflow. Thus, airborne pathogens were periodically expelled into the atmosphere via a fan shortly before the intake doors were opened, supported by a PLC unit. Additionally, the intake doors were electronically interlocked, ensuring that all other intake doors remained locked while any single door was in use, thereby ensuring user safety. In a sample refuse chute, numerical calculations were performed to evaluate parameters such as the static suitability of the chute body thickness, static compliance of the chute support dimensions, chute diameter, chute thickness, fan airflow rate, ventilation duct diameter, minimum rock wool thickness for human contact safety, and the required number of spare containers. Additionally, a MATLAB code was developed to facilitate these numerical calculations, with values optimized using the Fmincon function. This allowed for the easy calculation of outputs for the new refuse chute systems and enabled the conversion of existing systems, evaluating compatibility with the new design for cost-effective upgrades. This refuse chute design aims to serve as a resource for readers in case of infection risks and contribute to the literature. The new refuse chute design supports the global circular economy (CE) model by enabling waste disinfection under pandemic conditions and ensuring cleaner source separation and collection for recycling. Due to its adaptability to different pandemic conditions including pathogens beyond coronavirus and potential new virus strains, the designed system is intended to contribute to the global health framework. In addition to the health measures described, this study calls for future research on how evolving global health conditions might impact refuse chute design.

TOP2DFVT: An Efficient Matlab Implementation for Topology Optimization based on the Finite-Volume Theory.

The finite-volume theory has shown to be numerically efficient and stable for topology optimization of continuum elastic structures. The significant features of this numerical technique are the local satisfaction of equilibrium equations and the employment of compatibility conditions along edges in a surface-averaged sense. These are essential properties to adequately mitigate some numerical instabilities in the gradient version of topology optimization algorithms, such as checkerboard, mesh dependence, and local minima issues. Several computational tools have been proposed for topology optimization employing analysis domains discretized with essential features for finite-element approaches. However, this is the first contribution to offer a platform to generate optimized topologies by employing a Matlab code based on the finite-volume theory for compliance minimization problems. The Top2DFVT provides a platform to perform 2D topology optimization of structures in Matlab, from domain initialization for structured meshes to data post-processing. This contribution represents a significant advancement over earlier publications on topology optimization based on the finite-volume theory, which needed more efficient computational tools. Moreover, the Top2DFVT algorithm incorporates SIMP and RAMP material interpolation schemes alongside sensitivity and density filtering techniques, culminating in a notably enhanced optimization tool. The application of this algorithm to various illustrative cases confirms its efficacy and underscores its potential for advancing the field of structural optimization.

P18.42.B TAU AND AMYLOID BETA PATHOLOGY IN IDH-MUTANT GLIOMA

Abstract BACKGROUND With an aging population the incidence of brain tumors and neurodegenerative diseases is increasing. Although biological associations between glioma and Alzheimer’s disease (AD) have been suggested, the frequency of comorbidity is insufficiently defined. We here screen tumor-adjacent cortex of a representative cohort of patients with low grade glioma for AD neuropathological changes (ADNC) specifically for phosphorylated tau (pTau) and amyloid beta (Abeta) deposition, microglial activation, amyloid precursor protein (APP) expression, diffuse axonal injury, and cortical tumor cell infiltration. MATERIAL AND METHODS A total of 85 patients with astrocytoma, IDH mutant, CNS-WHO grade 2-3, (N=37, median age: 39) and oligodendroglioma, IDH mutant and 1p-19q co-deleted, CNS-WHO grades 2-3 (N=48 median age: 50) were included. 2µm sections were immunohistochemically stained for the markers b-A4, t-AT8, NeuN, APP and Iba1. Whole slide quantification was performed using Matlab and QuPath codes. For a subset of 15 patients, longitudinal samples were available for analysis of post-treatment effects. RESULTS The median cell density in the tumor-adjacent cortex was 1355/mm2 for astrocytoma and 1392/mm2 for oligodendroglioma, which was significantly higher than in non-tumor infiltrated cortex (median: 1030 cells/mm², p-value<0.0001). A total of 32 patients (38%) showed Abeta (14%, n=12/85) and/or pTau pathology (36%, n=31/85) in the tumor-adjacent cortex. No difference between astrocytoma and oligodendroglioma was reported. ADNC was mostly frequently observed in older patients but was also present in young individuals (median: 34 years, range: 17 to 77 years). pTau was significantly positively correlated with tumor cell infiltration (Kendall´s tau=0.13, p=0.002). Both Abeta and pTau deposits were frequently observed in the frontal cortex (30%, N=18/60). The tumor infiltration into the cortical regions was associated with neuronal loss (Kendall´s tau=-0.29, p<0.0001). Microglial activation depended on the degree of tumor infiltration (R=0.29), followed by increasing patient age (R=0.12). Diffuse axonal injury was associated with ADNC (Chi²=19.6, p<0.05). APP expression of tumor cells was highest in patients of old age (R=0.35). Longitudinal samples showed stable ANDC pathology in 15/15 subjects. CONCLUSION Our preliminary findings suggest presence of ADNC in roughly a third of patients with low-grade glioma, which increases with tumor cell infiltration into the cortex, microglial activation and patient age. Notably, isolated pTau pathology was more pronounced than Abeta pathology suggesting the acquisition of a secondary tauopathy.

Enhancing building energy efficiency: Numerical analysis of a hybrid thermoelectric ventilation system and earth-air heat exchange with photovoltaic-thermoelectric power integration for heating and cooling loads

The lack of studies on hybrid systems combining thermoelectric ventilation (TEV) and earth-air heat exchangers with a photovoltaic-thermoelectric generator (PV-TEG) as the power supply for the hybrid system is addressed in this research. This study aims to investigate the effectiveness and performance of such a combined system in providing the required thermal load of a building. To simulate the performance of TEV and PV-TEG systems, energy conservation equations for various components of the system were formulated for the quasi-state mode. The performance of the earth-air heat exchanger system was evaluated using the effectiveness-number of transfer units method. The resulting set of algebraic equations was then solved using a MATLAB code. Results indicated significant preheating (5.41–15.03 °C) and precooling (0.7–10.22 °C) effects during the daily hours of 15-February and 15-July, respectively. Additionally, the TEG component effectively reduced PV temperatures by 3.54 °C–16.99 °C in 15-February and 3.99 °C–21.54 °C in 15-July. The system also demonstrated a high monthly useful thermal energy output (1215–1584 kWh) in the cold months (December to March), contrasting with higher monthly useful thermal exergy (712–763 kWh) in the summer months. Furthermore, increasing the number of TEGs from 10 to 50 resulted in significant improvements in the yearly average energy and exergy proficiency indexes (PIya,en and PIya,ex) by 164.97 % and 459.82 %, respectively. The supply air mass flow rate and the length and depth of the earth-air duct were identified as the second and third most influential parameters affecting these indexes. Moreover, the system at CPV concentration ratio of 3 provided the highest PIya,en and PIya,ex of 1.512 and 14.65, respectively.

Topology optimization for metamaterials with negative thermal expansion coefficients using energy-based homogenization

Since the existing topology designs of negative thermal expansion metamaterials are primarily based on the asymptotic homogenization theory, this paper conducts a topology optimization method of negative thermal expansion metamaterials based on the computationally efficient energy-based homogenization for the first time. In this research, (1) a new effective thermal stress coefficient equation is pioneeringly proposed using energy-based homogenization frame, where its theoretical derivation process is presented as well as its effectiveness and computational efficiency are verified by comparative cases. Additionally, the matlab code is open-sourced for public learning. (2) A topology optimization design of both 2D and 3D metamaterials with negative thermal expansion properties is established innovatively with Discrete Material Optimization (DMO). Its advantages are illustrated compared with the convectional method and its results are validated by Finite Element Method simulations. The new methods have promising applications in the evaluation and optimization of thermal expansion properties of composites.

Non-orthogonal spectacle correction for irregular astigmatism.

To investigate the potential improvement in visual acuity and subjective perception of image quality in patients with keratoconus using non-orthogonal correction (NOC) cylinder trial lenses where the steep and flat power meridians are set at angles less or greater than 90°. A set of NOC plano/cylindrical trial lenses, where the axes between the power meridians were set at a range of non-orthogonal angles, were used to refract 18 participants with keratoconus in whom 23 eyes were used for testing. Corneal elevation data were processed by bespoke MATLAB code from Pentacam Scheimpflug tomographer scans. Each participant first underwent subjective refraction using standard orthogonal cylinder trial lenses, and the monocular best-corrected visual acuity (BCVA, logMAR) was recorded for each eye. They then underwent a second subjective refraction using NOC cylinder trial lenses created for the study and completed a questionnaire to elicit their subjective appraisal of letter clarity and ghosting. Fourteen (61%) eyes demonstrated an increase in objective BCVA with the NOC versus the orthogonal correction; seven (30%) eyes showed no change and in two (9%) eyes, the BCVA was slightly worse. Further, 87% and 79% experienced an increase in letter clarity and a reduction in ghosting, respectively, independent of changes in BCVA. The majority of non-orthogonal angles were in the range of 80°-85°, and it was possible to refine the cylinder and axis of the NOC further compared with the orthogonal correction. All but one of the participants said they would be interested in trying non-orthogonal spectacles if the opportunity arose. Correcting irregular astigmatism in keratoconic individuals with non-orthogonal spectacle correction may provide benefit in terms of increased visual acuity, improvements in letter clarity and a reduction of ghosting effects. This type of correction has the potential to improve the overall quality of life for patients with keratoconus.

FreeTO - Freeform 3D topology optimization using a structured mesh with smooth boundaries in Matlab

Topology optimization has revolutionized the design of structures for various applications, particularly with the advancement of additive manufacturing. However, existing open-source codes for topology optimization have limitations, such as restricted domain initialization and lack of a CAD output after optimization. A novel open-source Matlab code, FreeTO, is presented, and it addresses these limitations by enabling the initialization of 3D arbitrary geometries and providing an STL file post-optimization. FreeTO utilizes a structured mesh and a smooth-edge (boundary) algorithm to generate smooth topological boundaries. The code is demonstrated through six practical design cases, showcasing its effectiveness in compliance minimization, compliant mechanisms, and self-supporting problems. FreeTO offers a user-friendly, all-in-one topology optimization package, making it an invaluable tool for educators, researchers, and practitioners. Future developments will focus on eliminating a few geometrical deviations in the optimized topologies, incorporating speedups, and extending the code to apply to more applications.

Experimental and ANN modeling of kerosene fuel desulfurization using a manganese oxide-tin oxide catalyst

This research pioneered the use of a nanocatalyst composed of manganese oxide (MnO2) and stannic oxide (SnO2) to effectively remove dibenzothiophene (DBT) from kerosene fuel through the catalytic oxidative desulfurization (ODS) process, using hydrogen peroxide (H2O2) as the oxidant. Impregnating SnO2 with varying amounts of MnO2 was used to manufacture the catalyst. The oxidation experiment ran in a batch reactor with varying reaction times and temperatures to determine optimal conditions. High MnO2 dispersion over SnO2 was shown by catalyst characterization data. Under optimal operating parameters (catalyst type: 5 % MnO2/SnO2, reaction temperature: 75 °C, and reaction duration: 100 min), the results demonstrated a maximum DBT removal efficiency of 82.84 % from kerosene fuel. This research also provides the construction of Artificial Neural Network (ANN) model to simulate the upgrading of kerosene fuel via desulfurization process. There has been a growing trend toward the diversified use of ANN to represent steady state systems in chemical engineering. MATLAB's code was employed for matching the experimental data to the artificial neural network (ANN) model. The resulted data showed significant agreement between the experimental and predicted outcomes, with regression coefficients (R2) of 0.99902, 0.99986, and 0.99961 and mean square errors (MSE) of 0.266, 0.272, and 0.104 for 0 % MnO2/SnO2, 1 % MnO2/SnO2, and 5 % MnO2/SnO2 respectively. This interactive model provided a solid foundation for understanding the novel behavior of the oxidation process.

Design of N-doped C60-σ-B-doped C60 photodetector based on resonant tunneling diode using DFT and NEGF method.

Photodetectors utilizing donor/acceptor (D/A) molecules have the capacity to detect light through molecular interactions between a donor and an acceptor molecule. These devices leverage electronic or optical changes within molecules when exposed to light, resulting in observable modifications. The unique properties of photodetectors with D/A molecules make them valuable tools in various fields, including molecular electronics. This paper presents the modeling and simulation of a single-molecule photodetector based on a D/A molecule configuration. The acceptor molecule used is N-doped C60 fullerene, while the donor molecule is B-doped C60 fullerene. Initially, simulations were conducted at zero bias voltage to determine the energy and states of the bipartite molecule. Subsequently, the system's Hamiltonian was computed based on these results. The self-consistent field method (SCF) and optical self-energy coefficients were employed for modeling. Finally, the current-voltage curve of the device was derived for various input light frequencies. The simulation and modeling results demonstrated that the device exhibited negative differential resistances at bias voltages of 0.33 V, 1.58 V, and - 0.93 V, depending on the input light frequency. Furthermore, the designed device demonstrated the ability to detect and absorb waves with different frequencies. The number of current peaks in the current-voltage curve varied with by altering the number of optical modes. The computational work was conducted using the software package of Atomistix ToolKit (ATK-2018.06) and MATLAB code. The calculations were based on the density functional theory (DFT) approach and the self-consistent field method, specifically the non-equilibrium Green function (NEGF). The exchange correlation function was investigated using the generalized gradient approximation (GGA) proposed by Perdew, Burke, and Ernzerhof (PBE). For the calculations, we employed the double-ζ plus polarization (DZP) basis set. Initially, the structures of N doped-C60-σ-B-doped-C60 molecule underwent optimization using the DFT approach implemented in the ATK package. This optimization process allowed us to extract the parameters of the molecule. Subsequently, we utilized the NEGF formalism in MATLAB software to model and simulate photodetector based on the optimized molecule. We calculated important features of the photodetector, such as photocurrent, and compared the performance of the photodetector using photons with energies of 2 and 3 eV.

Implementing Superresolution of Nonstationary Tides with Wavelets: An Introduction to CWT_Multi

Abstract Tides are often nonstationary due to nonastronomical influences. Investigating variable tidal properties implies a trade-off between separating adjacent frequencies (using long analysis windows) and resolving their time variations (short analysis windows). Previous continuous wavelet transform (CWT) tidal methods resolved tidal species. Here, we present CWT_Multi, a MATLAB code that 1) uses CWT linearity (via the “response coefficient method”) to implement superresolution, i.e., resolving tidal constituents beyond the Rayleigh criterion; 2) provides a Munk–Hasselmann constituent selection criterion appropriate for superresolution; and 3) introduces an objective, time-variable form of inference (“dynamic inference”) based on time-varying data properties. CWT_Multi resolves tidal species on time scales of days, and multiple constituents per species with fortnightly filters. It outputs astronomical phase lags and admittances, analyzes multiple records, and provides power spectra of the signal(s), residual(s), and reconstruction(s); confidence limits; and signal-to-noise ratios. Artificial data and water levels from the Lower Columbia River Estuary (LCRE) and San Francisco Bay Delta (SFBD) are used to test CWT_Multi and compare it to harmonic analysis programs NS_Tide and UTide. CWT_Multi provides superior reconstruction, detiding, dynamic analysis utility, and time resolution of constituents (but with broader confidence limits). Dynamic inference resolves closely spaced constituents (like K1, S1, and P1) on fortnightly time scales, quantifying impacts of diel power peaking (with a 24-h period, like S1) on water levels in the LCRE. CWT_Multi also helps quantify the impacts of high flows and a salt barrier closing on tidal properties in the SFBD. On the other hand, CWT_Multi does not excel at prediction, and results depend on analysis details, as for any method applied to nonstationary data. Significance Statement Ocean tides, especially in coastal and estuarine systems, are often nonstationary, in the sense that the mean and standard deviation of tidal properties vary over time, usually in response to some nontidal process. We introduce here a MATLAB code, CWT_Multi, that uses wavelet transforms to resolve both tidal species and constituents on time scales from a few days to months. Our code accommodates multiple scalar time series and has typical tidal analysis features like constituent selection and inference, plus two forms of uncertainty analyses. It is flexible, allowing the user to adapt analysis properties to diverse datasets. CWT_Multi is applicable to many problems involving time-variable tides, including sea level rise, compound flooding, sediment transport, and wetland habitat analyses. Application to vector data is a straightforward extension, but further development of our uncertainty analysis is merited. Because nonstationary tidal analysis is rapidly advancing, we also define the features of a “well-formed” analysis code.

Flow and sensitivity analysis of MHD radiative hybrid nanofluid flow across a permeable wedge with slip condition: Response surface methodology

AbstractThis current paper has been written to investigate the effect of a hybrid Tiwari and Das nanofluid model along with optimizing the flow using sensitivity analysis. Heat transfer rates and skin friction assessments were examined with the supplementation of external effects like heat source, thermal radiation, buoyancy forces, velocity slip as well as magnetic effect with spherical‐shaped nanoparticles. The mathematical modeling was formulated using a system of nonlinear PDE. Similarity transformations were implemented in the governing equations for obtaining the final set of nondimensional equations which were then solved using bvp4c code in MATLAB. The results obtained were in close agreement with published results. Some of the significant results obtained included an increase in Nusselt number by 2.26% for an increase in velocity ratio parameter () from to 0.3 along with an increase in skin friction coefficient by 0.77% with magnetic () parameter to . The heat transfer profile was augmented by 0.79% with increasing radiation parameter to while an increase of 18.84% was observed for skin friction profile values recorded for the hybrid model as compared to the base fluid model. Nusselt number reduced by 0.51% for a transition to nanofluid model from regular fluid and by 1.76% for the hybrid model compared to the nanofluid model The unblocked face‐centered central composite design (CCD) was used for analyzing the sensitivity analysis of independent, continuous input parameters on the response parameters, skin friction coefficient, and Nusselt number. The high values for , and , for Nusselt number and skin friction coefficient values, respectively, validate the ANOVA results obtained using response surface methodology (RSM). Only shows positive sensitivity for both the Nusselt number and skin friction responses. The present hybrid nanofluid model finds extensive applications in bio‐toxicity studies, manufacturing of water heaters, and forging of hot exhaust valve heads.

A micro-channel cooling system with two-phase looped thermosyphon for a supercritical CO2 Brayton cycle

The supercritical Carbon Dioxide (sCO2) closed Brayton cycle is a promising power generation technology, while the efficient cooling of CO2 and precise control of Compressor Inlet Temperature (CIT) is crucial for the high cycle efficiency and stable compressor operation due to the acute variation of thermophysical properties in the near-critical region. In conventional indirect cooling scheme, an intermediate single-phase water circuit is used to transfer heat from CO2 to water at the precooler, and then from water to the environment at the cooling tower, having the disadvantage of large pumping work consumption and high thermal resistance. In this work, a self-driven two-phase looped thermosyphon that significantly enhances the heat transfer by internal evaporation and condensation, is proposed to replace the water circuit. An experimental ultra-compact cooling system, consisting of a looped thermosyphon combined with micro-channel evaporator and condenser, filled with R134a coolant, is designed, fabricated, and tested. Visualized observation of the two-phase flow pattern and simultaneous measurement of the temperature, pressure and mass flow rates are conducted. A nodal analysis method is adopted, and a MATLAB code is developed for analyzing the internal fluid flow and the coupled sCO2-R134a-Air heat transfer, which is validated by experiment data. The results show that, the CO2 temperature could be accurately maintained at a specified near-critical point with a fluctuation of less than 1 K, and the average heat-releasing temperature can be reduced, while considerable pumping work, usually accounting for 2–5 % of the rated power output can be saved, thus contributing to increased cycle efficiency and system compactness.

Trajectory analysis and flight modeling of combat aircrafts ejection seats

In this study, it is aimed to develop a generic model which calculates the trajectory of the ejection seat from the jet aircraft, by taking into account the parameters that will affect the seat movement such as the seat’s launch speed, ejection direction, ejection angle, altitude of the aircraft, distance/height from the aircraft rudder and canopy, pilot and ejection seat weight. With the model algorithm proposed, the ejection seat trajectory model was developed on MATLAB. The ejection seat trajectory model is based on point mass trajectory mathematical model. In this study, an analytical study of the problem has been made for modeling the flight trajectory of the ejection seat after it has been ejected. Past studies were used as a basis for validation and simulation. By writing a generic MATLAB code, a user interface was developed and presented to the user as a module. This generic code that has been developed could be used for simulations by users in the future by revising it in accordance with their own job descriptions.

Multi-objective topology optimization and numerical investigation of heat sinks based on triply periodic minimal surface lattices

In thermal management applications, such as heat sinks (HSs) for electronic devices, cellular materials have extensively been employed. In recent years, there has been a growing attention towards employing topology optimization for enhancing hydraulic and heat transfer performance of HSs by optimizing their topology. The utilization of triply periodic minimal surface (TPMS) based structures presents distinctive prospects for customizing the design and performance of HSs. However, their potential remains unexplored in the context of customizing additively manufactured porous optimized HSs. Consequently, there is a need for research aimed at examining their coupled hydraulic and thermal performance. Density mapping approaches are used to build a variable density TPMS-based HSs from the output of topology optimization by applying the TPMS level-set equations that relate relative density and the level-set constant. In this work, a relative density mapping methodology is applied to thermo-fluid optimization problem to design a TPMS-based convective cooling system. An in-house MATLAB code was developed to perform a multi-objective topology optimization. After that, uniform and variable density (mapped from topology optimization results) TPMS-based HSs are analyzed using Star-CCM + CFD software to investigate their hydraulic and heat transfer performance. An experimental setup was established, and the numerical results were validated using uniform TPMS-based heat sinks. Results showed that incorporating TPMS with topology optimization has a great potential in thermal management applications as pressure drop across the heat sink was reduced while maintaining the performance.

Mathematical Model of Fungal Growth with Hyphae Death and Substrate

One of the most crucial subjects in mathematics education is mathematical modeling, which involves connecting situations from the real world to mathematical formulas and vice versa. Our study will focus on how substrate affects fungal growth and lateral branching behaviors, hyphal death, and tip death due to overcrowding. In order to solve a mathematical model, we will employ the system's three-dimensional of equations (PDEs). using numerical solutions, traveling wave theory, stability, and non-dimensionality in our mathematical approach as well as we use trace and determine to find stable states.Computer programs, such as the Maple program and Pdep code in MATLAB, will also be used to aid in the solution process.

Natural gradient hybrid variational inference with application to deep mixed models

Stochastic models with global parameters and latent variables are common, and for which variational inference (VI) is popular. However, existing methods are often either slow or inaccurate in high dimensions. We suggest a fast and accurate VI method for this case that employs a well-defined natural gradient variational optimization that targets the joint posterior of the global parameters and latent variables. It is a hybrid method, where at each step the global parameters are updated using the natural gradient and the latent variables are generated from their conditional posterior. A fast to compute expression for the Tikhonov damped Fisher information matrix is used, along with the re-parameterization trick, to provide a stable natural gradient. We apply the approach to deep mixed models, which are an emerging class of Bayesian neural networks with random output layer coefficients to allow for heterogeneity. A range of simulations show that using the natural gradient is substantially more efficient than using the ordinary gradient, and that the approach is faster and more accurate than two cutting-edge natural gradient VI methods. In a financial application we show that accounting for industry level heterogeneity using the deep mixed model improves the accuracy of asset pricing models. MATLAB code to implement the method and replicate the results can be found at https://github.com/WeibenZhang07/NG-HVI

Numerical Analysis of 3D Unilateral Quasi-static Contact: Effect of Coating Thickness and Mechanical Properties

In this study, a numerical model for unilateral quasi-static contact between a rigid sphere and an elastic coating has been developed. The contact problem was solved using a numerical procedure based on the FFT technique, considering various coatings with different thicknesses and mechanical properties, implemented through a Matlab code. The model calculates the contact surface deformation and pressure field through a double iteration process. The first iteration solves the contact problem for a given indenter penetration, while the second iteration refines this penetration by minimizing the difference between the fixed and calculated loads. To achieve this, influence coefficients are derived from the elasto-static equations using the Papkovich-Neuber potentials. The study discusses the influence of coating thickness and friction coefficient value on the tribological behavior of the coating. The results indicate that contact pressure increases (ranging from 1.9 to 2.5) as the coating becomes thicker or more rigid (0.02 mm to 0.2 mm). Additionally, the tribological behavior of the coated surface is affected by the coating's thickness, hardness, and friction coefficient value. Importantly, this model demonstrates versatility by being applicable to both smooth and rough surfaces.

Can AI Predict the Magnitude and Direction of Ortho-K Contact Lens Decentration to Limit Induced HOAs and Astigmatism?

Background: The aim is to investigate induced higher-order aberrations (HOA)s and astigmatism as a result of non-toric ortho-k lens decentration and utilise artificial intelligence (AI) to predict its magnitude and direction. Methods: Medmont E300 Video topographer was used to scan 249 corneas before and after ortho-k wear. Custom-built MATLAB codes extracted topography data and determined lens decentration from the boundary and midpoint of the central flattened treatment zone (TZ). An evaluation was carried out by conducting Zernike polynomial fittings via a computer-coded digital signal processing procedure. Finally, an AI-based machine learning neural network algorithm was developed to predict the direction and magnitude of TZ decentration. Results: Analysis of the first 21 Zernike polynomial coefficients indicate that the four low-order and four higher-order aberration terms were changed significantly by ortho-k wear. While baseline astigmatism was not correlated with lens decentration (R = 0.09), post-ortho-k astigmatism was moderately correlated with decentration (R = 0.38) and the difference in astigmatism (R = 0.3). Decentration was classified into three groups: ≤0.50 mm, reduced astigmatism by -0.9 ± 1 D; 0.5~1 mm, increased astigmatism by 0.8 ± 0.1 D; >1 mm, increased astigmatism by 2.7 ± 1.6 D and over 50% of lenses were decentred >0.5 mm. For lenses decentred >1 mm, 29.8% of right and 42.7% of left lenses decentred temporal-inferiorly and 13.7% of right and 9.4% of left lenses decentred temporal-superiorly. AI-based prediction successfully identified the decentration direction with accuracies of 70.2% for right and 71.8% for left lenses and predicted the magnitude of decentration with root-mean-square (RMS) of 0.31 mm and 0.25 mm for right and left eyes, respectively. Conclusions: Ortho-k lens decentration is common when fitting non-toric ortho-k lenses, resulting in induced HOAs and astigmatism, with the magnitude being related to the amount of decentration. AI-based algorithms can effectively predict decentration, potentially allowing for better control over ortho-k fitting and, thus, preferred clinical outcomes.