Investigation Of Dynamics Research Articles

A comprehensive investigation of fractional glucose-insulin dynamics: existence, stability, and numerical comparisons using residual power series and generalized Runge-Kutta methods

The regulation of blood glucose levels involves complex interactions between glucose, insulin, and beta cells, where disruptions may lead to diabetes. Traditional integer-order models fail to capture the memory-dependent and hereditary properties of biological systems. This study develops a fractional-order glucose-insulin-beta cell model and investigates its dynamics using the Residual Power Series Method (RPSM) and the Generalized Runge-Kutta Method (GRKM). Theoretical analyses establish the model's existence, uniqueness, and boundedness of solutions, ensuring biological validity. Stability and bifurcation analyses reveal the critical role of fractional orders in shaping system dynamics. RPSM demonstrates computational efficiency with rapid convergence, while GRKM excels in stability and bifurcation studies. Comparative numerical simulations highlight the complementary strengths of these methods, providing a robust framework for predictive modeling in diabetes management. This work underscores the potential of fractional-order models to advance understanding and control of metabolic disorders.

Molecular Dynamics Investigation of the Shearing Behavior of Short-Chain Additives at Nanoscale Rough Ceramic/Polymer Interfaces: Implications for Biomedical Applications

Molecular Dynamics Investigation of the Shearing Behavior of Short-Chain Additives at Nanoscale Rough Ceramic/Polymer Interfaces: Implications for Biomedical Applications

All-fiber chirp tuning of ultrashort pulses via long chirped fiber Bragg grating pair

Chirp tuning of ultrashort pulses is crucial for nonlinear fiber amplification and nonlinear dynamics investigations. Here we demonstrate all-fiber chirp tuning via a chirped fiber Bragg grating (CFBG) pair. Two identically long CFBGs were placed reversely to cancel out most of their huge dispersion (∼40 ps/nm or ∼22.4 ps2 @1030 nm), while the controllable temperature gradient along them could be used for precise chirp tuning with a tuning range of ∼ps2, verified by dispersion measurement and ultrashort pulse broadening. This relatively large chirp tuning could be used in prechirp management in nonlinear fiber amplifiers, exemplified by the optical spectrum tailoring therein. In addition, we also show this precise chirp tuning capability could be very helpful for pulse temporal quality diagnosis, which is indispensable for seed pulse optimization. We believe this all-fiber chirp tuning technique would find wide applications in nonlinear amplification and ultrafast nonlinear dynamics investigations.

Investigation of the droplet dynamics and thermal performance during dropwise condensation in the wickless heat pipe condenser

Investigation of the droplet dynamics and thermal performance during dropwise condensation in the wickless heat pipe condenser

Numerical investigation of fluid dynamics in aquifers for seasonal large-scale hydrogen storage using compositional simulations

Numerical investigation of fluid dynamics in aquifers for seasonal large-scale hydrogen storage using compositional simulations

Machine Learning Prediction and Simulation of Drugs Targeting GSK-3β in Breast Cancer

Introduction and Objective: Breast cancer ranks as the second-most prevalent cause of death among women worldwide, with particularly elevated mortality rates in India. Breast cancer’s origin involves biochemical pathway alterations influenced by tumor-inducing proteins. Research has highlighted glycogen synthase kinase-3 beta (GSK-3β) as a crucial protein that regulates the expression of various genes in the cell cycle. Mutations in this protein have a significant impact on cellular development. As a consequence, it triggers aggressive subtypes of breast cancer, such as triple-negative breast cancer. So, the primary aim of this study is to identify novel chemicals targeting GSK-3β using machine learning methods, molecular modeling, and dynamic techniques. Materials and Methods: To achieve the study's objective, small molecules were screened using a Machine Learning (ML) approach, and subsequently, molecular docking and dynamic modelling investigations were conducted to explore interactions between drugs and GSK-3β. Results: The research findings highlighted a specific compound, piperidine, 4-(3,4- dichlorophenyl)-4-[4-(1H-pyrazol-4-yl) phenyl], which exhibited a superior docking score of -9.6 kcal/mol. Piperidine also formed conventional hydrogen bonds with the target protein. Furthermore, the calculated binding free energy of -12.46 kcal/mol suggested that this compound exhibited greater stability compared to commercially available drugs. Conclusion: These promising findings highlight the potential of piperidine and similar small molecules as promising candidates for targeting the tumor-inducing protein GSK-3β. Subsequent investigations, both in vitro and in vivo, will be essential to assess their effectiveness in combating breast cancer.

Exploring molecular dynamics investigations in PLA-based systems: A comprehensive review

Exploring molecular dynamics investigations in PLA-based systems: A comprehensive review

Molecular dynamics investigation of flue gas effects on CO2 diffusion and absorption in water lean amine-based solvents

Molecular dynamics investigation of flue gas effects on CO2 diffusion and absorption in water lean amine-based solvents

Calibration of GEDI footprint aboveground biomass models in Mediterranean forests with NFI plots: A comparison of approaches.

Observations from the NASA Global Ecosystem Dynamics Investigation (GEDI) provide global information on forest structure and biomass. Footprint-level predictions of aboveground biomass density (AGBD) in the GEDI mission are based on training data sourced from sparsely distributed field plots coincident with airborne laser scanning surveys. National Forest Inventories (NFI) are rarely used to calibrate GEDI footprint biomass models because their sampling and positional accuracy prevent accurate colocation with GEDI or ALS. This omission can limit the harmonization of jurisdictional biomass estimates from NFI's and GEDI; however, there are methods available to improve the colocation of NFI plots with GEDI footprints. Focusing on Mediterranean forests in Spain, we compared different approaches to the collocation of NFI and GEDI data: (i) simulated waveforms from ALS; (ii) nearest-neighbor on-orbit GEDI waveforms; and (iii) imputed GEDI waveforms imputed to NFI plot locations using a novel geostatistical method. These methods are potential solutions to improve the local performance of biomass models and address potential local systematic deviations between GEDI and NFI estimates. We assess the advantages and limitations of these methods to locally calibrate GEDI biomass models and quantify the impact of geolocation errors in reference NFI plot data. The new biomass models from each method were used to predict footprint level AGBD, which were then gridded for a province in the North-West of Spain. It was found that the imputation approach is not sensitive to common errors in NFI plot geolocation, but it can outperform ALS-based simulation in some cases, highlighting the benefit of information from multiple GEDI footprints proximate to NFI plots for improving biomass predictions. This research provides users with benchmark of available techniques to locally-calibrate GEDI footprint biomass models.

1H, 15N, 13C backbone resonance assignment of human Alkbh7.

The Alkbh7 protein, a member of the Alkylation B (AlkB) family of dioxygenases, plays a crucial role in epigenetic regulation of cellular metabolism. This paper focuses on the NMR backbone resonance assignment of Alkbh7, a fundamental step in understanding its three-dimensional structure and dynamic behavior at the atomic level. Herein, we report the backbone 1H, 15N, 13C chemical shift assignment of the full-length human Alkbh7. Experiments were acquired at 25°C by heteronuclear multidimensional NMR spectroscopy. Collectively, 70% of the backbone NH resonances were assigned, with 144 out of a possible 205 residues assigned in the 1H-15N TROSY spectrum. Interestingly, peaks from the active site and the C-terminal end of Alkbh7 are not NMR visible, suggesting that these regions are dynamic on the intermediate exchange regime. Using the program TALOS+, a secondary structure prediction was generated from the assigned backbone resonance that is consistent with the previously reported X-ray structure of the enzyme. The reported assignment will permit investigations of the protein structural dynamics anticipated to provide crucial insight regarding fundamental aspects in the recognition and enzyme regulation processes.

P-345. The Changing Dynamics of the Candida auris Outbreak in Southern California

Abstract Background The current global outbreak of Candida auris poses a serious public health threat. With both high levels of antifungal resistance and a propensity for nosocomial spread, Candida auris has the potential to cause devastating outbreaks within healthcare facilities, especially amongst long-term and intensive care patients. As in many viral and bacterial pathogens, pathogen genomics has proven to be an indispensable piece of the outbreak response toolkit by contributing information useful in diagnosis, drug-resistance testing, and epidemiologic investigations. Phylogenetic analysis of C. auris reveals introduction of Clade I to Southern CaliforniaFigure 1.A) Distribution of sequenced C. auris case clade membership during study period. B) Maximum-likelihood phylogenetic tree constructed from concatenated SNP sites of sequenced C. auris isolates with antifungal resistance data. MIC breakpoints: Fluconazole ≥ 32, Micafungin ≥ 4, Amphotericin B Intermediate ≥ 2, Resistant ≥ 4 Methods At our large healthcare system located in Los Angeles, patients admitted with epidemiologic risk factors such as residence in a long-term care facility undergo admission screening for C. auris via combined axillary/inguinal swab. Microbiologic data from 213 C. auris isolates collected either via admission screening or from clinical cultures, including 91 isolates with paired whole-genome sequencing data, were used to conduct an in-depth investigation of the changing dynamics of the local C. auris outbreak. Phylogenetic analysis was employed to investigate the relationships between isolates. Results Transmission of C. auris between 2021 and 2023 remained high, with an average of 59 patients per year determined to be colonized or infected with C. auris. While the local outbreak has overall been dominated by Clade III isolates that are resistant to fluconazole but susceptible to echinocandins and amphotericin B, phylogenetic analysis identified the first known introduction of Clade I to Southern California in late 2022, which may have led to the first amphotericin B resistant case within our healthcare system. Genotypes of ERG11 and FKS1 genes were found to correlate with fluconazole resistance and echinocandin susceptibility. Two cases identified nearly one month apart in an intensive care unit were genetically identical, supporting epidemiologic suspicion of in-hospital transmission. Conclusion Continued genomic surveillance of C. auris contributed to an enhanced understanding of local outbreak dynamics, helping inform and guide empirical treatments and epidemiologic investigations. Disclosures All Authors: No reported disclosures

Numerical and computational fluid dynamics experimental analysis of novel extended tetra-hybrid Tiwari and Das Sisko nanofluid passed a stenosed artery

The medical industry extensively uses nanoparticles for applications such as wound dressing, artificial organ components, drug delivery, tissue engineering, and cardiovascular disease treatment. The incorporation of nanoparticles into the base fluid enhances the rate of heat transmission and additionally decreases blood pressure. This study aims to examine the effects of a new tetra-hybrid nanofluid model, which includes nanoparticles, on the flow of blood through a stenosed artery with a circular shape. Model partial differential equations (PDEs) incorporate phenomena such as thermal radiation and viscous dissipation. Furthermore, we transform these modeled PDEs into dimensionless ordinary differential equations (ODEs) using self-similarity variables and numerically solve the proposed ODEs using the well-established Lobatto IIIa numerical technique. The impact of several dimensionless parameters on the heat transfer rate, skin friction, velocity, and temperature fields has been computed and analyzed using figures and tables. Moreover, we conducted a computational fluid dynamics (CFD) investigation on a tetra-hybrid nanofluid, using blood as the base fluid. The results indicate that heat transmission is higher in tetra-hybrid nanoparticles compared to tri-hybrid and di-hybrid nanofluids. The volume fraction of nanoparticles in the base fluid increases, resulting in a decrease in the surface drag coefficient and a decrease in the heat transport phenomenon. Amplification in the thermal radiation parameter improves heat transfer, helping to remove toxins and plaque from blood flowing through arteries. An increase in thermal radiation generates excess heat that dilates inflexible arteries, facilitating blood flow. From CFD analysis, it is observed that thermal conduction k and heat transfer coefficient h amplify by improving Reynolds number. Pressure at the outlet interface of the tube decreases from 3058 Pa to 2996 Pa for the case of a 10% volume fraction of nanoparticles. Velocity boundary layer thickness decreases from 1.46 to 1.37 with an increase in the volume fraction of nanoparticles from 1% to 10%. Heat deliverance rate amplifies in the case of tetrahybrid nanofluid 2.5394–2.6147 in contrast to trihybrid nanofluid 2.4008 to 2.4711 by amplifying Rd from 0.7 to 1.1. The surface drag coefficient amplifies by magnifying Re but the Nusselt number diminishes by improving the volume fraction of nanoparticles.

Dynamical investigation of the new (3+1)-dimensional Kadomtsev–Petviashvili–Sawada–Kotera–Ramani equation with conformable fractional derivative

This paper examines the conformable version of a recently proposed novel [Formula: see text]-dimensional Kadomtsev–Petviashvili–Sawada–Kotera–Ramani equation. First, some basic definitions and properties of the conformable derivative are provided. To find exact solutions to this problem, the modified extended tanh-function, exp([Formula: see text]-expansion, and Kudryashov R function methods are applied. The results are illustrated by utilizing some of the gathered data’s physical 3D, contour, and 2D surfaces, which sheds light on how geometric patterns are physically comprehended. These solutions contribute to understanding the potential practical applications of the examined model and other nonlinear representations in the physical sciences. These techniques can provide significant solutions for various fractional differential equations.

Molecular dynamics investigation of structural, thermal, and dynamic properties of maghemite through thermal cycling.

We analyzed the thermal, structural, and dynamic properties of maghemite using classical molecular dynamics, focusing on bulk and nanoparticle systems. We explored their behavior when heated to high temperatures (above the melting point) and during cooling, as well as under thermal cycles ending at intermediate temperatures. Our findings show that in the bulk system, both the tetrahedral and octahedral iron sub-lattices undergo a phase transition prior to melting. Cooling the system from above this transition, or from above the melting point, leads to the formation of different metastable maghemite structures. In contrast, this sub-lattice transition is absent in nanoparticles, where melting occurs through an interface-mediated process. At temperatures just above the transition, nanoparticles adopt an ellipsoidal shape, which is retained during cooling. In addition, the specific heat of both bulk and nanoparticle systems at temperatures above the Debye temperature is evaluated and compared with the available experimental data. Overall, our results highlight the complex thermal behavior of maghemite across a range of temperatures, which remains insufficiently explored experimentally. Further experimental investigations could also provide valuable feedback for model refinements.

Monitoring changes of forest height in California

Forests of California are undergoing large-scale disturbances from wildfire and tree mortality, caused by frequent droughts, insect infestations, and human activities. Mapping and monitoring the structure of these forests at high spatial resolution provides the necessary data to better manage forest health, mitigate wildfire risks, and improve carbon sequestration. Here, we use LiDAR measurements of top of canopy height metric (RH98) from NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission to map vegetation height across the entire California for two different time periods (2019–2020 and 2021–2022) and explore the impact of disturbance. Exploring the reliability of machine learning methods for temporal monitoring of forest is still a developing field. We train a deep neural network to predict forest height metrics at 10-m resolution from radar and optical satellite imagery. Model validation against independent airborne LiDAR data showed a R2≥0.65 for the top of canopy height outperforming existing GEDI-based height maps and with improved sensitivity for mapping tall trees (RH98 ≥ 50 m) across California. Height showed distinct spatial variations across forest types offering quantitative and spatial information to evaluate forest conditions. The model, trained on data from 2019 to 2020, showed a similar accuracy when applied to satellite imagery acquired in 2021–2022 allowing a robust detection of changes caused by natural and man-made disturbances of forest. Changes of height captured impacts of tree mortality and fire intensity, pointing to the influence of wildfire across landscapes. Fires caused more than 60% of the large forest disturbances between the two time periods. This study demonstrates the benefits of using locally trained ML models to rapidly modernize forest management techniques in the age of increasing climate risks.

In silico identification of a phosphate marine steroid from Indonesian marine compounds as a potential inhibitor of phosphatidylinositol mannosyltransferase (PimA) in Mycobacterium tuberculosis.

A higher death rate is associated with multiple factors, including medication resistance and co-infection with the human immunodeficiency virus (HIV). This shows the need to obtain new and effective drug candidates in improving tuberculosis (TB) treatment. In addition, the phosphatidylinositol mannosyltransferase (PimA) enzyme starts the production of phosphatidyl-myo-inositol. PimA has been identified as a key enzyme and an important area for further research in the development of anti-TB drugs. Previous research investigated various applications including marine resources driven by a deeper understanding of the distinctive features of the ecosystem and the diverse array of organisms. Therefore, this research aims to investigate the potential of Indonesian marine compounds as inhibitors of PimA, with a focus on binding energy, interaction modes, and stability using docking and molecular dynamics (MD) investigation methodologies. The results show that a total of 84 Indonesian marine compounds are effectively docked to the PimA to obtain compounds 21, 27, and 33 for further investigation. Based on the MD analysis, compound 27 (desulfohaplosamate) is the most promising candidate among the new MTB-PimA inhibitors. Compounds bind to PimA, as shown by a strong affinity of -30.09kJ/mol, and form hydrogen bonds with the key amino acid residue Gly16. Furthermore, a stable complex is formed to easily analyze the antibacterial agents targeting MTB in the future.

Effect of A-DNA and B-DNA Conformation on the Interplay between Local Excitations and Charge-Transfer States in the Ultrafast Decay of Guanine-Cytosine Stacked Dimers: A Quantum Dynamical Investigation.

We here simulate in the gas phase the population dynamics of guanine/cytosine (GC) and cytosine/guanine (CG) stacked dimers in B-DNA and A-DNA arrangement, following excitation in the lowest-energy band, and considering the four lowest-energy ππ* bright excited states, the three lowest-energy nπ* states, and the G → C charge-transfer (CT) state. We resort to a generalized Linear Vibronic Coupling (LVC) model parametrized with time-dependent density functional theory (TD-DFT) computations, exploiting a fragment-based diabatization and we run nonadiabatic quantum dynamical simulations with the multilayer version of the Multiconfigurational Time-Dependent Hartree (ML-MCTDH) approach. G → C CT results in a major decay process for GC in B-DNA but less in A-DNA arrangement, where also the population transfer to the lowest-energy excited state localized on C is an important intermonomer process. In CG arrangements, mostly intramonomeric decays take place. We simulate the dynamics of several other GC structures whose arrangement is intermediate between B-DNA and A-DNA, obtaining further insights on the effect that the sequence and, especially, the stacking geometry have on the population transfer to the G → C CT.

Analytical investigation of fireball dynamics and radiative heat damage in energetic explosions

The emergence of new energetic materials has significantly intensified the thermal damage effects at explosion sites, The traditional damage assessment methods face severe challenges. Static models used for thermal dose calculations rely on equivalence, failing to capture the dynamic characteristics of an explosive fireball. Furthermore, existing dynamic fireball models only characterize time as a variable, neglecting the energy characteristics. In this study, we obtain fireball diameter and central height data through thermal imaging which serve as essential inputs for our model construction. By integrating the geometric and physical characteristics of the explosion fireball, we develop a two-variable function model (time and equivalent) to predict changes in the fireball during its primary damage phase. The effective thermal region of the fireball is treated as a Lambertian body to derive an equivalent temperature. Using this model and equivalent temperature, the heat dose and corresponding damage effects of the fireball are calculated at five locations for four distinct equivalent masses using the Stefan-Boltzmann law. By applying the calculated thermal dose data to the pressure effect model in a static explosion field according to the explosion similarity law, we construct an equivalent damage model with an average error of 4.09%. This model, valid within a 1.17–4.76 m/kg1/3 range, reliably assesses thermal damage for high-energy materials. This method provides a better basis for the assessment of thermal damage caused by various types of explosions.

Numerical Investigation of Heat Transfer and Flow Dynamics in Tubes with DNA-Inspired Slotted Inserts

Within the realm of industrial energy conservation, the optimization of heat exchanger performance is paramount for the augmentation of energy utilization efficiency. This investigation employs computational fluid dynamics (CFD) simulations to elucidate the effects of an innovative DNA-Inspired Slotted Insert (DSI) on the convective heat transfer and pressure drop characteristics within heat exchange tubes. The study provides a thorough analysis of fully turbulent flow (Re = 6600−17,200), examining the effects of various DSI pitches, key lengths, and geometries. The findings reveal that the DSI instigates a three-dimensional spiral flow pattern, which is accompanied by an escalation in the Nusselt number (Nu) and friction factor (f) with increasing Reynolds numbers. An inverse relationship between Nu and both pitch and key length is observed; conversely, f exhibits a direct correlation with these parameters. The study identifies an optimal configuration characterized by a pitch of 10 mm and a key length of 1.5 mm, with square keys demonstrating superior heat transfer performance relative to other geometrical configurations. This research contributes significant design and application insights for double-helical inserts, which are pivotal for the enhancement of heat exchanger efficiency.

SMM Behavior in Distorted Trigonal Bipyramidal and Tetrahedral Cobalt(II) Complexes Based on Tripodal Tetradentate Phenolic Amines

Four CoII complexes [Co(L1)(MeOH)]‧MeOH (1‧MeOH), [Co(L2)(MeOH)]‧MeOH (2‧MeOH), [Co(L3)(H2O)]‧MeOH (3‧MeOH) and [Co(L4)] (4), derived from tripodal tetradentate phenolic amine arms, H2L1‐4, were synthesized and structurally characterized. The complexes 1·MeOH, 2·MeOH and 3·MeOH displayed distorted trigonal bipyramidal geometry, whereas 4 exhibited distorted tetrahedral geometry, depending on the substituents at the phenolate rings and amine arm. The variation of the coordination geometries and interatomic parameters around the CoII center has an impact on the magnetic behavior of the compounds. The complexes show magnetic anisotropy (ZFS) of the MS = ± ½ and ± 3/2 sub‐levels, with D = 29.1(2), 22.7(1), 28.8(2) and 30.9(5) cm‐1 for 1‧MeOH, 2‧MeOH, 3‧MeOH and 4, respectively. The results obtained from ab initio CASSF calculations match well with the experimental data, revealing the origin of magnetic anisotropy. The dynamic ac magnetic investigation of the magnetic susceptibility revealed a slow magnetic relaxation behavior for 2·MeOH, 3·MeOH and 4. The field‐induced slow relaxation of the magnetization occurred through combination of Raman and Direct processes, depending on the variation in the coordination geometries imposed by the coordinated ligand and/or the interatomic parameters around the CoII center, which in turn have definite impact on the magnetic features of the compounds.