Values Of Parameters Research Articles

Choice of Metric Divergence in Genome Sequence Comparison.

The paper introduces a novel probability descriptor for genome sequence comparison, employing a generalized form of Jensen-Shannon divergence. This divergence metric stems from a one-parameter family, comprising fractions up to a maximum value of half. Utilizing this metric as a distance measure, a distance matrix is computed for the new probability descriptor, shaping Phylogenetic trees via the neighbor-joining method. Initial exploration involves setting the parameter at half for various species. Assessing the impact of parameter variation, trees drawn at different parameter values (half, one-fourth, one-eighth). However, measurement scales decrease with parameter value increments, with higher similarity accuracy corresponding to lower scale values. Ultimately, the highest accuracy aligns with the maximum parameter value of half. Comparative analyses against previous methods, evaluating via Symmetric Distance (SD) values and rationalized perception, consistently favor the present approach's results. Notably, outcomes at the maximum parameter value exhibit the most accuracy, validating the method's efficacy against earlier approaches.

Triple-diffusive magneto-nanofluid flow over a nonlinearly stretching sheet

Magnetic nanofluid constitutes a special class of nanofluids that exhibit both magnetic and fluid properties. Due to its extensive applications, researchers used the magneto-nanofluid to investigate the thermos-diffusion effects on boundary layer flows. The impact on the thermos-diffusion of magneto-nanofluid over a linear stretching sheet was first introduced by Awad et al., (boundary value problems, 1–13, 2013), then Goyal et al., (microfluidics and nano fluidics, 17(3), 591–604) examined the effects of Brownian motion, thermophoresis, and cross-diffusion on the enhancement of convection features of the power-law stretching sheet. In the present study, we extend Goyal’s finding of the thermophoresis effect by including a magnetic field. Consequently, now five partial differential equations with the magnetic field parameter need to be solved, which are converted to ordinary differential equations with similarity transformations. Then, the resulting nonlinear coupled differential equations are solved analytically using the method of Directly Defining the inverse Mapping with the help of Maple software. This is the first time this novel method is used to solve a system with four non-linear coupled differential equations. The analytical results of the study are compared with the available numerical results for a special case and found good agreement. This leads to the conclusion that this novel method is accurate and can be applied to solve not only higher order but also a system of several non-linear differential equations. Further, the skin friction is analyzed for various values of the magnetic parameter. It is observed that, the velocity profile decreases as the magnetic parameter increases. However, quite the opposite is true on the skin friction.

Neural network and thermodynamic optimization of magnetized hybrid nanofluid dissipative radiative convective flow with energy activation

This article, motivated by hybrid magnetic coating manufacturing developments, utilizes a neural network-based computational program to study the dynamics of hybrid magnetic nanofluids with entropy generation. A new physico-chemo-mathematical model has been presented to simulate the hybrid magnetic nano-coating flow along a stretching surface to a porous medium with viscous heating. A Rosseland flux model is used for radiation heat transfer and Darcy’s model for the isotropic porous medium. The stretching sheet is porous, and wall suction or injection are possible. A robust neural network has been deployed to optimize the physical parameters controlling the transport characteristics of hybrid nanofluids. Specifically, two hybrid nanoparticle combinations are addressed, namely graphite oxide (GO)-molybdenum disulfide ( Mo S 2 ) and copper (Cu)-silicon dioxide ( Si O 2 ), both with engine oil as the base fluid. The dimensional boundary layer model is transformed via suitable scaling variables from a partial differential system into a dimensionless non-linear coupled ordinary differential system. The transformed boundary value problem is solved numerically with the BVP4C subroutine in the symbolic software MATLAB, which achieves exceptional accuracy. Validation with previous simpler studies is conducted and a good correlation is obtained. The neural network optimization analysis incorporates Bayesian regularization as the training algorithm. The Bejan entropy generation minimization (EGM) analysis shows that with increasing radiation parameter R d , both entropy generation rate and Bejan number are increased. Furthermore, an elevation in Brinkman number Br leads to an upsurge in entropy generation rate and a downtrend in the Bejan number. The numerical solution of the boundary value problem reveals that with an increment in nanoparticle solid volume fraction φ 2 , magnetic parameter M , inverse permeability parameter ϵ , surface injection parameter ( s < 0 ) , Eckert number Ec and radiation parameter R d and with a decrement in suction parameter ( s > 0 ) and Prandtl number Pr , there is a strong enhancement in temperature magnitude and thermal boundary layer thickness. With greater nanoparticle solid volume fraction φ 2 , magnetic parameter M , inverse permeability parameter ϵ , suction parameter s and a reduction in thermal buoyancy parameter λ , strong flow deceleration is induced, and momentum boundary layer thickness is increased. The skin friction coefficient is substantially boosted with lower values of magnetic parameter M , inverse permeability parameter ϵ , suction parameter s and higher values of thermal buoyancy parameter λ . There is a significant decrement also computed in Nusselt number with a greater radiation parameter R d . The simulations provide a good benchmark for future extensions that may consider non-Newtonian behavior.

Estimation of Peptide Helicity from Circular Dichroism Using the Ensemble Model.

An established method for the quantitation of the helix content in peptides using circular dichroism (CD) relies on the linear spectroscopic model. This model assumes an average value of the helix-length correction for all peptide conformers, irrespective of the length of the helical segment. Here we assess the validity of this approximation and introduce a more physically realistic ensemble-based analysis of the CD signal in which the length correction is assigned specifically to each ensemble conformer. We demonstrate that the linear model underestimates peptide helicity, with the difference depending on the ensemble composition. We developed a computer program that implements the ensemble model to estimate the peptide helicity. Using this model and the CD data set covering a broad range of helicities, we recalibrate CD baseline parameters and redetermine helix-coil parameters for the alanine-rich peptide. We show that the ensemble model leverages small differences in signal between conformers to extract more information from the experimental data, enabling the determination of several poorly defined quantities, such as the nucleation constant and heat capacity change associated with helix folding. Overall, the presented ensemble-based treatment of the CD signal, together with the recalibrated values of the spectroscopic baseline parameters, provides a coherent framework for the analysis of the peptide helix content.

Monitoring of multiple fabrication parameters of electrospun polymer fibers using mueller matrix analysis

Electrospun polymer fiber mats feature versatile applications in tissue engineering, drug delivery, water treatment and chemical processes. The orientation of fibers within these mats is a crucial factor that significantly influences their properties and performance. However, the analysis of fiber samples using scanning electron microscopy (SEM) has limitations such as time consumption, fixed assembly, and restricted field of vision. Therefore, a fast and reliable method for qualitative measurements of fiber orientation is required. Mueller matrix polarimetry, a well-established method for measuring orientation of chemical and biological species, was employed in this case. We investigated the effect of four important parameters of the electrospinning process, namely collector speed, applied voltage, needle-to-collector distance, and solution concentration, on fiber orientation using Mueller matrix polarimetry thus extending the range of parameters analyzed. Measurements were performed using two extreme values and a central optimized value for each fabrication parameter. Changes in matrix values were observed for each fabrication parameter, and their correlation with fiber orientation was analyzed based on the Lu-Chipman decomposition. The results were compared with SEM images, which served as the ground truth, and showed overall good agreement. In the future, the analysis of electrospun polymer fibers can be done by using Mueller matrix polarimetry as alternative to current technology and fabrication parameters, including solution concentration for the first time in this context and the production can quickly be adjusted based on the outcome of the measurements.

Pulmonary hypertension in newly diagnosed obstructive sleep apnea-chronic obstructive pulmonary disease overlap syndrome patients attending a tertiary care centre-a cross-sectional analysis.

Overlap syndrome is the association of obstructive sleep apnea (OSA) with chronic obstructive pulmonary disorder and with other chronic respiratory diseases. Patients with overlap syndrome have a worse prognosis compared with chronic obstructive pulmonary disorder or OSA alone. Additionally, patients with combined chronic obstructive pulmonary disorder and OSA are more likely to develop pulmonary hypertension and right heart failure much earlier than those without overlap. To assess the occurrence of pulmonary hypertension (PH) in newly diagnosed OSA-chronic obstructive pulmonary disorder overlap syndrome patients attending a tertiary care centre. This cross-sectional study was conducted at Department of Pulmonary Medicine, Government Medical College, Kozhikode, Kerala, South India, among patients with OSA above 40years of age who were proactively evaluated to pick out those with undiagnosed overlap. A period of 6months after getting ethical clearance from June 2018 was selected as the study period. Among patients with symptoms suggestive of OSA above 40years of age who gave the informed consent were enrolled after screening with Standard Sleep questionnaires (Berlin questionnaire, STOP BANG and Epworth Sleepiness Scale). Enrolled patients underwent routine spirometry and sleep study using the standard Level 1 overnight polysomnography (Level1 OPSG). Patients were classified in to two groups as OSA patients (group I), having an apnea hypopnea index (AHI) > 5/hr alone and the second group as those OSA patients (group II), with an obstructive spirometry pattern who were the overlap group. Arterial blood gas analysis (a sample of radial arterial blood was drawn with the patient awake and supine, and was analyzed for pH, PaCO2 and PaO2) and echocardiography (ECHO) of the two groups were compared as a non-invasive method to assess pulmonary artery hypertension and results were analyzed in a systematic manner. Among the 84 patients enrolled, 16.7% had overlap syndrome and the rest had OSA alone. Statistically significant higher mean weight and BMI for those with overlap syndrome compared to the OSA group were observed. Mean FVC (forced vital capacity), FEV1 (forced expiratory volume 1s), and FEV1/FVC were lower in those with overlap syndrome compared to OSA group. The mean values of ABG parameters revealed higher PaCO2 and lower PaO2 among the group with overlap syndrome which were statistically significant. However, there was no significant difference in resting room air SaO2 value between the two groups. The mean values of sleep duration and efficiency were significantly lower in those with overlap syndrome with a p value < 0.001. The mean value of arousal and REM (rapid eye movement) sleep percentage were significantly higher among those with overlap syndrome (p < 0.001). Mean value of NREM (non-rapid eye movement) sleep percentage was lower among the group with overlap syndrome compared with the OSA group, and this difference was statistically significant. The mean AHI value of the overlap syndrome group was 39.79 ± 7.54 and this was significantly higher than the OSA group (p < 0.004). Among the 14 patients who had ECHO evidence of pulmonary hypertension, 9 (64.3%) belonged to the overlap group which shows that they are a highly vulnerable group for developing pulmonary hypertension (PH) and requires early detection and more rigorous treatment. This study confirms that OSA patients with modest daytime level of hypoxemia and mild-moderate chronic airflow limitation have a high prevalence of PH. Chronic airway obstruction may be asymptomatic in some subjects and this stresses the necessity of pulmonary function test in OSA.

Low-carbon embodied alkali-activated materials for sustainable construction: A comparative study of single and ensemble learners

Abstract Popular and eco-friendly alkali-activated materials (AAMs) replace Portland cement concrete. Due to the considerable compositional variability of AAMs and the inability of established materials science methods to understand composition–performance relationships, accurate property forecasts have proved impossible. This study set out to develop AAM compressive strength (CS) evaluation machine learning (ML) models using techniques including extreme gradient boosting (XGB), bagging regressor (BR), and multi-layer perceptron neural network (MLPNN). Ten input variables were used with a large dataset of 676 points. Statistical and K-fold studies were also used to evaluate the developed models’ correctness. XGB predicted the CS of AAM the best, followed by BR and MLPNN. The MLPNN and BR models had R 2 values of 0.80 and 0.90, respectively, whereas the XGB model had 0.94. Results from statistical analyses and k-fold cross-validation of the used ML models further attest to their validity. The built models can potentially compute the CS of AAMs for a variety of input parameter values, reducing the requirement for costly and time-consuming laboratory testing. Researchers and businesses may find this study useful in determining the necessary quantities of AAMs’ raw components.

On mathematical problems of two-coefficient inverse problems of ultrasonic tomography

This paper proves the theorem of uniqueness for the solution of a coefficient inverse problem for the wave equation in (with two unknown coefficients: speed of sound and absorption. The original nonlinear coefficient inverse problem is reduced to an equivalent system of two uniquely solvable linear integral equations of the first kind with respect to the sound speed and absorption coefficients. Estimates are made, substantiating the multistage method for two unknown coefficients. These estimates show that given sufficiently low frequencies and small inhomogeneities, the residual functional for the nonlinear inverse problem approaches a convex one. This solution method for nonlinear coefficient inverse problems is not linked to the limit approach as frequency tends to zero, but assumes solving the inverse problem using sufficiently low, but not zero, frequencies at the first stage. For small inhomogeneities that are typical, for instance, for medical tasks, carrying out real experiments at such frequencies does not present major difficulties. The capabilities of the method are demonstrated on a model inverse problem with unknown sound speed and absorption coefficients. The method effectively solves the nonlinear problem with parameter values typical for tomographic diagnostics of soft tissues in medicine. A resolution of approximately 2 mm was achieved using an average sounding pulse wavelength of 5 mm.

Reactive axillary lymph nodes after COVID-19 mRNA vaccination: comparison of mRNA vs. attenuated whole-virus vaccines.

To compare the incidence and natural course of reactive axillary lymph nodes (RAL) between mRNA and attenuated whole-virus vaccines using Deauville criteria. In this multi-institutional PET-CT study comprising multiple vaccine types (Pfizer-BioNTech/Comirnaty, Moderna/Spikevax, Sinovac/CoronaVac and Janssen vaccines), we evaluated the incidence and natural course of RAL in a large cohort of oncological patients utilizing a standardized Deauville scaling system (n=522; 293 Female, Deauville 3-5 positive for RAL). Univariate and multivariate analyses were conducted to evaluate the predictive value of clinical parameters (absolute neutrophil count [ANC], platelets, age, sex, tumor type, and vaccine-to-PET interval) for PET positivity. Pfizer-BioNTech/Comirnaty and Moderna vaccines revealed similar RAL incidences for the first 20 days after the second dose of vaccine administration (44% for the first 10 days for both groups, 26% vs. 20% for 10-20 days, respectively for Moderna and Pfizer). However, Moderna recipients revealed significantly higher incidences of RAL after 20 days compared to Pfizer-BioNTech/Comirnaty, with nodal reactivity spanning up to the 9th week post-vaccination (15% vs. 4%, respectively P < 0.001). No RAL was observed in patients who received either a single dose of J&J vaccine or two doses of CroronaVac. Younger patients showed increased likelihood of RAL, otherwise, clinical/demographic parameters were not predictive of RAL ( P = 0.014 for age, P > 0.05 for additional clinical/demographic parameters). RAL based on strict PET criteria was observed with mRNA but not with attenuated whole-virus vaccines, in line with higher immunogenicity and stronger protection offered by mRNA vaccines.

The significance of chemical reaction, thermal buoyancy, and external heat source to optimization of heat transfer across the dynamics of Maxwell nanofluid via stretched surface

Energy loss during the transportation of energy is the main concern of researchers and industrialists. The primary cause of heat exchange gadget inefficiency during transportation was applied to traditional fluids with weak heat transfer characteristics. Instead, thermal devices worked much better when the fluids were changed to nanofluids that had good thermal transfer properties. A diverse range of nanoparticles were implemented on account of their elevated thermal conductivity. This research addresses the significance of MHD Maxwell nanofluid for heat transfer flow. The flow model comprised continuity, momentum, energy transport, and concentration equations in the form of PDEs. The developed model was converted into ODEs by using workable similarities. Numerical simulations in the MATLAB environment were employed to find the outcomes of velocity, thermal transportation, and concentration profiles. The effects of many parameters, such as Hartman, Deborah, buoyancy, the intensity of an external heat source, chemical reactions, and many others, were also evaluated. The presence of nanoparticles enhances temperature conduction. Also, the findings are compared with previously published research. In addition, the Nusselt number and skin friction increase as the variables associated with the Hartman number and buoyancy parameter grow. The respective transfer rates of heat are 28.26%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} and 38.19%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} respectively. As a result, the rate of heat transmission increased by 14.23%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}. The velocity profiles enhanced while temperature profiles declined for higher values of the Maxwell fluid parameter. As the external heat source increases, the temperature profile rises. Conversely, buoyancy parameters increase as it descends. This type of problem is applicable in many fields such as heat exchangers, cooling of electronic devices, and automotive cooling systems.

A Multifactor Combination Optimization Design Based on Orthogonality for a Two-Degree-of-Freedom Floating Machine Gun Vibration System

This paper introduces a novel type of floating machine gun that can be simplified as a self-balancing two-degree-of-freedom mechanical system with distinct vibration characteristics. The model accounts for intricate motion patterns and encompasses numerous potential influencing factors. Multifactor combination optimization of the system represents a pressing engineering challenge. After establishing a simulation model for the machine gun and validating it experimentally, seven factors were chosen as optimization variables. The maximum recoil displacement of the inner receiver (MRD) and the firing rate were chosen to be indicators. Orthogonal combinations and variance analyses were used, and the effects of multiple factors were analyzed using SPSS software; these processes led to a determination of the optimal combination. The results indicated that the piston cylinder pressure, the bi-directional buffer spring energy storage, and the inner receiver mass significantly affected the MRD. Furthermore, the automaton mass and the reset spring energy storage were found to substantially affect the firing rate. Careful analysis of the variance results facilitated the determination of the optimal combination of parameter values. Remarkably, the optimal combination chosen resulted in an MRD reduction of approximately 20.2% and a firing rate increase of approximately 26.6%.

Intrinsically low lattice thermal conductivity and thermoelectric performance of 2D Cu2Te

In this study, we employed density functional theory to investigate the structural, mechanical, dynamical, electronic, and thermal transport properties of 2D Cu2Te in the hexagonal P6/mm structure. Our results demonstrate that this structure is both mechanically and dynamically stable, and has a direct band gap, indicating its potential as a semiconductor. The high Grüneisen parameter value of 2D Cu2Te resulted in a lower lattice thermal conductivity compared to its bulk counterpart due to increased phonon scattering in the 2D structure. Furthermore, we observed that the Seebeck coefficient in 2D Cu2Te is higher in the p-type region, while the electrical conductivity is higher in the n-type region at lower temperatures. Two different approaches were used to calculate the lattice thermal conductivity, and it was found that the thermal conductivity decreases with dimension reduction in Cu2Te. Additionally, ultralow thermal conductivity was observed. Moreover, the lattice thermal conductivity plays a dominant role in the thermoelectric performance. The maximum ZT value for 2D Cu2Te was obtained as 1.28 at 700 K. Overall, our results suggest that 2D Cu2Te is a potential new candidate for high thermoelectric performance.

Simulation Study on the Influence of Chimney Structure on the Efficiency of Gravity Heat Pipes for Controlling Spontaneous Combustion in Coal Waste Rock Piles

ABSTRACT Heat pipe is an efficient heat transfer element. Using heat pipes to control spontaneous combustion of coal gangue piles is a simple and effective method. However, the heat conducted by the heat pipe is usually directly released into the atmosphere, causing waste of resources. In order to utilize this part of the heat, this paper studied the method of enhancing the heat dissipation of the heat pipe. Based on the chimney effect, a chimney that utilizes the heat conducted by the heat pipe to enhance its heat dissipation efficiency was designed. The effects of five different parameters, including the distance between the chimney and the ground, the length of the chimney expansion section, the radius of the chimney inlet, the distance between the heat pipe and the inner wall of the chimney, and the height of the chimney, on the enhancement of heat dissipation of the chimney effect by the heat pipe were mainly studied. A large number of numerical simulation studies were carried out for chimneys with different structural sizes. The results show that all of the above five parameters have certain effects on the enhancement of heat dissipation of the chimney by the heat pipe, and each parameter has an optimal value to achieve the best enhancement effect of the chimney on the heat dissipation of the heat pipe. The response surface analysis was used to optimize the parameter values. The final designed chimney can increase the heat dissipation efficiency of the heat pipe by three times, providing a new direction for heat dissipation of heat pipes and the utilization of waste energy.

Numerical simulation on the MHD time-dependent Williamson nanofluid flow with cross diffusion and heat generation/absorption over a stretching plate

This novel study unfolds the heat and mass transfer investigation of Williamson nanofluid (WNF) through a porous medium past a stretching plate, along with considering heat generation/absorption. Nanoparticles hold significant significance in thermal engineering, industrial operations, and biomedical advancements, contributing to enhanced heat transfer, cooling mechanisms, thermal extrusion processes, and applications in cancer treatment, particularly in addressing brain tumors. The coupled ordinary differential equations (ODEs) are gained from governing partial differential equations (PDEs) by applying sufficient transformations. Then the ODEs of a nonlinear nature, along with boundary conditions, are solved through bvp4c, a built-in MATLAB program. A good agreement has been found in comparing the present study with already published papers. Numerical values for skin friction, mass transfer, and heat transfer are shown through a table against involved physical parameters, especially for both injection [Formula: see text] and suction [Formula: see text] cases, by varying the values of unsteadiness parameter A and Weissenberg number [Formula: see text]. The effects of the physical parameters on velocity, temperature, and mass profile are graphically depicted and illustrated minutely. It is noted that both the magnetic and Williamson parameters cause the thickness of the boundary layer to be reduced. It can be deduced from these findings that the rate of heat transfer over the surface of the plate decreases as the unsteadiness parameter increases. Furthermore, it is observed that an increase in the parameters of thermophoresis and Brownian motion leads to a higher temperature of the nanofluid.

SPATIAL PREDICTION OF RECEIVED SIGNAL STRENGTH FOR CELLULAR COMMUNICATION USING SUPPORT VECTOR MACHINE AND K-NEAREST NEIGHBOURS REGRESSION

Abstract. Signal strength maps are of great importance for cellular system providers in network planning and operation. Accurate prediction of signal strength is important for solving problems such as link quality. In this study, Received Signal Strength (RSS) prediction model is proposed for the 900 MHz band in the Van Yüzüncü Yıl University campus environment by using machine learning regression methods such as K- Nearest Neıghbours (KNN) and Support Vector Regression (SVR) together with Geographic Information Systems. For the training of this model, signal strength values taken from the RF Spectrum Analyser at different locations and distances were used. In addition, spatial data sets such as the digital elevation model, location of base stations and measurement stations, building heights and location, and land use/cover were used in the model. The effect of these data sets on RSS power is included in the model. The model aims to predict RSS accurately, visualize the estimated signal strength, and analyze the signal field strength coverage. Different kernels from the SVR model such as Polynomial, , and Sigmoid were tested. To increase the success of the model, appropriate parameter values were selected and configured according to SVR and KNN methods. For 900 MHz, the performances of SVR and KNN models were compared and the results of the models were verified using root mean squares (RMSE). Among the measured data, the lowest prediction is found in KNN Manhattan. According to the results of the simulation was observed that the SVR model created with spatial data performs better for Signal Strength. Finally, the lowest RMSE value (1.71 dB) was obtained from the Sigmoid kernel in the best signal strength estimation SVR model. The SVR model is recommended for Campus Area signal strength estimation.

Creation of a Simulated Sequence of Dynamic Susceptibility Contrast—Magnetic Resonance Imaging Brain Scans as a Tool to Verify the Quality of Methods for Diagnosing Diseases Affecting Brain Tissue Perfusion

DSC-MRI examination is one of the best methods of diagnosis for brain diseases. For this purpose, the so-called perfusion parameters are defined, of which the most used are CBF, CBV, and MTT. There are many approaches to determining these parameters, but regardless of the approach, there is a problem with the quality assessment of methods. To solve this problem, this article proposes virtual DSC-MRI brain examination, which consists of two steps. The first step is to create curves that are typical for DSC-MRI studies and characteristic of different brain regions, i.e., the gray and white matter, and blood vessels. Using perfusion descriptors, the curves are classified into three sets, which give us the model curves for each of the three regions. The curves corresponding to the perfusion of different regions of the brain in a suitable arrangement (consistent with human anatomy) form a model of the DSC-MRI examination. In the created model, one knows in advance the values of the complex perfusion parameters, as well as basic perfusion descriptors. The shown model study can be disturbed in a controlled manner—not only by adding noise, but also by determining the location of disturbances that are characteristic of specific brain diseases.

Optimizing chemotherapy treatment outcomes using metaheuristic optimization algorithms: A case study.

This study explores the dynamics of a mathematical model, utilizing ordinary differential equations (ODE), to depict the interplay between cancer cells and effector cells under chemotherapy. The stability of the equilibrium points in the model is analysed using the Jacobian matrix and eigenvalues. Additionally, bifurcation analysis is conducted to determine the optimal values for the control parameters. To evaluate the performance of the model and control strategies, benchmarking simulations are performed using the PlatEMO platform. The Pure Multi-objective Optimal Control Problem (PMOCP) and the Hybrid Multi-objective Optimal Control Problem (HMOCP) are two different forms of optimal control problems that are solved using revolutionary metaheuristic optimisation algorithms. The utilization of the Hypervolume (HV) performance indicator allows for the comparison of various metaheuristic optimization algorithms in their efficacy for solving the PMOCP and HMOCP. Results indicate that the MOPSO algorithm excels in solving the HMOCP, with M-MOPSO outperforming for PMOCP in HV analysis. Despite not directly addressing immediate clinical concerns, these findings indicates that the stability shifts at critical thresholds may impact treatment efficacy.

High-precision 1′ × 1′ bathymetric model of Philippine Sea inversed from marine gravity anomalies

Abstract. The Philippine Sea, located at the edge of the northwestern Pacific Ocean, possesses complex seabed topography. Developing a high-precision bathymetric model for this region is of paramount importance, as it provides fundamental geoinformation essential for Earth observation and marine scientific research, including plate motion, ocean circulation, and hydrological characteristics. The gravity–geologic method (GGM), based on marine gravity anomalies, serves as an effective bathymetric prediction technique. To further strengthen the prediction accuracy of conventional GGM, we introduce the improved GGM (IGGM). The IGGM considers the effects of regional seafloor topography by employing weighted averaging to more accurately estimate the short-wavelength gravity component, along with refining the subsequent modeling of long-wavelength gravity component. In this paper, we focus on seafloor topography modeling in the Philippine Sea based on the IGGM, combining shipborne bathymetric data with the Scripps Institution of Oceanography (SIO) V32.1 gravity anomaly. To reduce computational complexity, the optimal parameter values required for IGGM are first calculated before the overall regional calculation, and then, based on the terrain characteristics and distribution of sounding data, we selected four representative local sea areas as the research objects to construct the corresponding bathymetric models using GGM and IGGM. The analysis indicates that the precision of the IGGM models in four regions is improved to varying degrees, and the optimal calculation radius is 2′. Based on the above finding, a high-precision 1′×1′ bathymetric model of the Philippine Sea (5–35° N, 120–150° E), known as the BAT_PS model, is constructed using IGGM. Results demonstrate that the BAT_PS model exhibits a higher overall precision compared to the General Bathymetric Chart of the Oceans (GEBCO), topo_25.1, and DTU18 models at single-beam shipborne bathymetric points.

Exploring Cooperative Mechanisms in the Chinese Agricultural Value Chain: A Game Model Analysis Based on Leading Enterprises and Small Farmers

In the context of agricultural modernization in China, this paper examines the micro-level perspective of agricultural value chains. Drawing from three primary models of agricultural value chain cooperation—namely, “leading enterprises + small farmers”, “leading enterprises + cooperatives + small farmers”, and “corporate integration”—it establishes four game models: the decentralized decision-making game model, the two revenue-sharing game models, and the centralized decision-making game model. It systematically analyzes the cooperation mechanisms between leading enterprises and small farmers in upstream production links of the agricultural value chain, aiming to improve the cooperation strategy between leading enterprises and small farmers, elevate the status of small farmers in the agricultural value chain, promote increased income for farmers, and strengthen the agricultural value chain. The research findings are as follows: Firstly, the traditional contract of “leading enterprise + smallholder farmers” is incomplete, which makes it difficult to avoid opportunism and moral hazard that may arise between the two parties. By comparing multiple parameter values, it is found that this model is at a lower level of agricultural value chain development. Secondly, the model of “leading enterprise + cooperative + smallholder farmers” improves the tightness and stability of cooperation between leading enterprises and smallholder farmers. This model explains to some extent the operability of smallholder farmers sharing the value of the agricultural value chain. Compared with various parameter values, this model is at a medium level between other models. Finally, the “corporate integration” model is a fully vertical integration model. Compared with various parameter values, this model is at an advanced stage of agricultural value chain development. Therefore, agricultural value chains will ultimately develop toward the direction of corporate integration. This study has positive practical significance for enhancing the status and claim rights of small farmers, promoting increased income for farmers, enhancing the consistency of values between leading enterprises and small farmers, strengthening the stability of the agricultural value chain, and ultimately achieving common prosperity and agricultural modernization in China.

Thermophoretic particle deposition in a nanofluid flow across a disc with non-fourier heat flux: An investigation using tangent hyperbolic model

Non-Newtonian nanofluids are attracting the attention of researchers and academics due to the high heat transmission rates that they possess. To improve thermal devices and exchangers, the researchers come up with innovative and expense-effective methods. One of the advanced technological approaches that can be utilized to enhance the thermal performance of devices is the utilization of nanofluids. This non- Newtonian nanofluid possesses the advanced properties of increasing heat transfer and destroying harmful bacteria. For this purpose, the present theoretical work is to explore the heat and thermophoretic particle deposition analysis on the tangent hyperbolic nanofluid flow over the rotating porous disk with presence of non-Fourier heat flux model and nanoparticle aggregation effect. Darcy-Forchheimer fluid model is adopted to develop the fluid flow system. Furthermore, the nanofluid’s viscosity and thermal conductivity are expressed through advanced modeling, employing the modified Krieger-Dougherty model for viscosity and the Maxwell-Bruggeman model for thermal conductivity. The nanoparticles and base fluid involved in this context are Molybdenum disulfide ( Mo S 2 ) and Water/carboxyl-methyl cellulose (CMC) respectively. The mathematical representation of the fluid flow and energy propagation involves a system of coupled partial differential equations (PDEs). The system of partial differential equations (PDEs) is transformed into non-dimensional ordinary differential equations (ODEs), which are then solved both analytically and numerically using the Homotopy Analysis Method(HAM) and Runge-Kutta-Fehlberg (RKF) method along with shooting technique. Exploring the graphical representation reveals a comprehensive analysis of key factors influencing velocity, temperature, and concentration. Through innovative visualization, these parameters take center stage, providing insights into their intricate interplay and dynamic relationships. The heat transfer rate is enhanced 0.24% and 1.4% by rising the values of thermal relaxation parameter and Weissenberg number. Also, the mass transfer rate is accelerated 0.8% and 0.14% due to enhancing the values of thermophoretic coefficient and thermophoresis parameter.