Accurate Results Research Articles

Should machine learning be applied in credit risk accessment

In the past, analysts evaluated whether to offer loans to particular applicants using rule-based approaches. However, due to the sudden rise in applicants and a labor shortage, financial institutions have created quantitative methods of decision-making. Credit scoring models are constructed. In this essay, random forest model, support vector machine regression model and Probit model are performed and compared according to the dataset from a major U.S. credit cards company. The result demonstrates that while machine learning techniques can improve the efficiency and accuracy of credit risk assessment, it does face some problems and limitations. Random forest model is capable of handling high-dimensional data and is not complicated to run. However, database with fewer features or samples will have lower classification accuracy. Support vector machine regression model has high accuracy and prevents overfitting to some degree. It is sensitive to the choice of kernel parameters and regularization term. By testing how important Mill Ratio is, the Probit model produces more accurate results. However, the model is more complex than the other two. In future research, we propose to enhance and extend our work by using more artificial intelligence algorithms and evaluation metrics.

Microscopic identification of foodborne bacterial pathogens based on deep learning method

Accurate and rapid detection of foodborne bacterial pathogens is critical for food quality control. Nowadays, tracking morphological bacterial properties using microscope is still a priority at the grass-roots food supervision department due to its simplicity and low cost. However, the method requires highly professional personnel and there are certain misjudgments in the process of analysis. Automatically recognizing foodborne pathogen using deep learning algorithm to replace manual microscopy will not only reduce expert cost, artificial misjudgment, and operation time in detection, but also provide more objective and accurate identification. Here, we firstly constructed a high-quality and large-scale dataset of foodborne pathogenic bacteria, allowing the deep learning-based model to be efficiently trained and achieve accurate identification. The deep convolutional neural network-based model is capable of identifying six common foodborne pathogens, including Escherichia coli (O157:H7), Vibrio parahaemolyticus, Staphylococcus aureus, Bacillus cereus, Salmonella typhi, and Streptococcus hemolyticus, with accuracy rates of 90%–100%. This method can assist or replace the manual microscopic inspection step in traditional detection methods, and is promising to break through the traditional approach that heavily relies on manual judgment, greatly reduce the cost of experts and human errors, and provide rapid, accurate, and powerful discriminatory results in large quantities for detection.

The Validity of Computed Tomography Guided Lung Tru- cut Biopsies in Achieving Accurate Tissue Results

Objective: This study aims to assess the efficacy of CT-guided true-cut biopsy as a less invasive and cost-effective diagnostic technique for peripherally placed lung lesions.
 
 Methods: fourty patients with solitary lung nodule were involved in this study, true cut biopsies under Ct guide was taken then processed for routine H&E staining.
 
 Results: different pathological features can be identified with different pathological features giving primary diagnostic screening for lung cancer
 
 Conclusion: CT guided thoracic lesion biopsy is very efficient, cost-effective and less invasive technique when compared with the thoracic surgery

Infant head and brain segmentation from magnetic resonance images using fusion-based deep learning strategies

Magnetic resonance (MR) imaging is widely used for assessing infant head and brain development and for diagnosing pathologies. The main goal of this work is the development of a segmentation framework to create patient-specific head and brain anatomical models from MR images for clinical evaluation. The proposed strategy consists of a fusion-based Deep Learning (DL) approach that combines the information of different image sequences within the MR acquisition protocol, including the axial T1w, sagittal T1w, and coronal T1w after contrast. These image sequences are used as input for different fusion encoder–decoder network architectures based on the well-established U-Net framework. Specifically, three different fusion strategies are proposed and evaluated, namely early, intermediate, and late fusion. In the early fusion approach, the images are integrated at the beginning of the encoder–decoder architecture. In the intermediate fusion strategy, each image sequence is processed by an independent encoder, and the resulting feature maps are then jointly processed by a single decoder. In the late fusion method, each image is individually processed by an encoder–decoder, and the resulting feature maps are then combined to generate the final segmentations. A clinical in-house dataset consisting of 19 MR scans was used and divided into training, validation, and testing sets, with 3 MR scans defined as a fixed validation set. For the remaining 16 MR scans, a cross-validation approach was adopted to assess the performance of the methods. The training and testing processes were carried out with a split ratio of 75% for the training set and 25% for the testing set. The results show that the early and intermediate fusion methodologies presented the better performance (Dice coefficient of 97.6 ± 1.5% and 97.3 ± 1.8% for the head and Dice of 94.5 ± 1.7% and 94.8 ± 1.8% for the brain, respectively), whereas the late fusion method generated slightly worst results (Dice of 95.5 ± 4.4% and 93.8 ± 3.1% for the head and brain, respectively). Nevertheless, the volumetric analysis showed that no statistically significant differences were found between the volumes of the models generated by all the segmentation strategies and the ground truths. Overall, the proposed frameworks demonstrate accurate segmentation results and prove to be feasible for anatomical model analysis in clinical practice.

Test mass charge estimation for the space inertial sensor with extended Kalman filter

Charge Management System (CMS), aimed at mitigating charge-induced noise on an isolated free-falling test mass (TM), is a crucial component of space inertial sensors in various spaceborne gravitational missions. The estimation of TM charge is one of the tasks of CMS, directly impacting CMS performance. However, current methods for TM charge estimation suffer from slow response and significant estimation errors. This paper presents a new charge estimation method that combines the force modulation principle with an extended Kalman filter (EKF). We analyzed the relationship between the TM surface potential and TM motion, then established a mathematical model. The estimation of TM charge is accomplished through the EKF algorithm. A charge estimation simulation model was developed in Simulink, and experiments were conducted to evaluate the performance of the charge estimation methods under varying charge conditions. The experimental results demonstrate that the EKF method obtains more accurate estimation results and faster convergence rates, compared with Kalman Filter (KF) and Quadrature demodulation (QD) methods. The KF and QD methods show larger errors when the estimated charge is greater than 106e order, meanwhile the QD method exhibits slower convergence rates. The presented method is expected to be beneficial for the CMS of on-orbit inertial sensors.

Improving the use of LLMs in radiology through prompt engineering: from precision prompts to zero-shot learning.

Large language models (LLMs) such as ChatGPT have shown significant potential in radiology. Their effectiveness often depends on prompt engineering, which optimizes the interaction with the chatbot for accurate results. Here, we highlight the critical role of prompt engineering in tailoring the LLMs' responses to specific medical tasks. Using a clinical case, we elucidate different prompting strategies to adapt the LLM ChatGPT using GPT4 to new tasks without additional training of the base model. These approaches range from precision prompts to advanced in-context methods such as few-shot and zero-shot learning. Additionally, the significance of embeddings, which serve as a data representation technique, is discussed. Prompt engineering substantially improved and focused the chatbot's output. Moreover, embedding of specialized knowledge allows for more transparent insight into the model's decision-making and thus enhances trust. Despite certain challenges, prompt engineering plays a pivotal role in harnessing the potential of LLMs for specialized tasks in the medical domain, particularly radiology. As LLMs continue to evolve, techniques like few-shot learning, zero-shot learning, and embedding-based retrieval mechanisms will become indispensable in delivering tailored outputs. · Large language models might impact radiological practice and decision-masking.. · However, implementation and performance are dependent on the assigned task.. · Optimization of prompting strategies can substantially improve model performance.. · Strategies for prompt engineering range from precision prompts to zero-shot learning..

High-order space–time parallel computing of the Navier–Stokes equations

Space–time parallel methods can leverage modern parallel computing architectures to further accelerate numerical simulations where parallelizing only in space limits concurrency. In this paper, we develop a high-order space–time parallel computing method for solving the Navier–Stokes equations. The method is based on revisionist integral deferred correction (RIDC) algorithm proposed by Christlieb, Macdonald and Ong [SIAM J. Sci. Comput., 32 (2010), pp. 818-835]. This parallel-in-time algorithm yields high-order accurate results within a wall-clock time comparable to that of a forward or backward Euler simulation. We extend the RIDC algorithm to a space–time parallel method and apply the method to a compressible fluid solver using the message passing interface (MPI) framework. The global communicator is split into space and time communicators to manage parallel computing in spatial and temporal domains, respectively. Accuracy tests show that the method is high-order accurate. Numerical results encompassing both smooth flows and flows with discontinuities demonstrate that the proposed method can accelerate the computation robustly. Additionally, a three-dimensional space–time parallel computation of flow around a sphere is performed. The results indicate that RIDC has the potential to be used in the field of computational fluid dynamics.

A data-driven model based on the numerical solution of the equivalent inclusion problem for the analysis of nonlinear short-fibre composites

The paper introduces a novel data-driven homogenization model developed for the computation of stress-strain curves for composites featuring arbitrary orientation distributions, aspect ratios, and volume fractions of fibres. The objective of the proposed model is to deliver both time-efficient and accurate results while taking into account a relatively small database. The database is generated through numerical solutions of the equivalent inclusion problem, encompassing results for predefined orientations and aspect ratios of the single inclusion (fibre). The database created for a specific matrix-fibre system can be employed by the surrogate model to predict outcomes for various cases involving different fibre configurations. The surrogate model comprises four key modules: aspect ratio interpolation, a discrete orientation averaging procedure involving a genetic algorithm, a spatial transformation procedure, and an iterative Mori-Tanaka scheme. The validity of the proposed data-driven model has been confirmed by comparing the obtained results with those acquired through analysis of the complex representative volume element (RVE) using Fast Fourier Transform (FFT)-based homogenization, as well as through comparisons with experimental results from the existing literature.

Exploration of Global and Specialized Near-Infrared Calibrations for the Quantification of Nutritional Content in Grapevine Organs, Berry Phenological Stages, and Shoot Lignification.

Current infrared spectroscopy applications in the field of viticulture are moving toward direct in-field measuring techniques. However, limited research is available on quantitative applications using direct measurement of fresh tissue. The few studies conducted have combined the spectral data from various cultivars, growing regions, grapevine organs, and phenological stages during model development. The spectral data from these heterogeneous samples are combined into a single data set and analyzed jointly during quantitative analysis. Combining the spectral information of these diverse samples into a global data set could be an unsuitable approach and could yield less accurate prediction results. Spectral differences among samples could be overlooked during model development and quantitative analysis. The development of specialized calibrations should be considered and could lead to more accurate quantitative analyses. This study explored a model optimization strategy attempting global and specialized calibrations. Global calibrations, containing data from multiple organs, berry phenological, and shoot lignification stages, were compared to specialized calibrations per organ or stage. The global calibration for organs contained data from shoots, leaves, and berries and produced moderately accurate prediction results for nitrogen, carbon, and hydrogen. The specialized calibrations per organ yielded more accurate calibrations with a coefficient of determination in validation (R2val) at 90.65% and a root mean square error of prediction (RMSEP) at 0.32% dry matter (DM) for the berries' carbon calibrations. The leaves and shoots carbon calibrations had R2val and RMSEP at 84.99%, 0.34% DM, and 90.06%, 0.37% DM, respectively. The specialized calibrations for nitrogen and hydrogen showed similar improvements in prediction accuracy per organ. Specialized calibrations per phenological and lignification stage were also explored. Not all stages showed improvement, however, most stages had comparable or improved results for the specialized calibrations compared to the global calibrations containing all phenological or lignification stages. The results indicated that both global and specialized calibrations should be considered during model development to optimize prediction accuracy.

Analysis of heat and mass transfer of a non-linear convective heat generating fluid flow in a porous medium with variable viscosity

The present study addresses the significance of analyzing the heat and mass transfer characteristics of a non-linear convective fluid flow featuring variable viscosity within a porous medium. Our focus lies on understanding the impact of varying viscosity on the hydromagnetic coupling stress, particularly in the presence of internal heat generation. The fluid is confined within a porous medium, and convection cooling takes place at both wall of the fluid. A key assumption in our investigation is the nonlinearity nature of the buoyancy force, specifically when there is a substantial difference in temperature and concentration within the fluid and its surroundings, the buoyancy force becomes quadratic. The exploration of couple stress fluids, alongside considerations of temperature-dependent internal heat generation and mass transfer, contributes to advancing our understanding of thermal system performance. By accounting for nonlinearity in density, temperature, and concentration, the research aims to provide more accurate results than previous linear models, crucial for optimizing energy consumption in practical applications such as nuclear reactors and heat exchangers. Dealing with this type of problem often pose significant challenges, as they often lack analytical solutions, necessitating the use of more advanced numerical methods or approximation techniques. We employ Spectral Quasilinearisation Method (SQLM) to solve the coupled non-linear boundary value problems (PDE) arising from the model, momentum equation, energy equation, concentration equation, and entropy generation. The obtained results are then validated using the Runge–Kutta method and a good agreement is achieved. The findings for the dimensionless velocity, temperature, mass concentration and entropy generation are presented in graphics and in table manner, and effects of controlling parameters are extensively explained. The results highlight that an increase in variable viscosity leads to an enhancement in the velocity profile, while simultaneously reducing the temperature in the system. Furthermore, increments in the non-linear convection parameter and coupling stress parameter result in an appreciation of the velocity profile, temperature profile, and entropy generation rate.

Monitoring internal heat fluxes on Pulsating Heat Pipes using Kalman filter – Numerical and experimental results

The present study proposes an approach to monitor the wall-to-fluid internal heat flux on Pulsating Heat Pipes (PHP), which provides meaningful information about the efficiency and limiting operation conditions of such devices. In this sense, the current work adopts the Kalman Filter (KF) to estimate the heat flux between the working fluid and the internal tube wall along the adiabatic section of a PHP device by solving an inverse heat conduction problem. The KF is a well-known tracking technique, and therefore the current study is attractive due to the possibility of real-time evaluation of internal heat fluxes, a prospect that is extremely difficult to realize by standard whole domain inverse problem techniques. A high-speed and high-resolution infrared camera performs the temperature acquisition on the external wall of the PHP device. Two mathematical heat conduction models are considered: complete and thin-wall models. The complete model determines the temperature and heat flux evolution over time, and the thin-wall model is used to correct the random walk parameter on the KF formulation. Since the time step needed to the KF evolution model is smaller than the measurements acquisition interval, a matrix manipulation is used during the prediction step, reducing the number of mathematical operations. Hence, instead of predicting the states until reaching the measurement times, a proper mathematical manipulation reduces several mathematical operations to a single one. Then, the KF estimation capability is evaluated by considering synthetic data with different noise levels and different heat flux values. Subsequently, real experimental data are employed, and the results are compared with the ones obtained via a standard whole domain technique. The results obtained with the KF presented good visual agreement with the exact solution. Once the KF presents a delayed response, which is a characteristic of some filtering techniques, the errors with synthetic data were calculated offsetting the delay of the estimation in time in order to make a fair comparison, showing a maximum estimation error lower than 36 % considering the highest noise level. The synthetic data considered 20 s of time experiment, and the KF was able to estimate the PHP internal heat flux in less than 12 s, including the smoothing temperature step, showing fast and accurate results. For the real experimental data, the agreement between the KF and a whole domain technique is highly commendable, demonstrating a deviation of 25.5 % between both techniques. Moreover, the KF required significantly less computational time. The proposed methodology for heat flux estimation on PHPs is promising to evaluate their thermal behavior in real-time due to a low CPU time and computational memory required, as well as its simplicity to be implemented, possibly on embarked devices. Such information may therefore provide a thermal indicator of the correct functioning of PHPs in real time applications.

Cash Processing Administration System In The Indonesian Red Cross Blood Transfusion Unit, Semarang City

The cash processing administration system in a company plays a very important role. One of the problems that often arises in a cash processing administration system is the inaccurate calculation results of cash administration reports. This can result in inaccurate calculations of a company's profits and losses. Besides that, the use of company funds for something is not necessary, because every income and expenditure of company funds can be seen clearly and can be checked at any time. In short, companies will be able to quickly and easily find out the income and expenditure of funds. Information technology which is developing rapidly at this time provides very helpful benefits for the development of a company's information system, especially with the use of computer-based information technology, because by using computer technology in an information system you will be able to process data more quickly with lower error rates. minimal and saves time. The procedures that will be processed consist of the administration section and the finance section. If there is an income or expenditure of money or funds, the data can simultaneously be entered into the computer and will automatically influence or add to the existing data in the company. In this way, the condition of the money or funds in the company can be known with certainty and accuracy.

Design of Computer Information Management System Based on Machine Learning Algorithms

In order to improve the efficiency of office automation, regulate work frequency, and improve office efficiency, this paper presents a computer information management system design based on machine learning technology. Firstly, the basic design principles of computer information management systems are analyzed, and secondly, risk prediction is studied. The risk of computer information management systems is caused by the cross influence of different risk factor indicators, and has linear and nonlinear characteristics. Using a single prediction model cannot obtain accurate prediction results. Therefore, the risk prediction method for computer information management based on machine learning technology. The risk prediction method is established by using Analytic Hierarchy Method in machine learning algorithms, and the historical data is collected according to the index system. The weight of the initial prediction is determined by the combination of subjective and objective weight; In machine learning algorithms, risk prediction and benefit prediction are used as input and output methods for cloud machine learning. Through training and training, a risk prediction model is established to obtain higher prediction efficiency. The simulation results show that the prediction accuracy of this method is 95.5%, which can estimate the hazard existing in computer information management and improve the method.

Security Enabled New Term Weight Measure Technique with Data Driven for Next Generation Mobile Computing Networks

In the field of ASIC and FPGA, Machine Learning (ML) techniques play a major role and become predominant for accurate results for different applications like big data analysis and automotive electronics, and driverless vehicles which are required speed and power savings. Due to increasing the demand for higher accuracy, low power, low area consumption, and higher throughput for the complexity of the designs in the latest technology, the proposed system is fulfilling these demands in ASIC and FPGA domains, reconfigurable hardware architecture has been proposed it consists of an ML-based Support Vector Machine (SVM), high-speed AHB protocol and Floating point (FP) operations and also the system has the flexibility to communicate with I2C and I2S protocols. In order to increase throughput with minimal latency, the proposed architecture with AHB protocol and AHB to APB bridge is incorporated between the fabric dynamically reconfigurable multi-processor (FDPM) and peripherals along with security algorithms using SHA-256bits and AES. In order to perform ML-based applications, the proposed system is incorporated double-precision floating point (DPFP) arithmetic operations. The overall proposed architecture is developed in Verilog HDL and quality checking using the LINT tool and Clock Domain Crossing (CDC) using Spyglass tool and synthesized using DC compiler for ASIC and Vivado Design Suite 2018.1 for FPGA implementation and verification. The entire design is interfaced with the Zynq processor and SDK tool to verify data transfer between hardware and software. The obtained results show the generated custom accelerator is able to compute any complex ML classifiers for a larger amount of data. The obtained results are compared with existing state-of-art results and found that 18 % improvement in throughput, a 21 % improvement in power consumption savings, and a 34 % reduction in latency.

PREDICTION OF INDUCED STRESSES WITHIN SOIL MASS USING ARTIFICIAL NEURAL NETWORK

In this paper, an Artificial Neural Network (ANN) is applied to predict the soil stress within a soil mass for a variety of depths and displacements under applied loading. The neural network model is created and applied for two cases, point load, and Uniform rectangular load.Theses two cases were selected among many other cases of loading as a representation of the capabilities of ANN in finding proper solutions. The first case needs one input to get one output, where in the second case we need two inputs, to get one output only.Results revealed that function approximation using neural network can be applied easily and can give accurate results by choosing the appropriate learning algorithm, number of layers, and number of neurons to solve the problem. ANN model can provide reasonable accuracy for civil engineering problems, and a more effective tool for engineering applications.

Application of a hierarchical quadrature element method to interface crack analysis in bi-material systems

This paper presents an extension of the hierarchical quadrature element method (HQEM) with p-convergence to evaluating fracture mechanical parameters of bi-material interface cracks. The numerical implementation of HQEM in combination with the virtual crack closure method (VCCM) is comprehensively illustrated, and the formulas for calculating the strain energy release rate (SERR) are rederived. The relationship between components of SERR and the complex stress intensity factors (SIFs) is established. In addition, the effectiveness of an auxiliary parameter related to the dual energy release rates in measuring the accuracy of the present method is examined.The feasibility of applying HQEM to the interface crack problems is demonstrated by investigating the benchmark problem of an infinite bi-material plate with a central interface crack. Two loading cases are considered, including pure tensile loading and mixed tensile-shear loading. The influence of the crack tip element size Δa on the convergence characteristics and computational accuracy of fracture parameters is quantitatively investigated to provide guidance for choosing the most reasonable value of Δa. Numerical results show that even with very coarse meshes, together with a virtual crack extension consisting of only one element, HQEM yields highly accurate results for the total SERR and the complex SIFs, making it suitable for complex, large-scale interface crack problems in dissimilar media.

Resistance of a mixed bacterial culture isolated from bee dysbiosis to disinfectant in laboratory conditions

Disinfection is one of the components of preventive actions to prevent disease in animals by infectious agents. Through the elimination of agents of infectious diseases in the environment, it is able to interrupt the mechanisms of the epizootic process. For the development of beekeeping and obtaining quality and products, it is necessary to carry out activities in apiaries, which include disinfection. The introduction of new disinfectants in the field is possible after preliminary laboratory tests. The aim of our research objective to investigate the resistance of a mixed bacterial culture isolated from bees with the symptoms of dysbiosis to disinfectant under laboratory conditions. Bacteriological investigations carried out in Petri dishes using meat-peptone agar and Mueller-Hinton agar. The disinfectant diluted with distilled water at concentrations of 0.25 %, 0.5 %, 1,25 % and 2,5 %. On the Mueller Hinton agar, the bacterial elimination zones recorded at 1.25 % (11,6 ± 0,45 mm) and 2.5 % (12,4 ± 0,27 mm) concentration of disinfectant at 1 day of investigation, and 7,4 ± 0,27 mm (1,25 %), 11,2 ± 0,42 mm (2.5 %) at 72 hours of the experiment, respectively. In addition, the bacterial growth inhibition effect increased with experimental time and ranged from 24.0 ± 0.35 mm (0.5 %) to 28.2 ± 0.22 mm (2.5 %) on day 3 of the study. Bacteriostatic and bactericidal effects appeared to be less pronounced on meat-peptone agar. The highest diameters of inhibition of the processes of reproduction and development of microorganisms from mixed microbial association on this medium were recorded at 1.25 % and 2.5 % concentrations of the disinfectant at the 3rd day of the investigation with diameters of the zones of inhibition of growth 15.6 ± 0.45 mm and 26.4 ± 0.27 mm, respectively. The specificity of diffusion of the investigated active substance from the discs into the agar medium thickness was also noted. This effect has a varying effect on infectious agents in in vitro experiments. Thus bacteriological laboratory experiments should be performed on several culture media in purpose of obtaining accurate results. The analysis of the obtained results indicates the demonstration of antimicrobial effect of the investigated disinfectant against mixed bacterial culture in the form of bactericidal and bacteriostatic actions. Therefore, a promising direction of research for veterinary doctors and apiary business owners is the search for effective and efficient disinfectants. Laboratory investigation of the activity of a remedy are the first step before it is approbation.

Modern software capabilities for shape optimization of shells

The article is devoted to the shape optimization of shell structures in Comsol Multiphysics using three gradient-based methods: IPOPT (Interior Point OPTimizer), SNOPT (Sparse Nonlinear OPTimizer) and MMA or GCMMA (the Method of Moving Asymptotes). Two types of complex shapes, such as right helicoid and developable helicoid are taken for the computational experiment. The task is to investigate the initial design and optimization process of two helicoids. To obtain a more accurate result and an interesting design solution, the calculation is carried out using three physics-controlled mesh sizes: extra coarse, fine and extra fine with varying values of special optimization settings, such as maximum displacement (dmax) and filter radius (Rmin). The results obtained using the three methods allow to conclude that the mesh element size and studied parameters dmax and Rmin have a significant impact on the final optimization result.

Stress-Based Model for Calculating the Opening Angle of Notch Cracks in a Magnesium Alloy under Multiaxial Fatigue

This paper presents a model to calculate the opening angle of crack initiation in notched fractures subjected to multiaxial loading. To validate the proposed model, a study was performed on polished AZ31B-F magnesium alloy specimens under multiaxial high-cycle fatigue loading. The specimens exhibited a notch in the smaller cross-sectional area, which was created with a special drilling jig to promote the formation of fatigue cracks in this localized area of the specimen. The load paths used in the experiments and numerical analyses were proportional and non-proportional, resulting in different stress states in the crack front opening, which were determined by finite element analysis to validate the proposed model. To obtain more accurate numerical results for these estimates, these finite element analyses were performed using the nonlinear Chaboche plasticity model of ABAQUS® 2021 software. A sensitivity analysis was also performed to determine which load component—axial or torsional—has a greater influence on the fatigue strength and contributes significantly to the crack opening process. The results show that the type of load path and the stress level of each load component—axial and torsional—has a strong influence on the opening angle of the notch crack and the fatigue lifetime of the specimen. This result is confirmed not only by the experimentally determined fatigue strength, but also by a fractographic analysis performed on the surface of the specimens for both load paths. Moreover, the results show an acceptable correlation between the experimental results and the estimates obtained with the proposed model and the stresses obtained with the finite element analysis.

Analytical Method for Quantifying Monoterpenoids (p-Cymene and Myrcene) in Nanoemulsions Using High-Performance Liquid Chromatography.

Myrcene and cymene, aromatic monoterpenes found in plants and essential oils, possess distinctive aromatic qualities. However, their volatility and limited solubility pose challenges in precise handling and formulation. Meanwhile, nanoemulsions emerge as promising drug delivery systems, improving the bioavailability and stability of these active ingredients. This article aimed to develop an HPLC method for the quantification of two monoterpenoids, p-cymene and myrcene, in nanoemulsions. The method used a Phenomenex® Synergi™ Fusion-RP column (150 mm × 4.6 mm id, 4 μm particle size) on an HPLC system with isocratic elution. The mobile phase was composed of acetonitrile and water (60:40, v/v) and was validated in terms of specificity, linearity, accuracy, precision, robustness, and selectivity. The method provided accurate and precise results with a correlation coefficient of 0.999 and RSD values of less than 2%. The method can be used for quality control of nanoemulsions containing these monoterpenoids and as a reference for future studies on their efficacy and stability. The study demonstrates the feasibility of using HPLC for the quantification of monoterpenoids in nanoemulsions and its potential as a quality control tool for nanoemulsion-based drug delivery systems. The method's accuracy, precision, and reliability, as evidenced by high correlation coefficients and low RSD values, underscore its suitability for ensuring the consistent formulation of these monoterpenoid-containing nanoemulsions, while also serving as a reference point for future research endeavors in this field.