Accuracy Of Model Research Articles

Integrating Artificial Intelligence for Enhanced Fault Detection in Power Transmission Systems: A Smart Grid Approach

Modern electrical systems rely heavily on sensors and relays for fault detection in three-phase transmission lines and distribution transformers. However, these devices often need more time complexity and are prone to false alarms (erroneous signals). The study was guided by the PRISMA guidelines and methodology. The study's objective was to compare the levels of accuracy of fault detection presented by different models studied between 2023 and 2024. The general objective was further divided into sub-objectives, which included determining types of faults in power transmission systems, examining fault detection machine-learning models in power transmission systems, and assessing levels of accuracy of fault detection techniques in power transmission systems. The study used a qualitative research design, which entailed a systematic literature review that involved identifying scholarly articles from respectable international journal websites that aligned with examining how the deep-learning model enhanced fault detection capabilities in a three-phase transmission line simulated dataset. The inclusion criteria were that the study used journals published between 2023 and 2024. The study also included journals that tested Artificial Intelligence systems' accuracy in fault detection in three-phase transmission lines and distribution transformers. The study excluded sources that were older than 2023. The sources of information were journals written by professionals and scholars in the field of Artificial intelligence and engineering. The sourced journals used simulated and real databases to test fault detection accuracy in three-phase transmission lines and distribution transformers. The source of data for review was obtained from the internet via Google search with various credible academic journal websites being searched by the researcher. A total of 12 sources were searched. To assess the risk of bias in the included studies, the researcher used the Robvis methods that visualized risk-of-bias during the inclusion criteria for sources used in the systematic review. The techniques used to present the findings were narratives, graphs, and tables. The method used to synthesize results was comparative data analysis. The study included two studies, the sample size from the three possible journals the researcher identified. The characteristics of the studies were that they both informed the study's general and specific sub-objectives. The two studies also used the same methods to arrive at their findings, which made them ideal for comparative analysis. Finally, both studies compared older systems to new systems regarding Artificial Intelligence accuracy in fault detection. The study's findings were as follows: The study ranked the Novel glass box-based model as the best artificial intelligence machine-learning approach for accurately detecting electric power transmission lines and systems faults. The Novel glass box-based approach's accuracy was 99%, followed by the Convolution Neural Network model's accuracy of 98%, the Gated Recurrent Unit model's accuracy of 92%, the Random Forest model's accuracy of 90%, the Logistic Regression model's accuracy of 74%, and the Support Vector Classifier model's accuracy of 63%. The review was adequate to conclude that the Novel glass-box-based proposed approach was the most advanced and suitable model for detecting faults in electric power transmission lines. The study had one limitation: it reported the results based on two sources, which made the exclusion criteria a point of bias. The survey results implied that builders of new Artificial intelligence systems for fault detection in electric line transmitters should aim to produce 99% accurate systems to meet the recommendations and fill the gap that this study has reported. This study was funded by the researcher using family contributions. The study's author is Nijat Taghizad (ORCID ID: 0009-0005-0505-8767).

Development and Validation of a Nomogram for Predicting Systemic Inflammatory Response Syndrome Following Percutaneous Nephrolithotomy.

Objective: To develop and validate a nomogram for predicting the occurrence of systemic inflammatory response syndrome (SIRS) following percutaneous nephrolithotomy (PCNL), aiming to enhance clinical decision-making and treatment planning. Methods: Clinical data of 1,047 patients undergoing PCNL at a single-center hospital between 2017 and 2023 were retrospectively analyzed. Independent risk factors influencing SIRS occurrence were identified through multi-variable logistic regression analysis, and a predictive model was constructed. The model's accuracy and reliability were evaluated through internal training and validation set. Results: Multi-variable regression analysis identified six key predictive factors: gender, diabetes, urine culture results, stone surface, staghorn stones, and operative time, leading to the establishment of a nomogram predictive model. Internal validation and validation set data demonstrated the model's high predictive accuracy and reliability, with areas under the receiver operating characteristic curve of 0.718 and 0.723, respectively. Conclusions: A nomogram predictive model for assessing SIRS following PCNL was successfully developed and validated. This model provides clinicians with a valuable tool for personalized treatment planning and implementing preventive measures.

Discrete element model construction and dehulling simulation verification based on harvested quinoa grains.

The dry dehulling process of quinoa grains faces challenges such as poor visibility and unclear dynamic response characteristics, leading to irrational settings of the shelling process and high losses of the final processed product. Simulation methods provide an effective solution to this issue, with the accuracy of the simulation model being crucial. In this study, using the Experts in Discrete Element Modeling (EDEM) simulation software, based on the biological characteristics and mechanical properties of the grains, a method was proposed to construct a discrete-element simulation model for the double-layer bonding of the quinoa grain using the three-axis spatial coordinates method. The Plackett-Burman method, the steepest rise method and the Box-Behnken method were used to simulate the compression test. The bonding parameters of the quinoa grain were calibrated. The parameters were further validated by constructing a mechanical shear test. Finally, the process of the dehulling quinoa grain was simulated in a dry dehulling equipment. The results showed that the relative error between the simulated and actual maximum compression force of the quinoa grains was 0.32%. The breaking shear force in the simulation had a relative error of no more than 5% compared to the actual measurements. In the simulation, the dehulling rate and broken rice rate were 78.54% and 1.38%, respectively, which exceeded the bench test values (75.32% for dehulling rate and 1.24% for broken rice rate) by 3.22% and 0.14%, respectively. The error was within the acceptable range. The calibration of the discrete element model parameters for quinoa grains was accurate, reflecting the mechanical properties differences between the rice and the pericarp effectively, providing a reliable basis for optimizing the design of dry dehulling mechanisms.

Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids.

The accurate and rapid diagnosis of bacterial vaginosis (BV) is crucial due to its high prevalence and association with serious health complications, including increased risk of sexually transmitted infections and adverse pregnancy outcomes. Although widely used, traditional diagnostic methods have significant limitations in subjectivity, complexity, and cost. The development of a novel diagnostic approach that integrates SERS with ML offers a promising solution. The CNN model's high prediction accuracy, cost-effectiveness, and extraordinary rapidity underscore its significant potential to enhance the diagnosis of BV in clinical settings. This method not only addresses the limitations of current diagnostic tools but also provides a more accessible and reliable option for healthcare providers, ultimately enhancing patient care and health outcomes.

A boundedly rational model for category learning

The computational modeling of category learning is typically evaluated in terms of the model's accuracy. For a model to accurately infer category membership of stimuli, it has to have sufficient representational precision. Thus, many category learning models infer category representations that guide decision-making and the model's fitness is evaluated by its ability to accurately choose. Substantial decision-making research, however, indicates that noise plays an important role. Specifically, noisy representations are assumed to introduce an element of stochasticity to decision-making. Noise can be minimized at the cost of cognitive resource expenditure. Thus, a more biologically plausible model of category learning should balance representational precision with costs. Here, we tested an autoencoder model that learns categories (the six category structures introduced by Roger Shepard and colleagues) by balancing the minimization of error with minimization of resource usage. By incorporating the goal of reducing category complexity, the currently proposed model biases category decisions toward previously learned central tendencies. We show that this model is still able to account for category learning performance in a traditional category learning benchmark. The currently proposed model additionally makes some novel predictions about category learning that future studies can test empirically. The goal of this paper is to make progress toward development of an ecologically and neurobiologically plausible model of category learning that can guide future studies and theoretical frameworks.

Amplitude stability research and experimental investigation of the actuation circuit of the inertial sensor for space gravitational wave detection

Abstract The primary measure of scientific performance for inertial sensors used in space gravitational wave detection is the residual acceleration noise of the test mass (TM). This residual noise arises from both the internal circuit and the external environment. The actuation circuit, a crucial component of the internal circuit, significantly affects the TM’s residual acceleration noise through its amplitude stability, thereby impacting the scientific performance of the inertial sensor. In this study, we designed the actuation circuit for an inertial sensor, developed a mathematical model to describe its amplitude stability, and experimentally verified the model's accuracy. Experimental results demonstrate that the current design enables the actuation circuit to achieve an amplitude stability of 3.6 ppm/Hz1/2 at 1 mHz, thereby offering theoretical support for achieving a higher amplitude stability in the millihertz frequency band.

Vehicle identification and analysis based on lightweight yolov5 on edge computing platform

Abstract With the accelerating process of urbanization, the role of intelligent transportation systems in urban traffic management has become more crucial. Nevertheless, traditional surveillance cameras only possess the ability to capture videos and lack the capacity for vehicle detection. Consequently, in order to enhance the intelligent capabilities of front-end surveillance cameras and achieve lightweight model deployment, this research has designed a lightweight detection model that can be deployed on embedded devices. Deploying detection models on embedded devices requires a high level of storage and system computing power, and there exist issues such as model speed, model accuracy, model size, and energy efficiency that are difficult to address in existing studies.Specifically, based on YOLOv5, we first use lightweight structures to replace the C3 module and introduce GSConv and Slim Neck structures to replace the Neck of YOLOv5 in order to reduce the model's parameters and computational complexity. After replacing the model structure, we use global channel pruning to remove redundant information, and use asymmetric quantization to convert model parameters from floating point numbers to fixed point numbers, significantly reducing the model's size and load time, and improving the inference speed. For the target tracking part, we combine the ByteTrack target tracking algorithm with the proposed model, effectively reducing the impact of target occlusion on detection and improving the correlation between video frames. In terms of data recording, detection information such as vehicle color, license plate number, and road blockage level can be uploaded to the database server, and the model's detection results can be encoded using the H.264 video encoding format and uploaded to the streaming media server through streaming media protocols. In the experimental part, we selected the embedded Rk3399pro as the deployment object and achieved 97.8% accuracy in the vehicle counting task, meeting the real-time performance requirements of 31FPS.

Solid-State NMR Validation of OPLS4: Structure of PC-Lipid Bilayers and Its Modulation by Dehydration.

Atomistic molecular dynamics (MD) simulations are a much-used tool for investigating the structure and dynamics of biomembranes with atomic resolution. The validity of the representations obtained is determined by the accuracy and realism of the MD model (force field). Here, we evaluated the proprietary OPLS4 force field of Schrödinger, Inc. against atomic-resolution experimental data, and compared its performance to CHARMM36, one of the best-performing openly available force fields. As a benchmark, we used high-resolution nuclear magnetic resonance (NMR) order parameters for C-H bonds─directly and reliably calculable from MD simulations─measured in phosphatidylcholine (PC) lipid bilayers under varying hydration conditions. Comparisons were made with two dehydration data sets: for saturated (1,2-dimyristoylphosphatidylcholine, DMPC) lipid bilayers from the literature and for unsaturated (1-palmitoyl-2-oleoylphosphatidylcholine, POPC) lipid bilayers measured here. Our findings indicate that OPLS4 reproduces the structure and dehydration response of PC-lipid bilayers fairly well, even slightly outperforming CHARMM36. Both models' main inaccuracies appear in (1) the order parameter magnitudes in the glycerol backbone and unsaturated carbon segments and (2) the qualitatively differing structural response of the PC headgroup to dehydration compared to experiments. In summary, this work underscores the importance of independent validation for (proprietary) force fields and highlights the striking similarities and nuanced differences between OPLS4 and CHARMM36 in describing biomembranes.

Genomic prediction of genotyped and non-genotyped Jeju black cattle using single- and multi-trait methods for carcass traits

Jeju black cattle (JBC) are recognised as one of Korea’s native breeds that are well-suited to the local environmental conditions and known for their excellent meat quality. This study investigated the variance components and genomic prediction accuracy for carcase traits such as carcase weight (CWT), eye muscle area (EMA), backfat thickness (BF) and marbling score (MS) in JBC using various statistical models. We employed single-step genomic best linear unbiased prediction (ssGBLUP) and single-step Bayesian regression (SSBR) models, which utilised imputed genotypes for non-genotyped individuals. Phenotypic data were collected from 2045 JBC, and 3759 JBC individuals were genotyped using the Affymetrix 160K SNP Axiom array. Estimated heritability ranged from 0.24 to 0.40 for both single-trait and multi-trait models. Multi-trait models consistently demonstrated higher genomic prediction accuracy compared to single-trait models, underscoring the value of integrating multiple traits. For instance, CWT accuracy improved from 0.24 in single-trait PBLUP (st-PBLUP) to 0.47 in multi-trait ssGBLUP. Similarly, SSBR models showed significant improvements, with single-trait SSBR Bayes B achieving 0.46 and its multi-trait version reaching 0.49. EMA accuracy improved from 0.21 in st-PBLUP to 0.39 in the multi-trait SSBR Bayes C model. BF and MS also exhibited consistent accuracy improvements with multi-trait Bayesian regression models. These results demonstrate the superior accuracy of ssGBLUP and SSBR models compared to traditional BLUP, particularly for traits with higher heritability values. This findings offer valuable insights for enhancing genomic selection strategies and improving carcase traits in JBC breeding programs, contributing to the breed’s economic sustainability and meat production efficiency.

ML-Based Pain Recognition Model Using Mixup Data Augmentation

Machine learning (ML) has revolutionized healthcare by enhancing diagnostic capabilities because of its ability to analyze large datasets and detect minor patterns often overlooked by humans. This is beneficial, especially in pain recognition, where patient communication may be limited. However, ML models often face challenges such as memorization and sensitivity to adversarial examples. Regularization techniques like mixup, which trains models on convex combinations of data pairs, address these issues by enhancing model generalization. While mixup has proven effective in image, speech, and text datasets, its application to time-series signals like electrodermal activity (EDA) is less explored. This research uses ML for pain recognition with EDA signals from the BioVid Heat Pain Database to distinguish pain by applying mixup regularization to manually extracted EDA features and using a support vector machine (SVM) for classification. The results show that this approach achieves an average accuracy of 75.87% using leave-one-subject-out cross-validation (LOSOCV) compared to 74.61% without mixup. This demonstrates mixup’s efficacy in improving ML model accuracy for pain recognition from EDA signals. This study highlights the potential of mixup in ML as a promising approach to enhance pain assessment in healthcare.

A field and remote sensing assessment of rates and drivers of tree cover loss in island catchments: variation in global model accuracy

This study seeks to shed light on the rates and drivers of tree cover loss in island catchments focusing on a case study from Fiji. Global Forest Watch (GFW) is critically assessed as a framework for quantifying terrestrial tree cover loss and associated forest carbon stocks. The study assesses the role of the drivers of tree cover loss identified by GFW global models including anthropogenic deforestation: shifting agriculture, wildfires, urbanisation and plantation forestry as well as other drivers outside of the globally standardised list. According to GFW, from 2000 to 2023, Fiji lost 530.9 km2 of tree cover equivalent to an annual average loss of 23.1 km2. The study finds that global models provide useful insights into the rates of tree cover loss at the national level. However, the impacts of local drivers of tree cover loss are found to diverge from global model predictions. Specifically, tropical cyclones driving and harvesting cycles within timber plantations driving 15.3–24.5% and 75.9–83.2% of tree cover loss in the Ba River Catchment, respectively. These ranges are significantly greater than the rates predicted by global models. Further collection of 5209 ground cover points, generation of land cover models and forest inventories across 115 sites and 1070 trees sheds light on biomass carbon implications of this tree cover loss. Based on the forest inventory estimates, the 40.5 km2 of tree cover resulted in approximately 466,000 tonnes of dry matter (t DM) or 856,000 tonnes carbon dioxide equivalent (tCO2e) in the Ba River Catchment between 2000 and 2023.

A review on inverse analysis models in steel material design

AbstractThis paper reviews various inverse analysis models used in steel material design, with a focus on integrating process, microstructure, and properties through advanced machine learning techniques. The study underscores the importance of establishing comprehensive models that effectively link these elements for enhanced materials engineering. Key models discussed include the convolutional neural network–artificial neural network‐coupled model, which employs convolutional neural networks for feature extraction; the Bayesian‐optimized generative adversarial network–conditional generative adversarial network model, which generates diverse virtual microstructures; the multi‐objective optimization model, which concentrates on process–property relationships; and the microstructure–process parallelization model, which correlates microstructural features with process conditions. Each model is assessed for its strengths and limitations, influencing its practical applicability in material design. The paper concludes by advocating for continued improvements in model accuracy and versatility, with the ultimate goal of enhancing steel properties and expanding the scope of data‐driven material development.

Active Light Weight Recommendation System for Social Networking Analysis Using a Novel Classifier Algorithm

The most popular way for people to share information is through social media. Several studies have been conducted using ML approaches like LSTM, SVM, BERT, GA, hybrid LSTM-SVM and Multi-View Attention Networks to recognize bogus news MVAN. Most traditional systems identify false news or true news exclusively, but discovering kind of false information and prioritizing false information is more difficult, and traditional algorithms offer poor textual classification accuracy. As a result, this study focuses on predicting COVID-19-related false information on Twitter along with prioritizing types of false information. The proposed lightweight recommendation-system consists of three phases such as preprocessing, feature extraction and classification. The preprocessing phase is performed to remove the unwanted data. After preprocessing, the BERT model is used to convert the word into binary vectors. Then these binary features are taken as the input of the classification phase. In this classification phase, a 4CL time distributed layer is introduced for effective feature selection to remove the detection burdens, and the Bi-GRU model is used in the classification phase. Proposed-method is implemented in Mat lab software and is carried out several performance-metrics, and there are three different datasets used for validating its performance. Proposed model's total accuracy is 97%, specificity is 98%, precision is 95%, and the error value is 0.02, demonstrating its effectiveness over current methods. The proposed social media research system can accurately predict false information, and recognized news may be offered to the user such that they can learn the truth about news on social media.

Privacy-by-Design with Federated Learning will drive future Rare Disease Research

Up to 6% of the global population is estimated to be affected by one of about 10,000 distinct rare diseases (RDs). RDs are, to this day, often not understood, and thus, patients are heavily underserved. Most RD studies are chronically underfunded, and research faces inherent difficulties in analyzing scarce data. Furthermore, the creation and analysis of representative datasets are often constrained by stringent data protection regulations, such as the EU General Data Protection Regulation. This review examines the potential of federated learning (FL) as a privacy-by-design approach to training machine learning on distributed datasets while ensuring data privacy by maintaining the local patient data and only sharing model parameters, which is particularly beneficial in the context of sensitive data that cannot be collected in a centralized manner. FL enhances model accuracy by leveraging diverse datasets without compromising data privacy. This is particularly relevant in rare diseases, where heterogeneity and small sample sizes impede the development of robust models. FL further has the potential to enable the discovery of novel biomarkers, enhance patient stratification, and facilitate the development of personalized treatment plans. This review illustrates how FL can facilitate large-scale, cross-institutional collaboration, thereby enabling the development of more accurate and generalizable models for improved diagnosis and treatment of rare diseases. However, challenges such as non-independently distributed data and significant computational and bandwidth requirements still need to be addressed. Future research must focus on applying FL technology for rare disease datasets while exploring standardized protocols for cross-border collaborations that can ultimately pave the way for a new era of privacy-preserving and distributed data-driven rare disease research.

An Adaptive Sliding-Mode Observer-Based Fuzzy PI Control Method for Temperature Control of Laser Soldering Process

Abstract In the process of electronic packaging, laser soldering exhibits advantages such as non-contact, minimal thermal affected zone, and rapid soldering process. Additionally, the precision of temperature control in laser soldering process is crucial as it determines the quality of the solder joints. However, the uncertain time-variant parameters and the unknown time-varying bounded disturbances make it challenging for high-precision temperature control to achieve high-quality solder joints. To aimed at the parametric uncertainties and disturbances, an adaptive sliding-mode observer-based fuzzy PI (ASMFPI) control method is proposed. In ASMFPI, an adaptive sliding-mode algorithm-based observer is designed to estimate temperature variations caused by uncertainties and disturbances, aiming to improve the accuracy of the temperature model. An observer-based compensation is employed to enhance the temperature control precision. Then, a fuzzy PI control algorithm is adopted to guarantee the track performence and robustness of the closedloop system. Experiments and comparisons are conducted on a laser soldering machine to validate the effectiveness of the proposed method. The results demonstrate that the designed observer significantly improves the accuracy of the thermodynamic model. Furthermore, comparisons of ASMFPI, well-tuned PI, and feed-forward PI methods indicate that ASMFPI exhibits superior temperature track performance compared to the other two methods.

Modeling and Forecasting the Prevalence of Human Immuno-Deficiency Virus (HIV) in Birnin Gwari Local Government Area of Kaduna State

Human immunodeficiency virus (HIV) has been prevalent in millions of people worldwide for more than 50 years. Therefore, its prevalence must be monitored to make well-informed decisions and create efficient health plans. This paper dwelt on the modeling and prediction of the prevalence of HIV among inpatients and outpatients visiting Jibril Mai-Gwari I Memorial Hospital in Birnin Gwari Local Government Area of Kaduna State. An expo-facto research methodology was adopted for this study through the collection of twelve (12) years of monthly data spanning the period 2010-2021 as extracted from the hospital’s inpatient and outpatient department records. Box and Jenkins analytical approach was used to model the data due to its presence of unit root, as a non-stationarity data cannot be modeled using the conventional time series models. Iterative methods were applied in the identification of several ARIMA models as ARIMA (2, 1, 5) model was found to be the best fit among the 25 iterated models based on its higher loglikelihood (-435.57), lower variance (24.95) and lower AIC (887.13). More so, the model was found to scale through the portmanteau tests of residual independence (Ljung-Box Chi-square 5.9261, p-value > 0.05) and normality (Wilk 0.9830, p-value > 0.05), a confirmatory test of model reliability and accuracy. Although the prognosis suggests that HIV prevalence may not change significantly in the upcoming years, the expanding uncertainty band suggests that it will be challenging to estimate HIV prevalence in the future with high confidence. To manage prevalence and lower the chance of an unanticipated increase, public health measures must be consistent and effective.

Radiomics for differential diagnosis of Bosniak II-IV renal masses via CT imaging

Rationale and ObjectivesThe management of complex renal cysts is guided by the Bosniak classification system, which may be inadequate for risk stratification of patients to determine the appropriate intervention. Radiomics models based on CT imaging may provide additional useful information.Materials and MethodsA total of 322 patients with Bosniak II-IV cysts were included in the study from January 2010 to December 2019. Contrast-enhanced CT scans were performed on all patients. ITK-snap was used for segmentation, and the PyRadiomics 3.0.1 package was used for feature extraction. The radiomics features were screened via the least absolute shrinkage and selection operator (LASSO) regression method. After feature selection, a logistic regression (LR) model, support vector machine (SVM) model and random forest (RF) model were constructed.ResultsIn the present study, 217 benign renal cysts (67.4%) and 105 cystic renal cell carcinomas (32.6%) were identified. According to the Bosniak classification, the sample included 179 (55.6%) Bosniak II cysts, 38 (11.8%) Bosniak IIF cysts, 44 (13.7%) Bosniak III cysts and 61 (18.9%) Bosniak IV cysts. A total of 1334 radiomics features were extracted from both unenhanced and cortical CT scans. After LASSO regression, all the models (LR, SVM and RF) showed satisfactory discrimination and reliability in both unenhanced and cortical CT scans (AUC > 0.950). In the Bosniak IIF-III subgroup analysis, the diagnostic accuracy of the LR model was very low for both the unenhanced and cortical scans. In contrast, the SVM model and RF model showed excellent and stable performance in classifying Bosniak IIF-III cysts. The AUCs of the models were all > 0.85, with a maximum of 0.941. The sensitivity, specificity, accuracy, and AUC of the RF model were 0.889, 0.913, 0.902, and 0.941, respectively.ConclusionOur data indicate that radiomics models can effectively distinguish between cystic renal cell carcinoma (cRCC) and complex renal cysts (Bosniak II-IV). Radiomics models may still have high diagnostic accuracy even for Bosniak IIF-III cysts that are clinically difficult to distinguish. However, external validation of these findings is still needed.

Performance Assessment of Individual and Ensemble Learning Models for Gully Erosion Susceptibility Mapping in a Mountainous and Semi-Arid Region

High-accuracy gully erosion susceptibility maps play a crucial role in erosion vulnerability assessment and risk management. The principal purpose of the present research is to evaluate the predictive power of individual machine learning models such as random forest (RF), decision tree (DT), and support vector machine (SVM), and ensemble machine learning approaches such as stacking, voting, bagging, and boosting with k-fold cross validation resampling techniques for modeling gully erosion susceptibility in the Oued El Abid watershed in the Moroccan High Atlas. A dataset comprising 200 gully points, identified through field observations and high-resolution Google Earth imagery, was used, alongside 21 gully erosion conditioning factors selected based on their importance, information gain, and multi-collinearity analysis. The exploratory results indicate that all derived gully erosion susceptibility maps had a good accuracy for both individual and ensemble models. Based on the receiver operating characteristic (ROC), the RF and the SVM models had better predictive performances, with AUC = 0.82, than the DT model. However, ensemble models significantly outperformed individual models. Among the ensembles, the RF-DT-SVM stacking model achieved the highest predictive accuracy, with an AUC value of 0.86, highlighting its robustness and superior predictive capability. The prioritization results also confirmed the RF-DT-SVM ensemble model as the best. These findings highlight the superiority of ensemble learning models over individual ones and underscore their potential for application in similar geo-environmental contexts.

Flutter Analysis Based on the New Numerical Model IAR-99 HAWK Empty Equipped Configuration Aircraft

After creating a new Finite Elements Model (FEM) of the aircraft IAR-99 HAWK using special elements, the team decided to validate the results by performing a free-free vibrations analysis on this model. In this presentation we have described the finite elements used and their peculiarities, the finite elements model obtained for the aircraft IAR-99 HAWK and the results of the free-free vibrations analysis obtained in the empty equipped configuration. The results of the theoretical free-free vibrations obtained for the IAR-99 HAWK in the empty equipped configuration are presented and compared to those obtained from ground tests, thus confirming the accuracy of the new Finite Element Model (FEM). The aerodynamic idealization of the IAR-99 aircraft was made using the DLM method for subsonic flow, also considering the load-bearing surface/fuselage interference and aims to determine the generalized aerodynamic forces that occur on one mode of vibration due to the influence of another mode of vibration. The flutter calculation for the IAR-99 HAWK empty equipped configuration aircraft was made using the p-K methodology presented in the MSC.Nastran program, using the vibration modes obtained on the basis of the new finite element model and the matrix of generalized aerodynamic forces in subsonic flow.

Prediction Method for Pomegranate Chlorophyll Content Based on Multi-feature Fusion of Unmanned Aerial Vehicle

The aim of this study was to obtain RGB and multispectral images of fruit tree canopy during the flowering period of pomegranate by multispectral unmanned aerial vehicle (UAV) to quickly and accurately predict the chlorophyll content in order to improve the monitoring efficiency of the orchard. A handheld chlorophyll meter was used to obtain the actual chlorophyll values, and image processing techniques were combined to extract parameters such as color features and texture features of the RGB images as well as vegetation index of the multispectral images. A chlorophyll content prediction method based on the support vector regression model and the convolutional neural network model (CNN-Attention) combined with the attention mechanism was established. The results showed that (1) the accuracy of the prediction model was improved by the fusion of RGB image features and multispectral image vegetation index. (2) After model comparison, the CNN-Attention model built under the fused features was the best in chlorophyll content prediction with R2, RE, and RMSE of 0.9699, 0.0052, and 0.6013, respectively.This study provides a more accurate method for fruit tree chlorophyll content prediction using UAVs, which provides a practical reference for orchard management.