Precise Values Research Articles

Performance Comparison of GLCM Features and Preprocessing Effect on Batik Image Retrieval

The use of the Grey-Level Co-occurrence Matrix (GLCM) for feature extraction in image retrieval with complex motifs, such as batik images, has been widely used. Some features often extracted include energy, entropy, correlation, and contrast. Other than these four features, the addition of dissimilarity and homogeneity features to the GLCM method is proposed in this study. Preprocessing methods such as Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) are also used to see whether the two methods can increase the precision value of the retrieval results. This study used the Batik 300 dataset, which consists of 50 classes. Batik was chosen because this type of image has complex patterns and motifs so that it will maximize the role of the GLCM method itself. In addition, Batik is also a world heritage art, so its sustainability needs to be maintained. The test results show that adding dissimilarity and homogeneity features and using the CLAHE method in the preprocessing step can improve model performance. Combining these two methods has produced higher precision values than not using either. Batik, a globally recognized art form, holds the status of a world heritage, necessitating the preservation of its sustainability. Test results have demonstrated that incorporating dissimilarity and homogeneity features, alongside using the CLAHE method during the preprocessing stage, leads to enhanced model performance. The amalgamation of these two methods has yielded precision values that surpass those achieved when either method is used in isolation.

Nessler Method Verification for Determining Ammonia in Shrimp Pond Wastewater and Its Application in the Ammonia Adsorption Test with Lampung Natural Zeolite

Ammonia levels exceeding 0.50 mg/L can threaten organisms in aquatic environments. The Nessler method is one of the ammonia analysis methods based on the reaction between ammonia in a basic solution and Nessler reagent (K<sub>2</sub>HgI<sub>4</sub>), forming a colloidal dispersion with a brownish-yellow colour. The colour intensity is determined by spectrophotometry. This research aims to verify the Nessler method for determining ammonia levels in shrimp pond wastewater. The research results indicate that the Nessler method shows good linearity in the range of ammonia concentrations from 1 to 5 mg/L, with a correlation coefficient (R<sup>2</sup>) value of 0.9962. The precision value was determined from repeatability, expressed as %RSD (Relative Standard Deviation), i.e., 1.92%, and it meets acceptance criteria, which should be less than 0.5 of the Horwitz RSD. The accuracy obtained from the standard addition method provides a percentage recovery value of 99.25%, meeting the AOAC acceptance criteria. The detection limit and quantification limit of the technique are 0.3883 mg/L and 1.2943 mg/L, respectively. The verified method is then applied to analyze shrimp pond wastewater samples from Sriminosari Village, East Lampung, resulting in an ammonia concentration of 1.52 mg/L. The ammonia levels were then reduced by adsorption with natural zeolite Lampung, decreasing ammonia levels by 20.30%. Meanwhile, adsorption with an activated zeolite reduced the ammonia levels by 45.30%.

Comparative analysis of electroencephalogram (EEG) data gathered from the frontal region with other brain regions affected by attention deficit hyperactivity disorder (ADHD) through multiresolution analysis and machine learning techniques

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by repeated patterns of hyperactivity, impulsivity, and inattention that limit daily functioning and development. Electroencephalography (EEG) anomalies correspond to changes in brain connection and activity. The authors propose utilizing empirical mode decomposition (EMD) and discrete wavelet transform (DWT) for feature extraction and machine learning (ML) algorithms to categorize ADHD and control subjects. For this study, the authors considered freely accessible ADHD data obtained from the IEEE data site. Studies have demonstrated a range of EEG anomalies in ADHD patients, such as variations in power spectra, coherence patterns, and event-related potentials (ERPs). Some of the studies claimed that the brain’s prefrontal cortex and frontal regions collaborate in intricate networks, and disorders in either of them exacerbate the symptoms of ADHD. , Based on the research that claimed the brain’s prefrontal cortex and frontal regions collaborate in intricate networks, and disorders in either of them exacerbate the symptoms of ADHD, the proposed study examines the optimal position of EEG electrode for identifying ADHD and in addition to monitoring accuracy on frontal/ prefrontal and other regions of brain our study also investigates the position groupings that have the highest effect on accurateness in identification of ADHD. The results demonstrate that the dataset classified with AdaBoost provided values for accuracy, precision, specificity, sensitivity, and F1-score as 1.00, 0.70, 0.70, 0.75, and 0.71, respectively, whereas using random forest (RF) it is 0.98, 0.64, 0.60, 0.81, and 0.71, respectively, in detecting ADHD. After detailed analysis, it is observed that the most accurate results included all electrodes. The authors believe the processes can detect various neurodevelopmental problems in children utilizing EEG signals.

Comparison of Classification Results of SVM, KNN, Decision Tree, and Ensemble Methods in Diabetes Diagnosis

This study aims to determine which algorithms and test techniques are the most optimal in detecting diabetes mellitus and obtaining the best results based on the value of accuracy, precision, and recall. In this study, approaches were used in early diagnosis of diabetes using KNN, SVM, Decision Tree, and Ensemble Majority Voting methods in Percentage Split and K-Fold Cross Validation methods. Diabetes is a disease characterized by high blood sugar (glucose) levels and can cause a variety of disease complications and damage to the body's organs if not treated immediately. Early diagnosis of diabetes is becoming crucial so that people can take immediate action to the hospital for immediate treatment. The data used is Healthcare-Diabetes from Kaggle. The results of this study have found that the K-Fold Cross Validation method is better because it can provide an average improvement in Ensemble accuracy of 13.42% compared to the Percentage Split method which only gives an average increase in Ensamble accuracy of 9.15%. The best algorithm for classifying diabetes disease is the Ensemble Majority Voting algorithm using the K-Fold Cross Validation method with a 98.81% accuracy rate. These excellent research results may contribute to detecting early symptoms of diabetes before it become too severe.

The Design of a Long-Distance Signal Transmission System in a Bistatic RCS Testing System

In wireless communication and radar systems, long-distance signal transmission poses significant challenges that affect overall system performance. In this paper, we propose an innovative bistatic radar cross section (RCS) testing system designed to address these challenges, with a particular focus on its long-distance signal transmission capabilities. This system is capable of accurately measuring the RCS of a target and improving multipath channel modeling accuracy by using precise RCS values. The integrated upper computer software extracts amplitude and phase information from received echo signals, processes these data, and provides detailed outputs including the target’s RCS, one-dimensional image, and two-dimensional image. The experimental results obtained prove that this system can not only achieve effective long-distance signal transmission but also substantially enhance the accuracy of RCS measurements, offering reliable support for multipath channel modeling. However, the conclusions drawn are preliminary and require further experimental validation to fully substantiate this system’s performance. Future work will focus on improving system accuracy, minimizing the impact of environmental noise, and optimizing data-processing methods to enhance the efficiency of wireless communication and radar applications.

A Robust [18F]-PSMA-1007 Radiomics Ensemble Model for Prostate Cancer Risk Stratification.

The aim of this study is to investigate the role of [18F]-PSMA-1007 PET in differentiating high- and low-risk prostate cancer (PCa) through a robust radiomics ensemble model. This retrospective study included 143 PCa patients who underwent [18F]-PSMA-1007 PET/CT imaging. PCa areas were manually contoured on PET images and 1781 image biomarker standardization initiative (IBSI)-compliant radiomics features were extracted. A 30 times iterated preliminary analysis pipeline, comprising of the least absolute shrinkage and selection operator (LASSO) for feature selection and fivefold cross-validation for model optimization, was adopted to identify the most robust features to dataset variations, select candidate models for ensemble modelling, and optimize hyperparameters. Thirteen subsets of selected features, 11 generated from the preliminary analysis plus two additional subsets, the first based on the combination of robust and fine-tuning features, and the second only on fine-tuning features were used to train the model ensemble. Accuracy, area under curve (AUC), sensitivity, specificity, precision, and f-score values were calculated to provide models' performance. Friedman test, followed by post hoc tests corrected with Dunn-Sidak correction for multiple comparisons, was used to verify if statistically significant differences were found in the different ensemble models over the 30 iterations. The model ensemble trained with the combination of robust and fine-tuning features obtained the highest average accuracy (79.52%), AUC (85.75%), specificity (84.29%), precision (82.85%), and f-score (78.26%). Statistically significant differences (p < 0.05) were found for some performance metrics. These findings support the role of [18F]-PSMA-1007 PET radiomics in improving risk stratification for PCa, by reducing dependence on biopsies.

Vehicle And Driver Recognition for Access Control

The following paper proposes an advanced vehicle and driver recognition system to enhance security at vehicular entry-exit points in response to the growing needs of more efficient and secure access control systems at these checkpoints. The proposed system will integrate license plate recognition and facial recognition technologies using state of-the-art machine-learning models from Ultralytics: YOLOv8 and EasyOCR, respectively. It works in three stages: license plate detection, character alpha-numeric identification, and then driver identity with facial recognition. Hence, the two-tier authentication process based on license plate detection and facial identification ensures further prevention from unauthorized entry. Preliminary evaluation of the system also produced very high values for precision, recall, and the mean average precision, indicating that the VDR system offers substantial advances in effectiveness and security when compared to conventional methodologies for access control.

Artificial Intelligence Detection of Cervical Spine Fractures Using Convolutional Neural Network Models.

To develop and evaluate a technique using convolutional neural networks (CNNs) for the computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. By leveraging deep learning techniques, the study might potentially lead to improved patient outcomes and clinical decision-making. This study obtained 500 lateral radiographic cervical spine x-ray images from standard open-source dataset repositories to develop a classification model using CNNs. All the images contained diagnostic information, including normal cervical radiographic images (n=250) and fracture images of the cervical spine fracture (n=250). The model would classify whether the patient had a cervical spine fracture or not. Seventy percent of the images were training data sets used for model training, and 30% were for testing. Konstanz Information Miner (KNIME)'s graphic user interface-based programming enabled class label annotation, data preprocessing, CNNs model training, and performance evaluation. The performance evaluation of a model for detecting cervical spine fractures presents compelling results across various metrics. This model exhibits high sensitivity (recall) values of 0.886 for fractures and 0.957 for normal cases, indicating its proficiency in identifying true positives. Precision values of 0.954 for fractures and 0.893 for normal cases highlight the model's ability to minimize false positives. With specificity values of 0.957 for fractures and 0.886 for normal cases, the model effectively identifies true negatives. The overall accuracy of 92.14% highlights its reliability in correctly classifying cases by the area under the receiver operating characteristic curve. We successfully used deep learning models for computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. This approach can assist the radiologist in screening, detecting, and diagnosing cervical spine fractures.

Analysis of Public Interest in Smartfren SIM Cards using the K-Nearest Neighbors Method

The use of Smartfren SIM cards is increasing along with the public's need for fast and stable internet services. However, a deep understanding of public interest in the SIM card is necessary to optimize marketing strategies and increase sales. Proper analysis can help companies identify potential target markets and develop effective marketing strategies. We chose the K-Nearest Neighbors method to analyze public interest in using Smartfren SIM cards. This study aims to develop and evaluate the K-Nearest Neighbors model in predicting public interest in using Smartfren SIM cards. This study uses a dataset containing information about Smartfren SIM card users. We divide the data into two sets: a training set for model building and a test set for evaluating model performance. We apply the K-Nearest Neighbors method to classify the data into two categories: interested and not interested. We evaluate the model performance using accuracy, precision, recall, and F1-score metrics. We present the evaluation results as a confusion matrix. The developed K-Nearest Neighbors model showed excellent performance with an accuracy of 94.29%, a precision of 94.20%, a recall of 100%, and an F1-score of 97.01%. These results indicate that the K-Nearest Neighbors model is effective in predicting people's interest in Smartfren SIM cards. The high recall value indicates that the model is able to identify all interested individuals without missing any, while the high precision value indicates that the model rarely makes false positive prediction errors. This study concludes that the K-Nearest Neighbors method is very effective for use in analyzing people's interest in using Smartfren SIM cards. We can rely on the developed model's strong performance for real-world applications in marketing strategies.

Analysis Of Public Interest In Smartfren SIM Cards Using The K-Nearest Neighbors Method

The use of Smartfren SIM cards is increasing along with the public's need for fast and stable internet services. However, a deep understanding of public interest in the SIM card is necessary to optimize marketing strategies and increase sales. Proper analysis can help companies identify potential target markets and develop effective marketing strategies. We chose the K-Nearest Neighbors method to analyze public interest in using Smartfren SIM cards. This study aims to develop and evaluate the K-Nearest Neighbors model in predicting public interest in using Smartfren SIM cards. This study uses a dataset containing information about Smartfren SIM card users. We divide the data into two sets: a training set for model building and a test set for evaluating model performance. We apply the K-Nearest Neighbors method to classify the data into two categories: interested and not interested. We evaluate the model performance using accuracy, precision, recall, and F1-score metrics. We present the evaluation results as a confusion matrix. The developed K-Nearest Neighbors model showed excellent performance with an accuracy of 94.29%, a precision of 94.20%, a recall of 100%, and an F1-score of 97.01%. These results indicate that the K-Nearest Neighbors model is effective in predicting people's interest in Smartfren SIM cards. The high recall value indicates that the model is able to identify all interested individuals without missing any, while the high precision value indicates that the model rarely makes false positive prediction errors. This study concludes that the K-Nearest Neighbors method is very effective for use in analyzing people's interest in using Smartfren SIM cards. We can rely on the developed model's strong performance for real-world applications in marketing strategies.

Soil Nutrient Analysis and Automatic Monitoring in Precision Agriculture

ABSTRACT The higher productivity from soil can be achieved through Wireless Sensor Networks (WSNs) technology; for this reason, the implementation of WSNs in precision agriculture is increasing day by day. Among the different technologies for crop monitoring, WSNs are recognized as a powerful option for collecting and processing data in the agricultural domain, with low cost and low energy consumption. The ultimate aim of this paper is to predict the precise values of soil nutrients nitrogen, phosphorus, and potassium (NPK) using a machine learning (ML)-based mathematical model formulation. The proposed system will use Global System for Mobile Communications (GSM) technology to automatically monitor soil parameters. It transfers soil data over the mobile network using a GSM modem. In the hardware part, sensor node units were developed to fetch and load data through the energy-efficient GSM, utilizing a bi-directional multi-level text messaging option to improve the alarm system’s efficiency. The system contains sensors such as potential of hydrogen and humidity (PH), which are used to monitor soil data collected from the sensors by the microcontroller. Additionally, as a novel contribution, machine learning based on regression models is applied to the NPK data. A novel data interpolation approach is proposed for data oversampling. The R2 values are compared, achieving nearly 99.5% accuracy in all cases. This paper focuses on various challenges and scalability issues encountered in research and discusses different results obtained by monitoring various soil parameters in a real-time agricultural environment.

Automatic Classification of Defective Photovoltaic Module Cells Based on a Novel CNN-PCA-SVM Deep Hybrid Model in Electroluminescence Images

In today’s world, the rapid development of photovoltaic (PV) power plants has facilitated sustainable energy production. Maintenance and fault detection play a crucial role in ensuring the continuity of energy production. Manual inspection of electroluminescence (EL) images of PV modules requires significant human resources and time investment. This study presents a method for automatic fault detection of PV cells in EL images using hybrid deep features optimized with the principal component analysis (PCA) feature selection algorithm. A lightweight and high-performance model that combines the strengths of convolutional neural network (CNN) architectures is proposed. Firstly, data augmentation techniques are employed due to the imbalance between defective and functional classes in the dataset containing EL images. In experimental studies conducted by integrating the PCA algorithm into MobileNetV2, DenseNet201, and InceptionV3 CNN models, accuracy, precision, recall, and F1-score values of 92.19%, 92%, 90% and 91%, respectively, were achieved. When the results were analyzed, it was observed that the proposed method had an effective performance in detecting faults in PV panel cells.

Comparative Analysis of K-Means and Divisive Clustering Techniques on Balanced Mental Health Data

Effective Clustering of mental health data can provide significant insights into patterns and relationships that are critical for understanding mental health conditions. This study investigated various clustering techniques applied to balanced mental health data to avoid biases associated with an imbalanced data. Clustering of the balanced mental health data was done with respect to the area of Residence feature. Firstly, Random undersampling and SMOTE techniques were incorporated to the imbalanced data set as balancing techniques so as to improve model performance. Random Undersampling Technique turned out to be the most ideal balancing technique with its accuracy, recall, precision and F-score values as 1. After balancing the data, two clustering techniques were applied to the Random Undersampled balanced data. The two techniques were namely: K-means and Divisive techniques. In order to select which of the two clustering techniques is ideal, two test statistics namely Internal Validation and Stability Validation were applied. Results showed that K-means clustering technique indicated slightly lower Average Propotion of None-overlap, Average Distance between Means and Figure Of Merit values given as 0.12, 0.41 and 0.9972 as compared to Divisive clustering technique which were 0.14, 0.42 and 0.9999. The conclusion was that K-means clustering has a better performance. This study's findings will help guide future researchers dealing with mental health data analysis on ways to improve model performance for better and more reliable predictions.

Influence of Head Circumference on the Accuracy of Facial Scanning: An In vitro Study

ObjectivesThis study aimed to investigate the impact of head circumference on the accuracy of three-dimensional (3D) facial scans, focusing on trueness and precision across three mannequin heads of different sizes. Material and methodsThree 3D-printed mannequin heads with circumferences of 30, 50, and 65 cm were used. Ten facial landmarks were identified to measure seven interlandmark distances and two angles. Direct anthropometric measurement, serving as the reference value, was taken using a digital vernier calliper for linear distance, and angle was calculated using the law of cosines. Each head was scanned six times using two systems: a dual-structured light facial scanner (iTom) and a stereophotogrammetry system (3dMD). Digital measurements were analysed using Meshlab and Blender for distances and angles, respectively. Trueness values were determined by comparing measurements to reference measurements, while precision values were derived from the variability among the six scans. Statistical analysis was performed using the Kruskal–Wallis test due to nonhomogeneous variances, followed by Bonferroni correction for pairwise group comparisons. ResultsFor each scanning system, overall deviations in trueness and precision significantly increased with head circumference. Five of the seven distances and one angle showed significant compromises in trueness with larger head circumferences. Most measurements exhibited significant precision decreasing due to head circumference changes, except for N-Pn and Pn-Sn. Additionally, 3dMD displayed higher overall trueness compared to iTom, with five out of seven linear measurements and one angular measurement showing better results. ConclusionHead circumference significantly affects both trueness and precision of 3D facial scans for both technologies, suggesting that facial imaging should be used with caution for larger faces. Selecting an appropriate scanning system, such as 3dMD, can help mitigate the negative effects of scanning larger objects.

Computing the Edge Irregularity Strength of Some Classes of Grid Graphs

Let G be a simple graph. A function ϕ:V(G)→{1,2,…,k} a vertex k-labeling which assigns labels to the vertices of G. For any edge xy in G, we define the weight of this edge as wϕ(xy)=ϕ(x)+ϕ(y). If all the edge weights are distinct, then ϕ is termed as an edge irregular k -labeling of G. The smallest possible value of k for which the graph G possesses an edge irregular k-labeling is denoted as the edge irregularity strength of G and is represented as es(G). In this paper, we investigate the edge irregular k-labeling of some classes of grid graphs, namely rhombic graph Rmn, triangular graph Lmn and octagonal graph Omn. As by-product, we obtain their precise value of edge irregularity strength.

Streamline-directed tunable deterministic lateral displacement chip: A numerical approach to efficient particle separation

In conventional Deterministic Lateral Displacement (DLD), the migration behavior of a particle of specific size is determined by the critical diameter (Dc), which is predefined by the device's geometry. In contrast to the typical approach that alters the angle between the pillar array and fluid streamlines by modifying the geometrical parameters, this study introduces a novel perspective that focuses on changing the direction of the streamlines. The proposed technique offers a tunable DLD chip featuring a straightforward design that allows for easy fabrication. This chip features one completely horizontal pillar array with two bypass channels on the top and bottom of the DLD chamber. The width of these bypass channels changes linearly from their inlet to their outlet. Two design configurations are suggested for this chip, characterized by either parallel or unparallel slopes of the bypass channels. This chip is capable of generating a wide range of Dc values by manipulating two distinct control parameters. The first control parameter involves adjusting the flow rates in the two bypass channels. The second control parameter entails controlling the slopes of these bypass channels. Both of these parameters influence the direction of particle-carrying streamlines resulting in a change in the path-line of the particles. By changing the angle of streamlines with pillar array, the Dc can be tuned. Prior to determining the Dc for each case, an initial estimation was made using a Python script that utilized the streamline coordinates. Subsequently, through FEM modeling of the particle trajectories, precise Dc values were ascertained and compared with the estimated values, revealing minimal disparities. By adjusting the flow rate and slope of the bypass channels, maximum Dc ranges of 4–10 μm and 8–13 μm can be achieved, respectively. This innovative chip enables the attainment of Dc values spanning from 0.5 to 14 μm.

The Effect of Image Count on Accuracy in Digital Measurements in Dentistry.

Objective: This study investigated how the number of images collected for digital measurements in dentistry affects accuracy compared with traditional methods. Methods: A Frasaco maxillary model was scanned using a SHINING 3D AutoScan-DS-MIX dental 3D scanner to create an STL file. The maxilla was molded 10 times using polyvinyl siloxane (Zhermack Elite HD+) to produce plaster models, which were scanned with the same reference scanner to generate 10 STL files. The Frasaco model was scanned 10 times, capturing images in intervals of 800-1000, 1000-1200, and 1200-1500 using a 3Shape TRIOS 3 intraoral scanner, creating additional STL files. These were analyzed with reverse engineering software. Results: The most accurate measurements were obtained using 1200-1500 images. Conventional impression techniques performed significantly worse. There was a significant difference between the groups Digital 1200-1500 and Plaster (p < 0.001) and between Digital 800-1000 and Plaster (p = 0.007). No significant difference was found when the digital groups were compared among themselves. There was also no significant difference between the Plaster and Digital 1000-1200 groups. To compare precision values that were normally distributed across three or more methods, a one-way ANOVA was used. Trueness values that were not normally distributed with three or more methods were compared employing the Kruskal-Wallis test. Conclusions: Different image counts affect digital measurement accuracy. The most accurate measurements were obtained when collecting 1200-1500 images. Conventional impression techniques were shown to perform significantly worse than digital impression.

An Experimental Methodology for Automated Detection of Surface Turbulence Features in Tidal Stream Environments

Tidal stream environments are important areas of marine habitat for the development of marine renewable energy (MRE) sources and as foraging hotspots for megafaunal species (seabirds and marine mammals). Hydrodynamic features can promote prey availability and foraging efficiency that influences megafaunal foraging success and behaviour, with the potential for animal interactions with MRE devices. Uncrewed aerial vehicles (UAVs) offer a novel tool for the fine-scale data collection of surface turbulence features and animals, which is not possible through other techniques, to provide information on the potential environmental impacts of anthropogenic developments. However, large imagery datasets are time-consuming to manually review and analyse. This study demonstrates an experimental methodology for the automated detection of turbulence features within UAV imagery. A deep learning architecture, specifically a Faster R-CNN model, was used to autonomously detect kolk-boils within UAV imagery of a tidal stream environment. The model was trained on pre-existing, labelled images of kolk-boils that were pre-treated using a suite of image enhancement techniques based on the environmental conditions present within each image. A 75-epoch model variant provided the highest average recall and precision values; however, it appeared to be limited by sub-optimal detections of false positive values. Although further development is required, including the creation of standardised image data pools, increased model benchmarking and the advancement of tailored pre-processing techniques, this work demonstrates the viability of utilising deep learning to automate the detection of surface turbulence features within a tidal stream environment.

Predicting pKa Values of Para-Substituted Aniline Radical Cations vs. Stable Anilinium Ions in Aqueous Media

The focus of pKa calculations has primarily been on stable molecules, with limited studies comparing radical cations and stable cations. In this study, we comprehensively investigate models with implicit solvent and explicit water molecules, direct and indirect calculation approaches, as well as methods for calculating free energy, solvation energy, and quasi-harmonic oscillator approximation for para-substituted aniline radical cations (R-PhNH2•+) and anilinium cations (R-PhNH3+) in the aqueous phase. Properly including and positioning explicit H2O molecules in the models is important for reliable pKa predictions. For R-PhNH2•+, precise pKa values were obtained using models with one or two explicit H2O molecules, resulting in a root mean square error (RMSE) of 0.563 and 0.384, respectively, for both the CBS-QB3 and M062X(D3)/ma-def2QZVP methods. Further improvement was achieved by adding H2O near oxygen-containing substituents, leading to the lowest RMSE of 0.310. Predicting pKa values for R-PhNH3+ was more challenging. CBS-QB3 provided an RMSE of 0.349 and the M062X(D3)/ma-def2QZVP method failed to calculate pKa accurately (RMSE &gt; 1). However, by adopting the double-hybrid functional method and adding H2O near the R substituent group, the calculations were significantly improved with an average absolute difference (ΔpKa) of 0.357 between the calculated and experimental pKa values. Our study offers efficient and reliable methods for pKa calculations of R-PhNH2•+ (especially) and R-PhNH3+ based on currently mature quantum chemistry software.

An Enhanced Symmetric Sand Cat Swarm Optimization with Multiple Strategies for Adaptive Infinite Impulse Response System Identification

An infinite impulse response (IIR) system might comprise a multimodal error surface and accurately discovering the appropriate filter parameters for system modeling remains complicated. The swarm intelligence algorithms facilitate the IIR filter’s parameters by exploring parameter domains and exploiting acceptable filter sets. This paper presents an enhanced symmetric sand cat swarm optimization with multiple strategies (MSSCSO) to achieve adaptive IIR system identification. The principal objective is to recognize the most appropriate regulating coefficients and to minimize the mean square error (MSE) between an unidentified system’s input and the IIR filter’s output. The MSSCSO with symmetric cooperative swarms integrates the ranking-based mutation operator, elite opposition-based learning strategy, and simplex method to capture supplementary advantages, disrupt regional extreme solutions, and identify the finest potential solutions. The MSSCSO not only receives extensive exploration and exploitation to refrain from precocious convergence and foster computational efficiency; it also endures robustness and reliability to facilitate demographic variability and elevate estimation precision. The experimental results manifest that the practicality and feasibility of the MSSCSO are superior to those of other methods in terms of convergence speed, calculation precision, detection efficiency, regulating coefficients, and MSE fitness value.