High Test Accuracy Research Articles

Arecanut Disease Detection

The tropical crop arecanut, sometimes referred to as betel nut, is primarily farmed in India. In terms of arecanut production and consumption, the nation ranks second in the world. The areca nut plant is vulnerable to numerous diseases that impact its roots, stem, leaves, and fruits throughout its life cycle. While some of these illnesses can be seen with the naked eye, others cannot. These illnesses are brought on by abrupt changes in temperature and other meteorological factors; early disease identification is crucial. In order to minimize losses for farmers, this work focuses on early and precise disease diagnosis. Using convolutional neural networks, we developed a system that assists in identifying arecanut, leaf, and trunk ailments and offers treatments. A Convolutional Neural Network (CNN) is a Deep Learning method that uses an image as input, gives different items in the image learnable weights and biases, and then uses the results to determine which objects are different. We took a dataset with 620 photos of arecanuts in both good and unhealthy conditions in order to train and evaluate the CNN model. An 80:20 ratio is used to separate the test and train data. Adam serves as the optimizer function, accuracy serves as a measure, and categorical cross-entropy serves as the loss function for the model compilation. To attain high validation and test accuracy with little loss, the model is trained over a total of 5 epochs. It was discovered that the suggested method was successful and 88.46 percent accurate in detecting arecanut illness. Diseases frequently seen in areca trees include Mahali Disease (Koleroga), Bud Rot Disorder, Stem Exudation, Yellow Leaf Blotch,Yellow Disease, which arises from persistent rainfall and climate alterations, these ailments need to be managed in the initial phase of infection; otherwise, it could lead to difficulties in oversight in the concluding phase that could result in detriment to the latter. To prevent this, we can utilize Machine Learning for disease detection. We will identify yellow leaf spot, stem bleeding, and Mahali disease (KoleRoga) in this project and provide treatments for the conditions we find. Key words: Convolution Neural Networks, Arecanut, and Machine Learning

Impact Damage Localization in Composite Structures Using Data-Driven Machine Learning Methods.

Due to the uncertainty of material properties of plate-like structures, many traditional methods are unable to locate the impact source on their surface in real time. It is important to study the impact source-localization problem for plate structures. In this paper, a data-driven machine learning method is proposed to detect impact sources in plate-like structures and its effectiveness is tested on three plate-like structures with different material properties. In order to collect data on the localization of the impact source, four piezoelectric transducers and an oscilloscope were utilized to construct an experimental platform for impulse response testing. Meanwhile, the position of the impact source on the surface of the test plate is generated by manually releasing the steel ball. The eigenvalue of arrival time in the time domain signal is extracted to build data sets for machine learning. This paper uses the Back Propagation (BP) neural network to learn the difference in the arrival time of each sensor and predict the location of the impact source. The results demonstrate that the machine learning method proposed in this paper can predict the location of the impact source in the plate-like structure without relying on the material properties, with high test accuracy and robustness. The research work in this paper can provide experimental methods and testing techniques for locating impact damage in composite material structures.

Artificial Neural Network-Based Validation, DFT, Thermal and Biological Evaluation of 4-Aminoantipyrine-Derived Ru(III) Complexes.

New methodologies have been evaluated for validating analytical characterization with artificial neural networks (ANNs). Compared to previous machine learning models, these provide more accurate and automated results with high testing accuracy. The Schiff base ruthenium complexes used in the proposed study were synthesized using 4-aminoantipyrine derivatives. 4-Aminoantipyrine is a biologically active pharmacophore. The geometry of the complexes was confirmed by IR, electronic, and magnetic measurements. XRD analysis pointed out the nanocrystalline behavior of the chelates. The molecular structures have been optimized using DFT calculations. The ruthenium complexes are one of the main chemotherapeutic agents in anticancer therapy over platinum drugs due to a wide range of peculiarities. Complexes exhibit octahedral geometry as confirmed by magnetic measurements exhibiting more biological activity. The complexes are redox active depicting high biological potency. The Ru chelates also display high photocatalytic efficiency. The chelates also adhere to Lipinski's rule of five as evidenced from mole inspiration calculations. Among the chelates, RuL3 exhibits high anticancer potency suggesting a valuable candidate for the treatment of cancer. RuL5 has high antibacterial efficiency, and RuL4 complex possesses high antifungal activity. The chelates may serve as potential antimicrobial agents.

Preferences of pregnant women and obstetric healthcare professionals on using noninvasive prenatal testing to predict adverse pregnancy outcomes: a discrete choice experiment

ObjectivesNoninvasive prenatal testing (NIPT) to screen for fetal aneuploidies by analysing cell-free DNA in maternal plasma is available to pregnant women worldwide. In the future, the scope of NIPT could...

G-tube contrast check: Transition from fluoroscopy to abdominal radiographs.

Reports of radiographic exam evaluation for G-tube malposition in children are limited. Evaluate effectiveness of a new 2-view abdominal radiograph exam protocol instituted to provide 24/7 coverage at 2 affiliated hospitals and replace the prior fluoroscopic G-tube contrast check exam. G-tube radiographic exams performed between December 2019 and May 2022 at 2 affiliated hospitals were identified and retrospective chart review was performed to delineate exam test yield, accuracy, sensitivity, specificity. Additional data collected included exam adherence to protocol, years of experience of the reporting pediatric radiologist, reporting time, and 30-day adverse events. 227 exams were performed in 186 patients. 2-view radiograph protocol was followed in 81.9% (186/227); Additional radiograph views were performed in 18.1% (41/227); additional contrast volume in 9.3% (21/227). Reporting time under 1 hour occurred in 79.7% (181/227). 5.7% (13/227) exams were reported as indeterminate adding a median time delay of 40 minutes (IQR 90). Indeterminate exam reporting did not correlate with years of experience of the reporting pediatric radiologist (p=0.19); reporting time over 1 hour occurred more often in the after-hours group (p= 0.032). Fluoroscopic G-tube contrast check was requested in 8 of 13 indeterminate readings. Following reclassification of indeterminate exams based on clinical suspicion, test performance: yield 94.3%, accuracy 97.3%, sensitivity 81.8%, specificity 98.2%, PPV 69.2%, NPV 99.1%. This new diagnostic exam performed well with high test yield, accuracy, specificity and negative predictive value. The exam mostly followed protocol, allowed timely resumption of G-tube use, and provided a needed 24/7 remote coverage option for the new affiliated hospital.

APPLICATION OF XCEPTION ARCHITECTURE FOR DEEP LEARNING-BASED FINGERPRINT RECOGNITION

This study proposes a convolutional neural network (CNN) method with an xception architecture model that is used to classify the types of fingerprint image patterns. The data used in this study uses data taken directly using a scanning tool made using FPM 10 A sensors and Arduino Uno. The dataset consists of five types of fingerprint image patterns, namely arch, ulnar loop, whorl, radial loop and twinted loop with a total of 1000 data. The research started from data collection, pre-processing, CNN architecture design, model training and evaluation. The application of the xception architecture shows the best performance with high test accuracy values, stable and consistent. The test scenario of this study is to compare different epoch values, namely 10.30 and 50 and use two learning rates, namely 0.0001 and 0.001. The best test results were obtained at epoch 30 with a learning rate of 0.0001, which was 92% and 93% at epoch 50 with a learning rate of 0.001.

Methods for Diagnosing Proteinuria: When to Use Which Test, and Why.

Proteinuria plays a central role in the diagnosis of kidney disease and has a high prognostic value. The test methods used differ considerably regarding impact on test accuracy, sensitivity, and specificity. Therefore, knowledge of the methodology is crucial for the interpretation of the results. In addition to the distinction between semi-quantitative and quantitative tests, there are also relevant differences within the two methods. In general, semi-quantitative tests are easy to handle but have limitations such as: i) incomplete quantification, ii) a lack of specificity regarding the type of proteinuria, iii) a high rate of false positive tests with the need for re-testing with a quantitative method for verification. In contrast, quantitative methods, especially immunoassays, have the advantages of: i) high test accuracy, ii) the possibility of targeted detection of specific protein molecules in addition to albumin. However, these methods are more expensive and require access to a laboratory or an electronic point of care device. In this review, the different types of tests for proteinuria, their underlying methodologies and their strengths and weaknesses are discussed in detail to allow a rational decision of use and a correct interpretation of the results depending on the clinical context.

MRI Brain Tumor Classification Using HOG Features Selected via Impurity Based Importance Measure

MRI is considered the primary method for confirming the diagnosis of brain tumors and choosing the appropriate treatment. Automating the process of detecting brain tumors in MRI images using deep models has become a popular trend in the scientific research community. However, deep neural networks require a large volume of data to avoid overfitting, which is not ideally available. This is where handcrafted features come in handy. In this paper, we present an efficient approach for brain tumor classification that can outperform deep CNN models. In the proposed system, the histogram of oriented gradients algorithm is used to extract feature descriptors from brain MRI images. The extracted features are processed using a random forest algorithm, where each decision tree performs the task of evaluating feature importances via the impurity metric, while all estimators collaborate in selecting the effective feature set. Finally, the Fine KNN algorithm is used to classify the types of brain tumors based on the numerical features obtained. The presented model achieved a high-test accuracy of 99.35% with an F1-score of 99.30%, outperforming many deep models.

Achieving High Standards in Clinical Biochemistry: Integrating Six Sigma, Quality Goal Index (QGI), and Operating Specifications (OPSpecs) for Targeted Quality Enhancement.

Background Sigma metrics, a cornerstone of quality control (QC) in manufacturing, have been increasingly adopted in analytical processes. In clinical biochemistry labs, Sigma analysis provides insights into the level of QC achieved and identifies deviations from perfection. Methods A prospective-retrospective observational study was conducted at the Central Diagnostic Laboratory of Shree Krishna Hospital between August 2021 and July 2022. Sigma metrics were calculated using the formula: Sigma = (TEa - Bias%) / CV%. Total allowable error (TEa) values were derived from guidelines including the Clinical Laboratory Improvement Amendments (CLIA), Royal College of Pathologists of Australasia (RCPA), and Rilibäk. Bias% was assessed using data from External Quality Assurance Services (EQAS), while the coefficient of variation (CV%) was calculated based on internal QCdata. For parameters with Sigma values less than 3, Quality Goal Index (QGI) ratios were computed to assess whether errors were primarily due to precision or accuracy issues. Operating Specifications (OPSpecs) charts were generated to guide adjustments to internal QC procedures for underperforming tests. Results The analysis revealed that high-density lipoprotein (HDL) cholesterol exhibited excellent performance with a Sigma value of ≥ 6 based on newer CLIA guidelines. Parameters such as alkaline phosphatase (ALP), low-density lipoprotein (LDL) cholesterol, creatine kinase, iron, and sodium achieved Sigma values between 4 and 6, indicating satisfactory QC. However, certain parameters, including urea, creatinine, thyroid-stimulating hormone (TSH), free thyroxine (FT4), vitamin B12, and chloride, demonstrated Sigma values less than 3, signaling the need for immediate quality improvement. QGI analysis highlighted precision issues in some tests, while others showed accuracy deficits. The OPSpecs charts provided a comprehensive framework for customizing QC protocols to address specific parameter deficiencies. Conclusions This study highlights the value of Sigma metrics in refining QC protocols in clinical biochemistry laboratories. Shifting from generalized to parameter-specific QC plans allows for better resource allocation, improved efficiency, and higher test accuracy. Tools like QGI and OPSpecs charts help identify underperforming tests, supporting continuous quality improvement in diagnostics.

Predicting the Physical Properties of Dark Matter Subhalos from Baryonic Parameters Using Machine Learning

Predicting the Physical Properties of Dark Matter Subhalos from Baryonic Parameters Using Machine Learning

Prediction of Potential Urban Hot Spots in Urban Environments Using Convolutional Neural Networks and Remote Sensing Techniques

Abstract. With the increase in built-up areas and rising urban populations, Land Surface Temperature (LST) has significantly increased, leading to the proliferation of Urban Hot Spots (UHS) in urban environments. To mitigate UHS proactively, researchers have conducted studies using various models to predict LST. However, current predictions are primarily based on data samples from isolated stations, making them unfeasible for continuous LST prediction on larger scales, such as regional levels. Therefore, this research aims to use Singapore as a case study to predict UHS on a regional scale using machine learning based on essential variables. Specifically, this research proposes training a Convolutional Neural Network (CNN) model using identified independent variables, including elevation, Normalized Difference Built-up Index (NDBI), Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), population, and Land Use and Land Cover (LULC), along with the target variable, UHS. After training, the model achieves high test accuracy and is fed projected data, subsequently producing projected UHS locations. The findings indicate that new UHS primarily appear in southwestern areas, Marina Bay, and the northwest regions of the country. This research predicts that 2.24 percent of the site could be classified as UHS by 2025, compared to the current percentage of 0.95 percent. Based on these projections, the research proposes preventative measures to proactively mitigate UHS. This research fills the gap by constructing a prediction model that can predict UHS locations on a regional scale.

Comparative analysis of speaker identification performance using deep learning, machine learning, and novel subspace classifiers with multiple feature extraction techniques

Comparative analysis of speaker identification performance using deep learning, machine learning, and novel subspace classifiers with multiple feature extraction techniques

A-327 Statistical Technique-based Image Analysis for Digital Reading of Immunochemistry Cassette

Abstract Background Covid-19 transformed healthcare and how we think about clinical diagnostics. People prefer at home testing options that enable them to focus on staying healthy at their own comfort and privacy. Fast and accurate lateral flow assays or rapid diagnostic test (RDT) meets that need, however ensuring high test specificity and accuracy are of utmost importance. Visual readout of RDTs is subjective and time consuming. With the current technological advancement, medical devices are smarter and can be used for automated readout. The core of such devices are the algorithms that process the information despite several limitations and unknown variations affecting input signals. Many such algorithms are developed and are used for similar applications. Here, a statistical model for any optical device that is designed to automatically read an RDT with one or more test lines is described. Methods The described LFA algorithm is a innovative way to analyze images acquired with Complementary Metal-Oxide-Semiconductor (CMOS) based optical system with low Signal-to-noise ratio (SNR), giving a semi quantitative result. This also takes care of variations in illumination sources with relatively reduced computation, by converting the images to a 1 Dimensional (1D) signal. Algorithm workflow: Image pre-processing: Affine transformations such as rotation, translation, etc. (depends on the inherent setup) are applied on the acquired images as a pre-processing step. Pattern matching techniques using Gaussian templates are applied to determine the window region of the LFA and precisely identify the test and control line regions. Image formatting: The sample region of the cassette is converted from RGB color space to the weighted gray (Gw) for digital interpretation of the primary biomarker present. Characterization of the Gw channel enables test peak detection with enhanced resolution. This helps in early call out of results and improved accuracy, thereby reducing chances of erroneous results. A second order Butterworth low pass filter is applied to reduce system noise and enhance SNR. The images are converted to 1D signal by averaging for mathematical read out. Mathematical interpretation: Maximum peak height (max) and minimum peak depth (min) of Gw channel values in the test line region (t-peak) are extracted from the 1D signal. The mathematical representation of the test line is calculated as: Test Peak Signal = {max(t-peak) - min(t-peak)} / Alpha; Normalization Factor, Alpha = (sum of all Gw channel values for any clear non-reactive part of the test device) / (number of Gw channel values) Results TPS values for various biomarkers from statistically significant replicates were computed at Low, Medium, and High levels. Two sample T-tests were performed, and respective p values are distinctly different as p-value<0.05. For a concentration level of Low to medium, p-value is 0.044 and for Medium - High p-value is 0.004. Conclusions This algorithm can run on any off-the-shelf available RDT coupled with a CMOS based optical system. The proposed algorithm can differentiate between low, medium, and high concentrations with high confidence. High throughput semiquantitative digital readouts are robust, accurate and are preferred for diagnostic test result interpretation.

Optimal layer selection for latent data augmentation

While data augmentation (DA) is generally applied to input data, several studies have reported that applying DA to hidden layers in neural networks, i.e., feature augmentation, can improve performance. However, in previous studies, the layers to which DA is applied have not been carefully considered, often being applied randomly and uniformly or only to a specific layer, leaving room for arbitrariness. Thus, in this study, we investigated the trends of suitable layers for applying DA in various experimental configurations, e.g., training from scratch, transfer learning, various dataset settings, and different models. In addition, to adjust the suitable layers for DA automatically, we propose the adaptive layer selection (AdaLASE) method, which updates the ratio to perform DA for each layer based on the gradient descent method during training. The experimental results obtained on several image classification datasets indicate that the proposed AdaLASE method altered the ratio as expected and achieved high overall test accuracy.

Error Analysis and Optimization of Structural Parameters of Spatial Coordinate Testing System Based on Position-Sensitive Detector.

For the research on real-time accurate testing technology for the explosion point spatial coordinate of munitions, its currently commonly used methods such as acoustic-electric detection or high-speed imaging are limited by the field conditions, response rate, cost, and other factors. In this paper, a method of spatial coordinate testing for the explosion point based on a 2D PSD (position-sensitive detector) intersection is proposed, which has the advantages of a faster response, better real-time performance, and a lower cost. Firstly, a mathematical model of the spatial coordinate testing system was constructed, and an error propagation model for structural parameters was developed. The influence of the position of the optical axes' intersection as well as the azimuth angle and pitch angle on the test accuracy of the system was simulated and analyzed, thus obtaining the distribution and variation trend of the overall error propagation coefficient of the system. Finally, experiments were designed to obtain the test error of the system for validation. The results show that the system test accuracy is high when the azimuth angle is 20°-50°, the overall error propagation coefficient does not exceed 48.80, and the average test error is 56.17 mm. When the pitch angle is -2.5°-2.5°, the system has a higher test accuracy, with the overall error propagation coefficient not exceeding 44.82, and the average test error is 41.87 mm. The test accuracy of the system is higher when the position of the optical axes' intersection is chosen to make sure that explosion points fall in the region of the negative half-axis of the Zw-axis of the world coordinate system, with an overall error propagation coefficient of less than 44.78 and an average test error of 73.38 mm. It is shown that a reasonable selection of system structure parameters can significantly improve the system test accuracy and optimize the system deployment mode under the long-distance field conditions so as to improve the deployment efficiency.

Addressing Imbalanced EEG Data for Improved Microsleep Detection: An ADASYN, FFT and LDA-Based Approach

Microsleep, brief lapses in consciousness lasting less than 15 seconds, are often accompanied by feelings of fatigue and are detectable through a deceleration in electroencephalogram (EEG) signal frequencies. Accurate identification of microsleep is critical for assessing driver alertness and preventing accidents. This paper introduces a novel approach to detecting driver microsleep by leveraging EEG signals and advanced machine learning techniques. The methodology begins with preprocessing raw EEG data to improve quality and balance, utilizing the ADASYN algorithm to address dataset imbalances. After preprocessing, features are extracted using Fast Fourier Transform (FFT), which provides a comprehensive frequency domain analysis of the EEG signals. For classification, Linear Discriminant Analysis (LDA) is employed to effectively distinguish between microsleep events and normal wakefulness based on the extracted features. The proposed framework was rigorously validated using a well-established publicly available EEG dataset, which included recordings from 76 healthy individuals. The validation results revealed a high testing accuracy of 92.71% in detecting microsleep episodes, demonstrating the effectiveness of the proposed approach. These results underscore the potential of combining EEG signal analysis with machine learning models for practical applications in monitoring driver alertness. The framework could significantly enhance driver safety by providing an effective tool for detecting microsleep and thereby reducing the risk of accidents caused by drowsy driving. This research highlights the promising application of advanced signal processing and machine learning techniques in the field of driver alertness monitoring.

A Multi-Dimensional Reverse Auction Mechanism for Volatile Federated Learning in the Mobile Edge Computing Systems

Federated learning (FL) can break the problem of data silos and allow multiple data owners to collaboratively train shared machine learning models without disclosing local data in mobile edge computing. However, how to incentivize these clients to actively participate in training and ensure efficient convergence and high test accuracy of the model has become an important issue. Traditional methods often use a reverse auction framework but ignore the consideration of client volatility. This paper proposes a multi-dimensional reverse auction mechanism (MRATR) that considers the uncertainty of client training time and reputation. First, we introduce reputation to objectively reflect the data quality and training stability of the client. Then, we transform the goal of maximizing social welfare into an optimization problem, which is proven to be NP-hard. Then, we propose a multi-dimensional auction mechanism MRATR that can find the optimal client selection and task allocation strategy considering clients’ volatility and data quality differences. The computational complexity of this mechanism is polynomial, which can promote the rapid convergence of FL task models while ensuring near-optimal social welfare maximization and achieving high test accuracy. Finally, the effectiveness of this mechanism is verified through simulation experiments. Compared with a series of other mechanisms, the MRATR mechanism has faster convergence speed and higher testing accuracy on both the CIFAR-10 and IMAGE-100 datasets.

Fault diagnosis method for distribution network based on improved deep learning

Abstract Due to the complexity of the grid structure, limited fault samples, and difficulty in obtaining them, there is a need to improve the accuracy of fault diagnosis in distribution networks. To address the issue of insufficient training samples in the target domain leading to poor training results, a fault diagnosis method for distribution networks based on improved deep learning is proposed. Firstly, finite element simulation is employed to generate fault data under different operating conditions, obtaining a sufficient number of samples for feature extraction and enhancement. Secondly, a deep convolutional neural network (DCNN) model is constructed, and the model is trained using the sample data. Finally, the model is used for fault diagnosis in distribution networks under different operating conditions. Experimental results demonstrate that the fault diagnosis model achieves high testing accuracy and is capable of detecting fault locations and types with high precision.

An attention-based deep learning for acute lymphoblastic leukemia classification

The bone marrow overproduces immature cells in the malignancy known as Acute Lymphoblastic Leukemia (ALL). In the United States, about 6500 occurrences of ALL are diagnosed each year in both children and adults, comprising nearly 25% of pediatric cancer cases. Recently, many computer-assisted diagnosis (CAD) systems have been proposed to aid hematologists in reducing workload, providing correct results, and managing enormous volumes of data. Traditional CAD systems rely on hematologists’ expertise, specialized features, and subject knowledge. Utilizing early detection of ALL can aid radiologists and doctors in making medical decisions. In this study, Deep Dilated Residual Convolutional Neural Network (DDRNet) is presented for the classification of blood cell images, focusing on eosinophils, lymphocytes, monocytes, and neutrophils. To tackle challenges like vanishing gradients and enhance feature extraction, the model incorporates Deep Residual Dilated Blocks (DRDB) for faster convergence. Conventional residual blocks are strategically placed between layers to preserve original information and extract general feature maps. Global and Local Feature Enhancement Blocks (GLFEB) balance weak contributions from shallow layers for improved feature normalization. The global feature from the initial convolution layer, when combined with GLFEB-processed features, reinforces classification representations. The Tanh function introduces non-linearity. A Channel and Spatial Attention Block (CSAB) is integrated into the neural network to emphasize or minimize specific feature channels, while fully connected layers transform the data. The use of a sigmoid activation function concentrates on relevant features for multiclass lymphoblastic leukemia classification The model was analyzed with Kaggle dataset (16,249 images) categorized into four classes, with a training and testing ratio of 80:20. Experimental results showed that DRDB, GLFEB and CSAB blocks’ feature discrimination ability boosted the DDRNet model F1 score to 0.96 with minimal computational complexity and optimum classification accuracy of 99.86% and 91.98% for training and testing data. The DDRNet model stands out from existing methods due to its high testing accuracy of 91.98%, F1 score of 0.96, minimal computational complexity, and enhanced feature discrimination ability. The strategic combination of these blocks (DRDB, GLFEB, and CSAB) are designed to address specific challenges in the classification process, leading to improved discrimination of features crucial for accurate multi-class blood cell image identification. Their effective integration within the model contributes to the superior performance of DDRNet.

An investigation of an athlete injury likelihood monitoring system using the random forest algorithm and DWT.

The main goal of sports science is to monitor sports injuries. Nevertheless, the existing sports injury monitoring projects have many expensive instruments and excessively extended monitoring periods, which makes it difficult to expand sports injury monitoring on a large scale. The advancement of machine learning algorithms opens up new avenues for the tracking of sports injuries. A training set of sports injuries was created using the Discrete Wavelet Transform (DWT) and Random Forest algorithms. Next, a basic analytic framework was created based on the lower-body movement of runners, and an athlete's injury likelihood monitoring system was established. First off, the wearable gyroscope device can efficiently plot the motion displacement curve and monitor the three-dimensional mechanics of the athlete's hips, thighs, and calves. Secondly, the system has a higher computational efficiency and an advantage over other classifier-based systems in terms of testing and training times. The suggested system framework identifies athletes' injury propensity, providing preventive recommendations based on displacement curves, and offering a low total cost and high testing accuracy, making it easy to implement and cost-effective. All things considered, the sports injury monitoring device is very accurate and reasonably priced, making it appropriate for widespread use.