Test Accuracy Research Articles

The diagnostic accuracy of community spine radiology for adolescent idiopathic scoliosis brace candidates.

The study aims to establish the diagnostic accuracy of community spine x-rays for brace candidates. A review of adolescent idiopathic scoliosis patients seen for initial visit at a tertiary care pediatric hospital was conducted (n = 170). The index test was the pre-referral community spine x-ray interpreted by a community radiologist. Measures of diagnostic accuracy for the index test were determined against the reference standard if images were obtained within 90 days (n = 111). The reference standard was the 3-foot standing EOS spine x-ray evaluated by spine specialists. Diagnostic criterion for a brace candidate was dichotomized by Cobb angle range (25-40°) according to Scoliosis Research Society criteria. Risser stage was not included given significant missing data in index reports. To mitigate the uncertainty around true progression, sensitivity analyses were conducted on a sub-sample of data when index test was within 60 days of the reference standard (n = 67). Accuracy of the community spine x-ray to detect a brace candidate was 65.8% (95% CI 56.2-74.5). Sensitivity of the index test was 65.4% with a false negative rate of 34.6%. Specificity was 66.1% with a false positive rate of 33.9%. Positive and negative predictive values were 63.0% and 68.4%, respectively. Of the total number of brace candidates (n = 52), 32.7% were missed because of underestimation in Cobb angle (95% CI 21.5-46.2). The proportion of missed brace candidates because of underestimation was unchanged with 60-day data (p = 0.37). Inaccuracies in community spine radiology may lead to missed opportunities for non-operative treatment.

Advancing Colorectal Cancer Detection With Blood-Based Tests: Qualitative Study and Discrete Choice Experiment to Elicit Population Preferences.

Colorectal cancer (CRC) is the second most deadly form of cancer, inducing an estimated 1.9 million incidence cases and 0.9 million deaths worldwide in 2020. Despite the availability of screening tests, their uptake remains suboptimal. However, blood-based tests that look for signs of cancer-specific markers in the body are increasingly available as an alternative for more invasive tests for cancer. Compared with existing tests, the benefits of blood-based tests for CRC include not needing pretest preparation, stool handling, and dietary or medication restrictions. This study aims to explore the population's preferences for CRC screening tests, with a focus on blood-based tests, and investigate the factors influencing test uptake. We used a mixed methods approach, combining semistructured interviews and a discrete choice experiment (DCE) survey. Interviews were analyzed using thematic analysis to identify salient attributes for CRC screening tests. These attributes informed the design of the DCE survey. The DCE data were analyzed using mixed logit and mixed-mixed multinomial logit models. Qualitative findings from 30 participants revealed that participants preferred blood-based tests due to their perceived low risk, minimal pain, and ease of sample collection. However, concerns about the test's lower accuracy were also expressed. The DCE survey was completed by 1189 participants. In the mixed logit model, participants demonstrated a stronger preference for blood-based tests over a 2-day stool-based test. The mixed-mixed multinomial logit model identified 2 classes, strong supporters and weak supporters, for CRC screening. Weak supporters, but not strong supporters, had a higher preference for blood-based tests. Women, ethnic Chinese, and people aged 40 to 60 years were more likely to be weak supporters. Both models highlighted the high influence of cost and test sensitivity on participants' preferences. Transitioning from a 2-day stool-based test to a blood-based test, assuming a national screening program at a base price of Singapore $5 (US $3.75), was estimated to have the potential to increase the relative uptake by 5.9% (95% CI 3.6%-8.2%). These findings contribute to our understanding of CRC screening preferences and provide insights into the factors driving test uptake. This study highlights the perceived advantages of blood-based tests and identifies areas of concern regarding their accuracy. Further research is needed to determine the actual increase in uptake rate when blood-based tests are made available.

Serum proteome signatures associated with liver steatosis in adolescents with obesity.

Childhood obesity, a pressing global health issue, significantly increases the risk of metabolic complications, including metabolic dysfunction associated with steatotic liver disease (MASLD). Accurate non-invasive tests for early detection and screening of steatosis are crucial. In this study, we explored the serum proteome, identifying proteins as potential biomarkers for inclusion in non-invasive steatosis diagnosis tests. Fifty-nine obese adolescents underwent ultrasonography to assess steatosis. Serum samples were collected and analyzed by targeted proteomics with the Proximity Extension Assay technology. Clinical and biochemical parameters were evaluated, and correlations among them, the individuated markers, and steatosis were performed. Receiver operating characteristic (ROC) curves were used to determine the steatosis diagnostic performance of the identified candidates, the fatty liver index (FLI), and their combination in a logistic regression model. Significant differences were observed between subjects with and without steatosis in various clinical and biochemical parameters. Gender-related differences in the serum proteome were also noted. Five circulating proteins, including Cathepsin O (CTSO), Cadherin 2 (CDH2), and Prolyl endopeptidase (FAP), were identified as biomarkers for steatosis. CDH2, CTSO, Leukocyte Immunoglobulin Like Receptor A5 (LILRA5), BMI, waist circumference, HOMA-IR, and FLI, among others, significantly correlated with the steatosis degree. CDH2, FAP, and LDL combined in a logit model achieved a diagnostic performance with an AUC of 0.91 (95% CI 0.75-0.97, 100% sensitivity, 84% specificity). CDH2 and FAP combined with other clinical parameters, represent useful tools for accurate diagnosis of fatty liver, emphasizing the importance of integrating novel markers into diagnostic algorithms for MASLD.

Enhancing intrusion detection: a hybrid machine and deep learning approach

The volume of data transferred across communication infrastructures has recently increased due to technological advancements in cloud computing, the Internet of Things (IoT), and automobile networks. The network systems transmit diverse and heterogeneous data in dispersed environments as communication technology develops. The communications using these networks and daily interactions depend on network security systems to provide secure and reliable information. On the other hand, attackers have increased their efforts to render systems on networks susceptible. An efficient intrusion detection system is essential since technological advancements embark on new kinds of attacks and security limitations. This paper implements a hybrid model for Intrusion Detection (ID) with Machine Learning (ML) and Deep Learning (DL) techniques to tackle these limitations. The proposed model makes use of Extreme Gradient Boosting (XGBoost) and convolutional neural networks (CNN) for feature extraction and then combines each of these with long short-term memory networks (LSTM) for classification. Four benchmark datasets CIC IDS 2017, UNSW NB15, NSL KDD, and WSN DS were used to train the model for binary and multi-class classification. With the increase in feature dimensions, current intrusion detection systems have trouble identifying new threats due to low test accuracy scores. To narrow down each dataset’s feature space, XGBoost, and CNN feature selection algorithms are used in this work for each separate model. The experimental findings demonstrate a high detection rate and good accuracy with a relatively low False Acceptance Rate (FAR) to prove the usefulness of the proposed hybrid model.

Comprehensive analysis of pathological and biochemical data of prostate tumors based on multiple machine learning models

This study conducts a comprehensive analysis of global prostate tumor cases, employing various machine learning models for classification. The research focuses on data preprocessing, feature selection, and model training, utilizing effective techniques for handling biochemical and pathological data. In model training, a range of algorithms such as XGBoost, Random Forest, LightGBM, and CatBoost were explored, ultimately achieving an 89% test accuracy through a Stacking model. For pathological data, strategies included addressing missing values, text cleaning, and segmentation, combining CountVectorizer with multiple algorithms. LightGBM demonstrated optimal performance with an accuracy of 82%. The Bert model, post-segmentation and Focal loss optimization, achieved an accuracy of 80.39%. In summary, this research leverages machine learning to provide a convenient approach for diagnosing and treating prostate tumors, offering robust model support for predicting related diseases.

Accuracy of pH strip testing and pH liquid testing versus standard pH meter of gastric contents in critically ill patients: a diagnostic accuracy study.

The utilisation of pH level measurements from gastric contents may indicate the preferred tip position of a nasogastric tube or monitor the efficacy of stress ulcer prophylaxis in critically ill patients. We aimed to determine the accuracy of pH strip (pHS) tests and pH liquid (pHL) tests compared with the standard pH meter (pHM). Diagnostic accuracy study. Gastric contents from medically critically ill patients. In total, 113 gastric samples were collected from 27 critically ill patients. The level of pH measured by pHM, pHS and pHL. The pH values measured by pHM, pHS and pHL were 5.83 (IQR 5.12-6.61), 5.50 (IQR 5.00-6.00) and 5.75 (IQR 5.25-6.25), respectively. The pHS test showed greater accuracy, exhibiting a more positive correlation with the standard pHM measurement than the pHL test, with Y=0.95*X+0.56; rho=0.91, p<0.001, and Y=1.09*X - 0.72; rho=0.75, p<0.001, respectively. However, the pHS test demonstrated less agreement with the pHM than the pHL test, with biases of -0.27 versus 0.18, respectively. Noticeably, a slight variation in pHL from the standard pH values was found when we measured gastric contents with a pH lower than 5. Both the pHS and pHL methods were good options for measuring gastric pH in critically ill patients. However, it was advisable to find alternative approaches to the pHL testing method when anticipated gastric acidity levels fall below 5. TCTR20220530004.

Diagnostic Prediction Model for Tuberculous Meningitis: An Individual Participant Data Meta-Analysis.

No accurate and rapid diagnostic test exists for tuberculous meningitis (TBM), leading to delayed diagnosis. We leveraged data from multiple studies to improve the predictive performance of diagnostic models across different populations, settings, and subgroups to develop a new predictive tool for TBM diagnosis. We conducted a systematic review to analyze eligible datasets with individual-level participant data (IPD). We imputed missing data and explored three approaches: stepwise logistic regression, classification and regression tree (CART), and random forest regression. We evaluated performance using calibration plots and C-statistics via internal-external cross-validation. We included 3,761 individual participants from 14 studies and nine countries. A total of 1,240 (33%) participants had "definite" (30%) or "probable" (3%) TBM by case definition. Important predictive variables included cerebrospinal fluid (CSF) glucose, blood glucose, CSF white cell count, CSF differential, cryptococcal antigen, HIV status, and fever presence. Internal validation showed that performance varied considerably between IPD datasets with C-statistic values between 0.60 and 0.89. In external validation, CART performed the worst (C = 0.82), and logistic regression and random forest had the same accuracy (C = 0.91). We developed a mobile app for TBM clinical prediction that accounted for heterogeneity and improved diagnostic performance (https://tbmcalc.github.io/tbmcalc). Further external validation is needed.

Blood CO Status Classification Using UV-VIS Spectroscopy and PSO-optimized 1D-CNN Model

Rapid and effective blood carbon monoxide (CO) assessment is of great importance, especially in estimating CO-related morbidity and instituting effective preventive measures. The conventional detection methods using CO breath analysis lack sensitivity, while collecting biological fluid samples for CO level measurement is prone to external contamination and expensive for frequent use. This study proposes a one-dimensional convolutional neural network (1D-CNN) consisting of three stacked biconvolutional layers for binary classification of blood CO status using the diffuse reflectance spectroscopy technique. Iterative particle swarm optimization (PSO) has efficiently found the best network parameters to learn important features from the reflectance spectroscopy data. The findings showed good testing accuracy, specificity, and precision of 92.9%, 90%, and 89.7%, respectively, and a high sensitivity of 96.3% in determining abnormal blood CO among smokers using the proposed CNN network. Comparisons with eight existing machine learning and deep learning models revealed the proposed method’s effectiveness in classifying blood CO status while reducing computing time by 8–13 folds. The findings of this work provide new insights that are valuable for researchers in neural network design automation, healthcare management, and skin-related research, specifically for application in nondestructive evaluation and clinical decision-making.

A traffic and road signal recognition method through deep learning

Before deep learning became popular, some researchers used traditional computer vision algorithms, including support vector machines, decision trees, and random forests to deal with traffic and road signal recognition problems. These methods often require manual design of features and may not perform well when dealing with complex scenarios and changing conditions. Therefore, traffic and road signal recognition have witnessed a transformative shift with the advent of deep learning technologies. Convolutional neural networks (CNNs) developed from deep learning has shown prominent capabilities in accurately detecting and interpreting traffic signs and signals from images and videos. In this project, the whole method is mainly based on two parts, which refers to the process of signal-capturing and the detecting process. The CNN model used in the latter part consists of various function layers, including max-pooling layer, linear layer, 2D convolution layer and batch- normalization layer to modify accuracy. This model is trained and tested with Chinese Traffic Sign Dataset, with 58 categories of over 6000 pictures. With the assistance of OpenCV, the model may detect real-time traffic signals and convey messages to vehicles. After adjusting parameters in the CNN model, high probability tags are obtained during the process, with both accuracy and average loss labeled after each epoch. The results demonstrate that the proposed model achieved the testing accuracy of over 95 percent, after undergoing 200 epochs.

Radiograph recognition of COVID-19 based on DenseNet

This paper presents a neural network approach for pneumonia image classification. Utilizing thousands of actual X-ray images from both pneumonia patients and healthy individuals, a classification model is developed. This neural network model, a prevalent machine learning technique, is employed as an adjunct tool to aid pneumonia diagnosis and pre-screening. The algorithms utilized in the study are comprehensively analyzed and detailed. Through an examination of the network's architecture, several effective methods for enhancing the model are proposed, leading to improved model structure and classification performance. The novel model structure proposed in this study is demonstrated and evaluated. The final training accuracy exceeds 95%, demonstrating the model's proficiency in learning from the training data. Moreover, the test accuracy hovers around 92%, indicating the model's robust performance in generalizing to unseen data.

Spectral sensor‐based device for real‐time detection and severity estimation of groundnut bud necrosis virus in tomato

AbstractA machine learning‐based approach was utilized to develop a device for groundnut bud necrosis virus (GBNV) disease severity detection and estimation in tomato plants (Solanum lycopersicum L.). The study involved inoculating tomato plants with GBNV, monitoring changes in morphological and spectral characteristics, evaluating machine learning algorithms (decision tree [DT] classifier) for analysis and classification of disease severity, and developing and validating a device for disease detection and severity estimation. Spectral data analysis revealed distinct patterns in reflectance, with notable peaks observed in the 680 and 760 nm bands, while reflectance remained low and constant beyond 900 nm. Machine learning techniques, specifically a DT model, were employed to classify disease severity based on spectral data with high accuracy (95.01% training accuracy and 93.65% testing accuracy). The model identified the near‐infrared band as highly correlated (correlation coefficient of 0.82) with disease severity. Furthermore, a compact handheld device integrating a spectral sensor, organic light‐emitting diode display, and Raspberry Pi 3B was developed for real‐time disease severity estimation. The device demonstrated robust performance, accurately predicting disease severity at different growth stages, even in the absence of visible symptoms. Additionally, disease severity percentages obtained via reverse transcription polymerase chain reaction were used to validate the accuracy of the device's estimations. Its responsive nature, with estimated response times ranging from milliseconds to seconds, facilitates timely interventions in agricultural settings. Overall, this interdisciplinary approach, combining spectral analysis, machine learning, and device development, presents a promising solution for efficient disease monitoring and management in agriculture, contributing to enhanced crop health and food security.

Comparison of four confirmatory tests for the diagnosis of primary aldosteronism: Bayesian analysis in the absence of a gold standard.

Captopril challenge test (CCT), seated saline infusion test (SSIT), oral sodium loading test (OSLT) and fludrocortisone suppression test (FST) are widely used diagnostic tests for primary aldosteronism (PA). These tests differ in terms of safety and complexity. Whether the simpler tests (CCT and SSIT) are comparable in diagnostic performance to the more complex ones (FST and OSLT) is unclear. To compare the diagnostic accuracy of the four tests. This is a retrospective study of hypertensive patients who were screened for PA and completed at least one confirmatory test. The patients were divided into two cohorts: one including those who completed one to three tests was used for the estimation of sensitivity and specificity. The other including those who completed four tests was used for the comparison of accuracy. Bayesian method was used to obtain the sensitivity, specificity, and Youden index of each test. The study included 1011 hypertensive patients. Among them, 895 patients completed one to three tests (including 889 CCT, 605 FST, 611 SSIT and 69 OSLT), and 116 patients completed four tests. SSIT had the highest sensitivity of 0.82(95% CI 0.78-0.86) but the lowest specificity of 0.76(0.70-0.80). OSLT had the lowest sensitivity of 0.65(0.56-0.75) but the highest specificity of 0.91(0.82-0.96). The sensitivity and specificity were 0.78 (95% CI, 0.75-0.82), 0.82 (95% CI, 0.78-0.85), for CCT, and 0.77 (95% CI, 0.73-0.81), 0.87 (95% CI, 0.82-0.91) for FST, respectively. The Youden index was not significantly different among the four tests[0.60(0.55-0.65) for CCT; 0.58(0.51-0.64) for SSIT; (0.64(0.57-0.69) for FST; 0.56(0.43-0.67) for OSLT]. The accuracy of simpler tests is comparable to the more complex ones. Considering the safety and simplicity of CCT, it may be a reasonable first choice when confirming the diagnosis of PA.

A novel optimized hybrid machine learning model to enhance the prediction accuracy of hourly building energy consumption

ABSTRACT Building energy consumption (BEC) prediction is crucial in efficient energy management. This paper proposes an optimized hybrid prediction model that combines a support vector regression (SVR), a newly evolved coati optimization algorithm (COA), and a recursive feature elimination with cross-validation (RFECV) implemented on an hourly new dataset. The SVR is selected based on the experimentations conducted in this work that outperform other models. The COA is used for optimizing the hyperparameters of SVR, and RFECV is used to optimize the dataset. The SVR COA performs better than the Harris hawk optimization and Gray wolf optimization. Later, the optimized SVR is implemented on the optimized dataset, showing better accuracy and faster prediction compared with the default SVR, SVR with feature elimination, and optimized SVR models. The error metrics MAE, MAPE, RMSE, and R2 are used for the model evaluation. The testing accuracy improved by 12.34%, 10.52%, 17.02%, and 0.09%, respectively, compared to the default model.

A stable and robust fault diagnosis method for bearing using lightweight batch normalization-free residual network

Abstract The conventional deep learning-based bearing fault diagnosis methods tend to utilize denoising modules to improve the fault diagnosis performance in noisy scenes. However, the addition of denoising modules will increase expensive computational costs, leading to a delayed acquisition of fault diagnosis results. This work proposed a lightweight batch normalization (BN)-free residual network without any denoising modules for bearing fault diagnosis which properly rescaled the weights in a standard initialization instead of BN to avoid the exploding gradient problem and vanishing gradient problem at the beginning of training for deep neural networks. Therefore, it prevents the undesirable properties caused by BN. Compared with other methods, the fault diagnosis performance of the proposed method can maintain a high level with different input sizes and batch sizes. Especially in noisy scenes, the testing accuracy of fault diagnosis on different bearing datasets can be improved by 13.54% and 7.74% using fewer parameters and floating point operations on different bearing datasets.

Systematic Review of Newborn Screening Programmes for Spinal Muscular Atrophy

Spinal muscular atrophy (SMA) is a genetic neuromuscular disorder causing the degeneration of motor neurons in the spinal cord. Recent studies suggest greater effectiveness of treatment in the presymptomatic stage. This systematic review synthesises findings from 37 studies (and 3 overviews) of newborn screening for SMA published up to November 2023 across 17 countries to understand the methodologies used; test accuracy performance; and timing, logistics and feasibility of screening. All studies screened for the homozygous deletion of SMN1 exon 7. Most (28 studies) used RT-PCR as the initial test on dried blood spots (DBSs), while nine studies also reported second-tier tests on DBSs for screen-positive cases. Babies testing positive on DBSs were referred for confirmatory testing via a range of methods. Observed SMA birth prevalence ranged from 1 in 4000 to 1 in 20,000. Most studies reported no false-negative or false-positive cases (therefore had a sensitivity and specificity of 100%). Five studies reported either one or two false-negative cases each (total of six cases; three compound heterozygotes and three due to system errors), although some false-negatives may have been missed due to lack of follow-up of negative results. Eleven studies reported false-positive cases, some being heterozygous carriers or potentially related to heparin use. Time to testing and treatment varied between studies. In conclusion, several countries have implemented newborn screening for SMA in the last 5 years using a variety of methods. Implementation considerations include processes for timely initial and confirmatory testing, partnerships between screening and neuromuscular centres, and timely treatment initiation.

Automatic Code Generation for Android Applications Based on Improved Pix2code

With the expansion of the Internet market, the traditional software development method has been difficult to meet the market demand due to the problems of long development cycle, tedious work and difficult system maintenance. Therefore, to improve software development efficiency, this study uses residual networks and bidirectional long short-term memory networks to improve the Pix2code model. The experiment results show that after improving the visual module of the Pix2code model using residual networks, the accuracy of the training set improves from 0.92 to 0.96, and the convergence time is shortened from 3 hours to 2 hours. After using a bidirectional long short-term memory network to improve the language module and decoding layer, the accuracy and convergence speed of the model have also been improved. The accuracy of the training set grew from 0.88 to 0.92, and the convergence time was shortened by 0.5 hours. However, models improved by bidirectional long short-term memory networks might exhibit over-fitting, and thus this study uses Dropout and Xavier normal distribution to improve the memory network. The results validate that the training set accuracy of the optimized bidirectional long short-term memory network remains around 0.92, but the accuracy of the test set has improved to a maximum of 85%. Dropout and Xavier normal distributions can effectively improve the over-fitting problem of bidirectional long short-term memory networks. Although they can also decrease the model’s stability, their gain is higher. The training and testing accuracy of the Pix2code improved by residual network and bidirectional long short-term memory network are 0.95 and 0.82, respectively, while the code generation accuracy of the original Pix2code is only 0.77. The above data indicates that the improved Pix2code model has improved the accuracy and stability of code automatic generation.

The prognostic test accuracy of the short and standard forms of the Montreal Cognitive Assessment.

Introduction Cognitive impairment is a critical concern in stroke care, and international guidelines recommend early cognitive screening. The aim of this study was to determine the prognostic accuracy of both the short and standard forms of the Montreal Cognitive Assessment in predicting long-term cognitive recovery following a stroke. Methods For this study, we used data from the "Efficacy of Fluoxetine - a Randomized Controlled Trial in Stroke" (EFFECTS) study, which encompassed stroke patients from 35 Swedish centers over the period from 2014 to 2019. Cognitive assessments were initially conducted at 2 to 15 days post-stroke, with follow-up data gathered at 6 months. We used the MoCA for objective cognitive evaluation. For assessing subjective cognitive impairment, we used the memory and thinking domain of the Stroke Impact Scale. For psychometric evaluation of the short Swedish version of MoCA (s-MoCA-SWE), we used cross tables and binary logistic regression. Results The study included 1141 patients (62.2% men; median [interquartile range, IQR] age, 72.3 [13.2] years; median [IQR] stroke severity, 3.0 [3.0]). At baseline, the prevalence of cognitive impairment was 71.7% according to the s-MoCA-SWE (≤12) and 67.0% according to the MoCA (≤25). The s-MoCA-SWE demonstrated a sensitivity of 92.3% for correctly identifying patients with objective cognitive impairment and 81.5% for identifying those with subjective impairments at 6 months. Although the s-MoCA-SWE had higher sensitivity, the MoCA had a more balanced sensitivity and specificity in detecting both subjective and objective cognitive impairments. In both crude and multivariable models, the s-MoCA-SWE was more strongly associated than the MoCA with cognitive impairment at 6 months. Conclusions Both the short and standard versions of the MoCA appear to be effective in identifying individuals likely to experience persistent cognitive issues following a stroke. Considering the limited time available in an acute stroke unit, the short-form version may be more practical. Nevertheless, further prospective studies are required to validate these findings.

A handheld rapid detector of soil total nitrogen based on phase-locked amplification technology

Soil total nitrogen (STN) serves as a critical indicator for agricultural productivity. Rapid STN detection is crucial for nutrient monitoring in farmland and for guiding precision fertilization. Traditional near-infrared spectroscopy-based STN detectors necessitate darkrooms or subterranean settings to negate ambient light interference, restricting their practical use. This study develops a handheld rapid STN detector employing phase-locked amplification technology to address this issue. The device comprises optical, circuit, and control units. The optical unit features a ring-shaped split fiber structure to mitigate soil surface irregularities’ effects on the signal, with a central InGaAs photodiode for signal reception. Eight wavelengths (970 nm, 1050 nm, 1085 nm, 1200 nm, 1300 nm, 1450 nm, 1550 nm, and 1600 nm) are selected for the light sources through an information theory optimized genetic algorithm. The circuit unit employs direct digital synthesis and operational amplifiers for light signal generation and modulation. Reflected soil light signals are demodulated using an InGaAs photodiode and a phase-locked amplifier, effectively filtering ambient light noise. The control unit integrates an STM32F103C8T6 microcontroller and a Raspberry Pi 4B. The STM32 microcontroller manages signal generation, channel selection, frequency control, A/D conversion, and display, while the Raspberry Pi handles data processing, uploading, and transmission. A BP neural network model optimized by genetic algorithms is embedded for real-time STN data acquisition. Cloud-based data storage and visualization are facilitated through the Alibaba Cloud IoT platform. Performance testing reveals the device’s stability and interference resistance, with a standard deviation of electrical signal under varying light conditions below 3 mV. Compared to commercial spectrometers, the correlation coefficient (r) for reflectance data at eight wavebands exceeds 0.9. Accuracy testing shows an R2 above 0.8 and an RMSE of 0.30 g/kg when comparing detector-obtained STN values with standard chemical methods. These results demonstrate the detector’s efficacy in ambient light isolation, stability, and accuracy in agricultural settings, offering technical support for variable fertilization in precision farming.

Willingness to pay for expanded non-invasive prenatal screening - An online discrete choice experiment from the perspective of women living in Western Australia.

Ongoing advances in genetic technology may soon provide prenatal screening for multiple genetic conditions. The aims were to investigate what prenatal screening test characteristics women prioritise and their willingness to pay for these tests. We designed an online survey incorporating a series of discrete choice scenarios. Dimensions and levels were selected based on existing prenatal tests and a hypothetical prenatal test that could non-invasively detect multiple genetic disorders in pregnancy. Participants were recruited from social media platforms. Data were analysed using conditional logistic regression and latent class analysis (LCA). A total of 219 women completed the survey. Women with higher incomes and those with a tertiary education were willing to pay more than other groups. The maximum willingness to pay was AUD1870 (95% confidence interval: 1630, 2112) for a hypothetical non-invasive test to detect multiple genetic conditions in early pregnancy. An LCA demonstrated considerable heterogeneity in preferences, differing in both overall preference for testing and test characteristics considered most attractive. Among the participants, decision factors cited by 14.5% of participants were the risk of pregnancy loss, making them less likely to undergo testing; for 32.1% participants, accuracy was a major factor, and they were very likely to have testing; for 12.9%, test availability early in pregnancy was a decision factor. If a non-invasive test that could detect the greatest number of genetic disorders in pregnancy was available, the priorities were test accuracy, risk of pregnancy loss and a test available early in pregnancy.

Towards Precise Water Quality Assessment : A Deep Learning Approach with Feature Selection in Smart Monitoring Systems

As water quality concerns intensify, the imperative for accurate monitoring systems grows. This study pioneers a novel approach to precise water quality assessment by integrating deep learning techniques and feature selection in smart monitoring systems. Utilizing k-Nearest Neighbors (KNN), Convolutional Neural Network (CNN), and Inception V3 for classification, along with Random Forest, AdaBoost, and XGBoost for feature selection, the study presents a detailed examination of their performance on water quality datasets. The results showcase notable improvements in both training and testing accuracies for KNN when coupled with Random Forest and varying numbers of estimators. The combination of CNN and AdaBoost exhibits robust performance, underscoring the impact of feature extraction on training and testing accuracies. Inception V3, when integrated with XGBoost, demonstrates nuanced results, emphasizing the significance of feature extraction in enhancing classification outcomes. Specifically, the performance metrics reveal a fusion model using XGBoost and Inception V3 achieving an accuracy of 65.82%, surpassing individual models like Inception V3 (60.05%). Similarly, the combination of AdaBoost and CNN attains a performance of 65.17%, outperforming individual models such as CNN (64.32%). Additionally, the integration of Artificial Neural Networks (ANN) with Random Forest yields a performance of 69.05%, showcasing improvement over standalone ANN (55.79%). The findings underscore the efficacy of deep learning models, particularly when integrated with appropriate feature selection algorithms, in advancing the precision of water quality assessment in smart monitoring systems. This study contributes valuable insights to the field of environmental monitoring, providing a basis for further exploration of synergies between deep learning and feature selection for enhanced accuracy in water quality assessment. The proposed approach holds promise for addressing the critical challenge of precise water quality monitoring in the face of escalating environmental concerns.