Test Data Research Articles

Examining the mediating and moderating roles of social marketing behavioural enhancers regarding consistent condom use.

Since their importance was highlighted by Ayikwa and De Jager, social marketing behavioural enhancers (SMBEs) have been investigated in terms of their causal and correlational relationships with the intention to use condoms (IUC) and consistent condom use (CCU), as people often fail to act on their intentions. However, scrutiny of their mediating and moderating roles could provide insights pertinent to the design of effective HIV and AIDS preventative programmes. This study examined whether perceived behavioural control (PBC) and IUC interact with exposure to HIV and AIDS information (EI), ease of access to condoms (EAC) and level of related knowledge (KN) in determining CCU. It also investigated whether PBC and IUC predict CCU through increasing EI, EAC, and KN. A quantitative approach was adopted and data were collected from 607 participants, aged at least 18, living in Gauteng Province, South Africa. The questionnaire administered included pre-existing items, validated through exploratory and confirmatory factor analysis procedures. Regression analyses of the data for mediation and moderation testing were performed using PROCESS macro software for SPSS. The results indicated that none of the SMBEs mediated the non-significant PBC-CCU relationship: B = -0.0258, SE = 0.0199, p = 0.195. Nor did they mediate the significant IUC-CCU relationship: B = 0.0395, SE = 0.0195, p = 0.043. Similarly, none of the SMBEs were found to moderate the PBC-CCU relationship (EI*PBC: B = 0.0034, SE = 0.0056, p = 0.540; KN*PBC: B = -0.0006, SE = 0.0064, p = 0.931; EAC*PBC: B = 0.0011, SE = 0.0059, p = 0.854) as IUC-CCU relationship (EI*IUC: B = 0.0036, SE = 0.0054, p = 0.513; KN*IUC: B = -0.0096, SE = 0.0060, p = 0.111; EAC*IUC: B = 0.0044, SE = 0.0061, p = 0.469). A recommendation is made to scrutinise the mediating and moderating roles of SMBEs in the context of health behavioural models other than the theory of planned behaviour, which was considered in this study.

Ocean water colour retrieval method based on directional polarimetric camera

ABSTRACT Chlorophyll-a (Chl-a) concentrations serve as a pivotal metric for assessing the biological content and eutrophication levels of waterbodies. In optical remote sensing, empirical and semi-analytical methods are commonly employed for Chl-a retrieval. Leveraging the Level-1B remote sensing data from the Directional Polarimetric Camera (DPC) aboard the Terrestrial Ecosystem Carbon Inventory Satellite (TECIS), this study amalgamates empirical algorithms (OC2, OC3, Aiken-P, and Aiken-C) and look-up-table (LUT) techniques to estimate Chl-a in open water. Test data acquired from DPC on 20 August 2022, are validated against the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the Terra satellite and AERONET-Ocean Color water-leaving radiance data for the same area and date. The results reveal that among the tested algorithms, particularly in the log10 scale space, the Aiken-P algorithm demonstrates the highest correlation coefficient (R2 = 0.6333), with the lowest bias of 1.0366 and a minimum mean absolute error (MAE) of 1.2097. This underscores the suitability and accuracy of the Aiken-P algorithm for water colour retrieval based on DPC-derived data, highlighting its practical applicability.

Predicting the financial performance of microfinance institutions with machine learning techniques

Purpose Given the growing emphasis among scholars, practitioners and policymakers on financial sustainability, this study aims to explore the applicability of machine learning techniques in predicting the financial performance of microfinance institutions (MFIs). Design/methodology/approach This study gathered 9,059 firm-year observations spanning from 2003 to 2018 from the World Bank's Mix Market database. To predict the financial performance of MFIs, the authors applied a range of machine learning regression approaches to both training and testing data sets. These included linear regression, partial least squares, linear regression with stepwise selection, elastic net, random forest, quantile random forest, Bayesian ridge regression, K-Nearest Neighbors and support vector regression. All models were implemented using Python. Findings The findings revealed the random forest model as the most suitable choice, outperforming the other models considered. The effectiveness of the random forest model varied depending on specific scenarios, particularly the balance between training and testing data set proportions. More importantly, the results identified operational self-sufficiency as the most critical factor influencing the financial performance of MFIs. Research limitations/implications This study leveraged machine learning on a well-defined data set to identify the factors predicting the financial performance of MFIs. These insights offer valuable guidance for MFIs aiming to predict their long-term financial sustainability. Investors and donors can also use these findings to make informed decisions when selecting their potential recipients. Furthermore, practitioners and policymakers can use these findings to identify potential financial performance vulnerabilities. Originality/value This study stands out by using a global data set to investigate the best model for predicting the financial performance of MFIs, a relatively scarce subject in the existing microfinance literature. Moreover, it uses advanced machine learning techniques to gain a deeper understanding of the factors affecting the financial performance of MFIs.

Untapped Potential of Poly(ADP-Ribose) Polymerase Inhibitors: Lessons Learned From the Real-World Clinical Homologous Recombination Repair Mutation Testing.

Testing for homologous recombination deficiency (HRD) mutations is pivotal to assess individual risk, to proact preventive measures in healthy carriers and to tailor treatments for cancer patients. Increasing prominence of poly(ADP-ribose) polymerase (PARP) inhibitors with remarkable impact on molecular-selected patient survival across diverse nosologies, ingrains testing for BRCA genes and beyond in clinical practice. Nevertheless, testing strategies remain a question of debate. While several pathogenic BRCA1/2 gene variants have been described as founder pathogenic mutations frequently found in patients from Russia, other homologous recombination repair (HRR) genes have not been sufficiently explored. In this study, we present real-world data of routine HRR gene testing in Russia. We evaluated clinical and sequencing data from cancer patients who had germline/somatic next-generation sequencing (NGS) HRR gene testing in Russia (BRCA1/2/ATM/CHEK2, or 15 HRR genes). The primary objectives of this study were to evaluate the frequency of BRCA1/2 and non-BRCA gene mutations in real-world unselected patients from Russia, and to determine whether testing beyond BRCA1/2 is feasible. Data of 2,032 patients were collected from February 2021 to February 2023. Most had breast (n = 715, 35.2%), ovarian (n = 259, 12.7%), pancreatic (n = 85, 4.2%), or prostate cancer (n = 58, 2.9%). We observed 586 variants of uncertain significance (VUS) and 372 deleterious variants (DVs) across 487 patients, with 17.6% HRR-mutation positivity. HRR testing identified 120 (11.8%) BRCA1/2-positive, and 172 (16.9%) HRR-positive patients. With 51 DVs identified in 242 formalin-fixed paraffin-embedded (FFPE), testing for variant origin clarification was required in one case (0.4%). Most BRCA1/2 germline variants were DV (121 DVs, 26 VUS); in non-BRCA1/2 genes, VUS were ubiquitous (53 DVs, 132 VUS). In silico prediction identified additional 4.9% HRR and 1.2% BRCA1/2/ATM/CHEK2 mutation patients. Our study represents one of the first reports about the incidence of DV and VUS in HRR genes, including genes beyond BRCA1/2, identified in cancer patients from Russia, assessed by NGS. In silico predictions of the observed HRR gene variants suggest that non-BRCA gene testing is likely to result in higher frequency of patients who are candidates for PARP inhibitor therapy. Continuing sequencing efforts should clarify interpretation of frequently observed non-BRCA VUS.

Prolonged treatment of dermatophytosis caused by Trichophyton indotinea with terbinafine or itraconazole impacts better outcomes irrespective of mutation in the squalene epoxidase gene.

Over the past decades, the increasing incidence of recurrent dermatophytosis associated with terbinafine-resistant Trichophyton has posed a serious challenge in management of dermatophytosis. Independent reports of failure of treatment and high minimum inhibitory concentrations (MIC) of antifungals are available, but data correlating MIC and clinical outcomes is still sparse. Therefore, the present study was conducted to evaluate the outcomes of systemic treatment of dermatophytosis and its correlation with MIC of the etiological agents isolated from such patients. Retrospective analysis of 587 consecutive patients with dermatophytosis was done from March 2017 to March 2019. Demographic and clinical details of the patients were noted, along with the results of direct microscopy and fungal culture. The isolates were identified by sequencing the internal transcribed spacer region of rDNA. Antifungal susceptibility testing was performed following the CLSI M38 protocol. Mutation in the squalene epoxidase (SE) gene was detected by DNA sequencing and ARMS-PCR. Based on the culture-positivity and prescribed systemic antifungal, patients were categorised into Group I culture-positive cases treated with systemic terbinafine and Group II culture-positive cases treated with systemic itraconazole, each for a total period of 12 weeks. In the present study, 477 (81.39%) were culture-positive; however, 12 weeks follow-up was available for 294 patients (Group I-157 and Group II-137) who were included for statistical analysis. In both groups [Group I-37/63 (51.4%) and Group II-14/54 (58.3%)], a better cure rate was observed if the initiation of therapy was performed within <6 months of illness. Treatment outcome revealed that if therapy was extended for 8-12 weeks, the odds of cure rate are significantly better (p < .001) with either itraconazole (Odd Ratio-15.5) or terbinafine (Odd Ratio-4.34). Higher MICs for terbinafine were noted in 41 cases (cured-18 and uncured-23) in Group I and 39 cases (cured-16 and uncured-23) in Group II. From cured (Group I-17/18; 94.4% and Group II-14/16; 87.5%) and uncured (Group I-20/23; 86.9% and Group II-21/23; 91.3%) cases had F397L mutation in the SE gene. No significant difference in cure rate was observed in patients with Trichophyton spp. having terbinafine MIC ≥ 1or <1 μg/mL (Group I-p = .712 and Group II-p = .69). This study revealed that prolonging terbinafine or itraconazole therapy for beyond 8 weeks rather than the standard 4 weeks significantly increases the cure rate. Moreover, no correlation has been observed between antifungal susceptibility and clinical outcomes. The MIC remains the primary parameter for defining antifungal activity and predicting the potency of antifungal agents against specific fungi. However, predicting therapeutic success based solely on the MIC of a fungal strain is not always reliable, as studies have shown a poor correlation between invitro data and invivo outcomes. To address this issue, further correlation of antifungal susceptibility testing (AFST) data with clinical outcomes and therapeutic drug monitoring is needed. It also highlights that initiation of the treatment within <6 months of illness increases cure rates and reduces recurrence. Extensive research is warranted to establish a better treatment regime for dermatophytosis.

Expanding molecular diagnostic coverage for tuberculosis by combining computer-aided chest radiography and sputum specimen pooling: a modeling study from four high-burden countries

BackgroundIn 2022, fewer than half of persons with tuberculosis (TB) had access to molecular diagnostic tests for TB due to their high costs. Studies have found that the use of artificial intelligence (AI) software for chest X-ray (CXR) interpretation and sputum specimen pooling can each reduce the cost of testing. We modeled the combination of both strategies to estimate potential savings in consumables that could be used to expand access to molecular diagnostics.MethodsWe obtained Xpert testing and positivity data segmented into deciles by AI probability scores for TB from the community- and healthcare facility-based active case finding conducted in Bangladesh, Nigeria, Viet Nam, and Zambia. AI scores in the model were based on CAD4TB version 7 (Zambia) and qXR (all other countries). We modeled four ordinal screening and testing approaches involving AI-aided CXR interpretation to indicate individual and pooled testing. Setting a false negative rate of 5%, for each approach we calculated additional and cumulative savings over the baseline of universal Xpert testing, as well as the theoretical expansion in diagnostic coverage.ResultsIn each country, the optimal screening and testing approach was to use AI to rule out testing in deciles with low AI scores and to guide pooled vs individual testing in persons with moderate and high AI scores, respectively. This approach yielded cumulative savings in Xpert tests over baseline ranging from 50.8% in Zambia to 57.5% in Nigeria and 61.5% in Bangladesh and Viet Nam. Using these savings, diagnostic coverage theoretically could be expanded by 34% to 160% across the different approaches and countries.ConclusionsUsing AI software data generated during CXR interpretation to inform a differentiated pooled testing strategy may optimize TB diagnostic test use, and could extend molecular tests to more people who need them. The optimal AI thresholds and pooled testing strategy varied across countries, which suggests that bespoke screening and testing approaches may be needed for differing populations and settings.

Evaluating the Quality of Multiple-Choice Test Questions in the Postlicensure Environment.

Multiple-choice test questions are among the main measures of knowledge used by educators in the postlicensure environment; however, these tests are often constructed in the absence of guidelines or the means to evaluate examination quality after administration. Previously, guidance was provided on constructing quality test items. Here we present instruction for professional development specialists to use postadministration test data for an item analysis, providing insight on test flaws and opportunities for iterative examination improvement. The topics of item difficulty, index of discrimination, and distractor analysis are covered for independent analysis, and topics such as reliability are addressed for those who have access to a formal program. Three levels of strategies are described: using a learning management system for item analysis, using free open-source software, and using a minimal standards method of evaluating test items. [J Contin Educ Nurs. 202x;5x(x):xx-xx.].

An improved three-dimensional extension of Hoek–Brown criterion for rocks

The Hoek–Brown (H–B) criterion has found widespread application in numerous rock engineering projects. However, its efficacy is compromised by an underestimation of rock strength due to its neglect of the influence of the intermediate principal stress (σ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma_{2}$$\\end{document}). Experimental evidence underscores the significant impact of σ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma_{2}$$\\end{document}. Consequently, there exists an imperative to formulate a three-dimensional (3D) criterion. In this study, a new deviatoric function with two additional parameters (k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k$$\\end{document} and A\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$A$$\\end{document}) is developed firstly, which ensure compliance with the prerequisites of smoothness and convexity. In addition, the parameters k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k$$\\end{document} and A\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$A$$\\end{document} are bonded with the weakening effect of the Lode angle (θσ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ heta_{\\sigma }$$\\end{document}) and the strengthening effect of the mean stress (σm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma_{m}$$\\end{document}), respectively. Then a new 3D strength criterion for rocks is proposed by combining this new deviatoric function and the triaxial compression meridian function of the original H–B criterion. Four distinct sets of test data encompassing various rock types are employed to validate the proposed criterion. The results demonstrate that the proposed criterion adeptly captures the strength characteristics of the four rock types, providing a good depiction of failure surfaces within the 3D principal stress space. Comparative analyses involve the utilization of several existing 3D H–B criteria for strength predictions. The proposed criterion exhibits superior fitting performance for all the selected rocks.

Impact of COVID-19 on human immunodeficiency virus tests, new diagnoses, and healthcare visits in the Republic of Korea: a retrospective study from 2016 to 2021.

Public health workers have been at the forefront of treating patients with coronavirus disease 2019 (COVID-19) and managing the pandemic. The redeployment of this workforce has limited or interrupted other public health services, including testing for human immunodeficiency virus (HIV). This study aims to examine the impact of COVID-19 on HIV testing and diagnosis in the Republic of Korea from 2016 to 2021, comparing data before and after the onset of COVID-19. Annual HIV testing data were collected from each institution through direct communication or from open-source databases. The annual number of new HIV cases was obtained from the official report of the Korea Disease Control and Prevention Agency. Data on healthcare visits for HIV diagnosis or treatment were extracted from the open-source database of the National Insurance Health Service of Korea. Interrupted time series regression was conducted, stratified by institution type. In 2020, HIV tests, diagnoses, and visits decreased. Notably, public health centers experienced a substantial reduction in 2020-2021 compared to previous years. The annual percentage change in HIV tests was -53.0%, while for HIV diagnoses, it was -31.6%. The decrease in visits for HIV was also most pronounced for public facilities: -33.3% in 2020 and -45.6% in 2021 relative to 2019. The numbers of tests, diagnoses, and healthcare visits for HIV at public health centers in the Republic of Korea substantially decreased in 2020 and 2021. The impacts of these changes on the early diagnosis and treatment of HIV necessitate further monitoring.

Text classification for distribution substation inspection based on BERT-TextRCNN model

With the advancement of source-load interaction in the new power systems, data-driven approaches have provided a foundational support for aggregating and interacting between sources and loads. However, with the widespread integration of distributed energy resources, fine-grained perception of intelligent sensing devices, and the inherent stochasticity of source-load dynamics, a massive amount of raw data is being recorded and accumulated in the data center. Valuable information is often dispersed across different paragraphs of the raw data, making it challenging to extract effectively. Distribution substation inspection plays a crucial role in ensuring the safe operation of the power system. Traditional methods for inspection report text classification typically rely on manual judgment and accumulated experience, resulting in low efficiency and a significant misjudgment rate. Therefore, this paper proposes a text classification method for inspection reports based on the pre-trained BERT-TextRCNN model. By utilizing the dense connection between the BERT embedding layer and the neural network, the proposed method improves the accuracy of matching long texts. This article collected 2,831 maintenance data for the first quarter of 2023 from the distribution room, including approximately 58 environmental testing data, 738 environmental box testing data, approximately 672 distribution room testing data, and approximately 1,363 box type substation testing data. A text corpus was constructed for experiments. Experimental results demonstrate that the proposed model automatically classifies a large volume of manually recorded inspection report data based on time, location, and faults, achieving a classification accuracy of 94.7%, precision of 92%, recall of 92%, and F1 score of 90.3%.

Sentiment analysis on twitter about the death penalty using the support vector machine method

It is estimated that 175 million people in Indonesia utilize the Internet, according to the most recent We Are Social survey. 160 million of them are internet users who utilize social media, according to this data. It is estimated that 19.5 million Indonesians use Twitter. This is consistent with the numerous tweets that users have posted on Twitter about a variety of topics, including politics, music, health, and education. The death penalty is still one of the most popular subjects that is addressed on Twitter. When a judge rules that someone will be executed as retribution for a crime they have committed, this is referred to as the death penalty. As a result, sentiment analysis utilizing the Support Vector Machine technique with linear kernel features and Python programming was used to study public opinions on the death sentence. To improve the accuracy of the results obtained, data labeling on 848 data that were received through the scraping process was done manually in this study. Positive data is categorized as belonging to the class that supports the death sentence, while negative data is categorized as belonging to the class that opposes it. The study that was done shows an 8:2 difference between the training and test data. After preprocessing a dataset containing 758 data points, of which 606 will be utilized for training and 152 for testing, we obtain 91% accuracy, 91% precision, 100% recall, and 95% f1-score

Breach diameter prediction of reinforced concrete wall under contact/near-contact explosives using deep neural network

In recent years, the use of Machine Learning (ML) has been expanded to several engineering fields with applications in structural engineering. Deep Neural Network (DNN) models have been widely implemented to predict the response of structural systems under conventional loading. The accuracy of these DNN models relies on the size of the dataset used, which can vary from hundreds to thousands of data points, typically formed by images, test data, and/or finite element models built for a specific environment. Such dependency becomes a limitation when DNN models are intended to be used in blast analysis and/or design, as data are typically scarce or restricted. This paper introduces the implementation of an ML/DNN model for the prediction of breach diameter of a reinforced concrete target subjected to contact and near-contact explosions. The DNN model predicts the breach diameter using experimentally collected concrete breach data. The DNN model considers several parameters related to the charge (e.g., type, geometry, orientation, casement properties) and wall target (e.g., material properties, geometry, reinforcement, fiber content), as well as proximity between the target and explosive. It was found that charge geometry, charge placement type, target thickness, scaled distance, and fiber content in the mix were important features affecting the breach diameter prediction.

Prediction of the level of crime cases using multiple linear regression in the city of Pontianak

This study aims to develop a predictive model for the crime rate in the Police Resort Area of Kota (POLRESTA) Pontianak using the Multiple Linear Regression method based on secondary data obtained from the Criminal Investigation Unit of POLRESTA Pontianak. The utilization of descriptive statistical techniques and data visualization aids in identifying relevant features that enrich the information within the model. The evaluation results indicate that this model performs well in both modeling and predicting crime rates in Kota Pontianak. Despite the variations in error rates between training and testing data, the model still demonstrates its proficiency in predicting known data. The testing results also reveal that the Mean Absolute Percentage Error (MAPE) values for each crime category exhibit variations in the testing dataset, with MAPE for "Berat" increasing to 12.91%, MAPE for "Sedang" increasing to 30.11%, and MAPE for "Ringan" increasing to 26.59%. Consequently, this study concludes that the Multiple Linear Regression method holds potential as an effective tool for decision-making and the development of strategies to combat criminal activities in Kota Pontianak

Sample-efficient and surrogate-based design optimization of underwater vehicle hulls

Physics simulations like computational fluid dynamics (CFD) are a computational bottleneck in computer-aided design (CAD) optimization processes. To overcome this bottleneck, one requires either an optimization framework that is highly sample-efficient, or a fast data-driven proxy (surrogate model) for long-running simulations. Both approaches have benefits and limitations. Bayesian optimization is often used for sample efficiency, but it solves one specific problem and struggles with transferability; alternatively, surrogate models can offer fast and often more generalizable solutions for CFD problems, but gathering data for and training such models can be computationally demanding. In this work, we leverage recent advances in optimization and artificial intelligence (AI) to explore both of these potential approaches, in the context of designing an optimal unmanned underwater vehicle (UUV) hull. For first approach, we investigate and compare the sample efficiency and convergence behavior of different optimization techniques with a standard CFD solver in the optimization loop. For the second approach, we develop a deep neural network (DNN) based surrogate model to approximate drag forces that would otherwise be computed via the CFD solver. The surrogate model is in turn used in the optimization loop of the hull design. Our study finds that the Bayesian Optimization—Lower Condition Bound (BO-LCB) algorithm is the most sample-efficient optimization framework and has the best convergence behavior of those considered. Subsequently, we show that our DNN-based surrogate model predicts drag force on test data in tight agreement with CFD simulations, with a mean absolute percentage error (MAPE) of 1.85%. Combining these results, we demonstrate a two-orders-of-magnitude speedup (with comparable accuracy) for the design optimization process when the surrogate model is used. To our knowledge, this is the first study applying Bayesian optimization and DNN-based surrogate modeling to the problem of UUV design optimization, and we share our developments as open-source software.

Generalization of Quantum Machine Learning Models Using Quantum Fisher Information Metric

Generalization is the ability of machine learning models to make accurate predictions on new data by learning from training data. However, understanding generalization of quantum machine learning models has been a major challenge. Here, we introduce the data quantum Fisher information metric (DQFIM). It describes the capacity of variational quantum algorithms depending on variational ansatz, training data, and their symmetries. We apply the DQFIM to quantify circuit parameters and training data needed to successfully train and generalize. Using the dynamical Lie algebra, we explain how to generalize using a low number of training states. Counterintuitively, breaking symmetries of the training data can help to improve generalization. Finally, we find that out-of-distribution generalization, where training and testing data are drawn from different data distributions, can be better than using the same distribution. Our work provides a useful framework to explore the power of quantum machine learning models. Published by the American Physical Society 2024

Weed Species Identification: Acquisition, Feature Analysis, and Evaluation of a Hyperspectral and RGB Dataset with Labeled Data

Site-specific weed management employs image data to generate maps through various methodologies that classify pixels corresponding to crop, soil, and weed. Further, many studies have focused on identifying specific weed species using spectral data. Nonetheless, the availability of open-access weed datasets remains limited. Remarkably, despite the extensive research employing hyperspectral imaging data to classify species under varying conditions, to the best of our knowledge, there are no open-access hyperspectral weed datasets. Consequently, accessible spectral weed datasets are primarily RGB or multispectral and mostly lack the temporal aspect, i.e., they contain a single measurement day. This paper introduces an open dataset for training and evaluating machine-learning methods and spectral features to classify weeds based on various biological traits. The dataset comprises 30 hyperspectral images, each containing thousands of pixels with 204 unique visible and near-infrared bands captured in a controlled environment. In addition, each scene includes a corresponding RGB image with a higher spatial resolution. We included three weed species in this dataset, representing different botanical groups and photosynthetic mechanisms. In addition, the dataset contains meticulously sampled labeled data for training and testing. The images represent a time series of the weed’s growth along its early stages, critical for precise herbicide application. We conducted an experimental evaluation to test the performance of a machine-learning approach, a deep-learning approach, and Spectral Mixture Analysis (SMA) to identify the different weed traits. In addition, we analyzed the importance of features using the random forest algorithm and evaluated the performance of the selected algorithms while using different sets of features.

Gamma-glutamyltransferase (GGT) is a predictor of NAFLD activity score for diagnosing non- alcoholic steatohepatitis (NASH)

Nonalcoholic fatty liver disease (NAFLD) encompasses a spectrum of conditions ranging from simple steatosis to steatohepatitis, advanced fibrosis and end stage liver disease. Despite the high prevalence and severity of hepatic illness, NAFLD remains underdiagnosed, because of few symptoms, lack of accurate laboratory markers. The study was aimed at to evaluate a bio-chemical score for diagnosing non-alcoholic steatohepatitis. A hospital based cross-sectional observational study was carried out for a period of two years from July 2013 to June 2015 in the Department of Hepatology, Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, Bangladesh. The study was conducted among 50 patients of NAFLD attending at department of Hepatology and underwent for biochemical investigations and liver biopsy with NAFLD Activity Score (NAS). All data were presented as mean ± SD and analyzed by SPSS (version 16). Qualitative data were analyzed by Chi-square test and quantitative data were analyzed by student’s t-test. Performance of the test were assessed by sensitivity and specificity test. Statistically significant result were considered when p value &lt; 0.05. Total patients were divided into two groups 25 were NASH and 25 were non- NASH. Mean GGT was found 73.6±48.6 U/L in NASH group and 49.9±25.4 U/L in non-NASH group. There was significant difference in the NAFLD activity score for diagnosing NASH between elevated and normal GGT (P value 0.035). Higher GGT values correlated with higher specificity. The Gamma-lutamyltransferase (GGT) has been proposed as a noninvasive and available marker for assessment of NASH. Bangladesh Med J. 2023 May; 52(2): 6-10

An improved autoencoder for denoising acoustic emission signals in rock fracturing

ABSTRACT Rock fracture acoustic emission (AE) signals are commonly used non-destructive testing data in geological exploration, resource exploitation, and engineering fields. However, these signals are often accompanied by noise interference caused by environmental factors. In this study, we propose an enhanced model for denoising rock fracture AE signals, called simplified fully convolutional denoising autoencoder (SFCDAE). This model is based on the denoising autoencoder principle in the field of deep learning neural networks. The SFCDAE model consists of only seven layers, with minimal preprocessing of data input. By comparing denoising performance evaluation indicators, higher peak signal-to-noise ratio (PSNR) and lower root mean square error (RMSE) were achieved. On average, PSNR increased by 5.575% and RMSE decreased by 22.225%. Using simulated environmental noise to validate the model, it was found that the model has good robustness and can remove artefacts from sudden noise. The practical application value of the LSTM classification model was validated using data containing real experimental noise, resulting in a higher classification accuracy of 80.083%. These results indicate that the proposed model has better denoising performance compared to existing intelligent models and has certain practical value.

Logistic Regression's Effectiveness in Feature Selection with Information Gain in Predicting Heart Failure Patients

Heart failure is a chronic illness that obstructs blood flow, which is necessary for the body to circulate oxygen. Patients with heart failure have a poor chance of survival, as evidenced by the high death rate. The hospital's infrastructure and medical facilities determine the degree of patient safety, and the patients' medical records play a significant role in ensuring that they receive the right care. As a result, a system that uses specific data to forecast the safety of heart failure patients is required. Machine learning, a computer-based approach, is one way to get around this. The logistic regression algorithm has been used to generate predictions in earlier studies. The approach for feature selection from the dataset that is suggested in this study is information gain. You can filter features that are significant to the dataset in this way. In addition, selection can enhance machine learning efficacy by decreasing the dimensions of the data. Five features—time, serum creatinine, ejection fraction, age, and serum sodium—are the outcome of information gain. After that, predictions were made using logistic regression, and a data sharing ratio of 70% training data and 30% test data resulted in an accuracy of 0.8556. This demonstrates how feature selection with Information Gain can improve the accuracy of the logistic regression model and is a very effective method.

Molecular Evolutionary Dynamics of Coxsackievirus A6 Causing Hand, Foot, and Mouth Disease From 2021 to 2023 in China: Genomic Epidemiology Study.

Hand, foot, and mouth disease (HFMD) is a global public health concern, notably within the Asia-Pacific region. Recently, the primary pathogen causing HFMD outbreaks across numerous countries, including China, is coxsackievirus (CV) A6, one of the most prevalent enteroviruses in the world. It is a new variant that has undergone genetic recombination and evolution, which might not only induce modifications in the clinical manifestations of HFMD but also heighten its pathogenicity because of nucleotide mutation accumulation. The study assessed the epidemiological characteristics of HFMD in China and characterized the molecular epidemiology of the major pathogen (CV-A6) causing HFMD. We attempted to establish the association between disease progression and viral genetic evolution through a molecular epidemiological study. Surveillance data from the Chinese Center for Disease Control and Prevention from 2021 to 2023 were used to analyze the epidemiological seasons and peaks of HFMD in Henan, China, and capture the results of HFMD pathogen typing. We analyzed the evolutionary characteristics of all full-length CV-A6 sequences in the NCBI database and the isolated sequences in Henan. To characterize the molecular evolution of CV-A6, time-scaled tree and historical population dynamics regarding CV-A6 sequences were estimated. Additionally, we analyzed the isolated strains for mutated or missing amino acid sites compared to the prototype CV-A6 strain. The 2021-2023 epidemic seasons for HFMD in Henan usually lasted from June to August, with peaks around June and July. The monthly case reporting rate during the peak period ranged from 20.7% (4854/23,440) to 35% (12,135/34,706) of the total annual number of cases. Analysis of the pathogen composition of 2850 laboratory-confirmed cases identified 8 enterovirus serotypes, among which CV-A6 accounted for the highest proportion (652/2850, 22.88%). CV-A6 emerged as the major pathogen for HFMD in 2022 (203/732, 27.73%) and 2023 (262/708, 37.01%). We analyzed all CV-A6 full-length sequences in the NCBI database and the evolutionary features of viruses isolated in Henan. In China, the D3 subtype gradually appeared from 2011, and by 2019, all CV-A6 virus strains belonged to the D3 subtype. The VP1 sequences analyzed in Henan showed that its subtypes were consistent with the national subtypes. Furthermore, we analyzed the molecular evolutionary features of CV-A6 using Bayesian phylogeny and found that the most recent common ancestor of CV-A6 D3 dates back to 2006 in China, earlier than the 2011 HFMD outbreak. Moreover, the strains isolated in 2023 had mutations at several amino acid sites compared to the original strain. The CV-A6 virus may have been introduced and circulating covertly within China prior to the large-scale HFMD outbreak. Our laboratory testing data confirmed the fluctuation and periodic patterns of CV-A6 prevalence. Our study provides valuable insights into understanding the evolutionary dynamics of CV-A6.