Level Of Accuracy Research Articles

Anatomy of the uncertainty of satellite vicarious calibration using radiosondes: concepts and preliminary results for microwave radiometric observations

Calibration of satellite observations is crucial for ensuring the quality of retrieved products essential for meteorological and climate applications. Calibration is obtained and monitored through a cascade of stages, including postlaunch vicarious calibration/validation activities through comparison with independent reference measurements. Here, the vicarious calibration method using radiative transfer simulations based on reference radiosondes is considered in the framework of the calibration/validation activities for the Microwave Imager (MWI) and the Ice Cloud Imager (ICI) to be launched with the Second Generation of the EUMETSAT Polar System. This paper presents an overview of the uncertainty characterizing the vicarious calibration of MWI and ICI using radiosondes as performed within the EUMETSAT-funded VICIRS study. The uncertainty characterization is pursued following a metrological approach, providing a preliminary estimation of all the identified sources. The same approach is used to develop a rigorous method for estimating the number of comparison pairs (i.e., observations vs. simulations) needed to reach a certain level of accuracy in bias determination.

Knowledge-tuning Large Language Models with Structured Medical Knowledge Bases for Trustworthy Response Generation in Chinese

Large Language Models (LLMs) have demonstrated remarkable success in diverse natural language processing (NLP) tasks in general domains. However, LLMs sometimes generate responses with the hallucination about medical facts due to limited domain knowledge. Such shortcomings pose potential risks in the utilization of LLMs within medical contexts. To address this challenge, we propose knowledge-tuning, which leverages structured medical knowledge bases for the LLMs to grasp domain knowledge efficiently and facilitate trustworthy response generation. We also release cMedKnowQA, a Chinese medical knowledge question-answering dataset constructed from medical knowledge bases to assess the medical knowledge proficiency of LLMs. Experimental results show that the LLMs which are knowledge-tuned with cMedKnowQA, can exhibit higher levels of accuracy in response generation compared with vanilla instruction-tuning and offer a new trustworthy way for the domain adaptation of LLMs. We release our code and data at https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese .

Prediction of lung adenocarcinoma prognosis and diagnosis with a novel model anchored in circadian clock-related genes

Lung adenocarcinoma is the most common primary lung cancer seen in the world, and identifying genetic markers is essential for predicting the prognosis of lung adenocarcinoma and improving treatment outcomes. It is well known that alterations in circadian rhythms are associated with a higher risk of cancer. Moreover, circadian rhythms play a regulatory role in the human body. Therefore, studying the changes in circadian rhythms in cancer patients is crucial for optimizing treatment. The gene expression data and clinical data were sourced from TCGA database, and we identified the circadian clock-related genes. We used the obtained TCGA-LUAD data set to build the model, and the other 647 lung adenocarcinoma patients’ data were collected from two GEO data sets for external verification. A risk score model for circadian clock-related genes was constructed, based on the identification of 8 genetically significant genes. Based on ROC analyses, the risk model demonstrated a high level of accuracy in predicting the overall survival times of lung adenocarcinoma patients in training folds, as well as external data sets. This study has successfully constructed a risk model for lung adenocarcinoma prognosis, utilizing circadian rhythm as its foundation. This model demonstrates a dependable capacity to forecast the outcome of the disease, which can further guide the relevant mechanism of lung adenocarcinoma and combine behavioral therapy with treatment to optimize treatment decision-making.

Selected Configuration Interaction for Resonances.

Electronic resonances are metastable states that can decay by electron loss. They are ubiquitous across various fields of science, such as chemistry, physics, and biology. However, current theoretical and computational models for resonances cannot yet rival the level of accuracy achieved by bound-state methodologies. Here, we generalize selected configuration interaction (SCI) to treat resonances by using the complex absorbing potential (CAP) technique. By modifying the selection procedure and the extrapolation protocol of standard SCI, the resulting CAP-SCI method yields resonance positions and widths of full configuration interaction quality. Initial results for the shape resonances of N2- and CO- reveal the important effect of high-order correlation, which shifts the values obtained with CAP-augmented equation-of-motion coupled-cluster with singles and doubles by more than 0.1 eV. The present CAP-SCI approach represents a cornerstone in the development of highly accurate methodologies for resonances.

Large-Area Film Thickness Identification of Transparent Glass by Hyperspectral Imaging

This study introduces a novel method for detecting and measuring transparent glass sheets using hyperspectral imaging (HSI). The main goal of this study is to create a conversion technique that can accurately display spectral information from collected images, particularly in the visible light spectrum (VIS) and near-infrared (NIR) areas. This technique enables the capture of relevant spectral data when used with images provided by industrial cameras. The next step in this investigation is using principal component analysis to examine the obtained hyperspectral images derived from different treated glass samples. This analytical procedure standardizes the magnitude of light wavelengths that are inherent in the HSI images. The simulated spectral profiles are obtained using the generalized inverse matrix technique on the normalized HSI images. These profiles are then matched with spectroscopic data obtained from microscopic imaging, resulting in the observation of distinct dispersion patterns. The novel use of images coloring methods effectively displays the thickness of the glass processing sheet in a visually noticeable way. Based on empirical research, changes in the thickness of the glass coating in the NIR-HSI range cause significant changes in the transmission of infrared light at different wavelengths within the NIR spectrum. This phenomenon serves as the foundation for the study of film thickness. The root mean square error inside the NIR area is impressively low, calculated to be just 0.02. This highlights the high level of accuracy achieved by the technique stated above. Potential areas of investigation that arise from this study are incorporating the proposed approach into the design of a real-time, wide-scale automated optical inspection system.

Assessment of the accuracy and reliability of clinical thermometers for body temperature measurements at the hospital: a laboratory study

Abstract This study aims to assess the accuracy and reliability of two different types of thermometers: contact thermometers (CT) and non-contact thermometers (NCIRT) at Maharaj Nakorn Chiang Mai Hospital. The comparison method was carried out in a hospital laboratory setting and followed a specific calibration procedure. The mean readings, errors, and measurement uncertainties were reported and categorized according to the manufacturer’s labeling models. The maximum permissible errors were defined by international standards and clinical acceptances for contact and NCIRTs, respectively. The results showed that thermometers of temperature probes (PTs) provided the highest level of accuracy. When compared to a reference standard thermometer (STD), the mean errors were only 0.04 °C at the calibration point of 37.0 °C. For digital CTs, the mean error at the calibration points of 37.0 °C was 0.24 °C. Most CT models exhibited high accuracy and reliability. However, the forehead (IRFTs) and ear (IRETs) readings significantly differed from the STD, with over half of all units not meeting the ISO 80601–2–56/ASTM E1965–98 standard requirements. These results suggest that CTs and PTs are highly accurate and reliable devices for measuring body temperature. However, both IRFTs and IRETs could be useful in certain situations, such as screening many people in a short amount of time, but their accuracy should be interpreted with caution and confirmed using a more precise method if necessary.

An integrated framework for rainfall prediction and analysis using a Stacked Heterogeneous Ensemble Model (SHEM)

Recent climate conditions in India have led to numerous disasters, including floods, overflowing rivers, broken dams, and reduced vegetation. Machine learning aims to train models to make predictions based on historical data. Although many studies have focused on predictions and model building, this research introduces a novel integrated framework using a Stacked Heterogeneous Ensemble Model (SHEM). It aims to enhance prediction accuracy by employing another novel approach for imputing missing values, the Variable Specific Hot Deck (VSHD) imputation method. The outcomes were contrasted with established machine learning techniques, including random forest, decision tree, k-nearest neighbor, and support vector machine. After completing the imputation, we proceeded to implement the SHEM model. Real-time climate data for the Cuddalore location in Tamil Nadu state, South India was collected from the NASA Power Access viewer portal to verify the accuracy level of the proposed model. Performance analysis indicates that the proposed imputation outperforms all four alternative models with a 30% average improvement in accuracy. Moreover, the developed SHEM model has a reduced RMSE value of 0.321 and an R-squared value of 0.952 and shows a 9% improvement in accuracy compared to the base model’s performance. Furthermore, these prediction results are compared to an LSTM (Long Short Term Memory), a deep learning model to calculate accuracy and loss, showing that the proposed achieves high accuracy and significant loss during validation. The obtained results can serve as a guideline for atmospheric scientists and various weather forecasting applications, helping them to choose the most appropriate machine learning method for their prediction task.

Comparative analysis of models (Altman, Grover, Zmijewski, Springate) in predicting company bankruptcy potential in the non-cyclical consumer sector

This research aims to conduct a comparative analysis of models (Altman, Grover, Zmijewski, Springate) in predicting potential bankruptcy for companies in the non-cyclical consumer sector listed on the IDX in 2020-2022. The main aim of this research is to test the results of the comparison of four models and test the accuracy of the prediction model in predicting bankruptcy. The data in this research is 240 data, namely 3 years of timeseries data and 80 companies using purposive sampling techniques according to certain criteria. Data analysis used the Kruskal Wallis Difference Test and Accuracy Level Test. The research results show that the Altman, Grover, Zmijewski, and Springate models have significant differences in results in predicting bankruptcy and the accuracy level test produces the Grover model with the best level of accuracy in predicting bankruptcy in non-cyclical consumer sector companies in 2020-2022.

Crop health assessment through hierarchical fuzzy rule-based status maps

AbstractPrecision agriculture is evolving toward a contemporary approach that involves multiple sensing techniques to monitor and enhance crop quality while minimizing losses and waste of no longer considered inexhaustible resources, such as soil and water supplies. To understand crop status, it is necessary to integrate data from heterogeneous sensors and employ advanced sensing devices that can assess crop and water status. This study presents a smart monitoring approach in agriculture, involving sensors that can be both stationary (such as soil moisture sensors) and mobile (such as sensor-equipped unmanned aerial vehicles). These sensors collect information from visual maps of crop production and water conditions, to comprehensively understand the crop area and spot any potential vegetation problems. A modular fuzzy control scheme has been designed to interpret spectral indices and vegetative parameters and, by applying fuzzy rules, return status maps about vegetation status. The rules are applied incrementally per a hierarchical design to correlate lower-level data (e.g., temperature, vegetation indices) with higher-level data (e.g., vapor pressure deficit) to robustly determine the vegetation status and the main parameters that have led to it. A case study was conducted, involving the collection of satellite images from artichoke crops in Salerno, Italy, to demonstrate the potential of incremental design and information integration in crop health monitoring. Subsequently, tests were conducted on vineyard regions of interest in Teano, Italy, to assess the efficacy of the framework in the assessment of plant status and water stress. Indeed, comparing the outcomes of our maps with those of cutting-edge machine learning (ML) semantic segmentation has indeed revealed a promising level of accuracy. Specifically, classification performance was compared to the output of conventional ML methods, demonstrating that our approach is consistent and achieves an accuracy of over 90% throughout various seasons of the year.

Discrimination of Fungicide-Contaminated Lettuces Based on Maximum Residue Limits Using Spectroscopy and Chemometrics

The fast and effective monitoring of agrochemical residues is essential for assuring food safety, since many agricultural products are sprayed with pesticides and commercialised without waiting for the pre-harvest interval. In this study, we investigated the use of spectral reflectance combined with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) to evaluate the discrimination of fungicide-contaminated lettuces, considering three maximum residue limits (MRLs) [3.5, 5, and 7 mg carbon disulphide (CS2) kg−1]. The non-systemic Mancozeb fungicide (dithiocarbamate) was adopted in this research. Spectral reflectance (Vis/NIR) was measured by a hand-held spectrometer connected to a clip probe with an integrating sphere. The lettuce spectra were pre-treated (centring, standard normal variate, and first derivative) before data processing. Our findings suggest that PCA recognised inherent similarities in the fungicide-contaminated lettuce spectra, categorising them into two distinct groups. The PLS-DA models for all MRLs resulted in high accuracy levels, with correct discriminations ranging from 94.5 to 100% for the external validation dataset. Overall, our study demonstrates that spectroscopy combined with discriminating methods is a promising tool for non-destructive and fast discrimination of fungicide-contaminated lettuces. This methodology can be used in industrial food processing, enabling large-scale individual analysis and real-time decision making.

The Role of Artificial Intelligence in Treatment and Diagnosis in Healthcare

Technology, specifically artificial intelligence (AI) is gradually but progressively creeping into the health sector and it’s perhaps the one that has been revolutionised most in diagnosis and treatment. This review brings out discussions on the practices of AI technologies in medical, the pros and the cons. First of all, an endeavour is made to elucidate the meaning of the term AI and its utilization in the field of healthcare. The specific AI techniques are described comprehensively focusing on the machine learning, deep learning, and natural language processing methods to be used in the project The role of multiple types of data in AI includes the EHR, medical images, and genomics data. Self-diagnosis: AI is improving the diagnosis approaches in the radiology and pathology fields and predicting the early-stage disease with better results in most of the cases, and enhancing the identification of genetic diseases. As for treatment, the enhancement of the use of AI has had an impact on issues such as; Prescribing and recommending drugs according to the characteristics of the patients, smart drug administration and management, robotic surgeries and simulations. Discussions are made using concrete and successful implementation of AI in cancer, cardiovascular, neurological and infectious diseases for the purpose of elucidating particular results. This also has to do with the ethical and legal problems like who has the liability to determine in the instance of complicated problems, patients’ information discretion, data privacy, and other legalities. In this article, we briefly mention the prosaic matters of AI, which deals with the engineering aspects of establishing AI such as the aspect of data and the ways and means of checking them and the interdisciplinary character of it. Concerning future developments, additional technologies like AI and connected devices in the field of health care, interdisciplinary at national and international level as well as data sharing is emphasized. Thus. AI has a very great perspective in healthcare, particularly in diagnostics and treatment of diseases due to the probability of increasing the level of accuracy, efficacy, and personalization. Despite these, they are tangible objectives with major challenges and require cooperation between nations with proper handling of Artificial Intelligence to practice clinical medication.

Seismic Inversion Techniques and Attribute Analysis for Accurate Well Placement in Offshore Niger Delta Basin, Nigeria

Before this study, simple methods like seismic attribute analysis have often been used for reservoir characterization with successes however, there is still the need to reduce exploration uncertainty to a negligible level and boost investors’ confidence. This study integrated seismic inversion with seismic attribute analysis to better characterize the reservoirs in MTW Field in deep-offshore Niger Delta Basin. Five (5) wells with complete suite of petrophysical logs, three-Dimensional (3-D) seismic data, checkshot and other well information were used. The well data were thoroughly quality checked, reservoirs were litho-stratigraphically delineated and used for petrophysical analysis across the wells. This was followed by seismic-to- well-tie, seismic interpretation, and seismic attribute analysis (Root Mean Square-RMS) generated using depth surface maps. 3-D static reservoir model and volumetric evaluation were carried out. Petrophysical properties were derived and distributed across the 3-D static model using sequential gaussian simulation algorithm to ascertain shale volume spread across the model. To improve the seismic resolution and reduce interpretation uncertainty at greater depth, model- based post-stack seismic inversion was performed to obtain acoustic impedance cube. Litho-stratigraphic and petrophysical analysis result revealed five reservoir sands (A, B, C, D, and E). Reservoir-A was seen to be more viable with a thickness of 13.42 m, high effective porosity of 27%, permeability of 3187.53 mD, low water saturation of 34% and low shale volume of 11% which are indication good reservoir quality and producibility. The seismic interpretation revealed thirty-one (31) growth and antithetic faults oriented in the NE-SW and NW-SE directions, respectively. The RMS result revealed high amplitude reflectivity which is a measure of zone of interest. Based on this, seven (7) prospects and three (3) leads were identified. The seismic inversion result shows a high level of accuracy with a correlation coefficient of 0.997; 0.997; 0.995; and 0.996 in MTW-001, MTW-003ST1, MTW-004ST1 and MTW-005 wells, respectively. The acoustic impedance successfully resolved and improved on the resolution of the seismic stacking velocity especially at reservoir layers and at depth deeper than 3600 ms. Acoustic impedance as a layer property has improved on the lateral and vertical resolutions of the data beyond what the usual seismic interval velocity could image thus, validating some of the prospects and leads identified in this study. This demonstrated that uncertainty can be reduced by a blend of RMS and seismic inversion in identifying reservoir for accurate placement of wells. It is therefore recommended that E and P operators should adopt the technique in their future hydrocarbon exploration endeavor in frontier and matured basins.

Accurate Prediction of Bangla Text Article Categorization by Utilizing Novel Bangla Stemmer

Text categorization involves assigning predefined category labels to an unlabeled document. With the exponential growth in the accessibility and availability of digital documents over the past decade, this field significantly attracted the scientific community that immensely demands rapid and accurate categorization of these documents. Relying on experts for manual classification is time-consuming and resource-intensive. Consequently, labeling unlabeled digital documents faster more accurately, and more efficiently is inescapable. One promising approach to addressing this demand is the use of machine learning algorithms. Training these algorithms on a large dataset of labeled texts lets them learn patterns and predicted unlabeled documents. This strategy might greatly expedite the categorizing process while retaining a substantial level of accuracy through leveraging artificial intelligence. These algorithms have also enhanced natural language processing techniques, making them more accurate at classifying unlabeled digital documents. In this study, we propose a novel machine-learning computational framework to address this challenge. Our framework incorporates a novel Bangla stemmer, which reduces words to their stems. We then employed TF-IDF for document vectorization, a statistical measure assessing word relevance for categorization purposes. Experimental results reveal that our framework significantly enhances prediction performance, achieving an impressive 95.3% prediction accuracy.

Multidimensional Evaluation of Altimetry Marine Gravity Models with Shipborne Gravity Data from a New Platform Marine Gravimeter

With the development of satellite altimetry technology and the application of new altimetry satellites, the accuracy and resolution of altimeter-derived gravity field models have improved over the last decades. Nowadays, they are close enough to shipborne gravimetry. In this paper, multi-source shipborne gravity data in the South China Sea were taken to evaluate the accuracies of two high-precision altimeter-derived marine gravity field models (SS V30.1, DTU17). In these shipborne gravity data, there are dozens of routes’ ship gravimetry data, obtained from the National Geophysical Data Center (NGDC); data were tracked from a marine survey with a commercial marine gravimeter (type KSS31M), and data were tracked from a marine gravimetry campaign that was conducted with a newly developed platform gravimeter (type JMG) in the South China Sea in September 2020. After various data filtering, processing, and calibrations, the shipborne gravity data were validated with crossover points analysis. Then, the processed shipborne data were employed to evaluate the accuracy of the altimeter-derived marine gravity field models. During this procedure, the quality of JMG shipborne gravity data was compared with the results of KSS31M and NGDC data. Analysis and evaluation results show that the crossover points verification accuracies of KSS31M and JMG are 0.70 mGal and 1.61 mGal, which are much better than the accuracy of NGDC, which is larger than 8.0 mGal. In the area where the bathymetry changes slowly, the root mean square error values between altimetry gravity models and KSS31M data are respectively 3.28 mGal and 4.54 mGal, and those of the JMG data are respectively 2.94 mGal and 2.60 mGal. According to the above results, we can conclude that the JMG has the same 1–2 mGal accuracy level as KSS31M and can meet the measurement requirements of marine gravity.

GRU-powered sleep stage classification with permutation-based EEG channel selection

We present a new approach to classifying the sleep stage that incorporates a computationally inexpensive method based on permutations for channel selection and takes advantage of deep learning power, specifically the gated recurrent unit (GRU) model, along with other deep learning methods. By systematically permuting the electroencephalographic (EEG) channels, different combinations of EEG channels are evaluated to identify the most informative subset for the classification of the 5-class sleep stage. For analysis, we used an EEG dataset that was collected at the International Institute for Integrative Sleep Medicine (WPI-IIIS) at the University of Tsukuba in Japan. The results of these explorations provide many new insights such as the (1) drastic decrease in performance when channels are fewer than 3, (2) 3-random channels selected by permutation provide the same or better prediction than the 3 channels recommended by the American Academy of Sleep Medicine (AASM), (3) N1 class suffers the most in prediction accuracy as the channels drop from 128 to 3 random or 3 AASM, and (4) no single channel provides acceptable levels of accuracy in the prediction of 5 classes. The results obtained show the GRU’s ability to retain essential temporal information from EEG data, which allows capturing the underlying patterns associated with each sleep stage effectively. Using permutation-based channel selection, we enhance or at least maintain as high model efficiency as when using high-density EEG, incorporating only the most informative EEG channels.

Bayesian-informed fatigue life prediction in shallow shell structures with the dual boundary element method

This study introduces an innovative approach for statistically inferring the Equivalent Initial Flaw Size Distribution (EIFSD) in shallow shell structures with the aim of predicting fatigue life. The proposed approach integrates Bayesian inference and surrogate modelling techniques, and utilizes a long crack growth model. The EIFSD is crucial for estimating fatigue life and ensuring structural durability. To showcase the proposed methodology, a numerical example featuring a fuselage window structure under multi-axial loading, employing the Dual Boundary Element Method (DBEM) for crack growth modelling, is presented. Efficiency gains in the inference of the EIFSD are achieved through the development of surrogate models, notably a Co-Kriging model that significantly reduces computational time and outperforms a Kriging model in error reduction. The study explores two assumptions for inspection data sourcing, leading to enhanced methodological effectiveness across varying uncertainty levels. It considers the separate effects of Paris Law constants and loading conditions under increased uncertainty, presenting cases that demonstrate the Co-Kriging model’s high accuracy and computational efficiency. The proposed methodology’s robustness is further validated by assessing the impact of prior distribution quality on Bayesian inference, showing the method’s adaptability to high uncertainty while maintaining a good level of accuracy. This study also demonstrates an approach for fatigue life estimation of structures using the inferred EIFSD. By evaluating the structure up to a critical crack length, the methodology allows for the accurate estimation of fatigue life, which, in turn, facilitates the determination of optimal inspection intervals. Notably, under conditions of high uncertainty, the method showcased a maximum difference of 1.51% in the estimated fatigue life when compared to the true EIFSD, underscoring the technique’s high accuracy and reliability.

A full operating conditions ejector model for refrigeration systems driven by low-grade heat sources

The ejector refrigeration technology driven by low-grade heat sources constitutes as one of the crucial means to solve the energy shortage in present-day society and to reduce the global environmental pollution. Thermodynamics ejector models are the basis of performance prediction, system control and structural design in ejector refrigeration systems driven by low-grade heat sources. However, the majority of present ejector models are unable to simultaneously meet the general feasibility of full operating conditions and the level of prediction accuracy for improving the energy efficiency of the refrigeration system. In this study, a novel theoretical model of ejectors is successfully derived based on the foundation of compound-choking theory and gas dynamic relations, which easily predicts the entrainment performance under the on-design and off-design conditions in ejector refrigeration systems. Indeed, a direct solution algorithm is presented to quickly and accurately solve this model and a linear correlation is employed between the mixing loss coefficient and the back pressure at the subcritical mode. The developed model is verified with experimental results in R245fa, R134a and R600a ejector refrigeration system. The results show that the model provides a precise estimation of the entrainment performance with the mean relative errors of 2.45%, 5.49% and 3.67% for R245fa, R134a and R600a refrigerants at full working conditions, while the prediction of critical condensing temperatures is considerably approximated by the experimental measurements. In the comparative analysis with the previous model, this model demonstrates a superior prediction performance, especially in the subcritical mode using the linear function mixing loss coefficient. This study will be a valuable aid in the improvement of energy efficiency in ejector refrigeration systems.

Plasma C-terminal agrin fragment concentrations across adulthood: Reference values and associations with skeletal muscle health.

Increasing interest surrounds the utility of blood-based biomarkers for diagnosing sarcopenia. C-terminal agrin fragment (CAF), a marker of neuromuscular junction stability, is amongst the most promising candidates; however, a dearth of reference data impedes the incorporation of its use in public health settings. This study aimed to establish reference values for plasma CAF concentrations across adulthood in a large, well-characterized cohort of healthy adults; and comprehensively examine the association between plasma CAF levels and skeletal muscle health. One thousand people aged between 18 and 87years took part in this study (mean age=50.4years; 51% females). Body composition and muscle strength were examined using DXA and hand dynamometry. Plasma CAF concentrations were measured, in duplicate, using commercially available ELISA kits. Sarcopenia and individual sarcopenia signatures [low skeletal muscle index (SMI) only/low grip strength only] were classified using the EWGSOP2 algorithm. Detailed reference CAF values, according to sex and age, are presented. A significant but modest age-related increase in plasma CAF concentration was observed (P=0.018). Across adulthood, CAF concentrations were negatively associated with grip strength and SMI (both P<0.001). In people ≥50years old, CAF concentrations were 22.6% higher in those with sarcopenia (P<0.001), 11.3% higher in those with low SMI (P=0.006) and 9.6% higher in those with low grip strength (P=0.0034), compared with controls. People in the highest CAF concentration quartile, had 3.25 greater odds for sarcopenia (95% CI=1.41-7.49, P=0.005), 2.76 greater odds for low SMI (95% CI=1.24-5.22, P=0.012), and 2.56 greater odds for low grip strength (95% CI=1.07-5.57, P=0.037), compared with those in the lowest quartile. People with a CAF Z-score ≥2 had 9.52 greater odds for sarcopenia (95% CI=3.01-30.05, P<0.001) compared with a Z-score <1. Plasma CAF concentration had an acceptable level of diagnostic accuracy for sarcopenia (AUC=0.772, 95% CI=0.733-0.807, P<0.001). The reference values presented herein may guide the clinical interpretation of circulating CAF and help identify people at risk of poor skeletal muscle outcomes for inclusion in therapeutic interventions. Our findings add clarity to existing data, demonstrating a robust relationship between circulating CAF and skeletal muscle integrity in the largest adult cohort to date, and support the use of CAF as an accessible, cost-effective screening tool for sarcopenia. However, further research into the prognostic utility of plasma CAF, and the establishment of normative data from other populations, are urgently needed if routine CAF screening is to be embedded into public healthcare settings.

Automatic notes based on video records of online meetings using the latent Dirichlet allocation method

Meeting minutes can also be used as a benchmark for whether the meeting objectives have been achieved or not. Minutes are taken during the meeting until the end of the meeting, which contain essential points from the meeting. Minutes in online meetings are currently still done manually, and generally, every meeting is recorded as documentation that requires more human resources to change the recording of the meeting file. Based on the problems above, a solution to this problem is needed by creating an automatic note-taking system that can assist the note-takers in concluding the meeting, especially in the Information Technology Department. This study uses the latent Dirichlet allocation (LDA) method to determine text summarization and topic modeling. Based on this research, the system calculation using the LDA method produces a pretty good accuracy value for text summarization of 57.91% and topic modeling with a coherence score of 64.56%. Based on this research, the implementation of the latent Dirichlet allocation method for text summarization and topic modeling provides a fairly good level of similarity accuracy when compared to the minutes that are written manually and can be implemented in the Information Technology Department.

Nomogram for predicting the risk and prognosis of lung metastasis of four subtypes of breast cancer: A population-based study from SEER

BackgroundBreast cancer (BC) is the most diagnosed cancer worldwide, and patients’ survival decreases with metastasis. We conducted a retrospective study using data derived from the Surveillance, Epidemiology, and End Results (SEER) database and clinicopathological data to construct a clinical predictive model to predict the risk and prognosis of lung metastasis (LM) in patients with different subtypes of BC and validate its performance. MethodsA total of 1650 patients from the SEER database between 2011 and 2015 were enrolled in this study. Cox regression analysis was performed to identify prognostic factors for breast cancer lung metastasis (BCLM). A nomogram was constructed using the independent prognostic factors. The concordance index (C-index), area under the curve (AUC) value, calibration curve, and decision curve analysis (DCA) were used to test the prediction accuracy of the nomogram. External validation was performed using clinical data from the Affiliated Hospital of Qinghai University and the General Hospital of Ningxia Medical University. ResultsMultivariate Cox regression analyses suggested that age, grade, surgery, chemotherapy, subtype, and liver, bone, and brain metastases were independent prognostic factors for overall survival (OS). Kaplan–Meier survival analysis showed that the median survival times of patients with human epidermal growth factor receptor 2 (HER2)-positive, luminal A, luminal B, and triple-negative BC were 25 (95% confidence interval [CI], 20–37), 27 (95% CI, 23–29), 35 (95% CI, 30–44), and 12 (95% CI, 11–14), respectively. The C-indexes of the nomogram for predicting OS of the SEER training, SEER validation, and clinical validation cohorts were 0.7, 0.6, and 0.6, respectively, and the calculated AUCs at 3 years were 0.765, 0.794, and 0.799, respectively. The calibration curve indicates that the nomogram possessed a high level of accuracy. ConclusionsOur nomogram demonstrates significant predictive value, indicating that molecular subtypes, brain metastasis, and liver metastasis are closely associated with the prognosis of patients with LM. This information can guide clinical practice.