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AbstractBackgroundMethodsResultsConclusionDiagnosing acute disease in older adults is challenged by vague and atypical symptoms. Point-of-care tests (POCTs) at home may improve diagnostics and clinical decision-making. We compared various POCT devices to routine testing in acutely ill older adults to assess their clinical reliability.We enrolled participants aged 65+ years requiring acute in-home assessment with signs of acute conditions. Venous and capillary blood samples were collected and analysed on-site using POCT, while identical samples were transported and analysed in a routine laboratory. Agreement between POCT and laboratory testing was assessed using scatter plots with linear regression, Pearson’s correlation coefficient (PCC), limits of agreement, and Bland-Altman plots. Misclassification rates were calculated based on clinically meaningful cut-offs to assess POCT’s clinical reliability.We included 100 participants with a mean age of 81.6 (±8.4 SD) years. Strong correlation was found between POCT and routine measurements (PCC: 0.76–0.94 for capillary samples and 0.85–0.98 for venous samples). Venous samples showed higher PCC than capillary, except for neutrophils (0.93 for capillary, 0.89 for venous). Misclassification occurred in capillary samples for haemoglobin (10/62) and total WBC (6/50), while in venous samples, misclassification was observed for haemoglobin (4/54), total WBC (4/50), K+ (5/47), urea (5/47), and creatinine (3/42). No misclassification was observed for Na+.POCT provides acceptable, clinically reliable measurements in acutely ill older adults, potentially enhancing diagnostics and treatments during in-home assessment. Venous blood testing is preferable due to a lower misclassification rate, but capillary blood remains a pragmatic alternative, despite higher variation and inaccuracy. [ABSTRACT FROM AUTHOR]

The document outlines the winners of the 2023 Technometrics Prizes, with Ying Jin, Shan Ba, Kungang Zhang, Anh Bui, and Daniel Apley receiving awards for their respective articles. The prizes include a certificate and a cash award. Additionally, the document provides a historical list of past winners of the Technometrics Prizes from 1969 to 2022, showcasing a diverse range of research topics and authors. [Extracted from the article]

The possibility of establishing relationships between the distribution of total concentrations and mobile forms of the elements in vineyard soils was studied using ICP spectrometry and chemometric analysis. The authenticity of wines based on varietal and geographical characteristics was studied based on the relationships between the elemental compositions of wine, grapes, and soil from the place where the grapes grew. The nature of the supply of the elements in the soil–grape chain and the degree of absorption of mobile forms of soil elements by grape berries were also assessed using the biological absorption coefficient. Each grape variety formed an elemental image due to the individual character of assimilation of the studied elements. The concentrations of K, Rb, and Ti found in grape samples were higher than the concentrations of mobile forms of these elements in the soil regardless of the variety. Scatter diagrams of canonical values and projections of observations onto the factor plane, constructed using multivariate statistical analysis methods for element concentrations, showed that each grape variety was localized in a certain part of the plane to form groups of homogeneous objects (clusters). The contributions of elements to the grape variety discrimination model decreased in the sequence Mo, Cu, K, Ni, Ba, Ca, Pb, Li, Mg, Fe, Ti, Zn, Rb, Al, and V or, on a regional basis, Rb, Al, K, Sr, Co, Na, Pb, Ca, and Ni. The results obtained can be used to determine markers responsible for the varietal and regional affiliations of wines. [ABSTRACT FROM AUTHOR]

Drought is a natural disaster characterised as precipitation much lower than the precipitation reported in actual periods. Many studies characterized drought as meteorological, hydrological, agricultural, or socioeconomic. When there is a long-term shortage of precipitation, deficits in surface and ground waters occur. In this study, a hydrological drought analysis has been performed for Wadi Mina Basin (4900 km2), which is the biggest sub-basin in Cheliff Basin, using the Streamflow Drought Index (SDI) for the time scales of 3, 6, 9, and 12-month. Monthly mean streamflow records for 05 stations are obtained from the National Water Resources Agency. The obtained SDI values were subjected to Run analysis and drought duration and severity values were calculated. According to the analysis, it has been observed that the maximum (duration: 70 months, severity: 92.78) and average (duration: 31 months, severity: 31.28) droughts occurred at the Sidi AEK Djillali station on a 12-month time scale. The average drought severity was 6.34, with a maximum value of 56.71 on a monthly time scale. However, on a 12-month time scale, the average drought severity increased to 31.28, with a maximum value of 92.78. Therefore, it can be said that the drought severity has increased with the increase in the time scale. When the temporal changes of drought indices are evaluated, it is noteworthy that extraordinary droughts prevailed in the basin in 2000 and 2007. When the scatter diagrams of drought characteristics were examined, it was seen that there was a significant linear relationship between drought duration and severity. In addition, the highest correlation was observed at the 9-month time scale at Ain Hamara (R:0.964) and Oued Abtal (R:0.904) stations. In contrast, the highest correlation was observed at the 12-month time scale at Sidi AEK Djillali (R:0.980) and Takhmaret (R:0.969) stations. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) evaluation is global NDT technique in which acoustic emissions takes place by loading the component at a specified threshold level. In this study, a crack propagation is studied by AE parameters.Specimens are tensile deformed and AE parameters are recorded. Three types of specimens, Type A, Type B and Type C, are designated as Heat affected zone, Parent metal and Weldment, respectively. The recorded data are analysed in three zones, Zone-1, Zone-2 and Zone-3, in terms of passive, active and critically active of a crack, respectively. Zone-1 is equivalent to the data arrangement up to 60% of Failure Load (FL), Zone-2 equates to 75% − 90% of F.L. and Zone-3 corresponds to fast-fracture region and equates to 90%-F.L. AE parameters – Amplitude, Ring down counts, Duration, Rise Time, Energy, RA Value, DA Value, Hit Rate, Count Rate and Energy Rate are tabulated for various failure loads. These parameters have vital importance in critical assessment of dynamic nature of cracks in Maraging steel. The scatter plots as qualitative indicators, and the rate graphs are used for understanding crack propagation. An abort criterion is developed by using combination of parameters that will serve as an early warning tool. [ABSTRACT FROM AUTHOR]

This study analyzes the depositional regime and textural properties of the sediments from the Shitalakshya River in Bangladesh, enabling us to comprehend how these sediments evolved in a river environment. For this investigation, 30 representative samples were taken from the Shitalakshya River, and their textures were analyzed. The cumulative frequency curve is obtained by using semilog graph paper to plot particle size (in phi scale) against cumulative percent. The statistical parameters such as median (Md), mode (Mo), mean (MZ), standard deviation (σi), skewness (SKi), and kurtosis (KG) were calculated using the percentile of phi values (1%, 5%, 16%, 25%, 50%, 75%, 84%, and 95%). The cumulative curves show that the sediments are deposited through the traction population (1.90%), saltation population (75.64%), and suspension population (22.46%). The median value varies between 0.7Ø and 4.85Ø, with an average of 2.738Ø indicating coarse sand to coarse silt. The analyzed samples have unimodal, bimodal, and polymodal distribution, which indicates the sediments are carried by different tributaries and distributaries in the Sitalakshya River system. The range of 1.46Ø–4.05Ø represented by the observed mean value suggests sand with medium to extremely fine grains. Most of the sediments indicate moderate sorting, which is shown by the standard deviation (sorting), which ranges from 0.399Ø to 1.48Ø. The skewness value ranges between −0.01Ø and 0.66Ø, suggesting near symmetrical skewed, while the kurtosis value ranges from 0.54Ø to 1.87Ø, indicating the sediments are 20% leptokurtic, 20% mesokurtic, and 60% platykurtic. According to the CM plot, the Shitalakshya River is mostly deposited in the area between the rolling and suspension fields, indicating a transportation regime of saltation. The scatter plots of skewness versus sorting and graphic mean versus sorting indicate that the sediments fall within the river sand zone. The energy process discriminant functions of the sediments show that they were deposited by a fluvial process. [ABSTRACT FROM AUTHOR]

The present work proposes a new model based on a convolutional neural network (CNN) to retrieve solar shortwave (SW) irradiance via the estimation of the cloud modification factor (CMF) from daytime sky images captured by all-sky cameras; this model is named CNN-CMF. To this end, a total of 237,669 sky images paired with SW irradiance measurements obtained by using pyranometers were selected at the following three sites: Valladolid and Izaña, Spain, and Lindenberg, Germany. This dataset was randomly split into training and testing sets, with the latter excluded from the training model in order to validate it using the same locations. Subsequently, the test dataset was compared with the corresponding SW irradiance measurements obtained by the pyranometers in scatter density plots. The linear fit shows a high determination coefficient ( R 2 ) of 0.99 . Statistical analyses based on the mean bias error (MBE) values and the standard deviation (SD) of the SW irradiance differences yield results close to − 2 % and 9 % , respectively. The MBE indicates a slight underestimation of the CNN-CMF model compared to the measurement values. After its validation, model performance was evaluated at the Antarctic station of Marambio (Argentina), a location not used in the training process. A similar comparison between the model-predicted SW irradiance and pyranometer measurements yielded R 2 = 0.95 , with an MBE of around 2 % and an SD of approximately 26 % . Although the precision provided by the SD at the Marambio station is lower, the MBE shows that the model's accuracy is similar to previous results but with a slight overestimation of the SW irradiance. Finally, the determination coefficient improved to 0.99 , and the MBE and SD are about 3 % and 11 % , respectively, when the CNN-CMF model is used to estimate daily SW irradiation values. [ABSTRACT FROM AUTHOR]

HLA antigens were historically defined according to the unique reactivity pattern of cells expressing HLA molecules with distinctive clusters of allo‐antisera and/or monoclonal antibodies. Subsequently, amino acid residues determining epitopes (DEP) in the HLA molecule were correlated with reactivity patterns. In current clinical practice, the presence of allo‐antibodies is assessed using Luminex‐based solid phase single antigen bead (SAB) assays for transplantation. Recently, novel antigens were proposed for HLA molecules with DEP patterns that do not match any serologically defined antigens recognised by the WHO Nomenclature Committee. To validate the antigens, mean fluorescence intensity values of SABs tested on >13,000 patients' sera were extracted from clinical databases and analysed by scatter plots using a linear regression model. We found that when two proteins were considered as the same antigen in the original study, for example, HLA‐A*02:01 and ‐A*02:06, their correlation ranked among the highest values at each locus. In contrast, discrete asymmetric outliers were observed when there were different antigens, for example, HLA‐A*30:01 and ‐A*30:02, allowing validation and confirmation of 20 novel antigens for HLA‐A, ‐B, ‐C and ‐DR. The outliers were confirmed to be true or false by flow cytometric crossmatches. In addition to the previously defined residues for antigen assignments, findings suggest that further distinction should be made for common antigens by including the substitutions at residue 67 of HLA‐B, 67 and 74 of ‐DR. These serologic analyses can be applied systematically to identify and confirm novel antigens. These developments will lead to designing optimal SAB panels and further improving virtual donor‐specific antibodies assessment. [ABSTRACT FROM AUTHOR]

Objective: Point‐of‐care tests (POCTs) are commonly utilized in emergency departments. The purpose of this study was to evaluate accuracy and clinical utility of capillary POCT Hemocue (POCT Hcue) in emergency department setting. Methods: This was a retrospective study. Accident and emergency department attendances ≥18 years old with a capillary POCT Hcue result and a paired venous hemoglobin (Hb) result were eligible. Statistical analyses including Pearson coefficient and Bland–Altman plot were performed to assess the relationship and agreement between capillary POCT Hcue and venous Hb. Results: One hundred and seven patients were included in the analysis. 59% were female. 67% of the patients were from triage category 3. Scatter plot revealed a linear relationship between capillary POCT Hcue and venous Hb, with a correlation coefficient calculated to be 0.95. The mean difference was −0.30 on the Bland–Altman plot with the limits of agreement from −1.83 to +1.22. Conclusion: Capillary POCT Hcue had acceptable agreement with venous Hb in emergency department setting. [ABSTRACT FROM AUTHOR]

Shallow-water mooring chains of floating offshore wind turbines pose challenges such as nonlinear tension characteristics, high cost and prominent fatigue issues, which seriously hinder the large-scale use of floating wind power. Based on the hydrological and meteorological data of an offshore wind farm in the South China Sea, a sensitivity analysis of the mooring chain fatigue of a semi-submersible floating offshore wind turbine in shallow water was conducted with different environmental loads and mooring chain design parameters. It was found that mooring fatigue is mainly caused by the wave condition within diagonal distribution of the wave dispersion diagram. The mooring chain fatigue calculated using the main monochromatic wave was more conservative than that calculated using the wave scatter diagram with a difference of about 28%. The mooring fatigue calculated using turbulent wind spectrum was greater than that calculated using steady wind. In contrast, waves are the main source of mooring fatigue, and the second-order wave force has an undeniable impact on the mooring fatigue of a semi-submersible floating offshore wind turbine, which accounts for approximately 26% of wave-induced mooring fatigue. In addition, the current-induced mooring fatigue should not be neglected, although its effect is not as large as that caused by the wave or wind loads. An increase in the cross-sectional diameter and the length of a mooring chain, as well as a reasonable clump weight arranged, all contribute to improving the fatigue life of mooring chains in shallow water, which provide useful technical references for the optimization of mooring lines fatigue in shallow-water areas. [ABSTRACT FROM AUTHOR]

Freestanding sculpture can be scored for abstraction using order scales such as the three-rank 'realistic,' 'mixed,' and 'abstract.' Subjective but invaluable, the ranks support ordering but not arithmetic. Numeric limitations can be addressed via information-theoretic metrics that measure collective uncertainty about a sculpture; to this end 60 participants view 25 images of sculpture via an internet questionnaire. They (i) score each depicted object and (ii) type a caption stating what impression the object evokes. Captions for the image are simplified to their English simple subject, sorted into classification categories and counted. A metric converts an image's categories and counts into viewers' uncertainty. The investigation examines four-rank and five-rank scales plus metrics CC (category count), H (entropy) and EC ≡ 2 H (equalized category count). Medians of scores or uncertainties reduce variability as appropriate. Comparisons of scores vs uncertainties show the two generally correlate well. Results also highlight inherent design tradeoffs. Scale/metric pairings affect both ranks (which should be statistically distinct) and rank/uncertainty correlations. Two good combinations are [four-rank; median CC ] and [five-rank; median EC ]. Metric CC is easy to compute and correlates slightly better than EC , although the latter supports five evenly separated ranks. Shifting focus to image scatterplots, the pair [four-rank median score; CC ] yields a very strong correlation. Once uncertainties are linked to ranks to 'calibrate' them, one gets a quantitative sense of the rank order scale. The augmented framework offers the speed and ease of scoring along with valid numeric estimates of uncertainty unavailable from ranks alone. [ABSTRACT FROM AUTHOR]

We have obtained Integral Field Unit (IFU) spectra of 75 Type Ia supernova (SN Ia) host galaxies from the Foundation Supernova Survey to search for correlations between the properties of individual galaxies and SN Hubble residuals. After standard corrections for light-curve width and SN colour have been applied, we find correlations between Hubble residuals and the equivalent width (EW) of the [ |${\rm O\, {\small II}}$| ] |$\lambda \lambda$| 3727, 3729 doublet (2.3 |$\sigma$|⁠), an indicator of the specific star formation rate. When splitting our sample by SN colour, we find no colour dependence impacting the correlation between EW[ |${\rm O\, {\small II}}$| ] and Hubble residual. However, when splitting by colour, we reveal a correlation between the Hubble residuals of blue SNe Ia and the Balmer decrement (2.2 |$\sigma$|⁠), an indicator of dust attenuation. These correlations remain after applying a mass-step correction, suggesting that the mass-step correction does not fully account for the limitations of the colour correction used to standardize SNe Ia. Rather than a mass correction, we apply a correction to SNe from star-forming galaxies based on their measurable EW[ |${\rm O\, {\small II}}$| ]. We find that this correction also removes the host galaxy mass-step, while also greatly reducing the significance of the correlation with the Balmer decrement for blue SNe Ia. We find that correcting for EW[ |${\rm O\, {\small II}}$| ], in addition to or in place of the mass-step, may further reduce the scatter in the Hubble diagram. [ABSTRACT FROM AUTHOR]

This document is a corrigendum for an article titled "Lateral prefrontal cortex thickness is associated with stress but not cognitive fatigue in exhaustion disorder." The corrigendum acknowledges errors in the original article, specifically in the computation of scores, tables, figures, and text. The corrections are provided for Table 1, Table 2, Table 3, Figure 4, and Figure 5. The authors apologize for the errors and state that they do not affect the scientific conclusions of the article. [Extracted from the article]

When field observations are lacking or are difficult to obtain, streamflow intermittency is usually assessed from the frequency of zeroes in streamflow time series, using some (often arbitrary) definition of the zero-count threshold signaling an intermittent flow regime. This research proposes alternative and robust indicators for differentiating between perennial and intermittent flow regimes from discharge time series. We perform inductive hydrologic classification of 136 rivers and streams in Greece and Cyprus at daily and monthly scales, using selected hydrologic signatures. PCA is used to reduce the dimensionality of the input data, followed by classification using hierarchical clustering to group rivers into classes of similar hydrological characteristics. Two superclusters are defined, interpreted as corresponding to perennial and intermittent river regimes respectively. A subset of monthly hydrological signatures explaining most of the variance in the flow record is used with univariate clustering to cluster the data into two groups with maximum homogeneity. The resulting cluster membership is validated by assessing similarity with the clusters from the multivariate hydrologic classifications. Seven signatures are identified (entropy of flow volumes, entropy of flow distribution, CV of mean annual flow, index of flow variability, variability of mean monthly flows, average maximum monthly flow, and 10th percentile of monthly flows) as the most useful in differentiating between perennial and intermittent river regimes; cluster separation is also assessed visually on scatterplots between signature pairs. Overall, the entropy-based signatures outperform the others. Finally, threshold values of the proposed hydrologic signatures are identified for streamflow intermittency assessment. [ABSTRACT FROM AUTHOR]

In addressing the complexity inherent in comparing socioeconomic indicators across diverse countries, a substantial barrier arises from the wide array of distinct calculation approaches. Consequently, a highly pertinent question emerges: how does one compare values when the underlying calculation methodologies are diverse? In this context, we propose a multidisciplinary and heterogeneous approach, employing numerical and visual comparisons to analyze socioeconomic indicators of municipalities in the US, Brazil and other Latin American countries. We identify independent-scale patterns in three stages: initially, by compiling a database sourced from respected institutions in each country and identifying correlations within this dataset; next, by creating graphical representations using a binning methodology for scatter plots of the identified patterns; finally, by transforming the dispersions from the initial stage into graphs using a variant of the Gravitational Clustering Algorithm technique from astrophysics. We find significant relationships, specifically in the logarithmic relationship between Municipal HDI and population size. In some countries, global minima were identified, suggesting the existence of a critical population density at which municipalities above this threshold exhibit a positive and significant correlation with an increase in their HDI. [ABSTRACT FROM AUTHOR]

It is a hot problem in epilepsy research to detect and predict seizures by EEG signals. Clinically, it is generally observed that there are only sudden abnormal signals during the ictal stage, but there is no significant difference in the EEG signal between the interictal and preictal stages. To solve the problem that preictal signals are difficult to recognize clinically, and then effectively improve the recognition efficiency of epileptic seizures, so, in this paper, some nonlinear methods are comprehensively used to extract the hidden information in the EEG signals in different stages, namely, phase space reconstruction (PSR), Poincaré section (PS), synchroextracting transform (SET), and machine learning for EEG signal analysis. First, PSR based on C-C method is used, and the results show that there are different diffuse attractor trajectories of the signals in different stages. Second, the confidence ellipse (CE) is constructed by using the scatter diagram of the corresponding trajectory on PS, and the aspect ratio and area of the ellipse are calculated. The results show that there is an interesting transitional phenomenon in preictal stage. To recognize ictal and preictal signals, time-frequency (TF) spectrums, which are processed by SET, are fed into the convolutional neural network (CNN) classifier. The accuracy of recognizing ictal and preictal signals reaches 99.7% and 93.7%, respectively. To summarize, our results based on nonlinear method provide new research ideas for seizure detection and prediction. NEW & NOTEWORTHY: Our results based on nonlinear method have better practical significance and clinical application value and improved the prediction efficiency of epileptic EEG signals effectively. This work provides direct insight into the application of these biomarkers for seizure detection and prediction. [ABSTRACT FROM AUTHOR]

Quantile-quantile plots in the precise sense of scatterplots showing corresponding quantiles of two variables have long been supported by official command qqplot. That command is generalized here in several ways in a new command, qqplotg. In this article, I explain the major features of qqplotg and give several examples of its use. Themes include the use of quantile-quantile plots to explore the possibilities for working on a transformed scale and the value of plotting difference between quantiles versus mean quantile or plotting position. Various historical and methodological remarks are sprinkled throughout. [ABSTRACT FROM AUTHOR]

The state of Rio Grande do Sul (RS) is the third largest sunflower producer in Brazil. This study verified the existence of linear relationships between grain yield, oil content, and oil yield in sunflower (Helianthus annuus L.) genotypes evaluated in RS. The data on grain yield (GY), oil content (OC), and oil yield (OY) were obtained from 73 sunflower cultivar trials in 21 municipalities in RS from 1999 to 2018. Scatter diagrams were createdto investigate the relationships between the variables GY, OC, and OY. Then, Pearson's linear correlation coefficient (r) was calculated between the variables, and the Student's t-test was performed at a 5% significance level. There is a positive and significant linear relationship between PG and OY. However, no linear relationship was found between grain yield and oil content in sunflower genotypes. Additionally, a positive linear association of high magnitude (r = 0.9672) was observed between grain yield and oil yield, while a low magnitude (r = 0.2245) was found between oil content and oil yield. [ABSTRACT FROM AUTHOR]

Evaluation metrics play a pivotal role in the calibration process of hydrological models, serving as objective functions that directly influence the final values of model parameters and significantly affect users' perceptions of model performance. However, the choice and interpretation of evaluation metrics are subjective; therefore, this study provides a more objective framework for assessing model performance. This paper initially explored the applicability of various commonly used evaluation metrics, providing an overview of their limitations. Following this, we decomposed errors by analysing their physical significance and geometric representation in scatter plots, categorizing them into systematic and unsystematic errors. Through the decomposition and derivation of the Nash–Sutcliffe efficiency (NSE) formula, we established the quantitative relationship among various evaluation metrics. The soil and water assessment tool (SWAT) model was utilized to simulate monthly runoff in the Baishan basin (China), for the period 1994–2017, with NSE serving as the objective function for calibration. Our findings are consistent with previous studies, indicating that the model tends to slightly underestimate high flows while significantly overestimating low flows. Further analysis through error decomposition and the examination of relationships among various evaluation metrics revealed that unsystematic errors were dominant during the spring snowmelt runoff period, while systematic errors prevailed in the dry season. By evaluating the runoff series based on the magnitude of runoff or by categorizing it according to seasons and months, a more stringent assessment of the model's performance was achieved. These findings not only highlight the necessity for careful selection of evaluation metrics but also underscore the significance of our methodological advancements in enhancing hydrological model precision and reliability. [ABSTRACT FROM AUTHOR]

The histogram remains a widely used tool for visualization of the distribution of a continuous variable, despite the disruption of binning the underlying continuity into somewhat arbitrarily sized discrete intervals imposed by the simplicity of its pre-computer origins. Alternatives include three visualizations, namely a smoothed density distribution such as a violin plot, a box plot, and the direct visualization of the individual data values as a one-dimensional scatter plot. To promote ease of use, the plotting function discussed in this work, Plot(x), automatically integrates these three visualizations of a continuous variable x into what is called a VBS plot here, tuning the resulting plot to the sample size and discreteness of the data. This integration complements the information derived from the histogram well and more easily generalizes to a multi-panel presentation at each level of a second categorical variable. [ABSTRACT FROM AUTHOR]

Using embedding methods, compounds with similar properties will be closely located in latent space, and these embedding vectors can be used to find other compounds with similar properties based on the distance between compounds. However, they often require computational resources and programming skills. Here we develop Dr.Emb Appyter, a user‐friendly web‐based chemical compound search platform for drug discovery without any technical barriers. It uses embedding vectors to identify compounds similar to a given query in the embedding space. Dr.Emb Appyter provides various types of embedding methods, such as fingerprinting, SMILES, and transcriptional response‐based methods, and embeds numerous compounds using them. The Faiss‐based search system efficiently finds the closest compounds of query in the library. Additionally, Dr.Emb Appyter offers information on the top compounds; visualizes the results with 3D scatter plots, heatmaps, and UpSet plots; and analyses the results using a drug‐set enrichment analysis. Dr.Emb Appyter is freely available at https://dremb.korea.ac.kr. [ABSTRACT FROM AUTHOR]

This software assistant aims at calculating the dose-response relations of tumors and normal tissues, or clinically assessing already determined values by other researchers. It can also indicate the optimal dose prescription by optimizing the expected treatment outcome. The software is developed solely in python programming language, and it employs PSFL license for its Graphical User Interface (GUI), NUMPY, MATPLOTLIB, and SCIPY libraries. It comprises of two components. The first is the Dose-response relations derivation component, which takes as input the dose volume histograms (DVHs) of patients and their recorded responses regarding a given clinical endpoint to determine the parameters of different tumor control probability (TCP) or normal tissue complication probability (NTCP) models. The second is the Treatment Plan Assessment component, which uses the DVHs of a plan and the dose-response parameters values of the involved tumors and organs at risk (OARs) to calculate their expected responses. Additionally, the overall probabilities of benefit (P B), injury (P I) and complication-free tumor control (P +) are calculated. The software calculates rapidly the corresponding generalized equivalent uniform doses (gEUD) and biologically effective uniform doses ( D ‾ ‾) for the Lyman-Kutcher-Burman (LKB), parallel volume (PV) and relative seriality (RS) models respectively, determining the model parameters. In the Dose-Response Relations Derivation component, the software plots the dose-response curves of the irradiated organ with the relevant confidence internals along with the data of the patients with and without toxicity. It also calculates the odds ratio (OR) and the area under the curve (AUC) of different dose metrics or model parameter values against the individual patient outcomes to determine their discrimination capacity. It also performs a goodness-of-fit evaluation of any model parameter set. The user has the option of viewing plots like Scatter, 3D surfaces, and Bootstrap plots. In the Treatment Plan Assessment part, the software calculates the TCP and NTCP values of the involved tumors and OARs, respectively. Furthermore, it plots the dose-response curves of the TCPs, NTCPs, P B , P I , and P + for a range of prescription doses for different treatment plans. The presented software is ideal for efficiently conducting studies of radiobiological modeling. Furthermore, it is ideal for performing treatment plan assessment, comparison, and optimization studies. [ABSTRACT FROM AUTHOR]

Deep learning models (DLMs), such as recurrent neural networks (RNN), long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), and gated recurrent unit (GRU), are superior for sequential data analysis due to their ability to learn complex patterns. This paper proposes enhancing performance of these models by applying fuzzy c-means (FCM) clustering on sequential data from a nonlinear plant and the stock market. FCM clustering helps to organize the data into clusters based on similarity, which improves the performance of the models. Thus, the proposed fuzzy c-means recurrent neural network (FCM-RNN), fuzzy c-means long short-term memory (FCM-LSTM), fuzzy c-means bidirectional long short-term memory (FCM-Bi-LSTM), and fuzzy c-means gated recurrent unit (FCM-GRU) models showed enhanced prediction results than RNN, LSTM, Bi-LSTM, and GRU models, respectively. This enhancement is validated using performance metrics such as root-mean-square error and mean absolute error and is further illustrated by scatter plots comparing actual versus predicted values for training, validation, and testing data. The experiment results confirm that integrating FCM clustering with DLMs shows the superiority of the proposed models. [ABSTRACT FROM AUTHOR]

The paper describes a method developed for the indirect cuffless estimation of arterial blood pressure (ABP) from two/three-channel photoplethysmography (PPG) signals. It is important when the actual ABPs cannot be measured, e.g., during scanning inside a magnetic resonance imager. The proposed procedure uses heart pulse transmission parameters (HPTPs) extracted from the second derivative PPG signals. The linear regression method was used to calculate the relation between the determined HPTPs and the ABPs measured in parallel by a blood pressure monitor. The ABP values were estimated by the inverse conversion characteristic calculated from these linear relations. Three auxiliary investigations were performed first to find appropriate settings for PPG signal processing. We tested the accuracy of ABP estimation using two small corpora of multi-channel PPG records sensed during our previous experiments. We also analyzed the distribution of the determined HPTP values depending on the hand and gender for the mapping of a mutual relationship of HPTPs and measured ABPs. The final estimation errors were evaluated graphically (by correlation scatter plots and Bland–Altman plots) and numerically (by a correlation coefficient between the measured and estimated ABPs and by enumeration of the relative estimation error). The obtained results achieve acceptable mean values of −2.6/−3.5 mm Hg for systolic/diastolic ABPs. [ABSTRACT FROM AUTHOR]

The document titled "Untitled" is a list of data points related to abdominal aortic aneurysm and thoracic aortic aneurysm. The data includes various analyses such as leave-one-out analyses, funnel plots, MR effect size, and scatter plots. The data is sourced from FinnGen, a genetic research project in Finland. [Extracted from the article]

In decentralized finance (DeFi), stablecoins like DAI are designed to offer a stable value amidst the fluctuating nature of cryptocurrencies. We examine the class of crypto-backed stable derivatives, focusing on mechanisms for price stabilization and exemplified by the well-known stablecoin DAI from MakerDAO. For simplicity, we consider a single-collateral setting. We introduce a belief parameter to the simulation model of DAI in a previous work (DAISIM), reflecting market sentiments about the value and stability of DAI, and show that it better matches the expected behavior when this parameter is set within a particular range of values. Our methods include comparing simulated data with real-world data, focusing on monthly correlations between ETH and DAI prices and scatter plots illustrating the relationship of their price trends over time. We also propose a simple mathematical model of DAI price to explain its stability and dependency on ETH price. Finally, we analyze possible risk factors associated with these stable derivatives to provide valuable insights for stakeholders in the DeFi ecosystem. [ABSTRACT FROM AUTHOR]

Background: Paragonimiasis, primarily caused by Paragonimus westermani and P. skrjabini in China, is a common food-borne parasitic zoonosis. However, the national distribution of Paragonimus spp. infection and its associated environmental determinants remain poorly understood. In this paper, we summarize the infection of P. westermani and P. skrjabini and describe key biogeographical characteristics of the endemic areas in China. Methods: Data on Paragonimus infection in humans and animal hosts were extracted from eight electronic databases, including CNKI, CWFD, Chongqing VIP, SinoMed, Medline, Embase, PubMed, and Web of Science. A random-effects meta-analysis model was used to estimate the pooled prevalence. All survey locations were georeferenced and plotted on China map, and scatter plots were used to illustrate the biogeographical characteristics of regions reporting Paragonimus infection. Results: A total of 28,948 cases of human paragonimiasis have been documented, with 2,401 cases reported after 2010. Among the 11,443 cases with reported ages, 88.05% were children or adolescents. The pooled prevalence of P. skrjabini is 0.45% (95% CI: 0.27–0.66%) in snails, 31.10% (95% CI: 24.77–37.80%) in the second intermediate host, and 20.31% (95% CI: 9.69–33.38%) in animal reservoirs. For P. westermani, the pooled prevalence is 0.06% (95% CI: 0.01–0.13%) in snails, 52.07% (95% CI: 43.56–60.52%) in the second intermediate host, and 21.40% (95% CI: 7.82–38.99%) in animal reservoirs. Paragonimus are primarily distributed in regions with low altitude, high temperature, and high precipitation. In northeastern China, only P. westermani infections have been documented, while in more southern areas, infections of both P. westermani and P. skrjabini have been reported. Conclusions: Paragonimiasis remains prevalent in China, particularly among children and adolescents. Variations exist in the intermediate hosts and geographical distribution of P. westermani and P. skrjabini. Additionally, altitude, temperature, and precipitation may influence the distribution of Paragonimus. Author summary: Paragonimiasis, a foodborne zoonotic parasitic disease caused by lung flukes (Paragonimus spp.), remains a significant neglected public health threat in many Asian countries, including China. Human infection occurs through the ingestion of raw or undercooked freshwater crab or crayfish containing the metacercariae stage. Given the popularity of consuming raw or undercooked freshwater products in many areas of China, understanding the infection status and spatial distribution of Paragonimus spp. in humans and animal hosts is crucial for controlling paragonimiasis. Our study provides a comprehensive summary of the infection levels of the two most important zoonotic Paragonimus species, P. westermani and P. skrjabini, in humans and animal hosts in China, along with a description of the spatial distribution and environmental characteristics of their endemic areas. We observe a wide distribution of Paragonimus infection in China, with a significant prevalence found in freshwater crabs and crayfish. Our findings underscore the importance of avoiding the consumption of raw or undercooked freshwater products to prevent foodborne diseases, including paragonimiasis. [ABSTRACT FROM AUTHOR]

Objective To investigate the causal relationship between body mass index (BMI) and lumbar disc herniation, to provide recommendations for clinical prevention and treatment. Methods Summary data from the Genome-wide Association Study (GWAS) in the UK Biobank of the IEU Open GW AS project (htt ps://gwas.mrcieu.ac.uk/) were used for analysis. Single nucleotide polymorphisms (SNPs) closely related to BMI were selected as instrumental variables, and lumbar disc herniation was the outcome variable. The sample data were extracted from European populations, with the BMI group consisting of 454 884 individuals and 9 851 867 SNPs, and the lumbar disc herniation group consisting of 462 933 individuals and 9 851 867 SNPs. The main analysis methods included inverse -variance weighted (IVW), MR -Egger regression, and weighted median estimator (WME), with the simple mode and weighted mode as auxiliary analyses. Mendelian randomization (MR) analysis was conducted to explore the causal relationship between BMI and lumbar disc herniation. Sensitivity analysis was performed using the leave -one -out method, and the MR -Egger regre ssion intercept test, CochranJ s Q test, funnel plot observation for heterogeneity, symmetry, and scatter plot observation for directional consistency were conducted. The odds ratio (OR) and 95% confidence interval (CI) were used to evaluate the causal relationship between BMI and lumbar disc herniation. Results A total of 419 SNPs associated with BMI (P<5xl0-8,产<0.001) were included as instiumental variables. The analysis using MR-Egger (O2-=1.002, 95%CI: 0.997-1.007), WME (0-=1,003, 95%CI: 1.000--1.006): IVW (O/-=1.004, 95%CI: 1.002-1.006), simple mode (OR=1.006: 95%CI: 0.998-1.014), and weighted mode (0-=1.004, 95%CI: 0.999-1.009) indicated that an increase in BMI was positively associated with lumbar disc herniation. The P values for WME and IVW were 0.030 and 335X10-6, respectively, with a significance level of a =0.05. The P values for MR-Egger, simple mode, and weighted mode were all >0.05, indicating no statistical significance. The consistent direction of effect among the five analysis methods suggested tliat an increase in BMI was associated with a higher risk of lumbar disc herniation. The intercept for MR- Egger regression was 4.2x10 巳 the standard error was 4.6x10 巴 and the P value was 0.364, indicating the absence of pleiotropy in the instrumental variables. A forest plot generated using the leave-one-out method showed no obvious bias, and the funnel plot exhibited overall symmetry. Conclusion There is a causal relationship between BMI and lumbar disc herniation, and an increase in BMI may be an important factor in triggering lumbar disc herniation. [ABSTRACT FROM AUTHOR]

Background and Aim: The study aims to introduce a novel indicator, effective withdrawal time (WTS), which measures the time spent actively searching for suspicious lesions during colonoscopy and to compare WTS and the conventional withdrawal time (WT). Methods: Colonoscopy video data from 472 patients across two hospitals were retrospectively analyzed. WTS was computed through a combination of artificial intelligence (AI) and manual verification. The results obtained through WTS were compared with those generated by the AI system. Patients were categorized into four groups based on the presence of polyps and whether resections or biopsies were performed. Bland Altman plots were utilized to compare AI‐computed WTS with manually verified WTS. Scatterplots were used to illustrate WTS within the four groups, among different hospitals, and across various physicians. A parallel box plot was employed to depict the proportions of WTS relative to WT within each of the four groups. Results: The study included 472 patients, with a median age of 55 years, and 57.8% were male. A significant correlation with manually verified WTS (r = 0.918) was observed in AI‐computed WTS. Significant differences in WTS/WT among the four groups were revealed by the parallel box plot (P < 0.001). The group with no detected polyps had the highest WTS/WT, with a median of 0.69 (interquartile range: 0.40, 0.97). WTS patterns were found to be varied between the two hospitals and among senior and junior physicians. Conclusions: A promising alternative to traditional WT for quality control and training assessment in colonoscopy is offered by AI‐assisted computation of WTS. [ABSTRACT FROM AUTHOR]

The Gaia mission has provided highly accurate observations that have significantly reduced the scatter in the colour–magnitude diagrams of open clusters. As a result of the improved isochrone sequence of the open cluster M67, we have created new stellar models that avoid commonly used simplifications in 1D stellar modelling, such as mass-independent core overshooting and a constant mixing length parameter. This has enabled us to deliver a precise isochrone specifically designed for M67, available for download. We follow a commonly used qualitative approach to adjust the input physics to match the well-defined colour–magnitude sequence, and we test the model-predicted masses against a known eclipsing binary system at the main sequence turnoff of the cluster. Despite using improvements in photometry and stellar physics we cannot match the masses of both binary components with the same theoretical isochrone. A |$\chi ^{2}$| -based isochrone fitting approach using our preferred input physics results in a cluster age of |$3.95^{+ 0.16}_{- 0.15}$| Gyr. [ABSTRACT FROM AUTHOR]

We consider situations in which the clustering of some multivariate data is desired, which establishes an ordering of the clusters with respect to an underlying latent variable. As our motivating example for a situation where such a technique is desirable, we consider scatterplots of traffic flow and speed, where a pattern of consecutive clusters can be thought to be linked by a latent variable, which is interpretable as traffic density. We focus on latent structures of linear or quadratic shapes, and present an estimation methodology based on expectation–maximization, which estimates both the latent subspace and the clusters along it. The directed clustering approach is summarized in two algorithms and applied to the traffic example outlined. Connections to related methodology, including principal curves, are briefly drawn. [ABSTRACT FROM AUTHOR]

While sensitivity analysis improves the transparency and reliability of mathematical models, its uptake by modelers is still scarce. This is partially explained by its technical requirements, which may be hard to understand and implement by the nonspecialist. Here we propose a sensitivity analysis approach based on the concept of discrepancy that is as easy to understand as the visual inspection of input-output scatterplots. First, we show that some discrepancy measures are able to rank the most influential parameters of a model almost as accurately as the variance-based total sensitivity index. We then introduce an ersatz-discrepancy whose performance as a sensitivity measure is similar that of the best-performing discrepancy algorithms, is simple to implement, easier to interpret and orders of magnitude faster. [ABSTRACT FROM AUTHOR]

Background: Chondrosarcoma (CHS), a bone malignancy, poses a significant challenge due to its heterogeneous nature and resistance to conventional treatments. There is a clear need for advanced prognostic instruments that can integrate multiple prognostic factors to deliver personalized survival predictions for individual patients. This study aimed to develop a novel prediction tool based on recursive partitioning analysis (RPA) to improve the estimation of overall survival for patients with CHS. Methods: Data from the Surveillance, Epidemiology, and End Results (SEER) database were analyzed, including demographic, clinical, and treatment details of patients diagnosed between 2000 and 2018. Using C5.0 algorithm, decision trees were created to predict survival probabilities at 12, 24, 60, and 120 months. The performance of the models was assessed through confusion scatter plot, accuracy rate, receiver operator characteristic (ROC) curve, and area under ROC curve (AUC). Results: The study identified tumor histology, surgery, age, visceral (brain/liver/lung) metastasis, chemotherapy, tumor grade, and sex as critical predictors. Decision trees revealed distinct patterns for survival prediction at each time point. The models showed high accuracy (82.40%–89.09% in training group, and 82.16%–88.74% in test group) and discriminatory power (AUC: 0.806–0.894 in training group, and 0.808–0.882 in test group) in both training and testing datasets. An interactive web‐based shiny APP (URL: https://yangxg1209.shinyapps.io/chondrosarcoma%5fsurvival%5fprediction/) was developed, simplifying the survival prediction process for clinicians. Conclusions: This study successfully employed RPA to develop a user‐friendly tool for personalized survival predictions in CHS. The decision tree models demonstrated robust predictive capabilities, with the interactive application facilitating clinical decision‐making. Future prospective studies are recommended to validate these findings and further refine the predictive model. [ABSTRACT FROM AUTHOR]

Background and Objectives: This study aims to analyze survival in peritoneal and hemodialysis patients using chest radiography and biochemical parameters, determine common dialysis etiologies and causes of death, reveal prognostic factors, and contribute to clinical practice. Materials and Methods: A retrospective cross-sectional study was conducted with data from 33 peritoneal dialysis and 37 hemodialysis patients collected between October 2018 and February 2020. Survival and mortality were retrospectively tracked over 70 months (October 2018–June 2024). Chest X-ray measurements (cardiothoracic index, pulmonary vascular pedicle width, right pulmonary artery diameter, diaphragmatic height) and biochemical parameters (urea, albumin, creatinine, parathormone, ferritin, hemoglobin, arterial blood gas, potassium) were analyzed for their impact on survival. Statistical analyses included descriptive statistics, chi-square test, Fisher's exact test, Bayesian analysis, McNemar test, Kaplan–Meier survival analysis, Cox regression, Bayesian correlation test, linear regression analysis (scatter plot), and ROC analysis. SPSS 20.0 was used for data analysis, with p < 0.05 considered statistically significant. Results: Hypertension, type 2 diabetes, and urogenital disorders were the main dialysis etiologies. Peritonitis (38.5%) and cardiovascular diseases (47.4%) were the leading causes of death in peritoneal and hemodialysis patients, respectively. Significant chest X-ray differences included pulmonary vascular pedicle width and pulmonary artery diameter in hemodialysis and diaphragm height in peritoneal dialysis. Kaplan–Meier showed no survival difference between methods. Cox regression identified age, intact parathormone levels, iPTH/PVPW ratio, and clinical status as survival and mortality factors. The iPTH/PVPW ratio cut-off for mortality prediction was ≤6.8. Conclusions: Age, intact parathormone levels, pulmonary vascular pedicle width, and clinical status significantly impact survival in dialysis patients. Management of hypertension and diabetes, management and follow-up of urogenital disorders, infection control, patient education, and regular cardiovascular check-ups may improve survival rates. Additionally, the iPTH/PVPW ratio can predict mortality risk. [ABSTRACT FROM AUTHOR]

A new method for the construction of both symmetric and asymmetric, absolutely continuous bivariate copulas is introduced. This research was inspired by the well-known cross ratio (CR), studied by Clayton (1978), which is a local dependence measure describing the association between the components of a bivariate random vector, as well as by an alternative risk ratio (RR) proposed recently by Abrams et al. (2022), which unravels the local relation between the two components of the vector. The practical meaning of RR is illustrated in two real data sets by the authors. In this article, various properties of the proposed parametric families of copulas are studied, for example quadrant dependence ordering, tail dependence coefficients and several copula-based measures of dependence. Some relationships of the copulas to RR are discussed and illustrative scatterplots are presented. [ABSTRACT FROM AUTHOR]

Predicting the groundwater level (GWL) is essential in water resource management and irrigation planning in arid and semi-arid areas. In this study, an artificial neural network (ANN) was combined with newly developed wild horse optimizer (WHO) and egret swarm optimization algorithm (ESOA) techniques to predict a one month lead-time GWL in the Tabriz plain of Iran. For the prediction of the GWL, the number of months and years, the one month lag of average temperature, evaporation, precipitation, and GWL were used as inputs. Model performances were compared using root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2), and relative strength ratio (RSR) statistical indicators and scatter diagrams, time series graph, violin graph, and Taylor diagram. As a result of the analysis, the most successful estimation results were obtained with the input combinations of year, month, average temperature, evaporation, precipitation, and GWL (t − 1) for the prediction of the one month lead-time GWL. According to the results of evaluation indicators in the testing phase, ANN with (R2 = 0.871, RMSE = 0.306 (m), NSE = 0.832, and RSR = 0.410), WHO–ANN (R2 = 0.932, RMSE = 0.200 (m), NSE = 0.929, and RSR = 0.267), and ESOA–ANN (R2 = 0.952, RMSE = 0.164 (m), NSE = 0.951, and RSR = 0.220). In addition, it was revealed that the ESOA–ANN hybrid model showed higher prediction success than the WHO–ANN and standalone ANN models. The study outputs contribute to decision-makers and planners for controlling land subsidence, assessing GWL and aquifer compaction, irrigation planning, and effective management of water resources. [ABSTRACT FROM AUTHOR]

Background: Male sexual dysfunction (MSD) is a common occurrence in diabetic patients, which is not routinely assessed in clinical settings. Aim: The aim of the study was to assess the prevalence of MSD and hypogonadism in young type 2 diabetes mellitus (T2DM) patients aged between 20 and 40 years and its association with duration, glycaemic control and complications of diabetes. Settings and Design: This is a cross-sectional study involving 70 men with T2DM of 20-40 years of age visiting the endocrinology department in a tertiary care centre. This study was conducted from November 2021 to March 2023. Materials and Methods: Seventy subjects satisfying inclusion criteria were included in the study and assessed using the International Index of Erectile Function-15 questionnaire, intravaginal ejaculation latency time, androgen deficiency in ageing male questionnaire, hormonal parameters (Luteinising Hormone, Follicle stimulating hormone, Testosterone and sex hormone-binding globulin) and penile Doppler. Statistical Analysis Used: The statistical analysis was done using SPSS version 21. A P < 0.05 is considered statistically significant. Data were checked for normality using Kolmogorov-Smirnov and Shapiro-Wilk tests. For determining statistical significance between continuous variables with non-normal data, the Mann-Whitney U test and Kruskal-Wallis tests were used. Pie charts, bar diagrams and scatter plots were used for descriptive statistics. Frequency, percentage, means, median, range and standard deviation were used for all quantitative data. The Chi-square test was used to find a significant association between categorical variables. Results: Sixty per cent of men had at least one form of MSD. Hypoactive sexual desire disorder (HSDD) - 44.28% was the most common MSD, followed by erectile dysfunction (ED) - 42.85% and ejaculatory disorders - 18.57%. Penile Doppler was done in 15 patients, of whom 9 patients had normal Doppler, venous incompetence was present in 3, followed by mixed (arterial and venous) in 2 and arterial insufficiency in 1 subject. Hypogonadism was present in 27.14% of men and secondary hypogonadism was the most common cause (84.21%). There was a significant association between neuropathy and ED and between hypogonadism and HSDD. There was also a significant association between fasting plasma glucose, post-prandial plasma glucose and MSD. Conclusion: MSD is common in young T2DM men, which should be given due importance in clinical practice to improve their quality of life. Glycaemic control is of utmost importance in these men. [ABSTRACT FROM AUTHOR]

The increase in the foreign population in municipalities with smaller populations of the Region of Valencia occurred after an intense migratory flow prior to the real estate and financial crisis. This process harbored the hope of stopping the demographic regression suffered in rural areas. After a period of population loss, in recent years a demographic recovery has been taking place in certain medium-sized and small municipalities. The objective of this article is to establish an analogy between the increase in the population of foreigners and economic diversification in these destination municipalities to promote population growth. Based on the use of a database of population and employment by sectors of activity at the municipal level, indicators have been found such as evolutionary rates, the Gibbs-Martin Index and different scatter plots to achieve the objectives established. The research shows that a further increase in the foreign population is taking place in medium-sized municipalities of between 2,000 and 10,000 inhabitants and that, unlike in the period prior to the crisis, their distribution coincides with the diversification of economic activity and the local labor market and not productive specialization. [ABSTRACT FROM AUTHOR]

In this unit, students explore the phylogeography of three-toed sloths (Bradypus pygmaeus and Bradypus variegatus) endemic to the Bocas del Toro islands off the Caribbean coast of Panama. After learning about the geologic history of this region, students measure model sloth skulls from each of the islands and compile that data into scatterplots. Students then analyze the scatterplots to make conclusions about which geographic factors best account for the regional variance in sloth body size. The sequence of activities was designed for high school life science classrooms using the Next Generation Science Standards (NGSS) framework and teaches the disciplinary core idea LS3.B Variation of Traits. This lesson was taught in an integrated co-teaching classroom, a special education classroom, and a ninth-grade general biology classroom. Students successfully answered the overarching research question and gained a deeper knowledge about natural selection and evolution in the process. [ABSTRACT FROM AUTHOR]

Rice plays an essential role in ensuring the food security of Indonesia. Hence, rice (paddy) field monitoring using synthetic aperture radar (SAR) satellite data is critical, particularly in tropical regions. This study presents a new algorithm to detect paddy fields in Subang, West Java, using Sentinel-1 SAR with a 12-day revisit acquisition. Three temporal phenological features of paddy growth were used, namely, the minimum and maximum backscatter, as well as their differences. Paddy fields were discriminated from other land covers using a simple thresholding algorithm based on their specific pattern of low minimum, high maximum, and high difference of vertical transmithorizontal receive polarization (VH) backscatter on a 2-dimensional (2D) scatter plot. The results showed that the proposed algorithm had an accuracy of 94.02%, comparable to that of the random forest algorithm and other studies using 3-dimensional (3D) parameters. The proposed algorithm reduces the dimensionality from 3D to 2D and is practical for mapping and monitoring paddy fields. In this context, the application of the algorithm to the surrounding regions of Karawang, Indramayu, and Bekasi achieved high accuracy rates of 93.37%, 92.87%, and 88.13%, respectively. [ABSTRACT FROM AUTHOR]

Effective partnership of education and business is a catalyst for innovation transfer, startup ecosystem development and sustainable economic growth. The research aims to identify the impact of university-industry R&D collaboration on startups performance, taking into account existing knots and gap intervals. The study sample was formed for 100 world countries, represented in the Global Startup Ecosystem Index rating. The formed base includes data of the Universityindustry R&D collaboration indicator within the Global Innovation Index 2023 by WIPO and data of the Global Startup Ecosystem Index 2023 by StartupBlink. In the first stage, the procedures for checking the significance of investigated indicators were applied, including descriptive statistics, checking the distribution law and regression analysis. The second stage covers the construction of medianspline plots, scatter plots and spline modelling using the STATA 18 program complex. The range of values of the University-industry R&D collaboration index was determined on the condition of reaching which the maximum statistically significant value of the indicator of startup productivity is predicted. The obtained results confirm the importance of an entrepreneurial university concept for innovation transfer and their commercialization and provide a view of the targeted value of the University-industry R&D collaboration on average at the world level. [ABSTRACT FROM AUTHOR]

In this study, due to their flexibility in forecasting, the capabilities of three regression-based machine learning models were explored, specifically random forest regression (RFr), generalized regression neural network (GRNN), and support vector regression (SVR). The above models were assessed for their suitability in modeling daily reference evapotranspiration (ETo), based only on temperature data (Tmin, Tmax, Tmean), by comparing their daily ETo results with those estimated by the conventional FAO 56 PM model, which requires a broad range of data that may not be available or may not be of reasonable quality. The RFr, GRNN, and SVR models were subjected to performance evaluation by using statistical criteria and scatter plots. Following the implementation of the ETo models' comparisons, it was observed that all regression-based machine learning models possess the capability to accurately estimate daily ETo based only on temperature data requirements. In particular, the RFr model outperformed the others, achieving the highest R value of 0.9924, while the SVR and GRNN models had R values of 0.9598 and 0.9576, respectively. Additionally, the RFr model recorded the lowest values in all error metrics. Once these regression-based machine learning models have been successfully developed, they will have the potential to serve as effective alternatives for estimating daily ETo, under current and climate change conditions, when temperature data are available. This information is crucial for effective water resources management and especially for predicting agricultural production in the context of climate change. [ABSTRACT FROM AUTHOR]

Effectively managing water resources in karst systems requires a thorough understanding of their general conduit network along with their seasonal dynamics. Their investigation has involved well construction or several advanced natural tracer data, most of which are not always available. Hence, this work showcases a pragmatic approach that makes use of basic hydrochemical variables of springs with coarse temporal resolution in characterising a karst system. In this study's example, physicochemical variables like major ion concentrations/ratios, Electrical Conductivity (EC), pH and water temperature (Tw) were measured on 20-day basis for a hydrological year at the Louros Catchment, Greece. We further performed the frequency distribution and variation analysis of EC and Tw, principal component analysis (PCA), scatter plots of carbonate ions vs sulphate and hydrochemographs to determine relevant hydrochemical processes and hydrogeological features. PCA and the scatter plots showed that the simple-type upper karst level is entirely dominated by carbonate dissolution, whereas the complex-type middle and lower levels also involve gypsum and dolomite dissolution. Presence of mixing between karst units was also detected. EC and Tw analyses revealed the degree of karstification of different units and relative depths of flow systems. Hydrochemographs reflected the seasonality of limestone and gypsum dissolution's contributions linked to the dominant flow type (conduit vs diffuse). This study thus was able to demonstrate the usefulness of such holistic hydrochemical analyses to better understand karst systems. Given their cost-effectiveness, they can be easily applied to any understudied karst system worldwide. [ABSTRACT FROM AUTHOR]

Introduction: There is currently no guidance on how to assess the calibration of multistate models used for risk prediction. We introduce several techniques that can be used to produce calibration plots for the transition probabilities of a multistate model, before assessing their performance in the presence of random and independent censoring through a simulation. Methods: We studied pseudo‐values based on the Aalen‐Johansen estimator, binary logistic regression with inverse probability of censoring weights (BLR‐IPCW), and multinomial logistic regression with inverse probability of censoring weights (MLR‐IPCW). The MLR‐IPCW approach results in a calibration scatter plot, providing extra insight about the calibration. We simulated data with varying levels of censoring and evaluated the ability of each method to estimate the calibration curve for a set of predicted transition probabilities. We also developed evaluated the calibration of a model predicting the incidence of cardiovascular disease, type 2 diabetes and chronic kidney disease among a cohort of patients derived from linked primary and secondary healthcare records. Results: The pseudo‐value, BLR‐IPCW, and MLR‐IPCW approaches give unbiased estimates of the calibration curves under random censoring. These methods remained predominately unbiased in the presence of independent censoring, even if the censoring mechanism was strongly associated with the outcome, with bias concentrated in low‐density regions of predicted transition probability. Conclusions: We recommend implementing either the pseudo‐value or BLR‐IPCW approaches to produce a calibration curve, combined with the MLR‐IPCW approach to produce a calibration scatter plot. The methods have been incorporated into the "calibmsm" R package available on CRAN. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cells (PEMFC) are widely acknowledged as a prospective power source, but durability problems have constrained development. Therefore, a compound prediction framework is proposed in this paper by integrating the locally weighted scatter plot smoothing method (LOESS), uniform information coefficient (UIC), and attention-based stacked generalization model (ASGM) with improved dung beetle optimization (IDBO). Firstly, LOESS is adopted to filter original degraded sequences. Then, UIC is applied to obtain critical information by selecting relevant factors of the processed degraded sequences. Subsequently, the critical information is input into the base models of ASGM, including kernel ridge regression (KRR), extreme learning machine (ELM), and the temporal convolutional network (TCN), to acquire corresponding prediction results. Finally, the prediction results are fused using the meta-model attention-based LSTM of ASGM to obtain future degradation trends (FDT) and the remaining useful life (RUL), in which the attention mechanism is introduced to deduce weight coefficients of the base model prediction results in LSTM. Meanwhile, IDBO based on Levy flight, adaptive mutation, and polynomial mutation strategies are proposed to search for optimal parameters in LSTM. The application of two different datasets and their comparison with five related models shows that the proposed framework is suitable and effective for forecasting the FDT and RUL of PEMFC. [ABSTRACT FROM AUTHOR]

Spray formed by a myriad of secondary droplets generated by the impact of raindrops on a deep-water pool is studied with a laboratory rain facility. Experiments are performed with two rain rates and raindrops fall on the water surface at a nearly constant velocity. The secondary droplets at various heights above the pool's water surface are recorded with a cinematic digital in-line holographic technique that consists of a high-speed camera, a pulsed Nd:YLF laser and associated optics. The experimental results show that in the heat-map scatter plots of radius versus velocity near the water surface of the pool, the droplets are distributed into three regions, corresponding to distinct physical mechanisms of droplet generation. It is found that the diameter distribution of the droplets in the rain field changes with height above the pool's water surfaces. Both numerical simulation and experimental data reveal that the liquid water content, due to the presence of secondary droplets, in the atmospheric surface layer decreases exponentially with increasing height. [ABSTRACT FROM AUTHOR]

Recent advancements in satellite remote sensing have led to increased spatial and temporal resolution of actual evapotranspiration (AET) estimates across scales. Yet, the accuracy of AET products remains unknown for many regions, prompting further investigation to guide selection. This study intercompares five AET products within Ethiopia's Bilate watershed, focusing on the 2009-2018 period. The products assessed include TerraClimate, Food and Agriculture Organization Water Productivity (FAO WaPOR), Moderate Resolution Imaging Spectroradiometer Operational Simplified Surface Energy Balance (ModisSSEBop), and Synthesis of Global AET. Reference evapotranspiration estimated using ground station climate data served as a basis for comparing the Satellite Products (SP). The intercomparison was conducted using descriptive statistics, scatter plots and Pearson's Correlation Coefficient to assess correlation, standard deviation, and root mean square error. Additional error statistics were also considered. Findings reveal higher AET values in the highlands compared to the lowlands of the Bilate watershed. A weak correlation (<0.35) exists between ETo and satellite-derived AET, potentially due to the averaging of AET values across diverse land cover classes, contrasting with point-scale reference measurements. The variance among AET products was varied across seasons and elevation ranges. While the annual patterns of AET were consistent across the products, large discrepancies in magnitude (average AET varies from 25 to 83 mm per month in the lower part) were detected. The ModisSSEBop global and continental products showed minimal mismatches, whereas the Synthesis of Global and FAO WaPOR products displayed slight differences. Notably, the FAO WaPOR's AET estimates showed relatively closer agreement with many products in terms of magnitude and variability of AET. In conclusion, the study highlights significant random and systematic differences between the AET products. The substantial mismatch between the products underscores the necessity for continued research to refine AET product accuracy through improved input dataset and revisiting the algorithms. [ABSTRACT FROM AUTHOR]