Search Results (4,236)

Background/Objectives: Regular follow-up treatment is important for the management of diabetes and to reduce the risk of complications. In this study, we aimed to evaluate the proportion of adult diabetic patients who miss appointments, in addition to the potential correlation between personal characteristics, personality, and the context of family structure and characteristics and missed appointments by adult patients with type 2 diabetes. Methods: This study was a cross-sectional descriptive study. The data were gathered through self-administered questionnaires and the patient medical records of 106 individuals who received healthcare services at the Khon Kaen Province primary care unit. Data were gathered from 1 November 2023 to 28 December 2023. Adjusted odds ratios (aORs) and Chi-Square statistics were used to evaluate the relationships with multivariate analyses via multinomial logistic regression and the Kruskal–Wallis test. Results: The majority of patients in the sample, 39.62%, regularly missed appointments. There was a significant association between occasionally missed appointments and middle adulthood (p-value 0.013) and regular exercise (p-value 0.025). A moderate level of the agreeableness personality trait showed a significant association with missed appointments (p-value 0.042). Conclusions: It is important to have a comprehensive understanding of the patient’s personality and family characteristics to effectively plan their healthcare and provide optimal support for diabetes treatment. Full article

The North–South Seismic Belt is one of the major regions in China where strong earthquakes frequently occur. Predicting the monthly maximum magnitude is of significant importance for proactive seismic hazard defense. This paper uses seismic catalog data from the North–South Seismic Belt since 1970 to calculate and extract multiple seismic parameters. The monthly maximum magnitude is processed using Variational Mode Decomposition (VMD) with sample segmentation to avoid information leakage. The decomposed multiple modal data and seismic parameters together form a new dataset. Based on these datasets, this paper employs four deep learning models and four time windows to predict the monthly maximum magnitude, using prediction accuracy (PA), False Alarm Rate (FAR), and Missed Alarm Rate (MR) as evaluation metrics. It is found that a time window of 12 generally yields better prediction results, with the PA for Ms 5.0–6.0 earthquakes reaching 77.27% and for earthquakes above Ms 6.0 reaching 12.5%. Compared to data not decomposed using VMD, traditional error metrics show only a slight improvement, but the model can better predict short-term trends in magnitude changes. Full article

Under the assumption of missing response data, empirical likelihood inference is studied via composite quantile regression. Firstly, three empirical likelihood ratios of composite quantile regression are given and proved to be asymptotically ${\chi }^2$. Secondly, without an estimation of the asymptotic covariance, confidence intervals are constructed for the regression coefficients. Thirdly, three estimators are presented for the regression parameters to obtain its asymptotic distribution. The finite sample performance is assessed through simulation studies, and the symmetry confidence intervals of the parametric are constructed. Finally, the effectiveness of the proposed methods is illustrated by analyzing a real-world data set. Full article

Preeclampsia is a pregnancy syndrome characterized by complex symptoms which cause maternal and fetal problems and deaths. The aim of this study is to achieve preeclampsia risk prediction and early risk prediction in Xinjiang, China, based on the placental growth factor measured using the SiMoA or Elecsys platform. A novel reliable calibration modeling method and missing data imputing method are proposed, in which different strategies are used to adapt to small samples, training data, test data, independent features, and dependent feature pairs. Multiple machine learning algorithms were applied to train models using various datasets, such as single-platform versus bi-platform data, early pregnancy versus early plus non-early pregnancy data, and real versus real plus augmented data. It was found that a combination of two types of mono-platform data could improve risk prediction performance, and non-early pregnancy data could enhance early risk prediction performance when limited early pregnancy data were available. Additionally, the inclusion of augmented data resulted in achieving a high but unstable performance. The models in this study significantly reduced the incidence of preeclampsia in the region from 7.2% to 2.0%, and the mortality rate was reduced to 0%. Full article

Accurate monitoring of estuarine turbidity patterns is important for maintaining aquatic ecological balance and devising informed estuarine management strategies. This study aimed to enhance the prediction of estuarine turbidity patterns by enhancing the performance of the multilayer perceptron (MLP) network through the introduction of stochastic gradient descent (SGD) and momentum gradient descent (MGD). To achieve this, Sentinel-2 multispectral imagery was used as the base on which spectral radiance properties of estuarine waters were analyzed against field-measured turbidity data. In this case, blue, green, red, red edge, near-infrared and shortwave spectral bands were selected for empirical relationship establishment and model development. Inverse distance weighting (IDW) spatial interpolation was employed to produce raster-based turbidity data of the study area based on field-measured data. The IDW image was subsequently binarized using the bi-level thresholding technique to produce a Boolean image. Prior to empirical model development, the selected spectral bands were calibrated to turbidity using multilayer perceptron neural network trained with the sigmoid activation function with stochastic gradient descent (SGD) optimizer and then with sigmoid activation function with momentum gradient descent optimizer. The Boolean image produced from IDW interpolation was used as the base on which the sigmoid activation function calibrated image pixels to turbidity. Empirical models were developed using selected uncalibrated and calibrated spectral bands. The results from all the selected models generally revealed a stronger relationship of the red spectral channel with measured turbidity than with other selected spectral bands. Among these models, the MLP trained with MGD produced a coefficient of determination (r²) value of 0.92 on the red spectral band, followed by the MLP with MGD on the green spectral band and SGD on the red spectral band, with r² values of 0.75 and 0.72, respectively. The relative error of mean (REM) and r² results revealed accurate turbidity prediction by the sigmoid with MGD compared to other models. Overall, this study demonstrated the prospect of deploying ensemble techniques on Sentinel-2 multispectral bands in spatially constructing missing estuarine turbidity data. Full article

This study presents an improved data-centric approach to short-term water demand forecasting using univariate time series from water reservoir levels. The dataset comprises water level recordings from 21 reservoirs in Eastern Thessaloniki collected over 15 months via a SCADA system provided by the water company EYATH S.A. The methodology involves data preprocessing, anomaly detection, data imputation, and the application of predictive models. Techniques such as the Interquartile Range method and moving standard deviation are employed to identify and handle anomalies. Missing values are imputed using LSTM networks optimized through the Optuna framework. This study emphasizes a data-centric approach in deep learning, focusing on improving data quality before model application, which has proven to enhance prediction accuracy. This strategy is crucial, especially in regions where reservoirs are the primary water source, and demand distribution cannot be solely determined by flow meter readings. LSTM, Random Forest Regressor, ARIMA, and SARIMA models are utilized to extract and analyze water level trends, enabling more accurate future water demand predictions. Results indicate that combining deep learning techniques with traditional statistical models significantly improves the accuracy and reliability of water demand predictions, providing a robust framework for optimizing water resource management. Full article

Wastewater treatment plants (WWTPs) connected to combined sewer systems must cope with high flows during wet-weather conditions, often leading to bypass and thus pollution of water bodies. Radar rainfall forecasts coupled with a rainfall-runoff model provides flow and volume forecasts that can be used for deciding when to switch from normal to wet-weather operation, which temporarily allows for higher inflow. However, forecasts are by definition uncertain and may lead to potential mismanagement, e.g., false alarms and misses. Our study focused on two years of operational data from the Damhuså sewer catchment and WWTP. We used the Relative Economic Value (REV) framework to optimize the control parameters of a baseline control strategy (thresholds on flow measurements and radar flow prognosis) and to test new control strategies based on volume instead of flow thresholds. We investigated two situations with different objective functions, considering higher negative impact from misses than false alarms and vice versa, and obtained in both cases a reduction of the rate of false alarms, higher flow thresholds and lower bypass compared to the baseline control. We also assess a new control strategy that employs thresholds of predicted accumulated volume instead of predicted flow and achieved even better results. Full article

Background: This review aims to explore the role of physiotherapy in early and traditional palliative care (PC) for oncology patients, focusing on its impact on six patient-reported outcomes (PROMs), namely fatigue, pain, cachexia, quality of life (QoL), physical functioning (PHF), and psychosocial functioning (PSF). The purpose is to assess the effectiveness of various physiotherapy interventions and identify gaps in the current research to understand their potential benefits in PC better. Methods: A systematic literature search was conducted across PubMed, Embase, and Web of Science, concluding on 21 December 2023. Two independent reviewers screened the articles for inclusion. The Cochrane Risk of Bias Tool 2 was employed to assess the risk of bias, while the GRADE approach was used to evaluate the certainty of the evidence. Results: Nine randomized controlled trials (RCTs) were included, with most showing a high risk of bias, particularly in outcome measurement and missing data. Cognitive behavioral therapy (CBT) was the only intervention that significantly reduced fatigue, enhanced PHF, and improved QoL and emotional functioning. Graded exercise therapy (GET) did not yield significant results. Combined interventions, such as education with problem-solving or nutritional counseling with physical activity, showed no significant effects. Massage significantly improved QoL and reduced pain, while physical application therapies were effective in pain reduction. Mindful breathing exercises (MBE) improved QoL but had a non-significant impact on appetite. The overall certainty of the evidence was low. Conclusions: Physiotherapy can positively influence PROMs in oncology PC; however, the low quality and high risk of bias in existing studies highlight the need for more rigorous research to confirm these findings and guide clinical practice. Full article

Ship contour extraction is vital for extracting the geometric features of ships, providing comprehensive information essential for ship recognition. The main factors affecting the contour extraction performance are speckle noise and amplitude inhomogeneity, which can lead to over-segmentation and missed detection of ship edges. Polarimetric synthetic aperture radar (PolSAR) images contain rich target scattering information. Under different transmitting and receiving polarization, the amplitude and phase of pixels can be different, which provides the potential to meet the uniform requirement. This paper proposes a novel ship contour extraction framework from PolSAR images based on polarization modulation. Firstly, the image is partitioned into the foreground and background using a super-pixel unsupervised clustering approach. Subsequently, an optimization criterion for target amplitude modulation to achieve uniformity is designed. Finally, the ship’s contour is extracted from the optimized image using an edge-detection operator and an adaptive edge extraction algorithm. Based on the contour, the geometric features of ships are extracted. Moreover, a PolSAR ship contour extraction dataset is established using Gaofen-3 PolSAR images, combined with expert knowledge and automatic identification system (AIS) data. With this dataset, we compare the accuracy of contour extraction and geometric features with state-of-the-art methods. The average errors of extracted length and width are reduced to 20.09 m and 8.96 m. The results demonstrate that the proposed method performs well in both accuracy and precision. Full article

Background: Training of machine learning algorithms on dish images collected in other countries requires possible sources of systematic discrepancies, including country-specific food composition databases (FCDBs), to be tackled. The US Nutrition5k project provides for ~5000 dish images and related dish- and ingredient-level information on mass, energy, and macronutrients from the US FCDB. The aim of this study is to (1) identify challenges/solutions in linking the nutritional composition of Italian foods with food images from Nutrition5k and (2) assess potential differences in nutrient content estimated across the Italian and US FCDBs and their determinants. Methods: After food matching, expert data curation, and handling of missing values, dish-level ingredients from Nutrition5k were integrated with the Italian-FCDB-specific nutritional composition (86 components); dish-specific nutrient content was calculated by summing the corresponding ingredient-specific nutritional values. Measures of agreement/difference were calculated between Italian- and US-FCDB-specific content of energy and macronutrients. Potential determinants of identified differences were investigated with multiple robust regression models. Results: Dishes showed a median mass of 145 g and included three ingredients in median. Energy, proteins, fats, and carbohydrates showed moderate-to-strong agreement between Italian- and US-FCDB-specific content; carbohydrates showed the worst performance, with the Italian FCDB providing smaller median values (median raw difference between the Italian and US FCDBs: −2.10 g). Regression models on dishes suggested a role for mass, number of ingredients, and presence of recreated recipes, alone or jointly with differential use of raw/cooked ingredients across the two FCDBs. Conclusions: In the era of machine learning approaches for food image recognition, manual data curation in the alignment of FCDBs is worth the effort. Full article

Misleading or unnecessary data can have out-sized impacts on the health or accuracy of Machine Learning (ML) models. We present a Bayesian sequential selection method, akin to Bayesian experimental design, that identifies critically important information within a dataset while ignoring data that are either misleading or bring unnecessary complexity to the surrogate model of choice. Our method improves sample-wise error convergence and eliminates instances where more data lead to worse performance and instabilities of the surrogate model, often termed sample-wise “double descent”. We find these instabilities are a result of the complexity of the underlying map and are linked to extreme events and heavy tails. Our approach has two key features. First, the selection algorithm dynamically couples the chosen model and data. Data is chosen based on its merits towards improving the selected model, rather than being compared strictly against other data. Second, a natural convergence of the method removes the need for dividing the data into training, testing, and validation sets. Instead, the selection metric inherently assesses testing and validation error through global statistics of the model. This ensures that key information is never wasted in testing or validation. The method is applied using both Gaussian process regression and deep neural network surrogate models. Full article

Background and aims: Acute uncomplicated diverticulitis (UD) of the left colon is common and mostly benign. Due to controversy over the definition of UD and the lack of adequate follow-up in most studies, good quality data to predict long-term outcomes after a first episode of UD are missing. The aim of this study was to assess the long-term risk for adverse outcomes after a first episode of UD. Methods: All consecutive patients with a CT-scan-documented first episode of acute UD (staged “uncomplicated” according to ESCP guidelines and/or modified Hinchey stages 0-1a, and/or CDD 1-2a) between January 2010 and June 2013 were included in the study. CT scans and clinical records were retrospectively reviewed. The primary endpoint was overall recurrence; the secondary endpoint was surgery for diverticular disease. Results: One hundred and five patients were included in the study with a median follow-up of 116.4 (4.9–154.7) months. Of these, 51 (48.5%) patients had a recurrence, 11 (10.4%) had 4 or more episodes. Twenty-one (20%) patients underwent sigmoidectomy, all in an elective setting, mostly due to multiple recurrent episodes. Male gender was the only independent risk factor for surgery (OR (95%CI): 0.301 (0.109–0.834), p = 0.021). Classification systems did not predict recurrence, but stage CDD 1a was protective for surgery (OR (95%CI): 0.201 (0.042–0.957), p = 0.044). Conclusions: After a decade of follow-up, almost half the patients experienced at least one recurrent episode after UD, higher than previously thought. None of those patients required emergency surgery, but one in five patients, mostly men, underwent elective sigmoidectomy for multiple recurrent episodes. Full article

Identifying surface sites with significant astrobiological potential on Mars requires a comprehensive understanding of past geological processes and conditions there, including the shallow subsurface region. Numerical modelling could distinguish between regions dominated by erosion and those characterized by sediment accumulation in ancient wet environments. The target area of Jezero Crater is relatively well explored and thus is an ideal site to evaluate model calculations; however, important works are still missing on expectations related to its shallow subsurface . In this work, the best available approaches were followed, and only surface morphology was considered (supposedly formed by the last fluvial episode). The shallow subsurface became an important target recently, and this model could provide new inputs in this area. Erosion–accumulation models are suitable for terrestrial surface features, but few have been applied to Mars. This work addresses this challenge using the SIMWE (SIMulated Water Erosion) model on the Jezero Crater delta, the landing site of the Perseverance rover. For calculations, the average grain size according to the THEMIS TI data was applied to the target area. The flow depth varied between 1.89 and 34.74 m (average of 12.66 m). The water-filled channel width ranged from 35.3 to 341.42 m. A flow velocity of 0.008–11.6 m/s, a maximum erosion rate of 5.98 g/m²/h, and a deposition 4.07 g/m²/h were estimated. These calculated values are close to the range of estimations from other authors assuming precipitation of 1–20 mm/h and discharges of 60–400 m³/s. The model was able to distinguish between erosion- and accumulation-dominated areas about 1 m above Jezero Crater’s delta that are not visible from above. This model helps to identify the accumulation-dominated areas with the finest grain size with good preservation capability for the shallow but invisible subsurface. Full article

The safety of lunar landing sites directly impacts the success of lunar exploration missions. This study develops a data-driven predictive model based on machine learning, focusing on engineering safety to assess the suitability of lunar landing sites and provide insights into key factors and feature representations. Six critical engineering factors were selected as constraints for evaluation: slope, elevation, roughness, hillshade, optical maturity, and rock abundance. The XGBoost model was employed to simulate and predict the characteristics of landing areas and Bayesian optimization was used to fine-tune the model’s key hyperparameters, enhancing its predictive performance. The results demonstrate that this method effectively extracts relevant features from multi-source remote sensing data and quantifies the suitability of landing zones, achieving an accuracy of 96% in identifying landing sites (at a resolution of 0.1° × 0.1°), with AUC values exceeding 95%. Notably, slope was recognized as the most critical factor affecting safety. Compared to assessment processes based on Convolutional Neural Networks (CNNs) and Random Forest (RF) models, XGBoost showed superior performance in handling missing values and evaluating feature importance accuracy. The findings suggest that the BO-XGBoost model shows notable classification performance in evaluating the suitability of lunar landing sites, which may provide valuable support for future landing missions and contribute to optimizing lunar exploration efforts. Full article

Background and Objectives: This study aimed to investigate the association between insulin resistance, as measured by the triglyceride–glucose index (TyG index), and hearing impairment in middle-aged women in Korea. Materials and Methods: This cross-sectional survey utilized data from the Korea National Health and Nutrition Examination Survey (KNHANES) IV (2007–2009), specifically from the period after July 21, 2009, when hearing test results became available, and from the KNHANES V (2010–2012). This study was conducted on 5416 women aged 40 to 69 who had completed both the health examination survey and audiometric tests, excluding those with missing data on menopausal status and the use of hormone replacement therapy. Results: In the study group, the prevalence of high-frequency hearing loss according to the TyG index was significantly higher in the mild hearing loss group (OR = 1.29; 95% CI: 1.12, 1.49, p < 0.001) and the moderate hearing loss group (OR = 1.27; 95% CI: 1.09, 1.48, p = 0.002). Conversely, the prevalence of low-frequency hearing loss did not show a significant difference in either the mild hearing loss group (OR = 1.17; 95% CI: 0.99, 1.37, p = 0.065) and the moderate hearing loss group (OR = 1.13; 95% CI: 0.94, 1.35, p = 0.199) Conclusions: Since diabetes can induce hearing impairment in women, it is recommended that women with a high TyG index undergo early hearing tests Full article