Distribution Of Characteristics Research Articles

Characteristics of rolling interface lubrication considering contact surface textures in mixed lubrication

This study comprehensively utilizes the mixed lubrication theory and rolling deformation theory to establish a finite element stiffness contact model that considers the surface texture characteristics of the rolling interface. The dynamic explicit finite element analysis is used to obtain the deformed solid, and the turbulent equation is used to control the fluid boundary conditions. The bidirectional Fluid-Structure Interacton (FSI) ansysis is used to study the influence of surface roughness, surface textures, and rolling parameters such as rolling speed on the distribution of oil film pressure and contact characteristics during the rolling process. The results confirm that transverse texture reduces the friction coefficient and dynamic pressure by enhancing the trapping effect of rough peaks and facilitating the entrainment of the fluid film. Under the same textural conditions, the contact area decreases with increasing roughness while the friction coefficient increases. As the rolling speed increases, the rolling pressure decreases to a certain extent, and the contact area ratio will decrease; Lubricants with higher viscosity can effectively reduce the friction and rolling force at the rolling interface. We believe that the insights obtained from the numerical simulations provided in this reaserch will lay a theoretical foundation for subsequent research on plate shape control.

Study of Distribution of Free Flow Speeds on Urban Road Sections Depending on Their Functional Purpose and One-Way Traffic—Evidence from Kharkiv (Ukraine)

Data on the distribution of the free flow speed (FFS) of cars are used to solve a wide range of tasks in the field of road transport, starting from road design and ending with the development of traffic modeling and simulation programs. The purpose of this study was to obtain the distribution of vehicle speeds on typical sections of the city road network, characterized by the presence of one-way traffic. The data were obtained by field observations using a portable radar. As a result, statistical characteristics and speed distribution laws for four sections of streets in the city of Kharkiv were analyzed. It was shown that the characteristics of FFS distributions differ depending on the functional class of the streets. Average FFS values on main street segments were on average 19 km/h higher. The one-way traffic has less impact on the FFS distribution, especially for arterial streets. The characteristics of FFS distributions differ depending on the type and functional class of streets; they can be described with sufficient accuracy by typical distribution laws, such as Normal, Log-normal, Gamma, and Chi-square. The results of this study can be useful for traffic modeling problems.

CFD analysis of turbulent flow characteristics of charge inside the combustion chamber of a dual–fuel homogeneous charge compression ignition engine under varying swirl ratios

This paper discusses the effects of the swirl ratio on the distribution of turbulent kinetic energy (TKE), combustion and emissions characteristics of a dual-fuel HCCI (Homogeneous charge compression ignition) engine fed with n-dodecane (premixed) and ethanol (injection). The performance of the Internal combustion (IC) engine is investigated using STAR-CD. ECFM-3Z (Extended Coherent Flame Combustion Model-3Zones) model is considered for the computational fluid dynamics (CFD) simulations. This study also includes the influence of swirl ratio on pressure and temperature of the combustion and hence the NOx (Nitrogen oxides), soot and CO2 (Carbon dioxide) emissions. The pressure, temperature and emissions of the combustion have been dropped significantly by increasing the swirl ratio. There is an increase in the distribution of air-fuel and hence the corresponding combustion flame by increasing the swirl ratio. It is observed that the reduced emissions of the combustion were achieved in the case of swirl ratio 3 and the values of the NOx emission, soot and CO2 emission are 0.0024 g/kg fuel, 0.0022 g/kg fuel and 5.12 g/kg fuel respectively. Eventually, the paper deals with the significance of the swirl ratio on NOx and CO2 emissions with limited loss of the piston work.

Computer Vision Technology for Short Fiber Segmentation and Measurement in Scanning Electron Microscopy Images

Computer vision technology for the automatic recognition and geometric characterization of carbon and glass fibers in scanning electron microscopy images is proposed. The proposed pipeline, combining the SAM model and DeepLabV3+, provides the generalizability and accuracy of the foundational SAM model and the ability to quickly train on a small amount of data via the DeepLabV3+ model. The pipeline was trained several times more rapidly with lower requirements for computing resources than fine-tuning the SAM model, with comparable inference time. On the basis of the pipeline, an end-to-end technology for processing images of electron microscopic fibers was developed, the input of which is images with metadata and the output of which is statistics on the distribution of the geometric characteristics of the fibers. This innovation is of great practical importance for modeling the physical characteristics of materials. This paper proposes a few-shot training procedure for the DeepLabV3+/SAM pipeline, combining the training of the DeepLabV3+ model weights and the SAM model parameters. It allows effective training of the pipeline using only 37 real labeled images. The pipeline was then adapted to a new type of fiber and background using 15 additional real labeled images. This article also proposes a method for generating synthetic data for training neural network models, which improves the quality of segmentation by the IoU and PixAcc metrics from 0.943 and 0.949 to 0.953 and 0.959, i.e., by 1% on average. The developed pipeline significantly reduces the time required to evaluate fiber length in scanning electron microscope images.

A comparison of the Information Content of Orthogonally Polarized Components of Lidar Echo Signal for Evaluating Hydrooptical Characteristics of the Near-Surface Layer

A series of lidar measurements were conducted at stations with a homogeneous vertical distribution of hydrooptical char acteristics in the near-surface layer using a two-channel shipborne polarization lidar PLD-1. Lidar sounding was accompanied by synchronous contact measurements of a number of hydrooptical characteristics. A large dataset of measurement data was obtained in waters where hydrooptical characteristics varied widely. As a result of the statistical processing of these data, regression relationships were obtained linking the seawater beam attenuation coefficient c, absorption coefficient a, and diffuse attenuation coefficient Kd to the lidar attenuation coefficients of the co- and cross-polarized components. In most cases, a linear relationship between hydrooptical characteristics and the lidar attenuation coefficients of the polarized components is observed. These rela tionships are characterized by high values of the coefficient of determination — from 0.8 to 0.95. An exception is the relationship between the seawater beam attenuation coefficient c and the lidar attenuation coefficient of the cross-polarized component, where a second-degree polynomial is used to describe this relationship (coefficient of determination is 0.88). Data on the hydrooptical characteristics obtained using the cross-polarized component of the lidar echo signal mostly duplicate the data of the co-polarized component. However, the use of a two-channel optical receiving system increases the reliability and accuracy of the obtained data and provides the possibility of controlling the homogeneity of the underwater section of the sounding path.

Enhancing Weather Target Detection with Non-Uniform Pulse Repetition Time (NPRT) Waveforms

The velocity/distance trade-off poses a fundamental challenge in pulsed Doppler weather radar systems and is known as the velocity/distance dilemma. Techniques such as multiple-pulse repetition frequency, staggered pulse repetition time (PRT), and pulse phase coding are commonly used to mitigate this issue. The current study evaluates the adaptability/capability of a specific type of low-capture signal called the non-uniform PRT (NPRT) through analyzing the weather target characteristics of typical velocity distributions. The spectral moments estimation (SME) signal-processing algorithm of the NPRT weather echo is designed to calculate the average power, velocity, and spectrum width of the target. A comprehensive error analysis is conducted to ascertain the efficacy of the NPRT processing algorithm under influencing factors. The results demonstrate that the spectral parameters of weather target echo with a velocity of [−50,50] m/s through random-jitter NPRT signals align with radar functionality requirements (RFRs). Notably, the NPRT waveform resolves the inherent conflicts between the maximum unambiguous distance and velocity and elevates the upper limit of the maximal observation velocity. The evaluation results confirm that nonlinear radar signal processing technology can improve a radar’s detection performance and provide a new method for realizing the multifunctional observation of radar in different applications.

Spatial distribution of soil pore structure and response characteristics of physical parameters in the subsidence area induced by shallow-buried extra-thick coal seam mining

The development of ground fissures in the subsidence area induced by shallow-buried extra-thick coal seam mining leads to a decrease in soil water-holding capacity and vegetation withering. The investigation of the spatial distribution characteristics of soil pore structure in mining-induced subsidence areas and the elucidation of the response mechanism between soil physical parameters and soil structure are essential prerequisites for achieving effective ecological environment protection and restoration in mining areas. In this study, three-dimensional visualization reconstruction of soil porosity, pore diameter, roundness rate and pore connectivity at different profile depths in subsidence area caused by ultra-thick coal seam mining was conducted by using the soil CT scanning technology. Additionally, a correlation analysis model was established between physical parameters such as soil moisture content, bulk density, and pore structure parameters. The results indicate that: (1) Compared to the non-subsidence area, the number, size, and proportion of soil pores significantly increase in the tension zone, compression zone, and neutral zone, and the pore network and connectivity are extensively interconnected, and the fissure development in the tension zone is most significant, and the soil pores in the subsidence compression zone are concentrated on one side due to the influence of soil deformation. (2) As the depth of the soil profile increases, the level of soil pore development significantly decreases. The large profile depth (40–100 cm) causes a significant decrease in pore development. (3) There is a significant correlation between soil pore structure parameters and soil physical parameters (P ≤ 0.05), a highly significant correlation between soil pore structure parameters and soil moisture content (P ≤ 0.01), and a highly significant correlation between soil texture and soil moisture content (P ≤ 0.01). This research is of great significance for guiding underground production layout and repairing damaged soil.

Patterns and Predictors of Orbitofrontal Sulcogyral Morphology in a Non-Clinical Population

Abstract Frequencies of orbitofrontal cortex (OFC) sulcogyral patterns in populations, with less common patterns identified at higher rates among those with psychopathology. Previous work has assumed demographic characteristics have no influence on OFC sulcogyral patterns. However, the influence of sociodemographic and health-related characteristics on OFC patterns within a neurotypical population has not been formally evaluated. We used structural brain magnetic resonance imaging (MRI) from a cohort from the Human Connectome Project (HCP) with existing OFC sulcogyral characterizations (n=238); none of the participants had psychiatric diagnoses. We evaluated distributions of participant demographics (i.e., age), socioeconomic factors (i.e., employment), and health history related factors (i.e., smoking history) by OFC sulcogyral pattern within each hemisphere. We then used logistic regression to estimate the odds of OFC sulcogyral pattern by participant characteristics. Distributions of study sample characteristics did not vary substantially by OFC sulcogyral pattern type within either hemisphere. Findings from logistic regression analyses suggest no association between OFC sulcogyral pattern and any of the demographic or socioeconomic characteristics. Two health history-related characteristics, body mass index (BMI) and smoking history, were associated with increased odds of having specific OFC pattern types. For example, individuals with obesity had a 2.65 increased odds (95% CI: 1.17, 6.65) of having OFC sulcogyral pattern Type II, III, or IV, compared to Type I in the left hemisphere with normal BMIs. We did not observe substantial influence of demographic or socioeconomic characteristics on OFC sulcogyral patterns. These results confirm assumptions made in previous work that demographic and socioeconomic characteristics do not seem to impact OFC patterns. We do show some evidence for an influence of health history related factors (obesity and smoking history); future work should evaluate whether these and other phenotypic risk factors interact to modify the relationship between psychiatric diagnoses and OFC sulcogyral patterns.

Sleep-Related Sudden Unexpected Infant Death Among Infants Prenatally Substance Exposed.

Maternal substance use during pregnancy heightens risk of sudden unexpected infant death (SUID), including through unsafe sleep practices. Families impacted by substance use frequently experience disproportionate social drivers of poor health and family vulnerability likely contributory to fatality risk. Characteristics of sleep-related SUID among infants born prenatally substance exposed versus nonexposed were compared to identify targeted prevention opportunities. Using the Sudden Death in the Young Registry, we examined SUID with sleep-related death between 2015 and 2020 across infants prenatally exposed versus nonexposed. Distribution of sleep environment characteristics, social drivers of poor health, and family vulnerability factors were examined using descriptive statistics and χ2. Of 2010 infants who experienced sleep-related deaths, 283 (14%) were prenatally exposed. More than half of deaths involved an adult bed (52%, n = 1045) or surface sharing with an adult (53%, n = 1074). Supervisors of prenatally exposed infants were disproportionately impaired at infant death versus nonexposed (34%, n = 97 vs 16%, n = 279). Statistically significant associations between prenatal exposure history and vulnerability factors (insurance, child welfare involvement, intimate partner violence, health care barriers) were identified (P < .05). Sleep-related SUID across infants prenatally exposed versus nonexposed differ in sleep environment characteristics and contributory social vulnerability. Disproportionate sleep environment hazards (surface sharing, supervisor impairment) are identified among prenatally exposed infants that should compel targeted prevention efforts, including safe sleep messaging, discouraging surface sharing, and engaging support persons during impairment periods. Addressing social needs and family vulnerability are also paramount to increase access to health care, safe sleep education, and material resource provision.

Analysis of Burn Casualties in the Swords of Iron War

Abstract The Swords of Iron (SOI) War is an armed conflict between Israel and Hamas, which has been taking place in Gaza from October 2023 until the present time of writing this article. This war is characterized by urban close contact combat. The combat equipment, weaponry, and protective measures employed, differ markedly from those in previously documented major conflicts, significantly impacting the types of injuries sustained by soldiers. This study examines the number of injured soldiers in combat, field mortality rates, incidence of burns and trauma, demographic details of the injured, causes and distribution of injuries, burn characteristics, hemodynamic status, and injury severity. This is a retrospective, registry-based cohort study. Military personnel treated by IDF medical teams with burn injuries from the ground maneuver in Gaza between October 27th, 2023 to April 2024 were included in the analysis. Diagnosis of burn injuries was made in the prehospital setting and recorded in the patient’s casualty cards and the IDF trauma registry and/or in the hospitals, and then recorded by the Israel National Trauma Registry. A total of 2,627 documented military personnel were included. Of them, there was a total of 249 burn casualties. They were all male combat soldiers and their mean age was 22 years. Most of the burn injuries were combined with other injuries, only 12% were defined as in shock state, and 14% received whole blood in the field. The burn degree was second and third degree for most cases. A third (34%) of burn injured soldiers had surgery (Table 2), and half of the total were sent to rehabilitation departments following their initial hospitalization. An analysis of total body surface area (TBSA)% affected by burns among SOI War casualties reveals a higher proportion of severe burns exceeding 20% TBSA, compared to previous conflicts. Additionally, a higher proportion of personnel suffered from second and third degree burns. This is likely attributed to the operational mode of the IDF in Gaza, where urban close-contact warfare exposes soldiers to heightened risks of explosive and blast injuries. A significant percentage of live casualties had TBSA percentages that were not life-threatening, which underscores the importance of updating emergency burn treatment protocols and employing rapid evacuation and medical response systems in the field. Most of the wounded that suffer from significant burns had other significant and life-threatening injuries, making the appropriate protective measures and the preparation for rapid evacuation the best way to improve burn casualty survival. The significance of this research lies in the timely analysis of accumulated combat data to derive lessons and conclusions relevant to ongoing and future conflicts, and to understand the clinical implications arising from combat situations. Given the relatively high prevalence of burn injuries, understanding their etiology, severity, and consequences is essential for developing effective prevention and treatment courses.

Design of Travel Route Planning Model Based on Deep Learning

Tourism route planning is affected by the factors such as tourism destination and tourists’ preference, which leads to poor automatic matching of routes. A mathematical model of tourism route planning based on deep learning is proposed. Under the condition of total route constraints, according to the historical tourism preference information and prior information of tourists, a big data model for the distribution of personalized characteristics of tourists is established, spatial constraints and time constraints parameters are input, and the feature matching of geographic location information and tourist interest parameter information is carried out by using the deep learning method, and the statistical feature quantity of the parameters of the tourism route planning model is extracted. Under the control of the deep learning and geographic information data set, Carry out multi-constraint and multi-objective hierarchical analysis of tourists and tourism destinations, and realize the optimization design of tourism route planning algorithm. The simulation results show that the accuracy of this method is high and the deviation is small. It improves the satisfaction level of tourists and helps users complete a series of visual analysis tasks such as route mining, route planning and destination analysis.

Association of Electrocardiographic Abnormalities With In-hospital Mortality in Adult Patients With COVID-19 Infection

OBJECTIVES: The study aimed to determine the association of electrocardiographic (ECG) abnormalities and in-hospital mortality of patients with coronavirus disease 2019 (COVID-19) infection admitted in a tertiary care hospital in the Philippines. METHODS: We conducted a retrospective study of confirmed COVID-19–infected patients. Demographic and clinical characteristics and clinical outcomes were extracted from the medical records. Electrocardiographic analysis was derived from the 12-lead electrocardiogram recorded upon admission. The frequencies and distributions of various clinical characteristics were described, and the ECG abnormalities associated with in-hospital mortality were investigated. RESULTS: A total of 163 patients were included in the study; most were female (52.7%) with a median age of 55 years. Sinus rhythm with any ECG abnormality (65%), nonspecific ST and T-wave changes (35%), and sinus tachycardia (22%) were the frequently reported ECG findings. The presence of any ECG abnormality was detected in 78.5% of patients, and it was significantly associated with in-hospital mortality (P = 0.038). The analysis revealed a statistically significant association between in-hospital mortality and having atrial fibrillation or flutter (P = 0.002), supraventricular tachycardia (P = 0.011), ventricular tachycardia (P = 0.011), third-degree atrioventricular block (P = 0.011), T-wave inversion (P = 0.005), and right ventricular hypertrophy (P = 0.011). CONCLUSION: The presence of any ECG abnormality in patients with COVID-19 infection was associated with in-hospital mortality. Electrocardiographic abnormalities that were associated with mortality were atrial fibrillation or flutter, supraventricular tachycardia, ventricular tachycardia, third-degree atrioventricular block, T-wave inversion, and right ventricular hypertrophy. KEYWORDS: COVID-19, electrocardiography, mortality, Philippines

Characteristics of body height, body weight and body mass index distributions in children aged 3-17 years in China

Objective: To investigate the distribution characteristics of body height, body weight and body mass index (BMI) in children aged 3-17 years in China. Methods: Data were obtained from the National Nutrition and Health Systematic Survey in 0-18 years old children in China. The study selected 70 853 children aged 3-17 years from 28 urban and rural survey sites in 14 provinces (autonomous regions and municipalities) in 7 regions of China with multi-stage stratified cluster random sampling. M (Q1, Q3) was used to describe the region, age and gender specific body height, body weight and BMI in the children aged 3-17 years. Wilcoxon rank sum test was used to compare the body height, body weight, and BMI between boys and girls in same age group. Kruskal-Wallis H rank sum test was used to compare the body height, body weight and BMI among boys in different age groups and among girls in different age groups, as well as among boys in same age group and among girls in same age group from different regions, and DSCF method was used for further pairwise comparisons. Results: In this study, the median body height and body weight were 172.0 cm and 62.9 kg in 17-year-old boys and 160.0 cm and 53.7 kg in 17-year-old girls. The median for children's body height, body weight, and BMI in most age groups were higher in northeastern and northern China than in southern China, and the differences could be observed until age 17 years. The differences in body weight and BMI in children in northeastern and northern China were greater in Q3 than in Q1 compared with southern China. Conclusions: The body height of children aged 3-17 years continues to increase in China. Northeastern and northern China have more children with higher bodyweight, showing an obvious body weight increase trend, to which close attention needs to be paid.

Influence of particle density on turbulence characteristics over a rough surface

The objective of the study is to use a 3D Acoustic Doppler Velocimeter (ADV) to gauge the typical flow and turbulence characteristics within a non-uniform open channel. The findings of experimental examinations of the subcritical flow along the channel are presented in this work. The behavior of sand grains in turbulent open channel flow across porous and rough bed surfaces was examined in laboratory research, and the results were obtained. The properties of turbulent flow, i.e., turbulence intensity, turbulent kinetic energy, and Reynolds shear stresses, are determined from ADV data. The continuity equation and the Reynolds equation of open-channel flow have been used to build theoretical formulations for the velocity distribution and Reynolds stress distribution in the vertical direction. Measured profiles of vertical velocity and Reynolds stress are compared to the derived expressions. The impact of the size of particles on the distribution of mean flow characteristics is discussed. This work provides a novel origin for the profile and analyzes the behavior of the vertical velocity distribution in the region where fully formed turbulence is dominating in open channels using the Navier–Stokes equations. In comparison to other sand roughness, Chopan sand bed (with greater density) exhibits the strongest turbulence intensities in both vertical and streamwise direction just next to the bed when away from the channel boundary. In contrast to flow across a rough surface, the variance ranges between 150 and 250% concerning the channel bed’s roughness type, impacting the velocity triple products that signifies transfer of turbulent kinetic energy.

Dementia Prevalence, Incidence, and Mortality Trends Among U.S. Adults Ages 72 and Older, 2011-2021.

U.S.-focused studies have reported decreasing dementia prevalence in recent decades, but have not yet focused on the implications of the coronavirus disease 2019 (COVID-19) pandemic for trends. We use the 2011-2021 National Health and Aging Trends Study (N = 48 065) to examine dementia prevalence, incidence, and mortality trends among adults ages 72 and older, and the contribution to prevalence trends of changes in the distribution of characteristics of the older population ("compositional shifts") during the full and prepandemic periods. To minimize classification error, individuals must meet dementia criteria for 2 consecutive rounds. The prevalence of probable dementia declined from 11.9% in 2011 to 9.2% in 2019 and 8.2% in 2021 (3.1% average annual decline). Pre-pandemic declines continued for women and non-Hispanic White individuals and emerged over the 2011-2021 period for men and those ages 80-89. Declines in dementia incidence were stronger for the 2011-2021 period than for the prepandemic period, while mortality among those with dementia rose sharply with the onset of the COVID-19 pandemic. Shifts in the composition of the older population accounted for a smaller fraction of the decline over the full period (27%) than over the prepandemic period (45%). Declines in dementia prevalence continued into years marked by onset of the COVID-19 pandemic, along with declines in incidence and sharp increases in mortality among those with dementia. However, declines are no longer largely attributable to compositional changes in the older population. Continued tracking of dementia prevalence, incidence, and mortality among those with and without dementia is needed to understand long-run consequences of the pandemic.

Predicting the risk of pulmonary infection after kidney transplantation using machine learning methods: a retrospective cohort study.

Pulmonary infection is the most common and serious complication after kidney transplantation that affects the survival of the transplanted kidney and the quality of life of patients. This study aims to construct a machine learning model for predicting the risk of pulmonary infection after kidney transplantation. We recruited 857 kidney transplant recipients from January 1, 2016, to December 31, 2021, in the Department of Nephrology, the First Affiliated Hospital of the University of Science and Technology of China. First, the distribution of baseline characteristics between patients with and without postoperative pulmonary infections was analyzed. Subsequently, six machine learning models were constructed to predict the risk of postoperative pulmonary infections. Finally, these models were subjected to external validation using an independent cohort. The performance of themodels was evaluated by area under the receiver operating characteristic curve (AUC). Among kidney transplant recipients, a total of 186 individuals developed pneumonia, with 144 cases in the training cohort and 42 cases in the external validation cohort. The AUC range of the six machine learning models for predicting the risk of postoperative pulmonary infection was 0.758-0.822 for the training cohort and 0.642-0.795 for the testing cohort. Among the models assessed, the gradient boosting machine demonstrated the most favorable predictive accuracy. Our study has developed a predictive model for assessing the risk of pulmonary infection after kidney transplantation, thereby providing a valuable foundation for the effective management of kidney transplant recipients.

Dissolved Nitrous Oxide and Methane in the Taiwan Strait: Distribution, Seasonal Variation, and Emission

AbstractThe global ocean plays an important role in the overall budgets of nitrous oxide (N2O) and methane (CH4), especially in continental estuaries and shelf areas. Four cruises were conducted between 2021 and 2022, covering the spring, summer, and fall seasons, to study the spatial and seasonal characteristics of N2O and CH4 distributions and emissions in the Taiwan Strait (TWS). The surface N2O and CH4 concentrations gradually decreased from the coast to the open sea, with maximum values (14.3 and 15.6 nmol L−1) occurring near the Jiulong and Minjiang estuaries, respectively. The mean surface N2O concentration (8.2 nmol L−1) was highest in the spring and approximately the same in the summer as in the fall. The mean surface concentrations of CH4 (8.8 nmol L−1) were greater in summer than in spring and fall, probably because of the high freshwater input in summer. Except for several stations in fall, surface waters were oversaturated with N2O and CH4 relative to the atmosphere in other seasons, and the TWS was a net source of atmospheric N2O and CH4 in the spring, summer, and fall. In situ production is the main source of N2O and CH4 in the TWS, with nitrification being the dominant mechanism of N2O production in the TWS. In contrast, physical influences (riverine inputs and water mass mixing) reshape the distributions of N2O and CH4. The annual emissions of N2O and CH4 from the TWS were estimated to be 0.9 × 10−3 ± 2.9 × 10−3 Tg yr−1 and 1.6 × 10−3 ± 3.0 × 10−3 Tg yr−1, respectively. Taken together, the TWS accounts for 0.025% of the surface area of the world's oceans and 0.022% ± 0.07% and 0.017% ± 0.033% of global oceanic N2O and CH4 emissions, respectively.

Long-term rice cultivation enhances root development and yields by improving the structural properties of soil aggregates in saline–alkaline environments

Rice cultivation has the ability to improve saline soils. However, the effects of long-term rice cultivation on saline soil chemistry, salt ions, root characteristics, and aggregate formation remain unclear. In this study, the impact of different planting durations (1, 3, 5, 8, and 10 years) on the spatial and temporal transport of soil salts, soil chemical properties, distribution and stability of aggregates, root characteristics, and yields, were assessed. Long-term rice cultivation reduced soil pH, exchangeable sodium percentage (ESP), and EC in the 0-60cm soil layer compared to 1Y (5.45%, 61.57%, and 81.87% reduction in topsoil (0-20cm) and 8.54%, 72.82%, and 71.94 in subsoil (20-60cm), respectively). However, the soil organic matter (OM), alkaline nitrogen (AN), phosphorus (AP), and potassium (AK) increased (217.09%, 90.18%, 89.23%, and 179.18% increase in topsoil and 223.72%, 81.24%, 174.28%, and 172.57% increase in subsoil, respectively). Additionally, the Cl-, K+, and Na+ in the soil gradually leached downward with the increase in the duration of rice cultivation (55.87%、36.08%、59.80%). Simultaneously, the increased duration markedly elevated the soil macroaggregate content (11.43%-20.04% by dry-sieving and 53.48%-288.09% by wet-sieving), clayey grain content (59.17%-198.33%), and soil aggregate stability, especially for 8Y and 10Y. The improved soil structure and root characteristic under long-term rice cultivation conditions increased the yield (28.89-54.81%). Partial least squares path model displayed that rice cultivation affected the aggregate stability, root characteristics, and rice yield via the corresponding chemical properties (pH, EC, and ESP), nutrient contents (AN, AP, AK, and SOM), and salt ion contents (Cl-, K+, and Na+). These findings are critical to understanding aggregate formation in saline soils.

The Distribution of Left Ventricular Ejection Fraction, Characteristics, and Clinical Outcomes of Patients with Newly Diagnosed Heart Failure in Taiwan.

Data regarding the distribution of left ventricular ejection fraction among patients with newly diagnosed heart failure (HF) and the outcomes of patients with heart failure with preserved ejection fraction (HFpEF) in Taiwan are limited. Patients with newly diagnosed HF were identified from a multi-institutional database between 2016 and 2020. Outcomes were compared between patients with HFpEF and heart failure with reduced ejection fraction (HFrEF) after propensity score matching (PSM). Of 7,736 newly diagnosed HF patients, 4,393 (56.8%) had HFpEF and 1,977 (25.6%) had HFrEF. The HFpEF group was older (71.5 vs. 64.2 years) and more likely to be female (48.9% vs. 31.1%). Comorbidities were more common in the HFpEF patients. Median follow-up was 2.1 years. Prior to PSM, the HFpEF patients had higher all-cause mortality risk [hazard ratio (HR) 1.21, 95% confidence interval (CI) 1.11-1.33] but lower cardiovascular (CV) death risk (HR 0.84, 95% CI 0.72-0.97) compared to those with HFrEF. The HFpEF group had a trend of higher overall hospitalization risk (HR 1.06, 95% CI 0.99-1.14), but lower HF hospitalization risk (HR 0.61, 95% CI 0.55- 0.67). After PSM, all-cause mortality and overall hospitalization were comparable. The HFpEF group had lower rates of CV death (HR 0.82, 95% CI 0.68-1.0) and HF hospitalization (HR 0.60, 95% CI 0.53-0.69) compared to the HFrEF group. Among patients with newly diagnosed HF, HFpEF is the predominant phenotype, characterized by older age, higher female prevalence, and increased comorbidities. After adjusting for these factors, all-cause death and hospitalization risks became similar between the HFpEF and HFrEF patients. The HFpEF patients had lower risks of CV death and HF hospitalization.

Libby-Novick Beta-Liouville Distribution for Enhanced Anomaly Detection in Proportional Data

We consider the problem of anomaly detection in proportional data by investigating the Libby-Novick Beta-Liouville distribution, a novel distribution merging the salient characteristics of Liouville and Libby-Novick Beta distributions. Its main benefit, compared to the typical distributions dedicated to proportional data such as Dirichlet and Beta-Liouville, is its adaptability and explanatory power when dealing with this kind of data. Our goal is to exploit this appropriateness for modeling proportional data to achieve great performance in the anomaly detection task. First, we develop generative models, namely finite mixture models of Libby-Novick Beta-Liouville distributions. Then, we propose two discriminative techniques: Normality scores based on selecting the given distribution to approximate the softmax output vector of a deep classifier and an improved version of Support Vector Machine (SVM) by suggesting a feature mapping approach. We demonstrate the benefits of the presented approaches through a variety of experiments on both image and non-image datasets. The results demonstrate that the proposed anomaly detectors based on the Libby-Novick Beta-Liouville distribution outperform the classical distributions as well as the baseline techniques.