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To develop and validate a predictive model for sarcopenia. A total of 240 subjects who visited our hospital between August 2021 and May 2023 were randomly divided by time of entry into a training set containing 2/3 of patients and a validation set containing 1/3 of patients. The muscle thickness (MT), echo intensity (EI), and shear wave velocity (SWV) of the medial gastrocnemius muscle were measured. Indicators that were meaningful in the univariate analysis in the training set were included in a binary logistic regression to derive a regression model, and the model was evaluated using a consistency index, calibration plot, and clinical validity curve. Diagnostic efficacy and clinical applicability were compared between the model and unifactorial indicators. Four meaningful variables, age, body mass index (BMI), MT, and SWV, were screened into the predictive model. The model was Logit Y = 21.292 + 0.065 × Age - 0.411 × BMI - 0.524 × MT - 3.072 × SWV. The model was well differentiated with an internally validated C-index of 0.924 and an external validation C-index of 0.914. The calibration plot predicted probabilities against actual probabilities showed excellent agreement. The specificity, sensitivity, and Youden's index of the model were 73.80%, 97.40%, and 71.20%, respectively, when using the diagnostic cut-off value of >0.279 for sarcopenia. The logistic model had higher diagnostic efficacy (p < 0.001) and higher net clinical benefit (p < 0.001) over the same threshold range compared to indicators. The logistic model of sarcopenia has been justified to have good discriminatory, calibrated, and clinical validity, and has higher diagnostic value than indicators. [ABSTRACT FROM AUTHOR]

A scoring system to discriminate between uncomplicated and complicated appendicitis is beneficial to determine the optimal treatment for acute appendicitis. We developed a scoring system to discriminate between uncomplicated and complicated appendicitis and assessed the clinical usefulness of the scoring system using external validation. A total of 299 patients with acute appendicitis were retrospectively reviewed. One hundred and ninety-nine patients were assigned to the model development group, while the other 100 patients were assigned to an external validation group. A scoring system for complicated appendicitis was created using a final multivariate logistic regression model with six independent predictors. The area under the receiver operating characteristic curve of the scoring system was 0.882 (95% confidence interval: 0.835–0.929). The cutoff point of the scoring system was 12, and the sensitivity and specificity were 82.9% and 86.2%, respectively. In the external validation group, the area under the receiver operating characteristic curve of the scoring system was 0.868 (95% confidence interval 0.794–0.942), and there was no significant difference between the groups in the area under the receiver operating characteristic curve (P = 0.750). Our newly developed scoring system may contribute to prompt determination of the optimal treatment for acute appendicitis. [ABSTRACT FROM AUTHOR]

With the advancement of urbanization, frequent geological disasters not only restrict the development of town in mountain areas, but also may cause major casualties and property losses. Consequently, the suspension of geological disasters is of great significance to predict and prevent regional geological disasters. In order to improve the accuracy and rationality of geological hazard susceptibility modeling, 11 key factors of geological hazards, such as topographic features, lithology, distance from fault and NDVI (normalized difference vegetation index), were selected by digital elevation model grid data, and the geological hazard susceptibility model of landslide and collapse in Macheng City was built by using information, information value-logistic regression coupling analysis, finally the comprehensive geological hazard susceptibility assessment model was extracted by the maximum value of grid. The ROC (receiver operating characteristic ) curve and rationality verification of the results of the single model and the coupling model show that the information value-logistic regression coupling model is more effective and more accurate than the traditional information value model, and the susceptibility of geological disasters in mountain areas can be evaluated more accurately by the coupling model. [ABSTRACT FROM AUTHOR]

With childhood hypertension emerging as a global public health concern, understanding its associated factors is crucial. This study investigated the prevalence and associated factors of hypertension among Chinese children. This cross-sectional investigation was conducted in Pinghu, Zhejiang province, involving 2,373 children aged 8–14 years from 12 schools. Anthropometric measurements were taken by trained staff. Blood pressure (BP) was measured in three separate occasions, with an interval of at least two weeks. Childhood hypertension was defined as systolic blood pressure (SBP) and/or diastolic blood pressure (DBP) ≥ age-, sex-, and height-specific 95th percentile, across all three visits. A self-administered questionnaire was utilized to collect demographic, socioeconomic, health behavioral, and parental information at the first visit of BP measurement. Random forest (RF) and multivariable logistic regression model were used collectively to identify associated factors. Additionally, population attributable fractions (PAFs) were calculated. The prevalence of childhood hypertension was 5.0% (95% confidence interval [CI]: 4.1–5.9%). Children with body mass index (BMI) ≥ 85th percentile were grouped into abnormal weight, and those with waist circumference (WC) > 90th percentile were sorted into central obesity. Normal weight with central obesity (NWCO, adjusted odds ratio [aOR] = 5.04, 95% CI: 1.96–12.98), abnormal weight with no central obesity (AWNCO, aOR = 4.60, 95% CI: 2.57–8.21), and abnormal weight with central obesity (AWCO, aOR = 9.94, 95% CI: 6.06–16.32) were associated with an increased risk of childhood hypertension. Childhood hypertension was attributable to AWCO mostly (PAF: 0.64, 95% CI: 0.50–0.75), followed by AWNCO (PAF: 0.34, 95% CI: 0.19–0.51), and NWCO (PAF: 0.13, 95% CI: 0.03–0.30). Our results indicated that obesity phenotype is associated with childhood hypertension, and the role of weight management could serve as potential target for intervention. [ABSTRACT FROM AUTHOR]

Background: Peri-urbanization, the expansion of large metropolitan centers into adjacent peri-urban regions, is a growing concern due to land scarcity and escalating housing costs. These zones, a blend of rural and urban features, blur the line between urban and rural areas, creating new landscapes. This study examines historical, present, and potential growth trends in the peri-urban area surrounding Durgapur Municipal Corporation (DMC). Analytical techniques and spatial metrics are used to track development intensity changes over time, including built-up density, Shannon's entropy, Landscape expansion index, Average Weighted Mean Expansion Index, Annual Built-Up Expansion Rate, Built-Up Expansion Intensity Index, and Built-Up Expansion Difference Index. Landscape indices like Patch Density, Edge Density, Landscape Shape Index, Largest Patch Index, Ratio of Open Space, and Area Weighted Mean Patch Fractal are used to understand fragmentation, connectivity, and spatial relationships. The Logistic Regression Model (LRM) is used to identify influencing factors and CA-Markov modeling for future built-up areas. Results: Between 1991 and 2001, built-up area in the region increased significantly, primarily due to urban development near industrial zones, roadways, and mining areas. The growth was primarily concentrated in the western sector and near National Highway-2 (NH-2). Urban sprawl was a continuous trend, with the highest built-up density in the South-South-East (SSE) direction from 1991 to 2011. Additionally, a key determinant of built-up development was the distance to the city core. By 2031, the built-up area is expected to concentrate in the western and southeast regions, reaching 177.90 km2. Conclusions: This expansion is attributed to urban development near industrial zones, roadways, mining areas, and other infrastructure. The study identifies distance to the city center as a significant influencing factor for built-up development. The results emphasize the need for inclusive urban planning methods prioritizing sustainable development principles and prudent resource management for future growth and efficient management in the DMC's peri-urban area. [ABSTRACT FROM AUTHOR]

A new liquefaction probability model based on shear wave velocity (Vs) was developed through a detailed comparative analysis of existing evaluation methods. Publicly available shear wave velocity liquefaction data were used to evaluate multiple existing liquefaction probability assessment methods under various probability contours and fines content levels. Significant performance differences were observed among the formulae under varying fines content levels. To construct the new model, the random forest feature importance ranking algorithm was employed to select the key parameters, including the effective stress-normalized shear wave velocity (Vs1), corrected cyclic resistance ratio (CSR7.5), magnitude (MW), depth (Z), and fines content (FC). Using these parameters, a new liquefaction probability assessment formula was developed utilizing the logistic regression model to predict the liquefaction probability. The new formula's performance was subsequently evaluated through a detailed case analysis and validation. The results demonstrate that the new formula achieves a higher accuracy (3–11%) for the liquefaction assessment compared to the existing formulae, performing consistently well across different probability contours and fines content levels, especially in areas with high fines content. This study provides theoretical support and empirical evidence for optimizing the shear wave velocity-based liquefaction probability assessment methods. [ABSTRACT FROM AUTHOR]

Background: A prediction model can be a useful tool to quantify the risk of a patient developing dementia in the next years and take risk-factor-targeted intervention. Numerous dementia prediction models have been developed, but few have been externally validated, likely limiting their clinical uptake. In our previous work, we had limited success in externally validating some of these existing models due to inadequate reporting. As a result, we are compelled to develop and externally validate novel models to predict dementia in the general population across a network of observational databases. We assess regularization methods to obtain parsimonious models that are of lower complexity and easier to implement. Methods: Logistic regression models were developed across a network of five observational databases with electronic health records (EHRs) and claims data to predict 5-year dementia risk in persons aged 55–84. The regularization methods L1 and Broken Adaptive Ridge (BAR) as well as three candidate predictor sets to optimize prediction performance were assessed. The predictor sets include a baseline set using only age and sex, a full set including all available candidate predictors, and a phenotype set which includes a limited number of clinically relevant predictors. Results: BAR can be used for variable selection, outperforming L1 when a parsimonious model is desired. Adding candidate predictors for disease diagnosis and drug exposure generally improves the performance of baseline models using only age and sex. While a model trained on German EHR data saw an increase in AUROC from 0.74 to 0.83 with additional predictors, a model trained on US EHR data showed only minimal improvement from 0.79 to 0.81 AUROC. Nevertheless, the latter model developed using BAR regularization on the clinically relevant predictor set was ultimately chosen as best performing model as it demonstrated more consistent external validation performance and improved calibration. Conclusions: We developed and externally validated patient-level models to predict dementia. Our results show that although dementia prediction is highly driven by demographic age, adding predictors based on condition diagnoses and drug exposures further improves prediction performance. BAR regularization outperforms L1 regularization to yield the most parsimonious yet still well-performing prediction model for dementia. [ABSTRACT FROM AUTHOR]

Rice is a globally important food crop, and it is crucial to accurately and conveniently obtain information on rice fields, understand their spatial patterns, and grasp their dynamic changes to address food security challenges. In this study, Chongqing's Yongchuan District was selected as the research area. By utilizing UAVs (Unmanned Aerial Vehicles) to collect multi-spectral remote sensing data during three seasons, the phenological characteristics of rice fields were analyzed using the NDVI (Normalized Difference Vegetation Index). Based on Sentinel data with a resolution of 10 m, the NDVI difference method was used to extract rice fields between 2019 and 2023. Furthermore, the reasons for changes in rice fields over the five years were also analyzed. First, a simulation model of the rice harvesting period was constructed using data from 32 sampling points through multiple regression analysis. Based on the model, the study area was classified into six categories, and the necessary data for each region were identified. Next, the NDVI values for the pre-harvest and post-harvest periods of rice fields, as well as the differences between them, were calculated for various regions. Additionally, every year, 35 samples of rice fields were chosen from high-resolution images provided by Google. The thresholds for extracting rice fields were determined by statistically analyzing the difference in NDVI values within the sample area. By utilizing these thresholds, rice fields corresponding to six harvesting regions were extracted separately. The rice fields extracted from different regions were merged to obtain the rice fields for the study area from 2019 to 2023, and the accuracy of the extraction results was verified. Then, based on five years of rice fields in the study area, we analyzed them from both temporal and spatial perspectives. In the temporal analysis, a transition matrix of rice field changes and the calculation of the rice fields' dynamic degree were utilized to examine the temporal changes. The spatial changes were analyzed by incorporating DEM (Digital Elevation Model) data. Finally, a logistic regression model was employed to investigate the causes of both temporal and spatial changes in the rice fields. The study results indicated the following: (1) The simulation model of the rice harvesting period can quickly and accurately determine the best period of remote sensing images needed to extract rice fields. (2) The confusion matrix shows the effectiveness of the NDVI difference method in extracting rice fields. (3) The total area of rice fields in the study area did not change much each year, but there were still significant spatial adjustments. Over the five years, the spatial distribution of gained rice fields was relatively uniform, while the lost rice fields showed obvious regional differences. In combination with the analysis of altitude, it tended to grow in lower areas. (4) The logistic regression analysis revealed that gained rice fields tended to be found in regions with convenient irrigation, flat terrain, lower altitude, and proximity to residential areas. Conversely, lost rice fields were typically located in areas with inconvenient irrigation, long distance from residential areas, low population, and negative topography. [ABSTRACT FROM AUTHOR]

The Land Use and Land Cover (LULC) changes are commonly used to determine the landscape conditions and have significant impacts on the earth's surface processes. During the last three decades, there has been an acceleration of LULC changes in Eastern DR Congo. However, there is no comprehensive overview of the drivers of these changes at the territorial level in this region, even though the knowledge of these drivers is important for land use planning and spatial modeling of environmental changes. Using the Kalehe Territory as a case study, this work sought to fill this gap by analyzing the drivers of LULC changes during the 1987-2020 period. A mixed approach combining remote sensing, Geographic Information System, and logistic regression modeling was used. The results indicated that the prominent LULC changes in the study area are deforestation, built-up area expansion, cropland expansion, and shrubland expansion. These changes are significantly influenced by biophysical factors (slope, altitude, and soil type), conservation zoning, population dynamics, and accessibility factors at different levels. The occurrence of conservation zones decreases the susceptibility to deforestation, built-up land, and cropland expansions. In contrast, the proximity factors (distance to road, artisanal mining, and locality) increase the susceptibility to LULC changes. These factors can be integrated into spatial models to forecast LULC changes susceptibility in this region. Furthermore, the establishment of future land use management policy at the territorial level in eastern DR Congo should be space-specific as the susceptibility of LULC changes shows a spatial trend. [ABSTRACT FROM AUTHOR]

Abstract A scoring system to discriminate between uncomplicated and complicated appendicitis is beneficial to determine the optimal treatment for acute appendicitis. We developed a scoring system to discriminate between uncomplicated and complicated appendicitis and assessed the clinical usefulness of the scoring system using external validation. A total of 299 patients with acute appendicitis were retrospectively reviewed. One hundred and ninety-nine patients were assigned to the model development group, while the other 100 patients were assigned to an external validation group. A scoring system for complicated appendicitis was created using a final multivariate logistic regression model with six independent predictors. The area under the receiver operating characteristic curve of the scoring system was 0.882 (95% confidence interval: 0.835–0.929). The cutoff point of the scoring system was 12, and the sensitivity and specificity were 82.9% and 86.2%, respectively. In the external validation group, the area under the receiver operating characteristic curve of the scoring system was 0.868 (95% confidence interval 0.794–0.942), and there was no significant difference between the groups in the area under the receiver operating characteristic curve (P = 0.750). Our newly developed scoring system may contribute to prompt determination of the optimal treatment for acute appendicitis.

Abstract Background Peri-urbanization, the expansion of large metropolitan centers into adjacent peri-urban regions, is a growing concern due to land scarcity and escalating housing costs. These zones, a blend of rural and urban features, blur the line between urban and rural areas, creating new landscapes. This study examines historical, present, and potential growth trends in the peri-urban area surrounding Durgapur Municipal Corporation (DMC). Analytical techniques and spatial metrics are used to track development intensity changes over time, including built-up density, Shannon’s entropy, Landscape expansion index, Average Weighted Mean Expansion Index, Annual Built-Up Expansion Rate, Built-Up Expansion Intensity Index, and Built-Up Expansion Difference Index. Landscape indices like Patch Density, Edge Density, Landscape Shape Index, Largest Patch Index, Ratio of Open Space, and Area Weighted Mean Patch Fractal are used to understand fragmentation, connectivity, and spatial relationships. The Logistic Regression Model (LRM) is used to identify influencing factors and CA-Markov modeling for future built-up areas. Results Between 1991 and 2001, built-up area in the region increased significantly, primarily due to urban development near industrial zones, roadways, and mining areas. The growth was primarily concentrated in the western sector and near National Highway-2 (NH-2). Urban sprawl was a continuous trend, with the highest built-up density in the South-South-East (SSE) direction from 1991 to 2011. Additionally, a key determinant of built-up development was the distance to the city core. By 2031, the built-up area is expected to concentrate in the western and southeast regions, reaching 177.90 km2. Conclusions This expansion is attributed to urban development near industrial zones, roadways, mining areas, and other infrastructure. The study identifies distance to the city center as a significant influencing factor for built-up development. The results emphasize the need for inclusive urban planning methods prioritizing sustainable development principles and prudent resource management for future growth and efficient management in the DMC’s peri-urban area.

Abstract With childhood hypertension emerging as a global public health concern, understanding its associated factors is crucial. This study investigated the prevalence and associated factors of hypertension among Chinese children. This cross-sectional investigation was conducted in Pinghu, Zhejiang province, involving 2,373 children aged 8–14 years from 12 schools. Anthropometric measurements were taken by trained staff. Blood pressure (BP) was measured in three separate occasions, with an interval of at least two weeks. Childhood hypertension was defined as systolic blood pressure (SBP) and/or diastolic blood pressure (DBP) ≥ age-, sex-, and height-specific 95th percentile, across all three visits. A self-administered questionnaire was utilized to collect demographic, socioeconomic, health behavioral, and parental information at the first visit of BP measurement. Random forest (RF) and multivariable logistic regression model were used collectively to identify associated factors. Additionally, population attributable fractions (PAFs) were calculated. The prevalence of childhood hypertension was 5.0% (95% confidence interval [CI]: 4.1–5.9%). Children with body mass index (BMI) ≥ 85th percentile were grouped into abnormal weight, and those with waist circumference (WC) > 90th percentile were sorted into central obesity. Normal weight with central obesity (NWCO, adjusted odds ratio [aOR] = 5.04, 95% CI: 1.96–12.98), abnormal weight with no central obesity (AWNCO, aOR = 4.60, 95% CI: 2.57–8.21), and abnormal weight with central obesity (AWCO, aOR = 9.94, 95% CI: 6.06–16.32) were associated with an increased risk of childhood hypertension. Childhood hypertension was attributable to AWCO mostly (PAF: 0.64, 95% CI: 0.50–0.75), followed by AWNCO (PAF: 0.34, 95% CI: 0.19–0.51), and NWCO (PAF: 0.13, 95% CI: 0.03–0.30). Our results indicated that obesity phenotype is associated with childhood hypertension, and the role of weight management could serve as potential target for intervention.

Abstract Background A prediction model can be a useful tool to quantify the risk of a patient developing dementia in the next years and take risk-factor-targeted intervention. Numerous dementia prediction models have been developed, but few have been externally validated, likely limiting their clinical uptake. In our previous work, we had limited success in externally validating some of these existing models due to inadequate reporting. As a result, we are compelled to develop and externally validate novel models to predict dementia in the general population across a network of observational databases. We assess regularization methods to obtain parsimonious models that are of lower complexity and easier to implement. Methods Logistic regression models were developed across a network of five observational databases with electronic health records (EHRs) and claims data to predict 5-year dementia risk in persons aged 55–84. The regularization methods L1 and Broken Adaptive Ridge (BAR) as well as three candidate predictor sets to optimize prediction performance were assessed. The predictor sets include a baseline set using only age and sex, a full set including all available candidate predictors, and a phenotype set which includes a limited number of clinically relevant predictors. Results BAR can be used for variable selection, outperforming L1 when a parsimonious model is desired. Adding candidate predictors for disease diagnosis and drug exposure generally improves the performance of baseline models using only age and sex. While a model trained on German EHR data saw an increase in AUROC from 0.74 to 0.83 with additional predictors, a model trained on US EHR data showed only minimal improvement from 0.79 to 0.81 AUROC. Nevertheless, the latter model developed using BAR regularization on the clinically relevant predictor set was ultimately chosen as best performing model as it demonstrated more consistent external validation performance and improved calibration. Conclusions We developed and externally validated patient-level models to predict dementia. Our results show that although dementia prediction is highly driven by demographic age, adding predictors based on condition diagnoses and drug exposures further improves prediction performance. BAR regularization outperforms L1 regularization to yield the most parsimonious yet still well-performing prediction model for dementia.

BackgroundArsenic, cobalt, barium, and other individual metal exposure have been confirmed to be associated with the incidence of kidney stones. However, there are few studies on the association between mixed metal exposure and kidney stones, especially in occupational groups. ObjectiveTo investigate the association between mixed metal exposure and kidney stones in an occupational population from a metal smelting plant. MethodsA questionnaire survey was conducted to collect sociodemographic characteristics, medical history, and lifestyle information of 1158 mixed metal-exposed workers in a metal smelting plant in Guangdong Province from July 2021 to January 2022. Midstream morning urine samples were collected from the workers, the concentrations of 18 metals including lithium, vanadium, chromium, manganese, cobalt, nickel, copper, zinc, arsenic, selenium, strontium, molybdenum, cadmium, cesium, barium, tungsten, titanium, and lead were measured by inductively coupled plasma mass spectrometry, and the urinary mercury levels were measured by cold atomic absorption spectroscopy. Based on predetermined inclusion criteria, a total of 919 mixed metal-exposed workers were included in the study, including 117 workers in the kidney stone group and 802 workers in the non-kidney stone group. With a detection rate of urinary metals greater than 80% as entry criterion, 16 eligible metals were finally included for further analysis. Parametric or non-parametric methods were used to compare the differences between continuous or categorical variables of the non-kidney stone group and the kidney stone group. Logistic regression models were constructed to explore the association between individual metal exposures and kidney stones. Weighted quantile sum (WQS) regression models were used to evaluate the association between mixed metal exposure and kidney stones, as well as the weights of each metal on kidney stones. Then Bayesian kernel machine regression (BKMR) models were used to explore the overall effect of mixed metal exposure on renal calculi and the potential interactions between metals. ResultsWe found that there were significant differences in sex, age, length of service, and body mass Index (BMI) between the non-kidney stone group and the kidney stone group (P

The Land Use and Land Cover (LULC) changes are commonly used to determine the landscape conditions and have significant impacts on the earth's surface processes. During the last three decades, there has been an acceleration of LULC changes in Eastern DR Congo. However, there is no comprehensive overview of the drivers of these changes at the territorial level in this region, even though the knowledge of these drivers is important for land use planning and spatial modeling of environmental changes. Using the Kalehe Territory as a case study, this work sought to fill this gap by analyzing the drivers of LULC changes during the 1987-2020 period. A mixed approach combining remote sensing, Geographic Information System, and logistic regression modeling was used. The results indicated that the prominent LULC changes in the study area are deforestation, built-up area expansion, cropland expansion, and shrubland expansion. These changes are significantly influenced by biophysical factors (slope, altitude, and soil type), conservation zoning, population dynamics, and accessibility factors at different levels. The occurrence of conservation zones decreases the susceptibility to deforestation, built-up land, and cropland expansions. In contrast, the proximity factors (distance to road, artisanal mining, and locality) increase the susceptibility to LULC changes. These factors can be integrated into spatial models to forecast LULC changes susceptibility in this region. Furthermore, the establishment of future land use management policy at the territorial level in eastern DR Congo should be space-specific as the susceptibility of LULC changes shows a spatial trend.

The study aimed to discuss the association between sleep duration and the risk of hyperhomocysteinemia (Hhcy). This cross-sectional study included 4173 adults (≥ 20 years) from the National Health and Nutrition Examination Survey 2005–2006. According to their sleep duration, participants were divided into five subgroups. Multivariate logistic regression analysis models and restrictive cubic spline regressions were used to explore the association between sleep duration and the risk of Hhcy. Compared with the participants who sleep 7 h, sleep deprivation (≤ 5 h) increased the risk of Hhcy, odds ratio (OR) 1.68 (95% confidence interval (CI) 1.06–2.68); Excessive sleep (≥ 9 h) also increased the risk of Hhcy, OR 1.86 (95% CI 1.09–3.14) after adjusting for a series of confounding factors in the entire population. The risk of Hhcy was distributed in a U-shape with sleep duration. Similar results were demonstrated in obese populations. The association between sleep duration and the risk of Hhcy is U-shaped. Both sleep deprivation and excessive sleep can increase the risk of Hhcy. [ABSTRACT FROM AUTHOR]

Aims: Proximal femur fractures treatment can involve anterograde nailing with a single or double cephalic screw. An undesirable failure for this fixation is screw cut-out. In a single-screw nail, a tip-apex distance (TAD) greater than 25 mm has been associated with an increased risk of cut-out. The aim of the study was to examine the role of TAD as a risk factor in a cephalic double-screw nail. Methods: A retrospective study was conducted on 112 patients treated for intertrochanteric femur fracture with a double proximal screw nail (Endovis BA2; EBA2) from January to September 2021. The analyzed variables were age, sex, BMI, comorbidities, fracture type, side, time of surgery, quality of reduction, pre-existing therapy with bisphosphonate for osteoporosis, screw placement in two different views, and TAD. The last follow-up was at 12 months. Logistic regression was used to study the potential factors of screw cut-out, and receiver operating characteristic curve to identify the threshold value. Results: A total of 98 of the 112 patients met the inclusion criteria. Overall, 65 patients were female (66.3%), the mean age was 83.23 years (SD 7.07), and the mean follow-up was 378 days (SD 36). Cut-out was observed in five patients (5.10%). The variables identified by univariate analysis with p < 0.05 were included in the multivariate logistic regression model were screw placement and TAD. The TAD was significant with an odds ratio (OR) 5.03 (p = 0.012) as the screw placement with an OR 4.35 (p = 0.043) in the anteroposterior view, and OR 10.61 (p = 0.037) in the lateral view. The TAD threshold value identified was 29.50 mm. Conclusion: Our study confirmed the risk factors for cut-out in the double-screw nail are comparable to those in the single screw. We found a TAD value of 29.50 mm to be associated with a risk of cut-out in double-screw nails, when good fracture reduction is granted. This value is higher than the one reported with single-screw nails. Therefore, we suggest the role of TAD should be reconsidered in well-reduced fractures treated with double-screw intramedullary nail. Cite this article: Bone Jt Open 2024;5(6):457–463.

Background. Early diagnosis of the mixed infection of ixodic tick-borne borreliosis and tick-borne encephalitis can be difficult without laboratory confirmation of the diagnosis.The aim. To assess clinical and laboratory predictors in patients for building the models of early differential diagnosis between mixed infection of erythema form of ixodic tick-borne borreliosis and febrile form of tick-borne encephalitis and monoinfection of erythema form of ixodic tick-borne borreliosis.Materials and methods. The clinical study included 26 patients with mixed infection of the erythema form of ixodic tick-borne borreliosis with tick-borne encephalitis (mean age – 46.7 years) and 30 patients with monoinfection of the erythema form of ixodic tick-borne borreliosis (mean age – 48.5 years). 59 clinical, epidemiological and laboratory parameters, including 6 leukocyte blood indices, standard and extended hemogram profiles were studied in these patients the first week of the disease. For statistical analysis, we used Pearson and Mann – Whitney goodness-of-fit tests, ROC analysis with calculation of the area under curve (AUC) and logistic regression. Results. Patients with mixed infection, compared to the patients with monoinfection, had increased odds of having febrile fever, weakness, headache and orbital pain; meningism symptoms occurred only in patients with mixed infection. Early predictors of a preliminary diagnosis of mixed infection were elevated values of fever (AUC = 0.87), intoxication syndrome score (AUC = 0.71), leukocyte count (AUC = 0.64) and neutrophils (AUC = 0.71) in the hemogram, levels of leukocyte intoxication index according to V.K. Ostrovsky (AUC = 0.67) and neutrophil-leukocyte index (AUC = 0.65), as well as a decrease in the number of platelets (AUC = 0.77), eosinophils (AUC = 0.72) and basophils (AUC = 0.79) in peripheral blood. Two prognostic models which included clinical and laboratory parameters demonstrated excellent prediction of mixed infection (AUC = 0.90 and AUC = 0.91).Conclusion. Early clinical and laboratory predictors have been established and models have been developed that make it possible, with excellent prognosis quality, to calculate in patients the probability of a preliminary diagnosis of mixed infection of the erythema form of ixodic tick-borne borreliosis with tick-borne encephalitis in comparison with borreliosis monoinfection.

This study aimed to identify the environmental factors of pedestrians’ preferred route and examine which factors are related to the choice of a healthy route. This study set the shortest and healthy routes in Ayanggyo neighborhood in Daegu Metropolitan City, South Korea. A healthy route is longer than the shortest route but has rich greenery and low traffic. A survey of 203 respondents was conducted from October 12 to 8 November 2022. This study used a logistic regression model with a walking route choice as the dependent variable. Results showed that 65% of participants chose a healthy route. People who considered low noise levels, presence of greenery and waterfront areas, low traffic speed, and presence of traffic safety facilities as important were likelier to choose healthy routes. The results suggest that pleasant and safe routes and built environments can make people walk longer. The findings of this study provide evidence to urban planners and policymakers that the built environmental conditions play an important role in encouraging residents’ walking behavior. This can contribute to urban policies supporting citizens’ active and healthy walking.

Previous studies on the susceptibility assessment of regional landslides mainly focused on comparing and improving the results of different evaluation models, while neglected the preservation of information on selected disaster-causing factors and the issue of how to unify factor dimensions. To explore the correlation and dimensionality of disaster causing-factors and their impact on susceptibility assessment, this study selected 12 factors such as elevation, slope, aspect, and terrain undulation, and used new factors extracted from principal component analysis in susceptibility assessment in the northern Jingyuan County. Data standardization, landslide density, and information quantity substitution methods were used to unify the dimensionality of disaster-causing factors. The landslide susceptibility zoning map was drawn based on the GIS platform in the study area. The accuracy of the susceptibility assessment of each mode was evaluated by the receiver operating characteristic curve. The results show that among the information model, the logistic regression model, and the perceptron model, the accuracy of the model evaluation obtained by the factors processed by principal component analysis is the highest. Using the information value substitution method to unify the dimensions of factors can further improve the accuracy of the evaluation of the logistic regression model and the perceptron model. The perceptron model has the highest accuracy (AUC=0.936 7), which is superior to the information model (AUC=0.917 3) and the logistic regression model (AUC=0.927 2). This is an ideal model for the landslide susceptibility assessment in the study area and should be given priority. The results can provide the basic theoretical information for disaster prevention and mitigation in similar areas.

Typically, in high dimensional data sets, many covariates are not significantly associated with a response. Moreover, those covariates are highly correlated, leading to a multicollinearity problem. Hence, the model is sparse since the coefficient of most covariates are likely to be zero. The classical frequentist or likelihood-based variable selection via any criterion such as Bayesian Information Criteria (BIC) and Akaike Information Criteria (AIC) or a stepwise subset selection becomes infeasible when the number of variables are large. An alternative solution is a Bayesian variable selection. In this study, we used a variable selection via a Bayesian variable selection and the least absolute shrinkage and selection operator (LASSO) method in the logistic regression model. Moreover, those methods were expanded to be applied to real datasets.

Background: Kawasaki disease shock syndrome (KDSS) is a critical manifestation of Kawasaki disease (KD). In recent years, a logistic regression prediction model has been widely used to predict the occurrence probability of various diseases. This study aimed to investigate the clinical characteristics of children with KD and develop and validate an individualized logistic regression model for predicting KDSS among children with KD. Methods: The clinical data of children diagnosed with KDSS and hospitalized between January 2021 and December 2023 were retrospectively analyzed. The best predictors were selected by logistic regression and lasso regression analyses. A logistic regression model was built of the training set (n = 162) to predict the occurrence of KDSS. The model prediction was further performed by logistic regression. A receiver operating characteristic curve was used to evaluate the performance of the logistic regression model. We built a nomogram model by visualizing the calibration curve using a 1000 bootstrap resampling program. The model was validated using an independent validation set (n = 68). Results: In the univariate analysis, among the 24 variables that differed significantly between the KDSS and KD groups, further logistic and Lasso regression analyses found that five variables were independently related to KDSS: rash, brain natriuretic peptide, serum Na, serum P, and aspartate aminotransferase. A logistic regression model was established of the training set (area under the receiver operating characteristic curve, 0.979; sensitivity=96.2%; specificity=97.2%). The calibration curve showed good consistency between the predicted values of the logistic regression model and the actual observed values in the training and validation sets. Conclusion: Here we established a feasible and highly accurate logistic regression model to predict the occurrence of KDSS, which will enable its early identification. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Extensive research has been conducted to identify key proteins governing stress responses, virulence, and antimicrobial resistance, as well as to elucidate their interactions within Listeria monocytogenes. While these proteins hold promise as potential targets for novel strategies to control L. monocytogenes, given their critical roles in regulating the pathogen's metabolism, additional analysis is needed to further assess their druggability—the chance of being effectively bound by small-molecule inhibitors. In this work, 535 binding pockets of 46 protein targets for known drugs (mainly antimicrobials) were first analyzed to extract 13 structural features (e.g., hydrophobicity) in a ligand–protein docking platform called Molsoft ICM Pro. The extracted features were used as inputs to develop a logistic regression model to assess the druggability of protein binding pockets, with a value of one if ligands can bind to the protein pocket. The developed druggability model was then used to evaluate 23 key proteins from L. monocytogenes that have been identified in the literature. The following proteins are predicted to be high-potential druggable targets: GroEL, FliH/FliI complex, FliG, FlhB, FlgL, FlgK, InlA, MogR, and PrfA. These findings serve as an initial point for future research to identify specific compounds that can inhibit druggable target proteins and to design experimental work to confirm their effectiveness as drug targets. [ABSTRACT FROM AUTHOR]

This study evaluates whether random forest (RF) models are as effective as traditional Logistic Regression (LR) models in predicting multidrug-resistant Gram-negative bacterial nosocomial infections. Data were collected from 541 patients with hospital-acquired Gram-negative bacterial infections at two tertiary-level hospitals in Urumqi, Xinjiang, China, from August 2022 to November 2023. Relevant literature informed the selection of significant predictors based on patients' pre-infection clinical information and medication history. The data were split into a training set of 379 cases and a validation set of 162 cases, adhering to a 7:3 ratio. Both RF and LR models were developed using the training set and subsequently evaluated on the validation set. The LR model achieved an accuracy of 84.57%, sensitivity of 82.89%, specificity of 80.10%, positive predictive value of 84%, negative predictive value of 85.06%, and a Yoden index of 0.69. In contrast, the RF model demonstrated superior performance with an accuracy of 89.51%, sensitivity of 90.79%, specificity of 88.37%, positive predictive value of 87.34%, negative predictive value of 91.57%, and a Yoden index of 0.79. Receiver operating characteristic curve analysis revealed an area under the curve of 0.91 for the LR model and 0.94 for the RF model. These findings indicate that the RF model surpasses the LR model in specificity, sensitivity, and accuracy in predicting hospital-acquired multidrug-resistant Gram-negative infections, showcasing its greater potential for clinical application. [ABSTRACT FROM AUTHOR]

Objective To investigate the potential relationship between dietary flavanone intake and asthma so as to provide reference for the prevention and control of asthma. Methods Adults over 20 years old in the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2008 were selected as research subjects. Multivariate logistic regression model was used to analyze the relationship between flavanone intake and asthma. Finally, subgroup analysis was used to explore the relationship between flavanone intake and asthma in different sex and age groups. Results A total of 5 415 subjects were included, of whom 710 (13.11%) had asthma. The median intake of flavanone was 027 mg/d. The intake of flavanone in patients with asthma was significantly lower than that in normal people, and the difference was statistically significant (Z-10.207, P < 0.001). After adjusting for age, sex, body mass index, race, education, drinking, smoking, montlily family poverty, hypertension, and diabetes, the incidence of asthma in the highest quantile was significantly lower than that in the lowest quantile (OJ=0.781, 95%CI: 0.615-0.991), and the risk of asthma decreased with the increase of flavanone intake (Ptrcnd = 0.015). Conclusion There is a negative correlation between flavanone intake and asthma. Flavanone may be a protective factor for asthma, and the correlation may be more obvious in women and young and middle-aged people under 50 years old. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology & Engineering Geology / Shuiwendizhi Gongchengdizhi is the property of Hydrogeology & Engineering Geology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, the decomposition of five different raw materials (maize, wheat and piney biomass, industrial wood chips and sunflower husk) were investigated using the TG-FTIR method to obtain raw data for model-based calculations. The data obtained from the thermogravimetric analysis served as a basis for kinetic analysis with three different isoconversional, model-free methods, which were the KAS, FWO and Friedman methods. Afterwards, the activation energy and the pre-exponential factor were determined, and no significant difference could be identified among the used methods (difference was under 5%), achieving 203–270 kJ/mol of Ea on average. Thereafter, the thermodynamic parameters were studied. Based on the TG-FTIR data, a logistic regression model was fitted to the data, which gives information about the thermal degradation and the obtained components with different heating rates. The FTIR analysis resulted in differential peaks corresponding to the studied components that were detected within the temperature range of 350–380 °C. The primary degradation processes occurred within a broader temperature range of 200–600 °C. Accordingly, in this work, the use of logistic mixture models as an alternative to traditional kinetic models for the description of the TGA process was also investigated, reaching adequate performance in fitting by a validation data coefficient of determination of R2 = 0.9988. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to use data mining methods to establish a simple and reliable predictive model based on the risk factors related to gallbladder stones (GS) to assist in their diagnosis and reduce medical costs. Materials and Methods: This was a retrospective cross-sectional study. A total of 4215 participants underwent annual health examinations between January 2019 and December 2019 at the Physical Examination Center of Shengjing Hospital Affiliated to China Medical University. After rigorous data screening, the records of 2105 medical examiners were included for the construction of J48, multilayer perceptron (MLP), Bayes Net, and Naïve Bayes algorithms. A ten-fold cross-validation method was used to verify the recognition model and determine the best classification algorithm for GS. Results: The performance of these models was evaluated using metrics of accuracy, precision, recall, F-measure, and area under the receiver operating characteristic curve. Comparison of the F-measure for each algorithm revealed that the F-measure values for MLP and J48 (0.867 and 0.858, respectively) were not statistically significantly different (p>0.05), although they were significantly higher than the F-measure values for Bayes Net and Naïve Bayes (0.824 and 0.831, respectively; p<0.05). Conclusion: The results of this study showed that MLP and J48 algorithms are effective at screening individuals for the risk of GS. The key attributes of data mining can further promote the prevention of GS through targeted community intervention, improve the outcome of GS, and reduce the burden on the medical system. [ABSTRACT FROM AUTHOR]

Aims: Total hip arthroplasty (THA) is a common procedure to address pain and enhance function in hip disorders such as osteoarthritis. Despite its success, postoperative patient recovery exhibits considerable heterogeneity. This study aimed to investigate whether patients follow distinct pain trajectories following THA and identify the patient characteristics linked to suboptimal trajectories. Methods: This retrospective cohort study analyzed THA patients at a large academic centre (NYU Langone Orthopedic Hospital, New York, USA) from January 2018 to January 2023, who completed the Patient-Reported Outcomes Measurement Information System (PROMIS) pain intensity questionnaires, collected preoperatively at one-, three-, six-, 12-, and 24-month follow-up times. Growth mixture modelling (GMM) was used to model the trajectories. Optimal model fit was determined by Bayesian information criterion (BIC), Vuong-Lo-Mendell-Rubin likelihood ratio test (VLMR-LRT), posterior probabilities, and entropy values. Association between trajectory groups and patient characteristics were measured by multinomial logistic regression using the three-step approach. Results: Among the 1,249 patients, a piecewise GMM model revealed three distinct pain trajectory groups: 56 patients (4.5%) in group 1; 1,144 patients (91.6%) in group 2; and 49 patients (3.9%) in group 3. Patients in group 2 experienced swift recovery post-THA and minimal preoperative pain. In contrast, groups 1 and 3 initiated with pronounced preoperative pain; however, only group 3 exhibited persistent long-term pain. Multinomial regression indicated African Americans were exceedingly likely to follow trajectory groups 1 (odds ratio (OR) 2.73) and 3 (OR 3.18). Additionally, odds of membership to group 3 increased by 12% for each BMI unit rise, by 19% for each added postoperative day, and by over four if discharged to rehabilitation services (OR 4.07). Conclusion: This study identified three distinct pain trajectories following THA, highlighting the role of individual patient factors in postoperative recovery. This emphasizes the importance of preoperatively addressing modifiable risk factors associated with suboptimal pain trajectories, particularly in at-risk patients. Cite this article: Bone Jt Open 2024;5(3):174–183.

Objective: to form a model for predicting the default of credit organizations under the modern conditions of the banking sector functioning.Methods: unidimensional analysis of variance, regression analysis of binary choice models.Results: in the modern economy, the banking system stability largely affects not only the financial sector, but also the economic and investment climate in the country. Understanding of the banks’ influence on the economy necessitates the formation of appropriate effective forecasting systems that allow identifying problem banks before revoking their licenses is necessary. The existing methodology of the Bank of Russia is characterized by subjectivity and inaccuracy of assessment. The analysis of studies on predicting bank defaults showed various approaches to the methodology of assessing the probability of credit institutions’ bankruptcy, though they have a number of shortcomings. Based on the selection of key factors affecting the bank’s financial stability, the logistic regression model for predicting bankruptcy of banks was formed. The methodology proposed in this article includes five predictors, selected on the basis of the improved methodology for selecting logit regression variables, and complements the existing methodologies.Scientific novelty: a methodology for assessing the probability of commercial banks’ bankruptcy in the Russian Federation was developed, which includes five key predictors for assessing the bank’s financial stability: return on assets, unit weight of liquid assets in the balance sheet currency, unit weight of the loan portfolio in the balance sheet currency, share of loans to the real sector in the balance sheet currency, and share of long-term placements in the loan portfolio. The logistic regression model of binary choice proposed in the paper allows distinguishing financially stable credit organizations from problem banks with a forecasting horizon of five months and a classification accuracy of 88,33 %.Practical significance: the relatively high classification accuracy of the model allows its use by the Bank of Russia in controlling the credit organizations functioning, as well as directly by the credit organization’s management, in order to assess the organization’s financial stability and to predict the default probability, as well as to form the bank’s development strategy.

Benggang (a fragmented erosion landform) have emerged as crucial factors restraining regional economic development, social stability, and ecological conservation due to their suddenness, explosiveness, and unpredictability. Monitoring the dynamic changes in distribution density, degree of development, and influencing factors of Benggang in large-scale natural environments can constitute preliminary references for studying the mechanisms, patterns, and influencing factors of Benggang in the natural environment, as well as providing insights for the coordination of regional socio-economic development and ecological environment governance. In this study, Sentinel-1/2 satellite constellation remote sensing images and other auxiliary data from 2016 to 2022 were combined with machine learning methods to identify Benggang erosion and measure the influencing factors of Benggang erosion in the middle and upper reaches of the Han River. The results indicate that: 1) The overall accuracy of the classification model in this study is 84%, with a Kappa coefficient of 0.8. The user's and producer's accuracy of Benggang erosion identification both exceed 95%, and their FScore is 0.97. However, smaller-scale Benggang erosion (< 100 m2) is easily confused with grassland and sparse vegetation, thereby reducing the Benggang identification accuracy. 2) Currently, the total area of Benggang erosion in the middle and upper reaches of the Han River is 435.5 km2. There are significant differences in Benggang erosion areas across counties, cities, and districts, with varying annual trends. In terms of spatial distribution, the Benggang erosion areas in Wuhua County, Xingning City, Dapu County, Fengshun County, Meixian District, Pingyuan County, Jiaoling County, and Meijiang District are 199.2, 70.1, 57.7, 50.1, 35.4, 14.9, 3.9, and 1.6 km2, respectively. In terms of time change trends, the average annual rates of change of Benggang erosion areas in Wuhua County, Xingning City, Dapu County, Fengshun County, Meixian District, Pingyuan County, Jiaoling County, and Meijiang District are 16.29, 6.51, 5.31, 4.94, 3.56, 1.39, 0.42, and 0.18 km2/a, respectively. 3) The occurrence probability of Benggang in the study area is significantly correlated (P < 0.001) with elevation, slope, vegetation coverage, geological type, population density, atmospheric pressure, rainfall, meridional wind speed, zonal wind speed, and wind speed. Within a certain range of variation in the influencing factors, elevation, slope, geological type, atmospheric pressure, meridional wind speed, zonal wind speed, and wind speed have a positive impact on Benggang occurrence probability in the study area, while vegetation coverage, population density, and rainfall have a negative impact on the occurrence of Benggang erosion. Furthermore, geological type, slope, atmospheric pressure, elevation, vegetation cover, population density and wind speed are the main influencing factors for the occurrence of Benggang in the study area. Therefore, strengthening the monitoring of these seven variables is necessary to enhance early warning capabilities for geological disasters due to Benggang in the study area.

Background and Aims: Gastric cancer (GC) is a leading cause of cancer incidence and mortality globally. Population screening is limited by the low incidence and prevalence of GC in the United States. A risk prediction algorithm to identify high-risk patients allows for targeted GC screening. We aimed to determine the feasibility and performance of a logistic regression model based on electronic health records to identify individuals at high risk for noncardia gastric cancer (NCGC). Methods: We included 614 patients who had a diagnosis of NCGC between ages 40 and 80 years and who were seen at our large tertiary medical center in multiple states between 2010 and 2021. Controls without a diagnosis of NCGC were randomly selected in a 1:10 ratio of cases to controls. Multiple imputation by chained equations for missing data followed by logistic regression on imputed datasets was used to estimate the probability of NCGC. Area under the curve and the 0.632 estimator was used as the estimate for discrimination. Results: The 0.632 estimator value was 0.731, indicating robust model performance. Probability of NCGC was higher with increasing age (odds ratio [OR] = 1.16, 95% confidence interval [CI]: 1.04–1.3), male sex (OR = 1.97; 95% CI: 1.64–2.36), Black (OR = 3.07; 95% CI: 2.46–3.83) or Asian race (OR = 4.39; 95% CI: 2.60–7.42), tobacco use (OR = 1.61; 95% CI: 1.34–1.94), anemia (OR = 1.35; 95% CI: 1.09–1.68), and pernicious anemia (OR = 6.12, 95% CI: 3.42–10.95). Conclusion: We demonstrate the feasibility and good performance of an electronic health record–based logistic regression model for estimating the probability of NCGC. Future studies will refine and validate this model, ultimately identifying a high-risk cohort who could be eligible for NCGC screening.

This article proposes some new estimators, namely Stein’s estimators for ridge regression and Kibria and Lukman estimator and compares their performance with some existing estimators, namely maximum likelihood estimator (MLE), ridge regression estimator, Liu estimator, almost unbiased ridge and Liu estimators, adjusted Liu estimator, James stein’s estimator, Kibria and Lukman estimator, Dorugade estimator, and modified ridge estimator for the logistic regression model to solve the multicollinearity problem. The bias, covariance matrix, and mean square error matrix for each of the estimators are provided. A Monte Carlo simulation has been conducted to compare the performance of different estimators. We consider the smaller mean squared error value as a performance criterion. From the simulation study, it is evident that all proposed estimators performed better than the MLE. Finally, a real-life data is analyzed to illustrate the findings of the article. Some promising estimators are recommended for the practitioners.

Copyright of SPORMETRE: The Journal of Physical Education & Sport Sciences / Beden Eğitimi ve Spor Bilimleri Dergisi is the property of SPORMETRE: The Journal of Physical Education & Sport Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Benggang (a fragmented erosion landform) have emerged as crucial factors restraining regional economic development, social stability, and ecological conservation due to their suddenness, explosiveness, and unpredictability. Monitoring the dynamic changes in distribution density, degree of development, and influencing factors of Benggang in large-scale natural environments can constitute preliminary references for studying the mechanisms, patterns, and influencing factors of Benggang in the natural environment, as well as providing insights for the coordination of regional socio-economic development and ecological environment governance. In this study, Sentinel-1/2 satellite constellation remote sensing images and other auxiliary data from 2016 to 2022 were combined with machine learning methods to identify Benggang erosion and measure the influencing factors of Benggang erosion in the middle and upper reaches of the Han River. The results indicate that: 1) The overall accuracy of the classification model in this study is 84%, with a Kappa coefficient of 0.8. The user's and producer's accuracy of Benggang erosion identification both exceed 95%, and their FScore is 0.97. However, smaller-scale Benggang erosion (< 100 m2) is easily confused with grassland and sparse vegetation, thereby reducing the Benggang identification accuracy. 2) Currently, the total area of Benggang erosion in the middle and upper reaches of the Han River is 435.5 km2. There are significant differences in Benggang erosion areas across counties, cities, and districts, with varying annual trends. In terms of spatial distribution, the Benggang erosion areas in Wuhua County, Xingning City, Dapu County, Fengshun County, Meixian District, Pingyuan County, Jiaoling County, and Meijiang District are 199.2, 70.1, 57.7, 50.1, 35.4, 14.9, 3.9, and 1.6 km2, respectively. In terms of time change trends, the average annual rates of change of Benggang erosion areas in Wuhua County, Xingning City, Dapu County, Fengshun County, Meixian District, Pingyuan County, Jiaoling County, and Meijiang District are 16.29, 6.51, 5.31, 4.94, 3.56, 1.39, 0.42, and 0.18 km2/a, respectively. 3) The occurrence probability of Benggang in the study area is significantly correlated (P < 0.001) with elevation, slope, vegetation coverage, geological type, population density, atmospheric pressure, rainfall, meridional wind speed, zonal wind speed, and wind speed. Within a certain range of variation in the influencing factors, elevation, slope, geological type, atmospheric pressure, meridional wind speed, zonal wind speed, and wind speed have a positive impact on Benggang occurrence probability in the study area, while vegetation coverage, population density, and rainfall have a negative impact on the occurrence of Benggang erosion. Furthermore, geological type, slope, atmospheric pressure, elevation, vegetation cover, population density and wind speed are the main influencing factors for the occurrence of Benggang in the study area. Therefore, strengthening the monitoring of these seven variables is necessary to enhance early warning capabilities for geological disasters due to Benggang in the study area. [ABSTRACT FROM AUTHOR]

Farmers are aware of challenges brought by climate change yet struggle with adaptation measures due to a lack of resources such as early warning systems, ecosystem restoration, climate-resilient infrastructure, and water supply. This study, undertaken to assess smallholder crop farmers' awareness of, and adaptation to, the impacts of climate change, was conducted in the three local municipalities of Mopani district in the Limpopo Province, South Africa: Giyani, Letaba and Maruleng. The proportionate sampling technique was used to obtain the study sample size of 291 farmers. A Statistical Package for Social Sciences (SPSS) version 28 was used to analyze data, along with descriptive statistics and a binary logistic regression model. The results indicated that 100% of smallholder crop farmers were aware of climate change but struggled to implement adaptation measures. The binary logistic regression results revealed that the variables gender, farm size and change of weather had a significant influence on farmers' adaptation measures. Ninety per cent of smallholder crop farmers were aware of, and implemented, climate change adaptation measures. The study recommends a heightened focus on improving the level of farmers' efforts to adapt, to mitigate the effects of climate change in the study area. [ABSTRACT FROM AUTHOR]

Featured Application: This study is an extension of the "Smart Computing Models, Sensors, and Early Diagnostic Speech and Language Deficits Indicators in Child Communication" project, abbreviated "SmartSpeech". The primary goal of SmartSpeech is to assist clinicians in identifying neurodevelopmental disorders in children. To that end, a serious game has been designed to evaluate the child's developmental profile. The current study aims to explore the enhancement of the SmartSpeech machine learning model efficacy capturing underlying patterns in the data. This study holds great potential for advancing our understanding of children's neurodevelopmental disorders and further upgrading the accuracy of digital diagnostic tools. Developmental domains like physical, verbal, cognitive, and social-emotional skills are crucial for monitoring a child's growth. However, identifying neurodevelopmental deficiencies can be challenging due to the high level of variability and overlap. Early detection is essential, and digital procedures can assist in the process. This study leverages the current advances in artificial intelligence to address the prediction of neurodevelopmental disorders through a comprehensive machine learning approach. A novel and recently developed serious game dataset, collecting various data on children's speech and linguistic responses, was used. The initial dataset comprised 520 instances, reduced to 473 participants after rigorous data preprocessing. Cluster analysis revealed distinct patterns and structures in the data, while reliability analysis ensured measurement consistency. A robust prediction model was developed using logistic regression. Applied to a subset of 184 participants with an average age of 7 years, the model demonstrated high accuracy, precision, recall, and F1-score, effectively distinguishing between instances with and without neurodevelopmental disorders. In conclusion, this research highlights the effectiveness of the machine learning approach in diagnosing neurodevelopmental disorders based on cognitive features, and offers new opportunities for decision making, classification, and clinical assessment, paving the way for early and personalized interventions for at-risk individuals. [ABSTRACT FROM AUTHOR]

Symptoms of COVID-19 are similar to the influenza virus, but because treatments and prognoses are different, it is important to accurately and rapidly differentiate these diseases. The aim of this study was to evaluate whether the analysis of complete blood count (CBC), including cellular population (CPD) data of leukocytes and automated flow cytometry analysis, could discriminate these pathologies. In total, 350 patients with COVID-19 and 102 patients with influenza were included between September 2021 and April 2022 in the tertiary hospital of Suresnes (France). Platelets were lower in patients with influenza than in patients with COVID-19, whereas the CD16pos monocyte count and the ratio of the CD16pos monocytes/total monocyte count were higher. Significant differences were observed for 9/56 CPD of COVID-19 and flu patients. A logistic regression model with 17 parameters, including among them 11 CPD, the haemoglobin level, the haematocrit, the red cell distribution width, and B-lymphocyte and CD16pos monocyte levels, discriminates COVID-19 patients from flu patients. The sensitivity and efficiency of the model were 96.2 and 86.6%, respectively, with an area under the curve of 0.862. Classical parameters of CBC are very similar among the three infections, but CPD, CD16pos monocytes, and B-lymphocyte levels can discriminate patients with COVID-19. [ABSTRACT FROM AUTHOR]