Geographic patterns and ecological causes of phylogenetic structure in mosses along an elevational gradient in the central Himalaya by Hong Qian, Oriol Grau

“…Our study shows that temperature-related variables and climate seasonality variables are more important drivers of phylogenetic dispersion in mosses in Nepal, compared with precipitation-related variables and climate extreme variables, respectively.…”
Get full text

INFLUENCE OF THE CRANK MECHANISM POSITION IN THE MOTION OF AN OSCILLATING SIEVE by EMILIAN MOSNEGUTU, NARCIS BARSAN, MIRELA PANAINTE-LEHADUS, DANA CHITIMUS, CLAUDIA TOMOZEI

“…From the obtained results analysis, it was found that in the case of 12.71° of the connecting rod angle, in relation to the horizontal, the lowest value of the angle described by the motion of the tie rod between the extreme points was obtained. Also, for the value of the connecting rod angle of approximately 6.5° equal angles described by the extreme positions of the sieve support in relation to the vertical were obtained. …”
Get full text

Causes of the exceptionally high number of fatalities in the Ahr valley, Germany, during the 2021 flood by B. Rhein, B. Rhein, H. Kreibich

“…We investigate what made this event so deadly in order to help improve flood risk management and prevent future fatalities. …”
Get full text

Older adults do not consistently overestimate their action opportunities across different settings by Isabel Bauer, Milena S. Gölz, Lisa Finkel, Maddalena Blasizzo, Sarah E. M. Stoll, Jennifer Randerath

“…We discuss potential links between more extreme judgments in older adults and higher reliance on learned patterns. …”
Get full text

Prediction of High-ozone Events Using GAM, SMOTE, and Tail Dependence Approaches in Texas (2005–2019) by Benjamin Brown-Steiner, Xiong Zhou, Matthew J. Alvarado, Brook T. Russell

“…We also find that the tail dependence approach is capable of predicting extreme ozone events, but algorithmic stability and configuration complexity can make this approach difficult to operationalize on a broad scale and that the selection of the threshold needs to be carefully considered. …”
Get full text

Predicting the heat capacity of strontium-praseodymium oxysilicate SrPr4(SiO4)3O using machine learning, deep learning, and hybrid models by Amir Hossein Sheikhshoaei, Ali Khoshsima, Davood Zabihzadeh

“…In this study, the capability of five advanced machine learning models, including Random Forest (RF), Gradient Boosting (GBoost), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Decision Tree (DT) models, and three deep learning models, TabNet, Deep Belief Network (DBN), and Deep Neural Network (DNN) was investigated. …”
Get full text

Verlossing as socio-politieke bevryding? by P. Robinson

“…Widely different approaches have developed of which the one extreme position sees salvation as "saving souls" in a clearly individualistic and spiritualistic way while on the other extreme one has the demand for total socio-political liberation. …”
Get full text

Pobreza hídrica y estancamiento socioeconómico: el caso de Manresa by Joan Carles Llurdés Coit, David Saurí Pujol, Rufí Cerdan Heredia

“…Además del ahorro extremo, la pobreza hídrica también estimula estrategias en forma de iniciativas para obtener subsidios y otras ayudas.  …”
Get full text

A Novel Classification of Uncertain Stream Data using Ant Colony Optimization Based on Radial Basis Function by Tahsin Ali Mohammed Amin, Sabah Robitan Mahmood, Rebar Dara Mohammed, Pshtiwan Jabar Karim

“…Finally, we evaluate our proposed method against some of the most popular ML methods, including a k-nearest neighbor, support vector machine, random forest, decision tree, logistic regression, and extreme gradient boosting (Xgboost). …”
Get full text

The dependency structure of international commodity and stock markets after the Russia-Ukraine war. by Cheng Zhang, Shuo Liu, Mimi Qin, Bin Gao

“…In this paper, the marginal density function of each series is constructed using the ARMA-GARCH-std method, and the R-Vine copula model is built based on the marginal density function to analyze the correlation relationship between each market. From the Tree1 of the Vine copula, it is found that crude oil becomes the core connecting each commodity market and the stock market during the Russia-Ukraine war. …”
Get full text

The Effects of Poverty on Rural Lively Hoods: A Case Study of Bufumbo Sub-County and Bufumeo Sub-Couny, Mbale District. by Muduwa, Jennifer

“…Uganda has made progress towards poverty elimination in rural areas having successfully achieved the Millennium Development goal target of halving the number of people in extreme poverty way ahead of the 2015 deadline. Uganda is on course to achieve its national target of reducing this number to 10% by 2017. …”
Get full text

Establishing a radiomics model using contrast-enhanced ultrasound for preoperative prediction of neoplastic gallbladder polyps exceeding 10 mm by Dong Jiang, Yi Qian, Yijun Gu, Ru Wang, Hua Yu, Zhenmeng Wang, Hui Dong, Dongyu Chen, Yan Chen, Haozheng Jiang, Yiran Li

“…This model, derived from machine learning frameworks including Support Vector Machine (SVM), Logistic Regression (LR), Multilayer Perceptron (MLP), k-Nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGBoost) with fivefold cross-validation, showed AUCs of 0.95 (95% CI: 0.90–0.99) and 0.87 (95% CI: 0.72–1.0) in internal validation. …”
Get full text

More than a feeling: A global economic valuation of subjective wellbeing damages resulting from rising temperatures. by Stephan Dietrich, Stafford Nichols

“…We use an experienced utility approach to measure how extreme heat affects subjective wellbeing. The data comes from a life evaluation question collected on nationally representative surveys covering 160 countries, conducted annually for 13 years. …”
Get full text

Sorghum yield prediction based on remote sensing and machine learning in conflict affected South Sudan by John Karongo, Joseph Ivivi Mwaniki, John Ndiritu, Victor Mokaya

“…We use five Machine Learning (ML) techniques, including Random Forest (RF), Decision Tree (DT), Extreme Gradient Boosting (XGboost), Support Vector Machine (SVM) and Artificial Neural Network (ANN) to predict 2021 end-of-season sorghum yield in conflict affected Upper Nile and Western Bahr El Gazal states. …”
Get full text

Efficient Feature Selection and Hyperparameter Tuning for Improved Speech Signal-Based Parkinson’s Disease Diagnosis via Machine Learning Techniques by Deepak Painuli, Suyash Bhardwaj, Utku Kose

“…This study investigates 12 machine learning models—logistic regression (LR), support vector machine (SVM, linear/RBF), K-nearest neighbor (KNN), Naïve bayes (NB), decision tree (DT), random forest (RF), extra trees (ET), gradient boosting (GbBoost), extreme gradient boosting (XgBoost), adaboost, and multi-layer perceptron (MLP)—to develop a robust ML model capable of reliably identifying PD cases. …”
Get full text

A Novel Ensemble Classifier Selection Method for Software Defect Prediction by Xin Dong, Jie Wang, Yan Liang

“…The experimental results demonstrate that the DFD ensemble learning-based software defect prediction model outperforms the ten other models, including five common machine learning (ML) classification algorithms (logistic regression (LR), naïve Bayes (NB), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM)), two deep learning (DL) algorithms (multi-layer perceptron (MLP) and convolutional neural network (CNN)), and three ensemble learning algorithms (random forest (RF), extreme gradient boosting (XGB), and stacking). …”
Get full text

Managing anxiety disorders with the neuro-biofeedback method of Brain Boy Universal Professional by Eleftheria Zafeiri, Vasileios Dedes, Kostantinos Tzirogiannis, Agapi Kandylaki, Maria Polikandrioti, Dimitris Panidis, Georgios I. Panoutsopoulos

“…Based on the SAS scale, before biofeedback, 42.4% of the individuals showed minimal to moderate anxiety, 21.2% marked severe anxiety and 36.5% most extreme anxiety. After the biofeedback, 68.2% of the individuals were within a normal range, 27.1% had minimal to moderate anxiety, 4.7% marked severe anxiety, and none in most extreme anxiety…”
Get full text

Weather Monitoring and Classification Tools Using Fuzzy Logic Method Based on Internet of Things for Agriculture by Muhammad Alfazri Avindra, Ahmad Taqwa, Suroso

“…Agriculture is a crucial sector that faces significant challenges due to climate change, such as altered rainfall patterns, increased temperatures, and extreme weather, which threaten productivity. This research aims to design and develop a weather monitoring and classification tool utilizing the Fuzzy Logic method based on the Internet of Things (IoT). …”
Get full text

The prevalence and prognostic significance of Sarcopenia and Adipopenia in Pleural Mesothelioma by Andrew C. Kidd, Gordon W. Cowell, Geoffrey A. Martin, Jenny Ferguson, Dean A. Fennell, Matt Evison, Kevin G. Blyth

“…Muscle/fat loss were defined by < 0 % change (%∆) between CT scans. Extreme muscle/fat loss were defined by <25th percentile of %∆. …”
Get full text

Individualized resting-state functional connectivity abnormalities unveil two major depressive disorder subtypes with contrasting abnormal patterns of abnormality by Keke Fang, Lianjie Niu, Baohong Wen, Liang Liu, Ya Tian, Huiting Yang, Ying Hou, Shaoqiang Han, Xianfu Sun, Wenzhou Zhang

“…In MDD patients, these extreme functional connections were linked to age of onset and reward-related processes. …”
Get full text