Physics-augmented deep learning models for improving evapotranspiration estimation in global land regions by Binrui Liu, Xinguang He, Wenkai Lyu, Lizhi Tao

“…A Physics-Data Learning (PDL) model is first formed by adding a complementary physical variable generated by Penman–Monteith (PM) equation to a deep learning (DL) model along with driving variables to regress latent heat flux. …”
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Performance of Machine Learning Classifiers for Diabetes Prediction by Mijala Manandhar, Shaikat Baidya, Babalpreet Kaur, Katia Atoji

“…The dataset included 768 instances with nine attributes, where the target variable indicated whether a patient tested positive for diabetes. …”
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AngleCam: Predicting the temporal variation of leaf angle distributions from image series with deep learning by Teja Kattenborn, Ronny Richter, Claudia Guimarães‐Steinicke, Hannes Feilhauer, Christian Wirth

“…The plausibility of the predicted leaf angle time series was underlined by its close relationship with environmental variables related to transpiration. The evaluations confirm that AngleCam is a robust and efficient method to track leaf angles under field conditions. …”
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Impact of Low-dose Imaging on PET Quantitative Accuracy and Image Quality by SU Xuesong, GENG Jianhua, ZHENG Rong, WANG Xuejuan

“…Subsequently, the recovery coefficient (RC), contrast recovery coefficient (CRC), contrast-to-noise ratio (CNR), percent background variability (PBV), background coefficient of variation (BCV), and residual error (RE) of the lung insert at different image planes were calculated within the phantom. …”

Effect of a mix of condense and hydrolysable tannins feed additive on lactating dairy cows’ services per conception and days open by Alejandro R Castillo, Julio A Di Rienzo, Damiano Cavallini

“…Services per conception was modeled as a Poisson count using a generalized linear model with log link function. Days open was modeled with a generalized linear model for a gamma variable with log link function. …”
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Synergistic Enhancement of Carbon Sinks and Connectivity: Restoration and Renewal of Ecological Networks in Nanjing, China by Renfei Zhang, Hongye Li, Zhicheng Liu

“…We propose an innovative carbon sink index (CSI) that integrates three indicators: capacity, efficiency, and variability. This index assesses the carbon sink function of ecological patches from both static and dynamic perspectives. …”
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Application of Optimization Algorithms in Voter Service Module Allocation by Edgar Jardón, Marcelo Romero, José-Raymundo Marcial-Romero

“…These findings align with principles of spatial justice and highlight the practical efficiency of heuristic-based solutions in administrative resource allocation. …”
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Using Fuzzy Feed-Forward Neural Network for Linguistic Processing by AbdulRahim K. Rah, Sozan S. Haydar

“…To determine the costs associated with fuzzy calculations and fuzzy objectives, developed a cost function. At that point, created a learning algorithm from the cost capacity to align the four variables of each trapezoidal fuzzy weight. …”
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Reliability analysis of landing gear operation based on improved support vector machine-based decomposed-coordinated by ZHANG Lei, CHEN Junyu, FENG Jun, FENG Yunwen, LIU Lei

“…The case results show that the proposed ISVM-DC can accurately decompose and coordinate the architecture of landing gear operational failure, and efficiently construct and solve the reliability performance function.…”
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Does charcoal act as a protective agent in fish nutrition to increase growth and physiological performance? A meta-analysis by Aliati Iswantari, Vitas Atmadi Prakoso, Nunak Nafiqoh, Rendy Ginanjar, Henti Hendalastuti Rachmat, Agung Irawan, Kurniawan Kurniawan, Mochammad Zamroni, Fathur Rochman, Danung Nur Adli, Mohammad Miftakhus Sholikin, Arif Wibowo, Anuraga Jayanegara

“…Charcoal notably reduced aspartate aminotransferase and increased immunoglobulin levels, suggesting benefits for liver function and immunity. Meta-regression revealed dose-dependent improvements with wood charcoal, whereas responses to bamboo and activated charcoal were variable. …”
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First-principles investigation on structural, electronic, and optical properties of (Cd1-xZnx)3As2 by Yu Deng, Xun Ge, Rongxiu Feng, Yunye Liang, Xiaohao Zhou

“…Calculations on the optical properties (dielectric function, absorption coefficient) of (Cd1-xZnx)3As2 show that it behaves well in the near infrared and far infrared. …”
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Inversion and validation of soil water-holding capacity in a wild fruit forest, using hyperspectral technology combined with machine learning by Tingwei Song, Liang Guo, Qian Sun, Guizhen Gao, Jing Chen, Qikun Zhang

“…The results revealed a significant negative correlation between SWHC and the smoothed leaf spectral reflectance, with the best correlation coefficient was r = − 0.59. The use of third-order derivatives and logarithmic second-order derivatives further enhanced this correlation, yielding optimal coefficients of r = − 0.78 and r = − 0.72, respectively. …”
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Enhancing Thermal–Hydraulic Performance in Nuclear Reactor Subchannels with Al₂O₃ Nanofluids: A CFD Analysis by Mohammad A. I. Sardar, Mushfiqur Rahman, Philip Rubini

“…This analysis has been carried out using the RNG k-epsilon turbulence model with standard wall function in ANSYS FLUENT 2022R1. The impact of various flow conditions and nanofluid concentrations has been examined. …”
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The Relationship Between Biometric Features of Trees and the Intensity of Birch Sap Leakage in Various Forest Sites by Szczepan Kopeć, Paweł Staniszewski, Robert Tomusiak, Maciej Bilek, Dariusz Zastocki, Tadeusz Moskalik

“…Research on the utility value of birch sap and the influence of a number of factors on its efficiency and quality has been carried out in many research centers, but so far, there are not many studies on the variability of such parameters as a function of time, taking into account the entire period of sap leakage. …”
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Correlation between Stressful Life Events and Suicidal Ideation among Patients with Schizophrenia in Remission Stage: A Cross-sectional Study by Ashok Silamban, Kailash Sureshkumar, Aravindh Manogaran, Shabeeba Z Kailash

“…Introduction: Patients with schizophrenia often exhibit impaired executive brain function, which disrupts their daily activities. Relapse in schizophrenia has been linked to stressful life events. …”
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Machine learning-based prediction of pervaporation permeation using physicochemical properties of permeant-membrane and process conditions by Muhammad Mujiburohman, Marwen Elkamel, Farzad Hourfar, Ali Elkamel

“…Two types of inputs were evaluated: regular variables (Type I) and dimensionless groups derived from these regular variables (Type II). …”
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Machine learning projection of climate and technology impacts on crops key to food security by Dan Li, Vassili Kitsios, David Newth, Terence John O’Kane

“…Additionally, a novel machine learning climate model emulator allows efficient estimation of crop production growth under a multitude of carbon equivalent emissions scenarios. …”
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Radiotherapy-induced fatigue in Palestinian breast cancer survivors by Hussein ALMasri, Omar Rimawi

“…Our purpose is to assess, compare, and correlate CRF in breast cancer survivors undergoing radiotherapy (RT) with study variables.Methods: CRF in breast cancer survivors was assessed using Functional Assessment of Chronic Illness Therapy – Fatigue Scale (FACIT-F) (version 4). …”
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Microbially‐Mediated Soil Carbon‐Nitrogen Dynamics in Response to Future Soil Moisture Change by Wanyu Li, Gangsheng Wang, Zirui Mu, Shanshan Qi, Shuhao Zhou, Daifeng Xiang

“…Furthermore, microbes and enzymes were key drivers of soil C‐N transformations, with soil enzymes displaying the highest correlation with SSI (nonlinear correlation coefficient based on mutual information = 0.81). This study establishes a foundational relationship between soil C‐N variables and soil moisture, accounting for lag effects, to enhance our understanding of the complex responses of these variables under future climate change scenarios.…”
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Analysis of Surface Roughness and Machine Learning-Based Modeling in Dry Turning of Super Duplex Stainless Steel Using Textured Tools by Shailendra Pawanr, Kapil Gupta

“…One of the most critical aspects of turning, and machining in general, is the surface roughness of the finished product, which directly influences the performance, functionality, and longevity of machined components. …”
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