Results 41 to 50 of about 125,586 (263)

In Silico Studies of Indole Derivatives as Antibacterial Agents

Journal of Pharmacopuncture, 2023
Objectives: Molecular docking and QSAR studies of indole derivatives as antibacterial agents. Methods: In this study, we used a multiple linear regressions (MLR) approach to construct a 2D quantitative structure activity relationship of 14 reported ...
Mridul Shah, Adarsh Kumar, Ankit Kumar Singh, Harshwardhan Singh, Balasubramanian Narasimhan, Pradeep Kumar   +5 more
doaj   +1 more source

PRESS values for all algorithms: (a) PLS; (b) SPA-MLR; (c) GA-MLR; (d) BVS; and (e) FA-MLR.

, 2014
PRESS values for all algorithms: (a) PLS; (b) SPA-MLR; (c) GA-MLR; (d) BVS; and (e) FA-MLR.
Alexandre C. B. Delbem (672578), Arlindo R. G. Filho (672580), Anderson S. Soares (672576), Lauro C. M. de Paula (672575), Telma W. de Lima (672577), Clarimar J. Coelho (672579)   +5 more
core   +1 more source

Online Appendix: Details on the MLR Process

, 2022
Online Appendix: Details on the MLR ...
Anonymous
core   +1 more source

Predicting the Reactivity of Acyclic Silylenes and Germylenes in Hydrogen Activation Using Machine Learning

Advanced Science, EarlyView.
Statistical models for the prediction of the energy profile of H2 activation by silylenes and germylenes were developed. A simple model based on increments of the elements in α‐position enabled robust energy prediction, extrapolation to new structures, and transferability to new tetrylenes and substrates.
Michelle Kleinhaus, Henning Remm, Prasenjit Palui, Alexander Linke, Viktoria H. Gessner   +4 more
wiley   +1 more source

ارزیابی یادگیری ماشین و سنجش از دور در تخمین تبخیر و تعرق مرجع [PDF]

مدیریت آب و آبیاری
تبخیر و تعرق مرجع (ETo) یکی از اساسی‌ترین متغیرها در تعیین نیاز آبی محصول و برنامه‌ریزی و طراحی سامانه‌های آبیاری است. مدل‌های یادگیری ماشین برای رفع محدودیت‌های مدل‌های تجربی و برآورد تبخیر تعرق (ET) توسعه داده‌ شده‌اند.
جوانشیر عزیزی مبصر, علی رسول‌زاده, امین اکبری مجد   +2 more
doaj   +1 more source

Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

Advanced Energy Materials, EarlyView.
In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu, Milica Zdravković, Dijana Milosavljević, Konstantin Köster, Olivier Guillon, Jyh‐Chiang Jiang, Payam Kaghazchi   +6 more
wiley   +1 more source

Immigrant mental health, safe work, discrimination, and state policies: From racism and xenophobia to health equity

American Journal of Community Psychology, EarlyView.
Abstract An ecological model was developed to examine the pathways linking immigration state policies to physically safe work conditions and work volition, interpersonal discrimination, and mental health distress. The ecological framework was tested among two subsamples totaling 529 Latinx immigrant participants: (1) immigrants who resided in states ...
Germán A. Cadenas, Thanharat Silamongkol, H. Kenny Nienhusser, Fiorella L. Carlos Chavez, Cristalís Capielo Rosario, Lucas Torres, Oswaldo Moreno   +6 more
wiley   +1 more source

MLR notations.

, 2018
MLR notations.
Azadeh Ansari (5814800), Leily Farrokhvar (5814797), Behrooz Kamali (5814803)   +2 more
core   +1 more source

Exclusion of the Locus for Autosomal Recessive Pseudohypoaldosteronism Type 1 from the Mineralocorticoid Receptor Gene Region on Human Chromosome 4q by Linkage Analysis. [PDF]

, 1995
Pseudohypoaldosteronism type 1 (PHA1) is an uncommon inherited disorder characterized by salt-wasting in infancy arising from target organ unresponsiveness to mineralocorticoids.
Hanukoglu, A., Gardiner, R.M., Bistritzer, T., Seckl, J., Chung, E., Kuhnle, U., Dillon, M., Gardiner, R. M., Hanukoglu, I., Rees, M., Thompson, R.   +10 more
core   +1 more source

Sensitivity analysis using LR and MLR models.

, 2022
Sensitivity analysis using LR and MLR models.
Hemn U. Ahmed (12635757), Azad A. Mohammed (10968689), Ahmed Mohammed (7004726)   +2 more
core   +1 more source