Results 111 to 120 of about 162,086 (291)

HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network

Ecological Informatics
Machine learning (ML) is emerging as a promising tool for modeling hydro-ecological processes due to the increasing availability of large environmental data.
Tam V. Nguyen, Vinh Ngoc Tran, Hoang Tran, Doan Van Binh, Toan D. Duong, Thanh Duc Dang, Pia Ebeling   +6 more
doaj   +1 more source

Teaching Concurrent Software Design: A Case Study Using Android

, 2017
In this article, we explore various parallel and distributed computing topics from a user-centric software engineering perspective. Specifically, in the context of mobile application development, we study the basic building blocks of interactive ...
Läufer, Konstantin, Thiruvathukal, George K.   +1 more
core  

Engineered Carbon Dots from a Traditional Herb Pair Orchestrate Concurrent Antioxidant and AP‐1‐Mediated Inflammation to Attenuate Renal Ischemia‐Reperfusion Injury

Advanced Science, EarlyView.
This study integrates two complementary traditional Chinese medicine components as precursors to synthesize bioactive AM‐AS@CDs. Compared with single‐component CDs, these dual‐component CDs exhibit enhanced antioxidant capacity and superior renoprotective effects.
Bixiao Liu, Fuying Zhu, Zhuqing Wang, Jiawen Chen, Xiaomiao Cui, Congzhong Yang, Yao Peng, Dengyuan Feng, Li Lu, Hui Wei, Xiaozhi Zhao   +10 more
wiley   +1 more source

Mechanism‐Informed Machine Learning Enables Discovery of Oncolytic Peptides for Cancer Immunotherapy

Advanced Science, EarlyView.
MISPOP integrates ensemble learning with membrane‐active physicochemical priors to identify Dermaseptin‐S9, a natural oncolytic peptide that disrupts tumor membranes, triggers immunogenic cell death, and shows strong antitumor activity. The study illustrates a mechanism‐informed route from peptide sequence data to cancer immunotherapy leads.
Wen Zhang, Shengxin Lu, Guangyong Zheng, Shensuo Li, Hongyu Chen, Mei Hong, Xiangru Zhou, Ruotian Tang, Ye Wu, Weidong Zhang, Dong Lu, Xin Luan   +11 more
wiley   +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

WTDetect: a third-party website tracking detection framework for android applications

Cybersecurity
With the development of HTML5, tracking technologies have evolved dramatically and gradually moved from cookies to browser fingerprinting. Previous research has shown that there are more serious privacy threats associated with tracking behavior on third ...
Wei Liu, Xinyu Liu, Yun Feng, Kerui Huang, Ze Jin, Yaqin Cao, Yuling Liu, Qixu Liu   +7 more
doaj   +1 more source

Robots and Robots and More [PDF]

Over the course of this year I worked on several projects across different departments, from physical robots at Swamp Works, to automated robots running functional tests on launch critical Graphical User Interfaces (GUIs).
Siegel, Alex
core   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

Accelerating Biosensor Discovery: A Computationally‐Driven Pipeline for Microplastics Monitoring

Advanced Intelligent Discovery, EarlyView.
A computationally guided pipeline unites molecular simulation, synthetic biology, electrochemical engineering, and machine learning to accelerate biosensor discovery. A Bacillus anthracis carbohydrate‐binding module is used to develop a high‐performance micro‐ and nanoplastics sensor with greatly reduced error and variability.
Gabriel X. Pereira, Marcos de C. Leite, Pedro H. M. Zanineli, Juliana N. Y. Costa, Débora M. Cunha, Sophia P. Serra, Pedro H. Sophia, Flávio M. Shimizu, Yasmin W. Moura, Célio C. Oliveira, Gabriel R. Schleder, Gabriela F. Persinoti, Leandro Merces, Renato S. Lima   +13 more
wiley   +1 more source

Enhancement of Damage Detectability in Aircraft Structures Using the Fusion of NDT Results

Fatigue of Aircraft Structures, 2017
Following the damage tolerance philosophy in aircraft design and operation, one of the most significant stages of maintenance is non-destructive testing of structures.
Zapłotny Marcin, Katunin Andrzej, Dragan Krzysztof   +2 more
doaj   +1 more source