Results 91 to 100 of about 3,428,862 (283)

Probe‐Free, Primer‐Programmed Amplicon Encoding Enables Nanopore‐Based Multiplex Detection With Deep Learning Decoding

Advanced Science, EarlyView.
Primer‐programmed amplicon encoding enables reporter‐probe‐free multiplex nucleic acid identification using glass nanopore sensing and deep neural network decoding. ABSTRACT Single‐reaction multiplex nucleic acid testing (NATs) must both co‐amplify multiple targets and distinguish the resulting amplicons within the product mixture.
Md. Ahasan Ahamed, Muhammad Asad Ullah Khalid, Wahid Uz Zaman, Anthony J. Politza, Weihua Guan   +4 more
wiley   +1 more source

Do Politically Motivated Import Bans Leave Lasting Scars? Evidence From Australian–Chinese Beef Trade

Agribusiness, EarlyView.
ABSTRACT In May 2020, China abruptly suspended imports from several major Australian beef processors, escalating a diplomatic dispute between the two countries. This trade measure disrupted one of the largest beef export relationships in the world almost overnight.
K. Aleks Schaefer, Youngjune Kim
wiley   +1 more source

Value of Information of Improved Traceability in Fresh Produce Markets

Agribusiness, EarlyView.
ABSTRACT Traceability plays an important role in promoting a safe food supply by fostering transparent information exchange along the food supply chain. New technological innovations have the potential to improve traceability outcomes, as greater transparency along the food supply chain can aid in pinpointing precise origins of the contamination ...
Kelsey Vourazeris, Mark R. Manfredo, Michael N. Kozicki   +2 more
wiley   +1 more source

The Geriatric Nutritional Risk Index Predicts Short‐ and Long‐Term Outcomes in the Oldest‐Old With Colorectal Cancer: A Multi‐Institutional Analysis of 225 Nonagenarians

Annals of Gastroenterological Surgery, EarlyView.
This multi‐institutional study evaluated the Geriatric Nutritional Risk Index (GNRI) in 225 nonagenarians undergoing surgical resection for colorectal cancer. A lower preoperative GNRI was independently associated with a longer hospital stay and poorer overall survival, even after adjustment for confounders using inverse‐probability‐of‐treatment ...
Toshiaki Toshima, Fuminori Teraishi, Satoe Takanaga, Toshiharu Mitsuhashi, Ryo Inada, Tsuyoshi Ohtani, Ryosuke Yoshida, Naoto Hori, Kaoru Shigemitsu, Sumiharu Yamamoto, Tetsushi Kubota, Yuka Okano, Tetsuji Nobuhisa, Fumitaka Taniguchi, Wataru Ishikawa, Ryohei Shoji, Tatsuo Matsuda, Tatsuo Umeoka, Toshiyoshi Fujiwara, The Setouchi Colorectal Neoplasm Registration Study Group Collaborators   +19 more
wiley   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

Advanced Intelligent Discovery, EarlyView.
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath, Shauryaraj K. Jilkar, Aditya Udgaonkar, Yu (Joe) Lu, Ali Mesbah   +4 more
wiley   +1 more source

Mixture of bivariate Poisson regression models with an application to insurance [PDF]

In a recent paper Bermúdez [2009] used bivariate Poisson regression models for ratemaking in car insurance, and included zero-inflated models to account for the excess of zeros and the overdispersion in the data set. In the present paper, we revisit this
Lluís Bermúdez, Dimitris Karlis
core  

From Data to Discovery: Machine Learning–Enabled Intelligent Characterization of Two‐Dimensional Materials

Advanced Intelligent Discovery, EarlyView.
Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley   +1 more source

Cellular Material Network: A General Machine Learning Architecture for Predicting Mechanical Properties of Cellular Materials

Advanced Intelligent Systems, EarlyView.
This study introduces Cellular Material Network (CM‐Net), a pioneering machine learning architecture integrating physical information, to predict the mechanical properties of cellular materials. Comprehensive validation through simulations and experiments demonstrates its accuracy in predicting nonlinear behaviors, including initial peak compression ...
Sicong Zhou, Qiang Gao, Liuchao Jin, Guoquan Xie, Kang Zhang, Wei‐Hsin Liao   +5 more
wiley   +1 more source

Weather based forewarning model for cotton pests using zero-inflated and hurdle regression models

Journal of Agrometeorology
Early forewarning of crop pest based on weather variables provides lead time to manage impending pest attacks that minimize crop loss, decrease the cost of pesticides and enhance the crop yield.
N. NARANAMMAL, S.R. KRISHNA PRIYA
doaj   +1 more source