Results 171 to 180 of about 455,878 (284)

Farmers' Preferences for Gene Editing Crops and Influencing Factors

open access: yesApplied Economic Perspectives and Policy, EarlyView.
ABSTRACT Gene editing (GE) is gaining momentum worldwide, but limited data on UK farmers' preferences hinders our understanding of its potential impact amid deregulation debates. Based on a survey of 200 English arable farmers, we employ a Latent Class Analysis and Multinomial Logit regressions to investigate current preferences for GE crops.
Bertolozzi‐Caredio Daniele   +1 more
wiley   +1 more source

Mapping the Innovation DNA of Agribusiness Firms: A Multi‐Method Analysis of Strategic Capabilities and Performance

open access: yesAgribusiness, EarlyView.
ABSTRACT Innovation is essential for competitiveness in agribusiness facing dynamic environments. This study examines how market orientation, marketing, relational, and social capabilities influence innovation performance. Using data from 751 Spanish firms and a multi‐method approach that integrates Structural Equation Modeling (PLS‐SEM), Necessary ...
Beatriz Corchuelo Martínez‐Azúa   +1 more
wiley   +1 more source

Model predictive control with inline parameter adaptation for direct crystal growth rate regulation

open access: yesAIChE Journal, EarlyView.
Abstract In batch cooling crystallization, many interactive factors, including supersaturation, reactor dimensions, and operating conditions, govern crystal growth and significantly influence product properties. Variables such as temperature or refractive index are used as surrogate control variables but have limitations in capturing growth rate ...
Huitian Yu, Jiewen Zhao, Heiko Briesen
wiley   +1 more source

Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art

open access: yesAdvanced Intelligent Discovery, EarlyView.
Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser   +6 more
wiley   +1 more source

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

open access: yesAdvanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong   +5 more
wiley   +1 more source

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