Results 11 to 20 of about 402 (93)

Trait-Relevant Tasks Improve Personality Prediction From Structural-Functional Brain Network Coupling. [PDF]

Hum Brain Mapp
Significant associations between the Big Five personality traits and structural‐functional brain network coupling are absent during resting state but emerge during trait‐relevant tasks. Carefully designed trait‐relevant tasks may amplify individual differences in trait‐related neural characteristics and thereby enhance their detectability.
Popp JL, Thiele JA, Faskowitz J, Seguin C, Sporns O, Hilger K.   +5 more
europepmc   +2 more sources

From Estimation to Discrimination: Algorithmic Bias, Predictive Uncertainty, and Anti‐Discrimination Law

The Modern Law Review, EarlyView.
Machine learning (ML) systems, increasingly deployed in high‐stakes decision‐making, inherently produce uncertain outputs that can lead to unlawful discrimination. This article provides the first legal analysis of how predictive uncertainty in ML systems interacts with UK anti‐discrimination law under the Equality Act 2010.
Holli Sargeant
wiley   +1 more source

Zinc Oxide Nanoparticles for Food Applications: From Synthesis and Functional Properties to Safety and Sustainability

Food Frontiers, Volume 7, Issue 4, July 2026.
Green and conventional synthesis routes produce ZnO nanoparticles with tunable physicochemical properties for food applications, ZnO‐NPs enhance antimicrobial activity, UV protection, and food preservation through ROS generation and ZN2+ release. However, particle dissolution, migration, cytotoxicity, and environmental concerns highlight the need for ...
Liza Akter, Md. Hassan Bin Nabi, Yusuf Chowdhury, Mohima Akther Any, Md Abu Rayhan, Abid Hassan Arnab, Wahidu Zzaman   +6 more
wiley   +1 more source

A Bayesian Location‐Scale Joint Model for Time‐To‐Event and Multivariate Longitudinal Data With Association Based on Within‐Individual Variability

Statistics in Medicine, Volume 45, Issue 10-12, May 2026.
ABSTRACT Within‐individual variability of health indicators measured over time is becoming commonly used to inform about disease progression. Simple summary statistics (e.g., the standard deviation for each individual) are often used but they are not suited to account for time changes.
Marco Palma, Omar El Makkaoui, Ruth H. Keogh, Siobhán B. Carr, Rhonda Szczesniak, David Taylor‐Robinson, Angela M. Wood, Graciela Muniz‐Terrera, Jessica K. Barrett   +8 more
wiley   +1 more source

Impact of screening programmes for type 1 diabetes in youth: A systematic review and meta‐analysis

Diabetic Medicine, Volume 43, Issue 5, May 2026.
Abstract Aim To evaluate the impact of anti‐islet antibody (IAb) screening on the general population and first‐degree relatives (FDRs)/high‐risk individuals and evidence‐based follow‐up modalities. Methods We performed this review through systematic searches of PubMed, EMBASE, Cochrane Library, Web of Science, ClinicalTrials.gov and International ...
Roberto Franceschi, Enza Mozzillo, Marco Marigliano, Giulio Maltoni, Ernesto Maddaloni, Rocco Amendolara, Luca Bernardini, Riccardo Bonfanti, Federica Ciambrelli, Francesca Di Candia, Ludovica Fedi, Alessandro Fierro, Dario Iafusco, Antonio Iannilli, Letizia Leonardi, Claudio Maffeis, Evelina Maines, Riccardo Pertile, Claudia Piona, Ivana Rabbone, Carlo Ripoli, Andrea Scozzarella, Valentina Tiberi, Raffaella Buzzetti, Valentino Cherubini   +24 more
wiley   +1 more source

Proposal for a Discourse Analysis Approach to Identify Greenwashing Practices Among Fruit Pulp Producers

Business Strategy &Development, Volume 9, Issue 1, March 2026.
ABSTRACT Frozen fruit pulp production addresses food availability, yet its manufacturing involves numerous legal, sanitary, and socio‐environmental considerations. This study aimed to analyze the relationship between the discourse of frozen fruit pulp producers on corporate websites and social media and their operational practices, aiming to identify ...
Italo Emmanoel Mesquita Oliveira de Moura, José Machado Moita Neto, Elaine Aparecida da Silva   +2 more
wiley   +1 more source

Affective Dimensions in Maternal Voice During Child Feeding in Mothers With and Without Eating Disorder History—Findings From a Machine Learning Analysis of Speech Data

European Eating Disorders Review, Volume 34, Issue 2, Page 388-397, March 2026.
ABSTRACT Objective Eating disorder (ED) history may impact mother‐child communication during mealtimes and contribute to transgenerational transmission of ED. This study employed machine learning (ML) to identify speech characteristics during mother‐child feeding interactions, aiming for investigating whether vocalised affective characteristics differ ...
Jana Katharina Throm, Manuel Milling, Andreas Triantafyllopoulos, Alexander Kathan, Annica Franziska Dörsam, Johanna Löchner, Björn Schuller, Katrin Elisabeth Giel   +7 more
wiley   +1 more source

Cognitive Networks for Knowledge Modeling: A Gentle Introduction for Data‐ and Cognitive Scientists

WIREs Cognitive Science, Volume 17, Issue 2, March/April 2026.
Cognitive network science helps organize associative knowledge—that is, the connections between concepts. These connections play a key role in cognitive processes such as language understanding and context interpretation, even though they are not obvious in language use.
Edith Haim, Massimo Stella
wiley   +1 more source

Listen Closely: Measuring Vocal Tone in Corporate Disclosures

Journal of Accounting Research, Volume 64, Issue 1, Page 229-277, March 2026.
ABSTRACT We examine the usefulness of machine learning approaches for measuring vocal tone in corporate disclosures. We document a substantial mismatch between the widely adopted actor‐based training data underlying these approaches and speech in corporate disclosures.
Jonas Ewertz, Charlotte Knickrehm, Martin Nienhaus, Doron Reichmann   +3 more
wiley   +1 more source

AI‐Enabled Intelligent Monitoring of Mental Health Indicators During Physical Activity Among Jiangsu Vocational College Students

Engineering Reports, Volume 8, Issue 2, February 2026.
This study presents an AI‐driven framework integrating wearable technology and machine learning to monitor and predict mental health indicators in vocational college students during physical activity, enabling real‐time stress detection, personalized interventions, and early prevention strategies to enhance student well‐being and mental health outcomes.
Yanfeng Shang, Yanxia Shang, Yutong Shang   +2 more
wiley   +1 more source