Results 121 to 130 of about 25,305 (197)

Smart food‐sharing platforms for social sustainability: a heuristic algorithm approach

International Transactions in Operational Research, Volume 33, Issue 5, Page 2893-2920, September 2026.
Abstract This study examines how smart food‐sharing platforms (SFSP) can help reduce food waste and suggests a method for using smart contracts to share extra food among different partners effectively. For smart contracts to work automatically and prevent food wastage, artificial intelligence systems can recognize how smart clauses should be executed ...
Behzad Maleki Vishkaei, Pietro De Giovanni   +1 more
wiley   +1 more source

Enhanced colorectal gland segmentation through multi-scale attention and contextual feature fusion. [PDF]

Sci Rep
B K P, N L, Ajibesin AA.
europepmc   +1 more source

SMAA‐Based FITradeoff: An Efficient Framework for Pairwise Elicitation in Multicriteria Decision Analysis

Journal of Multi-Criteria Decision Analysis, Volume 33, Issue 2, August 2026.
ABSTRACT The Flexible and Interactive Tradeoff Elicitation (FITradeoff) method is a Multi‐Attribute Decision‐Making (MADM) approach designed to capture the preferences of a Decision Maker (DM) while minimising cognitive effort. To reduce the frequency of interactions and optimise the preference elicitation process, this paper introduces an innovative ...
Qian Zhao, Elia Balugani, Rita Gamberini, Francesco Lolli   +3 more
wiley   +1 more source

Artificial intelligence-enhanced microsurgical training: a systematic review. [PDF]

NPJ Digit Med
Jamel WA, Jameel M, Riaz I, Yousif YF, Perez H R, de la Torre V, Seth I.   +6 more
europepmc   +1 more source

The impacts of biological invasions

Biological Reviews, EarlyView.
ABSTRACT The Anthropocene is characterised by a continuous human‐mediated reshuffling of the distributions of species globally. Both intentional and unintentional introductions have resulted in numerous species being translocated beyond their native ranges, often leading to their establishment and subsequent spread – a process referred to as biological
Phillip J. Haubrock, Teun Everts, Neil Angelo S. Abreo, Jamie Bojko, Victor Deklerck, James W. E. Dickey, Ana Clara S. Franco, Emili García‐Berthou, Stelios Katsanevakis, Natalia I. Kirichenko, Stefano Mammola, Martin A. Nuñez, Ben Parker, Riccardo Scalera, Ismael Soto, Diederik Strubbe, Ali Serhan Tarkan, Lorenzo Vilizzi, Tim Adriaens, Paride Balzani, Dagmara Błońska, Elizabeta Briski, Rein Brys, Amy L. Burgess, James E. Byers, Carlos Cano‐Barbacil, Giuseppe Castaldelli, Jaimie T.A. Dick, Victoria Dominguez Almela, Romina D. Dimarco, Margarita Florencio, Antonín Kouba, Melina Kourantidou, Irmak Kurtul, Irene Martín‐Forés, Olivier Morissette, Julian D. Olden, Bruno E. Soares, Jakub Truszkowski, Hugo Verreycken, Marc Kenis, Ronaldo Sousa, J. Robert Britton   +42 more
wiley   +1 more source

A novel deep learning framework with artificial protozoa optimization-based adaptive environmental response for wind power prediction. [PDF]

Sci Rep
Lee S, Almomani MH, Alomari SA, Saleem K, Smerat A, Snasel V, Gandomi AH, Abualigah L.   +7 more
europepmc   +1 more source

MetTeL: A Generic Tableau Prover. [PDF]

, 2011
Khodadadi, Mohammad, Schmidt, Renate A, Tishkovsky, Dmitry   +2 more
core  

The Rise of Human–Computer Integration in Marketing: A Theory Synthesis

Psychology &Marketing, Volume 43, Issue 6, Page 1343-1380, June 2026.
ABSTRACT Human–computer integration (HCInt) technologies, which merge human bodily, cognitive, and sensory functions with computational processes, are reshaping the foundations of consumer experience. Unlike traditional human–computer interaction, HCInt entails adaptive and reciprocal coupling through AI‐driven augmentation, wearables, muscle–computer ...
Carlos Velasco, Kosuke Motoki, Olivia Petit, Aram Nikolai Andersen, Marzieh Alaei, Anders Gustafsson   +5 more
wiley   +1 more source

Revisiting Turing's Chemical Basis of Morphogenesis. [PDF]

Bull Math Biol
Tyson JJ.
europepmc   +1 more source

Personalized Differential Privacy for Ridge Regression Under Output Perturbation

Naval Research Logistics (NRL), Volume 73, Issue 4, Page 525-537, June 2026.
ABSTRACT The increased application of machine learning (ML) in sensitive domains requires protecting the training data through privacy frameworks, such as differential privacy (DP). Traditional DP enforces a uniform privacy level ε$$ \varepsilon $$, which bounds the maximum privacy loss that each data point in the dataset is allowed to incur.
Krishna Acharya, Franziska Boenisch, Rakshit Naidu, Juba Ziani   +3 more
wiley   +1 more source