Results 141 to 150 of about 403 (203)

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

open access: yesPsychology &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   +5 more
wiley   +1 more source

Deep Learning Unlocks Behavioral Prediction and Neurobehavioral Decoding

open access: yesMed Research, Volume 2, Issue 2, Page 371-387, June 2026.
This review evaluates deep learning frameworks that surmount conventional limitations through high‐dimensional nonlinear modeling, spatiotemporal dependency capture, and multimodal information integration. Focusing on biological behavior forecasting and neural mechanism decoding, we delineate cutting‐edge applications, including real‐time action ...
Tianzhe Han   +5 more
wiley   +1 more source

The logic induced by effect algebras. [PDF]

open access: yesSoft comput, 2020
Chajda I, Halaš R, Länger H.
europepmc   +1 more source

Quantum Information Measures of a Dirichlet Waveguide with Neumann Window(s)

open access: yesAdvanced Quantum Technologies, Volume 9, Issue 6, June 2026.
ABSTRACT Engineering boundary conditions in low‐dimensional structures provides a simple yet powerful way of shaping how quantum information is stored and transported. We investigate a flat 2D Dirichlet waveguide containing one or two finite Neumann windows and compute the bound states in both position and momentum space as functions of the window ...
Firoz Chogle, Berihu Teklu
wiley   +1 more source

Wasserstein Regression, Forecasting, and Change‐Point Detection for Daily Traffic Flow Distributions

open access: yesStatistical Analysis and Data Mining: An ASA Data Science Journal, Volume 19, Issue 3, June 2026.
ABSTRACT We develop a distribution‐valued framework for modeling, forecasting, and monitoring traffic flow counts by treating each day as a probability distribution summarized by jittered empirical quantile signatures. Inference is conducted under the 2‐Wasserstein geometry, which in one dimension is isometric to the L2(0,1)$$ {L}^2\left(0,1\right ...
Abdolnasser Sadeghkhani
wiley   +1 more source

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