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Experimental evidence that exerting effort increases meaning.

Cognition

January 2025

Department of Psychology, University of Toronto,; Rotman School of Management, University of Toronto.

Efficiency demands that we work smarter and not harder, but is this better for our wellbeing? Here, we ask if exerting effort on a task can increase feelings of meaning and purpose. In six studies (N = 2883), we manipulated how much effort participants exerted on a task and then assessed how meaningful they found those tasks. In Studies 1 and 2, we presented hypothetical scenarios whereby participants imagined themselves (or others) exerting more or less effort on a writing task, and then asked participants how much meaning they believed they (or others) would derive.

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This paper presents the findings from a survey on factors influencing the adoption of agricultural Decision Support Systems (DSS). Our study focuses on examining the influence of behavioural, socioeconomic and farm specific characteristics on DSS adoption. Using two structural equation models, we investigate how these factors influence the willingness to pay (WTP) and willingness to adopt.

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Objectives: To investigate how studies determine the sample size when developing radiomics prediction models for binary outcomes, and whether the sample size meets the estimates obtained by using established criteria.

Methods: We identified radiomics studies that were published from 01 January 2023 to 31 December 2023 in seven leading peer-reviewed radiological journals. We reviewed the sample size justification methods, and actual sample size used.

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The Two-Spin Enigma: From the Helium Atom to Quantum Ontology.

Entropy (Basel)

November 2024

EPITA Research Laboratory, 14-16 Rue Voltaire, 94270 Le Kremlin-Bicêtre, France.

The purpose of this article is to provide a novel approach and justification of the idea that classical physics and quantum physics can neither function nor even be conceived without the other-in line with ideas attributed to, e.g., Niels Bohr or Lev Landau.

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This study proposes an image enhancement detection technique based on Adltformer (Adaptive dynamic learning transformer) team-training with Detr (Detection transformer) to improve model accuracy in suboptimal conditions, addressing the challenge of detecting cattle in real pastures under complex lighting conditions-including backlighting, non-uniform lighting, and low light. This often results in the loss of image details and structural information, color distortion, and noise artifacts, thereby compromising the visual quality of captured images and reducing model accuracy. To train the Adltformer enhancement model, the day-to-night image synthesis (DTN-Synthesis) algorithm generates low-light image pairs that are precisely aligned with normal light images and include controlled noise levels.

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