comfort evaluation
Recently Published Documents


TOTAL DOCUMENTS

341
(FIVE YEARS 94)

H-INDEX

17
(FIVE YEARS 4)

Urban Climate ◽  
2022 ◽  
Vol 41 ◽  
pp. 101033
Author(s):  
Zhengrong Li ◽  
Xiwen Feng ◽  
Xueke Fan ◽  
Jingting Sun ◽  
Zhaosong Fang

2021 ◽  
Author(s):  
Feiyan Zhou ◽  
Dewen Cheng ◽  
Wenrui Shen ◽  
Yongtian Wang

2021 ◽  
Author(s):  
Kai Feng ◽  
Luoxi Hao ◽  
Shujian Dai

LED wide beam angle lamps have been widely used but might leading to glare or light pollution easily than traditional floodlighting lamps. Standards for wide beam angle products is not enough and a laboratory experiment was carried out in which visual and emotional comfort was used as evaluation items. 3 linear lamps (3000K/4000K/5000K) were used to evaluate emotional and visual comfort changes by performing different brightness or dynamic speed. Results showed that both brightness and dynamic speed could lead to negative feelings while emotional discomfort always occurs behind the eye’s discomfort. A higher brightness could leading to more negative evaluations, while some people think that medium brightness gives a more comfort feeling. A faster speed leads to more negative evaluation while some subjects prefer a medium speed (both in shading and erasure situations); In different lighting scenes, the significance of different indicators is different.


2021 ◽  
pp. 082585972110636
Author(s):  
Loïc Bauschert ◽  
Chloé Prod’homme ◽  
Magali Pierrat ◽  
Luc Chevalier ◽  
Hélène Lesaffre ◽  
...  

Background: Comfort evaluation is one of the major challenges in the palliative care setting, particularly when it comes to non-communicative patients. For this specific population, validated tools for comfort evaluation are scarce and healthcare professionals have to rely on their clinical sense and experience. Objectives: To provide arguments for the use of Analgesia/Nociception Index (ANI) monitoring in order to improve clinical comfort evaluation. Methods: We conducted a retrospective cohort study of non-communicative patients at the end of their lives whose comfort was evaluated clinically and with ANI. We focused on the coherence or discordance of clinical and ANI evaluations and on pharmacological interventions driven by them. Results: 58 evaluations from 33 patients were analyzed. Clinical and demographic characteristics were highly variable. Simultaneous clinical and ANI evaluations were concordant in 45 measurements (77.58%), leading mostly to no treatment modification when indicating comfort and to increasing anxiolytic or pain-relief treatments when indicating discomfort. Thirteen (22.41%) evaluations were discordant, leading mostly to treatment incrementation. Conclusion: We suggest that the ANI monitor is a reliable tool in the palliative setting and may help provide patients with the best symptom relief and the most appropriate therapeutics.


2021 ◽  
pp. 1-13
Author(s):  
Hang Zhao ◽  
Jianjie Chu ◽  
Rong Mo ◽  
Chen Chen ◽  
Ning Ding

At present, high-speed trains have become popular modern transportation. As a significant part of the high-speed train riding activity, the stowing and unloading luggage task has its characteristics. To comprehensively and reasonably evaluate passenger comfort of the stowing and unloading luggage task in high-speed trains. In this paper, passenger behavior characteristics are firstly analyzed by the author, the theoretical architecture of passenger comfort evaluation is constructed with the perspective of product aesthetics and ergonomics, and then the process of the passenger comfort evaluation is put forward. Secondly, a combination of Rough Number (RN) and Decision Making Trial and Evaluation Laboratory (DEMATEL) (i.e. R-DEMATEL) is utilized to solve the centrality degree of comfort influencing factors and determine comfort evaluation indexes. Furthermore, the passenger comfort evaluation model with Fuzzy Neural Network (FNN) is constructed and trained. After that, the sample data of the evaluation are collected through the simulated experiment of the stowing and unloading luggage task, and they are trained with FNN comparing to Back Propagation Neural Network (BPNN). Eventually, the result of examples testing is verified that the effectiveness of the proposed method.


Sign in / Sign up

Export Citation Format

Share Document