Analysis of differences in consumption values by gender of home training consumers

2021 ◽  
Vol 26 (6) ◽  
pp. 34-47
Author(s):  
Na-Kyung Lee ◽  
Young-Nam Chung
2020 ◽  
Author(s):  
Da-Wei Zhang ◽  
Stuart J. Johnstone ◽  
Hui Li ◽  
Xiangsheng Li ◽  
Li Sun

The current study used behavioral and electroencephalograph measures to compare the transferability of cognitive training (CT), neurofeedback training (NFT), and CT combined with NFT in children with AD/HD. Following a multiple-baseline single-case experimental design, twelve children were randomized to a training condition. Each child completed a baseline phase, followed by an intervention phase. The intervention phase consisted of 20 sessions of at-home training. Tau-U analysis and standardized visual analysis were adopted to detect effects. CT improved inhibitory function, and NFT showed improved alpha activity and working memory. The combined condition, who was a reduced 'dose' of CT and NFT, did not show any improvements. The three conditions did not alleviate AD/HD symptoms. While CT and NFT may have near transfer effects, considering the lack of improvement in symptoms, this study does not support CT and NFT on their own as a treatment for children with AD/HD.


2021 ◽  
pp. 026921552198901
Author(s):  
Nathalia Cristina de Souza Borges ◽  
Ariane Hidalgo Mansano Pletsch ◽  
Mariana Barbosa Buzato ◽  
Natalia Akemi Yamada Terada ◽  
Fernanda Maria Ferreira da Cruz ◽  
...  

Objective: Analyze postural control in the bipedal position as well as during gait and functional tests in patients with type 2 diabetes mellitus after supervised and unsupervised proprioceptive training. Design: A three-group randomized controlled trial. Setting: Physiotherapeutic Resources Lab, Department of Health Sciences, Ribeirão Preto Medical School, University of São Paulo Subjects: Eighty patients with type 2 diabetes allocated to three groups: control, home training, and supervised training. Interventions: The supervised and home training groups performed two weekly sessions of proprioceptive exercises for 12 weeks. The control group was not submitted to any of treatment. Main measures: Bipedal balance, gait, and performance on functional tests were evaluated before and after 12 weeks using the Balance Evaluation Systems Test (BESTest) and the force plate. Results: No significant improvements were found regarding postural control, gait, or performance on the functional tests, as evidenced by the inter-group comparisons of the total BESTest score [control: 90.7 (81.5–92.6); home training: 85.2 (77.8–90.3); supervised training: 88.4 (82.6–91.4), P > 0.05] as well as the tests performed on the force plate ( P > 0.05). The clinical effect size of the proposed intervention was less than 0.2, demonstrating no effect for the main outcome variable evaluated by the “Sensory Orientation” item of the BESTest and by the mCTSIB (pressure plate). Conclusions: The proposed proprioceptive training did not lead to improvements in postural control in patients with type 2 diabetes with no clinical signs of diabetic distal polyneuropathy when analyzed using the BESTest clinical evaluation and a force plate. Trial registration: NCT01861392 (clinicaltrials.gov).


2021 ◽  
Vol 13 (2) ◽  
pp. 693
Author(s):  
Elnaz Azizi ◽  
Mohammad T. H. Beheshti ◽  
Sadegh Bolouki

Nowadays, energy management aims to propose different strategies to utilize available energy resources, resulting in sustainability of energy systems and development of smart sustainable cities. As an effective approach toward energy management, non-intrusive load monitoring (NILM), aims to infer the power profiles of appliances from the aggregated power signal via purely analytical methods. Existing NILM methods are susceptible to various issues such as the noise and transient spikes of the power signal, overshoots at the mode transition times, close consumption values by different appliances, and unavailability of a large training dataset. This paper proposes a novel event-based NILM classification algorithm mitigating these issues. The proposed algorithm (i) filters power signals and accurately detects all events; (ii) extracts specific features of appliances, such as operation modes and their respective power intervals, from their power signals in the training dataset; and (iii) labels with high accuracy each detected event of the aggregated signal with an appliance mode transition. The algorithm is validated using REDD with the results showing its effectiveness to accurately disaggregate low-frequency measured data by existing smart meters.


Author(s):  
Ioannis Rizomyliotis ◽  
Athanasios Poulis ◽  
Kleopatra Konstantoulaki ◽  
Apostolos Giovanis

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