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short-and long-term effects of architecture on the brain:Toward theoretical formalization

Frontiers of Architectural Research 2019年第4期8卷 564-571页

作者： Andrea de Paiva Richard Jedon Institute for Educational Development Fundagao Getulio VargasFGVSao PauloBrazil Urban Plannins and Development Institute of the City of Pilsen PUsenCzech Republic

The physical environm ent affects people’s behavior and *** effects can be easily noticed through observation,whereas others require an in-depth study to be understood and *** many alterations can be positive,some can also negatively influence wellbeing,decision-m aking,and m ental and physical *** of these effects are not easily associated with physical ***,people may be unaware of the real triggers for changes in behavior,mood,and *** many studies have been performed on environmental psychology,detailed research to understand the im pacts of architecture on the brain using neuroscience is *** difficulties experienced by researchers in this field are on the isolation of each stimulus to understand its effects individually and m easurem ent of brain changes in people interacting with the environm ent because some brain scans,such as fMRI,require people to be inside the ***,the several ways a space can impact its users should be discussed to understand how architecture influences in dividuals and to help architects and urban planners in designing efficient and healthy *** study aims to describe and analyze the results of previous research works and propose a way of organizing them to facilitate further investigation on this field.

关键词： Brain Neuroarchitecture Neuroscience Behavior short term Long term

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Hybrid Deep Learning Model for short-term Wind Speed Forecasting Based on Time Series Decomposition and Gated Recurrent Unit

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Complex System Modeling and Simulation 2021年第4期1卷 308-321页

作者： Changtong Wang Zhaohua Liu Hualiang Wei Lei Chen Hongqiang Zhang School of Information and Electrical Engineering Hunan University of Science and TechnologyXiangtan 411201China Department of Automatic Control and Systems Engineering the University of SheffieldSheffieldS13JDUK.

Accurate wind speed prediction has been becoming an indispensable technology in system security,wind energy utilization,and power grid dispatching in recent ***,it is an arduous task to predict wind speed due to its variable and random *** the objective to enhance the performance of forecasting short-term wind speed,this work puts forward a hybrid deep learning model mixing time series decomposition algorithm and gated recurrent unit(GRU).The time series decomposition algorithm combines the following two parts:(1)the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),and(2)wavelet packet decomposition(WPD).Firstly,the normalized wind speed time series(WSTS)are handled by CEEMDAN to gain pure fixed-frequency components and a residual *** WPD algorithm conducts the second-order decomposition to the first component that contains complex and high frequency signal of raw ***,GRU networks are established for all the relevant components of the signals,and the predicted wind speeds are obtained by superimposing the prediction of each *** from two case studies,adopting wind data from laboratory and wind farm,respectively,suggest that the related trend of the WSTS can be separated effectively by the proposed time series decomposition algorithm,and the accuracy of short-time wind speed prediction can be heightened significantly mixing the time series decomposition algorithm and GRU networks.

关键词： deep learning complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) gated recurrent unit(GRU) short term wavelet packet decomposition wind speed prediction

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Threefold Optimized Forecasting of Electricity Consumption in Higher Education Institutions

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Computers, Materials & Continua 2022年第11期73卷 2351-2370页

作者： Majida Kazmi Hashim Raza Khan Lubaba Mohammad Hashir Bin Khalid Saad Ahmed Qazi Faculty of Electrical and Computer Engineering NED University of Engineering and TechnologyKarachi75270Pakistan Neurocomputation Lab National Center of Artificial IntelligenceKarachi75270Pakistan

Energy management benefits both consumers and utility companiesalike. Utility companies remain interested in identifying and reducing energywaste and theft, whereas consumers’ interest remain in lowering their energyexpenses. A large supply-demand gap of over 6 GW exists in Pakistan asreported in 2018. Reducing this gap from the supply side is an expensiveand complex task. However, efficient energy management and distributionon demand side has potential to reduce this gap economically. Electricityload forecasting models are increasingly used by energy managers in takingreal-time tactical decisions to ensure efficient use of resources. Advancementin Machine-learning (ML) technology has enabled accurate forecasting ofelectricity consumption. However, the impact of computation cost affordedby these ML models is often ignored in favour of accuracy. This studyconsiders both accuracy and computation cost as concurrently significantfactors because together they shape the technology environment as well ascreate economic impact. Thus, a three-fold optimized load forecasting modelis proposed which includes (1) application specific parameters selection, (2)impact of different dataset granularities and (3) implementation of specificdata preparation. It deploys and compares the widely used back-propagationArtificial Neural Network (ANN) and Random Forest (RF) models for theprediction of electricity consumption of buildings within a university. In addition to the temporal and historical power consumption date as input parameters, the study also embeds weather data as well as university operationalcalendars resulting in improved performance. The outcomes are indicativethat the granularity i.e. the scale of details in data, and set of reduced and fullinput parameters impact performance accuracies differently for ANN and RFmodels. Experimental results show that overall RF model performed betterboth in terms of accuracy as well as computational time for a 1-min, 15-minand 1-h dataset

关键词： Electricity forecasting short term higher educational institution artificial neural network random forest accuracy computational time

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Mobile Phone Usage and the Risk of Brain Tumors

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Occupational Diseases and Environmental Medicine 2016年第3期4卷 47-55页

作者： Naina Sawal Amir Hossein Mortazavi Entesab Saba University School of Medicine Saba Netherlands

Purpose: This literature review investigated the possible association between the use of mobile phones and brain tumors. Methods: In brief, 11 publications were retrieved from JSTOR, PubMed, Google Scholar and Summon in order to compare the association between the usage of mobile phones in patients with a brain tumor and those without. Papers published in English, and after 2001 were selected for. There was no limit on age, gender, geographical location and type of brain tumor. Results: For regular mobile phone usage, the combined odds ratios (OR) (95% confidence intervals) for three studies are: 1.5 (1.2 - 1.8), 1.3 (0.95 - 1.9), and 1.1 (0.8 - 1.4), respectively. Furthermore, the odds ratio did not increase, regardless of mobile phone use duration. Additionally, Lonn et al. (2005) observed that the risk also did not significantly increase when assessing the laterality (ipsilateral or contralateral) of the tumor in relation to side of head used for the mobile phone. Kan et al. (2007) observed an OR of 1.22 when comparing analog phone to digital phone use. Conclusion: This review concludes that there is no current association between mobile phone use and the development of brain tumors. Although certain studies speak in favor of an increased risk, many are plagued with either: sampling bias, misclassification bias, or issues concerning risk estimates. Further research needs to be done in order to evaluate the long-term effect of mobile phone usage on the risk of developing a brain tumor.

关键词： Cellular Phone Cancer Tumor Brain Mobile Phone short term Long term Cross-Sectional Meta-Analysis Radiofrequencies Glioma Meningioma and Schwannoma

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