Results 21 to 30 of about 3,937,174 (257)

Schizophrenia: Is it time to replace the term? [PDF]

Psychiatry and Clinical Neurosciences, 1999
AbstractThe attitudes of Japanese psychiatrists toward their patients who suffer from schizophrenia were investigated. We were concerned specifically with whether the psychiatrists inform their patients of the suspected diagnosis. We discuss how the term ‘schizophrenia’ may influence a psychiatrist’s decision to inform his patients of the diagnosis.
Y, Ono, Y, Satsumi, Y, Kim, T, Iwadate, K, Moriyama, Y, Nakane, T, Nakata, K, Okagami, T, Sakai, M, Sato, T, Someya, S, Takagi, S, Ushijima, K, Yamauchi, K, Yoshimura   +14 more
openaire   +2 more sources

Short‐term traffic travel time forecasting using ensemble approach based on long short‐term memory networks

IET Intelligent Transport Systems, 2023
To enhance the control effect of intelligent transportation system (ITS) of expressway, avoid blind travel causing traffic congestion, improve road capacity, and achieve the purpose of smooth and efficient operation of the network, 3σ criterion and 2σ ...
Xingli Jia, Wuxiao Zhou, Hongzhi Yang, Shuangqing Li, Xingpeng Chen   +4 more
doaj   +1 more source

NUMERICAL METHODS FOR SOLVING THE MULTI-TERM TIME-FRACTIONAL WAVE-DIFFUSION EQUATION. [PDF]

Fract Calc Appl Anal, 2013
Liu F, Meerschaert MM, McGough RJ, Zhuang P, Liu Q.   +4 more
europepmc   +2 more sources

A decomposition‐based multi‐time dimension long short‐term memory model for short‐term electric load forecasting

IET Generation, Transmission & Distribution, 2021
Short‐term load forecasting is essential to power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate the intrinsic time‐related dimensions of electric load data and the decomposition methods ...
Jiehui Huang, Zhiwang Zhou, Chunquan Li, Zhiyuan Liao, Peter X. Liu   +4 more
doaj   +1 more source

Long-Short Term Echo State Network for Time Series Prediction

IEEE Access, 2020
The Echo State Networks (ESNs) is an efficient recurrent neural network consisting of a randomly generated reservoir (a large number of neurons with sparse random recurrent connections) and a trainable linear layer.
Kaihong Zheng, Bin Qian, Sen Li, Yong Xiao, Wanqing Zhuang, Qianli Ma   +5 more
doaj   +1 more source

Within trial comparison of survival time projections from short‐term follow‐up with long‐term follow‐up findings

ESC Heart Failure, 2022
Aims Data on long‐term treatment effects are scarce, despite the intent to use new therapies for many years and the need of patients, physicians and payers to have a better understanding of the lifetime benefits of treatments.
João Pedro Ferreira, Brian L. Claggett, Kieran F. Docherty, Pardeep S. Jhund, Faiez Zannad, Scott D. Solomon, John J.V. McMurray   +6 more
doaj   +1 more source

Swimming exercise improves short‐ and long‐term memories: Time‐course changes

Physiological Reports, 2021
The beneficial effects of exercise training on memory formation are well documented. However, the memory enhancement profile following the time‐course of exercise training remains unknown.
Mahmoud A. Alomari, Karem H. Alzoubi, Omar F. Khabour   +2 more
doaj   +1 more source

Real‐time long‐term tracking with reliability assessment and object recovery

IET Image Processing, 2021
In recent years, many visual tracking algorithms based on discriminative correlation filters have been proposed and proved to be successful in short‐term tracking.
Jun Liu, Ping Ye, Xingchen Zhang, Gang Xiao   +3 more
doaj   +1 more source

Lower bounds for blow up time of the p-Laplacian equation with damping term [PDF]

Mathematica Moravica, 2021
In this work deals with the p-Laplacian wave equation with damping terms in a bounded domain. Under suitable conditions, we obtain a lower bounds for the blow up time.
Dınç Yavuz, Pışk˙ın Erhan, Tunç Cem˙ıl   +2 more
doaj   +1 more source

Temporal Disaggregation of Time Series with Regularization Term

Barometr Regionalny, 2016
Methods of temporal disaggregation are used to obtain high frequency time series from the same low frequency time series — so-called disaggregation—with respect to some additional consistency conditions between low and high frequency series.
Sebastian Wójcik
doaj   +1 more source