Analysis Of Time Series Data Sets Research Articles

Curbing environmental degradation to balance sustainable development: Evidence from China

To achieve the goals of sustainable development, it is crucial to check the balance of increased level of international trade along with financial development, foreign direct investment (FDI), energy consumption, and institutional advancement with the quality of the environment. This study focuses on how these variables have caused environmental degradation in China. To achieve Goal 13 of the Sustainable Development Goals (SDGs), i.e. to increase the nation's resilience to natural disasters and hazards related to climate change, to promote climate action and safeguard life as part of sustainable development, this involves the analysis of time-series data sets from 1975 to 2021 and the use of the dynamic version of ARDL (Autoregressive Distributed Lag) cointegration or simulation model. The results show that FDI is essential for lowering carbon dioxide (CO2) emissions over the long term, but financial development, the volume of international trade, and the level of energy use or consumption contribute to environmental deterioration. Moreover, China's environmental degradation is decreased by high institutional quality. To accomplish the SDGs and reduce environmental degradation, it is essential to take into account how these elements interact and to adopt a holistic strategy that includes regulations, policies, and education. On the basis of our trustworthy findings, suggestions are made to keep the sustainable development.

Epileptic seizure detection and prediction using stacked bidirectional long short term memory

Epilepsy is a not a disease but a neurological disorder. But people with epilepsy cannot lead a social life like others. Proper diagnosis and advance prediction of epileptic seizures definitely improves the life of epilepsy patients. In this paper an effort is made to develop a seizure detection and prediction method using stacked bidirectional long short term memory technique. This is the most suitable technique for the analysis of time series datasets as it overcomes the vanishing gradient problem identified in recurrent neural network. The dataset for detection and prediction experiments was taken from Bonn University. Our model could perform the seizure detection with the highest accuracy of 99.08% with 98% precision, 99.5% recall and ROC AUC: 0.984346. A binary classification method with AUC more than 0.9 is considered to be outstanding. Seizure prediction was conducted using the same dataset by classifying preictal states of EEG from interictal and ictal states. For the case of prediction our model could identify preictal states with the overall sensitivity: 89.21% and false prediction rate: 0.06. In future the model could be used to program the wearable devices like wrist watch which can be used by epileptic patients for seizure prediction. The device could be programmed to fire alarm during the detection of the preictal EEG signal before the onset of the seizure.

Investigation of the influences of atmospheric conditions on the variability of radon and radon progeny in buildings

Analysis of time-series data sets, collected in vernacular buildings and workplaces linked with radium source bedrock has identified a number of internal and external pressure characteristics linking meteorological parameters with the variability of radon gas and its progeny. The cellars in these buildings are excavated from the bedrock associated with the radium source and have relatively high levels of radon concentration along with largely stable microclimatic conditions, which differ from those of the buildings’ ground and upper levels. In workplace environments cyclical characteristics are apparent, associated with heating and ventilation related to working hours. Comparative radon concentration data, collected within buildings and similarities identified between buildings, suggest the need to distinguish between short and longer-term influences. From a range of comparative data studied, water vapour pressure, a partial pressure of barometric pressure, is indicated as a principal determinant of the short-term variability of radon gas concentrations, with barometric pressure determining the trend or general longer term level, both linked to temperature. Wind speed appears to have the potential for a dual influence on radon variability: directly, through wind pressure differences and indirectly, through changes to the water vapour component.

Investigation of the air pressure characteristics influencing the variability of radon gas and radon progeny in domestic vernacular buildings.

Analysis of time-series data sets collected in vernacular buildings linked with radium source bedrock has identified a number of internal and external pressure characteristics linking meteorological parameters with the variability of radon gas and its progeny. The buildings' cellars built into the bedrock associated with the radium source have relatively high levels of radon concentration. These cellars have essentially stable microclimatic conditions, unlike the ground and upper levels of the buildings. Comparative radon concentration data collected from various comparable buildings suggest the need to distinguish between short and longer-term influences on radon concentrations. Water vapor pressure is inferred to be a principal determinant of the short-term variability of radon gas concentrations. Barometric pressure is suggested as determining the trend or general longer-term level of radon. Both of these pressure components are related to temperature. Wind speed appears to have a dual influence on radon variability: directly, through wind pressure relative to the ground and building structure particularly associated with low-pressure weather regimes; and indirectly, through changes to the water vapor pressure component.