Spatial skyline query problem in Euclidean and road-network spaces

With the growth of data-intensive applications, along with the increase of both size and dimensionality of data, queries with advanced semantics have recently drawn researchers’ attention. Skyline query problem is one of them, which produces optimal results based on user preferences. In this thesis, we study the problem of spatial skyline query in the Euclidean and road network spaces. For a given data set P, we are required to compute the spatial skyline points of P with respect to an arbitrary query set Q. A point p ∈ P is a spatial skyline point if and only if, for any other data point r ∈ P , p is closer to at least one query point q ∈ Q as compared to r and has in the best case the same distance as r to the rest of the query points. We propose several efficient algorithms that outperform the existing algorithms

Time-Series Deep Learning Models for Reservoir Scheduling Problems Based on LSTM and Wavelet Transformation

In 2022, as a result of the historically exceptional high temperatures that have been observed this summer in several parts of China, particularly in the province of Sichuan, residential demand for energy has increased. Up to 70% of Sichuan’s electricity comes from hydropower, thus creating a sensible and practical reservoir scheduling plan is essential to maximizing reservoir power generating efficiency. However, classical optimization, such as back propagation (BP) neural network, does not take into account the correlation of samples in time while generating reservoir scheduling rules. We proposed a prediction model based on LSTM neural network coupled with wavelet transformation (WT-LSTM) to address the problem. In order to extract the reservoir scheduling rules, this paper first gathers the scheduling operation data from the Xiluodu hydropower station and creates a dataset. Next, it uses the feature of the time-series prediction model with the realization of a complex nonlinear mapping function, time-series learning capability, and high prediction accuracy. The results demonstrate that the time-series deep learning network has high learning capability for reservoir scheduling by comparing evaluation indexes such as root mean square error (RMSE), rank-sum ratio (RSR), and Nash–Sutcliffe efficiency (NSE). The WT-LSTM network model put forward in this research offers better prediction accuracy than conventional recurrent neural networks and serves as a reference base for scheduling decisions by learning previous scheduling data to produce outflow solutions, which has some theoretical and practical benefits

Time-Series Deep Learning Models for Reservoir Scheduling Problems Based on LSTM and Wavelet Transformation

In 2022, as a result of the historically exceptional high temperatures that have been observed this summer in several parts of China, particularly in the province of Sichuan, residential demand for energy has increased. Up to 70% of Sichuan’s electricity comes from hydropower, thus creating a sensible and practical reservoir scheduling plan is essential to maximizing reservoir power generating efficiency. However, classical optimization, such as back propagation (BP) neural network, does not take into account the correlation of samples in time while generating reservoir scheduling rules. We proposed a prediction model based on LSTM neural network coupled with wavelet transformation (WT-LSTM) to address the problem. In order to extract the reservoir scheduling rules, this paper first gathers the scheduling operation data from the Xiluodu hydropower station and creates a dataset. Next, it uses the feature of the time-series prediction model with the realization of a complex nonlinear mapping function, time-series learning capability, and high prediction accuracy. The results demonstrate that the time-series deep learning network has high learning capability for reservoir scheduling by comparing evaluation indexes such as root mean square error (RMSE), rank-sum ratio (RSR), and Nash–Sutcliffe efficiency (NSE). The WT-LSTM network model put forward in this research offers better prediction accuracy than conventional recurrent neural networks and serves as a reference base for scheduling decisions by learning previous scheduling data to produce outflow solutions, which has some theoretical and practical benefits

Hinfp is a guardian of the somatic genome by repressing transposable elements

Germ cells possess the Piwi-interacting RNA pathway to repress transposable elements and maintain genome stability across generations. Transposable element mobilization in somatic cells does not affect future generations, but nonetheless can lead to pathological outcomes in host tissues. We show here that loss of function of the conserved zinc-finger transcription factor Hinfp causes dysregulation of many host genes and derepression of most transposable elements. There is also substantial DNA damage in somatic tissues of Drosophila after loss of Hinfp. Interference of transposable element mobilization by reverse-transcriptase inhibitors can suppress some of the DNA damage phenotypes. The key cell-autonomous target of Hinfp in this process is Histone1, which encodes linker histones essential for higher-order chromatin assembly. Transgenic expression of Hinfp or Histone1, but not Histone4 of core nucleosome, is sufficient to rescue the defects in repressing transposable elements and host genes. Loss of Hinfp enhances Ras-induced tissue growth and aging-related phenotypes. Therefore, Hinfp is a physiological regulator of Histone1-dependent silencing of most transposable elements, as well as many host genes, and serves as a venue for studying genome instability, cancer progression, neurodegeneration, and aging