A Novel Decomposition and Combination Technique for Forecasting Monthly Electricity Consumption

With the share of electricity in total final energy consumption increasing quickly, the world is becoming increasingly dependent on electricity, which makes it more and more important to improve the forecasting accuracy of electricity consumption to ensure the normal operation of economic activities. In this paper, a novel decomposition and combination technique to forecast monthly electricity consumption is proposed. First, we use STL decomposition to obtain the trend, season, and residual components of the time series. Second, we use SARIMA, SVR, ANN, and LSTM to forecast trend, season, and residual component, respectively. Third, we use time correlation principle to improve the forecasting accuracy of season component. Fourth, we integrated the residual component predicted by SARIMA, SVR, ANN, and LSTM into a new sequence to improve the forecasting accuracy of residual component. In order to verify the performance of the proposed forecast model, monthly electricity consumption data in China is introduced as an example for empirical analysis. The results show that after STL decomposition, time correlation modification, and residual modification, the forecasting accuracy of each model has been gradually improved. We believe that the proposed forecast model in this paper can also be used to solve other mid- and long-term forecasting problems with obvious seasonal characteristics.

Transcriptomic Alterations Related to Epilepsy and Tumor Suppression in the Peritumoral Area of a Rat Glioma Model

Patients with glioma often demonstrate epilepsy. We previously found burst discharges in the peritumoral area in patents with malignant brain tumors during biopsy. Therefore, we hypothesized that the peritumoral area may possess an epileptic focus and that biological alterations in the peritumoral area may cause epileptic symptoms in patients with glioma. To test our hypothesis, we developed a rat model of glioma and characterized it at the cellular and molecular levels. We first labeled rat C6 glioma cells with tdTomato, a red fluorescent protein (C6-tdTomato) and implanted them into the somatosensory cortex of VGAT-Venus rats, which specifically expressed Venus, a yellow fluorescent protein in GABAergic neurons. We observed that the density of GABAergic neurons was significantly decreased in the peritumoral area of rats with glioma compared with the contralateral healthy side. By using a combination technique of laser capture microdissection and RNA sequencing(LCM-seq) of paraformaldehyde-fixed brain sections, we demonstrated that 19 genes were differentially expressed in the peritumoral area and that five of them were associated with epilepsy and neurodevelopmental disorders. In addition, the canonical pathways actively altered in the peritumoral area were predicted to cause a reduction in GABAergic neurons. These results suggest that biological alterations in the peritumoral area may be a cause of glioma-related epilepsy.

Designing a Block Cipher in Galois Extension Fields for IoT Security

This paper focuses on a block cipher adaptation of the Galois Extension Fields (GEF) combination technique for PRNGs and targets application in the Internet of Things (IoT) space, an area where the combination technique was concluded as a quality stream cipher. Electronic Codebook (ECB) and Cipher Feedback (CFB) variations of the cryptographic algorithm are discussed. Both modes offer computationally efficient, scalable cryptographic algorithms for use over a simple combination technique like XOR. The cryptographic algorithm relies on the use of quality PRNGs, but adds an additional layer of security while preserving maximal entropy and near-uniform distributions. The use of matrices with entries drawn from a Galois field extends this technique to block size chunks of plaintext, increasing diffusion, while only requiring linear operations that are quick to perform. The process of calculating the inverse differs only in using the modular inverse of the determinant, but this can be expedited by a look-up table. We validate this GEF block cipher with the NIST test suite. Additional statistical tests indicate the condensed plaintext results in a near-uniform distributed ciphertext across the entire field. The block cipher implemented on an MSP430 offers a faster, more power-efficient alternative to the Advanced Encryption Standard (AES) system. This cryptosystem is a secure, scalable option for IoT devices that must be mindful of time and power consumption.

Using Combination Technique for Land Cover Classification of Optical Multispectral Images

The need for efficient planning of the land is exponentially increasing because of the unplanned human activities, especially in the urban areas. A land cover map gives a detailed report on temporal dynamics of a given geographical area. The land cover map can be obtained by using machine learning classifiers on the raw satellite images. In this work, the authors propose a combination method for the land cover classification. This method combines the outputs of two classifiers, namely, random forests (RF) and support vector machines (SVM), using Dempster-Shafer combination theory (DSCT), also called the theory of evidence. This combination is possible because of the inherent uncertainties associated with the output of each classifier. The experimental results indicate an improved accuracy (89.6%, kappa = 0.86 as versus accuracy of RF [87.31%, kappa = 0.83] and SVM [82.144%, kappa = 0.76]). The results are validated using the normalized difference vegetation index (NDVI), and the overall accuracy (OA) has been used as a comparison basis.

Evaluation of effectiveness various seed processing combination technique to produce True Shallot Seed (TSS) with good quality

The seed processing technique is essential to maintain quality and suppress seed deterioration rate as long as processing time. This research aims to evaluate various seed processing combination techniques to produce TSS with good quality. The study was conducted at Indonesian Vegetables Research Institute, Lembang (1,250 m sal) from March until December 2018. The research used a Randomized Complete Block Design (RCBD) with three replications. The treatment consisted of 12 combinations of seed processing, including the technique of drying, capsule breaking, and sorting. The research result showed that the best treatment was a combination technique with umbels drying in the room at RH 50 % and 30-35°C for 72 hours, breaking capsule by hand manually, and seed sorting by winnower followed by hand manually. The produced seed quality in this treatment showed the seed germination was 75%, the moisture content was 7.5%, and the physical purity was 99.9%. This research implies that the availability of TSS processing technology can be carried out by massal, but still can produce good seed quality.