An Online Resource Scheduling for Maximizing Quality-of-Experience in Meta Computing

Meta Computing is a new computing paradigm, which aims to solve the problem of computing islands in current edge computing paradigms and integrate all the resources on a network by incorporating cloud, edge, and particularly terminal-end devices. It throws light on solving the problem of lacking computing power. However, at this stage, due to technical limitations, it is impossible to integrate the resources of the whole network. Thus, we create a new meta computing architecture composed of multiple meta computers, each of which integrates the resources in a small-scale network. To make meta computing widely applied in society, the service quality and user experience of meta computing cannot be ignored. Consider a meta computing system providing services for users by scheduling meta computers, how to choose from multiple meta computers to achieve maximum Quality-of-Experience (QoE) with limited budgets especially when the true expected QoE of each meta computer is not known as a priori? The existing studies, however, usually ignore the costs and budgets and barely consider the ubiquitous law of diminishing marginal utility. In this paper, we formulate a resource scheduling problem from the perspective of the multi-armed bandit (MAB). To determine a scheduling strategy that can maximize the total QoE utility under a limited budget, we propose an upper confidence bound (UCB) based algorithm and model the utility of service by using a concave function of total QoE to characterize the marginal utility in the real world. We theoretically upper bound the regret of our proposed algorithm with sublinear growth to the budget. Finally, extensive experiments are conducted, and the results indicate the correctness and effectiveness of our algorithm

Effect of ximenynic acid on cell cycle arrest and apoptosis and COX-1 in HepG2 cells

Ximenynic acid is a conjugated enyne fatty acid, which is currently of interest due to its anti-inflammatory activity. Due to the association between inflammation and cancer, the present study was designed to investigate the anti-cancer activity of ximenynic acid in the HepG2 human hepatoma cell line and the underlying mechanisms. The current study demonstrated the anti-proliferation and pro-apoptosis activities of ximenynic acid by cell viability assay and flow cytometry analysis. The expression of anti-apoptosis protein silent information regulator T1 (SIRT1) was significantly suppressed by ximenynic acid. Furthermore, ximenynic acid blocked G1/S phase transition by inhibiting the protein expression of the cell cycle-associated protein general control of amino acid synthesis yeast homolog like 2 (GCN5L2), and the mRNA expression of cyclin D3 and cyclin E1. Furthermore, ximenynic acid suppressed the expression of angiogenesis-associated genes, including vascular endothelial growth factor (VEGF)-B and VEGF-C. Finally, ximenynic acid significantly inhibited the expression of cyclooxygenase-1 (COX-1) mRNA and protein, however COX-2 expression was not reduced. The results of the present study suggested that ximenynic acid may inhibit growth of HepG2 cells by selective inhibition of COX-1 expression, which leads to cell cycle arrest, and alters the apoptosis pathway and expression of angiogenic factors. The current study aimed to investigate whether ximenynic acid might be developed as novel anticancer agent

A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization

Influence Maximization (IM) is a classical combinatorial optimization problem, which can be widely used in mobile networks, social computing, and recommendation systems. It aims at selecting a small number of users such that maximizing the influence spread across the online social network. Because of its potential commercial and academic value, there are a lot of researchers focusing on studying the IM problem from different perspectives. The main challenge comes from the NP-hardness of the IM problem and \#P-hardness of estimating the influence spread, thus traditional algorithms for overcoming them can be categorized into two classes: heuristic algorithms and approximation algorithms. However, there is no theoretical guarantee for heuristic algorithms, and the theoretical design is close to the limit. Therefore, it is almost impossible to further optimize and improve their performance. With the rapid development of artificial intelligence, the technology based on Machine Learning (ML) has achieved remarkable achievements in many fields. In view of this, in recent years, a number of new methods have emerged to solve combinatorial optimization problems by using ML-based techniques. These methods have the advantages of fast solving speed and strong generalization ability to unknown graphs, which provide a brand-new direction for solving combinatorial optimization problems. Therefore, we abandon the traditional algorithms based on iterative search and review the recent development of ML-based methods, especially Deep Reinforcement Learning, to solve the IM problem and other variants in social networks. We focus on summarizing the relevant background knowledge, basic principles, common methods, and applied research. Finally, the challenges that need to be solved urgently in future IM research are pointed out.Comment: 45 page

A Multi-Dimensional Matrix Pencil-Based Channel Prediction Method for Massive MIMO with Mobility

This paper addresses the mobility problem in massive multiple-input multiple-output systems, which leads to significant performance losses in the practical deployment of the fifth generation mobile communication networks. We propose a novel channel prediction method based on multi-dimensional matrix pencil (MDMP), which estimates the path parameters by exploiting the angular-frequency-domain and angular-time-domain structures of the wideband channel. The MDMP method also entails a novel path pairing scheme to pair the delay and Doppler, based on the super-resolution property of the angle estimation. Our method is able to deal with the realistic constraint of time-varying path delays introduced by user movements, which has not been considered so far in the literature. We prove theoretically that in the scenario with time-varying path delays, the prediction error converges to zero with the increasing number of the base station (BS) antennas, providing that only two arbitrary channel samples are known. We also derive a lower-bound of the number of the BS antennas to achieve a satisfactory performance. Simulation results under the industrial channel model of 3GPP demonstrate that our proposed MDMP method approaches the performance of the stationary scenario even when the users' velocity reaches 120 km/h and the latency of the channel state information is as large as 16 ms

Pengembangan Modul Berbasis Bounded Inquiry Laboratory (Lab) Untuk Meningkatkan Literasi Sains Dimensi Proses Pada Materi Sistem Pencernaan Kelas XI

Penelitian bertujuan untuk: 1) Mengetahui karakteristik modul berbasis bounded inquiry laboratory (lab) untuk meningkatkan literasi sains dimensi proses; 2) Menguji kelayakan modul berbasis bounded inquiry laboratory (lab) untuk meningkatkan literasi sains dimensi proses; 3) Menguji keefektivan penggunaan modul berbasis bounded inquiry laboratory (lab) untuk meningkatkan literasi sains dimensi proses pada materi Sistem Pencernaan kelas XI. Penelitian ini menggunakan metode Research and Development (R & D) mengacu pada model Borg and Gall (1983) yang dimodifikasi. Instrumen yang digunakan adalah lembar analisis, lembar observasi, angket, lembar validasi, wawancara, dan tes. Data penelitian dianalisis dengan metode deskriptif kualitatif dan literasi sains dimensi proses dianalisis dengan N-gain ternormalisasi untuk mengetahui keefektivan modul berbasis bounded inquiry laboratory (lab), dan uji Wilcoxon untuk mengetahui literasi sains dimensi proses. Hasil penelitian dan pengembangan menunjukkan bahwa: 1) Modul berbasis bounded inquiry laboratory (lab) untuk meningkatkan literasi sains dimensi proses pada materi Sistem Pencernaan dikembangkan sesuai dengan tahapan bounded inquiry laboratory (lab) (observasi, manipulasi, generalisasi, verifikasi, aplikasi) dan pendekatan saintifik; 2) Hasil pengembangan modul berbasis bounded inquiry laboratory (lab) layak untuk diterapkan pada materi Sistem Pencernaan. Kelayakan modul berbasis bounded inquiry laboratory (lab) pada materi Sistem Pencernaan berdasarkan validasi ahli memperoleh kategori “sangat baik” dengan persentase 98,21%, validasi praktisi memperoleh kategori “sangat baik” dengan persentase 99,22%, dan responden uji coba skala kecil memperoleh kategori “baik” dengan persentase 77,34%, sehingga layak digunakan kelas XI; 3) Modul berbasis bounded inquiry laboratory (lab) pada materi Sistem Pencernaan efektif untuk meningkatkan literasi sains dimensi proses yang ditunjukkan dengan hasil uji Wilcoxon yaitu diperoleh probabilitas (p) sebesar 0,000 (p < 0,05), H0 ditolak, sehingga ada perbedaan literasi sains dimensi proses sebelum dan setelah menggunakan modul bounded inquiry laboratory (lab) pada materi sistem pencernaan. Berdasarkan hasil penelitian dapat disimpulkan bahwa karakteristik modul berbasis bounded inquiry laboratory (lab) sesuai tahapan bounded inquiry laboratory (lab) (observasi, manipulasi, generalisasi, verifikasi, aplikasi) dan pendekatan saintifik; layak dan efektif untuk meningkatkan literasi sains dimensi proses pada materi Sistem Pencernaan kelas XI

Deficiency and excess of groundwater iodine and their health associations

More than two billion people worldwide have suffered thyroid disorders from either iodine deficiency or excess. By creating the national map of groundwater iodine throughout China, we reveal the spatial responses of diverse health risks to iodine in continental groundwater. Greater non-carcinogenic risks relevant to lower iodine more likely occur in the areas of higher altitude, while those associated with high groundwater iodine are concentrated in the areas suffered from transgressions enhanced by land over-use and intensive anthropogenic overexploitation. The potential roles of groundwater iodine species are also explored: iodide might be associated with subclinical hypothyroidism particularly in higher iodine regions, whereas iodate impacts on thyroid risks in presence of universal salt iodization exhibit high uncertainties in lower iodine regions. This implies that accurate iodine supply depending on spatial heterogeneity and dietary iodine structure optimization are highly needed to mitigate thyroid risks in iodine-deficient and -excess areas globally

Highly pathogenic avian influenza H5N6 viruses exhibit enhanced affinity for human type sialic acid receptor and in-contact transmission in model ferrets

Since May 2014, highly pathogenic avian influenza H5N6 virus has been reported to cause six severe human infections three of which were fatal. The biological properties of this subtype, in particular its relative pathogenicity and transmissibility in mammals, are not known. We characterized the virus receptor-binding affinity, pathogenicity, and transmissibility in mice and ferrets of four H5N6 isolates derived from waterfowl in China from 2013-2014. All four H5N6 viruses have acquired a binding affinity for human-like SA alpha 2,6Gal-linked receptor to be able to attach to human tracheal epithelial and alveolar cells. The emergent H5N6 viruses, which share high sequence similarity with the human isolate A/Guangzhou/39715/2014 (H5N6), were fully infective and highly transmissible by direct contact in ferrets but showed less-severe pathogenicity than the parental H5N1 virus. The present results highlight the threat of emergent H5N6 viruses to poultry and human health and the need to closely track their continual adaptation in humans