Wireless Resource Management and Resilience Optimization of the M2M-Oriented Mobile Communication System

Machine-to-machine (M2M) communication technology is an emerging technology that can connect smart wireless devices. The most obvious feature of M2M is that the communication between devices does not require human intervention. Therefore, ensuring the low-energy consumption of M2M devices is a necessary condition for prolonging the survival time of their devices. This paper first considers the coexistence of M2M and H2H scenarios. Aimed at the energy efficiency of M2M equipment and the channel capacity of H2H equipment, a multiobjective problem is constructed for joint spectrum and power resource management, and a weighted Chebyshev algorithm is proposed to solve this problem. Secondly, in view of the additional interference problems caused by the introduction of M2M communication, the intercell cooperative link selection algorithm is used to optimize its resilience. The effectiveness of the algorithm is proven by simulation results.

STUDY ON PROPAGATION LAW OF ACOUSTIC EMISSION SIGNALS ON ANISOTROPIC WOOD SURFACE

In order to explore the influence of wood’s anisotropic characteristics on Acoustic Emission (AE) signals’ propagation, the law of AE signals’ propagation velocity along different directions was studied. First, The center of the specimen’s surface was took as the AE source,then 24 directions were chose one by one every 15º around the center,and 2 AE sensors were arranged in each direction to collect the original AE signals. Second, the wavelet analysis was used to denoise the original AE signals, then the AE signals were reconstructedby Empirical Mode Decomposition (EMD). Finally, time difference location method was utilized to calculate AE signals’ propagation velocity. The results demonstrate that AE signals’ propagation velocity has obvious feature of quadratic function. In the range of 90º, as the angle of propagation direction increases, the propagation velocity of the AE signals presents a downward trend.

Effectiveness of electromagnetic monitoring in studying earthquakes

Numerous researches conducted in connection with the study of earthquakes have shown that electromagnetic monitoring studies have led to some important results. From the Loma Prieta earthquake to the Guam earthquake, electromagnetic monitoring studies led to significant results. Since then, there have been numerous reports of possible elect-romagnetic precursors to earthquakes, some of which have involved frequencies covered by ELF/VLF (10—32 kHz) monitoring system Fraser-Smith et al. [1990]. Sometime later, they retrieved and started processing their ULF data. They had less reason to expect electromagnetic precursors in this latter data, because previous reports of precursory signals at frequencies below the ELF/VLF range have, with few exceptions, involved frequencies either below or predominantly below their ULF range (0.01—10 Hz) of operation. They found out that ELF/VLF data do not appear to show precursory activity, whereas ULF data contain a number of anomalous features that may prove to be earthquake precursors. The lack of observation of precursory ELF/VLF noise so close to the epicenters of several mode-rate to moderately-large earthquakes showed that ELF/VLF noise need not be a strong or obvious feature of every earthquake, as Fraser-Smith et al. [1990] reported in their paper. At present, numerous studies have been conducted in this area and researches are being improved. From my experience as a young researcher, it became clear that electromagnetic monitoring research is necessary, and that more important and significant results can be achieved if continuous research is conducted in a certain area. Thus, these studies may play a significant role in the detection of earthquake precursors.

Study of the Bible in Chinese Academia

Since Christianity came to China, the Bible has been translated, preached, and studied, and church-based universities have been instrumental in the Bible being taught and studied in Chinese institutions of higher learning. For the mainland Chinese academic system, however, the history of studying and teaching the Bible is relatively short, getting traction only in the past four decades by this period surveyed in this essay. This essay observes that, since there is no clear distinction between “religious or non-religious” classics in the Chinese tradition, and because of Confucius’ emphasis on “inspiration from the Poems, steady with the ritual, and fulfillment in music,” both factors give literary reading a crucial role in the study of religious texts. Although the studying and teaching of the Bible in China’s academia also can be found in the fields of philosophy, ethics, history, and art, the “Bible as literature” is the most obvious feature, thus becoming the focus of this paper.

Visualizing and quantifying data from timelapse imaging experiments

One obvious feature of life is that it is highly dynamic. The dynamics can be captured by movies that are made by acquiring images at regular time intervals, a method that is also known as timelapse imaging. Looking at movies is a great way to learn more about the dynamics in cells, tissue and organisms. However, science is different from Netflix, in that it aims for a quantitative understanding of the dynamics. The quantification is important for the comparison of dynamics and to study effects of perturbations. Here, we provide detailed processing and analysis methods that we commonly use to analyze and visualize our timelapse imaging data. All methods use freely available open-source software and use example data that is available from an online data repository. The step-by-step guides together with example data allow for fully reproducible workflows that can be modified and adjusted to visualize and quantify other data from timelapse imaging experiments.Abstract Figure

A Correlation Analysis between SNPs and ROIs of Alzheimer’s Disease Based on Deep Learning

Motivation. At present, the research methods for image genetics of Alzheimer’s disease based on machine learning are mainly divided into three steps: the first step is to preprocess the original image and gene information into digital signals that are easy to calculate; the second step is feature selection aiming at eliminating redundant signals and obtain representative features; and the third step is to build a learning model and predict the unknown data with regression or bivariate correlation analysis. This type of method requires manual extraction of feature single-nucleotide polymorphisms (SNPs), and the extraction process relies on empirical knowledge to a certain extent, such as linkage imbalance and gene function information in a group sparse model, which puts forward certain requirements for applicable scenarios and application personnel. To solve the problems of insufficient biological significance and large errors in the previous methods of association analysis and disease diagnosis, this paper presents a method of correlation analysis and disease diagnosis between SNP and region of interest (ROI) based on a deep learning model. It is a data-driven method, which has no obvious feature selection process. Results. The deep learning method adopted in this paper has no obvious feature extraction process relying on prior knowledge and model assumptions. From the results of correlation analysis between SNP and ROI, this method is complementary to other regression model methods in application scenarios. In order to improve the disease diagnosis performance of deep learning, we use the deep learning model to integrate SNP characteristics and ROI characteristics. The SNP feature, ROI feature, and SNP-ROI joint feature were input into the deep learning model and trained by cross-validation technique. The experimental results show that the SNP-ROI joint feature describes the information of the samples from different angles, which makes the diagnosis accuracy higher.

Fire, humans and climate as drivers of environmental change on Broughton Island, New South Wales, Australia

In Australia, the drivers of precolonial fire regimes remain contentious, with some advocating an anthropogenic-dominated regime, and others highlighting the importance of climate, climatic variability or alternatively some nexus between climate and human activity. Here, we explore the inter-relationships between fire, humans and vegetation using macroscopic charcoal, archaeology and palynology over the last ~5430 cal. year BP from Broughton Island, a small, near-shore island located in eastern Australia. We find a clear link between fire and the reduction of arboreal pollen and rainforest indicators on the island, especially at ~4.0 ka and in the last ~1000 years. Similarities with comparable palaeoenvironmental records of fire in the region and a record of strong El Niño (dry, fire-prone) events supports the contention that climate was a significant influence on the fire regimes of Broughton Island. However, two periods of enhanced fire activity, at ~4000 years BP and ~<600 years BP have weaker links to climate, and perhaps reflect anthropogenic activity. Changes to the fire regime in the last ~600 years corresponds with the earliest evidence of Indigenous archaeology on the island, and coincides with implications that Polynesian people were present in the region. After the mid-Twentieth Century a human-dominated fire regime is also an obvious feature of the reconstructed fire record on Broughton Island.

Optical Communication with Invisible Photons

It has always been a self-evident and obvious feature of any kind of communication that there should be an exchange of objects like photons or electrons between the sender and the receiver to convey any information. In this chapter a protocol is presented in which information is transmitted between a sender and receiver with no particles in the transmission channel. The basic building block of this counterfactual communication protocol, the Mach–Zehnder interferometer, is discussed. The concept of interaction-free measurement is also introduced.

On the Innovation of Journalism Practice Teaching in the Media Convergence Environment

<p>The current media environment has undergone great changes, which benefit from the continuous development of Internet technology. The gap between different media is gradually being eliminated, and the concept and approach of news transmission have also undergone tremendous changes. With the integration of different media, the way of news dissemination also changes. In the current education of journalism and communication in China, the most obvious feature is the practicability of journalism practice. This paper mainly discusses the innovation and change of journalism practice teaching under the media convergence environment.</p>

Synthesis, characterization and electrochemical properties of isoflavone substituted zinc(II), cobalt(II), and metal-free phthalocyanines

In this study, novel tetrasubstituted metallo- and metal-free phthalocyanines containing 7-hydroxy-4′-methoxyisoflavonoxy moieties at peripheral and non-peripheral positions have been prepared by cyclotetramerization of corresponding phthalonitriles. The most obvious feature of these quaternized complexes is their extensive solubility and non-aggregated species (especially non-peripherally substituted) in organic solvents such as chloroform, tetrahydrofuran, dimethylformamide and dimethylsulfoxide, which makes them candidates for use in many applications in different fields. The new compounds have been characterized by elemental analysis, FT-IR, UV-vis, 1H and [Formula: see text]C NMR and MS (Maldi-TOF MS). Voltammetric and in situ spectroelectrochemical measurements have been performed with the aim of characterizing the electron transfer properties of the compounds on Pt in dimethylsulfoxide/tetrabutylammonium perchlorate, compared to those of previously reported corresponding compounds with tetra 6-hydroxyflavonoxy substituents. The effect of aggregation on the redox character of these complexes was also discussed.