Sedimentation rates of the middle Miocene Clarkia Lake deposit, Nothern Idaho, USA

A global warming phase related to the onset of the Columbia River volcanism in the USA is recorded in the middle Miocene Clarkia Lake deposit, which yields abundant fossil leaves of subtropical to warm-termperate species preserved in extraordinary conditions [1]. These leaf fossils are found in varve-like laminated successions that presumably represent seasonal phases interleaved with volcanic-ash layers [2]. Despite being studied for over four decades, this paleolake deposit remains poorly constrained in its time-scale. Defining its sedimentation rate is pivotal for reconstructing the paleoclimatic conditions during the middle Miocene. X-Ray Fluorescence (XRF) scanning of key intervals offered insights about the elemental ratio distribution in the Clarkia Lake deposit, which might hold the answer to the sedimentation rate question. Accelerating voltages of 10, 30, and 50 kV detected counts of Mg, Al, Si, P, S, Cl, Ar, K, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Br, Rb, Sr, Y, Zr, Nb, Mo, and Ba. Plots of ratios using 702 element-combinations show a strong, positive correlation between the observed varve-like structures and S/Rb and Zr/Rb ratios. The former ratio is interpreted as a tracer of fluvial dilution of the presumably constant rate of reduced sulfur deposition, and the latter denotes variation in the grain-size distribution. For both ratios, low countings represent light-colored, coarse-grained, and quartz-rich layers while high countings correspond to dark-colored, fine-grained, and organic-rich layers. Volcanic ash-layers are distinguishable by enhanced signals of Si, Al, Ti, Zn, and Rb as well as low counts of Fe and Mn. Ratios of Zn and trace elements remarkably detect the extension of these layers along the profiles. Preliminary statistic treatment of this XRF data, employing spectral analysis, suggests depositional cycles at every 1.

A Fair Resource Allocation Algorithm for Data and Energy Integrated Communication Networks

With the rapid advancement of wireless network technologies and the rapid increase in the number of mobile devices, mobile users (MUs) have an increasing high demand to access the Internet with guaranteed quality-of-service (QoS). Data and energy integrated communication networks (DEINs) are emerging as a new type of wireless networks that have the potential to simultaneously transfer wireless energy and information via the same base station (BS). This means that a physical BS is virtualized into two parts: one is transferring energy and the other is transferring information. The former is called virtual energy base station (eBS) and the latter is named as data base station (dBS). One important issue in such setting is dynamic resource allocation. Here the resource concerned includes both power and time. In this paper, we propose a fair data-and-energy resource allocation algorithm for DEINs by jointly designing the downlink energy beamforming and a power-and-time allocation scheme, with the consideration of finite capacity batteries at MUs and power sensitivity of radio frequency (RF) to direct current (DC) conversion circuits. Simulation results demonstrate that our proposed algorithm outperforms the existing algorithms in terms of fairness, beamforming design, sensitivity, and average throughput.</jats:p

Data and Energy Integrated Communication Networks for Wireless Big Data

This paper describes a new type of communication network called data and energy integrated communication networks (DEINs), which integrates the traditionally separate two processes, i.e., wireless information transfer (WIT) and wireless energy transfer (WET), fulfilling co-transmission of data and energy. In particular, the energy transmission using radio frequency is for the purpose of energy harvesting (EH) rather than information decoding. One driving force of the advent of DEINs is wireless big data, which comes from wireless sensors that produce a large amount of small piece of data. These sensors are typically powered by battery that drains sooner or later and will have to be taken out and then replaced or recharged. EH has emerged as a technology to wirelessly charge batteries in a contactless way. Recent research work has attempted to combine WET with WIT, typically under the label of simultaneous wireless information and power transfer. Such work in the literature largely focuses on the communication side of the whole wireless networks with particular emphasis on power allocation. The DEIN communication network proposed in this paper regards the convergence of WIT and WET as a full system that considers not only the physical layer but also the higher layers, such as media access control and information routing. After describing the DEIN concept and its high-level architecture/protocol stack, this paper presents two use cases focusing on the lower layer and the higher layer of a DEIN network, respectively. The lower layer use case is about a fair resource allocation algorithm, whereas the high-layer section introduces an efficient data forwarding scheme in combination with EH. The two case studies aim to give a better explanation of the DEIN concept. Some future research directions and challenges are also pointed out

Understanding Time Series Anomaly State Detection through One-Class Classification

For a long time, research on time series anomaly detection has mainly focused on finding outliers within a given time series. Admittedly, this is consistent with some practical problems, but in other practical application scenarios, people are concerned about: assuming a standard time series is given, how to judge whether another test time series deviates from the standard time series, which is more similar to the problem discussed in one-class classification (OCC). Therefore, in this article, we try to re-understand and define the time series anomaly detection problem through OCC, which we call 'time series anomaly state detection problem'. We first use stochastic processes and hypothesis testing to strictly define the 'time series anomaly state detection problem', and its corresponding anomalies. Then, we use the time series classification dataset to construct an artificial dataset corresponding to the problem. We compile 38 anomaly detection algorithms and correct some of the algorithms to adapt to handle this problem. Finally, through a large number of experiments, we fairly compare the actual performance of various time series anomaly detection algorithms, providing insights and directions for future research by researchers

A hybrid ensemble forecasting model of passenger flow based on improved variational mode decomposition and boosting

An accurate passenger flow forecast can provide key information for intelligent transportation and smart cities, and help promote the development of smart cities. In this paper, a mixed passenger flow forecasting model based on the golden jackal optimization algorithm (GJO), variational mode decomposition (VMD) and boosting algorithm was proposed. First, the data characteristics of the original passenger flow sequence were extended. Second, an improved variational modal decomposition method based on the Sobol sequence improved GJO algorithm was proposed. Next, according to the sample entropy of each intrinsic mode function (IMF), IMF with similar complexity is combined into a new subsequence. Finally, according to the determination rules of the sub-sequence prediction model, the boosting modeling and prediction of different sub-sequences were carried out, and the final passenger flow prediction result was obtained. Based on the experimental results of three scenic spots, the mean absolute percentage error (MAPE) of the mixed set model is 0.0797, 0.0424 and 0.0849, respectively. The fitting degree reached 95.33%, 95.63% and 95.97% simultaneously. The results show that the hybrid model proposed in this study has high prediction accuracy and can provide reliable information sources for relevant departments, scenic spot managers and tourists

Variations of stomatal frequency in Taxodium and Metasequoia populations at the mid-Miocene Clarkia Lake deposits: Implications for atmospheric CO2 reconstruction

Stomatal frequency (such as stomatal index-SI and stomatal density-SD) has been widely used to reconstruct atmospheric CO2 levels in geological history as it is one of the most reliable proxies of paleo-CO2 that predate the oldest icecore records. However, living plants show large variations on stomatal frequency within the same species, potentially generating large error margins for estimated paleo-CO2 levels using limited fossil specimen(s). The extraordinarily wellpreserved and abundant fossil leaves from the mid-Miocene (~15Ma) Clarkia Lake deposits in northern Idaho, the USA, allow us to test variations within a population of a fossil species and to compare that cross different contemporary species. Our preliminary results from the SD of 15 cuticular membranes of Taxodium revealed a range of variation leading to CO2 levels of 345-445 parts per million (ppm). The SI of eight cuticular membranes of Metasequoia from the same fossiliferous layers reconstructed CO2 levels of 290-345 ppm. These wide and discrepant ranges imply that randomly selected fossil leaves with limited sample numbers may give a large range of CO2 reconstructions and different methods (such as SD or SI) and different plant taxa (such as Taxodium and Metasequoia) may result in different CO2 results. A better understanding of stomatal frequency variations within populations and consistent sampling method will reduce errors in paleo-CO2 reconstruction

CD74-dependent Deregulation of the Tumor Suppressor Scribble in Human Epithelial and Breast Cancer Cells

The γ subunit of the major histocompatibility complex (MHC) class II complex, CD74, is overexpressed in a significant proportion of metastatic breast tumors, but the mechanistic foundation and biologic significance of this phenomenon are not fully understood. Here, we show that when CD74 is overexpressed in human cancer and noncancerous epithelial cells, it interacts and interferes with the function of Scribble, a product of a well-known tumor suppressor gene. Furthermore, using epithelial cell lines expressing CD74 under the control of tetracycline-inducible promoter and quantitative high-resolution mass spectrometry, we demonstrate that, as a result of CD74 overexpression, the phosphorylation pattern of the C-terminal part of Scribble undergoes specific changes. This is accompanied with a translocation of the protein from the sites of cell-to-cell contacts at the plasma membrane to the cytoplasm, which is likely to effectively enhance the motility and invasiveness of the cancer cells. © 2013 Neoplasia Press, Inc. All rights reserved