Cosmological Luminosity Evolution of QSO/AGN Population

We apply the observed optical/X-ray spectral states of the Galactic black hole candidates (GBHCs) to the cosmological QSO luminosity evolution under the assumptions that QSOs and GBHCs are powered by similar accretion processes and that their emission mechanisms are also similar. The QSO luminosity function (LF) evolution in various energy bands is strongly affected by the spectral evolution which is tightly correlated with the luminosity evolution. We generate a random sample of QSOs born nearly synchronously by allowing the QSOs to have redshifts in a narrow range around an initial high redshift, black hole masses according to a power-law, and mass accretion rates near Eddington rates. The QSOs evolve as a single long-lived population on the cosmological time scale. The pure luminosity evolution results in distinct luminosity evolution features due to the strong spectral evolution. Most notably, different energy bands (optical/UV, soft X-ray, and hard X-ray) show different evolutionary trends and the hard X-ray LF in particular shows an apparent reversal of the luminosity evolution (from decreasing to increasing luminosity) at low redshifts, which is not seen in the conventional pure luminosity evolution scenario without spectral evolution. The resulting mass function of black holes (BHs), which is qualitatively consistent with the observed QSO LF evolution, shows that QSO remnants are likely to be found as BHs with masses in the range 10**8-5x10**10 solar masses. The long-lived single population of QSOs are expected to leave their remnants as supermassive BHs residing in rare, giant elliptical galaxies.Comment: 9 pages, 2 figures, ApJ

Design of oversampling current steering DAC with 640MHz equivalent clock frequency

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references (leaves 59-62).Issued also on microfiche from Lange Micrographics.A DAC (Digital-to-Analog Converter) architecture based on the current steering method is proposed. The architecture exploits the first order sigma-delta modulator, oversampling technique, multi-bit and MASH (multi stage noise shaping) configuration. The DAC adopted MASH structure requires two current steering 6-bit D/A converters whose current references are properly scaled. The two output currents are combined at the output node to achieve the output signal. For high frequency operation, we modified sigma-delta structure to parallel four paths digital sigma-delta modulator by concerning the data stream in the conventional sigma-delta architecture. Each path has the 160MHz-clock frequency so that total output data stream can work at 640MHz-clock frequency. Since the DAC employs multi-bit solution, the dynamic matching of element technique is applied to current cells in the DAC. Rotated data weight averaging algorithm, one of the matching techniques, is used for mismatch error reduction. The DAC operates with an oversampling factor equal to 8 and 40 MHz band-width (clock frequency 640 MHz) and the possible output SNR (signal-to-noise ratio) reaches an SNR as large as 86 dB

Collision Avoidance Geographic P2P-RPL in Multi-Hop Indoor Wireless Networks

In home and building automation applications, wireless sensor devices need to be connected via unreliable wireless links within a few hundred milliseconds. Routing protocols in Low-power and Lossy Networks (LLNs) need to support reliable data transmission with an energy-efficient manner and short routing convergence time. IETF standardized the Point-to-Point RPL (P2P-RPL) routing protocol, in which P2P-RPL propagates the route discovery messages over the whole network. This leads to significant routing control packet overhead and a large amount of energy consumption. P2P-RPL uses the trickle algorithm to control the transmission rate of routing control packets. The non-deterministic message suppression nature of the trickle algorithm may generate a sub-optimal routing path. The listen-only period of the trickle algorithm may lead to a long network convergence time. In this paper, we propose Collision Avoidance Geographic P2P-RPL, which achieves energy-efficient P2P data delivery with a fast routing request procedure. The proposed algorithm uses the location information to limit the network search space for the desired route discovery to a smaller location-constrained forwarding zone. The Collision Avoidance Geographic P2P-RPL also dynamically selects the listen-only period of the trickle timer algorithm based on the transmission priority related to geographic position information. The location information of each node is obtained from the Impulse-Response Ultra-WideBand (IR-UWB)-based cooperative multi-hop self localization algorithm. We implement Collision Avoidance Geographic P2P-RPL on Contiki OS, an open-source operating system for LLNs and the Internet of Things. The performance results show that the Collision Avoidance Geographic P2P-RPL reduced the routing control packet overheads, energy consumption, and network convergence time significantly. The cooperative multi-hop self localization algorithm improved the practical implementation characteristics of the P2P-RPL protocol in real world environments. The collision avoidance algorithm using the dynamic trickle timer increased the operation efficiency of the P2P-RPL under various wireless channel conditions with a location-constrained routing space

River Stage Modeling by Combining Maximal Overlap Discrete Wavelet Transform, Support Vector Machines and Genetic Algorithm

This paper proposes a river stage modeling approach combining maximal overlap discrete wavelet transform (MODWT), support vector machines (SVMs) and genetic algorithm (GA). The MODWT decomposes original river stage time series into sub-time series (detail and approximation components). The SVM computes daily river stage values using the decomposed sub-time series. The GA searches for the optimal hyperparameters of SVM. The performance of MODWT–SVM models is evaluated using efficiency and effectiveness indices; and compared with that of a single model (multilayer perceptron (MLP) and SVM), discrete wavelet transform (DWT)-based models (DWT–MLP and DWT–SVM) and MODWT–MLP models. The conjunction of MODWT, SVM and GA improves the performance of the SVM model and outperforms the single models. The MODWT–based models using the SVM model enhance model performance and accuracy compared to those of using MLP model. Also, hybrid models coupling MODWT, SVM and GA improve model performance and accuracy in daily river stage modeling as compared with those combined with DWT. The MODWT–SVM model using the Coiflet 12 (c12) mother wavelet, MODWT–SVM-c12, produces the best efficiency and effectiveness among all models. Therefore, the conjunction of MODWT, SVM and GA can be an efficient and effective approach for modeling daily river stages

Engineering Therapeutic Strategies in Cancer Immunotherapy via Exogenous Delivery of Toll-like Receptor Agonists

As a currently spotlighted method for cancer treatment, cancer immunotherapy has made a lot of progress in recent years. Among tremendous cancer immunotherapy boosters available nowadays, Toll-like receptor (TLR) agonists were specifically selected, because of their effective activation of innate and adaptive immune cells, such as dendritic cells (DCs), T cells, and macrophages. TLR agonists can activate signaling pathways of DCs to express CD80 and CD86 molecules, and secrete various cytokines and chemokines. The maturation of DCs stimulates naïve T cells to differentiate into functional cells, and induces B cell activation. Although TLR agonists have anti-tumor ability by activating the immune system of the host, their drawbacks, which include poor efficiency and remarkably short retention time in the body, must be overcome. In this review, we classify and summarize the recently reported delivery strategies using (1) exogenous TLR agonists to maintain the biological and physiological signaling activities of cargo agonists, (2) usage of multiple TLR agonists for synergistic immune responses, and (3) co-delivery using the combination with other immunomodulators or stimulants. In contrast to naked TLR agonists, these exogenous TLR delivery strategies successfully facilitated immune responses and subsequently mediated anti-tumor efficacy

Short-Term Water Demand Forecasting Model Combining Variational Mode Decomposition and Extreme Learning Machine

Accurate water demand forecasting is essential to operate urban water supply facilities efficiently and ensure water demands for urban residents. This study proposes an extreme learning machine (ELM) coupled with variational mode decomposition (VMD) for short-term water demand forecasting in six cities (Anseong-si, Hwaseong-si, Pyeongtaek-si, Osan-si, Suwon-si, and Yongin-si), South Korea. The performance of VMD-ELM model is investigated based on performance indices and graphical analysis and compared with that of artificial neural network (ANN), ELM, and VMD-ANN models. VMD is employed for multi-scale time series decomposition and ANN and ELM models are used for sub-time series forecasting. As a result, ELM model outperforms ANN model. VMD-ANN and VMD-ELM models outperform ANN and ELM models, and the VMD-ELM model produces the best performance among all the models. The results obtained from this study reveal that the coupling of VMD and ELM can be an effective forecasting tool for short-term water demands with strong nonlinearity and non-stationarity and contribute to operating urban water supply facilities efficiently

Location-Aware Point-to-Point RPL in Indoor IR-UWB Networks

Wireless multi-hop ad hoc routing is one of the critical design factors that determine the network performance of various wireless IoT applications. IETF has standardized the point-to-point RPL (P2P-RPL) routing protocol to overcome the inefficient routing overheads of RPL. However, P2P-RPL propagates the route discovery forwarding packets throughout the whole network. P2P-RPL suffers from the high energy consumption and the huge route discovery overhead in low-power and lossy networks (LLNs). In this paper, we propose a novel Location-Aware P2P-RPL (LA P2P-RPL), which achieves the energy-efficient P2P data delivery without reducing the networking reliability. The proposed algorithm introduces the Impulse-Response UWB (IR-UWB) based cooperative multi-hop self localization algorithm and the Location-Aware P2P-RPL algorithm for Indoor IR-UWB based networks. To increase the localization accuracy, smartphone based Inertial Navigation System (INS) with particle filtering is used in indoor multi-hop environments. The performance evaluations for Location-Aware P2P-RPL algorithm are compared with the traditional P2P-RPL and the ER-RPL algorithm to show the significant performance improvements for route discovery overheads and energy consumptions in LLNs