Volatility clustering in land markets

Purpose– The purpose of this paper is to investigate the volatility clustering in the return of land markets through both theoretical and empirical approaches.Design/methodology/approach– Using extensive monthly panel data at the provincial level from 1986 to 2013, the authors identify the existence of time-correlated and time-varying returns in Canadian land markets.Findings– Consistent with the proposed theory, volatility clustering in land markets tends to be observed in more populated areas.Originality/value– The result has significant implications for portfolio management, economic theory and government policy by revealing the systematic pattern of volatility clustering in land markets.This is the author accepted manuscript. The final version is available from Emerald via http://dx.doi.org/10.1108/PM-02-2014-000

A belief propagation approach for distributed user association in heterogeneous networks

© 2014 IEEE. In heterogeneous networks (HetNets), the load between macro-cell base stations (MBSs) and small-cell BSs (SBSs) is imbalanced due to transmit power disparities and ad-hoc deployment of SBSs. This significantly impacts the system performance and user experience. Associating more users to the SBSs is an effective way to solve this problem. In this paper, we formulate the user-BS association problem as a distributed optimization problem with proportional fairness as the objective. Specifically, we propose a novel distribute algorithm based on the belief propagation (BP) method to solve the user-BS association problem via iteratively message passing between the users and BSs. Also, we develop an approximation calculation in the BP method to reduce the computational complexity and transmission overhead of message passing. Simulation results show that the proposed algorithm well approaches the optimal system performance (by exhausting search) with low complexity and fast convergence

Synthesis of Polycyclic Aromatic Hydrocarbons by Phenyl Addition-Dehydrocyclization: The Third Way.

Polycyclic aromatic hydrocarbons (PAHs) represent the link between resonance-stabilized free radicals and carbonaceous nanoparticles generated in incomplete combustion processes and in circumstellar envelopes of carbon rich asymptotic giant branch (AGB) stars. Although these PAHs resemble building blocks of complex carbonaceous nanostructures, their fundamental formation mechanisms have remained elusive. By exploring these reaction mechanisms of the phenyl radical with biphenyl/naphthalene theoretically and experimentally, we provide compelling evidence on a novel phenyl-addition/dehydrocyclization (PAC) pathway leading to prototype PAHs: triphenylene and fluoranthene. PAC operates efficiently at high temperatures leading through rapid molecular mass growth processes to complex aromatic structures, which are difficult to synthesize by traditional pathways such as hydrogen-abstraction/acetylene-addition. The elucidation of the fundamental reactions leading to PAHs is necessary to facilitate an understanding of the origin and evolution of the molecular universe and of carbon in our galaxy

Innovative sponge-based moving bed-osmotic membrane bioreactor hybrid system using a new class of draw solution for municipal wastewater treatment

© 2016 Elsevier Ltd. For the first time, an innovative concept of combining sponge-based moving bed (SMB) and an osmotic membrane bioreactor (OsMBR), known as the SMB-OsMBR hybrid system, were investigated using Triton X-114 surfactant coupled with MgCl2 salt as the draw solution. Compared to traditional activated sludge OsMBR, the SMB-OsMBR system was able to remove more nutrients due to the thick-biofilm layer on sponge carriers. Subsequently less membrane fouling was observed during the wastewater treatment process. A water flux of 11.38 L/(m2 h) and a negligible reverse salt flux were documented when deionized water served as the feed solution and a mixture of 1.5 M MgCl2 and 1.5 mM Triton X-114 was used as the draw solution. The SMB-OsMBR hybrid system indicated that a stable water flux of 10.5 L/(m2 h) and low salt accumulation were achieved in a 90-day operation. Moreover, the nutrient removal efficiency of the proposed system was close to 100%, confirming the effectiveness of simultaneous nitrification and denitrification in the biofilm layer on sponge carriers. The overall performance of the SMB-OsMBR hybrid system using MgCl2 coupled with Triton X-114 as the draw solution demonstrates its potential application in wastewater treatment

Prediction of the functional class of metal-binding proteins from sequence derived physicochemical properties by support vector machine approach

Metal-binding proteins play important roles in structural stability, signaling, regulation, transport, immune response, metabolism control, and metal homeostasis. Because of their functional and sequence diversity, it is desirable to explore additional methods for predicting metal-binding proteins irrespective of sequence similarity. This work explores support vector machines (SVM) as such a method. SVM prediction systems were developed by using 53,333 metal-binding and 147,347 non-metal-binding proteins, and evaluated by an independent set of 31,448 metal-binding and 79,051 non-metal-binding proteins. The computed prediction accuracy is 86.3%, 81.6%, 83.5%, 94.0%, 81.2%, 85.4%, 77.6%, 90.4%, 90.9%, 74.9% and 78.1% for calcium-binding, cobalt-binding, copper-binding, iron-binding, magnesium-binding, manganese-binding, nickel-binding, potassium-binding, sodium-binding, zinc-binding, and all metal-binding proteins respectively. The accuracy for the non-member proteins of each class is 88.2%, 99.9%, 98.1%, 91.4%, 87.9%, 94.5%, 99.2%, 99.9%, 99.9%, 98.0%, and 88.0% respectively. Comparable accuracies were obtained by using a different SVM kernel function. Our method predicts 67% of the 87 metal-binding proteins non-homologous to any protein in the Swissprot database and 85.3% of the 333 proteins of known metal-binding domains as metal-binding. These suggest the usefulness of SVM for facilitating the prediction of metal-binding proteins. Our software can be accessed at the SVMProt server

Genomic Expansion of Magnetotactic Bacteria Reveals an Early Common Origin of Magnetotaxis with Lineage-specific Evolution

The origin and evolution of magnetoreception, which in diverse prokaryotes and protozoa is known as magnetotaxis and enables these microorganisms to detect Earth’s magnetic field for orientation and navigation, is not well understood in evolutionary biology. The only known prokaryotes capable of sensing the geomagnetic field are magnetotactic bacteria (MTB), motile microorganisms that biomineralize intracellular, membrane-bounded magnetic single-domain crystals of either magnetite (Fe3O4) or greigite (Fe3S4) called magnetosomes. Magnetosomes are responsible for magnetotaxis in MTB. Here we report the first large-scale metagenomic survey of MTB from both northern and southern hemispheres combined with 28 genomes from uncultivated MTB. These genomes expand greatly the coverage of MTB in the Proteobacteria, Nitrospirae, and Omnitrophica phyla, and provide the first genomic evidence of MTB belonging to the Zetaproteobacteria and “Candidatus Lambdaproteobacteria” classes. The gene content and organization of magnetosome gene clusters, which are physically grouped genes that encode proteins for magnetosome biosynthesis and organization, are more conserved within phylogenetically similar groups than between different taxonomic lineages. Moreover, the phylogenies of core magnetosome proteins form monophyletic clades. Together, these results suggest a common ancient origin of iron-based (Fe3O4 and Fe3S4) magnetotaxis in the domain Bacteria that underwent lineage-specific evolution, shedding new light on the origin and evolution of biomineralization and magnetotaxis, and expanding significantly the phylogenomic representation of MTB

Single channel wireless EEG device for real-time fatigue level detection

© 2015 IEEE. Driver fatigue problem is one of the important factors of traffic accidents. Recent years, many research had investigated that using EEG signals can effectively detect driver's drowsiness level. However, real-time monitoring system is required to apply these fatigue level detection techniques in the practical application, especially in the real-road driving. Therefore, it required less channels, portable and wireless, real-time monitoring and processing techniques for developing the real-time monitoring system. In this study, we develop a single channel wireless EEG device which can real-time detect driver's fatigue level on the mobile device such as smart phone or tablet. The developed device is investigated to obtain a better and precise understanding of brain activities of mental fatigue under driving, which is of great benefit for devolvement of detection of driving fatigue system. This system consists of a Bluetooth-enabled one channel EEG, a regression model, and smartphone, which was a platform recording and transforming the raw EEG data to useful driving status. In the experiment, this was a sustained-attention driving task to implement in a virtual-reality (VR) driving simulator. To training model and develop the system, we were performed for 15 subjects to study Electroencephalography (EEG) brain dynamics by using a mobile and wireless EEG device. Based on the outstanding training results, the leave-one-subject-out cross validation test obtained 90% fatigue detection accuracy. These results indicate that the combination of a smartphone and wireless EEG device constitutes an effective and easy wearable solution for detecting and preventing driver fatigue in real driving environments