Spatio-temporal environmental monitoring for smart buildings

© 2017 IEEE. The paper addresses the problem of efficiently monitoring environmental fields in a smart building by the use of a network of wireless noisy sensors that take discretely-predefined measurements at their locations through time. It is proposed that the indoor environmental fields are statistically modeled by spatio-temporal non-parametric Gaussian processes. The proposed models are able to effectively predict and estimate the indoor climate parameters at any time and at any locations of interest, which can be utilized to create timely maps of indoor environments. More importantly, the monitoring results are practically crucial for building management systems to efficiently control energy consumption and maximally improve human comfort in the building. The proposed approach was implemented in a real tested space in a university building, where the obtained results are highly promising

Efficient spatio-temporal sensor deployments: A smart building application

© 2017 IEEE. The paper addresses the problem of efficiently deploying sensors in spatial environments, e.g. smart buildings, for the purpose of monitoring environmental phenomena. By modelling the environmental fields using spatio-temporal Gaussian processes, a new and efficient optimality criterion of minimizing prediction uncertainties is proposed to find the best sensor locations. Though the environmental processes spatially and temporally vary, the proposed approach of choosing sensor positions is not affected by time variations, which significantly reduces computational complexity of the optimization problem. The sensor deployment problem is then solved by a practically and feasibly polynomial algorithm, where its solutions are guaranteed. The proposed approaches were implemented in a real tested space in a university building, where the obtained results are highly promising

Very Constrained Minimal Supersymmetric Standard Models

We consider very constrained versions of the minimal supersymmetric extension of the Standard Model (VCMSSMs) which, in addition to constraining the scalar masses m_0 and gaugino masses m_{1/2} to be universal at some input scale, impose relations between the trilinear and bilinear soft supersymmetry breaking parameters A_0 and B_0. These relations may be linear, as in simple minimal supergravity models, or nonlinear, as in the Giudice-Masiero mechanism for generating the Higgs-mixing mu term. We discuss the application of the electroweak vacuum conditions in VCMSSMs, which may be used to make a prediction for tan beta as a function of m_0 and m_{1/2} that is usually unique. We baseline the discussion of the parameter spaces allowed in VCMSSMs by updating the parameter space allowed in the CMSSM for fixed values of tan beta with no relation between A_0 and B_0 assumed {\it a priori}, displaying contours of B_0 for a fixed input value of A_0, incorporating the latest CDF/D0 measurement of m_t and the latest BNL measurement of g_mu - 2. We emphasize that phenomenological studies of the CMSSM are frequently not applicable to specific VCMSSMs, notably those based on minimal supergravity, which require m_0 = m_{3/2} as well as A_0 = B_0 + m_0. We then display (m_{1/2}, m_0) planes for selected VCMSSMs, treating in a unified way the parameter regions where either a neutralino or the gravitino is the LSP. In particular, we examine in detail the allowed parameter space for the Giudice-Masiero model.Comment: 26 pages, 32 eps figure

Rewiring of the phosphoproteome executes two meiotic divisions in budding yeast

The cell cycle is ordered by a controlled network of kinases and phosphatases. To generate gametes via meiosis, two distinct and sequential chromosome segregation events occur without an intervening S phase. How canonical cell cycle controls are modified for meiosis is not well understood. Here, using highly synchronous budding yeast populations, we reveal how the global proteome and phosphoproteome change during the meiotic divisions. While protein abundance changes are limited to key cell cycle regulators, dynamic phosphorylation changes are pervasive. Our data indicate that two waves of cyclin-dependent kinase (Cdc28Cdk1) and Polo (Cdc5Polo) kinase activity drive successive meiotic divisions. These two distinct phases of phosphorylation are ensured by the meiosis-specific Spo13 protein, which rewires the phosphoproteome. Spo13 binds to Cdc5Polo to promote phosphorylation in meiosis I, particularly of substrates containing a variant of the canonical Cdc5Polo motif. Overall, our findings reveal that a master regulator of meiosis directs the activity of a kinase to change the phosphorylation landscape and elicit a developmental cascade.</p

Non-linear dynamic response of a cable system with a tuned mass damper to stochastic base excitation via equivalent linearization technique

Abstract: Non-linear dynamic model of a cable–mass system with a transverse tuned mass damper is considered. The system is moving in a vertical host structure therefore the cable length varies slowly over time. Under the time-dependent external loads the sway of host structure with low frequencies and high amplitudes can be observed. That yields the base excitation which in turn results in the excitation of a cable system. The original model is governed by a system of non-linear partial differential equations with corresponding boundary conditions defined in a slowly time-variant space domain. To discretise the continuous model the Galerkin method is used. The assumption of the analysis is that the lateral displacements of the cable are coupled with its longitudinal elastic stretching. This brings the quadratic couplings between the longitudinal and transverse modes and cubic nonlinear terms due to the couplings between the transverse modes. To mitigate the dynamic response of the cable in the resonance region the tuned mass damper is applied. The stochastic base excitation, assumed as a narrow-band process mean-square equivalent to the harmonic process, is idealized with the aid of two linear filters: one second-order and one first-order. To determine the stochastic response the equivalent linearization technique is used. Mean values and variances of particular random state variable have been calculated numerically under various operational conditions. The stochastic results have been compared with the deterministic response to a harmonic process base excitation

Partial wave treatment of Supersymmetric Dark Matter in the presence of CP - violation

We present an improved partial wave analysis of the dominant LSP annihilation channel to a fermion-antifermion pair which avoids the non-relativistic expansion being therefore applicable near thresholds and poles. The method we develop allows of contributions of any partial wave in the total angular momentum J in contrast to partial wave analyses in terms of the orbital angular momentum L of the initial state, which is usually truncated to p-waves, and yields very accurate results. The method is formulated in such a way as to allow easy handling of CP-violating phases residing in supersymmetric parameters. We apply this refined partial wave technique in order to calculate the neutralino relic density in the constrained MSSM (CMSSM) in the presence of CP-violating terms occurring in the Higgs - mixing parameter \mu and trilinear A coupling for large tanb. The inclusion of CP-violating phases in mu and A does not upset significantly the picture and the annihilation of the LSP's to a b b_bar, through Higgs exchange, is still the dominant mechanism in obtaining cosmologically acceptable neutralino relic densities in regions far from the stau-coannihilation and the `focus point'. Significant changes can occur if we allow for phases in the gaugino masses and in particular the gluino mass.Comment: 23 pages LaTeX, 10 eps figures, version to appear in PR

Tuning of Reciprocal Carbon-Electrode Properties for an Optimized Hydrogen Evolution.

Closing the material cycle for harmful and rare resources is a key criterion for sustainable and green energy systems. The concept of using scalable biomass-derived carbon electrodes to produce hydrogen from water was proposed here, satisfying the need for sustainability in the field of chemical energy conversion. The carbon electrodes exhibited not only water oxidation activity but also a strong self-oxidation when being used as anode for water splitting. The carbon oxidation, which is more energy-favorable, was intentionally allowed to occur for an improvement of the total current, thus enhancing the hydrogen production on the cathode side. By introducing different earth-abundant metals, the electrode could be well adjusted to achieve an optimized water/carbon oxidation ratio and an appreciable reactivity for practical applications. This promising methodology may become a very large driver for carbon chemistry when waste organic materials or biomass can be converted using its intrinsic energy content of carbon. Such a process could open a safe path for sub-zero CO2 emission control. The concept of how and which parameter of a carbon-based electrode can be optimized was presented and discussed in this paper

Sparse Deterministic Approximation of Bayesian Inverse Problems

We present a parametric deterministic formulation of Bayesian inverse problems with input parameter from infinite dimensional, separable Banach spaces. In this formulation, the forward problems are parametric, deterministic elliptic partial differential equations, and the inverse problem is to determine the unknown, parametric deterministic coefficients from noisy observations comprising linear functionals of the solution. We prove a generalized polynomial chaos representation of the posterior density with respect to the prior measure, given noisy observational data. We analyze the sparsity of the posterior density in terms of the summability of the input data's coefficient sequence. To this end, we estimate the fluctuations in the prior. We exhibit sufficient conditions on the prior model in order for approximations of the posterior density to converge at a given algebraic rate, in terms of the number $N$ of unknowns appearing in the parameteric representation of the prior measure. Similar sparsity and approximation results are also exhibited for the solution and covariance of the elliptic partial differential equation under the posterior. These results then form the basis for efficient uncertainty quantification, in the presence of data with noise

CDK4/6 inhibitors induce replication stress to cause long-term cell cycle withdrawal

CDK4/6 inhibitors arrest the cell cycle in G1‐phase. They are approved to treat breast cancer and are also undergoing clinical trials against a range of other tumour types. To facilitate these efforts, it is important to understand why a cytostatic arrest in G1 causes long‐lasting effects on tumour growth. Here, we demonstrate that a prolonged G1 arrest following CDK4/6 inhibition downregulates replisome components and impairs origin licencing. Upon release from that arrest, many cells fail to complete DNA replication and exit the cell cycle in a p53‐dependent manner. If cells fail to withdraw from the cell cycle following DNA replication problems, they enter mitosis and missegregate chromosomes causing excessive DNA damage, which further limits their proliferative potential. These effects are observed in a range of tumour types, including breast cancer, implying that genotoxic stress is a common outcome of CDK4/6 inhibition. This unanticipated ability of CDK4/6 inhibitors to induce DNA damage now provides a rationale to better predict responsive tumour types and effective combination therapies, as demonstrated by the fact that CDK4/6 inhibition induces sensitivity to chemotherapeutics that also cause replication stress