A numerical filtering method for linear state-space models with Markov switching

A class of discrete-time random processes arising in engineering and econometrics applications consists of a linear state-space model whose parameters are modulated by the state of a finite-state Markov chain. Typical filtering approaches are collapsing methods, which approximate filtered distributions by mixtures of Gaussians, each Gaussian corresponding to one possibility of the recent history of the Markov chain, and particle methods. This article presents an alternative approach to filtering these processes based on keeping track of the values of the underlying filtered density and its characteristic function on grids. We prove that it has favorable convergence properties under certain assumptions. On the other hand, as a grid method, it suffers from the curse of dimensionality, and so is only suitable for low-dimensional systems. We compare our method to collapsing filters and a particle filter with examples, and find that it can outperform them on 1- and 2-dimensional problems, but loses its speed advantage on 3-dimensional systems. Meanwhile, our method has a proven theoretical convergence rate that is probably not achieved by collapsing and particle methods.     

Soft sensing of water depth in combined sewers using LSTM neural networks with missing observations

Information and communication technologies combined with in-situ sensors are increasingly being used in the management of urban drainage systems. The large amount of data collected in these systems can be used to train a data-driven soft sensor, which can supplement the physical sensor. Artificial Neural Networks have long been used for time series forecasting given their ability to recognize patterns in the data. Long Short-Term Memory (LSTM) neural networks are equipped with memory gates to help them learn time dependencies in a data series and have been proven to outperform other type of networks in predicting water levels in urban drainage systems. When used for soft sensing, neural networks typically receive antecedent observations as input, as these are good predictors of the current value. However, the antecedent observations may be missing due to transmission errors or deemed anomalous due to errors that are not easily explained. This study quantifies and compares the predictive accuracy of LSTM networks in scenarios of limited or missing antecedent observations. We applied these scenarios to an 11-month observation series from a combined sewer overflow chamber in Copenhagen, Denmark. We observed that i) LSTM predictions generally displayed large variability across training runs, which may be reduced by improving the selection of hyperparameters (non-trainable parameters); ii) when the most recent observations were known, adding information on the past did not improve the prediction accuracy; iii) when gaps were introduced in the antecedent water depth observations, LSTM networks were capable of compensating for the missing information with the other available input features (time of the day and rainfall intensity); iv) LSTM networks trained without antecedent water depth observations yielded larger prediction errors, but still comparable with other scenarios and captured both dry and wet weather behaviors. Therefore, we concluded that LSTM neural network may be trained to act as soft sensors in urban drainage systems even when observations from the physical sensors are missing.     

Integrating social and cognitive aspects of belief dynamics: towards a unifying framework

Belief change and spread have been studied in many disciplines—from psychology, sociology, economics and philosophy, to biology, computer science and statistical physics—but we still do not have a firm grasp on why some beliefs change more easily and spread faster than others. To fully capture the complex social-cognitive system that gives rise to belief dynamics, we first review insights about structural components and processes of belief dynamics studied within different disciplines. We then outline a unifying quantitative framework that enables theoretical and empirical comparisons of different belief dynamic models. This framework uses a statistical physics formalism, grounded in cognitive and social theory, as well as empirical observations. We show how this framework can be used to integrate extant knowledge and develop a more comprehensive understanding of belief dynamics.     

Patterns of exits from housing in a homelessness system of care: the case of Calgary,Alberta

Public officials around the world seek to target subsidized housing as purposely and efficiently as possible. With limited availability of subsidized housing, it is helpful to know which household types require specific types of housing support and for how long. With this in mind, we undertake survival analysis and hazard models on clients placed into housing funded by the Calgary Homeless Foundation (CHF) to characterize patterns of exit from Calgary’s homeless system of care. To do this, we use data from Calgary’s Homelessness Management Information System from 1 April 2012 until 31 March 2015. We find singles without dependents to require housing support for the longest period of time, while families require the support for the least amount of time. One important finding is that women require housing support for longer periods of time than men (even though we control for employment and income).     

Thermomechanical surface instability at the origin of surface fissure patterns on heated circular MDF samples

When a flat sample of medium density fibreboard (MDF) is exposed to radiant heat in an inert atmosphere, primary crack patterns suddenly start to appear over the entire surface before pyrolysis and any charring occurs. Contrary to common belief that crack formation is due to drying and shrinkage, it was demonstrated for square samples that this results from thermomechanical instability. In the present paper, new experimental data are presented for circular samples of the same MDF material. The sample was exposed to radiant heating at 20 or 50 kW/m², and completely different crack patterns with independent eigenmodes were observed at the two heat fluxes. We show that the two patterns can be reproduced with a full 3‐D thermomechanical surface instability model of a hot layer adhered to an elastic colder foundation in an axisymmetric domain. Analytical and numerical solutions of a simplified 2‐D formulation of the same problem provide excellent qualitative agreement between observed and calculated patterns. Previous data for square samples, together with the results reported in the present paper for circular samples, confirm the validity of the model for qualitative predictions and indicate that further refinements can be made to improve its quantitative predictive capability.     

Physicochemical stability of antilisterial proteins from P. polymyxa Kp10 as potential food biopreservative

Listeria monocytogenes has continuously become a significant threat to consumers worldwide. The use of chemical-derived preservatives that are commonly associated with safety and nutritional issues has prompted the use of natural-based preservatives as a better alternative. Many bacterial strains including Paenibacillus polymyxa Kp10 have been reported to produce various antimicrobial proteins and compounds that are considered more natural. However, their stability in various physicochemical conditions should be examined before being applied in various types of food. In this study, the stability of four previously identified antilisterial proteins in P. polymyxa Kp10 upon exposure to several physicochemical conditions was examined. More than 80% residual antilisterial activity is conserved upon heat and proteinase K treatment. But, sensitivity to 24 h trypsin digestion has been observed. P1 and P2 proteins (histone-like DNA binding proteins HU) were sensitive to alkaline pH (pH 10-12) as compared with other proteins. More than 70% and 97% residual antilisterial activity were recovered after incubation in raw beef homogenates and simulated meat gravy model, respectively. However, the antilisterial activity of most proteins was highly compromised in chicken and salmon meat homogenates, and UHT cow milk. Inoculation of these proteins into Listeria-contaminated simulated meat gravy showed that all proteins exerted a bactericidal action against L. monocytogenes. P1 and P2 shared almost similar antilisterial activity rates, while P4 was the most potent antilisterial protein. The findings in this study could provide important preliminary data for future applications of these proteins as preservative in food products especially beef-based meat products.     

Pittsburgh's translocal social movement: A case of the new public water

This paper contributes to the water privatization and governance literatures by examining the case of Pittsburgh where renewed efforts of water privatization have emerged after a failed private management arrangement between the local water authority and Veolia ended abruptly in 2015. We argue that, as the water authority aims to rebuild trust in the community, resident mobilization has played a key role in hindering further privatizing arrangements and has exerted influence in defining what constitutes the “new public” water in Pittsburgh, by advocating for increased transparency and accountability in governance, infrastructure improvements and protections for low-income residents.     

A Comprehensive Review of Genetically Engineered Mouse Models for Prader-Willi Syndrome Research

Prader-Willi syndrome (PWS) is a neurogenetic multifactorial disorder caused by the deletion or inactivation of paternally imprinted genes on human chromosome 15q11-q13. The affected homologous locus is on mouse chromosome 7C. The positional conservation and organization of genes including the imprinting pattern between mice and men implies similar physiological functions of this locus. Therefore, considerable efforts to recreate the pathogenesis of PWS have been accomplished in mouse models. We provide a summary of different mouse models that were generated for the analysis of PWS and discuss their impact on our current understanding of corresponding genes, their putative functions and the pathogenesis of PWS. Murine models of PWS unveiled the contribution of each affected gene to this multi-facetted disease, and also enabled the establishment of the minimal critical genomic region (PWScr) responsible for core symptoms, highlighting the importance of non-protein coding genes in the PWS locus. Although the underlying disease-causing mechanisms of PWS remain widely unresolved and existing mouse models do not fully capture the entire spectrum of the human PWS disorder, continuous improvements of genetically engineered mouse models have proven to be very powerful and valuable tools in PWS research.     

阳泉地区“老窑水”现状及治理对策探究

阳泉地区的“老窑水”目前已在部分区域出露,对区域地表水和喀斯特地下水水质造成了一定的影响。因此,本文在充分了解“老窑水”成因及对阳泉地区的影响的基础上,针对区域现状,按照分类制定、综合施策的原则,系统探究了“源头控、末端治”有效结合的治理方案,可为当地“老窑水”治理提供参考,以期为其他类似地区的“老窑水”治理提供借鉴。