Optimized Indoor Positioning for static mode smart devices using BLE

Bluetooth Low Energy (BLE) technology and BLE-based devices such as iBeacons have become popular recently. In this work, an optimized indoor positioning approach using BLE for detecting a smart device’s location in an indoor environment is proposed. The first stage of the proposed approach is a calibration stage for initialization. The Received Signal Strength Indicator (RSSI) is collected and pre-processed for a stable outcome, in the second stage. Then the distance is estimated by using the processed RSSI and calibrated factors in the third stage. The final stage is the position estimation using the outputs from the previous steps. The positioning technique, which is an improved Least Square estimation is evaluated against the other well-known techniques such as, Trilateration-Centroid, classic Least Square Estimation in estimating the user’s location in the 2D plane. Experimental results show that our proposed approach has promising results by achieving an accuracy of positioning within 0.2 to 0.35m

Parameterized Model-Checking for Timed-Systems with Conjunctive Guards (Extended Version)

In this work we extend the Emerson and Kahlon's cutoff theorems for process skeletons with conjunctive guards to Parameterized Networks of Timed Automata, i.e. systems obtained by an \emph{apriori} unknown number of Timed Automata instantiated from a finite set $U_1, \dots, U_n$ of Timed Automata templates. In this way we aim at giving a tool to universally verify software systems where an unknown number of software components (i.e. processes) interact with continuous time temporal constraints. It is often the case, indeed, that distributed algorithms show an heterogeneous nature, combining dynamic aspects with real-time aspects. In the paper we will also show how to model check a protocol that uses special variables storing identifiers of the participating processes (i.e. PIDs) in Timed Automata with conjunctive guards. This is non-trivial, since solutions to the parameterized verification problem often relies on the processes to be symmetric, i.e. indistinguishable. On the other side, many popular distributed algorithms make use of PIDs and thus cannot directly apply those solutions

A Novel Online Dynamic Temporal Context Neural Network Framework for the Prediction of Road Traffic Flow

Traffic flow exhibits different magnitudes of temporal patterns, such as short-term (daily and weekly) and long-term (monthly and yearly). Existing research into road traffic flow prediction has focused on short-term patterns; little research has been done to determine the effect of different long-term patterns on road traffic flow prediction. Providing more temporal contextual information through the use of different temporal data segments, could improve prediction results. In this paper, we have investigated different magnitudes of temporal patterns, such as short-term and long-term, through the use of different temporal data segments to understand how contextual temporal data can improve prediction. Furthermore, to learn temporal patterns dynamically, we have proposed a novel online dynamic temporal context neural network framework. The framework uses different temporal data segments as input features, and during online learning, the updating scheme dynamically determines how useful a temporal data segment (short and long-term temporal patterns) is for prediction, and weights it accordingly for use in the regression model. Therefore, the framework can include short-term and relevant long-term patterns in the regression model leading to improved prediction results. We have conducted a thorough experimental evaluation with a real dataset containing daily, monthly and yearly data segments. The experiment results show that both short and long-term temporal patterns improved prediction accuracy. In addition, the proposed online dynamical framework improved predication results by 10.8% when compared with a deep gated recurrent model

Alzheimer's disease biomarkers in Black and non-Hispanic White cohorts: A contextualized review of the evidence

Black Americans are disproportionately affected by dementia. To expand our understanding of mechanisms of this disparity, we look to Alzheimer's disease (AD) biomarkers. In this review, we summarize current data, comparing the few studies presenting these findings. Further, we contextualize the data using two influential frameworks: the National Institute on Aging–Alzheimer's Association (NIA-AA) Research Framework and NIA's Health Disparities Research Framework. The NIA-AA Research Framework provides a biological definition of AD that can be measured in vivo. However, current cut-points for determining pathological versus non-pathological status were developed using predominantly White cohorts—a serious limitation. The NIA's Health Disparities Research Framework is used to contextualize findings from studies identifying racial differences in biomarker levels, because studying biomakers in isolation cannot explain or reduce inequities. We offer recommendations to expand study beyond initial reports of racial differences. Specifically, life course experiences associated with racialization and commonly used study enrollment practices may better account for observations than exclusively biological explanations

On the Legendre differential equation with uncertainties at the regular-singular point 1: Lp random power series solution and approximation of its statistical moments

"This is the peer reviewed version of the following article: Calatayud, J, Cortés, J-;C, Jornet, M. On the Legendre differential equation with uncertainties at the regular-singular point 1: Lp random power series solution and approximation of its statistical moments. Comp and Math Methods. 2019; 1:e1045. https://doi.org/10.1002/cmm4.1045 , which has been published in final form at https://doi.org/10.1002/cmm4.1045. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."[EN] In this paper, we construct two linearly independent response processes to the random Legendre differential equation on (-1,1)U(1,3), consisting of Lp(omega) convergent random power series around the regular¿singular point 1. A theorem on the existence and uniqueness of Lp(omega) solution to the random Legendre differential equation on the intervals (-1,1) and (1,3) is obtained. The hypotheses assumed are simple: initial conditions in Lp(omega) and random input A in L infinite(omega) (this is equivalent to A having absolute moments that grow at most exponentially). Thus, this paper extends the deterministic theory to a random framework. Uncertainty quantification for the solution stochastic process is performed by truncating the random series and taking limits in Lp(omega). In the numerical experiments, we approximate its expectation and variance for certain forms of the differential equation. The reliability of our approach is compared with Monte Carlo simulations and generalized polynomial chaos expansions.Spanish Ministerio de Economía y Competitividad, Grant/Award Number: MTM2017-89664-P; Programa de Ayudas de Investigación y Desarrollo; Universitat Politècnica de ValènciaCalatayud-Gregori, J.; Cortés, J.; Jornet-Sanz, M. (2019). On the Legendre differential equation with uncertainties at the regular-singular point 1: Lp random power series solution and approximation of its statistical moments. Computational and Mathematical Methods. 1(4):1-12. https://doi.org/10.1002/cmm4.1045S11214Calbo, G., Cortés, J.-C., Jódar, L., & Villafuerte, L. (2011). Solving the random Legendre differential equation: Mean square power series solution and its statistical functions. Computers & Mathematics with Applications, 61(9), 2782-2792. doi:10.1016/j.camwa.2011.03.045Villafuerte, L., Braumann, C. A., Cortés, J.-C., & Jódar, L. (2010). Random differential operational calculus: Theory and applications. Computers & Mathematics with Applications, 59(1), 115-125. doi:10.1016/j.camwa.2009.08.061Wong, E., & Hajek, B. (1985). Stochastic Processes in Engineering Systems. Springer Texts in Electrical Engineering. doi:10.1007/978-1-4612-5060-9Nouri, K., & Ranjbar, H. (2014). Mean Square Convergence of the Numerical Solution of Random Differential Equations. Mediterranean Journal of Mathematics, 12(3), 1123-1140. doi:10.1007/s00009-014-0452-8Lupulescu, V., O’Regan, D., & ur Rahman, G. (2014). Existence results for random fractional differential equations. Opuscula Mathematica, 34(4), 813. doi:10.7494/opmath.2014.34.4.813Villafuerte, L., & Chen-Charpentier, B. M. (2012). A random differential transform method: Theory and applications. Applied Mathematics Letters, 25(10), 1490-1494. doi:10.1016/j.aml.2011.12.033Licea, J. A., Villafuerte, L., & Chen-Charpentier, B. M. (2013). Analytic and numerical solutions of a Riccati differential equation with random coefficients. Journal of Computational and Applied Mathematics, 239, 208-219. doi:10.1016/j.cam.2012.09.040Lang, S. (1997). Undergraduate Analysis. Undergraduate Texts in Mathematics. doi:10.1007/978-1-4757-2698-5Cortés, J.-C., Romero, J.-V., Roselló, M.-D., Santonja, F.-J., & Villanueva, R.-J. (2013). Solving Continuous Models with Dependent Uncertainty: A Computational Approach. Abstract and Applied Analysis, 2013, 1-10. doi:10.1155/2013/983839Calatayud, J., Cortés, J. C., Jornet, M., & Villanueva, R. J. (2018). Computational uncertainty quantification for random time-discrete epidemiological models using adaptive gPC. Mathematical Methods in the Applied Sciences, 41(18), 9618-9627. doi:10.1002/mma.531

Human hippocampal CA3 damage disrupts both recent and remote episodic memories

Neocortical-hippocampal interactions support new episodic (event) memories, but there is conflicting evidence about the dependence of remote episodic memories on the hippocampus. In line with systems consolidation and computational theories of episodic memory, evidence from model organisms suggests that the cornu ammonis 3 (CA3) hippocampal subfield supports recent, but not remote, episodic retrieval. In this study, we demonstrated that recent and remote memories were susceptible to a loss of episodic detail in human participants with focal bilateral damage to CA3. Graph theoretic analyses of 7.0-Tesla resting-state fMRI data revealed that CA3 damage disrupted functional integration across the medial temporal lobe (MTL) subsystem of the default network. The loss of functional integration in MTL subsystem regions was predictive of autobiographical episodic retrieval performance. We conclude that human CA3 is necessary for the retrieval of episodic memories long after their initial acquisition and functional integration of the default network is important for autobiographical episodic memory performance

Are autistic traits measured equivalently in individuals with and without an Autism Spectrum Disorder?:An invariance analysis of the Autism Spectrum Quotient Short Form

It is common to administer measures of autistic traits to those without autism spectrum disorders (ASDs) with, for example, the aim of understanding autistic personality characteristics in non-autistic individuals. Little research has examined the extent to which measures of autistic traits actually measure the same traits in the same way across those with and without an ASD. We addressed this question using a multi-group confirmatory factor invariance analysis of the Autism Quotient Short Form (AQ-S: Hoekstra et al. in J Autism Dev Disord 41(5):589-596, 2011) across those with (n = 148) and without (n = 168) ASD. Metric variance (equality of factor loadings), but not scalar invariance (equality of thresholds), held suggesting that the AQ-S measures the same latent traits in both groups, but with a bias in the manner in which trait levels are estimated. We, therefore, argue that the AQ-S can be used to investigate possible causes and consequences of autistic traits in both groups separately, but caution is due when combining or comparing levels of autistic traits across the two group

Quantitative model for inferring dynamic regulation of the tumour suppressor gene p53

Background: The availability of various "omics" datasets creates a prospect of performing the study of genome-wide genetic regulatory networks. However, one of the major challenges of using mathematical models to infer genetic regulation from microarray datasets is the lack of information for protein concentrations and activities. Most of the previous researches were based on an assumption that the mRNA levels of a gene are consistent with its protein activities, though it is not always the case. Therefore, a more sophisticated modelling framework together with the corresponding inference methods is needed to accurately estimate genetic regulation from "omics" datasets. Results: This work developed a novel approach, which is based on a nonlinear mathematical model, to infer genetic regulation from microarray gene expression data. By using the p53 network as a test system, we used the nonlinear model to estimate the activities of transcription factor (TF) p53 from the expression levels of its target genes, and to identify the activation/inhibition status of p53 to its target genes. The predicted top 317 putative p53 target genes were supported by DNA sequence analysis. A comparison between our prediction and the other published predictions of p53 targets suggests that most of putative p53 targets may share a common depleted or enriched sequence signal on their upstream non-coding region. Conclusions: The proposed quantitative model can not only be used to infer the regulatory relationship between TF and its down-stream genes, but also be applied to estimate the protein activities of TF from the expression levels of its target genes

Grounding knowledge and normative valuation in agent-based action and scientific commitment

Philosophical investigation in synthetic biology has focused on the knowledge-seeking questions pursued, the kind of engineering techniques used, and on the ethical impact of the products produced. However, little work has been done to investigate the processes by which these epistemological, metaphysical, and ethical forms of inquiry arise in the course of synthetic biology research. An attempt at this work relying on a particular area of synthetic biology will be the aim of this chapter. I focus on the reengineering of metabolic pathways through the manipulation and construction of small DNA-based devices and systems synthetic biology. Rather than focusing on the engineered products or ethical principles that result, I will investigate the processes by which these arise. As such, the attention will be directed to the activities of practitioners, their manipulation of tools, and the use they make of techniques to construct new metabolic devices. Using a science-in-practice approach, I investigate problems at the intersection of science, philosophy of science, and sociology of science. I consider how practitioners within this area of synthetic biology reconfigure biological understanding and ethical categories through active modelling and manipulation of known functional parts, biological pathways for use in the design of microbial machines to solve problems in medicine, technology, and the environment. We might describe this kind of problem-solving as relying on what Helen Longino referred to as “social cognition” or the type of scientific work done within what Hasok Chang calls “systems of practice”. My aim in this chapter will be to investigate the relationship that holds between systems of practice within metabolic engineering research and social cognition. I will attempt to show how knowledge and normative valuation are generated from this particular network of practitioners. In doing so, I suggest that the social nature of scientific inquiry is ineliminable to both knowledge acquisition and ethical evaluations

Hedgehog pathway mutations drive oncogenic transformation in high-risk T-cell acute lymphoblastic leukemia.

The role of Hedgehog signaling in normal and malignant T-cell development is controversial. Recently, Hedgehog pathway mutations have been described in T-ALL, but whether mutational activation of Hedgehog signaling drives T-cell transformation is unknown, hindering the rationale for therapeutic intervention. Here, we show that Hedgehog pathway mutations predict chemotherapy resistance in human T-ALL, and drive oncogenic transformation in a zebrafish model of the disease. We found Hedgehog pathway mutations in 16% of 109 childhood T-ALL cases, most commonly affecting its negative regulator PTCH1. Hedgehog mutations were associated with resistance to induction chemotherapy (P = 0.009). Transduction of wild-type PTCH1 into PTCH1-mutant T-ALL cells induced apoptosis (P = 0.005), a phenotype that was reversed by downstream Hedgehog pathway activation (P = 0.007). Transduction of most mutant PTCH1, SUFU, and GLI alleles into mammalian cells induced aberrant regulation of Hedgehog signaling, indicating that these mutations are pathogenic. Using a CRISPR/Cas9 system for lineage-restricted gene disruption in transgenic zebrafish, we found that ptch1 mutations accelerated the onset of notch1-induced T-ALL (P = 0.0001), and pharmacologic Hedgehog pathway inhibition had therapeutic activity. Thus, Hedgehog-activating mutations are driver oncogenic alterations in high-risk T-ALL, providing a molecular rationale for targeted therapy in this disease