A Guard Code Scheme for Handover Traffic Management in WCDMA Systems

A key performance indicator of mobile wireless networks is failure probability of handover calls. In this paper, we propose a Call Admission Control policy which prioritizes handover calls over new calls in WCDMA systems. The OVSF code occupancy of the system is modeled by a Markov chain and the differentiation between handover and new calls is performed at the code level by introducing a “guard code” scheme. The scheme belongs to the well-known family of guard channel schemes and reserves some code capacity to favor the continuation of handover calls over the new calls. As the management of the general case is intractable, we solve certain numerical instances of the problem and manage to calculate several performance metrics like new call blocking and handover failure probabilities and code utilization. We complete our study with simulation results in the case of higher OVSF code tree capacity.

A general class of nonparametric control charts

In this article, we introduce a new general class of nonparametric Shewhart‐type control charts using the lengths of runs of test sample observations between successive observations of a reference sample. Several control charts that have appeared in the literature are members of the new family. In addition, 3 new nonparametric control charts that belong to the class are introduced and studied. Numerical results depict that the proposed charts attain competitive in‐control and out‐of‐control performance as compared with existing nonparametric charts.     

PNET “ONDM 2019” special issue

Photonic Network Communications -     

A Semi-Parametric Non-linear Neural Network Filter: Theory and Empirical Evidence

In this work, we decompose a time series into trend and cycle by introducing a novel de-trending approach based on a family of semi-parametric artificial neural networks. Based on this powerful approach, we propose a relevant filter and show that the proposed trend specification is a global approximation to any arbitrary trend. Furthermore, we prove formally a famous claim by Kydland and Prescott (1981, 1997) that over long time periods, the average value of the cycles is zero. A simple procedure for the econometric estimation of the model is developed as a seven-step algorithm, which relies on standard techniques, where all relevant measures may be computed routinely. Next, using relevant DGPs, we compare and show by means of Monte Carlo simulations that our approach is superior to Hodrick–Prescott (HP) and Baxter and King (BK) regarding the generated distortionary effects and the ability to operate in various frequencies, including changes in volatility, amplitudes and phase. In fact, while keeping the structure of the model relatively simple, our approach is perfectly capable of addressing the case of stochastic trend, in the sense that the generated distortionary effects in the near unit root case are minimal and, by all means, considerably fewer than those generated by HP and BK. Application to EU15 business cycles clustering is presented and the empirical results are consistent with the rigorous theoretical framework developed in this work.     

Tensor splines for interpolation and approximation of DT-MRI with applications to segmentation of isolated rat hippocampi

In this paper, we present novel algorithms for statistically robust interpolation and approximation of diffusion tensors-which are symmetric positive definite (SPD) matrices-and use them in developing a significant extension to an existing probabilistic algorithm for scalar field segmentation, in order to segment diffusion tensor magnetic resonance imaging (DT-MRI) datasets. Using the Riemannian metric on the space of SPD matrices, we present a novel and robust higher order (cubic) continuous tensor product of B-splines algorithm to approximate the SPD diffusion tensor fields. The resulting approximations are appropriately dubbed tensor splines. Next, we segment the diffusion tensor field by jointly estimating the label (assigned to each voxel) field, which is modeled by a Gauss Markov measure field (GMMF) and the parameters of each smooth tensor spline model representing the labeled regions. Results of interpolation, approximation, and segmentation are presented for synthetic data and real diffusion tensor fields from an isolated rat hippocampus, along with validation. We also present comparisons of our algorithms with existing methods and show significantly improved results in the presence of noise as well as outliers.     

Liquid Biopsy: A New Translational Diagnostic and Monitoring Tool for Musculoskeletal Tumors

Soft tissue and bone sarcomas represent a group of aggressive neoplasms often accompanied by dismal patient prognosis, especially when distant metastases are present. Moreover, effective treatment can pose a challenge, as recurrences are frequent and almost half of patients present with advanced disease. Researchers have unveiled the molecular abnormalities implicated in sarcomas’ carcinogenesis, paving the way for novel treatment strategies based on each individual tumor’s characteristics. Therefore, the development of new techniques aiding in early disease detection and tumor molecular profiling is imperative. Liquid biopsy refers to the sampling and analysis of patients’ fluids, such as blood, to identify tumor biomarkers, through a variety of methods, including qRT-PCR, qPCR, droplet digital PCR, magnetic microbeads and digital PCR. Assessment of circulating tumor cells (CTCs), circulating free DNA (ctDNA), micro RNAs (miRs), long non-coding RNAs (lncRNAs), exosomes and exosome–associated proteins can yield a plethora of information on tumor molecular signature, histologic type and disease stage. In addition, the minimal invasiveness of the procedure renders possible its wide application in the clinical setting, and, therefore, the early detection of the presence of tumors. In this review of the literature, we gathered information on biomarkers assessed through liquid biopsy in soft tissue and bone sarcoma patients and we present the information they can yield for each individual tumor type.     

A computer filtering method to drive out tiny genes from the yeast genome

The authors of the first yeast chromosome sequence defined a minimum threshold requirement of 100 codons, above which an open reading frame (ORF) is retained as a putative coding sequence. However, at least 58 yeast genes shorter than 100 codons have an assigned protein function. Therefore, the yeast genome may contain other tiny but functionally important genes that are discarded from analyses by this simple filtering rule. We have established discriminant functions from the in-phase hexamer frequencies of functional genes and of simulated ORFs derived from a stationary Markov chain model. Fifty-two out of the 58 genes were recognized as coding ORFs by our discriminating method. The test was also applied to all the small ORFs (36 to 100 codons) found in the intergenic regions of published chromosomes. It retained 140 new potential tiny coding sequences, among which we identified seven new genes by similarity searches. Our method, used conjointly with similarity searches, can also highlight sequencing errors resulting from the disruption of the coding frame of longer ORFs. This method, by its ability to detect potential coding ORFs, can be a very useful tool for functional analysis.     

A molecular perspective of water at metal interfaces

Water/solid interfaces are relevant to a broad range of physicochemical phenomena and technological processes such as corrosion, lubrication, heterogeneous catalysis and electrochemistry. Although many fields have contributed to rapid progress in the fundamental knowledge of water at interfaces, detailed molecular-level understanding of water/solid interfaces comes mainly from studies on flat metal substrates. These studies have recently shown that a remarkably rich variety of structures form at the interface between water and even seemingly simple flat surfaces. In this Review we discuss the most exciting work in this area, in particular the emerging physical insight and general concepts about how water binds to metal surfaces. We also provide a perspective on outstanding problems, challenges and open questions.