Survey of switching techniques in high-speed networks and their performance

One of the most promising approaches for high speed networks for integrated service applications is fast packet switching, or ATM (Asynchronous Transfer Mode). ATM can be characterized by very high speed transmission links and simple, hard wired protocols within a network. To match the transmission speed of the network links, and to minimize the overhead due to the processing of network protocols, the switching of cells is done in hardware switching fabrics in ATM networks.A number of designs has been proposed for implementing ATM switches. While many differences exist among the proposals, the vast majority of them is based on self-routing multi-stage interconnection networks. This is because of the desirable features of multi-stage interconnection networks such as self-routing capability and suitability for VLSI implementation.Existing ATM switch architectures can be classified into two major classes: blocking switches, where blockings of cells may occur within a switch when more than one cell contends for the same internal link, and non-blocking switches, where no internal blocking occurs. A large number of techniques has also been proposed to improve the performance of blocking and nonblocking switches. In this paper, we present an extensive survey of the existing proposals for ATM switch architectures, focusing on their performance issues

Multiple changepoint analysis of COVID-19 infection progression and related deaths in the small island state of Malta

In December 2019, in the city of Wuhan (China), Severe Acute Respiratory Syndrome Coronavirus - 2 (SARS-CoV−2), a virus that causes what is known as Coronavirus Disease 2019 (better known as COVID-19), emerged. In a few months the virus spread around the world becoming a global pandemic that has shaken the world. On Malta (a nation consisting of an archipelago of islands of approximately 500000 people), which is the case study of this analysis, the first case was identified on 7/3/2020. In this paper, we shall fit a piecewise linear trend model to the log-scale of cumulative cases and deaths due to COVID-19 in Malta by implementing the SN-NOT changepoint model. This model combines the self-normalisation (SN) technique, which is used to test whether there is a single change-point in the linear trend of a time series, with the Narrowest Over Threshold algorithm (NOT) to achieve multiple change-point in the linear trend. Through analysis of news reports and other sources of information, estimated change-points are then compared to potential factors such as health restrictions, mass events, government policy and population behaviour that have affected these changes, in order to determine the efffect of these factors on the spread of the disease.peer-reviewe

Investigation of copper metal presence in cattle fodder, milk, hair and manure in Malta

This study investigated the concentration of copper (Cu) present during the lifecycle of cattle in Malta to determine how metals enter and exit the animal, and their impact via the food chain on the human consumer. The determination of Cu in 10 cows from a dairy cattle farm located in Salina, Malta was estimated by taking milk (100), hair (100), fodder (15) and manure (10) samples over 10 weeks between September and November 2016 and their Cu concentration was determined using atomic absorption spectroscopy. Conventional methods were used to prepare all these samples. Cu concentration in milk samples showed high levels (0.02 to 0.07mg/L) which is higher than the maximum level recommended (0.01mg/L) by FAO and WHO. Cu concentration was significantly higher in summer than in autumn. However, hair samples results (3.04–6.88mg/kg) did not provide a clear impact by season. The concentration of Cu in different fodder types varied significantly (1.08–16.06 mg/Kg). Manure Cu concentrations ranged from 10.70to 16.63 mg/kg but there were no distinctions between weeks or seasons. Considering the discrepancy of copper concentrations at inputs (feed) and outputs (manure, hair, milk) for a dairy cow, it can be concluded that there are other factors contributing to the copper concentration within the cow system.peer-reviewe

Identification of candidates for cyclotide biosynthesis and cyclisation by expressed sequence tag analysis of Oldenlandia affinis

<p>Abstract</p> <p>Background</p> <p>Cyclotides are a family of circular peptides that exhibit a range of biological activities, including anti-bacterial, cytotoxic, anti-HIV activities, and are proposed to function in plant defence. Their high stability has motivated their development as scaffolds for the stabilisation of peptide drugs. <it>Oldenlandia affinis</it> is a member of the Rubiaceae (coffee) family from which 18 cyclotides have been sequenced to date, but the details of their processing from precursor proteins have only begun to be elucidated. To increase the speed at which genes involved in cyclotide biosynthesis and processing are being discovered, an expressed sequence tag (EST) project was initiated to survey the transcript profile of <it>O. affinis</it> and to propose some future directions of research on in vivo protein cyclisation.</p> <p>Results</p> <p>Using flow cytometry the holoploid genome size (1C-value) of <it>O. affinis </it>was estimated to be 4,210 - 4,284 Mbp, one of the largest genomes of the Rubiaceae family. High-quality ESTs were identified, 1,117 in total, from leaf cDNAs and assembled into 502 contigs, comprising 202 consensus sequences and 300 singletons. ESTs encoding the cyclotide precursors for kalata B1 (<it>Oak1</it>) and kalata B2 (<it>Oak4</it>) were among the 20 most abundant ESTs. In total, 31 ESTs encoded cyclotide precursors, representing a distinct commitment of 2.8% of the <it>O. affinis </it>transcriptome to cyclotide biosynthesis. The high expression levels of cyclotide precursor transcripts are consistent with the abundance of mature cyclic peptides in <it>O. affinis</it>. A new cyclotide precursor named <it>Oak5 </it>was isolated and represents the first cDNA for the bracelet class of cyclotides in <it>O. affinis</it>. Clones encoding enzymes potentially involved in processing cyclotides were also identified and include enzymes involved in oxidative folding and proteolytic processing.</p> <p>Conclusion</p> <p>The EST library generated in this study provides a valuable resource for the study of the cyclisation of plant peptides. Further analysis of the candidates for cyclotide processing discovered in this work will increase our understanding and aid in reconstructing cyclotide production using transgenic systems and will benefit their development in pharmaceutical applications and insect-resistant crop plants.</p

Comparing market phase features for cryptocurrency and benchmark stock index using HMM and HSMM filtering

A desirable aspect of financial time series analysis is that of successfully detecting (in real time) market phases. In this paper we implement HMMs and HSMMs with normal state-dependent distributions to Bitcoin/USD price dynamics, and also compare this with S&P 500 price dynamics, the latter being a benchmark in traditional stock market behaviour which most literature resorts to. Furthermore, we test our models’ adequacy at detecting bullish and bearish regimes by devising mock investment strategies on our models and assessing how profitable they are with unseen data in comparison to a buy-and-hold approach. We ultimately show that while our modelling approach yields positive results in both Bitcoin/USD and S&P 500, and both are best modelled by four-state HSMMs, Bitcoin/USD so far shows different regime volatility and persistence patterns to the one we are used to seeing in traditional stock markets.peer-reviewe

Long- and medium-term financial strategies on equities using dynamic Bayesian networks

Devising a financial trading strategy that allows for long-term gains is a very common problem in finance. This paper aims to formulate a mathematically rigorous framework for the problem and compare and contrast the results obtained. The main approach considered is based on Dynamic Bayesian Networks (DBNs). Within the DBN setting, a long-term as well as a shortterm trading strategy are considered and applied on twelve equities obtained from developed and developing markets. It is concluded that both the long-term and the medium-term strategies proposed in this paper outperform the benchmark buy-and-hold (B&H) trading strategy. Despite the clear advantages of the former trading strategies, the limitations of this model are discussed along with possible improvements.peer-reviewe

COVID-19 vaccination attitudes across the European continent

This study was conducted to determine the predictors of COVID-19 vaccination attitudes across multiple waves in seven countries geographically spread across the European continent, using data from a COVID-19 survey provided by the Massachusetts Institute of Technology COVID-19. Facebook users from across the globe participated in this survey which collected information on their knowledge of COVID-19, attitudes towards risk and available information, and their willingness or lack thereof to take the vaccine. In this secondary data analysis study, neural networks were used with special attention given to the importance of the predictors of COVID-19 vaccination attitudes. Perception of social norms regarding COVID-19 vaccination was found to be the most important predictor of vaccine acceptance. Country of residence and wave of data collection were among the important predictors, with different patterns for each country emerging across different waves. Other strong predictors included attitudes towards masks and mask wearing; attitudes towards the influenza vaccine; distrust in government health authorities and scientists; and level of knowledge of existing treatments for COVID-19. The results of this study can inform effective public health prevention and intervention efforts against infectious diseases.peer-reviewe

Star Formation Timescales of the Halo Populations from Asteroseismology and Chemical Abundances

We combine asteroseismology, optical high-resolution spectroscopy, and kinematic analysis for 26 halo red giant branch stars in the \textit{Kepler} field in the range of $-2.5<[\mathrm{{Fe}/{H}}]<-0.6$. After applying theoretically motivated corrections to the seismic scaling relations, we obtain an average mass of $0.97\pm 0.03\,\mathrm{M_{\odot}}$ for our sample of halo stars. Although this maps into an age of $\sim 7\,\mathrm{Gyr}$, significantly younger than independent age estimates of the Milky Way stellar halo, we considerer this apparently young age is due to the overestimation of stellar mass in the scaling relations. There is no significant mass dispersion among lower red giant branch stars ($\log g>2$), which constrains a relative age dispersion to $<18\%$, corresponding to $<2\,\mathrm{Gyr}$. The precise chemical abundances allow us to separate the stars with [{Fe}/{H}]$>-1.7$ into two [{Mg}/{Fe}] groups. While [$\alpha$/{Fe}] and [{Eu}/{Mg}] ratios are different between the two subsamples, [$s$/Eu], where $s$ stands for Ba, La, Ce, and Nd, does not show a significant difference. These abundance ratios suggest that the chemical evolution of the low-Mg population is contributed by type~Ia supernovae, but not by low-to-intermediate mass asymptotic giant branch stars, providing a constraint on its star formation timescale as $100\,\mathrm{Myr}<\tau<300\,\mathrm{Myr}$. We also do not detect any significant mass difference between the two [{Mg}/{Fe}] groups, thus suggesting that their formation epochs are not separated by more than 1.5 Gyr.Comment: 44 pages. accepted versio

Predicting motor policy loss – a ZAIG model or a two stage neural network approach?

Artificial neural networks have increasingly being applied to solve problems which traditionally would have fallen under the domain of more classical statistical methodology, and the latter has long been a staple of popular actuarial methodology. We aim to compare a two-stage artificial neural network approach with the zero-adjusted inverse Gaussian model for predicting the claim of a motor insurance policy, which is a popular method with actuaries. The performance of both approaches is analysed by means of K-fold cross-validation. The conclusion reached is that our approach provides a comparable, if not superior, overall performance in predicting policy loss which is more robust to extreme observations.peer-reviewe