The e-Dimensionality of Genetic Information

This paper provides an explanation for why the assignment of codons to amino acids, which range from 1 to 6, is non-uniform. Since mathematical coding theory demands a near uniform assignment, the answer to this question is important to understand deeper aspects of the structure of the genetic code. Our analysis points to 20 different covering regions in an e-dimensional information space, which is equal to the number of amino acids. It is also shown that the assignment of the codons to the amino acids is fractal-like that is well modeled by the Zipf distribution. It is remarkable that the Zipf distribution that holds for the letter frequencies of words in a language also applies to the rank order of triplets the code for amino acids.

The e-Dimensionality of Genetic Information

This paper provides an explanation for why the assignment of codons to amino acids, which range from 1 to 6, is non-uniform. Since mathematical coding theory demands a near uniform assignment, the answer to this question is important to understand deeper aspects of the structure of the genetic code. Our analysis points to 20 different covering regions in an e-dimensional information space, which is equal to the number of amino acids. It is also shown that the assignment of the codons to the amino acids is fractal-like that is well modeled by the Zipf distribution. It is remarkable that the Zipf distribution that holds for the letter frequencies of words in a language also applies to the rank order of triplets the code for amino acids.

Economic Transition and the Evolution of City-Size Distribution of China’s Urban System

The evolution of city size distribution in China has gained a great deal of scholarly attention. However, little is known about the effect of economic transition on the reorganization of city size distribution in China. Using an urban hierarchy with cascade structure model, we decompose Zipf’s law into two exponential functions that provide a new way of examining the dynamic processes of urban system evolution. This study aims to investigate the dominating latent forces that affect China’s city size distribution through mathematical modeling of the hierarchical scaling laws based on census data of 1982, 1990, 2000, and 2010. A number of features of China’s city size distribution are found. First, the size distribution of Chinese cities displayed a clear trend of evolving toward the Zipf distribution, which is the result of economic transition from planned to market. Second, the rank-size pattern still deviates slightly from the standard Zipf distribution, as indicated by the narrow scaling range and departure of the scaling exponent from the theoretically expected value. We argue that the top-down state regulation is a critical cause of deviation of China’s city size distribution from Zipf’s law.

Scaling Performance Analysis and Optimization Based on the Node Spatial Distribution in Mobile Content-Centric Networks

Content-centric networks (CCNs) have become a promising technology for relieving the increasing wireless traffic demands. In this paper, we explore the scaling performance of mobile content-centric networks based on the nonuniform spatial distribution of nodes, where each node moves around its own home point and requests the desired content according to a Zipf distribution. We assume each mobile node is equipped with a finite local cache, which is applied to cache contents following a static cache allocation scheme. According to the nonuniform spatial distribution of cache-enabled nodes, we introduce two kinds of clustered models, i.e., the clustered grid model and the clustered random model. In each clustered model, we analyze throughput and delay performance when the number of nodes goes infinity by means of the proposed cell-partition scheduling scheme and the distributed multihop routing scheme. We show that the node mobility degree and the clustering behavior play the fundamental roles in the aforementioned asymptotic performance. Finally, we study the optimal cache allocation problem in the two kinds of clustered models. Our findings provide a guidance for developing the optimal caching scheme. We further perform the numerical simulations to validate the theoretical scaling laws.

Dynamic Cache Management of Cloud RAN and Multi-Access Edge Computing for 5G Networks

Using a cache to improve efficiency and to save on the cost of a computer system has been a field that attracts many researchers, including those in the area of cellular network systems. The first part of this chapter focuses on adaptive cache management schemes for cloud radio access networks (CRAN) and multi-access edge computing (MEC) of 5G mobile technologies. Experimental results run through CloudSim show that the proposed adaptive algorithms are effective in increasing cache hit rate, guaranteeing QoS, and in reducing algorithm execution time. In second part of this chapter, a new cache management algorithm using Zipf distribution to address dynamic input is proposed for CRAN and MEC models. A performance test is also run using iFogSim to show the improvement made by the proposed algorithm over the original versions. This work contributes in the support of 5G for IoT by enhancing CRAN and MEC performance; it also contributes to how novel caching algorithms can resolve the unbalanced input load caused by changing distributions of the input traffic.

Handling data-skewness in character based string similarity join using Hadoop

The scalability of similarity joins is threatened by the unexpected data characteristic of data skewness. This is a pervasive problem in scientific data. Due to skewness, the uneven distribution of attributes occurs, and it can cause a severe load imbalance problem. When database join operations are applied to these datasets, skewness occurs exponentially. All the algorithms developed to date for the implementation of database joins are highly skew sensitive. This paper presents a new approach for handling data-skewness in a character- based string similarity join using the MapReduce framework. In the literature, no such work exists to handle data skewness in character-based string similarity join, although work for set based string similarity joins exists. Proposed work has been divided into three stages, and every stage is further divided into mapper and reducer phases, which are dedicated to a specific task. The first stage is dedicated to finding the length of strings from a dataset. For valid candidate pair generation, MR-Pass Join framework has been suggested in the second stage. MRFA concepts are incorporated for string similarity join, which is named as “MRFA-SSJ” (MapReduce Frequency Adaptive – String Similarity Join) in the third stage which is further divided into four MapReduce phases. Hence, MRFA-SSJ has been proposed to handle skewness in the string similarity join. The experiments have been implemented on three different datasets namely: DBLP, Query log and a real dataset of IP addresses & Cookies by deploying Hadoop framework. The proposed algorithm has been compared with three known algorithms and it has been noticed that all these algorithms fail when data is highly skewed, whereas our proposed method handles highly skewed data without any problem. A set-up of the 15-node cluster has been used in this experiment, and we are following the Zipf distribution law for the analysis of skewness factor. Also, a comparison among existing and proposed techniques has been shown. Existing techniques survived till Zipf factor 0.5 whereas the proposed algorithm survives up to Zipf factor 1. Hence the proposed algorithm is skew insensitive and ensures scalability with a reasonable query processing time for string similarity database join. It also ensures the even distribution of attributes.

Modelling societal landslide risk in Italy

<p>Landslides cause every year worldwide severe damages to the population. A quantitative knowledge of the impact of landsliding phenomena on the society is fundamental for a proper and accurate assessment of the risk posed by such natural hazards. In this work, a novel approach is proposed to evaluate the spatial and temporal distribution of societal landslide risk from historical, sparse, point information on fatal landslides and their direct human consequence.s (Rossi et al., Accepted). The approach was tested in Italy, using a detailed catalogue listing 5571 fatalities caused by 1017 landslides at 958 sites across Italy, in the 155-year period 1861 – 2015. The model adopting a Zipf distribution to evaluate societal landslide risk for the whole of Italy, and for seven physiographic and 20 administrative subdivisions of Italy. The model is able to provide estimates of the frequency (and the probability) of fatal landslides, based on the parameters, namely (i) the largest magnitude landslide F, (ii) the number of fatal events E, and (iii) the scaling exponent of the Zipf distribution s, which controls the relative proportion of low vs. large magnitude landslides. Different grid spacings, g and circular kernel sizes, r were tested finally adopting g = 10 km and r = 55 km. Using such geometrical model configuration, the values of the F, E and s parameters were derived for each grid cells revealing the complexity of landslide risk in Italy, which cannot be described properly with a single set of such parameters. Based on such modeling configuration. This model configuration allowed to estimate different risk scenarios for landslides of increasing magnitudes, which were validated checking the anticipated return period of the fatal events against information on 130 fatal landslides between 1000 and 1860, and eleven fatal landslides between January 2016 and August 2018. Despite incompleteness in the old part of the record for the low magnitude landslides, and the short length and limited number of events in the recent period 2016 – 2018, the anticipated return periods are in good agreement with the occurrence of fatal landslides in both validation periods. Despite the known difficulty in modelling sparse datasets, the proposed approach was able to provide a coherent and realistic representation and new insight on the spatial and temporal variations of societal landslide risk in Italy.</p>

Dynamic Cache Management of Cloud RAN and Multi-Access Edge Computing for 5G Networks

Using a cache to improve efficiency and to save on the cost of a computer system has been a field that attracts many researchers, including those in the area of cellular network systems. The first part of this chapter focuses on adaptive cache management schemes for cloud radio access networks (CRAN) and multi-access edge computing (MEC) of 5G mobile technologies. Experimental results run through CloudSim show that the proposed adaptive algorithms are effective in increasing cache hit rate, guaranteeing QoS, and in reducing algorithm execution time. In second part of this chapter, a new cache management algorithm using Zipf distribution to address dynamic input is proposed for CRAN and MEC models. A performance test is also run using iFogSim to show the improvement made by the proposed algorithm over the original versions. This work contributes in the support of 5G for IoT by enhancing CRAN and MEC performance; it also contributes to how novel caching algorithms can resolve the unbalanced input load caused by changing distributions of the input traffic.