Comparative Characterization of Mitogenomes From Five Orders of Cestodes (Eucestoda: Tapeworms)

The recognized potential of using mitogenomics in phylogenetics and the more accessible use of high-throughput sequencing (HTS) offer an opportunity to investigate groups of neglected organisms. Here, we leveraged HTS to execute the most comprehensive documentation of mitogenomes for cestodes based on the number of terminals sequenced. We adopted modern approaches to obtain the complete mitogenome sequences of 86 specimens representing five orders of cestodes (three reported for the first time: Phyllobothriidea, “Tetraphyllidea” and Trypanorhyncha). These complete mitogenomes represent an increase of 41% of the mitogenomes available for cestodes (61–147) and an addition of 33% in the representativeness of the cestode orders. The complete mitochondrial genomes are conserved, circular, encoded in the same strand, and transcribed in the same direction, following the pattern observed previously for tapeworms. Their length varies from 13,369 to 13,795 bp, containing 36 genes in total. Except for the Trypanorhyncha specimen, the gene order of the other four cestode orders sequenced here suggests that it could be a synapomorphy for the acetabulate group (with a reversion for taenids). Our results also suggest that no single gene can tell all the evolutionary history contained in the mitogenome. Therefore, cestodes phylogenies based on a single mitochondrial marker may fail to capture their evolutionary history. We predict that such phylogenies would be improved if conducted under a total evidence framework. The characterization of the new mitochondrial genomes is the first step to provide a valuable resource for future studies on the evolutionary relationships of these groups of parasites.

Application of least squares support vector machines in medium-term load forecasting

Power load forecasting is essential in the task scheduling of every electricity production and distribution facility. In this project, we study the applications of modern artificial intelligence techniques in power load forecasting. We first investigate the application of principal component analysis (PCA) to least squares support vector machines (LS-SVM) in a week-ahead load forecasting problem. Then, we study a variety of tuning techniques for optimizing the least squares support vector machines' (LS-SVM) hyper-parameters. The construction of any effective and accurate LS-SVM model depends on carefully setting the associated hyper-parameters. Poplular optimization techniques including Genetic Algorithm (GA), Simulated Annealing (SA), Bayesian Evidence Framework and Cross Validation (CV) are applied to the target application and then compared for performance time, accuracy and computational cost. Analysis of the experimental results proves that LS-SVM by feature extraction using PCA can achieve greater accuracy and faster speed than other models including LS-SVM without feature extraction and the popular feed forward neural network (FFNN). Also, it is observed that optimized LS-SVM by Bayesian Evidence Framework can achieve greater accuracy and faster speed than other techniques including LS-SVM tuned with genetic algorithm, simulated annealing and 10-fold cross validation.

Application of least squares support vector machines in medium-term load forecasting

Power load forecasting is essential in the task scheduling of every electricity production and distribution facility. In this project, we study the applications of modern artificial intelligence techniques in power load forecasting. We first investigate the application of principal component analysis (PCA) to least squares support vector machines (LS-SVM) in a week-ahead load forecasting problem. Then, we study a variety of tuning techniques for optimizing the least squares support vector machines' (LS-SVM) hyper-parameters. The construction of any effective and accurate LS-SVM model depends on carefully setting the associated hyper-parameters. Poplular optimization techniques including Genetic Algorithm (GA), Simulated Annealing (SA), Bayesian Evidence Framework and Cross Validation (CV) are applied to the target application and then compared for performance time, accuracy and computational cost. Analysis of the experimental results proves that LS-SVM by feature extraction using PCA can achieve greater accuracy and faster speed than other models including LS-SVM without feature extraction and the popular feed forward neural network (FFNN). Also, it is observed that optimized LS-SVM by Bayesian Evidence Framework can achieve greater accuracy and faster speed than other techniques including LS-SVM tuned with genetic algorithm, simulated annealing and 10-fold cross validation.

Social Impact Archaeology: Pontefract Castle and the Gatehouse Project

Archaeology is said to add value to development, creating a deeper sense of place, community identity as well as improving health and wellbeing. Accentuating these wider social values has been welcomed by a profession keen to broaden its public relevance and legitimacy and protect its seat at the table in modern cultural life, but how much, if at all, do the public actually benefit from developer-led archaeology? Benefits to individuals and communities from archaeology projects are often abstract, intangible and difficult to attribute, and the discipline arguably lacks a satisfactory frame of reference around which it can express and design for these additional social values. Drawing on the language of social impact investing, this article will explore how the UK-based collaborative platform, DigVentures, has addressed this challenge. It introduces a 'Theory of Change' and 'Standards of Evidence' framework to account for the impact of development-led archaeology programmes, illustrating the causal links between activity and change through the case of the Pontefract Castle Gatehouse Project. It is complemented by a short documentary film exploring the spectrum of digital and physical opportunities for participation by the public alongside a team of highly experienced professional field archaeologists, demonstrating how development-led archaeology can be designed to accomplish far more than answer a planning brief.

CustodyBlock: A Distributed Chain of Custody Evidence Framework

With the increasing number of cybercrimes, the digital forensics team has no choice but to implement more robust and resilient evidence-handling mechanisms. The capturing of digital evidence, which is a tangible and probative piece of information that can be presented in court and used in trial, is very challenging due to its volatility and improper handling procedures. When computer systems get compromised, digital forensics comes into play to analyze, discover, extract, and preserve all relevant evidence. Therefore, it is imperative to maintain efficient evidence management to guarantee the credibility and admissibility of digital evidence in a court of law. A critical component of this process is to utilize an adequate chain of custody (CoC) approach to preserve the evidence in its original state from compromise and/or contamination. In this paper, a practical and secure CustodyBlock (CB) model using private blockchain protocol and smart contracts to support the control, transfer, analysis, and preservation monitoring is proposed. The smart contracts in CB are utilized to enhance the model automation process for better and more secure evidence preservation and handling. A further research direction in terms of implementing blockchain-based evidence management ecosystems, and the implications on other different areas, are discussed.

Synonymisation of the male-based ant genus Phaulomyrma (Hymenoptera:Formicidae) with Leptanilla based upon Bayesian total-evidence phylogenetic inference

Although molecular data have proven indispensable in confidently resolving the phylogeny of many clades across the tree of life, these data may be inaccessible for certain taxa. The resolution of taxonomy in the ant subfamily Leptanillinae is made problematic by the absence of DNA sequence data for leptanilline taxa that are known only from male specimens, including the monotypic genus Phaulomyrma Wheeler & Wheeler. Focusing upon the considerable diversity of undescribed male leptanilline morphospecies, the phylogeny of 35 putative morphospecies sampled from across the Leptanillinae, plus an outgroup, is inferred from 11 nuclear loci and 41 discrete male morphological characters using a Bayesian total-evidence framework, with Phaulomyrma represented by morphological data only. Based upon the results of this analysis Phaulomyrma is synonymised with Leptanilla Emery, and male-based diagnoses for Leptanilla that are grounded in phylogeny are provided, under both broad and narrow circumscriptions of that genus. This demonstrates the potential utility of a total-evidence approach in inferring the phylogeny of rare extant taxa for which molecular data are unavailable and begins a long-overdue systematic revision of the Leptanillinae that is focused on male material.