TechNews digests: Jan - Nov 2009

TechNews is a technology, news and analysis service aimed at anyone in the education sector keen to stay informed about technology developments, trends and issues. TechNews focuses on emerging technologies and other technology news. TechNews service : digests september 2004 till May 2010 Analysis pieces and News combined publish every 2 to 3 month

Topics in Power Usage in Network Services

The rapid advance of computing technology has created a world powered by millions of computers. Often these computers are idly consuming energy unnecessarily in spite of all the efforts of hardware manufacturers. This thesis examines proposals to determine when to power down computers without negatively impacting on the service they are used to deliver, compares and contrasts the efficiency of virtualisation with containerisation, and investigates the energy efficiency of the popular cryptocurrency Bitcoin. We begin by examining the current corpus of literature and defining the key terms we need to proceed. Then we propose a technique for improving the energy consumption of servers by moving them into a sleep state and employing a low powered device to act as a proxy in its place. After this we move on to investigate the energy efficiency of virtualisation and compare the energy efficiency of two of the most common means used to do this. Moving on from this we look at the cryptocurrency Bitcoin. We consider the energy consumption of bitcoin mining and if this compared with the value of bitcoin makes this profitable. Finally we conclude by summarising the results and findings of this thesis. This work increases our understanding of some of the challenges of energy efficient computation as well as proposing novel mechanisms to save energy

Remote Sensing of Cell-Culture Assays

This chapter describes a full system developed to perform the remote sensing of cell-culture experiments from any access point with internet connection. The proposed system allows the real-time monitoring of cell assays thanks to bioimpedance measurement circuits developed to count the number of cell present in a culture. Cell-culture characterization is performed through the measurement of the increasing bioimpedance parameter over time. The circuit implementation is based on the oscillation-based test (OBT) methodology. Bioimpedance of cell cultures is measured in terms of the oscillation parameters (frequency, amplitude, phase, etc.) and used as empirical markers to carry out an appropriate interpretation in terms of cell size identification, cell counting, cell growth, growth rhythm, etc. The device is capable of managing the whole sensing task and performs wireless communication through a Bluetooth module. Data are interpreted and displayed on a computer or a mobile phone through a web application. The system has its practical application in drug development processes, offering a label-free, high-throughput, and high-content screening method for cellular research, avoiding the classical end-point techniques and a significant workload and cost material reduction

Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table

FeatureScan: revealing property-dependent similarity of nucleotide sequences

FeatureScan is a software package aiming to reveal novel types of DNA sequence similarity by comparing physico-chemical properties. Thirty-eight different parameters of DNA double strands such as charge, melting enthalpy, conformational parameters and the like are provided. As input FeatureScan requires two sequences, a pattern sequence and a target sequence, search conditions are set by selecting a specific DNA parameter and a threshold value. Search results are displayed in FASTA format and directly linked to external genome databases/browsers (ENSEMBL, NCBI, UCSC). An Internet version of FeatureScan is accessible at . As part of the HOBIT initiative () FeatureScan is also accessible as a web service at its above home page. Currently, several preloaded genomes are provided at this Internet website (Homo sapiens, Mus musculus, Rattus norvegicus and four strains of Escherichia coli) as target sequences. Standalone executables of FeatureScan are available on request

Comparative Analysis of Malware Behavior in Hardware and Virtual Sandboxes

openMalicious software, or malware, continues to be a pervasive threat to computer systems and networks worldwide. As malware constantly evolves and becomes more sophisticated, it is crucial to develop effective methods for its detection and analysis. Sandboxing technology has emerged as a valuable tool in the field of cybersecurity, allowing researchers to safely execute and observe malware behavior in controlled environments. This thesis presents a comprehensive investigation into the behavior of malware samples when executed in both hardware and virtual sandboxes. The primary objective is to assess the effectiveness of hardware sandboxing in capturing and analyzing malware behaviors compared to traditional virtual sandboxes. The research methodology involves the execution of various malware samples in both hardware and virtual sandboxes, followed by the analysis of key parameters, including memory changes, file system logs, and network traffic. By comparing the results obtained from the two sandboxing approaches, this study aims to provide insights into the advantages and limitations of each method. Furthermore, the research delves into the potential evasion techniques employed by malware to bypass detection in either sandboxing environment. Identifying such evasion strategies is vital for enhancing the overall security posture and developing more robust defense mechanisms against evolving malware threats. The findings of this research contribute to the field of cybersecurity by shedding light on the strengths and weaknesses of hardware and virtual sandboxes for malware analysis. Ultimately, this work serves as a valuable resource for security practitioners and researchers seeking to improve malware detection and analysis techniques in the ever-evolving landscape of cybersecurity threats.Malicious software, or malware, continues to be a pervasive threat to computer systems and networks worldwide. As malware constantly evolves and becomes more sophisticated, it is crucial to develop effective methods for its detection and analysis. Sandboxing technology has emerged as a valuable tool in the field of cybersecurity, allowing researchers to safely execute and observe malware behavior in controlled environments. This thesis presents a comprehensive investigation into the behavior of malware samples when executed in both hardware and virtual sandboxes. The primary objective is to assess the effectiveness of hardware sandboxing in capturing and analyzing malware behaviors compared to traditional virtual sandboxes. The research methodology involves the execution of various malware samples in both hardware and virtual sandboxes, followed by the analysis of key parameters, including memory changes, file system logs, and network traffic. By comparing the results obtained from the two sandboxing approaches, this study aims to provide insights into the advantages and limitations of each method. Furthermore, the research delves into the potential evasion techniques employed by malware to bypass detection in either sandboxing environment. Identifying such evasion strategies is vital for enhancing the overall security posture and developing more robust defense mechanisms against evolving malware threats. The findings of this research contribute to the field of cybersecurity by shedding light on the strengths and weaknesses of hardware and virtual sandboxes for malware analysis. Ultimately, this work serves as a valuable resource for security practitioners and researchers seeking to improve malware detection and analysis techniques in the ever-evolving landscape of cybersecurity threats