Tolerance of juvenile Mytilus galloprovincialis to experimental seawater acidification

Coastal ocean acidification is expected to interfere with the physiology of marine bivalves. In this work, the effects of acidification on the physiology of juvenile mussels Mytilus galloprovincialis were tested by means of controlled CO2 perturbation experiments. The carbonate chemistry of natural (control) seawater was manipulated by injecting CO2 to attain 2 reduced pH levels: -0.3 and -0.6 pH units as compared with the control seawater. After 78 d of exposure, we found that the absorption efficiency and ammonium excretion rate of juveniles were inversely related to pH. Significant differences among treatments were not observed in clearance, ingestion and respiration rates. Coherently, the maximal scope for growth and tissue dry weight were observed in mussels exposed to the pH reduction delta pH=-0.6, suggesting that M. galloprovincialis could be tolerant to CO2 acidification, at least in the highly alkaline coastal waters of Ria Formosa (SW Portugal).

Experimental data on photophysiology and growth rates of the invasive foraminifera Amphistegina lobifera showing high thermal tolerance from the Eastern Mediterranean and the Gulf of Aqaba

Invasive species allow an investigation of trait retention and adaptations after exposure to new habitats. Recent work on corals from the Gulf of Aqaba (GoA) shows that tolerance to high temperature persists thousands of years after invasion, without any apparent adaptive advantage. Here we test whether thermal tolerance retention also occurs in another symbiont-bearing calcifying organism. To this end, we investigate the thermal tolerance of the benthic foraminifera Amphistegina lobifera from the GoA (29° 30.14167 N 34° 55.085 E) and compare it to a recent "Lessepsian invader population" from the Eastern Mediterranean (EaM) (32° 37.386 N, 34°55.169 E). We first established that the studied populations are genetically homogenous but distinct from a population in Australia, and that they contain a similar consortium of diatom symbionts, confirming their recent common descent. Thereafter, we exposed specimens from GoA and EaM to elevated temperatures for three weeks and monitored survivorship, growth rates and photophysiology. Both populations exhibited a similar pattern of temperature tolerance. A consistent reduction of photosynthetic dark yields was observed at 34°C and reduced growth was observed at 32°C. The apparent tolerance to sustained exposure to high temperature cannot have a direct adaptive importance, as peak summer temperatures in both locations remain <32°C. Instead, it seems that in the studied foraminifera tolerance to high temperature is a conservative trait and the EaM population retained this trait since its recent invasion. Such pre-adaptation to higher temperatures confers A. lobifera a clear adaptive advantage in shallow and episodically high temperature environments in the Mediterranean under further warming.

Eradication and phenotypic tolerance of Burkholderia cenocepacia biofilms exposed to atmospheric pressure non-thermal plasma.

Chronic lung infection with bacteria from the Burkholderia cepacia complex (BCC), and in particular B. cenocepacia, is associated with significant morbidity and mortality in patients with cystic fibrosis (CF). B. cenocepacia can spread from person to person and exhibits intrinsic broad-spectrum antibiotic resistance. Recently, atmospheric pressure non-thermal plasmas (APNTPs) have gained increasing attention as a novel approach to the prevention and treatment of a variety of hospital-acquired infections. In this study, we evaluated an in-house-designed kHz-driven plasma source for the treatment of biofilms of a number of clinical CF B. cenocepacia isolates. The results demonstrated that APNTP is an effective and efficient tool for the eradication of B. cenocepacia biofilms but that efficacy is highly variable across different isolates. Determination of phenotypic differences between isolates in an attempt to understand variability in plasma tolerance revealed that isolates which are highly tolerant to APNTP typically produce biofilms of greater biomass than their more sensitive counterparts. This indicates a potential role for biofilm matrix components in biofilm tolerance to APNTP exposure. Furthermore, significant isolate-dependent differences in catalase activity in planktonic bacteria positively correlated with phenotypic resistance to APNTP by isolates grown in biofilms.

Intelligent DMU creation: Nominal CAD model preparation for automated tolerance allocation

This paper examines the integration of a tolerance design process within the Computer-Aided Design (CAD) environment having identified the potential to create an intelligent Digital Mock-Up [1]. The tolerancing process is complex in nature and as such reliance on Computer-Aided Tolerancing (CAT) software and domain experts can create a disconnect between the design and manufacturing disciplines It is necessary to implement the tolerance design procedure at the earliest opportunity to integrate both disciplines and to reduce workload in tolerance analysis and allocation at critical stages in product development when production is imminent.
The work seeks to develop a methodology that will allow for a preliminary tolerance allocation procedure within CAD. An approach to tolerance allocation based on sensitivity analysis is implemented on a simple assembly to review its contribution to an intelligent DMU. The procedure is developed using Python scripting for CATIA V5, with analysis results aligning with those in literature. A review of its implementation and requirements is presented.

Multidimensional Intrusion Detection System for IEC 61850 based SCADA Networks

Emerging cybersecurity vulnerabilities in supervisory control and data acquisition (SCADA) systems are becoming urgent engineering issues for modern substations. This paper proposes a novel intrusion detection system (IDS) tailored for cybersecurity of IEC 61850 based substations. The proposed IDS integrates physical knowledge, protocol specifications and logical behaviours to provide a comprehensive and effective solution that is able to mitigate various cyberattacks. The proposed approach comprises access control detection, protocol whitelisting, model-based detection, and multi-parameter based detection. This SCADA-specific IDS is implemented and validated using a comprehensive and realistic cyber-physical test-bed and data from a real 500kV smart substation.

Addressing conflict and tolerance through the curriculum

Political, religious and national divisions in Northern Ireland go back many hundreds of years so it is not surprising that the lack of a common national narrative has made the teaching of history in schools difficult. The fact that schools have largely been organized on a denominational basis has added to the challenge. When political violence broke out in the late 1960s many looked to schools to contribute to the promotion of reconciliation and the way history had been taught received significant critical attention. This chapter will outline the evolving nature of the history curriculum and review evidence on the impact of this curriculum on the historical understanding of students and young people. In addition, the chapter will briefly consider other ways in which students engage with historical issues through the teaching of citizenship, and wider family and community influences. Whereas the teaching of history in the past either was largely absent or often took on a partisan character, the development of a statutory curriculum in the 1990s helped promote a more dispassionate, skills-based approach which emphasized critical engagement with evidence and a multiperspectivity. While this represented a significant improvement on what had gone before, evaluation of the impact of this approach has highlighted the need for a consideration of the emotional impact of historical understanding and the need better to connect the lessons of history to contemporary society.

Data Mining for Network Intrusion Detection : A comparison of data mining algorithms and an analysis of relevant features for detecting cyber-attacks

Data mining can be defined as the extraction of implicit, previously un-known, and potentially useful information from data. Numerous re-searchers have been developing security technology and exploring new methods to detect cyber-attacks with the DARPA 1998 dataset for Intrusion Detection and the modified versions of this dataset KDDCup99 and NSL-KDD, but until now no one have examined the performance of the Top 10 data mining algorithms selected by experts in data mining. The compared classification learning algorithms in this thesis are: C4.5, CART, k-NN and Naïve Bayes. The performance of these algorithms are compared with accuracy, error rate and average cost on modified versions of NSL-KDD train and test dataset where the instances are classified into normal and four cyber-attack categories: DoS, Probing, R2L and U2R. Additionally the most important features to detect cyber-attacks in all categories and in each category are evaluated with Weka’s Attribute Evaluator and ranked according to Information Gain. The results show that the classification algorithm with best performance on the dataset is the k-NN algorithm. The most important features to detect cyber-attacks are basic features such as the number of seconds of a network connection, the protocol used for the connection, the network service used, normal or error status of the connection and the number of data bytes sent. The most important features to detect DoS, Probing and R2L attacks are basic features and the least important features are content features. Unlike U2R attacks, where the content features are the most important features to detect attacks.

Shopping Cart System: Load Balancing and Fault Tolerance in the OSGi Service Platform

The main purpose of this paper was to find a simple solution for load balancing and fault tolerance in OSGi. The challenge was to implement a highly available web application such as a shopping cart system with load balancing and fault tolerance, without having to change the core of OSGi.

A survey of intrusion detection system technologies

This paper provides an overview of IDS types and how they work as well as configuration considerations and issues that affect them. Advanced methods of increasing the performance of an IDS are explored such as specification based IDS for protecting Supervisory Control And Data Acquisition (SCADA) and Cloud networks. Also by providing a review of varied studies ranging from issues in configuration and specific problems to custom techniques and cutting edge studies a reference can be provided to others interested in learning about and developing IDS solutions. Intrusion Detection is an area of much required study to provide solutions to satisfy evolving services and networks and systems that support them. This paper aims to be a reference for IDS technologies other researchers and developers interested in the field of intrusion detection.

Threat analysis of IoT networks using artificial neural network intrusion detection system

The Internet of things (IoT) is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles. However, as a paradigm, it is susceptible to a range of significant intrusion threats. This paper presents a threat analysis of the IoT and uses an Artificial Neural Network (ANN) to combat these threats. A multi-level perceptron, a type of supervised ANN, is trained using internet packet traces, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the classification of normal and threat patterns on an IoT Network. The ANN procedure is validated against a simulated IoT network. The experimental results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS attacks.

Thermal Tolerance of the Coffee Berry Borer Hypothenemus hampei: Predictions of Climate Change Impact on a Tropical Insect Pest

Coffee is predicted to be severely affected by climate change. We determined the thermal tolerance of the coffee berry borer, Hypothenemus hampei, the most devastating pest of coffee worldwide, and make inferences on the possible effects of climate change using climatic data from Colombia, Kenya, Tanzania, and Ethiopia. For this, the effect of eight temperature regimes (15, 20, 23, 25, 27, 30, 33 and 35 degrees C) on the bionomics of H. hampei was studied. Successful egg to adult development occurred between 20-30 degrees C. Using linear regression and a modified Logan model, the lower and upper thresholds for development were estimated at 14.9 and 32 degrees C, respectively. In Kenya and Colombia, the number of pest generations per year was considerably and positively correlated with the warming tolerance. Analysing 32 years of climatic data from Jimma (Ethiopia) revealed that before 1984 it was too cold for H. hampei to complete even one generation per year, but thereafter, because of rising temperatures in the area, 1-2 generations per year/coffee season could be completed. Calculated data on warming tolerance and thermal safety margins of H. hampei for the three East African locations showed considerably high variability compared to the Colombian site. The model indicates that for every 1 degrees C rise in thermal optimum (T(opt)), the maximum intrinsic rate of increase (r(max)) will increase by an average of 8.5%. The effects of climate change on the further range of H. hampei distribution and possible adaption strategies are discussed. Abstracts in Spanish and French are provided as supplementary material Abstract S1 and Abstract S2.

'Danger Theory: The Link between AIS and IDS?'

Abstract We present ideas about creating a next generation Intrusion Detection System (IDS) based on the latest immunological theories. The central challenge with computer security is determining the difference between normal and potentially harmful activity. For half a century, developers have protected their systems by coding rules that identify and block specific events. However, the nature of current and future threats in conjunction with ever larger IT systems urgently requires the development of automated and adaptive defensive tools. A promising solution is emerging in the form of Artificial Immune Systems (AIS): The Human Immune System (HIS) can detect and defend against harmful and previously unseen invaders, so can we not build a similar Intrusion Detection System (IDS) for our computers? Presumably, those systems would then have the same beneficial properties as HIS like error tolerance, adaptation and self-monitoring. Current AIS have been successful on test systems, but the algorithms rely on self-nonself discrimination, as stipulated in classical immunology. However, immunologist are increasingly finding fault with traditional self-nonself thinking and a new 'Danger Theory' (DT) is emerging. This new theory suggests that the immune system reacts to threats based on the correlation of various (danger) signals and it provides a method of 'grounding' the immune response, i.e. linking it directly to the attacker. Little is currently understood of the precise nature and correlation of these signals and the theory is a topic of hot debate. It is the aim of this research to investigate this correlation and to translate the DT into the realms of computer security, thereby creating AIS that are no longer limited by self-nonself discrimination. It should be noted that we do not intend to defend this controversial theory per se, although as a deliverable this project will add to the body of knowledge in this area. Rather we are interested in its merits for scaling up AIS applications by overcoming self-nonself discrimination problems.

Detecting Danger: Applying a Novel Immunological Concept to Intrusion Detection Systems'

INTRODUCTION In recent years computer systems have become increasingly complex and consequently the challenge of protecting these systems has become increasingly difficult. Various techniques have been implemented to counteract the misuse of computer systems in the form of firewalls, antivirus software and intrusion detection systems. The complexity of networks and dynamic nature of computer systems leaves current methods with significant room for improvement. Computer scientists have recently drawn inspiration from mechanisms found in biological systems and, in the context of computer security, have focused on the human immune system (HIS). The human immune system provides an example of a robust, distributed system that provides a high level of protection from constant attacks. By examining the precise mechanisms of the human immune system, it is hoped the paradigm will improve the performance of real intrusion detection systems. This paper presents an introduction to recent developments in the field of immunology. It discusses the incorporation of a novel immunological paradigm, Danger Theory, and how this concept is inspiring artificial immune systems (AIS). Applications within the context of computer security are outlined drawing direct reference to the underlying principles of Danger Theory and finally, the current state of intrusion detection systems is discussed and improvements suggested.