The use of artificial neural networks for identifying sustainable biodiesel feedstocks

Over the past few decades, biodiesel produced from oilseed crops and animal fat is receiving much attention as a renewable and sustainable alternative for automobile engine fuels, and particularly petroleum diesel. However, current biodiesel production is heavily dependent on edible oil feedstocks which are unlikely to be sustainable in the longer term due to the rising food prices and the concerns about automobile engine durability. Therefore, there is an urgent need for researchers to identify and develop sustainable biodiesel feedstocks which overcome the disadvantages of current ones. On the other hand, artificial neural network (ANN) modeling has been successfully used in recent years to gain new knowledge in various disciplines. The main goal of this article is to review recent literatures and assess the state of the art on the use of ANN as a modeling tool for future generation biodiesel feedstocks. Biodiesel feedstocks, production processes, chemical compositions, standards, physio-chemical properties and in-use performance are discussed. Limitations of current biodiesel feedstocks over future generation biodiesel feedstock have been identified. The application of ANN in modeling key biodiesel quality parameters and combustion performance in automobile engines is also discussed. This review has determined that ANN modeling has a high potential to contribute to the development of renewable energy systems by accelerating biodiesel research.

An envirogenomic signature is associated with risk of IBD-related surgery in a population-based Crohn’s Disease cohort

BACKGROUND AND AIMS: Crohn's disease (CD) is an inflammatory bowel disease (IBD) caused by a combination of genetic, clinical, and environmental factors. Identification of CD patients at high risk of requiring surgery may assist clinicians to decide on a top-down or step-up treatment approach. METHODS: We conducted a retrospective case-control analysis of a population-based cohort of 503 CD patients. A regression-based data reduction approach was used to systematically analyse 63 genomic, clinical and environmental factors for association with IBD-related surgery as the primary outcome variable. RESULTS: A multi-factor model was identified that yielded the highest predictive accuracy for need for surgery. The factors included in the model were the NOD2 genotype (OR = 1.607, P = 2.3 × 10(-5)), having ever had perianal disease (OR = 2.847, P = 4 × 10(-6)), being post-diagnosis smokers (OR = 6.312, P = 7.4 × 10(-3)), being an ex-smoker at diagnosis (OR = 2.405, P = 1.1 × 10(-3)) and age (OR = 1.012, P = 4.4 × 10(-3)). Diagnostic testing for this multi-factor model produced an area under the curve of 0.681 (P = 1 × 10(-4)) and an odds ratio of 3.169, (95 % CI P = 1 × 10(-4)) which was higher than any factor considered independently. CONCLUSIONS: The results of this study require validation in other populations but represent a step forward in the development of more accurate prognostic tests for clinicians to prescribe the most optimal treatment approach for complicated CD patients.

Understanding and measuring information security culture in developing countries : case of Saudi Arabia

The purpose of the current study was to develop a measurement of information security culture in developing countries such as Saudi Arabia. In order to achieve this goal, the study commenced with a comprehensive review of the literature, the outcome being the development of a conceptual model as a reference base. The literature review revealed a lack of academic and professional research into information security culture in developing countries and more specifically in Saudi Arabia. Given the increasing importance and significant investment developing countries are making in information technology, there is a clear need to investigate information security culture from developing countries perspective such as Saudi Arabia. Furthermore, our analysis indicated a lack of clear conceptualization and distinction between factors that constitute information security culture and factors that influence information security culture. Our research aims to fill this gap by developing and validating a measurement model of information security culture, as well as developing initial understanding of factors that influence security culture. A sequential mixed method consisting of a qualitative phase to explore the conceptualisation of information security culture, and a quantitative phase to validate the model is adopted for this research. In the qualitative phase, eight interviews with information security experts in eight different Saudi organisations were conducted, revealing that security culture can be constituted as reflection of security awareness, security compliance and security ownership. Additionally, the qualitative interviews have revealed that factors that influence security culture are top management involvement, policy enforcement, policy maintenance, training and ethical conduct policies. These factors were confirmed by the literature review as being critical and important for the creation of security culture and formed the basis for our initial information security culture model, which was operationalised and tested in different Saudi Arabian organisations. Using data from two hundred and fifty-four valid responses, we demonstrated the validity and reliability of the information security culture model through Exploratory Factor Analysis (EFA), followed by Confirmatory Factor Analysis (CFA.) In addition, using Structural Equation Modelling (SEM) we were further able to demonstrate the validity of the model in a nomological net, as well as provide some preliminary findings on the factors that influence information security culture. The current study contributes to the existing body of knowledge in two major ways: firstly, it develops an information security culture measurement model; secondly, it presents empirical evidence for the nomological validity for the security culture measurement model and discovery of factors that influence information security culture. The current study also indicates possible future related research needs.

Flexural capacity of hollow flange beams

The hollow flange beam (HFB) is a unique cold-formed steel section developed in Australia for use as a flexural member. Research has identified that the HFB section's flexural capacity for intermediate span members is limited by lateral distortional buckling, which is characterized by simultaneous lateral deflection, twist, and web distortion. This buckling behaviour is mainly due to the unique geometry of the section, comprising two torsionally stiff triangular flanges connected by a slender web. This paper presents a finite element analytical model suitable for non-linear analysis of HFB flexural members. The model includes all significant effects that may influence the ultimate capacity of such members, including material inelasticity, local buckling, member instability, web distortion, residual stresses, and geometric imperfections. It was found to accurately predict both the elastic lateral distortional buckling moments and the ultimate capacities of HFB flexural members, and was therefore used in the development of design curves and suitable design procedures.

Pro-osteogenic topographical cues promote early activation of osteoprogenitor differentiation via enhanced TGFβ, Wnt, and Notch signaling

Objectives Titanium implant surfaces with modified topographies have improved osteogenic properties in vivo. However, the molecular mechanisms remain obscure. This study explored the signaling pathways responsible for the pro-osteogenic properties of micro-roughened (SLA) and chemically/nanostructurally (modSLA) modified titanium surfaces on human alveolar bone-derived osteoprogenitor cells (BCs) in vitro. Materials and methods The activation of stem cell signaling pathways (TGFβ/BMP, Wnt, FGF, Hedgehog, Notch) was investigated following early exposure (24 and 72 h) of BCs to SLA and modSLA surfaces in the absence of osteogenic cell culture supplements. Results Key regulatory genes from the TGFβ/BMP (TGFBR2, BMPR2, BMPR1B, ACVR1B, SMAD1, SMAD5), Wnt (Wnt/β-catenin and Wnt/Ca2+) (FZD1, FZD3, FZD5, LRP5, NFATC1, NFATC2, NFATC4, PYGO2, LEF1) and Notch (NOTCH1, NOTCH2, NOTCH4, PSEN1, PSEN2, PSENEN) pathways were upregulated on the modified surfaces. These findings correlated with a higher expression of osteogenic markers bone sialoprotein (IBSP) and osteocalcin (BGLAP), and bone differentiation factors BMP2, BMP6, and GDF15, as observed on the modified surfaces. Conclusions These findings demonstrate that the activation of the pro-osteogenic cell signaling pathways by modSLA and SLA surfaces leads to enhanced osteogenic differentiation as evidenced after 7 and 14 days culture in osteogenic media and provides a mechanistic insight into the superior osseointegration on the modified surfaces observed in vivo.

Array of subarrays using adaptive element patterns

This paper explores the use of subarrays as array elements. Benefits of such a concept include improved gain in any direction without significantly increasing the overall size of the array and enhanced pattern control. The architecture for an array of subarrays will be discussed via a systems approach. Individual system designs are explored in further details and proof of principle is illustrated through a manufactured examples.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Typology of phishing email victims based on their behavioural response

A victim of phishing emails could be subjected to money loss and identity theft. This paper investigates the different types of phishing email victims, with the goal of increasing such victims' defences. To obtain this kind of information, an experiment which involves sending a phishing email to participants is conducted. Quantitative and qualitative methods are also used to collect users' information. A model for detecting deception has been employed to understand victims' behaviour. This paper reports the qualitative results. The findings suggest that victims of phishing emails do not always exhibit the same vulnerability. The cause of being a victim is a result of three weaknesses in the detection process: (1) lack of knowledge; (2) weak confirmation channel, and; (3) victims' high propensity towards risk-taking. Therefore, it is suggested that users be provided with suitable confirmation channels and be more risk averse in their behaviour so that they would not fall victim to phishing emails.

Occupy Oakland and #oo : uses of Twitter within the Occupy movement

Social media have become crucial tools for political activists and protest movements, providing another channel for promoting messages and garnering support. Twitter, in particular, has been identified as a noteworthy medium for protests in countries including Iran and Egypt to receive global attention. The Occupy movement, originating with protests in, and the physical occupation of, Wall Street, and inspiring similar demonstrations in other U.S. cities and around the world, has been intrinsically linked with social media through location-specific hashtags: #ows for Occupy Wall Street, #occupysf for San Francisco, and so on. While the individual protests have a specific geographical focus-highlighted by the physical occupation of parks, buildings, and other urban areas-Twitter provides a means for these different movements to be linked and promoted through tweets containing multiple hashtags. It also serves as a channel for tactical communications during actions and as a space in which movement debates take place. This paper examines Twitter's use within the Occupy Oakland movement. We use a mixture of ethnographic research through interviews with activists and participant observation of the movements' activities, and a dataset of public tweets containing the #oo hashtag from early 2012. This research methodology allows us to develop a more accurate and nuanced understanding of how movement activists use Twitter by cross-checking trends in the online data with observations and activists' own reported use of Twitter. We also study the connections between a geographically focused movement such as Occupy Oakland and related, but physically distant, protests taking place concurrently in other cities. This study forms part of a wider research project, Mapping Movements, exploring the politics of place, investigating how social movements are composed and sustained, and the uses of online communication within these movements.

Chlamydia pneumoniae infection of Dentritic cells and its role in asthma exacerbations

Chlamydia infections are associated with exacerbations of asthma however the mechanisms are poorly understood. In this thesis we infected dendritic cells from healthy controls and asthmatic patients to determine if the immune response to chlamydial infection by these key immune cells could explain this association of chlamydial infection with asthma attacks. Infected dendritic cells from asthmatic patients showed increased expression of multiple inflammatory cytokine genes and genes for several tissue remodelling proteins, suggesting that infected dendritic cells play a central role in driving the airways damage associated with asthma. The findings provide a greater understanding of the role of infections in asthma and may provide a basis for new therapies to treat this important disease.

A gene encoding a new cold-active lipase from an Antarctic isolate of Penicillium expansum

Cold-active lipases are of significant interest as biocatalysts in industrial processes. We have identified a lipase that displayed activity towards long carbon-chain-p-nitrophenyl substrates (C12–C18) at 25 °C from the culture supernatant of an Antarctic Penicillium expansum strain assigned P. expansum SM3. Zymography revealed a protein band of around 30 kDa with activity towards olive oil. DNA fragments of a lipase gene designated as lipPE were isolated from the genomic DNA of P. expansum SM3 by genomic walking PCR. Subsequently, the complete genomic lipPE gene was amplified using gene-specific primers designed from the 5′- and 3′-regions. Reverse transcription PCR was used to amplify the lipPE cDNA. The deduced amino acid sequence consisted of 285 residues that included a predicted signal peptide. Three peptides identified by LC/MS/MS analysis of the proteins in the culture supernatant of P. expansum were also present in the deduced amino acid sequence of the lipPE gene suggesting that this gene encoded the lipase identified by initial zymogram activity analysis. Full analysis of the nucleotide and the deduced amino acid sequences indicated that the lipPE gene encodes a novel P. expansum lipase. The lipPE gene was expressed in E. coli for further characterization of the enzyme with a view of assessing its suitability for industrial applications.

Enzyme activities and biotechnological applications of cold-active microfungi

Fungi are eukaryotic organisms and considered to be less adaptable to extreme environments when compared to bacteria. While there are no thermophilic microfungi in a strict sense, some fungi have adapted to life in the cold. Cold-active microfungi have been isolated from the Antarctic and their enzyme activities explored with a view to finding new candidates for industrial use. On another front, environmental pollution by petroleum products in the Antarctic has led to a search for, and the subsequent discovery of, fungal isolates capable of degrading hydrocarbons. The work has paved the way to developing a bioremedial approach to containing this type of contamination in cold climates. Here we discuss our efforts to map the capability of Antarctic microfungi to degrade oil and also introduce a novel cold-active fungal lipase enzyme.

A comprehensive analysis of parameter settings for novelty-biased cumulative gain

In the TREC Web Diversity track, novelty-biased cumulative gain (α-NDCG) is one of the official measures to assess retrieval performance of IR systems. The measure is characterised by a parameter, α, the effect of which has not been thoroughly investigated. We find that common settings of α, i.e. α=0.5, may prevent the measure from behaving as desired when evaluating result diversification. This is because it excessively penalises systems that cover many intents while it rewards those that redundantly cover only few intents. This issue is crucial since it highly influences systems at top ranks. We revisit our previously proposed threshold, suggesting α be set on a query-basis. The intuitiveness of the measure is then studied by examining actual rankings from TREC 09-10 Web track submissions. By varying α according to our query-based threshold, the discriminative power of α-NDCG is not harmed and in fact, our approach improves α-NDCG's robustness. Experimental results show that the threshold for α can turn the measure to be more intuitive than using its common settings.