Estimation of the factor model by unweighted least squares (ULS) is distribution free, yields consistent estimates, and is computationally fast if the Minimum Residuals (MinRes) algorithm is employed. MinRes algorithms produce a converging sequence of monotonically decreasing ULS function values. Various suggestions for algorithms of the MinRes type are made for confirmatory as well as for exploratory factor analysis. These suggestions include the implementation of inequality constraints and the prevention of Heywood cases. A simulation study, comparing the bootstrap standard deviations for the parameters with the standard errors from maximum likelihood, indicates that these are virtually equal when the score vectors are sampled from the normal distribution. Two empirical examples demonstrate the usefulness of constrained exploratory and confirmatory factor analysis by ULS used in conjunction with the bootstrap method.
Research in the Netherlands on the success of students in higher education is highly influenced by Tinto’s integration theory. This paper is part of a broader PhD research, in which I investigate the possible influence of the use of social media (Facebook) by students. By comparing various studies conducted in line with the tradition of the integration theory, this paper focuses on a limited amount of variables. By taking the best-proven variables to represent satisfaction, this research measures and test these variables, derived from Tinto’s theory as one latent variable. The internal consistency is tested by factor analysis and the reliability is measured with Cronbach’s alpha and Guttman’s lambda-2. All data is collected by digital surveys among the first year students in 2011-2012 at the Amsterdam University of Applied Sciences with a limited enrollment of 904. Ultimately, this paper provides insight into the potential use of a simplified version of the integration theory and to include, in later study, the role of social media as a factor of integration in education. In addition, it will open the possibility to create a model for student success, which will have a better fit with the present generation of students in the developed world.
Mycotoxins are small (MW approximately 700), toxic chemical products formed as secondary metabolites by a few fungal species that readily colonise crops and contaminate them with toxins in the field or after harvest. Ochratoxins and Aflatoxins are mycotoxins of major significance and hence there has been significant research on broad range of analytical and detection techniques that could be useful and practical. Due to the variety of structures of these toxins, it is impossible to use one standard technique for analysis and/or detection. Practical requirements for high-sensitivity analysis and the need for a specialist laboratory setting create challenges for routine analysis. Several existing analytical techniques, which offer flexible and broad-based methods of analysis and in some cases detection, have been discussed in this manuscript. There are a number of methods used, of which many are lab-based, but to our knowledge there seems to be no single technique that stands out above the rest, although analytical liquid chromatography, commonly linked with mass spectroscopy is likely to be popular. This review manuscript discusses (a) sample pre-treatment methods such as liquid-liquid extraction (LLE), supercritical fluid extraction (SFE), solid phase extraction (SPE), (b) separation methods such as (TLC), high performance liquid chromatography (HPLC), gas chromatography (GC), and capillary electrophoresis (CE) and (c) others such as ELISA. Further currents trends, advantages and disadvantages and future prospects of these methods have been discussed.
Micro and macro algae are a rich source of lipids, proteins and carbohydrates, but also of secondary metabolites like phytosterols. Phytosterols have important health effects such as prevention of cardiovascular diseases. Global phytosterol market size was estimated at USD 709.7 million in 2019 and is expected to grow with a CAGR of 8.7% until 2027. Growing adoption of healthy lifestyle has bolstered demand for nutraceutical products. This is expected to be a major factor driving demand for phytosterols.Residues from algae are found in algae farming and processing, are found as beachings and are pruning residues from underwater Giant Kelp forests. Large amounts of brown seaweed beaches in the province of Zeeland and are discarded as waste. Pruning residues from Giant Kelp Forests harvests for the Namibian coast provide large amounts of biomass. ALGOL project considers all these biomass residues as raw material for added value creation.The ALGOL feasibility project will develop and evaluate green technologies for phytosterol extraction from algae biomass in a biocascading approach. Fucosterol is chosen because of its high added value, whereas lipids, protein and carbohydrates are lower in value and will hence be evaluated in follow-up projects. ALGOL will develop subcritical water, supercritical CO2 with modifiers and ethanol extraction technologies and compare these with conventional petroleum-based extractions and asses its technical, economic and environmental feasibility. Prototype nutraceutical/cosmeceutical products will be developed to demonstrate possible applications with fucosterol.A network of Dutch and African partners will supply micro and macro algae biomass, evaluate developed technologies and will prototype products with it, which are relevant to their own business interests. ALGOL project will create added value by taking a biocascading approach where first high-interest components are processed into high added value products as nutraceutical or cosmeceutical.
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
Today, embedded devices such as banking/transportation cards, car keys, and mobile phones use cryptographic techniques to protect personal information and communication. Such devices are increasingly becoming the targets of attacks trying to capture the underlying secret information, e.g., cryptographic keys. Attacks not targeting the cryptographic algorithm but its implementation are especially devastating and the best-known examples are so-called side-channel and fault injection attacks. Such attacks, often jointly coined as physical (implementation) attacks, are difficult to preclude and if the key (or other data) is recovered the device is useless. To mitigate such attacks, security evaluators use the same techniques as attackers and look for possible weaknesses in order to “fix” them before deployment. Unfortunately, the attackers’ resourcefulness on the one hand and usually a short amount of time the security evaluators have (and human errors factor) on the other hand, makes this not a fair race. Consequently, researchers are looking into possible ways of making security evaluations more reliable and faster. To that end, machine learning techniques showed to be a viable candidate although the challenge is far from solved. Our project aims at the development of automatic frameworks able to assess various potential side-channel and fault injection threats coming from diverse sources. Such systems will enable security evaluators, and above all companies producing chips for security applications, an option to find the potential weaknesses early and to assess the trade-off between making the product more secure versus making the product more implementation-friendly. To this end, we plan to use machine learning techniques coupled with novel techniques not explored before for side-channel and fault analysis. In addition, we will design new techniques specially tailored to improve the performance of this evaluation process. Our research fills the gap between what is known in academia on physical attacks and what is needed in the industry to prevent such attacks. In the end, once our frameworks become operational, they could be also a useful tool for mitigating other types of threats like ransomware or rootkits.
