Changes in physiology, gene expression and ethylene biosynthesis in MDMV-infected sweet corn primed by small RNA pre-treatment

The physiological condition of plants is significantly affected by viral infections. Viral proliferation occurs at the expense of the energy and protein stores in infected plant cells. At the same time, plants invest much of their remaining resources in the fight against infection, making them even less capable of normal growth processes. Thus, the slowdown in the development and growth processes of plants leads to a large-scale decrease in plant biomass and yields, which may be a perceptible problem even at the level of the national economy. One form of protection against viral infections is treatment with small interfering RNA (siRNA) molecules, which can directly reduce the amount of virus that multiplies in plant cells by enhancing the process of highly conserved RNA interference in plants. The present work demonstrated how pre-treatment with siRNA may provide protection against MDMV (Maize dwarf mosaic virus) infection in sweet corn (Zea mays cv. saccharata var. Honey Koern). In addition to monitoring the physiological condition of the maize plants, the accumulation of the virus in young leaves was examined, parallel, with changes in the plant RNA interference system and the ethylene (ET) biosynthetic pathway. The siRNA pre-treatment activated the plant antiviral defence system, thus significantly reducing viral RNA and coat protein levels in the youngest leaves of the plants. The lower initial amount of virus meant a weaker stress load, which allowed the plants to devote more energy to their growth and development. In contrast, small RNA pre-treatment did not initially have a significant effect on the ET biosynthetic pathway, but later a significant decrease was observed both in the level of transcription of genes responsible for ET production and, in the amount of ACC (1-aminocyclopropane-1-carboxylic acid) metabolite. The significantly better physiological condition, enhanced RNAi response and lower quantity of virus particles in siRNA pretreated plants, suggested that siRNA pre-treatment stimulated the antiviral defence mechanisms in MDMV infected plants. In addition, the consistently lower ACC content of the plants pre-treated with siRNA suggest that ET does not significantly contribute to the successful defence in this maize hybrid type against MDMV

Privacy-Preserving Authentication: A Homomorphic Encryption Approach

The importance of privacy for individuals has become increasingly evident in recent years as the amount of personal data being collected, stored and used by both private companies and government institutions has grown exponentially. The potential for this data to be misused or mishandled has led to widespread concern among individuals about the protection of their personal information. In response to these concerns, there has been a rise in the development of privacy-preserving technologies, which aim to protect personal data while still allowing it to be used for legitimate purposes. These technologies are necessary not only to address the concerns of individuals, but also to meet the legal requirements of institutions that handle personal information. Many applications using personal information as a commodity can benefit from privacy-preserving technologies. The research presented in this thesis targets a commonly used Internet application in which privacy-enhancing technologies can play a key role: biometric-based authentication. Authentication is the establishment of one party’s identity to the other. Biometric data, such as faces, fingerprints or iris, are used more and more commonly as a means of providing personal identification and authentication. However, authentication protocols using biometric data face serious privacy concerns, as the data involved is sensitive or personally-identifiable, which makes it necessary for data holders to protect its privacy. The widespread use of this application, and the need to protect user privacy, motivated us to examine how homomorphic encryption, a privacy-preserving technology, can be used and deployed to enhance privacy in such an application. Homomorphic encryption is a form of encryption that allows arbitrary computations to be performed on encrypted data, resulting in an encrypted result that, when decrypted, is the same as if the computation had been performed on the corresponding cleartext data. This means that entire computational processes can be executed on encrypted data without requiring the decryption key, thereby maintaining the privacy of the data involved. This can address both concerns from individuals regarding the protection of their personal and sensitive data, and legal requirements that institutions must meet. Homomorphic encryption can be used in an authentication protocol to allow a server to verify the authenticity of a client’s credentials without having access to the cleartext values of the credentials. In this thesis, we describe and prove secure two novel biometric-based authentication protocols that use homomorphic encryption to preserve the confidentiality of the biometric data both in storage and during use. These protocols ensure the privacy of the biometric information, while still allowing it to be used for authentication purposes. Users of the protocols encrypt their own biometric data and send it to a remote server that performs computations, including the biometric matching, solely on encrypted data. One of the protocols is designed to protect biometric data privacy against a honest-but-curious server and the other against a malicious server. Additionally, in both cases the user is securely authenticated by the server. For both the protocols, implementation and performance results using public homomorphic encryption libraries are presented along with a security and usability assessment, including an evaluation analysis against industry-standard biometric-based authentication schemes. In the most efficient implementation, the active authentication phase takes no more than three seconds to complete

Sensitivity of NEXT-100 detector to neutrinoless double beta decay

Nesta tese estúdiase a sensibilidade do detector NEXT-100 á desintegración dobre beta sen neutrinos. Existe un gran interese na busca desta desintegración xa que podería respostar preguntas fundamentais en física de neutrinos. O detector constitúe a terceira fase do experimento NEXT, colaboración na que se desenrolou esta tese. A continuación inclúese un resumo de cada un dos capítulos nos que se divide a tese. Comézase introducindo o marco teórico e experimental nas seccións Física de neutrinos, A busca da desintegración dobre beta sen neutrinos e O experimento NEXT. Posteriormente descríbense a parte principal do análise da tese en Simulación do detector, Procesamento de datos e Sensibilidade do detector NEXT-100

A framework for preserving privacy in e-government

Today the world is relying heavily on the use of Information and Communication Technologies (ICT) in performing daily tasks and governments are no exception. Governments around the world are utilising latest ICT to provide government services in the form of electronic services (e-services) in a phenomena called the electronic government (e-government). These services vary from providing general information to the provision of advanced services. However, one of the major obstacles facing the adoption of e-government services is the challenging privacy issues arising from the sharing of user’s information between government agencies and third parties. Many privacy frameworks have been proposed by governments and researchers to tackle these issues, however, the adoption of these frameworks is limited as they lack the consideration of users’ perspective. This thesis uses Soft Systems Methodology (SSM) to investigate the concepts relevant to e-government, and preserving privacy in the context of e-government. Using SSM, Conceptual Models(CMs) relevant to the concepts under investigation were developed and used to review and to identify the limitations of existing frameworks in the literature and to determine the requirements for preserving privacy in an e-government context. A general framework for Privacy REquirements in E-GOVernment (PRE_EGOV) is proposed based on the developed CMs. The proposed framework considers the perspectives of relevant stakeholders and the ownership rights of information about users. The CM relevant to preserving privacy and the elements of the PRE_EGOV framework were evaluated against stakeholders’ perspectives using a survey. The applicability of the proposed framework is demonstrated by applying it on a real world case study. The insight gained from the analysis of the case study and the survey’s results increased confidence in the usefulness of the proposed framework and showed that a system thinking approach to tackle such complex, multi-disciplinary problem can result in a promising solution that is more likely to be accepted by involved stakeholders. The work in this research has been published in three full papers and a poster. The developed Conceptual Models and proposed framework have found acceptance in E-government research community [1, 2, 3, 4] as well as in other research communities [5]

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Challenges for engineering students working with authentic complex problems

Engineers are important participants in solving societal, environmental and technical problems. However, due to an increasing complexity in relation to these problems new interdisciplinary competences are needed in engineering. Instead of students working with monodisciplinary problems, a situation where students work with authentic complex problems in interdisciplinary teams together with a company may scaffold development of new competences. The question is: What are the challenges for students structuring the work on authentic interdisciplinary problems? This study explores a three-day event where 7 students from Aalborg University (AAU) from four different faculties and one student from University College North Denmark (UCN), (6th-10th semester), worked in two groups at a large Danish company, solving authentic complex problems. The event was structured as a Hackathon where the students for three days worked with problem identification, problem analysis and finalizing with a pitch competition presenting their findings. During the event the students had workshops to support the work and they had the opportunity to use employees from the company as facilitators. It was an extracurricular activity during the summer holiday season. The methodology used for data collection was qualitative both in terms of observations and participants’ reflection reports. The students were observed during the whole event. Findings from this part of a larger study indicated, that students experience inability to transfer and transform project competences from their previous disciplinary experiences to an interdisciplinary setting