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Unveiling the Evolution: Analysing Generational Variances in Malware Families
No full textThe evolution of malware presents an ever-growing challenge to cybersecurity, impacting individuals, organisations, and nations alike. As malicious actors continue to adapt their tactics to bypass security measures, it becomes imperative to understand the evolutionary patterns of malware to stay ahead in the ongoing arms race between defenders and attackers. The complexity and sophistication of modern malware pose significant difficulties in detection and mitigation, making it crucial to unravel its evolving nature to enhance the existing defensive capabilities.This research focuses on studying the evolutionary dynamics of malware, examining how variants emerge to circumvent existing security measures. Understanding the mechanisms through which malware evolves makes it possible to identify common patterns and develop strategies to predict the behaviour of certain malware. This work mainly encompasses Windows ransomware, particularly the Conti family, with an additional examination of the WannaCry and Ryuk families. The analysis was conducted primarily by applying dynamic malware analysis techniques to the samples. A total of 143 true-positive Conti samples, alongside 75 WannaCry and 21 Ryuk samples, were collected from reputable sources such as VX-Underground and Malware Bazaar. By utilising the ANY.RUN interactive sandbox for dynamic behavioural analysis, malware samples can be executed in a controlled environment and real-time behaviours, such as file modifications or registry changes, can be collected to discern the malware's underlying functionality and potential impact. In addition, the results obtained from Virustotal, a widely-used online malware scanning platform, are considered to get insights into the detection status of the analysed samples across multiple antivirus engines. Finally, Microsoft Defender Antivirus is utilised to classify the variants and eliminate false positives as much as possible. The tactics and techniques outlined in the MITRE ATT&CK Matrix are used to assess sample behaviour. This framework provides valuable insights into the observed behaviour of samples and the methods employed to achieve specific objectives.The results answer the question "How do different variants of the malware families succeed in bypassing security measures?" and split the answer into three smaller ones. Overall, it can be observed that different ransomware share common traits, but differences over time and between variants and families can be seen. Some differences exist between the version of the operating system in which the malware is executed. Malware evolves, and the changes of the malware authors are reflected in their malware's behaviour and structure. Some changes persist, whereas new ways quickly replace others. By understanding the evolution and analysing the patterns that emerge, we can build our defences in a way that predicts incoming threats and creates a safer space for everyone.https://github.com/FBroy/thesis GitHub repository of the thesisComputer Science | Cyber Securit
Biobased short chain fatty acid production: Exploring microbial community dynamics and metabolic networks through kinetic and microbial modeling approaches
No full textIn recent years, there has been growing interest in harnessing anaerobic digestion technology for resource recovery from waste streams. This approach has evolved beyond its traditional role in energy generation to encompass the production of valuable carboxylic acids, especially volatile fatty acids (VFAs) like acetic acid, propionic acid, and butyric acid. VFAs hold great potential for various industries and biobased applications due to their versatile properties. Despite increasing global demand, over 90% of VFAs are currently produced synthetically from petrochemicals. Realizing the potential of large-scale biobased VFA production from waste streams offers significant eco-friendly opportunities but comes with several key challenges. These include low VFA production yields, unstable acid compositions, complex and expensive purification methods, and post-processing needs. Among these, production yield and acid composition stand out as the most critical obstacles impacting economic viability and competitiveness. This paper seeks to offer a comprehensive view of combining complementary modeling approaches, including kinetic and microbial modeling, to understand the workings of microbial communities and metabolic pathways in VFA production, enhance production efficiency, and regulate acid profiles through the integration of omics and bioreactor data.BT/Environmental Biotechnolog
Decision-making in urban drainage asset management
No full textDecision-making is at the core of urban drainage asset management (UDAM), but its importance is often underestimated, leading to a lack of improvement of decision quality in practice. Therefore, our objective is to present fundamental concepts and theories of decision-making from literature and compare them with real-world experiences of observing, supporting, and participating in UDAM decisions in the Netherlands. The observations are contrasted against selected observations from other nations to illustrate the potential impact of key factors on decision-making processes and outcomes. From this, we observe that despite the available UDAM literature and experiences suggesting otherwise, decision-making in UDAM practice tends to focus on information acquisition, cognitive processing, and judgmental processes. This can lead to known decision biases such as protection of mindset and following fragmented, path-dependent processes influenced by formal and informal structures or institutions. To improve decision-making in UDAM, it is necessary to look beyond optimization of existing assets within the pre-existing technical paradigm and instead work toward aligning it with governing structures and processes for effective decision-making at a system level. While the existing evidence – although limited and mostly anecdotal – is compelling, it does not allow for generalization or validation of theoretical propositions against practical findings and vice versa. We therefore see a need for strengthened efforts into a more systematic study of current UDAM practices that incorporates existing theories and empirical insights on decision-making from several disciplines. This will foster accumulation of knowledge and mutual learning to enhance the research and practice of UDAM decision-making.Policy Analysi
Global epistasis and the emergence of function in microbial consortia
No full textThe many functions of microbial communities emerge from a complex web of interactions between organisms and their environment. This poses a significant obstacle to engineering microbial consortia, hindering our ability to harness the potential of microorganisms for biotechnological applications. In this study, we demonstrate that the collective effect of ecological interactions between microbes in a community can be captured by simple statistical models that predict how adding a new species to a community will affect its function. These predictive models mirror the patterns of global epistasis reported in genetics, and they can be quantitatively interpreted in terms of pairwise interactions between community members. Our results illuminate an unexplored path to quantitatively predicting the function of microbial consortia from their composition, paving the way to optimizing desirable community properties and bringing the tasks of predicting biological function at the genetic, organismal, and ecological scales under the same quantitative formalism.BT/Industriele Microbiologi
Responsible learning organizations: a framework to embed responsible innovation within organizations
No full textPurpose: The purpose of this paper is to explore the extent to which the concept of learning organization can support the embedding of responsible innovation (RI) in organizations. Design/methodology/approach: Based on literature in the fields of corporate social responsibility, learning organizations and quadruple helix collaborations, the authors constructed the responsible learning organization (RLO) framework for RI. With the framework, the authors want to show that the RLO can enable RI within organizations. Findings: Based on this framework, the distinction is made between, on the one hand, the learning processes inside the organization, which resemble reflexivity, and, on the other hand, the learning processes that take place with stakeholders outside the organization, which resemble the other three core processes of RI: anticipation, inclusion and responsiveness. Based on these insights, the authors argue that if an organization wants to do good on innovation, which is seen as the core of RI, organization’s core values should guide that. Practical implications: Organizational core values should be developed by means of learning inside the organization. Therefore, the process of reflexivity should be stressed more, and employees should be empowered to take part in developing these values, which in return can guide the organization as a compass through all the uncertainty it will encounter during the learning outside the organization when interacting with stakeholders. Originality/value: The RLO framework for RI shows what learning processes organizations should facilitate first and what content should be at stake during these learning processes to embed RI. Furthermore, the framework puts emphasis on reflexivity as a condition for responsiveness, inclusion and anticipation.Ethics & Philosophy of Technolog
Data Augmentation for Sample Efficient and Robust Document Ranking
No full textContextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models tend to be data-hungry and require large amounts of data even for fine-tuning. In this article, we propose data-augmentation methods for effective and robust ranking performance. One of the key benefits of using data augmentation is in achieving sample efficiency or learning effectively when we have only a small amount of training data. We propose supervised and unsupervised data augmentation schemes by creating training data using parts of the relevant documents in the query-document pairs. We then adapt a family of contrastive losses for the document ranking task that can exploit the augmented data to learn an effective ranking model. Our extensive experiments on subsets of the MS MARCO and TREC-DL test sets show that data augmentation, along with the ranking-adapted contrastive losses, results in performance improvements under most dataset sizes. Apart from sample efficiency, we conclusively show that data augmentation results in robust models when transferred to out-of-domain benchmarks. Our performance improvements in in-domain and more prominently in out-of-domain benchmarks show that augmentation regularizes the ranking model and improves its robustness and generalization capability.Web Information System
Quantum Magnetism of the Iron Core in Ferritin Proteins: A Re-Evaluation of the Giant-Spin Model
No full textThe electron–electron, or zero-field interaction (ZFI) in the electron paramagnetic resonance (EPR) of high-spin transition ions in metalloproteins and coordination complexes, is commonly described by a simple spin Hamiltonian that is second-order in the spin S: ℋ=[2−(+1)/3+(2−2). Symmetry considerations, however, allow for fourth-order terms when S ≥ 2. In metalloprotein EPR studies, these terms have rarely been explored. Metal ions can cluster via non-metal bridges, as, for example, in iron-sulfur clusters, in which exchange interaction can result in higher system spin, and this would allow for sixth- and higher-order ZFI terms. For metalloproteins, these have thus far been completely ignored. Single-molecule magnets (SMMs) are multi-metal ion high spin complexes, in which the ZFI usually has a negative sign, thus affording a ground state level pair with maximal spin quantum number mS = ±S, giving rise to unusual magnetic properties at low temperatures. The description of EPR from SMMs is commonly cast in terms of the ‘giant-spin model’, which assumes a magnetically isolated system spin, and in which fourth-order, and recently, even sixth-order ZFI terms have been found to be required. A special version of the giant-spin model, adopted for scaling-up to system spins of order S ≈ 103–104, has been applied to the ubiquitous iron-storage protein ferritin, which has an internal core containing Fe3+ ions whose individual high spins couple in a way to create a superparamagnet at ambient temperature with very high system spin reminiscent to that of ferromagnetic nanoparticles. This scaled giant-spin model is critically evaluated; limitations and future possibilities are explicitly formulated.BT/Biocatalysi
Investigating Effects of Environmental Treatments on Transpiration
No full textTranspiration is a crucial flux in greenhouses that directly affects plant growth. However, there are currently no direct measurements available. Sap flow is the most widely used method for quantifying transpiration, and calibration can enhance the accuracy of these estimates. Despite the existence of numerous recommendations for applying sap flow calibrations, few reports have employed them. Furthermore, plants frequently encounter various stressors during their growth and development, including drought, salinity, and reduced leaf area. These stressors can impede plant growth and, in extreme cases, can even result in plant death. Plant growth is contingent upon transpiration, and thus, stress conditions can also influence transpiration. However, there is a paucity of research on the effects of these stress conditions on transpiration. The objective of this report is to calibrate the sap flow and subsequently investigate the effects of stresses, such as salinity, drought and leaf removal on transpiration in order to enhance irrigation strategies. Calibration was initially conducted by comparing daily sap flow with transpiration estimated by a water balance model using linear regression analysis. The corrected sap flow for the controlled and treated gutter was then compared to investigate the impact of stresses on transpiration. The corrected sap flow coefficients were 2.30, 1.76, 1.64 and 3.58 for the controlled plants (1 and 2) and the treated plants (1 and 2), respectively. The corrected sap flow may be employed as an indicator of transpiration. It has been observed that salinity levels and a reduction in leaf area result in a decrease in transpiration, particularly when transpiration rates are already high. There was no significant difference in transpiration even when irrigation was reduced to 33% of the original amount. In the event of salinity, drought and leaf area reduction, it is recommended that the irrigation frequency be increased and the irrigation amount be reduced. Furthermore, it is recommended that salt leaching during the non-planting period be undertaken.Water Management | Hydrolog
Modelling and Design Guidelines for Ram Air Ducts Using the Meredith Effect: Applied to an Organic Rankine Cycle Waste Heat Recovery System
No full textWith climate change posing increasing risks, the Advisory Council for Aviation Research and Innovation in Europe (ACARE) aims to reduce CO2 emissions by 75% and NOx emissions by 90% per passenger kilometer by 2050, compared to a baseline aircraft from 2000. The ARENA project addresses this by developing a waste heat recovery system using aircraft engine exhaust gases, improving fuel efficiency. This thesis focuses on integrating the condenser of such a system into the propulsion unit, aiming to minimize drag from the ram air cooling duct by evaluating different heat exchanger topologies and duct designs.The study uses the IMOTHEP Distributed fans Research Aircraft with electric Generators by ONERA (DRAGON) concept, a hybrid electric aircraft with two tail-mounted turbogenerators. The research proceeds in several stages. Initially, a multipass-condenser's potential to reduce pressure drop was examined by adjusting the heat exchanger blockage factor per pass, but results showed no reduction in pressure drop.Next, a lumped parameter model was developed to analyze drag, pressure drop, temperature increase, and ram air duct length. This model evaluated the sensitivity of duct geometrical parameters on the drag recovery factor—a dimensionless number indicating net thrust. Findings revealed that inclining the heat exchanger efficiently increases the drag recovery factor, while the diffuser area ratio has a similar effect but is less space-efficient. The mass flow rate ratio showed less sensitivity, and fin height or pitch had the smallest effect on drag recovery.Using the lumped parameter model, an optimal preliminary ram air duct design was identified for different spatial constraints. The study found that inline plain tube bundle and flat tube offset strip fin heat exchangers provided more compact solutions with higher drag recovery factors. For large diffuser-blocked area fractions, optimal duct geometry remained independent of heat exchanger type, characterized by a 70-degree maximum inclination angle, a 0.7 mass flow rate ratio, and maximized diffuser area ratio within spatial constraints.A verification study of the lumped parameter model was conducted using a two-dimensional Reynolds Averaging Navier Stokes (RANS) computational fluid dynamics (CFD) analysis with k-ω SST turbulence and a porous zone to mimic the heat exchanger. The CFD analysis confirmed the lumped parameter model's predictions, with a 0.8% difference in drag recovery factor for optimal duct geometry. Across various geometries, the mean absolute difference in drag recovery factor between models was 1.082%, with a standard deviation of 0.584%.In conclusion, integrating the condenser in the propulsion unit within spatial constraints results in positive thrust, thus supporting Meredith's 1935[2] claim. This integration reduces net drag and improves overall efficiency, contributing to significant emission reductions in line with ACARE's targets.ARENAAerospace Engineerin
Non-destructive inspection technologies for repair assessment in materials and structures
No full textAging infrastructure globally faces degradation, posing risks and requiring substantial repair investment. Strategic maintenance practices are crucial for evaluating structural conditions and ensuring sustainability. The growing demands on modern materials and structures necessitate enhanced health monitoring approaches. Shifting from reactive to proactive maintenance methodologies is paramount, due to lower investment while keeping the structural performance at acceptable standards. However, quantitative assurance of repair/reinforcement/retrofit programs or self-healing effect in structures is similarly crucial for the operation of the infrastructure. Non-destructive testing (NDT) techniques, such as ultrasound, acoustic emission, and optical methods, play a vital role in assessing structural health. Through real-world case studies, the effectiveness of repair in addition to damage assessment are evaluated, encouraging a more systematic approach to monitoring structural repair efficacy. The paper intends to address the research gap in monitoring the repair effectiveness in civil structures and materials and provides valuable insights to enhance repair strategies in civil engineering.Concrete Structure