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On enhancing the security against memory disclosure attacks
No full textMain memory is a critical component for storing computational data across various applications, including highly sensitive information such as banking transactions, encryption keys, and authentication credentials. However, memory systems are susceptible to side-channel attacks, making it imperative to implement effective countermeasures against unauthorized data extraction. Additionally, counterfeit memory controllers and memory devices pose a significant security risk, as they can facilitate data leakage even in the presence of conventional encryption mechanisms. Traditional security solutions, such as AES encryption integrated into memory controllers, provide strong cryptographic protection; however, they introduce substantial latency and area overhead. This paper presents a novel security countermeasure that ensures secure communication between the memory controller and the memory device by employing XOR-based encryption and decryption at both ends. The proposed technique remains secure even in the presence of unauthenticated memory components, preventing unauthorized data access. Experimental evaluations demonstrate that the proposed approach achieves a high level of security (2512) while significantly reducing area overhead and incurring no additional access latency
Ein Projekt der Fördermaßnahme „MobilitätsWerkStadt 2025“ (Förderkennzeichen 01UV2122A-E; Laufzeit: 01.08.2021-31.07.2024)
No full textThe KoGoMo project aims to strengthen municipal governance capabilities in relation to private-sector sharing and mobility providers, and to foster cooperation in order to establish sustainable mobility services in urban areas. Within the framework of a real-world laboratory in the Hamburg district of Harburg, various new mobility products were successfully implemented. This includes the expansion of carsharing services in collaboration with Hochbahn, with 13 hvv switch mobility points established that also accommodate free-floating carsharing providers. In addition, pilot projects with the providers Cambio and Dorfstromer were initiated for station-based carsharing with a minimum revenue guarantee to ensure economic viability during the initial phase. Furthermore, a cargo bike rental service with hourly rentals was established in Harburg’s city center, with its user structure evaluated by TUHH. An on-demand service by hvv hop was also introduced, while simulations for a MOIA ridepooling service demonstrated that such a service is not feasible in the area. Close cooperation between the Harburg District Office, TUHH, and the Authority for Transport and Mobility Transition enabled structured and accelerated site planning and the gradual expansion of mobility services. The offerings developed within the project will be continued beyond the project duration and have received positive feedback from the population, although mobility behavior has not yet changed significantly. The insights gained are being incorporated into a governance strategy that is intended to serve as a guideline for other districts and municipalities to facilitate the introduction of sustainable mobility services and to transfer the project’s successes
Self-assembly of bent-core nematics in nanopores
No full textBent-core nematic liquid crystals exhibit unique properties, including giant flexoelectricity and polar electro-optic responses, making them ideal for energy conversion and electro-optic applications. When confined in nanopores, they can stabilize chiral nanostructures, enhance polar order, and enable defect-driven switching -- offering potential in nanofluidics, sensing, and adaptive optics. Here we examine the thermotropic ordering of the bent-core dimer CB7CB confined in anodic aluminum oxide (AAO) and silica membranes with precisely engineered cylindrical nanochannels -- ranging from just a few nanometers to several hundred nanometers. These well-aligned nanochannels enable high-resolution polarimetry studies of optical anisotropy, revealing how geometric confinement affects molecular organization and phase behavior. Under weak confinement, CB7CB forms a layered heterophase structure, with nematic, splay-bent, and twist-bent heliconical phases likely arranged concentrically. As confinement increases, a Landau-de Gennes analysis shows that ordered phases are suppressed, leaving only a paranematic phase under strong spatial constraints. Remarkably, temperature-dependent changes in optical birefringence under confinement closely resemble those seen under applied electric fields, revealing a parallel between geometric and electro-optic effects. Overall, our work demonstrates how nanoconfinement allows one to systematically tailor the self-assembly and optical behavior of bent-core nematics, enabling novel functionalities in responsive and anisotropic materials
Prompting techniques for secure code generation: a systematic investigation
No full textLarge Language Models (LLMs) are gaining momentum in software development with prompt-driven programming enabling developers to create code from Natural Language (NL) instructions. However, studies have questioned their ability to produce secure code and, thereby, the quality of prompt-generated software. Alongside, various prompting techniques that carefully tailor prompts have emerged to elicit optimal responses from LLMs. Still, the interplay between such prompting strategies and secure code generation remains underexplored and calls for further investigations. Objective: In this study, we investigate the impact of different prompting techniques on the security of code generated from NL instructions by LLMs. Method: First, we perform a systematic literature review to identify the existing prompting techniques that can be used for code generation tasks. A subset of these techniques are evaluated on GPT-3, GPT-3.5, and GPT-4 models for secure code generation. For this, we used an existing dataset consisting of 150 NL security-relevant code generation prompts. Results: Our work (i) classifies potential prompting techniques for code generation (ii) adapts and evaluates a subset of the identified techniques for secure code generation tasks, and (iii) observes a reduction in security weaknesses across the tested LLMs, especially after using an existing technique called Recursive Criticism and Improvement (RCI), contributing valuable insights to the ongoing discourse on LLM-generated code security
Effects of soil, climatic, and anthropogenic drivers on the abundance, richness, and diversity of soil microbial communities: A global perspective
No full textDiverse microbial communities are fundamental to healthy and productive soils, accommodating essential ecosystem services including nutrient cycling, organic matter decomposition, land-atmosphere carbon exchange, water and climate regulation, and contaminant control. The immense taxonomic and functional diversity of soil microorganisms makes deciphering the intricate interactions between soil, its inhabitants, and the far-extending effects for life on earth a complex challenge. Advances in the analysis of eDNA, like metabarcoding to determine community composition from soil samples, enable large-scale assessments across manifold habitat conditions. Based on the LUCAS 2018 soil biodiversity datasets, we aim to (i) identify key drivers shaping soil microbial community composition, and (ii) quantify marginal changes in soil microbial abundance, richness, and diversity forced by soil properties, climatic, and anthropogenic pressures. To improve the understanding of interactions between external drivers and soil microbial communities, we employ machine learning algorithms, in particular generalized additive models for increased interpretability (Hassani et al., 2024), to investigate and identify the parameters influencing the observed soil microbial diversity and richness in the LUCAS datasets. Our modeling efforts will enable us to predict changes in soil biodiversity under the influence of anthropogenic pressures and projected climate scenarios. Such an analysis can further support decision-making in land management with potential policy implications on a pan-European scale.European Commissio
Ontology-based knowledge representation for wire arc additive manufacturing and composite extrusion modeling
No full textKnowledge representation in additive manufacturing (AM) is essential for data management, enabling semantic interoperability and decision-making. Wire arc additive manufacturing (WAAM) and composite extrusion modeling (CEM) are advanced manufacturing methods employed within metal-based additive manufacturing. Although knowledge representation in AM has been widely explored, there is a notable gap in research addressing knowledge representation tailored to WAAM and CEM. Aiming to advance knowledge representation for WAAM and CEM, this paper proposes an ontology-based knowledge representation approach. Two ontologies, the Wire Arc Additive Manufacturing Application Ontology (WAAMAO) and the Composite Extrusion Modeling Application Ontology (CEMAO), are proposed, following a well-known ontology engineering methodology to ensure a rigorous and systematic ontology design process. To validate the proposed approach, a manufacturing information system utilizing both ontologies is presented. The findings highlight the capability of WAAMAO and CEMAO in knowledge representation, enabling efficient data management and supporting semantic interoperability in metal-based AM processes
Ray tracing via Snellius
No full textA wave travelling between two media denoted by different wave propagation velocities is subject to refraction at the interface between the media. The refraction is regulated by the Snellius law, where the interface is assumed infinitesimally thin. The jump in propagation velocity at the interface results in a discontinuous propagation direction for the wave. We consider a continuously changing medium, where the wave propagation velocity is assumed to be a continuous field. We reduce the Snellius law to its linear expansion at the interface between two regions of the medium with infinitesimally different propagation velocities. The linearised Snellius law connects the curvilinear coordinates associated with the propagation process from a point source and the spatial distribution of the propagation velocity. The coordinates map the rays evolving from the source and the wavefronts, orthogonal to the rays. Curved rays determine local osculating planes, spanned by the tangent to the ray and the gradient of the propagation velocity. The wavefront curvature is determined parallel to the tracing of each ray. Intersections of the wavefront are considered, with the osculating plane and with the longitudinal plane of the ray. For curved rays, the determined wavefront curvatures are different for the different planes. A numerical implementation of the model is used to approach an exemplary test case, regarding sound radiation in a stratified medium
Gershgorin-type spectral inclusions for matrices
No full textIn this paper we derive sequences of Gershgorin-type inclusion sets for the spectra and pseudospectra of finite matrices. In common with previous generalisations of the classical Gershgorin bound for the spectrum, our inclusion sets are based on a block decomposition. In contrast to previous generalisations that treat the matrix as a perturbation of a block-diagonal submatrix, our arguments treat the matrix as a perturbation of a block-tridiagonal matrix, which can lead to sharp spectral bounds, as we show for the example of large Toeplitz matrices. Our inclusion sets, which take the form of unions of pseudospectra of square or rectangular submatrices, build on our own recent work on inclusion sets for bi-infinite matrices in Chandler-Wilde et al. (2024
Analytical and machine learning-based fatigue life prediction of welded joints under multiaxial loading
No full textEvaluating the fatigue life of welded joints under multiaxial loading is a key challenge in structural engineering. This study explores machine learning (ML) methods for predicting fatigue life and compares their performance against the novel super ellipse criterion, which is an analytical approach that aims to improve current design standard methods (e.g., Eurocode 3, IIW). Using a dataset of uniaxial and multiaxial fatigue tests with varying phase angles, ML models—including artificial neural networks and extreme gradient boosting (XGBoost)—are trained on features like stress amplitudes, phase differences, and material properties. Artificial neural networks provide high accuracy, while tree-based models like XGBoost offer better interpretability via model agnostic interpretation using Explainable Artificial Intelligence. Results show ML models can outperform traditional criteria, especially under non-proportional loading, but face limitations near the edges of the training data. This work highlights the potential and challenges of ML in fatigue prediction and highlights their value for enhancing the safety and reliability of welded structures