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Optical photothermal microscopy is a powerful, emerging method that overcomes the diffraction limit in infrared hyperspectral imaging by utilizing a visible probe laser beam to detect local temperature-induced modulation at the visible diffraction limit. However, the spectral range of this technique has been limited by the tuning range of infrared sources, which is generally restricted to the fingerprint window with commercially available quantum cascade lasers. In this work, ultra-broadband synchrotron infrared radiation was used for infrared photothermal imaging and spectroscopy, spanning the entire mid-infrared range. Both optical- and fluorescence-detected photothermal modalities were performed, demonstrating improved spectral range when compared to optical photothermal microscopy using commercial sources and improved spatial resolution when compared to synchrotron micro-spectroscopy measurements. Following these initial validation studies, synchrotron Fourier-transform optical photothermal infrared spectroscopy (FT-OPTIR) in combination with synchrotron micro-spectroscopy measurements were used to differentiate cells in mouse brain tissue sections.

3D reconstruction is a fundamental task in robotics that gained attention due to its major impact in a wide variety of practical settings, including agriculture, underwater, and urban environments. This task can be carried out via view planning (VP), which aims to optimally place a certain number of cameras in positions that maximize the visual information, improving the resulting 3D reconstruction. Nonetheless, in most real-world settings, existing environmental noise can significantly affect the performance of 3D reconstruction. To that end, this work advocates a novel geometric-based reconstruction quality function for VP, that accounts for the existing noise of the environment, without requiring its closed-form expression. With no analytic expression of the objective function, this work puts forth an adaptive Bayesian optimization algorithm for accurate 3D reconstruction in the presence of noise. Numerical tests on noisy agricultural environments showcase the merits of the proposed approach for 3D reconstruction with even a small number of available cameras.

In the context of digital twins, structural health monitoring (SHM) constitutes the backbone of condition-based maintenance, facilitating the interconnection between virtual and physical assets. Guided wave propagation (GWP) is commonly employed for the inspection of structures in SHM. However, GWP is sensitive to variations in the material properties of the structure, leading to false alarms. In this direction, uncertainty quantification (UQ) is regularly applied to improve the reliability of predictions. Computational mechanics is a useful tool for the simulation of GWP, and is often applied for UQ. Even so, the application of UQ methods requires numerous simulations, while large-scale, transient numerical GWP solutions increase the computational cost. Reduced order models (ROMs) are commonly employed to provide numerical results in a limited amount of time. In this paper, we propose a machine learning (ML)-based ROM, mentioned as BO-ML-ROM, to decrease the computational time related to the simulation of the GWP. The ROM is integrated with a Bayesian optimization (BO) framework, to adaptively sample the parameters for the ROM training. The finite element method is used for the simulation of the high-fidelity models. The formulated ROM is used for forward UQ of the GWP in an aluminum plate with varying material properties. To determine the influence of each parameter perturbation, a global, variance-based sensitivity analysis is implemented based on Sobol' indices. It is shown that Bayesian optimization outperforms one-shot sampling methods, both in terms of accuracy and speed-up. The predicted results reveal the efficiency of BO-ML-ROM for GWP and demonstrate its value for UQ.

InterPlanetary File System~(IPFS) is one of the most promising decentralized off-chain storage mechanisms, particularly relevant for blockchains, aiming to store the content forever, thus it is crucial to understand its composition, deduce actor intent and investigate its operation and impact. Beyond the network functionality that IPFS offers, assessing the quality of nodes, i.e. analysing and categorising node software and data, is essential to mitigate possible risks and exploitation of IPFS. To this end, in this work we took three daily snapshots of IPFS nodes within a month and analysed each node (by IP address) individually, using threat intelligence feeds. The above enabled us to quantify the number of potentially malicious and/or abused nodes. The outcomes lead us to consider using a filter to isolate malicious nodes from the network, an approach we implemented as a prototype and used for assessment of effectiveness., Comment: To appear at the 38th International Conference on ICT Systems Security and Privacy Protection (IFIP SEC 2023)

Greece constitutes a coastal country with a lot of geomorphologic, climatic, cultural and historic peculiarities favoring the development of many aspects of tourism. Within this framework, this article examines what are the effects of tourism in Greece and how determinative these effects are, by applying a macroscopic analysis on empirical data for the estimation of the contribution of tourism in the Greek Economy. The available data regard records of the Balance of Payments in Greece and of the major components of the Balance of the Invisible Revenues, where a measurable aspect of tourism, the Travel or Tourism Exchange, is included. At the time period of the available data (2000-2012) two events of the recent Greek history are distinguished as the most significant (the Olympic Games in the year 2004 and the economic crisis initiated in the year 2009) and their impact on the diachronic evolution in the tourism is discussed. Under an overall assessment, the analysis illustrated that tourism is a sector of the Greek economy, which is described by a significant resilience, but it seems that it has not yet been submitted to an effective developmental plan exploiting the endogenous tourism dynamics of the country, suggesting currently a promising investment of low risk for the economic growth of country and the exit of the economic crisis

Strain gradient elasticity and nonlocal elasticity are two enhanced elastic theories intensively used over the last fifty years to explain static and dynamic phenomena that classical elasticity fails to do. The nonlocal elastic theory has a clear differentiation from the classical case by considering stresses in a point of the continuum as an integral of all stresses defined in the treated elastic body. On the other hand, strain gradient elasticity is characterized as a non-classical theory because considers both potential and kinetic energy densities as not only functions of strains and velocities, respectively but also functions of their gradients. Although the two considerations seem to be completely different from each other, it is a common belief that strain gradient elasticity has a lot in common with nonlocal elasticity. The goal of the present work is to derive all the strain gradient elastic theories appearing so far in the literature via nonlocal definitions of the potential and kinetic energy densities. Such a derivation demonstrates the common roots of the two theories and explains the nature of the involved intrinsic parameters in strain gradient elastic theories. For the sake of simplicity and brevity, only one-dimensional wave propagation phenomena are considered., Comment: 46 pages, 9 figures

As IoT becomes omnipresent vast amounts of data are generated, which can be used for building innovative applications. However,interoperability issues and security concerns, prevent harvesting the full potentials of these data. In this paper we consider the use case of data generated by smart buildings. Buildings are becoming ever "smarter" by integrating IoT devices that improve comfort through sensing and automation. However, these devices and their data are usually siloed in specific applications or manufacturers, even though they can be valuable for various interested stakeholders who provide different types of "over the top" services, e.g., energy management. Most data sharing techniques follow an "all or nothing" approach, creating significant security and privacy threats, when even partially revealed, privacy-preserving, data subsets can fuel innovative applications. With these in mind we develop a platform that enables controlled, privacy-preserving sharing of data items. Our system innovates in two directions: Firstly, it provides a framework for allowing discovery and selective disclosure of IoT data without violating their integrity. Secondly, it provides a user-friendly, intuitive mechanisms allowing efficient, fine-grained access control over the shared data. Our solution leverages recent advances in the areas of Self-Sovereign Identities, Verifiable Credentials, and Zero-Knowledge Proofs, and it integrates them in a platform that combines the industry-standard authorization framework OAuth 2.0 and the Web of Things specifications., Comment: to appear in ESORICS Workshop on Emerging Technologies for Authorization and Authentication, ETAA 2022

Digital twins have emerged as a key technology for optimizing the performance of engineering products and systems. High-fidelity numerical simulations constitute the backbone of engineering design, providing an accurate insight into the performance of complex systems. However, large-scale, dynamic, non-linear models require significant computational resources and are prohibitive for real-time digital twin applications. To this end, reduced order models (ROMs) are employed, to approximate the high-fidelity solutions while accurately capturing the dominant aspects of the physical behavior. The present work proposes a new machine learning (ML) platform for the development of ROMs, to handle large-scale numerical problems dealing with transient nonlinear partial differential equations. Our framework, mentioned as $\textit{FastSVD-ML-ROM}$, utilizes $\textit{(i)}$ a singular value decomposition (SVD) update methodology, to compute a linear subspace of the multi-fidelity solutions during the simulation process, $\textit{(ii)}$ convolutional autoencoders for nonlinear dimensionality reduction, $\textit{(iii)}$ feed-forward neural networks to map the input parameters to the latent spaces, and $\textit{(iv)}$ long short-term memory networks to predict and forecast the dynamics of parametric solutions. The efficiency of the $\textit{FastSVD-ML-ROM}$ framework is demonstrated for a 2D linear convection-diffusion equation, the problem of fluid around a cylinder, and the 3D blood flow inside an arterial segment. The accuracy of the reconstructed results demonstrates the robustness and assesses the efficiency of the proposed approach., Comment: 35 pages, 22 figures