National Technical University of Athens

During maneuvers of a space manipulator system (SMS), thruster fuel in its spacecraft (S/C) results in sloshing effects which impact its dynamics and performance. In this work, sloshing effects are modeled by a 3D mass-spring-damper. A novel system identification scheme is developed, which identifies all parameters required for the reconstruction of system dynamics despite the unmeasurable sloshing states. This is achieved by two identification experiments. In Exp. 1, the SMS translates with locked joints allowing for the elimination of the unmeasurable sloshing states. As a result, all sloshing model parameters and the mass of the SMS base are identified. In Exp. 2, the SMS operates in free-floating mode, with the reaction wheels (RWs) and the manipulator(s) active. Based on kinematics and conservation of momentum, a method is developed which eliminates the unmeasurable sloshing states and identifies all the required SMS inertial parameters. The estimated parameters render the system dynamics fully identified and available to advanced model-based control algorithms. The effect of sloshing parameter errors is also discussed. The developed scheme is validated by simulation and experiments.

We consider double phase problems with unbalanced and balanced growth and logistic reaction of the superdiffusive type. We prove existence and multiplicity results and discuss how the two cases differ.

Acetyl substitutions are common on the hemicellulosic structures of lignocellulose, which up until recently were known to inhibit xylanase activity. Emerging data, however, suggest that xylanases are able to accommodate acetyl side-groups within their catalytic site. In the present work, a fungal GH30 xylanase from Thermothelomyces thermophila, namely TtXyn30A, was shown to release acetylated xylobiose when acting on pretreated lignocellulosic substrate. The released disaccharides could be acetylated at the 2-OH, 3-OH or both positions of the non-reducing end xylose, but the existence of the acetylation on the reducing end cannot be excluded. The synergy of TtXyn30A with acetyl esterases indicates that particular subsites within its active site cannot tolerate acetylated xylopyranose residues. Molecular docking showed that acetyl group can be accommodated on the 2- or 3-OH position of the non-reducing end xylose, unlike the reducing-end xylose (subsite -1), where only 3-OH decoration can be accommodated. Such insight into the catalytic activity of TtXyn30A could contribute to a better understanding of its biological role and thus lead to a more sufficient biotechnological utilization.

The implementation of scarcity pricing is underway in the Belgian balancing market. The market design proposed in this paper aims at transposing the first principles of scarcity pricing theory to the boundary conditions of European balancing markets. One relevant boundary condition is the absence of real-time co-optimization of energy and reserves in Europe. As international experience demonstrates, the fact that energy and reserves are not co-optimized in balancing markets does not preclude the implementation of scarcity pricing. The mechanism can be implemented implicitly, and a concrete path has been proposed in the context of the Belgian balancing market. The Belgian design proposal, which is based on the implicit trading of reserve in real time, has raised questions related to financial implications for market stakeholders, the pricing of multiple reserve products, cross-border interactions, the financing of the mechanism, compatibility with EU law, and the coexistence of the proposed mechanism with capacity markets. We attempt to address these questions in the present work by drawing comparisons between the explicit co-optimization of energy and reserves and the implicit trading of reserve capacity.

In the present work, a thorough description of the creep response of polymers in both linear and nonlinear viscoelastic domains is presented. According to the proposed model, the polymeric structure is considered as an ensemble of meso-regions linked with each other while they can cooperatively relax and change their positions. Each meso-region has its own energy barrier that needs to be overcome for a transition to occur. It was found that the distribution function, followed by the energy barriers, attains a decisive role, given that it is associated with the distribution of retardation times and with their particular effect on the materials’ time evolution. The crucial role of the imposed stress in a creep experiment by its influence on the retardation time spectrum of the polymeric structure was extensively analyzed. The proposed model has been successfully validated by a series of creep data in a variety of temperatures and stress levels for polymeric materials, studied experimentally elsewhere. Furthermore, the model’s capability to predict the long-term creep response was analytically shown.

Political polarization has become an alarming trend observed in various countries. In the effort to produce more consistent simulations of the process, insights from the foundations of physics are adopted. The work presented here looks at a simple model of political polarization amongst agents which influence their immediate locality and how a entropy trace of the political discourse can be produced. From this model an isolated system representation can be formulated in respect to the changes in the entropy values across all variables of the system over simulation time. It is shown that a constant entropy value for the system can be calculated so that as the agents coalesce their opinions, the entropy trace in regards to political engagements decreases as the entropy value across non-political engagements increase. This relies upon an intrinsic constraint upon agents imposing a fixed number of activities per time point. As a result the simulation respects the second law of thermodynamics and provides insight into political polarization as a basin of entropy within an isolated system without making assumptions about external activities.

Keratins (KRTs) are the intermediate filament-forming proteins of epithelial cells, classified, according to their physicochemical properties, into “soft” and “hard” keratins. They have a key role in several aspects of cancer pathophysiology, including cancer cell invasion and metastasis, and several members of the KRT family serve as diagnostic or prognostic markers. The human genome contains both, functional KRT genes and non-functional KRT pseudogenes, arranged in two uninterrupted clusters on chromosomes 12 and 17. This characteristic renders KRTs ideal for evolutionary studies. Herein, comprehensive phylogenetic analyses of KRT homologous proteins in the genomes of major taxonomic divisions were performed, so as to fill a gap in knowledge regarding the functional implications of keratins in cancer biology among tumor-bearing species. The differential expression profiles of KRTs in diverse types of cancers were investigated by analyzing high-throughput data, as well. Several KRT genes, including the phylogenetically conserved ones, were found to be deregulated across several cancer types and to participate in a common protein-protein interaction network. This indicates that, at least in cancer-bearing species, these genes might have been under similar evolutionary pressure, perhaps to support the same important function(s). In addition, semantic relations between KRTs and cancer were detected through extensive text mining. Therefore, by applying an integrative in silico pipeline, the evolutionary history of KRTs was reconstructed in the context of cancer, and the potential of using non-mammalian species as model organisms in functional studies on human cancer-associated KRT genes was uncovered.

Distribution-free or nonparametric control charts are used for monitoring the process parameters when there is a lack of knowledge about the underlying distribution. In this paper, we investigate a single distribution-free triple exponentially weighted moving average control chart based on the Lepage statistic (referred as TL chart) for simultaneously monitoring shifts in the unknown location and scale parameters of a univariate continuous distribution. The design and implementation of the proposed chart are discussed using time-varying and steady-state control limits for the zero-state case. The run-length distribution of the TL chart is evaluated by performing Monte Carlo simulations. The performance of the proposed chart is compared to those of the existing EWMA-Lepage (EL) and DEWMA-Lepage (DL) charts. It is observed that the TL chart with a time-varying control limit is superior to its competitors, especially for small to moderate shifts in the process parameters. We also provide a real example from a manufacturing process to illustrate the application of the proposed chart.

In this paper, the dynamic properties of three types of Persian brick masonry arches, semi-circular, ordinary pointed and ordinary four-centred have been studied. These arches were constructed with clay brick, and gypsum mortar in the laboratory and experimental tests were conducted. First, the mechanical properties of the used materials were determined. Then, operational modal analysis was used to measure the dynamic properties of the constructed arches. Afterwards, a horizontal displacement was applied to the arch support to create a crack. The arches were repaired then, and dynamic identification was performed for each case. Several damage detection methods were used to evaluate their ability to detect damage in brick masonry arches. Finite element model updating was used to track changes in the material properties of arches and to match numerical results with dynamic laboratory results. The study showed that damage has a significant effect on the dynamic properties of arches. Repairing the damage partially restores the dynamic parameters to the undamaged condition, but it cannot completely transform the arch into an arch without damage. Damage detection methods were able to identify the occurrence of damage to the arches, but there are limitations in the use of these methods. Diagrams were generated to estimate the static moduli of brick and of gypsum mortar from the dynamic modulus of elasticity of the assemblage of brick and gypsum mortar.

Crop phenology is crucial information for crop yield estimation and agricultural management. Traditionally, phenology has been observed from the ground; however Earth observation, weather and soil data have been used to capture the physiological growth of crops. In this work, we propose a new approach for the within-season phenology estimation for cotton at the field level. For this, we exploit a variety of Earth observation vegetation indices (derived from Sentinel-2) and numerical simulations of atmospheric and soil parameters. Our method is unsupervised to address the ever-present problem of sparse and scarce ground truth data that makes most supervised alternatives impractical in real-world scenarios. We applied fuzzy c-means clustering to identify the principal phenological stages of cotton and then used the cluster membership weights to further predict the transitional phases between adjacent stages. In order to evaluate our models, we collected 1,285 crop growth ground observations in Orchomenos, Greece. We introduced a new collection protocol, assigning up to two phenology labels that represent the primary and secondary growth stage in the field and thus indicate when stages are transitioning. Our model was tested against a baseline model that allowed to isolate the random agreement and evaluate its true competence. The results showed that our model considerably outperforms the baseline one, which is promising considering the unsupervised nature of the approach. The limitations and the relevant future work are thoroughly discussed. The ground observations are formatted in an ready-to-use dataset and will be available at https://github.com/Agri-Hub/cotton-phenology-dataset upon publication.

In this paper we first introduce a new concept of a functional equation called multi-Drygas equation. We deal with the generalized hyperstability results of the multi-Drygas functional equation on a restricted domain by applying the Brzdȩk’s fixed point theorem (Brzdȩk et al. in Nonlinear Anal. 74: 6728–6732, 2011, Theorem 1). Our main results improve and generalize results obtained in Aiemsombonn and Sintunavarat (Bull Aust Math Soc 92: 269–280, 2016), El-Fassi(J Fixed Point Theory Appl 9: 2529–2540, 2017), Piszczek, Szczawińska(J Funct Spaces Appl 2013: 912718, 2013) . Some applications of our results are also provided.

We consider a double phase (unbalanced growth) Dirichlet problem with a Carathéodory reaction f ( z , x ) which is superlinear in x but without satisfying the AR-condition. Using the symmetric mountain pass theorem, we produce a whole sequence of distinct bounded solutions which diverge to infinity.

The rapid growth in the technological advancements of the smartphone industry has classified contemporary smartphones as a low-cost and high quality indoor positioning tools requiring no additional infrastructure or equipment. In recent years, the fine time measurement (FTM) protocol, achieved through the Wi-Fi round trip time (RTT) observable, available in the most recent models, has gained the interest of many research teams worldwide, especially those concerned with indoor localization problems. However, as the Wi-Fi RTT technology is still new, there is a limited number of studies addressing its potential and limitations relative to the positioning problem. This paper presents an investigation and performance evaluation of Wi-Fi RTT capability with a focus on range quality assessment. A set of experimental tests was carried out, considering 1D and 2D space, operating different smartphone devices at various operational settings and observation conditions. Furthermore, in order to address device-dependent and other type of biases in the raw ranges, alternative correction models were developed and tested. The obtained results indicate that Wi-Fi RTT is a promising technology capable of achieving a meter-level accuracy for ranges both in line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, subject to suitable corrections identification and adaptation. From 1D ranging tests, an average mean absolute error (MAE) of 0.85 m and 1.24 m is achieved, for LOS and NLOS conditions, respectively, for 80% of the validation sample data. In 2D-space ranging tests, an average root mean square error (RMSE) of 1.1m is accomplished across the different devices. Furthermore, the analysis has shown that the selection of the bandwidth and the initiator–responder pair are crucial for the correction model selection, whilst knowledge of the type of operating environment (LOS and/or NLOS) can further contribute to Wi-Fi RTT range performance enhancement.

Citation: Mitro, N.; Argyri, K.; Pavlopoulos, L.; Kosyvas, D.; Karagiannidis, L.; Kostovasili, M.; Misichroni, F.; Ouzounoglou, E.; Amditis, A. AI-Enabled Smart Wristband Providing Real-Time Vital Signs and Stress Monitoring. Sensors 2023, 23, 2821. https://doi. Abstract: This work introduces the design, architecture, implementation, and testing of a low-cost and machine-learning-enabled device to be worn on the wrist. The suggested wearable device has been developed for use during emergency incidents of large passenger ship evacuations, and enables the real-time monitoring of the passengers' physiological state, and stress detection. Based on a properly preprocessed PPG signal, the device provides essential biometric data (pulse rate and oxygen saturation level) and an efficient unimodal machine learning pipeline. The stress detecting machine learning pipeline is based on ultra-short-term pulse rate variability, and has been successfully integrated into the microcontroller of the developed embedded device. As a result, the presented smart wristband is able to provide real-time stress detection. The stress detection system has been trained with the use of the publicly available WESAD dataset, and its performance has been tested through a two-stage process. Initially, evaluation of the lightweight machine learning pipeline on a previously unseen subset of the WESAD dataset was performed, reaching an accuracy score equal to 91%. Subsequently, external validation was conducted, through a dedicated laboratory study of 15 volunteers subjected to well-acknowledged cognitive stressors while wearing the smart wristband, which yielded an accuracy score equal to 76%.

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak.

Resource disaggregation offers a cost effective solution to resource scaling, utilization, and failure-handling in data centers by physically separating hardware devices in a server. Servers are architected as pools of processor, memory, and storage devices, organized as independent failure-isolated components interconnected by a high-bandwidth network. A critical challenge, however, is the high performance penalty of accessing data from a remote memory module over the network. Addressing this challenge is difficult as disaggregated systems have high runtime variability in network latencies/bandwidth, and page migration can significantly delay critical path cache line accesses in other pages. This paper conducts a characterization analysis on different data movement strategies in fully disaggregated systems, evaluates their performance overheads in a variety of workloads, and introduces DaeMon, the first software-transparent mechanism to significantly alleviate data movement overheads in fully disaggregated systems. First, to enable scalability to multiple hardware components in the system, we enhance each compute and memory unit with specialized engines that transparently handle data migrations. Second, to achieve high performance and provide robustness across various network, architecture and application characteristics, we implement a synergistic approach of bandwidth partitioning, link compression, decoupled data movement of multiple granularities, and adaptive granularity selection in data movements. We evaluate DaeMon in a wide variety of workloads at different network and architecture configurations using a state-of-the-art simulator. DaeMon improves system performance and data access costs by 2.39× and 3.06×, respectively, over the widely-adopted approach of moving data at page granularity.

The aim of this study is to analyze the freight relationships between three major mainland Greek ports (Piraeus, Thessaloniki and Volos), based on containers’ transportation. The methodology used is the Global Vector Autoregressive (GVAR) model. The empirical analysis is based on time series data spanning the period January 1998–December 2012. The most important finding is that a shock in one port doesn’t have, in general, a significant impact on any other port and the corresponding effects settle down relatively quickly, usually in less than two months.

This study provides insights into the experience gained from investigating the thermodynamic behavior of well and reservoir fluids during acid gas injection (AGI) in a hydrocarbon field to enhance oil recovery (EOR) and to reduce greenhouse gas emissions. Unlike conventional water and natural gas injection, AGI involves complicated phase changes and physical property variations of the acid gas and reservoir fluids at various pressure-temperature (P-T) conditions and compositions, and both constitute crucial parts of the EOR chain. A workflow is developed to deal with the reservoir fluid and acid gas thermodynamics, which is a key requirement for a successful design and operation. The workflow focuses firstly on the development of the thermodynamic models (EoS) to simulate the behavior of the reservoir fluids and of the injected acid gas and their integration in the field and in well dynamic models. Subsequently, the workflow proposes the thermodynamic simulation of the fluids’ interaction to determine the Minimum Miscibility Pressure (MMP), yielding the dynamic evolution of the fluids’ miscibility that may appear within the reservoir. Flow assurance in the acid gas transportation lines and in the wellbore is also considered by estimating the hydrate formation conditions.

The phenomenon of resonances in large circular arrays of electrically short and thick dipoles was studied intensively in the nineties by Harvard’s Antenna Group, led by R. W. P. King and T. T. Wu. Here, we propose these arrays as a benchmark for modern 3D Computational Electromagnetics (CEM) solvers. Our proposed benchmark is challenging because it exhibits resonances which, for judicious choices of the parameters, can be extremely narrow. We present exploratory simulations of our proposed benchmark using COMSOL Multiphysics and ANSYS HFSS, and give detailed comparisons with previously published results.

Optoelectronic technology is expected to be the cornerstone of sub-THz communication systems, enabling access to and use of the vast frequency resources found in this portion of the spectrum. In this work we demonstrate a photonics-enabled sub-THz wireless link operating in real-time settings, using a PIN-PD-based THz emitter, and a THz receiver based on an ultra-fast photoconductor. The real-time generation and detection of the information signal is performed by an intermediate frequency (IF) unit based on a commercially available mmWave platform, operating at 1.6 GBaud. The evaluation of our setup takes place on two phases. Firstly, a homodyne scenario is demonstrated, where the same pair of lasers is used at the transmitter and receiver side. Secondly, we demonstrate a heterodyne scheme, employing optical phase locking techniques at the receiver. Error-free operation was achieved in both scenarios at a bit rate of 3.2 Gb/s, over 1 m of free-space with ambient air. The broadband characteristics of our setup were validated, achieving error-free transmission over a 0.22 THz range, spanning from 90 up to 310 GHz. Finally, the stability of our real-time link was successfully demonstrated, showing stable SNR performance at the receiver with adaptive capabilities, over a time period of 5 min and 22 sec.