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Paraoxonase 1 (PON1) is one of the most significant antioxidative enzymes associated with high-density lipoprotein (HDL). It has been proved that is involved in the pathogenesis of many diseases including chronic kidney disease (CKD). The association between PON1 and CKD seems to be mutual, such that the disease produces a significant decrease in PON1 activity levels, while the genetics of PON1 may affect the risk of susceptibility to CKD. Recent studies reveal that the decrease in serum PON1 activity observed in non-dialyzed and dialyzed CKD patients as well as in renal transplant (RT) patients is linked to an increased vulnerability to atherosclerosis. We intend to summarize current literature concerning PON1 activity in CKD, highlighting on the main determinants of PON1 activity, its association with oxidative stress, the impact of its genetic polymorphism on the disease development, the effect of drugs and nutritional state. Furthermore, evidence supporting the implication of reduced PON1 activity in the incident of cardiovascular disease in CKD patients, is also examined. It appears that despite the lack of standardization of PON1 activity measurement, PON1 remains a valuable biomarker for the researchers through the last decades, which contributes to the assessment of the antioxidant status having prognostic benefit on adverse clinical outcomes at various stages and etiologies of kidney disease.

Composite curing through infrared radiation (IR) has become a popular autoclave alternative due to lower energy costs and short curing cycles. As such, understanding and measuring the effect of all parameters involved in the process can aid in selecting the proper constituents as well as curing cycles to produce parts with a high degree of cure and low curing time. In this work, a numerical model that takes inputs such as part geometry, material properties, curing-related properties and applied curing cycle is created. Its outputs include the degree of cure, maximum curing temperature and total curing time. A genetic algorithm and a design of experiments (DOE) sequence cover the range of each input variable and multiple designs are evaluated. Correlations are examined and factor analysis on each output is performed, indicating that the most important inputs are activation energy, specimen precuring, applied curing temperature and curing duration, while all the others can be considered constant. Finally, response surfaces are created in order to effectively map and provide estimations of the design space, resulting in a curing cycle optimizer given certain restrictions over the input parameters.

As the environmental crisis intensifies, the demand for energy-efficient systems has never been greater. Vortex generators have emerged as an effective method for enhancing heat transfer within tubes. While extensive research has been conducted on their application in straight tubes, studies focusing on their performance in curved tubes remain limited. This simulation study examined three different arrangements of triangular vortex generators in a common flow-down configuration within a U-turn tube to optimize heat transfer. The analysis conducted under constant wall temperature conditions across a range of Reynolds numbers spans both laminar and turbulent flow regimes to evaluate the broader impacts of vortex generators on flow and thermal fields. The efficiency of each arrangement was evaluated based on the Nusselt number and friction factor. Results show a remarkable increase in the Nusselt number, reaching up to 115% for the configuration with the highest number of vortex generators. However, this enhancement was accompanied by a significant increase in the friction factor, rising by up to 383% at higher Reynolds numbers. Overall, vortex generators demonstrated their highest effectiveness in curved tubes during the laminar-to-turbulent flow transition. In fully turbulent flow, the friction factor increased disproportionately to the modest gains in heat transfer. Despite these limitations, the use of vortex generators in curved tubes offers promising efficiency improvements and merits further exploration.

The aerodynamic performance of an aircraft can be significantly enhanced by incorporating wingtip devices, such as winglets, which primarily reduce lift-induced drag caused by wingtip vortices. This study introduces a comprehensive optimization framework for designing winglets on a Class I fixed-wing mini-UAV, aiming to maximize aerodynamic efficiency and operational performance. Using surrogate-based optimization (SBO) techniques, this research developed winglet geometries with varying geometric parameters such as length, cant angle, and sweep angle with their performance being evaluated through high-fidelity Computational Fluid Dynamics (CFD) simulations. These simulations utilized Reynolds-Averaged Navier–Stokes (RANS) equations coupled with the Spalart–Allmaras turbulence model to capture the intricate flow dynamics around the UAV in different flight phases. The integration of SBO techniques allowed for an efficient exploration of the design space while reducing computational costs associated with iterative high-fidelity simulations. In particular, the proposed SBO framework optimized the UAV’s aerodynamic characteristics, including lift-to-drag ratio and drag reduction, followed by a stability and control analyses to ensure balanced performance for the optimal configurations. Dynamic stability evaluations revealed improved flight characteristics, maintaining control across operational envelopes. The results demonstrated a significant improvement in aerodynamic coefficients, range, endurance, and reduction in battery consumption throughout the entire UAV operational envelope, underscoring the potential of innovative winglet designs to enhance UAV performance across diverse mission profiles.

The reduction in the inner diameter of the artery due to the creation of atheromatic plaque on the artery lumen, known as artery stenosis, disrupts the blood flow, leading to medical complications, which can be fatal. The angioplasty procedure aims to reopen the artery and uses a stent to keep it open. In this study, an effort is made to determine the point of the stent, the plaque and the artery during the expansion phase of the angioplasty using the in silico Finite Element Analysis method. A literature-based design was chosen for the stent geometry, whereas simplified shapes of the balloon and the two artery layers were used. Additionally, two plaque designs were the benchmark for the eight distinct artery stenosis models within the Abaqus environment. In the context of stent angioplasty simulations, failure patterns were investigated. An inverse relationship was observed between artery stenosis and pressure at the artery failure point, while an increased danger of interlaminar failure was detected in models with larger artery stenosis. This study verifies the necessity for the inclusion of interlaminar failure in future angioplasty research.

Computational methods were combined with an experimental setup in order to investigate the response of human umbilical cord stem cells to 3D electrospun and printed scaffolds, when dynamically stimulated in a bioreactor. Key parameters associated to bioreactor working conditions were computationally investigated using Comsol software to use the output for the planned experimental setup. Based on the theoretical observations, the influence of the inlet velocity, cell number, and exposure time in the bioreactor were analyzed and the in vitro parameters were adjusted accordingly. MSCs were seeded in different numbers in the 3D porous scaffolds and stimulated in the bioreactor (0.5 and 2 h duration, 3 and 6 mm/s inlet velocity). Polycaprolactone 3D electrospun, and polyurethane and polylactic acid 3D-printed scaffolds were fabricated and fibronectin-coated. The computational study predicted initial events in the process of cells deposition and attachment. Total protein, osteopontin, and osteocalcin levels in cells deposited in scaffolds were investigated; SEM and confocal imaging confirmed the biomarker analysis. MSCs proliferated well in PCL. Polyurethane enabled extremely rapid proliferation followed by differentiation, while PLA induced a moderate proliferation and parallel mineralization. The scaffolds stiffness has been found as the key enabling parameter decisive for cells feedback.

Runway Safety Assistant Foreseeing Excursions (RUNSAFE) is a complete embedded system solution, that predicts a potential runway overrun of a civil aviation aircraft during takeoff and landing. This work examines the feasibility of such a system, through the algorithms and computations that predict the overruns. The system executes both static and dynamic calculations, with the former being dependent on and the latter independent to the user’s inputs. Their outcomes and the runway’s length are compared in real time to assess if the process will end up in an overrun. All inputs are specifically selected to either be available to the pilots or be retrieved from the existing avionics systems of the cockpit. A performance evaluation is conducted on both static and dynamic calculations, and metrics unveil the accuracy of the predictions and the time needed to converge to a reliable result. The solution is adapted for a Boeing 737-800 aircraft with CFM56-7B engines, but the calculations also apply for similar aircraft equipped with tricycle landing gear and turbofan engines, namely the whole Boeing 737 family, the Airbus A320 family, etc. The system is aligned with current standards and certification specifications, where applicable.

Aviation authorities require, from aircraft seat manufacturers, specific performance metrics that maximize the occupants’ chances of survival in the case of an emergency landing and allow for the safe evacuation of the aircraft cabin. Therefore, aircraft seats must comply with specific requirements with respect to their structural integrity and potential occupant injuries, which are certified through the conduction of costly, full-scale tests. To reduce certification costs, computer-aided methods such as finite element analysis can simulate and predict the responses of different seat configuration concepts and potentially save time and development costs. This work presents one of the major steps of an aircraft seat development, which is the design and study of preliminary design concepts, whose structural and biomechanical response will determine whether the concept seat model is approved for the next steps of development. More specifically, a three-occupant aircraft seat configuration is studied for crash landing load cases and is subjected to modification iterations from a baseline design to a composite one for its structural performance, its weight reduction and the reduction of forces transmitted to the passengers.

The pursuit of more efficient transport has led engineers to develop a wide variety of aircraft configurations with the aim of reducing fuel consumption and emissions. However, these innovative designs introduce significant aeroelastic couplings that can potentially lead to structural failure. Consequently, aeroelastic analysis and optimization have become an integral part of modern aircraft design. In addition, aeroelastic testing of scaled models is a critical phase in aircraft development, requiring the accurate prediction of aeroelastic behavior during scaled model construction to reduce costs and mitigate the risks associated with full-scale flight testing. Achieving a high degree of similarity between the stiffness, mass distribution and flow field characteristics of scaled models and their full-scale counterparts is of paramount importance. However, achieving similarity is not always straightforward due to the variety of configurations of modern lightweight aircraft, as identical geometry cannot always be directly scaled down. This configuration diversity has a direct impact on the aeroelastic response, necessitating the use of computational aeroelasticity tools and optimization algorithms. This paper presents the development of an aeroelastic scaling framework using multidisciplinary optimization. Specifically, a parametric Finite Element Model (FEM) of the wing is created, incorporating the parameterization of both thickness and geometry, primarily using shell elements. Aerodynamic loads are calculated using the Doublet Lattice Method (DLM) employing twist and camber correction factors, and aeroelastic coupling is established using infinite plate splines. The aeroelastic model is then integrated within an Ant Colony Optimization (ACO) algorithm to achieve static and dynamic similarity between the scaled model and the reference wing. A notable contribution of this work is the incorporation of internal geometry parameterization into the framework, increasing its versatility and effectiveness.

A combined computational and experimental study of 3D-printed scaffolds made from hybrid nanocomposite materials for potential applications in bone tissue engineering is presented. Polycaprolactone (PCL) and polylactic acid (PLA), enhanced with chitosan (CS) and multiwalled carbon nanotubes (MWCNTs), were investigated in respect of their mechanical characteristics and responses in fluidic environments. A novel scaffold geometry was designed, considering the requirements of cellular proliferation and mechanical properties. Specimens with the same dimensions and porosity of 45% were studied to fully describe and understand the yielding behavior. Mechanical testing indicated higher apparent moduli in the PLA-based scaffolds, while compressive strength decreased with CS/MWCNTs reinforcement due to nanoscale challenges in 3D printing. Mechanical modeling revealed lower stresses in the PLA scaffolds, attributed to the molecular mass of the filler. Despite modeling challenges, adjustments improved simulation accuracy, aligning well with experimental values. Material and reinforcement choices significantly influenced responses to mechanical loads, emphasizing optimal structural robustness. Computational fluid dynamics emphasized the significance of scaffold permeability and wall shear stress in influencing bone tissue growth. For an inlet velocity of 0.1 mm/s, the permeability value was estimated at 4.41 × 10−9 m2, which is in the acceptable range close to human natural bone permeability. The average wall shear stress (WSS) value that indicates the mechanical stimuli produced by cells was calculated to be 2.48 mPa, which is within the range of the reported literature values for promoting a higher proliferation rate and improving osteogenic differentiation. Overall, a holistic approach was utilized to achieve a delicate balance between structural robustness and optimal fluidic conditions, in order to enhance the overall performance of scaffolds in tissue engineering applications.

Remote sensing of particulate matter (PM) absolute concentration levels can address the need for continuous wide-area monitoring in urban environments, which arises from the adverse effects of air pollution on human health. Raymetrics PMeye is a unique aerosol monitoring system designed around a state-of-the-art polarization scanning UV lidar that offers large-area PM concentration monitoring and high spatial resolution source localization. The PMeye lidar employs a novel inversion scheme for converting raw lidar signals to PM concentrations. This study demonstrates the effectiveness and accuracy of remote monitoring PM concentration measurement results in the region of Attica, Greece. Potential synergistic use with inversion modeling techniques and dispersion models to support an advanced warning system for the population and local authorities of the Athens metropolitan area is also discussed.

The race towards cleaner and more efficient commercial aviation demands novel designs featuring improved aerodynamic and structural characteristics, the main pillars that drive aircraft efficiency. Among the many proposed and introduced, the increase in the aspect ratio of the wings enables greater fuel efficiency by reducing induced drag. Nevertheless, such structures are characterised by elevated flexibility, aggravating static and dynamic aeroelastic phenomena. Consequently, the preliminary and conceptual design and optimization stages using high-fidelity numerical tools is rendered extremely intricate and prohibitive in terms of computational cost. Low-fidelity tools, contrastingly, enable computational-burden alleviation. In our approach, a computational framework for the low-fidelity steady-state static aeroelastic optimization of a composite high-aspect-ratio commercial aircraft wing via surrogate modelling is proposed. The methodology starts with the development of the 3D panel method as well of the elements of the surrogate model. The design variables, objective function and constraints which formulate the optimization problem are then provided. Moreover, comparison against rigid aerodynamics indicate the significant load-alleviation capabilities of the present case study. The effect of structural nonlinearities is also explored. The optimization framework is executed and optimal laminates for the structural members are obtained. The optimal structure was deemed critical in panel buckling.

Recent advances both in hardware and software have facilitated the embedded intelligence (EI) research field, and enabled machine learning and decision-making integration in resource-scarce IoT devices and systems, realizing “conscious” and self-explanatory objects (smart objects). In the context of the broad use of WSNs in advanced IoT applications, this is the first work to provide an extreme-edge system, to address structural health monitoring (SHM) on polymethyl methacrylate (PPMA) thin-plate. To the best of our knowledge, state-of-the-art solutions primarily utilize impact positioning methods based on the time of arrival of the stress wave, while in the last decade machine learning data analysis has been performed, by more expensive and resource-abundant equipment than general/development purpose IoT devices, both for the collection and the inference stages of the monitoring system. In contrast to the existing systems, we propose a methodology and a system, implemented by a low-cost device, with the benefit of performing an online and on-device impact localization service from an agnostic perspective, regarding the material and the sensors’ location (as none of those attributes are used). Thus, a design of experiments and the corresponding methodology to build an experimental time-series dataset for impact detection and localization is proposed, using ceramic piezoelectric transducers (PZTs). The system is excited with a steel ball, varying the height from which it is released. Based on TinyML technology for embedding intelligence in low-power devices, we implement and validate random forest and shallow neural network models to localize in real-time (less than 400 ms latency) any occurring impacts on the structure, achieving higher than 90% accuracy.

In this work, UV-curable resin poly (ethylene glycol) diacrylate (PEGDA) was reinforced with three different types of nanofillers: pristine graphene (G), multiwalled carbon nanotubes (MWNTs), and a hybrid of MWNTs and graphene 70/30 in mass ratio (Hyb). PEGDA was mixed homogenously with the nanofiller oligomer by shear mixing and then photopolymerized, affording thin, stable films. The thermomechanical properties of the afforded nanocomposites indicated the superior reinforcing ability of pristine graphene compared with MWNTs and an intermediate behavior of the hybrid.

Autologous cell therapy uses patients’ own cells to deliver precise and ideal treatment through a personalized medicine approach. Isolation of patients’ cells from residual tissue extracted during surgery involves specific planning and lab steps. In the present manuscript, a path from isolation to in vitro research with human mesenchymal stem cells (MSCs) obtained from residual bone tissues is described as performed by a medical unit in collaboration with a research center. Ethical issues have been addressed by formulating appropriate harvesting protocols according to European regulations. Samples were collected from 19 patients; 10 of them were viable and after processing resulted in MSCs. MSCs were further differentiated in osteoblasts to investigate the biocompatibility of several 3D scaffolds produced by electrospinning and 3D printing technologies; traditional orthopedic titanium and nanostructured titanium substrates were also tested. 3D printed scaffolds proved superior compared to other substrates, enabling significantly improved response in osteoblast cells, indicating that their biomimetic structure and properties make them suitable for synthetic tissue engineering. The present research is a proof of concept that describes the process of primary stem cells isolation for in vitro research and opens avenues for the development of personalized cell platforms in the case of patients with orthopedic trauma. The demonstration model has promising perspectives in personalized medicine practices.

This paper addresses the problem of singularity avoidance for a 4-Control Moment Gyroscope (CMG) pyramid cluster, as used for the attitude control of a satellite using machine learning (ML) techniques. A data-set, generated using a heuristic algorithm, relates the initial gimbal configuration and the desired maneuver—inputs—to a number of null space motions the gimbals have to execute—output. Two ML techniques—Deep Neural Network (DNN) and Random Forest Classifier (RFC)—are utilized to predict the required null motion for trajectories that are not included in the training set. The principal advantage of this approach is the exploitation of global information gathered from the whole maneuver compared to conventional steering laws that consider only some local information, near the current gimbal configuration for optimization and are prone to local extrema. The data-set generation and the predictions of the ML systems can be made offline, so no further calculations are needed on board, providing the possibility to inspect the way the system responds to any commanded maneuver before its execution. The RFC technique demonstrates enhanced accuracy for the test data compared to the DNN, validating that it is possible to correctly predict the null motion even for maneuvers that are not included in the training data.

In this paper, a low-cost, miniature spacecraft attitude control simulator is presented for testing miniature actuators such as Nano Control Moment Gyroscopes (CMGs) for simple maneuvers. The experimental setup is composed by an attitude control system (ACS) that mainly consists of a four-CMG cluster in a pyramid configuration and a custom-made air bearing. The one-degree-of-freedom (DoF) air bearing is fabricated to reproduce the frictionless conditions of a nano-satellite in orbit. The ACS is made exclusively using low-cost commercial-off-the-shelf (COTS) components, whilst the air bearing is made using 3D-printed parts. Both hardware and software implementations are described in detail and the performance of the developed simulator is evaluated by two maneuver experiments. Despite the manufacturing imperfections, the ACS is capable of providing higher angular velocities than previously presented in the literature while following the theoretical or simulation data. The results indicate that it is possible to manufacture a low-cost, miniature actuator such as a CMG, using COTS components to demonstrate the operation of an agile nano-satellite. Any deviations from the theoretical values are addressed and several improvements are discussed to further enhance the performance of the air bearing testing platform.

EuroDRONE is an Unmanned Traffic Management (UTM) demonstration project, funded by the EU’s SESAR organization, and its aim is to test and validate key UTM technologies for Europe’s ‘U-Space’ UTM program. The EuroDRONE UTM architecture comprises cloud software (DroNav) and hardware (transponder) to be installed on drones. The proposed EuroDRONE system is a Highly Automated Air Traffic Management System for small UAVs operating at low altitudes. It is a sophisticated, self-learning system based on software and hardware elements, operating in a distributed computing environment, offering multiple levels of redundancy, fail-safe algorithms for conflict prevention/resolution and assets management. EuroDRONE focuses its work on functionalities which involve the use of new communication links, the use of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) technology to communicate information between drones and operators for safe and effective UTM functionality. Practical demonstrations that took place in Patras/Messolonghi in 2019 are presented and show the benefits and shortcomings of near-term UTM implementation in Europe.

Efficient optimization is a prerequisite to realize the full potential of an aeronautical structure. The success of an optimization framework is predominately influenced by the ability to capture all relevant physics. Furthermore, high computational efficiency allows a greater number of runs during the design optimization process to support decision-making. The efficiency can be improved by the selection of highly optimized algorithms and by reducing the dimensionality of the optimization problem by formulating it using a finite number of significant parameters. A plethora of variable-fidelity tools, dictated by each design stage, are commonly used, ranging from costly high-fidelity to low-cost, low-fidelity methods. Unfortunately, despite rapid solution times, an optimization framework utilizing low-fidelity tools does not necessarily capture the physical problem accurately. At the same time, high-fidelity solution methods incur a very high computational cost. Aiming to bridge the gap and combine the best of both worlds, a multi-fidelity optimization framework was constructed in this research paper. In our approach, the low-fidelity modules and especially the equivalent-plate methodology structural representation, capable of drastically reducing the associated computational time, form the backbone of the optimization framework and a MIDACO optimizer is tasked with providing an initial optimized design. The higher fidelity modules are then employed to explore possible further gains in performance. The developed framework was applied to a benchmark airliner wing. As demonstrated, reasonable mass reduction was obtained for a current state of the art configuration.

This paper addresses the problem of singularity avoidance in a cluster of four Single-Gimbal Control Moment Gyroscopes (SGCMGs) in a pyramid configuration when used for the attitude control of a satellite by introducing a new gimballed control moment gyroscope (GCMG) cluster scheme. Four SGCMGs were used in a pyramid configuration, along with an additional small and simple stepper motor that was used to gimbal the full cluster around its vertical (z) axis. Contrary to the use of four variable-speed control moment gyroscopes (VSCMGs), where eight degrees of freedom are available for singularity avoidance, the proposed GCMG design uses only five degrees of freedom (DoFs), and a modified steering law was designed for the new setup. The proposed design offers the advantages of SGCMGs, such as a low weight, size, and reduced complexity, with the additional benefit of overcoming the internal elliptic singularities, which create a minor attitude error. A comparison with the four-VSCMG cluster was conducted through numerical simulations, and the results indicated that the GCMG design was considerably more efficient in terms of power while achieving a better gimbal configuration at the end of the simulation, which is essential when it is desired for different manoeuvres to be consecutively executed. Additionally, for a nano-satellite of a few kilograms, the results prove that it is feasible to manufacture the GCMG concept by using affordable and lightweight commercial off-the-shelf (COTS) stepper motors.

The solution electrospinning process (SEP) is a cost-effective technique in which a wide range of polymeric materials can be electrospun. Electrospun materials can also be easily modified during the solution preparation process (prior SEP). Based on this, the aim of the current work is the fabrication and nanomodification of scaffolds using SEP, and the investigation of their porosity and physical and mechanical properties. In this study, polylactic acid (PLA) was selected for scaffold fabrication, and further modified with multi-walled carbon nanotubes (MWCNTs) and hydroxyapatite (HAP) nanoparticles. After fabrication, porosity calculation and physical and mechanical characterization for all scaffold types were conducted. More precisely, the morphology of the fibers (in terms of fiber diameter), the surface properties (in terms of contact angle) and the mechanical properties under the tensile mode of the fabricated scaffolds have been investigated and further compared against pristine PLA scaffolds (without nanofillers). Finally, the scaffold with the optimal properties was proposed as the candidate material for potential future cell culturing.

Microcapsule-based carbon fiber reinforced composites were manufactured by wet layup, in order to assess their mechanical properties and determine their healing efficiency. Microcapsules at 10%wt. containing bisphenol-A epoxy, encapsulated in a urea formaldehyde (UF) shell, were employed with Scandium (III) Triflate (Sc (OTf)3) as the catalyst. The investigation was deployed with two main directions. The first monitored changes to the mechanical performance due to the presence of the healing agent within the composite. More precisely, a minor decrease in interlaminar fracture toughness (GIIC) (−14%), flexural strength (−12%) and modulus (−4%) compared to the reference material was reported. The second direction evaluated the healing efficiency. The experimental results showed significant recovery in fracture toughness up to 84% after the healing process, while flexural strength and modulus healing rates reached up to 14% and 23%, respectively. The Acoustic Emission technique was used to support the experimental results by the onsite monitoring.

The design optimization, development, and verification by analysis and testing of the 1st Greek cubesat, developed by the University of Patras and Libre Space Foundation (UPSat (University of Patras Satellite)), is presented. The key innovative approach includes the replacement of the aluminum side faces with structural composite components, keeping the commonly used aluminum frame. A “hybrid” double-unit (2U) cubesat structure was optimized, built, and tested for all launch and thermal loads/specifications required for launch and mission operations as imposed from the EU-funded FP7-QB50 project. Results show that the new design of the structure using CFRP can offer similar levels of performance in terms of stiffness, while saving 30% of the mass, for the entire cubesat platform.

Ceramic-coated materials used in different engineering sectors are the focus of world-wide interest and have generated a need for inspection techniques that detect very small structural anomalies. Non-destructive testing is increasingly being used to evaluate coating thickness and to test for coating flaws. The main pros of non-destructive testing is that the tested object remains intact and available for continued use afterward. This paper reports on an integrated, non-destructive testing approach that combines infrared thermography and acousto-ultrasonics to evaluate advanced aerospace sandwich structure materials with the aim of exploring any potential for detecting defects of more than one type. Combined, these two techniques successfully detected fabrication defects, including inclusions and material loss.