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The change in heating and cooling needs of Greece in the near future due to the climate change is assessed in the present study. Global and regional climate models and two different representative concentration pathways (RCPs) are used to simulate the expected change in temperature. A widely used methodology of computation of heating degree days (HDDs) and cooling degree days (CDDs) is employed with a base temperature of 18 °C. In agreement with the expected temperature rise in the near future, an HDD decrease and CDD increase under both RCPs is also expected. The changes under RCP8.5 are stronger compared to those under RCP4.5. Differences related to topography are noted. The HDD decrease is stronger than CDD increase but the relative increase in CDDs is higher than the relative increase in HDDs. The highest absolute decreases in HDDs are expected for February and March while the highest absolute increases in CDDs are expected during the three summer months.

Heart failure (HF) remains a major cause of morbidity and mortality worldwide. Recently, significant advances have been made in its treatment; however, diuretics remain the cornerstone in managing congestion in HF. Although diuretic resistance poses a significant challenge in the management of HF and is associated with poor outcomes, only limited alternative pharmaceutical options are available in clinical practice. The objective of this narrative review is to provide a comprehensive analysis of the current evidence on the effects of sodium-glucose co-transporter-2 (SGLT-2) inhibitors on diuretic resistance in HF patients. The primary emphasis is placed on clinical data that assess the impact of SGLT-2 inhibitors on fluid balance, symptom improvement, and clinical outcomes and secondarily on safety profile and potential adverse effects associated with SGLT-2 inhibitor use in acute decompensated HF. The current evidence on the efficacy of SGLT-2 on diuretic resistance remains controversial. Findings from observational and randomized studies are quite heterogenous; however, they converge on the notion that although SGLT-2 inhibitors show promise for mitigating diuretic resistance in HF, their diuretic effect may not be potent enough to be widely used to relieve objective signs of congestion in patients with HF. Importantly, the introduction of SGLT-2 inhibitors in HF treatment appears to be generally well tolerated, with manageable adverse effects. Further research is needed to investigate the underlying mechanisms and the possible beneficial impact of SGLT-2 inhibitors on diuretic resistance in HF.

Background: This systematic review explores the effects of landiolol administration in individuals presenting with supraventricular tachyarrhythmia (SVT) and concurrent left ventricular dysfunction, without being septic or in a peri-operative period. Methods: We systematically searched PubMed, Cochrane, Web of Science, and Scopus databases, retrieving a total of 15 eligible studies according to prespecified eligibility criteria. Results: Patients treated with landiolol experienced a substantial reduction in heart rate (HR) (mean HR reduction: 42 bpm, 95% confidence intervals (CIs): 37–47, I2 = 82%) and were more likely to achieve the target HR compared to those receiving alternative antiarrhythmic therapy (pooled odds ratio (OR): 5.37, 95% CIs: 2.87–10.05, I2 = 0%). Adverse events, primarily hypotension, occurred in 14.7% of patients receiving landiolol, but no significant difference was observed between the landiolol and alternative antiarrhythmic receiving groups (pooled OR: 1.02, 95% CI: 0.57–1.83, I2 = 0%). No significant difference was observed between the two groups concerning sinus rhythm restoration (pooled OR: 0.97, 95% CI: 0.25–3.78, I2 = 0%) and drug discontinuation due to adverse events (pooled OR: 5.09, 95% CI: 0.6–43.38, I2 = 0%). Conclusion: While further research is warranted, this systematic review highlights the potential benefits of landiolol administration in the management of SVTs in the context of left ventricular dysfunction.

Background: Polycystic ovary syndrome (PCOS) is closely related to various adverse cardiovascular manifestations and increased cardiovascular risk. However, atrial fibrillation (AF) development and atrial conduction abnormalities have not been thoroughly studied in patients with PCOS. Methods: This meta-analysis (CRD42021261375) was conducted in accordance with the PRISMA guidelines. Our aim was to investigate associations between PCOS and disorders in atrial conduction parameters linked with an increased risk for AF occurrence. Results: Five cohort studies with aggregate data on 406 adult women (229 with PCOS and 177 age-matched without PCOS) were included in this analysis. Our results showed a significantly increased mean difference in P-wave maximum duration (+7.63 ± 7.07 msec; p < 0.01) and P-wave dispersion (+11.42 ± 5.22 msec; p = 0.03) of patients with PCOS compared to healthy women. The mean difference in P-wave minimum duration (−2.22 ± 2.68 msec; p = 0.11) did not reach the statistical threshold between the compared groups. Echocardiographic measurements of atrial electromechanical delay (AED) also indicated a statistically significant mean difference in favour of the PCOS group in all assessed parameters, except for atrial electromechanical coupling (PA) in the tricuspid annulus. Particularly, PCOS was associated with increased lateral PA, septal PA, inter- and intra-AED durations (mean difference: +17.31 ± 9.02 msec; p < 0.01, +11.63 ± 7.42 msec; p < 0.01, +15.31 ± 9.18 msec; p < 0.01, +9.31 ± 6.85 msec; p < 0.01, respectively). Conclusions: PCOS is strongly associated with alterations in several electrocardiographic and echocardiographic parameters indicating abnormal atrial conduction. Therefore, PCOS could be considered as a causal or triggering factor of AF. Larger studies are needed to confirm these results and investigate direct associations between PCOS and AF.

Background: Acute myocardial infarction (AMI) remains a major cause of death worldwide. Survivors of AMI are particularly at high risk for additional cardiovascular events. Consequently, a comprehensive approach to secondary prevention is necessary to mitigate the occurrence of downstream complications. This may be achieved through a multiparametric tailored risk stratification by incorporating clinical, laboratory and echocardiographic parameters. Methods: The ‘‘CLEAR-AMI Study’’ (ClinicalTrials.gov Identifier: NCT05791916) is a non-interventional, prospective study including consecutive patients with AMI without a known history of coronary artery disease. All patients satisfying these inclusion criteria are enrolled in the present study. The rationale of this study is to refine risk stratification by using clinical, laboratory and novel echocardiographic biomarkers. All the patients undergo a comprehensive transthoracic echocardiographic assessment, including strain and myocardial work analysis of the left and right heart chambers, within 48 h of admission after coronary angiography. Their laboratory profile focusing on systemic inflammation is captured during the first 24 h upon admission, and their demographic characteristics, past medical history, and therapeutic management are recorded. The angioplasty details are documented, the non-culprit coronary lesions are archived, and the SYNTAX score is employed to evaluate the complexity of coronary artery disease. A 24-month follow-up period will be recorded for all patients recruited. Conclusion: The ‘‘CLEAR-AMI” study is an ongoing prospective registry endeavoring to refine risk assessment in patients with AMI without a known history of coronary artery disease, by incorporating echocardiographic parameters, biochemical indices, and clinical and coronary characteristics in the acute phase of AMI.

Next generation in-to-out-of body biomedical applications have adopted optical wireless communications (OWCs). However, by delving into the published literature, a gap is recognized in modeling the in-to-out-of channel, since most published contributions neglect the particularities of different types of tissues. In this paper, we present a novel pathloss and scattering models for in-to-out-of OWC links. Specifically, we derive extract analytical expressions that accurately describe the absorption of the five main tissues’ constituents, namely fat, water, melanin, and oxygenated and de-oxygenated blood. Moreover, we formulate a model for the calculation of the absorption coefficient of any generic biological tissue. Next, by incorporating the impact of scattering in the aforementioned model, we formulate the complete pathloss model. The developed model is verified by means of comparisons between the estimated pathloss and experimental measurements from independent research works. Finally, we illustrate the accuracy of the proposed model in estimating the optical properties of any generic tissue based on its constitution. The extracted channel model is expected to enable link budget analysis, performance analysis, and theoretical framework development, which will boost the design of optimized communication protocols for a plethora of biomedical applications.

The Byzantine religious tradition includes Greek Orthodox Church hymns, which significantly differ from other cultures’ religious music. Since the deep learning revolution, audio and music signal processing are often approached as computer vision problems. This work trains from scratch three different novel convolutional neural networks on a hymns dataset to perform hymns classification for mobile applications. The audio data are first transformed into Mel-spectrograms and then fed as input to the model. To study in more detail our models’ performance, two state-of-the-art (SOTA) deep learning models were trained on the same dataset. Our approach outperforms the SOTA models both in terms of accuracy and their characteristics. Additional statistical analysis was conducted to validate the results obtained.

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%.

This work studies the effects of graphene nanoplatelets (GNPs) and hexagonal boron nitride (hBN) on the thermal and mechanical properties of polydimethylsiloxane (PDMS) matrix for use as thermal interface materials (TIMs). Solution mixing of 10 wt% GNPs and 40 wt% hBN with PDMS produced TIMs with through-plane thermal conductivity (TC) of 1.24 W m−1 K−1 showing a 519% increase relative to the neat matrix. A synergistic effect between GNPs and hBN was particularly demonstrated when in samples with 48 wt% total filler content, 8 wt% hBN was replaced by GNPs, and the TC was increased by 33%. The elongation at break of the hBN (40 wt%)/GNP (8 wt%) samples reached 151%, representing a 160% increase when compared to samples with only 40 wt% hBN. Moreover, the shore hardness of samples containing 40 wt% hBN was 68 A, and the introduction of 8 wt% GNPs caused a decrease to 38 A. DSC measurements on samples filled with hBN and GNPs showed a reduction up to 65% in Ultimate Heat of Curing, which was attributed to the presence of graphene flakes interfering with the curing of the matrix. Graphene was found to be an efficient filler in tuning the thermal and mechanical properties of TIMs.

The evacuation and abandonment of large passenger ships, involving thousands of passengers, is a safety-critical task where techniques and systems that can improve the complex decision-making process and the timely response to emergencies on board are of vital importance. Current evacuation systems and processes are based on predefined and static exit signs, information provided to the passengers in the form of evacuation drills, emergency information leaflets and public announcements systems. It is mandatory for passengers to wear lifejackets during an evacuation, which are made of buoyant or inflatable material to keep them afloat in the water. Time is the most critical attribute in ship evacuation and can significantly affect the overall evacuation process in case passengers do not reach their embarkation stations in a timely manner. Moreover, extreme conditions and hazards, such as fire or flooding, can prevent and hinder the timely evacuation process. To improve the current evacuation systems onboard large passenger ships, a smart lifejacket has been designed and implemented within the context of the project SafePASS. The proposed smart lifejacket integrates indoor localization and navigation functionality to assist passengers during the evacuation process. Once the passenger location is calculated within the ship, the navigation feature guides the passengers along an escape route using vibration motors attached to the lifejacket. This is done in the form of haptic cues to help passengers reach their destination, especially in low-visibility conditions and in case they are left behind or lost. This can increase passenger safety and reduce the total evacuation time, as well as support dynamic evacuation scenarios where the predefined routes and static exit routes may not be available due to fire or flooding incidents.

The implementation of smart networks has made great progress due to the development of the Internet of Things (IoT). LoRa is one of the most prominent technologies in the Internet of Things industry, primarily due to its ability to achieve long-distance transmission while consuming less power. In this work, we modeled different environments and assessed the performances of networks by observing the effects of various factors and network parameters. The path loss model, the deployment area size, the transmission power, the spreading factor, the number of nodes and gateways, and the antenna gain have a significant effect on the main performance metrics such as the energy consumption and the data extraction rate of a LoRa network. In order to examine these parameters, we performed simulations in OMNeT++ using the open source framework FLoRa. The scenarios which were investigated in this work include the simulation of rural and urban environments and a parking area model. The results indicate that the optimization of the key parameters could have a huge impact on the deployment of smart networks.

The algorithm of the Convective Rainfall Rate with Microphysical Properties (CRRPh) product of the 2021 version of the Nowcasting and Very Short Range Forecasting Satellite Application Facility (NWC SAF) presents innovative characteristics. It was developed employing principal components analysis to reduce the number of utilized parameters and uses the same mathematical scheme for day and night, simulating the missing visual channels and satellite-derived cloud water path information that is unavailable during nighttime. Applying adequate statistical methodologies and scores and using rain gauge data as ground truth, it is shown that the new algorithm appears to be significantly improved compared to its predecessors in regard to the delineation of the precipitation areas. In addition, it minimizes the day–night difference in the estimation efficiency, which is a remarkable achievement. The new product suffers from slightly higher errors in the precipitation accumulations. Finally, it is shown that topography does not seem to affect the estimation efficiency of the product. In light of these results, it is argued that, overall, the new algorithm outperforms its predecessors and, possibly after adequate adaptations, can be used as a real-time total precipitation product.

Advances in Artificial intelligence (AI) and embedded systems have resulted on a recent increase in use of image processing applications for smart cities’ safety. This enables a cost-adequate scale of automated video surveillance, increasing the data available and releasing human intervention. At the same time, although deep learning is a very intensive task in terms of computing resources, hardware and software improvements have emerged, allowing embedded systems to implement sophisticated machine learning algorithms at the edge. Additionally, new lightweight open-source middleware for constrained resource devices, such as EdgeX Foundry, have appeared to facilitate the collection and processing of data at sensor level, with communication capabilities to exchange data with a cloud enterprise application. The objective of this work is to show and describe the development of two Edge Smart Camera Systems for safety of Smart cities within S4AllCities H2020 project. Hence, the work presents hardware and software modules developed within the project, including a custom hardware platform specifically developed for the deployment of deep learning models based on the I.MX8 Plus from NXP, which considerably reduces processing and inference times; a custom Video Analytics Edge Computing (VAEC) system deployed on a commercial NVIDIA Jetson TX2 platform, which provides high level results on person detection processes; and an edge computing framework for the management of those two edge devices, namely Distributed Edge Computing framework, DECIoT. To verify the utility and functionality of the systems, extended experiments were performed. The results highlight their potential to provide enhanced situational awareness and demonstrate the suitability for edge machine vision applications for safety in smart cities.

The prognostic value of health status metrics in patients with adult congenital heart disease (ACHD) and atrial arrhythmias is unclear. In this retrospective cohort study of an ongoing national, multicenter registry (PROTECT-AR, NCT03854149), ACHD patients with atrial arrhythmias on apixaban are included. At baseline, health metrics were assessed using the physical component summary (PCS), the mental component summary (MCS) of the Short-Form-36 (SF-36) Health Survey, and the modified European Heart Rhythm Association (mEHRA) score. Patients were divided into groups according to their SF-36 PCS and MCS scores, using the normalized population mean of 50 on the PCS and MCS as a threshold. The primary outcome was the composite of mortality from any cause, major thromboembolic events, major/clinically relevant non-major bleedings, or hospitalizations. Multivariable Cox-regression analyses using clinically relevant parameters (age greater than 60 years, anatomic complexity, ejection fraction of the systemic ventricle, and CHA₂DS₂-VASc and HAS-BLED scores) were performed to examine the association of health metrics with the composite outcome. Over a median follow-up period of 20 months, the composite outcome occurred in 50 of 158 (32%) patients. The risk of the outcome was significantly higher in patients with SF-36 PCS ≤ 50 compared with those with PCS > 50 (adjusted hazard ratio (aHR), 1.98; 95% confidence interval [CI], 1.02–3.84; p = 0.04) after adjusting for possible confounders. The SF-36 MCS ≤ 50 was not associated with the outcome. The mEHRA score was incrementally associated with a higher risk of the composite outcome (aHR = 1.44 per 1 unit increase in score; 95% CI, 1.03–2.00; p = 0.03) in multivariable analysis. In ACHD patients with atrial arrhythmias, the SF-36 PCS ≤ 50 and mEHRA scores predicted an increased risk of adverse events.

Developing risk assessment tools for CAD prediction remains challenging nowadays. We developed an ML predictive algorithm based on metabolic and clinical data for determining the severity of CAD, as assessed via the SYNTAX score. Analytical methods were developed to determine serum blood levels of specific ceramides, acyl-carnitines, fatty acids, and proteins such as galectin-3, adiponectin, and APOB/APOA1 ratio. Patients were grouped into: obstructive CAD (SS > 0) and non-obstructive CAD (SS = 0). A risk prediction algorithm (boosted ensemble algorithm XGBoost) was developed by combining clinical characteristics with established and novel biomarkers to identify patients at high risk for complex CAD. The study population comprised 958 patients (CorLipid trial (NCT04580173)), with no prior CAD, who underwent coronary angiography. Of them, 533 (55.6%) suffered ACS, 170 (17.7%) presented with NSTEMI, 222 (23.2%) with STEMI, and 141 (14.7%) with unstable angina. Of the total sample, 681 (71%) had obstructive CAD. The algorithm dataset was 73 biochemical parameters and metabolic biomarkers as well as anthropometric and medical history variables. The performance of the XGBoost algorithm had an AUC value of 0.725 (95% CI: 0.691–0.759). Thus, a ML model incorporating clinical features in addition to certain metabolic features can estimate the pre-test likelihood of obstructive CAD.

In this study, atmospheric dynamical processes, which govern the intensification of wildfire activity and the associated increase in low-level ozone concentrations, were studied using images, advanced products and vertical profiles derived from satellite observations. The analyses confirm that the influence of deep stratospheric intrusions, identified in the satellite water vapor imagery, on a fire-risk area contributes to the increase in fire activity. The depth of dry stratospheric intrusions, the associated synoptic evolution and the enhanced low-level ozone concentrations caused by vertical transport of stratospheric air and/or related to biomass burning emissions were analyzed using satellite measurements from SEVIRI, IASI and CrIS instruments, complemented with surface observations near the wildfires’ locations. It is shown that the spatial and vertical resolutions of these soundings provide a way of identifying areas of enhanced ozone downwind of wildfires. Influences of the upper-troposphere dynamics and the wind field evolution as factors of uncertainty and complexity in studying the ozone production from wildfire emissions are considered. The combination of satellite soundings and satellite estimations of fire radiative energy and WV imagery may contribute to better understand the ozone enhancement associated with stratospheric intrusion and wildfire emissions.

Nowadays, the transformations of cities into smart cities is a crucial factor in improving the living conditions of the inhabitants as well as addressing emergency situations under the concept of public safety and property loss. In this context, many sensing systems have been designed and developed that provide fire detection and gas leakage alerts. On the other hand, new technologies such edge computing have gained significant attention in recent years. Moreover, the development of recent intelligent applications in IoT aims to integrate several types of systems with automated next-generation emergency calls in case of a serious accident. Currently, there is a lack of studies that combine all the aforementioned technologies. The proposed smart building sensor system, SB112, combines a small-size multisensor-based (temperature, humidity, smoke, flame, CO, LPG, and CNG) scheme with an open-source edge computing framework and automated Next Generation (NG) 112 emergency call functionality. It involves crucial actors such as IoT devices, a Public Safety Answering Point (PSAP), the middleware of a smart city platform, and relevant operators in an end-to-end manner for real-world scenarios. To verify the utility and functionality of the proposed system, a representative end-to-end experiment was performed, publishing raw measurements from sensors as well as a fire alert in real time and with low latency (average latency of 32 ms) to the middleware of a smart city platform. Once the fire was detected, a fully automatic NG112 emergency call to a PSAP was performed. The proposed methodology highlights the potential of the SΒ112 system for exploitation by decision-makers or city authorities.

The 2021 fire season in Greece was the worst of the past 13 years, resulting in more than 130,000 ha of burnt area, with about 70% consumed by five wildfires that ignited and spread in early August. Common to these wildfires was the occurrence of violent pyroconvection. This work presents a meteorological analysis of this outbreak of extreme pyroconvective wildfires. Our analysis shows that dry and warm antecedent weather preconditioned fuels in the fire-affected areas, creating a fire environment that alone could effectively support intense wildfire activity. Analysis of surface conditions revealed that the ignition and the most active spread of all wildfires coincided with the most adverse fire weather since the beginning of the fire season. Further, the atmospheric environment was conducive to violent pyroconvection, as atmospheric instability gradually increased amid the breakdown of an upper-air ridge ahead of an approaching long-wave trough. In summary, we highlight that the severity and extent of the 2021 Greek wildfires were not surprising considering the fire weather potential for the period when they ignited. Continuous monitoring of the large- and local-scale conditions that promote extreme fire behavior is imperative for improving Greece’s capacity for managing extreme wildfires.

Maritime operations rely heavily on surveillance and require reliable and timely data that can inform decisions and planning. Critical information in such cases includes the exact location of objects in the water, such as vessels, persons, and others. Due to the unique characteristics of the maritime environment, the location of even inert objects changes through time, depending on the weather conditions, water currents, etc. Unmanned aerial vehicles (UAVs) can be used to support maritime operations by providing live video streams and images from the area of operations. Machine learning algorithms can be developed, trained, and used to automatically detect and track objects of specific types and characteristics. EFFECTOR is an EU-funded project, developing an Interoperability Framework for maritime surveillance. Within the project, we developed an embedded system that employs machine learning algorithms, allowing a UAV to autonomously detect objects in the water and keep track of their changing position through time. Using the on-board computation unit of the UAV, we ran and present the results of a series of comparative tests among possible architecture sizes and training datasets for the detection and tracking of objects in the maritime environment. We tested architectures based on their efficiency, accuracy, and speed. A combined solution for training the datasets is suggested, providing optimal efficiency and accuracy.

Visual sensing of the environment is crucial for flying an unmanned aerial vehicle (UAV) and is a centerpiece of many related applications. The ability to run computer vision and machine learning algorithms onboard an unmanned aerial system (UAS) is becoming more of a necessity in an effort to alleviate the communication burden of high-resolution video streaming, to provide flying aids, such as obstacle avoidance and automated landing, and to create autonomous machines. Thus, there is a growing interest on the part of many researchers in developing and validating solutions that are suitable for deployment on a UAV system by following the general trend of edge processing and airborne computing, which transforms UAVs from moving sensors into intelligent nodes that are capable of local processing. In this paper, we present, in a rigorous way, the design and implementation of a 12.85 kg UAV system equipped with the necessary computational power and sensors to serve as a testbed for image processing and machine learning applications, explain the rationale behind our decisions, highlight selected implementation details, and showcase the usefulness of our system by providing an example of how a sample computer vision application can be deployed on our platform.

MicroRNAs (miRNAs) create systems networks and gene-expression circuits through molecular signaling and cell interactions that contribute to health imbalance and the emergence of cardiovascular disorders (CVDs). Because the clinical phenotypes of CVD patients present a diversity in their pathophysiology and heterogeneity at the molecular level, it is essential to establish genomic signatures to delineate multifactorial correlations, and to unveil the variability seen in therapeutic intervention outcomes. The clinically validated miRNA biomarkers, along with the relevant SNPs identified, have to be suitably implemented in the clinical setting in order to enhance patient stratification capacity, to contribute to a better understanding of the underlying pathophysiological mechanisms, to guide the selection of innovative therapeutic schemes, and to identify innovative drugs and delivery systems. In this article, the miRNA–gene networks and the genomic signatures resulting from the SNPs will be analyzed as a method of highlighting specific gene-signaling circuits as sources of molecular knowledge which is relevant to CVDs. In concordance with this concept, and as a case study, the design of the clinical trial GESS (NCT03150680) is referenced. The latter is presented in a manner to provide a direction for the improvement of the implementation of pharmacogenomics and precision cardiovascular medicine trials.

Recent studies support that acylcarnitines exert a significant role in cardiovascular disease development and progression. The aim of this metabolomics-based study was to investigate the association of serum acylcarnitine levels with coronary artery disease (CAD) severity, as assessed via SYNTAX Score. Within the context of the prospective CorLipid trial (NCT04580173), the levels of 13 circulating acylcarnitines were accurately determined through a newly developed HILIC-MS/MS method in 958 patients undergoing coronary angiography in the AHEPA University Hospital of Thessaloniki, Greece. Patients presenting with acute coronary syndrome had significantly lower median acylcarnitine C8, C10, C16, C18:1 and C18:2 values, compared to patients with chronic coronary syndrome (p = 0.012, 0.007, 0.018, 0.011 and p = 0.026), while median C10, C16, C18:1 and C18:2 levels were higher in stable angina compared to STEMI (p = 0.019 p = 0.012, p = 0.013 and p < 0.001, respectively). Moreover, median C2, C3, C4 and C8 levels were significantly elevated in patients with diabetes mellitus (p < 0.001, p-values less than 0.05). With regard to CAD severity, median C4 and C5 levels were elevated and C16 and C18:2 levels were reduced in the high CAD complexity group with SYNTAX Score > 22 (p = 0.002, 0.024, 0.044 and 0.012, respectively), indicating a potential prognostic capability of those metabolites and of the ratio C4/C18:2 for the prediction of CAD severity. In conclusion, serum acylcarnitines could serve as clinically useful biomarkers leading to a more individualized management of patients with CAD, once further clinically oriented metabolomics-based studies provide similar evidence.

Ensuring citizens’ safety and security has been identified as the number one priority for city authorities when it comes to the use of smart city technologies. Automatic understanding of the scene, and the associated provision of situational awareness for emergency situations, are able to efficiently contribute to such domains. In this study, a Video Analytics Edge Computing (VAEC) system is presented that performs real-time enhanced situation awareness for person detection in a video surveillance manner that is also able to share geolocated person detection alerts and other accompanied crucial information. The VAEC system adopts state-of-the-art object detection and tracking algorithms, and it is integrated with the proposed Distribute Edge Computing Internet of Things (DECIoT) platform. The aforementioned alerts and information are able to be shared, though the DECIoT, to smart city platforms utilizing proper middleware. To verify the utility and functionality of the VAEC system, extended experiments were performed (i) in several light conditions, (ii) using several camera sensors, and (iii) in several use cases, such as installed in fixed position of a building or mounted to a car. The results highlight the potential of VAEC system to be exploited by decision-makers or city authorities, providing enhanced situational awareness.

In this work, we report the synthesis and study of nanocomposites with a biobased epoxy/amine (Epilok 60-600G/Curamine 30-952) matrix reinforced with reduced graphene oxide (rGO) or functionalised with 3-glycidoxypropyltrimethoxysilane (GLYMO-rGO). These graphene related materials (GRMs) were first dispersed into a Curamine hardener using bath ultrasonication, followed by the addition of epoxy resin. Curing kinetics were studied by DSC under non-isothermal and isothermal conditions. The addition of 1.5 wt% of GLYMO-rGO into the epoxy matrix was found to increase the degree of cure by up to 12% and glass transition temperature by 14 °C. Mechanical testing showed that the addition of 0.05 wt% GLYMO-rGO improves Young’s modulus and tensile strength by 60% and 16%, respectively, compared to neat epoxy. Carbon fibre reinforced polymer (CFRP) laminates were prepared via hand lay up, using the nanocomposite system GRM/Epilok/Curamine as matrix, and were cut as CFRP adherents for lap shear joints. GRM/Epilok/Curamine was also used as adhesive to bond CFRP/CFRP and CFRP/aluminium adherents. The addition of 0.1 wt% GLYMO-rGO into the adhesive and CRFP adherents showed improved lap shear strength by 23.6% compared to neat resin, while in the case of CFRP/Aluminium joints the increase was 21.2%.

This paper is devoted to the discussion of the practice of storm naming that has been initiated in January 2017 for the first time in the Eastern Mediterranean Region. Namely the METEO Unit at NOA, taking into consideration that storm naming facilitates meteorologists, researchers, authorities, civil protection officers, the media and citizens to communicate the forecasts of high-impact weather events, started storm naming in January 2017 and has named 35 storms up to September 2021. The criteria of storm naming are discussed, and a synopsis of the events is presented. The monthly distribution shows that 57% of the named storms occurred during the winter period, with January being the month with the highest percentage of occurrence of named storms (28%). The impact of storm naming on citizens risk perception and increased awareness has been also assessed through an internet-based questionnaire that was launched on the fourth year of the storm naming practice in Greece. Overall, results indicate a significant impact of storm naming on the readiness of citizens through the activation of perceptual and cognitive mechanisms.

Micro-computed tomography (micro-CT) is a promising novel medical imaging modality that allows for non-destructive volumetric imaging of surgical tissue specimens at high spatial resolution. The aim of this study is to provide a comprehensive assessment of the clinical applications of micro-CT for the tissue-based diagnosis of lung diseases. This scoping review was conducted in accordance with the PRISMA Extension for Scoping Reviews, aiming to include every clinical study reporting on micro-CT imaging of human lung tissues. A literature search yielded 570 candidate articles, out of which 37 were finally included in the review. Of the selected studies, 9 studies explored via micro-CT imaging the morphology and anatomy of normal human lung tissue; 21 studies investigated microanatomic pulmonary alterations due to obstructive or restrictive lung diseases, such as chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, and cystic fibrosis; and 7 studies examined the utility of micro-CT imaging in assessing lung cancer lesions (n = 4) or in transplantation-related pulmonary alterations (n = 3). The selected studies reported that micro-CT could successfully detect several lung diseases providing three-dimensional images of greater detail and resolution than routine optical slide microscopy, and could additionally provide valuable volumetric insight in both restrictive and obstructive lung diseases. In conclusion, micro-CT-based volumetric measurements and qualitative evaluations of pulmonary tissue structures can be utilized for the clinical management of a variety of lung diseases. With micro-CT devices becoming more accessible, the technology has the potential to establish itself as a core diagnostic imaging modality in pathology and to enable integrated histopathologic and radiologic assessment of lung cancer and other lung diseases.

Graft copolymers, deproteinized natural rubber-graft-polystyrene (DPNR-g-PS) and deproteinized natural rubber-graft-polyacrylonitrile (DPNR-g-PAN), were prepared by the grafting of styrene (St) or acrylonitrile (AN) monomers onto DPNR latex via emulsion copolymerization. Then, ultrafine fully vulcanized powdered natural rubbers (UFPNRs) were produced by electron beam irradiation of the graft copolymers in the presence of di-trimethylolpropane tetra-acrylate (DTMPTA) as a crosslinking agent and, subsequently, a fast spray drying process. The effects of St or AN monomer contents and the radiation doses on the chemical structure, thermal stability, and physical properties of the graft copolymers and UFPNRs were investigated. The results showed that solvent resistance and grafting efficiency of DPNR-g-PS and DPNR-g-PAN were enhanced with increasing monomer content. SEM morphology of the UFPNRs showed separated and much less agglomerated particles with an average size about 6 μm. Therefore, it is possible that the developed UFPNRs grafted copolymers with good solvent resistance and rather high thermal stability can be used easily as toughening modifiers for polymers and their composites.

Several imaging techniques are used in biological and biomedical studies. Micro-computed tomography (micro-CT) is a non-destructive imaging technique that allows the rapid digitisation of internal and external structures of a sample in three dimensions and with great resolution. In this review, the strengths and weaknesses of some common imaging techniques applied in biological and biomedical fields, such as optical microscopy, confocal laser scanning microscopy, and scanning electron microscopy, are presented and compared with the micro-CT technique through five use cases. Finally, the ability of micro-CT to create non-destructively 3D anatomical and morphological data in sub-micron resolution and the necessity to develop complementary methods with other imaging techniques, in order to overcome limitations caused by each technique, is emphasised.

The GRACE score constitutes a useful tool for risk stratification in patients with acute coronary syndrome (ACS), while the SYNTAX score determines the complexity of coronary artery disease (CAD). This study sought to correlate these scores and assess the accuracy of the GRACE score in predicting the extent of CAD. A total of 539 patients with ACS undergoing coronary angiography were included in this analysis. The patients were classified into those with a SYNTAX score < 33 and a SYNTAX score ≥ 33. Spearman’s correlation and receiver operator characteristic analysis were conducted to investigate the role of the GRACE score as a predictor of the SYNTAX score. There was a significantly positive correlation between the SYNTAX and the GRACE scores (r = 0.32, p < 0.001). The GRACE score predicted severe CAD (SYNTAX ≥ 33) moderately well (the area under the curve was 0.595 (0.522–0.667)). A GRACE score of 126 was documented as the optimal cut-off for the prediction of a SYNTAX score ≥ 33 (sensitivity = 53.5% and specificity = 66%). Therefore, our study reports a significantly positive correlation between the GRACE and the SYNTAX score in patients with ACS. Notably, NSTEMI patients with a high-risk coronary anatomy have higher calculated GRACE scores. A multidisciplinary approach by a heart team could possibly alter the therapeutic approach and management in patients presenting with ACS and a high calculated GRACE score.

With the emergence of wireless networks, cooperation for secrecy is recognized as an attractive way to establish secure communications. Departing from cryptographic techniques, secrecy can be provided by exploiting the wireless channel characteristics; that is, some error-correcting codes besides reliability have been shown to achieve information-theoretic security. In this paper, we propose a polar-coding-based technique for the primitive relay wiretap channel and show that this technique is suitable to provide information-theoretic security. Specifically, we integrate at the relay an additional functionality, which allows it to smartly decide whether it will cooperate or not based on the decoding detector result. In the case of cooperation, the relay operates in a decode-and-forward mode and assists the communication by transmitting a complementary message to the destination in order to correctly decode the initial source’s message. Otherwise, the communication is completed with direct transmission from source to the destination. Finally, we first prove that the proposed encoding scheme achieves weak secrecy, then, in order to overcome the obstacle of misaligned bits, we implement a double-chaining construction, which achieves strong secrecy.

The internet-of-things (IoT) is expected to have a transformative impact in several different domains, including energy management in smart grids, manufacturing, transportation, smart cities and communities, smart food and farming, and healthcare. To this direction, the maintenance cost of IoT deployments has been identified as one of the main challenges, which is directly related to energy efficiency and autonomy of IoT solutions. In order to increase the energy sustainability of next-generation IoT, wireless power transfer (WPT) emerged as a promising technology; however, its effectiveness is hindered as the distance between the base station and the wireless powered IoT devices increases. To counter this effect, decentralized approaches based on the use of distributed densely deployed remote radio heads (RRHs) can be utilized to diminish the distance between the transmitting and the receiving nodes. A trade-off ensues from the use of RRHs as power beacons (PBs) or access points (APs) that enable either energy transfer during downlink or information reception during uplink, respectively. To balance this trade-off, in this work, the maximization of the ergodic rate in ultra-dense wireless powered networks is investigated. In more detail, three different protocols are introduced, optimized, and compared to each other: density splitting, time splitting, and hybrid time and density splitting, which are based on the optimization of the portion of the number of RRHs that are employed as PBs or APs at a specific time instance. Additionally, two different policies are taken into account regarding the PBs’ power constraint. The formulated problems that correspond to the combination of the proposed protocols with each of the two considered power constraint policies are optimally solved by using convex optimization tools and closed-form solutions are derived that result to useful insights. Finally, numerical results are provided, which illustrate the ergodic rate achieved by each of the proposed protocols and offer interesting conclusions regarding their comparison, which are directly linked to design guidelines and the required capital and operational expenses.

Situational awareness is a critical aspect of the decision-making process in emergency response and civil protection and requires the availability of up-to-date information on the current situation. In this context, the related research should not only encompass developing innovative single solutions for (real-time) data collection, but also on the aspect of transforming data into information so that the latter can be considered as a basis for action and decision making. Unmanned systems (UxV) as data acquisition platforms and autonomous or semi-autonomous measurement instruments have become attractive for many applications in emergency operations. This paper proposes a multipurpose situational awareness platform by exploiting advanced on-board processing capabilities and efficient computer vision, image processing, and machine learning techniques. The main pillars of the proposed platform are: (1) a modular architecture that exploits unmanned aerial vehicle (UAV) and terrestrial assets; (2) deployment of on-board data capturing and processing; (3) provision of geolocalized object detection and tracking events; and (4) a user-friendly operational interface for standalone deployment and seamless integration with external systems. Experimental results are provided using RGB and thermal video datasets and applying novel object detection and tracking algorithms. The results show the utility and the potential of the proposed platform, and future directions for extension and optimization are presented.

ST-elevation myocardial infarction (STEMI) remains one of the leading causes of mortality worldwide. The identification of novel metabolic and imaging biomarkers could unveil key pathophysiological mechanisms at the molecular level and promote personalized care in patients with acute coronary syndromes. We studied 38 patients with STEMI who underwent primary percutaneous coronary intervention and thrombus aspiration. We sought to correlate serum ceramide levels with micro-CT quantified aspirated thrombus volume and relevant angiographic outcomes, including modified TIMI thrombus grade and pre- or post-procedural TIMI flow. Higher ceramide C16:0 levels were significantly but weakly correlated with larger aspirated thrombus volume (Spearman r = 0.326, p = 0.046), larger intracoronary thrombus burden (TB; p = 0.030) and worse pre- and post-procedural TIMI flow (p = 0.049 and p = 0.039, respectively). Ceramides C24:0 and C24:1 were also significantly associated with larger intracoronary TB (p = 0.008 and p = 0.001, respectively). Receiver operating characteristic analysis demonstrated that ceramides C24:0 and C24:1 could significantly predict higher intracoronary TB (area under the curve: 0.788, 95% CI: 0.629–0.946 and 0.846, 95% CI: 0.706–0.985, respectively). In conclusion, serum ceramide levels were higher among patients with larger intracoronary and aspirated TB. This suggests that quantification of serum ceramides might improve risk-stratification of patients with STEMI and facilitate an individualized approach in clinical practice.

This study aims to develop a lightweight ballistic helmet based on nanocomposite with matrix of the copolymer of benzoxazine with an urethane prepolymer [poly(BA-a-co-PU)], at mass ratio 80/20, reinforced with aramid fabric and multi-walled carbon nanotubes (MWCNTs). This has a protection level II according to the National Institute of Justice (NIJ) 0106.01 standard. The effects of MWCNTs mass content in a range of 0 to 2 wt% on tensile, physical and ballistic impact properties of the nanocomposite were investigated. The results revealed that the introduction of MWCNTs enhanced the tensile strength and energy at break of the nanocomposite; the highest values were obtained at 0.25 wt%. In addition, the nanocomposite laminate with 20 plies of aramid fabric showed the lowest back face deformation of 8 mm which was much lower than that specified by the NIJ standard. According to Military Standard (MIL-STD) 662F, the simulation prediction revealed that the ballistic limit of the ballistic helmet nanocomposite was as high as 632 m s−1. The developed laminates made of aramid fabric impregnated with poly(BA-a-co-PU) 80/20 containing 0.25 wt% MWCNTs showed great promise for use as a light weight and high-performance ballistic helmet.

Micro-Fibrillated Cellulose (MFC) is a new type of bio-based additive, coming from wood cellulose. It can compete and substitute oil derived chemicals in several application fields. In the present work, the use of micro-fibrillated cellulose, in waterborne adhesive systems applied in the manufacture of composite wood-based panels was evaluated. Research was conducted to test the potential of improving the performance of wood-based panel types such as particleboard, waferboard or randomly-oriented strand board and plywood, by the application of MFC and the substitution of conventional and non-renewable chemical compounds. The approaches followed to introduce MFC into the adhesive systems were three, i.e., MFC 2% suspension added during the adhesive resin synthesis, MFC 10% paste admixed with the already prepared adhesive resin and MFC 2% suspension admixed with the already prepared resin. It was found that MFC improves not only the performance of the final wood panel products but also the behaviour of the applied adhesive polymer colloids (e.g., rheology improvement), especially when admixed with the already prepared resins. Moreover, it was proven that when MFC is introduced into the adhesive resin system, there is a possibility of decreasing the resin consumption, by maintaining the board performance. MFC’s robustness to pH, shear and temperature makes it a highly interesting new additive for adhesive producers. In addition, its natural origin can give adhesive producers the opportunity to move over to more environmentally friendly product solutions.

Tumor-associated macrophages (TAMs) in the gastrointestinal tumor microenvironment (TME) are known to polarize into populations exhibiting pro- or anti-tumoral activity in response to stimuli such as growth factors and cytokines. Our previous work has recognized granulocyte colony-stimulating factor (G-CSF) as a cytokine capable of influencing immune cells of the TME exhibiting pro-tumoral activity. Here, we aimed to focus on how G-CSF regulates TAM phenotype and function and the effects on gastrointestinal (GI) tumor progression. Thus, wildtype (WT) and G-CSFR−/− macrophages were examined for cytokine production, gene expression, and transcription factor activity. Adoptive transfer of WT or G-CSFR−/− macrophages into tumor-bearing mice was performed to study their influence in the progression of colon (MC38) and pancreatic (PK5L1940) tumor mouse models. Finally, the difference in cytotoxic potential between WT and G-CSFR−/− macrophages was examined both in vitro and in vivo. Our results indicate that G-CSF promotes increased IL-10 production and decreased IL-12 production, which was reversed in G-CSFR−/− macrophages for a pro-inflammatory phenotype. Furthermore, G-CSFR−/− macrophages were characterized by higher levels of NOS2 expression and NO production, which led to greater tumor related cytotoxicity both in vitro and in vivo. Our results suggest that in the absence of G-CSFR, macrophage-related tumor cytotoxicity was amplified. These findings, along with our previous reports, pinpoint G-CSF /G-CSFR as a prominent target for possible clinical applications that aim to control the TME and the GI tumor progression.

During February 2019, two severe storms affected the island of Crete, located in south Greece. Both storms produced excessive rainfall, provoking severe damages, especially in the western part of Crete. The role of the prevailing synoptic patterns and the interaction of the flow with the high mountains of Crete were investigated. For this purpose, a variety of observational and numerical model data were exploited, including data from a dense rain gauge network, satellite imagery, and model analysis of various parameters describing the stability of the impinging flow. The first storm was a long-lasting event, with convective outbreaks embedded in a more stratiform rainfall pattern. The second storm was brief but mostly convection dominated. The analysis of the available data underlined the role of the low-level convergence upstream of the mountains during both storms, highlighting similarities and differences, as well as the role of the stability of the impinging flow. High soil moisture content was also evidenced as a key ingredient for the severe flooding that occurred during the second storm. This work complements similar studies on the role of Mediterranean islands and their topography on the spatial and temporal distribution of extreme rainfall.

The smart grid provides advanced functionalities, including real-time monitoring, dynamic energy management, advanced pricing mechanisms, and self-healing, by enabling the two-way flow of power and data, as well as the use of Internet of Things (IoT) technologies and devices. However, converting the traditional power grids to smart grids poses severe security challenges and makes their components and services prone to cyber attacks. To this end, advanced techniques are required to mitigate the impact of the potential attacks. In this paper, we investigate the use of honeypots, which are considered to mimic the common services of the smart grid and are able to detect unauthorized accesses, collect evidence, and help hide the real devices. More specifically, the interaction of an attacker and a defender is considered, who both optimize the number of attacks and the defending system configuration, i.e., the number of real devices and honeypots, respectively, with the aim to maximize their individual payoffs. To solve this problem, game theoretic tools are used, considering an one-shot game and a repeated game with uncertainty about the payoff of the attacker, where the Nash Equilibrium (NE) and the Bayesian NE are derived, respectively. Finally, simulation results are provided, which illustrate the effectiveness of the proposed framework.