Showing papers by "University of Piraeus published in 2021"

Will climate mitigation ambitions lead to carbon neutrality? An analysis of the local-level plans of 327 cities in the EU

Abstract: Cities across the globe recognise their role in climate mitigation and are acting to reduce carbon emissions. Knowing whether cities set ambitious climate and energy targets is critical for determining their contribution towards the global 1.5 °C target, partly because it helps to identify areas where further action is necessary. This paper presents a comparative analysis of the mitigation targets of 327 European cities, as declared in their local climate plans. The sample encompasses over 25% of the EU population and includes cities of all sizes across all Member States, plus the UK. The study analyses whether the type of plan, city size, membership of climate networks, and its regional location are associated with different levels of mitigation ambition. Results reveal that 78% of the cities have a GHG emissions reduction target. However, with an average target of 47%, European cities are not on track to reach the Paris Agreement: they need to roughly double their ambitions and efforts. Some cities are ambitious, e.g. 25% of our sample (81) aim to reach carbon neutrality, with the earliest target date being 2020.90% of these cities are members of the Climate Alliance and 75% of the Covenant of Mayors. City size is the strongest predictor for carbon neutrality, whilst climate network(s) membership, combining adaptation and mitigation into a single strategy, and local motivation also play a role. The methods, data, results and analysis of this study can serve as a reference and baseline for tracking climate mitigation ambitions across European and global cities.

Blockchain-based food supply chain traceability: a case study in the dairy sector

Abstract: Traceability has become a critical element in supply chain management, particularly in safety-sensitive sectors like food, pharmaceuticals, etc. Upstream (manufacturers, producers, etc.) and downst...

How many FIDO protocols are needed? Surveying the design, security and market perspectives.

Abstract: Unequivocally, a single man in possession of a strong password is not enough to solve the issue of security. Studies indicate that passwords have been subjected to various attacks, regardless of the applied protection mechanisms due to the human factor. The keystone for the adoption of more efficient authentication methods by the different markets is the trade-off between security and usability. To bridge the gap between user-friendly interfaces and advanced security features, the Fast Identity Online (FIDO) alliance defined several authentication protocols. Although FIDO's biometric-based authentication is not a novel concept, still daunts end users and developers, which may be a contributor factor obstructing FIDO's complete dominance of the digital authentication market. This paper traces the evolution of FIDO protocols, by identifying the technical characteristics and security requirements of the FIDO protocols throughout the different versions while providing a comprehensive study on the different markets (e.g., digital banking, social networks, e-government, etc.), applicability, ease of use, extensibility and future security considerations. From the analysis, we conclude that there is currently no dominant version of a FIDO protocol and more importantly, earlier FIDO protocols are still applicable to emerging vertical services.

Re-examining Bitcoin Volatility: A CAViaR-based Approach

Abstract: The article aims to explore the heterogeneous feature in the determination of Bitcoin volatility using a Markov regime-switching model and test its forecasting ability. The forecasting methodology ...

Vitamin D Status is Associated With In-hospital Mortality and Mechanical Ventilation: A Cohort of COVID-19 Hospitalized Patients

Abstract: Objective To explore the possible associations of serum 25-hydroxyvitamin D [25(OH)D] concentration with coronavirus disease 2019 (COVID-19) in-hospital mortality and need for invasive mechanical ventilation. Patients and Methods A retrospective, observational, cohort study was conducted at 2 tertiary academic medical centers in Boston and New York. Eligible participants were hospitalized adult patients with laboratory-confirmed COVID-19 between February 1, 2020, and May 15, 2020. Demographic and clinical characteristics, comorbidities, medications, and disease-related outcomes were extracted from electronic medical records. Results The final analysis included 144 patients with confirmed COVID-19 (median age, 66 years; 64 [44.4%] male). Overall mortality was 18%, whereas patients with 25(OH)D levels of 30 ng/mL (to convert to nmol/L, multiply by 2.496) and higher had lower rates of mortality compared with those with 25(OH)D levels below 30 ng/mL (9.2% vs 25.3%; P=.02). In the adjusted multivariable analyses, 25(OH)D as a continuous variable was independently significantly associated with lower in-hospital mortality (odds ratio, 0.94; 95% CI, 0.90 to 0.98; P=.007) and need for invasive mechanical ventilation (odds ratio, 0.96; 95% CI, 0.93 to 0.99; P=.01). Similar data were obtained when 25(OH)D was studied as a continuous variable after logarithm transformation and as a dichotomous ( Conclusion Among patients admitted with laboratory-confirmed COVID-19, 25(OH)D levels were inversely associated with in-hospital mortality and the need for invasive mechanical ventilation. Further observational studies are needed to confirm these findings, and randomized clinical trials must be conducted to assess the role of vitamin D administration in improving the morbidity and mortality of COVID-19.

Diabetes is associated with increased risk for in-hospital mortality in patients with COVID-19: a systematic review and meta-analysis comprising 18,506 patients.

Abstract: Infectious diseases are more frequent and can be associated with worse outcomes in patients with diabetes. The aim of this study was to systematically review and conduct a meta-analysis of the available observational studies reporting the effect of diabetes on mortality among hospitalized patients with COVID-19. The Medline, Embase, Google Scholar, and medRxiv databases were reviewed for identification of eligible studies. A random effects model meta-analysis was used, and I2 was utilized to assess the heterogeneity. In-hospital mortality was defined as the endpoint. Sensitivity, subgroup, and meta-regression analyses were performed. A total of 18,506 patients were included in this meta-analysis (3713 diabetics and 14,793 non-diabetics). Patients with diabetes were associated with a higher risk of death compared with patients without diabetes (OR 1.65; 95% CI 1.35–1.96; I2 77.4%). The heterogeneity was high. A study-level meta-regression analysis was performed for all the important covariates, and no significant interactions were found between the covariates and the outcome of mortality. This meta-analysis shows that that the likelihood of death seems to be higher in diabetic patients hospitalized with COVID-19 compared with non-diabetic patients. Further studies are needed to assess whether this association is independent or not, as well as to investigate the role of adequate glycemic control prior to infection with COVID-19.

Model-based policymaking or policy-based modelling? How energy models and energy policy interact

Abstract: As energy models become more and more powerful, they are increasingly used to support energy policymaking. Although modelling has been used for policy advice for many years, there is little knowledge about how computer-based models actually influence policymaking, and to what extent policymakers influence the modelling process. Here, we empirically investigate (i) whether, how and when models influence the policymaking process, and (ii) whether, how and when policymakers influence the design, use and results of energy modelling. We analysed modelling and policy documents and conducted thirty-two interviews with different stakeholder groups in five different European jurisdictions. We show that models are used and have an impact on policymaking, especially by assessing impacts and supporting target setting, and sometimes by exploring policy options to reach these targets. We also show that policymakers influence models and modellers, especially by affecting data and assumptions, as well as the study scope, and by deciding how the modelling results are used. Hence, energy modelling and policymaking influence each other. In their exploratory mode, models can help investigate policy options and ambitious target setting. However, models can also be instrumentalised to justify already decided policies and targets. Our study implies that greater transparency, including open-source code and open data, and transdisciplinary elements in modelling could increase model legitimacy and impact in policymaking.

Coverage Analysis of Reconfigurable Intelligent Surface Assisted THz Wireless Systems

Abstract: This paper presents a connectivity analysis of reconfigurable intelligent surface (RIS) assisted terahertz (THz) wireless systems. Specifically, a system model that accommodates the particularities of THz band links as well as the characteristics of the RIS is reported, accompanied by a novel general end-to-end (e2e) channel attenuation formula. Based on this formula, we derive a closed-form expression that returns the optimal phase shifting of each reflection unit (RU) of the RIS. Moreover, we provide a tractable e2e channel coefficient approximation that is suitable for analyzing the RIS-assisted THz wireless system performance. Building upon the aforementioned approximation as well as the assumption that the user equipments are located in random positions within a circular cluster, we present the theoretical framework that quantifies the coverage performance of the system under investigation. In more detail, we deliver a novel closed-form expression for the coverage probability that reveals that there exists a minimum transmission power that guarantees 100% coverage probability. Both the derived channel model as well as the coverage probability are validated through extensive simulations and reveal the importance of taking into account both the THz channel particularities and the RIS characteristics, when assessing the system's performance and designing RIS-assisted THz wireless systems.

Smoking prevalence among hospitalized COVID-19 patients and its association with disease severity and mortality: an expanded re-analysis of a recent publication.

Abstract: There is a lot of debate about the effects of smoking on COVID-19. A recent fixed-effects meta-analysis found smoking to be associated with disease severity among hospitalized patients, but other studies report an unusually low prevalence of smoking among hospitalized patients. The purpose of this study was to expand the analysis by calculating the prevalence odds ratio (POR) of smoking among hospitalized COVID-19 patients, while the association between smoking and disease severity and mortality was examined by random-effects meta-analyses considering the highly heterogeneous study populations. The same studies as examined in the previous meta-analysis were analyzed (N = 22, 20 studies from China and 2 from USA). The POR relative to the expected smoking prevalence was calculated using gender and age-adjusted population smoking rates. Random-effects meta-analyses were used for all other associations. A total of 7162 patients were included, with 482 being smokers. The POR was 0.24 (95%CI 0.19–0.30). Unlike the original study, the association between smoking and disease severity was not statistically significant using random-effects meta-analysis (OR 1.40, 95%CI 0.98–1.98). In agreement with the original study, no statistically significant association was found between smoking and mortality (OR 1.86, 95%CI 0.88–3.94). An unusually low prevalence of smoking, approximately 1/4th the expected prevalence, was observed among hospitalized COVID-19 patients. Any association between smoking and COVID-19 severity cannot be generalized but should refer to the seemingly low proportion of smokers who develop severe COVID-19 that requires hospitalization. Smokers should be advised to quit due to long-term health risks, but pharmaceutical nicotine or other nicotinic cholinergic agonists should be explored as potential therapeutic options, based on a recently presented hypothesis.

Performance Determinants in LoRa Networks: A Literature Review

Abstract: The LoRa radio technology is one of the most prominent choices in the Internet of Things Low-Power Wide Area Networks (LPWANs) industry due to its versatile and robust technical characteristics along with its ability to achieve long communication ranges combined with low energy consumption and reduced cost. One of the main issues in LoRa networks is how many end-devices can be reporting efficiently while meeting the requirements set by the application they support. This is known as the capacity metric and it is affected by many network parameters and various factors. A literature overview is presented in this work, studying works on LoRa-based networks, outlining their behavior and categorizing them based on their technological breakthroughs. Throughout this survey, a number of performance determinants that stand out are highlighted. These factors span five main categories that encompass physical layer characteristics, deployment and hardware features, end device transmission settings, LoRa MAC protocols, and application requirements. Discussion follows the presentation of each of the factors pinpointing the relevant research, and describing the impact of each one of them on the achieved network efficiency focusing especially on the capacity metric. Open issues and research directions are also highlighted for each of the five identified categories.

Reconfigurable Intelligent Surface Optimal Placement in Millimeter-Wave Networks

Abstract: This work discusses the optimal reconfigurable intelligent surface placement in highly-directional millimeter wave links In particular, we present a novel system model that takes into account the relationship between the transmission beam footprint at the RIS plane and the RIS size Subsequently, based on the model we derive the end-to-end expression of the received signal power and, furthermore, provide approximate closed-form expressions in the case that the RIS size is either much smaller or at least equal to the transmission beam footprint Moreover, building upon the expressions, we derive the optimal RIS placement that maximizes the end-to-end signal-to-noise ratio Finally, we substantiate the analytical findings by means of simulations, which reveal important trends regarding the optimal RIS placement according to the system parameters

Quantum-Fourier-transform-based quantum arithmetic with qudits

Abstract: We present some basic integer arithmetic quantum circuits, such as adders and multiplier-accumulators of various forms, which operate on multilevel qudits. The integers to be processed are represented in an alternative basis after they have been Fourier transformed. Several arithmetic circuits operating on Fourier-transformed integers have appeared in the literature for two-level qubits. Here we extend these techniques to multilevel qudits, as they may offer some advantages relative to qubit implementations. The arithmetic circuits presented here can be used as basic building blocks for higher level algorithms such as quantum phase estimation, quantum simulation, quantum optimization, etc. Detailed decomposition is given down to elementary two-level single- and two-qudit gates as such gates are the most appropriate for physical implementation. A complexity analysis is given after this decomposition step and it is shown that the depth of the circuits is linear in the number of qudits employed and quadratic in the dimension of each qudit while their quantum cost is quadratic in the number of the qudits and quadratic in the dimension.

Parallel and scalable processing of spatio-temporal RDF queries using Spark

Abstract: The ever-increasing size of data emanating from mobile devices and sensors, dictates the use of distributed systems for storing and querying these data. Typically, such data sources provide some spatio-temporal information, alongside other useful data. The RDF data model can be used to interlink and exchange data originating from heterogeneous sources in a uniform manner. For example, consider the case where vessels report their spatio-temporal position, on a regular basis, by using various surveillance systems. In this scenario, a user might be interested to know which vessels were moving in a specific area for a given temporal range. In this paper, we address the problem of efficiently storing and querying spatio-temporal RDF data in parallel. We specifically study the case of SPARQL queries with spatio-temporal constraints, by proposing the DiStRDF system, which is comprised of a Storage and a Processing Layer. The DiStRDF Storage Layer is responsible for efficiently storing large amount of historical spatio-temporal RDF data of moving objects. On top of it, we devise our DiStRDF Processing Layer, which parses a SPARQL query and produces corresponding logical and physical execution plans. We use Spark, a well-known distributed in-memory processing framework, as the underlying processing engine. Our experimental evaluation, on real data from both aviation and maritime domains, demonstrates the efficiency of our DiStRDF system, when using various spatio-temporal range constraints.

Applying a dynamic ARDL approach to the Environmental Phillips Curve (EPC) hypothesis amid monetary, fiscal, and trade policy uncertainty in the USA

Abstract: It is well known that unemployment and environmental degradation are two critical issues across the globe. However, there is an extended dearth of literature that explores the nexus between unemployment and environmental degradation. Kashem and Rahman (Environ. Sci. Pollut. Res. 27(101): 31153–31170, 2020) put forward the Environmental Phillips Curve (EPC) hypothesis, which depicts a negative relationship between unemployment and environmental degradation. This study further explores the validity of the EPC hypothesis in the case of the USA. It also investigates the impact of monetary policy uncertainty (MU), fiscal policy uncertainty (FU), and trade policy uncertainty (TU) on carbon dioxide emissions. To this end, the analysis employs the novel methodology of the dynamic ARDL model. The results document that EPC does not hold in the short run, but it does in the long run. Furthermore, both in the short and long run, MU escalates CO2 emissions, while FU plunges emissions in both the short and long run. Finally, TU does not alter the level of CO2 emissions.

Soluble Angiotensin Converting Enzyme 2 (ACE2) Is Upregulated and Soluble Endothelial Nitric Oxide Synthase (eNOS) Is Downregulated in COVID-19-induced Acute Respiratory Distress Syndrome (ARDS).

Abstract: A damaged endothelium is an underlying condition of the many complications of COVID-19 patients. The increased mortality risk associated with diseases that have underlying endothelial dysfunction, such as acute respiratory distress syndrome (ARDS), suggests that endothelial (e) nitric oxide synthase (NOS)-derived nitric oxide could be an important defense mechanism. Additionally, intravenous recombinant angiotensin converting enzyme 2 (ACE2) was recently reported as an effective therapy in severe COVID-19, by blocking viral entry, and thus reducing lung injury. Very few studies exist on the prognostic value of endothelium-related protective molecules in severe COVID-19 disease. To this end, serum levels of eNOS, inducible (i) NOS, adrenomedullin (ADM), soluble (s) ACE2 levels, and serum (s) ACE activity were measured on hospital admission in 89 COVID-19 patients, hospitalized either in a ward or ICU, of whom 68 had ARDS, while 21 did not. In our cohort, the COVID-19-ARDS patients had considerably lower eNOS levels compared to the COVID-19 non-ARDS patients. On the other hand, sACE2 was significantly higher in the ARDS patients. iNOS, ADM and sACE activity did not differ. Our results might support the notion of two distinct defense mechanisms in COVID-19-derived ARDS; eNOS-derived nitric oxide could be one of them, while the dramatic rise in sACE2 may also represent an endogenous mechanism involved in severe COVID-19 complications, such as ARDS. These results could provide insight to therapeutical applications in COVID-19.

Dissecting Climate Risks: Are they Reflected in Stock Prices?

Abstract: We provide first-time evidence on whether market-wide physical or transition climate risks are priced in US stocks Textual and narrative analysis of Reuters climate-change news over 2000-2018, uncovers four novel risk factors related to natural disasters, global warming, international summits, and US climate policy, respectively Only the climate-policy factor is priced, especially post-2012 The documented risk premium is consistent with investors hedging the imminent transition risks from government intervention, rather than the direct risks from climate change itself Firms that are most exposed to transition risks tend to be polluting businesses which show no strong intention of becoming greener

Risk Assessment Methodologies for the Internet of Medical Things: A Survey and Comparative Appraisal

Abstract: The Internet of Medical Things (IoMT) has revolutionized health care services by providing significant benefits in terms of patient well being and relevant costs. Traditional risk assessment methodologies, however, cannot be effectively applied in the IoMT context since IoMT devices form part of a distributed and trustless environment and naturally support functionalities that favor reliability and usability instead of security. In this work we present a survey of risk assessment and mitigation methodologies for IoMT. For conducting the survey, we assess two streams of literature. First, we systematically review and classify the current scientific research in IoMT risk assessment methodologies. Second, we review existing standards/best practices for IoMT security assessment and mitigation in order to i) provide a comparative assessment of these standards/best practices on the basis of predefined criteria (scope and/or coverage, maturity level, and relevant risk methodology applied) and ii) identify common themes for IoMT security controls. Based on the analysis, we provide various IoMT research and implementation gaps along with a road map of fruitful areas for future research. The paper could be of significant value to security assessment researchers and policymakers/stakeholders in the health care industry.

An experimentally validated fading model for THz wireless systems.

Abstract: As the wireless world moves towards the sixth generation (6G) era, the demand of supporting bandwidth-hungry applications in ultra-dense deployments becomes more and more imperative. Driven by this requirement, both the research and development communities have turned their attention into the terahertz (THz) band, where more than [Formula: see text] of contiguous bandwidth can be exploited. As a result, novel wireless system and network architectures have been reported promising excellence in terms of reliability, massive connectivity, and data-rates. To assess their feasibility and efficiency, it is necessary to develop stochastic channel models that account for the small-scale fading. However, to the best of our knowledge, only initial steps have been so far performed. Motivated by this, this contribution is devoted to take a new look to fading in THz wireless systems, based on three sets of experimental measurements. In more detail, measurements, which have been conducted in a shopping mall, an airport check-in area, and an entrance hall of a university towards different time periods, are used to accurately model the fading distribution. Interestingly, our analysis shows that conventional distributions, such as Rayleigh, Rice, and Nakagami-m, lack fitting accuracy, whereas, the more general, yet tractable, [Formula: see text]-[Formula: see text] distribution has an almost-excellent fit. In order to quantify their fitting efficiency, we used two well-defined and widely-accepted tests, namely the Kolmogorov-Smirnov and the Kullback-Leibler tests. By accurately modeling the THz wireless channel, this work creates the fundamental tools of developing the theoretical and optimization frameworks for such systems and networks.

M-learning in the COVID-19 era: physical vs digital class.

Abstract: Emerging technologies, such as the development of the Internet of Things and the transition to smart cities, and innovative handheld devices have led to big changes in many aspects of our lives, while more changes were imminent. Education is also a sector that has undergone huge changes due to the spreading of those devices. Even at the era of feature phones, it started to become clear that portable devices with access to the internet can be used for learning. The process of learning with the use of mobile phones was then in an early stage, due to the limitations of feature phones. Whereas, with the introduction of smartphones, education is expected to be drastically altered in the future, in most parts of the world. New, radical, and controversial in some cases, approaches have been developed, over the past years, in an effort to implement a mobile learning process in real life conditions. Intelligent tutoring systems have had rapid growth, especially in the COVID-19 era, while a significant increase in online courses via social networks has also been noted. This paper focuses on presenting the most important research parameters of m-learning during the last decade, while it also incorporates a novel empirical study in the domain. The utilization of educational data has been taken into consideration and is presented, aiming at ways to improve human interaction in the digital classroom.

Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning

Abstract: The fingerprinting technique is a popular approach to reveal location of persons, instruments or devices in an indoor environment. Typically based on signal strength measurement, a power level map is created first in the learning phase to align with measured values in the inference. Second, the location is determined by taking the point for which the recorded received power level is closest to the power level actually measured. The biggest limit of this technique is the reliability of power measurements, which may lack accuracy in many wireless systems. To this end, this work extends the power level measurement by using multiple anchors and multiple radio channels and, consequently, considers different approaches to aligning the actual measurements with the recorded values. The dataset is available online. This article focuses on the very popular radio technology Bluetooth Low Energy to explore the possible improvement of the system accuracy through different machine learning approaches. It shows how the accuracy–complexity trade-off influences the possible candidate algorithms on an example of three-channel Bluetooth received signal strength based fingerprinting in a one dimensional environment with four static anchors and in a two dimensional environment with the same set of anchors. We provide a literature survey to identify the machine learning algorithms applied in the literature to show that the studies available can not be compared directly. Then, we implement and analyze the performance of four most popular supervised learning techniques, namely k Nearest Neighbors, Support Vector Machines, Random Forest, and Artificial Neural Network. In our scenario, the most promising machine learning technique being the Random Forest with classification accuracy over 99%.

Investigation of efficiency in the UK hotel industry: a network data envelopment analysis approach

Abstract: This paper aims to investigate efficiency in the UK hotel industry and further evaluate the impacts of hotel characteristics and industry environment on efficiency.,The network data envelopment (DEA) weak link approach is used for the efficiency analysis, while the determinants of efficiency are evaluated by bootstrapped truncated regression.,The findings show that the UK hotel industry is very inefficient. The results of overall efficiency deconstruction show that the second-stage production process experiences an even lower level of efficiency than that of the first stage. The second-phase analysis shows that both the hotel-specific characteristics and the industry-specific characteristics are significantly related to UK hotel efficiency.,The robustness of the results is affected because a single set of input-intermediate product-outputs and a single DEA method were used. Therefore, further studies can use alternate inputs, intermediate measures and outputs in the efficiency analysis. In addition, the robustness of the efficiency score can be checked using alternate parametric or non-parametric methods.,Hotels in the UK should focus on cost reduction, business diversification, improvement in the capital level and labor productivity, while at industry and macroeconomic level, discounts are recommended to be provided to international tourism and the tourism industry should be further opened.,The weak-link approach has been applied to estimate the efficiency level, as this provides more robust and accurate results compared to other non-parametric methods in the existing empirical studies and unique hotel-specific and industry-specific determinants of efficiency are considered in the second-stage analysis.

SoK: Blockchain Solutions for Forensics

Abstract: As the digitization of information-intensive processes gains momentum in nowadays, the concern is growing about how to deal with the ever-growing problem of cybercrime. To this end, law enforcement officials and security firms use sophisticated digital forensics techniques for analysing and investigating cybercrimes. However, multi-jurisdictional mandates, interoperability issues, the massive amount of evidence gathered (multimedia, text, etc.) and multiple stakeholders involved (law enforcement agencies, security firms, etc.) are just a few among the various challenges that hinder the adoption and implementation of sound digital forensics schemes. Blockchain technology has been recently proposed as a viable solution for developing robust digital forensics mechanisms. In this chapter, we provide an overview and classification of the available blockchain-based digital forensic tools, and we further describe their main features. We also offer a thorough analysis of the various benefits and challenges of the symbiotic relationship between blockchain technology and the current digital forensics approaches, as proposed in the available literature. Based on the findings, we identify various research gaps, and we suggest future research directions that are expected to be of significant value both for academics and practitioners in the field of digital forensics.

Learning Programming by Creating Games through the Use of Structured Activities in Secondary Education in Greece.

Abstract: The effective teaching of the concept of programming, where critical thinking is an important factor, is not so easy in secondary education. New teaching approaches, including, game-based learning, may provide a solution due to their inclusion of more fun and diverse activities but they still lack the active participation of the students in the creation of the material. In this context, we develop new teaching and learning materials to teach programming principles, like conditionals, loops and variables, to secondary education students based mainly on the constructivistic philosophy. The aim is to help students learn the basics of programming though creating games using a block-type programming environment and not only through the use and the playing of games. This approach combines the use of game design and creation with learning and results to the developing of basic programming skills. In order to evaluate the produced material quantitative and qualitative methods, such as questionnaires, classroom observations and discussions have been used. The results depict an improvement of the students’ knowledge and skills in programming through this game creation process.