Search

Employing the Lumerical Suite, which encompasses FDE, FDTD, and EME tools, we engineered waveguide chips made of SOI and SiN for methane detection at a wavelength of 1650nm. This document details our extensive simulation methodology and tactics for optimizing parameters.

Naturalistic driving studies (NDS) have recently gained attention as a way of instrumenting vehicles in an unobtrusive way and collecting driving data over long periods of time. Aiming at eventually modeling driving behavior, NDS are often a part of larger scale studies. These studies involve several stakeholders who are responsible for different components of the data collection and analysis, and thus are inevitably confronted with challenges in the data management pipeline. The aim of this paper is to develop standard protocols that could be used as guidelines for data handling in the context of NDS. In the development of these protocols, we first review data handling strategies used in previous studies, focusing on data collection, preparation, storage, as well as ethical and legal considerations. This review helps us draw lessons, based on which methods are developed to answer the gaps and challenges arising from handling NDS data. We then introduce a case study, the i–DREAMS project, to show the applicability of the data handling framework. Finally, we showcase standard protocols for data handling, that could serve as data handling guidelines for future studies., Safety and Security Science

Osteoarthritis is a degenerative joint disease affecting middle-aged and elderly patients. It mainly involves weight-bearing joints such as the hip, knee and spine as well as the basilar joint of the thumb, causing dysfunction and painful symptoms. Often, joint arthritis is accompanied by cartilage defects, joint space narrowing, osteophytes, bone sclerosis and subchondral bone cysts (SBC). The aim of the present study was to explore the pathophysiology responsible for the development of SBCs as well as the association between SBCs and disease progress, the level of clinical symptoms and their impact on postoperative outcomes and risk of possible complications following joint replacements if left untreated. A literature review on PubMed articles was conducted to retrieve and evaluate all available evidence related to the main objective mentioned above. A few theories have been put forth to explain the formation process of SBCs. These involve MMPs secretion, angiogenesis, and enhanced bone turnover as a biological response to abnormal mechanical loads causing repeated injuries on cartilage and subchondral tissue during the development of arthritis. However, the application of novel therapeutics, celecoxib-coated microspheres, local administration of IGF-1 and activated chondrocytes following surgical debridement of SBCs hinders the expansion of SBCs and prevents the progression of osteoarthritis. © 2023 by the authors.

School closures were enforced as measures to restrain the COVID-19 pandemic, based on the assumption that young children may play a key role in SARS-CoV-2 spread. This study aims to determine the prevalence of SARS-CoV-2 IgG antibodies in children and corresponding parents, in order to improve surveillance and estimate the prevalence of asymptomatic or subclinical COVID-19 cases. A prospective multicenter study was conducted between March and June 2021 in Greece. Children admitted to the hospital or examined in outpatient clinics for reasons other than COVID-19 and their parents were tested for anti-Spike SARS-CoV-2 IgG in serum. A questionnaire about clinical and demographic data was completed. The study included 823 participants: 427 children and 396 corresponding parents. The overall seroprevalence was 16.4% in parents and 13.8% in children. Among families with ≥ 1 seropositive child or parent, the combination of a seropositive parent and a corresponding seronegative child was 29.6%, a seronegative parent and a corresponding seropositive child was 24.7%, and a seropositive child with a corresponding seropositive parent was 45.7%. Age, level of education, and school or work attendance were not significantly associated with increased seropositivity. On the contrary, ethnic minority of Roma, close contact with known COVID-19 case, previous symptoms consistent with COVID-19, and mass gatherings were risk factors for seropositivity. Conclusion: The spread of SARS-CoV-2 during a period of lockdown in Greece was low in children and comparable to adults most likely due to intrafamilial transmission. Accordingly, it is unlikely that children have boosted virus transmission.What is Known:• In the earliest months of the pandemic, it was demonstrated that children had significantly lower seroprevalence rates than the older age groups, due to the fact that children had decreased exposure to the virus, because of early public health interventions, such as school and day care c

Background: Multiple HIV outbreaks have been recorded among people who inject drugs (PWID) since 2010. During an intervention for PWID in 2019–2021 in Thessaloniki, Greece, an increasing number of HIV cases was documented. Here, we provide an analysis of this new outbreak. Methods: ALEXANDROS was a community-based program and participation included interviewing, rapid HIV/HCV tests, counselling and linkage to care. PWID were recruited through Respondent-Driven Sampling (RDS) in five sampling rounds. Crude and RDS-weighted HIV prevalence estimates were obtained. HIV incidence was estimated from data on 380 initially seronegative PWID with at least two tests. Multivariable Cox proportional hazards model was used to assess risk factors for HIV seroconversion. Results: In total, 1,101 PWID were recruited. At first participation, 53.7% were current PWID, 20.1% homeless, 20.3% on opioid substitution treatment and 4.8% had received syringes in the past 12 months. HIV prevalence (95% CI) was 7.0% (5.6–8.7%) and an increasing trend was observed over 2019–2021 (p = 0.002). Two-thirds of the cases (67.5%) were new diagnoses. HIV incidence was 7.0 new infections/100 person-years (95% CI:4.8–10.2). Homelessness in the past 12 months (HR:2.68; 95% CI:1.24–5.81) and receptive syringe sharing (HR:3.86; 95% CI:1.75–8.51) were independently associated with increased risk of seroconversion. By the end of the program, 67.3% of the newly diagnosed cases initiated antiretroviral treatment. Conclusions: A new HIV outbreak among PWID was documented in Greece during the COVID-19 pandemic with homelessness and syringe sharing being associated with increased risk of HIV acquisition. Peer-driven programs targeting the population of high-risk underserved PWID can be used to early identify emerging outbreaks and to improve linkage to HIV care. © 2023 Elsevier B.V.

The safety of maritime autonomous surface ships (MASS) in mixed waterborne transport system (MWTS) depends on effective situational awareness (SA) distribution among MASS, manned ships, and various stakeholders, such as Vessel Traffic Service (VTS), Remote Control Center (RCC) and Fairway Shipping Agency. This paper focuses on the research question: How can situational awareness be effectively distributed among these entities in mixed waterborne transport? The research objective is to develop a distributed situational awareness framework that unifies SA among these stakeholders, ensuring safe navigation and compatibility with users of different roles. To achieve this objective, the proposed framework incorporates three key concepts: individual SA, authority-based SA, and distributed SA. Individual SA, previously introduced in our study, is responsible for each ship's SA, while authority-based SA accounts for the SA of human operators supervising the waterborne transport system, such as VTS operators and fairway agency personnel. Distributed SA generates guiding messages for ships based on the situational awareness from both individual SA and authority-based SA, thereby enabling regulation-based and traffic control-based recommendations for waterborne transport (e.g., ship speed and course adjustments). The research methodology employs ontology-based modelling to implement the framework, constructing a domain knowledge network. A case study is conducted as an essential part of the research methodology, presenting how the framework perform the situational awareness from different aspects and inconsistency detection among manned ships, MASS, VTS operators, and so on. Semantic Web Rule Language (SWRL) is utilized to detect inconsistencies and generate guidance messages for ships. Through these cases, we demonstrate how the proposed Ontology-based framework can reconcile inconsistencies between individual and authority-based SA, leading to a safer and more effective wate, Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Safety and Security Science, Transport Engineering and Logistics

Although signal countdown timers (SCTs) are likely to enhance efficiency at signalized intersections, there is little research on how they affect road users’ behavior. The present study explores factors associated with driver behavior through two approaches to examine how SCTs influence drivers’ actions toward pedestrians violating red lights. In the first approach, through an on-road questionnaire survey, the self-reported behavior of 369 drivers when crossing an intersection enabled with SCTs was analyzed. In the second approach, the drivers’ behavior was studied through naturalistic driving studies at two signalized intersections equipped with SCTs in Babol, Iran. Analyzing vehicle–pedestrian conflicts indicated that the presence of SCTs had a significant influence on driving behavior. Also, the ending seconds of green lights, as critical times of the SCTs, led to changes in driving behavior. Increasing the vehicle speed, changing lanes, and concurrent increases of speed and changing lanes were the common driver actions affected by critical times of the SCTs. Finally, the effect of critical times on drivers’ actions during conflicts was modeled by using the binary and multinomial logistic methods. The results show that SCTs are an external factor that can lead to risky driver behavior, such as errors and violations that might increase the potential for pedestrian accidents., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Safety and Security Science

Driving pattern recognition has been applied for the purposes of driving styles identification and harsh driving events detection. However, the evolution of driving behavior around and especially before such events has not been investigated at a microscopic level. The objective of this research is to reveal existing driving patterns around harsh events at the driving ‘pulse’ level i.e. a few seconds before and after the event. For that purpose, a time-series clustering approach is applied on speed and heart rate metrics of individual drivers using data collected from a large naturalistic driving study. Results show that there are distinct speed patterns before harsh braking, harsh acceleration, and harsh cornering events. A deceleration is identified shortly before most harsh acceleration and cornering events, which possibly indicates reckless behavior, i.e. drivers not dedicating enough time to smoothly brake before cornering, or of a brief ‘decision-making’ moment before the harsh manoeuvre. On the contrary, speed seems to be steady before harsh braking events. Regarding heart rate, the analysis revealed certain patterns only after raw data were cleansed and filtered. These patterns may show increasing, decreasing or variable heart rate trends, which may correspond to different stress patterns of drivers around harsh events. Finally, we introduce the concept of driving pattern consistency, which can reveal the share of individual drivers that follow the same harsh event pattern. It is indicated that more than half of the drivers are not consistent, suggesting that driving patterns around harsh events may be more context-related than driver personality-related., Policy & Strategy, Safety and Security Science

Driver behavior analytics is an important concept that plays a significant role in the understanding of road crashes. This paper investigates the optimal number of driver profiles to understand the most important characteristics that differentiate drivers and extract useful insights on the value of using different clustering approaches in profile recognition. To this end, two Machine Learning clustering algorithms, the K-Means and OPTICS algorithms, are applied on driving data from a large naturalistic experiment using almost 18 K trips recorded from 130 drivers. The results revealed 3 profiles, the less risky drivers, the modest drivers and the more aggressive drivers. Clustering was based on 3 important driving behavior characteristics, namely the number of speeding, headway and harsh events per 100 km. The less risky drivers profile was revealed by both algorithms, whereas drivers of higher aggressiveness are distinguished by K-Means based on the driving feature that dominates the rest. The OPTICS algorithm showed that many drivers, especially the aggressive ones, present unique behavior that cannot be grouped together with other drivers. The interpretability of driver profiles resulting from the application of these unsupervised learning techniques is worsened as the number of clusters increases. The association between driver profiles and individual characteristics leads to the conclusion that aggressiveness is mainly driven by personality traits and less by specific characteristics such as gender, age or past accident history. The results of this study can be potentially used to develop profile-specific applications that provide feedback to drivers and reduce their crash risk., Safety and Security Science

Driver anticipation plays a crucial role in crashes along horizontal curves. Anticipation is related to road predictability and can be influenced by roadway geometric design. Therefore, it is essential to understand which geometric design elements can influence anticipation and cause the road to be (un)predictable. This exercise, however, is not straightforward because anticipation is individual-specific whereas road geometric design is location-specific; anticipation is latent and measuring it may not be trivial; anticipation may have several stages from the preceding tangent until the midst of the curve; and not all drivers anticipate in the same way and thus there may well be unobserved heterogeneity in the effect of anticipation on crash risk. Despite methodological advancements in crash risk modelling, there is no econometric model that can adequately explain the above complexities. This study aims to fill this gap by developing an econometric model with a new latent variable, named ‘predictability’ that is measured by individual-specific driving behaviour indicators and predicted by location-specific road geometric factors. The model is specified with random parameters to account for unobserved heterogeneity and is empirically tested by a unique dataset including detailed geometric design and driver behaviour data obtained for 156 curves in the Netherlands. Results indicate that higher exposure and uphill vertical grade are associated with increased likelihood of vehicle crashes along horizontal curves, whereas adequate superelevation and higher predictability are associated with decreased likelihood of those crashes. Pavement friction influences this likelihood too but it has varied effects. Road predictability is influenced by the differences in angle of horizontal curves, vertical grades, and width of consecutive road segments., Safety and Security Science, Transport and Planning

Recent research in transport safety focuses on the processing of large amounts of available data by means of intelligent systems, in order to decrease the number of accidents for transportation users. Several Machine Learning (ML) and Artificial Intelligence (AI) applications have been developed to address safety problems and improve efficiency of transportation systems. However exchange of knowledge between transport modes has been limited. This paper reviews the ML and AI methods used in different transport modes (road, rail, maritime and aviation) to address safety problems, in order to identify good practices and experiences that can be transferable between transport modes. The methods examined include statistical and econometric methods, algorithmic approaches, classification and clustering methods, artificial neural networks (ANN) as well as optimization and dimension reduction techniques. Our research reveals the increasing interest of transportation researchers and practitioners in AI applications for crash prediction, incident/failure detection, pattern identification, driver/operator or route assistance, as well as optimization problems. The most popular and efficient methods used in all transport modes are ANN, SVM, Hidden Markov Models and Bayesian models. The type of the analytical technique is mainly driven by the purpose of the safety analysis performed. Finally, a wider variety of AI and ML methodologies is observed in road transport mode, which also appears to concentrate a higher, and constantly increasing, number of studies compared to the other modes., Safety and Security Science

This study reviews the Artificial Intelligence and Machine Learning approaches developed thus far for driver profile and driving pattern recognition, representing a set of macroscopic and microscopic behaviors respectively, to enhance the understanding of human factors in road safety, and therefore reduce the number of crashes. It provides a definition of the two scientific fields in terms of safety, and identifies the most efficient approaches used regarding methodology, data collection and driving metrics. Results show that K-means and Neural Networks are the most commonly used methodologies for driver profile identification, and Dynamic Time Warping for driving pattern detection. Most studies discovered driver profiles related to aggressiveness, considering mainly speed and acceleration as driving metrics. Based on the gaps and challenges identified, this paper provides a new framework for combining microscopic and macroscopic driving behavior analysis, instead of examining them separately as is the state-of-theart. Such combined results can potentially improve the development of traffic risk models, which could be exploited in applications that monitor drivers in real-time and provide feedback. These models will represent human behavior more accurately, which can eventually lead to the recognition of 'optimal' human driving patterns that Automated Vehicles (AV) could 'mimic' to become safer., Safety and Security Science

Background: Two rotavirus (RV) vaccines were licensed in Greece in late 2006 and included in the national immunisation programme in 2012. Aim: To study the epidemiology and genotype distribution of RV in children during the post-vaccination period and assess the impact of increased vaccination coverage. Methods: In a prospective multicentre hospital-based study, hospitalised children (≤ 16 years) with an RV-positive faecal sample were recruited. Epidemiological and genotyping analyses were performed; periods of low (2008- 12) and moderate (2012-20) RV vaccination coverage were compared. Statistical analysis was performed with a chi-squared or Mann-Whitney U test and logistic regression. Results: A total of 3,874 children (55.6% male; n = 2,153) with median age of 1.4 years (IQR: 0.5-3.3) were studied during 2008-20. Most RV-infected children were aged ≤ 3 years (72.2%) and hospitalised during December-May (69.1%). Common RV genotypes (G1P[8], G2P[4], G3P[8], G4P[8], G9P[8], G12P[8]) were detected in 92.2% of samples; G-P combinations with prevalence above 1% were G4P[8] (44.1%), G1P[8] (25.4%), G2P[4] (14.9%), G9P[8] (3.5%), G12P[8] (2.2%), G3P[8] (2.1%), other (4.3%) and mixed (3.5%). Of all samples, 97.6% were homotypic or partially heterotypic to vaccines' genotypes. With moderate vaccination coverage, the seasonal peak was detected earlier, children were older and partially or fully heterotypic genotypes were increased (p < 0.001). Conclusions: In the era of moderate RV vaccination coverage in Greece, epidemiology of RV in hospitalised children seemed to change. However, most circulating genotypes remain homotypic or partially heterotypic to RV vaccines. Continuous epidemiological surveillance and genotyping are important to monitor possible changes arising from RV vaccines' implementation. © 2022 Authors. All rights reserved.

Complement dysregulation has been documented in adults with COVID‐19 and impli-cated in relevant pediatric inflammatory responses against SARS‐CoV‐2. We propose that signa-tures of complement missense coding SNPs associated with dysregulation could also be identified in children with multisystem inflammatory syndrome (MIS‐C). We investigated 71 pediatric patients with RT‐PCR validated SARS‐CoV‐2 hospitalized in pediatric COVID‐19 care units (Novem-ber 2020–March 2021) in three major groups. Seven (7) patients suffered from MIS‐C (MIS‐C group), 32 suffered from COVID‐19 and were hospitalized (admitted group), whereas 32 suffered from COVID‐19, but were sent home. All patients survived and were genotyped for variations in the C3, C5, CFB, CFD, CFH, CFHR1, CFI, CD46, CD55, MASP1, MASP2, MBL2, COLEC11, FCN1, and FCN3 genes. Upon evaluation of the missense coding SNP distribution patterns along the three study groups, we noticed similarities, but also considerably increased frequencies of the alternative pathway (AP) associated with SNPs rs12614 CFB, rs1061170, and rs1065489 CFH in the MIS‐C patients. Our analysis suggests that the corresponding substitutions potentially reduce the C3b‐inactivation efficiency and promote slower and weaker AP C3bBb pre‐convertase assembly on virions. Under these circumstances, the complement AP opsonization capacity may be impaired, leading to com-promised immune clearance and systemic inflammation in the MIS‐C syndrome. © 2022 by the authors.

Road safety research is largely focused on prediction and prevention of technical, human or organisational failures that may result in critical conflicts or crashes. Indicators of traffic risk aim to capture the passage to unsafe states. However, research in other industries has shown that it is meaningful to analyse safety along the whole spectrum of behaviours. Knowing the causes and patterns of “successful” interactions, rather than failures, could give new insights on the complexity of the system and the adaptability and resilience of its users in handling the inherent risks. The concept is known as Safety-II and has been extensively explored in the aviation, healthcare and process engineering domains. In this paper, we explore a new Safety-II paradigm for road safety research. We briefly review Safety-II applications in other sectors. We then present a Safety-II model for road safety, by means of an inverse version of Hyden's “safety pyramid”. Furthermore, we discuss a number of key road safety goals, theories, analysis methods and data sources and map them into a tentative taxonomy of Safety-I and Safety-II applications. It is concluded that there can be opportunities and benefits from adopting this new mindset, in order to complement existing approaches., Safety and Security Science

Automated vehicles (AVs) aim to dramatically improve traffic safety by reducing or eliminating human error, which remains the leading cause of road crashes. However, commonly accepted standards for the ‘safe driving behaviour of machines’ are pending and urgently needed. Unless a common understanding of safety as a design value is achieved, different manufacturers’ driving styles may emerge, resulting in inconsistent, unpredictable and potentially unsafe ‘behaviour’ of AVs in certain situations. This paper aims to explore the main gaps and challenges towards establishing shared safety standards for the ‘behaviour’ of AVs, and contribute to their responsible traffic integration, by reviewing the state-of-the-art on AV safety in the core relevant disciplines: ethics of technology, safety science (engineering & human factors), and standardisation. The ethical and safety aspects investigated include the users’ perception of AV safety, the ethical trade-offs in critical decision-making contexts, the pertinence of data-driven approaches for AVs to mimic human behaviour, and the responsibilities of various actors. Moreover, the paper reviews the current safety patterns, metrics (surrogate measures of safety – SMoS) and their thresholds introduced in existing research for three use cases: mixed traffic of AV and conventional vehicles, AV interaction with pedestrians and cyclists, and transition of control from machine to human driver. The results reveal several knowledge gaps within each discipline and highlights the lack of common understanding of safety across disciplines. On the basis of the results, the paper proposes a framework for further research on AV safety, identifying concrete opportunities for interdisciplinary research, with common goals and methodologies, and explicitly indicating the path for transfer of knowledge between sectors., Safety and Security Science, Transport and Planning, Economics of Technology and Innovation, Ethics & Philosophy of Technology

Many projects related to maritime autonomous surface ships (MASS) have been proceeding to date, which promotes the commercialization of MASS. It is anticipated that there will be ships with different degrees of autonomy coexisting in a waterborne transport system (WTS) in the near future, forming a mixed waterborne transport system (MWTS). To ensure navigational safety, the ship needs to be well aware of the situation in real time, i.e. be able to exhibit situation awareness. The inconsistency of comprehension within SA of the same situation could occur in the MWTS, leading to a potentially dangerous situation. As such, it is essential to unify the SA framework for MASS to eliminate the inconsistency with human operators. It is challenging but necessary for a MASS to accomplish the process of situation awareness involving perception, comprehension, and projection. Especially the part of comprehension is the core element that needs to be addressed well and enhanced further. One possible way to reach it is to integrate the information given by the perception layer, projection layer, as well as additional domain knowledge like navigational rules to conduct further analysis. Accordingly, the current paper proposes a method for knowledge integration of SA for the MASS. The method realizes four capabilities of SA to satisfy relevant requirements in maritime domain: a general map, risk assessment enrichment, temporal and dynamic features, as well as a supplement of domain knowledge. For that purpose, the paper takes two steps: (i) constructing a SA framework for MASS, in which the entities related to SA are classified to different categories. (ii) proposing an ontology-based SA comprehension model where the information of entities are integrated together and then the SA can be depicted for MASS in real time. A case study is provided to how the model can be applied in maritime domain, in which a MASS is approaching a port executing its tasks. As a result, the proposed metho, Safety and Security Science, Transport Engineering and Logistics

Ship behavior is the semantic expression of corresponding trajectory in spatial-temporal space. The intelligent identification of ship behavior is critical for safety supervision in the waterborne transport. In particular, the complicated behavior reflects the long-term intentions of a ship, but it is challenging to recognize it automatically for computers without a proper understanding. For this purpose, this study provides a method to model the behavior for computers from the perspective of knowledge modeling that is explainable. Based on our previous work, a semantic model for ship behavior representation is given considering the multi-scale features of ship behavior in cognitive space. Firstly, the multi-scale features of ship behavior are analyzed in spatial-temporal dimension and semantic dimension individually. Then, a method for multi-scale behaviors modeling from the perspective of semantics is determined, which divides the behavior scale into four sub-scales in cognitive space, considering spatial and temporal dimensions: action, activity, process, and event. Furthermore, an ontology model is introduced to construct the multi-scale semantic model for ship behavior, where behaviors with different semantic scales are expressed using the functions of ontology from a microscopic perspective to a macroscopic perspective consecutively. To validate the model, a case study is conducted in which ship behavior with different scales occurred in port water areas. Typical behaviors, which include leveraging the axioms expression and semantic web rule language (SWRL) of the ontology, are then deduced using a reasoner, such as Pellet. The results show that the model is reasonable and feasible to represent multi-scale ship behavior in various scenarios and provides the potential to construct a smart supervision network for maritime authorities., Safety and Security Science