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To enable Computer Science students to develop employability and ‘work-ready’ skills it is important to consider both the technical skills aligned to their discipline and the soft skills desired by employers. Research has identified that students in Computer Science would benefit from further support to develop the latter. This chapter considers how these skills can be developed through a variety of work experience opportunities including work-based learning and work-related learning; in class activities and alternative teaching approaches such as project, inquiry and problem-based learning; and through scaffolding both soft skills development and reflective practice, how students can become more confident in articulating these skills when applying for graduate work.

In the past few decades, technology has completely transformed the world around us. Indeed, experts believe that the next big digital transformation in how we live, communicate, work, trade and learn will be driven by Artificial Intelligence (AI) [83]. This paper presents a high-level industrial and academic overview of AI in Education (AIEd). It presents the focus of latest research in AIEd on reducing teachers’ workload, contextualized learning for students, revolutionizing assessments and developments in intelligent tutoring systems. It also discusses the ethical dimension of AIEd and the potential impact of the Covid-19 pandemic on the future of AIEd’s research and practice. The intended readership of this article is policy makers and institutional leaders who are looking for an introductory state of play in AIEd.

Purpose of ReviewThis review offers a discussion on how energy storage deployment advances equitable outcomes for the power system. It catalogues the four tenets of the energy justice concept—distributive, recognition, procedural, and restorative—and shows how they relate to inequities in energy affordability, availability, due process, sustainability, and responsibility.Recent FindingsEnergy storage systems have been deployed to support grid reliability and renewable resource integration, but there is additional emerging value in considering the connections between energy storage applications and equity challenges in the power system. Through a thorough review of the energy justice and energy transitions literature, this paper offers the equity dimensions of storage project design and implementations.SummaryEmerging energy programs and projects are utilizing energy storage in pursuit of improved equity outcomes. Future research and policy design should integrate energy justice principles to align storage penetration with desired equity outcomes.

Interactive and immersive technologies can significantly enhance the fruition of museums and exhibits. Several studies have proved that multimedia installations can attract visitors, presenting cultural and scientific information in an appealing way. In this article, we present our workflow for achieving a gaze-based interaction with artwork imagery. We designed both a tool for creating interactive “gaze-aware” images and an eye tracking application conceived to interact with those images with the gaze. Users can display different pictures, perform pan and zoom operations, and search for regions of interest with associated multimedia content (text, image, audio, or video). Besides being an assistive technology for motor impaired people (like most gaze-based interaction applications), our solution can also be a valid alternative to the common touch screen panels present in museums, in accordance with the new safety guidelines imposed by the COVID-19 pandemic. Experiments carried out with a panel of volunteer testers have shown that the tool is usable, effective, and easy to learn.

Computed Tomography (CT) is a widely use medical image modality in clinical medicine, because it produces excellent visualizations of fine structural details of the human body. In clinical procedures, it is desirable to acquire CT scans by minimizing the X-ray flux to prevent patients from being exposed to high radiation. However, these Low-Dose CT (LDCT) scanning protocols compromise the signal-to-noise ratio of the CT images because of noise and artifacts over the image space. Thus, various restoration methods have been published over the past 3 decades to produce high-quality CT images from these LDCT images. More recently, as opposed to conventional LDCT restoration methods, Deep Learning (DL)-based LDCT restoration approaches have been rather common due to their characteristics of being data-driven, high-performance, and fast execution. Thus, this study aims to elaborate on the role of DL techniques in LDCT restoration and critically review the applications of DL-based approaches for LDCT restoration. To achieve this aim, different aspects of DL-based LDCT restoration applications were analyzed. These include DL architectures, performance gains, functional requirements, and the diversity of objective functions. The outcome of the study highlights the existing limitations and future directions for DL-based LDCT restoration. To the best of our knowledge, there have been no previous reviews, which specifically address this topic.

The literature on blockchain-enabled use cases has grown exponentially over recent years. Yet, studies are missing that apply bibliometrics and visualization techniques to unravel the dynamics and current discussions pertaining to the nexus of blockchain technology (BCT) and the healthcare field. To close this knowledge gap, we examine the knowledge base and research hotspots of BCT research in the field of healthcare. We carry out a series of bibliometric analyses on the extant literature, including the scholarly production, developmental pattern of the annual total number of authors, and identification of productive academic institutions, countries, and leading authors. Additionally, we conduct a keyword co-occurrence analysis and identify the major research hotspots and trends for the future. The findings of this research are valuable for scholars and practitioners who seek to better understand the development status, dynamics, and trends pertaining to BCT in healthcare.

Objective During the first months of 2020, the coronavirus disease of 2019 (COVID-19) spread rapidly and soon reached a pandemic level. With the increasing number of hospitalizations, medical and nursing personnel resources were soon inadequate. As a consequence, medical volunteers became a key human resource and young medical residents in any specialty were hired on a voluntary basis to contribute to take care of patients with COVID-19. This study reports on the lived experience of residents in child neuropsychiatry who volunteered in Italian hotspot COVID-19-designated hospitals during the epidemic outbreak. Methods A phenomenological, qualitative approach using semi-structured interviews with open-ended questions was used to obtain in-depth narratives of the experience of residents in child neuropsychiatry volunteering in North Italy COVID-19-designated hospitals. All residents (n = 8) participated in the study. Interviews were conducted by an expert researcher trained in qualitative methods. Data analysis was performed by independent coders. Results Five core themes could be identified from the interviews: acting as mediators on two fronts, facing the shock of COVID-19 reality, capitalizing from specialty education, growing as persons and professionals, and humanizing medical care. Conclusions This study is unique in providing an in-depth understanding of the experience of young residents in child neuropsychiatry volunteering in general hospitals during the COVID-19 pandemic in Northern Italy. The findings suggest that this experience may be highly beneficial for both the residents and the hospital quality of care. Insights for an accurate planning of residents’ engagement in future healthcare emergencies are provided.

Purpose Automatic identification of interventional devices in X-ray (XR) fluoroscopy offers the potential of improved navigation during transcatheter endovascular procedures. This paper presents a prototype implementation of fully automatic 3D reconstruction of a cryo-balloon catheter during pulmonary vein isolation (PVI) procedures by deep learning approaches. Methods We employ convolutional neural networks (CNN) to automatically identify the cryo-balloon XR marker and catheter shaft in 2D fluoroscopy during PVI. Training data are generated exploiting established semiautomatic techniques, including template-matching and analytical graph building. A first network of U-net architecture uses a single grayscale XR image as input and yields the mask of the XR marker. A second network of the similar architecture is trained using the mask of the XR marker as additional input to the grayscale XR image for the segmentation of the cryo-balloon catheter shaft mask. The structures automatically identified in two 2D images with different angulations are then used to reconstruct the cryo-balloon in 3D. Results Automatic identification of the XR marker was successful in 78% of test cases and in 100% for the catheter shaft. Training of the model for prediction of the XR marker mask was successful with 3426 training samples. Incorporation of the XR marker mask as additional input for the model predicting the catheter shaft allowed to achieve good training result with only 805 training samples. The average prediction time per frame was 14.47 ms for the XR marker and 78.22 ms for the catheter shaft. Localization accuracy for the XR marker yielded on average 1.52 pixels or 0.56 mm. Conclusions In this paper, we report a novel method for automatic detection and 3D reconstruction of the cryo-balloon catheter shaft and marker from 2D fluoroscopic images. Initial evaluation yields promising results thus indicating the high potential of CNNs as alternatives to the current state-of-the-art solutions.

Bone age assessment using hand-wrist X-ray images is fundamental when diagnosing growth disorders of a child or providing a more patient-specific treatment. However, as clinical procedures are a subjective assessment, the accuracy depends highly on the doctor’s experience. Motivated by this, a deep learning-based computer-aided diagnosis method was proposed for performing bone age assessment. Inspired by clinical approaches and aimed to reduce expensive manual annotations, informative regions localization based on a complete unsupervised learning method was firstly performed and an image-processing pipeline was proposed. Subsequently, an image model with pre-trained weights as a backbone was utilized to enhance the reliability of prediction. The prediction head was implemented by a Multiple Layer Perceptron with one hidden layer. In compliance with clinical studies, gender information was an additional input to the prediction head by embedded into the feature vector calculated from the backbone model. After the experimental comparison study, the best results showed a mean absolute error of 6.2 months on the public RSNA dataset and 5.1 months on the additional dataset using MobileNetV3 as the backbone.

Due to the impact of the COVID-19 pandemic, on-demand grocery delivery service that combines mobile technology and city logistics has gained tremendous popularity among grocery shoppers as a substitute to self-service grocery shopping in the store. This paper proposes an intelligent comparative approach where fuzzy logic and the analytical hierarchy process (AHP) method are combined to determine the importance weights of the criteria for marketing mix elements (7Ps) of the on-demand grocery delivery service for the period before COVID-19 and during COVID-19. In addition to its comprehensive theoretical insight, this paper provides a practical contribution to decision makers who create a marketing mix for the on-demand grocery delivery service and other similar online grocery businesses in terms of efficient allocation of resources to the development of marketing mix elements. The study’s findings can also provide clues for the decision makers in times of similar pandemics and crises that are likely to be seen in the future.

The main assay tool of COVID-19, as a pandemic, still has significant faults. To ameliorate the current situation, all facilities and tools in this realm should be implemented to encounter this epidemic. The current study has endeavored to propose a self-assessment decision support system (DSS) for distinguishing the severity of the COVID-19 between confirmed cases to optimize the patient care process. For this purpose, a DSS has been developed by the combination of the data-driven Bayesian network (BN) and the Fuzzy Cognitive Map (FCM). First, all of the data are utilized to extract the evidence-based paired (EBP) relationships between symptoms and symptoms’ impact probability. Then, the results are evaluated in both independent and combined scenarios. After categorizing data in the triple severity levels by self-organizing map, the EBP relationships between symptoms are extracted by BN, and their significance is achieved and ranked by FCM. The results show that the most common symptoms necessarily do not have the key role in distinguishing the severity of the COVID-19, and extracting the EBP relationships could have better insight into the severity of the disease.

Accurate prediction is a fundamental and leading work of the emergency medicine reserve management. Given that the emergency medicine reserve demand is affected by various factors during the public health events and thus the observed data are composed of different but hard-to-distinguish components, the traditional demand forecasting method is not competent for this case. To bridge this gap, this paper proposes the EMD-ELMAN-ARIMA (ELA) model which first utilizes Empirical Mode Decomposition (EMD) to decompose the original series into various components. The Elman neural network and ARIMA models are employed to forecast the identified components and the final forecast values are generated by integrating the individual component predictions. For the purpose of validation, an empirical study is carried out based on the influenza data of Beijing from 2014 to 2018. The results clearly show the superiority of the proposed ELA algorithm over its two rivals including the ARIMA and ELMAN models.

Spherical fuzzy set (SFS) is also one of the fundamental concepts for address more uncertainties in decision problems than the existing structures of fuzzy sets, and thus its implementation was more substantial. The well-known sine trigonometric function maintains the periodicity and symmetry of the origin in nature and thus satisfies the expectations of the experts over the multi parameters. Taking this feature and the significance of the SFSs into the consideration, the main objective of the article is to describe some reliable sine trigonometric laws for SFSs. Associated with these laws, we develop new average and geometric aggregation operators to aggregate the Spherical fuzzy numbers. Then, we presented a group decision-making strategy to address the multi-attribute group decision-making problem using the developed aggregation operators. To verify the value of the defined operators, a MAGDM strategy is provided along with an application for the selection of an authentic COVID-19 laboratory. Moreover, a comparative study is also performed to present the effectiveness of the developed approach.

Objective In this study, the authors aim to compare perceptions of remote learning versus in-person learning among faculty and trainees at a single institution during the COVID-19 pandemic and to evaluate the impact that a brief faculty training on best practices in online teaching would have on faculty attitudes towards remote learning. Methods The authors conducted an attitude survey on remote learning among trainees and faculty members approximately 3 months after the transition from in-person to remote learning. The authors then conducted a faculty training on best practices in online teaching followed by an evaluation survey. Study findings were examined descriptively and by Fisher’s exact testing. Results The response rates for the attitudes survey were 68% among trainees and 61% among faculty. Trainees and faculty perceived in-person learning more favorably than remote learning across a variety of domains, including overall enjoyment, interpersonal connection, ability to communicate, and concentration. Despite these trends, only 10% of trainees and 14% of faculty felt that all lectures would be most effectively delivered in-person when this becomes possible again. The response rate for the faculty training evaluation survey was 16%. Compared to non-attendees, faculty attendees reported more confidence in their ability to teach remotely (89% vs 56%, p=0.02) and but not increased optimism (89% vs 63%, p=0.06). Conclusions The study findings suggest that both trainees and faculty perceive remote learning negatively compared to in-person learning but still feel that some lectures should be delivered remotely even after a return to in-person learning is possible.

We present a hybrid algorithm to estimate lung nodule malignancy that combines imaging biomarkers from Radiologist's annotation with image classification of CT scans. Our algorithm employs a 3D Convolutional Neural Network (CNN) as well as a Random Forest in order to combine CT imagery with biomarker annotation and volumetric radiomic features. We analyze and compare the performance of the algorithm using only imagery, only biomarkers, combined imagery + biomarkers, combined imagery + volumetric radiomic features and finally the combination of imagery + biomarkers + volumetric features in order to classify the suspicion level of nodule malignancy. The National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) IDRI dataset is used to train and evaluate the classification task. We show that the incorporation of semi-supervised learning by means of K-Nearest-Neighbors (KNN) can increase the available training sample size of the LIDC-IDRI thereby further improving the accuracy of malignancy estimation of most of the models tested although there is no significant improvement with the use of KNN semi-supervised learning if image classification with CNNs and volumetric features are combined with descriptive biomarkers. Unexpectedly, we also show that a model using image biomarkers alone is more accurate than one that combines biomarkers with volumetric radiomics, 3D CNNs, and semi-supervised learning. We discuss the possibility that this result may be influenced by cognitive bias in LIDC-IDRI because malignancy estimates were recorded by the same radiologist panel as biomarkers, as well as future work to incorporate pathology information over a subset of study participants., This paper has been submitted to the Journal of Digital Imaging (JDI 2020). The poster of this paper has received the 2nd prize for the Research Poster Award. Link: https://siim.org/page/20m_p_lung_node_malignancy

In this study, we characterized the dynamics and analyzed the degree of synchronization of the time series of daily closing prices and volumes in US$ of three cryptocurrencies, Bitcoin, Ethereum, and Litecoin, over the period September 1,2015–March 31, 2020. Time series were first mapped into a complex network by the horizontal visibility algorithm in order to revel the structure of their temporal characters and dynamics. Then, the synchrony of the time series was investigated to determine the possibility that the cryptocurrencies under study co-bubble simultaneously. Findings reveal similar complex structures for the three virtual currencies in terms of number and internal composition of communities. To the aim of our analysis, such result proves that price and volume dynamics of the cryptocurrencies were characterized by cyclical patterns of similar wavelength and amplitude over the time period considered. Yet, the value of the slope parameter associated with the exponential distributions fitted to the data suggests a higher stability and predictability for Bitcoin and Litecoin than for Ethereum. The study of synchrony between the time series investigated displayed a different degree of synchronization between the three cryptocurrencies before and after a collapse event. These results could be of interest for investors who might prefer to switch from one cryptocurrency to another to exploit the potential opportunities of profit generated by the dynamics of price and volumes in the market of virtual currencies.

The workload of the static security guards has doubled due to the Covid-19 outbreak. In addition to their regular duties, they undertake some additional tasks to evaluate each individual's body temperature and welcome them with a hand sanitizer. In this scenario, their situational awareness is hugely desirable to perform these activities for the entire campus's safety. This situational awareness of guards means their ability to observe, inspect, and make the right decisions. However, due to their fatigue and other secondary activities, such as cell phone use, they cannot perform their duties correctly. In this context, this paper presents a method for sending random alarms in real-time to the on-duty guards, who are executing their work at the campus gates, remotely monitoring the alertness throughout the day from the head security office. For alertness detection, the system uses a simple client-server model. The system is designed using NodeMCU Wi-Fi modules. The algorithm of the Client, server, and repeater has been developed. The prototype has been tested by placing it on the working individuals' desk inside the departmental lab inside the campus. The system records the response time of the working individuals. These data are further used to calculate their percentage of alertness. In addition, an alertness-rating/scoring method has been developed to improve their work performance. This system can be an economical solution to enable the awareness of on-duty guards.

Neighborhood systems are used to approximate graphs as finite topological structures. Throughout this article, we construct new types of eight neighborhoods for vertices of an arbitrary graph, say, j-adhesion neighborhoods. Both notions of Allam et al. and Yao will be extended via j-adhesion neighborhoods. We investigate new types of j-lower approximations and j-upper approximations for any subgraph of a given graph. Then, the accuracy of these approximations will be calculated. Moreover, a comparison between accuracy measures and boundary regions for different kinds of approximations will be discussed. To generate j-adhesion neighborhoods and rough sets on graphs, some algorithms will be introduced. Finally, a sample of a chemical example for Walczak will be introduced to illustrate our proposed methods.

Parkinson’s disease (PD) is a progressive disorder of the central nervous system that causes motor dysfunctions in affected patients. Objective assessment of symptoms can support neurologists in fine evaluations, improving patients’ quality of care. Herein, this study aimed to develop data-driven models based on regression algorithms to investigate the potential of kinematic features to predict PD severity levels. Sixty-four patients with PD (PwPD) and 50 healthy subjects of control (HC) were asked to perform 13 motor tasks from the MDS-UPDRS III while wearing wearable inertial sensors. Simultaneously, the clinician provided the evaluation of the tasks based on the MDS-UPDRS scores. One hundred-ninety kinematic features were extracted from the inertial motor data. Data processing and statistical analysis identified a set of parameters able to distinguish between HC and PwPD. Then, multiple feature selection methods allowed selecting the best subset of parameters for obtaining the greatest accuracy when used as input for several predicting regression algorithms. The maximum correlation coefficient, equal to 0.814, was obtained with the adaptive neuro-fuzzy inference system (ANFIS). Therefore, this predictive model could be useful as a decision support system for a reliable objective assessment of PD severity levels based on motion performance, improving patients monitoring over time. Electronic supplementary material The online version of this article (10.1007/s10439-020-02628-4) contains supplementary material, which is available to authorized users.

Bioethics Education in the Anglo-European context developed since 1970 and was incorporated into the undergraduate and postgraduate education, residency training, and continuous education. In the Asia-Pacific region, bioethics education is less structured and often dependent on contextual constraints. This paper provides a cross-sectional analysis, describing institutional experiences in developing bioethics curriculum at two medical schools in Malaysia and Hong Kong. The medical programmes of the two institutions are distinctive in terms curriculum framework, teaching approach, and topic selection, and common challenges include implementation of bioethics courses, students’ resistance to bioethics, and limited teaching capacity, emerged as they evolve. The reported experiences revealed that there is room for improvement regarding how medical schools integrate bioethics education in regions where curriculum development remains at an early stage. At least, a bioethics education requires both top-down support from the faculty to improve teaching and educational quality, as well as from the bottom-up approach to empower students to raise awareness and concerns toward bioethics, and helps students developing reasoning through challenging issues.

Purpose of ReviewTo present the multi-faceted aspects of communication between robot and humans (HRI), putting in evidence that it is not limited to language-based interaction, but it includes all aspects that are relevant in communication among physical beings, exploiting all the available sensor channels.Recent FindingsFor specific purposes, machine learning algorithms could be exploited when data sets and appropriate algorithms are available.SummaryTogether with linguistic aspects, physical aspects play an important role in HRI and make the difference with respect to the more limited human-computer interaction (HCI). A review of the recent literature about the exploitation of different interaction channels is presented. The interpretation of signals and the production of appropriate communication actions require to consider psychological, sociological, and practical aspects, which may affect the performance. Communication is just one of the functionalities of an interactive robot and, as all the others, will need to be benchmarked to support the possibility for social robots to reach a real market.

Objective Medical errors affect trainees. Existing curricula emphasize tenets of patient safety but rarely address the impact of medical errors on health care providers, particularly at the undergraduate level. The authors developed an interactive session exploring the professional and personal effects of medical errors for 300 second-year medical students. Methods The session utilized case-based learning, small group discussion, and video vignettes. Physicians in multiple specialties were interviewed, sharing narrative accounts of their experience with medical errors and adverse events. Discussions were facilitated by local physicians, who delivered content and shared personal anecdotes. Surveys using a 5-point Likert scale were conducted before and after the session. Results Pre- and post-test surveys resulted in 91 responses in 2016 and 174 in 2017. In 2016, results showed significant change in student responses by 0.34 units (SD 0.35 units; p < 0.001). Two survey items addressing student awareness and three items addressing confidence were significant. The domains assessing students’ attitudes and knowledge regarding medical errors were not significant. In 2017, the overall mean change was 0.33 with a lower standard error, 0.03 (p < 0.001), showing significance in both years the session was delivered. Conclusion A 1-h, small-group session increased medical students’ understanding of the impact of medical errors and adverse events on providers and the supportive resources available. Recruitment of local faculty to facilitate discussions and share personal anecdotes enhanced the success of the session.

Purpose In surgical oncology, complete cancer resection and lymph node identification are challenging due to the lack of reliable intraoperative visualization. Recently, endoscopic radio-guided cancer resection has been introduced where a novel tethered laparoscopic gamma detector can be used to determine the location of tracer activity, which can complement preoperative nuclear imaging data and endoscopic imaging. However, these probes do not clearly indicate where on the tissue surface the activity originates, making localization of pathological sites difficult and increasing the mental workload of the surgeons. Therefore, a robust real-time gamma probe tracking system integrated with augmented reality is proposed. Methods A dual-pattern marker has been attached to the gamma probe, which combines chessboard vertices and circular dots for higher detection accuracy. Both patterns are detected simultaneously based on blob detection and the pixel intensity-based vertices detector and used to estimate the pose of the probe. Temporal information is incorporated into the framework to reduce tracking failure. Furthermore, we utilized the 3D point cloud generated from structure from motion to find the intersection between the probe axis and the tissue surface. When presented as an augmented image, this can provide visual feedback to the surgeons. Results The method has been validated with ground truth probe pose data generated using the OptiTrack system. When detecting the orientation of the pose using circular dots and chessboard dots alone, the mean error obtained is $$0.05^{\circ }$$ 0 . 05 ∘ and $$0.06^{\circ }$$ 0 . 06 ∘ , respectively. As for the translation, the mean error for each pattern is 1.78 mm and 1.81 mm. The detection limits for pitch, roll and yaw are $$360^{\circ }, 360^{\circ }$$ 360 ∘ , 360 ∘ and $$8^{\circ }$$ 8 ∘ –$$82^{\circ }\cup 188^{\circ }$$ 82 ∘ ∪ 188 ∘ –$$352^{\circ }$$ 352 ∘ . Conclusion The performance evaluation results show that this dual-pattern marker can provide high detection rates, as well as more accurate pose estimation and a larger workspace than the previously proposed hybrid markers. The augmented reality will be used to provide visual feedback to the surgeons on the location of the affected lymph nodes or tumor.

Background There is a worldwide recruitment and retention crisis in general practice. Workforce planning has identified the need to train more general practitioners as an urgent priority. Exposure of medical students to general practice as part of the formal and hidden curriculum, the use of longitudinal integrated clerkships, and positive experiences and role models in general practice are all thought to be contributing factors to doctors choosing careers in general practice. Aim The aim of this study was to identify career destinations of medical school graduates in a medical school with an 18-week longitudinal integrated clerkship in general practice. Design and setting This study was conducted in a single graduate entry medical school at the University of Limerick, Ireland. Participants Medical school alumni 6–8 years after graduation. Method A survey of graduating cohorts of the medical school from 2011 to 2013 was conducted through email and telephone. Results There were a total of 175 alumni for the period 2011 to 2013. Data was collected on 92% (161/175) through an online survey, follow-up email and telephone interview, and was triangulated with searches of professional registration databases and information from key informants. Between 6 and 8 years after graduation, a total of 43% of alumni were engaged in general practice as a career. Conclusion The reform of the delivery of general practice within medical school curricula should be considered by medical schools, curriculum designers and policy-makers as part of an overall strategy to address the recruitment and retention of general practitioners as part of the global healthcare workforce.

Introduction and hypothesis Previous studies have found high prevalence rates of anxiety and depression in women with urinary incontinence (UI). This study investigates the prevalence in women who had turned to eHealth for treatment of UI and identifies possible factors associated with depression. Methods We analyzed data from two randomized controlled trials evaluating eHealth treatment for UI, including 373 women with stress UI (SUI), urgency UI (UUI), or mixed UI (MUI). We used the Hospital Anxiety and Depression Scale (HADS) and defined a score of ≥8 as depression or anxiety. The ICIQ-UI-SF questionnaire was used to score incontinence severity. Logistic regression was used to determine factors associated with depression and anxiety. Results Women with UUI or MUI were older than women with SUI, mean age 58.3 vs 48.6 years (p = Conclusion The odds of depression in women with MUI/UUI were increased compared with SUI. The prevalence of anxiety and depression was considerably lower than reported in large cross-sectional surveys. Socioeconomic differences may partly explain this finding, as the use of eHealth still is more common among highly educated women.

This chapter defines the IoT protocol stack and compares it to the existing Internet protocol stack. It provides a layer-by-layer walkthrough of that stack, and, for each such layer, discusses the challenges brought forward by the IoT requirements of the previous chapter, the industry progress made to address those challenges and the remaining gaps that require future work.

In this paper, we develop and submit a novel model to the Neureka 2020 challenge11https://neureka-challenge.com/ that can predict seizures from an EEG signal. This challenge places emphasis on limiting the number of false positives, or reducing the places in which background signal is erroneously labeled as a seizure. As a result, our model is intended to act as a tool to aid neurologists as opposed to supplanting them completely. The goal is to allow a doctor to move more quickly through EEG data, enabling the doctor to treat more patients and devote more time toward treatment. Our model is a transformer based neural network architecture. We call it TABS, after Transformer Based Seizure detector. TABS scored a sensitivity of 9.03% and a false alarm rate of 31.21 per 24 hours when evaluated using the Time-Aligned Event Score.

Conference on Lasers and Electro-Optics. -- CLEO: Applications and Technology 2017, San Jose, California United States, 14–19 May 2017. -- https://www.osapublishing.org/abstract.cfm?uri=cleo_at-2017-AF1A.2&origin=search, We provide a general overview of spectral broadening and SC generation applications in fiber-optic telecommunications from a historical perspective, with a particular focus on the most recent developments.

The recent proliferation of social media has enabled users to post views about entities, individuals, events, and topics in a variety of formal and informal settings. Examples of such settings include reviews, forums, social media posts, blogs, and discussion boards. The problem of opinion mining and sentiment analysis is defined as the computational analytics associated with such text.

The purpose of an integration-oriented ontology is to provide a conceptualization of a domain of interest for automating the data integration of an evolving and heterogeneous set of sources using Semantic Web technologies. It links domain concepts to each of the underlying data sources via schema mappings. Data analysts, who are domain experts but not necessarily have technical data management skills, pose ontology-mediated queries over the conceptualization, which are automatically translated to the appropriate query language for the sources at hand. Following well-established rules when designing schema mappings allows to automate the process of query rewriting and execution.

Most document collections are defined by document-term matrices in which the rows (or columns) are highly correlated with one another. These correlations can be leveraged to create a low-dimensional representation of the data, and this process is referred to as dimensionality reduction.

Complying with the European Union (EU) perspective on human rights goes or should go together with handling ethical, social and legal challenges arising due to the use of biometrics technology as border control technology. While there is no doubt that the biometrics technology at European borders is a valuable element of border control systems, these technologies lead to issues of fundamental rights and personal privacy, among others. This paper discusses various ethical, social and legal challenges arising due to the use of biometrics technology in border control. First, a set of specific challenges and values affected were identified and then, generic considerations related to mitigation of these issues within a framework is provided. The framework is expected to meet the emergent need for supplying interoperability among multiple information systems used for border control.

Objective The aim of this study was to ascertain the relationships between perspective-taking, empathic concern, and self-rating of empathy as a physician among medical students. Methods This study analyzed the questionnaire responses of 152 medical students enrolled in Ajou University School of Medicine, Suwon, Republic of Korea, in 2018. As measurement instruments, the authors applied the Interpersonal Reactivity Index (IRI) and Korean Student Version of the Jefferson Scale of Physician Empathy (Korean JSPE-S), and then examined participant characteristic variables based on the obtained data and conducted subsequent correlation analyses of subscales, one-way ANOVA, and regression analyses. Results Medical students with clinical clerkship experience demonstrated higher levels of perspective-taking and empathy as physicians than did students without experience. Moreover, perspective-taking and empathic concern were significant predictors of medical students’ empathy as physicians in the regression model. Conclusions Medical students with higher scores in perspective-taking and empathic concern demonstrated higher levels of perception regarding the necessity and importance of empathy as a physician in patient-physician relationships. Therefore, in actual medical situations with patient-centered therapy, to enhance the levels of physician empathy, medical education should focus on the understanding of other persons’ opinions and interpersonal interactions accompanied by empathic concern.

Dynamic consent aims to empower research partners and facilitate active participation in the research process. Used within the context of biobanking, it gives individuals access to information and control to determine how and where their biospecimens and data should be used. We present Dwarna—a web portal for ‘dynamic consent’ that acts as a hub connecting the different stakeholders of the Malta Biobank: biobank managers, researchers, research partners, and the general public. The portal stores research partners’ consent in a blockchain to create an immutable audit trail of research partners’ consent changes. Dwarna’s structure also presents a solution to the European Union’s General Data Protection Regulation’s right to erasure—a right that is seemingly incompatible with the blockchain model. Dwarna’s transparent structure increases trustworthiness in the biobanking process by giving research partners more control over which research studies they participate in, by facilitating the withdrawal of consent and by making it possible to request that the biospecimen and associated data are destroyed., peer-reviewed

International audience; This paper proposes a composable "Just in Time Architecture" for Data Science (DS) Pipelines named JITA-4DS and associated resource management techniques for configuring disaggregated data centers (DCs). DCs under our approach are composable based on vertical integration of the application, middleware/operating system, and hardware layers customized dynamically to meet application Service Level Objectives (SLO-application-aware management). Thereby, pipelines utilize a set of flexible building blocks that can be dynamically and automatically assembled and reassembled to meet the dynamic changes in the workload's SLOs. To assess disaggregated DC's, we study how to model and validate their performance in large-scale settings.

Under the scope of UK’s net zero emissions target by 2050, decarbonisation of the housing sector is necessary. With the need to move to a smarter energy system with flexible demand, there is a drive to install batteries in homes to maximise the utilisation of local renewables. This paper empirically examines the effectiveness of distributed energy resources (DERs) comprising smart home batteries coupled with rooftop solar PV on actual energy use and peak demand in three dwellings designed to high thermal standards in York, England. The paper also explores the change in daily energy use and performance of DERs during the Covid-19 lockdown period (23 March to 31 May 2020). All three dwellings were occupied continuously by families and have identical heating systems (district heating), rooftop solar PV (4 kWp) and home batteries (14 kWh). The energy use, generation and charge-discharge of the batteries were monitored every 5 min using remote sensors.

Currently, internet is the most wanted medium of communication and most used by people or entities. Unquestionably, it has transformed significantly consumer behaviour, namely in the access to information or in the way a product or service is purchased.

International audience; We extend the existing encoding of abstract argumentation frameworks in DL-PA (Dynamic Logic of Propositional Assignments) in order to capture different formalisms for arguing with qualitative forms of uncertainty. More in particular, we encode the main reasoning tasks of (rich) incomplete argumentation frameworks and control argumentation frameworks. After that, and inspired by our encoding, we define and study a new class of structures that are shown to be maximally expressive: constrained incomplete argumentation frameworks.

OpenSim is a widely used biomechanics simulator with several anatomically accurate human musculo-skeletal models. While OpenSim provides useful tools to analyse human movement, it is not fast enough to be routinely used for emerging research directions, e.g., learning and simulating motor control through deep neural networks and Reinforcement Learning (RL). We propose a framework for converting OpenSim models to MuJoCo, the de facto simulator in machine learning research, which itself lacks accurate musculo-skeletal human models. We show that with a few simple approximations of anatomical details, an OpenSim model can be automatically converted to a MuJoCo version that runs up to 600 times faster. We also demonstrate an approach to computationally optimize MuJoCo model parameters so that forward simulations of both simulators produce similar results.

International audience; Although predictive forward gait simulators have existed for a little while, only few sensitivity studies have been proposed. We present a study analysing precision, accuracy, stability and sensitivity of such simulator. We studied a gait simulator based on the trajectory mimicking method proposed by Lee et al. [1]. We assessed the effect of motion variation on the resulting gait simulation using the sensitivity analysis method proposed by Saltelli et al. [2]. Our method supports the use of this simulator for prediction.

This contribution is concerned with the motion planning for underactuated Euler-Bernoulli beams. The design of the feedforward control is based on a differential parametrization of the beam, where all system variables are expressed in terms of a free time function and its infinitely many derivatives. In the paper, we derive an advantageous representation of the set of all formal differential parametrizations of the beam. Based on this representation, we identify a well-known parametrization, for the first time without the use of operational calculus. This parametrization is a flat one, as the corresponding series representations of the system variables converge. Furthermore, we discuss a formal differential parametrization where the free time function allows a physical interpretation as the bending moment at the unactuated boundary. Even though the corresponding series do not converge, a numerical simulation using a least term summation illustrates the usefulness of this formal differential parametrization for the motion planning.