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The conventional wisdom is that American universities transfer technologies more rapidly and more effectively than their European counterparts. While this appears to be true, it is important to note that there are substantial cultural, legal, and regulatory differences between the two regions, and even within Europe. We believe that European universities can enhance their effectiveness in technology transfer if this function is given more visibility and prestige, enhanced public support, and formal procedures are implemented to facilitate benchmarking. In this paper, we examine some of the differences in university technology transfer in Europe and the U.S., and discuss these new approaches, which have recently been fully supported by the European Commission.

This paper presents an experimental investigation of comparisons in attitudes towards risk between risk managers and non-risk managers. The two groups of managers responded to a number of hypothetical business decisions and their risk-taking attitude in each decision was measured by expressing the degree to which their actual choice differed from the expected monetary value as a fraction of the absolute value of the expected monetary value. Fifty per cent of the decisions involved a prospect of profit or break even, while the remainder held out only the prospect of a loss or no loss. A significant difference was detected in the attitudes of managers in each group. There was a great similarity in attitude over all subjects in those decisions involving profit but in potential loss-producing problems the risk managers were considerably more risk averse. Rather than resulting in a contempt for loss the risk managers' familiarity with loss-producing events would seem to have led them to a far higher degree of caution. The adaptability of non-risk managers to the loss situation together with the methods of management education are both called into question by these findings.

Purpose Proximal hamstring tendinopathy (PHT) presents as localised lower buttock pain with tasks such as squatting and sitting. It is a condition that occurs at all ages and levels of sporting participation and can cause disability with sport, work, and activities of daily living. This paper details a pilot trial protocol for investigating the effectiveness of individualised physiotherapy compared to extracorporeal shockwave therapy (ESWT) on pain and strength in people with PHT. Methods The study is an assessor-blinded, pilot randomised controlled trial (RCT). One hundred participants with PHT will be recruited from the local community and sporting clubs. Participants will be randomised to receive six sessions of either individualised physiotherapy or ESWT, with both groups also receiving standardised education and advice. Primary outcomes will be global rating of change on a 7-point Likert scale, and the Victorian Institute of Sport—Hamstring (VISA-H) scale, measured at 0, 4, 12, 26 and 52 weeks. Secondary outcomes will include sitting tolerance, the modified Physical Activity Level Scale, eccentric hamstring strength, modified Tampa scale for kinesiophobia, the Örebro Musculoskeletal Pain Screening Questionnaire Short Form (ÖMPSQ-SF), Numerical Pain Rating Scale (NPRS) for average and worst pain, participant adherence, the Pain Catastrophizing scale, satisfaction scores, and quality of life. Data will be analysed on an intention to treat basis, with between-group effects estimated using linear mixed models for continuous data and Mann Whitney U tests for ordinal data. Conclusions This pilot RCT will compare individualised physiotherapy versus ESWT for PHT. The trial will determine feasibility and estimated treatment effects to inform a definitive trial in the future. Trial registration The trial has been prospectively registered with the Australia & New Zealand Clinical Trials Registry (ACTRN12621000846820), registered 1 July 2021, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=373085

Women who were previously treated for breast cancer (BC) are an important particular subgroup of women at intermediate BC risk. Their breast follow-up should be planned taking in consideration a 1.0-1.5 % annual rate of loco-regional recurrences and new ipsilateral or contralateral BCs during 15-20 years, and be based on a regional/district invitation system. This activity should be carried out by a Department of Radiology integrating screening and diagnostics in the context of a Breast Unit. We recommend the adoption of protocols dedicated to women previously treated for BC, with a clear definition of responsibilities, methods for invitation, site(s) of visits, methods for clinical and radiological evaluation, follow-up duration, role and function of family doctors and specialists. These women will be invited to get a mammogram in dedicated sessions starting from the year after the end of treatment. The planned follow-up duration will be at least 10 years and will be defined on the basis of patient's age and preferences, taking into consideration organizational matters. Special agreements can be defined in the case of women who have their follow-up planned at other qualified centers. Dedicated screening sessions should include: evaluation of familial/personal history (if previously not done) for identifying high-risk conditions which could indicate a different screening strategy; immediate evaluation of mammograms by one or, when possible, two breast radiologists with possible addition of supplemental mammographic views, digital breast tomosynthesis, clinical breast examination, breast ultrasound; and prompt planning of possible further workup. Results of these screening sessions should be set apart from those of general female population screening and presented in dedicated reports. The following research issues are suggested: further risk stratification and effectiveness of follow-up protocols differentiated also for BC pathologic subtype and molecular classification, and evaluation of different models of survivorship care, also in terms of cost-effectiveness.

Localization of sensor nodes in wireless sensor networks (WSNs) is very important since it associates spatial context with the data collected by sensor nodes and used in applications. Rapid development of wireless sensor technologies and wide applications of wireless sensor networks have also made security in sensor localization a primary concern as well as a great challenge. Without adequate security measures, the performance of sensor localization, e.g., the accuracy of localization results, cannot be ensured in hostile environments. In this paper, we propose a trust-based secure sensor localization scheme (TSLS) for WSNs following the theory of neural network (NN). The proposed TSLS scheme can ensure that unknown sensor nodes will get credible information to perform localization through the evaluation of beacon nodes in a WSN. The evaluation model is comprised of the evaluation of both the identity and the behavior of beacon nodes as well as a filtering mechanism to deal with the slander behaviors between beacon nodes. Simulation results show that the proposed TSLS scheme can improve the accuracy of sensor localization in hostile environments for both static and dynamic WSNs.

This paper reports on current initiation and prevalence rates, in Western Australia, differentiating 'any' breastfeeding with 'exclusive' breastfeeding whilst exploring patterns and reasons for stopping breastfeeding. The results presented are part of a larger study examining women's perceptions of care and wellbeing in the early postnatal period. A cross sectional survey was used to examine infant feeding practices during the hospital stay and at 9 weeks post birth from Western Australian women with a registered live birth between February and June 2006. Data obtained from 2,669 women revealed a 93% (n = 2,472) initiation rate of any breastfeeding. More multiparous women (73.5%) were exclusively breastfeeding in hospital compared to primiparous women (65.2%), which decreased to 57.1 and 49.2%, respectively at 9 weeks. Of those who had ceased by 9 weeks, more multiparous women (71.1%) ceased before 3 weeks. Reasons cited for ceasing in order of frequency were insufficient milk supply, infant related reasons, pain and discomfort and emotional reasons. Younger maternal age, primiparous women, lower maternal education levels, offering a combination of breast milk and formula in hospital and caesarean birth were significant independent predictors of early cessation. Although initiation rates including "any" breast milk are meeting NHMRC dietary guidelines of 90%, the 60% target of exclusive breastfeeding is not being achieved for 3 months or in fact at 9 weeks. Targeted support for at risk groups such as younger, less well-educated, primiparous women must continue. Evidence based policies to protect breastfeeding must address the practice of offering formula to breastfed infants in hospital and the impact of increasing interventions such as caesarean births.

The teaching–learning-based optimization algorithm (TLBO) is an efficient optimizer. However, it has several shortcomings such as premature convergence and stagnation at local optima. In this paper, the strengthened teaching–learning-based optimization algorithm (STLBO) is proposed to enhance the basic TLBO’s exploration and exploitation properties by introducing three strengthening mechanisms: the linear increasing teaching factor, the elite system composed of new teacher and class leader, and the Cauchy mutation. Subsequently, seven variants of STLBO are designed based on the combined deployment of the three improved mechanisms. Performance of the novel STLBOs is evaluated by implementing them on thirteen numerical optimization tasks. The results show that STLBO7 is at the top of the list, significantly better than the original TLBO. Moreover, the remaining six variants of STLBO also outperform TLBO. Finally, a set of comparisons are implemented between STLBO7 and other advanced optimization techniques. The numerical results and convergence curves prove that STLBO7 clearly outperforms other competitors, has stronger local optimal avoidance, faster convergence speed and higher solution accuracy. All the above manifests that STLBOs has improved the search performance of TLBO. Data Availability Statements: All data generated or analyzed during this study are included in this published article.

PIM-SM (Protocol Independent Multicast-Sparse Mode) is a main multicast routing protocol in the IPv6 (Internet Protocol version 6). It can use either a shared tree or a shortest path tree to deliver data packets, consequently the multicast IP lookup engine requires, in some cases, two searches to get a correct lookup result according to its multicast forwarding rule, and it may result in a new requirement of doubling the lookup speed of the lookup engine. The ordinary method to satisfy this requirement in TCAM (Ternary Content Addressable Memory) based lookup engines is to exploit parallelism among multiple TCAMs. However, traditional parallel methods always induce more resources and higher design difficulty. We propose in this paper a novel approach to solve this problem. By arranging multicast forwarding table in class sequence in TCAM and making full use of the intrinsic characteristic of the TCAM, our approach can get the right lookup result with just one search and a single TCAM, while keeping the hardware of lookup engine unchanged. Experimental results have shown that the approach make it possible to satisfy forwarding IPv6 multicast packets at the full link rate of 20 Gb/s with just one TCAM with the current TCAM chip.

This paper presents a split Hopkinson pressure bar technique to obtain compressive stress-strain data for rock materials. This technique modifies the conventional split Hopkinson bar apparatus by placing a thin copper disk on the impact surface of the incident bar. When the striker bar impacts the copper disk, a nondispersive ramp pulse propagates in the incident bar and produces a nearly constant strain rate in a rock sample. Data from experiments with limestone show that the samples are in dynamic stress equilibrium and have constant strain rates over most of the test durations. In addition, the ramp pulse durations can be controlled such that samples are unloaded just prior to failure. Thus, intact samples that experience strains beyond the elastic region and postpeak stresses can be retrieved for microstructural evaluations. The paper also presents analytical models that predict the time durations for sample equilibrium and constant strain rate. Model predictions are in good agreement with measurements.

For medical students first entering the clinical space in July 2020, the unique challenges related to the coronavirus pandemic threatened to amplify the psychological distress associated with clerkship rotations. This study aimed to characterize the mental health of third-year medical students starting clinical clerkships in the midst of a pandemic by assessing symptoms of major depressive disorder (MDD), generalized anxiety disorder (GAD), and posttraumatic stress disorder (PTSD) as well as risk, coping, and protective factors associated with psychological outcomes. Of 147 third-year medical students at the Icahn School of Medicine at Mount Sinai in New York City, 110 (75%) participated in this prospective survey-based study with 108 included in the final analysis. 43 (39.8%) respondents screened positive for symptoms of either MDD, GAD, or PTSD. Multiple regression analyses revealed that greater overall symptom severity was associated with more avoidant coping, more traumatic events witnessed, poorer student and leisure functioning, lower trait emotional stability, and lower social support. Worries related to COVID-19 did not significantly influence outcome variables. To better understand the role of the pandemic on psychological outcomes in third-year medical students, additional research should focus on the trajectory of these outcomes over the year during the coronavirus pandemic. Supplementary Information The online version contains supplementary material available at 10.1007/s11126-021-09955-2.

Irinotecan-based regimens are used worldwide for the treatment of several recurrent or advanced gastrointestinal malignancies. In this paper we describe the cases of four patients treated in our institution who developed acute dysarthria while receiving intravenous infusion of irinotecan. In all our cases, dysarthria occurred during the infusion of the first course of irinotecan, and then resolved rapidly without any sequelae. Imaging of the brain was performed, but failed to show any evidence of an acute neurological event. We also reviewed the literature on this very uncommon adverse event. The pathogenesis of irinotecan-induced dysarthria is still unknown and is not completely elucidated by the current pharmacodynamic or kinetic explanations; therefore, we could only hypothesize some assumptions.

Transfer learning is a widely investigated learning paradigm that is initially proposed to reuse informative knowledge from related domains, as supervised information in the target domain is scarce while it is sufficiently available in the multiple source domains. One of the challenging issues in transfer learning is how to handle the distribution differences between the source domains and the target domain. Most studies in the research field implicitly assume that data distributions from the source domains and the target domain are similar in a well-designed feature space. However, it is often the case that label assignments for data in the source domains and the target domain are significantly different. Therefore, in reality even if the distribution difference between a source domain and a target domain is reduced, the knowledge from multiple source domains is not well transferred to the target domain unless the label information is carefully considered. In addition, noisy data often emerge in real world applications. Therefore, considering how to handle noisy data in the transfer learning setting is a challenging problem, as noisy data inevitably cause a side effect during the knowledge transfer. Due to the above reasons, in this paper, we are motivated to propose a robust framework against noise in the transfer learning setting. We also explicitly consider the difference in data distributions and label assignments among multiple source domains and the target domain. Experimental results on one synthetic data set, three UCI data sets and one real world text data set in different noise levels demonstrate the effectiveness of our method.

Regression testing is required in each iteration of microservice systems. Regression testing selection, which reduces testing costs by selecting a subset from the original test cases, is one of the main techniques to optimize regression testing. Existing techniques mainly rely on the information retrieved from artifacts such as code files and system models. For microservice systems with service autonomy, development method diversity and a large amount of services, such artifacts are too difficultly obtained and costly processed to apply those approaches. This paper presents a regression testing selection approach called MRTS-BP, which needs the API gateway layer logs instead of code files and system models as inputs. By parsing the API gateway layer logs, our approach establishes the service dependency matrix, which in further is transformed into a directed graph with the services as nodes. Then, to find out which test cases are affected by service changes, an algorithm based on belief propagation is presented to compute the quantitative results of service-change propagation from the directed graph. Finally, the relationships between original test cases and service-change propagation results are established to select test cases with three strategies. To evaluate the efficiency of MRTS-BP, the empirical study based on four microservice systems is presented. A typical technique RTS-CFG is compared with MRTS-CFG and four experiments are setup to investigate four research questions. The results show that MRTS-BP can not only reduce the number of test cases by half compared with the retest-all strategy while ensuring the safety, but also save at least 20% testing time costs more than that of RTS-CFG. MRTS-BP is more practical than the techniques relying on the artifacts when the latter cannot be implemented due to the artifacts are difficult to obtain and process.

Industrial control applications are usually designed by domain experts instead of software engineers. These experts frequently use visual programming languages based on standards such as IEC 61131-3 and IEC 61499. The standards apply model-based engineering concepts to abstract from hardware and low-level communication. Developing industrial control software is challenging due the fact that such systems are usually one-of-a-kind systems that have to be maintained for many years. These challenges, together with the growing complexity of control software, require very usable model-based development environments for visual programming languages. However, so far only little empirical research exists on the practical usefulness of such environments, i.e., their usability and utility. In this paper, we discuss common control software maintenance tasks and tool capabilities based on existing research and show the realization of these capabilities in 4diac IDE. We first performed a walkthrough of the demonstrated capabilities using the cognitive dimensions of notations framework from the field of human-computer interaction. We then improved the tool and conducted a user study involving ten industrial automation engineers, who used 4diac IDE in a realistic control software maintenance scenario. Our findings demonstrate how the usefulness of IDEs can be successfully investigated using a multi-phase approach that includes a walkthrough and a user study. We discuss lessons learned and derive general implications with respect to large-scale applications for developers of IDEs that we deem applicable in the context of (visual) model-based engineering tools.

The transition of classical power distribution grids towards actively operated smart grids locates new functionality into intelligent secondary substations. Increased computational power and newly attained communication infrastructure in thousands of secondary substations allow for the distributed realization of sophisticated functions, which were inconceivable a few years ago. These novel functions (e.g., voltage and reactive power control, distributed generation optimization or decentralized market interaction) can primarily be realized by software components operated on powerful automation devices located on secondary substation level. The effective and safe operation of such software is crucial and has a broad set of requirements. In this paper, we present a flexible and modular software ecosystem for automation devices of substations, which is able to handle these requirements. This ecosystem contains means for high performance data exchange and unification, automatic application provisioning and configuration functions, dependency management, and others. The application of the ecosystem is demonstrated in the context of a field operation example, which has been developed within an Austrian smart grid research project.

In this paper, we proposed a new low-light image enhancementapproach to overcome the above limitations. Theproposed algorithm is named as Nature Preserving LowlightImage Enhancement (NPLIE). NPLIE estimates initialillumination and performs optimal refinement. The proposedalgorithm computes the reflectance component through anelement-wise division of input image by illumination. The enhanced image is obtained as a product of adjusted illumination and reflectance component. In this work, we estimate initialilluminance from structure-aware smoothening of a low-lightimage using guided filters of variable box sizes. We computerefined illumination by solving the proposed multi-objective

This paper builds on previous work that investigated anticancer drugs as 'informed materials', i.e., substances that undergo an informational enrichment that situates them in a dense relational web of qualifications and measurements generated by clinical experiments and clinical trials. The paper analyzes the recent transformation of anticancer drugs from 'informed' to 'informing material'. Briefly put: in the post-genomic era, anti-cancer drugs have become instruments for the production of new biological, pathological, and therapeutic insights into the underlying etiology and evolution of cancer. Genomic platforms characterize individual patients' tumors based on their mutational landscapes. As part of this new approach, drugs targeting specific mutations transcend informational enrichment to become tools for informing (and destabilizing) their targets, while also problematizing the very notion of a 'target'. In other words, they have become tools for the exploration of cancer pathways and mechanisms. While several studies in the philosophy and history of biomedicine have called attention to the heuristic relevance and experimental use of drugs, few have investigated concrete instances of this role of drugs in clinical research.

Repairing technologies enable a huge economical potential, especially in high-technology sectors like the aerospace industry with complex capital goods. Companies in these industries retain their knowledge concerning repair technologies, in order to maintain the advantage over their competitors. Thus, the quantity of publications dealing with technological challenges and scientific basics for repairing are limited. The main purpose of this literature review is to collect published information on common technologies regarding the regeneration of turbine blades to extract scientific basics and to present challenges that are currently unattended. The attention of this paper is focused particularly on the machining process. Due to the variety of damages on engine blades, the general process chain has to be adapted partly or in whole for each component. In the second part of this paper, a general process chain for regeneration is presented, including a classification of repair methods. The main interest of the third part addresses the recontouring, which is the final shape cutting of repaired engine blades. At this point, the previous research and occurring challenges are pointed out in two stages related to the machining process—before and during machining. In the last part, the achievable workpiece quality after machining is discussed.

This paper introduces a novel iterative approach for interactive single or multiple foreground co-segmentation using semantic information. A quadratic cost function based on a graph model is proposed. The cost function includes a ‘smoothness’ and a ‘label-information’ terms. The ‘label-information’ term propagates the feature-level and contextual information. This information is updated based on the features and neighborhood patterns of all the images after each iteration. The approach can be easily implemented with a few scribbles on a few random images. The paper also proposes a model called Neighborhood Pattern Model (NPM) for contextual information. Along with feature level information, NPM helps to give semantic meanings to the labels (i.e., foreground(s) and background). Moreover, in the case of insufficient features (i.e., same features for different labels), NPM can be effective to distinct the labels. Experimental results on two benchmark datasets, iCoseg and FlickrMFC, illustrate the better performance of the proposed approach over the current state-of-the-art co-segmentation methods.

Background Despite an abundance of information on the risk factors of SARS-CoV-2, there have been few US-wide studies of long-term effects. In this paper we analyzed a large medical claims database of US based individuals to identify common long-term effects as well as their associations with various social and medical risk factors. Methods The medical claims database was obtained from a prominent US based claims data processing company, namely Change Healthcare. In addition to the claims data, the dataset also consisted of various social determinants of health such as race, income, education level and veteran status of the individuals. A self-controlled cohort design (SCCD) observational study was performed to identify ICD-10 codes whose proportion was significantly increased in the outcome period compared to the control period to identify significant long-term effects. A logistic regression-based association analysis was then performed between identified long-term effects and social determinants of health. Results Among the over 1.37 million COVID patients in our datasets we found 36 out of 1724 3-digit ICD-10 codes to be statistically significantly increased in the post-COVID period (p-value p-value p-value Conclusion We identified several long-term effects of SARS-CoV-2 through a self-controlled study on a cohort of over one million patients. Furthermore, we found that while age and gender are commonly associated with the long-term effects, other social determinants of health such as race, income and education levels have rare or no significant associations.

The field of Internet of Things (IoT) and Remote Patient Monitoring (RPM) system has been developing rapidly causing these technologies to be adopted in multiple domains all around the world. This paper presents the design of a real-time remote monitoring system for vulnerable patients. The proposed device measures body temperature, heart rate, blood pressure, Oxygen saturation level and electrocardiogram signals (ECG) of patients admitted into Intensive Care Unit (ICU) or Operating Room (OR), and automatically updates all these data on a corresponding hybrid cloud server in every 45 seconds to which a corresponding physician has access. More than 2000 cycles are conducted within a day where all five biomedical parameters are measured and stored on each cycle. Furthermore, the system includes alarm triggering system and emergency Short Message Service (SMS) alert service using GSM technology in order to initiate proper control actions in case of emergencies. This device facilitates doctors, nurses, relatives of patients, or anyone around the world to monitor real-time parameters of patients. The process was tested on 10 patients obtaining a satisfactory outcome. The system proposed in this paper reveals a new vision of biomedical engineering which tends to the era of remotely controllable ICU environment with more interactive, efficient and fast response of both patient and corresponding physicians.

In this paper we investigate the innovative patterns of Chilean productive sectors applying multivariate analysis methods such as factor and cluster analysis. Three main patterns are detected in our sample according to the type of innovation introduced and the motivation behind it: product strategy innovators, cost strategy innovators and non-innovators. Starting on this clustering, we analyze the relationship between innovative pattern (or type of innovation introduced) and wage for professional category. We found a positive impact of product innovations on wages for all professional groups except unskilled manual workers. Controlling for unobserved firm heterogeneity through a fixed effect panel estimator, coefficients are not significant anymore for all professional categories. Only for innovations in marketing we can still register a wage premium earned by high skilled workers. Finally, we do not register a polarization effect as the one detected for Europe by recent literature.

This paper presents the experimental validation of an active control approach to improve the damping of a spindle system for wood machining. The active control was implemented using piezoelectric actuators in push–pull configuration with real-time control capabilities. The adaptive control strategy based on the linear quadratic Gaussian is first modeled within MATLAB/Simulink and then implemented on the real spindle system. Experimental tests were performed on the small-scale planer prototype to validate the simulation models. Moreover a cutting force observer which can be used to continuously monitor the cutting process is also implemented and its performance is presented.

Recent work in qualitative shape recovery and object recognition has focused on solving the “what is it” problem, while avoiding the “where is it” problem. In contrast, typical CAD-based recognition systems have focused on the “where is it” problem, while assuming they know what the object is. Although each approach addresses an important aspect of the 3-D object recognition problem, each falls short in addressing the complete problem of recognizing and localizing 3-D objects from a large database. In this paper, we first synthesize a new approach to shape recovery for 3-D object recognition that decouples recognition from localization by combining basic elements from these two approaches. Specifically, we use qualitative shape recovery and recognition techniques to provide strong fitting constraints on physics-based deformable model recovery techniques. Secondly, we extend our previously developed technique of fitting deformable models to occluding image contours to the case of image data captured under general orthographic, perspective, and stereo projections. On one hand, integrating qualitative knowledge of the object being fitted to the data, along with knowledge of occlusion supports a much more robust and accurate quantitative fitting. On the other hand, recovering object pose and quantitative surface shape not only provides a richer description for indexing, but supports interaction with the world when object manipulation is required. This paper presents the approach in detail and applies it to real imagery.

The outbreak of Coronavirus Disease 2019 (COVID-19) occurred at the end of 2019, and it has continued to be a source of misery for millions of people and companies well into 2020. There is a surge of concern among all persons, especially those who wish to resume in person activities, as the globe recovers from the epidemic and intends to return to a level of normalcy. Wearing a face mask greatly decreases the likelihood of viral transmission and gives a sense of security, according to studies. However, manually tracking the execution of this regulation is not possible. The key to this is technology. We present a Deep Learning-based system that can detect instances of improper use of face masks. A dual-stage Convolutional Neural Network (CNN)architecture is used in our system to recognie masked and unmasked faces. This will aid in the tracking of safety breaches, the promotion of face mask use, and the maintenance of a safe working environment. This paper will automate the tasks of mask detection in public places when incorporated with CCTV cameras and will alert the system manager when a person without mask or wearing incorrect mask tries to enter. This paper includes multi face detection model which has the potential to target and identify a group of people whether they are wearing masks or not. We tried to collect various facial pictures and tried to identify the face Region of Interest (ROI), and then we separated it. Applying facial milestones, to permit the restriction the eyes, nose, mouth, and so. face was then completed and we tried to detect the presence of mask. To prepare a custom face cover locator, breaking our venture into two unmistakable stages was required, each with its own separate sub-steps. 1. Preparing: Here, stacking our face veil discovery dataset from plate, preparing a model on this dataset, and afterward serializing the face cover locator to circle was the focus. 2. Sending: Once the face veil identifier is prepared, the accompanying advance of stacking the cover finder, performing face recognition, and afterward characterizing each face as with veil or without veil, can be executed.

Communication is the main driving force behind the emerging intelligent transportation systems, which are expected to make traveling safer, more ecological, and faster. The most challenging among all the different communication technologies that will be used are the direct vehicle-to-vehicle communications, because vehicles move with high speeds and in different directions. Apart from that, it is expected that vehicles should be able to organize themselves in an ad hoc network without any assistance from outside entities, such as road side units. To make the ad hoc network less dynamic and communications more reliable, vehicles with similar movement patterns can be grouped together in clusters. Clustering is a well-known method for organizing ad hoc networks and is used in mobile and wireless sensor networks, but with different constraints and goals, so new clustering solutions for vehicular ad hoc networks (VANET) have to be developed. In recent years, this has been a hot topic among researchers and many different clustering algorithms for VANET have been proposed. In this paper, we propose a new clustering metric for VANET, named vehicle interconnection metric, which is based on sending periodic beacons among vehicles and reflects the communication abilities between them. We also propose a new clustering algorithm whose primary goal is increased connectivity and lower number of disconnects. The working principle of this algorithm is also inverted compared to others and uses unneeded cluster head elimination instead of cluster head election. Mathematical analysis of memory usage and communication overhead are provided, predicting low-resource usage. Simulation results, obtained with the ns-3 network simulator and the SUMO vehicle movement simulator, have confirmed the analysis and expected performance in terms of cluster head duration, number of connectivity losses and role switches.

The position-based routing of Vehicular Ad hoc Network (VANET) vulnerable to various security attacks because of dependency on computing, control, and communication technologies. The Internet of Things (IoT)-enabled VANET application leads to the challenges such as integrity, access control, availability, privacy protection, non-repudiation, and confidentiality. Several security solutions have been introduced for two decades in two categories as cryptography-based and trust-based. Due to the high computation complexity, cryptography-based solutions are outperformed by recent intelligent trust-based mechanisms. The trust-based techniques are lightweight and effective against the well-known security threats in VANET. The objective of this paper has to design a novel position-based routing in which the conduct of vehicles assessed to accomplish reliable VANET communications. Attack Resilient Position-based VANET Protocol (ARPVP) proposed to detect and prevent malicious vehicles in the network using the trust evaluation technique and artificial intelligence (AI). In the first phase of ARPVP, the periodic self-trust assessment algorithm has designed using various trust parameters to detect unreliable vehicles in the network. In the second phase of ARPVP, the position-based route formation algorithm has designed using the AI technique Ant Colony Optimization (ACO). ACO solves the problem of reliable route formation by neglecting the attacker's using a trust-based fitness function. The trust parameters of each vehicle as mobility, buffer occupancy, and link quality parameters had measured in both phases of ARPVP. Simulation outcomes of the proposed model outperformed state-of-art protocols in terms of average throughput, communication delay, overhead, and Packet Delivery Ratio (PDR).

The worldwide epidemic of obesity and the metabolic syndrome has made nonalcoholic fatty liver disease (NAFLD), one of the most important liver diseases of our time. NAFLD is now the commonest cause of abnormal liver test results in industrialized countries and its incidence is rising. The current treatment of nonalcoholic steatohepatitis (NASH) has focused on lifestyle modification to achieve weight loss and modification of risk factors, such as insulin resistance, dyslipidemia, and hyperglycemia associated with the metabolic syndrome. With our increasing understanding of the pathogenesis of NASH, have come a plethora of new pharmacologic options with great potential to modify the natural history of NAFLD and NASH. This article focuses on a number of novel molecular targets for the treatment of NASH as well as the evidence for currently available therapy. It should be noted, however, that in part because of the long natural history of NASH and NAFLD, no therapy to date has been shown to unequivocally alter liver-related morbidity and mortality in these patients.

To sustain competitive performance in dynamic business, technical, economic and social environments, change capability is an essential characteristic property of any small- to medium-sized enterprise (SME). It follows that the business, engineering and production systems deployed by SMEs must be transformable, reconfigurable and interoperable. Likewise the models we deploy to engineer, change and operate SME systems must themselves be transformable, reconfigurable and interoperable. This paper reports on the application of new modelling concepts and modelling methods that are designed to facilitate change capability in SMEs. ‘Role’ and ‘resource’ modelling concepts are used in complementary forms to describe the ‘how’ and ‘who’ of SME systems in such ways that inherent complexities and change requirements can be faced by managers and engineers responsible for the life cycle engineering of SMEs. With respect to ‘role’ modelling the paper describes how an extension of standard enterprise modelling techniques can enable the decomposition and formal multi-level of abstraction description of sets of process-oriented roles; such as those required in any specific SME case to realise changing products and services for customers. Regarding resource modelling the paper describes how the ‘Dynamic Producer Unit’ (DPU) concept enables decomposition and multi-level of abstraction description of people, machine and IT system elements in a coherent manner. The paper goes on to describe a novel method of applying ‘role’ and ‘DPU’ concepts. This novel method takes the form of an ‘Integrated Production Control Modelling Approach’ and a support ‘Simulator’. By being based upon ‘role’ and ‘DPU’ modelling concepts the ‘coherent and flexibly integrated’ use of static enterprise models, causal loop models, simulation models and workflow models is facilitated. Finally, the paper exemplifies the use of the IPCMA and IPCMS with reference to the changing requirements and systems of an SME making many types of furniture product with a common set of resources.

This paper reasseses the original data of Stigler and Friedland's influential 1962 article on the effects of regulation, which studied U.S. electricity rates in the early 1900's. Whereas Stigler and Friedland contended the economic effects of regulation serve those regulated regardless of the statutory characteristics of the regulations, a critical look at their data suggests the regulations studied did have their intended effect.

Spatial downsampling layers are favored in convolutional neural networks (CNNs) to downscale feature maps for larger receptive fields and less memory consumption. However, for discriminative tasks, there is a possibility that these layers lose the discriminative details due to improper pooling strategies, which could hinder the learning process and eventually result in suboptimal models. In this paper, we present a unified framework over the existing downsampling layers (e.g., average pooling, max pooling, and strided convolution) from a local importance view. In this framework, we analyze the issues of these widely-used pooling layers and figure out the criteria for designing an effective downsampling layer. According to this analysis, we propose a conceptually simple, general, and effective pooling layer based on local importance modeling, termed as {\em Local Importance-based Pooling} (LIP). LIP can automatically enhance discriminative features during the downsampling procedure by learning adaptive importance weights based on inputs. Experiment results show that LIP consistently yields notable gains with different depths and different architectures on ImageNet classification. In the challenging MS COCO dataset, detectors with our LIP-ResNets as backbones obtain a consistent improvement ($\ge 1.4\%$) over the vanilla ResNets, and especially achieve the current state-of-the-art performance in detecting small objects under the single-scale testing scheme., Comment: ICCV 2019 camera ready version. Code: https://github.com/sebgao/LIP

In this paper, we propose a stereo method specifically designed for image-based rendering. For effective image-based rendering, the interpolated views need only be visually plausible. The implication is that the extracted depths do not need to be correct, as long as the recovered views appear to be correct. Our stereo algorithm relies on over-segmenting the source images. Computing match values over entire segments rather than single pixels provides robustness to noise and intensity bias. Color-based segmentation also helps to more precisely delineate object boundaries, which is important for reducing boundary artifacts in synthesized views. The depths of the segments for each image are computed using loopy belief propagation within a Markov Random Field framework. Neighboring MRFs are used for occlusion reasoning and ensuring that neighboring depth maps are consistent. We tested our stereo algorithm on several stereo pairs from the Middlebury data set, and show rendering results based on two of these data sets. We also show results for video-based rendering.

Background In India, the prevalence of overweight among adolescents is on the rise, setting the stage for an increase in metabolic syndrome (MS). This paper presents the national prevalence of MS in adolescents in India. Methods A nationally representative data of adolescents (10–19 years) from the Comprehensive National Nutrition Survey was used. MS was defined based on the NCEP–ATP III criteria for adolescents. Bivariate analysis was used to report socio-demographic differentials in prevalence and to assess interstate variability. Multivariate logistic regression model was constructed to measure the association between socio-demographic characteristics and prevalence of MS. Census data from 2011 was projected to 2017 to calculate burden. Results The prevalence of MS was 5.2% among adolescents. 11.9%, 15.4%, 26.0%, 31.9% and 3.7% had central obesity, high blood pressure, hypertriglyceridemia, low HDL-cholesterol and high fasting glucose, respectively. The prevalence was higher among males (5.7% vs. 4.7%, adjusted odds ratio (AOR): 1.3, 95% confidence interval [CI]: 1.0, 1.6), those residing in urban areas (7.9% vs 4.2%, AOR: 1.4, 95% CI: 1.1, 1.8), and from wealthier households as compared to their counterparts (8.3% vs. 2.4%, AOR: 3.4, 95% CI: 2.1, 5.5). There was wide interstate variability in the prevalence of MS (0.5% – 16.5%). In 2017, 14.2 million adolescents had MS in India. Conclusions The prevalence of MS among adolescents in India is low and clustered in urban areas and richer households. Early prevention interventions promoting a healthy lifestyle, especially in high prevalence areas, are needed to keep MS from becoming a public health issue.

In the current era of the Internet of Things (IoT), various devices can provide more services by connecting to the Internet. However, the explosive growth of connected devices will cause the cloud core overload and significant network delays. To overcome these problems, the Mobile Edge Computing (MEC) network is proposed to provide most of the computing and storage near the radio access network to reduce the traffic of the core cloud network and provide lower latency for the terminal.Mobile edge computing can work with third parties to develop multiple services, such as mobile big data analysis and context-aware services. However, when there is a large amount of popular data accessed in a short period, the system must generate many replicas, which will not only reduce access efficiency but also cause additional traffic overhead. To improve the above problems, an Adaptive Replica Configuration Mechanism (ARCM) is proposed in this paper to predict the popularity of the file and make a replica to the low-blocking node. This method spreads the subsequent access workload by copying the popular file in advance to improve the overall performance of the system.

Background Since its discovery in 2012, over 1700 confirmed cases of Middle East Respiratory Syndrome (MERS) have been documented worldwide and more than a third of those cases have died. While the greatest number of cases has occurred in Saudi Arabia, the recent export of MERS-coronavirus (MERS-CoV) to Republic of Korea showed that a pandemic is a possibility that cannot be ignored. Due to the deficit of knowledge in transmission methodology, targeted treatment and possible vaccines, understanding this virus should be a priority. Our aim was to combine epidemiological data from literature with genetic information from viruses sequenced around the world to present a phylodynamic picture of MERS spread molecular level to global scale. Methods We performed a qualitative meta-analysis of all laboratory confirmed cases worldwide to date based on literature, with emphasis on international transmission and healthcare associated infections. In parallel, we used publicly available MERS-CoV genomes from GenBank to create a phylogeographic tree, detailing geospatial timeline of viral evolution. Results Several healthcare associated outbreaks starting with the retrospectively identified hospital outbreak in Jordan to the most recent outbreak in Riyadh, Saudi Arabia have occurred. MERS has also crossed many oceans, entering multiple nations in eight waves between 2012 and 2015. In this paper, the spatiotemporal history of MERS cases, as documented epidemiologically, was examined by Bayesian phylogenetic analysis. Distribution of sequences into geographic clusters and interleaving of MERS-CoV sequences from camels among those isolated from humans indicated that multiple zoonotic introductions occurred in endemic nations. We also report a summary of basic reproduction numbers for MERS-CoV in humans and camels. Conclusion Together, these analyses can help us identify factors associated with viral evolution and spread as well as establish efficacy of infection control measures. The results are especially pertinent to countries without current MERS-CoV endemic, since their unfamiliarity makes them particularly susceptible to uncontrollable spread of a virus that may be imported by travelers. Electronic supplementary material The online version of this article (doi:10.1186/s41256-016-0014-7) contains supplementary material, which is available to authorized users.

In this paper, we examine how mutual fund managers behave to fluctuations in market volatility. We use a sample of daily return from countries around the world to evaluate how manager perform to publicly available information. There is a lack of empirical studies that examine the relation between conditional market returns and conditional volatility on a global scale; we provide evidence across countries to answer this question. Our study provides new evidence about conditional mutual fund performance across countries. We find that during periods of high market volatility mutual funds reduce market exposure across all countries; this implies that systemic risk is particularly sensitive to changes in market volatility around the world.

This paper describes the development of a recognition system based on a simple ultrasonic range finder mounted on the end-effector of a robot arm. The system can recognize components based on both real and CAD generated image data. The latter provides a route to off-line training of the recognition system. A description of the requirements of a second-generation ultrasonic recognition system is given including the use of CAD data.

Background This paper describes the process and results of a health promotion intervention to engage lay communities using telephone and online communication, to improve their current responses to the spread of COVID-19. Methods An intervention was conducted from March to July 2020 in three districts of Sri Lanka. Seven ‘trigger’ stories were shared through telephone or online communication to stimulate brainstorming and to engage selected community members. Determinants were identified and prioritised through discussions, and potentially beneficial actions were implemented as agreed by participants. The process was monitored, outcomes were evaluated monthly, and activities were modified according to ongoing observations. Results A total of 638 families (both adults and children) involved actively in implementing useful actions and reported an increased sense of personal control. Potential risk groups, best feasible community safety precautions and preparation to face challenges in the event of infection reaching their community were identified during brainstorming sessions with community mobilisers. A majority reported that they felt more confident, united and less anxious about handling potential risks and problems. Other beneficial outcomes include lifestyle changes leading to healthier behaviours and a sense of greater control over the conditions that govern their lives. Conclusion Use of telephone and online communication was effective in generating desirable community changes.

The challenge of unsupervised person re-identification (ReID) lies in learning discriminative features without true labels. This paper formulates unsupervised person ReID as a multi-label classification task to progressively seek true labels. Our method starts by assigning each person image with a single-class label, then evolves to multi-label classification by leveraging the updated ReID model for label prediction. The label prediction comprises similarity computation and cycle consistency to ensure the quality of predicted labels. To boost the ReID model training efficiency in multi-label classification, we further propose the memory-based multi-label classification loss (MMCL). MMCL works with memory-based non-parametric classifier and integrates multi-label classification and single-label classification in a unified framework. Our label prediction and MMCL work iteratively and substantially boost the ReID performance. Experiments on several large-scale person ReID datasets demonstrate the superiority of our method in unsupervised person ReID. Our method also allows to use labeled person images in other domains. Under this transfer learning setting, our method also achieves state-of-the-art performance., Comment: CVPR2020

This paper presents a research on the effectiveness of digital learning in organisations and how it can be measured. This research aims to bridge the gap between scientific and practice-oriented research about digital learning. The focus will be on learning analytics and measuring its impacts on organisations with the goal to identify the potential of digital learning for organisations. This research is based on a systematic literature review and a survey analysis to answer the following research questions: What are the contexts in which digital learning can take place? What are the main metrics which allows measuring the efficiency of digital learning in organisational contexts? Is there a positive influence of the digital learning contexts supported by organisations, on skills development and impacts on performance analytics that measure the impacts in organisations? And what is the digital learning analytics that measures the impacts on organisations? What is the digital learning analytics that measures the impacts on organisations? The results showed that digital learning analytics can enhance the efficacy of the learning process impact in organisations and that contexts for learning supported on mobile technologies, tablet and smartphone applications become more and more popular among the employees. Additionally, the results helped to create a model of learning analytics and identify impacts of the learning process.

Pythagorean fuzzy set (PFS) is a broadening of intuitionistic fuzzy set that can represent the situations where the sum of membership and the non-membership values exceeds one. Adding parameterization to PFS we obtain a structure named as Pythagorean fuzzy soft set (PFSS). It has a higher capacity to deal with vagueness as it captures both the structures of a PFS and a soft set. Several practical situations demand the measure of similarity between two structures, whose sum of membership value and non-membership value exceeds one. There are no existing tools to measure the similarity between PFSS and this paper put forward similarity measures for PFSS. An axiomatic definition for similarity measure is proposed for PFSS and certain expressions for similarity measure are introduced. Further, some theorems which express the properties of similarity measures are proved. A comparative study between proposed expressions for similarity measure is carried out. Also, a clustering algorithm based on PFSS is introduced by utilizing the proposed similarity measure.

Different applications of Wireless Sensor Networks (WSNs) have different expectations from the working of Medium Access Control (MAC) protocols. Some value reliability more than delay incurred while some demand a fair trade-off for the factors like: Throughput, Bit Error Rate (BER) etc. This paper evaluates the performance of Wireless Personal Area Networks (WPANs) from 802.15.4 group for WSNs with modified algorithm which helps in reducing the Medium Access Delay and Delay in reaching of the packet from one end to another end. In this paper certain modifications to existing algorithm have been proposed for reducing the Medium Access Delay and to reduce the number of packets dropped. The result comparisons on the performance parameters like: network output load, generated acknowledged traffic, media access delay, battery consumed and delay in packet transmission from one end to another end that the back-off number & exponent values used for transmission play vital role for improving the performance of WSNs as they directly affect the number of packets dropped, successfully acknowledged and Medium Access Delay.

As of late, Convolutional Neural Networks have been very successful in segmentation and classification tasks. Magnetic resonance imaging (MRI) is a favored medical imaging method that comes up with interesting information for the diagnosis of different diseases.MR method is getting to be exceptionally well-known due to its non-invasive rule and for this reason, automated processing of this sort of image is getting noticed. MRI is effectively and widely used for tumor detection. Brain tumor detection is a popular medical application of MRI. Automating segmentation using CNN assists radiologists to lessen the high manual workload of tumor evaluation. CNN classification accuracy depends on network parameters and training data. CNN has the benefit of learning image features automatically directly out of multi-modal MRI images. In this survey paper, we have presented a summary of CNN's recent advancement in its technique applied on MRI images. The aim of this survey is to discuss various architectures and factors affecting the performance of CNN for learning features from different available MRI datasets. Based on the survey, section III (CNN for MRI analysis) comprises three subsections: A) MRI data and processing, B) CNN dimensionality, C) CNN architectures.

Real-time data transmission is one of the objectives of MANET (mobile ad-hoc network) to handle emergencies like a forest fire, flood, and earthquake. In this scenario, quick delivery of data is itself a challenging task for MANET and there is a possibility of load imbalance due to packet transmission in between source-destination pairs via the shortest path due to congestion or control overhead. In this paper, a novel routing protocol, called ACOLBR (ACO based load balancing routing), is design to control the congestion and balancing the load among the multiple paths in between source to destination. The similarity between the environment of Ant Colony and the MANET inspires the authors to apply ACO (Ant colony optimization) technique during routing in MANET to control congestion and balance the load in the network. In our proposal, two colonies of ants (red/blue) carry their packets based on the network condition. A decision variable is designed to select red/blue ant for transferring packets based on different network parameters such as bandwidth, energy, mobility, and distance. The selection of red ant means the route where the concentration of red pheromone is maximum and reverse is true for blue ant. This protocol is also concerns about the link failure during packet transmission in a route. Simulation results using OMNET++ show that ACOLBR outperforms ARA, ANTHOCNET, FACO, AODV, DSDV, DSR, CA-ARTT, and MOAODV in terms of load balancing efficiently in the route for data transmission in between source to destination.

In the present study, attempts were made to clarify the existence of a correlation between visually perceived and instrumentally measured specular gloss of a series of achromatic samples. To this end, seven achromatic physical scales of specular gloss each consisting of 10 or 11 samples were prepared using lithographically printed black, white, and five in between gray papers. The samples were visually assessed and subsequently quantified in terms of a visually uniform color constant lightness scale, by a panel of 14 observers in an especially designed unidirectional light compartment at three illumination/observation geometries, namely 20°/20°, 60°/60°, and 85°/85°. Four statistical parameters were utilized to determine the correlation between visually perceived and instrumentally measured specular gloss. The results show that the instrumental 60°/60° geometry is capable of efficiently quantifying the equivalent specular gloss as perceived by a human observer. Surprisingly, it was also possible to accurately predict the visually quantified specular gloss both at the 20° and the 85° geometries by the aid of applying special linear relationships derived from the instrumentally measured specular gloss of the 60° geometry.

Background Chronic disease is the leading cause of premature death globally, and many of these deaths are preventable by modifying some key behavioural and metabolic risk factors. This study examines changes in health behaviours among men and women at risk of diabetes or cardiovascular disease (CVD) who participated in a 6-month lifestyle intervention called the My health for life program. Methods The My health for life program is a Queensland Government-funded multi-component program designed to reduce chronic disease risk factors amongst at-risk adults in Queensland, Australia. The intervention comprises six sessions over a 6-month period, delivered by a trained facilitator or telephone health coach. The analysis presented in this paper stems from 9,372 participants who participated in the program between July 2017 and December 2019. Primary outcomes included fruit and vegetable intake, consumption of sugar-sweetened drinks and take-away, alcohol consumption, tobacco smoking, and physical activity. Variables were summed to form a single Healthy Lifestyle Index (HLI) ranging from 0 to 13, with higher scores denoting healthier behaviours. Longitudinal associations between lifestyle indices, program characteristics and socio-demographic characteristics were assessed using Gaussian Generalized Estimating Equations (GEE) models with an identity link and robust standard errors. Results Improvements in HLI scores were noted between baseline (Md = 8.8; IQR = 7.0, 10.0) and 26-weeks (Md = 10.0; IQR = 9.0, 11.0) which corresponded with increases in fruit and vegetable consumption and decreases in takeaway frequency (p p p p p Conclusions While participants showed improvements in dietary indicators, changes in alcohol consumption and physical activity were less amenable to the program. Additional research is needed to help understand the multi-level barriers and facilitators of behaviour change in this context to further tailor the intervention for priority groups.

Bioinformatics aims at applying computer science methods to the wealth of data collected in a variety of experiments in life sciences (e.g. cell and molecular biology, biochemistry, medicine, etc.) in order to help analysing such data and eliciting new knowledge from it. In addition to string processing bioinformatics is often identified with machine learning used for mining the large banks of bio-data available in electronic format, namely in a number of web servers. Nevertheless, there are opportunities of applying other computational techniques in some bioinformatics applications. In this paper, we report the application of constraint programming to address two structural bioinformatics problems, protein structure prediction and protein interaction (docking). The efficient application of constraint programming requires innovative modelling of these problems, as well as the development of advanced propagation techniques (e.g. global reasoning and propagation), which were adopted in Chemera, a system that is currently used to support biochemists in their research.

A novel architecture with full-duplex direct communications(FDDC) between any two ONUs for optical access network is proposed in this paper. By designing combination switch(CS) ingeniously, the architecture is finished to realize FDDC between any two ONUs in wavelength division multiplexing passive optical networks(WDM-PONs). By utilizing interconnected optical fibers between ONUs, the network has achieved three-level protections(feeder optical fiber protection, distributed optical fiber protection and interconnected optical fiber protection). By constructing expansion module(EM), the network can be flexibly expanded and optimized, and the CapEx and OpEx of network can be greatly reduced. Finally, by the simulation and analysis, the effectiveness of the proposed architecture is demonstrated.

Some structures may be subjected to blast loading while in service. This may cause damage or failure to the structural elements. This paper examines the performance of reinforced concrete beams using carbon fiber reinforced polymer (CFRP) when subjected to blast loading. The experimental data including damage and deflection were collected from a previous investigation and numerical analysis was then performed using ABAQUS software. Furthermore, the single degree of freedom (SDOF) model was used to complement the findings from numerical analysis. Following the good correlation between the experimental and numerical data, further analysis was performed on reinforced concrete beams strengthened with carbon fiber-reinforced polymer (CFRP). Using CFRP was found to enhance the load capacity and energy absorption and to reduce the central deflection. In addition, Iso-Damage curves were produced for each beam, thus allowing the assessment of damage to be predicted.

We propose a new summary measure of population health (SMPH), the well-being-adjusted health expectancy (WAHE). WAHE belongs to a subgroup of health-adjusted life expectancy indicators and gives the number of life years equivalent to full health. WAHE combines health and mortality information into a single indicator with weights that quantify the reduction in well-being associated with decreased health. WAHE's advantage over other SMPHs lies in its ability to differentiate between the consequences of health limitations at various levels of severity and its transparent, simple valuation function. Following the guidelines of a Committee on Summary Measures of Population Health, we discuss WAHE's validity, universality, feasibility sensitivity and ensure its reproducibility. We evaluate WAHE's performance compared to life expectancy, the most commonly used indicators of health expectancy (HE) and disability-adjusted life expectancy (DALE) in an empirical application for 29 European countries. Data on health and well-being are taken from the 2018 EU-SILC, and the life tables are from Eurostat. DALE is taken from the database of the Global Burden of Disease Programme. WAHE's sensitivity to univariate and multivariate state specifications is studied using the three Minimum European Health Module health dimensions: chronic morbidity, limitations in activities of daily living, and self-rated health. The empirical tests of the indicators' correspondence reveal that WAHE has the strongest correlation with the other SMPHs. Moreover, WAHE estimates are in agreement with all other SMPHs. Additionally, WAHE and all other SMPHs form a group of reliable indicators for studying population health in European countries. Finally, WAHE estimates are robust, regardless of whether health is defined across one or multiple simultaneous dimensions of health. We conclude that WAHE is a useful and reliable indicator of population health and performs at least as well as other commonly used SMPHs.The online version contains supplementary material available at 10.1007/s10680-022-09628-1.