title: Telehealth Training Is Essential to Care for Underserved Populations: a Medical Student Perspective

text: Telehealth is an area of medicine which has magnified the ability to treat patients remotely. Presently the education of medical professionals pertaining to the value, use, and implementation of telehealth is not adequate to harness the potential of available technologies. Patients engaging in telehealth experience time and cost savings, improved disease management through remote monitoring programs, and high-quality care regardless of geographic location. Despite this, medical education has been slow to evolve. It is therefore imperative that medical curricula incorporate training for this rapidly advancing mode of healthcare delivery to enable students to best care for their future patient population.


title: Economic menace of diabetes in India: a systematic review

text: AIM: Diabetes mellitus is recognised as a major chronic pandemic disease that does not consider any ethnic and monetary background. There is a dearth of literature on the cost of diabetes in the Indian context. Therefore, the present study aims to capture the evidence from the literature on the cost of diabetes mellitus in India. METHODS: An extensive literature was reviewed from ACADEMIA, NCBI, PubMed, ProQuest, EBSCO, Springer, JSTOR, Scopus and Google Scholar. The eligibility criterion is based on ‘PICOS’ procedure, and only those studies which are available in the English language, published between 1999 and February 2019, indexed in ABDC, EBSCO, ProQuest, Scopus and peer-reviewed journals are included. RESULTS: A total of thirty-two studies were included in the present study. The result indicates that the median direct cost of diabetes was estimated to be ₹18,890/- p.a. for the north zone, ₹10,585/- p.a. for the south zone, ₹45,792/- p.a. for the north-east zone and ₹8822/- p.a. for the west zone. Similarly, the median indirect cost of diabetes was ₹18,146/- p.a. for the north zone, ₹1198/- p.a. for the south zone, ₹18,707/- p.a. for the north-east and ₹3949/- p.a. for the west zone. CONCLUSION: The present study highlighted that diabetes poses a high economic burden on individuals/households. The study directed the need to arrange awareness campaign regarding diabetes and associated risk factors in order to minimise the burden of diabetes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s13410-020-00838-z) contains supplementary material, which is available to authorized users.


title: An Epidemiological Framework for Investigating Organized Crime and Terrorist Networks

text: In the last two decades, organized crime and terrorism has grown more complex, posing evolving challenges to law enforcement nationally, regionally and globally. Within the US ‘…there is a transformation in how criminals are using technology to invent new types of crime, and are creating new methods for committing traditional crimes’ (Wexler C. Critical issues in policing series new national commitment required: The changing nature of crime and criminal investigations police executive research forum, Washington, D.C. 20036 retrieved from https://www.policeforum.org/assets/ChangingNatureofCrime.pdf: 2018). In addition, criminals and terrorists have adopted more agile structural models and internationalized their operations. The UK regards organized crime as a threat to national security which costs the UK at least £32 billion each year, leads to loss of life, and can deprive people of their security and prosperity (https://www.gov.uk/government/publications/official-development-assistance-oda-fco-programme-spend-objectives-2017-to-2018/combatting-serious-and-organised-crime-objectives-2017-to-2018). Given this threat and risk landscape, it has been argued that the ‘…science of criminal investigations is changing rapidly, and many law enforcement agencies are not prepared for the changes that are taking place’ (Wexler C. Critical issues in policing series new national commitment required: The changing nature of crime and criminal investigations police executive research forum, Washington, D.C. 20036 retrieved from https://www.policeforum.org/assets/ChangingNatureofCrime.pdf: 2018). The inherent complexity associated with serious and organized crime and terrorism calls for intervention and investigative strategies and practices rooted in a science-informed, evidence-based approach. Epidemiology, the science of public health, provides a unique approach that can be leveraged and tailored to support policing operations. The systems lens is key to the application of epidemiological models, and hence the Cynefin framework is introduced to help frame the policing problem space in terms of complexity. This paper presents a conceptual model that illustrates and operationalizes epidemiological practices to support policing operations.


title: Inhaler with electrostatic sterilizer and use of cationic amphiphilic peptides may accelerate recovery from COVID-19

text: We explore the design of a smart inhaler with electrostatic sterilizer and propose the utilization of cationic amphiphilic peptides, independently or in conjunction with a bronchodilator, for COVID-19 patients to quickly improve wellbeing while maintaining a strategic distance to protect healthcare personnel from virus-containing aerosol or droplets during the process of inhalation.


title: Beta Thalassemia: New Therapeutic Options Beyond Transfusion and Iron Chelation

text: Hemoglobinopathies are among the most common monogenic diseases worldwide. Approximately 1–5% of the global population are carriers for a genetic thalassemia mutation. The thalassemias are characterized by autosomal recessive inherited defects in the production of hemoglobin. They are highly prevalent in the Mediterranean, Middle East, Indian subcontinent, and East and Southeast Asia. Due to recent migrations, however, the thalassemias are now becoming more common in Europe and North America, making this disease a global health concern. Currently available conventional therapies in thalassemia have many challenges and limitations. A better understanding of the pathophysiology of β-thalassemia in addition to key developments in optimizing transfusion programs and iron-chelation therapy has led to an increase in the life span of thalassemia patients and paved the way for new therapeutic strategies. These can be classified into three categories based on their efforts to address different features of the underlying pathophysiology of β-thalassemia: correction of the globin chain imbalance, addressing ineffective erythropoiesis, and improving iron overload. In this review, we provide an overview of the novel therapeutic approaches that are currently in development for β-thalassemia.


title: Cerebral oxygenation during pediatric congenital cardiac surgery and its association with outcome: a retrospective observational study

text: PURPOSE: Non-invasive cerebral oxygen saturation (ScO(2)) monitoring is an established tool in the intraoperative phase of pediatric congenital cardiac surgery (CCS). This study investigated the association between ScO(2) and postoperative outcome by investigating both baseline ScO(2) values and intraoperative desaturations from baseline. METHODS: All CCS procedures performed in the period 2010-2017 in our institution in which ScO(2) was monitored were included in this historical cohort study. Baseline ScO(2) was determined after tracheal intubation, before surgical incision. Subgroups were based on cardiac pathology and degree of intracardiac shunting. Poor outcome was defined based on length of stay (LOS) in the intensive care unit (ICU)/hospital, duration of mechanical ventilation (MV), and 30-day mortality. Intraoperatively, ScO(2) total time below baseline (TBBL) and ScO(2) time-weighted average (TWA) were calculated. RESULTS: Data from 565 patients were analyzed. Baseline ScO(2) was significantly associated with LOS in ICU (odds ratio [OR] per percentage decrease in baseline ScO(2), 0.95; 95% confidence interval [CI], 0.93 to 0.97; P < 0.001), with LOS in hospital (OR, 0.93; 95% CI, 0.91 to 0.96; P < 0.001), with MV duration (OR, 0.92; 95% CI, 0.90 to 0.95; P < 0.001) and with 30-day mortality (OR, 0.94; 95% CI, 0.91 to 0.98; P = 0.007). Cerebral oxygen saturation TWA had no associations, while ScO(2) TBBL had only a small association with LOS in ICU (OR, 1.02; 95% CI, 1.01 to 1.03; P < 0.001), MV duration (OR,1.02; 95% CI, 1.01 to 1.03; P = 0.002), and LOS in hospital (OR, 1.02; 95% CI, 1.01 to 1.04; P < 0.001). CONCLUSION: In pediatric patients undergoing cardiac surgery, low baseline ScO(2) values measured after tracheal intubation were associated with several adverse postoperative outcomes. In contrast, the severity of actual intraoperative cerebral desaturation was not associated with postoperative outcomes. Baseline ScO(2) measured after tracheal intubation may help identify patients at increased perioperative risk.


title: Containing the risk of catastrophic climate change

text: ABSTRACT: There is a general perception that limiting emissions of carbon dioxide will be sufficient to avoid catastrophic climate change. The reality could well be that the many of the world’s wealthy coastal cities which historically developed as ports will be inundated repeatedly to disappear eventually below the sea, with large swathes of the earth being flooded. Other major conurbations may cease to be liveable without air conditioning, while large numbers of people could well starve as a result of disruption of ecosystems. To the extent that this possibility is recognized, it is nevertheless perceived as a gradual process with the worst results in a distance future with any sea-level rise a gradual process. Limiting emissions is unlikely to be sufficient, because the level of carbon dioxide in the earth’s atmosphere is now much higher than ever before since humans started to exploit fossil fuels. We know this from the study of ancient air bubbles in Antarctic ice cores. There is a lack of balance between the energy transmitted by the incoming sunlight and the earth’s outgoing infrared heat radiation. So far this imbalance is absorbed by the enormous thermal mass of the oceans. As to the speed of sea-level rise, a period of extremely rapid sea-level rise of about 1.4 cm per year has occurred in the prehistoric past and disintegration of ice sheets as is happening currently may well be a plausible explanation of this fact. In fact, it is straightforward to create substantial amounts of negative emissions of carbon dioxide. Nevertheless, making it happen will require an unprecedented degree of global cooperation, a high level of taxation on the extraction of coal and crude oil, and the use of pressurized liquid petrol gas as aviation fuel. GRAPHIC ABSTRACT: [Image: see text]


title: Age-Structured Population Dynamics with Nonlocal Diffusion

text: Random diffusive age-structured population models have been studied by many researchers. Though nonlocal diffusion processes are more applicable to many biological and physical problems compared with random diffusion processes, there are very few theoretical results on age-structured population models with nonlocal diffusion. In this paper our objective is to develop basic theory for age-structured population dynamics with nonlocal diffusion. In particular, we study the semigroup of linear operators associated to an age-structured model with nonlocal diffusion and use the spectral properties of its infinitesimal generator to determine the stability of the zero steady state. It is shown that (i) the structure of the semigroup for the age-structured model with nonlocal diffusion is essentially determined by that of the semigroups for the age-structured model without diffusion and the nonlocal operator when both birth and death rates are independent of spatial variables; (ii) the asymptotic behavior can be determined by the sign of spectral bound of the infinitesimal generator when both birth and death rates are dependent on spatial variables; (iii) the weak solution and comparison principle can be established when both birth and death rates are dependent on spatial variables and time; and (iv) the above results can be generalized to an age-size structured model. In addition, we compare our results with the age-structured model with Laplacian diffusion in the first two cases (i) and (ii).


A Stay-at-Home Scientist’s Strategy for Synthesizing Sustainable Support Structures


LARAM School 2020 goes online: the 15th doctoral school on “LAndslide Risk Assessment and Mitigation”


title: The Role of Theory in Behavior Analysis: A Response to Unfinished Business, Travis Thompson’s Review of Staddon's New Behaviorism (2(nd) edition)

text: Travis Thompson’s lengthy review of Staddon’s The New Behaviorism requires several corrections and extensions. This response discusses Staddon’s analysis of Herrnstein’s matching law and concludes that Thompson misinterprets a gentle critique as a paean. The response goes on to defend the utility of models and “internal states” (i.e., postulated processes that are not directly measurable) as “formal representation[s] of the data reduced to a minimal number of terms,” a position similar to one of B. F. Skinner’s statements. The response ends with a defense of Skinner’s empirical brilliance, but a critique of his sweeping societal prescriptions.


title: Development and evaluation of a novel protective device for upper gastrointestinal endoscopy in the COVID-19 pandemic: the EBOX

text: BACKGROUND: During the COVID-19 pandemic, aerosol-generating procedures such as upper gastrointestinal endoscopy (UGIE) have been considered high risk. We designed a novel acrylic box (endoscopy box (EBOX)) with the intention of limiting aerosol and droplet spread during such procedures. We evaluated clinical utility, impact on the endoscopy team and also assessed the impact of the EBOX on macroscopic droplet spread from a simulated cough during UGIE. METHODS: Clinical utility was evaluated prospectively via EBOX use on 15 patients undergoing endoscopic retrograde cholangiopancreatography (13) or endoscopic ultrasound (2). Feedback was recorded from the endoscopy team regarding ease of positioning, impact of the EBOX on procedural performance and cleaning. A cough was simulated via explosion of a hyperinflated balloon containing 0.75 mL of ultraviolet disclosing lotion within the oral cavity of a mannequin, with and without the EBOX. Macroscopic spread was then evaluated with a ultraviolet torch. RESULTS: Three endoscopists and the team members found that the EBOX did not hamper the procedure and felt it was a useful adjunct to full personal protective equipment (PPE). Simulated cough without the EBOX identified macroscopic spread up to 2.3 m away from the patient’s mouth as well as onto key areas such as the exposed neck of the endoscopist, which is not considered in current PPE guidance. Simulated cough using the EBOX significantly reduced macroscopic spread onto key areas of the healthcare workers. CONCLUSIONS: The EBOX is a valuable adjunct to recommended PPE for UGIE, but still allows these procedures to be performed in the standard manner.


Ten minutes with Mrs Katya Tambe, Joint Head of Service, Ophthalmology Department, Nottingham University Hospitals NHS Trust, Nottingham, UK


COVID-19 has no effect on gravity


title: Structuring mental health support for frontline caregivers during COVID-19: lessons from organisational scholarship on unit-aligned support

text: BACKGROUND: Although the COVID-19 pandemic exposes frontline caregivers to severe prolonged stresses and trauma, there has been little clarity on how healthcare organisations can structure support to address these mental health needs. This article translates organisational scholarship on professionals working in organisations to elucidate why traditional approaches to supporting employee mental health, which often ask employees to seek assistance from centralised resources that separate mental health personnel from frontline units, may be insufficient under crisis conditions. We identify a critical but often overlooked aspect of employee mental health support: how frontline professionals respond to mental health services. In high-risk, high-pressure fields, frontline professionals may perceive mental health support as coming at the expense of urgent frontline work goals (ie, patient care) and as clashing with their central professional identities (ie, as expert, self-reliant ironmen/women). FINDINGS: To address these pervasive goal and identity conflicts in professional organisations, we translate the results of a multiyear research study examining the US Army’s efforts to transform its mental health support during the wars in Iraq and Afghanistan. We highlight parallels between providing support to frontline military units and frontline healthcare units during COVID-19 and surface implications for structuring mental health supports during a crisis. We describe how an intentional organisational design used by the US Army that assigned specific mental health personnel to frontline units helped to mitigate professional goal and identity conflicts by creating personalised relationships and contextualising mental health offerings. CONCLUSION: Addressing frontline caregivers’ mental health needs is a vital part of health delivery organisations’ response to COVID-19, but without thoughtful organisational design, well-intentioned efforts may fall short. An approach that assigns individual mental health personnel to support specific frontline units may be particularly promising.


Knowledge synthesis and translation in global food and nutrition security to evaluate and accelerate priority actions


"Panic first!"


Einmalige Chancen: Wenn Geschäftsideen offene Grenzen überschreiten


Gezielte Schnitte


Wirksam wiederbeleben


Konto in Sekunden


Vorsorge mildert Krisenschäden


Eigenverwaltung als Option


Schnelles Fahrverbot droht


Ideen neuer Netzwerke


Hypofractionated chemoradiotherapy for locally advanced non-small cell lung cancer: Is split-dose chemotherapy safer than full dose chemotherapy?


title: Structure-Based Design of Nipah Virus Vaccines: A Generalizable Approach to Paramyxovirus Immunogen Development

text: Licensed vaccines or therapeutics are rarely available for pathogens with epidemic or pandemic potential. Developing interventions for specific pathogens and defining generalizable approaches for related pathogens is a global priority and inherent to the UN Sustainable Development Goals. Nipah virus (NiV) poses a significant epidemic threat, and zoonotic transmission from bats-to-humans with high fatality rates occurs almost annually. Human-to-human transmission of NiV has been documented in recent outbreaks leading public health officials and government agencies to declare an urgent need for effective vaccines and therapeutics. Here, we evaluate NiV vaccine antigen design options including the fusion glycoprotein (F) and the major attachment glycoprotein (G). A stabilized prefusion F (pre-F), multimeric G constructs, and chimeric proteins containing both pre-F and G were developed as protein subunit candidate vaccines. The proteins were evaluated for antigenicity and structural integrity using kinetic binding assays, electron microscopy, and other biophysical properties. Immunogenicity of the vaccine antigens was evaluated in mice. The stabilized pre-F trimer and hexameric G immunogens both induced serum neutralizing activity in mice, while the post-F trimer immunogen did not elicit neutralizing activity. The pre-F trimer covalently linked to three G monomers (pre-F/G) induced potent neutralizing antibody activity, elicited responses to the greatest diversity of antigenic sites, and is the lead candidate for clinical development. The specific stabilizing mutations and immunogen designs utilized for NiV were successfully applied to other henipaviruses, supporting the concept of identifying generalizable solutions for prototype pathogens as an approach to pandemic preparedness.


G-BA gestattet Ausdehnung der Untersuchungszeiträume U6-U9


Welche Verdachtsdiagnose stellen Sie?


title: Risk assessment for influenza D in Europe

text: Recent studies have identified a new genus of the Orthomyxoviridae family, Influenza D virus (IDV). This virus was shown to infect farm animals including swine and cattle, and to efficiently replicate and transmit in ferrets, the animal model of choice for transmission of influenza A virus to humans. This partnering grant on IDV addressed the need for capacity building at EU level to improve the EU's scientific assessment capacity and international competitiveness. We have promoted cross‐disciplinary cooperation between the partner institutes representing six Member States (BE, FR, IT, LU, NL and SE). We have shown that the available antigen and genome test systems allow reliable influenza D diagnostics in partners laboratories, while for a few of the applied antibody testing methods adjustments are recommended. Tools were developed to study virus‐host range, with a gain of knowledge on host and tissue tropism of IDV in farm animals but also in wild life and very preliminary data was generated on human tissues. Serological results in European cattle suggest that influenza D virus is enzootic. Virus diversity is still unfolding: new virus introductions were identified, as well as new reassortants whose differential clinical impact or cross‐protection levels are still poorly understood. Considering drivers of emergence, IDV was in the top five in comparison with 29 other diseases. The main risk factors of IDV in cattle are related to the animal density, presence of respiratory clinical signs in cattle and contact rates between animals. Simplified quantitative IDV risk assessment exposure model indicated a possible infection of human by IDV through aerosols in cattle farms. Further studies are warranted to fully assess the risk of IDV for both animal and Human health in Europe.


title: A reflection on the mass production of scenarios in response to COVID‐19

text: In this short paper, we reflect on the recent accumulation of scenarios that have been developed in response to the COVID‐19 pandemic. Will they be of use to worried individuals and organizations? Or, are they artifacts of the very recent past? We conclude that these quickly‐produced scenarios are not embedded in the realities of affected communities and that the scenario development process should be enhanced to either involve power‐holding stakeholders or, more straightforwardly, to activate local consideration of how the affected community might/might not wish to react to localized dilemmas. Both enhancements will provide action‐orientated insights, beyond that of simply appreciating possible global/regional futures.


Editor's Notes: Key Success Factors in Strengthening and Supporting Institutionwide Goals for Learning: Key Success Factor #2, Professional Development


Industry Assistance During the Pandemic


title: The new normal?: A pandemic of task engagement in language learning

text: This article demonstrates how, at a time when learners may be experiencing fear and chaos in other aspects of their lives, a focus on language task engagement is essential across both online and offline language learning contexts. It presents a model of language task engagement and describes why and how teachers can use it to support learner achievement during the current crisis and in the future.


World Bank: Covid‐19 Response


title: MADVent: A low‐cost ventilator for patients with COVID‐19

text: The COVID‐19 pandemic has produced critical shortages of ventilators worldwide. There is an unmet need for rapidly deployable, emergency‐use ventilators with sufficient functionality to manage COVID‐19 patients with severe Acute Respiratory Distress Syndrome. Here we show the development and validation of a simple, portable, and low‐cost ventilator that may be rapidly manufactured with minimal susceptibility to supply chain disruptions. This single‐mode continuous, mandatory, closed‐loop, pressure‐controlled, time‐terminated emergency ventilator offers robust safety and functionality absent in existing solutions to the ventilator shortage. Validated using certified test lungs over a wide range of compliances, pressures, volumes and resistances to meet U.S. Food and Drug Administration standards of safety and efficacy, an Emergency Use Authorization is in review for this system. This emergency ventilator could eliminate controversial ventilator rationing or splitting to serve multiple patients. All design and validation information is provided to facilitate ventilator production even in resource‐limited settings.


title: How Can Local Governments Address Pandemic Inequities?

text: COVID‐19 is exposing a nexus between communities disproportionately suffering from underlying health conditions, policy‐reinforced disparities, and susceptibility to the disease. As the virus spreads, policy responses will need to shift from focusing on surveillance and mitigation to recovery and prevention. Local governments, with their histories of mutual aid and familiarity with local communities, are capable of meeting these challenges. However, funding must flow in a flexible enough fashion for local governments to tailor their efforts to preserve vital services and rebuild local economies. We argue in this article that the Community Development Block Grant (CDBG) and the Energy Efficiency and Conservation Block Grant (EECBG) programs are mechanisms for how to provide funds in a manner adaptable to local context while also focusing on increasing social equity. Administrators must emphasize the fourth pillar of public administration ‐‐ social equity ‐‐ in framing government responses to the pandemic.


title: Experts weigh in on economic sustainability in light of pandemic's effect on athletics

text: Yes, we can all expect the COVID‐19 crisis to have a significant financial impact on college athletics and some very real costs for schools nationwide. But that doesn't mean there's nothing you can do to soften the blow. In fact, you can make a big difference by taking some very practical steps recommended by financial experts during a virtual forum hosted by Tom McMillen, President and Chief Executive Officer of the LEAD1 Association. Here's an overview of the guidance provided by Ronald Nahass, C.F.E., A.S.A., Director of Financial Consulting; and Steve Mermelstein, M.B.A., Director of Management Consulting, both at RSM US LLP, an audit, tax, and consulting firm.


title: In Case You Haven't Heard…

text: The American Academy of Social Work and Social Welfare (AASWSW) on June 2 adopted the following resolution by vote of its members: “AASWSW objects strenuously to the Trump Administration decision to withdraw the United States from WHO [the World Health Organization]. This decision is ill‐founded and must be reversed. It will harm the health of Americans and will lead to additional deaths during the COVID‐19 pandemic.” The Trump administration's decision to withdraw the United States from WHO threatens the health of people everywhere, the AASWSW stated. The withdrawal of the United States will greatly weaken WHO's efforts to eradicate disease and improve health care on a worldwide basis. It also will particularly harm public health efforts in the United States to combat the COVID‐19 pandemic, they stated.


title: Focus on transparency, strategic planning to effectively manage challenges resulting from pandemic

text: Transparency and strategic planning will play critical roles in leading athletics departments through the uncertain and unusual times surrounding the COVID‐19 crisis. That's according to Jacque Bruns, M.B.A., Senior Associate Athletics Director and Chief Financial Officer at Oregon State University; and Jonathan Butros, M.B.A., Senior Associate AD and Chief Financial Officer at Texas Tech University, who shared their views during a virtual forum hosted by Tom McMillen, President and Chief Executive Officer of the LEAD1 Association.


title: Review colleagues’ insights, guidance for managing impact of COVID‐19 crisis

text: The COVID‐19 crisis sent college athletics administrators hurtling into a whole new realm. And it happened so completely out of the blue that there was very little time to prepare, and nothing to compare it to. But, once again, college athletics administrators throughout the country have excelled at adapting to and learning from the circumstances of a crisis.


title: Co‐production during and after the Covid‐19 pandemic: will it last?

text: Co‐production is blooming under Covid‐19, but can we expect it to last? Most likely, in post‐COVID‐19 times people and institutions will easily slip back into ‘business as usual’. This article addresses the relevance of co‐production under COVID‐19 and argues for the need of co‐production initiatives to persist well beyond the pandemic. The conditions that made co‐production emerge are likely to change, as emergency regulations and funds are abandoned and as the sense of urgency disappears. Areas of public life where there could be a more lasting effect are those where the basic conditions for successful co‐production are already in place and where only a push was necessary for co‐production to take off. These conditions include basic commitment, complementarity and supportive regulative frameworks, all of which can be sustained beyond the crisis with targeted choices and sufficient support.


Industry News


title: In Case You Haven't Heard…

text: There are many ways regulatory agencies have moved aside to make way for a safer public during the COVID‐19 pandemic: more methadone take‐homes and even home deliveries, video buprenorphine inductions, insurance‐covered telehealth and takeaway alcohol (all safer because consumers and patients don't have to go into crowded areas where the virus could be transmitted). There's great hope by some that some of these will be continued post‐pandemic, especially the lowered barriers to treatment access. But what about takeaway drinks? In Texas, at least, this may be what's happening. Alcohol‐to‐go is very popular. Despite all the implications, including more DUIs and alcohol use disorders, it is so popular it may become permanent in that state, according to a report on the Houston Press website. And the alcoholic beverage industry — not surprisingly — supports it, as do restaurants. Stay tuned. For the report, go to https://www.houstonpress.com/restaurants/could‐alcohol‐to‐go‐become‐permanent‐in‐texas‐11474023.


title: Understand role of COVID‐19 in contractual obligations

text: It's common knowledge: COVID‐19’s impact on student‐athletes, coaches, and staff members of every athletics department has been substantial. At the same time, however, the legal and business sides of an athletics department's operations are truly at the epicenter of much of the required activity. Indeed, in regard to other campus departments, only a few have as many daily, weekly, and ongoing contractual obligations with outside entities as athletics. The true magnitude of the business side of athletics became quickly apparent when existing arrangements for trips, hotels, buses, planes, equipment, supplies, food, services, and support personnel for numerous teams had to be canceled within a short period of time. Although cancellations took care of the short‐term commitments, athletics departments still need to address many broader and potentially more impactful contractual obligations.


Tunisia – Covid‐19


title: Gender gap in women authors is not worse during COVID‐19 pandemic: Results from Research and Practice in Thrombosis and Haemostasis

text: Scientific discovery and dissemination has radically transformed during the coronavirus disease 2019 (COVID‐19) pandemic. For COVID‐19 research, this includes rapid research funding opportunities with streamlined application processes, self‐funded research, fast‐tracked clinical trials and research studies being completed in a fraction of the normal time. Unfortunately the downside has been complete closure of research laboratories and stoppage of clinical trials not related to COVID‐19. Hematologists and scientists in the thrombosis, hemostasis and vascular biology fields are front and center in this science owing to the sinister coagulopathy associated with severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection [1].


title: Network of networks: preliminary lessons from the Antwerp Port Authority on crisis management and network governance to deal with the COVID‐19 pandemic

text: In this article we describe and illustrate what we call a network of networks perspective and map the development of a Lead network of the Antwerp Port Authority that governs various organizations and networks in the port community before and during the COVID‐19 pandemic. We find that setting a collective focus and selective integration to be crucial in the creation and reproduction of an effective system to adequately deal with a wicked problem like the COVID‐19 pandemic. We use the findings on crisis management and network governance to engage practitioners and public policy planners to revisit current design and governance of organizational networks within organizational fields that have been hit by the COVID‐19 pandemic.


title: Emerging zoonotic diseases originating in mammals: a systematic review of effects of anthropogenic land‐use change

text: 1. Zoonotic pathogens and parasites that are transmitted from vertebrates to humans are a major public health risk with high associated global economic costs. The spread of these pathogens and risk of transmission accelerate with recent anthropogenic land‐use changes (LUC) such as deforestation, urbanisation, and agricultural intensification, factors that are expected to increase in the future due to human population expansion and increasing demand for resources. 2. We systematically review the literature on anthropogenic LUC and zoonotic diseases, highlighting the most prominent mammalian reservoirs and pathogens, and identifying avenues for future research. 3. The majority of studies were global reviews that did not focus on specific taxa. South America and Asia were the most‐studied regions, while the most‐studied LUC was urbanisation. Livestock were studied more within the context of agricultural intensification, carnivores with urbanisation and helminths, bats with deforestation and viruses, and primates with habitat fragmentation and protozoa. 4. Research into specific animal reservoirs has improved our understanding of how the spread of zoonotic diseases is affected by LUC. The behaviour of hosts can be altered when their habitats are changed, impacting the pathogens they carry and the probability of disease spreading to humans. Understanding this has enabled the identification of factors that alter the risk of emergence (such as virulence, pathogen diversity, and ease of transmission). Yet, many pathogens and impacts of LUC other than urbanisation have been understudied. 5. Predicting how zoonotic diseases emerge and spread in response to anthropogenic LUC requires more empirical and data synthesis studies that link host ecology and responses with pathogen ecology and disease spread. The link between anthropogenic impacts on the natural environment and the recent COVID‐19 pandemic highlights the urgent need to understand how anthropogenic LUC affects the risk of spillover to humans and spread of zoonotic diseases originating in mammals.


ETHIOPIA: A Million Jobs at Risk


title: Emphasize communication during times of change, crisis

text: After the COVID‐19 crisis brought sports activities to a sudden halt, college athletics staff worked on developing new ways to handle daily responsibilities. This included adapting to new communication methods in the midst of difficult circumstances.


The Singaporean State and Community Care in the Time of Corona


Morocco – Virus Hits Business


Science as a virulent myth archive


SOMALIA: Coronavirus Response Plan


Meeting the Challenge: Keen oversight and flexible operations keep manufacturers viable and productive despite daunting business conditions


title: New research launching to identify psychological effects of COVID‐19

text: A team of more than 200 scientists representing diverse academic organizations from more than 35 countries recently announced plans to assess the impact COVID‐19 has had on global mental wellness and what can be learned from it.


title: De dokter gevloerd: Hoe voorkomen we burn-out bij huisartsen?

text: Stress is onverbrekelijk verbonden met het vak, maar huisartsen werken tegenwoordig wel onder hoogspanning en het aantal burn-outs neemt toe. Dat huisartsen gemotiveerd, gezond, vakbekwaam en productief blijven is hun eigen verantwoordelijkheid, maar ook die van de organisatie waarbinnen ze werken. Tijdig onderkennen van de eerste symptomen en een veilig werkklimaat verkleinen de kans op burn-out.


title: Simplified, minimally invasive, beating-heart technique for redo isolated tricuspid valve surgery

text: BACKGROUND: Redo isolated tricuspid valve surgery is associated with a high morbidity and mortality, and its optimal timing remains controversial. Hence, here we reviewed the early and midterm results of simplified, minimally invasive, beating-heart technique for redo isolated tricuspid valve surgery in patients at high risk. METHODS: A total of 32 consecutive patients underwent a redo isolated tricuspid valve surgery using minimally invasive, beating-heart technique through a right lateral thoracotomy in our center between June 2016 and April 2020. The mean age of patients was 57.4 ± 8.3 years, and 18 patients (56.3%) were women. The mean preoperative EuroSCORE was 7.8 ± 1.4 (range: 6–11). Follow-up was 87.1% complete, with a mean duration of 26.3 ± 12.3 months. RESULTS: Both in-hospital and 30-day mortalities were 3.1%. Tricuspid valve replacement with bioprosthesis was performed in 30 patients (93.8%), and the remaining two patients (6.2%) underwent tricuspid repair (annuloplasty and leaflet reconstruction). The mean cardiopulmonary bypass time was 81.5 ± 29.0 min. The overall in-hospital duration and intensive care unit (ICU) times were 13.6 ± 7.6 days and 4.1 ± 2.8 days, respectively. Postoperative complications included prolonged ventilation in six patients (18.8%), acute kidney injury in three patients (9.4%), and neurologic event, wound infection, or permanent third-degree atrioventricular block, in one patient (3.1%) each. A total of 96.9% patients were discharged uneventfully. Four patients were lost to follow-up; there were no midterm deaths in patients who were followed up. CONCLUSIONS: Simplified, minimally invasive, beating-heart technique for redo tricuspid valve surgery is both feasible and safe, and the early and midterm results are excellent.


Acute cardiovascular diseases may be less likely to be considered because of the COVID-19 pandemic – our duty is first to alert, then to analyse more deeply: Response to a letter entitled “Severity of cardiovascular diseases during the COVID-19 pandemic” from T. Imamura


title: Early warning score validation methodologies and performance metrics: a systematic review

text: BACKGROUND: Early warning scores (EWS) have been developed as clinical prognostication tools to identify acutely deteriorating patients. In the past few years, there has been a proliferation of studies that describe the development and validation of novel machine learning-based EWS. Systematic reviews of published studies which focus on evaluating performance of both well-established and novel EWS have shown conflicting conclusions. A possible reason is the heterogeneity in validation methods applied. In this review, we aim to examine the methodologies and metrics used in studies which perform EWS validation. METHODS: A systematic review of all eligible studies from the MEDLINE database and other sources, was performed. Studies were eligible if they performed validation on at least one EWS and reported associations between EWS scores and inpatient mortality, intensive care unit (ICU) transfers, or cardiac arrest (CA) of adults. Two reviewers independently did a full-text review and performed data abstraction by using standardized data-worksheet based on the TRIPOD (Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) checklist. Meta-analysis was not performed due to heterogeneity. RESULTS: The key differences in validation methodologies identified were (1) validation dataset used, (2) outcomes of interest, (3) case definition, time of EWS use and aggregation methods, and (4) handling of missing values. In terms of case definition, among the 48 eligible studies, 34 used the patient episode case definition while 12 used the observation set case definition, and 2 did the validation using both case definitions. Of those that used the patient episode case definition, 18 studies validated the EWS at a single point of time, mostly using the first recorded observation. The review also found more than 10 different performance metrics reported among the studies. CONCLUSIONS: Methodologies and performance metrics used in studies performing validation on EWS were heterogeneous hence making it difficult to interpret and compare EWS performance. Standardizing EWS validation methodology and reporting can potentially address this issue.


Phytotherapie – Ganzheit im Licht von Biodiversität


title: Weight convergence analysis of DV-hop localization algorithm with GA

text: The distance vector-hop (DV-hop) is a typical localization algorithm. It estimates sensor nodes location through detecting the hop count between nodes. To enhance the positional precision, the weight is used to estimate position, and the conventional wisdom is that the more hop counts are, the smaller value of weight will be. However, there has been no clear mathematical model among positioning error, hop count, and weight. This paper constructs a mathematical model between the weights and hops and analyzes the convergence of this model. Finally, the genetic algorithm is used to solve this mathematical weighted DV-hop (MW-GADV-hop) positioning model, the simulation results illustrate that the model construction is logical, and the positioning error of the model converges to 1/4R.


Ageusia, gastrointestinal symptoms and marked asthenia in late December. A single case report with positive SARS-Cov2 IgG in Italy


title: Corona im Knast: Auswirkungen der COVID-19-Pandemie auf die Soziale Arbeit im Justizvollzug des Landes Nordrhein-Westfalen

text: Die Corona-Pandemie hat gravierende Auswirkungen auf das System Justizvollzug, die Soziale Arbeit und das Übergangsmanagement. Kriseninterventionen statt Behandlungsangebote sind angesagt. Die Pandemie zeigt die Schwachstellen im gesellschaftlichen System radikal auf. Betroffen sind sowohl Klient_innen als auch Mitarbeiter_innen.


title: Automatic Management of Cloud Applications with Use of Proximal Policy Optimization

text: Reinforcement learning is a very active field of research with many practical applications. Success in many cases is driven by combining it with Deep Learning. In this paper we present the results of our attempt to use modern advancements in this area for automated management of resources used to host distributed software. We describe the use of an autonomous agent that employs a policy trained with use of Proximal Policy Optimization algorithm. The agent is managing a cloud infrastructure used to process a sample workload. We present the design and architecture of a complete autonomous management system and explain how the management policy was trained. Finally, we compare the performance to the traditional automatic management approach exploited in AWS stack and discuss feasibility to use the presented approach in other scenarios.


title: An Empirical Analysis of Predictors for Workload Estimation in Healthcare

text: The limited availability of resources makes the resource allocation strategy a pivotal aspect for every clinical department. Allocation is usually done on the basis of a workload estimation, which is performed by human experts. Experts have to dedicate a significant amount of time to the workload estimation, and the usefulness of estimations depends on the expert’s ability to understand very different conditions and situations. Machine learning-based predictors can help in reduce the burden on human experts, and can provide some guarantees at least in terms of repeatability of the delivered performance. However, it is unclear how good their estimations would be, compared to those of experts. In this paper we address this question by exploiting 6 algorithms for estimating the workload of future activities of a real-world department. Results suggest that this is a promising avenue for future investigations aimed to optimising the use of resources of clinical departments.


title: Enabling Hardware Affinity in JVM-Based Applications: A Case Study for Big Data

text: Java has been the backbone of Big Data processing for more than a decade due to its interesting features such as object orientation, cross-platform portability and good programming productivity. In fact, most popular Big Data frameworks such as Hadoop and Spark are implemented in Java or using other languages designed to run on the Java Virtual Machine (JVM) such as Scala. However, modern computing hardware is increasingly complex, featuring multiple processing cores aggregated into one or more CPUs that are usually organized as a Non-Uniform Memory Access (NUMA) architecture. The platform-independent features of the JVM come at the cost of hardware abstraction, which makes it more difficult for Big Data developers to take advantage of hardware-aware optimizations based on managing CPU or NUMA affinities. In this paper we introduce jhwloc, a Java library for easily managing such affinities in JVM-based applications and gathering information about the underlying hardware topology. To demonstrate the functionality and benefits of our proposal, we have extended Flame-MR, our Java-based MapReduce framework, to provide support for setting CPU affinities through jhwloc. The experimental evaluation using representative Big Data workloads has shown that performance can be improved by up to 17% when efficiently exploiting the hardware. jhwloc is publicly available to download at https://github.com/rreye/jhwloc.


title: Identifying Influential Spreaders On a Weighted Network Using HookeRank Method

text: Influence maximization is a significant research problem that requires the selection of influential users who are capable of spreading information in the network such that it can reach to a large number of people. Many real-world networks like Road Network, Email Networks are weighted networks. Influence maximization on weighted networks is more challenging than an unweighted network. Many methods, such as weighted-degree rank, weighted-voteRank, weighted-eigenvalue rank, and weighted-betweenness rank methods, have been used to rank the nodes in weighted networks with certain limitations. In this manuscript, we propose a Hooke’s law-based approach named HookeRank method to identify spreaders in a weighted network. We model edge weights as spring constants. The edges present in the network are modeled as springs, which are connected in series and parallel. They elongate by a distance under the effect of a given constant force following Hooke’s law of elasticity, and this is the equivalent propagation distance between nodes in the network. The proposed the model finds relevant influential nodes, that can propagate the information to other nodes. A higher HookeRank score implies the greater influential capability of the node in the network. We compared our proposed algorithm with state-of-the-art models and found that it performs reasonably well on real-life data-sets using epidemic spreading Susceptible-Infected-Recovered model.


title: Towards Modeling of Information Processing Within Business-Processes of Service-Providing Organizations

text: The paper presents an ongoing research project aimed towards the development of an approach to modeling of information spreading in complex organizations during providing service to customers and performing domain-specific business processes (BP). The approach is based on the idea of information spreading modeling and includes the view of this process in three main levels (physical, informational, and technical). The multi-layer view enables the integration and automation of obtained models in order to resolve domain-specific tasks of BP understanding, optimization, and extension. Two domain-specific case studies from healthcare and HR-management were considered within the approach proposing a way to resolve existing problems.


title: Look Who’s Talking: Modeling Decision Making Based on Source Credibility

text: Understanding how humans evaluate credibility is an important scientific question in the era of fake news. Source credibility is among the most important aspects of credibility evaluations. One of the most direct ways to understand source credibility is to use measurements of brain activity of humans who make credibility evaluations. Nevertheless, source credibility has never been investigated using such a method before. This article reports the results of an experiment during which we have measured brain activity during source credibility evaluation using EEG. The experiment allowed for identification of brain areas that were active when a participant made positive or negative source credibility evaluations. Based on experimental data, we modelled and predicted human source credibility evaluations using EEG brain activity measurements with F1 score exceeding 0.7 (using 10-fold cross-validation).


title: Fault Injection, Detection and Treatment in Simulated Autonomous Vehicles

text: In the last few years autonomous vehicles have been on the rise. This increase in popularity lead by new technology advancements and availability to the regular consumer has put them in a position where safety must now be a top priority. With the objective of increasing the reliability and safety of these vehicles, fault detection and treatment modules for autonomous vehicles were developed for an existing multi-agent platform that coordinates them to perform high-level missions. Additionally, a fault injection tool was also developed to facilitate the study of said modules alongside a fault categorization system to help the treatment module select the best course of action. The results obtained show the potential of the developed work, with it being able to detect all the injected faults during the tests in a small enough time frame to be able to adequately treat these faults.


title: Extracting Backbone Structure of a Road Network from Raw Data

text: The representation of roads as a networked system of nodes and edges has attracted significant interest in the network literature, generating a large number of studies over the years. Such representation requires a proper identification of what constitute an edge or a node. Intuitively, nodes represent the junctions where roads intersect, and edges are the road segments connecting these junctions. In practice, however, such simplified presentation is not trivial to achieve due to extra details of individual roads. In this paper, we present a set of novel and efficient computational techniques based on elementary geometry and graph theory that can be employed to obtain the essential structure of a road network, while also retaining the crucial geometry of roads, such as shape and length. This is done by dissecting the network into clusters of nodes of degree other than 2 and curves, which contain consecutive nodes of degree 2, connecting these clusters. These clusters of nodes and curves will be collapsed into cleaned nodes and paths, respectively in a simplified mathematical graph. We apply this method to obtain the simplification of road network in Punggol new town in the northeast region of Singapore, and show that application of network analyses such as centrality measures could be performed in a more meaningful and concise manner than on the original road network.


title: Using Cellular Automata to Model High Density Pedestrian Dynamics

text: The article presents a model of pedestrian dynamics based on Cellular Automata, dedicated especially for high density conditions. Using the proposed representation, it is possible to map different behaviors of agents in various conditions. The article describes mechanisms of a pedestrian’s movement for different pedestrian’s shapes when a pedestrian occupies multiple cells, compared to the classical representation where a pedestrian occupies a single square cell. A greater number of cells per pedestrian can describe a pedestrian with e.g. a backpack, a suitcase on wheels, or on a bicycle more precisely than when s/he is represented by one cell. More accurate discretizations are particularly useful when high density of pedestrians occurs. The article focuses on the following aspects of a pedestrian’s movement: changing direction, movement speed, a passing scheme, and an overtaking scheme.


title: A Dynamic Vote-Rank Based Approach for Effective Sequential Initialization of Information Spreading Processes Within Complex Networks

text: Seed selection is one of the key factors influencing information spread within networks. Whereas most solutions are based on single-stage seeding at the beginning of the process, performance increases when additional seeds are used. This enables the acquisition of knowledge about ongoing processes and activating new nodes for further influence maximisation. This paper describes an approach based on the Vote-Rank algorithm with dynamic rankings for sequential seed selection. The results prove the increased performance of dynamic rankings compared to the static version and show how the frequency of ranking updates affects both performance and computational costs.


title: On the Planarity of Validated Complexes of Model Organisms in Protein-Protein Interaction Networks

text: Leveraging protein-protein interaction networks to identify groups of proteins and their common functionality is an important problem in bioinformatics. Systems-level analysis of protein-protein interactions is made possible through network science and modeling of high-throughput data. From these analyses, small protein complexes are traditionally represented graphically as complete graphs or dense clusters of nodes. However, there are certain graph theoretic properties that have not been extensively studied in PPI networks, especially as they pertain to cluster discovery, such as planarity. Planarity of graphs have been used to reflect the physical constraints of real-world systems outside of bioinformatics, in areas such as mapping and imaging. Here, we investigate the planarity property in network models of protein complexes. We hypothesize that complexes represented as PPI subgraphs will tend to be planar, reflecting the actual physical interface and limits of components in the complex. When testing the planarity of known complex subgraphs in S. cerevisiae and selected mammalian PPIs, we find that a majority of validated complexes possess this planar property. We discuss the biological motivation of planar versus nonplanar subgraphs, observing that planar subgraphs tend to have longer protein components. Functional classification of planar versus nonplanar complex subgraphs reveals differences in annotation of these groups relating to cellular component organization, structural molecule activity, catalytic activity, and nucleic acid binding. These results provide a new quantitative and biologically motivated measure of real protein complexes in the network model, important for the development of future complex-finding algorithms in PPIs. Accounting for this property paves the way to new means for discovering new protein complexes and uncovering the functionality of unknown or novel proteins.


title: An Optimizing Multi-platform Source-to-source Compiler Framework for the NEURON MODeling Language

text: Domain-specific languages (DSLs) play an increasingly important role in the generation of high performing software. They allow the user to exploit domain knowledge for the generation of more efficient code on target architectures. Here, we describe a new code generation framework (NMODL) for an existing DSL in the NEURON framework, a widely used software for massively parallel simulation of biophysically detailed brain tissue models. Existing NMODL DSL transpilers lack either essential features to generate optimized code or capability to parse the diversity of existing models in the user community. Our NMODL framework has been tested against a large number of previously published user models and offers high-level domain-specific optimizations and symbolic algebraic simplifications before target code generation. NMODL implements multiple SIMD and SPMD targets optimized for modern hardware. When comparing NMODL-generated kernels with NEURON we observe a speedup of up to 20[Formula: see text], resulting in overall speedups of two different production simulations by [Formula: see text]. When compared to SIMD optimized kernels that heavily relied on auto-vectorization by the compiler still a speedup of up to [Formula: see text] is observed.


title: Parallel Numerical Solution of a 2D Chemotaxis-Stokes System on GPUs Technology

text: The aim of this paper is the numerical solution of a 2D chemotaxis model by a parallel numerical scheme, implemented on a GPU technology. The numerical discretization relies on the utilization of a finite difference scheme for the spatial part and the explicit Euler method for the time integration. Accuracy and stability properties are provided. The effectiveness of the approach, as well as the coherence of the results with respect to the modeled phenomenon, is provided through numerical evidence, also giving a performance analysis of the serial and the parallel implementations.


title: Stochastic Volatility and Early Warning Indicator

text: We extend Merton’s framework by adopting stochastic volatility to propose an early warning indicator for banks’ credit risk. Bayesian inference is employed to estimate the parameters of Heston model. We provide empirical evidence and demonstrate the comparative strength of our risk measure over others.


title: Estimation of Tipping Points for Critical and Transitional Regimes in the Evolution of Complex Interbank Network

text: We consider an agent-based model of dynamic interbank network, evolving under several influential factors. Co-evolution is formally performed by the connection between node states and topology of interaction, and vice versa. During the simulation, network evolves to critical regime, corresponding to cascading behaviour in the system, through transitional one. Results show these global regimes correspond to dynamics at micro-level with three types of node states. On the base of formal model of system evolution and regimes formal definitions we estimate the starting point of cascading behaviour and determine number of iterations before its early warning signal – the start of transitional regime. Experiment is made for the interbank market model, nevertheless, possible applications are not restricted by the case. We show, that the obtained estimations allow for appropriate prediction of starting points of critical and transitional regimes (which correspond to cascading behaviour and its early warning signal) and explanation of observed dynamics in the evolution of banking system model under fund infusion scenario.


title: DDNE: Discriminative Distance Metric Learning for Network Embedding

text: Network embedding is a method to learn low-dimensional representations of nodes in networks, which aims to capture and preserve network structure. Most of the existing methods learn network embedding based on distributional similarity hypothesis while ignoring adjacency similarity property, which may cause distance bias problem in the network embedding space. To solve this problem, this paper proposes a unified framework to encode distributional similarity and measure adjacency similarity simultaneously, named DDNE. The proposed DDNE trains a siamese neural network which learns a set of non-linear transforms to project the node pairs into the same low-dimensional space based on their first-order proximity. Meanwhile, a distance constraint is used to make the distance between a pair of adjacent nodes smaller than a threshold and that of each non-adjacent nodes larger than the same threshold, which highlight the adjacency similarity. We conduct extensive experiments on four real-world datasets in three social network analysis tasks, including network reconstruction, attribute prediction and recommendation. The experimental results demonstrate the competitive and superior performance of our approach in generating effective network embedding vectors over baselines.


title: Social Recommendation in Heterogeneous Evolving Relation Network

text: The appearance and growth of social networking brings an exponential growth of information. One of the main solutions proposed for this information overload problem are recommender systems, which provide personalized results. Most existing social recommendation approaches consider relation information to improve recommendation performance in the static context. However, relations are likely to evolve over time in the dynamic network. Therefore, temporal information is an essential ingredient to making social recommendation. In this paper, we propose a novel social recommendation model based on evolving relation network, named SoERec. The learned evolving relation network is a heterogeneous information network, where the strength of relation between users is a sum of the influence of all historical events. We incorporate temporally evolving relations into the recommendation algorithm. We empirically evaluate the proposed method on two widely-used datasets. Experimental results show that the proposed model outperforms the state-of-the-art social recommendation methods.


title: A Novel Metric to Evaluate In Situ Workflows

text: Performance evaluation is crucial to understanding the behavior of scientific workflows and efficiently utilizing resources on high-performance computing architectures. In this study, we target an emerging type of workflow, called in situ workflows. Through an analysis of the state-of-the-art research on in situ workflows, we model a theoretical framework that helps characterize such workflows. We further propose a lightweight metric for assessing resource usage efficiency of an in situ workflow execution. By applying this metric to a simple, yet representative, synthetic workflow, we explore two possible scenarios (Idle Simulation and Idle Analyzer) for the execution of real in situ workflows. Experimental results show that there is no substantial difference in the performance of both the in transit placement (analytics on dedicated nodes) and the helper-core configuration (analytics co-allocated with simulation) on our target system.


title: Improving Performance of the Hypre Iterative Solver for Uintah Combustion Codes on Manycore Architectures Using MPI Endpoints and Kernel Consolidation

text: The solution of large-scale combustion problems with codes such as the Arches component of Uintah on next generation computer architectures requires the use of a many and multi-core threaded approach and/or GPUs to achieve performance. Such codes often use a low-Mach number approximation, that require the iterative solution of a large system of linear equations at every time step. While the discretization routines in such a code can be improved by the use of, say, OpenMP or Cuda Approaches, it is important that the linear solver be able to perform well too. For Uintah the Hypre iterative solver has proved to solve such systems in a scalable way. The use of Hypre with OpenMP leads to at least 2x slowdowns due to OpenMP overheads, however. This behavior is analyzed and a solution proposed by using the MPI Endpoints approach is implemented within Hypre, where each team of threads acts as a different MPI rank. This approach minimized OpenMP synchronization overhead, avoided slowdowns, performed as fast or (up to 1.5x) faster than Hypre’s MPI only version, and allowed the rest of Uintah to be optimized using OpenMP. Profiling of the GPU version of Hypre showed the bottleneck to be the launch overhead of thousands of micro-kernels. The GPU performance was improved by fusing these micro kernels and was further optimized by using Cuda-aware MPI. The overall speedup of 1.26x to 1.44x was observed compared to the baseline GPU implementation.


title: An Adaptive Network Model for Burnout and Dreaming

text: As burnouts grow increasingly common, the necessity for a model describing burnout dynamics becomes increasingly apparent. The model discussed in this paper builds on previous research by adding dreams, a component that has been shown to have an adaptive regulating effect on emotions. The proposed model is a first-order adaptive temporal-causal network model, incorporating emotions, exercise, sleep, and dreams. The model was validated against given patterns found in empirical literature and it may be used to gain a better understanding of burnout dynamics.


title: Enabling EASEY Deployment of Containerized Applications for Future HPC Systems

text: The upcoming exascale era will push the changes in computing architecture from classical CPU-based systems towards hybrid GPU-heavy systems with much higher levels of complexity. While such clusters are expected to improve the performance of certain optimized HPC applications, it will also increase the difficulties for those users who have yet to adapt their codes or are starting from scratch with new programming paradigms. Since there are still no comprehensive automatic assistance mechanisms to enhance application performance on such systems, we propose a support framework for future HPC architectures, called EASEY (Enable exASclae for EverYone). Our solution builds on a layered software architecture, which offers different mechanisms on each layer for different tasks of tuning, including a workflow management system. This enables users to adjust the parameters on each of the layers, thereby enhancing specific characteristics of their codes. We introduce the framework with a Charliecloud-based solution, showcasing the LULESH benchmark on the upper layers of our framework. Our approach can automatically deploy optimized container computations with negligible overhead and at the same time reduce the time a scientist needs to spent on manual job submission configurations.


title: Community Aware Models of Meme Spreading in Micro-blog Social Networks

text: We propose the new models of meme spreading over social network constructed from Twitter mention relations. Our models combine two groups of diffusion factors relevant for complex contagions: network structure and social constraints. In particular, we study the effect of perceptive limitations caused by information overexposure. This effect was not yet measured in the classical models of community-aware meme spreading. Limiting our study to hashtags acting as specific, concise memes, we propose different ways of reflecting information overexposure: by limited hashtag usage or global/local increase of hashtag generation probability. Based on simulations of meme spreading, we provide quantitative comparison of our models with three other models known from literature, and additionally, with the ground truth, constructed from hashtag popularity data retrieved from Twitter. The dynamics of hashtag propagation is analyzed using frequency charts of adoption dominance and usage dominance measures. We conclude that our models are closer to real-world dynamics of hashtags for a hashtag occurrence range up to [Formula: see text].


title: From Conditional Independence to Parallel Execution in Hierarchical Models

text: Hierarchical models describe phenomena by grouping data into multiple levels. Due to the size of these models, parallel execution is required to avoid prohibitively long computing time. While it is occasionally possible to specify some of these models using parallel building blocks, this limits expressivity. Therefore, a more general generative specification is preferred. To leverage parallel computing capacity, these specifications can be annotated, but doing so effectively assumes that the modeler has expertise from computer science. This paper outlines how to identify parallel parts automatically by leveraging the conditional independence property in the graphical model extracted from the dataflow graph of model specifications. Computation related to random variables with the same depth in the graphical model are identified as candidates for parallel execution. Since subsequent proposals in the parameter space exploration of the model are clustered together, the results show that the well known longest processing time scheduling heuristic deals adequately with load imbalance. The proposed parallelization is evaluated on two pharmacometrics models, a domain where hierarchical models with load imbalance are common due to the numeric simulation of pharmacokinetics and pharmacodynamics of human subjects. The varying number of measurements taken per subject further exacerbates load imbalance.


title: Modeling of Fire Spread Including Different Heat Transfer Mechanisms Using Cellular Automata

text: The article presents a new method of modeling the spread of fire using Cellular Automata based on 3D mesh. The models based on different heat transport mechanisms were used in the study, including conductivity, convection and thermal radiation. General mechanisms of fire spread are exemplified by a fire inside a building. The tests of the created simulator for fire in rooms were conducted, and the reference point was the known simulator based on CFD, namely FDS Fire Dynamics Simulator.


title: An Efficient New Static Scheduling Heuristic for Accelerated Architectures

text: Heterogeneous architectures that use Graphics Processing Units (GPUs) for general computations, in addition to multicore CPUs, are increasingly common in high-performance computing. However many of the existing methods for scheduling precedence-constrained tasks on such platforms were intended for more diversely heterogeneous clusters, such as the classic Heterogeneous Earliest Finish Time (HEFT) heuristic. We propose a new static scheduling heuristic called Heterogeneous Optimistic Finish Time (HOFT) which exploits the binary heterogeneity of accelerated platforms. Through extensive experimentation with custom software for simulating task scheduling problems on user-defined CPU-GPU platforms, we show that HOFT can obtain schedules at least [Formula: see text] shorter than HEFT’s for medium-to-large numerical linear algebra application task graphs and around [Formula: see text] shorter on average for a large collection of randomly-generated graphs.


title: How You Say or What You Say? Neural Activity in Message Credibility Evaluation

text: In the Internet era, understanding why humans find messages from unknown receivers credible (such as fake news) is an important research topic. Message credibility is an important theoretical aspect of credibility evaluation that relies only on message contents and design. For the first time in the field, we study message credibility by directly measuring brain activity of humans who make credibility evaluations in an experiment that controls message design. Brain activity as measured using EEG is used to investigate areas of the brain involved in message credibility evaluation. We also model and predict human message credibility evaluations using EEG brain activity measurements.


title: A Probabilistic Infection Model for Efficient Trace-Prediction of Disease Outbreaks in Contact Networks

text: We propose a novel method which we call the Probabilistic Infection Model (PIM). Instead of stochastically assigning exactly one state to each agent at a time, PIM tracks the likelihood of each agent being in a particular state. Thus, a particular agent can exist in multiple disease states concurrently. Our model gives an improved resolution of transitions between states, and allows for a more comprehensive view of outbreak dynamics at the individual level. Moreover, by using a probabilistic approach, our model gives a representative understanding of the overall trajectories of simulated outbreaks without the need for numerous (order of hundreds) of repeated Monte Carlo simulations. We simulate our model over a contact network constructed using registration data of university students. We model three diseases; measles and two strains of influenza. We compare the results obtained by PIM with those obtained by simulating stochastic SEIR models over the same the contact network. The results demonstrate that the PIM can successfully replicate the averaged results from numerous simulations of a stochastic model in a single deterministic simulation.


title: Multilevel Parallel Computations for Solving Multistage Multicriteria Optimization Problems

text: In the present paper, a novel approach for solving the computationally costly multicriteria optimization problems is considered. Within the framework of the developed approach, the obtaining of the efficient decisions is ensured by means of several different methods for the scalarization of the efficiency criteria. The proposed approach provides an opportunity to alter the scalarization methods and the parameters of these ones in the course of computations that results in the necessity of multiple solving the time-consuming global optimization problems. Overcoming the computational complexity is provided by reusing the computed search information and efficient parallel computing on high-performance computing systems. The performed numerical experiments confirmed the developed approach to allow reducing the amount and time of computations for solving the time-consuming multicriteria optimization problems.


title: Look Deep into the New Deep Network: A Measurement Study on the ZeroNet

text: ZeroNet is a new decentralized web-like network of peer-to-peer users created in 2015. ZeroNet, called an open, free and robust network, has attracted a rising number of users. Most of P2P networks are applied into file-sharing systems or data-storage system and the characteristics of these networks are widely investigated. However, there are obvious differences between ZeroNet and conventional P2P networks. Existing researches rarely involve ZeroNet and the characteristics and robustness of ZeroNet are unknown. To tackle the aforementioned problem, the present study measures the ZeroNet peer resources and site resources separately, and at the same time, proposes collection methods for both. No like other simulation experiments, the experiments of this paper are set on real-world environment. This is also the first measurement study about ZeroNet. Experimental results show that the topology of the peer network in ZeroNet has scarce edges, short distances and low clustering coefficients, and its degree distribution exhibits a special distribution. These indicate that the peer network of ZeroNet has poor robustness and the experimental results of the ZeroNet resilience verify this issue. In addition, this paper represents an improved peer exchange method to enhance the robustness of the ZeroNet. We also measure the topology characteristics, languages, sizes and versions of the sites in ZeroNet. We find that the size of the sites and the client version are also the reasons for the low robustness of the ZeroNet.


title: Reproducibility of Computational Experiments on Kubernetes-Managed Container Clouds with HyperFlow

text: We propose a comprehensive solution for reproducibility of scientific workflows. We focus particularly on Kubernetes-managed container clouds, increasingly important in scientific computing. Our solution addresses conservation of the scientific procedure, scientific data, execution environment and experiment deployment, while using standard tools in order to avoid maintainability issues that can obstruct reproducibility. We introduce an Experiment Digital Object (EDO), a record published in an open science repository that contains artifacts required to reproduce an experiment. We demonstrate a variety of reproducibility scenarios including experiment repetition (same experiment and conditions), replication (same experiment, different conditions), and propose a smart reuse scenario in which a previous experiment is partially replayed and partially re-executed. The approach is implemented in the HyperFlow workflow management system and experimentally evaluated using a genomic scientific workflow. The experiment is published as an EDO record on the Zenodo platform.


title: Autonomous Vehicles as Local Traffic Optimizers

text: This paper explores the interaction between autonomous and human-driven cars on a microscopic level using an agent-based traffic simulator. More specifically, it deals with the design of driving logic models of “socially-aware” autonomous vehicles that can improve the performance of surrounding vehicles on the road. Congestion waves, which are created as a result of an abrupt stopping or a car joining a highway, are a known phenomenon in current traffic systems. Experiments performed, demonstrate how the presence of intelligent social vehicles on the road can reduce such effects by acting as a flexible medium between human-driven cars. Metrics to evaluate benefits ot our AV behaviour models under various states of traffic conditions/congestion are also proposed. Finally, results showing the effectiveness of these models are presented.


title: Learning Mixed Traffic Signatures in Shared Networks

text: On shared, wide-area networks (WAN), it can be difficult to characterise the current traffic. There can be different protocols in use, by multiple data streams, producing a mix of different traffic signatures. Furthermore, bottlenecks and protocols can change dynamically. Yet, if it were possible to determine the protocols (e.g., congestion control algorithms (CCAs)) or the applications in use by the background traffic, appropriate optimisations for the foreground traffic might be taken by operating systems, users, or administrators. We extend previous work in predicting network protocols via signatures based on a time-series of round-trip times (RTT). Gathering RTTs is minimally intrusive and does not require administrative privilege. Although there have been successes in using machine learning (ML) to classify protocols, the use cases have been relatively simple or have focused on the foreground traffic. We show that both k-nearest-neighbour (K-NN) with dynamic time warp (DTW), and multi-layer perceptrons (MLP), can classify (with useful accuracy) background traffic signatures with a range of bottleneck bandwidths.


title: Modeling Helping Behavior in Emergency Evacuations Using Volunteer’s Dilemma Game

text: People often help others who are in trouble, especially in emergency evacuation situations. For instance, during the 2005 London bombings, it was reported that evacuees helped injured persons to escape the place of danger. In terms of game theory, it can be understood that such helping behavior provides a collective good while it is a costly behavior because the volunteers spend extra time to assist the injured persons in case of emergency evacuations. In order to study the collective effects of helping behavior in emergency evacuations, we have performed numerical simulations of helping behavior among evacuees in a room evacuation scenario. Our simulation model is based on the volunteer’s dilemma game reflecting volunteering cost. The game theoretic model is coupled with a social force model to understand the relationship between the spatial and social dynamics of evacuation scenarios. By systematically changing the cost parameter of helping behavior, we observed different patterns of collective helping behaviors and these collective patterns are summarized with a phase diagram.


title: GPU-Accelerated RDP Algorithm for Data Segmentation

text: The Ramer-Douglas-Peucker (RDP) algorithm applies a recursive split-and-merge strategy, which can generate fast, compact and precise data compression for time-critical systems. The use of GPU parallelism accelerates the execution of RDP, but the recursive behavior and the dynamic size of the generated sub-tasks, requires adapting the algorithm to use the GPU resources efficiently. While previous research approaches propose the exploitation of task-based parallelism, our research advocates a general fine-grained solution, which avoids the dynamic and recursive execution of kernels. The segmentation of depth images, a typical application used on autonomous driving, reaches speeds of almost 1000 frames per second for typical workloads using our massively parallel proposal on low-consumption, embedded GPUs. The GPU-accelerated solution is at least an order of magnitude faster than the execution of the same program on multiple CPU cores with similar energy consumption.


title: Utilizing GPU Performance Counters to Characterize GPU Kernels via Machine Learning

text: GPU computing kernels are relatively simple to write if achieving the best performance is not of the highest priority. However, it can quickly become a much more daunting task when users try to tune and optimize their kernels to obtain the highest performance. This is due to GPUs’ massive degree of parallelism, complex memory hierarchy, fine grain synchronization, and long memory access latency. Hence, users must carry out the complex tasks of profiling, analyzing, and tuning to reduce performance bottlenecks. Today’s GPUs can generate hundreds of performance events that comprehensively quantify the behavior of a kernel. Instead of relying on experts’ manual analysis, this paper targets using machine learning methods to generalize GPU performance counter data to determine the characteristics of a GPU kernel as they will reveal possible reasons for low performance. We choose a set of problem-independent counters as our inputs to design and compare three machine learning methods to automatically classify the execution behavior of a kernel. The experimental results on stencil computing kernels and sparse matrix multiplications show the machine learning models’ good accuracy, and demonstrate a feasible approach that is capable of classifying a kernel’s characterizations and suggesting changes to a skilled user, who can subsequently improve kernel performance with less guessing.


title: QEScalor: Quantitative Elastic Scaling Framework in Distributed Streaming Processing

text: Recently, researchers usually use the elastic scaling techniques as a powerful means of the distributed stream processing systems to deal with the high-speed data stream which arrives continuously and fluctuates constantly. The existing methods allocate the same amount of resources to the instances of the same operator, but they ignore the correlation between the operator performance and resource provision. It may lead to the waste of the resources caused by the over-provision or the huge overhead of the scheduling caused by the under-provision. To solve the above problems, we present a quantitative elastic scaling framework, named QEScalor, to allocate resources for the operator instances quantitatively based on the actual performance requirements. The experimental results show that compared with the existing works, the QEScalor can not only achieve resource-efficient elastic scaling with lower cost, but also it can enhance the total performance of the DSPAs.


title: Modelling the Effects of Antibiotics on Gut Flora Using a Nonlinear Compartment Model with Uncertain Parameters

text: We present a mathematical model of a microbial community in the human colon, including transport, exchange and metabolic processes. The colon is represented as a bioreactor with separate lumen and mucus microhabitats. The microbial community in the colon is grouped into four biomass functional groups based on their metabolic activity. While computational models present a challenge in the selection of model parameters, they also provide a more systematic approach to addressing uncertainties when compared to in vivo and in vitro experiments. We conduct an exploratory study on the uncertainty of our input parameters and a simulation study of the perturbation and recovery of gut microbiota from antibiotic exposure. We consider our parameters as random variables drawn from a uniform random distribution to reflect the diversity of gut microbial composition and variability between individuals.


title: Detecting Critical Transitions in the Human Innate Immune System Post-cardiac Surgery

text: Coronary artery bypass grafting with cardiopulmonary bypass activates the human innate immune system (HIIS) and invokes a vigorous inflammatory response that is systemic. This massive inflammatory reaction can contribute to the development of postoperative complications that could topple the state of the system from health to disease, or even to some extent, death. The body, after all, is in a state where majority of its immune cell populations have been depleted, and sometimes needs days or even longer to recuperate. To obtain a deeper understanding on how HIIS responds to complications after cardiac surgery, we perturb the immune system model that we have developed in an earlier work in-silico by adding another source of inflammation triggering moieties (ITMs) hours after surgery in various regimes. A critical transition occurs upon the addition of a critical concentration of ITMs when the insult is sustained for approximately 3 h – a total concentration that corresponds to the fatal concentration of ITMs documented in literature. By perturbing HIIS in-silico with additional sources of ITMs to mimic persistent and recurring episodes of post-surgery complications, we are able to specify under which conditions critical transitions occur in HIIS, as well as pinpoint important blood parameters that exhibit critical transitions in our model. More importantly, by applying early warning signals on the clinical trial data used to calibrate and validate HIIS model, we are able to detect blood parameters that exhibit critical transitions in patients who died post-surgery, where pro-inflammatory cytokines are deemed potential markers for critical transitions.


title: heFFTe: Highly Efficient FFT for Exascale

text: Exascale computing aspires to meet the increasing demands from large scientific applications. Software targeting exascale is typically designed for heterogeneous architectures; henceforth, it is not only important to develop well-designed software, but also make it aware of the hardware architecture and efficiently exploit its power. Currently, several and diverse applications, such as those part of the Exascale Computing Project (ECP) in the United States, rely on efficient computation of the Fast Fourier Transform (FFT). In this context, we present the design and implementation of heFFTe (Highly Efficient FFT for Exascale) library, which targets the upcoming exascale supercomputers. We provide highly (linearly) scalable GPU kernels that achieve more than [Formula: see text] speedup with respect to local kernels from CPU state-of-the-art libraries, and over [Formula: see text] speedup for the whole FFT computation. A communication model for parallel FFTs is also provided to analyze the bottleneck for large-scale problems. We show experiments obtained on Summit supercomputer at Oak Ridge National Laboratory, using up to 24,576 IBM Power9 cores and 6,144 NVIDIA V-100 GPUs.


title: A Massively Parallel Algorithm for the Three-Dimensional Navier-Stokes-Boussinesq Simulations of the Atmospheric Phenomena

text: We present a massively parallel solver using the direction splitting technique and stabilized time-integration schemes for the solution of the three-dimensional non-stationary Navier-Stokes-Boussinesq equations. The model can be used for modeling atmospheric phenomena. The time integration scheme utilized enables for efficient direction splitting algorithm with finite difference solver. We show how to incorporate the terrain geometry into the simulation and how to perform the domain decomposition. The computational cost is linear [Formula: see text] over each sub-domain, and near to [Formula: see text] in parallel over 1024 processors, where N is the number of unknowns and c is the number of cores. This is even if we run the parallel simulator over complex terrain geometry. We analyze the parallel scalability experimentally up to 1024 processors over a PROMETHEUS Linux cluster with multi-core processors. The weak scalability of the code shows that increasing the number of sub-domains and processors from 4 to 1024, where each processor processes the subdomain of [Formula: see text] internal points ([Formula: see text] box), results in the increase of the total computational time from 120 s to 178 s for a single time step. Thus, we can perform a single time step with over 1,128,000,000 unknowns within 3 min. The number of unknowns results from the fact that we have three components of the velocity vector field, one component of the pressure, and one component of the temperature scalar field over 256,000,000 mesh points. The computation of the one time step takes 3 min on a Linux cluster. The direction splitting solver is not an iterative solver; it solves the system accurately since it is equivalent to Gaussian elimination. Our code is interfaced with the mesh generator reading the NASA database and providing the Earth terrain map. The goal of the project is to provide a reliable tool for parallel, fully three-dimensional computations of the atmospheric phenomena.


title: Sparse Matrix-Based HPC Tomography

text: Tomographic imaging has benefited from advances in X-ray sources, detectors and optics to enable novel observations in science, engineering and medicine. These advances have come with a dramatic increase of input data in the form of faster frame rates, larger fields of view or higher resolution, so high performance solutions are currently widely used for analysis. Tomographic instruments can vary significantly from one to another, including the hardware employed for reconstruction: from single CPU workstations to large scale hybrid CPU/GPU supercomputers. Flexibility on the software interfaces and reconstruction engines are also highly valued to allow for easy development and prototyping. This paper presents a novel software framework for tomographic analysis that tackles all aforementioned requirements. The proposed solution capitalizes on the increased performance of sparse matrix-vector multiplication and exploits multi-CPU and GPU reconstruction over MPI. The solution is implemented in Python and relies on CuPy for fast GPU operators and CUDA kernel integration, and on SciPy for CPU sparse matrix computation. As opposed to previous tomography solutions that are tailor-made for specific use cases or hardware, the proposed software is designed to provide flexible, portable and high-performance operators that can be used for continuous integration at different production environments, but also for prototyping new experimental settings or for algorithmic development. The experimental results demonstrate how our implementation can even outperform state-of-the-art software packages used at advanced X-ray sources worldwide.


title: Narrow Passage Problem Solution for Motion Planning

text: The paper introduces a new randomized sampling-based method of motion planning suitable for the problem of narrow passages. The proposed method was inspired by the method of exit points for cavities in protein models and is based on the Rapidly Exploring Random Tree (RRT). Unlike other methods, it can also provide locations of the exact positions of narrow passages. This information is extremely important as it helps to solve this part of space in more detail and even to decide whether a path through this bottleneck exists or not. For data with narrow passages, the proposed method finds more paths in a shorter time, for data without narrow passages, the proposed method is slower but still provides correct paths.


title: Reconstruction of Low Energy Neutrino Events with GPUs at IceCube

text: IceCube is a cubic kilometer neutrino observatory located at the South Pole that produces massive amounts of data by measuring individual Cherenkov photons from neutrino interaction events in the energy range from few GeV to several PeV. The actual reconstruction of neutrino events in the GeV range is computationally challenging due to the scarcity of data produced by single events. This can lead to run times of several weeks for the state-of-the-art reconstruction method – Pegleg – on CPUs for typical workloads of many ten-thousand events. We propose a GPU version of Pegleg that probes the likelihood space with several hypotheses in parallel while adapting the amount of parallel sampled hypotheses dynamically in order to reduce computation time significantly. Our results show an average speedup of 14 (with a maximum of over 200) for 5262 reconstructed neutrino events of different flavors on a Titan V GPU compared to the multithreaded CPU version, which enables quicker and broader analysis of IceCube events.


title: Scalable Workflow-Driven Hydrologic Analysis in HydroFrame

text: The HydroFrame project is a community platform designed to facilitate integrated hydrologic modeling across the US. As a part of HydroFrame, we seek to design innovative workflow solutions that create pathways to enable hydrologic analysis for three target user groups: the modeler, the analyzer, and the domain science educator. We present the initial progress on the HydroFrame community platform using an automated Kepler workflow. This workflow performs end-to-end hydrology simulations involving data ingestion, preprocessing, analysis, modeling, and visualization. We demonstrate how different modules of the workflow can be reused and repurposed for the three target user groups. The Kepler workflow ensures complete reproducibility through a built-in provenance framework that collects workflow specific parameters, software versions, and hardware system configuration. In addition, we aim to optimize the utilization of large-scale computational resources to adjust to the needs of all three user groups. Towards this goal, we present a design that leverages provenance data and machine learning techniques to predict performance and forecast failures using an automatic performance collection component of the pipeline.


title: Boost and Burst: Bubbles in the Bitcoin Market

text: This study investigates bubbles and crashes in the cryptocurrency market. In particular, using the log-periodic power law, we estimate the critical time of bubbles in the Bitcoin market. The results indicate that Bitcoin bubbles clearly exist, and our forecast of critical times can be verified with high accuracy. We further claim that bubbles could originate from the mining process, investor sentiment, global economic trend, and even regulation. For policy makers, the findings suggest the necessity of monitoring the signatures of bubbles and their progress in the market place.


title: Using Individual-Based Models to Look Beyond the Horizon: The Changing Effects of Household-Based Clustering of Susceptibility to Measles in the Next 20 Years

text: Recent measles outbreaks in regions with a high overall vaccination coverage have drawn attention to other factors - aside from the overall immunity level - determining the spread of measles in a population, such as heterogeneous social mixing behavior and vaccination behavior. As households are an important context for measles transmission, the clustering of susceptible individuals within households can have a decisive effect on the risk for measles outbreaks. However, as the population ages and household constitutions change over the next 20 years, that effect may change as well. To adequately plan for the control and eventual elimination of measles, we need to understand how the effect of within-household susceptibility clustering will evolve. Individual-based models enable us to represent the different levels of heterogeneity in a population that are necessary to understand the spread of a disease in a highly immunized population. In this paper, we use such an individual-based model to investigate how the effect of household-based susceptibility clustering is expected to change over the next two decades in Flanders, Belgium. We compare different scenarios regarding the level of within-household susceptibility clustering for three different calendar years between 2020 and 2040, using projections for the age distribution of the population, the constitution of households and age-specific immunity levels. We find that a higher level of susceptibility clustering within households increases the risk for measles outbreaks and their potential to spread through the population, in current as well as in future populations.


title: Computational Analysis of the Adaptive Causal Relationships Between Cannabis, Anxiety and Sleep

text: In this paper an adaptive computational temporal-causal network model is presented to analyse the dynamic and adaptive relationships between cannabis usage, anxiety and sleep. The model has been used to simulate different well-known scenarios varying from intermittent usage to longer periods of usage interrupted by attempts to quit and to constant usage based on full addiction. It is described how the model has been verified and validated by empirical information from the literature.