Hartman effect in presence of Aharanov Bohm flux

The Hartman effect for the tunneling particle implies the independence of group delay time on the opaque barrier width, with superluminal velocities as a consequence. This effect is further examined on a quantum ring geometry in the presence of Aharonov-Bohm flux. We show that while tunneling through an opaque barrier the group delay time for given incident energy becomes independent of the barrier thickness as well as the magnitude of the flux. The Hartman effect is thereby extended beyond one dimension and in the presence of Aharonov-Bohm flux.Comment: 4 pages, 4 figure

Analysis of genetic diversity and molecular evolution of human group B rotaviruses based on whole genome segments

Group B rotavirus (GBR) is a rare enteric pathogen that causes severe diarrhoea, primarily in adults. Nearly full-length sequences of all 11 RNA segments were determined for human GBRs detected recently in India (IDH-084 in 2007, IC-008 in 2008), Bangladesh (Bang117 in 2003) and Myanmar (MMR-B1 in 2007), and analysed phylogenetically with the sequence data of GBRs reported previously. All RNA segments of GBR strains from India, Bangladesh and Myanmar showed >95 % nucleotide sequence identities. Among the 11 RNA segments, the VP6 and NSP2 genes showed the highest identities (>98 %), whilst the lowest identities were observed in the NSP4 gene (96.1 %), NSP5 gene (95.6 %) and VP8*-encoding region of the VP4 gene (95.9 %). Divergent or conserved regions in the deduced amino acid sequences of GBR VP1–VP4 and NSP1–NSP5 were similar to those in group A rotaviruses (GARs), and the functionally important motifs and structural characteristics in viral proteins known for GAR were conserved in all of the human GBRs. These findings suggest that, whilst the degree of genetic evolution may be dependent on each RNA segment, human GBR may have been evolving in a similar manner to GAR, associated with the similar functional roles of individual viral proteins

Ebola virus disease in West Africa — the first 9 Months of the epidemic and forward projections

BACKGROUND On March 23, 2014, the World Health Organization (WHO) was notified of an outbreak of Ebola virus disease (EVD) in Guinea. On August 8, the WHO declared the epidemic to be a "public health emergency of international concern." METHODS By September 14, 2014, a total of 4507 probable and confirmed cases, including 2296 deaths from EVD (Zaire species) had been reported from five countries in West Africa - Guinea, Liberia, Nigeria, Senegal, and Sierra Leone. We analyzed a detailed subset of data on 3343 confirmed and 667 probable Ebola cases collected in Guinea, Liberia, Nigeria, and Sierra Leone as of September 14. RESULTS The majority of patients are 15 to 44 years of age (49.9% male), and we estimate that the case fatality rate is 70.8% (95% confidence interval [CI], 69 to 73) among persons with known clinical outcome of infection. The course of infection, including signs and symptoms, incubation period (11.4 days), and serial interval (15.3 days), is similar to that reported in previous outbreaks of EVD. On the basis of the initial periods of exponential growth, the estimated basic reproduction numbers (R-0) are 1.71 (95% CI, 1.44 to 2.01) for Guinea, 1.83 (95% CI, 1.72 to 1.94) for Liberia, and 2.02 (95% CI, 1.79 to 2.26) for Sierra Leone. The estimated current reproduction numbers (R) are 1.81 (95% CI, 1.60 to 2.03) for Guinea, 1.51 (95% CI, 1.41 to 1.60) for Liberia, and 1.38 (95% CI, 1.27 to 1.51) for Sierra Leone; the corresponding doubling times are 15.7 days (95% CI, 12.9 to 20.3) for Guinea, 23.6 days (95% CI, 20.2 to 28.2) for Liberia, and 30.2 days (95% CI, 23.6 to 42.3) for Sierra Leone. Assuming no change in the control measures for this epidemic, by November 2, 2014, the cumulative reported numbers of confirmed and probable cases are predicted to be 5740 in Guinea, 9890 in Liberia, and 5000 in Sierra Leone, exceeding 20,000 in total. CONCLUSIONS These data indicate that without drastic improvements in control measures, the numbers of cases of and deaths from EVD are expected to continue increasing from hundreds to thousands per week in the coming months

Rehabilitation versus surgical reconstruction for non-acute anterior cruciate ligament injury (ACL SNNAP): a pragmatic randomised controlled trial

BackgroundAnterior cruciate ligament (ACL) rupture is a common debilitating injury that can cause instability of the knee. We aimed to investigate the best management strategy between reconstructive surgery and non-surgical treatment for patients with a non-acute ACL injury and persistent symptoms of instability.MethodsWe did a pragmatic, multicentre, superiority, randomised controlled trial in 29 secondary care National Health Service orthopaedic units in the UK. Patients with symptomatic knee problems (instability) consistent with an ACL injury were eligible. We excluded patients with meniscal pathology with characteristics that indicate immediate surgery. Patients were randomly assigned (1:1) by computer to either surgery (reconstruction) or rehabilitation (physiotherapy but with subsequent reconstruction permitted if instability persisted after treatment), stratified by site and baseline Knee Injury and Osteoarthritis Outcome Score—4 domain version (KOOS4). This management design represented normal practice. The primary outcome was KOOS4 at 18 months after randomisation. The principal analyses were intention-to-treat based, with KOOS4 results analysed using linear regression. This trial is registered with ISRCTN, ISRCTN10110685, and ClinicalTrials.gov, NCT02980367.FindingsBetween Feb 1, 2017, and April 12, 2020, we recruited 316 patients. 156 (49%) participants were randomly assigned to the surgical reconstruction group and 160 (51%) to the rehabilitation group. Mean KOOS4 at 18 months was 73·0 (SD 18·3) in the surgical group and 64·6 (21·6) in the rehabilitation group. The adjusted mean difference was 7·9 (95% CI 2·5–13·2; p=0·0053) in favour of surgical management. 65 (41%) of 160 patients allocated to rehabilitation underwent subsequent surgery according to protocol within 18 months. 43 (28%) of 156 patients allocated to surgery did not receive their allocated treatment. We found no differences between groups in the proportion of intervention-related complications.InterpretationSurgical reconstruction as a management strategy for patients with non-acute ACL injury with persistent symptoms of instability was clinically superior and more cost-effective in comparison with rehabilitation management

50 Days of Lockdown: Measuring India’s Success in Arresting COVID-19

As India completes 50 days of lockdown, this report presents the findings of a data-driven enquiry into the extent to which the lockdown has achieved its health objectives and arrested the spread of COVID-19. The success is measured on four parameters: flattening the curve, reducing the growth rate of new cases, containing the spread, and improving healthcare capacity. The findings show that while the lockdown has flattened the curve to an extent, it has failed to reverse the trend or contain the disease. Significant changes in strategy would have to be adopted to arrest the spread of COVID-19

Fast Data for Faster Decision-making: The Utility of High-frequency Economic Indicators

The COVID-19 pandemic is posing unique challenges to policymakers across the globe, necessitating efficient action in short timeframes. During such crises, having the right data at the right time is crucial to making informed policy decisions. Traditional economic indicators can be inadequate owing to issues of timeliness, granularity, and difficulty in collection. There is a need therefore for higher-frequency and more granular data to track economic activities. These “alternative” or “proxy” high-frequency indicators could help assess the economic impact triggered by COVID-19, shape new economic policies, and understand the road to recovery. This brief argues for the creation of a publicly accessible, multi-sectoral dashboard based on sectoral, high-frequency indicators

Operationalizing algorithmic explainability in the context of risk profiling done by robo financial advisory apps

Robo Advisors are financial advisory apps that profile users into risk classes before providing financial advice. This risk profiling of users is of functional importance and is legally mandatory. Irregularities at this primary step will lead to incorrect recommenda- tions for the users. Further, lack of transparency and explanations for these automated decisions makes it tougher for users and regulators to understand the rationale behind the advice given by these apps, leading to a trust deficit. Regulators monitor this pro- filing but possess no independent toolkit to “demystify” the black box or adequately explain the decision-making process of the robo financial advisor. Our paper proposes an approach towards developing a ‘RegTech tool’ that can explain the robo advisors decision making. We use machine learning models to reverse engi- neer the importance of features in the black-box algorithm used by the robo advisor for risk profiling and provide three levels of explanation. First, we find the importance of inputs used in the risk profiling algorithm. Second, we infer relationships between inputs and with the assigned risk classes. Third, we allow regulators to explain decisions for any given user profile, in order to ‘spot check’ a random data point. With these three explanation methods, we provide regulators, who lack the technical knowledge to understand algorithmic decisions, a method to understand it and ensure that the risk-profiling done by robo advisory applications comply with the regulations they are subjected to