Closed atraumatic flexor hallucis longus tendon rupture following hallux valgus correction repaired using a turn down flap

A case report of closed atraumatic rupture of flexor hallucis longus (FHL) tendon few months after hallux valgus correction in a high functioning individual is presented. There have been only two cases of FHL tendon rupture reported following hallux valgus correction in literature till now. Our patient underwent Hallux valgus corrective osteotomy, 4 months after which he presented with rupture of the FHL tendon, he subsequently underwent successful surgical turn down flap repair with good clinical outcome. Closed atraumatic rupture of FHL tendon as an isolated injury is a rare event evidenced by systematic review reporting only 10 cases in literature till now. Low clinical suspicion of FHL rupture in closed foot injuries could be one factor resulting in fewer cases being reported in literature. Acute rupture of FHL tendon following open foot injuries and partial closed rupture due to tendinitis in dancers have been reported frequently in literature. In conclusion, we emphasize careful handling of FHL tendon while performing corrective osteotomy of the hallux in any patient. Although, turn down flap is a well-documented technique to bridge gaps and repair chronic tendo-achilles rupture, we were able to replicate the same technique in our patient and produce good functional result using this effective tendon repair technique to bridge segmental gap as evidence by return of almost normal power of great toe plantar flexion

COVID-19 Vaccination: Public Health Lessons from a Large Indoor Gathering

COVID-19 transmission rates among vaccinated persons attending large gatherings have not been reported widely. This research was intended to track the potential incidence of COVID-19 among physicians and their families who attended a large in-person gathering in Atlanta in August 2021. After the successful conclusion of a large-scale indoor gathering, we encouraged all attendees to self-report the incidence of COVID-19 illness. In addition, an online questionnaire was disseminated to collect basic information about age, gender, place of residence, vaccination status including the number of doses, type, and date of each dose as well as behavioral and convention factors that would have contributed to the infection rates. Information about current COVID-19 infection status, symptoms, and severity were also collected. We also contacted the attendees through telephone to gather pending information about their COVID-19 status, after attending the meeting. Most attendees were physicians, employees in the healthcare industry or family members of healthcare professionals. Among the 520 participants of the meeting, no COVID-19 illness was reported up to six weeks after attending the convention. As a sub-group analysis, we obtained demographic data from 143 attendees, through an online survey. Among the survey respondents, 43% were over the age of 60 years, 10% over the age of 70 years, 29% and 14% each between 31-45 years and 12-30 years. 53% were women. Almost 99% had received both doses of COVID-19 mRNA vaccine in January and February of 2021. Public health measures including the use of indoor masks, social distancing, and personal hygiene were followed by 76%. None of the convention attendees who responded had any symptoms or tested positive for COVID-19 infection six weeks after leaving the convention. None reported being diagnosed with COVID-19 for at least 30 days before attending the convention. This report confirms the efficacy of COVID-19 mRNA vaccines against protection from COVID-19 illness among participants of large scale indoor gatherings. Our findings support the notion that large-scale events can be successfully conducted among fully vaccinated persons who follow public health guidelines

URA*: Uncertainty-aware Path Planning using Image-based Aerial-to-Ground Traversability Estimation for Off-road Environments

A major challenge with off-road autonomous navigation is the lack of maps or road markings that can be used to plan a path for autonomous robots. Classical path planning methods mostly assume a perfectly known environment without accounting for the inherent perception and sensing uncertainty from detecting terrain and obstacles in off-road environments. Recent work in computer vision and deep neural networks has advanced the capability of terrain traversability segmentation from raw images; however, the feasibility of using these noisy segmentation maps for navigation and path planning has not been adequately explored. To address this problem, this research proposes an uncertainty-aware path planning method, URA* using aerial images for autonomous navigation in off-road environments. An ensemble convolutional neural network (CNN) model is first used to perform pixel-level traversability estimation from aerial images of the region of interest. The traversability predictions are represented as a grid of traversal probability values. An uncertainty-aware planner is then applied to compute the best path from a start point to a goal point given these noisy traversal probability estimates. The proposed planner also incorporates replanning techniques to allow rapid replanning during online robot operation. The proposed method is evaluated on the Massachusetts Road Dataset, the DeepGlobe dataset, as well as a dataset of aerial images from off-road proving grounds at Mississippi State University. Results show that the proposed image segmentation and planning methods outperform conventional planning algorithms in terms of the quality and feasibility of the initial path, as well as the quality of replanned paths

Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities

Robotics and Artificial Intelligence (AI) have been inextricably intertwined since their inception. Today, AI-Robotics systems have become an integral part of our daily lives, from robotic vacuum cleaners to semi-autonomous cars. These systems are built upon three fundamental architectural elements: perception, navigation and planning, and control. However, while the integration of AI-Robotics systems has enhanced the quality our lives, it has also presented a serious problem - these systems are vulnerable to security attacks. The physical components, algorithms, and data that make up AI-Robotics systems can be exploited by malicious actors, potentially leading to dire consequences. Motivated by the need to address the security concerns in AI-Robotics systems, this paper presents a comprehensive survey and taxonomy across three dimensions: attack surfaces, ethical and legal concerns, and Human-Robot Interaction (HRI) security. Our goal is to provide users, developers and other stakeholders with a holistic understanding of these areas to enhance the overall AI-Robotics system security. We begin by surveying potential attack surfaces and provide mitigating defensive strategies. We then delve into ethical issues, such as dependency and psychological impact, as well as the legal concerns regarding accountability for these systems. Besides, emerging trends such as HRI are discussed, considering privacy, integrity, safety, trustworthiness, and explainability concerns. Finally, we present our vision for future research directions in this dynamic and promising field

New insights into the efficient charge transfer of ternary chalcogenides composites of TiO2

Abstract A two-step solvothermal synthesis was adopted to prepare AgXSe2-TiO2 (X = In, Bi) composites. DFT study of the pristine parent samples showed the formation of the hexagonal phase of AgBiSe2, and tetragonal phase of AgInSe2 and TiO2, which corroborated the experimentally synthesised structures. Both the AgBiSe2-TiO2 and AgInSe2-TiO2 composites displayed enhanced visible light absorption and reduced band gap in the UV-DRS patterns. The XPS results exhibited a shift in binding energy values and the TEM results showed the formation of spherical nanoparticles of both AgBiSe2 and AgInSe2. The PL signals displayed delayed recombination of the photogenerated excitons. The as synthesised materials were studied for their photocatalytic efficiency, by hydrogen generation, degradation of doxycycline, and antimicrobial disinfection (E. coli and S. aureus). The composite samples illustrated more than 95 % degradation results within 180 min and showed 5 log reductions of bacterial strains within 30 min of light irradiation. The hydrogen production outcomes were significantly improved as the AgBiSe2 and AgInSe2 based composites illustrated 180-fold and 250-fold enhanced output compared to their parent samples. The enhanced photocatalytic efficiency displayed is attributed to the delayed charge recombination of the photogenerated electron-hole pairs in the AgXSe2-TiO2 interface. Formation of a p-n nano heterojunction for AgBiSe2-TiO2 and type II heterojunction for AgInSe2-TiO2 composite are explained

Special Seminar: How to deal with the media: maximising opportunity and minimising threat

This session will cover how to work effectively with the media including print, radio and TV. You'll get an insight into how journalists and news rooms operate and what they would like from you. You'll learn to recognise both soft balls and traps - and develop the techniques for dealing with them. The challenges of explaining and positioning CERN to any media outlet whether local, national or international will be dealt with too. This interactive presentation, given by Jessica Pryce-Jones, Managing Director of the consultancy iOpener Ltd, and by Nisha Pillai, news anchor for BBC World, will be illustrated with various case studies.Tea and coffee will be served from 16:00</i

Artificial Intelligence Models for Zoonotic Pathogens: A Survey

Zoonotic diseases or zoonoses are infections due to the natural transmission of pathogens between species (animals and humans). More than 70% of emerging infectious diseases are attributed to animal origin. Artificial Intelligence (AI) models have been used for studying zoonotic pathogens and the factors that contribute to their spread. The aim of this literature survey is to synthesize and analyze machine learning, and deep learning approaches applied to study zoonotic diseases to understand predictive models to help researchers identify the risk factors, and develop mitigation strategies. Based on our survey findings, machine learning and deep learning are commonly used for the prediction of both foodborne and zoonotic pathogens as well as the factors associated with the presence of the pathogens

Critical behavior and magnetocaloric effect in La0.67Ca0.33Mn1-xCrxO3 (x=0.1, 0.25)

Universal behavior of the magnetocaloric effect along with structural and critical exponent analysis in mixed manganite La0.67Ca0.33Mn1-xCrxO3 (x = 0.1, 0.25), [LCMCr0.1 and LCMCr0.25] exhibiting second order phase transition are investigated. Structural study using Reitveld refinement of XRD patterns indicates orthorhombic structure with Pnma space group. Modified Arrott plot method has been adopted to study the critical behavior of the compounds at their transition region, which gives values of beta = 0.555(6), gamma = 1.17(4) and delta = 2.7096(7) at T-C = 232.5 K for LCMCr0.1 and beta = 0.68 (1), gamma = 1.09(3) and delta = 2.9362(4) at T-C = 202.5 K for LCMCr0.25. The values are close to those expected for mean field ferromagnets with long range order. With increase in Cr content, the temperature corresponding to the maximum entropy change as well as the magnetic transition temperature gradually shifts to low temperatures. The maximum magnetic entropy change was found to be 3.5 J/kg K for x = 0.1 and 2.2 J/kg K for x = 0.25 for a field change of 5 T. The field dependence of the magnetic entropy change is also analyzed, which shows the power law dependence namely, Delta S-M alpha H-n, n = 0.9086(5) at T-C = 232.5 K and n = 0.849(7) at T-C = 202.5 K for LCMCr0.1 and LCMCr0.25 respectively. Relative cooling power was found to be about 147 J/kg for LCMCr0.1 and 88 J/kg for LCMCr0.25. The field dependence of the relative cooling power for both the compounds shows a H1+1/delta dependence with the delta values in agreement with the mean field model. (C) 2011 Elsevier Masson SAS. All rights reserved