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Laser bending is used to induce precise bend angle in sheets and foils by controlled laser irradiations. In this paper an analytical model is developed to set up a relationship between bend angle and process parameters in terms of laser power and scan speed during laser sheet bending. The proposed model estimates the bend angle considering laser parameters and thermo-mechanical properties of the workpiece material. The analytical model is validated by conducting experiments on low carbon steel, and the results showed that the developed analytical model is in good agreement with the experimental results. The average absolute error between experimental and analytical bend angle is about 9.51%. [ABSTRACT FROM AUTHOR]

A novel and an efficient technique is developed in this work to obtain a large bend angle with a single laser scan. The proposed technique consists of laser-assisted bending with moving pre-displacement. A thermomechanical model is developed for laser-assisted bending with moving pre-displacement using the finite element method (FEM). The numerical model is validated with the developed experimental results and is found to be in good agreement with the experimental bend angle. The validated model is used to analyse the effect of pre-displacement on temperature distribution, stress-strain distribution, bend angle, edge effect and springback. Laser-assisted bending is compared with the laser bending process without external mechanical load. Results showed that the bend angle, edge effect and springback are significantly affected by the applied predisplacement. [ABSTRACT FROM AUTHOR]

Purpose: To document the demographic profile of the SHAPU outbreak amidst the COVID-19 pandemic., Methods: A multicentric cross-sectional study of the 2021 SHAPU outbreak during the second phase of the COVID-19 outbreak., Results: A total of 135 patients were diagnosed with SHAPU from August to December 2021, 77 (57%) were children <16 years, males 54.8% and 34.8% had direct physical contact with white moths and 41.5% had severe type of SHAPU. Dramatic increment in the moth abundance was noted in these outbreak sites. Few cases presented with atypical ocular findings, unlike past outbreaks. Due to the ongoing COVID-19 pandemic with restrictions on travel and transportation, timely management was difficult and good visual outcome was achieved only in mild-moderate cases with an early presentation., Conclusion: The surge in the number of SHAPU patients, its occurrence in areas previously unreported, and some atypical presentation added raised suspicion of a possible link between COVID-19 and SHAPU.

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: APP report grants from Metabiota Inc outside the submitted work. J.S. and Columbia University declare partial ownership of SK Analytics. No other authors have competing interests to declare., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022., Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1-4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models' predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models' forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models' past predictive performance., Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models' forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models' forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models' forecasts of deaths (N=763 predictions from 20 models). Across a 1-4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models., Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks., Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z)., Competing Interests: KS, HG, RG, HJ, RN, BP, FS, JD, DW, SA, AU, GG, ER, NR, DS, YW, NW, LW, JT, GO, TS, DT, LP, EK, JM, MB, NL, JM, JS, JW, JF, BL, IR, JB, RW, IV, VT, MS, MZ, CP, BR, NB, SM, LC, GF, IM, DO, PA, AM, VE, AK, DK, LP, BD, ML, SS, JD, SM, VP, GR, BB, GA, NP, GZ, WB, SH, TH, DS, MG, JA, DM, SA, EA, DL, CP, JB, AR, TZ, AK, JZ, FP, FD, MC, GL, PG, BT, FB, GJ, MM, AF, AS, PM, AA, BH, BL, MM, PP, SV, RB, FD, AG, KG, MG, BK, AM, KN, JN, MR, FR, MS, ES, JZ, JK, BP, KH, YK, MS, BP, MB, MF, RI, TK, TO, JB, SF No competing interests declared, PB, HG, SS, BS Affiliated with IEM, Inc. The author has no financial interests to declare

Introduction: Classification are essential part of scientific methodology and has important role in medical reporting system. Even after having 46 years long history, Seasonal Hyperacute Panuveitis (SHAPU), the blinding diseases reported mainly from Nepal lacks a standard classification system. Thus, we aim to contribute in the ophthalmic nosology by purposing a classification system for SHAPU., Methodology: The classification is suggested on the background of prolonged experience of this entity by the group of investigators who have dedicated years of research on this topic., Conclusion: We are optimistic that the proposed classification system will help in effective planning and evaluation of this ocular emergency condition and deliver the appropriate and reliable information for timely management and prevention of complications.

Background: Seasonal Hyperacute Panuveitis (SHAPU) is an eye disease of unclear aetiology occurring cyclically during the autumn in odd years in Nepal causing blindness within a week. This study is the first of its type to investigate the risk factors of SHAPU., Methods: A multicentric national level case-control study was performed during the 2017 SHAPU outbreak. Cases were matched to controls in a 1:3 ratio based on age, sex and geographic area.Questionnaire-based personal interview was used and risk factors were categorized as biological and behavioral. For univariate analysis, frequency, median and interquartile range was calculated. Chi-squared test with/without continuity correction and Fisher's exact test were used. Multivariate conditional logistic regressions were used for all the independent variables for p <0.1 in the univariate analyses., Results: We identified 35 cases and 105 controls; 71.4% were children≤16 years (38-day infant to 50-year-old). All were immunocompetent individuals, males were 57.1% and females 42.9%. Potential risks such as visible moths/butterfly activity, contact with livestock, and attending mass gatherings of people were not reported more frequently in cases vs controls in univariate analyses. Differences in possibly protective factors such as self-reported mosquito net use, light off at night while sleeping, and habit of hands/face washing after physical contact/touch with any insects/butterflies/birds were not statistically significant between both groups. In multivariate model, SHAPU cases were significantly more likely than controls to report physical contact with butterflies/white moths (Adjusted OR:6.89; CI:2.79-17.01, p < .001)., Conclusions: Direct physical contact with butterflies/moths was associated with significantly increased odds of SHAPU cases.

Purpose: This study has attempted to assess the profile and treatment outcome of patients with OSSN and report the recurrence rate following treatment of primary OSSN., Materials and Methods: This was a retrospective, non-comparative hospital based observational study. The case files of OSSN patients from Jan 2010 to Dec 2017were assessed. The study site was Himalaya Eye Hospital, Gharipatan, Pokhara. This hospital is the only eye hospital in province 4, Nepal. All the files of suspicious cases of conjunctival mass or OSSN were reviewed. Data regarding demographic characteristics, vision acuity, involved eye, clinical features, conjunctival lesion type, duration of symptoms, preoperative clinical diagnosis, history, other eye disease, associated systemic diseases, occupation as well as treatment modalities of the patients and its outcome were taken., Results: Out of 40 cases, females were 21(52.5%) vesus 19(47.5%) male. Amongthem, 19 patients had keratinized stratified squamous epithelium, 17 had conjunctiva intraepithelial squamous neoplasia and 4 had invasive squalors cell carcinoma. Predominance age group was 15-59 age (25/40, 62.5%), among which 12 had keratinized stratified squamous epithelium, 11 conjunctival intraepithelial squamousneoplasia and 2 had invasive squamous cell carcinoma conjunctival lesion. The mean duration of symptoms was about nine months (S.D. = 5.972). The mean size of lesion mass was 3.28 mm (SD=1.569). More than half of the cases (55%, 22/40) had the mass located at nasal limbus. 95% (38/40) cases underwent lesion total excision and direct closure and 5% (2/40) underwent lesion excision followed by Amniotic Membrane grafting. Only 5% (2/40) cases, those who had undergone lesion excision, the lesion recurrence was observed during follow up period of 31 months., Conclusion: The outcome of the OSSN following the treatment was found to be satisfactory. Further studies with prolonged follow-up are recommended to evaluate the risk of recurrence and other adverse effects., (© NEPjOPH.)