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Background During the COVID-19 pandemic there has been a strong interest in forecasts of the short-term development of epidemiological indicators to inform decision makers. In this study we evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland for the period from January through April 2021. Methods We evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland. These were issued by 15 different forecasting models, run by independent research teams. Moreover, we study the performance of combined ensemble forecasts. Evaluation of probabilistic forecasts is based on proper scoring rules, along with interval coverage proportions to assess calibration. The presented work is part of a pre-registered evaluation study. Results We find that many, though not all, models outperform a simple baseline model up to four weeks ahead for the considered targets. Ensemble methods show very good relative performance. The addressed time period is characterized by rather stable non-pharmaceutical interventions in both countries, making short-term predictions more straightforward than in previous periods. However, major trend changes in reported cases, like the rebound in cases due to the rise of the B.1.1.7 (Alpha) variant in March 2021, prove challenging to predict. Conclusions Multi-model approaches can help to improve the performance of epidemiological forecasts. However, while death numbers can be predicted with some success based on current case and hospitalization data, predictability of case numbers remains low beyond quite short time horizons. Additional data sources including sequencing and mobility data, which were not extensively used in the present study, may help to improve performance. Plain language summary We compare forecasts of weekly case and death numbers for COVID-19 in Germany and Poland based on 15 different modelling approaches. These cover the period from January to April 2021 and address numbers of cases and deaths one and two weeks into the future, along with the respective uncertainties. We find that combining different forecasts into one forecast can enable better predictions. However, case numbers over longer periods were challenging to predict. Additional data sources, such as information about different versions of the SARS-CoV-2 virus present in the population, might improve forecasts in the future. Bracher et al. compare 15 forecasting models of COVID-19 cases and deaths in Germany and Poland between January and mid-April 2021. Many, though not all, models outperform a simple baseline model up to four weeks ahead, with ensemble methods showing very good relative performance.

Disease modelling has had considerable policy impact during the ongoing COVID-19 pandemic, and it is increasingly acknowledged that combining multiple models can improve the reliability of outputs. Here we report insights from ten weeks of collaborative short-term forecasting of COVID-19 in Germany and Poland (12 October–19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic. Forecasting models have been used extensively to inform decision making during the COVID-19 pandemic. In this preregistered and prospective study, the authors evaluated 14 short-term models for Germany and Poland, finding considerable heterogeneity in predictions and highlighting the benefits of combined forecasts.

Deep learning techniques, especially Convolutional Neural Networks (CNN), dominate the benchmarks for most computer vision tasks. These state-of-the-art results are typically obtained through supervised learning, for which large annotated datasets are required. However, acquiring such datasets for manufacturing applications remains a challenging proposition due to the time and costs involved in their collection. To overcome this disadvantage, a novel framework is proposed for data augmentation by creating synthetic images using Generative Adversarial Networks (GANs). The generator synthesizes new surface defect images from random noise which is trained over time to get realistic fakes. These synthetic images can be used further for training of classification algorithms. Three GAN architectures are trained, and the entire data augmentation pipeline is implemented for the Northeastern University (China) Classification (NEU-CLS) dataset for hot-rolled steel strips from NEU Surface Defect Database. The classification accuracy of a simple CNN architecture is measured on synthetic augmented data and further it is compared with similar state-of-the-arts. It is observed that the proposed GANs-based augmentation scheme significantly improves the performance of CNN for classification of surface defects. The classically augmented CNN yields sensitivity and specificity of 90.28% and 98.06% respectively. In contrast, the synthetically augmented CNN yields better results, with sensitivity and specificity of 95.33% and 99.16% respectively. Also, the use of GANs is demonstrated to disentangle the representation space and to add additional domain knowledge through synthetic augmentation that can be difficult to replicate through classic augmentation. The proposed framework demonstrates high generalization capability. It may be applied to other supervised surface inspection tasks, and thus facilitate the development of advanced vision-based inspection instruments for manufacturing applications.

Solar power in India is growing at a tremendous pace. India's solar power capacity is 20 GW and has grown 8-fold since 2014. Assessing the solar potential in India is thus the need of the hour. The objective of this study is to make an optimized prediction model of the monthly potential of solar irradiance of the Indian Subcontinent, by utilizing hour-wise unstructured voluminous (80 million line item) satellite-based data from 609 locations for 15 years. The variables chosen are temperature, pressure, relative humidity, month, year, latitude, longitude, altitude, DHI, DNI, and GHI. Combining predictive models using combinations of SVM, ANN, and RF for factors affecting solar irradiance. This model's performance has been evaluated by its accuracy. Accuracy for DHI, DNI, GHI values on testing data evaluated through the SVM model is 95.11%, 93.25%, and 96.88%, respectively, whereas accuracy evaluated through the ANN model is 94.18%, 91.60%, and 95.90%, respectively. The achieved high prediction accuracy makes the SVM, ANN, and RF model very robust. This model with a sustainable financial model can thus be used to identify major locations to set up solar farms in the present and future and the feasibility of its establishment, wherever local meteorological data measuring facilities are not available in India. Along with the air temperature, air pressure, and humidity predictive interrelation model created to aid the irradiance model this can be used for climate predictions in the Indian sub-continental region.

This study explores the development of an explainable music recommendation system with enhanced user control. Leveraging a hybrid of collaborative filtering and content-based filtering, we address the challenges of opaque recommendation logic and lack of user influence on results. We present a novel approach combining advanced algorithms and an interactive user interface. Our methodology integrates Spotify data with user preference analytics to tailor music suggestions. Evaluation through RMSE and user studies underscores the efficacy and user satisfaction with our system. The paper concludes with potential directions for future enhancements in group recommendations and dynamic feedback integration.