Reviewerly/exHarmony huggingface dataset:
Using Satellite Information to Evaluate Cloud Forecast from WRF-Solar EPS&amp;#160;

&amp;lt;p&amp;gt;Cloud forecasting is an enormous challenge in numerical weather prediction (NWP) models because of the complex physical processes, high variability in spatial and temporal scales, and lack of observations for model evaluation. The capability to represent cloud fields in the NWP models directly impacts the accuracy in predicting surface solar irradiance. Verifying cloud forecasts from NWP models is essential to investigate the source of uncertainty and error stemming from predicting various types of clouds. However, it requires high-quality observations containing various information of clouds and in-depth analysis over a wide range of regions to assess the cloud fields. In this study, we produce day-ahead cloud forecast over the contiguous United States (CONUS) for 2018 using the Weather Research and Forecasting-Solar Ensemble Prediction System (WRF-Solar EPS) which is a state-of-the-art ensemble NWP model specialized for solar applications. The strengths and limitations of WRF-Solar EPS in reproducing cloud fields is diagnosed using satellite observations from the National Solar Radiation Database (NSRDB). The frequency of clouds and various cloud detection metrics including the probability of detection (POD), the false alarm rate (FAR), the hit rate (HR), Kuiper&amp;amp;#8217;s skill scores (KSS), and mismatched cloud frequency (MCF) are calculated to assess the performance of WRF-Solar EPS. In the first part of this study, we focus on monthly analysis using the detection metrics to account for seasonal performance of WRF-Solar EPS. In the second part, the MCF classified by cloud top height and cloud optical depth is analyzed to investigate the model&amp;amp;#8217;s capability to predict nine different types of clouds. The study exhibits that the WRF-Solar EPS has difficulty predicting optically thin clouds; overall MCFs show 46% (cumulus), 34% (stratocumulus), 19% (stratus), 33% (altocumulus), 23% (altostratus), 16% (nimbostratus), 27% (cirrus), 13% (cirrostratus), and 8% (deep convective) for the nine cloud types.&amp;lt;/p&amp;gt;
A Study on Disclosure Items of Safety and Health Management System for Major Injury Prevention

The Serious Accidents Punishment Act was enacted in a surprise move to raise corporate managers' interest and awareness of fatal accidents, effective on January 2022. The government policy to reduce serious disasters by punishments has significant limitations. Thus, it is important to conduct a study on estimating industrial accident costs in the safety and health management system so that companies can voluntarily check the workplace realities. In order to strengthen the occupational safety and health (OSH) management system, this study focuses on the development of safety accident cost disclosure items, estimating industrial accident loss costs, both direct and indirect. The OSH disclosure items can be divided into general information of companies, OSH management system, OSH budget and expenses, certification status, and corporate industrial accident information. And also the OSH disclosure survey form was developed.&#x0D;
ICA&amp;D: Climate services across borders

&amp;lt;p&amp;gt;The International Climate Assessment &amp;amp; Dataset (ICA&amp;amp;D) contributes to the provision of climate services in regions across the world. ICA&amp;amp;D contributes both to the provision of climate data and to climate monitoring, thereby supporting the WMO Regional Climate Centres in carrying-out their mandatory functions.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In WMO Regional Association VI (Europe and the Middle East), the European Climate Assessment &amp;amp; Dataset (ECA&amp;amp;D) fulfills this role and from this the ICA&amp;amp;D concept is derived. ICA&amp;amp;D has already been implemented in Indonesia as the Southeast Asian Climate Assessment &amp;amp; Dataset (SACA&amp;amp;D).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;ICA&amp;amp;D facilitates the sharing of daily meteorological surface observations from countries in a given region with meteorological services in that region and with scientists worldwide, and deriving climate monitoring products from these observations. Examples are the climate indices of extremes, such as the number of warm or dry days, which can be monitored over time in view of climate change. ICA&amp;amp;D complements national meteorological databases in having only a daily resolution and containing information from multiple countries in the region in the same format.&amp;lt;br&amp;gt;&amp;amp;#160;&amp;lt;br&amp;gt;With support from the EU-funded ClimSA project, under which WMO is implementing a EUR 5.5 million grant, ICA&amp;amp;D will be implemented in the Caribbean, the Pacific and 5 sub-regions in Africa. ICA&amp;amp;D will be set-up in selected Regional Climate Centres that form part of the WMO institutional network. Within the ClimSA project, the ICA&amp;amp;D website has seen an upgrade and has been made more user friendly. Furthermore, e-learning modules are being developed to aid the ICA&amp;amp;D maintainers in the region in updating their ICA&amp;amp;D database and website.&amp;lt;/p&amp;gt;
Benchmarking different approaches to convert surface solar irradiance into PV power production : a case study with an operational forecast system for a roof-top PV farm

&amp;lt;p&amp;gt;Photovoltaic production of a given solar power plant is mainly correlated with solar irradiance reaching the solar panel and to a lesser extent with air temperature and wind speed. Therefore, available energy in a building with roof-top photovoltaic (PV) panels in self-consumption can be highly variable due to cloud cover stochastic behaviour. Accurate irradiance forecast within the next hours are useful to help the energy management system of the building to cope with this variability and thus to maximize the consumption of the locally generated electricity at the expense of grid energy, then reducing financial and environmental costs of the overall building energy consumption.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Any solar energy forecast solution presents several sources of uncertainty at each main steps of the process: cloud forecast, radiative transfer of cloud and aerosols, irradiance conversion into power. Reducing the uncertainty of PV power modelled from irradiance forecast is a specific issue because it depends on certain conditions of the forecast application. PV performance models convert irradiance into PV power if PV cells characteristics are known and correctly specified by manufacturer, which is not always the case. Moreover, unexpected shadowing, panel surface soiling or aging of materials cannot be easily taken into account. If a consistent historical record of PV power data is available, a model output statistics (MOS) helps to decrease these induced uncertainties. If only real-time data are available, adaptive calibration using Kalman filter can improve the accuracy. If both historical and real-time data access are available, various machine learning approaches can set up more accurate MOS.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The start-up incubator of &amp;lt;em&amp;gt;Institut Polytechnique de Paris&amp;lt;/em&amp;gt; is a building equipped with a roof-top PV farm with a total capacity of 17 kWp, made up with 53 using 8 different technologies with several tilted angle and some panels are equipped with reflectors. Minute PV power data have been continuously recorded from July 2020 onwards, collocated with the irradiance measurements of the Palaiseau BSRN station. This exceptional testbed enabled us to benchmark the several approaches for PV power modelling. We implement reference algorithms (linear regression, Kalman filter and autoregression) simulating respectively the availability data conditions (historical, real-time, both) and compared them with more complex machine-learning approaches. Results according to data availability, season, solar zenith angle, air temperature and irradiance variability are discussed. One of the outcome is that availability of real-time power measurements improves significantly forecasting results in most weather situations.&amp;lt;/p&amp;gt;
Solar Power Nowcasting in the Presence of Sahara dust: Can Deep Learning based on Satellite and Synthetic Production Data Recognize the Production-Offsets?

&amp;lt;p&amp;gt;Fostering solar power as a sustainable, as fossil fuel free as possible, energy source demands accurate, location-optimized, and highly resolved forecasts of power production. For this study, we consider the issue of production offsets due to Sahara dust events in large areas of Central Europe as well as data coverage and inconsistency issues with evolving solar power sites. In the presented case study we investigate subhourly nowcasts using machine learning for (i) specific solar power plants in Central Europe (ii) the suitability of synthetically generated production using NWP grid points in the studied areas. &amp;amp;#160;&amp;lt;br&amp;gt;Deep learning enables us to consider complex timeseries from historic data to model highly variable (spatial, temporal) diurnal and seasonal changes in the expected power production. In particular, we investigate how to exploit the spatio-temporal relationships in highly resolved forecasts by a sequence-to-sequence encoder-decoder inspired LSTM (long short-term memory) artificial neural network. We optimize the performance of our deep learning approach by tuning hyper-parameters, network weighting, and loss as well as addressing the input feature selection accordingly. Our preprocessing steps transform available data into a suitable representation for learning efficiently from a combination of multiple, very heterogeneous data sources with varying temporal availability and spatial resolution. For instance, we utilize 3D-fields from other weather prediction models, satellite data and remote sensing products, and observation time-series as well as their generated climatologies. A key objective is to properly process the differing temporal and spatial resolution while still generating nowcasts efficiently. To extend the historic training data set of complex models, we generate synthetic solar production data using machine learning and consider climatological driven data transformation. We investigate transfer learning as a further option in our deep learning setup.&amp;lt;br&amp;gt;Results obtained by the developed method generally yield high forecast-skills, where the best model setups are shown in our analysis. We compare the forecast results of up to 6 hours ahead obtained through this machine learning approach to available forecast methods, e.g., forecasts generated with python pvlib driven with AROME.&amp;lt;/p&amp;gt;
“You're on Your Own”

In this chapter, the author performs a final review of the goals of the book, its research findings, limitations, and avenues for future research. In a nutshell, the book attempted to answer the following three questions: 1) What is SC? 2) How is it done, and what can it help us achieve? 3) How can we improve it? Most of the book's research data is derived from this author's life-story context, as well as his thoughts and self-directed communications. The other data is from a qualitative basic meta-review of cognitive-science scholarly articles, and articles of instruction for data-science and other related topics—e.g., theorization about learning and creativity—curated over a period of two years. Overall, the research findings suggest that SC is a powerful tool for various functions—including learning, ICT, and AI. However, its most helpful function might be to support our resilience and mental health.
Abductive Reasoning in Intuitionistic Propositional Logic via Theorem Synthesis

Abstract With help of a compact Prolog-based theorem prover for Intuitionistic Propositional Logic, we synthesize minimal assumptions under which a given formula formula becomes a theorem. After applying our synthesis algorithm to cover basic abductive reasoning mechanisms, we synthesize conjunctions of literals that mimic rows of truth tables in classical or intermediate logics and we abduce conditional hypotheses that turn the theorems of classical or intermediate logics into theorems in intuitionistic logic. One step further, we generalize our abductive reasoning mechanism to synthesize more expressive sequent premises using a minimal set of canonical formulas, to which arbitrary formulas in the calculus can be reduced while preserving their provability. Organized as a self-contained literate Prolog program, the paper supports interactive exploration of its content and ensures full replicability of our results.
Problems and Prospects of Formation of Readiness for Physical Self-improvement of Cadets of the University

The article presents a study of the problems and prospects of formation of readiness for physical self-improvement of cadets and students of higher education institutions of the Ministry of Internal Affairs, considers a three-component structure of readiness for the implementation of any activity, reveals the readiness of a cadet for physical self-improvement from the standpoint of professional readiness, analyzes the results of the survey, formulates the problems identified in as a result of studying the answers of respondents. The article is focused on experimental and pedagogical research of potential problems (external and internal) of students’ professional self-improvement. Methodology. The theoretical basis of the work was the study of the problems of forming personnel of the structure of the Ministry of Internal Affairs for various areas of professional activity (A. V. Barabanshchikov, V. N. Gerasimov, V. P. Davydov, G. A. Davydov, A. I. Kamenev, N. N. Konyukhov, N. F. Fedenko), aspects of the formation of readiness of military personnel (I. A. Lipsky, V. A. Mikhailovsky, P. A. Korchemny, L. G. Laptev), readiness to carry out any type of professional activity, having a three-component structure (G. A. Bokareva, M. I. Dyachenko, L. A. Kandybovich, O. A., Kozlov, L. G. Laptev, A. K. Markova). Works in the field of readiness for military-professional self-improvement (G. A. Volkovitsky, I. V. Biochinsky, Yu. V. Mukhanov), studying the aspects of professional readiness (V. G. Astafiev, V. P. Bespalko, G. A. Krushnina, T. D. Kalistratova, A. M. Mitina, G. K. Parinova, V. A. Slastenin, N. A. Sorokina). The article concludes about the need of students for self-improvement, but at the same time, about the lack of psychological and pedagogical support for self-improvement of a cadet in formal and non-formal education. The problems of uniformity of forms and methods of teaching the cycle of physical culture disciplines along with the proven effectiveness of interactive pedagogical technologies were revealed. There is definitely a problem in the absence of a system of teaching the basics of self-organization and self-improvement.
Blockchain Enabled Product Tracking for Supply Chain Management

The journey of the product from initial raw material to final goods is known as supply chain and the way it is managed is known as supply chain management (SCM). The importance of SCM has been growing, now more than ever, due to the COVID-19 pandemic that has brought the entire world to a standstill. On the other hand, BlockChain Technology (BCT) has been spreading its wings and has become a part of almost every industry today. However, their amalgamation has been an area of constant critical concern. The removal of a regulatory body from a network that involves humongous transactions at times has found its detractors. Implementation of BCT in the SCM has its share of pros and cons but the pros far outweigh the cons. With digital being the cynosure of the new fast-emerging world, it is only apt to shift to a platform, being backed by one that speeds up the pace of our lives while not compromising on the trust, safety and privacy of the users. On the commercial side of the implementation, the trail of the products throughout their entire journey from the raw materials to the end-users is tracked but, in this chapter, we propose and attempt to implement it on a smaller but more affordable scale.
Graph neural networks for solar energy nowcasting and intra-day prediction in Central Europe

&amp;lt;p&amp;gt;With the transition towards an increased use of renewable energy system, especially in solar energy, accurate predictions are essential to maintain and secure grid stability; grid feed-in scheduling of PV production as well as in intra-day and day-ahead trading. To provide forecasts in the nowcasting and intra-day range a combination of different heterogeneous data sources such as satellite data, observations, power productions, and numerical weather prediction data is needed.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this study, a graph neural network model (GNN) with adapted loss function is evaluated against more &amp;amp;#8220;classical&amp;amp;#8221; deep learning and statistical approaches such as a ConvLSTM architecture for nowcasting and intra-day prediction of PV production. Besides the GNN and the ConvLSTM a baseline forecast using an observation-based analog method is implemented. The analog method includes a Euclidean distance measure and dimensionality reduction to include not only temporal analogs but also spatial analogs surrounding the target site. For selected sites, which are included in the NWP domain, a simple NWP to PV forecast is used as baesline using the Python library PVLIB. The target region for the models is a belt in Central Europe stretching from Luxembourg to Austria with PV sites in different altitude and climatic regions. Heterogeneous data sources with varying temporal availability and spatial resolution are used as inputData pre-processing and careful feature selection are needed to not overfit the different methods.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Results show that the graph neural network is able to outperform the standard and baseline methods as well as the other two methods for most of the selected cases.&amp;lt;/p&amp;gt;
Assessment of direct normal irradiance assessment from cloud optical depth of geostationary meteorological satellites in Germany

&amp;lt;p&amp;gt;The assessment of Direct Normal Irradiance (DNI) is essential for the solar power industry. Satellite estimations offer many advantages over ground-based measurements with immediate and affordable installation (no hardware installation), inexistant operation and maintenance of sensors, and efficient detection of any malfunction by comparing the production measured on the meter with the satellite estimation. In this work, we compare two estimations models of DNI from geostationary meteorological satellites in Germany.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The first model is provided by Copernicus Atmosphere Monitoring Service (CAMS) solar radiation services. In its latest v4 update, this model uses the APOLLO_NG (APOLLO_NextGeneration) cloud processing scheme that is a probabilistic interpretation of the original APOLLO (AVHRR Processing scheme Over cLouds, Land and Ocean) method to infer the Cloud Optical Depth (COD).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The second model is based on COD of the &amp;amp;#8220;Satellite Application Facility on support to NoWCasting and very short range forecasting&amp;amp;#8221; (SAFNWC) with the version 2018.1. The algorithm is based on a multispectral threshold technique applied to each pixel of the satellite image and uses Numerical Weather Prediction (NWP) input data for surface temperature and total atmospheric water vapour content.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Both models use a Cloud Mask (CMA) product to delineate all cloud-free pixels in a satellite scene with a high confidence, then DNI is forced to the value obtained by the McClear clear sky model when CMA indicates &amp;quot;cloud free&amp;quot;. Otherwise, the COD is combined with the clear sky model in order to compute the effective DNI.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Moreover, we take into account the circumsolar radiation to avoid underestimation of the forward scattered radiation in DNI when compared to ground observations.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The model outputs are compared to 10-minute solar radiation measurements from Deutscher Wetterdienst (DWD) stations located in Germany over the period 2021-04-01 and 2022-03-31. This network measures the Global Horizontal Irradiance (GHI) and Diffuse Horizontal Irradiance (DHI). DNI is computed out of the quality-checked measurements of GHI and DHI. The results are expressed for all available measurement data in terms of relative Root Mean Scare Error (RMSE), RMSE Skill Score, Mean Absolute Error (MAE), MAE Skill Score, and mean bias error.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We conclude that the two models offer similar performances with a MAE between 25% and 35% for Germany.&amp;lt;/p&amp;gt;
A handbook of mathematical methods and problem-solving tools for introductory physics (second edition)

"A handbook of mathematical methods and problem-solving tools for introductory physics (second edition)." Contemporary Physics, ahead-of-print(ahead-of-print), p. 1
Transformation Fast and Slow

AbstractThis volume is based upon a selection of papers that were presented at the online EAIR Forum in 2021. The book brings together scholars, practitioners and policymakers in higher education, and sets out the theme of transformation in three key areas: digitalisation, quality and trust. Herewith this volume presents a stimulating and careful analysis of the opportunities and associated challenges of transformation in higher education systems and institutions.
Helping Students in the COVID Crisis

AbstractIn March 2020 the World Health Organization declared the spread of COVID-19 a pandemic. Subsequently, universities had to make many decisions in response to the changing situation. Now, almost two years later, university management as well as quality management departments need information to learn from these experiences and to react to new developments in an informed manner. In this context, Hochschule für Technik und Wirtschaft (HTW) Berlin, a university of applied sciences with almost 14,000 students, used its business intelligence software based on its Campus Management System (CMS) data to gain insights into changes in student dropout rates, exam registrations, and credit point gains.The findings indicate fewer dropouts at the beginning of the pandemic and lower numbers of exam registrations compared to pre-pandemic semesters, but also that there might be delayed effects that are starting to emerge. HTW Berlin will use the results of its monitoring of study programmes as a quality management tool to discuss future improvements.