dataesr/scientific-paragraphs-categorization huggingface dataset:
Disease-Associated Behaviour and Awareness Studies Planning and implementing approaches to address diseases at a population-scale requires careful assessment of the overall awareness of the diseases and the factors that increase risk. Although a plethora of studies have reported on these aspects in non-African populations, the varied socio-cultural settings in Africa have the potential to result in unique behavioural patterns that require careful investigation across countries and communities. In parallel to collecting phenotype information related to diseases, some of the H3Africa groups, based on questionnaires and interviews, have also collected valuable data on awareness, psychological and behavioural attitudes of individuals, families and communities towards various diseases. This is well described in the reports from the SIREN group that provide key insights into awareness levels and attitudes towards stroke and stroke-associated risk factors  (Ojagbemi et al.
Bioinformatics and Genomics Capacity Development Prior to the formation of the H3Africa Consortium, limited large scale genomic studies were being conducted on the African continent. As a result, there were few African-based researchers with the skills required to efficiently organize, store and analyse the large-scale biological datasets anticipated from the various H3Africa projects. With this in mind, the H3Africa Bioinformatics Network (H3ABioNet) was established to support H3Africa researchers through the development of bioinformatics capacity. H3ABioNet was established with over 30 nodes across 15 African countries with bioinformatics capacity ranging from established to intermediate, to little or no bioinformatics capacity. The initial overarching aim was to work towards building a network of nodes across Africa that would have the necessary skilled personnel and computational infrastructure to analyse the large H3Africa datasets.
A major focus area of the initial activity for the network was core capacity development through the provision of teaching and training events, coupled with building core computational infrastructure at developing nodes (Table  9. 1). This involved addressing some low-middle-income country (LMIC) specific challenges, such as limited/ unstable network connectivity. Another major challenge was to monitor, follow up and advance the training imparted both at the participant and the node level, which was done through node accreditation in specific skills areas (e. g. GWAS and NGS workflows), internships and webinars. In addition, tools and resources were developed to facilitate communication between H3Africa projects and to ensure that a framework and policy guidelines were developed and implemented for the efficient organization, storage and analysis of the various datasets being generated (Table  9.
Evaluation of capacity development -Node accreditation exercises were designed to assess the capacity of H3ABioNet nodes to properly process and analyse a variety of datasets  (Jongeneel et al. 2017 ). -NetCapDB, a database for automated capture of quantitative metrics for bioinformatics capacity at each of the nodes, was established  (Bendou et al. 2016 ) -HtrainDB, a database to track the career progression of individuals who have attended training events, was developed  (Mulder et al. 2018 ) Development of computational resources and research support -Pipelines and workflows: skilled individuals were tasked with using the latest workflow languages to develop workflows for variant calling from NGS data, 16s rRNA sequence analysis for metagenomics, GWAS genotype calling and data analysis and imputation for SNP genotyping arrays  (Baichoo et al.
Although still a challenging area, H3ABioNet has made great strides in building large-scale data analysis capacity on the continent. Against the backdrop of a critical lack of bioinformatics expertise in Africa, a major accomplishment for the network was the development of a large number of resources and tools to support the analysis and exploration of data being generated by the larger H3Africa Consortium (Table  9. 1). The tools include a novel post-GWAS approach called ancGWAS that was developed to improve the detection of disease variants with small effects by integrating the signals from a GWAS dataset, the local ancestry information and the SNP pairwise linkage disequilibrium in admixed populations (4-way complex admixture) into a protein-protein interaction network  (Chimusa et al. , 2016).
Further exploration of the limitations of the current methods available in this area of post-GWAS analysis in admixed populations has been conducted  (Chimusa et al. , 2018; Geza et al. , 2019; Awany, Allali, and Chimusa 2019; Chimusa et al. , 2019)  to identify the caveats and develop better suited methods to identify variants with small to moderate effects. The inability to access and use high-performance computing (HPC) clusters via a command line terminal is usually a limiting factor for most researchers wanting to analyse large datasets. Job Management System (JMS) was developed as a web-based front-end to an HPC cluster that allows users to create workflows based on different tools and to run, manage and monitor jobs on an HPC via an intuitive web-based graphical user interface  (Brown et al.
, 2015). Several projects within the Consortium are centred around the identification and functional impact of African-specific genetic variants. The network also contributed to the development of specialized ontologies for sickle cell disease  (Mulder et al. , 2016; Adekile et al. , 2019)  and hearing impairment  (Hotchkiss et al. , 2019). A Human Mutation Analysis (HUMA) web server and database was designed to integrate sequence data, protein structure variation and disease data into a single connected database  (Brown and Tastan Bishop 2018). HUMA allows for the uploading and interrogation of genetic variant data and the prediction of the impact at the protein structural level. A number of additional tools have been developed examining novel methods for modelling the molecular dynamics of a protein including MD-TASK and MODE-TASK  (Brown et al.
This indicates that the 24-SNP barcode method is efficient at monitoring changes in parasite population diversity and divergence over time and can be extended to explore transmission patterns and drug resistance in future studies  (Bankole et al. , 2018). Tuberculosis is another major illness that impacts millions of people in Africa. A number of studies based on an Ethiopian cohort, generated insights into aspects of tuberculosis infection, diagnosis and treatment. Studies on Mycobacterium tuberculosis complex (MBTC) diversity  (Nuru et al. , 2015; Bedewi et al. , 2017)  showed significant geographic diversity of bacterial strains within a country and reported several strains that were not present in the molecular genotyping databases of MTBC. As the accurate diagnosis of TB and latent TB infection (LTBI) play an integral role in administering the correct treatment regime to tackle the infection, the detection of LTBI plays a key role in addressing the disease.
This population-cross sectional dataset, generated primarily for studying cardio-metabolic diseases  (Ramsay et al. , 2016) , included most of the major South African Bantu-speaking groups, was used to investigate population structure. The analyses of genome-wide genetic data demonstrated a clear population structure within the South-Eastern Bantu-speaking (SEB) ethnolinguistic groups in the country. The structure showed correspondence with geographic location and languages and provided insights into the migration and admixture events that may have contributed to the current distribution of SEB groups across the country. Moreover, by recording parental and grandparental ethnicity it became apparent that recent inter-group admixture has made the study of population structure more complex and that this would need to be considered when performing disease-association studies.
The second study is based on whole-genome sequence data that was generated primarily for the design of a genotyping array for the Consortium, and resulted in a landmark population genetics paper for H3Africa. This pan-African study included participants from 50 ethnolinguistic groups sampled across 13 countries and provided the most comprehensive description of genetic diversity and population structure across African populations  ( Choudhury et al. , 2020). Despite the recent inclusion of variants from thousands of African genomes in current databases, the study identified over 3 million novel variants from only 300 genomes. Some of the novel findings included the detection of gene flow events that identified East African gene flow into a population in Nigeria, and rain-forest forager gene flow into a population from Uganda.
and DS were supported by the AWI-Gen grant. SA is a member of H3ABioNet funded by the US National Institutes of Health Common Fund Grant Numbers U41HG006941 and U24HG009641. We sincerely thank Michelle Skelton and her team at the H3Africa Coordinating Centre for the map in Figure  9. 1 and the latest statistics on the consortium and Harry Wedel for sharing the search terms for the PubMed search.
Acknowledgements H3Africa is primarily funded by the  US National Institutes of Health  and the  UK Wellcome Trust , with support from the  African Academy of Sciences. The authors are members of the  AWI-Gen Collaborative Centre  funded by the  National Human Genome Research Institute (NHGRI) ,  Office of the Director (OD) ,  Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD) , the  National Institute of Environmental Health Sciences (NIEHS) , the  Office of AIDS Research (OAR)  and the National.
A Qarnot smart heater derives heat by executing programs on computational processing units like CPU and graphics cards. The programs are usually high performance compute loads or cloud jobs. We propose a novel method of a near real-time forecast by incorporating the intrinsic properties of heating systems such as the heat-sink temperature and power consumption. There are many contexts where time series forecasting have been applied such as to predict wind speed  [8] , or enrolments  [9]. And there are studies that compare methodologies taken as conventional with other considered modern  [10]. Focused on temperature time series forecasting, it is also found in the literature several works based on different techniques 2 https://gitlab. com/andersonandrei/forecasting smart heaters temperature/ such as Neural Networks  [11] -  [14] , Support Vector Machines  [15] , Fuzzy Time Series  [16] -  [18] , Auto Regressive (AR) processes  [19] , ARIMA and SARIMA  [20].
V. CONCLUSIONS AND FUTURE WORK In this work, we shed light on understanding how one can perform ambient temperature predictions of environments heated by a Cloud/Edge infrastructure of smart heaters, a network of computing machines designed to dissipate their heat in the same manner as a standard heater. We developed an ambient temperature forecasting method based on shortterm time series prediction, which considers the historical data of the ambient temperature and the smart heater's properties, notably its power consumption and heat-sink temperature. We implemented our method using the Prophet [24] time-series forecasting framework, and we considered the Cloud/Edge smart heaters infrastructure of Qarnot Computing as a case example. Our experimental results show that a relatively simple trained time-series prediction model is capable of accurately predicting the ambient temperature of an environment heated by a smart heater.
This work is a step towards learning how well standard processors can recycle their dissipated heat to fulfill the everlasting heat requirements of buildings, thus using energy more efficiently and contributing with ways to deal with the growth of power demands for both heating and maintaining data centers. An ambient temperature prediction method is a crucial component for better resource management of a smart heater computing infrastructure. With accurate predictions, one can better balance the smart heaters' computing workload to provide optimal heat. For future work, we plan to advance in two main axes. The first axis is to evaluate more sophisticated time-series prediction models such as Long Short-Term Memory (LSTM) and better ways to deal with missing data sections in the historical data.
The authors show that PV shares of 50% and 75% of the annual charging electricity are feasible, whereas a 100% PV share is possible but could lead to high system costs. In  [19] , an extended method of coordination distribution is proposed, based on power prediction for the microgrid PV power generation with plug-in EVs to improve the local consumption of renewable energy in the microgrid by guiding the orderly charging of EVs. In this method, a neural network and clustering algorithm are used to construct a power predicting model to characterize the PV generation uncertainty and EV charging profile. In  [20] , a linear programming approach is developed to share the PV energy with EVs in order to satisfy users and also maximizes PV utilization. They evaluate the performance of the approach on a real case and synthetically datasets to demonstrate that it distributes the available electric charge between EVs through the seasons with varying profiles of demand.
In  [21] , the focus lies on the economic aspects and feasibility of EV charging system made up of PV sources connected to the grid and equipped with stationary storage. In this case, a support tool was modeled and developed, making it possible to size and manage EV charging stations with only a few input parameters. A technical and economic analysis using the tool HOMER (Hybrid Optimization Model for Multiple Energy Resources) software of PVCS under different irradiation conditions is presented in  [22]. They show that a region with strong sunshine is more likely to invest in PVCS than other regions with lower solar irradiation, but the charging type is not investigated to assess more the performance of PV energy in recharging EV. As in slow charging, the PV benefits can be improved, where the EVs can depend more on PV and less on power grid whereas in fast charging, the energy demand is high, which increases the dependency on the power grid.
The optimal technical design of PV and stationary storage system for charging EVs is decisive to ensure their economic feasibility, which corresponds to the system components sizing with minimum cost  [23]. In  [23]  the study is realized using HOMER software to analyze the economic and technical performances for standalone PVCS associated with battery as stationary storage system. The off-grid PVCS can be considered as a good solution to reduce the CO 2 emissions, but it generally works with slow charging terminals and for EVs with a short daily trip. In  [24] , an analysis is made of PV car parks that meet the aforementioned benefits. Following this techno-economic feasibility study, the PV installation is not always economically feasible, especially for areas where electricity prices are relatively low.
The tool is developed in Excel, with the Visual Basic for Applications language. The assumptions of this methodology are listed below: • The consumption of an EV is assumed to be 15 kWh/100 km for "normal" driving and 10 kWh/100 km for economical driving  [26] ; • Maximum Power Point Tracking (MPPT) for PV power is considered to be al-ways optimal; • Losses due to microgrid system are estimated at 14% (average value given by PVGIS software); • All considered charging terminals can offer a charging power less than or equal to 22 kW. This limitation is chosen in order to reduce the dependency on the power grid in favor of PV energy in the EV charging  [17].
Requisite Data The necessary input data, requested from the user during phase 1, are the following: • Available maximum public grid power P G , which corresponds to the maximum value of the power, in kW, that the power grid can supply the future PVCS; • PV orientation Υ and PV inclination α, which will make it possible to determine the irradiations received by the PV panels; • Depending on the location, latitude and longitude, the weather data profile is generated through a download link allowing to obtain the solar irradiation data from Photovoltaic Geographical Information System (PVGIS) software  [27]. These aforementioned inputs data are entered according to the interface depicted in Figure  2. • PV orientation ϒ and PV inclination α , which will make it possible to determine the irradiations received by the PV panels; • Depending on the location, latitude and longitude, the weather data profile is generated through a download link allowing to obtain the solar irradiation data from Photovoltaic Geographical Information System (PVGIS) software  [27].
1. 5. Fourth Step: Sizing of the Storage The solar irradiation G data for the optimum location, orientation and inclination are obtained from the PVGIS tool, whose data are available at present for every 10 min on any day, for the years from 2005 to 2016. The corrected irradiation value G is calculated according to a correction factor k and G: G = k • G (5) where the factor k depends on and α, as summarized in Table  1. The total PV power P produced by the PVCS is calculated according to Equation (  6 ): P = N PV-max • G G STC • P STC (6) where P STC is the PV panel power at standard test estimated from a database of different available ranges of PV panels. This power calculation is made for the best sunny day in terms of irradiation, according to data from PVGIS. The obtained power is corrected by considering the losses due to the infrastructure estimated at 14%.
• The cost of charging terminals depends on the type of terminals (on the ground for the roof type and suspended for the shade type) and on the real maximum number of terminals NT max. This cost is determined using a database establishing an average price for the two types of terminals. Although the data included in Tables 2-4 as well as the cost of charging terminals come from French databases, this does not influence the proposed methodology, and other costs can be entered to agree with other references. Concerning the phase 1, the weakness of the approach lies in its dependency on the construction of the PVGIS download link. Indeed, PVGIS changes the construction logic; the tool will then generate a bad link, and the download cannot therefore be carried out. Finally, as presented in Figure  3 , the first phase of the tool offers a maximum sizing of the PVCS (the cursors of adjustment of PV panels quantity, of terminals number and of the stationary battery capacity are at the maximum positions).
General Assumptions Additional working assumptions are formulated for phase 3. Firstly, the PVCS sizing obtained at the end of phase 2 is an input to phase 3, and the solar irradiation data used in phase 1 is also used in this phase. The storage of the excess produced PV energy is ensured by stationary batteries within its storage capacity limits, which maximum capacity is obtained or chosen at the end of phase 2. The SOC of the stationary batteries, at the start of the day at 8:00 am, is considered to be 50%. It is assumed that the charge and discharge of the stationary storage are equally distributed among all the batteries that compose it. In addition, in this study, it is considered that users cover a distance of 43 km daily, an average value among EV users according to the U. S. National Household Travel Survey NHTS dataset, as in  [26].
Conclusions This study aims to improve the management of energy within the growth of PV benefits, to which contributes the development of a technical-economic tool intended for the local stakeholders, giving them the main characteristics necessary for the installation of a PVCS. The development of such methodology was translated by an algorithm, resulting, phase by phase, in the constitution of the techno-economic tool, which was coded on an easy-to-use interface. Through the results, concerning the implementation of a PVCS, the proposed methodology allows the tool to fulfill its function of supporting local stakeholders and authorities, by facilitating the decision-making on the deployment of PV-powered charging stations. Indeed, the user of the tool, by entering an easily accessible dataset, obtains the pre-sizing of the infrastructure, a budget associated with this sizing that can be adjusted and finally a qualitative evaluation simulating the ecological character of its infrastructure thus dimensioned.
Propagation Phase Contrast Synchrotron Microtomography (PPC-SRµCT) is the gold standard for non-invasive and non-destructive access to internal structures of archaeological remains. In this analysis, the virtual specimen needs to be segmented to separate different parts or materials, a process that normally requires considerable human effort. In the Automated SEgmentation of Microtomography Imaging (ASEMI) project, we developed a tool to automatically segment these volumetric images, using manually segmented samples to tune and train a machine learning model. For a set of four specimens of ancient Egyptian animal mummies we achieve an overall accuracy of 94-98% when compared with manually segmented slices, approaching the results of off-the-shelf commercial software using deep learning (97-99%) at much lower complexity.
In industrial applications automatic segmentation will be useful in non-destructive metrology and detection of voids, cracks and defects. In biomedical research, it would be useful in small animal and tumour imaging, amongst others. Other applications include nanotechnology, geology, electronics and food science. The main objective of this work is to develop and use artificial intelligence techniques to automatically segment volumetric microtomography images of Egyptian mummies, and label each voxel as textiles, organic tissues or bones. The main advantages of our proposed system are that it can achieve high accuracy at lower complexity, allowing it to scale well for very large volumes. All software and datasets used to generate the results in this work are available for free download. The input and output datasets are available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.
Image acquisition The animal mummies used in this work were scanned at the European Synchrotron Radiation Facility (ESRF) in Grenoble, France. The properties of the synchrotron source allows Propagation Phase Contrast tomography, which gives images with higher resolution and better contrast than conventional sources. In our case, three configurations were used, from 24. 19 µm to 50. 72 µm voxel size. For each scan, the volumetric images were reconstructed using a single distance phase-retrieval algorithm coupled with filtered back projection implemented in the PyHST2 software package  [35, 36]  and ring artefact corrections were applied. Then, the scanned sections were vertically concatenated and the final files were saved in 16-bit JPEG2000 format, using lossy compression with a target compression rate of 10, four levels of wavelet decomposition, and a tile size that covers the entire image.
Acknowledgment This project has received funding from the  ATTRACT  project funded by the  EC  under Grant Agreement 777222. Authors thank  Alessandro Mirone  for the initial GPU implementation of the 3D histogram algorithm, and  Andy Götz  for project management support. We would like to acknowledge the  Museum d'Histoire Naturelle de Grenoble (France) , in particular  Philippe Candegabe , for the opportunity to scan the mummies MHNGr. ET. 1017,MHNGr. ET. 1023,MHNGr. ET. 1456. We also want to thank the  Musée de Grenoble (France)  in the person of  Valérie Huss  for providing access to the sealed jar MG. 2038.