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This dataset contains supplementary materials for the paper "Demand response through decentralized optimization in residential areas with wind and photovoltaics". The following data is included: • Mathematical description of all optimization problems with explanations of the equations • Resulting load and temperature profiles of the buildings for the base case scenarios • Result tables with detailed information about the scenarios and their results • Commented code (in the modelling language GAMS) of the decentralized optimization problems for the different building types and the centralized optimization problem In case of any questions or comments regarding the files please feel free to contact: Thomas Dengiz Chair of Energy Economics, Karlsruhe Institute of Technology (KIT) in Germany Webpage: https://www.iip.kit.edu/english/86_3459.php E-Mail: thomas.dengiz@kit.edu
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Daily precipitation of RCM data is downscaled to station based hourly precipitation over South Korea. The data contains base, RCP2.6, RCP4.5, RCP6.0, and RCP9.5 for 62 stations. Unit of the data is 10^-1 mm/hr.
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This data file contains the results from a comprehensive systematic review of interventions addressing depression in men during the perinatal period.
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The codes and algorithms of bi-DCSR and HPTM.
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This data set consists of (6672) histograms of original voice recordings and fake voice recordings obtained by Imitation [1, 2] and Deep Voice [3]. The histograms provided in this dataset can be used to train a machine learning system to classify original and fake voice recordings obtained with the imitation and Deep Voice algorithms. Each directory has the following composition: -- corrupted images have been fixed -- Training_fake: 2088 histograms of fake voice recordings (2016 with Imitation and with 72 Deep Voice) Training_original: 2020 histograms of original voice recordings Validation_fake: 864 histograms of fake voice recordings (all with Imitation) Validation_original: 864 histograms of original voice recordings External_test1: 760 histograms (380 original + 380 fake with Imitation) External_test2: 76 histograms (4 original + 72 fake with Deep Voice) References: [1] DM Ballesteros L, JM Moreno A. Highly transparent steganography model of speech signals using Efficient Wavelet Masking. Expert Systems with Applications 39 (10), 2012, 9141-9149, https://doi.org/10.1016/j.eswa.2012.02.066 [2] DM Ballesteros L, JM Moreno A. On the ability of adaptation of speech signals and data hiding, Expert Systems with Applications 39 (16), 2012, 12574-12579, https://doi.org/10.1016/j.eswa.2012.05.027 [3] S.O. Arik, M. Chrzanowski, A. Coates, G. Diamos, A. Gibiansky, Y. Kang, X. Li, J. Miller, A. Ng, J. Raiman, S. Sengupta, M. Shoeybi. Deep Voice: Real-time Neural Text-to-Speech. 2017. https://arxiv.org/abs/1702.07825
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These files are most of the raw data used for our paper on the effect or HCl on chloride binding.
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This dataset is the extension of Naderi and Ruiz (2010) benchmark set for distributed permutation flowshop scheduling problem (DPFSP) with due dates. It can be utilized for DPFSP with total tardiness criterion.
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This code allows for the simulation of delamnination or debonding of DCB specimens. It is based on the macro-element technique.
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Background Snakebite envenoming is a major neglected tropical disease that affects millions of people every year. The only effective treatment against snakebite envenoming consists of unspecified cocktails of polyclonal antibodies purified from the plasma of immunized production animals. Currently, little data exists on the molecular interactions between venom toxin epitopes and antivenom antibody paratopes. To address this issue, high-density peptide microarray (hdpm) technology has recently been adapted to the field of toxinology. However, analysis of such valuable datasets requires expert understanding and, thus, complicates its broad application within the field. Results In the present study, we developed a user-friendly, and high-throughput web application named “Snake Toxin and Antivenom Binding Profiles” (STAB Profiles), to allow straight-forward analysis of hdpm datasets. To test our tool and evaluate its performance with a large dataset, we conducted hdpm assays using all African snake toxin protein sequences available in the UniProt database at the time of study design, together with eight commercial antivenoms in clinical use in Africa, thus representing the largest venom-antivenom dataset to date. Furthermore, we introduced a novel method for evaluating raw signals from a peptide microarray experiment and a data normalization protocol enabling inter-microarray and even intra-microarray chip comparisons. Finally, these data, alongside all the data from previous similar studies by Engmark et al., were preprocessed according to our newly developed protocol and made publicly available for download through the STAB Profiles web application (https://snake.shinyapps.io/STAB_Profiles/). With these data and our tool, we were able to gain key insights into toxin-antivenom interactions and were able to differentiate the ability of different antivenoms to interact with certain toxins of interest. Conclusions The data, as well as the web application, we present in this article should be of significant value to the venom-antivenom research community. Insights gained from our current and future analyses of this dataset carry the potential to guide the improvement and optimization of current antivenoms for maximum patient benefit, as well as aid the development of next generation antivenoms.
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This dataset contains 295 labelled images for computer vision based detection and identification of packaged products in a vending machine. In addition, the dataset contains data from a user study with 61 participants on the impact of mixed reality headset mediated interventions on food choices. The results indicate that the display of Nutri-Score during the product selection process can alter decisions towards healthier choices. References: * Fuchs, K., Grundmann, T., Fleisch, E., Towards Identification of Packaged Products via Computer Vision, in The 9th International Conference on the Internet of Things (IoT 2019), Bilbao, Spain. * Fuchs, K., Grundmann, T., Haldimann, M., Fleisch, E., Impact of Mixed Reality Food Labels on Product Selection: Insights from a User Study using Headset-mediated Food Labels at a Vending Machine, in 5th International Workshop on Multimedia Assisted Dietary Management In conjunction with the 27th ACM International Conference on Multimedia (ACMMM2019).
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