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opencc-by-4.0Oct 2022View details →

Journal data for European scholarly journals

This relates to the following study: https://doi.org/10.5281/zenodo.5909512 The methodology is described in the linked manuscript.

opencc-by-4.0Oct 2022View details →

Data set for the journal article: Tandem electrocatalytic CO2 reduction with Fe-porphyrins and Cu nanocubes enhances ethylene production

Copper-based tandem schemes have emerged as promising strategies to promote the formation of multi-carbon products of the electrocatalytic CO2 reduction reaction. In such approaches, the CO-generating component of the tandem catalyst increases the local concentration of CO and thereby enhances the intrinsic carbon-carbon (C-C) coupling on copper. However, the optimal characteristics of the CO-generating catalyst for maximizing eventual C2 production are currently unknown. In this work, we developed tunable tandem catalysts comprising iron porphyrin (Fe-Por), as the CO-generating component, and Cu nanocubes (Cucub) to understand how the turnover frequency for CO (TOFCO) of the molecular catalysts impacts C-C coupling on the Cu surface. First, we tuned the TOFCO of the Fe-Por by varying the number of orbitals involved in the π-system. Then, by coupling these molecular catalysts with the Cucub, we assessed the current densities and faradaic efficiencies, discovering that all of the designed Fe- Por boost ethylene production. The most efficient Cucub/Fe-Por tandem catalyst was the one including the Fe-Por with the highest TOFCO and exhibited a nearly 22-fold increase in the ethylene selectivity and 100 mV positive shift of the onset potential with respect to the pristine Cucub. These results reveal that coupling the TOFCO tunability of molecular catalysts along with copper nanocatalysts opens up new possibilities towards the development of Cu-based catalysts with enhanced selectivity for multi-carbon product generation at low overpotential.

opencc-zeroJul 2023View details →

A Year of Journal of Open Humanities Data

I asked myself, "What can I learn by applying distant reading computing techniques against a single year of content from the Journal of Open Humanities Data?" In a sentence, I learned a great deal about the Journal, and it very much lives up to is name.

opencc-by-4.0Jun 2024View details →

Data Set for the Journal Article "Heron: Visualizing and Controlling Chemical Reaction Explorations and Networks"

This data archive contains all data newly created in the following publication: Charlotte H. Müller, Miguel Steiner, Jan P. Unsleber, Thomas Weymuth, Moritz Bensberg, Katja- Sophia Csizi, Maximilian Mörchen, Paul L. Türtscher, and Markus Reiher, "Heron: Visualizing and Controlling Chemical Reaction Explorations and Networks", in preparation. The directory contents are as follows: <ul> <li>steered_eschenmoser.tar.xz: Dump of the database created during the steered exploration</li> <li>steered_exploration_protocol_chemoton_3.1.json: Protocol used for the steered exploration</li> </ul>

opencc-by-4.0Jul 2024View details →

Associated data for "The Roasting Marshmallows Program with IGRINS on Gemini South II -- WASP-121 b has super-stellar C/O and refractory-to-volatile ratios" Published in The Astronomical Journal

<table> <tbody> <tr> <td>File Name</td> <td>Description</td> </tr> <tr> <td>w121_1DRC_H2O_ONLY.txt</td> <td>Self consistent, solar composition model spectrum with only H2O opacity.</td> </tr> <tr> <td>w121_1DRC_OH_ONLY.txt</td> <td>Self consistent, solar composition model spectrum with only OH opacity.</td> </tr> <tr> <td>w121_1DRC_CO_ONLY.txt</td> <td>Self consistent, solar composition model spectrum with only CO opacity.</td> </tr> <tr> <td>w121_1DRC_EVERYTHING.txt</td> <td>Self consistent, solar composition model spectrum with all sources of opacity.</td> </tr> <tr> <td>pre_.pic</td> <td>Pre-eclipse data in data cuboid of shape N_order, N_frame, N_pixel</td> </tr> <tr> <td>pre_variance.pic</td> <td>Associated per-pixel variance for the pre-eclipse data.</td> </tr> <tr> <td>post_cube.pic</td> <td>Post-eclipse data in data cuboid of shape N_order, N_frame, N_pixel</td> </tr> <tr> <td>pre_time_BJD.pic</td> <td>Average frame time in BJD for the pre-eclipse sequence.</td> </tr> <tr> <td>pre_rvel.pic</td> <td>Stellar radial velocity, including barycentric correction, per frame for the pre-eclipse sequence.</td> </tr> <tr> <td>post_ph.pic</td> <td>Orbital phase per frame for the post-eclipse sequence.</td> </tr> <tr> <td>post_time_BJD.pic</td> <td>Average frame time in BJD for the post-eclipse sequence.</td> </tr> <tr> <td>post_rvel.pic</td> <td>Stellar radial velocity, including barycentric correction, per frame for the post-eclipse sequence</td> </tr> </tbody> </table>

BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 8. Performance in 1st Approach for three data set

BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 9. Performance in 2nd Approach for three data set

This work also deals with classification of multi class images under different constraints of data set. The first experiment is carried out on images without noise, second with Gaussian noise and filtered data set in third experiment. Performance of the classifier using statistical texture features for two approaches are presented in the table 3. It is observed that performance n the first experiment is best in the first data set i.e. data set without noise in both the approach, while the performance is decreased if the same images are affected by Gaussian noise. This is because the texture feature of the original images consists Gaussian pattern also. Filtering of the noise from the second data set improves the result. The table also shows that feature extraction using blocking of the image enhance the average classification rate in all the case.

BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 8 . The Comparision of Run Times

That is distinct that dynamic mutation rate or reduction idea for mutation operator is more better of fixed rate. In fact obtain to high accuracy is result of our idea for mutation operator. The number of hidden layer neurone is important problem for NN. The natural selection by GA help finding the number of hidden layer neurone and it progress on duration generations. The structured model of GANN finds better answer than NN but with much run time in simulation. The learning of GA is much better than NN with back propagation because BP is a method based on gradient descend and local optimum is a serious risk for that. We hope that the number of training samples is more accurate without error, the new algorithm is better. Tests show that the combination of genetic algorithms and neural networks to an acceptable level solves the problem of overfitting.

BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 7 . Test Accuracy with prograess generation

In the training phase, the neural network weights errors are minimized and network design problem which the objective function to an acceptable level. In test step we have better results because weights of neural network are adjusted by genetic algorithm and back propagation method. Of course achievement to accuracy with 83.5% is reason using of good feature with minimum error.

BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 6. Training Accuracy with prograess generation

There are many features will reduce the efficiency of the algorithm and its complexity. Among the methods for selecting the appropriate features, the algorithm is a GA. One of the important parameters for testing methods is accuracy rate on progress generation. In fact accuracy is reverse error in algorithm results. As reader can compare the results of our paper with another works. Figure 4 show that accuracy present for Training step. We achieve to best answers of 800 generation to after generation.

BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 4. Structural Crossover

Guided crossover operator is based on the two point separation from parents are selected Left and right parts of them are related to each other by the condition to be meaningful With this new child of his parents is that. But a new generation of the random choice to have reached this stage. The crossover rate is fixed for our algorithm.

BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 5. Insertion and Deletion Hidden Layer in NN

Change in NN structure is other method that we used to optimization of solution[18]. Insertion a hidden layer caused to mutation operator is much natural. As connection with father and mother nodes is easily[20],[21]. Weights of node and errors automatically calculated. For each stage of the implementation of the mutation operator in genetic algorithms, neural networks, only one of the nodes in the hidden layer is selected and inserted. These layers are inserted on condition that the definition does not harm the network structure and the action is meaningful. As an added layer can adjust the weights and the connection to the parent node of a network layer to be removed.

BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 3. The Structure of Neural Network

A neural network (NN), in the case of artificial neurons called artificial neural network (ANN) or simulated neural network (SNN), is an interconnected group of natural or artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network[9]. In more practical terms neural networks are nonlinear statistical data modelling or decision making tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data. Two neurons neural network active in memory (ON or 1) or disable (Off or 0), and each edge (synapses or connections between nodes) is a weight. Edges with positive weight, stimulate or activate next active node, and edges with negative weight, disable or inhibit the next connected node (if it is active) ones.

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 9b. Translation of a tab for all languages

After checking a few (or all!) languages for instance and pressing the Ok button , we obtain the windows shown in figure 9a, or respectively 9b for all languages. Here we can add one or more missing translations, or modify one or more of the existing translations accordingly. All the data within the view cluster can be translated into any language supported by the system.

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 8. GoTo -> Translation Option

For example, we select in the view cluster the tab called “Forecasting” and then choose Goto -> Translation (figure 8). After selecting Goto -> Translation, we obtain a selecting window for the desired languages where the user can check one or more languages to translate those tabs or areas into.

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 9a. Translation of a tab for certain languages

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 7. Dialog structure of the View Cluster

Field dependency has been generated automatically and as a result the following desired structure has been achieved (figure 7). After populating the database tables with data in different languages with the help of the view cluster, the popup has been adapted in order to support the translation of the tabs and areas. Supplementary internal tables have been defined in the function module POPUP_FLEX, in order to copy data from the translation tables. The corresponding SELECT statements have been embedded in TRY-CATCH blocks, in order to prevent short dumps due to faulty selection processes.

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 6. View cluster TAFC

For all these tables and views, table maintenance generators have been created and activated, in order to have the possibility to manage individual datasets in every table and view. The corresponding names of those function groups for the table maintenance generators are the same names as those for the views. The purpose of these maintenance views is only to take care of the input data more efficiently. These views will be used later in the view cluster, which ensures a hierarchical order of the data. Therefore, the maintenance views will also include the predecessor, in order to facilitate linking in the field dependency tab of the view cluster (Swapna, 2007). These three maintenance views are embedded in the following view cluster (figure 6)

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 5. Maintenance Views

Beside the tabs and the areas, the database table FLD also contains the fields TABLENAME and FIELD, which suggest the related parameters, whom input may be updated at runtime. Through standard SAP functionality the tables in BASIS, respectively their fields are by default translated in the login language of the user. So in the fields TABLENAME and FIELD of the FLD table, we will obtain, in the user login language, the names of the tables and fields from BASIS via the foreign keys to the table DD03L for TABLENAME and to the table DD02L for FIELD. Beside the database tables, 3 maintenance views have been created, TABV, AREV and FLDV, for the tabs, areas and fields of the popup (figure 5). We have chosen maintenance views instead of database views to be able to use them in the view cluster.

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 2. Content of the single database table in the previous implementation

In the previous implementation there has been used a single database table which did not provide a consistent overview of existing tabs, areas and fields, as well as of the languages, in which a specific field of an area or tab was translated. So, it was difficult to maintain this database table by the customizing end-users in different languages, because every update of a tab, area or field required a number of actions in this table which had to be done manually and very carefully, requiring much time and attention. The number of rows of this table was very large and the content looked like the one shown in figure 2.