Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
39
datasets available to search
ShareScore release 0.9.0
Dataset results
39 results for “Product design”
SGS-LTER Standard Production Data: 1983-2008 Annual Aboveground Net Primary Production on the Central Plains Experimental Range, Nunn, Colorado, USA 1983-2008, ARS Study Number 6 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/700/1. The abstract below was extracted from the Level 0 data package and is included for context: This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. The objective of the long-term ANPP study is to monitor long-term net above ground primary production of the shortgrass steppe community by species. There are 6 sites: ridgetop (ridge), midslope (mid), swale, ESA (replicate 1 not 2), Section 25 (SEC 25), and owl-creek (OC). Each site is located in a different landscape position or soil type on the shortgrass steppe and may be grazed or not. Ridgetop, midslope and swale are grazed and are sampled along a catena. Section 25 is grazed and is located in an upload grassland. ESA is an ungrazed upland grassland an is the control from the Ecosystem Stress Area experiment. Owl Creek is ungrazed and is located in the lowland along the owl creek drainage. There are 3 transects with 5 plots in each transect. Plots in the grazed locations are protected by cages. Because this is a monitoring effort, true replicates across the landscape are not
FabWave Product Design Knowlsdge Graph (FPD-KG)
<p>FabWave Product Design Knowledge Graph: A Knowledge Graph for product design & manufacturing, constructed using the openly available and academia-sourced 3D CAD data. (Starly, Binil; Bharadwaj, Akshay; Angrish, Atin. (2019). FabWave CAD Repository Categorized Part Classes. 10.13140/RG.2.2.31167.87201.)</p>
Dataset for Process design within planetary boundaries: Application to CO2 based methanol production
<p>Dataset for the journal article: Process design within planetary boundaries: Application to CO2 based methanol production</p>
Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design
<p>Dataset related to the article: Virtanen, E.A., Lappalainen, J., Nurmi, M., Viitasalo, M., Tikanmäki, M., Heinonen, J., Atlaskin, E., Kallasvuo, M., Tikkanen, H., Moilanen, A. (2022) Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design. Renewable and Sustainable Energy Reviews 158, 112087.</p> <p>Dataset includes suitability maps for offshore windfarms, where priority values are scaled between 0-1 (note the reversed value scale): analysis solution (A) economy, (B) society, (C) biodiversity, (D) restrictions, (E) A+B+C without restrictions and (F) A+B+C with restrictions. Dataset includes also the conflict map (and R script), where each three main solutions (A, B, C) are mapped onto an RGB color composite map. </p> <p>Additional details can be found from the published article: <a href="https://doi.org/10.1016/j.rser.2022.112087">https://doi.org/10.1016/j.rser.2022.112087</a></p>
BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 1. Science branches involved in translating product features to human needs
<p>Approach notwithstanding, designing product features involves basically a two-step process: (a) detecting and recognizing emotional information and (b) exhibiting a suitable reaction to the previously considered input. Since needs are reflected in the emotional impact, a method to measure the emotions is necessary (Abraham & Michie, 2008) to correctly assess the impact. Referring strictly to a software product, in order to implement the capability to sense the users’ emotional state, the first step should be developing an affective database (Tao & Tan, 2005), to allow correct identification of the affective status. This results in a translation between the affective status of the user and the computer, therefore allowing the program to process the user emotion just like any other input, successfully “digitizing” emotion. </p>
A parametric life cycle framework to promote sustainable-by-design product development: Application to a hydrogen production technology
<p>The European Ecodesign Directive is an effective normative framework that has been extensively proven to support the energy transition of numerous European industrial sectors. From an analytical standpoint, it provides practitioners with the EcoReport tool, a simplified life cycle spreadsheet that is aimed at guiding the development of ecodesign measures of mandatory compliance in European countries. In this regard, several studies have highlighted the limitations of the EcoReport tool when addressing emerging technologies like those tied to the hydrogen sector. These works also propose to further integrate material criticality and social metrics in order to enlarge the scope of the European Directive and foster the shift from ecodesign to sustainable-by-design product development. In this situation, building upon the principles of the EcoReport tool and recognizing the outcomes of the aforementioned critical analyses, the conceptualization of a novel sustainable-by-design framework is presented and applied to a Solid Oxide Electrolysis Cell (SOEC) stack for hydrogen production. The operationalization of the framework is conducted, for the first time in the context of sustainable design, by combining the use of the <em>Brightway2</em> and <em>lca_algebraic</em> Python packages. Overall, the proposed approach succeeds in providing a complete sustainability perspective to the design of emerging technologies. Regarding the tangible lessons learned on the hydrogen-related case study, product concepts are proven to progressively improve the sustainability performance of the technology. It is noticeable that the enhancement of the economic competitivity is more limited than that achieved at the remaining sustainability indicators (i.e., environmental, social and material criticality metrics). In line with the outcomes of the life cycle contribution assessment, multi-criteria decision analysis ratings lead to concluding that a sustainable-by-design SOEC stack product concept should prioritize limiting its material intensity.</p>
Dataset of "Theoretical and practical aspects of the design and production of synthetic holograms for transmission electron microscopy"
<p>Dataset with script and article images published in https://doi.org/10.1063/5.0067528</p>
OPLA-Tool v2.0: a Tool for Product Line Architecture Design Optimization
<p>The Multi-objective Optimization Approach for Product Line Architecture Design (MOA4PLA) is the seminal approach that successfully optimizes Product Line Architecture (PLA) design using search algorithms. The tool named OPLA-Tool was developed in order to automate the use of MOA4PLA. Over time, the customization of the tool to suit the needs of new research and application scenarios led to several problems. The main problems identified in the original version of OPLA-Tool are environment configuration, maintainability and usability problems, and PLA design modeling and visualization. Such problems motivated the development of a new version of this tool: OPLA-Tool v2.0, presented in this work. In this version, those problems were solved by the source code refactoring, migration to a web-based graphical user interface (GUI) and inclusion of a new support tool for PLA modeling and visualization. Furthermore, OPLA-Tool v2.0 has new functionalities, such as new objective functions, new search operators, intelligent interaction with users during the optimization process, multi-user authentication and simultaneous execution of several experiments to PLA optimization. Such a new version of OPLA-Tool is an important achievement to PLA design optimization as it provides an easier and more complete way to automate this task. </p>
Supporting user preferences in search-based product line architecture design using Machine Learning
<p>The Product Line Architecture (PLA) is one of the most important artifacts of a Software Product Line. PLA design requires intensive human effort as it involves several conflicting factors. In order to support this task, an interactive search-based approach, automated by a tool named OPLA-Tool, was proposed in a previous work. Through this tool the software architect evaluates the generated solutions during the optimization process. Considering that evaluating PLA is a complex task and search-based algorithms demand a high number of generations, the evaluation of all solutions in all generations cause human fatigue. In this work, we incorporated in OPLA-Tool a Machine Learning (ML) model to represent the architect in some moments during the optimization process aiming to decrease the architect's effort. Through the execution of a quanti-qualitative exploratory study it was possible to demonstrate the reduction of the fatigue problem and that the solutions produced at the end of the process, in most cases, met the architect’s needs.</p>
Simulation Results of the Paper entitled "The Design of a Zone-Picking System with Cooperation Area between Neighboring Zones and Its Cooperation Methods" (submitted to International Journal of Production Research)
<p>Simulation Results of the Paper entitled “The Design of a Zone-Picking System with Cooperation Area between Neighboring Zones and Its Cooperation Methods” (submitted to International Journal of Production Research)</p>
Design principles a heterogeneously catalyzed autothermal reactor with enhanced heat and mass transfer for hydrogen production
<p><strong>Design principles a</strong> <strong>heterogeneously catalyzed autothermal reactor with enhanced heat and mass transfer for hydrogen production</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>Catalysis, in chemistry, is the modification of the rate of a chemical reaction, usually an acceleration, by addition of a substance not consumed during the reaction. The rates of chemical reactions, that is, the velocities at which they occur, depend upon a number of factors, including the chemical nature of the reacting species and the external conditions to which they are exposed. A particular phenomenon associated with the rates of chemical reactions that is of great theoretical and practical interest is catalysis, the acceleration of chemical reactions by substances not consumed in the reactions themselves, substances known as catalysts. The study of catalysis is of interest theoretically because of what it reveals about the fundamental nature of chemical reactions; in practice, the study of catalysis is important because many industrial processes depend upon catalysts for their success. In a catalyzed reaction, the catalyst generally enters into chemical combination with the reactants but is ultimately regenerated, so the amount of catalyst remains unchanged. Since the catalyst is not consumed, each catalyst molecule may induce the transformation of many molecules of reactants. For an active catalyst, the number of molecules transformed per minute by one molecule of catalyst may be as large as several million. Where a given substance or a combination of substances undergoes two or more simultaneous reactions that yield different products, the distribution of products may be influenced by the use of a catalyst that selectively accelerates one reaction relative to the other(s). By choosing the appropriate catalyst, a particular reaction can be made to occur to the extent of practically excluding another. Many important applications of catalysis are based on selectivity of this kind.</p> <p>Streamwise distance (millimeters), Reforming channel centerline temperature (degrees kelvin)</p> <p>0 373</p> <p>0.00025 373.048742</p> <p>0.0005 373.2382942</p> <p>0.00075 373.7354626</p> <p>0.001 374.7081362</p> <p>0.00125 376.2548823</p> <p>0.0015 378.3951976</p> <p>0.00175 381.0814232</p> <p>0.002 384.2247405</p> <p>0.00225 387.7222496</p> <p>0.0025 391.465635</p> <p>0.00275 395.3617454</p> <p>0.003 399.3347598</p> <p>0.00325 403.3218552</p> <p>0.0035 407.2775391</p> <p>0.00375 411.1628179</p> <p>0.004 414.9538621</p> <p>0.00425 418.6311751</p> <p>0.0045 422.1828419</p> <p>0.00475 425.6012807</p> <p>0.005 428.8821587</p> <p>0.00525 432.025476</p> <p>0.0055 435.0312325</p> <p>0.00575 437.9015946</p> <p>0.006 440.6398117</p> <p>0.00625 443.2502165</p> <p>0.0065 445.7371415</p> <p>0.00675 448.1049195</p> <p>0.007 450.3600492</p> <p>0.00725 452.5057803</p> <p>0.0075 454.5475284</p> <p>0.00775 456.4896262</p> <p>0.008 458.3374895</p> <p>0.00825 460.0954509</p> <p>0.0085 461.7689261</p> <p>0.00875 463.3600816</p> <p>0.009 464.874333</p> <p>0.00925 466.3149298</p> <p>0.0095 467.6862047</p> <p>0.00975 468.9924902</p> <p>0.01 470.2348696</p> <p>0.01025 471.4176754</p> <p>0.0105 472.5441571</p> <p>0.01075 473.6175642</p> <p>0.011 474.6389798</p> <p>0.01125 475.6138198</p> <p>0.0115 476.5431672</p> <p>0.01175 477.4302716</p> <p>0.012 478.276216</p> <p>0.01225 479.0831669</p> <p>0.0125 479.8543736</p> <p>0.01275 480.5909193</p> <p>0.013 481.2938872</p> <p>0.01325 481.9665268</p> <p>0.0135 482.6099212</p> <p>0.01375 483.2251535</p> <p>0.014 483.8143901</p> <p>0.01425 484.377631</p> <p>0.0145 484.9170424</p> <p>0.01475 485.4347907</p> <p>0.015 485.930876</p> <p>0.01525 486.4063812</p> <p>0.0155 486.8623897</p> <p>0.01575 487.3010677</p> <p>0.016 487.721332</p> <p>0.01625 488.1264322</p> <p>0.0165 488.515285</p> <p>0.01675 488.8889736</p> <p>0.017 489.2496644</p> <p>0.01725 489.5962742</p> <p>0.0175 489.9309692</p> <p>0.01775 490.2526664</p> <p>0.018 490.5646152</p> <p>0.01825 490.8646493</p> <p>0.0185 491.1560181</p> <p>0.01875 491.4365554</p> <p>0.019 491.7073443</p> <p>0.01925 491.970551</p> <p>0.0195 492.2250926</p> <p>0.01975 492.472052</p> <p>0.02 492.7114294</p> <p>0.02025 492.9432247</p> <p>0.0205 493.1696042</p> <p>0.02075 493.3884016</p> <p>0.021 493.6017832</p> <p>0.02125 493.8086659</p> <p>0.0215 494.0101329</p> <p>0.02175 494.2072672</p> <p>0.022 494.3989857</p> <p>0.02225 494.5863716</p> <p>0.0225 494.7683417</p> <p>0.02275 494.9470624</p> <p>0.023 495.1214504</p> <p>0.02325 495.2915058</p> <p>0.0235 495.4583118</p> <p>0.02375 495.6218682</p> <p>0.024 495.7810921</p> <p>0.02425 495.9381497</p> <p>0.0245 496.0919577</p> <p>0.02475 496.2425163</p> <p>0.025 496.3898255</p> <p>0.02525 496.5360515</p> <p>0.0255 496.679028</p> <p>0.02575 496.8187551</p> <p>0.026 496.9563158</p> <p>0.02625 497.0927934</p> <p>0.0265 497.2260215</p> <p>0.02675 497.3570834</p> <p>0.027 497.4859789</p> <p>0.02725 497.6137912</p> <p>0.0275 497.7383541</p> <p>0.02775 497.8618338</p> <p>0.028 497.9831472</p> <p>0.02825 498.1033775</p> <p>0.0285 498.2203583</p> <p>0.02875 498.3362559</p> <p>0.029 498.4499873</p> <p>0.02925 498.5637186</p> <p>0.0295 498.6666184</p> <p>0.02975 498.796597</p> <p>0.03 498.88</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Design principles an autothermal chemical reactor with enhanced momentum transport for hydrogen production
<p><strong>Design principles an autothermal chemical reactor with enhanced momentum transport for hydrogen production</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>Chemical engineering is the development of processes and the design and operation of plants in which materials undergo changes in their physical or chemical state. Applied throughout the process industries, it is founded on the principles of chemistry, physics, and mathematics. The laws of physical chemistry and physics govern the practicability and efficiency of chemical engineering operations. Energy changes, deriving from thermodynamic considerations, are particularly important. Mathematics is a basic tool in optimization and modeling. Optimization means arranging materials, facilities, and energy to yield as productive and economical an operation as possible. Modeling is the construction of theoretical mathematical prototypes of complex process systems, commonly with the aid of computers. Study of the fundamental phenomena upon which chemical engineering is based has necessitated their description in mathematical form and has led to more sophisticated mathematical techniques. The advent of digital computers has allowed laborious design calculations to be performed rapidly, opening the way to accurate optimization of industrial processes. Variations due to different parameters, such as energy source used, plant layout, and environmental factors, can be predicted accurately and quickly so that the best combination can be chosen.</p> <p>Streamwise distance (millimeters), Reforming channel centerline temperature (degrees kelvin)</p> <p>0 373.004</p> <p>0.00025 373.049</p> <p>0.0005 373.224</p> <p>0.00075 373.683</p> <p>0.001 374.581</p> <p>0.00125 376.009</p> <p>0.0015 377.985</p> <p>0.00175 380.465</p> <p>0.002 383.367</p> <p>0.00225 386.596</p> <p>0.0025 390.052</p> <p>0.00275 393.649</p> <p>0.003 397.317</p> <p>0.00325 400.998</p> <p>0.0035 404.65</p> <p>0.00375 408.237</p> <p>0.004 411.737</p> <p>0.00425 415.132</p> <p>0.0045 418.411</p> <p>0.00475 421.567</p> <p>0.005 424.596</p> <p>0.00525 427.498</p> <p>0.0055 430.273</p> <p>0.00575 432.923</p> <p>0.006 435.451</p> <p>0.00625 437.861</p> <p>0.0065 440.157</p> <p>0.00675 442.343</p> <p>0.007 444.425</p> <p>0.00725 446.406</p> <p>0.0075 448.291</p> <p>0.00775 450.084</p> <p>0.008 451.79</p> <p>0.00825 453.413</p> <p>0.0085 454.958</p> <p>0.00875 456.427</p> <p>0.009 457.825</p> <p>0.00925 459.155</p> <p>0.0095 460.421</p> <p>0.00975 461.627</p> <p>0.01 462.774</p> <p>0.01025 463.866</p> <p>0.0105 464.906</p> <p>0.01075 465.897</p> <p>0.011 466.84</p> <p>0.01125 467.74</p> <p>0.0115 468.598</p> <p>0.01175 469.417</p> <p>0.012 470.198</p> <p>0.01225 470.943</p> <p>0.0125 471.655</p> <p>0.01275 472.335</p> <p>0.013 472.984</p> <p>0.01325 473.605</p> <p>0.0135 474.199</p> <p>0.01375 474.767</p> <p>0.014 475.311</p> <p>0.01425 475.831</p> <p>0.0145 476.329</p> <p>0.01475 476.807</p> <p>0.015 477.265</p> <p>0.01525 477.704</p> <p>0.0155 478.125</p> <p>0.01575 478.53</p> <p>0.016 478.918</p> <p>0.01625 479.292</p> <p>0.0165 479.651</p> <p>0.01675 479.996</p> <p>0.017 480.329</p> <p>0.01725 480.649</p> <p>0.0175 480.958</p> <p>0.01775 481.255</p> <p>0.018 481.543</p> <p>0.01825 481.82</p> <p>0.0185 482.089</p> <p>0.01875 482.348</p> <p>0.019 482.598</p> <p>0.01925 482.841</p> <p>0.0195 483.076</p> <p>0.01975 483.304</p> <p>0.02 483.525</p> <p>0.02025 483.739</p> <p>0.0205 483.948</p> <p>0.02075 484.15</p> <p>0.021 484.347</p> <p>0.02125 484.538</p> <p>0.0215 484.724</p> <p>0.02175 484.906</p> <p>0.022 485.083</p> <p>0.02225 485.256</p> <p>0.0225 485.424</p> <p>0.02275 485.589</p> <p>0.023 485.75</p> <p>0.02325 485.907</p> <p>0.0235 486.061</p> <p>0.02375 486.212</p> <p>0.024 486.359</p> <p>0.02425 486.504</p> <p>0.0245 486.646</p> <p>0.02475 486.785</p> <p>0.025 486.921</p> <p>0.02525 487.056</p> <p>0.0255 487.188</p> <p>0.02575 487.317</p> <p>0.026 487.444</p> <p>0.02625 487.57</p> <p>0.0265 487.693</p> <p>0.02675 487.814</p> <p>0.027 487.933</p> <p>0.02725 488.051</p> <p>0.0275 488.166</p> <p>0.02775 488.28</p> <p>0.028 488.392</p> <p>0.02825 488.503</p> <p>0.0285 488.611</p> <p>0.02875 488.718</p> <p>0.029 488.823</p> <p>0.02925 488.928</p> <p>0.0295 489.023</p> <p>0.02975 489.143</p> <p>0.03 489.22</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Design principles an autothermal reactor with enhanced mass transfer for hydrogen production
<p><strong>Design principles an autothermal reactor with enhanced mass transfer for hydrogen production</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>Chemical engineers are employed in the design and development of both processes and plant items. In each case, data and predictions often have to be obtained or confirmed with pilot experiments. Plant operation and control is increasingly the sphere of the chemical engineer rather than the chemist. Chemical engineering provides an ideal background for the economic evaluation of new projects and, in the plant construction sector, for marketing. The fundamental principles of chemical engineering underlie the operation of processes extending well beyond the boundaries of the chemical industry, and chemical engineers are employed in a range of operations outside traditional areas. Plastics, polymers, and synthetic fibres involve chemical-reaction engineering problems in their manufacture, with fluid flow and heat transfer considerations dominating their fabrication. The dyeing of a fibre is a mass-transfer problem. Pulp manufacture involve considerations of fluid flow and heat transfer. The nuclear industry makes similar demands on the chemical engineer, particularly for fuel manufacture and reprocessing. Chemical engineers are involved in many sectors of the metals processing industry, which extends from steel manufacture to separation of rare metals.</p> <p>Streamwise distance (millimeters), Reforming channel centerline temperature (degrees kelvin)</p> <p>0 373.004</p> <p>0.00025 373.059</p> <p>0.0005 373.261</p> <p>0.00075 373.771</p> <p>0.001 374.741</p> <p>0.00125 376.228</p> <p>0.0015 378.219</p> <p>0.00175 380.651</p> <p>0.002 383.434</p> <p>0.00225 386.47</p> <p>0.0025 389.674</p> <p>0.00275 393.013</p> <p>0.003 396.47</p> <p>0.00325 400.02</p> <p>0.0035 403.634</p> <p>0.00375 407.275</p> <p>0.004 410.898</p> <p>0.00425 414.453</p> <p>0.0045 417.9</p> <p>0.00475 421.231</p> <p>0.005 424.454</p> <p>0.00525 427.574</p> <p>0.0055 430.596</p> <p>0.00575 433.522</p> <p>0.006 436.342</p> <p>0.00625 439.041</p> <p>0.0065 441.606</p> <p>0.00675 444.041</p> <p>0.007 446.359</p> <p>0.00725 448.572</p> <p>0.0075 450.693</p> <p>0.00775 452.727</p> <p>0.008 454.676</p> <p>0.00825 456.532</p> <p>0.0085 458.289</p> <p>0.00875 459.952</p> <p>0.009 461.531</p> <p>0.00925 463.036</p> <p>0.0095 464.477</p> <p>0.00975 465.858</p> <p>0.01 467.18</p> <p>0.01025 468.441</p> <p>0.0105 469.635</p> <p>0.01075 470.767</p> <p>0.011 471.843</p> <p>0.01125 472.873</p> <p>0.0115 473.859</p> <p>0.01175 474.805</p> <p>0.012 475.713</p> <p>0.01225 476.579</p> <p>0.0125 477.403</p> <p>0.01275 478.186</p> <p>0.013 478.933</p> <p>0.01325 479.649</p> <p>0.0135 480.337</p> <p>0.01375 480.998</p> <p>0.014 481.634</p> <p>0.01425 482.243</p> <p>0.0145 482.823</p> <p>0.01475 483.376</p> <p>0.015 483.906</p> <p>0.01525 484.415</p> <p>0.0155 484.905</p> <p>0.01575 485.379</p> <p>0.016 485.835</p> <p>0.01625 486.273</p> <p>0.0165 486.692</p> <p>0.01675 487.093</p> <p>0.017 487.477</p> <p>0.01725 487.848</p> <p>0.0175 488.206</p> <p>0.01775 488.553</p> <p>0.018 488.889</p> <p>0.01825 489.212</p> <p>0.0185 489.522</p> <p>0.01875 489.82</p> <p>0.019 490.106</p> <p>0.01925 490.383</p> <p>0.0195 490.651</p> <p>0.01975 490.913</p> <p>0.02 491.168</p> <p>0.02025 491.413</p> <p>0.0205 491.648</p> <p>0.02075 491.875</p> <p>0.021 492.093</p> <p>0.02125 492.306</p> <p>0.0215 492.513</p> <p>0.02175 492.716</p> <p>0.022 492.915</p> <p>0.02225 493.106</p> <p>0.0225 493.291</p> <p>0.02275 493.469</p> <p>0.023 493.642</p> <p>0.02325 493.811</p> <p>0.0235 493.977</p> <p>0.02375 494.141</p> <p>0.024 494.301</p> <p>0.02425 494.456</p> <p>0.0245 494.607</p> <p>0.02475 494.753</p> <p>0.025 494.895</p> <p>0.02525 495.036</p> <p>0.0255 495.174</p> <p>0.02575 495.311</p> <p>0.026 495.446</p> <p>0.02625 495.578</p> <p>0.0265 495.706</p> <p>0.02675 495.831</p> <p>0.027 495.953</p> <p>0.02725 496.074</p> <p>0.0275 496.195</p> <p>0.02775 496.315</p> <p>0.028 496.433</p> <p>0.02825 496.549</p> <p>0.0285 496.661</p> <p>0.02875 496.772</p> <p>0.029 496.881</p> <p>0.02925 496.99</p> <p>0.0295 497.092</p> <p>0.02975 497.232</p> <p>0.03 497.324</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Computer-aided design of optimal environmentally benign solvent-based adhesive products
<p>The files contain all the product design problems implemented in GAMS for this publication. All models were run on a single core of a dual 8 core Intel(R) Xeon(R) CPU E5-2650 machine at 3.52 GHz with 125GB of memory.</p> <p> </p> <p><strong>Abstract</strong></p> <p>In this work, a general systematic methodology for the design of optimal adhesive products with low environmental impact is presented. The proposed approach integrates computer-aided design tools and Generalised Disjunctive Programming to formulate and solve the product design problem. Key design decisions in product design (number of ingredients, identity of compounds and their proportions) are optimised simultaneously. This methodology is applied to the design of solvent-based acrylic adhesives, which are commonly used in construction. First, optimal product formulations are determined with the aim to minimize toxicity. This reveals that that high performance can be achieved by investigating different number of components as well as by optimising all ingredients simultaneously rather than sequentially. The relation between two competing objectives is then explored by obtaining a set of Pareto optimal solutions. This leads to significant trade-offs and large areas of discontinuity driven by discrete changes in the list of optimal product ingredients.</p>
Supplementary material "Designing robust transformation toward a sustainable circular battery production"
<p>Supplementary material for the publication " Designing robust transformation toward a sustainable circular battery production" in Procedia CIRP. The paper is available at <a href="https://doi.org/10.1016/j.procir.2023.02.069">https://doi.org/10.1016/j.procir.2023.02.069</a>.</p> <p> </p> <p>The underlying research of this publication was funded by the German Federal Ministry of Education and Research within the Competence Cluster Recycling & Green Battery (greenBatt) (03XP0302A) and the research project EffizientNutzen (033R240C). The authors are responsible for the content of this publication.</p> <p><em>Accepted for publication</em></p>
Oxbryta® Product Registry An Observational Study Designed to Evaluate the Effect of Oxbryta in Individuals With SCD
ClinicalTrials.gov study NCT04930445. IPD Sharing: NO. Countries: 1. Publications: 0.
Plasmid Design for Tunable Two‐Enzyme Co‐Expression Promotes Whole‐Cell Production of Cellobiose
<p>We provide underlying data for the publication "Plasmid design for tunable two-enzyme co-expression promotes whole-cell production of cellobiose". Please find the abstract below.</p> <p>Catalyst development for biochemical cascade reactions often follows a “whole cell approach” in which a single microbial cell is made to express all of the required enzyme activities. Although attractive in principle, the approach can encounter limitations when efficient overall flux from substrate to product necessitates precise balancing between the individual activities. Here, we show effective integration of major design strategies from synthetic biology to a coherent development of plasmid vectors enabling tunable two‐enzyme co‐expression in <em>E. coli </em>, for the purpose of whole‐cell production of cellobiose. Flux efficiency in the transformation of sucrose and glucose into cellobiose by a parallel (countercurrent) cascade of disaccharide phosphorylases requires the enzyme co‐expression cope with large differences in the specific activity of cellobiose phosphorylase (14 U mg<sup>−1</sup>) and sucrose phosphorylase (122 U mg<sup>−1</sup>). Comparing mono‐ and bicistronic co‐expression strategies, we analyze genetic elements controlling transcription, transcription‐translation coupling or plasmid replication for effect on activity, and also stable producibility, of the whole cell catalyst. We discover a key role of the <em>bom </em>(basis of mobility) site for plasmid stability dependent on the origin of replication and demonstrate the importance of RBS (ribosome binding site) strength for balanced bicistronic co‐expression. Whole cell catalysts show high specific rates (460 μmol cellobiose min<sup>−1</sup> g<sup>−1</sup> dry cells) and performance metrics (30 g L<sup>−1</sup>; ∼82% yield; 3.8 g L<sup>−1</sup> h<sup>−1</sup> overall productivity) promising for cellobiose production.</p>
Supporting user preferences in Search-Based Product Line Architecture Design using Machine Learning
<p>Presentation of the paper Supporting user preferences in Search-Based Product Line Architecture Design using Machine Learning to the SBCARS 2020.</p>
The Impact of Website Design, E-Service Quality, Satisfaction, Trust to Intention to Purchase Skin Care Products in the E-Marketplace
<p><strong><span>The study aims to identify factors influencing pricing, understand how pricing affects purchase intention and customer satisfaction, and provide valuable insights for skincare businesses to develop effective pricing strategies in Indonesia’s skincare market. The study used a purposive sampling approach, collecting data from 123 respondents who were internet users in Indonesia in January 2024. These respondents have engaged with online retailers to purchase skin care products. The results found that website design affects e-service quality, affecting customer satisfaction and trust. Customer satisfaction and trust have a significant impact on repurchase intentions. This study recommends that skincare businesses focus on website design, provide high-quality e-services, and implement customer satisfaction and loyalty programs. Future research can explore additional factors and conduct similar studies in different locations or product categories.<span> </span></span></strong></p> <p><span>Keywords—Website design, skin care, e-marketplace, purchase intention, customer satisfaction, customer trust, e-service quality.</span></p>
Rational design of optimal bimetallic and trimetallic nickel-based single-atom alloys for bio-oil upgrading toward hydrogen production
<p>Supplementary Data for the "Rational design of optimal <em>bimetallic</em> and <em>trimetallic</em> nickel-based single-atom alloys for bio-oil upgrading toward hydrogen production".</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.