5,484 research outputs found
An Open Data Repository for Engineering Design: Using Text Mining with Open Government Data
No full textEngineering Design (ED) is a complex process in which the reuse of knowledge is crucial: applying the knowledge consolidated in previous design activities to future design activities means performing them in a better way. The relevance of data in ED is even more crucial in a business context in which Data Science (DS) is literally revolutionizing the way companies operate and therefore also the way data are analyzed.
Despite having been recognized as crucial for ED processes, data still remain closed in the domain and accessible only to their owners due to several constraints related to the private and proprietary nature of the acquired data. An answer to these challenges could be found in Open Data, but at the state of the art an operational Engineering Design framework to embrace them is still far to be achieved by both academia and industry.
Given these issues, the aim of this paper is to give evidence that Text Mining can help to make a complex open database more effective to be used for the ED process, taking U.S. Open Government Data (OGD) repository as a case study. Open access to methods and data used within this research is provided.
The results of this study allow us to understand for which purposes it is possible to apply the datasets and to comprehend the expertise and the data science methods needed for processing different data formats. Moreover, this work opens relevant implications and challenges for researchers, practitioners and policy makers operating in ED and DS domains that could become opportunities for future research and industrial application
Scaled gradient projection methods for X-rays CT image reconstruction from reduced sampling
No full textTowards Automatic building of Human-Machine Conversational System to support Maintenance Processes
No full textCompanies are dealing with many cognitive changes with the introduction of the Industry 4.0 paradigm. In this constantly changing environment, knowledge management is a key factor. Dialog systems, being able to hold a conversation with humans, could support the knowledge management in business environment. Although, these systems are currently hand-coded and need the intervention of a human being in writing all the possible questions and answers, and then planning the interactions. This process, besides being time-consuming, is not scalable. Conversely, a dialog system, also referred to as chatbot, can be built from scratch by simply extracting rules from technical documentation. So, the goal of this research is designing a methodology for automatic building of human-machine conversational system, able to interact in an industrial environment. An initial taxonomy, containing entities expected to be found in maintenance manuals, is used to identify the relevant sentences of a manual provided by the company BOBST SA and applying text mining techniques, it is automatically expanded. The final result is a taxonomy network representing the entities and their relation, that will be used in future works for managing the interactions of a maintenance chatbot
AgandCuloadedonTiO2/graphite as a catalyst for �Escherichia coli- contaminated water disinfection
No full textTiO2 film was synthesized by means of the chemical bath deposition (CBD) method from TiCl4
as a precursor and surfactant cetyl trimethyl ammonium bromide (CTAB) as a linking and assem-
bling agent of the titanium hydroxide network on a graphite substrate. Ag and Cu were loaded
on the TiO2 film by means of electrodeposition at various applied currents. Photoelectrochemical
testing on the composite of Ag–TiO2/G and Cu–TiO2/G was used to define the composite for
Escherichia coli-contaminated water disinfection. Disinfection efficiency and the rate of disinfection
of E. coli-contaminated water with Ag–TiO2/G as a catalyst was higher than that observed for
Cu–TiO2/G in all disinfection methods including photocatalysis (PC), electrocatalysis (EC), and
photoelectrocatalysis (PEC). The highest rate constant was achieved by the PEC method using
Ag–TiO2/G, k was 6.49 × 10−2
CFU mL−1
min−1
. Effective disinfection times of 24 h (EDT24)
and 48 h (EDT48) were achieved in all methods except the EC method using Cu–TiO2/G.
Keywords: Ag–TiO2/G, Cu–TiO2/G, Escherichia coli, disinfectio
Perceiving molecular evolution processes in Escherichia coli by comprehensive metabolite and gene expression profiling
No full textVijayendran C, Barsch A, Friehs K, Niehaus K, Becker A, Flaschel E. Perceiving molecular evolution processes in Escherichia coli by comprehensive metabolite and gene expression profiling. GENOME BIOLOGY. 2008;9(4):R72
Unveiling the role of Artificial Intelligence in Search Strategy: a Patent analysis through Natural Language Processing
No full textThis study investigates the impact of Artificial Intelligence (AI) adoption on firms' exploration and exploitation strategies in innovation. Amidst a rapidly evolving competitive landscape, firms must innovate to survive, either through incremental refinement of existing capabilities (incremental innovation) or by making significant leaps (radical innovation). The ability of AI to process diverse and voluminous data sets suggests it could significantly shift the traditional balance between exploration and exploitation, potentially redefining strategic outcomes in terms of innovation efficiency and firm performance.
To investigates the impact of AI on firms' exploration and exploitation strategies, we need to identify firms that have adopted AI technologies and assess their levels of exploration and exploitation. We use patent activity as a proxy to determine AI adoption. Whereas, to understand the level of exploration and exploitation of firms we analysed the text of patents using Natural Language Processing. We categorize firms into those with AI-related patents and those without, employing a difference-in-difference methodology to explore changes in their innovation search strategies over time. This approach helps to isolate the impacts of AI on firms' strategic behaviors in terms of exploration and exploitation.
Preliminary findings indicate that AI adoption correlates with a marked increase in explorative activities, suggesting that AI not only supports but actively promotes exploration over exploitation
Development of a framework to evaluate the economic feasibility of digitalization investments in MSMEs
No full textThe Fourth Industrial Revolution has presented the micro, small, and medium enterprises (MSMEs) with many challenges, especially when investing in digitalization to keep up with the pace of Industry 4.0. Due to limited resources and market information, MSMEs must evaluate their digital investments carefully to remain competitive. To help MSMEs quantify the economic benefits of digitalization, the paper develops a framework consisting of five layers. This framework is applied to seven Italian MSMEs, which were selected based on their well-defined and valuable digitalization interventions that had been evaluated and revised by a panel of experts.
The paper makes a significant contribution to the field by offering a framework for assessing the economic feasibility of digital investments in MSMEs. This framework provides a useful tool for MSMEs and professionals to translate the technology impact into quantified elements, both ex ante (when estimating impacts) and ex post (when measuring impacts). The main finding of the work is that the framework is successful in guiding MSMEs step by step in calculating the total value created by their potential investments.
Overall, this paper offers valuable insights and practical guidance for MSMEs seeking to invest in digitalization in a cost-effective and informed manner, which can ultimately enhance their competitiveness and growth prospects in the current business landscape
DATA FOR ENGINEERING DESIGN: MAPS AND GAPS
No full textData, information and knowledge are strongly involved in Engineering Design (ED) process. Despite the crucial role played by data in the design process, there is a lack of studies about how different data are used and generated by the various phases of the ED process. This study is a first attempt to fill this gap by mapping which data types are involved in the different ED phases from a research perspective.
In order to achieve this objective, we used a methodology based on Text Mining. Firstly, we retrieve a corpus of scientific papers related to ED; then, we build two lexicons to recognize ED phases and data types; finally, we collect these entities within ED papers and map the relations between them.
The methodology application allows the building of a network graph for visualizing the relations among data lexicon and ED lexicon. Then, we investigate the specific relations among data types and ED phases by building a heatmap to investigate data types from 3 different perspective.
The insight coming from our analysis shows that ED studies have a great potential in the usage of many data sources, but also that there exist some gaps to be solved in order to reach a more effective data usage in the context of ED
Unveiling the role of Artificial Intelligence in Search Strategy: a Patent analysis through Natural Language Processing
No full textThis study investigates the impact of Artificial Intelligence (AI) adoption on firms' exploration and exploitation strategies in innovation. Amidst a rapidly evolving competitive landscape, firms must innovate to survive, either through incremental refinement of existing capabilities (incremental innovation) or by making significant leaps (radical innovation). The ability of AI to process diverse and voluminous data sets suggests it could significantly shift the traditional balance between exploration and exploitation, potentially redefining strategic outcomes in terms of innovation efficiency and firm performance.
To investigates the impact of AI on firms' exploration and exploitation strategies, we need to identify firms that have adopted AI technologies and assess their levels of exploration and exploitation. We use patent activity as a proxy to determine AI adoption. Whereas, to understand the level of exploration and exploitation of firms we analysed the text of patents using Natural Language Processing. We categorize firms into those with AI-related patents and those without, employing a difference-in-difference methodology to explore changes in their innovation search strategies over time. This approach helps to isolate the impacts of AI on firms' strategic behaviors in terms of exploration and exploitation.
Preliminary findings indicate that AI adoption correlates with a marked increase in explorative activities, suggesting that AI not only supports but actively promotes exploration over exploitation
Unveiling the role of Artificial Intelligence in Search Strategy: a Patent analysis through Natural Language Processing
No full textThis study investigates the impact of Artificial Intelligence (AI) adoption on firms' exploration and exploitation strategies in innovation. Amidst a rapidly evolving competitive landscape, firms must innovate to survive, either through incremental refinement of existing capabilities (incremental innovation) or by making significant leaps (radical innovation). The ability of AI to process diverse and voluminous data sets suggests it could significantly shift the traditional balance between exploration and exploitation, potentially redefining strategic outcomes in terms of innovation efficiency and firm performance. To investigate the impact of AI on firms' exploration and exploitation strategies, we need to identify firms that have adopted AI technologies and assess their levels of exploration and exploitation. We use patent activity as a proxy to determine AI adoption. Whereas, to understand the level of exploration and exploitation of firms we analysed the text of patents using Natural Language Processing. We categorize firms into those with AI-related patents and those without, employing a difference-in-difference methodology to explore changes in their innovation search strategies over time. This approach helps to isolate the impacts of AI on firms' strategic behaviors in terms of exploration and exploitation. Preliminary findings indicate that AI adoption correlates with a marked increase in explorative activities, suggesting that AI not only supports but actively promotes exploration over exploitation
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