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7 results for “industrial transformation”
Data set for publication: A New Industry-Oriented Technique for the Wideband Characterization of Voltage Transformers
<p>This is dataset for paper published:</p> <p>G. Crotti, D. Giordano, G. D'Avanzo, P.S. Letizia, M. Luiso, "A New Industry-Oriented Technique for the Wideband Characterization of Voltage Transformers", Measurement, Volume 182, 2021, 109674, ISSN 0263-2241,<br> https://doi.org/10.1016/j.measurement.2021.109674.</p> <p> </p> <p>Excel file provides data for Figure 3, 4, 6 and 7.</p>
TRANSFORMING CUSTOMER RETENTION IN FINTECH INDUSTRY THROUGH PREDICTIVE ANALYTICS AND MACHINE LEARNING
<p>In recent years, the fintech industry has experienced rapid growth, driven by technological advancements and evolving consumer expectations. Fintech companies offer innovative financial services, such as digital banking, investment platforms, and payment solutions, catering to the needs of a tech-savvy customer base. However, as competition intensifies, customer retention has emerged as a critical challenge for these companies. According to a study by Ransom (2021), acquiring a new customer can cost five times more than retaining an existing one, making it imperative for fintech organizations to focus on strategies that enhance customer loyalty. The financial technology (fintech) sector has experienced unprecedented growth in recent years, fundamentally transforming how individuals and businesses access and manage financial services. Characterized by the integration of technology with financial services, fintech encompasses a wide array of offerings, including digital banking, peer-to-peer lending, robo-advisory services, and payment processing. As of 2023, the global fintech market was valued at approximately $309 billion and is projected to reach around $1.5 trillion by 2030, according to a report by Fortune Business Insights. This remarkable growth is largely attributed to advancements in digital technology, increasing smartphone penetration, and a growing consumer preference for online financial solutions. Moreover, the COVID-19 pandemic accelerated the adoption of digital financial services, as consumers sought contactless transactions and remote banking options.</p>
Example data and scripts for the paper: Team maturity and reorganization during a very large-scale agile transformation in the automotive industry
<p>Example data and scripts for the paper: Team maturity and reorganization during a very large-scale agile transformation in the automotive industry. </p>
Case study input data set for article "Stochastic planning of energy system transformation pathways under uncertain industry demands"
<p>The data set contains input data for the model EMPRISE of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. </p>
FTIR-Plastics: a Fourier Transform Infrared Spectroscopy dataset for the six most prevalent industrial plastic polymers.
<p><span><span>Two datasets are presented: FTIR-Plastics-C4 and FTIR-Plastics-C8, comprising 6,000 spectra obtained through Fourier Transform Infrared Spectroscopy (FTIR) applied to the six most used synthetic polymers: Polyethylene Terephthalate (PET), High-Density Polyethylene (HDPE), Polyvinyl Chloride (PVC), Low-Density Polyethylene (LDPE), Polypropylene (PP), and Polystyrene (PS). The key feature of the datasets lies in the FTIR analysis, which reports the percentage transmittance as the intensity measure as a function of the wavelength of an Infrared light source, expressed as wavenumber (with units in cm</span></span><sup><span><span>-1</span></span></sup><span><span>). FTIR analysis was performed using a Jasco FTIR PRO 4x spectrophotometer with a wavenumber resolution setting of 8 cm</span></span><sup><span><span>-1</span></span></sup><span><span> for FTIR-Plastics-C8 and 4 cm</span></span><sup><span><span>-1</span></span></sup><span><span> for FTIR-Plastics-C4, both employing a configuration of 32 scans and a range from 4000 to 400 cm</span></span><sup><span><span>-1</span></span></sup><span><span>. The datasets are presented in CSV (comma-separated values) format, including the following information (per each column):</span></span></p> <ul> <li> <p><span><span><strong>IDE</strong></span></span><span><span>: unique identifier of the sample.</span></span></p> </li> <li> <p><span><span><strong>Polymer: </strong></span></span><span><span>type of synthetic polymer (PET, HDPE, PVC, LDPE, PP, or PS).</span></span></p> </li> <li> <p><span><span><strong>Technique: </strong></span></span><span><span>Type of technique used (FTIR).</span></span></p> </li> <li> <p><span><span><strong>Sample: </strong></span></span><span><span>polymer sample number.</span></span></p> </li> <li> <p><span><span><strong>BR</strong></span></span><span><span>: scanning configuration (32).</span></span></p> </li> <li> <p><span><span><strong>RST</strong></span></span><span><span>: resolution configuration (8 or 4 cm</span></span><sup><span><span>-1</span></span></sup><span><span>).</span></span></p> </li> <li> <p><span><span><strong>Data (x) y Data(y): </strong></span></span><span><span>1884 pairs of columns for FTIR-Plastics-C8 and 3751 pairs of columns for FTIR-Plastics-C4, representing values on the "x" axis (wavenumber) and the "y" axis values associated with molecular vibration intensities, indicating the transmittance (%), which differentiates each polymer.</span></span></p> </li> </ul> <p><span><span>Additionally, the files generated by the Jasco spectrophotometer for each polymer are provided, which were standardized by adding a header with the following structure:</span></span></p> <ul> <li> <p><span><span>TITLE SAMPLE NAME: referring to the name of the analyzed polymer.</span></span></p> </li> <li> <p><span><span>DATA TYPE: specifying the characterization technique.</span></span></p> </li> <li> <p><span><span>MEASUREMENT INFORMATION: equipment used for data collection.</span></span></p> </li> <li> <p><span><span>MODEL NAME: name of the equipment used.</span></span></p> </li> <li> <p><span><span>SERIAL No: serial number assigned to the equipment used.</span></span></p> </li> <li> <p><span><span>ACCESSORY: complementary device integrated into the equipment.</span></span></p> </li> <li> <p><span><span>LIGHT SOURCE: standardized light source related to the DLATGS detector.</span></span></p> </li> <li> <p><span><span>RESOLUTION: parameters are used to distinguish the wavenumber in the analyzed materials.</span></span></p> </li> <li> <p><span><span>XUNIT/HORIZONTAL AXIS: referring to the unit’s title assigned on the x-axis.</span></span></p> </li> <li> <p><span><span>YUNITS/VERTICAL AXIS: referring to the unit’s title designated on the y-axis.</span></span></p> </li> <li> <p><span><span>FIRSTX: initial value set for the x-axis.</span></span></p> </li> <li> <p><span><span>FIRSTY: initial value set for the y-axis.</span></span></p> </li> <li> <p><span><span>LASTX: final value set for the x-axis.</span></span></p> </li> <li> <p><span><span>LASTY: final value set for the y-axis.</span></span></p> </li> <li> <p><span><span>NPOINTS: total data points in the file.</span></span></p> </li> </ul> <p><span><span>Data collection was carried out meticulously, following specific steps to ensure the accuracy and reliability of the results. The calibration certificates issued by the supplier (calibration_certificate.pdf) corresponding to the equipment used in the experiments and data collection that give rise to these databases are attached.</span></span></p>
FedCSIS 2023 Classifying Industrial Sectors with a Domain Adapted Transformer - Datasets and Configuration files
<p>Supplementary material for the FedCSIS 2023 regular paper "Classifying Industrial Sectors from German Textual Data with a Domain Adapted Transformer". We provide the datasets containing raw .csv and .spacy binaries, mapping (i) German Wikipedia Text and (ii) Jobpostings to WZ2008 sectors and divisions. Additionally, spacy model configuration files are provided.</p>
Does local government tax competition promote industrial transformation and upgrading
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