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  1. Arai, K. (Ed.)
    Integral digitalization aims to liaise with Universal interface for human-computer interaction, assemble Brewing aggregation via online analytical processing, and engage Centered user experience (UBC), which enables wiseCIO to orchestrate “Anything-as-a-Service” (XaaS). This paper presents three important concepts such as iDATA, iDEA and ACTiVE that together orchestrate XaaS on wiseCIO. iDATA stands for “integral digitalization via archival transformation and analytics” in support of content management, iDEA denotes “intelligence-driven efficient automation” for UBC processing with little coding required via machine learning automata, and ACTiVE represents “accessible, contextual and traceable information for vast engagement” with content delivery. Where iDATA is central to XaaS through computational thinking applied to multidimensional online analytical processing (mOLAP). Case studies are through discussed on the massive basis through iDATA over broad fields, such as manageable ARM (archival repository for manageable accessibility), animated BUS (biological understanding from STEM), sensible DASH (deliveries assembled for fast search & hits), smart DIGIA (digital intelligence governing instruction and administering), informative HARP (historical archives & religious preachings), vivid MATH (mathematical apps in teaching and hands-on exercise), and engaging SHARE (studies via hands-on assignment, review/revision and evaluation). As a result, iDATA-orchestrated wiseCIO is in favor of archival content management (ACM) and massive content delivery (MCD). Most recently, the comprehensive online teaching and learning (COTL) has been prepared and published as ACTiVE courseware with various multimedia and the student online profiles for paperless homework, labs and submissions. The ACTiVE courseware is integrated with a capacity equivalent to 10,000 + traditional web pages and broadly used for advanced remote learning (ARL) in both synchronous model and asynchronous model with great ease. 
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  2. Abstract. As cloud-based web services get more and more capable, available, and powerful (CAP), data science and engineering is pulled toward the frontline because DATA means almost anything-as-a-service (XaaS) via Digital Archiving and Transformed Analytics. In general, a web service (via a website) serves customers with web documents in HTML, JSON, XML, and multimedia via interactive (request) and responsive (reply) ways for specific domain problem solving over the Internet. In particular, a web service is deeply involved with UI & UX (user interface and user experience) plus considerate regulations on QoS (Quality of Service) as well, which refers to both information synthesis and security, namely availability and reliability for providential web services. This paper, based on the novel wiseCIO as a Platform-as-a-Service (PaaS), presents digital archiving 3 and transformed analytics (DATA) via machine learning, one of the most practical aspects of artificial intelligence. Machine learning is the science of data analysis that automates analytical model building and online analytical processing (OLAP) that enables computers to act without being explicitly programmed through CTMP. Computational thinking combined with manageable processing is 4 thoroughly discussed and utilized for FAST solutions in a feasible, analytical, scalable and testable approach. DATA is central to information synthesis and analytics (ISA), and digitized archives plays a key role in transformed analytics on intelligence for business, education and entertainment (iBEE). Case studies as applicable examples are discussed over broad fields where archival digitization is required for analytical transformation via machine learning, such as scalable ARM (archival repository for manageable accessibility), visual BUS (biological understanding from STEM), schooling DIGIA (digital intelligence governing instruction and administering), viewable HARP (historical archives & religious preachings), vivid MATH (mathematical apps in teaching and hands-on exercise), and SHARE (studies via hands-on assignment, revision and evaluation). As a result, wiseCIO promotes DATA service by providing ubiquitous web services of analytical processing via universal interface and user-centric experience in favor of logical organization of web content and relational information groupings that are vital steps in the ability of an archivist or librarian to recommend and retrieve information for a researcher. More important, wiseCIO also plays a key role as a content management system and delivery platform with capacity of hosting 10,000+ traditional web pages with great ease. 
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  3. Abstract

    Xenon dual-phase time projections chambers (TPCs) have proven to be a successful technology in studying physical phenomena that require low-background conditions. With$$40\,\textrm{t}$$40tof liquid xenon (LXe) in the TPC baseline design, DARWIN will have a high sensitivity for the detection of particle dark matter, neutrinoless double beta decay ($$0\upnu \upbeta \upbeta $$0νββ), and axion-like particles (ALPs). Although cosmic muons are a source of background that cannot be entirely eliminated, they may be greatly diminished by placing the detector deep underground. In this study, we used Monte Carlo simulations to model the cosmogenic background expected for the DARWIN observatory at four underground laboratories: Laboratori Nazionali del Gran Sasso (LNGS), Sanford Underground Research Facility (SURF), Laboratoire Souterrain de Modane (LSM) and SNOLAB. We present here the results of simulations performed to determine the production rate of$${}^{137}$$137Xe, the most crucial isotope in the search for$$0\upnu \upbeta \upbeta $$0νββof$${}^{136}$$136Xe. Additionally, we explore the contribution that other muon-induced spallation products, such as other unstable xenon isotopes and tritium, may have on the cosmogenic background.

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  4. null (Ed.)
  5. Precisely forecasting wind speed is essential for wind power producers and grid operators. However, this task is challenging due to the stochasticity of wind speed. To accurately predict short-term wind speed under uncertainties, this paper proposed a multi-variable stacked LSTMs model (MSLSTM). The proposed method utilizes multiple historical meteorological variables, such as wind speed, temperature, humidity, pressure, dew point, and solar radiation to accurately predict wind speeds. The prediction performance is extensively assessed using real data collected in West Texas, USA. The experimental results show that the proposed MSLSTM can preferably capture and learn uncertainties while output competitive performance. 
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  6. Free, publicly-accessible full text available July 1, 2024