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In this era of technology, online data is increasing enormously in various forms be it images, videos, texts, sound, etc. Now with increasing data, there arises the issue of storage and that's the time when Bigdata and its tools come into the picture. To analyze the big data and process it with the available traditional methods, becomes an extremely strenuous task. And to overcome this drawback, there are various big data tools and techniques available. Some of the techniques that are used to analyze big data are the techniques of data mining such as clustering, classification, division, and prediction. The tools that are used in analyzing the big data are MongoDB, NoSQL, HPCC, Apache Storm, Spark, Apache Hadoop, also these tools are used in handling big data. In this paper, analysis of big data tools and techniques has been done with different examples and instances. To understand the tools better, their summaries along with examples are presented. [ABSTRACT FROM AUTHOR]

The 10 billion J/ψ decays collected with the BESIII experiment offer a unique opportunity to investigate the decays of η and η′ mesons produced in the radiative J/ψ → γη(′) transitions. Using this clean production mechanism, the BESIII experiment is making important contributions to precision studies of the strong and electromagnetic interactions in η(′) decays. Three papers are presented in which the decays η → π+π−π0, η → π0π0π0, η′ → ηπ0π0π0, and η′ → π+π−e+e− are studied to test C and CP symmetry, as well as the predictions of different effective field theories. [ABSTRACT FROM AUTHOR]

To innovate firms, need to source knowledge and technology from different actors, including universities. The present paper is a systematic literature review of the benefits obtained by the firms from the collaboration with universities. Out of an initial sample of 4100 articles, 88 empirical studies which measured benefits obtained by the firms from University-industry (U-I) collaboration were selected. The analysis showed that by engaging in U-I collaboration firms may access six types of benefits and, in turn, the size of these benefits is influenced by five groups of factors. It was identified also the high diversity of measurement instruments which makes it harder to obtain statistical comparability between different studies. Most studies were conducted in high-tech sector showing the lack of studies in less technological-intensive sectors. The paper also suggests a list of existing research streams and avenues for future research. [ABSTRACT FROM AUTHOR]

Foresight and anticipation of technology convergence has been of interest to innovation management scholars and practitioners for almost three decades. Since then, the prevailing data source for analysing convergence movements and for delimiting them from fusion dynamics are patents. However, increasing criticism arose about the limited forecasting power of established analysis methods, insinuating they rather monitor technology convergence than anticipating it. Our ongoing research argues that the discipline is in need of a new data source for analysing technology convergence and thus proposes online job postings as more forward-looking and future-oriented data. This research-inprogress paper provides a brief literature review on the theoretical background of technology convergence and outlines the preliminary design of our ongoing research on anticipating fusion and/or convergence movements in the AI technology field. Next to describing our keyword-based job postings analysis we also introduce a novel interpretation approach for differentiating between technology fusion and convergence. [ABSTRACT FROM AUTHOR]

This paper examines the challenge industrial companies face in transforming pilot AI projects into successful, scalable new businesses models. An evaluation of international industrial firms has revealed common difficulties in maintaining the objectives of AI business models and achieving alignment between AI initiatives and overarching strategic goals. These misalignments result in significant challenges, including scalability issues, unclear return on investment (ROI), and a general misalignment with business objectives. To address these challenges, a comprehensive methodical framework is introduced that guides companies through the complexity of scalable AI implementation in industrial domain. This framework not only provides use-case-specific guidelines, but also defines AI-specific strategies and addresses critical cultural and organizational considerations necessary for successful AI integration. [ABSTRACT FROM AUTHOR]

Our understanding of how stars evolve relies on two critical factors: energy production and chemical evolution. These factors are influenced by thermonuclear reactions. However, directly measuring these reactions in a laboratory setting can be challenging due to small reaction cross sections or the need for radioactive targets that are impractical to handle. To overcome these challenges, scientists have developed indirect techniques. One such method is the Trojan Horse Method (THM), which utilizes transfer reactions to determine cross sections of reactions relevant to stellar burning processes. In this paper, we will delve into the features of the Trojan Horse Method, its application in nuclear astrophysics, and provide examples of THM measurements. [ABSTRACT FROM AUTHOR]

The KLOE/KLOE-2 Collaboration ended its data taking in 2018, about 20 years after the detector was turned on. In two different periods of datataking about 8 fb−1 at the peak of the ϕ(1020) resonance have been collected. During the years many results of the study of the light mesons have been published. The analysis of this unique data sample continues and the results of the latest measurements concerning kaons, and vector and pseudoscalar mesons are presented in this paper. [ABSTRACT FROM AUTHOR]

Geography is one of the defining factors in scientific collaboration. Despite the voluminous evidence for how geographical proximity shapes the formation of collaboration in research articles, it has been rarely examined in the emerging genre of data papers, one that describes research data and has enjoyed growing attention in the data‐driven paradigm of research. This poster presents preliminary findings from our project that aims to evaluate the geographical dynamics behind the production of data papers. We analyze how researchers from different countries collaborate with one another using 6,821 data papers published in Scientific Data and Data in Brief between 2014 and 2020. We found that data papers rely heavily upon domestic collaboration and the collaboration pattern largely mirrors that of research articles, although some distinctiveness was also observed. We discuss future work in conclusion, with the ultimate goal of opening a more meaningful conversation about the relationship between the data‐driven paradigm and knowledge production. [ABSTRACT FROM AUTHOR]

The Details of accidents on roadways, in workplaces, or public areas will be addressed in this study paper, it can have severe repercussions. In recent years, there has been an increase in interest in studying mishap data in a bid to comprehend their causes as well as create effective protection strategies. This paper will investigate the different approaches and methods used in the analysis of mishap data and associated effects, emphasizing the advantages and disadvantages of this approach, and giving instances of effective uses in various contexts. Analyzing mishap data and associated factors aids in the identification of patterns and trends, the development of predictive models, and the creation of measures to reduce the risk of accidents as well as improve overall safety. [ABSTRACT FROM AUTHOR]

Nuclear reaction study is important to understand elemental abundance in the universe and stability of stars. For several reactions due to the paucity of experimental data or diffculties in performing the direct measurements, we depend upon theoretical estimates and therefore one needs reliable theoretical models with minimum inputs. This paper is a short review of our work on nuclear reactions and their applications to astrophysics. In particular, we discuss the nuclear transfer and breakup reaction while focusing on our theoretical modelings and techniques involved. [ABSTRACT FROM AUTHOR]

Climate change is getting more attention in recent years as it is bringing more devastating natural disasters such as heat waves, drought and floods around the world. Human activities such as the burning of fossil fuels, which produce heat-trapping gases, are the main driver of climate change. The enormous amount of greenhouse gases that are released into the atmosphere accelerates the pace of global warming. On the other hand, renewable energy is usually clean and sustainable. Thus, turning down the role of fossil fuel in the energy sector should be a priority and facilitating the use of renewable energy is the best solution to mitigate climate change. To have a clear picture on the current situation, the paper will include a literature review about climate change, greenhouse gases and renewable energy. The methodology, analysis methods and results will be included in the paper. Data collection, pre-processing, data analysis and data visualisation dashboard will be involved during the process. A data analysis dashboard will be created to incorporate the background of the story and the results of the data analysis. The users will be exploring the trends of renewable energy and greenhouse gas emissions with data analysis tools to gain useful insights from the data. [ABSTRACT FROM AUTHOR]

The Box-Cox (BC) transformation is widely used in data analysis for achieving approximate normality in the trans-formed scale. The transformation is only possible for non-negative data. This positiveness requirement implies a truncation to the distribution on the transformed scale and the distribution in the transformed scale is truncated normal. This fact has consequences for the estimation of the parameters specially if the truncated probability is high. In the seminal paper Box and Cox proposed to estimate parameters using the normal distribution which in practice means to ignore any consequences of the truncation on the estimation process. In this work we present the framework for exact likelihood estimation on the PN distribution to which we call method m1 and how to calculate the parameters estimates using consistent estimators. We also present a pseudo-Likelihood function for the same model not taking into account truncation and allowing to replace parameters μ and σ for their estimates. We call m2 to this estimation method. We conclude that for cases where the truncated probability is low both methods give good estimation results. However for larger values of the truncated probability the m2 method does not present the same efficiency. [ABSTRACT FROM AUTHOR]

The BABAR experiment collected electron-positron collisions at the SLAC National Accelerator Laboratory (SLAC) from 1999-2008. Although data taking has stopped 15 years ago, the collaboration is still actively doing data analyses, publishing results, and giving presentations at international conferences. Special considerations were needed to do analyses using a computing environment that was developed decades ago. A framework is required that preserves the data, data access, and the capability of doing analyses using a well defined and preserved environment. Also, BABAR's support by SLAC ended at the beginning of 2021. Fortunately, the High Energy Physics Research Computing group at the University of Victoria (UVic), Canada, offered to provide the new home for the main BABAR computing infrastructure, the Grid Computing Centre Karlsruhe offered to host all data for access by analyses running at UVic, and CERN and the IN2P3 Computing Centre offered to store a backup of all data. This paper presents what was done at BABAR to preserve the data and analysis capabilities and what was needed to move the whole computing infrastructure, including collaboration tools and data files, away from SLAC. It will be shown how BABAR preserved the ability to continue to do data analyses and also have a working collaboration tools infrastructure. This paper will describe on BABAR's experience with such a big change in its infrastructure and what was learned from it, which may be useful to other experiments which are interested in long term analysis support and data preservation in general. [ABSTRACT FROM AUTHOR]

Human-AI collaboration (HAIC) is a promising strategy to transform engineering design and innovation, yet how to design artificial intelligence (AI) to boost HAIC remains unclear. Accordingly, this paper provides a new, unified, and comprehensive scheme for classifying AI roles. On this basis, we develop an AI design framework that outlines expected AI capabilities, interactive attributes, and trust enablers across various HAIC scenarios, offering guidance for integrating AI into human teams effectively. We also discuss current advancements, challenges, and prospects for future research. [ABSTRACT FROM AUTHOR]

In the past decade, there has been an exponential adoption and ownership of smartphones by billions of users worldwide. However, as smartphone usage increases, criminals have taken advantage of them for illicit or criminal purposes. In criminal investigations, smartphone data has become an invaluable source of information. This study focuses on constructing a snapshot of Android-operated smartphone data to assist investigators in answering critical investigative questions. A thorough review of the literature with regard to the use of smartphone evidence in criminal cases, with the goal of emphasising the investigative phase and the supportive role of mobile data evidence in guiding investigations will be performed. In order to identify additional persons of interest and develop a thorough understanding of the case, the methodology will entail analysing user profiles, smartphone usage patterns, communication logs, application usage, geographic lookups, and device interactions. By conducting a thorough examination of relevant literature, designing a suitable model, and executing a case study, the study intends to offer valuable perspectives on the creation of timelines or visual representations derived from smartphone data. The results of this study will contribute to improving the efficacy of mobile forensics in assisting investigators and facilitating the use of Android-operated smartphone data as supporting evidence in criminal investigations. [ABSTRACT FROM AUTHOR]

Information practices comprise both seeking and avoidance. Although information practices scholars use qualitative and visual methods to understand seeking practices, they rarely do so to understand avoidance. This paper proposes a new visual method that supports revealing and explaining the complex interplay of seeking and avoidance in rich qualitative data. We introduce seven dimensions of information seeking and avoidance practice (intensity, granularity, engagement, control, relevance, quality, and timeliness). We conceptualize these visually as axes radiating outward from a central origin. We use Excel radar charts to depict these dimensions, allowing us to identify and characterize the shapes of information seeking and avoidance in everyday information practices. We apply our approach to two cases from published literature to show how mapping reveals the interplay between seeking and avoidance at one point in time and over time. We propose potential data collection and analysis applications of this method for information practices research. [ABSTRACT FROM AUTHOR]

Existing evidence indicates that Free/Libre Open-Source Software (FLOSS) ecosystems offer extensive learning opportunities. Community members actively participate in various activities, both during their interactions with peers and while utilizing these environments. Given that FLOSS repositories contain valuable data on participant interactions and activities, our study focuses on analyzing knowledge exchange and interactions within emails to track learning activities across different phases of the learning process, with a focus on the first phase (Initiation). In this paper, we leverage Natural Language Processing (NLP) and Machine Learning (ML) techniques within a process mining framework. Specifically, we employ NLP techniques to analyze the contents of emails and messages exchanged in these FLOSS repositories to generate event logs for the purpose of modeling learning patterns. Subsequently, we construct corresponding event logs, which serve as input to Disco, the process mining tool, for learning process discovery in these environments. The output comprises visual workflow nets that we interpret as representations of learning activity traces within FLOSS, capturing their sequential occurrences. To enhance the understanding of these models, we incorporate additional statistical details for contextualization and description. This approach enables a nuanced exploration of learning dynamics within FLOSS environments, emphasizing the role of NLP and ML in uncovering valuable insights on how FLOSS participants acquire and exchange knowledge. [ABSTRACT FROM AUTHOR]

Vocational Education and Training (VET) suffers from an increasing rate of student dropout in the school sector, while it is largely neglected in the learning analytics (LA) research. At the same time school dropout rates are consistently rising globally and researchers are trying to better understand this rising phenomenon. Some concentrate on its relation to chronic absenteeism from school and to the student socioeconomic background. In addition, other contextual factors can come into play to explain this complex and challenging social phenome-non. Research also shows that the use of LA could contribute to alleviating school dropout. The main purpose of this paper is to present a LA system that visualizes student dropout in the VET school context providing the rea-sons behind it. The paper describes both the backend and the frontend of the system. Regarding the former, the paper focuses on the LA system design and architecture. Regarding the latter, it presents a set of interactive LA visualisations provided by our system based on existing student data on school dropout. In particular, the paper demonstrates its LA visualisations via a case study in a large VET school in Greece based on student data gathered from four consecutive school years. The LA visualisations show students' dropouts across years in the school unit, trajectories of students' dropouts in a specific school year, and the degree of the association between some geographical area (as the area of student residence) with chronic absenteeism. [ABSTRACT FROM AUTHOR]

The fourth industrial revolution was a radically new round in the evolution of many processes in society, including management processes. The digital revolution has provoked the development of new management models and concepts. Agile manufacturing is one of such managerial production concepts that meets modern challenges and requirements of the business environment. Literature review as a research method plays an important role in the studies. This paper presents a systematic literature review of the papers published in SCOPUS and Web of Science database about agile manufacturing since 2000. Specifically, we aim at analysing the research methods on this very popular topic on the economy and management. We believe that by defining the methods used in the agile field we may understand the nature of the research. It is well known that agile manufacturing methods are meant to be upfront in terms of efficiency, but in this paper we want to make a review on research methods to check how research has been made. Therefore, we believe this research is useful for scientists and practitioners. [ABSTRACT FROM AUTHOR]

The need for data is growing at an extremely steep rate in the ever-digital realm, where terms like "big data" are becoming a thing of the past. All this development requires the use of modern and advanced data handling techniques, where users and researchers can analyze and predict vast amounts of data efficiently. Data warehouses are centralized repositories of data used for business intelligence activities such as analysis and reporting. In this paper, a comparative emphasis has been laid down on two different types of data warehouses, on-premises, and cloud data warehouses. The on-premises are known to be physically housed inside an organization's infrastructure. Cloud data warehouses are online-accessible repositories for data that is stored on cloud platforms. This paper provides a comparative analysis of both types in the context of deployment, scalability, flexibility, query management, cost analysis, access and integration, data security, data storage, data recovery, self-service capabilities and nonetheless, speed and performance. This article further highlights the evolution of data warehouses onto cloud and accentuates the growing demand for an efficient data warehouse, due to the amplification of volume, velocity, variety, value, and veracity of the incoming data in all realms. Furthermore, it provides an in-depth analysis of the advantages of the most suitable data warehouse and discusses the limitations of both. [ABSTRACT FROM AUTHOR]

With containers being the de-facto standard to package, distribute, and run applications, Helm charts are on the rise for application deployment in managed clusters (e.g., Kubernetes, OpenShift), providing developers and operators with a rich ecosystem of tools to utilize, as well as the means to configure applications and roll changes out in a programmatic way. This paper describes the reboot of the ScienceBox project: The containerized software bundle providing the ability to deploy CERN storage and analysis services on any cloud or local infrastructure. While the service offering provided through ScienceBox remains unchanged, we evolved the original implementation to make use of Helm charts across the entire stack and incorporated a major architectural update to CERNBox, replacing the previous PHP backend with a distributed microservices architecture. ScienceBox maintains the flexibility to be deployed in a few clicks for demonstration purposes, or scaled out on managed clusters for the provisioning of a multi-user service. It also removes external dependencies by deploying additional services (e.g., IDP, DBs) and facilitates the configuration of the service stack by providing automated configuration scripts, making the package fully self-contained and easy to use. [ABSTRACT FROM AUTHOR]

The increasingly larger data volumes that the LHC experiments will accumulate in the coming years, especially in the High-Luminosity LHC era, call for a paradigm shift in the way experimental datasets are accessed and analyzed. The current model, based on data reduction on the Grid infrastructure, followed by interactive data analysis of manageable size samples on the physicists' individual computers, will be superseded by the adoption of Analysis Facilities. This rapidly evolving concept is converging to include dedicated hardware infrastructures and computing services optimized for the effective analysis of large HEP data samples. This paper describes the actual implementation of this new analysis facility model at the CIEMAT institute, in Spain, to support the local CMS experiment community. Our work details the deployment of dedicated highly performant hardware, the operation of data staging and caching services ensuring prompt and efficient access to CMS physics analysis datasets, and the integration and optimization of a custom analysis framework based on ROOT's RDataFrame and CMS NanoAOD format. Finally, performance results obtained by benchmarking the deployed infrastructure and software against a CMS analysis workflow are summarized. [ABSTRACT FROM AUTHOR]

Computational science, data management and analysis have been key factors in the success of Brookhaven National Laboratory's scientific programs at the Relativistic Heavy Ion Collider (RHIC), the National Synchrotron Light Source (NSLS-II), the Center for Functional Nanomaterials (CFN), and in biological, atmospheric, and energy systems science, Lattice Quantum Chromodynamics (LQCD) and Materials Science, as well as our participation in international research collaborations, such as the ATLAS Experiment at Europe's Large Hadron Collider (LHC) at CERN (Switzerland) and the Belle II Experiment at KEK (Japan). The construction of a new data center is an acknowledgement of the increasing demand for computing and storage services at BNL in the near term and enable the Lab to address the needs of the future experiments at the High-Luminosity LHC at CERN and the Electron-Ion Collider (EIC) at BNL in the long term. The Computing Facility Revitalization (CFR) project is aimed at repurposing the former National Synchrotron Light Source (NSLS-I) building as the new data center for BNL. The construction of the new data center was finished in 2021Q3, and it was delivered for production in early FY2022 for all collaborations supported by the Scientific Data and Computing Center (SDCC), including STAR, PHENIX and sPHENIX experiments at RHIC collider at BNL, the Belle II Experiment at KEK (Japan), and the Computational Science Initiative at BNL (CSI). This paper highlights the key mechanical, electrical, and networking components of the new data center in its final configuration as used in production since 2021Q4 and gives an overview for the extension of the central network systems into the new data center and the migration of a significant portion of IT load and services from the old data center to the new data center carried out in 20212023, with expected completion of the main phase of the gradual IT equipment replacement and migration from the old data center into the new one set to the end of FY2023 (Sep 30, 2023). [ABSTRACT FROM AUTHOR]

Many current physics experiments require precision reconstruction of low-energy particles. One example is the Mu2e experiment, which requires reconstructing an isolated 105 MeV electron with better than 500 KeV/c momentum resolution. Mu2e uses a low-mass straw tube tracker, and a CsI crystal calorimeter, to reconstruct tracks. In this paper, we present the design and performance of a track reconstruction algorithm optimized for Mu2e's unusual requirements. The algorithm is based on the KinKal [1] kinematic Kalman filter track fit package. KinKal supports multiple track parameterizations, including one optimized for looping tracks, such as Mu2e signal tracks, and others optimized for straight or slightly-curved tracks, such as the high-momentum (>1 GeV/c) cosmic ray muons used to calibrate and align the Mu2e detectors. All KinKal track parameterizations include the track origin time, to correctly model correlations arising from measurements that couple time and space, such as the straw drift time or the calorimeter cluster time. KinKal employs magnetic field inhomogeneity and material effect correction algorithms with 10−4 fractional precision. The Mu2e fit uses Artificial Neural Net functions to discriminate background hits from signal hits, and to resolve the straw tube hit left-right ambiguity, while iterating the extended Kalman filter. The efficiency, accuracy, and precision of the Mu2e track reconstruction, as tested on detailed simulations of Mu2e data, are presented. [ABSTRACT FROM AUTHOR]

A critical challenge of performing data transfers or remote reads is to be as fast and efficient as possible while, at the same time, keeping the usage of system resources as low as possible. Ideally, the software that manages these data transfers should be able to organize them so that one can have them run up to the hardware limits. Significant portions of LHC analysis use the same datasets, running over each file or dataset multiple times. By utilizing "ondemand" based regional caches, we can improve CPU Efficiency and reduce the wide area network usage. Speeding up user analysis and reducing network usage (and hiding latency from jobs by caching most essential files on demand) are significant challenges for HL-LHC, where the data volume increases to an exabyte level. In this paper, we will describe our journey and tests with the CMS XCache project (SoCal Cache), which will compare job performance and CPU efficiency using different storage solutions (Hadoop, Ceph, Local Disk, Named Data Networking). It will also provide insights into our tests over a wide area network and possible storage and network usage savings. [ABSTRACT FROM AUTHOR]

Monitoring services play a crucial role in the day-to-day operation of distributed computing systems. The ATLAS Experiment at LHC uses the Production and Distributed Analysis workload management system (PanDA WMS), which allows a million computational jobs to run daily at over 170 computing centers of the WLCG and opportunistic resources, utilizing 600k cores simultaneously on average. The BigPanDA monitor is an essential part of the monitoring infrastructure for the ATLAS Experiment that provides a wide range of views, from top-level summaries to a single computational job and its logs. Over the past few years of the PanDA WMS advancement in the ATLAS Experiment, several new components were developed, such as Harvester, iDDS, Data Carousel, and Global Shares. Due to its modular architecture, the BigPanDA monitor naturally grew into a platform where the relevant data from all PanDA WMS components and accompanying services are accumulated and displayed in the form of interactive charts and tables. Moreover the system has been adopted by other experiments beyond HEP. In this paper we describe the evolution of the BigPanDA monitor system, the development of new modules, and the integration process into other experiments. [ABSTRACT FROM AUTHOR]

The ATLAS experiment is expected to deliver an unprecedented amount of scientific data in the High Luminosity(HL-LHC) era. As the demand for disk storage capacity in ATLAS continues to rise steadily, the BNL Scientific Data and Computing Center (SDCC) faces challenges in terms of cost implications for maintaining multiple disk copies and adapting to the coming ATLAS storage requirements. To address these challenges, the SDCC Storage team has undertaken a thorough analysis of the ATLAS experiment's requirements, matching them to suitable storage options and strategies, and has explored alternatives to enhance or replace the current storage solution. This paper aims to present the main challenges encountered while supporting big data experiments such as ATLAS. We describe the experiment's specific requirements and priorities, particularly focusing on the critical storage system characteristics of the high-luminosity run and how the key storage components provided by the Storage team work together: the dCache disk storage system; its archival back-end, HPSS; and its OS-level backend Storage. Specifically, we investigate a novel approach to integrate Lustre and XRootD. In this setup, Lustre serves as backend storage and XRootD acts as an access layer frontend, supporting various grid access protocols. Additionally, we also describe the validation and commissioning tests, including the performance comparison between dCache and XRootd. Furthermore, we provide a performance and cost analysis comparing OpenZFS and LINUX MD RAID, evaluate different storage software stacks, and showcase stress tests conducted to validate Third Party Copy (TPC) functionality. [ABSTRACT FROM AUTHOR]

Increases in data volumes are forcing high-energy and nuclear physics experiments to store more frequently accessed data on tape. Extracting the maximum performance from tape drives is critical to make this viable from a data availability and system cost standpoint. The nature of data ingest and retrieval in an experimental physics environment make achieving high access performance difficult given the inherent limitations of magnetic tape. Tailoring the layout of data on tape is one key to improving read performance. This paper highlights the work in progress to characterize ATLAS data ingested in the tape system, understand how data layout, i.e. file co-location on tape and file distribution over tapes, affect read performance and how optimal data layout might be achieved in a production environment. [ABSTRACT FROM AUTHOR]

In the HEP community the prediction of Data Popularity is a topic that has been approached for many years. Nonetheless, while facing increasing data storage challenges, especially in the upcoming HL-LHC era, there is still the need for better predictive models to answer the questions of whether particular data should be kept, replicated, or deleted. Caches have proven to be a convenient technique for partially automating storage management, potentially eliminating some of these questions. On the one hand, one can benefit even from simple cache eviction policies like LRU, on the other hand, we show that incorporation of knowledge about future access patterns has the potential to greatly improve cache performance. In this paper, we study data popularity on the file level, where the special relation between files belonging to the same dataset could be used in addition to the standard attributes. We turn to Machine Learning algorithms, such as Random Forest, which is well suited to work with Big Data: it can be parallelized, is more lightweight and easier to interpret than Deep Neural Networks. Finally, we compare the results with standard cache eviction algorithms and the theoretical optimum. [ABSTRACT FROM AUTHOR]

The ALICE experiment at CERN has undergone a substantial detector, readout and software upgrade for the LHC Run 3. A signature part of the upgrade is the triggerless detector readout, which necessitates a real time lossy data compression from 1.1 TB/s to 100 GB/s performed on a GPU/CPU cluster of 250 nodes. To perform this compression, a significant part of the software, which traditionally is considered off-line, was moved to the front-end of the experiment data acquisition system, for example the detector tracking. This is the case also for the various configuration and conditions databases of the experiment, which are now replaced with a single homogeneous service, serving both the real-time compression, online data quality checks and the subsequent secondary data passes, Monte-Carlo simulation and data analysis. The new service is called CCDB (for Calibration and Conditions Database). It receives, stores and distributes objects and their metadata, created from online detector calibration tasks and control systems, from offline (Grid) workflows or by users. CCDB propagates the new objects in real time to the Online cluster and asynchronously replicates all content to Grid storage elements for later access by Grid jobs or by collaboration members. The access to the metadata and objects is done via a REST API and a ROOT-based C++ client interface which streamlines the interaction with this service from compiled code while plain curl command line calls are a simple access alternative. In this paper we will present the architecture and implementation details of the components that manage frequent updates of objects with millisecond-resolution intervals of validity and how we have achieved an independent operation of the Online cluster while also making all objects available to Grid computing nodes. [ABSTRACT FROM AUTHOR]

With the shift from mechanical value delivery to mechatronic value delivery, development environments are becoming more complex. Intuitive decision-making in development management is becoming increasingly challenging. Meanwhile, the use project management software is spreading, bringing about a new level of project data for development projects, holding to potential to enhance human decision making. To this end, the paper presents an extension to factor analysis of mixed data, which can facilitate usage of exploratory data analysis to improve decision-making in development project planning. [ABSTRACT FROM AUTHOR]

Artificial Intelligence-based Computer Vision models (AI-CV models) for object detection can support various applications over the entire lifecycle of machines and plants such as monitoring or maintenance tasks. Despite ongoing research on using engineering data to synthesize training data for AI-CV model development, there is a lack of process guidelines for the creation of such data. This paper proposes a synthetic training data creation process tailored to the particularities of an engineering context addressing challenges such as the domain gap and methods like domain randomization. [ABSTRACT FROM AUTHOR]

This theoretical study offers an extensive exploration of the ecosystem approach to tourism destination management. Ecosystems have gained prominence in contemporary discourse across various domains, including business and the academic world. The study examines tourism destinations through the lens of a business ecosystem, shedding light on the intricate interplay of elements within such ecosystems. For this purpose, first, the concept of a business ecosystem is defined and delimitated, laying the foundation for its application to the unique context of tourism destinations. Ecosystems, both in biology and in business, are complex and spontaneous structures, which raises profound questions about the feasibility of managing an ecosystem, the responsible entities, and the methods entailed. This paper argues that rather than a traditional management approach, it is plausible to adopt a governance perspective when dealing with the tourism destination ecosystem. By synthesizing existing bibliographical data on the characteristics of the ecosystem, the roles and functions of actors in it, as well as unique attributes of tourism enriching the understanding of the ecosystem approach, the research concludes on the important aspects that could be used for successful governance. This study serves as the initial phase of a broader research endeavor, which overarching goal is to contribute to the socioeconomic understanding of tourism management, casting it through the prism of business ecosystems by expanding on these foundational insights and conducting further empirical investigations on particular types of tourism. [ABSTRACT FROM AUTHOR]

Interoperability platforms (IOPs) have been and are continuously designed, deployed and used for a variety of scopes, from simple data integration, reducing heterogeneity between data sources, data management systems without and with archival function up to allowing for process support and supporting data analysis. Furthermore, the scope depends on the intended use in acute care or research or both of them as well as on the stakeholders involved from health care professionals, external cooperating organizations and up to the involvement of patients. The objective of the paper is to derive a functional classification reflecting not only the IOP development over the last years but also anticipating upcoming designs. As such, four functional levels which build on top of each other have been identified: (i) management of unstructured data, (ii) management of structured data, (iii) process support, and (iv) data analysis. For each level, the respective system architecture is sketched, including functional components and references to relevant standards, profiles, and means for semantic annotation. Furthermore, the functional classification is applied to a set of projects and initiatives and allows assigning them to functional levels, thus enabling functional discrimination. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to provide a conceptual framework to further stimulate and progress the ongoing discussion and innovation around the challenges and opportunities for educational programs in Game Design and Interactive/Immersive Media. As a reflective paper it provides a curated literature overview that illustrates key drivers in this conversation and thus highlights critical aspects of recent trends that have emerged. It then situates this within the curriculum contexts and realities that presently exists within the higher education (HE) sector specifically outlining mechanisms and approaches that can be infused/adopted to enhance the quality of the learning experience and graduate outcomes in the field and allied professions. Effectively addressing these issues further provides much added value to new proposals and evolving programs in Game Design. Indeed, when examining available data it could reasonably be theorized that interdisciplinary programs in Game Design and Interactive Media could become a ubiquitous feature of the higher and continuing education landscape not dissimilar to the rise of other major fields of endeavor in recent decades. [ABSTRACT FROM AUTHOR]

The field of AI has witnessed considerable advances in recent years, driven by improvements in computing power and the development of various algorithms. A significant proportion of game development time is consumed by level design, which has led to a great deal of research aimed at reducing this time. However, many content generation algorithms depend on training data, making it challenging to efficiently apply them to reduce game development time. This paper presents a novel approach that uses reinforcement learning to train a level generation agent based on difficulty. The method does not require training data, making it particularly useful in scenarios where limited or no training data is available. Our experiments demonstrated the agent's ability to generate levels at high speed. [ABSTRACT FROM AUTHOR]

This paper presents a targeted reflection on the evolving research area of Data Physicalization (DP) within the Human-Computer Interaction (HCI) domain. In recent years substantial works have been done to define this relevant research area and to address its opportunities and challenges. Nonetheless, literature shows that there is not an unequivocal agreement on a definition of DP, with scholars proposing closely related areas that approach the "physicalization" of data in varied ways. This work aims to clarify the many definitions appearing in literature. Authors chose to perform a scoping review to select, compare, and classify DP definitions. The proposed map classifies the definitions progressively, according to the ways data is integrated into the physical system or object: data rendered, data encoded, and data embodied. Finally, this work suggests an extended definition for DP including the dimensions of embodiment and dynamicity, to be further supported by empirical validation and through user-centered design experimentations. [ABSTRACT FROM AUTHOR]

To carry out a literature review often involves hard and tedious work. There is a tradition of using facilitating tools, that extended to the AI field in 2018 when iris.ai appeared. Today, in the emerging field of Generative AI tools based on Large Language Models, there has been rapid development of new literature search tools and approaches. This study has the aim of exploring this vast array of Generative AI tools, in a literature study where some of the found tools were used to facilitate the selection of relevant publication. Three research questions guided this study: RQ1) "What Generative AI tools can be found in literature?", RQ2) "Which of these tools could be of use in the literature review conducted in this study, and how?", and RQ3) "What are the ethical aspects of using Generative AI tools in literature studies?" The approach has been a scoping review, built around a search that combined the keywords: "AI supported", "AI generated", "AI based" and "Literature review". An initial result set was filtered with inclusion exclusion criteria in a strive for an interesting quality answer to the research questions. However, most publications that passed the filtering lacked any potential to contribute to answer the research questions. The most interesting finding in the first search was a hint about the new feature 'Scopus AI'. A new search with the Scopus AI tool resulted in a small but very relevant set of publications. These publications were analysed in a deductive inductive thematic analysis, and primary sorted into the categories of: 'Generative AI Tools', 'Supportive AI Techniques', and 'Ethical Issues'. Findings indicate that there is a wide variety of tools that can facilitate the skimming process of a literature, and to provide adequate summaries of retrieved publication. However, authors recommendation is to keep the tools on the facilitating support level, and that the main analysis and conclusion should be human conducted. With this, rather traditional approach, researchers will have clearly less ethical issues to consider. Finally, the ethical aspects of Generative AI tools in research ought to be investigated more in detail, in a separate future study. [ABSTRACT FROM AUTHOR]

This paper explores the integration of word cloud technology within an Educational Library Management System. Word clouds, used in data analysis and text mining, offer a user-friendly visual/graphical images of textual data. The term "word clouds" refers to visual representations that quickly capture the key themes or words from written text, spanning various sources such as books, articles, speeches, and survey responses. With the advent of internet and software advancements, students now possess the capability and flexibility to access a myriad of literary resources via library management software. These resources encompass both printed and digital texts, utilized across various software platforms. Presently, numerous software tools serve this purpose within college library systems. This study proposes the implementation of a novel system leveraging word cloud technology within an Educational Library Management System. [ABSTRACT FROM AUTHOR]

This study conducts a comprehensive bibliometric examination to analyze Model-Based Customer Churn Research. Through Scopus, PRISMA, and VOSviewer. last two decades, academic publications and interest have increased. Empirical research, growth, and trends are examined in this paper. Evidence suggests churn prediction models using advanced data analytics, machine learning, and industry-specific methods. This shows scientific collaboration to predict industry churn. The study highlights "Expert Systems with Applications" and "ACM International Conference Proceedings Series," which spurred field dialogue. These publications provide cutting-edge research and expert opinions. Geographically, we study China, India, and the US. Global churn research matters. Bibliometric research shows model-based customer churns evolve. The resource can help academic and business researchers develop innovative client retention methods in different markets. By analyzing Model-Based Customer Churn the research studies, growth, and trends are examined. Evidence suggests churn prediction models use advanced data analytics, machine learning, and industry-specific methods. This shows scientific collaboration to predict industry churn. The study highlights "Expert Systems with Applications" and "ACM International Conference Proceedings Series," which spurred field dialogue. These publications provide cutting-edge research and expert opinions. Geographically, we study China, India, and the US. Global churn research matters. Bibliometric research shows that model-based customer churns evolve. The resource significantly assists academic and business researchers in developing innovative client retention methods in different markets. [ABSTRACT FROM AUTHOR]

This paper explores the operational efficacy of an Iron Ore sample preparation laboratory using the Overall Equipment Effectiveness (OEE) model. Addressing the discrepancy between the laboratory's production capacity and the client's expectation of reduced turnaround time, the study investigates the impact of operational inefficiencies on customer satisfaction. Employing a mixed-methods approach, the research combines quantitative data from a time and motion study with qualitative insights from interviews and observations. The findings reveal that by eliminating planned stops, an increased target of six batches per day could be achieved, enhancing the monthly output, and addressing the current backlog. Quality checks embedded in the Laboratory Information Management System (LIMS) ensured 100% process quality with no reworks observed during the study period. The study concludes that optimizing the performance metric across workstations is pivotal to improving productivity and overall operational efficacy, with the potential to reduce sample turnaround time significantly. Recommendations include comprehensive training for personnel, implementation of pre-emptive screen tests, and ergonomic adjustments to the equipment. For future research, the paper suggests comparative studies across diverse laboratory settings, the adoption of experimental designs for empirical validation of improvements, and the use of simulations and qualitative methods to predict outcomes and understand operational challenges comprehensively. [ABSTRACT FROM AUTHOR]

when conducting business and management research, the most common strategy employed for collecting primary data is either interviews or questionnaires (or both). When questionnaires are used to collect primary data a very common approach used is to construct questions which can be called Likert-type questions or Likert scales. Numbers are allocated to the question responses of each question. Often, this is followed by performing arithmetic or statistical operations on the allocated numbers. In many cases encountered by the author, the analysis has even included techniques such as parametric statistics and factor analysis. This paper explains why such simplistic approaches are completely invalid and should never be used. It goes on to explain how analysis can be done whilst avoiding typical hazards. Often, the writers of such papers do not understand or explain levels of measurement. Of course, Likert scales are at an ordinal level of measurement which would normally preclude the use of arithmetic, statistical, factor analysis techniques there is an additional problem of reliability because different people will interpret terms such as strongly disagree and disagree, differently. Likert was aware of these problems when writing the original paper in 1932 although at that time the term "levels of measurement" was not in use. This paper provides approaches and suggestions for avoiding the problems of data analysis when using Likert-type questions. This paper should be of assistance to those who intend to use Likert-type questions in the questionnaire. [ABSTRACT FROM AUTHOR]

The hybrid education room is an important content in the art course, but the teaching method has the problem of contradictory teaching path and teaching method. At present, artificial intelligence is widely used in the field of education, which also has an impact on blended teaching and solves the problem of incompatibility between methods and paths in blended education. Therefore, this paper applies artificial intelligence to the teaching of art courses and optimizes the teaching methods and teaching paths. First, hybrid teaching methods are used for data analysis to form initial data and incorporate uncertainty. Then, artificial intelligence methods are used to plan the hybrid path, form a teaching path that meets the requirements, and verify the effectiveness of the path. The actual simulation results show that the artificial intelligence method can improve the effectiveness of the hybrid teaching method and optimize the teaching and art teaching methods of art courses. [ABSTRACT FROM AUTHOR]

The neutron Time-of-Flight (TOF) research facility at CERN, n_TOF, has been a pioneering platform for neutron cross-section measurements since its inception in 2001. It boasts three distinct experimental areas, each tailored to address a specific range of neutron energies. This paper delves into the intricacies of the n_TOF facility, including its recent upgrade during the Long Shutdown 2 (LS2) at CERN. Additionally, it highlights the key characteristics of the detectors employed for capture and fission cross-section measurements, paving the way for future research endeavors. [ABSTRACT FROM AUTHOR]

The advent of extensive data analysis has fundamentally transformed data processing, equipping organizations with the capacity to efficiently manage vast data volumes. It delves into the significance of technological synergies in harnessing the poten-tial of extensive data and its relevance across diverse sectors, including social networks, agriculture, and real estate. Furthermore, the paper scrutinizes the current state of extensive data analysis, appraising its applications and advancements. Additionally, it explores emerging trends in extensive data analysis, such as intelligent farming and electronic commerce, spotlighting the forth-coming potential of this technology. [ABSTRACT FROM AUTHOR]

Crime is considered one of the largest troubles in our society and its prevention could be very crucial undertaking to all of us. Every day there are big numbers of crimes happening in our society. This calls for preserve tracking of all the crimes and maintaining a database which can be used for future reference. The contemporary issues facing are keeping the proper dataset of crime and studying this information to assist in predicting and fixing crimes in destiny. The goal of this paper is to research dataset which may consist of several crimes and predicting the kind of crime which could take place in future depending upon numerous conditions [ABSTRACT FROM AUTHOR]

In the modern era, artificial intelligence and autonomous weapon systems represent an important step in the evolution of military conflicts. The ability of these technologies to make fast and accurate decisions in real time brings significant benefits to the military, improving the efficiency of actions and protecting the lives of soldiers. Artificial intelligence can analyze vast amounts of data and information in a much shorter time than humans, thus providing critical information for decision-making in order to surprise the adversary and achieve strategic objectives. Artificial intelligence is becoming an indispensable tool for modern armed forces because it allows actions to be carried out with maximum efficiency that would involve a high consumption of human resources, time and would involve numerous risks for human operators, from data analysis to identifying enemy targets and anticipating adversary movements or even engaging adversary forces under certain conditions. At the same time, autonomous weapon systems, equipped with AI technologies, have the ability to act independently, reducing the dependence on human intervention and speeding up the response time in critical situations. These systems can detect and neutralize threats faster and more efficiently than humans could. However, the use of artificial intelligence in military conflicts raises numerous ethical and moral challenges. Concerns about the loss of control over the technology and the possibility that it could be mistakenly used against civilians or against protected targets have generated intense debate in the international community. [ABSTRACT FROM AUTHOR]

The bibliographic science papers with high citations rates are often used as an indication of the science impact of an observatory. These high impact papers are presented as examples of the best science being done with an observatory's data. But, is the number of citations by itself a good indicator of the scientific impact of the paper, and is impact a good indicator of the scientific impact of the observatory? In this paper we will present results from a recent study of Chandra high impact papers and suggest some alternative methods for identifying such papers. This work has been supported by NASA under contract NAS 8-03060 to the Smithsonian Astrophysical Observatory for operation of the Chandra X-ray Center. [ABSTRACT FROM AUTHOR]

In the current era, one of the key challenges in utilizing the Holt-Winters Method of forecasting is the accurate selection of smoothing coefficients. To address this issue, researchers have explored an optimization approach that aims to minimize forecasting errors, such as Mean Squared Errors (MSE) or Mean Absolute Deviation (MAD). This paper presents a novel methodology that employs a Genetic Algorithm (GA) to optimize the forecasting error by determining the optimal smoothing coefficients for the Holt-Winters Method. The objective value of the optimization problem is the Mean Square Error (MSE), which serves as a measure of the accuracy of the forecast. To evaluate the effectiveness of the proposed approach, actual test cases based on rice stock commercial commodity in the Philippines during the COVID-19 pandemic were utilized. The paper examines different variants of the Holt-Winters Method and assesses their suitability for capturing the characteristics of the rice stock data. The findings indicate that an additive seasonal effect is more appropriate for modeling the seasonal patterns observed in the rice stock data. Furthermore, the performance of the proposed GA-based approach is compared to other forecasting models to ascertain its efficacy. The results demonstrate promising outcomes, suggesting that the GA-based optimization approach for determining the smoothing coefficients in the Holt-Winters Method improves the accuracy of rice stock forecasting during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]