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Grinding surface quality is a key indicator to determine the performance of parts and the reliability of products. During the grinding process, the pre-control technology makes appropriate adjustments in advance on its key aspects to improve the grinding surface quality. Therefore, this paper constructs a data-driven pre-control system for grinding surface quality including target layer, acquisition layer, analysis layer, and application layer. From the perspective of this system, the research progress of grinding surface quality by data-driven is reviewed. In the target layer, the connotation, formation mechanism, and main influencing indexes of grinding surface quality are described in detail. In the acquisition layer, the acquisition and processing methods of grinding data and their advantages and disadvantages are comprehensively analyzed. In the analysis layer, the methods of using result data, process data, and real-time signal data to characterize grinding surface quality are introduced. In the application layer, the research status of grinding surface quality prediction, optimization, and control are comprehensively and systematically summarized. Finally, the paper presented a summary and outlook on the improvement of grinding surface quality by data-driven. [ABSTRACT FROM AUTHOR]

An ensemble n-sub-epidemic modeling framework that integrates sub-epidemics to capture complex temporal dynamics has demonstrated powerful forecasting capability in previous works. This modeling framework can characterize complex epidemic patterns, including plateaus, epidemic resurgences, and epidemic waves characterized by multiple peaks of different sizes. In this tutorial paper, we introduce and illustrate SubEpiPredict, a user-friendly MATLAB toolbox for fitting and forecasting time series data using an ensemble n-sub-epidemic modeling framework. The toolbox can be used for model fitting, forecasting, and evaluation of model performance of the calibration and forecasting periods using metrics such as the weighted interval score (WIS). We also provide a detailed description of these methods including the concept of the n-sub-epidemic model, constructing ensemble forecasts from the top-ranking models, etc. For the illustration of the toolbox, we utilize publicly available daily COVID-19 death data at the national level for the United States. The MATLAB toolbox introduced in this paper can be very useful for a wider group of audiences, including policymakers, and can be easily utilized by those without extensive coding and modeling backgrounds. [ABSTRACT FROM AUTHOR]

As data and computing power have surged in recent decades, statistical modeling has become an important tool for understanding ecological patterns and processes. Statistical modeling in ecology faces two major challenges. First, ecological data may not conform to traditional methods, and second, professional ecologists often do not receive extensive statistical training. In response to these challenges, the journal Ecology has published many innovative statistical ecology papers that introduced novel modeling methods and provided accessible guides to statistical best practices. In this paper, we reflect on Ecology's history and its role in the emergence of the subdiscipline of statistical ecology, which we define as the study of ecological systems using mathematical equations, probability, and empirical data. We showcase 36 influential statistical ecology papers that have been published in Ecology over the last century and, in so doing, comment on the evolution of the field. As data and computing power continue to increase, we anticipate continued growth in statistical ecology to tackle complex analyses and an expanding role for Ecology to publish innovative and influential papers, advancing the discipline and guiding practicing ecologists. [ABSTRACT FROM AUTHOR]

In the ongoing academic discussion regarding what happens to our data after we die, how our data are utilised for commercial profit-making purposes, and what kinds of death-related practices our posthumous data figure in, the notion of digital afterlife is attracting increasing attention. While the concept of digital afterlife has been approached in different ways, the main focus remains on the level of individual loss. The emphasis tends to be on the role of posthumous digital artefacts in grief practices and death-related rituals or on data management issues relating to death. Building on a socio-technical view of digital afterlife, this paper offers, as a novel contribution, an understanding of digital afterlife as a techno-affective assemblage. It argues for the necessity of examining technological and social factors as mutually shaping and brings into the discussion of digital afterlife the notions of relationality, materiality, and the affective potential of data. The paper ends with ruminations about digital afterlife as a posthumanist project. [ABSTRACT FROM AUTHOR]

In promoting the development of China's digital economy, relevant departments have given full play to the underpinning role of the rule of law, actively promoted the formulation of laws and regulations in the field of data, and put in place a foundational legal system for data that combines vertical and horizontal aspects. Firstly, this paper introduces the basic overview of China's data legal system, including both horizontal system and vertical system. Secondly, it analyzes the vertical system, including data legislation at central and local levels, and covering laws, administrative regulations, departmental rules, local regulations and local administrative rules. Thirdly, it analyzes the horizontal system, including legal systems in the fields of data security and development, personal information protection, commercial data circulation and government data management. Finally, this paper summarizes the achievements of China's data legislation and deficiencies of the current data law system, and puts forward suggestions for improvement. [ABSTRACT FROM AUTHOR]

Background: the number of infants and children who receive artificial nutrition at home has been steadily increasing over the last decades, as better outcomes for children with chronic conditions have been achieved. In order to evaluate the need of resources to implement the technique it is necessary to know how many patients benefit from home artificial nutrition. This information can be estimated from the register of patients, when available. Methods: in this paper the characteristics of all registers were reviewed, especially those devoted to pediatric patients. Results: only two pediatric registers are active in 2023: the Canadian register and the Spanish one. NADYA register from the Spanish Society for Clinical Nutrition and Metabolism (SENPE) and the recent REPAFI, form the Spanish Society of Pediatric Gastroenterology, Hepatology and Nutrition. The most valuable register from the British Society, BANS, stopped providing information in 2018. Conclusion: despite the fact of acknowledging the importance of having gathered information on the prevalence and incidence of home artificial nutrition, to fit resources to necessities, the number of active registers is quite short. [ABSTRACT FROM AUTHOR]

Ship collision avoidance has always been a concern and it is crucial for achieving safe navigation of ships at sea. There are many studies on ship collision avoidance in open water, but less attention on coastal waters considering the uncertainty of target ships due to the complexity of the environment and traffic flow. In this paper, collision avoidance decision-making research in coastal waters considering the uncertainty of target ships was proposed. Firstly, accurate ship trajectories are obtained by preprocessing the raw Automatic Identification System (AIS) data. Subsequently, the processed trajectories are clustered using the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm and Hausdorff distance, acquiring a dataset for trajectory prediction of target ships. Then, a mixed Gaussian model is utilized to calculate the prior probability distribution of the prediction model, thus establishing a trajectory prediction model that considers the uncertainty of the target ship. Finally, ship maneuverability is simulated using the Mathematical Model Group (MMG) and Proportion Integration Differentiation (PID) models, and a collision avoidance decision-making model for ships is constructed. The proposed algorithm has been tested and verified in a case study. The results show that the approach effectively predicts the trajectory of the target ship and facilitates informed collision avoidance decision-making. [ABSTRACT FROM AUTHOR]

An increasing number of studies on non-contact vital sign detection using radar are now beginning to turn to data-driven neural network approaches rather than traditional signal-processing methods. However, there are few radar datasets available for deep learning due to the difficulty of acquiring and labeling the data, which require specialized equipment and physician collaboration. This paper presents a new model of heartbeat-induced chest wall motion (CWM) with the goal of generating a large amount of simulation data to support deep learning methods. An in-depth analysis of published CWM data collected by the VICON Infrared (IR) motion capture system and continuous wave (CW) radar system during respiratory hold was used to summarize the motion characteristics of each stage within a cardiac cycle. In combination with the physiological properties of the heartbeat, appropriate mathematical functions were selected to describe these movement properties. The model produced simulation data that closely matched the measured data as evaluated by dynamic time warping (DTW) and the root-mean-squared error (RMSE). By adjusting the model parameters, the heartbeat signals of different individuals were simulated. This will accelerate the application of data-driven deep learning methods in radar-based non-contact vital sign detection research and further advance the field. [ABSTRACT FROM AUTHOR]

Introduction/purpose Reliability and availability are important especially for military, medical, and other professional equipment. Reliability and availability management and/or prognostic reliability calculations have always been data driven. This article focuses on the analysis of the data impact on reliability and availability. Methods: This research is based mostly on the articles published by the author of this work as well as on some other papers. Results: This paper results in a discussion on the definition of the datadriven concept, preceded by brief definitions of reliability and availability, and followed by the analysis of the main impacts of uncertain data on prognostic reliability calculations as well as by reliability and availability of data used in reliability calculations. Conclusions: Reliability and availability are still very important. Reliability and availability have always been data driven while valid and relevant data have always been the main problem. Without good data, prognostic reliability is useless in spite of a good reliability model. [ABSTRACT FROM AUTHOR]

Minerals are information-rich materials that offer researchers a glimpse into the evolution of planetary bodies. Thus, it is important to extract, analyze, and interpret this abundance of information to improve our understanding of the planetary bodies in our solar system and the role our planet's geosphere played in the origin and evolution of life. Over the past several decades, data-driven efforts in mineralogy have seen a gradual increase. The development and application of data science and analytics methods to mineralogy, while extremely promising, has also been somewhat ad hoc in nature. To systematize and synthesize the direction of these efforts, we introduce the concept of "Mineral Informatics," which is the next frontier for researchers working with mineral data. In this paper, we present our vision for Mineral Informatics and the X-Informatics underpinnings that led to its conception, as well as the needs, challenges, opportunities, and future directions of the field. The intention of this paper is not to create a new specific field or a sub-field as a separate silo, but to document the needs of researchers studying minerals in various contexts and fields of study, to demonstrate how the systemization and enhanced access to mineralogical data will increase cross- and interdisciplinary studies, and how data science and informatics methods are a key next step in integrative mineralogical studies. [ABSTRACT FROM AUTHOR]

The Parliament of Kenya enacted the Data Protection legislation which came into effect on November 25, 2019. The new law was passed given the provisions of Article 31 of the Constitution of Kenya, 2010 which guarantee the right to privacy as a fundamental right. Data Protection and citizens' right to privacy is now a topical concern as evident around the world in many jurisdictions. The increasing globalization, cross-border transactions, internet penetration and the use of social media and digital platforms among citizens, governments and private institutions raises several data security and privacy concerns that breaches may amount to loss of reputation, identity, safety concerns, legal penalties or compensation for damages or loss of business. As a result, the enactment of Data Protection legislations plays a resounding role in providing the requisite legal framework to regulate the activities of market players, government and private entities, in collecting, storing, processing, accessing, transmitting, sharing and disposing of personal or corporate data among other subjects. This paper reviews the crucial provisions of Kenya's Data Protection law which covers regulated actions seeking compliance by data controllers and processors under the stewardship of the Office of the Data Protection Commissioner ('ODPC'). The paper will also provide a comparative analysis of the practice in other jurisdictions, case laws and court decisions, merits and demerits of data protection legislations and areas of possible data breach targets. [ABSTRACT FROM AUTHOR]

Intelligent water usage is required in order to target the challenging goals for 2030 and 2050. Hydroelectric power plants represent processes wherein water is exploited as a renewable resource and a source for energy production. Hydroelectric power plants usually include reservoirs, valves, gates, and energy production devices, e.g., turbines. In this context, monitoring and maintenance policies together with control and optimization strategies, at the different levels of the automation hierarchy, may represent strategic tools and drivers for energy efficiency improvement. Nowadays, these strategies rely on different basic concepts and elements, which must be assessed and investigated in order to provide a reliable background. This paper focuses on a review of the state of the art associated with these basic concepts and elements, i.e., digitalization, Industry 4.0, data, KPIs, modelization, and forecast. [ABSTRACT FROM AUTHOR]

Background: This paper discusses how collective intelligence (CI) methods can be implemented to improve government data infrastructures, not only to support understanding and primary use of complex national data but also to increase the dissemination and secondary impact of research based on these data. The case study uses the Northern Ireland Longitudinal Study (NILS), a member of the UK family of census/administrative data longitudinal studies (UKLS). Methods: A stakeholder-engaged CI approach was applied to inform the transformation of the NILS Research Support Unit (RSU) infrastructure to support researchers in their use of government data, including collaborative decision-making and better dissemination of research outputs. Results: We provide an overview of NILS RSU infrastructure design changes that have been implemented to date, focusing on a website redesign to meet user information requirements and the formation of better working partnerships between data users and providers within the Northern Ireland data landscape. We also discuss the key challenges faced by the design team during this project of transformation. Conclusion: Our primary objective to improve government data infrastructure and to increase dissemination and the impact of research based on data was a complex and multifaceted challenge due to the number of stakeholders involved and their often conflicting perspectives. Results from this CI approach have been pivotal in highlighting how NILS RSU can work collaboratively with users to maximize the potential of this data, in terms of forming multidisciplinary networks to ensure the research is utilized in policy and in the literature and providing academic support and resources to attract new researchers. [ABSTRACT FROM AUTHOR]

Phenomenology presents itself not as an explanation or interpretation of phenomena but as a description of them. Describing experience means making its internal structure explicit, which, in phenomenology, is an eidetic structure. The method of phenomenological explication or clarification is, however, by no means univocal. This paper aims to isolate the two fundamental ways in which phenomenological description is achieved. The first refers to a phenomenology of manifestation, based on the concept of determination or datum, which is realized in the phenomenological-static approach and, in particular, on the concept of extensive quality. The second refers to a phenomenology of disposition, based on the concept of tension or force – which is realized in the genetic approach as well as in Merleau-Ponty's phenomenology of perception – and, in particular on the concept of intensive or forceful quality. The analysis of the difference between the two approaches allows us to introduce the crucial distinction between digital and analogue dimensions within phenomenology. [ABSTRACT FROM AUTHOR]

According to how AI has defined itself from its beginning, thinking in non-living matter, i.e., without life, is possible. The premise of symbolic AI is that operating on representations of reality machines can understand it. When this assumption did not work as expected, the mathematical model of the neuron became the engine of artificial "brains." Connectionism followed. Currently, in the context of Machine Learning success, attempts are made at integrating the symbolic and connectionist paths. There is hope that Artificial General Intelligence (AGI) performance can be achieved. As encouraging as neuro-symbolic AI seems to be, it remains unclear whether AGI is actually a moving target as long as AI itself remains ambiguously defined. This paper makes the argument that the intelligence of machines, expressed in their performance, reflects how adequate the means used for achieving it are. Therefore, energy use and the amount of data necessary qualify as a good metric for comparing natural and artificial performance. [ABSTRACT FROM AUTHOR]

Digital objects are now pervasively used across the heritage sciences, often as 3D models. However, the theoretical discussion of what these objects are, ontologically speaking, can be diverse, ranging, and inconclusive. This paper will focus on the Cherish Project, a European research initiative that used a range of methods—including drone-based photogrammetry and laser scanning— to create 3D models of coastal heritage landscapes that are at risk due to climate change. In specifically attending to the database storage schemes and software/platforms employed by Cherish, this paper explores how digital heritage objects can more broadly be discussed in terms of their ontological multiplicity, the multi-sitedness of their production and circulation, and their mobility across interfaces as they are formalised and circulated. In tracing these specific factors, this paper arrives at epistemological insights about how digital heritage objects factor into knowledge producing practices like Cherish, foregrounding critical questions about how these practices might be differently discussed, pursued, or imagined. [ABSTRACT FROM AUTHOR]

The sustainable development of cities relies on the implementation of multi-sectoral actions towards carbon neutrality, reducing the air pollutants emissions. The actions' decision-making process for cities transformation should be supported by lessons learnt from previous interventions and KPIs (Key Performance Indicators). To do so, gathering real data becomes pivotal, complementing simulation tools (currently used), solving the inherent uncertainties due to assumptions. Data collection methodologies are then necessary, being the main driver for digital cities and providing better mechanisms for informed decision-making. Most of the cities still operate in silos and do not always implement the strategic plans supported with a digitalization of the municipal processes. Within this perspective, this paper presents a methodology to support cities in the preparation of monitoring programmes to collect real data in a robust and feasible manner. Taking the KPIs and the Smart Cities urban strategies into account, this paper concludes with some lessons learnt within cities to deploy monitoring approaches. From the city challenges to the review of the plans, all the process is driven by real data and KPIs. The methodology has been applied in the mySMARTLife project (Grant Agreement #731297) and deployed into the cities of Nantes (France), Hamburg (Germany) and Helsinki (Finland). [ABSTRACT FROM AUTHOR]

Brazil has a great diversity of mites of the Phytoseiidae family. The recognition of the hosts associated with this family is essential for applied biological control studies. The objective of this work is to compile data from the literature on the occurrence of phytoseiid mites Caatinga areas, in order to provide information on the current state of knowledge of the acarofauna in this biome. The data were obtained from the Phytoseiidae Database. Twenty-six papers were compiled, covering 7 states and 84 municipalities. A total of 65 phytoseiid species belonging to 22 genera were recorded. The genera Amblyseius (18%), Neoseiulus (16%) and Euseius (9%) had the highest occurrence; at the species level E. concordis (33%), E. citrifolius (32%) and Neoseiulus idaeus (30%) had the highest number of records. Regarding hosts, 71 species from 24 different families belonging to 46 botanical genera were recorded in the study. The Solanaceae family (46%) had the most records, followed by Myrtaceae (20%) and Euphorbiaceae (18%). Solanum (38%) was the genus with more compiled records, at the species level Solanum paniculatum (23%) had more records. The present study contributed to the knowledge of Phytoeiidae species that occur in Caatinga areas. [ABSTRACT FROM AUTHOR]

Design flood estimation (DFE) methods are used to limit the risk of failure and ensure the safe design of hydrological and related infrastructure, and to inform water resources management. In order to improve DFE methods which are based on event or continuous simulation rainfall–runoff models, it is generally necessary to use sub-daily rainfall data. However, sub-daily rainfall gauges are relatively sparse and have shorter record lengths than daily rainfall gauges in South Africa. Rainfall temporal disaggregation (RTD) techniques can be used to produce finer resolution data from coarser resolution daily rainfall data. Several RTD approaches have been developed and are used in South Africa. However, there is a need to review and assess the performance of the available RTD methods. This paper contains an overview of selected RTD approaches and the performance of the methods at selected sites in South Africa, for disaggregating daily rainfall into 15-min intervals. Temporal distributions of rainfall were represented by dimensionless Huff curves, which served as the basis for comparison of observed and disaggregated rainfall. In a pilot study it was found that the SCS-SA (Soil Conservation Service model South Africa) distributions and the Knoesen model approaches performed considerably better than the other approaches. The RTD approaches were further assessed using data from 14 additional rainfall stations. For the additional stations, the Knoesen model and SCS-SA disaggregated rainfall generally provided the most realistic temporal distributions. [ABSTRACT FROM AUTHOR]

The expansion of bio-based value chains is prioritized through various European Union (EU) policy initiatives. Due to the growing awareness of the importance of a sustainable bioeconomy in Europe, the need to increase the availability and quality of statistics is increasing. There are several essential aspects lacking, including (i) comprehensive databases and statistics for bio-based sectors; (ii) transparent methodology for bio-based data collection; and (iii) integrated value chain data and indicators that illustrate the flows of different bio-based commodities. The aim of this paper is to develop a bio-based material flow monitor to measure the physical contribution of industries to the bioeconomy. The material flow monitor describes physical material flows (including biomass) to, from, and within the economy. It is recorded in the form of supply and use tables. To measure the bioeconomy, the BioSAM database, along with disaggregated commodities and activities, are used. Data regarding waste generation/treatment and CO2 emissions/sequestrations are added to assess the impact on climate change. The results indicate that the bioeconomy in the EU is underreported due to a lack of data, leading to an insufficient understanding of its contribution to the economy. It can also be concluded that the data from the BioSAM tables are the most complete and have the highest disaggregation level for commodities and sectors, allowing one to measure the significance of the bioeconomy. [ABSTRACT FROM AUTHOR]

In the era of digital economy, the importance of data as a factor of production is becoming increasingly prominent. Data has become an indispensable input for many business innovations related to the digital economy. Non-competitive characteristics in the process of data use, complementarity with data development tools and synergy with other elements, economy of scale under certain conditions and possible economy of scope, and universality of use make data theoretically serve as a core element. This paper, from the theory of core elements and the development of digital economy, provides a comprehensive overview and analysis of the theoretical logic of data as an essential facility, responds to the debate of data as the necessity of the essential facility, then puts forward the policy framework design and specific suggestions to support data as an essential facility: first, establishing data as the essential facility of the review organization, review standards and constituent elements. Second, establishing a dynamic balance mechanism between data privacy protection and data essential facility. Third, making an in-depth study of the data value creation process, and accelerating the construction of data factor market. Fourth, coordinating the theory of essential facility with data's portability, interoperability and other related policies and measures. [ABSTRACT FROM AUTHOR]

The UK has been a global leader in the development and regulation of biobanks and bio-databases that facilitate clinical and laboratory access to tissue, blood samples, DNA and data. Yet the persistent barrier to mobilise non-White communities into actively contributing to and, subsequently benefiting from structural and scientific advantages that the UK can offer constitutes a ‘Silent Crisis’. This paper builds on ongoing research on stem cell donations carried out by the authors in the UK. We underline the centrality of the concept of ‘relatedness’ in donor recruitment, and the tricky role it has played, both as a uniting and an alienating force within and between different ethnic communities. We argue that the building of a thick societal relatedness or what we term as ‘polyphonic relatedness’ offers a constructive guidance to overcome the racial disparity in biomaterial donations. [ABSTRACT FROM AUTHOR]

Healthcare organizations have realized that Artificial intelligence (AI) can provide a competitive edge through personalized patient experiences, improved patient outcomes, early diagnosis, augmented clinician capabilities, enhanced operational efficiencies, or improved medical service accessibility. However, deploying AI-driven tools in the healthcare ecosystem could be challenging. This paper categorizes AI applications in healthcare and comprehensively examines the challenges associated with deploying AI in medical practices at scale. As AI continues to make strides in healthcare, its integration presents various challenges, including production timelines, trust generation, privacy concerns, algorithmic biases, and data scarcity. The paper highlights that flawed business models and wrong workflows in healthcare practices cannot be rectified merely by deploying AI-driven tools. Healthcare organizations should re-evaluate root problems such as misaligned financial incentives (e.g., fee-for-service models), dysfunctional medical workflows (e.g., high rates of patient readmissions), poor care coordination between different providers, fragmented electronic health records systems, and inadequate patient education and engagement models in tandem with AI adoption. This study also explores the need for a cultural shift in viewing AI not as a threat but as an enabler that can enhance healthcare delivery and create new employment opportunities while emphasizing the importance of addressing underlying operational issues. The necessity of investments beyond finance is discussed, emphasizing the importance of human capital, continuous learning, and a supportive environment for AI integration. The paper also highlights the crucial role of clear regulations in building trust, ensuring safety, and guiding the ethical use of AI, calling for coherent frameworks addressing transparency, model accuracy, data quality control, liability, and ethics. Furthermore, this paper underscores the importance of advancing AI literacy within academia to prepare future healthcare professionals for an AI-driven landscape. Through careful navigation and proactive measures addressing these challenges, the healthcare community can harness AI's transformative power responsibly and effectively, revolutionizing healthcare delivery and patient care. The paper concludes with a vision and strategic suggestions for the future of healthcare with AI, emphasizing thoughtful, responsible, and innovative engagement as the pathway to realizing its full potential to unlock immense benefits for healthcare organizations, physicians, nurses, and patients while proactively mitigating risks. • This study categorizes AI applications in healthcare and analyzes their deployment challenges at scale in medical practices. • The paper emphasizes a cultural shift to view AI as a healthcare delivery enhancer and job creator, not a threat. • To integrate AI, investments in finance and human capital, continuous learning, and a supportive environment are needed. • Clear regulatory frameworks should be developed to build trust, ensure safety, and guide the ethical use of AI in healthcare. • The study provides strategic suggestions for responsible AI use in healthcare to maximize benefits and minimize risks. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has presented unique challenges for rapidly designing, initiating, and delivering therapeutic clinical trials. PRINCIPLE (Platform Randomised Trial of Treatments in the Community for Epidemic and Pandemic Illnesses) is the UK national platform investigating repurposed therapies for COVID-19 treatment of older people in the community at high risk of complications. Standard methods of patient recruitment were failing to meet the required pace and scale of enrolment. This paper describes the development and appraisal of a near real-time, data-driven, ethical approach for enhancing recruitment in community care by contacting people with a recent COVID-19 positive test result from the central NHS Test and Trace service within approximately 24-48 h of their test result.Methods: A multi-disciplinary team was formed to solve the technical, ethical, public perception, logistical and information governance issues required to provide a near-real time (approximately within 24-48 h of receiving a positive test) feed of potential trial participants from test result data to the research team. PRINCIPLE was also given unique access to the Summary Care Record (SCR) to ensure safe prescribing, and to enable the trial team to quickly and safely bring consented patients into the trial. A survey of the public was used to understand public perceptions of the use of test data for this proposed methodology.Results: Prior to establishing the data service, PRINCIPLE registered on average 87 participants per week. This increased by up to 87 additional people registered per week from the test data, contributing to an increase from 1013 recruits to PRINCIPLE at the start of October 2020 to 2802 recruits by 20 December 2020. Whilst procedural caveats were identified by the public consultation, out of 2639 people contacted by PRINCIPLE following a positive test result, no one raised a concern about being approached.Conclusions: This paper describes a novel approach to using near-real time NHS operational data to recruit community-based patients within a few days of presentation with acute illness. This approach increased recruitment and reduced time between positive test and randomisation, allowing more rapid evaluation of treatments and increased safety for participants. End-to-end public and patient involvement in the design of the approach provided evidence to inform information governance decisions.Trial Registration: PRINCIPLE is funded by UK Research and Innovation and the Department of Health and Social Care through the National Institute for Health Research. EudraCT number: 2020-001209-22 . 26/03/2020 ISRCTN registry: ISRCTN86534580 . 20/03/2020 REC number: 20/SC/058 IRAS number: 281958. [ABSTRACT FROM AUTHOR]

This paper presents the findings of a study on readiness of the University of Zambia in participating in research data management services in the COVID-19 and post Covid eras. The study adopted a qualitative research method and employed purposive sampling. In-depth interviews were conducted with key informants in the University. Findings reveal a lack of awareness and knowledge about research data management among senior officers and inadequate skills among library staff. On a positive note, the University has a robust information and communication technology (ICT) infrastructure supported by qualified information technology (IT) staff. It was further discovered that the University did not have a research data management policy. This paper provides a framework for the University to operationalize research data management services. The study recommends development of a research data management policy; conducting awareness campaigns; investing in skills training for library staff, and constituting research teams to deal with issues of research data management. [ABSTRACT FROM AUTHOR]

With the increasing demands for data, the subscription scheme came into being in the face of pricing for an extensive and unfixed number of data items. However, in the existing subscription scheme, a diversity of customers in the real market may lead to the lack of stability, which means risking the failure of pricing. Additionally, the study involves arbitrage-free, an essential economics concept, which is not reasonable on data items. To address these problems, this paper provides insights for designing an improved subscription scheme that includes two components: the calculation and the specific validity. On the one hand, the calculation improves the existing scheme by building a new structure that combines different customers' behaviours instead of the separated calculation in the existing scheme, and can steadily set prices for subscriptions to maximise the sellers' profit even in a real market. On the other hand, the specific validity shows the improvement towards arbitrage-free by taking the characteristics of data subscriptions into account. In other words, the specific validity endows the scheme with more rationality. [ABSTRACT FROM AUTHOR]

This discussion paper will reflect on the contribution of digital humanities (DH) to a complex interdisciplinary project like the Congruence Engine. It begins by considering how DH has developed within the larger history of interdisciplinarity in the humanities, crossing boundaries within and between disciplines and sectors, and facilitating collaboration and knowledge exchange. It discusses the growth of large-scale digital projects in the humanities, shaped by the nature and scope of the data increasingly available to humanities researchers, by the new kinds of research questions that can be asked, but also by changes in broader funding and policy landscapes. It considers three recent projects which exemplify the value of DH in interdisciplinary contexts, before reflecting on how DH methods and approaches have influenced the shape of the Congruence Engine. It situates the practices of the Congruence Engine in the wider context of knowledge exchange, focusing in particular on the concept of 'trading zones', and draws out the complementarity between the bridging or translational role of DH and the systemic action research framing of the project. Finally, it highlights the value of responsible openness not just in relation to published research outputs but to research practice and process. [ABSTRACT FROM AUTHOR]

The aging of the world's population, the willingness of elderly to remain independent, and the recent COVID-19 pandemic have demonstrated the urgent need for home-based diagnostic and patient monitoring systems to reduce the financial and organizational burdens that impact healthcare organizations and professionals. The Internet of Medical Things (IoMT), i.e., all medical devices and applications that connect to health information systems through online computer networks. The IoMT is one of the domains of IoT where real-time processing of data and reliability are crucial. In this paper, we propose RAMi, which is a Real-Time Architecture for the Monitoring of elderly patients thanks to the Internet of Medical Things. This new architecture includes a Things layer where data are retrieved from sensors or smartphone, a Fog layer built on a smart gateway, Mobile Edge Computing (MEC), a cloud component, blockchain, and Artificial Intelligence (AI) to address the specific problems of IoMT. Data are processed at Fog level, MEC or cloud in function of the workload, resource requirements, and the level of confidentiality. A local blockchain allows workload orchestration between Fog, MEC, and Cloud while a global blockchain secures exchanges and data sharing by means of smart contracts. Our architecture allows to follow elderly persons and patients during and after their hospitalization. In addition, our architecture allows the use of federated learning to train AI algorithms while respecting privacy and data confidentiality. AI is also used to detect patterns of intrusion. [ABSTRACT FROM AUTHOR]

The use of information technology during the COVID-19 pandemic raises significant questions around the protection of personal data in a disaster. This paper considers how the clear benefits of using and sharing such data in disaster scenarios can be achieved while recognising an individual's right to privacy through examining the experiences of Taiwan and New Zealand. These states represent two successful COVID-19 response strategies which utilised different approaches to the use of technology. In Taiwan, the response made significant use of personal data and information technology. New Zealand, by contrast, has relied upon stringent lockdowns and extreme limits upon personal freedoms. The paper considers the different approaches to data and privacy that underpinned these responses and considers whether New Zealand can learn from the Taiwanese experience in future disaster planning. In doing so, the paper concludes by examining the wider question of when and if the community's expectation of a safe environment should trump the rights of individuals to retain personal data both in the context of pandemics and in other emergency or disaster scenarios. It also raises deeper questions, exposed by the COVID-19 response, about whether our current approach to privacy is sustainable in the digital age. [ABSTRACT FROM AUTHOR]

Is the survival of constitutional democracy in America at serious risk? Given the actions of the Trump administration, and given the decline of democracy and concomitant rise of authoritarianism the world over, there is genuine cause for alarm. In light of these fears, it is worth remembering that the authors of The Federalist Papers were notably pessimistic about the survival chances of republican government. To what extent have their constitutional design innovations contributed to present woes, and conversely, to what extent will the Constitution ensure the survival of democracy? This Essay argues that while the design of the Constitution is both helpful and harmful, much will ultimately turn on the political dimension. In particular, this Essay claims that the republic will survive only if the ongoing practices of democracy re-affirm its central values. But there are significant challenges to maintaining constitutional democracy. In addition to President Trump in the White House, current challenges include the decline in the democratic norms of civility and compromise, the rise of ideological warfare and hyperpartisanship, and the ever-deepening polarization between opposing camps--not only in government but also in the public at large. In the coming years, the continued vigilance and resistance of individuals and institutions will be crucially important to ensure the survival of constitutional democracy in America. [ABSTRACT FROM AUTHOR]

This paper examines the materiality of digitally mapping eviction and landlord geographies, focusing on struggles and contradictions that critical GIS and counter-mapping collectives encounter in attempting to produce data, maps, and tools useful for housing justice organizing. I look at how public restrictions of parcel ownership and eviction data, along with limited accessibility of free and open source mapping software, often instantiate increased reliance upon technocapitalist data and infrastructure. Many of these systems are precipitative of gentrification geographies, thereby at odds with the politics of antidisplacement mapping. Meanwhile, there are a growing number of cartographic labs and companies that produce geospatial data related to evictions and property ownership, but that prioritize data accumulation and scalability over grounded housing justice. By exploring paradoxes within this space, I theorize the conjuncture of datafied property and propertied data landscapes. Homing in on San Francisco landscapes of the Tech Boom 2.0., I draw upon my own experience working with the Anti-Eviction Mapping Project and partner groups. I conclude by looking to housing justice and land rematriation practices based upon grounded relationalities - models that offer emancipatory trajectories for digitally mapping property and dispossession. [ABSTRACT FROM AUTHOR]

The practice of modern craniomaxillofacial surgery has been defined by emergent technologies allowing for the acquisition, storage, utilization, and transfer of massive amounts of sensitive and identifiable patient data. This alone has thrust providers into an unlikely and unprecedented role as the stewards of vast databases of digital information. This data powers the potent surgical tool of virtual surgical planning, a method by which craniomaxillofacial surgeons plan and simulate procedural outcomes in a digital environment. Further complicating this new terrain is the involvement of third‐party contractors—a necessary presence in bringing raw data to bear in the office, virtual space, and operating room. The individual privileges and responsibilities of patients, providers, and vendors towards data are situated within the most recent U.S. court rulings and regulations. This paper offers guidance for overseeing the safe and responsible transfer to third‐party contractors, and provides suggestions for negotiating the trinary relationship between physicians, their patients, and the vendors offering this transformative technology. [ABSTRACT FROM AUTHOR]

This paper uses survey‐based data for 16 South Korean regions to refine the application of Flegg's location quotient (FLQ) and its variant, the sector‐specific FLQ (SFLQ). These regions vary markedly in terms of size. Especial attention is paid to the problem of choosing appropriate values for the unknown parameter δ in these formulae. Alternative approaches to this problem are evaluated and tested. Our paper adds to earlier research that aims to find a cost‐effective way of adapting national coefficients, so as to produce a satisfactory initial set of regional input coefficients for regions where survey‐based data are unavailable. [ABSTRACT FROM AUTHOR]

Considering the significant progress made on RGB-based deep salient object detection (SOD) methods, this paper seeks to bridge the gap between those 2D methods and 4D light field data, instead of implementing specific 4D methods. We observe that the performance of 2D methods changes dramatically with the input refocusing on different depths. This paper attempts to make the 2D methods available for light field SOD by learning to select the best single image from the 4D tensor. Given a 2D method, a deep model is proposed to explicitly compare pairs of SOD results on one light field sample. Moreover, a comparator module is designed to integrate the features from a pair, which provides more discriminative representations to classify. Experiments over 13 latest 2D methods and 2 datasets demonstrate the proposed method can bring about 24.0% and 5.3% average improvement of mean absolute error and F-measure, and outperform state-of-the-art 4D methods by a large margin. [ABSTRACT FROM AUTHOR]

Background: The existing Z-test for uncertainty events does not give information about the measure of indeterminacy/uncertainty associated with the test. Methods: This paper introduces the Z-test for uncertainty events under neutrosophic statistics. The test statistic of the existing test is modified under the philosophy of the Neutrosophy. The testing process is introduced and applied to the Covid-19 data. Results: Based on the information, the proposed test is interpreted as the probability that there is no reduction in uncertainty of Covid-19 is accepted with a probability of 0.95, committing a type-I error is 0.05 with the measure of an indeterminacy 0.10. Based on the analysis, it is concluded that the proposed test is informative than the existing test. The proposed test is also better than the Z-test for uncertainty under fuzzy-logic as the test using fuzz-logic gives the value of the statistic from 2.20 to 2.42 without any information about the measure of indeterminacy. The test under interval statistic only considers the values within the interval rather than the crisp value. Conclusions: From the Covid-19 data analysis, it is found that the proposed Z-test for uncertainty events under the neutrosophic statistics is efficient than the existing tests under classical statistics, fuzzy approach, and interval statistics in terms of information, flexibility, power of the test, and adequacy. [ABSTRACT FROM AUTHOR]

With the increasing demands for data, the subscription scheme came into being in the face of pricing for an extensive and unfixed number of data items. However, in the existing subscription scheme, a diversity of customers in the real market may lead to the lack of stability, which means risking the failure of pricing. Additionally, the study involves arbitrage-free, an essential economics concept, which is not reasonable on data items. To address these problems, this paper provides insights for designing an improved subscription scheme that includes two components: the calculation and the specific validity. On the one hand, the calculation improves the existing scheme by building a new structure that combines different customers' behaviours instead of the separated calculation in the existing scheme, and can steadily set prices for subscriptions to maximise the sellers' profit even in a real market. On the other hand, the specific validity shows the improvement towards arbitrage-free by taking the characteristics of data subscriptions into account. In other words, the specific validity endows the scheme with more rationality. [ABSTRACT FROM AUTHOR]

• The parameters can be identified successfully using incomplete frequency domain data. • The approach in this paper can result in good identification results from noisy data. • The optimal weight matrix indeed improves the identification accuracy. • The proposed approach is more efficient than that in the time domain-based approach. The technique to identify the system parameters thereof has attracted extensive research interest, since knowing the parameters would enable effective system control strategy and accurate response prediction. In this paper, a novel approach is developed to identify the parameters of the linear time-delay differential system by analyzing the complex system response in the frequency domain. Firstly, the complex frequency response of the time-delay system is expressed as a function of physical parameters and time-delay parameters, forming a typical optimization problem. Subsequently, the sensitivities with respect to the unknown parameters are derived. A novel sensitivity-based algorithm is adopted in the identification procedure. Trust-region constraint is implemented and hence tackled by Tikhonov regularization, which effectively enhances the efficiency of the algorithm. The feasibility and robustness of the identification procedure are evaluated by identifying the parameters of two numerical time-delay systems and an experimental case. [ABSTRACT FROM AUTHOR]

The current "AI Summer" is marked by scientific breakthroughs and economic successes in the fields of research, development, and application of systems with artificial intelligence. But, aside from the great hopes and promises associated with artificial intelligence, there are a number of challenges, shortcomings and even limitations of the technology. For one, these challenges arise from methodological and epistemological misconceptions about the capabilities of artificial intelligence. Secondly, they result from restrictions of the social context in which the development of applications of machine learning is embedded. And third, they are a consequence of current technical limitations in the development and use of artificial intelligence. The paper intends to provide an overview of current challenges which the research and development of applications in the field of artificial intelligence and machine learning have to face, whereas all three mentioned areas are to be further explored in this paper. [ABSTRACT FROM AUTHOR]

Up to now, the Chinese government has only made very general comments on the application of international humanitarian law to cyberspace. There are indeed Chinese academic papers concerning this issue, but the discussion of the principle of distinction is limited both in length and in academic depth. Compared with the West, research by Chinese scholars on this topic is still in a relatively preliminary stage. At present, there is no specific deconstruction or clarification of the application of the principle of distinction in cyberspace in Chinese academia. As the first paper written by Chinese scholars specifically devoted to this question, this piece provides a different perspective by injecting the positions of Chinese officials and the views of Chinese scholars. The authors aim to clarify whether the existing rules are still completely applicable in the cyber context, and if needed, to find out what kind of improvements and clarifications can be made. Weighing in on these debates, we argue that despite the potential technical challenges and uncertainties, the principle of distinction should be applied to cyberspace. It should also be carefully re-examined and clarified from the standpoint of preventing over-militarization and maximizing the protection of the interests of civilians. For human targets, the elements of combatant status identified in customary international law and relevant treaties are not well suited to the digital battlefield. Nevertheless, cyber combatants are still obligated to distinguish themselves from civilians. In applying the principle of distinction, we argue that it makes more sense to focus on substantive elements over formal elements such as carrying arms openly or having a fixed distinctive sign recognizable at a distance. In interpreting "direct participation in hostilities", the threshold of harm requires an objective likelihood instead of mere subjective intention; the belligerent nexus should be confirmed, and the causal link should be proximate. Applying the "cyber kill chain" model by analogy helps us to grasp the whole process of direct participation in hostilities during cyber warfare. For non-human targets, all military objectives must cumulatively fulfil both the "effective contribution" and "definite military advantage" criteria, which are equally indispensable. The same requirements apply to dual-use objects. Furthermore, certain data should fall within the ambit of civilian objects. [ABSTRACT FROM AUTHOR]

This paper explores the concept of digital modernity, the extension of narratives of modernity with the special affordances of digital networked technology. Digital modernity produces a new narrative which can be taken in many ways: to be descriptive of reality; a teleological account of an inexorable process; or a normative account of an ideal sociotechnical state. However, it is understood that narratives of digital modernity help shape reality via commercial and political decision-makers, and examples are given from the politics and society of the United Kingdom. The paper argues that digital modernity has two dimensions, of progression through time and progression through space, and these two dimensions can be in contradiction. Contradictions can also be found between ideas of digital modernity and modernity itself, and also between digital modernity and some of the basic pre-modern concepts that underlie the whole technology industry. Therefore, digital modernity may not be a sustainable goal for technology development. [ABSTRACT FROM AUTHOR]

Toric gerbes are étale gerbes over toric Deligne-Mumford stacks which are constructed out of suitably chosen toric data. In this paper we study the genus 0 Gromov-Witten theory of toric gerbes. Our main result equates the genus 0 Gromov-Witten theory of a toric gerbe with a suitable twist of the genus 0 Gromov-Witten theory of a disjoint union of several copies of the base. Our result can be interpreted in the context of the decomposition conjecture in physics. The main tool used in this paper is the calculation of Gromov-Witten theory of toric Deligne-Mumford stacks by Coates-Corti-Iritani-Tseng. [ABSTRACT FROM AUTHOR]

A common divide-and-conquer approach for Bayesian computation with big data is to partition the data, perform local inference for each piece separately, and combine the results to obtain a global posterior approximation. While being conceptually and computationally appealing, this method involves the problematic need to also split the prior for the local inferences; these weakened priors may not provide enough regularization for each separate computation, thus eliminating one of the key advantages of Bayesian methods. To resolve this dilemma while still retaining the generalizability of the underlying local inference method, we apply the idea of expectation propagation (EP) as a framework for distributed Bayesian inference. The central idea is to iteratively update approximations to the local likelihoods given the state of the other approximations and the prior. The present paper has two roles: we review the steps that are needed to keep EP algorithms numerically stable, and we suggest a general approach, inspired by EP, for approaching data partitioning problems in a way that achieves the computational benefits of parallelism while allowing each local update to make use of relevant information from the other sites. In addition, we demonstrate how the method can be applied in a hierarchical context to make use of partitioning of both data and parameters. The paper describes a general algorithmic framework, rather than a specific algorithm, and presents an example implementation for it. [ABSTRACT FROM AUTHOR]

In this paper, a new modification of the Lomax distribution is considered named as Lomax exponential distribution (LE). The proposed distribution is quite flexible in modeling the lifetime data with both decreasing and increasing shapes (non-monotonic). We derive the explicit expressions for the incomplete moments, quantile function, the density function for the order statistics etc. The Renyi entropy for the proposed distribution is also obtained. Moreover, the paper discusses the estimates of the parameters by the usual maximum likelihood estimation method along with determining the information matrix. In addition, the potentiality of the proposed distribution is illustrated using two real data sets. To judge the performance of the model, the goodness of fit measures, AIC, CAIC, BIC, and HQIC are used. Form the results it is concluded that the proposed model performs better than the Lomax distribution, Weibull Lomax distribution, and exponential Lomax distribution. [ABSTRACT FROM AUTHOR]

The paper offers an overview of ethical issues about cataloguing. After a general introduction about the topic in library science, the paper analyses two principal issues: access to information, in particular about semantic access, and data. Ethical issues are a part of the cataloguing process and affect the way cataloguers create metadata for the users. The last part of the paper presents an analysis of the proposal for a specific code of ethics for cataloguers. [ABSTRACT FROM AUTHOR]

This paper is a think piece about the possible future of bibliographic control; it provides a brief introduction to the Semantic Web and defines related terms, and it discusses granularity and structure issues and the lack of standards for the efficient display and indexing of bibliographic data. It is also a report on a work in progress—an experiment in building a Resource Description Framework (RDF) model of more FRBRized cataloging rules than those about to be introduced to the library community (Resource Description and Access) and in creating an RDF data model for the rules. I am now in the process of trying to model my cataloging rules in the form of an RDF model, which can also be inspected at http://myee. bol.ucla.edu/. In the process of doing this, I have discovered a number of areas in which I am not sure that RDF is sophisticated enough yet to deal with our data. This article is an attempt to identify some of those areas and explore whether or not the problems I have encountered are soluble—in other words, whether or not our data might be able to live on the Semantic Web. In this paper, I am focusing on raising the questions about the suitability of RDF to our data that have come up in the course of my work. [ABSTRACT FROM AUTHOR]