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Concerns about research’s environmental impacts have been articulated in the research arena, but questions remain about what types of role responsibilities are appropriate to place on researchers, if any. The research question of this paper is: what are the views of UK health researchers who use data-intensive methods on their responsibilities to consider the environmental impacts of their research? Twenty-six interviews were conducted with UK health researchers using data-intensive methods. Participants expressed a desire to take responsibility for the environmental impacts of their research, however, they were unable to consolidate this because there were often obstacles that prevented them from taking such role responsibilities. They suggested strategies to address this, predominantly related to the need for regulation to monitor their own behaviour. This paper discusses the implications of adopting such a regulatory approach as a mechanism to promote researchers’ role responsibilities using a neo-liberal critique. [ABSTRACT FROM AUTHOR]

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]

Any tool, apparatus, machine, appliance, implant, reagent for in vitro usage, software, material, or other like or related item that the producer intends to be used, either alone or in combination, for medical purposes is considered a medical device. A thorough review was conducted for the purpose of revealing the medical device data and medical device industry in the current paper. Some of the data incorporated in this paper has been taken from Government Websites, Press Releases, Media Reports, Deloitte Report, Union Budget 2022-23 and dully citation was mentioned. [ABSTRACT FROM AUTHOR]

The paper explores how Generative AI intersects with Competition Law, focusing on Foundation Models (FMs) and Large Language Models (LLMs). It examines industry dynamics and identifies key competition issues like entry barriers, tying, leveraging, and acquisitions. It highlights the supply chain's importance and looks at how FMs are integrated into search software, chatbots, and productivity tools, particularly noting entry barriers such as computing power and data collection. It suggests that FMs might require new approaches to market delineation, possibly creating a separate relevant market for data. The paper also discusses various cases pertaining to tying and leveraging and highlights the difficulty in proving tying due to the blurred lines between traditional search engines and AI chatbots. It illustrates how competition assessments for acquisitions may require changes due to data being a highly flexible commodity for the industry. The paper concludes by calling for increased scrutiny and regulation for the industry. [ABSTRACT FROM AUTHOR]

Relating electoral laws to electoral integrity has long been a focus of academic research. Using the strategic-relational approach, as outlined in this special issue, it is possible to better understand how electoral laws shape the voter experience and electoral outcomes. This paper contributes to this understanding by looking at Voter Identification (ID) laws. With no consolidated dataset of voter ID laws existing outside the U.S.A. it is difficult to answer research questions put forward in this special issue, especially the second. This paper begins to address this shortfall by presenting the Comparative Voter ID Law (CVIL) index. Which has collected data on 246 individual electoral jurisdictions. Data presented show how voter ID laws are distributed globally, regionally, by regime type and level of democracy. The second part of the analysis goes on to describe voter ID laws by whether a jurisdiction has compulsory ID laws, how many different types of ID are accepted and the minimum number of ID documents that must be shown. Thirdly, other variables within the dataset are described. The CVIL will provide opportunities to understand how Voter ID laws are part of institutional design, are used by actors, shape the voter experience and electoral out-comes. [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 this paper I argue through the double entendre of ‘data‐bility’ that how dateable one is on a dating app relies on data. This techno‐social framework enables an understanding of how dating apps are reconfiguring a politics of sexuality, circumscribed by digital technologies and data. Drawing on research with middle‐class women and gender‐minority dating app users in Mumbai and one dating app executive, the paper investigates how algorithms and users' digital behaviour together constitute data‐bility in three ways. First, dating app algorithms are designed to match those of similar social identities to one another. Second, dating app users engage with others' digital data on profiles and through message chats, reading class through these processes, deciding who to match/reject and correspondingly who is data‐ble. Third, users and algorithmic infrastructures come together to create new regimes of verification, through deeming some users ‘real’ and others ‘fake’ on dating apps, extending violent legacies of categorisation. Together, these processes result in data‐bility, a techno‐social order of digital dating oriented around the exclusion of those labelled ‘creeps’ along class and caste lines. [ABSTRACT FROM AUTHOR]

Humanitarian organisations are increasingly utilising biometric data. However, we know little about the extent and scope of this practice, as its benefits and risks have attracted all the attention so far. This paper explores the biometric practices of the United Nations Refugee Agency, the United Nations World Food Programme, the International Committee of the Red Cross, Médecins Sans Frontières, and World Vision International. The study analysed relevant documents published over the past two decades and 17 semi‐structured interviews with humanitarian workers conducted between June 2021 and June 2022. The findings reveal that humanitarian organisations use diverse types and functions of biometric data for different services, collaborate with many actors, and employ various data protection measures. Ultimately, challenging the straightforward generalisations about the use of such data, the paper argues that variational applications of biometrics in the humanitarian context require case‐by‐case analysis, as each instance will likely produce a different outcome. [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]

New tools are being explored to provide collective and participatory means of governing data to promote the management of data in ways that benefit those from whom data is collected. This paper discusses whether data trusts are feasible structures in an African context by outlining specific considerations that should be prioritised in the development of bottom-up and collective models of data governance on the continent. Making use of international instruments, principles and established values like Ubuntu, the paper analyses the importance of collective decision-making through collective and participatory governance, women's empowerment, and capacity-building, and how the alignment of data trusts to African contexts could help balance historical power differentials, and emphasise heterogeneity as the starting point of all discussions in the digital age. [ABSTRACT FROM AUTHOR]

The current epoch in fisheries science has been driven by continual advances in laboratory techniques and increasingly sophisticated approaches to analysing datasets. We now have the scientific knowledge and tools to proactively identify obstacles to the sustainable management of marine resources. However, in addition to technological advances, there are predicted global environmental changes, each with inherent implications for fisheries. The 2023 symposium of the Fisheries Society of the British Isles called for "open and constructive knowledge exchange between scientists, stakeholders, managers and policymakers" (https://fsbi.org.uk/symposium-2023/), a nexus of collaborative groups best placed to identify issues and solutions. Arguably, the Centre of Environment, Aquaculture and Fisheries Science (Cefas) and their Scientific Advice for Fisheries Management (SAFM) Team sit at the centre of such a network. SAFM regularly engages with managers and stakeholders, undertakes scientific research, provides fisheries advice to the UK government, and are leading experts within the International Council for the Exploration of the Sea (ICES). As such, this paper is an opinion piece, linked to individual authors specialisms, that aims to highlight emerging issues affecting fisheries and suggest where research efforts could be focused that contribute to sustainable fisheries. [ABSTRACT FROM AUTHOR]

Over 10 million ED visits occur each year across Australia and Aotearoa New Zealand. Outside basic administrative data focused on time‐based targets, there is minimal information about clinical performance, quality of care, patient outcomes, or equity in emergency care. The lack of a timely, accurate or clinically useful data collection represents a missed opportunity to improve the care we deliver each day. The present paper outlines a proposal for a National Acute Care Secure Health Data Environment, including design, possible applications, and the steps taken to date by the Australasian College for Emergency Medicine ED Epidemiology Network in collaboration with the College of Emergency Nursing Australasia. Optimal use of the existing information collected routinely during clinical care of emergency patients has the potential to enable data‐driven quality improvement and research, leading to better care and better outcomes for millions of patients and families each year. [ABSTRACT FROM AUTHOR]

Data have become a vital device of border governance and security. Recent scholarship on the datafication of borders and migration at the intersection of science and technology studies and critical security studies has privileged concepts attuned to messiness, contingency, and friction such as data assemblages and infrastructures. This paper proposes to revisit and expand the analytical vocabulary of script analysis to understand what comes to count as data, what forms of data come to matter and how "drawing data together" reconfigures power and agency at Europe's borders. Empirically, we analyze controversies about the practices of asylum decision-making and age assessment in Greece. We show that agency of "users" is unequally distributed through anticipations of subscription and dis-inscription, while asylum seekers are conscripted within security scripts that restrict their agency. Moreover, as a multiplicity of inscriptions are produced, migrants' claims can be disqualified through circumscriptions of data and ascriptions of expertise. [ABSTRACT FROM AUTHOR]

This paper examines the Saudi Food and Drug Authority's (SFDA) Guidance on Artificial Intelligence (AI) and Machine Learning (ML) technologies based Medical Devices (the MDS-G010). The SFDA has pioneered binding requirements designed for manufacturers to obtain Medical Device Marketing Authorization. The regulation of AI in health is at an early stage worldwide. Therefore, it is critical to examine the scope and nature of the MDS-G010, its influences, and its future directions. It is argued that the guidance is a patchwork of existing international best practices concerning AI regulation, incorporates adapted forms of non-AI-based guidelines, and builds on existing legal requirements in the SFDA's existing regulatory architecture. There is particular congruence with the approaches of the US Food and Drug Administration (FDA) and the International Medical Device Regulators Forum (IMDRF), but the SFDA goes beyond those approaches to incorporate other best practices into its guidance. Additionally, the binding nature of the MDS-G010 is complex. There are binding 'components' within the guidance, but the incorporation of non-binding international best practices which are subordinate to national law results in a lack of clarity about how penalties for non-compliance will operate. [ABSTRACT FROM AUTHOR]

Just as the Frankfurt School responded to the radicalisation of the working class in Germany and the rise of post-war consumerism in the United States, today, we are confronted by platform monopolies, automated hyper-consumption and technological control. Critical approaches to digital media have exposed the structural coupling of Internet use and capital accumulation for almost two decades. However, many authors building on this tradition can struggle to understand how online social interaction is controlled beyond the worn-out critique of false consciousness or beyond conceptualising all digital activity mediated by data as labour. This paper will attempt to theoretically untangle the Marxian ontology of labour and the Frankfurt School-inspired critique of everyday life. This is not just theoretical nit-picking. Society becomes completely dominated if we accept no difference between wage labour and lifeworld activities. Each contains its internal struggles. The value form regulates both in different ways. [ABSTRACT FROM AUTHOR]

Most secondary (high) schools in a broad range of jurisdictions internationally engage in various forms of high stakes, standardized assessment and related qualifications. In this paper, we interrogate how educational achievement regimes – especially via the reporting of curriculum and assessment ‘data’ – continue to mobilize particular gender norms. Drawing on Derrida’s notion of haunting we explore how such regimes impose and reinscribe stable and binary gendered patterning and create what Barad has named ‘entangled relationalities of inheritance’ [https://doi.org/10.3366/drt.2010.0206,] despite young people (and many schools) moving towards greater recognition of non-binary genders. Drawing on assessment data from Aotearoa New Zealand, we look at both generalized reporting of educational achievement data along the lines of ‘male’ and ‘female’ and on reporting of a single (historically gendered) curriculum subject – health education. We argue that such systems are ‘haunted’ by stable gender categorizations and hierarchies and we ask what this means for the reporting of educational assessment data and the erasure of identities that don’t align with the binary. [ABSTRACT FROM AUTHOR]

This paper explores the mechanisms by which companies can gain access to data necessary for automated decision-making in scenarios without direct contractual agreements, focusing on market-driven approaches. It introduces the concept of the essential facilities doctrine under EU competition law and examines its applicability to sets of data, alongside an examination of current ex-ante regulatory instruments which grant data access rights, such as the Type Approval Regulation, the Open Data Directive, the Electricity Directive, the Digital Markets Act, and the Data Act. These legal instruments are analysed in terms of their ability to facilitate access to data necessary for the automation of decision-making processes. In addition, the study looks at the challenges and opportunities presented by these legal instruments, including the nuances of applying the essential facilities doctrine to data. The article concludes that the most efficient way for a company to gain access to sets of data required for automated decision-making (in the absence of a contractual agreement) is to base its data access claim on an act of ex-ante regulation. If, however, such legal basis does not exist, a company could still base its data access claim on the essential facilities doctrine. The practical applicability of the doctrine to sets of data, however, remains unclear. [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]

This paper outlines an example of Special Collections materials being used to support master's-level students' data and digital skills development. It evidences positive student learning outcomes through an unconventional interdepartmental collaboration between a digital media teaching team and Special Collections staff, which combined digital practices and tangible Special Collections material. This paper outlines the readings used for the course under review, the assignments used to evaluate students, student experiences of those assignments, and staff observations of student success and the logistics of course delivery. Findings show that in completing their assignments students (a) negotiated uncertainty; (b) increased understandings of data and digital methods; (c) embraced the lifeworlds of data; and (d) blurred the boundaries of analog and digital. More generally, this work finds that Special Collections material can be effectively used to support data and digital skills development, as well as help students gain broader understandings of digital contexts and media. Value is, however, dependent upon embracing uncertainty: something that universities tend to avoid. But, as is shown, staff and students alike can thrive in states of bewilderment. [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]

Prediction models are being increasingly used in the medical field to identify risk factors and possible outcomes. Some of these are presently being used to develop guidelines for improving clinical practice. The application of machine learning (ML), comprising a powerful set of computational tools for analysing data, has been clearly expanding in the role of predictive modelling. This paper reviews the latest developments of supervised ML techniques that have been used to analyse data related to post‐operative total hip and knee replacements. The aim was to review the most recent findings of relevant published studies by outlining the methodologies employed (most‐widely used supervised ML techniques), data sources, domains, limitations of predictive analytics and the quality of predictions. [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]

In this paper, the authors have used Block Chain technology for tie-line power transferred data in respect to trustworthy monitoring, non-tamper proof and traceability. Nepanagar–Dharni tie-line between MP Electricity Board and Maharashtra Electricity Board is considered. Also, an example of a solar power plant connected to the grid and a consumer is considered to show the security concern using Block Chain Technology. The intrinsic features of decentralized block chain technology enable the grid owner, utility and consumer to maintain the privacy and security of the data set and Image sharing of tie-line power transferred data and Image. The smart contract can register and authorize the grid corporation authority to access the tie-line power transferred during the period and make payments to the concern in compliance with the regulatory and involved parties' consent policy. Security's role as a trust model is used to sort out the discrepancy in the exchange for power monitored by the phase measurement unit. Distributed and scalable data models based on the block chain are implemented using a heterogeneous set of MYSQL database management systems, hosted on the AWS cloud. If the power is transferred to a tie line connected to the Railway traction system, the data and Image of the train and its status can be secured with block chain. [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]

We present an intriguing topic in a mathematical modelling course where Lanchester models are taught to our students. Lanchester models are some of the earliest and most important models used for combat modelling. We describe modelling activities and the use of technology that can be implemented in teaching this topic in this paper. [ABSTRACT FROM AUTHOR]

The adoption of blockchain technology can guarantee the safe and reliable storage of IoT data. In the agricultural products traceability system based on blockchain + Internet of Things, the storage method used in most existing schemes is to save the original data in the off-chain database system, but the real data cannot be agreed on the blockchain. However, if all the data in the production Internet of things are stored directly on the blockchain, it will cause great storage pressure. In order to balance the reliability and storage pressure of the IoT data stored on blockchain in the agricultural industry chain, a high-value density blockchain compression storage scheme for agricultural IoT data is proposed. This paper connects all production links of the agricultural product chain based on consortium blockchain and collects field data based on the Internet of Things composed of sensors. Firstly, a blockchain storage framework for agricultural Internet of Things data is proposed. The collected Internet of Things data is summarized through gateways or edge server devices before being stored in the blockchain. The value density of on-chain data is increased by compression method. An adaptive lossy compression method with outliers is designed for the data compression process. The agricultural IoT data is divided into normal values and outliers based on the outlier separation method. According to the time correlation between sensor data and the sparse of abnormal data, an adaptive compression algorithm for normal data and outlier data compression algorithm is designed to compress normal data and outlier data respectively. Compressed data are stored in the blockchain instead of original data, reducing storage space occupation; The application service platform can obtain the data on the blockchain and reconstruct the data that meets the accuracy need. The results of experiments show that when the proposed scheme is used for data compression, k values can be adaptively selected for different data sets, and good compression effects can be obtained, on-chain storage space can be reduced, and the value density of data can be improved, satisfying the reconstruction accuracy of users. This method has a good compression effect for data with low change frequency, such as temperature and humidity data, but fails to achieve a high compression effect for data with high change frequency such as wind speed data. In the scheme, storing data on the blockchain can solve the problem of traceability data caused by off-chain storage. In view of the storage redundancy caused by storing all the data on the blockchain, lossy compression method is adopted to reduce the data volume and improve the value density of on-chain data within the acceptable error range of the Internet of Things data provider. Experiments show that suppose the compression ratio of normal data is 10:1, this method saves about 85% of on-chain storage space and increases the corresponding data value density by about 85%. The scheme only stores data with a compression factor through the blockchain network, reducing the storage and management overhead of on-chain data. At the same time, the scheme has the characteristics of availability, data traceability and non-repudiation. Apart from being a mere technical solution, this research endeavor emerges as a robust response to the imperatives of secure and reliable IoT data storage within the agricultural product traceability landscape. Looking ahead to the agricultural product traceability scenario based on blockchain, this innovative solution calls for the possibility of harmonious integration with technologies such as sidechain and data offloading. This integration will further mitigate blockchain storage redundancy while ensuring data consensus on the blockchain. The ultimate goal is to ensure the transparency and traceability of the entire agricultural product data traceability process. With the development of the industry and the continuous progress of the technology field, it provides technical support for the realization of distributed agricultural Internet of Things data compression storage. [ABSTRACT FROM AUTHOR]

Since the 21st century, the digital economy has developed by leaps and bounds. As a key factor of production in the digital economy era, data plays an important role in China's green transformation. Based on the panel data of 282 prefecture-level cities in China from 2011 to 2019, this paper studies the relationship between data elements and green transformation. The results show that data elements can significantly promote green transformation by improving technical efficiency and achieving technological progress. Based on the above conclusions, all regions should speed up the infrastructure construction of digital economy, train digital talents, reduce resource dependence, improve the green, low-carbon and circular development system, formulate a digital economy development strategy considering local economic development levels, improve resource utilization efficiency and labor productivity, and actively promote green transformation. [ABSTRACT FROM AUTHOR]

In the past few decades, earth observation has experienced rapid development, which provides necessary theoretical knowledge, research means and methods for global sustainable development. By searching the Web of Science core collection database and using CiteSpace, a knowledge map tool, this study conducted a bibliometric analysis of sustainable development research based on earth observation from 1991 to 2021. This study analyzed the research progress based on the literature output trends, publishing regions and cooperation network, high-frequency terms and terms with the strongest citation bursts, and proposed future research directions. The results show that: ①The number of articles published in the field of "earth observation technology and sustainable development" is generally on the rise, which can be divided into germination stage, growth stage and rapid development stage. ②There is close international cooperation in the study of earth observation technology and sustainable development, among which, United States and Germany are in a leading position in terms of the number and influence of published paper. These countries are rich in scientific research. China and other developing countries have a dominant position in the number of publications, but their international influence still needs to be improved. ③At present, research focuses on the environmental protection related contents such as land use/land cover change, climate change, natural resources and water resources. In recent years, the research field has been constantly integrating artificial intelligence, mathematical statistics and other emerging methods, and the research content tends to be diversified. In the future research, it is necessary to fully achieve the supporting role of earth observation technology and contribute to the achievement of the Sustainable Development Goals through a Decade of Action, and to deepen the study of earth observation data and facilitate data sharing in order to open up a new path for wide application. The combination of earth observation data with traditional statistical data should be strengthened to achieve the fully functioning of the potential of earth observation in SDGs monitoring. We can focus on international cooperation and take global action. These can help us to provide effective strategic and decision reference for the realization of the Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

As a mesh-free approach, the incompressible Smoothed Particle Hydrodynamics (ISPH) method has been often used for simulating wave-structure interaction problems. In the conventional ISPH method, the pressure-projection phase of solving the pressure Poisson's equation (PPE) is the most time-consuming. In recent years, the machine learning (ML) techniques has gradually shown their potential in accelerating the computational fluid dynamics. In this paper, the graph neural network (GNN) supported ISPH method (ISPH_GNN), in which the GNN replaces solving the PPE for the fluid pressure in the conventional ISPH, is adopted for numerical simulations of wave-floater interactions. To the best of the authors' knowledge, this is the first work to study the wave-floater interactions by using GNN supported ISPH method. More importantly, this paper demonstrates that the GNN trained only on data for simpler wave-only cases (i.e. no structure in the wave fields) can be satisfactorily applied to the cases for wave-floater interactions. More specifically, the paper will show this by using the ISPH_GNN with such trained GNN model to simulate various different cases, including the decay tests of a box, a floating box subjected to a wave, the interaction between wave and a moored floating breakwater and the violent green water impact on a floating structure. In most of the cases, the numerical results are validated by comparing with experimental data. Agreement between them is surprisingly satisfactory, being as good as those obtained by the conventional ISPH. The paper will also show that the ISPH_GNN requires much less computational time (97 times less for the cases concerned) than the conventional ISPH for estimating pressure involved in wave-floater interactions. This reveals a great potential that one can train the GNN using the datasets for simpler cases and then use the ISPH_GNN to simulate wave-floater interaction problems. • The first work to study the wave-floater interactions by using the graph neural network (GNN) supported ISPH method. • The GNN trained only on data for simpler wave-only cases can be applied to more complex cases for wave-floater interactions. • The ISPH_GNN can give satisfactory results for the relatively complex cases involving wave-floater interactions. • The ISPH_GNN takes much less computation time than the conventional ISPH for estimating pressure. [ABSTRACT FROM AUTHOR]

In this article it will be concluded that systematic menstrual tracking in women’s sport has the potential to cause harm to athletes. Since the ruling of Dobbs v. Jackson Women’s Health Organization (2022) in the United States, concerns regarding menstrual health tracking have arisen. Research suggests that the menstrual tracking of female athletes presents potential risks to “women’s autonomy, privacy, and safety in sport” (Casto 2022, 1725). At present, the repercussions of systematic menstrual tracking are particularly under-scrutinized, and this paper seeks to combine novel research in the sport sciences with present ethical debates in the philosophy of sports. Utilizing Beauvoir’s feminist philosophy (2011), this paper argues that systematic menstrual tracking may contribute to the wider system of women’s oppression by exploiting female athletes, as well as enabling the internalization of submissive behaviour in cultures where athletes are expected to comply unquestioningly. Five policy recommendations are made concerning autonomy, informed consent, education, safeguarding and data access. The overall findings of this paper propose that a more in-depth understanding of the links between data, privacy, and the menstrual cycle are required by sports organizations and governing bodies if athletes are to be protected in a future where systematic menstrual tracking is inevitable. [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]

The way in which consumers engage with, utilise, or discard the technologies in their lives is constantly being reassessed and changed. This paper questions what role the emergent "right to repair" could play in resolving issues posed by the increasing ubiquity of the Internet of Things (IoT). The right gives consumers the ability and freedom to fix their devices, or to fair access to appropriate services that can carry out repair on their behalf. In this paper, firstly we establish the problem space surrounding consumer IoT – i.e., devices that are interconnected via the internet, enabling them to send and receive data. We reflect on hardware, software, and data components that pose legal and policy challenges for data protection, security, and sustainability. Through a literature review we then reflect on the current socio-legal developments that support or oppose changes in the consumer IoT market in regards to repair. We then highlight gaps in the existing literature that should inform future research trajectories in this area. This includes exploring disparities between environmental and consumer autonomy approaches, assessing consistency in regulatory developments, and market prioritisation. Finally, the paper concludes with a series of key insights and recommendations from our analysis including: recognition of the growing e-Waste problem and the inequalities it exacerbates and perpetuates; the need for identification and argumentation for different formulations of "repair" and how these may impact the implementation of a right going forward; the need for identification of the reasoning behind disparities in governmental approaches to the right to repair; and the need to practically translate better IoT design practices into reality. [ABSTRACT FROM AUTHOR]

The energy of Wireless Sensor Network (WSN) in edge network is limited. In order to save energy, data bundling is proposed to reduce the energy consumed in setting up connection links by joining several packets into a single unit. However, although current data bundling schemes can reduce energy consumption, they will increase the delay of data routing, so it makes the multi-hop end to end delay a lot in WSN. Also, there is no appropriate solution to tackle the delayed growing problem caused by data bundling. And the energy consumption of the Acknowledgment (ACK) transmission is not comprehensively considered either. Regarding to those issues of previous researches, in this paper, a Data and ACK Bundling Scheme (DAB) is proposed to aggregate several data or ACK packets into a single unit to reduce the energy consumption as well as the end to end delay of the WSN. In the proposed DAB scheme, not only multiple packets but also ACK bundling into a single unit can reduce energy consumption. More importantly, the parameters of bundling technology are optimized from a global perspective in DAB scheme, so that WSN can not only improve the network lifetime, but also reduce the end to end delay which is impossible in previous studies. The theoretical analysis and experimental results illustrate that, while ensuring the reliability of data collection, our DAB scheme improves the energy efficiency by 29.03 % and reduces the delay by 22.29 % when compared with our traditional data collection scheme. [ABSTRACT FROM AUTHOR]