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The AUD2IT-algorithm is a tool to structure the data, which is collected during an emergency treatment. The goal is on the one hand to structure the documentation of the data and on the other hand to give a standardised data structure for the report during handover of an emergency patient. AUD2IT-algorithm was developed to provide residents a documentation aid, which helps to structure the medical reports without getting lost in unimportant details or forgetting important information. The sequence of anamnesis, clinical examination, considering a differential diagnosis, technical diagnostics, interpretation and therapy is rather an academic classification than a description of the real workflow. In a real setting, most of these steps take place simultaneously. Therefore, the application of the AUD2IT-algorithm should also be carried out according to the real processes. A big advantage of the AUD2IT-algorithm is that it can be used as a structure for the entire treatment process and also is entirely usable as a handover protocol within this process to make sure, that the existing state of knowledge is ensured at each point of a team-timeout. PR-E-(AUD2IT)-algorithm makes it possible to document a treatment process that, in principle, does not have to be limited to the field of emergency medicine. Also, in the outpatient treatment the PR-E-(AUD2IT)-algorithm could be used and further developed. One example could be the preparation and allocation of needed resources at the general practitioner. The algorithm is a standardised tool that can be used by healthcare professionals of any level of training. It gives the user a sense of security in their daily work.

This paper proposes an online learning platform learner DM method based on the improved fuzzy C clustering (FCM) algorithm, constructs a learner feature database, and combines clustering analysis and SPSS statistical methods to statistically summarize the big data of law, thus improving the deficiencies of static and absolute classification of students in the student model. In the experiment paper, the improved algorithm is implemented and the experimental data is analyzed. The results show that the learner behavior feature extraction model in this paper has fewer errors and higher recall rate. Compared with the traditional CF algorithm, the error rate is reduced by 19.64% and the recall rate is increased by 22.85%. This study provides better targeted teaching programs and case resources for legal case teaching and promotes the innovation of legal case teaching mode.

Education is seen as a resource at a global level but is currently considered to be in crisis in many parts of the world. This constitutes a significant drawback in terms of humanity's prosperity and well-being since education is the key not only to an educated workforce but also to humane, collaborative, and caring societies. Even within this dim landscape, there are certain educational systems that defy the odds and perform significantly higher than their otherwise comparable systems. This paper proposes using an unusual lens for educational policy comparative studies, that of positive deviance, to aid us in progressing towards a more stable educational state of affairs. Using a positive deviance methodology, which focuses on learning what is working well in systems that defy and overcome substantial challenges, this study investigates the patterns, attitudes, and actions of three selected cases: Massachusetts as a positive deviant in the US, Estonia as a positive deviant in Europe, and Castile-Leon as a positive deviant in Spain. The purpose is, by analysing educational policies, laws, and other related documents, to find commonalities that explain why these systems outperform others. The results of the comparative analysis pinpoint areas and strategies informative to those leading struggling educational systems, such as a strong commitment to equity and justice, placing teachers at the centre of reforms, using assessment as a tool for process monitoring and summative inquiry, and making preschool education accessible to all.

Chinese students studying abroad have been increasing rapidly in the past decades and become a significant financial contribution to receiving countries. Accordingly, understanding their enrollment choice is essential to facilitate college marketing and admission strategies. Though the decision process is believed to be different from domestic students, empirical analysis of Chinese students' enrollment choices is still lacking. This paper fills the void by examining the influential factors of Chinese students' enrollment choice with novel student-level data. We find that in addition to factors domestic students typically consider, such as financial aid and academic quality, Chinese students particularly emphasize college ranking, reputation, and location in their decision process. Furthermore, unlike domestic students who usually prefer colleges with proximity to home, Chinese students' location preference is linked to job prosperity. We also find that the impact of the factors varies for students from different regions of China, which can be attributable to uneven economic development within the country.

OBE concept is a new teaching mode which emphasizes the improvement of students' subjective initiative and professional practice ability. The teaching of animation course is based on drawing and computer, which requires teachers to understand the OBE mode of animation course, carry out targeted teaching innovation of animation course, and adjust the traditional teaching methods, teaching contents and teaching assessment methods. Based on the MOOC platform from the perspective of big data, this paper analyzes the teaching status and innovation process of animation course, and puts forward a hybrid animation course teaching method. Through the research of 686 primary and secondary school teachers, the results show that the hybrid animation course teaching based on OBE and MOOC from the perspective of big data has a better effect than the traditional teaching method, which improves students' initiative in learning animation courses and greatly enhances students' acceptability in learning animation courses.

The level of information technology is increasing, and technology is developed. University English teaching has also changed under its influence. Different from the traditional teaching in the past, more and more students adopt the mode of "Internet + Smartphone" to learn English. This paper proposes a teaching mode evaluation method in the context of big data. Through the K-means algorithm based on it, the data clustering of business English teaching mode is completed. According to the data obtained from clustering, combined with the standardization of positive and negative indicators, the business English teaching mode system is constructed. Experiments show that when the clustering K value is 16, the clustering accuracy of this method is 99.2%, and it can effectively get the index weights, and the evaluation time is short, which is better than the other two comparison methods.

India has produced several drafts of data policies. In this work, they are referred to [1] JBNSCR 2018, [2] DPDPR 2018, [3] NSAI 2018, [4] RAITF 2018, [5] PDPB 2019, [6] PRAI 2021, [7] JPCR 2021, [8] IDAUP 2022, [9] IDABNUP 2022. All of them consider Artificial Intelligence (AI) a social problem solver at the societal level, let alone an incentive for economic growth. However, these policy drafts warn of the social disruptions caused by algorithms and encourage the careful use of computational technologies in various social contexts. Hence, the emerging data society and its implications in India's social contexts demand immense social science attention, which needs to be improved in the policy drafts, primarily because they are creations of industry stakeholders, technocrats, bureaucrats, and experts from tech schools. In the larger social milieu of digital infrastructure emerging, the fundamental question is whether India's national philosophy envisioned in the Indian constitution is reflected in the policy papers. The paper enquires whether the national data policy upholds the core values dispersed through the philosophy of the Indian constitution, which, among other things, is not confined only to inclusion, diversity, rights, liberty, justice and equality. By focusing on constitutional values, the paper seeks to offer a broader and more critical understanding of India's approach to AI policy by bringing together analyses of a wide array of policy documents available in the public realm. [ABSTRACT FROM AUTHOR]

This paper outlines an initial analysis of 20 years of data held on an electronic bridge management database for approximately 3500 arch bridges across Northern Ireland (NI) by the Department for Infrastructure. Arch bridges represent the largest group of bridge types, making up nearly 56% of the total bridge stock in NI. This initial analysis aims to identify trends that might help inform maintenance decisions in the future. Consideration of the Bridge Condition Indicator (BCI) average value for the overall arch bridge stock indicates the potential for regional variations in the overall condition and the potential for human bias in inspections. The paper presents the most prevalent structural elements and associated defects recorded in the inspections of arch bridges. This indicated a link to scour and undermining for the worst-conditioned arch bridges. An Analysis of Variance (ANOVA) analysis identified function, number of spans, and deck width as significant factors during the various deterioration stages in a bridge's lifecycle. [ABSTRACT FROM AUTHOR]

This paper focuses on the importance of equality, diversity, and inclusion (EDI) in the aquaculture industry, with a particular emphasis on the sector in Scotland. Aquaculture is a particularly important industry when it comes to EDI, given its potential to address Sustainable Development Goals on gender equality and diversity. The paper highlights the increasing attention being paid to EDI in many areas and the significant benefits to businesses that adopt EDI policies, including improved reputation, increased innovation, and greater profitability. This paper draws on a survey of EDI in Scottish aquaculture, a workshop and interviews with industry experts to suggest concrete actions that could improve EDI in the sector. A key priority is the collection and publication of workforce data in Scottish aquaculture as well as industry champions who raise awarenes and promote EDI, and supporting cross-sector organizations who provide EDI training. We conclude by calling for more research to support the development of EDI in Scottish aquaculture, which will contribute to future resilience and fairness as well as a dynamic, relevant, and accessible industry. [ABSTRACT FROM AUTHOR]

Through an ethnographic study of Chinese IT professionals who integrate a form of data culture into the digital platforms they design, maintain, and operate daily within one of China's tech giants, this paper reveals numerous overlaps and interrelations between the data practices of Chinese IT professionals and the broader social implications that arise from them. The aim is to foster a more productive dialogue between the social studies of quantification and platform studies. This original research proposes the backstage as a potent methodology for inquiring into the role of Chinese IT professionals and domestic tech giants in advancing measuring systems and audit culture. This paper concludes by suggesting that such an approach can also be applied to wider studies of the paradox in quantification between its general claims and specific effects. [ABSTRACT FROM AUTHOR]

This article discusses the potential of new environmental data to improve the governance and sustainability of cities. It highlights the use of distributed sensors and real-time data to monitor and manage urban systems, inform policy and planning, and address urban challenges. The article also explores the practicalities, politics, and governance of using environmental data in cities. The document includes eight papers that cover topics such as urban observatories, governance issues, and public perceptions of real-time data. The authors emphasize the need for interdisciplinary collaboration and new modes of governance to fully realize the benefits of these technologies in urban settings. [Extracted from the article]

The purpose of this paper is to address the extraction of entities and relationships from unstructured Chinese text, with a particular emphasis on the challenges of Named Entity Recognition (NER) and Relation Extraction (RE). This will be achieved by integrating external lexical information and utilizing the abundant semantic information available in Chinese. We utilize a pipeline model that is applied separately to NER and RE by introducing an innovative NER model that integrates Chinese pinyin, characters, and words to enhance recognition capabilities. Simultaneously, we incorporate information such as entity distance, sentence length, and part-of-speech to improve the performance of relation extraction. We also delve into the interactions among data, models, and inference algorithms to improve learning efficiency in addressing this challenge. In comparison to existing methods, our model has achieved significant results. [ABSTRACT FROM AUTHOR]

Building on work which has shown the role of digital technologies in reframing environmental relations, this paper explores ethnographically how environmental data is reconfiguring the concept of place. The paper takes as its focus an action-research project within a UK based, citizen-oriented initiative called Newtown Energy Futures, in which we sought to enfold climate and energy data into a social-justice informed attempt at climate action. By exploring how the project used data as an invitation for citizens to engage with and participate in local infrastructural and environmental dynamics, the paper sheds light on how environmental data came to participate in the making of place and in doing so raised questions about how to rebuild the socio-material relations through which 'a sense of place' might be reproduced. As climate and energy data increasingly demand that places become enrolled into environmental projects our findings suggest that data enables place to emerge as a 'socio-technical potentiality' an observation that has implications both for both engagement with, and the study of data and place. In practical terms, we suggest that this refiguration of place has the effect of creating hopeful trajectories for change, whilst also posing difficult questions about the limits of participation in a data-infused form of place-based politics. [ABSTRACT FROM AUTHOR]

The subject of this article is the terminology concerning all that is digital and intangible. In the past decades, many similar notions describing the essentially same phenomenon appeared - virtual property, digital content, digital assets, or data. However, most of these terms are not codified legal notions and as a result, their understanding in the literature or soft law instruments differs. Various bodies, including UNIDROIT, ELI, and the UK Law Commission, have recently addressed the topic of digital assets and private law and provided their definitions of the notion. Moreover, with the new EU digital legal framework, we may see attempts to codify notions such as data, digital content, or cryptoassets. The aim of this paper is to analyse the understanding of the notion of digital assets, put it into a broader context and identify how this notion overlaps with others. The main argument is that the difference between the notions is unclear which puts the policymakers into a tough position in which it is difficult to avoid overlapping regulation. [ABSTRACT FROM AUTHOR]

This paper will discuss Data, Information, Knowledge, and Wisdom, which is commonly referred to as DIKW. The DIKW Pyramid Model is a hierarchical model that is often referenced in both academic and practitioner circles. This model will be discussed and shown to be faulty on several levels, including a lack of definitional agreement. A new DIKW framework with systems orientation will be proposed that focuses on what the DIKW elements do in the way humans think, not what they are by definition. Information as a replacement for wasted physical resources in goal-oriented tasks will be a central organizing point. The paper will move the DIKW discussion to the computer-based concept of Digital Twins (DTs) and its augmentation of how we can use DIKW to be more effective and efficient. This will especially be the case as we move toward Intelligent Digital Twins (IDTs) with Artificial Intelligence (AI). [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]

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]

Multiple sclerosis (MS) is a devastating immune-mediated disorder of the central nervous system resulting in progressive disability accumulation. As there is no cure available yet for MS, the primary therapeutic objective is to reduce relapses and to slow down disability progression as early as possible during the disease to maintain and/or improve health-related quality of life. However, optimizing treatment for people with MS (pwMS) is complex and challenging due to the many factors involved and in particular, the high degree of clinical and subclinical heterogeneity in disease progression among pwMS. In this paper, we discuss these many different challenges complicating treatment optimization for pwMS as well as how a shift towards a more pro-active, data-driven and personalized medicine approach could potentially improve patient outcomes for pwMS. We describe how the ‘Clinical Impact through AI-assisted MS Care’ (CLAIMS) project serves as a recent example of how to realize such a shift towards personalized treatment optimization for pwMS through the development of a platform that offers a holistic view of all relevant patient data and biomarkers, and then using this data to enable AI-supported prognostic modelling. [ABSTRACT FROM AUTHOR]

Monitoring microclimate variables within cities with high resolution and accuracy is crucial for enhancing urban resilience to climate change. Assessing intra-urban characteristics is essential for ensuring satisfactory living standards. This paper presents a comprehensive methodology for studying urban heat islands (UHIs) on a university campus, emphasizing the importance of multi-modal and multi-temporal data collection. The methodology integrates mobile surveys, stationary sensor networks, and drone-based thermal imaging, providing a detailed analysis of temperature variations within urban microenvironments. The preliminary findings confirm the presence of a UHI on the campus and identify several hotspots. This comprehensive approach enhances the accuracy and reliability of UHI assessments, offering a cost-effective, fine-resolution approach that facilitates more effective urban planning and heat mitigation strategies. [ABSTRACT FROM AUTHOR]

The sensitivity and exclusivity attached to personal health records make such records a prime target for cyber intruders, as unauthorized access causes unfathomable repudiation and public defamation. In reality, most medical records are micro-managed by different healthcare providers, exposing them to various security issues, especially unauthorized third-party access. Over time, substantial progress has been made in preventing unauthorized access to this critical and highly classified information. This review investigated the mainstream security challenges associated with the transmissibility of medical records, the evolutionary security strategies for maintaining confidentiality, and the existential enablers of trustworthy and transparent authorization and authentication before data transmission can be carried out. The review adopted the PRSIMA-SPIDER methodology for a systematic review of 122 articles, comprising 9 surveys (7.37%) for qualitative analysis, 109 technical papers (89.34%), and 4 online reports (3.27%) for quantitative studies. The review outcome indicates that the sensitivity and confidentiality of a highly classified document, such as a medical record, demand unabridged authorization by the owner, unquestionable preservation by the host, untainted transparency in transmission, unbiased traceability, and ubiquitous security, which blockchain technology guarantees, although at the infancy stage. Therefore, developing blockchain-assisted frameworks for digital medical record preservation and addressing inherent technological hitches in blockchain will further accelerate transparent and trustworthy preservation, user authorization, and authentication of medical records before they are transmitted by the host for third-party access. [ABSTRACT FROM AUTHOR]

This paper is concerned with the spreading properties for a reaction-diffusion equation with free boundary condition. We obtained a complete description of the long-time dynamical behavior of this problem. By introducing a parameter σ in the initial data, we revealed a threshold value σ ∗ such that spreading happens when σ > σ ∗ and vanishing happens when σ ≤ σ ∗ . There exists a unique L ∗ > 0 independent of the initial data such that σ ∗ = 0 if and only if the length of initial occupying interval is no smaller than 2 L ∗ . These theoretical results may have important implications for prediction and prevention of biological invasions. This paper is concerned with the spreading properties for a reaction-diffusion equation with free boundary condition. We obtained a complete description of the long-time dynamical behavior of this problem. By introducing a parameter in the initial data, we revealed a threshold value such that spreading happens when and vanishing happens when . There exists a unique independent of the initial data such that if and only if the length of initial occupying interval is no smaller than . These theoretical results may have important implications for prediction and prevention of biological invasions. [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]

There is an increasing desire to study neurodevelopmental disorders (NDDs) together to understand commonalities to develop generic health promotion strategies and improve clinical treatment. Common data elements (CDEs) collected across studies involving children with NDDs afford an opportunity to answer clinically meaningful questions. We undertook a retrospective, secondary analysis of data pertaining to sleep in children with different NDDs collected through various research studies. The objective of this paper is to share lessons learned for data management, collation, and harmonization from a sleep study in children within and across NDDs from large, collaborative research networks in the Ontario Brain Institute (OBI). Three collaborative research networks contributed demographic data and data pertaining to sleep, internalizing symptoms, health-related quality of life, and severity of disorder for children with six different NDDs: autism spectrum disorder; attention deficit/hyperactivity disorder; obsessive compulsive disorder; intellectual disability; cerebral palsy; and epilepsy. Procedures for data harmonization, derivations, and merging were shared and examples pertaining to severity of disorder and sleep disturbances were described in detail. Important lessons emerged from data harmonizing procedures: prioritizing the collection of CDEs to ensure data completeness; ensuring unprocessed data are uploaded for harmonization in order to facilitate timely analytic procedures; the value of maintaining variable naming that is consistent with data dictionaries at time of project validation; and the value of regular meetings with the research networks to discuss and overcome challenges with data harmonization. Buy-in from all research networks involved at study inception and oversight from a centralized infrastructure (OBI) identified the importance of collaboration to collect CDEs and facilitate data harmonization to improve outcomes for children with NDDs. [ABSTRACT FROM AUTHOR]

The original vision on what later became LifeWatch ERIC started about a quarter of a century ago in 1996. In those days, the promise of digital technologies entered biodiversity and ecosystem research. Not only by digitiing relevant information, but also with applications to process such data. While several (inter)national initiatives embarked on specific topics, there was also an idea that the upcoming view on grid computing provided attractive solutions for federated data sources, together with a strong computing capacity. This paper presents the history from conception to early actions, until actual preparations towards a research infrastructure on the European scale. [ABSTRACT FROM AUTHOR]

Argo floats have been deployed in the global ocean for over 20 years. The Core mission of the Argo program (Core Argo) has contributed well over 2 million profiles of salinity and temperature of the upper 2000 m of the water column for a variety of operational and scientific applications. Core Argo floats have evolved such that the program currently consists of more than eight types of Core Argo float, some of which belong to second or third generation developments, three unique satellite communication systems (Argos, Iridium and Beidou) and two types of Conductivity, Temperature and Depth (CTD) sensor systems (Seabird and RBR). This, together with a well-established data management system, delayed mode data quality control, FAIR and open data access, make the program a very successful ocean observing network. Here we present Part 1 of the Best Practices for Core Argo floats in terms of how users can get started in the program, recommended metadata parameters and the data management system. The objective is to encourage new and developing scientists, research teams and institutions to contribute to the OneArgo Program, specifically to the Core Argo mission. Only by leveraging sustained contributions from current Core Argo float groups with new and emerging Argo teams and users who are eager to get involved and are actively encouraged to do so, can the OneArgo initiative be realized. This paper presents a list of best practices to get started in the program, set up the recommended metadata, implement the data management system with the aim to encourage new scientists, countries and research teams to contribute to the OneArgo Program. [ABSTRACT FROM AUTHOR]

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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]

Drilling vibrations significantly impact drilling operations with high costs due to early downhole equipment failure and loss of productive time. Stick-slip vibrations, a severe form of torsional vibrations, is known to be present up to 50% of total drilling time, making it a topic of immense concern and research. An ongoing discussion in the industry is regarding the reliability of surface measurements for early detection of severe downhole bit sticking. Moreover, most surface measurements are sampled at lower frequency rates closer to 1 Hz. Recently, the implementation of advanced data acquisition modules in downhole subs has greatly improved our understanding of drilling vibrations through high resolution data, sampled up to 10 kHz. However, with a wide range of sampling frequency to choose from different available tools, a critical question remains unanswered. What is an optimal and adequate sampling frequency for early detection of downhole vibrations using both surface and downhole measurements? The paper addresses the question with a focus on stick-slip vibrations through an experimental investigation. Stick slip tests are repeated for different sampling frequencies of surface and downhole measurements and the stick slip index for each case is calculated. The stick-slip index varies for different sampling frequency even though the vibration tests remain completely identical. It was inferred that sampling frequency of measurements greatly impact the detection of downhole vibrations. Even though stick-slip vibrations are characteristically low frequency vibrations (-2Hz), a minimum of 10Hz sampling frequency is recommended for detection of stick-slip vibrations. Moreover, all characteristics of stick-slip vibrations including bit sticking, bit RPM peaks and negative bit RPMs are clearly observed at a minimum of 100Hz sampling rate. [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]

The proposal of Industry 5.0 has made sustainability, human-centric and resilience the core of digital manufacturing, which also puts forward new requirements for the human-machine interaction (HMI) paradigm in human-centric smart manufacturing (HCSM). In the manufacturing scenario, the process of HMI can be divided into four parts: 1) Sensors and hardware, where the environment information and input signals are collected, 2) Data processing, where the signals are converted into data, 3) Transmission mechanism, where the data is transmitted to the processing centre, and 4) Interaction and collaboration. Among them, sensors and data are expected to become breakthrough points in optimising HMI. This is not only due to the emergence of new research, innovation and technologies but also because they are closely influenced by the new design concepts brought about by Industry 5.0. This paper analyses the latest studies and technologies in the sensor field and their possible applications in HCSM scenarios. Then, opportunities and challenges of data analysis in the HMI in Industry 5.0 are discussed. Finally, based on the design concepts and requirements of Industry 5.0, this paper demonstrates how they will become the key points for future HMI development. [ABSTRACT FROM AUTHOR]

This work develops a new method for vector data augmentation. The proposed method applies principal component analysis (PCA), determines the eigenvectors of a set of training vectors for a machine learning (ML) method and uses them to generate the distilled vectors. The training and PCA-distilled vectors have the same dimension. The user chooses the number of vectors to be distilled and augmented to the set of training vectors. A statistical approach determines the lowest number of vectors to be distilled such that when augmented to the original vectors, the extended set trains an ML classifier to achieve a required accuracy. Hence, the novelty of this study is the distillation of vectors with the PCA method and their use to augment the original set of vectors. The advantage that comes from the novelty is that it increases the statistics of ML classifiers. To validate the advantage, we conducted experiments with four public databases and applied four classifiers: a neural network, logistic regression and support vector machine with linear and polynomial kernels. For the purpose of augmentation, we conducted several distillations, including nested distillation (double distillation). The latter notion means that new vectors were distilled from already distilled vectors. We trained the classifiers with three sets of vectors: the original vectors, original vectors augmented with vectors distilled by PCA and original vectors augmented with distilled PCA vectors and double distilled by PCA vectors. The experimental results are presented in the paper, and they confirm the advantage of the PCA-distilled vectors increasing the classification statistics of ML methods if the distilled vectors augment the original training vectors. [ABSTRACT FROM AUTHOR]

This paper presents aspects concerning the e-learning courses for the Romanian textile sector in the context of digital transition. In the framework of the ADDTEX Erasmus+ project, INCDTP developed three interactive lessons covering digital aspects of the textile industry. The proposed courses for digital transition cover aspects related to data acquisition, visualization, and analytics-based descriptive, diagnostic, predictive, prescriptive, augmented, and real-time analytics, including mathematical and business modelling by artificial intelligence. The proposed courses gradually introduce the learner to digital knowledge, starting from basic principles necessary for technicians, graduates, engineers, professionals, managers, and mentors. In order to achieve the objectives of the ADDTEX project, the developed courses and certification tools for different specialization levels cover digital aspects that could support innovation in the development of new products based on mathematical models and business modelling, promotion of entrepreneurship and improving the quality and relevance of employers’ skills. In addition, data visualization of historical data record stored in data warehouse for analysis and data visualization using specific tools (scatter, combo, list or stacked plots) help in quick visualization of the aspects related to performance and improves the business outcomes. The courses related to digital transition are available on the Addtex project website and offer learners certificates for completing the courses after the courses after completing the course modules and the electronic tests assigned to each module. [ABSTRACT FROM AUTHOR]

In this paper, we studied the dynamical behavior of various phases of breast cancer using the Caputo Fabrizio (CF) fractional order derivative operator. The Picard-Lindelof (PL) method was used to investigate the existence and uniqueness of the proposed system. Moreover, we investigated the stability of the system in the sense of Ulam Hyers (UH) criteria. In addition, the two-step Adams-Bashforth (AB) technique was employed to simulate our methodology. The fractional model was then simulated using real data, which includes reported breast cancer incidences among females of Saudi Arabia from 2004 to 2016. The real data was used to determine the values of the parameters that were fitted using the least squares method. Also, residuals were computed for the integer as well as fractional-order models. Based on the results obtained, the CF model's efficacy rates were greater than those of the existing classical model. Graphical representations were used to illustrate numerical results by examining different choices of fractional order parameters, then the dynamical behavior of several phases of breast cancer was quantified to show how fractional order affects breast cancer behavior and how chemotherapy rate affects breast cancer behavior. We provided graphical results for a breast cancer model with effective parameters, resulting in fewer future incidences in the population of phases Ⅲ and Ⅳ as well as the disease-free state. Chemotherapy often raises the risk of cardiotoxicity, and our proposed model output reflected this. The goal of this study was to reduce the incidence of cardiotoxicity in chemotherapy patients while also increasing the pace of patient recovery. This research has the potential to significantly improve outcomes of patients and provide information of treatment strategies for breast cancer patients. [ABSTRACT FROM AUTHOR]

The pandemic has exacerbated the effects of the digital transformation: the extractive economy is steadily giving way to the new economic space—the digital economy. This transformation shakes the very foundations of the existence and purpose of law, i.e., the regulation of social relations. However, today, the consequences of developing tech in an unsustainable manner are becoming obvious. Despite the internet's many benefits, it also erodes trust and fuels misinformation, polarization, and inequality. These developments occur partly because the algorithms that shape our economies, society, and even public discourse were developed with few legal restrictions or commonly held ethical standards. It is becoming increasingly obvious that the technologies that currently shape our socio-economic relations must be consistent with both our shared norms and values and the existing rules. The main question of this study is how to correctly introduce tech that is legal by design, but also, and especially, liable and sustainable by design. The underlying questions are hence, firstly: to this end, do we need (to create) a whole new body of norms, law, and regulation? Alternatively, are there already legal fields, concepts, and tools that can help us lay comprehensive groundwork for tech that is liable and sustainable by design? The central object of this study is to address this problem with regard to the types of organization that is in any way involved in or at least related to tech and innovation, essentially, the Web 3-4 actors. My principal method is systems analysis, which engages with a system as a whole. The construct of regulatory compliant and sustainable tech is thus analysed both functionally and institutionally, with concepts including norm-setting and law-making, formal application and enforcement, case law, real-world effects, and limitations. The objective of the article is to first synthesize the pre-existing legal and regulatory fields and constructs, and then analyse in a succinct yet systematic manner the conditions for their applicability to, and efficiency for, regulation of Web 3.0 (and soon, Web 4.0), as well as their limits. In the course of the study, I found that there are a few pre-existing legal fields, concepts, and tools that can pave the way to creating a Web that is liable and sustainable by design. I have also identified two key developments that arise from the digital transformation: (i) the digital economic space creates the so-called governance and regulatory gaps; and (ii) some of these gaps are rapidly filled (at times, successfully and at times, less so) by a burgeoning newest legal framework (national and supranational targeted regulation, legislation, and case law), which has been growing especially rapidly since the global digital 'leap' facilitated by the COVID-19 pandemic in the beginning of the 2020s. To conclude, the article summarizes both pre-existing and new tools and thus offers a ready-to-use toolkit for a regulatory compliant and sustainable tech (including a table summarizing the toolkit), which is the key aim of this paper. [ABSTRACT FROM AUTHOR]

Background/Objectives: Metrics drive diagnosis, and metrics will also drive our response to the challenge of climate change. Recognising how current scientific research defines and uses metrics of the environmental impact of human diets is essential to understand which foods, food groups, or dietary patterns are associated with a higher environmental impact. Methods: This research, aided by artificial intelligence (AI), aimed to search, map, and synthesise current evidence on the commonly used definitions and metrics of the environmental impacts of human diets. Results: We identified 466 studies measuring the environmental impact of diets. Most studies were from North American or European countries (67%), with data mainly from high-income countries (81%). Most studies did not include methods to recall the provenance of the foods consumed. Most (53%) of the studies only used one metric to estimate the environmental impact of human diets, with 82% of the studies using GHGE. Conclusions: Agreement on how the environmental impact of diets is measured and more comprehensive and accurate data on the environmental impact of single foods is essential to better understand what changes in food systems are needed, at a consumer and policy level, to make a well-meaning change towards a more sustainable diet. [ABSTRACT FROM AUTHOR]

We run a field experiment to study the relative performance of human curation and automated personalized recommendation technology in the context of online news. We build a simple theoretical model that captures the relative efficacy of personalized algorithmic recommendations and curation based on human expertise. We highlight a critical tension between detailed, yet potentially narrow, information available to the algorithm versus broad (often private), but not scalable, information available to the human editor. Empirically, we show that, on average, algorithmic recommendations can outperform human curation with respect to clicks, but there is significant heterogeneity in this treatment effect. The human editor performs relatively better in the absence of sufficient personal data and when there is greater variation in preferences. These results suggest that reverting to human curation can mitigate the drawbacks of personalized algorithmic recommendations. Our computations show that the optimal combination of human curation and automated recommendation technology can lead to an increase of up to 13% in clicks. In absolute terms, we provide thresholds for when the estimated gains are larger than our estimate of implementation costs. This paper was accepted by Chris Forman, information systems. Funding: C. Peukert acknowledges funding from the Swiss National Science Foundation [Grant No. 100013_197807]. Supplemental Material: The e-companion and data files are available at https://doi.org/10.1287/mnsc.2023.4954. [ABSTRACT FROM AUTHOR]

As firms accumulate more data, users' data-sharing decisions may polarize. Some users may share all data, whereas others may share no data, becoming "digital hermits." When users share multidimensional data about themselves with a firm, the firm learns about the correlations between different dimensions of user data. We incorporate this type of learning into a model of a data market in which a firm acquires data from users with privacy concerns. Each user can share no data, only nonsensitive data, or their full data with the firm. As the firm collects more data and becomes better at drawing inferences about a user's privacy-sensitive data from their nonsensitive data, the share of new users who share no data ("digital hermits") grows. This growth of digital hermits occurs even though the firm offers higher compensation for a user's nonsensitive data and a user's full data as its ability to draw inferences improves. At the same time, the share of new users who share their full data also grows. The model thus predicts a polarization of users' data-sharing choices away from nonsensitive data sharing to no sharing and full sharing. Our model suggests that recent privacy policies, which are focused on control of data rather than inferences, may be misplaced. History: Anthony Dukes served as the senior editor. This paper was accepted through the Marketing Science: Frontiers review process. Funding: Partial financial support was received from the Social Sciences and Humanities Research Council of Canada [Grant 435-2023-0492]. [ABSTRACT FROM AUTHOR]

The interaction of wood and moisture has to be considered in many industrial sectors. Wood is highly hygroscopic material while the absorbed moisture affects all its technical properties. One of them is a moisture permeability which is further affected by the sorption hysteresis and also differs in the three wood anatomical directions – radial, tangential, and axial. For the prediction of the dynamic hygro-thermal behaviour of wood can be used numerical simulation tools. However, data from carefully designed and controlled experiments are needed for reliable validation of these tools. This paper presents data from a 45-day dynamic laboratory experiment. The one-dimensional moisture transport in spruce wood in the tangential and radial directions under isothermal conditions was studied. The samples were exposed to cyclic step-changes in relative humidity 72–95 % at 23 °C. Data show the rate of stabilisation of moisture content in the samples, the effect of sorption hysteresis, and changes in the temperature of samples due to moisture sorption. In addition, the paper also presents material functions describing the sorption properties and moisture permeability of spruce wood. These properties were determined based on laboratory measurements using the spruce wood of the same origin as used for the dynamic experiment. The dynamic data, together with the proposed material functions can be used in the development or verification of hygro-thermal numerical simulation tools.

This paper will discuss Data, Information, Knowledge, and Wisdom, which is commonly referred to as DIKW. The DIKW Pyramid Model is a hierarchical model that is often referenced in both academic and practitioner circles. This model will be discussed and shown to be faulty on several levels, including a lack of definitional agreement. A new DIKW framework with systems orientation will be proposed that focuses on what the DIKW elements do in the way humans think, not what they are by definition. Information as a replacement for wasted physical resources in goal-oriented tasks will be a central organizing point. The paper will move the DIKW discussion to the computer-based concept of Digital Twins (DTs) and its augmentation of how we can use DIKW to be more effective and efficient. This will especially be the case as we move toward Intelligent Digital Twins (IDTs) with Artificial Intelligence (AI).

Abstract Through an ethnographic study of Chinese IT professionals who integrate a form of data culture into the digital platforms they design, maintain, and operate daily within one of China’s tech giants, this paper reveals numerous overlaps and interrelations between the data practices of Chinese IT professionals and the broader social implications that arise from them. The aim is to foster a more productive dialogue between the social studies of quantification and platform studies. This original research proposes the backstage as a potent methodology for inquiring into the role of Chinese IT professionals and domestic tech giants in advancing measuring systems and audit culture. This paper concludes by suggesting that such an approach can also be applied to wider studies of the paradox in quantification between its general claims and specific effects.

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]

The recently released draft South African Ethics in Health Research Guidelines: Principles, Processes and Structures (Draft Guidelines) by the National Health Research Ethics Council recognize open data and provide guiding principles for this in the context of health research in South Africa. While its inclusion is a positive development, there is room for improvement. Although the Draft Guidelines leverage the Draft National Policy on Data and Cloud, it lacks incorporation of other relevant government policies, notably the Draft National Open Science Policy, and fails to sufficiently detail the principles of open science and open access. This limited scope and lack of comprehensive definition and detailed guidance present challenges for researchers in conducting ethical and responsible health research in South Africa. It constrains the Draft Guidelines from fully aligning with national imperatives and from fostering African-centric approaches. To address these issues, it is recommended that the Draft Guidelines integrate broader policies and principles, enhance clarity through comprehensive definitions, provide detailed guidance on open access, and promote African-centric approaches. Implementing these solutions will strengthen the Draft Guidelines, aligning them with national visions of open science, and thereby harnessing the full potential of South Africa's diverse scientific community in advancing health research. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic illustrates that we are never far away from situations that have a scale and impact, which are difficult to predict. Positioned at the intersection of crisis management and resilience, this insider case-study provides the opportunity for a more complete understanding of the organisation-adversity relationship (Williams et al., 2017), by focusing on the third Covid19 wave in Ireland (Dec 2020) and resulting response by an Intensive Care Unit crisis team. The study examines the evolution of seven data supply chains that were developed to support the ICU crisis team through the surge of cases which put the highest level of strain on the Irish health system since the pandemic began. The study focuses on 289 data reviews, which triggered 63 changes each requiring a new iteration of a data supply chain. Incorporating Organisational Mindfulness as the theoretical framework, the study provides an insight into the realities of data management during a crisis but also provides a rich awareness of the complexities of data management that often go unrecognised. In doing so, the study contributes the concept of 'mindful data', which aids managers to understand the key characteristics of resilient data supply chains. The study also provides a rare first-hand insight into how mindful data was constructed, presented, and evolved into an essential element within the critical care environment. [ABSTRACT FROM AUTHOR]

Context and background Land Monitoring is critical for continuous implementation of land reforms and general growth and development in the sector. It guides the deployment of policy by identifying and highlighting the fundamental problems affecting sustainable land development. Kenya in the recent past, has been involved in land monitoring activities including Land Governance Assessment Framework (LGAF) by the World Bank and the Global Land Indicators Initiative (GLII) by UN Habitat. The reporting under these monitoring activities have been affected by unavailability of data. Goal and Objectives: The objectives of the exercise were: to establish the existence of comprehensive policies and extent to which the policy formulation process is consultative; Determine the extent of land tenure security and proportion of women and men with legally recognized documentation; Establish the extent of land disputes and the access to effective resolution; Establish the efficiency of land administration services and generation of revenue by government from the land services; and Determine the capacity of land administration systems and determine sustainability of land use. Methodology: The exercise was carried out by a multi-agency National Land Monitoring Working Group (NLMWG) whose membership was drawn from technical staff from the various Government Agencies in Kenya. The basis of data collection was a land monitoring framework with the six indicators areas. Secondary data from administrative sources was collected and key informant interviews were carried among the land registrars, surveyors, physical planners, valuers, land adjudicators and judiciary registrars. The quantitative data collected was analyzed using descriptive statistics while qualitative data was analyzed using content analysis, narrative analysis and framework analysis. Results: The total area of land under cadastral maps/land information system was approximately 11,288,916.12 ha, representing 19.38 % of the total land area. The exercise further established that 1.58 % of the total filed court cases were land related in 2021/2022. In the same year, 2.17 % and 2.60 % of resolved and pending respectively of the total court cases were land related. The exercise further established that forest land decreased by 0.5 percent (255,851 ha) between the year 2008 and 2018. The data indicated that 10 out of 47 Counties (21.27 %) have prepared and approved County Physical and Land Use Plans. The exercise recommended the need to disaggregate land related data at the point of collection, design and fund new strategies for collection and managing data, develop National Land use/Land cover mapping and modernize land information management to allow for easier collection, collation, storage and dissemination of land information. [ABSTRACT FROM AUTHOR]

Developing Urban Building Energy Models (UBEM) to manage heat decarbonisation at the neighbourhood level is both crucial and challenging. Our study addresses key research challenges: quantifying peak heat energy demand and developing reliable and replicable urban energy demand microsimulations. We applied our UBEM approach to a case study area of 228 houses served by the Ridgeway New Low Voltage (LV) electrical substation in Newcastle upon Tyne, to estimate the difference between existing peak heat and electricity demand at this scale. Results show that peak heat demand is 5 to 14 times greater than peak electricity demand, significantly exceeding estimates from national studies. Furthermore, our work follows a comprehensive validation framework, comparing our simulation results against all relevant and available UK datasets. This comparison demonstrates that our model is an overall good fit at the declared Level of Detail. However, our paper identifies significant challenges, particularly in validation, that need to be addressed to improve the reliability and usability of future UBEMs. Finally, we reflect on all our findings and make policy recommendations, as we believe addressing the issues raised in this paper is vital for enabling area-based planning of residential heating electrification, particularly in the UK. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) systems are trained to solve complex problems and learn to perform specific tasks by using large volumes of data, such as prediction, classification, recognition, decision-making, etc. In the past three decades, AI research has focused mostly on the model-centric approach compared to the data-centric approach. In the model-centric approach, the focus is to improve the code or model architecture to enhance performance, whereas in data-centric AI, the focus is to improve the dataset to enhance performance. Data is food for AI. As a result, there has been a recent push in the AI community toward data-centric AI from model-centric AI. This paper provides a comprehensive and critical analysis of the current state of research in data-centric AI, presenting insights into the latest developments in this rapidly evolving field. By emphasizing the importance of data in AI, the paper identifies the key challenges and opportunities that must be addressed to improve the effectiveness of AI systems. Finally, this paper gives some recommendations for research opportunities in data-centric AI.