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Data brokers use black-box methods to profile and segment individuals for ad targeting, often with mixed success. We present evidence from 5 complementary field tests and 15 data brokers that differences in profiling accuracy and coverage for these attributes mainly depend on who is being profiled. Consumers who are better off—for example, those with higher incomes or living in affluent areas—are both more likely to be profiled and more likely to be profiled accurately. Occupational status (white-collar versus blue-collar jobs), race and ethnicity, gender, and household arrangements often affect the accuracy and likelihood of having profile information available, although this varies by country and whether we consider online or offline coverage of profile attributes. Our analyses suggest that successful consumer-background profiling can be linked to the scope of an individual's digital footprint from how much time they spend online and the number of digital devices they own. Those who come from lower-income backgrounds have a narrower digital footprint, leading to a "data desert" for such individuals. Vendor characteristics, including differences in profiling methods, explain virtually none of the variation in profiling accuracy for our data, but explain variation in the likelihood of who is profiled. Vendor differences due to unique networks and partnerships also affect profiling outcomes indirectly due to differential access to individuals with different backgrounds. We discuss the implications of our findings for policy and marketing practice. This paper was accepted by David Simchi-Levi, marketing. Funding: Financial support from the National Science Foundation [CAREER Award 6923256] and an anonymous panel company is gratefully acknowledged. Supplemental Material: The web appendix and data files are available at https://doi.org/10.1287/mnsc.2023.4979. [ABSTRACT FROM AUTHOR]

The escalating human pressures on natural ecosystems necessitate urgent and effective conservation strategies to safeguard biodiversity and ecosystem functions. This review explored current techniques for mapping human pressure, with a particular focus on their application in nature conservation, especially within protected areas (PAs). Specifically, we analyzed the impacts of seven major types of human pressures on nature conservation within PAs. Additionally, we discussed four key methods for mapping human pressure, including land use intensity, human footprint, digital human footprint, and other proxies, examining their distinct characteristics and respective advantages and disadvantages. Additionally, our research explored the application of human pressure mapping for nature conservation, assessing its suitability for conservation applications and delineating directions for future work. These insights contributed to better support nature conservation and the management of PAs. [ABSTRACT FROM AUTHOR]

This study aims to assess how the construction patterns within residential communities influence the adolescent myopia using general survey. In a private high school from a megacity in mid-west China, a questionnaire gathered data on the 10th-grade students' level of myopia, home address, and some potential confounding factors. Additionally, satellite digital images were utilized to calculate the proportion of shadow area (PSA) and the proportion of greenness area (PGA) within a 500 m×500 m area centered on each student's home address. Correlations between myopia levels and PSA, along with other variables, were analyzed. The prevalence of mild, moderate, and high myopia were 39.2%, 32.5%, and 8.3%, respectively. A negative correlation was observed between myopia levels and PSA, albeit marginally significant (r=-0.189*, P = 0.05). Upon dividing the sample into higher and lower PSA groups using a cut-off point of 20%, a significant difference in myopia levels was evident (χ2 = 8.361, P = 0.038), while other confounding factors remained comparable. In conclusion, high-rise apartment constructions, which often cast more shadows on digital satellite maps, may not exacerbate myopia progression. Instead, they could potentially serve as a protective factor against adolescent myopia in densely populated megacities, as they allow for more ground space allocation. [ABSTRACT FROM AUTHOR]

Digitalization appears as a lever to enhance agriculture sustainability. However, existing works on digital agriculture's own sustainability remain scarce, disregarding the environmental effects of deploying digital devices on a large scale. We propose a bottom‐up method to estimate the carbon footprint of digital agriculture scenarios considering deployment of devices over a diversity of farm sizes. It is applied to two use‐cases and demonstrates that digital agriculture encompasses a diversity of devices with heterogeneous carbon footprints and that more complex devices yield higher footprints not always compensated by better performances or scaling gains. By emphasizing the necessity of considering the multiplicity of devices, and the territorial distribution of farm sizes when modeling digital agriculture deployments, this study highlights the need for further exploration of the first‐order effects of digital technologies in agriculture. [ABSTRACT FROM AUTHOR]

The Spherical Tokamak for Energy Production (STEP) programme is an ambitious but challenging endeavour to design and deliver a prototype fusion power plant. It is a rapid, fast-moving programme, designing a first of a kind device in a Volatile, Uncertain, Complex and Ambiguous (VUCA) environment, and digital tools play a pivotal role in managing and navigating this space. Digital helps manage the complexity and sheer volume of information. Advanced modelling and simulation techniques provide a platform for designers to explore various scenarios and iteratively refine designs, providing insights into the intricate interplay of requirements, constraints and design factors across physics, technology and engineering domains and aiding informed decision-making amidst uncertainties. It also provides a means of building confidence in the new scientific, technological and engineering solutions, given that a full-scale-integrated precursor test is not feasible, almost by definition. The digital strategy for STEP is built around a vision of a digital twin of the whole plant. This will evolve from the current digital shadow formed by system architecting codes and complex workflows and will be underpinned by developing capabilities in plasma, materials and engineering simulation, data management, advanced control, industrial cybersecurity, regulation, digital technologies and related digital disciplines. These capabilities will help address the key challenges of managing the complexity and quantity of information, improving the reliability and robustness of the current digital shadow and developing an understanding of its validity and performance. This article is part of the theme issue 'Delivering Fusion Energy – The Spherical Tokamak for Energy Production (STEP)'. [ABSTRACT FROM AUTHOR]

Online reviews reflect consumer needs and generate an electronic word-of-mouth (eWOM) effect influencing businesses' financial performance. Existing literature on online reviews largely assumes consumers have a consistent conversion rate of writing and posting online reviews after a consumption experience. Using dual-source data of consumers' footprints and online restaurant reviews, we challenge this assumption and quantify the online review conversion rate by depicting a comprehensive pathway from consumers' visitation to their review writing. We find that consumer overall satisfaction has a nonlinear impact on the online review conversion rate. Extremely dissatisfied consumers are more likely to post online reviews to complain than generally dissatisfied consumers. In contrast, generally satisfied consumers are more likely to post online reviews than extremely satisfied consumers. Additionally, consumer engagement in the platform stimulates their online review behavior. Further, consumers' geographical distance from providers has a U-shaped impact on online review conversion rate. Local nearby consumers and consumers from a relatively long distance have a higher chance to post online reviews than consumers from a moderate distance. Moreover, price promotions from providers facilitate consumers' review behavior. The findings guide businesses to prioritize reading and responding to online consumer reviews based on these influential factors. Companies thus can use more targeted actions to encourage consumers to post positive reviews and spread a positive eWOM effect to attract consumers and enhance financial performance [ABSTRACT FROM AUTHOR]

A major contributor to climate change is the emissions from Information and Communication Technology (ICT) devices and digitalisation. Energy use, heat production, and the operation of assets all contribute to the production of harmful emissions. However, indirect emissions, such as production and disposal, also play a role. We rely mainly on the output of small and medium-sized enterprises (SMEs). This paper focuses on the emissions of SMEs. Is it certain that cloud services (remote data storage and management) leave a much smaller carbon footprint than ICT devices for own use? These two solutions lead to a paradox: using more modern devices to produce less emissions requires more energy and generates more heat. This article analyses how to resolve this paradox for SMEs. [ABSTRACT FROM AUTHOR]

Vaccine manufacturing fosters the prevention, control, and eradication of infectious diseases. Recombinant DNA and in vitro (IVT) mRNA vaccine manufacturing technologies were enforced to combat the recent pandemic. Despite the impact of these technologies, there exists no scientific announcement that compares them. Digital Shadows are employed in this study to simulate each technology, investigating root cause deviations, technical merits, and liabilities, evaluating cost scenarios. Under this lens we provide an unbiased, advanced comparative technoeconomic study, one that determines which of these manufacturing platforms are suited for the two types of vaccines considered (monoclonal antibodies or antigens). We find recombinant DNA technology to exhibit higher Profitability Index due to lower capital and starting material requirements, pertaining to lower Minimum Selling Price per Dose values, delivering products of established quality. However, the potency of the mRNA, the streamlined and scalable synthetic processes involved and the raw material availability, facilitate faster market penetration and product flexibility, constituting these vaccines preferable whenever short product development cycles become a necessity. [ABSTRACT FROM AUTHOR]

Digital footprints provide crucial insights into individuals' behaviors and preferences. Their role in credit scoring is becoming increasingly significant. Therefore, it is crucial to combine digital footprint data with traditional data for personal credit scoring. This paper proposes a novel credit-scoring model. First, lasso-logistic regression is used to select key variables that significantly impact the prediction results. Then, digital footprint variables are categorized based on business understanding, and candidate models are constructed from various combinations of these groups. Finally, the optimal weight is selected by minimizing the Kullback–Leibler loss. Subsequently, the final prediction model is constructed. Empirical analysis validates the advantages and feasibility of the proposed method in variable selection, coefficient estimation, and predictive accuracy. Furthermore, the model-averaging method provides the weights for each candidate model, providing managerial implications to identify beneficial variable combinations for credit scoring. [ABSTRACT FROM AUTHOR]

The digitization of social interactions and daily activities means that multiple aspects of our daily lives are documented and stored, and social interactions leave digital traces. The accumulated data do not evaporate upon death, and questions about posthumous privacy and impression management arise. Drawing on eight focus groups comprised of Israeli Internet users from various backgrounds, the article points to the perceived interrelation between posthumous impression management and respect toward the dead and identifies a pervasive normative stance that advocates for the continuation of privacy management from life to after death. We call it the continuity principle. The living users position their personal data across two axes of public–private and in life–after death and manage access to their data accordingly. The findings suggest that given a digital footprint and possibilities to access digital remains, the separation between life and death erodes. However, users opine that in-life norms should linger and survive death. [ABSTRACT FROM AUTHOR]

This paper explores the use of digital footprints data to improve on the limited information available on the size and distribution of the residential housing stock in the United Kingdom over the past two decades. We use a subset of a large dataset consisting of the names and addresses of more than one billion individuals dating back to 1997 (LCRs: Linked Consumer Registers) to calibrate property lifecycle information within an authoritative geolocated address and property dataset. We validate the results of our novel calibration method against official estimates by the UK government covering the same period. We further show that our calibration method, based on individual level data, captures a much larger share of the residential housing stock since 1997 than would have been possible with other available data sources. We argue that these housing stock estimates are fundamental to the development of a comprehensive digital footprints data infrastructure that has the potential to significantly enhance population, migration, and other social statistics in the UK. More generally, this study highlights the benefits of using digital footprints data in refining, calibrating, and extending existing datasets. [ABSTRACT FROM AUTHOR]

Prizren, a pivotal region within Kosovo, stands out due to its amalgamation of natural beauty and cultural heritage, positioning it as a prime destination for discerning travelers. In the contemporary landscape, the power of modern mass media in marketing destinations is unparalleled, offering global reach, interactive engagement, and rapid communication channels with both current and prospective visitors. The digital footprint of a destination, encompassing a professional website, online commerce capabilities, and dynamic presence on major social media platforms, is now fundamental for travel entities aiming to thrive in the competitive tourism sector. Social media, in particular, has evolved into a critical marketing medium, facilitating direct interaction with the market and fostering relationships built on trust and loyalty, essential for attracting and retaining visitors. This study aims to explore the role of the internet and its myriad tools in marketing and enhancing the visibility of Prizren's tourism offerings. Through an empirical investigation gathering insights from tourists, hotel operators, and travel agencies in Prizren, coupled with an analysis of marketing content at both local and national levels, the research has assessed current practices. The goal is to identify strategies that can elevate Prizren's profile in the global tourism market, ensuring it is recognized not only for its inherent attractions but also for its innovative and engaging marketing approaches. Recommendations are proposed to refine and augment the marketing efforts dedicated to promoting Prizren as a leading tourist destination. [ABSTRACT FROM AUTHOR]

The uninterrupted operation of induction motors is crucial for industries, ensuring reliability and continuous functionality. To achieve this, we propose an innovative approach that utilizes an efficiency model-based digital shadow system for in situ failure detection and diagnosis (FDD) in induction motors (IMs). The shadow model accurately estimates IM losses and efficiency across various operational conditions. Our proposed method utilizes efficiency as the primary indicator for fault detection, while losses serve as condition indicators for fault diagnosis based on real-time motor parameters and loss sources. We introduce a bond graph as a fault diagnosis network, linking loss sources, motor parameters, and faults. This interconnected approach is the key aspect of our proposed diagnostic method and aims to be used in fault diagnosis as a general method. A case study of a broken rotor bar is used to validate the proposed method using a dataset of five motors. Among these, one motor operates without failure, while the remaining four exhibit broken rotor faults categorized as 1, 2, 3, and 4. The proposed method achieves 99.99% precision in identifying one to four defective rotor bars in IMs. Comparative analysis demonstrates good performance compared to vibration-based FDD approaches. Moreover, our methodology is computationally efficient and aligned with Industry 4.0 requirements. [ABSTRACT FROM AUTHOR]

This study applies a forensic examination to six distinct Android fitness applications centered around monitoring running activities. The applications are Adidas Running, MapMyWalk, Nike Run Club, Pumatrac, Runkeeper and Strava. Specifically, we perform a post mortem analysis of each application to find and document artifacts such as timelines and Global Positioning System (GPS) coordinates of running workouts that could prove helpful in digital forensic investigations. First, we focused on the Nike Run Club application and used the gained knowledge to analyze the other applications, taking advantage of their similarity. We began by creating a test environment and using each application during a fixed period. This procedure allowed us to gather testing data, and, to ensure access to all data generated by the apps, we used a rooted Android smartphone. For the forensic analysis, we examined the data stored by the smartphone application and documented the forensic artifacts found. To ease forensic data processing, we created several Python modules for the well-known Android Logs Events And Protobuf Parser (ALEAPP) digital forensic framework. These modules process the data sources, creating reports with the primary digital artifacts, which include the workout activities and related GPS data. [ABSTRACT FROM AUTHOR]

The evolution and wide use of modern technologies, tools and methods of communication are an integral part of the modern information society. In particular, current communication and information technologies provide an opportunity to create and extract a digital footprint for an individual or a certain group. In relation to the public authorities’ activities, the digital footprint can be used to analyse the reaction of society to their actions more effectively. By researching the digital footprint of social media actors in a particular region, it is possible to determine how the population treats various decisions and measures taken by the authorities. Due to digital participation, citizens have the opportunity and a tool to facilitate the management process at the stages of decision-making. The digital participation generates a digital footprint, the analysis of which provides feedback on the actions of the authorities. The purpose of the article is to propose a communicative and information model for the inclusion of the digital participation in the evaluation of social processes, consisting of mechanisms and interfaces for obtaining information alongside with mechanisms and interfaces for receiving feedback for evaluating the activities of public authorities in the region. The efficiency of the model has been practically proved by the example of a socially significant event in the Oryol region. Using the example of regional management centres, the expediency of introducing the model for evaluating the activities of public authorities in the region through receiving feedback is shown. This will increase the involvement of citizens in the regulation of social processes in the region and improve the quality of management.

Disparity in the quality of life of people living in rural and urban areas is among the major problems, often leading to greater depression among the rural population compared to those in urban areas who have access to a high standard of living. The goal of this study was to identify the issues of the quality of life that are fundamental from the point of view of rural settlements and typical for the rural population in various regions of the Russian Federation with diverse geographical, socioeconomic, and demographic characteristics. The practical relevance of our study is based on the identification of the scope of typical problems related to the quality of life in rural areas. The fundamental value of this study is the significance of digital tracks, which can serve as a source of information. The data sources included messages and posts discussing various quality of life aspects of the rural population published by VKontakte. For this study, we used messages and posts with negative implications from communities in rural settlements in ten Russian regions. These issues include housing infrastructure and utilities, transportation, the environment, telecommunications, banking, healthcare, and education.

Purpose. One of the main directions of teaching methodology is the creation of a personalized educational route. In the context of the development of the digital educational environment, it is necessary to explore the possibilities of combining individualization of learning with modern digital platforms. The key concept in the study of this topic is the "digital footprint". The article presents a model for displaying a digital footprint in the form of a digital profile, a universal diagnosis of the understanding of the educational text is compiled to create its own text field and it is recorded that the secondary educational text can be a digital artifact that remains in cloud storage and forms part of the digital footprint for a certain period of time. Methodology. The research is based on the analysis and synthesis of psychological, pedagogical and educational literature, on modeling the process of fixing secondary educational texts in the form of a digital artifact, which will be part of the digital footprint, as well as displaying the levels of subject, meta-subject and personal results. Results. The concepts of digital footprint and digital artifact, secondary educational text, and semantic reading are defined. A digital footprint model consisting of digital artifacts in the form of secondary educational texts has been developed, and a universal diagnostic of the understanding of the educational text has been created to create a secondary text by students. Practical implications. The results can be applied in creating a digital educational environment of an educational organization based on the compilation and accumulation of a digital footprint of each student, in the analysis of digital artifacts presented in the form of secondary educational texts, in the development of a system for evaluating educational results achieved using modern technologies.

Introduction. The article is devoted to the problem of digital identity. The development of the information society and digitalization processes poses to each Internet user the challenge of matching the digital profile to its real owner. There is often a big difference between a real person and his digital avatars, which leads not only to confusion and ambiguous perception of the narrator and the author by the other, but also to depersonalization of the author. Theoretical analysis. Having a digital identity allows you to successfully self-realize in the virtual space and in the real world, be it finding a job or finding a life partner. However, the development of artificial intelligence and neural networks makes it possible to create many fakes, impersonate one narrator for another, and replace the image of the author. On the other hand, the author’s desire to create a different virtual image for himself due to a number of complexes or intrigues can lead to a split personality. Conclusion. Modern digitalization processes lead to the awareness of the need for digital identity. The formation of digital identity can take place both in an authoritarian way and in a softer educational way. In any case, its presence will give the user of the digital environment undeniable advantages in personal and professional activities.

Implementing the IIoT concept in industry involves the development and implementation of online systems monitoring the technical state of electromechanical equipment. This is achieved through the use of digital twins and digital shadows (object state observers). The tasks of mastering new rolling profiles and optimizing plate mill rolling programs require improved methods for calculating equivalent motor currents and torques. Known methods are generally based on calculations using smoothed load diagrams, which are assumed to be identical for the upper and lower main drive (UMD and LMD) rolls. These methods do not consider the differences in actual loads (currents or torques) in steady rolling states. Experiments performed on the 5000 plate mill have shown that due to speed mismatches, the UMD and LMD torques differ three times or more. This causes overheating of the more heavily loaded motor, insulation life reduction, and premature failure. Therefore, the problem of developing and implementing techniques for monitoring the load and thermal regimes of motors using digital observers is relevant. The paper's contribution is the first justification of the concept of object-oriented digital shadows. They are developed for specific classes of industrial units using open-source software. This research justifies a methodology for assessing motor load and temperature by processing arrays of motor currents or torques generated during rolling. An equivalent load observer and a temperature observer were proposed and implemented using Matlab-Simulink resources. The algorithm was implemented on the mill 5000 and tuned using an earlier-developed virtual commissioning methodology with digital twins. Thermal regimes were studied, proving that torque alignment ensures equal motor temperatures. The proposed considerations contribute to the development of the theory and practice for creating digital systems to monitor the technical condition of electromechanical and mechatronic systems and implementing the Industry 4.0 concept at industrial enterprises. [ABSTRACT FROM AUTHOR]

[Display omitted] • Fusion of varied data sources are used to develop more informed flood extent maps. • SVM outscored the MLE, RF, Iso cluster, and Otsu's algorithms in all strategies. • PCA-based technique outperforms the other three approaches. • First and Second approaches performed better in VV than VH polarisation. Accurate and timely mapping of flood extent is crucial for effective disaster response and management. However, existing methods often face challenges in integrating diverse data sources and leveraging advanced techniques for precise inundation mapping. This research aimed to address this gap by proposing and evaluating four different methods for improving flood extent mapping in Sindh, Pakistan, by integrating various data sources, including precipitation, Synthetic Aperture Radar (SAR), buildings, population, and optical data. The first strategy involves categorizing post-flood SAR data immediately using non-parametric (Support Vector Machine (SVM) and Random Forest (RF)), parametric (Maximum Likelihood Estimation (MLE)), and cluster (ISO) algorithms for Vertical transmit and Vertical received (VV) and Vertical transmit and Horizontal received (VH) polarisations. The second method relies on Otsu's method for automatic thresholding on post-flood event VH and VV data. The third approach utilizes both pre- and post-flood event data by creating a stack of Pre-event VH, Pre-event VV, Post-event VH, and Post-event VV data, which is then classified using the SVM, RF, MLE, and ISO algorithms. Lastly, the fourth method employs the stack of Principal Component Analysis (PCA) bands, consisting of PCA components 1, 2, and 3, to categorize data for flooding using the SVM, RF, MLE, and ISO algorithms. The rationale behind evaluating these diverse methods was to identify the most effective approach for accurately mapping flood extent by exploiting the strengths of different algorithms and data processing techniques. The accuracy and precision of these approaches were rigorously evaluated using Landsat-9 Normalized Difference Water Index (NDWI) as a reference dataset, along with error matrix and compound value (C v) metrics. The findings show that a PCA-based technique with SVM is marginally superior (based on the overall accuracy and Kappa coefficient with a value of 92.20 % and kappa coefficient 0.821) than the other approaches tested in the study. By reducing the dimensionality of the dataset while preserving relevant information, PCA offers a computationally efficient approach to flood extent mapping. Among all the approaches, the collective performance of the algorithms was computed using the C v metric. The SVM algorithm with a C v of 1.25 demonstrated the best performance, followed by the RF algorithm with C v of 1.5, and the MLE algorithm with C v of 2.5. The worst performing algorithm among all is found to be ISO with C v of 4. It comes into view that first and second approach underperformed in VH polarisation compared to the VV polarisation and provide a more accurate representation of flooded area in VV polarisation. The flood extent with highest accuracy, together with the world's gridded population, latest Microsoft's Global ML Building Footprints, and OpenStreetMap building data are used in conjunction to estimate the number of people and buildings at risk within the study area. [ABSTRACT FROM AUTHOR]

With the increasing pervasiveness of Information and Communication Technology (ICT) in the fabric of economic activities, the corporate digital divide has become a crucial issue for the assessment of Information Technology (IT) competencies and the digital gap between firms and territories. With little granular data available to measure the phenomenon, most studies have used survey data. To address this empirical gap, we scanned the homepages of 182,705 Italian companies and extracted ten characteristics related to their digital footprint to develop a new index for the corporate digital assessment. Our results show a significant digital divide between Italian companies according to size, sector and geographical location, opening new perspectives for monitoring and data-driven analysis. [ABSTRACT FROM AUTHOR]

The development and proliferation of the Internet, GPS, cell phones, social media, and the associated data that support these now ubiquitous technologies have created a new ecosystem of information making up a person's digital identity. Our digital footprints have traditionally been subject to different levels of privacy protection depending upon the kind of data at issue. Over time, court decisions have revealed tensions and a lack of consistency on the question of how the protections guaranteed by the Fourth Amendment apply to an individual's digital footprint and their reasonable expectations of privacy over it. This Comment will examine the gaps in the current landscape of U.S. privacy protections in the absence of federally explicit legislative protections. First, it will examine current federal statutory privacy law and the piecemeal approach through which certain areas of information or categories of individuals are protected. Next, it will examine the development of the Supreme Court's Fourth Amendment jurisprudence as applied to individual privacy rights. It will then analyze the gaps in privacy protection in both statutory and case law and recommend a unified federal statutory approach to ensure that currently legal uses of data do not, when aggregated, yield an impermissibly intrusive infringement of the privacy rights of U.S. citizens in violation of the spirit of the Fourth Amendment's protections. It will recommend a legislative solution to fill those gaps, provide a clear expression of how certain kinds of data can and cannot be used, and ensure these critical protections are applied equally to all, regardless of the state in which any individual lives. [ABSTRACT FROM AUTHOR]

Purpose: The paper addresses the privacy–personalisation paradox in the post-GDPR-2018 era. As the regulation came in a bid to regulate the collection and use of personal data, its implications remain underexplored. The research question is: How do consumers perceive the matter of personal data collection for the use of highly targeted and personalised ads post-GDPR-2018? The invasion of privacy vs the benefits of highly personalised digital marketing. Design/methodology/approach: To address the research question, this qualitative study conducts semi-structured interviews with 14 individuals, consisting of average users and digital experts. Findings: This paper reports on increasing consumer vulnerability post-GDPR-2018 due to increased awareness of personal data collection yet incessant lack of control, particularly regarding the repercussions of the digital footprint. The privacy paradox remains an issue except among experts, and personalisation remains necessary, yet critical challenges arise (e.g. filter bubbles and intrusion). Practical implications: Policy implications include education, regulating consent platforms and encouraging consensual sharing of personal data. Originality/value: While the privacy–personalisation paradox has been widely studied, the impact of GDPR-2018 has rarely been addressed in the literature. GDPR-2018 has seemingly had little impact on instilling a sense of security for consumers; if anything, this paper highlights greater concerns for privacy as users sign away their rights on consent forms to access websites, thus contributing novel insights to this area of research. [ABSTRACT FROM AUTHOR]

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Development of complex systems through the application of Model‐Based Systems Engineering (MBSE) will require the creation and maintenance of multiple models, created using multiple languages. Hence, structuring the models such that there is efficient support for incremental development requires some foresight. Having models with multiple or unclear purposes may introduce situations where parts of the organisation will ask for modification for representing a desired future system state, whereas other parts of the organisation require it to remain unchanged in order to represent the present state. This paper introduces a structure with associated terms and characteristics of the different model types in order to provide guidance to an organisation. In particular, it extends previous work on model tenses, capturing different time perspectives/purposes on the models, to also include model usage for keeping track of the actual state of a realised system instance. For example, the concepts of Digital Twin and Digital Shadow are discussed and conceptualised. As such, the contributions made in the paper allows an organisation to reason about the completeness of their modelling methodology and its applicability to support incremental development. Above all, the paper spells out clearly that for a complex heterogeneous system, any request for an update to "the model" will prompt the response "Excuse me Sir/Madam, which model?". [ABSTRACT FROM AUTHOR]

In the ongoing discussion about how learning analytics can effectively support self-regulated student learning and which types of data are most suitable for this purpose, this empirical study aligns with the framework who advocated the inclusion of both behavioral trace data and survey data in learning analytics studies. By incorporating learning dispositions in our learning analytics modeling, this research aims to investigate and understand how students engage with learning tasks, tools, and materials in their academic endeavors. This is achieved by analyzing trace data, which captures digital footprints of students' interactions with digital tools, along with survey responses from the Study of Learning Questionnaire (SLQ), to comprehensively examine their preferred learning strategies. Additionally, the study explores the relationship between these strategies and students' learning dispositions measured at the start of the course. An innovative aspect of this investigation lies in its emphasis on understanding how learning dispositions act as antecedents and potentially predict the utilization of specific learning strategies. The data is scrutinized to identify patterns and clusters of such patterns between students' learning disposition and their preferred strategies. Data is gathered from two cohorts of students, comprising 2,400 first year students. This analytical approach aims to uncover predictive insights, offering potential indicators to predict and understand students' learning strategy preferences, which holds value for teachers, educational scientists, and educational designers. Understanding students' regulation of their own learning process holds promise to recognize students with less beneficial learning strategies and target interventions aimed to improve these. A crucial takeaway from our research underscores the significance of flexibility, which entails the ability to adjust preferred learning strategies according to the learning environment. While it is imperative to instruct our students in deep learning strategies and encourage autonomous regulation of learning, this should not come at the expense of acknowledging situations where surface strategies and controlled regulation may prove to be more effective. [ABSTRACT FROM AUTHOR]

Digitalization is a core component of the green transition. Today's focus is on quantifying and predicting the climate effects of digitalization through various life-cycle assessments and baseline scenario methodologies. Here we argue that this is a mistake. Most attempts at prediction are based on three implicit assumptions: (a) the digital carbon footprint can be quantified, (b) business-as-usual with episodic change leading to a new era of stability, and (c) investments in digitalization will be delivered within the cost, timeframe, and benefits described in their business cases. We problematize each assumption within the context of digitalization and argue that the digital carbon footprint is inherently unpredictable. We build on uncertainty literature to show that even if you cannot predict, you can still mitigate. On that basis, we propose to rethink practice on the digital carbon footprint from prediction to mitigation. [ABSTRACT FROM AUTHOR]

Healthcare 4.0 is about collecting huge amounts of data and getting it to work in applications, enabling healthcare management decisions well‐informed while providing for important gains in effectiveness and cost control. Diagnostics based on the digital footprint depend on wearable technology's ability to gather and extract essential patient data. Artificial intelligence (AI) technologies allow the analysis of real‐time observed data and continuously developing from data to understand the world surrounding them. To connect and access intelligent healthcare services, people, and devices at any time, a secure wireless mobile communication system is essential. This article suggests a mHealth‐based patient monitoring system (mHealth‐PMS) based on AI for healthcare 4.0. Mobile healthcare applications motorized by Convolutional Neural Network (CNN) have enabled people to triage their conditions and preemptive treatment decisions. Information collected has been analysed for substantiating cause, and alert and preventive messages have been immediately sent through the mobile application. The performance analysis has been executed, and the proposed mobile application‐based surveillance provided much‐enhanced reporting of information quickly on diseases, symptoms, factors, and more. The mHealth‐PMS strategy shows an accuracy ratio of 95.6%, monitoring ratio of 93.5%, data management ratio of 94.4%, data security ratio of 91.7%, data privacy ratio of 92.1%, prediction ratio of 95.3%, a cost‐effective ratio of 25.5% compared to the existing methods. [ABSTRACT FROM AUTHOR]

To address the difficulty of accurately characterizing the fluctuations in equipment energy consumption and the dynamic evolution of whole energy consumption in low-carbon workshops, a low-carbon-operation-oriented construction method of the energy footprint model (EFM) for a digital twin workshop (DTW) is proposed. With a focus on considering the fluctuations in equipment energy consumption and the correlation between multiple pieces of equipment at the workshop production process level (CBMEatWPPL), the EFM of a DTW is obtained to characterize the dynamic evolution of whole energy consumption in the workshop. Taking a production unit as a case, on the one hand, an EFM of the production unit is constructed, which achieved the characterization and visualization of the fluctuations in equipment energy consumption and the dynamic evolution of whole energy consumption in the production unit; on the other hand, based on the EFM, an objective function of workshop energy consumption is established, which is combined with the tool life, robot motion stability, and production time to formulate a multi-objective optimization function. The bee colony algorithm is adopted to solve the multi-objective optimization function, achieving collaborative optimization of cross-equipment process parameters and effectively reducing energy consumption in the production unit. The effectiveness of the proposed method and constructed EFM is demonstrated from the above two aspects. [ABSTRACT FROM AUTHOR]

Background: The detailed complexity of triceps brachii insertional footprint continues to challenge surgeons as evidenced by continued reports of triceps-associated complications following elbow procedures. The purpose of this study is to describe the three-dimensional footprint of the triceps brachii at its olecranon insertion at the elbow. Methods: 22 cadaveric elbows were dissected leaving only the distal insertion of the triceps intact. The insertion was defined and probed with a three-dimensional digitizer to create a digital three-dimensional footprint allowing width, height, and surface area of the footprint to be recorded relative to the bare area. The insertional soft tissues of tendon versus muscle along with the shape of the footprints were qualitatively described. Results: The mean width and surface area of the lateral segment was greater in males than in females (30.07 mm vs. 24.37 mm, p = 0.0339 and 282.1 mm vs. 211. 56 mm, p = 0.0181, respectively). No other statistically significant differences between the sexes were noted. The triceps insertional footprint was "crescent-shaped" and consisted of three regions: central tendon, medial muscular extension, and lateral muscular extension. Discussion: These findings can help explain the importance of avoiding these muscular structures during triceps-off approaches and provides the framework for future clinical studies. Clinical Relevance: Basic Science, anatomy study, cadaver dissection. [ABSTRACT FROM AUTHOR]

The unprecedented growth of 'Artificial Intelligence' (hereinafter referred to as AI) has brought immense benefits but at the same time has posed complex challenges that has impacted users' lives, including privacy and data security, particularly, children who are vulnerable to these problems. This paper examines privacy of children in the era of AI and the legal framework's adequacy in protecting children's privacy, focusing on India, the world's most populous nation in 2024,1 with over 833.7 million² internet users, accounting for more than half of its population. With the advent of AI, unprecedented accumulation, processing, and analysis of massive datasets has become possible by algorithms applying predictive analytics on discrete datasets. Nevertheless, AI's pattern recognition ability has blurred privacy boundaries which has enabled it to feed on sensitive information such as that concerning health, emotions, interests, and behaviours. Due to innate curiosity and digital immersion, children are more susceptible to privacy violations in this 'AI-driven' digital era. Since children possess a limited understanding of privacy risks, they are more likely to share information online. Consequently, there is an urgent need to address the issue concerning the increased digital footprint of children and the associated conflict between the 'age of consent' and the 'age of contractual capacity' for the purpose of fixing the 'digital age' of the child. Such a requirement can be potentially addressed through legislative intervention by enacting a comprehensive piece of legislation to regulate the ubiquitous collection of data. Facial recognition, predictive analytics, autonomous systems, and other AI applications, could be the reason for the apprehensions that systemic discrimination could occur and governance is also at stake that points out the need for transparency and accountability. While AI brings with itself exponential growth, there is also a need to underscore the importance of protecting children's right to privacy, given their vulnerability. A comprehensive legislative framework, responsible corporate policies, and increased awareness can help strike a balance, allowing children to harness AI's benefits while safeguarding their fundamental rights. [ABSTRACT FROM AUTHOR]

International heritage management approaches have developed into a more inclusive process wherein public participation is identified as a pivotal tool. Thus, determining how to assess public interests and include the public's ideas in heritage protection has become a technical issue, but relevant research still remains limited. This paper aims to test the digital footprints of social media users as a public participatory tool, with the objectives of identifying industrial heritage landscape attributes and assessing associated values. Targeting the Sanxian industrial heritage landscape of Liangdancheng in China as a case study, in this research the data from user-generated content on social media platforms Ctrip, Weibo, and Meituan were collected and processed with ROST CM 6 and NVivo 12, and content analysis (CA) and importance-performance analysis (IPA) were conducted. Results revealed that the industrial heritage landscape of Liangdancheng encompasses various built, cultural, and natural environmental resources, including both tangible and intangible attributes such as architectural constructions, historic artifacts, cultural events, and plants. These attributes were assessed and categorized into four quadrants of importance–performance characteristics, wherein cultural environmental resources show relatively high performance but built environmental resources need further actions to improve their value perception and interpretation among the public. This research demonstrated that the digital footprints of social media users as a participatory tool can work well in terms of data accessibility, value identification, and public representation, advancing the theoretical framework of Chinese industrial heritage management and global practices. [ABSTRACT FROM AUTHOR]

Student retention is a significant challenge for higher education institutions (HEIs). The fact that a considerable number of dropouts from universities are primarily due to academic underperformance motivates universities to develop learning analytics tools based on models for predicting learning success. However, the scalability of such models is limited since students' academic performance and engagement, as well as the factors influencing them, are largely determined by the educational environment. The article proposes a hybrid approach to forecasting success in completing an academic semester, which involves creating a set of predictive models. Some of the models use historical student data, while others are intended to refine the forecast using current data on student performance and engagement, which are regularly extracted from available sources. Based on this approach, we developed an ensemble of machine learning models and the Markov-process model that simultaneously address the tasks of forecasting success in mastering a course and success in completing a semester. The models utilize digital footprint data, digital educational history, and digital personality portraits of students extracted from the databases of Siberian Federal University, and the resulting ensemble demonstrates a high quality of the forecast. The proposed approach can be utilized by other HEIs as a framework for creating mutually complementary forecasting models based on different types of accessible educational data. [ABSTRACT FROM AUTHOR]

Network analytical metrics often seek to capture the structural dimension of social capital, but such data collections using traditional social research tools often suffer from biases like interviewer effects and are usually only suitable to study small groups of participants. Digital sources of social relations might offer great potential for facilitating such measures though, because they digitally store unprecedented amounts of relational data, free from the limitations associated with self-reported data. This study therefore compares individual node degrees collected through a contact diary (i.e., overall-social capital), and a counterpart extracted from digital footprint data from the social media network, Facebook (i.e., social media network-social capital). The findings suggest that researchers conducting empirical studies involving the concept thus should not ignore social media network-social capital as a practical alternative measure of overall-social capital; it provides a sound approximation but only after controlling for other influential factors. In particular, our results highlight that the usability of the digital social capital metric is conditional on the three-way interaction between the variables gender, age, and social media network-social capital. Thus, the evidence from our study, in turn, also intimates that individuals act heterogeneously in the digital sphere with respect to their networking behaviour. [ABSTRACT FROM AUTHOR]

In this study, the Digital Shadow (DS) of the Shell and Tube Heat Exchanger (STHE) is designed and analyzed for numerous disturbances that occur when the system is in a running condition. The disruptive segregation of the heat exchanger is related to the DS for its operation, and thus a realistic DS was developed for the STHE. Fuzzy Logic (FL) was used to identify and segregate the disturbance signals from the process output. The Response Optimization (RO) algorithm was adopted and modified to work on the STHE. The observer-based residual generator design was implemented to prevent system failure and defective conditions. Model Predictive Controller (MPC), Transposed System Controller (TSC), and a looping-based control technique called Unity Response Loop (URL) were also implemented, and the results are discussed. The findings of this study contribute to the improvement of the overall performance of nonlinear systems in industrial processes and the avoidance of defects. [ABSTRACT FROM AUTHOR]

Chinese shadow puppetry has been recognized as a world intangible cultural heritage. However, it faces substantial challenges in its preservation and advancement due to the intricate and labor-intensive nature of crafting shadow puppets. To ensure the inheritance and development of this cultural heritage, it is imperative to enable traditional art to flourish in the digital era. This paper presents an Interactive Collaborative Creation System for shadow puppets, designed to facilitate the creation of high-quality shadow puppet images with greater ease. The system comprises four key functions: Image contour extraction, intelligent reference recommendation, generation network, and color adjustment, all aimed at assisting users in various aspects of the creative process, including drawing, inspiration, and content generation. Additionally, we propose an enhanced algorithm called Smooth Generative Adversarial Networks (SmoothGAN), which exhibits more stable gradient training and a greater capacity for generating high-resolution shadow puppet images. Furthermore, we have built a new dataset comprising high-quality shadow puppet images to train the shadow puppet generation model. Both qualitative and quantitative experimental results demonstrate that SmoothGAN significantly improves the quality of image generation, while our system efficiently assists users in creating high-quality shadow puppet images, with a SUS scale score of 84.4. This study provides a valuable theoretical and practical reference for the digital creation of shadow puppet art. [ABSTRACT FROM AUTHOR]

Longitudinal population studies (LPS) have a long history of providing insights into how individual consumption patterns and other lifestyle choices affect health and socio-economic outcomes. LPS typically operate as research databanks, integrating rich and deep phenotypic data - covering diverse aspects of individual, family and household status - with genomic data and linked records on health and socio-economic outcomes. However, individual consumption and behavioural choices are traditionally studied solely using self-report methods which are prone to known biases. We propose to enrich LPS databanks with a new form of digital footprint data – individual shopping history records. These are collected by supermarkets through “loyalty” card schemes and can provide a new perspective on real world behaviours and history of consumption. However, as a novel class of data in the context of longitudinal research, our ability to assess the quality and completeness of the data is unknown, as is our ability to effectively triangulate between self-reported and linked data. This paper describes a protocol for linking individual level shopping history data into a LPS using Avon Longitudinal Study of Parents and Children (ALSPAC) as a testbed. The protocol covers the process of establishing participant fair processing, an ethical and legal basis for the linkage framework itself, and how these data will be integrated into the ALSPAC databank. It does not cover the subsequent research use of these data. The protocol was built on an extensive participant engagement and acceptability work and has been approved by the ALSPAC Law and Ethics committee.

Eddie Webster, a renowned academic in the field of sociology of work, passed away unexpectedly at the age of 81. He was known for his contributions to academia and activism, advocating for collective action and democratic trade unions as vehicles for social change. His seminal work, "Cast in a Racial Mould," analyzed the intersection of race and class in South Africa's engineering industry and laid the foundation for "social movement unionism." Eddie's influence extended globally, and he played a key role in establishing the Global Labour University and the Global Labour Journal. His dedication to social justice and empowering individuals was evident throughout his life, and his legacy continues to inspire scholars and activists worldwide. [Extracted from the article]

The COVID-19 pandemic has changed the way learning is organized around the world. Russian universities have also been faced with the need to quickly transfer all teaching to an online format. The importance of student satisfaction with the education quality in online learning is increasing, since it is an important condition for motivation. The paper shows that based on the analysis of students’ messages in the social network, it is possible to observe and analyze the overall dynamics and trends in student community satisfaction with the quality of the learning / the efficiency of universities and conduct a comparative analysis of the identified characteristic data groups with their totality. It is shown that the data on the reaction of students of a particular university may have significant deviations from the totality of data, which reflects the characteristics of the reaction of students of a particular university to events occurring at the same time. This may indicate the internal differences of the university, which form an appropriate response to external events.To understand the satisfaction of students in the transition to a new implementation format of the learning. The digital traces of students from the social network VKontakte were analyzed using individual Big Data tools on the PolyAnalyst software platform. This made it possible to trace changes in the mood of students and, on the example of a single university, to identify and explain deviations in the attitude of students to the implementation of the learning, as well as to verify the methodology. The methodology developed by authors makes it possible to detect problematic issues in the university, including the moment of their occurrence, relevance, degree of concern of students. Such content analysis can be used not only to assess students’ satisfaction with the quality of the learning, but also to monitor the emergence of any problems that cause concern and strong reactions on the part of the student community, as well as other communities and individual groups.

The paper presents the results of an empirical research on the topic of personal data awareness, conducted through a survey of a representative sample of the Italian population. Considering five self-perceived levels of digital competence, we investigate the perception of digital footprints people leave online, how they act when apps and platforms demand to share personal data, and whether they are concerned about privacy issues. We discuss these topics and the research data in the light of theoretical frameworks regarding the emerging forms of new digital inequalities, which are broadening to include the awareness gaps, among different users, about how apps and online platforms collect and use personal data. [ABSTRACT FROM AUTHOR]

This article explores the growing role of traces and footprints in social science, giving an outline of the main concepts and empirical questions discussed in this special issue. Remarkably, the study of traces (unobtrusive, necessarily interpretative, theory-driven and data-fueled at the same time) considers them as unwillingly insightful and strategic materials. However, the difference between information "intentionally transmitted" and information "accidentally leaked" gets fuzzy especially in online communication and social network. In this sense, by interpreting the "digital data deluge" as a social trace deluge, issues of algorithmic environments, users' participation, datafied biases, and transparency are addressed. Finally, it is argued that traces, data exhaust, and residual information help overcoming standard dichotomies in social science (e.g., human vs. non-human realms, quantitative vs. qualitative, online vs. offline activities), both methodologically and theoretically. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of modern manufacturing, the pursuit of efficiency, reliability, and optimal performance has prompted the integration of cutting-edge technologies. Among these, digital twins (DT) have emerged as transformative tools, offering a virtual representation of physical processes, systems, and equipment. This paper delves into the pivotal role of digital twins in advancing the monitoring and supervision of manufacturing processes, focusing specifically on process digital twin (PDT) and its application in the domain of froth flotation in minerals processing. Our data-driven digital twin, replicating the behavior of a flotation cell, was developed using a combination of industrial and simulation data anchored by Artificial Neural Networks. This approach provides precise process emulation of the flotation process. Industrial evaluations of the AI model within the Digital Shadow demonstrated an overall 94% accuracy in estimating insightful information regarding the flotation cell operations with 2 s in response time. This research significantly contributes to the practical implementation of digital twins in industrial processes, highlighting their potential to revolutionize process control and enhance efficiency in the industrial sector. [ABSTRACT FROM AUTHOR]

Generative Artificial Intelligence (AI) systems, like ChatGPT, have the potential to perpetuate and amplify cultural biases embedded in their training data, which are predominantly produced by dominant cultural groups. This paper explores the philosophical and technical challenges of detecting and mitigating cultural bias in generative AI, drawing on Plato's Allegory of the Cave to frame the issue as a problem of limited and distorted representation. We propose a multifaceted approach combining technical interventions, such as data diversification and culturally aware model constraints, with a deeper engagement with the cultural and philosophical dimensions of the problem. Drawing on theories of extended cognition and situated knowledge, we argue that mitigating AI biases requires a reflexive interrogation of the cultural contexts of AI development and a commitment to empowering marginalized voices and perspectives. We claim that controlling cultural bias in generative AI is inseparable from the larger project of promoting equity, diversity, and inclusion in AI development and governance. By bridging philosophical reflection with technical innovation, this paper contributes to the growing discourse on responsible and inclusive AI, offering a roadmap for detecting and mitigating cultural biases while grappling with the profound cultural implications of these powerful technologies. [ABSTRACT FROM AUTHOR]

This article discusses the concept of reference rot, which refers to the breakdown of active or functional website components, leading to the loss of access to websites, hyperlinks, or source data. The author explores the implications of reference rot for media scholars, particularly in relation to discriminatory media. They argue that while the removal of hateful media is something to celebrate, it also raises questions about the preservation and analysis of such material. The author suggests that media scholars can use contact tracing and qualitative methods to study the afterlives of discriminatory data and its impact on digital cultures. They emphasize the importance of actively archiving discriminatory media while also considering the ethical and political implications of doing so. [Extracted from the article]