Mesoscale simulation of spherulite growth during polymer crystallization by use of a cellular automaton

The kinetics and topology of spherulite growth during crystallization of isotactic polypropylene (iPP) has been studied by using a three-dimensional cellular automaton model. The automaton cells can assume one out of two discrete states, i.e. melt or spherulite. The transformation kinetics governing the individual cell switches from melt to spherulite is formulated according to the Hoffman–Lauritzen secondary surface nucleation and growth theory for spherulite expansion. It is used to calculate the switching probability of each cell as a function of its previous state and the state of the neighbour cells. The actual switching decision is made by evaluating the Hoffman–Lauritzen switching probability calculated locally by using a Monte Carlo step. The growth rule is scaled by the ratio of the local and maximum occurring Gibbs free energy of transformation, the local and maximum occurring temperature and by the size of the automaton cells. The Hoffman–Lauritzen scaling procedure provides a real time and a real space scale. The simulations are conducted for quiescent and for weakly sheared melts. The kinetics predicted are in excellent agreement with those obtained for iPP from experiments.

Mesoscale simulation of the kinetics and topology of spherulite growth during crystallization of isotactic polypropylene (iPP) by using a cellular automaton

It is well-known that most users do not read privacy policies, but almost always tick the box to agree with them. While the length and readability of privacy policies have been well studied, and many approaches for policy analysis based on natural language processing have been proposed, existing studies are limited in their depth and scope, often focusing on a small number of data practices at single point in time. In this paper, we fill this gap by analyzing the 25-year history of privacy policies using machine learning and natural language processing and presenting a comprehensive analysis of policy contents. Specifically, we collect a large-scale longitudinal corpus of privacy policies from 1996 to 2021 and analyze their content in terms of the data practices they describe, the rights they grant to users, and the rights they reserve for their organizations. We pay particular attention to changes in response to recent privacy regulations such as the GDPR and CCPA. We observe some positive changes, such as reductions in data collection post-GDPR, but also a range of concerning data practices, such as widespread implicit data collection for which users have no meaningful choices or access rights. Our work is an important step towards making privacy policies machine-readable on the user-side, which would help users match their privacy preferences against the policies offered by web services.

Privacy Policies Across the Ages: Content of Privacy Policies 1996–2021

This research explores the intersection of emerging technologies, including Artificial Intelligence (AI), blockchain, and biotechnology, and the imperative for adaptive legal frameworks to regulate and encourage responsible innovation. Examining historical contexts of technological regulation, the study identifies key lessons from past approaches, emphasizing the need for flexibility, adaptability, and interdisciplinary collaboration. The economic dynamics of emerging technologies reveal both opportunities and challenges, with innovation playing a pivotal role in economic growth. Legal responses and current frameworks are scrutinized, prompting a call for ongoing evaluation and adaptation to the evolving technological landscape. Case studies highlight the effectiveness of existing regulations, while gaps in the regulatory landscape are identified. The concept of an adaptive legal framework emerges as a recommendation, promoting flexibility and agility to address the intricacies of emerging technologies. Balancing stability with adaptability, incentivizing responsible practices, and fostering collaboration among legal, technological, and economic experts are crucial components of effective regulatory measures. The research advocates for building a culture of corporate responsibility, aligning business goals with ethical standards, and promoting education and awareness across all stakeholders. The importance of training for decision-makers, especially policymakers, is underscored as pivotal for informed and ethical governance. In conclusion, this research emphasizes the urgent need for adaptive legal frameworks, interdisciplinary collaboration, continuous education, and a commitment to aligning technological progress with ethical considerations to ensure responsible and ethical innovation in the ever-evolving landscape of emerging technologies. 

Adaptive Legal Frameworks and Economic Dynamics in Emerging Tech-nologies: Navigating the Intersection for Responsible Innovation

Consenting on the Web, in the context of online privacy and data protection, is universally accepted as a difficult problem, mainly because of its cross-disciplinarity. For example, any approach to online Consenting needs to meet usability, legal, regulatory, technical, and business requirements. To date, effort has been predominantly focused on meeting compliance with regulations and automation, and less on the true re-empowerment of users with respect to their personal data. One approach that has not seen sufficient research is the use of ’Consent Receipts’, which offer a new paradigm of recording interactions concerning consent and using them as proofs in future actions, similar to familiar use of a common shopping receipt. In addition to being a record, receipts encourage accountability in how technology handles consent and is beneficial for all involved stakeholders. For organisations, it assists with legal requirements for demonstration of valid consent, while for users it provides transparency and accountability by being a proof to be used against malpractices related to consent. Receipts also have uses in addition to those related to consent, such as for authorising the holder in exercising related rights. This paper analyses the requirements, uses, and benefits offered by Consent Receipts with an extensive and broad literature review. Since receipts are a novel concept, we identify properties and requirements, and then new mechanisms necessary for the Web to support receipts. We then demonstrate feasibility of receipts through proof-of-concepts in three common real-world use-cases: (a) acceptance of a privacy policy and its subsequent changes; (b) choices expressed via consent dialogues or cookie banners; and (c) verbal interactions with Amazon Alexa.

/pdf/consent-receipts-for-a-usable-and-auditable-web-of-personal-7k2jm2b4.pdf

Consent Receipts For a Usable And Auditable Web of Personal Data

The General Data Protection Regulation (GDPR) is in effect since May of 2018. As one of the most comprehensive pieces of legislation concerning privacy, it sparked a lot of discussion on the effect it would have on users and providers of online services in particular, due to the large amount of personal data processed in this context. Almost three years later, we are interested in revisiting this question to summarize the impact this new regulation has had on actors in the World Wide Web. Using Scopus, we obtain a vast corpus of academic work to survey studies related to changes on websites since and around the time the GDPR went into force. Our findings show that the emphasis on privacy increased w.r.t. online services, but plenty potential for improvements remains. Although online services are on average more transparent regarding data processing practices in their public data policies, a majority of these policies still either lack information required by the GDPR (e.g., contact information for users to file privacy inquiries) or do not provide this information in a user-friendly form. Additionally, we summarize that online services more often provide means for their users to opt out of data processing, but regularly obstruct convenient access to such means through unnecessarily complex and sometimes illegitimate interface design. Our survey further details that this situation contradicts the preferences expressed by users both verbally and through their actions, and researchers have proposed multiple approaches to facilitate GDPR-conform data processing without negatively impacting the user experience. Thus, we compiled reoccurring points of criticism by privacy researchers and data protection authorities into a list of four guidelines for service providers to consider.

Cookie Banners and Privacy Policies: Measuring the Impact of the GDPR on the Web

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Predicting the Mircostructure in Semi-Crystalline Thermoplastics using Software for the Simulation of Recrystallization in Metals†

Ahstract- The collaboration of Wikipedia editors is well researched, covered by scientific works of many different fields. There is a growing interest to implement recommender systems that guide inexperienced editors to projects which fit their interests in certain topical domains. Although there have been numerous studies focusing on editing behavior in Wikipedia the role of topical domains in this regard is still unclear. In particular, topical aspects of co-authorship are generally neglected. In this paper, we want to determine by which criteria editors usually choose articles they want to contribute to. We analyzed three different language editions of Wikipedia (Vietnamese, Hebrew, and Serbo-Croatian) by building social networks and running community detection algorithms on them, i.e. editors are grouped based on their shared involvement in Wikipedia articles using social network analysis techniques. Then, we related this to the topical domains of these articles based on Wikipedia's user defined category network. Our results demonstrated that communities in Wikipedia tend to edit articles with a higher than average topical relatedness. But the significance and quality of these results vary considerably in the different language versions of Wikipedia. Topical relations between contributors and articles are a complex matter and influenced by a number of different factors, e.g. by culture.

Using Topical Networks to Detect Editor Communities in Wikipedias

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Cookie Banners and Privacy Policies: Measuring the Impact of the GDPR on the Web

Predicting the Mircostructure in Semi-Crystalline Thermoplastics using Software for the Simulation of Recrystallization in Metals†

Using Topical Networks to Detect Editor Communities in Wikipedias