Search Results (405)

Legal text summarization presents distinct challenges due to the intricate and domain-specific nature of legal language. This paper introduces a novel framework integrating dynamic Retrieval-Augmented Generation (RAG) with domain-specific adaptation to enhance the accuracy and contextual relevance of legal document summaries. The proposed Dynamic Legal RAG system achieves a vital form of symmetry between information retrieval and content generation, ensuring that retrieved legal knowledge is both comprehensive and precise. Using the BM25 retriever with top-3 chunk selection, the system optimizes relevance and efficiency, minimizing redundancy while maximizing legally pertinent content. with top-3 chunk selection, the system optimizes relevance and efficiency, minimizing redundancy while maximizing legally pertinent content. A key design feature is the compression ratio constraint (0.05 to 0.5), maintaining structural symmetry between the original judgment and its summary by balancing representation and information density. Extensive evaluations establish BM25 as the most effective retriever, striking an optimal balance between precision and recall. A comparative analysis of transformer-based (Decoder-only) models—DeepSeek-7B, LLaMA 2-7B, and LLaMA 3.1-8B—demonstrates that LLaMA 3.1-8B, enriched with Legal Named Entity Recognition (NER) and the Dynamic RAG system, achieves superior performance with a BERTScore of 0.89. This study lays a strong foundation for future research in hybrid retrieval models, adaptive chunking strategies, and legal-specific evaluation metrics, with practical implications for case law analysis and automated legal drafting. Full article

The abandonment of agricultural land is now considered one of the primary land use changes driven by complex interactions between social, economic, and environmental factors. To understand and manage this process, a holistic approach that integrates multidimensional methodologies and interactions is essential. This study examines the key driving factors behind agricultural land abandonment in Lithuania using two methodological approaches. First, seventeen highly qualified land management experts were surveyed, and their insights were analysed using in-depth qualitative interviews, focusing on agricultural land abandonment and its underlying factors. Second, the development of agricultural land abandonment in a representative Lithuanian municipality was modelled using Markov chain models, incorporating freely available geographic data as factors influencing land use transformation. Actual areas of abandoned agricultural land were mapped using orthophotos from 2012, 2018, and 2021, for both model development and validation. The importance of predictors in the model was then assessed in relation to their significance as drivers of agricultural land abandonment. The findings indicate that natural factors, such as the proximity of forests and topographical constraints, play a significant role in explaining land abandonment processes. Additionally, agricultural land abandonment is influenced by social, economic, and legal factors, including land ownership structures, migration, and infrastructure accessibility. The importance of soil quality, productivity, and the presence of nearby arable land was found to vary depending on data accuracy and local environmental conditions, highlighting the complexity of agricultural land use patterns. The chosen mixed-method approach, combining qualitative surveys with numerical spatial modelling, demonstrates potential for identifying critical land use areas and providing insights to improve land management policies and decision making. Full article

Emerging technologies and the so-called information and communication technologies (ICT or IT) are transforming society, interpersonal relationships, and our way of understanding the world and, by extension, also law and the legal profession. Emerging technologies will have a significant impact on society in the coming years and will pose new challenges and legal issues in the legal sector that will surely affect the development, evolution, and way of understanding the legal practice. The future of the legal industry will be comprise occupations that do not yet exist, or areas and subjects that are little or not yet known or even explored. The key for law firms will therefore be to specialize in these sectors. This Topic has become a window into the new challenges of law and policies in relation to emerging technologies. Full article

The rapid advancement of artificial intelligence (AI) has introduced unprecedented opportunities and challenges, necessitating a robust ethical and regulatory framework to guide its development. This study reviews key ethical concerns such as algorithmic bias, transparency, accountability, and the tension between automation and human oversight. It discusses the concept of algor-ethics—a framework for embedding ethical considerations throughout the AI lifecycle—as an antidote to algocracy, where power is concentrated in those who control data and algorithms. The study also examines AI’s transformative potential in diverse sectors, including healthcare, Insurtech, environmental sustainability, and space exploration, underscoring the need for ethical alignment. Ultimately, it advocates for a global, transdisciplinary approach to AI governance that integrates legal, ethical, and technical perspectives, ensuring AI serves humanity while upholding democratic values and social justice. In the second part of the paper, the author offers a synoptic view of AI governance across six major jurisdictions—the United States, China, the European Union, Japan, Canada, and Brazil—highlighting their distinct regulatory approaches. While the EU’s AI Act as well as Japan’s and Canada’s frameworks prioritize fundamental rights and risk-based regulation, the US’s strategy leans towards fostering innovation with executive directives and sector-specific oversight. In contrast, China’s framework integrates AI governance with state-driven ideological imperatives, enforcing compliance with socialist core values, whereas Brazil’s framework is still lacking the institutional depth of the more mature ones mentioned above, despite its commitment to fairness and democratic oversight. Eventually, strategic and governance considerations that should help chief data/AI officers and AI managers are provided in order to successfully leverage the transformative potential of AI for value creation purposes, also in view of the emerging international standards in terms of AI. Full article

Religious places represent one of the most significant categories of protected heritage. In Italy, however, places of worship belonging to minority communities often remain inconspicuous and are not legally recognized as part of the nation’s cultural heritage. Consequently, the histories of these communities face challenges in securing a space within the collective memory. This contribution, through a spatial approach and an interdisciplinary methodology, highlights the richness of the hidden heritage—both tangible and intangible—of the Greek Orthodox Church of the Nativity of St. John the Baptist in Turin. In particular, this research explores the role of the Greek language, which constitutes a significant element of intangible heritage for the community. Since the 1960s, regular celebrations in the Byzantine rite and the Greek language have been held in the Piedmontese capital. These biritual practices emerged in response to the demands of numerous Greek university students and families who revitalized the Orthodox presence in the territory during those years. In 2000, the Catholic Archdiocese granted the Greek Orthodox community the use of a church in the city’s historic center. This church is interpreted as a shared religious space, having undergone a transformation of identity over time: its Orthodox identity remains architecturally invisible, as the community continues to worship in a former Catholic church. Full article

For many years, countries around the world have been struggling with the problem of storing asbestos waste, especially in, those countries where the production and use of asbestos products have been legally banned. Following the adoption of plans for cleaning up asbestos waste, countries are struggling with the problem of its disposal, which mainly involves storing it in specialist landfills. At the same time, scientists are looking for alternatives to this type of “disposal” of asbestos by developing methods for degrading the harmful fibers. Particular attention has been paid to methods based on the thermal treatment of this waste, which results in hazardous asbestos fibers being thermally decomposed. This work focuses on the kinetic study of the thermal decomposition process of cement–asbestos using an exsitu thermal treatment. The results obtained made it possible to interpret this thermal transformation kinetically. Kinetic analysis of the isothermal data using an Avrami–Erofeev model yielded values for the overall reaction order. On this basis, the value of the apparent activation energy of the thermal decomposition process of the tested cement–asbestos samples was obtained, which was approximately 140–180 kJ mol⁻¹. Full article

The swift global proliferation of solar photovoltaic (PV) technology has significantly contributed to the acceleration of the transition to renewable energy. Projections indicate a significant rise in installed capacity by 2050, suggesting that the extensive implementation of solar panels is transforming energy systems while simultaneously highlighting important issues regarding end-of-life waste management and long-term sustainability. The environmental advantages of photovoltaic (PV) systems are overshadowed by the prevalent reliance on landfilling and inadequate recycling practices, revealing a substantial deficiency in sustainable waste management, especially in areas with underdeveloped policy frameworks. This research study examines the solar panel supply chain, highlighting critical stages, sources of waste generation, existing management practices, and potential areas for enhancement. Waste is classified into four categories, solid, hazardous, electronic (WEEE), and environmental, each necessitating specific management strategies. Regions such as Europe exhibit comprehensive legal frameworks and advanced recycling technologies, whereas others, including Latin America and certain areas of Asia, continue to encounter deficits in policy and infrastructure. The research highlights the implementation of the 6R principles—Recycle, Recover, Reduce, Reuse, Repair, and Refine—within a circular economy framework to improve sustainability, optimize resource utilization, and reduce environmental impact. The findings highlight the necessity for coordinated policies, technological innovation, and international collaboration to ensure a sustainable future for solar energy. This study offers important insights for policymakers, industry stakeholders, and researchers focused on enhancing circularity and sustainability within the photovoltaic sector. Full article

The legal and social perception of sexually motivated crimes has undergone profound transformations in the Czech lands from the Middle Ages to the present. Acts once considered grave moral transgressions, punishable by death, have been gradually decriminalised or even integrated into the realm of personal identity and cultural self-expression. This article examines the evolving legal frameworks and societal attitudes towards such offences, with a particular focus on their implications for family structures, inheritance rights, and genealogical continuity. By analysing historical judicial records—primarily early modern pitch books—alongside contemporary legislation, we highlight the shifting boundaries between crime, morality, and individual rights. Methodologically, this study combines a historical–legal analysis with comparative criminology to elucidate the changing regulatory mechanisms governing sexual behaviour. The findings illustrate that, while legal norms have progressively moved away from religious morality toward individual freedoms, some taboos persist, reflecting enduring social anxieties. The Czech case serves as a model for broader European trends, offering valuable insights into the interplay between law, social norms, and genealogical structures across different historical periods. Full article

With the rapid growth in maritime traffic, navigational safety has become a pressing concern. Some vessels deliberately manipulate their type information to evade regulatory oversight, either to circumvent legal sanctions or engage in illicit activities. Such practices not only undermine the accuracy of maritime supervision but also pose significant risks to maritime traffic management and safety. Therefore, accurately identifying vessel types is essential for effective maritime traffic regulation, combating maritime crimes, and ensuring safe maritime transportation. However, the existing methods fail to fully exploit the long-term sequential dependencies and intricate mobility patterns embedded in vessel trajectory data, leading to suboptimal identification accuracy and reliability. To address these limitations, we propose MESTR, a Multi-Task Enhanced Ship-Type Recognition model based on Automatic Identification System (AIS) data. MESTR leverages a Transformer-based deep learning framework with a motion-pattern-aware trajectory segment masking strategy. By jointly optimizing two learning tasks—trajectory segment masking prediction and ship-type prediction—MESTR effectively captures deep spatiotemporal features of various vessel types. This approach enables the accurate classification of six common vessel categories: tug, sailing, fishing, passenger, tanker, and cargo. Experimental evaluations on real-world maritime datasets demonstrate the effectiveness of MESTR, achieving an average accuracy improvement of 12.04% over the existing methods. Full article

Frequent ambiguities in contract terms often lead to costly legal disputes and project delays in the construction industry. Large Language Models (LLMs) offer a promising solution, enhancing accuracy and reducing misinterpretations. As studies pointed out, many professionals use LLMs, such as ChatGPT, to assist with their professional tasks at a minor level, such as information retrieval from the Internet and content editing. With access to a construction regulation database, LLMs can automate contract interpretation. However, the lack of Artificial Intelligence tools tailored to industry regulations hinders their adoption in the construction sector. This research addresses the gap by developing and deploying a publicly available specialised chatbot using the ChatGPT language model. The development process includes architectural design, data preparation, vector embeddings, and model integration. The study uses qualitative and quantitative methodologies to evaluate the chatbot’s role in resolving contract-related issues through standardised tests. The specialised chatbot, trained on construction-specific legal information, achieved an average score of 88%, significantly outperforming ChatGPT’s 36%. The integration of a domain-specific language model promises to revolutionise construction practices through increased precision, efficiency, and innovation. These findings demonstrate the potential of optimised language models to transform construction practices. Full article

Large Language Models (LLMs) have revolutionized natural language processing but present significant technical and legal challenges when confronted with the General Data Protection Regulation (GDPR). This paper examines the complexities involved in reconciling the design and operation of LLMs with GDPR requirements. In particular, we analyze how key GDPR provisions—including the Right to Erasure, Right of Access, Right to Rectification, and restrictions on Automated Decision-Making—are challenged by the opaque and distributed nature of LLMs. We discuss issues such as the transformation of personal data into non-interpretable model parameters, difficulties in ensuring transparency and accountability, and the risks of bias and data over-collection. Moreover, the paper explores potential technical solutions such as machine unlearning, explainable AI (XAI), differential privacy, and federated learning, alongside strategies for embedding privacy-by-design principles and automated compliance tools into LLM development. The analysis is further enriched by considering the implications of emerging regulations like the EU’s Artificial Intelligence Act. In addition, we propose a four-layer governance framework that addresses data governance, technical privacy enhancements, continuous compliance monitoring, and explainability and oversight, thereby offering a practical roadmap for GDPR alignment in LLM systems. Through this comprehensive examination, we aim to bridge the gap between the technical capabilities of LLMs and the stringent data protection standards mandated by GDPR, ultimately contributing to more responsible and ethical AI practices. Full article

Recent advances in legal language processing have highlighted limitations in correlation-based artificial intelligence approaches, prompting exploration of Causal Artificial Intelligence (AI) techniques for improved legal reasoning. This systematic review examines the challenges, limitations, and potential impact of Causal AI in legal language processing compared to traditional correlation-based methods. Following the Joanna Briggs Institute methodology, we analyzed 47 papers from 2017 to 2024 across academic databases, private sector publications, and policy documents, evaluating their contributions through a rigorous scoring framework assessing Causal AI implementation, legal relevance, interpretation capabilities, and methodological quality. Our findings reveal that while Causal AI frameworks demonstrate superior capability in capturing legal reasoning compared to correlation-based methods, significant challenges remain in handling legal uncertainty, computational scalability, and potential algorithmic bias. The scarcity of comprehensive real-world implementations and overemphasis on transformer architectures without causal reasoning capabilities represent critical gaps in current research. Future development requires balanced integration of AI innovation with law’s narrative functions, particularly focusing on scalable architectures for maintaining causal coherence while preserving interpretability in legal analysis. Full article

Decentralized Autonomous Organizations (DAOs) represent a transformative shift in organizational structures, combining decentralized governance with blockchain-based smart contracts. While DAOs present significant opportunities for innovation, they are confronted with several unresolved challenges, such as the centralization of power, the design of effective governance mechanisms, and the legal uncertainties surrounding their operation. Drawing on insights from recent studies and discussions presented in July 2024 at DAWO24, the first European DAO Workshop, this article explores these issues. The purpose of this article is to identify and analyze the critical research streams in DAO studies, particularly in governance mechanisms, technical frameworks, value assessment, and legal dimensions. A systematic approach, following the PRISMA methodology, was employed to analyze contributions from 14 extended abstracts and 11 full papers presented at DAWO24. The findings highlight the need for more equitable governance structures, secure and scalable technical frameworks, standardized tools for assessing DAOs’ value, and coherent legal frameworks to support decentralized operations. The article concludes by outlining future research directions, urging interdisciplinary collaboration to address current gaps and optimize DAO design, operation, and regulation. Full article

Sustainable development is a key concept that has been formulated over many years and is currently transforming our world. Decisions made in its spirit are influencing the economic and legal order and the daily lives of people in Europe and around the world. In Poland, achieving sustainable development requires a number of difficult decisions, and one of them is to transform the energy system toward low carbon. Poland’s energy transition is not an easy task in a country where, for many years, the dominant energy resource in terms of availability, resources and price has been coal. In view of such conditions, the Polish energy system has been based on coal, which in Polish conditions is still of strategic importance in meeting energy needs. For this reason, Poland’s planned move away from coal raises many controversies and concerns, especially in areas where mines operate. At the same time, it should be remembered that the mining industry, in addition to mining companies, brings together a large group of mining-related companies working for the benefit of mining. Due to the fact that it is in the territory of the Upper Silesian Coal Basin that about 80% of the documented balance resources of Polish hard coal are located, it was justified to conduct a survey among the residents of the Silesian Province as the group most likely to be affected by this decision. The aim of the survey was to find out the target group’s opinion on Poland’s transition away from coal. In turn, the main research problem was an attempt to answer the question of what percentage of households in the Silesian Province are opposed to Poland’s transition away from coal and what are the most significant factors influencing their opinion. Hence, this study presents the results of an empirical survey conducted among a randomly selected group of residents of the Silesian Province. The size of the research sample was 385 people. The study took into account factors such as age, place of residence, income, the square footage of the dwelling and the method of heating it, as well as respondents’ professional affiliation with the mining, mining-related, gas or energy industry. The results of the survey and analyses show that the vast majority of Upper Silesian residents are against the departure from coal, which is being planned in Poland’s energy transition. In addition, the most significant factors influencing respondents’ opinion on Poland’s move away from coal were identified and evaluated, revealing two social groups with differing views: one group opposes the move away from coal, prioritizing energy independence, energy security, energy prices and jobs over environmental issues; the other group advocates for the transition mainly for environmental reasons. Full article

(1) Background: Each year, significant volumes of healthcare data are generated through both research and care. Since fundamental digital processes cannot function effectively without essential data competencies, the challenge lies in enhancing the quality of data management by establishing data literacy among professionals in outpatient healthcare and research. (2) Methods: Within the DIM.RUHR project (Data Competence Center for Interprofessional Use of Health Data in the Ruhr Metropolis), a didactic concept for interprofessional data literacy education is developed, structured as a learning objectives matrix. Initially conceived through a literature review, this concept has been continually developed through collaboration with interprofessional project partners. The study was conducted between February 2023 and June 2024. (3) Results: The foundational structure and content of the didactic concept are based on various scientific studies related to general data literacy and the outcomes of an interactive workshop with project partners. Eight distinct subject areas have been developed to encompass the data literacy required in healthcare professions: (1) Fundamentals and general concepts, (2) ethical, legal, and social considerations, (3) establishing a data culture, (4) acquiring data, (5) managing data, (6) analyzing data, (7) interpreting data, and (8) deriving actions. Within these, learners’ data literacy is assessed across the four competency areas: basic, intermediate, advanced, and highly specialized. (4) Conclusions: The learning objectives matrix is anticipated to serve as a solid foundation for the development of teaching and learning modules aimed at enhancing data literacy across healthcare professions, enabling them to effectively manage data processes while addressing the challenges associated with digital transformation. Full article