Type of the article: Research Article
This study examines whether knowledge management practices in commercial banks influence employees’ knowledge of environmental law, providing empirical evidence from Vietnam’s banking sector as an emerging Asian economy. Data were collected from 568 employees across 35 Vietnamese commercial banks in the first quarter of 2025, and the hypotheses were tested using partial least squares structural equation modeling (PLS-SEM). The results indicate that all three knowledge management components (information-sharing systems, a learning-oriented organizational culture, and training and skill development) have positive effects on both work motivation and perceived organizational support. Information-sharing systems exert a strong influence on organizational support (β = 0.188) and work motivation (β = 0.207), while a learning-oriented culture significantly enhances organizational support (β = 0.204) and work motivation (β = 0.165). Training and skill development show the strongest effects on work motivation (β = 0.240) and organizational support (β = 0.185). In turn, work motivation and organizational support directly and positively affect employees’ environmental law knowledge (β = 0.343 and β = 0.363, respectively). Moreover, both variables significantly mediate the relationships between KM components and environmental law knowledge. These findings underscore the relevance of social exchange theory in explaining how effective knowledge management practices enhance employees’ legal knowledge, thereby contributing to more sustainable organizational development. Accordingly, practical implications are proposed for bank managers to strengthen knowledge management implementation.
Acknowledgment The authors would like to thank the Editor-in Chief and a reviewer for their helpful comments that in our view have helped to improve the quality of the manuscript significantly. Beside, this study is the result of collaboration between researchers from the University of Law, Hue University, and School of Business and Economics, Duy Tan University. The authors would like to thank both institutions for their support and facilitation in the publication of this research.
The GDPR contains an accuracy principle, as most data privacy laws in the world do. In principle, data controllers must ensure that personal data they use are accurate. Some have argued that the accuracy principle does not apply to personal data in the form of opinions about data subjects. We argue, however, from a positive law perspective, that the accuracy principle does apply to opinions. We further argue, from a normative perspective, that the accuracy principle should apply to opinions.
L. Thomas van Binsbergen, Marten C. Steketee, Milen G. Kebede
et al.
Compliance with the GDPR privacy regulation places a significant burden on organisations regarding the handling of personal data. The perceived efforts and risks of complying with the GDPR further increase when data processing activities span across organisational boundaries, as is the case in both small-scale data sharing settings and in large-scale international data spaces. This paper addresses these concerns by proposing a case-generic method for automated normative reasoning that establishes legal arguments for the lawfulness of data processing activities. The arguments are established on the basis of case-specific legal qualifications made by privacy experts, bringing the human in the loop. The obtained expert system promotes transparency and accountability, remains adaptable to extended or altered interpretations of the GDPR, and integrates into novel or existing distributed data processing systems. This result is achieved by defining a formal ontology and semantics for automated normative reasoning based on an analysis of the purpose-limitation principle of the GDPR. The ontology and semantics are implemented in eFLINT, a domain-specific language for specifying and reasoning with norms. The XACML architecture standard, applicable to both access and usage control, is extended, demonstrating how GDPR-based normative reasoning can integrate into (existing, distributed) systems for data processing. The resulting system is designed and critically assessed in reference to requirements extracted from the GPDR.
Artificial intelligence is reshaping science, society, and power. Yet many debates over its likely impact remain fixated on extremes: utopian visions of universal benefit and dystopian fears of existential doom, or an arms race between the U.S. and China, or the Global North and Global South. What's missing is a serious conversation about distribution - who gains, who loses, and who decides. The global AI landscape is increasingly defined not just by geopolitical divides, but by the deepening imbalance between public governance and private control. As governments struggle to keep up, power is consolidating in the hands of a few tech firms whose influence now rivals that of states. If the twentieth century saw the rise of international institutions, the twenty-first may be witnessing their eclipse - replaced not by a new world order, but by a digital oligarchy. This essay explores what that shift means for international law, global equity, and the future of democratic oversight in an age of silicon sovereignty.
Heather J. Alexander, Jonathan A. Simon, Frédéric Pinard
The law draws a sharp distinction between objects and persons, and between two kinds of persons, the ''fictional'' kind (i.e. corporations), and the ''non-fictional'' kind (individual or ''natural'' persons). This paper will assess whether we maximize overall long-term legal coherence by (A) maintaining an object classification for all future AI systems, (B) creating fictional legal persons associated with suitably advanced, individuated AI systems (giving these fictional legal persons derogable rights and duties associated with certified groups of existing persons, potentially including free speech, contract rights, and standing to sue ''on behalf of'' the AI system), or (C) recognizing non-fictional legal personhood through legal identity for suitably advanced, individuated AI systems (recognizing them as entities meriting legal standing with non-derogable rights which for the human case include life, due process, habeas corpus, freedom from slavery, and freedom of conscience). We will clarify the meaning and implications of each option along the way, considering liability, copyright, family law, fundamental rights, civil rights, citizenship, and AI safety regulation. We will tentatively find that the non-fictional personhood approach may be best from a coherence perspective, for at least some advanced AI systems. An object approach may prove untenable for sufficiently humanoid advanced systems, though we suggest that it is adequate for currently existing systems as of 2025. While fictional personhood would resolve some coherence issues for future systems, it would create others and provide solutions that are neither durable nor fit for purpose. Finally, our review will suggest that ''hybrid'' approaches are likely to fail and lead to further incoherence: the choice between object, fictional person and non-fictional person is unavoidable.
As it is the case with almost every modern legal system, mediation performs an important role in civil dispute resolution in Türkiye. Even though mediation in the modern sense was introduced in Turkish law a little more than a decade ago, its development and adoption was swift. Probably the main factor behind this quick adoption in legal circles is the push by the Government and the Legislator; particularly through implementing a form of mandatory mediation in 2017. While there are two main approaches to the implementation of proper mandatory mediation, Turkish Legislator preferred the procedural requirement option. After the first implementation regarding labor disputes, there were three more major expansions to the extent of mediation as procedural requirement; including commercial disputes, consumer disputes and various disputes within the jurisdiction of civil courts of peace. Since the expansion trend is still ongoing, in this study, the effectiveness of mandatory mediation, as well as probable future developments regarding the subject are examined and discussed.
Surrogacy is a complex and evolving practice that sits at the intersection of reproductive medicine, ethics, law, and social policy. This comprehensive review traces the historical, medical, and ethical foundations of surrogacy, from its roots in early IVF experiments to its current global practice. It explores the distinctions between traditional and gestational surrogacy, outlines medical and social indications, and evaluates associated perinatal and psychological outcomes. Central to the analysis is the debate between altruistic and commercial surrogacy, highlighting the ethical tensions surrounding compensation, autonomy, and potential exploitation. The review examines global legal frameworks, with detailed attention to Israel’s uniquely regulated system, which balances reproductive liberty with strict oversight. The paper also considers religious perspectives, showing how cultural and theological differences shape national policy. The experiences of gestational carriers, commissioning couples, and children born through surrogacy are examined to underscore the psychological and social implications. While surrogacy offers hope to many, it raises profound questions about the commodification of reproduction, the fragmentation of motherhood, and the responsibilities of professionals involved in care. Ultimately, the review argues for nuanced, culturally sensitive regulation that protects all parties particularly the child, whose interests must remain central throughout the surrogacy process.
Wood, steel, and concrete constitute the three predominant structural materials employed in contemporary commercial and residential construction. In composite applications, bond interfaces between these materials represent critical structural junctures that frequently exhibit a reduced load-bearing capacity, rendering them susceptible to the initiation of cracks. To elucidate the fracture propagation mechanisms at composite material interfaces, this study implements the cohesive zone method (CZM) to numerically simulate interfacial cracking behavior in two material systems: glued laminated timber (GLT) and reinforced concrete (RC). The adopted CZM framework utilizes a progressive delamination approach through cohesive elements governed by a bilinear traction–separation constitutive law. This methodology enables the simulation of interfacial failure through three distinct fracture modes: mode I (pure normal separation), mode II (pure in-plane shear), and mixed-mode (mode m) failure. Numerical models were developed for GLT beams, RC beams, and RC slab structures to investigate the propagation of interfacial cracks under monotonic loading conditions. The simulation results demonstrate strong agreement with experimental cracking observations in GLT structures, validating the CZM’s efficacy in characterizing both mechanical behavior and crack displacement fields. The model successfully captures transverse tensile failure (mode I) parallel to wood grain, longitudinal shear failure (mode II), and mixed-mode failure (mode m) in GLT specimens. Subsequent application of the CZM to RC structural components revealed a comparable predictive accuracy in simulating the interfacial mechanical response and crack displacement patterns at concrete composite interfaces. These findings collectively substantiate the robustness of the proposed CZM framework in modeling complex fracture phenomena across diverse construction material systems.
Jelena Matijašević, Nenad Bingulac, Darko Marinković
In the last decade, digital evidence in criminal proceedings has become a key tool in modern forensic investigation, with the help of which it is possible to identify, analyze and verify information that can be crucial for making a decision in court proceedings. Digital data, through the perception of various forms of electronic records, are increasingly becoming the basic evidence in criminal cases, and because of this, no distinction is made between existing material evidence and modern digital evidence. In this research, the importance of digital evidence, its advantages and challenges in collection and processing, as well as the legal and ethical aspects of its use in criminal proceedings were pointed out and indicated. The importance of the methodological approach in the forensic analysis of digital evidence was also pointed out, all in order to achieve the admissibility of digital evidence before a criminal court. The challenges and opportunities presented by this evidence are drawn throughout the research and pointed out. The hypothesis of this research is contained in its title and refers precisely to controversial issues and the legislative application of digital evidence in criminal proceedings. The aim of the work is to consider this topic by looking at the new regulation and directive that have been passed and will only be adopted in 2026, but in principle to consider the issue of digital evidence through practice because this topic remains crucial for the further development of the judicial system in the digital age. During the writing of this research, an analytical method was used in order to consider the legislation of the European Union, and make a comparison with the domestic legislation. Then, the normative method was used when considering the criminal procedure and defining digital evidence, and certainly the deductive method was also used.
We present preliminary results about Legistix, a tool we are developing to automatically consolidate the French and European law. Legistix is based both on regular expressions used in several compound grammars, similar to the successive passes of a compiler, and on a new specialized language of functional type, allowing to describe the changes applied to the texts. Instead of creating manually a full consolidated version of a text at each modification date, Legistix generates automatically programs from legal documents written in natural language to automatically create the consolidated versions.
Our project aims at helping and supporting stakeholders in refugee status adjudications, such as lawyers, judges, governing bodies, and claimants, in order to make better decisions through data-driven intelligence and increase the understanding and transparency of the refugee application process for all involved parties. This PhD project has two primary objectives: (1) to retrieve past cases, and (2) to analyze legal decision-making processes on a dataset of Canadian cases. In this paper, we present the current state of our work, which includes a completed experiment on part (1) and ongoing efforts related to part (2). We believe that NLP-based solutions are well-suited to address these challenges, and we investigate the feasibility of automating all steps involved. In addition, we introduce a novel benchmark for future NLP research in refugee law. Our methodology aims to be inclusive to all end-users and stakeholders, with expected benefits including reduced time-to-decision, fairer and more transparent outcomes, and improved decision quality.
The serious mismatch between industrialization and urbanization has led to the emergence of ghost cities. Industry-and-city integration aims to agglomerate industries and the population simultaneously by coordinating the planning and construction, and by mixing different functional areas including industry, office, living, and commercial functions. Based on the population spatial vector database of Jimei District in Xiamen in 2020, this paper empirically analyzes the effects of spatial patterns between industry and city, in terms of residential location and job accessibility, on the housing occupancy rate in new towns and cities. The findings demonstrate that: (1) The attraction of residential location to population varies among three different urban expansion models. The housing occupancy rate of residential areas that meet the concentric circle model is the highest, followed by the sector model, and the multiple nuclei model is the lowest; (2) The jobs–housing relationship has a stable and positive impact on the occupancy rate of commercial housing in the new town, which verifies that job accessibility is the basic demand for families’ residential location choice; (3) There is a significant pattern difference in the influence of job accessibility on the occupancy rate. The occupancy rate of the sector model residential area is highly dependent on job accessibility: the higher the job accessibility, the lower the occupancy rate of the concentric residential area, while job accessibility has a weak impact on the occupancy rate of the multiple nuclei residential area. The conclusions suggest that the spatial planning of new towns should include a clear population absorbing strategy, and the residential location should follow the expansion law of the urban residential functional area, balance the relationship between industrial agglomeration and the job–housing relationship, and allocate life factors in a targeted manner according to the actual impact of job accessibility.
Tujuan penelitian ini untuk mengkaji perlindungan hak cipta lagu dan musik yang digunakan secara komersial oleh orang lain berdasarkan Undang-Undang Hak Cipta dan peran dari Lembaga Manajemen Kolektif Nasional terhadap penggunaan karya ciptaan bagi perlindungan hak cipta. Lagu dan musik ialah karya ciptaan yang mudah disalahgunakan secara ilegal, sehingga dalam hal ini perlindungan terhadap hak cipta sangat dibutuhkan. Seperti yang terjadi di Surabaya, tempat hiburan karaoke menggunakan karya ciptaan berupa fonogram tanpa ada izin dari pencipta lagu. Tujuan dari penelitian ini ialah untuk mengetahui perlindungan yang dapat dilakukan terhadap penggunaan karya ciptaan di tempat karaoke dan peran Lembaga Manajemen Kolektif Nasional (LMKN) dalam perlindungan karya cipta. Jenis penelitian bersifat yuridis normatif, dengan kajian pustaka dan pendekatan perundang-undangan yang digunakan dalam sumber penelitian. Hasil penelitian ini menunjukkan bahwa perlindungan karya cipta untuk penggunaan komersial diatur dalam Undang-Undang Hak Cipta Tahun 2014 dan Peraturan Pemerintah Nomor 56 Tahun 2021 tentang Pengelolaan Royalti Hak Cipta Lagu dan/atau Musik. Upaya perlindungan hak cipta dapat dilakukan dengan dua cara yaitu upaya pencegahan dan penindakan. Direktorat Jenderal Kekayaan Intelektual yang berada di bawah Kemenkumham telah membentuk suatu lembaga untuk melindungi dan menegakkan hukum yang mengatur penggunaan karya berhak cipta yang dibuat oleh pemerintah. lembaga non-APBN yang dibentuk adalah LMK (Lembaga Manajemen Kolektif) dan LMKN (Lembaga Manajemen Kolektif Nasional) sebagai lembaga yang diberdayakan untuk mengelola hak cipta. LMK dan LMKN bertanggung jawab untuk mengumpulkan dan mendistribusikan royalti. Dengan demikian, dalam perlindungan hak cipta, lembaga ini memainkan peran tertentu, karena berwenang untuk mengelola hak ekonomi pencipta.
Kata kunci:
Abstract
The purpose of this study is to examine the copyright protection of songs and music that are used commercially by other people based on the Copyright Act and the role of Management Institutions. Songs and music are creations that are easily misused illegally, so in this case, copyright protection is needed. In Surabaya, karaoke entertainment venues use phonograms without the permission of the songwriters. The purpose of this research is to regulate the protection that can be carried out against the use of works of creation in karaoke venues and the role of the National Collective Management Institute (LMKN) in protecting copyrighted works. This type of research is normative juridical, with literature review and statutory approaches used in research sources. The results of this study indicate that the protection of copyrighted works for commercial use is regulated in the Copyright Law 2014 and Government Regulation Number 56 of 2021 concerning the Management of Song and/or Music Copyright Royalties. Efforts to protect copyright can be carried out in two method, prevention and enforcement efforts. The Directorate General of Intellectual Property is under the Ministry of Law and Human Rights has established an institution to protect and enforce laws governing the use of copyrighted works created by the government. The non-APBN institutions formed are the LMK (Collective Management Institute) and LMKN (National Collective Management Institute) as institutions empowered to manage copyrights. LMK and LMKN are responsible for collecting and distributing royalties. In copyright protection, this institution have a role, because it is authorized to administer the economic rights of the creator.
Power-law distributions are essential in computational and statistical investigations of extreme events and complex systems. The usual technique to generate power-law distributed data is to first infer the scale exponent $α$ using the observed data of interest and then sample from the associated distribution. This approach has important limitations because it relies on a fixed $α$ (e.g., it has limited applicability in testing the {\it family} of power-law distributions) and on the hypothesis of independent observations (e.g., it ignores temporal correlations and other constraints typically present in complex systems data). Here we propose a constrained surrogate method that overcomes these limitations by choosing uniformly at random from a set of sequences exactly as likely to be observed under a discrete power-law as the original sequence (i.e., regardless of $α$) and by showing how additional constraints can be imposed in the sequence (e.g., the Markov transition probability between states). This non-parametric approach involves redistributing observed prime factors to randomize values in accordance with a power-law model but without restricting ourselves to independent observations or to a particular $α$. We test our results in simulated and real data, ranging from the intensity of earthquakes to the number of fatalities in disasters.
This work extends the proof of a local version of the linearized second law involving an entropy current with non-negative divergence by including the arbitrary non-minimal coupling of scalar and $U(1)$ gauge fields with gravity. In recent works, the construction of entropy current to prove the linearized second law rested on an important assumption about the possible matter couplings to gravity: the corresponding matter stress tensor was assumed to satisfy the null energy conditions. However, the null energy condition can be violated, even classically, when the non-minimal coupling of matter fields to gravity is considered. Considering small dynamical perturbations around stationary black holes in diffeomorphism invariant theories of gravity with non-minimal coupling to scalar or gauge fields, we prove that an entropy current with non-negative divergence can still be constructed. The additional non-minimal couplings that we have incorporated contribute to the entropy current, which may even survive in the equilibrium limit. We also obtain a spatial current on the horizon apart from the entropy density in out-of-equilibrium situations. We achieve this by using a boost symmetry of the near horizon geometry, which constraints the off-shell structure of a specific component of the equations of motion with newer terms due to the non-minimal couplings. The final expression for the entropy current is $U(1)$ gauge-invariant for gauge fields coupled to gravity. We explicitly check that the entropy current obtained from our abstract arguments is consistent with the expressions already available in the literature for specific model theories involving non-minimal coupling of matter with higher derivative theories of gravity. Finally, we also argue that the physical process version of the first law holds for these theories with arbitrary non-minimal matter couplings.
Given a small training data set and a learning algorithm, how much more data is necessary to reach a target validation or test performance? This question is of critical importance in applications such as autonomous driving or medical imaging where collecting data is expensive and time-consuming. Overestimating or underestimating data requirements incurs substantial costs that could be avoided with an adequate budget. Prior work on neural scaling laws suggest that the power-law function can fit the validation performance curve and extrapolate it to larger data set sizes. We find that this does not immediately translate to the more difficult downstream task of estimating the required data set size to meet a target performance. In this work, we consider a broad class of computer vision tasks and systematically investigate a family of functions that generalize the power-law function to allow for better estimation of data requirements. Finally, we show that incorporating a tuned correction factor and collecting over multiple rounds significantly improves the performance of the data estimators. Using our guidelines, practitioners can accurately estimate data requirements of machine learning systems to gain savings in both development time and data acquisition costs.
There are many previous studies on the Hopf algebra $K(n)_*(K(n))$, the stable cooperations of $n$th Morava $K$-theory at an odd prime. Whereas the main part of $K(n)_*(K(n))$ corepresents the group-valued functor consisting of strict automorphisms of the Honda formal group law of height $n$, relations between the whole structure of $K(n)_*(K(n))$ including the exterior part and formal group laws have not been investigated well. Firstly, we constitute a functor $C(-)$ which is given by the fiber product of two natural homomorphism between subgroups of automorphisms of formal group laws, and the Hopf algebra $C_*$ corepresenting $C(-)$. Next, we construct a Hopf algebra homomorphism $κ^*:C_*\to K(n)_*(K(n))$ naturally. To relate $C_*$ to $K(n)_*(K(n))$, we use stable comodule algebras which are introduced by Boardman. From the algebra structure of $K(n)_*(K(n))$ which is given by Würgler and Yagita, we see that $κ^*$ is an isomorphism. Since we formulate $C_*$ by using formal group laws, the isomorphism $κ^*$ clarifies relationship between the Hopf algebra structure of $K(n)_*(K(n))$ including the exterior algebra part and formal group laws.
El objetivo es determinar la vulneración del principio internacional de no discriminación por la reforma al impuesto departamental al consumo de licores en Colombia, en comparación con la legislación española, como referente normativo, en tres momentos: i) analizar los aspectos constitucionales y normativos del IDC en Colombia y en España, ii) determinar la existencia de presión internacional para la modificación de la normativa interna con fundamento en el principio de no discriminación en el caso de Colombia, y iii) analizar la nueva legislación en el Estado colombiano respecto a la aplicación del principio de no discriminación. Para abordar el trabajo se tuvo como metodología de investigación el analítico-deductivo y documental tomando como fuentes la doctrina internacional, las constituciones de Colombia y España, normas internas de los Estados, y la doctrina nacional colombiana. Se concluye que la reforma ha sido totalmente infortunada.
Peter Henderson, Ben Chugg, Brandon Anderson
et al.
We explore the promises and challenges of employing sequential decision-making algorithms -- such as bandits, reinforcement learning, and active learning -- in law and public policy. While such algorithms have well-characterized performance in the private sector (e.g., online advertising), the tendency to naively apply algorithms motivated by one domain, often online advertisements, can be called the "advertisement fallacy." Our main thesis is that law and public policy pose distinct methodological challenges that the machine learning community has not yet addressed. Machine learning will need to address these methodological problems to move "beyond ads." Public law, for instance, can pose multiple objectives, necessitate batched and delayed feedback, and require systems to learn rational, causal decision-making policies, each of which presents novel questions at the research frontier. We discuss a wide range of potential applications of sequential decision-making algorithms in regulation and governance, including public health, environmental protection, tax administration, occupational safety, and benefits adjudication. We use these examples to highlight research needed to render sequential decision making policy-compliant, adaptable, and effective in the public sector. We also note the potential risks of such deployments and describe how sequential decision systems can also facilitate the discovery of harms. We hope our work inspires more investigation of sequential decision making in law and public policy, which provide unique challenges for machine learning researchers with potential for significant social benefit.