ethicalabs/Research-Intent-Collab huggingface dataset:
On the Protection via Neighboring Right of Artificial Intelligence Generated Content (AIGC)

The understanding and application of copyright law with regards to AI generated images has created significant challenges. China and the United States demonstrate that the judicial bodies of the two countries have distinct differences in their attitude towards copyright protection of AI generated material. The reason is that the generation process of artificial intelligence generator includes two stages of user input instruction and automatic generation of artificial intelligence program. The discussion focuses on the personalized characteristics of the instructions in the user input stage and the automatic characteristics stage, which will determine whether the artificial intelligence generated material can be protected by copyright. This paper argues that the neighboring rights approach can better integrate these two stages. By comparing the object of rights, the subject of rights, and the content of rights between AI generated works and existing neighboring rights, it can be found that AI generated works are in conformity with neighboring rights, which can resolve the dilemma of "either this or that" choice faced by the copyright approach. However, neighboring rights are typical statutory rights and need to be coordinated with the copyright protection of the existing works used by artificial intelligence. Whether it is necessary to legislate on neighboring rights for artificial intelligence generated works still awaits further observation over time.
Thermodynamics of large-scale chemical reaction networks

Chemical and biological networks can describe a wide variety of processes, from gene regulatory networks to biochemical oscillations. Modeled by chemical master equations, these processes are inherently stochastic, as fluctuations dominate deterministic order at mesoscopic scales. These classic many-body processes suffer from the so-called curse of high dimensionality, which makes exact mathematical descriptions exponentially expensive to compute. The exponential cost renders the study of the thermodynamic properties of such systems out of equilibrium intractable and forces approximations of system noise or assumptions of continuous particle numbers. Here, we use tensor networks to numerically explore the thermodynamics of chemical processes by directly solving the ensemble solution of the chemical master equation with efficient (sub-exponential) computational cost. We provide accurate estimates of the entropy production rate, heat flux, chemical work, and nonequilibrium thermodynamic potentials, free from sampling errors or mean-field approximations. We illustrate our results through a dissipative self-assembly model. In this way, we show how tensor networks can inform the design of efficient chemical processes in previously unattainable regimes.
Climate projections for glacier change modelling over the Himalayas

AbstractGlaciers are of key importance to freshwater supplies in the Himalayan region. Their growth or decline is among other factors determined by an interaction of 2‐m air temperature (TAS) and precipitation rate (PR) and thereof derived positive degree days (PDD) and snow and ice accumulation (SAC). To investigate determining factors in climate projections, we use a model ensemble consisting of 36 CMIP5 general circulation models (GCMs) and 13 regional climate models (RCMs) of two Asian CORDEX domains for two different representative concentration pathways (RCP4.5 and RCP8.5). First, we downsize the ensemble in respect to the models' ability to correctly reproduce dominant circulation patterns (i.e., the Indian summer monsoon [ISM] and western disturbances [WDs]) as well as elevation‐dependent trend signals in winter. Within this evaluation, a newly produced data set for the Indus, Ganges and Brahmaputra catchments is used as observational data. The reanalyses WFDEI, ERA‐Interim, NCEP/NCAR and JRA‐55 are used to further account for observational uncertainty. In a next step, remaining TAS and PR data are bias corrected applying a new bias adjustment method, scale distribution mapping, and subsequently PDD and SAC computed. Finally, we identify and quantify projected climate change effects. Until the end of the century, the ensemble indicates a rise of PDD, especially during summer and for lower altitudes. Also TAS is rising, though the highest increases are shown for higher altitudes and between December and April (DJFMA). PRs connected to the ISM are projected to robustly increase, while signals for PR changes during DJFMA show a higher level of uncertainty and spatial heterogeneity. However, a robust decline in solid precipitation is projected over our research domain, with the exception of a small area in the high mountain Indus catchment where no clear signal emerges.
NO sparks off chromatin: Tales of a multifaceted epigenetic regulator

The discovery of nitric oxide (NO) revealed its ambiguous nature, which is related to its pleiotropic activities that control the homeostasis of every organism from bacteria to mammals in several physiological and pathological situations. The wide range of action of NO basically depends on two features: 1) the variety of chemical reactions depending on NO, and 2) the differential cellular responses elicited by distinct NO concentrations. Despite the increasing body of knowledge regarding its chemistry, biology and NO-dependent signaling pathways, little information is available on the nuclear actions of NO in terms of gene expression regulation. Indeed, studies of a putative role for this diatomic compound in regulating chromatin remodeling are still in their infancy. Only recently has the role of NO in epigenetics emerged, and some of its putative epigenetic properties are still only hypothetical. In the present review, we discuss the current evidence for NO-related mechanisms of epigenetic gene expression regulation. We link some of the well known NO chemical reactions and metabolic processes (e.g., S-nitrosylation of thiols, tyrosine nitration, cGMP production) to chromatin modification and address the most recent, striking hypothesis about NO and the control of chromosomes structure.
The strong-interaction limit of density functional theory

This is a comprehensive review of the strong-interaction limit of density functional theory. It covers the derivation of the limiting strictly correlated electrons (SCE) functional from exact Hohenberg-Kohn DFT, basic aspects of SCE physics such as the nonlocal dependence of the SCE potential on the density, equivalent formulations and the mathematical interpretation as optimal transport with Coulomb cost, rigorous results (including exactly soluble cases), approximations, numerical methods, integration into Kohn-Sham DFT (KS SCE), and applications to molecular systems, an example being that KS SCE, unlike the local density approximation or generalized gradient approximations, dissociates H$_2$ correctly. We have made an effort to make this review accessible to a broad audience of physicists, chemists, and mathematicians.
Assessing the Usefulness and Quality of Artificial Intelligence-Generated Content on Bone Graft Materials in Dentistry Based on Patient Questions

Background: This study evaluated the performance of three Artificial Intelligence (AI) conversational agents-ChatGPT-4, DeepSeek and Copilot-in providing clinically relevant information on bone graft materials in dentistry.  Methodology: A standardized set of questions related to bone graft types, indications, effectiveness, risks and patient comfort was posed to each model and these questions were derived from commonly asked patient inquiries. Responses were independently assessed by calibrated reviewers using validated tools: CLEAR criteria, a modified Global Quality Score (mGQS), a 5-point Likert scale for accuracy, a 4-point usefulness scale and readability metrics (Flesch Reading Ease and Flesch-Kincaid Grade Level).  Results: ChatGPT-4 outperformed Copilot in CLEAR scores (p = 0.030) and exceeded both DeepSeek and Copilot in mGQS (p = 0.022 and p = 0.017, respectively). However, no significant differences were observed in accuracy or readability (p &gt; 0.05). ChatGPT-4 scored significantly lower in usefulness compared to DeepSeek and Copilot (p &lt; 0.05). Negative correlations were found between mGQS and usefulness (r = -0.807) and between readability and grade level (r = -0.938).  Conclusion: ChatGPT-4 provided higher-quality and more comprehensive answers, but all models showed limitations in readability and usefulness. Improving the clarity and practical relevance of AI-generated content is essential to support dental education and enhance patient communication.
The Rule of Law, the Separation of Powers and Judicial Independence in Canada

Abstract                This chapter considers the meaning, scope, and application of three constitutional principles of surpassing importance in Canada. The rule of law is foundational to Canada’s constitutional framework and may properly be characterized as the first principle of Canadian constitutional law. It is linked to, and in some respects, forms the underpinning for other fundamental principles, including constitutionalism, federalism, democracy, and parliamentary sovereignty. As the latter principles are the focus of chapters by other commentators in this Handbook, this chapter will examine the rule of law primarily in relation to the separation of powers and judicial independence. The principle of judicial independence is also essential to the functioning and structure of the Constitution, given the role the courts are called upon to play in policing the constitutional limits of legislative power and administrative action. The separation of powers is still an emerging principle in Canada, but also increasingly viewed as fundamental.
BIOLOGIC THERAPY AND QUALITY OF LIFE IN NSAID-EXACERBATED RESPIRATORY DISEASE: A SYSTEMATIC REVIEW OF SNOT-22 PATIENT-REPORTED OUTCOMES

Background: Nonsteroidal anti-inflammatory drug-exacerbated respiratory disease (N-ERD) is a severe, refractory phenotype of chronic rhinosinusitis with nasal polyps (CRSwNP) and asthma. Traditional treatments, including repeated endoscopic sinus surgeries and aspirin therapy after desensitization (ATAD), are frequently limited by high recurrence and discontinuation rates, leaving a critical unmet need for durable symptom control. Objective: To systematically evaluate the impact of targeted biologic therapies on patient-reported quality of life in the N-ERD population, utilizing the 22-item Sino-Nasal Outcome Test (SNOT-22). Methods: A systematic literature search of PubMed and Scopus was conducted between January and March 2026. Eligibility criteria strictly isolated studies reporting longitudinal, continuous SNOT-22 data for adult N-ERD cohorts treated with monoclonal antibodies. Results: Eight studies met the inclusion criteria. Pre-treatment SNOT-22 scores confirmed severe baseline disability across all cohorts.Treatment with the anti-IL-4Rα agent dupilumab rapidly transformed patient outcomes, yielding clinically meaningful SNOT-22 score reductions ranging from 27 to 48 points, which were sustained for up to two years.Dupilumab demonstrated robust efficacy in restoring olfactory function and improving asthma control. While anti-IL-5 therapies (mepolizumab) provided clinical benefit, comparative data indicated they were less consistently effective at normalizing olfaction and achieving comprehensive symptom resolution than dupilumab. Conclusion: Targeted biologic therapies, particularly dupilumab, deliver significant improvements in quality of life for patients with N-ERD. They represent a highly effective, non-surgical step-up therapy capable of halting the surgical cycle and achieving profound symptom relief in treatment-refractory patients.
High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting

This paper explores the application of parallel algorithms and high-performance computing (HPC) in the processing and forecasting of large-scale water demand data. Building upon prior work, which identified the need for more robust and scalable forecasting models, this study integrates parallel computing frameworks such as Apache Spark for distributed data processing, Message Passing Interface (MPI) for fine-grained parallel execution, and CUDA-enabled GPUs for deep learning acceleration. These advancements significantly improve model training and deployment speed, enabling near-real-time data processing. Apache Spark’s in-memory computing and distributed data handling optimize data preprocessing and model execution, while MPI provides enhanced control over custom parallel algorithms, ensuring high performance in complex simulations. By leveraging these techniques, urban water utilities can implement scalable, efficient, and reliable forecasting solutions critical for sustainable water resource management in increasingly complex environments. Additionally, expanding these models to larger datasets and diverse regional contexts will be essential for validating their robustness and applicability in different urban settings. Addressing these challenges will help bridge the gap between theoretical advancements and practical implementation, ensuring that HPC-driven forecasting models provide actionable insights for real-world water management decision-making.
The Good, the Bad, and the Risky: Can Birds Be Incorporated as Biological Control Agents into Integrated Pest Management Programs?

AbstractSome bird species often benefit farmers by suppressing invertebrate crop pests, yet birds are rarely considered in integrated pest management (IPM) strategies. This is likely because some bird species pose risks to farmers through crop damage, intraguild predation, and food safety concerns. Nonetheless, the benefits of some bird species on crop production are often substantial. Therefore, understanding when birds are most likely to enhance crop production (and when they are most likely to depress it) is crucial for designing effective IPM strategies. Here, we briefly review the literature on birds in agricultural systems, discuss examples of how birds can provide services and disservices to crops, examine factors that influence the net effects of birds, and discuss emerging tools that will help fill key knowledge gaps surrounding the complex roles of birds in agricultural systems.
Respecting the principles of the rule of law and judicial independence through the prism of European Union policy

The essence of the ideas of the rule of law is to assert the sovereignty of the people as a source of power, to guarantee its freedom, to subordinate the state of society. In general, the rule of law can be determined as the state in which there is supremacy of law and law. In other words, the rule of law is a legal form of organization and activity of political power in its correlation with individuals as subjects of law. The issue of respect for the principles of the rule of law and the freedom of the judiciary is also true for some Western democracies. Thus, EU Member States that do not comply with Community standards on the rule of law could lose access to finance. For the Republic of Moldova, the promotion of the principles of the rule of law and the independence of the judiciary in the conditions of the implementation of the provisions of the Association Agreement implies the deepening of the essential reforms in order to adopt the European standrds.
‘Encore, encore!’ – secondary use of health data for research: A practical guide to POPIA

Health data hold immense potential for advancing medical science and informing public health strategies; however, their secondary use for research purposes has been poorly exploited, partly due to uncertainty and compliance concerns brought about by the Protection of Personal Information Act (POPIA). Researchers often assume that re-consent is the only option for secondary use. Yet POPIA provides several lawful grounds for processing special personal information, which includes health data, without necessarily obtaining re-consent. The present article serves as a practical guide for researchers navigating POPIA’s requirements for the secondary use of health data for research. A systematic approach is recommended: first, researchers should establish whether the original consent encompasses the intended secondary use. If it does not, they should determine which lawful grounds for processing special personal information best suit their context and can be relied upon. Options include obtaining re-consent, relying on a research purpose, or seeking authorisation from the Information Regulator. In addition, researchers may consider de-identifying the dataset, which would exempt it from the ambit of POPIA. Situations where pseudonymisation might achieve a similar outcome are also unpacked. By taking proactive, well-documented steps, researchers can better capitalise on existing health datasets to advance vital health research in South Africa.
Advancements in diffusion models for high-resolution image and short form video generation

This paper offers an in-depth review of the most recent advancements in diffusion models, particularly highlighting their transformative role in high-resolution image generation and their emerging applications in short-form video generation. Diffusion models, a class of generative models rooted in probabilistic frameworks, have rapidly gained prominence due to their ability to produce photorealistic and detailed outputs by reversing a noise-infusion process. Their strength lies in their capacity to generate high-quality media that exceeds previous limitations of generative models like GANs, especially in terms of diversity and training stability. The study utilized five scientific databases and a systematic search strategy to identify research articles on PubMed, Google Scholar, Scopus, IEEE, and Science Direct relating to the topic. Furthermore, books, dissertations, master's theses, and conference proceedings were utilized in this study. This study encompassed all publications published until 2024. The review begins by delving into the fundamental principles underlying diffusion models, which revolve around the process of gradually adding and removing noise from an image or video over a series of time steps. This section emphasizes the mathematical foundation of diffusion processes, particularly the forward process of noise addition and the reverse process of denoising, which enables these models to generate media with fine detail. A significant portion of this review is dedicated to the impact of diffusion models on high-resolution image and short-form video generation as well as success metrics for evaluating short-form video generation, curation, and summarization, areas where they have been especially transformative. Conclusively, this paper provides a comprehensive exploration of how diffusion models have reshaped the landscape of media generation. From their foundational principles and technical evolution to their applications in high-resolution media and short-form video, the paper highlights both the profound potential of these models and the ongoing challenges that must be addressed for their responsible and scalable use.
Alternative Dispute Resolution: Concept, Criticism and Future of Arbitration and Mediation

Settlement of disputes either in the courtroom or outside the courtroom requires the expeditious dispensation of justice as “justice delayed is justice denied” and “justice hurried is justice buried.” While maintaining the thin line between hurried and delayed dispensation of justice, the alternative dispute resolution processes are in trends in legal markets and are embellished with the characteristics of amicable, inexpensive, and expeditious resolution of conflict, hence, attracting the aggrieved parties to resolve their disputes through “arbitration, mediation, conciliation, and hybrid processes”. With this intent, this article explores the concept of alternative dispute resolution processes in general and arbitration and mediation in particular. Rendering qualitative methods, this article critically describes the difference between arbitration and mediation and spent ink to point out the in-depth criticism of the processes and to predict the future of techniques of alternative dispute resolution.
The relationship between manager leadership behavior and organizational pessimistic considering the mediating role of organizational justice in education

This study aime to examine the mediating role of organizational justice in the relation of manager leadership behavior to organizational pessimistic in education and training staff. This research was a descriptive study of correlational type focusing on structural equation modeling In this stydy, the statistical population consisted of all male and female education and training staff of B.on city in Charmahal Bakhtiyari province that a sample of 300 teachers (140 women and 160 men) was selected by random sampling method. Participants completed the Leadership Behavior Descriptive Questionnaire, the Standard Pessimistic Organizational Inventory (SPOI), and the Organizational Justice Questionnaire . The data were analyzed using Pearson correlation and structural equation modeling . The results showed that there was a significant negative correlations between manager leadership behavior and its components including supportive leadership and authoritative leadership with organizational pessimism . The manager leadership behavior and its components were positively associated with organizational justice. Organizational justice and its components including distributive justice, procedural justice and interactional justice had significant negative relationships with organizational pessimism . All three components of organizational justice mediated the relationship between manager leadership behavior and its component including supportive leadership with organizational pessimism. Organizational justice components, except for interactional justice, mediated the relation of authoritative leadership to organizational pessimism . On the whole, it can be concluded that the manager leadership behavior including supportive leadership and authoritative leadership can lead to reduction in organizational pessimism through organizational justice in in education and training staff.
Error Correction for Reliable Quantum Computing

Quantum computers herald the arrival of a new era in which previously intractable computational problems will be solved efficiently. However, quantum technology is held down by decoherence, a phenomenon that is omnipresent in the quantum paradigm and that renders quantum information useless when left unchecked. The science of quantum error correction, a discipline that seeks to combine and protect quantum information from the effects of decoherence using structures known as codes, has arisen to meet this challenge. Stabilizer codes, a particular subclass of quantum codes, have enabled fast progress in the field of quantum error correction by allowing parallels to be drawn with the widely studied field of classical error correction. This has resulted in the construction of the quantum counterparts of well-known capacity-approaching classical codes like sparse codes and quantum turbo codes. However, quantum codes obtained in this manner do not entirely evoke the stupendous error correcting abilities of their classical counterparts. This occurs because classical strategies ignore important differences between the quantum and classical paradigms, an issue that needs to be addressed if quantum error correction is to succeed in its battle with decoherence. In this dissertation we study a phenomenon exclusive to the quantum paradigm, known as degeneracy, and its effects on the performance of sparse quantum codes. Furthermore, we also analyze and present methods to improve the performance of a specific family of sparse quantum codes in various different scenarios.