ethicalabs/Research-Intent-Collab huggingface dataset:
Inter-correlation between Human Rights and Environmental Justice: A Discourse of Right to a Good and Healthy Environment in Indonesia

The right to a good and healthy environment is part of human rights. Where the right to the environment as a human right to live in an environment with a minimum quality that allows the realization of a dignified and prosperous life. As part of human rights, the state must respect, fulfill, and protect the right to a good and healthy environment. To fulfill the right to a good and healthy environment, everyone is given the right to obtain environmental education, access to information, access to participation, and access to justice. In other words, without access to information, participation, and justice, the right to a good and healthy environment will be difficult to fulfill. The government needs a mechanism or instrument for these three accesses to fulfill the right to a good and proper environment for its citizens. In the next section, we will review mechanisms or instruments for access to information, as an effort to fulfill the right to a good and healthy environment, especially in the context of controlling air pollution, as well as the shortcomings and alternatives of these mechanisms or instruments. This study aims to analyze the discourse on the right to a good and healthy environment in Indonesia in the study of human rights and environmental law in Indonesia.
Implementing a quality management system in dietetics for improving patient safety

Background Providing quality, patient-centered nutrition care is the main priority of dieticians working in clinical settings. Within the last decades there was a development of numerous new fields of work for clinical dieticians, including enteral and parenteral nutrition as well as the challenge integrating current evidence-based findings into daily work. To ensure continuous improvement of ongoing processes, a quality management system is crucial. This systematic approach leads to high quality nutrition care and in further consequence patient safety. Therefore it was the aim of the Department of Dietetics and Applied Nutrition (University Hospital Graz) to implement a quality management system and the certification through an official accreditation organization (qualityaustria, ISO 9001:2008) [1].
Facilitating the Use of Data From Multiple Sources for Formative Learning in the Context of Digital Assessments: Informing the Design and Development of Learning Analytic Dashboards

Learning analytic dashboards (LADs) are data visualization systems that use dynamic data in digital learning environments to provide students, teachers, and administrators with a wealth of information about student’s engagement, experiences, and performance on tasks. LADs have become increasingly popular, particularly in formative learning contexts, and help teachers make data-informed decisions about a student’s developing skills on a topic. LADs afford the possibility for teachers to obtain real-time data on student performance, response processes, and progress on academic learning tasks. However, data presented on LADs are often not based on an evaluation of stakeholder needs, and have been found to not be clearly interpretable and actionable for teachers to readily adapt their pedagogical actions based on these insights. We elaborate on how insights from research focused on interpretation and use of Score Reporting systems and research on open learner models (OLMs) can be used to inform a research agenda aimed at exploring the design and evaluation of LADs.
Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting

Deep learning is often criticized by two serious issues which rarely exist in natural nervous systems: overfitting and catastrophic forgetting. It can even memorize randomly labelled data, which has little knowledge behind the instance-label pairs. When a deep network continually learns over time by accommodating new tasks, it usually quickly overwrites the knowledge learned from previous tasks. Referred to as the {\it neural variability}, it is well-known in neuroscience that human brain reactions exhibit substantial variability even in response to the same stimulus. This mechanism balances accuracy and plasticity/flexibility in the motor learning of natural nervous systems. Thus it motivates us to design a similar mechanism named {\it artificial neural variability} (ANV), which helps artificial neural networks learn some advantages from ``natural'' neural networks. We rigorously prove that ANV plays as an implicit regularizer of the mutual information between the training data and the learned model. This result theoretically guarantees ANV a strictly improved generalizability, robustness to label noise, and robustness to catastrophic forgetting. We then devise a {\it neural variable risk minimization} (NVRM) framework and {\it neural variable optimizers} to achieve ANV for conventional network architectures in practice. The empirical studies demonstrate that NVRM can effectively relieve overfitting, label noise memorization, and catastrophic forgetting at negligible costs. \footnote{Code: \url{https://github.com/zeke-xie/artificial-neural-variability-for-deep-learning}.
MHD analysis on the physical designs of CFETR and HFRC

The China Fusion Engineering Test Reactor (CFETR) and the Huazhong Field Reversed Configuration (HFRC), currently both under intensive physical and engineering designs in China, are the two major projects representative of the low-density steady-state and high-density pulsed pathways to fusion. One of the primary tasks of the physics designs for both CFETR and HFRC is the assessment and analysis of the magnetohydrodynamic (MHD) stability of the proposed design schemes. Comprehensive efforts on the assessment of MHD stability of CFETR and HFRC baseline scenarios have led to preliminary progresses that may further benefit engineering designs.
Insights into DeepSeek-V3: Scaling Challenges and Reflections on Hardware for AI Architectures

The rapid scaling of large language models (LLMs) has unveiled critical limitations in current hardware architectures, including constraints in memory capacity, computational efficiency, and interconnection bandwidth. DeepSeek-V3, trained on 2,048 NVIDIA H800 GPUs, demonstrates how hardware-aware model co-design can effectively address these challenges, enabling cost-efficient training and inference at scale. This paper presents an in-depth analysis of the DeepSeek-V3/R1 model architecture and its AI infrastructure, highlighting key innovations such as Multi-head Latent Attention (MLA) for enhanced memory efficiency, Mixture of Experts (MoE) architectures for optimized computation-communication trade-offs, FP8 mixed-precision training to unlock the full potential of hardware capabilities, and a Multi-Plane Network Topology to minimize cluster-level network overhead. Building on the hardware bottlenecks encountered during DeepSeek-V3's development, we engage in a broader discussion with academic and industry peers on potential future hardware directions, including precise low-precision computation units, scale-up and scale-out convergence, and innovations in low-latency communication fabrics. These insights underscore the critical role of hardware and model co-design in meeting the escalating demands of AI workloads, offering a practical blueprint for innovation in next-generation AI systems.
Integration of generative machine learning with the heuristic crystal structure prediction code FUSE

We integrate generative machine learning with heuristic crystal structure prediction in FUSE. The combined result shows superior performance over both components, accelerating the pace at which we will be able to predict and discover new compounds.
Natural language processing to identify lupus nephritis phenotype in electronic health records

Abstract                 Background                 Systemic lupus erythematosus (SLE) is a rare autoimmune disorder characterized by an unpredictable course of flares and remission with diverse manifestations. Lupus nephritis, one of the major disease manifestations of SLE for organ damage and mortality, is a key component of lupus classification criteria. Accurately identifying lupus nephritis in electronic health records (EHRs) would therefore benefit large cohort observational studies and clinical trials where characterization of the patient population is critical for recruitment, study design, and analysis. Lupus nephritis can be recognized through procedure codes and structured data, such as laboratory tests. However, other critical information documenting lupus nephritis, such as histologic reports from kidney biopsies and prior medical history narratives, require sophisticated text processing to mine information from pathology reports and clinical notes. In this study, we developed algorithms to identify lupus nephritis with and without natural language processing (NLP) using EHR data from the Northwestern Medicine Enterprise Data Warehouse (NMEDW).                                Methods                 We developed five algorithms: a rule-based algorithm using only structured data (baseline algorithm) and four algorithms using different NLP models. The first NLP model applied simple regular expression for keywords search combined with structured data. The other three NLP models were based on regularized logistic regression and used different sets of features including positive mention of concept unique identifiers (CUIs), number of appearances of CUIs, and a mixture of three components (i.e. a curated list of CUIs, regular expression concepts, structured data) respectively. The baseline algorithm and the best performing NLP algorithm were externally validated on a dataset from Vanderbilt University Medical Center (VUMC).                                Results                 Our best performing NLP model incorporated features from both structured data, regular expression concepts, and mapped concept unique identifiers (CUIs) and showed improved F measure in both the NMEDW (0.41 vs 0.79) and VUMC (0.52 vs 0.93) datasets compared to the baseline lupus nephritis algorithm.                                Conclusion                 Our NLP MetaMap mixed model improved the F-measure greatly compared to the structured data only algorithm in both internal and external validation datasets. The NLP algorithms can serve as powerful tools to accurately identify lupus nephritis phenotype in EHR for clinical research and better targeted therapies.
Ballon pathfinders for ultra-high energy cosmic ray detection from space : optics performance and search for air showers

Démonstrateurs ballon pour la détection de rayons cosmiques d'ultra haute énergie depuis l'espace : performance optique et recherche de gerbes atmosphériques                 Les Rayons Cosmiques d'Ultra Haute Énergie (RCUHE) sont des particules chargées venant de l'espace avec des énergies &gt;1018 eV jusqu'à une énergie mesurée de 3×1020 eV. Ils sont les messagers cosmiques les plus énergétiques, cependant leur origine et les mécanismes permettant leur accélération restent inconnus. Leur observation est difficile à cause de leur très faible flux (1 particule par stéradian par km2 par siècle) et nécessite des techniques de détection indirectes, utilisant l'atmosphère comme un calorimètre. Le projet JEM-EUSO a pour but le développement d'un télescope spatial capable d'observer les gerbes atmosphériques produites par les RCUHE par le biais de leur émission de fluorescence en UV (300 - 400 nm). Pour atteindre ces objectifs, deux projets ballons démonstrateurs ont été développés afin de tester la technologie et les méthodes requises: EUSO-Balloon et EUSO-SPB1, qui ont volé en 2014 et 2017 respectivement. Une technologie clé de ces démonstrateurs est leur système d'optique réfractive composé de deux larges lentilles de Fresnel (presque égal à 1 m2). EUSO-Balloon a volé pendant une nuit et son système optique a contribué à l'observation de traces de laser et la mesure du bruit de fond UV. Néanmoins, la performance des optiques est restée mal comprise, i.e. l'efficacité et sa fonction d'étalement du point (PSF). Ce travail explique la méthode utilisée pour caractériser la performance de l'optique et l'efficacité globale des lentilles de Fresnel. La performance mesurée peut être comprise par la combinaison d'un modèle de diffusion semi-empirique avec une simulation classique de tracé de rayon. EUSO-SPB1 a collecté des données pendant 12 nuits. On présente l'analyse des événements enregistrés suite au déclenchement de l'algorithme de "trigger". On classifie ces événements en différentes catégories et on discute leurs caractéristiques. On montre que la majorité des événements enregistrés sont des rayons cosmiques qui interagissent directement avec le détecteur ainsi que des défauts instrumentaux sur les tubes photomultiplicateurs du détecteur. Aucune gerbe atmosphérique n'a été trouvé dans cette analyse.
Nano delivery systems in stem cell therapy: Transforming regenerative medicine and overcoming clinical challenges

Stem cell therapy has emerged as a promising approach in regenerative medicine, offering potential treatments for various degenerative diseases and injuries. However, the clinical application of stem cell therapy faces challenges such as low cell viability, inefficient delivery to target sites, and immune rejection. Nanodelivery systems (NDS) have the potential to address these limitations and enhance the efficacy of stem cell-based treatments. This review looks at how NDS can help stem cell therapy work well by creating a safe environment, allowing targeted delivery, and making it easier to control the release of therapeutic factors. The article discusses various types of NDS, including liposomes, polymeric nanoparticles, mesoporous silica nanoparticles, gold nanoparticles, and magnetic nanoparticles, highlighting their unique properties and advantages in stem cell therapy applications. Furthermore, the review examines the potential of NDS in specific areas of regenerative medicine, such as cardiovascular regeneration, neurodegenerative diseases, musculoskeletal tissue repair, and wound healing. The article also addresses the challenges and limitations associated with NDS, such as biocompatibility, toxicity, manufacturing scalability, and regulatory hurdles. Finally, the article explores the future trajectory of nanotechnology in stem cell therapy, discussing the utilization of intelligent nanoparticles, precision genetic modifications, and the benefits of personalized nanomedicine.
Robust Language Identification for Romansh Varieties

The Romansh language has several regional varieties, called idioms, which sometimes have limited mutual intelligibility. Despite this linguistic diversity, there has been a lack of documented efforts to build a language identification (LID) system that can distinguish between these idioms. Since Romansh LID should also be able to recognize Rumantsch Grischun, a supra-regional variety that combines elements of several idioms, this makes for a novel and interesting classification problem. In this paper, we present a LID system for Romansh idioms based on an SVM approach. We evaluate our model on a newly curated benchmark across two domains and find that it reaches an average in-domain accuracy of 97%, enabling applications such as idiom-aware spell checking or machine translation. Our classifier is publicly available.
The place of the central bank digital currency in the classification of types of money

In the light of the active development and implementation of the central bank digital currency by central banks of different countries, one of the most significant theoretical issues facing the scientific community is the question of finding a place for the central bank digital currency in the existing classification of types of money. The object of this study is the central bank digital currency. The subject of this study is the place of the central bank digital currency in the classification of types of money. The purpose of the study is to analyze the phenomenon of the central bank digital currency, its relationship to the category of money from the point of view of substantiating the possibilities of this currency performing a basic set of monetary functions, as well as finding its place in the classification of money types. In the course of the research, comparative and logical analysis, study, generalization and systematization of the research results of domestic and foreign specialists and scientists were used. The paper presents a generalization and systematization of the existing classifications of forms and types of money. Based on these classifications and the nature and essence of the central bank digital currency the thesis about it as a separate and independent form of money is put forward and substantiated in the work. The paper provides points of view confirming this thesis, presented both in the scientific community and among representatives of the largest financial institutions. The scientific value and novelty of this study lies in the generalization of the existing points of view in the scientific knowledge, substantiating the legitimacy of classifying central bank digital currency as "money" as a new and independent form, complementing the existing classification of money types.
An exposition of Africa’s regional environmental laws, policies and systems for safeguarding the human rights to a clean, healthy and secured environment

In Africa, there has been a growing concern about the high rate of environmental degradation and the consequential effect on the ecosystem. The environmental issues in Africa range from climate change; air, water and soil pollution; harmful mining and extractive activities; biodiversity and land degradation; deforestation; and adverse agricultural practices. In terms of governance, several measures have been undertaken at the regional level to conserve the environment and respond to some of these most critical challenges. The creation of regional environmental treaties, initiatives and institutions in Africa has ushered in significant milestones in the development of the African regional environmental governance framework. These treaties, organisations and policy strategies have played a crucial role in shaping the region’s environmental governance. The establishment of these frameworks has fostered cooperation among African countries in addressing environmental challenges at the regional level. It has further provided a platform for the exchange of knowledge, best practices and resources, as well as paved the way for judicial environmentalism. This article examines some of these legal instruments and governance frameworks in place and also highlights their strengths and weaknesses. It indicates other areas of improvement for maximum effectiveness and management. The analysis also reveals that while there are some mechanisms in place to address certain environmental issues, gaps exist in the enforcement and implementation of these regulations and policies.
Emancipatory accounting in China:does alternative social reporting of non-governmental organisations address corporate social responsibility problems?

With the increasing number of corporate scandals such as Enron, Corporate Social Responsibility (CSR) movement has become a global phenomenon and attracted ever more attention nowadays. Although a large number of corporations have begun to choose corporate social reporting to provide social and environmental information for proclaiming their social responsibilities, corporate social reporting is widely regarded as a public relation opportunity to respond to social pressures. Treating corporate social reporting as an addendum to conventional accounting which serves for ruling classes in society, critical accounting researchers suggest radical changes of current social accounting practices and alternative accounting practices to go beyond the conventional accounting practices and to have positive impact on CSR development. Focusing on the potential transformative nature of accounting and alternative social reporting of Non-governmental Organisations (NGOs), this paper investigates roles of NGOs’ social reporting in generating emancipatory changes of accounting as well as promoting CSR development in China, from both theoretical and empirical ways. On the basis of empirical work, the paper concludes that NGO’s social reporting, as a form of emancipatory accounting practices in China, plays a positive role in CSR development; however, its influence is restricted due to several reasons.
A deep convolutional neural network for segmentation of whole-slide pathology images identifies novel tumour cell-perivascular niche interactions that are associated with poor survival in glioblastoma

AbstractBackgroundGlioblastoma is the most aggressive type of brain cancer with high-levels of intra- and inter-tumour heterogeneity that contribute to its rapid growth and invasion within the brain. However, a spatial characterisation of gene signatures and the cell types expressing these in different tumour locations is still lacking.MethodsWe have used a deep convolutional neural network (DCNN) as a semantic segmentation model to segment seven different tumour regions including leading edge (LE), infiltrating tumour (IT), cellular tumour (CT), cellular tumour microvascular proliferation (CTmvp), cellular tumour pseudopalisading region around necrosis (CTpan), cellular tumour perinecrotic zones (CTpnz) and cellular tumour necrosis (CTne) in digitised glioblastoma histopathological slides from The Cancer Genome Atlas (TCGA). Correlation analysis between segmentation results from tumour images together with matched RNA expression data was performed to identify genetic signatures that are specific to different tumour regions.ResultsWe found that spatially resolved gene signatures were strongly correlated with survival in patients with defined genetic mutations. Further in silico cell ontology analysis along with single-cell RNA sequencing data from resected glioblastoma tissue samples showed that these tumour regions had different gene signatures, whose expression was driven by different cell types in the regional tumour microenvironment. Our results further pointed to a key role for interactions between microglia/pericytes/monocytes and tumour cells that occur in the IT and CTmvp regions, which may contribute to poor patient survival.ConclusionsThis work identified key histopathological features that correlate with patient survival and detected spatially associated genetic signatures that contribute to tumour-stroma interactions and which should be investigated as new targets in glioblastoma. The source codes and datasets used are available in GitHub:https://github.com/amin20/GBM_WSSM.
United Nations Security Council Resolutions 2240 (2015), 2312 (2016) &amp; 2380 (2017)

In response to the large number of irregular migrants crossing the Mediterranean Sea from Libya to southern Italy, frequently using overcrowded, unseaworthy vessels, and often facilitated by migrant smugglers, on October 9, 2015, the United Nations Security Council (UNSC) adopted Resolution 2240. This resolution authorizes member states to intercept, inspect, and seize vessels suspected of being used to smuggle migrants or to traffic persons. Initially limited to one year, these measures were renewed through two further UNSC resolutions on October 6, 2016, and October 5, 2017.