ethicalabs/Research-Intent-Collab huggingface dataset:
Low-Mass X-ray Binaries

A large fraction of X-ray sources in our Galaxy are low-mass X-ray binaries, containing a black hole or a neutron star accreting from a gravitationally bound low-mass ($\leq$1 M$_\odot$) companion star. These systems are among the older population of stars and accreting systems in the Galaxy, and typically have long accretion histories. Low-mass X-ray binaries are categorized into various sub-classes based on their observed properties such as X-ray variability and brightness, nature of the companion star and/or the compact object, and binary configuration. In this Chapter, we review the phenomenology of sub-classes of these systems and summarize observational finding regarding their characteristics, populations, and their distribution in the Galaxy.
International Environmental Law for the 21st Century: The Constitutionalization of the Right to a Healthy Environment in the Inter-American Court of Human Rights Advisory Opinion No. 23

This article analyses the recent Advisory Opinion of the Inter-American Court of Human Rights on Environment and Human Rights and argues that it constitutes a milestone effectively reorientating international environmental law.   The article is divided as follows.  First, it analyses the most salient aspects of the Advisory Opinion inter alia (1) The right to a healthy environment as binding law and; (2) The Advisory Opinion as a landmark in the gradual development of international jurisprudence on cross-border (or “diagonal”) human rights obligations (i.e. the possibility for human rights claims to be brought by individuals not under the territorial jurisdiction of the State whose international responsibility for environmental harm is invoked).  Second, it contextualizes the Advisory Opinion by discussing what we consider to be four key vectors currently affecting the trajectory of the ongoing development of international environmental law and how the advances made in the Advisory Opinion fit with those developments.  Third, it places the Advisory Opinion in the wider context of developments moving towards a needed reorientation in international environmental law, in particular: integration (or de-fragmentation) of international law, the operationalization of environmental principles into working-level legal norms, and a focus on practical remedies.   It is argued that as the world experiences the pressure for more effective environmental law and accountability, some of the most sophisticated and innovative thinking on international environmental law today, is emanating from countries in the Southern hemisphere, as attested to by the Advisory Opinion.
Organic crystal structure prediction via coupled generative adversarial networks and graph convolutional networks

Organic crystal structures exert a profound impact on the physicochemical properties and biological effects of organic compounds. Quantum mechanics (QM)-based crystal structure predictions (CSPs) have somewhat alleviated the dilemma that experimental crystal structure investigations struggle to conduct complete polymorphism studies, but the high computing cost poses a challenge to its widespread application. The present study aims to construct DeepCSP, a feasible pure machine learning framework for minute-scale rapid organic CSP. Initially, based on 177,746 data entries from the Cambridge Crystal Structure Database, a generative adversarial network was built to conditionally generate trial crystal structures under selected feature constraints for the given molecule. Simultaneously, a graph convolutional attention network was used to predict the density of stable crystal structures for the input molecule. Subsequently, the distances between the predicted density and the definition-based calculated density would be considered to be the crystal structure screening and ranking basis, and finally, the density-based crystal structure ranking would be output. Two such distinct algorithms, performing the generation and ranking functionalities, respectively, collectively constitute the DeepCSP, which has demonstrated compelling performance in marketed drug validations, achieving an accuracy rate exceeding 80% and a hit rate surpassing 85%. Inspiringly, the computing speed of the pure machine learning methodology demonstrates the potential of artificial intelligence in advancing CSP research.
Visualization of Neural Network Predictions for Weather Forecasting

AbstractRecurrent neural networks are prime candidates for learning evolutions in multi‐dimensional time series data. The performance of such a network is judged by the loss function, which is aggregated into a scalar value that decreases during training. Observing only this number hides the variation that occurs within the typically large training and testing data sets. Understanding these variations is of highest importance to adjust network hyper‐parameters, such as the number of neurons, number of layers or to adjust the training set to include more representative examples. In this paper, we design a comprehensive and interactive system that allows users to study the output of recurrent neural networks on both the complete training data and testing data. We follow a coarse‐to‐fine strategy, providing overviews of annual, monthly and daily patterns in the time series and directly support a comparison of different hyper‐parameter settings. We applied our method to a recurrent convolutional neural network that was trained and tested on 25 years of climate data to forecast meteorological attributes, such as temperature, pressure and wind velocity. We further visualize the quality of the forecasting models, when applied to various locations on the Earth and we examine the combination of several forecasting models.
Decision-Making Under Scientific Uncertainty: The Economics of the Precautionary Principle

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Protection of the rights of minors in case of divorce of parents

в 2023 году Президентом РФ был подписан Указ, согласно которому 2024 год объявлен Годом семьи. Он направлен на сохранение традиционных семейных ценностей и популяризацию государственной политики в сфере защиты семьи. Семья является залогом стабильности и процветания гражданского общества. Именно она формирует личность и закладывает основные фундаментальные ценности такие, как любовь, верность, уважение, взаимопонимание и поддержка. Особое значение в настоящее время в Российской Федерации приобретает вопрос судебной защиты прав и интересов ребенка при расторжении брака родителей, поскольку современная статистика показывает, что на 1 053 884 зарегистрированных браков приходится 683 111 разводов, что составляет примерно 65% от общего числа. Поэтому основной задачей в данном случае является защита интересов несовершеннолетних. Суд стремится к достижению наилучшего решения для ребенка, учитывая его потребности, возможности родителей и общие обстоятельства. Важно, чтобы дети чувствовали себя защищенными и получали необходимую заботу и поддержку со стороны обоих родителей. В данной статье рассматриваются вопросы о правах несовершеннолетних при расторжении брака родителей, которые являются наиболее актуальными в Российской Федерации.                                                                                             in 2023, the President of the Russian Federation signed a decree according to which 2024 was declared the Year of the Family. It is aimed at preserving traditional family values and popularizing state policy in the field of family protection. The family is the key to the stability and prosperity of civil society. It is it who forms a personality and lays down the basic fundamental values such as love, loyalty, respect, mutual understanding and support. The issue of judicial protection of the rights and interests of a child during the dissolution of a parent's marriage is of particular importance at present in the Russian Federation, since modern statistics show that 683,111 divorces account for 1,053,884 registered marriages, which is approximately 65% of the total number. Therefore, the main task in this case is to protect the interests of minors. The Court strives to achieve the best solution for the child, taking into account his needs, the capabilities of the parents and the general circumstances. It is important that children feel protected and receive the necessary care and support from both parents. This article discusses the issues of the rights of minors in the dissolution of parental marriage, which are the most relevant in the Russian Federation.
A reliability risk analysis method based on the fuzzy fault tree and fuzzy event tree

Based on the analyses systems of fuzzy fault tree and event tree, this paper proposes a method for a reliability risk analysis to mitigate problems associated with risk modeling of fuzzy and uncertain information. Risk event occurrence probabilities were obtained with fuzzy linguistic variables instead of exact values used in fault tree analysis. Quantitative analysis was applied to the fuzzy fault tree to determine the fuzzy importance degree of each basic event, which were then ranked and divided to distinguish the influence of these basic events. Based on this analysis, a fuzzy event tree was constructed and linguistic terms used to evaluate occurrence probabilities and outcomes. Mitigation measures were put forward for risk events and the expected risk magnitudes were calculated under the mitigation strategies. This ensured the mitigation measures were intuitive and accurate. Finally, an application example was illustrated to verify that the proposed method was effective and feasible.
The Monte Carlo Computational Summit -- October 25 & 26, 2023 -- Notre Dame, Indiana, USA

The Monte Carlo Computational Summit was held on the campus of the University of Notre Dame in South Bend, Indiana, USA on 25--26 October 2023. The goals of the summit were to discuss algorithmic and software alterations required for successfully porting respective code bases to exascale-class computing hardware, compare software engineering techniques used by various code teams, and consider the adoption of industry-standard benchmark problems to better facilitate code-to-code performance comparisons. A large portion of the meeting included candid discussions of direct experiences with approaches that have and have not worked. Participants reported that identifying and implementing suitable Monte Carlo algorithms for GPUs continues to be a sticking point. They also report significant difficulty porting existing algorithms between GPU APIs (specifically Nvidia CUDA to AMD ROCm). To better compare code-to-code performance, participants decided to design a C5G7-like benchmark problem with a defined figure of merit, with the expectation of adding more benchmarks in the future. Problem specifications and results will eventually be hosted in a public repository and will be open to submissions by all Monte Carlo transport codes capable of running the benchmark problem. The participants also identified the need to explore the intermediate and long-term future of the Monte Carlo neutron transport community and how best to modernize and contextualize Monte Carlo as a useful tool in modern industry. Overall the summit was considered to be a success by the organizers and participants, and the group shared a strong desire for future, potentially larger, Monte Carlo summits.
Predicting and explaining karst spring dissolved oxygen using interpretable deep learning approach

AbstractDissolved oxygen (DO) is one of the most important indicators of water quality and an essential measure for the aquatic organisms and the local ecosystem. DO concentrations in karst spring flow involves complex hydrological processes because of the heterogeneous nature of the karst system. In this study, an interpretable and explainable model that integrates the shapley additive explanations (SHAP) algorithm with the long short‐term memory network model (LSTM) is proposed to evaluate the contributions of karst spring discharge, precipitation, water temperature, and specific conductance to DO concentrations in karst spring flow. The hybrid model can predict the temporal fluctuations of DO levels and provide a robust characterization of DO behaviours. To demonstrate the applicability of the proposed model, we adopted the hydrological and meteorological data from Barton Springs, situated within a highly karstified segment of the Edwards aquifer characterized by extensive fractures and openings. The optimal prediction performance is achieved with a 14‐day time step, which is considered as the effective response time between various hydrological processes and DO concentrations at Barton Springs. It reveals that the influence of karst spring discharge, precipitation, water temperature, and specific conductance in previous 14 days collectively contribute to the current DO concentration in karst spring flow. The SHAP values of input features provide both local and global explanations, demonstrating the magnitude and the direction of each feature's impact on DO levels in karst spring flow. In descending order, the contributions of various hydrological processes to DO are ranked as follows: precipitation, karst spring discharge, temperature, and specific conductance. Precipitation and discharge exhibit positive SHAP values, indicating that increases in these hydrological processes contribute to higher DO levels in the karst flow. Water temperature and specific conductance have negative SHAP values, suggesting that higher water temperature and specific conductance will lead to decreased DO levels in the karst flow.
Brain Tumor Classification from 3D MRI Using Persistent Homology and Betti Features: A Topological Data Analysis Approach on BraTS2020

Accurate and interpretable brain tumor classification from medical imaging remains a challenging problem due to the high dimensionality and complex structural patterns present in magnetic resonance imaging (MRI). In this study, we propose a topology-driven framework for brain tumor classification based on Topological Data Analysis (TDA) applied directly to three-dimensional (3D) MRI volumes. Specifically, we analyze 3D Fluid Attenuated Inversion Recovery (FLAIR) images from the BraTS 2020 dataset and extract interpretable topological descriptors using persistent homology.  Persistent homology captures intrinsic geometric and structural characteristics of the data through Betti numbers, which describe connected components (Betti-0), loops (Betti-1), and voids (Betti-2). From the 3D MRI volumes, we derive a compact set of 100 topological features that summarize the underlying topology of brain tumor structures. These descriptors represent complex 3D tumor morphology while significantly reducing data dimensionality.  Unlike many deep learning approaches that require large-scale training data or complex architectures, the proposed framework relies on computationally efficient topological features extracted directly from the images. These features are used to train classical machine learning classifiers, including Random Forest and XGBoost, for binary classification of high-grade glioma (HGG) and low-grade glioma (LGG). Experimental results on the BraTS 2020 dataset show that the Random Forest classifier combined with selected Betti features achieves an accuracy of 89.19%. These findings highlight the potential of persistent homology as an effective and interpretable approach for analyzing complex 3D medical images and performing brain tumor classification.
AI-Driven Early Warning Systems for Wildfire Disaster Risk Reduction in a Changing Climate

Wildfires have turned into one of the most devastating and fast spreading forms of climate changes, posing a threat to ecosystems and human habitats, as well as carbon regimes around the world. Conventional approaches to early warning of wildfires, which are limited to a limited number of sensors and are designed reactive responses to risk conditions, are becoming less sufficient in the context of the growing fire seasons and new fire behavior. Artificial intelligence (AI) can be used to increase wildfire disaster risk reduction (DRR) with transformative capabilities through the fusion of real-time data, modeling of predictive fire behavior, and the automatic dissemination of early warning. In this chapter, the author focuses on the intersections of AI technologies, such as machine learning (ML), deep learning (DL), remote sensing analytics, and Internet of Things (IoT) sensor integration, to construct next-generation early warning systems (EWS) of wildfires.
Development of a new mRNA vaccine platform for tuberculosis

Background Tuberculosis (TB), caused by Mycobacterium tuberculosis (M.tb), is the frst cause of death by an infectious disease worldwide, killed 1.6 million people in 2021. Bacillus Calmette-Guerin (BCG) is the only approved vaccine for TB to date. However, while BCG is efective in preventing severe forms in children, its efcacy in adults is inconsistent and it does not prevent transmission, highlighting the need for new vaccine development [1]. The recent success of COVID-19 vaccines raised the interest for mRNA-based vaccines, as they are efective, safe and easy to produce. This project aims to develop a new mRNA vaccine platform for TB, based on mRNA coding for antigenic peptides from BCG and M.tb identifed by immunopeptidomics [2], and formulated with a patented technology of lipid nanoemulsions (NE) (WO2019138139A1), adapted for efcient intracellular delivery of mRNA [3]. Materials and methods We tested diferent prototypes of NE-mRNA formulations, coding for EGFP, in vitro. Human alveolar basal epithelial cells (A549), human monocytic cells (THP-1), and primary human monocyte-derived macrophages, were transfected with NE-mRNA formulations. Transfection efciency was assessed by measuring the percentage of transfected cells, and the intensity of GFP fuorescence. The cytotoxicity of the formulations was evaluated using AlamarBlue, and by 7-AAD viability staining. Results In vitro preliminary data using EGFP-mRNA-NE formulations indicate that NE formulations can efciently deliver mRNA and induce expression of the encoded protein in diferent cell types, with low cytotoxicity. Conclusions The NE technology presented here is safe, stable, and can efciently deliver mRNA to various cell types. Selected NE formulations will be used as a carrier for a new vaccine candidate against TB, based on mRNA encoding relevant antigenic peptides. These will be tested in mice for safety, immunogenicity, efcacy and dose optimization in order to generate an efective and sustained humoral and cellular immune response against TB. The mRNA vaccines are rapid and relatively simple to produce. The vaccine platform described here could be adapted to develop vaccines against other infectious diseases, particularly to quickly respond to emerging pathogens.
Role of transgenic Bt-crops in promoting biological control and integrated pest management

Since their commercial introduction in 1996 in the USA, the insect resistant transgenic  Bt  crops, notably  Bt -cotton and  Bt -corn, have given effective control of target pests and found overwhelming adoption in several countries. As of 2017, these  Bt  crops were cultivated in 14 countries on 100 m ha, including 11.4 m ha of  Bt -cotton in India, which comprised 53% of 189.8 m ha of all GM crops grown in 24 countries. Such extensive cultivation of  Bt  crops, incorporated with genes derived from the soil bacterium,  Bacillus thuringiensis  ( Bt ), modified to express host-specific insecticidal crystalline (Cry) proteins, has resulted in higher crop yields by 22%, increased farmers’ profit by 68% and reduced chemical insecticide applications by 37%, thereby providing social, economic, health and environmental benefits. The reduced chemical sprays have contributed to conservation of parasitoids and predators leading to enhanced biological control in crop systems. Feeding tests carried out with predators like ladybird beetles and green lacewing and also with hymenopteran parasitoids have demonstrated  Bt  proteins to be safe to these natural enemies. The value of  Bt  crops in promoting biological control and integrated pest management is discussed.
Actuarial activities in life insurance and critical illness insurance (including health insurance): features of the theory and practice of genetic discrimination

This article provides an in-depth examination of prevailing trends in the conceptual and applied dimensions of genetic discrimination within actuarial practices governing life insurance and critical illness insurance, including health insurance. Particular emphasis is placed on the expanding role of genetic information in underwriting procedures and actuarial risk stratification. The study systematizes categories of diseases for which genetic data concerning the probability of disease manifestation are most frequently utilized by insurance undertakings in the evaluation of insurance risks. It is convincingly argued that the valid and methodologically sound interpretation of genetic information necessitates a holistic assessment of the insurance applicant’s overall health profile, incorporating clinical, environmental, and behavioral determinants. The article demonstrates that the fragmented or erroneous interpretation of genetic data may result in distorted actuarial estimations, institutionalized forms of genetic discrimination, and economically unjustified adjustments in insurance premiums. Empirical cases of genetic discrimination arising in actuarial activities within the domains of life and health insurance are subjected to systematic analysis. The findings reveal that the most recurrent manifestations of genetic discrimination in insurance markets encompass: (1) outright denial of insurance coverage or the imposition of actuarially inflated premiums, particularly in relation to African Americans carrying the sickle cell anemia gene; (2) adverse modification of contractual conditions or refusal to insure individuals exhibiting a genetic predisposition to Huntington’s disease; and (3) exclusion from coverage under health insurance policies for individuals possessing gene mutations associated with an elevated risk of developing Long QT syndrome. The article further substantiates a set of regulatory and financial mechanisms aimed at mitigating genetic discrimination in insurance practice. These mechanisms include the application of individualized premium-setting models, the establishment of segregated insurance risk pools, and the partial subsidization of life insurance costs through public budgetary instruments.
Time for Implementation of the Right to Water and Sanitation—e.g. The Missing Implementation in Germany

Access to safe water supplies and basic sanitation are necessary for maintaining public health, and water is needed to support healthy ecosystems, which in turn provide critical environmental goods and services. As water demand and availability become more uncertain, all societies become more vulnerable to a wide range of risks associated with inadequate water supply, including hunger and thirst, high rates of disease and death, economic crises, and degraded ecosystems. This endangers the enforcement of the Human Right to Water and Sanitation. Against this background the paper reviews the current political development to strengthen the legal enforcement of the Right to Water; describes the importance of its legal implementation regarding poor populations in Europe; exemplifies the need for implementation and legal action in view of Germany; and addresses to strengthen enforcement of the Human Right to Water and Sanitation with a view to Environmental Justice.