ethicalabs/Research-Intent-Collab huggingface dataset:
A global scavenging and circulation ocean model of thorium-230 and protactinium-231 with realistic particle dynamics (NEMO-ProThorP 0.1)

In this paper, we set forth a 3-D ocean model of the radioactive trace isotopes Th-230 and Pa-231. The interest arises from the fact that these isotopes are extensively used for investigating particle transport in the ocean and reconstructing past ocean circulation. The tracers are reversibly scavenged by biogenic and lithogenic particles.   Our simulations of Th-230 and Pa-231 are based on the NEMO-PISCES ocean biogeochemistry general circulation model, which includes biogenic particles, namely small and big particulate organic carbon, calcium carbonate and biogenic silica. Small and big lithogenic particles from dust deposition are included in our model as well. Their distributions generally compare well with the small and big lithogenic particle concentrations from recent observations from the GEOTRACES programme, except for boundary nepheloid layers for which, as up to today, there are no non-trivial, prognostic models available on a global scale. Our simulations reproduce Th-230 and Pa-231 dissolved concentrations: they compare well with recent GEOTRACES observations in many parts of the ocean. Particulate Th-230 and Pa-231 concentrations are significantly improved compared to previous studies, but they are still too low because of missing particles from nepheloid layers. Our simulation reproduces the main characteristics of the Pa-231/Th-230 ratio observed in the sediments, and supports a moderate affinity of Pa-231 to biogenic silica as suggested by recent observations, relative to Th-230.   Future model development may further improve understanding, especially when this will include a more complete representation of all particles, including different size classes, manganese hydroxides and nepheloid layers. This can be done based on our model, as its source code is readily available.
Environmental Problems and the State of Compliance with the Right to a Healthy Environment in a Mining Region of México

AbstractThe objective of this article is to review the environmental problems in Mexican state of San Luis Potosí (mining region) and the state of compliance with the right to a healthy environment. Our study helps to demonstrate complexity of the environmental impact in a broader context, if it is repeated in other mining regions of the non-developing world. The findings include heavy metals as lead and arsenic, in soil, with neurotoxic and carcinogenic properties. Impact on the biota as a whole, decrease of the biological activity and enzymatic inhibition. Heavy metals, including arsenic, mercury, cadmium and lead, as a product of the metallurgical and foundry industry were detected in children of the city of San Luis Potosí. Water contaminated with fluorine and arsenic, product of the extensive drilling of water wells and the transfer of contaminants from the mining and metallurgical industry. Air contaminated with heavy metals product of mining and metallurgy and hydrocarbons in urban and rural areas. Plastics as a global problem, but with the absence of local diagnosis, despite having one of the main pollution factors: industrial development. Hydrocarbons as pollution with very little diagnosis, beyond environmental emergencies. There is no evidence that the right to a healthy environment is fulfilled in the State of San Luis Potosí.
AlphaFold prediction of structural ensembles of disordered proteins

Abstract                   Deep learning methods of predicting protein structures have reached an accuracy comparable to that of high-resolution experimental methods. It is thus possible to generate accurate models of the native states of hundreds of millions of proteins. An open question, however, concerns whether these advances can be translated to disordered proteins, which should be represented as structural ensembles because of their heterogeneous and dynamical nature. To address this problem, we introduce the AlphaFold-Metainference method to use AlphaFold-derived distances as structural restraints in molecular dynamics simulations to construct structural ensembles of ordered and disordered proteins. The results obtained using AlphaFold-Metainference illustrate the possibility of making predictions of the conformational properties of disordered proteins using deep learning methods trained on the large structural databases available for folded proteins.
LncRNAs as Emerging Diagnostic Biomarkers for Cardiovascular Diseases: Evidence Synthesis Through Systematic Review and Meta-Analysis

Background                     Cardiovascular diseases (CVDs) are the leading cause of global morbidity and mortality. Long non-coding RNAs (lncRNAs) have emerged as potential diagnostic biomarkers for CVDs.                                                           Aim                     To systematically evaluate the diagnostic value of lncRNAs for CVDs through a systematic review and meta-analysis.                                                           Methods                     Studies focusing on lncRNAs as diagnostic markers for CVDs were searched from PubMed, Embase, and Web of Science databases. The meta-analysis was conducted according to the PRISMA 2020 guidelines. The diagnostic value of lncRNAs was calculated by pooled sensitivity, specificity, and area under the summary receiver operating characteristic (SROC) curve (AUC). Meta-regression and subgroup analyses were performed to explore heterogeneity sources. Publication bias and sensitivity analysis were also conducted.                                                           Results                     A total of 16 articles (18 lncRNAs, 1125 controls, and 1855 cases) were included. LncRNAs showed pooled sensitivity of 0.81 (95% CI = 0.76-0.85) and specificity of 0.86 (95% CI = 0.80-0.90) for CVDs diagnosis. The SROC analysis revealed an AUC of 0.90 (95% CI = 0.87-0.92), indicating high diagnostic accuracy. The positive diagnostic likelihood ratio (DLR) was 5.73 (95% CI =4.07-8.08), and the negative DLR was 0.22 (95% CI = 0.18-0.28). The diagnostic score was 3.25 (95% CI = 2.79-3.71), the diagnostic odds ratio was 25.77 (95% CI = 16.22-40.93). The expression trend of lncRNAs, control type, and disease type were the main sources of heterogeneity.                                                           Conclusion                     LncRNAs had high sensitivity and specificity for CVDs diagnosis, showing great potential as auxiliary diagnostic tools.
Automation in Cloud-Based DevOps: A Guide to CI/CD Pipelines and Infrastructure as Code (IaC) with Terraform and Jenkins

This study offers a comprehensive analysis of automation in cloud-based DevOps, focusing on the role of Continuous Integration/Continuous Delivery (CI/CD) pipelines and Infrastructure as Code (IaC) in streamlining software development processes. Employing tools like Jenkins and Terraform, the research aims to demonstrate how automation can significantly enhance operational efficiency, scalability, and security in cloud deployments. Through a detailed examination of CI/CD components and their integration with IaC, this paper identifies key findings, including the reduction of manual errors, improved deployment consistency across environments, and enhanced security through DevSecOps practices. The study further explores challenges such as configuration complexity and compliance, proposing best practices like proactive monitoring, encrypted secrets management, and version control to mitigate these issues. Conclusively, the research recommends the adoption of AI-driven analytics and robust security frameworks to optimize cloud-based CI/CD automation. This work not only highlights current methodologies but also anticipates future trends, providing a strategic roadmap for organizations aiming to leverage DevOps automation effectively.
NUMERICAL IMPLEMENTATION OF A PARALLEL ALGORITHM FOR SOLVING THE PROBLEM OF POLLUTANT TRANSPORT IN A RESERVOIR ON A HIGH-PERFORMANCE COMPUTER SYSTEM

The purpose of this work is to create a software package for a distributed solution of the problem of transporting a pollutant in a reservoir with complex bathymetry and the presence of technological structures. An algorithm has been developed for the parallel solution of the problem of transporting a pollutant (pollutant) in a reservoir on a graphics accelerator controlled by the CUDA (Compute Unified Device Architecture) system; a comparative analysis of the operation of algorithms on a CPU (Central Processing Unit) and on a graphics accelerator GPU (Graphics Processing Unit) made it possible to evaluate their performance. The software implementation of the modules included in the complex is described, the main classes and implemented methods are documented. The results of numerical experiments showed that solving of pollutant transport’s problem based on the CUDA technology is ineffective for small grids (up to 100 ´ 100 computational nodes). In the case of large grids (1000 ´ 1000 computational nodes), the use of CUDA technology reduces the computation time by an order of magnitude. An analysis of the experiments carried out with the developed components of software showed that the maximum value of the ratio of the algorithm operating time that implements the set task of transferring matter in a shallow water on a GPU to the operating time of a similar algorithm on the CPU was 24.92 times, which is achieved on a grid of 1000 ´ 1000 computational nodes. Implementation of methods for decomposition of grid regions is proposed for solving computationally laborious problems of diffusion-convection, including the problem of transporting pollutants in a reservoir with complex bathymetry with technological objects that take into account the architecture and parameters of a MSC (Multiprocessor Computing System) located on the basis of the infrastructure facility of the STU (Scientific and Technological University) “Sirius” (Sochi, Russia). Consideration was made for such a property of a computing system as the time it takes to transmit and receive floating point data. An algorithm for the parallel solution of the task under the control of MPI (Message Passing Interface) technology has been developed, and its efficiency has been assessed. The acceleration values of the proposed algorithm are obtained depending on the number of involved computers (processors) and the size of the computational grid. The maximum number of computers used is 24, the maximum size of the computational grid was 10 000 ´ 10 000 computational nodes. The developed algorithm showed low efficiency for small computational grids (up to 100 ´ 100 computational nodes). In the case of large computational grids ( from 1000  1000 computational nodes), the use of MPI reduces the computation time by several times.
A new database for time-series monitoring data: the NitroEurope approach

The NitroEurope Integrated Project (2006-2011) aimed to gain a better understanding of the nitrogen cycle and its impacts on the European greenhouse gas balance. Over sixty project partners collected large volumes of data from sites all over Europe. There are currently more than 15 forest sites in 11 countries, including beech, oak, spruce and mixed forests. The collected data are at different temporal resolutions, from one-off measurements to 30-minute time-series data. Around 500 variables (plus attributes) are measured, including soil and plant data, details of cultivation management for natural and manipulated sites, long-term datasets of flux and concentrations of greenhouse gas and pollutants and their precursors, and micrometeorology data. The NitroEurope research community therefore needed a database to provide easy upload and extraction of data for analysis, interpretation and modelling. A bespoke database with a web-based graphical user interface (GUI) in a user-friendly and attractive online environment was produced by the NitroEurope (NitroEurope 2011) database development team. The software provides a flexible interface between Microsoft Excel workbooks and an Oracle database. The database “Form Manager” enables construction of database forms which reflect the project’s Excel data workbook templates. The software locates data according to Excel worksheet and cell references. The database “Uploader” extracts the specified data from completed Excel templates and uploads data directly to the Oracle relational database tables through the web front-end. Data are run through a series of automatic checks on upload which can be verified using an online graphing tool. Data are then validated and made available to the NitroEurope community for downloading. The database “Reporting” tools enable data from different sites and activities to be brought together and datasets can be previewed and graphed before they are downloaded. The database has almost 300 users and currently contains over eight million rows of data. The data will be made available to the wider scientific community two years after the project ends (Spring 2013). The database structure is extremely flexible and has been used for the storage and reporting of other time-series project data at our institute.
Climate smart disaster risk reduction: Indigenous knowledge practiced for early warning in Coastal Bangladesh

Background: The coastal region of Bangladesh faces severe vulnerability to climate change and climate-induced natural disasters annually. Inhabitants in these areas have developed a wealth of indigenous knowledge over generations, comprising techniques to mitigate the impact of these disasters. This study, conducted in Dashmina Upazila within the Patuakhali District of the southern coastal region of Bangladesh, aimed to identify, evaluate, and scale up existing indigenous knowledge practices for early warning in Climate Smart Disaster Risk Reduction. Methods: Information was gathered through various methods such as direct observations, key informant interviews, group discussions, case studies, and a comprehensive literature review. There are 14 Focus Group Discussions (FGD) and communal meetings; 16 Key Informant Interviews (KII) and 5 case histories were conducted. SWOT analysis employed to assess the strengths, weaknesses, opportunities, and threats of these practices. Data were analyzed using Microsoft Excel 2010 for descriptive qualitative analysis, with a SWOT analysis employed to assess the strengths, weaknesses, opportunities, and threats of these practices. Findings: The study revealed several indigenous indicators for early warning, including the observation that ants climbing into houses with stored food signify imminent heavy rainfall, excessive frog barking indicating forthcoming heavy rain, the belief that crows soar high into the sky to summon rain, a combination of heavy clouds with strong wind suggesting little or no rainfall, muddy river waters signaling potential large floods or storm surges, peculiar crowing sounds at night predicting cyclones, and the occurrence of hot winds from specific directions along with black clouds hinting at imminent norwesters. Conclusion: The study strongly recommends integrating this indigenous knowledge into decision-making processes for developing disaster risk reduction strategies and incorporating them into future development projects within the region. The study faces recall bias, the study has a single-district scope, and it lacks quantitative validation. Novelty/Originality of this article: This research is novel because it systematically documents the indigenous early-warning knowledge of coastal communities in Dashmina Upazila, Bangladesh knowledge that has rarely recorded or scientifically analyzed and invisible to outsiders. It uniquely integrates these traditional cues with modern disaster-risk-reduction perspectives, offering new insights for community-based early warning systems.
Developing a Natural Language Processing Strategy to Avoid Biased Data in Electronic Health Record Suicide Risk Modeling

Objective                     Unstructured electronic health record (EHR) data is increasingly used to enhance suicide risk modeling. Unfortunately, EHR reported death dates are frequently inaccurate. Including EHR data from after patients' deaths, or after suicidal actions which led to their deaths, potentially biases suicide prediction models. In contrast to prior methods which withheld all data from 5‐day before reported death, this study investigates using natural language processing to improve the accuracy of detecting EHR reported death dates.                                                           Methods                                            We selected all Veterans Affairs patients who died by suicide with EHR data during 5‐day before reported death date (                       n                        = 1620) during 2017–2018 and extracted all interval EHR texts (texts = 9127). We randomly sub‐selected corpus to develop code to identify if texts were written before or after death or suicidal action and utilized this approach in our full corpus.                                                                                Results                     In the full corpus, we identified 1742 texts entered on reported death date, 274 texts after death date, and 1556 texts that did not reference death or suicidal action but were entered chronologically after other texts indicating death. In contrast to the prior method, which excluded all interval texts, our derived approach retained 60.9% of interval data.                                                           Conclusions                     Our approach improved detection of valid EHR data in the interval before patient death. Relevance to clinical practice: This study operationalizes a method to detect immediate pre‐mortem EHR data that could contribute to less bias in suicide risk modeling. This utilization can improve risk prediction and in turn bolster prevention services.
Research on Learning Algorithm of Spiking Neural Network

The spiking neural network is a new artificial neural network that expresses and transmits biological information through spiking sequences based on precise time coding. Compared with the traditional frequency-based neural network, spiking neural network has stronger bionics and computing power, which can more accurately simulate human brain and brain activity. This paper introduces the typical unsupervised spiking neural network learning method-STDP (Spike-Timing-Dependent Plasticity) and the classic supervised spiking neural network learning method-Tempotron and ReSuMe (Remote Supervised Method), combines the spatio- temporal transmission characteristics of biological neural information to analyze the characteristics and performance of various existing spiking sequence learning methods. This review contributes to the applicability research of various spiking neural network models.
On Different Types of Uncertainties in the Context of the Precautionary Principle

Few policies for risk management have created more controversy than the precautionary principle. A main problem is the extreme number of different definitions and interpretations. Almost all definitions of the precautionary principle identify “scientific uncertainties” as the trigger or criterion for its invocation; however, the meaning of this concept is not clear. For applying the precautionary principle it is not sufficient that the threats or hazards are uncertain. A stronger requirement is needed. This article provides an in‐depth analysis of this issue. We question how the scientific uncertainties are linked to the interpretation of the probability concept, expected values, the results from probabilistic risk assessments, the common distinction between aleatory uncertainties and epistemic uncertainties, and the problem of establishing an accurate prediction model (cause‐effect relationship). A new classification structure is suggested to define what scientific uncertainties mean.
Modulation region of galactic cosmic rays in the heliosphere: Estimation of dimension, radial diffusion coefficient, intensity out of region

ABSTRACT     On the basis of neutron monitor data as well as solar activity data for four solar cycles 19-22 the hysteresis phenomenon is investigated by taking into account convection-diffusion and drifts, and estimated the dimension of modulation region, radial diffusion coefficient, expected cosmic ray intensity in the interstellar medium and absolute level of galactic cosmic ray modulation observed on the Earth’s orbit. It was estimated that the average time of particle propagation from the boundary of modulation region to the Earth’s orbit is much smaller than characteristic time-lag in hysteresis phenomenon, what supports the using in this research of quasi-stationary model of high-energy particle modulation in the Heliosphere.
Phase transitions and statistical mechanics for BPS Black Holes in AdS/CFT

Using the general framework developed in hep-th/0607056, we study in detail the phase space of BPS Black Holes in AdS, for the case where all three electric charges are equal. Although these solitons are supersymmetric with zero Hawking temperature, it turns out that these Black Holes have rich phase structure with sharp phase transitions associated to a corresponding critical generalized temperature. We are able to rewrite the gravity variables in terms of dual CFT variables and compare the gravity phase diagram with the free dual CFT phase diagram. In particular, the elusive supergravity constraint characteristic of these Black Holes is particulary simple and in fact appears naturally in the dual CFT in the definition of the BPS Index. Armed with this constraint, we find perfect match between BH and free CFT charges up to expected constant factors.
Patient Safety in Critical Care Unit: Development of a Nursing Quality Indicator System

This is a methodological study and technological production that aims to describe the development of a computerized system of nursing care quality indicators for the Intensive Care Unit. The study population consisted of a systems analyst and fifteen critical care nurses. For the development of the system we adopted some of the best practices of the Unified Process methodology using the Unified Modeling Language and the programming language Java Enterprise Edition 7. The system consists of an access menu with the following functions: Home (presents general information), New Record (records the indicator), Record (record search), Census (add information and indicators of the patient), Report (generates report of the indicators) and Annex (accesses the Braden Scale). This information system allows for measurement of the quality of nursing care and to evaluate patient safety in intensive care unit by monitoring quality indicators in nursing.
Governed GitOps: Converging Infrastructure-as-Code, Policy-as-Code, and Progressive Delivery

Governed GitOpsrepresents a transformative paradigm that unifies traditionally fragmented practices in cloud infrastructure management. By converging Infrastructure-as-Code modules, Policy-as-Code enforcement, and Progressive Delivery mechanisms into a cohesive framework, organizations can address endemic challenges including configuration drift, compliance friction, and subjective release decisions. Git repositories serve as the definitive source of truth, while controllers continuously reconcile desired and actual states, enabling automated drift detection and correction. The architecture implements defense-in-depth through multi-layered policy validation, cryptographic supply-chain verification, and SLO-gated deployment progression. This integration transforms governance from periodic assessment to continuous validation, making security and compliance inherent properties of the system rather than external constraints. The resulting platform enables organizations to simultaneously improve deployment frequency, lead time, change failure rate, and audit readiness while establishing clear maturity pathways from manual processes to automated, evidence-based governance.