ethicalabs/Research-Intent-Collab huggingface dataset:
Object Detection for Computer Vision Using a Robust Genetic Algorithm

This work is concerned with the development and implementation of an image pattern recognition approach to support computational vision systems when it is necessary to automatically check the presence of specific objects on a scene, and, besides, to describe their position, orientation and scale. The developed methodology involves the use of a genetic algorithm to find known 2D object views in the image. The proposed approach is fast and presented a robust performance in several test instances including multiobject scenes, with or without partial occlusion.
Best practices for estimating and reporting epidemiological delay distributions of infectious diseases using public health surveillance and healthcare data

Epidemiological delays, such as incubation periods, serial intervals, and hospital lengths of stay, are among key quantities in infectious disease epidemiology that inform public health policy and clinical practice. This information is used to inform mathematical and statistical models, which in turn can inform control strategies. There are three main challenges that make delay distributions difficult to estimate. First, the data are commonly censored (e.g., symptom onset may only be reported by date instead of the exact time of day). Second, delays are often right truncated when being estimated in real time (not all events that have occurred have been observed yet). Third, during a rapidly growing or declining outbreak, overrepresentation or underrepresentation, respectively, of recently infected cases in the data can lead to bias in estimates. Studies that estimate delays rarely address all these factors and sometimes report several estimates using different combinations of adjustments, which can lead to conflicting answers and confusion about which estimates are most accurate. In this work, we formulate a checklist of best practices for estimating and reporting epidemiological delays with a focus on the incubation period and serial interval. We also propose strategies for handling common biases and identify areas where more work is needed. Our recommendations can help improve the robustness and utility of reported estimates and provide guidance for the evaluation of estimates for downstream use in transmission models or other analyses.
A Sentinel-2 Multiyear, Multicountry Benchmark Dataset for Crop Classification and Segmentation With Deep Learning

In this work we introduce Sen4AgriNet, a Sentinel-2 based time series multi country benchmark dataset, tailored for agricultural monitoring applications with Machine and Deep Learning. Sen4AgriNet dataset is annotated from farmer declarations collected via the Land Parcel Identification System (LPIS) for harmonizing country wide labels. These declarations have only recently been made available as open data, allowing for the first time the labeling of satellite imagery from ground truth data. We proceed to propose and standardise a new crop type taxonomy across Europe that address Common Agriculture Policy (CAP) needs, based on the Food and Agriculture Organization (FAO) Indicative Crop Classification scheme. Sen4AgriNet is the only multi-country, multi-year dataset that includes all spectral information. It is constructed to cover the period 2016-2020 for Catalonia and France, while it can be extended to include additional countries. Currently, it contains 42.5 million parcels, which makes it significantly larger than other available archives. We extract two sub-datasets to highlight its value for diverse Deep Learning applications; the Object Aggregated Dataset (OAD) and the Patches Assembled Dataset (PAD). OAD capitalizes zonal statistics of each parcel, thus creating a powerful label-to-features instance for classification algorithms. On the other hand, PAD structure generalizes the classification problem to parcel extraction and semantic segmentation and labeling. The PAD and OAD are examined under three different scenarios to showcase and model the effects of spatial and temporal variability across different years and different countries.
Surface processes and phase transitions from ab initio atomistic thermodynamics and statistical mechanics

Abstract   Knowledge of the surface composition and atomic geometry is a prerequisite for understanding the physical and chemical properties of modern materials such as those used in, for example, heterogeneous catalysis, corrosion resistance, sensors, and fuel cells. To understand the function of surfaces under realistic conditions, it is crucial to take into account the influence of environmental gas at finite (possibly high) temperatures and pressures. Recent experimental and theoretical studies have shown that when transition metal surfaces are exposed to high oxygen pressures, thin oxide-like structures can form which may have little similarity to the bulk oxides, and thus possess unique chemical and physical properties. Given that technological oxidation catalysis typically involves oxygen-rich conditions, such structures may be present, and in fact be the active material for the reaction, as opposed to the traditionally believed pure metal. Using the approach of ab initio atomistic thermodynamics, free energy phase-diagrams for oxygen/transition metal systems in (T, p)-space ranging from ultra-high vacuum to technically relevant pressures, p, and temperatures, T, are discussed. In addition, results of ab initio statistical mechanical schemes, namely, the Lattice-gas Hamiltonian plus Monte Carlo (MC) simulations, are presented for oxygen/transition metal and alkali-atom/metal systems, where for the latter, the recently introduced “Wang–Landau” algorithm is employed, which affords an accurate estimation of the density of (configurational) states, therefore allowing a direct determination of all major thermodynamic functions.
Statistical Analysis Plan for Primary and Selected Secondary Health Endpoints of the SEARCH-Youth Study

This document provides the statistical analytic plan (SAP) for evaluating health outcomes in the SEARCH-Youth study, a cluster randomized trial designed to evaluate the effect of a combination intervention on HIV viral suppression among adolescents and young adults with HIV in rural Uganda and Kenya (Clinicaltrials.gov: NCT03848728). The SAP was locked prior to unblinding and effect estimation. This SAP was embargoed until November 04, 2022 when it was submitted to arXiv.
185 Testing Cost reduction to Improve Quality of Care and Patient Safety in The Municipal Public Health System Based Ambulatory Care Clinics In New York City

Abstract                                        Introduction/Objective                     Our New York City Municipal Public Health System based multi-site ambulatory clinics that offer various waived POCT (point of care tests) and provider-performed microscopy (PPM) to our communities, ensure standardization and quality of POC testing performed in-house and optimization of the appropriate all other send-out tests to the reference laboratories across our health system. Our laboratory service conducts system-wide centralized implementation, monitoring and oversight of the laboratory operations regarding regulatory compliance, test performance, quality control and training. As part of the Laboratory Quality improvement (QI)/Quality Assurance (QA) program, we conducted a comprehensive cost analysis of laboratory testing to identify areas where costs can be reduced or optimized and can be implemented system-wide to improve the quality of care without affecting patient safety.                                                           Methods/Case Report                     We focused on identifying areas where resources are being overused, wasted and or less utilized leading to opportunities for cost savings through better resource management and through reducing unnecessary testing.                                                           Results                     Among the performed in-house POCT tests, urine toxicology has been identified, which is least utilized per resource allocation. The tuberculosis screening test: an interferon-γ release assay (IGRA) was found as the most overutilized among the send out tests. Removal of the least utilized POCT would save the cost of the kit or reagent, technical oversight and time and optimization of the overutilized send out test would make sure that lab tests are performed appropriately and only when clinically indicated and compliant to the policy, leading to fewer testing and lower costs.                                                           Conclusion                     Since cost reduction is an important component of clinical laboratory quality improvement (QI) efforts and an ongoing process, by identifying and implementing new ways our laboratory services quality initiatives ensure efficiency and reduce expenses, the quality of testing for patient safety and care across our health system.
Time complexity analysis of quantum difference methods for the multiscale transport equations

We investigate time complexities of finite difference methods for solving the multiscale transport equation with quantum algorithms. We find that the time complexities of both the classical treatment and quantum treatment for a standard explicit scheme scale as $\mathcal{O}(1/\varepsilon)$, where $\varepsilon$ is the small scaling parameter, while the complexities for the even-odd parity based Asymptotic-Preserving (AP) scheme do not depend on $\varepsilon$. This indicates that it is still of great importance to use AP (and probably other efficient multiscale) schemes for multiscale problems in quantum computing when solving multiscale transport or kinetic equations.
Magnetic field induced incommensurate resonance in cuprate superconductors

The influence of a uniform external magnetic field on the dynamical spin response of cuprate superconductors in the superconducting state is studied based on the kinetic energy driven superconducting mechanism. It is shown that the magnetic scattering around low and intermediate energies is dramatically changed with a modest external magnetic field. With increasing the external magnetic field, although the incommensurate magnetic scattering from both low and high energies is rather robust, the commensurate magnetic resonance scattering peak is broadened. The part of the spin excitation dispersion seems to be an hourglass-like dispersion, which breaks down at the heavily low energy regime. The theory also predicts that the commensurate resonance scattering at zero external magnetic field is induced into the incommensurate resonance scattering by applying an external magnetic field large enough.
Role of Nursing Informatics Competencies in Quality Nursing Care and Patient Safety: A Review and Concept Paper

Nursing Informatics, which comprises nursing, computer, and information systems sciences, is crucial to improving quality and safety. Electronic health records, telemedicine, big data, and analytics have redesigned the decision-making process for clinical work and practices. Nursing Informatics involves understanding information technology systems, science, and computer literacy, preparing registered nurses to use healthcare technology effectively. This paper reviews the historical development of Nursing Informatics, from the analytical statistics initiated by Florence Nightingale to set data standards focusing on Nursing Informatics in the contemporary nursing curriculum. Although Donabedian’s Model of Quality Care and Benner’s Novice-to-Expert Theory have complex approaches, one concept in each theory stresses the value of structured learning to gain experience in developing Nursing Informatics competencies. The main challenges encountered are limited resource availability, poor training, and uneven supply between the urban and rural health facilities. Research examines nursing informatics’ ability to improve Healthcare, reduce errors, and improve patient care. The challenge is prioritizing policy support, infrastructure investment, and continued education to achieve sustainable success in Nursing Informatics. The paper reviews the approaches enabling healthcare systems to integrate Nursing Informatics for better quality care and safety, particularly in regions with limited resources.
Two-View Graph Neural Networks for Knowledge Graph Completion

To appear in Proceedings of ESWC 2023; 17 pages; 4 tables; 4 figures
The Politics of Surveillance: Upholding the Privacy Rights of Racialized Peoples in the Digital Age

&lt;p&gt;This paper explores the prevalence of AI technology and biometric data collection within institutions and its implications on democratic nations. Of particular interest, is the potential infringement on the constitutional rights to privacy. The experiences of racialized communities when met with government sanctioned surveillance schemas will also be discussed. A comparative analysis of privacy policies in North America and Europe will be examined along with a case study on the biometric data collection firm, Clearview AI.&lt;/p&gt;
Quantification, Monetary Valuation, and Recognition of Mangrove Blue Carbon Assets: An Environmental Accounting Study and Natural Capital Potential in Surumana Village, Donggala Regency

This research aims to internalize the ecological value of Blue Carbon in the mangroves of Surumana Village, Donggala Regency, into an accounting framework based on the principles of Environmental Accounting and Carbon Economic Value (NEK). A descriptive quantitative research method was employed, utilizing Systematic Stratified Sampling (10 x 10 m plots) for field inventory. Above Ground Biomass (AGB) data were calculated using Multivariate Allometric Equations and converted into Carbon Dioxide Equivalent (CO2e) stocks using IPCC standard factors. The subsequent key stage involved Monetary Valuation of the total CO2e using a proxy price from IDX Carbon (IDR 144,000/tCO2e). Physical quantification results indicate that the total CO2e stock stored in the Surumana Mangroves (4.34 Ha) is 28,764.48 Ton CO2e, dominated by the Nypa fruticans species (71.95%). The Monetary Valuation (Fair Value) resulted in a total asset value of IDR 4,141,376,012.67. This valuation figure meets the Level 1 Input criteria in the Fair Value measurement hierarchy (PSAK 113) as it utilizes active market transaction prices.From an accounting perspective, this Blue Carbon stock fulfills the criteria for Biological Assets and is recognized as regional Natural Capital. The recognition of this IDR 4.14 billion asset is essential for supporting the preparation of the regional Natural Resource Balance Sheet (NSA) and enhancing local government accountability. Consequently, mangrove degradation can now be recorded as a measurable impairment of asset value, fostering conservation protection based on measurable economic value.
Peace at last? Appraisal of the Addis Ababa Peace and Security Cooperation Framework and the United Nations Security Council Resolution 2098 for the Democratic Republic of the Congo

The seizure of Goma by the Mouvement du 23 Mars rebellion in November 2012 has precipitated a review of the peace strategy implemented in the Democratic Republic of the Congo (DRC) and the Great Lakes region thus far. As a result, the Congolese government and other peace stakeholders in the Great Lakes region and beyond signed the Addis Ababa Peace and Security Cooperation (PSC) Framework in February 2013, paving the way to the adoption, a month later, of Resolution 2098 by the United Nations (UN) Security Council. Notwithstanding the constructive criticisms these two policy documents have received due to, amongst other things, the shortcomings they may harbour, this article argues that the Addis Ababa PSC Framework and Resolution 2098 have the potential of instilling a new dynamics into the search for peace and stability in the DRC and the Great Lakes region. Rather than dismissing them for their possible inadequacies, these two policy documents ought to be embraced as important stepping stones in the much needed overhaul of the peace strategy that has dominated peacemaking and peacebuilding initiatives in the DRC and the Great Lakes region in the past two decades.
IMPACT OF ANTIMICROBIAL STEWARDSHIP PROGRAMMES ONBACTERIAL RESISTANCE LEVEL IN THE UAE: A SYSTEMATICREVIEW

Antimicrobial resistance is a global problem that needs to be addressed immediately due to its huge clinical and economic burden ((WHO, 2021a). The death toll associated with antimicrobial resistance approached 5 million cases in 2019 while total cost that can impact the whole society is around Â£10 billion each year (Antimicrobial Resistance Collaborators, 2022: Smith and Coast, 2012). Eventually, this has motivated the World Health Organization (WHO) to set a global action plan to counteract the antimicrobial resistance all-over the world as a priority issue (WHO, 2015). In 2015, the United Nations (UN) released the updated Sustainability Development Goals (SDG) that is aiming to improve the public health and general well-being of the globe through tackling 17 topics. Treatment and prevention for multiple common infections such as Human Immunodeficiency Virus (HIV), malaria, tuberculosis and others were among the main targets in its agenda (UN, 2019). In UAE, although several initiatives have been adopted such as launching antimicrobial stewardship programmes and surveillance programmes, antimicrobial resistance is still an unresolved issue (National sub-committee for AMR surveillance, 2021 UAE Higher Committee for Antimicrobial Resistance, 2019). The resistance of Acinetobacter species to several cephalosporins and penicillins exceeded 20% (National sub-committee for AMR surveillance, 2021). Current evidence available publicly does not include a systematic review that is evaluating the effect of antimicrobial stewardship programmes on bacterial resistance level in UAE. With the scarcity of data and complexity of the issue, it is important to conduct this systematic review to assess the impact of these programmes on bacterial resistance level and identify gaps and challenges in the implementation.
Probing magnetic ordering in air stable iron-rich van der Waals minerals

In the rapidly expanding field of two-dimensional materials, magnetic monolayers show great promise for the future applications in nanoelectronics, data storage, and sensing. The research in intrinsically magnetic two-dimensional materials mainly focuses on synthetic iodide and telluride based compounds, which inherently suffer from the lack of ambient stability. So far, naturally occurring layered magnetic materials have been vastly overlooked. These minerals offer a unique opportunity to explore air-stable complex layered systems with high concentration of local moment bearing ions. We demonstrate magnetic ordering in iron-rich two-dimensional phyllosilicates, focusing on mineral species of minnesotaite, annite, and biotite. These are naturally occurring van der Waals magnetic materials which integrate local moment baring ions of iron via magnesium/aluminium substitution in their octahedral sites. Due to self-inherent capping by silicate/aluminate tetrahedral groups, ultra-thin layers are air-stable. Chemical characterization, quantitative elemental analysis, and iron oxidation states were determined via Raman spectroscopy, wavelength disperse X-ray spectroscopy, X-ray absorption spectroscopy, and X-ray photoelectron spectroscopy. Superconducting quantum interference device magnetometry measurements were performed to examine the magnetic ordering. These layered materials exhibit paramagnetic or superparamagnetic characteristics at room temperature. At low temperature ferrimagnetic or antiferromagnetic ordering occurs, with the critical ordering temperature of 38.7 K for minnesotaite, 36.1 K for annite, and 4.9 K for biotite. In-field magnetic force microscopy on iron bearing phyllosilicates confirmed the paramagnetic response at room temperature, present down to monolayers.
Advances, opportunities, and challenges of using modern artificial general intelligence and artificial intelligence-generated content technologies in depression and related disorders – Systematic review

Artificial general intelligence (AGI) and artificial intelligence-generated content (AIGC) technologies are transforming mental health care by enabling early diagnosis, personalized treatment, and innovative therapeutic interventions. This systematic review evaluates the applications, benefits, and challenges of AGI and AIGC in diagnosing and managing depression and related disorders. A comprehensive literature search was conducted across PubMed, PsycINFO, EMBASE, Scopus, and Web of Science, with the last update on June 2, 2024. Studies were included if they assessed the role of AGI or AIGC in screening, diagnosis, treatment, or monitoring of depression. Exclusion criteria included non-English publications, review articles, and studies unrelated to artificial intelligence (AI) applications in mental health. Risk of bias was evaluated using standardized assessment tools, and findings were synthesized qualitatively. Of 246 identified articles, 34 met the inclusion criteria. Key findings indicate that AGI enhances diagnostic accuracy by integrating multimodal data (e.g. neuroimaging, wearable devices, and behavioral analysis), whereas AI-driven tools improve treatment personalization and real-time monitoring. AI-assisted psychotherapy and drug discovery models show promise in optimizing mental health interventions. However, challenges remain regarding algorithmic bias, data privacy, regulatory compliance, and ethical concerns. AGI and AIGC offer transformative potential in mental health care, improving diagnostic precision and personalized treatment strategies. However, further research is required to validate AI-driven interventions, mitigate bias, and establish ethical frameworks for clinical integration. Ensuring equitable access and robust validation will be essential for the responsible adoption of AI in psychiatry.
Development of methodologies for secondary use of health data for research, management, and assessment of care quality

The implementation of medical software over the past few decades has generated a significant amount of clinical data, stored in electronic format. These software programs, and their associated databases, were designed to collect and retrieve data for care, patient stay administration, and care billing purposes. They also provide opportunities for reuse beyond direct care. For example, data reuse offers opportunities for research, quality of care, business management or public health. However, data reuse encounters many obstacles that we are trying to overcome through this research project. My research focus is on the development of methodologies for the reuse of health data. This theme is part of the continuity of my university thesis work, defended in 2015 and entitled "Contribution to the prevention of risks related to anesthesia by the promotion of hospital information within a data warehouse". The development of methodologies for the reuse of data primarily involves four key stages: data collection, data integration and standardization, feature extraction, and their exploitation for feedback to the user. In the coming years, the outlook for this research project is to: (i) automate data collection, integration, and data quality assessment, especially from social networks or emerging new data sources; (ii) formalize feature extraction and propose tools and methods to enhance the reproducibility of this process; (iii) propose an epidemiological analysis pipeline to guide optimal statistical analysis strategies and facilitate access to and understanding of results; (iv) promote the implementation of common data models and the development and sharing of open-source methods and tools dedicated to research; (v) train healthcare professionals and data scientists in research and foster collaboration among different specialties