ethicalabs/Research-Intent-Collab huggingface dataset:
Monitoring of Air Quality with Low-Cost Electrochemical Sensors and the Use of Artificial Neural Networks for the Atmospheric Pollutants Concentration Levels Prediction

This paper shows the preliminary results of the monitoring and estimation of air pollutants at a strategic point within the district of San Isidro, Lima - Peru. Low-cost, portable, wireless and geo-locatable electrochemical sensors were used to capture reliable contamination levels in real-time which could be used not only to quantify atmospheric pollution exposure but also for prevention and control, and even for legislative purposes. For the prediction of \(CO{_2}\) and \(SO{_2}\) levels, computational intelligence algorithms were applied and validated with experimental data. We proved that the use of Artificial Neural Networks (ANNs) has a high potential as a tool to use it as a forecast methodology in the area of air pollution.
Measuring Dark Matter Power Spectrum from Cosmic Microwave Background

4 pages, 2 figures
A Stormy War of Position: An Investigation of the Use of Human Right to Water and Sanitation Discourse to Legitimate Accumulation by Dispossession

This thesis examines the corporate appropriation of human right to water and sanitation discourse. David Harvey’s concept of accumulation by dispossession provides the political- economic basis for this analysis while enabling a discussion of water conflicts that looks at neoliberalization strategies beyond the privatization of services. Inspired by Gramsci’s notion of a “war of position”, this thesis investigates the role of corporate appropriation of human right to water and sanitation discourse in legitimating strategies of accumulation by dispossession. Through content and critical discourse analysis of publications of a corporate policy consortium called the 2030 Water Resources Group the investigation concludes that this lobby group simultaneously undermines human rights while actively using key elements of rights discourse to advance its neoliberal water policy objectives.
Systematic review of instruments for the assessment of patient reported outcomes and quality of life in childhood glaucoma patients

Topic To identify patient-reported outcome measures (PROMs), that have been used in children and adolescents with glaucoma, and to evaluate their methodological quality. Clinical relevance Childhood glaucoma impairs vision and quality of life throughout all stages of life. Thus, a PROM needs to cover many different age groups and topics. Various instruments have been used to evaluate patient-reported outcomes in patients with childhood glaucoma, however it is unclear which PROM has the highest methodological quality and complies best with the needs of childhood glaucoma patients. Methods A systematic literature review was performed searching MEDLINE (PubMed), The Cochrane Library, Web of Science and PsycINFO (EBSCO). We included peer-reviewed full-text articles of the past ten years in English, German or Spanish language that reported PROMs in children with glaucoma. The study selection and methodological quality assessment of the identified PROMs was performed by two independent reviewers using a seven-point checklist. The content was mapped onto the World Health Organization International Classification of Functioning, Disability and Health. The systematic review was prospectively registered in PROSPERO (ID CRD42022353936). Results The search strategy retrieved 3295 matches. 2901 studies were screened, and 11 relevant articles were identified using ten different instruments. The instruments addressed functional visual ability (FVA), vision-related quality of life (VRQoL), health-related QoL (HRQoL), and life satisfaction (LS). Six instruments were applicable for the use in children. Seven of the questionnaires received the highest number of positive ratings (5/7). None of the instruments considered the views of childhood glaucoma patients during their development. Conclusion This systematic review provides a descriptive catalogue of vision-specific and generic health PRO instruments that have been used in childhood glaucoma cohorts. An instrument specifically developed for childhood glaucoma is lacking which might result in missing important factors, such as permanent treatment with eye drops, repeated surgeries and heritability of the disease, when investigating the quality of life in children with glaucoma.
Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis

The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence environmental monitoring stations are typically sparsely deployed, resulting in limited spatial resolution for measurements. Recently, low-cost air quality sensors have emerged as an alternative that can improve the granularity of monitoring. The use of low-cost air quality sensors, however, presents several challenges: they suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors, such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Periodic re-calibration can improve the accuracy of low-cost sensors, particularly with machine-learning-based calibration, which has shown great promise due to its capability to calibrate sensors in-field. In this article, we survey the rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques. We also identify open research challenges and present directions for future research.
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks

In recent years, machine learning has established itself as a powerful tool for high-resolution weather forecasting. While most current machine learning models focus on deterministic forecasts, accurately capturing the uncertainty in the chaotic weather system calls for probabilistic modeling. We propose a probabilistic weather forecasting model called Graph-EFM, combining a flexible latent-variable formulation with the successful graph-based forecasting framework. The use of a hierarchical graph construction allows for efficient sampling of spatially coherent forecasts. Requiring only a single forward pass per time step, Graph-EFM allows for fast generation of arbitrarily large ensembles. We experiment with the model on both global and limited area forecasting. Ensemble forecasts from Graph-EFM achieve equivalent or lower errors than comparable deterministic models, with the added benefit of accurately capturing forecast uncertainty.
The Effect of Six Sigma Lean Strategy Management on Improving the Quality of Information Therapy Services at Pediatric Medical Center

Introduction: In the last decade, Six Sigma has emerged as a systematic and powerful approach to improving the quality of health care and controlling costs. The purpose of this article is to determine the effect of Six Sigma strategy management on improving the quality of information services in the Children's Medical Center Hospital.Research Method: The methodology of the present study, which is an applied study in terms of purpose, is a descriptive correlational survey. The statistical population of this study consists of all managers, supervisors and experts of the Children's Medical Center Hospital, whose number is 149 people. Cochran's formula has been used to determine the sample size, which according to the statistical population is 94 people. The method of selecting statistical samples is simple random sampling according to the subject and nature of the research. The required data were collected using a standard questionnaire and the data were analyzed using regression analysis through SPSS 23 software.Findings: Research findings showed that fact-based management can account for 26% of changes in the quality of information services, management of resource providers can account for 38% of changes in the quality of information services and management based on facts and information Predict 39% of changes in the quality of information services.Conclusion: Six Sigma is a systematic and statistical approach that aims to identify the shortcomings of process performance and reduce the error of those work and clinical processes that are performed with long time, high cost and poor outcome.
Using optimization methods to align food production and biodiversity conservation beyond land sharing and land sparing

Aligning food production with biodiversity conservation is one of the greatest challenges of our time. One framing of this challenge is the land‐sharing vs. land‐sparing debate. Much empirical research has focused on identifying the relationship between agricultural yields and species populations, and using the relative number of species with particular relationships to inform landscape‐level management. We feel this is misguided, as such an approach does not guarantee the existence of every species of conservation concern. Here, we show that constrained optimization methods can be used to identify landscape‐level solutions which maximize agricultural yields and populations for any number of species. Our results suggest that the relative number of species with particular yield–density curves is not a good indicator as to how landscapes should be managed. Likewise, choosing between blanket sharing or sparing strategies leads to suboptimal outcomes at the landscape scale in many cases. Our framework makes maximum use of the rich information contained in yield–density curves to move beyond black‐and‐white choices and toward more nuanced, context‐specific solutions to aligning biodiversity conservation and agricultural production. Such optimal landscapes will likely have features of both sharing and sparing strategies.
NATURAL LANGUAGE PROCESSING UNTUK EKSTRAKSI INFORMASI ADVERSE DRUG REACTIONS DARI ELECTRONIC HEALTH RECORDS: SYSTEMATIC REVIEW

Adverse drug events (ADEs) berkontribusi terhadap 5-10% hospitalisasi dan menghabiskan biaya sekitar USD 30 miliar per tahun, namun sistem pelaporan spontan hanya menangkap 5-10% dari ADEs aktual akibat underreporting yang parah. Systematic review ini menganalisis 60 studi peer-reviewed (2019-2025) mengenai metode natural language processing (NLP) untuk mengekstrak informasi ADEs dari catatan klinis electronic health records (EHR), mengikuti pedoman PRISMA 2020 di lima basis data (PubMed, IEEE Xplore, ACL Anthology, Scopus, Web of Science). Hasil menunjukkan bahwa model berbasis transformer, khususnya BioBERT dan ClinicalBERT, merepresentasikan state-of-the-art dengan F1-score 0,85-0,92 pada dataset acuan (n2c2 2018, MIMIC-III, MADE1.0), secara signifikan melampaui sistem berbasis aturan (+15-20%) dan metode machine learning tradisional (+8-12%). Pre-training spesifik domain pada teks klinis terbukti krusial, meningkatkan performa 3-5% dibanding model BERT umum. Namun demikian, tantangan kritis masih ada: deteksi negasi dan spekulasi (30-40% penyebutan medis memerlukan disambiguasi kontekstual), penalaran temporal untuk menentukan onset ADEs relatif terhadap paparan obat, resolusi ambiguitas singkatan medis, dan asesmen kausalitas. Kesenjangan signifikan antara laboratorium dan klinis sebesar 10-15% degradasi performa teridentifikasi, dengan hanya 8% studi melaporkan pengalaman deployment klinis aktual. Reproduksibilitas tetap menjadi masalah, dengan hanya 23% studi membagikan kode dan 15% menyediakan model terlatih. Prioritas masa depan meliputi pengembangan pendekatan few-shot learning untuk mengatasi keterbatasan data berlabel (~5.000 catatan klinis teranotasi yang tersedia publik), peningkatan interpretabilitas model melalui metode explainable AI, pelaksanaan studi validasi eksternal multi-pusat, dan penetapan protokol evaluasi terstandarisasi. Tinjauan ini menyediakan panduan berbasis bukti bagi peneliti yang mengembangkan metode NLP, praktisi yang mengimplementasikan sistem deteksi ADEs, dan pembuat kebijakan yang merumuskan standar untuk farmakovigilans berbasis NLP.
PSO Variants for Localization Challenge in Wireless Sensor Network and Internet Of Thing

Wireless Sensor Network (WSN) has recently been extensively investigated due to their numerous applications where processes have to be spread over a large area. One of several technical aspects of WSNs is the node localization. Most approaches in the recent literature rely on algorithms that maximize the localization rate with a minimum runtime. In this paper, we introduce a comparative study about the PSO (particle swarm optimization) and its variants, The node localization problem is seen as an optimization problem in a multi-dimensional space, PSO computes iteratively (through evolution) the nodes positions using the Euclidian distance as fitness. Deploying this algorithm on a large WSN with hundreds of sensors shows pretty good performance in terms of node localization, two network topology are treated in this paper for contributing the advantages of range communication as an influencing factor.
Genetic discrimination in life insurance: a human rights issue

In this issue of Journal of Medical Ethics, Pugh1 offers a pluralist justice-based argument in support of the spirit, if not the precise letter, of the UK approach to the use of genetic test results (GTRs) to underwrite life insurance.  We agree with Dr Pugh’s general contention that there is ethical and philosophical support for curtailment of insurers’ access to, and use of, applicants’ GTR in underwriting. However, we disagree with the contention that broad revisionary implications of certain theories of justice render them unpersuasive. In fact, despite the competing theories, the United Nations Universal Declaration on the Human Genome and Human Rights (UDHGHR) has already made a clear statement on this issue. Article 6 of the Declaration,2 unanimously adopted in 1997 by 77 countries (including Australia and the UK), along with a resolution for its implementation,3 states, ‘No one shall be subjected to discrimination based on genetic characteristics that is intended to infringe or has the effect of infringing human rights, fundamental freedoms and human dignity.’ Further, Article 25 of the UN Convention on the Rights of Persons with Disabilities (CRPD) (which Australia and the UK have confirmed) prohibits discrimination against persons with disabilities in the provision of life insurance. These statements are not contingent on the acceptability of the degree of revisionary implications. They are clear, unambiguous statements about the obligations of signatory countries. Despite this, few countries have taken steps commensurate with this expectation, possibly due to the scale of changes required for proper ratification. However, genetic discrimination is recognised as one …
Tinker-HP: Accelerating Molecular Dynamics Simulations of Large Complex Systems with Advanced Point Dipole Polarizable Force Fields Using GPUs and Multi-GPU Systems

We present the extension of the Tinker-HP package (Lagardère et al., Chem. Sci., 2018,9, 956-972) to the use of Graphics Processing Unit (GPU) cards to accelerate molecular dynamics simulations using polarizable many-body force fields. The new high-performance module allows for an efficient use of single- and multi-GPU architectures ranging from research laboratories to modern supercomputer centers. After detailing an analysis of our general scalable strategy that relies on OpenACC and CUDA, we discuss the various capabilities of the package. Among them, the multi-precision possibilities of the code are discussed. If an efficient double precision implementation is provided to preserve the possibility of fast reference computations, we show that a lower precision arithmetic is preferred providing a similar accuracy for molecular dynamics while exhibiting superior performances. As Tinker-HP is mainly dedicated to accelerate simulations using new generation point dipole polarizable force field, we focus our study on the implementation of the AMOEBA model. Testing various NVIDIA platforms including 2080Ti, 3090, V100 and A100 cards, we provide illustrative benchmarks of the code for single- and multi-cards simulations on large biosystems encompassing up to millions of atoms. The new code strongly reduces time to solution and offers the best performances to date obtained using the AMOEBA polarizable force field. Perspectives toward the strong-scaling performance of our multi-node massive parallelization strategy, unsupervised adaptive sampling and large scale applicability of the Tinker-HP code in biophysics are discussed. The present software has been released in phase advance on GitHub in link with the High Performance Computing community COVID-19 research efforts and is free for Academics (see https://github.com/TinkerTools/tinker-hp).
Infrastructure as Code for Performance Engineering: Automating Deployment and Testing with Terraform and Ansible

Infrastructure as Code (IaC) has become an important part of current performance engineering that makes automation and consistency possible. Setting up performance testing setups by hand is generally not consistent, takes longer, and gives incorrect results. This theoretical study investigates the function of Infrastructure as Code (IaC) in automating deployment and performance testing with Terraform and Ansible. The suggested method looks at deployment efficiency, configuration consistency, and performance test reliability by comparing human infrastructure management to an automated IaC-driven approach. To see if deployment speed, configuration accuracy, and consistency of performance results have been better, we employ percentage-based frequency analysis. The results show that IaC cuts down on deployment time, keeps configuration drift to a minimum, and makes performance testing more repeatable. The study shows how important it is to use Terraform for setting up infrastructure and Ansible for managing configurations to create performance engineering workflows that are scalable, dependable, and efficient.
Beach nourishment provides resilient protection for critical coastal infrastructure

Hurricanes this century have produced almost a trillion dollars in damages in the U.S., often to critical infrastructure that requires large costs and unacceptable times to repair or replace. There is a need for resilient protection of critical infrastructure where the protection must have the “ability to anticipate, absorb, adapt to, and/or rapidly recover from a potentially disruptive event” and be cost-effective. Coastal infrastructure can be protected by moving infrastructure back from the ocean (retreat), building protective structures, or by beach nourishment to effectively move the ocean back from the infrastructure. Retreat is very costly and highly unpopular on developed shores. Structures are expensive and lack resilience because their failure is usually catastrophic and repair slow. Beach nourishment is the favored protection option on developed shorelines because it significantly reduces infrastructure damage, provides resilient protection, and is cost effective with a high return on investment. The aftermath of Hurricane Sandy provided an example of the tremendous reduction of infrastructure damage due to beach nourishment. Dr. Stewart Farrell, director of the Coastal Research Center in New Jersey, reported: “It really, really works. Where there was a federal beach fill in place, there was no major damage — no homes destroyed. Where there was no beach nourishment, the destruction was complete. Older homes were ripped from foundations and tossed about.” A post-Sandy analysis showed that Corps of Engineers’ beach nourishment projects saved an estimated $1.3 billion in avoided damages. Beach nourishment provides resilient protection. It is partially self-healing, because during a storm some of the sand moves to offshore bars where it causes waves to break and reduces infrastructure damage, and then much of it returns to shore after the storm passes. It can be repaired relatively rapidly by use of a mobile dredge to replace net sand loss. It is a highly adaptable approach to climate change because its rate of sand placement can be varied to raise profiles to offset increased sea level rise. Beach nourishment pays for itself as nice, wide beaches sit in readiness to protect critical coastal infrastructure from storms, while in the meantime, tourists typically generate more than $100 in taxes annually to local, state, and the federal governments for every $1 these governments invest in beach nourishment.
Investigation and Root Cause Analysis Guideline for Undetected Cracking Incidents in Safety-of-Flight Aircraft Structure

Abstract : This document provides guidance for investigating and conducting root cause analyses of safety-related incidents associated with Air Force maintenance actions. The impetus for and sole focus of this document are incidents in which detectable cracks in safety-of-flight aircraft structure have been missed by approved nondestructive inspection (NDI) techniques. The tools defined herein, however, can be adapted and utilized for the investigation of any maintenance-related incident. The role of root cause analysis in incident investigation is emphasized in this document. Root cause analysis is intended to answer the questions "What?," "How?," and, most importantly, "Why?" regarding an incident. A successful root cause analysis will identify the, controllable, causal factors that can be corrected to eliminate the recurrence of similar incidents in the future.
Impact of Nurse Burnout on Patient Safety and Quality of Care in Intensive Care Units in Sub-Saharan Africa

Nurse burnout has become a pressing global health workforce issue, and its implications on patient safety and quality of care are increasingly recognised. In sub-Saharan Africa (SSA), there are great resource constraints and workforce shortages, especially in high acuity settings such as high dependency units (HDUs) and intensive care units (ICUs). Such has contributed to stress within the working environs on nursing staff in most health facilities. This scoping review aims to examine the prevalence and determinants of nurse burnout in Sub-Saharan Africa (SSA) and explores how burnout may impact patient safety and care quality in High-Dependency Unit (HDU)/Intensive Care Unit (ICU) settings. The methodology outlines a proposed survey instrument using a validated burnout questionnaire combined with metrics of patient safety and quality of care, and statistical analysis methods. Findings from the literature indicate a high prevalence of burnout among nurses in SSA (for instance, emotional exhaustion resulting to 66 %) (Owuor et al., 2020) and suggest the relationship between nurse workload assigned, burnout, and the adverse patient outcomes. The discussion elaborates on the implications for ICU/HDU nursing, putting emphasis on the mechanisms by which burnout may degrade the quality of care, for instance, through increased operational errors, decreased vigilance, as well as missed care, and proposes directions for future research work and policy formulation in the health sector across the SSA. In conclusion, addressing nurse burnout is not only a workforce well-being imperative but also central to safeguarding patient safety and improving care quality in critical care settings in SSA.
Scalable Mitigation of Measurement Errors on Quantum Computers

9 pages, 8 figures, 1 table