ethicalabs/Research-Intent-Collab huggingface dataset:
Design and Implementation of a Nursing Clinical Decision Support System to Promote Guideline Adherence

This article describes the development and implementation of a nursing clinical decision support system that prompts nurses to place mechanically ventilated patients in a semirecumbent position in the absence of contraindications. A literature review is provided regarding (1) ventilator-associated pneumonia and recommendations from expert panels to maintain mechanically ventilated patients in a semirecumbent position (unless contraindicated) and (2) clinical decision support systems. Use of clinical decision support systems has primarily been developed for physicians; however, there is growing interest in expanding this technology to nurses working in the clinical area. Despite recommendations from organizations such as the Center for Disease Control and Prevention to maintain mechanically ventilated patients in a head-elevated position, there is evidence that the practice is not adequately adhered to. Therefore, a nursing clinical decision support system (in the form of a reminder) that prompts nurses to adhere to this recommendation was designed and implemented.
Land use planning based on the connectivity of tree species does not ensure the conservation of forest biodiversity

Abstract   Amidst rapid global climate change and at a time when many adaptive policies and strategies have been implemented, reductionism in forest management decisions may have detrimental effects on forest biodiversity. Stancioiu et al. (2018) made an important contribution by assessing forest connectivity based on tree species populations in Romania. Their study concluded that current management policies and inherited guidelines from the past likely promoted the conservation of regional biodiversity. In this viewpoint paper, we present a different perspective on their conclusion. We show that current management policies are threatening biodiversity in Romania, and changes in forest management policies are urgently needed to halt the loss of habitats of protected species. Although recent management policies likely did lead to good tree species connectivity, habitats of protected species are becoming increasingly fragmented by logging. Adopting policies to protect forest habitats of protected species, limiting large-scale salvage logging within protected areas, and restricting road building to reduce forest fragmentation are crucial steps towards the long-term persistence of biodiversity hotspots in the Romanian Carpathians.
Improvement in Cell Imaging by Applying a New Natural Dye from Beet Root Extraction

Fluorescent dyes are getting popularity for last few decades due to their extraordinary applications in cell imaging. We have discovered organic Beet root extracted fluorescent (BREF) dye as an efficient pigment for effective cell imaging. By applying this dye to different types of human cells we obtained very good results in fluorescence cell imaging in case of all types of cells. We also have seen that this dye takes very less internalization time to give very good image of cells and remains stable for sufficiently long period.
PDNS-Net: A Large Heterogeneous Graph Benchmark Dataset of Network Resolutions for Graph Learning

In order to advance the state of the art in graph learning algorithms, it is necessary to construct large real-world datasets. While there are many benchmark datasets for homogeneous graphs, only a few of them are available for heterogeneous graphs. Furthermore, the latter graphs are small in size rendering them insufficient to understand how graph learning algorithms perform in terms of classification metrics and computational resource utilization. We introduce, PDNS-Net, the largest public heterogeneous graph dataset containing 447K nodes and 897K edges for the malicious domain classification task. Compared to the popular heterogeneous datasets IMDB and DBLP, PDNS-Net is 38 and 17 times bigger respectively. We provide a detailed analysis of PDNS-Net including the data collection methodology, heterogeneous graph construction, descriptive statistics and preliminary graph classification performance. The dataset is publicly available at https://github.com/qcri/PDNS-Net. Our preliminary evaluation of both popular homogeneous and heterogeneous graph neural networks on PDNS-Net reveals that further research is required to improve the performance of these models on large heterogeneous graphs.
GPU-Based High Performance Computing: Employing Massively Parallel Processors for Speeding-Up Compute Intensive Algorithms

One-dimensional blood flow models have been used extensively for hemodynamic computations in the human arterial circulation. In this chapter we introduce a high performance computing solution based Graphics Processing Units (GPU). Novel GPU only and hybrid CPU-GPU solutions are proposed and evaluated. Physiologically sound periodic (structured tree) and non-periodic (windkessel) boundary conditions are considered, in combination with both elastic and viscoelastic arterial wall laws, and different second-order accurate numerical solutions schemes. Both the GPU only and the hybrid solutions lead to significantly smaller execution times.
A new model of Port Safety Management: The analysis and risk management based on the Formal Safety Assessment (FSA)

The present study aims to create a new model of port safety management in our country based on the analysis and assessment of the risk with the Formal Safety Assessment methodology to make it a first level instrument in the planning of the safety management of Port safety and also maritime port emergencies.
Is access to essential medicines as part of the fulfilment of the right to health enforceable through the courts?

Most countries in the world have become States parties to one or more international human rights treaties, thus creating an obligation by the State to its people towards the realisation of the right to health, which includes access to essential medicines. But whether such access is enforceable in practice is unknown.We did a systematic search to identify completed court cases in low-income and middle-income countries in which individuals or groups had claimed access to essential medicines with reference to the right to health in general, or to specific human rights treaties ratified by the government. We identified and analysed 71 court cases from 12 countries in which access to essential medicines was claimed with reference to the right to health.In 59 cases, access to essential medicines as part of the fulfilment of the right to health could indeed be enforced through the courts, with most coming from Central and Latin America. Success was mainly linked to constitutional provisions on the right to health, supported by the human rights treaties. Other success factors were a link between the right to health and the right to life, and support by public-interest non-government organisations. Individual cases have generated entitlements across a population group, the right to health was not restricted by limitations in social security coverage, and government policies have successfully been challenged in court.Skilful litigation can help to ensure that governments fulfil their constitutional and international treaty obligations. Such assurances are especially valuable in countries in which social security systems are still being developed. However, redress mechanisms through the courts should be used as a last resort. Rather, policymakers should ensure that human rights standards guide their health policies and programmes from the outset.
The hybrid Fuzzy Fault and Event Tree analysis in the geotechnical risk management in HDD projects

Horizontal Directional Drilling (HDD) technology is one of the most popular trenchless construction methods for installing pipelines under various obstacles. The geotechnical risk level of HDD proj...
Digital science communication practices and public engagement

In the current context of the democratisation of science, scientists are increasingly engaging in digital forms of communication aimed at broad publics. In this study, we applied genre and activity theory (Bazerman 2004, Spinuzzi 2001, Wickman and Fitzgerald 2018) and used survey data from a large sample of individuals (n=842 scientists) to identify scientists’ digital practices in sharing, communicating and disseminating their research beyond the expert scientific community on the Internet and social media.  The results show a mixed picture of scientists’ involvement in public-facing digital communication activities. Around half of the respondents actively disseminate research to non-specialist audiences using personal and research group websites, blogs, social media, online science popularisation journals, specialised forums (e.g., Reddit) and crowdfunding and citizen science e-platforms. Around 30% also express the intention to engage in such activities in the future. Meanwhile, circa 60% are already involved in activities to promote science culture and open science, such as radio and TV appearances, podcasts, infographics, YouTube educational videos and explainer videos, with 25% planning to do so in the future. The survey also reveals that these activities are not highly valued in the professional context of the respondents, which explains the relatively low level of engagement, despite the scientists themselves attaching greater value to them. Notably, the use of digital tools and resources (e.g., AI-assisted software, content/image editing tools, multimedia apps) was also relatively low among the scientists, indicating a potential gap in the ability to effectively adapt scientific content for wide dissemination of science. Unsurprisingly, these tools were used by less than 30% of respondents, with programmes for adapting specialist content for non-specialist audiences and software for creating podcasts/vidcasts scoring the lowest percentages (15% or less). The study findings suggest that scientists could benefit from training in adapting scientific content for different audiences, and in accommodating science to broad publics in order to enhance public understanding. In conclusion, the study highlights the need for targeted support and training to enable scientists to leverage digital technologies for effective public science communication, ultimately promoting broader engagement and understanding of scientific issues online.
How dependent basic events with interval‐valued probabilities affect fault tree analysis

AbstractThis paper studies the effect of the dependence state between basic events (BEs) on fault tree analysis (FTA) when the probabilities of events are characterized by interval values. The well‐known Frèchent bounds are extended for modeling six different types of dependence states between BEs. Three indices, called average dependence effect (ADE), location effect (LE) and size effect (SE), are defined for evaluating the effect of the dependence states between BEs on the probability of top event (TE) and identifying influential and non‐influential dependence states. Then, the proposed method is applied to fault tree (FT) examples, thereby explaining the dependence problem in FTA. To further verify the practicability of the method, FTA of the unilateral asymmetric movement failure of an aircraft flap mechanism is performed. The results show that: (i) the opposite and negative dependence contribute to the reliability of a parallel system while the perfect and positive dependence reduce it, (ii) the perfect and positive dependence contribute to the reliability of a series system while the opposite and negative dependence reduce it, and (iii) parallel systems are more reliable than series systems regardless of the dependence between BEs.
The role of trust and risk communication in determining residents' flood risk perception and preparedness intention: Evidence from Wuhan, China

Flood risk perception and preparedness intention are critical intrinsic drivers of private flood protection behaviors. This study highlights that governance factors, such as trust and risk communication, play a significant role in shaping these perceptions and intentions. Despite their importance, limited research has examined how these governance dynamics influence individual's understanding of flood risk mitigation. To address this gap, the study develops a conceptual model to explore the interaction mechanisms among key variables, including flood risk perception, flood preparedness intention, trust in public flood protection capacity, and risk communication. These relationships are empirically tested using
Chapter 4 A Contemporary Interpretation of the Principles of Sovereignty, Territorial Integrity and Self-Determination, and the Kosovo Conundrum

This chapter discusses the place of Kosovo in between main principles of international law: self determination, on one hand, and sovereignty and territorial integrity, on the other. It tries to discuss the Kosovo conundrum in between these principles to develop a case where the people of Kosovo would be eligible to use the right to self-determination and secede, as a last resort, from a state where their fundamental human rights and freedoms were denied persistently. The chapter approaches legal, historical and political development of Kosovo from the dissolution of the Ottoman Empire up to its current status. It focuses on the challenges to Kosovo after the declaration of independence and its struggle to enter into international relations. It also discusses the prospects of entering some of the most important international organizations for Kosovo; the United Nations, International Monetary Fund, World Bank, European Union and NATO.  Keywords:European Union; international law; International Monetary Fund; Kosovo Conundrum; NATO; Ottoman Empire; Sovereignty; territorial integrity; United Nations; World Bank
Analysis of the Use of Human Resource Information Systems (HRIS) in Improving Human Resource Management Efficiency

Effective Human Resources (HR) management is crucial for achieving Sustainable Development Goals (SDGs). The Human Resource Information System (HRIS) is an IT tool designed to enhance HR functions efficiently. This study uses the Systematic Literature Review (SLR) method to analyze HRIS's impact on HR management. Data were collected from academic databases like Google Scholar. The findings show that HRIS significantly boosts operational efficiency by automating tasks such as payroll, attendance management, and recruitment. It also supports data-driven decision-making through real-time access to detailed employee data. However, challenges include high implementation costs, system integration complexities, and employee resistance to change. These challenges can be managed with good planning and effective change management. Overall, HRIS is a highly effective tool for improving HR management efficiency, supporting better decision-making, and enhancing employee data management. Organizations must strategically address implementation challenges to maximize HRIS benefits
Evaluating Facial Expression Recognition Datasets for Deep Learning: a Benchmark Study with Novel Similarity Metrics

This study investigates the key characteristics and suitability of widely used Facial Expression Recognition (FER) datasets for training deep learning models. In the field of affective computing, FER is essential for interpreting human emotions, yet the performance of FER systems is highly contingent on the quality and diversity of the underlying datasets. To address this issue, we compiled and analyzed 24 FER datasets, including those targeting specific age groups such as children, adults, and the elderly, and processed them through a comprehensive normalization pipeline. In addition, we enriched the datasets with automatic annotations for age and gender, enabling a more nuanced evaluation of their demographic properties. To further assess dataset efficacy, we introduce three novel metricsLocal, Global, and Paired Similarity, which quantitatively measure dataset difficulty, generalization capability, and cross-dataset transferability. Benchmark experiments using state-of-the-art neural networks reveal that large-scale, automatically collected datasets (e.g., AffectNet, FER2013) tend to generalize better, despite issues with labeling noise and demographic biases, whereas controlled datasets offer higher annotation quality but limited variability. Our findings provide actionable recommendations for dataset selection and design, advancing the development of more robust, fair, and effective FER systems.
Priorities for Sustainably Managing Sri Lanka’s Marine Fisheries, Coastal Aquaculture, and the Ecosystems that Support Them

The World Bank has engaged with the government of Sri Lanka in an advisory services and analytics (ASA) work entitled priorities for sustainably managing Sri Lanka’s marine fisheries, coastal aquaculture and the ecosystems that support them. The development objective of this ASA is to support the government in prioritizing investment and policy actions to enhance the welfare and resilience of coastal fishing communities and fisheries’ contribution to the national economy, including in light of the Coronavirus (COVID-19) pandemic and climate risks. This ASA forms part of the Bank’s support to the government in its effort to respond to, recover from, and build back better after the Coronavirus (COVID-19) pandemic, given the important role that the fisheries sector plays in the livelihoods of the coastal communities and food security of the nation as a whole. The government took swift measures to provide immediate relief and restore domestic fisheries production and the associated value chain in the first phase of the pandemic. For the recovery and build-back-better phases, the government is prioritizing sustainability and resilience, as well as infrastructure enhancement, adoption of innovative technology, institutional strengthening, and skill development to enhance productivity and global competitiveness as a means to generate employment and local value added and contribute to the balance of payments.
Cold + Hot and Cold Dark Matter Cosmologies: Analysis of Numerical Simulations

We present a series of four simulations of Cold Dark Matter (CDM) and Cold + Hot Dark Matter (CHDM) cosmologies. We discuss the power spectrum and correlation functions in real and redshift space, with comparisons to the CfA2 and IRAS redshift data, the pairwise velocity of galaxies, and the distribution of hot and cold particles in CHDM simulations. We confirm that CHDM with cold/hot/baryon density ratios $Ω_c/Ω_ν/Ω_b= 0.6/0.3/0.1$ is a good fit to a wide variety of present-epoch data, much better than CDM. In particular, with reasonable assumptions about identification of galaxies and biasing, we find that the power spectrum agrees rather well with both the CfA2 and IRAS power specta in both the nonlinear and linear regimes. New CHDM models (e.g., with $Ω_ν=0.2$ or with two massive neutrinos) predict a significantly larger rate of formation of galaxies at high redshift, which may be needed to explain some observational data. At the same time, the difference between the variants is rather small at $z=0$. The results presented in this paper are interesting for two purposes: (i) For a comparison with other classes of models (like CDM and $Λ$CDM) at $z=0$.(ii) As a reference point for comparison between different variants of the CHDM model.
Ultra High Energy Cosmic Rays, Z-Shower and Neutrino Astronomy by Horizontal-Upward Tau Air-Showers

24 pages, 19 figures, Invited talk at the X International Workshop on Neutrino Telescopes, Venice, Italy, March 11-14, 2003
New Quantum Chemistry Methods for Open-Shell Systems and Their Applications in Spin-Polarized Conceptual Density Functional Theory

Motivated by our frustration with the lack of quantum chemistry methods for strongly-correlated open-shell systems, we develop quantitative methods for computing the electronic structure of such systems and qualitative tools for analyzing their chemical properties and reactivity. Specifically, we present a modern framework for performing sparse configuration interaction (CI) computations with arbitrary (Slater determinant) N-electron basis sets, using restricted or generalized spin-orbitals, and including computation of spin-polarized 1- and 2- electron reduced density matrices (RDMs). This framework is then used to implement the flexible ansätze for N-electron CI (FanCI) method more efficiently, via increased vectorization in the FanCI equations and use of sparse CI algorithms. We also extended the FanCI approach, including least-squares and stochastic optimization techniques, the computation of spin-polarized 1- and 2- electron RDMs, and transition energies (ionization potentials, electron affinities, and excitation energies). We use these tools to compare various open-shell CI methods and FanCI methods based on various antisymmetrized product of nonorthogonal geminals ansätze. To translate the vast amount of quantitative data present in the energies and (spin-polarized) density matrices of multiple open-shell states, we present a new, internally consistent and unambiguous framework for spin-polarized conceptual density-functional theory (SP-DFT) that reduces to a sensible formulation of spin-free CDFT in an appropriate limit. Using this framework, we were able to generalize the (non-spin-polarized) Parr function. We can also, using this framework, construct promolecules with proatoms having non-integer charges and multiplicities. Finally, we describe an equations-of-motion-based method for computing spin-polarized reactivity descriptors of a chemical system from only the ground state energy and the 1- and 2- electron RDMs from a single-point electronic structure computation, and show some benchmark computations for this method based on various CI and FanCI electronic structure methods.
Text-to-Speech Synthesis Techniques for MIDI-to-Audio Synthesis

Speech synthesis and music audio generation from symbolic input differ in many aspects but share some similarities. In this study, we investigate how text-to-speech synthesis techniques can be used for piano MIDI-to-audio synthesis tasks. Our investigation includes Tacotron and neural source-filter waveform models as the basic components, with which we build MIDI-to-audio synthesis systems in similar ways to TTS frameworks. We also include reference systems using conventional sound modeling techniques such as sample-based and physical-modeling-based methods. The subjective experimental results demonstrate that the investigated TTS components can be applied to piano MIDI-to-audio synthesis with minor modifications. The results also reveal the performance bottleneck -- while the waveform model can synthesize high quality piano sound given natural acoustic features, the conversion from MIDI to acoustic features is challenging. The full MIDI-to-audio synthesis system is still inferior to the sample-based or physical-modeling-based approaches, but we encourage TTS researchers to test their TTS models for this new task and improve the performance.