ethicalabs/Research-Intent-Collab huggingface dataset:
GDPR Compliance Challenges for Interoperable Health Information Exchanges (HIEs) and Trustworthy Research Environments (TREs)

Background: We present our current approaches to improving personal data protection in (i) large (regional/ national/international) scale health information exchanges (HIEs) and (ii) UK NHS IG toolkit and ISO 27001-compliant trustworthy research environments (TREs) for discovery science communities. In particular we examine impacts of the General Data Protection Regulation (GDPR) on these technology designs and developments and the responses we have made to control complexity.  Methods: The paper discusses multiple requirements to implement the key GDPR principles of “data protection by design” and “data protection by default”, each requiring new capabilities to embed multiple security tests and data protection tools in common deployable infrastructures. Methods are presented for consistent implementation of diverse data processing use cases.  Results: We describe how modular compositions of GDPRcompliant data processing software have been used to implement use case(s) and deliver information governance (IG) requirements transparently. Security surveillance analysis is embedded throughout the application lifecycle, namely at design, implementation and operation (runtime) phases. A solution is described to the challenge of integrating coherent research (analytic) environments for authorized researchers to access data and analytic tools without compromising security or privacy.  Conclusion: We recognise the need for wider implementation of rigorous interoperability standards concerning privacy and security management. Standards can be disseminated within low-cost commodity infrastructures that are shared across consortium partners. Comprehensive model-based approaches to information management will be fundamental to guaranteeing security and privacy in challenging areas such as ethical use of artificial intelligence in medicine. The target architecture is still in evolution but needs a number of communitycollaborative API developments to couple advanced specifications fulfilling all IG requirements.
Applying Attention Mechanism and Deep Neural Network for Medical Object Segmentation and Classification in X-Ray Fluoroscopy Images

We study how to apply attention mechanism and deep neural network for real-time segmentation and classification of balloon objects from X-ray fluoroscopy images during percutaneous balloon compression (PBC) surgical procedures. Fast and accurate identification of balloon shape and its relative location to the Meckel’s cave can be of significant benefit to the success of the PBC procedure. In this work, we combine the most successful region-based convolutional neural network pipeline with attention mechanism to address these challenges.
Using Natural Language Processing and the Electronic Health Record for Appendicitis Risk Stratification

This study evaluated an automated approach for appendicitis risk stratification of pediatric Emergency Department patients using Conditional Random Fields, rules and Support Vector Machines. The results show that the approach is very promising for appendicitis risk stratification.
Perceived Performance of the Human Resource Information Systems (HRIS) and Perceived Performance of the Management of Human Resources (HRM)

The changing business environment and increasing technology is redefining the role of the human resources function. Nowadays, corporations have to consistently advance the value of human assets in their own organizations for maintaining their competitiveness. One of the technological changes in this regard is the appearance of Human Resources Information systems (HRIS). How to improve the efficiency of the HR and enhance its status in the organizations has become the top agenda to enterprises. The development of Information Technology (IT) transforms the role of the HR Dept in the organization. It enables HR to be a real strategic partner of corporations through the process of Organization Development. Thus, one of the challenges that face managers nowadays in regard to these emerging technologies, is the need to determine the success factors that play an important role in the implementation of an HRIS and how these factors, that some are Technological, Individual, and Organizational, affect the perceived performance of a HRIS and to measure the impact of this perceived performance on the perceived performance of the HRM.
Uninsured migrants: Health insurance coverage and access to care among Mexican return migrants

Background: Despite an expansive body of research on health and access to medical care among Mexican immigrants in the United States, research on return migrants focuses primarily on their labor market mobility and contributions to local development.  Objective: Motivated by recent scholarship that documents poor mental and physical health among Mexican return migrants, this study investigates return migrants' health insurance coverage and access to medical care.  Methods: I use descriptive and multivariate techniques to analyze data from the 2009 and 2014 rounds of Mexico's National Survey of Demographic Dynamics (ENADID, combined n=632,678).  Results: Analyses reveal a large and persistent gap between recent return migrants and non-migrants, despite rising overall health coverage in Mexico. Multivariate analyses suggest that unemployment among recent arrivals contributes to their lack of insurance. Relative to non-migrants, recently returned migrants rely disproportionately on private clinics, pharmacies, self-medication, or have no regular source of care. Mediation analysis suggests that returnees' high rate of uninsurance contributes to their inadequate access to care.  Conclusion: This study reveals limited access to medical care among the growing population of Mexican return migrants, highlighting the need for targeted policies to facilitate successful reintegration and ensure access to vital resources such as health care.
Protecting the human rights of sexual minorities in contemporary Africa

This collection of essays, Protecting the human rights of sexual minorities in contemporary Africa, contains papers that were first presented at a colloquium on sexual minority rights in Africa, which took place at the Centre for Human Rights, University of Pretoria, in December 2014. This event was made possible with the generous support of the Government of the Kingdom of Norway, through its Embassy in South Africa. These papers were subsequently peer-reviewed and reworked. Viewing homosexuality through a legal and rights-based prism, this volume brings together fourteen essays focusing on various aspects of homosexuality, covering a wide rage of countries from across the continent. The situation in nine countries (Botswana, Cameroon, Kenya, Mauritius, Mozambique, Nigeria, South Africa, Uganda and Zimbabwe) is reviewed; while other contributions are more regional in their perspective. This makes this publication one of the most comprehensive collections of African voices on this topic. For too long African voices have been silent on the fledgling discourse on sexual minorities. This volume seeks to amend this shortcoming. The editors and authors and contributors are not only African, but also, with a few exceptions, graduates of the Centre’s Master’s programme in Human Rights and Democratisation in Africa. The publisher is an African-based publisher, the Pretoria University Law Press (PULP), based at the Centre for Human Rights.
Non-contrastive self-supervised learning of utterance-level speech representations

Accepted at Interspeech 2022
Understanding farmers vulnerability to climate change and their adaptation strategies for climate resilience

The Bundelkhand region is historically more vulnerable to climate change and experienced drought once every 16 years during the 18th and 19th centuries, whereas it increased thrice from 1968 to 1992, and now it has become a recurrent annual phenomenon. The study was conducted from February to June 2023, and primary data from 180 farm households across eight blocks in Banda, Hamirpur, and Mahoba districts of Bundelkhand were collected using a pretested structured interview schedule. The research used the vulnerability analysis method of the IPCC and the climate change vulnerability index of the entire three districts. The results revealed that the “index values” of biological exposition/exposure sub-indicators show that farmers (D3) were highly exposed to climate change with a vulnerability index value of “0.77,” and index values of the Farm susceptibility sub-indicators indicate that farmers (D3) exhibit a high susceptibility to climate change “(0.86),” while index values of the Institutional adaptive capacity sub-indicators indicate that farmers (D1) exhibit a high exposure to climate change, with a vulnerability index of “0.93.” The findings revealed that farmers predominantly adopted strategies such as crop diversification, varietal changes, calendar adjustments, and crop insurance. Binary probit analysis highlighted several significant factors influencing adaptation decisions, including age, education, farm size, access to credit, household size, extension services, and perceptions of changing rainfall and flooding patterns. The findings emphasize the need to strengthen agricultural extension services, enhance credit availability, and improve educational outreach to enable effective, context-specific climate adaptation. The study calls for targeted policy support to boost climate resilience among vulnerable farming populations in Bundelkhand and similar ecologically fragile regions, ensuring their livelihoods remain sustainable amid growing climatic uncertainties.
Explainable AI for Alzheimer’s Prediction Diagnosis: Developing an Interpretable Deep Learning Pipeline with 3D Visualization

Deep learning methods are growing in complexity and inherently trading interpretability for accuracy. While accuracy is important, equal emphasis should be placed on explaining the model’s internal workings to justify its decisions. In this work, an explainable pipeline was developed for a CNN model for predicting Alzheimer’s diagnosis. Specifically, it determines the brain region’s relevance in classifying cognitively normal (CN) and Alzheimer’s disease (AD) using 3D structural MRI images. The XAI methods used in the implementation were DeepLIFT, Layer Gradient x Activation, Occlusion, and XGrad-CAM to obtain 3D visual explanations of discriminative regions derived from the model’s internal workings. The regional relevance was assessed in a cross-sectional comparison between CN and AD subjects and a longitudinal comparison of subjects transitioning from CN to AD, tracking the relevance in time. A correlation analysis between regional relevance and mean standardized uptake value ratio (SUVR) was also conducted to investigate structural and bioactive overlap. The results showed that the model uses a relatively concentrated region centered around the temporal lobe to predict CN subjects and a much larger and disipated area for the AD predictions. The longitudinal comparison found the model attributing increasing importance to the caudate, putamen, and pallidum. The Occlusion relevance of the hippocampus and ventricles showed a positive correlation with the Amyloid PET mean SUVR. This supports the connection between anatomical brain changes and neuropathologic biomarkers used to diagnose Alzheimer’s disease.
Enhancing Microwave Photonic Interrogation Accuracy for Fiber-Optic Temperature Sensors via Artificial Neural Network Integration

In this paper, an application of an artificial neural network algorithm is proposed to enhance the accuracy of temperature measurement using a fiber-optic sensor based on a Fabry–Perot interferometer (FPI). It is assumed that the interrogation of the FPI is carried out using an optical comb generator realizing a microwave photonic approach. Firstly, modelling of the reflection spectrum of a Fabry–Perot interferometer is implemented. Secondly, probing of the obtained spectrum using a comb-generator model is performed. The resulting electrical signal of the photodetector is processed and is used to create a sample for artificial neural network training aimed at temperature detection. It is demonstrated that the artificial neural network implementation can predict temperature variations with an accuracy equal to 0.018 °C in the range from −10 to +10 °C and 0.147 in the range from −15 to +15 °C.
Cybertheology and the Ethical Dimensions of Artificial Superintelligence: A Theological Inquiry into Existential Risks

Purpose: This study explores the role of cybertheology in addressing the ethical and societal challenges posed by Artificial Superintelligence (ASI), which has the potential to surpass human cognitive capabilities, heralding a profound cultural and existential crisis. It integrates theological anthropology to assess the implications of a posthuman future. Methodology: Utilising a comprehensive literature review, the research examines technological, philosophical, and theological perspectives through primary and secondary sources, including influential works by futurists and ethicists. The methodology aims to uncover the nuanced discourse surrounding the development of ASI and its potential impacts. Findings: The analysis reveals a narrative marked by speculative optimism and significant existential concerns regarding ASI. A critical gap in the existing ethical discourse is identified, highlighting the necessity for a grounded ethical framework that addresses the profound implications of superintelligent entities on human dignity and societal norms. Research Implications: The findings emphasise the urgent need to incorporate robust ethical considerations into the development and deployment of ASI. Cybertheology is presented as a vital framework for ensuring that ASI technologies align with human values and theological insights, thus providing a valuable lens through which to view the integration of superintelligence into society. Originality/Value: This paper contributes to academic and policy discussions on ASI by promoting cybertheology as a crucial perspective in ethical deliberations. It enriches scholarly dialogues by linking technological advancements with theological and ethical evaluations, proposing that cybertheology can play a pivotal role in shaping policies that govern ASI technologies. This approach ensures that technological progress is compatible with humanistic values, fostering a holistic understanding of ASI's potential impact on humanity.
Effects of cluster expansion on the locations of phase transition boundary as a first step to quantify uncertainty in first principles statistical mechanics framework

Abstract   Predicting phase diagrams from first principle calculations eliminates the need of tedious experimental trials and errors. Fully automating first principle phase diagram calculations without any sort of human intervention has been a long daunting task and troubling scientific communities for decades. This grand problem remains not fully resolved, largely due to the vastly high-dimensional parameter space associated with density functional theory, cluster expansion, lattice Monte Carlo, and the substantial uncertainty propagating through a set of complex simulations. As a first step to tackle this grand problem, we reported a first demonstration of how sensitive phase boundary locations can be to various cluster expansion fittings and input DFT training data. To the best knowledge of the authors, this study reported the first ever attempt to quantify uncertainty in first principles statistical mechanics framework. In addition, a semi-automated phase transition detection algorithm has been devised in this paper to deal with the associated statistical errors and uncertainties from Monte Carlo method and its predecessor DFT calculations and cluster expansions. This algorithm has been applied in a classical cluster expansion Mg-Cd binary alloy system to detect phase transitions at various locations of phase diagram using different cluster expansions to demonstrate its predictive power and quantify uncertainties. The results suggested that using Chebyshev basis function shifted the transition locations toward the dilute solid solution phase and expanded the phase stability of concentrated ordered phases to wider composition range. The addition of perturbed defect configurations into training data set lowered the order-disorder transition temperature to be away from true transition temperature, suggesting the transition nature is indeed configurational disorder dominated rather than defect assisted. We found that the weighting has negligible effect on the transition locations, except for the case of using Chebyshev basis function to fit all non-weighted configurations that can be susceptible to Monte Carlo sampling hysteresis.
A Model Driven Approach to Automate the Implementation of Clinical Guidelines in Decision Support Systems

Clinical guidelines and protocols are used to aid decision making in health care. This paper presents a model- based approach to the development of decision support systems for clinical guidelines. The guidelines are modelled using the UML Statechart language and the final system is automatically generated using an automated model transformation. Our approach is illustrated with a Spanish clinical guideline developed on the basis of a guideline published by the well-known National Guideline Clearing House (NGC).
Human Rights to Water and Sanitation in Burkina Faso : Assessing human rights and use of human rights based approaches in theory and practice

Sida Helpdesk for Environment and Climate ChangeQC 20150624
Gamma‐Ray Burst Afterglows as Probes of Environment and Blast Wave Physics. I. Absorption by Host‐Galaxy Gas and Dust

25 pages, 5 figures, ApJ accepted. Minor revisions to Table 1, Sections 4.1.4 and 5.1.5
Rice Farming Performance for Sustainable Agriculture and Food Security in West Kalimantan

For more than three decades, domestic rice production was not able to meet its growing demand and showed in poor performance. This research aims to analyze the performance of rice farming in West Kalimantan. The research was conducted using descriptive methods through interviews. The data was analyzed to describe farmer personal character, skill and knowlwdge, risk analysis, and technical efficiency employing stochastic frontier model.  This study proved that farmer had 3 to 6 years of pupilage attainment; used rainfed; widely used Ciherang seeds; most of them had 0.25-1.25 ha; only 20% of rice farming could achieve over 6.5 tons/ha. The distribution of technical efficiency showed that only 17.41% of rice farmer is able to achieve sufficient; while 60-70% of technical efficiency is achieved by 30.37% of rice farmer. However, the risk of production, prices, and income each was than 0.5.
On the Possibility of Melting Water Ice during the Recent Past of Mars: Application to the Formation of Gullies

The formation of gullies on Mars has often been attributed to the melting of (sub)surface water ice. However, melting-based hypotheses generally overlook key processes: (1) sublimation cooling by latent heat absorption, (2) the non-stability of ice where melting conditions can be reached, and (3) the particular microclimates of gullied slopes. Using state-of-the-art climate simulations, we reassess ice melting scenarios over the past four million years (obliquity <= 35°), beyond the estimated period of gully formation. We find that the melting of opaque water snow or ice at the surface of Mars is unlikely anywhere due to sublimation cooling, while (quasi-) stable subsurface ice is typically too deep to reach melting temperatures. We propose an alternative mechanism in which seasonal CO2 frost sublimation destabilizes the regolith and brings the underlying water ice close to the surface, allowing rapid heating. Even under these optimal conditions, melting requires unrealistic assumptions. Ice containing a small amount of dust could melt via a solid-state greenhouse effect, but both its possibility and frequency in Mars' recent past remain uncertain.
Communicating science and technology while engaging the public at the Illinois Basin – Decatur Project

AbstractThe Midwest Geological Sequestration Consortium communication initiative at the Illinois Basin – Decatur Project (IBDP) draws on multiple carbon capture and storage (CCS) best practices, guidelines, and international project experiences to be both proactive and responsive toward the engagement of multiple stakeholders. The IBDP communications strategy was developed early in the project to actively reduce associated risks by creating and implementing a communication plan, training communicators, and providing a structure for the communications team. Formalized approaches to knowledge sharing and capacity building have generated additional opportunities to further outreach and impact from IBDP experiences. The initial challenge for the communications team was to provide easy‐to‐understand, scientifically accurate, and consistent information for stakeholders to carry throughout the project. Consistent, factual information was developed and incorporated into project planning, and provides the basis of public communications through the alignment of goals across communications, risk mitigation, and project management. The refinement of the communication strategy and plan is ongoing due to the changing communication needs that are encountered throughout the lifetime of the project.
Human Ecology of Vulnerability, Resilience and Adaptation: Case Studies of Climate Change from High Latitudes and Altitudes

Human societies in Arctic (high latitude) and mountain (high altitude) regions contributed little to the causes of climate change, yet they are among the first to observe and respond to its impacts (Crate & Nuttall, 2009; Krupnik et al., 2004; Orlove et al., 2008). Many of these communities are indigenous and maintain dynamic relations within their local ecologies through subsistence activities. This means they are keen observers of their habitat, and raises concern that their food and livelihood securities are vulnerable to adverse impacts of climate change (Kassam, 2009a, 2009b). The risk that climate change impacts may overwhelm high latitude and high altitude communities is increased by other chronic stressors, including legacies of colonialism, economic imperialism that constrains local economies, recurring natural disasters, shifting and conflicting political alliances, and war. Put tersely, climate change is an additional layer of complexity on already existing inequities. Social scientists are compelled to address issues of justice, so it is appropriate that significant research efforts be devoted to the regions of the world most affected by climate change. This paper examines the concepts of vulnerability and adaptation through a human ecological lens that was developed in the contexts of Arctic and sub-Arctic communities (Kassam, 2009a), and has been applied in the Afghan and Tajik Pamirs (Kassam , 2009b)1. Two case studies will focus on the human ecology of vulnerability and adaptation in those regions. Emphasis is placed on possibilities to integrate diverse indigenous and scientific knowledge systems towards the reduction of specific vulnerabilities to climate change impacts.