ethicalabs/Research-Intent-Collab huggingface dataset:
Detection of selective sweeps in cattle using genome-wide SNP data

The domestication and subsequent selection by humans to create breeds of cattle undoubtedly altered the patterning of variation within their genomes. Strong selection to fix advantageous large-effect mutations underlying domesticability, breed characteristics or productivity created selective sweeps in which variation was lost in the chromosomal region flanking the selected allele. Selective sweeps have been identified in the genomes of many species including humans, dogs, horses, and chickens. We attempt to identify regions of the bovine genome that have been subjected to selective sweeps. Two datasets were used for the discovery and validation of selective sweeps via the fixation of alleles at a series of contiguous SNP loci. BovineSNP50 data were used to identify 28 putative sweep regions among 14 cattle breeds. Affymetrix BOS 1 prescreening assay data for five breeds were used to identify 114 regions and validate 5 regions identified using the BovineSNP50 data. Many genes are located within these regions; however, phenotypes that we predict to have historically been under strong selection include horned-polled, coat color, stature, ear morphology, and behavior. The identified selective sweeps represent recent events associated with breed formation rather than ancient events associated with domestication. No sweep regions were shared between indicine and taurine breeds reflecting their divergent selection histories. A primary finding of this study is the sensitivity of results to assay resolution. Despite the bias towards common SNPs in the BovineSNP50 design, false positive sweep regions appear to be common due to the limited resolution of the assay. This assay design bias leads to the detection of breed-specific sweep regions, or regions shared by a small number of breeds, restricting the suite of selected phenotypes detected to primarily those associated with breed characteristics.
Rotation curves in Bose-Einstein Condensate Dark Matter Halos

The study of the rotation curves of spiral galaxies reveals a nearly constant cored density distribution of Cold Dark Matter. N-body simulations however lead to a cuspy distribution on the galactic scale, with a central peak. A Bose-Einstein condensate (BEC) of light particles naturally solves this problem by predicting a repulsive force, obstructing the formation of the peak. After succinctly presenting the BEC model, we test it against rotation curve data for a set of 3 High Surface Brightness (HSB), 3 Low Surface Brightness (LSB) and 3 dwarf galaxies. The BEC model gives a similar fit to the Navarro-Frenk-White (NFW) dark matter model for all HSB and LSB galaxies in the sample. For dark matter dominated dwarf galaxies the addition of the BEC component improved more upon the purely baryonic fit than the NFW component. Thus despite the sharp cut-off of the halo density, the BEC dark matter candidate is consistent with the rotation curve data of all types of galaxies.
Intensive and critical care nurses patient safety, care quality, professional self‐efficacy, and missed nursing care: Structural equation model analysis

AbstractBackgroundGlobally, nurses' patient safety, care quality, and missed nursing care are well documented. However, there is a paucity of studies on the mediating roles of care quality and professional self‐efficacy, particularly among intensive and critical care unit (ICCU) nurses in developing countries like the Philippines.AimTo test a model of the interrelationships of patient safety, care quality, professional self‐efficacy, and missed nursing care among ICCU nurses.MethodsA cross‐sectional, correlational design study was used. ICCU nurses (n = 335) were recruited via consecutive sampling from August to December 2023 and completed four validated self‐report scales. Spearman Rho, structural equation modeling, mediation, and path analyses were utilized for data analysis.ResultsThe emerging model demonstrated acceptable fit parameters. Patient safety positively influenced care quality (β = .34, p = .002) and professional self‐efficacy (β = .18, p = .011), while negatively affecting missed nursing care (β = −.34, p = .003). Care quality positively and negatively influenced professional self‐efficacy (β = .40, p = .003) and missed nursing care (β = −.13, p = .003), respectively. Professional self‐efficacy indirectly impacted missed nursing care (β = −.32, p = .003). Care quality (β = −.10, p = .003) and professional self‐efficacy (β = .13, p = .003) showed mediating effects between patient safety and missed nursing care.Linking Evidence to ActionICCU nurses' care quality and professional self‐efficacy are essential mediating factors that can bolster patient safety practices, hence reducing missed nursing care. Therefore, healthcare organizations, nurse managers, and policymakers should cultivate care quality and self‐efficacy by creating support programs and providing a positive practice environment. Nurses and nurse supervisors could directly observe missed nursing care in the ICCU to understand its underreported causes.
A survey on big data-driven digital phenotyping of mental health

Abstract   The landscape of mental health has undergone tremendous changes within the last two decades, but the research on mental health is still at the initial stage with substantial knowledge gaps and the lack of precise diagnosis. Nowadays, big data and artificial intelligence offer new opportunities for the screening and prediction of mental problems. In this review paper, we outline the vision of digital phenotyping of mental health (DPMH) by fusing the enriched data from ubiquitous sensors, social media and healthcare systems, and present a broad overview of DPMH from sensing and computing perspectives. We first conduct a systematical literature review and propose the research framework, which highlights the key aspects related with mental health, and discuss the challenges elicited by the enriched data for digital phenotyping. Next, five key research strands including affect recognition, cognitive analytics, behavioral anomaly detection, social analytics, and biomarker analytics are unfolded in the psychiatric context. Finally, we discuss various open issues and the corresponding solutions to underpin the digital phenotyping of mental health.
SPATIAL ANALYSIS OF ROAD TRAFFIC ACCIDENT HOTSPOTS AND PATTERNS IN MUSCAT, OMAN: AN EXPLORATORY RISK MANAGEMENT ASSESSMENT

Oman is grappling with a significant challenge posed by road accidents, with approximately 1,539 accidents recorded in 2021. To tackle this issue, this study showcases the potential of Geographical Information Systems (GIS) technology in mapping and analysing road accident hotspots and distribution patterns in Muscat, Oman. The data (from 2019 to 2021) was gathered from the Omani Royal Police Department, with ArcGIS Pro serving as the geographical representation and analysis platform. The study identified high-risk locations for road accidents in specific areas of Mutrah, Bawshar, and Al-Amerat, which were attributed to densely populated areas and heavy traffic flow. The spatial pattern of accidents appeared dispersed, with Fridays and Mondays recording the highest number of accidents due to increased traffic associated with social gatherings and commuting to work or school after the weekend. Qualitative effect analysis revealed that factors such as roadway characteristics, environmental conditions, traffic volume, driver behaviour, and vulnerable road users may influence local accident hotspots. These GIS-based road accident analyses can enhance road accident guidelines and traffic prevention strategies in the Muscat region.
Flexibility in patient-reported outcome and health-related quality of life measurement

The development of the first European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Group (QLG) health-related quality of life (HRQoL) questionnaires contributed to the systematic uptake of HRQoL as an endpoint in cancer clinical trials, and to the measurement of HRQoL for individual assessment in routine care. Following a modular approach, these patient-reported outcome (PRO) measures (PROMs) ensure that both generic and disease-specific issues are assessed, enabling comparison of PROs across groups and studies. The application of a comprehensive and continually refined methodology for developing and updating these PROMs has been crucial in supporting their psychometric and cross-cultural validity, and their continued implementation in clinical research. However, the advancement of measurement science, the more widespread implementation of PROMs, and the significant evolution of anti-cancer therapies over the last decades have highlighted the need to adopt more flexible approaches to PRO assessment to ensure that PROMs remain relevant and fit-for-purpose. The QLG has responded to this call by implementing more tailored PRO measurement approaches through the development and release of the computerised adaptive test (CAT) version of the EORTC QLQ-C30 (i.e., the EORTC CAT Core) and the EORTC Item Library. The EORTC Item Library is an interactive online platform that allows for the creation of customised questionnaires (item lists) from the pool of available items derived from established EORTC QLG PROMs. The aim of this article is to describe the current EORTC QLG approach to PRO measurement in oncology, covering important historical developments and best practice recommendations.
A Quality end of Life from A Palliative Care Patient's Perspective

This qualitative study explored the meaning of quality of life (QOL) from the perspective of palliative care patients by examining their lived experiences and their perceptions of what contributes to their QOL. Ten in-patients — five women and five men — took part in in-depth, semi-structured interviews. When asked about their QOL, they spoke about three distinct aspects: the meaning of a quality life at this stage in their illness trajectory, the experience of living with the illness, and factors that contributed to their QOL. Patients’ approach to life, illness life, and ideal quality life were found to create a sphere of influence that shaped their end-of-life experience. At the heart of living a quality end of life for these patients was their ability to “do the things that I usually do,” “be helpful to others,” and “live in a caring environment,” The findings highlight the importance of understanding each of these factors: patients’ approach to life, illness life, and ideal quality of life, and what they mean to patients in order to tailor interventions to enhance their QOL.
Smart Wildfire Prediction and Risk Mapping System Using AI and Remote Sensing Data

Wildfires are increasing in frequency and intensity due to climate change and environmental factors. This paper presents a Smart Wildfire Prediction and Risk Mapping System that integrates real-time wildfire detection with batch-based historical risk prediction using artificial intelligence and remote sensing data. The proposed system combines machine learning models with satellite imagery to analyze environmental conditions and predict wildfire risk zones. Experimental results demonstrate the effectiveness of the approach in improving wildfire detection accuracy and supporting early warning systems for disaster management.
The forced eviction of the Ogieks indigenous people from their ancestral land in Kenya: the intervention of the African Court on Human and Peoples' Rights

The Application and the judgement of the African Court on Human and Peoples’Rights issued on 26 May2017 are in respect of the Ogieks, a historically disadvantaged indigenous community of the Mau forest, which in 2009 have been victim of an eviction and forced relocation perpetrated by the Republic of Kenya. The eviction act came as the last of a series of other similar actions undertaken by the Government since thecolonialism period, as well as the lack of legal recognition of the Ogieks as indigenous group. Given the veryclose relationship between the Ogieks and their ancestral territories, the eviction resulted in a substantial violation of both their basic human right as to live in their homeland and to their cultural, religious, socialand economic rights as indigenous people. These rights are protected under international human rights lawinstruments such as the United Nation Declaration on the Rights of Indigenous Peoples (UNDRIP) and the African Charter on Human and Peoples’ Rights (the “Charter” ).
Automation system for measurement of gamma-ray spectra of induced activity for multi-element high volume neutron activation analysis at the reactor IBR-2 of Frank Laboratory of Neutron Physics at the joint institute for nuclear research

The automation system for measurement of induced activity of gamma-ray spectra for multi-element high volume neutron activation analysis (NAA) was designed, developed and implemented at the reactor IBR-2 at the Frank Laboratory of Neutron Physics. The system consists of three devices of automatic sample changers for three Canberra HPGe detector-based gamma spectrometry systems. Each sample changer consists of two-axis of linear positioning module M202A by DriveSet company and disk with 45 slots for containers with samples. Control of automatic sample changer is performed by the Xemo S360U controller by Systec company. Positioning accuracy can reach 0.1 mm. Special software performs automatic changing of samples and measurement of gamma spectra at constant interaction with the NAA database.
Heroes, landmarks and blind alleys in personality assessment: Early implicit measures and the first psycho-lexical trait studies

In the third of a series of articles to mark the centenary of the First World War, the author discusses some early implicit measures and outlines the early psycho-lexical trait studies and their implications on today’s test users.
The Human Right to Clean Water and Sanitation - A Perspective from Nigeria

The human right to clean water and sanitation has currently become a global issue. From Brazil to Sudan, Tibet to Turkana, water for living has gradually become a consistent discourse. There is indeed a perception shared by many scholars and international bodies that access to safe, clean or wholesome water has attained the status of a fundamental human right. The rationale behind most of this critical mass of opinion is that water is an essential need for life. Take away water and life is an empty shell. Without clean and wholesome water for drinking and sanitation, the other rights which are hinged on life become meaningless. This perception reached a crescendo in the year 2010, when the United Nations General Assembly passed Resolution 64/292, clearly recognizing the human right to water and sanitation. The Resolution also acknowledged that clean drinking water and sanitation are essential to the realization of all human rights. However, despite this there appears to be a huge gap between this global perception and the reality of access to clean water and sanitation as a human right today. Like all other environmental and human right concerns of mankind, the actualization of this premium right lies majorly at the local level. Thus while having an eye on the global scene, there is an essential need to act locally. This explains our present attempt herein to explore the concept of a human right to clean water and sanitation using Nigeria as the prism. The work is therefore aimed at ascertaining the extent of recognition, enforcement and availability of the human right to clean water and sanitation in Nigeria. Ultimately it is recommend that critical social engagement, active citizenship participation, legislative intervention and corporate social responsibility are possible measures to solve the problem. It is also hoped that a careful study of the Nigerian situation would help in the formulation of a human right to clean water and sanitation policy template. It is further hoped that this will in the end assist the realization of the purport and essence of the United Nations resolution on the human right to clean water and sanitation.
The Use of Intelligence in the Protection of Critical Infrastructure: From Reactive Protection to Anticipatory Resilience

The study analyzes the role of intelligence in the protection and resilience of critical infrastructure within a dynamic security environment characterized by technological interdependencies, accelerated digitalization, and multidimensional threats. A qualitative, theoretical-analytical, and interdisciplinary approach was adopted, with the main objective of constructing an integrated conceptual framework that highlights the contribution of intelligence - across its operational, tactical, and strategic analytical dimensions - to the transition from a reactive security model to one of anticipatory resilience. The research demonstrates that the coherent integration of intelligence processes and products into infrastructural risk governance enhances the capacity to anticipate, detect, and prevent incidents with systemic potential, providing decision-making and operational support under conditions of heightened uncertainty. Furthermore, the study underscores the need to optimize the intelligence cycle, leverage emerging technologies, develop human capital, and standardize information dissemination. The general conclusion is that intelligence must be conceived as a central pillar of infrastructural resilience - not merely as an informational support tool - in order to ensure the continuity of the essential functions of the state and society.
Perancangan Data Warehouse Online Analytical Processing (OLAP) Data Hasil Kerja PT. ABC

PT. ABC merupakan salah satu perusahaan jasa yang bergerak dibidang Hutan Tanaman Industri. Perusahaan ini memiliki banyak tim yang tersebar di semua lokasi kerja sehingga data yang masuk semakin banyak pula, data-data tersebut tentunya perlu disimpan, diolah, dan dianalisis untuk menghasilkan suatu informasi yang berguna bagi perusahaan, dan dilaporkan kepada manajer untuk mengetahui keadaan perusahaan pada periode waktu tertentu. Dengan demikian dibutuhkanlah Data Warehouse yang digunakan untuk mendukung data yang dapat dimanfaatkan sebagai sumber informasi ketika menganalisis data. Perancangan Data Warehouse ini menggunakan metode Nine Step Kimball dengan model diagram Star Schema. Dalam mendukung perancangan data warehouse maka dibutuhkan tools yaitu Pentaho data Integration dan Tableau. Dengan menggunakan tools tersebut dapat dibangun sebuah Data Warehouse hasil tim dengan mengumpulkan data-data hasil kerja meliputi jenis pekerjaan, lokasi kerja, tanggal, petak kerja dan nama tim kerja yang berasal dari Ms. Excel lalu dimasukan kedalam database MySQL. Hasil dari penelitian ini adalah sebuah dashboard Online Analytical Processing (OLAP) yang berasal dari database MySQL lalu diolah ke berbagai informasi yang butuhkan, sehingga membentuk sebuah dashboard yang memberikan informasi kepada pihak eksekutif.
Microphone Array Signal Processing and Deep Learning for Speech Enhancement

Multi-channel acoustic signal processing is a well-established and powerful tool to exploit the spatial diversity between a target signal and non-target or noise sources for signal enhancement. However, the textbook solutions for optimal data-dependent spatial filtering rest on the knowledge of second-order statistical moments of the signals, which have traditionally been difficult to acquire. In this contribution, we compare model-based, purely data-driven, and hybrid approaches to parameter estimation and filtering, where the latter tries to combine the benefits of model-based signal processing and data-driven deep learning to overcome their individual deficiencies. We illustrate the underlying design principles with examples from noise reduction, source separation, and dereverberation.
Innovative Application of Artificial Intelligence Algorithms in Credit Scoring: Taking specific products in the field of consumer finance as the research object

This paper focuses on specific products in consumer finance and explores the innovative application of artificial intelligence algorithms in credit scoring. By analyzing the technical foundation and current situation, this paper expounds the core technological innovations such as deep learning, tree model integration, and graph neural networks, and solves the problems of data fusion, model optimization, and interpretability. Empirical research shows that artificial intelligence algorithms outperform traditional models in multiple indicators.
Travelling in antique lands: using past famines to develop an adaptability/resilience framework to identify food systems vulnerable to climate change

This paper builds on existing theory and proposes a framework to identify vulnerability to climate change in food systems by examining historic cases where common environmental problems caused famine. Cases presented are (1) Ireland’s Potato Famine, (2) El Nino induced famines during the Colonial period, and (3) Ethiopia between 1965 and 1997. Three factors stand out as common in each. Prior to each famine: (1) there were very few ways that people could obtain a living in the worst affected regions; (2) livelihoods in famine stricken communities came to depend on highly specialized agro-ecosystems that were sensitive to environmental change; (3) institutions failed to provide adequate safety nets to protect livelihoods from failure. This analysis suggests that vulnerability to climate change in food systems can be assessed by looking at agro-ecosystems, livelihoods and institutions. Local conditions, however, mean that ways of measuring these three factors will vary from place to place. As a result, direct comparisons are difficult. By conceptualizing these three variables as the axes of a three dimensional “vulnerability” space, it is possible to compare regions and look at trends over time by studying the paths through this “space” as traced by changes at the agro-ecosystem, livelihood, and institutional scale.