ethicalabs/Research-Intent-Collab huggingface dataset:
476 Associations between hospital mergers and hospital level nursing factors, nurse well-being, and nurse-sensitive patient safety and quality of care outcomes: A scoping review

Objectives/Goals:                     Hospital mergers have proliferated in the USA, yet a comprehensive understanding of how mergers affect nurses’ well-being and their ability to provide safe, high-quality care is lacking. We examined the associations among hospital mergers, nursing, nurse well-being, and patient safety and quality of care to guide policy or hospital interventions.                     Methods/Study Population:                     Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for scoping reviews reporting guidelines, we conducted a scoping review by systematically searching 10 databases comprised of both business and healthcare literature up to January 2025. Eligible studies met the following inclusion criteria: (1) US-based sample, (2) specify a hospital merger and one nursing-related criteria, (3) use empirical data, (4) peer-reviewed, and (5) in English. Conference abstracts, protocols, interventions, non-empirical studies, theses or dissertations, quality improvement, and systematic review articles were excluded. Two reviewers independently screened articles and extracted data to focus on empirical research that examined hospital mergers and nursing.                     Results/Anticipated Results:                     Our search yielded 1,772 articles; 10 met our inclusion criteria and were included in the analysis: 5 quantitative, 3 qualitative, and 2 multi-methods. Only three studies analyzed data from 2003 or later. Although the data were sparse and older, we found that hospital mergers introduced organizational changes among the nursing workforce – nurse staffing, leadership, and operations. Nurse-sensitive patient safety and quality of care outcomes may not improve post-merger. Qualitatively, nurses described poor communication, a perceived lack of trust in leadership, a loss of identity and unity, decreased morale, increased fear or uncertainty, and issues with equal and/or respectful treatment. The identified study methods were unable to draw strong causal and/or detailed conclusions, and few variables were examined.                     Discussion/Significance of Impact:                     This review highlights potential links between US hospital mergers and nursing, nurse well-being, and patient safety and quality of care and emphasizes the lack of robust, up-to-date studies to guide policymakers and hospital administrators in making informed decisions – potentially leading to negative consequences.
Surgical Robot Learning: From Demonstration and Simulation to World Models-A Review

Robot-assisted minimally invasive surgery is widely adopted, yet systems are predominantly teleoperated, and progress towards autonomy remains challenging. Expert demonstrations are limited and costly, while existing simulators struggle to capture contactrich tissue dynamics and intraoperative variability, contributing to sim-to-real gaps. This review surveys over 100 studies on surgical robot learning for manipulation tasks, organised by learning paradigms, data strategies, and model architectures. We find that, despite notable advances-including strong perception performance and demonstrations of complete ex vivo procedures-scaling remains limited by data scarcity and restricted environmental diversity. We discuss Surgical World Models (SWMs) as one possible direction: models that learn environment dynamics from endoscopic video and robot trajectories and can be used to synthesise large, diverse, and parameterizable training scenarios. Such approaches could complement physics-based simulation and targeted data collection; their practical value will depend on rollout horizon, fidelity under distribution shift, and verification and validation requirements for safety-critical use.
A REVIEW ON HUMAN RESOURCE INFORMATION SYSTEM (HRIS) IN CONSONANCE WITH POTENTIALITIES OF BIG DATA FOR CORPORATE COMMUNICATION AND ITS EFFECTIVENESS IN STEEL INDUSTRIES OF INDIA

Human resource is a prime variable without which the entire equation of profit and productivity becomes imbalanced. It can appreciate in value. Entrusted with the successful operation of a business, human resource management is an extremely complex and demanding duty.  According to Farndale et al. (2010) and Troshani et al. (2011), HRIS systems that support decision-making about essential HR functions can be used to create strategic value." Strategic planners could not have predicted future labor supply and demand without the data provided by HRIS. Planning for human resources is the process by which problems with them are connected to the business requirements of the company.  The results of this study will help future HRIS users utilize the system's functionalities for HR planning to the fullest instead of just using it for administrative tasks. The two HRD subsystems that make up the study framework are HRIS Corporate Communication and HRIS Human Resource Planning.  Better plans have a big impact on a company's survival and performance, which is why businesses have started concentrating on tactfully use of its Human Resource and other resources with a concern for its customers satisfactions.  Organizations were better equipped to handle the intense competition brought on by globalization when they applied strategic management with information systems. There are several examples of businesses that have failed and others that have flourished.  With the advent of big data, businesses now handle, analyze, and derive conclusions from massive amounts of data in a fundamentally different way. An extensive overview of big data is provided, along with information on its applications, challenges, and strategies for handling its complexities. The study looks at all the industries that depend on big data, including government, healthcare, industry, and education. It also examines the challenges that big data presents, such as data storage, scalability, privacy, and security. These problems are addressed by a wide range of technologies and techniques, including cloud computing, machine learning, and data mining.
Machine Learning: A Historical and Methodological Analysis

Machine learning has always been an integral part of artificial intelligence, and its methodology has evolved in concert with the major concerns of the field. In response to the difficulties of encoding ever-increasing volumes of knowledge in modern AI systems, many researchers have recently turned their attention to machine learning as a means to overcome the knowledge acquisition bottleneck. This article presents a taxonomic analysis of machine learning organized primarily by learning strategies and secondarily by knowledge representation and application areas. A historical survey outlining the development of various approaches to machine learning is presented from early neural networks to present knowledge-intensive techniques.
Unveiling the impact of intrapartum antimicrobial prophylaxis in the early microbiome through metagenomics analysis

Trabajo presentado en el IX Workshop de la Sociedad Española de Probióticos y Prebióticos (SEPyP), celebrado en Zaragoza (España) el 15 y 16 de febrero de 2018
The Allen Telescope Array Twenty-centimeter Survey - A 690-Square-Degree, 12-Epoch Radio Dataset - I: Catalog and Long-Duration Transient Statistics

We present the Allen Telescope Array Twenty-centimeter Survey (ATATS), a multi-epoch (12 visits), 690 square degree radio image and catalog at 1.4GHz. The survey is designed to detect rare, very bright transients as well as to verify the capabilities of the ATA to form large mosaics. The combined image using data from all 12 ATATS epochs has RMS noise sigma = 3.94mJy / beam and dynamic range 180, with a circular beam of 150 arcsec FWHM. It contains 4408 sources to a limiting sensitivity of S = 20 mJy / beam. We compare the catalog generated from this 12-epoch combined image to the NRAO VLA Sky Survey (NVSS), a legacy survey at the same frequency, and find that we can measure source positions to better than ~20 arcsec. For sources above the ATATS completeness limit, the median flux density is 97% of the median value for matched NVSS sources, indicative of an accurate overall flux calibration. We examine the effects of source confusion due to the effects of differing resolution between ATATS and NVSS on our ability to compare flux densities. We detect no transients at flux densities greater than 40 mJy in comparison with NVSS, and place a 2-sigma upper limit on the transient rate for such sources of 0.004 per square degree. These results suggest that the &gt; 1 Jy transients reported by Matsumura et al. (2009) may not be true transients, but rather variable sources at their flux density threshold.
Sampling of Stochastic Differential Equations using the Karhunen-Loève Expansion and Matrix Functions

19 pages, 4 figures
Challenges of Space Debris Detection, Tracking, and Monitoring in Near-Earth Orbit: Overview of Current Status and Mitigation Strategies

The accumulation of space debris in near-Earth orbit, particularly in Low Earth Orbit (LEO), poses an increasing threat to satellite operations, communication infrastructures, and long-term space sustainability. As modern constellations expand and incorporate advanced satellite technologies, including sensing and wireless communications, artificial intelligence-of-things (AIoT), enabled payloads, and edge computing for on-orbit data processing, the risk profile grows. This paper reviews the current debris environment and existing sensing and monitoring techniques, highlights major collision events and deliberate debris-generating activities, and analyzes the role of both governmental and commercial satellite constellations in exacerbating and mitigating the challenges. Emerging space surveillance and tracking (SST) techniques, leveraging radar, optical sensors, and interferometric SAR for enhanced intelligence, surveillance, and reconnaissance (ISR), are highlighted alongside software-defined networking (SDN) approaches and cloud communication technology that enable coordinated debris-avoidance maneuvers. Key international regulatory frameworks, tracking architectures, and mitigation measures, including alignment with ISO 24113 standards, advanced TT&amp;C capabilities, and evolving active debris removal technologies, are examined. The study emphasizes the necessity of a global, interoperable ecosystem that integrates AI/ML (artificial intelligence and machine learning)-driven situational awareness, secure SATCOM links with AJ/LPI/LPD (anti-jamming/low probability of interception/low probability of detection) characteristics, and collaborative protocols among space agencies, commercial operators, and regulatory bodies to ensure the sustainable use of orbital space for future generations.
Water and the Arab Uprisings: the human right to water and sanitation in post-transition Egypt

Second semester University: University College Dublin
Evaluating the effectiveness of data governance frameworks in ensuring security and privacy of healthcare data: A quantitative analysis of ISO standards, GDPR, and HIPAA in blockchain technology

Blockchain technology is widely used in almost every domain of life nowadays including healthcare sector. Although there are existing frameworks to govern healthcare data but they have certain limitations in effectiveness of data governance to ensure security and privacy. This study aimed to evaluate effectiveness of health care data governance frameworks, examining security and privacy concerns and limitations within the existing frameworks of ISO Standards, GDPR, and HIPAA. In this study quantitative research approach was followed. A sample of 250 participants from Islamabad, Lahore and Karachi based healthcare experts, IT specialist, blockchain research and developer, administrator was selected. The collected data was analyzed though frequencies and descriptive statistical tests with the help of SPSS. The results revealed un-satisfaction for data governance frameworks, i.e., ISO standards, GDPR, and HIPAA in terms of security concerns, i.e., data encryption, access controls, audit trails, interoperability and standards, smart contracts for compliance, data integrity, regulatory compliance monitoring and privacy concerns, i.e., consent management, anonymization and pseudonymization, data minimization. The participants agreed that there is a need of integration of reliable data governance framework in health care data management. Various personalized governance techniques, targeted security upgrades, and continuous improvement in the specific customized data governance framework has been presented based on the findings of the study. An implementation of blockchain-based systems is recommended in order to ensure and expand the security and privacy of healthcare data management.
Weighed Domain-Invariant Representation Learning for Cross-domain Sentiment Analysis

Cross-domain sentiment analysis is currently a hot topic in the research and engineering areas. One of the most popular frameworks in this field is the domain-invariant representation learning (DIRL) paradigm, which aims to learn a distribution-invariant feature representation across domains. However, in this work, we find out that applying DIRL may harm domain adaptation when the label distribution $\rm{P}(\rm{Y})$ changes across domains. To address this problem, we propose a modification to DIRL, obtaining a novel weighted domain-invariant representation learning (WDIRL) framework. We show that it is easy to transfer existing SOTA DIRL models to WDIRL. Empirical studies on extensive cross-domain sentiment analysis tasks verified our statements and showed the effectiveness of our proposed solution.
CRIMINOLOGICAL FACTORS OF RECIDIVISM IN UZBEKISTAN: CHALLENGES OF SOCIAL REINTEGRATION AND CRIME PREVENTION

Recidivism remains one of the most pressing challenges facing contemporary criminal justice systems worldwide. The repeated commission of crimes by previously convicted individuals not only undermines public security but also indicates shortcomings in rehabilitation and reintegration mechanisms. This article examines the criminological factors contributing to recidivism in Uzbekistan and analyzes the challenges of social reintegration faced by formerly incarcerated individuals. Particular attention is paid to socio-economic, psychological, legal, and institutional determinants of repeat offending. The study also evaluates the role of probation services, employment opportunities, family support, and community involvement in preventing recidivism. Based on comparative international practices and the current legal framework of Uzbekistan, the article proposes recommendations aimed at strengthening post-penitentiary reintegration and enhancing crime prevention policies.
PENGARUH LIKUIDITAS TERHADAP CORPORATE SOCIAL RESPONSIBILITY PADA PERUSAHAAN PENGHARGAAN ANNUAL REPORT AWARD 2019 YANG TERDAFTAR DI BURSA EFEK INDONESIA TAHUN 2015 – 2019

This study aims to examine and analyze the effect of liquidity on corporate social responsibility. The independent variable is liquidity by measuring the current ratio and liquidity by measuring the cash ratio while the dependent variable is corporate social responsibility.  The object of research in this study is the 2019 Annual Report Award winning companies listed on the Indonesia Stock Exchange in 2015 - 2019. The population of this study were 32 companies that won the 2019 Annual Report Award listed on the Indonesia Stock Exchange in 2015 - 2019. The research sample was selected using the method purposive sampling with certain criteria and obtained as many as 14 companies that meet the criteria. The analytical method used is multiple regression analysis.  The results of the analysis show that the liquidity variable using the current ratio measurement has a significant negative effect on corporate social responsibility, the liquidity variable using the cash ratio measurement has a significant negative effect on corporate social responsibility, this shows that the smaller the liquidity of a company, the disclosure of corporate social responsibility information will be more low. Meanwhile, simultaneously, the liquidity variable using the current ratio and cash ratio measurements has a significant negative effect on corporate social responsibility, meaning that the higher the company's liquidity, the higher the disclosure of corporate social responsibility information will be.
The RSNA Abdominal Traumatic Injury CT (RATIC) Dataset

The RSNA Abdominal Traumatic Injury CT (RATIC) dataset is the largest publicly available collection of adult abdominal CT studies annotated for traumatic injuries. This dataset includes 4,274 studies from 23 institutions across 14 countries. The dataset is freely available for non-commercial use via Kaggle at https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection. Created for the RSNA 2023 Abdominal Trauma Detection competition, the dataset encourages the development of advanced machine learning models for detecting abdominal injuries on CT scans. The dataset encompasses detection and classification of traumatic injuries across multiple organs, including the liver, spleen, kidneys, bowel, and mesentery. Annotations were created by expert radiologists from the American Society of Emergency Radiology (ASER) and Society of Abdominal Radiology (SAR). The dataset is annotated at multiple levels, including the presence of injuries in three solid organs with injury grading, image-level annotations for active extravasations and bowel injury, and voxelwise segmentations of each of the potentially injured organs. With the release of this dataset, we hope to facilitate research and development in machine learning and abdominal trauma that can lead to improved patient care and outcomes.
Ecological restoration for biodiversity conservation improves habitat quality for an insectivorous passerine in boreal forest

Abstract   It is increasingly recognized that successful biodiversity conservation will necessitate active ecological restoration measures. In boreal forests, emulating natural disturbances is commonly used as a restoration tool for improving habitat quality for a range of sensitive species. We assessed the consequences of prescribed burning and artificial gap creation on the demographic parameters of an insectivorous bird, the pied flycatcher ( Ficedula hypoleuca ). This to improve our ability to develop strategies for successful ecological restoration aiming at improving the conservation status of boreal forest birds. Pied flycatchers reproduced equally well in nature reserves, forests subjected to ecological restoration and untreated control stands. Nestling body weight was found to be higher in stands restored through prescribed burning. Considering that nestling condition at the time of fledging is known to be positively related to survival rates after fledging, our results suggest a positive effect of prescribed burning on population dynamics and on local habitat quality. Our findings should encourage forest managers to actively use prescribed burning as a management tool in boreal forests to complement other conservation measures. However, one should be careful with generalizing these results to other bird species as they only are directly applicable to pied flycatchers. Still, they may potentially apply also to other insectivorous bird species with similar habitat requirements. In addition, it should be stressed that to maintain diverse boreal forest bird assemblages, heterogeneous landscapes are needed including both burned and unburned forest of different successional stages.
Sequential Inference for Gaussian Processes: A Signal Processing Perspective

The proliferation of capable and efficient machine learning (ML) models marks one of the strongest methodological shifts in signal processing (SP) in its nearly 100-year history. ML models support the development of SP systems that represent complex, nonlinear relationships with high predictive accuracy. Adapting these models often requires sequential inference, which differs both theoretically and methodologically from the usual paradigm of ML, where data are often assumed independent and identically distributed. Gaussian processes (GPs) are a flexible yet principled framework for modeling random functions, and they have become increasingly relevant to SP as statistical and ML methods assume a more prominent role. We provide a self-contained, tutorial-style overview of GPs, with a particular focus on recent methodological advances in sequential, incremental, or streaming inference. We introduce these techniques from a signal-processing perspective while bridging them to recent advances in ML. Many of the developments we survey have direct applications to state-space modeling, sequential regression and forecasting, anomaly detection in time series, sequential Bayesian optimization, adaptive and active sensing, and sequential detection and decision-making. By organizing these advances from a signal-processing perspective, we intend to equip practitioners with practical tools and a coherent roadmap for deploying sequential GP models in real-world systems.
Developing measurement indices to enhance protection and resilience of critical infrastructure and key resources

The US Department of Homeland Security (DHS) is developing indices to better assist in the risk management of critical infrastructures. The first of these indices is the Protective Measures Index — a quantitative index that measures overall protection across component categories: physical security, security management, security force, information sharing, protective measures and dependencies. The Protective Measures Index, which can also be recalculated as the Vulnerability Index, is a way to compare differing protective measures (eg fence versus security training). The second of these indices is the Resilience Index, which assesses a site’s resilience and consists of three primary components: robustness, resourcefulness and recovery. The third index is the Criticality Index, which assesses the importance of a facility. The Criticality Index includes economic, human, governance and mass evacuation impacts. The Protective Measures Index, Resilience Index and Criticality Index are being developed as part of the Enhanced Critical Infrastructure Protection initiative that DHS protective security advisers implement across the nation at critical facilities. This paper describes two core themes: determination of the vulnerability, resilience and criticality of a facility and comparison of the indices at different facilities.
Central Bank digital currency in the inside-outside money competition mechanism

A presente tese estuda como uma CBDC acessível pelo público e com juros afeta o mecanismo de competição pela moeda e, por sua vez, os equilíbrios monetários. A percepção dos consumidores sobre a CBDC como alternativa aos depósitos bancários e a estrutura do setor bancário são fatores cruciais para determinar o papel da CBDC nesse mecanismo. Dada a concorrência imperfeita no setor bancário, constata-se que o CBDC é um instrumento eficaz de política monetária capaz de influenciar os agregados familiares diretamente através do canal de taxa de depósito. Em particular, quanto maior a substituibilidade entre as duas formas de moeda digital, maior a correlação entre as suas taxas de juros