ethicalabs/Research-Intent-Collab huggingface dataset:
Towards Resilience by Design: Systematic Review of Critical Infrastructure Protection Against Systemic Risks

Abstract         Critical Infrastructures (CI) are essential cyber-physical systems providing vital services to society, with key examples including the energy, transport, and health sectors. Our work focuses specifically on the risks emerging from the deep integration of information and communication technologies (ICT) within these infrastructures. While we exclude purely physical threats like natural disasters, our scope addresses the operational and systemic risks that can originate in or propagate through the cyber domain to impact physical operations. These inherent interdependencies create systemic risks prone to cascading effects across sectors, a threat that traditional risk management struggles to address. This gap requires a fundamental shift in strategy from traditional protection to 'Resilience by Design. This approach focuses not just on preventing failures, but on proactively embedding the capacity to adapt and recover when they inevitably occur. This Systematic Literature Review (SLR) bridges this gap by synthesizing 41 primary research articles (2017–2025) to build a coherent framework for CI protection against systemic risk.  The central finding is that effective protection requires moving beyond traditional methods and embedding resilience across the entire incident lifecycle. We consolidate the literature into an integrated framework organized around three critical stages: 1) pre-incident preparedness, 2) during-incident response, and 3) post-incident recovery. For each stage, we identify key strategies, trends, and research gaps.  Unlike previous reviews that focus on fragmented aspects of risk, this study is the first to systematically structure CI protection around a holistic, three-stage resilience lifecycle. It offers a clear roadmap for developing robust strategies capable of managing the complex, interconnected nature of modern infrastructure.
Life Cycle Assessment (LCA) and Carbon Footprint Reduction Potential of Bio-CNG

This study explores the Life Cycle Assessment (LCA) and carbon footprint reduction potential of Bio-CNG,a renewable fuel derived from organic waste, aimed at mitigating the environmental impacts of fossil fuels.Through a cradle-to-grave approach, the LCA evaluates the energy inputs, greenhouse gas (GHG) emissions,and resource use across the Bio-CNG production chain, from biomass collection to biogas upgrading and usein transport or energy sectors. The findings highlight the significant reductions in CO₂, methane, and otherpollutants when compared to conventional fossil fuels. The research introduces a Bio-CNG Index, a compositemetric that quantifies the fuel's carbon footprint reduction potential, energy return, and sustainabilityperformance. This study emphasizes Bio-CNG as a key player in achieving climate action goals and fosteringsustainable energy transitions, particularly for regions seeking low-carbon alternatives to fossil-derived naturalgas and diesel. The results offer valuable insights for policy development and industrial adoption.
Versatility, value and limitations of using Health and Demographic Surveillance Site data for secondary analyses: guidance for researchers using examples from existing analyses

Health and Demographic Surveillance sites (HDSS) are geographic open cohorts operating in countries with absent/incomplete vital registration. Data on demographic events, socio-demographic indicators, and certain health conditions are regularly gathered on the whole population of a small area, sometimes for decades. In the same countries there are often also nationally-representative demographic data available from roughly quinquennial Demographic and Health Surveys (DHS). This paper uses a comparison with DHS data, and an in-depth review of complex HDSS analyses to demonstrate the utility of using HDSS data for secondary analyses, and to provide guidance on the conduct and reporting of these analyses. DHS data has advantages in terms of representativeness and data access, and HDSS in terms of scope for complex longitudinal analyses which may take household and familial contexts into account. HDSS also have issues which make interpretation of data and conclusions challenging: lack of data on in- and out-migrants when they are outside of the area, and repeatedly collected data may result in inconsistencies and/or more reliable data for longer-term residents. Despite these challenges, the reviewed HDSS data analyses demonstrate the flexibility and unique strengths of HDSS data. HDSS data users are recommended to clearly state their methods, particularly how they approached the issues specific to HDSS analyses of handling repeated data, migration and missing data: while there were interesting ways used to approach these issues, they were often not discussed. HDSS data producers are further encouraged to ensure that the data is being used to its full potential.
Climate Change Litigation and Environmental Law Enforcement in South Africa: A Systematic Review

Climate change litigation in South Africa has emerged as a significant legal avenue for environmental protection, often complementing traditional regulatory mechanisms. A comprehensive search strategy was employed across multiple databases including HeinOnline, LexisNexis, and Web of Science. Studies were screened based on predefined inclusion criteria focusing on legal outcomes and enforcement actions. The review identified a growing trend towards litigation driven by non-governmental organizations (NGOs) and community groups seeking to hold polluters accountable for environmental damage. A notable proportion—35%—of climate change cases resulted in significant fines or remediation orders, indicating the effectiveness of this legal route. The review underscores the pivotal role of litigation in strengthening enforcement efforts against non-compliant entities and highlights a need for further research to assess long-term impacts on environmental policy reform. Regulatory bodies should consider integrating climate change litigation into their enforcement strategies, alongside traditional legal mechanisms. Collaboration between NGOs, communities, and judicial authorities is essential for maximising the impact of these proceedings. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
APLIKASI DATA WAREHOUSE DAN ON-LINE ANALYTICAL PROCESSING (OLAP) (STUDI KASUS: PERMINTAAN DAN PEMAKAIAN OBAT DI PUSKESMAS POASIA KOTA KENDARI)

AbstrakPenelitian ini bertujuan untuk merancang dan membangun Aplikasi Data Warehouse dan On-Line Analytical Processing (OLAP) yang dapat digunakan untuk permintaan dan pemakaian obat yang dapat dijadikan sebagai sarana yang dapat membantu dalam proses pembuatan laporan di Puskesmas Poasia kota Kendari.Dalam pembuatan perangkat lunak digunakan sistem operasi Windows 10 pro x64, dengan database menggunakan Microsoft SQL Server 2016, untuk ETL Tools menggunakan SQL Server Integartion Services (SSIS), menggunakan Bahasa Pemograman C#, dan untuk Editor menggunakan Microsoft Visual Studio 2013. Aplikasi yang dibuat diharapkan dapat membantu pihak Petugas dan Pimpinan Puskesmas dalam pembuatan laporan.Dalam pembuatan laporan yang skala besar menyulitkan pihak Puskesmas Poasia dalam melakukan analisis terhadap data tersebut. Untuk dapat menangani data dalam jumlah besar dan memanfaatkannya semaksimal mungkin, diperlukan perancangan teknologi informasi yang lebih lanjut untuk dapat mengatasinya, yaitu Data Warehouse. Kata kunci— Data, Data Warehouse, Informasi.
Heliospheric modulation of cosmic rays during prolonged solar minima deduced from cosmogenic radioisotopes in meteorites

Abstract   Production of cosmogenic radioisotopes in meteorites depends on the galactic cosmic ray flux, which is controlled by the heliospheric magnetic field (HMF), which, in turn, depends on the solar activity. The radioisotopes can, therefore, serve as proxy records of the behaviour of the heliosphere in the past, in particular over prolonged solar quiet periods, such as during Gleissberg or Maunder minima, when the production rate of radioisotopes is expected to change significantly. For this purpose we have measured the  44 Ti (T 1/2 =66.6 yr) in ten stony meteorites, which fell between 1840–1992. The results indicate that the production of  44 Ti was significantly higher when the solar activity was persistently low, indicating that the HMF was weak during such periods, enabling higher flux of cosmic rays to enter the inner heliosphere.
Generative Adversarial Networks for Crystal Structure Prediction

The constant demand for new functional materials calls for efficient strategies to accelerate the materials design and discovery. In addressing this challenge, machine learning generative models can offer promising opportunities since they allow for the continuous navigation of chemical space via low dimensional latent spaces. In this work, we employ a crystal representation that is inversion-free with a low memory requirement based on unit cell information and fractional atomic coordinates, and build the generative adversarial network (GAN) for crystal structures. The proposed model is then applied to the Mg-Mn-O ternary inorganic materials system to generate novel structures with application as potential water-splitting photoanodes, and combined with the evaluation of their photoanode properties for high-throughput virtual screening (HTVS). The generative-HTVS system that we built predicts 23 new crystal structures with a reasonable predicted stability and bandgap. These findings suggest that the proposed generative model can be an effective way to explore hidden portions of the chemical space, an area that is usually unreachable when conventional substitution-based discovery is employed.
Advancing health equity through CTSA programs: Opportunities for interaction between health equity, dissemination and implementation, and translational science

AbstractDissemination and implementation (D&amp;I) science is dedicated to studying how to effectively translate and apply research in real-world contexts. There has been increasing interest in health equity within the D&amp;I field to ensure the equitable implementation of evidence-based programs/practices across a range of diverse populations and settings. At the same time, health equity researchers recognize the potential of D&amp;I science to promote the more widespread dissemination, implementation, and sustainment of evidence-based interventions to address health inequities. The National Center for Accelerating Clinical and Translational Science Clinical and Translational Science Award (CTSA) Program has been a champion for community engagement and translational scholarship in its mission to improve individual and population health. The overall CTSA infrastructure and resources within and among CTSA hubs are well-equipped to facilitate a health equity focus to D&amp;I across the phases of translational research. This paper proposes a framework that demonstrates the interaction and opportunities between health equity and D&amp;I science and highlights how CTSAs can support and facilitate wider efforts in translational research with a focus on equitable D&amp;I.
Multiradar Collaborative Task Scheduling Algorithm Based on Graph Neural Networks with Model Knowledge Embedding

Modern radar systems face increasingly complex challenges in tasks such as detection, tracking, and identification. The diversity of task types, limited data resources, and strict execution time requirements make radar task scheduling a strongly NP-hard problem. However, existing scheduling algorithms struggle to efficiently handle multiradar collaborative tasks involving complex logical constraints. Therefore, Artificial Intelligence (AI)-based scheduling algorithms have gained significant attention. However, their efficiency is heavily dependent on effectively extracting the key features of the problem. The ability to quickly and comprehensively extract common features of multiradar scheduling problems is essential for improving the efficiency of such AI scheduling algorithms. Therefore, this paper proposes a Model Knowledge Embedded Graph Neural Network (MKEGNN) scheduling algorithm. This method frames the radar task collaborative scheduling problem as a heterogeneous network graph, leveraging model knowledge to optimize the training process of the Graph Neural Network (GNN) algorithm. A key innovation of this algorithm is its capability to capture critical model knowledge using low-complexity calculations, which helps to further optimize the GNN model. During the feature extraction stage, the algorithm employs a random unitary matrix transformation. This approach utilizes the spectral features of the random Laplacian matrix from the task’s heterogeneous graph as global features, enhancing the GNN’s ability to extract shared problem features while downplaying individual characteristics. In the parameterized decision-making stage, the algorithm leverages the upper and lower bound knowledge derived from guiding and empirical solutions of the problem model. This strategy significantly reduces the decision space, enabling the network to optimize quickly and accelerating the learning process. Extensive simulation experiments confirm the effectiveness of the MKEGNN algorithm. Compared to existing approaches, it demonstrates improved stability and accuracy across all task sets, boosting the scheduling success rate by 3%～10% and the weighted success rate by 5%～15%. For particularly challenging task sets involving complex multiradar collaborations, the success rate improves by over 4%. The results highlight the algorithm’s stability and robustness.
Child sexual abuse material (CSAM): understanding the problem, the offender, &amp; risk concerns

Users of Child Sexual Abuse Material (CSAM), Child Exploitive Sexual Materials (CESM), child porn and Child Related Erotica (CRE) pose a risk to communities everywhere. Erotica is anything that an individual finds sexually arousing. This can include child pornography, Child Sexually Exploitive Material (CSEM), Child Sexual Abuse Material (CSAM), and any CRE (e.g., pictures of clothed or partially clothed minors, children’s underwear, toys, teaching or sports paraphernalia designed for minors, etc.).  The use of any CRE/CSEM/CSAM strengthens deviant sexual arousal towards minors. Those who believe that continued use of CRE/CSEM helps prevent actual touch offenses against children and teenagers fail to understand theories of habituation, learning theory, and behavioral and cognitive theories. To put it simply, practice makes perfect. A small number of articles and research finds that those convicted of use or possession of child porn/CSEM/CSAM are not dangerous and pose little risk of engaging in contact offenses; however, these sources fall short of solid methodological standards, mainly that they rely on offender self-report and a lack of any sex related conviction for a contact sex offense. Most studies fail to utilize objective measures such as polygraph to offer support of the offender’s claim to not have any undetected contact victims.
Compassion fatigue in palliative care: Exploring Its comprehensive impact on geriatric nursing well-being and care quality in end-of-life

This study examines the experiences of geriatric nurses in palliative care. It aims to understand how these experiences influence their well-being and the quality of care.Conducted in the Alahsa region of Saudi Arabia, the study employs qualitative methods, utilizing in-depth interviews with 12 geriatric nurses. The analysis employs a thematic approach, enriched by iterative reflections within a multidisciplinary research team.The analysis reveals main themes: 1) the deep emotional connections between nurses and their patients; 2) the challenges faced, including compassion fatigue, high patient mortality, and communication hurdles; 3) the impact of these challenges on the quality of care, highlighting issues like diminished empathy; 4) the coping strategies used by nurses, such as self-care practices and continuous education.The study concludes that coping strategies, including self-care and ongoing professional development, are vital for sustaining the nurses' well-being and ensuring the continued provision of high-quality care to Older Adults patients.
"Low-state" Black Hole Accretion in Nearby Galaxies

I summarize the main observational properties of low-luminosity AGNs in nearby galaxies to argue that they are the high-mass analogs of black hole X-ray binaries in the "low/hard" state. The principal characteristics of low-state AGNs can be accommodated with a scenario in which the central engine is comprised of three components: an optically thick, geometrically accretion disk with a truncated inner radius, a radiatively inefficient flow, and a compact jet.
Victor's Justice or the Law? Judging And Punishing At The International Criminal Tribunal For The Former Yugoslavia

The development of fair and impartial criteria for judging those accused of international crimes is one of the most critical issues facing the international community and the International Criminal Tribunal for the Former Yugoslavia (ICTY). Are the resources, experience, and moral force so weighted in favor of the international community that the accused cannot obtain a fair contest? Are international criminal trials legalistic exercises that cloak a victor's justice, or do such courts premise their decisions on fair criteria? Data on ICTY verdicts and punishment of convicted war criminals show that the ICTY judges follow a "legal" model and that punishment is based primarily on the gravity of the crimes committed and the defendant's level of responsibility in the political and military chain of command. Political factors largely do not explain verdicts or sentences.
Fundamentals of occupational health and safety practices outside the workplace among health professionals in Ghana: An assessment of determinants of crash helmet use in the Tamale Teaching Hospital

Abstract                 Objectives  This study investigates the determinants of crash helmet use among healthcare professionals at Tamale Teaching Hospital (TTH) in Ghana. Grounded in the Health Belief Model, the Theory of Planned Behavior, and Social Cognitive Theory, the study emphasizes the roles of personal attitudes, perceived risks, and social influences in promoting safety behavior. By integrating these theoretical frameworks, the study highlights how individual beliefs and societal factors can impact the adoption of safety measures. Methods  Employing a cross-sectional quantitative design, the study aimed to assess helmet usage among healthcare professionals. The target population consisted of approximately 4,000 staff members at TTH. Using the Krejcie and Morgan table, a sample size of 351 participants was determined. Stratified random sampling was implemented to ensure adequate representation across various job roles, including doctors, nurses, and paramedics. The dependent variable in this study was helmet usage, categorized as binary (uses/does not use), while the independent variables included job role, safety training (received/not received), years of experience, and attitudes towards safety. A structured questionnaire, incorporating closed and Likert-scale questions, was utilized to capture demographic data, helmet use habits, and safety attitudes. Statistical analyses were conducted using SPSS version 27, applying logistic regression, chi-square tests, and correlation analysis to assess the relationships between the variables. Results  The findings indicated that safety training (β = 0.80, p &lt; 0.001) and positive attitudes towards safety (β = 0.005, p = 0.005) significantly influenced helmet use. Medical doctors emerged as the most compliant group, demonstrating a higher likelihood of helmet usage (β = 0.43, p &lt; 0.001). Years of experience exhibited varying effects on helmet compliance, with professionals at different career stages showing distinct usage patterns. Conclusions  The study underscores the need for targeted interventions aimed at enhancing safety training and cultivating positive safety attitudes to improve helmet compliance among healthcare professionals. The study emphasizes that, to bolster helmet usage, healthcare institutions should expand safety training programs, promote a culture of safety, and develop tailored strategies to address the unique needs of various job roles within the healthcare setting.
The regulation of racially derogatory speech in the Promotion of Equality and Prevention of Unfair Discrimination Act 4 of 2000

AbstractA significant number of hate speech cases in South Africa involve the use of racially derogatory epithets in the form of inter-personal racial slurs. The Promotion of Equality and Prevention of Unfair Discrimination Act 4 of 2000 (PEPUDA), as a transformative human rights and anti-discriminatory statute, is ideally suited to regulate the use of such speech and to provide a means grounded in law to overcome the harm caused thereby both to its victims and to the broader societal good, which includes the constitutional ideal of an equal and pluralistic society embracing tolerance. Section 7(a) of PEPUDA, however, is an inappropriate tool for the regulation of racially derogatory epithets, which are more suitably addressed through the medium of the narrowed down hate speech regulator proposed for section 10(1) of PEPUDA.
Algorithms used for the Cell Segmentation Benchmark Competition at ISBI 2019 by RWTH-GE

The presented algorithms for segmentation and tracking follow a 3-step approach where we detect, track and finally segment nuclei. In the preprocessing phase, we detect centroids of the cell nuclei using a convolutional neural network (CNN) for the 2D images and a Laplacian-of-Gaussian Scale Space Maximum Projection approach for the 3D data sets. Tracking was performed in a backwards fashion on the predicted seed points, i.e., starting at the last frame and sequentially connecting corresponding objects until the first frame was reached. Correspondences were identified by propagating detections of a frame t to its preceding frame t-1 and by combining redundant detections using a hierarchical clustering approach. The tracked centroids were then used as input to variants of the seeded watershed algorithm to obtain the final segmentation.
A mathematical framework for thermodynamic computing with applications to chemical reaction networks

Abstract                   The widespread adoption of energy-intensive computing applications has led to a growing need for energy-efficient computing approaches. Thermodynamic computing offers a promising approach for low-energy computation by leveraging the intrinsic computational capabilities of physical, chemical, or biological systems. However, the mathematical foundations of thermodynamic computing require further development to fully realize the potential energy efficiencies, as well as to assess factors like noise and operational speed. In this paper, we establish a mathematical framework for utilizing thermodynamic processes to perform fundamental operations, including addition, subtraction, multiplication, and division. We highlight the use of chemical reactions as potential computational units and explore synthetic chemical and biochemical systems as practical implementations. Additionally, we demonstrate how these principles can be applied to solving complex mathematical problems, such as ordinary differential equations (ODEs) and suggest the necessary components to implement the thermodynamic computing framework using chemical reactions based in a microfluidic device. This work enhances our understanding of thermodynamic processes for natural computing as a basis for scalable, energy-efficient computation in paradigm disruptive next-generation systems.