ethicalabs/Research-Intent-Collab huggingface dataset:
Current State of Sexual Rights Protection for Male Minors in China

This research paper provides an overview of the current state of sexual rights protection for male minors inChina and makes recommendations to strengthen existing protection mechanisms. It examines the legal framework,the prevalence of sexual offenses, and the current state of sex education in the context of the rights of male minors. Byanalyzing these aspects, the paper aims to reveal the challenges and opportunities for strengthening the protection of thesexual rights of this vulnerable group.
AI IN TRANSLATION, INTERPRETATION AND MULTILINGUAL COMMUNICATION

Artificial intelligence has become an important part of our lives. People in all sectors, from governments to businesses, are increasingly using AI to solve problems and improve processes. Artificial intelligence (AI) has played a significant role in the development of language. It is not only helping in the preservation and revitalization of languages but is also promoting translation, language education, communication systems, and inclusive language use. Artificial intelligence is a type of technology that helps computers and machines think and learn like humans. Artificial intelligence helps computers perform tasks that typically require human intelligence, such as understanding different written languages, recognizing patterns in images, solving logical and critical problems, and making decisions. AI-based tools and applications are making linguistic communication simpler, more effective, and more accessible. Through this, linguistic diversity is being integrated into the technological world, making the global development of language possible. This paper focused on AI in translation, interpretation and multilingual communication.
Improved multiscale computational strategies for delamination

This paper presents a three-scale computational strategy for the study of composite modeled at the mesoscale so that delamination can be reliably simulated. The solver is based on a LaTIn approach so that nonlinearities can be tackled at the local scale. We show how search directions which are parameters of the method need to be updated according to the delamination status of interfaces. We also present the parallelization of the macro problem which transmits long-range phenomena and warranty the scalabilty of the method; this parallelization is based on the balancing domain decomposition method, it leads to the introduction of a third (super-macro) scale.
Learning by doing: Highlighting the successful collaboration between an open access peer-reviewed journal, a scholarly communication class, and a supportive library

As the “NASIG Core Competencies for Scholarly Communication Librarians” makes clear, the responsibilities associated with scholarly communication work in libraries are so “broad and amorphous” that the “full suite of competencies is beyond the reach of even the most accomplished librarian.” It is, therefore, increasingly important for all academic librarians to have not just a theoretical understanding of scholarly communication topics, but also the ability to actively engage in and manage related projects. The question then is how are master of library and information science (MLIS) programs preparing aspiring academic librarians for these roles?
Investigation of eye splash and needlestick incidents from an HIV-positive donor on an intensive care unit using root cause analysis

Two doctors working on a busy intensive care unit sustained injuries whilst removing a chest drain from an HIV-positive patient. One doctor had a needlestick injury into his finger whilst the other sustained an eyesplash when the chest drain was pulled out.Following Department of Health format 'Doing less harm', a root cause and human factor analysis of the incident was carried out. The aim was to explore the underlying issues.Training, cultural and organizational issues were exposed, and are now being addressed. This approach has led to a far more effective dialogue with the National Health Trust concerned than was previously experienced, and there is early evidence of progress on important aspects of health and safety management at organizational level. Lack of health and safety training of doctors at undergraduate and postgraduate level needs to be addressed.
SYSTEMATIC RISK ASSESSMENT AND MANAGEMENT IN MODERN SHIPPING: A COMPREHENSIVE APPROACH TO SAFETY ANALYSIS

The monograph is devoted to a comprehensive study of the risk management system in modern maritime shipping. The work presents a holistic scientific and methodological approach to risk analysis and control, taking into account technical, organisational and human aspects of maritime safety. The evolution of risk management concepts - from isolated protective measures to integrated risk management systems - is revealed. The author substantiates the need to move from a reactive to a proactive approach, which involves the early detection and elimination of potential threats. Particular attention is paid to the human element as a key factor in ensuring the safety of shipping. The modern methods of risk identification and assessment are investigated. The peculiarities of applying the ALARP (As Low As Reasonably Practicable) concept to determine the acceptable level of risk are considered. The mechanisms for integrating risk assessment methods into the safety management system of shipping companies are analysed. The article analyses in detail the specifics of managing various categories of risks in maritime activities - navigation, technical, commercial, and environmental. Their interrelation and the need for an integrated approach to their control are shown. Particular emphasis is placed on the human element as a critical factor in maritime safety. The psychophysiological aspects of seafarers' activities, problems of fatigue, stress, and intercultural communication in multinational crews are considered. The article presents innovative approaches to risk management using modern digital technologies, such as decision support systems, predictive analytics, and virtual simulators. Attention is paid to the development of a proactive safety culture in maritime organisations. The importance of developing common values, open communication, and a system for reporting incidents and potentially dangerous situations is shown. The role of leadership and involvement of personnel at all levels in ensuring effective risk management is considered.
Synthesis of titanate nanostructures using amorphous precursor material and their adsorption/photocatalytic properties

This paper reports on a new and swift hydrothermal chemical route to prepare titanate nanostructures (TNS) avoiding the use of crystalline TiO2 as starting material. The synthesis approach uses a commercial solution of TiCl3 as titanium source to prepare an amorphous precursor, circumventing the use of hazardous chemical compounds. The influence of the reaction temperature and dwell autoclave time on the structure and morphology of the synthesised materials was studied. Homogeneous titanate nanotubes with a high length/diameter aspect ratio were synthesised at 160^{\circ}C and 24 h. A band gap of 3.06\pm0.03 eV was determined for the TNS samples prepared in these experimental conditions. This value is red shifted by 0.14 eV compared to the band gap value usually reported for the TiO2 anatase. Moreover, such samples show better adsorption capacity and photocatalytic performance on the dye rhodamine 6G (R6G) photodegradation process than TiO2 nanoparticles. A 98% reduction of the R6G concentration was achieved after 45 minutes of irradiation of a 10 ppm dye aqueous solution and 1 g/L of TNS catalyst.
Determination green human resource management: Analysis green training, green behavior, green leadership, and green organizational culture (study literature review)

Purpose: The purpose of this study is to develop hypotheses related to factors that influence green human resource management, especially in the campus environment/world of education. Methodology: The approach used in this literature review is descriptive qualitative. The data collection technique is to use literature studies or review previous relevant articles. The data used in this descriptive qualitative approach are derived from previous studies relevant to this research and sourced from academic online media such as Thomson Reuters Journals, Springer, Taylor &amp; Francis, Scopus Emerald, Elsevier, Sage Q2-Q4, Web of Science, Sinta Journals, DOAJ, EBSCO, Google Scholar, and digital reference books. Results: The results of this literature review are as follows: 1) Green training influences green human resource management; 2) Green behavior influences green human resource management; 3) Green leadership influences green human resource management; and 4) Green organizational culture influences green human resource management. Conclusions: This study concludes that green training, green behavior, green leadership, and green organizational culture significantly influence Green Human Resource Management in higher education, fostering sustainable practices, enhancing HR quality, and strengthening universities’ commitment to green campus initiatives. Limitations: This study is limited to green HRM in the campus/educational setting. Contribution: This study contributes to the understanding of factors influencing green HRM, focusing on factors such as green training, green behavior, green leadership, and green organizational culture.
Carbon Footprint and Uncertainties of Geopolymer Concrete Production: A Comprehensive Life Cycle Assessment (LCA)

This study aims to estimate the carbon footprint and relative uncertainties for design components of conventional and geopolymer concrete. All the design components of alkaline-activated geopolymer concrete, such as fly ash, ground granulated blast furnace slag, sodium hydroxide (NaOH), sodium silicate (Na2SiO3), superplasticizer, and others, are assessed to reflect the actual scenarios of the carbon footprint. The conjugate application of the life cycle assessment (LCA) tool SimPro 9.4 and @RISK Monte Carlo simulation justifies the variations in carbon emissions rather than a specific determined value for concrete binders, precursors, and filler materials. A reduction of 43% in carbon emissions has been observed by replacing cement with alkali-activated binders. However, the associative uncertainties of chemical admixtures reveal that even a slight increase may cause significant environmental damage rather than its benefit. Pearson correlations of carbon footprint with three admixtures, namely sodium silicate (r = 0.80), sodium hydroxide (r = 0.52), and superplasticizer (r = 0.19), indicate that the shift from cement to alkaline activation needs additional precaution for excessive use. Therefore, a suitable method of manufacturing chemical activators utilizing renewable energy sources may ensure long-term sustainability.
Optimal Sensor and Actuator Selection for Factored Markov Decision Processes: Complexity, Approximability and Algorithms

Factored Markov Decision Processes (fMDPs) are a class of Markov Decision Processes (MDPs) in which the states (and actions) can be factored into a set of state (and action) variables and can be encoded compactly using a factored representation. In this paper, we consider a setting where the state of the fMDP is not directly observable, and the agent relies on a set of potential sensors to gather information. Each sensor has a selection cost and the designer must select a subset of sensors under a limited budget. We formulate the problem of selecting a set of sensors for fMDPs (under a budget) to maximize the infinite-horizon discounted return provided by the optimal policy. We show the fundamental result that it is NP-hard to approximate this problem to within any non-trivial factor. Our inapproximability results for optimal sensor selection also extend to a general class of Partially Observable MDPs (POMDPs). We then study the dual problem of budgeted actuator selection (at design-time) to maximize the expected return under the optimal policy. Again, we show that it is NP-hard to approximate this problem to within any non-trivial factor. Furthermore, with explicit examples, we show the failure of greedy algorithms for both the sensor and actuator selection problems and provide insights into the factors that cause these problems to be challenging. Despite this, through extensive simulations, we show the practical effectiveness and near-optimal performance of the greedy algorithm for actuator and sensor selection in many real-world and randomly generated instances.
Defining the Middle Corona

The middle corona, the region roughly spanning heliocentric altitudes from $1.5$ to $6\,R_\odot$, encompasses almost all of the influential physical transitions and processes that govern the behavior of coronal outflow into the heliosphere. Eruptions that could disrupt the near-Earth environment propagate through it. Importantly, it modulates inflow from above that can drive dynamic changes at lower heights in the inner corona. Consequently, this region is essential for comprehensively connecting the corona to the heliosphere and for developing corresponding global models. Nonetheless, because it is challenging to observe, the middle corona has been poorly studied by major solar remote sensing missions and instruments, extending back to the Solar and Heliospheric Observatory (SoHO) era. Thanks to recent advances in instrumentation, observational processing techniques, and a realization of the importance of the region, interest in the middle corona has increased. Although the region cannot be intrinsically separated from other regions of the solar atmosphere, there has emerged a need to define the region in terms of its location and extension in the solar atmosphere, its composition, the physical transitions it covers, and the underlying physics believed to be encapsulated by the region. This paper aims to define the middle corona and give an overview of the processes that occur there.
Trace element and isotope geochemistry of macroelongatoolithid eggs as an indicator of palaeoenvironmental reconstruction from the Late Cretaceous Xixia Basin, China

Abstract   The trace elements and isotopic compositions of macroelongatoolithid eggshells provide insights into the palaeoenvironment of these huge eggs preservation. We analysed the geochemical characteristics of newly discovered macroelongatoolithid eggs from the early Cretaceous Zhaoying Formation in the Xixia Basin, Henan Province, China. X-ray diffraction results reveal that the minerals of these eggs are predominately composed of calcite. The major elements in eggshells are Ca, Mg, K, Na, Al, Fe and Ti, and trace elements mainly include Sr, Ba, Mn, La, As, Sc, V, Cr, Co, Ni, Cu, Pb, Zn, Cd, U, and Ir. The high Sr and Ir concentrations could be caused by food materials and drinking water of dinosaurs. δ13C values of eggshells varying between −7.19‰ and −5.08‰ (average −5.69‰) indicate the C3 plants for dinosaurs with relatively high atmospheric CO2 concentration and low mean annual precipitation (MAP); further researches show that the carbon isotopic composition of atmospheric CO2 was around −4.11‰ to −2.19‰, and the MAP was lower than 10 mm/yr. δ18O values varying between −5.72‰ and −3.28‰ (average −4.53‰) show the temperature of nesting area were about 23 °C and the drinking water was dominated by leaf water with few meteoric water. The palaeoenvironment in which these huge eggs were preserved could be interpreted as continental deposit with extremely arid climate in the early Late Cretaceous Xixia Basin based on the high Fe contents of eggshells, the low palaeoclimate index of surrounding rocks, and the extremely low MAP shown by oxygen isotope.
DEAL: Deep Evidential Active Learning for Image Classification

Convolutional Neural Networks (CNNs) have proven to be state-of-the-art models for supervised computer vision tasks, such as image classification. However, large labeled data sets are generally needed for the training and validation of such models. In many domains, unlabeled data is available but labeling is expensive, for instance when specific expert knowledge is required. Active Learning (AL) is one approach to mitigate the problem of limited labeled data. Through selecting the most informative and representative data instances for labeling, AL can contribute to more efficient learning of the model. Recent AL methods for CNNs propose different solutions for the selection of instances to be labeled. However, they do not perform consistently well and are often computationally expensive. In this paper, we propose a novel AL algorithm that efficiently learns from unlabeled data by capturing high prediction uncertainty. By replacing the softmax standard output of a CNN with the parameters of a Dirichlet density, the model learns to identify data instances that contribute efficiently to improving model performance during training. We demonstrate in several experiments with publicly available data that our method consistently outperforms other state-of-the-art AL approaches. It can be easily implemented and does not require extensive computational resources for training. Additionally, we are able to show the benefits of the approach on a real-world medical use case in the field of automated detection of visual signals for pneumonia on chest radiographs.
Relativistic density functional theory for finite nuclei and neutron stars

The main goal of the present contribution is a pedagogical introduction to the fascinating world of neutron stars by relying on relativistic density functional theory. Density functional theory provides a powerful--and perhaps unique--framework for the calculation of both the properties of finite nuclei and neutron stars. Given the enormous densities that may be reached in the core of neutron stars, it is essential that such theoretical framework incorporates from the outset the basic principles of Lorentz covariance and special relativity. After a brief historical perspective, we present the necessary details required to compute the equation of state of dense, neutron-rich matter. As the equation of state is all that is needed to compute the structure of neutron stars, we discuss how nuclear physics--particularly certain kind of laboratory experiments--can provide significant constrains on the behavior of neutron-rich matter.
Improving the complexities of approximation algorithms for optimization problems

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Learning Graph Representation via Formal Concept Analysis

We present a novel method that can learn a graph representation from multivariate data. In our representation, each node represents a cluster of data points and each edge represents the subset-superset relationship between clusters, which can be mutually overlapped. The key to our method is to use formal concept analysis (FCA), which can extract hierarchical relationships between clusters based on the algebraic closedness property. We empirically show that our method can effectively extract hierarchical structures of clusters compared to the baseline method.
Implications of Climate Change Litigation for Environmental Law and Policy in Ghana

Purpose: The aim of the study was to examine implications of climate change litigation for environmental law and policy in Ghana  Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.  Findings: The study found that the implications of climate change litigation for environmental law and policy in Ghana underscore the urgent need for robust legal frameworks and adaptive policy responses to address the multifaceted challenges posed by climate change. While Ghana has made strides in enacting environmental legislation and participating in international climate agreements, the increasing frequency and severity of climate-related impacts necessitate greater legal clarity, enforcement mechanisms, and stakeholder engagement to enhance climate resilience and adaptation efforts. Climate change litigation provides a crucial avenue for affected communities, civil society organizations, and environmental advocates to hold governments and corporations accountable for their contributions to climate change and their obligations to protect the environment and uphold human rights.  Unique Contribution to Theory, Practice and Policy: Legal Mobilization Theory, Policy Diffusion Theory &amp; Ecological Modernization Theory may be used to anchor future studies on implications of climate change litigation for environmental law and policy in Ghana. Encourage strategic litigation that targets key actors, industries, and policy arenas to maximize the impact of legal interventions on environmental protection and climate action. This involves identifying leverage points within existing legal frameworks and leveraging innovative legal strategies, such as public trust doctrines and rights-based approaches, to advance environmental objectives. Foster greater integration of climate change litigation outcomes into national and international environmental policy-making processes. Governments and intergovernmental organizations should proactively incorporate legal precedents, court decisions, and settlement agreements into policy development, implementation, and evaluation.
Water: commodity, common good or right? contradictions in the implementation of public policies connected with the human right to water and sanitation (in Portuguese)

This article aims to contribute to the critical debate on the forms of appropriation of water as a commodity. Through the application of content analysis to the normative texts of the International System of Human Rights, and based on the opposition to the neoliberal model in the critical literature and in the positions of social movements, it is argued that the content of these international norms is in agreement and coherent with the neoliberal water project, because they do not exclude private participation from the control of water supply and sewerage services. Thinking about the contradictions between this normative content and the philosophy of Living in Plenitude (Buen Vivir) reveals a stimulating research agenda for construction of strategies in the struggle for a “new right” that is constituted not just formally, as it has been in Bolivia and Ecuador, but also in everyday practices.
A Structural Model of Educational Management Practices as Estimated by Organizational Citizenship Behavior, Emotional Leadership and Work Life Balance

This quantitative study utilized Structural Equation Modeling (SEM) to validate a multivariate structural model that predicts Educational Management Practices (EMP) among school leaders and administrators in Local and State Universities and Colleges in Davao, Region XI. The research investigated the direct and indirect influence of Organizational Citizenship Behavior (OCB), Emotional Leadership (EL), and Work-Life Balance (WLB) on EMP. The final, revised model demonstrated an excellent global fit to the data, confirming its reliability. Findings established that OCB is the most potent and direct predictor of effective EMP (β=.83, p&lt;.001). Crucially, the positive influence of Emotional Leadership on EMP was found to be fully mediated through OCB (β=.581, p&lt;.001), demonstrating that a leader's emotional support is effective by successfully boosting discretionary staff effort. Conversely, the model confirmed that Work-Life Balance does not have a statistically significant total effect on educational management practices (β=.083, p=.184). This validated structure provides a data-driven framework for academic institutions, highlighting that fostering positive staff behaviors is the primary mechanism for achieving high-quality management outcomes.