ethicalabs/Research-Intent-Collab huggingface dataset:
Applications of trace element and isotope geochemistry in revealing origin and evolution of thermal spring water and ore deposit

Abstract                Geochemistry is normally referred to as the study of Earth’s chemistry by exploring properties of materials and natural processes from deep beneath the Earth’s surface to the upper atmosphere, demonstrating the natural phenomena nowadays and discovering the history of nature. This study describes a subtopic of geochemistry: inorganic isotopes and trace elements, and discusses the application of the isotopes and trace elements in studies of geothermal spring water and ore deposit. The study of water samples in Hengjing, China was accomplished with the help of gas isotope analysis. The carbon isotope ratios and gas compositions indicate that the thermal waters are mostly sourced from meteoric and mantle sources in this area. The He isotope could evolute from mantle fluid, and CO2 is a product of the carbonate decomposition of rocks at high temperatures. The other study, conducted on the Zhaoxian ore deposit, revealed the origin and processes of formation of the different compositions of the ore. The magmatic-hydrothermal activity in this tectonically active area contributes much to the mineralization process of the ore, as shown by the analysis of trace elements of pyrite. The fluid inclusion He-Ar isotope shows that the ore was characterized by crust-mantle mixing with a little participation of meteoric water.
The Role of Job Satisfaction as a Mediator on the Implementation of Human Resource Information System (HRIS) On Employee Performance at PT. Hardo Soloplast

This study investigates the influence of Human Resource Information Systems (HRIS) on employee performance with job satisfaction as a mediating variable at PT. Hardo Soloplast, within the broader context of digital transformation in HR management. Utilizing a quantitative approach and Partial Least Squares-Structural Equation Modeling (PLS-SEM) via SmartPLS 4, the research collected data from 64 randomly selected employees. Results indicate that HRIS significantly enhances both job satisfaction and employee performance, with job satisfaction partially mediating this relationship. While the study confirms the positive impact of HRIS, it treats HRIS as a singular construct without detailing how specific functionalities such as self-service portals, performance tracking, or automated workflows affect satisfaction and performance outcomes. Moreover, the reliance on simple random sampling within a single company limits the generalizability of findings, suggesting that future research should include multi-company comparisons or more diverse sampling strategies. The literature review could be enriched by addressing conflicting evidence and exploring conditions under which HRIS might yield negative effects, such as user resistance or inadequate training. This would provide a more comprehensive and critical understanding of HRIS implementation and its varied outcomes across organizational contexts.
Implementation of three-phase transformer model in radial load-flow analysis

AbstractThis paper presents an efficient approach for developing three-phase transformer admittance matrices in the radial power-flow analysis. The proposed transformer model overcomes the singularity problem of the nodal admittance submatrices of ungrounded transformer configurations. This has been achieved by applying symmetrical components modeling. The classical (6×6) transformer nodal admittance matrix written in phase components is converted to sequence components instead of the (3×3) admittance submatrices. In this model, the phase shifts accompanied with special transformer connections are included in the radial power-flow solution process without any convergence problems. The final model of the transformer is represented by a generalized power-flow equation written in phase components. The developed equation is applicable for all transformer connections. The transformer model is integrated into the radial power-flow and tested using the IEEE radial feeders. The results have shown that the developed transformer model is very efficient and the radial power-flow has robust convergence characteristics.
Impact of Nurse Staffing Levels on Patient Safety and Quality of Care

Nurse staffing levels play a critical role in ensuring patient safety and enhancing the quality of care delivered in healthcare settings. Studies have shown that higher nurse-to-patient ratios are directly associated with better patient outcomes, including reduced rates of hospital-acquired infections, fewer medication errors, and lower incidences of adverse events. When nurses are overloaded with patients, their ability to effectively monitor and respond to individual patient needs diminishes, leading to increased risks of complications and negative health outcomes. In contrast, adequate staffing allows nurses to spend more time on patient assessments, education, and direct care, which ultimately contributes to improved recovery times and patient satisfaction. Furthermore, nurse staffing levels significantly influence the work environment and overall job satisfaction among nursing staff, which can further impact patient care. When nurses work in understaffed conditions, they experience higher levels of stress and burnout, which can lead to decreased morale and increased turnover rates. These dynamics create a cyclical problem, where inadequate staffing not only affects patient care but also contributes to an ongoing shortage of experienced nursing professionals. Therefore, healthcare administrators must recognize the importance of appropriate nurse staffing levels as a fundamental component of patient safety strategies and quality of care, necessitating ongoing assessment and investment in nursing resources to ensure optimal healthcare delivery.
Field Investigation and Numerical Analysis of Residential Building Damaged by Expansive Soil Movement Caused by Tree Root Drying

Trees have long been known to cause damages to pavements and buildings as a result of soil desiccation by tree roots. This paper presents a case study of a residential house damaged by expansive soil movement caused by tree root drying. Daily transpiration and water uptake of a large Eucalypt tree, located 7 m from the northwest corner of the house, was monitored by using a sap flow meter. A numerical analysis was conducted to estimate the soil suction distribution and soil movement under influence of the tree. The measured soil suction profiles indicated that the large Eucalypt tree on the site had influenced the soil moisture condition to a depth of approximately 3.5 m, i.e., 1.5 m below the level of underpins. The case study revealed that the damage was most likely caused by tree root drying, and the differential foundation movements that had occurred after installation of underpins could be attributed to relatively shallow depth of underpins.
Efficient Particle Smoothing for Bayesian Inference in Dynamic Survival Models

This article proposes an efficient Bayesian inference for piecewise exponential hazard (PEH) models, which allow the effect of a covariate on the survival time to vary over time. The proposed inference methodology is based on a particle smoothing (PS) algorithm that depends on three particle filters. Efficient proposal (importance) distributions for the particle filters tailored to the nature of survival data and PEH models are developed using the Laplace approximation of the posterior distribution and linear Bayes theory. The algorithm is applied to both simulated and real data, and the results show that it generates an effective sample size that is more than two orders of magnitude larger than a state-of-the-art MCMC sampler for the same computing time, and scales well in high-dimensional and relatively large data.
Lattice Density-Functional Theory for Quantum Chemistry

We propose a lattice density-functional theory for {\it ab initio} quantum chemistry or physics as a route to an efficient approach that approximates the full configuration interaction energy and orbital occupations for molecules with strongly-correlated electrons. We build on lattice density-functional theory for the Hubbard model by deriving Kohn-Sham equations for a reduced then full quantum chemistry Hamiltonian, and demonstrate the method on the potential energy curves for the challenging problem of modelling elongating bonds in a linear chain of six hydrogen atoms. Here the accuracy of the Bethe-ansatz local-density approximation is tested for this quantum chemistry system and we find that, despite this approximate functional being designed for the Hubbard model, the shapes of the potential curves generally agree with the full configuration interaction results. Although there is a discrepancy for very stretched bonds, this is lower than when using standard density-functional theory with the local-density approximation.
Creating a Digital Health Smartphone App and Digital Phenotyping Platform for Mental Health and Diverse Healthcare Needs: an Interdisciplinary and Collaborative Approach

As the potential of smartphone apps and sensors for healthcare and clinical research continues to expand, there is a concomitant need for open, accessible, and scalable digital tools. While many current app platforms offer useful solutions for either clinicians or patients, fewer seek to serve both and support the therapeutic relationship between them. Thus, we aimed to create a novel smartphone platform at the intersection of patient demands for trust, control, and community and clinician demands for transparent, data driven, and translational tools. The resulting LAMP platform has evolved through numerous iterations and with much feedback from patients, designers, sociologists, advocates, clinicians, researchers, app developers, and philanthropists. As an open and free tool, the LAMP platform continues to evolve as reflected in its current diverse use cases across research and clinical care in psychiatry, neurology, anesthesia, and psychology. In this paper, we explore the motivation, features, current progress, and next steps to pair the platform for use in a new digital psychiatry clinic, to advance digital interventions for youth mental health, and to bridge gaps in available mental health care for underserved patient groups. The code for the LAMP platform is freely shared with this paper to encourage others to adapt and improve on our team’s efforts.
Transfer Learning between Motor Imagery Datasets using Deep Learning -- Validation of Framework and Comparison of Datasets

We present a simple deep learning-based framework commonly used in computer vision and demonstrate its effectiveness for cross-dataset transfer learning in mental imagery decoding tasks that are common in the field of Brain-Computer Interfaces (BCI). We investigate, on a large selection of 12 motor-imagery datasets, which ones are well suited for transfer, both as donors and as receivers. Challenges. Deep learning models typically require long training times and are data-hungry, which impedes their use for BCI systems that have to minimize the recording time for (training) examples and are subject to constraints induced by experiments involving human subjects. A solution to both issues is transfer learning, but it comes with its own challenge, i.e., substantial data distribution shifts between datasets, subjects and even between subsequent sessions of the same subject. Approach. For every pair of pre-training (donor) and test (receiver) dataset, we first train a model on the donor before training merely an additional new linear classification layer based on a few receiver trials. Performance of this transfer approach is then tested on other trials of the receiver dataset. Significance. First, we lower the threshold to use transfer learning between motor imagery datasets: the overall framework is extremely simple and nevertheless obtains decent classification scores. Second, we demonstrate that deep learning models are a good option for motor imagery cross-dataset transfer both for the reasons outlined in the first point and because the framework presented is viable in online scenarios. Finally, analysing which datasets are best suited for transfer learning can be used as a reference for future researchers to determine which to use for pre-training or benchmarking.
Machine Learning Methodology

There is a variety of patterns we desire to learn from radiation oncologic data. The previous chapters described how these various learning objectives can commonly be formulated in theoretical nomenclatures. This chapter introduces different machine learning algorithms that could cater to readers’ specific learning goals. We intend to provide conceptual outlines of some of the widely used algorithms with minimal mathematical conundrum and examples drawn from the radiotherapy literature. In this chapter we classify the algorithms into three types, based on the availability of information: unsupervised, supervised, and reinforcement learning. The methods illustrated in this chapter include principal component analysis and clustering (unsupervised), logistic regression, neural network, support vector machine, decision tree, Bayesian networks, and naive Bayes (supervised) in addition to reinforcement learning.
Is Corporate Social Responsibility Ethical? - Recognising Ethical Issues In Business

Corporate Social Responsibility is all about companies playing their responsible part in society and giving back to society and a big part of that is fund raising for valuable cause. Companies engage in Corporate Social Responsibility when they grant benefits to the communities where they work which are neither required by law nor an integral part of their primary objective is profit oriented company. Today, however businesses must also reflect on the legal, ethical, moral and social consequences of their decisions. The purpose of working on this paper is to identify the ethical issues faced by the organization and how the unethical behavior in organization is excessively aggressive to achieve business objectives. Evidences suggest that social corporate responsibility and the maintenance of high ethical standards is not an option but an obligation for all businesses. This paper also draws the inferences that often have the ethical implications of an action are overlooked for personal gain and the benefits are usually material. This frequently manifests itself in companies that attempt to cheat environmental regulations.
International Organisations and Indigenous Peoples' Issues: Rights over Land and Natural Resources

Abstract: The relationship between land and indigenous peoples give rise to various questions which have been largely connected to the idea of rights. These rights, whether individual or collective, have formed further issues of debates within various international organisations. Therefore, how these organisations, like International Labour Organisation and the United Nations, have deliberated on matters related to indigenous peoples' access to their lands and the rights that are to be conferred upon them form an interesting field of enquiry which have been sought to be understood through this paper.
Comparison of Neural Network Models for Weather Forecasting

Weather forecasting has a big impact on people’s lives from event planning to cultivation. Conventionally, it has been performed by simulating physical conditions of the atmosphere. Due to nonlinear and irregular kind of weather data, machine learning methodologies can be seen as an alternative of the physical model for forecasting weather. This paper traverses the potential of deep neural networks in the field of weather prediction. It compares the performance of two different neural network models. First model uses the feed- forward network while other uses recurrent neural network to feed the weather data. The models illustrate that neural network models are emulative with the conventional methods and can be perused as a better alternative to predict general meteorological conditions.
Summary of the proceedings of the international forum 2016: “Imaging referral guidelines and clinical decision support - how can radiologists implement imaging referral guidelines in clinical routine?”

The International Forum is held once a year by the ESR and its international radiological partner societies with the aim to address and discuss selected subjects of global relevance in radiology. In 2016, the issue of implementing imaging referral guidelines in clinical routine was analysed. The legal environment in the USA requires that after January 1, 2017, physicians must consult government-approved, evidence-based appropriate-use criteria through a clinical decision support system when ordering advanced diagnostic imaging exams. The ESR and the National Decision Support Company are developing "ESR iGuide", a clinical decision support system for European imaging referral guidelines using ESR imaging referral guidelines based on ACR Appropriateness Criteria. In many regions of the world, the situation is different and quite diverse, depending on the specific features of health care systems in different countries, but there are, unlike in the USA and EU, no legal obligations to implement imaging referral guidelines into the clinical practice. Imaging referral guidelines and clinical decision support implementation is a complex issue everywhere and the legal environment surrounding it even more so; how they will be implemented into the clinical practice in different areas of the world needs yet to be decided.• Implementation of imaging referral guidelines in clinical routine is needed. • Potential benefits are improved appropriateness in referrals and reduction of unnecessary radiation exposure. • The educational benefits include new insights through data collection and reporting. • The system will potentially highlight the lack in quality or availability of equipment.
Using secondary analysis of qualitative data of patient experiences of health care to inform health services research and policy

Qualitative research is recognized as an important method for including patients' voices and experiences in health services research and policy-making, yet the considerable potential to analyse existing qualitative data to inform health policy and practice has been little realized. This failure may partly be explained by: a lack of awareness amongst health policy makers of the increasing wealth of qualitative data available; and around 15 years of internal debates among qualitative researchers on the strengths, limitations and validity of re-use of qualitative data. Whilst acknowledging the challenges of qualitative secondary data analysis, we argue that there is a growing imperative to be pragmatic and to undertake analysis of existing qualitative data collections where they have the potential to contribute to health policy formulation. Time pressures are inherent in the policy-making process and in many circumstances it is not possible to seek funding, conduct and analyse new qualitative studies of patients' experiences in time to inform a specific policy. The danger then is that the patient voice, and the experiences of relatives and carers, is either excluded or included in a way that is easily dismissed as ‘unrepresentative’. We argue that secondary analysis of qualitative data collections may sometimes be an effective means to enable patient experiences to inform policy decision-making.
Report on the European platform for excellence in corporate social responsibility research

info:eu-repo/semantics/nonPublished
Aligning Large Language Models with Recommendation Knowledge

Accepted to the NAACL 2024 Findings
A Study on Demand Analysis and Forecasting and its Impact on Inventory Management in Supply Chain Logistics

Modern supply chains operate within an increasingly globalized and unpredictable marketplace where the margin for error is razor-thin. Demand Analysis and Forecasting have emerged as the backbone of strategic and operational success, serving as the primary link between market requirements and logistical execution. The ability to anticipate customer needs with high precision is fundamental to maintaining a lean supply chain, as it directly governs the most critical aspects of business operations: procurement, production scheduling, and inventory optimization.
Integrating Ethical Frameworks for Artificial Intelligence Reasoning in Business Decision-Making: A Qualitative Review of Transparency and Accountability Practices

The adoption of Artificial Intelligence (AI) in organisational decision-making processes has significantly transformed multiple industries by enhancing operational efficiency and scalability. Nonetheless, ethical concerns related to AI’s reasoning capabilities—especially in terms of transparency and accountability—are still not comprehensively explored. This study explores these ethical concerns within AI systems used in enterprise decision-making, focusing on the impact of transparency and accountability frameworks. A qualitative literature review method was employed to gather data from academic articles, industry reports, and case studies. A thematic analysis was employed to uncover recurring themes and critical concerns associated with transparency, accountability, and potential biases within AI systems. The findings show that while AI enhances operational efficiency, its "black-box" nature often undermines transparency, leading to a lack of trust in its decisions. Additionally, accountability remains vague, with organisations frequently not taking responsibility for AI-induced harm. AI system biases, stemming from prejudiced datasets and algorithmic structures, intensify discriminatory practices, especially in critical domains like recruitment and medical services. In conclusion, this study advocates for advancing well-rounded ethical guidelines for AI that emphasise openness, responsibility, and the reduction of bias. It highlights the significance of integrating ethical principles at each phase of the AI development process, with ongoing collaboration between developers, regulators, and stakeholders. Future research should focus on creating global accountability standards and integrating ethics-by-design principles to promote justice and equal treatment within AI technologies.
ENN's Roadmap for Proton-Boron Fusion Based on Spherical Torus

ENN Science and Technology Development Co., Ltd. (ENN) is committed to generating fusion energy in an environmentally friendly and cost-effective manner, which requires abundant aneutronic fuel. Proton-boron ( p-$^{11}$B or p-B) fusion is considered an ideal choice for this purpose. Recent studies have suggested that p-B fusion, although challenging, is feasible based on new cross-section data, provided that a hot ion mode and high wall reflection can be achieved to reduce electron radiation loss. The high beta and good confinement of the spherical torus (ST) make it an ideal candidate for p-B fusion. By utilizing the new spherical torus energy confinement scaling law, a reactor with a major radius $R_0=4$ m, central magnetic field $B_0=6$ T, central temperature $T_{i0}=150$ keV, plasma current $I_p=30$ MA, and hot ion mode $T_i/T_e=4$ can yield p-B fusion with $Q>10$. A roadmap for p-B fusion has been developed, with the next-generation device named EHL-2. EHL stands for ENN He-Long, which literally means ``peaceful Chinese Loong". The main target parameters include $R_0\simeq1.05$ m, $A\simeq1.85$, $B_0\simeq3$ T, $T_{i0}\simeq30$ keV, $I_p\simeq3$ MA, and $T_i/T_e\geq2$. The existing ST device EXL-50 was simultaneously upgraded to provide experimental support for the new roadmap, involving the installation and upgrading of the central solenoid, vacuum chamber, and magnetic systems. The construction of the upgraded ST fusion device, EXL-50U, was completed at the end of 2023, and it achieved its first plasma in January 2024. The construction of EHL-2 is estimated to be completed by 2026.