ethicalabs/Research-Intent-Collab huggingface dataset:
Transitions and thermodynamics on species graphs of chemical reaction networks

Chemical reaction networks (CRNs) exhibit complex dynamics governed by their underlying network structure. In this paper, we propose a novel approach to study the dynamics of CRNs by representing them on species graphs (S-graphs). By scaling concentrations by conservation laws, we obtain a graph representation of transitions compatible with the S-graph, which allows us to treat the dynamics in CRNs as transitions between chemicals. We also define thermodynamic-like quantities on the S-graph from the introduced transitions and investigate their properties, including the relationship between specieswise forces, activities, and conventional thermodynamic quantities. Remarkably, we demonstrate that this formulation can be developed for a class of irreversible CRNs, while for reversible CRNs, it is related to conventional thermodynamic quantities associated with reactions. The behavior of these specieswise quantities is numerically validated using an oscillating system (Brusselator). Our work provides a novel methodology for studying dynamics on S-graphs, paving the way for a deeper understanding of the intricate interplay between the structure and dynamics of chemical reaction networks.
Access to Inclusive Education for Students with Autism: An Analysis of Canada’s Compliance with the United Nations Convention on the Rights of Persons with Disabilities

This article argues that Canada fails to meet its obligation under article 24 of the United Nations Convention on the Rights of Persons with Disabilities to provide students with autism with access to inclusive education. Moving beyond Canadian legislation, under which every province and territory recognises the right of all students to an inclusive education, it analyses Canada’s education system and the implementation of the goal of inclusive education. It points out the effect of five interrelated factors on the inclusiveness of the Canadian education system and its accessibility for students with autism: reductions in funding for education; the inadequacy of individual support measures and parent participation; the lack of education and training for teachers; the use of language indicative of the medical model of disability by governments; and "voluntary segregation" – the voluntary removal of children from the public education system by their parents. It concludes that Canada likely does not meet its obligations under the United Nations Convention on the Rights of Persons with Disabilities.
Knowledge Discovery in Geographical Sciences—A Systematic Survey of Various Machine Learning Algorithms for Rainfall Prediction

One of the biggest challenges faced by humanity over time is weather prediction. Rainfall prediction plays a critical role in agricultural sciences, besides it is pivotal in the prediction of droughts and floods. Over the last few decades, weather, especially rain has become much more unexpected with primarily the blame being climate change. This review paper presents an introduction to the implementation of machine learning on the geographical datasets: How it started, how much has been achieved, and what is the current scenario of machine learning in geographical sciences. The main area covering in this paper are meteorological data, Ensemble models, regression, classification including Neural Networks, Support vector machine (SVM), Decision trees, Naive Bayes, J48, CART, and ID3. However, the main thrust area has been the implementation of various neural network models which includes BPNN, FFNN, GWLM-NARX, RNN, and TDNN in geographical data sciences.
The Interplay between Transparency, Accountability, and Corruption: A Contemporary Discourse

The purpose of this study is to explore the intricate interplay between transparency, accountability, and corruption within contemporary governance systems. Employing a multidisciplinary approach, the research design incorporates empirical evidence and theoretical frameworks from diverse fields to elucidate the complex dynamics of this nexus. Methodologically, the study synthesizes existing literature and conducts comparative analyses to identify key patterns and insights regarding the effectiveness of transparency-enhancing measures and accountability mechanisms in combating corruption. The findings underscore a compelling association between increased transparency and reduced levels of corruption, highlighting the pivotal role of robust accountability structures in ensuring integrity within governance systems. Implications of the study extend to both academic scholarship and practical policymaking, emphasizing the importance of adopting a holistic approach that integrates transparency and accountability measures to effectively address corruption challenges. Key implications include the need for tailored interventions that consider contextual factors, such as institutional quality and societal norms, and the imperative of leveraging technological innovations and social mobilization to empower citizens and confront corruption. This study contributes to advancing our understanding of governance dynamics and informs evidence-based strategies for promoting transparency, strengthening accountability, and combating corruption in diverse contexts.
Emotional Intelligence in the Workplace : A study on Emotional Intelligence in Workers’ Occupational Health, Safety and Environment (HSE) in the workplace

Work-related accidents emerge from potential hazards that can cause different negative outcomes in different situations. Human errors are specific actions that can either directly (active errors) or indirectly (latent errors) cause an accident in the workplace. Nowadays in order to establish an applicable system in the way of maintenance and preferment of a work environment without any accidents that are trying to develop the HSE system. In fact, this management system has been using as a significant tool to control and improve the performance of health and safety and the environment in all development programs of industries and organizations. In this term, one of the important perspectives of HSE management is Emotional Intelligence which deals with the management’s ability and safety performance in the workplace. The purpose of this study is to investigate the relationship between Emotional Intelligence and Occupational Health, Safety and Environment (HSE) management in the workplace, to reduce industrial incidents of human factors. Therefore, there is a requirement for a better understanding of how Emotional Intelligence factors influence health and safety performance in the workplace. A qualitative study has been done to achieve this purpose. In this case, data has been collected through eight semi-structured interviews with HSE managers and officers that participated from different industries around the world.  The main focus of this collection data was extracting the perspectives of the individual’s views. Afterward, to create a theory, the data has been analyzed according to different steps for a grounded analysis regarding discovering how the Emotional Intelligence factors of employees impact their health and safety performance in the workplace.   The results of this study have shown that there are mainly two areas to study which are key roles of Emotional Intelligence in safety performance and key roles for effective Healthy, Safety, and Environment management. It has shown that the key roles of Emotional Intelligence in safety performance is being able to manage your own and being able to deal with other’s emotions. Besides, key roles for effective Healthy, Safety, and Environment management only three factors have been important to improve the safety act which is being able to make the correct decision in the emergency situation’ and ability to prevent incidents at the workplace as well as the level of perception of risk. These results demonstrated that strong factors of Emotional Intelligence are vital to improve the health and safety performance at the workplace and the improvement of these abilities should be approached for the workplace.
Antibiotic susceptibility and resistance patterns of diarrhoeagenic Escherichia Coli, Shigella and Salmonella species: A need for antimicrobial stewardship and surveillance programmes

Background Diarrhoeal diseases caused by bacterial pathogens are a major cause of increased morbidity and mortality rates worldwide, especially in vulnerable populations such as children. The emergence of antibiotic resistance has affected antibiotics commonly used in the management of diarrhoea such as ampicillin, co-trimoxazole and tetracyclines. We assessed the antibiotic susceptibility and resistance patterns of diarrhoeagenic Escherichia coli, Shigella, and Salmonella species based on published studies. Method This was a narrative review in which PubMed, Google Scholar, and EMBASE databases were used to search for studies published between January 2010 and January 2021. Results This review shows that diarrhoeagenic Escherichia coli, Shigella species, and Salmonella species are among the microorganisms which have developed high resistance to antibiotics including ampicillin, co-trimoxazole and tetracyclines. However, the three diarrhoeagenic bacteria have a low resistance to ciprofloxacin, norfloxacin, and ceftriaxone and hence can be used as the drugs of choice in diarrhoeal infections. Conclusion There is a high prevalence of diarrhoea caused by Escherichia coli, Shigella species and Salmonella species. Many diarrhoeagenic bacteria have developed multi-drug resistance to antibiotics, more especially to ampicillin, co-trimoxazole and tetracyclines. Antibiotic susceptibility tests of diarrhoeagenic bacteria must be carried out before antibiotics are prescribed. More importantly, antimicrobial stewardship programmes and surveillance systems must be promoted to curb the emergence and spread of antimicrobial resistance both in public and private practicing sites.
Estimation of fading channels modeled by stochastic differential equations from unevenly sampled data

A stochastic differential equation model for wireless fading channels is considered and the problem of estimating the model parameters from irregularly sampled data is studied. A computationally very efficient estimator based on the least squares method and the introduction of a differentiation operator is suggested. It is described how the operator should be chosen to obtain a consistent estimator as the number of data tends to infinity and the upper bound on the irregular sampling interval goes to zero. Attention is given to the computation of the operator. An online implementation of the estimator is also included.
Inclusivity, Access, Opportunities, Rights, and Support Needed by Learners and Students With Diverse Disabilities

This study delves into the inclusive education (IE) landscape, emphasizing its role in providing access, opportunities, and rights for learners with diverse needs. IE is considered a fundamental human right and a cornerstone for social justice. The research focused on thirteen teachers in Umgungundlovu District using probability sampling. Interviews, including one conducted by telephone, formed the data collection method. Analysis revealed that while IE embodies ubuntu, social justice, and human rights, challenges persist, including implementation issues, insufficient teacher training, large class sizes, and resource constraints. Approximately thirty percent of participants displayed negative attitudes toward accommodating diverse needs. Some teachers are open to inclusion with addressed concerns, highlighting the need for improved understanding. Urgent implementation of Salamanca commitments is recommended, with compulsory quarterly in-service training for teachers. Practical inclusion, coupled with informed parental engagement, is crucial for realizing IE opportunities.
Parents as Teachers Health Literacy Demonstration Project

The Parents as Teachers (PAT) Health Literacy Demonstration project assessed the impact of integrating data-driven reflective practices into the PAT home visitation model to promote maternal health literacy. PAT is a federally approved Maternal, Infant, Early Childhood Home Visiting program with the goal of promoting school readiness and healthy child development. This 2-year demonstration project used an open-cohort longitudinal design to promote parents’ interactive and reflective skills, enhance health education, and provide direct assistance to personalize and act on information by integrating an empowerment paradigm into PAT’s parent education model. Eight parent educators used the Life Skills Progression instrument to tailor the intervention to each of 103 parent–child dyads. Repeated-measures analysis of variance, paired t tests, and logistic regression combined with qualitative data demonstrated that mothers achieved overall significant improvements in health literacy, and that home visitors are important catalysts for these improvements. These findings support the use of an empowerment model of health education, skill building, and direct information support to enable parents to better manage personal and child health and health care.
Designing Ethical Learning Analytics Frameworks to Support Decision Making and Equity in Technology Enhanced Higher Education Environments

This study presents an ethical framework for learning analytics aimed at addressing key challenges related to the collection and use of student data in higher education. Learning analytics, a powerful tool for improving student outcomes and institutional decision-making, has raised ethical concerns regarding data privacy, transparency, fairness, and equity. The proposed framework integrates four core principles: data privacy, informed consent, transparency, and fairness, ensuring that institutions use learning analytics responsibly while safeguarding student rights. A central feature of the framework is its focus on promoting equitable decision-making, minimizing bias, and preventing the reinforcement of existing inequalities in algorithmic and data-driven decisions. The framework also emphasizes the importance of continuous ethical oversight, holding institutions accountable for ethical data use and adapting practices as technology evolves. The study concludes that the framework offers a comprehensive solution to the ethical challenges in learning analytics, providing institutions with a practical guide to embedding ethical principles in data practices. Additionally, the research discusses its potential to foster fairness, equity, and transparency in decision-making processes. Future research is recommended to refine the framework and explore its application across various educational contexts, ensuring responsible and inclusive use of learning analytics.
A machine learning methodology for medical imaging anonymization

Privacy protection is a major requirement for the complete success of EHR systems, becoming even more critical in collaborative scenarios, where data is shared among institutions and practitioners. While textual data can be easily de-identified, patient data in medical images implies a more elaborate approach. In this work we present a solution for sensitive word identification in medical images based on a combination of two machine-learning models, achieving a F1-score of 0.94. Three experts evaluated the system performance. They analyzed the output of the present methodology and categorized the studies in three groups: studies that had their sensitive words removed (true positive), studies with complete patient identity (false negative) and studies with mistakenly removed data (false positive). The experts were unanimous regarding the relevance of the present tool in collaborative medical environments, as it may improve the exchange of anonymized patient data between institutions.
Data protection-compliant broad consent for secondary use of health care data and human biosamples for (bio)medical research: Towards a new German national standard

Background: The secondary use of deidentified but not anonymized patient data is a promising approach for enabling precision medicine and learning health care systems. In most national jurisdictions (e.g., in Europe and North America), this type of secondary use requires patient consent. While various ethical, legal, and technical analyses have stressed the opportunities and challenges for different types of consent over the past decade, no country has yet established a national consent standard accepted by the relevant authorities.Methods: A working group of the national Medical Informatics Initiative in Germany conducted a requirements analysis and developed a GDPR-compliant broad consent standard involving all relevant stakeholder groups and authorities.Results: This paper presents the broad consent text together with a guidance document on mandatory safeguards for broad consent implementation. The mandatory safeguards comprise i) independent review of individual research projects, ii) organizational measures to protect patients from involuntary disclosure of protected information, and iii) comprehensive information for patients and public transparency. This paper further describes the key issues discussed with the relevant authorities, especially the position on additional or alternative consent approaches such as dynamic consent.Discussion: Both the resulting broad consent text and the national consensus process are relevant for similar activities internationally. A key challenge of aligning consent documents with the various stakeholders was explaining and justifying the decision to use broad consent and the decision against using alternative models such as dynamic consent. Public transparency for all secondary use projects and their results emerged as a key factor in this justification. While currently largely limited to academic medicine in Germany, the first steps for extending this broad consent approach to wider areas of application, including smaller institutions and medical practices, are currently under consideration.
Self-Organization, Emergence, and Constraint in Complex Natural Systems

Contemporary complexity theory has been instrumental in providing novel rigorous definitions for some classic philosophical concepts, including emergence. In an attempt to provide an account of emergence that is consistent with complexity and dynamical systems theory, several authors have turned to the notion of constraints on state transitions. Drawing on complexity theory directly, this paper builds on those accounts, further developing the constraint-based interpretation of emergence and arguing that such accounts recover many of the features of more traditional accounts. We show that the constraint-based account of emergence also leads naturally into a meaningful definition of self-organization, another concept that has received increasing attention recently. Along the way, we distinguish between order and organization, two concepts which are frequently conflated. Finally, we consider possibilities for future research in the philosophy of complex systems, as well as applications of the distinctions made in this paper.
Systematic Identification of Caregivers of Patients Living With Dementia in the Electronic Health Record: Known Contacts and Natural Language Processing Cohort Study

Background                     Systemically identifying caregivers in the electronic health record (EHR) is a critical step for delivering patient-centered care, enhancing care coordination, and advancing research and population health efforts in caregiving. Despite EHRs being effective in identifying patients through standardized data fields like demographics, laboratory results, medications, and diagnoses, identifying caregivers through the EHR is challenging in the absence of specific caregiver fields.                                                           Objective                     Recognizing the complexity of identifying caregiving networks of people living with dementia, this study aims to systematically capture caregiver information by combining EHR structured fields, unstructured notes, and free text.                                                           Methods                     Among a cohort of people living with dementia aged 60 years and older from Kaiser Permanente Colorado, caregiver names were identified by combining structured patient contact fields, that is, known contacts, with name-matching and natural language processing techniques of unstructured notes and patient portal messages containing caregiver terms.                                                           Results                     Among the cohort of 789 people living with dementia, 95% (n=749) had at least 1 caregiver name listed in structured fields (mean 2.1 SD 1.1). Over 95% of the cohort had caregiver terms mentioned near a known contact name in unstructured encounter notes, with 35% having a full name match in unstructured patient portal messages. The natural language processing model identified 7556 “new” names in the unstructured EHR text containing caregiver terms among 99% of the cohort with high accuracy and reliability (F1-score=.85; precision=.89; recall=.82). Overall, 87% of the cohort had a new name identified ≥2 near a caregiver term in their notes and portal messages.                                                           Conclusions                     Patterns in caregiver-related information were distributed across structured and unstructured EHR fields, emphasizing the importance of integrating both data sources for a comprehensive understanding of caregiving networks. A framework was developed to systematically identify potential caregivers across caregiving networks using structured and unstructured EHR data. This approach has the potential to improve health services for people living with dementia and their caregivers.
ABC Align: Large Language Model Alignment for Safety &amp; Accuracy

23 pages, 4 figures
(In)equality at the workplace? Differences in occupational safety and health measures during the Covid-19 pandemic and related employee health

Based on data from a supplementary survey of the German Socio-Economic Panel on the Covid-19 pandemic (SOEP-CoV), this article examines differences in the implementation of various person-related and organizational occupational safety and health measures during the Covid-19 pandemic and their relationship to individual health. We first provide a brief outline of social inequalities in workplace-related health risks before and during the pandemic and describe the implementation of different occupational health and infection protection measures during the pandemic. We argue that the infection and occupational health measures were not only successful in mitigating the risk of infection, but that their compliance may also contribute to a safety climate in the workplace, which in turn may be associated with better employee health. Accordingly, the analyses show that the mere number of organizational or person-related measures taken is not directly related to health, but perceived compliance with the measures significantly correlates with employee health (satisfaction). The OLS results also reveal social stratification in the occupational safety and health measures taken.