ethicalabs/Research-Intent-Collab huggingface dataset:
Detecting and understanding stigmatizing language in electronic health records using natural language processing

Stigmatizing language such as negative descriptors, misgendering, or expressions of disbelief used in Electronic Health Records (EHRs) can perpetuate bias, erode patient trust, and reinforce healthcare disparities. Building upon the growing intersection of health informatics and natural language processing (NLP), this dissertation integrates methodological innovation with fairness-oriented analysis across two complementary case studies to document, detect, and counter stigmatizing language in EHRs. Case 1 introduces a multi-stage transfer learning (MSTL) framework that sequentially adapts transformer-based models through semantic, syntactic, and task-specific fine-tuning. Using datasets spanning hate speech, clinical phenotypes, and stigmatizing language corpora, the framework achieved an accuracy of 89.83% and F1 = 93.18, significantly outperforming traditional baselines and large-language-model comparisons (e.g., GPT-4o). Statistical validation through Wilcoxon-Mann-Whitney tests with Bonferroni correction confirmed the robustness of performance gains (p &lt; .05). The MSTL-Longformer model demonstrated consistent accuracy across demographic subgroups, highlighting its capacity to detect subtle and context-dependent forms of stigma in long clinical narratives. Case 2 extends this framework to fairness auditing, baseline modeling, and interpretive contextualization for gender-expansive patient (GEP) documentation. Multivariable logistic-regression and odds-ratio analyses revealed that GEP and Black/African American patients had the highest adjusted odds of stigmatizing documentation, underscoring intersectional disparities in clinical narratives. Baseline models for SL detection among GEPs established comparative benchmarks for future studies, while comprehensive annotation guidelines were developed to standardize identification of ambiguous expressions, family-attributed remarks, and misgendering. Together, these studies demonstrate that detecting linguistic bias in EHRs is both a computational and ethical challenge. The proposed frameworks show that domain-progressive transfer learning can substantially improve model accuracy while fairness-aware evaluation exposes structural inequities in documentation. By combining advanced NLP modeling with ethical inquiry, this dissertation contributes new methodological and conceptual tools for responsible AI in healthcare. The findings illustrate how linguistic equity can be operationalized through data-driven innovations, ensuring that the language of medicine and the algorithms can promote inclusion, transparency, and justice in patient care.
Resilience, vulnerability and adaptive capacity of an inland rural town prone to flooding: a climate change adaptation case study of Charleville, Queensland, Australia

Australia is currently experiencing climate change effects in the form of higher temperatures and more frequent extreme events, such as floods. Floods are its costliest form of natural disaster accounting for losses estimated at over $300 million per annum. This article presents an historical case study of climate adaptation of an Australian town that is subject to frequent flooding. Charleville is a small, inland rural town in Queensland situated on an extensive flood plain, with no significant elevated areas available for relocation. The study aimed to gain an understanding of the vulnerability, resilience and adaptive capacity of this community by studying the 2008 flood event. Structured questionnaires were administered in personal interviews in February 2010 to householders and businesses affected by the 2008 flood, and to institutional personnel servicing the region (n = 91). Data were analysed using appropriate quantitative and qualitative techniques. Charleville was found to be staunchly resilient, with high levels of organisation and cooperation, and well-developed and functioning social and institutional networks. The community is committed to remaining in the town despite the prospect of continued future flooding. Its main vulnerabilities included low levels of insurance cover (32% residents, 43% businesses had cover) and limited monitoring data to warn of impending flooding. Detailed flood modelling and additional river height gauging stations are needed to enable more targeted evacuations. Further mitigation works (e.g., investigate desilting Bradley’s Gully and carry out an engineering assessment) and more affordable insurance products are needed. Regular information on how residents can prepare for floods and the roles different organisations play are suggested. A key finding was that residents believe they have a personal responsibility for preparation and personal mitigation activities, and these activities contribute substantially to Charleville’s ability to respond to and cope with flood events. More research into the psychological impacts of floods is recommended. Charleville is a valuable representation of climate change adaptation and how communities facing natural disasters should organise and operate.
Enabling communication between Wireless Sensor Networks and The Internet-of-Things : A CoAP communication stack

The thesis focuses on enabling the communication between Wireless Sensor Networks and Internet-of-Things applications.  In order to achieve this goal, the first step has been to investigate the concept of the Internet-of-Things and then to understand how this scenario could be used to interconnect multiple Wireless Sensor Networks in order to develop context-aware applications which could handle sensor data coming from this type of network.  The second step was to design and implement a communication stack which enabled Wireless Sensor Networks to communicate with an Internet-of-Things platform. The CoAP protocol has been used as application protocol for the communication with the Wireless Sensor Networks. The solution has been developed in Java programming language and extended the sensor and actuator layer of the Sensible Things platform.  The third step of this thesis has been to investigate in which real world applications the developed solution could have been used. Next a Proof of Concept application has been implemented in order to simulate a simple fire detection system, where multiple Wireless Sensor Networks collaborate to send their temperature data to a control center. The last step was to evaluate the whole system, specifically the responsiveness and the overhead introduced by the developed communication stack.
A pointwise ergodic theorem for Fuchsian groups

We use Series' Markovian coding for words in Fuchsian groups and the Bowen-Series coding of limit sets to prove an ergodic theorem for Cesaro averages of spherical averages in a Fuchsian group.
Achieving sustainable development in rural areas in Colombia: Future scenarios for biodiversity conservation under land use change

Agricultural expansion is a complex land use change phenomenon with deep environmental and socio-economic consequences, especially across tropical countries where most of this expansion is occurring. Here we use scenario and network analysis combined with sustainability assessment to understand the drivers of landscape change and their effects on sustainable development in Colombia’s rural areas, using the Central Magdalena region as a case study, and ultimately informing strategies to reconcile agricultural expansion with biodiversity conservation and rural development. Using this approach we investigated three environmental and agricultural policy scenarios: the Business as Usual scenario, enforcing a stronger regulatory framework, and adopting incentives. Our analysis show that the Business as Usual scenario is not supported by stakeholders and negatively affects most sustainability objectives with the predominant agricultural sectors in the region (cattle ranching and oil palm) not improving social inequality, and threatening biodiversity, natural resources, and food security. Both alternative scenarios improve overall sustainability, including biodiversity. Therefore to reconcile agricultural expansion, biodiversity and sustainable development, it is important to adopt a stronger regulatory and enforcement framework at different administrative levels, as well as incentive schemes focusing on small holders. Our study also shows that history cannot be ignored when thinking about the future and sustainability especially in areas with legacies of strong inequalities caused by armed conflict. Finally, we suggest that combining scenario analysis, network analysis, and sustainability assessment is a useful methodology for studying land use changes holistically, exploring complex systems at different scales, and informing locally-relevant strategies and recommendations, ultimately enabling science to be proactive.
The Impact of Batch Size on Deep Learning-Based Galaxy Morphology Classification Using ResNet50

Galaxy morphology classification is a fundamental task in astronomy, which helps to gain a deeper understanding of the formation and evolution of galaxies. This paper adopts a convolutional neural network as the backbone model to achieve the basic requirements of galaxy classification. In this study, a frozen convolutional base ResNet50 is used as the backbone network to systematically explore the impact of learning rate and batch size on the classification accuracy of 10 types of galaxy morphologies. The results show that when the batch sizes are 16, 32, 64, and 128, the overall accuracies are 74.75%, 74.93%, 75.01%, and 74.90% respectively. A medium batch size of 64 achieves the best balance between optimization stability and generalization ability. This study provides a reference direction for the impact of batch size on the model and offers new ideas for better solving the problem of galaxy classification. Future research may extend this work by incorporating transfer learning or self-supervised methods to further enhance model robustness and accuracy across larger astronomical datasets.
Hybrid Quantum--Classical Machine Learning Potential with Variational Quantum Circuits

26+6 pages, 6+4 figures
AutoML: A systematic review on automated machine learning with neural architecture search

AutoML (Automated Machine Learning) is an emerging field that aims to automate the process of building machine learning models. AutoML emerged to increase productivity and efficiency by automating as much as possible the inefficient work that occurs while repeating this process whenever machine learning is applied. In particular, research has been conducted for a long time on technologies that can effectively develop high-quality models by minimizing the intervention of model developers in the process from data preprocessing to algorithm selection and tuning. In this semantic review research, we summarize the data processing requirements for AutoML approaches and provide a detailed explanation. We place greater emphasis on neural architecture search (NAS) as it currently represents a highly popular sub-topic within the field of AutoML. NAS methods use machine learning algorithms to search through a large space of possible architectures and find the one that performs best on a given task. We provide a summary of the performance achieved by representative NAS algorithms on the CIFAR-10, CIFAR-100, ImageNet and well-known benchmark datasets. Additionally, we delve into several noteworthy research directions in NAS methods including one/two-stage NAS, one-shot NAS and joint hyperparameter with architecture optimization. We discussed how the search space size and complexity in NAS can vary depending on the specific problem being addressed. To conclude, we examine several open problems (SOTA problems) within current AutoML methods that assure further investigation in future research.
Human Metabolic Network: Reconstruction, Simulation, and Applications in Systems Biology

Metabolism is crucial to cell growth and proliferation. Deficiency or alterations in metabolic functions are known to be involved in many human diseases. Therefore, understanding the human metabolic system is important for the study and treatment of complex diseases. Current reconstructions of the global human metabolic network provide a computational platform to integrate genome-scale information on metabolism. The platform enables a systematic study of the regulation and is applicable to a wide variety of cases, wherein one could rely on in silico perturbations to predict novel targets, interpret systemic effects, and identify alterations in the metabolic states to better understand the genotype-phenotype relationships. In this review, we describe the reconstruction of the human metabolic network, introduce the constraint based modeling approach to analyze metabolic networks, and discuss systems biology applications to study human physiology and pathology. We highlight the challenges and opportunities in network reconstruction and systems modeling of the human metabolic system.
Service architecting and dynamic composition in pervasive smart ecosystems for the Internet of things based on sensor network technology

Why pervasive awareness and Ambient Intelligence are perceived by a great part of the academia and industry as a massive revolution in the short-term? In our best knowledge, a cornerstone of this thought is based on the fact that the ultimate nature of the smart environment paradigm is not in the technology itself, but on a people-centered approach. Perhaps, is in this apparently simple conception where precisely lies the boldness of this promising vision, which has been consolidated in recent years with the emerging proliferation of mobile, personal, portable, wearable and sensory computing: to reach everyone and everywhere. On the one hand, it touches our daily lives in a close manner, minimizing the required attention from the users, anticipating to their needs with the main intention of redefining our idea of Quality of Experience. On the other hand, this new wave impacts everywhere at both global and personal scales allowing expanded connectivity between devices and smart objects, in a dynamic and ubiquitous manner, as a natural extension of the physical world around us. According to the above, this doctoral dissertation focuses on contributing to the integration of software and networking engineering advances in the field of pervasive smart spaces and environment using sensor networks. This is founded on the convergence of some information technology and computer science paradigms, such as service and agent orientation, semantic technologies and knowledge management in the framework of pervasive computing and the Internet of Things. To this end, the nSOM (nano Service-Oriented Middleware) and nSOL (nano Semantics-Oriented Language) approaches are presented. Firstly, the nSOM proposal defines a service-oriented platform for the implementation, deployment and exposure of agent-based in-network services to the Internet cloud on heterogeneous sensor devices. Secondly, the nSOL solution enables an abstraction for supporting ubiquitous service composition based on semantic knowledge management. The integration of both contributions leads to the formal modelling and practical development of adaptive virtual sensor services for pervasive Ambient Intelligence ecosystems. This work includes also the related performance characterization of the resulting prototype according to several metrics such as code size, volatile memory footprint, CPU overhead, service time delay and battery lifetime. Main foundations and outcomes presented in this essay are contextualized in the following European Research Projects: μSWN (FP6 code: IST-034642), DiYSE (ITEA2 code: 08005) and LifeWear (ITEA2 code: 09026). --------------------
The Relationship Between Knowledge Risk Management and Sustainable Organizational Performance: The Mediating and Moderating Role of Leadership Behavior

Abstract                Despite the vital role of organizations’ knowledge management and its diverse influence on achieving sustainable organizational performance (SOP), as well as the impact of leadership behavior (LB), it is remarkable that no previous study has addressed this subject matter comprehensively. As a result, this study aims to investigate the relationship between SOP and knowledge risk management (KRM) while examining the role of LB. It adopts a quantitative approach and gathers data from Nigerian companies through an online questionnaire distributed between November 2019 and September 2020. Structural equation modeling (SEM) is utilized to test the hypotheses. Remarkably, no prior research has inspected the mediating and moderating role of LB in the connection between SOP and KRM until this study. Empirical results indicate that: (i) LB and KRM positively influence SOP; (ii) KRM has a positive impact on LB; (iii) LB moderates the relationship between KRM and SOP; and (iv) LB serves as a mediator between KRM and SOP. The findings of this empirical research will enhance managers’ understanding of the significance of LB in the relationship between KRM and SOP.
Toward Millimeter Wave Joint Radar-Communications: A Signal Processing Perspective

Synergistic design of communications and radar systems with common spectral and hardware resources is heralding a new era of efficiently utilizing a limited radio-frequency spectrum. Such a joint radar-communications (JRC) model has advantages of low-cost, compact size, less power consumption, spectrum sharing, improved performance, and safety due to enhanced information sharing. Today, millimeter-wave (mm-wave) communications have emerged as the preferred technology for short distance wireless links because they provide transmission bandwidth that is several gigahertz wide. This band is also promising for short-range radar applications, which benefit from the high-range resolution arising from large transmit signal bandwidths. Signal processing techniques are critical in implementation of mmWave JRC systems. Major challenges are joint waveform design and performance criteria that would optimally trade-off between communications and radar functionalities. Novel multiple-input-multiple-output (MIMO) signal processing techniques are required because mmWave JRC systems employ large antenna arrays. There are opportunities to exploit recent advances in cognition, compressed sensing, and machine learning to reduce required resources and dynamically allocate them with low overheads. This article provides a signal processing perspective of mmWave JRC systems with an emphasis on waveform design.
Topological Phase Transitions in Condensed Matter — Experimental Predictions

⚠️ SUPERSEDED: This paper has been superseded by Paper XXVIII: Condensed Matter Analogues of Hopf Soliton Physics (DOI: 10.5281/zenodo.19349018) and Paper XCI: From Electrons to Ferrofluids — Condensed Matter Realizations of Hopf Soliton Physics (DOI: 10.5281/zenodo.19555616), which together provide more complete and updated treatments. This paper is retained for historical reference. Please cite the successor papers instead.              Abstract      The topological soliton framework developed in this series identifies particles as Hopf-fibred field configurations with topological invariants $\pi_3(S^2) = \mathbb{Z}$. While the fundamental solitons operate at the Compton scale ($\bar{\lambda}_C \sim 10^{-13}$ m), the same mathematical structures — Hopf fibrations, linking numbers, topological charges — appear in accessible condensed matter systems. We present detailed experimental protocols for observing and characterizing Hopf solitons in three platforms: cholesteric liquid crystals (CLCs), spinor Bose-Einstein condensates (BECs), and frustrated magnetic materials (spin ice). For each platform, we specify sample preparation, creation protocols, imaging techniques, and quantitative predictions including soliton size ($1$--$50\,\mu$m in CLCs, $5$--$20\,\mu$m in BECs, $1$--$10$ nm in spin ice), energy barriers for topological transitions ($10$--$100\,k_BT$ in CLCs), and expected lifetimes ($10^2$--$10^6$ s in CLCs at room temperature). We identify cholesteric liquid crystals as the most promising near-term platform, building on the pioneering observations of Ackerman and Smalyukh [1]. The key experimental signature distinguishing Hopf solitons from other topological textures is the linking number $H = 1$ of preimage curves, measurable via three-dimensional polarization microscopy reconstruction.        Keywords           physics       topology       solitons       condensed matter       experiments       liquid crystals       BEC       spin ice                      Type        Preprint                   License        CC BY 4.0                   Date        2026-03-28                   Subject        Theoretical Physics                   DOI        10.5281/zenodo.19295021                   © 2026 Alexander Novickis. Licensed under     Creative Commons Attribution 4.0 International.
A crop simulation model to predict fruit yield and quality on mango tree: overview, progresses and perspectives

Our aim is to develop a crop model for mango that synthesizes the knowledge acquired on the processes involved in fruit yield and quality development, and biotic and abiotic factors that affect them. To our knowledge, a crop model predicting fruit yield and quality development for a perennial tropical fruit crop is unique.The crop model combines complementary phenological, architectural and eco-physiological viewpoints and relies on two sub-models developed for the cultivar Cogshall in Réunion Island.The first sub-model accounts for carbon and water processes occurring at the branch level during the fruit growing season. It considers weather and source–sink factors and predicts fruit growth and quality development. It is currently completed with fruit maturation processes. It is also being extended to the tree scale and growing cycle by accounting for carbohydrates exchanges at these scales and through its linkage to the second sub-model. This latter accounts for architectural development and phenology. It is based on endogenous factors and temperature-controlled laws. It predicts the number and budburst date of vegetative growth units and inflorescences at the tree scale over successive growing cycles, and represents their development and growth on dynamic 3D models. The crop model will then be linked to a pests model, in particular fruit flies, through phenological stages and the level of fruit maturity.From an applied point of view, the global model will allow simulation-based design of management solutions able to improve mango performances by handling the processes involved in fruit yield and quality development.
The Risk Management Practices of Health Research Ethics Committees May Undermine Citizen Science to Address Basic Human Rights

Abstract                Lack of supportive workplaces may be depriving babies and mothers of the health advantages of breastfeeding. This citizen science pilot project set out to engage women in photographing and sharing information on the available facilities for breastfeeding and expressing and storing breastmilk in Australian workplaces. While some useful insights were gained, the project failed in the sense that 234 people ‘liked’ the project Facebook page set up to recruit participants, but only nine photographs were submitted. The heaviest loss of participation occurred after the ethics committee requirements were explained, in particular, the requirement for employer consent to take a photograph, or alternatively, for a participant to agree to bear the risk of taking a photo without permission. The ethics committee also made stipulations about the formality of language to be used. Thus, the project’s potential role as an enabler for health action was neutered. We argue that the ethics requirements worked contrary to our purpose and possibly acted to reinforce the powerlessness some women experience at work.