ethicalabs/Research-Intent-Collab huggingface dataset:
Design for the additive manufacture of a control lever with multimaterial 3D printing

In this master's project, the design for the additive manufacturing of a joystick has been created to later be able to materialize it through multi-material 3D printing so as to obtain a sustainable and simple model to print. The printing technique that has been used is called Fused Deposition Modelling (FDM). The model contains integrated sensors to allow linear and circular displacement. Two different materials have been combined, one conductive for the manufacture of the sensors and another structural for the manufacture of the rest of components. In the sensory part, resistive sensors have been designed to identify linear motion so that when the joystick is moved in one direction, the sensors perceive a traction or compression tension to give an electronic response. For circular motion, capacitive sensors have been designed so that one electrode is integrated into the lever and the other electrode into the joystick body. Theoretically, when a torque is applied to the lever, these electrodes form a capacitor that varies according the applied torque. In the initial phase of the project, bibliographical research has been carried out and the necessary printing parameters have been identified. After this, the design phase has been carried out, where different models have been created analysing all the components and their functionalities in order to obtain an effective and simple model. Some initial tests have been done with PLA to analyse the finish and quality before proceeding to the final printing. Three different geometry and component designs have been created. Design 3 has been the definitive one to be printed. The materials to be used for the printing were structural TPU and conductive TPU. Once printed, the sensory part has been configured. It has focused on circular motion using capacitive sensors since resistive sensors were developed in previous projects, so the linear motion was not included in the project scope. Four possible methods have been identified for measuring capacitance. Three methods based on the RC circuit and the last one based on the LC circuit. The first RC method identified distinct values but only in high value ranges. The second method using a 555 timer has failed due to unidentified internal electronic errors. However, the third RC method, which uses a 741 comparator, works correctly from 100 picofarads measures an above. The last method, based on LC circuit, has also been effective with similar range to the 741 method. It has been tried to test the joystick and no measurements could be detected. For this reason, it has returned to the design phase to rectify the last design and reduce the probability of failing in the measurement, although due to lack of time it was not possible to print the last design or analyse the results. Finally, the economic analysis and the environmental impact produced by the manufacture of the model have been carried out
Models in Science Communication: Formatting Public Engagement and Expertise

Three models of expert-public interaction in science and technology communication are central: the dissemination model (often called the deficit model), the dialogue model, and the participation model. These three models constitute a multi-model framework for studying science and technology communication and are often described along an evolutionary continuum, from dissemination to dialogue, and finally to participation. Underlying this description is an evaluation claiming that the two latter are “better” than the first. However, these three models can coexist as policy instruments, and do not exclude each other. Since 1975, concerns with public engagement over time have led to a mode that is more dialogical across the three models within science and technology communication policy in Norway. Through an active policy, sponsored hybrid forums that encourage participation have gradually been developed. In addition, social media increasingly allows for spontaneous public involvement in an increasing number of hybrid forums. Dialogue and participation thus have become crucial parts of science and technology communication and format public engagement and expertise.
A computational study on the reaction mechanisms of N-formylation of amines under a Lewis acid catalysis

The mechanisms of the title reaction have been studied by density functional theory (DFT) and the second order Moller–Pleset (MP2) method. Two possible reaction channels including the non-catalyzed channel (channel 1) and the other catalyzed channel (promoted by Lewis acid ZnCl2, channel 2) are shown. The calculated results indicate that the catalyzed channel is more energy favorable than the non-catalyzed channel, and the energy barrier of channel 2 (31.62 kcal/mol at the B3LYP/6-31G(d,p) level and 30.32 kcal/mol at the MP2/6-311++G(2d,2p) level) is not a high energy barrier for the experimental condition (343 K), thus, we think the Lewis acid ZnCl2 would play an important role in making the title reaction easier to occur, which is in good agreement with the experimental results.
Ensemble density functional theory of the fractional quantum Hall effect

We develop an ensemble density functional theory for the fractional quantum Hall effect using a local density approximation. Model calculations for edge reconstructions of a spin-polarized quantum dot give results in good agreement with semiclassical and Hartree-Fock calculations, and with small system numerical diagonalizations. This establishes the usefulness of density functional theory to study the fractional quantum Hall effect, which opens up the possibility of studying inhomegeneous systems with many more electrons than has heretofore been possible.
From manual search to artificial intelligence: A methodological framework for developing patient journey models from electronic health records using natural language processing

Abstract                 Improving care pathways for chronic conditions requires a holistic, data-driven understanding of the patient journey. This study introduces a novel framework that reconstructs and simulates patient journeys using structured medication data and probabilistic modeling. Chronic spontaneous urticaria (CSU) was selected as a use case, with symptom onset estimated via natural language processing (NLP) and treatment cycles identified through Anatomical Therapeutic Chemical (ATC) code sequences—an approach not previously applied in this context. A Markov model was built to simulate transitions across health states using de-identified EHR data from 74,664 CSU patients. Results revealed significant delays in diagnosis and variability in treatment initiation, with 29.7% of patients receiving no therapy. The model enables simulation of targeted interventions to streamline care, reduce delays, and optimize resource use. While intentionally simple, the framework is scalable across chronic conditions and supports value-based care through data-driven decision-making.
Strategies for Leveraging Interoperable Health Information Exchange Systems Among Healthcare Communities

The panel will share international Health Information Exchange (HIE) projects to improve quality and lower costs in healthcare communities (i.e. hospitals, clinician practices, and aged care facilities). HIE allows healthcare professionals and patients to appropriately access and securely share a patient's vital medical information electronically within and across organizations. Intended audience: Researchers, consumers, practitioners, vendors, care providers, and policy makers with interests in technology design, development, implementation, and management, particularly focused on HIE.
Prompting Neural-Guided Equation Discovery Based on Residuals

Neural-guided equation discovery systems use a data set as prompt and predict an equation that describes the data set without extensive search. However, if the equation does not meet the user's expectations, there are few options for getting other equation suggestions without intensive work with the system. To fill this gap, we propose Residuals for Equation Discovery (RED), a post-processing method that improves a given equation in a targeted manner, based on its residuals. By parsing the initial equation to a syntax tree, we can use node-based calculation rules to compute the residual for each subequation of the initial equation. It is then possible to use this residual as new target variable in the original data set and generate a new prompt. If, with the new prompt, the equation discovery system suggests a subequation better than the old subequation on a validation set, we replace the latter by the former. RED is usable with any equation discovery system, is fast to calculate, and is easy to extend for new mathematical operations. In experiments on 53 equations from the Feynman benchmark, we show that it not only helps to improve all tested neural-guided systems, but also all tested classical genetic programming systems.
An online platform for interactive feedback in biomedical machine learning

Machine learning models have great potential in biomedical applications. A new platform called GradioHub offers an interactive and intuitive way for clinicians and biomedical researchers to try out models and test their reliability on real-world, out-of-training data.
Dynamical aspects of Galactic habitability in N-body simulations

AbstractRecent studies of Galactic evolution revealed that the dynamics of the stellar component might be one of the key factors when considering galactic habitability. We run an N-body simulation model of the Milky Way, which we evolve for 10 Gyr, to study the secular evolution of stellar orbits and the resulting galactic habitability related properties, i.e., the density of the stellar component and close stellar encounters. The results indicate that radial migrations are not negligible, even in a simple axisymmetric model with mild levels of dynamical heating, and that the net outward diffusion of the stellar component can populate galactic outskirts with habitable systems. Habitable environment is also likely even at sub-Solar galactocentric radii, because the rate of close encounters should not significantly degrade habitability. Stars that evolve from non-circular to stable nearly circular orbits typically migrate outwards, settling down in a broad Solar neighbourhood. The region between$R \approx 3$kpc and$R \approx 12$kpc represents the zone of radial mixing, which can blur the boundaries of the Galactic Habitable Zone (GHZ), as it has been conventionally understood. The present-day stable population of the stars in the Solar neighbourhood originates from this radial mixing zone, with most of the stars coming from the inner regions. The Solar system can be considered as a typical Milky Way habitable system because it migrated outwards from the metal-rich inner regions of the Disk and has a circular orbit in the present epoch. We conclude that the boundaries of the GHZ cannot be sharply confined for a given epoch because of the mixing caused by the stellar migrations and secular evolution of stellar orbits.
A Holistic Natural Language Generation Framework for the Semantic Web

With the ever-growing generation of data for the Semantic Web comes an increasing demand for this data to be made available to non-semantic Web experts. One way of achieving this goal is to translate the languages of the Semantic Web into natural language. We present LD2NL, a framework for verbalizing the three key languages of the Semantic Web, i.e., RDF, OWL, and SPARQL. Our framework is based on a bottom-up approach to verbalization. We evaluated LD2NL in an open survey with 86 persons. Our results suggest that our framework can generate verbalizations that are close to natural languages and that can be easily understood by non-experts. Therewith, it enables non-domain experts to interpret Semantic Web data with more than 91\% of the accuracy of domain experts.
Using ultra-high energy cosmic rays and air showers to test Lorentz invariance within modified Maxwell theory

Cosmic rays and air showers at ultra-high energy are unique tools to test the validity of Lorentz invariance. A brief overview is given on such tests focusing on isotropic, non-birefringent Lorentz violation (LV) in the photon sector. Based on the apparent absence of vacuum Cherenkov radiation and photon decay, the LV parameter $κ$ is bound to $-0.6 \cdot 10^{-20} &lt; κ&lt; 6 \cdot 10^{-20}$ (98\% CL). We report an updated limit from cosmic-ray photon observations and preliminary results on testing vacuum Cherenkov radiation in air showers.
Empowering Agency: Western Sydney Migrant and Refugee Youths' Perspectives on Sexual and Reproductive Health and Rights

The population of migrant and refugee youth in Greater Western Sydney is increasing exponentially each year. Little is understood about these young people's understanding of and ability to exert their sexual and reproductive health and rights. By centering their voices, we can better understand the social ecology of the barriers they encounter and the factors that facilitate informed sexual and reproductive health decision-making. This will result in a youth-determined model for policy and programming aimed at improving migrant and refugee sexual and reproductive health literacy, wellbeing and agency.
Contribution of lean and Six Sigma to effective cost of quality management

In today’s competitive market, most of the organisations promote quality as a critical success factor to achieve sustainable competitive advantages when the high quality of delivered product and service is considered extensively by the customers as a vital factor besides a reasonable price. Hence, most organisations would invest a lot of money to achieve the desired quality, at the same time, set a competitive price. Although, various quality improvement models, methods and tools can be applied to determine the most effective areas and the level of investment needed to measure, control and manage the cost of quality (COQ). This paper tries to present and summarise COQ models to identify COQ elements by emphasising the optimal quality cost model to see how lean and six sigma approaches can contribute to achieve further improvement in terms of reducing waste, time, defects and variation while targeting and obtaining the minimum total quality costs.
Environmental NGOs and the political ecology of biodiversity conservation in Mexican forests

Mexico is a multi-cultural megadiverse country, which due to its geographical and climatic characteristics also represents a strategic potential refuge for endangered species. It is considered a biodiversity hotspot in which there exist complex socio-political factors that impact forests and are contributing to their decline. Efforts to halt biodiversity conflict with the economic interests of the national government and private companies, while also framed within the collective ownership of Mexican forests in the form of ejidos. This thesis aims to reveal the intricate power dynamics of multi-actor governance within Mexican forests through the perspectives of environmental NGOs. The political ecology approach intends to provide insights concerning the socio-political and environmental conflicts that contribute to the lack of progress in tackling biodiversity loss trends. The analysis of the diverse roles that Mexican ENGOs play simultaneously, reveals their understanding of the social, political, and economic drivers of biodiversity loss and their orientation towards field-based action and policy reform albeit constrained by political change. In this sense, ENGOs are practitioners of political ecology theory, and their experience navigating the socio-political structures of Mexico offers an asset to create multi-stakeholder partnerships.
Do androids dream of electric sorghum?: Predicting Phenotypes from Multi-Scale Genomic and Environmental Data using Neural Networks and Knowledge Graphs

The interplay between an organism's genes, its environment, and the expressed phenotype is dynamic. These interactions within ecosystems are shaped by non-linear multi-scale effects that are difficult to disentangle into discrete components. In the face of anthropogenic climate chance, it is critical to understand environmental and genotypic influences on plant phenotypes and phenophase transitions. However, it is difficult to integrate and interoperate between these datasets. Advances in the fields of ontologies, unsupervised learning, and genomics may overcome the disparate data schema. Here we present a framework to better link phenotypes, environments, and genotypes of plant species across ecosystem scales. This approach utilizing phenotypic data, knowledge graphing, and deep learning, serves as the groundwork for a new scientific sub-discipline: “Computational Ecogenomics”
The Design of Circuit-Measuring Collaborative Learning System with Embedded Broker

Recently, the academic community has been giving much attention to Cooperative Learning System, a group learning method combined with pedagogy and social psychology. It allows group members to gain knowledge through collaborations and interactions. Nowadays, most Internet cooperative learning systems are designed to provide students mainly with a convenient online environment to study theoretical courses but rarely with an online environment to operate practical instruments. Hence, this paper designed a 3D online cooperative learning system for operating virtual instruments with circuit-measuring function. By integrating with Virtual Reality, Remote Control Parameter Transmission and embedded system techniques, this system gives learners not only a cooperative learning environment via networking to jointly operate the 3D virtual instruments (for example, multi-meters, power supplies and oscilloscopes) but also the functions of instant messages and 3D puzzles to interact with one another. Therefore, learners can effectively improve learning interests and results.
18 Utilizing lean six sigma and quality improvement (QI) to increase diabetes self-management education referrals within the endocrinology clinic

Background  Diabetes self-management education (DSME) is the ongoing process of facilitating the knowledge, skill, and ability necessary for diabetes self-care. Despite evidence that DSME improves clinical outcomes, health status, and quality of life for persons with diabetes referrals to DSME continue to be low in our population.  Objectives  To increase referrals to Diabetes Self-Management Education (DSME) program from 26% at baseline to at least 40% in 12 months in our Endocrinology Clinic.  Methods  We utilized Six Sigma DMAIC (Define-Measure-Analyze-Improve-Control) methodology to problem solve and perform process improvement. We created a multi-functional team including clinic staff, providers and quality improvement personnel to assist in the design and development of the new processes. In define phase we generated a project charter and communication plan. During the measure phase we determined the baseline referral volume, completed a current state process map and completed measurement system analysis. During the analyze phase we brainstormed reasons for low referrals (root cause analysis) and analyzed referral volume for trends. During improve phase we finalized future state workflow, implemented pre-visit planning, standing medical orders for referrals and provided training to clinic staff and providers. We continued bi-weekly chart audits for appropriate referrals being placed for 12 months.  Results  As of August 2020, we were able to meet our goal at 42% with 1,813 referrals placed for DSME. Of these, 1,086 patients completed their education sessions (figure 1).  Conclusions  Quality improvement tools and methodologies can be successfully utilized to improve care for diabetes in clinical settings. We were able to create a process that has maintained success, even during the uncertain times of COVID-19. The processes created and put in place were sustainable and have allowed the staff to continue placing referrals during tele-health visits and patients completing DSME virtually.
Near-Field Signal Processing: Unleashing the Power of Proximity

After nearly a century of specialized applications in optics, remote sensing, and acoustics, the near-field (NF) electromagnetic propagation zone is experiencing a resurgence in research interest. This renewed attention is fueled by the emergence of promising applications in various fields such as wireless communications, holography, medical imaging, and quantum-inspired systems. Signal processing within NF sensing and wireless communications environments entails addressing issues related to extended scatterers, range-dependent beampatterns, spherical wavefronts, mutual coupling effects, and the presence of both reactive and radiative fields. Recent investigations have focused on these aspects in the context of extremely large arrays and wide bandwidths, giving rise to novel challenges in channel estimation, beamforming, beam training, sensing, and localization. While NF optics has a longstanding history, advancements in NF phase retrieval techniques and their applications have lately garnered significant research attention. Similarly, utilizing NF localization with acoustic arrays represents a contemporary extension of established principles in NF acoustic array signal processing. This article aims to provide an overview of state-of-the-art signal processing techniques within the NF domain, offering a comprehensive perspective on recent advances in diverse applications.
An intelligent framework for simulating robot-assisted surgical operations

In this paper, a system based on several intelligent techniques, including speech recognition, natural language processing and linear planning is described. These techniques have been employed to generate a sequence of operations understandable by the control system of a robot that is to perform a semi-automatic surgical task. Thus, a system has been implemented that translates some surgeon's 'natural' language into robot-executable commands. A robotic simulator has then been implemented in order to test the planned sequence in a virtual environment.
Occupational Health and Safety at Workplace: A Reflective Review of Nepal’s TVET Sector

Ensuring occupational health and safety (OHS) is essential for maintaining a secure and healthy work environment. It encompasses multiple aspects, such as occupational health and safety-related training and awareness among the formal and informal workers, integration of hands-on practical practices in Technical and Vocational Education and Training (TVET), and proper use of occupation-wise Personal Protective Equipment (PPE) at workplace. Although it is one of the major components of decent work, Nepal still has limited access to occupational health and safety along with the use of PPE at workplace. This review paper was prepared through relevant literature review and searches on Google Scholar to access a diverse range of articles, research papers and reports. The findings reveal significant gaps in OHS awareness, training and practical implementation, particularly in the TVET sector. Despite increasing emphasis on employee skills development, workplace safety remains inadequately addressed. Insufficient infrastructures, lack of occupation-specific PPE, limited OHS hands-on practical processes in training curricula and the absence of structured monitoring systems contribute to unsafe working conditions and unprepared workers in Nepal. The study emphasizes the need to integrate practical OHS practices into TVET curricula, engage certified OHS experts, improve the quality and accessibility of PPE, and raise awareness among workers, employers and government entities. These measures, combined with an effective enforcement of safety regulations, are essential for fostering a safe work environment, enhancing workers' well-being and promoting sustainable economic growth.