ethicalabs/Research-Intent-Collab huggingface dataset:
Cost-effectiveness of emicizumab prophylaxis for haemophilia A with inhibitors: an adaptive health technology assessment for the Indian setting

Objective                     To assess the cost-effectiveness of emicizumab prophylaxis for patients having haemophilia A with inhibitors in the Indian context using an adaptive health technology assessment (aHTA) methodology.                                                           Design                     Economic evaluation using multiple approaches aimed at adjusting previously generated cost-effectiveness results based on (1) price differences only (‘simple’) and (2) differences in cost and expected treatment duration (‘moderate’) and differences in cost, inflation and life expectancy (‘complex’).                                                           Setting                     Typical haemophilia care in India.                                                           Participants                     Patients with haemophilia A and inhibitors.                                                           Intervention                     Emicizumab prophylaxis using two vial strengths (30 or 150 mg/mL) in comparison to no prophylaxis.                                                           Main outcome measures                     Adjusted incremental cost-effectiveness ratio (ICERa), incremental costs and incremental quality-adjusted life years associated with emicizumab prophylaxis from both the health system and societal perspectives.                                                           Results                     Using the simple ICER adjustment method, emicizumab prophylaxis resulted in potential cost savings from the payers’ perspective for both vial strengths in patients aged ≥12 and &lt;12 years. However, from a societal perspective, emicizumab prophylaxis was not cost-effective. Using the moderate adjustment method, emicizumab prophylaxis showed potential cost saving from the health system perspective. The complex adjustment method also revealed cost savings for emicizumab prophylaxis from the health system and societal perspectives across different age groups.                                                           Conclusion                     We found that implementing emicizumab prophylaxis for patients with haemophilia A and inhibitors in India has the potential to result in cost savings. This study highlights the feasibility of using the expanded aHTA methodology for rapid evidence generation in the Indian context. However, it is crucial to address certain research gaps, including data limitations, challenges in translating international evidence to Indian context and associated uncertainties. Additionally, conducting a comprehensive budget impact analysis is necessary. These findings hold significant implications for decision-making regarding the potential provision of emicizumab prophylaxis through federal or/and state government-funded programmes and institutions in India.
Artificial Neural Network for Weather Forecasting: A Review

Forecasting is considered one of the most imperative and perplex proceedings in the present-day. Weather forecasting is a natural process which entails the prediction of the change in the atmospheric conditions with the passage of time. It has become a core area of studies and analysis for the scientist owing to the brusque forecasts and approaches of weather. Weather forecasting has always been a challenging task. In several cases, scientists and researchers had attempted in establishing a linear association between the input and targeted data of the weather. Since, weather is non-linear and dynamic in nature, the target has shifted to prediction of non-linear weather data. Though weather prediction is relatively a statistical measure and is automated, with the traditional tools, its result is rather uncertain and not always accurate. Due to its non-linearity and complex process, the best approach for resolving such problems is with the use of Artificial Neural Network (ANN). ANN simplify the weather predictions with its better efficiency, reliability and accuracy. The features of ANN are not just to analyse the past data but also to acquire future predictions rendering to be much ideal for weather forecasting. NN is rather a complicated network which are pliable and flexible in nature. It is autodidactic with its existing training data consequently forging a new smart pattern useful for predicting the weather. Survey of various techniques of NN for weather prediction is provided and it is observed that by simply increasing the number of hidden layers the trained NN can predict and classify the weather variables with minimal error. In this paper, predictive analysis algorithm is incorporated with back propagation network(BPN) to predict future weather by training the network. The technical milestone, where various researchers have acquired on this discipline has been reviewed and presented in these surveys.
Performance Evaluation of AI Based Load Balancing Algorithm (Reinforcement Learning) with other load balancing algorithms in a JPPF Grid: E.coli Genome Sequence Alignment Problem

Recent development of computational analysis in the field of genomics and proteomics has necessitated the use of high performance computing architectures such as grid, cluster and parallel computing. In this paper an attempt has been made to study the performance enhancement by optimization of various Load Balancing Algorithms (LBAs) and E.coli genome sequence alignment (using PAM 120 substitution matrix) was performed on the grid. We have achieved a performance gain of 11.72 times with just 8 processing nodes in comparison to serial execution time using artificial intelligence based LBA parameter optimization.
Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models

This paper deals with the problem of efficient sampling from a stochastic differential equation, given the drift function and the diffusion matrix. The proposed approach leverages a recent model for probabilities \cite{rudi2021psd} (the positive semi-definite -- PSD model) from which it is possible to obtain independent and identically distributed (i.i.d.) samples at precision $\varepsilon$ with a cost that is $m^2 d \log(1/\varepsilon)$ where $m$ is the dimension of the model, $d$ the dimension of the space. The proposed approach consists in: first, computing the PSD model that satisfies the Fokker-Planck equation (or its fractional variant) associated with the SDE, up to error $\varepsilon$, and then sampling from the resulting PSD model. Assuming some regularity of the Fokker-Planck solution (i.e. $β$-times differentiability plus some geometric condition on its zeros) We obtain an algorithm that: (a) in the preparatory phase obtains a PSD model with L2 distance $\varepsilon$ from the solution of the equation, with a model of dimension $m = \varepsilon^{-(d+1)/(β-2s)} (\log(1/\varepsilon))^{d+1}$ where $1/2\leq s\leq1$ is the fractional power to the Laplacian, and total computational complexity of $O(m^{3.5} \log(1/\varepsilon))$ and then (b) for Fokker-Planck equation, it is able to produce i.i.d.\ samples with error $\varepsilon$ in Wasserstein-1 distance, with a cost that is $O(d \varepsilon^{-2(d+1)/β-2} \log(1/\varepsilon)^{2d+3})$ per sample. This means that, if the probability associated with the SDE is somewhat regular, i.e. $β\geq 4d+2$, then the algorithm requires $O(\varepsilon^{-0.88} \log(1/\varepsilon)^{4.5d})$ in the preparatory phase, and $O(\varepsilon^{-1/2}\log(1/\varepsilon)^{2d+2})$ for each sample. Our results suggest that as the true solution gets smoother, we can circumvent the curse of dimensionality without requiring any sort of convexity.
Enhancing Upper Limb Rehabilitation in Hospital Occupational Therapy Using a Machine Learning Human-Robot Interaction (HRI) Platform Integrated with Real-time Visual Feedback

Robotics and machine learning algorithms can potentially enhance upper limb rehabilitation, addressing the limitations of traditional therapy methods. This study presents a Human-Robot Interaction (HRI) platform designed to improve the occupational therapy environment in clinical settings. Eight participants engaged in grasping and reaching tasks with the assistance of an upper limb robotic system, guided by visual feedback. The tasks were structured across three levels of difficulty. Machine learning models were employed to classify task difficulty based on muscle activity patterns in the moving arm. Participant performance was evaluated using average trajectory deviation and non-dimensional squared jerk, providing insight into movement accuracy and smoothness, which corresponded with the predefined task difficulty levels. This platform represents a meaningful step toward delivering more personalized and effective rehabilitation for individuals recovering from stroke-related upper limb impairments. Future advancements in rehabilitation technology must prioritize patient-centered design, accessibility, and data-driven innovation to ensure impactful and inclusive care.
An investigation into the use of the Glasgow Coma Scale in one case using root cause analysis

The Glasgow Coma Scale observation chart is well established nationally and internationally as the primary tool that enables nurses to make quick, repeated evaluations of several key indicators of neurological status. However, coroners' inquiries have reported incidents in which the GCS has been inaccurately and ineffectively used in some areas of clinical practice. This article presents a clinical incident study to examine why practitioners may find difficulties in using the GCS. Root cause analysis has been used to investigate the attributes and antecedents of an adverse incident to try and raise awareness of some of the problems and pitfalls of recording Glasgow Coma Scale observations.
EXAMINING THE IMPACTS OF PUBLIC POLICY CHANGES ON IMMIGRANT CHILDREN’S INSURANCE COVERAGE, ACCESS TO CARE, AND HEALTH OUTCOMES

A large body of evidence shows that public policies that expanding public insurance eligibility to children would lead to improvements in health care access and health outcomes during childhood. Growing up in the US, immigrant children face multifaceted barriers related to “not from here”, including economic constrains, acculturation pressure, immigration status of self and family members. It is unclear if immigrant children may respond to a public policy that expand public insurance eligibility to them. Prior research showed that immigrants would adjust their participation in public programs and health care utilization based on their perceived immigration climate. However, less is known about the impact of a national immigration enforcement program on immigrant children’s health care access and health outcomes. In this study, I examined two public policies: Children’s Health Insurance Program Reauthorization Act of 2009, the public policy that expanded public insurance eligibility to immigrant children under the five-year bar; and Secure Communities, a national immigration enforcement program that linked federal immigration enforcement activities to local authorities. I found that immigrant children in states that adopted CHIPRA’s option experienced a 6.35 percentage points decrease in uninsurance, and 8.1 percentage points increase in public insurance coverage, while estimated changes on private coverage were not statistically significant. I did not observe any statistically significant effects of CHIPRA on immigrant children’s access to care and health outcomes. My estimates suggested that activation of SC significantly decreased immigrant children’s public insurance coverage by 8.2 percentage points, while the estimates on other outcomes were not statistically significant.
Accelerated Reactive Force Field Molecular Dynamics Simulation on the Diffusion Process in Ni/YSZ Anode of SOFCs with Collective Variable-Driven Hyperdynamics

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Due to advantages such as high efficiency, low emissions, and fuel flexibility, solid oxide fuel cells (SOFCs) have garnered significant attention as promising power sources for automotive applications. Nickel/yttria-stabilized zirconia (Ni/YSZ) is one of the most widely used anode materials in SOFCs, as it can catalyze both chemical and electrochemical reactions of carbon-containing fuels. However, the direct use of carbon-containing fuels can lead to carbon deposition on the Ni/YSZ anode, negatively impacting the performance and reliability of automotive SOFC systems. The diffusion of carbon atoms within nickel plays a crucial role in the carbon deposition process and requires further investigation. The oxygen atoms that spillover from YSZ also participate in main reactions such as carbon deposition and electrochemical reactions in Ni. Molecular dynamics (MD) is one of the main methods for studying atomic diffusion in crystalline structures. In this study, reactive force field molecular dynamics (ReaxFF-MD) models of Ni and Ni/YSZ crystal structures are established. The operating temperature of automotive SOFCs is typically 500-700°C, which results in a low frequency of diffusion events in the ReaxFF-MD model, leading to high computational costs. Therefore, an acceleration technique known as collective variable-driven hyperdynamics (CVHD) is employed to extend the time scale of the simulations. The diffusion coefficient of carbon atoms in nickel at low temperatures is calculated. Additionally, the spillover process of oxygen atoms in YSZ is considered, and the diffusion coefficients of oxygen atoms in both Ni and YSZ are calculated. The Arrhenius equation parameters for carbon and oxygen diffusion in nickel are determined, laying the foundation for further studies on the mechanisms of carbon deposition and oxidation on Ni/YSZ anode materials.&lt;/div&gt;&lt;/div&gt;
Microgravity Experiments in Sounding Rockets, Spacelab and EURECA

Launch date 13 Dec. 1977; number of experiments 11.      • Aluminum-Lead Alloy. E. Hodes, M. Steeg, Glyco-Wiesbaden      • Reaction Kinetics in Glass Melts. H.J. Barklage-Hilgefort, G.H. Frischat, Technical University of Clausthal      • Skin Technology. H. Sprenger, MAN, Munchen, Schweitzer, MTU, Munchen      • Powder Metallurgy. H.U. Walter, G. Ziegler, DFVLR, Koln      • Primary Crystallization. H. Ahlborn, Technical University of Hamburg      • Hydrogen Electrolysis. C.J. Raub, W. Wopersnow, FI Edelmetalle, Schwa-bisch-Gmund      • Dispersion Electrolysis. J. Ehrhardt, Battelle, Frankfurt      • Interface Convection. R. Bruckner, Technical University of Berlin      • Dendrite Growth. H. Fredriksson, T. Carlberg, RIT, Stockholm      • Alloy with Oxide Inclusions. G. Neuschutz, Krupp FI, Essen      • Acoustic Positioning. E.G. Lierke, Battelle, Frankfurt
Deep Learning Approaches for Sparse Recovery in Compressive Sensing

Compressive sensing enables sparse signals recovery by less measurements than required by the Nyquist rate, so leading to energy and processing saving. Accuracy and complexity improvements can be achieved applying neural network to sparse linear inverse problem. This work focuses on sparse recovery with deep network. Improvements to the Learned Iterative Shrinkage-Thresholding Algorithm (LISTA) and a novel neural network are proposed. Results show that these propositions can decrease up to 10.8 dB the NMSE value and require fewer layers than if only LISTA is used to estimate the signal.
Maximum Lebesgue Extension of Monotone Convex Functions

Given a monotone convex function on the space of essentially bounded random variables with the Lebesgue property (order continuity), we consider its extension preserving the Lebesgue property to as big solid vector space of random variables as possible. We show that there exists a maximum such extension, with explicit construction, where the maximum domain of extension is obtained as a (possibly proper) subspace of a natural Orlicz-type space, characterized by a certain uniform integrability property. As an application, we provide a characterization of the Lebesgue property of monotone convex function on arbitrary solid spaces of random variables in terms of uniform integrability and a nice dual representation of the function.
Desain Dashboard Keuangan dan Informasi di GMIM Wilayah Manado Barat Daya

Di era Teknologi Informasi dan Komunikasi (TIK) saat ini yang semakin pesat, manusia disuguhkan dengan kemudahan-kemudahan dalam beraktifitas. Perkembangan Teknologi begitu pesat saat ini telah meluas pada semua kalangan, mulai dari anak-anak sampai pada orang dewasa dan bahkan lanjut usia. Kehadiran TIK dalam era perkembangan informasi saat ini memberikan dampak bagi masyarakat. Proses sentralisasi keuangan dalam lingkup pelayanan Gereja Masehi Injili di Minahasa (GMIM) masih dilakukan secara konvensional. Untuk menjawab permasalahan di atas dan memenuhi kebutuhan pengguna maka dianggap perlu untuk memberikan suatu inovasi berupa merancang sistem informasi keuangan jemaat yang terintegrasi. Rancangan sistem informasi ini berupa website dalam bentuk Dashboard dengan menggunakan bahasa pemrograman JavaScript dengan framework ReactJS untuk bagian Front-End dan NodeJS untuk bagian Back-End untuk kemudian di integrasikan ke aplikasi keuangan yang ada di setiap jemaat di wilayah Manado Barat Daya (E-JIB) menggunakan API.
Natural Language Processing in Computational Creativity: A Systematic Review

The intersection of Natural Language Processing (NLP) and computational creativity has emerged as a dynamic and increasingly significant research domain, reshaping foundational questions about authorship, artistic expression, and machine intelligence. This systematic review explores the evolution, current capabilities, and future directions of NLP systems in the generation and simulation of creative writing and language-based art. Through a structured analysis of peer-reviewed literature, preprints, and relevant grey sources from 2005 to 2024, this paper categorizes and evaluates a wide range of methodologies that enable machines to exhibit behavior that can be described as “creative” within linguistic contexts. Key technologies investigated include rule-based symbolic systems, statistical learning models, encoder-decoder architectures, and large-scale transformer-based language models such as GPT, BERT, T5, and their successors. The review examines the shifting paradigms from syntactic and semantic generation to stylistically nuanced, contextually aware, and affectively resonant textual output. It highlights the significant strides made in automatic poetry generation, narrative construction, metaphor synthesis, and stylistic imitation—fields traditionally reserved for human ingenuity. Evaluation metrics remain one of the most critical challenges, as creativity eludes easy quantification. The study synthesizes both computational approaches (e.g., BLEU, ROUGE, perplexity, MAUVE, Distinct-N) and qualitative frameworks (e.g., expert literary assessment, human-AI interaction analysis, and user perception studies), emphasizing the need for hybrid, multi-dimensional evaluation paradigms. It also addresses ethical considerations, including bias in training corpora, originality vs. replication, cultural homogenization, and the philosophical debate around machine authorship and intentionality. The review concludes that while NLP models have achieved impressive simulation of creative behavior, true computational creativity remains contextually constrained and lacks the self-awareness, intentionality, and socio-cultural grounding inherent to human artistic practice. Nonetheless, these systems offer compelling possibilities for co-creative applications, content augmentation, and cognitive modeling. The paper proposes a framework for future research that integrates symbolic reasoning, affective computing, narrative cognition, and participatory design to advance more transparent, inclusive, and semantically aware creative AI systems. By providing a critical synthesis of the current landscape, this systematic review contributes to the scholarly discourse on computational creativity and serves as a foundation for interdisciplinary collaboration across artificial intelligence, digital humanities, creative writing, and ethics. It argues that the future of machine-mediated creativity lies not in replacing the human imagination, but in reimagining the tools that support and extend it.
Dynamic Generation of Website Content Based on User Segmentation Using Artificial Intelligence

The aim of this work is the segmentation of website users on the basis of artificial intelligence with the aim of dynamically modifying the content of the website for users, in accordance with the objectives of Web 4.0, and in this way enabling quick and optimal display of content follow­ing their needs. User classification will be based on click events on catego­ries/subcategories and articles. Based on that information, using Konon­en’s neural network, the user will be classified into one of the n categories to which the neural network was initially trained. Based on the detected type of the user’s classification, the content of the site is dynamically changed to the user, and the categories and products for which the majority of users of that type of classification have expressed greater interest are initially dis­played and offered. The goal is to adapt the content of the site to the needs of the user and in this way the user can easily and quickly find the desired product.
Applied Study of Medical Image Segmentation Based on Convolutional Neural Network

Medical image segmentation is one of the hot topics in today's research, and researchers have found that medical image segmentation has important application prospects and potentials in the diagnosis and treatment of diseases, however, there are still some challenges and problems in the practical application. Therefore, the research topic of this paper is the research of medical image segmentation applications based on convolutional neural networks. The research methodology of this paper is to analyze the application of existing deep learning algorithms in image segmentation. It is found that the medical image segmentation method based on the convolutional neural network can effectively improve the accuracy and efficiency of segmentation results. The reliability of segmentation can be further improved by introducing medical experts' knowledge and labeled data. In summary, the research in this paper shows that the medical image segmentation method based on convolutional neural networks has the potential and advantages to improve the accuracy and efficiency of segmentation.