ethicalabs/Research-Intent-Collab huggingface dataset:
Cost-Effectiveness Modelling of Multiple System Atrophy for Early Health Technology Assessment

Multiple system atrophy is a neurodegenerative and rapidly progressing disease. Although symptoms often overlap, diagnostic criteria define two motor phenotypes: cerebellar and Parkinsonian. Little is known about available health-economic parameters for multiple system atrophy, and it remains unclear if the current available data are sufficient to construct an early health technology assessment. This assessment considered (i) if data gaps can be minimised or cost-effectiveness modelling can be facilitated by conducting literature searches for disease analogues, (ii) if an early health technology assessment is feasible with the currently available data, and (iii) the potential for developing user-friendly model interfaces in rare diseases.Literature searches were conducted for economic evaluations and health-economic parameters (including costs, utilities, natural history of disease) for multiple system atrophy. The searches for disease analogues focussed on economic evaluations and showed a widespread use of functional or disability scales to inform model structures; we therefore used the unified multiple system atrophy rating scale part IV (global disability scale) to inform the model structure. Natural history of disease data were used to inform utilities and transition probabilities. Analyses of a hypothetical intervention were conducted from the perspective of the United Kingdom National Health Service with a cost-effectiveness threshold of £30,000 per quality-adjusted life year. The cost-effectiveness analysis was presented using a user-friendly interface in R Shiny, which provides stakeholders with an opportunity to explore results.Cost-effectiveness analyses were not identified for multiple system atrophy; however, two cost-of-illness, 20 quality-of-life, and nine natural history of disease studies were identified. Health-economic parameters were estimated from registry data, such as utilities and transition probabilities. A cost-effectiveness analysis was constructed for a hypothetical intervention despite evidence shortcomings, for example, aggregated cost data. An R Shiny app with a user-friendly interface was developed for stakeholders to change inputs and evaluate results.Literature searches for disease analogues proved useful for early modelling in multiple system atrophy, for example, to inform model structures, but surrogate data could only be used when sufficient granularity was available (e.g., micro costing). These findings suggest that early conceptual modelling can help identify data gaps and guide future evidence generation, such as selecting appropriate endpoints for natural history of disease studies. Cost-effectiveness results can be highly uncertain for rare diseases at these early stages and should be considered as part of an iterative modelling framework.
General Computational Algorithms for Ab Initio Crystal Structure Prediction for Organic Molecules

The prediction of the possible crystal structure(s) of organic molecules is an important activity for the pharmaceutical and agrochemical industries, among others, due to the prevalence of crystalline products. This chapter considers the general requirements that crystal structure prediction (CSP) methodologies need to fulfil in order to be able to achieve reliable predictions over a wide range of organic systems. It also reviews the current status of a multistage CSP methodology that has recently proved successful for a number of systems of practical interest. Emphasis is placed on recent developments that allow a reconciliation of conflicting needs for, on the one hand, accurate evaluation of the energy of a proposed crystal structure and on the other hand, comprehensive search of the energy landscape for the reliable identification of all low-energy minima. Finally, based on the experience gained from this work, current limitations and opportunities for further research in this area are identified. We also consider issues relating to the use of empirical models derived from experimental data in conjunction with ab initio CSP.
Personalized Hierarchical Federated Learning Algorithm Based on Adaptive Differential Privacy

This paper proposes ADP-PHFL, an Adaptive Differential Privacy-based Personalized Hierarchical Federated Learning framework. It integrates hierarchical federated learning with personalized training via knowledge transfer, mitigating performance degradation caused by directly applying a global model while reducing communication overhead. An adaptive differential privacy mechanism is introduced to allocate privacy budgets more effectively, avoiding the drawbacks of uniform noise injection and enhancing privacy protection against data leakage. Theoretical analysis establishes the convergence and privacy guarantees of the proposed method. Experimental results demonstrate that ADP-PHFL achieves improved communication efficiency and higher model accuracy.
Heliospheric modulation of high-energy cosmic rays: II. Deformation of the neutral surface

Analysis of neutron monitor data in the periods of negative and positive polarities of the general solar magnetic field has revealed a dependence of the long-term cosmic-ray modulation on both deformation of the neutral surface of the interplanetary magnetic field and solar activity level. The effects of solar activity and neutral surface deformation dominate at positive and negative polarities, respectively. The magnetic drift of cosmic rays with different trajectories in the epochs of positive and negative polarities is responsible for this behavior of the 11-year and annual density modulations.
A melting mode of frozen sessile droplets with unmelted ice layer deposited at the bottom

Water-repellent properties of superhydrophobic surfaces make them promising for anti-icing and deicing applications. Through experimental visualization of frozen sessile droplets undergoing melting on superhydrophobic surfaces, we identify a melting mode with the unmelted ice layer deposited at the bottom of the melting droplet, even though the density of ice is lower than that of water. In the deposited mode of the melting process, the time required for the frozen droplet to melt completely is much shorter than that in the floating mode. Force analysis shows that the melted fluid flows along the gas-liquid interface toward the top of the melting droplet, thereby exerting force and then suppressing the upward movement of the unmelted ice layer. Moreover, the flow within the liquid film formed between the unmelted ice layer and the heating wall is dominated by the viscous force, which has a lubrication effect and maintains the deposition of the unmelted ice layer. High heating temperature, large contact angle, and low particle concentration are helpful for the occurrence of the deposited mode.
Biological Control and Host Plant Resistance as Pillars of Integrated Pest Management in Cabbage (Brassica oleracea var. capitata): A Global Review

Cabbage and allied brassicaceous crops are among the most economically important vegetables cultivated globally, yet they are subjected to severe yield losses caused by a diverse assemblage of insect pests. Conventional chemical control, whilst effective in the short term, has engendered widespread insecticide resistance, environmental degradation, and negative consequences for non-target organisms and human health. Against this backdrop, biological control strategies and the deployment of resistant plant varieties have emerged as pivotal components of sustainable pest management. This review synthesises evidence from peer-reviewed literature on (i) the major insect pest species threatening cabbage production worldwide, (ii) the efficacy and limitations of biological control agents including parasitoids, predators, entomopathogenic microorganisms, and botanical biopesticides, and (iii) the mechanisms and applications of host plant resistance in Brassica species. Special attention is given to the glucosinolate myrosinase defence system, the role of physical plant traits, and advances in molecular and biotechnological breeding. The review further discusses the integration of biological control and resistant varieties within comprehensive integrated pest management (IPM) frameworks, including trap cropping, habitat manipulation, and pheromone-based monitoring. Key findings indicate that whilst substantial progress has been made in individual biological control tactics, the greatest sustainable gains are achieved through multi-component IPM programmes that harness the complementarity of diverse pest management tools. Critical challenges persist regarding the inconsistent field performance of biopesticides under varying environmental conditions, the potential for resistance evolution to plant-derived defences, and the limited adoption of IPM in smallholder farming systems.
Automated individual decision-making and profiling  [on case C-634/21 - SCHUFA (Scoring)]

Automated decision-making and profiling are finally being considered before the Court of Justice of the European Union (CJEU). Article 22 GDPR states that “the data subject shall have the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning him or her or similarly significantly affects him or her” – but its provisions raise many doubts to the legal doctrine – and to the referring court in the SCHUFA case. The problem with this case law lies in the opacity of inferences or predictions resulting from data analysis, particularly by AI systems – inferences whose application to everyday situations determines how each of us, as personal data subjects, are perceived and evaluated by others. The CJEU has the opportunity to assess the existence of legal remedies to challenge operations which result in automated inferences that are not reasonably justified. However, the effectiveness of the application of the GDPR to inferred data faces several obstacles. This has to do with fact that the GDPR was designed for data provided directly by the data subject – and not for data inferred by digital technologies such as AI systems. This is the difficulty behind the Advocate General’s Opinion.
Learning Analytics Research in Relation to Educational Technology: Capturing Learning Analytics Contributions with Bibliometric Analysis

In this study the authors conducted an empirical, bibliometric analysis of current literature in learning analytics. The authors performed a citation network analysis and found three dominant clusters of research. A qualitative thematic review of publications in these clusters revealed distinct context, goals, and topics. The largest cluster focused on predicting student success and failure, the second largest on using analytics to inform instructional design, and the third on concerns in implementing learning analytics systems. The authors suggest that further collaboration with educational technology researchers and practitioners may be necessary for learning analytics to reach its interdisciplinary goal. The authors also note that learning analytics currently does not often take place in K-12 settings, and that the burden of creating learning interventions still seemed to reside mainly with practitioners.
Additional file 14: of LaminA/C regulates epigenetic and chromatin architecture changes upon aging of hematopoietic stem cells

Review history. (DOCX 48 kb)
The precautionary principle in environmental policy and the theory of choice under uncertainty

The precautionary principle, presented as a guide to environmental policy decisions in the presence of uncertainty, has been the subject of vigorous debate. However, the has generally not been discussed in relation to formal theories of choice under uncertainty developed as generalizations of the expected utility model. In this paper, it is argued that a formal basis for the precautionary principle may be found in an incompleteness hypothesis regarding formal models of choice under uncertainty. The incompleteness hypothesis states that estimates derived from formal models of choice under uncertainty will generally be over-optimistic and that the errors will be greater, the less well-understood is the problem in question.
AI and Assistive Technology in Special Education: Transforming Learning Access for Students with Disabilities

The integration of Artificial Intelligence (AI) and Assistive Technology (AT) in special education has emerged as a pivotal solution in bridging learning gaps for students with disabilities. Despite advancements in inclusive education, students with disabilities continue to face substantial barriers in accessing educational content. AI and AT offer transformative potential in addressing these barriers by personalizing learning experiences and enhancing educational outcomes for these students. This review explores the impact of AI and AT in transforming learning access for students with disabilities, focusing on the integration of AI-powered tools and AT to provide personalized, adaptive learning experiences. In carrying out this review, Google Scholar was the primary literature source. Findings from the included literature were analyzed and synthesized thematically, focusing on studies related to AI and AT applications in special education, and their effectiveness in enhancing accessibility and academic performance. The review identifies significant improvements in academic engagement, independence, and social inclusion among students using AI-powered assistive tools. Case studies highlight the effectiveness of AI in fostering personalized learning environments for students with learning disabilities, autism, and motor impairments. AI and AT have substantial potential to enhance learning outcomes for students with disabilities. However, challenges such as economic barriers, technological inequalities, and resistance to adoption must be addressed to ensure broader accessibility and equity in educational settings.
Designing Experiments Informed by Observational Studies

The increasing availability of passively observed data has yielded a growing methodological interest in "data fusion." These methods involve merging data from observational and experimental sources to draw causal conclusions -- and they typically require a precarious tradeoff between the unknown bias in the observational dataset and the often-large variance in the experimental dataset. We propose an alternative approach to leveraging observational data, which avoids this tradeoff: rather than using observational data for inference, we use it to design a more efficient experiment. We consider the case of a stratified experiment with a binary outcome, and suppose pilot estimates for the stratum potential outcome variances can be obtained from the observational study. We extend results from Zhao et al. (2019) in order to generate confidence sets for these variances, while accounting for the possibility of unmeasured confounding. Then, we pose the experimental design problem as one of regret minimization, subject to the constraints imposed by our confidence sets. We show that this problem can be converted into a convex minimization and solved using conventional methods. Lastly, we demonstrate the practical utility of our methods using data from the Women's Health Initiative.
The Identity of the EU Legal Order as a “Shield” for Judicial Independence in the (Polish) Rule of Law Crisis

By Maciej Taborowski This article is a shortened version of the re:constitution working paper, which was originally published by the Forum Transregionale Studien in 2023 and can be read here. Marciej Taborowski. Photo: Łukasz Sokół. Introduction The original purpose of my re:constitution project was to answer the question whether the value of the rule of law (Article 2 TEU) could be regarded as having a comparable weight for the EU legal order to that of the national identity clause (Art. 4 (2)...
Optimal design of frame structures with mixed categorical and continuous design variables using the Gumbel-Softmax method

In optimizing real-world structures, due to fabrication or budgetary restraints, the design variables may be restricted to a set of standard engineering choices. Such variables, commonly called categorical variables, are discrete and unordered in essence, precluding the utilization of gradient-based optimizers for the problems containing them. In this paper, incorporating the Gumbel-Softmax (GSM) method, we propose a new gradient-based optimizer for handling such variables in the optimal design of large-scale frame structures. The GSM method provides a means to draw differentiable samples from categorical distributions, thereby enabling sensitivity analysis for the variables generated from such distributions. The sensitivity information can greatly reduce the computational cost of traversing high-dimensional and discrete design spaces in comparison to employing gradient-free optimization methods. In addition, since the developed optimizer is gradient-based, it can naturally handle the simultaneous optimization of categorical and continuous design variables. Through three numerical case studies, different aspects of the proposed optimizer are studied and its advantages over population-based optimizers, specifically a genetic algorithm, are demonstrated.
Impact of Expanded Hemodialysis Using Medium Cut-off Dialyzer on Quality of Life: Application of Dynamic Patient-Reported Outcome Measurement ToolPlain-Language Summary

Rationale &amp; Objective: Current hemodialysis (HD) treatments have limited ability to clear larger-molecular-weight uremic toxins. Retention is associated with increased symptom burden, low health-related quality of life (HRQoL), and high mortality. Improved clearance, using novel medium cut-off dialyzers, termed expanded HD (HDx), may be associated with improved subjective experience. We have previously developed a dynamic patient-reported outcome measure (PROM) instrument to allow iterative recording to better appreciate the overall burden of disease and assess the impact of therapy changes. Study Design: Single-center interventional pilot study. Setting &amp; Participants: 28 patients established on maintenance HD, London, Ontario, Canada. Intervention: Initial study consisting of 2-week observation (baseline-conventional high-flux HD) followed by 12 weeks of HDx. HRQoL was assessed using the dynamic PROM instrument thrice weekly (enabled in a dedicated app as the London Evaluation of Illness [LEVIL]). Extension phase; 2-week baseline with 24 weeks of HDx and 8-week washout. Outcomes: Principal aim was to establish whether HDx therapy was associated with improved HRQoL, evidence of dose-dependant response, and whether effects were durable over time, using LEVIL. Results: Patients with lower LEVIL scores (<70/100) at baseline showed improvement in overall HRQoL after 8 weeks of therapy with similar carryover effect. General well-being, energy, and sleep quality were improved significantly as a consequence of HDx therapy. There were no detrimental effects of HDx detected in patients with higher baseline HRQoL. Limitations: Small nonrandomized sample size. The coronavirus disease 2019 pandemic interfered with the extension phase. Conclusions: Dynamic PROM assessment effectively identified patients with lower HRQoL and higher symptom burden, demonstrating durable time/dose-dependent improvements across a range of symptom domains. The use of this instrument may allow targeted selection of patients most likely to benefit from HDx therapy and assist in monitoring response and defining effect size and treatment duration to allow optimal design of further definitive randomized controlled trials of this newly introduced technology. Funding: Baxter Healthcare Canada. Trial Registration: ClinicalTrials.gov ID: NCT03640858.
Root cause analysis of a failure in a flange-pipe welded joint in a steam line in an ammonia plant: Experimental investigation and simulation assessment

Abstract   This study aims to investigate the root cause of a failure in an ASTM A106 pipe-ASTM A105 flange welded joint, welded to a pump in an ammonia plant. Samples were taken from the failed joint. These joints were exposed to steam at the temperature of 148 °C and internal pressure of 7 bar. The changes in metal microstructures along the welded metal showed that root pass region consists of large grain-size structure, containing Widmanstatten, grain boundary and polygonal ferrite structures. On the other hand, the areas on the middle and upper sections of the weld mostly contained acicular ferrites. The results of hardness tests indicated that the root pass region has lower hardness compared to other areas which is due to large grain size structure in this region. In addition, simulation results showed that a rather large residual tensile stress exists at the root of the weldment. Besides, results of fractography showed that areas close to the weld root consisted of a quasi-cleavage structure. Based on the results, it can be postulated that the main reason for the failure was unsuitable welding parameters which have resulted in the formation of large grain structures with low strength.
Additive Manufacturing Of Highly Efficient Electric Sheets Of Fe6,5Si By 3D Screen-Printing

Energy-efficient electric motors are crucial for the progress of electromobility. Soft magnetic materials with a high silicon content, such as Fe6.5Si, offer the possibility of high electrical resistance, high saturation magnetization and comparatively low power losses. However, due to the brittleness of Fe6.5Si, this material cannot be processed using conventional manufacturing methods (such as stamping).  By means of 3D screen printing process, this material can be processed through a powder metallurgical processing route. Thus, electric steel sheets with low thickness, high alloying content and high productivity can be realized. We present results of printed and sintered Fe6,5Si electric steel sheets with thicknesses between 100 and 350 µm. The power loss can be reduced below 35 W kg-1 at 1000 Hz and 1 T. The magnetic properties of the sheets will be discussed in dependence on the sintering parameters and the powder properties.
Bayesian deep learning framework for uncertainty quantification in high dimensions

16 pages, 8 figures
Implementing reactivity in molecular dynamics simulations with harmonic force fields

The simulation of chemical reactions and mechanical properties including failure from atoms to the micrometer scale remains a longstanding challenge in chemistry and materials science. Bottlenecks include computational feasibility, reliability, and cost. We introduce a method for reactive molecular dynamics simulations using a clean replacement of non-reactive classical harmonic bond potentials with reactive, energy-conserving Morse potentials, called the Reactive INTERFACE Force Field (IFF-R). IFF-R is compatible with force fields for organic and inorganic compounds such as IFF, CHARMM, PCFF, OPLS-AA, and AMBER. Bond dissociation is enabled by three interpretable Morse parameters per bond type and zero energy upon disconnect. Use cases for bond breaking in molecules, failure of polymers, carbon nanostructures, proteins, composite materials, and metals are shown. The simulation of bond forming reactions was included via template-based methods. IFF-R maintains the accuracy of the corresponding non-reactive force fields and is about 30 times faster than prior reactive simulation methods.