ethicalabs/Research-Intent-Collab huggingface dataset:
Systematic versus on-demand early palliative care: A randomised clinical trial assessing quality of care and treatment aggressiveness near the end of life

Early palliative care (EPC) in oncology has shown sparse evidence of a positive impact on patient outcomes, quality of care outcomes and costs.Data for this secondary analysis were taken from a trial of 207 outpatients with metastatic pancreatic cancer randomly assigned to receive standard cancer care plus on-demand EPC (standard arm) or standard cancer care plus systematic EPC (interventional arm). After 20 months' follow-up, 149 (80%) had died. Outcome measures were frequency, type and timing of chemotherapy administration, use of resources, place of death and overall survival.Some indices of end-of-life (EoL) aggressiveness had a favourable impact from systematic EPC. Interventional arm patients showed higher use of hospice services: a significantly longer median and mean period of hospice care (P = 0.025 for both indexes) and a significantly higher median and mean number of hospice admissions (both P < 0.010). In the experimental arm, chemotherapy was performed in the last 30 days of life in a significantly inferior rate with respect to control arm: 18.7% versus 27.8% (adjusted P = 0.036). Other non-significant differences were seen in favour of experimental arm.Systematic EPC showed a significant impact on some indicators of EoL treatment aggressiveness. These data, reinforced by multiple non-significant differences in most of the other items, suggest that quality of care is improved by this approach. This study is registered on ClinicalTrials.gov (NCT01996540).
Parametrical Neural Networks and Some Other Similar Architectures

A review of works on associative neural networks accomplished during last four years in the Institute of Optical Neural Technologies RAS is given. The presentation is based on description of parametrical neural networks (PNN). For today PNN have record recognizing characteristics (storage capacity, noise immunity and speed of operation). Presentation of basic ideas and principles is accentuated.
A computational framework for the morpho-elastic development of molluskan shells by surface and volume growth

Mollusk shells are an ideal model system for understanding the morpho-elastic basis of morphological evolution of invertebrates' exoskeletons. During the formation of the shell, the mantle tissue secretes proteins and minerals that calcify to form a new incremental layer of the exoskeleton. Most of the existing literature on the morphology of mollusks is descriptive. The mathematical understanding of the underlying coupling between pre-existing shell morphology, de novo surface deposition and morpho-elastic volume growth is at a nascent stage, primarily limited to reduced geometric representations. Here, we propose a general, three-dimensional computational framework coupling pre-existing morphology, incremental surface growth by accretion, and morpho-elastic volume growth. We exercise this framework by applying it to explain the stepwise morphogenesis of seashells during growth: new material surfaces are laid down by accretive growth on the mantle whose form is determined by its morpho-elastic growth. Calcification of the newest surfaces extends the shell as well as creates a new scaffold that constrains the next growth step. We study the effects of surface and volumetric growth rates, and of previously deposited shell geometries on the resulting modes of mantle deformation, and therefore of the developing shell's morphology. Connections are made to a range of complex shells ornamentations.
Space, matter and topology

An old branch of mathematics, Topology, has opened the road to the discovery of new phases of matter. A hidden topology in the energy spectrum is the key for novel conducting/insulating properties of topological matter.
A Multi-scale Generalized Shrinkage Threshold Network for Image Blind Deblurring in Remote Sensing

Remote sensing images are essential for many applications of the earth's sciences, but their quality can usually be degraded due to limitations in sensor technology and complex imaging environments. To address this, various remote sensing image deblurring methods have been developed to restore sharp and high-quality images from degraded observational data. However, most traditional model-based deblurring methods usually require predefined {hand-crafted} prior assumptions, which are difficult to handle in complex applications. On the other hand, deep learning-based deblurring methods are often considered as black boxes, lacking transparency and interpretability. In this work, we propose a new blind deblurring learning framework that utilizes alternating iterations of shrinkage thresholds. This framework involves updating blurring kernels and images, with a theoretical foundation in network design. Additionally, we propose a learnable blur kernel proximal mapping module to improve the accuracy of the blur kernel reconstruction. Furthermore, we propose a deep proximal mapping module in the image domain, which combines a generalized shrinkage threshold with a multi-scale prior feature extraction block. This module also incorporates an attention mechanism to learn adaptively the importance of prior information, improving the flexibility and robustness of prior terms, and avoiding limitations similar to hand-crafted image prior terms. Consequently, we design a novel multi-scale generalized shrinkage threshold network (MGSTNet) that focuses specifically on learning deep geometric prior features to enhance image restoration. Experimental results on real and synthetic remote sensing image datasets demonstrate the superiority of our MGSTNet framework compared to existing deblurring methods.
Population Transformer: Learning Population-level Representations of Neural Activity

We present a self-supervised framework that learns population-level codes for arbitrary ensembles of neural recordings at scale. We address key challenges in scaling models with neural time-series data, namely, sparse and variable electrode distribution across subjects and datasets. The Population Transformer (PopT) stacks on top of pretrained temporal embeddings and enhances downstream decoding by enabling learned aggregation of multiple spatially-sparse data channels. The pretrained PopT lowers the amount of data required for downstream decoding experiments, while increasing accuracy, even on held-out subjects and tasks. Compared to end-to-end methods, this approach is computationally lightweight, while achieving similar or better decoding performance. We further show how our framework is generalizable to multiple time-series embeddings and neural data modalities. Beyond decoding, we interpret the pretrained and fine-tuned PopT models to show how they can be used to extract neuroscience insights from large amounts of data. We release our code as well as a pretrained PopT to enable off-the-shelf improvements in multi-channel intracranial data decoding and interpretability. Code is available at https://github.com/czlwang/PopulationTransformer.
Evaluation of Learning Outcomes in Higher Education through the Integration of Interactive Mobile Technology and Big Data Analytics

With the rapid advancement of mobile Internet technology, interactive learning platforms, and big data analytics, they have become essential tools in modern higher education, particularly in the evaluation of learning outcomes. Traditional evaluation methods primarily rely on offline testing and questionnaire surveys, which often lack real-time adaptability, specificity, and multi-dimensional analysis, making it difficult to comprehensively reflect learners’ academic progress. Recent developments in big data technology and artificial intelligence have led researchers to explore data-driven approaches for evaluating learning outcomes. However, existing methodologies remain limited in feature selection, feature fusion, and model optimization, hindering their ability to fully capture learners’ behavioral features and academic performance. To address these limitations, a learning outcome evaluation model based on multi-semantic feature interaction and big data analytics was proposed. The model was designed with a feature embedding module, a multi-semantic feature interaction module, a two-dimensional squeeze-and-excitation module, and a feature fusion module to enhance evaluation accuracy and comprehensiveness. The feature embedding module extracts latent features by embedding multidimensional learner behavior data. The multi-semantic feature interaction module captures complex learning patterns by facilitating interactions among various features. The two-dimensional squeeze-and-excitation module optimizes feature representation, improving evaluation sensitivity. The feature fusion module integrates diverse features to enhance evaluation accuracy. Through these innovations, this study not only introduces a novel perspective for evaluating learning outcomes in higher education but also provides valuable insights for further research in educational technology.
Enamine/Dienamine and Brønsted Acid Catalysis: Elusive Intermediates, Reaction Mechanisms, and Stereoinduction Modes Based on in Situ NMR Spectroscopy and Computational Studies

Over the years, the field of enantioselective organocatalysis has seen unparalleled growth in the development of novel synthetic applications with respect to mechanistic investigations. Reaction optimization appeared to be rather empirical than rational. This offset between synthetic development and mechanistic understanding was and is generally due to the difficulties in detecting reactive intermediates and the inability to experimentally evaluate transition states. Thus, the first key point for mechanistic studies is detecting elusive intermediates and characterizing them in terms of their structure, stability, formation pathways, and kinetic properties. The second key point is evaluating the importance of these intermediates and their properties in the transition state. In the past 7 years, our group has addressed the problems with detecting elusive intermediates in organocatalysis by means of NMR spectroscopy and eventually theoretical calculations. Two main activation modes were extensively investigated: secondary amine catalysis and, very recently, Brønsted acid catalysis. Using these examples, we discuss potential methods to stabilize intermediates via intermolecular interactions; to elucidate their structures, formation pathways and kinetics; to change the kinetics of the reactions; and to address their relevance in transition states. The elusive enamine in proline-catalyzed aldol reactions is used as an example of the stabilization of intermediates via inter- and intramolecular interactions; the determination of kinetics on its formation pathway is discussed. Classical structural characterization of intermediates is described using prolinol and prolinol ether enamines and dienamines. The Z/E dilemma for the second double bond of the dienamines shows how the kinetics of a reaction can be changed to allow for the detection of reaction intermediates. We recently started to investigate substrate-catalyst complexes in the field of Brønsted acid catalysis. These studies on imine/chiral phosphoric acid complexes show that an appropriate combination of highly developed NMR and theoretical methods can provide detailed insights into the complicated structures, exchange kinetics, and H-bonding properties of chiral ion pairs. Furthermore, the merging of these structural investigations and photoisomerization even allowed the active transition state combinations to be determined for the first time on the basis of experimental data only, which is the gold standard in mechanistic investigations and was previously thought to be exclusively the domain of theoretical calculations. Thus, this Account summarizes our recent mechanistic work in the field of organocatalysis and explains the potential methods for addressing the central questions in mechanistic studies: stabilization of intermediates, elucidation of structures and formation pathways, and addressing transition state combinations experimentally.
Optimal experimental designs for dose–response studies with continuous endpoints

In most areas of clinical and preclinical research, the required sample size determines the costs and effort for any project, and thus, optimizing sample size is of primary importance. An experimental design of dose-response studies is determined by the number and choice of dose levels as well as the allocation of sample size to each level. The experimental design of toxicological studies tends to be motivated by convention. Statistical optimal design theory, however, allows the setting of experimental conditions (dose levels, measurement times, etc.) in a way which minimizes the number of required measurements and subjects to obtain the desired precision of the results. While the general theory is well established, the mathematical complexity of the problem so far prevents widespread use of these techniques in practical studies. The paper explains the concepts of statistical optimal design theory with a minimum of mathematical terminology and uses these concepts to generate concrete usable D-optimal experimental designs for dose-response studies on the basis of three common dose-response functions in toxicology: log-logistic, log-normal and Weibull functions with four parameters each. The resulting designs usually require control plus only three dose levels and are quite intuitively plausible. The optimal designs are compared to traditional designs such as the typical setup of cytotoxicity studies for 96-well plates. As the optimal design depends on prior estimates of the dose-response function parameters, it is shown what loss of efficiency occurs if the parameters for design determination are misspecified, and how Bayes optimal designs can improve the situation.
Mechanical and Kinematic Design of Surgical Mini Robotic Manipulator used into SP-LAP Multi-DOF Platform for Training and Simulation

Surgical robotics is the new age of minimally invasive procedures, for this reason, it is important to acquire fundamental skills using training and simulation. Therefore, a research study was developed to present the design of 2 robotic arms, which are used into SP-LAP training platform for remote control haptic manipulation in order to magnify natural eye-hand-instrument alignment, boost motor dexterity and minimally invasive, enhance ergonomics, ensure feasibility, safety, and patient risk reduction. The proposed medical robotic manipulator has 3 degrees of freedom (DOF), 4 links and 3 joints, which have 9 DOF in combination with the multi-DOF system, to promote the application for new surgical techniques and approaches, based on the ergonomics during triangulation. In conclusion, the analysis is focused on mechanical 3D design and kinematics. The next step of the project is to evaluate adding 2 DOF more, and also work on the mechatronic integration of sensors and actuators, and then, perform test and validation of the cyber-physical system.
Electrochemistry of anode materials in lithium- and sodium-ion batteries

Lithium-ion batteries (LIBs) have a high energy and power density, making them attractive for electric vehicles (EVs) and portable electronic devices. In commercially available LIBs, graphite and transition metal oxides (LiCoO2) are used as anode and cathode materials, respectively. Unfortunately, graphite has a safety concern related to dendrite formation at low voltage and also has low rate-capability issues, restricting its high-power demand. Li4Ti5O12 (LTO) is considered an alternative anode and a good contender for LIBs due to its high reversibility and zero structural changes during the lithiation/(de)lithiation process. Its high operating voltage (∼1.55 V vs. Li+/Li) helps avoid dendritic formations, thereby ensuring safe cycling. Despite these advantages, LTO has low electronic conductivity, relatively low capability at high current rates due to large polarization, and sluggish Li-ion diffusion. The work provides a solution to overcome these drawbacks and improve the LTO performance at high currents by modifying the crystal and electronic structure and reducing particle size. To accomplish these goals, the structural characteristics and electrochemical behavior of LTO-based materials have been systematically and intensively discussed. In this chapter, three different ways of doping in LTO are discussed that are already been synthesized by a simple solid-state method, co-doped LTO electrode exhibits outstanding cycling stability, having higher capacity retention of ∼98.79% after 300 cycles at high currents. While considering the practical advantages, this study provides two more benefits: (1) it sheds light on the doping strategy; (2) it elucidates the relations among the material composition, structure, and electrochemical performances in LIBs.
Deepfakes and the Geneva Conventions: Does Deceptive AI-Generated Misinformation Directed at an Enemy During Armed Conflict Violate International Humanitarian Law? A Critical Discussion

‘Deepfakes’ and other forms of digital communications disinformation are now on the virtual frontlines of many armed conflicts. Military commanders can potentially gain significant tactical advantages by misleading enemy forces, opposing governments, and civilian populations into believing X when Y is the true state of affairs. Distinct from military propaganda, deliberate deceptions and subterfuge have long been part of warfare. However, a powerful claim is advanced that deepfakes such as announcing surrender, truce declarations, or similar messages that place soldiers and civilians at greater risk are international humanitarian law (IHL) violations, notably under the 1907 Hague Convention and the 1977 Additional Protocol I to the Geneva Conventions. This four-section critical discussion considers whether, or to what extent, deepfakes are IHL compliant. Selected examples taken from the ongoing Russia–Ukraine war are highlighted to illustrate the potentially grave dangers that deepfakes represent for innocent civilian populations. IHL reform recommendations are made that would reduce deepfake harm—if such reforms are embraced by the international community (an admittedly doubtful prospect).
A Modelling Framework for Addressing the Synergies Between Global Conventions Through Land Use Changes: Carbon Sequestration, Biodiversity Conservation, Prevention of Land Degradation and Food Security in Agricultural and Forested Lands in Developing Countries

This paper proposed a methodological framework for the assessment of carbon stocks and the development and identification of land use, land use change and land management scenarios, whereby enhancing carbon sequestration synergistically increases biodiversity, the prevention of land degradation and food security through the increases in crop yields. The framework integrates satellite image interpretation, computer modelling tools (i.e. software customization of off-the-shelf soil organic matter turnover simulation models) and Geographical Information Systems (GIS). The framework addresses directly and indirectly the cross-cutting ecological concerns foci of major global conventions: climate change, biodiversity, the combat of desertification and food security. Their synergies are targeted by providing procedures for assessing and identifying simultaneously carbon sinks, potential increases in plant diversity, measures to prevent land degradation and enhancements in food security through crop yields, implicit in each land use change and land management scenario. The scenarios aim at providing win-win options to decision makers through the frameworks decision support tools. Issues concerning complex model parameterization and spatial representation were tackled through tight coupling soil carbon models to GIS via software customization. Results of applying the framework in the field in two developing countries indicate that reasonably accurate estimates of carbon sequestration can be obtained through modeling; and that alternative best soil organic matter management practices that arrest shifting slash-and-burn cultivation and prevent burning and emissions, can be identified. Such options also result in increased crop yields and food security for an average family size in the area, while enhancing biodiversity and preventing land degradation. These options demonstrate that the judicious management of organic matter is central to greenhouse gas mitigation and the attainment of synergistic ecological benefits, which is the concern of global conventions. The framework is to be further developed through successive approximations and refinement in future, extending its applicability to other landscapes.
Observation of a time lag in solar modulation of cosmic rays in the heliosphere

The solar modulation effect of Galactic cosmic rays is a time-dependent phenomenon that is caused by the transport of these particles through the magnetized plasma of the heliosphere. Using a data-driven model of cosmic-ray transport in the heliosphere, in combination with a large collection of data, we report the evidence for a eight-month time lag between observations of solar activity and measurements of cosmic-ray fluxes in space. As we will discuss, this result enables us to forecast the cosmic ray flux at Earth well in advance by monitoring solar activity. We also compare our predictions with the new multi-channel measurements of cosmic rays operated by the AMS experiment in space.
Secondary Use of Clinical Trials Data in Health Research: A Practical Guide

This document presents a theoretical framework for the use of clinical trials and other health data for secondary research purposes, which was derived from research papers, consultation with stakeholders and the research community.    Four overall scenarios for data reuse were identified; scenario 1: evidence synthesis, scenario 2: secondary analyses, scenario 3: reproducibility, replication and validation, and scenario 4: education and methods development.  View an online version of this pdf document on the ARDC website.
&lt;b&gt;Artificial Intelligence in Islamic Philosophy: a Qur’anic Epistemological Framework for AI Ethics &lt;/b&gt;

This study examines the epistemological relationship between Artificial Intelligence (AI) and the values of the Qur’an, with the aim of formulating a model for the Islamization of AI within the framework of the Islamic worldview. This study constructs a normative-conceptual framework for the development of AI that aligns with the principles of tawhid, maqasid al-shari’ah, and Qur’anic ethics. This study employs a literature-based qualitative design with a philosophical-analytical approach. The findings indicate that, ontologically, AI is a neutral tool (mubah), where its ethical status depends on the worldview and intentions guiding its use. Epistemologically, the primary distinction between the secular paradigm and Islamic epistemology lies in the integration of revelation as a legitimate and authoritative source of knowledge, which shapes the value orientation in technological development. This study proposes a model for the Islamization of AI based on three Qur’anic principles: reason (‘aql), knowledge (‘ilm), and order (nazm). These principles emphasize the importance of accountable algorithmic governance, authoritative knowledge validation, and continuous human oversight. Practically, this model provides a foundational framework for the ethical use of AI, guiding Muslim scholars, technology experts, and policymakers in designing AI systems that align with Islamic ethical values