ethicalabs/Research-Intent-Collab huggingface dataset:
The Assessment of Quality of life at the End of Life (AQEL) questionnaire: a brief but comprehensive instrument for use in patients with cancer in palliative care

The aim of the study was to further validate the Assessment of Quality of life at the End of Life (AQEL) questionnaire, to explore whether conceptually consistent scales could be confirmed and how the instrument relates to other widely used and validated HRQL instruments.A total of 106 patients with lung cancer in palliative care completed the 20-item AQEL, the EORTC QLQ-C30, Hospital Anxiety and Depression Scale (HADS), Sense of Coherence (SOC-13) and Social Support Survey (SSS). Construct validity of the AQEL was tested by multitrait scaling, principal components analysis, inter-scale correlations and known-groups comparisons.Five scales were supported by multitrait scaling: Basic function, Activity, Emotional function, Cognitive function and Existential needs, with satisfactory (>.7) internal consistency for the latter three and intermediate (>.5) for the remaining two. A Social support scale was supported by principal components analysis, but exhibited a low internal consistency. Items on health care issues and physical symptoms worked best as individual items. Expected correlations with other instruments were found for all scales. Activity was sensitive to different levels of functioning as defined by performance status.This study provided some evidence for the validity of the AQEL and its feasibility in patients with cancer in palliative care.
PVAFN: Point-Voxel Attention Fusion Network with Multi-Pooling Enhancing for 3D Object Detection

The integration of point and voxel representations is becoming more common in LiDAR-based 3D object detection. However, this combination often struggles with capturing semantic information effectively. Moreover, relying solely on point features within regions of interest can lead to information loss and limitations in local feature representation. To tackle these challenges, we propose a novel two-stage 3D object detector, called Point-Voxel Attention Fusion Network (PVAFN). PVAFN leverages an attention mechanism to improve multi-modal feature fusion during the feature extraction phase. In the refinement stage, it utilizes a multi-pooling strategy to integrate both multi-scale and region-specific information effectively. The point-voxel attention mechanism adaptively combines point cloud and voxel-based Bird's-Eye-View (BEV) features, resulting in richer object representations that help to reduce false detections. Additionally, a multi-pooling enhancement module is introduced to boost the model's perception capabilities. This module employs cluster pooling and pyramid pooling techniques to efficiently capture key geometric details and fine-grained shape structures, thereby enhancing the integration of local and global features. Extensive experiments on the KITTI and Waymo datasets demonstrate that the proposed PVAFN achieves competitive performance. The code and models will be available.
Latent Space Data Fusion Outperforms Early Fusion in Multimodal Mental Health Digital Phenotyping Data

Background: Mental illnesses such as depression and anxiety require improved methods for early detection and personalized intervention. Traditional predictive models often rely on unimodal data or early fusion strategies that fail to capture the complex, multimodal nature of psychiatric data. Advanced integration techniques, such as intermediate (latent space) fusion, may offer better accuracy and clinical utility. Methods: Using data from the BRIGHTEN clinical trial, we evaluated intermediate (latent space) fusion for predicting daily depressive symptoms (PHQ-2 scores). We compared early fusion implemented with a Random Forest (RF) model and intermediate fusion implemented via a Combined Model (CM) using autoencoders and a neural network. The dataset included behavioral (smartphone-based), demographic, and clinical features. Experiments were conducted across multiple temporal splits and data stream combinations. Performance was evaluated using mean squared error (MSE) and coefficient of determination (R2). Results: The CM outperformed both RF and Linear Regression (LR) baselines across all setups, achieving lower MSE (0.4985 vs. 0.5305 with RF) and higher R2 (0.4695 vs. 0.4356). The RF model showed signs of overfitting, with a large gap between training and test performance, while the CM maintained consistent generalization. Performance was best when integrating all data modalities in the CM (in contradistinction to RF), underscoring the value of latent space fusion for capturing non-linear interactions in complex psychiatric datasets. Conclusion: Latent space fusion offers a robust alternative to traditional fusion methods for prediction with multimodal mental health data. Future work should explore model interpretability and individual-level prediction for clinical deployment.
Disparities in Health Insurance Coverage and Access to Care by English Language Proficiency in the USA, 2006–2016

In the USA, people with limited English proficiency (LEP) disproportionately experience gaps in health insurance coverage and access to care. The Patient Protection and Affordable Care Act (ACA) of 2010 included reforms that could improve these outcomes.To describe changes in insurance coverage and access to health care by English language proficiency over 2006-2016.We used regression models to estimate changes in coverage and access after 2010 for adults with high vs. limited English proficiency, adjusting for socio-economic status, demographic characteristics, and health care needs. We used difference-in-differences models to assess adjusted changes in disparities by English proficiency after 2010. Supplemental analyses used nearest-neighbor propensity score matching to balance the characteristics of respondents.Respondents aged 18-64 in the Medical Expenditure Panel Survey over 2006-2016, with high (n = 174,214) or limited (n = 16,484) English language proficiency.Insurance coverage was a binary variable indicating any health insurance coverage during the past 12 months. Access to care was measured using binary variables indicating whether the respondent had a usual source of care and received necessary medical, dental, and preventive care.Gains in health insurance coverage after 2010 were significant for adults with high English proficiency (1.7 percentage points, p < 0.001) and adults with limited English proficiency (4.6 percentage points, p = 0.007); gains did not significantly vary by English proficiency. Adults with LEP showed larger improvements than adults with high English proficiency in having a usual source of care (5 percentage points, p = 0.007) and receiving needed medical care and dental care (1.4 percentage points, p = 0.013, and 2.8 percentage points, p = 0.009, respectively). Findings remained similar when matching was used to balance the measured characteristics of respondents with high vs. limited English proficiency.Disparities in health care access by English proficiency narrowed after 2010, the year of passage of the ACA.
Trade Justice at a Crossroads: Barriers and Bottlenecks in Dispute Settlement Mechanism of the World Trade Organization

It is well known that a global disagreement attracts greater attention than the national one, but a trade dispute is more sensitive and need more active consideration. Dispute settlement process of the World Trade Organization (WTO) is a distinctive feature exclusively designed to ensure trade equity and a proactive forum for resolving global trade issues. The World Trade Organization (WTO), which succeeded the General Agreement on Tariffs and Trade (GATT) framework, has been in the force in ensuring stability and efficiency in global commercial relations. As times evolve, global trade is increasingly intricate and linked; its boundaries are becoming more apparent. The dispute settlement process in World Trade Organization (WTO) is not outdated in its function; instead, it calls for modernization to address evolving demands and growing trade patterns. This study examines the structural and procedural dimensions while emphasizing certain practical restrictions, including the exclusion of non-governmental entities and the conditions for inclusion. The research emphasizes procedural issues and implementation inadequacies, suggesting modifications to improve its efficacy, flexibility, productivity, and legitimacy.
An evolutionary perspective on carotenoids and apocarotenoids in the sustainability era: versatile bioactive isoprenoids for agriculture, food security, nutrition, and health promotion

Abstract                                        Background                     Carotenoids were invented millions of years ago to interact with light in cyanobacterial photosynthesis. Curiously, extraordinary levels of them are accumulated in the human retina at the location where light reaches with the highest intensity. Beyond their fundamental functions in photosynthesis, the primal food factory, many organisms harnessed carotenoids and their derivatives for different purposes. In this review, aspects such as carotenoid distribution, their role in food security, dietary importance, bioavailability, biological actions related to health and safety are summarized. Sustainability aspects and research needs are also pinpointed.                                                           Conclusions                     It is indisputable that carotenoids are essential for food production in terrestrial and aquatic ecosystems. Studies of different nature provide ample evidence that they are involved in health benefits. Indeed, their intake and/or circulating levels are often associated to lower all-cause and cause-specific mortality. Research on carotenoids is undoubtedly pivotal in the current health-promoting and sustainable foods era. Given their importance in different industries (agriculture, food, feed, pharma, cosmetics) working toward reducing the overall environmental impact of the production of (apo)carotenoids is a timely research priority. Other important needs are the establishment of recommended intakes for health promotion and cross-talk with research networks on other bioactives to assess interactions among them and better understand their health benefits. Considering that only the tip of the iceberg of all carotenoids is being studied in depth and that they can form a myriad of mostly unknown apocarotenoids, there are many chances for innovation in the field.
Sustainability Research of Building Systems Based on Neural Network Predictive Models and Life Cycle Assessment (LCA)–Emergy–Carbon Footprint Method

Facing the abnormal climate changes and the goal of carbon neutrality, the ecological sustainability research of building systems has become a focus of attention for experts in this field. However, the definition of sustainable buildings is broad. This article discusses the quantitative analysis of sustainable buildings from the perspectives of an ecological emergy and carbon footprint. It also establishes the long-term sustainability of buildings through predictive neural networks. The research findings indicate that the emergy and carbon emissions during the operational and materials phases dominate the entire system. The calculation and analysis of the emergy sustainability indicator (ESI) demonstrate a decreasing trend in the sustainability of the building system over three time periods (10 years, 20 years, and 30 years), with results of 0.58, 0.238, and 0.089, respectively. As the operational time increases, carbon emissions from the building system also increase, further exacerbating the pressure on the building and reducing its overall sustainability. To address this dilemma, sustainable retrofit measures have been proposed, such as rainwater harvesting and embedded applications of distributed energy sources, which reduce the burden of emergy and carbon emissions. The effectiveness of these measures has been validated in this article, demonstrating their potential to enhance building sustainability and providing references for architects and building managers.
Perancangan Data Warehouse Sistem Penerimaan Siswa Baru Menggunakan Online Analytical Processing (OLAP) di TK IT Mutiara

Peningkatan jumlah siswa pada TK IT Mutiara membuat sekolah kesulitan dalam melakukan pengelolaan data pendaftaran sehingga harus membutuhkan waktu untuk mendapatkan informasi. Pembuatan aplikasi OnLine Analytical Processing (OLAP) Data Warehouse dapat membantu mengekstrak dan memudahkan dalam mencari informasi penting dari beberapa sistem informasi yang berbeda. Metode penelitian yang dilakukan adalah kualitatif dengan teknik perolehan data wawancara dengan seorang narasumber di TK IT Mutiara dan studi pustaka melalui jurnal yang relevan dengan topik penulis dan juga menggunakan metode nine step Kimball. Tujuan penelitian ini adalah untuk merancang sistem yang memudahkan kepala sekolah  untuk melakukan analisis terhadap data siswa baru menggunakan Aplikasi OLAP Data Warehouse. Hasil penelitian ini adalah terciptanya aplikasi OLAP Data Warehouse yang dapat memberikan kemudahan kepada kepala sekolah dalam melakukan analisis terhadap data siswa baru sehingga dapat membantu proses pengambilan keputusan, serta dapat menampilkan bentuk laporan yang mudah dibaca dan dipahami.
Hybridized Fuzzy Based Clustering For Wireless Sensor Networks Based On Cognitive Internet Of Things

Wireless sensor network explosive growth has increased demand for radio spectrum and has created problems with spectrum shortage since different wireless services and technologies have already been assigned the full range of wireless sensor networks. Cognitive radio has become a promising solution for resource-controlled wireless sensor network to access the reserved under-used frequency bands resourcefully. Artificial intelligence algorithms allow sensor nodes to avoid crowded congested bands by detecting under utilized licensed bands and to decide to adapt their transmission parameters. However, clusters are based on fixed spectrum distribution and cannot deal with the dynamic spectrum allocation required for future generation networks. Clusters are used to reduce power usage and support scalability of sensor networks. This article proposes an Hybridized Fuzzy Clustering (HFC), which groups adjacent nodes with comparable sets of idle channels and optimally forming power-efficient clusters based on three fuzzy energy parameters, proximity to the base station, and the level of the node to determine the possibility of each node being a cluster head.
Additional file 1 of Using co-design to understand consumer’s health information-seeking behaviours and design preferences for a new digital clinical dashboard in aged care

Supplementary Material 1
Potential Global Sequestration of Atmospheric Carbon Dioxide by Drylands Forestation

Drylands forestation offers the potential for significant long-term sequestration of atmospheric CO$_2$. We consider sequestration of organic and inorganic carbon by a planted semi-arid forest, based on carbon that originates from atmospheric CO$_2$. Measurements at Israels Yatir forest give a sequestration rate of $\sim$550 g CO$_2$ m$^{-2}$ yr$^{-1}$ as organic carbon in the trees biomass. The inorganic carbon precipitation rate gives an additional 216 g CO$_2$ m$^{-2}$ yr$^{-1}$ globally, via calcite (CaCO$_3$) precipitation in soil. This sequestration is due to a combination of microbial activity on organic soil carbon, and the formation of soil carbonic acid (H$_2$CO$_3$) that arises from the reaction of soil water with CO$_2$ exhaled from tree roots. Published estimates restrict the potential drylands surface available for sustainable forestation to $\sim$4.5 million km$^2$, only $\sim$10$\%$ of the global drylands. The dominant limitation is the apparent lack of water. However, immediately under many drylands, there are paleowaters (fossil water) that had recharged underlying aquifers during prior wetter climatic regimes. Conservatively, including fossil water, at least $\sim$9.0 million km$^2$ is available for afforestation. Measurements at Yatir show that drip irrigation to $\sim$18$\%$ average Soil Moisture (higher than the rainfed $\sim$12$\%$ SM) would double the organic carbon sequestration rate. In addition, the tree density could be increased, which would independently double the organic carbon sequestration rate. The potential total annual sequestration rate is then estimated as 20.0 Gt CO$_2$. Measurements at Israels Yatir forest give a sequestration rate of $\sim$20.0 Gt CO$_2$ yr$^{-1}$, divided between 14.0 Gt CO$_2$ yr$^{-1}$ (organic) and 6.0 Gt CO$_2$ yr$^{-1}$ (inorganic). This corresponds to $\sim$100$\%$ of the annual rate of atmospheric CO$_2$ increase.
Benchmarking Deep Learning Models for Laryngeal Cancer Staging Using the LaryngealCT Dataset

Laryngeal cancer imaging research lacks standardised datasets to enable reproducible deep learning (DL) model development. We present LaryngealCT, a curated benchmark of 1,029 computed tomography (CT) scans aggregated from six collections from The Cancer Imaging Archive (TCIA). Uniform 1 mm isotropic volumes of interest encompassing the larynx were extracted using a weakly supervised parameter search framework validated by clinical experts. 3D DL architectures (3D CNN, ResNet18,50,101, DenseNet121) were benchmarked on (i) early (Tis,T1,T2) vs. advanced (T3,T4) and (ii) T4 vs. non-T4 classification tasks. 3D CNN (AUC-0.881, F1-macro-0.821) and ResNet18 (AUC-0.892, F1-macro-0.646) respectively outperformed the other models in the two tasks. Model explainability assessed using 3D GradCAMs with thyroid cartilage overlays revealed greater peri-cartilage attention in non-T4 cases and focal activations in T4 predictions. Through open-source data, pretrained models, and integrated explainability tools, LaryngealCT offers a reproducible foundation for AI-driven research to support clinical decisions in laryngeal oncology.
Heliospheric modulation of galactic cosmic rays during grand solar minima: Past and future variations: COSMIC RAYS IN GRAND SOLAR MINIMA

Galactic cosmic ray flux at Earth is modulated by the heliospheric magnetic field. Heliospheric modulation potential, Φ, during grand solar minima is investigated using an open solar flux (OSF) model with OSF source based on sunspot number, R, and OSF loss on heliospheric current sheet inclination. Changing dominance between source and loss means Φ varies in- (anti-) phase with R during strong (weak) cycles, in agreement with Φ estimates from ice core records of10Be concentration, which are in-phase during most of the last 300 years, but anti-phase during the Maunder Minimum. Model results suggest "flat" OSF cycles, such as solar cycle 20 result from OSF source and loss terms temporarily balancing throughout the cycle. Thus even if solar activity continues to decline steadily, the long-term drop in OSF through SC21 to SC23 may plateau during SC24, though reemerge in SC25 with the inverted phase relation
Molecular dynamics simulations of the properties of water-methanol mixtures. Effects of force fields

Isothermal-isobaric molecular dynamics simulations are used to examine the microscopic structure and some properties of water-methanol liquid mixture. The TIP4P/2005 and SPC/E water models are combined with the united atom TraPPE and the all-atom force field model for methanol. Our principal focus is to evaluate the quality of predictions of different combinations of model force fields concerning the composition dependence of basic properties of this system. Specifically, we explored the composition effects on density, excess molar volume and excess entropy, as well as on the surface tension and static dielectric constant. In addition, the structural properties are described in terms of the coordination numbers and the average number of hydrogen bonds between molecules of constituent species. Finally, the composition dependence of self-diffusion coefficients of the species is evaluated. All theoretical predictions are tested with respect to experimental data.
Beyond Learning Analytics: Framework for Technology-Enhanced Evidence-Based Education and Learning

Currently eLearning infrastructure across various institutions often includes aLearning Management System (LMS), various ubiquitous and classroom learning tools, Learning Record Stores (LRS) and Learning Analytics Dashboards (LAD). Such aninfrastructure can apply Learning Analytics (LA) methods to process log data and supportvarious stakeholders. Teachers can refine their instructional practices, learners can enhancelearning experiences and researchers can study the dynamics of the teaching-learningprocess with it. While LA platforms gathers and analyses the data, there is a lack of specificdesign framework to capture the technology-enhanced teaching-learning practices. Thisposition paper focuses the research agenda on evidence in a data-driven educationalscenario. We propose the Learning Evidence Analytics Framework (LEAF) and present theresearch challenges involved. 26th International Conference on Computers in Education, Metro Manila, Philippines, November 26-30, 2018.
An Architecture and Reference Implementation of an Open Health Information Mediator: Enabling Interoperability in the Rwandan Health Information Exchange

Rwanda, one of the smallest and most densely populated countries in Africa, has made rapid and substantial progress towards designing and deploying a national health information system. One of the more challenging aspects of the system is the design of an architecture to support: interoperability between existing health information systems already in use in the country; incremental extension into a fully integrated national health information system without substantial re-engineering; and scaling, from a single district in the initial phase, to national level without requiring a fundamental change in technology or design paradigm. This paper describes the key requirements and the design of the current architecture using the ISO/IEC/IEEE 42010 standard architecture descriptions. The architecture takes an Enterprise Service Bus approach. A partial implementation and preliminary analysis of the architecture is given. Since these challenges are experienced by other developing African countries, the next steps involves creating a generic architecture that can be reused for health information exchange in other developing African countries.
Effects of Observational Uncertainty and Models Similarity on Climate Change Projections

Abstract         Climate change projections (CCPs) are based on the multimodel means of individual climate model simulations that are assumed to be independent. However, model similarity leads to projections biased toward the largest set of similar models and the underestimation of uncertainties. We assessed the influence of similarities in CMIP6 through CMIP3 CCPs. We ascertained model similarity due to shared physics/dynamics and initial conditions by comparing simulated spatial temperature and precipitation with the corresponding observed patterns and accounting for inter-model spread relative to the spread across observational datasets. After accounting for similarity, the information from 57 CMIP6, 47 CMIP5, and 24 CMIP3 models could be explained by just 11 effective models, without significant differences in globally averaged climate change statistics. The effective models showed a smaller globally averaged temperature rise of 0.25ºC (~0.5ºC–1ºC in some regions) by the end of 21 century relative to the multimodel mean of all models for socioeconomic pathways 5–8.5.