ethicalabs/Research-Intent-Collab huggingface dataset:
pencal: an R Package for the Dynamic Prediction of Survival with Many Longitudinal Predictors

In survival analysis, longitudinal information on the health status of a patient can be used to dynamically update the predicted probability that a patient will experience an event of interest. Traditional approaches to dynamic prediction such as joint models become computationally unfeasible with more than a handful of longitudinal covariates, warranting the development of methods that can handle a larger number of longitudinal covariates. We introduce the R package pencal, which implements a Penalized Regression Calibration (PRC) approach that makes it possible to handle many longitudinal covariates as predictors of survival. pencal uses mixed-effects models to summarize the trajectories of the longitudinal covariates up to a prespecified landmark time, and a penalized Cox model to predict survival based on both baseline covariates and summary measures of the longitudinal covariates. This article illustrates the structure of the R package, provides a step by step example showing how to estimate PRC, compute dynamic predictions of survival and validate performance, and shows how parallelization can be used to significantly reduce computing time.
Probing the electrochemistry of MXene (Ti2CTx)/electrolytic manganese dioxide (EMD) composites as anode materials for lithium-ion batteries

Abstract   The performance of MXenes (Ti2CTx) combined with electrolytic manganese dioxide (EMD) in three different weight ratios (i.e. MXene:EMD = 20:80; 50:50; 80:20) were examined as anode material for Lithium-ion batteries (LIBs). A study of the structure, composition and morphology of the synthesized materials was conducted. The materials were further investigated for their electrochemical properties in a half-cell configuration using impedance spectroscopy measurements, cyclic voltammetry and galvanostatic charge-discharge cycling. Results showed that the combined MXene/EMD material has a greater cycling stability, capacity and rate capability as compared to the EMD. The best ratio was found to be MXene:EMD = 80:20. The capacity obtained for this material after 200 cycles is 460 mA h g−1 at a current density of 100 mA g−1. The Li-ion accessibility improved with cycling. This study provides a first insight into the viability of using one of the lightest known MXenes and EMD composite for improved LIB anodes. As EMD is a low cost and abundant material, it provides great opportunities for improved capabilities for lightweight applications at an affordable cost.
Philosophy of Teaching by Michael A. Milton (updated to include a statement on ethical use of artificial intelligence in teaching and learning)

This white paper, "A Philosophy of Teaching," by Michael A. Milton, PhD, outlines the core principles and methodologies that undergird his approach to education, reflecting a robust integration of Christian faith and pedagogical practice. Dr. Milton anchors his philosophy in the Triune God revealed in Holy Scripture, following the Puritan poet John Milton's conviction that the end of learning is to repair the ruins of our first parents by regaining to know God aright — and from that knowledge, to love and imitate Him.Dr. Milton articulates his educational philosophy through a series of well-defined chapters: "One Sacred Moment," "A Strong Foundation for Lifelong Learning," "A Dialogical Method," "The Incarnate Means," "A Learning Environment Built on Integrity and Inclusivity," "Mastery Through Purposeful Progression," "The Golden Goal," and "Preparation for Real-World Application." His approach emphasizes a relational, dialogical method of instruction rooted in a theological framework that fosters deep, transformative learning experiences.Classical and theological perspectives deeply influence Milton's philosophy. He draws upon the Socratic tradition, the teaching example of Jesus of Nazareth as the Great Teacher, and the insights of Parker J. Palmer on authentic presence in the classroom. He advocates a scaffolding approach in which the professor first serves as a subject-matter expert, then gradually withdraws as the student constructs her own model of the subject, ultimately assuming the role of mentor. Milton's vision of teaching is profoundly incarnational, insisting that deep learning occurs only when the teacher becomes a full participant in the learning community.The document addresses the ethical use of artificial intelligence in both publication and the classroom, articulating a principled distinction between technology as a servant of the educator's voice and the illegitimate outsourcing of thought that, in Dr. Milton's phrase, produces the laundering of ignorance into the appearance of competence. A concluding postscript grounds the entire philosophy in Dr. Milton's Christian confession, his vocation as a learner-teacher, and his prayer that his teaching flow from an authentic center where Jesus of Nazareth teaches him still.Milton's philosophy is encapsulated in his dedication to fostering an environment where education serves the glory of God, the well-being of humanity, and the healing of creation — and in the single phrase he offers as its distillation: to pass along that which he received, a license to learn.
Conditional Image-to-Video Generation with Latent Flow Diffusion Models

CVPR 2023
A global view of the modulation of cosmic ray protons in the heliosphere for the solar minimum period up to 2009

The heliosphere was in a state of ideal solar minimum conditions for at least three years up to the end of 2009. During this period the highest ever recorded cosmic ray spectra were observed at the Earth. Fortunately, the PAMELA and Ulysses KET instruments simultaneously observed proton intensities for most of the period between July 2006 and June 2009, while Voyager 1 made observations in the outer heliosphere. This provides a good opportunity to compare the basic features of a comprehensive numerical model for the global modulation of cosmic rays in the heliosphere with these observations. Global gradients for protons are computed with the model for this prolonged solar minimum of cycle 23/24. This is done for both radial and latitudinal gradients, with the latter possible because Ulysses changed its position significantly in the heliocentric meridional plane during this period. The modulation model is set up for the conditions that prevailed during this unusual solar minimum period so that insight is gained on what role particle drifts played in establishing the observed gradients for this period. Good agreement is found between computed and observed gradients so that we conclude that the model gives a most reasonable representation of modulation conditions from the Earth to the heliopause for the mentioned period. These results can be used to refine the theory for diffusion, particle drifts and turbulence in the heliosphere.
Reviewing the evidence used in cost-effectiveness models in health technology assessment: a qualitative investigation of current concerns and future research priorities

Objectives: Health technology assessments typically include a systematic review of clinical efficacy evidence and a cost-effectiveness model. The development of the model always requires additional information beyond clinical efficacy. Depending on the timing, size and number of information requirements, the researcher faces considerable difficulties ensuring that reviewing activity to inform model development is both timely and systematic. There is a tension in terms of the need to ensure that this process is transparent and reproducible. This paper uses qualitative methods to identify these issues and to explore options for their resolution.\ud \ud Methods: A focus group was held with 1 7experienced systematic reviewers, information specialists and health economic modellers. Framework analysis was used to analyse themes within the qualitative data.\ud \ud Results: Six key themes were identified including: 1) problem structuring, 2) current practice, 3) adequate information, 4) timing, 5) ideal practice and 6) areas for further research. Reviewing, searching and modelling were seen as integrated tasks and the respondents felt that the whole team should be involved in structuring the decision problem. Good communication was deemed to be essential with more time spent on the most important information requirements. Assessments of the quality and relevance of information were also considered important by the focus group members. Future research needs include training for focussed searching, problem structuring, quality assessment and the validation of parameter estimates.\ud \ud Conclusions: This preliminary investigation highlights numerous concerns and potential deficiencies in the process of identifying, selecting and using evidence to inform models. Further guidance is required to ensure that such research activity is transparent, timely and rigorous.\ud \ud
Southern African Climate Change: Processes, Models, and Projections

ABSTRACT                   Southern Africa, along with the Mediterranean and eastern South America, is one of three major land regions projected to dry in the future. Confidence in the drying trend relies on a clear understanding of the regional climate and evaluation of the climate models used for projections. This paper reviews our understanding of southern African climate and evaluates the performance of successive climate model generations. A key finding is improved confidence in projections of early summer drying. The drying results from a delayed seasonal cycle, with regional features such as the Congo Air Boundary and heat lows increasing in frequency in the future. Future drying is matched by emerging observed trends indicating large temperature increases and drying in early summer, in addition to projections of increases in extreme rainfall, heatwaves, and dry spells. There is greater uncertainty in climate changes in the core rainy season (December to February), in part due to systematic climate model biases in this season, which have worsened through model generations. Model biases are compounded by a lack of in situ observations of key regional climate features, including heat lows, tropical lows, and mesoscale convective systems. The review recommends a focus on quantifying changes in circulation features and rainfall systems, which are well known but lack comprehensive assessment of trends and future projections. The future of tropical cyclone risk is a priority, especially given the increase in the frequency of intense tropical cyclones in the Mozambique Channel over the last 40 years.                                        This article is categorized under:                                                                     Paleoclimates and Current Trends &gt; Modern Climate Change
Mental health of Canadian youth: A systematic review and meta-analysis of studies examining changes in depression, anxiety, and suicide-related outcomes during the COVID-19 pandemic

This systematic review and meta-analysis aims to describe Canadian youth mental health during the COVID-19 pandemic, focusing on changes in anxiety and depressive symptoms and suicidality.We searched four databases up to February 2023 for longitudinal or repeated cross-sectional studies reporting on changes in depressive and anxiety symptoms, suicidality, or related services utilization among young people under 25 years old residing in Canada during the COVID-19 pandemic. Random-effects meta-analyses were performed for studies comparing depressive and anxiety symptoms from before to during the first, second, and third COVID-19 waves (up to June 2021), and between COVID-19 waves. Other studies were described narratively. Risk of bias was assessed using an adapted Joanna Briggs Institute Checklist.Of the 7916 records screened, 35 articles met inclusion criteria for this review. Included studies were highly heterogeneous in design, population, and type of change investigated, and many had a high risk of bias. The meta-analyses found that depressive symptoms worsened minimally from pre-pandemic to wave 1 but returned to pre-pandemic levels by wave 2. Anxiety symptoms were broadly comparable from pre-pandemic to waves 1 and 2 but worsened from waves 1 to 3 and from pre-pandemic to wave 1 for girls. The narrative review included several studies that provided inconclusive evidence of increases in services utilization.The current evidence is limited and highly heterogeneous, making it insufficient to draw definitive conclusions regarding the short- to medium-term impact of the pandemic on youth mental health in Canada. Obtaining better mental health surveillance among Canadian youth is imperative.
Disaster risk reduction through early warning systems

Disaster Risk Reduction (DRR) is critical to safeguarding the safety and well-being of communities in coastal regions and communities. An early warning system (EWS) is crucial for mitigating catastrophic risks associated with flooding because an effective EWS can allow communities to take the required steps to reduce the impacts of an impending hazard. It can therefore be implied that one of the reasons for the devastating effects of flooding in coastal communities, may be associated with the lack of EWS as witnessed in the October 2022 flooding in Nigeria. This paper advocates for more effective EWS and better technology integration into emergency operations in Nigeria's emergency services to minimise the impacts of flood risk amidst the cascading effects of climate emergency. A combination of primary and secondary research methodologies was employed to conduct the study. This paper used the constructivist philosophical perspective to explain the snowball sampling method utilised for the research. The method also involves distributing open-ended questionnaires to Nigerian individuals in flood-prone regions, to obtain accurate information on flood occurrences. The selection criteria required individuals to have resided in Nigeria for at least five years. The survey involved 45 participants aged 20-60, with a majority being corporate workers (69.2%). Drawing from secondary data on the October 2022 flooding in Nigeria, and collated primary data, the result displayed a lack of effective Early Warning System (EWS)and limited response capabilities especially in coastal towns and cities. The findings from this evaluative process indicate the need to drastically improve EWS and capabilities for response in coastal communities in Nigeria. This paper recommends incorporating disaster risk reduction (DRR) into development planning and policy to ensure the long-term viability of disaster risk reduction efforts. Beyond the policy implication of this paper, the impact on practice includes reviewing existing structural and non-structural measures to incorporate applicable and more effective EWS for coastal communities, and technology into regional emergency operations centres in the country.
Context-Based Feature Technique for Sarcasm Identification in Benchmark Datasets Using Deep Learning and BERT Model

Le sarcasme est un terme linguistique compliqué que l'on trouve couramment dans le commerce électronique et les sites de médias sociaux. Le fait de ne pas identifier les énoncés sarcastiques dans les applications de traitement du langage naturel telles que l'analyse des sentiments et l'exploration d'opinions confondra les algorithmes de classification et générera de faux résultats. Plusieurs études sur la détection du sarcasme ont utilisé différents algorithmes d'apprentissage. Cependant, la plupart de ces modèles d'apprentissage se sont toujours concentrés sur le contenu de l'expression uniquement, laissant les informations contextuelles isolées. En conséquence, ils n'ont pas réussi à capturer les informations contextuelles dans l'expression sarcastique. Deuxièmement, de nombreuses méthodes d'apprentissage en profondeur en PNL utilisent un algorithme d'apprentissage par intégration de mots comme approche standard pour la représentation vectorielle des caractéristiques, qui ignore la polarité des sentiments des mots dans l'expression sarcastique. Cette étude propose une technique de caractéristiques basée sur le contexte pour l'identification du sarcasme en utilisant le modèle d'apprentissage en profondeur, le modèle BERT et l'apprentissage automatique conventionnel pour résoudre les problèmes mentionnés ci-dessus. Deux ensembles de données de référence Twitter et Internet Argument Corpus, version deux (IAC-v2) ont été utilisés pour la classification en utilisant les trois modèles d'apprentissage. Le premier modèle utilise une représentation basée sur l'intégration via un modèle d'apprentissage en profondeur avec un long court bidirectionnel mémoire de termes (Bi-LSTM), une variante du réseau neuronal récurrent (RNN), en appliquant la représentation vectorielle globale (GloVe) pour la construction de l'intégration de mots et l'apprentissage contextuel. Le deuxième modèle est basé sur Transformer en utilisant une représentation d'encodeur bidirectionnel et un transformateur (BERT) pré-entraînés. En revanche, le troisième modèle est basé sur la fusion de fonctionnalités qui comprenait la fonctionnalité BERT, la fonctionnalité liée au sentiment, la fonctionnalité syntaxique et l'intégration GloVe avec l'apprentissage automatique conventionnel. L'efficacité de cette technique est testée avec diverses expériences d'évaluation. Cependant, l'évaluation de la technique sur deux ensembles de données de référence Twitter a atteint 98,5 % et 98,0 % de précision la plus élevée, respectivement. L'ensemble de données IAC-v2, d'autre part, a atteint la plus grande précision de 81,2 %, ce qui montre l'importance de la technique proposée par rapport aux approches de base pour l'analyse du sarcasme.
The Faint Optical Afterglow and Host Galaxy of GRB 020124: Implications for the Nature of Dark Gamma‐Ray Bursts

We present ground-based optical observations of GRB 020124 starting 1.6 hr after the burst, as well as subsequent Very Large Array and Hubble Space Telescope (HST) observations. The optical afterglow of GRB 020124 is one of the faintest afterglows detected to date, and it exhibits a relatively rapid decay, Fv ∝ t-1.60±0.04, followed by further steepening. In addition, a weak radio source was found coincident with the optical afterglow. The HST observations reveal that a positionally coincident host galaxy must be the faintest host to date, R ≳ 29.5 mag. The afterglow observations can be explained by several models requiring little or no extinction within the host galaxy, AVhost ≈ 0-0.9 mag. These observations have significant implications for the interpretation of the so-called dark bursts (bursts for which no optical afterglow is detected), which are usually attributed to dust extinction within the host galaxy. The faintness and relatively rapid decay of the afterglow of GRB 020124, combined with the low inferred extinction, indicate that some dark bursts are intrinsically dim and not dust obscured. Thus, the diversity in the underlying properties of optical afterglows must be observationally determined before substantive inferences can be drawn from the statistics of dark bursts.
An Overview of 3D Printing (Additive Manufacturing in Powder-Based Methods) Materials, Methods, Mechanical Properties, and Applications

The multi-component alloys with special technique of additive manufacturing or 3D printing creates the novel material to enhance the mechanical characteristics, excellent formability, and maximum potency. Because these techniques were able to compose the layer-by-layer process with various materials like titanium, nickel alloys, and aluminium matrix materials, for creating the complex based geometry shapes, this additive technique recreates the material with layer-by-layer on the substrate with the help of powder materials with selected process parameters. The selected materials of additive manufacturing possess oxidation performances, creep resistances, high hardness, hydrogen properties, compressive strength, and tensile strength are in maximum level, and the post-heat treatments are well built on the substrate layers. Therefore, this chapter was utilized to identify the correctness of manufacturing procedures, selection of materials, and application-oriented areas.
Learning Curves for Decision Making in Supervised Machine Learning: A Survey

Learning curves are a concept from social sciences that has been adopted in the context of machine learning to assess the performance of a learning algorithm with respect to a certain resource, e.g., the number of training examples or the number of training iterations. Learning curves have important applications in several machine learning contexts, most notably in data acquisition, early stopping of model training, and model selection. For instance, learning curves can be used to model the performance of the combination of an algorithm and its hyperparameter configuration, providing insights into their potential suitability at an early stage and often expediting the algorithm selection process. Various learning curve models have been proposed to use learning curves for decision making. Some of these models answer the binary decision question of whether a given algorithm at a certain budget will outperform a certain reference performance, whereas more complex models predict the entire learning curve of an algorithm. We contribute a framework that categorises learning curve approaches using three criteria: the decision-making situation they address, the intrinsic learning curve question they answer and the type of resources they use. We survey papers from the literature and classify them into this framework.
Black Hole Thermodynamics via Tsallis Statistical Mechanics and Phase Transitions Probed by Optical Characteristics

We develop a non-extensive thermodynamic framework for Reissner--Nordström black holes based on a near-horizon photon-gas model within Tsallis statistics. We derive the generalized Bekenstein--Hawking entropy based on such an approach, consistent with the Bekenstein--Hawking area law in the extensive limit, $q \rightarrow 1$. The induced deformation gives rise to a rich thermodynamic structure consisting of small, intermediate, and large black-hole branches, exhibiting Van der Waals-like phase transitions characterized by mean-field critical exponents. We further establish an optical--thermodynamic analogy by relating photon-sphere observables, including orbital periods and Lyapunov exponents, to thermodynamic variables. These optical signatures qualitatively track the thermodynamic critical behavior and phase structure, suggesting their potential relevance as observational probes in future high-resolution measurements. These results may shed light on a conceptual connection between non-extensive entropy, black-hole critical phenomena, and strong-gravity optics.