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Continuing professional development for teachers in the physical sciences is crucial to maintaining high-quality instruction, especially when addressing modern physics. Nevertheless, the teaching of these topics often relies on theoretical models that may seem abstract and removed from practical applications. In this context, research in astrophysics provides many valuable insights into the nature of light and its fundamental properties, such as continuous and discrete spectra, blackbody radiation, and atomic orbitals. This paper, aimed at both high school and university-level physics teachers, examines the peculiarities of the emission and absorption spectra of various types of astronomical objects and demonstrates how spectroscopy is applied in astrophysics research. From this perspective, the study conceptually illustrates how astrophysicists, by measuring light spectra, determine the composition, physical properties, origin, and evolution of celestial bodies and, by extension, of the universe as a whole. By understanding not only the theory but also the direct applications of astronomical spectroscopy, teachers will be better prepared to guide their students, thereby showcasing

The United States Food and Drug Administration (FDA) conducted a benefit-risk assessment for Moderna's COVID vaccine mRNA-1273 prior to its full approval, announced 1/31/2022. The FDA's assessment focused on males of ages 18-64 years because the agency's risk analysis was limited to vaccine-attributable myocarditis/pericarditis (VAM/P) given the excess risk among males. The FDA's analysis concluded that vaccine benefits clearly outweighed risks, even for 18-25-year-old males (those at highest VAM/P risk). We reanalyze the FDA's benefit-risk assessment using information available through the third week of January 2022 and focusing on 18-25-year-old males. We use the FDA's framework but extend its model by accounting for protection derived from prior COVID infection, finer age-stratification in COVID-hospitalization rates, and incidental hospitalizations (those of patients who test positive for COVID but are being treated for something else). We also use more realistic projections of Omicron-infection rates and more accurate rates of VAM/P. With hospitalizations as the principal endpoint of the analysis (those prevented by vaccination vs. those caused by VAM/P), our model finds vacci

The thought-provoking analogy between AI and electricity, made by computer scientist and entrepreneur Andrew Ng, summarizes the deep transformation that recent advances in Artificial Intelligence (AI) have triggered in the world. This chapter presents an overview of the ever-evolving landscape of AI, written in Portuguese. With no intent to exhaust the subject, we explore the AI applications that are redefining sectors of the economy, impacting society and humanity. We analyze the risks that may come along with rapid technological progress and future trends in AI, an area that is on the path to becoming a general-purpose technology, just like electricity, which revolutionized society in the 19th and 20th centuries. A provocativa comparação entre IA e eletricidade, feita pelo cientista da computação e empreendedor Andrew Ng, resume a profunda transformação que os recentes avanços em Inteligência Artificial (IA) têm desencadeado no mundo. Este capítulo apresenta uma visão geral pela paisagem em constante evolução da IA. Sem pretensões de exaurir o assunto, exploramos as aplicações que estão redefinindo setores da economia, impactando a sociedade e a humanidade. Analisamos os riscos q

The test-negative design (TND) is a resource-efficient observational study design that can assess vaccine effectiveness and exposure-proximal immune correlates of disease. The TND enrolls symptomatic individuals seeking diagnostic testing and compares case status by an exposure variable, such as vaccination status or immune marker level, that is measured at testing. While the TND reduces confounding by healthcare-seeking behavior, other sources of confounding may remain. TND studies may also have missing data in the exposure variable due to incomplete records or two-phase sampling designs. We present a targeted maximum likelihood estimation approach involving a semiparametric logistic regression model that targets a causal conditional risk ratio of symptomatic disease in the healthcare-seeking population. Under causal and missing at random assumptions, our method produces an efficient, asymptotically linear estimator that provides flexible, data-driven confounding control and valid causal inference when analyzing TND studies with missing exposure variable data. We evaluate our method's finite sample properties using plasmode simulations of a two-phase TND immune correlates study. W

Understanding how vaccine effectiveness (VE) changes over time can provide evidence-based guidance for public health decision making. While commonly reported by practitioners, time-varying VE estimates obtained using Cox regression are vul- nerable to hidden biases. To address these limitations, we describe how to leverage vaccine-irrelevant infections to identify hazard-based, time-varying VE in the pres- ence of unmeasured confounding and selection bias. We articulate assumptions under which our approach identifies a causal effect of an intervention deferring vaccination and interaction with the community in which infections circulate. We develop sieve and efficient influence curve-based estimators and discuss imposing monotone shape constraints and estimating VE against multiple variants. As a case study, we examine the observational booster phase of the Coronavirus Vaccine Efficacy (COVE) trial of the Moderna mRNA-1273 COVID-19 vaccine which used symptom-triggered multi- plex PCR testing to identify acute respiratory illnesses (ARIs) caused by SARS-CoV-2 and 20 off-target pathogens previously identified as compelling negative controls for COVID-19. Accounting for vaccine-irrele

This study examines the roles of public and private sector actors in the development of mRNA vaccines, a breakthrough innovation in modern medicine. Using a dataset of 151 core patent families and 2,416 antecedent (cited) patents, we analyze the structure and dynamics of the mRNA vaccine knowledge network through network theory. Our findings highlight the central role of biotechnology firms, such as Moderna and BioNTech, alongside the crucial contributions of universities and public research organizations (PROs) in providing foundational knowledge.We develop a novel credit allocation framework, showing that universities, PROs, government and research centers account for at least 27% of the external technological knowledge base behind mRNA vaccine breakthroughs - representing a minimum threshold of their overall contribution. Our study offers new insights into pharmaceutical and biotechnology innovation dynamics, emphasizing how Moderna and BioNTech's mRNA technologies have benefited from academic institutions, with notable differences in their institutional knowledge sources.

Numerous successes have been achieved in combating the COVID-19 pandemic, initially using various precautionary measures like lockdowns, social distancing, and the use of face masks. More recently, various vaccinations have been developed to aid in the prevention or reduction of the severity of the COVID-19 infection. Despite the effectiveness of the precautionary measures and the vaccines, there are several controversies that are massively shared on social media platforms like Twitter. In this paper, we explore the use of state-of-the-art transformer-based language models to study people's acceptance of vaccines in Nigeria. We developed a novel dataset by crawling multi-lingual tweets using relevant hashtags and keywords. Our analysis and visualizations revealed that most tweets expressed neutral sentiments about COVID-19 vaccines, with some individuals expressing positive views, and there was no strong preference for specific vaccine types, although Moderna received slightly more positive sentiment. We also found out that fine-tuning a pre-trained LLM with an appropriate dataset can yield competitive results, even if the LLM was not initially pre-trained on the specific language

Categorical data are common in educational and social science research; however, methods for its analysis are generally not covered in introductory statistics courses. This chapter overviews fundamental concepts and methods in categorical data analysis. It describes and illustrates the analysis of contingency tables given different sampling processes and distributions, estimation of probabilities, hypothesis testing, measures of associations, and tests of no association with nominal variables, as well as the test of linear association with ordinal variables. Three data sets illustrate fatal police shootings in the United States, clinical trials of the Moderna vaccine, and responses to General Social Survey questions.

The standard model of modern cosmology provides a description of a wide range of astrophysical and astronomical data. However, despite this impressive success, several discrepancies have have persisted. Most strikingly, the emerging tension in the observed and inferred values of the Hubble constant. This constant parameterizes the rate of expansion of the cosmos and thus provides clues about its energy content. In this article we examine the origin of this discrepancy and we explore possible solutions to overcome the problem.

The importance of implementing unitarity constraints in meson spectroscopy is very briefly outlined for Portuguese students of engineering sciences and therefore non-experts in the field. After explaining the profound differences between meson spectroscopy and atomic spectroscopy, attention is paid to the shortcomings of standard Breit-Wigner parametrisations in the case of broad and/or overlapping resonances. Finally, the manifestly unitary Resonance-Spectrum-Expansion model, which lies at the heart of a recent invited review paper by the present authors, is graphically presented, together with a simple yet typical application to the long-controversial $K_0^\star(700)$ resonance.

Learning the Individual Treatment Effect (ITE) is essential for personalized decision-making, yet causal inference has traditionally focused on aggregated treatment effects. While integrating conformal prediction with causal inference can provide valid uncertainty quantification for ITEs, the resulting prediction intervals are often excessively wide, limiting their practical utility. To address this limitation, we introduce \underline{S}urrogate-assisted \underline{C}onformal \underline{I}nference for \underline{E}fficient I\underline{N}dividual \underline{C}ausal \underline{E}ffects (SCIENCE), a framework designed to construct more efficient prediction intervals for ITEs. SCIENCE accommodates the covariate shifts between source data and target data and applies to various data configurations, including semi-supervised and surrogate-assisted semi-supervised learning. Leveraging semi-parametric efficiency theory, SCIENCE produces rate double-robust prediction intervals under mild rate convergence conditions, permitting the use of flexible non-parametric models to estimate nuisance functions. We quantify efficiency gains by comparing semi-parametric efficiency bounds with and without

Metascientific criteria used for explaining or constraining physical space dimensionality and their historical relationship to prevailing causal systems are discussed. The important contributions by Aristotle, Kant and Ehrenfest to the dimensionality of space problem are considered and shown to be grounded on different causal explanations: {\it causa materialis} for Aristotle, {\it causa efficiens} for young Kant and an ingenious combination of {\it causa efficiens} and {\it causa formalis} for Ehrenfest. The prominent and growing rôle played by {\it causa formalis} in modern physical approaches to this problem is emphasized.

A brief introduction to gravitational waves, addressing those questions that nowadays more and more frequently arise within a large audience (not only of insiders): what is a gravity wave, how can it be detected and, above all, what impact on modern physical theories may have their recent discovery? Interested readers can also find a few simple technical details and insights in the final Appendices.

A pandemic, the worldwide spread of a disease, can threaten human beings from the social as well as biological perspectives and paralyze existing living habits. To stave off the more devastating disaster and return to a normal life, people make tremendous efforts at multiscale levels from individual to worldwide: paying attention to hand hygiene, developing social policies such as wearing masks, social distancing, quarantine, and inventing vaccines and remedy. Regarding the current severe pandemic, namely the coronavirus disease 2019, we explore the spreading-suppression effect when adopting the aforementioned efforts. Especially the quarantine and vaccination are considered since they are representative primary treatments for block spreading and prevention at the government level. We establish a compartment model consisting of susceptible (S), vaccination (V), exposed (E), infected (I), quarantined (Q), and recovered (R) compartments, called SVEIQR model. We look into the infected cases in Seoul and consider three kinds of vaccines, Pfizer, Moderna, and AstraZeneca. The values of the relevant parameters are obtained from empirical data from Seoul and clinical data for vaccines and

Statistical methods are developed for analysis of clinical and virus genetics data from phase 3 randomized, placebo-controlled trials of vaccines against novel coronavirus COVID-19. Vaccine efficacy (VE) of a vaccine to prevent COVID-19 caused by one of finitely many genetic strains of SARS-CoV-2 may vary by strain. The problem of assessing differential VE by viral genetics can be formulated under a competing risks model where the endpoint is virologically confirmed COVID-19 and the cause-of-failure is the infecting SARS-CoV-2 genotype. Strain-specific VE is defined as one minus the cause-specific hazard ratio (vaccine/placebo). For the COVID-19 VE trials, the time to COVID-19 is right-censored, and a substantial percentage of failure cases are missing the infecting virus genotype. We develop estimation and hypothesis testing procedures for strain-specific VE when the failure time is subject to right censoring and the cause-of-failure is subject to missingness, focusing on $J \ge 2$ discrete categorical unordered or ordered virus genotypes. The stratified Cox proportional hazards model is used to relate the cause-specific outcomes to explanatory variables. The inverse probability w

The COVID-19 pandemic due to the novel coronavirus SARS CoV-2 has inspired remarkable breakthroughs in development of vaccines against the virus and the launch of several phase 3 vaccine trials in Summer 2020 to evaluate vaccine efficacy (VE). Trials of vaccine candidates using mRNA delivery systems developed by Pfizer-BioNTech and Moderna have shown substantial VEs of 94-95%, leading the US Food and Drug Administration to issue Emergency Use Authorizations and subsequent widespread administration of the vaccines. As the trials continue, a key issue is the possibility that VE may wane over time. Ethical considerations dictate that all trial participants be unblinded and those randomized to placebo be offered vaccine, leading to trial protocol amendments specifying unblinding strategies. Crossover of placebo subjects to vaccine complicates inference on waning of VE. We focus on the particular features of the Moderna trial and propose a statistical framework based on a potential outcomes formulation within which we develop methods for inference on whether or not VE wanes over time and estimation of VE at any post-vaccination time. The framework clarifies assumptions made regarding in

I present here longitudinal evaluation of T and B cell immunity to SARS-CoV2 and variants of concern (VOC) from a single subject (me) over an entire year post vaccination. After enrolling in the Moderna phase III clinical trial, I collected my own biological samples pre- and post-immunization in the event of being a recipient of the experimental vaccine. The evidence strongly supports the conclusion that I did not receive the placebo. The analysis is admittedly limited to an n of 1, but the results fit well with data taken from published works and represent one of the more comprehensive longitudinal evaluations of vaccine-elicited immunity within a single individual yet to be undertaken. Though the data amount to a well-documented anecdote, given its granularity, it is not without its insights and may be of further use in directing future longitudinal studies that have actual statistical significance.

According to the World Health Organization, development of the COVID-19 vaccine is occurring in record time. Administration of the vaccine has started the same year as the declaration of the COVID-19 pandemic. The United Nations emphasized the importance of providing COVID-19 vaccines as "a global public good", which is accessible and affordable world-wide. Pricing the COVID-19 vaccines is a controversial topic. We use optimization and game theoretic approaches to model the COVID-19 U.S. vaccine market as a duopoly with two manufacturers Pfizer-BioNTech and Moderna. The results suggest that even in the context of very high production and distribution costs, the government can negotiate prices with the manufacturers to keep public sector prices as low as possible while meeting demand and ensuring each manufacturer earns a target profit. Furthermore, these prices are consistent with those currently predicted in the media.

Federated learning of causal estimands may greatly improve estimation efficiency by leveraging data from multiple study sites, but robustness to heterogeneity and model misspecifications is vital for ensuring validity. We develop a Federated Adaptive Causal Estimation (FACE) framework to incorporate heterogeneous data from multiple sites to provide treatment effect estimation and inference for a flexibly specified target population of interest. FACE accounts for site-level heterogeneity in the distribution of covariates through density ratio weighting. To safely incorporate source sites and avoid negative transfer, we introduce an adaptive weighting procedure via a penalized regression, which achieves both consistency and optimal efficiency. Our strategy is communication-efficient and privacy-preserving, allowing participating sites to share summary statistics only once with other sites. We conduct both theoretical and numerical evaluations of FACE and apply it to conduct a comparative effectiveness study of BNT162b2 (Pfizer) and mRNA-1273 (Moderna) vaccines on COVID-19 outcomes in U.S. veterans using electronic health records from five VA regional sites. We show that compared to t