A New Cosmological Model: Exploring the Evolution of the Universe and Unveiling Super-Accelerated Expansion

In this paper, we present a cosmological model designed to study the evolution of the universe based on a new parametrization of the deceleration parameter. The model considers a spatially flat, homogeneous, and isotropic Friedmann-Lema\^itre-Robertson-Walker (FLRW) universe filled with radiation, dark matter (DM), and dark energy (DE). We derive the Friedmann equations and the energy conservation equation for the universe, accounting for separate conservation equations for radiation, DM, and DE. Our proposed deceleration parameter is given by a formula involving constants $H_{0}$, $\Omega_{r0}$, $\Omega_{m0}$, $q_{2}$, $q_{1}$, $q_{0}$, $\alpha$ and $\beta$. which we subsequently fit to observational data. To assess the model's viability, we compare it with a diverse range of observational data, including cosmic chronometers, type Ia supernovae, baryon acoustic oscillations, and cosmic microwave background measurements. Employing the chi-square statistic and a Markov Chain Monte Carlo (MCMC) method, we estimate the best-fit values for the free parameters and investigate the constraints imposed by observational data on the model. Our results indicate that our cosmological model provides an excellent fit to the observed data and exhibits a remarkable agreement with the standard $\Lambda$CDM paradigm at higher redshifts. However, the most intriguing discovery lies in the model's prediction of a super-accelerated expansion in the distant future, in contrast to the de Sitter phase predicted by $\Lambda$CDM. This implies the presence of dark energy driving the universe's accelerated expansion. These findings suggest that our proposed cosmological model offers a compelling alternative to the $\Lambda$CDM paradigm, shedding new light on the nature of dark energy and the future fate of the cosmos.Comment: 10 figures, 2 table

Exploring Tidal Force Effects and Shadow Constraints for Schwarzschild-like Black Hole in Starobinsky-Bel-Robinson Gravity

The current manuscript deals with the tidal force effects, geodesic deviation, and shadow constraints of the Schwarzschild-like black hole theorised in Starobinsky-Bel-Robinson gravity exhibiting M-theory compactification. In the current analysis, we explore the radial and angular tidal force effects on a radially in-falling particle by the central black hole, which is located in this spacetime. We also numerically solve the geodesic deviation equation and study the variation of the geodesic separation vector with the radial coordinate for two nearby geodesics using suitable initial conditions. All the obtained results are tested for Sag A* and M87* by constraining the value of the stringy gravity parameter $\beta$ using the shadow data from the event horizon telescope observations. All the results are compared with Schwarzschild black hole spacetime. In our study, we found that both the radial and angular tidal forces experienced by a particle switch their initial behaviour and turn compressive and stretching, respectively, before reaching the event horizon. The geodesic deviation shows an oscillating trend as well for the chosen initial condition. For the constrained value of $\beta$, we see that the spacetime geometry generated by Sag A* and M87* is effectively same for both Schwarzschild and Starobinsky-Bel-Robinson black hole. Furthermore, we also calculated the angular diameter of the shadow in Starobinsky-Bel-Robinson black hole and compared with the Schwarzschild black hole. It is observed that the angular diameter of shadow for M87* and Sgr A* in Starobinsky-Bel-Robinson black hole is smaller than the Schwarzschild black hole. The calculated results satisfy the event horizon telescope observational constraints. Finally, we have concluding remarks.Comment: 12 pages, 18 figures, accepted for publication in European Physical Journal

Machine Reading Comprehension using Case-based Reasoning

We present an accurate and interpretable method for answer extraction in machine reading comprehension that is reminiscent of case-based reasoning (CBR) from classical AI. Our method (CBR-MRC) builds on the hypothesis that contextualized answers to similar questions share semantic similarities with each other. Given a target question, CBR-MRC retrieves a set of similar questions from a memory of observed cases and predicts an answer by selecting the span in the target context that is most similar to the contextualized representations of answers in the retrieved cases. The semi-parametric nature of our approach allows CBR-MRC to attribute a prediction to the specific set of cases used during inference, making it a desirable choice for building reliable and debuggable QA systems. We show that CBR-MRC achieves high test accuracy comparable with large reader models, outperforming baselines by 11.5 and 8.4 EM on NaturalQuestions and NewsQA, respectively. Further, we also demonstrate the ability of CBR-MRC in identifying not just the correct answer tokens but also the span with the most relevant supporting evidence. Lastly, we observe that contexts for certain question types show higher lexical diversity than others and find CBR-MRC to be robust to these variations while performance using fully-parametric methods drops.Comment: 9 pages, 2 figure

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Exploring tidal force effects and shadow constraints for Schwarzschild-like black hole in Starobinsky–Bel-Robinson gravity

Abstract The current manuscript deals with the tidal force effects, geodesic deviation, and shadow constraints of the Schwarzschild-like black hole theorised in Starobinsky–Bel-Robinson gravity exhibiting M-theory compactification. In the current analysis, we explore the radial and angular tidal force effects on a radially in-falling particle by the central black hole, which is located in this spacetime. We also numerically solve the geodesic deviation equation and study the variation of the geodesic separation vector with the radial coordinate for two nearby geodesics using suitable initial conditions. All the obtained results are tested for Sag A* and M87* by constraining the value of the stringy gravity parameter $$\beta$$ β using the shadow data from the event horizon telescope observations. All the results are compared with Schwarzschild black hole spacetime. In our study, we found that both the radial and angular tidal forces experienced by a particle switch their initial behaviour and turn compressive and stretching, respectively, before reaching the event horizon. The geodesic deviation shows an oscillating trend as well for the chosen initial condition. For the constrained value of $$\beta$$ β , we see that the spacetime geometry generated by Sag A* and M87* is effectively same for both Schwarzschild and Starobinsky–Bel-Robinson black hole. Furthermore, we also calculated the angular diameter of the shadow in Starobinsky–Bel-Robinson black hole and compared with the Schwarzschild black hole. It is observed that the angular diameter of shadow for M87* and Sgr A* in Starobinsky–Bel-Robinson black hole is smaller than the Schwarzschild black hole. The calculated results satisfy the event horizon telescope observational constraints

Using artificial intelligence for improving stroke diagnosis in emergency departments: a practical framework.

Stroke is the fifth leading cause of death in the United States and a major cause of severe disability worldwide. Yet, recognizing the signs of stroke in an acute setting is still challenging and leads to loss of opportunity to intervene, given the narrow therapeutic window. A decision support system using artificial intelligence (AI) and clinical data from electronic health records combined with patients presenting symptoms can be designed to support emergency department providers in stroke diagnosis and subsequently reduce the treatment delay. In this article, we present a practical framework to develop a decision support system using AI by reflecting on the various stages, which could eventually improve patient care and outcome. We also discuss the technical, operational, and ethical challenges of the process

Using artificial intelligence for improving stroke diagnosis in emergency departments: a practical framework.

Stroke is the fifth leading cause of death in the United States and a major cause of severe disability worldwide. Yet, recognizing the signs of stroke in an acute setting is still challenging and leads to loss of opportunity to intervene, given the narrow therapeutic window. A decision support system using artificial intelligence (AI) and clinical data from electronic health records combined with patients\u27 presenting symptoms can be designed to support emergency department providers in stroke diagnosis and subsequently reduce the treatment delay. In this article, we present a practical framework to develop a decision support system using AI by reflecting on the various stages, which could eventually improve patient care and outcome. We also discuss the technical, operational, and ethical challenges of the process

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