Classifying Exoplanets with Gaussian Mixture Model

Recently, Odrzywolek and Rafelski (arXiv:1612.03556) have found three distinct categories of exoplanets, when they are classified based on density. We first carry out a similar classification of exoplanets according to their density using the Gaussian Mixture Model, followed by information theoretic criterion (AIC and BIC) to determine the optimum number of components. Such a one-dimensional classification favors two components using AIC and three using BIC, but the statistical significance from both the tests is not significant enough to decisively pick the best model between two and three components. We then extend this GMM-based classification to two dimensions by using both the density and the Earth similarity index (arXiv:1702.03678), which is a measure of how similar each planet is compared to the Earth. For this two-dimensional classification, both AIC and BIC provide decisive evidence in favor of three components.Comment: 8 pages, 7 figure

Two Dimensional Clustering of Gamma-Ray Bursts using durations and hardness

Gamma-Ray Bursts (GRBs) have been conventionally bifurcated into two distinct categories: ``short'' and ``long'' with durations less than and greater than two seconds respectively. However, there is a lot of literature (although with conflicting results) regarding the existence of a third intermediate class. To investigate this issue, we extend a recent study (arXiv:1612.08235) on classification of GRBs to two dimensions by incorporating the GRB hardness in addition to the observed durations. We carry out this unified analysis on GRB datasets from four detectors, viz. BATSE, RHESSI, Swift (observed and intrinsic frame), and Fermi-GBM. We consider the duration and hardness features in log-scale for each of these datasets and determine the best-fit parameters using Gaussian Mixture Model. This is followed by information theoretic criterion (AIC and BIC) to determine if a three-component fit is favored compared to a two-component one or vice-versa. For BATSE, we find that both AIC and BIC show preference for three components with decisive significance. For Fermi and RHESSI, both AIC and BIC show preference for two components, although the significance is marginal from AIC, but decisive using BIC. For Swift dataset in both the observed and rest frame, we find that three components are favored according to AIC with decisive significance, and two are preferred with BIC with marginal to strong significance.Comment: 13 pages, 10 figures. This is an extension of arXiv:1612.08235 to two-dimension

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

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

Dataset for evaluating fitness index using Adaptive Neuro-Fuzzy Inference System

With the current global downturn, the organizations need to develop new strategies and innovative approaches to ensure that every aspect of sustainability is achieved. For this purpose, the organizations need an indicator that measures the fitness if an organization. The purpose of this project is to analyze the ‘Fitness’ of an organization using the dataset related to leanness, agility and sustainability in ANFIS (Adaptive Neuro-Fuzzy Inference System) in order to determine whether the company is fit enough to sustain in global markets or not. The project does so by integrating both neural networks and fuzzy logic principles with lean, agility and sustainability principles. FIT manufacturing is the integration of Lean, Agile and sustainability manufacturing in one system as a whole which would help in attaining maximum output and sustain effectively in global markets. FIT Manufacturing adopts an integrated approach towards the use of Lean, Agility and Sustainability to achieve a level of fitness that is unique to each company. The database in the paper contains lean, agile and sustainable indices reviewed by experts. FIT does not prescribe that every aspect of Lean, Agile and Sustainability methodologies must be applied to every company, but a selective mix of components will provide the optimum conditions for a company to prosper

Study of Various Key Process Parameters of FDM 3D Printed Parts using Ultimaker 2+ 3D Printer

This research paper aims to investigate the effect of different Fused Deposition Modelling (FDM) printing parameters on the mechanical properties of printed parts. FDM is one of the most widely used 3D printing technologies due to its versatility and low cost. However, the mechanical properties of FDM printed parts depend largely on the printing parameters used. A series of tensile tests were conducted on FDM printed parts with varying printing parameters such as layer height, infill density, print speed, and nozzle temperature. The results showed that increasing the layer height and infill density improved the mechanical properties of the printed parts, while increasing the print speed decreased the mechanical properties. Nozzle temperature also had a significant effect on the mechanical properties of the printed parts, with a higher temperature resulting in stronger parts. Overall, this research provides valuable insights into the effects of different FDM printing parameters on the mechanical properties of printed parts and can be used to optimize FDM printing for specific applications. The research value of this case study is to obtain the best suitable key process parameters for FDM printing