ClimaX: A foundation model for weather and climate

Most state-of-the-art approaches for weather and climate modeling are based on physics-informed numerical models of the atmosphere. These approaches aim to model the non-linear dynamics and complex interactions between multiple variables, which are challenging to approximate. Additionally, many such numerical models are computationally intensive, especially when modeling the atmospheric phenomenon at a fine-grained spatial and temporal resolution. Recent data-driven approaches based on machine learning instead aim to directly solve a downstream forecasting or projection task by learning a data-driven functional mapping using deep neural networks. However, these networks are trained using curated and homogeneous climate datasets for specific spatiotemporal tasks, and thus lack the generality of numerical models. We develop and demonstrate ClimaX, a flexible and generalizable deep learning model for weather and climate science that can be trained using heterogeneous datasets spanning different variables, spatio-temporal coverage, and physical groundings. ClimaX extends the Transformer architecture with novel encoding and aggregation blocks that allow effective use of available compute while maintaining general utility. ClimaX is pre-trained with a self-supervised learning objective on climate datasets derived from CMIP6. The pre-trained ClimaX can then be fine-tuned to address a breadth of climate and weather tasks, including those that involve atmospheric variables and spatio-temporal scales unseen during pretraining. Compared to existing data-driven baselines, we show that this generality in ClimaX results in superior performance on benchmarks for weather forecasting and climate projections, even when pretrained at lower resolutions and compute budgets. The source code is available at https://github.com/microsoft/ClimaX.Comment: International Conference on Machine Learning 202

A Hybrid Convolutional Network and Long Short-Term Memory (HBCNLS) model for Sentiment Analysis on Movie Reviews

This paper proposes a hybrid model (HBCNLS) for sentiment analysis that combines the strengths of multiple machine learning approaches. The model consists of a convolutional neural network (CNN) for feature extraction, a long short-term memory (LSTM) network for capturing sequential dependencies, and a fully connected layer for classification on movie review dataset. We evaluate the performance of the HBCNLS on the IMDb movie review dataset and compare it to other state-of-the-art models, including BERT. Our results show that the hybrid model outperforms the other models in terms of accuracy, precision, and recall, demonstrating the effectiveness of the hybrid approach. The research work also compares the performance of BERT, a pre-trained transformer model, with long short-term memory (LSTM) networks and convolutional neural networks (CNNs) for the task of sentiment analysis on a movie review dataset.

Gender-Associated Oral and Periodontal Health Based on Retrospective Panoramic Radiographic Analysis of Alveolar Bone Loss

Gender-based heterogeneity in periodontal disease has been witnessed in the recent past with huge mounting evidence. The composite effect of sex-based genetic structure and the sex steroid hormones runs in line with the corresponding gender-related differences in risk for chronic periodontitis. Since estrogens, the predominant sex hormones in women, show immune protective and anti-inflammatory effects in hormonally active premenopausal women, they show better periodontal status compared to age-matched men. Conversely, after menopause with a weakening estrogen signal, women may show an equal or even more serious periodontal status compared to men. Periodontal status of postmenopausal women may be improved by menopausal hormone therapy. Alveolar bone loss, an irreversible sign of past periodontal disease activity can be easily observed on radiographs in an objective manner. Orthopantomographs provide a fairly accurate assessment of the status of alveolar bone in the whole mouth. A cross-sectional retrospective panoramic radiographic analysis has been carried out in a north Indian dental institute to decipher the gender-based distribution of periodontal bone loss. The current chapter shall provide an update on gender-based differences in oral health, underlying mechanisms, differences in patterns and distribution of alveolar bone loss (case study), and potential gender-specific disease protection and management strategies

An overview of chronic disease models: a systematic literature review

AIMS: The objective of our study was to examine various existing chronic disease models, their elements and their role in the management of Diabetes, Chronic Obstructive Pulmonary Disease (COPD), and Cardiovascular diseases (CVD)

An Overview of Chronic Disease Models: A Systematic Literature Review

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A need for a health IT portal to disseminate information about national health programmes in India

The objective of our study is to evaluate information about the national health programmes in India, available over the internet and to study the challenges faced while acquiring this information. To achieve our objective, we used the key words National Health Programmes OR Public Health Programmes OR Health Programs AND India in Google from January 1-January 10 2012, to find information about the existing Health Programmes. We chose first 20 web links across all the three search terms to yield 60 websites, which were then reviewed for their relevance. Only 16 websites were found to be relevant that met the inclusion criteria. The study showed that there was inadequate information about the existing national health programmes in India. This suggests a need to develop a National Public Health IT portal that can disseminate information about the various health programmes in a more structured manner and which is tailored to the needs of diverse group of stakeholders. Copyright © 2013 Inderscience Enterprises Ltd

Landscape Analysis of Public Health Jobs in India to Develop an Evidence-Based Public Health Curriculum

The increase in communicable and non-communicable disease incidence and prevalence, changing population demographics, along with concerns about pandemics, natural disasters, and wars, have highlighted the challenges faced by health systems. The study aims to identify data on publicly posted public health jobs available to applicants eligible to work in India to identify the public health and allied fields workforce needs, skills, and expertise in India. A cross-sectional study was done in June–July 2021. The data was collected from eleven common job portals in India. Descriptive and content analysis was done to identify the most common job titles, educational level preferred/desired, skills, and experience required in the public health jobs in India. In total 382 unique public health and related fields jobs were analyzed. Job postings were most commonly classified as manager (n = 68), officer/lead (n = 61), analyst (n = 49), and consultant (n = 44). Around one-fifth of the jobs were based in Delhi (n = 98, 24%). About a quarter of the job postings required more than 8 years of experience (26%, n = 100). More than half of the job postings mentioned having the knowledge and understanding of data analysis and statistical approaches (n = 116, 64%). Around 15% (n = 193) of the job posting wanted the candidate to have expertise in communication. Skills were classified into various types such as software, technical, and language. Timely assessment of the curriculum should be done to impart skills related to the needs of the employers and prepare a skilled and competent public health workforce to address the 21st century public health challenges