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National levels of personal health-care access and quality can be approximated by measuring mortality rates from causes that should not be fatal in the presence of effective medical care (ie, amenable mortality). Previous analyses of mortality amenable to health care only focused on high-income countries and faced several methodological challenges. In the present analysis, we use the highly standardised cause of death and risk factor estimates generated through the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) to improve and expand the quantification of personal health-care access and quality for 195 countries and territories from 1990 to 2015. We mapped the most widely used list of causes amenable to personal health care developed by Nolte and McKee to 32 GBD causes. We accounted for variations in cause of death certification and misclassifications through the extensive data standardisation processes and redistribution algorithms developed for GBD. To isolate the effects of personal health-care access and quality, we risk-standardised cause-specific mortality rates for each geography-year by removing the joint effects of local environmental and behavioural risks, and adding back the global levels of risk exposure as estimated for GBD 2015. We employed principal component analysis to create a single, interpretable summary measure–the Healthcare Quality and Access (HAQ) Index–on a scale of 0 to 100. The HAQ Index showed strong convergence validity as compared with other health-system indicators, including health expenditure per capita (r=0·88), an index of 11 universal health coverage interventions (r=0·83), and human resources for health per 1000 (r=0·77). We used free disposal hull analysis with bootstrapping to produce a frontier based on the relationship between the HAQ Index and the Socio-demographic Index (SDI), a measure of overall development consisting of income per capita, average years of education, and total fertility rates. This frontier allowed us to better quantify the maximum levels of personal health-care access and quality achieved across the development spectrum, and pinpoint geographies where gaps between observed and potential levels have narrowed or widened over time. Between 1990 and 2015, nearly all countries and territories saw their HAQ Index values improve; nonetheless, the difference between the highest and lowest observed HAQ Index was larger in 2015 than in 1990, ranging from 28·6 to 94·6. Of 195 geographies, 167 had statistically significant increases in HAQ Index levels since 1990, with South Korea, Turkey, Peru, China, and the Maldives recording among the largest gains by 2015. Performance on the HAQ Index and individual causes showed distinct patterns by region and level of development, yet substantial heterogeneities emerged for several causes, including cancers in highest-SDI countries; chronic kidney disease, diabetes, diarrhoeal diseases, and lower respiratory infections among middle-SDI countries; and measles and tetanus among lowest-SDI countries. While the global HAQ Index average rose from 40·7 (95% uncertainty interval, 39·0–42·8) in 1990 to 53·7 (52·2–55·4) in 2015, far less progress occurred in narrowing the gap between observed HAQ Index values and maximum levels achieved; at the global level, the difference between the observed and frontier HAQ Index only decreased from 21·2 in 1990 to 20·1 in 2015. If every country and territory had achieved the highest observed HAQ Index by their corresponding level of SDI, the global average would have been 73·8 in 2015. Several countries, particularly in eastern and western sub-Saharan Africa, reached HAQ Index values similar to or beyond their development levels, whereas others, namely in southern sub-Saharan Africa, the Middle East, and south Asia, lagged behind what geographies of similar development attained between 1990 and 2015. This novel extension of the GBD Study shows the untapped potential for personal health-care access and quality improvement across the development spectrum. Amid substantive advances in personal health care at the national level, heterogeneous patterns for individual causes in given countries or territories suggest that few places have consistently achieved optimal health-care access and quality across health-system functions and therapeutic areas. This is especially evident in middle-SDI countries, many of which have recently undergone or are currently experiencing epidemiological transitions. The HAQ Index, if paired with other measures of health-system characteristics such as intervention coverage, could provide a robust avenue for tracking progress on universal health coverage and identifying local priorities for strengthening personal health-care quality and access throughout the world. Bill & Melinda Gates Foundation.

Climate change has increased the frequency and intensity of extreme weather events worldwide, amplifying the global need for reliable weather prediction. Cyclones and flash floods pose serious threats to human life and infrastructure, with advance forecasts and flash flood guidance provided by numerical weather models for disaster mitigation. These models depend on data from satellites and/or ground instruments. In the greater part of the world, less industrialized countries rely widely on the free ubiquitous satellite data, amid limited availability of rain gauges and radars. Assessing the reliability of satellite data during an extreme event in diverse geographic regions is therefore vital. Here we present the assessment of satellite rainfall estimates during tropical cyclone Shaheen, which hit the Arabian Sea and the Arabian Peninsula, in particular, Oman, where flash flood prediction is highly dependent on the MWGHE satellite estimator. We show that satellite data overestimate gauge cumulative precipitation, with daily totals showing a consistent positive bias. Six-hourly analysis reveals fluctuations, with overestimation varying by intensity and timing. Comparisons with UAE radar observations further highlight these discrepancies, particularly in the spatial distribution and intensity of precipitation. Our study underscores the need for a full range of products and/or enhancements of current satellite estimators.

Tropical Cyclone (TC) Gulab-Shaheen in the Northern Indian Ocean in September 2021 marked an extraordinary and rare climatic event that formed during the monsoon season, making it uncommon. The western North Pacific subtropical High (WNPSH) branch over the northeastern part of India is responsible for the westward movement of the Gulab cyclone in the Bay of Bengal (BoB). The extension of WNPSH and availability of soil moisture influences the cyclone's movement over the Indian land, and it eventually emerges as a westward-moving cyclone Shaheen in the Arabian Sea (AS). Furthermore, the fourth stage of the Madden-Julian Oscillation propagated over southeast Asia is responsible for aiding cyclogenesis by increasing convective activities and maintaining higher humidity with lower wind shear. Due to this, TC Gulab remains a basic cyclonic flow over the land and moves westward after landfall. In the case of Shaheen, higher tropical cyclone heat potential and SST assisted the remnant of Gulab in rejuvenating over the AS, and lower VWS lets the storm's center line up vertically, which helps it get more robust. Therefore, this study demonstrates the causes of the cyclone formation in BoB and its rejuvenation in the AS after surviving over the peninsular India.

The Sultanate of Oman is in the south-eastern part of the Persian Gulf. Oman's coast causes Oman to be exposed to tropical cyclones occasionally. The damages occur when hurricanes reach the land, but some hurricanes dissipate in the sea without any noticeable harm. Generally, these strong storms hit Oman every 3 or 4 years between June and October, and it is mostly in the southern part of the Sultanate. One of these cyclones was Gonu on June 1, 2007, which caused 50 deaths in total and the damaged areas cost around $4.2 billion (2007 US dollars). This paper reviews and tracks the tropical cyclone Shaheen, which hit the Omani coast on October 2, 2021. We have used the hydrological data of previous cyclones to state the level of damage the cyclone caused. As expected, the cyclone caused a lot of human and material losses in a very short period due to the inadequate flood drain systems in Al-Khaboura city. Results showed the necessity of a proper stormwater drainage system to be installed in the northern cities of Oman. These effects were followed by a 2-day holiday for both government and private sectors for the sake of people's safety.

A poem is presented.

Tropical Cyclone Shaheen (TCS), originating in the Arabian Sea on 30 September 2021, followed an east-to-west trajectory and made landfall as a category-1 cyclone in northern Oman on 3 October 2021, causing severe floods and damages before dissipating in the United Arab Emirates. This study aims to analyze the synoptic and dynamical conditions influencing Shaheen’s genesis and evolution. Utilizing ERA5 reanalysis data, SEVIRI-EUMETSAT imagery, and Sorbonne University Atmospheric Forecasting System (SUAFS) outputs, it was found that Shaheen manifested as a warm-core cyclone with moderate vertical wind shear within the eyewall. Distinctive features included a trajectory aligned with rising sea surface temperatures and increased specific humidity levels at 700 hPa in the Arabian Sea. As Shaheen approached the Gulf of Oman, a significant increase in rainfall rates occurred, correlated with variations in sea surface temperatures and vertical wind shear. Comparative analysis between SUAFS and ERA5 data revealed a slight northward shift in the SUAFS track and landfall. Advance warnings highlighted heavy rainfall, rough seas, and strong winds. This study provides valuable insights into the meteorological factors contributing to Shaheen’s formation and impact.

The Bay of Bengal and Arabian Sea are annually exposed to severe tropical cyclones, which impose massive infrastructure damages and cause the loss of life in coastal regions. Cyclone Shaheen originally generated in the Bay of Bengal in 2021 and translated a rare east-to-west path toward the Arabian Sea. Although the cyclone’s wind field can be obtained from reanalysis datasets such as ERA5 (fifth generation European Centre for Medium-Range Weather Forecasts), the wind speed cannot be reproduced with realistic details in the regions close to the center of cyclone due to spatial resolution. In this study, to address this problem, the high-resolution advanced Weather Research and Forecasting (WRF) model is used for simulation of Shaheen’s wind field. As a critical part of the study, the sensitivity of the results to the planetary boundary layer (PBL) parameterization in terms of the track, intensity, strength and structure of the cyclone Shaheen is investigated. Five experiments are considered with five PBL schemes: Yonsei University (YSU); Mellor–Yamada–Janjić (MYJ); Mellor–Yamada–Nakanishi–Niino level 2.5 (MYNN); Asymmetric Convective Model version 2 (ACM2); Quasi-Normal Scale Elimination (QNSE). The track, intensity, and strength of the experiments are compared with the wind fields obtained from the Joint Typhoon Warning Centre (JTWC) dataset. The results imply the high dependency of the track, intensity, and strength of the cyclone to the PBL parameterization. Simulated tracks with non-local PBL schemes (YSU and ACM2) outperformed those of the local PBL schemes (MYJ, MYNN, and QNSE), especially during the rapid intensification phase of Shaheen before landfall. The YSU produced highly intensified storm, while the ACM2 results are in better agreement with the JTWC data. The most accurate track was obtained from the ERA5 data; however, this dataset overestimated the spatial size and underestimated the wind speed. The WRF model using either YSU or ACM2 overestimated the wind speed compared to that of the altimeter data. The YSU and ACM2 schemes were able to reproduce the observed increase in wind speed and pressure drop at in situ stations. The wind data from EAR5 and cyclone parametric model were applied to the SWAN model to simulate the wave regime in the Arabian Sea during the time that Shaheen was translating across the region. Janssen formulation for wind input and whitecapping dissipation source terms in combination with both ERA5 and hybrid wind were used and the minimum combined error in the prediction of significant wave height (Hs) and zero up-crossing wave period (Tz) was examined. The maximum significant wave height for hybrid wind is higher than that of ERA5, while the cyclone development was successfully inferred from the wave field of the hybrid data.

In today's dynamic educational environment, the application of Artificial Intelligence (AI) has gained popularity and has enormous potential for transforming the learning environment across a wide range of subjects. The purpose of this research is to gain a deeper understanding of the application of AI in professional courses, namely in Master of Business Administration (MBA), Bachelor of Computer Application (BCA), and Bachelor of Technology (B.Tech) programs. The present study employed a questionnaire survey with 356 students as respondents from different engineering and management colleges and universities in Bihar. The purpose of the research is to explore the various factors that affect the adoption and acceptance of AIbased technologies among BCA, B.Tech, and MBA students and its effects on their retention rates, academic achievements, and student engagement. It is widely recognized that scant literature is available on this specific research area, i.e., the application of AI in MBA, BCA, and B.Tech programs in Bihar. Despite the high potential of AI-based technological applications in higher education, there is a paucity of context-specific research that acknowledges the prospects and challenges faced by these professional students. The present research investigates the acceptance and adoption of AI among higher education students in Bihar by employing the Technology Acceptance Model (TAM). Further, it incorporates Teacher Support and Training as a moderating factor between students' Attitude and Intention to adopt AI. The findings of the study highlight the significance of perceived ease of use and perceived usefulness in fostering favorable attitudes towards the adoption of AI in higher education in Bihar. Additionally, it underscores the essential role of teacher support and training in aligning student attitudes with their intentions to engage with AI technologies. These outcomes enrich the current discussions surrounding the integration of AI in educational settings, providing actionable recommendations for stakeholders, educators, and policymakers aimed at enhancing the adoption of AI among higher education students in Bihar.