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The constellation approach underpinning precipitation products such as the Integrated Multi‐satellitE Retrievals for GPM (IMERG) is key to achieving high resolution, but the use of data from multiple sources can unintentionally incorporate instrumental artifacts. Here, we introduce a machine learning–based anomaly detection scheme called SPEEDe, which processes a two‐dimensional precipitation field into a re‐estimated precipitation field that can be compared with the input. Large differences identify IMERG fields with bad orbit data, separating most of the bad cases from the good cases. When modified to process the passive microwave inputs, SPEEDe can pick out orbits with bad data, enabling quality control on these IMERG inputs. SPEEDe works by producing a locally realistic‐looking precipitation field when given unphysical data, which results in a larger‐than‐normal difference between the input and the output. SPEEDe is implemented as an automated quality control for GPM precipitation products. Plain Language Summary: A machine learning–based scheme, SPEEDe, has been developed to identify bad data in satellite precipitation products. This scheme is able to pick out precipitation maps that have unrealistic patterns better than a conventional approach based on the distribution of values. It can be run as the precipitation data are being produced, thus allowing for automated quality control. Avoiding manual intervention is important in computing quality‐controlled precipitation data in near‐real time for applications such as flood monitoring that require highly reliable data with minimal delay. Key Points: A machine learning scheme called SPEEDe can detect artifacts in satellite precipitation fields based on an aggregate grid‐wide scoreSPEEDe identifies bad data by modifying anomalous inputs substantially to produce realistic‐looking outputs, leading to a large scoreSPEEDe is being applied operationally in GPM data production for quality control [ABSTRACT FROM AUTHOR]

The Strengthening Program for Rural Banks (SPRB) in the Philippines has allowed universal banks to purchase rural banks, and this policy highlights the possible advantages of expanding business combinations for intangible assets. Considering the SPRB, this paper examines the intangible motivation behind banks' actions to purchase capital-deficient financial institutions. This study aims to address sources of intangible assets for Philippine banks as Tobin's q measures. Using Tobin's q as a measure of intangible value allows for identifying contributing factors that determine it based on financial data. The study subjects are actively traded Philippine Universal Banks between 2009 and 2018. The influence of variables in the study on intangible asset value, as Tobin's q measures, was determined using an Ordinary Least Squares model. Operational factors significantly influencing intangible value are investments in subsidiaries and associates, the return on assets, and bank deposits. Activities intended to reach target customers, such as those captured by marketing and advertising expenditures, positively influence intangible value. These findings imply that merely purchasing more assets does not improve the value of intangible resources. Instead, asset productivity, funds to make more loans, and informing the public of bank activities benefit intangible asset values. The implications of these findings offer insight into the sources of intangible assets that investors value. [ABSTRACT FROM AUTHOR]

Objective: Coronavirus disease (COVID-19) vaccinations have been shown to prevent infection with efficacies ranging from 50% to 95%. This study assesses the impact of vaccination on the clinical severity of COVID-19 during the second wave in Brunei Darussalam in 2021, which was due to the Delta variant. Methods: Patients included in this study were randomly selected from those who were admitted with COVID-19 to the National Isolation Centre between 7 August and 6 October 2021. Cases were categorized as asymptomatic, mild (symptomatic without pneumonia), moderate (pneumonia), severe (needing supplemental oxygen therapy) or critical (needing mechanical ventilation) but for statistical analysis purposes were dichotomized into asymptomatic/mild or moderate/severe/critical cases. Univariate and multivariable analyses were conducted to identify risk factors associated with moderate/severe/critical disease. Propensity score-matched analysis was also performed to evaluate the impact of vaccination on disease severity. Results: The study cohort of 788 cases (mean age: 42.1 ± 14.6 years; 400 males) comprised 471 (59.8%) asymptomatic/ mild and 317 (40.2%) moderate/severe/critical cases. Multivariable logistic regression analysis showed older age group (≥45 years), diabetes mellitus, overweight/obesity and vaccination status to be associated with increased severity of disease. In propensity score-matched analysis, the relative risk of developing moderate/severe/critical COVID-19 for fully vaccinated (two doses) and partially vaccinated (one dose) cases was 0.33 (95% confidence interval [CI]: 0.16-0.69) and 0.62 (95% CI: 0.46-0.82), respectively, compared with a control group of non-vaccinated cases. The corresponding relative risk reduction (RRR) values were 66.5% and 38.4%, respectively. Vaccination was also protective against moderate/severe/ critical disease in a subgroup of overweight/obese patients (RRR: 37.2%, P = 0.007). Discussion: Among those who contracted COVID-19, older age, having diabetes, being overweight/obese and being unvaccinated were significant risk factors for moderate/severe/critical disease. Vaccination, even partial, was protective against moderate/severe/critical disease. [ABSTRACT FROM AUTHOR]

NASA's multisatellite precipitation product from the Global Precipitation Measurement (GPM) mission, the Integrated Multi-satellitE Retrievals for GPM (IMERG) product, is validated over tropical and high-latitude oceans from June 2014 to August 2021. This oceanic study uses the GPM Validation Network's island-based radars to assess IMERG when the GPM Core Observatory's Microwave Imager (GMI) observes precipitation at these sites (i.e., IMERG-GMI). Error tracing from the Level 3 (gridded) IMERG V06B product back through to the input Level 2 (satellite footprint) Goddard Profiling Algorithm GMI V05 climate (GPROF-CLIM) product quantifies the errors separately associated with each step in the gridding and calibration of the estimates from GPROF-CLIM to IMERG-GMI. Mean relative bias results indicate that IMERG-GMI V06B overestimates Alaskan high-latitude oceanic precipitation by +147% and tropical oceanic precipitation by +12% with respect to surface radars. GPROF-CLIM V05 overestimates Alaskan oceanic precipitation by +15%, showing that the IMERG algorithm's calibration adjustments to the input GPROF-CLIM precipitation estimates increase the mean relative bias in this region. In contrast, IMERG adjustments are minimal over tropical waters with GPROF-CLIM overestimating oceanic precipitation by +14%. This study discovered that the IMERG V06B gridding process incorrectly geolocated GPROF-CLIM V05 precipitation estimates by 0.1° eastward in the latitude band 75°N–75°S, which has been rectified in the IMERG V07 algorithm. Correcting for the geolocation error in IMERG-GMI V06B improved oceanic statistics, with improvements greater in tropical waters than Alaskan waters. This error tracing approach enables a high-precision diagnosis of how different IMERG algorithm steps contribute to and mitigate errors, demonstrating the importance of collaboration between evaluation studies and algorithm developers. Significance Statement: Evaluation of IMERG's oceanic performance is very limited to date. This study uses the GPM Validation Network to conduct the first extensive assessment of IMERG V06B at its native resolution over both high-latitude and tropical oceans, and traces errors in IMERG-GMI back through to the input GPROF-CLIM GMI product. IMERG-GMI overestimates tropical oceanic precipitation (+12%) and strongly overestimates Alaskan oceanic precipitation (+147%) with respect to the island-based radars studied. IMERG's GMI estimates are assessed as these should be the optimal estimates within the multisatellite product due to the GMI's status as calibrator of the GPM passive microwave constellation. [ABSTRACT FROM AUTHOR]