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Frontiers | Job Insecurity, Work-Related Flow, and Financial Anxiety in the Midst of COVID-19 Pandemic and Economic Downturn

Frontiers | Job Insecurity, Work-Related Flow, and Financial Anxiety in the Midst of COVID-19 Pandemic and Economic Downturn
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New yorkUnited statesThousand oaksEteläuomen läiSaudi arabiaSan diegoUnited kingdomCity ofMihali csikszentmihalyiLos angelesAmerican psychiatric associationYour healthPalgrave macmillanUs census bureauUmm al qura universityAnxiety disorder american psychiatric association

Pharmaceutical Patent Perceptions in Japan

Background: The recent trend of pharmaceutical companies commercializing new objects as new drugs based on the findings of academic medical researchers, commonly categorizing them as “academic drug discovery” is increasingly gaining popularity in the pharmaceutical industry. Studies state that academic researchers based in universities have lower motivation to apply for patents. However, none of the studies evaluated the existence and extent of the “motivation for patent” in academic researchers, being lower than that of pharmaceutical companies. This study assesses two hypotheses; H1: academic medical researchers are less likely to believe that the patent system is necessary for pharmaceuticals, and thus have diminished interest in the commercialization of their research findings when compared to those in the pharmaceutical industry, H2: apprehension of the raison d’être of the patent system affects positive impressions on patents among academic medical researchers.

United statesHoward floreyAlexander flemingJikei university school of medicineKyoto universityProperty association of japanSapporo medical universitySapporo medical university in hokkaidoKanazawa institute of technologyJapan pharmaceutical manufacturers associationOsaka institute of technologyJapan agency for medical researchMeiji universityTokyo university of scienceCore research facilitiesPatent law

Geostationary Earth Orbit Hyperspectral Infrared Radiance data improve local severe storm forecasts proofed by using a new Hybrid OSSE method

Scientists are developing data assimilation methods for Numerical Weather Prediction models that will increase the quality of initialization data from satellites. The Observing System Simulation Experiment (OSSE) is designed to use data assimilation to investigate the potential impact of future atmospheric observing systems. Traditional OSSE processes require significant effort to compute, simulate, and calibrate information, then assimilate the data to produce a forecast. Therefore, model meteorologists are working to make this process more efficient.

United statesJun liUniversity of wisconsinMadison cooperative institute for meteorological satelliteAdvances in atmospheric sciencesObserving system simulation experimentWisconsin madison cooperative instituteMeteorological satelliteAtmospheric sciencesFengyun meteorological satellitesGreat plainsMidwestern united statesRoot mean square errorAtmospheric scienceEarth scienceSpace planetary science

Improving rock mechanical properties estimation using machine learning by Ruizhi Zhong, Matt Tsang et al

ABSTRACT: Rock mechanical properties (e.g., uniaxial compressive strength or UCS, Young’s modulus, and Poisson’s ratio) are important input parameters for geotechnical assessment and excavation designs. Two common methods used to obtain these parameters are laboratory testing and geophysical logging. The former delivers probably the most reliable results, but can be costly and time-consuming and for a lot of the time it is challenging to source sufficient samples. Alternative ways to better predict rock mechanical properties are needed. In this case study, the XGBoost machine learning algorithm was applied to correlate laboratory and geophysical logging data with the three mechanaical properties of UCS, Young’s modulus, and Poisson’s ratio. The proposed machine learning approach better predicted UCS values with a smaller Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) and a larger R2. Similarly, better results were obtained for the Young’s modulus prediction using

Mean absolute errorRoot mean square errorசராசரி பிழைவேர் சராசரி சதுரம் பிழை