Multi layer perceptron model in pattern recognition of surface parameters during pre-monsoon thunderstorm

The concept of Multi Layer Perceptron and Fuzzy logic is introduced in this paper to recognize the pattern of surface parameters pertaining to forecast the occurrence of pre-monsoon thunderstorms over Kolkata (22 ° 32¢ , 88 ° 20¢ ). The results reveal that surface temperature fluctuates significantly from Fuzzy Multi Layer Perceptron (FMLP) model values on thunderstorm days whereas on non-thunderstorm days FMLP model fits well with the surface temperature. The results further indicate that no definite pattern could be made available with surface dew point temperature and surface pressure that can help in forecasting the occurrence of these storms.

A Fundamental Equation of State for Chloroethene for Temperatures from the Triple Point to 430 K and Pressures to 100 MPa

A fundamental equation of state in terms of the Helmholtz energy is presented for chloroethene (vinyl chloride). Due to its fundamental nature, it can be used to consistently calculate all thermodynamic state properties in the fluid region. Based on the underlying experimental database, it is valid from the triple-point temperature 119.31 K to 430 K with a maximum pressure of 100 MPa. In addition to the accurate reproduction of experimental data, correct extrapolation behavior during the development of the equation was attained. This enables the equation to be applied in modern mixture frameworks.

Observed and Projected Scaling of Daily Extreme Precipitation with Dew Point Temperature at Annual and Seasonal Scales across the Northeast United Sates

This study investigates how extreme precipitation scales with dew point temperature across the Northeast U.S., both in the observational record (1948-2020) and in a set of downscaled climate projections in the state of Massachusetts (2006-2099). Spatiotemporal relationships between dew point temperature and extreme precipitation are assessed, and extreme precipitation – temperature scaling rates are evaluated on annual and seasonal scales using non-stationary extreme value analysis for annual maxima and partial duration series, respectively. A hierarchical Bayesian model is then developed to partially pool data across sites and estimate regional scaling rates, with uncertainty. Based on the observations, the estimated annual scaling rate is 5.5% per °C, but this varies by season, with most non-zero scaling rates in summer and fall and the largest rates (∼7.3% per °C) in the summer. Dew point temperatures and extreme precipitation also exhibit the most consistent regional relationships in the summer and fall. Downscaled climate projections exhibited different scaling rates compared to the observations, ranging between -2.5 and 6.2% per °C at an annual scale. These scaling rates are related to the consistency between trends in projected precipitation and dew point temperature over the 21st century. At the seasonal scale, climate models project larger scaling rates for the winter compared to the observations (1.6% per °C). Overall, the observations suggest that extreme daily precipitation in the Northeast U.S. only thermodynamic scales with dew point temperature in the warm season, but climate projections indicate some degree of scaling is possible in the cold season under warming.

Establishing Coupled Models for Estimating Daily Dew Point Temperature Using Nature-Inspired Optimization Algorithms

Potential of a classic adaptive neuro-fuzzy inference system (ANFIS) was evaluated in the current study for estimating the daily dew point temperature (Tdew). The study area consists of two stations located in Iran, namely the Rasht and Urmia. The daily Tdew time series of the studied stations were modeled through the other effective variables comprising minimum air temperature (Tmin), extraterrestrial radiation (Ra), vapor pressure deficit (VPD), sunshine duration (n), and relative humidity (RH). The correlation coefficients between the input and output parameters were utilized to determine the most effective inputs. Furthermore, novel hybrid models were proposed in this study in order to increase the estimation accuracy of Tdew. For this purpose, two optimization algorithms named bee colony optimization (BCO) and dragonfly algorithm (DFA) were coupled on the classic ANFIS. It was concluded that the hybrid models (i.e., ANFIS-BCO and ANFIS-DFA) demonstrated better performances compared to the classic ANFIS. The full-input pattern of the coupled models, specifically the ANFIS-DFA, was found to present the most accurate results for both the selected stations. Therefore, the developed hybrid models can be proposed as alternatives to the classic ANFIS to accurately estimate the daily Tdew.

Relationship between weather parameters and risk of exertional heat injuries during military training

Introduction: The Singapore Armed Forces (SAF) collaborated with the Meteorological Service Singapore (MSS) to study the relationship between weather parameters and the incidents of exertional heat injury (EHI) to mitigate the risk of EHI in a practical manner. Methods: Data from the SAF’s heat injury registry and MSS’ meteorological data from 2012 to 2018 were used to establish a consolidated dataset of EHI incidents and same-day weather parameters rank-ordered in deciles. Poisson regression modelling was used to determine the incidence rate ratios (IRRs) of the EHI, referencing the first decile of weather parameters. Two frames of analysis were performed - the first described the relationship between the weather parameters and the adjusted IRR for the same day (D), and the second described the relationship between the weather parameters and the adjusted IRR on the following day (D+1). Results: For wet-bulb temperature, the IRR on D+1 approximated unity for the first nine deciles but rose to 3.09 at the tenth decile. For dew-point temperature, the IRR on D+1 approximated unity for the first nine deciles but rose to 3.48 at the tenth decile. By designating a single dew-point temperature cut-off at  25.1°C (transition between the ninth and tenth decile), the adjusted IRR on D +1 was 2.26 on days with dew-point temperature  25.1°C,. Conclusion: Integrating the data from the SAF and MSS demonstrated that a dew-point temperature ≥ 25.1°C on D correlates statistically with the risk of EHI on D +1and could be used to supplement the risk mitigation system.

Study on the Mechanism of Solid-Phase Oxidant Action in Tribochemical Mechanical Polishing of SiC Single Crystal Substrate

Na2CO3—1.5 H2O2, KClO3, KMnO4, KIO3, and NaOH were selected for dry polishing tests with a 6H-SiC single crystal substrate on a polyurethane polishing pad. The research results showed that all the solid-phase oxidants, except NaOH, could decompose to produce oxygen under the frictional action. After polishing with the five solid-phase oxidants, oxygen was found on the surface of SiC, indicating that all five solid-phase oxidants can have complex tribochemical reactions with SiC. Their reaction products are mainly SiO2 and (SiO2)x. Under the action of friction, due to the high flash point temperature of the polishing interface, the oxygen generated by the decomposition of the solid-phase oxidant could oxidize the surface of SiC and generate a SiO2 oxide layer on the surface of SiC. On the other hand, SiC reacted with H2O and generated a SiO2 oxide layer on the surface of SiC. After polishing with NaOH, the SiO2 oxide layer and soluble Na2SiO3 could be generated on the SiC surface; therefore, the surface material removal rate (MRR) was the highest, and the surface roughness was the largest, after polishing. The lowest MRR was achieved after the dry polishing of SiC with KClO3.