Key properties of inorganic thermoelectric materials – tables (version 1)

This paper presents tables of key thermoelectric properties, which define thermoelectric conversion efficiency, for a wide range of inorganic materials. The 12 families of materials included in these tables are primarily selected on the basis of well established, internationally-recognised performance and their promise for current and future applications: Tellurides, Skutterudites, Half Heuslers, Zintls, Mg-Sb Antimonides, Clathrates, FeGa3–type materials, Actinides and Lanthanides, Oxides, Sulfides, Selenides, Silicides, Borides and Carbides. As thermoelectric properties vary with temperature, data are presented at room temperature to enable ready comparison, and also at a higher temperature appropriate to peak performance. An individual table of data and commentary are provided for each family of materials plus source references for all the data.

A statistical bias correction technique for global climate model predicted near-surface temperature in India using the generalized regression neural network

Global climate models (GCMs) developed by the numerical simulation of physical processes in the atmosphere, ocean, and land are useful tools for climate prediction studies. However, these models involve parameterizations and assumptions for the simulation of complex phenomena, which lead to random and structural errors called biases. So, the GCM outputs need to be bias-corrected with respect to observed data before applying these model outputs for future climate prediction. This study develops a statistical bias correction approach using a four-layer feedforward radial basis neural network – a generalized regression neural network (GRNN) to reduce the biases of the near-surface temperature data in the Indian mainland. The input to the network is the CNRM-CM5 model output gridded data of near-surface temperature for the period 1951–2005, and the target to the model used for bias correcting the input data is the gridded near-surface temperature developed by the Indian Meteorological Department for the same period. Results show that the trained GRNN model can improve the inherent biases of the GCM modelled output with significant accuracy, and a good correlation is seen between the test statistics of observed and bias-corrected data for both the training and testing period. The trained GRNN model developed is then used for bias correction of CNRM-CM5 modelled projected near-surface temperature for 2006–2100 corresponding to the RCP4.5 and RCP8.5 emission scenarios. It is observed that the model can adapt well to the nature of unseen future temperature data and correct the biases of future data, assuming quasi-stationarity of future temperature data for both emission scenarios. The model captures the seasonal variation in near-surface temperature over the Indian mainland, having diverse topography appreciably, and this is evident from the bias-corrected output.

Supplemental Material: Middle-lower crustal flow in response to the India-Eurasia collision: Structural evidence from the southern Chong Shan belt within the Sundaland block, southeastern Tibetan Plateau

S1: Analytical Methods; Table S1: Summary of Mineral assemblages, microstructures and temperature data; Table S2: Zircon U-Pb LA-ICP-MS data of the granitic rocks from the Chong Shan structural belt.

Supplemental Material: Middle-lower crustal flow in response to the India-Eurasia collision: Structural evidence from the southern Chong Shan belt within the Sundaland block, southeastern Tibetan Plateau

S1: Analytical Methods; Table S1: Summary of Mineral assemblages, microstructures and temperature data; Table S2: Zircon U-Pb LA-ICP-MS data of the granitic rocks from the Chong Shan structural belt.

loT-based temperature and humidity monitoring system for smart garden

The main objective of this paper is to design an IoT (Internet of Things) to monitor temperature and humidity for smart gardens. Temperature sensors and humidity sensors measure environmental conditions and are processed by a microcontroller. The actuator used is a spray pump that is used to spray water into the air to lower the temperature. Data from the sensors and status from the actuators are sent to the server and can be monitored via a smartphone. The data collected can be analyzed for various purposes. The result obtained is the effect of spraying on temperature reduction.

Assessing Forecasting Performance of Daily Mean Temperature at 1st and 2nd Perak Station, Surabaya Using ARIMA and VARIMA Model with Outlier Detection

Air temperature is an important data for several sectors. The demand of fast, exact and accurate forecast on temperature data is getting extremely important since it is useful for planning of several important sectors. In order to forecast mean daily temperature data at 1st and 2nd Perak BMKG Station in Surabaya, this study used the univariate method, ARIMA model and multivariate method, VARIMA model with outlier detection. The best ARIMA model was selected using in-sample criteria, i.e. AIC and BIC. While for VAR model, the minimum information criterion namely AICc value was considered. The RMSE values of several forecasting horizons of out-sample data showed that the overall best model for mean daily temperature at 1st and 2nd Perak Station was the multivariate model, i.e. VARX (10,1) with four outliers incorporated in the model, indicated that it was necessary to consider the temperature from the nearest stations to improve the forecasting performance. This study recommends performing the overall best model only for short term forecasting, i.e. two weeks at maximum. By using the one week-step ahead and one day-step ahead forecasting scheme, the forecasting performance is significantly improved compared to default the k-step ahead forecasting scheme.

Sosialisasi penerapan Smart e-Monitoring untuk pasien Covid-19 berbasis IoT di STIPARK NTB

The Merdeka Campus Competition Program, held by the Ministry of Education, Culture, Research, and Technology, is a financing program that aims to facilitate, encourage, and accelerate the transformation of higher education institutions. Informatics Engineering Study Program, Hamzanwadi University, as one of the universities that received the grant, developed an Internet of Things-based application to monitor the progress of Covid-19 patients who are self-isolating called SMART e-MONITORING. Therefore, this activity aims to socialize the Smart e-Monitoring application at STIPARK NTB as partners. This activity was held over four months. It’s were starting from product presentations to system testing and product launching. The activity results show that the developed application can run well and display the patient's condition in real-time. The recorded data include body temperature data, oxygen levels (SPO2), and heart rate (BPM). This data is used to facilitate the handling of self-isolated patients in real-time and without direct contact with Covid-19 patients.