Magneto-Photo-Thermoelastic Excitation Rotating Semiconductor Medium Based on Moisture Diffusivity

In this research,we focus on the free-surface deformation of a one-dimensional elastic semiconductor medium as a function of magnetic field and moisture diffusivity.The problem aims to analyze the interconnection between plasma and moisture diffusivity processes,as well as thermo-elastic waves.The study examines the photothermoelasticity transport process while considering the impact of moisture diffusivity.By employing Laplace’s transformation technique,we derive the governing equations of the photo-thermo-elastic medium.These equations include the equations for carrier density,elastic waves,moisture transport,heat conduction,and constitutive relationships.Mechanical stresses,thermal conditions,and plasma boundary conditions are used to calculate the fundamental physical parameters in the Laplace domain.By employing numerical techniques,the Laplace transform is inverted to get complete time-domain solutions for the primary physical domains under study.Referencemoisture,thermoelastic,and thermoelectric characteristics are employed in conjunction with a graphical analysis that takes into consideration the effects of applied forces on displacement,moisture concentration,carrier density,stress due to forces,and temperature distribution.

Calibration Method Based on RBF Neural Networks for Soil Moisture Content Sensor

Temporal and spatial variation of soil moisture content is significant for crop growth,climate change and the other fields.In order to overcome shortage of non-linear output voltage of TDR3 soil moisture content sensor and increase soil moisture content data collection and computational efficiency,this paper presents a RBF neural network calibration method of soil moisture content based on TDR3 soil moisture sensor and wireless sensor networks.Experiment results show that the calibration method is effective...

Time-resolved multiomics analysis of the genetic regulation of maize kernel moisture

Maize kernel moisture content(KMC)at harvest greatly affects mechanical harvesting,transport and storage.KMC is correlated with kernel dehydration rate(KDR)before and after physiological maturity.KMC and KDR are complex traits governed by multiple quantitative trait loci(QTL).Their genetic architecture is incompletely understood.We used a multiomics integration approach with an association panel to identify genes influencing KMC and KDR.A genome-wide association study using time-series KMC data from 7 to 70 days after pollination and their transformed KDR data revealed respectively 98and 279 loci significantly associated with KMC and KDR.Time-series transcriptome and proteome datasets were generated to construct KMC correlation networks,from which respectively 3111 and 759 module genes and proteins were identified as highly associated with KMC.Integrating multiomics analysis,several promising candidate genes for KMC and KDR,including Zm00001d047799 and Zm00001d035920,were identified.Further mutant experiments showed that Zm00001d047799,a gene encoding heat shock 70 kDa protein 5,reduced KMC in the late stage of kernel development.Our study provides resources for the identification of candidate genes influencing maize KMC and KDR,shedding light on the genetic architecture of dynamic changes in maize KMC.

An Arduino-Based Wireless Sensor Network for Soil Moisture Monitoring Using Decagon EC-5 Sensors

It is undeniable that wireless communication technology has become a very important component of modern society. One aspect of modern society in which application of wireless communication technologies has tremendous potential is in agricultural production. This is especially true in the area of sensing and transmission of relevant farming information such as weather, crop development, water quantity and quality, among others, which would allow farmers to make more accurate and timely farming decisions. A good example would be the application of wireless communication technology to transmit soil moisture data in real time to help farmers make irrigation scheduling decisions. Although many systems are commercially available for soil moisture monitoring, there are still many important factors, such as cost, limiting widespread adoption of this technology among growers. Our objective in this study was, therefore, to develop and test an affordable wireless communication system for monitoring soil moisture using Decagon EC-5 sensors. The new system uses Arduino-compatible microcontrollers and communication systems to sample and transmit values from four Decagon EC-5 soil moisture sensors. Developing the system required conducting lab calibrations for the EC-5 sensors for the microcontroller operating in either 10-bit or 12-bit analog-to-digital converter (ADC) resolution. The system was successfully tested in the field and reliably collected and transmitted data from a wheat field for more than two months.

Distribution prediction of moisture content of dead fuel on the forest floor of Maoershan national forest, China using a LoRa wireless network

The moisture content of dead forest fuel is an important indicator of risk levels of forest fires and prediction of fire spread. Moisture distribution is important to determine wild fire rating. However, it is often difficult to predict moisture distribution because of a complex terrain, changeable environments and low cover of commercial communication signals inside the forest. This study proposes a moisture content prediction system composed of environmental data collected using a long range radio frequency band 433 MHz wireless sensor network and data processing for moisture prediction based on a BP (back-propagation) neural network. In the fall of 2019, twenty nodes for the collection of environmental data were placed in four forest stands of Maoershan National Forest for a month;7440 sets of data including temperature, humidity, wind speed and air pressure were obtained. Half the data were used as a training set, the other as a testing set for a BP neural network. The results show that the average absolute error between the predicted value and the real value of moisture content of fuels of Larix gmelini, Betula platyphylla, Juglans mandshurica, and Quercus mongolica stands was 0.94%, 0.21%, 0.86%, 0.97%, respectively. The prediction accuracy was relatively high. The proposed distributed moisture content prediction method has the advantages of wide coverage and good real-time performance;at the same time, it is not limited by commercial signals and so it is especially suitable for forest fire prediction in remote mountainous areas.

Drying kinetics of soy protein isolate-corn starch film during preparation and its moisture adsorption characteristics during storage

To better understand the mass transfer process of moisture in the soy protein isolate-corn starch(SPI-CS)films during preparation and storage process,the drying kinetics model of SPI-CS films with different formation conditions during the drying process and the moisture adsorption characteristics of the SPI-CS films under different humidity conditions were investigated.Within the range of experimental conditions,the moisture migration rule in the SPI-CS films during the drying preparation was combined with the Page model which was expressed as MR=exp(-kt^(n)).It was found that the adsorption equilibrium needed shorter time(about 3 h)when the SPI-CS films existed in the environment with lower humidity(RH<54%).Additionally,the secondorder adsorption kinetic equation was successful to describe the moisture adsorption characteristic of the SPICS films during storage under different humidity conditions.

Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models

Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes.In addition to PB models,deep learning(DL)models have been widely used in SM predictions recently.However,few pure DL models have notably high success rates due to lacking physical information.Thus,we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM predictions.To this end,we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale(attention model).We further built an ensemble model that combined the advantages of different hybrid schemes(ensemble model).We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory(ConvLSTM)model for 1–16 days of SM predictions.The performances of the proposed hybrid models were investigated and compared with two existing hybrid models.The results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid models.Moreover,the ensemble model performed best among all hybrid models at all forecast time scales and different soil conditions.It is highlighted that the ensemble model outperformed the pure DL model over 79.5%of in situ stations for 16-day predictions.These findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.

Time-lagged Effects of the Spring Atmospheric Heat Source over the Tibetan Plateau on Summer Precipitation in Northeast China during 1961–2020:Role of Soil Moisture

The spring atmospheric heat source(AHS)over the Tibetan Plateau(TP)has been suggested to affect the Asian summer monsoon and summer precipitation over South China.However,its influence on the summer precipitation in Northeast China(NEC)remains unknown.The connection between spring TP AHS and subsequent summer precipitation over NEC from 1961 to 2020 is analyzed in this study.Results illustrate that stronger spring TP AHS can enhance subsequent summer NEC precipitation,and higher soil moisture in the Yellow River Valley-North China region(YRVNC)acts as a bridge.During spring,the strong TP AHS could strengthen the transportation of water vapor to East China and lead to excessive rainfall in the YRVNC.Thus,soil moisture increases,which regulates local thermal conditions by decreasing local surface skin temperature and sensible heat.Owing to the memory of soil moisture,the lower spring sensible heat over the YRVNC can last until mid-summer,decrease the land–sea thermal contrast,and weaken the southerly winds over the East Asia–western Pacific region and convective activities over the South China Sea and tropical western Pacific.This modulates the East Asia–Pacific teleconnection pattern,which leads to a cyclonic anomaly and excessive summer precipitation over NEC.

Effect of the Vertical Diffusion of Moisture in the Planetary Boundary Layer on an Idealized Tropical Cyclone

Previous numerical studies have focused on the combined effect of momentum and scalar eddy diffusivity on the intensity and structure of tropical cyclones.The separate impact of eddy diffusivity estimated by planetary boundary layer(PBL)parameterization on the tropical cyclones has not yet been systematically examined.We have examined the impacts of eddy diffusion of moisture on idealized tropical cyclones using the Advanced Research Weather Research and Forecasting model with the Yonsei University PBL scheme.Our results show nonlinear effects of moisture eddy diffusivity on the simulation of idealized tropical cyclones.Increasing the eddy diffusion of moisture increases the moisture content of the PBL,with three different effects on tropical cyclones:(1)an decrease in the depth of the PBL;(2)an increase in convection in the inner rain band and eyewall;and(3)drying of the lowest region of the PBL and then increasing the surface latent heat flux.These three processes have different effects on the intensity and structure of the tropical cyclone through various physical mechanisms.The increased surface latent heat flux is mainly responsible for the decrease in pressure.Results show that moisture eddy diffusivity has clear effects on the pressure in tropical cyclones,but contributes little to the intensity of wind.This largely influences the wind-pressure relationship,which is crucial in tropical cyclones simulation.These results improve our understanding of moisture eddy diffusivity in the PBL and its influence on tropical cyclones,and provides guidance for interpreting the variation of moisture in the PBL for tropical cyclone simulations.

Automated Soil Moisture Monitoring Wireless Sensor Network for Long-Term Cal/Val Applications

The design and development of a wireless sensor network for soil moisture measurement in an unlevelled 10 km × 10 km area, is described. It was specifically deployed for the characterization of a reference area, in campaigns of calibration and validation of the space mission SMOS (Soil Moisture and Ocean Salinity), but the system is easily extensible to monitor other climatic or environmental variables, as well as to other regions of ecological interest. The network consists of a number of automatic measurement stations, strategically placed following soil homogeneity and land uses criteria. Every station includes acquisition, conditioning and communication systems. The electronics are battery operated with the help of solar cells, in order to have a total autonomous system. The collected data is then transmitted through long radio links, with ling ranges above 8 km. A standard PC linked to internet is finally used in order to control the whole network, to store the data, and to allow the remote access to the real-time data.

Late Holocene Moisture Changes in the Core Area of Arid Central Asia Reflected by Rock Magnetic Records of Glacier Lake Kalakuli Sediments in the Westernmost Tibetan Plateau and their Influences on the Evolution of Ancient Silk Road

The evolution of Ancient Silk Road(ASR) was deeply influenced by late Holocene moisture changes in Arid Central Asia(ACA). Nevertheless, controversies in Holocene moisture change pattern of ACA and poorly–constrained age models of related studies have made the discussion about late Holocene moisture changes in ACA and their influences on the evolution of ASR difficult. Recently, a high–resolution age model during the late Holocene was established for Kalakuli Lake, a small glacier lake located in the core area of ACA. A thorough rock magnetic investigation was carried out on Kalakuli Lake sediments based on this age model. The magnetic mineral assemblage of Kalakuli Lake sediments is still dominated by primary magnetite despite minor diagenetic effects. Comparisons of rock magnetic records to parameters previously used as indicator of glacier fluctuations suggest that clastic input to Kalakuli Lake was high(low) and magnetic grain size is relatively larger(smaller), when glaciers on Muztagh Ata advanced(retreated). The ARM/SIRM ratio, a magnetic grain size proxy, is directly related to lake hydrodynamics, which are ultimately controlled by glacier fluctuations on Muztagh Ata as the result of regional moisture changes. Late Holocene moisture changes indicated by the ARM/SIRM ratio are consistent with cool/wet and warm/dry oscillations indicated by the unweighted average of biomarker hydrogen isotopic data of the C26 and C28 n–alkanoic acids in a previous study about Kalakuli Lake, most moisture change records of the core area of ACA and winter insolation of the Northern Hemisphere, but opposite to Asian monsoon evolution. Given Asian monsoon and the westerlies are mutually inhibited, we propose that late Holocene moisture changes in the core area of ACA were controlled by the intensity of Asian monsoon versus the westerlies under the governance of solar insolation. Generally increased moisture since the late Holocene indicated by the ARM/SIRM ratio favored cultural exchange and integration between the western and the eastern Eurasia, which paved the way for the formation of ASR. Coincidence between significant increase in moisture at ~200 BC suggested by the ARM/SIRM ratio and the formation of ASR indicates moisture as an important factor that facilitated the formation of ASR. The onsets of three prosperity periods of ASR in the history generally correspond to periods when moisture was relatively high, nevertheless, stagnant periods of ASR do not coincide with periods when moisture was relatively low in the core area of ACA. Disorganized correlations between stagnant periods of ASR and moisture changes in the core area of ACA suggest that moisture is not the decisive factor influencing the evolution of ASR.

The Soil Moisture and Net Primary Production Affected by CO_2 and Climate Change Using a Coupled Model

In this paper, a coupled model was used to estimate the responses of soil moisture and net primary production of vegetation(NPP) to increasing atmospheric CO2 concentration and climate change. The analysis uses three experiments simulated by the second-generation Earth System Model(CanESM2) of the Canadian Centre for Climate Modelling and Analysis(CCCma), which are part of the phase 5 of the Coupled Model Intercomparison Project(CMIP5). The authors focus on the magnitude and evolution of responses in soil moisture and NPP using simulations modeled by CanESM, in which the individual effects of increasing CO2 concentration and climate change and their combined effect are separately accounted for. When considering only the single effect of climate change, the soil moisture and NPP have a linear trend of 0.03 kg m–2 yr–1 and –0.14 gC m–2 yr–2, respectively. However, such a reduction in the global NPP results from the decrease of NPP at lower latitudes and in the Southern Hemisphere, although increased NPP has been shown in high northern latitudes. The largest negative trend is located in the Amazon basin at –1.79 gC m–2 yr–2. For the individual effect of increasing CO2 concentration, both soil moisture and NPP show increases, with an elevated linear trend of 0.02 kg m–2 yr–1 and 0.84 gC m–2 yr–2, respectively. Most regions show an increasing NPP, except Alaska. For the combined effect of increasing atmospheric CO2 and climate change, the increased soil moisture and NPP exhibit a linear trend of 0.04 kg m–2 yr–1 and 0.83 gC m–2 yr–2 at a global scale. In the Amazon basin, the higher reduction in soil moisture is illustrated by the model, with a linear trend of –0.39 kg m–2 yr–1, for the combined effect. Such a change in soil moisture is caused by a weakened Walker circulation simulated by this coupled model, compared with the single effect of increasing CO2 concentration(experiment M2), and a consequence of the reduction in NPP is also shown in this area, with a linear trend of-0.16 gC m-2 yr-2.

Artificial neural network techniques to predict the moisture ratio content during hot air drying and vacuum drying of Radix isatidis extract

Background:To predict the moisture ratio of Radix isatidis extract during drying.Methods:Artificial neural networks were designed using the MATLAB neural network toolbox to produce a moisture ratio prediction model of Radix isatidis extract during hot air drying and vacuum drying,where regression values and mean squared error were used as evaluation indexes to optimize the number of hidden layer nodes and determine the topological structure of artificial neural networks model.In addition,the drying curves for the different drying parameters were analyzed.Results:The optimal topological structure of the moisture ratio prediction model for hot air drying and vacuum drying of Radix isatidis extract were“4-9-1”and“5-9-1”respectively,and the regression values between the predicted value and the experimental value is close to 1.This indicates that it has a high prediction accuracy.The moisture ratio gradually decreases with an increase in the drying time,reducing the loading,initial moisture content,increasing the temperature,and pressure can shorten the drying time and improve the drying efficiency.Conclusion:Artificial neural networks technology has the advantages of rapid and accurate prediction,and can provide a theoretical basis and technical support for online prediction during the drying process of the extract.

BP Neural Network Based on Microwave Method for Measuring the Moisture Content of Textile

The moisture content of yarn and fabric is an important factor in textiles industry.A novel microwave method used for material moisture content measurements is described in this paper.It can estimate the moisture content of the yarn roll with a standard deviation of 1.58% in the range of 0% to 90.00%.According to the actual size of the yarn,the yarn roll simulation model is established.The microwave attenuation variations arising from the changes in the conductivity and dielectric constant of the wet cone yarn from1.8 GHz to 5.0 GHz frequency are obtained by ultra-wideband antenna.The measured data are analyzed using the BP neural network.The result shows that it is a non-contact and online method to solve the moisture content of the yarn in the wide moisture content range.

Genetic Variation for Grain Yield and Yield Related Traits in Tef [<i>Eragrostis tef</i>(Zucc.)Trotter] under Moisture Stress and Non-Stress Environments

Tef [Eragrostis tef (Zucc.)Trotter] is an ancient and major cereal crop in Ethiopia. Increasing tef grain yield partly requires developing cultivars that are adapted to drought stress environment. An experiment was carried out using 18 tef genotypes grown during September to December, 2010, under two water supply environments (stress during grain filling period, and non-stress) to identify genetic variation, heritability and correlations of grain yield and yield related traits. Broad-sense heritability values under respective stress and non-stress environments were grain yield (g/m2) 0.80 and 0.89, total biomass (g/m2) 0.89 and 0.73, harvest index 0.69 and 0.79, panicle weight (g/plant) 0.93 and 0.92, and seed weight (g/plant) 0.96 and 0.86. The correlations of grain yield under respective stress and non-stress environments were total biomass rp = 0.64, rg = 0.70, and rp = 0.48, rg = 0.56, harvest index rp = 0.70, rg = 0.64, and rp = 0.87, rg = 0.90, panicle weight rp = 0.98, rg = 1.00, and rp = 0.96, rg = 1.00, and seed weight/plant rp = 0.98, rg = 1.00, and rp = 0.90, rg = 1.00. The present experiment showed that either grain yield per se, or seed weight/plant could be used to improve grain yield under stress and non-stress environments.

Relating Cone Penetration and Rutting Resistance to Variations in Forest Soil Properties and Daily Moisture Fluctuations

Soil resistance to penetration and rutting depends on variations in soil texture, density and weather-affected changes in moisture content. It is therefore difficult to know when and where off-road traffic could lead to rutting-induced soil disturbances. To establish some of the empirical means needed to enable the “when” and “where” determinations, an effort was made to model the soil resistance to penetration over time for three contrasting forest locations in Fredericton, New Brunswick: a loam and a clay loam on ablation/ basal till, and a sandy loam on alluvium. Measurements were taken manually with a soil moisture probe and a cone penetrometer from spring to fall at weekly intervals. Soil moisture was measured at 7.5 cm soil depth, and modelled at 15, 30, 45 and 60 cm depth using the Forest Hydrology Model (ForHyM). Cone penetration in the form of the cone index (CI) was determined at the same depths. These determinations were not only correlated with measured soil moisture but were also affected by soil density (or pore space), texture, and coarse fragment and organic matter content (R2 = 0.54;all locations and soil depths). The resulting regression-derived CI model was used to emulate how CI would generally change at each of the three locations based on daily weather records for rain, snow, and air temperature. This was done through location-initialized and calibrated hydrological and geospatial modelling. For practical interpretation purposes, the resulting CI projections were transformed into rut-depth estimates regarding multi-pass off-road all-terrain vehicle traffic.

The Impact of Soil Moisture Availability upon the Partition of Net Radiation into Sensible and Latent Heat Fluxes

The impact of soil moisture availability on the Bowen ratio and on the partition of net radiation flux into sensible, latent and soil heat fluxes was investigated by using one-dimensional primitive equations with a refined soil parameterization scheme. Simulation results presented that as soil moisture availability increases, the Bowen ratio and the partition of net radiation flux into sensible and soil heat fluxes decrease. The partition of net radiation flux into latent heat flux, however, increases. Quantitative relationships between Bowen ratio and the partitions with soil moisture availability were also given in this study.

Analyzing and Projecting Soil Moisture and Cone Penetrability Variations in Forest Soils

This article details how forest soil moisture content (MC) and subsequent resistances to cone penetration (referred below as Cone Index, CI) vary by daily weather, season, topography, site and soil properties across eleven harvest blocks in northwestern New Brunswick. The MC- and CI-affecting soil variables refer to density, texture, organic matter content, coarse fragment content, and topographic position (i.e., elevation, and the seasonally affected cartographic depth-to-water (DTW) pattern). The harvest blocks were transect-sampled inside and outside their wood-forwarding tracks at varying times throughout the year. In detail, 61% of the pore-filled moisture content (MCPS) determinations inside and outside the tracks could be related to topographic position, coarse fragments, bulk density, and forest cover type specifications. In addition, 40% of the CI variations could be related to soil depth, MCPS, and block-specific cover type. Actual versus model-projected uncertainties amounted to ΔMCPS ≤ ± 15% and ΔCI ≤ ± 0.5 MPa, 8 times out of 10. Block-centered MC and CI projections were obtained through: 1) daily hydrological modelling using daily precipitation and air temperature weather-station records nearest each block, and 2) digitally mapped variations in soil properties, elevation, DTW and forest cover type, done at 10 m resolution.

Development and Application of Cell-Phone-Based Internet of Things (IoT) Systems for Soil Moisture Monitoring

Active soil moisture monitoring is an important consideration in irrigation water management. A permanent and readily accessible record of changes in soil moisture can be used to improve future water management decision-making. Similarly, accessing stored soil moisture data in near-real-time is also essential for making timely farming and management decisions, such as where, when, and how much irrigation to apply. Access to reliable communication systems and delivery of real-time data can be affected by its availability near production fields. Therefore, soil moisture monitoring systems with real-time data functionality that can meet the needs of farmers at an affordable cost are currently needed. The objective of the study was to develop and fieldtest affordable cell-phone-based Internet of things (IoT) systems for soil moisture monitoring. These IoT systems were designed using low-cost hardware components and open-source software to transmit soil moisture data from the Watermark 200SS or ECH2O EC-5 sensors. These monitoring systems utilized either Particle Electron or Particle Proton Arduino-compatible devices for data communication. The IoT soil moisture monitoring systems have been deployed and operated successfully over the last three years in South Carolina.

Monitoring Soil Moisture under Wheat Growth through a Wireless Sensor Network in Dry Conditions

Drought research requires data on precipitation and actual soil moisture of fields because precipitation is variable among years and the soil textures differ with crop fields. Measurement of soil water content in the field is simple but labor-intensive. A prototype of an automatic field data monitoring system has been recently developed to collect data more efficiently. Using this system, data of soil water contents was successfully transmitted onto the personal computer approximately 700 m away from wheat field plots, for the period from March to May which was critical for soil drying and wheat growth. In addition, sample data of soil water content and grain yield was obtained from field plots of three bread wheat genotypes.