Reveal the kernel dehydration mechanisms in maize based on proteomic and metabolomic analysis.

BACKGROUND: Kernel dehydration is an important factor for the mechanized harvest in maize. Kernel moisture content (KMC) and kernel dehydration rate (KDR) are important indicators for kernel dehydration. Although quantitative trait loci and genes related to KMC have been identified, where most of them only focus on the KMC at harvest, these are still far from sufficient to explain all genetic variations, and the relevant regulatory mechanisms are still unclear. In this study, we tried to reveal the key proteins and metabolites related to kernel dehydration in proteome and metabolome levels. Moreover, we preliminarily explored the relevant metabolic pathways that affect kernel dehydration combined proteome and metabolome. These results could accelerate the development of further mechanized maize technologies. RESULTS: In this study, three maize inbred lines (KB182, KB207, and KB020) with different KMC and KDR were subjected to proteomic analysis 35, 42, and 49 days after pollination (DAP). In total, 8,358 proteins were quantified, and 2,779 of them were differentially expressed proteins in different inbred lines or at different stages. By comparative analysis, K-means cluster, and weighted gene co-expression network analysis based on the proteome data, some important proteins were identified, which are involved in carbohydrate metabolism, stress and defense response, lipid metabolism, and seed development. Through metabolomics analysis of KB182 and KB020 kernels at 42 DAP, 18 significantly different metabolites, including glucose, fructose, proline, and glycerol, were identified. CONCLUSIONS: In sum, we inferred that kernel dehydration could be regulated through carbohydrate metabolism, antioxidant systems, and late embryogenesis abundant protein and heat shock protein expression, all of which were considered as important regulatory factors during kernel dehydration process. These results shed light on kernel dehydration and provide new insights into developing cultivars with low moisture content.

Tree species explain only half of explained spatial variability in plant water sensitivity.

Spatiotemporal patterns of plant water uptake, loss, and storage exert a first-order control on photosynthesis and evapotranspiration. Many studies of plant responses to water stress have focused on differences between species because of their different stomatal closure, xylem conductance, and root traits. However, several other ecohydrological factors are also relevant, including soil hydraulics, topographically driven redistribution of water, plant adaptation to local climatic variations, and changes in vegetation density. Here, we seek to understand the relative importance of the dominant species for regional-scale variations in woody plant responses to water stress. We map plant water sensitivity (PWS) based on the response of remotely sensed live fuel moisture content to variations in hydrometeorology using an auto-regressive model. Live fuel moisture content dynamics are informative of PWS because they directly reflect vegetation water content and therefore patterns of plant water uptake and evapotranspiration. The PWS is studied using 21,455 wooded locations containing U.S. Forest Service Forest Inventory and Analysis plots across the western United States, where species cover is known and where a single species is locally dominant. Using a species-specific mean PWS value explains 23% of observed PWS variability. By contrast, a random forest driven by mean vegetation density, mean climate, soil properties, and topographic descriptors explains 43% of observed PWS variability. Thus, the dominant species explains only 53% (23% compared to 43%) of explainable variations in PWS. Mean climate and mean NDVI also exert significant influence on PWS. Our results suggest that studies of differences between species should explicitly consider the environments (climate, soil, topography) in which observations for each species are made, and whether those environments are representative of the entire species range.

Wood trait preferences of Neotropical xylophagous beetles (Coleoptera: Cerambycidae).

Tree life history strategies are correlated with functional plant traits, such as wood density, moisture content, bark thickness, and nitrogen content; these traits affect the nutrients available to xylophagous insects. Cerambycid beetles feed on substrates that vary in these traits, but little is known about how they affect community composition. The goal of this project is to explore the community composition of two cerambycid subfamilies (Cerambycinae and Lamiinae) according to the wood traits in the wood they eat. In a salvage project conducted adjacent to the Panama Canal, trees were felled and exposed to Cerambycidae for oviposition. Disks from branches of differing thickness from the same plant individuals were used to calculate wood density, moisture content, and bark thickness in the field; nitrogen data were acquired offsite. Thick and thin branches tended to differ in wood trait values; therefore, data were analyzed separately in subsequent analyses. In thin branches, cerambycid abundance and species richness were higher in samples with less dense, moister wood, and thicker bark. Thick branches showed similar trends, but the wood traits accounted for little variability in beetle abundance or species richness. There were no significant regressions between beetle data and nitrogen. Cerambycines emerged more slowly, and from denser, drier wood, than lamiines. Cerambycines might be more drought-tolerant than lamiines, and therefore more resistant to the longer, more severe dry seasons that are predicted to occur due to climate change.

La historia de vida de los árboles se correlaciona con los rasgos funcionales de la planta, como la densidad de la madera, el contenido de humedad, el grosor de la corteza, y el contenido de nitrógeno; estos rasgos afectan los nutrientes disponibles para los insectos xilófagos. Los escarabajos cerambícidos se alimentan de sustratos que varían en estos rasgos, pero se sabe poco sobre cómo afectan la composición de la comunidad. El objetivo de este proyecto es explorarla composición comunitaria de dos subfamilias de cerambícidos (Cerambycinae y Lamiinae) según las características de la madera que consumen. En un proyecto de salvamento realizado junto al Canal de Panamá, se talaron árboles y se expusieron a Cerambycidae para la oviposición. Se usaron discos de ramas de diferente grosor de las mismas plantas para calcular la densidad de la madera, el contenido de humedad y el grosor de la corteza en el campo; los datos de nitrógeno fueron adquiridos fuera del sitio. Las ramas gruesas y delgadas tendieron a diferir en los valores de las características de la madera; por lo tanto, los datos se analizaron por separado en análisis posteriores. En ramas delgadas, la abundancia de cerambícidos y la riqueza de especies fueron mayores en muestras con madera menos densa, más húmeda y con corteza más gruesa. Las ramas gruesas mostraron tendencias similares, pero las características de la madera explicaron poca variabilidad en la abundancia de escarabajos o la riqueza de especies. No hubo regresión significativas entre los datos del escarabajo y el nitrógeno. Cerambycines surgieron más lentamente y de maderas más densas y secas que los lamiines. Cerambycines podrían ser más tolerantes a la sequía que lamiines y, por lo tanto, más resistentes a las estaciones secas más largas y severas que se prevé que ocurran debido al cambio climático.

Pinpointing Moisture: The Capacitive Detection for Standing Tree Health.

BACKGROUND: the feasibility of the capacitance method for detecting the water content in standing tree trunks was investigated using capacitance-based equipment that was designed for measuring the water content of standing tree trunks. METHODS: In laboratory experiments, the best insertion depth of the probe for standing wood was determined by measurement experiments conducted at various depths. The bark was to be peeled when specimens and standing wood were being measured. The actual water content of the test object was obtained by specimens being weighed and the standing wood being weighed after the wood core was extracted. RESULTS: A forecast of the moisture content of standing wood within a range of 0 to 180% was achieved by the measuring instrument. The feasibility of the device for basswood and fir trees is preliminarily studied. When compared to the drying method, the average error of the test results was found to be less than 8%, with basswood at 7.75%, and fir at 7.35%. CONCLUSIONS: It was concluded that the measuring instrument has a wide measuring range and is suitable for measuring wood with low moisture content, as well as standing timber with high moisture content. The measuring instrument, being small in size, easy to carry, and capable of switching modes, is considered to have a good application prospect in the field of forest precision monitoring and quality improvement.

Rapid and Non-Destructive Prediction of Moisture Content in Maize Seeds Using Hyperspectral Imaging.

The moisture content of corn seeds is a crucial indicator for evaluating seed quality and is also a fundamental aspect of grain testing. In this experiment, 80 corn samples of various varieties were selected and their moisture content was determined using the direct drying method. The hyperspectral imaging system was employed to capture the spectral images of corn seeds within the wavelength range of 1100-2498 nm. By utilizing seven preprocessing techniques, including moving average, S-G smoothing, baseline, normalization, SNV, MSC, and detrending, we preprocessed the spectral data and then established a PLSR model for comparison. The results show that the model established using the normalization preprocessing method has the best prediction performance. To remove spectral redundancy and simplify the prediction model, we utilized SPA, CASR, and UVE algorithms to extract feature wavelengths. Based on three algorithms (PLSR, PCR, and SVM), we constructed 12 predictive models. Upon evaluating these models, it was determined that the normalization-SPA-PLSR algorithm produced the most accurate prediction. This model boasts high RC2 and RP2 values of 0.9917 and 0.9914, respectively, along with low RMSEP and RMSECV values of 0.0343 and 0.0257, respectively, indicating its exceptional stability and predictive capabilities. This suggests that the model can precisely estimate the moisture content of maize seeds. The results showed that hyperspectral imaging technology provides technical support for rapid and non-destructive prediction of corn seed moisture content and new methods in seed quality evaluation.

Prediction of moisture content for a single maize kernel based on viscoelastic properties.

BACKGROUND: The rapid and accurate detection of moisture content is important to ensure maize quality. However, existing technologies for rapidly detecting moisture content often suffer from the use of costly equipment, stringent environmental requirements, or limited accuracy. This study proposes a simple and effective method for detecting the moisture content of single maize kernels based on viscoelastic properties. RESULTS: Two types of viscoelastic experiments were conducted involving three different parameters: relaxation tests (initial loads: 60, 80, 100 N) and frequency-sweep tests (frequencies: 0.6, 0.8, 1 Hz). These experiments generated corresponding force-time graphs and viscoelastic parameters were extracted based on the four-element Maxwell model. Then, viscoelastic parameters and data of force-time graphs were employed as input variables to explore the relationships with moisture content separately. The impact of different preprocessing methods and feature time variables on model accuracy was explored based on force-time graphs. The results indicate that models utilizing the force-time data were more accurate than those utilizing viscoelastic parameters. The best model was established by partial least squares regression based on S-G smoothing data from relaxation tests conducted with initial force of 100 N. The correlation coefficient and the root mean square error of the calibration set were 0.954 and 0.021, respectively. The corresponding values of the prediction set were 0.905 and 0.029, respectively. CONCLUSIONS: This study confirms the potential for accurate and fast detection of moisture content in single maize kernels using viscoelastic properties, which provides a novel approach for the detection of various components in cereals. © 2024 Society of Chemical Industry.

[Application of microwave vacuum drying with process management in extract drying of personalized traditional Chinese medicine preparations].

Extracts are important intermediates in the production of traditional Chinese medicines preparations. The drying effect of extracts will directly affect the subsequent production process and the quality of the preparation. To meet the requirements of high drug loading, short time consumption, and simple production process of personalized traditional Chinese medicine preparations, this study explored the application of multi-program microwave vacuum drying process in the extract drying of personalized traditional Chinese medicine preparations. The influencing factors of microwave vacuum drying process were investigated for 5 excipients and 40 prescriptions. Taking the feasibility of drying, drying rate, drying time, and dried extract status as indicators, this study investigated the feeding requirements of microwave vacuum drying. With the dried extract status as the evaluation indicator, the three drying programs(A, B, and C) were compared to obtain the optimal drying condition. The experimental results showed that the optimal feeding conditions for microwave vacuum drying were material layer thickness of 2 cm and C program(a total of 7 drying processes), which solved the problem of easy scorching in microwave drying with process management. Furthermore, the preset moisture content of the dried extract in microwave drying should be 4%-5%, so that the dried extract of traditional Chinese medicine preparation had uniform quality, complete drying, and no scorching. This study lays a foundation for the application of microwave drying in the production of traditional Chinese medicine preparations, promoting the high-quality development of personalized traditional Chinese medicine preparations.

Plant hydraulic modelling of leaf and canopy fuel moisture content reveals increasing vulnerability of a Mediterranean forest to wildfires under extreme drought.

Fuel moisture content (FMC) is a crucial driver of forest fires in many regions world-wide. Yet, the dynamics of FMC in forest canopies as well as their physiological and environmental determinants remain poorly understood, especially under extreme drought. We embedded a FMC module in the trait-based, plant-hydraulic SurEau-Ecos model to provide innovative process-based predictions of leaf live fuel moisture content (LFMC) and canopy fuel moisture content (CFMC) based on leaf water potential ( ψ Leaf ). SurEau-Ecos-FMC relies on pressure-volume (p-v) curves to simulate LFMC and vulnerability curves to cavitation to simulate foliage mortality. SurEau-Ecos-FMC accurately reproduced ψ Leaf and LFMC dynamics as well as the occurrence of foliage mortality in a Mediterranean Quercus ilex forest. Several traits related to water use (leaf area index, available soil water, and transpiration regulation), vulnerability to cavitation, and p-v curves (full turgor osmotic potential) had the greatest influence on LFMC and CFMC dynamics. As the climate gets drier, our results showed that drought-induced foliage mortality is expected to increase, thereby significantly decreasing CFMC. Our results represent an important advance in our capacity to understand and predict the sensitivity of forests to wildfires.

Unraveling the Positive Effect of Soil Moisture on the Bioaugmentation of Petroleum-Contaminated Soil Using Bioinformatics.

Petroleum contamination is a severe threat to the soil environment. Previous studies have demonstrated that petroleum degradation efficiency is promoted by enhancing soil moisture content (MC). However, the effects of MC on soil microbial ecological functions during bioremediation remain unclear. Here, we investigated the impacts of 5% and 15% of moisture contents on petroleum degradation, soil microbial structures and functions, and the related genes using high-throughput sequencing and gene function prediction. Results indicated that petroleum biodegradation efficiency was increased by 8.06% in the soils with 15% MC when compared to that with 5% of MC. The complexity and stability of soil microbial community structures with 15% MC were higher than those in the soils with 5% MC when hydrocarbon-degrading bacterial flora (HDBF) were inoculated into the soils. Fifteen percent of moisture content strengthened the interaction of the bacterial community network and reduced the loss of some key bacteria species including Mycobacterium, Sphingomonas, and Gemmatimonas. Some downregulated gene pathways relating to bioaugmentation were enhanced in the soils with 15% MC. The results suggested that the dynamic balances of microbial communities and the metabolic interactions by 15% MC treatment are the driving forces for the enhancement of bioremediation in petroleum-contaminated soil.

Isolation and application of pH- and salt-resistant Bacillus strains to reduce ammonia emission from pig manure during the storage period.

AIM: Ammonia released during the storage period from pig manure causes severe air pollution and odor issues, ultimately leading to nitrogen loss in the manure. In this study, we investigated the application of 13 Bacillus spp. strains isolated from paddy soil and their potential to minimize reactive nitrogen loss during pig manure storage at 28°C and initial moisture content at 76.45%. METHODS AND RESULTS: We selected five strains of Bacillus spp. named H3-1, H4-10, H5-5, H5-9, and Y3-28, capable of reducing ammonia emissions by 23.58%, 24.65%, 25.58%, 25.36%, and 26.82% in pig manure over 60 days compared to control. We further tested their ability on various pH, salinity, and ammonium-nitrogen concentrations for future field applications. Our investigation revealed that certain bacteria could survive and grow at pH 6, 8, and 10; 4, 8, and 10% salinity and up to 8 g l-1 of ammonium-nitrogen concentration. CONCLUSIONS: The results from our study show that saline and ammonium-nitrogen tolerant Bacillus strains isolated from soil can potentially reduce ammonia emissions in pig manure, even at high moisture content during their storage period.

Artificial intelligence model substantially improves stratum corneum moisture content prediction from visible-light skin images and skin feature factors.

BACKGROUND: Appropriate skin treatment and care warrants an accurate prediction of skin moisture. However, current diagnostic tools are costly and time-consuming. Stratum corneum moisture content has been measured with moisture content meters or from a near-infrared image. OBJECTIVE: Here, we establish an artificial intelligence (AI) alternative for conventional skin moisture content measurements. METHODS: Skin feature factors positively or negatively correlated with the skin moisture content were created and selected by using the PolynomialFeatures(3) of scikit-learn. Then, an integrated AI model using, as inputs, a visible-light skin image and the skin feature factors were trained with 914 skin images, the corresponding skin feature factors, and the corresponding skin moisture contents. RESULTS: A regression-type AI model using only a visible-light skin-containing image was insufficiently implemented. To improve the accuracy of the prediction of skin moisture content, we searched for new features through feature engineering ("creation of new factors") correlated with the moisture content from various combinations of the existing skin features, and have found that factors created by combining the brown spot count, the pore count, and/or the visually assessed skin roughness give significant correlation coefficients. Then, an integrated AI deep-learning model using a visible-light skin image and these factors resulted in significantly improved skin moisture content prediction. CONCLUSION: Skin moisture content interacts with the brown spot count, the pore count, and/or the visually assessed skin roughness so that better inference of stratum corneum moisture content can be provided using a common visible-light skin photo image and skin feature factors.

On the Feasibility of Monitoring Power Transformer's Winding Vibration and Temperature along with Moisture in Oil Using Optical Sensors.

Despite major progress in the design of power transformers, the Achilles' heel remains the insulation system, which is affected by various parameters including moisture, heat, and vibrations. These important machines require extreme reliability to guarantee electricity distribution to end users. In this contribution, a fiber optic sensor (FOS), consisting of a Fabry-Perot cavity made up of two identical fiber Bragg gratings (FBGs), is proposed, to monitor the temperature and vibration of power transformer windings. A phase shifted gratings recoated sensor, with multilayers of polyimide films, is used to monitor the moisture content in oil. The feasibility is investigated using an experimental laboratory transformer model, especially fabricated for this application. The moisture contents are well correlated with those measured by a Karl Fisher titrator, while the values of temperature compare well with those recorded from thermocouples. It is also shown that the sensors can be used to concurrently detect vibration, as assessed by sensitivity to the loading current. The possibility of dynamically measuring humidity, vibrations, and temperatures right next to the winding, appears to be a new insight that was previously unavailable. This approach, with its triple ability, can help to reduce the required number of sensors and therefore simplify the wiring layout.

Estimating Scalp Moisture in a Hat Using Wearable Sensors.

Hair quality is easily affected by the scalp moisture content, and hair loss and dandruff will occur when the scalp surface becomes dry. Therefore, it is essential to monitor scalp moisture content constantly. In this study, we developed a hat-shaped device equipped with wearable sensors that can continuously collect scalp data in daily life for estimating scalp moisture with machine learning. We established four machine learning models, two based on learning with non-time-series data and two based on learning with time-series data collected by the hat-shaped device. Learning data were obtained in a specially designed space with a controlled environmental temperature and humidity. The inter-subject evaluation showed a Mean Absolute Error (MAE) of 8.50 using Support Vector Machine (SVM) with 5-fold cross-validation with 15 subjects. Moreover, the intra-subject evaluation showed an average MAE of 3.29 in all subjects using Random Forest (RF). The achievement of this study is using a hat-shaped device with cheap wearable sensors attached to estimate scalp moisture content, which avoids the purchase of a high-priced moisture meter or a professional scalp analyzer for individuals.

Rapid and Non-Destructive Estimation of Moisture Content in Caragana Korshinskii Pellet Feed Using Hyperspectral Imaging.

Moisture content is an important parameter for estimating the quality of pellet feed, which is vital in nutrition, storage, and taste. The ranges of moisture content serve as an index for factors such as safe storage and nutrition stability. A rapid and non-destructive model for the measurement of moisture content in pellet feed was developed. To achieve this, 144 samples of Caragana korshinskii pellet feed from various regions in Inner Mongolia Autonomous Region underwent separate moisture content control, measurement using standard methods, and captured their images using a hyperspectral imaging (HSI) system in the spectral range of 935.5-2539 nm. The Monte Carlo cross validation (MCCV) was used to eliminate abnormal sample data from the spectral data for better model accuracy, and a global model of moisture content was built by using partial least squares regression (PLSR) with seven preprocessing techniques and two spectral feature extraction techniques. The results showed that the regression model developed by PLSR based on second derivative (SD) and competitive adaptive reweighted sampling (CARS) resulted in better performance for moisture content. The model showed predictive abilities for moisture content with a coefficient of determination of 0.9075 and a root mean square error (RMSE) of 0.4828 for the training set; and a coefficient of determination of 0.907 and a root mean square error (RMSE) of 0.5267 for the test set; and a relative prediction error of 3.3 and the standard error of 0.307.

Backscattering Echo Intensity Characteristics of Laser in Soil Explosion Dust.

Soil dust generated by explosions can lead to the absorption and scattering of lasers, resulting in low detection and recognition accuracy for laser-based devices. Field tests to assess laser transmission characteristics in soil explosion dust are dangerous and involve uncontrollable environmental conditions. Instead, we propose using high-speed cameras and an indoor explosion chamber to assess the backscattering echo intensity characteristics of lasers in dust generated by small-scale explosive blasts in soil. We analyzed the influence of the mass of the explosive, depth of burial, and soil moisture content on crater features and temporal and spatial distributions of soil explosion dust. We also measured the backscattering echo intensity of a 905 nm laser at different heights. The results showed that the concentration of soil explosion dust was highest in the first 500 ms. The minimum normalized peak echo voltage ranged from 0.318 to 0.658. The backscattering echo intensity of the laser was found to be strongly correlated with the mean gray value of the monochrome image of soil explosion dust. This study provides experimental data and a theoretical basis for the accurate detection and recognition of lasers in soil explosion dust environments.

Design and Experiment of Capacitive Rice Online Moisture Detection Device.

To solve the problems of poor stability and low monitoring precision in the online detection of rice moisture in the drying tower, we designed an online detection device for rice moisture at the outlet of the drying tower. The structure of a tri-plate capacitor was adopted, and the electrostatic field of the tri-plate capacitor was simulated using COMSOL software. A central composite design of three factors and five levels was carried out with the thickness, spacing, and area of the plates as the influencing factors and the capacitance-specific sensitivity as the test index. This device was composed of a dynamic acquisition device and a detection system. The dynamic sampling device was found to achieve dynamic continuous sampling and static intermittent measurements of rice using a ten-shaped leaf plate structure. The hardware circuit of the inspection system with STM32F407ZGT6 as the main control chip was designed to realize stable communication between the master and slave computers. Additionally, an optimized BP neural network prediction model based on the genetic algorithm was established using the MATLAB software. Indoor static and dynamic verification tests were also carried out. The results showed that the optimal plate structure parameter combination includes a plate thickness of 1 mm, plate spacing of 100 mm, and relative area of 18,000.069 mm2 while satisfying the mechanical design and practical application needs of the device. The structure of the BP neural network was 2-90-1, the length of individual code in the genetic algorithm was 361, and the prediction model was trained 765 times to obtain a minimum MSE value of 1.9683 × 10-5, which was lower than that of the unoptimized BP neural network with an MSE of 7.1215 × 10-4. The mean relative error of the device was 1.44% under the static test and 2.103% under the dynamic test, which met the accuracy requirements for the design of the device.

Multilayer Model in Soil Moisture Content Retrieval Using GNSS Interferometric Reflectometry.

The global navigation satellite system-interferometric reflectometry (GNSS-IR) was developed more than a decade ago to monitor soil moisture content (SMC); a system that is essentially finished has emerged. The standard GNSS-IR model typically considers soil to be a single layer of medium and measures the average SMC between 1 and 10 cm below the soil surface. The majority of the SMC is not distributed uniformly along the longitudinal axis. This study is based on a simulation platform and suggests a SMC-stratified measurement model that can be used to recover the SMC at different depths in the sink and reverse osmosis to address the issue that conventional techniques cannot accurately measure soil moisture at different depths. The soil moisture of each layer was assessed by utilizing the GNSS signals reflected by various soil layers, and this study employed total transmission when the vertical linearly polarized component of the electromagnetic wave was conveyed by the GNSS signal reflected by the soil. This work employed the Hilbert transform to obtain the interference signal envelope, which increases the visibility of the interference signal's "notch" and reduces the burr impact of the interference signal brought on by ambient noise. The accuracy of the SMC measurement at the bottom declines due to the soil's attenuation of the GNSS signal power, but the correlation between the predetermined value and SMC retrieved by the GNSS-IR multilayer SMC measurement model similarly approached 0.92.

Accurate Assessment of Moisture Content and Degree of Polymerization in Power Transformers via Dielectric Response Sensing.

Power transformers are essential apparatuses used to transfer electrical energy from one voltage-level circuit to another. For reliable systems, preventive maintenance of the transformers is required to ensure good services of all mechanical, electrical, and insulation parts. Oil-immersed paper is most often used for transformer insulation. To ensure such good insulation performance and for assessing insulation conditions, advanced transformer sensing, monitoring, and effective assessment techniques are required. This paper introduces an effective technique for assessing the insulation conditions in power transformers, which are crucial for ensuring reliable energy transfer. The method utilizes advanced transformer sensing and monitoring, focusing on oil-immersed paper insulation commonly used in transformers. The technique employs dielectric response sensing, obtained from frequency-domain spectroscopy tests, to estimate degrees of polymerization (DP) and percentages of moisture content (PMCs) in the oil-immersed paper insulation. These parameters are well-known indicators of insulation performance. The approach is based on the weighted k-nearest neighbor regression, using a database of dielectric loss factors at low frequency and oil conductivities. To overcome limited data availability, linear interpolation and extrapolation techniques are applied to enlarge the database. Experimental verification and comparison with a previously developed method demonstrate the proposed technique's superiority in accuracy and complexity. The maximum deviations of DP and PMC in the validation cases are 6.2% and 18.7%, respectively. In addition, to evaluate the validity of our proposed method in the case of a real power transformer, a comparative analysis of the DP and PMC values determined by the proposed method with those obtained through a previously developed and complicated approach was performed. The predicted results indicate that the DP and PMC values of the oil-immersed insulation fall within the ranges of 800 to 1000 and 1.5 to 2.0, respectively, which agree with the results determined by the complicated approach and closely align with real conditions. By offering a reliable and advanced means of assessing insulation conditions, this technique contributes to the preventive maintenance and overall efficiency of power transformers.

Recent Advances in THz Detection of Water.

The frequency range of terahertz waves (THz waves) is between 0.1 and 10 THz and they have properties such as low energy, penetration, transients, and spectral fingerprints, which are especially sensitive to water. Terahertz, as a frontier technology, have great potential in interpreting the structure of water molecules and detecting biological water conditions, and the use of terahertz technology for water detection is currently frontier research, which is of great significance. Firstly, this paper introduces the theory of terahertz technology and summarizes the current terahertz systems used for water detection. Secondly, an overview of theoretical approaches, such as the relaxation model and effective medium theory related to water detection, the relationship between water molecular networks and terahertz spectra, and the research progress of the terahertz detection of water content and water distribution visualization, are elaborated. Finally, the challenge and outlook of applications related to the terahertz wave detection of water are discussed. The purpose of this paper is to explore the research domains on water and its related applications using terahertz technology, as well as provide a reference for innovative applications of terahertz technology in moisture detection.

A first-order kinetic model for simulating the aerobic degradation of municipal solid waste.

Aerobic degradation models are important tools for investigating the aerobic degradation behavior of municipal solid waste (MSW). In this paper, a first-order kinetic model for aerobic degradation of MSW was developed. The model comprehensively considers the aerobic degradation of five substrates, i.e., holocellulose, non-cellulosic sugars, proteins, lipids and lignin. The proportion ranges of the five substrates are summarized with the recommended values given. The effects of temperature, moisture content, oxygen concentration and free air space (FAS) on the reaction rates are considered, and the effect of settlement is accounted for in the FAS correction function. The reliability of the model was verified by comparing simulations of the aerobic degradation of low food waste content (LFWC-) and high food waste content (HFWC-) MSWs to the literature. Afterwards, a sensitivity analysis was carried out to establish the relative importance of aeration rate (AR), volumetric moisture content (VMC), and temperature. VMC had the greatest influence on the aerobic degradation of LFWC-MSW, followed by temperature and then AR; for HFWC-MSW, temperature was the most important factor, then VMC and last was AR. The degradation ratio of LFWC-MSW can reach 98.0% after 100 days degradation under its optimal conditions (i.e., temperature: 55 °C, VMC: 40%, AR: 0.16 L min-1 kg-1 DM), while it is slightly higher as 99.5% for HFWC-MSW under its optimal conditions (i.e., temperature: 55 °C, VMC: 40%, AR: 0.20 L min-1 kg-1 DM).