Controlled drainage in the Nile River delta of Egypt:a promising approach for decreasing drainage off-site effects and enhancing yield and water use efficiency of wheat

North Africa is one of the most regions impacted by water shortage.The implementation of controlled drainage(CD)in the northern Nile River delta of Egypt is one strategy to decrease irrigation,thus alleviating the negative impact of water shortage.This study investigated the impacts of CD at different levels on drainage outflow,water table level,nitrate loss,grain yield,and water use efficiency(WUE)of various wheat cultivars.Two levels of CD,i.e.,0.4 m below the soil surface(CD-0.4)and 0.8 m below the soil surface(CD-0.8),were compared with subsurface free drainage(SFD)at 1.2 m below the soil surface(SFD-1.2).Under each drainage treatment,four wheat cultivars were grown for two growing seasons(November 2018–April 2019 and November 2019–April 2020).Compared with SFD-1.2,CD-0.4 and CD-0.8 decreased irrigation water by 42.0%and 19.9%,drainage outflow by 40.3%and 27.3%,and nitrate loss by 35.3%and 20.8%,respectively.Under CD treatments,plants absorbed a significant portion of their evapotranspiration from shallow groundwater(22.0%and 8.0%for CD-0.4 and CD-0.8,respectively).All wheat cultivars positively responded to CD treatments,and the highest grain yield and straw yield were obtained under CD-0.4 treatment.Using the initial soil salinity as a reference,the soil salinity under CD-0.4 treatment increased two-fold by the end of the second growing season without negative impacts on wheat yield.Modifying the drainage system by raising the outlet elevation and considering shallow groundwater contribution to crop evapotranspiration promoted water-saving and WUE.Different responses could be obtained based on the different plant tolerance to salinity and water stress,crop characteristics,and growth stage.Site-specific soil salinity management practices will be required to avoid soil salinization due to the adoption of long-term shallow groundwater in Egypt and other similar agroecosystems.

Data-Driven Models for Predicting Solar Radiation in Semi-Arid Regions

Solar energy represents one of themost important renewable energy sources contributing to the energy transition process.Considering that the observation of daily global solar radiation(GSR)is not affordable in some parts of the globe,there is an imperative need to develop alternative ways to predict it.Therefore,the main objective of this study is to evaluate the performance of different hybrid data-driven techniques in predicting daily GSR in semi-arid regions,such as the majority of Spanish territory.Here,four ensemble-based hybrid models were developed by hybridizing Additive Regression(AR)with Random Forest(RF),Locally Weighted Linear Regression(LWLR),Random Subspace(RS),and M5P.The base algorithms of the developed models are scarcely applied in previous studies to predict solar radiation.The testing phase outcomes demonstrated that the ARRF models outperform all other hybrid models.The provided models were validated by statisticalmetrics,such as the correlation coefficient(R)and root mean square error(RMSE).The results proved that Scenario#6,utilizing extraterrestrial solar radiation,relative humidity,wind speed,and mean,maximum,and minimum ambient air temperatures as the model inputs,leads to the most accurate predictions among all scenarios(R=0.968–0.988 and RMSE=1.274–1.403 MJ/m^(2)・d).Also,Scenario#3 stood in the next rank of accuracy for predicting the solar radiation in both validating stations.The AD-RF model was the best predictive,followed by AD-RS and AD-LWLR.Hence,this study recommends new effective methods to predict GSR in semiarid regions.

机织结构预成型体本构关系和剪切性能

综述了国内外纺织预成型体的剪切性能及其本构关系的研究进展。在简要介绍机织预成型体的分类及其结构和特性的基础上,从实验方法、本构关系以及变形模拟等方面概述了纺织预成型体剪切性能的研究进展,对比分析了纺织预成型体剪切性能的测试方法,描述了纺织预成型体剪切变形的几何非线性和材料非线性的特征,总结了纺织预成型体的本构模型以及可成型性数值模拟方面的研究成果,并提出了目前存在的问题和今后的发展方向。

基于二维条码和ARM的谷物溯源采集传输系统(英文)

谷物在生产供应链中按照等级和用途进行混合配送和分销,多源头性决定了谷物追溯系统中信息识别的难点。针对这个问题,该文提出以表面喷印二维条码的追溯颗粒作为谷物追溯信息的载体。基于嵌入式系统开发了二维条码、GPS 定位信息和谷物产地环境信息采集终端,终端采集到的数据通过 GPRS 网络传输。系统实现了谷物标示信息及产地环境信息(温度、湿度、光照、二氧化碳)的自动采集和传输,可作为快速、低成本的谷物追溯系统,为确保谷物质量与安全生产提供一种有效手段。

水中悬浮物浓度的检测方法研究进展

水中悬浮物是重要的农业非点源污染物之一,也是水质评价的重要研究对象,对水中悬浮物浓度的有效检测有助于确定相关水域的悬浮物最大日负荷(total maximum daily loads,TMDL)以及相应的最佳管理操作(best management practice,BMP)。对水中悬浮物浓度的检测方法,包括传统称质量法、光学传感器、激光衍射、遥感、声学、图像处理、电容等方法进行了总结和归纳,分析各自的优势和存在的问题并提出了建议。结果表明,在利用遥感技术、水中传感器研究的基础上,从空间、地面进行信息采集和融合,并开展多源实时监测水中悬浮物浓度的研究是未来的发展方向。

食品中核磁共振状态图的应用(英文)

食品中质量感官指标和货架期与它的理化特性有关,水是食品原料中重要组成部分,它的含量与食品的理化特性有关,并在其中扮演着重要角色。“玻璃态转化” 的概念是在研究食品原料和产品理化特性的聚合物理论时提出的。食品玻璃态转化状态图通常用来描述水含量与食品理化特性的关系。状态图能有效地反映在不同温度和水分含量下的物质的理化特性。本文提出一个新的观念—“核磁共振状态图” 概念,核磁共振状态图是指核磁共振弛豫时间(通常指自旋-自旋弛豫时间或T2)和温度之间的曲线图,它放映食品的核磁共振弛豫现象与食品的理化特性关系。我们在研究时发现这条曲线图与物质的许多理化特性有着良好的相关性。利用核磁共振状态图可以在以下方面得到应用:(1)通过分析产品的成分,确定产品变化的规律;(2)预测产品的理化性质的变化(质地、粘性、结块、水分和油脂的迁移等)的趋势,营养成分降解和微生物代谢与食品中水分活度之间的关系;(3)结合核磁共振成像技术能很好地帮助理解水分(特别游离水分)对食品的质量与安全的影响。本文描述了核磁共振的基本原理、核磁共振弛豫现象的特征与食品特性的关系,并为研究食品体系提供新的研究方法和途径。

基于模糊综合分析方法的谷物溯源颗粒优化设计

身份标识技术是食品溯源系统中的关键技术之一,有效的身份标识技术应具有唯一性、识别鲁棒性、耐磨损性的特点。针对粮食生产中谷物生产多源头混合配送和分销而难以追溯到源产地的问题,该文提出采用溯源颗粒标识谷物身份信息的方法,以生产工艺参数压力及配方为影响因素,设计了影响基于糖类型和基于纤维素类型2种溯源颗粒的表面粗糙度的正交试验;并应用模糊综合评判方法分析2种溯源颗粒最优工艺参数组合。该研究探索了具有较光滑表面的追溯颗粒生产过程中生产参数的优化策略,为进一步研究谷物溯源颗粒的信息标识及提高生产工艺提供技术支持。

基于高光谱的牛奶脂肪质量浓度预测模型建立与评价

以牛奶中脂肪质量浓度的检测为研究对象,应用图像处理技术分析高光谱数据,选取样品分析区域,提取分析区域的平均光谱,建立了PLS、N-PLS预测模型对牛奶中脂肪质量浓度进行分析。结果表明,PLS预测模型中校正集与预测集的相关系数分别为0.9851和0.9913,而N-PLS预测模型中校正集与预测集的相关系数分别为0.9999和0.9976。两个模型对比发现,N-PLS模型预测精度高于PLS模型,尤其是牛奶中脂肪质量浓度较少的情况。

Advances in Effects of Sound Waves on Plants

Sound waves technology has been applied to different plants.It has been found that sound waves were at different frequencies,sound pressure levels(SPLs),exposure periods,and distances from the source of sound infl uence plant growth.Experiments have been conducted in the open field and under greenhouse growing conditions with different levels of audible sound frequencies and sound pressure levels.Sound waves at 1 kHz and 100 dB for 1 h within a distance of 0.20 m could significantly promote the division and cell wall fl uidity of callus cells and also significantly enhance the activity of protective enzymes and endogenous hormones.Sound waves stimulation could increase the plant plasma-membrane H+-ATPase activity,the contents of soluble sugar,soluble protein,and amylase activity of callus.Moreover,sound waves could increase the content of RNA and the level of transcription.Stress-induced genes could switch on under sound stimulation.Sound waves at 0.1-1 kHz and SPL of(70±5)dB for 3 h from plant acoustic frequency technology(PAFT)generator within a distance ranged from 30 to 60 m every other day significantly increased the yield of sweet pepper,cucumber and tomato by 30.05,37.1and 13.2%,respectively.Furthermore,the yield of lettuce,spinach,cotton,rice,and wheat were increased by 19.6,22.7,11.4,5.7,and 17.0%,respectively.Sound waves may also strengthen plant immune systems.It has been proved that spider mite,aphids,gray mold,late blight and virus disease of tomatoes in the greenhouses decreased by 6.0,8.0,9.0,11.0,and 8.0%,respectively,and the sheath blight of rice was reduced by 50%.This paper provides an overview of literature for the effects of sound waves on various growth parameters of plant at different growth stages.

Artificial senses for characterization of food quality

Food quality is of primary concern in the food industry and to the consumer. Systems that mimic human senses have been developed and applied to the characterization of food quality. The five primary senses are: vision, hearing, smell, taste and touch. In the characterization of food quality, people assess the samples sensorially and differentiate “good”from “bad”on a continuum. However, the human sensory system is subjective, with mental and physical inconsistencies, and needs time to work. Artificial senses such as machine vision, the electronic ear, electronic nose, electronic tongue, artificial mouth and even artificial the head have been developed that mimic the human senses. These artificial senses are coordinated individually or collectively by a pat- tern recognition technique, typically artificial neural networks, which have been developed based on studies of the mechanism of the human brain. Such a structure has been used to formulate methods for rapid characterization of food quality. This research presents and discusses individual artificial sensing systems. With the concept of multi-sensor data fusion these sensor systems can work collectively in some way. Two such fused systems, artificial mouth and artificial head, are described and discussed. It indicates that each of the individual systems has their own artificially sensing ability to differentiate food samples. It further indicates that with a more complete mimic of human intelligence the fused systems are more powerful than the individual sys- tems in differentiation of food samples.

In-depth observations of fermentative hydrogen production from liquid swine manure using an anaerobic sequencing batch reactor

In this study,experiments were designed to reveal in-depth information of the effect of pH and hydraulic retention time(HRT)on biohydrogen fermentation from liquid swine manure supplemented with glucose using an Anaerobic Sequencing Batch Reactor(ASBR)System.Five values of HRT(8,12,16,20,and 24 h)were first tested and the best HRT determined was further studied at five p H levels(4.4,4.7,5.0,5.3,and 5.6).The results showed that for HRT 24 h,there was a dividing H_2 content(around 37%)related to the total biogas production rate for the ASBR System running at p H 5.0.When the H_2 content went beyond 37%,an appreciable decline in biogas production rate was observed,implying that there might exist an H_2 content limit in the biogas.For other HRTs(8 through 20 h),an average H_2 content of 42%could be achieved.In the second experiment(HRT 12 h),the highest H_2 content(35%)in the biogas was found to be associated with p H 5.0.The upswing of p H from 5.0 to 5.6 had a significantly more impact on biogas H_2 content than the downswing of p H from5.0 to 4.3.The results also indicated good linear relationships of biogas and H_2 production rates with HRT(r=0.9971 and0.9967,respectively).Since the optimal ASBR operating conditions were different for the biogas/H_2 production rates and the H_2 yield,a compromised combination of the running parameters was determined to be HRT 12 h and pH 5.0 in order to achieve good biogas/H_2 productions.

Study on evaluation of gamma oryzanol of germinated brown rice by near infrared spectroscopy

Germinated brown rice(GBR)is rich in gamma oryzanol which increase its consumption popularity,particularly in the health food market.The objective of this research was to apply the near infraredspectroscopy(NIRS)for evaluation of gamma oryzanol of the germinated brown rice.The germinated brown rice samples were prepared from germinated rough rice(soaked for 24 and 48 h,incubated for 0,6,12,18,24,30 and 36 h)and purchased from local supermar kets.The germinated brown rice sampleswere subjected to NIR scanning before the evaluation of gamma oryzanol by using partial extractionmet hodology.The prediction model was established by partial least square regression(PLSR)andvalidated by full cross validation method.The NIRS model established from various varieties of germinated brown rice bought from diferent markets by first derivatives+vector normalizationpretreated spectra showed the optimal prediction with the correlation of determination(R?),root mean squared error of cross validation(RMSECV),and bias of 0.934,8.84×10^(-5) mg/100 g drymatter and 1.06×10^(-5) mg/100 g dry matter,respectively.This is the first report on the application of NIRS in the evaluation of gamma oryzanol of the germinated brown rice.This information is veryuseful to the germinated brown rice production factory and consumers.

Eating quality of cooked rice determination using Fourier transform near infrared spectroscopy

The goal of this research was to study the relationship between the eating quality of cooked rice and near infrared spectra measured by a Fourier Transform near infrared(FT-NIR)Spee-trometer.Samples of milled:parboiled rioe,white rioe,new Jasmine rice(harvested in 2012)and aged Jasmine rice(harvested in 2006 or during the period 2007-2011)were used in this study.The eating quality of the cooked rioe,ie,adhesiveness,hardness,dryness,whiteness and aroma,were evaluated by trained sensory panelists.FT-NIR spectroscopy models for predicting the eating quality of cooked rioe were established using the partial least squares regression.Among the eating quality,the stickiness model indicated its highest prediction ability(ie,R2a=0.71;.RMSEP=0.65;Bias=0.00;RPD=1.87)and SEP/SD of 2.In addition,it was clear that the water content did not affect the eating quality of cooked rice,rather the main chemical com-ponent implicated was starch.

Multichannel imaging for monitoring chemical composition and germination capacity of cowpea(Vigna unguiculata) seeds during development and maturation

This study aimed to set a computer-integrated multichannel spectral imaging system as a high-throughput phenotyping tool for the analysis of individual cowpea seeds harvested at different developmental stages. The changes in germination capacity and variations in moisture, protein and different sugars during twelve stages of seed development from 10 to 32 days after anthesis were nondestructively monitored. Multispectral data at 20 discrete wavelengths in the ultraviolet, visible and near infrared regions were extracted from individual seeds and then modelled using partial least squares regression and linear discriminant analysis(LDA) models. The developed multivariate models were accurate enough for monitoring all possible changes occurred in moisture, protein and sugar contents with coefficients of determination in prediction R^(2) of 0.93, 0.80 and 0.78 and root mean square errors in prediction(RMSEP) of 6.045%, 2.236% and 0.890%, respectively. The accuracy of PLS models in predicting individual sugars such as verbascose and stachyose was reasonable with R~2 of 0.87 and 0.87 and RMSEP of 0.071%and 0.485%, respectively;but for the prediction of sucrose and raffinose the accuracy was relatively limited with R^(2) of 0.24 and 0.66 and RMSEP of 0.567% and 0.045%, respectively. The developed LDA model was robust in classifying the seeds based on their germination capacity with overall correct classification of96.33% and 95.67% in the training and validation datasets, respectively. With these levels of accuracy,the proposed multichannel spectral imaging system designed for single seeds could be an effective choice as a rapid screening and non-destructive technique for identifying the ideal harvesting time of cowpea seeds based on their chemical composition and germination capacity. Moreover, the development of chemical images of the major constituents along with classification images confirmed the usefulness of the proposed technique as a non-destructive tool for estimating the concentrations and spatial distributions of moisture, protein and sugars during different developmental stages of cowpea seeds.

Evaluation of the moisture content of tapioca starch using near-infrared spectroscopy

The purpose of this study was to develop a calibration model to evaluate the moisture content of tapioca starch using the near infrared(NIR)spectral data in conjunction with partial least square(PIS)regression.The prediction ability was asessed using a separate prediction data set.Three groups of tapioca starch samples were used in this study:tapioca starch cake,dried tapioca starch and combined tapioca starch.The opt imum model obtained from the baseline ofset spectra of dried tapioca starch samples at the outlet of the factory drying process provided a cofficient of determination(R^(2)),standard error of prediction(SEP),bias and residual prediction deviation(RPD)of 0.974,0.16%,-0.092%and 7.4,respectively.The NIR spectroscopy protocol developed in this study could be a rapid method for evaluation of the moisture content of the tapioca starch in factory laboratories.It indicated the possibility of real-tine online monitoring and control of the tapioca starch cake feeder in the drying process.In addition,it was determined that there was.a stronger influence of the NIR absorption of both water and starch on the prediction of moisture content of the model.

Evaluation of Wastewater Quality Compared to Well Water in Irrigation

Water resources’ scarcity and increasing demand for water consumption have necessitated the use of unconventional water for optimized and sus-tainable consumption of water resources. The present project1 was con-ducted to investigate effects of domestic wastewater in comparison to well water, in a randomized complete block design with four treatments in three replications during three years. Concentrations of cations and anions in soil and plants as well as plants’ fresh and dry weight were measured in the present research per harvesting time. Results showed that the concentration of elements in alfalfa was higher in treatments being irrigated with wastewater resulting in the significant differences in nitrogen, phosphorus, sodium and chloride. The mean of elements’ concentration at soil with the depth of 0 - 30 cm in wastewater treatments was much more than those treatments irrigated with groundwater. Concentrations of elements in wastewater treatment with the depth of 30 - 60 cm were higher than well water treatments. Electrical conductivity, pH, total nitrogen, copper and iron significantly differed in different years and different treatments. Treatments irrigated with wastewater showed higher yield compared to treatments irrigated with well water (29,158 and 1252.5 kg per ha). Using wastewater of domestic treatment plant increased production per unit area and subsequently increased elements concentration in soil and plant.

Physiological traits and anatomic structures of the seed for two short cotton season genotypes(Gossypium hirsutum L.) under water stress

Cotton(Gossypium hirsutum L.) is utilized commercially in the production of textile, clothing, and household goods. Its growth is affected by various environmental conditions such as soil, climate, and water supply. Irrigation is one of the most important factors for crop management. This research was designed with the aim of studying the biochemical and anatomical features of two genotypes of cottonseed(Latif and Golestan) in order to determine optimum irrigation. Protein, starch, gossypol gland, total soluble protein, starch content, and the anatomical structure of the cotton seeds were investigated. The data were analyzed using the SAS Software. The results showed that when rainfed, Golestan genotype seeds had a larger number of secretory cavities, soluble protein, and starch compared to Latif seeds. There was also a noticeable difference in the size of the seeds. Cellulosic cell walls and protein particles were also observed in the seed structure under induced water stress conditions. It can therefore be concluded that in response to water shortage, there was a noticeable change in the morphometric, anatomic, and biochemical features of the cottonseeds. It can be concluded that when rainfed, the Golestan genotype of cottonseed has more compatibility than the Latif genotype. In general, the application of different levels of irrigation showed that at moderate levels of irrigation, anatomical features appeared more normal. The observations indicate that at high levels of irrigation, some cells begin to dehisce due to osmotic stress, which results in a lack of accurate formation of tissue structures. The Golestan genotype is therefore the best suited for dryland farming.

Design parameters for a small-scale batch in-bin maize dryer

Early season maize is harvested with high moisture content that makes it impossible to store. The sale of early season maize in green form is uneconomical to the farmer. Experience had shown that farmers could hardly make the cost of production from their sales. Also, grain losses are high when maize is harvested green. To minimize grain losses and thereby increase value and the profit margin of the farmer, a grain dryer is necessary for wet grains. Therefore, this paper presents the design and development of a batch in-bin maize grain dryer. Some properties of maize such as moisture content and bulk density were determined to get information required for design of the dryer. The dimension of drying chamber, amount of moisture to be removed in a batch, quantity of air required to effect drying, volume of air required to effect drying, blower capacity, quantity of heat required to effect drying and actual heat used to effect drying were all designed for. A maize dryer was developed with a batch size of100 kg of threshed wet maize. The dryer can be used in laboratory for experimental purpose as well as on the farm for commercial purposes. The dryer can be used to measure drying rates of maize at different initial moisture contents, drying air temperatures, drying air velocities and grain beds. The effects of different drying temperature, air velocity, loading and agitating speed on the quality of dried maize can be investigated with the dryer.

水文模拟的熵理论(英文)

借助Shannon熵和最大熵原理提出了熵理论,并基于此理论,提出了一种水文模拟方法,结合两个实例阐述了该方法在下渗和流速分布模拟中的应用.