Effect of Gum Arabic from Acacia senegal var. kerensis as an Improver on the Rheological Properties of Wheat Flour Dough

Dough improvers are substances with functional characteristics used in baking industry to enhance dough properties. Currently, the baking industry is faced with increasing demand for natural ingredients owing to increasing consumer awareness, thus contributing to the rising demand for natural hydrocolloids. Gum Arabic from Acacia senegal var. kerensis is a natural gum exhibiting excellent water binding and emulsification capacity. However, very little is reported on how it affects the rheological properties of wheat dough. The aim of this study was therefore, to determine the rheological properties of wheat dough with partial additions of gum Arabic as an improver. Six treatments were analyzed comprising of: flour-gum blends prepared by adding gum Arabic to wheat flour at different levels (1%, 2% and 3%), plain wheat flour (negative control), commercial bread flour and commercial chapati flour (positive controls). The rheological properties were determined using Brabender Farinograph, Brabender Extensograph and Brabender Viscograph. Results showed that addition of gum Arabic significantly (p chapati. These findings support the need to utilize gum Arabic from Acacia senegal var. kerensis as a dough improver.

Poly-Acrylic Acid Derivatives as Diesel Flow Improver for Paraffin-Based Daqing Diesel

Since the diesel products from paraffin-based Daqing crude oil showed low sensitivity to certain commercial diesel pour point depressant （PPDs） that resulted from the high content of paraffin, certain poly-acrylic acid derivatives （PADE） with-COOR, -COOH,-CONHR, and -COO-NH3^＋R groups by molecular design on the mechanics of diesel; PPDs were synthesized and evaluated as cold flow improver for Daqing 0^# diesel in this paper. The pure PADE was superior to the commercial PPDs and displayed a substantial ability of wax crystals dispersion. There was a synergistic effect among the PADE and T1804 and secondary amine. The synergism clearly improved the low temperature performance of Daqing diesel products and could reduce the cold filter plugging point of 0^# diesel by 6-7 ℃.

A new alternating copolymerized derivative as a cold flow improver for diesel fuel

Synthesis of a cold flow improver （MAVA-a） for diesel fuel and its effect on solidifying point （SP） and cold filter plugging point （CFPP） of diesel fuels were investigated, The cold flow improver was prepared by using maleic anhydride （MA） and vinyl acetate （VA） as raw materials, toluene as solvent, dibenzoyl peroxide （BPO） as initiator, through alternating polymerization under nitrogen to obtain a binary-polymer and then through aminolysis by using a higher carbon amine as aminating agent at a temperature of 80 ℃. A cold flow improver was designed and prepared for No. 0 diesel fuel from Zhang Jia-Gang Petrochemical Company according to the contents of n-paraffin and its carbon number distribution in the No. 0 diesel fuel. It was also used together with two kinds of ethene-vinyl acetate copolymer improvers （EVA） separately. The test result showed that the CFPP of the No. 0 diesel fuel could be lowered by 3-5 ℃ when the improver MAVA-a was used. The CFPP was lowered by 8℃ when the improver MAVA-a was used together with EVA-2.

Chloroaluminate ionic liquid catalyzed isomerization of n-pentane in the presence of product distribution improver

The ionic liquid Et3NHC1-2AIC13 can catalyze the isomerization of n-pentane. This paper investigates the ionic liquid catalyzed isomerization performance of n-pentane with several product distribution improvers in an autoclave. The product distribution of n-pentane isomerization could be improved by such improvers as benzene, cyelohexane, and methyl cyclohexane at the expense of conversion of n-pentane. The optimal product distribution improver was determined to be cyclohexane. The yield of isomerate and the selectivity of liquid isoalkanes increased, whereas the conversion of n-pentane decreased, with increasing cyclohexane amount. At the same conversion of n-pentane, the research octane number （RON） of liquid product, with cyclohexane amount of 0.5 wt%, was about 1.3 higher than that without cyclohexane. At the same reaction conditions, the presence of cyclohexane enhanced the yield of isomerate, the selectivity of liquid isoalkanes, and the RON of the liquid product, but reduced the conversion of n-pentane.

Co-adsorption behaviors of asphaltenes and different flow improvers and their impacts on the interfacial viscoelasticity

Commonly used flow improvers in oilfields,such as ethylene–vinyl acetate copolymer(EVA),poly(octadecyl acrylate)(POA),and polymethylsilsesquioxane(PMSQ)are proven to be effective to enhance the flowability of crude oil.However,the addition of these flow improvers may change the stability of the emulsion and make the crude oil treatment process challenging.In this research,the impacts of different flow improvers on the interfacial properties of the emulsions containing asphaltenes are systematically investigated.The co-adsorption behaviors of the flow improvers and asphaltenes are analyzed through dynamic interfacial tension(DIFT).The rheological properties of the interfacial layer after the adsorption are explored via dilational viscoelasticity.Significant difference is observed in the structural properties of the interface adsorbed by different flow improvers,which is attributed to different interactions between the flow improvers and asphaltenes.To investigate these interactions,conductivity,asphaltenes precipitation,dynamic light scattering(DLS),and contact angle experiments are conducted systematically.Results show that EVA and POA can alter the interfacial properties by changing the asphaltene dispersion state.The interaction between EVA and asphaltenes is stronger than that between POA and asphaltenes due to the difference in molecular structures.Unlike EVA and POA,the change of interfacial property with the addition of PMSQ is attributed to the partial adsorption of asphaltenes on PMSQ.

How Polymers Behave as Viscosity Index Improvers in Lubricating Oils

One of the requirements of engine lubricating oil is that it must have a low enough viscosity at low temperatures to assist in cold starting and a high enough viscosity at high temperatures to maintain its load-bearing characteristics. Viscosity Index (VI) is one approach used widely in the lubricating field to assess the variation of viscosity with temperature. The VI of both mineral and synthetic base oils can be improved by the addition of polymeric viscosity modifiers (VMs). VI improvement by VMs is widely attributed to the polymer coil size expanding with increasing temperature. However, there is very little physical data supporting this generally accepted mechanism. To address this issue, intrinsic viscosity measurements and Small-Angle Neutron Scattering (SANS) have been used to study the variation of polymer coil size with changing temperature and concentration in a selection of solvents. The results will show that coil size expansion with temperature is not necessary to achieve significant elevation of viscosity index.

Aminated Copolymers as Flow Improvers for Super-viscous Crude Oils

The new flow improvers for super-viscous crude oils were developed via esterification of polybasic high carbon alcohol with methacrylate and copolymerizafion of monomers followed by amination of copolymers. The structure of the synthesized polymer flow improver additive was confirmed by IR spectroscopy and the crystal structure of the flow improver additives were determined by X-ray diffrac- tion analysis. The structure of wax crystals was also studied at the same time. The results showed that the wax crystal structure was closely related with the crystal structure of the flow improver, which could change the pour point depression and viscosity reduction behavior of the crude oil. When the wax crystal structure matched well with that of the additive, the wax crystals were dispersed satisfactorily, resulting in favorable effects in terms of pour point depression and viscosity reduction. The new synthe- sized aminated polymer flow improver additive was most efficient for treating super-viscous crude oils. The super-viscous crude oil had a high content of resins and asphaltenes, which might aggregate onto the surface of wax crystals to form blocks to limit the crude oil fluidity. However, amination of copolymers having similar structure with the resins and asphaltenes contained in crude oil could dissolve the huge polar groups to make the deposit formation difficult.

Synthesis and Properties of Dendritic Long-Chain Esters as Crude Oil Flow Improver Additives

The efficiencies of 6 kinds of macromolecules with dendritic structure in improving the flow properties of crude oil were investigated. The dendritic additives were synthesized using low-generation dendritic poly(amidoamine) and alkyl longchain acrylic esters as starting materials, and their structures were characterized by the Fourier transform infrared spectroscopy, 1H-nuclear magnetic resonance and elemental analysis. The effects on the pour point and rheological properties of crude oil samples were studied. Efficiencies of dendritic long-chain esters were not only influenced by the alky chain length, but also by the generation of dendrimer. The longer the alkyl chain of dendritic long-chain ester was, the better the effect in the reduction of pour point and apparent viscosity was. Efficiencies of 1.5 generation dendritic long-chain ester with 8 branched chains for the reduction of pour point and apparent viscosity were superior to those of 0.5 generation dendritic long-chain ester with 4 branched chains. Under the same conditions, efficiencies of 1.5 generation dendritic eighteen ester were superior to those of other 1.5 generation dendritic long-chain esters for the reduction of pour point and viscosity of crude oil.

Effects of Soil Improver on Wheat in Saline-Alkali Lands in the Yellow River Delta

Field experiment carried out to test the effects of soil improver on wheat yield and soil physical-chemical properties. The results indicated that soil improver could optimize soil aggregates structure, decrease soil bulk density, soil pH and soil salt content, increase soil organic matter and 1 000-grain weight, thereby enhancing wheat yield. With the increase of soil improver application amount, soil physical-chemical properties became better and wheat yield increased. However, there was no significant difference in the treatments with the application amounts of 3%, 4% and 5%. In addition, the treatment of reducing nitrogen showed no superiority in soil physical-chemical properties and wheat yield, indicating that sufficient nitrogen was essential for the growth of wheat.

Mechanism of La2O3 as combustion improver in blast furnace coal injection

La2O3 is a combustion improver suitable for burning pulverized coal in blast furnace. La2O3 forms the active species La3＋（CO-）3 that weakens the bridge adhesion of carbon structural units and alters the lattice structures, thus reducing the activation energy of the pulverized coal and accelerating the burning process. Research shows that La2O3 can form the active species La3＋（CO-）3, which weakens the bridge adhesion of carbon structural units and alters the lattice structures of the fixed carbon, hence decreasing the activation energy of the pulverized coal and accelerating the burning process.

Effects of Cereal Malts Used as Improver on Physico-Chemical, Nutritional and Sensory Characteristics of Wheat and Millet Composites Breads

The aim of this study was to optimize composite breads of wheat and whole millet flour by the use of natural improvers. Three types of local malted cereals were used as natural improvers. The millet flour was fermented with EPSs producing LAB strain prior to use. The technological characteristics of the composite flours were determined using an alveograph. The physico-chemical and nutritional characteristics of the composite breads were determined using standard methods and their sensory profiles were evaluated by a panel of 35 consumers. The alveograph results showed an increase in dough resistance, deformation and a decrease in extensibility and elasticity with the level of incorporation of millet flour. From the results of physico-chemical analyses of composite breads, no significant difference (p > 0.05) was observed in the use of the three types of local cereal malts except for the incorporation of 50% of the millet flour. The control sample presented the lowest acidity and dry matter value, the highest water content and pH value. No significant difference (p > 0.05) was observed in the use of the three types of cereal malts for the macronutrient contents of the composite bread samples. However, differences were observed according to the levels of incorporation. Macronutrients results showed an increase in protein content (11.17% ± 0.28% - 14.01% ± 0.10%/DM);crude fat content (1.86% ± 0.05% - 2.48% ± 0.20%/DM) and a decrease in carbohydrates content (85.36% ± 0.54% - 81.06% ± 0.36%/ DM). Regarding the content of mineral elements, significant differences (p < 0.05) were observed in the use of the three types of cereal malts for the incorporation of 15% (Mg and Fe content), 30% (Fe, Zn and K) and 50% (content of Zn, K, Na and Mg) of millet flour. The free amino acid profile revealed three essential amino acids such as valine, isoleucine and lysine. Breads incorporated with 30% of whole millet flour were the most appreciated by consumers.

Rheological Study of Polymers Used as Viscosity Index Improvers for Automotive Lubricant Oils

In this work, a rheological study of automotive lubricant oils, neutral light paraffinic (NLP) and neutral medium paraffinic (NMP), additivated with two commercial viscosity index improvers-VIIs, based on hydrogenated poly (isoprene-co-styrene), at different concentrations, was carried out. The study included the determination of the kinematic viscosity of the mixtures, the calculation of the viscosity indexes and their behavior when subjected to various rheometric experiments, including stress sweep, steady state flow, creep and recovery, and temperature ramp oscillatory tests. The obtained results showed the efficacy of the additives as viscosity index improvers, enabling a significant increase in the viscosity index of the pure oils at relatively low concentrations. The determination of the storage (G’) and of the loss (G”) moduli for the mixtures oil/VII additives showed a large predominance of the viscous effect over the elastic one. Except when under low shear stress, the mixtures showed a pseudoplastic behavior, with the flow curves being adjusted to fit the Ostwald-de-Waele model (Power Law), with negative flow behavior indexes.

中美大陆区域气溶胶成分消光贡献研究综述

气溶胶消光作用是影响大气能见度的主控因素,气溶胶浓度、成分与其散射和吸收特性的非线性关系导致其对能见度的影响存在较大不确定性。1988~2008年美国IMPROVE(Interagency Monitoring of Protected Visual Environments)能见度监测网络各区域的重构细颗粒物(RCFM)浓度范围为1.4~19.4μg m^(-3),重构气溶胶消光系数为10.0~172.5 Mm^(-1)(1 Mm^(-1)=10^(-6) m^(-1))。2006~2018年中国各地区已有观测的平均细颗粒物PM2.5浓度为14.3~188.3μg m^(-3),对应的重构消光系数为52.6~1044.0 Mm^(-1)。美国地区PM2.5浓度水平与我国三亚地区相当;硫酸盐是气溶胶消光的最大贡献成分,占比可高达77%;其次是有机物,最大可达50%;而硝酸盐只有在南加州对气溶胶消光的贡献较大,超过了30%。同时,由于东部的相对湿度高于西部,东部和西部的消光差异比气溶胶浓度的差异更显著。在我国,硫酸盐和有机物同样是消光的主要成分,贡献分别为21%~57%和21%~39%;硝酸盐只在华中地区是主要的消光成分,超过了30%。我国气溶胶浓度和消光水平显著高于美国地区,当前广泛使用的气溶胶消光IMPROVE方程存在较高误差,消光系数低值高估和高值低估范围可达-60%~35%;并且污染越严重,消光低估越显著。采用较大误差的气溶胶消光算法将直接影响我国大气污染防治与蓝天计划的准确实施,随着我国大气污染防治工作的深入开展,急需针对我国各类环境和气候区域进行气溶胶成分消光的精准核算与溯源研究,为我国大气能见度提升提供关键科技支撑。

Prospects and limitations of soil amendment and irrigation techniques for the water-saving public urban greenery and ephemeral weed management in the sandy soils of the United Arab Emirates

Public urban greenery greatly contributes to the residential and tourist value of cities in the Gulf Region,but due to the hyper-arid climatic conditions,the cost of irrigation and plant maintenance is very high.Existing strategies to reduce the monetary and ecological costs involve the cultivation of native xerophytic plantations,and/or the use of soil improvers to increase water-and nutrient-holding capacity of the sandy soils.Various soil improvers based on mineral,organic,or synthetic materials have entered the United Arab Emirates(UAE)market in recent years,but there is considerable uncertainty about how they should best be used in combination with ornamental plant stands involving xerophytic native plants.The present study investigated the effect of soil amendment and deep pipe irrigation on perennial ornamental plant stands involving native plants(Tephrosia appolinea(Gel.)Link in combination with Aerva javanica(Burm.f.)Juss.ex Schult.)and native-exotic plants(T.appolinea in combination with Ruelia simplex C.Wright)either or not topsoil and subsoil amendment with bentonite and hydrophobic sand under the irrigation water supply of less than 50%of reference evapotranspiration(ET0).After one year of cultivation,T.appolinea and A.javanica(native vs.native)produced high biomass and exhibited high water use efficiency(WUE)as compared with T.appolinea and R.simplex(native vs.exotic)combination given that no significant differences were found under the soil amendment treatments.All stands thrived under irrigation water supply far below what is usually supplied to exotic ornamental stands in public parks of the Al Ain City,the UAE.However,subsoil amendment in combination with deep pipe irrigation reduced the occurrence of weeds and increased the overall plant rooting depth.Our results suggest that subsoil amendment and irrigation up to 60-80 cm depth can potentially control ephemeral weed infestation,which is a great challenge in various plant production systems of the Gulf Region.The results of the present study suggest that the impact of soil amendment on the WUE of exotic plants is marginal and might not be economically justified.Replacing exotic with native ornamental plant species seems to have a far greater water-saving potential than the amendment of the soil,while weeds can be suppressed in the absence of topsoil moisture.

Integrating artificial intelligence and high-throughput phenotyping for crop improvement

Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.

Assessment of compressive strength of jet grouting by machine learning

Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the properties of the improved material leads to designers assuming a conservative,arbitrary and unjustified strength,which is even sometimes subjected to the results of the test fields.The present paper presents an approach for prediction of the uniaxial compressive strength(UCS)of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers.The selected machine learning model(extremely randomized trees)relates the soil type and various parameters of the technique to the value of the compressive strength.Despite the complex mechanism that surrounds the jet grouting process,evidenced by the high dispersion and low correlation of the variables studied,the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works.Consequently,this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.

Application of GNSS-PPP on Dynamic Deformation Monitoring of Offshore Platforms

The real-time dynamic deformation monitoring of offshore platforms under environmental excitation is crucial to their safe operation.Although Global Navigation Satellite System-Precise Point Positioning(GNSS-PPP)has been considered for this purpose,its monitoring accuracy is relatively low.Moreover,the influence of background noise on the dynamic monitoring accuracy of GNSS-PPP remains unclear.Hence,it is imperative to further validate the feasibility of GNSS-PPP for deformation monitoring of offshore platforms.To address these concerns,vibration table tests with different amplitudes and frequencies are conducted.The results demonstrate that GNSS-PPP can effectively monitor horizontal vibration displacement as low as±30 mm,which is consistent with GNSS-RTK.Furthermore,the spectral characteristic of background noise in GNSS-PPP is similar to that of GNSS-RTK(Real Time Kinematic).Building on this observation,an improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)has been proposed to de-noise the data and enhance the dynamic monitoring accuracy of GNSS-PPP.Field monitoring application research is also undertaken,successfully extracting and analyzing the dynamic deformation of an offshore platform structure under environmental excitation using GNSS-PPP monitoring in conjunction with improved CEEMDAN de-noising.By comparing the de-noised dynamic deformation trajectories of the offshore platform during different periods,it is observed that the platform exhibits reversible alternating vibration responses under environmental excitation,with more pronounced displacement deformation in the direction of load action.The research results confirm the feasibility and potential of GNSS-PPP for dynamic deformation monitoring of offshore platforms.

Rock mass quality prediction on tunnel faces with incomplete multi-source dataset via tree-augmented naive Bayesian network

Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.

Identification of SSR markers linked to the abscission of cotton boll traits and mining germplasm in Cotton

Background Cotton is an economically important crop.It is crucial to find an effective method to improve cotton yield,and one approach is to decrease the abscission of cotton bolls and buds.However,the lack of knowledge of the genetic and molecular mechanisms underlying cotton boll abscission traits has hindered genetic improvements.Results Pearson’s correlation analysis revealed a significant positive correlation between boll abscission rates 1(AR1)and boll abscission rates 2(AR2).A genome-wide association study was conducted on 145 loci that exhibited high polymorphism and were uniformly distributed across 26 chromosomes(pair).The study revealed 18,46,and 62 markers that were significantly associated with boll abscission,fiber quality,and yield traits(P<0.05),explaining 1.75%–7.13%,1.16%–9.58%,and 1.40%–5.44%of the phenotypic variation,respectively.Notably,the marker MON_SHIN-1584b was associated with the cotton boll abscission trait,whereas MON_CGR5732a was associated with cotton boll abscission and fiber quality traits.Thirteen of the marker loci identified in this study had been previously reported.Based on phenotypic effects,six typical cultivars with elite alleles related to cotton boll abscission,fiber quality,and yield traits were identified.These cultivars hold great promise for widespread utilization in breeding programs.Conclusions These results lay the foundation for understanding the molecular regulatory mechanism of cotton boll abscission and provide data for the future improvement of cotton breeding.

An Underwater Target Detection Algorithm Based on Attention Mechanism and Improved YOLOv7

For underwater robots in the process of performing target detection tasks,the color distortion and the uneven quality of underwater images lead to great difficulties in the feature extraction process of the model,which is prone to issues like error detection,omission detection,and poor accuracy.Therefore,this paper proposed the CER-YOLOv7(CBAM-EIOU-RepVGG-YOLOv7)underwater target detection algorithm.To improve the algorithm’s capability to retain valid features from both spatial and channel perspectives during the feature extraction phase,we have added a Convolutional Block Attention Module(CBAM)to the backbone network.The Reparameterization Visual Geometry Group(RepVGG)module is inserted into the backbone to improve the training and inference capabilities.The Efficient Intersection over Union(EIoU)loss is also used as the localization loss function,which reduces the error detection rate and missed detection rate of the algorithm.The experimental results of the CER-YOLOv7 algorithm on the UPRC(Underwater Robot Prototype Competition)dataset show that the mAP(mean Average Precision)score of the algorithm is 86.1%,which is a 2.2%improvement compared to the YOLOv7.The feasibility and validity of the CER-YOLOv7 are proved through ablation and comparison experiments,and it is more suitable for underwater target detection.