Combined Insights from Leachate Structure and Microstructure Characteristics for Eating Quality of Convenience Rice Processed by Super-Heated and Pressurized Steam Technologies

Convenience rice has become widely popular due to its easy availability for cooking. This study investigated the starch structure and composition of leachate and the microstructure of reheated convenience rice using novel processing technologies: super-heated steaming(SHS), auto-electric cooking(AEC), and pressurized-steam cooking(PSC). Additionally, the effect of two different target water contents(58% and 63%) was also evaluated. The PSC_63% sample had the highest total solids and amylopectin amount in the leachate. The amylopectin amount in the leachate differed significantly based on the targeted water content. Morphological characterization revealed that the swelling of starch and the coated layer on the surface of rice grains were most pronounced in the PSC_63% sample due to the pressure processing. The textural hardness of the AEC_58% sample was much higher than that of the other samples. The PSC_63% sample had the highest textural adhesiveness value, which can be attributed to the highest amylopectin amount in the leachate. Sensory characterization showed that the PSC_63% sample had the highest glossiness, whiteness, moistness, and overall acceptability. The principal component analysis score plots presented substantial differences in the leachate and textural and sensory characteristics of reheated convenience rice among the different processing technologies.

Prediction of corrosion rate for friction stir processed WE43 alloy by combining PSO-based virtual sample generation and machine learning

The corrosion rate is a crucial factor that impacts the longevity of materials in different applications.After undergoing friction stir processing(FSP),the refined grain structure leads to a notable decrease in corrosion rate.However,a better understanding of the correlation between the FSP process parameters and the corrosion rate is still lacking.The current study used machine learning to establish the relationship between the corrosion rate and FSP process parameters(rotational speed,traverse speed,and shoulder diameter)for WE43 alloy.The Taguchi L27 design of experiments was used for the experimental analysis.In addition,synthetic data was generated using particle swarm optimization for virtual sample generation(VSG).The application of VSG has led to an increase in the prediction accuracy of machine learning models.A sensitivity analysis was performed using Shapley Additive Explanations to determine the key factors affecting the corrosion rate.The shoulder diameter had a significant impact in comparison to the traverse speed.A graphical user interface(GUI)has been created to predict the corrosion rate using the identified factors.This study focuses on the WE43 alloy,but its findings can also be used to predict the corrosion rate of other magnesium alloys.

Enhancing performance of low-temperature processed CsPbI2Br all-inorganic perovskite solar cells using polyethylene oxide-modified TiO_(2)

CsPbX_(3)-based(X=I,Br,Cl)inorganic perovskite solar cells(PSCs)prepared by low-temperature process have attracted much attention because of their low cost and excellent thermal stability.However,the high trap state density and serious charge recombination between low-temperature processed TiO_(2)film and inorganic perovskite layer interface seriously restrict the performance of all-inorganic PSCs.Here a thin polyethylene oxide(PEO)layer is employed to modify TiO_(2)film to passivate traps and promote carrier collection.The impacts of PEO layer on microstructure and photoelectric characteristics of TiO_(2)film and related devices are systematically studied.Characterization results suggest that PEO modification can reduce the surface roughness of TiO_(2)film,decrease its average surface potential,and passivate trap states.At optimal conditions,the champion efficiency of CsPbI_(2)Br PSCs with PEO-modified TiO_(2)(PEO-PSCs)has been improved to 11.24%from 9.03%of reference PSCs.Moreover,the hysteresis behavior and charge recombination have been suppressed in PEO-PSCs.

Colorectal cancer’s burden attributable to a diet high in processed meat in the Belt and Road Initiative countries

BACKGROUND Colorectal cancer(CRC)plays a significant role in morbidity,mortality,and economic cost in the Belt and Road Initiative(“B and R”)countries.In addition,these countries have a substantial consumption of processed meat.However,the burden and trend of CRC in relation to the consumption of a diet high in processed meat(DHPM-CRC)in these“B and R”countries remain unknown.AIM To analyze the burden and trend of DHPM-CRC in the“B and R”countries from 1990 to 2019.METHODS We used the 2019 Global Burden of Disease Study to collate information regarding the burden of DHPM-CRC.Numbers and age-standardized rates(ASRs)of deaths along with the disability-adjusted life years(DALYs)were determined among the“B and R”countries in 1990 and 2019.Using joinpoint regression analysis,the average annual percent change(AAPC)was used to analyze the temporal trends of age-standardized DALYs rate(ASDALR)from 1990 to 2019 and in the final decade(2010–2019).RESULTS We found geographical differences in the burden of DHPM-CRC among“B and R”countries,with the three highest-ranking countries being the Russian Federation,China,and Ukraine in 1990,and China,the Russian Federation,and Poland in 2019.The burden of DHPM-CRC generally increased in most member countries from 1990 to 2019(all P<0.05).The absolute number of deaths and DALYs in DHPM-CRC were 3151.15[95%uncertainty interval(UI)665.74-5696.64]and 83249.31(95%UI 15628.64-151956.31)in China in 2019.However,the number of deaths(2627.57-2528.51)and DALYs(65867.39-55378.65)for DHPM-CRC in the Russian Federation has declined.The fastest increase in ASDALR for DHPM-CRC was observed in Vietnam,Southeast Asia,with an AAPC value of 3.90%[95%confidence interval(CI):3.63%-4.16%],whereas the fastest decline was observed in Kyrgyzstan,Central Asia,with an AAPC value of-2.05%(95%CI:-2.37%to-1.73%).A substantial upward trend in ASR of mortality,years lived with disability,years of life lost,and DALYs from DHPM-CRC changes in 1990-2019 and the final decade(2010-2019)for most Maritime Silk Route members in East Asia,South Asia,Southeast Asia,North Africa,and the Middle East,as well as Central Europe,while those of the most Land Silk Route members in Central Asia and Eastern Europe have decreased markedly(all P<0.05).The ASDALR for DHPM-CRC increased more in males than in females(all P<0.05).For those aged 50-74 years,the ASDALR for DHPM-CRC in 40 members exhibited an increasing trend,except for 20 members,including 7 members in Central Asia,Maldives,and 12 high or high-middle social development index(SDI)members in other regions(all P<0.05).CONCLUSION The burden of DHPM-CRC varies substantially across“B and R”countries and threatens public health.Relevant evidence-based policies and interventions tailored to the different trends of countries in SDIs or Silk Routes should be adopted to reduce the future burden of CRC in“B and R”countries via extensive collaboration.

Green‑Solvent Processed Blade‑Coating Organic Solar Cells with an Efficiency Approaching 19%Enabled by Alkyl‑Tailored Acceptors

Power-conversion-efficiencies(PCEs)of organic solar cells(OSCs)in laboratory,normally processed by spin-coating technology with toxic halogenated solvents,have reached over 19%.However,there is usually a marked PCE drop when the bladecoating and/or green-solvents toward large-scale printing are used instead,which hampers the practical development of OSCs.Here,a new series of N-alkyl-tailored small molecule acceptors named YR-SeNF with a same molecular main backbone are developed by combining selenium-fused central-core and naphthalene-fused endgroup.Thanks to the N-alkyl engineering,NIR-absorbing YR-SeNF series show different crystallinity,packing patterns,and miscibility with polymeric donor.The studies exhibit that the molecular packing,crystallinity,and vertical distribution of active layer morphologies are well optimized by introducing newly designed guest acceptor associated with tailored N-alkyl chains,providing the improved charge transfer dynamics and stability for the PM6:L8-BO:YRSeNF-based OSCs.As a result,a record-high PCE approaching 19%is achieved in the blade-coating OSCs fabricated from a greensolvent o-xylene with high-boiling point.Notably,ternary OSCs offer robust operating stability under maximum-power-point tracking and well-keep>80%of the initial PCEs for even over 400 h.Our alkyl-tailored guest acceptor strategy provides a unique approach to develop green-solvent and blade-coating processed high-efficiency and operating stable OSCs,which paves a way for industrial development.

Effect of bubble morphology and behavior on power consumption in non-Newtonian fluids’aeration process

Due to a prolonged operation time and low mass transfer efficiency, the primary challenge in the aeration process of non-Newtonian fluids is the high energy consumption, which is closely related to the form and rate of impeller, ventilation, rheological properties and bubble morphology in the reactor. In this perspective, through optimal computational fluid dynamics models and experiments, the relationship between power consumption, volumetric mass transfer rate(kLa) and initial bubble size(d0) was constructed to establish an efficient operation mode for the aeration process of non-Newtonian fluids. It was found that reducing the d0could significantly increase the oxygen mass transfer rate, resulting in an obvious decrease in the ventilation volume and impeller speed. When d0was regulated within 2-5 mm,an optimal kLa could be achieved, and 21% of power consumption could be saved, compared to the case of bubbles with a diameter of 10 mm.

Multi-component quantitative and feed-forward neural network for pattern classification of raw and wine-processed Corni Fructus

Background:To promote the quality evaluation,clarify the processing mechanism and distinguish origins of Corni Fructus(cornus)from different regions.Methods:This study developed a high performance liquid chromatography method for simultaneous determination of 5-hydroxymethylfurfural,2 phenolic acids and 4 iridoid glycosides and the reference fingerprint of cornus from different regions.In addition,the feedforward neural network model provided a pattern classification of sample regions.Results:The content of morroniside and loganin were the highest in all raw cornus samples ranging from 9.45μg/mg to 16.3μg/mg and 6.64μg/mg to 13.7μg/mg,respectively.The level of sweroside in raw cornus from Henan(0.83μg/mg^(-1).39μg/mg)and Zhejiang(0.64μg/mg^(-1).17μg/mg)were greater than other origins.After wine-processing,the glucose or fructose were dehydrated to increase the levels of 5-hydroxymethylfurfural.The C-4 position of-COOCH3 of hot-sensitive iridoid glycosides was hydrolyzed to generate-COOH as stable components.Polyphenol derivatives may be degraded to increase the content of phenolic acid.Subsequently,an excellent feedforward neural network model for identification of raw cornus and wine-prepared cornus was established which could distinguish the sample origins.Conclusion:This work provided a trustworthy method to evaluate the quality and distinguish the sources of cornus.Meanwhile,the clear processing mechanism provided a scientific foundation for controlling the cornus quality during wine-processing.

Influence of tool rotational speed on mechanical and corrosion behaviour of friction stir processed AZ31/Al_(2)O_(3)nanocomposite

Nano-sized reinforcements improved the mechanical characteristics efficiently by promoting more implicit particle hardening mechanisms compared to micron-sized reinforcements.Nano-sized particles lessen the critical particle solidification velocity for swamp and thus offers better dispersal.In the present investigation,the friction stir processing(FSP)is utilized to produce AZ31/Al_(2)O_(3)nanocomposites at various tool rotation speeds(i.e.,900,1200,and 1500 rpm)with an optimized 1.5%volume alumina(Al_(2)O_(3))reinforcement ratio.The mechanical and corrosion behavior of AZ31/Al_(2)O_(3)-developed nanocomposites was investigated and compared with that of the AZ31 base alloy.The AZ31 alloy experienced a comprehensive dynamic recrystallization during FSP,causing substantial grain refinement.Grain-size strengthening is the primary factor contributed to the enhancement in the strength of the fabricated nanocomposite.Tensile strength and yield strength values were lower than those for the base metal matrix,although an upward trend in both values has been observed with an increase in tool rotation speed.An 19.72%increase in hardness along with superior corrosion resistance was achieved compared to the base alloy at a tool rotational speed of 1500 rpm.The corrosion currents(Jcorr)of all samples dropped with increase in the rotational speed,in contrast to the corrosion potentials(Ecorr),which increased.The values of Jcorr of AZ31/Al_(2)O_(3)were 42.3%,56.8%,and 65.5%lower than those of AZ31 alloy at the chosen rotating speeds of 900,1200,and 1500 rpm,respectively.The corrosion behavior of friction stir processed nanocomposites have been addressed in this manuscript which has not been given sufficient attention in the existing literature.Further,this work offers an effective choice for the quality assurance of the FSP process of AZ31/Al_(2)O_(3)nanocomposites.The obtained results are relevant to the development of lightweight automobile and aerospace structures and components.

Reinforcement Learning in Process Industries:Review and Perspective

This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control hierarchy. The survey paper presents a comprehensive overview of RL algorithms,including fundamental concepts like Markov decision processes and different approaches to RL, such as value-based, policy-based, and actor-critic methods, while also discussing the relationship between classical control and RL. It further reviews the wide-ranging applications of RL in process industries, such as soft sensors, low-level control, high-level control, distributed process control, fault detection and fault tolerant control, optimization,planning, scheduling, and supply chain. The survey paper discusses the limitations and advantages, trends and new applications, and opportunities and future prospects for RL in process industries. Moreover, it highlights the need for a holistic approach in complex systems due to the growing importance of digitalization in the process industries.

NADARAYA-WATSON ESTIMATORS FOR REFLECTED STOCHASTIC PROCESSES

We study the Nadaraya-Watson estimators for the drift function of two-sided reflected stochastic differential equations.The estimates,based on either the continuously observed process or the discretely observed process,are considered.Under certain conditions,we prove the strong consistency and the asymptotic normality of the two estimators.Our method is also suitable for one-sided reflected stochastic differential equations.Simulation results demonstrate that the performance of our estimator is superior to that of the estimator proposed by Cholaquidis et al.(Stat Sin,2021,31:29-51).Several real data sets of the currency exchange rate are used to illustrate our proposed methodology.

Detection of Oscillations in Process Control Loops From Visual Image Space Using Deep Convolutional Networks

Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.

Operational optimization of copper flotation process based on the weighted Gaussian process regression and index-oriented adaptive differential evolution algorithm

Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation industrial processes.This paper addresses the fluctuation problem of CCG through an operational optimization method.Firstly,a density-based affinity propagationalgorithm is proposed so that more ideal working condition categories can be obtained for the complex raw ore properties.Next,a Bayesian network(BN)is applied to explore the relationship between the operational variables and the CCG.Based on the analysis results of BN,a weighted Gaussian process regression model is constructed to predict the CCG that a higher prediction accuracy can be obtained.To ensure the predicted CCG is close to the set value with a smaller magnitude of the operation adjustments and a smaller uncertainty of the prediction results,an index-oriented adaptive differential evolution(IOADE)algorithm is proposed,and the convergence performance of IOADE is superior to the traditional differential evolution and adaptive differential evolution methods.Finally,the effectiveness and feasibility of the proposed methods are verified by the experiments on a copper flotation industrial process.

Dendritic spine degeneration:a primary mechanism in the aging process

Recent reports suggest that aging is not solely a physiological process in living beings;instead, it should be considered a pathological process or disease(Amorim et al., 2022). Consequently, this process involves a wide range of factors, spanning from genetic to environmental factors, and even includes the gut microbiome(GM)(Mayer et al., 2022). All these processes coincide at some point in the inflammatory process, oxidative stress, and apoptosis, at different degrees in various organs and systems that constitute a living organism(Mayer et al., 2022;AguilarHernández et al., 2023).

Multimodal Data-Driven Reinforcement Learning for Operational Decision-Making in Industrial Processes

Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and operating conditions in the industrial processes, existing data-driven methods cannot effectively adjust the operational variables. In addition, multimodal data such as images, audio.

Volumetric lattice Boltzmann method for pore-scale mass diffusionadvection process in geopolymer porous structures

Porous materials present significant advantages for absorbing radioactive isotopes in nuclear waste streams.To improve absorption efficiency in nuclear waste treatment,a thorough understanding of the diffusion-advection process within porous structures is essential for material design.In this study,we present advancements in the volumetric lattice Boltzmann method(VLBM)for modeling and simulating pore-scale diffusion-advection of radioactive isotopes within geopolymer porous structures.These structures are created using the phase field method(PFM)to precisely control pore architectures.In our VLBM approach,we introduce a concentration field of an isotope seamlessly coupled with the velocity field and solve it by the time evolution of its particle population function.To address the computational intensity inherent in the coupled lattice Boltzmann equations for velocity and concentration fields,we implement graphics processing unit(GPU)parallelization.Validation of the developed model involves examining the flow and diffusion fields in porous structures.Remarkably,good agreement is observed for both the velocity field from VLBM and multiphysics object-oriented simulation environment(MOOSE),and the concentration field from VLBM and the finite difference method(FDM).Furthermore,we investigate the effects of background flow,species diffusivity,and porosity on the diffusion-advection behavior by varying the background flow velocity,diffusion coefficient,and pore volume fraction,respectively.Notably,all three parameters exert an influence on the diffusion-advection process.Increased background flow and diffusivity markedly accelerate the process due to increased advection intensity and enhanced diffusion capability,respectively.Conversely,increasing the porosity has a less significant effect,causing a slight slowdown of the diffusion-advection process due to the expanded pore volume.This comprehensive parametric study provides valuable insights into the kinetics of isotope uptake in porous structures,facilitating the development of porous materials for nuclear waste treatment applications.

Correlation of Microbiological Stability with Redox Processes in White Wines

In this paper, the authors analyzed the correlation between the microbiological stability of white wines and the content of sulfur dioxide, which influences the main redox processes that take place in the technological stages of the wine. The consecutive, parallel and spontaneous development of several redox processes and their impact on the quality, microbiological and crystalline stability of white wines were examined. The reduction of additive and subtractive technological interventions, of the amounts of adjuvants (sulphurous anhydride) is essential for the production of organic wines.

Comparative metabolomics analysis in the clean label ingredient of NFC spine grape juice processed by mild heating vs high pressure processing

Not from concentrate(NFC)fruit juice is the crucial clean label ingredient for new-style tea-making due to its pleasant color and fresh aroma.Here,we compared the effects of mild heating(MH)and high pressure processing(HPP)on physicochemical characters and phytochemicals in NFC spine grape juice based on metabolomics analysis.Similar compound profiles were observed between HPP-treated and fresh juices.The richer phytochemical compounds comprised malvidin-3-O-glucoside,malvidin-3,5-di-O-glucoside,quercetin-3-O-rhamnoside,quercetin-3-Oglucuronide,catechin,caffeic acid,ferulic acid,procyanidin B1,procyanidin B2 were obtained after MH treatment.Nine marker phenolics and two marker tripeptides(i.e.,Glu-Val-Phe and Leu-Leu-Tyr)were identified to differentiate MH from HPP treatment,of which higher contents occurred in the MH group.Storage time experiments showed that the Glu-Val-Phe could serve as potential markers for monitoring storage of spine grape juice.These results provide new insights into the effects of processing on individual phytochemical changes and the guide for commercial application of production of spine grape NFC juice.

Development and Validation of Integrated Nutrient Management Practices of Industrial Processing Varieties: Asterix and Courage in Bangladesh

An experiment was meticulously conducted at the research field of Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur, Bangladesh, during the 2011-2012 potato growing season to develop integrated crop management practices for the potato seed production of industrial processing varieties Asterix and Courage. Significantly, higher growth and yield parameters were found in the BADC-recommended practice. Later, another experiment was conducted to validate the BADC practice during the 2013-2014 potato growing season in two locations in Bangladesh. Results showed that the production of tuber per hill, tuber weight per hill as well as gross tuber yield per plot, higher proportion of storable seed tubers, and more quality seed potatoes (A-grade and B-grade) seed tubers were found significantly higher in the “BADC developed practice” compared to other treatments. Viral diseases (PLRV and PVY) prevalence was lower in “BADC developed practice”. Moreover, “BADC developed practice” contributed more economic yield by minimizing input cost compared to “Munshiganj advanced farmers’ practice”. Therefore, the “BADC developed practice” was found “superior” regarding yield, quality, and profitability in seed potato production of industrial varieties—Asterix and Courage in Bangladesh.

基于Arduino与Processing的心率检测计设计研究

随着社会经济的高速发展,人们的物质生活也有了极大的提高,但同时也伴随着各种疾病的到来,身体健康已经成为人们普遍关注的焦点,因此,心率检测仪、血压计、血糖仪等各种家用医疗监测仪器已经逐渐融入日常生活。心脏病是人们难以预防的突发致命疾病之一,本文介绍的是一款基于Arduino【是基于易用硬件和软件的原型开源平台,包由可编程的电路板(简称微控制器),以及集成开发环境(称为Arduino IDE)的现成软件组成】Processing(开源编程语言,包括编辑器、编译器、展示器)的简易心率检测计系统,其功能实用、操作简单,可以测量心率,当超出正常心率范围时及时预警,是一款便携的实时心率测试仪。

Cookie Baking Process Optimization and Quality Analysis Based on Food 3D Printing

In order to obtain better quality cookies, food 3D printing technology was employed to prepare cookies. The texture, color, deformation, moisture content, and temperature of the cookie as evaluation indicators, the influences of baking process parameters, such as baking time, surface heating temperature and bottom heating temperature, on the quality of the cookie were studied to optimize the baking process parameters. The results showed that the baking process parameters had obvious effects on the texture, color, deformation, moisture content, and temperature of the cookie. All of the roasting surface heating temperature, bottom heating temperature and baking time had positive influences on the hardness, crunchiness, crispiness, and the total color difference(ΔE) of the cookie. When the heating temperatures of the surfac and bottom increased, the diameter and thickness deformation rate of the cookie increased. However,with the extension of baking time, the diameter and thickness deformation rate of the cookie first increased and then decreased. With the surface heating temperature of 180 ℃, the bottom heating temperature of 150 ℃, and baking time of 15 min, the cookie was crisp and moderate with moderate deformation and uniform color. There was no burnt phenomenon with the desired quality. Research results provided a theoretical basis for cookie manufactory based on food 3D printing technology.