Valorization of Camellia oleifera oil processing byproducts to value-added chemicals and biobased materials: A critical review

The C.oleifera oil processing industry generates large amounts of solid wastes,including C.oleifera shell(COS)and C.oleifera cake(COC).Distinct from generally acknowledged lignocellulosic biomass(corn stover,bamboo,birch,etc.),Camellia wastes contain diverse bioactive substances in addition to the abundant lignocellulosic components,and thus,the biorefinery utilization of C.oleifera processing byproducts involves complicated processing technologies.This reviewfirst summarizes various technologies for extracting and converting the main components in C.oleifera oil processing byproducts into value-added chemicals and biobased materials,as well as their potential applications.Microwave,ultrasound,and Soxhlet extractions are compared for the extraction of functional bioactive components(tannin,flavonoid,saponin,etc.),while solvothermal conversion and pyrolysis are discussed for the conversion of lignocellulosic components into value-added chemicals.The application areas of these chemicals according to their properties are introduced in detail,including utilizing antioxidant and anti-in-flammatory properties of the bioactive substances for the specific application,as well as drop-in chemicals for the substitution of unrenewable fossil fuel-derived products.In addition to chemical production,biochar fabricated from COS and its applications in thefields of adsorption,supercapacitor,soil remediation and wood composites are comprehensively reviewed and discussed.Finally,based on the compositions and structural characteristics of C.oleifera byproducts,the development of full-component valorization strategies and the expansion of the appli-cationfields are proposed.

Upcycling plastic wastes into value-added products via electrocatalysis and photoelectrocatalysis

Plastic,renowned for its versatility,durability,and cost-effectiveness,is indispensable in modern society.Nevertheless,the annual production of nearly 400 million tons of plastic,coupled with a recycling rate of only 9%,has led to a monumental environmental crisis.Plastic recycling has emerged as a vital response to this crisis,offering sustainable solutions to mitigate its environmental impact.Among these recycling efforts,plastic upcycling has garnered attention,which elevates discarded plastics into higher-value products.Here,electrocatalytic and photoelectrocatalytic treatments stand at the forefront of advanced plastic upcycling.Electrocatalytic or photoelectrocatalytic treatments involve chemical reactions that facilitate electron transfer through the electrode/electrolyte interface,driven by electrical or solar energy,respectively.These methods enable precise control of chemical reactions,harnessing potential,current density,or light to yield valuable chemical products.This review explores recent progress in plastic upcycling through electrocatalytic and photoelectrocatalytic pathways,offering promising solutions to the plastic waste crisis and advancing sustainability in the plastics industry.

Storage time affects the level and diagnostic efficacy of plasma biomarkers for neurodegenerative diseases

Several promising plasma biomarker proteins,such as amyloid-β(Aβ),tau,neurofilament light chain,and glial fibrillary acidic protein,are widely used for the diagnosis of neurodegenerative diseases.However,little is known about the long-term stability of these biomarker proteins in plasma samples stored at-80°C.We aimed to explore how storage time would affect the diagnostic accuracy of these biomarkers using a large cohort.Plasma samples from 229 cognitively unimpaired individuals,encompassing healthy controls and those experiencing subjective cognitive decline,as well as 99 patients with cognitive impairment,comprising those with mild cognitive impairment and dementia,were acquired from the Sino Longitudinal Study on Cognitive Decline project.These samples were stored at-80°C for up to 6 years before being used in this study.Our results showed that plasma levels of Aβ42,Aβ40,neurofilament light chain,and glial fibrillary acidic protein were not significantly correlated with sample storage time.However,the level of total tau showed a negative correlation with sample storage time.Notably,in individuals without cognitive impairment,plasma levels of total protein and tau phosphorylated protein threonine 181(p-tau181)also showed a negative correlation with sample storage time.This was not observed in individuals with cognitive impairment.Consequently,we speculate that the diagnostic accuracy of plasma p-tau181 and the p-tau181 to total tau ratio may be influenced by sample storage time.Therefore,caution is advised when using these plasma biomarkers for the identification of neurodegenerative diseases,such as Alzheimer's disease.Furthermore,in cohort studies,it is important to consider the impact of storage time on the overall results.

4种植物源性成分多重real-time PCR检测方法的建立及其在食用淀粉中的应用

建立一种可同时快速检测红薯、木薯、马铃薯、玉米源性成分的多重实时聚合酶链式反应(real-time polymerase chain reaction,real-time PCR)方法。分别以红薯g3pdh基因、木薯g3pdh基因、马铃薯UGPase基因、玉米zSSIIb基因为靶基因设计特异性引物和TaqMan探针,以18S rRNA基因为内参基因,建立多重real-time PCR方法,开展方法学验证,并对不同掺入比例模拟样品和实际淀粉样品进行检测。结果显示,该方法具有高通量、特异性强、灵敏度高等优点。与15种非目标源性均无交叉反应;对目标DNA的检测灵敏度可达到3×10^(-3) ng/μL,且具有良好的线性关系和扩增效率;对淀粉样品的检出限可达0.1%,对50份实际样品进行检测,结果与参比方法一致,说明建立的多重real-time PCR法可用于食用淀粉种类掺假鉴别检测。

Artificial photosynthesis for high-value-added chemicals:Old material,new opportunity

Solar energy utilization has drawn attention due to ever-increasing environmental and energy issues.Photoelectrochemical(PEC)and photocatalytic(PC)water splitting for hydrogen production,which is the most popular and well-established solar-to-chemical conversion process,has been studied thoroughly to date but is now facing limitations related to low conversion efficiency.To resolve this issue,research in PEC cells or photocatalysts has recently aimed to produce alternative value-added chemicals by modifying their redox reactions,which potentially enables high economic reward to compensate for the low efficiency.Here,various kinds of redox reactions that decouple classic water splitting reactions to produce value-added chemicals via PEC and PC processes are introduced.Successful coupling of CO_(2) reduction,O_(2) reduction and organic synthesis with either water oxidation or water reduction is comprehensively discussed from the perspective of basic fundamental and product selectivity in terms of the band structure of materials,cocatalyst design,and thermodynamics and kinetics of the reactions.Throughout the review,future challenges and opportunities are suggested with respect to the redesigned artificial synthesis,which might be an alternative development for the commercialization of PEC or PC value-added chemical production technologies in the near future.

Food processing wastewater purification by microalgae cultivation associated with high value-added compounds production——A review

Microalgae have been considered as an efficient microorganism for wastewater treatment with simultaneously bioenergy and high value-added compounds production.However,the high energy cost associated with complicated biorefinery(e.g.microalgae cultivation,harvesting,drying,extraction,conversion,and purification)is a critical challenge that inhibits its large-scale application.Among different nutrition(e.g.carbon,nitrogen and phosphorous)sources,food processing wastewater is a relative safe and suitable one for microalgae cultivation due to its high organic content and low toxicity.In this review,the characteristic of different food wastewater is summarized and compared.The potential routes of value-added products(i.e.biofuel,pigment,polysaccharide,and amino acid)production along with wastewater purification are introduced.The existing challenges(e.g.biorefinery cost,efficiency and mechanism)of microalgal-based wastewater treatment are also discussed.The prospective of microalgae-based food processing wastewater treatment strategies(such as microalgae-bacteria consortium,poly-generation of bioenergy and value-added products)is forecasted.It can be observed that food wastewater treatment by microalgae could be a promising strategy to commercially realize waste source reduce,conversion and reutilization.

Influence of Pt/Ru anodic ratio on the valorization of ethanol by PEM electrocatalytic reforming towards value-added products

The ethanol electro-reforming process was studied over PtRu/C catalysts synthesized by the modified polyol method with different compositions.In particular,this work reports the influence of anodic Pt:Ru ratio(5:1,2:1 and 1:2)on the organic product distribution(acetaldehyde,acetic acid and ethyl acetate)and pure hydrogen generation at different current densities operation levels.Physicochemical characterization of the catalysts was made by X-ray diffraction(XRD),temperature-programmed reduction(TPR)and N_(2) adsorption-desorption measurements.XRD patterns showed that Ru is introduced into the Pt structure,forming an alloy between both metals.Also,the degree of alloy was higher by increasing the Ru amounts.From TPR profiles Pt was found to be properly reduced while Ru was both in metallic state and forming RuO2.The electrochemical behaviour of each catalyst towards ethanol electroreforming process was investigated through electrochemical techniques in a half cell and a single proton exchange membrane(PEM)cell systems.An intermediate Pt:Ru ratio was found to result in high current density and electrochemical surface area(ECSA)values along with lower amounts of adsorbed species.Also,Ru addition seems to diminish the degree of degradation of the catalyst.Based on characterization and in agreement with essays carried out in a PEM cell at mild conditions(80℃ and 1 atm),PtRu/C 2:1 anode provided the best electrocatalytic results in terms of current density(740 mA cm^(-2)),hydrogen production and selectivity toward acetic acid(up to 15%apart from acetaldehyde and ethyl acetate)while requiring the lowest energy consumption.

ICON/MIGHTI与TIMED/SABER探测温度数据的对比

ICON卫星为临近空间环境特性研究、建模和预报提供了新数据.通过对ICON/MIGHTI与TIMED/SABER在90~105 km高度探测温度数据的比较,计算两者的年平均温度偏差和均方根误差,同时分析月平均温度偏差在不同月份中随高度和纬度的分布情况,为MIGHTI和SABER温度探测数据在临近空间大气建模和预报应用提供参考依据.结果表明,MIGHTI和SABER的温度垂直廓线变化趋势基本吻合,数值上有所差异.在12°S-42°N范围内,MIGHTI探测温度与SABER相比,在90~93 km时偏低,偏差最大值约2.5 K,在93~105 km偏高,偏差的绝对值最大约10 K.在不同季节,白天的温度偏差通常高于夜晚.SABER和MIGHTI的月平均温度偏差随季节和纬度的变化显著,夏季时的月平均温度偏差最大,且温度的均方根误差最大.

Partition-Time Masking:一种唇语识别数据增强方法

提出了一种唇语识别数据增强方法Partition-Time Masking。该方法直接作用于输入数据,通过将输入划分为多个子序列再分别进行Mask操作最后再将各子序列按序拼接,使得模型能对部分帧缺失的输入具有更强的鲁棒性,从而增强泛化能力。实验前根据划分的子序列数目与掩码值来源不同而设计了5种增强策略,并与唇语识别研究中最重要的数据增强方法Time Masking进行了对比实验。实验在LRW数据集和LRW1000数据集上进行,实验结果表明Partition-Time Masking方法对模型性能提升的效果要优于Time Masking方法,其中子序列数目为3、掩码值选择各子序列平均帧时为最优策略,该策略使得目前最佳的唇语识别模型DC-TCN的性能从89.6%提高到90.0%。

Brief analysis on value-added tax transformation and its influence on enterprises

From Jan. 1st, 2009, the value-added tax transformation will be performed in all industries around the country. Based on value-added tax types and retrospection of reform practices, this article analyzes the background of the national value-added tax transformation and points out the influence of full implementation of the value-added tax transformation on various enterprises.

TimeGAN-Informer长时机场能见度预测

能见度的预测对机场的业务决策、保障飞机的安全起降具有重要的意义。针对现有能见度预测模型预测时间较短的问题,提出一种基于TimeGAN Informer(Time Generative Adversarial Network-Informer)的机场能见度预测方法。利用2018—2022年气象和污染物数据,通过相关系数法和递归特征消除法提取出能见度的主要影响因素,使用TimeGAN时间序列生成对抗网络对数据进行扩充,并将Informer长时间序列预测模型应用于能见度预测。结果显示:当预测步长为1 d、2 d、3 d时,TimeGAN Informer的绝对误差(Mean Absolute Error,MAE)分别为2.42、3.13、3.57,比Informer分别降低了0.29、0.27、0.28,比长短时记忆网络(Long Short-Term Memory,LSTM)分别降低了0.28、0.49、0.63;均方根误差(Root Mean Square Error,RMSE)分别为3.03、3.7、4.09,比Informer分别降低了0.38、0.22、0.24,比长短时记忆网络(LSTM)分别降低了0.3、0.5、1.04;百分误差小于30%的分别占测试样本集的78.07%、70.68%、63.84%。尽管随着步长的增加预测效果变差,但在预测步长为3 d时,多数样本的预测误差仍小于30%,实现了对机场区域较为准确的长时能见度预测。

Evolution trends and structural decomposition of China's green value-added over 40 years of reform and opening-up

China's 40-year history of reform and opening-up includes rapid economic development as well as pollution and environmental governance.Using a four-stage division,this study explores the evolution trend and structural decomposition of China's green value-added by constructing a non-competitive input-output table for environmental pollution from 1978 to 2017.The results indicate that pollution production coefficients increased continuously,and the green value-added index decreased.Additionally,the structural decomposition showed that investment and export were critical for economic growth during the period,though they were accompanied by serious pollution problems.The pollution generated by the raw material(represented by coal mining)and processing industries(represented by the textiles)were not controlled effectively.Pollution treatment for these industries should be strengthened in the future.The study has implications for government officials,policy makers,and academics.First,China should make green development a core concept for economic development,increase environmental pollution governance,develop a“green GDP,”incorporate the external costs of environmental pollution into the national economic accounting system.Second,it must change the investment and export structure as well as the traditional economic development pattern that exacerbates pollution.Specifically,the country should develop industries with low pollution and promote the export of industries producing high value-added products and increase green GDP per capita.Third,it should closely monitor the development of highly polluting industries.Upgrading technology to reduce pollution and strengthening pollution treatment will reduce the number of polluting industries and improve environmental governance efficiency.

基于TimeGAN数据增强的复杂过程故障分类方法

针对传统基于重构的故障分类方法在故障样本稀疏或失衡情况下效果不佳、故障子空间区分能力弱的问题,提出基于TimeGAN数据增强的复杂过程故障分类方法.针对小子样故障,使用TimeGAN对历史故障数据进行数据增强,生成与历史数据分布相似的虚拟故障样本;采用马氏距离评估虚拟样本的质量,剔除不可信样本,构造平衡的故障样本集.将故障样本映射到高维核空间,并在核空间中提取故障子空间.设计故障分类策略并定义4种故障分类性能评估指标以定量衡量算法的分类性能.Tennessee Eastman应用结果表明,所提数据增强方法可以有效扩充故障样本,进而提高故障重构率.与WGAN-GP和SMOTE方法进行对比,发现基于TimeGAN数据增强的故障分类方法具有更好的分类性能.

Reactive ball-milling synthesis of Co-Fe bimetallic catalyst for efficient hydrogenation of carbon dioxide to value-added hydrocarbons

Catalytic hydrogenation of CO_(2) using renewable hydrogen not only reduces greenhouse gas emissions,but also provides industrial chemicals.Herein,a Co-Fe bimetallic catalyst was developed by a facile reactive ball-milling method for highly active and selective hydrogenation of CO_(2) to value-added hydrocarbons.When reacted at 320℃,1.0 MPa and 9600 mL h^(-1) g_(cat)^(-1),the selectivity to light olefin(C_(2)^(=)-C_(4)^(=)) and C_(5)+ species achieves 57.3% and 22.3%,respectively,at a CO_(2) co nversion of 31.4%,which is superior to previous Fe-based catalysts.The CO_(2) activation can be promoted by the CoFe phase formed by reactive ball milling of the Fe-Co_(3)O_(4) mixture,and the in-situ Co_(2)C and Fe_(5)C_(2) formed during hydrogenation are beneficial for the C-C coupling reaction.The initial C-C coupling is related to the combination of CO species with the surface carbon of Fe/Co carbides,and the sustained C-C coupling is maintained by self-recovery of defective carbides.This new strategy contributes to the development of efficient catalysts for the hydrogenation of CO_(2) to value-added hydrocarbons.

PoC: An IMS-based Wireless Value-added Service

This article introduces the Push-to-talk over Cellular( PoC) service, a new mobile value-added service based on the IP Multimedia Subsystem (IMS). Its implementation scheme is discussed and its Session Initiation Protocol (SIP) signaling exchange flow is described. Target user groups are predicted based on the analysis of the strengths and weaknesses of the PoC service. The article purports that the PoC system could undoubtedly be used as a platform for new services such as multimedia messaging, instant messaging, presence, and picture receiving and sending. Just like the short message service, the PoC service will help the terminal vendors, equipment vendors, content providers and operators setup a win-for-all industrial value chain.

Brain Time Stack图像融合技术在CT中的应用

目的:分析Brain Time Stack图像融合技术在CT中的应用。方法:选取2021年3月—2022年9月衡水市第四人民医院收治的50例CT检查患者作为研究对象。所有患者进行CT检查并进行Brain Time Stack后处理。比较四组不同部位CT值、标准差(SD)、信噪比(SNR)。比较四组图像主观质量评分。分析不同部位CT值、SD、SNR与图像主观质量评分的相关性。结果:B组的延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显低于A组;C组的延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值高于A组;D组延髓、额叶灰质、颞肌肌肉CT值明显低于A组,脑室、额叶白质、小脑外侧CT值明显高于A组;C组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显高于B组;D组延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显高于B组;D组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显低于C组;D组脑室CT值明显高于C组,差异有统计学意义(P<0.05)。B组、C组、D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SD值明显低于A组;C组延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SD值均明显高于B组;C组额叶灰质SD明显低于B组;D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、肌肉SD均明显低于B组、C组,差异有统计学意义(P<0.05)。B组、C组、D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR均明显高于A组;C组、D组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR值明显高于B组;C组、D组脑室SNR明显低于B组;D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR明显高于C组,差异有统计学意义(P<0.05)。D组图像主观质量评分最高,差异有统计学意义(P<0.05)。延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧及颞肌肌肉SD与主观质量评分呈明显负相关,SNR与主观质量评分间呈明显正相关,差异有统计学意义(P<0.05)。结论:利用Brain Time Stack图像融合技术对头部CT扫描检查图像处理,动脉期结合前一期及后一期的图像数据在处理后具有更好的质量和更少的噪音。

Export Value-Added of China’s Manufacturing Sector:Estimation and Mechanism Analysis

Estimation of domestic and overseas value-added of manufacturing sector is an important and difficult subject for the science-based evaluation of a country＇s trade interests under global value chain. Traditional HIY approach overestimates the domestic value- added of export. Although Koopman＇s method made certain improvements, it cannot utilize traditional I/O matrix and direct input coefficient matrix under the condition of incomplete information. By creating GAMS model, this paper addresses the above-mentioned problems and employs an improved model for the estimation of variations in domestic and overseas value-added of Chinese exports between 2002 and 2012. Our results indicate that by neglecting the export of processing trade, HIY approach overestimates the domestic value- added ratio of Chinese exports. As more imported intermediate inputs have been used in the export of processing trade, the estimation result of this paper have corrected deviations in the forecast of overseas value-added ratio and its tendencies based on HIY method Further analysis of specific factors of domestic value-added of export led to the discovery that the domestic value-added of export of processing trade and mixed trade is highly vulnerable to the impact of international capital inflow. It can be seen that the improved method for the estimation of value-added has indeed corrected the deviations in the estimation of China＇s value-added. In conclusion, China should accelerate the development of export of non- processing trade and trade in high-end services, and balance the relationship of export between local firms and foreign-funded firms, with a view to improving trade dependence and increasing the trade status of Chinese manufaeturing firms in global value chain.

Association of daily sitting time and leisure-time physical activity with body fat among U.S.adults

Background:Prolonged sitting and reduced physical activity lead to low energy expenditures.However,little is known about the joint impact of daily sitting time and physical activity on body fat distribution.We investigated the independent and joint associations of daily sitting time and physical activity with body fat among adults.Methods:This was a cross-sectional analysis of U.S.nationally representative data from the National Health and Nutrition Examination Survey2011-2018 among adults aged 20 years or older.Daily sitting time and leisure-time physical activity(LTPA)were self-reported using the Global Physical Activity Questionnaire.Body fat(total and trunk fat percentage)was determined via dual X-ray absorptiometry.Results:Among 10,808 adults,about 54.6%spent 6 h/day or more sitting;more than one-half reported no LTPA(inactive)or less than 150 min/week LTPA(insufficiently active)with only 43.3%reported 150 min/week or more LTPA(active)in the past week.After fully adjusting for sociodemographic data,lifestyle behaviors,and chronic conditions,prolonged sitting time and low levels of LTPA were associated with higher total and trunk fat percentages in both sexes.When stratifying by LTPA,the association between daily sitting time and body fat appeared to be stronger in those who were inactive/insuufficiently active.In the joint analyses,inactive/insuufficiently active adults who reported sitting more than 8 h/day had the highest total(female:3.99%(95%confidence interval(95%CI):3.09%-4.88%);male:3.79%(95%CI:2.75%-4.82%))and trunk body fat percentages(female:4.21%(95%CI:3.09%-5.32%);male:4.07%(95%CI:2.95%-5.19%))when compared with those who were active and sitting less than 4 h/day.Conclusion:Prolonged daily sitting time was associated with increased body fat among U.S.adults.The higher body fat associated with 6 h/day sitting may not be offset by achieving recommended levels of physical activity.

Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network

The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.

Prescribed Performance Tracking Control of Time-Delay Nonlinear Systems With Output Constraints

The problem of prescribed performance tracking control for unknown time-delay nonlinear systems subject to output constraints is dealt with in this paper. In contrast with related works, only the most fundamental requirements, i.e., boundedness and the local Lipschitz condition, are assumed for the allowable time delays. Moreover, we focus on the case where the reference is unknown beforehand, which renders the standard prescribed performance control designs under output constraints infeasible. To conquer these challenges, a novel robust prescribed performance control approach is put forward in this paper.Herein, a reverse tuning function is skillfully constructed and automatically generates a performance envelop for the tracking error. In addition, a unified performance analysis framework based on proof by contradiction and the barrier function is established to reveal the inherent robustness of the control system against the time delays. It turns out that the system output tracks the reference with a preassigned settling time and good accuracy,without constraint violations. A comparative simulation on a two-stage chemical reactor is carried out to illustrate the above theoretical findings.