Batch crystallization in acidic aqueous solution of cephalexin was conducted by reactive crystallization with or without seeding. Supersaturation was generated by mixing ammonia and acidic aqueous solution of cephalex...Batch crystallization in acidic aqueous solution of cephalexin was conducted by reactive crystallization with or without seeding. Supersaturation was generated by mixing ammonia and acidic aqueous solution of cephalexin and controlled by solution feeding rate and seeding conditions. UV and Morphologi G3 were used to measure supersaturation and aspect ratio. Experimental results demonstrated that burst nucleation occurred and the products were needle-like at high supersaturation;meanwhile, the products were plate-like and had high aspect ratio at low supersaturation. Analysis of the measured supersaturation profi les and corresponding aspect ratio explained the mechanisms governing the aspect ratio. The optimized operating parameters were also proposed (seeding supersaturation is equal to 1.3, seed mass ratio 8% and feeding rate 368 μL/min).展开更多
With the high-quality requirements for cephalexin monohydrate,developing a robust and practical crystallization process to produce cephalexin monohydrate with good crystal habit,appropriate aspect ratio and high bulk ...With the high-quality requirements for cephalexin monohydrate,developing a robust and practical crystallization process to produce cephalexin monohydrate with good crystal habit,appropriate aspect ratio and high bulk density as well as suitable flowability is urgently needed.This research has explored the influence of ultrasound on crystallization of cephalexin monohydrate in terms of nucleation mechanism and crystal habit control.The results of metastable zone width and induction time measurement showed the presence of ultrasound irradiation can narrow the metastable zone and shorten induction time.Cavitation phenomena generated by ultrasound were used to qualitatively explain the mechanism of ultrasound promoting nucleation of cephalexin monohydrate.Furthermore,on the basis of classical nucleation theory and induction time data,a series of nucleation-related parameters(such as crystalliquid interfacial tension,radius of the critical nucleus and etc.)were calculated and showed a decreasing trend under ultrasound irradiation.The diffusion coefficient of the studied system was also determined to increase by 72.73%under ultrasound.The changes in these parameters have quantitatively confirmed the mechanism of ultrasound influence on the nucleation process.In further,the calculated surface entropy factor has confirmed that the growth of cephalexin monohydrate follows continuous growth mechanism under the research conditions of this work.Through the exploration of crystallization conditions,it is found that suitable ultrasonic treatment,seeding,supersaturation control and removal of fine crystals are conducive to improving the quality of cephalexin monohydrate product.Optimizing the crystallization process coupled continuous ultrasound irradiation with fine-crystal dissolution policy has achieved the controllable production of monodisperse cephalexin monohydrate crystal with good performance.展开更多
Developing effective strategies to earn excess returns in the stock market is a cutting-edge topic in the field of economics.At the same time,stock price forecasting that supports trading strategies is considered one ...Developing effective strategies to earn excess returns in the stock market is a cutting-edge topic in the field of economics.At the same time,stock price forecasting that supports trading strategies is considered one of the most challenging tasks.Therefore,this study analyzes and extracts news media data,expert comments,social opinion data,and pandemic text data using natural language processing,and then combines the data with a deep learning model to forecast future stock price patterns based on historical stock prices.An interval constraint-based trading strategy is constructed.Using data from several typical stocks in the Chinese stock market during the COVID-19 period,the empirical studies and trading simulations show,first,that the sentiment composite index and the deep learning model can improve the accuracy of stock price forecasting.Second,the interval constraint-based trading strategy based on the proposed approach can effectively enhance returns and thus,can assist investors in decision-making.展开更多
The hot central-plant recycling(HCPR)technology has been widely concerned by researchers in pavement engineering because of its excellent economic benefits and positive environmental outcomes.Over the last few years,t...The hot central-plant recycling(HCPR)technology has been widely concerned by researchers in pavement engineering because of its excellent economic benefits and positive environmental outcomes.Over the last few years,the application of HCPR technology in highway construction and maintenance has increasingly expanded.However,despite this wider adoption,critical issues concerning the composition of raw materials and performance-influencing factors of hot central-plant recycled asphalt mixtures(HCPRAM)necessitate careful consideration and deeper understanding.Therefore,conducting a detailed interpretation and systematic analysis of each component material and performance-influencing factors holds tremendous significance for advancing the design methodology,optimizing the production process,and enhancing the overall quality and durability of HCPRAM.This paper comprehensively reviews the current state-of-the-art research pertaining to the raw material composition and the crucial factors affecting the road performance of HCPRAM.Firstly,the functionality of recycled asphalt pavement(RAP)materials,virgin asphalt,virgin aggregates,rejuvenators,and fibers in the mixtures are introduced.Then,the influencing factors of the performance of HRAM are described in detail from both internal and external factors.Finally,the paper further discusses persistent challenges and knowledge gaps identified in the current research landscape of HCPR technology.Based on this critical analysis,pertinent recommendations are suggested to guide productive avenues for future research and development.展开更多
In protein engineering,while computational models are increasingly used to predict mutation effects,their evaluations primarily rely on high-throughput deep mutational scanning(DMS)experiments that use surrogate reado...In protein engineering,while computational models are increasingly used to predict mutation effects,their evaluations primarily rely on high-throughput deep mutational scanning(DMS)experiments that use surrogate readouts,which may not adequately capture the complex biochemical properties of interest.Many proteins and their functions cannot be assessed through high-throughput methods due to technical limitations or the nature of the desired properties,and this is particularly true for the real industrial application scenario.Therefore,the desired testing datasets,will be small-size(∼10–100)experimental data for each protein,and involve as many proteins as possible and as many properties as possible,which is,however,lacking.Here,we present VenusMutHub,a comprehensive benchmark study using 905 small-scale experimental datasets curated from published literature and public databases,spanning 527 proteins across diverse functional properties including stability,activity,binding affinity,and selectivity.These datasets feature direct biochemical measurements rather than surrogate readouts,providing a more rigorous assessment of model performance in predicting mutations that affect specific molecular functions.We evaluate 23 computational models across various methodological paradigms,such as sequence-based,structure-informed and evolutionary approaches.This benchmark provides practical guidance for selecting appropriate prediction methods in protein engineering applications where accurate prediction of specific functional properties is crucial.展开更多
基金supported by the Major National Science and Technology Projects (No. 2017ZX07402003)Major National Scientifi c Instrument Development Project (No. 21527812)
文摘Batch crystallization in acidic aqueous solution of cephalexin was conducted by reactive crystallization with or without seeding. Supersaturation was generated by mixing ammonia and acidic aqueous solution of cephalexin and controlled by solution feeding rate and seeding conditions. UV and Morphologi G3 were used to measure supersaturation and aspect ratio. Experimental results demonstrated that burst nucleation occurred and the products were needle-like at high supersaturation;meanwhile, the products were plate-like and had high aspect ratio at low supersaturation. Analysis of the measured supersaturation profi les and corresponding aspect ratio explained the mechanisms governing the aspect ratio. The optimized operating parameters were also proposed (seeding supersaturation is equal to 1.3, seed mass ratio 8% and feeding rate 368 μL/min).
基金the financial support of National Natural Science Foundation of China(22078238)Special Project for the Transformation of Major Scientific and Technology Achievements of Hebei Province(19042822Z).
文摘With the high-quality requirements for cephalexin monohydrate,developing a robust and practical crystallization process to produce cephalexin monohydrate with good crystal habit,appropriate aspect ratio and high bulk density as well as suitable flowability is urgently needed.This research has explored the influence of ultrasound on crystallization of cephalexin monohydrate in terms of nucleation mechanism and crystal habit control.The results of metastable zone width and induction time measurement showed the presence of ultrasound irradiation can narrow the metastable zone and shorten induction time.Cavitation phenomena generated by ultrasound were used to qualitatively explain the mechanism of ultrasound promoting nucleation of cephalexin monohydrate.Furthermore,on the basis of classical nucleation theory and induction time data,a series of nucleation-related parameters(such as crystalliquid interfacial tension,radius of the critical nucleus and etc.)were calculated and showed a decreasing trend under ultrasound irradiation.The diffusion coefficient of the studied system was also determined to increase by 72.73%under ultrasound.The changes in these parameters have quantitatively confirmed the mechanism of ultrasound influence on the nucleation process.In further,the calculated surface entropy factor has confirmed that the growth of cephalexin monohydrate follows continuous growth mechanism under the research conditions of this work.Through the exploration of crystallization conditions,it is found that suitable ultrasonic treatment,seeding,supersaturation control and removal of fine crystals are conducive to improving the quality of cephalexin monohydrate product.Optimizing the crystallization process coupled continuous ultrasound irradiation with fine-crystal dissolution policy has achieved the controllable production of monodisperse cephalexin monohydrate crystal with good performance.
基金partly supported by the National Natural Science Foundation of China under Grants No.72171223,No.71801213,and No.71988101the National Key R&D Program of China under Grants No.2021ZD0111204。
文摘Developing effective strategies to earn excess returns in the stock market is a cutting-edge topic in the field of economics.At the same time,stock price forecasting that supports trading strategies is considered one of the most challenging tasks.Therefore,this study analyzes and extracts news media data,expert comments,social opinion data,and pandemic text data using natural language processing,and then combines the data with a deep learning model to forecast future stock price patterns based on historical stock prices.An interval constraint-based trading strategy is constructed.Using data from several typical stocks in the Chinese stock market during the COVID-19 period,the empirical studies and trading simulations show,first,that the sentiment composite index and the deep learning model can improve the accuracy of stock price forecasting.Second,the interval constraint-based trading strategy based on the proposed approach can effectively enhance returns and thus,can assist investors in decision-making.
基金supported by the National Natural Science Foundation of China(grant number 52108394)the Key Research and Development Program of Shaanxi,China(grant number 2023-YBGY-491)+2 种基金the Spring Sunshine Program of the Ministry of Education of China(grant number 202200441)the Key Laboratory of Road and Traffic Engineering of the Ministry of Education,Tongji University(grant number K202307)the Fundamental Research Funds for the Central Universities,CHD(grant number 300102214209).
文摘The hot central-plant recycling(HCPR)technology has been widely concerned by researchers in pavement engineering because of its excellent economic benefits and positive environmental outcomes.Over the last few years,the application of HCPR technology in highway construction and maintenance has increasingly expanded.However,despite this wider adoption,critical issues concerning the composition of raw materials and performance-influencing factors of hot central-plant recycled asphalt mixtures(HCPRAM)necessitate careful consideration and deeper understanding.Therefore,conducting a detailed interpretation and systematic analysis of each component material and performance-influencing factors holds tremendous significance for advancing the design methodology,optimizing the production process,and enhancing the overall quality and durability of HCPRAM.This paper comprehensively reviews the current state-of-the-art research pertaining to the raw material composition and the crucial factors affecting the road performance of HCPRAM.Firstly,the functionality of recycled asphalt pavement(RAP)materials,virgin asphalt,virgin aggregates,rejuvenators,and fibers in the mixtures are introduced.Then,the influencing factors of the performance of HRAM are described in detail from both internal and external factors.Finally,the paper further discusses persistent challenges and knowledge gaps identified in the current research landscape of HCPR technology.Based on this critical analysis,pertinent recommendations are suggested to guide productive avenues for future research and development.
基金supported by Science and Technology Innovation Key R&D Program of Chongqing(CSTB2024TIAD-STX0032,China)the Computational Biology Key Program of Shanghai Science and Technology Commission(23JS1400600,China)+3 种基金Shanghai Jiao Tong University Scientific and Technological Innovation Funds(21X010200843,China)and Science and Technology Innovation Key R&D Program of Chongqing(CSTB2022TIAD-STX0017,China)the Postdoctoral Fellowship Program of CPSF under Grant Number GZC20241010the Student Innovation Center at Shanghai Jiao Tong University,and Shanghai Artificial Intelligence Laboratory.
文摘In protein engineering,while computational models are increasingly used to predict mutation effects,their evaluations primarily rely on high-throughput deep mutational scanning(DMS)experiments that use surrogate readouts,which may not adequately capture the complex biochemical properties of interest.Many proteins and their functions cannot be assessed through high-throughput methods due to technical limitations or the nature of the desired properties,and this is particularly true for the real industrial application scenario.Therefore,the desired testing datasets,will be small-size(∼10–100)experimental data for each protein,and involve as many proteins as possible and as many properties as possible,which is,however,lacking.Here,we present VenusMutHub,a comprehensive benchmark study using 905 small-scale experimental datasets curated from published literature and public databases,spanning 527 proteins across diverse functional properties including stability,activity,binding affinity,and selectivity.These datasets feature direct biochemical measurements rather than surrogate readouts,providing a more rigorous assessment of model performance in predicting mutations that affect specific molecular functions.We evaluate 23 computational models across various methodological paradigms,such as sequence-based,structure-informed and evolutionary approaches.This benchmark provides practical guidance for selecting appropriate prediction methods in protein engineering applications where accurate prediction of specific functional properties is crucial.
