Multi-stage, charge conversional, stimuli-responsive nanogels for therapeutic protein delivery

2015 ◽  
Vol 3 (11) ◽  
pp. 1487-1496 ◽  
Author(s):  
Xuejiao Zhang ◽  
Kai Zhang ◽  
Rainer Haag
Keyword(s):  
Cellular Uptake ◽  
Protein Delivery ◽  
Therapeutic Protein ◽  
Extracellular Ph ◽  
Endosomal Escape ◽  
Stimuli Responsive ◽  
Two Stage ◽  
Second Stage ◽  
Multi Stage ◽  
Intracellular Organelles

A two-stage charge conversional nanogel with ATP/pH-sensitivity was generated. The first-stage charge conversion at tumor extracellular pH can enhance the tumor cellular uptake and the second-stage charge conversion in the acidic intracellular organelles (endo/lysosome) can result in the endosomal escape.

scholarly journals Formulation of Two-Stage Stochastic Programming with Fixed Recourse

2019 ◽  
Vol 1 (1) ◽  
pp. 18-21
Author(s):  
Hashnayne Ahmed
Keyword(s):  
Stochastic Programming ◽  
Objective Function ◽  
Past Experience ◽  
Optimization Techniques ◽  
Random Parameters ◽  
Two Stage ◽  
Second Stage ◽  
Multi Stage ◽  
Time Period ◽  
Primary Model

Stochastic Programming is an asset for the next world researchers due to its uncertainty calculations, which has been skipped in deterministic world experiments as it includes complicated calculations. Two-stage stochastic programming concerns two time period decisions based on some random parameters obtained from past experience or some sort of survey. The objective function for formulating two-stage stochastic programming with fixed recourse includes two parts: first-stage forecast and second-stage fixed decisions based on the experiment results. The constraints are similar to the normal optimization techniques rather some adjustments of requirements and technology assets. The fixed recourse decisions are sort of decisions from the deterministic world.  Formulation techniques of two-stage stochastic programming with fixed recourse may be used for further complications arises in stochastic programming like complete recourse problems, multi-stage problems, etc. And that’s why Two-stage stochastic programming with fixed recourse is called the primary model for stochastic programming.

Scientific Approach to Planning and Implementation of Blind Refracturing in Horizontal Wells with MSF Completion in Low-Permeability Reservoirs

2021 ◽  
Author(s):  
Almaz Makhmutovich Sadykov ◽  
Sergey Anatolyevich Erastov ◽  
Maxim Sergeevich Antonov ◽  
Denis Vagizovich Kashapov ◽  
Tagir Ramilevich Salakhov ◽  
...  
Keyword(s):  
Hydraulic Fracturing ◽  
Surface Pressure ◽  
Horizontal Wells ◽  
Well Logging ◽  
Low Permeability ◽  
Geomechanical Model ◽  
Two Stage ◽  
Second Stage ◽  
Multi Stage ◽  
Low Permeability Reservoirs

Abstract One of the fundamental methods of developing low-permeability reservoirs is the use of multi-stage hydraulic fracturing in horizontal wells. Decreasing wells productivity requires geological and technical measures, where one of the methods is "blind" refracturing. Often, only one "blind" hydraulic fracturing is carried out for all ports of multistage hydraulic fracturing, the possibility of carrying out two or more stages of "blind" hydraulic fracturing is considered in this article. The purpose of the article is to increase the productivity of horizontal wells with multi-stage hydraulic fracturing by the "blind" refracturing method. A one-stage and two-stage approach was implemented when planning and performing "blind" hydraulic fracturing with analysis of treatment pressures, indicating a possibility for reorientation of the fracture during the second stage in a horizontal wellbore. Based on the experience of the "blind" hydraulic fracturing performed at the Kondinskoye field, "NK "Kondaneft" JSC carried out pilot work on "blind" refracturing at four horizontal wells of the Zapadno -Erginskoye field. A geomechanical model was used, built based on well logging and core studies carried out at "RN-BashNIPIneft" LLC. The total mass of the planned proppant per well was 280-290 tons, while this tonnage was pumped in one or more stages. A one-stage "blind" refracturing approach was successfully performed in one well, two-stage hydraulic fracturing was implemented in three wells, where in one of the wells, after two stages to open ports, initial hydraulic fracturing was also carried out to the last, previously non-activated port. In the case of two-stage hydraulic fracturing, the first stage purpose was to saturate the reservoir-fracture system with the injection of a "sand plug" with a high concentration of proppant at the end of the job to isolate the initial injectivity interval, determined based on the interpretation of well logging data and analysis of the wellhead treatment pressure. The second stage purpose was the initiation and possible reorientation of the fracture in a new interval, confirmed by an increase in surface pressure during hydraulic fracturing and instantaneous shut-in pressure. This article summarizes the results and lessons learned from the pilot works carried out using the geomechanical model and well productivity assessment before and after "blind" fracturing. The analysis of surface pressure based on production data indicating fracture reorientation is presented. The recommendations and accumulated experience presented in this work should increase the effectiveness of repeated "blind" refracturing in horizontal wells with multi-stage hydraulic fracturing.

Venture investment under multi-stage incentive model based on fairness concerns

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yadong Shu ◽  
Ying Dai ◽  
Zujun Ma ◽  
Zhijun Hu
Keyword(s):  
Venture Capital ◽  
Two Stage ◽  
Content Type ◽  
Effort Level ◽  
Asymmetrical Information ◽  
Second Stage ◽  
Venture Investment ◽  
Multi Stage ◽  
Fairness Concerns ◽  
The Impact

PurposeThis study explores the impact of EN's (venture entrepreneurs, simplified as EN) jealousy fairness concerns coefficient on two-stage venture capital decision-making in cases of symmetrical and asymmetrical information. It discusses the equilibrium solution of two-stage venture.Design/methodology/approachThe principal-agent model was established based on multiple periods, and differentiated contracts were established at different stages. The validity of the models and the contract was verified by numerical simulation.FindingsThe results suggest that with the increase in the EN fairness concerns coefficient, the effort level of EN decreases continuously and decreases faster in the second stage because this is the last stage. The level of VC's (venture capitalist, simplified as VC) effort declines first and then increases; that is, VC will increase the effort level when the fairness concerns coefficient increases to a certain threshold. To motivate EN to pay more effort, VC will increase the incentive to EN in the first stage. However, it will reduce the level of incentive to EN in the second stage. In the limited stage of venture investment, consider that the fairness concerns of EN do not make the profits of EN and VC achieve Pareto improvement simultaneously.Originality/valueFirst, the authors implanted fairness concerns into multi-stage venture capital and discussed the impact of fairness concerns on the efforts and returns of both parties. Second, among the influencing factors of the project output, the authors consider the bilateral efforts of EN and VC, the working capacity of EN, the initial investment scale, and the external uncertain environment.

Multi-stage sampling

2019 ◽  
pp. 173-199
Author(s):  
David G. Hankin ◽  
Michael S. Mohr ◽  
Ken B. Newman
Keyword(s):  
Natural Resource ◽  
Optimal Allocation ◽  
Sampling Strategy ◽  
Cluster Sampling ◽  
Sampling Variance ◽  
Two Stage ◽  
Unequal Size ◽  
Second Stage ◽  
Resource Setting ◽  
Multi Stage

In multi-stage sampling, there are two or more stages of sampling and the simplest version, which the chapter emphasizes is called two-stage sampling. In two-stage sampling, an initial first-stage sample of n primary units (or clusters) is selected. Then, at the second stage of sampling, m i subunits are selected from the M i subunits in the selected primary units. First- and second-stage units may be selected with equal or unequal probabilities and a wide variety of estimators may be used to estimate totals within selected primary units and to estimate the total of the target variable in the finite population. Illustrative sample spaces are provided for equal sized two-stage cluster sampling with SRS selection at both stages, and for two-stage unequal size cluster sampling, with clusters selected by PPSWOR and units within clusters selected by SRS. Sampling variance is shown to originate from two sources: variation between primary unit totals or means (first-stage variance), and errors of estimation of primary units totals (second-stage variance). Topics of optimal allocation and net relative efficiency are addressed in the two-stage context with equal and unequal size clusters. General expressions for sampling variance are presented for three or more stages of sampling. The multi-stage framework can take powerful advantage of all of the concepts and sampling designs considered in previous chapters and the ecologist or natural resource scientist can apply everything he/she knows about an ecological or natural resource setting to guide development of an intelligent multi-stage sampling strategy.

scholarly journals Formulation of Two-Stage Stochastic Programming with Fixed Recourse

2019 ◽  
Author(s):  
Hashnayne Ahmed
Keyword(s):  
Stochastic Programming ◽  
Objective Function ◽  
Past Experience ◽  
Optimization Techniques ◽  
Random Parameters ◽  
Two Stage ◽  
Second Stage ◽  
Multi Stage ◽  
Time Period ◽  
Primary Model

Stochastic Programming is an asset for the next world researchers due to its uncertainty calculations, which has been skipped in deterministic world experiments as it includes complicated calculations. Two-stage stochastic programming concerns two time period decisions based on some random parameters obtained from past experience or some sort of survey. The objective function for formulating two-stage stochastic programming with fixed recourse includes two parts: first-stage forecast and second-stage fixed decisions based on the experiment results. The constraints are similar to the normal optimization techniques rather some adjustments of requirements and technology assets. The fixed recourse decisions are sort of decisions from the deterministic world. Formulation techniques of two-stage stochastic programming with fixed recourse may be used for further complications arises in stochastic programming like complete recourse problems, multi-stage problems, etc. And that’s why Two-stage stochastic programming with fixed recourse is called the primary model for stochastic programming.

Understanding the contribution of surface roughness and hydrophobic modification of silica nanoparticles to enhanced therapeutic protein delivery

2016 ◽  
Vol 4 (2) ◽  
pp. 212-219 ◽  
Author(s):  
Yuting Niu ◽  
Meihua Yu ◽  
Anand Meka ◽  
Yang Liu ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Sustained Release ◽  
Silica Nanoparticles ◽  
Cellular Uptake ◽  
Protein Delivery ◽  
Therapeutic Protein ◽  
Hydrophobic Modification

The contribution of the surface roughness and hydrophobic modification of silica nanoparticles to enhanced therapeutic protein delivery, including adsorption, sustained release, cellular uptake and endo/lysosomal escape.

An Enhanced Two Stage MMSE Equalizer for Coded FBMC/OQAM Systems

2019 ◽  
Vol 10 (1) ◽  
pp. 69-82 ◽  
Author(s):  
Mohammad Rizk Assaf ◽  
Abdel-Nasser Assimi
Keyword(s):  
Error Propagation ◽  
Channel Model ◽  
Error Correcting Codes ◽  
Two Stage ◽  
Probability Of Error ◽  
Second Stage ◽  
Different Types ◽  
Mmse Equalizer ◽  
And Performance ◽  
Ici Cancellation

In this article, the authors investigate the enhanced two stage MMSE (TS-MMSE) equalizer in bit-interleaved coded FBMC/OQAM system which gives a tradeoff between complexity and performance, since error correcting codes limits error propagation, so this allows the equalizer to remove not only ICI but also ISI in the second stage. The proposed equalizer has shown less design complexity compared to the other MMSE equalizers. The obtained results show that the probability of error is improved where SNR gain reaches 2 dB measured at BER compared with ICI cancellation for different types of modulation schemes and ITU Vehicular B channel model. Some simulation results are provided to illustrate the effectiveness of the proposed equalizer.

Personalised attraction recommendation for enhancing topic diversity and accuracy

2021 ◽  
pp. 016555152199980
Author(s):  
Yuanyuan Lin ◽  
Chao Huang ◽  
Wei Yao ◽  
Yifei Shao
Keyword(s):  
Real World ◽  
Information Overload ◽  
Two Stage ◽  
Misclassification Cost ◽  
Second Stage ◽  
Low Visibility ◽  
Recommendation Diversity ◽  
Optimisation Model ◽  
Definition Of ◽  
Improved Methods

Attraction recommendation plays an important role in tourism, such as solving information overload problems and recommending proper attractions to users. Currently, most recommendation methods are dedicated to improving the accuracy of recommendations. However, recommendation methods only focusing on accuracy tend to recommend popular items that are often purchased by users, which results in a lack of diversity and low visibility of non-popular items. Hence, many studies have suggested the importance of recommendation diversity and proposed improved methods, but there is room for improvement. First, the definition of diversity for different items requires consideration for domain characteristics. Second, the existing algorithms for improving diversity sacrifice the accuracy of recommendations. Therefore, the article utilises the topic ‘features of attractions’ to define the calculation method of recommendation diversity. We developed a two-stage optimisation model to enhance recommendation diversity while maintaining the accuracy of recommendations. In the first stage, an optimisation model considering topic diversity is proposed to increase recommendation diversity and generate candidate attractions. In the second stage, we propose a minimisation misclassification cost optimisation model to balance recommendation diversity and accuracy. To assess the performance of the proposed method, experiments are conducted with real-world travel data. The results indicate that the proposed two-stage optimisation model can significantly improve the diversity and accuracy of recommendations.

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