Averaging level control to reduce off-spec material in a continuous pharmaceutical pilot plant

The judicious use of buffering capacity is important in the development of future continuous pharmaceutical manufacturing processes. The potential benefits are investigated of using optimal-averaging level control for tanks that have buffering capacity for a section of a continuous pharmaceutical pilot plant involving two crystallizers, a combined filtration and washing stage and a buffer tank. A closed-loop dynamic model is utilized to represent the experimental operation, with the relevant model parameters and initial conditions estimated from experimental data that contained a significant disturbance and a change in setpoint of a concentration control loop. The performance of conventional proportional-integral (PI) level controllers is compared with optimal-averaging level controllers. The aim is to reduce the production of off-spec material in a tubular reactor by minimizing the variations in the outlet flow rate of its upstream buffer tank. The results show a distinct difference in behavior, with the optimal-averaging level controllers strongly outperforming the PI controllers. In general, the results stress the importance of dynamic process modeling for the design of future continuous pharmaceutical processes

Hydrogen Sulfide Triggered Charge-Reversal Micelles for Cancer-Targeted Drug Delivery and Imaging

Currently, the development of polymeric micelles combining diagnosis and targeted therapy is theoretically and practically significant in cancer treatment. In addition, it has been reported that cancer cells can produce large amounts of hydrogen sulfide (H₂S) and their survival depends on the content of H₂S. In this study, a series of <i>N</i>-(2-hydroxyethyl)-4-azide-1,8-naphthalimide ended amphiphilic diblock copolymer poly­(2-hydroxyethyl methacrylate)-<i>block</i>-poly­(methyl methacrylate) (N₃-Nap-PHEMA-<i>b</i>-PMMA-N₃) micelles were prepared. Around cancer tissues, the N₃-Nap-PHEMA₄₅-<i>b</i>-PMMA₄₂-N₃ micelles exhibited dual characteristics of monitoring H₂S and H₂S triggered charge reversal with the reduction of the azido group. The surface charge of N₃-Nap-PHEMA₄₅-<i>b</i>-PMMA₄₂-N₃ micelles reversed from negative to positive after monitoring H₂S. With H₂S triggered charge reversal, the cellular uptake of DOX-loaded N₃-Nap-PHEMA₄₅-<i>b</i>-PMMA₄₂-N₃ micelles was effectively enhanced through electrostatic attraction mediated targeting, and a fast doxorubicin (DOX) release rate was observed. The MTT assay demonstrated that N₃-Nap-PHEMA₄₅-<i>b</i>-PMMA₄₂-N₃ micelles were biocompatible to HeLa cells, and DOX-loaded N₃-Nap-PHEMA₄₅-<i>b</i>-PMMA₄₂-N₃ micelles showed enhanced cytotoxicity in HeLa cells in the presence of H₂S. Furthermore, in vivo fluorescence imaging and biodistribution experiments revealed that DOX-loaded N₃-Nap-PHEMA₄₅-<i>b</i>-PMMA₄₂-N₃ micelles could provide good tumor imaging and accumulate in tumor tissue. Therefore, N₃-Nap-PHEMA₄₅-<i>b</i>-PMMA₄₂-N₃ micelles can be used as a promising platform for tumor diagnosis and therapy

Kinetic Modeling of Acetic Acid Hydrogenation to Ethanol over K‑Modified PtSn Catalyst Supported on Alumina

Experiments for acetic acid hydrogenation and catalyst characterizations were conducted to study the kinetics of hydrogenation and esterification over PtSn impregnated on Al₂O₃ catalyst (PtSn/Al₂O₃), K doped on PtSn/Al₂O₃ catalyst (K/PtSn/Al₂O₃), and PtSn impregnated on K/Al₂O₃ catalyst (PtSn/K/Al₂O₃). Kinetic parameters such as activation energy, dissociation enthalpy, and adsorption heat of elementary reaction for hydrogenation and esterification were determined on the basis of Langmuir–Hinshelwood model. After the addition of 0.5 wt % K to PtSn/Al₂O₃, activation energy of hydrogenation decreased from 20.8 to 11.5 kJ/mol and activation energy of esterification increased from 22.3 to 31.7 kJ/mol. The results of dissociation energy and adsorption heat reflect that K/PtSn/Al₂O₃ can improve spillover of hydrogen and desorption of ethanol. PtSn/K/Al₂O₃ had the opposite effect. Potassium also enhanced dissociation of C–OH bond to form more acetyl species. Good agreement was obtained upon comparison of the experimental and calculated data

Sei pezzi meno facili: relatività einsteiniana, simmetria, spazio-tempo

Una scelta di testi "meno facili" operata tra le "Lectures on Physics" di Feynman. Filo conduttore di questo volume è una teoria tanto popolare quanto poco compresa: la teoria della relatività di Einstein. Come disse il fisico Freeman Dyson, che fu suo allievo al Caltech, in Feynman "il pensiero profondo e il fare burlesco e giocoso non erano parti separate di una personalità divisa... egli faceva le due cose contemporaneamente". Seguire una di queste lezioni richiede una costante attenzione ai trabocchetti che il fisico tende di continuo, avvalendosi di uno stile dialogico degno di un filosofo antico. In Feynman nessun concetto è così ovvio o elementare da non meritare un supplemento di indagine, un'analisi più attenta

Location Prediction Based on Transition Probability Matrices Constructing from Sequential Rules for Spatial-Temporal K-Anonymity Dataset

<div><p>Spatial-temporal k-anonymity has become a mainstream approach among techniques for protection of users’ privacy in location-based services (LBS) applications, and has been applied to several variants such as LBS snapshot queries and continuous queries. Analyzing large-scale spatial-temporal anonymity sets may benefit several LBS applications. In this paper, we propose two location prediction methods based on transition probability matrices constructing from sequential rules for spatial-temporal k-anonymity dataset. First, we define single-step sequential rules mined from sequential spatial-temporal k-anonymity datasets generated from continuous LBS queries for multiple users. We then construct transition probability matrices from mined single-step sequential rules, and normalize the transition probabilities in the transition matrices. Next, we regard a mobility model for an LBS requester as a stationary stochastic process and compute the n-step transition probability matrices by raising the normalized transition probability matrices to the power n. Furthermore, we propose two location prediction methods: rough prediction and accurate prediction. The former achieves the probabilities of arriving at target locations along simple paths those include only current locations, target locations and transition steps. By iteratively combining the probabilities for simple paths with n steps and the probabilities for detailed paths with n-1 steps, the latter method calculates transition probabilities for detailed paths with n steps from current locations to target locations. Finally, we conduct extensive experiments, and correctness and flexibility of our proposed algorithm have been verified.</p></div

A novel on-line spatial-temporal k-anonymity method for location privacy protection from sequence rules-based inference attacks

<div><p>Analyzing large-scale spatial-temporal k-anonymity datasets recorded in location-based service (LBS) application servers can benefit some LBS applications. However, such analyses can allow adversaries to make inference attacks that cannot be handled by spatial-temporal k-anonymity methods or other methods for protecting sensitive knowledge. In response to this challenge, first we defined a destination location prediction attack model based on privacy-sensitive sequence rules mined from large scale anonymity datasets. Then we proposed a novel on-line spatial-temporal k-anonymity method that can resist such inference attacks. Our anti-attack technique generates new anonymity datasets with awareness of privacy-sensitive sequence rules. The new datasets extend the original sequence database of anonymity datasets to hide the privacy-sensitive rules progressively. The process includes two phases: off-line analysis and on-line application. In the off-line phase, sequence rules are mined from an original sequence database of anonymity datasets, and privacy-sensitive sequence rules are developed by correlating privacy-sensitive spatial regions with spatial grid cells among the sequence rules. In the on-line phase, new anonymity datasets are generated upon LBS requests by adopting specific generalization and avoidance principles to hide the privacy-sensitive sequence rules progressively from the extended sequence anonymity datasets database. We conducted extensive experiments to test the performance of the proposed method, and to explore the influence of the parameter <i>K</i> value. The results demonstrated that our proposed approach is faster and more effective for hiding privacy-sensitive sequence rules in terms of hiding sensitive rules ratios to eliminate inference attacks. Our method also had fewer side effects in terms of generating new sensitive rules ratios than the traditional spatial-temporal k-anonymity method, and had basically the same side effects in terms of non-sensitive rules variation ratios with the traditional spatial-temporal k-anonymity method. Furthermore, we also found the performance variation tendency from the parameter <i>K</i> value, which can help achieve the goal of hiding the maximum number of original sensitive rules while generating a minimum of new sensitive rules and affecting a minimum number of non-sensitive rules.</p></div

La biofisica

Un vasto progetto che, prendendo le mosse da una nuova teoria del mondo della vita, dà nuove interpretazioni dell'uomo, del suo vivere, del suo pensare

Basic characteristics of simulated anonymity datasets for LBS continuous queries.

<p>Basic characteristics of simulated anonymity datasets for LBS continuous queries.</p

Comparison of differences between confidence values and normalized values for the sequential rules with confidence thresholds 0.2, 0.22 and 0.24.

<p>Comparison of differences between confidence values and normalized values for the sequential rules with confidence thresholds 0.2, 0.22 and 0.24.</p

Sequential rules mined from simulated anonymity datasets for LBS continuous queries.

<p>Sequential rules mined from simulated anonymity datasets for LBS continuous queries.</p