Optimising drug solubilisation in amorphous polymer dispersions: rational selection of hot-melt extrusion processing parameters

The aim of this article was to construct a T–ϕ phase diagram for a model drug (FD) and amorphous polymer (Eudragit® EPO) and to use this information to understand the impact of how temperature–composition coordinates influenced the final properties of the extrudate. Defining process boundaries and understanding drug solubility in polymeric carriers is of utmost importance and will help in the successful manufacture of new delivery platforms for BCS class II drugs. Physically mixed felodipine (FD)–Eudragit® EPO (EPO) binary mixtures with pre-determined weight fractions were analysed using DSC to measure the endset of melting and glass transition temperature. Extrudates of 10 wt% FD–EPO were processed using temperatures (110°C, 126°C, 140°C and 150°C) selected from the temperature–composition (T–ϕ) phase diagrams and processing screw speed of 20, 100 and 200rpm. Extrudates were characterised using powder X-ray diffraction (PXRD), optical, polarised light and Raman microscopy. To ensure formation of a binary amorphous drug dispersion (ADD) at a specific composition, HME processing temperatures should at least be equal to, or exceed, the corresponding temperature value on the liquid–solid curve in a F–H T–ϕ phase diagram. If extruded between the spinodal and liquid–solid curve, the lack of thermodynamic forces to attain complete drug amorphisation may be compensated for through the use of an increased screw speed. Constructing F–H T–ϕ phase diagrams are valuable not only in the understanding drug–polymer miscibility behaviour but also in rationalising the selection of important processing parameters for HME to ensure miscibility of drug and polymer.

Optimization of Controller Parameters for Energy Saving

Among various technologies to tackle the twin challenges of sustainable energy supply and climate change, energy saving through advanced control plays a crucial role in decarbonizing the whole energy system. Modern control technologies, such as optimal control and model predictive control do provide a framework to simultaneously regulate the system performance and limit control energy. However, few have been done so far to exploit the full potential of controller design in reducing the energy consumption while maintaining desirable system performance. This paper investigates the correlations between control energy consumption and system performance using two popular control approaches widely used in the industry, namely the PI control and subspace model predictive control. Our investigation shows that the controller design is a delicate synthesis procedure in achieving better trade-o between system performance and energy saving, and proper choice of values for the control parameters may potentially save a significant amount of energy

Detection of Bridge Dynamic Parameters using an Instrumented Vehicle

Highway structures such as bridges are subject to continuous degradation primarily due to ageing and environmental factors. A rational transport policy requires the monitoring of this transport infrastructure to provide adequate maintenance and guarantee the required levels of transport service and safety. In Europe, this is now a legal requirement - a European Directive requires all member states of the European Union to implement a Bridge Management System. However, the process is expensive, requiring the installation of sensing equipment and data acquisition electronics on the bridge. This paper investigates the use of an instrumented vehicle fitted with accelerometers on its axles to monitor the dynamic behaviour of bridges as an indicator of its structural condition. This approach eliminates the need for any on-site installation of measurement equipment. A simplified half-car vehicle-bridge interaction model is used in theoretical simulations to test the possibility of extracting the dynamic parameters of the bridge from the spectra of the vehicle accelerations. The effect of vehicle speed, vehicle mass and bridge span length on the detection of the bridge dynamic parameters are investigated. The algorithm is highly sensitive to the condition of the road profile and simulations are carried out for both smooth and rough profiles

Experimental Investigation of the Detection of Bridge Dynamic Parameters using a Moving Vehicle

This paper investigates the feasibility of using an instrumented vehicle to detect bridge dynamic parameters, such as natural frequency and structural damping, in a scaled laboratory experiment. In the experiment, a scaled vehicle model crosses a steel girder which has been adopted as the bridge model. The bridge model also includes a scaled road surface profile. The effects of varying vehicle model mass and speed are investigated. The damping of the girder is also varied. The bridge frequency and changes in damping are detected in the vehicle acceleration response in the presence of a rough road surface profile.

Identification of Small Signal Oscillation Mode Parameters from Simulated and Actual PMU Ringdown Data

This paper presents the practical use of Prony Analysis to identify small signal oscillation mode parameters from simulated and actual phasor measurement unit (PMU) ringdown data. A well-known two-area four-machine power system was considered as a study case while the latest PMU ringdown data were collected from a double circuit 275 kV main interconnector on the Irish power system. The eigenvalue analysis and power spectral density were also conducted for the purpose of comparison. The capability of Prony Analysis to identify the mode parameters from three different types of simulated PMU ringdown data has been shown successfully. Furthermore, the results indicate that the Irish power system has dominant frequency modes at different frequencies. However, each mode has good system damping.

Hidden Markov Models With Set-Valued Parameters

Hidden Markov models (HMMs) are widely used probabilistic models of sequential data. As with other probabilistic models, they require the specification of local conditional probability distributions, whose assessment can be too difficult and error-prone, especially when data are scarce or costly to acquire. The imprecise HMM (iHMM) generalizes HMMs by allowing the quantification to be done by sets of, instead of single, probability distributions. iHMMs have the ability to suspend judgment when there is not enough statistical evidence, and can serve as a sensitivity analysis tool for standard non-stationary HMMs. In this paper, we consider iHMMs under the strong independence interpretation, for which we develop efficient inference algorithms to address standard HMM usage such as the computation of likelihoods and most probable explanations, as well as performing filtering and predictive inference. Experiments with real data show that iHMMs produce more reliable inferences without compromising the computational efficiency.

Test-retest variability in structural and functional parameters of glaucoma damage in the glaucoma imaging longitudinal study.

PURPOSE:

To determine the test-retest variability in perimetric, optic disc, and macular thickness parameters in a cohort of treated patients with established glaucoma.

PATIENTS AND METHODS:

In this cohort study, the authors analyzed the imaging studies and visual field tests at the baseline and 6-month visits of 162 eyes of 162 participant in the Glaucoma Imaging Longitudinal Study (GILS). They assessed the difference, expressed as the standard error of measurement, of Humphrey field analyzer II (HFA) Swedish Interactive Threshold Algorithm fast, Heidelberg retinal tomograph (HRT) II, and retinal thickness analyzer (RTA) parameters between the two visits and assumed that this difference was due to measurement variability, not pathologic change. A statistically significant change was defined as twice the standard error of measurement.

RESULTS:

In this cohort of treated glaucoma patients, it was found that statistically significant changes were 3.2 dB for mean deviation (MD), 2.2 for pattern standard deviation (PSD), 0.12 for cup shape measure, 0.26 mm for rim area, and 32.8 microm and 31.8 microm for superior and inferior macular thickness, respectively. On the basis of these values, it was estimated that the number of potential progression events detectable in this cohort by the parameters of MD, PSD, cup shape measure, rim area, superior macular thickness, and inferior macular thickness was 7.5, 6.0, 2.3, 5.7, 3.1, and 3.4, respectively.

CONCLUSIONS:

The variability of the measurements of MD, PSD, and rim area, relative to the range of possible values, is less than the variability of cup shape measure or macular thickness measurements. Therefore, the former measurements may be more useful global measurements for assessing progressive glaucoma damage.

Influence of corneal structure, corneal responsiveness, and other ocular parameters on tonometric measurement of intraocular pressure.

PURPOSE: To estimate the relationships between ocular parameters and tonometrically measured intraocular pressure (IOP), to determine the influence of ocular parameters on different instrument measurements of IOP, and to evaluate the association of ocular parameters with a parameter called hysteresis. METHODS: Patients presenting at a glaucoma clinic were recruited for this study. Subjects underwent IOP measurement with the Goldmann applanation tonometer (GAT), the TonoPen, and the Reichert Ocular Response Analyzer (ORA), and also measurements of central corneal thickness (CCT), axial length, corneal curvature, corneal astigmatism, central visual acuity, and refractive error. Chart information was reviewed to determine glaucoma treatment history. The ORA instrument provided a measurement called corneal hysteresis. The association between measured IOP and the other ocular characteristics was estimated using generalized estimating equations. RESULTS: Among 230 patients, IOP measurements from the TonoPen read lowest, and ORA read highest, and GAT measurements were closest to the mean IOP of the 3 instruments. In a multiple regression model adjusting for age, sex, race, and other ocular characteristics, a 10 microm increase in CCT was associated with an increase of 0.79 mm Hg measured IOP in untreated eyes (P<0.0001). Of the 3 tonometers, GAT was the least affected by CCT (0.66 mm Hg/10 mum, P<0.0001). Hysteresis was significantly correlated with CCT with a modest correlation coefficient (r=0.20, P<0.0007). CONCLUSIONS: Among parameters related to measured IOP, features in addition to CCT, such as hysteresis and corneal curvature, may also be important. Tonometric instruments seem to be affected differently by various physiologic characteristics.