Managing housing needs in post conflict housing reconstruction in Sri Lanka: gaps versus recommendations

Addressing housing needs in post conflict housing reconstruction leads to successful housing reconstruction. As part of a study of investigating how the housing needs can be effectively addressed in post conflict housing reconstruction, this paper identifies the gaps in managing housing needs in post conflict housing reconstruction within the context of Sri Lanka and presents the recommendations to minimise such gaps. Data was collected through un-structured interviews conducted with 37 participants, comprising policy makers, practitioners, academics and beneficiaries who engaged in post conflict housing reconstruction in Sri Lanka. Gaps were mainly found in conflict sensitivity, measures related to physical housing, performance of implementing agencies, policy and practice issues. On the job training, application of ‘do no harm’ principles, enhanced beneficiary participation, enhanced accountability, effective monitoring, enhanced knowledge sharing, adequate drinking water facilities, irrigation development and initiatives for material manufacturing were suggested as recommendations to minimise these gaps. Identification of gaps in managing housing needs in post conflict housing reconstruction and recommendations to minimise them inform policy makers to address the housing needs effectively through incorporating these aspects into the related policies. This in turn enhances the sustainability in housing development after conflicts

Time Varying Apparent Volume of Distribution and Drug Half-Lives Following Intravenous Bolus Injections.

We present a model that generalizes the apparent volume of distribution and half-life as functions of time following intravenous bolus injection. This generalized model defines a time varying apparent volume of drug distribution. The half-lives of drug remaining in the body vary in time and become longer as time elapses, eventually converging to the terminal half-life. Two example fit models were substituted into the general model: biexponential models from the least relative concentration error, and gamma variate models using adaptive regularization for least relative error of clearance. Using adult population parameters from 41 studies of the renal glomerular filtration marker 169Yb-DTPA, simulations of extracellular fluid volumes of 5, 10, 15 and 20 litres and plasma clearances of 40 and 100 ml/min were obtained. Of these models, the adaptively obtained gamma variate models had longer times to 95% of terminal volume and longer half-lives

A Gamma-Distribution Convolution Model of 99Mtc-Mibi Thyroid Time-Activity Curves

Background The convolution approach to thyroid time-activity curve (TAC) data fitting with a gamma distribution convolution (GDC) TAC model following bolus intravenous injection is presented and applied to 99mTc-MIBI data. The GDC model is a convolution of two gamma distribution functions that simultaneously models the distribution and washout kinetics of the radiotracer., The GDC model was fitted to thyroid region of interest (ROI) TAC data from 1 min per frame 99mTc-MIBI image series for 90 min; GDC models were generated for three patients having left and right thyroid lobe and total thyroid ROIs, and were contrasted with washout-only models, i.e., less complete models. GDC model accuracy was tested using 10 Monte Carlo simulations for each clinical ROI. Results The nine clinical GDC models, obtained from least counting error of counting, exhibited corrected (for 6 parameters) fit errors ranging from 0.998% to 1.82%. The range of all thyroid mean residence times (MRTs) was 212 to 699 min, which from noise injected simulations of each case had an average coefficient of variation of 0.7% and a not statistically significant accuracy error of 0.5% (p = 0.5, 2-sample paired t test). The slowest MRT value (699 min) was from a single thyroid lobe with a tissue diagnosed parathyroid adenoma also seen on scanning as retained marker. The two total thyroid ROIs without substantial pathology had MRT values of 278 and 350 min overlapping a published 99mTc-MIBI thyroid MRT value. One combined value and four unrelated washout-only models were tested and exhibited R-squared values for MRT with the GDC, i.e., a more complete concentration model, ranging from 0.0183 to 0.9395. Conclusions The GDC models had a small enough TAC noise-image misregistration (0.8%) that they have a plausible use as simulations of thyroid activity for querying performance of other models such as washout models, for altered ROI size, noise, administered dose, and image framing rates. Indeed, of the four washout-only models tested, no single model approached the apparent accuracy of the GDC model using only 90 min of data. Ninety minutes is a long gamma-camera acquisition time for a patient, but a short a time for most kinetic models. Consequently, the results should be regarded as preliminary.PubMedWoSScopu

Concentration versus time curve for E2 and GV models for four <i>V</i>_E values at <i>CL</i> of 100 ml/min (left panel) and 40 ml/min (right panel).

<p>Concentration versus time curve for E2 and GV models for four <i>V</i>_E values at <i>CL</i> of 100 ml/min (left panel) and 40 ml/min (right panel).</p

Pharmacokinetic parameters from the weighted biexponential (E2) and the Tk-GV models.

<p>Comparable measures boxed.</p

Time to achieve apparent volume of distribution to 95% of <i>V</i>_area after the intravenous bolus of the drug and terminal half-life of the drug from E2 and GV models.

<p>Time to achieve apparent volume of distribution to 95% of <i>V</i>_area after the intravenous bolus of the drug and terminal half-life of the drug from E2 and GV models.</p

Schematic diagram showing E2 compartmental and GV variable volume models of drug distribution.

<p>The E2 model could also be drawn as a variable volume model in which case a scale factor <i>α</i>_exp = <i>V</i>_E/<i>V</i>_d(∞) < 1 would define the physical volume at time <i>t</i> to be <i>α</i>_exp<i>V</i>_d(<i>t</i>). Similarly, for the variable volume adaptively obtained GV model, one can define <i>α</i> = <i>V</i>_E/<i>V</i>_d(∞) < 1, and an expanding physical volume <i>αV</i>_<i>d</i>(<i>t</i>). Note, both <i>α</i>_exp and <i>α</i> are constants at all times for their respective models. The term <i>V</i>_<i>SS</i> can be confusing because 1) <i>V</i>_<i>SS</i> implies that <i>V</i>_E is always a steady state volume, which is not the case as the GV model <i>αV</i>_<i>d</i>(<i>t</i>) <i>< V</i>_E is concentration depleted at late time, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158798#pone.0158798.e034" target="_blank">Eq (30)</a>. 2) <i>V</i>_<i>SS</i> implies that <i>V</i>_E only exists at <i>t</i> = ∞, whereas <i>V</i>_E is defined all of the time, i.e., on <i>t</i> = [0,∞) by Eqs (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158798#pone.0158798.e010" target="_blank">8</a> & <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158798#pone.0158798.e007" target="_blank">7</a>). Finally, 3) <i>V</i>_<i>SS</i> implies an expected physical volume of distribution for sums of exponential term bolus models, and the apparent volume of distribution for a constant infusion experiment, whereas <i>V</i>_E applies to more models as the expected volume of physical distribution of a drug for both the bolus and constant infusion experiments.</p