OBJECTIVE:To assess the feasibility and performance of direct visual inspection (DVI) of the cervix as a primary tool for the detection of premalignant lesions of the cervix (HPV/CIN 1 and CIN 2,3).SETTING:The early cancer detection unit at the Ain Shams University Maternity Hospital in Cairo, Egypt.SUBJECTS AND METHODS:The study included 2049 women. Cervical smears were obtained from all women for cytologic evaluation followed by direct visual inspection (DVI) of the cervix after painting with 5% acetic acid. Women whose smear reports showed abnormal cells suggestive of squamous intraepithelial lesion (SIL) or human papillomavirus (HPV) infection or those who showed abnormalities or acetowhite areas on direct visual inspection subsequently were referred for colposcopy and biopsy when appropriate. Colposcopy also was performed for women with negative DVI and negative smears if they had contact bleeding or chronic per vaginal discharge.RESULTS:Mean (SD) age of women included in the study was 39.9 (10.2) years with their mean (SD) parity 2.9 (1.1). Results of DVI were normal in 1916 women (93.4%) and showed abnormal acetowhite appearance in 133 (6.6%) women. There were 458 (22.4%) colposcopic examinations and 130 biopsies (6.34%) were carried out, picking up 83 cases of premalignant lesions (4.0%). Premalignant lesions were 80 HPV/CIN 1 and CIN 2,3. Direct visual inspection detected 71 of the 83 premalignant lesions (sensitivity, 85.5%; specificity, 96.8%; positive predictive value, 52.6%). Direct visual inspection missed one of the three samples showing CIN 2,3. Cervical cytologic analysis showed abnormal cells in 60 (2.9%) and identified only 14 of the premalignant lesions (sensitivity, 16.9%; specificity, 97.8%; positive predictive value, 23.3%). Twelve of the premalignant lesions had positive smear results and a negative DVI, with none of them being of a high grade.CONCLUSIONS:Direct visual inspection is feasible and had superior sensitivity compare with cervical cytologic analysis in detecting premalignant lesions of the cervix. Direct visual inspection can be used as a primary screening tool with a satisfactory low biopsy rate in low-resource settings or where cytologic services are suboptimal.

The subject of this paper is modelling and optimal control of consumption and explorationof non‐renewable resources via piecewise‐deterministic Markov processes (PDP). Ingeneral, one assumes that new reserves are found at random times T1 , T2 , T3 ,..., where theinter‐arrival times are exponentially distributed. The amount of resource found at each T i is also a random variable with a given distribution. This process falls into the category ofPDP. In this paper, we clearly show how one can apply the PDP optimal control theory tothe optimal consumption and exploration of non‐renewable resources under uncertain exploration.The advantage of using a PDP model is that no matter how complicated thedynamics, utility function and distribution functions are, as long as they satisfy some mildassumptions, one can easily transform the problem into a deterministic optimal controlproblem, which can be solved iteratively with guaranteed convergence for the value function.

This article concerns the finite horizon optimal control of continuous-time deterministic processes in the presence of model-reality differences. The latter occurs either due to the complexity of the real physical process and hence inability in accurate and exact modelling or the desire of the control engineers in employing simplified models, e.g. linear time varying models. A typical optimal control algorithm in the presence of model-reality differences may be as follows. A nominal control signal is applied to the process and all the states in the process are measured for the duration of time horizon (let us assume this is possible). Using the state measurements, one can construct a linear time-varying model for the small variations in state and control around the nominal trajectories, i.e. a small signal model. Now by solving a minimisation problem based on the linear time-varying model, one can calculate a new control signal which reduces the objective function compared to the previous step. The new control signal is applied to the process and all the calculations are repeated again. These calculations continue iteratively until the control signal converges to its optimal value. In this algorithm instead of using the exact physical model of the process, a linear time-varying model is used which is updated after each iteration. This makes the calculation of optimal control in each iteration much easier

An iterative computational method for determining the value function of an optimal control problem, related to target tracking, is presented. The target is assumed to be located in a fixed known position in space, but its identity (hostile or friendly) is known only with a prior probability. An observation of the target can be made at any location, and its error has position-dependent probability. The objective is finding the optimal navigation and observation strategy which leads to a final decision (i.e., the target is friendly or hostile), The value function is shown to be the unique viscosity solution of a variational inequality Furthermore it is the unique fixed point of a nondecreasing concave operator.