Tubal stump pregnancy in ART patients two cases of ectopic stump pregnancy after IVF-ET

Ectopic pregnancy (EP) is a complication of pregnancy in which the embryo attaches outside the uterus. The rate of ectopic pregnancy is about 1 and 2% that of live births, though it may be as high as 4% among those using assisted reproductive technology (ART). We present two cases of interstitial stump pregnancies in patients who previously underwent salpingectomy for ectopic pregnancies, and a review of the literature. One patient has been treated with methotrexate (MTX) before the removal of the tubal stump, while the second has gone directly to laparoscopic (LPS) surgery. Transvaginal ultrasound examination is essential for early and accurate management of this condition. It should be quickly performed to rule out a stump interstitial pregnancy in women who conceive by ART after bilateral salpingectomy. A correct attitude towards this condition is not yet internationally standardized and both medical and surgical options should be promptly considere

Mild solutions to the dynamic programming equation for stochastic optimal control problems

We show via the nonlinear semigroup theory in $L^1(\mathbb{R})$ that the $1$-D dynamic programming equation associated with a stochastic optimal control problem with multiplicative noise has a unique mild solution $\varphi\in C([0,T];W^{1,\infty}(\mathbb{R}))$ with $\varphi_{xx}\in C([0,T];L^1(\mathbb{R}))$. The $n$-dimensional case is also investigated

DEVELOPMENT OF PREDICTIVE MODELS FOR QUALITY CONTROL OF CARROTS DURING DRYING

This thesis research project is aimed at setting up prediction models based on NIR spectroscopy, for quality control of organic carrot discs (Daucus carota L., var. Romance) during hot-air drying process (horizontal flow) up to 8 h. Hot-water blanching was tested at 95°C for 1.5 min, as pre-treatment to control the occurrence of enzymatic browning during drying. Hot-water blanching had a positive impact on the appearance of the carrot discs. PLS regression showed good performances for the prediction of aw (RMSE = 0.04; R2 = 0.96), moisture (RMSE = 0.04; R2 = 0.98), SSC (RMSE = 4.32-4.40 °Brix; R2 =0.88), carotenoids (RMSE = 21.75-23.10; R2 = 0.96) and changes in color (RMSE = 1.40-1.46; R2 = 0.85-0.86) during drying. Also PLSDA classification showed very good metrics (total accuracy 92.38%) in recognising 3-drying steps, both for control and hot-water blanched samples. Features selection by iPLS and iPLSDA algorithms showed results better/equal than models based on full spectrum. For these results, the implementation of low-cost NIR sensors on drier device, seems feasible

Mean field games with controlled jump-diffusion dynamics: Existence results and an illiquid interbank market model

We study a family of mean field games with a state variable evolving as a multivariate jump diffusion process. The jump component is driven by a Poisson process with a time-dependent intensity function. All coefficients, i.e. drift, volatility and jump size, are controlled. Under fairly general conditions, we establish existence of a solution in a relaxed version of the mean field game and give conditions under which the optimal strategies are in fact Markovian, hence extending to a jump-diffusion setting previous results established in [30]. The proofs rely upon the notions of relaxed controls and martingale problems. Finally, to complement the abstract existence results, we study a simple illiquid inter-bank market model, where the banks can change their reserves only at the jump times of some exogenous Poisson processes with a common constant intensity, and provide some numerical results.Comment: 37 pages, 6 figure

Incremental Predictive Process Monitoring: How to Deal with the Variability of Real Environments

A characteristic of existing predictive process monitoring techniques is to first construct a predictive model based on past process executions, and then use it to predict the future of new ongoing cases, without the possibility of updating it with new cases when they complete their execution. This can make predictive process monitoring too rigid to deal with the variability of processes working in real environments that continuously evolve and/or exhibit new variant behaviors over time. As a solution to this problem, we propose the use of algorithms that allow the incremental construction of the predictive model. These incremental learning algorithms update the model whenever new cases become available so that the predictive model evolves over time to fit the current circumstances. The algorithms have been implemented using different case encoding strategies and evaluated on a number of real and synthetic datasets. The results provide a first evidence of the potential of incremental learning strategies for predicting process monitoring in real environments, and of the impact of different case encoding strategies in this setting