Congenital choledochal cysts

Cystic dilation of the common bile duct is a rare congenital anomaly. The cases of two patients, 2 1/2 and 18 years old, respectively, illustrate the difficulty in preoperative diagnosis, as well as the value of long-term postoperative follow-up. Ascending cholangitis developed in one of the patients after a Roux-en-Y cystojejunostomy nine years after operation. A follow-up of three years after cystoduodenostomy and 14 years after a hepaticojejunostomy is reported. In infants and small children, a cystoduodenostomy is usually done; otherwise, cystojejunostomy should be the preferred initial operation. Excision may be attempted if the cyst is very small, and in selected cases.     

Transboundary pollution,R&;D spillovers and international trade

We consider a non-cooperative and symmetric three-stage game played by two regulator-firm hierarchies. We show that R&D spillovers and the competition of firms on the common market help non-cooperating countries to better internalize transboundary pollution. Surprisingly, international competition increases the per-unit emission-tax and decreases the per-unit R&D subsidy. It also leads to a higher investment in R&D and production, and to a lower emission ratio. In most cases, pollution in common market is lower than in autarky, implying a greater social welfare, and the contrary occurs in some other cases.     

Opinion mining and sentiment analysis for Arabic on-line texts: application on the political domain

Since the continuous proliferation of the journalistic content online and the changing political landscape in many Arabic countries, we started our current research in order to implement a media monitoring system about the opinion mining in political field. This system allows political actors, despite of the large volume of online data, to be constantly informed about opinions expressed on the web in order to properly monitor their actual standing, orient their communication strategy and prepare the election campaigns. The developed system is based on a linguistic approach using NooJ’s linguistic engine to formalize the automatic recognition rules and apply them to a dynamic corpus composed of journalistic articles. The first implemented rules allow identifying and annotating the different political entities (political actors and organizations). Then these annotations are used in our system of media monitoring in order to identify the opinions associated with the extracted named entities. The system is mainly based on a set of local grammars developed for the identification of different structures of the political opinion phrases. These grammars are using the entries of the opinion lexicon that contain the different opinion words (verbs, adjectives, nouns) where each entry is associated with the corresponding semantic marker (polarity and intensity). Our developed system is able to identify and properly annotate the opinion holder, the opinion target and the polarity (positive or negative) of the phraseological expression (nominal or verbal) expressing the opinion. Our experiments showed that the adopted method of extraction is consistent with 0.83 F-measure.     

An Artificial Intelligence Approach for the Stochastic Management of Coastal Aquifers

Aquifer recharge rates and patterns are often uncertain, especially in arid areas due to sporadic and erratic rainfall. Therefore, determining the optimal groundwater abstraction using classical approaches such as Monte Carlo Simulation (MCS) requires a large number of groundwater simulations and exorbitant computational efforts. The problem becomes even more complex and time consuming for regional coastal aquifers whose domains must be discretized using high-resolution meshes. In fact, even fast evolutionary multi-objective optimization techniques generally require a large number of simulations to determine the Pareto-front among the objectives. This study explores the performance of a Decision Tree (DT) approach for the generation of the Pareto optimal solutions of groundwater extraction. This paper applies the DTs for the optimal management of the Al-Khoud coastal aquifer in Oman. The learning process of the developed DT-based model uses the output of a numerical simulation model to assess the aquifer response based on different abstraction policies. The trained DT network then utilizes the NSGA-II to determine the Pareto-optimal solutions. The simulation show that the general flux pattern in the study area is toward the sea and the hydraulic head following a similar pattern in both best and worst recharging scenarios downstream of the studied recharging dam. Statistical tests showed a good correlation between the DT-based and simulation-based results and demonstrate the capability of the DT approach to obtain high-quality solutions by incorporating a large number of recharge scenarios. Moreover, the required runtime of the DT-based approach is extremely low (5 min) compared to that of the simulation-based method (several days). This means that including additional Monte-Carlo simulations can be readily done in few minutes using the obtained DTs, instead of the long computational time needed by the simulation-based approach.