Mahmoud Abd El Hafez M. Adam

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Nephropathic cystinosis is rare genetic disease characterized by defective lysosomal cystine transport and increased lysosomal cystine. Corneal Cystine Crystal Scoring (CCCS) for diagnosis of nephropathic cystinosis was studied in all suspected children with renal Fanconi syndrome and siblings of diagnosed cases over a two year period. In addition to oral cysteamine, cysteamine eye drops were provided to all diagnosed patients and CCCS was followed up on a quarterly basis. Of 33 screened cases, 14 had corneal cystine crystals. Crystals were absent in two cystinotic patients under the age of 20 months. The mean age at diagnosis was 52.7 months and five patients had ERSD. After six months of treatment, the mean CCCS did not increase from the initial value of 1.81; associated with a decrease of 0.5 in two cases and a similar increase in two others. Scores decreased in two other patients after 12 months. Compliance was generally inadequate due to the high frequency of administration and the need for multi-drug regimen. CCCS is a simple and reasonably sensitive method for diagnosis of nephropathic cystinosis above two years of age. Topical treatment with cysteamine eye drops prevents progression of deposits and may decrease it with adequate compliance. Further follow up is still recommended to monitor long term effects of both systemic and topical cysteamine therapy.

Name identification has been worked on quite intensively for the past few years, and has been incorporated into several products revolving around natural language processing tasks. Many researchers have attacked the name identification problem in a variety of languages, but only a few limited research efforts have focused on named entity recognition for Arabic script. This is due to the lack of resources for Arabic named entities and the limited amount of progress made in Arabic natural language processing in general. In this article, we present the results of our attempt at the recognition and extraction of the 10 most important categories of named entities in Arabic script: the person name, location, company, date, time, price, measurement, phone number, ISBN, and file name. We developed the system Named Entity Recognition for Arabic (NERA) using a rule-based approach. The resources created are: a Whitelist representing a dictionary of names, and a grammar, in the form of regular expressions, which are responsible for recognizing the named entities. A filtration mechanism is used that serves two different purposes: (a) revision of the results from a named entity extractor by using metadata, in terms of a Blacklist or rejecter, about ill-formed named entities and (b) disambiguation of identical or overlapping textual matches returned by different name entity extractors to get the correct choice. In NERA, we addressed major challenges posed by NER in the Arabic language arising due to the complexity of the language, peculiarities in the Arabic orthographic system, non-standardization of the written text, ambiguity, and lack of resources. NERA has been effectively evaluated using our own tagged corpus; it achieved satisfactory results in terms of precision, recall, and F-measure.}

The coupling capacitor voltage transformers transient response during faults can cause protective relay mal-operation or even prevent tripping. This paper presents the CCVT transient response errors and the use of artificial neural network (ANN) to correct the CCVT secondary waveform distortion. In this paper, an ANN program is developed to recover the primary voltage from the distorted secondary voltage. The ANN is trained to achieve the inverse transfer function of the coupling capacitor voltage transformer (CCVT), which provides a good estimate of the true primary voltage from the distorted secondary voltage. The neural network is developed and trained using MATLAB simulations. The accuracy of the simulation program is
confirmed by comparison of its response with that of the target value from the simulation data.