Appendagite épiploïque primitive: à propos de cinq cas

La torsion de frange épiploïque (ou appendagite) est une pathologie rare qui survient principalement chez les adultes entre 20 et 50 ans.L'incidence de cette pathologie n'est pas réellement connue et elle varie de 2 à 7% chez les patients hospitalisés pour suspicion d'appendicite oude sigmoïdite. Nous rapportons cinq cas d'appendagites dont nous précisons les particularités cliniques, radiologiques et thérapeutiques. Il s'agit de 5 patients dont l'âge moyen est de 34.6 ans (24-55). Le sexe ratio est de 1.5. Le principal motif de consultation était un syndrome douloureux de l'abdomen principalement au niveau de la fosse iliaque droite. L'examen abdominal montrait toujours une sensibilité localisée. La fièvre était présente chez 3 patients. Le bilan biologique révèle un syndrome inflammatoire biologique chez trois patients. Les examens complémentaires radiologiques en particulier échographie abdominale et TDM abdominale ont éliminé formellement une urgence chirurgicale et ont évoqué le diagnostic d'appendagite dans trois cas. Trois patients ont bénéficié d'une coelioscopie diagnostique confirmant le diagnostic  d'appendagite. L'évolution était favorable chez tous les patients. Les appendagites épiploïques primitives sont des étiologies rares et  sous-estimées de syndrome abdominal aigu. Le diagnostic peut être affirmé par imagerie notamment avec le scanner hélicoïdal injecté,  permettant d'instaurer ainsi un traitement médical premier et d'éviter un traitement chirurgical et des hospitalisations excessives

Current Opinion and Practice on Peritoneal Carcinomatosis Management: The North African Perspective.

The status of peritoneal surface malignancy (PSM) management in North Africa is undetermined. The aim of this study was to assess and compare current practice and knowledge regarding PSM and examine satisfaction with available treatment options and need for alternative therapies in North Africa. This is a qualitative study involving specialists participating in PSM management in North Africa. The survey analyzed demographic characteristics and current knowledge and opinions regarding PSM management in different institutions. We also looked at goals and priorities, satisfaction with treatment modalities and heated intraperitoneal chemotherapy (HIPEC) usefulness according to specialty, country, years of experience, and activity sector. One-hundred and three participants responded to the survey (response rate of 57%), including oncologists and surgeons. 59.2% of respondents had more than 10 years experience and 45.6% treated 20-50 PSM cases annually. Participants satisfaction with PSM treatment modalities was mild for gastric cancer (3/10 [IQR 2-3]) and moderate for colorectal (5/10 [IQR 3-5]), ovarian (5/10 [IQR 3-5]), and pseudomyxoma peritonei (5/10 [IQR 3-5]) type of malignancies. Good quality of life and symptom relief were rated as main priorities for treatment and the need for new treatment modalities was rated 9/10 [IQR 8-9]. The perceived usefulness of systemic chemotherapy in first intention was described as high by 42.7 and 39.8% of respondents for PSM of colorectal and gastric origins, while HIPEC was described as highly useful for ovarian (49.5%) and PMP (73.8) malignancies. The management of PSM in the North African region has distinct differences in knowledge, treatments availability and priorities. Disparities are also noted according to specialty, country, years of expertise, and activity sector. The creation of referral structures and PSM networks could be a step forward to standardized PSM management in the region

Machine Failure Prediction using Joint Reserve Intelligence with Feature Selection Technique

A model with high accuracy of machine failure prediction is important for any machine life cycle. In this paper, a prediction model based on machine learning methods is proposed. The used method is a combination of machine learning algorithms and techniques. The Machine learning algorithm is a data mining technique that has been widely used as a prediction model for classifying problems. Five algorithms have been tested including JRIP, logistic, KStar, bayes network and decision table machine learning. The evaluation process is done by applying the algorithms on a predictive dataset using different performance measures. In the proposed model, the feature selection and voting techniques are used and applied in the classification process for each classifier. From the comparison of the result, the feature selection shows the best performance result. Paired t-test evaluation measures were considered to confirm our conclusion. The best accuracy result among the five classifiers shows that joint reserve intelligence classifier can be used to predict the failure with an accuracy high as 0.983. Applying classifier subset evaluation using the JRIP classifier can enhance the accuracy result to be 0.985. The finding shows that the proposed model improves the results of the classifiers

Adopting Security Practices in Software Development Process: Security Testing Framework for Sustainable Smart Cities

The dependence on smart city applications has expanded in recent years. Consequently, the number of cyberattack attempts to exploit smart application vulnerabilities significantly increases. Therefore, improving smart application security during the software development process is mandatory to ensure sustainable smart cities. But the challenge is how to adopt security practices in the software development process. There are Several established and mature security testing frameworks exist that consider security requirements and testing during Several already established and mature security testing frameworks exist that consider security requirements and testing during Software Development Life Cycle (SDLC), but there is a unique challenges posed by smart city applications and the need for a comprehensive approach to address the evolving threat landscape in this context. This paper proposed a framework that adopts security testing practices in all phases of the software development process. The proposed framework identifies several security activities and steps that can be applied in each phase of the software development process

Contents

The Kernel Fisher’s Discriminant (KFD) is a non-linear classifier which has proven to be powerful and competitive to several state-of-the-art classifiers. Its main ingredient is the kernel trick which allows the efficient computation of Fisher’s Linear Discriminant in feature space. However, it is assuming equal covariance structure for all transformed classes, which is not true in many applications. In this paper, we propose a novel Bayesian Kernel Logistic Discriminant model (BKLD) which goes one step further by representing each transformed class by its own covariance matrix. This can allow more flexibility and better classification performances than the KFD. The posterior distribution of the BKLD model is elegantly approximated by a tractable Gaussian form using variational transformation and Jensen’s inequality, which allow a straightforward computation of the weights. An extensive comparison of the BKLD to the KFD and to other state-of-the-art non-linear classifiers is performed