Proliferation of technologies such as IoT, Smartphone, and cheaper internet connectivity has increased the threat perceptions due to DDoS attacks. DDoS attacks have achieved a sophistication level for they hardly get detected in the traditional network scenario. Software Defined Networking is emerging and brags of improved security, management and control over the traditional network but are equally prone to disruptions due to DDoS attacks. Machine Learning models can help in identifying malicious traffic flows from genuine network traffic. Their performance relies on extensive training on Intrusion Detection dataset. The major hurdle in training of Machine Learning models is the unavailability of SDN datasets in the public domain. We show an end to end synthesis of SDN dataset having a mix of normal and DDoS traffic. Furthermore, we used three metrics to investigate the influence of feature selection on Machine Learning Classifiers to detect DDoS attacks. Network Security Metrics ideally should be simple, automated, and effective. Considering this, we in this study, present three metrics to not only classify the network traffic but also the analyses of the misclassification, unlike other studies, which talk about classification only. Our results illustrate over 99% accuracy in identifying the DDoS attacks in SDN scenario despite selecting only 12/15 features out of total 45 and comparison of the results with others’ work validates its edge over others. We also present intelligent SDN architecture based on machine learning which can detect DDoS attacks in the live network traffic.

A total of 1449 tuberculosis (TB) suspected samples were collected from different hospital and/or TB centers of Khyber Pakhtunkhwa. The samples were subjected to fluorescent microscopy (FM), GeneXpert assay (GXA) and liquid culture method (LCM) for the investigation of TB. Out of the total suspected samples, we have got 129 (8.90%), 186 (12.84%) and 200 (13.80%) TB positive through FM, GXA and LCM respectively. The LCM positive culture were subjected to AgMPT64 based immunochromatographic assay (ICA), and 193 (96.5%) samples were further confirmed as TB culprit, while 7 (3.5%) samples showed unexpected negative results. Interestingly, these 7 negative samples were positive on FM, GXA and LCM. Thus it was decided to reveal the mystery of these 7 samples at genetic level. As ICA is based on AgMPT64 monoclonal antibody therefore, MPT64/Rv1980c gene was sequenced using NGS technology to disclose the hidden story of variation in the gene. The results showed a paradigm shift, as among 14 samples (7 ICA negative and 7 ICA positive), 13 samples were identical to the reference sequence while one ICA positive sample showed a C477A point mutation, resulted in amino acid alteration at F159L. Thus, it is concluded that the performance of ICA influenced by MPT64 gene polymorphism depends whether the alteration at genetic level is leading to any significant alteration in structure topology of antigen or not that eventually affecting its biological function. Moreover, the conservation of MPT64 gene is encouraging towards its protein product (AgMPT64) to be considered as a potent vaccine candidate.

An improved model and its computer program for Drainage, Evaporation and Runoff (DEaR) from bare soils are presented. The model, which is the successor of the model originally known as E-DiGOR, adequately represents the physical processes important in estimating actual soil evaporation, soil water storage, direct surface runoff, infiltrated rainfall, and subsurface flow. The model is useful for quantifying these components of soil water balance with a few parameters, and for the descriptions and predictions of the past, present and future dynamics if climate data are available. Although physical credibility of the model is quite high, a consistent set of values suitable for the calculations are required. The input variables of the computer program are climate data (sunshine duration, air-temperature, relative humidity, wind speed, and precipitation) and environmental data/soil properties (albedo, psychrometric constant, latitude, fraction of radiation, height for resistance, tortuosity, average diffusivity for drying soil, volumetric water content at field capacity, air-dry water content, threshold potential, reset threshold, profile depth, initial water content of the profile, porosity, slope gradient, and ratio of saturated hydraulic conductivity to maximum rainfall intensity) to account for specific soil-climate combinations. The computer program developed is a useful tool for a fast and precise simulation, and consists of three modules. The computer program has been developed as a MATLAB™ application. The software can be obtained upon request.

In this paper an approach for a comparative evaluation of different options for energy system development in small country’s specific context is studied. Considerations address mainly small countries without nuclear power interested in nuclear energy as an option for the future energy development as well as small countries, users of nuclear technology.\nThe performed evaluation is based on the methodology developed in the frame of the IAEA International Project on Innovative Nuclear Reactors and Fuel Cycles and its Collaborative Project “Key indicators for innovative nuclear energy systems” (KIND).\nThe presented analysis is performed using available public information for small countries of the Balkan region such as Macedonia and Bulgaria as reference countries and the experts’ judgment of Key Indicators for different energy systems development. \nA comparative evaluation of two hypothetical energy options has been performed: \n-Nuclear energy option and\n-Non-nuclear energy option \nThe comparison has been done by means of an evaluation tool (KIND – ET tool) based on Multi-Attribute Value Function method. The philosophy of the methodology is briefly presented. The results of the comparative evaluation have been presented and interpreted. This study could be used to support decision makers in energy policy at national level, demonstrating the application of methodology

Pheochromocytomas are neuroendocrine tumors developed at the expense of cells derived from the neural crest. They occur sporadically in 50–55% of cases, but they can be familial in 5 to 10% of cases and then be isolated or integrated into an inherited syndrome of multiple neuroendocrinopathy. The association of von Recklinghausen\'s disease and a pheochromocytoma is found in 10% of cases. The properly operated pheochromocytoma is radically and definitively cured as long as it is benign and sporadic. We report through an observation the case of a pheochromocytoma observed in the context of a type 1 neurofibromatosis of Von Recklinghausen. 43 year old patient with a family history of NF1 (the mother) consults for hypertension with sweating, palpitations, evoking a pheochromocytoma. It presents “cafe au lait” spots, axillary lentigines, Lisch nodules and disseminated cutaneous neurofibromas, the largest of which measures 30 cm Biology finds a significant rise in methoxylated derivatives: normetanephrines = 2550nmol / 24 h (44–213), metanephrines: 1005 nmol / 24 h (40–228) and chromagranin A: 900 ng / mL (20–115). The thoraco – abdomo – pelvic CT finds a right adrenal mass of 62 × 60 × 45 mm, oval and well limited, discreetly hypodense, increasing heterogeneously after injection of the contrast agent. MIBG scintigraphy favored a neuro-ectodermal process. In addition, he had neurofibromas at the posterior mediastinal, pleural level. The cerebral CT was normal. Doppler ultrasound of the renal arteries did not show stenosis. This patient is referred to surgery for excision of pheochromocytoma and bulky neurofibroma with anatomopathology study.

Water is vital to humans and other life forms. The increase in human population and their developmental activities increases the demand for water. Water is becoming a more valuable and scarce commodity nowadays. The general health and life expectancy of the public is much affected due to lack of clean drinking water in many developing and under-developing countries of the world. In irrigation, poor water quality affects the physical conditions of the soil and the yield of a crop. Since the dependence on groundwater has increased tremendously in India due to monsoonal effect and shortage of surface water. The rainfall data from various rain gauge stations in the study area for ten years from 2006 to 2015 for thirteen locations were obtained from the public works department. It was categorized in the form of monthly, yearly and seasonally and it was analysed spatially using GIS. In the study area, the small amount of precipitation was observed in the year 2008. The maximum precipitation was noticed in the year 2015. The rainfall data spatial distribution maps reveal that the Erode area received the highest rainfall (1060.66 mm) whereas Chennimalai area received only 709 mm. spatially, the central part of the study area falls in high rainfall zone.