Fuzzy Logic-based Trusted and Power-aware Routing Protocol in Mobile Ad-hoc Networks

Mobile ad-hoc networks (MANETs) have attracted much attention from researchers lately because MANETs are able to provide networks in areas with unavailable fixed network infrastructure. However, some mobile nodes may misbehave by dropping packets to conserve power usage because mobile ad-hoc networks nodes are usually battery operated. In this paper, a fuzzy logic-based routing protocol that considers the battery level of nodes, hop count, and trust among the nodes is proposed. The proposed routing protocol adaptively selects routes that use minimum hop count with the highest level of trust and a sufficient battery level to enhance the reliability of route selection while maintaining the percentage of successfully delivered packets. The result of the simulation shows that the proposed protocol can achieve a high ratio of successfully delivered packets, a lower average end-to-end delay, and a normalized routing load

Performance analysis of black hole and worm hole attacks in MANETs

A Mobile Ad Hoc Network MANET is composed of a freely and mobility set of mobile nodes. They form a temporary dynamic wireless network without any infrastructure. Since the nodes act as both host and router in their communication, they act as a router provide connectivity by forwarding data packets among intermediate nodes to the destination. The routing protocol is used to grove their communication and connectivity as example, the Ad On-demand distance vector (AODV) routing protocol. However, due to the lack of security vulnerabilities of routing protocols and the absence of infrastructure, MANET is vulnerable to various security threats and attacks. This paper examines the impact of two types of attacks on AODV routing protocol using Network Simulator version 2 (NS2) environment. These attacks are Blackhole and Wormhole Attacks. The aim of both of them is to prevent data packets to reach the destination node and dropping all the traffic.

Increased myeloperoxidase activity as an indicator of neutrophil-induced inflammation and sepsis in neonates

Background: MPO is an enzyme that contains heme secreted by phagocytic cells after the respiratory burst system activation. MPO is expressed mainly by neutrophils and monocytes in small quantities and it is very important to determine further process of hydrogen peroxide.Objective: to evaluate neutrophils activation and the MPO enzyme activity in plasma as an indicator of sepsis as well as sepsis severity in neonates with sepsis with correlation to their clinical and laboratory findings.Methods: were classified into 2 groups: sepsis group: included 45 neonates with gestational ages 28-40 weeks with sepsis, 15 of them had been subjected to follow up samples, control group: included 30 neonates proved to be free of sepsis. All neonates were subjected to history taking, clinical examination and measurement of plasma MPO enzyme.Results: this study revealed that MPO activity and neutrophil cell count are increased in sepsis group compared to the non-septic neonates. The ROC curve showed that the best cut off for MPO in prediction of septic patients and mortality was found >54 mu/ml and >83 mu/ml respectively. There was positive correlation between MPO enzyme activity and the total leukocyte count and neutrophil count. By following up some of sepsis group neonates there was significant decrease in MPO activity goes along with improvement in clinical state of neonates with sepsis. MPO enzyme activity was found to be low in septic shock patients who also have pancytopenia compared to septic patients without shock.Conclusion: plasma MPO enzyme proved to be a good marker of sepsis in neonates, with a good prognostic value in severe cases.Keywords: MPO, inflammatory response, neonates, sepsi

International survey of Cronobacter sakazakii and other Cronobacter spp. in follow up formulas and infant food

A coordinated survey for Cronobacter and related organisms in powdered infant formula, follow up formula and infant foods was undertaken by 8 laboratories in 7 countries in recognition of and in response to the data needs identified in an FAO/WHO call for data in order to develop global risk management guidance for these products. The products (domestic and imported) were purchased from the local market and were categorised according to their principle ingredients. A total of 290 products were analysed using a standardised procedure of pre-enrichment in 225 ml Buffered Peptone Water (BPW), followed by enrichment in Enterobacteriaceae Enrichment (EE) broth, plating on the chromogenic Cronobacter Druggan–Forsythe–Iversen (DFI) agar and presumptive identification with ID 32 E. Presumptive Cronobacter isolates were identified using 16S rRNA gene sequence analysis. Aerobic plate counts (APC) of the products were also determined on nutrient agar. Fourteen samples had APC > 105 cfu/g, 3 of which contained probiotic cultures. C. sakazakii was isolated from 27 products; 3/91 (3%) follow up formulas (as defined by Codex Alimentarius Commission), and 24/199 (12%) infant foods and drinks. Hence C. sakazakii was less prevalent in follow up formula than other foods given to infants over the same age range. A range of other bacteria were also isolated from follow up formulas, including Acinetobacter baumannii, Enterobacter cloacae, Klebsiella pneumoniae, Citrobacter freundii, and Serratia ficaria. There was significant variation in the reconstitution instructions for follow up formulas. These included using water at temperatures which would enable bacterial growth. Additionally, the definition of follow up formula varied between countries

Henry Gas Solubility Optimization Double Machine Learning Classifier for Neurosurgical Patients

This study aims to predict head trauma outcome for Neurosurgical patients in children, adults, and elderly people. As Machine Learning (ML) algorithms are helpful in healthcare field, a comparative study of various ML techniques is developed. Several algorithms are utilized such as k-nearest neighbor, Random Forest (RF), C4.5, Artificial Neural Network, and Support Vector Machine (SVM). Their performance is assessed using anonymous patients\u27 data. Then, a proposed double classifier based on Henry Gas Solubility Optimization (HGSO) is developed with Aquila optimizer (AQO). It is implemented for feature selection to classify patients\u27 outcome status into four states. Those are mortality, morbidity, improved, or the same. The double classifiers are evaluated via various performance metrics including recall, precision, F-measure, accuracy, and sensitivity. Another contribution of this research is the original use of hybrid technique based on RF-SVM and HGSO to predict patient outcome status with high accuracy. It determines outcome status relationship with age and mode of trauma. The algorithm is tested on more than 1000 anonymous patients\u27 data taken from a Neurosurgical unit of Mansoura International Hospital, Egypt. Experimental results show that the proposed method has the highest accuracy of 99.2% (with population size = 30) compared with other classifiers

A game-based corpus for analysing the interplay between game context and player experience

Recognizing players’ affective state while playing video games has been the focus of many recent research studies. In this paper we describe the process that has been followed to build a corpus based on game events and recorded video sessions from human players while playing Super Mario Bros. We present different types of information that have been extracted from game context, player preferences and perception of the game, as well as user features, automatically extracted from video recordings. We run a number of initial experiments to analyse players’ behavior while playing video games as a case study of the possible use of the corpus.peer-reviewe

A silent gigantic solitary fibrous tumor of the pleura: case report

Solitary fibrous tumor of the pleura is a rare mesenchymal tumor, representing less than 5% of all neoplasms associated with the pleura. A 57-year-old man had general malaise without chest symptoms for 1 month. A chest roentgenogram and computed tomography showed a giant mass in the left thorax. Although the tumor compressed the descending aorta and other mediastinal structures strongly, thereby shifting them to the right side, the patient had no symptoms except malaise. The tumor was successfully resected via two separate thoracotomies. The tumor was measured (20 cm × 19 cm × 15 cm) and weighed (2150 g). The tumor was histologically and immunohistochemically diagnosed as benign. Although SFT is benign, a long follow-up period is essential as even patients with complete resection are at risk of recurrence many years after surgery

Association between celiac sprue and cryopyrin associated autoinflammatory disorders: a case report

Cryopyrin-associated diseases may be characterized by rashes, fever, and sensorineural deafness, while celiac disease may present with symptoms of malabsorption and fatigue. Arthritis is seen in both conditions. We report a young child with histologically diagnosed celiac disease and a cryopyrinopathy

Measurement of νˉμ\bar{\nu}_{\mu} and νμ\nu_{\mu} charged current inclusive cross sections and their ratio with the T2K off-axis near detector

We report a measurement of cross section $\sigma(\nu_{\mu}+{\rm nucleus}\rightarrow\mu^{-}+X)$ and the first measurements of the cross section $\sigma(\bar{\nu}_{\mu}+{\rm nucleus}\rightarrow\mu^{+}+X)$ and their ratio $R(\frac{\sigma(\bar \nu)}{\sigma(\nu)})$ at (anti-)neutrino energies below 1.5 GeV. We determine the single momentum bin cross section measurements, averaged over the T2K $\bar{\nu}/\nu$-flux, for the detector target material (mainly Carbon, Oxygen, Hydrogen and Copper) with phase space restricted laboratory frame kinematics of $\theta_{\mu}$500 MeV/c. The results are $\sigma(\bar{\nu})=\left( 0.900\pm0.029{\rm (stat.)}\pm0.088{\rm (syst.)}\right)\times10^{-39}$ and $\sigma(\nu)=\left( 2.41\ \pm0.022{\rm{(stat.)}}\pm0.231{\rm (syst.)}\ \right)\times10^{-39}$in units of cm$^{2}$/nucleon and$R\left(\frac{\sigma(\bar{\nu})}{\sigma(\nu)}\right)= 0.373\pm0.012{\rm (stat.)}\pm0.015{\rm (syst.)}$.Comment: 18 pages, 8 figure