A distributed,multi‐hop,adaptive, tree‐based energy‐balanced routing approach

To accomplish the primary objective of data sensing and collection of wireless sensor networks (WSN), the design of an energy efficient routing algorithm is very important. However, the energy constrained sensing nodes along with the intrinsic properties of the (WSN) environment makes the routing a challenging task. To overcome this routing dilemma, an improved distributed, multi‐hop, adaptive, tree‐based energy‐balanced (DMATEB) routing scheme is proposed in this paper. In this scheme, a relay node is selected in view of minimum distance and high energy from a current sensing node. Further, the parent node is chosen among the selected relay nodes on the basis of high residual energy and less power consumption with due consideration of its associated child nodes. As each sensing node itself selects its parent among the available alternatives, the proposed scheme offers a distributive and adaptive approach. Moreover, the proposed system does not overload any selected parent of a particular branch as it starts acting as a child whenever its energy lowers among the other available relay nodes. This leads to uniform energy utilization of nodes that offers a better energy balance mechanism and improves the network lifespan by 20% to 30% as compared with its predecessors.     

Study of trust‐based mechanism and its component model in MANET: Current research state,issues, and future recommendation

Security against routing attacks in MANET is most critical issue and traditional concepts of cryptography, authentication, or Watchdog are not that significant in mitigation of these attacks. In recent years, trust‐based approach provides a reputation system that compose of different components to provide security against routing attacks. In this paper, we provide an in‐depth analysis of various components that are used in trust‐based mechanism and techniques deployed in effective and efficient execution of task by the components. Trust‐based mechanisms generally have five components: monitoring and information gathering, trust calculation and evaluation unit, trust recommendation unit, decision‐making and dissemination of detection unit. All or some of these units cooperate together to provide a reliable communication environment with the aim to prevent routing attacks from participating in route formation process and detect attacking nodes simultaneously. Different routing attacks and measures in trust‐based mechanism along with some published work are discussed to understand the implementation of this type of mechanism. Various related issues are explored, discussed, and recommendations are pointed out for future research work in this field.     

Behavioral and performance analysis of feeding system using stochastic reward nets

This paper presents an approach based on Petri nets for studying the behavior of a real industrial system and evaluating its performance. Stochastic reward nets, a class of high level Petri nets, have been used to model dynamic interaction among active or standby equipment in the system, and the performance parameters have been evaluated using Markovian approach. The performance analysis of the system in long-run conditions is carried out using the data related to equipment’s operational behavior of a real industrial system. The methodology adopted in this paper provides a better understanding of the behavior of the system under varying operating conditions. Reliability analysis of a feeding system of a paper industry presented in this paper will help the management in deciding upon the maintenance strategy to be adopted to improve the performance of the system and consequently reduce the operation and maintenance cost.     

Epidemiology and transmission of V. cholerae O1 and V. cholerae O139 infections in Delhi in 1993

In 1993, rectal swabs from clinically suspected cases of cholera admitted to the Infectious Diseases Hospital (IDH), Delhi were examined for Vibrio cholerae O1 and O139. Epidemiological data of 396 cholera cases were collected before the patients' discharge from IDH. Of the 1528 laboratory-confirmed cholera cases, 46% and 54% were caused by serotype O1 and O139 respectively. Both serotypes appeared and disappeared simultaneously, and peaked during the same time of the year. However, the two serotypes affected persons of different age groups; about 65% of the O1 cases occurred in children aged less than 10 years, whereas this age group accounted for 40% of the cases due to V. cholerae O139. Although there were some focal outbreaks due to serotype O139, both serotypes had almost similar geographical distributions. Important risk factors for transmission of cholera were almost equally prevalent in the majority of both types of cholera cases. Since the seasonality, geographical distribution, and risk factors for transmission were similar for both serotypes, the study indicates that the preventive and control measures are also likely to be similar. The study also shows that the emergence of V. cholerae O139 in 1993 did not affect the incidence, seasonality, and epidemiology of endemic V. cholerae O1 E1 Tor strains in Delhi.     

Impact of fortification with iron salts and vitamin A on the physicochemical properties of laboratory‐pasteurised toned milk and bioaccessibility of the added nutrients

The sensory and physicochemical attributes of pasteurised milk fortified with soluble ferric pyrophosphate (FPP) (25 mg/L iron) and vitamin A (2500 IU) were comparable to unfortified control samples. The heat coagulation time (HCT) of fortified milks was comparable to the control and exhibited a type A curve with HCT–pH maxima at the acidic side of the natural pH. The distribution pattern of iron in different milk fractions was similar in control and fortified milk samples. The bioaccessibility of iron, estimated through in vitro digestibility of milk fortified with vitamin A and iron, was found to be slightly higher than that of milk fortified with iron alone.     

Enc-Unet: A novel method for Glioma segmentation

The diagnosis' treatment planning, follow-up and prognostication of Gliomas is significantly enhanced on Magnetic Resonance Imaging. In the present research, deep learning-based variant of convolutional neural network methodology is proposed for glioma segmentation where pretrained autoencoder acts as backbone to the 3D-Unet which performs the segmentation task as well as image restoration. Further, Unet accepts input as the combination of three non-native MR images (T2, T1CE, and FLAIR) to extract maximum and superior features for segmenting tumor regions. Further, weighted dice loss employed, focusses on segregating tumor region into three regions of interest namely whole tumor with oedema (WT), enhancing tumor (ET), and tumor core (TC). The optimizer preferred in the proposed methodology is Adam and the learning rate is initially set to $1e-4$, progressively reduced by a cosine decay after 50 epochs. The learning parameters are reduced to a larger extent (up to 9.8 M as compared to 27 M). The experimental results show that the proposed model achieved Dice similarity coefficients: 0.77, 0.92, and 0.84; sensitivity: 0.90, 0.95, and 0.89; specificity: 0.97, 0.99, and 0.99; Hausdorff95: 5.74, 4.89, and 6.00, in the three regions including ET, WT, TC. This proposed Glioma segmentation method is efficient for segregation of tumors.