Routing Protocols for Underwater Wireless Sensor Networks: Taxonomy, Research Challenges, Routing Strategies and Future Directions.

Recent research in underwater wireless sensor networks (UWSNs) has gained the attention of researchers in academia and industry for a number of applications. They include disaster and earthquake prediction, water quality and environment monitoring, leakage and mine detection, military surveillance and underwater navigation. However, the aquatic medium is associated with a number of limitations and challenges: long multipath delay, high interference and noise, harsh environment, low bandwidth and limited battery life of the sensor nodes. These challenges demand research techniques and strategies to be overcome in an efficient and effective fashion. The design of routing protocols for UWSNs is one of the promising solutions to cope with these challenges. This paper presents a survey of the routing protocols for UWSNs. For the ease of description, the addressed routing protocols are classified into two groups: localization-based and localization-free protocols. These groups are further subdivided according to the problems they address or the major parameters they consider during routing. Unlike the existing surveys, this survey considers only the latest and state-of-the-art routing protocols. In addition, every protocol is described in terms of its routing strategy and the problem it addresses and solves. The merit(s) of each protocol is (are) highlighted along with the cost. A description of the protocols in this fashion has a number of advantages for researchers, as compared to the existing surveys. Firstly, the description of the routing strategy of each protocol makes its routing operation easily understandable. Secondly, the demerit(s) of a protocol provides (provide) insight into overcoming its flaw(s) in future investigation. This, in turn, leads to the foundation of new protocols that are more intelligent, robust and efficient with respect to the desired parameters. Thirdly, a protocol can be selected for the appropriate application based on its described merit(s). Finally, open challenges and research directions are presented for future investigation.

Pharmacological rational of dry ripe fruit of Aegle marmelos L. as an anti-nociceptive agent in different painful conditions.

The aim of study is to investigate central and peripheral analgesic effects of methanolic extract of dry ripe fruit of Aegle marmelos Linn. Corea (Am. Cr) by two methods, tail flick test and acetic acid induced writhing test at 100, 250 and 500mg/kg doses in animal models. Analgesic activity against tail flick test revealed that Am. Cr induced significant increase in latency period in dose dependent manner i.e. 65.38% at 100mg/kg, 395.37% at 250mg/kg (p<0.01) and 459.25% at 500mg/kg (p<0.01) body weight at 1hr after drug delivery while at 2hr effect decreased i.e. 61.53% at 100mg/kg, 161.11% (p<0.01) at 250mg/kg and 165.74% (p<0.01) at 500mg/kg but interestingly again there is an elongation in latency period at 3hr i.e. 106.15% at 100mg/kg dose, 251.85% (p<0.01) at 250mg/kg and 293.51% (p<0.05) at 500mg/kg respectively. The standard drug Diclofenac sodium at the dose of 5mg/kg continuously increased the latency period but less significantly as compared to the test substance i.e. 79.43%, 113.08% and 222.42% (p<0.05) respectively. Acetic acid induced writhing test produced highest significant activity at the dose of 100mg/kg i.e. 89.83% (p<0.01) as compared to Diclofenic sodium (standard drug) at a dose of 5mg/kg body weight i.e 63.63% (p<0.01). It is concluded that dry ripe fruit of A. marmelos possesses significant dual analgesic activities i.e. central and peripheral.

Direct mapping from PET coincidence data to proton-dose and positron activity using a deep learning approach.

Objective. Obtaining the intrinsic dose distributions in particle therapy is a challenging problem that needs to be addressed by imaging algorithms to take advantage of secondary particle detectors. In this work, we investigate the utility of deep learning methods for achieving direct mapping from detector data to the intrinsic dose distribution.Approach. We performed Monte Carlo simulations using GATE/Geant4 10.4 simulation toolkits to generate a dataset using human CT phantom irradiated with high-energy protons and imaged with compact in-beam PET for realistic beam delivery in a single-fraction (∼2 Gy). We developed a neural network model based on conditional generative adversarial networks to generate dose maps conditioned on coincidence distributions in the detector. The model performance is evaluated by the mean relative error, absolute dose fraction difference, and shift in Bragg peak position.Main results. The relative deviation in the dose and range of the distributions predicted by the model from the true values for mono-energetic irradiation between 50 and 122 MeV lie within 1% and 2%, respectively. This was achieved using 105coincidences acquired five minutes after irradiation. The relative deviation in the dose and range for spread-out Bragg peak distributions were within 1% and 2.6% uncertainties, respectively.Significance. An important aspect of this study is the demonstration of a method for direct mapping from detector counts to dose domain using the low count data of compact detectors suited for practical implementation in particle therapy. Including additional prior information in the future can further expand the scope of our model and also extend its application to other areas of medical imaging.