EETP-MAC: energy efficient traffic prioritization for medium access control in wireless body area networks

Wireless body area network (WBAN) has witnessed significant attentions in the healthcare domain using biomedical sensor-based monitoring of heterogeneous nature of vital signs of a patient’s body. The design of frequency band, MAC superframe structure, and slots allocation to the heterogeneous nature of the patient’s packets have become the challenging problems in WBAN due to the diverse QoS requirements. In this context, this paper proposes an Energy Efficient Traffic Prioritization for Medium Access Control (EETP-MAC) protocol, which provides sufficient slots with higher bandwidth and guard bands to avoid channels interference causing longer delay. Specifically, the design of EETP-MAC is broadly divided in to four folds. Firstly, patient data traffic prioritization is presented with broad categorization including Non-Constrained Data (NCD), Delay-Constrained Data (DCD), Reliability-Constrained Data (RCD) and Critical Data (CD). Secondly, a modified superframe structure design is proposed for effectively handling the traffic prioritization. Thirdly, threshold based slot allocation technique is developed to reduce contention by effectively quantifying criticality on patient data. Forth, an energy efficient frame design is presented focusing on beacon interval, superframe duration, and packet size and inactive period. Simulations are performed to comparatively evaluate the performance of the proposed EETP-MAC with the state-of-the-art MAC protocols. The comparative evaluation attests the benefit of EETP-MAC in terms of efficient slot allocation resulting in lower delay and energy consumption.

     

Automated Patient Discomfort Detection Using Deep Learning

The Internet of Things (IoT) has been transformed almost all fields of life, but its impact on the healthcare sector has been notable. Various IoT-based sensors are used in the healthcare sector and offer quality and safe care to patients. This work presents a deep learning-based automated patient discomfort detection system in which patients’ discomfort is non-invasively detected. To do this, the overhead view patients’ data set has been recorded. For testing and evaluation purposes, we investigate the power of deep learning by choosing a Convolution Neural Network (CNN) based model. The model uses confidence maps and detects 18 different key points at various locations of the body of the patient. Applying association rules and part affinity fields, the detected key points are later converted into six main body organs. Furthermore, the distance of subsequent key points is measured using coordinates information. Finally, distance and the time-based threshold are used for the classification of movements associated with discomfort or normal conditions. The accuracy of the proposed system is assessed on various test sequences. The experimental outcomes reveal the worth of the proposed system’ by obtaining a True Positive Rate of 98% with a 2% False Positive Rate.     

Learning curve analysis of concentrated photovoltaic systems

Price declines and volume growth of concentrated photovoltaic (CPV) systems are analysed using the learning curve methodology and compared with other forms of solar electricity generation. Logarithmic regression analysis determines a learning rate of 18% for CPV systems with 90% confidence of that rate being between 14 and 22%, which is higher than the learning rates of other solar generation systems (11% for CSP and 12 to 14% for PV). Current CPV system prices are competitive with PV and CSP, which, when combined with the higher learning rate, indicates that CPV is likely to further improve its marketability. A target price of 1 $/W in 2020 could be achieved with a compound growth rate of 67% for the total deployed volume between 2014 and 2020, which would realize a cumulative deployed volume of 7900 MW. Other projections of deployment volumes from commercial sources are converted using the learning rate into future price scenarios, resulting in predicted prices in the range of 1.1 to 1.3 $/W in 2020. © 2014 The Authors. Progress in Photovoltaics: Research and Applications published by John Wiley & Sons Ltd.     

Stabilization of the Reductase Domain in the Catalytically Self‐Sufficient Cytochrome P450BM3 by Consensus‐Guided Mutagenesis

The multidomain, catalytically self‐sufficient cytochrome P450 BM‐3 from Bacillus megaterium (P450_BM3) constitutes a versatile enzyme for the oxyfunctionalization of organic molecules and natural products. However, the limited stability of the diflavin reductase domain limits the utility of this enzyme for synthetic applications. In this work, a consensus‐guided mutagenesis approach was applied to enhance the thermal stability of the reductase domain of P450_BM3. Upon phylogenetic analysis of a set of distantly related P450s (>38 % identity), a total of 14 amino acid substitutions were identified and evaluated in terms of their stabilizing effects relative to the wild‐type reductase domain. Recombination of the six most stabilizing mutations generated two thermostable variants featuring up to tenfold longer half‐lives at 50 °C and increased catalytic performance at elevated temperatures. Further characterization of the engineered P450_BM3 variants indicated that the introduced mutations increased the thermal stability of the FAD‐binding domain and that the optimal temperature (T_opt) of the enzyme had shifted from 25 to 40 °C. This work demonstrates the effectiveness of consensus mutagenesis for enhancing the stability of the reductase component of a multidomain P450. The stabilized P450_BM3 variants developed here could potentially provide more robust scaffolds for the engineering of oxidation biocatalysts.     

Non-oxidative Decomposition of CH4 Over CeO2 and CeO2–SiO2 Supported Bimetallic Ni–Mo Catalysts

Catalysis Letters - The development of highly active and durable catalysts for H2 production through CH4 decomposition process is still a great challenge. In this study, CeO2 and CeO2–SiO2...     

Impact of graphene oxide nanoparticles and carbon black on the gamma radiation sensitization of acrylonitrile–butadiene rubber seal materials

Seals prepared from acrylonitrile–butadiene rubber (NBR) are primarily used in nuclear services. Nevertheless, at relatively high ionizing radiation doses, NBR seal materials may undergo radiation-induced degradation processes, leading to adverse effects on the sealing ability life. Herein, to strengthen the functional characteristics of NBR seals against radiation, graphene oxide (GO) nanoparticles were prepared and characterized by transmission electron microscopy, X-ray diffraction (XRD), Fourier transform infrared (FTIR), and ultraviolet/visible spectroscopies. Various NBR/GO composites fabricated with different ratios of GO nanoparticles and in the presence or absence of carbon black (CB) were investigated via cross-linking density, scanning electron microscopy, XRD, FTIR, and mechanical and thermal stability analyses. The synergistic effect of the simultaneous presence of GO and CB on the NBR seal sensitization to gamma radiation up to a dose of 1 MGy was studied. The physicomechanical properties were enhanced by adding GO nanosheets up to 3 phr and by incorporating 35 phr of a CB with GO until 5 phr. Further, the application of γ-irradiation resulted in an overall enhancement in the mechanical, physical, and thermal stability of the prepared composites up to 0.5 and 1 MGy with GO nanosheets in the absence or presence of CB particles, respectively. The mechanical measurements indicated significant increments by loading with GO nanosheets in the absence and presence of CB as well as by irradiation. The tensile strength elevated up to about 121%, 336%, and 366% by adding 3 phr GO, 3 GO:35 CB phr, and 5 GO:35 CB phr, respectively.     

Enhancement of glass-ceramic performance by TiO2 doping: In vitro cell viability,proliferation, and differentiation

A new TiO₂-containing bioactive glass and glass-ceramics based on 50SiO₂-(45-X)CaO-(XTiO₂)-5P₂O₅ system was designed using a sol–gel technique (where X = 5, 7.5 and 10 wt %). The roles of the crystallization behavior and physicochemical characteristics of the designed glass and glass-ceramics which were played in the introduction of TiO₂ substitutions were investigated. Moreover, cell proliferation and differentiation were evaluated against human osteosarcoma cells (Saos-2). The TiO₂/CaO replacements led to the formation of a stronger glass structure and thus increased thermal parameters and the chemical stabilization of the designed materials. The FTIR data confirmed the existence of Ti within the glass and glass-ceramics samples, and no remarkable effect on their chemical integrity was observed. The XRD patterns indicated that calcium-containing minerals, including Ca₂SiO₄,Ca₃(PO₄)₂, Ca(Ti,Si)O₅, CaTiSiO₅, and Ca₁₅(PO₄)₂·(SiO₄)₆ phases were developed as a role of structure/texture under the applied heat-treatment. The results of the cytotoxicity test proved that a safe sample dose is 12–50 μg/ml, at which cell viability is ≥ 85%. The cell differentiation determined by ALP test proved the superiority of glass-ceramics compared with their native glasses. Therefore, the obtained materials could be safely used as novel biocompatible materials for the regeneration of bone tissue.     

Performance of ultrasonic – Treated nano-zeolites employed in the preparation of dimethyl ether

Catalytic dehydration of methanol to dimethyl ether was carried out over nano-zeolites and their modified samples via ultrasonic technique. Parent H-Beta and Parent H-Mordenite zeolites are used as synthesized and after sonication. H-Mordenite sonicated for 20 and 120 min whereas H-Beta sonicated for 20 min only. The reaction temperature was varied between 100 and 225 °C at three different contact times. The different catalyst samples were characterized using: XRD, FTIR, TEM, SEM and NH₃-TPD techniques. The results revealed that sonication of parent zeolite samples affects on the unit cell dimensions and their crystal size. FTIR-spectroscopic analysis indicated that sonication may decrease the pore opening and cause framework structure defects. TEM and SEM micrographs showed that sonication broke-up and re-ordered zeolite crystals with longer time resulted in a different morphology relative to parents and also change the particle size. Sonicated samples have a good performance in methanol dehydration with complete conversion and complete selectivity to dimethyl ether at lower temperature relative to the corresponding parent zeolites. TPD results indicated that the concentration of strong acid sites decreased in sonicated H-Mordenite samples leading to an increase of their catalytic activity and the selectivity to DME. On the other hand, ultrasonic treatment of H-Beta sample decreased its catalytic activity.     

Novel algorithms for the network lifetime problem in wireless settings

One of the major concerns in wireless ad-hoc networks design is energy efficiency. Wireless devices are typically equipped with a limited energy supply sufficient only for a limited amount of time which is reversely proportional to the transmission power of the device. The network lifetime is defined as the time the first device runs out of its initial energy charge. In this paper we study the maximum network lifetime problem for broadcast and data gathering in wireless settings. We provide polynomial time approximation algorithms, with guaranteed performance bounds while considering omnidirectional and unidirectional transmissions. We also consider an extended variant of the maximum lifetime problem, which simultaneously satisfies additional constraints, such as bounded hop-diameter and degree of the routing tree, and minimizing the total energy used in a single transmission. Finally, we evaluate the performance of some of our algorithms through simulations.