Multi‐hop medium access control protocol for low energy critical infrastructure monitoring networks using wake‐up radio

The IEEE 802.15.4K Task Group was formed recently to address the low energy critical infrastructure monitoring networks. The aim is to collect scheduled and event data from a large number of non‐mains powered endpoints that are widely dispersed. The application requirements include reliable data transfer, energy efficiency, and long deployment lifetime. To meet the low energy critical infrastructure monitoring network requirements, we propose a multihop medium access control protocol where the scheduled or event data are routed to the coordinator through the cluster heads. The power consumption of the cluster heads is critical as they use more power than the normal endpoints. Our protocol uses the wake‐up radio approach from cluster head to cluster head communication and an efficient guaranteed time slots allocation scheme to minimize the power consumption of the cluster heads. We derive analytical expressions for the average power consumption of cluster heads as well as ordinary endpoints. The results show that our proposed protocol outperforms the IEEE 802.15.4 MAC and SCP MAC in terms of power consumption. High power efficiency is achieved in both the cluster heads and normal endpoints. Copyright © 2012 John Wiley & Sons, Ltd.     

A MAC protocol for the mixed scenario of omnidirectional and directional nodes in an ad hoc network

In ad hoc networks, directional antennas offer many benefits compared with classical omnidirectional antennas. Those include significant increases in spatial reuse, coverage range, and subsequently network capacity as a whole. Most of the proposed directional medium access control methods assume that all the nodes in the network have homogeneous antenna types. However, it is quite unusual that a sudden change will occur, and every laptop, palmtop, computer, and other similar device will be turned into a directional antenna holding device. There will always be a possibility of heterogeneous antenna used in the deployed nodes of the network. In this paper, we have proposed a medium access control protocol that deals with the new challenges introduced into heterogeneous networks in the form of deaf and hidden node problems. Using Optimized Network Engineering Tools 16.0, we have simulated three ad hoc network scenarios. These scenarios have different ratios of omnidirectional and directional nodes. We evaluated the performance of these scenarios by varying the ratio. We found that when we increase the directional nodes in the network, the performance increases. Copyright © 2013 John Wiley & Sons, Ltd.     

Modeling and percept of transcorneal electrical stimulation in humans

Retinal activation via transcorneal electrical stimulation (TcES) in normal humans was investigated by comparing subject perception, model predictions, and brain activation patterns. The preferential location of retinal stimulation was predicted from 3-D admittance modeling. Visual cortex activation was measured using positron emission tomography (PET) and (18)F-fluorodeoxyglucose (FDG). Two different corneal electrodes were investigated: DTL-Plus and ERG-Jet. Modeling results predicted preferential stimulation of the peripheral, inferior, nasal retina during right eye TcES using DTL-Plus, but more extensive activation of peripheral, nasal hemiretina using ERG-Jet. The results from human FDG PET study using both corneal electrodes showed areas of visual cortex activation that consistently corresponded with the reported phosphene percept and modeling predictions. ERG-Jet was able to generate brighter phosphene percept than DTL-Plus and elicited retinotopically mapped primary visual cortex activation. This study demonstrates that admittance modeling and PET imaging consistently predict the perceived location of electrically elicited phosphenes produced during TcES.     

Properly aligned band structures in B-TiO2/MIL53(Fe)/g-C3N4 ternary nanocomposite can drastically improve its photocatalytic activity for H2 evolution: Investigations based on the experimental results

The development of new tools that could meet the demand of sustainable energy production has attracted worldwide scientific attention. Over the past few decades, significant research efforts have been carried out to efficiently reduce water to H₂ (green fuel) over semiconductor photocatalysts. Numerous semiconductor photocatalysts have been employed in photocatalysis for optimum H₂ production. All the techniques were chosen based on their flexibility, cost-effectiveness, and ease of availability. Recently, polymeric carbon nitride (g-C₃N₄) received worldwide attention in visible light photocatalysis for energy and environmental applications due to its low price, robust nature, and superior thermal stability. Nevertheless, g-C₃N₄ (CN) exhibits shortfalls such as high charge carrier's recombination rate and weak reduction ability. To overcome these drawbacks, herein, for the first time we have fabricated B-TiO₂/MIL-53(Fe)/CN ternary composite via hydrothermal and wet-chemical methods. The resultant B-TiO₂/MIL53(Fe)/CN ternary composite shows drastically improved photocatalytic activity for hydrogen evolution compared to the bare CN, B-TiO_2, and MIL53(Fe) components. The B-TiO₂/MIL53(Fe)/CN ternary composite produced approximately 166.3 and 581.2 μmol h^?1 g^?1 of hydrogen under visible light and UV–visible light irradiations, respectively, with the assistance of co-catalyst Pt. Photo-luminescence (PL) and the fluorescence (FL) spectroscopy measurements reveal that the enhanced photoactivity is due to the greatly promoted charge carrier's separation and transfer at the interfacial contact of the well-aligned three-component systems. This work will promote the design and development of efficient photocatalyst based on CN for clean energy production and environmental purification.     

Calculation of bright roof-tops for solar PV applications in Dhaka Megacity,Bangladesh

Bangladesh has already been known as the country of power crisis. Although the country's electricity generation capacity is 4275 MW, around 3000–3500 MW of electricity can be generated against the demand of more than 5000 MW. The country's power is being generated mostly with conventional fuel (82% indigenous natural gas, 9% imported oil, 5% coal) and renewable sources (4% hydropower and solar). But recently a remarkable decline of the indigenous gas takes place, which rapidly aggravates electricity generation. Dhaka, the capital as well as prime city of the country with its nearly 14 million populations faces the worst situation due to the shortfall of electricity. Around 1000–1200 MW of electricity is supplied to Dhaka Megacity, while the existing demand is nearly 2000 MW. As a result frequent load shedding takes place and most of the service sectors in the city are interrupted, which has recently created immense dissatisfaction among the city-dwellers. Given the city's power crisis and geophysical situations, applications of either stand-alone or grid connected PV systems would be very effective and pragmatic for power supplement. The conservative calculation of bright roof-tops from the Quickbird Scene 2006 of Dhaka city indicates that the city offers 10.554 km² of bright roof-tops within the Dhaka City Corporation (DCC) ward area (134.282 km²). The application of stand-alone PV systems with 75 Wp solar modules can generate nearly 1000 MW of electrical power, which can substantially meet the city's power demand.     

Residue analysis of picoxystrobin in oriental melon using gas chromatography coupled with electron capture detection and mass spectrometric confirmation: application to dissipation kinetics and risk assessment

Food Science and Biotechnology - This study was carried out to determine the residual amounts of picoxystrobin in oriental melon (Cucumis melo L.) grown under plastic house conditions at two...     

Decline pattern and risk assessment of cyenopyrafen in different varieties of Asian pear using liquid chromatography and tandem mass spectrometry

Food Science and Biotechnology - The dissipation pattern of a commercial cyenopyrafen formulation sprayed at the recommended dose on Asian pears (two different species) grown at two different sites...     

Prediction of COVID-19 Cases Using Machine Learning for Effective Public Health Management

COVID-19 is a pandemic that has affected nearly every country in the world. At present, sustainable development in the area of public health is considered vital to securing a promising and prosperous future for humans. However, widespread diseases, such as COVID-19, create numerous challenges to this goal, and some of those challenges are not yet defined. In this study, a Shallow Single-Layer Perceptron Neural Network (SSLPNN) and Gaussian Process Regression (GPR) model were used for the classification and prediction of confirmed COVID-19 cases in five geographically distributed regions of Asia with diverse settings and environmental conditions: namely, China, South Korea, Japan, Saudi Arabia, and Pakistan. Significant environmental and non-environmental features were taken as the input dataset, and confirmed COVID-19 cases were taken as the output dataset. A correlation analysis was done to identify patterns in the cases related to fluctuations in the associated variables. The results of this study established that the population and air quality index of a region had a statistically significant influence on the cases. However, age and the human development index had a negative influence on the cases. The proposed SSLPNN-based classification model performed well when predicting the classes of confirmed cases. During training, the binary classification model was highly accurate, with a Root Mean Square Error (RMSE) of 0.91. Likewise, the results of the regression analysis using the GPR technique with Matern 5/2 were highly accurate (RMSE = 0.95239) when predicting the number of confirmed COVID-19 cases in an area. However, dynamic management has occupied a core place in studies on the sustainable development of public health but dynamic management depends on proactive strategies based on statistically verified approaches, like Artificial Intelligence (AI). In this study, an SSLPNN model has been trained to fit public health associated data into an appropriate class, allowing GPR to predict the number of confirmed COVID-19 cases in an area based on the given values of selected parameters. Therefore, this tool can help authorities in different ecological settings effectively manage COVID-19.     

Antibacterial Role of SO(4), NO(3), C(2)O(4) and CH(3)CO(2) Anions on Cu(II) and Zn(II) Complexes of a Thiadiazole-derived Pyrrolyl Schiff Base

A condensation reaction of 2-amino-1,3,4-thiadiazole with 2-pyrrolecarboxaldehyde to form tridentate NNN donor Schiff base has been performed. The prepared Schiff base was further used for the formation of metal complexes having stoichiometry [M(L)(2)]X(n), where M=Cu(II) or Zn(II), L=N-(2-pyrrolylmethylene)-2-amino-1,3,4-thiadiazole, X=SO(4) (2-), NO(3) (-), C(2)O(4) (2-) or CH(3)CO(2-) and n=1 or 2. The new compounds described here have been characterized by their physical, spectral and analytical data, and have been screened against several bacterial strains such as Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa. The antibacterial potency of the Schiff base increased upon chelation/complexation, having the same metal ion (cation) but different anions opening up a novel approach in finding new ways to fight against antibiotic resistant strains.     

Nuclear spatial and spectral features based evolutionary method for meningioma subtypes classification in histopathology

Meningioma subtypes classification is a real‐world multiclass problem from the realm of neuropathology. The major challenge in solving this problem is the inherent complexity due to high intra‐class variability and low inter‐class variation in tissue samples. The development of computational methods to assist pathologists in characterization of these tissue samples would have great diagnostic and prognostic value. In this article, we proposed an optimized evolutionary framework for the classification of benign meningioma into four subtypes. This framework investigates the imperative role of RGB color channels for discrimination of tumor subtypes and compute structural, statistical and spectral phenotypes. An evolutionary technique, Genetic Algorithm, in combination with Support Vector Machine is applied to tune classifier parameters and to select the best possible combination of extracted phenotypes that improved the classification accuracy (94.88%) on meningioma histology dataset, provided by the Institute of Neuropathology, Bielefeld. These statistics show that computational framework can robustly discriminate four subtypes of benign meningioma and may aid pathologists in the diagnosis and classification of these lesions.