Prolong network lifetime and improve efficiency in WSN‐UAV systems using new clustering parameters and CSMA modification

Since unmanned aerial vehicles (UAVs) have been introduced as mobile nodes for data gathering, wireless sensor networks (WSNs) have progressed considerably. The resulting WSN‐UAV systems are employed for emergency applications and also for remote monitoring purposes. WSN‐UAV systems yield an optimum data gathering method using the WSN. In the proposed method, the nodes' data are transferred using a remotely operated vehicle (drone) rather than the conventional data transferring methods like the direct and hop‐to‐hop data transmission approaches. Then, the gathered data are delivered in the pre‐determined destination point. WSN‐UAV systems, in fact, are a special case of the systems with the mobile sink in which the sink path is previously specified and controlled. In this paper, the effects of clustering parameters on the WSNs are studied; then, the network's lifetime is prolonged by applying some parameters. In addition, the network's performance is enhanced to some extent by assigning some changes in the media access control (MAC) layer. Also, the effect of drone's path pattern on the lifetime of the network is studied.     

Queue length‐based load balancing in data center networks

Traffic load balancing in data centers is an important requirement. Traffic dynamics and possibilities of changes in the topology (e.g., failures and asymmetries) make load balancing a challenging task. Existing end‐host–based schemes either employ the predominantly used ECN or combine it with RTT to get congestion information of paths. Both congestion signals, ECN and RTT, have limitations; ECN only tells whether the queue length is above or below a threshold value but does not inform about the extent of congestion; similarly, RTT in data center networks is on the scale of up to few hundreds of microseconds, and current data center operating systems lack fine‐grained microsecond‐level timers. Therefore, there is a need of a new congestion signal which should give accurate information of congestion along the path. Furthermore, in end‐host–based schemes, detecting asymmetries in the topology is challenging due to the inability to accurately measure RTT on the scale of microseconds. This paper presents QLLB, an end‐host–based, queue length–based load balancing scheme. QLLB employs a new queue length–based congestion signal that gives an exact measure of congestion along the paths. Furthermore, QLLB uses relative‐RTT to detect asymmetries in the topology. QLLB is implemented in ns‐3 and compared with ECMP, CONGA, and Hermes. The results show that QLLB significantly improves performance of short flows over the other schemes and performs within acceptable level, of CONGA and Hermes, for long flows. In addition, QLLB effectively detects asymmetric paths and performs better than Hermes under high loads.     

A dual‐band case‐printed planar inverted‐F antenna design with independent resonance control for wearable short range telemetric systems

In this article, a novel 3D meandered planar inverted‐F antenna (PIFA) is proposed for dual band application targeting Wireless Body Area Network (WBAN). The proposed antenna is printed on the casing of a 3D‐base‐station model having a size of 88 × 95 × 10.2 mm³. The proposed PIFA covers two bands including medical implant communication service (MICS) (402‐405 MHz), as well as the industrial, scientific, and medical (ISM) (2.4‐2.48 GHz) bands. Each of the two bands can be controlled independently. The 3D configuration contains two linked meandered resonators to downsize the structure. Due to its conformal shape, omnidirectional radiation pattern, and low‐profile nature, the proposed PIFA is a potential candidate for targeting the WBAN applications. The proposed antenna, covering the MICS and ISM bands, works with an optimally matching (VSWR<2) at the aforementioned bands. The design concept was validated by fabricating the antenna prototype and measuring its characteristics.     

A metrics suite for UML model stability

Software and Systems Modeling - Software metrics have become an essential part of software development because of their importance in estimating cost, effort, and time during the development phase....     

Efficient Oxygen Evolution and Gas Bubble Release Achieved by a Low Gas Bubble Adhesive Iron–Nickel Vanadate Electrocatalyst

Surface chemistry is a pivotal prerequisite besides catalyst composition toward advanced water electrolysis. Here, an evident enhancement of the oxygen evolution reaction (OER) is demonstrated on a vanadate‐modified iron–nickel catalyst synthesized by a successive ionic layer adsorption and reaction method, which demonstrates ultralow overpotentials of 274 and 310 mV for delivering large current densities of 100 and 400 mA cm^?2, respectively, in 1 m KOH, where vigorous gas bubble evolution occurs. Vanadate modification augments the OER activity by i) increasing the electrochemical surface area and intrinsic activity of the active sites, ii) having an electronic interplay with Fe and Ni catalytic centers, and iii) inducing a high surface wettability and a low‐gas bubble‐adhesion for accelerated mass transport and gas bubble dissipation at large current densities. Ex situ and operando Raman study reveals the structural evolution of β‐NiOOH and γ‐FeOOH phases during the OER through vanadate‐active site synergistic interactions. Operando dynamic specific resistance measurement evidences an accelerated gas bubble dissipation by a significant decrease in the variation of the interfacial resistance during the OER for the vanadate‐modified surface. Achievement of a high catalytic turnover of 0.12 s^?1 suggests metallic oxo‐anion modification as a versatile catalyst design strategy for advanced water oxidation.     

Information extraction from scientific articles: a survey

In last few decades, with the advent of World Wide Web (WWW), world is being overloaded with huge data. This huge data carries potential information that once extracted, can be used for betterment of humanity. Information from this data can be extracted using manual and automatic analysis. Manual analysis is not scalable and efficient, whereas, the automatic analysis involves computing mechanisms that aid in automatic information extraction over huge amount of data. WWW has also affected overall growth in scientific literature that makes the process of literature review quite laborious, time consuming and cumbersome job for researchers. Hence a dire need is felt to automatically extract potential information out of immense set of scientific articles to automate the process of literature review. Therefore, in this study, aim is to present the overall progress concerning automatic information extraction from scientific articles. The information insights extracted from scientific articles are classified in two broad categories i.e. metadata and key-insights. As available benchmark datasets carry a significant role in overall development in this research domain, existing datasets against both categories are extensively reviewed. Later, research studies in literature that have applied various computational approaches applied on these datasets are consolidated. Major computational approaches in this regard include Rule-based approaches, Hidden Markov Models, Conditional Random Fields, Support Vector Machines, Naïve-Bayes classification and Deep Learning approaches. Currently, there are multiple projects going on that are focused towards the dataset construction tailored to specific information needs from scientific articles. Hence, in this study, state-of-the-art regarding information extraction from scientific articles is covered. This study also consolidates evolving datasets as well as various toolkits and code-bases that can be used for information extraction from scientific articles.

     

Thresholded Multivariate Principal Component Analysis for Phase I Multichannel Profile Monitoring

Monitoring multichannel profiles has important applications in manufacturing systems improvement, but it is nontrivial to develop efficient statistical methods because profiles are high-dimensional functional data with intrinsic inner- and interchannel correlations, and that the change might only affect a few unknown features of multichannel profiles. To tackle these challenges, we propose a novel thresholded multivariate principal component analysis (PCA) method for multichannel profile monitoring. Our proposed method consists of two steps of dimension reduction: It first applies the functional PCA to extract a reasonably large number of features under the in-control state, and then uses the soft-thresholding techniques to further select significant features capturing profile information under the out-of-control state. The choice of tuning parameter for soft-thresholding is provided based on asymptotic analysis, and extensive numerical studies are conducted to illustrate the efficacy of our proposed thresholded PCA methodology.     

Microstructure characterization,mechanical properties,compressibility and sintering behavior of Al-B4C nanocomposite powders

In the present work, Al-xB₄C nanocomposite (x = 0, 1, 2, 3, 4 and 5 in wt%, having the average B₄C size of 50 nm) were prepared using a high-energy ball mill. The milling times up to 16 h were applied. Then, the microstructural evolutions, mechanical properties, compressibility and sintering behavior of nanocomposites were investigated. The changes in powders morphology and microstructure during the milling process were characterized by laser diffraction particle size analyzer (LDA), SEM, XRD, EDS and TEM techniques. Compressibility and sintering behavior of milled powders compacted under different pressures (100–900 MPa) and at different sintering temperatures (500, 550 and 600 °C) were also studied. The pressing behavior of the nanocomposites was analyzed using linear compaction equations developed by Heckel, Panelli-Filho and Ge. The results showed the significant effects of B₄C amounts and sintering temperatures on the compressibility and sintering behavior of nanocomposites. The increase in the B₄C amount led to a decrease in both the compressibility rate and the sinterability of specimens. The maximum compression strength of 265 MPa and Vickers hardness of 165 VHN were obtained for Al-5 wt.% B₄C nanocomposite milled for 16 h followed by sintering at 600 °C.     

Synthesis and characterization of yttrium aluminum garnet nanostructures by cathodic electrodeposition method

Pure nanostructures of yttrium aluminum garnet (YAG) was prepared based on the cathodic electrodeposition method from the mixture of YCl₃ and AlCl₃ dissolved in water/ethanol solution. At first, hydroxide precursors cathodically were grown on the steel substrates then, the hydroxide powders heat treated at 850 °C for 4 h in dry air atmosphere. The formation of crystalline YAG nanopowder was confirmed by X-ray diffraction (XRD), thermogravimetric analysis (DSC-TGA), scanning electron microscopy (SEM) and Fourier transformed infrared spectroscopy (FT-IR). The results of the SEM showed that applied current density and bath temperature have the prominent effect on the morphology and particle size of the products. The results revealed that cathodic electrodeposition followed by heat-treatment can be used as a facile method for preparation of YAG nanostructures with different morphology.     

Scientific impact of an author and role of self-citations

Scientometrics - In bibliometric and scientometric research, the quantitative assessment of scientific impact has boomed over the past few decades. Citations, being playing a major role in...