Synthesis,evolution and hydrogen storage properties of ZnV2O4 glomerulus nano/microspheres: A prospective material for energy storage

We present first study on ZnV₂O₄ glomerulus nano/microspheres synthesize by template free route to expose its hydrogen storage potential. Besides this the evolution of nano/microspheres has been investigated in detail. To reveal possible growth mechanism of these spheres, time-dependent experiments are performed. Reitveld analysis is taken into account for calculation of lattice parameters, crystallite size and strain. From our results, a correlation between lattice parameters and crystallite size is observed. The strain decreases with the increase in reaction time. Hydrogen storage measurement reveals potential of ZnV₂O₄ nanospheres as a prospective material for energy storage applications. These studies can open new avenue of research for hydrogen storage in spinel oxide materials.     

A novel symmetric 10 Gbit/s architecture with a single feeder fiber for WDM-PON based on chirp-managed laser

We propose the single feeder fiber architecture for wavelength division multiplexing passive optical network(WDM-PON)based on directly modulated chirp managed laser(CML).The downlink(DL)signal output from the laser is converted to return-to-zero(RZ)differential phase shift signal using a pulse carver.The downstream signal is reused as a carrier for the upstream using intensity modulation technique.Simulation results show the error-free performance at symmetric data rate of 10 Gbit/s per channel with negligible power penalty and improved receiver sensitivity for the uplink(UL),over 25 km standard single-mode fiber(SSMF).A low-cost and reduced circuitry network design is implemented on a single feeder fiber with the elimination of differential encoder and one external modulator.     

The last two decades of computer vision technologies in water resource management: A bibliometric analysis

Efficient management of water resources is an important task given the significance of water in daily lives and economic growth. Water resource management is a specific field of study which deals with the efficient management of water resources towards fulfilling the needs of society and preventing from water-related disasters. Many activities within this domain are getting benefitted with the recent technological advancements. Within many others, computer vision-based solutions have emerged as disruptive technologies to address complex real-world problems within the water resource management domain (e.g., flood detection and mapping, satellite-based water bodies monitoring, monitoring and inspection of hydraulic structures, blockage detection and assessment, drainage inspection and sewer monitoring). However, there are still many aspects within the water resource management domain which can be explored using computer vision technologies. Therefore, it is important to investigate the trends in current research related to these technologies to inform the new researchers in this domain. In this context, this paper presents the bibliometric analysis of the literature from the last two decades where computer vision technologies have been used for addressing problems within the water resource management domain. The analysis is presented in two categories: (a) performance analysis demonstrating highlighted trends in the number of publications, number of citations, top contributing countries, top publishing journals, top contributing institutions and top publishers and (b) science mapping to demonstrate the relation between the bibliographic records based on the co-occurrence of keywords, co-authorship analysis, co-citation analysis and bibliographic coupling analysis. Bibliographic records (i.e., 1059) are exported from the Web of Science (WoS) core collection database using a comprehensive query of keywords. VOSviewer opensource tool is used to generate the network and overlay maps for the science mapping of bibliographic records. Results highlighted important trends and valuable insights related to the use of computer vision technologies in water resource management. An increasing trend in the number of publications and focus on deep learning/artificial intelligence (AI)-based approaches has been reported from the analysis. Further, flood mapping, crack/fracture detection, coastal flood detection, blockage detection and drainage inspections are highlighted as active areas of research.     

Electrical Contacts With 2D Materials: Current Developments and Future Prospects

Current electrical contact models are occasionally insufficient at the nanoscale owing to the wide variations in outcomes between 2D mono and multi-layered and bulk materials that result from their distinctive electrostatics and geometries. Contrarily, devices based on 2D semiconductors present a significant challenge due to the requirement for electrical contact with resistances close to the quantum limit. The next generation of low-power devices is already hindered by the lack of high-quality and low-contact-resistance contacts on 2D materials. The physics and materials science of electrical contact resistance in 2D materials-based nanoelectronics, interface configurations, charge injection mechanisms, and numerical modeling of electrical contacts, as well as the most pressing issues that need to be resolved in the field of research and development, will all be covered in this review.     

A new lossless electroencephalogram compression technique for fog computing-based IoHT networks

The ability to design smarter, more predictive healthcare solutions for use in the community (at work and at home) and in healthcare facilities has been greatly enhanced by recent developments in the Internet of Health Things (IoHT) and cyber physical systems (CPS). These data collected by such medical sensors will be sent in a large quantity to the fog gateway of IoHT networks. These data should be forwarded to far cloud for further analysis and processing. Therefore, sending all of these data over the IoHT network to the data center will impose a significant burden on the IoHT network. This paper proposed a new lossless electroencephalogram (EEG) compression technique (NeLECoT) for fog computing-based IoHT networks. It encodes the data of the patient at the fog gateway prior to sending it to the data center, thereby reducing the data's size. In the fog node, the NeLECoT combines three efficient techniques: clustering based on density-based spatial clustering of applications with noise (DBSCAN), RLE (run length encoding), and Huffman encoding (HE). The clustering based on DBSCAN separates a massive volume of captured data into small groups of closely related (or similar) data. RLE encodes clustered EEG data, and the resulting file is encoded with HE. The fog gateway then transmits the encoded file to the cloud. Numerous simulation experiments were carried out, and the findings demonstrated that the suggested NeLECoT achieved better results than the competing techniques in terms of transmitted data size, compression ratio, compression power, compression time, decompression time, and average compression power.