PAINT: Priority aware interference mitigation techniques for improving coexistence in home area networks of smart grid

As smart grid (SG) home area networks (HANs) communicate with various smart devices such as meters, sensors, and actuators on a 2.4‐GHz unlicensed band, the coexistence of different wireless technologies in such networks is a common phenomenon due to the overlapping of channels. In this research, homogeneous and heterogeneous interference are considered to address the coexistence problem in smart utility networks (SUNs). The homogeneous interference is mitigated by utilizing the contention free period (CFP) and the contention access period (CAP) of the MAC layer superframe of IEEE 802.15.4g designed for SUNs. This frame is used to get access for the channel. For this, a slotted CSMA/CA algorithm is used for various priority levels of data with adjustable backoff period (BP) and clear channel assessment (CCA) period in order that nodes (devices) with high priority can achieve high probability of channel access. By modeling the proposed scheme using the Markov chain, the exactness of the proposed scheme is assessed based on throughput, channel access delay, energy consumption per bit, and probability of successful data transmission and collision. A performance evaluation of the proposed scheme is further investigated by comparing it with the existing scheme PA‐MAC. In addition, a channel switching mechanism is explored to mitigate the heterogeneous interference with the help of a Naive Bayes classifier prediction. Finally, the prediction indicates that by choosing the non‐coexisting and the non‐overlapping channel, the proposed channel switching mechanism effectively mitigates the heterogeneous interference.     

Small Molecules in Light‐Emitting Electrochemical Cells: Promising Light‐Emitting Materials

Light‐emitting electrochemical cells (LECs) are solid‐state lighting devices that convert electric current to light within electroluminescent organic semiconductors, and these devices have recently attracted significant attention. Introduced in 1995, LECs are considered a great breakthrough in the field of light‐emitting devices for their applications in scalable and adaptable fabrication processes aimed at producing cost‐efficient devices. Since then, LECs have evolved through the discovery of new suitable emitters, understanding the working mechanism of devices, and the development of various fabrication methods. LECs are best known for their simple architecture and easy, low‐cost fabrication techniques. The key feature of their fabrication is the use of air stable electrodes and a single active layer consisting of mobile ions that enable efficient charge injection and transport processes within LEC devices. More importantly, LEC devices can be operated at low voltages with high efficiencies, contributing to their widespread interest. This review provides a general overview of the development of LECs and discusses how small molecules can be utilized in LEC applications by overcoming the use of traditional lighting materials like polymers and ionic transition metal complexes. The achievements of each study concerning small molecule LECs are discussed.     

A hybrid cuckoo search and genetic algorithm for reliability–redundancy allocation problems

Solving reliability and redundancy allocation problems via meta-heuristic algorithms has attracted increasing attention in recent years. In this study, a recently developed meta-heuristic optimization algorithm cuckoo search (CS) is hybridized with well-known genetic algorithm (GA) called CS–GA is proposed to solve the reliability and redundancy allocation problem. By embedding the genetic operators in standard CS, the balance between the exploration and exploitation ability further improved and more search space are observed during the algorithms’ performance. The computational results carried out on four classical reliability–redundancy allocation problems taken from the literature confirm the validity of the proposed algorithm. Experimental results are presented and compared with the best known solutions. The comparison results with other evolutionary optimization methods demonstrate that the proposed CS–GA algorithm proves to be extremely effective and efficient at locating optimal solutions.     

Lidar remote sensing of the mixed layer height over a tropical urban Indian station

Mixed layer is an important parameter which controls meteorological conditions in the lower atmosphere. Transport and diffusion of pollutants in the lower atmosphere is highly dependant on the structure of the planetary boundary layer, one important feature of which is the height of the well-mixed layer. In the present study, continuous wave, bistatic argon ion lidar-derived scattered signal strength from different heights in the lower troposphere over Pune (18? 32′ N, 73? 51′ E, 559 m above mean sea level), India during the period April 2007–January 2008 has been recorded remotely and by employing simple statistical tools, the mixed layer height (MLH) and transition layer thickness (TLT) have been estimated. The results show that sufficient mixing of atmospheric constituents such as aerosols exists in the boundary layer in the post-sunset hours during the summer season, enabling estimation of MLH and TLT. On the other hand, during winter months as mixing ceases/weakens by late evening hours, the mixed layer depth is either low or not easily discernible. In view of the importance of mixed layer depth information for various atmospheric applications, the remote sensing tool used and the simple methodology followed here seem promising.     

Experimental investigation on performance of additive mixed dielectric during micro-electric discharge drilling on 316L stainless steel

This study investigates the impact of machining factors on the performance of additive mixed micro-electric discharge drilling of 316L stainless steel. The effects of three kinds of powder, powder concentration, voltage, capacitance, feed rate, and speed on tool wear rate, material removal rate, taper angle, and overcut (OC) were also investigated. The experimental results show that adding additives to the dielectric enhanced rate of material removal and reduced rate of tool wear significantly. Mixing powder with dielectric increased the length of the sparking, resulting in significant OC, and process performance also improved with the increase in amount of powder added. Further, scanning electron microscopy analysis was carried out to examine the surface characteristics and material migration properties, which confirmed that the properties of the machined surface are indeed significantly improved.     

A Novel Solution of Using Deep Learning for White Blood Cells Classification: Enhanced Loss Function with Regularization and Weighted Loss (ELFRWL)

Deep learning has been successfully applied in classification of white blood cells (WBCs), however, accuracy and processing time are found to be less than optimal hindering it from getting its full potential. This is due to imbalanced dataset, intra-class compactness, inter-class separability and overfitting problems. The main research idea is to enhance the classification and prediction accuracy of blood images while lowering processing time through the use of deep convolutional neural network (DCNN) architecture by using the modified loss function. The proposed system consists of a deep neural convolution network (DCNN) that will improve the classification accuracy by using modified loss function along with regularization. Firstly, images are pre-processed and fed through DCNN that contains different layers with different activation function for the feature extraction and classification. Along with modified loss function with regularization, weight function aids in the classification of WBCs by considering weights of samples belonging to each class for compensating the error arising due to imbalanced dataset. The processing time will be counted by each image to check the time enhancement. The classification accuracy and processing time are achieved using the dataset-master. Our proposed solution obtains better classification performance in the given dataset comparing with other previous methods. The proposed system enhanced the classification accuracy of 98.92% from 96.1% and a decrease in processing time from 0.354 to 0.216 s. Less time will be required by our proposed solution for achieving the model convergence with 9 epochs against the current convergence time of 13.5 epochs on average, epoch is the formation white blood cells (WBCs) and the development of granular cells. The proposed solution modified loss function to solve the adverse effect caused due to imbalance dataset by considering weight and use regularization technique for overfitting problem.

     

Impact of interfacial charges on analog and RF performance of Mg2Si source heterojunction double-gate tunnel field effect transistor

Journal of Materials Science: Materials in Electronics - Tunnel field effect transistors (TFETs) have proved themselves as a better choice for the replacement of MOSFET due to provision of...     

Sintering behaviour of Copper/carbon nanotube composites and their characterization

The potential usage of Copper (Cu) is very limited, where combined mechanical and thermal properties are desirable, which can be overcome by using carbon nanotube (CNT) as a reinforcement. An attempt was made to synthesize Cu/CNT composites by varying CNT diameter and its concentration through a molecular level mixing technique followed by uniaxial compaction and conventional sintering. The sintering behaviour of Cu and Cu/CNT composites was studied to understand the influence of different parameters. The sintering duration of Copper was decreased with an increase of CNT diameter. The maximum enhancement of properties was achieved at 0.25 wt.% CNT irrespective of its diameter, where the thermal conductivity and hardness were obtained as 328 W/mK at 20–40 nm diameter CNT composites and 81.2 ± 2.9 VHN at 40–60 nm diameter CNT composites, respectively. The conventional method of synthesize can generate the desired characteristics of composites at par with high end techniques, such as SPS.     

Investigation of Characterization and Mechanical Performances of Al<Subscript>2</Subscript>O<Subscript>3</Subscript> and SiC Reinforced PA6 Hybrid Composites

In this study, the influence of different weight percentages of alumina oxide (Al₂O₃) and silicon carbide (SiC) reinforcement on the mechanical properties of Polyamide (PA6) composite is investigated. Test specimens of pure PA6, 85 wt% PA6 + 10 wt% Al₂O₃ + 5 wt% SiC and 85 wt% PA6 +10 wt% SiC + 5 wt% Al₂O₃ are prepared using an injection molding machine. To investigate the mechanical behaviors tensile test, impact test, flexural test, and hardness test were conducted in accordance with ASTM standards. Experimental results indicated that the mechanical properties, such as tensile, impact, hardness, and flexural strength were considerably higher than the pure PA6. The tensile fracture morphology and the characterization of PA6 hybrid composites were observed by scanning electron microscope and Fourier transform infrared spectroscopic method. Further, thermogravimetric analysis confirms the thermal stability of PA6 hybrid composites. The reinforcing effects of Al₂O₃ and SiC on the mechanical properties of PA6 hybrid composites were compared and interpreted in this paper. Improved mechanical and thermal characteristics were observed by the addition of small amount of Al₂O₃ and SiC simultaneously reinforced with the pure PA6.     

Impact of ammonia fiber expansion (AFEX) pretreatment on energy consumption during drying,grinding, and pelletization of corn stover

Pretreatment and densification of biomass can increase the viability of bioenergy production by providing a feedstock that is readily hydrolyzed and able to be transported over greater distances. Ammonia fiber expansion (AFEX?) is one such method targeted for use at distributed depots to create a value-added and densified feedstock for bioenergy use. However, the pretreatment process results in a high-moisture material that must be dried, further size reduced, and pelletized, all of which are energy-intensive processes. This work quantifies the energy consumption required to dry, grind, and densify AFEX-pretreated corn stover compared to non-pretreated stover and explores the potential of reduced drying as a means to conserve energy. The purpose of this work is to understand whether material property changes resulting from AFEX pretreatment influence the material performance in downstream formatting operations. Material properties, heat balance equations, and a rotary drum dryer model were used to model a commercial-scale rotary drum dryer for AFEX-pretreated corn stover, showing the potential to reduce dryer energy consumption by up to 36% compared to non-pretreated corn stover. Laboratory-measured grinding and pelleting energies were both very sensitive to material moisture content. Overall, the total energy required for drying, grinding, and pelleting amounts to a savings of up to 23 kWh/dry Mg for the AFEX-pretreated material when dried to a low moisture content, equating to up to 0.61 $/Mg savings for gas and electricity. Grinding and pelleting of high-moisture AFEX-pretreated stover was shown to be more costlier than the savings collected through reduced drying. Although the energy and cost savings shown here are modest, the results help to highlight operational challenges and opportunities for continued improvement.