• Energy Research

The magnetic levitation (MAGLEV) train uses magnetic field to suspend, guide, and propel vehicle onto the track. The MAGLEV train provides a sustainable and cleaner solution for train transportation by significantly reducing the energy usage and greenhouse gas emissions as compared to traditional train transportation systems. In this paper, we propose an advanced control mechanism using an Arduino microcontroller that selectively energizes the electromagnets in a MAGLEV train system to provide dynamic stability and energy efficiency. We also design the prototype of an energy-efficient MAGLEV train that leverages our proposed control mechanism. In our MAGLEV train prototype, the levitation is achieved by creating a repulsive magnetic field between the train and the track using magnets mounted on the top-side of the track and bottom-side of the vehicle. The propulsion is performed by creating a repulsive magnetic field between the permanent magnets attached on the sides of the vehicle and electromagnets mounted at the center of the track using electrodynamic suspension (EDS). The electromagnets are energized via a control mechanism that is applied through an Arduino microcontroller. The Arduino microcontroller is programmed in such a way to propel and guide the vehicle onto the track by appropriate switching of the electromagnets. We use an infrared-based remote-control device for controlling the power, speed, and direction of the vehicle in both the forward and the backward direction. The proposed MAGLEV train control mechanism is novel, and according to the best of our knowledge is the first study of its kind that uses an Arduino-based microcontroller system for control mechanism. Experimental results illustrate that the designed prototype consumes only 144 W-hour (Wh) of energy as compared to a conventionally designed MAGLEV train prototype that consumes 1200 Wh. Results reveal that our proposed control mechanism and prototype model can reduce the total power consumption by 8.3 × as compared to the traditional MAGLEV train prototype, and can be applied to practical MAGLEV trains with necessary modifications. Thus, our proposed prototype and control mechanism serves as a first step towards cleaner engineering of train transportation systems.

Fiber optic communications are inevitable to achieve higher data rates of modern telecom networks. After utilization of Wavelength division multiplexing, higher order modulations and polarization multiplexing, mode division multiplexing is a new dimension to achieve higher transmission capacity for optical fiber communication links. Different spatial distributions of optical energy along cross sectional area of optical fiber allows simultaneous transmission of data by considering each mode as an independent channel. During such simultaneous transmissions, possibility of mixing of signals amongst modes causes signal degradations and acts as limiting factor for bandwidth – distance product of the link. This effect of mode coupling has been explored in this article by presenting its mathematical formulations. A simulation has been performed to study the impact of fiber constructional parameters on mode coupling using optical wavelengths used for telecommunication systems. The observations help to develop fibers for reduced mode coupling for particular group of modes and operating wavelengths. This article paves the way forward for study of mode coupling in micro and macro bending conditions for forthcoming research endeavours.

The Internet of things (IoT) enables a diverse set of applications such as distribution automation, smart cities, wireless sensor networks, and advanced metering infrastructure (AMI). In smart grids (SGs), quality of service (QoS) and AMI traffic management need to be considered in the design of efficient AMI architectures. In this article, we propose a QoS-aware machine-learning-based framework for AMI applications in smart grids. Our proposed framework comprises a three-tier hierarchical architecture for AMI applications, a machine-learning-based hierarchical clustering approach, and a priority-based scheduling technique to ensure QoS in AMI applications in smart grids. We introduce a three-tier hierarchical architecture for AMI applications in smart grids to take advantage of IoT communication technologies and the cloud infrastructure. In this architecture, smart meters are deployed over a georeferenced area where the control center has remote access over the Internet to these network devices. More specifically, these devices can be digitally controlled and monitored using simple web interfaces such as REST APIs. We modify the existing K-means algorithm to construct a hierarchical clustering topology that employs Wi-SUN technology for bi-directional communication between smart meters and data concentrators. Further, we develop a queuing model in which different priorities are assigned to each item of the critical and normal AMI traffic based on its latency and packet size. The critical AMI traffic is scheduled first using priority-based scheduling while the normal traffic is scheduled with a first-in–first-out scheduling scheme to ensure the QoS requirements of both traffic classes in the smart grid network. The numerical results demonstrate that the target coverage and connectivity requirements of all smart meters are fulfilled with the least number of data concentrators in the design. Additionally, the numerical results show that the architectural cost is reduced, and the bottleneck problem of the data concentrator is eliminated. Furthermore, the performance of the proposed framework is evaluated and validated on the CloudSim simulator. The simulation results of our proposed framework show efficient performance in terms of CPU utilization compared to a traditional framework that uses single-hop communication from smart meters to data concentrators with a first-in–first-out scheduling scheme.

Continuous application of phosphate (P) mineral to soil renders apatite addition during each crop growing season which is of great concern from a sustainable agriculture viewpoint. Use of efficient phosphate solubilizing microbes (PSB) is one of the most effective ways to solubilize this apatite mineral in the soil. The current study targeted hydroxyapatite mines to explore, isolate and characterize efficient P solubilizers to solubilize apatite in the soil. Efficiency of isolated microbes to solubilize rock phosphate (hydroxyapatite) and tri-calcium phosphate (TCP) as well as indole-3-acetic acid (IAA) and 1-aminocyclopropane-1-carboxylate deaminase (ACC) activity were tested. Identification and phylogenetic analysis of bacterial and fungal isolates were carried out by 16S rRNA and internal transcribed spacer (ITS) rDNA sequence analyses, respectively. The isolated bacterial strains were identified as Staphylococcus sp., Bacillus firmus, Bacillus safensis, and Bacillus licheniformis whereas fungal isolates were identified as Penicillium sp. and Penicillium oxalicum. Results showed that the impact of identified strains in combination with three phosphate fertilizers sources (compost, rock phosphate and diammonium phosphate (DAP)) was conspicuous on maize crop grown in pot. Both bacterial and fungal strains increased the P uptake by plants as well as recorded with higher available P in post-harvested soil. Penicillium sp. in combination with compost resulted in maximum P-uptake by plants and post-harvest soil P contents, compared to other combinations of P sources and bio-inoculants. Screening and application of efficient P solubilizers can be a better option to utilize the indigenous phosphate reserves of soil as well as organic amendments for sustainable agriculture.