Minimum spanning tree problem (MSTP) has allured many researchers and practitioners due to its varied range of applications in real world scenarios. Modelling these applications involves the incorporation of indeterminate phenomena based on their subjective estimations. Such phenomena can be represented rationally using uncertainty theory. Being a more realistic variant of MSTP, in this article, based on the principles of the uncertainty theory, we have studied a multi-objective minimum spanning tree problem (MMSTP) with indeterminate problem parameters. Subsequently, two uncertain programming models of the proposed uncertain multi-objective minimum spanning tree problem (UMMSTP) are developed and their corresponding crisp equivalence models are investigated, and eventually solved using a classical multi-objective solution technique, the epsilon-constraint method. Additionally, two multi-objective evolutionary algorithms (MOEAs), non-dominated sorting genetic algorithm II (NSGAII) and duplicate elimination non-dominated sorting evolutionary algorithm (DENSEA) are also employed as solution methodologies. With the help of the proposed UMMSTP models, the practical problem of optimizing the distribution of petroleum products was solved, consisting in the search for symmetry (balance) between the transportation cost and the transportation time. Thereafter, the performance of the MOEAs is analyzed on five randomly developed instances of the proposed problem.

In this letter, a miniaturized slotted microstrip antenna with modified ground plane for multiresonant wideband operation is proposed. The miniaturized antenna dimension is only about 24 (0.44λr) ×22 (0.403λr) mm ² , where, λr is the wavelength of the resonant frequency of the conventional antenna (i.e., 5.5 GHz). The measured result shows two distinct resonant frequencies at 2 and 3.3 GHz. A -10-dB wide-impedance bandwidth of 3100 MHz ranging from 4.2-7.3 GHz is also achieved for the proposed antenna structure. The measured result shows that the size of the proposed antenna has been reduced by 67% in comparison to the conventional rectangular microstrip antenna with same area, and the operating bandwidth is also enhanced up to 53.9%.

In this article, we present dielectric modulation-based label free detection of biomolecules at a cavity under gate metal located near the tunneling junction of charge plasma based dual metal-double gate tunnel FET (CP-DM DGTFET). The biomolecules that settle down in the cavity act as dielectric above the tunneling junction and cause drain current to increase/ decrease. To eliminate the short channel impact and to enhance I _ON /I _OFF ratio without compromising any other device features, dual metal gate architecture with laterally split dielectric is used. The biosensor is capable of recognizing neutral, positive, and negative charges with the highest drain current sensitivity of 2.9×10 ⁹ Ccm ^-2 and highest I _ON /I _OFF ratio of 9.02×10 ¹¹ for biomolecule gelatin with dielectric constant k = 12. This work explores the effectiveness of charge-plasma based DM DGTFET for detection of biomolecules such as gelatin (k = 12), keratin (k = 8), bacteriophage T7 (k = 6.3), and APTES (k = 3.57) with varied charge distribution at various biasing. The drain current sensitivity, subthreshold slope, drain current, ON/OFF current ratio are computed for various biomolecules and their variations with respect to cavity length, biomolecule charge, etc. are used to detect unknown biomolecules. Finally, RF analysis such as gain bandwidth product, cut-off frequency, transit time, and linearity/selectivity of biosensors are incorporated. The device shows better linearity and less distortion for high –k biomolecules.

The Indian Railways Reservation System (IRRS) is one of the world’s busiest reservation systems of railway tickets. Recently, the COVID-19 pandemic situation has severely impacted the Indian Railway’s (IR) transportation, which eventually has enforced the IR to alter the passenger reservation system. This research attempts to evaluate and analyse the factors that modify the IRRS. In this research, a rough set-based Data Mining Scaffolding (DMS) has been proposed. Here, the relevant preferential information related to the IRRS is managed by introducing a multi-criteria decision-making (MCDM), where a decision-maker (DM) can make a decision based on several decision rules. The effectiveness of the proposed DMS is explained by gathering realistic data of 26 trains, which run between railway stations of two metro cities of India during the COVID-19 pandemic period.