National Institute of Technology Mizoram (NIT Mizoram, or NITMZ) is one of the 31 National Institutes of Technology in India. Located in Aizawl, NIT Mizoram was one of the ten new NITs established by the Ministry of Human Resources Development (part of the Government of India, via order no. F. 23-13-2009-TS-III, dated 30 October 2009 and 3 March 2010).The primary objective of NIT Mizoram is to provide education through research and training in undergraduate and graduate programs including PhD. The school was declared an "Institute of National Importance" by the Indian Parliament. Students are admitted through the All India Entrance Exam - Joint Entrance Exam (JEE Main).
In this paper, we consider a nondifferentiable multiobjective interval-valued bilevel optimization problem where the involved functions are characterized by their tangential subdifferentials. We establish optimality conditions for this nonsmooth extremum problem by transforming its hierarchical model into a single-level one by using the optimal value reformulation. Namely, under a nonsmooth version of Zangwill’s constraint qualification, we derive necessary optimality conditions of the Karush-Kuhn-Tucker type in terms of the tangential subdifferentials of the involved functions. Further, we prove sufficient optimality conditions under the assumption that the functions involved are generalized invex functions which are characterized by their tangential subdifferentials. The applicability of the established optimality conditions is demonstrated through illustrative examples of nondifferentiable multiobjective interval-valued bilevel optimization problems.
Three indigenous lignocellulosic residues from Mizoram (areca nut fibre, wood chips, and mixed tree leaves) were characterised through multi-analytical methods (proximate, ultimate, XRD, FTIR, TG/DSC) and experimentally gasified in a 10 kWe downdraft gasifier at a common equivalence ratio of 0.33. Among the three feedstocks, areca nut fibre exhibited the most favourable combination of low ash, high volatile matter, and lower O/C ratio. In common-anchor experimental gasification at ER = 0.33, areca nut fibre produced the highest cold-gas efficiency (CGE = 49.4
Sm-doped BiFeO3 (Bi1-xSmxFeO₃, x = 0.05 and 0.1) were fabricated using the sol-gel approach to systematically investigate the influence of rare earth substitution on structural distortion, optical, thermal, microstructural, dielectrical relaxation, and electrical properties. X-ray diffraction (XRD) analysis confirms the formation of the R3c phase and reveals a reduction in the lattice volume as the Sm concentration increases from 5
GaN HEMTs are essential devices in high-voltage, fast switching operation in 5G and millimeter-wave applications. This research presents an AlGaN/GaN HEMT without buffer (BF-HEMT) featuring an AlGaN back barrier and stacked HfO2/SiN passivation. The device’s characteristics were evaluated using the Sentaurus TCAD tool. Incorporating a 100 nm AlGaN back barrier with 7
In this paper, we propose a neural network based on an augmented Lagrangian function to solve a challenging class of constrained optimization problems called Mathematical Programs with Equilibrium Constraints (MPEC). The original MPEC problem, which includes equilibrium constraints, is first transformed into an equivalent nonlinear programming formulation using a smoothing technique. A neural network inspired by the augmented Lagrangian method is then employed to solve the reformulated problem. The proposed approach is rigorously analyzed in terms of stability, convergence, and computational performance, ensuring its effectiveness and reliability. Under specific assumptions, it is demonstrated that the neural network converges to an equilibrium point that satisfies the Karush-Kuhn-Tucker (KKT) conditions of the underlying nonlinear programming problem, guaranteeing the optimality of the obtained solution. The stability of the network is further established through the Lyapunov function method. To validate the approach, several numerical simulations are conducted, and the reported results are compared with the Lagrange Programming Neural Network. The proposed model is also applied to compute Stackelberg-Cournot-Nash equilibria to demonstrate its versatility and practical relevance.
