Effective & Efficient Implementation of OBE Framework within Constrained Pakistani Environment to Attain Desired Learning Outcomes

Outcome-based-education (OBE) is an educational theory that bases each part of an educational system around goals (outcomes). Pakistan Engineering Council (PEC) introduced the OBE in Pakistani engineering higher-education-institutions (HEIs),from 2018, in the replacement of previously followed Syllabus& Curriculum based education system. Key to success of OBE System is its continuous-quality-improvement (CQI) mechanism. The CQI mechanism design should be based on accurate assessment process and flawless evaluation of OBE System parameters; otherwise the OBE System’s benefits cannot be achieved. However, some HEIs have not devised an effective CQI mechanism and they still using classical procedures and academic tools in implementation of the OBE Framework, which cannot give desired results from the new System. This paper presents that; instead of presently employed loose-control, a modified stringent control strategy for academic activity and employment of latest technology, can make the CQI mechanism, more effective and efficient. This paper therefore presents a two-fold contribution. At first, the implementation problems of the OBE System are critically analyzed. Secondly, few modifications in the existing approaches have been introduced to achieve the desired results from the recently adopted OBE system.

An atomistic model for the thermal resistance of a liquid–solid interface

The thermal resistance associated with the interface between a solid and a liquid is analysed from an atomistic point of view. Partial evaluation of the associated Green–Kubo integral elucidates the various factors governing heat transport across the interface and leads to a quantitative model for the thermal resistance in terms of atomistic-level system parameters. The model is validated using molecular dynamics simulations.

Machine learning of phase transitions in nonlinear polariton lattices

Polaritonic lattices offer a unique testbed for studying nonlinear driven-dissipative physics. They show qualitative changes of their steady state as a function of system parameters, which resemble non-equilibrium phase transitions. Unlike their equilibrium counterparts, these transitions cannot be characterised by conventional statistical physics methods. Here, we study a lattice of square-arranged polariton condensates with nearest-neighbour coupling, and simulate the polarisation (pseudospin) dynamics of the polariton lattice, observing regions with distinct steady-state polarisation patterns. We classify these patterns using machine learning methods and determine the boundaries separating different regions. First, we use unsupervised data mining techniques to sketch the boundaries of phase transitions. We then apply learning by confusion, a neural network-based method for learning labels in a dataset, and extract the polaritonic phase diagram. Our work takes a step towards AI-enabled studies of polaritonic systems.

Velocity Multistability vs. Ergodicity Breaking in a Biased Periodic Potential

Multistability, i.e., the coexistence of several attractors for a given set of system parameters, is one of the most important phenomena occurring in dynamical systems. We consider it in the velocity dynamics of a Brownian particle, driven by thermal fluctuations and moving in a biased periodic potential. In doing so, we focus on the impact of ergodicity—A concept which lies at the core of statistical mechanics. The latter implies that a single trajectory of the system is representative for the whole ensemble and, as a consequence, the initial conditions of the dynamics are fully forgotten. The ergodicity of the deterministic counterpart is strongly broken, and we discuss how the velocity multistability depends on the starting position and velocity of the particle. While for non-zero temperatures the ergodicity is, in principle, restored, in the low temperature regime the velocity dynamics is still affected by initial conditions due to weak ergodicity breaking. For moderate and high temperatures, the multistability is robust with respect to the choice of the starting position and velocity of the particle.

Matched Queues with Flexible Customers and Impatient Customers

This paper studies a double-ended queue with four Poisson inputs and flexible customers, and its stability is guaranteed by customers’ impatient behavior. We show that such a queue can be expressed as a quasi birth-and-death (QBD) process with infinitely many phases. For this purpose, we provide a detailed analysis for the QBD process, including the system stability, the stationary probability vector, the sojourn time, and so forth. Finally, numerical examples are employed to verify the correctness of our theoretical results, and demonstrate how the performance measures of this queue are influenced by key system parameters. We believe that the methodology and results described in this paper can be applied to analyze many practical issues, such as those encountered in sharing economy, organ transplantation, employee recruitment, onlinedating, and so on.