Lahore University of Management Sciences

About: Lahore University of Management Sciences is a education organization based out in Lahore, Pakistan. It is known for research contribution in the topics: Fixed point & Metric space. The organization has 1524 authors who have published 3015 publications receiving 42665 citations. The organization is also known as: LUMS.

On solitons: the biomolecular nonlinear transmission line models with constant and time variable coefficients

Abstract: The article studies the dynamics of solitons in electrical microtubule model, which describes the propagation of waves in nonlinear dynamical system. Microtubules are not only a passive support of a cell but also they have highly dynamic structures involved in cell motility, intracellular transport and signaling. The underlying model has been considered with constant and variable coefficients of time function. The solitary wave ansatz has been applied successfully to extract these solitons. The corresponding integrability criteria, also known as constraint conditions, naturally emerge from the analysis of these models.

Individual’s financial investment decision-making in reward-based crowdfunding: evidence from China

Abstract: This article extends the work of Cecere et al. (Appl. Econ., 49(57): 5802–5813, 2017) and explores the antecedents of backers’ decision to invest in projects from eight categories on a reward-based crowdfunding platform in China. We extract data from 2011 to 2016 from the pioneer Chinese reward-based crowdfunding site ‘Demohour’. Our empirical investigation using OLS reveals that feedback score, social capital (followers on Weibo, project sharing on social media) and project quality (number of updates) are key motivating factors in investment decision and subsequently, project success or failure. Robustness tests also confirm the findings.

A 0.5V PPG-based Heart Rate and Variability Detection System

Abstract: Heart Rate Variability (HRV) is a diagnostic and predictive tool for multiple health conditions. A Photoplethysmographic (PPG) based HRV detection system with a low-power readout together with an embedded feature extraction processor to measure heart rate (HR) and HRV is presented. The proposed system proposes a low power LED driver with novel clock bootstrapping switching. The LED current is digitally controlled for adjusting LED brightness using Pulse Density Modulation (PDM) with >90% efficiency. It also introduces a highly compact low power/low noise transimpedance amplifier for an input base current range (0.5nA-4.5µA). The proposed system eliminates the motion artifacts without the need for an accelerometer. The complete system is simulated using 65nm CMOS technology with 0.5V supply. It consumes 3 µW/1.1mW of power for the HRV detection and LED driver, respectively.

Evaluation of solar module equivalent models under real operating conditions—A review

Abstract: A number of mathematical models are available to model the performance of solar modules under varying operating conditions. Most commonly recognized and used models include (a) the basic three-parameter model, (b) the five-parameter model, and (c) the seven-parameter model. The basic three-parameter model does not incorporate series and shunt resistance for IV curves. The five-parameter model incorporates the effect of series and shunt resistance, and the seven-parameter model further includes the additional effect of temperature and irradiance variation on solar cell parameters. While all these models reasonably predict IV profiles of solar modules at small variations from standard testing conditions (STCs), their performance in modeling the module performance at low irradiances and high temperatures is far from ideal. This work primarily reviews the accuracy of available models for various module technologies not only under STC conditions but also over a wide range of operating conditions. The accuracy of modeled results is quantified (with datasheet results) for 10 crystalline silicon (c-Si) based modules as well as 9 thin film module (TF) samples (commercial modules) at multiple irradiance conditions. The results show that the three-parameter model generally overestimates the power output both for c-Si and TF modules. The five-parameter model predicts TF technology more accurately compared to the other two available models, whereas the seven-parameter model is most accurate for c-Si module modeling under varying operations.