RESUMEN

In the conventional finite control set model predictive torque control, the cost function consists of different control objectives with varying units of measurements. Due to presence of diverse variables in cost function, weighting factors are used to set the relative importance of these objectives. However, selection of these weighting factors in predictive control of electric drives and power converters still remains an open research challenge. Improper selection of weighting factors can lead to deterioration of the controller performance. This work proposes a novel weighting factor tuning method based on the Multi-Criteria-Decision-Making (MCDM) technique called the Entropy method. This technique has several advantages for multi-objective problem optimization. It provides a quantitive approach and incorporates uncertainties and adaptability to assess the relative importance of different criteria or objectives. This technique performs the online tuning of the weighting factor by forming a data set of the control objectives, i.e., electromagnetic torque and stator flux magnitude. After obtaining the error set of control variables, the objective matrix is normalized, and the entropy technique is applied to design the corresponding weights. An experimental setup based on the dSpace dS1104 controller is used to validate the effectiveness of the proposed method for a two-level, three-phase voltage source inverter (2L-3P) fed induction motor drive. The dynamic response of the proposed technique is compared with the previously proposed MCDM-based weighting factor tuning technique and conventional MPTC. The results reveal that the proposed method provides an improved dynamic response of the drive under changing operating conditions with a reduction of 28% in computational burden and 38% in total harmonic distortion, respectively.

RESUMEN

Estimated age-adjusted comparative diabetes prevalence in adults (20-79 years) in Pakistan from the year 2011 to 2021.

Asunto(s)

Isquemia Encefálica , Accidente Cerebrovascular , Humanos , Tenecteplasa/uso terapéutico , Pakistán , Accidente Cerebrovascular/tratamiento farmacológico , Activador de Tejido Plasminógeno/uso terapéutico , Fibrinolíticos/uso terapéutico , Isquemia Encefálica/tratamiento farmacológico , Resultado del Tratamiento

RESUMEN

Diphtheria, a vaccine-preventable disease, remains a concern in Pakistan as cases have risen post-COVID-19 pandemic causing more than 45 deaths in Pakistan in the year 2022. The respiratory variant of the disease is more common and can lead to serious complications, such as myocarditis and respiratory insufficiency. Diphtheria has caused havoc in the past killing millions of people worldwide before the development of its vaccine. Although the diphtheria toxoid vaccine is effective against toxigenic strains, there have been cases of treatment-resistant strains, particularly the non-toxigenic strains of C. diphtheriae. Pakistan's economic and health systems have suffered setbacks, which have been exacerbated by the COVID-19 pandemic. The pandemic has disrupted routine vaccination programs, and recent floods have contributed to an increase in diphtheria cases and rendered millions homeless. Poor immunization services, inadequate training of vaccination teams, and wealth inequality have all contributed to unequal vaccination coverage in Pakistan. The rising cases of diphtheria call for prompt action, including booster shots, updating vaccination records and administering immediate doses of the toxoid to close contacts.

RESUMEN

Model Predictive Control (MPC) is an effective method of driving motors and power converters due to its quick response, integrity, and multivariable control adaptability. A model-predictive torque control (MPTC) technique for permanent magnet synchronous motors (IPMSMs), which is computationally efficient and of low complexity, is presented in this paper. The proposed technique designs a lookup table that is independent of flux angle and torque deviation. For each control instant, this technique has to evaluate four voltage space vectors (VSV) from the lookup table, resulting in a substantial reduction in switching frequency and computational burden without compromising the performance. A maximum torque per ampere (MTPA) technique generates reference currents. The controller's complexity is minimized by eliminating the flux weighting factor from the cost function, saving time on offline weighting factor adjustments. Moreover, duty cycle optimization is performed using the mean torque control technique to minimize torque and flux ripples. The proposed method has been experimentally validated using a real-time simulator hardware in loop (HIL) with a TMS320F28335 floating-point digital signal processor on a prototype IPMSM drive. Furthermore, the proposed MPTC scheme is compared to conventional MPTC and direct torque control (DTC).

RESUMEN

Additively manufactured nano-MEH systems are widely used to harvest energy from renewable and sustainable energy sources such as wind, ocean, sunlight, raindrops, and ambient vibrations. A comprehensive study focusing on in-depth technology evolution, applications, problems, and future trends of specifically 3D printed nano-MEH systems with an energy point of view is rarely conducted. Therefore, this paper looks into the state-of-the-art technologies, energy harvesting sources/methods, performance, implementations, emerging applications, potential challenges, and future perspectives of additively manufactured nano-mechanical energy harvesting (3DP-NMEH) systems. The prevailing challenges concerning renewable energy harvesting capacities, optimal energy scavenging, power management, material functionalization, sustainable prototyping strategies, new materials, commercialization, and hybridization are discussed. A novel solution is proposed for renewable energy generation and medicinal purposes based on the sustainable utilization of recyclable municipal and medical waste generated during the COVID-19 pandemic. Finally, recommendations for future research are presented concerning the cutting-edge issues hurdling the optimal exploitation of renewable energy resources through NMEHs. China and the USA are the most significant leading forces in enhancing 3DP-NMEH technology, with more than 75% contributions collectively. The reported output energy capacities of additively manufactured nano-MEH systems were 0.5-32 mW, 0.0002-45.6 mW, and 0.3-4.67 mW for electromagnetic, piezoelectric, and triboelectric nanogenerators, respectively. The optimal strategies and techniques to enhance these energy capacities are compiled in this paper.