A deep learning based encoder-decoder model for speed planning of autonomous electric truck platoons.

Electric truck platooning offers a promising solution to extend the range of electric vehicles during long-haul operations. However, optimizing the platoon speed to ensure efficient energy utilization remains a critical challenge. The existing research on implementing data-driven solutions for truck platooning remains limited and implementing first principles solution is still a challenge. However, recognizing the resemblance of truck platoon data to a time series serves as a compelling motivation to explore suitable analytical techniques to address the problem. This paper presents a novel deep learning approach using a sequence-to-sequence encoder-decoder model to obtain the speed profile to be followed by an autonomous electric truck platoon considering various constraints such as the available state of charge (SOC) in the batteries along with other vehicles and road conditions while ensuring that the platoon is string stable. To ensure that the framework is suitable for long-haul highway operation, the model has been trained using various known highway drive cycles. Encoder-decoder models were trained and hyperparameter tuning was performed for the same. Finally, the most suitable model has been chosen for the application. For testing the entire framework, drive cycle/speed prediction corresponding to different desired SOC profiles has been presented. A case study showing the relevance of the proposed framework in predicting the drive cycle on various routes and its impact on taking critical policy decisions during the planning of electric truck platoons has also been presented. This study would help to efficiently plan the feasible routes for electric trucks considering multiple constraints such as battery capacity, expected discharge rate, charging infrastructure availability, route length/travel time, and other on-road operating conditions while also maintaining stability.

Influence of chitosan thioglycolic acid conjugate in improving bioavailability of an antiparkinson drug; Rasagiline Mesylate from transdermal patch.

OBJECTIVE: Parkinson disease (PD) is a chronic disorder of central nervous system mainly affecting the motor systems. The drug of choice to treat PD is Rasagiline Mesylate (RM) and it belongs to BCS class III drug. The objective of the present study was the preparation of transdermal drug delivery system for RM. Several permeation enhancers were screened to be included in the formulation. To achieve desired flux a new strategy was developed by including in-house prepared CTC to enhance the permeation of RM. METHODS: The CTC was prepared by reaction between chitosan and thioglycolicacid, characterized by determining physical properties and applying analytical tools. Seven permeation enhancers with different mechanisms were screened. The transdermal patches were prepared with chitosan along with permeation enhancer IPM, various proportions of CTC and evaluated for physical and permeation studies. The optimized transdermal patch was obtained by two factors and three responses to obtain the design space and further evaluated for pharmacokinetic studies. RESULTS: The results of the present study confirmed the formation of CTC, IPM was best permeation enhancer among all. The presence of CTC in the formulations significantly improved the permeation of RM to achieve desired steady-state flux. The relative bioavailability of optimized transdermal patch was determined and it was observed that improved bioavailability as compared to marketed conventional tablets. CONCLUSION: The study was concluded that CTC has significant influence on permeation enhancing ability of IPM.

An Augmented SEIR Model with Protective and Hospital Quarantine Dynamics for the Control of COVID-19 Spread

In this work, an attempt is made to analyse the dynamics of COVID-19 outbreak mathematically using a modified SEIR model with additional compartments and a nonlinear incidence rate with the help of bifurcation theory. Existence of a forward bifurcation point is presented by deriving conditions in terms of parameters for the existence of disease free and endemic equilibrium points. The significance of having two additional compartments, viz., protective and hospital quarantine compartments, is then illustrated via numerical simulations. From the analysis and results, it is observed that, by properly selecting transfer functions to place exposed and infected individuals in protective and hospital quarantine compartments, respectively, and with apt governmental action, it is possible to contain the COVID-19 spread effectively. Finally, the capability of the proposed model in predicting/representing the COVID-19 dynamics is presented by comparing with real-time data.

Dynamics and control of COVID-19 pandemic with nonlinear incidence rates.

World Health Organization (WHO) has declared COVID-19 a pandemic on March 11, 2020. As of May 23, 2020, according to WHO, there are 213 countries, areas or territories with COVID-19 positive cases. To effectively address this situation, it is imperative to have a clear understanding of the COVID-19 transmission dynamics and to concoct efficient control measures to mitigate/contain the spread. In this work, the COVID-19 dynamics is modelled using susceptible-exposed-infectious-removed model with a nonlinear incidence rate. In order to control the transmission, the coefficient of nonlinear incidence function is adopted as the Governmental control input. To adequately understand the COVID-19 dynamics, bifurcation analysis is performed and the effect of varying reproduction number on the COVID-19 transmission is studied. The inadequacy of an open-loop approach in controlling the disease spread is validated via numerical simulations and a robust closed-loop control methodology using sliding mode control is also presented. The proposed SMC strategy could bring the basic reproduction number closer to 1 from an initial value of 2.5, thus limiting the exposed and infected individuals to a controllable threshold value. The model and the proposed control strategy are then compared with real-time data in order to verify its efficacy.

Systemic Mastocytosis with Associated Clonal Hematological Non-Mast Cell Lineage Disorder (MDS-RCMD): A Difficult Disease to Diagnose and Treat.

Systemic mastocytosis is a rare and recalcitrant disorder with nonspecific clinical features. Hence, a high index of suspicion is required. Here, we report the case of a 64 years old male presenting with chronic diarrhoea that was evaluated at different centres and treated with multiple lines of therapy. The diagnosis of aggressive systemic mastocytosis was finally clinched following a holistic work up that included a Jejunal biopsy and a laparoscopic lymph node biopsy. Treatment of this disorder is difficult, responses are transient and most patients will eventually relapse, as illustrated by this case. Cladribine, Interferon α, steroids and imatinib have limited success in the management of this disease. The role of stem cell transplant is uncertain.