Comparative study of vacuum-assisted closure therapy versus vacuum-assisted closure therapy supplemented with vitamin C in compound wound healing

Background: It is imperative for early and precise management of the compound wound for preventing further complication and delaying definitive management. As we all know superiority of vacuum assisted closure (VAC) therapy in wound management over any other method, but adding vitamin C has been shown to accelerate wound healing, reducing hospital stay, and cost of management and prevention of delaying definitive management of wound due to some conspicuous property of vitamin C that serve as superior adjuvant in wound healing. Methods: A case series of 40 patients who have been inflicted with compound wounds with most following road traffic accidents. We then categorised patients and tried to observe any difference in rate of satisfactorily healing of wound with 20 patients put on VAC therapy alone and other 20 patients put on VAC therapy supplemented with vit C. Results: Patients who were undergoing VAC dressing and supplemented with vitamin C, not only portrayed a better result of wound healing but also reduced the amount of vacuum dressing sittings. Conclusions: It was observed that, in general, patients who were undergoing VAC dressing and supplemented with vitamin C, not only portrayed a better result of wound healing but also reduced amount of vacuum dressing sittings, improved rate of granulation tissue, reduced hospital stay, early definitive fixation of associated fracture and skin grafting and showed superior outcomes in terms of better tissue recovery

Maximizing Seaweed Growth on Autonomous Farms: A Dynamic Programming Approach for Underactuated Systems Navigating on Uncertain Ocean Currents

Seaweed biomass offers significant potential for climate mitigation, but large-scale, autonomous open-ocean farms are required to fully exploit it. Such farms typically have low propulsion and are heavily influenced by ocean currents. We want to design a controller that maximizes seaweed growth over months by taking advantage of the non-linear time-varying ocean currents for reaching high-growth regions. The complex dynamics and underactuation make this challenging even when the currents are known. This is even harder when only short-term imperfect forecasts with increasing uncertainty are available. We propose a dynamic programming-based method to efficiently solve for the optimal growth value function when true currents are known. We additionally present three extensions when as in reality only forecasts are known: (1) our methods resulting value function can be used as feedback policy to obtain the growth-optimal control for all states and times, allowing closed-loop control equivalent to re-planning at every time step hence mitigating forecast errors, (2) a feedback policy for long-term optimal growth beyond forecast horizons using seasonal average current data as terminal reward, and (3) a discounted finite-time Dynamic Programming (DP) formulation to account for increasing ocean current estimate uncertainty. We evaluate our approach through 30-day simulations of floating seaweed farms in realistic Pacific Ocean current scenarios. Our method demonstrates an achievement of 95.8% of the best possible growth using only 5-day forecasts. This confirms the feasibility of using low-power propulsion and optimal control for enhanced seaweed growth on floating farms under real-world conditions.Comment: 8 pages, submitted to 2023 IEEE 62th Annual Conference on Decision and Control (CDC) Matthias Killer and Marius Wiggert contributed equally to this wor

Energy-time optimal path planning in strong dynamic flows

Thesis: S.M., Massachusetts Institute of Technology, Center for Computational Science & Engineering, February, 2021Cataloged from the official PDF version of thesis.Includes bibliographical references (pages 55-61).We develop an exact partial differential equation-based methodology that predicts time-energy optimal paths for autonomous vehicles navigating in dynamic environments. The differential equations solve the multi-objective optimization problem of navigating a vehicle autonomously in a dynamic flow field to any destination with the goal of minimizing travel time and energy use. Based on Hamilton-Jacobi theory for reachability and the level set method, the methodology computes the exact Pareto optimal solutions to the multi-objective path planning problem, numerically solving the equations governing time-energy reachability fronts and optimal paths. Our approach is applicable to path planning in various scenarios, however we primarily present examples of navigating in dynamic marine environments. First, we validate the methodology through a benchmark case of crossing a steady front (a highway flow) for which we compare our results to semi-analytical optimal path solutions. We then consider more complex unsteady environments and solve for time-energy optimal missions in a quasi-geostrophic double-gyre ocean flow field.by Manan Doshi.S.M.S.M. Massachusetts Institute of Technology, Center for Computational Science & Engineerin

Over-the-Air Design of GAN Training for mmWave MIMO Channel Estimation

Future wireless systems are trending towards higher carrier frequencies that offer larger communication bandwidth but necessitate the use of large antenna arrays. Existing signal processing techniques for channel estimation do not scale well to this "high-dimensional" regime in terms of performance and pilot overhead. Meanwhile, training deep learning based approaches for channel estimation requires large labeled datasets mapping pilot measurements to clean channel realizations, which can only be generated offline using simulated channels. In this paper, we develop a novel unsupervised over-the-air (OTA) algorithm that utilizes noisy received pilot measurements to train a deep generative model to output beamspace MIMO channel realizations. Our approach leverages Generative Adversarial Networks (GAN), while using a conditional input to distinguish between Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) channel realizations. We also present a federated implementation of the OTA algorithm that distributes the GAN training over multiple users and greatly reduces the user side computation. We then formulate channel estimation from a limited number of pilot measurements as an inverse problem and reconstruct the channel by optimizing the input vector of the trained generative model. Our proposed approach significantly outperforms Orthogonal Matching Pursuit on both LOS and NLOS channel models, and EM-GM-AMP -- an Approximate Message Passing algorithm -- on LOS channel models, while achieving comparable performance on NLOS channel models in terms of the normalized channel reconstruction error. More importantly, our proposed framework has the potential to be trained online using real noisy pilot measurements, is not restricted to a specific channel model and can even be utilized for a federated OTA design of a dataset generator from noisy data.Comment: 34 pages, 12 figures, 5 tables. Under review for publication in IEEE Journal of Sel. Areas in Information Theor