The Renin–Angiotensin-Aldosterone System,Nitric Oxide,and Hydrogen Sulfide at the Crossroads of Hypertension and COVID-19: Racial Disparities and Outcomes

Coronavirus disease 2019 is caused by SARS-CoV-2 and is more severe in the elderly, racial minorities, and those with comorbidities such as hypertension and diabetes. These pathologies are often controlled with medications involving the renin–angiotensin–aldosterone system (RAAS). RAAS is an endocrine system involved in maintaining blood pressure and blood volume through components of the system. SARS-CoV-2 enters the cells through ACE2, a membrane-bound protein related to RAAS. Therefore, the use of RAAS inhibitors could worsen the severity of COVID-19’s symptoms, especially amongst those with pre-existing comorbidities. Although a vaccine is currently available to prevent and reduce the symptom severity of COVID-19, other options, such as nitric oxide and hydrogen sulfide, may also have utility to prevent and treat this virus.     

Grid Integration of Wind Generation Considering Remote Wind Farms: Hybrid Markovian and Interval Unit Commitment

Grid integration of wind power is essential to reduce fossil fuel usage but challenging in view of the intermittent nature of wind. Recently, we developed a hybrid Markovian and interval approach for the unit commitment and economic dispatch problem where power generation of conventional units is linked to local wind states to dampen the effects of wind uncertainties. Also, to reduce complexity, extreme and expected states are considered as interval modeling. Although this approach is effective, the fact that major wind farms are often located in remote locations and not accompanied by conventional units leads to conservative results. Furthermore, weights of extreme and expected states in the objective function are difficult to tune, resulting in significant differences between optimization and simulation costs. In this paper, each remote wind farm is paired with a conventional unit to dampen the effects of wind uncertainties without using expensive utility-scaled battery storage, and extra constraints are innovatively established to model pairing. Additionally, proper weights are derived through a novel quadratic fit of cost functions. The problem is solved by using a creative integration of our recent surrogate Lagrangian relaxation and branch-and-cut. Results demonstrate modeling accuracy, computational efficiency, and significant reduction of conservativeness of the previous approach.      

Teaching the User By Learning From the User:Personalizing Movement Control in Physical Human-robot Interaction

This paper proposes a novel approach for physical human-robot interactions (pHRI), where a robot provides guidance forces to a user based on the user performance. This framework tunes the forces in regards to behavior of each user in coping with different tasks, where lower performance results in higher intervention from the robot. This personalized physical human-robot interaction (p2HRI) method incorporates adaptive modeling of the interaction between the human and the robot as well as learning from demonstration (LfD) techniques to adapt to the users' performance. This approach is based on model predictive control where the system optimizes the rendered forces by predicting the performance of the user. Moreover, continuous learning of the user behavior is added so that the models and personalized considerations are updated based on the change of user performance over time. Applying this framework to a field such as haptic guidance for skill improvement, allows a more personalized learning experience where the interaction between the robot as the intelligent tutor and the student as the user, is better adjusted based on the skill level of the individual and their gradual improvement. The results suggest that the precision of the model of the interaction is improved using this proposed method, and the addition of the considered personalized factors to a more adaptive strategy for rendering of guidance forces.      

Challenges and alternatives for unmanned underwater vehicular research in the Amazon basin: Towards a more sustainable management of water resources and the environment

The Amazon basin is one of the largest hydrographic systems in the world, possessing a great diversity of natural resources in need of more sustainable water and environmental management. However, as much of what lies beneath the surface of Amazonian waters is still unexplored, underwater exploration technologies are seen as a means of research and monitoring. The present work therefore aims to answer two research questions: What are the current technologies that could be implemented for unmanned underwater research in the Amazon waters? What are the main limitations and existing alternatives for using unmanned underwater vehicles in the rivers of this region to provide more sustainable water and environment management? Our results demonstrate that remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs) are possible options in the short and long terms, respectively. The main challenges in implementing these technologies are related to the variations in hydraulic geometry, current velocities and turbidity of the rivers, as well as the preservation of the region's biodiversity. This research can be taken as a starting point for planners and decision makers seeking more sustainable underwater and environmental exploration of the Amazon river system.     

Monitoring a labeled degree-corrected stochastic block model

The stochastic block model (SBM) is a random graph model that focuses on partitioning the nodes into blocks or communities. A degree-corrected stochastic block model (DCSBM) considers degree heterogeneity within nodes. Investigation of the type of edge label can be useful for studying networks. We have proposed a labeled degree-corrected stochastic block model (LDCSBM), added the probability of the occurrence of each edge label, and monitored the behavior of this network. The LDCSBM is a dynamic network that varies over time; thus, we applied the monitoring process to both the US Senate voting network and simulated networks by defining structural changes. We used the Shewhart control chart for detecting changes and studied the effect of Phase I parameter estimation on Phase II performance. The efficiency of the model for surveillance has been evaluated using the average run length for estimated parameters.     

Analysis of interaction effects of persulphate and peroxymonosulphate on solar photocatalytic degradation of cortisone acetate

The degradation of environmental contaminants using sustainable solar energy is one of the most promising applications of the photocatalytic process. In this research, the individual and interaction effects of persulphate (PS) and peroxymonosulphate (PMS) oxidants on the solar photocatalytic degradation of cortisone acetate (CA) were studied. Concentrations of PS, PMS, and photocatalyst were independent variables, and the response was CA degradation efficiency. Response surface methodology (RSM) and artificial neural network (ANN) models were developed to predict the CA degradation efficiency. The analysis of the current results revealed that the optimum amounts of individual variables were highly affected by the other variables, which confirmed their significant interaction effect. As an example, by increasing the PS concentration, not only were the required concentrations of PMS and photocatalyst decreased, but also the degradation efficiency was enhanced. Then, the overall optimum concentration of the photocatalyst, PS, and PMS were found to be respectively 328.7, 119.1, and 194.2 mg/L using the genetic algorithm method. The maximum CA degradation efficiency at the optimum condition was 95.6% after only 30 min of solar radiation. Finally, investigation of relative importance of the variables showed that the concentrations of both oxidants affected the degradation efficiency almost equally.     

Tunable Terahertz Wavelength Conversion Based on Optofluidic Infiltrated Rib Silicon Waveguides

Silicon - Terahertz (THz) sources have attracted special attention for various applications. Compared to the standard silica-based optical fibers, silicon waveguides have more advantages such as...