The Effects of Insulin-Like Growth Factor I and BTP-2 on Acute Lung Injury

Acute lung injury (ALI) afflicts approximately 200,000 patients annually and has a 40% mortality rate. The COVID-19 pandemic has massively increased the rate of ALI incidence. The pathogenesis of ALI involves tissue damage from invading microbes and, in severe cases, the overexpression of inflammatory cytokines such as tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β). This study aimed to develop a therapy to normalize the excess production of inflammatory cytokines and promote tissue repair in the lipopolysaccharide (LPS)-induced ALI. Based on our previous studies, we tested the insulin-like growth factor I (IGF-I) and BTP-2 therapies. IGF-I was selected, because we and others have shown that elevated inflammatory cytokines suppress the expression of growth hormone receptors in the liver, leading to a decrease in the circulating IGF-I. IGF-I is a growth factor that increases vascular protection, enhances tissue repair, and decreases pro-inflammatory cytokines. It is also required to produce anti-inflammatory 1,25-dihydroxyvitamin D. BTP-2, an inhibitor of cytosolic calcium, was used to suppress the LPS-induced increase in cytosolic calcium, which otherwise leads to an increase in proinflammatory cytokines. We showed that LPS increased the expression of the primary inflammatory mediators such as toll like receptor-4 (TLR-4), IL-1β, interleukin-17 (IL-17), TNF-α, and interferon-γ (IFN-γ), which were normalized by the IGF-I + BTP-2 dual therapy in the lungs, along with improved vascular gene expression markers. The histologic lung injury score was markedly elevated by LPS and reduced to normal by the combination therapy. In conclusion, the LPS-induced increases in inflammatory cytokines, vascular injuries, and lung injuries were all improved by IGF-I + BTP-2 combination therapy.     

Perceiving,learning, and exploiting object affordances for autonomous pile manipulation

Autonomous manipulation in unstructured environments will enable a large variety of exciting and important applications. Despite its promise, autonomous manipulation remains largely unsolved. Even the most rudimentary manipulation task—such as removing objects from a pile—remains challenging for robots. We identify three major challenges that must be addressed to enable autonomous manipulation: object segmentation, action selection, and motion generation. These challenges become more pronounced when unknown man-made or natural objects are cluttered together in a pile. We present a system capable of manipulating unknown objects in such an environment. Our robot is tasked with clearing a table by removing objects from a pile and placing them into a bin. To that end, we address the three aforementioned challenges. Our robot perceives the environment with an RGB-D sensor, segmenting the pile into object hypotheses using non-parametric surface models. Our system then computes the affordances of each object, and selects the best affordance and its associated action to execute. Finally, our robot instantiates the proper compliant motion primitive to safely execute the desired action. For efficient and reliable action selection, we developed a framework for supervised learning of manipulation expertise. To verify the performance of our system, we conducted dozens of trials and report on several hours of experiments involving more than 1,500 interactions. The results show that our learning-based approach for pile manipulation outperforms a common sense heuristic as well as a random strategy, and is on par with human action selection.     

Investigation of effective dimensionless numbers on initiation of instability in combustion of moisty organic dust简

In this work, the effect of various effective dimensionless numbers and moisture contents on initiation of instability in combustion of moisty organic dust is calculated. To have reliable model, effect of thermal radiation is taken into account. One- dimensional flame structure is divided into three zones： preheat zone, reaction zone and post-flame zone. To investigate pulsating characteristics of flame, governing equations are rewritten in dimensionless space-time （（, r/, ~） coordinates. By solving these newly achieved governing equations and combining them, which is completely discussed in body of article, a new expression is obtained. By solving this equation, it is possible to predict initiation of instability in organic dust flame. According to the obtained results by increasing Lewis number, threshold of instability happens sooner. On the other hand, pulsating is postponed by increasing Damk6hler number, pyrolysis temperature or moisture content. Also, by considering thermal radiation effect, burning velocity predicted by our model is closer to experimental results.     

Fracture mechanics‐based progressive damage modelling of adhesively bonded fibre‐reinforced polymer joints

A quasi‐static progressive damage model for prediction of the fracture behaviour and strength of adhesively bonded fibre‐reinforced polymer joints is introduced in this paper. The model is based on the development of a mixed‐mode failure criterion as a function of a master R‐curve derived from the experimental results obtained from standard fracture mechanics joints. Consequently, the developed failure criterion is crack‐length and mode‐mixity dependent, and it takes into account the contribution of the fibre‐bridging effect. Energy release rate values for adhesively bonded double‐lap joints are obtained by using the virtual crack closure technique method in a finite element model, and the numerically obtained strain energy release rate is compared to the critical strain energy release rate given by the mixed‐mode failure criterion. The entire procedure is implemented in a numerical algorithm, which was successfully used for predicting the strength and R‐curve response of adhesively bonded double‐lap structural joints made of pultruded glass fibre‐reinforced polymers and epoxy adhesives.     

A parameter-tuned genetic algorithm for the resource investment problem with discounted cash flows and generalized precedence relations

A resource investment problem with discounted cash flows (RIPDCF) is a project-scheduling problem in which (a) the availability levels of the resources are considered decision variables and (b) the goal is to find a schedule such that the net present value of the project cash flows optimizes. In this paper, the RIPDCF in which the activities are subject to generalized precedence relations is first modeled. Then, a genetic algorithm (GA) is proposed to solve this model. In addition, design of experiments and response surface methodology are employed to both tune the GA parameters and to evaluate the performance of the proposed method in 240 test problems. The results of the performance analysis show that the efficiency of the proposed GA method is relatively well.     

A robust learning based evolutionary approach for thermal-economic optimization of compact heat exchangers

This paper presents a robust, efficient and parameter-setting-free evolutionary approach for the optimal design of compact heat exchangers. A learning automata based particle swarm optimization (LAPSO) is developed for optimization task. Seven design parameters, including discreet and continuous ones, are considered as optimization variables. To make the constraint handling straightforward, a self-adaptive penalty function method is employed. The efficiency and the accuracy of the proposed method are demonstrated through two illustrative examples that include three objectives, namely minimum total annual cost, minimum weight and minimum number of entropy generation units. Numerical results indicate that the presented approach generates the optimum configuration with higher accuracy and a higher success rate when compared with genetic algorithms (GAs) and particle swarm optimization (PSO).     

Accurate analysis of spectral regrowth of nonlinear power amplifier driven by cyclostationary modulated signals

In this paper we present an exact analytical expression to calculate the spectral regrowth at the output of a nonlinear power amplifier (PA) using the higher order cumulants and Poisson summation formula. This PA is driven by the filtered digitally modulated signals. To improve the accuracy of the calculations, the cyclosationarity of the input signal is considered. Moreover, closed-form expressions for the 1-dB compression and saturation points are extracted as a function of the PA model parameters, higher order statistics of the input signal, and the transfer function of the pulse shaping filter. In addition, an analytical expression for the adjacent channel power (ACP) and a closed-form expression of the ACP ratio are derived. This is followed by investigation of the effect of the PA nonlinearity on the performance of receiver. Simulation studies are carried out to verify the accuracy of the derived expressions. Excellent agreement between the analytical and simulation results is achieved.