Macrophage Immunomodulation Through New Polymers that Recapitulate Functional Effects of Itaconate as a Power House of Innate Immunity

Itaconate (ITA) is an emerging powerhouse of innate immunity with therapeutic potential that is limited in its ability to be administered in a soluble form. A library of polyester materials that incorporate ITA into polymer backbones resulting in materials with inherent immunoregulatory behavior is developed. Harnessing hydrolytic degradation release from polyester backbones, ITA polymers result in the mechanism specific immunoregulatory properties on macrophage polarization in vitro. In a functional assay, the polymer-released ITA inhibits bacterial growth on acetate. Translation to an in vivo model of biomaterial associated inflammation, intraperitoneal injection of ITA polymers demonstrate a rapid resolution of inflammation in comparison to a control polymer silicone, demonstrating the value of sustained biomimetic presentation of ITA.     

Compile-time copy elimination

Single-assignment and functional languages have value semantics that do not permit side-effects. This lack of side-effects makes automatic detection of parallelism and optimization for data locality in programs much easier. However, the same property poses a challenge in implementing these languages efficiently. This paper describes an optimizing compiler system that solves the key problem of aggregate copy elimination. The methods developed rely exclusively on compile-time algorithms, including interprocedural analysis, that are applied to an intermediate data flow representation. By dividing the problem into update-in-place and build-in-place analysis, a small set of relatively simple techniques—edge substitution, graph pattern matching, substructure sharing and substructure targeting—was found to be very powerful. If combined properly and implemented carefully, the algorithms eliminate unnecessary copy operations to a very high degree. No run-time overhead is imposed on the compiled programs.     

Reliability-based failure cause assessment of collapsed bridge during construction

Until now, in many forensic reports, the failure cause assessments are usually carried out by a deterministic approach so far. However, it may be possible for the forensic investigation to lead to unreasonable results far from the real collapse scenario, because the deterministic approach does not systematically take into account any information on the uncertainties involved in the failures of structures.Reliability-based failure cause assessment (reliability-based forensic engineering) methodology is developed which can incorporate the uncertainties involved in structural failures and structures, and to apply them to the collapsed bridge in order to identify the most critical failure scenario and find the cause that triggered the bridge collapse. Moreover, to save the time and cost of evaluation, an algorithm of automated event tree analysis (ETA) is proposed and possible to automatically calculate the failure probabilities of the failure events and the occurrence probabilities of failure scenarios. Also, for reliability analysis, uncertainties are estimated more reasonably by using the Bayesian approach based on the experimental laboratory testing data in the forensic report. For the applicability, the proposed approach is applied to the Hang-ju Grand Bridge, which collapsed during construction, and compared with deterministic approach.     

A STOCHASTIC CHARACTERIZATION OF THE MACHINE TOOL WORKPIECE SYSTEM IN BTA OEEP HOLE MACHINING Part I: Mathematical Modelling and Analysis

A three dimensional model of the BTA deep-hole machining system is presented by modelling each of the components separately and later combining to represent the total system. A model for the interaction between the workpiece and the cutting tool is also included. Such a model can determine the response of any component of the machine tool as well as the individual influence on the system performance. Based on this, physical models representing the three working methods in the BTA process can be studied, from which stochastic differential equations are derived to represent the resultant force system on the machine tool

A physical model for the stationary workpiece and rotating cutting tool working method is developed. The assumed modes method along with the Lagrange' equation is used to obtain the stochastic differential equation to represent the influence of axial force and torque, in order to obtain the response of the system under the action of the axial force and torque to predict the stability behaviour.     

Scattering from a thin perfectly-conducting square plate

Extensive numerical experiments on the scattering from a thin perfectly conducting square plate have been carried out to assess the performance of the exact analytical expressions for the electromagnetic field of a rectangular patch with uniform and linear distributions of current, in connection with the method of moments. Two solution schemes, employing pulses and roof-top functions for approximating the surface current on the plate, have been used. Convergence rates and results for the two solution schemes are compared with each other, as well as with an efficient solution by A.W. Glisson and D.R. Wilton (1980). The overall performance indicated by the numerical experiments suggests that it would be useful to use the exact numerical expressions in the solution of problems where accurate computation of the field radiated by such current sources is required     

Recent Advances in Hierarchical Reinforcement Learning

Reinforcement learning is bedeviled by the curse of dimensionality: the number of parameters to be learned grows exponentially with the size of any compact encoding of a state. Recent attempts to combat the curse of dimensionality have turned to principled ways of exploiting temporal abstraction, where decisions are not required at each step, but rather invoke the execution of temporally-extended activities which follow their own policies until termination. This leads naturally to hierarchical control architectures and associated learning algorithms. We review several approaches to temporal abstraction and hierarchical organization that machine learning researchers have recently developed. Common to these approaches is a reliance on the theory of semi-Markov decision processes, which we emphasize in our review. We then discuss extensions of these ideas to concurrent activities, multiagent coordination, and hierarchical memory for addressing partial observability. Concluding remarks address open challenges facing the further development of reinforcement learning in a hierarchical setting.