E7766, a Macrocycle-Bridged Stimulator of Interferon Genes (STING) Agonist with Potent Pan-Genotypic Activity

A strategy for creating potent and pan-genotypic stimulator of interferon genes (STING) agonists is described. Locking a bioactive U-shaped conformation of cyclic dinucleotides by introducing a transannular macrocyclic bridge between the nucleic acid bases leads to a topologically novel macrocycle-bridged STING agonist (MBSA). In addition to substantially enhanced potency, the newly designed MBSAs, exemplified by clinical candidate E7766 , exhibit broad pan-genotypic activity in all major human STING variants. E7766 is shown to have potent antitumor activity with long lasting immune memory response in a mouse liver metastatic tumor model. Two complementary stereoselective synthetic routes to E7766 are also described.     

A modelling framework to support design of complex engineering systems in early design stages

Production, assembly or logistic systems exist in widespread domains. It is agreed that more than 50% of life-cycle performance, costs and environmental impacts of such systems are due to those decisions that are made in their early design stages (Reich, Res Eng Design 28(4):411–419, https://doi.org/10.1007/s00163-017-0270-7, 2017). However, the large scale and multi-disciplinary essence of such systems make their design considerably challenging. Most of the design approaches follow a sequential approach such that the design in each lower level is finalized/frozen before proceeding to the next level. However, such approaches do not properly address the interaction between different design disciplines which may later lead to design inconsistencies. Therefore, this paper aimed to propose a modelling framework that allows having an integrated approach in the early design stages of such systems. To this end, first the framework prescribed developing an executable meta-architecture that can embody all the design requirements. Second, the framework describes the interconnections between the meta-architecture with certain supporting algorithms and optimization models. This allows generating and simulating different design alternatives and observing the impact of different design decisions on system integrated performance. Therefore, the proposed framework with its providing outcomes can be used to support the decision making in early design stages of such systems. The framework is applied in a real case study from the warehousing domain, which serves to show the practical application of the proposed framework.     

Effects of different dwell-times under low pelletization pressure on the physical properties of the Bi-2212 ceramics

Journal of Materials Science: Materials in Electronics - We reported the evaluation of superconducting properties of Bi-2212 ceramics prepared under different dwell times between 12 and...     

Transport phenomena in electrolyte solutions: Nonequilibrium thermodynamics and statistical mechanics

The theory of transport phenomena in multicomponent electrolyte solutions is presented here through the integration of continuum mechanics, electromagnetism, and nonequilibrium thermodynamics. The governing equations of irreversible thermodynamics, including balance laws, Maxwell's equations, internal entropy production, and linear laws relating the thermodynamic forces and fluxes, are derived. Green–Kubo relations for the transport coefficients connecting electrochemical potential gradients and diffusive fluxes are obtained in terms of the flux–flux time correlations. The relationship between the derived transport coefficients and those of the Stefan–Maxwell and infinitely dilute frameworks are presented, and the connection between the transport matrix and experimentally measurable quantities is described. To exemplify the application of the derived Green–Kubo relations in molecular simulations, the matrix of transport coefficients for lithium and chloride ions in dimethyl sulfoxide is computed using classical molecular dynamics and compared with experimental measurements.     

DETERMINATION OF COPPER(II) IN NATURAL WATERS BY EXTRACTION USING N-O-VANILLIDINE-2-AMINO-P-CRESOL AND FLAME ATOMIC ABSORPTION SPECTROMETRY

A simple and accurate solvent extraction method was developed for the separation and preconcentration of trace levels of copper(II) in water using N-o-vanillidine-2-amino-p-cresol prior to its determination using flame atomic absorption spectrometry. Analytical parameters such as the pH of the aqueous phase, extraction time, concentration of the reagent, concentration of the nitric acid back extraction agent, volumes of aqueous phase and back extraction agent, and concomitant ions on the extraction yield of the copper(II) were investigated and optimized. Under the optimal conditions, the detection limit (3σ) was 0.61 µ g L^?1 for a 25 mL blank solution, yielding a preconcentration factor of 12.5. The method was succesfully applied to the determination of copper(II) in tap water, river water, and seawater. The accuracy of the method was verified by analyzing two certified reference materials and spiked water samples. The results obtained were in agreement with the certified values and the recoveries for spiked water samples were between 98 and 112%.     

Benders Decomposition Algorithms for Two Variants of the Single Allocation Hub Location Problem

Networks and Spatial Economics - The hub location problem (HLP) is a special type of the facility location problem with numerous applications in the airline industry, postal services, and computer...     

Numerical Modeling of Exit-Ply Delamination During Drilling of CFRPs Considering Thermal Effects

Abstract

A finite element (FE) model for exit-ply delamination during drilling carbon fiber reinforced polymers (CFRPs) laminates is presented. The current FE model is developed to predict critical thrust force at the onset of delamination for 1 and 2 plies under the twist drill for various cutting temperatures. The interface behavior for delamination onset is modeled using surface based cohesive zone model (CZM). The numerical predictions for critical thrust force are compared with experimental thrust forces for various number of plies under the twist drill over a range of cutting temperature. Thrust force predictions were found to match with experimental data.     

Refinement of some basic features of Zr surface-layered Bi-2223 superconductor with diffusion annealing temperature

This study aims to investigate the influences of diffusion annealing temperatures on structural, morphological, electrical, and superconducting features of Zr surface-layered Bi-2223 ceramics. The present study also covers an in-depth understanding of correlations between disorders and transition temperatures. The Zr diffusion is carried out via an annealing process between 650 and 840 °C. The observed results depict that the Zr ions can easily diffuse into the deeper level of Bi-ceramics and possible Zr/Bi substitution has occurred due to the driving force of high thermal energy. Besides, it is found that the Zr diffusion improves the general crystallinity quantities of Bi-2223 ceramic up to 800 °C annealing temperature. In addition, better intergranular couplings with a smoother plate-like structure are extensively observed in surface morphology for the samples annealed at 800 °C. Significant refinements of both basic electrical resistivity, hole carrier densities, and critical temperatures with narrow transitions are also obtained for the Zr surface-layered Bi-2223 ceramics after the 800 °C annealing process. The obtained improvements in critical fundamental features can be attributed to the optimum pairing mechanism, best crystal structure quality, ideal Cu–O₂ interlayer coupling strengths, and enhanced interaction between adjacent superconductive layers. Besides, the first-order derivative of electrical resistivity versus temperature graphs indicates that the best annealing temperature enables to triggers to stabilize the superconductivity in the homogeneous regions. It can be concluded that the Zr impurity diffusion at 800 °C is promising for the improvement in the basic features of Bi-2223 superconducting systems for future applications in superconductor technology.

     

Learning-based symbolic assume-guarantee reasoning for Markov decision process by using interval Markov process

Many real-life critical systems are described with large models and exhibit both probabilistic and non-deterministic behaviour. Verification of such systems requires techniques to avoid the state space explosion problem. Symbolic model checking and compositional verification such as assume-guarantee reasoning are two promising techniques to overcome this barrier. In this paper, we propose a probabilistic symbolic compositional verification approach (PSCV) to verify probabilistic systems where each component is a Markov decision process (MDP). PSCV starts by encoding implicitly the system components using compact data structures. To establish the symbolic compositional verification process, we propose a sound and complete symbolic assume-guarantee reasoning rule. To attain completeness of the symbolic assume-guarantee reasoning rule, we propose to model assumptions using interval MDP. In addition, we give a symbolic MTBDD-learning algorithm to generate automatically the symbolic assumptions. Moreover, we propose to use causality to generate small counterexamples in order to refine the conjecture assumptions. Experimental results suggest promising outlooks for our probabilistic symbolic compositional approach.     

Passenger Flow Prediction Based on Newly Adopted Algorithms

Passenger flow forecasting is an essential part of transportation systems. Neural networks in the transportation field have been applied to passenger demand prediction. In this paper, we developed two hybrid methods, known as parlimentary optimization algorithm-artificial neural network (POA-ANN), and intelligent water drops algorithm-ANN (IWD algorithm-ANN). In addition, we applied the proposed algorithms to illustrate the effect of precise prediction for passenger queues. We mainly focus on predicting passenger demand by comparing the genetic algorithm-ANN (GA-ANN) with POA-ANN and IWD-ANN. The results of prediction methods suggest that both POA-ANN and IWD-ANN provide a better forecasting performance, which is obtained via mean square error (MSE), than GA-ANN in the field of passenger flow prediction. This study illustrates that the newly adopted algorithms exhibit good performance for passenger prediction.