Synthesis of phosphorus‐ and phenyl‐based ROMP polymers and investigation of their effects on the thermomechanical and flammability properties of a polypropylene–IFR system

In this study, we present an approach for the synthesis of novel phosphorous‐ or phenyl‐ containing polymers, 2_phenyl, 3_phospho, and 2_phenyl‐co‐3_phospho, derived from ring‐opening metathesis polymerization (ROMP), to reduce the flammability of polypropylene (PP). The composites were processed by melt‐blending ROMP polymers and octaphenyl–polyhedral oligomeric silsesquioxane with PP/intumescent flame retardant (IFR) compounds at different compositions. The composites were characterized by limiting oxygen index (LOI), UL‐94, and mechanical tests as well as thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). The molecular structures of phosphorous‐ and phenyl‐containing polymers were proved by ¹H‐NMR, ¹³C‐NMR, and Fourier transform infrared spectroscopy. The online rheological measurements indicated that the addition of additives to the PP/IFR system increased the melt viscosity of the compounds regardless of the type. The DSC analysis showed that the addition of ROMP polymers to the PP/IFR system influenced the crystal perfection and degree of crystallization. TGA analysis of the composites revealed that the addition of ROMP polymers to PP/IFR compounds deteriorated the thermal stability as the amount of phosphorus increased in the matrix. Dynamic mechanical properties such as storage modulus (E′) and loss modulus (E″) of the composites were lowered by the addition of ROMP polymers. The LOI and UL‐94 rating of PP/IFR were enhanced by the addition of ROMP polymers. It was successfully demonstrated that the novel phosphorous‐ or phenyl‐containing polymers were highly potent additives in optimizing the flammability of PP composites. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 45998.     

Enhancement of pacemaker induced stochastic resonance by an autapse in a scale-free neuronal network

An autapse is an unusual synapse that occurs between the axon and the soma of the same neuron. Mathematically, it can be described as a self-delayed feedback loop that is defined by a specific time-delay and the so-called autaptic coupling strength. Recently, the role and function of autapses within the nervous system has been studied extensively. Here, we extend the scope of theoretical research by investigating the effects of an autapse on the transmission of a weak localized pacemaker activity in a scale-free neuronal network. Our results reveal that by mediating the spiking activity of the pacemaker neuron, an autapse increases the propagation of its rhythm across the whole network, if only the autaptic time delay and the autaptic coupling strength are properly adjusted. We show that the autapse-induced enhancement of the transmission of pacemaker activity occurs only when the autaptic time delay is close to an integer multiple of the intrinsic oscillation time of the neurons that form the network. In particular, we demonstrate the emergence of multiple resonances involving the weak signal, the intrinsic oscillations, and the time scale that is dictated by the autapse. Interestingly, we also show that the enhancement of the pacemaker rhythm across the network is the strongest if the degree of the pacemaker neuron is lowest. This is because the dissipation of the localized rhythm is contained to the few directly linked neurons, and only afterwards, through the secondary neurons, it propagates further. If the pacemaker neuron has a high degree, then its rhythm is simply too weak to excite all the neighboring neurons, and propagation therefore fails.     

Patterning SiC nanoprecipitate in Si single crystals by simultaneous dual- beam ion implantation

β–SiC nanoprecipitates can be patterned in crystalline silicon with an almost monomodal size distribution by simultaneous-dual-beam of C⁺ and Si⁺ ion implantations at 550 °C. Their shape appears as spherical (average diameter ~4–5 nm) ,and they are in epitaxial relationship with the crystalline silicon matrix. The narrow size distribution follows the left wing of the carbon distribution where the nuclear ion stopping, and thus the point defect generation rate is largest. This observation allows us to conclude that the induced damage act as sinks for C atoms leading to the SiC nanoprecipitates formation centered at the maximum of the simulated damage distribution. The nuclear reaction analysis, X-ray diffraction, Raman spectroscopy, and transmission electron microscopy techniques were used to characterize the samples.     

On deterministic approximation of DNF

We develop several quasi-polynomial-time deterministic algorithms for approximating the fraction of truth assignments that satisfy a disjunctive normal form formula. The most efficient algorithm computes for a given DNF formulaF onn variables withm clauses and > 0 an estimateY such that ¦Pr[F] –Y¦ in time which is , for any constant. Although the algorithms themselves are deterministic, their analysis is probabilistic and uses the notion of limited independence between random variables.Research supported in part by National Science Foundation Operating Grant CCR-9016468, National Science Foundation Operating Grant CCR-9304722, United States-Israel Binational Science Foundation Grant No. 89-00312, United States-Israel Binational Science Foundation Grant No. 92-00226, and ESPRIT Basic Research Grant EC-US 030.Research partially done while visiting the International Computer Science Institute and while at Carnegie Mellon University.     

Generative adversarial network (GAN) and enhanced root mean square error (ERMSE): deep learning for stock price movement prediction

The prediction of stock price movement direction is significant in financial circles and academic. Stock price contains complex, incomplete, and fuzzy information which makes it an extremely difficult task to predict its development trend. Predicting and analysing financial data is a nonlinear, time-dependent problem. With rapid development in machine learning and deep learning, this task can be performed more effectively by a purposely designed network. This paper aims to improve prediction accuracy and minimizing forecasting error loss through deep learning architecture by using Generative Adversarial Networks. It was proposed a generic model consisting of Phase-space Reconstruction (PSR) method for reconstructing price series and Generative Adversarial Network (GAN) which is a combination of two neural networks which are Long Short-Term Memory (LSTM) as Generative model and Convolutional Neural Network (CNN) as Discriminative model for adversarial training to forecast the stock market. LSTM will generate new instances based on historical basic indicators information and then CNN will estimate whether the data is predicted by LSTM or is real. It was found that the Generative Adversarial Network (GAN) has performed well on the enhanced root mean square error to LSTM, as it was 4.35% more accurate in predicting the direction and reduced processing time and RMSE by 78 s and 0.029, respectively. This study provides a better result in the accuracy of the stock index. It seems that the proposed system concentrates on minimizing the root mean square error and processing time and improving the direction prediction accuracy, and provides a better result in the accuracy of the stock index.

     

Efficient CO2 Capture by Ultra‐high Microporous Activated Carbon Made from Natural Coal

Efficient CO₂ capture capabilities of activated carbons prepared from natural coal are presented. The preparation method involved simple chemical activation using wet impregnation or dry physical mixing of the raw sample with activating agents like KOH or NaOH. The activated materials were characterized for their structural and textural properties by different analysis techniques. The activated samples exhibited well‐developed porosity, large surface area, and high pore volumes and had other active elements like oxygenated functional groups. These groups modified the surface energy of the resultant samples. The superior performance of the activated carbons was attributed to several factors, including large surface area, presence of narrow micropores, and oxygenated functional groups on the surface.     

Camouflaged Hybrid Cancer Cell-Platelet Fusion Membrane Nanovesicles Deliver Therapeutic MicroRNAs to Presensitize Triple-Negative Breast Cancer to Doxorubicin

Camouflaged cell-membrane-based nanoparticles have gained increasing attention owing to their improved biocompatibility and immunomodulatory properties. Using nanoparticles prepared from the membranes of specific cell types or fusions derived from different cells membranes, their functional performance could be improved in several aspects. Here, cell membranes extracted from breast cancer cells and platelets are used to fabricate a hybrid-membrane vesicle (cancer cell-platelet-fusion-membrane vesicle, CPMV) loaded with therapeutic microRNAs (miRNAs) for the treatment of triple-negative breast cancer (TNBC). A clinically scalable microfluidic platform is presented for fusion of cell membranes. The reconstitution process during synthesis allows for efficient loading of miRNAs into CPMVs. Conditions for preparation of miRNA-loaded CPMVs are systematically optimized and their property of homing to source cells is demonstrated using in vitro experiments and therapeutic evaluation in vivo. In vitro, the CPMVs exhibit significant recognition of their source cells and avoided engulfment by macrophages. After systemic delivery in mice, CPMVs show a prolonged circulation time and site-specific accumulation at implanted TNBC-xenografts. The delivered antimiRNAs are sensitized TNBCs to doxorubicin, resulting in an improved therapeutic response and survival rate. This strategy has considerable potential for clinical translation to improve personalized therapy for breast cancer and other malignancies.