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The threats of drug resistance and new emerging pathogens have led to an urgent need to develop alternative treatment therapies. Recently, considerable research efforts have focused on membrane-active peptides (MAPs), a category of peptides in drug discovery with antimicrobial, anticancer, and cell penetration activities that have demonstrated their potential to be multifunctional agents. Nonetheless, natural MAPs have encountered various disadvantages, which mainly include poor bioavailability, the lack of a secondary structure in short peptides, and high production costs for long peptide sequences. Hence, an “all-hydrocarbon stapling system” has been applied to these peptides and proven to effectively stabilize the helical conformations, improving proteolytic resistance and increa... |
Since parallel robots are the multibody systems with closed-loop structures, their movement equations usually are in the complex form of redundant coordinates and their dynamics parameters are usually uncertain. The aim of this paper is to improve the control quality for the parallel robot by applying RBF neutron network. Firstly, the movement equations of Rostock Delta robot are established as differential–algebraic systems of equations with redundant generalized coordinates. Then, the stableness of the control method based on sliding mode control law using neural network is proved. Finally, the error in tracking control of a specific Rostock Delta robot is simulated by using this method. |
This work develops a Neural Network (NN) model for the prediction of the tensile modulus of carbon nanotube (CN)/polymer nanocomposites, based on experimental database. A data set composed of 282 configurations is collected from available resources. Considered input variables of the dataset are such as mechanical properties of separated phases, density of polymer matrix, processing method, geometry of CN, modification method at the CN surface, etc. while the problem output is the tensile modulus of nanocomposite. Parametric studies have been performed in finding optimum architecture of the proposed NN model. |
Nitride red phosphors of Sr2Si5N8:Eu2+ are synthesized in mild conditions and exhibit excellent luminescence properties; yet, the occurrence of thermal degradation results in a decrease of their luminous intensity and quantum efficiency at the high working temperature of wLED. This work shows that Sr2Si5N8:Eu2+ phosphors, prepared by a high-temperature solid-state reaction, undergo severe thermal degradation at high temperatures, not only in air atmosphere, but even in highly pure N2 gas. In order to shed light on the degradation mechanism, the materials obtained after heat-treatment at various temperatures up to 600 °C were thoroughly characterized at the investigated temperatures with the aid of several experimental techniques, such as in-situ HRTEM, along with XPS, luminescence s... |
We here invent an implicitly new optical device - cylindrical Fresnel lenses historically used in the decades-old lighthouse concentrator to find application in the field of concentrator photovoltaic, with numerous demonstrations of exotic features in optics. The precious attribute of such a solar concentrator is its ability to grant a focal point sequence, essentially distributed over an arc at any light incidence. The other striking merit of this design is its high acceptance angle, about 60°, at hand and even up to 90°, ideally. We have applied this approach to a simplified principle-of-concept model that closely depicts the octagonal cylindrical Fresnel lens in essence. This convergence feature was evidenced in a mock-up model through optical simulation and further consolidated ... |
Herein, we have designed and synthesized a series of the novel (E)-N'-((1-(4-chlorobenzyl)- 1H-indol-3-yl)methylene)-2-(4-oxoquinazolin-3(4H)-yl)acetohydrazides (5) as potent small molecules activating procaspase- 3. The compounds were designed by the amalgamation of structural features of PAC-1 (the first procaspase-3 activator) and oncrasin-1, one potential anticancer agent |
Tuned mass dampers (TMDs) to improve long-span bridges' critical flutter wind speed have been widely investigated. However, the optimization of the parameters of TMDs to increase the aerodynamic stability of the long-span bridge girders in the most efficient way has not been properly considered. The present study shows a novel approach in optimizing the parameters of TMDs installed in the sectional model of the long-span bridge to maximize the critical flutter wind speed of the system based on a multi-objective optimization method. The objective functions, maximizing critical flutter wind speed and minimizing TMDs’ mass, are included simultaneously. The configuration parameters of TMDs are considered as design variables. Balancing composite motion optimization (BCMO), a recently pub... |
A new observation is that fundamental particles possess a dynamical dark charge, which governs both neutrino mass and dark matter, where the neutrino mass is determined via a canonical seesaw, while the dark matter stability is ensured by electric and color charge conservations |
In this article, we consider the information leakage and outage probabilities of a multiple-input single-output (MISO) energy-harvesting (EH) Internet of Things (IoT) system in which a multiantenna ground base station (GBS) transmits messages to legitimate IoT destinations (LIDs) with the help of IoT relays (IRs) using non-orthogonal multiple access (NOMA) in the presence of a malicious jammer (MJ) and eavesdroppers (EAVs). The communication protocol is separated into two phases. In the EH phase, the IRs harvest energy from a power beacon (PB). In the information transmission (IT) phase, the communication process is further divided into two subphases: 1) The GBS broadcasts signals to the IRs using NOMA. Simultaneously, the MJ sends interfering signals to attack the IRs while the EAV... |
Future Internet involves several emerging technologies such as 5G and beyond 5G networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of Things (IoTs). Moreover, the future Internet becomes heterogeneous and decentralized with a large number of involved network entities. Each entity may need to make its local decision to improve the network performance under dynamic and uncertain network environments. Standard learning algorithms such as single-agent Reinforcement Learning (RL) or Deep Reinforcement Learning (DRL) have been recently used to enable each network entity as an agent to learn an optimal decision-making policy adaptively through interacting with the unknown environments. However, such an algorithm fails to model the cooperations or competition... |
