Pattern Synthesis of Linear Antenna Array Using Improved Differential Evolution Algorithm with SPS Framework.

In this paper, an improved differential evolution (DE) algorithm with the successful-parent-selecting (SPS) framework, named SPS-JADE, is applied to the pattern synthesis of linear antenna arrays. Here, the pattern synthesis of the linear antenna arrays is viewed as an optimization problem with excitation amplitudes being the optimization variables and attaining sidelobe suppression and null depth being the optimization objectives. For this optimization problem, an improved DE algorithm named JADE is introduced, and the SPS framework is used to solve the stagnation problem of the DE algorithm, which further improves the DE algorithm's performance. Finally, the combined SPS-JADE algorithm is verified in simulation experiments of the pattern synthesis of an antenna array, and the results are compared with those obtained by other state-of-the-art random optimization algorithms. The results demonstrate that the proposed SPS-JADE algorithm is superior to other algorithms in the pattern synthesis performance with a lower sidelobe level and a more satisfactory null depth under the constraint of beamwidth requirement

Reinforcement learning based anti-jamming schedule in cyber-physical systems

In this paper, the security issue of cyber-physical systems is investigated, where the observation data is transmitted from a sensor to an estimator through wireless channels disturbed by an attacker. The failure of this data transmission occurs, when the sensor accesses the channel that happens to be attacked by the jammer. Since the system performance measured by the estimation error depends on whether the data transmission is a success, the problem of selecting the channel to alleviate the attack effect is studied. Moreover, the state of each channel is time-variant due to various factors, such as path loss and shadowing. Motivated by energy conservation, the problem of selecting the channel with the best state is also considered. With the help of cognitive radio technique, the sensor has the ability of selecting a sequence of channels dynamically. Based on this, the problem of selecting the channel is resolved by means of reinforcement learning to jointly avoid the attack and enjoy the channel with the best state. A corresponding algorithm is presented to obtain the sequence of channels for the sensor, and its effectiveness is proved analytically. Numerical simulations further verify the derived results

Integration of Polymer Synthesis and Self-Assembly for Controlled Periodicity and Photonic Properties

Materials chemistry and self‐assembly properties are usually treated separately, largely limiting the real‐time control of their nanostructures and resulting macroscopic properties in advanced self‐assembled materials. This study shows a model system that integrates synthesis and self‐assembly to achieve controlled periodicity and photonic properties in block copolymers (BCPs) at defined locations. First, the BCP thin films containing a pre‐dissolved photo‐initiator are swollen with monomer vapors. Upon exposure to UV light, the photoreaction synthesizes homopolymers within the film, which simultaneously blend with the BCPs and modify the periodicity in the exposed regions. This technique is successfully adapted to cylindrical polystyrene‐b‐polyisoprene‐b‐polystyrene and lamellar polystyrene‐b‐poly(2‐vinyl pyridine) BCP thin films. This capability is especially useful in the practical application of photonic crystals, where it is shown that the stop band position of polystyrene‐b‐quaternized‐poly(2‐vinyl pyridine) photonic gel films can be successfully modulated in situ. A large‐scale pattern is then fabricated by using a photomask. This study provides a model system for integrating materials synthesis and self‐assembly to achieve spatially defined control over structural periodicity and macroscopic properties in self‐assembled materials

Folic Acid-Decorated <i>β</i>-Cyclodextrin-Based Poly(ε-caprolactone)-dextran Star Polymer with Disulfide Bond-Linker as Theranostic Nanoparticle for Tumor-Targeted MRI and Chemotherapy

β-cyclodextrin(βCD)-based star polymers have attracted much interest because of their unique structures and potential biomedical and biological applications. Herein, a well-defined folic acid (FA)-conjugated and disulfide bond-linked star polymer ((FA-Dex-SS)-βCD-(PCL)14) was synthesized via a couple reaction between βCD-based 14 arms poly(ε-caprolactone) (βCD-(PCL)14) and disulfide-containing α-alkyne dextran (alkyne-SS-Dex), and acted as theranostic nanoparticles for tumor-targeted MRI and chemotherapy. Theranostic nanoparticles were obtained by loading doxorubicin (DOX), and superparamagnetic iron oxide (SPIO) particles were loaded into the star polymer nanoparticles to obtain ((FA-Dex-SS)-βCD-(PCL)14@DOX-SPIO) theranostic nanoparticles. In vitro drug release studies showed that approximately 100% of the DOX was released from disulfide bond-linked theranostic nanoparticles within 24 h under a reducing environment in the presence of 10.0 mM GSH. DOX and SPIO could be delivered into HepG2 cells efficiently, owing to the folate receptor-mediated endocytosis process of the nanoparticles and glutathione (GSH), which triggered disulfide-bonds cleaving. Moreover, (FA-Dex-SS)-βCD-(PCL)14@DOX-SPIO showed strong MRI contrast enhancement properties. In conclusion, folic acid-decorated reduction-sensitive star polymeric nanoparticles are a potential theranostic nanoparticle candidate for tumor-targeted MRI and chemotherapy

Using combined network-based approaches to analyze risk interactions in R&D alliance

Previous researches on risks in R&D alliance have treated risks independently. They mainly focus on risk identification and the impact of risk on the objectives of alliance. However, most risks are not isolated but interdependent in reality. To address this gap, a combined method that integrates fuzzy DEMATEL and social network analysis is presented to assess and analyze risk interactions in R&D alliance. A case study of a real R&D alliance is conducted to identify key risks and their interactions, together with the corresponding mitigation actions. By unveiling risks and their interactions, this method assists professionals to make better decisions regarding risk mitigation, and further helps them to achieve higher performances in R&D alliance risk management

Mitochondrial Division Inhibitor 1 Attenuates Mitophagy in a Rat Model of Acute Lung Injury

The regulation of intracellular mitochondria degradation is mediated by mitophagy. While studies have shown that mitophagy can lead to mitochondrial dysfunction and cell damage, the role of Mdivi-1 and mitophagy remains unclear in acute lung injury (ALI) pathogenesis. In this study, we demonstrated that Mdivi-1, which is widely used as an inhibitor of mitophagy, ameliorated acute lung injury assessed by HE staining, pulmonary microvascular permeability assay, measurement of wet/dry weight (W/D) ratio, and oxygenation index (PaO2/FiO2) analysis. Then, the mitophagy related proteins were evaluated by western blot. The results indicated that LPS-induced activation of mitophagy was inhibited by Mdivi-1 treatment. In addition, we found that Mdivi-1 protected A549 cells against LPS-induced mitochondrial dysfunction. We also found that Mdivi-1 reduced pulmonary cell apoptosis in the LPS-challenged rats and protected pulmonary tissues from oxidative stress (represented by the content of superoxide dismutase, malondialdehyde and lipid peroxides in lung). Moreover, Mdivi-1 treatment ameliorated LPS-induced lung inflammatory response and cells recruitment. These findings indicate that Mdivi-1 mitigates LPS-induced apoptosis, oxidative stress, and inflammation in ALI, which may be associated with mitophagy inhibition. Thus, the inhibition of mitophagy may represent a potential therapy for treating ALI

Effect of Curcumin as Feed Supplement on Immune Response and Pathological Changes of Broilers Exposed to Aflatoxin B1

In this study, we examined the protective effects of curcumin against the AFB1-induced immune response of and pathological changes in broilers. Histopathology examinations showed that at day 28, AFB1 (5 mg/kg) exposure leads to severe histological changes in the spleen, thymus and bursa of Fabricius with a decrease in the number and karyoplasmic area ratio of plasma cells. Curcumin alleviated the AFB1-induced immune organs&rsquo; damage as well as the changes in plasma cells in a dose-dependent manner. RT-PCR data showed that AFB1 significantly downregulated the IL-2 and IFN-&gamma; mRNA expression levels in the thymus, spleen and bursa of Fabricius. However, curcumin supplementation improved the AFB1-induced immune organs&rsquo; damage via upregulated cytokines&rsquo; expression. Intriguingly, similar trends were noticed in abnormal morphological changes and the immune response at day 35 after the withdrawal of AFB1 and curcumin from the diet, suggesting the protective effects and immunomodulatory function against AFB1 in broilers. The current study provides a scientific experimental basis for the application of curcumin as a therapeutic drug or additive in animal husbandry productive practice

Optimizing attack schedules based on energy dispatch over two-hop relay networks

In this article, the security issue of remote state estimation in cyber-physical systems (CPS) for a two-hop relay network is investigated. As the system performance depends on communication topology and communication environment over each channel, we explore the channel selection problem to maximize the attack effect, from the perspective of the jammer. Furthermore, for an energy-constrained jammer, there exists the optimal strategy to decide the attack number and the dropout rate, since the amount of attack number decreases and the dropout rate increases when the energy launched at each attack time becomes larger. For this consideration, the problem of energy dispatch, aiming to derive the optimal tradeoff between the attack number and the energy launched at each attack time, is studied along with the channel selection problem. We first formulate this problem as a mixed integer programming problem to derive the optimal attack schedule including the channel selection, the attack number, and the corresponding energy dispatch. Then, using the optimality equations based on the Markov decision process allows us to present the characteristics of the optimal energy dispatch policy for a given attack number, and further propose the dynamic energy dispatch algorithm with low complexity to approximate the optimal schedule. Besides, for the constant dispatch (CD) case, the optimum solution in an analytical form for the channel selection problem can be obtained, and we thus present a CD algorithm to acquire the optimal schedule. Last, numerical results are given to validate the theoretical findings and the effectiveness of the proposed algorithms