Biomorphic porous graphitic carbon for electromagnetic interference shielding

Using a kind of plant biomass (coconut shell) as template and carbon precursor, we prepare biomorphic porous carbon materials for electromagnetic interference shielding application. The carbon frameworks of the porous carbon materials are modified by in situ formation of graphitic nanostructures in a catalytic graphitization process, leading to well-tailored electrical conductivity of the resultant materials. The modified porous carbon materials exhibit shielding effectiveness of 40 dB over the X-band frequency, and the shielding by absorption is as high as 91%, indicating an absorptiondominant shielding mechanism. The high absorption contribution is attributed to the coupled effects of the biomorphic porous structures and graphitic nanostructures of the porous graphitic carbon

Gender-Based Deep Learning Firefly Optimization Method for Test Data Generation.

Software testing is a widespread validation means of software quality assurance in industry. Intelligent optimization algorithms have been proved to be an effective way of automatic test data generation. Firefly algorithm has received extensive attention and been widely used to solve optimization problems because of less parameters and simple implement. To overcome slow convergence rate and low accuracy of the firefly algorithm, a novel firefly algorithm with deep learning is proposed to generate structural test data. Initially, the population is divided into male subgroup and female subgroup. Following the randomly attracted model, each male firefly will be attracted by another randomly selected female firefly to focus on global search in whole space. Each female firefly implements local search under the leadership of the general center firefly, constructed based on historical experience with deep learning. At the final period of searching, chaos search is conducted near the best firefly to improve search accuracy. Simulation results show that the proposed algorithm can achieve better performance in terms of success coverage rate, coverage time, and diversity of solutions

Adaptive Backstepping Control for Air-Breathing Hypersonic Vehicles with Input Nonlinearities

This paper addresses the control problem of air-breathing hypersonic vehicles subject to input nonlinearities, aerodynamic uncertainties and flexible modes. An adaptive backstepping controller and a dynamic inverse controller are developed for the altitude subsystem and the velocity subsystem, respectively, where the former eliminates the problem of “explosion of terms” inherent in backstepping control. Moreover, a modified smooth inverse of the dead-zone is proposed to compensate for the dead-zone effects and reduce the computational burden. Based on this smooth inverse, an input nonlinear pre-compensator is designed to handle input saturation and dead-zone nonlinearities, which leads to a simpler control design for the altitude subsystem subject to these two input nonlinearities. It is proved that the proposed controllers can guarantee that all closed-loop signals are bounded and the tracking errors converge to an arbitrarily small residual set. Simulation results are carried out to demonstrate the effectiveness of the proposed control scheme

A Novel Algorithm for Intrusion Detection Based on RASL Model Checking

The interval temporal logic (ITL) model checking (MC) technique enhances the power of intrusion detection systems (IDSs) to detect concurrent attacks due to the strong expressive power of ITL. However, an ITL formula suffers from difficulty in the description of the time constraints between different actions in the same attack. To address this problem, we formalize a novel real-time interval temporal logic—real-time attack signature logic (RASL). Based on such a new logic, we put forward a RASL model checking algorithm. Furthermore, we use RASL formulas to describe attack signatures and employ discrete timed automata to create an audit log. As a result, RASL model checking algorithm can be used to automatically verify whether the automata satisfy the formulas, that is, whether the audit log coincides with the attack signatures. The simulation experiments show that the new approach effectively enhances the detection power of the MC-based intrusion detection methods for a number of telnet attacks, p-trace attacks, and the other sixteen types of attacks. And these experiments indicate that the new algorithm can find several types of real-time attacks, whereas the existing MC-based intrusion detection approaches cannot do that

Optimized operational approach for multi-type reactive power compensation to enhance the grid integration strength of new energy clusters

The insufficient system strength in the high-proportion new energy access area has gradually emerged as a crucial factor contributing to the transient overvoltage issue. Therefore, it is imperative to propose a reactive power optimization operation mode that takes into consideration both the power grid strength and system operating voltage of the new energy cluster system. Firstly, the relationship between the evaluation index of power grid strength and the performance of system voltage response is elucidated, while analyzing the influence mechanism of various reactive power compensation devices on the power grid strength of new energy cluster systems. Then, a reactive power operation optimization model is proposed to maximize the strength of the system grid and minimize the voltage deviation. To solve this problem, a hybrid approach combining genetic algorithm and CPLEX solver is employed. Finally, the effectiveness of the proposed method is validated through a typical simulation example

Stable Internal Reference Genes for Normalizing Real-Time Quantitative PCR in Baphicacanthus cusia under Hormonal Stimuli and UV Irradiation, and in Different Plant Organs

Baphicacanthus cusia (Nees) Bremek, the plant source for many kinds of drugs in traditional Chinese medicine, is widely distributed in South China, especially in Fujian. Recent studies about B. cusia mainly focus on its chemical composition and pharmacological effects, but further analysis of the plant's gene functions and expression is required to better understand the synthesis of its effective compounds. Real-time quantitative polymerase chain reaction (RT-qPCR) is a powerful method for gene expression analysis. It is necessary to select a suitable reference gene for expression normalization to ensure the accuracy of RT-qPCR results. Ten candidate reference genes were selected from the transcriptome datasets of B. cusia in this study, and the expression stability was assessed across 60 samples representing different tissues and organs under various conditions, including ultraviolet (UV) irradiation, hormonal stimuli (jasmonic acid methyl ester and abscisic acid), and in different plant organs. By employing different algorithms, such as geNorm, NormFinder, and BestKeeper, which are complementary approaches based on different statistical procedures, 18S rRNA was found to be the most stable gene under UV irradiation and hormonal stimuli, whereas ubiquitin-conjugating enzyme E2 was the best suitable gene for different plant organs. This novel study aimed to screen for suitable reference genes and corresponding primer pairs specifically designed for gene expression studies in B. cusia, in particular for RT-qPCR analyses

Semaphorin 4C accelerates disease progression and enables disease detection in breast cancer

Semaphorins constitute a diverse family of widely expressed transmembrane, diffusible, and GPI-linked proteins with versatile physiologic functions in orchestrating nerve system development, immune homeostasis, angiogenesis, and cell metabolism. Accumulating evidence highlights semaphorins as essential regulators of tumorigenesis by coordinating the cell-cell communications in the tumor microenvironment. Semaphorin 4C (SEMA4C) is a member of the fourth class of semaphorins with high affinity to Plexin-B2 and its interplay with cancer has long been a significant knowledge gap. Here, this perspective summarizes the recent progress in the understanding of SEMA4C in cancer and comprehensively delineates the discovery of SEMA4C in lymphatic vessels of breast cancer, the mechanisms by which SEMA4C promotes the invasiveness, proliferation, metastasis, and drug resistance of breast cancer, and the explorations of leveraging serum SEMA4C in breast cancer detection, highlighting SEMA4C as a critical driver of breast cancer progression, an effective biomarker for breast cancer diagnosis, and potential therapeutic target for breast cancer treatment

Long Noncoding RNA HOTTIP Promotes Mouse Hepatic Stellate Cell Activation via Downregulating miR-148a

Background/Aims: HOTTIP is a critical modulator in human diseases including liver cancer, but its role and molecular biological mechanisms in liver fibrosis are still unclear. Methods: The expression profile of HOTTIP during the progression of liver fibrosis was detected in human liver samples and in CCl4-treated mice using qRT-PCR. The expressing sh-HOTTIP adenoviral vector was used to reduce HOTTIP levels in vivo. Dual-Luciferase Reporter Assay was performed to validate the interaction between miR-148a and HOTTIP, TGFBR1, or TGFBR2. Results: HOTTIP expressions in fibrotic liver samples and cirrhotic liver samples were significantly upregulated compared with healthy liver controls, and cirrhotic samples exhibited the highest levels of HOTTIP. Moreover, HOTTIP expressions were substantially induced in the liver tissues and hepatic stellate cells (HSC) of CCl4-treated mice. Ad-shHOTTIP delivery could alleviate CCl4- induced liver fibrosis in mice. Down-regulation of HOTTIP inhibited the viability and activation of HSCs in vitro, and HOTTIP negatively regulated miR-148a expression in HSCs. miR-148a had a negative effect on HSC activation by targeting TGFBR1 and TGFBR2. Conclusion: HOTTIP is involved in the progression of liver fibrosis by promoting HSC activation. The high level of HOTTIP downregulates miR-148a, thus to increase the level of TGFBR1 and TGFBR2 and contribute to liver fibrosis

Wireless, battery-free optoelectronic systems as subdermal implants for local tissue oximetry

Monitoring regional tissue oxygenation in animal models and potentially in human subjects can yield insights into the underlying mechanisms of local O2-mediated physiological processes and provide diagnostic and therapeutic guidance for relevant disease states. Existing technologies for tissue oxygenation assessments involve some combination of disadvantages in requirements for physical tethers, anesthetics, and special apparatus, often with confounding effects on the natural behaviors of test subjects. This work introduces an entirely wireless and fully implantable platform incorporating (i) microscale optoelectronics for continuous sensing of local hemoglobin dynamics and (ii) advanced designs in continuous, wireless power delivery and data output for tether-free operation. These features support in vivo, highly localized tissue oximetry at sites of interest, including deep brain regions of mice, on untethered, awake animal models. The results create many opportunities for studying various O2-mediated processes in naturally behaving subjects, with implications in biomedical research and clinical practice.Center for Bio-Integrated Electronics at Northwestern University; Soft and Hybrid Nanotechnology Experimental (SHyNE) Resource (NSF) [ECCS-1542205]; Materials Research Science and Engineering Center [DMR-1720139]; State of Illinois; Northwestern University; Developmental Therapeutics Core at Northwestern University; Robert H. Lurie Comprehensive Cancer Center [NCI CA060553]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]