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Advances in blockchain technology have attracted significant attention across the world. The practical blockchain applications emerging in various domains, ranging from finance, healthcare, and entertainment, have quickly become attractive targets for adversaries. The novelty of the technology coupled with the high degree of anonymity it provides made malicious activities even less visible in the blockchain environment. This made their robust detection challenging. This article presents EtherShield, a novel approach for identifying malicious activity on the Ethereum blockchain. By combining temporal transaction information and contract code characteristics, EtherShield can detect various types of threats and provide insight into the behavior of contracts. The time-interval-based analysis used by EtherShield enables expedited detection, achieving comparable accuracy to other approaches with significantly less data. Our validation analysis, which involved over 15,000 Ethereum accounts, demonstrated that EtherShield can significantly expedite the detection of malicious activity while maintaining high accuracy levels (86.52% accuracy with 1 hour of transaction history data and 91.33% accuracy with 1 year of transaction history data). [ABSTRACT FROM AUTHOR]

Summary: The equivalent consumption minimization strategy is widely used in real‐time energy optimization for fuel cell electric vehicles due to its approximate optimality and good real‐time performance. However, equivalent factors have always been difficult to select in the research of this algorithm, and the existing methods have insufficient adaptability and robustness. In this paper, an equivalent factor predictor based on a neural network is proposed, which can predict the equivalent factor in real time by considering various operating conditions and vehicle states. In addition, we improve the real‐time performance of the equivalent consumption minimum strategy. A new method to quickly solve the continuous optimal solution of the algorithm by transforming the objective function into the quadratic function of the control variable is studied. Simulation and experimental results show that the designed equivalent consumption minimization strategy can ensure state of charge of the battery in a better range, and the energy saving effect is approximately 3% more than that of dynamic programming and 8% to 10% less than that of the traditional rule algorithm. Finally, the proposed method saves 2 to 3 orders of magnitude in computation time compared with the traditional equivalent consumption minimum strategy. [ABSTRACT FROM AUTHOR]

Deep learning is increasingly being used for many tasks in wireless communications, such as modulation classification. However, it has been shown to be vulnerable to adversarial attacks, which introduce specially crafted imperceptible perturbations, inducing models to make mistakes. This letter proposes an input-agnostic adversarial attack technique that is based on generative adversarial networks (GANs) and multi-task loss. Our results show that our technique reduces the accuracy of a modulation classifier more than a jamming attack and other adversarial attack techniques. Furthermore, it generates adversarial samples at least 335 times faster than the other techniques evaluated, which raises serious concerns about using deep learning-based modulation classifiers. [ABSTRACT FROM AUTHOR]

Antimicrobial peptides have received increased attention due to the increasing prevalence of antibiotic‐resistant bacteria. However, the development of antimicrobial peptides for clinical applications remains a huge challenge. SPA ([D‐rg1, D‐Trp5,7,9, Leu11]SP), an analog of substance P, is a broad‐spectrum neuropeptide antagonist. In this study, we found that SPA could efficiently kill Gram‐positive and Gram‐negative bacteria by membrane disruption, similar to antimicrobial peptides. In addition, SPA showed high killing activity toward bacteria rather than mammalian cells. Our results also demonstrated that SPA could significantly decrease the expression of proinflammatory cytokines and rescue mice from lethal septic shock induced by lipopolysaccharide (LPS). The impressive therapeutic potential of SPA, as indicated in this study, makes it a good template for developing effective antibiotics. Meanwhile, our study provides a new idea for developing multifunctional therapeutic agents to combat bacterial infections. [ABSTRACT FROM AUTHOR]

Camptothecin (CPT), a DNA‐toxin drug, exerts anticancer activity by inhibiting topoisomerase I. Targeted delivery of CPT into the cancer cell nucleus is important for enhancing its therapeutic efficiency. In this study, a new type of acid‐activated cell‐penetrating peptide (CPP) with nuclear localization capacity was constructed by conjugating six histidine residues and a hydrophobic peptide sequence, PFVYLI, to the nuclear localization sequence (NLS). Our results indicated that HNLS‐3 displayed significant pH‐dependent cellular uptake efficiency, endosomal escape ability, and nuclear localization activity. More importantly, the HNLS‐3–CPT conjugate showed obviously enhanced cytotoxicity and selectivity compared with CPT. Taken together, our findings provide an effective approach to develop efficient CPPs with both cancer‐ and nucleus‐targeting properties. [ABSTRACT FROM AUTHOR]

A high-quality and large-sized GdB6 single crystal was successfully prepared by using the optical floating zone method to accurately control the temperature and composition of the molten zone. A GdB6 [100] single crystal 25 mm in length and 5 mm in diameter was obtained. The full width at half-maximum (FWHM) of the X-ray rocking curves was 0.227°, which indicated that the as-grown GdB6 single crystal had a high quality. The maximum thermionic emission current density of the GdB6 single crystal was 6.73 A cm−2 at U = 4000 V and T = 1873 K, and the average work function was 2.99 eV. Combining first-principles calculations and experiments, the work function was found to monotonically increase as the size of the GdB6 single crystal changed from nanostructure (1D) to bulk (3D). [ABSTRACT FROM AUTHOR]

Telecommunication networks already store a large amount of subscriber data (e.g., subscriber profile, location data, presence information), and even more data could be captured. Although this data is a key asset of telecommunication network operators, it is seldom fully exploited. This article proposes a novel architecture to enable the provisioning of new value-added services, based on subscriber data. This architecture uses semantic web to build a knowledge base from the subscriber data available in individual telecommunication networks. KBs are then federated across telecommunication networks to provide a single and unified interface to service providers for accessing the different KBs. The article also illustrates the use of the proposed architecture in a concrete scenario, and presents a proof-of-concept prototype. [ABSTRACT FROM AUTHOR]

For reducing the work load of calibration, improving its accuracy, and realizing the automation and intelligentization of calibration process, a model-based universal calibration and optimization plafform is proposed, its experiment design system is developed, and based on which a reasonable test program is worked Out. Then the calibration test management system performs the automatic calibration operation according to the test program. Finally an optimal control map is obtained with optimization system based on the diesel engine model constructed from test data. The results of an automatic calibration optimization test on a high-pressure common rail diesel engine with 13 working modes indicate that the platform developed works stably and reliably with calibration efficiency raised and calibration time shortened. [ABSTRACT FROM AUTHOR]