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Background: KRAS and BRAF targets are involved in the epidermal growth factor receptor pathway. KRAS and BRAF targets are the most frequent driver mutations in cancer. Objective: The objective of the study was to present the recent developments in the KRAS target and the BRAF target. Method: KRAS target and BRAF target were analyzed by US patent analysis. All US granted patent documents from January 2002 to November 2021 were retrieved. Results: The results showed both KRAS and BRAF targets to be attractive targets for developing anticancer drugs. The technology of RNA interference has been developed for drug discovery related to the KRAS target. Our study indicates that the structural screening of inhibitors between the KRAS target and the BRAF target should be an inverse option. Conclusion: The chemical structures of inhibitors of BRAF target exhibited a unique classification of C07D405. The inhibitors of BRAF target could be used for the treatment of various cancers. However, the inhibitors of KRAS target did not show this feature. The present study provides new insight into drug discovery involving KRAS and BRAF targets.

To solve the problem of poor face recognition performance in unrestricted environments. A face recognition algorithm based on improved residual IR-ResNet-SE is designed. Firstly, the IR structure is added to the 34-layer residual network to reduce the variability of different features; Secondly, we add the channel attention module to increase the weight of important channel features; Finally, the Arcface loss function is used to improve the classification ability of the model. The LFW, AgeDB, and AR datasets reflect unrestricted factors such as pose, age, expression, occlusion, and illumination. The algorithm proposed in this paper is experimented on these three datasets. The experimental results show that the IR-ResNet-SE algorithm proposed in this paper can achieve 99.74% accuracy in the dataset LFW. And it has excellent robustness in face recognition under unrestricted conditions. &nbsp

A new species Eterusia sinoraja Huang & Horie sp. n. is described from western Sichuan Province, western China. The comparison is made with the reminiscent E. raja Moore, 1859 from northeastern India. The adult and genitalia of both sexes of the aforementioned taxa are illustrated.

Multi-access edge computing (MEC) has been recently considered in challenging environments lacking available terrestrial infrastructures by extending the computing resources to the air for further enhancing the computation capability of the new aerial user equipment (AUE). Additionally, wireless power transfer (WPT) is a promising solution to prolong the battery lifetime of energy-constrained wireless devices like AUEs. In this paper, we investigate the integration of laser-beamed WPT in the high-altitude platform (HAP) aided MEC systems for the HAP-connected AUEs. By discretizing the three-dimensional coverage space of the HAP, we present a multi-tier tile grid-based spatial structure to provide aerial locations for laser charging. With this setup, we identify a new privacy vulnerability caused by the openness during the air-to-air transmission of WPT signaling messages in the presence of a terrestrial adversary. A privacy-aware laser-powered aerial MEC framework is developed that addresses this vulnerability and enhances the location privacy of AUEs for laser WPT. Specifically, the interaction between the HAP as a defender and the adversary in their tile grid allocation as charging locations to AUEs is formulated as a Colonel Blotto game, which models the competition of the players for limited resources over multiple battlefields for a finite time horizon. Moreover, we derive the mixed-strategy Nash equilibria of the tile grid allocation game for both symmetric and asymmetric tile grids between the defender and the adversary. Simulations results show that the proposed framework significantly outperforms the design baselines with a given privacy protection level in terms of system-wide expected total utilities.

Efficient mode-selective manipulation in multimode photonics has drawn much attention as a key technology for realizing scalable and flexible mode-division multiplexing (MDM) systems. A mode-selective manipulation scheme based on the modal-field redistribution assisted with subwavelength grating (SWG) structures is proposed and demonstrated for the first time. In particular, the proposed scheme focuses on manipulating the coupling coefficient κ as well as the ratio δ/κ for different mode channels. The SWG structures are used to engineer the refractive-index profile and redistribute the modal field distributions in the multimode bus waveguide, so that different modes are localized in different local regions. In this way, the undesired mode coupling can be suppressed significantly while the desired mode coupling can be enhanced. With such mode manipulation scheme, the fundamental and higher-order mode channels in the bus waveguide can be added/dropped independently and freely. As a proof of concept, a three-channel mode-selective add-drop coupler utilizing the proposed scheme is fabricated and demonstrated experimentally on silicon. The fabricated devices show low excess losses ranging from 0.1 to 1.9 dB over a wavelength range of 70 nm. The inter-mode crosstalks are lower than −19.4 dB in the wavelength range of 1525–1600 nm. The crosstalks for the drop and through ports (i.e., the residual power) are suppressed to be as low as −18 ∼ −30 dB in the wavelength range of ∼60 nm with the assistance of an additional coupler in cascade for performance improvement. The present concept of manipulating the evanescent coupling of the mode-channels paves the way for designing multimode silicon photonic devices with flexible mode-selective manipulation for MDM systems.

This study considers the seasonal solar thermal storage and heating system in the Jilin region of China, and a system model is established by using the TRNSYS software under heat storage and heating modes. The operating characteristics of the system are analysed during the complete heat storage and heating seasons, and the economic cost of the system is minimised by optimising the ratio of the collector area to the water tank volume. The results show that the most heat is stored in the system during May, and the maximum heat loss from the tank occurs during October. The maximum solar fraction is 47.85%, the maximum heat loss rate of the water tank is 29.82% and the energy efficiency ratio of the system is 1.70. The optimised ratio of the heat storage tank volume to solar collector area is 4.5, energy efficiency ratio of the system is 1.67 and the annual cost about ¥440 000 (£52 860).

In this paper, we attempt to deal with the routing problem in a cognitive unmanned aerial vehicle (UAV) swarm (CU-SWARM), which applies the cognitive radio into a swarm of UAVs within a three-hierarchical aerial-ground integrated network architecture for emergency communications. In particular, the flexibly converged architecture utilizes a UAV swarm and a high-altitude platform to support aerial sensing and access, respectively, over the disaster-affected areas. We develop a Q-learning framework to achieve the intelligent routing with maximum utility for CU-SWARM. To characterize the reward function, we take into account both the routing metric design and the candidate UAV selection optimization. The routing metric is determined by maximizing the utility, which jointly captures the achievable rate of UAV pair and the residual energy of UAV. Besides, under the location, arc, and direction constraints, the circular sector is modeled by properly choosing the central angle and the acceptable signal-to-noise ratio for the UAV. With this setup, we further propose a low-complexity iterative algorithm using the dynamic learning rate to update Q-values during the training process for achieving a fast convergence speed. Extensive simulation results are provided to assess the potential of the Q-learning framework of intelligent routing as well as to verify our overall iterative algorithm via the dynamic learning rate for training procedure. Our findings reveal that the proposed algorithm can significantly increase the accumulated rewards significantly with practical complexity compared to other benchmark schemes with fixed and decaying learning rates.