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Many biological structures exhibit intriguing morphological patterns adapted to environmental cues, which contribute to their important biological functions and also inspire material designs. Here, we report a chiral wrinkling topography in shrinking core–shell spheres, as observed in excessively dehydrated passion fruit and experimentally demonstrated in silicon core–shells under air extraction. Upon shrinkage deformation, the surface initially buckles into a buckyball pattern (periodic hexagons and pentagons) and then transforms into a chiral mode. The neighbouring chiral cellular patterns can further interact with each other, resulting in secondary symmetry breaking and the formation of two types of topological network. We develop a core–shell model and derive a universal scaling law to understand the underlying morphoelastic mechanism and to effectively describe and predict such chiral symmetry breaking far beyond the critical instability threshold. Moreover, we show experimentally that the chiral characteristic adapted to local perturbation can be harnessed to effectively and stably grasp small-sized objects of various shapes and made of different stiff and soft materials. Our results not only reveal chiral instability topographies, providing fundamental insights into the surface morphogenesis of the deformed core–shell spheres that are ubiquitous in the real world, but also demonstrate potential applications of adaptive grasping based on delicate chiral localization.

The unmanned aerial vehicle (UAV) assisted communication can significantly improve the coverage and spectrum efficiency of the Internet of Things (IoT). One main feature of IoT is short packet communication (SPC), in which the data transmission uses finite block-length codewords. The transmission rate and the packet error rate will affect the effective throughput of the IoT. In this paper, we investigate the impact of SPC on the performance of UAV-enabled spectrum sharing network. Specifically, the optimization of energy efficiency (EE) in the UAV communication system is considered since the battery of the UAV is usually limited. We design the packet error rate, the sensing duration, the normalized sensing threshold and the UAV’s transmit power to maximize the EE under the constraint that the primary user is sufficiently protected. Since the system parameters are intertwined with each other, we employ a successive optimization algorithm to solve the optimization problem by dividing it into three subproblems. Then, an efficient iterative algorithm is proposed to obtain the optimal parameters. Simulation results reveal that the proposed algorithm can converge to global optimal value and has better EE performance than other benchmark schemes. And there is a fundamental tradeoff between the throughput and the EE.

Unmanned aerial vehicle (UAV)-enabled mobile relaying is regarded as an important wireless connectivity component in areas without infrastructure coverage due to its rapid response, strong mobility, and low cost. This paper studies a delay tolerant UAV-enabled mobile relaying system and adopts the load-carry-and-deliver paradigm. The UAV is employed to assist in the information transmission from a ground transmitter to a ground receiver with their direct link blocked. Two kinds of UAV flight trajectories are proposed in this system, i.e., a straight line and circular trajectory. Suppose that the UAV employs time-division duplexing (TDD)-based decode-and-forward (DF) relaying. This paper then aims to maximize the spectrum efficiency (SE) and energy efficiency (EE) in of the UAV-enabled relaying system by jointly optimizing the time allocation, flight speed, and the flying radius of the circular trajectory. Then, we develop an efficient algorithm by leveraging the bisection method and particle swarm optimization (PSO) algorithm. Simulation results show the superiority of the proposed algorithm as compared to other benchmark schemes. In addition, numerical results show that, when the communication distance is 1000 m, the SE and EE performance of the circular trajectory is better than the SLF trajectory when the obstacle height is greater than 300 m. Thus, the height of the obstacle between the communication nodes and the trade-off between the SE and EE should be taken into account when we design the optimal trajectory of the UAV-enabled mobile relaying system.

Relay selection for Relay Assisted (RA) networks is an economical and effective method to improve the spectrum efficiency. Relay selection performs especially well when the source node has accurate and timely Channel State Information (CSI). However, since perfect CSI knowledge is rarely available, research of relay selection with partial (statistical) CSI is of paramount importance. In this paper, relay selection for RA networks with statistical CSI is formulated as a Multiple-Decision (MD) problem. And, the cost of obtaining the CSI is also considered in the formulated problem. Two relay selection schemes, Maximal Selection Probability (MSP) and Maximal Spectrum Efficiency Expectation (MSEE), are proposed to solve the formulated MD problem under different optimal criteria assumptions based on the optimal stopping theory. The MSP scheme maximizes the probability that the Best Assisted Relay Candidate (BARC) can be selected, whereas the MSEE scheme provides the maximal expectation of the spectrum efficiency. Experimental results show that the proposed schemes effectively improve the spectrum efficiency, and the MSEE scheme is more suitable for stable communication cases. Meanwhile, the MSP scheme is more suitable for burst communication cases.

In this study, the authors investigate the performance of an energy harvesting cognitive cooperative multiple relays network with spectrum sensing, where secondary user accesses the licensed spectrum according to the spectrum sensing results. Particularly, if the spectrum hole is detected, the secondary nodes harvest energy from the ambient environment, otherwise, the secondary source and relays transmit with the energy collected from the primary signals. Taking the imperfect spectrum sensing results into account, exact closed-form expressions for the total outage probability and capacity of the secondary network are derived under the context of Rayleigh fading channels, where the proposed modified selective cooperative decode-and-forward relaying transmission schemes are adopted. Compared with the conventional cognitive relay network, the authors must undertake the correlations among the received signal-to-noise ratios or signal-to-interference-plus-noise ratios caused by both the presence of primary user's interference and energy harvesting for the system understudy. It is shown that spectrum sensing phase and data transmission phase are mutual influence and mutual restraint.

In recent years, the development of wireless communication leads to an explosive growth of energy demand, widely application of smart devices and rapid emergence of services. So, the energy efficient communication is expected urgently to save power and prolong the lifetime of the resource-constrained terminal devices. Especially in 5G age, the excellent spectrum efficiency provides more opportunity to save power by adjusting the transmission power of the delay tolerable (DT) service. Meanwhile, although the tradeoff between energy efficiency and service delay plays a non-negligible role in the energy efficient communication, it is not exploited sufficiently due to the time variation and randomness of wireless communication channel. For this reason, the fundamental tradeoff between energy efficiency and delay of the DT service is investigated and analyzed. And, the optimal problem of energy efficient communication for DT service is formulated as a Markov Decision Process (MDP) which can be solved effectively by statistical dynamic programming (SDP) since the perfect channel state information (CSI) is hard to obtain. To improve the utility of research, the approximate SDP (ASDP) and Q-learning are also investigated to overcome the limitation of the curse of dimensionality and model-based algorithm respectively.

With the consolidation of the Internet of Things (IoT), the unmanned aerial vehicle- (UAV-) based IoT has attracted much attention in recent years. In the IoT, cognitive UAV can not only overcome the problem of spectrum scarcity but also improve the communication quality of the edge nodes. However, due to the generation of massive and redundant IoT data, it is difficult to realize the mutual understanding between UAV and ground nodes. At the same time, the performance of the UAV is severely limited by its battery capacity. In order to form an autonomous and energy-efficient IoT system, we investigate semantically driven cognitive UAV networks to maximize the energy efficiency (EE). The semantic device model for cognitive UAV-assisted IoT communication is constructed. And the sensing time, the flight speed of UAV, and the coverage range of UAV communication are jointly optimized to maximize the EE. Then, an efficient alternative algorithm is proposed to solve the optimization problem. Finally, we provide computer simulations to validate the proposed algorithm. The performance of the joint optimization scheme based on the proposed algorithm is compared to some benchmark schemes. And the simulation results show that the proposed scheme can obtain the optimal system parameters and can significantly improve the EE.

This paper studies the secure transmit design for a downlink multiple-input-single-output cognitive radio network (CRN) with a practical energy harvesting (EH) model. Due to the open architecture of a CRN, the confidential information intended for the secondary receiver is prone to be eavesdropped by external eavesdroppers (Eves). Specifically, considering imperfect channel state information, our aim is to jointly design the transmit beamforming vector and the power splitting ratio, such that the outage-constrained secrecy rate is maximized under the constraints of total power consumption, secrecy rate outage probability, EH and quality of service requirements. The original problem is not convex and intractable in general, in order to tackle this non-convexity issue, we utilize semidefinite relaxation and Bernstein-type inequality to transform the outage constraints into a deterministic form. We show that the optimal solution can be obtained by alternately optimizing two convex subproblems. Also, we prove that the optimal solution of the relaxed problem will always be rank one. Furthermore, we consider a large deviation inequality (LDI)-based approach to obtain a sub-optimal solution, which can significantly reduce the computational complexity. Finally, simulation results have been provided to demonstrate the performance of our proposed designs.

The performance of an underlay cognitive radio network equipped with multiple decode-and-forward relays is investigated in the presence of multiple primary transmitters and multiple primary receivers. The allowed maximum transmission powers for the secondary nodes are obtained according to the outage constraint of multiple primary users (PUs). The authors study selective cooperation scheme for the scenarios of non-direct relay transmission, direct and relay transmission with selection combining, direct and relay transmission with maximum ratio combining, and incremental relay transmission. Considering the dependence among the received signal-to-interference-plus-noise ratios caused by multiple PUs, exact and asymptotical expressions for the outage probability of the proposed selective cooperation schemes are derived over Rayleigh fading channels under underlay spectrum sharing constraints. It is shown that the diversity order for the underlay cognitive relay network with multiple primary transmitters and multiple primary receivers is zero. Finally, simulation results are presented to verify the correctness of the authors’ theoretical derivations.

Energy harvesting techniques are promising in next generation wireless communication systems. However, most of the existing works are based on an ideal linear energy harvesting model. In this paper, a multiple-input single-output cognitive radio network is studied under a practical non-linear energy harvesting model. In order to improve the security of both the primary network and the secondary network, a cooperative jamming scheme is proposed. A robust artificial noise aided beamforming design problem is formulated under the bounded channel state information error model. The formulated problem is non-convex and challenging to be solved. Using ${\mathcal{ S}}\text {-Procedure} $ and the semidefinite relaxation method, a suboptimal beamforming can be obtained. Simulation results show that the performance achieved under the non-linear energy harvesting model may be better than that obtained under the linear energy harvesting model. It is also shown that the cooperation between the primary network and the secondary network can obtain a performance gain compared with that without this cooperation.

Motivation: A number of methods have been reported that predict protein–protein interactions (PPIs) with high accuracy using only simple sequence-based features such as amino acid 3mer content. This is surprising, given that many protein interactions have high specificity that depends on detailed atomic recognition between physiochemically complementary surfaces. Are the reported high accuracies realistic? Results: We find that the reported accuracies of the predictions are significantly over-estimated, and strongly dependent on the structure of the training and testing datasets used. The choice of which protein pairs are deemed as non-interactions in the training data has a variable impact on the accuracy estimates, and the accuracies can be artificially inflated by a bias towards dominant samples in the positive data which result from the presence of hub proteins in the protein interaction network. To address this bias, we propose a positive set-specific method to create a ‘balanced’ negative set maintaining the degree distribution for each protein, leading to the conclusion that simple sequence-based features contain insufficient information to be useful for predicting PPIs, but that protein domain-based features have some predictive value. Availability: Our method, named ‘BRS-nonint’, is available at http://www.bioinformatics.leeds.ac.uk/BRS-nonint/. All the datasets used in this study are derived from publicly available data, and are available at http://www.bioinformatics.leeds.ac.uk/BRS-nonint/PPI_RandomBalance.html Contact: maozuguo@hit.edu.cn; d.r.westhead@leeds.ac.uk

Motivation: MicroRNAs (miRNAs) are a set of short (21–24 nt) non-coding RNAs that play significant roles as post-transcriptional regulators in animals and plants. While some existing methods use comparative genomic approaches to identify plant precursor miRNAs (pre-miRNAs), others are based on the complementarity characteristics between miRNAs and their target mRNAs sequences. However, they can only identify the homologous miRNAs or the limited complementary miRNAs. Furthermore, since the plant pre-miRNAs are quite different from the animal pre-miRNAs, all the ab initio methods for animals cannot be applied to plants. Therefore, it is essential to develop a method based on machine learning to classify real plant pre-miRNAs and pseudo genome hairpins. Results: A novel classification method based on support vector machine (SVM) is proposed specifically for predicting plant pre-miRNAs. To make efficient prediction, we extract the pseudo hairpin sequences from the protein coding sequences of Arabidopsis thaliana and Glycine max, respectively. These pseudo pre-miRNAs are extracted in this study for the first time. A set of informative features are selected to improve the classification accuracy. The training samples are selected according to their distributions in the high-dimensional sample space. Our classifier PlantMiRNAPred achieves >90% accuracy on the plant datasets from eight plant species, including A.thaliana, Oryza sativa, Populus trichocarpa, Physcomitrella patens, Medicago truncatula, Sorghum bicolor, Zea mays and G.max. The superior performance of the proposed classifier can be attributed to the extracted plant pseudo pre-miRNAs, the selected training dataset and the carefully selected features. The ability of PlantMiRNAPred to discern real and pseudo pre-miRNAs provides a viable method for discovering new non-homologous plant pre-miRNAs. Availability: The web service of PlantMiRNAPred, the training datasets, the testing datasets and the selected features are freely available at http://nclab.hit.edu.cn/PlantMiRNAPred/. Contact: maozuguo@hit.edu.cn; yufei.huang@utsa.edu

In order to improve the homogeneity and dispersity of the sericite in methylphenyl-silicone resin, the agglomerate state of the sericites was controlled effectively. The dispersive model of the sericite in methylphenyl-silicone resin was designed also. First, the modified sericite was prepared using hexadecyl trimethyl ammonium bromide as the intercalating agent. Then, functional sericite was incorporated into methylphenyl-silicone by terminal hydroxyl. The structure and dispersive performance of the hybrid polymers was charactered by analytical instruments. Scanning electron microscopy and Transmission electron microscope, Laser scanning confocal microscope and X-ray diffraction analysis showed that functional sericite was dispersed homogeneously in methylphenyl-silicone resin matrix. X-ray photoelectron spectroscopy analysis showed that the absorption peaks of the Si-OH band of methylphenyl-silicone resin were decreased and the Si-O-Si band was increased. This change evidently showed a significant role to enhance the reaction degree of the functional sericite in methylphenyl-silicone resin.

Background MicroRNAs (miRNAs) are a set of short (19∼24 nt) non-coding RNAs that play significant roles as posttranscriptional regulators in animals and plants. The ab initio prediction methods show excellent performance for discovering new pre-miRNAs. While most of these methods can distinguish real pre-miRNAs from pseudo pre-miRNAs, few can predict the positions of miRNAs. Among the existing methods that can also predict the miRNA positions, most of them are designed for mammalian miRNAs, including human and mouse. Minority of methods can predict the positions of plant miRNAs. Accurate prediction of the miRNA positions remains a challenge, especially for plant miRNAs. This motivates us to develop MaturePred, a machine learning method based on support vector machine, to predict the positions of plant miRNAs for the new plant pre-miRNA candidates. Methodology/Principal Findings A miRNA:miRNA* duplex is regarded as a whole to capture the binding characteristics of miRNAs. We extract the position-specific features, the energy related features, the structure related features, and stability related features from real/pseudo miRNA:miRNA* duplexes. A set of informative features are selected to improve the prediction accuracy. Two-stage sample selection algorithm is proposed to combat the serious imbalance problem between real and pseudo miRNA:miRNA* duplexes. The prediction method, MaturePred, can accurately predict plant miRNAs and achieve higher prediction accuracy compared with the existing methods. Further, we trained a prediction model with animal data to predict animal miRNAs. The model also achieves higher prediction performance. It further confirms the efficiency of our miRNA prediction method. Conclusions The superior performance of the proposed prediction model can be attributed to the extracted features of plant miRNAs and miRNA*s, the selected training dataset, and the carefully selected features. The web service of MaturePred, the training datasets, the testing datasets, and the selected features are freely available at http://nclab.hit.edu.cn/maturepred/.