Reconfiguring hydrogel assemblies using a photocontrolled metallopolymer adhesive for multiple customized functions.

Stimuli-responsive hydrogels with programmable shape changes are promising materials for soft robots, four-dimensional printing, biomedical devices and artificial intelligence systems. However, these applications require the fabrication of hydrogels with complex, heterogeneous and reconfigurable structures and customizable functions. Here we report the fabrication of hydrogel assemblies with these features by reversibly gluing hydrogel units using a photocontrolled metallopolymer adhesive. The metallopolymer adhesive firmly attached individual hydrogel units via metal-ligand coordination and polymer chain entanglement. Hydrogel assemblies containing temperature- and pH-responsive hydrogel units showed controllable shape changes and motions in response to these external stimuli. To reconfigure their structures, the hydrogel assemblies were disassembled by irradiating the metallopolymer adhesive with light; the disassembled hydrogel units were then reassembled using the metallopolymer adhesive with heating. The shape change and structure reconfiguration abilities allow us to reprogramme the functions of hydrogel assemblies. The development of reconfigurable hydrogel assemblies using reversible adhesives provides a strategy for designing intelligent materials and soft robots with user-defined functions.

A Method for Detecting LDoS Attacks in SDWSN Based on Compressed Hilbert-Huang Transform and Convolutional Neural Networks.

Currently, Low-Rate Denial of Service (LDoS) attacks are one of the main threats faced by Software-Defined Wireless Sensor Networks (SDWSNs). This type of attack uses a lot of low-rate requests to occupy network resources and hard to detect. An efficient detection method has been proposed for LDoS attacks with the features of small signals. The non-smooth small signals generated by LDoS attacks are analyzed employing the time-frequency analysis method based on Hilbert-Huang Transform (HHT). In this paper, redundant and similar Intrinsic Mode Functions (IMFs) are removed from standard HHT to save computational resources and to eliminate modal mixing. The compressed HHT transformed one-dimensional dataflow features into two-dimensional temporal-spectral features, which are further input into a Convolutional Neural Network (CNN) to detect LDoS attacks. To evaluate the detection performance of the method, various LDoS attacks are simulated in the Network Simulator-3 (NS-3) experimental environment. The experimental results show that the method has 99.8% detection accuracy for complex and diverse LDoS attacks.

Functional identification of PhMATE1 in flower color formation in petunia.

Multidrug and toxic compound extrusion (MATE) transporter proteins are a class of secondary transporter proteins that can transport flavonoids. Anthocyanins, a kind of flavonoid, are important secondary metabolites widely found in higher plants; they determine the flower color of most angiosperms. TT12 in Arabidopsis was the first MATE protein identified to be involved in flavonoid transport. Petunia (Petunia hybrida) is an important ornamental plant and is one of the ideal plants for studying plant flower color. However, there are few reports on anthocyanin transport in petunia. In this study, we characterized a homolog of Arabidopsis TT12 in the petunia genome, PhMATE1, that shares the highest amino acid sequence identity with Arabidopsis TT12. PhMATE1 protein contained 11 transmembrane helices. PhMATE1 showed a high transcription level in corollas. The silencing of PhMATE1 mediated by both virus-induced gene silence and RNA interference changed flower color and reduced anthocyanin content in petunia, suggesting that PhMATE1 is involved in anthocyanin transport in petunia. Furthermore, PhMATE1 silencing downregulated the expression of the structural genes of the anthocyanin synthesis pathway. The results of this study supported the hypothesis that MATEs are involved in the sequestration of anthocyanins during flower color formation.

A Photopatternable Conjugated Polymer with Thermal-Annealing-Promoted Interchain Stacking for Highly Stable Anti-Counterfeiting Materials.

Photoresponsive polymers can be conveniently used to fabricate anti-counterfeiting materials through photopatterning. However, an unsolved problem is that ambient light and heat can damage anti-counterfeiting patterns on photoresponsive polymers. Herein, photo- and thermostable anti-counterfeiting materials are developed by photopatterning and thermal annealing of a photoresponsive conjugated polymer (MC-Azo). MC-Azo contains alternating azobenzene and fluorene units in the polymer backbone. To prepare an anti-counterfeiting material, an MC-Azo film is irradiated with polarized blue light through a photomask, and then thermally annealed under the pressure of a photonic stamp. This strategy generates a highly secure anti-counterfeiting material with dual patterns, which is stable to sunlight and heat over 200 °C. A key for the stability is that thermal annealing promotes interchain stacking, which converts photoresponsive MC-Azo to a photostable material. Another key for the stability is that the conjugated structure endows MC-Azo with desirable thermal properties. This study shows that the design of photopatternable conjugated polymers with thermal-annealing-promoted interchain stacking provides a new strategy for the development of highly stable and secure anti-counterfeiting materials.

Designing Rewritable Dual-Mode Patterns using a Stretchable Photoresponsive Polymer via Orthogonal Photopatterning.

The fabrication of dual-mode patterns in the same region of a material is a promising approach for high-density information storage, new anti-counterfeiting technologies, and highly secure encryption. However, dual-mode patterns are difficult to achieve because the two patterns in one material may interfere with each other during fabrication and usage. The development of noninterfering dual-mode patterns requires new materials and patterning techniques. Herein, a novel orthogonal photopatterning technique is reported for the fabrication of noninterfering dual-mode patterns on an azopolymer P1. P1 is a unique material that exhibits both photoinduced reversible solid-to-liquid transitions and good stretchability. In the first step of orthogonal photopatterning, patterned photonic structures are fabricated on a P1 film via masked nanoimprinting controlled by photoinduced reversible solid-to-liquid transitions. In the second step, the P1 film is stretched and irradiated with polarized light through a photomask, which generates a chromatic polarization pattern. In particular, the photonic structures and chromatic polarization in the dual-mode pattern are noninterfering. Another feature of dual-mode patterns is that they are rewritable via photo-, thermal, or solution reprocessing, which are useful for recycling and reprogramming. This study opens an avenue for the development of novel materials and techniques for photopatterning.

Zero-Shot Image Classification Based on a Learnable Deep Metric.

The supervised model based on deep learning has made great achievements in the field of image classification after training with a large number of labeled samples. However, there are many categories without or only with a few labeled training samples in practice, and some categories even have no training samples at all. The proposed zero-shot learning greatly reduces the dependence on labeled training samples for image classification models. Nevertheless, there are limitations in learning the similarity of visual features and semantic features with a predefined fixed metric (e.g., as Euclidean distance), as well as the problem of semantic gap in the mapping process. To address these problems, a new zero-shot image classification method based on an end-to-end learnable deep metric is proposed in this paper. First, the common space embedding is adopted to map the visual features and semantic features into a common space. Second, an end-to-end learnable deep metric, that is, the relation network is utilized to learn the similarity of visual features and semantic features. Finally, the invisible images are classified, according to the similarity score. Extensive experiments are carried out on four datasets and the results indicate the effectiveness of the proposed method.

ECG Heartbeat Classification Based on an Improved ResNet-18 Model.

Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 model for heartbeat classification of electrocardiogram (ECG) signals through appropriate model training and parameter adjustment. Due to the unique residual structure of the model, the utilized CNN layered structure can be deepened in order to achieve better classification performance. The results of applying the proposed model to the MIT-BIH arrhythmia database demonstrate that the model achieves higher accuracy (96.50%) compared to other state-of-the-art classification models, while specifically for the ventricular ectopic heartbeat class, its sensitivity is 93.83% and the precision is 97.44%.

A Partition Based Gradient Compression Algorithm for Distributed Training in AIoT.

Running Deep Neural Networks (DNNs) in distributed Internet of Things (IoT) nodes is a promising scheme to enhance the performance of IoT systems. However, due to the limited computing and communication resources of the IoT nodes, the communication efficiency of the distributed DNN training strategy is a problem demanding a prompt solution. In this paper, an adaptive compression strategy based on gradient partition is proposed to solve the problem of high communication overhead between nodes during the distributed training procedure. Firstly, a neural network is trained to predict the gradient distribution of its parameters. According to the distribution characteristics of the gradient, the gradient is divided into the key region and the sparse region. At the same time, combined with the information entropy of gradient distribution, a reasonable threshold is selected to filter the gradient value in the partition, and only the gradient value greater than the threshold is transmitted and updated, to reduce the traffic and improve the distributed training efficiency. The strategy uses gradient sparsity to achieve the maximum compression ratio of 37.1 times, which improves the training efficiency to a certain extent.

A DNA-Threaded ZIF-8 Membrane with High Proton Conductivity and Low Methanol Permeability.

Natural biomolecules have potential as proton-conducting materials, in which the hydrogen-bond networks can facilitate proton transportation. Herein, a biomolecule/metal-organic framework (MOF) approach to develop hybrid proton-conductive membranes is reported. Single-strand DNA molecules are introduced into DNA@ZIF-8 membranes through a solid-confined conversion process. The DNA-threaded ZIF-8 membrane exhibits high proton conductivity of 3.40 × 10-4 S cm-1 at 25 °C and the highest one ever reported of 0.17 S cm-1 at 75 °C, under 97% relatively humidity, attributed to the formed hydrogen-bond networks between the DNA molecules and the water molecules inside the cavities of the ZIF-8, but very low methanol permeability of 1.25 × 10-8 cm2 s-1 due to the small pore entrance of the DNA@ZIF-8 membranes. The selectivity of the DNA@ZIF-8 membrane is thus significantly higher than that of developed proton-exchange membranes for fuel cells. After assembling the DNA@ZIF-8 hybrid membrane into direct methanol fuel cells, it exhibits a power density of 9.87 mW cm-2 . This is the first MOF-based proton-conductivity membrane used for direct methanol fuel cells, providing bright promise for such hybrid membranes in this application.

Flexible and Binder-Free Hierarchical Porous Carbon Film for Supercapacitor Electrodes Derived from MOFs/CNT.

Rational design of free-standing porous carbon materials with large specific surface area and high conductivity is a great need for ligh-weight suprecapacitors. Here, we report a flexible porous carbon film composed of metal-organic framework (MOF)-derived porous carbon polyhedrons and carbon nanotubes (CNTs) as binder-free supercapacitor electrode for the first time. Due to the synergistic combination of carbon polyhedrons and CNT, the obtained carbon electrode shows a specific capacitance of 381.2 F g-1 at 5 mV s-1 and 194.8 F g-1 at 2 A g-1 and outstanding cycling stability with a Coulombic effciency above 95% after 10000 cycles at 10 A g-1. The assembled aqueous symmetrical supercapacitor exhibits an energy density of 9.1 Wh kg-1 with a power density of 3500 W kg-1. The work opens a new way to design flexible MOF-based hierarchical porous carbon film as binder-free electrodes for high-performance energy storage devices.

Strings of Porous Carbon Polyhedrons as Self-Standing Cathode Host for High-Energy-Density Lithium-Sulfur Batteries.

Rational design of cathode hosts with high electrical conductivity and strong sulfur confinement is a great need for high-performance lithium-sulfur batteries. Herein, we report a self-standing, hybrid-nanostructured cathode host comprised of metal-organic framework (MOF)-derived porous carbon polyhedrons and carbon nanotubes (CNTs) for the significant improvement of both the electrode cyclability and energy density. The strong coupling of the intertwined CNTs and strung porous carbon polyhedrons as a binder-free thin film significantly enhances the long-range electronic conductivity and provides abundant active interfaces as well as robust electrode integrity for sulfur electrochemistry. Attributed to the synergistic combination of the CNTs and carbon polyhedrons, the obtained sulfur electrodes exhibit outstanding cyclability, an excellent high-rate response up to 10 C, and an ultra-high volumetric capacity of 960 Ah L-1 .

Hyper-branched anion exchange membranes with high conductivity and chemical stability.

In the manuscript, we report the design and preparation of hyper-branched polymer electrolytes intended for alkaline anion exchange membrane fuel cells. The resulting membrane exhibits high conductivity, lower water swelling and shows prolonged chemical stability under alkaline conditions.

Distributed Task Offloading in Heterogeneous Vehicular Crowd Sensing.

The ability of road vehicles to efficiently execute different sensing tasks varies because of the heterogeneity in their sensing ability and trajectories. Therefore, the data collection sensing task, which requires tempo-spatial sensing data, becomes a serious problem in vehicular sensing systems, particularly those with limited sensing capabilities. A utility-based sensing task decomposition and offloading algorithm is proposed in this paper. The utility function for a task executed by a certain vehicle is built according to the mobility traces and sensing interfaces of the vehicle, as well as the sensing data type and tempo-spatial coverage requirements of the sensing task. Then, the sensing tasks are decomposed and offloaded to neighboring vehicles according to the utilities of the neighboring vehicles to the decomposed sensing tasks. Real trace-driven simulation shows that the proposed task offloading is able to collect much more comprehensive and uniformly distributed sensing data than other algorithms.

Synthesis and evaluations of an acid-cleavable, fluorescently labeled nucleotide as a reversible terminator for DNA sequencing.

An acid-cleavable linker based on a dimethylketal moiety was synthesized and used to connect a nucleotide with a fluorophore to produce a 3'-OH unblocked nucleotide analogue as an excellent reversible terminator for DNA sequencing by synthesis.

Highly hydroxide conductive ionomers with fullerene functionalities.

A novel ionomer was designed that will not poison the catalyst in alkaline fuel cells, by incorporating for the first time N-methyl pyrrolidine-C60 cation in polymeric anion exchange ionomers. The resultant fullerene-based anion exchange ionomer shows an extremely high hydroxide conductivity (182 mS cm(-1)) at a low cation concentration (0.62 mmol g(-1)).

Design and synthesis of fluorescence-labeled nucleotide with a cleavable azo linker for DNA sequencing.

A cleavable azo linker was synthesized and reacted with 5-(6)-carboxytetramethyl rhodamine succinimidyl ester, followed by further reactions with di(N-succinimidyl) carbonate and 5-(3-amino-1-propynyl)-2'-deoxyuridine 5'-triphosphate [dUTP(AP3)] to obtain the terminal product dUTP-azo linker-TAMRA as a potential reversible terminator for DNA sequencing by synthesis with no need for 3'-OH blocking.

Heterogeneous Participant Recruitment for Comprehensive Vehicle Sensing.

Widely distributed mobile vehicles wherein various sensing devices and wireless communication interfaces are installed bring vehicular participatory sensing into practice. However, the heterogeneity of vehicles in terms of sensing capability and mobility, and the participants' expectations on the incentives blackmake the collection of comprehensive sensing data a challenging task. A sensing data quality-oriented optimal heterogeneous participant recruitment strategy is proposed in this paper for vehicular participatory sensing. In the proposed strategy, the differences between the sensing data requirements and the collected sensing data are modeled. An optimization formula is established to model the optimal participant recruitment problem, and a participant utility analysis scheme is built based on the sensing and mobility features of vehicles. Besides, a greedy algorithm is then designed according to the utility of vehicles to recruit the most efficient vehicles with a limited total incentive budget. Real trace-driven simulations show that the proposed strategy can collect 85.4% of available sensing data with 34% incentive budget.

Design and synthesis of new acid cleavable linkers for DNA sequencing by synthesis.

A new kind of acid sensitive tetrahydrofuranyl (THF) linker was synthesized and then reacted with 5-(6)-carboxytetramethylrhodaminesuccinimidyl ester (5(6)-TAMRA, SE), followed by di(N-succinimidyl) carbonate (DSC) and modified 2'-deoxyuridine triphosphate (dUTP); the final product, as a reversible terminator for DNA sequencing by synthesis (DNA SBS), was given obtained and confirmed by 1H-NMR, 31P-NMR, and HRMS with purity of up to 99%. The synthesized dye-labeled terminator incorporated into DNA strand successfully, and the fluorophore was cleaved completely under acidic conditions. The preliminary results encourage us to explore more acid-sensitive linkers for DNA SBS to increase the cleavage efficiency under weakly acidic conditions.