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As a widely adopted model of infrastructure and public service provision, the goal of PPP projects is gradually shifting from traditional economic orientation to sustainable development orientation. During this process, contractual governance and relational governance are regarded as effective ways to improve the sustainable performance of PPP projects. This paper extends contractual governance (control, coordination, adaptation) and relational governance (trust, communication, reciprocity, and industry practices) to second-order constructs. On this basis, partial least squares structural equation model (PLS-SEM) is adopted to analyze the questionnaire data of 114 PPP project practitioners, aiming to study the influence of contractual and relational governance on the sustainable performance of PPP projects. The results of PLS-SEM verify the support effect of contractual and relational governance on sustainable performance, and the effect of relational governance is more significant. This paper enriches the governance mechanism of PPP projects from the perspective of sustainable development. At the same time, the research results can also help PPP project managers rationally use the governance mechanism of both contract and relationship to promote the project to achieve its sustainable performance.

In this article, a process named NaAlO2-Dawsonite-Pseudo-boehmite was proposed. By using dawsonite as an intermediate, pseudo-boehmite (PB) with high pore volume could be synthesized through this p...

Hydrogen sulfide (H

Early-life inflammation increases the risk for depression in later life. Here, we demonstrate how early-life inflammation causes adolescent depressive-like symptoms: by altering the long-term neuronal spine engulfment capacity of microglia. For mice exposed to lipopolysaccharide (LPS)-induced inflammation via the Toll-like receptor 4/NF-κB signaling pathway at postnatal day (P) 14, ongoing longitudinal imaging of the living brain revealed that later stress (delivered during adolescence on P45) increases the extent of microglial engulfment around anterior cingulate cortex (ACC) glutamatergic neuronal (ACCGlu) spines. When the ACC microglia of LPS-treated mice were deleted or chemically inhibited, the mice did not exhibit depressive-like behaviors during adolescence. Moreover, we show that the fractalkine receptor CX3CR1 mediates stress-induced engulfment of ACCGlu neuronal spines. Together, our findings establish that early-life inflammation causes dysregulation of microglial engulfment capacity, which encodes long-lasting maladaptation of ACCGlu neurons to stress, thus promoting development of depression-like symptoms during adolescence.

It has been a long-term challenge to improve the phase stability of Ni-rich LiNixMnyCo1-x-yO2 (x ≥ 0.6) transition metal (TM) oxides for large-scale applications. Herein, a new structure engineering strategy is utilized to optimize the structural arrangement of Li1+x(Ni0.88Mn0.06Co0.06)1-xO2 (NMC88) with a different Li-excess content. It was found that structure stability and particle sizes can be tuned with suitable Li-excess contents. NMC88 with an actual Li-excess of 2.7% (x = 0.027, Li/TM = 1.055) exhibits a high discharge capacity (209.1 mAh g-1 at 3.0-4.3 V, 0.1 C) and maintains 91.7% after the 100th cycle at 1 C compared with the NMC88 sample free of Li-excess. It also performs a delayed voltage decay and a good rate capacity, delivering 145.8 mAh g-1 at a high rate of 10 C. Multiscale characterization technologies including ex/in situ X-ray diffraction (XRD), focused ion beam (FIB) cutting-scanning electronic microscopy (SEM), and transmission electron microscopy (TEM) results show that a proper Li-excess (2.7%) content contributes to the formation of a broader Li slab, optimized cation mixing ratio, and even particle sizes. Therefore, NMC88 with a proper Li-excess is a good choice for next-generation cathode materials.

With the rise in popularity of artificial intelligence, the technology of verbal communication between man and machine has received an increasing amount of attention, but generating a good conversation remains a difficult task. The key factor in human-machine conversation is whether the machine can give good responses that are appropriate not only at the content level (relevant and grammatical) but also at the emotion level (consistent emotional expression). In our paper, we propose a new model based on long short-term memory, which is used to achieve an encoder-decoder framework, and we address the emotional factor of conversation generation by changing the model’s input using a series of input transformations: a sequence without an emotional category, a sequence with an emotional category for the input sentence, and a sequence with an emotional category for the output responses. We perform a comparison between our work and related work and find that we can obtain slightly better results with respect to emotion consistency. Although in terms of content coherence our result is lower than those of related work, in the present stage of research, our method can generally generate emotional responses in order to control and improve the user’s emotion. Our experiment shows that through the introduction of emotional intelligence, our model can generate responses appropriate not only in content but also in emotion.

A fuzzy clustering method based on FCM algorithm for working status of mineral flotation process is proposed to help workers control mineral flotation process better. The working status of mineral floatation process can be determined by judging dosing records, in terms of copper sulphate, lead nitrate, xanthate, 2# oil, black medicine, etc., or observing the visual features of foam layer including the average grayscale, R-means, G-means, B-means, the average bubble size, the skewness of bubble size, the standard deviation of bubble size and bubble stability. Nearly 6,000 continuous dosing data were collected from an automatic dosing system installed in a gold mine flotation workshop and normalised to range of [0, 1]. Those dosing data were first pre-classified into steady condition and unsteady condition according to the degree of change in neighbouring data and then clustered respectively. 96 categories of steady condition and 24 categories of unsteady condition were obtained by an FCM program using Euclidean distance. Similarity coefficient analysis on mean, standard deviation, and variation coefficient indicate that the dosing clusters in steady condition are more trustworthy to dosing operators than those in unsteady condition. Meanwhile, FCM algorithm is suitable for dosing data clustering with high consistency.

Most of the existing fingerprint retrieval systems are based on the overall characteristics and detailed features of fingerprints, and their performance is poor in the cases of low-quality fingerprint images, such as incomplete fingerprint images. In order to improve the recognition speed, accuracy, and robustness of automated fingerprint recognition systems based on large-scale fingerprint databases, in this paper, we propose a fast fingerprint classification retrieval and identification method based on Scale Invariant Feature Transform (SIFT) and Local Sensitive Hash (LSH) algorithms. A method based on scale space theory extracts SIFT feature point descriptors of relatively high-quality fingerprint images in accordance with the principle of a greater matching contribution rate, uses multi-template image feature fusion technology to build a stable fingerprint feature template database, achieves the storage and retrieval of highdimensional SIFT features using the LSH algorithm, and carries out matching progressively by level on the basis of SIFT’s matching principle of close neighboring priority scale. Experimental results show that the proposed method has strong penetration, high retrieval efficiency, good recognition accuracy, and strong robustness, thereby providing a fast and efficient retrieval and matching mechanism for the automated recognition of the large-scale fingerprint database, with strong practicality.

With the rise and popular of artificial intelligence,the technology of conversation between human and machine get more and more attention. Using neural network model on the Encoder-Decoder framework has been wildly used in translation and human-machine conversation. This paper we propose a new hybrid neural network model (HNN) which consists of some essential neural network models (that is RNN, LSTM, and CNN). At the same time, according to the number of words that each sentence contains, we will get three sub-datasets from the original dataset. Then training and testing our models on different sub-datasets. Experimental results show that the best accuracy belongs to different hybrid models on different sub-datasets, which indicate the proposed approach can make use of each models advantages aim at different sub-dataset.

A novel distributed quantum packet transmission scheme is proposed with teleportation in the non-maximally entangled relay channel. Quantum signals are encoded as quantum signal packets and transmitted from the source to the destination with assistance of the relay. The TDM (Time Division Multiplexing) technique is applied in order to improve the efficiency of quantum communications. The security depends on the security of signal particles and an upper bound of attackers’ intercepting quantum information under their strongest collective attacks is derived. The communication efficiency can be improved to four times of the traditional schemes, and the timesaving percentage may approach to 75 %. It has a wide application in quantum networks as well.

ADP-glucose pyrophosphorylase (AGPase) catalyzes the first committed step of starch synthesis. AGPase is a heterotetramer composed of two large subunits and two small subunits, has cytosolic and plastidial isoforms, and is detected mainly in the cytosol of endosperm in cereal crops. To investigate the effects of AGPase cytosolic large subunit gene (LSU I) on starch biosynthesis in higher plant, in this study, a TaLSU I gene from wheat was overexpressed under the control of an endosperm-specific promoter in a wheat cultivar (Yumai 34). PCR, Southern blot, and real-time RT-PCR analyses indicated that the transgene was integrated into the genome of transgenic plants and was overexpressed in their progeny. The overexpression of the TaLSU I gene remarkably enhanced AGPase activity, endosperm starch weight, grain number per spike, and single grain weight. Therefore, we conclude that overexpression of the TaLSU I gene enhances the starch biosynthesis in endosperm of wheat grains, having potential applications in wheat breeding to develop a high-yield wheat cultivar with high starch weight and kernel weight.

Treatment with 0.5 mM salicylic acid (SA) significantly alleviated growth inhibition induced by drought in wheat seedlings, manifested by less decreassed fresh mass, dry mass, plant height, root length, and less increased lipid peroxidation. Under drought stress, SA significantly increased the content of ascorbate (ASA) and glutathione (GSH). We determined the full-length cDNA sequences of genes encoding the glutathione-S-transferase 1 (GST1) and 2 (GST2) and we also measured the transcription of eight genes related to ASA-GSH cycle. The results indicated that exogenous SA significantly enhanced the transcription of GST1, GST2, glutathione reductase (GR), and monodehydroascorbate reductase (MDHAR) genes during almost the entire drought period, but only increased those of dehydroascorbate reductase (DHAR) at 12 h, glutathione peroxidase (GPX1) at 48 h, phospholipid hydroperoxide glutathione peroxidase (GPX2) at 12 and 24 h, and glutathione synthetase (GSHS) at 12, 24, and 48 h. This implies that SA alleviates the detrimental effects of drought stress on wheat seedling growth by influencing the ASA-GSH cycle.

Proteomic studies were performed to identify the protein species involved in copper (Cu) stress responses in common wheat. Two-week-old wheat seedlings were exposed to 100 μM CuSO4 treatment for 3 days. Growth of shoots and roots was markedly inhibited and lipid peroxidation was greatly increased. Cu was readily absorbed by wheat seedlings, with greater Cu contents in roots than in leaves. Using 2-DE method, 98 protein spots showed significantly enhanced or reduced abundance, of which 93 were successfully identified. Of these identified protein species, 49 and 44 were found in roots and leaves, respectively. Abundance of most of identified protein species, which function in signal transduction, stress defense, and energy production, was significantly enhanced, while that of many protein species involved in carbohydrate metabolism, protein metabolism, and photosynthesis was severely reduced. The Cu-responsive protein interaction network revealed 36 key proteins, most of which may be regulated by abscisic acid (ABA), ethylene, jasmonic acid (JA), and so on. Exogenous JA application showed a protective effect against Cu stress and significantly increased transcripts of the glutathione S-transferase (GST) gene. This study provides insight into the molecular mechanisms of Cu responses in higher plants.

Exogenous salicylic acid (SA) significantly improved abiotic tolerance in higher plants, and ascorbate (ASA) and glutathione (GSH) play important roles in abiotic tolerance. In this study, SA (0.5mM) markedly increased the contents of ASA and GSH in SA-treated plants during salt stress (250mM NaCl). The transcript levels of the genes encoding ASA and GSH cycle enzymes were measured using quantitative real-time PCR. The results indicated that, during salt stress, exogenous SA significantly enhanced the transcripts of glutathione peroxidase (GPX1), phospholipid hydroperoxide glutathione peroxidase (GPX2) and dehydroascorbate reductase (DHAR) genes at 12h, glutathione reductase (GR) at 24h, 48h and 72h, glutathione-S-transferase 1 (GST1), 2 (GST2), monodehydroascorbate reductase (MDHAR) and glutathione synthetase (GS) at the 48h and 72h after salt stress, respectively. The results implied that SA temporally regulated the transcript levels of the genes encoding ASA-GSH cycle enzymes, resulting in the increased contents of GSH and ASA and enhanced salt tolerance.

Pretreatment with 0.5 mM salicylic acid (SA) for 3 days significantly enhanced the growth and tolerance to subsequent drought stress (PEG-6000, 15%) in wheat seedlings, manifesting as increased shoot and root dry weights, and decreased lipid peroxidation. Total proteins from wheat leaves exposed to (i) 0.5 mM SA pretreatment, (ii) drought stress, and (iii) 0.5 mM SA treatment plus drought-stress treatments were analyzed using a proteomics method. Eighty-two stress-responsive protein spots showed significant changes, of which 76 were successfully identified by MALDI-TOF-TOF. Analysis of protein expression patterns revealed that proteins associated with signal transduction, stress defense, photosynthesis, carbohydrate metabolism, protein metabolism, and energy production could by involved in SA-induced growth and drought tolerance in wheat seedlings. Furthermore, the SA-responsive protein interaction network revealed 35 key proteins, suggesting that these proteins are critical for SA-induced tolerance.

Along with the development of social network, more and more people know the world by reading news. The problem about what kind of emotion is inspired when people read news is very worthy of discussion. This paper will mix Deep Belief Networks (DBN) model and Support Vector Machine (SVM) to a hybrid neural network model by using the Contrast Divergence (CD) algorithm to estimate the weights when training a generating model, ensure that each layer of the Restricted Boltzmann Machine (RBM) mapping the features of the inputs to the best. At the same time, we cascade the last layer of DBN and a SVM classifier to adjust judging performance. And a set of tags will be attached to the top (Associative Memory), through a process of parameter tuning, learn the identifying weights to obtain a network for the task of text classification. The experimental results show that the hybrid neural network model works better than the traditional text categorization method based on simple characteristics (such as CHI), and it is more suitable for extracting text semantic characteristics.

Computation intelligence is becoming an essential technology during the development of human society. There are many family members in computation intelligence. Many researchers have already studied the mathematical inherent rules of these biology-inspired algorithms to found methods to improve the capacity of the algorithms. In the family of computation intelligence, differential evolution (DE) algorithm shows performance optimization ability. In order to explore the reason why DE could have stable and robust quality. We analyzed the parallelism of the evolutionary process in the iterative process of DE algorithm based on graph theory. By the knowledge of graph theory, it will directly exhibit the essential reason of differential evolution in the algorithm. The research will reveal that the superior DE algorithm have more extent parallelism ability than the elementary algorithm.

Motivated by the space-time diversity transmission technique in wireless communications, a novel probabilistic quantum relay communication scheme in the quantum noisy channel is proposed in order to maximize the correct information transmission and the range of quantum communication, in which quantum signal sequences that carrying two-particle entangled states are transmitted from two senders to two relays and then retransmitted to the receiver after space-time encoded by relays. The quantum signal states can be restored via filtering out the channel noise with two-dimensional Bell measurements by the receiver. Analysis and discussions indicate that our scheme can increase and approximately double the range of quantum communication while not to reduce too much quantum signal-to-noise ratio, and meanwhile the security can be guaranteed under strongest collective attacks and LOCC attacks.

Three kinds of quantum relay communication models are proposed, i.e., the quantum single relay model, quantum serial multi-relay model and quantum parallel multi-relay model. The channel capacities of those three kinds of systems are analyzed with the theory of quantum Markov trace-preserving process and the generalized theory of simple multi-hop channel in quantum system. Motivated by the quantum Fano inequality, the lower bounds of that channel capacities are derived. The illustration and simulation present the trends of the lower bounds on the channel capacities of different quantum relay systems based on the depolarizing noisy channel.

A novel (t,n)-threshold scheme for the multi-party quantum group signature is proposed based on the irregular quantum Fourier transform, in which every t-qubit quantum message needs n participants to generate the quantum group signature. All the quantum operation gates in the quantum circuit can be distributed and arranged randomly in the irregular QFT algorithm, which can increase the von Neumann entropy of the signed quantum message and the randomicity of the quantum signature generation significantly. The generation and verification of the quantum group signature can be both performed in quantum circuits with the parallel algorithm. Security analysis shows that an available and legal quantum (t,n)-threshold group signature can be achieved.