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Regional promotion of school-based research and study to improve the relevant systems, Focusing on the actual situation of rural schools, Found that school-based study in rural schools, Uneven development, Lack of teachers in rural schools, Insufficient funds for school-based research and training, The reward mechanism causes the vicious competition among teachers, The knowledge structure of the left-behind teachers is old, Teachers have limited competence, School-based teaching and research interest is not high, The lack of organizational forms of school-based training, The ways to solve these problems include the following aspects: turning regional resources into teaching materials, Integrating school-based research and training to promote teachers' professional development, Give full play to the practical advantages of the region, Promote the diversification of school-based research and study forms, Establish a mechanism for sustainable development, Realize the normalization of regional school-based research and training.

Aiming at the inconsistency of manual detection of mobile phone screen defects, the image feature extraction of traditional machine learning is often set based on experience, resulting in unsatisfactory detection results. Therefore, a mobile phone screen defect detection model (Ghostbackbone) which is proposed by this paper based on YOLOv5 s and Ghostbottleneck. The bottleneck of Ghostbackbone mainly uses and improves the Ghostbottleneck of GhostNet. The attention module of Ghostbackbone uses Coordinated Attention and Depthwise Separable Convolution for parameter reduction. Finally, Ghostbackbone uses YOLOv5 as the object detector to train the mobile phone screen defect dataset. The experimental results show that the parameter quantity of Ghostbackbone is 24% of that of YOLOv5 s, the average time of detecting a single picture is only 2% lower than that of YOLOv5 s, and the mAP0.5 : 0.95 is 2% higher than that of MobilenetV3 s.

Atomically precise synthesis of topological molecular carbons such as Möbius carbon nanobelts (MCNBs) represents a big challenge in synthetic organic chemistry as it requires careful control of both twist and strain. So far, only one singly twisted MCNB has been reported and the MCNBs with more than two twists remain unknown. Herein, we report the facile synthesis of the first triply twisted MCNB by rational design and via a synthetic route involving Suzuki coupling-mediated macrocyclization, Bi(OTf)3-catalyzed cyclization of vinyl ethers, followed by oxidative dehydrogenation. The Möbius band-like structure was unambiguously confirmed by X-ray crystallographic analysis, which also revealed coexistence of (P,P,P)- and (M,M,M)- enantiomers. The racemic isomers were resolved by chiral HPLC and the isolated enantiomers exhibited a large absorption dissymmetry factor (|gabs| = 0.019) according to circular dichroism spectroscopy, which can be explained by the fully conjugated structure and the desirable orientation of the electric and magnetic transition moments.

Background During our clinical practice, some patients with Chlamydia psittaci pneumonia were found to have secondary pulmonary fungal infections; consequently, this study analyzed the risk factors for their combined pulmonary fungal infections. We also compared the efficacy of tetracycline and moxifloxacin in the treatment of C. psittaci pneumonia to guide the treatment of this disease.Methods Eighty cases of C. psittaci pneumonia in Chongqing were selected and divided into a non-fungal group (56 cases) and a fungal group (24 cases) according to the combination of pulmonary fungal infection. The risk factors for combined pulmonary fungal infection were analyzed. According to the type of medication used, the patients were divided into five treatment groups, including (I) 12 cases of omadacycline, (II) 12 cases of tigecycline, (III) 10 cases of doxycycline, (IV) 8 cases of minocycline, and (V) 38 cases of moxifloxacin, and the efficacy of the drugs was compared.Results (1) Comparisons of factors, including hospitalization cost, length of stay, and duration of fever, showed that, first, the prognosis of the non-fungal group was better than that of the fungal group, and second, there were statistically significant differences between the two groups in the use of broad-spectrum antibacterial drugs; the co-administration of antimicrobials; combined structural lung disease, diabetes mellitus, and liver disease; and serum ferritin levels at admission. A logistic regression analysis showed the co-administration of antimicrobials; combined structural lung disease, diabetes mellitus, and liver disease; and relatively low serum ferritin were independent risk factors for combined pulmonary fungal infections. (2) Comparison of drug efficacy: The white blood cell count in Group I was better than in groups II and V, while fever, imaging improvement, and calcitoninogen decrease time in Group I were significantly better than in groups II, III, IV, and V. The cost in Group V was lower than in groups I, II, and III, and no significant difference was seen in the occurrence of major adverse reactions.Conclusions Patients with C. psittaci pneumonia who are co-administered with antimicrobials and are combined with structural lung disease, diabetes mellitus, and liver disease along with relatively low serum ferritin need to be alerted to combined pulmonary fungal infections. The efficacy of tetracyclines in treating such infections is more pronounced than that of other drugs, while the cost of moxifloxacin is lower than that of other antibiotics.

In this article, we report a tungsten disulphide (WS2) nanosheet sensing platform based on chemiluminescence resonance energy transfer (CRET) for light on detection of ochratoxin A (OTA). The CRET system involves OTA aptamer-12-mer linker-DNAzyme, phosphate-DNA, lambda exonuclease (λexo), hemin, H2O2, luminol and a WS2 nanosheet. Based on the change in chemiluminescence intensity resulting from the OTA aptamer-12-mer linker-G-Quadruplexes/Hemin DNAzymes affinity for or desorption from the WS2 nanosheet surface in the presence and absence of OTA, an ultra-sensitive CRET-based sensor system is proposed, in which the OTA aptamer-12-mer linker-G-Quadruplexes/Hemin DNAzymes–H2O2–luminol and WS2 nanosheets act as CRET donors and acceptors, respectively. Various factors affecting the detection of OTA, including the incubation time of the OTA aptamer-12 mer linker-G-Quadruplexes/Hemin DNAzyme and WS2 nanosheet, the amount of λexo, concentrations of the WS2 nanosheet, luminol and H2O2, and pH value of the chemiluminescence reaction solution were investigated in detail. Under the optimal experimental conditions, the relative luminesecence intensity of the sensing system exhibited a good linear correlation with the OTA concentration in the range of 1.0–10.0 ng mL–1 with a LOD of 0.13 ng mL–1. The proposed CRET-based sensing system also exhibited excellent recoveries of 85.7–93.0% in rice samples and showed good application prospects for the analysis and detection of OTA, which is also easily extended to assay other biomolecules by simply changing the recognition sequence with the substrate aptamer.

An aptamer sensor based on manganese dioxide (MnO2) nanosheets was developed for the detection of zearalenone (ZEN).

An aptamer sensor based on manganese dioxide (MnO

Twisted carbon nanobelts could display persistent chirality, which is desirable for material applications, but their synthesis is very challenging. Herein, we report the successful synthesis and chiral resolution of such a kind of molecules (1-H and 1) with a figure-eight configuration. 1-H was synthesized first by macrocyclization through Suzuki coupling reaction followed by benzannulation via Bi(OTf)3-mediated cyclization reaction of vinyl ether. Oxidative dehydrogenation of 1-H gave the fully π-conjugated 1. Their twisted structures were confirmed by X-ray crystallographic analysis and calculations, and they can be resolved by chiral high-performance liquid chromatography. The isolated enantiomers showed persistent chiroptical properties according to the circular dichroism measurements, with moderate |gabs| values (0.0016 for 1-H and 0.005-0.007 for 1). Their photophysical properties were also briefly studied.

Recently, there exist growing concerns about the interpretability of deep learning models. While few of these models have been applied to a duplicate question detection task, which aims at finding semantically equivalent question pairs of question answering forum. In this paper, based on an attention mechanism, we propose two modularized interpretable deep neural network models for such tasks. During the word precessing procedure, a filter operation is employed to enhance the relevant information contained in the pre-trained word embeddings. Regarding the word matching and sentence representation process, vanilla attention and structured attention mechanisms are utilized, respectively. Benefiting from the interpretability of attention techniques, our models can illustrate how the words match between sentence pairs and what aspects of the sentences are extracted to have an effect on the final decision. The attention visualization furnishes us with detailed representation at word and sentence level. And experimental results show that our models are comparable with other reported models.

Synergistic delivery of two drugs is a desirable choice to overcome drug resistance. Resveratrol (RES) and xanthohumol (XAN) which are both new drugs originated from plants exhibit great potential in the treatment of cancer, but there are few studies on combining them in a delivery system. In this work, a new core/shell fiber mesh containing RES and XAN was designed out via coaxial electrospinning, and the effect of drug content on the medicated fibers was discovered. RES/polyethylene oxide (PEO) could be well combined with XAN/Poly(lactide-glycolide) (PLGA) to make core/shell fibers, but fibrous morphology worsened with increasing drug content of the core or shell to some extent. The core/shell was amorphous nature, and changing drug content had little influence on the wetability of the core/shell fibers. The release accelerated with drug content increasing in the fibers, and RES and XAN showed gradient release, which the release rate of RES was slower than XAN. These two drugs could sustain release for 350 h. When the blend ratios of RES/PEO and XAN/PLGA were 50/50 and 10/90 in the fibers, the fiber mesh showed the best mechanical properties, of which the tensile strength and elongation at break were 2.85 ± 0.10 MPa and 55.23 ± 2.53%, respectively. The medicated fibers presented good characteristics of inhibiting breast cancer cell activity.

Graph convolutional neural networks(GCNs) have achieved impressive results on a variety of graph-based tasks. However, due to the problem of over-smoothing, the output representation of each node in the graph converges to the same asymptotically with increasing depth of GCN, so the vast majority of GCN models in practical applications are shallow. In this paper, we investigate deep GCNs and propose a PageRank-GCN(PR-GCN) model, which is an extension of DropEdge model. PR-GCN uses PageRank to get the top e neighbors that are most easily accessed by nodes and adds them back to the adjacency matrix after DropEdge processing. The results of a series of experiments on real-world datasets show that the PR-GCN model outperforms the state-of-the-art baseline approach in semi-supervised node classification tasks. The experiments also validate that the proposed method can prevent over-smoothing in deep GCNs while mitigating the information loss caused by DropEdge.

ive summarization has become the mainstream of automatic text summarization because of its unique flexibility. Pointer-Generator Network(PGN) has already been the de facto standard for the text summarization model. However, this model still has two main shortcomings: first, since the word embeddings obtained from the word embedding matrix lack of contextual semantic information, the model tends to ignore the global information of the input text when generating the summary. Second, the embedding matrix is only learned on a single corpus from scratch, which decreases the convergence speed of the PGN model and affects the generalization ability of the word embeddings. In this paper, we propose a new text summarization framework, Two-Stage Encoder for pointer-generator network with Pretrained Embeddings, which aims to solve the problems mentioned above. We introduce an encoder based on the multi-head self-attention for the first-stage encoding. This encoder module can improve the word representation by solving the polysemy problem in the PGN model and alleviate the problem of ignoring global information. Besides, we apply the pretrained word embeddings GloVe to initialize the word embedding matrix and then fine-tune it. A range of experiments are conducted on the CNN/Daily Mail dataset. For the CNN/Daily mail dataset, comparing with the baseline model, the proposed model enhances the ROUGE metrics by 0.75, 0.74 and 0.77 respectively.

Thermo-responsive shape memory polymers which are a class of important smart materials can recover permanent shape from temporary shape by simple thermal stimulus, but most of the polymers lack of biodegradability and biocompatibility. Poly(L-lactide) (PLLA) is a green degradable thermoplastic, however, only limited studies involve controlling its shape change. In the work, a new x-shaped copolymer with defined architecture has been devised by using tetramethyltetravinylcyclotetrasiloxane (D4vi) as centronucleus and polytetrahydrofuran-co-poly(e-caprolactone) (PTMG-co-PCL) as branch to achieve PLLA shape memory. When the weight ratio of DAPP and PLLA is 50/50, the composite exhibits good thermo-responsive shape memory capacity because a balance between Tm of the x-shaped polymer rand Tg of PLLA is kept. The shape fixation ratio is 84% at 15 °C, and the recovery ratio 94% at 55 °C. And the shape memory capability does not change evidently after the reversible spiral transition is repeated for 30 times. At the same time, the composite shows great flexibility. So the obtained composite is promising for medical and bionic devices, self-healing systems, self-assembly structures.

The leading cause of genetic dilated cardiomyopathy (DCM) is due to mutations in the TTN gene, impacting approximately 15-20% of familial and 18% of sporadic DCM cases. Currently, there is potential for a personalized RNA-based therapeutic approach in titin-based DCM, utilizing antisense oligonucleotide (AON) mediated exon-skipping, which attempts to reframe mutated titin transcripts, resulting in shortened, functional protein. However, the TTN gene is massive with 363 exons; each newly identified TTN exon mutation provides a challenge to address when considering the potential application of AON mediated exon skipping. In the initial phase of this strategy, the mutated TTN exon requires specific AON design and evaluation to assess the exon skipping effectiveness for subsequent experiments. Here, we present a detailed protocol to effectively assemble and evaluate AONs for efficient exon-skipping in targeted TTN exons. We chose a previously identified TTN 1-bp deletion mutation in exon 335 as an exemplary target exon, which causes a frameshift mutation leading to truncated A-band titin in DCM. We designed two specific AONs to mask the Ttn exon 335 and confirmed successfully mediated exon skipping without disrupting the Ttn reading frame. In addition, we evaluated and confirmed AON-treated HL-1 cells show maintained store-operated calcium entry, fractional shortening as well as preserved sarcomeric formation in comparison to control samples, indicating the treated cardiomyocytes retain adequate, essential cell function and structure, proving the treated cells can compensate for the loss of exon 335. These results indicate our method offers the first systematic protocol in designing and evaluating AONs specifically for mutated TTN target exons, expanding the framework of future advancements in the therapeutic potential of antisense-mediated exon skipping in titin-based DCM.

The problem of time series prediction has been well explored in the community of data mining. However, little research attention has been paid to the case of predicting the movement of a collection of related time series data. In this work, we study the problem of simultaneously predicting multiple time series data using joint predictive models. We observe that in real-world applications, strong relationships between different time-sensitive variables are often held, either explicitly predefined or implicitly covered in nature of the application. Such relationships indicate that the prediction on the trajectory of one given time series could be improved by incorporating the properties of other related time series data into predictive models. The key challenge is to capture the temporal dynamics of these relationships to jointly predict multiple time series. In this research, we propose a predictive model for multiple time series forecasting and apply it to the domain of inventory management. The relationships among multiple time series are modeled as a class of constraints, and in turn, refine the predictions on the corresponding time series. Experimental results on real-world data reveal that the proposed algorithms outperform well-established methods of time series forecasting.

Storyline generation has emerged to be an effective method to describe the evolution of disaster. However, due to the temporal-spatial, heterogeneous, and information overload, most of the existing storylines are only based on textual data and deliver limited information. In this paper, we introduce a novel framework for generating multimedia storylines to provide more concise and vivid information and deeper understanding of real-time events. We first adopt generative adversarial networks to implement an unsupervised bilingual document summarizing model. Then, we transform image and text incorporation problem into a multi-label learning problem and use convolutional neural networks to train a classification model. And finally, the bilingual documents and images are jointly summarized and embedded into a two-layer storyline generating framework. The experiments on real Hurricane data sets demonstrate the effectiveness of the proposed methods in each level and the overall framework.

Alkaline rocks are generally enriched in rare metals (e.g., Nb, Ta, and Zr) and rare earth elements (REE), but the key factors controlling Nb-Ta-REE enrichment remain unclear. The Boziguoer Nb (Ta-Zr-Rb-REE) deposit in Southwest Tianshan (northern margin of Tarim Basin) is China’s largest, with reserves of 0.32 Mt Nb2O5 and 0.02 Mt Ta2O5. It is an alkaline felsic complex 4.45 km in length and 0.5–1.3 km in width, composed of alkalic granite and syenite, which can be subdivided into syenite I and syenite II. The main minerals in each lithofacies are the same (albite, K-feldspar, quartz, arfvedsonite and aegirine). The Nb in the deposit is mainly hosted in pyrochlore supergroup minerals, ubiquitous in alkalic granite and syenite of the Boziguoer deposit. The wide variation in cations (Ca, Na, REE, U, Th) in the A-site further classifies the Boziguoer pyrochlore supergroup minerals as fluornatropyrochlore, fluorcalciopyrochlore and fluorkenopyrochlore. All Boziguoer pyrochlore supergroup minerals are Nb-rich and Ta-poor at the B-site and dominated by F at the Y-site. These cation occurrence illustrate a new mechanism of substitution in the Boziguoer pyrochlore supergroup minerals (2Ca2+ +Ti4+ +4Ta5+ = REE3+ +A-V + 5Nb5+, where A-V is the A-site vacancy). This substitution mechanism is different from that in the pyrochlore supergroup minerals from other rocks such as carbonatite and nepheline syenite, which are dominated by the replacement of Ba (Rb, Sr) with Ca+ Na + A-V. In addition, the substitution of REE (mainly La, Ce) for Ca in the Boziguoer pyrochlore supergroup minerals is likely a result of either REE enrichment or a change in the REE partition coefficient during the evolution of the alkaline magma. Both the pyrochlore supergroup minerals and their host rocks display negative large ion lithophile element (LILE; K, Rb, Sr, and Ba) anomalies, positive high-field-strength element (HFSE) anomalies and light rare earth element (LREE) enrichment with negative Eu anomalies. This is consistent with the crystallization of the pyrochlore supergroup minerals from the magma rather than from hydrothermal fluids, suggesting a magmatic origin. These findings indicate that the mechanisms of pyrochlore supergroup minerals crystallization in alkaline magma may be significantly different from those in carbonatite and nepheline syenite, and that magmatic differentiation processes may have played a role in the enrichment of the Boziguoer deposit by Nb.

The mineralogical studies of rare-element (REL) pegmatites are important for unraveling the ore-forming process and evaluating REL mineralization potential. The Chinese Altai orogenic belt hosting more than 100,000 pegmatite dykes is famous for rare-metal resources worldwide and diverse REL mineralization types. In this paper, we present the results of EMPA and LA-ICP-MS for muscovite from the typical REL pegmatite dykes of the Chinese Altai. The studied pegmatites are Li-Be-Nb-Ta, Li-Nb-Ta, Nb-Ta, Be-Nb-Ta, Be and barren pegmatites. The Li+ accompanied with Fe, Mg and Mn substitute for Al3+ at the octahedral site in muscovite from the REL pegmatites, and the substitution of Rb by Cs at the interlayer space is identified in muscovite from the Be pegmatites. The P and B contents increase with evolution degree and the lenses from the Nb-Ta pegmatite are produced at late fluid-rich stage with high fluxes (P and B). The enrichment of HFSE in muscovite indicates a Nb-Ta-Sn-W rich pegmatite magma for the Be-Nb-Ta pegmatite. From barren pegmatite, beryl-bearing zone, to spodumene-bearing zone, the evolution degrees of pegmatite-forming magmas progressively increase. In the Chinese Altai, the possible indicators of muscovite for REL mineralization types include Rb (ca. 400–600 ppm, barren pegmatite; ca. 1200–4000 ppm, Be pegmatite; >4500 ppm, Li pegmatite), Cs (ca. 5–50 ppm, barren pegmatite; ca. 100–500 ppm, Be pegmatite; >300 ppm, Li pegmatite) and Ge (

It is critical for airlines to develop an effective fuel-saving strategy because the fuel cost accounts for a large proportion of the operating costs in air transport industry. This paper aims to optimize the flight extra fuel predicted by dispatchers via data mining techniques on the consideration of both economy and safety. We first establish a random forest (RF) model from history data to predict the consumption of trip fuel. Then to ensure safety index, we add a margin to the output of the model as the modified prediction value. Finally, the experimental results on real flight data show that the proposed method can make a more accurate prediction for the extra fuel adding amount under the condition of ensuring safety. It indicates that the proposed method is beneficial for airlines in economic cost saving.

In this study, the −1 mm fine coal is treated in a laboratory Knelson concentrator. The influence of various parameters (e.g., feed rate, solid content, centrifugal force, and the fluidization wate...

Effectively capturing the status information and improving situational awareness is the most important task in disaster information management. Due to the rapid increase of online information, this task becomes very challenging. Existing information retrieval and text summarization methods can solve information overload problem to some extent, however, they suffer from some limitations: lacking theme structure, ignoring spatial information, and unable to update information on the real time events. In this paper, we propose a dynamic disaster storyline generation framework, which generates a global storyline describing the evolution of the disaster events in the high-level layer and provides condensed information about specific regions affected by the disaster in the local-level layer. The proposed framework considers both uniqueness and relevance for representative document selection, uses Maximal Marginal Relevance to generate summaries from each local document set, and utilizes dynamic Steiner tree to implement the information update. Comprehensive experiments on typhoons data sets demonstrate the effectiveness of the proposed methods in each level and the overall framework.

The East-Qinling pegmatite district located in the North Qinling terrane is the largest one in the Qinling orogen, Central China. It is famous for its rare-element (REL) reserves, but its genesis is unraveled. We present in-situ major- and trace-element chemistry and U-Pb dating analysis of columbite-group minerals (CGM) and wodginite from representative Li-, Be-mineralized and barren pegmatite dykes. The mineralogical researches reveal that Li-mineralized pegmatite formed from a highly-evolved fluxes-rich magma, experiencing a magmatic stage at undercooling condition and a post-magmatic stage involving disturbance by alkali liquid or hydrothermal fluid. The lithium mineralization is accomplished by fractional crystallization after emplacement, alkali-fluid replacement or overprint of two Li-rich magmas. CGM Fe-Mn fractionation and REE distribution patterns with tetrad effect are considered as indicators to identify generations of CGM and trace the evolution of pegmatite formation. The CGM and zircon U-Pb ages suggest that the REL pegmatites formed in 422–410 Ma (Period I) and 399–384 Ma (Period II) and an early pegmatite magma activity occurred in 447–443 Ma. The late Ordovician-early Silurian and Period II pegmatite magmas stem from partial melting of North Qinling unit in collision and post-orogenic extensional settings, respectively, whereas Period I pegmatites formed in a weakening convergence setting by anatexis of fertile metasedimentary rocks or extremely fractional crystallization of granitic magma. The early-Paleozoic evolution of the North Qinling terrane contributes to the formation of large-scale East-Qinling REL pegmatites on the aspects of magma production and subsequent emplacement by crustal thickening, crustal reworking and dynamic extension during crust uplift.

Venue recommendation has attracted a lot of research attention with the rapid development of Location-Based Social Networks. The effectiveness of venue recommendation largely depends on how well it captures users’ contexts or preferences. However, it is quite difficult, if not impossible, to capture the whole information about users’ preferences. In addition, users’ preferences are often heterogeneous (i.e., some preferences are static and common to all users while some preferences are dynamic and diverse). Existing venue recommendation does not well address the aforementioned issues and often recommends the most popular, the cheapest, or the closest venues based on simple contexts. In this paper, we cast the venue recommendation as a ranking problem and propose a recommendation framework named VRer (Context-Based V enue R ecommendation using e mbedded space r anking SVM) employing an embedded space ranking SVM model to separate the venues in terms of different characteristics. Our proposed approach makes use of ‘check-in’ data to capture users’ preferences and utilizes a machine learning model to tune the importance of different factors in ranking. The major contribution of this paper are: (1) VRer combines various contexts (e.g., the temporal influence and the category of locations) with the check-in records to capture individual heterogeneous preferences; (2) we propose an embedded space ranking SVM optimizing the learning function to reduce the time consumption of training the personalized recommendation model for each group or user; (3) we evaluate our proposed approach against a real world LBSN and compare it with other baseline methods. Experimental results demonstrate the benefits of our proposed approach.

Dilated cardiomyopathy (DCM) is the result of maladaptive cardiac remodeling, which involves microRNA regulation. In turn, microRNAs can contribute to the remodeling process by post-transcriptional modulation of gene expression networks. The exact role of microRNAs in the pathogenesis of DCM is largely unknown. Here, we used an inducible DCM mouse model that carries a human truncation mutation in the sarcomeric protein titin to dissect microRNA pathways in DCM development.MicroRNA microarray studies revealed up-regulation of microRNA-208b in the myocardium of DCM mice and DCM patients (p0.05 compared to controls). In order to investigate the effect of microRNA-208b on cardiac remodeling, loss-of-function and gain-of-function studies were performed by repetitive injections of LNA-modified microRNA-208b mimics and antimiR-208b. MiR-208b overexpression resulted in cardiac hypertrophy, whereas miR-208b antagonisation prevented transition of adaptive to maladaptive remodeling in the DCM mouse model. In vitro studies identified several pro-hypertrophic transcription factors as potential targets of miR-208b, suggesting that microRNA-208b plays an important role in cardiac development and growth. MiR-208b was also upregulated in DCM patients, but not in heart failure patients due to ischemic heart disease or myocarditis.Our data suggests that miR-208b is involved in the remodeling process and pathogenesis of DCM by post-transcriptional gene expression modulation. MicroRNA-208b might be a novel therapeutic target for DCM.

Several factors (e.g., balancedness, good correlation immunity) are considered as important properties of Boolean functions for using in cryptographic primitives. A Boolean function is perfect algebraic immune if it is with perfect immunity against algebraic and fast algebraic attacks. There is an increasing interest in construction of Boolean function that is perfect algebraic immune combined with other characteristics, like resiliency. A resilient function is a balanced correlation-immune function. This paper uses bivariate representation of Boolean function and theory of finite field to construct a generalized and new class of Boolean functions on even variables by extending the Carlet-Feng functions. We show that the functions generated by this construction support cryptographic properties of 1-resiliency and (sub)optimal algebraic immunity and further propose the sufficient condition of achieving optimal algebraic immunity. Compared experimentally with Carlet-Feng functions and the functions constructed by the method of first-order concatenation existing in the literature on even (from 6 to 16) variables, these functions have better immunity against fast algebraic attacks. Implementation results also show that they are almost perfect algebraic immune functions.

The advent of Big Data era drives data analysts from different domains to use data mining techniques for data analysis. However, performing data analysis in a specific domain is not trivial; it often requires complex task configuration, onerous integration of algorithms, and efficient execution in distributed environments. Few efforts have been paid on developing effective tools to facilitate data analysts in conducting complex data analysis tasks. In this paper, we design and implement FIU-Miner, a Fast, Integrated, and User-friendly system to ease data analysis. FIU-Miner allows users to rapidly configure a complex data analysis task without writing a single line of code. It also helps users conveniently import and integrate different analysis programs. Further, it significantly balances resource utilization and task execution in heterogeneous environments. Case studies of real-world applications demonstrate the efficacy and effectiveness of our proposed system.

The Chinese Altai is renowned for its rich rare-element resources. Nine representative rare-element (REL) pegmatites were dated using LA-ICP-MS and40Ar–39Ar methods. The columbite grains yield a weighted mean206Pb/238U age of 239.6±3.8 Ma for the Dakalasu (Be-Nb-Ta) pegmatite and concordia U–Pb ages of 258.1±3.1 Ma and 262.3±2.5 Ma for the Xiaokalasu (Li-Nb-Ta) pegmatite. The zircons display a weighted mean206Pb/238U age of 198.5±2.5 Ma for the Husite (Be) pegmatite and concordia U–Pb ages of 194.3±1.6 Ma and 248.2±2.2 Ma for the Qunkuer (Be) and Taerlang (barren) pegmatites. The muscovite40Ar–39Ar dating gives plateau ages of 286.4±1.6 Ma, 297.0±2.6 Ma, 265.2±1.5 Ma, 178.8±1.0 Ma, 162.2±0.9 Ma, 237.7±1.3 Ma, 237.4±1.2 Ma and 231.9±1.2 Ma for the Talate (Li-Be-Nb-Ta), Baicheng (Nb-Ta), Kangmunagong (barren), Husite (Be), Qunkuer (Be-Nb-Ta), Xiaokalasu (Li-Nb-Ta), Weizigou (Be) and Taerlang (barren) pegmatites, respectively. These new ages coupled with previous geochronological work suggest that the REL pegmatites in the Chinese Altai formed during early Permain – Late Jurassic time. The REL pegmatites located in the Central Altaishan terrane are younger than those in the Qiongkuer–Abagong terrane, showing a correlation with the coeval and adjacent granites. The formation of the REL pegmatites and these granites indicates frequent and strong magmatic activity in the post-orogenic and anorogenic setting. The spatial and temporal distribution of pegmatites and granites reveals a magmatism path from the SE (of age early–middle Permian), to the NW (middle Permian – Middle Triassic) and finally to the central part (Middle Triassic – Jurassic) of the Chinese Altai.

The rapid development of information era has influenced to realize the notion of Smart Bank via approaches like paperless services and interactive self-service systems. Since the traditional methods of identity verification are insecure and cumbersome for supporting these services, Smart Bank has been questioned. To overcome the limitations of current identity verification, it is imperative to explore an effective recognition strategy considering the trade-off between security and customer experience, which can conveniently collect identity information and accurately distinguish people. However, few research and existing systems have been reported for an integrated solution of identity verification satisfying convenient and secure banking environment. In this paper, we propose, implement, and deploy an integrated system named FIRST (Face Identity Recognition in SmarT Bank), which is a customized platform for the identity verification via face recognition in Smart Bank. FIRST uses Gabor surface feature and Fisherfaces, which can provide accurate face recognition within acceptable training time. For information acquisition, our system employs the patch-based face quality assessment, which efficiently extracts valuable faces (i.e. front face) from video streams. Furthermore, FIRST provides a distributed environment to effectively manage recognition tasks and massive data. Since March 2015, FIRST has been successfully deployed on 1800 self-service terminals in Jiangsu Province by ABC (Agriculture Bank of China), and is under deployment by State Grid in China.

With the increase of data dimension in many application fields, feature selection, as an essential step to avoid the curse of dimensionality and enhanced the generalization of the model, is attracting more and more research attention. However, most existing feature selection methods always assume the features have the same cost. These research efforts mainly focus on features’ relevance to learning performance while neglecting the cost to obtain them. Feature cost is a crucial factor need to be considered in feature selection problem especially for the real world applications. For example, in the process of medical diagnosis, each feature may have a very different testing cost. To select low-cost subsets of informative features, in this paper, we propose a stratified random forest-based cost-sensitive feature selection method. Unlike commonly used two-step cost-sensitive feature selection approaches, in our model, the cost of features is incorporated into the construction process of the base decision tree, that is, the cost and the performance of each feature are optimized simultaneously. Moreover, we adopt a stratified sample method to enhance the performance of the feature subset for high-dimensional data. A series of experimental results show that compared with the state-of-the-art methods, the proposed approach can lower the cost of the selected feature subset while maintaining comparable learning performance.

Web medium service has emerged to be a dominant way for billions of individuals sharing and spreading instant news and information. Meanwhile, the challenging issues for delivering high-quality disaster related information are also brought up for the information overload, fast response and dynamic of disaster events. In this paper, we explore the problem of generating storyline from huge amount of web information and propose a knowledge graph-based disaster storyline generating framework. The deep learning technique and the semi-supervision method are first used to extract two kinds of triples from disaster news. Then the evolution of disasters is summarized in a way of graph-based which can clearly show the sketch of disaster development. Finally, the location entities are extracted by the name entity recognized model. Compared with commonly used text summarization storyline, the proposed framework can provide a better user situational awareness and deeper understanding on disaster events by the presentation of graph.

A successful taxi route recommendation system is helpful to achieve a win-win situation for both increasing drivers’ income and improving passengers’ satisfaction. The critical problem in this system is how to find the optimal routes under the highly time-varying and complex traffic environment. By investigating the main factors and comparing various route recommendation methods, in this paper, we handle the taxi route recommendation issue from a new perspective. The relationships between the cruising taxis and passengers are regarded as attraction or repulsion between electric charges. Then based on urban traffic charge heat map, we propose a simple yet effective taxi route recommendation method named TaxiC. TaxiC considers four key factors: the number of passengers, travel distance, traffic conditions, vacant competition, and then recommends driving direction in real time for drivers to help them find the next passengers more efficiently and reduce the cruising time. The experimental results on a real-world data set extracted from 5398 taxis in Xiamen city demonstrate the effectiveness of the proposed method.

Poly(L-lactide) (PLLA) was one of the most well-known biodegradable polyesters. Although its thermo-responsive shape memory (SM) function has been studied by proper program, high transformation temperature is not suitable in some biomedical applications. Water-responsive SM polymer is an alternative choice and exhibits great potential because it is harmless to the cells and tissues of organisms. In the work, a novel PCL-(PMVS-s-PAA)-PCL-PTMG-PCL-(PMVS-s-PAA)-PCL copolymer was devised as an additive to endow PLLA with water-responsive SM capability. The incorporated PLLA was characterized by cross-section, water contact angle, differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA). Inserted PTMG segment and assisted ambient temperature were contributed to its SM behaviors. The shape fixation ratio and shape recovery ratio were 92.0% and 94.2%, respectively. The schematic diagram between internal microstructures and macroscopic properties was proposed. At the same time, the incorporated PLLA showed excellent toughness. So the obtained SM material would be promising for medical and biomimetic fields.

The mathematical model of partition curve is fundamental to evaluate and predict the gravity separation performance in coal cleaning industry. Existing models have achieved good fitting accuracy but lack explicit physical meanings. From the perspective of probability and error distribution, the common characteristics of actual partition curve and its differential curve were analyzed according to the generalized central limit theorem. A skew normal-Laplace (SNL) model with three parameters was established based on asymmetry and fat tail characteristics. Parameters in the SNL model represent location, scale and skewness of partition curve, which reflect separation density and error distribution, therefore it is convenient to evaluate the efficiency of the whole process. Compared with the existing models, the SNL model achieved smaller deviation with fewer parameters and conformed better to physical reality. For practical application, the parameters transformation law when separation density changed was provided according to their properties.

Feature selection aims to select a small subset of informative features that contain most of the information related to a given task. Existing feature selection methods often assume that all the features have the same cost. However, in many real world applications, different features may have different costs (e.g., different tests a patient might take in medical diagnosis). Ignoring the feature cost may produce good feature subsets in theory but they can not be used in practice. In this paper, we propose a random forest-based feature selection algorithm that incorporates the feature cost into the base decision tree construction process to produce low-cost feature subsets. In particular, when constructing a base tree, a feature is randomly selected with a probability inversely proportional to its associated cost. We evaluate the proposed method on a number of UCI datasets and apply it to a medical diagnosis problem where the real feature costs are estimated by experts. The experimental results demonstrate that our feature-cost-sensitive random forest (FCS-RF) is able to select a low-cost subset of informative features and achieves better performance than other state-of-art feature selection methods in real-world problems.

The Koktokay pegmatite-type rare-metal-bearing ore district in the Altai orogen is famous for both its large scale and its diversity of rare metals. However, the emplacement mechanisms of the ore-bearing pegmatite intrusions in the Koktokay ore district are still unclear. Based on field observations, the emplacement of the ore-bearing pegmatite intrusions falls into two types. The first type is typical of the formation of dykes and sills, whereby they intruded into fan shaped, moderate dipping, joints within plutonic rocks. The second type involves the formation of a punched laccolith that was fed by a pegmatite sill. Magmatic stoping is the main mechanism of the laccolith emplacement. The peripheral faults played an important role in helping the emplacement of the laccolith. The trend of dykes and sills indicate two potential prospecting areas, which are located in the western and northern regions of the Koktokay ore district.

In this communication, we propose a gold nanorod-based SERS nanotracker to monitor the local pH change during photothermal therapy (PTT). The dynamic SERS analysis indicated that the local pH of the lysosome in living cells increased during the PTT which would be helpful to understand the cellular metabolic processes and further facilitate the photosensitizer screening and optimization.