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In this information era of high-speed Internet development, the volume of information has grown exponentially along with the expansion in Internet usage by the general people and the steady strengthening of business and institutional information technology. How to find the needed information quickly and accurately from the vast amount of digital information has become an urgent demand of the people of the whole country. The term "open source search engine" simply denotes that anyone may use and alter the source code as they see fit. Open source search engines provide an excellent way and material for people to learn, study and master search technology. This study aims to compare different open source search engines. In this paper compares MeiliSearch and Apache Solr; ElasticSearch and Typesense. Introduce how these engines work and the advantage and disadvantage of theses open source search engines. The section four gives a case study of Apache Lucene.

This study used the coordinated development of the regional economy and ecological environment in the Yellow River basin as the research object and selected 13 indicators representing the regional economic subsystem that reflect economic scale, economic structure, and economic efficiency in order to explore a path of high-quality economic development suitable for China’s national conditions and realize the modernization of China with common prosperity for all people. The coupling weights of each indicator were then calculated separately, along with the degree of orderliness between the two subsystems of the regional economy and ecological environment. Finally, the degree of synergy between the two subsystems was calculated. The subjective weights and objective weights of each indicator were first estimated using hierarchical analysis (AHP) and conflicting correlation among criteria (CRITIC). The findings reveal a strong growth tendency in the regional economic development of the Yellow River basin and a wave-like growth pattern in ecological environment indicators. Since it overtook the ecological economy in 2017, the orderliness of the regional economic subsystem has continued this growth trend, demonstrating that the regional economy is in front of the ecological environment and is in a dominant position. The Yellow River basin’s composite system of regional economic and natural environment exhibits a low-level synergy stage and an M-shaped development pattern of alternating increases and decreases. As a result, many solutions are suggested, including promoting high-quality growth with greater levels of opening, modifying the regional economic structure, and stepping up ecological environmental protection.

Microbially-synthesized protein-based materials are attractive replacements for petroleum-derived synthetic polymers. However, the high molecular weight, high repetitiveness, and highly-biased amino acid composition of high-performance protein-based materials have restricted their production and widespread use. Here we present a general strategy for enhancing both strength and toughness of low-molecular-weight protein-based materials by fusing intrinsically-disordered mussel foot protein fragments to their termini, thereby promoting end-to-end protein-protein interactions. We demonstrate that fibers of a ~60 kDa bi-terminally fused amyloid-silk protein exhibit ultimate tensile strength up to 481 ± 31 MPa and toughness of 179 ± 39 MJ*m−3, while achieving a high titer of 8.0 ± 0.70 g/L by bioreactor production. We show that bi-terminal fusion of Mfp5 fragments significantly enhances the alignment of β-nanocrystals, and intermolecular interactions are promoted by cation-π and π-π interactions between terminal fragments. Our approach highlights the advantage of self-interacting intrinsically-disordered proteins in enhancing material mechanical properties and can be applied to a wide range of protein-based materials.

The human visual system can recognize object categories accurately and efficiently and is robust to complex textures and noises. To mimic the analogy-detail dual-pathway human visual cognitive mechanism revealed in recent cognitive science studies, in this article, we propose a novel convolutional neural network (CNN) architecture named analogy-detail networks (ADNets) for accurate object recognition. ADNets disentangle the visual information and process them separately using two pathways: the analogy pathway extracts coarse and global features representing the gist (i.e., shape and topology) of the object, while the detail pathway extracts fine and local features representing the details (i.e., texture and edges) for determining object categories. We modularize the architecture and encapsulate the two pathways into the analogy-detail block as the CNN building block to construct ADNets. For implementation, we propose a general principle that transmutes typical CNN structures into the ADNet architecture and applies the transmutation on representative baseline CNNs. Extensive experiments on CIFAR10, CIFAR100, street view house numbers, and ImageNet data sets demonstrate that ADNets significantly reduce the test error rates of the baseline CNNs by up to 5.76% and outperform other state-of-the-art architectures. Comprehensive analysis and visualizations further demonstrate that ADNets are interpretable and have a better shape-texture tradeoff for recognizing the objects with complex textures.

Manmade high-performance polymers are typically non-biodegradable and derived from petroleum feedstock through energy intensive processes involving toxic solvents and byproducts. While engineered microbes have been used for renewable production of many small molecules, direct microbial synthesis of high-performance polymeric materials remains a major challenge. Here we engineer microbial production of megadalton muscle titin polymers yielding high-performance fibers that not only recapture highly desirable properties of natural titin (i.e., high damping capacity and mechanical recovery) but also exhibit high strength, toughness, and damping energy — outperforming many synthetic and natural polymers. Structural analyses and molecular modeling suggest these properties derive from unique inter-chain crystallization of folded immunoglobulin-like domains that resists inter-chain slippage while permitting intra-chain unfolding. These fibers have potential applications in areas from biomedicine to textiles, and the developed approach, coupled with the structure-function insights, promises to accelerate further innovation in microbial production of high-performance materials., Here, the authors engineer microbial production of muscle titin fibers with highly desirable mechanical properties and provide structural analyses that explain the molecular mechanisms underlying high performance of this polymer with potential uses in biomedicine and textile industries, among others.

In this paper, we focus on the challenging few-shot class incremental learning (FSCIL) problem, which requires to transfer knowledge from old tasks to new ones and solves catastrophic forgetting. We propose the exemplar relation distillation incremental learning framework to balance the tasks of old-knowledge preserving and new-knowledge adaptation. First, we construct an exemplar relation graph to represent the knowledge learned by the original network and update gradually for new tasks learning. Then an exemplar relation loss function for discovering the relation knowledge between different classes is introduced to learn and transfer the structural information in relation graph. A large number of experiments demonstrate that relation knowledge does exist in the exemplars and our approach outperforms other state-of-the-art class-incremental learning methods on the CIFAR100, miniImageNet, and CUB200 datasets.

In this paper, we propose a novel single-task continual learning framework named Bi-Objective Continual Learning (BOCL). BOCL aims at both consolidating historical knowledge and learning from new data. On one hand, we propose to preserve the old knowledge using a small set of pillars, and develop the pillar consolidation (PLC) loss to preserve the old knowledge and to alleviate the catastrophic forgetting problem. On the other hand, we develop the contrastive pillar (CPL) loss term to improve the classification performance, and examine several data sampling strategies for efficient onsite learning from ‘new’ with a reasonable amount of computational resources. Comprehensive experiments on CIFAR10/100, CORe50 and a subset of ImageNet validate the BOCL framework. We also reveal the performance accuracy of different sampling strategies when used to finetune a given CNN model. The code will be released.

Grain chalkiness, an undesirable trait caused by complex factors, has great negative impacts on the quality and economic value of rice. However, little is known about the regulatory mechanism of grain chalkiness, particularly the effect of endoplasmic reticulum (ER) stress. Here, a genome-wide association study (GWAS) reveals that the transcription factor OsbZIP60 is a vital regulator of rice grain chalkiness. Genetic analysis demonstrates that knockout of OsbZIP60 results in extremely high grain chalkiness and aberrant structure of storage substances. Notably, the expression of unfolded protein response (UPR) genes, such as OsbZIP50, OsBiP1, OsBiP2 and OsBiP3, is up-regulated in the endosperm cells of osbzip60, and overexpression of all these UPR genes causes various degrees of chalkiness. Furthermore, OsbZIP60 is found to activate the expression of key genes related to grain chalkiness, such as GPA3, FSE1, FLO7, Chalk5, OsNF-YB1, and OsPK2, whose expression is significantly suppressed in osbzip60 and overexpression lines of OsbZIP50, OsBiP1, OsBiP2, and OsBiP3. Our study provides novel insights into the function of OsbZIP60 and the role of the UPR pathway in the formation of grain chalkiness in rice.

Numerous methods have been proposed for person re-identification (Re-ID) with promising performances. While most of them neglect the matching efficiency which is crucial in real-world applications. Recently, several hashing based approaches have been developed, which consider the importance of matching speed in large-scale datasets. Despite the considerable efficiency of these traditional and deep learning based hashing methods, the concomitant matching accuracy reduction is unacceptable in practical application. Towards this end, we propose a novel deep hashing framework, namely Consistency-Preserving Deep Hashing (CPDH), aiming to bridge the gap between the effective high-dimensional feature and low-dimensional binary vector by focusing on the consistency preservation of hash code. First, CPDH designs a new hash structure to extract the hash code. Next, a noise consistency cost is proposed to improve robustness of both hash code and high-dimensional feature. Finally, a topology consistency cost is provided to maintain the ordinal relation between the high-dimensional feature space and Hamming space. Comprehensive experimental results on three widely-used benchmark datasets demonstrate the superior performance of proposed method as compared with existing state-of-the-art approaches.

Class-incremental learning (CIL) aims to incrementally learn a unified classifier for new classes emerging, which suffers from the catastrophic forgetting problem. To alleviate forgetting and improve the recognition performance, we propose a novel CIL framework, named the topological schemas model (TSM). TSM consists of a Gaussian mixture model arranged on 2D grids (2D-GMM) as the memory of the learned knowledge. To train the 2D-GMM model, we develop a novel competitive expectation-maximization (CEM) method, which contains a global topology embedding step and a local expectation-maximization fine-tuning step. Meanwhile, we choose the image samples of old classes that have the maximum posterior probability with respect to each Gaussian distribution as the episodic points. When finetuning for new classes, we propose the memory preservation loss (MPL) term to ensure episodic points still have maximum probabilities with respect to the corresponding Gaussian distribution. MPL preserves the distribution of 2D-GMM for old knowledge during incremental learning and alleviates catastrophic forgetting. Comprehensive experimental evaluations on two popular CIL benchmarks CIFAR100 and subImageNet demonstrate the superiority of our TSM.

With the continuous development of the national big data strategy and the construction of "Digital China", cities have been given new connotations and requirements. This paper takes the real estate price changes in Hainan Province as the background, collects relevant information and data, and makes relevant analysis and reasonable predictions for the housing price changes in Hainan Province. This paper comprehensively considers the prediction defects of MEA_BP neural network, Elman neural network and Wavelet neural network, and uses a combination algorithm based on entropy method to comprehensively consider the results of all prediction models. On the one hand, it can improve the prediction accuracy of the model, and on the other hand, it can reduce the sensitivity of the prediction results to changes in a certain factor, making the prediction results more reliable. This paper establishes a multiple and effective new era smart city prediction model, which provides a reference for building a new city database supported by a new generation of information technology.

The ability to incrementally learn new classes is crucial to the development of real-world artificial intelligence systems. In this paper, we focus on a challenging but practical few-shot class-incremental learning (FSCIL) problem. FSCIL requires CNN models to incrementally learn new classes from very few labelled samples, without forgetting the previously learned ones. To address this problem, we represent the knowledge using a neural gas (NG) network, which can learn and preserve the topology of the feature manifold formed by different classes. On this basis, we propose the TOpology-Preserving knowledge InCrementer (TOPIC) framework. TOPIC mitigates the forgetting of the old classes by stabilizing NG's topology and improves the representation learning for few-shot new classes by growing and adapting NG to new training samples. Comprehensive experimental results demonstrate that our proposed method significantly outperforms other state-of-the-art class-incremental learning methods on CIFAR100, miniImageNet, and CUB200 datasets., Accepted by CVPR 2020 (oral)

A well-known issue for class-incremental learning is the catastrophic forgetting phenomenon, where the network’s recognition performance on old classes degrades severely when incrementally learning new classes. To alleviate forgetting, we put forward to preserve the old class knowledge by maintaining the topology of the network’s feature space. On this basis, we propose a novel topology-preserving class-incremental learning (TPCIL) framework. TPCIL uses an elastic Hebbian graph (EHG) to model the feature space topology, which is constructed with the competitive Hebbian learning rule. To maintain the topology, we develop the topology-preserving loss (TPL) that penalizes the changes of EHG’s neighboring relationships during incremental learning phases. Comprehensive experiments on CIFAR100, ImageNet, and subImageNet datasets demonstrate the power of the TPCIL for continuously learning new classes with less forgetting. The code will be released.

In-field molecular diagnosis of plant diseases via nucleic acid amplification is currently limited by cumbersome protocols for extracting and isolating pathogenic DNA from plant tissues. To address this challenge, a rapid plant DNA extraction method was developed using a disposable polymeric microneedle (MN) patch. By applying MN patches on plant leaves, amplification-assay-ready DNA can be extracted within a minute from different plant species. MN-extracted DNA was used for direct polymerase chain reaction amplification of plant plastid DNA without purification. Furthermore, using this patch device, extraction of plant pathogen DNA ( Phytophthora infestans) from both laboratory-inoculated and field-infected leaf samples was performed for detection of late blight disease in tomato. MN extraction achieved 100% detection rate of late blight infections for samples after 3 days of inoculation when compared to the conventional gold standard cetyltrimethylammonium bromide (CTAB)-based DNA extraction method and 100% detection rate for all blind field samples tested. This simple, cell-lysis-free, and purification-free DNA extraction method could be a transformative approach to facilitate rapid sample preparation for molecular diagnosis of various plant diseases directly in the field.

Semi-supervised learning is important and has become more widespread because obtaining labeled data is expensive and labor-intensive. In this paper, we focus on the challenging semi-supervised person Re-identification (ReID) task, which is a metric learning problem based on the assumption that unlabeled data is open-set. To address this problem, we propose the Transductive Semi-Supervised Metric Learning (TSSML) framework. In TSSML, we propose a graph-based transductive hard mining method for deeply mining hard triplets in unlabeled data and a degree-based relationship confidence scoring method for further reducing incorrect triplets. Moreover, we investigate the feature consistency loss (FCL) and adopt the curriculum learning strategy to improve the representation learning for semi-supervised ReID. Extensive experiments have been conducted on three large-scale ReID datasets and demonstrate the effectiveness of our TSSML framework.

The objective of this research was to study plant cell wall degradation enzymes from Fusarium sp. Q7-31T.Strain was cultured in liquid medium with 1% (W/V) peptone as nitrogen source, 0.5% (W/V) oat straw as carbon source, 120 r/min shaking at 20 °C for 3 days. The endoglucanase Egn21 was purified by using Sephacry S-100 chromatography and DEAE-sepharose ion-exchange column chromatography. Then the enzymatic properties and MADIL-TOF-TOF identification were analyzed.The molecular weight and isoelectric point (pI) of Egn21 was 44.25 kDa and 4.91, respectively. Egn21 had optimal activity with carboxymethyl cellulose at 40 °C and pH 6.0, stable at 45 °C and pH between 5.0 and 8.0, inhibited by Fe2+, Ca2+, K+, Na+, Mn2+ and inactivated by Hg2+, whereas Co2+, Zn2+ and Mg2+ had no effect.The enzymatic properties and MADIL-TOF-TOF results suggested that Egn21 belongs to GH5 family.

Metal chemical fractions obtained by optimized BCR three-stage extraction procedure and multivariate analysis techniques were exploited for assessing 7 heavy metals (Cr, Pb, Cd, Co, Cu, Zn and Ni) in sediments from Gansu province, Ningxia and Inner Mongolia Autonomous Regions of the Yellow River in Northern China. The results indicated that higher susceptibility and bioavailability of Cr and Cd with a strong anthropogenic source were due to their higher availability in the exchangeable fraction. A portion of Pb, Cd, Co, Zn, and Ni in reducible fraction may be due to the fact that they can form stable complexes with Fe and Mn oxides. Substantial amount of Pb, Co, Ni and Cu was observed as oxidizable fraction because of their strong affinity to the organic matters so that they can complex with humic substances in sediments. The high geo-accumulation indexes (I(geo)) for Cr and Cd showed their higher environmental risk to the aquatic biota. Principal component analysis (PCA) revealed that high toxic Cr and Cd in polluted sites (Cd in S10, S11 and Cr in S13) may be contributed to anthropogenic sources, it was consistent with the results of dual hierarchical clustering analysis (DHCA), which could give more details about contributing sources.

A method IR-CGC (infrared-cryogenic gas chromatography) was established directly through the combination of infrared spectrum (IR) and cryogenic gas chromatography (CGC) to measure the electrolytic separation factor (α) between protium and deuterium. The n1,liq(D)/n2,liq(H) ratio of alkalic aqueous solution after electrolysis was tested quantitatively by the IR method, and the n1,gas(D)/n2,gas(H) ratio of mixed gases from electrolysis was determined by CGC. Then α of different electrode materials were obtained according to the formula of electrolytic separation factor. As a result, it provides an accurate, simple, feasible, suitable approach for evaluating the electrolytic protium-deuterium separation ability of different electrode materials, which is helpful for searching excellent hydrogen isotopes separation materials.

Photocatalytic activities of three composites, namely nickel foam/Ag-TiO2, nickel foam/Ag-ZnO, and nickel foam/Ag-WO3, were evaluated for toluene degradation in a home-made cuboid photoreactor. Scanning electron microscopy and energy dispersive spectrometry were employed to characterize these photocatalysts. The photodegradation effects of the three photocatalysts peaked under 60% relative humidity, with nickel foam/Ag-TiO2 exhibiting the highest rate of photocatalytic toluene degradation. Controlling humidity could enhance the photocatalytic toluene degradation activities of the three composites, which are significantly affected by a wider band gap. In this study, a dynamic model was proposed and analyzed according to the time-dependent photodegradation of toluene.

The endoglucanase from Fusarium sp. Q7-31T was isolated, purified, identified and characterized to provide data for enzyme system of Fusarium sp. . [Methods] Strain was cultured in liquid fermentation with oat straw as carbon source, the endoglucanase was purified by using Sephacry S-100 chromatography and DEAE-sepharose ion-exchange column chromatography and the enzymatic properties were studied. The protein was identified using MADIL-TOF-TOF.An endoglucanase was purified and named Egn20. The molecular weight was 55.37 kDa and isoelectric point (pI) was 7.44. Egn20 had optimal activity with carboxymethyl cellulose at 40 degrees C and pH 6.0, stabilized at 45 degrees C and pH 5.0 - 7.0, activated by Fe2+, inhibited by Na+, Ca2+, Mg2+, Zn2+, K+ and inactivated by Hg2+. The enzymatic properties and MADIL-TOF-TOF results suggested that Egn20 belongs to GH7 family.Our results may provide important data for the study of Fusarium sp. enzyme system.