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Wind Power Forecasting has emerged as a critical and dynamic research area in response to the growing demand for renewable energy. The unpredictable and stochastic nature of wind conditions, encompassing factors such as wind speed, wind direction, air temperature, and barometric pressure, poses unique challenges for accurate forecasting of wind power generation. Reliable wind power generation forecasts are essential for optimizing energy grid management, ensuring grid stability, and facilitating the integration of wind energy with existing power systems. To address these challenges, this research introduces Powerformer, a Transformer-based model designed to improve the accuracy of wind power prediction. Powerformer utilizes the infrastructure of the Transformer with innovative modifications to address the complexity of wind power prediction, enhancing temporal feature extraction capabilities while reducing complexity. The research in this study includes a comprehensive set of experiments, revealing that Powerformer achieves superior results among all models. Furthermore, the model exhibits stronger robustness, as confirmed through a series of ablation experiments validating the reasonableness of the model design.

SiC power semiconductor devices exhibit the potential to operate at high temperatures; however, research regarding their high-temperature characteristics is currently insufficient. By using a high-temperature experimental platform, the temperature-dependent characteristics of SiC power diodes in an ultra-wide temperature range of 25–425 °C are studied in this article. Two high-temperature bipolar phenomena of SiC power diodes are discovered and thoroughly studied, that is, the reverse recovery “high temperature dramatic increase (HTDI)” of SiC MOSFET body diode (MBD) and the “high temperature bipolar transition (HTBT)” of SiC Schottky barrier diode (SBD). Furthermore, for high-temperature applications, the high-temperature characteristics of the package form of SiC MBD and SiC SBD in parallel (SiC MNSP) are characterized and analyzed in this article, and its advantages are proven from the perspective of switching energy loss. The experimental results indicate that the maximum improvement effect on the peak reverse recovery current ( $I_{\text {rr}}$ ) is approximately one-third, and that the switching energy loss ( $E_{\text {sw}}$ ) can be reduced by nearly half at most. This article provides guidance for future high-temperature applications and packaging forms of SiC-based modules.

Deep cross-media hashing provides an efficient semantic representation learning solution for large-scale cross-media retrieval. The existing methods only consider the inter-media or intra-media semantic association learning, ignore the guiding of semantic structure information, and have weak reasoning ability for implicit fine-grained semantic associations. To tackle this problem, we propose a novel structures aware fine-grained contrastive adversarial hashing method for cross-media retrieval. A novel cross-media contrastive adversarial hash network is constructed for the first time, which integrates the cross-media and intra-media contrastive learning and multi-modal adversarial learning, aiming at maximizing the semantic association between different modalities, and improving the semantic discrimination and consistency of cross-media unified hash representation, thereby the inter-media and intra-media semantic preserving ability can be well enhanced; A fine-grained cross-media semantic feature learning method based on fine-grained semantic reasoning with transformers is proposed, which captures fine-grained salient features of different modalities for semantic association learning, and enhances the reasoning ability of fine-grained implicit semantic association; A semantic label graph convolutional network guided cross-media semantic association learning strategy is proposed, which makes full use of semantic structure information to enhance the learning ability of implicit cross-media semantic associations. Extensive experiments on several large-scale cross-media benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art methods.

During the training process of federated learning models, the domain information of the target test data on the server can differ greatly from the training data of each client, leading to a decrease in the performance of the federated model. Additionally, due to privacy protection during federated training, clients cannot see the target domain test data, and the distribution information of the target data cannot be used. This poses a new challenge for federated learning. Domain generalization techniques are often used in centralized frameworks to resolve such problems. In recent years, the domain generalization method based on feature decorrelation has enabled models to learn knowledge with a stronger generalization ability in unseen target domain data. However, existing methods require data centralization in the feature decorrelation process, which conflicts with data privacy protection in federated learning. To address these issues, we propose Reinforcement Federated Domain Generalization (RFDG), which incorporates domain generalization in federated learning via reinforcement learning. RFDG can improve the generalization ability of the federated model of unseen target domain test data. We design a reinforcement federated feature decorrelation policy that uses reinforcement learning technology to transform the sample reweight work into a parameterized sample reweight policy that can be shared among federated learning clients. We develop reinforcement federated experience replay techniques to supplement the feature information loss of local data due to the mini-batch mechanism during the policy learning process. When the policy is shared by each client, those features can be decorrelated from a global perspective, allowing the model to focus on capturing the fundamental association between features and labels to learn domain-invariant knowledge. We verified the effectiveness of our method through extensive experiments using four publicly available datasets.

• Hot-rolled Zn-2Cu- x Li (x =0.2,0.5, 0.8) alloys obtain first-class mechanical properties in 300 MPa (yield strength) grade rolled biodegradable Zn alloys. • New ε′-(Cu0.5, Li0.5)Zn4 and β′-(Li0.9, Cu0.1)Zn4 ternary phases are characterized. • Microstructure evolution of phases with different Li contents in alloys is observed and summarized. • Zn-2Cu- x Li alloys have proper biocompatibility. Although a few high-strength biodegradable Zn alloys with yield strengths (YSs) over 300 MPa in rolled state have been developed, their elongations (ELs) are generally less than 30%. This study developed rolled Zn-2Cu- x Li (x = 0.2 wt.%, 0.5 wt.%, 0.8 wt.%) alloys with YSs of 316–335 MPa and ELs of 44%–61%. Three-dimensional atom probe (3DAP) and time of flight secondary ion mass spectrometry (TOF-SIMS) were employed to characterize Li distribution. Three kinds of Zn-Cu-Li ternary phases are identified, which are blocky ε′-(Cu 0.5 , Li 0.5)Zn 4 , blocky β′-(Li 0.9 , Cu 0.1)Zn 4 , and small round γ particles with high Li content in the annealed state. Other identified phases are Zn, β-LiZn 4 , and ε-CuZn 4 phases. With the increase of Li content in the alloys, ε′ phase with 6.50 at.% Cu transforms into β′ phase with 2.12 at.% Cu, i.e., the average level in the alloys. Within ε′ phase, there exist nano-scale Li clusters and ε phase, resulting in ε′/ε structure. Dense Zn laths precipitate from β′ phase, resulting in β′/Zn lamellar structure. The lamellar structure is the matrix of Zn-2Cu-0.8Li and leads to near-isotropic plasticity. Electrochemistry tests show that degradation rates fall in the range of 153–196 μm/year, which decrease with Li content. All the alloys exert positive effects on the growth of MC3T3-E1 cells with 10% extract. This research reveals how microstructure evolves in Zn-2Cu- x Li alloys, which lays the foundation for their future applications. [Display omitted] [ABSTRACT FROM AUTHOR]

This work reveals how trace Mn significantly improves high temperature strength of pure Zn. The peak stress of pure Zn increases from 46 MPa to 84 MPa at 300 °C/0.1 s−1after 0.8 wt.% Mn addition. The as-compressed Zn-0.8Mn alloy has a bimodal grain structure with fine grains surrounding a coarse grain. Transmission electron microscopy results show that Mn addition promotes activation of non-basal slip and pile-up of dislocations near coarse/fine grain boundary or MnZn13/Zn interface or on the MnZn13particles. First-principles calculations indicate that Mn addition can reduce stacking fault energy values of basal and prismatic slip systems of Zn, therefore activating slip. Piled dislocations generate forward and back stresses, resulting in hetero-deformation induced strengthening. It serves as a suggestion for designing bimodal grain structures to improve strength of Zn alloys.

Zinc (Zn) has emerged as a promising biodegradable metal with moderate degradation profile and good biocompatibility. The wider application of biodegradable Zn in clinics relies on further optimization of its biodegradation behavior, biological activity, and other functions. In recent years, various surface modification strategies have been applied to biodegradable Zn in the orthopedics and cardiovascular fields, with significant progress made in cytocompatibility, osteogenesis, antibacterial performance, and degradation control. In this review, we summarized various surface modification techniques as well as their formation features for biodegradable Zn. Meanwhile, we proposed combining the surface modification strategy with porous and complex structured Zn alloy. We also discussed the effects of different coatings on the degrading behavior and biocompatibility of the Zn alloys. While most surface modifications attempt to improve the degradation resistance of Zn, plasma electrolytic oxidation and mechanical methods, in particular, can increase the degradation rate of Zn alloys, shortening the implantation period under specific degradation requirements. An ideal surface modification should slow down Zn degradation at the beginning, while speeding it up after the healing process. Regarding biological properties, the biocompatibility, osteointegration, and antibacterial properties of biodegradable Zn have been enhanced through surface modifications. It can be envisioned that, with proper surface design, biodegradation adaptive and biocompatibility adaptive Zn alloys hold great potential for many biomedical applications.

• Whole fabricating process of biodegradable Zn-Li alloy rib plates is developed. • Zn-0.5Li alloy rib plates have mechanical properties comparable to pure Ti. • Uniform corrosion dominates during immersion tests in SBF up to 30 days. • The Zn alloy rib plates have better biocompatibility than pure Zn rib plates. Trauma kills more than four million people worldwide each year, with chest trauma accounting for 25% of these deaths. Rib fractures are the main manifestation of chest trauma. Biodegradable Zn alloys offer a new option to overcome clinical problems caused by permanent rib fracture internal fixation materials, e.g., long-term stress masking and secondary surgery. In this study, the fabrication procedure of biodegradable Zn-0.5Li alloy rib plates is successfully developed, which consists of casting, hot-warm rolling, cutting, and pressing sequentially. Biomechanical three-point bending performance of the Zn alloy rib plates is comparable to that of commercial pure Ti rib plates, much higher than that of pure Zn rib plates. In addition, the Zn alloy exhibits the best antibacterial ability against E. coli and S. aureus among the three materials. Although the Zn alloy exhibits a weaker MC3T3 cytocompatibility than pure Ti, it is better than pure Zn. This study provides a foundation for the future development of various biodegradable Zn alloy rib plates. [ABSTRACT FROM AUTHOR]

By a ligand-oriented assembly approach, the heterometallic–organic framework [Pb4Cu2I2(pdc)4(DMF)6]n(1; H2pdc = 3,5-pyridinedicarboxylic acid, DMF = N,N-dimethylformamide) has been successfully fabricated with the assistance of a N-/O-containing H2pdc molecule. 1displays a 3D architecture constructed from the combination of discrete [Cu2I2] moieties and infinite Pb–O chains with the organic linkers. A bright yellow luminescence emission of 1can be observed, and temperature-dependent emission spectra indicate the huge potential of 1to serve as a molecular luminescence thermometer. In addition, through a luminescence turn-off effect, compound 1can be considered to be a superior sensor for the sensitive detection of Fe3+. Moreover, photocatalysis experiments have been performed on 1, suggesting that the title catalyst could induce and accelerate the decomposition of dye pollutants.

This work reports a novel Mo-based SAC catalyst (Mo@C9N4), which exhibits excellent activity and selectivity toward electrochemical nitrogen reduction reaction with favorable limiting potential of 0.40 V.

This work builds up a picture (activity trends, electronic origins and design strategies) of TMN4unit supported on graphene as a high-performance bifunctional electrocatalyst for OER and ORR.

Nickel/cobalt-layered double hydroxides (NiCo-LDH) have been attracted increasing interest in the applications of anode materials for lithium ion battery (LIB), but the low cycle stability and rate performance are still limited its practice applications. To achieve high performance LIB, the surface-confined strategy has been applied to design and fabricate a new anode material of NiCo-LDH nanosheet anchored on the surface of Ti3C2MXene (NiCo-LDH/Ti3C2). The ultra-thin, bended and wrinkled α-phase crystal with an interlayer spacing of 8.1 Å can arrange on the conductive substrates Ti3C2MXene directly, resulting in high electrolyte diffusion ability and low internal resistance. Furthermore, chemical bond interactions between the highly conductive Ti3C2MXene and NiCo-LDH nanosheets can greatly increase the ion and electron transport and reduce the volume expansion of NiCo-LDH during Li ion intercalation. As expected, the discharge capacity of 562 mAh g−1at 5.0 A g−1for 800 cycles without degradation can be achieved, rate capability and cycle performance are better than that of NiCo-LDH (~100 mAh g−1). Furthermore, the density function theory (DFT) calculations were performed to demonstrate that NiCo-LDH/Ti3C2system can be used as a highly desirable and promising anode material for lithium ion battery.

To observe changes in the molecular expression of the NLR Family Pyrin Domain Containing Protein 3 (NLRP3) pathway in depressed rats after treatment with Xiaoyaosan, and identify the regulatory mechanism of this compound.

Cross-media search from large-scale social network big data has become increasingly valuable in our daily life because it can support querying different data modalities. Deep hash networks have shown high potential in achieving efficient and effective cross-media search performance. However, due to the fact that social network data often exhibit text sparsity, diversity, and noise characteristics, the search performance of existing methods often degrades when dealing with this data. In order to address this problem, this article proposes a novel end-to-end cross-media semantic correlation learning model based on a deep hash network and semantic expansion for social network cross-media search (DHNS). The approach combines deep network feature learning and hash-code quantization learning for multimodal data into a unified optimization architecture, which successfully preserves both intramedia similarity and intermedia correlation, by minimizing both cross-media correlation loss and binary hash quantization loss. In addition, our approach realizes semantic relationship expansion by constructing the image–word relation graph and mining the potential semantic relationship between images and words, and obtaining the semantic embedding based on both internal graph deep walk and an external knowledge base. Experimental results demonstrate that DHNS yields better cross-media search performance on standard benchmarks.

Impurity segregation even small amounts, can drastically change the cohesive properties of the grain boundaries (GB), eventually leading to intergranular embrittlement and failure of the materials, thereby effectively controlling the types and the concentrations of the impurity is very important. In this work, the nonmetallic impurities (C, H, O, N) segregation and their effects on the strength of Zr (10 1 ¯ 2) GB were thoroughly investigated using first-principles calculations based on density functional theory. A comprehensive analysis of the interstitial configurations and the relative site energies indicating that C, N and O overwhelmingly prefer the octahedral sites, only H, prefers to reside in the tetrahedral sites. Moreover, the strengthening/embrittlement potency of impurity atoms on the GB was estimated using both the Rice-Wang model and first-principles tensile test calculations. The results show that all impurities, exhibit a strong segregation tendency near the GB region. The segregation of C, N and O has a remarkable strengthening effect on strength of the GB, whereas the presence of impurity H weaken the GB. Most importantly, the underlying mechanism of the strength change of the GBs due to the segregation of impurities was profoundly discussed by charge density and the bond lengths analyses, revealing that the strengthening effect especially for C-doped GB, mainly comes from an enhancement of the charge density across the GB plane. In the end, we expect that our results will be certainly useful for future theoretical and experimental investigations on Zr and its alloys. [ABSTRACT FROM AUTHOR]

Storage and transportation are critical processes that significantly affect food quality, with bacterial proliferation serving as a major contributor to deterioration. Preservative films are commonly used in food transportation and preservation. However, the approval process for contact-type preservative films remains challenging due to inconclusive toxicity assessments. In this work, we synthesized antimicrobial zeolitic imidazolate frameworks (ZIFs) with ZnO microtube heterostructures (ZZHs) for contactless biological preservation using the optical vapor phase supersaturated precipitation (OVSP) method, combined with an in situ solvothermal approach. This ZZH exhibited excellent antimicrobial activity and achieved efficiencies of 90.47% and 98.24% against E. coliand S. aureus, respectively. We also developed a highly flexible ZZH/PDMS film that demonstrated exceptional structural stability under various temperature, acid, and alkali conditions, which supported the potential for stable intrinsic antimicrobial activity. The hydrophilic nature and high specific surface area of the ZZH/PDMS film were beneficial to capture the dispersed water vapor, bacteria, and other harmful substances, which enhanced the efficiency of the antimicrobial functional sites. This strategy indirectly inhibited bacterial proliferation by controlling ambient humidity, thus avoiding direct contact between the film and the fruit. This process was defined as a contactless mechanism. This work offers an avenue for the development of highly flexible and durable antimicrobial heterostructure agents for contactless biological preservation in future applications.

The retention of self-released chalcogenates at reconstructed Co oxyhydroxides derived from selenides can be finely promoted by self-transformed carbonates derived from carbon dopants for boosted oxygen evolution performance.

Images can be represented by multiple views and each view describes a specific visual appearance. Compared with single view learning method, multi-view methods can integrate information of different views to generate better clustering performance. Most of the existing multi-view methods assume that the importance of each view is the same to all the images. However, since visual appearance of images are different, the description abilities of different features vary with images. To solve this problem, a group-aware multi-view fusion approach is proposed in this paper. Specifically, images are partitioned into groups according to their visual appearance, and different fusion weights are assigned to different groups. We develop two paradigms under our group-aware fusion framework: pair-wise fusion and center-wise fusion. The former focuses on generating more accurate fusion results, while the latter achieves lower computational complexity. We design an optimization objective function which combines consensus and discrimination criterion to select more reliable and discriminative views for multi-view fusion. The clustering results and the fusion weights are learned by an iterative optimization algorithm. Experiments on four real-world image datasets indicate that our approach achieves promising image clustering performance over the existing methods. [ABSTRACT FROM AUTHOR]

Abstract Multi-view clustering aims to incorporate complementary information from different data views for more effective clustering. However, it is difficult to obtain the true categories of data based on complex distribution and diversified latent attributes of multi-view data. In this paper, we propose a new multi-view clustering method that integrates deep matrix factorization, low-rank subspace learning, and multiple subspace ensemble in a unified framework, which we term as the Deep Low-Rank Subspace Ensemble (DLRSE) method. DLRSE first employs deep matrix factorization to capture diverse and hierarchical structures in data for robust multi-view multi-layer data representations. Then, low-rank subspace representations are learned from the extracted factors to further reveal their correlations, from which more explicit clustering structure can be obtained. We further develop a subspace ensemble learning method with structured sparsity regularization to aggregate different subspaces into a consensus subspace, which can incorporate the intrinsic clustering structure across multiple views better. Extensive experiments on several datasets demonstrate that the proposed method can effectively explore the diversified clustering structure inherent in data and exploit multi-view complementary information, and achieve considerable improvement in performance over existing methods. [ABSTRACT FROM AUTHOR]

The local crowd density and the crowd distribution estimation tasks are useful but challenging. There are two main problems which limit the performance of existing algorithms. The first one is that there are not enough labeled training samples to build the high-performance estimation model. Another one is that existing methods lack the supports of physical theories of the crowd. To remedy them, a novel crowd density field model is proposed, which is deduced by jointing crowd dynamics theory and social force model. A crowd counting method based on the proposed crowd density field model is introduced to measure the proposed crowd density field model. Extensive experiments confirm the effectiveness of the proposed plan.

A family of two-dimensional (2D) Zn-based metal–organic frameworks (MOFs) with exitonic emission have been successfully synthesized under hydrothermal conditions. When isophthalic acid ligands with different substitutions are introduced, the crystal structures and fluorescence properties are significantly changed. Hirshfeld surface calculation is used to study the nuances of diverse substitutions during the construction of all of the crystals. The solid fluorescence results indicate that there are obvious two-channel emissions, including intralayer excimers and interlayer trapped excitons, both in 1and 2with a double-layer structure and in 3with a single-layer structure, mainly exhibiting intralayer emission. Furthermore, the fluorescence changes on morphology transformations are explored after mechanical exfoliation consisting of grinding and ultrasonicaation of MOFs 1–3. The regulation and control of crystal structure and morphology can suppress emission based on interlayer excitons, achieving adjustment of the overall emitting color. To the best of our knowledge, this is the first report of 2D bilayer MOFs with dual-channel emissions, which provides a new structural model for synthesizing new exciton materials.

Developing a low-cost photocatalyst with efficient performance is significant for practical application of solar-to-fuel conversion. Here, we first adopt a facile method to synthesize Bi2O2CO3-modified g-C3N4heterojunction via in situ thermal growth. Bi2O2CO3nanoparticles on g-C3N4nanosheets play a vital role in improving the photocatalytic activity of splitting water for hydrogen production. The activity of Bi2O2CO3/g-C3N4heterojunction during 5 h reaches 965 μmol·g–1·h–1, which is much higher than that of pure g-C3N4(337 μmol·g–1·h–1) or other modified g-C3N4materials. The significantly enhanced photocatalytic activity is attributed to direct Z-scheme system construction, resulting in a superior charge carrier separation ability. Theoretical calculations further reveal the redistribution of charge carrier at interface between Bi2O2CO3and g-C3N4. This work provides new direction to synthesize g-C3N4-based heterojunction with high photocatalytic performance for alleviating energy and environmental issues.

Hydrogen can be the potential substitute energy carrier for fuel while electrolysis water with hydrogen evolution reaction (HER) is an efficient way to produce hydrogen. Highly active and robust electrocatalysts composed by earth abundant elements are required. Herein, nickel–copper–phosphorus (Ni–Cu–P) electrocatalysts are designed and synthesized by a facile one-step electrodeposition method. A unique pine-needle-like dendrite nanotube morphology of Ni–Cu–P electrocatalyst can be synthesized when copper content changed and impressive HER activity obtained in alkaline and acidic media. Briefly, the overpotential reaches 120 mV in 1 M KOH and 150 mV in 0.5 M H2SO4at the current density of 10 mA cm–2, with the corresponding Tafel slope reaching 69 mV dec–1. The results are close to that of commercial Pt/C catalysts (37 mV in 1 M KOH). Furthermore, the density functional theory calculations also demonstrate that P-incorporated Ni–Cu, Cu-incorporated Ni–P, and Ni-incorporated Cu–P have the optimized hydrogen adsorption free energy (ΔGH*) of −0.066, −0.157, and −0.003 eV, respectively, which are more suitable than those of Ni–Cu, Ni–P, and Cu–P, respectively. The Ni-incorporated Cu–P even has a much smaller ΔGH*of −0.003 than that of Pt (∼−0.09 eV). We believe that our study will provide a new strategy to design non-noble metal alloy materials for practical applications in catalysis and energy fields.

Titanium carbide @ carbon-doped titanium dioxide (TiC@C-TiO2) core-shell nanostructures are designed, prepared and demonstrated for the application in lithium ion battery anode. Synthesis of these specific core-shell nanostructures is achieved via a facile, novel, and one-pot approach using oxidative growth of C-TiO2onto TiC nanoparticles, which has a higher electrochemical activity than those of pure P25 and TiC nanoparticles. The core-shell nanostructured anodes exhibit a high lithium storage capacity (352.8 mAh g−1at 100 mA g−1), good rate capability (253.6 mAh g−1at 1 A g−1, 158.1 mAh g−1at 10 A g−1), and outstanding cycle stability in lithium ion batteries (LIBs) (~ 150 mAh g−1at 10 A g−1after 400 cycles), which is about 48 times and 7 times higher than that of TiO2electrode (~ 3.3 mAh g−1at 10 A g−1) and TiC (~ 25 mAh g−1at 10 A g−1). According to the first-principle calculation, the ultrahigh capacity and cycle stability of the as-prepared anode is ascribed to the enhancement of Li+absorption and diffusion ability through formation of C-TiO2porous layer onto the conductive TiC particles. Moreover, the increase of electron density around the Fermi level is found to be mainly caused by the core-shell nanostructures. The results demonstrate that the presence of TiC plays an important role in providing high conductivity and the novel core-shell nanostructure can buffer the huge volume expansion and contraction during prolonged cycling, resulting in great potential applications in LIBs.

Image representation and annotation are two key tasks in practical applications such as image search. Existing methods have tried to learn an effective representation or to predict tags directly using multi-view low-level visual features, which usually contain redundant information. However, these two tasks are closely related and interact on each other. A suitable image representation can yield better image annotation results, which in turn can effectively guide the image representation learning. In this paper, we propose to jointly conduct multi-view representation and image annotation via optimal predictive subspace learning, making the two tasks promote each other. Specifically, for subspace learning, visual structure and semantic information of images are exploited to make the learned subspace more discriminative and compact. For tag prediction, support vector machines (SVM) is adopted to obtain better tag prediction results. Then to simultaneously learn image representation, tag predictors and projection function, the three subproblems are combined into a unified optimization objective function and an alternative optimization algorithm is derived to solve it. Experimental results on four image datasets illustrate that our method is superior to the other image annotation methods. [ABSTRACT FROM AUTHOR]

Graphitic carbon nitride (g-C3N4) photocatalysts have attracted much attention towards harvesting solar energy for applications in energy and environment sectors. However, separation of electron-hole pairs is an intrinsic problem for the bulk g-C3N4. Here, we report the tiny amount of carbon doped TiO2modified g-C3N4(C-TiO2/g-C3N4) with a narrow bandgap and prolonged lifetime of charge carriers. This heterojunction photocatalysts were successfully fabricated via a facile heat treatment under atmosphere. The enhanced separation of photogenerated charge carriers and narrow band gap confer superior photocatalytic activities with 5.728mmol/g photogenerated hydrogen gas for 5h and 52.395mmol/g for 64h in triethanolamine aqueous solution. The apparent quantum efficiency of C-TiO2/g-C3N4is ~ 6.2% under 420nm irradiation, which is about 2.4 times higher than the corresponding value 2.6% of pristine g-C3N4. This photocatalyst with excellent photocatalytic performance and photo-stability can work as a promising candidate to applicate in solar-to-fuel conversion and environmental remediation.

Node classification has a wide range of application scenarios such as citation analysis and social network analysis. In many real-world attributed networks, a large portion of classes only contain limited labeled nodes. Most of the existing node classification methods cannot be used for few-shot node classification. To train the model effectively and improve the robustness and reliability of the model with scarce labeled samples, in this paper, we propose a local adaptive discriminant structure learning (LADSL) method for few-shot node classification. LADSL aims to properly represent the nodes in the attributed graphs and learn a metric space with a strong discriminating power by reducing the intra-class variations and enlarging inter-class differences. Extensive experiments conducted on various attributed networks datasets demonstrate that LADSL is superior to the other methods on few-shot node classification task.

Visual information has been widely used for image representation. Although proven very effective, the visual representation lacks explicit semantics. However, how to generate a proper semantic space for image representation is still an open problem that needs to be solved. To jointly model the visual and semantic representations of images, we propose a boosted random contextual semantic space based image representation method. Images are initially represented using local feature’s distribution histograms. The semantic space is generated by randomly selecting training images. Images are then mapped into the semantic space accordingly. Semantic context is explored to model the correlations of different semantics which is then used for classification. The classification results are used to re-weight training images in a boosted way. The re-weighted images are used to construct new semantic space for classification. In this way, we are able to jointly consider the visual and semantic information of images. Image classification experiments on several public datasets show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Verification and validation (V&V) are the primary means to assess the accuracy and reliability in computational fluid dynamics (CFD) simulation. V&V of the multi-medium detonation CFD model is conducted by using our independently-developed software --- Lagrangian adaptive hydrodynamics code in the 2D space (LAD2D) as well as a large number of benchmark testing models. Specifically, the verification of computational model is based on the basic theory of the computational scheme and mathematical physics equations, and validation of the physical model is accomplished by comparing the numerical solution with the experimental data. Finally, some suggestions are given about V&V of the detonation CFD model.

Dichloro(1,2-diaminocyclohexane)platinum(II) (DACHPt), encapsulated by PEGylated hyaluronan (mPEG-HA), assembled to nanoparticle in aqueous solution, which significantly inhibited the growth of A549 xenografts in nude mice.

Anabaena sp. and M. aeruginosa were used to examine the toxic mechanism of nano-ZnO to them, as well as the toxicity. Typical chlorophyll fluorescence parameters, including effective quantum yield, photosynthetic efficiency and maximum electron transport rate, were measured by a pulse amplitude modulated fluorometer. Results showed that nano-ZnO could inhibit Anabaena sp. and M. aeruginosa growth with the EC50 (concentration for 50% of maximal effect) of 0.74±0.01 and 1.68±0.01 mg/L respectively. The toxicity of nano-ZnO to Anabaena sp. is higher than that to M. aeruginosa, which can be proved by the malondialdehyde content in Anabaena sp. and M. aeruginosa cells.

Coal mine electromechanical equipment plays an important role in the process of producing coal.For the lack of scientific management philosophy and effective management methods, China’s Coal mine electromechanical equipment is now facing severer and severer challenges in its way of moving ahead-shortage of investment, hysteresis of management-hazards emerge in every single seconds in the production of coal mine. These hazards,however,does not merely affect the normal production, but will also increase the risks of accidents.Therefore,in order to improve the management level of electromechanical equipment and optimize the utilization of mechanical and electrical equipment, coal mine managers should pay their attentions to the following three aspects:the importance of coal mining equipment ,the importance of training mechanical and electrical personnels and the importance of accelerating the construction of mechanical and electrical information management system [1].

Coal Mine Safety Monitoring System is an important material guarantee for Coal Mine Safety Production. At present,most China's coal mines have established safety monitoring system, but in the use of safety monitoring system we've found many problems. In order to solve these problems ,our paper put forward some practical solutions. [1].

With the rapid development of social media, the topics emerge and propagate in a variety of media websites. Although much work has been done since NIST proposed the problem of topic detection and tracking (TDT), most of them focus on single media data and are mainly based on unsupervised clustering method, which does not use some side information to help detecting topics. Therefore, traditional TDT approaches are not competent for cross-media topic detection. To efficiently use the information contained in multi-modal data from different sources and the prior knowledge, we propose a semi-supervised co-clustering approach for cross-media topic detection by a constrained non-negative matrix factorization. The correctness and convergence of our approach are proved to demonstrate its mathematical rigorousness. Experiments on the cross-media dataset verify the effectiveness of our proposed approach.

Located in Shanxi, Datong Province, the Lu Garden is the earliest Royal Garden of grassland type for the purpose of hunting. From beginning to end with the Northern Wei Dynasty, the Lu Garden has been developing for more than 100 years and currently it is damaged after being through so many sufferings. In this paper, on the basis of investigating literatures of the Lu Garden massively, the start and the end, as well as the function of the Lu Garden are introduced. In addition, the historical status of the Lu Garden and its architecture materials are concluded.

Tunnel roof separation monitoring is an important research content of tunnel supporting technology, it is of great significance to guarantee the safety of mine production. The traditional detection methods of roof separation have disadvantages of low precision, poor reliability, high cost, and the comprehensive analysis of measurement results is difficult. In the paper, a roof separation monitoring device based on STC12C5A is proposed. It has the core of STC12C5A single-chip microcomputer, and realizes the accurate measurement of displacement by using the fine drag-rope displacement sensor, and realizes the data communication and sharing by using bus interface. The device can achieve real-time, reliable monitoring of roof separation condition and also can make pre-caution to the possible production accident.

As the application of the information network technology continuously extended, library management service platform is experiencing a shift to the information service system. In the library information management field, researchers are seeking a library identification system which is more convenient and comprehensive. Under the circumstance of the information technology conditions, Radio Frequency Identification (RFID) technology completes the automatic identification of the electronic identification tags--the two-dimensional code. The RFID technology is a contactless scanning recognition technology. And the core technology of RFID can be described as follows: using two-dimensional code as the filter identification tool according to backup data of the book management.

As it's known to all, taking full advantage of useful information in university information management systems can improve the quality and efficiency of university management. With the aim at the characteristic of the information management system, this paper proposes practical applications of the Data Mining in university information management by analyzing the process, main workload and main technical methods of data mining. And finally, we provide some guidance information and reference information for the university management staffs to help them accomplish the university management quickly and efficiently.

Based on the disadvantages and problems of current detect ways, come up the testing system which using numerical controlled oscillator (NOC), Digital Radio Frequency Memory (DRFM) and digital down-covert to intermediate frequency (IF) processing, introduce the background information, main functions, total structure and design implementation.

The dynamic performance of modern machine tools is more and more highly emphasized. The research purpose of this paper is to research dynamic performance of gantry machining center through modal experiment. In this paper, the theory of relative excitation test on the machine tool is expounded, and relative excitation test is carried on gantry machining center and then the modal parameters of this machine tool are obtained through experimental modal analysis. Analyze the weak link of this gantry machining center, show that the stiffness of Z direction of Z to feed component of this machine tool is insufficient and the stiffness of X, Y direction of ram is insufficient.

The Cu-based SS-O solar spectrum flat-plate selective absorption coating system was prepared by the method of direct current reactive sputtering coating films, in which, two layers are SS-O absorbing layer and one is Si-O anti-reflection layer. The reflectance spectra of the absorption coating system was measured by UV3600 spectrophotometer, the reflectance and absorptance of the absorption layer and the anti-reflection layer were calculated, and certain experimental tests were conducted on the prepared absorption coating system in terms of the salt spray resistance performances, thermal stability and damp heat stability. The results show that the SS-O selective absorption coating which was prepared by magnetron sputtering technique possesses excellent processing property, corrosion resistance and thermal stability, whose absorption rate and the emission ratio have reached the level of the German blue tape series. It can meet the domestic demand for high-level flat-plate market.

In this study, we propose a new detection method of nanoparticle-enhanced human papillomavirus genotyping microarrays using a DVD optical pick-up with a photodiode. The HPV genotyping DNA chip was labeled using Au/Ag core-shell nanoparticles, prepared on a treatment glass substrate. Then, the bio information of the HPV genotyping target DNA was detected by measuring the difference of the optical signals between the DNA spots and the background parts for cervical cancer diagnosis. Moreover the approximate linear relationship between the concentration of the HPV genotyping target DNA and the optical signal depending on the density of Au/Ag core-shell nanoparticles was obtained by performing a spot finding algorithm. It is shown that the nanoparticle-labeled HPV genotyping target DNA can be measured and quantified by collecting the low-cost photodiode signal on the treatment glass chip, replacing high-cost fluorescence microarray scanners using a photomultiplier tube.

This work describes a simulation research concerning dynamic effect of multi-pad hydrostatic thrust bearing having rectangular recess, sector recess, ellipse recess and I-shaped recess in order to solve the loading capacity of the hydrostatic thrust bearing. Three-dimensional pressure field of gap fluid between the rotation worktable and the base has been computed by using the Finite Volume Method. This study theoretically analyzes the influence of recess shape on dynamic effect of the bearing according to computational fluid dynamics and lubricating theory, and the simulation results indicate that an improved characteristic will be affected by recess shape easily. Through this method, the safety of a multi-pad hydrostatic thrust bearing having different cavities can be forecasted, and the optimal loading capacity of such products can be achieved, so it can provide reasonable data for design, lubrication, experience and thermal deformation computation for hydrostatic thrust bearing.

In this article a multi-dimensional coupled electro-thermal model of a LFP battery is proposed. The model includes a 1-d model based on porous-electrode theory and a 3-d battery model solving the temperature distribution of the battery. These models are coupled through heat generation rate during discharging process. Moreover, an experiment is designed to check the inside temperature information of the battery.

Gene silencing induced by RNA interference using small interfering RNA (siRNA) provides a promising therapeutic approach for cancers. However, the lack of siRNA delivery vector has limited the development of siRNA therapy. The purpose of this study was to use the novel copolymer (mPEG5k-PCL1.2k)1.4-g-PEI10kto prepare siRNA-loaded nanoparticles for siRNA delivery. The results suggested that (mPEG5k-PCL1.2k)1.4-g-PEI10kcould load siRNA to form nanoparticles with particle size less than 200 nm in a narrow distribution. Moreover, a certain density of positive charge existed onto the surfaces of nanoparticles. MTT assay results demonstrated that (mPEG5k-PCL1.2k)1.4-g-PEI10k/siRNA nanoparticles showed very low cytotoxicity. The gene silencing efficiency of (mPEG5k-PCL1.2k)1.4-g-PEI10k/siRNA nanoparticles was investigated through luciferase reporter gene assays. The expression of exogenous luciferase gene was significantly down-regulated at a range of N/P ratio from 50 to 125, and was maximally inhibited at the N/P ratio of 125 with 54% and 59% reduction in MCF-7 and HepG2 cells, respectively. In the 4T1-luc cell line expressing luciferase stably, the silencing of endogenous luciferase gene also has a similar overall profile with maximal 54% reduction of luciferase expression. These results suggested that (mPEG5k-PCL1.2k)1.4-g-PEI10k/siRNA nanoparticles could serve as a kind of highly efficient siRNA delivery system for down-regulating the expression of exogenous and endogenous target genes.

Signal source with high precision and programmable is needed in modern scientific research, communication system, teaching apparatus, electronic measurement and so on. In the paper, a portable signal generator is proposed, which has a core of AT89C51 and programmable DDS integrated chip AD9833. The paper also introduces the principle and implementation of system project. The generator can output sinusoidal wave, square wave, triangle wave, dc voltage, and it has manual and SPC two working mode, can implement PC SPC output through RS232 communication interface. Experiments show that the system has advantages of high precision, wide scope, small size and flexible operation etc.

This paper introduces a method of manufacturing process of resin concrete, made three tubular cast-iron specimens filled with resin concrete. This paper detected the amplitude-frequency response curve of specimens in experimental method, and calculated the damping ratio of specimens through half power bandwidth method. The damping ratio of specimens is compared. It provided a good reference for the design of the machine tool on the dynamic characteristics.

Linear rolling guide has gained wide application in various CNC machines. As a main joint of the machine tools, it makes great impact on the dynamic characteristics of machine tools. It is therefore necessary to study the dynamic behavior of linear rolling guide. And external load is a factor that affects the dynamic characteristics of linear rolling guide. In this paper experimental approach was used with the aim of studying effects of external load on dynamic behavior of linear rolling guide. The experiment contained some modal tests of linear rolling guide ways when they were loaded forces of different sizes.

Vehicle safety is one of the most important design elements for all types of vehicles designed for use on public streets or roadways. Compared with traditional vehicles, besides other safety issues to be considered, a new issue that is the high voltage management problem should be taken in consideration very carefully in the electric vehicle development. This paper analyses the high voltage system in the electric vehicle and proposed a high voltage management system which implements several functions like power-up and power-down control of the high voltage system, high-voltage-circuit interlock protection, protection of electric leakage of the HV system etc. Because of the high voltage management, the electrical safety goals could be achieved and the protection of human being from high voltage hazardous becomes possible.