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Buying stocks based on relatively accurate predictions to make a profit is what investors have been yearning for. However, due to the volatility and stochastic intrinsic of the stock, the price is also full of uncertainty, which is difficult to predict. With the improvement of computer performance and the popularization of machine learning methods nowadays, effective stock prediction methods emerge one after another. In this paper, random forest, XGBoost, and LSTM techniques are utilized for predicting the closing price of Netflix, which is one of the major stocks in Nasdaq and has been fluctuating this year. The closing price of the previous five days and the last day’s volume are used as inputs. The models are evaluated by MSE and R-square. According to the analysis, similar model evaluation results in random forest and LSTM indicate that the models are efficient. Furthermore, the prediction could be improved by considering the fluctuations of stocks simultaneously and introducing more variables in various dimensions. These results shed light on guiding further exploration of predicting stock price movements based on advanced machine learning methods.

The effect of different heat inputs of 1.45 kJ/mm, 1.78 kJ/mm and 2.31 kJ/mm on the microstructure and mechanical properties of deposited metals of the self-developed AWS A5.28 E120C-K4 high strength steel flux-cored wire was studied by optical microscope, scanning electron microscope and mechanical property test. With the increase in heat input, the results showed that the microstructure of deposited metals became coarse. Acicular ferrite increased at first and then decreased, granular bainite increased and degenerated upper bainite and martensite decreased slightly. Under the low heat input of 1.45 kJ/mm, the cooling rate was fast and the element diffusion was uneven, which caused composition segregation and easy to form large size inclusions SiO2-TiC-CeAlO3 with weak binding to the matrix. Under the middle heat input of 1.78 kJ/mm, the composite rare earth inclusions in dimples were mainly TiC-CeAlO3. The dimples were small and uniformly distributed, and the dimple fracture mainly depended on the wall-breaking connection between medium-sized dimples rather than an intermediate media. Under the high heat input of 2.31 kJ/mm, SiO2 was easy to adhere to high melting point Al2O3 oxides to form irregular composite inclusions. Such irregular inclusions do not need to absorb too much energy to form necking. Finally, the integrated effects of microstructure and inclusions resulted in the optimum mechanical properties of deposited metals with a heat input of 1.78 kJ/mm, which was a tensile strength of 793 MPa and an average impact toughness at −40 °C of 56 J.

To meet the demand for high-performance magnetic abrasive particles (MAPs) for finishing of difficult-to-machine materials, CBN/Fe-based spherical composite MAPs were prepared via gas atomization under different process conditions. The effects of nozzle type (single-stage and two-stage), second-stage gas pressure (2, 4, 6, and 8 MPa), and the grain size of the CBN abrasive (W7 and W40) on the MAP quality were studied. The MAPs were analysed through scanning electron microscopy and energy-dispersive spectroscopy. The results indicate that the quality of MAPs prepared using two-stage atomizing nozzles is better than that of MAPs prepared using single-stage nozzles. When the second-stage gas pressure was 6 MPa, the prepared MAPs had the best sphericity, and the CBN abrasive was embedded uniformly and densely on the surface of the iron matrix. For MAPs with the same particle size, the iron matrix wrapped more effectively around CBN abrasives with smaller grain sizes. This research is can serve as a methodological reference for understanding the preparation of ceramic/metal-based spherical composite MAPs via gas atomization.

The staircase effect, balling effect, and powder adhesion as well as other problems in additive manufacturing (AM) forming all lead to the poor uniformity and high roughness of the sample surfaces. Therefore, there exist differences in the physical and mechanical properties of the samples. In this paper, the spherical composite magnetic abrasive particles (MAPs) are used for magnetic abrasive finishing (MAF) experimental investigations and process optimization of the AM sample surface. According to the Box-Behnken design principle of the response surface methodology, the finishing effects of different process parameters (spindle speed, feed speed, and machining gap) of MAF on the samples prepared by selective laser melting (SLM) with different formed angles are studied, the quadratic regression equations are established, and the validity of the equations are assessed by ANOVA and 3D response surfaces. After that, MAPs with smaller size were selected for the fine MAF experiments with the optimized parameters. Finally, we found that the optimal parameters of MAF for the same material with different forming angles are similar, but the polishing time consumed is quite different. After the MAF experiments, the changes of the surface Vickers hardness, roughness (Ra), and microscopic morphology are analyzed. The surface roughness of each sample is reduced from the initial 4–10 μm to about 100 nm, and the diversities in hardness are also reduced. MAF significantly improves the defects of poor surface uniformity caused by inconsistent forming angles.

The precipitation accelerates the migration of water and salt in the soil, and aggravates the deterioration of saline earthen sites. Based on the freeze-thaw tests of soil column, the water and sal...

Crowd counting is challenging due to unconstrained imaging factors, e.g. , background clutters, non-uniform distribution of people, large scale and perspective variations. Dealing with these problems using deep neural networks requires rich prior knowledge and multi-scale contextual representations. In this paper, we propose a Cross-stage Refinement Network (CRNet) that can refine predicted density maps progressively based on hierarchical multi-level density priors. In particular, CRNet is composed of several fully convolutional networks. They are stacked together recursively with the previous output as the next input, and each of them serves to utilize previous density output to gradually correct prediction errors of crowd areas and refine the predicted density maps at different stages. Cross-stage multi-level density priors are further exploited in our recurrent framework by the cross-stage skip layers based on ConvLSTM. To cope with different challenges of unconstrained crowd scenes, we explore different crowd-specific data augmentation methods to mimic real-world scenarios and enrich crowd feature representations from different aspects. Extensive experiments show the proposed method achieves superior performances against state-of-the-art methods on four widely-used challenging benchmarks in terms of counting accuracy and density map quality. Code and models are available at this https://github.com/lytgftyf/Crowd-Counting-via-Cross-stage-Refinement-Networks .

In this paper, AlSi10Mg alloy powder is selected as the forming powder of Selective Laser Melting technology, and the AlSi10Mg alloy SLM curved surface sample is constructed by setting the internal and external layering parameters. In view of the relatively rough surface roughness of SLM technology molded parts, this paper selects the magnetic finishing technology with higher flexibility characteristics to perform surface finishing and finishing on the formed curved surface samples. Explore the feasibility of magnetic finishing technology on the finishing of SLM shaped curved parts, and test and analyze the surface roughness, surface hardness and hydrophobicity of the finishing permanent magnet tools and the curved surface samples before and after finishing. Finally, it was found that the use of a 75° trapezoidal slotted permanent magnet finishing tool to absorb spherical Al2O3 magnetic abrasives for flexible finishing of AlSi10Mg alloy SLM shaped curved surface samples can achieve better finishing results.In this paper, the orthogonal experiment method is used to optimize the finishing experiment. It is found that the finishing parameters of the spindle speed is 1800 r/min, the feed rate is 5 mm/min, the gap is 2 mm, and the abrasive consumption is 7g to form the AlSi10Mg alloy SLM. The surface roughness Ra=0.279μm can be obtained by magnetic finishing of the curved sample, and the surface morphology of the sample has been greatly improved. At the same time, it is found that the magnetic finishing technology improves the surface roughness of the AlSi10Mg alloy SLM forming surface sample, while it does not change the surface hardness of the sample, but it can significantly improve the hydrophobicity of the sample surface.

Unsupervised video object segmentation (UVOS) aims at segmenting the primary objects in videos without any human intervention. Due to the lack of prior knowledge about the primary objects, identifying them from videos is the major challenge of UVOS. Previous methods often regard the moving objects as primary ones and rely on optical flow to capture the motion cues in videos, but the flow information alone is insufficient to distinguish the primary objects from the background objects that move together. This is because, when the noisy motion features are combined with the appearance features, the localization of the primary objects is misguided. To address this problem, we propose a novel reciprocal transformation network to discover primary objects by correlating three key factors: the intra-frame contrast, the motion cues, and temporal coherence of recurring objects. Each corresponds to a representative type of primary object, and our reciprocal mechanism enables an organic coordination of them to effectively remove ambiguous distractions from videos. Additionally, to exclude the information of the moving background objects from motion features, our transformation module enables to reciprocally transform the appearance features to enhance the motion features, so as to focus on the moving objects with salient appearance while removing the co-moving outliers. Experiments on the public benchmarks demonstrate that our model significantly outperforms the state-of-the-art methods. Code is available at https://github.com/OliverRensu/RTNet.

This paper tackles the task of Few-Shot Video Object Segmentation (FSVOS), i.e., segmenting objects in the query videos with certain class specified in a few labeled support images. The key is to model the relationship between the query videos and the support images for propagating the object information. This is a many-to-many problem and often relies on full-rank attention, which is computationally intensive. In this paper, we propose a novel Domain Agent Network (DAN), breaking down the full-rank attention into two smaller ones. We consider one single frame of the query video as the domain agent, bridging between the support images and the query video. Our DAN allows a linear space and time complexity as opposed to the original quadratic form with no loss of performance. In addition, we introduce a learning strategy by combining meta-learning with online learning to further improve the segmentation accuracy. We build a FSVOS benchmark on the Youtube-VIS dataset and conduct experiments to demonstrate that our method outperforms baselines on both computational cost and accuracy, achieving the state-of-the-art performance. Code is available at https://github.com/scutpaul/DANet.

This paper presents a constant current regulation for discontinuous conduction mode primary-side controlled flyback converter. It consists of a demagnetization time detector and a peak current threshold generator. Based on peak current control, the regulation is achieved by fixing the product of the peak current and the ratio of the demagnetization time to the switching period constant. Using a reference voltage and the demagnetization time provided by the demagnetization time detector, the peak current threshold generator generates the threshold signal, which is relative to the ratio of the demagnetization time to the switching period. The circuit is simulated in software of Spectre based on the nuvoton 0.35um BCD process and a control IC has been fabricated. Experimental results of a 7V/1.5A circuit prototype shows that the constant output current precision is within 5.4% when the input voltage changes from 100V to 320V and while the load changes from 1.6H to 4.5H, the output current changes by 4.1%.

We developed an innovative approach that couples headspace solid-phase microextraction (SPME) with surface-enhanced Raman spectroscopy (SERS) to detect a volatile pesticide (i.e., fonofos) in a liquid complex matrix (i.e., apple juice). A gold nanoparticles-coated fiber was fabricated by reducing gold(III) on a chemically etched stainless steel wire to extract pesticide, using SPME. The fabricated fibers were then tested by a headspace-SPME method and a dip-SPME method, followed by SERS detection of fonofos in water and apple juice samples. Using the headspace-SPME method, we can detect as low as 5 ppb of fonofos in water and apple juice, compared with the dip-SPME method, which cannot detect lower than 10 ppb in water and 50 ppb in apple juice. This study demonstrated the potential capability of the headspace-SPME-SERS method for rapid (within 30 min) and sensitive detection of volatile and vaporizable compounds in complex matrices. The developed method could be a potential alternative approach to the gas chromatography method. Future work is needed to optimize the fiber by minimizing signal variation, and it should be tested in a variety of targeted compounds and matrices.

The increasing market demand for Ready-To-Eat fresh produce promotes the keen interest in developing a rapid, sensitive and reliable method for determining the shelf life of fresh-cut produce. In this study, we developed a non-destructive headspace detection approach using gold nanoparticles (AuNPs) coated fibers coupled with surface-enhanced Raman spectroscopy (SERS), to detect volatile biochemical changes during postharvest storage of arugula leaves. The headspace detection revealed significant spectral changes during the freshness decline, in the shifts around 500, 950 and 1030 cm−1. These changes were analyzed using principal component analysis (PCA) to classify and establish a prediction model for remaining shelf life determination. Through analyzing reference standard volatile organic compounds (VOCs), we identified dimethyl disulfide (DMDS), methanethiol (MT) and 1-propanethiol were most likely to account for the signature spectra of arugula headspace at the late storage period due to the growth of spoilage bacteria. In conclusion, the headspace detection based on SERS fibers provides a new strategy for monitoring the quality and shelf life of fresh produce and thus reducing food waste.

This paper proposes four newly-designed antenna configurations for the applications of short-distance wireless power transfer. The antennas are developed from a traditional circular loop antenna, which have a structural feature in common that all of them are in enclosed shape. Four proposed designs aim to improve the transfer efficiency. In this work, the transmitting antenna faces to the receiving antenna with a distance of 12 cm in the wireless power transfer system. Results show that two of them have better performance of transferring power than the other two and the conventional design.

In this manuscript, porous Co3O4 nanorods are prepared through a two-step approach which is composed of hydrothermal process and heating treatment as high performance anode for lithium-ion battery. Benefiting from the porous structure and 1-dimensional features, the product becomes robust and exhibits high reversible capability, good cycling performance, and excellent rate performance.

Monodisperse LiFePO 4 (LFP) microspheres have been prepared successfully by a low-temperature and template-free solvothermal method on a large scale. And a possible formation progress of the spherical LFP microparticles has been presented. The uniform microspheres are composed of aggregated nanoparticles with an open three-dimensional interconnected porous microstructure. These micro/nanostructured LFP microspheres exhibit a high tap density (1.5 g cm −3 ) and, as cathode materials, show remarkably high rate capability as high as 109 mAh g −1 at 5C rate and 99 mAh g −1 at 10C rate, and good cycle stability. The porous LFP electrode materials can find their way for the application in high-power lithium-ion batteries.

Mesoporous Co3O4 nanorods as high performance anode for Li-ion batteries are prepared by a two-step approach comprising hydrothermal processing of CoCl2 and urea (autoclave, 100 °C, 12 h) followed by calcination in air (300 °C, 2 h).

With the further market reform of electric power industry, customer-oriented marketing concept has been more and more important in electric power enterprises. After doing research on customer service data of an electric power enterprise, we pick up some key factors which might be related to customer satisfaction, and utilize a developed association rule model to mine association rules between these key factors and customer satisfaction, which reflects the close relationship between them. Furthermore, we design metrics that can reflect the changing trend of rule confidence, which helps business officers have an insight into how these factors influence customer satisfaction as time goes on, to effectively improve customer service quality and then win praise of customers. As practical experiments prove, this method can fulfill the goal very well.

Mesoporous core–shell nanocomposites with porous α-Fe2O3 nanorods as the core and porous TiO2 as the shell are synthesized by calcination treatment of FeOOH@TiO2 core–shell nanorods. Due to the high capacity of Fe2O3 core and durable outer shell of TiO2, as well as the specific mesoporous nanostructures, the products deliver an excellent electrochemical performance towards lithium storage, including stable reversible capacity as high as ∼860 mA h g−1 at high current density of 1 A g−1, excellent cyclic stability with ultralong cycling life over 1000 cycles, and outstanding rate capability. The disclosure of the relationship between the components/structure and the lithium storage properties will greatly benefit the rational design and synthesis of high-performance nanocomposites for lithium-ion batteries.