Dynamic Influence of Network Public Opinions on Price Fluctuation of Small Agricultural Products Based on NLP-TVP-VAR Model—Taking Garlic as an Example

In recent years, the price of small agricultural products has both plummeted and skyrocketed, which has a great impact on people’s lives. Studying the factors affecting the price fluctuation of small agricultural products is of great significance for stabilizing their price. With the development and application of social media, farmers and consumers are more greatly influenced by online public opinion, resulting in irrational planting behavior or purchasing behavior, which has a complex impact on the price of small agricultural products. Taking garlic as an example, we crawled through network public opinions about garlic price from January 2015 to December 2020 using web crawler technology. Then, the network public opinions were quantified using a natural language processing and time-varying parameter vector autoregression (NLP-TVP-VAR) model to empirically analyze their dynamic influence on garlic price fluctuation. It was found that both public attitude and public attention have a short-term influence on garlic price fluctuation, and the influences of each differ according to direction, intensity and timing. The influence of public attitude on garlic price fluctuation is positive, while the influence of public attention on garlic price fluctuation is largely negative. The influence intensity of public attitude is stronger than of public attention on garlic price fluctuation. The influence of public attitude on garlic price fluctuation shows a trend of intensifying, while that of public attention has been weaker than in previous years. In addition, based on the results of our study, we present some recommendations for improving the comprehensive information platform and price fluctuation early warning system for the whole industry chain of small agricultural products

Dynamic Influence of Network Public Opinions on Price Fluctuation of Small Agricultural Products Based on NLP-TVP-VAR Model—Taking Garlic as an Example

In recent years, the price of small agricultural products has both plummeted and skyrocketed, which has a great impact on people’s lives. Studying the factors affecting the price fluctuation of small agricultural products is of great significance for stabilizing their price. With the development and application of social media, farmers and consumers are more greatly influenced by online public opinion, resulting in irrational planting behavior or purchasing behavior, which has a complex impact on the price of small agricultural products. Taking garlic as an example, we crawled through network public opinions about garlic price from January 2015 to December 2020 using web crawler technology. Then, the network public opinions were quantified using a natural language processing and time-varying parameter vector autoregression (NLP-TVP-VAR) model to empirically analyze their dynamic influence on garlic price fluctuation. It was found that both public attitude and public attention have a short-term influence on garlic price fluctuation, and the influences of each differ according to direction, intensity and timing. The influence of public attitude on garlic price fluctuation is positive, while the influence of public attention on garlic price fluctuation is largely negative. The influence intensity of public attitude is stronger than of public attention on garlic price fluctuation. The influence of public attitude on garlic price fluctuation shows a trend of intensifying, while that of public attention has been weaker than in previous years. In addition, based on the results of our study, we present some recommendations for improving the comprehensive information platform and price fluctuation early warning system for the whole industry chain of small agricultural products

Can Deep Learning Identify Tomato Leaf Disease?

This paper applies deep convolutional neural network (CNN) to identify tomato leaf disease by transfer learning. AlexNet, GoogLeNet, and ResNet were used as backbone of the CNN. The best combined model was utilized to change the structure, aiming at exploring the performance of full training and fine-tuning of CNN. The highest accuracy of 97.28% for identifying tomato leaf disease is achieved by the optimal model ResNet with stochastic gradient descent (SGD), the number of batch size of 16, the number of iterations of 4992, and the training layers from the 37 layer to the fully connected layer (denote as “fc”). The experimental results show that the proposed technique is effective in identifying tomato leaf disease and could be generalized to identify other plant diseases

Effects of Preparation Methods on the Thermoelectric Performance of SWCNT/Bi2Te3 Bulk Composites

Single-walled carbon nanotube (SWCNT)/Bi2Te3 composite powders were fabricated via a one-step in situ reductive method, and their corresponding bulk composites were prepared by a cold-pressing combing pressureless sintering process or a hot-pressing process. The influences of the preparation methods on the thermoelectric properties of the SWCNT/Bi2Te3 bulk composites were investigated. All the bulk composites showed negative Seebeck coefficients, indicating n-type conduction. A maximum power factor of 891.6 μWm−1K−2 at 340 K was achieved for the SWCNT/Bi2Te3 bulk composites with 0.5 wt % SWCNTs prepared by a hot-pressing process, which was ~5 times higher than that of the bulk composites (167.7 μWm−1K−2 at 300 K) prepared by a cold-pressing combing pressureless sintering process, and ~23 times higher than that of the bulk composites (38.6 μWm−1K−2 at 300 K) prepared by a cold-pressing process, mainly due to the enhanced density of the hot-pressed bulk composites

Flexible ternary carbon black/Bi2Te3 based alloy/polylactic acid thermoelectric composites fabricated by additive manufacturing

Flexible ternary carbon black/Bi2Te3 based alloy/polylactic acid (CB/BTBA/PLA) composites were fabricated by additive manufacturing and their thermoelectric properties were investigated from 300 K to 360 K. At 300 K, as the mass ratios of BTBAs in the composites increased from 38.5% to 71.4%, both the electrical conductivity and Seebeck coefficient of the composites increased from 5.8 S/cm to 13.3 S/cm, and from 60.2 mV/K to 119.9 mV/K, respectively, and the thermal conductivity slightly increased from 0.15 W m(-1)K(-1) to 0.25 W m(-1)K(-1), as a result, the ZT value of the composites increased from 0.004 to 0.023. As the temperature increased from 300 K to 360 K, the electrical conductivity of all the composites slightly decreased, while the thermal conductivity slowly increased, and a highest ZT value of 0.024 was achieved for the composites with 71.4% BTBAs at 320 K. Unlike traditional sterolithography, fused deposition modeling, selective laser melting, etc., this additive manufacturing process can directly print the solutions which contain inorganic fillers and polymer matrixes into almost any designed intricate geometries of thermoelectric composites, therefore this process has great potential to be used for fabrication of flexible polymer based thermoelectric composites and devices. (C) 2020 The Chinese Ceramic Society. Production and hosting by Elsevier B.V.Funding Agencies|Shanghai Innovation Action Plan Project [17090503600]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China [11811530636, 61504081, 61611530550]; Program for Professor of Special Appointment (Young Eastern Scholar Program) at Shanghai Institutions of Higher Learning [QD2015039]; Swedish Research CouncilSwedish Research Council [2016-3365]; Swedish Energy AgencySwedish Energy Agency [46519-1]; Knut and Alice Wallenberg Foundation through the Wallenberg Academy Fellows program; Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoping University (Faculty Grant SFO-Mat-LiU) [2009 00971]</p

Flexible Thermoelectric Double‐Layer Inorganic/Organic Composites Synthesized by Additive Manufacturing

This study shows an approach to combine a high electrical conductivity of one composite layer with a high Seebeck coefficient of another composite layer in a double-layer composite, resulting in high thermoelectric power factor. Flexible double-layer-composites, made from Bi2Te3-based-alloy/polylactic acid (BTBA/PLA) composites and Ag/PLA composites, are synthesized by solution additive manufacturing. With the increase in Ag volume-ratio from 26.3% to 41.7% in Ag/PLA layers, the conductivity of the double-layer composites increases from 12 S cm(-1)to 1170 S cm(-1), while the Seebeck coefficient remains approximate to 80 mu V K(-1)at 300 K. With further increase in volume ratio of Ag until 45.6% in Ag/PLA composite layer, the electrical conductivity of the double-layer composites increases to 1710 S cm(-1), however, with a slight decrease of the Seebeck coefficient to 64 mu V K-1. The electrical conductivity and Seebeck coefficient vary only to a limited extent with the temperature. The high Seebeck coefficient is due to scattering of low energy charge carriers across compositionally graded interfaces. A power factor of 875 mu W m(-1) K(-2)is achieved at 360 K for 41.7 vol.% Ag in the Ag/PLA layers. Solution additive manufacturing can directly print this double-layer composite into intricate geometries, making this process is promising for large-scale fabrication of thermoelectric composites.Funding Agencies|Shanghai Innovation Action Plan Project [17090503600]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China [11811530636, 61504081, 61611530550]; Program for Professor of Special Appointment (Young Eastern Scholar Program) at Shanghai Institutions of Higher Learning [QD2015039]; Swedish Research Council (VR)Swedish Research Council [2016-3365]; Swedish Energy AgencySwedish Energy Agency [46519-1]; Knut and Alice Wallenberg Foundation through the Wallenberg Academy Fellows program; Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoping University [2009 00971]</p

Thermoelectric Characteristics of Self-Supporting WSe2-Nanosheet/PEDOT-Nanowire Composite Films

Conducting polymer poly(3,4-ethylenedioxythiophene) nanowires(PEDOTNWs) were synthesized by a modified self-assembled micellar soft-templatemethod, followed by fabrication by vacuum filtration of self-supportingexfoliated WSe2-nanosheet (NS)/PEDOT-NW composite films.The results showed that as the mass fractions of WSe2 NSsincreased from 0 to 20 wt % in the composite films, the electricalconductivity of the samples decreased from &amp; SIM;1700 to &amp; SIM;400S cm(-1), and the Seebeck coefficient increased from12.3 to 23.1 &amp; mu;V K-1 at 300 K. A room-temperaturepower factor of 44.5 &amp; mu;W m(-1) K-2 was achieved at 300 K for the sample containing 5 wt % WSe2 NSs, and a power factor of 67.3 &amp; mu;W m(-1) K-2 was obtained at 380 K. The composite film containing5 wt % WSe2 NSs was mechanically flexible, as shown byits resistance change ratio of 7.1% after bending for 500 cycles ata bending radius of 4 mm. A flexible thermoelectric (TE) power generatorcontaining four TE legs could generate an output power of 52.1 nWat a temperature difference of 28.5 K, corresponding to a power densityof &amp; SIM;0.33 W/m(2). This work demonstrates that the fabricationof inorganic nanosheet/organic nanowire TE composites is an approachto improve the TE properties of conducting polymers.Funding Agencies|Program for Professor of Special Appointment at Shanghai Institutions of Higher Learning [TP2020068]; Shanghai Sailing Program [20YF1447300]; Natural Science Foundation of Shanghai [21ZR1462300]; Shanghai Innovation Action Plan Project [17090503600]; Knut and Alice Wallenberg Foundation through the Wallenberg Academy Fellows program [KAW 2020.0196]; Swedish Research Council [2021-03826]; Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoping University [2009 00971]; Swedish Energy Agency [46519-1]</p

Microstructure and Mechanical Properties of Multicomponent Metal Ti(C,N)-Based Cermets

Ti(C,N)-based cermets with multicomponent ingredients were prepared using vacuum sintering technology. The effect of molding agents, binder phase and sintering temperature on Ti(C,N)-based cermets were studied. The optimum molding performance was obtained by adding 2% polyvinyl alcohol (PVA-1788). The microstructure and mechanical properties of Ti(C,N)-based cermets were investigated. The Ti(C,N)-based cermet with a weight percentage of TiC:TiN:Ni:Co:Mo:WC:Cr3C2:C = 40:10:20:10:7:8:4:1 and sintered at 1450 &deg;C had the optimal mechanical properties. The relative bending strength, Vickers hardness, elastic modulus and wear resistance were 2010 MPa, 15.01 GPa, 483.57 GPa and 27 mg, respectively. Additionally, X-ray diffraction (XRD), backscatter scanning electron microscopy pictures (SEM&ndash;BSE), energy dispersive spectrometry (EDS) and optical micrographs of Ti(C,N)-based cermets were characterized