Data-based Polymer-Unit Fingerprint (PUFp): A Newly Accessible Expression of Polymer Organic Semiconductors for Machine Learning

In the process of finding high-performance organic semiconductors (OSCs), it is of paramount importance in material development to identify important functional units that play key roles in material performance and subsequently establish substructure-property relationships. Herein, we describe a polymer-unit fingerprint (PUFp) generation framework. Machine learning (ML) models can be used to determine structure-mobility relationships by using PUFp information as structural input with 678 pieces of collected OSC data. A polymer-unit library consisting of 445 units is constructed, and the key polymer units for the mobility of OSCs are identified. By investigating the combinations of polymer units with mobility performance, a scheme for designing polymer OSC materials by combining ML approaches and PUFp information is proposed to not only passively predict OSC mobility but also actively provide structural guidance for new high-mobility OSC material design. The proposed scheme demonstrates the ability to screen new materials through pre-evaluation and classification ML steps and is an alternative methodology for applying ML in new high-mobility OSC discovery.Comment: 42 pages, 13 figure

Indoor solid fuel use and tuberculosis in China: a matched case-control study

<p>Abstract</p> <p>Background</p> <p>China ranks second among the 22 high burden countries for tuberculosis. A modeling exercise showed that reduction of indoor air pollution could help advance tuberculosis control in China. However, the association between indoor air pollution and tuberculosis is not yet well established. A case control study was conducted in Anhui, China to investigate whether use of solid fuel is associated with tuberculosis.</p> <p>Methods</p> <p>Cases were new sputum smear positive tuberculosis patients. Two controls were selected from the neighborhood of each case matched by age and sex using a pre-determined procedure. A questionnaire containing demographic information, smoking habits and use of solid fuel for cooking or heating was used for interview. Solid fuel (coal and biomass) included coal/lignite, charcoal, wood, straw/shrubs/grass, animal dung, and agricultural crop residue. A household that used solid fuel either for cooking and (/or) heating was classified as exposure to combustion of solid fuel (indoor air pollution). Odds ratios and their corresponding 95% confidence limits for categorical variables were determined by Mantel-Haenszel estimate and multivariate conditional logistic regression.</p> <p>Results</p> <p>There were 202 new smear positive tuberculosis cases and 404 neighborhood controls enrolled in this study. The proportion of participants who used solid fuels for cooking was high (73.8% among cases and 72.5% among controls). The majority reported using a griddle stove (85.2% among cases and 86.7% among controls), had smoke removed by a hood or chimney (92.0% among cases and 92.8% among controls), and cooked in a separate room (24.8% among cases and 28.0% among controls) or a separate building (67.8% among cases and 67.6% among controls). Neither using solid fuel for cooking (odds ratio (OR) 1.08, 95% CI 0.62-1.87) nor using solid fuel for heating (OR 1.04, 95% CI 0.54-2.02) was significantly associated with tuberculosis. Determinants significantly associated with tuberculosis were household tuberculosis contact (adjusted OR, 27.23, 95% CI 8.19-90.58) and ever smoking tobacco (adjusted OR 1.64, 96% CI 1.01-2.66).</p> <p>Conclusion</p> <p>In a population where the majority had proper ventilation in cooking places, the association between use of solid fuel for cooking or for heating and tuberculosis was not statistically significant.</p

A Small Ship Target Detection Method Based on Polarimetric SAR

The detection of small fishing ships is very important for maritime fishery supervision. However, it is difficult to detect small ships using synthetic aperture radar (SAR), due to the weak target scattering and very small number of pixels. Polarimetric synthetic aperture radar (PolSAR) has been widely used in maritime ship detection due to its abundant target scattering information. In the present paper, a new ship detector, named &Lambda;M, is developed based on the analysis of polarization scattering differences between ship and sea, then combined with the two-parameter constant false alarm rate method (TP-CFAR) algorithm to conduct ship detection. The goals of the detector construction are to fully consider the ship&rsquo;s depolarization effect, and further amplify it through sliding window processing. First, the signal-to-clutter ratio (SCR) enhancement performance of &Lambda;M for ships with different lengths ranging from 8 to 230 m under 90 different combinations of windows are analyzed in detail using three set of RADARSAT-2 quad-polarization data, then the appropriate window size is determined. In addition, the SCR enlargement between &Lambda;M and some typical polarization features is compared. Among these, for ships of length greater than 35 m, the average contrast of &Lambda;M is 33.7 dB, which is 20 dB greater than that of the HV channel. For small vessels of length less than 16 m, the average contrast of &Lambda;M is 16 dB higher than that of HV channel on average. Finally, the RADARSAT-2 data including nonmetallic small vessels are used to perform ship detection tests, and the detection ability for conventional and small ships of some classic algorithms are compared and analyzed. For large vessels of length greater than 35 m, the method proposed in this paper is able to obtain a superior detection result, maintain the ship contour well, and suppress false alarms caused by the cross side lobe in the SAR image. For small vessels of length less than 16 m, the method proposed in this paper can reduce the number of missed targets, while also obtaining superior detection results, especially for small nonmetallic vessels

Managing production systems with machine learning: a case analysis of Suzhou GCL photovoltaic technology

Production control in the manufacturing industry involves complex circumstances and high demand for timeliness. Unlike traditional production control methods, the approach that integrates machine learning and industrial big data can enable manufacturing industries to dynamically adapt to the changing environment and respond in a timely manner to market changes due to production optimisation and improve economic benefits. In order to explore the relationship between production system optimisation and lean strategic planning based on machine learning and big data, the paper conducts an exploratory case analysis based on Suzhou GCL Photovoltaic Technology, a successful company in the photovoltaic industry in China. We sort and investigate the first-hand interview data and second-hand news and video data, and then use the qualitative research method. Based on the analysis and observation, we find that machine learning has a positive impact on quality management. Data, information, knowledge, intelligence collectively impact the performance of intelligence production systems. Our research provides valuable insights for practitioners to effectively accelerate the transformation to intelligent manufacturing

Experimental study of C-band microwave scattering characteristics during the emulsification process of oil spills

In this study, oil spill experiments were performed in a water tank to determine changes in the surface scattering characteristics during the emulsification of oil spills. A C-band fully-polarimetric microwave scatterometer and a vector network analyzer were used to observe films of the following oils: crude oil with an asphalt content below 3% that is prone to emulsification (type A), fresh crude oil extracted from an oilfield (type B), and industrial crude oil that was dehydrated and purified (type C). The difference in the backscatter results between the emulsified oil film and the calm water surface under C-band microwaves and the influence of the emulsification of the oil film on the backscatter were analyzed in detail. The results demonstrate that under a low-wind and no-waves condition (the maximum wave height was below than 3 mm), the emulsification of crude oil could modulated the backscatter through changes in the surface roughness and the dielectric constant, where the surface roughness had the dominant effect. The surface backscatters of the type B oil were greater than that of the type C oil in both the emulsified and non-emulsified states. In the non-emulsified state, the average differences in the backscatter between the type B and C oils were 2.19 dB, 2.63 dB, and 2.21 dB for the polarization modes of VV, HH, and HV/VH, respectively. Smaller corresponding average differences of 0.98 dB, 1.49 dB, and 1.5 dB were found for the emulsified state with a 20% moisture constant for the oil film. The results demonstrated that the surface roughness of the different oil films could vary due to the differences in the oil compositions and the oil film properties, which in turn affect the backscatter of the oil film surface

Evaluation of Arctic Sea Ice Drift Products Based on FY-3, HY-2, AMSR2, and SSMIS Radiometer Data

Different radiometer sensors have different frequencies, spatial resolutions, and time resolutions, which lead to inconsistencies in ice drift products retrieved by radiometer sensors. Based on the continuous maximum cross-correlation method, in this paper, we used China&rsquo;s FY-3 and HY-2 satellite radiometer data to generate sea ice drift products; we further evaluated the consistency between them and sea ice drift products retrieved from AMSR2 and SSMIS satellite radiometer data, which could help in future retrieval accuracies of more radiometer sea ice drift products. The results show that ice drift products with good reliability can be obtained by retrievals using 37 and 89 GHz channels of FY-3 and HY-2 radiometer bright temperature data. Compared with the buoy data, the root mean square errors (RMSEs) of the 37 GHz HY-2 sea ice drift product (at an interval of 6 days) were 1.40 cm/s and 7.31&deg; for speed and direction, respectively, and the relative errors (REs) were 5.78% and 6.44%, respectively. The RMSEs of the 37 GHz FY-3 sea ice drift product were 0.77 cm/s and 6.49&deg; for speed and direction, respectively, and the REs were 4.38% and 9.23%, respectively. Moreover, comparisons between sea ice drift vectors derived from AMSR2 and SSMIS satellites showed good quantitative agreement

Evaluation of Arctic Sea Ice Drift Products Based on FY-3, HY-2, AMSR2, and SSMIS Radiometer Data

Different radiometer sensors have different frequencies, spatial resolutions, and time resolutions, which lead to inconsistencies in ice drift products retrieved by radiometer sensors. Based on the continuous maximum cross-correlation method, in this paper, we used China’s FY-3 and HY-2 satellite radiometer data to generate sea ice drift products; we further evaluated the consistency between them and sea ice drift products retrieved from AMSR2 and SSMIS satellite radiometer data, which could help in future retrieval accuracies of more radiometer sea ice drift products. The results show that ice drift products with good reliability can be obtained by retrievals using 37 and 89 GHz channels of FY-3 and HY-2 radiometer bright temperature data. Compared with the buoy data, the root mean square errors (RMSEs) of the 37 GHz HY-2 sea ice drift product (at an interval of 6 days) were 1.40 cm/s and 7.31° for speed and direction, respectively, and the relative errors (REs) were 5.78% and 6.44%, respectively. The RMSEs of the 37 GHz FY-3 sea ice drift product were 0.77 cm/s and 6.49° for speed and direction, respectively, and the REs were 4.38% and 9.23%, respectively. Moreover, comparisons between sea ice drift vectors derived from AMSR2 and SSMIS satellites showed good quantitative agreement

One-pot synthesis of polyaniline/Fe

One-pot synthesis of polyaniline/Fe3O4 nanocomposite in 1-methyl-3-alkylcarboxylic acid imidazolium chloride ([CMMIm]Cl) ionic liquid (IL) was introduced for the first time in this work. Transmission electron microscopy (TEM), X-ray diffraction (XRD), four probes method and vibrating sample magnetometer (VSM) were used to explore the influence of IL on the structure, conductivity and magnetic properties of polyaniline/Fe3O4 composite. Compared with Fe3O4 particles prepared in water, the results show that Fe3O4 particles prepare in imidazolium-based ionic liquid were more regular in shape and dispersed uniformly. So the Fe3O4 nanoparticles prepared in IL can easier serve as cores to form the polyaniline/Fe3O4 nanocomposite via in situ chemical oxidative polymerization of aniline molecule. The saturation magnetization of polyaniline/Fe3O4 nanocomposite prepared in ionic liquid shows about 2 times higher than polyaniline/Fe3O4 composite prepared in water. And the conductivities of PANI/Fe3O4 composite prepared in IL decreased and the saturated magnetization increased with the increasing amount of Fe3O4

Experimental research on oil film thickness and its microwave scattering during emulsification

Synthetic Aperture Radar (SAR) plays a major role in identifying oil spills on the sea surface. However, obtaining information of oil spill thickness (volume) is still a challenge. Emulsification is an important process affecting the thickness and normalized radar cross section (NRCS) of oil film. Experiments of crude oil emulsification with C-band fully-polarized scatterometer were conducted combining airborne hyperspectral imaging spectrometer and 3D laser scanner observation data, to provide experimental parameters and method to support accurate remote sensing monitoring on marine oil spill. It is further proved that through quantitative homogeneous emulsified oil spill experiments, to a certain extent, the NRCS of oil film increased during the emulsification process of crude oil. The backscattering mechanism of crude oil emulsification was explored using a semi-empirical model (SEM); the change of oil film NRCS was modulated by its dielectric constant and surface roughness, in which the dielectric constant showed a dominant effect. The relationship between thickness and NRCS of oil film was studied under two experimental conditions. The differences of NRCS between oil film and adjacent seawater (Delta sigma(0)) and the damping ratio (DR) were found to have a linear relationship with oil thickness, which were best in the vertical polarization mode (VV) at 45 degrees incident angle during the quantitative crude oil homogeneous emulsification process. In the natural emulsification process of continuous oil spill in which oil film was mixed with both crude oil and emulsified oil, an empirical equation of oil film thickness is preliminarily established. The Delta sigma(0), DR, and the empirical equation of oil film thickness were applied to the marine continuous oil spill incident on a 19-3 oil platform with spaceborne SAR image and successfully explained the distribution of the relative thickness of the oil film

Smart recommendation for tourist hotels based on multidimensional information: a deep neural network model

Most hotel recommendation systems currently rely on text-based information or meta-data. We develop a deep network recommendation model with three modalities–picture, review, and scoring.We propose a unifified deep neural network including an embedding layer, pooling layer, and fully connected layer. Comparing with other algorithms, we verify its efficacy in improving travel recommendations based on the hotel data crawled from Ctrip and the major evaluation indicators. Our study contributes to the literature by building a knowledge model for tourist hotels based on the analysis of user-generated data and providing practical guidance for hotel managers and users