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High-performance p-type silicon target materials of Co-doped B and Al elements were produced using Ga and P contaminated upgraded metallurgical-grade silicon (UMG-Si) at the industrial scale. The purity of silicon ingots is above 5.5 N after the directional solidification process, which meets market demand. The segregation behavior of elements and compensation effect on the resistivity are discussed. The effective segregation coefficients of B, Al, Ga, and P for ingot No. 1 were approximately 0.66, 0.14, 0.38, and 0.49, respectively. The segregation coefficients of P, Ga, and Al become larger, the segregation effect tends to become smaller, which is attributed to the doped and contaminated elements that have the recombination effect on the holes and electrons. The distribution of resistivity can be regulated precisely by the compensation difference [NA–ND] along the solidified fraction. The mean resistivity of the ingots is approximately 0.013 Ω cm. Prolonging melting time is conducive to the uniform distribution of doping elements.

   In order to stabilize the thermal field at solid/liquid (S/L) interface front micro region, a novel method for introducing direct current (DC) to the melt has been proposed. Based on the Joule heat effect, a micro heat source was introduced into the S/L interface front, and the melt temperature of interface front was increased, which was confirmed by the actual measured results. Furthermore, benefit from the optimized thermal field, the stability of the S/L interface during directional solidification (DS) was improved. It was conducive to the crystal growth of mono-like crystalline silicon and the regulation mechanism for DC on crystal growth was discussed. This technology overcomes the bottleneck of micro thermal field control, which is expected to achieve the adjustment of crystal growth for silicon, including mono-like crystalline silicon, cast multi-crystalline silicon, and Czochralski (Cz) single crystalline silicon.

In the production of multicrystalline silicon solar cell, diamond wire sawing method (DWS) is an important technique, which has already completely replaced multiwire slurry sawing (MWSS) method. And the making texture surface is one of the crucial steps for preparing silicon solar cells. Acid etching method with additives is an effective way to make texture surface on DWS multicrystalline silicon wafer surface. The texture structure was obtained in an acid solution by adding NaNO 2 , a mixture solution of PEG and PVA, DBSA and sodium citrate solution, and the volume ratio of HF, HNO 3 and H 2 O for acid solution is 1:5:3. The reflectivity of silicon surface after etching can reduce to 20.06%, and the depth-to-width ratio of texture structure is 0.55. The photovoltaic conversion efficiency of solar cell obtained under this condition is the highest, the maximum value is 18.69%.

A silicon target material with the purity of 99.999 wt% (99.999%) was prepared by adding Al–B master alloy in directional solidification. The segregation behavior of the dopant and the effect on the resistivity were studied in this work. It was revealed that the AlB 2 particles in the Al–B master alloy will generate the clusters of [B] and [Al] in molten silicon at 1723 K spontaneously. The concentrations of B and Al were increasing gradually along the solidified fraction in the silicon ingot. The measured values of B were in good agreement with the curve of the Scheil’s equation below 85% of the solidified fraction. The measured values of Al were fitting well with the curve of the Scheil’s equation when the effective segregation coefficient is 0.00378. It was found that the resistivity of the silicon target material was regulated by B co-doped Al simultaneously in directional solidification.

The silicon target material was prepared by adding Al-6B master alloy in directional solidification. The microstructure was characterized and the resistivity was studied in this work. The results showed that the purity of the silicon target material was more than 99.999% (5N). The resistivity was ranges from 0.002 to 0.030 Ω·cm along the ingot height. It was revealed that the particles of AlB2 in Al-6B master alloy would react spontaneously and generate clusters of [B] and [Al] in molten silicon at 1723 K. After directional solidification, the content of B and Al were increasing gradually with the increase of solidified fraction. The measured values of B were in good agreement with the curve of the Scheil equation below 80% of the ingot height. The mean concentration of B was about 17.20 ppmw and the mean concentration of Al was about 8.07 ppmw after directional solidification. The measured values of Al were fitting well with the curve of values which the effective segregation coefficient was 0.00378. It was observed that B co-doped Al in directional solidification polysilicon could regulate resistivity mutually. This work provides the theoretical basis and technical support for industrial production of the silicon target material.

Large temperature gradient was introduced to improve the removal rate of metal impurity in silicon ingot during direction solidification. The concentration of metal impurities in the silicon ingot with a large temperature gradient is 0.96 ppmw. The solidification time is reduced by 20% due to the fast speed of crystal growth improved; meanwhile the purity is increased by 64%.

The tendency for men to find younger adult women more attractive has been hypothesised to reflect preferences for cues of reproductive potential. To further test this hypothesis, we investigated the possible relationship between heterosexual men and women's reported desire to have children and their preferences for cues of youth in face images of potential mates. Contrary to our expectation that reported desire to have children would be correlated with stronger preferences for younger adult faces, and that this pattern of results would be particularly pronounced when men judged women's attractiveness, both men and women who reported greater desire to have children actually demonstrated weaker preferences for the faces of younger potential mates. Follow-up work suggested that this pattern of results was unlikely to have occurred because individuals with older faces were perceived as more likely to be better parents (Study 2) or wealthier (Study 3). While the role of individual differences in desire to have children in preferences for facial cues of age remains somewhat unclear, our results show no evidence for the widely held view that strong reliable sex differences exist in preferences for cues of youth.

Accurate and rapid estimation of the crop yield is essential to precision agriculture. Critical to crop improvement, yield is a primary index for selecting excellent genotypes in crop breeding. Recently developed unmanned aerial vehicle (UAV) platforms and advanced algorithms can provide powerful tools for plant breeders. Genotype category information such as the maturity group information (M) can significantly influence soybean yield estimation using remote sensing data. The objective of this study was to improve soybean yield prediction by combining M with UAV-based multi-sensor data using machine learning methods. We investigated three types of maturity groups (Early, Median and Late) of soybean, and collected the UAV-based hyperspectral and red–green–blue (RGB) images at three key growth stages. Vegetation indices (VI) and texture features (Te) were extracted and combined with M to predict yield using partial least square regression (PLSR), Gaussian process regression (GPR), random forest regression (RFR) and kernel ridge regression (KRR). The results showed that (1) the method of combining M with remote sensing data could significantly improve the estimation performances of soybean yield. (2) The combinations of three variables (VI, Te and M) gave the best estimation accuracy. Meanwhile, the flowering stage was the optimal single time point for yield estimation (R2 = 0.689, RMSE = 408.099 kg/hm2), while using multiple growth stages produced the best estimation performance (R2 = 0.700, RMSE = 400.946 kg/hm2). (3) By comparing the models constructed by different algorithms for different growth stages, it showed that the models built by GPR showed the best performances. Overall, the results of this study provide insights into soybean yield estimation based on UAV remote sensing data and maturity information.

Facial attractiveness influences important social outcomes and most studies investigating possible predictors of facial attractiveness have tested for effects of shape symmetry, averageness (i.e., the converse of distinctiveness), and sexual dimorphism (i.e., masculinity–femininity). These studies have typically either tested for these possible effects by experimentally manipulating shape characteristics in faces images or have tested only for bivariate correlations between shape characteristics and attractiveness judgments. However, these two approaches have been criticised for lacking ecological validity and providing little insight into the independent contributions of symmetry, averageness, and sexual dimorphism, respectively. Moreover, the few studies that have investigated the independent contributions of symmetry, averageness, and sexual dimorphism have reported mixed results. Here we measured shape symmetry, averageness, and sexual dimorphism from face images and assessed their independent contribution to attractiveness ratings. Linear mixed effects models showed that facial attractiveness was significantly predicted by averageness in male and female faces and femininity in female faces, but not by masculinity in male faces or symmetry. These results are consistent with other recent work suggesting that averageness and femininity, rather than symmetry and masculinity, predict facial attractiveness.

The breeding of high-yield varieties is a core objective of soybean breeding programs, and phenotypic trait-based selection offers an effective pathway to achieve this goal. The aim of this study was to identify the key phenotypic traits of high-yield soybean varieties and to utilize these traits for screening high-yield soybean varieties. In this study, the UAV (unmanned aerial vehicle)- and field-based phenotypic data were collected from 1923 and 1015 soybean breeding plots at the Xuzhou experimental site in 2022 and 2023, respectively. First, the soybean varieties were grouped by using a self-organizing map and K-means clustering to investigate the relationships between various traits and soybean yield and to identify the key ones for selecting high-yield soybean varieties. It was shown that the duration of canopy coverage remaining above 90% (Tcc90) was a critical phenotypic trait for selecting high-yield varieties. Moreover, high-yield soybean varieties typically exhibited several key phenotypic traits such as rapid development of canopy coverage (Tcc90r, the time when canopy coverage first reached 90%), prolonged duration of high canopy coverage (Tcc90), a delayed decline in canopy coverage (Tcc90d, the time when canopy coverage began to decline below 90%), and moderate-to-high plant height (PH) and hundred-grain weight (HGW). Based on these findings, a method for screening high-yield soybean varieties was proposed, through which 87% and 72% of high-yield varieties (top 5%) in 2022 and 2023, respectively, were successfully selected. Additionally, about 9% (in 2022) and 10% (in 2023) of the low-yielding (bottom 60%) were misclassified as high-yielding. This study demonstrates the benefit of high-throughput phenotyping for soybean yield-related traits and variety screening and provides helpful insights into identifying high-yield soybean varieties in breeding programs.