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With AI and other technologies widely applied, knowledge management paradigms are being systemically reconstructed. How to effectively leverage digital technologies to manage knowledge and activate employees’ innovative behaviors has become key for enterprises’ sustainable development. This article explores the influence pathways of digital knowledge management on employees’ innovative behavior, conducting cross-level transmission mechanism research based on digital knowledge management, organizational learning, and employee innovation behavior. Drawing on 325 questionnaires and hierarchical regression, this study finds that: digital knowledge management positively effects employees’ innovative behavior; exploitative learning mediates more strongly than exploratory learning between digital knowledge management and employees’ innovative behavior; challenging technostress weakens the link between organizational learning and innovation. This paper also uses fsQCA analysis to identify three pathways to high employee innovation behavior: exploration-driven innovation based on full knowledge chain collaboration, dual-driven innovation oriented towards knowledge transformation, and dual-driven innovation oriented towards knowledge sharing. The conclusions of this study are intended to promote the application and development of digital knowledge management in enterprises and provide practical insights for enterprises to foster employee innovation and achieve sustainable development.

Over the past decade, the application of frozen-thawed embryo transfer treatment cycles has increased substantially. Hormone replacement therapy and the natural cycle are two popular methods for preparing the endometrium. Hormone replacement therapy is now used at the discretion of the doctors because it is easy to coordinate the timing of embryo thawing and transfer with the schedules of the in-vitro fertilization lab, the treating doctors, and the patient. However, current results suggest that establishing a pregnancy in the absence of a corpus luteum as a result of anovulation may pose significant maternal and fetal risks. Therefore, a ‘back to nature’ approach that advocates an expanded use of natural cycle FET in ovulatory women has been suggested. Currently, there is increasing interest in how the method of endometrial preparation may influence frozen embryo transfer outcomes specifically, especially when it comes to details such as different types of ovulation monitoring and different luteal support in natural cycles, and the ideal exogenous hormone administration route as well as the endocrine monitoring in hormone replacement cycles. In addition to improving implantation rates and ensuring the safety of the fetus, addressing these points will allow for individualized endometrial preparation, also as few cycles as possible would be canceled.

To evaluate the impact of secondary host selection by the autoparasitoid E. sophia on the fitness and biological control potential of its offspring, we compared the demographic traits, parasitism capacity, and host-feeding rates of populations reared on different secondary hosts: the heterospecific E. formosa and the conspecific E. sophia. Analyses conducted with TWOSEX-MSChart, CONSUME-MSChart, and TIMING-MSChart showed that the population reared on E. formosa and E. sophia as secondary hosts. The E. sophia population reared on E. formosa exhibited significantly shorter developmental times, extended adult longevity, and enhanced female reproductive output, characterized by higher fecundity and longer oviposition periods than the conspecific-reared group. This group also displayed superior host consumption, accelerated population growth, a shorter mean generation time, and improved biocontrol efficacy. These findings underscore the importance of secondary host optimization in mass rearing and offer a theoretical basis for improving the field performance of E. sophia.

In order to enable the calibration model to be effectively transferred among multiple instruments and correct the differences between the spectra measured by different instruments, a new feature transfer model based on partial least squares regression (PLS) subspace (PLSCT) is proposed in this paper. Firstly, the PLS model of the master instrument is built, meanwhile a PLS subspace is constructed by the feature vectors. Then the master spectra and the slave spectra are projected into the PLS subspace, and the features of the spectra are also extracted at the same time. In the subspace, the pseudo predicted feature of the slave spectra is transferred by the ordinary least squares method so that it matches the predicted feature of the master spectra. Finally, a feature transfer relationship model is constructed through the feature transfer of the PLS subspace. This PLS-based subspace transfer provides an efficient method for performing calibration transfer with only a small number of standard samples. The performance of the PLSCT was compared and assessed with slope and bias correction (SBC), piecewise direct standardization (PDS), calibration transfer method based on canonical correlation analysis (CCACT), generalized least squares (GLSW), multiplicative signal correction (MSC) methods in three real datasets, statistically tested by the Wilcoxon signed rank test. The obtained experimental results indicate that PLSCT method based on the PLS subspace is more stable and can acquire more accurate prediction results.

Calibration transfer is an important field for near-infrared (NIR) spectroscopy in practical applications. However, most transfer methods are constructed with standard samples, which are expensive and difficult to obtain. Taking this problem into account, this paper proposes a calibration transfer method based on affine invariance without transfer standards (CTAI). Our method can be utilized to adjust the difference between two instruments by affine transformation. CTAI firstly establishes a partial least squares (PLS) model of the master instrument to obtain score matrices and predicted values of the two instruments, and then the regression coefficients between each of the score vectors and predicted values are computed for the master instrument and the slave instrument, respectively. Next, angles and biases are calculated between the regression coefficients of the master instrument and the corresponding regression coefficients of the slave instrument, respectively. Finally, by introducing affine transformation, new samples are predicted based on the obtained angles and biases. A comparative study between CTAI and the other five methods was conducted, and the performances of these algorithms were tested with two NIR spectral datasets. The obtained experimental results show clearly that, in general CTAI is more robust and can also achieve the best Root Mean Square Error of test sets (RMSEPs). In addition, the results of statistical difference with the Wilcoxon signed rank test show that CTAI is generally better than the others, and at least statistically the same.

In the present study, the structural, mechanical, electronic and optical properties of all-inorganic vacancy-ordered double perovskites A2Sn1−xTixY6 (A = K, Rb, Cs; Y = Cl, Br, I) are explored by density functional theory. The structural and thermodynamic stabilities are confirmed by the tolerance factor and negative formation energy. Moreover, by doping Ti ions into vacancy-ordered double perovskite A2SnY6, the effect of Ti doping on the electronic and optical properties was investigated in detail. Then, according to the requirement of practical applications in photovoltaics, the optimal concentration of Ti ions and the most suitable halide element are determined to screen the right compositions. In addition, the mechanical, electronic and optical properties of the selected compositions are discussed, exhibiting the maximum optical absorption both in the visible and ultraviolet energy ranges; thus, the selected compositions can be considered as promising materials for application in solar photovoltaics. The results suggest a great potential of A2Sn1−xTixY6 (A = K, Rb, Cs; Y = Cl, Br, I) for further theoretical research as well as experimental research on the photovoltaic performance of stable and toxic-free perovskite solar cells.

In order to figure out the migration and deposition of impurities on the first wall of HL-2A/2M tokamak, Peking University and Southwestern Institute of Physics are co-developing a deuteron RFQ as part of the in situ ion-beam diagnostic for the material. The RFQ, which operates at 162.5 MHz, is designed to accelerate a 10-mA deuteron beam from 40 keV up to 1.5 MeV. Key design considerations and the final design parameters are presented. The RFQ has been conditioned at a 1% duty factor for 80 h at RF cavity power of 55 kW. The specific shunt impedance of the cavity is 221 kΩ·m by measuring the bremsstrahlung spectrum. The intrinsic Q-value after the high-power tests measured by the Ring-Down method is 13,780. Beam commissioning has been taken place during the first half of 2021, and the beam measurements include beam current and energy of 2H+ ion. A 10 mA 2H+ beam was successfully accelerated through the RFQ.

With the rapid development of society, the railway system plays an important role in human life, and the safety of railways has become an extremely important task. As we all know, the switch is one of the important equipment to ensure the safe operation of trains. Real-time detection of the turnout current plays a vital role in train safety. However, the previous signal-based processing methods require a large number of feature engineering, which greatly increases the workload of pre-processing. Some neural network-based methods show good performance, but for the time series data of the switch, these methods cannot fully extract its local features, resulting in poor information loss and poor prediction accuracy. Based on the generational confrontation network, this paper proposes a kind of unsupervised anomaly detection system. We combine the one-dimensional convolution with the Generative Adversarial Networks (GAN). The one-dimensional convolution network can effectively extract the local features of the time series. The GAN can self-game learning to sample distribution and is better than self-encoder and other models, which improves the accuracy of prediction. In the real railway system, abnormal data is extremely rare and varied, while unsupervised learning does not require label data, and it can well learn the distribution of normal samples. The system improves the efficiency of the staff, accurately diagnoses the switch, greatly shortens the processing time, and avoids the blindness in maintenance. Using our model on the turnout data, the accuracy rate is 0.992, the recall rate is 0.815, and the F1 score is 0.895.

We here report a rapid, sensitive, selective and label-free fluorescence detection method for cysteine (Cys). The conformation of mercury-specific DNA (MSD) changes from a random coil form to a hairpin structure in the presence of Hg2+ due to the formation of a thymine-Hg2+-thymine (T-Hg2+-T) complex. Cys can selectively coordinate with Hg2+ and extract it from the thymine-Hg2+-thymine complex. The hairpin structure dehybridizes and the fluorescence intensity of Sybr Green I (SG) decreases upon addition of Cys because SG efficiently discriminates mercury-specific DNA and mercury-specific DNA/Hg2+ complex. The detection can be finished within 5 min with high sensitivity and selectivity. In addition, we can obtain variable dynamic ranges for Cys by changing the concentration of MSD/Hg2+.

Gao, S., Chen, J., and Zhou, Y., 2019. Relationship between team knowledge heterogeneity and corporate innovation performance: An empirical study in coastal areas of East China. In: Li, L.; Wan, X.; and Huang, X. (eds.), Recent Developments in Practices and Research on Coastal Regions: Transportation, Environment and Economy. Journal of Coastal Research Journal of Coastal Research, Special Issue No. 98, pp. 320–324. Coconut Creek (Florida), ISSN 0749-0208. This paper studies the internal relationship among team knowledge heterogeneity, organizational learning, and corporate innovation performance in coastal areas of east China. The results show that different heterogeneity characteristics of team members have different effects on corporate innovation performance. There is an inverted U-shaped relationship between knowledge and skills heterogeneity and corporate innovation performance. Organization learning has a positive impact on corporate innovation performance. Exploitive learning plays a part of mediating role in the relationship between team heterogeneity and corporate innovation performance.