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Indirect matrix converters (IMC) feature an AC-AC configuration without an intermediate DC link and wide application prospects in wind power generation, AC transmission, and various other fields, known for their sinusoidal input/output, high power density, and superior operating efficiency. However, these converters inevitably generate common-mode voltages during modulation operations, causing motor damage and affecting the reliable functioning of the systems. This paper aims to enhance the operational reliability of IMCs by suppressing common-mode voltage output, and focuses on five classical modulation methods for suppressing common-mode voltage peaks by 42.3% in IMCs, including the modulation method for synthesizing reference output voltage vectors using adjacent four effective voltage vectors at the inverter stage, that for synthesizing reference output voltage vectors using adjacent three effective voltage vectors at the inverter stage, that for synthesizing reference current vectors using adjacent three effective current vectors at the rectifier stage, that for synthesizing reference current vectors using two adjacent 120° effective current vectors plus a zero-current vector at the rectifier stage, and that for synthesizing reference current vectors using weighted three effective current vectors at the rectifier stage. By modeling the effective value (root mean square, rms) of output common-mode voltages, which incorporates the influence of voltage transfer ratios on these values, a quantitative analysis was conducted to explore the variation patterns of the rms value of common-mode voltages. Following this, an evaluation was performed on the suppression capability of different modulation methods on common-mode voltages. The comparative analysis results indicated that the space vector modulation method of synthesizing reference voltage using adjacent four effective voltage vectors at the inverter stage exhibited the best suppression effect on the rms value of common-mode voltages in IMCs. Additionally, the correctness of the theoretical analysis results was verified through simulations and experiments.

Abstract Background Hormone receptor‐positive (HR+)/human epidermal growth factor receptor 2‐positive (HER2+) breast cancer exhibits considerable heterogeneity, and it is of great interest whether patients with premenopausal HR+/HER2+ breast cancer treated with trastuzumab can benefit from ovarian function suppression (OFS) therapy similarly to HR+/HER2‐ breast cancer. Here, we conducted a real‐world study in this population to identify both who would derive substantial benefits from the addition of OFS and clinicopathological factors with potential prognostic value. Methods Multicenter data from 253 premenopausal patients with HR+/HER2+ early‐stage breast cancer who received trastuzumab from October 2009 to October 2018 were retrospectively included. The Kaplan–Meier method was used for survival analysis, while the log‐rank test was used to compare the survival rates. Univariate and multifactor Cox regression analyses were performed to analyze the independent risk factors affecting invasive disease‐free survival (IDFS). Results After a median follow‐up of 98.50 months, compared with tamoxifen/toremifene alone, tamoxifen/toremifene/aromatase inhibitors plus OFS demonstrated significant benefits in the overall study population (HR = 0.289, 95% CI: 0.100–0.835, p = 0.022, 8‐year IDFS rate: 90.78% vs. 95.54%), especially in the lymph node‐positive subgroup and age ≤40 years subgroup. Age ≤40 years, histological grade >2, lymph node involvement, PR ≤50%, and tamoxifen alone were independent prognostic factors. Conclusions For premenopausal HR+ breast cancer patients, HER2 positivity alone is an indication for the addition of OFS in adjuvant endocrine therapy. Age, histological grade, lymph node status, the expression of PR, and OFS treatment were independent prognostic factors in this population.

In recent years, microfluidic bioreactors have become important in chemical production; however, the production cost is high, and it becomes difficult to balance scale, efficiency, and cost. Therefore, a low-cost, efficient, high-delivery-capacity, green-fluid reactor is urgently required. Herein, a cellulose bioreactor with dual-scale directional channels and a porous inner wall is reported for the continuous catalytic conversion of polydatin into resveratrol. The dual-scale directional channel structure endows the reactor with high mass transfer speed and large transport capacity, resulting in fast fluid diffusion rate and more severe "disturbance" inside the reactor. It accelerates the mass transfer state of the whole reaction and further improves the catalytic performance compared to the uniform channel reactor. The reactor can achieve a high conversion rate of 98.58% of resveratrol within 2 h and exhibits excellent stability, owing to which it has broad application prospects in catalytic reactions, and helps derive a new approach for constructing fluid catalysis bioreactors. [ABSTRACT FROM AUTHOR]

This paper explores the variations in cross-correlations among the carbon trading market, geopolitical risk (GPR) and economic policy uncertainty (EPU), focusing on their multifractality and asymmetric properties. Therefore, the study employs the multifractal detrended cross-correlation analysis (MF-DCCA) and multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) approaches to examine these relationships from a three-dimensional perspective. Also, the study looks into cross-correlations at different phases of carbon trading. Our findings reveal that the multifractal cross-correlations between the carbon trading market and GPR or EPU exhibit anti-persistence and asymmetry. The anti-persistent cross-correlations are notably stronger when GPR experiences a downtrend or when EPU undergoes an uptrend. Furthermore, distinct fluctuation patterns emerge across various trends and scales on Hurst surfaces. In the short term, the carbon trading market exhibits heightened sensitivity to changes in GPR or EPU when they rise or fall. The cross-correlations remain multifractal, anti-persistent and asymmetric across different stages of carbon trading. Notably, the multifractality of cross-correlations is most pronounced for the series pair EUA/GPR in Phase II and for the series pair EUA/UK EPU in Phase III and Phase IV. Except for the series pair EUA/UK EPU in Phase II, the anti-persistent cross-correlations are more pronounced during uptrend than downtrend for the other series pairs. Moreover, the series pair EUA/UK EPU exhibits the highest degree of asymmetry at all stages. [ABSTRACT FROM AUTHOR]

The segmentation of abnormal regions is vital in smart manufacturing. The blurring sauce-packet leakage segmentation task (BSLST) is designed to distinguish the sauce packet and the leakage's foreground and background at the pixel level. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination. This issue adversely affects segmentation performance, thereby hindering the measurements of leakage area and impeding the automated sauce-packet production. To alleviate this issue, we propose the two-stage illumination-aware sauce-packet leakage segmentation (ISLS) method for intelligent sensors. The ISLS comprises two main stages: illumination-aware region enhancement and leakage region segmentation. In the first stage, YOLO-Fastestv2 is employed to capture the Region of Interest (ROI), which reduces redundancy computations. Additionally, we propose image enhancement to relieve the impact of uneven illumination, enhancing the texture details of the ROI. In the second stage, we propose a novel feature extraction network. Specifically, we propose the multi-scale feature fusion module (MFFM) and the Sequential Self-Attention Mechanism (SSAM) to capture discriminative representations of leakage. The multi-level features are fused by the MFFM with a small number of parameters, which capture leakage semantics at different scales. The SSAM realizes the enhancement of valid features and the suppression of invalid features by the adaptive weighting of spatial and channel dimensions. Furthermore, we generate a self-built dataset of sauce packets, including 606 images with various leakage areas. Comprehensive experiments demonstrate that our ISLS method shows better results than several state-of-the-art methods, with additional performance analyses deployed on intelligent sensors to affirm the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

The growing preference for incorporating microbial aspartic proteases in industries is due to their high catalytic function and high degree of substrate selectivity. These properties, however, are attributable to molecular alterations in their structure and a variety of other characteristics. Molecular tools, functional genomics, and genome editing technologies coupled with other biotechnological approaches have aided in improving the potential of industrially important microbial proteases by addressing some of their major limitations, such as: low catalytic efficiency, low conversion rates, low thermostability, and less enzyme yield. However, the native folding within their full domain is dependent on a surrounding structure which challenges their functionality in substrate conversion, mainly due to their mutual interactions in the context of complex systems. Hence, manipulating their structure and controlling their expression systems could potentially produce enzymes with high selectivity and catalytic functions. The proteins produced by microbial aspartic proteases are industrially capable and far-reaching in regulating certain harmful distinctive industrial processes and the benefits of being eco-friendly. This review provides: an update on current trends and gaps in microbial protease biotechnology, exploring the relevant recombinant strategies and molecular technologies widely used in expression platforms for engineering microbial aspartic proteases, as well as their potential industrial and biotechnological applications. [ABSTRACT FROM AUTHOR]

BACKGROUND: With the requirements of environmental, cost and economic sustainability, new sources of alternative proteins in the livestock industry are receiving increasing attention. Mulberry (Morus alba L.) leaves are a unique feed resource because of their high protein content and large availability. Therefore, mining sustainable protein suitable for the animal husbandry industry in sericulture resources could achieve a win–win situation. RESULTS: The protein content in mulberry leaves is 232.10–386.16 g kg−1, and the mean value of crude fat content is 43.76 ± 8.48 g kg−1, which has the advantages of protein content and energy. In addition, the average content of phytic acid in mulberry leaves is only 1.88 ± 0.56 g kg−1, which means that it is not inhibited in terms of nutrient absorption. Meanwhile, the digestibility of protein was Bean pulp > Sample 8 ≈ Alfalfa ≈ Sample 13 ≈ Cottonseed meal > Fish meal, and the β‐turn and particle size of mulberry leaf protein are more conducive to digestion in vitro. Furthermore, the protein of Sample 13 had the richest essential amino acids (252.00 g kg−1) and the highest essential amino acid index (EAAI), which was superior to conventional feed protein. In addition, the partial substitution of mulberry leaf protein (15%) significantly increased the EAAI value of conventional feed protein. However, to balance nutrition, it is necessary to combine mulberry leaf protein with other proteins to further broaden its application field. CONCLUSION: Mulberry leaves are a new source of feed protein, which helps to alleviate the two major problems of mulberry resource surplus and feed protein resource shortage. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

According to the traditional blasting theory and practice, the slotted cartridge blasting can form straight cracks extending along the slot direction and achieve directional fracture. In this paper, an experimental study has been carried out to determine the influence of incident stress waves and reflected stress waves. Under the influence of stress waves, the propagation path of the main crack in slotted cartridge blasting is not straight, and the directional fracture effect is inadequate. Under the experimental conditions in this paper, the propagation of main cracks mainly experienced three stages: directional propagation stage, counterclockwise deflecting propagation stage, and clockwise deflecting propagation stage. The difference between incident stress waves and reflected stress waves results in the difference in the deflecting direction, transforming the main crack into a wing shape. In rock roadway drilling and blasting engineering, the propagation path of main cracks produced by peripheral boreholes using the slotted cartridge blasting is also affected by the incident and reflected stress waves. These stress waves are inevitably generated from free faces and adjacent boreholes; however, these are rarely considered in the design of blasting parameters. Therefore, the design and optimization of blasting parameters considering the interaction of stress waves and main cracks in the slotted cartridge blasting need to be further developed. [ABSTRACT FROM AUTHOR]

Learning-enabled spectroscopic analysis, promising for automated real-time analysis of chemicals, is facing several challenges. Firstly, a typical machine learning model requires a large number of training samples that physical systems can not provide. Secondly, it requires the testing samples to be in range with the training samples, which often is not the case in the real world. Further, a spectroscopy device is limited by its memory size, computing power, and battery capacity. That requires highly efficient learning models for on-site analysis. In this paper, by analyzing multi-gas mixtures and multi-molecule suspensions, we first show that orders of magnitude reduction of data dimension can be achieved as the number of principal components that need to be retained is the same as the independent constituents in the mixture. From this principle, we designed highly compact models in which the essential principal components can be directly extracted from the interrelations between the individual chemical properties and principal components; and only a few training samples are required. Our model can predict the constituent concentrations that have not been seen in the training dataset and provide estimations of measurement noises. This approach can be extended as an effectively standardized method for principle component extraction.

FAPbI 3 stands out as an ideal candidate for the photoabsorbing layer of perovskite solar cells (PSCs), showcasing outstanding photovoltaic properties. Nonetheless, stabilizing photoactive α-FAPbI 3 remains a challenge due to the lower formation energy of the competitive photoinactive δ-phase. In this study, we employ tetraethylphosphonium lead tribromide (TEPPbBr 3 ) single crystals as templates for the epitaxial growth of PbI 2 . The strategic use of TEPPbBr 3 optimizes the evolution of intermediates and the crystallization kinetics of perovskites, leading to high-quality and phase-stable α-FAPbI 3 films. The TEPPbBr 3 -modified perovskite exhibits optimized carrier dynamics, yielding a champion efficiency of 25.13% with a small voltage loss of 0.34 V. Furthermore, the target device maintains 90% of its initial PCE under maximum power point (MPP) tracking over 1000 h. This work establishes a promising pathway through single crystal seed based epitaxial growth for achieving satisfactory crystallization regulation and phase stabilization of α-FAPbI 3 perovskites toward high-efficiency and stable PSCs.