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In the realm of five-axis Computer Numerical Control (CNC) machining, the challenge of minimizing velocity, acceleration, and jerk fluctuations has prompted the development of various local corner smoothing methods. However, existing techniques often rely on symmetrical spline curves or impose pre-set transition length constraints, limiting their effectiveness in reducing curvature and maintaining velocity at critical corners. To comprehensively address these limitations comprehensively, a novel approach for local smoothing of five-axis linear toolpaths is presented in this paper. The proposed method introduces two asymmetrical B-splines at corners, effectively smoothing both the tool-tip position in the workpiece coordinate system and the tool orientation in the machine coordinate system. To fine-tune transition curve scales and minimize velocity disparities between adjacent corners, an overlap elimination scheme is employed. Furthermore, the two-step strategy emphasizes the synchronization of tool-tip position and tool orientation while considering maximal approximation error. The outcome is a blended five-axis toolpath that significantly reduces curvature extremes in the smoothed path, achieving reductions ranging from 36.28 % to 45.51 %. Additionally, the proposed method streamlines the interpolation process, resulting in time savings ranging from 5.68 % to 8.78 %, all the while adhering to the same geometric and kinematic constraints. [ABSTRACT FROM AUTHOR]

Highlights: [•] Whole-cell biocatalyst was used to catalyze the transesterification. [•] Study on reaction mode was performed for improving PG yield. [•] This study presented an ecofriendly and efficient way for propyl gallate production. [ABSTRACT FROM AUTHOR]

Purpose: Previously, we demonstrated that heavy ions kill more cells at the same dose than X-rays because DNA-clustered lesions produced by heavy ions affect nonhomologous end-joining (NHEJ) repair but not homologous recombination repair (HRR). We have also shown that our designed artificial microRNAs (amiRs) could efficiently target XRCC4 (an essential factor for NHEJ) or XRCC2 (an essential factor for HRR) and sensitize human tumor cells to X-rays. Based on these data, we were interested in testing the hypothesis that combining heavy ions and amiRs to target HRR but not NHEJ should more efficiently kill human tumor cells. Methods and Materials: Human tumor cell lines (U87MG, a brain tumor cell line, and A549, a lung cancer cell line) and their counterparts, overexpressed with amiR to target XRCC2, XRCC4 or both, were used in this study. Survival sensitivities were examined using a clonogenic assay after these cells were exposed to X-rays or heavy ions. In addition, these cell lines were subcutaneously injected into nude mice to form xenografts and the tumor size was compared after the tumor areas were exposed to X-rays or heavy ions. Results: Although targeting either XRCC4 (NHEJ factor) or XRCC2 (HRR factor) sensitized the human tumor cells to X-rays, in vitro and the xenograft animal model, targeting only XRCC2 but not XRCC4 sensitized the human tumor cells to heavy ions in vitro and in the xenograft animal model. Conclusions: Combining heavy ions with targeting the HRR pathway, but not the NHEJ pathway, could significantly improve the efficiency of tumor cell death. [Copyright &y& Elsevier]

Abstract: This paper deals with the problem of computing Lyapunov functions for asymptotic stability analysis of autonomous polynomial systems of differential equations. We propose a new semi-algebraic approach by making advantage of the local property of the Lyapunov function as well as its derivative. This is done by first constructing a semi-algebraic system and then solving this semi-algebraic system in an adaptive way. Experiment results show that our semi-algebraic approach is more efficient in practice, especially for low-order systems. [Copyright &y& Elsevier]

Abstract: In this paper, we first generalize reachability constraints for linear differential equations, then tighten them by using the information of multiplicity of the eigenvalues and eigenvectors of such equations and put them into our constraint based approach for safety verification of hybrid systems that employs recursive reasoning to improve the method of constraint propagation based abstraction refinement. [Copyright &y& Elsevier]

• Proposed a novel maintenance cycle predictive framework for large energy equipment based data-oriented. • The multi-level adversarial domain adaptation model generated a feature subspace, ensuring the transfer foundation of degradation knowledge. • The federated learning strategy improved predictive performance with limited training samples while ensuring data security. Failures in natural gas compressor units can lead to significant production incidents that impact energy security, making intelligent preventative maintenance of this equipment critically important. Given the limited and sensitive data from critical energy equipment like natural gas compressor units, this paper conducts preventative maintenance research based on domain adaptation and federated learning (DFPM), and proposes a novel maintenance cycle prediction framework. The feasibility of the proposed method was validated through historical operational data from field natural gas compressor units, providing a data-oriented reference for maintenance decision-making. Verification results demonstrate that the proposed multi-level adversarial domain adaptation method possesses superior feature representation capability, enhancing the preservation of the original degradation trend for time series data compared to traditional DA models. The novel federated adversarial strategy presented in this paper can effectively transfer source domain knowledge to the target domain, further enhancing the cross-unit predictive performance of the target domain model with limited samples. Additionally, this strategy with the chaotic multi-mode predictive model further improves the limitations of low parallelism and efficiency in traditional RNN-based prediction models while ensuring data security. This research provides a new perspective for the intelligent operation and maintenance of large energy equipment, which is of significance in practical engineering. [ABSTRACT FROM AUTHOR]

• A facile and efficient approach was used to prepare magnetic nanocomposite. • The immobilized NovQ exhibits good tolerance towards pH and temperature. • Immobilized NovQ displays improved storage stability and reusability. • The nanoparticles occupy an inessential domain away from active center of NovQ. Prenyltransferase NovQ immobilized carboxymethyl cellulose magnetic nanoparticles (NCMNs) were successfully synthesized via a valuable approach integrated from nanocomposite preparation, and applied for the production of vitamin K 2 using menadione hydroquinol and dimethylallyl diphosphate (DMAPP) as substrates. To investigate the interaction between nanoparticles and NovQ, we characterized the nanocomposite, and revealed that carboxymethyl cellulose (CMC) and Fe 3 O 4 formed a core-shell structure to absorb NovQ in the reaction systems, resulting from the high affinity of immobilized materials. Meanwhile, NCMNs with excellent pH and temperature tolerance, enhanced prenylated activity, and improved stability were found. Molecular docking analysis was also conducted to justify the contribution of multiple amino acids and effect of nanoparticles on catalytic properties of NovQ. Taken together, our study introduces a promising strategy to prepare magnetic nanoparticles and improve the performance of catalyst, which aims for opening new orientations for synthesis of magnetic nanoparticles used for prenyltransferase immobilization. [ABSTRACT FROM AUTHOR]

• It is the first report on the purification of menaquinone-7 from Bacillus subtilis natto fermentation. • A large of menaquinone-7 was extracted from wet-cell by two-stage extraction with ethanol. • A simple, economic, and efficient preparation of manquinone-7 from Bacillus subtilis natto fermentation. • The method possesses great potential for large-scale preparation of vitamin K 2. Meanquinone-7 (MK-7) can be produced by the fermentation of Bacillus subtilis (natto), which is known for its nutraceutical and pharmacological properties. This study focused on the separation and purification of meanquinone-7 from wet cells produced by Bacillus subtilis (natto) fermentation. Using ethanol to extract menaquinone-7 twice directly from the crude cells after fermentation produced a menaquinone-7 yield of 1.47mg/g. The crude extract was purified using macroporous resin and subsequent crystallization. After treatment with HPD722 resin, the purification of menaquinone-7 were 7.17-fold increased with recovery yields of 97.2%. Menaquinone-7 with purities of more than 96% were produced with recovery yield of 99.3% by recrystallized with 95% ethanol. The developed procedure can produce high-purity of menaquinone-7 as well as high recoveries. This is an economical, efficient and simple process to large-scale preparation of high-purity menaquinone-7. [ABSTRACT FROM AUTHOR]

Background: The incidence of thyroid cancer is rising steadily because of overdiagnosis and overtreatment conferred by widespread use of sensitive imaging techniques for screening. This overall incidence growth is especially driven by increased diagnosis of indolent and well-differentiated papillary subtype and early-stage thyroid cancer, whereas the incidence of advanced-stage thyroid cancer has increased marginally. Thyroid ultrasound is frequently used to diagnose thyroid cancer. The aim of this study was to use deep convolutional neural network (DCNN) models to improve the diagnostic accuracy of thyroid cancer by analysing sonographic imaging data from clinical ultrasounds.Methods: We did a retrospective, multicohort, diagnostic study using ultrasound images sets from three hospitals in China. We developed and trained the DCNN model on the training set, 131 731 ultrasound images from 17 627 patients with thyroid cancer and 180 668 images from 25 325 controls from the thyroid imaging database at Tianjin Cancer Hospital. Clinical diagnosis of the training set was made by 16 radiologists from Tianjin Cancer Hospital. Images from anatomical sites that were judged as not having cancer were excluded from the training set and only individuals with suspected thyroid cancer underwent pathological examination to confirm diagnosis. The model's diagnostic performance was validated in an internal validation set from Tianjin Cancer Hospital (8606 images from 1118 patients) and two external datasets in China (the Integrated Traditional Chinese and Western Medicine Hospital, Jilin, 741 images from 154 patients; and the Weihai Municipal Hospital, Shandong, 11 039 images from 1420 patients). All individuals with suspected thyroid cancer after clinical examination in the validation sets had pathological examination. We also compared the specificity and sensitivity of the DCNN model with the performance of six skilled thyroid ultrasound radiologists on the three validation sets.Findings: Between Jan 1, 2012, and March 28, 2018, ultrasound images for the four study cohorts were obtained. The model achieved high performance in identifying thyroid cancer patients in the validation sets tested, with area under the curve values of 0·947 (95% CI 0·935-0·959) for the Tianjin internal validation set, 0·912 (95% CI 0·865-0·958) for the Jilin external validation set, and 0·908 (95% CI 0·891-0·925) for the Weihai external validation set. The DCNN model also showed improved performance in identifying thyroid cancer patients versus skilled radiologists. For the Tianjin internal validation set, sensitivity was 93·4% (95% CI 89·6-96·1) versus 96·9% (93·9-98·6; p=0·003) and specificity was 86·1% (81·1-90·2) versus 59·4% (53·0-65·6; p<0·0001). For the Jilin external validation set, sensitivity was 84·3% (95% CI 73·6-91·9) versus 92·9% (84·1-97·6; p=0·048) and specificity was 86·9% (95% CI 77·8-93·3) versus 57·1% (45·9-67·9; p<0·0001). For the Weihai external validation set, sensitivity was 84·7% (95% CI 77·0-90·7) versus 89·0% (81·9-94·0; p=0·25) and specificity was 87·8% (95% CI 81·6-92·5) versus 68·6% (60·7-75·8; p<0·0001).Interpretation: The DCNN model showed similar sensitivity and improved specificity in identifying patients with thyroid cancer compared with a group of skilled radiologists. The improved technical performance of the DCNN model warrants further investigation as part of randomised clinical trials.Funding: The Program for Changjiang Scholars and Innovative Research Team in University in China, and National Natural Science Foundation of China. [ABSTRACT FROM AUTHOR]

Abstract The extinction characteristics of microbial materials have always played an important role in identification of microbial materials. To evaluate the extinction properties of microbial materials comprehensively, a method via combined static and dynamics analysis extinction performance is presented, combining with the reflection spectra measured by a Nicolet FT-IR spectrometer, and transmittance determined by smoke box test of microbial materials in 3 ∼ 5 μ m. In the case of static analysis, the complex refractive indices of microbial spores and mycelia were retrieved by using Kramers-Kronig (KK) transform and an iterative method on the basis of the reflection spectrum exponential extrapolation method. Based on Mie theory, the mass extinction coefficients of microbial spores are 15.58%, 28.07%, and 36.72% greater than those of the corresponding mycelia respectively. While in the dynamic analysis, in the terms of Beer-Lambert law, the mass extinction coefficients of microbial spores are 31.83%, 21.63%, and 68.42% greater than those of the corresponding mycelia respectively. It is shown that mid-infrared extinction performance of microbial spores are always better than those of corresponding microbial mycelia. The results show that the method is practicable and play an important role in finding strong mid-infrared extinction microbial materials. [ABSTRACT FROM AUTHOR]