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Background Epidemiologic studies examining the relations between dairy product and calcium intakes and breast cancer have been inconclusive, especially for tumor subtypes. Objective To evaluate the associations between intakes of specific dairy products and calcium and risk of breast cancer overall and for subtypes defined by estrogen receptor (ER) status. Method We pooled the individual-level data of over 1 million women who were followed for a maximum of 8–20 years across studies. Associations were evaluated for dairy product and calcium intakes and risk of incident invasive breast cancer overall (n  = 37,861 cases) and by subtypes defined by ER status. Study-specific multivariable hazard ratios (HRs) were estimated and then combined using random-effects models. Results Overall, no clear association was observed between the consumption of specific dairy foods, dietary (from foods only) calcium, and total (from foods and supplements) calcium, and risk of overall breast cancer. Although each dairy product showed a null or very weak inverse association with risk of overall breast cancer (P , test for trend >0.05 for all), differences by ER status were suggested for yogurt and cottage/ricotta cheese with associations observed for ER-negative tumors only (pooled HR = 0.90, 95% CI: 0.83, 0.98 comparing ≥60 g/d with <1 g/d of yogurt and 0.85, 95% CI: 0.76, 0.95 comparing ≥25 g/d with <1 g/d of cottage/ricotta cheese). Dietary calcium intake was only weakly associated with breast cancer risk (pooled HR = 0.98, 95% CI: 0.97, 0.99 per 350 mg/d). Conclusion Our study shows that adult dairy or calcium consumption is unlikely to associate with a higher risk of breast cancer and that higher yogurt and cottage/ricotta cheese intakes were inversely associated with the risk of ER-negative breast cancer, a less hormonally dependent subtype with poor prognosis. Future studies on fermented dairy products, earlier life exposures, ER-negative breast cancer, and different racial/ethnic populations may further elucidate the relation. [ABSTRACT FROM AUTHOR]

• A novel digital twin-driven fault diagnosis method is proposed. • A high-fidelity digital twin model is built for the gearbox. • A physical-virtual data fusion is designed to improve the virtual data quality. • One case study for a truck transmission is conducted. The acknowledged challenge of intelligent fault diagnosis methods is that constructing a reliable diagnosis model requires numerous labeled datasets as training data, which is difficult to collect such high-quality labeled data in the practical industry. The digital twin methodology provides a brand-new and potentially powerful solution to mitigate this challenge. However, during the practical application of digital twin-driven fault diagnosis methods, an information gap can exist between the virtual and physical spaces and poses a hurdle in adopting these methods. Therefore, this paper proposes a novel digital twin-driven approach based on physical-virtual data fusion for gearbox fault diagnosis to enhance the diagnosis performance with insufficient collected fault data. One case study on a truck transmission is carried out. First, a digital twin model of the transmission is established, which can effectively mirror the vibration characteristics and generate vibration data with different health states. Second, a physical-virtual data fusion method based on the Wasserstein generative adversarial networks with gradient penalty is designed to improve the quality of the virtual fault data further. Finally, the virtual fault data through the physical-virtual fusion are used to train a fault diagnosis model. The experimental results indicate that the proposed method significantly enhances the diagnostic performance when few measured fault data from the physical space are available. [ABSTRACT FROM AUTHOR]

Catastrophic forgetting of learned knowledges and distribution discrepancy of different data are two key problems within fault diagnosis fields of rotating machinery. However, existing intelligent fault diagnosis methods generally tackle either the catastrophic forgetting problem or the domain adaptation problem. In complex industrial environments, both the catastrophic forgetting problem and the domain adaptation problem will occur simultaneously, which is termed as continual transfer problem. Therefore, it is necessary to investigate a more practical and challenging task where the number of fault categories are constantly increasing with industrial streaming data under varying operation conditions. To address the continual transfer problem, a novel framework named deep continual transfer learning network with dynamic weight aggregation (DCTLN-DWA) is proposed in this study. The DWA module is used to retain the diagnostic knowledge learned from previous phases and learn new knowledge from the new samples. The adversarial training strategy is applied to eliminate the data distribution discrepancy between source and target domains. The effectiveness of the proposed framework is investigated on an automobile transmission dataset. The experimental results demonstrate that the proposed framework can effectively handle the industrial streaming data under different working conditions and can be utilized as a promising tool for solving actual industrial problem. [ABSTRACT FROM AUTHOR]

Straw used for biogas fermentation raw materials is an important way to reduce the environmental pollution and utilize straw resource. Owing to waxy layer on the straw particles surface during the fermentation process, biogas production and pollutant removal rate is not high enough, restricting the further promotion. In order to achieve high-efficient fermentation and biogas production, new type biogas fermentation material liquid circulation fluidization scheme is proposed in this study. Based on hydraulic mixing, the raw straw surface loose flocculating organization is broken, the dense part is exposed, the surface waxy layer formed in the long time of fermentation is teared, so as to enhance surface contact effect between solid straw and liquid material, increase organic matter and bacteria in the slurry of biochemical reaction, then promote efficiency of anaerobic co-digestion. Through the comparison experiment of three 635.5 L anaerobic digesters, it's found that the high centralized pressure outlet scheme and the high distributed pressure outlet scheme increase the average daily gas production rate by 176% and 253%, the removal rate of total solid (TS) increase by 41% and 69%, and the removal rate of volatile solid (VS) increases by 51% and 71%, respectively, compared with the non-mixing scheme. • New type biogas fermentation circulation fluidization scheme is proposed. • Hydraulic mixing shear stress can remove straw particles surface spore structure. • Hydraulic mixing shear stress can tear surface wax layer. • Hydraulic mixing shear stress can improve anaerobic co-digestion efficiency. [ABSTRACT FROM AUTHOR]

Ganglioside GM3 is well known as a tumor-associated carbohydrate antigen on several types of tumors. Many studies have demonstrated that GM3 plays roles in cells proliferation, adhesion, motility and differentiation, which is involved in the process of cancer development. In the present study, we developed methods to synthesize GM3 analogues conveniently. By enzymatic hydrolysis and chemical procedures, two novel analogues and two known analogues were synthesized, containing lactose and glucosamine. Then anti-proliferation and anti-migration activities were evaluated by cytotoxicity assays and wound healing tests, and the data demonstrated that these analogues exhibited anticancer activities. Based on our previous studies, the structure-activity relationships were discussed. This study could provide valuable sight to find new antitumor agents for cancer therapy. Results exhibited that these analogues can inhibit cancer cell growth and migration. Image 1 • Two novel GM3 analogues containing lactose and glucosamine were synthesized. • GM3 analogues showed antitumor activities by cytotoxicity assays and wound healing tests. • This work provided valuable information to study carbohydrate derivatives as potential agents for cancer therapy. [ABSTRACT FROM AUTHOR]

• A self-adaptive dynamic clustering approach is proposed for automatically selecting and fusing multimodal data. • A cluster-ensemble transfer regression method is developed for RUL prediction under cross working conditions. • A multi-level feature learning strategy is provided to learn the domain-invariant temporal degradation knowledge. • The method outperforms other SOAT RUL prediction methods on N CMAPSS dataset released in 2021. Remaining useful life (RUL) prediction based on multimodal sensing data is indispensable for predictive maintenance of aero-engine under cross-working conditions. Although data-driven methods have emerged as a powerful tool in RUL prediction, it is still limited in industrial applications because the majority of existing methods manually select or fuse multisensory data and ignore the inconsistency of the sensing data collected from different engines. Therefore, an intelligent RUL prediction approach is proposed for aero-engine by integrating multimodal data fusion methodology and ensemble transfer learning technology to dynamically select sensing data and make a robust RUL prediction under cross-working conditions. Specifically, a self-adaptive dynamic clustering approach is developed to select useful multimodal data into different clusters, each of which has a consistent degradation tendency. Furthermore, a cluster-ensemble transfer regression network is constructed by building multiple regressors for different clusters to predict the RUL values of aero-engine under cross-working conditions, where a multi-level feature learning strategy is provided to learn the domain-invariant temporal degradation knowledge. Comparative experiments are conducted on the N CMAPSS dataset released in 2021. The results show that the proposed method outperforms other state-of-the-art RUL prediction methods. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI)-driven fault diagnosis methods are crucial for ensuring rotating machinery's safety and effective operation. The success of most current methods relies on the assumption that sufficient high-quality labeled datasets can be obtained for model training. However, in real-world industrial scenarios, obtaining such datasets is difficult or nearly impossible, thereby hindering the practical implementation of these methods. The integration of virtual modeling and transfer learning offers a powerful approach to meet the above challenge. Abundant virtual data of different fault categories can be acquired in the virtual space with highly flexible and at a low cost, and transfer learning can enhance the practical utility of these virtual data for contributing to the construction of diagnosis models. Therefore, this paper proposes a digital twin-driven partial domain fault diagnosis method based on unlabeled physical data and labeled virtual data. First, a virtual model of rotating machinery is built to generate labeled virtual fault data with enough fault types. Then, an adversarial transfer learning network is developed to leverage the effective knowledge from the virtual and physical data. Meanwhile, a weighting learning module is introduced to reduce the negative effect caused by the redundant fault categories in the virtual space. Finally, the proposed digital twin-driven transfer learning network is trained with the labeled virtual data and unlabeled physical data. Experiments on a light truck transmission system demonstrate that the proposed method achieves satisfactory diagnostic performance even without labeled physical fault data, contributing to the advancement of AI engineering applications. [ABSTRACT FROM AUTHOR]

Decentralized biogas facilities have become an important component of agricultural sectors in Chinese rural area. This study is the first attempt to investigate how transition in Chinese rural areas affects the revolution of decentralized biogas facilities and its sustainability. The characteristics of the household biogas digesters (HBD), simple biogas plants (SBP), and well equipped biogas plants (WEBP) with higher level of technologies were clarified, and their practical feasibility was correspondingly investigated through a comprehensive survey of 15 selected villages. More effort was devoted to the adoption of feedstock availability, energy supply stability, and digestate distribution optimization for each facility. The results showed that feedstock collection and insufficient biogas production were the main restraining factors for HBD, while SBP found to be ineffective facilities for competitive energy production due to the un-guaranteed biogas supply. The survey results demonstrated that the average biogas expenditure of dwellers, who were connected to WEBP, accounted for 39.61% commercial energy cost. According to the one-year operation data collected from a 52-household club revealed that WEBP would effectively maintain stability and continuous biogas supply with the average feeding interval of 6.2 d. Accordingly, WEBP groups were found as the best option for regional development respected with the professional management unit. Overall, commercial biogas is the orientation of decentralized biogas facility construction. However, to form a robust biogas-centric industrial chain, future research should focus on how to establish relative policies and regulations supporting the comprehensive operation framework of a multi-participant collaboration leadership system. • Decentralized biogas facilities in Chinese rural area have been scrutinized. • Household digesters, simple, and equipped plants are prevalent biogas facilities. • Feedstock collection and low gas production were restraining factors in HBD. • WEBPs were found as the best option for regional development. [ABSTRACT FROM AUTHOR]

Abstract Fermentation material fluid mixing is an indispensable subsidiary technology for modern biogas engineering, which can increase fermentation efficiency substantially. Taking advantage of computational fluid dynamics (CFD), the flowing progress of biogas fermentation material fluid in the anaerobic digesters can be reappeared, the distribution curve of flow pattern can be obtained, the visualization of flow pattern can be achieved, the features and defect of flow configuration can be differentiated on the basis of the flow pattern visualization. Scholars quantified and evaluated advantages and disadvantages of fermentation material fluid mixing schemes by using CFD and setting the parameters of "poor mixing zone", "dead zone" and so on, which became the important basis of modern biogas engineering design. Meanwhile, the mixing form and running parameters can be excellently designed and thus no longer added at random. On the basis of the classic k-ε model, researchers have developed a variety of improved models, which are more suitable for complex and diverse conditions of biogas fermentation material fluid. In this present study, the applicable algorithm models of various working conditions are proposed after subdivision research. During the first decade of the 21st century, one research upsurge of simulating biogas fermentation material mixing by using the CFD simulation is growing up in the biogas field, which makes a great progress on the aspect of optimal design of digesters shape, mixing form, mixing running parameters and methods of fluid, promotes the research levels of biogas science and becomes one of the most important stage of the biogas science phylogeny. All of these represent the development direction of future, accordingly, the summary of progress related with above research aspects is given in this present study. Graphical abstract fx1 Highlights • The measured results of many flow field parameters can be fitted with simulation. • The methods of setting the mixing parameters by CFD are listed. • Successful examples of optimization of biogas engineering design by CFD are presented. • The applicable algorithm models of various working conditions are proposed. • The role of CFD in promoting the upgrading of biogas project is discussed. [ABSTRACT FROM AUTHOR]

• A generalized open-set fault diagnosis (OSFD) scenario is defined. • A multiple metric weighting learning network is constructed to address two OSFD issues simultaneously. • Enhanced domain similarity measurement is designed to leverage class label information. • Ensemble uncertainty measurement is developed to reduce misclassification interference. • Twenty-seven OSFD diagnosis tasks are used to evaluate the proposed approach. The problem of practical open-set domain adaptation diagnosis has gained great attention considering unobserved fault categories in target domain. However, existing studies assume that the label space of source domain is a subset of target domain, ignoring that source domain may also contain private fault categories. This generalized open-set diagnosis issue is more challenging, making existing techniques less effective. To tackle this problem, a novel approach is proposed that focuses on addressing two open-set diagnosis issues simultaneously. A multiple metric weighting learning strategy with the integration of an enhanced domain similarity measurement and an ensemble uncertainty measurement is constructed to adaptively weight the importance of samples across domains. Then, weighted adversarial training with multiple metric weight functions is implemented to learn domain-invariant features by performing alignment across different distributions. As such, both unknown and known fault categories can be simultaneously and effectively recognized. Experiments on three bearing datasets are carried out. Results demonstrate the proposed approach can effectively deal with generalized open-set diagnosis tasks, outperforming existing diagnosis approaches. [ABSTRACT FROM AUTHOR]

Six positional isomers of triptolide–glucose conjugates (TG1α, TG1β, TG2, TG3, TG4 and TG6) were designed and synthesized. These conjugates exhibited better water solubility, and had selective cytotoxicity between tumor cells with high expression of glucose transport-1 (Glut-1) and non-tumor cells with low expression of Glut-1, in which TG2 formed by triptolide (TPL) and d -glucose C2–OH had the strongest cytotoxicity to tumor cells and lowest toxicity in non-tumor cells, therefore the highest relative therapeutic index, which was 5.7 times that of triptolide and consequent the most powerful selective antitumor activity in vitro. The cytotoxicity of TG2 was highly correlated with Glut-1 function. As a prodrug of triptolide, TG2 could promote RNA Pol II degradation and induce apoptosis as TPL does. TG2 had a stronger dose-dependent antitumor effect in vivo than TPL and no adverse reaction occurred when its tumor inhibition was higher than 90%, which was associated with its selective distribution in tumor tissues. TG2 could be used as a promising drug candidate for the treatment of solid tumors with high expression of Glut-1, which is worthy of further study. [Display omitted] TG2 conjugated by triptolide and d -glucose C2–OH has a stronger selective antitumor activity than triptolide due to selective transport of TG2 into tumor cells via glucose transporters. • ●Triptolide-Glucose Conjugate TG2 formed by triptolide C14–OH and d -glucose C2–OH. • ●The cytotoxicity of TG2 is highly dependent on Glut-1 function. • ●TG2 distributes in tumor selectively and has a highly selective antitumor activity. [ABSTRACT FROM AUTHOR]