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In the process industry, an alarm system is one of the important ways of condition monitoring. Due to the complexity and irregularity of process information in condition monitoring, there are too many false alarms in the current alarm system. In order to solve the problem of designing an alarm system, this paper proposes a multivariate alarm design method based on the evidence reasoning (ER) rule, considering interval-valued reliability, which can make full use of process information to make accurate alarm decisions. Firstly, the referential evidence matrixes (REMs) are constructed based on the training samples of process variables, and the real-time samples of the process variables are converted into alarm evidence by activating the REMs. Alarm evidence is then fused by the ER rule. In this fusion process, in order to better describe the uncertainty of the process information, the reliability of the alarm evidence is characterized by random variables with certain probability distributions, and it can be adjusted in dynamic intervals according to the real-time change of alarm evidence. Finally, the reactor fault case is implemented in the Tennessee Eastman (TE) process, which shows that the adjustment of interval-valued reliability can adapt to the irregular change of process information and obtains consistent alarm results to further improve the accuracy of alarm decisions. [ABSTRACT FROM AUTHOR]

The chemical compositions of zircons have been used as magma fertility indicators for porphyry copper mineralization potential. Here, we trained and tested two machine learning (ML) models: support vector machines and random forest to classify the metallogenic fertility (fertile vs. unfertile) based on igneous zircon chemistry. Both models achieved good classification accuracy (~ 93%) against the testing dataset, suggesting the feasibility and effectiveness of using the ML methods to discriminate whether zircons are from fertile or unfertile magmas. The feature importance analysis of the RF classifiers identified that the most important parameters of fertile magma are Eu, Ce, and Dy, further validating previous studies of the genesis of porphyry copper deposit-related magma. As a case study, we predicted the metallogenic fertility of the detrital zircons in the eastern Gangdese belt in southern Tibet by applying the ML models trained by igneous zircon labeled data. Our ML models suggest that the magmas that formed after 50 Ma, especially from 30 to 10 Ma, have higher fertile percentages. Taking this together with the local singularity analysis of detrital zircon U–Pb ages, we suggest that magmatic flare-ups with high FP and singularity index values are more favorable for porphyry copper formation. Our ML models and defined FP may help reveal temporal variation of the favorability for porphyry copper formation. [ABSTRACT FROM AUTHOR]

Nonpharmaceutical interventions (NPIs) have been commonly deployed to prevent and control the spread of the coronavirus disease 2019 (COVID-19), resulting in a worldwide decline in influenza prevalence. However, the influenza risk in China warrants cautious assessment. We conducted a cross-sectional, seroepidemiological study in Shandong Province, Northern China in mid-2021. Hemagglutination inhibition was performed to test antibodies against four influenza vaccine strains. A combination of descriptive and meta-analyses was adopted to compare the seroprevalence of influenza antibodies before and during the COVID-19 pandemic. The overall seroprevalence values against A/H1N1pdm09, A/H3N2, B/Victoria, and B/Yamagata were 17.8% (95% CI 16.2%–19.5%), 23.5% (95% CI 21.7%–25.4%), 7.6% (95% CI 6.6%–8.7%), and 15.0 (95% CI 13.5%–16.5%), respectively, in the study period. The overall vaccination rate was extremely low (2.6%). Our results revealed that antibody titers in vaccinated participants were significantly higher than those in unvaccinated individuals (P < 0.001). Notably, the meta-analysis showed that antibodies against A/H1N1pdm09 and A/H3N2 were significantly low in adults after the COVID-19 pandemic (P < 0.01). Increasing vaccination rates and maintaining NPIs are recommended to prevent an elevated influenza risk in China. [ABSTRACT FROM AUTHOR]

AAV-delivered CRISPR/Cas9 (AAV-CRISPR) has shown promising potentials in preclinical models to efficiently insert therapeutic gene sequences in somatic tissues. However, the AAV input doses required were prohibitively high and posed serious risk of toxicity. Here, we performed AAV-CRISPR mediated homology-independent knock-in at a new target site in mAlb 3'UTR and demonstrated that single dose of AAVs enabled long-term integration and expression of hF9 transgene in both adult and neonatal hemophilia B mice (mF9 −/−), yielding high levels of circulating human Factor IX (hFIX) and stable hemostasis restoration during entire 48-week observation period. Furthermore, we achieved hemostasis correction with a significantly lower AAV dose (2 × 109 vg/neonate and 1 × 1010 vg/adult mouse) through liver-specific gene knock-in using hyperactive hF9R338L variant. The plasma antibodies against Cas9 and AAV in the neonatal mice receiving low-dose AAV-CRISPR were negligible, which lent support to the development of AAV-CRISPR mediated somatic knock-in for treating inherited diseases. Here, the authors develop an AAV-CRISPR mediated somatic knock-in. They apply this system to restore hemostasis in neonatal hemophilia B mice and show liver-specificity of the knock-in and low serum antibody production. [ABSTRACT FROM AUTHOR]

Developing new strategies to enhance adsorbents' gas separation performance is highly desirable. In this contribution, we created a post-synthetic modification approach to enhance the C2H2/CO2 separation performance for an ultramicroporous MOF (NKMOF-2) platform that underwent a single-crystal-to-single-crystal in the counter anion exchange process. Substitution of Cl− with BF4− not only greatly improved the water stability of the NKMOF-2 platform but also enhanced their adsorption capacity and separation performance. Mixture gas breakthrough experiments revealed that the anion exchange strategy was a powerful approach for boosting the selective adsorption of C2H2 over CO2. Moreover, we first observed that the C2H2/CO2 separation performance could be enhanced dramatically by elevating the test temperature. This study not only enriches the toolbox to create efficient MOF adsorbents for gas separation but also provides a fundamental understanding of the enhanced gas uptake and separation performance. [ABSTRACT FROM AUTHOR]

Vertical track irregularity is one of the most significant indicators to evaluate track health. Accurate identification of vertical track irregularity is beneficial to achieve precise maintenance of the track and thus avoid accidents. However, the continuous variation of the track irregularity and the imbalance of the abnormal/normal data samples make it difficult to guarantee the accuracy of identification models. Therefore, by considering the interaction between train and track, a multi-level evidential reasoning (M-ER) rule model is proposed to build the nonlinear causal relationship of vibration signals and vertical track irregularity. In the modeling process of M-ER, the referential evidence matrix (REM) and fusion parameters (i.e., reliability factors and importance weights) are determined and optimized. In the model, the reliability factor of evidence is determined through trend analysis, while the importance weights of evidence and REM are optimized by sequential quadratic programming (SQP). In the inference process of M-ER, sample expansion strategy and two-level evidence fusion mechanism are designed. Specifically, in the first level, samples on each vibration signal are fused with their nearest neighboring historical samples obtained by K-Nearest Neighbor(K-NN) method. In the second-level, the results generated in the first-level are integrated by ER rule. We evaluate the M-ER rule model with an actual data set from China railway. The experimental results show that the model can identify the vertical track irregularity more accurately compared with the single-level ER rule model and other typical machine learning based models. [ABSTRACT FROM AUTHOR]

In order to move towards sustainable development, the discovery of energy-efficient and environmentally friendly materials has become increasingly imperative. Covalent organic frameworks (COFs) as emerging designable crystalline porous materials have captured increasing attention for a wide array of clean-energy and environmental applications, attributed to their attractive advantages of low density, high surface area, adjustable and periodic pores, and functional skeletons. This review attempts to highlight the key advancements made in the green synthesis of COFs, processing of COFs, energy and environment-related applications, including gas storage, water treatment, the separation of gas mixture and organic molecules, catalysis, supercapacitors, fuel cell, and rechargeable batteries. Finally, a perspective regarding the remaining challenges and future directions on the synthesis and promising application for green chemical engineering of COFs has also been presented based on current achievements. [ABSTRACT FROM AUTHOR]

Covalent organic frameworks (COFs)‐based photocatalysts have received growing attention for photocatalytic hydrogen (H2) production. One of the big challenges in the field is to find ways to promote energy/electron transfer and exciton dissociation. Addressing this challenge, herein, a series of olefin‐linked 2D COFs is fabricated with high crystallinity, porosity, and robustness using a melt polymerization method without adding volatile organic solvents. It is found that regulation of the spatial distances between the acceptor units (triazine and 2, 2'‐bipyridine) of COFs to match the charge carrier diffusion length can dramatically promote the exciton dissociation, hence leading to outstanding photocatalytic H2 evolution performance. The COF with the appropriate acceptor distance achieves exceptional photocatalytic H2 evolution with an apparent quantum yield of 56.2% at 475 nm, the second highest value among all COF photocatalysts and 70 times higher than the well‐studied polymer carbon nitride. Various experimental and computation studies are then conducted to in‐depth unveil the mechanism behind the enhanced performance. This study will provide important guidance for the design of highly efficient organic semiconductor photocatalysts. [ABSTRACT FROM AUTHOR]

The key task of fault diagnosis is to establish the nonlinear mapping relationship between fault feature sequences (FFSs) and fault modes (FMs). Therefore, it is usually necessary to detect transit jump points of FFS to separate feature samples with the different trends. These samples and the their fault labels are combined to form training samples for a fault diagnosis model (FDM). However, due to changes in fault status and uncertainty in measurement, some feature samples will imprecisely point to multiple FMs (fault labels), hardly adopted in FDM. In order to solve such imprecision problem, this paper presents an information fusion method via One vs Rest (OvR) evidence classifiers. For each FFS, the multiple OvR evidence classifiers are designed to model the imprecise relationship between the samples and FMs. Then, a two-level fusion framework is proposed to integrate the evidence of all FFSs. By using the intersection operation between One (one FM) and Rest (the other FMs), the imprecision can be significantly reduced. A rotating machinery fault diagnosis experiment is given to illustrate that the proposed method have better fault classification accuracy compared with the traditional FDMs including SVM, BPNN, KNN, FCNN, RF and Bayes. • To address the challenge of modeling imprecise feature samples in fault classification, the OvR-REM method is introduced to accurately represent the relationship between fault features and modes. • Based on the ER rule, a two-level fusion mechanism is proposed to integrate the OvR evidence classifiers of all "perfect" and "imperfect" features, the imprecision can be significantly reduced. • Experimental results from rotating machinery fault diagnosis demonstrate the proposed method's superior classification accuracy compared to traditional models like SVM, BPNN, KNN, FCNN, RF and Bayes. [ABSTRACT FROM AUTHOR]

• The temperature field model of heterogeneous materials was simulated by the Level-Set method. • The mechanisms of material removal and the formation of thermal damage were studied. • We proposed a spray-mist-assisted laser processing of the CFRP based on the cooling characteristics of the water–air. • The state of balance between cold and heat has been realized by optimizing the process. To regulate the balance between heat and cold during laser processing, a method of combining water and air to form a spray-mist is proposed to assist in the laser processing of CFRP. The characteristics of the temperature field and flow field distribution are analyzed by the finite element model. And the machining mechanism is revealed. The results show that the material will be removed by vaporization under the coupling of heat accumulation and heat conduction. The heat-affected zone (HAZ) will be generated when the removal of heterogeneous material is inconsistent. The temperature of the material steam is up to 2000 K, so the timely removal of the high-temperature steam can effectively reduce the HAZ. The uneven thickness distribution of the water film formed by the spray mist impacting the wall will change the laser distribution. Fortunately, there exists a region suitable for laser processing where the water film thickness is about 100 μm. It can provide effective cooling and a low laser power loss rate. Finally, the balance between heat and cold is realized by regulating the heat input, reducing the HAZ. This study can provide a reference for laser processing of composite materials with low damage. [ABSTRACT FROM AUTHOR]