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Abstract Growing evidence confirms the potential of PDE4 inhibitors for the treatment of Parkinson's disease. Our reported PDE4 inhibitors FCPR16 and FCPR03 have displayed neuroprotective effects in SH-SY5Y cells, but have very low oral bioavailability. To access analogues with improved bioavailability, a new series of arylbenzylamine derivatives were designed and synthesized. Preliminary screening results of the series showed that arylbenzylamine derivatives bearing a pyridin-3-amine side chain displayed good inhibitory activities against human PDE4B1 and PDE4D7 isoforms. Moreover, kinetic studies revealed that the most potent compounds 11r and 11s with mid-nanomolar IC 50 values partially bind to PDE4B1 (I max = 93% and 90% respectively). Molecular docking results revealed the possible interactions of compounds 11r and 11s with upstream conserved region 2 (UCR2) of PDE4B1, which illuminate possible reasons for their partial inhibition against PDE4. Using a cell-based model of PD, compounds 11r and 11s were found to alleviate cellular apoptosis in SH-SY5Y cells induced by MPP+ (1-methyl-4-phenylpyridinium), with this neuroprotective effect being greater than PDE4 inhibitor rolipram. Furthermore, compound 11r displayed nearly sevenfold oral bioavailability (8.20%) than FCPR03 (1.23%). Graphical abstract Image 1 Highlights • A series of arylbenzylamines was designed and synthesized as PDE4 inhibitors. • Arylbenzylamines bearing pyridin-3-amine side chain displayed good inhibitory activities. • Compounds 11r and 11s with mid-nanomolar IC 50 value partially bound PDE4B1. • Compounds 11r and 11s alleviated MPP+-induced apoptosis in Parkinson's cell model. • Compared with FCPR03, compound 11r displayed improved oral bioavailability. [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptation methods have recently achieved satisfactory results in detecting mechanical faults with slight class relevance. However, in engineering, decision confusion caused by strong class relevance is ubiquitous. As a typical application neglected in most existing studies, the unsupervised domain adaptation scenario with compound faults considers interrelated and cross-influenced fault types under distribution shift, intensifying class confusion and threatening fault diagnosis reliability inevitably. To this challenge, an innovative sample-level distance metric, termed uncertainty relevance (UR), is proposed to overcome class confusion. Specifically, the metric is constructed from the class relevance matrix and uncertainty weighting to measure discrepancies between predictions, whose max–min optimization enhances discriminability and more tradeoffs on multiclass information. Combined with the metric, a novel gradual inference domain adaptation method is developed, whose backbone, termed gradual inference, consists of a multilayer extractor and multiple classifiers, structurally achieving prediction diversity. Functionally, optimizing UR among multiple classifiers enables class-level domain adaptation to reduce class confusion, simultaneously treating classifiers as domain discriminators to construct hierarchical domain adversarial reaches global-level domain adaptation. Moreover, the theoretical risk upper bound is provided by introducing Rademacher complexity. High-precision performance on extensive trials demonstrates the proposed method improves the decision reliability in mechanical fault diagnosis. • UR - GIDA tackles class confusion caused by domain shift and strong class relevance. • The proposed discrepancy metric, UR, induces adaptation. • Hierarchical domain adversarial achieves multi-tendency global domain adaptation. • Rademacher complexity is introduced to provide theoretical generalization bound. • Two across-bearing fault diagnosis cases are organized to method verification. [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptation has achieved certain success in recent cross-domain fault diagnosis research. As a widely used transfer strategy, the distribution alignment often occurs with the problems of too few valid alignment samples, too low confidence of predicted labels, and the inadequate alignment of marginal or conditional distributions. Therefore, this paper proposes a statistical distribution recalibration method of soft labels (SDRS). First, SDRS defines the valid samples and confusion interval in the statistical distribution of per-class predicted probabilities. Then, from the perspective of binary classification, a recalibration space in the confusion interval is further optimized by a center distance metric, to improve predicted confidence and valid distribution alignment. Built on SDRS, a novel cross-domain fault diagnosis approach named SDRS-DAN is constructed, where dynamic distribution adaptation is used to match and adjust the marginal and conditional distribution discrepancies adaptively. Extensive experiments prove the effectiveness of SDRS-DAN in cross-location and cross-machine scenarios. • The proposed SDRS improves the domain adaptation of unsupervised fault diagnosis. • The SDRS provides more valid samples for distribution alignment of fault data. • The recalibration space of SDRS can be optimized by center distance metric. • SDRS-DAN method obtains higher accuracy on cross-machine fault diagnosis tasks. • The dynamic adaptation in SDRS-DAN can adjust the distribution alignment better. [ABSTRACT FROM AUTHOR]

• The optimal band selection is applied to design a network module. • A method is proposed for evaluating and optimizing the convolutional kernel. • The optimal kernels and features show semantics and make the network robust. • The impulsiveness or cyclostationarity can be used as semantics. • An improvement of 4% and 3% is achieved under two scenarios, respectively. Deep data-driven methods for fault diagnosis, as an engineering-oriented approach, rely heavily on target data. For engineering applications, the working conditions of rotating machinery fluctuate from time to time and a collection for any working conditions is impossible. To tackle this problem, a robust network with optimal kernels named EnvelopeNet is proposed for extracting solid information and eliminating the influence of fluctuations. In the EnvelopeNet, a feature evaluation building block named envelope module is constructed based on optimal band selection theory to optimize the kernels. Compared with the kurtogram, the learned optimal kernels and features show strong semantics which helps the network become robust. The EnvelopeNet is validated under the approximate speed and mixed speed scenarios. The results show that the EnvelopeNet could provide admirable generalization ability for fluctuating working conditions and an average improvement of about 4% and 3% over existing approaches under two scenarios respectively. [ABSTRACT FROM AUTHOR]

In complex working site, bearings used as the important part of machine, could simultaneously have faults on several positions. Consequently, multi-label learning approach considering fully the correlation between different faulted positions of bearings becomes the popular learning pattern. Deep reinforcement learning (DRL) combining the perception ability of deep learning and the decision-making ability of reinforcement learning, could be adapted to the compound fault diagnosis while having a strong ability extracting the fault feature from the raw data. However, DRL is difficult to converge and easily falls into the unstable training problem. Therefore, this paper integrates the feature extraction ability of DRL and the knowledge transfer ability of transfer learning (TL), and proposes the multi-label transfer reinforcement learning (ML-TRL). In detail, the proposed method utilizes the improved trust region policy optimization (TRPO) as the basic DRL framework and pre-trains the fixed convolutional networks of ML-TRL using the multi-label convolutional neural network method. In compound fault experiment, the final results demonstrate powerfully that the proposed method could have the higher accuracy than other multi-label learning methods. Hence, the proposed method is a remarkable alternative when recognizing the compound fault of bearings. • The proposed method is based on the DRL and the TL. • A pre-trained ML-CNN is designed to enhance the ability of ML-TRL. • An end-to-end mode is developed by training directly the 1D signal. [ABSTRACT FROM AUTHOR]

Highlights: [•] The characteristics of the DC filter are used for the fault locating of MTDC transmission lines. [•] The inevitable one-end protection blind zones are deduced in the distributed parameter model. [•] The fault line section selection is important to MTDC control strategy. [•] We propose a fault location criterion based on complex wavelet transform. [•] The algorithm is successfully tested and the results are compared with those from other algorithms. [Copyright &y& Elsevier]

Abstract: Face stability is critical for ground settlement and construction safety control in shield tunneling. In this paper, a series of 3D large-scale model tests with a tunnel of 1m diameter were conducted in dry sand for various cover-to-diameter ratios C/D =0.5, 1, and 2 (i.e., relative depth; C is the cover depth and D is the diameter of tunnel). Each test provided a measurement of the support pressure and the ground settlement with the advance of face displacement. The evolution of soil arching during face failure was investigated by monitoring the redistribution of earth pressure in front of the face in the test case of C/D =2. In the displacement-controlled face failure tests in the medium density sands, the support pressure dropped steeply to the minimum value, then increased to a steady state with the continuing increase in the face displacement. Relationships between the support pressure and face displacement for various cover depths were also verified by the numerical analysis using the finite difference program, FLAC3D (Itasca, 2005). The limit support pressure increases with the increase of the relative depth C/D and then tends to be constant. A significant rotation of principal stress axes in the upward arches in the soil during face failure was found in the tests. A two-stage failure pattern is proposed based on the observation of earth pressure. The theoretical and empirical formulas for estimating limit support pressure were verified by the tests results. [Copyright &y& Elsevier]

• Novel DES by coordinating FeCl 3 in choline chloride (ChCl)/glycerol DES. • DES with FeCl 3 approvingly retained most of cellulose in pretreated Hybrid Pennisetum. • Hemicelluloses and lignin were effectively removed during the proposed pretreatment. • The excellent pretreatment performance keeps almost unchanged after recycling process. Deep eutectic solvent (DES) with protonic acid shows the great potential for biomass valorization. However, the acid corrosion and recycling are still severe challenges in biorefinery. Herein, a novel DES by coordinating FeCl 3 in choline chloride/glycerol DES was designed for effective and recyclable pretreatment. As compared to DESs with FeCl 2 , ZnCl 2 , AlCl 3 and CuCl 2 , DES with FeCl 3 approvingly retained most of cellulose in pretreated Hybrid Pennisetum (95.2%). Meanwhile, the cellulose saccharification significantly increased to 99.5%, which was six-fold higher than that of raw biomass. The excellent pretreatment performance was mainly attributed to the high removal of lignin (78.88 wt%) and hemicelluloses (93.63 wt%) under the synergistic effect of Lewis acid and proper hydrogen-bond interaction of DES with FeCl 3. Furthermore, almost all cellulose still can be converted into glucose after five recycling process. Overall, the process demonstrated designed pretreatment was great potential for the low-cost biorefinery and boost the biofuel development. [ABSTRACT FROM AUTHOR]