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Therapeutic perturbation of cyclin-dependent kinase 12 (CDK12) is proposed to have pleiotropic effects in ovarian cancer, including direct cytotoxicity against tumor cells and indirect induction of immunogenicity that confer synthetic sensitivity to immune-based treatment. However, formal testing of this hypothesis has been hindered by an insufficient mechanistic understanding of CDK12 and its close homolog CDK13, as well as generally unfavorable pharmacokinetics of available CDK12/CDK13 covalent inhibitors. In this study, we used an innovative arsenous warhead modality to develop an orally bioavailable CDK12/CDK13 covalent compound. The dual CDK12/CDK13 inhibitors ZSQ836 exerted potent anticancer activity in cell culture and mouse models and induced transcriptional reprogramming, including downregulation of DNA damage response genes. CDK12 and CDK13 were both ubiquitously expressed in primary and metastatic ovarian cancer, and the two kinases performed independent and synergistic functions to promote tumorigenicity. Unexpectedly, although ZSQ836 triggered genomic instability in malignant cells, it counterintuitively impaired lymphocytic infiltration in neoplastic lesions by interfering with T-cell proliferation and activation. These findings highlight the Janus-faced effects of dual CDK12/CDK13 inhibitors by simultaneously suppressing tumor and immune cells, offering valuable insights into the future direction of drug discovery to pharmacologically target CDK12.Significance:This study dissects the specific roles of CDK12 and CDK13 in ovarian cancer and develops a CDK12/CDK13 inhibitor that impairs both tumor and immune cells, which could guide future CDK12 inhibitor development.

Supplementary Data from Dual Inhibition of CDK12/CDK13 Targets Both Tumor and Immune Cells in Ovarian Cancer

Amphibious species of frogs are notable candidates to mimic for amphibious robotic design, as their swimming and sprawling locomotion is generated by the united propulsive mechanisms in which the hindlimbs play the dominant role. Although the propulsive system of frogs is not as complex as other amphibians, it is still difficult to employ the propulsive mechanism in robotic design due to the numerous degrees of freedom (DoF). This paper proposes a novel united propulsive mechanism to acquire the amphibious function inspired by the frog's hindlimb. The mechanism is a hybrid design combining a planar six-bar linkage, which is designed based on homotopy continuation and a spatial four-bar linkage. The DoF of the hindlimb-like mechanism are dramatically decreased to 2, with 1 each in the two sub-chains. Forward analysis is conducted to find the pose of the foot when two actuations are input. The improved analysis based on the geometrical features overcomes the multiplicity from the numerical computation. The inverse kinematic analysis is conducted to find the rotation of the input with a given pose of the foot. The aquatic function of the united propulsive mechanism is demonstrated based on the blade element theory, where the rotational speed and the projected area of the foot are fully active. The terrestrial function of the propulsive mechanism is validated with a specific gait.

The control cost is an important and inevitable problem for network control. In existing work on finite-time (FnT) synchronization on stochastic multilayer networks (MNs), the pth moment synchronization based on energy consumption estimation has not been involved. The paper firstly proposes two new FnT pth moment quasi-stability theorems of stochastic systems, which expand the existing value range of p. Second, novel sufficient criteria are obtained for FnT pth moment quasi-synchronization of stochastic MNs. Compared with some existing FnT synchronization of stochastic MNs, the paper not only gives the range of the number of layers about MNs under the minimum convergence time, but also gives the estimation of energy consumption. Finally, numerical simulation shows that the theoretical analysis is effective.

Nonideal switching characteristics of voltage source inverter (VSI) will result in distortions in both phase currents and reference voltages of a permanent magnet synchronous machine (PMSM) drive system, which will also consequently introduce ripples into the output speed and shaft torque, and result in a performance deterioration in the state observation control. To solve this issue, a method of directly extracting and compensating the nonlinear voltage disturbance due to VSI nonlinearity employed in PMSM drives is proposed in this paper. Firstly, the distorted voltage due to VSI nonlinearity is directly extracted via a harmonic separation scheme, which can be easily compensated by a proportional integral controller afterwards. Since both 6th harmonic and DC terms of dq-axis distorted voltages due to VSI nonlinearity are well compensated, the proposed method shows sufficiently good performance under both $i_{d}=0$ and maximum torque per ampere controls ( $i_{d} < 0$ ), as verified on two prototype PMSMs being salient pole and non-salient pole, respectively, both developed for a low-speed rotary actuator. In addition, the application of proposed method to state observation controls such as the model-based sensorless control and the deadbeat predictive current control, is finally investigated. Test results manifest that the proposed method can effectively improve the accuracy of observed rotor position/speed, and the ability of current tracking.

Aim: To explore the prognostic value of the systemic inflammatory marker (SIM) based on neutrophil, lymphocyte and monocyte counts in head and neck squamous cell carcinoma (HNSCC) patients. Patients & methods: We retrospectively collected the data of 367 patients with HNSCC who underwent surgery. The Kaplan–Meier survival analysis and Cox regression analysis were conducted on disease-free survival and overall survival. Results: A high SIM (>1.34) was associated with larger tumor size, advanced clinical stage and shorter survival time. The survival analysis showed that only clinical stage and SIM were independent prognostic indicators of disease-free survival and overall survival. Conclusion: The SIM positively correlated with tumor progression and might be a powerful prognostic indicator of poor outcome in patients with HNSCC.

Extracting original data in scientific figures embedded in documents precisely and efficiently remains a challenge, especially when it needs to be performed in high throughput way. To solve the automatic curve recognition problem, this paper proposes an integrated tool for extracting digital data from curve figures in portable documents, which mainly consists of picking/recognition/summary parts. During each extracting process, trained neural network firstly picks up figure pieces; the X-Y axes are then located by horizontal and vertical image projection and their labels are read using character recognition, which is followed by curve data recovery point by point; and finally the recognition result are summarized and sent back to the requester. This open tool is accessible and testable by anyone around the world via email, with open source on Github.

Therapeutic perturbation of cyclin-dependent kinase 12 (CDK12) is proposed to have pleiotropic effects in ovarian cancer, including direct cytotoxicity against tumor cells and indirect induction of immunogenicity that confer synthetic sensitivity to immune-based treatment. However, formal testing of this hypothesis has been hindered by an insufficient mechanistic understanding of CDK12 and its close homolog CDK13, as well as generally unfavorable pharmacokinetics of available CDK12/CDK13 covalent inhibitors. In this study, we used an innovative arsenous warhead modality to develop an orally bioavailable CDK12/CDK13 covalent compound. The dual CDK12/CDK13 inhibitors ZSQ836 exerted potent anticancer activity in cell culture and mouse models and induced transcriptional reprogramming, including downregulation of DNA damage response genes. CDK12 and CDK13 were both ubiquitously expressed in primary and metastatic ovarian cancer, and the two kinases performed independent and synergistic functions to promote tumorigenicity. Unexpectedly, although ZSQ836 triggered genomic instability in malignant cells, it counterintuitively impaired lymphocytic infiltration in neoplastic lesions by interfering with T-cell proliferation and activation. These findings highlight the Janus-faced effects of dual CDK12/CDK13 inhibitors by simultaneously suppressing tumor and immune cells, offering valuable insights into the future direction of drug discovery to pharmacologically target CDK12. Significance: This study dissects the specific roles of CDK12 and CDK13 in ovarian cancer and develops a CDK12/CDK13 inhibitor that impairs both tumor and immune cells, which could guide future CDK12 inhibitor development.

The use of supervised neural networks is a new approach to solving digital image correlation (DIC) problems, but the existing methods solely adopt the black-box neural network, i.e., the mapping from speckle image pair (reference image and deformed image) to multiple deformation fields (displacement fields and strain fields) is directly established without considering the physical constraints between the fields, causing a low level of accuracy that is even inferior to that of Subset-DIC. In this work, we proposed a deep learning model by introducing strain-displacement relations into a neural network, in which the effect of errors both in displacement and strain are considered in the network training. The back-propagation process of the proposed model is derived, and the solution scheme is implemented by Python. The performance of the proposed model is evaluated by simulation and real DIC experiments, and the results show that adding physical constraints to the neural network can significantly improve prediction accuracy.

Current anchor-free object detectors do not rely on anchors and obtain comparable accuracy with anchor-based detectors. However, anchor-free object detectors that adopt a single-level feature map and lack a feature pyramid network (FPN) prior information about an object’s scale; thus, they insufficiently adapt to large object scale variation, especially for autonomous driving in complex road scenes. To address this problem, we propose a divide-and-conquer solution and attempt to introduce some prior information about object scale variation into the model when maintaining a streamlined network structure. Specifically, for small-scale objects, we add some dense layer jump connections between the shallow high-resolution feature layers and the deep high-semantic feature layers. For large-scale objects, dilated convolution is used as an ingredient to cover the features of large-scale objects. Based on this, a scale adaptation module is proposed. In this module, different dilated convolution expansion rates are utilized to change the network’s receptive field size, which can adapt to changes from small-scale to large-scale. The experimental results show that the proposed model has better detection performance with different object scales than existing detectors.

Background: To investigate the relationship of circulating tumor cells (CTCs) and the clinical characteristic parameters and prognosis in patients with head and neck squamous cell carcinoma (HNSCC). Methods: The retrospective clinical study included 95 patients with HNSCC who after surgery in Shanghai Ninth People’s Hospital affiliated to Shanghai Jiao Tong University School of Medicine between December 2015 and December 2016. All patients were followed up for survival until the end of June 2019. The CTCs detection was performed by negative enrichment (NE) immunofluorescence-in situ hybridization (im-FISH) of chromosome 8. Results: Patients with higher CTCs counts are associated with a worse prognosis with an area under the receiver operator characteristic (ROC) curve of 0.756 [95% confidence interval (CI) 0.640-0.872, p = 0.001]. The CTCs-positive rate of HNSCC patients was 58.9% (56/95) by using the cut-point of 3. Both the chi-square test and binary logistic regression analysis showed that the N stage and clinical stage were significantly associated with CTCs-positive in patients with HNSCC ( p < 0.05). Further Non-parametric test analysis indicated that more CTCs counts were detected in late N and clinical stages patients ( p < 0.001). The Kaplan-Meier survival analysis indicated that CTCs-positive were correlated with shorter progression-free survival (PFS) ( p < 0.001) and overall survival (OS) ( p = 0.001). Further, the CTCs-positive was an independent prognostic factor for PFS and OS according to the Cox multivariate regression analysis ( p < 0.05). Conclusion: More CTCs were associated with N stage, clinical stage, poor prognosis in patients with HNSCC, which might be used as a prognostic biomarker.

The resilience assessment method of hybrid AC-DC distribution grid is the key and basis of measuring the tolerance of the distribution network in face of extreme events and testing the effect of the resilience improvement strategy of the distribution network. This paper fully considers the characteristics of different extreme natural disasters and proposes a targeted resilience assessment method. For natural disasters, the fault rate model of the line under the influence of the disaster is constructed, and the fault set is generated based on Monte Carlo sampling. According to the operating characteristics, a set of evaluation indicators focusing on the core characteristics of resilience, including capability of strain, defense and recoverability, is constructed. The objective weight of each indicator and the comprehensive assessment results are obtained based relatively on the entropy weight method and the approximate ideal solution sorting method.

Digital cameras represented by industrial cameras are widely used as image acquisition sensors in the field of image-based mechanics measurement, and their thermal effect inevitably induces thermal-induced errors of the mechanics measurement. To deeply understand the errors, the research for digital camera&rsquo, s thermal effect is necessary. This study systematically investigated the heat transfer processes and temperature characteristics of a working digital camera. Concretely, based on the temperature distribution of a typical working digital camera, the heat transfer of the working digital camera was investigated, and a model describing the temperature variation and distribution was presented and verified experimentally. With this model, the thermal equilibrium time and thermal equilibrium temperature of the camera system were calculated. Then, the influences of thermal parameters of digital camera and environmental temperature on the temperature characteristics of working digital camera were simulated and experimentally investigated. The theory analysis and experimental results demonstrate that the presented model can accurately describe the temperature characteristics and further calculate the thermal equilibrium state of working digital camera, all of which contribute to guiding mechanics measurement and thermal design based on such camera sensors.

In this study, the extent and effect of fluctuations in the proximate composition of carbon materials on specific electrical energy consumption and exergy efficiency in the production process of silicon furnaces was evaluated using a novel artificial neural network approach. The neural network architecture 4-5-2 with hyperbolic sigmoid in the hidden layer and linear function in the output layer was used. The proposed model successfully predicts the values of the specific electrical energy consumption and the exergy efficiency through the correlation coefficient (0.93 and 0.87) of the actual and predicted values for the various proximate compositions of carbon materials. The interactive effects of components in the mixtures of carbon materials were investigated via contour diagrams. Fixed carbon had the biggest impact on electrical energy consumption and exergy efficiency in the mixtures of carbon materials, followed by moisture. Compared with volatile matter from woodchips without any pretreatment, volatile matter from petcoke, charcoal, and coal had a significant effect on electrical energy consumption and exergy efficiency.

The objective of this study was to use a statistical approach to determine the correlation of fixed carbon, volatile matters and moisture with the specific electrical consumption and exergy efficiency by collecting and analyzing an abundance of actual production data generated in silicon plants during the melting process of silicon produced in the submerged arc furnace (SAF). A high positive correlation (the r maximum value of 0.82) between the fixed carbon composition and the specific electrical consumption, as well as the perfect negative correlation (the r maximum value of −0.92) between the fixed carbon and the exergy efficiency were found, especially when the carbon materials contained a very high amount of graphitized carbon. Compared with the carbon materials without woodchips, the high correlation among the volatile matters, the specific electrical consumption and exergy efficiency was observed when using the carbon materials with woodchips. The influence of the proximate composition on the electrical consumption and exergy efficiency becomes stronger with the increase of the graphitized carbon content in the carbon materials. The analysis results can be used to support decisions when optimizing the proximate composition of the carbonaceous reductants in order to increase exergy efficiency and decrease electrical consumption costs, which is one of the biggest operating costs in the silicon furnace.

In this paper, we propose a supervised learning strategy for the fluid motion estimation problem (i.e., extracting the velocity fields from particle images). The purpose of this work is to design a convolutional neural network (CNN) for estimating dense motion field for particle image velocimetry (PIV), which allows to improve the computational efficiency without reducing the accuracy. First, the network model is developed based on FlowNetS, which is recently proposed for end-to-end optical flow estimation in the computer vision community. The input of the network is a particle image pair and the output is a velocity field with displacement vectors at every pixel. Second, a synthetic dataset of fluid flow images is generated to train the CNN model. To our knowledge, this is the first time a CNN has been used as a global motion estimator for particle image velocimetry. Experimental evaluations indicate that the trained CNN model can provide satisfactory results in both artificial and laboratory PIV images. The proposed estimator is also applied to the experiment of turbulent boundary layer. In addition, the computational efficiency of the CNN estimator is much superior to those of the traditional cross-correction and optical flow methods.

This study aimed to evaluate the prognostic value of circulating tumor cells (CTCs) in advanced colorectal cancer (CRC) patients during chemotherapy course.From January 2016 to September 2017, the clinicopathological variables, such as gender, age, tumor location, tumor de-differentiation, depth of invasion, lymphatic invasion, distant metastasis, TNM stage, CTCs enumeration during 2-6 cycles of chemotherapy, and serum carcinoembryonic antigen (CEA) level during the same period, of 121 newly acquired and histopathologically confirmed CRC patients were collected from the Shanghai Ninth People's Hospital affiliated to Shanghai Jiao Tong University School of Medicine. All patients were followed up for survival until the end of November 2018. Statistical analysis focused on the associations between CTCs counts and clinicopathological variables. Overall survival (OS) and progression-free survival (PFS) among different prognostic factors were calculated using the Kaplan-Meier method, and the differences between the survival curves were compared by using the log-rank test. Factors of prognostic significance were investigated with the multivariate Cox regression analysis.Here, 71 of 121 patients were CTC-positive, in which CTC-positive rate was positively correlated with the depth of invasion, lymphatic invasion, distant metastasis, TNM stage, and serum CEA level (P 0.05 for all). However, no significant difference was found between CTC-positive and other clinicopathological variables (P 0.05 for all), such as gender, age, tumor location, and tumor de-differentiation. CTCs counts gradually increased with the advancement of depth of invasion (P = 0.002), lymphatic invasion (P = 0.004), distant metastasis (P = 0.007), TNM stage (P = 0.001), serum CEA level (P = 0.001), and decreased tumor de-differentiation (P = 0.011). Furthermore, the Kaplan-Meier survival curves showed that patients with CTC-positive had a significantly unfavorable PFS (14 vs. 23 months, P = 0.001) and OS (18 vs. 25 months, P = 0.003). The multivariate Cox regression analyses revealed that the presence of CTCs during chemotherapy was an independent factor for unfavorable PFS (hazard ratio (HR) 2.682, P = 0.017, 95% confidence interval (CI) 1.193-6.029) and OS (HR 2.790, P = 0.048, 95% CI 1.010-7.705) in advanced CRC patients.This study provided an evidence that the presence of CTCs may be valuable for predicting survival outcome, and CTCs was associated with unfavorable survival in advanced CRC patients during chemotherapy.

Understory vegetation plays a crucial role in nutrient turnover and cycling in plantations, but it also competes for nutrients with crop trees when only a single species is present due to its specific nutrient requirements. However, it remains unclear whether this competition can be alleviated when the species richness of understory vegetation increases. In this study, we tested different gradients of understory vegetation species richness, including understory vegetation removal (UR), the retention of a single main understory vegetation species (RS), and the retention of natural diverse understory vegetation (RD) as part of a poplar (Populus deltoides ‘Nanlin-3804′) plantation, to study their effects on poplar growth, and to evaluate nitrogen (N) usage and how this was affected by the interactions between the poplar and understory vegetation. The results showed a generally lower periodic growth, and a significant decline in the foliar chlorophyll content and glutamine synthetase activity of poplar under treatment with RS and RD compared to those under UR treatment conducted in July 2019, which clearly indicated N competition between the understory vegetation and poplar trees. However, no significant difference was detected in the foliar chlorophyll content and glutamine synthetase activity of poplar under RD and RS treatment, only the nitrate reductase activity in poplar leaves under RD treatment declined significantly, by 22.25%, in June 2019. On the contrary, the diameter at breast height (DBH) of the poplar under RD treatment showed an increase of 34.69% from July to August 2019, compared with that under RS treatment. Furthermore, the increase in the species richness of understory vegetation resulted in an increase in the δ15N values in the poplar leaves, which was strongly regulated by the NH4+-N pool in the 10–20 cm soil layer, indicating the effective coordination of N utilization between poplar and understory vegetation when diversified understory plant species were present. These findings demonstrate the essential role of understory vegetation species diversity in alleviating N competition with crop trees, and provide guidance for understory vegetation management in poplar plantations.

In this work, the fatigue behavior of a low-cost Zr58Cu15.46Ni12.74Al10.34Nb2.76Y0.5 (at%) bulk metallic glass (BMG) fabricated by industrial-grade Zirconium raw material was investigated under three-point bending loading mode. X-ray, fatigue tests under different stress amplitude and fatigue fractography were conducted in order to characterize the amorphous structure, fatigue stress-life (S-N) curve and fracture mechanism, respectively. It is found that the X-ray diffraction (XRD) result showed a fully amorphous structure due to high glass-forming ability, cracks initiated from inclusions near the rectangular corners at tensile surfaces and the fatigue endurance limit (~168 MPa) and fatigue ratio (~0.13) termed as fatigue endurance limit divided by ultimate tensile strength in stress amplitude were comparable to the similar BMG prepared by high pure raw materials.

In this study, we evaluated the characteristics and applications of carbonaceous materials, which play a vital role in silicon production. Specifically, we conducted an exploratory study on biomass and petroleum coke (PC) performance improvement in response to different grinding media, and discussed the outcomes of our experimental results in conjunction with thermodynamic and kinetic analysis. Furthermore, we comprehensively analyzed the strengthening mechanism using Fourier-transform infrared (FTIR) spectroscopy, Raman spectroscopy, and scanning electron microscopy (SEM). The results showed that the initial reduction reaction temperature was significantly lowered by using the wet-grinding method. Even more remarkably, the maximum reaction rate increased from 62% to 157% when organic solvent was added to the mixture during the wet-grinding process and the proportion of decreased activation energy increased from 15.34% to 22.35%, as compared with dry-blending. Thus, the mixtures’ reactivity improved by using wet-grinding, especially after adding organic solvent, which increased the carbothermic reduction utilization of PC and biomass. A potential primary cause may be related to the “like dissolves like” interaction between organic solvents and organic functional groups in carbon materials. This study has important implications for developing low-cost, low energy consumption of silicon, as well as promoting clean, efficient production.

High energy consumption and high pollution arising from the production of a compound reductant with high-coke and high-coal has drawn universal attention and urgently requires a solution. In this study, activated carbon (AC) as a carbonaceous reductant produced via combined microwave heating and additive inclusion (K2CO3 and Na2CO3) was investigated during carbothermic reduction of silicon. The effect of microwave-assisted treatment on the kinetic and thermal behavior of AC mixed with alkali carbonate was evaluated by thermogravimetric analysis. By using microscopy techniques such as Raman spectroscopy, Fourier-transform infrared spectroscopy, and field-emission scanning electron microscopy (FESEM), the influence of specific factors on the chemical properties of the AC were analyzed. The results showed that the properties of the silicon produced via carbothermic reduction changed after microwave-assisted treatment, which was attributable to changes in structure of the aromatic ring and carbon chain in the AC. In the presence of different alkali carbonates, AC subjected to microwave-assisted treatment exhibited varied in performance because of the different degrees of microwave absorption of the different alkali carbonates. For the additive K2CO3, weight loss improved by 54.35%, and activated energy was reduced by 31.32% during reduction reaction under microwave-assisted treatment. Similarly, the weight loss increased by 42.01%, and the activated energy decreased by 14.30% for the additive Na2CO3. These data indicated that the largest improvement was achieved under microwave-assisted treatment, which contributed substantially to energy-saving. Carbonaceous reductants are important raw materials for silicon production, which currently present a major technical problem. These research results may provide new insights into the use of AC loaded with alkali metal compounds as a carbon reducing agent in production systems.

Bcl-2-associated athanogene 3 (BAG3) is a cochaperone protein and a central player of the cellular protein quality control system. BAG3 is prominently expressed in the heart and plays an essential role in cardiac protein homeostasis by interacting with chaperone heat shock proteins (HSPs) in large, functionally distinct multichaperone complexes. The BAG3 mutation of proline 209 to leucine (P209L), which resides in a critical region that mediates the direct interaction between BAG3 and small HSPs (sHSPs), is associated with cardiomyopathy in humans. However, the mechanism by which the BAG3 P209L missense mutation leads to cardiomyopathy remains unknown. To determine the molecular basis underlying the cardiomyopathy caused by the BAG3 P209L mutation, we generated a knockin (KI) mouse model in which the endogenous Bag3 gene was replaced with mutant Bag3 containing the P215L mutation, which is equivalent to the human P209L mutation. We performed physiological, histological, and biochemical analyses of Bag3 P209L KI mice to determine the functional, morphological, and molecular consequences of the P209L mutation. We found that Bag3 P209L KI mice exhibited normal cardiac function and morphology up to 16 mo of age. Western blot analysis further revealed that levels of sHSPs, stress-inducible HSPs, ubiquitinated proteins, and autophagy were unaffected in P209L mutant mouse hearts. In conclusion, the P209L mutation in Bag3 does not cause cardiomyopathy in mice up to 16 mo of age under baseline conditions. NEW & NOTEWORTHY Bcl-2-associated athanogene 3 (BAG3) P209L mutation is associated with human cardiomyopathy. A recent study reported that transgenic mice overexpressing human BAG3 P209L in cardiomyocytes have cardiac dysfunction. In contrast, our P209L mice that express mutant BAG3 at the same level as that of wild-type mice displayed no overt phenotype. Our results suggest that human cardiomyopathy may result from species-specific requirements for the conserved motif that is disrupted by P209L mutation or from genetic background-dependent effects.

Extreme learning machine (ELM) has been extensively studied, due to its fast training and good generalization. Unfortunately, the existing ELM-based feature representation methods are uncompetitive with state-of-the-art deep neural networks (DNNs) when conducting some complex visual recognition tasks. This weakness is mainly caused by two critical defects: (1) random feature mappings (RFM) by ad hoc probability distribution is unable to well project various input data into discriminative feature spaces; (2) in the ELM-based hierarchical architectures, features from previous layer are scattered via RFM in the current layer, which leads to abstracting higher level features ineffectively. To address these issues, we aim to take advantage of label information for optimizing random mapping in the ELM, utilizing an efficient label alignment metric to learn a conditional random feature mapping (CRFM) in a supervised manner. Moreover, we proposed a new CRFM-based single-layer ELM (CELM) and then extended CELM to the supervised multi-layer learning architecture (ML-CELM). Extensive experiments on various widely used datasets demonstrate our approach is more effective than original ELM-based and other existing DNN feature representation methods with rapid training/testing speed. The proposed CELM and ML-CELM are able to achieve discriminative and robust feature representation, and have shown superiority in various simulations in terms of generalization and speed.

To better clarify and understand the anisotropic elastic properties of Ca–Pb intermetallic compounds, the structural and elastic properties of the Ca–Pb compounds (Ca3Pb, Ca2Pb, Ca5Pb3, CaPb and CaPb3) have been investigated by using the first-principles calculations based on density functional theory. The calculated equilibrium structural parameters are in good agreement with the available experimental data. Elastic properties including bulk modulus, shear modulus, Young’s modulus and Poisson’s ratio, are obtained by Voigt–Reuss–Hill approximation. All Ca–Pb compounds are mechanically stable. The brittle and ductile properties are discussed by using B/G and Poisson’s ratio. The elastic anisotropies of Ca–Pb compounds are investigated via the anisotropic index and the 3D figures of directional dependences of reciprocals of elastic moduli. Finally, the anisotropic sound velocities and Debye temperatures of the Ca–Pb compounds are predicted.

To better clarify and understand the phase stability and elastic properties of stable Zr–Al binary intermetallic compounds, the structural properties, phase stability, elastic properties, and electronic structures of these compounds in Zr–Al system have been systematically investigated by using first-principles calculations. The calculated equilibrium structures and enthalpies of formation in present work are in good agreement with the available experimental and other theoretical data, and the results of enthalpies of formation show that ZrAl 2 is the most stable. The elastic properties, including elastic constants, Poisson’s ratio and anisotropy index, and Debye temperatures were also investigated. It is found that ZrAl is the most anisotropic in Zr–Al binary compounds. Furthermore, the electronic structures were discussed to reveal the bonding characteristics of the compounds.

The effects of high pressure on elastic properties and electronic structures of L12 structure trialuminide Al3Sc and Al3Y compounds have been investigated by first-principles calculations within the local density approximation. The elastic properties and Debye temperature for Al3Sc and Al3Y increase with the increasing pressure, and the calculated values in elastic properties and Debye temperatures which obtained at 0 GPa are in good agreement with the available experiment data. The mechanical anisotropic properties are discussed using universal anisotropic index AU and Zener anisotropy index AZ. The calculated electronic properties suggest that a significant amount of charge in the interstitial region gives rise to an enhanced directional covalent-type bonding between the Sc (Y) and Al atoms as the pressure is up to 40 GPa.

With the diversification of power market users, reasonable profit distribution among members becomes the focus of government regulation. Under the background of demand-side liberalization, with the introduction of “peak-regulating device” represented by battery, the member operation characteristics of small and medium-sized power users become more and more complex, and the traditional method of profit allocation based on power generation assessment is no longer applicable. In order to solve this problem, this paper proposes a profit-sharing method based on weighted Shapley value, which takes the contribution of marginal conditional value-at-risk under the participation of small and medium-sized users as the weight to improve the traditional Shapley value method. An example is given to illustrate the feasibility and rationality of the improved method.