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A generalized varying-coefficient model is proposed to estimate a population size at a specific time from multiple lists of an open population. The research datasets have millions of records with a very long time span (38 years), bringing challenges to calculations. The authors develop a regularization iterative algorithm to overcome this difficulty. The asymptotic distribution of the proposed estimators is derived. Simulation studies show that the procedure works well. The method is applied to estimate the number of drug abusers in Hong Kong, China over the period 1977–2014.

Blade tip timing (BTT) is a noncontact blade vibration measurement method for rotating blade condition monitoring. However, it is difficult to install a once-per-revolution (OPR) probe due to the internal environment limitation of an engine. To solve this problem, an OPR-free BTT method is proposed in this article. Compared with the existing OPR-free methods that are based on blade displacement, this method is based on blade spacing change (BSC). In addition, a probe layout selection method based on minimum redundancy linear array (MRLA) is introduced to reduce the redundancy of the acquired signal. Simulations are conducted to prove that the proposed probe layout selection method has a stronger signal extraction capability and better robustness. The experiment is conducted to show that BSC is an alternative way for rotating blade condition monitoring and the technical route of this article is feasible.

Blade health monitoring (BHM) is an important technology to ensure safe operation of turbine machinery. The blade tip timing (BTT) method is a non-contact approach of vibration monitoring that is widely used for aero-engines. A BTT signal can be characterized as nonuniform and sub-Nyquist sampled (i.e., under-sampled). For nonuniform sampling, the least squares (LS) fitting method has long been used to reconstruct the spectrum. However, the under-sampling of the BTT signal causes the spectrum obtained by LS fitting aliasing. The iterative reweighted LS periodogram (IRLSP) can eliminate the aliasing caused by sub-Nyquist and nonuniform sampling. This method weakens the frequency aliasing through the method of reweighted iteration, but the consequence of this operation is that the amount of calculation is greatly increased. Thus, we propose an adaptive iterative approach (AIA) based on the IRLSP that combines a priori information of the blade. The main contribution of this method is to improve the computational efficiency for processing the BTT signal and the effect of anti-aliasing. These two points mean that this method is expected to become a real-time blade vibration monitoring method. We combined a finite element model with modal testing to perform simulations and verify the improvement with AIA. A large number of experiments were also performed to verify the effectiveness of our approach for BHM.

Blades are weak and essential parts of an aeroengine. Monitoring the running state of blades is essential for ensuring the safety of aeroengine operation. Blade tip timing (BTT) is considered a promising technique for operational blade vibration monitoring for turbomachinery; however, it is difficult to analyze the BTT signal because of the inherent undersampled limitation. To overcome this limitation, the minimal sensor layout scheme is restricted to be “three.” Usually, more than five probes are required for good signal revolution. This requirement is an installation restriction that limits the application of BTT; however, for users, the conflict between probe layout optimization and real installation restriction is also a serious problem. Motivated by these issues, a single-probe BTT (SP-BTT) method is proposed; the SP-BTT method could extract the shift between natural frequencies, which could be used as a damage index for blade condition monitoring. To validate the effectiveness of the proposed method, numerical simulations and experimental tests are performed.

Hunting for a type of green phosphor with outstanding properties is of much significance for improving the performance of display backlights. In this work, a series of efficient Eu2+-activated RbLi...

Blade tip timing (BTT) is an effective noncontact measurement technology for rotating blade health monitoring. However, due to the mismatching between the high-speed rotation and the limited amount of probes, the signal collected from the BTT system is severely undersampled, which induces the difficulty in feature extraction. Multiple signal classification (MUSIC) has the potential to overcome the undersampled problem once the probes are properly placed. Whereas, if traditional MUSIC is directly used in BTT, the accuracy of frequency identification cannot be high enough and the identified number of frequency components is also severely restrained. To address these two problems, an improved MUSIC is proposed as an alternative methodology to extract the blade vibration frequency for BTT. Based on the orthogonality of the signal subspace and the noise subspace from undersampled signal, the presented method can effectively identify the vibration frequency components from the undersampled signal of BTT.

Background: Lenvatinib and transarterial chemoembolization (TACE) are first-line treatments for unresectable hepatocellular carcinoma (HCC), but the objective response rate (ORR) is not satisfactory. We aimed to predict the response to lenvatinib combined with TACE before treatment for unresectable HCC using machine learning (ML) algorithms based on clinical data. Methods: Patients with unresectable HCC receiving the combination therapy of lenvatinib combined with TACE from two medical centers were retrospectively collected from January 2020 to December 2021. The response to the combination therapy was evaluated over the following 4–12 weeks. Five types of ML algorithms were applied to develop the predictive models, including classification and regression tree (CART), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM). The performance of the models was assessed by the receiver operating characteristic (ROC) curve and area under the receiver operating characteristic curve (AUC). The Shapley Additive exPlanation (SHAP) method was applied to explain the model. Results: A total of 125 unresectable HCC patients were included in the analysis after the inclusion and exclusion criteria, among which 42 (33.6%) patients showed progression disease (PD), 49 (39.2%) showed stable disease (SD), and 34 (27.2%) achieved partial response (PR). The nonresponse group (PD + SD) included 91 patients, while the response group (PR) included 34 patients. The top 40 most important features from all 64 clinical features were selected using the recursive feature elimination (RFE) algorithm to develop the predictive models. The predictive power was satisfactory, with AUCs of 0.74 to 0.91. The SVM model and RF model showed the highest accuracy (86.5%), and the RF model showed the largest AUC (0.91, 95% confidence interval (CI): 0.61–0.95). The SHAP summary plot and decision plot illustrated the impact of the top 40 features on the efficacy of the combination therapy, and the SHAP force plot successfully predicted the efficacy at the individualized level. Conclusions: A new predictive model based on clinical data was developed using ML algorithms, which showed favorable performance in predicting the response to lenvatinib combined with TACE for unresectable HCC. Combining ML with SHAP could provide an explicit explanation of the efficacy prediction.

A generalized partial linear regression model is proposed to estimate population size at a specific time from multiple lists of a time-varying and heterogeneous population. The challenge is that we have millions of records and hundreds of parameters for a long period of time. This presents a challenge for data analysis, mainly due to the limitation of computer memory, computational convergence and infeasibility. In the paper, an analytical methodology is proposed for modeling a large data set with a large number of parameters. The basic idea is to apply the maximum likelihood estimator to data observed at each time separately, and then combine these results via weighted averages so that the final estimator becomes the maximum likelihood estimator of the whole data set (full MLE). The asymptotic distribution and inference of the proposed estimators is derived. Simulation studies show that the proposed procedure gives exactly the same performance as the full MLE, but the proposed method is computationally feasible while the full MLE is not, and has much lower computational cost than the full MLE if both methods work. The proposed method is applied to estimate the number of drug-abusers in Hong Kong over the period 1977–2014.

Rabies, caused by rabies virus (RABV), is fatal to both humans and animals around the world. Effective clinical therapy for rabies has not been achieved, and vaccination is the most effective means of preventing and controlling rabies. Although different vaccines, such as live attenuated and inactivated vaccines, can induce different immune responses, different expressions of pattern recognition receptors (PRRs) also cause diverse immune responses. Toll-like receptor 4 (TLR4) is a pivotal PRR that induces cytokine production and bridges innate and adaptive immunity. Importantly, TLR4 recognizes various virus-derived pathogen-associated molecular patterns (PAMPs) and virus-induced damage-associated molecular patterns (DAMPs), usually leading to the activation of immune cells. However, the role of TLR4 in the humoral immune response induced by RABV has not yet been revealed. Based on TLR4-deficient (TLR4(−/−)) and wild-type (WT) mouse models, we report that TLR4-dependent recruitment of the conventional type 2 dendritic cells (CD8α(−) CD11b(+) cDC2) into secondary lymph organs (SLOs) is critical for antigen presentation. cDC2-initiated differentiation of follicular helper T (Tfh) cells promotes the proliferation of germinal center (GC) B cells, the formation of GCs, and the production of plasma cells (PCs), all of which contribute to the production of RABV-specific IgG and virus-neutralizing antibodies (VNAs). Collectively, our work demonstrates that TLR4 is necessary for the recruitment of cDC2 and for the induction of RABV-induced humoral immunity, which is regulated by the cDC2-Tfh-GC B axis. IMPORTANCE Vaccination is the most efficient method to prevent rabies. TLR4, a well-known immune sensor, plays a critical role in initiating innate immune response. Here, we found that TLR4-deficient (TLR4(−/−)) mice suppressed the induction of humoral immune response after immunization with rabies virus (RABV), including reduced production of VNAs and RABV-specific IgG compared to that occurred in wild-type (WT) mice. As a consequence, TLR4(−/−) mice exhibited higher mortality than that of WT mice after challenge with virulent RABV. Importantly, further investigation found that TLR4 signaling promoted the recruitment of cDC2 (CD8α(+) CD11b(−)), a subset of cDCs known to induce CD4(+) T-cell immunity through their MHC-II presentation machinery. Our results imply that TLR4 is indispensable for an efficient humoral response to rabies vaccine, which provides new insight into the development of novel rabies vaccines.

Foreign object damage (FOD) is a frequent but dangerous failure of the compressor. Blade tip timing (BTT) is regarded as a promising on-line monitoring method for blades, but it is difficult to obtain the real FOD data in a laboratory environment. This paper proposes a virtual BTT measuremnt method for FOD. Firstly, we establish the dynamic response model of the multi-degree of freedom damped system, and use the modal superposition method to infer its response displacement under the impact of a foreign object. Secondly, we use the Johnson-Cook model to calculate the stress limit of the blade model. Thirdly, finite element analysis is employed to calculate the influence of different impact positions on the blade tip displacement and the blade failure. Finally, we use the Simulink module to simulate the blade vibration displacement signal, and the measurement process according to the principle of tip timing. By this means, the laboratory simulation of BTT measurement under FOD is realized.

Engine compressor blades play important roles in aeroengine operation. They are easily injured by foreign objects due to direct contact with the outside air. Blade tip timing (BTT) is a non-contacting vibration measurement method for rotating blade health monitoring. However, the under sampled problem is an inevitable obstacle in the feature extraction of BTT signal. An alternative feature extraction method called autocorrelation method for asynchronous vibration analysis is proposed in this paper to identify the vibration amplitude and frequency of the under sampled BTT signal simultaneously. Based on the assumption of single frequency vibration, the signals collected by three probes are used to form two equations, and the vibration frequency and amplitude are obtained by solving them. Additionally, the discussion on the probe layout required by the proposed method is conducted. The verification using simulation signal (the noised signal with one frequency component) and experimental signal (signal collected under constant rotating speed) shows that the amplitude and frequency can be accurately extracted by the proposed method.

Polydopamine (PDA) is a biopolymer inspired by the mussel chemistry. Due to its capability to bind various metal-ions, PDA has been explored in several potential applications including absorbents for heavy metal contaminants and templates for nanoparticle growth. In this study, metal-ion-containing PDA was prepared by introducing Cu, Ni, and Co cations during synthesis. We discovered that nanoparticles (NPs) formed under intense electron beam irradiation. For Cu-containing PDA, both equiaxed NPs and rod-like nanocrystals were observed. In contrast, Ni and Co NPs were mainly equiaxed with smaller diameters. The formation of metal NPs is likely a result of electron beam induced reduction. We also demonstrated that electron beam could induce growth of Cu NPs on the surface of PDA-coated SiO2 particles. This result suggested that PDA coating could be used as a platform to grow metal NPs on ceramics and potentially other substrates.

The efficient single-photon upconversion photoluminescence (UCPL) feature of lead halide perovskite semiconductors makes it promising for developing laser cooling devices. This is an attractive potential application, but the underlying physics still remains unclear so far. By using the all-inorganic CsPbX3 (X = Br, I) nanocrystal samples, this phenomenon was investigated by photoluminescence (PL) and time-resolved PL under different temperatures and various excitation conditions. A broad emission band located at the low-energy side of the free exciton (FE) peak was detected and deduced to be from the self-trapped exciton (STE). The lifetime of STE emission was found to be 171 ns at 10 K, much longer than that of FE. The UCPL phenomenon was then attributed to thermal activation of transformation from STEs to FEs, and the energy barrier was derived to be 103.7 meV for CsPbBr3 and 45.2 meV for CsPb(Br/I)3, respectively. The transformation also can be seen from the fluorescence decay processes.

Sintering is an essential step to obtain bulk components from aluminum powders, which enables powder metallurgy-based fabrication methods such as additive manufacturing technologies. Copper (Cu) ca...

High-performance fibers made of poly-(p-phenylene terephthalamide) (PPTA) with high stiffness and high strength are widely used in body armor for protection due to their high degree of molecular chain alignment along the fiber direction. However, their poor mechanical properties in the transverse direction and low surface friction are undesirable for applications requiring resistance to ballistic impact. Here we provide a simple yet effective surface engineering strategy to improve both the transverse mechanical properties and the tribological property by coating PPTA fibers with ultra-high molecular weight polyethylene (UHMWPE) embedded with silica nanoparticles. The coated-PPTA fiber shows remarkable enhancement in transverse mechanical properties including ~127% increase of Young’s modulus, which is attributed to both the alignment of UHMWPE chains in the transverse direction and the embeded ceramic nanoparticles. Meanwhile, the surface friction of the coated fiber increases twofold as a result of the ceramic nanoparticles. In addition, the coated fibers exhibit an enhanced chemical resistance to external harsh environment. The improved transverse mechanical properties, surface frictional characteristics, and chemical resistance demonstrate that coating with UHMWPE and ceramic nanoparticles can be used as an effective approach to enhance the performance of PPTA and other high-performance polymer fibers for body armor applications.

Introduction of nanocarbon into metal matrices has shown promising improvement over the base metals. In this study, we examine the effect of a new nanocarbon phase – carbonized polydopamine (cPDA) – on the electrical, thermal, and mechanical properties of sintered copper. The composite samples exhibited better electrical conductivity, thermal diffusivity, hardness and wear resistance. Composites with a low cPDA concentration (0.4 vol%) possessed the best electrical conductivity and mechanical properties, while composite samples with higher cPDA concentrations (3.5 vol %) showed significantly enhanced thermal diffusivity as high as 104.5 mm2/s that is 63.5% higher than the sintered Cu sample.

Polydopamine (PDA) is a biopolymer, which can form uniform thin films on almost all solid substrates as well as at the liquid/air interface. Carbonized polydopamine possesses graphite-like structure and exhibits high electrical conductivity, which makes it a potential carbon-based thin film conductor. However, studies on mechanical behavior of PDA and its derived materials are very limited. In this study, PDA samples were synthesized through self-assembly of dopamine in aqueous solution. Elastic modulus of thin films was measured using the nanoindentation technique. It is shown that the Young’s modulus of PDA thin film increased with increasing heat treatment temperature (up to 600°C). Doping with Cu ions also increased the Young’s modulus of PDA. Furthermore, all PDA thin films, with and without Cu, exhibited creep behavior.

Polydopamine (PDA) is a biopolymer that can be synthesized under mild conditions. Thermal annealing can convert PDA into a conductive phase, the so-called carbonized PDA (cPDA). This work studied the effect of three metal ions, i.e., Cu2+, Mg2+, and Na+, on the synthesis of PDA and its conversion to cPDA. Both Cu2+ and Mg2+ could interact with PDA, which in turn influenced (1) the growth of PDA thin film, (2) morphology change of PDA particles upon thermal annealing, and (3) the electrical properties of heat-treated thin films. In contrast, the presence of Na+ ion during the synthesis of PDA did not show any effect. In this study, the morphology of PDA thin films and powder particles was examined using SEM and TEM; their chemical compositions were studied by EDS and ICP-MS; the structure was investigated using electron diffraction and Raman spectroscopy; and the properties were evaluated with respect to the electrical conductivity and thermoelectric Seebeck coefficient. The results from this work provide a potential approach to control the structure and properties of PDA and cPDA materials through metal ion doping.

Since ancient times, Chinese households have known by life experience that washing rice water and wheat floury soup may be used to wash oil dishes, face and hair. However, till today, a detailed scientific explanation is still lacking. Herein, we demonstrated that the native starch granules may play the role of a Pickering emulsifier, enabling them to remove oil. We evaluated the properties of native starch granules from rice, wheat and potato stabilizing corn oil and anisole emulsion in water. It was found that rice starch, with the smallest granule size, had the best emulsifying performance. After pre-treating the starch granules in heating conditions in water, however, we found that wheat starch pre-treated at 70 °C demonstrated a much more extended ability to stabilize anisole droplets. Compared with starch from rice or wheat, intact potato starch granules were larger in average and displayed poor emulsion stabilization. In order to explore the role of size factor behind this difference, we fractionated potato starch granules into six fractions by size. It seems that a smaller granule size means better emulsifying ability. In addition, by optical microscopy, the native starch granules were observed to form a particular monolayer arrangement to stabilize the oil droplets, revealing the Pickering emulsion mechanism. Although the emulsion stability for native starch is short in general, it is still promising to apply them as complete environment-friendly detergents for daily usage.

In this paper, we study functional regression with a random response curve and vector covariates. We propose a supervised least squares estimation procedure after utilizing B-spline functions to approximate the unknown functions and establish the asymptotic normality of the proposed estimators. The method has an analytic form and is easily implemented. Compared to existing methods, it does not rely on a normality assumption and can be broadly applied to sparse or non-sparse, equally or non-equally spaced, and balanced or unbalanced observations. We assess the numerical performance of the proposed procedure through simulation experiments and illustrate its performance on a real example.

Key challenges in developing generalized automatic emotion recognition systems include scarcity of labeled data and lack of gold-standard references. Even for the cues that are labeled as the same emotion category, the variability of associated expressions can be high depending on the elicitation context e.g., emotion elicited during improvised conversations vs. acted sessions with predefined scripts. In this work, we regard the emotion elicitation approach as domain knowledge, and explore domain transfer learning techniques on emotional utterances collected under different emotion elicitation approaches, particularly with limited labeled target samples. Our emotion recognition model combines the gradient reversal technique with an entropy loss function as well as the softlabel loss, and the experiment results show that domain transfer learning methods can be employed to alleviate the domain mismatch between different elicitation approaches. Our work provides new insights into emotion data collection, particularly the impact of its elicitation strategies, and the importance of domain adaptation in emotion recognition aiming for generalized systems., paper accepted by INTERSPEECH2021

Background: The CCDC family plays a significant role in the development and progression of malignant tumors. However, the relationship between CCDC family members and HCC progression is incompletely known. This study used bioinformatics analysis to investigate the expression as well as clinical prognostic value of CCDC family members in HCC and to predict the role of CCDCs family in the development and progression of HCC. Methods: This study utilized the data from two platforms databases to explore the diagnostic value and prognostic significance of CCDC family members by Cox proportional hazards regression analysis, Kaplan-Meier curve and log-rank test, ROC and nomogram diagnostic and prognostic analysis methods. GSEA and tumor microenvironment analysis were employed to investigate the underlying mechanisms and cell-cell interactions of CCDCs family in the development and progression of HCC. The relationship between mutational signatures and CCDCs family were evaluated in HCC patients with somatic mutation. Results: Five CCDC family members (CCDC34, CCDC137, CCDC77, CCDC93 and CCDC21) mRNA expression showed significantly higher in HCC tissues than in normal tissues and high expression levels of these genes predicted poor prognosis in HCC patients. The combined effect analysis of five CCDCs family prognostic markers suggests that the prognosis difference for CCDC family members combination was more significant than that for any individual CCDC family genes. We then developed a risk score model that could predict the prognosis of HCC, and nomogram gene expression was visualized with the probability of predicting the prognosis of HCC by clinical factors. GSEA revealed that, while five CCDCs family combined high expression was associated with increased cell cycle progression and low expression was associated with complement activation pathway. Mutation analysis showed that the combined high expression group had a higher TP53 mutation rate than the combined low expression group, and the high expression group showed higher TMB, which was associated with a better prognosis than high TMB. Conclusions: Our data suggest that the expression of CCDC34, CCDC137, CCDC77, CCDC93 and CCDC21 may be potential prognostic markers in HCC and in combination have a strong interaction and better predictive value for HCC prognosis.

Speech encodes a wealth of information related to human behavior and has been used in a variety of automated behavior recognition tasks. However, extracting behavioral information from speech remains challenging including due to inadequate training data resources stemming from the often low occurrence frequencies of specific behavioral patterns. Moreover, supervised behavioral modeling typically relies on domain-specific construct definitions and corresponding manually-annotated data, rendering generalizing across domains challenging. In this paper, we exploit the stationary properties of human behavior within an interaction and present a representation learning method to capture behavioral information from speech in an unsupervised way. We hypothesize that nearby segments of speech share the same behavioral context and hence map onto similar underlying behavioral representations. We present an encoder-decoder based Deep Contextualized Network (DCN) as well as a Triplet-Enhanced DCN (TE-DCN) framework to capture the behavioral context and derive a manifold representation, where speech frames with similar behaviors are closer while frames of different behaviors maintain larger distances. The models are trained on movie audio data and validated on diverse domains including on a couples therapy corpus and other publicly collected data (e.g., stand-up comedy). With encouraging results, our proposed framework shows the feasibility of unsupervised learning within cross-domain behavioral modeling.

The primary characteristic of robust speaker representations is that they are invariant to factors of variability not related to speaker identity. Disentanglement of speaker representations is one of the techniques used to improve robustness of speaker representations to both intrinsic factors that are acquired during speech production (e.g., emotion, lexical content) and extrinsic factors that are acquired during signal capture (e.g., channel, noise). Disentanglement in neural speaker representations can be achieved either in a supervised fashion with annotations of the nuisance factors (factors not related to speaker identity) or in an unsupervised fashion without labels of the factors to be removed. In either case it is important to understand the extent to which the various factors of variability are entangled in the representations. In this work, we examine speaker representations with and without unsupervised disentanglement for the amount of information they capture related to a suite of factors. Using classification experiments we provide empirical evidence that disentanglement reduces the information with respect to nuisance factors from speaker representations, while retaining speaker information. This is further validated by speaker verification experiments on the VOiCES corpus in several challenging acoustic conditions. We also show improved robustness in speaker verification tasks using data augmentation during training of disentangled speaker embeddings. Finally, based on our findings, we provide insights into the factors that can be effectively separated using the unsupervised disentanglement technique and discuss potential future directions., Comment: Submitted to Speaker Odyssey 2020

Transparent ceramics (TCs) represent a new family of functional hard materials. In this Letter, steady-state and time-resolved upconversion photoluminescence in

To provide a simple numerical formulation based on fixed grids, a wavelet element method for fluid–solid modelling is introduced in this work. Compared with the classical wavelet finite element method, the presented method can potentially handle more complex shapes. Considering the differences between the solid and fluid regions, a damping-like interface based on wavelet elements is designed, in order to ensure consistency between the two parts. The inner regions are constructed with the same wavelet function in space. In the time and spatial domains, a partitioned approach based on Jacobi iteration is combined with the pseudo-parallel calculation method. Numerical convergence analyses show that the method can serve as an alternative choice for fluid–solid coupling modelling.

Polydopamine (PDA) is an emerging nature-inspired biopolymer material that possesses many interesting properties including self-assembly and universal adhesion. PDA is also able to form coordination bonds with various metal ions, which can be reduced to metal nanoparticles (NPs) as a result of thermal annealing under protective environment. In this study, PDA has been utilized as a support material to synthesize Pt NPs in an aqueous solution at room temperature. The catalytic performance of the resulting PDA-Pt nanocomposite was evaluated using an electrochemical workstation which showed comparable activity to Pt/C material for hydrogen evolution reaction (HER). Furthermore, Cu, Ni, and Cu–Ni NPs supported on PDA were also obtained using this strategy with assistance of subsequent thermal annealing. The phase evolution of the NPs was studied by in-situ X-ray diffraction while the morphology of the nanoparticles was investigated using electron microscopic techniques. Preliminary results showed the NPs supported on PDA also possessed HER activity. This work demonstrates that PDA can be utilized as a potential support for synthesis of metal NPs that can be exploited in engineering applications such as catalysts.

Compressor blades often operate in high temper-ature, high pressure, and heavy load environments. They are in direct contact with the outside air, which causes their life to be severely limited. Blade vibration is an important factor that reflecting the state of the blade. Therefore, effective blade vibration monitoring methods are needed. Blade tip timing (BTT) is a non-contact blade vibration measurement method that has the potential to achieve real-time condition monitoring. However, one of the biggest obstacles to real-time condition monitoring is the once per revolution (OPR) sensor in BTT. Due to the internal environment limitations of aero-engines, installing the OPR sensor in an actual engine is extremely expensive, and it is almost impossible to install the OPR sensor. To solve this problem, the relationship between the blade spacing change (BSC) and the blade vibration is analyzed and the measurement principle of BSC is presented in this study. Based on BSC, we proposed a novel BTT measurement method that does not need the OPR signal. The simulation and experiment are conducted to show the rationality of the proposed method.

The harsh working environment of the compressor hinders the condition monitoring of rotating blades. Blade faults are mostly induced by foreign object damage or high cycle failure due to initial defects. Detecting these failures before blades being broken will not only ensure engine safety but largely reduce repair costs. As a non-contact measurement technique, Blade Tip Timing(BTT) becomes a more popular method recently for health monitoring and fault diagnosis. By analyzing BTT data, many blade vibration parameters could be obtained. In this paper, we discuss how to extract first bend natural frequency, amplitude and static offset from BTT data. Then the change rule of these parameters around blade fault is illustrated. Afterward, a clustering method is employed to combine these parameters. In addition, a test with two faulty blades is introduced and used to verify the effectiveness of the proposed method.

Suicide is a major societal challenge globally, with a wide range of risk factors, from individual health, psychological and behavioral elements to socio-economic aspects. Military personnel, in particular, are at especially high risk. Crisis resources, while helpful, are often constrained by access to clinical visits or therapist availability, especially when needed in a timely manner. There have hence been efforts on identifying whether communication patterns between couples at home can provide preliminary information about potential suicidal behaviors, prior to intervention. In this work, we investigate whether acoustic, lexical, behavior and turn-taking cues from military couples' conversations can provide meaningful markers of suicidal risk. We test their effectiveness in real-world noisy conditions by extracting these cues through an automatic diarization and speech recognition front-end. Evaluation is performed by classifying 3 degrees of suicidal risk: none, ideation, attempt. Our automatic system performs significantly better than chance in all classification scenarios and we find that behavior and turn-taking cues are the most informative ones. We also observe that conditioning on factors such as speaker gender and topic of discussion tends to improve classification performance., submitted to ICASSP 2020

Supplementary document

Natural frequency is an essential parameter for rotating blade condition monitoring. Various previous simulations and experiments have shown that multiple signal classification (MUSIC) has the advantage of filtering out the synchronous frequency component to make the natural frequency more prominent in the frequency domain. However, the negative effect of this characteristic is the poor performance in the resonance area because of the overlap between the synchronous frequency component and the natural frequency. This effect results in the disconnection of the natural frequency line in the resonance area. Thus, morphological filtering and mean absolute error (MAE)-based curve fitting are applied to robustly extract and restore the natural frequency line. Based on this method, automatic tracking of the natural frequency in the time–frequency domain is proposed in this study. Additionally, the mathematical principle of the inherent characteristic of MUSIC to filter out synchronous frequency components is first mentioned and explored herein. Furthermore, simulations and experiments under variable rotating frequencies are conducted to show that the proposed method can track the natural frequency under variable operating conditions.

Rabies, as one of the most threatening zoonoses in the world, causes a fatal central nervous system (CNS) disease. So far, vaccination with rabies vaccines has been the most effective measure to prevent and control this disease. At present, inactivated rabies vaccines are widely used in humans and domestic animals. However, humoral immune responses induced by inactivated rabies vaccines are relatively low and multiple shots are required to achieve protective immunity. Supplementation with an adjuvant is a practical way to improve the immunogenicity of inactivated rabies vaccines. In this study, we found that monophosphoryl-lipid A (MPLA), a well-known TLR4 agonist, could significantly promote the maturation of bone marrow-derived dendritic cells (BMDC) through a TLR4-dependent pathway in vitro and the maturation of conventional DCs (cDCs) in vivo. We also found that MPLA, serving as an adjuvant for inactivated rabies vaccines, could significantly facilitate the generation of T follicular helper (Tfh) cells, germinal center (GC) B cells, and plasma cells (PCs), consequently enhancing the production of RABV-specific total-IgG, IgG2a, IgG2b, and the virus-neutralizing antibodies (VNAs). Furthermore, MPLA could increase the survival ratio of mice challenged with virulent RABV. In conclusion, our results demonstrate that MPLA serving as an adjuvant enhances the intensity of humoral immune responses by activating the cDC&ndash, Tfh&ndash, GC B axis. Our findings will contribute to the improvement of the efficiency of traditional rabies vaccines.

Two-dimensional (2D) conducting polymers are expected to offer emergent topological, structural, and physical properties, which has become the “holy grail” for the development of plastic electronics. Here, we report the assembly of a free-floating metallic polymer layer, consisting of poly(3,4-ethylenedioxythiophene) complexed with poly(styrenesulfonate) anions, directed by electrostatic recognition, amphiphilicity, and aromatic interactions. The obtained large-area crystalline nanosheets exhibit excellent environmental stability and mechanical robustness, meanwhile showing an electrical conductivity of 1216 S·cm–1, the highest among the nanometer-thick conducting polymers. The kirigami-inspired freestanding polymer thermoelectrics, repeatedly stretching up to 200% strain, is demonstrated with high Seebeck coefficient with an optimized power factor of 95 μW m–1 K–2. The large-scale assembly and aqueous compatibility of 2D conducting polymers provide an exciting platform for integrating thermoelectricity i...

This article expresses the constant elasticity of variance stock loans pricing formula in terms of the noncentral chi-square distribution. By using the numerical solutions of the integral equation ...

Metal matrix composites with nanocarbon phases, such carbon nanotube (CNT) and graphene, have shown potentials to achieve improved mechanical, thermal, and electrical properties. However, incorporation of these nanocarbons into the metal matrix usually involves complicated processes. This study explored a new processing method to fabricate copper (Cu) matrix composite by coating Cu powder particles with nanometer-thick polydopamine (PDA) thin films and sintering of the powder compacts. For sintering temperatures between 300 °C and 750 °C, the Cu-PDA composite samples showed higher electrical conductivity and thermal conductivity than the uncoated Cu samples, which is likely related to the higher mass densities of the composite samples. After being sintered at 950 °C, the thermal conductivity of the Cu-PDA sample was approximately 12% higher than the Cu sample, while the electrical conductivity did not show significant difference. On the other hand, Knoop micro-hardness values were comparable between the Cu-PDA and Cu samples sintered at the same temperatures.

In this study, copper (Cu)/polydopamine (PDA) composite was fabricated by vacuum hot-press sintering of micrometer-sized Cu particles covered with PDA film tailored at the nanometer scale thickness. The resultant compacts exhibited much higher electrical conductivity than the uncoated counterparts. Analyses of sintered samples using scanning and transmission electron microscopy, Raman spectroscopy, and X-ray photoelectron spectroscopy revealed the conversion of PDA into carbonized PDA (cPDA). The increased mass density and grain growth are likely responsible for improved electrical conductivity of the composite material.