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Inspired by the success of classical neural networks, there has been tremendous effort to develop classical effective neural networks into quantum concept. In this paper, a novel hybrid quantum–classical neural network with deep residual learning (Res-HQCNN) is proposed. We firstly analyse how to connect residual block structure with a quantum neural network, and give the corresponding training algorithm. At the same time, the advantages and disadvantages of transforming deep residual learning into quantum concept are provided. As a result, the model can be trained in an end-to-end fashion, analogue to the backpropagation in classical neural networks. To explore the effectiveness of Res-HQCNN , we perform extensive experiments for quantum data with or without noisy on classical computer. The experimental results show the Res-HQCNN performs better to learn an unknown unitary transformation and has stronger robustness for noisy data, when compared to state of the arts. Moreover, the possible methods of combining residual learning with quantum neural networks are also discussed.

In many countries, outpatients generally visit a major hospital without a referral from health professionals due to the shortage of family physicians. Not knowing at which medical specialty department to register, outpatients have to wait in long queues to consult receptionists. We propose to alleviate this situation via a computer system offering an automatic recommendation of departments (ARD) to outpatients, which identifies the appropriate medical department for outpatients according to their chief complaints. Besides, ARD systems can boost the emerging services of online hospital registration and online medical diagnosis, which require that the outpatients know the correct department first. ARD is a typical problem of text classification. Nevertheless, off-the-shelf tools for text processing may not suit ARD, because the chief complaints of outpatients are generally brief and contain much noisy information. To solve this problem, we propose ARD-K, a deep learning framework incorporating external medical knowledge sources. We also propose a dual-attention mechanism to mitigate the interference of noisy words and knowledge entities. The performance of ARD-K is compared with some off-the-shelf techniques on a real-world dataset. The results demonstrate the effectiveness of ARD-K for the automatic recommendation of departments to outpatients.

Dielectric tunable properties in ferroelectric ceramics have been optimized by a variety of approaches, e.g., isovalent/aliovalent substitution, system composite et al., while the study of non-stoichiometric regulation on the performance of BCZT ceramics was rarely concerned. Herein, a series of novel non-stoichiometric (Ba0·91Ca0.09)x (Zr0·18Ti0.82)O3 (BCZTx) ceramic specimens were successfully prepared via solid-state reaction. The microstructures of BCZTx ceramics are simultaneously investigated through XRD and SEM. The diffuse phase transition (DPT) behaviour of BCZTx ceramics are studied by the Lorentz-type empirical formula. The significant enhancement of DPT behaviour is observed at x = 1.03, which probably due to the coaction of the generation of partial Schottky defects and prominent reduction of grain size. Furthermore, a high tunability (k) 87.80%, low dielectric loss (tan δ) 0.141%, and a remarkably enhanced FOM of 623 are achieved in x = 0.99 at a low DC bias electric fields (BEFs) of 7.28 kV/cm and room temperature (RT), which is superior to that of the stoichiometric BCZT ceramics and other available reported BT-based ceramics systems in term of the dielectric tunable properties. Meanwhile, it shows that the temperature dependent CQF value of x = 0.99 remained advantageous around the RT. These findings suggested that non-stoichiometric BCZTx ceramics with x = 0.99 are significantly competitive in the applications of dielectric tunable devices at RT. The non-stoichiometric regulation is an effective approach in improving the dielectric tunability properties of BCZT ceramics.

Dielectric tunable devices with improved overall tunability properties are in urgent demand for tunable applications. Hence, a series of Pb-free dielectric tunable ceramics based on (Ba1−xCax) (Zr0·18Ti0.82)O3 (abbreviated as BCZTx, x = 0.05–0.21, corresponding to BCZT05 to BCZT21) were carefully prepared using the traditional solid-state route in this work. The crystal structure, surface morphology, dielectric properties, and tunable performance of different BCZTx ceramics at room temperature (RT) were systematically studied. The temperature-dependent dielectric tunable performance was further investigated. The phase evolutions were co-confirmed by XRD and d e r d T -T curves. Interesting, high tunability (86.09%), together with a relatively low dielectric loss (~0.19% @7.42 kV/cm) were obtained in BCZT09 ceramics at 1 kHz, resulting in a giant figure-of-merit (FOM) of 448, which implied these ceramics are promising matrix for dielectric tunable applications that operated at RT. From the Tunability−T and FOM−T curves, it can be found that Ca2+ incorporation can improve the temperature stability of dielectric tunable performance to a certain extent. In addition, the FOM−T curve of BCZT09 showed advantages over other compositions in the temperature range of −20 °C–85 °C, and its maximum FOM (~886) was reached at 40 °C. These observations suggest the BCZT09 ceramic as a promising matrix for application in dielectric tunable devices operating at RT. This work may guide the design of novel high-performance tunable ceramic materials.

In the process of building electrical load data collection, it is inevitable to introduce different kinds of noises, which makes the observation values deviate from the actual values, thus resulting in high levels of uncertainties. And such uncertainties make it difficult to achieve accurate point prediction of the short-term building electrical load. To improve the rationality of the prediction results and offer more effective information for decision makers, this paper proposes a novel multi-objective algorithm optimized modular fuzzy method which can accomplish the interval prediction for the short-term electrical load. First, one novel single-input-rule-modules (SIRMs)-based distributed interval fuzzy model (SIRM-DIFM) is proposed by replacing the original functional weights of the traditional SIRMs-based fuzzy inference system (SIRM-FIS) with the interval functional weights. Then, a data-driven learning scheme is presented for constructing the SIRM-DIFM. This learning sheme includes two main steps. The first step utilizes the iterative least square method to generate fuzzy rules for the SIRMs and determine the centers of the interval functional weights, while in the second step, the genetic algorithm (GA)-based multi-objective optimization algorithm is adopted to determine the widths of the interval functional weights. Through these two steps, accurate point estimation and reasonable interval prediction results can be achieved. Finally, two building electrical load prediction experiments are conducted to verify the effectiveness of the presented SIRM-DIFM. Simulation results indicate that the proposed SIRM-DIFM can compensate the shortcomings of the low accuracy of the point estimation and the predicted interval can effectively cover the observed data, providing the decision-makers more reliable and useful information.

In order to simultaneously detect high-frequency dynamic parameters and low-frequency quasi-static parameters through spectrum acquisition, a novel frequency-scanned parallel phase-shifting interferometry is proposed in this work. A Michelson interferometer (MI) composed of a 3 × 3 coupler is used for high-frequency vibration measurement. Different from conventional 3 × 3 fiber coupler based demodulation methods, a frequency swept laser source with flat intensity output is used. The time-varying vibrations will be encoded on the phase shifted spectra measured at two return ports of the coupler. Using a modified phase shifting algorithm, the optical path difference (OPD) changes along optical frequency can be retrieved, thereby significantly improving the sampling rate of the dynamic MI. Thanks to the full-spectrum scanning, the asymmetric parameters of non-ideal 3 × 3 couplers can be directly measured. Moreover, it provides the possibility of spectral demodulation of multiple quasi-static parameters. Another sensing channel containing multiple fiber Bragg grating (FBG) sensors is used to achieve quasi-distributed temperature measurement. Experimental results demonstrate its effectiveness in dynamic/static multi-parameter measurement.

When the cavity changes during spectrum acquisition process, Doppler phenomena will occur and cause conventional scanning white-light interferometry (WLI) to fail. To absolutely demodulate the cavity length of the dynamic low-finesse Fabry–Perot sensor, a novel laser frequency modulated phase-shifting WLI (PS-WLI) is proposed. Two phase-shifted spectra can be obtained by introducing a fixed phase-shift after each scanning frequency. The phase results containing modulations of frequency scanning and dynamic signals are retrieved based on quadrature demodulation. Although the full-spectrum acquisition frequency of the proposed PS-WLI is only 50 Hz, 0.5∼20 kHz vibration signals can be demodulated. It combines the advantages of frequency-switched two-step phase shifting interferometry (PSI) and scanning WLI to achieve absolute measurement with high sampling rate. Numerical simulations and experiments verify its effectiveness.

Optical fiber hot-wire anemometer is an emerging technique for wind speed measurement. In many practical applications, both the magnitude and the direction of the flow need to be obtained. However, most of previously reported fiber optic anemometers show omnidirectional response, rendering difficulty in recognizing the flow direction. Here, we propose a simple and effective fiber-optic flow sensor with directional response based on an asymmetric coating of single-wall carbon nanotubes (SWCNTs) on the surface of tilted fiber Bragg grating (TFBG), paving the way for the vector analysis of wind flow field. FLUENT software was employed to analyze the wind speed field, temperature distribution field, and vortex street. The temperature field shows asymmetrical distribution directly related to the wind directions, causing directional response of the sensor to the wind flow. In the experiments, with optimized 12˚ TFBG with 1.3 μm thick asymmetric SWCNTs coating, three wind directions at different angle (0˚, 180˚, 270˚) were clearly distinguished. Moreover, high sensitivity and efficiency were also achieved by investigating and optimizing the system parameters like pumping power, coating thickness. In addition to directional response, the proposed sensor exhibits many advantages such as simple structure, low power consumption, small size, and high sensitivity, which has good potential application for infield static wind field and slowly varying wind (SVW) measurement.

Herein, we report the simultaneously application of surface plasmon resonance (SPR) spectroscopy and electrochemistry (EC) technology for the real-time quantitative investigation of heavy metal ions, with high selectivity and sensitivity. A surface of optical fiber coated over with nanometric gold film and then modified with a dithiol self-assembled monolayer of 1,6-hexanedithiol (HDT) was developed as the dual mode electrochemical-surface plasmon resonance (EC-SPR) optical fiber sensor. The limit of detection of the proposed sensor exposed in Hg2+ ions solution without and with the multiple cyclic voltammetry (CV) scans to activate the HDT monolayer can be improved from 0.47 μM to 0.22 μM. More importantly, the CV scans can activate the redox reaction of Hg2+ ions, and then easily strip the Hg2+ ions from the HDT monolayer to restore the active sites, and endow the designed sensor the characteristics of repeated use. Under simultaneous SPR monitoring, the differential pulse voltammetry (DPV) currents of HDT-functionalized optical fiber can also act as a microelectrode to develop electrochemical sensor platform for monitoring Cu2+, with a limit of detection of 94 nM and a dynamic range up to 90 μM. As a comparison, the EC-SPR sensor still has good analysis performance for both Hg2+ and Cu2+ ions in presence of Ni2+, Fe2+, Co2+, Pb2+, K+, among others. We therefore demonstrated that the proposed fiber-based EC-SPR sensor has the advantages of real-time, miniaturized, and cost-effective sensing, paving the way for environmental monitoring applications for on site inspection.

The characteristics of how graphite particles adhere to the walls of high-temperature gas-cooled reactors are very important for analyzing reactor source terms. In the present study, atomic force microscopy (AFM) is used to measure the particle adhesion force and the wall morphology, then Lennard-Jones potential theory and the finite element method (FEM) are coupled to calculate the adhesion force between different particles and rough walls. The obvious deviations between the AFM measurements and the theoretical models are due to the contact hypothesis of a sphere with a smooth flat plane in the latter. The FEM results reveal the formation of maximum adhesion at the pull-off point of the particle and the corresponding stress distribution. For aspherical particles, the local curvature of the particle contact interface is the main factor affecting the adhesion force. In addition, for rough walls, different contact regimes correspond to different adhesion trends. When discrete wall roughness with hemispheres and truncated cones is used, the FEM predictions are closer to the AFM measurements, indicating that roughness dominates the adhesion weakening.

The isolation and detection of rare circulating tumor cells (CTCs) from patient peripheral blood can help early diagnosis of cancer and evaluation of therapeutic outcomes. At present, most of the available strategies for enriching CTCs face serious problems with purity due to the nonspecific interactions between the capture medium and leukocytes. Inspired by the immune evasion ability of homologous red blood cells (RBCs), we modified the tumor-targeting molecule folic acid (FA) on the surface of RBCs by hydrophobic interactions. Under the treatment of polybrene, the charges on the surface of RBCs are neutralized, which reduces the mutual repulsion force. Furthermore, RBCs treated with polyethylene also have excellent deformability, thereby enabling engineered RBCs to form a dense bionic layer on the adhesive glass slide, which can greatly inhibit the nonspecific adhesion of leukocytes. The bionic layer can achieve high-purity enrichment of tumor cells in phosphate-buffered saline (PBS), and we can achieve high-activity release in plasma. The cell count showed over 80% capture efficiency and over 70% release rate, and the purity of CTCs obtained in the artificial blood sample after release was higher than 90%. The RBC bionic surface coating is notably cost-effective and highly applicable for CTC isolation in clinic practice, and thus provides new prospects for designing cell-material interfaces for advanced cell-based biomedical studies in the future.

The strength of backfill plays an important role in the stability and safe mining of goaf. Considering the influence of underground acid environment, the physical characteristics and corrosion mechanism of cemented tailings backfill (CTB) are studied. Based on the accelerated test method of the acid environment, the mass change fraction, longitudinal wave velocity, porosity, volume and appearance change in sulfuric acid solution of the CTB in different corrosion periods were measured and analyzed. In addition, the concentration of H+ in solution was monitored, and the surface material of the corroded backfill was analyzed by X-ray diffraction and scanning electron microscopy. The results show that acidic corrosion increases the porosity, longitudinal wave velocity, and volume of the CTB after soaked 60 days. Acidic corrosion also increases the quality of the CTB in the early stage of corrosion. The quality of the CTB deceased with day of corrosion in long term. Corrosion causes the surface of the CTB to bulge and fall off. The corrosion mechanism of the CTB was found to be generated by two kinds of destructive factors: the dissolution reaction of H+ ion and S042− ion resulted in the swelling material.

In extreme heat transfer environments, functionally graded materials (FGMs) have aroused great concern due to the excellent thermal shock resistance. With the development of micro-scale devices, the size-dependent effect has become an important issue. However, the classical continuum mechanical model fails on the micro-scale due to the influence of the size-dependent effect. Meanwhile, for thermoelastic behaviors limited to small-scale problems, Fourier’s heat conduction law cannot explain the thermal wave effect. In order to capture the size-dependent effect and the thermal wave effect, the nonlocal generalized thermoelastic theory for the formulation of an FGM microbeam is adopted in the present work. For numerical validation, the transient responses for a simply supported FGM microbeam heated by the ramp-type heating are considered. The governing equations are formulated and solved by employing the Laplace transform techniques. In the numerical results, the effects of the ramp-heating time parameter, the nonlocal parameter, and the power-law index on the considered physical quantities are presented and discussed in detail.

Oxygen vacancy in oxide ferroelectrics can be strongly coupled to the polar order via local strain and electric fields, thus holding the capability of producing and stabilizing exotic polarization patterns. However, despite intense theoretical studies, an explicit microscopic picture to correlate the polarization pattern and the distribution of oxygen vacancies remains absent in experiments. Here we show that in a high-quality, uniaxial ferroelectric system, i.e., compressively strained BaTiO3 ultrathin films (below 10 nm), nanoscale polarization structures can be created by intentionally introducing oxygen vacancies in the film while maintaining structure integrity (namely no extended lattice defects). Using scanning transmission electron microscopy, we reveal that the nanodomain is composed of swirling electric dipoles in the vicinity of clustered oxygen vacancies. This finding opens a new path toward the creation and understanding of the long-sought topological polar objects such as vortices and skyrmions.

Shock waves can significantly affect the film cooling for supersonic flow and shock waves may have different influence when impinging in different regions. The present study numerically compared the results of shock wave impinging in three different regions and analyzed the effect of impinging region. The shock wave generators were located at x/s = 5, 25, 45 with 4°, 7° and 10° shock wave incidence. The mainstream Mach number was 3.2 and the coolant Mach number was 1.2 or 1.5. The numerical results illustrated that the shock wave impinged in the further upstream region led to a larger high-pressure region and a larger vortex in the boundary layer. Moreover, placing the shock wave generator upstream resulted in the lower mass fraction of coolant in the downstream region. The velocity in the upstream part of the cooling layer was lower than the midstream and downstream part, which resulted in the less ability to resist the shock wave impingement. Therefore, the upstream impingement deteriorated the cooling performance to a greater extent. The study also manifested that the stronger shock wave had a larger effect on supersonic film cooling. Increasing the coolant inlet Mach number can increase the blowing ratio and reduce the mixing, which was of benefit to improve cooling effect.

Background Some proposed methods for identifying essential proteins have better results by using biological information. Gene expression data is generally used to identify essential proteins. However, gene expression data is prone to fluctuations, which may affect the accuracy of essential protein identification. Therefore, we propose an essential protein identification method based on gene expression and the PPI network data to calculate the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network. Our experiments show that the method can improve the accuracy in predicting essential proteins. Results In this paper, we propose a new measure named JDC, which is based on the PPI network data and gene expression data. The JDC method offers a dynamic threshold method to binarize gene expression data. After that, it combines the degree centrality and Jaccard similarity index to calculate the JDC score for each protein in the PPI network. We benchmark the JDC method on four organisms respectively, and evaluate our method by using ROC analysis, modular analysis, jackknife analysis, overlapping analysis, top analysis, and accuracy analysis. The results show that the performance of JDC is better than DC, IC, EC, SC, BC, CC, NC, PeC, and WDC. We compare JDC with both NF-PIN and TS-PIN methods, which predict essential proteins through active PPI networks constructed from dynamic gene expression. Conclusions We demonstrate that the new centrality measure, JDC, is more efficient than state-of-the-art prediction methods with same input. The main ideas behind JDC are as follows: (1) Essential proteins are generally densely connected clusters in the PPI network. (2) Binarizing gene expression data can screen out fluctuations in gene expression profiles. (3) The essentiality of the protein depends on the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network.

Citrus peels are considered as an agricultural waste with negative environmental impacts. In the present study, yellow (ripe) and green (unripe) Citrus reticulata peels were dried in a hot air impi...

Ceramics' micromorphology plays a pivotal role on their dielectric and tunable properties. Thus, many methods are used to modify the micromorphology of ceramics to improve the dielectric and tunable properties. However, the mechanisms about the influence of raw materials' particle diameters on the dielectric and tunable properties have seldom been investigated. In this study, the (Ba0.91Ca0.09)(Zr0.2Ti0.8)O3 ceramics were synthesized using the nanoscale BaTiO3 (BT) powders as Ti sources to study the effect of the BT particle diameters on the structure, micromorphology, dielectric properties, and tunable performance. When the BT particle diameters decreased from 500 nm (BT05) to 400 nm (BT04), the grain size sharply increased and then slightly decreased as the particle diameter further decreased to 100 nm (BT01) due to the influence of oxygen vacancies. The generated oxygen vacancies, which were verified by the XPS results, introduced compressive stress, resulting in the reduction of the cell volume and Curie temperature. A large e value (~17,500) was achieved at room temperature when the particle diameters were 100 nm. The large e contributed to the high tunability (~70%), which was greater than that of the BT05 ceramic (~49%). Due to the high tunability, the BT01 ceramic's figure of merit was more than twice as that of the BT05 ceramic. This study demonstrated that the dielectric and tunable properties of (Ba0.91Ca0.09)(Zr0.2Ti0.8)O3 ceramics can also be controlled by the raw materials' particle diameters.

Spin-orbit torques (SOTs) from transition metal dichalcogenides systems (TMDs) in conjunction with ferromagnetic materials are recently attractive in spintronics for their versatile features. However, most of the previously studied crystalline TMDs are prepared by mechanical exfoliation, which limits their potentials for industrial applications. Here we show that amorphous WTe2 heterostructures deposited by magnetron sputtering possess a sizable damping-like SOT efficiency {\xi}_DL^WTe2 ~ 0.20 and low damping constant {\alpha} = 0.009/pm0.001. Only an extremely low critical switching current density J_c ~ 7.05\times10^9 A/m^2 is required to achieve SOT-driven magnetization switching. The SOT efficiency is further proved to depend on the W and Te relative compositions in the co-sputtered W_100-xTe_x samples, from which a sign change of {\xi}_DL^WTe2 is observed. Besides, the electronic transport in amorphous WTe2 is found to be semiconducting and is governed by a hopping mechanism. With the above advantages and rich tunability, amorphous and semiconducting WTe2 serves as a unique SOT source for future spintronics applications.

The development of efficient and stable catalysts for the oxygen reduction reaction (ORR) at low cost is crucial for realizing the large-scale application of metal-air batteries. Herein, we report an efficient ORR catalyst of bimetallic copper and cobalt fluoride heterojunctions, which are uniformly dispersed in nitrogen-fluorine-oxygen triply doped porous carbon nanofibers (PCNFs) that contain hierarchical macro-meso-micro pores. The composite catalyst materials are fabricated with a facile and green method of electrospinning with water as the solvent. By using poly(tetrafluoroethylene) as the pore inducer to anchor electropositive copper and cobalt salts in the electrospun hybrid nanofibers, bimetallic fluoride heterojunctions can be directly formed in PCNFs after calcination. The hierachical porous structures provide an effective way to transport matter, while the bimetallic fluorides expose abundant electroactive sites, both of which result in stable ORR activities with a high half-wave potential of 0.84 V. The study proposes a feasible strategy for the fabrication of nonprecious catalysts.

In this study, we investigated the dielectric tunable performance of (BaxCa1-x)(Zr0.2Ti0.8)O3 ceramics (x = 0.95, 0.91 and 0.87) using Landau-Devonshire theory. According to the dielectric constant-temperature (e-T) curves, the Curie temperature is found to be 15.0 °C, 5.0 °C and 2.5 °C for x = 0.95, 0.91 and 0.87, respectively. It indicates that all the prepared ceramics possess a low loss tangent ( tan δ ) at room temperature, which is in favor of achieving a high figure of merit (FOM) and commutation quality factor (CQF). For a better understanding of the dielectric tunable performance, the Landau-Devonshire theory is used. The associated Landau coefficients have been derived based on the e-T and tunability-electric field (k-E) curves. According to the calculated free energy-electric field and polarization-electric field curves, the prepared ceramics are in the paraelectric phase at room temperature, which is in accord with the e-T curves. Meanwhile, the calculated results indicate that a high dielectric constant is obtained in the (Ba0.91Ca0.09)(Zr0.2Ti0.8)O3 ceramic at room temperature. Owing to the high e and low tan δ , a high value of FOM and CQF are obtained in the (Ba0.91Ca0.09)(Zr0.2Ti0.8)O3 ceramic at ~740 kV/m. The high value of FOM (197.7) and CQF (43130) indicates that the (Ba0.91Ca0.09)(Zr0.2Ti0.8)O3 ceramic has a potential application in tunable devices.

Having an accurate understanding of the individual’s Kansei needs and afterwards designing products that match these needs are particularly important in the era of mass personalisation. Although customers’ Kansei needs have been addressed by Kansei engineering, difficulties remain in handling the differences of individual Kansei. In this paper, individual Kansei variance is considered to transform the Kansei words into multisensory design elements, to help designers better understand the individual's Kansei needs. First, a fuzzy cognitive model is proposed to identify the individual Kansei differences in Kansei words by taking customers’ characteristics and purchasing motives into consideration. Second, a fuzzy cognitive model-based mapping method is proposed to interpret Kansei words into multisensory design elements. The method incorporates a fuzzy clustering method and basic-emotion systems to identify Kansei variance and to determine design elements’ membership of Kansei words dynamically. Finally, the prototype application of the proposed method on a compact SUV is illustrated. The results suggest that individual differences in Kansei terms do exist among customers in the same market segment, and the proposed method has good feasibility and practicability in handling individual Kansei differences in emotional design. Those Kansei dimensions that are more prominent in individual Kansei variance are highly recommended for further digging, which would benefit carrying out personalised customisation and differentiated design.

Relaxor ferroelectrics are attracting an increasing interest in the application of pulse power systems due to their excellent energy storage performance. In this paper, the (1-x)(Ba0·85Ca0.15)(Zr0·1Ti0.9)O3-xBi(Mg0·5Ti0.5)O3 ((1-x)BCZT-xBMT, x ≤ 0.2) relaxor ceramics are prepared by the solid state method. The influence of BMT on the microstructure, dielectric and energy storage properties of the prepared ceramics is investigated. The XRD results show that the peak intensity of impurities (Bi2O3, TiO2 and Ba2Bi4Ti5O18) is gradually stronger than that of BCZT phase with x increasing. Meanwhile, the grain size of (1-x)BCZT-xBMT ceramics gradually increases on account of the appearance of impurities Bi2O3. Influenced by the impurities and BMT, the dielectric constant of prepared ceramics gradually decreases with x increasing. A large Wrec value of 0.65 J/cm3 with an ultrahigh η value of 97.89% is achieved at x = 0.15 due to the high breakdown strength and slim P-E hysteresis loop. Meanwhile, the η is insensitive to the electric field. The ultrahigh η leads to lesser energy loss during the charge and discharge process. It makes the 0.85BCZT-0.15BMT ceramic more attractive in the application of pulse power systems.

In this paper, we propose and demonstrate a high-performance mercury ion sensor with sub-nM detection limit, high selectivity, and strong practicability based on the small molecule of the 4-mercaptopyridine (4-MPY) modified tilted fiber Bragg grating surface plasmon resonance (TFBG-SPR) sensing platform. The TFBG-SPR sensor has a rich mode field distribution and a narrow bandwidth, which can detect the microscopic physical and chemical reactions on the sensor surface with high sensitivity without being disturbed by the external temperature. For the environmental compatibility and highly efficient capture of the toxic mercury ion, 4-MPY is modified on the sensor surface forming a stable (4-MPY)-Hg-(4-MPY) structure due to the specific combination between the nitrogen of the pyridine moiety and the Hg2+ via multidentate N-bonding. Moreover, gold nanoparticles (AuNPs) are connected to the sensor surface through the (4-MPY)-Hg-(4-MPY) structure, which could play an important role for signal amplification. Under the optimized conditions, the limit of detection of the sensor for mercury ions detection in the solution is as low as 1.643×10−10 M (0.1643nM), and the detection range is 1×10−9 M − 1×10−5 M. At the same time, the mercury ion spiked detection with tap water shows that the sensor has the good selectivity and reliability in actual water samples. We develop a valuable sensing technology for on-time environmental Hg2+ detection and in-vivo point of care testing in clinic applications.

Strong electromagnetic coupling among plasmonic nanostructures paves a new route toward efficient manipulation of photons. Particularly, plasmon-waveguide systems exhibit remarkable optical properties by simply tailoring the interaction among elementary elements. In this paper, we propose and demonstrate a freestanding bilayer plasmonic-waveguide structure exhibiting an extremely narrow transmission peak with efficiency up to 92%, the linewidth of only 0.14 nm and an excellent out of band rejection. The unexpected optical behavior considering metal loss is consistent with that of electromagnetic induced transparency, arising from the destructive interference of super-radiative nanowire dipolar mode and transversal magnetic waveguide mode. Furthermore, for slow light application, the designed plasmonic-waveguide structure has a high group index of approximately 1.2 × 105 at the maximum of the transmission band. In sensing application, its lowest sensing figure of merit is achieved up to 8500 due to the ultra-narrow linewidth of the transmission band. This work provides a valuable photonics design for developing high performance nano-photonic devices.

The electrochemical reduction of CO2 to an ethanol product is regarded as a highly promising route for CO2 utilization. However, the poor selectivity is still a critical challenge for increasing the yield of the specific ethanol. As a CO2 reduction catalyst, the hierarchically nanoporous copper integrated with vanadium oxide can achieve a 30.1% faradaic efficiency for CO2-to-ethanol production and an ethanol partial current density of −16 mA cm−2 at −0.62 V vs. RHE, corresponding to a 4-fold increase in activity compared to bare nanoporous Cu. It even delivers an ethanol partial current density that exceeds −39 mA cm−2 at −0.8 V vs. RHE in a flow-cell reactor. The hierarchically nanoporous Cu skeleton not only facilitates both electron and electrolyte transport but also provides a large specific surface area for high active site density. Density functional theory reveals that the vanadium oxide decorated Cu surface can facilitate water dissociation and optimize the hydrogen adsorption energy on Cu, lowering the energy barrier for the protonation of carbon dioxide and C–C coupling. Meanwhile, it can increase hydrogen proton coverage on the catalyst surface and inhibit dehydration, which are beneficial for breaking the CC bond of the *HCCOH intermediate, thus enhancing the faradaic efficiency of ethanol significantly. The highly efficient conversion of CO2 to ethanol demonstrates that the hybrid electrocatalyst is considered as a promising candidate for practical electrocatalytic CO2RR applications.

The development of minimally invasive and easy-to-use sensor devices is of current interest for ultrasensitive detection and signal recognition of Alzheimer's disease (AD) biomarkers. Over the years, tremendous effort has been made on diagnostic platforms specifically targeting neurological markers for AD in order to replace the conventional, laborious, and invasive sampling-based approaches. However, the sophistication of analytical outcomes, marker inaccessibility, and material validity strongly limit the current strategies towards effectively predicting AD. Recently, with the promising progress in biosensor technology, the realization of a clinically applicable sensing platform has become a potential option to enable early diagnosis of AD and other neurodegenerative diseases. In this review, various types of biosensors, which include electrochemical, fluorescent, plasmonic, photoelectrochemical, and field-effect transistor (FET)-based sensor configurations, with better clinical applicability and analytical performance towards AD are highlighted. Moreover, the feasibility of these sensors to achieve point-of-care (POC) diagnosis is also discussed. Furthermore, by grafting nanoscale materials into biosensor architecture, the remarkable enhancement in durability, functionality, and analytical outcome of sensor devices is presented. Finally, future perspectives on further translational and commercialization pathways of clinically driven biosensor devices for AD are discussed and summarized.

Interferometric fiber-optic microphones (FOMs) with outstanding sensitivity are ideal method to detect weak acoustic signals. In this work, we demonstrated an ultrasensitive fiber-optic Michelson microphone with dual enhancement by using a diaphragm-free acoustic transducer and a high-resolution optical path difference (OPD) demodulation device. A cylindrical polyvinyl chloride (PVC) tube wrapped around by the optical fiber was employed as a transducer to enhance the responsivity to weak acoustic wave. A high-resolution spectrum demodulation-based white-light interferometer (WLI) was utilized to measure the variations of OPD. In theory, the FOM owns features to overcome defects of the conventional diaphragm-based or intensity demodulation method-based FOMs, such as quadrated point ( ${Q}$ -point) drifting and being sensitive to environmental interference. Experimental results showed that this FOM had an ultrahigh sensitivity of ~6800 nm/Pa at the resonance frequency of 1455 Hz. The sensitivity and the noise-limited minimum detectable sound pressure level (MDSPL) at 1000 Hz were calculated to be 315.3 nm/Pa and − 20.4 dB/Hz1/2 ( $1.9~\mu $ Pa/Hz1/2), respectively.

This work mainly focuses on modeling the impact of bulk resistivity $ \rho_b $ on the bifacial n -type passivated emitter rear and totally diffused rear-junction ( n -PERT-RJ) solar cells using Quokka3. Through simulation, under the front illumination, the light-generated excess carrier flow behavior in the bulk is studied. The regulatory function of the rear-junction solar cells’ bulk on the electron flows distributary is discussed. Finally, how the different $ \rho_b $ values affect the hole-current flow behavior and the FSF performance is revealed. This explains very well the reasons for decreasing short-circuit current J SC because of the low $ \rho_b $ wafers in n -PERT-RJ solar cells. The studies also provide insights on the lowest value of $ \rho_b $ that can be applied to n -PERT-RJ solar cells without the efficiency deterioration. We also simulated the n -PERT-RJ solar cell performance and bifaciality when applying different $ \rho_b $ values under rear illumination. Last but not the least, investigation on how $ \rho_b $ affects the output performance will be extended to when n- PERT-RJ solar cells are served as the bottom subcell in a four-terminal tandem configuration.

Driven by the lifestyle habits of modern people, such as excessive smoking, drinking, and chewing betel nut and other cancer-causing foods, the incidence of oral cancer has increased sharply and has a trend of becoming younger. Given the current mainstream treatment means of surgical resection will cause serious damage to many oral organs, so that patients lose the ability to chew, speak, and so on, it is urgent to develop new oral cancer treatment methods. Based on the strong killing effect of photothermal therapy on exposed superficial tumors, we developed a pH-responsive charge reversal nanomedicine system for oral cancer which is a kind of classic superficial tumor. With excellent photothermal properties of polydopamine (PDA) modified black phosphorus nanosheets (BP NSs) as basal material, then used polyacrylamide hydrochloride-dimethylmaleic acid (PAH-DMMA) charge reversal system for further surface modification, which can be negatively charged at blood circulation, and become a positive surface charge in the tumor site weakly acidic conditions due to the breaking of dimethylmaleic amide. Therefore, the uptake of oral cancer cells was enhanced and the therapeutic effect was improved. It can be proved that this nanomedicine has excellent photothermal properties and tumor enrichment ability, as well as a good killing effect on oral cancer cells through in vitro cytotoxicity test and in vivo photothermal test, which may become a very promising new model of oral cancer treatment.

Benefited from the powerful ability of spatial temporal Graph Convolutional Networks (ST-GCNs), skeleton-based human action recognition has gained promising success. However, the node interaction through message propagation does not always provide complementary information. Instead, it May even produce destructive noise and thus make learned representations indistinguishable. Inevitably, the graph representation would also become over-smoothing especially when multiple GCN layers are stacked. This paper proposes spatial-temporal graph deconvolutional networks (ST-GDNs), a novel and flexible graph deconvolution technique, to alleviate this issue. At its core, this method provides a better message aggregation by removing the embedding redundancy of the input graphs from either node-wise, frame-wise or element-wise at different network layers. Extensive experiments on three current most challenging benchmarks verify that ST-GDN consistently improves the performance and largely reduce the model size on these datasets.

The development of earth-abundant, non-noble, high-performance hydrogen evolution reaction (HER) electrocatalysts is still a highly challenging but vitally important issue for energy conversion system. Herein, we reported a self-supported Mo modified hierarchical nanoporous Cu as an efficient electrocatalyst for hydrogen evolution. The optimized nanoporous Cu-Mo electrocatalysts with extremely dilute Mo content exhibits a high HER activity with a negligible onset potential, a small Tafel slope, and an excellent durability in alkaline solution. The dealloying process provides nanoporous Cu-Mo electrocatalysts a unique three-dimensional interconnected bicontinuous nanoporous architecture, which can not only offer high-density catalytic active sites for HER, but also accelerate the desorption of hydrogen molecule from catalysts surface. Density functional theory (DFT) calculations reveal that the introducing of Mo into Cu matrix can accelerate water adsorption and dissociation and optimize adsorption-desorption energetics of H intermediates, thus improving the intrinsic HER activity of nanoporous Cu-Mo electrocatalysts.

Compared with ground views and direct overhead views (for orbital satellites), aerial robotics allow for capturing videos from diverse viewpoints and scenes, thus, the content of aerial image is complex and changeable, and aerial video has complex inter-frame transforms stemming from the blend of camera motion, platform motion and jitter. In addition, poor quality and similar texture are common in long-distance and large-scale aerial video surveillance. All of these interferences make image registration of aerial video difficult. This article puts forward one image registration method suited to aerial video on the basis of the hypothesize-and-verify of RANSAC. The proposed accelerated RANSAC, named PSSC-RANSAC (Prior Sampling & Sample Check RANSAC), incorporates prior sampling, which comes from three levels of sample evaluation, including texture magnitude, spatial consistency and feature similarity, to generate more possibly correct samples in priority. Furthermore, prior information of sample, quality of sample subset and subset invariability are together used to check the sample subsets, and the incompatible arrangements of subsets are immediately ruled out in sample check stage, which speeds up the iteration further. Results of the experiment have proved the good performance of the presented PSSC-RANSAC at 90% contamination level. For typical image pairs, the number of iterations is reduced by at least 16.67% and evaluation computation is reduced by at least 11.01% compared with SVH-RANSAC, and the re-projection error is decreased by at least 4.44% and 6.31% compared with RANSAC and SVH-RANSAC, respectively. It can overcome the interferences, and is very suitable for image registration of aerial images.

The applicability of the generalized thermoelastic theories with integer-order derivative in solving anomalous or transient thermoelastic diffusion problems is questionable. Due to the feature of m...

In this work, the influences of the intumescent flame retardant (IFR) composed of tri-(2-hydroxyethyl) isocyanurate (THEIC) and ammonium polyphosphate (APP) on the coal were studied in order to eff...

Well-dispersed and dense layers of gold nanorods (AuNRs) on optical fibers are shown to regulate the longitudinal peak wavelength and enhance the sensing performances of localized surface plasmon resonance (LSPR) biosensors. A simple self-assembly method relying on a brush-like monolayer of poly(styrene)-b-poly(acrylic acid) (PS-b-PAA) diblock copolymer was used to immobilize AuNRs with various aspect ratios from 2.33 to 4.60 on optical fibers. Both the experimental and simulation results illustrated that the particle aspect ratio, deposition time (related to the coverage of AuNRs), and interparticle gap significantly affected the optical properties of the fiber-based LSPR biosensors. The highest refractive index (RI) sensitivity of the sensor was 753 nm/RIU, while the limit of detection for human IgG was as low as 0.8 nM. Compared with standard nanoparticle deposition methods of polyelectrolytes or alkoxysilanes, the RI sensitivity of the PS-b-PAA dip-coating method was approximately 3-fold better, a consequence of the higher particle coverage and fewer AuNR aggregates. The presented AuNR-based LSPR sensors could regulate the detection range by tuning the aspect ratios of AuNRs. Applicability is demonstrated via quantitative analysis of antigen-antibody interactions, DNA sensing, and surface-enhanced Raman scattering.

In this work, we investigated the applicability and effectiveness of ultrasound detection using laser frequency modulated phase shifting interferometry. A quasi-continuous quadrature frequency modulation scheme is proposed to improve the time resolution and dynamic range. Based on a modulated grating Y-branch (MG-Y) laser, we achieve fast and discrete frequency switching with flat intensity output. Selective data sampling is used to eliminate the instability caused by the abrupt changes in drive currents, so as to obtain an effective frequency modulation rate of 10 MHz. Through numerical simulation, the demodulation errors induced by time delay are analyzed. The 5B-step phase shifting algorithm (PSA) shows a better performance. Simulation and experimental results demonstrate that this technique can be effectively used to demodulate extrinsic Fabry–Perot interferometric (EFPI) ultrasonic sensors. With programmable frequency modulation, essential data for signal recovery can be obtained in nanoseconds, which provides a novel perspective for high-speed and high-performance fiber-optic sensing.

In this work, a facile KOH activation was explored to prepare porous carbonaceous CO2 adsorbents using water chestnut shell as the raw material. This strategy integrates the carbonization and activation process together, which can avoid tedious multiple-step synthesis. The resultant adsorbents possess highly developed porous structures, which leads to good CO2 uptake under 1 bar, up to 3.61 and 5.23 mmol/g at 1 bar, 25 °C and 0 °C, respectively. Additional investigation was conducted to point out the main factors that determine the CO2 uptake of as-synthesized adsorbents. It is found that in addition to the volume of narrow micropores, pore size and pore size distribution of the sorbents play indispensable roles in their CO2 adsorption capacities. Thus, carbonaceous sorbents with high CO2 uptake should have the features of high amount of narrow micropores, together with small and narrow-distributed pore size. Aside from large CO2 uptake, these water chestnut shell-based carbons also hold stable recyclability, good CO2/N2 selectivity, suitable heat of adsorption, fast adsorption kinetics, and fair dynamic adsorption capacity. This study hopes to provide useful information that might profit the CO2 capture by biomass-derived carbons in realistic application.

We present an all-optical photoacoustic (PA) multigas analyzer by using a digital fiber-optic acoustic detector, which consists of a spectrum demodulation based fiber Fabry–Perot (F–P) pressure sensor and a digital virtual lock-in amplifier. The concentration of multigas is quantified by using an intensity-modulated infrared thermal source and two wavelength-modulated laser diodes. Light beams from the three sources are coupled to a single nonresonant PA cell. The generated PA pressure is detected by the fiber F-P pressure sensor, which is demodulated by a near-infrared spectrometer. A lock-in white-light interferometer is developed for detecting first-harmonic ( $1f$ ) and second-harmonic ( $2f$ ) signals. The noise equivalent detection limits with a 100-s averaging time are 37, 9, 6, 17, 4, and 60 ppb for CH4, C2H2, C2H4, C2H6, CO, and CO2, respectively.

Recently, hyperbolic deep neural networks (HDNNs) have been gaining momentum as the deep representations in the hyperbolic space provide high fidelity embeddings with few dimensions, especially for data possessing hierarchical structure. Such a hyperbolic neural architecture is quickly extended to many different scientific fields, including natural language processing, single-cell RNA-sequence analysis, graph embedding, financial analysis, and computer vision. The promising results demonstrate its superior capability, significant compactness of the model, and a substantially better physical interpretability than its counterpart in the Euclidean space. To stimulate future research, this paper presents a coherent and a comprehensive review of the literature around the neural components in the construction of HDNN, as well as the generalization of the leading deep approaches to the hyperbolic space. It also presents current applications of various tasks, together with insightful observations and identifying open questions and promising future directions.

Temperature is a crucial factor for clinker quality in the Industrial Rotary Alumina Kiln Process(IRAKP). However, the characteristic of the high temperature, complex kinetics, multivariable, non-linearreaction kinetics, long-time delayed reaction and various raw materials make it difficult to accurately controlthe temperature in IRAKP through an existing control technology. This paper proposes a dual-responsesurface-based process control (DRSPC) system for the IRAKP in a novel manner. In the DRSPC, instead ofthe more precise and complicated nonlinear equations, the dual response surface models are fitted to describethe reaction kinetics in the IRAKP and track their standard deviations for stable operation purpose. Because asimultaneous consideration of multiple control targets could address the problem of unstable operation inkilns; the objectives of the DRSPC study are designed as optimizing product quality, minimizing energyconsumption and temperature fluctuations. Therefore, the proposed DRSPC goals are to achieve a uniformquality clinker, a high fuel efficiency, and a long refractory life. A weight optimization approach is used tohandle these multiple objective functions. The proposed DRSPC can estimate the working conditions of a kilnand predict some optimal manipulated variables to the control system in each control time interval forimproving the efficiency of IRAKP. The DRSPC is applied to a real IRAKP for demonstrating itsapplicability and advantages.

Automatic tongue image segmentation and tongue image classification are two crucial tongue characterization tasks in traditional Chinese medicine (TCM). Due to the complexity of tongue segmentation and fine-grained traits of tongue image classification, both tasks are challenging. Fortunately, from the perspective of computer vision, these two tasks are highly interrelated, making them compatible with the idea of Multi-Task Joint learning (MTL). By sharing the underlying parameters and adding two different task loss functions, an MTL method for segmenting and classifying tongue images is proposed in this paper. Moreover, two state-of-the-art deep neural network variants (UNET and Discriminative Filter Learning (DFL)) are fused into the MTL to perform these two tasks. To the best of our knowledge, our method is the first attempt to manage both tasks simultaneously with MTL. We conducted extensive experiments with the proposed method. The experimental results show that our joint method outperforms the existing tongue characterization methods. Besides, visualizations and ablation studies are provided to aid in understanding our approach, which suggest that our method is highly consistent with human perception.

Suffering from both high computational complexity and high memory bandwidth is the major challenge in realizing the deep neural network in low-power for real-time applications. Binarizing the feature maps as well as the filter coefficients in deep neural network is an efficient way to reduce the high power consumption in deep learning object detection, however, it greatly scarifies the detection accuracy when reducing the bit-width of a 32-bit word to a binary bit in a floating-point deep neural network. This paper proposes a hybrid fixed point/binary deep neural network design methodology for object detection to achieve low-power consumption by taking advantage of both the fixed-point and binary deep neural networks, which allocates enough bit-width to design the hardware datapath in different layers of deep neural network. The proposed methodology combines dynamic fixed-point quantization and binarization techniques together to extremely compress the object detection model to result in a compact hybrid fixed-point/binary detection neural network, which achieves lower bandwidth and lower computational complexity. An automation tool based on the proposed methodology is also developed to train a hybrid deep neural network under a specified quality loss range. Taking MobileNet-SSD as an example, using the proposed methodology, the resulted model achieves 91% model size reduction and 75.8% memory bandwidth reduction at the cost of less than 1% mAP quality degradation. The proposed design methodology for hybrid fixed-point/binary deep neural networks achieves a good balance on detection accuracy, model size compression ratio and feature map reduction for low-power deep learning object detection applications.

An efficient and low-cost method was developed for the preparation of pedunculoside from the dried barks of Ilex rotunda using crystallization combined with polyamide column chromatography. The kin...

This article analyzes a switched-capacitor-based seven-level inverter modulated by a hybrid of level- and phase-shifted pulsewidth modulation (PWM) algorithm in detail. The optimal design of circuit parameters is also presented to ensure this hybrid PWM algorithm gives full play to its advantages. The two capacitors employed in this inverter operate alternately in charging and discharging states at carriers' frequency of this PWM algorithm, to provide different output voltage levels. With reasonable parameter design as addressed in this article, both capacitors can be fully charged every time and the phenomenon of capacitors' voltage ripple accumulation existed in other works is no longer found in this seven-level inverter. All transistors employed in this inverter are switched under low switching stress, resulting in less switching loss. Both simulation and experimental results are provided to demonstrate the correctness of theoretical analysis, and the maximum measured efficiency is up to 96.4%.

In high-temperature gas-cooled reactors, graphite dust particles within the reactor core and the heat transfer equipment experience large temperature gradients. Under such conditions, thermophoresis may play an important role in determining aerosol evolution. This study presents a theoretical and numerical analysis of the thermophoretic effects on aerosol coagulation within these reactors. The coagulation rates for Brownian versus thermophoretic coagulation are calculated and compared for various temperature gradients. Our results show that thermophoretic coagulation dominates over Brownian coagulation for large temperature gradients. We defined an enhancement factor to evaluate the role of thermophoretic coagulation under various reactor conditions. The enhancement factor increased dramatically with increasing temperature gradient, decreasing pressure and increasing particle diameter, but was not very sensitive to temperature change. The time evolution of the particle size distribution related to combined Brownian and thermophoretic coagulation was simulated using a log-skew-normal method of moments. The simulation results indicate that aerosol evolution can be strongly accelerated by thermophoretic coagulation under large temperature gradients.

Reconsidering the Bertrand duopoly game based on the concept of long short-term memory, we construct a fractional-order Bertrand duopoly game by extending the integer-order game to its corresponding fractional-order form. We build such a Bertrand duopoly game, in which both players can make their decisions with long-memory effects. Then, we investigate its Nash equilibria, local stability, and numerical solutions. Using the bifurcation diagram, the phase portrait, time series, and the 0-1 test for chaos, we numerically validate these results and illustrate its complex phenomena, such as bifurcation and chaos.

A graphite pebble lifting test platform was used to study the aerodynamic characteristics and collision behavior of graphite pebble in the lifting pipe. The impact strength of the pipe wall during pneumatic transport was measured by acceleration sensors and the number of collisions is counted by another visualization experiment. Meanwhile, hydrodynamic theory was combined with a dynamics model of the motion to predict the aerodynamic characteristics and trajectories of the pebble in the lifting pipe. The results show that the aerodynamic force on the graphite pebble increases with increasing inlet velocity and offset. Each graphite pebble in the pipeline follows an upward spiral trajectory due to the aerodynamic forces. The number of graphite pebble impacts decreases with increasing helium pressure due to the shorter residence time, which is in good agreement with experimental results. The present method can provide an efficient method for estimating the impact wear in real HTGR.

In this study, three novel main-chain liquid-crystal polyurethanes based on mesogenic diol 4,4ʹ-bis(6-hydroxyhexoxy)biphenyl (BP6), azo diol 4,4ʹ-bis(6-hydroxyhexyloxy)-azobenzene (BHHAB) and 4,4ʹ-...