The controlled preparation of hexagonal tungsten trioxide(h-WO_(3))nanostructures was achieved by adjusting the pH of the precursor *** effect of the pH on the morphology,elemental composition,and photocatalytic perfo...
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The controlled preparation of hexagonal tungsten trioxide(h-WO_(3))nanostructures was achieved by adjusting the pH of the precursor *** effect of the pH on the morphology,elemental composition,and photocatalytic performance of the samples was characterized via X-ray diffraction(XRD),scanning electron microscopy,energy dispersive X-ray spectroscopy,and Raman ***-visible(UV-Vis)spectra were used to evaluate the absorbance and the photocatalytic performance of methylene ***(PL),electrochemical impedance spectroscopy,photocurrent response and Brunauer-Emmett-Teller(BET)were used to study the optical properties,electrical performance,and specific surface area of the WO_(3)-nanostructures,*** results indicate that the WO_(3) nanorods prepared at pH=1.0 exhibit the highest photocatalytic performance(87.4%in 1 h),whereas the WO_(3) nanoblocks prepared at p H=3.0 show the *** photocatalytic performance of the one dimensional(1 D)-nanorods can be attributed to their high specific surface area and charge transfer *** h-WO_(3) nanostructures were synthesized via a simple method and without a capping *** show an excellent photocatalytic performance,which is promising for their application in environment purification.
The scarcity of public resources and environmental pollution caused by rapid urbanization highlight the practical significance of parks in ensuring the sustainable development of a ***,the social equity of parks warra...
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The scarcity of public resources and environmental pollution caused by rapid urbanization highlight the practical significance of parks in ensuring the sustainable development of a ***,the social equity of parks warrants further *** paper proposes a fine-grained comprehensive evaluation framework that combines geographic accessibility models,geo-statistical analysis,and machine learning algorithms to explore social inequity in Taiyuan,*** this framework,gini coefficient and lorentz curve express spatial equality,accessibility shows spatial equity,and ridge regression model handles the interdependence of variables with different dimensions to quantify the relative effects of local participants on changes in park *** this basis,the imbalance between vulnerable groups and park supply is analyzed to further understand the core concept of social *** highlight serious spatial inequality in all three types of parks allocation of six urban areas,especially in commu-nity *** actual access level of people to parks is also stratified by their demographic and socioeconomic characteristics,revealing the social inequity in access to *** distribution is indeed not conducive to some social vulnerable groups,whose contradiction between supply and demand is highly prominent in urban-rural junctions and new urban *** paper also confirms the unfair layout of public facilities can be observed in second-tier cities of China by highlighting the social inequity of parks in *** findings of this work have profound implications for urban planning and sustainable development.
The localized faults of rolling bearings can be diagnosed by its vibration impulsive ***,it is always a challenge to extract the impulsive feature under background noise and non-stationary *** paper investigates impul...
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The localized faults of rolling bearings can be diagnosed by its vibration impulsive ***,it is always a challenge to extract the impulsive feature under background noise and non-stationary *** paper investigates impulsive signals detection of a single-point defect rolling bearing and presents a novel data-driven detection approach based on dictionary *** overcome the effects harmonic and noise components,we propose an autoregressive-minimum entropy deconvolution model to separate harmonic and deconvolve the effect of the transmission *** address the shortcomings of conventional sparse representation under the changeable operation environment,we propose an approach that combines K-clustering with singular value decomposition(K-SVD)and split-Bregman to extract impulsive components *** experiments on synthetic signals and real run-to-failure signals,the excellent performance for different impulsive signals detection verifies the effectiveness and robustness of the proposed ***,a comparison with the state-of-the-art methods is illustrated,which shows that the proposed approach can provide more accurate detected impulsive signals.
Automatically generated questions often suffer from problems such as unclear expression or factual inaccuracies, requiring a reliable and comprehensive evaluation of their quality. Human evaluation is widely used in t...
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Accurate estimation of the remaining useful life(RUL)and health state for rollers is of great significance to hot rolling *** can provide decision support for roller management so as to improve the productivity of the...
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Accurate estimation of the remaining useful life(RUL)and health state for rollers is of great significance to hot rolling *** can provide decision support for roller management so as to improve the productivity of the hot rolling *** addition,the RUL prediction for rollers is helpful in transitioning from the current regular maintenance strategy to conditional-based ***,a new method that can extract coarse-grained and fine-grained features from batch data to predict the RUL of the rollers is proposed in this ***,a new deep learning network architecture based on recurrent neural networks that can make full use of the extracted coarsegrained fine-grained features to estimate the heath indicator(HI)is developed,where the HI is able to indicate the health state of the *** that,a state-space model is constructed to describe the HI,and the probabilistic distribution of RUL can be estimated by extrapolating the HI degradation model to a predefined failure ***,application to a hot strip mill is given to verify the effectiveness of the proposed methods using data collected from an industrial site,and the relatively low RMSE and MAE values demonstrate its advantages compared with some other popular deep learning methods.
This paper considers the adaptive neuro-fuzzy control scheme to solve the output tracking problem for a class of strict-feedback nonlinear *** asymmetric output constraints and input saturation are *** asymmetric barr...
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This paper considers the adaptive neuro-fuzzy control scheme to solve the output tracking problem for a class of strict-feedback nonlinear *** asymmetric output constraints and input saturation are *** asymmetric barrier Lyapunov function with time-varying prescribed performance is presented to tackle the output-tracking error constraints.A high-gain observer is employed to relax the requirement of the Lipschitz continuity about the nonlinear *** avoid the"explosion of complexity",the dynamic surface control(DSC)technique is employed to filter the virtual control signal of each *** deal with the actuator saturation,an additional auxiliary dynamical system is *** is theoretically investigated that the parameter estimation and output tracking error are semi-global uniformly ultimately *** simulation examples are conducted to verify the presented adaptive fuzzy controller design.
Diagnostic pathology,historically dependent on visual scrutiny by experts,is essential for disease *** digital pathology and developments in computer vision technology have led to the application of artificialintellig...
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Diagnostic pathology,historically dependent on visual scrutiny by experts,is essential for disease *** digital pathology and developments in computer vision technology have led to the application of artificialintelligence(AI)in this *** these advancements,the variability in pathologists’subjective interpretations ofdiagnostic criteria can lead to inconsistent *** meet the need for precision in cancer therapies,there is anincreasing demand for accurate pathological ***,traditional diagnostic pathology is evolvingtowards“next-generation diagnostic pathology”,prioritizing on the development of a multi-dimensional,intelligentdiagnostic *** nonlinear optical effects arising from the interaction of light with biological tissues,multiphoton microscopy(MPM)enables high-resolution label-free imaging of multiple intrinsic components acrossvarious human pathological ***-empowered MPM further improves the accuracy and efficiency of diagnosis,holding promise for providing auxiliary pathology diagnostic methods based on multiphoton diagnostic *** review,we systematically outline the applications of MPM in pathological diagnosis across various human diseases,and summarize common multiphoton diagnostic ***,we examine the significant role of AI inenhancing multiphoton pathological diagnosis,including aspects such as image preprocessing,refined differentialdiagnosis,and the prognostication of *** also discuss the challenges and perspectives faced by theintegration of MPM and AI,encompassing equipment,datasets,analytical models,and integration into the existingclinical ***,the review explores the synergy between AI and label-free MPM to forge novel diagnosticframeworks,aiming to accelerate the adoption and implementation of intelligent multiphoton pathology systems inclinical settings.
In recent years, with the increasingly serious quality problems of human sperm, the performance of traditional sperm analysis methods has been unable to meet the growing demand for diagnosis and treatment. The purpose...
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When employing the network architecture search approach for designing a steel surface defect detector, there are issues with conflicting evaluation metrics and limited computational resources. To address this challeng...
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Underwater optical imaging produces images with high resolution and abundant information and hence has outstanding advantages in short-distance underwater target ***,low-light and high-noise scenarios pose great chall...
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Underwater optical imaging produces images with high resolution and abundant information and hence has outstanding advantages in short-distance underwater target ***,low-light and high-noise scenarios pose great challenges in un-derwater image and video *** improve the accuracy and anti-noise performance of underwater target image edge detection,an underwater target edge detection method based on ant colony optimization and reinforcement learning is proposed in this ***,the reinforcement learning concept is integrated into artificial ants’movements,and a variable radius sensing strategy is pro-posed to calculate the transition probability of each *** methods aim to avoid undetection and misdetection of some pixels in image ***,a double-population ant colony strategy is proposed,where the search process takes into account global search and local search *** results show that the algorithm can effectively extract the contour information of underwater targets and keep the image texture well and also has ideal anti-interference performance.
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