There is a continuing need for artificial bone substitutes for bone repair and reconstruction, Magnesium phosphate bone cement (MPC) has exceptional degradable properties and exhibits promising biocompatibility. However, its mechanical strength needs improved and its low osteo-inductive potential limits its therapeutic application in bone regeneration. We functionally modified MPC by using a polymeric carboxymethyl chitosan-sodium alginate (CMCS/SA) gel network. This had the advantages of: improved compressive strength, ease of handling, and an optimized interface for bioactive bone in-growth. The new composites with 2% CMCS/SA showed the most favorable physicochemical properties, including mechanical strength, wash-out resistance, setting time, injectable time and heat release. Biologically, the composite promoted the attachment and proliferation of osteoblast cells. It was also found to induce osteogenic differentiation in vitro, as verified by expression of osteogenic markers. In terms of molecular mechanisms, data showed that new bone cement activated the Wnt pathway through inhibition of the phosphorylation of β-catenin, which is dependent on focal adhesion kinase. Through micro-computed tomography and histological analysis, we found that the MPC-CMCS/SA scaffolds, compared with MPC alone, showed increased bone regeneration in a rat calvarial defect model. Overall, our study suggested that the novel composite had potential to help repair critical bone defects in clinical practice.
Deviant tourist behavior (DTB) among Chinese outbound tourists has sparked concerns that such behavior not only exposes destinations to various negative impacts but also damages the international image of China. Hence, it is necessary to explore how to reduce such behavior. Social identity cues are an effective inhibitor of DTB; however, previous research has focused on the influence of the interdependent and interactive nature of social identities on DTB, neglecting the inclusive nature of social identities. To fill this gap, the current study focuses on how Chinese outbound tourists’ identity breadth affects their deviant behavior in international tourism contexts. In Study 1, we examine a distinctive feature of international tourism contexts and find that tourists have high face consciousness. Second, we propose and document that Chinese outbound tourists primed with a broad (vs. narrow) identity develop higher face consciousness and a lower intention to engage in deviant behavior (Studies 1 and 2), with face consciousness mediating this process (Study 3). Finally, Study 4 finds that the number of fellow tourists with the same identity moderates this effect. The influence of identity breadth on DTB is manifest when there are few in-group members present. Our findings provide meaningful practical and theoretical value regarding how to reduce tourists’ deviant behavior through identity-related cues.
In marine operations, the performance of model-based automatic control design and decision support systems highly relies on the accuracy of the representative mathematical models. Model fidelity can be crucial for safe voyages and offshore operations. This paper proposes a data-driven parametric model identification of a ship with 6 degrees of freedom (6DOF) exposed to waves using sparse regression according to the vessel motion measurements. The features of the complex ship dynamics are extracted and expressed as a linear combination of several functions. Thruster inputs and environmental loads are considered. The hydrodynamic coefficients and wave-induced loads are simultaneously estimated. Unlike earlier studies using a limited number of unknown functions, a library of abundant candidate functions is applied to fully consider the coupling effects among all DOFs. The benefit of the proposed method is that it does not require the exact construction of the library functions. Based on the estimated model, short-term motion prediction is achievable. The algorithm is verified through experiments. The method can be extended to other types of floating structures.
The evaluation of automobile sound quality is not only related to the inherent properties of the sound, but also to the psychological and physiological state of the evaluator, which makes the evaluation of sound quality become an interdisciplinary research. However, there are some deficiencies regarding the existing studies as follows: (1) it is imprecise enough to visualize the preference of evaluators for sound on the scale; (2) the evaluators without acoustic experience are prone to deviations in their evaluations, resulting in the universally and unapplicable results; (3) the automobile sound quality cannot be fully reflected only by physical and psychological parameters, and most of the existing evaluation models of sound quality are only characterized by shallow architectures. To alleviate the above flaws, a hybrid deep neural network (HDNN) is constructed to achieve the evaluation of automobile interior acceleration sound fused with physiological signals in this paper. An EEG test paradigm under the stimulation of automobile sound is meanwhile designed for a feasibility confirmation of the proposed method. In addition, the acquired EEG datum are respectively analyzed from two perspectives: the manual extraction of typical EEG features and the adaptive extraction of EEG features based on HDNN model. Furthermore, the performance of the proposed HDNN is also validated by comparing with conventional convolutional neural network (CNN) and long short time memory (LSTM). The results indicate that the adaptively extracted EEG features are superior to the manually extracted EEG features, and the proposed HDNN outperforms the CNN and LSTM in terms of accuracy, sensitivity as well as precision, and the optimal accuracy of HDNN is up to 88.1%. Thus, the constructed HDNN contributes to achieving accurate evaluation of automobile sound quality with EEG signal.
Scheduling scheme is one of the critical factors affecting the production efficiency. In the actual production, anomalies will lead to scheduling deviation and influence scheme execution, which makes the traditional job shop scheduling methods are not sufficient to meet the needs of real-time and accuracy. By introducing digital twin (DT), further convergence between physical and virtual space can be achieved, which enormously reinforces real-time performance of job shop scheduling. For flexible job shop, an anomaly detection and dynamic scheduling framework based on DT is proposed in this paper. Previously, a multi-level production process monitoring model is proposed to detect anomaly. Then, a real-time optimization strategy of scheduling scheme based on rolling window mechanism is explored to enforce dynamic scheduling optimization. Finally, the improved grey wolf optimization algorithm is introduced to solve the scheduling problem. Under this framework, it is possible to monitor the deviation between the actual processing state and the planned processing state in real time and effectively reduce the deviation. An equipment manufacturing job shop is taken as a case study to illustrate the effectiveness and advantages of the proposed framework.
A numerical study was performed for the combustion process of a spark-ignition engine fueled with ammonia to investigate the effects of hydrogen-rich reformate addition on the combustion and emission characteristics of the engine. For the combustion characteristics, the results show that the in-cylinder pressure is increased, and the combustion duration is shortened with the addition of hydrogen-rich reformate. Moreover, when blending 10.0% of the reformate by volume, the combustion efficiency and thermal efficiency of the engine at stoichiometric conditions are improved to 96.3% and 43.6%, respectively. However, increasing the reformate content in the mixture to 12.5% leads to negative work during the compression stroke and a reduction in the power. For the emission characteristics, it is found that the NOx emissions can meet the IMO Tier III limit at rich-burn conditions for all the reformate blending ratios (Rre). The NH3 and N2O emissions decrease monotonically with the increase of Rre. In addition, the chemical kinetic analyses demonstrate that the concentration of H and OH radicals increase with the addition of hydrogen-rich reformate thus accelerating the elementary reaction rates in the NH3 consumption pathway. Consequently, the combustion phase is advanced and the unburned NH3 emissions are reduced with the increase of Rre. The present study concludes that 7.5% – 10.0% is the recommended blending ratio of hydrogen-rich reformate for enhanced combustion with low NOx and NH3 emissions in ammonia-fueled engines.
The fluorescent dye 4′,6-diamidino-2-phenylindole (DAPI) has been widely used to stain microorganisms in various environment media. We applied DAPI fluorescence enumeration to airborne microorganisms and found that non-biological particles, including organic compounds, minerals, and soot, were also visible upon exposure to UV excitation under fluorescence microscope. Using laboratory-prepared biological particles as the control, we investigated the feasibility of identifying both biological and non-biological particles in the same sample with DAPI staining. We prepared biological (bacterial, fungi, and plant detritus) and non-biological (biochar, soot, mineral, metal, fly ash, salt) particles in the laboratory and enumerated the particles and their mixture with DAPI. We found that mineral particles were transparent, and biochar, soot, metals and fly ash particles were black under a filter set at excitation 350/50 nm and emission 460/50 nm bandpass (DAPI-BP), while biological particles were blue, as expected. Particles of the water-soluble salts NaCl and (NH4)2SO4 were yellow under a filter set at excitation 340–380 nm and emission 425 nm long pass (DAPI-LP). Case studies with samples of dustfall, atmospheric aerosols and surface soils could allow for the quantification of the relative number of different types of particles and particles with organic matter or salt coating as well. Fluorescence enumeration with DAPI stain is thus able to identify the co-existence of biological and non-biological particles in the air, at least to the extent of those examined in this study.
Passive thermal management systems (BTMS) based on phase change material (PCM) have been proposed in many articles, but more attention has been paid to the temperature effect of BTMS, and the influence of BTMS on the electro-thermal performance of battery module has rarely been studied. Therefore, composite PCM composed of lauric acid, expand graphite (EG) and graphene (GR) with a mass ratio of 8:1.5:0.5 is prepared and used for temperature control of Li-ion battery. Then, a series of experiments are performed to research the performance of the thermal management module based on the cPCM, including thermal property analysis, discharge tests under standard and extreme conditions. In addition, the Gaussian process regression (GPR) model is trained and tested with internal resistance data measured over a wide temperature range, and then combined with an electro-thermal model to estimate the heat generation of batteries and further research the effect of the BTMS on discharge performance. The experimental results show that the maximum temperature (Tmax) of the batteries with cPCM is 47.41 °C, and the maximum temperature difference (ΔTmax) between the batteries is 1.46 °C in the 2C discharge process at 40 °C. The estimation results show that the heat generation of batteries with cPCM is higher than that of the batteries without cPCM under the same working conditions, and it is more sensitive to ambient temperature even without phase transition.
Metal–organic frameworks (MOFs) MIL-100 (Cr, Fe) synthesized by the hydrothermal method were subjected to heat treatment under an H2 flow. The MIL-100 (Cr) samples had specific surface areas and pore volumes approximately-two times higher than those of the MIL-100 (Fe) samples. The H2 heat treatment enhanced the crystallinity, especially for MIL-100 (Fe). The resulting adsorbents exhibited a superior CO2 adsorption capacity. Furthermore, the grand canonical Monte Carlo method was used to examine the adsorption mechanism at a molecular level. The heat contribution of each atom type of MOFs was demonstrated. The highest heat was found at low loadings (Henry’s law region) among other regions owing to CO2 adsorption inside the supertetrahedron. The most active atoms were C atoms (CO > CC > CH), followed by the O atom (COunsaturated metal), the unsaturated metal site, the other O atom (COsaturated metal), the saturated metal site, the H atom of the ligands, and the O atom of the metal cluster, respectively. This study can provide insights into the determination of the atomic heat contributions of other adsorbents for a better understanding of material design and the mechanism of adsorption.
As a non-toxic copolymer of isobutylene and maleic anhydride, Isobam is successfully used as a dispersant and a gelling agent for fabricating porous Si3N4 ceramics by gel casting. The dispersity and rheological properties of the Si3N4 slurry are influenced by the pH, milling time, and Isobam content which varies from 0.1 wt.% to 0.6 wt.%, and these factors are investigated. The slurry with 40 vol.% solid content and milled for 4 h has a high zeta potential at pH 12 (adjusted by Tetramethyl ammonium hydroxide (TMAH)), which means that the particles are well dispersed. The mechanisms of TMAH are electrostatic repulsion and steric hindrance. The viscosity of the slurry increases with the increase of Isobam content. After pressureless sintering at 1700 °C for 2 h, a uniform unique interlocking microstructure of rod-like β-Si3N4 grains is observed, which may improve the flexural strength of the ceramics by intergranular fracture and particle pullout of β-Si3N4 grains. The density and porosity of the samples fluctuate negligibly with the increase of Isobam content, and the Si3N4 ceramic with 0.1 wt.% Isobam exhibits the highest bending strength of 251.6 MPa among all samples.
Indigenous microorganisms can affect coal slurry settling by polyacrylamide biodegradation; however, they have rarely been examined in detail. In this study, the microbial community composition in coal slurry was explored using 16S rDNA, and the microorganisms are assigned to 34 phyla and 98 genera. The predicted function results show that microorganisms in coal slurry are closely related to the organic matter biodegradation. To prove it, the Sphingomonas was isolated from the coal slurry, which was used to biodegrade polyacrylamide in coal slurry, and the biodegradation rate reached 66.4 % within 76 h. The results of SEM, AFM, UV spectrometry, FTIR, and HPLC explain that the high molecular weight polyacrylamide is degraded into the smaller molecular weight organic products in the biodegradation process, and the coal slurry settling tests reflect that the settling rate and settling layer height are obviously inhibited by polyacrylamide biodegradation.
The Fourth Industrial Revolution, also known as Industry 4.0, stems from the rapid advancement of digital technologies such as the Internet of Things and Cyber-Physical Production Systems. It has the potential to weave positive changes to firms and impact organizational structure layers. Therefore, it provides an impetus for the collaboration of factories, suppliers, and customers. Nevertheless, due to the difference of Industry 4.0 vision among companies, there is a lack of unified perception and approach of its implementation roadmap. Therefore, many firms in both developed and developing countries that step in the way of digital transformation encounter not only organizational, technological, and operational challenges but are also compelled to cope with a large deal of confusion. Hence, this paper aims to identify the main concepts, characteristics, and technology enablers related to Industry 4.0 to provide stakeholders with a clear understanding of this paradigm. It then clusters and matches the derived concepts and characteristics associated with Industry 4.0. Further, the paper provides an analysis of how these clusters are supported by technology enablers of Industry 4.0, as well as managerial implications. © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
The interactions of NH3, NOx and O2 with two Fe3O4 (1 1 1) surfaces named Fetet- and Feoct-tet-terminated, respectively, were investigated by first principle calculation. The results indicated that the Feoct-tet-terminated surface had a stronger effect on the adsorption and activation of these small molecules overall. Specifically, NH3, NO and O2 tend to be stably adsorbed on the octahedral Fe site on the Feoct-tet-terminated surface. The energy released by O2 adsorption is the most among them, and it is easy to form abundant surface adsorbed oxygen on the catalyst surface, which further co-adsorb or combine with NO to form nitrate and nitrite species with various coordination structures. The chelated and bridged bidentate-coordinated nitrate species with the highest adsorption energies are important reaction intermediates in NH3 selective catalytic reduction (NH3-SCR). In addition, due to the horizontal adsorption configuration, NH3 is significantly affected by the nearest lattice oxygen, which makes the oxidative dehydrogenation reaction more likely to occur, although the adsorption energy of NH3 on Feoct-tet-terminated surface is relatively lower. These results provide a basis for understanding the interactions of NH3, NOx and O2 on Fe3O4 (1 1 1) surfaces in NH3-SCR reaction.
The deformation and reconstruction of the composite propeller under the static load in the laboratory is studied so as to provide the basic research for the deformation and reconstruction of the underwater deformed propeller. The fiber Bragg grating (FBG) sensor is proposed to be used for strain monitoring and deformation reconstruction of the carbon fiber reinforced polymer (CFRP) propeller, and a reconstruction algorithm of structural curvature deformation of the CFRP propeller based on strain information is presented. The reconstruction algorithm is verified by using variable-thickness CFRP laminates in the finite element software. The results show that the relative error of the reconstruction algorithm is within 8%. Then, an experimental system of strain monitoring and deformation reconstruction for the CFRP propeller based on the FBG sensor network is built. The propeller blade is loaded in the form of the cantilever beam, and the blade deformation is reconstructed by the strain measured by the FBG sensor network. Compared with the blade deformation measured by three coordinate scanners, the reconstruction relative error is within 15%.
Hydrogen production from water splitting provides an effective method to alleviate the ever-growing global energy crisis. In this work, delafossite CuGaO 2 (CGO) crystal was synthesized through hydrothermal routes with Cu(NO 3 ) 2 ·3H 2 O and Ga(NO 3 ) 3 · x H 2 O used as reactants. The addition of cetyltrimethylammonium bromide (CTAB) was found to play an important role in modifying the morphology of CuGaO 2 (CGO-CTAB). With the addition of CTAB, the morphology of CGO-CTAB samples changed from irregular flake to typical hexagonal sheet microstructure, with an average size of 1–2 μm and a thickness of around 100 nm. Furthermore, the electrocatalytic activity of CGO-CTAB crystals for oxygen evolution reaction (OER) was also studied and compared with that of CGO crystals. CGO-CTAB samples exhibited better activity than CGO. An overpotential of 391.5 mV was shown to be able to generate a current density of 10 mA/cm ² . The as-prepared samples also demonstrate good stability for water oxidation and relatively fast OER kinetics with a Tafel slope of 56.4 mV/dec. This work highlights the significant role of modification of CTAB surfactants in preparing CGO related crystals, and the introduction of CTAB was found to help to improve their electrocatalytic activity for OER. Graphical abstract
Lamb wave-based signals from sparse-distributed sensors are complicated and difficult to process for structural health monitoring (SHM), not only due to their dispersive and multi-mode nature, but also due to the increasing complexity of materials and structures. Deep learning (DL) has attracted huge attention to help solve physical problems with a high level of automation and accuracy. However, its reliability and robustness are still questioned when performing the case-by-case model trained by inadequate datasets for practical scenarios, where many variables exist. In this study, a hierarchical deep convolutional regression framework is proposed to solve the impact source localization problem by acoustic emission signals. One-dimensional (1D) network is used due to its capability to process fast with raw time-series data. The window length of input data and the target of output results are discussed to improve the over-fitting issue. The sensor network fail-safe mechanism is designed via generalizing the model to handle abnormal situations with random faulty channels. Data augmentation and transfer learning techniques are utilized to train the fail-safe model without the need for additional experimental data. Pristine case and multiple random-faulty-channel cases are used to test and validate the adaptation performance of the fail-safe model. The whole framework combines both pristine and fail-safe models to achieve high accuracy of impact localization results of both a simple homogeneous plate and a complex inhomogeneous plate with geometric features. The proposed DL framework of greatly improved reliability and robustness, also short processing time, is well suitable for real-time and in-situ SHM applications.
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