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IntroductionTo address the challenges related to bone defects, including osteoinductivity deficiency and post-implantation infection risk, this study developed the collagen composite scaffolds (CUR-GO-COL) with multifunctionality by integrating the curcumin-loaded graphene oxide with collagen through a freeze-drying-cross-linking process.MethodsThe morphological and structural characteristics of the composite scaffolds were analyzed, along with their physicochemical properties, including water absorption capacity, water retention rate, porosity, in vitro degradation, and curcumin release. To evaluate the biocompatibility, cell viability, proliferation, and adhesion capabilities of the composite scaffolds, as well as their osteogenic and antimicrobial properties, in vitro cell and bacterial assays were conducted. These assays were designed to assess the impact of the composite scaffolds on cell behavior and bacterial growth, thereby providing insights into their potential for promoting osteogenesis and inhibiting infection.ResultsThe CUR-GO-COL composite scaffold with a CUR-GO concentration of 0.05% (w/v) exhibits optimal biological compatibility and stable and slow curcumin release rate. Furthermore, in vitro cell and bacterial tests demonstrated that the prepared CUR-GO-COL composite scaffolds enhance cell viability, proliferation and adhesion, and offer superior osteogenic and antimicrobial properties compared with the CUR-GO composite scaffold, confirming the osteogenesis promotion and antimicrobial effects.DiscussionThe introduction of CUR-GO into collagen scaffold creates a bone-friendly microenvironment, and offers a theoretical foundation for the design, investigation and utilization of multifunctional bone tissue biomaterials.

Simulation of subsurface energy system involves multi-physical processes such as thermal, hydraulical, and mechanical (THM) processes, and requires a so-called THM coupled modeling approach. THM coupled modeling is commonly performed in geothermal energy production. However, for hydrocarbon extraction, we need to consider multiphase flow additionally. In this paper, we describe a three-dimensional numerical model of non-isothermal two-phase flow in the deformable porous medium by integrating governing equations of two-phase mixture in the porous media flow in the reservoir. To account for inter-woven impacts in subsurface conditions, we introduced a temperature-dependent fluid viscosity and a fluid density along with a strain-dependent reservoir permeability. Subsequently, we performed numerical experiments of a ten-year water flooding process employing the open-source parallelized code, OpenGeoSys. We considered different well patterns with colder water injection in realistic scenarios. Our results demonstrate that our model can simulate complex interactions of temperature, pore pressure, subsurface stress and water saturation simultaneously to evaluate the recovery performance. High temperature can promote fluid flow while cold water injection under non-isothermal conditions causes the normal stress reduction by significant thermal stress. Under different well patterns the displacement efficiency will be changed by the relative location between injection and production wells. This finding has provided the important reference for fluid flow and induced stress evolution during hydrocarbon exploitation under the environment of large reservoir depth and high temperature.

Abstract Flexible perovskite solar cells (f‐PSCs) as a promising power source have grabbed surging attention from academia and industry specialists by integrating with different wearable and portable electronics. With the development of low‐temperature solution preparation technology and the application of different engineering strategies, the power conversion efficiency of f‐PSCs has approached 24%. Due to the inherent properties and application scenarios of f‐PSCs, the study of strain in these devices is recognized as one of the key factors in obtaining ideal devices and promoting commercialization. The strains mainly from the change of bond and lattice volume can promote phase transformation, induce decomposition of perovskite film, decrease mechanical stability, etc. However, the effect of strain on the performance of f‐PSCs has not been systematically summarized yet. Herein, the sources of strain, evaluation methods, impacts on f‐PSCs, and the engineering strategies to modulate strain are summarized. Furthermore, the problems and future challenges in this regard are raised, and solutions and outlooks are offered. This review is dedicated to summarizing and enhancing the research into the strain of f‐PSCs to provide some new insights that can further improve the optoelectronic performance and stability of flexible devices.

Danpness-eliminating decoction was fermented by Bacillus sp. DU-106 and Lactobacillus plantarum. The effects of fermentation on physical and chemical properties and flavor substances of Danpness-eliminating decoction were compared, and sensory evaluation was conducted. Physical and chemical properties included pH, reducing sugars, total acids, polysaccharides, proteins, polyphenols, flavonoids, essential amino acids and organic acids. The results showed that after 4 days of fermentation, the pH of Danpness-eliminating decoction decreased to below 3.0, and the total acid content was increased by consuming reducing sugar, up to 11.00 g/kg. The contents of polysaccharides, proteins, polyphenols and flavonoids in Danpness-eliminating decoction increased by probiotic fermentation, and the maximum values were 20.95, 1.66 mg/mL, 52.45 and 53.26 μg/mL respectively on the 7th day. Probiotic fermentation also significantly (P

It is increasing concerned that the plateau environment will potentially increase fuel consumption in engines. Despite this, the current state of research on high altitude engines is still inadequate in providing sufficient and detailed information to counteract the decrease in engine efficiency. In addition, one of the novelty of this study was that the investigated altitudes is up to 4500 m, of which the previous studies was limited. Therefore, this study aims to investigate the effect of altitude on in-cylinder spray, combustion, and soot formation processes in diesel engines. A calibrated three-dimensional (3D) computational fluid dynamics (CFD) model of a single-cylinder, four-strokes, direct injection (DI) compression ignition, intake boost research diesel engine is established. The numerical CFD model used in this study has been validated through comparison with experimental data. It effectively investigates the in-cylinder activities and provides insights into the causes behind combustion and soot emission deterioration. The simulation is operated at altitudes of 0, 1500, 3000, 4500 m with corresponding intake pressure. It has been observed that engine performance and soot emissions deteriorate as altitude increases, and a sharp drop occurs when the altitude exceeds 3000 m, which can be attributed to the dramatic decline in combustion efficiency. The extended spray leads to a decrease in air utilization, which ultimately results in a sudden drop in combustion efficiency at altitudes above 3000 m. Overall, this study has identified that the extended penetration of the spray, caused by reduced pressure and air density, leads to poor air utilization, resulting in decreased performance and increased emissions. Hence, it is recommended that optimization of the combustion chamber geometry and injection strategies of diesel engines operating at high altitudes be undertaken to improve air utilization and combustion quality. This will serve as an avenue for further research in the future.

By reviewing the development history of shale oil reservoir stimulation technology of PetroChina Company Limited (PetroChina), we have systematically summarized the main progress of shale oil reservoir stimulation technology of CNPC in five aspects: reservoir stimulation mechanism, fracture-controlled fracturing, geological-engineering integrated reservoir stimulation design platform, low-cost materials, and large well-pad three-dimensional development mode. It is made clear that the major stimulation technology for shale oil reservoir is the high density multi-cluster and fracture-controlled staged fracturing aiming to increase fracture-controlled reserves, lower operation costs and increase economic benefits. Based on comprehensive analysis of the challenges shale oil reservoir stimulation technology faces in three-dimensional development, stimulation parameter optimization for fracture-controlled fracturing, refracturing and low-cost stimulation technology, we proposed five development directions of the stimulation technology: (1) Strengthen the research on integration of geology and engineering to make full use of reservoir stimulation. (2) Deepen the study on fracture-controlled fracturing to improve reserves development degree. (3) Promote horizontal well three-dimensional development of shale oil to realize the production of multiple layers vertically. (4) Research refracturing technology of shale oil reservoir through horizontal well to efficiently tap the remaining reserves between fractures. (5) Develop low-cost stimulation supporting technology to help reduce the cost and increase economic benefit of oilfield development.

Recent research shows that generative adversarial network (GAN) based deep learning derived frameworks can improve the accuracy of hyperspectral image (HSI) classification on limited labeled samples. However, several studies point out that existing GAN-based methods are heavily affected by the complexity and inefficient description issues of HSIs. The discriminator in GAN always attempts to interpret high-dimensional nonlinear spectral knowledge of HSIs, thus resulting in the Hughes phenomenon. Another critical issue is sample generation. The generator is only used as a regularizer for the discriminator, which seriously restricts the performance for classification. In this article, we propose SSAT-GAN, a semisupervised spectral–spatial attention feature extraction approach based on the GAN that feeds raw data into a deep learning framework, in an end-to-end fashion. First, the unlabeled data is added into the discriminator to alleviate the problems of training samples and supplies a reconstructed real HSI data distribution through adversarial training. Second, to enhance the description of HSIs, we build spectral–spatial attention modules (SSAT) and extend them to the discriminator and the generator to extract discriminative characteristics from abundant spatial contexts and spectral signatures. The SSAT modules learn a three-dimensional filter bank with spectral–spatial attention weights to obtain meaningful feature maps to improve the discrimination of the feature representation. In terms of the mode collapse of GANs, the mean minimization loss is employed for unsupervised learning. Experimental results from three real datasets indicate that SSAT-GAN has certain advantages over the state-of-the-art methods.

The spray atomization of an injector significantly influences the performance and working life span of a bipropellant thruster of a spacecraft. Deep space exploration requires the thruster to be able to operate reliably at a low temperature range from −40 °C to 0 °C, so the effect of low temperature conditions on the atomization characteristics of injector spray is motivated to be comprehensively investigated. To study the swirl atomization characteristics of MMH (methylhydrazine), which is more difficult to atomize than NTO (nitrogen tetroxide), numerical simulations were conducted, employing the methods of VOF (volume of fluid) and LES (large eddy simulation) under low temperature conditions. The physical model with a nozzle size of 0.5 mm and boundary conditions with a velocity inlet of 3.89 m/s both follow the actual operation of thrusters. The development of spray atomization at low temperatures was observed through parametric comparisons, such as spray velocity, liquid total surface area, droplet particle size distribution, spray cone angle and breakup distance. When the temperature decreased from 20 °C to −40 °C at the same condition of flowrate inlet, those atomization characteristics of MMH propellant vary following these rules: the spray ejection velocity of MMH is significantly reduced by 7.7%, and gas-liquid disturbance sequentially decreases; the liquid film development is more stable, with a negative influence on atomization quality, causing difficulties for primary and secondary breakup, so the total surface area of droplets also decreases by 6.4%; the spatial distribution characteristics, spray cone angle and breakup distance vary less than 5%. Therefore, the low temperature condition can directly lower the combustion efficiency of thrusters with obvious performance degradation, but there are no significant changes in the propellant mixing and liquid film cooling. It is concluded that the bipropellant thruster can reliably work at low temperatures around −40 °C for deep space probe operation.

Abstract Background Saccharomyces cerevisiae AN120 osw2∆ spores were used as a host with good resistance to unfavorable environment. This work was undertaken to develop a new yeast spore-encapsulation of Candida parapsilosis Glu228Ser/(S)-carbonyl reductase II and Bacillus sp. YX-1 glucose dehydrogenase for efficient chiral synthesis in organic solvents. Results The spore microencapsulation of E228S/SCR II and GDH in S. cerevisiae AN120 osw2∆ catalyzed (R)-phenylethanol in a good yield with an excellent enantioselectivity (up to 99%) within 4 h. It presented good resistance and catalytic functions under extreme temperature and pH conditions. The encapsulation produced several chiral products with over 70% yield and over 99% enantioselectivity in ethyl acetate after being recycled for 4–6 times. It increased substrate concentration over threefold and reduced the reaction time two to threefolds compared to the recombinant Escherichia coli containing E228S and glucose dehydrogenase. Conclusions This work first described sustainable enantioselective synthesis without exogenous cofactors in organic solvents using yeast spore-microencapsulation of coupled alcohol dehydrogenases.

The indisputable wide use of the Eddy Dissipation Concept (EDC) implies that the resulting mean reaction rate is reasonably well modeled. To model turbulent combustions, an amount of EDC constants that differ from the original values was proposed. However, most of them were used without following the nature of the model or considering the effects of the modification. Starting with the energy cascade and the EDC models, the exact original primary and secondary constants are deduced in detail in this work. The mean reaction rate is then formulated from the primary constants or the secondary constants. Based on the physical meaning of fine structures, the limits of the EDC constants are presented and can be used to direct the EDC constant modifications. The effects of the secondary constant on the mean reaction rate are presented and the limiting turbulence Reynolds number used for the validity of EDC is discussed. To show the effects of the constants of the EDC model on the mean reaction rate, 20 combinations of the primary constants are used to simulate a laboratory-scale turbulent jet flame, i.e., Sandia Flame D. After a thorough and careful comparison with experiments, case 8, with a secondary constant of 6 and primary constants of 0.1357 and 0.11, can aptly reproduce this flame, except for in the over-predicted mean OH mass fraction.

Abstract Bimetal catalysts are good alternatives for non-enzymatic glucose sensors owing to their low cost, high activity, good conductivity, and ease of fabrication. In the present study, a self-supported CuNi/C electrode prepared by electrodepositing Cu nanoparticles on a Ni-based metal–organic framework (MOF) derivate was used as a non-enzymatic glucose sensor. The porous construction and carbon scaffold inherited from the Ni-MOF guarantee good kinetics of the electrode process in electrochemical glucose detection. Furthermore, Cu nanoparticles disturb the array structure of MOF derived films and evidently enhance their electrochemical performances in glucose detection. Electrochemical measurements indicate that the CuNi/C electrode possesses a high sensitivity of 17.12 mA mM−1 cm−2, a low detection limit of 66.67 nM, and a wider linearity range from 0.20 to 2.72 mM. Additionally, the electrode exhibits good reusability, reproducibility, and stability, thereby catering to the practical use of glucose sensors. Similar values of glucose concentrations in human blood serum samples are detected with our electrode and with the method involving glucose-6-phosphate dehydrogenase; the results further demonstrate the practical feasibility of our electrode.

Electroencephalography neurofeedback (EEG-NFB) training can induce changes in the power of targeted EEG bands. The objective of this study is to enhance and evaluate the specific changes of EEG power spectral density that the brain-machine interface (BMI) users can reliably generate for power augmentation through EEG-NFB training. First, we constructed an EEG-NFB training system for power augmentation. Then, three subjects were assigned to three NFB training stages, based on a 6-day consecutive training session as one stage. The subjects received real-time feedback from their EEG signals by a robotic arm while conducting flexion and extension movement with their elbow and shoulder joints, respectively. EEG signals were compared with each NFB training stage. The training results showed that EEG beta (12–40 Hz) power increased after the NFB training for both the elbow and the shoulder joints’ movements. EEG beta power showed sustained improvements during the 3-stage training, which revealed that even the short-term training could improve EEG signals significantly. Moreover, the training effect of the shoulder joints was more obvious than that of the elbow joints. These results suggest that NFB training can improve EEG signals and clarify the specific EEG changes during the movement. Our results may even provide insights into how the neural effects of NFB can be better applied to the BMI power augmentation system and improve the performance of healthy individuals.

Recently, generative adversarial network (GAN)-based methods for hyperspectral image (HSI) classification have attracted research attention due to their ability to alleviate the challenges brought by having limited labeled samples. However, several studies have demonstrated that existing GAN-based HSI classification methods are limited in redundant spectral knowledge and cannot extract discriminative characteristics, thus affecting classification performance. In addition, GAN-based methods always suffer from the model collapse, which seriously hinders their development. In this study, we proposed a semi-supervised adaptive weighting feature fusion generative adversarial network (AWF2-GAN) to alleviate these problems. We introduced unlabeled data to address the issue of having a small number of samples. First, to build valid spectral–spatial feature engineering, the discriminator learns both the dense global spectrum and neighboring separable spatial context via well-designed extractors. Second, a lightweight adaptive feature weighting component is proposed for feature fusion; it considers four predictive fusion options, that is, adding or concatenating feature maps with similar or adaptive weights. Finally, for the mode collapse, the proposed AWF2-GAN combines supervised central loss and unsupervised mean minimization loss for optimization. Quantitative results on two HSI datasets show that our AWF2-GAN achieves superior performance over state-of-the-art GAN-based methods.

Brain–Machine Interfaces (BMIs) have attracted much attention in recent decades, mainly for their applications involving severely disabled people. Recently, research has been directed at enhancing the ability of healthy people by connecting their brains to external devices. However, there are currently no successful research reports focused on robotic power augmentation using electroencephalography (EEG) signals for the shoulder joint. In this study, a method is proposed to estimate the shoulder’s electromyography (EMG) signals from EEG signals based on the concept of a virtual flexor–extensor muscle. In addition, the EMG signal of the deltoid muscle is used as the virtual EMG signal to establish the EMG estimation model and evaluate the experimental results. Thus, the shoulder’s power can be augmented by estimated virtual EMG signals for the people wearing an EMG-based power augmentation exoskeleton robot. The estimated EMG signal is expressed via a linear combination of the features of EEG signals extracted by Independent Component Analysis, Short-time Fourier Transform, and Principal Component Analysis. The proposed method was verified experimentally, and the average of the estimation correlation coefficient across different subjects was 0.78 (±0.037). These results demonstrate the feasibility and potential of using EEG signals to provide power augmentation through BMI technology.

A power-assisted exoskeleton should be capable of reducing the burden on the wearer’s body or rendering his or her work improved and efficient. More specifically, the exoskeleton should be easy to wear, be simple to use, and provide power assistance without hindering the wearer’s movement. Therefore, it is necessary to evaluate the backdrivability, range of motion, and power-assist capability of such an exoskeleton. This evaluation identifies the pros and cons of the exoskeleton, and it serves as the basis for its subsequent development. In this study, a lightweight upper-limb power-assisted exoskeleton with high backdrivability was developed. Moreover, a motion capture system was adopted to measure and analyze the workspace of the wearer’s upper limb after the exoskeleton was worn. The results were used to evaluate the exoskeleton’s ability to support the wearer’s movement. Furthermore, a small and compact three-axis force sensor was used for power assistance, and the effect of the power assistance was evaluated by means of measuring the wearer’s surface electromyography, force, and joint angle signals. Overall, the study showed that the exoskeleton could achieve power assistance and did not affect the wearer’s movements.

In this paper, aiming to estimate force/torque information from brain activity to help and support the daily lives of human beings, we estimate the human muscular activity from EEG (Electroencephalogram) by PCA (Principal Component Analysis) and RLS (Recursive Least Squares). EEG and EMG (Electromyogram) are measured when a subject is flexing and extending his arm, and their linear model is established by PCA. Then, this linear model between EEG and EMG is updated by the angle, the angular velocity and the angular acceleration of the robot arm. Finally, EMG is estimated from EEG using the updated model. The results show that the estimation of EMG from EEG is possible, and using EEG support human's activities has a great potential.

Brain-Machine Interface (BMI) has been considered as an effective way to help and support both the disabled rehabilitation and healthy individuals’ daily lives to use their brain activity information instead of their bodies. In order to reduce costs and control exoskeleton robots better, we aim to estimate the necessary torque information for a subject from his/her electroencephalography (EEG) signals when using an exoskeleton robot to perform the power assistance of the upper limb without using external torque sensors nor electromyography (EMG) sensors. In this paper, we focus on extracting the motion-relevant EEG signals’ features of the shoulder joint, which is the most complex joint in the human’s body, to construct a power assistance system using wearable upper limb exoskeleton robots with BMI technology. We extract the characteristic EEG signals when the shoulder joint is doing flexion and extension movement freely which are the main motions of the shoulder joint needed to be assisted. Independent component analysis (ICA) is used to extract the source information of neural components, and then the average method is used to extract the characteristic signals that are fundamental to achieve the control. The proposed approach has been experimentally verified. The results show that EEG signals begin to increase at 300–400 ms before the motion and then decrease at the beginning of the generation of EMG signals, and the peaks appear at about one second after the motion. At the same time, we also confirmed the relationship between the change of EMG signals and the EEG signals on the time dimension, and these results also provide a theoretical basis for the delay parameter in the linear model which will be used to estimate the necessary torque information in future. Our results suggest that the estimation of torque information based on EEG signals is feasible, and demonstrate the potential of using EEG signals via the control of brain-machine interface to support human activities continuously.

The NADPH-dependent (S)-carbonyl reductaseII from Candida parapsilosis catalyzes acetophenone to chiral phenylethanol in a very low yield of 3.2%. Site-directed mutagenesis was used to design two mutants Ala220Asp and Glu228Ser, inside or adjacent to the substrate-binding pocket. Both mutations caused a significant enantioselectivity shift toward (R)-phenylethanol in the reduction of acetophenone. The variant E228S produced (R)-phenylethanol with an optical purity above 99%, in 80.2% yield. The E228S mutation resulted in a 4.6-fold decrease in the K M value, but nearly 5-fold and 21-fold increases in the k cat and k cat/K M values with respect to the wild type. For NADPH regeneration, Bacillus sp. YX-1 glucose dehydrogenase was introduced into the (R)-phenylethanol pathway. A coexpression system containing E228S and glucose dehydrogenase was constructed. The system was optimized by altering the coding gene order on the plasmid and using the Shine-Dalgarno sequence and the aligned spacing sequence as a linker between them. The presence of glucose dehydrogenase increased the NADPH concentration slightly and decreased NADP(+) pool 2- to 4-fold; the NADPH/NADP(+) ratio was improved 2- to 5-fold. The recombinant Escherichia coli/pET-MS-SD-AS-G, with E228S located upstream and glucose dehydrogenase downstream, showed excellent performance, giving (R)-phenylethanol of an optical purity of 99.5 % in 92.2% yield in 12 h in the absence of an external cofactor. When 0.06 mM NADP(+) was added at the beginning of the reaction, the reaction duration was reduced to 1 h. Optimization of the coexpression system stimulated an over 30-fold increase in the yield of (R)-phenylethanol, and simultaneously reduced the reaction time 48-fold compared with the wild-type enzyme. This report describes possible mechanisms for alteration of the enantiopreferences of carbonyl reductases by site mutation, and cofactor rebalancing pathways for efficient chiral alcohols production.

The Diesel engine is expected to be available for operation at high altitude. However, power loss and emission deterioration have been plaguing highland Diesel engines. Therefore, this study aimed to investigate the impact of altitude on the performance and combustion characteristics of Diesel engines that is limited discussed in existing studies. The research was conducted by varying the altitude from 0-4500 m using a research Diesel engine and analyzing the combustion characteristics at different combustion phases with the help of triple Wiebe function. The results indicated a noticeable drop in power output with increasing altitude, and the deterioration of performance and emissions became significant when the altitude exceeded 3000 m. Specifically, the indicated specific CO, unburned hydrocarbon, and soot emissions increased while nitrogen NOx showed a reverse trend. Additionally, it was found that the lower cylinder pressure at high altitude extended the ignition delay and caused a higher heat release rate in the premixed combustion stage. Moreover, the high altitude condition shortened the duration of combustion and reduced the energy release fraction in the diffusion phase. Furthermore, the late combustion phase occurred earlier and lasted longer at high altitude, which consequently reduced the combustion and thermal efficiency. The most important finding is that the engine performance, especially the combustion efficiency, shows an abrupt degradation with altitudes above 3000 m. As a result, engines operating at extremely high altitudes require multi-stage turbocharging to compensate for combustion deterioration. [ABSTRACT FROM AUTHOR]