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Guided wave ultrasonic testing (GWUT) within the realm of pipeline inspection is an efficacious approach; however, current GWUT systems are characterized by high costs and power consumption, and their detection results are significantly susceptible to noise interference. Addressing these issues, this study introduces a GWUT system predicated on adaptive wavelet threshold denoising, centered around a low-power main controller, achieving cost-effective and low-power-consumption pipeline non-destructive testing (NDT) with clear results. The system employs an STM32 as the main controller and utilizes direct digital frequency synthesis (DDS) technology to generate ultrasonic excitation signals. These signals, after power amplifier processing, ensure high-stability output for the driving signal. In conjunction with the signal acquisition module, digital filtering of the collected signals is executed via the host computer. Empirical validation has demonstrated that the system can achieve an output amplitude of up to 90 Vpp within an excitation frequency range of 20 kHz–400 kHz, directly driving piezoelectric transducers. The optimal threshold is identified using the butterfly optimization algorithm, enabling the wavelet threshold function to adaptively denoise the echo signals, thereby significantly enhancing the capability to identify pipeline damage. [ABSTRACT FROM AUTHOR]

To address the challenges of sinking, imbalance, and complex control systems faced by hexapod robots walking on lunar soil, this study develops an umbrella-shaped foot lunar exploration hexapod robot. The overall structure of the robot is designed to mimic the body structure of insects. By incorporating a four-bar linkage mechanism to replace the commonly used naked joints in traditional hexapod robots, the robot reduces the number of degrees of freedom and simplifies control complexity. Additionally, an extension mechanism is added to the robot's foot, unfolding into an umbrella shape to provide a larger support area, effectively addressing the issue of foot sinking instability during walking. This study adopts and simplifies the Central Pattern Generator (CPG) model to generate stable periodic control signals for the robot's legs. Precise control of the extension mechanism's unfolding period is achieved through mapping functions. A joint simulation platform using Solid Works and Matlab is established to analyze the stability of the robot's walking. Finally, walking experiments are conducted on the prototype, confirming the smooth walking of the lunar exploration hexapod robot. The results indicate that the designed lunar exploration hexapod robot has a reasonable structure, excellent stability in motion, and the CPG control scheme is feasible. [ABSTRACT FROM AUTHOR]

As a typical intelligent device, magnetorheological (MR) dampers have been widely applied in vibration control and mitigation. However, the inherent hysteresis characteristics of magnetic materials can cause significant time delays and fluctuations, affecting the controllability and damping performance of MR dampers. Most existing mathematical models have not considered the adverse effects of magnetic hysteresis characteristics, and this study aims to consider such effects in MR damper models. Based on the magnetic circuit analysis of MR dampers, the Jiles–Atherton (J-A) model is adopted to characterize the magnetic hysteresis properties. Then, a weight adaptive particle swarm optimization algorithm (PSO) is introduced to the J-A model for efficient parameter identifications of this model, in which the differential evolution and the Cauchy variation are combined to improve the diversity of the population and the ability to jump out of the local optimal solution. The results obtained from the improved J-A model are compared with the experimental data under different working conditions, and it shows that the proposed J-A model can accurately predict the damping performance of MR dampers with magnetic hysteresis characteristics. [ABSTRACT FROM AUTHOR]

Nowadays, high-pressure hydrogen storage is the most commercially used technology owing to its high hydrogen purity, rapid charging/discharging of hydrogen, and low-cost manufacturing. Despite numerous reviews on hydrogen storage technologies, there is a relative scarcity of comprehensive examinations specifically focused on high-pressure gaseous hydrogen storage and its associated materials. This article systematically presents the manufacturing processes and materials used for a variety of high-pressure hydrogen storage containers, including metal cylinders, carbon fiber composite cylinders, and emerging glass material-based hydrogen storage containers. Furthermore, it introduces the relevant principles and theoretical studies, showcasing their advantages and disadvantages compared to conventional high-pressure hydrogen storage containers. Finally, this article provides an outlook on the future development of high-pressure hydrogen storage containers. [ABSTRACT FROM AUTHOR]

To address the challenges posed by the loose lunar surface structure, including the adhesive nature of lunar soil, strong corrosiveness and the slow walking speed of robots using traditional tripod gaits, this paper proposes the design of a small lunar exploration hexapod robot with hollow legs, employing anti-corrosive aerospace materials throughout. Additionally, an inverted gait motion mode is introduced. Simulation analysis is conducted on the displacement, angular velocity, angular acceleration and joint torque of the robot's body under both traditional tripod gaits and the "inverted gait" motion mode. A physical prototype of the robot is developed to validate the rationality of its structure. Our research results indicate that the designed lunar exploration hexapod robot's body structure is reasonable, enabling it to stand and walk normally on the unstructured lunar terrain. The hollow design reduces the adhesion of lunar soil. The inverted gait motion mode expands the effective swinging range of the robot's legs and increases the effective step length during leg swing. Additionally, it improves the robot's movement speed, eliminates vibrations at the joints during motion and improves the robot's stability during the support phase. [ABSTRACT FROM AUTHOR]

Building information models (BIMs) offer advantages, such as visualization and collaboration, making them widely used in the management of existing buildings. Currently, most BIMs for existing indoor spaces are manually created, consuming a significant amount of manpower and time, severely impacting the efficiency of building operations and maintenance management. To address this issue, this study proposes an automated reconstruction method for an indoor scene BIM based on a feature-enhanced point transformer and an octree. This method enhances the semantic segmentation performance of point clouds by using feature position encoding to strengthen the point transformer network. Subsequently, the data are partitioned into multiple segments using an octree, collecting the geometric and spatial information of individual objects in the indoor scene. Finally, the BIM is automatically reconstructed using Dynamo in Revit. The research results indicate that the proposed feature-enhanced point transformer algorithm achieves a high segmentation accuracy of 71.3% mIoU on the S3DIS dataset. The BIM automatically generated from the field point cloud data, when compared to the original data, has an average error of ±1.276 mm, demonstrating a good reconstruction quality. This method achieves the high-precision, automated reconstruction of the indoor BIM for existing buildings, avoiding extensive manual operations and promoting the application of BIMs for the maintenance processes of existing buildings. [ABSTRACT FROM AUTHOR]

Carrier phase measurements currently play a crucial role in achieving rapid and highly accurate positioning of global navigation satellite systems (GNSS). Resolving the integer ambiguity correctly is one of the key steps in this process. To address the inefficiency and slow search problem during ambiguity solving, we propose a single-frequency GNSS integer ambiguity solving based on an adaptive genetic particle swarm optimization (AGPSO) algorithm. Initially, we solve for the floating-point solution and its corresponding covariance matrix using the carrier-phase double difference equation. Subsequently, we decorrelate it using the inverse integer Cholesky algorithm. Furthermore, we introduce an improved fitness function to enhance convergence and search performance. Finally, we combine a particle swarm optimization algorithm with adaptive weights to conduct an integer ambiguity search, where each generation selectively undergoes half-random crossover and mutation operations to facilitate escaping local optima. Comparative studies against traditional algorithms and other intelligent algorithms demonstrate that the AGPSO algorithm exhibits faster convergence rates, improved stability in integer ambiguity search results, and in practical experiments the baseline accuracy of the solution is within 0.02 m, which has some application value in the practical situation of short baselines. [ABSTRACT FROM AUTHOR]

The low light conditions, abundant dust, and rocky terrain on the lunar surface pose challenges for scientific research. To effectively perceive the surrounding environment, lunar rovers are equipped with binocular cameras. In this paper, with the aim of accurately detect obstacles on the lunar surface under complex conditions, an Improved Semi-Global Matching (I-SGM) algorithm for the binocular cameras is proposed. The proposed method first carries out a cost calculation based on the improved Census transform and an adaptive window based on a connected component. Then, cost aggregation is performed using cross-based cost aggregation in the AD-Census algorithm and the initial disparity of the image is calculated via the Winner-Takes-All (WTA) strategy. Finally, disparity optimization is performed using left–right consistency detection and disparity padding. Utilizing standard test image pairs provided by the Middleburry website, the results of the test reveal that the algorithm can effectively improve the matching accuracy of the SGM algorithm, while reducing the running time of the program and enhancing noise immunity. Furthermore, when applying the I-SGM algorithm to the simulated lunar environment, the results show that the I-SGM algorithm is applicable in dim conditions on the lunar surface and can better help a lunar rover to detect obstacles during its travel. [ABSTRACT FROM AUTHOR]

A novel neural network (NN)-based parallel model predictive control (PMPC) method is proposed to deal with the tracking problem of the quadrotor unmanned aerial vehicles (Q-UAVs) system in this article. It is well known that the dynamics of Q-UAVs are changeable while the system is operating in some specific environments. In this case, traditional NN-based MPC methods are not applicable because their model networks are pre-trained and kept constant throughout the process. To solve this problem, we propose the PMPC algorithm, which introduces parallel control structure and experience pool replay technology into the MPC method. In this algorithm, an NN-based artificial system runs in parallel with the UAV system to reconstruct its dynamics model. Furthermore, the experience replay technology is used to maintain the accuracy of the reconstructed model, so as to ensure the effectiveness of the model prediction algorithm. Furthermore, a convergence proof of the artificial system is also given in this paper. Finally, numerical results and analysis are given to demonstrate the effectiveness of the PMPC algorithm. [ABSTRACT FROM AUTHOR]

Tomato is generally cultivated by transplanting seedlings in ridges and furrows. During growth, there are various types of tomato pests and diseases, making it challenging to identify them simultaneously. To address this issue, conventional convolutional neural networks have been investigated, but they have a large number of parameters and are time-consuming. In this paper, we proposed a lightweight multi-scale tomato pest and disease classification network, called CNNA. Firstly, we constructed a dataset of tomato diseases and pests consisting of 27,193 images with 18 categories. Then, we compressed and optimized the ConvNeXt-Tiny network structure to maintain accuracy while significantly reducing the number of parameters. In addition, we proposed a multi-scale feature fusion module to improve the feature extraction ability of the model for different spot sizes and pests, and we proposed a global channel attention mechanism to enhance the sensitivity of the network model to spot and pest features. Finally, the model was trained and deployed to the Jetson TX2 NX for inference of tomato pests and diseases in video stream data. The experimental results showed that the proposed CNNA model outperformed the pre-trained lightweight models such as MobileNetV3, MobileVit, and ShuffleNetV2 in terms of accuracy and all parameters, with a recognition accuracy of 98.96%. Meanwhile, the error rate, inference time for a single image, network parameters, FLOPs, and model size were only 1%, 47.35 ms, 0.37 M, 237.61 M, and 1.47 MB, respectively. [ABSTRACT FROM AUTHOR]

Thermal expansion is inevitable for space structures under the alternating temperature of outer space around the earth. This may lead to the thermal stress and deformation due to the mismatch of the coefficient of thermal expansion. Near-zero thermal expansion (Near-ZTE) is a vitally essential demand for large-scale space telescopes or antennas to preserve their spatial precision and resolution. Recently, mechanical metamaterials with superior and tailorable properties have attracted significant interest with regard to developing negative materials or ultra-property materials. In this paper, the near-ZTE space structure architected by a dual-hourglass bi-material lattice is achieved by the structural optimization method with the gradient-based algorithm. First, an hourglass lattice with adjustable structural parameters is optimized to seek the design of effective negative thermal expansion (NTE) in the thickness direction. Then, two building blocks with both NTE and legacy positive thermal expansion (PTE) are combined as a dual-layered lattice to obtain the near-ZTE. Finally, a structure with near-ZTE of about ~10−9 m/(m·K) is obtained. Furthermore, the various lattice configurations, such as the hexagonal pyramid and triangle pyramid, are investigated in detail. Finally, the natural frequencies of two near-ZTE lattices are calculated by the modal analysis method, and the stiffness is discussed for the optimal solution of space applications. This work demonstrates that the near-ZTE structure can be achieved by utilizing the negative metamaterial and structural optimization method. It provides a novel solution to design the large-scale space structures with the near-zero thermal induced deformation, and may be constructed and assembled by the on-orbit fabrication technology. [ABSTRACT FROM AUTHOR]

In order to improve the seismic performance of damaged reinforced concrete joints, a new retrofitting method using viscoelastic materials and steel plates is proposed. A reversal cyclic loading test was carried out on four T-shaped RC joints to investigate the reliability of this method. Seismic performance, including the strength, stiffness, ductility, and energy dissipation capacity of the original non-damaged joint and different repaired and retrofitted joints are then compared. The results show that this new method can significantly improve the strength of the retrofitted joints, and the joints can be loaded to a larger displacement value. After retrofitting, the energy dissipation of the joints increases, and the stiffness degradation decreases. The test results indicate that the retrofitting method is effective in repairing seismically damaged RC joints. [ABSTRACT FROM AUTHOR]

Saprobic hyphomycetes are highly diverse on plant debris. Over the course of our mycological surveys in southern China, three new Helminthosporium species, H. guanshanense sp. nov., H. jiulianshanense sp. nov. and H. meilingense sp. nov., collected on dead branches of unidentified plants, were introduced by morphological and molecular phylogenetic analyses. Multi-loci (ITS, LSU, SSU, RPB2 and TEF1) phylogenetic analyses were performed using maximum-likelihood and Bayesian inference to infer their taxonomic positions within Massarinaceae. Both molecular analyses and morphological data supported H. guanshanense, H. jiulianshanense and H. meilingense as three independent taxa within Helminthosporium. A list of accepted Helminthosporium species with major morphological features, host information, locality and sequence data was provided. This work expands our understanding of the diversity of Helminthosporium-like taxa in Jiangxi Province, China. [ABSTRACT FROM AUTHOR]

Pollutants from construction activities of building projects can have serious negative impacts on the natural environment and human health. Carrying out monitoring of environmental pollutants during the construction period can effectively mitigate environmental problems caused by construction activities and achieve sustainable development of the construction industry. However, the current environmental monitoring method relying only on various sensors is relatively singlar which is unable to cope with a complex on-site environment We propose a mechanism for environmental pollutants identification combining association rule mining and ontology-based reasoning and using random forest algorithm to improve the accuracy of identification. Firstly, the ontology model of environmental pollutants monitoring indicator in the construction site is built in order to integrate and share the relative knowledge. Secondly, the improved Apriori algorithm with added subjective and objective constraints is used for association rule mining among environmental pollutants monitoring indicators, and the random forest algorithm is applied to further filter the strong association rules. Finally, the ontology database and rule database are loaded into a Jena reasoning machine for inference to establish an identification mechanism of environmental pollutants. The results of running on a real estate development project in Jiangning District, Nanjing, prove that this identification mechanism can effectively tap the potential knowledge in the field of environmental pollutants monitoring, explore the relationship between environmental pollutants monitoring indicators and then overcome the shortcomings of traditional monitoring methods that only rely on sensors to provide new ideas and methods for making intelligent decisions on environmental pollutants in a construction site. [ABSTRACT FROM AUTHOR]

Environmental monitoring is a crucial part of environmental management on construction sites. With the increasing integration of environmental-monitoring systems and cyber-physical systems (CPS), the environmental-monitoring cyber-physical system (E-CPS) has been developed, but it still suffers from uncertainty problems and a lack of robustness. In this study, ontology is utilized to establish an E-CPS model that can realize the integration and interaction of physical space, cyberspace, and social space, and the E-CPS model contains perception, transportation, fusion, and decision-making layers. Three uncertainty scenarios are then identified in four layers of the E-CPS to address the current E-CPS shortcomings. The proposed E-CPS model is applied in a construction project, and simulation experiments are then conducted on construction sites. The results show that the abnormal-data-recognition algorithm based on spatiotemporal correlation, whose detection rate is stable around 96%, improves the system's anti-interference ability against anomalous data entering the perception layer and the transportation layer. This algorithm ensures the accuracy of environmental monitoring for early warning. The sensory data-fusion results based on the belief function method vary from 52.16 to 52.50, with a decrease rate reduced to 0.65%. Finally, the decision-fusion algorithm based on the improved Dempster–Shafer (D-S) evidence theory achieves robust performance. This study could enhance the robustness of the E-CPS in uncertainty conditions and aid the project managers to make decisions and take targeted measures according to the environmental monitoring results and experts' decisions. [ABSTRACT FROM AUTHOR]

Exosomes are nano-sized vesicles that contain a variety of mRNAs, miRNAs, and proteins. They are capable of being released by a variety of cells and are essential for cell–cell communication. The exosomes produced by cells have shown protective benefits against spinal cord damage (SCI). Recently, it was discovered that M2 macrophages aid in the angiogenesis of numerous illnesses. However, the functional role of M2 macrophage-derived exosomes on SCI is unclear. Here, we investigate the pro-angiogenesis of M2 macrophage-derived exosomes on SCI. We founded that M2 macrophage exosomes alleviated tissue damage and enhanced functional recovery post-SCI. We discovered that M2 macrophage exosome administration increased angiogenesis after SCI in vivo using immunohistochemistry, immunofluorescence labeling, and Western blot analysis. Additionally, the expression of the pro-angiogenesis factors, HIF-1α and VEGF, were enhanced with the treatment of the M2 macrophage exosomes. Furthermore, we found that M2 macrophage exosomes enhanced neurogenesis after SCI in vivo. In vitro, we found that M2 macrophage exosomes can be taken up by the brain endothelial cell line (bEnd.3) and that they enhanced the tube formation, migration, and proliferation of bEnd.3 cells. Furthermore, by using special siRNA to inhibit HIF-1α expression, we observed that the expression of VEGF decreased, and the tube formation, migration, and proliferation of bEnd.3 cells were attenuated with the treatment of HIF-1α-siRNA. In conclusion, our findings reveal that M2 macrophage exosomes improve neurological functional recovery and angiogenesis post-SCI, and this process is partially associated with the activation of the HIF-1/VEGF signaling pathway. [ABSTRACT FROM AUTHOR]

Traditional medicine is critical in disease treatment and management. Herbs are gaining popularity for disease management and treatment. Therefore, they can be utilised as complementary and alternative treatment (CAT) ingredients. Atopic dermatitis (AD) is one of the common non-communicable diseases. It is characterised by chronic inflammatory skin disease with intense pruritus and eczematous lesions. AD is associated with oxidative stress, microbial infection, and upregulation of inflammatory cytokines. Both children and adults could be affected by this skin disorder. The prevalence of AD is increasing along with the country's level of development. This review revisited the literature on four medicinal herbs widely used as complementary medicine to manage AD. These therapeutic herbs are commonly eaten as food and used as spices in Asian cuisine. The four food herbs reviewed are Cassia alata, Coriandrum sativum, Curcuma longa Linn, and Azadirachta indica. Their traditional uses and phytochemical content will be covered. Four relevant pharmacological and biological activities of the plants crucial in AD management have been reviewed and discussed, including anti-inflammatory, anti-microbial, antioxidant, and wound recovery. [ABSTRACT FROM AUTHOR]

A real-time hybrid simulation (RTHS) is a promising technique to investigate a complicated or large-scale structure by dividing it into numerical and physical substructures and conducting cyber-physical tests on it. The control system design of an RTHS is a challenging topic due to the additional feedback between the physical and numerical substructures, and the complexity of the physical control plant. This paper proposes a novel RTHS control strategy by combining the theories of adaptive control and robust control, where a reformed plant which is highly simplified compared to the physical plant can be used to design the control system without compromising the control performance. The adaptation and robustness features of the control system are realized by the bounded-gain forgetting least-squares estimator and the sliding mode controller, respectively. The control strategy is validated by investigating an RTHS benchmark problem of a nonlinear three-story steel frame The proposed control strategy could simplify the control system design and does not require a precise physical plant; thus, it is an efficient and practical option for an RTHS. [ABSTRACT FROM AUTHOR]

Contemporary engineering in the construction field has put forward higher requirements on the value utilization of building information models, and as the prefabricated building is the core of construction industrialization, using BIM (Building Information Model) technology to realize the forward design of prefabricated buildings and maximize the value of BIM is in urgent demand in the current construction industry. However, in the application process of forward design, there is a lack of standardized implementation guidance and mature technical support, leading to many problems such as a redundancy of model information, heterogeneity of data information, and low efficiency of transmission. Based on this, this paper proposes a model view delivery method of forward design for prefabricated buildings. Firstly, a simplified assembly model and an assembly knowledge model are designed and extended with IFC (Industry Foundation Classes), then we realize the knowledge visualization expression by combining with an ontology semantic system. For the model view of a prefabricated building domain, this paper realizes the reusable concept module by ontology IDM (Information Delivery Manual) and properties selection of knowledge graphs, and then completes the model view delivery through data mapping and IfcDoc (Ifc Documentation Generator) tool output. Finally, the implementation model of information delivery management for forward design is built with model view delivery as the central link. [ABSTRACT FROM AUTHOR]

The identification of wildfires is a very complex task due to their different shapes, textures, and colours. Traditional image processing methods need to manually design feature extraction algorithms based on prior knowledge, and because fires at different stages have different characteristics, manually designed feature extraction algorithms often have insufficient generalization capabilities. A convolutional neural network (CNN) can automatically extract the deeper features of an image, avoiding the complexity and blindness of the feature extraction phase. Therefore, a wildfire identification method based on an improved two-channel CNN is proposed in this paper. Firstly, in order to solve the problem of the insufficient dataset, the dataset is processed by using PCA_Jittering, transfer learning is used to train the model and then the accuracy of the model is improved by using segmented training. Secondly, in order to achieve the effective coverage of the model for fire scenes of different sizes, a two-channel CNN based on feature fusion is designed, in which the fully connected layers are replaced by a support vector machine (SVM). Finally, in order to reduce the delay time of the model, Lasso_SVM is designed to replace the SVM in the original model. The results show that the method has the advantages of high accuracy and low latency. The accuracy of wildfire identification is 98.47% and the average delay time of fire identification is 0.051 s/frame. The wildfire identification method designed in this paper improves the accuracy of identifying wildfires and reduces the delay time in identifying them. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for construction component classification by designing a feature-based deep learning network to tackle the automation problem in construction digitization. Although scholars have proposed a variety of ways to achieve the use of deep learning to classify point clouds, there are few practical engineering applications in the construction industry. However, in the process of building digitization, the level of manual participation has significantly reduced the efficiency of digitization and increased the application restrictions. To address this problem, we propose a robust classification method using deep learning networks, which is combined with traditional shape features for the point cloud of construction components. The proposed method starts with local and global feature extraction, where global features processed by the neural network and the traditional shape features are processed separately. Then, we generate a feature map and perform deep convolution to achieve feature fusion. Finally, experiments are designed to prove the efficiency of the proposed method based on the construction dataset we establish. This paper fills in the lack of deep learning applications of point clouds in construction component classification. Additionally, this paper provides a feasible solution to improve the construction digitization efficiency and provides an available dataset for future work. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries (LIBs) as a predominant power source are widely used in large-scale energy storage fields. For the next-generation energy storage LIBs, it is primary to seek the high capacity and long lifespan electrode materials. Nickel and purified terephthalic acid-based MOF (Ni-PTA) with a series amounts of zinc dopant (0, 20, 50%) are successfully synthesized in this work and evaluated as anode materials for lithium-ion batteries. Among them, the 20% atom fraction Zn-doped Ni-PTA (Zn0.2-Ni-PTA) exhibits a high specific capacity of 921.4 mA h g−1 and 739.6 mA h g−1 at different current densities of 100 and 500 mA g−1 after 100 cycles. The optimized electrochemical performance of Zn0.2-Ni-PTA can be attributed to its low charge transfer resistance and high lithium-ion diffusion rate resulting from expanded interplanar spacing after moderate Zn doping. Moreover, a full cell is fabricated based on the LiFePO4 cathode and as-prepared MOF. The Zn0.2-Ni-PTA shows a reversible specific capacity of 97.9 mA h g−1 with 86.1% capacity retention (0.5 C) after 100 cycles, demonstrating the superior electrochemical performance of Zn0.2-Ni-PTA anode as a promising candidate for practical lithium-ion batteries. [ABSTRACT FROM AUTHOR]

The management of prefabricated component staging and turnover lacks the effective integration of informatization and complexity, as relevant information is stored in the heterogeneous systems of various stakeholders. BIM and its underlying data schema, IFC, provide for information collaboration and sharing. In this paper, an automatic classification and coding system for prefabricated building, based on BIM technology and Random Forest, is developed so as to enable the unique representation of components. The proposed approach starts with classifying and coding information regarding the overall design of the components. With the classification criteria, the required attributes of the components are extracted, and the process of attribute extraction is illustrated in detail using wall components as an example. The Random Forest model is then employed for IFC building component classification training and testing, which includes the selection of the datasets, the construction of CART, and the voting of the component classification results. The experiment results illustrate that the approach can automate the uniform and unique coding of each component on a Python basis, while also reducing the workload of designers. Finally, based on the IFC physical file, an extended implementation process for component encoding information is designed to achieve information integrity for prefabricated component descriptions. Additionally, in the subsequent research, it can be further combined with Internet-of-Things technology to achieve the real-time collection of construction process information and the real-time control of building components. [ABSTRACT FROM AUTHOR]

As a result of the deepening of China's power system reform, the market-oriented business development of power grid enterprises is in full swing. However, most of the existing research has focused on the regulatory risks of the regulatory business for power grid enterprises, while ignoring the regulatory risks faced by market-oriented businesses. In order to promote the sustainable development of market-oriented business, a comprehensive regulatory risk assessment framework was constructed for the market business of power grid enterprises. First, the risk assessment index system was constructed from the perspectives of policy risk, business isolation risk, market risk, and safety risk. Then, a novel hybrid multi-criteria decision-making (MCDM) model based on the Bayesian best-worst method and the measurement alternatives and ranking according to the compromise solution approach, was adopted. Finally, eight market-oriented businesses were selected as case studies. The result indicates that organizational isolation risk and operational security risk are the key regulatory risks of the market-oriented business for power grid enterprises. Compared with two other MCDM models, the proposed hybrid MCDM model has good applicability and effectiveness for risk evaluation of the regulatory business. The results of this research can provide support for power enterprises to deal with market-oriented business supervision, and can also provide a reference for power industry regulators. [ABSTRACT FROM AUTHOR]

Research on blue and green water footprints (WF) for China has typically been carried out based on bottom-up and top-down approach using a single-region input–output table. However, this research typically lacks detail on the sectoral interrelationships which exist between China and its trading partners in other countries/regions of the world. Here, a multi-region input–output approach using the WIOD database was applied to quantify the blue and green WF for China in 2009. The quantification was conducted from both production (WFP) and consumption (WFC) perspectives. The results show that the total WFP for China in 2009 was 1152.2 km3, second only to India. At 1070.9 km3, China had the largest WFC volume in the world. The internal WF was 953.5 km3, taking the substantial share for both the WFC and WFP. Overall, China’s trade resulted in a net export of 53.5 km3 virtual water. In contrast, the agricultural sector resulted in a net import of 70.6 km3 virtual water to China, with United States, Brazil, and Canada acting as major suppliers. This study suggests that quantifying the WF of China at global level through a MRIO framework is a necessary step towards achieving sustainability for China’s water management. [ABSTRACT FROM AUTHOR]

A simple and rapid ultra-performance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS) coupled with a one-step liquid-liquid extraction method has been developed and validated for the simultaneous determination of two flavonoids (chrysin, tectochrysin) from Alpinia oxyphylla fruits extract in rat plasma. Plasma samples were extracted with diethyl ether and separated on an ACQUITY UPLC BEH C18 column (100 mm × 2.1 mm, 1.7 μm) using gradient elution consisting of 0.1% formic acid in water (A) and methanol (B). The multiple reaction monitoring (MRM) mode with electrospray ionization in the positive ion mode was used for detection. The linear range was 0.1 ng/mL to 50 ng/mL for chrysin and tectochrysin, respectively. The accuracy (relative error, RE%) ranged from −8.8% to 7.5% and the intra-day and inter-day precision were within 15% and had a mean extraction recovery rate of 80.3% to 86.7%. The validated method was applied to a comparative pharmacokinetic study after oral administration of Alpinia oxyphylla fruit ethanol extract between normal rats and dementia rats. The area under the curve (AUC) and peak plasma concentration (Cmax) of the two constituents were remarkably increased in dementia rats than in normal rats. [ABSTRACT FROM AUTHOR]

The integration of renewables is fast-growing, in light of smart grid technology development. As a result, the uncertain nature of renewables and load demand poses significant technical challenges to distribution network (DN) daily operation. To alleviate such issues, price-sensitive demand response and distributed generators can be coordinated to accommodate the renewable energy. However, the investment cost for demand response facilities, i.e., load control switch and advanced metering infrastructure, cannot be ignored, especially when the responsive demand is large. In this paper, an optimal coordinated investment for distributed generator and demand response facilities is proposed, based on a linearized, price-elastic demand response model. To hedge against the uncertainties of renewables and load demand, a two-stage robust investment scheme is proposed, where the investment decisions are optimized in the first stage, and the demand response participation with the coordination of distributed generators is adjusted in the second stage. Simulations on the modified IEEE 33-node and 123-node DN demonstrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Adsorption of hydrocarbons may significantly affect hydrocarbon migration in unconventional reservoirs. Clay minerals form the primary adsorbent surfaces for hydrocarbons adsorbed in mudstone/shale. To study the adsorption properties of hydrocarbons (n-decane (C10H22), methyl cyclohexane (C7H14) and toluene (C7H8)) on clay minerals (i.e., cookeite, ripidolite, kaolinite, illite, illite/smectite mixed-layer, Na-montmorillonite and Ca-montmorillonite), hydrocarbon vapor adsorption (HVA) tests were conducted at 298.15 K. The results showed that (i) the adsorption amounts of C10H22, C7H14 and C7H8 ranged from 0.45-1.03 mg/m², 0.28-0.90 mg/m² and 0.16-0.53 mg/m², respectively; (ii) for cookeite, ripidolite and kaolinite, the adsorption capacity of C10H22 was less than C7H14, which was less than C7H8; (iii) for illite, Na-montmorillonite and Ca-montmorillonite, the adsorption capacity of C10H22 was greater than that of C7H8, and the adsorption capacity of C7H14 was the lowest; (iv) for an illite/smectite mixed-layer, C7H14 had the highest adsorption capacity, followed by C10H22, and C7H8 had the lowest capacity. Adsorption properties were correlated with the microscopic parameters of pores in clay minerals and with experimental pressure. Finally, the weighted average method was applied to evaluate the adsorption properties of C10H22, C7H14 and C7H8 on clay minerals in oil-bearing shale from the Shahejie Formation of Dongying Sag in the Bohai Bay Basin, China. For these samples, the adsorbed amounts of C7H14 ranged from 18.03-28.02 mg/g (mean 23.33 mg/g), which is larger than that of C10H22, which ranges from 15.40-21.72 mg/g (mean 18.82 mg/g). The adsorption capacity of C7H8 was slightly low, ranging from 10.51-14.60 mg/g (mean 12.78 mg/g). [ABSTRACT FROM AUTHOR]

This study seeks to address the problems of major geo-environmental hazards caused by high-intensive coal mining in China's western eco-environment frangible area including strong mining pressure, surface subsidence, soil and water loss, and land desertification. Using the high-intensive mining at the Xiao-jihan Coal Mine, this paper investigates the compaction characteristics of aeolian sand-based backfilling materials, and then the evolution of water-conducting fractures and surface deformation laws with different backfill material's compression ratios (BMCRs) by using physical simulation and numerical simulation analysis methods. This study presents the technical system of water-preserved and environmental protection with rapid-backfilling methods in China's western eco-environment frangible area. The backfill coal mining technique and application prospects are assessed and discussed. The results will be helpful for coordinated development of coal resources exploitation and environmental protection in China's western eco-environment frangible area. [ABSTRACT FROM AUTHOR]

Clinically-informative biomarkers of ischemic stroke are needed for rapid diagnosis and timely treatment. In the present study, APOA1 unique peptide (APOA1-UP), a novel peptide biomarker, was identified and quantified by multiple reaction monitoring (MRM) using labeled reference peptide (LRP). Serum samples of 94 patients in the ischemic stroke group and 37 patients in the non-stroke group were analyzed for the levels of total APOA1-UP, low density lipoprotein cholesterol (LDL-C), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and total cholesterol (TC). Median ratio of total APOA1-UP/LRP was 2.14 (interquartile range, 0.40) in the non-stroke group and 1.32 (0.44) in the ischemic stroke group (p < 0.0001). The serum level of total APOA1-UP was independently correlated with the presence of ischemic stroke by multivariate logistic regression analysis (p < 0.0001). From the receiver operating characteristic (ROC) curve, the area under the curve (AUC) was 0.9750 and the optimal cutoff value of the serum APOA1-UP level was 1.80, which yielded a sensitivity of 90.63% and a specificity of 97.14%. The diagnostic efficiency of HDL-C was lower, with an AUC of 0.7488. Therefore, the serum level of APOA1-UP is a diagnostic biomarker candidate for ischemic stroke in the early stage. [ABSTRACT FROM AUTHOR]

Optimizing international oil exploration projects is one of the main challenges for oil companies in obtaining investment benefits. This paper establishes an integrated evaluation model to maximize investment benefits within the constraints of technical and economic factors, including geological factors, resource quality, geographic conditions, the investment environment, and oil contracts. The paper also proposes a dynamic calculation method of indicators' weight associated with oil prices. The analysis describes the effects of contract terms and the investment environment on project value and quantifies the contractor income ratio for different types of contracts and the investment environment of the host country. Oil exploration projects in Africa are illustrated as examples in which the evaluation indicator Adjusted Concept Reserves (ACR) is calculated for each project. The results show that remaining recoverable reserves and contract terms exert tremendous influences on ACR, and remaining recoverable reserves is the essential factor. Simultaneously, changes in oil prices lead to various rates of change in the contractor income ratio, which is determined by different fiscal terms. This study is important in helping oil companies optimize international oil projects and design reasonable investment strategies. [ABSTRACT FROM AUTHOR]

Hedging is widely used to mitigate severe water shortages in the operation of reservoirs during droughts. Rationing is usually instituted with one hedging policy, which is based only on one trigger, i.e., initial storage level or current water availability. It may perform poorly in balancing the benefits of a release during the current period versus those of carryover storage during future droughts. This study proposes a novel hedging rule to improve the efficiency of a reservoir operated to supply water, in which, based on two triggers, hedging is initiated with three different hedging sub-rules through a two-step approach. In the first step, the sub-rule is triggered based on the relationship between the initial reservoir storage level and the level of the target rule curve or the firm rule curve at the end of the current period. This step is mainly concerned with increasing the water level or not in the current period. Hedging is then triggered under the sub-rule based on current water availability in the second step, in which the trigger implicitly considers both initial and ending reservoir storage levels in the current period. Moreover, the amount of hedging is analytically derived based on the Karush–Kuhn–Tucker (KKT) conditions. In addition, the hedging parameters are optimized using the improved particle swarm optimization (IPSO) algorithm coupled with a rule-based simulation. A single water-supply reservoir located in Hubei Province in central China is selected as a case study. The operation results show that the proposed rule is reasonable and significantly improves the reservoir operation performance for both long-term and critical periods relative to other operation policies, such as the standard operating policy (SOP) and the most commonly used hedging rules. [ABSTRACT FROM AUTHOR]

Identifying the main sources of conflict and understanding the relationships between protected areas and local communities are critical to conflict resolution related to protected area management. We surveyed and assessed the perceptions of local people related to forest values of the Bulong Nature Reserve (BNR), Yunnan, China, and their attitudes toward forest management of the recently-established nature reserve. Factors influencing the differences in perceptions and attitudes were investigated. Our results indicated local residents preferred the economic value of forests, and they perceived more forest values after reserve establishment than before, especially for ecological services. Interviewees believed the value of the forests have increased, and predicted that the forest will have increased importance to their livelihoods and health in the future. Nonetheless, the majority of interviewees were dissatisfied with current reserve management. Variation in these perceptions and attitudes were related to several social variables: age, gender, education, and distance from the reserve. Embedding the findings of the present study, specifically the perceptions and attitudes of local people, into management guidelines for mitigation of potential conflicts is proposed and should help managers to achieve biodiversity-related goals. This study offers new insights related to the identification of the underlying sources of conflict in forest management and provides a better understanding of the relationship between local people and protected areas. The study also contributes to the literature on forest values of indigenous populations by examining those of remote, rural populations in China. [ABSTRACT FROM AUTHOR]

The search to date for accurate protein biomarkers in acute ischemic stroke has taken into consideration the stage and/or the size of infarction, but has not accounted for the site of stroke. In the present study, multiple reaction monitoring using labeled reference peptide (LRP) following laser capture microdissection (LCM) is used to identify site-specific protein biomarker candidates. In middle cerebral artery occlusion (MCAO) rat models, both intact and infarcted brain tissue was collected by LCM, followed by on-film digestion and semi-quantification using triple-quadrupole mass spectrometry. Thirty-four unique peptides were detected for the verification of 12 proteins in both tissue homogenates and LCM-captured samples. Six insoluble proteins, including neurofilament light polypeptide (NEFL), alpha-internexin (INA), microtubule-associated protein 2 (MAP2), myelin basic protein (MBP), myelin proteolipid protein (PLP) and 2',3'-cyclic-nucleotide 3'-phosphodiesterase (CNP), were found to be site-specific. Soluble proteins, such as neuron-specific enolase (NSE) and ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1), and some insoluble proteins, including neurofilament heavy polypeptide (NEFH), glial fibrillary acidic protein (GFAP), microtubule-associated protein tau (MAPT) and tubulin ß-3 chain (TUBB3), were found to be evenly distributed in the brain. Therefore, we conclude that some insoluble protein biomarkers for stroke are site-specific, and would make excellent candidates for the design and analysis of relevant clinical studies in the future. [ABSTRACT FROM AUTHOR]

Smart textile with IR radiative cooling is of paramount importance for reducing energy consumption and improving the thermal comfort of individuals. However, wearable textile via facile methods for indoor/outdoor thermal management is still challenging. Here we present a novel simple, yet effective method for versatile thermal management via silver-coated polyamide (PA) textile. Infrared transmittance of coated fabric is greatly enhanced by 150% due to the multi-order reflection of silver coating. Based on their IR radiative cooling, indoors and outdoors, the skin surface temperature is lower by 1.1 and 0.9 °C than normal PA cloth, allowing the textile to be used in multiple environments. Moreover, the coated fabric is capable of active warming up under low voltage, which can be used in low-temperature conditions. These promising results exemplify the practicability of using silver-coated textile as a personal thermal management cloth in versatile environments. [ABSTRACT FROM AUTHOR]

Calmodulin-binding protein 60 (CBP60) members constitute a plant-specific protein family that plays an important role in plant growth and development. In the soybean genome, nineteen CBP60 members were identified and analyzed for their corresponding sequences and structures to explore their functions. Among GmCBP60A-1, which primarily locates in the cytomembrane, was significantly induced by drought and salt stresses. The overexpression of GmCBP60A-1 enhanced drought and salt tolerance in Arabidopsis, which showed better state in the germination of seeds and the root growth of seedlings. In the soybean hairy roots experiment, the overexpression of GmCBP60A-1 increased proline content, lowered water loss rate and malondialdehyde (MDA) content, all of which likely enhanced the drought and salt tolerance of soybean seedlings. Under stress conditions, drought and salt response-related genes showed significant differences in expression in hairy root soybean plants of GmCBP60A-1-overexpressing and hairy root soybean plants of RNAi. The present study identified GmCBP60A-1 as an important gene in response to salt and drought stresses based on the functional analysis of this gene and its potential underlying mechanisms in soybean stress-tolerance. [ABSTRACT FROM AUTHOR]

In China, a new-round marketization reform of electricity industry is in progress, and the electricity transmission and distribution tariff reform is the core and important task. Currently, the electricity transmission and distribution tariff regulation has gone to the second round in China, and the electric power grid enterprises are facing a closed-loop regulatory system and an increasingly strict regulatory environment. Therefore, it is urgent to evaluate the risk of electric power grid enterprise that is related to electricity transmission and distribution tariff regulation, which can aid the electricity regulators and electric power grid enterprise operators to manage risk and promote the sustainable development of electric power industry. In this paper, a hybrid novel multi-criteria decision making (MCDM) method combining the fuzzy Best-Worst method (FBWM) and improved fuzzy comprehensive evaluation method based on a vague set is proposed for the risk evaluation of electric power grid enterprise related to electricity transmission and distribution tariff regulation. The risk evaluation index system is built. Subsequently, the FBWM is utilized to determine the optimal weights of electric power grid enterprise risk criteria, and the improved fuzzy comprehensive evaluation method that is based on vague set is employed to rank the comprehensive risk grade of electric power grid enterprise related to electricity transmission and distribution tariff regulation. The risk of a province-level electric power grid enterprise that is located in Northern China is empirically evaluated using the proposed MCDM method, and the result indicates that the overall risk of this province-level electric power grid enterprise belongs to 'High' grade, but it is very close to 'Very High' grade. The results indicate that the proposed hybrid novel MCDM method in this paper is effective and practical. Meanwhile, it provides a new view for the risk evaluation of electric power grid enterprise that is related to electricity transmission and distribution tariff regulation. [ABSTRACT FROM AUTHOR]

With the breadth of applications and analysis performed over the last few decades, it would not be an exaggeration to call piezoelectric materials "the top of the crop" of smart materials. Piezoelectric materials have emerged as the most researched materials for practical applications among the numerous smart materials. They owe it to a few main reasons, including low cost, high bandwidth of service, availability in a variety of formats, and ease of handling and execution. Several authors have used piezoelectric materials as sensors and actuators to effectively control structural vibrations, noise, and active control, as well as for structural health monitoring, over the last three decades. These studies cover a wide range of engineering disciplines, from vast space systems to aerospace, automotive, civil, and biomedical engineering. Therefore, in this review, a study has been reported on piezoelectric materials and their advantages in engineering fields with fundamental modeling and applications. Next, the new approaches and hypotheses suggested by different scholars are also explored for control/repair methods and the structural health monitoring of engineering structures. Lastly, the challenges and opportunities has been discussed based on the exhaustive literature studies for future work. As a result, this review can serve as a guideline for the researchers who want to use piezoelectric materials for engineering structures. [ABSTRACT FROM AUTHOR]

Road roughness is an important factor in road network maintenance and ride quality. This paper proposes a road-roughness estimation method using the frequency response function (FRF) of a vehicle. First, based on the motion equation of the vehicle and the time shift property of the Fourier transform, the vehicle FRF with respect to the displacements of vehicle–road contact points, which describes the relationship between the measured response and road roughness, is deduced and simplified. The key to road roughness estimation is the vehicle FRF, which can be estimated directly using the measured response and the designed shape of the road based on the least-squares method. To eliminate the singular data in the estimated FRF, the shape function method was employed to improve the local curve of the FRF. Moreover, the road roughness can be estimated online by combining the estimated roughness in the overlapping time periods. Finally, a half-car model was used to numerically validate the proposed methods of road roughness estimation. Driving tests of a vehicle passing over a known-sized hump were designed to estimate the vehicle FRF, and the simulated vehicle accelerations were taken as the measured responses considering a 5% Gaussian white noise. Based on the directly estimated vehicle FRF and updated FRF, the road roughness estimation, which considers the influence of the sensors and quantity of measured data at different vehicle speeds, is discussed and compared. The results show that road roughness can be estimated using the proposed method with acceptable accuracy and robustness. [ABSTRACT FROM AUTHOR]

The stable working window of a dual-fuel engine is narrow, and it is prone to knock during operation. The occurrence of knock limits the load and torque output of a dual-fuel engine, and even causes engine damage in severe cases. The existing volumetric model of marine dual-fuel engine has little research on the related problems of knock simulation. In order to analyze the causes of knock phenomenon and the influence of operating parameter changes on knock, under the Matlab/Simulink simulation environment, a quasi-dimensional model was established with MAN 8L51/60DF dual-fuel engine as the prototype, and the model was calibrated using the bench data. The knock intensity index coefficient (KI) was used as the evaluation index of knock intensity. Three parameters, the intake air temperature, compression ratio, and natural gas intake, were selected as variables to simulate the engine. According to the analysis of the simulation results, the influence of the parameter changes on the occurrence of engine knock phenomenon and knock intensity could be further studied. The results showed that the combination of the KI model and the quasi-dimensional model could effectively and accurately predict the engine performance and knock trend. The change of gas inlet quality, compression ratio, and inlet temperature could promote the occurrence of detonation, the engine knock could be avoided by controlling the intake air temperature below 336 K, compression ratio not exceeding 15 or the intake volume of natural gas per cycle not exceeding 11.25 g/cycle. [ABSTRACT FROM AUTHOR]

Background: With the ever-increasing availability of data and a higher level of automation and simulation, production scheduling in the factory for prefabrication can no longer be seen as an autonomous solution. Concepts such as building information modelling (BIM), graphic techniques, databases, and interface development as well as heightened emphasis on overall-process optimization topics increase the pressure to connect to and interact with interrelated tasks and procedures. Methods: The automated optimization framework detailed in this study intended to generate optimal schedule of prefabricated component production based on the manufacturing process model and genetic algorithm method. An extraction and segmentation approach based on industry foundation classes (IFC) for prefabricated component production is discussed. During this process, the position and geometric information of the prefabricated components are adjusted and output in the extracted IFC file. Then, the production process and the completion time of each process have been examined and simulated with the genetic algorithm. Lastly, the automated optimization solution can be formed by the linking production scheduling database and the computational environment. Results: This shows that the implementation of the automated optimization framework for the production scheduling of the prefabricated elements improves the operability and accuracy of the production process. Conclusions: Based on the integration technique discussed above, the data transmission and integration in the mating application program is achieved by linking the Python-based application, the Structured Query Language (SQL) database and the computational environment. The implementation of the automated optimization framework model enables BIM models to play a better foundational role in patching up the technical gaps between prefabricated building designers and element producers. [ABSTRACT FROM AUTHOR]

This work uses the sliding mode control method to conduct the finite-time synchronization of chaotic systems. The utilized parameter selection principle differs from conventional methods. The designed controller selects the unknown parameters independently from the system model. These parameters enable tracking and prediction of the additional variables that affect the chaotic motion but are difficult to measure. Consequently, the proposed approach avoids the limitations of selecting the unknown parameters that are challenging to measure or modeling the parameters solely within the relevant system. This paper proposes a novel nonsingular terminal sliding surface and demonstrates its finite-time convergence. Then, the adaptive law of unknown parameters is presented. Next, the adaptive sliding mode controller based on the finite-time control idea is proposed, and its finite-time convergence and stability are discussed. Finally, the paper presents numerical simulations of chaotic systems with either the same or different structures, thus verifying the proposed method's applicability and effectiveness. [ABSTRACT FROM AUTHOR]

Vascular plant one-zinc-finger (VOZ) transcription factor, a plant specific one-zinc-finger-type transcriptional activator, is involved in regulating numerous biological processes such as floral induction and development, defense against pathogens, and response to multiple types of abiotic stress. Six VOZ transcription factor-encoding genes (GmVOZs) have been reported to exist in the soybean (Glycine max) genome. In spite of this, little information is currently available regarding GmVOZs. In this study, GmVOZs were cloned and characterized. GmVOZ genes encode proteins possessing transcriptional activation activity in yeast cells. GmVOZ1E, GmVOZ2B, and GmVOZ2D gene products were widely dispersed in the cytosol, while GmVOZ1G was primarily located in the nucleus. GmVOZs displayed a differential expression profile under dehydration, salt, and salicylic acid (SA) stress conditions. Among them, GmVOZ1G showed a significantly induced expression in response to all stress treatments. Overexpression of GmVOZ1G in soybean hairy roots resulted in a greater tolerance to drought and salt stress. In contrast, RNA interference (RNAi) soybean hairy roots suppressing GmVOZ1G were more sensitive to both of these stresses. Under drought treatment, soybean composite plants with an overexpression of hairy roots had higher relative water content (RWC). In response to drought and salt stress, lower malondialdehyde (MDA) accumulation and higher peroxidase (POD) and superoxide dismutase (SOD) activities were observed in soybean composite seedlings with an overexpression of hairy roots. The opposite results for each physiological parameter were obtained in RNAi lines. In conclusion, GmVOZ1G positively regulates drought and salt stress tolerance in soybean hairy roots. Our results will be valuable for the functional characterization of soybean VOZ transcription factors under abiotic stress. [ABSTRACT FROM AUTHOR]

Interspecific hybridization creates genetic variation useful for crop improvement. However, whether pollen from a different genus affects the genomic stability and/or transcriptome of the recipient species during intergeneric pollination has not been investigated. Here, we crossed japonica rice cv. Z12 with the maize accession B73 (pollen donor) and obtained a morphologically stable line, MU1, exhibiting moderate dwarfism, higher tiller number, and increased grain weight compared with Z12. To reveal the genetic basis of these morphological changes in MU1, we performed whole-genome resequencing of MU1 and Z12. Compared with Z12, MU1 showed 107,250 single nucleotide polymorphisms (SNPs) and 23,278 insertion/deletions (InDels). Additionally, 5'-upstream regulatory regions (5'UTRs) of 429 and 309 differentially expressed genes (DEGs) in MU1 contained SNPs and InDels, respectively, suggesting that a subset of these DEGs account for the variation in 5'UTRs. Transcriptome analysis revealed 2190 DEGs in MU1 compared with Z12. Genes up-regulated in MU1 were mainly involved in photosynthesis, generation of precursor metabolites, and energy and cellular biosynthetic processes; whereas those down-regulated in MU1 were involved in plant hormone signal transduction pathway and response to stimuli and stress processes. Quantitative PCR (qPCR) further identified the expression levels of the up- or down-regulated gene in plant hormone signal transduction pathway. The expression level changes of plant hormone signal transduction pathway may be significant for plant growth and development. These findings suggest that mutations caused by intergeneric pollination could be the important reason for changes of MU1 in agronomic traits. [ABSTRACT FROM AUTHOR]