Search

A nanocrystalline alloy, with an iron-based composition (Fe58.5Si16.7B6.5Nb5.1Cu13.2) and a Curie temperature of 570 °C, was investigated for its effectiveness as magnetic shielding films in an induction heating system. The primary focus of the research was to evaluate the shielding performance of the 3-turned (9-layered) shielding films with dimensions of 135 mm × 17 mm × 0.15 mm. Upon winding, these films formed a cylindrical structure that enveloped the coil, with a diameter of 13.9 mm and a height of 17 mm. The results showed that increasing the degree of fragmentation within the nanocrystalline shielding films significantly reduced the magnetic permeability by decreasing the real component from 11,500 to 400 and the imaginary part from 2800 to 20. However, a lower degree of fragmentation led to a 10 % increase in the resistance (Rs) of the heating module, although this effect was less pronounced as the relative permeability continued to increase. Furthermore, observations on preheating time to a set temperature of 400 °C and total energy consumption over a duration of 250s revealed an initial downward trend, followed by a rapid increase that even exceeded the initial values as the magnetic permeability of the nanocrystalline shielding films augmented. Notably, the study emphasized that nanocrystalline shielding films with a relative permeability value of 1000 demonstrated exceptional magnetic shielding performance, resulting in a 12.5 % reduction in preheat time and 7 % less energy consumption during preheating. In addition to empirical findings, the study developed a theoretical model elucidating the shielding mechanism inherent in induction heating systems. This model serves as a robust framework for the application of nanocrystalline shielding materials in such systems, laying the groundwork for enhanced magnetic shielding capabilities in future applications.

To tackle the challenges associated with variability and uncertainty in distributed power generation, as well as the complexities of solving high-dimensional energy management mathematical models in mi-crogrid energy optimization, a microgrid energy optimization management method is proposed based on an improved soft actor-critic algorithm. In the proposed method, the improved soft actor-critic algorithm employs an entropy-based objective function to encourage target exploration without assigning signifi-cantly higher probabilities to any part of the action space, which can simplify the analysis process of distributed power generation variability and uncertainty while effectively mitigating the convergence fragility issues in solving the high-dimensional mathematical model of microgrid energy management. The effectiveness of the proposed method is validated through a case study analysis of microgrid energy op-timization management. The results revealed an increase of 51.20%, 52.38%, 13.43%, 16.50%, 58.26%, and 36.33% in the total profits of a microgrid compared with the Deep Q-network algorithm, the state-action-reward-state-action algorithm, the proximal policy optimization algorithm, the ant-colony based algorithm, a microgrid energy optimization management strategy based on the genetic algorithm and the fuzzy inference system, and the theoretical retailer stragety, respectively. Additionally, com-pared with other methods and strategies, the proposed method can learn more optimal microgrid energy management behaviors and anticipate fluctuations in electricity prices and demand.

The data integration of high voltage (HV) and medium voltage (MV) distribution power grids is a development trend in future energy management systems. This integration brings about challenges such as handling large-scale networks and bad data, which can impact the speed and accuracy of state estimation. To address these issues, a forward/backward sweep (FBS) robust state estimation algorithm (FB-SE) is proposed, particularly suitable for simple loop and radial distribution systems. The algorithm consists of two stages: 1) Bad Data Pre-processing (BDP); and 2) Branch Power Flow State Estimation; Both stages follow the FBS strategy. The preprocessing of bad data only involves one FBS calculation, aiming to identify and rectify obvious bad data; The branch power flow state estimation introduces the weights of state estimation and uses a classification normalized residual index function to achieve weight allocation. The state estimation algorithm proposed in this paper avoids solving Jacobian matrices and linear equations. It can also handle simple ring networks by opening the loop and performing power compensation. The simulation results based on IEEE examples show that this algorithm has advantages in computation speed, robustness, and convergence.

Oasis ecosystems in dryland regions take on crucial significance in human survival and development since the quality of these regions that are characterized by ecological environments directly affects the well-being and socio-economic progress of their residents. Accordingly, it is of utmost importance to evaluate changes in ecological environment quality (EEQ) and gain insights into how land use transformation affects the local ecological condition, thus guiding the sustainable development of the dryland oasis. In this study, we evaluated the EEQ of the Middle Heihe River (MHR) from 1985 to 2020 by constructing the dryland oasis ecological index (DOEI) through the Google Earth Engine (GEE) platform. Moreover, we investigated how land use transformation influenced the EEQ of the MHR. Results suggested that (1) the DOEI can effectively capture the comprehensive dynamics of long-term EEQ changes in MHR, providing a more accurate representation of land type characteristics. (2) The EEQ in MHR generally exhibited a trend of initial improvement, followed by deterioration. The desert-oasis transition zone acts as the most significant area of EEQ changes in the MHR. The mutual transformation among cropland, grassland and desert is the primary factor for EEQ in the MHR. (3) The expansion of cropland in the MHR pronouncedly affected the EEQ. However, this continuous expansion may impose greater stress on water resources while potentially threatening ecological degradation in the lower reaches of the river basin. The DOEI built through the GEE platform is capable of indicating the spatiotemporal status of the EEQ in the dryland oasis, which holds significant implications for studying the interaction between human activities and EEQ in the dryland oasis.

Fast and accurate self-healing after faults is an important way to improve the reliability of distribution networks. To improve the self-healing speed and accuracy of distribution networks, this paper proposes a fast and accurate self-healing scheme for distribution networks using mixed integer linear programming. Firstly, this method constructs a centralized 5G communication network architecture, which can effectively reduce the communication delay during the self-healing process. Secondly, the principle of logical algebraic transformation is utilized to linearize the switch function in the fault location model, thereby achieving equivalent transformation of the fault location model. Thirdly, using the polyhedral approximation method, the mixed integer second-order cone programming of the power supply recovery model is transformed into a mixed integer linear programming. The proposed method was validated by distribution network systems with different nodes in MATLAB/Simulink, and the results showed that the proposed method significantly improved self-healing speed and accuracy.

Abstract A large number of renewable distributed generation (DG) systems connect to the distribution network, affecting the structure of the traditional distribution network and forming an active distribution network (ADN). Although the accelerating grid penetration of DGs brings significant challenges to the distribution network operation, the islanded operation capability of DGs provides a flexible solution to the self‐healing of ADNs from faults. To ensure that ADN can quickly recover and reconfigure in the event of a fault and continue to maintain safe, economical, and reliable operation, this paper proposes a dynamic microgrid formation method for ADNs combined with the dynamic network reconfiguration and intentional islanding operation of DGs. An optimization model is designed to represent the proposed self‐healing method, maximizing the load restoration and minimizing the DG cost, line network loss, and voltage excursion simultaneously. A binary hybrid optimization solver is applied to pursue the optimal self‐healing schedules from the optimization model. The self‐healing method is evaluated on the Institute of Electrical and Electronics Engineers (IEEE) 33‐node system and the IEEE 123‐node system, which indicate its rationality and effectiveness fully verified. By optimizing the on and off states of the normally open switches and the on‐grid and off‐grid operation states of DGs, ADNs not only get healed with minimum load curtailment, but also achieve minimal DG generation cost, network loss, and node voltage deviation. In addition, compared with traditional solvers, the proposed solver has a slightly higher computational time than the corresponding solver.

A data-driven channel prediction method for distribution automation master is proposed to address the poor quality of communication network and communication system transmission problems in distribution network communication. In this paper, an adaptive broad learning network (ABLN) consisting of a standard broad learning network and a hybrid learning network is introduced to predict the channel state information of the communication system. Among them, the hybrid learning network is used to solve the ill-conditioned solution problem when estimating the output weight matrix of the standard broad learning network. Therefore, the ABLN produces sparse output weight matrices and provides excellent prediction performance. In the simulation analysis, the outdoor and indoor scenes are considered based on OFDM system. The prediction performance of ABLN is subsequently evaluated in one step prediction and multistep prediction. The results show that for the prediction performance is concerned, the maximum improvement of ABLN is about 96.49% as compared to other evaluation models, indicating that the CSI is effectively predicted by the ABLN to support the adaptive transmission of the main station of the distribution automation and to satisfy the quality of the communication network of the distribution network.

With the rapid development of microgrids with distributed generations (DGs) and energy storage system (ESS), it is important to study energy management methods to improve the operation economy of microgrids. However, there is currently a lack of research on microgrid’s energy management models including multi-party groups such as wind turbines, photovoltaics and ESS. This paper proposed an energy trading management method of microgrids based on Stackelberg game real-time pricing mechanism, which can solve the more complex optimization operation problem of microgrids. First, the rolling optimization was carried out to determine the charging and discharging behavior of ESS for maximizing the total benefit microgrid in the next few time slots. Further, a Stackelberg game real-time pricing model was built. The electricity prices of different entities in the microgrid in the next time slot were optimized by microgrid operator (MGO) to determine the load demand of each DG, preference parameter in the utility function of DGs was improved to promote the internal energy interaction and the economic benefits of the microgrid. Finally, the results show that our method can effectively improve DGs’ total utility and stimulate energy trading within the microgrid. Compared with no optimization and traditional method, the daily profit of MGO obtained by our method was increased by 31.89% and 5.4% respectively, verifying the economics of the proposed method.

In the context of vigorously developing new energy sources, the economic dispatch(ED) of active distribution network (ADN) is essential. Due to the single fault affecting the global and high communication cost in centralized scheduling, we propose a distributed gradient method, namely the fast Nesterov accelerated gradient method(FNAGM), which can solve the economic dispatch problem(EDP) in ADN. The distributed architecture does not need to collect global information and only uses a sparse communication network to complete communication exchanges. It is the distributed architecture that can protect users’ private information and reduce communication pressure. By constructing the acceleration matrix based on the upper limit of the second derivative, the convergence speed can be effectively improved while satisfying the equality constraints. Eventually, the FNAGM combined with the historical momentum information and the forecast gradient, which is simulated in the bi-layer model of ADN via MATLAB. The verification results show that the algorithm with the historical momentum information and the forecast gradient can complete the optimal scheduling of controllable distributed generator(DG). What is more, the convergence efficiency performance is greatly improved.

Automatic distribution master stations (ADMSs) are software platforms that can be used to monitor energy supply and consumption of power demand-side resources as well as to guarantee grid security. They play a vital role in maintaining the optimal performance and stability of electrical power systems. However, the stability of ADMSs is usually determined by the performance of the information interaction, which includes various challenges and complexities. Therefore, an efficient multiple-indicator filtering-based information interaction evaluation method for the ADMS is proposed in this paper. The proposed method first utilizes the principal component analysis (PCA) to filter the indicators of the ADMS. Then, the data envelopment method and the analytic network process (ANP) are employed to remove the invalid indicators and to determine the indicator weights. In this regard, the secondary screening of the indicators is completed, the effective indicators are obtained, and the accuracy of the evaluation system is improved. Results of the simulation show that the proposed method works well when evaluating information interaction performance, and it offers satisfactory performance evaluation of ADMSs.

The alpine wetlands are vital in regulating climate, maintaining biodiversity, stabilizing ecosystem balance, maintaining water resources and ecological security. However, influenced by extreme climate and human activities, the ecological degradation of alpine wetlands seriously threatens the sustainable development of the region. Nowadays, it is urgent to develop an ecological monitoring index suitable for high-altitude alpine wetlands, and timely carry out long-term remote sensing monitoring and evaluation on the ecological degradation and restoration of alpine wetlands. Based on Landsat images of Google Earth Engine (GEE) year by year, we constructed the Alpine Wetland Degradation Index (AWDI) and studied the ecological change process of alpine wetland in the first meander of the Yellow River during 1990–2022 with the help of the Unary linear regression, the Theil-Sen median and the Mann-Kendall test methods. In addition, we also discussed the main driving factors of ecological degradation of alpine wetlands with the help of the Pearson correlation analysis and Geodetector. Our research found that the AWDI based on long-time series MNDWI, NDVI, FVC and WET has significant accuracy in evaluating the ecological degradation of alpine wetlands (POA = 95.69 %, Kappa = 0.9460). In the past 33 years, the ecological situation of the alpine wetland in the first meander of the Yellow River has experienced a process of degradation first and then improvement. Precipitation is the main factor that affects the ecological change of the alpine wetland, and the role of human activities can not be ignored. Our AWDI can quickly and accurately reveal the ecological degradation process of alpine wetlands, and then provide a scientific basis and case reference for the evaluation, restoration, management and protection of alpine wetland degradation.

Objectives The respiratory variations will lead to inconsistency between the actual delivery dose and the planning dose. How the minor interfractional amplitude changes affect the geometry and dose delivery accuracy remains to be investigated in the context of lung adaptive radiotherapy. Methods Planning 4-dimensional-computed tomography and kV-cone beam computed tomography were scanned based on the Computerized Imaging Reference Systems phantom, which was employed to simulate the minor interfractional amplitude variations. The corresponding synthetic computed tomography for a particular motion pattern can be generated from Velocity program. Then a clinically meaningful synthetic computed tomography was analyzed through the geometrical and dosimetric assessment. Results The image quality of synthetic computed tomography was improved obviously compared with cone beam computed tomography. Mean absolute error was minimized when no significant interfractional motion occurs and Velocity can be qualified for dealing with the regular breathing motion patterns. The mean percent hounsfield unit difference of the synthetic hounsfield unit values per organ relative to the planning 4-dimensional-computed tomography image was 22.3%. Under the same conditions, the mean percent hounsfield unit difference of the cone beam computed tomography hounsfield unit values per organ, relative to the planning 4-dimensional-computed tomography image was 83.9%. Overall, the accuracy of hounsfield unit in synthetic computed tomography was improved obviously and the variability of the synthetic image correlates with the planning 4-dimensional-computed tomography image variability. Meanwhile, the dose-volume histograms between planning 4-dimensional-computed tomography and synthetic computed tomography almost coincided each other, which indicates that Velocity program can qualify lung adaptive radiotherapy well when there were no interfractional respiratory variations. However, for cases with obvious interfractional amplitude change, the volume covered at least by 100% of the prescription dose was only 59.6% for that synthetic image. Conclusion The synthetic computed tomography images generated from Velocity were close to the real images in anatomy and dosimetry, which can make clinical lung adaptive radiotherapy possible based on the actual patient anatomy during treatment.

Salt stress detrimentally impacts plant growth, imperiling crop yield and food quality. Ameliorating plant resilience and productivity in saline environments is critical for global food security. Here, we report the positive effect of Arthrospira (Spirulina) on plant growth and salt tolerance in Arabidopsis and sweet sorghum. Arthrospira application greatly promotes seed germination and seedling growth in both species under salt stress conditions in a dosage-dependent manner. Application of 6 mg Arthrospira per plate significantly enhances K+/Na+ equilibrium and reactive oxygen species (ROS) scavenging in Arabidopsis, reducing salt-induced toxicity. The primary root length, survival rate, chlorophyll content, photosynthesis, plant height, biomass and yield were all improved in both species. Concurrently, Arthrospira demonstrated the synthesis of compatible solutes, such as trehalose (Tre) and glucosylglycerol (GG), contributing to heightened stress tolerance when co-cultivated with Arabidopsis on plates. Transcriptome analysis revealed dramatic up-/down- regulation of genes involved in phytohormone signal transduction, chlorophyll and photosynthesis metabolism, and phenylpropanoid metabolism in Arabidopsis. Furthermore, the application of Arthrospira exerted a positive influence on the rhizosphere bacteriome structure in sweet sorghum, crucial for nutrient cycling and soil health enhancement. Our findings uncovered the underlying mechanisms of algae-plants interaction in saline soil, proposing strategies to enhance crop productivity and soil quality, thereby addressing the urgent need for sustainable agriculture practices to mitigate salinity’s repercussions amidst climate change challenges.

Identifying and monitoring open-surface water bodies are significant for understanding regional water resource distribution and evolving trends. This understanding carries profound value for comprehensively assessing the effects of climate change and human social activities on water resources and maintaining water security. This study pioneers’ improvements in surface water detection rules with the central aim of constructing a Method with Enhanced Impurity Control, specifically tailored for complex environmental scenarios (OA = 98.97 % ± 0.06). This is achieved by incorporating intricate exponential constraints and targeted data masks. Our analysis is grounded in the examination of 21,709 preprocessed high-quality images, covering the period from 1990 to 2021 across the northern slope of the Kunlun Mountains. Through this comprehensive study, we have gleaned insightful results regarding the spatiotemporal dynamics of open-surface water bodies. In the past 32 years, the surface water body area on the northern slope of the Kunlun Mountains has exhibited a significantly increasing trend. Specifically, the size of permanent water bodies expanded by 71.03 % (with a 95 % confidence interval: ± 27.73 %), while the area of seasonal water bodies grew by a substantial 170.63 % (with a 95 % confidence interval: ± 17.50 %). Open-surface water bodies display discernible spatiotemporal differences. Stable and continuous permanent water bodies are predominantly in mountainous areas, whereas seasonal water bodies with pronounced fluctuations are chiefly found in desert-oasis areas. Climate change is the dominant factor that affects the changes of water bodies on the surface of mountain regions, while human social activities also have a significant impact on desert-oasis areas. The improved methodology adopted in this study offers a robust and scalable framework that can be tailored for studying changes in open-surface water bodies in intricate environments. We performed a comprehensive analysis of the changes and drivers of open-surface water bodies on the north side of the Kunlun Mountains over time and space. This work is aimed at providing data support for scientifically appraising the development potential of water resources in Southern Xinjiang and addressing the disparity between water resource supply and demand.

Combined bodies of rock-like material and rock are widely encountered in geotechnical engineering, such as tunnels and mines. The existing theoretical models describing the stress-strain relationship of a combined body lack a binary feature. Based on effective medium theory, this paper presents the governing equation of the “elastic modulus” for combined and single bodies under triaxial compressive tests. A binary effective medium model is then established. Based on the compressive experiment of concrete-granite combined bodies, the feasibility of determining the stress threshold based on crack axial strain is discussed, and the model is verified. The model is further extended to coal-rock combined bodies of more diverse types, and the variation laws of the compressive mechanical parameters are then discussed. The results show that the fitting accuracy of the model with the experimental curves of the concrete-granite combined bodies and various types of coal-rock combined bodies are over 95%. The crack axial strain method can replace the crack volumetric strain method, which clarifies the physical meanings of the model parameters. The variation laws of matrix parameters and crack parameters are discussed in depth and are expected to be more widely used in geotechnical engineering.

Wetlands are among the most productive ecosystems on Earth, providing various key ecosystem services to humans and other organisms. However, an insufficient understanding of the economic value and importance of wetland resources has seriously weakened the ecosystem service value of wetlands. Thus, a more comprehensive understanding of wetland ecosystem services must be revealed in order to propose appropriate conservation policies. Therefore, to achieve the abovementioned goals, we used the emergy value accounting method to account for the ecosystem service value of the Maqu alpine wetlands, and the Logarithmic Mean Divisia Index was used to analyze the driving forces affecting changes in ecosystem services. The results indicate that the ecosystem service value of the Maqu alpine wetlands showed a trend of decline and then rebound during 1990–2020; its unit area emergy value decreased from 3.85 × 1010 sej/m2/y to 3.04 × 1010 sej/m2/y during 1990–2000, and after 2010, it began to gradually rebound. Moreover, the monetary ecosystem service value of the Maqu alpine wetlands continued to rise during the study period, reaching 41.45 USD/m2/y in 2020, thus reflecting the great potential value of wetland ecosystems. Based on our analysis, the key drivers affecting the changes in ecosystem services include economic scale and emergy benefit. Rapid and intensive human activities have boosted local economic development, but these developments have come at the cost of environmental degradation. Finally, some suggestions are proposed. This study suggests that the alpine wetlands in Maqu occupy an important ecological position within the whole Yellow River basin. Therefore, it is worthwhile to invest a significant amount of funding in their restoration and protection.

Non-intrusive load monitoring is a novel and cost-effective technology for monitoring details of electricity consumption and identifying the operating status of appliances. It supports the construction of the energy internet and big data on electricity consumption in smart cities. However, one of the most challenging problems in this area is that machine learning algorithms often require large amounts of labeled data. In this paper, a non-intrusive load monitoring model based on the Self-supervised Regularization is proposed. The model reduces the pre-processing stage compared to the traditional methods. We make full use of the unlabeled data by using them to generate proxy labels to participate in the model training together with the true labels. We performed experiments on the common data set PLAID to compare performance with the existing method Mean Teacher and CoMatch. The experimental results show that: 1) when using all labeled data, the model with self-supervised regularization significantly improves the traditional supervised classifier with a recognition accuracy of 0.965; 2) when coupled with unlabeled data, our model produces good semi-supervised performance. It is highly competitive with current state-of-the-art Mean Teacher and Contrastive Learning.

In this paper, a W-band single-layer reflectarray antenna is proposed with the advantages of broadband and high aperture efficiency. A novel dual rotation-symmetry windmill element is designed in the W-band, which consists two layers of copper patches printed on a Rogers RT5880 substrate. Based on the analysis of the proposed metasurface element, a square reflectarray with $13.85\lambda _{0}$ (44.2 mm) aperture diameter at 94 GHz center frequency was designed, fabricated and measured. The designed reflectarray antenna shows a 1-dB gain bandwidth of 25.5% (89-113 GHz) in the W-band. Moreover, the measured gain is 31.1 dBi at 94 GHz and the maximum aperture efficiency is 53.4%. The result shows that the proposed design has prospects in wireless communication applications.

NbxCu0.3Cr2Fe2Ni3Mn2 (x = 0, 0.2, and 0.4; named Nb0, Nb0.2, and Nb0.4, respectively) high-entropy alloys (HEAs) were prepared using arc melting process. The effect of Nb content on the microstructure and corrosion behavior of HEAs in HNO3 solution was studied. Nb0 and Nb0.2 alloys are composed of a single FCC phase, whereas Nb0.4 alloy is composed of FCC + Laves phase. In 10% and 30% HNO3 solutions, the immersion corrosion rate and potentiodynamic polarization corrosion rate of the HEAs showed a trend of first increasing and then decreasing with the increase in Nb content. After 30 days of immersion corrosion, Nb0.4 alloy exhibited the lowest average corrosion rate in 10% and 30% HNO3 solutions, with values of 4.6 × 10−3 and 2.8 × 10−3 mm/yr, respectively. Potentiodynamic polarization corrosion showed that Nb0.4 alloy also had the lowest corrosion current density and average corrosion rate in 10% and 30% HNO3 solutions, with values of 1.74 × 10−5, 5.56 × 10−6 A/cm2 and 0.18, 0.06 mm/yr, respectively. In addition, The Rpo of Nb0.4 alloy are 91 times and 8737 times higher than those of Nb0 alloy, respectively, and has the largest relative thickness of the passivation film. Therefore, Nb0.4 alloy exhibited the best resistance to HNO3 corrosion, mainly because adding Nb reduced the segregation of Cu, thereby weakening the corrosion effect of the primary battery. Meanwhile, Nb could play a stabilizing role in the passivation film of the alloy. The formation of corrosion-resistant Laves phase also correspondingly improved the HNO3 corrosion resistance of the alloy.

China holds large-scale lead–zinc mineral resources; however, mining activities often cause severe contamination by heavy metals. This study systemically assessed contamination by eight heavy metals (Cu, Zn, Cd, Pb, Cr, Hg, Ni, and As) in mine tailings, soil, and groundwater from 27 contaminated sites across China. Regarding mine tailings, 1% of the mine tailing samples were hazardous waste and 20% were class II non-hazardous waste. Regarding soil, Zn and Pb showed the highest mean concentrations, at 5574.67 mg/kg and 2034.88 mg/kg, respectively. The indexes of geo-accumulation (Igeo) of eight heavy metals ranged from −3.62 to 7.67, while Zn, Pb, and Cd showed the highest environmental risk levels as the priority pollutants. The contamination levels of these heavy metals in groundwater were generally in the order of Zn>As>Pb>Ni>Cd>Cu>Hg>Cr. In this study, 20% of the soil and 10% of the groundwater samples exceeded the corresponding quality limits. The content of heavy metals in soil, groundwater, and mine tailing were positively correlated, demonstrating the main pollution source and transport paths. The pollution levels of heavy metals in soil and groundwater were listed in the foremost and moderate positions compared with similar sites from other countries, respectively. These results may help determine the pollution levels of lead–zinc mining regions and direct the remediation activities of target sites to support the environmental management of abandoned mining and tailing waste in China.

Inflammatory response plays a critical role in myocardial infarction (MI) repair. The neutrophil apoptosis and subsequent macrophage ingestion can result in inflammation resolution and initiate regeneration, while the therapeutic strategy that simulates and enhances this natural process has not been established. Here, we constructed engineered neutrophil apoptotic bodies (eNABs) to simulate natural neutrophil apoptosis, which regulated inflammation response and enhanced MI repair. The eNABs were fabricated by combining natural neutrophil apoptotic body membrane which has excellent inflammation-tropism and immunoregulatory properties, and mesoporous silica nanoparticles loaded with hexyl 5-aminolevulinate hydrochloride (HAL). The eNABs actively targeted to macrophages and the encapsulated HAL simultaneously initiated the biosynthesis pathway of heme to produce anti-inflammatory bilirubin after intracellular release, thereby further enhancing the anti-inflammation effects. In in vivo studies, the eNABs efficiently modulated inflammation responses in the infarcted region to ameliorate cardiac function. This study demonstrates an effective biomimetic construction strategy to regulate macrophage functions for MI repair.

Much zinc residue is produced during the traditional processes involved in zinc hydrometallurgy in the leaching stage: its composition is complex and valuable metals are difficult to recover therefrom. If not handled properly, it can lead to a waste of resources and environmental pollution. To solve this problem, zinc leach residue specimens were treated using the carbothermal reduction method (CTR) that is easy to operate and has a high energy utilisation rate. The methods, such as X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and scanning transmission electron microscopy (STEM) were used for analytical characterisation. Based on this, this research investigated a structure-function relationship between microstructures and microwave-absorbing properties of ZnO smoke from CTR-treated zinc leach residue. The results demonstrate that microstructures and macro-properties of ZnO smoke obtained at different temperatures differ greatly. Under conditions including a calcination temperature of 1250°C, holding time of 60 min, and addition of 50% and 10% of powdered coal and CaO separately, the ZnO content in the obtained smoke is 99.14%, with regular micron-sized ZnO particles therein. For these particles, the minimum reflection loss (RLmin) reached –25.56 dB at a frequency of 15.84 GHz with a matching thickness of 5 mm. Moreover, frequency bandwidth corresponding to RL < –10 dB can reach 2.0 GHz. ZnO smoke obtained using this method is found to have excellent microwave-absorbing performance, which provides a new idea for high-value applications of zinc-rich residue.

Healthy and sustainable urban agglomerations development relies heavily on land use optimization. However, there is insufficient scientific basis and reliable quantitative analysis for land use pattern identification and optimal prediction in embryonic urban agglomeration. Therefore, taking the Lanzhou–Xining (LX) region, a typical primary developing urban agglomeration, as the study area, we first assessed the land ecological suitability (LES). Then, we embedded the LES evaluation results in the land optimization process and constructed the MCR-MOP- Dyna-CLUE model framework, simulating and optimizing land use patterns for the year 2035 under ecological optimization and business as usual scenarios, which aimed to explore a sustainable land use pattern for embryonic urban agglomerations. The results indicated that the ecological optimization scenario based on LES had a more rational land use pattern. It appropriately controlled the expansion rate of construction land and effectively alleviated the problems of construction land encroaching on farming land and ecological land. Meanwhile, the ecosystem services increased in value based on adequately addressing the need for food security and economic development. Compared to the business as usual scenario, the construction land under the ecological optimization scenario was 19,622.69 ha less, and the cultivated land was 32,103.29 ha more. Moreover, the ecological benefit and the economic benefit increased by 187,490.4595 million yuan and 151,808,605.1 million yuan from 2020 to 2035, respectively, under the ecological optimization scenario. Our research is of great value for making decisions on sustainable land use and land resource management in initial developing agglomerations.

Abstract The allogeneic transplantation of primordial germ cells (PGCs) derived from somatic cells overcomes the limitation of avian cloning. Here, we transdifferentiate chicken embryo fibroblasts (CEFs) from black feathered Langshan chickens to PGCs and transplant them into White Plymouth Rock chicken embryos to produce viable offspring with characteristics inherited from the donor. We express Oct4/Sox2/Nanog/Lin28A (OSNL) to reprogram CEFs to induced pluripotent stem cells (iPSCs), which are further induced to differentiate into PGCs by BMP4/BMP8b/EGF. DNA demethylation, histone acetylation and glycolytic activation elevate the iPSC induction efficiency, while histone acetylation and glycolytic inhibition facilitate PGCs formation. The induced PGCs (iPGCs) are transplanted into the recipients, which are self-crossed to produce 189/509 somatic cells derived chicken with the donor’s characteristics. Microsatellite analysis and genome sequencing confirm the inheritance of genetic information from the donor. Thus, we demonstrate the feasibility of avian cloning from somatic cells.

Abstract Lactobacillus casei f1, L. paracasei f2 and L. paracasei f3 with lipolytic activity were isolated and identified from vinasses according to the morphological–physiological properties detection and 16S rDNA analysis. These three strains showed obvious lipase activities to olive oil and L. casei f1 performed highest enzyme activity of 17.8 U/mL. L. casei f1, L. paracasei f2 and L. paracasei f3 could lipolyze the blending oils, peanut oil and sesame oil with diverse degrading rates. The degrading rates to the preferred oils, L. casei f1 to blending oils, L. paracasei f2 to peanut oil and L. paracasei f3 to sesame oil, were 21.2%, 27.3% and 39.6%, respectively. The corresponding oil degrading rates increased as the cell growth and the highest degrading rates were obtained at the stationary phase with the viable count more than 7.5 LogCFU/mL. By GC–MS analysis, L. casei f1, L. paracasei f2 and L. paracasei f3 performed diverse lipolytic capacities to the 12 kinds of fat acids and all of them preferred to hydrolyze the linoleic acid with the degrading rate of 49.11%, 31.83% and 64.44%, respectively. These three strains showed considerable probiotic properties, displaying higher than 106 CFU/mL desirable viable count though the simulated gastrointestinal tract, as well as inhibiting six indicator bacteria. These results suggested that the three isolated strains could be considered as novel probiotic candidates and applied in the food industry.

Non-intrusive load monitoring (NILM) can obtain fine-grained electricity consumption information of each appliance by analyzing the voltage and current data measured at a single point on the bus, which is of great significance for promoting and improving the efficiency and sustainability of the power grid and enhancing the energy efficiency of users. NILM mainly includes data collection and preprocessing, event detection, feature extraction, and appliance identification. One of the most critical steps in NILM is signature extraction, which is the basis for all algorithms to achieve good state detection and energy disaggregation. With the generalization of machine learning algorithms, different algorithms have also been used to extract unique signatures of appliances. Recently, the development and deployment of the voltage–current (V-I) trajectory signatures applied for appliance identification motivated us to present a comprehensive review in this domain. The V-I trajectory signatures have the potential to be an intermediate domain between computer vision and NILM. By identifying the V-I trajectory, we can detect the operating state of the appliance. We also summarize existing papers based on V-I trajectories and look forward to future research directions that help to promote the field’s development.

Bovine mastitis is perplexing the dairy industry since the initiation of intensive dairy farming, which has caused a reduction in the productivity of cows and an escalation in costs. The use of antibiotics causes a series of problems, especially the formation of bacterial antimicrobial resistance. However, there are limited antibiotic-free therapeutic strategies that can effectively relieve bacterial infection of bovine mammary glands. Hence, in this study, we constructed a mammary gland tissue-specific expression vector carrying the antimicrobial peptide of bovine-derived tracheal antimicrobial peptide (TAP) and evaluated it in both primary bovine mammary epithelial cells (pBMECs) and mice. The results showed that the vector driven by the β-lactoglobulin gene (BLG) promoter could efficiently direct the expression of TAP in pBMECs and the mammary gland tissue of mice. In addition, significant antibacterial effects were observed in both in vitro and in vivo experiments when introducing this vector to bovine-associated Staphylococcus aureus-treated pBMECs and mice, respectively. This study demonstrated that the mammary gland tissue-specific expression vector could be used to introduce antimicrobial peptide both in in vitro and in vivo and will provide a new therapeutic strategy in the treatment of bovine mastitis.

The existing continuation power flow (CPF) methods, which mainly focus on regional independent systems, are not suitable for multi-area interconnected bulk systems in the electricity market environment. These existing CPF models cannot simulate the control behaviors of active/reactive power exchange among subsystems, and the corresponding CPF algorithms cannot satisfy the requirements of data sharing. This study presents a novel CPF model and its corresponding distributed algorithm for interconnected systems in which the influence of the electricity market is considered. This CPF method has the following unique features: 1) Regarding the bilateral power trading contracts (BPTCs) among regional subsystems, the nonlinear constraint equations of the directional trading active power via the interface are derived, and a multi-balancing machine strategy is introduced for realizing the active power balance of each subsystem. 2) Based on the simulation of the constant-voltage control behavior of the pilot buses in the regional automatic voltage control (AVC) system, the constant-voltage control behavior of the boundary buses of the tie-lines is further simulated to realize the reactive power balance among the regional subsystems. 3) According to the characteristics of the proposed CPF model, a novel distributed CPF algorithm based on block matrix computations is presented for realizing the decomposition and coordination calculation of multiple regional subsystems. This distributed algorithm preserves the precision and convergence of integrated CPF algorithms and has an advantage in terms of the calculation speed. The performance of the proposed CPF model and distributed algorithm is demonstrated via case studies and comparative analyses.

Many works have reported that protein folding rates are influenced by the characteristics of amino acid sequences and protein structures. However, few reports on the problem of whether the corresponding mRNA sequences are related to the protein folding rates can be found. An mRNA sequence is regarded as a kind of genetic language, and its vocabulary and phraseology must provide influential information regarding the protein folding rate. In the present work, linear regressions on the parameters of the vocabulary and phraseology of mRNA sequences and the corresponding protein folding rates were analyzed. The results indicated that D2 (the adjacent base-related information redundancy) values and the GC content values of the corresponding mRNA sequences exhibit significant negative relations with the protein folding rates, but D1 (the single base information redundancy) values exhibit significant positive relations with the protein folding rates. In addition, the results show that the relationships between the parameters of the genetic language and the corresponding protein folding rates are obviously different for different protein groups. Some useful parameters that are related to protein folding rates were found. The results indicate that when predicting protein folding rates, the information from protein structures and their amino acid sequences is insufficient, and some information for regulating the protein folding rates must be derived from the mRNA sequences.

Naringenin (NAR) is a major flavanone in citrus fruits that has multiple pharmacological attributes such as anticancer and antiatherogenic. This study aims to investigate the mechanism of NAR in high-fat-diet (HFD)-induced atherosclerosis (AS) in apolipoprotein E-knockout (ApoE-/-) mice. A HFD-induced AS ApoE-/- mouse model was established. The mice were treated with HFD, different doses of NAR and simvastatin (Simv). After drug treatment, the levels of total cholesterol (TC), triglyceride (TG), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), glutathione peroxidase (GSH-Px), malondialdehyde (MDA), superoxide dismutase (SOD), and alanine aminotransferase (ALT) were determined. The expression of interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) was detected using qRT-PCR and enzyme-linked immunosorbent assay. The plaque area of the aorta of AS mice was determined using oil red O staining. Western blot analysis was applied to measure the levels of autophagy-related proteins [protein 1 light chain 3B (LC3B), beclin 1, and p62]. The TC, TG, LDL-C, TNF-α, ALT, and MDA levels were significantly increased while the HDL-C, SOD, and GSH-Px levels were decreased in the HFD-induced AS ApoE-/- mice. NAR treatment reversed the expression of the above indicators in mice. After they were treated with different doses of NAR, the LC3B and beclin 1 levels were improved while the p62 protein level was decreased. This study suggested that NAR could promote cell autophagy to improve HFD-induced AS in ApoE-/- mice.

The microstructure, mechanical properties, and texture characteristics of marine titanium alloy Ti6321 sheets with four different initial microstructures were investigated contrastively in this paper. The results indicate that the strength of hot-rolled, equiaxed, and bimodal microstructure sheets exhibits significant anisotropy in the RD and TD directions. The hot rolled sheet shows the strongest anisotropy of tensile properties, that is, the transverse yield strength is 91 MPa higher than that of the rolling direction. The morphology characteristics of the macroscopic tensile fracture are also significantly different in RD and TD. The strength anisotropy of Ti6321 alloy sheet decreases with the increase of annealing temperature. The mechanical properties of the widmanstätten microstructure sheet are close to isotropic, and the plasticity is the worst among the four initial microstructures. The root cause for the properties anisotropy was analyzed by XRD and EBSD. There is a strong (1¯21¯0)[101¯0] prismatic texture and a relatively weak (1¯21¯0)[0001] basal texture in the anisotropic sheet. The anisotropic characteristics of the tensile deformation of Ti6321 alloy sheet at room temperature are mainly attributed to the difference in the Schmid factor of the basal slip system and prismatic slip system in TD and RD specimens.

Somatic cells can be reprogrammed into induced pluripotent stem cells (iPSCs) in vitro. Previously, a lentivirus induction strategy of introducing Oct4, Sox2, Nanog and Lin28 (OSNL) into the iPSC process has been shown as a possible way to produce chicken iPSCs from chicken embryonic fibroblasts, but the induction efficiency of this method was found to be significantly limiting. In order to help resolve this efficiency obstacle, this study seeks to clarify the associated regulation mechanisms and optimizes the reprogramming strategy of chicken iPSCs. This study showed that glycolysis and the expression of glycolysis-related genes correlate with a more efficient reprogramming process. At the same time, the transcription factors Oct4, Sox2 and Nanog were found to activate the expression of glycolysis-related genes. In addition, we introduced two small-molecule inhibitors (2i-SP) as a “glycolysis activator” together with the OSNL cocktail, and found that this significantly improved the induction efficiency of the iPSC process. As such, the study identifies direct molecular connections between core pluripotency factors and glycolysis during the chicken iPSC induction process and, with its results, provides a theoretical basis and technical support for chicken somatic reprogramming.

The present study established an efficient genome editing approach for the construction of stable transgenic cell lines of the domestic chicken (Gallus gallus domesticus). Our objectives were to facilitate the breeding of high-yield, high-quality chicken strains, and to investigate gene function in chicken stem cells. Three guide RNA (gRNAs) were designed to knockout the C2EIP gene, and knockout efficiency was evaluated in DF-1 chicken fibroblasts and chicken ESCs using the luciferase single-strand annealing (SSA) recombination assay, T7 endonuclease I (T7EI) assay, and TA clone sequencing. In addition, the polyethylenimine-encapsulated Cas9/gRNA plasmid was injected into fresh fertilized eggs. At 4.5 d later, frozen sections of the embryos were prepared, and knockout efficiency was evaluated by the T7EI assay. SSA assay results showed that luciferase activity of the vector expressing gRNA-3 was double that of the control. Results of the T7EI assay and TA clone sequencing indicated that Cas9/gRNA vector-mediated gene knockdown efficiency was approximately 27% in both DF-1 cells and ESCs. The CRISPR/Cas9 vector was also expressed in chicken embryos, resulting in gene knockdown in three of the 20 embryos (gene knockdown efficiency 15%). Taken together, our results indicate that the CRISPR/Cas9 system can mediate stable gene knockdown at the cell and embryo levels in domestic chickens.

Time synchronization (TS) is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things (IoT). Multi-clock source time synchronization (MTS) has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales, and the coupling of synchronization latency jitter and pulse phase difference. In this paper, the multi-timescale MTS model is conducted, and the reinforcement learning (RL) and analytic hierarchy process (AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of synchronization latency jitter standard deviation and average pulse phase difference. Specifically, the multi-clock source selection is optimized based on Softmax in the large timescale, and the clock source weight calculation is optimized based on lower confidence bound-assisted AHP in the small timescale. Simulation shows that the proposed algorithm can effectively reduce time synchronization delay standard deviation and average pulse phase difference. [ABSTRACT FROM AUTHOR]