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Organic amine carbon dioxide capture technology is widely used for industrial carbon capture due to its mature technology, however, the emission of absorbent and degradation products can affect the carbon dioxide capture efficiency and increase the environmental health risk. This paper summarizes the types and current status of pollutant emissions from chemical absorption systems, and explores the effects of flue gas condensation nucleation occurring in different salt solutions, carbon dioxide concentration in the flue gas, lean liquid loading, liquid-to-gas ratio, flue gas inlet temperature of the absorber tower, and temperature of the lean liquid on pollutant emissions from carbon dioxide chemical absorption systems by means of a small test experiment with a flue gas treatment design capacity of 3.6 Nm3/h. At the same time, amine escape from a coal-fired power plant with a flue gas treatment capacity of about 1.4 million Nm3/h was also studied, and the control effects of different pollutant emission control measures were compared, and the best pollutant emission control measure was the combined control process of “secondary water washing + 7-meter dry bed”, with a reduction of organic amine absorber loss by 716.5 mg/Nm3, a reduction of 71.65 % compared with the “primary water washing” control process.

Abstract Complex oxide heterointerfaces provide a spacious arena for creating emergent phenomena that are unattainable in the constituent bulk counterparts. Herein, The BaTiO3/La1‐xSrxMnO3 [BTO/LSMO (x)] superlattice (SL) as a model system to investigate the intimately coupled interfacial effects and their resultant phenomena is focused on. The experimental and Density Functional Theory (DFT) calculations reveal that the induced magnetism on Ti originates from both the charge transfer process between Mn and Ti at the titanate/manganite heterointerfaces and the cation intermixing, both contributing comparably to the overall magnetic effect. Upon changing x, the orbital reconstruction of the Mn 3d electrons, tailored by the strain state of the LSMO (x) layers, efficiently modifies the magnetic exchange coupling between Mn–Ti and Mn–Mn at the interfaces, which are proved to account for the modulations of the macroscopic magnetism of the SLs. This work demonstrates the significance of interfacial reconstructions for magnetism, paving a new pathway to manipulate the magnetic properties of artificial oxide heterostructures.

Purposes The Hutuo Group belongs to the Early Proterozoic strata. As one of the most typical Paleoproterozoic strata in North China, the timing redefinition of the Hutuo Group is of great significance to the establishment of the early Precambrian stratigraphic chronology framework, the discussion of the early Precambrian geological evolution process in North China and the study of plate assembly. Methods This article conducts a detailed study of the Dongye Subgroup strata of the Hutuo Group, collecting 24 detrital zircon samples from the Jian'ancun Formation of the Hutuo Group. From 23 similar age detrital zircons, we define the weighted average age of the bottom boundary of the Hutuo Group is 2 469 Ma, and regional tectonic movements occurred in the Wutai area around 1 855 Ma. According to the 1 109 zircon ages in Hutuo Group from previous studies, we considered that the ages of >2.5 Ga are from underlying Gaofan subgroup and the maximum depositional age of Hutuo Group was early Paleoproterozoic. Findings The results show that the crust of the Wutai area was unstable at 2 376, 2 170, 2 066, 1 988, and 1 825 Ma, resulting in multiple magmatic events. The 1 825 Ma zircon age can further indicate the occurrence of the Lyuliang movement, and provide a basis for the early Proterozoic North China Craton integration.

Highlights Digital holography can realize the in situ observation of electrode/electrolyte interface and provide dynamic evolution information of the liquid phase of electrode, which is both efficient and effective in investing the interficial electrochemical mechanism and screening electrolyte additives. Thioacetamide electrolyte additive effectively enhances the electrochemical performance of Zn anode by regulating the interficial ion flux to induce dendrite-free Zn deposition and constructing adsorption molecule layers to inhibit side reactions.

Highlights The engineering design principles for enhancing interfacial contact between the electrodes (Li anodes and S cathode) and solid-state electrolytes in solid-state Li–S batteries are classified and discussed. Research progresses of experimental strategies for reducing interfacial impedance in solid-state Li–S batteries are summarized. Challenges and future perspectives of rational interfacial strategies in solid-state Li–S batteries are highlighted.

The recently proposed ambient signal-based load modeling approach offers an important and effective idea to study the time-varying and distributed characteristics of power loads. Meanwhile, it also brings new problems. Since the load model parameters of power loads can be obtained in real-time for each load bus, the numerous identified parameters make parameter application difficult. In order to obtain the parameters suitable for off-line applications, load model parameter selection (LMPS) is first introduced in this paper. Meanwhile, the convolution neural network (CNN) is adopted to achieve the selection purpose from the perspective of short-term voltage stability. To begin with, the field phasor measurement unit (PMU) data from China Southern Power Grid are obtained for load model parameter identification, and the identification results of different substations during different times indicate the necessity of LMPS. Meanwhile, the simulation case of Guangdong Power Grid shows the process of LMPS, and the results from the CNN-based LMPS confirm its effectiveness.

This paper introduces the mixed-strategy Harris hawk optimization (MSHHO) algorithm as an enhancement to address the limitations of the conventional Harris hawk optimization (HHO) algorithm in solving complex optimization problems. HHO often faces challenges such as susceptibility to local optima, slow convergence, and inadequate precision in global solution-seeking. MSHHO integrates four innovative strategies to bolster HHO’s effectiveness in both local exploitation and global exploration. These include a positive charge repulsion strategy for diverse population initialization, a nonlinear decreasing parameter to heighten competitiveness, the introduction of Gaussian random walk, and mutual benefit-based position updates to enhance mobility and escape local optima. Empirical validation on 12 benchmark functions from CEC2005 and comparison with 10 established algorithms affirm MSHHO’s superior performance. Applications to three real-world engineering problems and UAV flight trajectory optimization further demonstrate MSHHO’s efficacy in overcoming complex optimization challenges. This study underscores MSHHO as a robust framework with enhanced global exploration capabilities, significantly improving convergence accuracy and speed in engineering applications.

The advance of the working face in coal seams alters the local stress field and may give rise to fractures in the vicinity of the excavation. In this study, a constitutive model in which damage is defined as a function of volumetric strain was established and utilized in a numerical model to prognosticate the fracture development around the excavation. The predicted fractures that emerged in the overlying rock mass were found to exhibit hybrid characteristics. A permeability model was also constructed, taking into account both tension- and shear-induced fracture development. The permeability increase of the upper adjacent coal seam is most notable within 40 m from the goaf boundary. As the working face progresses, the permeability of the upper adjacent coal seam is further enhanced while that of the lower adjacent coal seam remains unaffected. The permeability at the goaf boundary is high and reaches its maximum at the rear of the working face, indicating that for the permeability change, the effect of shear-induced dilation plays a more crucial role than that of pressure-dependent compaction. This study can be used to guide the design of coal seam methane drainage for the mining of closely spaced coal seams.

The shape of a building profoundly impacts its energy consumption throughout its life and is a critical consideration in early architectural design. Despite its significance, the influence of building shape on heating and air conditioning energy usage remains insufficiently understood. This study systematically analyzes the relationship between building shape and energy consumption, grounded in objective facts about building energy performance from the perspective of architects during the initial design phases. This analysis aids designers in making informed decisions. Key parameters, notably the widely used building shape coefficient, are examined. The relationship between building shape and energy consumption across various global and China’s diverse climate zones is synthesized. Current simulation tools and methodologies are assessed to guide future research. Findings reveal a predominant reliance on simulations for comparing energy use across specific building shapes. The academic understanding of the shape−energy relationship remains superficial, complicating standardization. Future research should prioritize extensive, multi-parameter simulations to enhance understanding of building performance, thereby facilitating energy-efficient design.

This study offers a modular isolated grid-connected DC/DC medium-voltage DC aggregation converter to support offshore full DC wind farms’ need for lightweight and highly efficient power aggregation and transmission. The converter can simultaneously have a smaller transformer size and lower switching frequency during operation through the dual-voltage stabilization three-loop control strategy and phase-shift modulation strategy, which greatly reduces the space occupied by the converter and lowers the switching loss, Additionally, the use of a two-level structure at a lower switching frequency has lower loss, which effectively reduces the cost of the power device compared with the commonly used three-level converter. The input series output series connection between the converter sub-modules effectively lowers the voltage stress on each power switching device and facilitates expansion into a multi-module structure, expanding its application in high-voltage and large-capacity environments. This study analyzes the two working modes of the DC/DC converter and its control approach, in addition to providing a detailed introduction to the application scenarios of this converter. Ultimately, the efficacy and practicability of the suggested topology and control scheme are confirmed by simulations and experiments.

Accurate monitoring of lithium-ion battery temperature is essential to ensure these batteries’ efficient and safe operation. This paper proposes a relevance-based reconstruction-oriented EMD-Informer machine learning model, which combines empirical mode decomposition (EMD) and the Informer framework to estimate the surface temperature of 18,650 lithium-ion batteries during charging and discharging processes under complex operating conditions. Initially, based on 9000 data points from the U.S. NASA Prognostics Center of Excellence’s random battery-usage dataset, where each data point includes three features: temperature, voltage, and current, EMD is used to decompose the temperature data into intrinsic mode functions (IMFs). Subsequently, the IMFs are reconstructed into low-, medium-, and high-correlation components based on their correlation with the original data. These components, along with voltage and current data, are fed into sub-models. Finally, the model captures the long-term dependencies among temperature, voltage, and current. The experimental results show that, in single-step prediction, the mean squared error, mean absolute error, and maximum absolute error of the model’s predictions are 0.00095, 0.02114, and 0.32164 °C; these metrics indicate the accurate prediction of the surface temperature of lithium-ion batteries. In multi-step predictions, when the prediction horizon is set to 12 steps, the model achieves a hit rate of 93.57% where the maximum absolute error is within 0.5 °C; under these conditions, the model combines high predictive accuracy with a broad predictive range, which is conducive to the effective prevention of thermal runaway in lithium-ion batteries.

The enrichment of organic matter in high-quality marine shale is generally controlled by factors such as the redox conditions of sedimentary environments, productivity levels, terrigenous input, and ancient productivity. However, the controlling effect of the sedimentary environment on organic matter enrichment in intracratonic sag is still unclear. This study takes samples from the Qiongzhusi formation shale in southern Sichuan Basin as the research object, focusing on trace elements as well as rare earth elements in different stratigraphic intervals. The provenance of the Qiongzhusi formation shale is mainly terrigenous, with sediment sources mainly consisting of sedimentary rocks and granites. The primary sedimentary environment transitions from a continental margin setting, influenced by rift-related tectonic activity and sediment influx from adjacent landmasses, to an open oceanic environment characterized by mid-ocean ridge processes and oceanic plate subduction zones. During sedimentation, saline water was present, with predominant sedimentary environments ranging from shallow water to deep water continental shelves. The shale in the study area is characterized by a higher content of silicates and a lower content of carbonate minerals. Its siliceous sources are mainly influenced by biogenic and terrigenous debris, indicating higher ancient primary productivity and representing a favorable target for shale gas exploration.

This study explores the efficient decolorization and complete mineralization of the diazo dye Evans blue, using an integrated aerobic bioreactor system coupled with a double-chamber microbial fuel cell (DCMFC) including a bio-cathode and acetate as a cosubstrate. The research addresses the environmental challenges posed by dye-laden industrial effluents, focusing on achieving high decolorization efficiency and understanding the microbial communities involved. The study utilized mixed strains of actinomycetes, isolated from garden compost, to treat initial dye concentrations of 100 mg/L and 200 mg/L. Decolorization efficiency and microbial community composition were evaluated using 16S rRNA sequencing, and electrochemical impedance spectroscopy (EIS) was used to assess anode and DCMFC resistance. The results demonstrated decolorization efficiencies ranging from 90 ± 2% to 98 ± 1.9% for 100 mg/L and from 79 ± 2% to 87% ± 1% for 200 mg/L. An anode resistance of 12.48 Ω indicated a well-developed biofilm and enhanced electron transfer. The microbial community analysis revealed a significant presence of Pseudomonadota (45.5% in dye-acclimated cultures and 32% in inoculum cultures), with key genera including Actinomarinicola (13.75%), Thermochromatium (4.82%), and Geobacter (4.52%). This study highlights the potential of the integrated DCMFC–aerobic system, utilizing mixed actinomycetes strains, for the effective treatment of industrial dye effluents, offering both environmental and bioenergy benefits.

With the large-scale penetration of distributed generation (DG), the volatility problems of active distribution networks (ADNs) have become more prominent, which can no longer be met by traditional regulation means and need to be regulated by introducing flexible resources. Soft open points (SOP) and energy storage systems (ESS) can regulate the tidal currents on spatial and temporal scales, respectively, to improve the flexibility of ADN. To this end, in-depth consideration of DG admission is given to establish flexibility assessment indicators from the power side of ADN. The conditional deep convolution generative adversarial network (C-DCGAN) is used to generate the output scenario of DG. On this basis, the SOP and ESS two-layer planning models, which take account of the potential for improvement in the flexibility of ADN, are constructed. Among them, the upper layer is the site selection and volume determination layer, which considers the economy of the system with the optimization objective of minimizing the annual integrated cost; the lower layer is the operation optimization layer, which considers the flexibility of the system and takes the highest average daily flexibility level as the optimization objective. The planning model is solved using genetic algorithm-particle swarm optimization (GA-PSO) and second-order cone programming (SOCP). The case analysis verifies the rationality and effectiveness of the planning model.

This study addresses the limitations of traditional methods used to analyze factors influencing submarine cable burial depth and emphasizes the underutilization of cable construction data. To overcome these limitations, a machine learning-based model is proposed. The model utilizes cable construction data from the East China Sea to predict the weight of factors influencing cable burial depth. Pearson correlation analysis and principal component analysis are initially employed to eliminate feature correlations. The random forest method is then used to determine the weights of factors, followed by the construction of an optimized backpropagation (BP) neural network using the ISOA-BP hybrid optimization algorithm. The model’s performance is compared with other machine learning algorithms, including support vector regression, decision tree, gradient decision tree, and the BP network before optimization. The results show that the random forest method effectively quantifies the impact of each factor, with water depth, cable length, deviation, geographic coordinates, and cable laying tension as the significant factors. The constructed ISOA-BP model achieves higher prediction accuracy than traditional algorithms, demonstrating its potential for quality control in cable laying construction and data-driven prediction of cable burial depth. This research provides valuable theoretical and practical implications in the field.

Oil and natural gas resources are crucial energy sources formed during the geological and biological evolution of the Earth [...]

The prevailing approach to the wind resistance design of transmission towers is rooted in the quasi-static method. However, this methodology faces criticism for neglecting tower-line coupling dynamics. Despite efforts to boost structural wind resilience, the research on tower failure mechanisms, especially under extreme winds considering tower-line coupling, is limited. To address this gap, the wind-induced dynamic failure modes of the transmission tower-line system are investigated in this paper. The consistent discrete random flow generation method is employed to simulate the fluctuating wind field for transmission lines. Incorporating the compressive buckling mode of angle steel, the plastic hinge model of the frame element is employed to simulate mechanical nonlinearity. A typical transmission tower-line system is concerned, with a finite element model established for a three-tower, four-line coupled configuration. The findings reveal that the wind-induced collapse of the transmission tower is directly triggered by the buckling failure of the compressed main members, with the vulnerable section located beneath the lower cross-arm. The transmission tower experiences bidirectional bending and compression instability under an unfavorable wind direction. In contrast, the traditional pushover collapse modes of the transmission tower cannot fully capture the characteristics of the collapse failure, mainly due to the ignorance of the transverse wind force action induced by the coupling effect. This research underscores the importance of incorporating lateral dynamic considerations into transmission tower designs and advocates for optimizing strategies to mitigate wind-induced collapse modes.

In the current landscape of medical data management, processing data across diverse institutions and maximizing their value are paramount. However, traditional methods lack a secure and efficient mechanism for end-to-end traceability and supervision, posing challenges in distributed scenarios lacking mutual trust. Leveraging blockchain’s decentralized, tamper-proof, and traceable features, this paper introduces a blockchain-based medical data management platform. This platform enables full-process management of raw data, operational behaviors, intermediate data, and final data, meeting the needs of trusted storage and supervision of data. We propose two methods, namely, naive method and DAG-based method, to realize forward tracking and backward tracing of medical data stored on the blockchain, respectively. We validated and analyzed the storage and query performance of the medical data management platform on real medical data, and we also conducted experimental analyses on the efficiency of the proposed traceability algorithm under different data scales and processing path lengths. The results demonstrate that our platform and traceability methods effectively meet the management needs of medical data distributed across institutions.

The Venus flytrap, which possesses a number of mechano-sensitive trigger hairs, is a typical carnivorous plant that effectively senses and catches insects to survive in nutrient-poor habitats. When insects touch the trigger hairs on a leaf, once they reach the threshold, the Venus flytrap induces an action potential and sharply closes to capture the prey. In this paper, the trigger hairs obtain a special cantilever beam structure with a stiff hair lever and a flexible basal podium, and there is a noticeable notched structure at the basal podium, which differs from a common homogeneous hair. Based on the characteristics of the Venus flytrap trigger hairs, we established a three-dimensional model and conducted theoretical and finite element analysis. The results show that the unique hollow heterogeneous cantilever structure of the Venus flytrap trigger hair can achieve high sensitivity and optimal tactile perception. Overall, the morphology, structure and mechanical characteristics of Venus flytrap trigger hairs were characterized in detail, which may provide a deeper understanding of the trigger hairs’ tactile perception mechanism. And the mechanical simulation and optimization analysis of Venus flytrap trigger hairs had an important theoretical basis and parameter support for the further design of state-of-the-art tactile sensors with high sensitivity inspired by Venus flytrap trigger hairs.

In order to enhance the utilization of alkaline carbide slag (CS) and steel slag (SS) in solid waste-based cementitious materials (SWBCM). In this study, slag (GGBS) was utilized as the primary material, phosphogypsum (PG) as the sulfat activator, and carbide slag (CS) and steel slag (SS) as the alkali activators to prepare carbide slag-steel slag-slag-phosphogypsum composite cementitious material (CS-SS-GGBS-PG). The impact of partial SS (mass fraction) substitution by CS on the flowability, mechanical properties, softening coefficient, pore solution pH value, hydration heat, and microstructure of CS-SS-GGBS-PG was studied. The hydration products, microstructure, and pore structure of CS-SS-GGBS-PG were studied via XRD, FTIR, TG-DTG, SEM, and MIP. The results show that CS replacement with SS decreases the flowability of CS-SS-GGBS-PG compared to the specimen without CS doping (A0). The addition of CS provides an abundance of Ca2+ and OH− to facilitate the hydration reaction in the system, prompting CS-SS-GGBS-PG to form more C-(A)-S-H gels and ettringite (AFt) in the early stages. This significantly enhances the 3 and 7 days strength of CS-SS-GGBS-PG. Compared to A0, the sample group with 5% CS substitution for SS showed a respective increase of 400% and 1,150% in flexural strength at 3 and 7 days, and an increase of 800% and 1,633% in compressive strength. However, if CS is substituted for SS in excess, the system’s late hydration process will be inhibited, increasing the volume of harmful pores in the specimen’s microstructure and lowering its degree of compactness. This leads to a decrease of 28 days of strength and a deterioration of the water resistance of CS-SS-GGBS-PG. It is advised that the blending ratio of SS to CS be 3:1 in order to maintain equilibrium between the early strength enhancement of CS-SS-GGBS-PG and the stability of strength and water resistance in the latter stage. This study has a positive impact on improving the utilization of CS and SS in SWBCM, reducing the dependence of SWBCM on traditional high-angstrom chemical alkali activators, and promoting the development of SWBCM.

Benefiting from the ultra-high specific energy that is even comparable to gasoline, metal-air batteries (MABs) showcase great promising practical prospects in next-generation battery technologies that beyond the commercial Li-ion batteries. Even though the advances witnessed in MABs in the last decades, there are still numerous critical scientific challenges that need to be solved for their future practical applications. Especially, ascribed to the sluggish reaction kinetics and high overpotentials of air cathodes, the performances of MABs are less than ideal, which also makes its industrialization prospect unclear. Noble metal-based catalysts often show the superior catalytic activity in MABs, but the corresponding systematic review is still not given. Our review mainly focuses on the relationship between the MABs performances and the composite and structure of the noble metal catalysts regarding their size from nano to atom levels. In this review, we first introduced the synthesis strategies of the noble metal-based nano-catalysts followed up by discussing the influences of the size, morphology, and support of noble metal-based catalysts as well as their applications in a variety of MABs. In the following sections, the main advanced characterization techniques for single atom catalysts (SACs) and the roles and recent progress of the noble metal-based SACs were also discussed in detail. Finally, we provided personal outlook and perspectives towards the challenges and future development of MABs and noble metal-based catalysts.

Metal-organic framework (MOF)-based mixed matrix membranes (MMMs) have attracted significant attentions for their distinguished characteristics in pervaporation such as enhanced selectivity, increased permeability and improved mechanical strength through the synergistic integration of polymeric matrices and inorganic fillers. Although many publications have emerged in recent years focusing on MOF-based MMMs, this review specifically emphasizes the improvement of MOF-based pervaporation membranes through the design of dimension of fillers and microstructure. The challenges encountered in MOF-based MMMs for pervaporation and the essential requirements for practical separation applications are addressed. A brief summary of strategies is provided for designing MOF-based MMMs with desired microstructure, macrostructure and multicomponent characteristics by using MOFs as fillers. The latest progresses in novel MOF-based MMMs with specific compositions, controllable pore structure and improved compatibility for recovery of organics are also displayed. The broad application prospects of MOF-based MMMs in pervaporation are introduced, including recovery of ethyl alcohol, butanol and other organics. Moreover, the challenges faced in the practical application of MOF-based MMMs for recovery of organics are presented and the promising future directions are outlined.

The split delivery vehicle routing problem (SDVRP) is a classic combinatorial optimization problem, which is usually solved using a heuristic algorithm. The ant colony optimization algorithm is an excellent heuristic algorithm that has been successfully applied to solve various practical problems, and it has achieved good results. However, in the existing ant colony optimization algorithms, there are issues with weak targeting of different customer selection strategies, difficulty in balancing convergence speed and global search ability, and a predisposition to become trapped in local optima. To solve these problems, this paper proposes an improved ant colony algorithm (IACA). First, in terms of customer point selection, the initial customer and noninitial customer selection strategies are proposed for different customers, and the adaptive selection threshold is designed. Second, in terms of pheromone processing, an initial pheromone distribution method based on a greedy strategy, a pheromone backtracking mechanism, and an adaptive pheromone volatile factor are proposed. Finally, based on the 2-opt local search method, vehicle path self-search and intervehicle path search are proposed to further improve the quality of the solution. This paper tests the performance of the IACA on datasets of different scales. The experimental results show that compared with the clustering algorithm, artificial bee colony algorithm, particle swarm optimization algorithm, traditional ant colony algorithm, and other algorithms, the IACA can achieve more competitive results. Specifically, compared to the path length calculated by other algorithms, the path length calculated by IACA decreased by an average of 1.58%, 4.28%, and 3.64% in small, medium, and large-scale tests, respectively.

Due to the deterioration caused by repeated freeze–thaw cycles, concrete materials in cold regions often develop cracks, which have serious effects and challenge the durability of concrete-based structures. Therefore, it is worthwhile to repair and strengthen freeze–thaw-damaged concrete to extend the service duration of the structure. In the present study, to investigate the restorative effect of surface reinforcer on freeze–thaw-deteriorated concrete specimens, the effects of the surface reinforcer type, its action duration, and the number of applications on the strength and deformation parameters of repaired specimens were systematically studied. Moreover, pore size distribution, pore structure at different depths, and the micromorphology characteristics were investigated by nuclear magnetic resonance (NMR), pore structure, and scanning electron microscope (SEM) tests to reveal the repair mechanism of different surface reinforcer types. The results indicated that the compressive strength of freeze–thaw-deteriorated concrete could be increased by up to 15.67% after the application of the surface reinforcer. Both the values of compressive strength and deformation modulus E50 increased with the increase in the action duration. In addition, the surface reinforcer could efficiently penetrate the interior of the deteriorated specimen and was able to decrease the total proportion of multi-harmful pores and harmful pores. Furthermore, the pore structure parameters could be significantly improved at a depth of 10 mm; however, the reparative effect of the surface reinforcer gradually decreased with the increase in the action depth. The surface reinforcer could efficiently promote the second hydration of cement and generate more cementitious materials to fill the microvoids, thereby improving the compactness and mechanical properties of the repaired specimens.

The beetle, of the order Coleoptera, possesses outstanding flight capabilities. After completing flight, they can fold their hindwings under the elytra and swiftly unfold them again when they take off. This sophisticated hindwing structure is a result of biological evolution, showcasing the strong environmental adaptability of this species. The beetle’s hindwings can provide biomimetic inspiration for the design of flapping-wing micro air vehicles (FWMAVs). In this study, the Asian ladybird (Harmonia axyridis Pallas) was chosen as the bionic research object. Various kinematic parameters of its flapping flight were analyzed, including the flight characteristics of the hindwings, wing tip motion trajectories, and aerodynamic characteristics. Based on these results, a flapping kinematic model of the Asian ladybird was established. Then, three bionic deployable wing models were designed and their structural mechanical properties were analyzed. The results show that the structure of wing vein bars determined the mechanical properties of the bionic wing. This study can provide a theoretical basis and technical reference for further bionic wing design.

Active sonar can usually only directly measure the distance and bearing information of underwater targets, and cannot directly obtain target velocity, acceleration and other information. Therefore, the amount of information is relatively small, making it difficult to support the construction of complex motion models. At the same time, the motion state of underwater maneuvering targets is changeable. In response to the problem of detecting and tracking underwater moving targets by active sonar, this paper proposes a target transient model correction (TMC) filtering tracking method. Based on the conventional Kalman filter (KF) estimation, residual covariance is used as a signal quantity. When there is a large change in it, a transient filter with constant gain is adopted to filter the measurement value. The filtered output is used to correct the KF gain matrix and the target motion state model, to avoid the problem of increasing or even diverging KF estimation errors caused by changes in process noise. Using this method can solve the problem of maintaining stability and filtering estimation accuracy of active sonar tracking of underwater maneuvering targets with less computational and engineering costs.

Extralow-permeability reservoirs have emerged as a significant area of focus for CO2 geological sequestration due to their stable subterranean structure and expansive storage capacity, offering substantial potential in addressing global climate change. However, the full extent of CO2 geological sequestration potential within these extralow-permeability reservoirs remains largely unexplored. To address this gap, this paper utilizes the Shahejie Formation (Es1 member) of the Shuang 229 block in the Liaohe oilfield, Bohai Bay Basin, as a case study. This section is characterized by its abundant oil-gas reserves and serves as an exemplar for conducting experimental research on CO2 storage within extralow-permeability reservoirs. The results demonstrate that the reservoir lithology of the Es1 member is fine sandstone and siltstone, with high compositional and structural maturity. Moreover, the average porosity is 14.8%, the average permeability is 1.48 mD, and the coefficient of variation of the reservoir is approximately 0.5, which indicates a low- to extralow-permeability homogeneous reservoir. In addition, the overburden pressure is >2.0 MPa, the fault can withstand a maximum gas column height of >200 m, and the reservoir exhibits favorable overburden and fault sealing characteristics. Notably, stepwise increasing gas injection in the Shuang 229-36-62 well reveals that the injected liquid CO2 near the wellhead exhibits a relatively high density, close to 1.0 g/cm3, which gradually decreases to approximately 0.78 g/cm3 near a depth of 2000 m underground. The injected fluid changes into a supercritical state upon entering the formation, and the CO2 injection speed is optimal, at 0.08 HCPV/a. According to these findings, it is predicted that the highest burial CO2 volume via the injection of 1.5 HCPVs in the Wa 128 block area is 1.11 × 105 t/year, and the cumulative burial volume reaches approximately 2.16 × 106 t. This shows that the CO2 sequestration potential of extralow-permeability reservoirs is considerable, providing confidence for similar instances worldwide.

For linear viscoelastic materials, this paper proposes a finite element analysis method based on an integral constitutive relationship that can simultaneously consider the relaxation behavior of the elastic modulus and the creep Poisson’s ratio. Firstly, the generalized Maxwell model is employed to depict the relaxation characteristics of the elastic modulus, while the generalized Kelvin model is used to represent the creep Poisson’s ratio. Subsequently, the element relaxation stiffness matrix is established, thereby forming a convolutional finite element equation. Furthermore, the recursive calculation of the convolutional integral is derived, and the calculation steps of the finite element for viscoelasticity considering the time-dependent nature of both the elastic modulus and Poisson’s ratio are established. Finally, the accuracy of the proposed algorithm is verified through two numerical examples with linear viscoelastic material. The results indicate that the proposed variable stiffness method for the finite element analysis of linear viscoelastic materials can simultaneously consider the changes in the elastic modulus and Poisson’s ratio over time, thereby establishing a new path and idea for the more accurate simulation of viscoelastic materials’ mechanical properties. Compared with the initial strain method for linear viscoelastic materials, the variable stiffness method proposed in this paper effectively avoids the assumption of constant stress during the micro time interval, thus significantly enhancing the finite element calculation accuracy of linear viscoelastic materials. The proposed method establishes a simulation algorithm that matches existing commercial software with viscoelastic material experiments by considering the elastic modulus and Poisson’s ratio as material parameters.

Helium is a scarce strategic resource. Currently, all economically valuable helium resources are found in natural gas reservoirs. Owing to helium’s different formation and migration processes compared to natural gas’s, the traditional method of collecting wellhead gas to detect helium concentration may miss helium-rich layers in the vertical direction, which will not only cause the waste of helium resources, but also restrict the study of helium migration and accumulation mechanisms. To solve this problem, we designed a helium detector based on a quadrupole mass spectrometer. Through the combination of different inlet valves, we avoided gas mixing between different vertical layers during the inlet process and realized high-spatial-resolution helium concentration detection. We applied the helium detector to the Dongsheng gas field in the northern Ordos Basin, and the instrumental detection results were consistent with the laboratory analysis results of the wellhead gas, which demonstrated the stability of the helium detector in the field environment and the reliability of the data. Meanwhile, the results showed that the distribution of helium in the plane is highly heterogeneous, and the natural gas dessert layers and the helium dessert layers do not coincide in the vertical direction. In addition, we found a good correlation between helium and hydrogen concentrations. Combining our results with previous data, we propose a hydrogen–helium migration and accumulation model, which enriches the understanding of helium accumulation mechanisms and provides a basis for future helium resource exploration.

In this paper, the authors address the issue of large solid waste output and surface subsidence in the western coal base. They focus on the 110904 filling working face of Renjiazhuang Mine and employ a combination of theoretical analysis, numerical simulation, and field practice to analyze the limit caving step and cycle pressure step of the working face. The authors propose a mining-filling coordination scheme called “three mining and one charging”. Through numerical simulation, they demonstrate that this scheme ensures the time-space continuity of the mining-filling cycle and is theoretically feasible. Additionally, the authors conduct nine groups of orthogonal simulation tests to identify the main factors affecting the filling effect. They conclude that the filling rate is the primary controlling factor, and there is a positive correlation between the filling rate and 1-day strength. The authors implement hanging pipe filling in the return air roadway of the 110904 working face, achieving a filling material 1-day strength of 2 MPa and a filling ratio of 80%. The mining-filling synergy scheme proves effective and feasible. However, the issue of pipe plugging during the filling process requires further optimization of the particle gradation in the subsequent filling material ratio scheme. Overall, the proposed mining-filling collaborative process serves as a theoretical reference for similar filling faces.

The performance of wind turbines directly determines the profitability of wind farms. However, the complex environmental conditions and influences of various uncertain factors make it difficult to accurately assess and monitor the actual power generation performance of wind turbines. A data-driven approach is proposed to intelligently monitor the power generation performance evolution of wind turbines based on operational data. Considering the inherent nonlinearity and structural complexity of wind turbine systems, a data-derived characteristic construction and dimensionality reduction method based on KPCA is adopted as a prerequisite. Additionally, an AdaBoost-enhanced regressor is applied to wind power prediction with adequate inputs, and day-oriented deviation indicators are further constructed for quantifying performance fluctuations. The final validation phase includes two application cases: In the first case, the results show that the proposed method is sensitive enough to capture the early characteristics of blade damage faults. In the second case, an uncertainty error within ±0.5% demonstrates that the proposed method has high-level accuracy in the quantitative assessment of the power performance and good practical effectiveness in real engineering applications.

In order to improve the reliability and maintainability of rod control power cabinets in nuclear power plants, this paper uses insulated gate bipolar transistors (IGBTs), the key power device of rod control power cabinets, as the object of research on cross-working-condition fault prediction. An improved transfer learning (TL) model based on a temporal convolutional network (TCN) is proposed to solve the problem of low fault prediction accuracy across operating conditions. First, the peak emitter voltage of an IGBT aging dataset is selected as the source domain failure characteristic, and the TCN model is trained after the removal of outliers and noise reduction. Then, the time–frequency features are extracted according to the characteristics of the target domain data, and the target domain representation data are obtained using kernel principal component analysis (KPCA) for dimensionality reduction. Finally, the TCN model trained on the source domain is transferred; the model is fine-tuned according to the target domain data, and the learning rate, the number of hidden layer nodes, and the number of training times in the network model are optimized using the dung beetle optimization (DBO) algorithm to obtain the optimal network, making it more suitable for target sample fault prediction. The prediction results of this TCN model, the long short-term memory (LSTM) model, the gated recurrent unit (GRU) model, and the recursive neural network (RNN) model are compared and analyzed by selecting prediction performance evaluation indexes. The results show that the TCN model has a better predictive effect. Comparing the prediction results of the TCN-based optimized transfer learning model with those of the directly trained TCN model, the mean square error, root mean square error, and mean absolute error are reduced by a factor of two to three, which provides an effective solution for fault prediction across operating conditions.

Abstract The high‐temperature molten salt pump (MSP) is the core equipment of the thermal storage system for concentrating solar power plants, which circulates the molten salt of the thermal storage medium. This study employs numerical simulation of a two‐stage MSP with a storage tank to find the correlation between pressure pulsations and the evolutions of vortical structures based on renormalization group k‐ε turbulence model. It is shown that rotor/stator interaction is the most significant excitation source of pressure pulsations with the prominent excitations being the diffuser passing frequency (fDPF) in the impeller and the blade passing frequency (fBPF). Furthermore, Q‐criterion is employed to capture the periodic shedding vortexes with a frequency of about 3fR at the suction surface and the trailing edge in diffusers. Impinging vortex and shedding vortex with the frequency 6fR on the leading edge of the diffuser is captured. The vortex shedding characteristics are significantly different under various conditions, and the separation vortexes appear at the leading edge of pressure surface in the diffusers at higher flow rates. Finally, it is obvious that the excited components and their magnitudes in the pressure spectrum are closely associated with the unsteady vortical structures within the model pump.

Abstract With the development of optoelectronic devices toward miniaturization, flexibility, and large‐scale integration, conventional submillimeter rigid encapsulation techniques rarely achieve conformational functionality while blocking water and oxygen. At the same time, the sensitivity of electronic devices with organic/metal/semiconductor components to humidity and oxygen severely impairs their operational stability and lifetime. Here, a nanometer to micrometer scale organic/inorganic hybrid thin film encapsulation (TFE) with the self‐cleaning ability for flexible encapsulation is developed. The water vapor transmittance rate of polyethylene terephthalate substrate coated with the TFE is as low as 1.65 × 10−4 g m−2 day−1, and the barrier improvement factor reaches 104 at 38 °C and 90% relative humidity. This value is equivalent to 9.81 × 10−6 g m−2 day−1 at ambient conditions, sufficient to improve the lifetime of water‐sensitive electronic devices. Meanwhile, this TFE shows a super‐hydrophobic performance, with a water contact angle of 168.4°. In addition, the resulting barrier films exhibit outstanding optical properties, with an average optical transmittance of 86.88% in the visible region. This versatile TFE can promote the development of optoelectronic devices toward miniaturization and large‐scale integration in the future.

Two different business models of 5GtoB were analyzed, the current toB business tend to adopt the project business model rather than the product business model, which will become an important obstacle in the large-scale development of 5GtoB.On this basis, three types of innovation modes in the current wave of digital transformation were analyzed, including process innovation, product innovation and service innovation, and integrating product innovation is the key path for 5GtoB to realize product business model and large-scale development, and service innovation will further expand the application field of 5GtoB and drive the development of platform.

Abstract Aqueous zinc-ion batteries (AZIBs) can be one of the most promising electrochemical energy storage devices for being non-flammable, low-cost, and sustainable. However, the challenges of AZIBs, including dendrite growth, hydrogen evolution, corrosion, and passivation of zinc anode during charging and discharging processes, must be overcome to achieve high cycling performance and stability in practical applications. In this work, we utilize a dual-functional organic additive cyclohexanedodecol (CHD) to firstly establish [Zn(H2O)5(CHD)]2+ complex ion in an aqueous Zn electrolyte and secondly build a robust protection layer on the Zn surface to overcome these dilemmas. Systematic experiments and theoretical calculations are carried out to interpret the working mechanism of CHD. At a very low concentration of 0.1 mg mL−1 CHD, long-term reversible Zn plating/stripping could be achieved up to 2200 h at 2 mA cm−2, 1000 h at 5 mA cm−2, and 650 h at 10 mA cm−2 at the fixed capacity of 1 mAh cm−2. When matched with V2O5 cathode, the resultant AZIBs full cell with the CHD-modified electrolyte presents a high capacity of 175 mAh g−1 with the capacity retention of 92% after 2000 cycles under 2 A g−1. Such a performance could enable the commercialization of AZIBs for applications in grid energy storage and industrial energy storage.

This study employs unmanned aerial vehicles (UAVs) to detect mouse holes in grasslands, offering an effective tool for grassland ecological conservation. We introduce the specially designed CGT-YOLOv5n model, addressing long-standing challenges UAVs face, particularly the decreased detection accuracy in complex grassland environments due to shadows and obstructions. The model incorporates a Context Augmentation Module (CAM) focused on improving the detection of small mouse holes and mitigating the interference of shadows. Additionally, to enhance the model’s ability to recognize mouse holes of varied morphologies, we have integrated an omni-dimensional dynamic convolution (ODConv), thereby increasing the model’s adaptability to diverse image features. Furthermore, the model includes a Task-Specific Context Decoupling (TSCODE) module, independently refining the contextual semantics and spatial details for classification and regression tasks and significantly improving the detection accuracy. The empirical results show that when the intersection over union (IoU) threshold is set at 0.5, the model’s mean average precision (mAP_0.5) for detection accuracy reaches 92.8%. The mean average precision (mAP_0.5:0.95), calculated over different IoU thresholds ranging from 0.5 to 0.95 in increments of 0.05, is 46.2%. These represent improvements of 3.3% and 4.3%, respectively, compared to the original model. Thus, this model contributes significantly to grassland ecological conservation and provides an effective tool for grassland management and mouse pest control in pastoral areas.

Given a textual query, text-based person re-identification is supposed to search for the targeted pedestrian images from a large-scale visual database. Due to the inherent heterogeneity between different modalities, it is challenging to measure the cross-modal affinity between visual and textual data. Existing works typically employ single-granular methods to extract local features and align image regions with relevant words/phrases. Nevertheless, the limited robustness of single-granular methods cannot adapt to the imprecision and variances of visual and textual features, which are usually influenced by the background clutter, position transformation, posture diversity, and occlusion in surveillance videos, thereby leading to the deterioration of cross-modal matching accuracy. In this paper, we propose a Pyramidal Multi-Granular matching network (PMG) that incorporates a gradual transition process between the coarsest global information and the finest local information by a coarse-to-fine pyramidal method for multi-granular cross-modal features extraction and affinities learning. For each body part of a pedestrian, PMG is adequate in ensuring the integrity of local information while minimizing the surrounding interference signals at a certain scale and can adapt to capture discriminative signals of different body parts and achieve semantically alignment between image strips with relevant textual descriptions, thus suppressing the variances of feature extraction and improving the robustness of feature matching. Comprehensive experiments are conducted on the CUHK-PEDES and RSTPReid datasets to validate the effectiveness of the proposed method and results show that PMG outperforms state-of-the-art (SOTA) methods significantly and yields competitive accuracy of cross-modal retrieval.

Recently, deep learning techniques have been used for low-dose CT (LDCT) reconstruction to reduce the radiation risk for patients. Despite the improvement in performance, the network models used for LDCT reconstruction are becoming increasingly complex and computationally expensive under the mantra of “deeper is better”. However, in clinical settings, lightweight models with a low computational cost and short reconstruction times are more popular. For this reason, this paper proposes a computationally efficient CNN model with a simple structure for sparse-view LDCT reconstruction. Inspired by super-resolution networks for natural images, the proposed model interpolates projection data directly in the sinogram domain with a fully convolutional neural network that consists of only four convolution layers. The proposed model can be used directly for sparse-view CT reconstruction by concatenating the classic filtered back-projection (FBP) module, or it can be incorporated into existing dual-domain reconstruction frameworks as a generic sinogram domain module. The proposed model is validated on both the 2016 NIH-AAPM-Mayo Clinic LDCT Grand Challenge dataset and The Lung Image Database Consortium dataset. It is shown that despite the computational simplicity of the proposed model, its reconstruction performance at lower sparsity levels (1/2 and 1/4 radiation dose) is comparable to that of the sophisticated baseline models and shows some advantages at higher sparsity levels (1/8 and 1/15 radiation dose). Compared to existing sinogram domain baseline models, the proposed model is computationally efficient and easy to train on small training datasets, and is thus well suited for clinical real-time reconstruction tasks.

Abstract Endo-β-mannanases are important enzymes for degrading lignocellulosic biomass to generate mannan, which has significant health effects as a prebiotic that promotes the development of gut microbiota. Here, a novel endo-β-mannanase belonging to glycoside hydrolase (GH) family 113 from Paenibacillus cineris (PcMan113) was cloned, expressed and characterized, as one of only a few reported GH113 family β-mannanases. Compared to other functionally and structurally characterized GH113 mannanases, recombinant PcMan113 showed a broader substrate spectrum and a better performance. Based on a structural homology model, the highly active mutant PcMT3 (F110E/N246Y) was obtained, with 4.60- and 5.53-fold increases of enzyme activity (towards KG) and catalytic efficiency (k cat/K m, against M5) compared with the WT enzyme, respectively. Furthermore, molecular dynamics (MD) simulations were conducted to precisely explore the differences of catalytic activity between WT and PcMT3, which revealed that PcMT3 has a less flexible conformation, as well as an enlarged substrate-binding channel with decreased steric hindrance and increased binding energy in substrate recognition. In conclusion, we obtained a highly active variant of PcMan113 with potential for commercial application in the manufacture of manno-oligosaccharides. Graphical Abstract

Abstract Circulating tumor cells (CTCs) have tremendous potential to indicate disease progression and monitor therapeutic response using minimally invasive approaches. Considering the limitations of affinity strategies based on their cost, effectiveness, and simplicity, size-based enrichment methods that involve low-cost, label-free, and relatively simple protocols have been further promoted. Nevertheless, the key challenges of these methods are clogging issues and cell aggregation, which reduce the recovery rates and purity. Inspired by the natural phenomenon that the airflow around a windmill is disturbed, in this study, a windmill-like hole array on the SU-8 membrane was designed to perturb the fluid such that cells in a fluid would be able to self-mix and that the pressure acting on cells or the membrane would be dispersed to allow a greater velocity. In addition, based on the advantages of fluid coatings, a lipid coating was used to modify the membrane surface to prevent cell aggregation and clogging of the holes. Under the optimal conditions, recovery rates of 93% and 90% were found for A549 and HeLa cells in a clinical simulation test of our platform with a CTC concentration of 20–100 cells per milliliter of blood. The white blood cell (WBC) depletion rate was 98.7% (n = 15), and the CTC detection limit was less than 10 cells per milliliter of blood (n = 6). Moreover, compared with conventional membrane filtration, the advantages of the proposed device for the rapid (2 mL/min) and efficient enrichment of CTCs without clogging were shown both experimentally and theoretically. Due to its advantages in the efficient, rapid, uniform, and clog-free enrichment of CTCs, our platform offers great potential for metastatic detection and therapy analyses.

Abstract A feasibility study was carried out on generation of hydrochloric acid and lithium hydroxide from the simulated lithium chloride solution using EX3B model bipolar membrane electrodialysis (BMED). The influence of a series of process parameters, such as feed concentration, initial acid and base concentration in device component, feed solution volume, and current density were investigated. In addition, the maximum achievable concentrations of HCl and LiOH, the average current efficiency, and specific energy consumption were also studied and compared in this paper to the existing literature. Higher LiCl concentrations in the feed solution were found to be beneficial in increasing the final concentrations of HCl and LiOH, as well as improving current efficiency while decreasing specific energy consumption. However, when its concentration was less than 4 g/L, the membrane stack voltage curve of BMED increased rapidly, attributed to the higher solution resistance. Also low initial concentration of acid and base employed in device component can improve the current efficiency. Increasing of the initial concentration of acid and base solution lowered energy consumption. Moreover, a high current density could rapidly increase HCl and LiOH concentration and enhance water movements of BMED process, but reduced the current efficiency. The maximum achievable concentration of HCl and LiOH generated from 130 g/L LiCl solution were close to 3.24 mol/L and 3.57 mol/L, respectively. In summary, the present study confirmed the feasible application for the generation of HCl and LiOH from simulated lithium chloride solution with BMED.

Abstract The wettability and adhesion of biological surfaces often depend on their periodically arranged hybrid micro–nano array structures. Various fabrication processes are designed to mimic biomimetic micro–nano array surfaces. This review summarizes the types of micro–nano array structures and analyzes fabrication methods based on top‐down and bottom‐up construction, including templating, etching, self‐assembly, and electrospinning/electrospraying. This review focuses on the shape reconfiguration of surface micro–nano array structures under physical stimuli, as well as the changes in material surface properties during the reconfiguration process. In addition, the applications of biomimetic micro–nano array composite surfaces in the fields of droplet transport, adhesion properties, sensors, and soft robotics are also discussed, and the current challenges and prospects in this field are identified.

The fast development of urbanization emits huge amount of domestic wastewater, of which the high contents of chemical oxygen demand(COD), NH4-N, total nitrogen and phosphorous lead to the difficulty in their complete removal and the recycling of the water. To solve this problem, the simulated domestic sewage treatment efficiency was investigated via scenedesmus quadricauda and chlorella pyrenoidosa in terms of COD, NH4-N, total nitrogen (TN), and total phosphorous (TP). The influence of the dosage of the two microalgae (100 mg/L and 200 mg/L) on wastewater treatment and the relationship of the indexes COD, NH4-N, TN, and TP with handling time were examined. The surface morphology and chemistry before and after water treatment were characterized through SEM and FTIR, respectively. Scenedesmus quadricauda and chlorella pyrenoidosa show significant removal efficiency towards COD, with removal rates of 77.92% and 80.43%, respectively. Chlorella pyrenoidosa shows better NH4-N (58.34%) and TP (51.09%) removal efficiency than scenedesmus quadricauda for NH4-N (43.66%) and TP (41.52%). Scenedesmus quadricauda shows better TN removal efficiency (78.33%) than chlorella pyrenoidosa (76.67%). The results might provide fundamental and technical support for the application of algal microorganism.

Abstract Ferredoxin (Fdx) is regarded as the main electron carrier in biological electron transfer and acts as an electron donor in metabolic pathways of many organisms. Here, we screened a self-sufficient P450-derived reductase PRF with promising production yield of 9OHAD (9α-hydroxy4-androstene-3,17-dione) from AD, and further proved the importance of [2Fe–2S] clusters of ferredoxin-oxidoreductase in transferring electrons in steroidal conversion. The results of truncated Fdx domain in all oxidoreductases and mutagenesis data elucidated the indispensable role of [2Fe–2S] clusters in the electron transfer process. By adding the independent plant-type Fdx to the reaction system, the AD (4-androstene-3,17-dione) conversion rate have been significantly improved. A novel efficient electron transfer pathway of PRF + Fdx + KshA (KshA, Rieske-type oxygenase of 3-ketosteroid-9-hydroxylase) in the reaction system rather than KshAB complex system was proposed based on analysis of protein–protein interactions and redox potential measurement. Adding free Fdx created a new conduit for electrons to travel from reductase to oxygenase. This electron transfer pathway provides new insight for the development of efficient exogenous Fdx as an electron carrier. Graphical Abstract

Real-time detection of satellite remote sensing images is one of the key technologies in the field of remote sensing, which requires not only high-efficiency algorithms, but also low-power and high-performance hardware deployment platforms. At present, the image processing hardware acceleration platform mainly uses an image processing unit (GPU), but the GPU has the problem of large power consumption, and it is difficult to apply to micro-nano satellites and other devices with limited volume, weight, computing power, and power consumption. At the same time, the deep learning algorithm model has the problem of too many parameters, and it is difficult to directly deploy it on embedded devices. In order to solve the above problems, we propose a YOLOv4-MobileNetv3 field programmable gate array (FPGA) deployment scheme based on channel layer pruning. Experiments show that the acceleration strategy proposed by us can reduce the number of model parameters by 91.11%, and on the aerial remote sensing dataset DIOR, the average accuracy of the design scheme in this paper reaches 82.61%, the FPS reaches 48.14, and the average power consumption is 7.2 W, which is 317.88% FPS higher than the CPU and reduces the power consumption by 81.91%. Compared to the GPU, it reduces power consumption by 91.85% and improves FPS by 8.50%. Compared with CPUs and GPUs, our proposed lightweight algorithm model is more energy-efficient and more real-time, and is suitable for application in spaceborne remote sensing image processing systems.

In this paper, a novel two-dimensional direction of arrival (2D-DOA) estimation method with total variation regularization is proposed to deal with the problem of sparse DOA estimation for spatially extended sources. In a general sparse framework, the sparse 2D-DOA estimation problem is formulated with the regularization of extended source characteristics including spatial position grouping, acoustic signal block sparse, and correlation features. An extended sources acoustic model, two-dimensional array manifold and its complete representation, total variation regularization penalty term, and the regularization equation are built, and are utilized to seek the solutions where the non-zero coefficients are grouped together with optimum sparseness. A total variation sparse 2D-DOA estimation model is constructed by combining total variation regularization with LASSO. The model can be easily solved by the convex optimization algorithm, and the solving process can promote the sparsity of the solution on the spatial derivatives and the solution itself. The theoretical analysis results show that the steps of decorrelation processing and angle matching of traditional 2D-DOA estimation methods could be avoided when adopting the proposed method. The proposed method has better robustness to noise, better sparsity, and faster estimation speed with higher resolution than traditional methods. It is promising to provide a coherent sources sparse representation of a non-strictly sparse field.

The frequency synthesizer plays a crucial role in atomic clock technology. In this study, we demonstrate a direct microwave frequency synthesizer for a cesium atomic clock, employing frequency multiplication and a dual-phase-locked loop mode. A mathematical model of the frequency synthesis chain is established to estimate its performance. The phase-settling time and system stability are analyzed and studied in detail, and the obtained results are verified by experiments. An optimized realization of the frequency synthesizer shows that the phase-settling time can be adjusted within the range of 644.5 µs to 1.5 ms. Additionally, we measure the absolute phase noise values to be −63.7 dBc/Hz, −75.7 dBc/Hz, −107.1 dBc/Hz, and −122.5 dBc/Hz at 1 Hz, 10 Hz, 1 kHz, and 10 kHz offset frequencies, respectively.