Inherent Relationship Research Articles

Hydrodynamic dipole-driven theory for active flow control in heterogeneous porous media

Although significant efforts have been directed toward refining active control methods for porous media flows, limited explorations have been devoted to the effects of heterogeneous permeability on fluid flow in such environments. These gaps in understanding pose a challenge in developing effective strategies for regulating flow states in porous media with varying permeability. To address these issues, we propose a hydrodynamic dipole-driven theory, solely leveraging a pair of hydrodynamic point source and sink, to rectify flow in heterogeneous porous media systems, thus enabling precise manipulation of the flow field. By carefully tuning the moment of the hydrodynamic dipole, we demonstrate the complete elimination of flow disturbances arising from permeability heterogeneity, and this restoration of the original uniform flow state effectively homogenizes overall permeability. Furthermore, our theory transcends limitations associated with electroosmotic and magnetic methods that require fluids respond to such physical fields, offering broader applicability and minimizing potential contamination risks. Finally, the inherent relation between potential function and pressure distributions in Dracy's law is established with rigorous theoretical analysis, which lays the foundation for active hydrodynamic metamaterials assisted with hydrodynamic dipole strategy. We anticipate that our findings will significantly advance the field of active flow control, particularly in addressing heterogeneous permeability in complex porous media flows, and provide valuable insights for the development of hydrodynamic metamaterial without reliance on heterogeneous or anisotropic materials.

Visions of posthumanity: a posthumanist narrative study on Rebuild of Evangelion

Rebuild of Evangelion, the tetralogy of animated films created by Hideaki Anno, envisions and discusses the form and attributes of the posthuman in the near future, showing the inherent vitality of as the core feature, as well as the dynamic relationality in terms of geography and technology. The films critique the “Human Instrumentality Project” as a transhumanist ideal for its disembodied inclination, anthropocentric nature, and predatory dichotomy. Through the characterization and narratives of the films, the posthumanist values and ethics advocated by Rosi Braidotti are presented as Anno’s key message, that is, in the era of posthumanism that has already come, we should establish a monistic materialistic attitude, highlight the inherent relationality, embeddedness, and transversality among all beings, and establish a posthuman ethics that advocates egalitarian care basing on universal vitality.

Scale-specific controls of monthly suspended sediment load in a typical inland river

Sediment transport is a multi-scale and non-linear process controlled by multiple variables. Given the intricate nature of sediment transport mechanisms and the overlap of different factors’ effects on it at various time scales, unraveling the relationship between sediment load and its influencing factors is challenging. This study aimed to elucidate the multi-timescale effects of factors on monthly suspended sediment load in a seasonal inland river. In this study, monthly sediment loads measured by the depth-integrating method were obtained from the Tabu River Basin in northern China from 2007 to 2021, alongside data on four controlling factors: runoff, precipitation, water surface evaporation, and the normalized difference vegetation index (NDVI). The results showed that sediment load, runoff, and precipitation showed strong variability during 2007–2021, while water surface evaporation and NDVI exhibited moderate variability. Multivariate empirical mode decomposition (MEMD) decomposed the temporal patterns of sediment load and associated variables into five intrinsic mode functions (IMF) and a residue. IMF1 (4.7 months), IMF2 (9.0 months) and IMF3 (14.2 months) collectively explained 115.88 %, 91.73 %, 91.28 %, 107.69 %, and 106.09 % of the total variability in sediment load, runoff, precipitation, water surface evaporation, and NDVI, respectively. Subsequently, the time-dependent intrinsic correlation (TDIC) was utilized to illustrate the inherent relationships between sediment load and its controlling factors across various time scales. The associations between sediment load and the controlling factors changed significantly with the scale. Runoff was the dominant factor controlling sediment load at IMF1 (4.7 months), IMF2 (9.0 months), IMF3 (14.2 months), and IMF4 (25.1 months). Water surface evaporation controlled the sediment transport process at IMF5 (63.6 months). The prediction of sediment load on the measurement scale, using scale-specific controls analyzed with MEMD (R2 = 0.993), demonstrated superior performance compared to traditional multiple linear regression prediction (R2 = 0.748). This study will provide a theoretical basis for effective prevention and control of soil erosion and watershed management in inland river basins.

GeoExplainer: Interpreting Graph Convolutional Networks with geometric masking

For Graph Convolutional Network (GCN), the inherent geometric topology and relationships within spatio-temporal graphs are critical to factors affect its predictive performance. Understanding the decision-making process of GCN remains a challenging task. Existing perturbation-based explanation methods are primarily tailored for Graph Neural Network (GNN) models and focus on how the information of nodes or subgraphs in static graphs impacts GNN predictions. Due to the geometric properties of spatio-temporal graphs, screw theory serves as crucial mathematical tools for describing the geometric relationships and patterns of motion within the geometric space of spatio-temporal graphs. Therefore,we propose GeoExplainer, which utilizes screw theory to geometric masking for spatio-temporal graphs with strong geometric correlations, generating explanations for GCN. Our method perturbs spatio-temporal graphs using geometric masking, and ultimately generating explanations in the form of geometric masks serve as importance mappings for model prediction results. Firstly, we design a screw motion vector model with geometric constraints to describe the spatio-temporal graph in screw system. Then, we design a geometric screw perturbation to generate smooth geometric masks. Finally, we propose a geometric masking approach to optimize and update the geometric mixture mask. We utilize pointwise mutual information (PMI) to maximize the correlation between the model predictions after geometric perturbation and the original predictions, obtaining the optimal geometric mask as an explanation for GCN. The interpretable experiments of ST-GCN, HD-GCN, GCN and GIN models on four datasets with geometric relationships demonstrate that GeoExplainer outperforms eight competitive interpretation methods in quantitative and visualization analysis.

An MPPT Control Strategy Based on Current Constraint Relationships for a Photovoltaic System with a Battery or Supercapacitor

When the battery or supercapacitor is connected to the output of a PV system, the conventional voltage equation expressing its mathematical model usually must be replaced by the current relationship to study the maximum power point tracking (MPPT) control theory. However, hitherto, there is a lack of an attempt to disclose the current constraint relationships at the maximum power point (MPP), which leads to the potential risk of MPPT failure. To solve this problem, in this paper, the MPPT constraint conditions on the basis of currents are built and then a new MPPT control strategy is proposed. In this strategy, a linearized model parameter of a PV cell is used as the bridge to find the current relationships. On the basis of them, some expressions involving the duty cycle are built to directly calculate the control signal of the MPPT controller. Meanwhile, an implementation method is designed to match this proposed MPPT strategy. Finally, some simulation experiments are conducted. The simulation results verify that the proposed MPPT constraint expressions are accurate and workable and that the proposed MPPT strategy and its implementation process are feasible and available. In addition, the simulation results also show that the proposed MPPT strategy has a better MPPT speed and the same MPPT accuracy when the P&O method and fuzzy algorithm are compared. By this work, the MPPT constraint conditions based on current relationships are first found, representing a breakthrough in disclosing the inherent relationships between different currents when the PV system is operating around the MPP.

Analysing A/O Possession in Māori-Language Tweets

This article contributes the first corpus-based study of possession in Māori, the indigenous language of Aotearoa New Zealand. Like most Polynesian languages, Māori has a dual possessive system involving a choice between the so-called A and O categories. While Māori grammars describe these categories in terms of the inherent semantic relationship between the possessum and possessor, there have been no large-scale corpus analyses demonstrating their use in natural contexts. Social media provide invaluable opportunities for such linguistic studies, capturing contemporary language use while alleviating the burden of gathering data through traditional means. We operationalise semantic distinctions to investigate possession in Māori-language tweets, focusing on the [possessum a/o possessor] construction (e.g., te tīmatanga o te wiki ‘the beginning of the week’). In our corpus comprising 2500 tweets produced by more than 200 individuals, we find that users leverage a wide array of noun types encompassing many different semantic relationships. We observe not only the expected predominance of the O category, but also a tendency for examples described by Māori grammars as A-marked to instead be O-marked (59%). Although the A category persists in the corpus, our findings suggest that language change could be underway. Our primary dataset can be explored interactively online.

Digital payments and relational embedding: Turning relations into data and data into relations

Research on the digital economy has highlighted the assetization of data. This article argues for expanding existing research on data and datafication processes by focusing on how relationships are made and unmade through and from data. We introduce a general analytic model of “relationing” and show how relationships between users, companies, and products are created in three different moments—entanglement, dissection, and matching—first in the digital economy, then in physical stores. We show how payments with mobile phones connect the digital to the brick-and-mortar economy. Applying our model, we illustrate how a mobile phone's various data streams, money's record-keeping function, and retailers’ loyalty programs produce qualitatively and quantitatively new relations between customers, retailers, banks, app providers, and payment intermediaries. We argue that “relational embedding” captures the inherent relationality between users, their data points, and other economic actors: algorithmically relating users’ data profiles to other users’ profiles yields personalized recommendations, ads, or rebates, continuing the relationship between retailers and customers.

Analysis of Mechanical Damage in Dance Training Under Artificial Intelligence Behavior Constraints

This study focuses on the application of artificial intelligence behavior constraints in the analysis of mechanical injuries in dance training, aiming to accurately analyze and effectively prevent mechanical injuries during dance training through the introduction of artificial intelligence technology. Dance, as a highly dependent art form on physical skills, often comes with a certain risk of mechanical injury during its training process. In this study, we first reviewed the relevant theories of mechanical injuries in dance training and analyzed the inherent relationship between dance movements and mechanical injuries. Subsequently, we utilized artificial intelligence technology to conduct behavior constraint analysis on the dance training process. By constructing a dance action recognition model, we achieved real-time monitoring and evaluation of dance training actions. On this basis, we further utilize the principles of mechanics to quantitatively analyze dance movements and extract key factors that affect mechanical damage. Through in-depth analysis and comparison, this study found that mechanical injuries in dance training are mainly influenced by various factors such as movement standardization, training intensity, and individual differences. We applied the theory of sports biomechanics to study sports dance injuries and analyzed the causes of athlete injuries. By exploring more scientific training methods and means, the correlation coefficients between main joint muscle strength, proprioception, tibialis anterior muscle imbalance response time, and rotational stability were measured to study proprioception training suitable for sports dance, adhering to the principle of gradual progression. In future sports dance rotation training, corresponding training should be carried out according to the characteristics of different rotation steps, providing reference for strengthening leg training and ankle training.

Do We Really Believe That “More Is Better”? Mapping Implicit and Explicit Associations Between Quantity and Quality

ABSTRACTThe dimensions of quantity and quality play a crucial role in shaping our judgments and decisions. How these dimensions are perceived in relation to each other is of extreme importance when it comes to those decisions and judgments. The presumed positive link between them, embodied in the notion that “the more, the better,” is a common thread in decision‐making. However, owing to the diverse contexts within which decisions unfold, individuals appear to acquire the understanding that such relationship is not universally applicable, leading to profess the belief that “quantity is not quality.” This dichotomy establishes a dissociation between the implicit and explicit associations formed regarding the connection between quantity and quality. In two studies, we put this hypothesis to the test. Initially, we explore the nature of this association through an Implicit Association Test (Study 1), followed by an investigation into the modulation of this association within an ecological context (Study 2). The results show that, explicitly, participants assert no inherent relationship between quantity and quality. However, at an implicit level, with consequential impacts on behavior, a robust positive association between quantity and quality persists, providing challenging to overturn.

SML: A Skeleton-based multi-feature learning method for sign language recognition

Sign language recognition (SLR) is an effective solution to communication barriers experienced by hearing and vocally impaired individuals with other communities. Its applications extend to human–robot interaction (HRI), virtual reality (VR), and augmented reality (AR). However, the diverse nature of sign languages, stemming from varying user habits and geographical regions, poses significant challenges. To address these challenges, we propose the Skeleton-based Multi-feature Learning method (SML). This method comprises a Multi Feature Aggregation (MFA) module, designed to capture the inherent relationships between different skeleton-based features, enabling effective fusion of complementary information. Furthermore, we propose the Self knowledge distillation Guided Adaptive Residual Decoupled Graph Convolutional Network (SGAR-DGCN) for feature extraction. SGAR-DGCN consists of three components: a Self Knowledge Distillation (SKD) mechanism to enhance model training, convergence, and accuracy; a DGCN-Block, incorporating Decoupled GCN and Spatio Temporal Channel attention (STC) for efficient feature extraction; and an Adaptive Residual Block (ARes-Block) for cross-layer information fusion. Experimental results demonstrate that our SML method outperforms state-of-the-art approaches on the WLASL (55.85%) and AUTSL (96.85%) datasets, solely utilizing skeleton data. Code is available at https://github.com/DzwFine37/SML.

HTNet for micro-expression recognition

Facial expression is related to facial muscle contractions and different muscle movements correspond to different emotional states. For micro-expression recognition, the muscle movements are usually subtle, which has a negative impact on the performance of current facial emotion recognition algorithms. Most existing methods use self-attention mechanisms to capture relationships between tokens in a sequence, but they do not take into account the inherent spatial relationships between facial landmarks. This can result in sub-optimal performance on micro-expression recognition tasks. Therefore, learning to recognize facial muscle movements is a key challenge in the area of micro-expression recognition. In this paper, we propose a Hierarchical Transformer Network (HTNet) to identify critical areas of facial muscle movement. HTNet includes two major components: a transformer layer that leverages the local temporal features and an aggregation layer that extracts local and global semantical facial features. Specifically, HTNet divides the face into four different facial areas: left lip area, left eye area, right eye area and right lip area. The transformer layer is used to focus on representing local minor muscle movement with local self-attention in each area. The aggregation layer is used to learn the interactions between eye areas and lip areas. The experiments on four publicly available micro-expression datasets show that the proposed approach outperforms previous methods by a large margin. The codes and models are available at: https://github.com/wangzhifengharrison/HTNet.

The Relation between Soil Moisture Phase Transitions and Soil Pore Structure under Freeze–Thaw Cycling

The process of soil moisture phase transitions (SMPT) under freeze–thaw cycling is considered a key factor driving changes in soil pore structure. However, there is still no consensus on which indicators related to SMPT affect the soil pore structure. The objectives of this study were to compare SMPT and soil pore characteristics under freeze–thaw cycling, and to analyze the inherent relationship between them as affected by different bulk densities. Hence, we employed thermal pulse time-domain reflection technology (T-TDR) and X-ray CT scanning technology (X-CT) to quantitatively study the process of SMPT and pore characteristics of soil core samples (60 mm diameter, 100 mm height) repacked with three different bulk density levels: 1.10 g·cm−3 (NC), 1.30 g·cm−3 (LC) and their combination (1.10 g·cm−3 for upper half, 1.30 g·cm−3 for lower half, SC) under freeze–thaw cycling. Our results showed that compared with NC, the porosity of LC’s 0–5 cm soil column decreased by 0.070 cm3·cm−3, the imaged porosity (ϕ>60μm) decreased by 0.034 cm3·cm−3, and the maximum soil ice content (MIC) decreased by 0.030 cm3·cm−3. The pores within the range of 200−300 mm (ϕ2) and 300–400 mm (ϕ3) contribute the most significantly to ϕ>60μm (50–60%). Soil initial moisture content (IMC) and MIC explained 50.1% of the change in ϕ2, and the bulk density explained 49.3% of the change in ϕ3. During the melting process, higher moisture content promotes the thaw collapse of soil particles, resulting in a decrease in ϕ>60μm. The mean pore radius of the limiting layer (MRLL) and the hydraulic radius (HR) show that changes in bulk density from 1.10 g·cm−3 to 1.30 g·cm−3 do not have significant differences. Our results show the relationship between SMPT and pore structure change during freeze–thaw cycles as affected by initial soil bulk density and moisture condition.

Tunable magnetic synapse for reliable neuromorphic computing

Artificial neural networks (ANNs), inspired by the structure and function of the human brain, have achieved remarkable success in various fields. However, ANNs implemented using conventional complementary metal oxide semiconductor technology face significant limitations. This has prompted exploration of nonvolatile memory technologies as potential solutions to overcome these limitations by integrating storage and computation within a single device. These emerging technologies can retain resistance values without power, allowing them to serve as analog weights in ANNs, mimicking the behavior of biological synapses. While promising, these nonvolatile devices often exhibit inherent nonlinear relationships between resistance and applied voltage, complicating training processes and potentially impacting learning accuracy. This article proposes a magnetic synapse device based on the spin–orbit torque effect with geometrically controlled linear and nonlinear response characteristics. The device consists of a magnetic multilayer stack patterned into a designed shape, where the width variation along the current flow direction allows for controllable magnetic domain wall propagation. Through finite element method simulations and experimental studies, we demonstrate that by engineering the device geometry, a linear relationship between the applied current and the resulting Hall resistance can be achieved, mimicking the desired linear weight-input behavior in artificial neural networks. Additionally, this study explores the influence of current pulse width on the response curves, revealing a deviation from linearity at longer pulse durations. The geometric tunability of the magnetic synapse device offers a promising approach for realizing reliable and energy-efficient neuromorphic computing architectures.

Advancement in transformer fault diagnosis technology

The transformer plays a critical role in maintaining the stability and smooth operation of the entire power system, particularly in power transmission and distribution. The paper begins by providing an overview of traditional fault diagnosis methods for transformers, including dissolved gas analysis and vibration analysis techniques, elucidating their developmental trajectory. Building upon these traditional methods, numerous researchers have aimed to enhance and optimize them through intelligent technologies such as neural networks, machine learning, and support vector machines. These researchers have addressed common issues in traditional fault diagnosis methods, such as the low correlation between characteristic parameters and faults, ambiguous fault descriptions, and the complexity of feature analysis. However, due to the complexity of transformer structures and the uncertainties in operating environments, the collection and analysis of characteristic parameters becomes highly intricate. Researchers have further refined algorithms and feature values based on intelligent diagnostic algorithms for transformers. The goal is to improve diagnostic speed, mitigate the impact of measurement noise, and further advance the adaptability of artificial intelligence technology in the field of transformers. On the other hand, the excellent multi-parameter analysis capability of artificial intelligence technology is more suitable for transformer diagnostic techniques that involve the fusion of multiple information sources. Through the powerful data acquisition, processing, and decision-making capabilities provided by intelligent algorithms, it can comprehensively analyze non-electrical parameters such as oil and gas characteristics, vibration signals, temperature, along with electrical parameters like short-circuit reactance and load ratio. Moreover, it can automatically analyze the inherent relationship between faults and characteristic quantities and provide decision-making suggestions. This technique plays a pivotal role in ensuring transformer safety and power network security, emerging as a prominent direction in transformer fault diagnosis research.

Nonlinear Optimal Control Based on FBDEs and its Application to AGV.

This article focuses on solving a finite-horizon nonlinear optimal control problem by using the Pontryagin's maximum principle. In practical applications, linearization is a common approach for solving nonlinear dynamical systems. However, it is not universally applicable due to various reasons, such as instability and low accuracy. In contrast to linearization, the inherent challenge in directly solving the above nonlinear optimal control problem lies in addressing the highly coupled nonlinear forward and backward differential equations. In order to address this problem, an equivalent relationship is established between these equations and a new optimization problem. By exploiting the inherent relationship between supervised learning and an optimization problem from the view of a dynamical system, a deep neural network framework is constructed for describing the new optimization problem. Furthermore, a numerical algorithm for optimal control, which is very powerful for a large variety of nonlinear dynamical systems, is implemented by training a deep residual network. Finally, the effectiveness of the algorithm is demonstrated by solving a trajectory tracking control problem for automatic guided vehicle. The obtained results reveal that the proposed control scheme can achieve high-precision tracking.

Energy band engineering of graphitic carbon nitride for photocatalytic hydrogen peroxide production

AbstractHydrogen peroxide (H2O2) is one of the 100 most important chemicals in the world with high energy density and environmental friendliness. Compared with anthraquinone oxidation, direct synthesis of H2O2 with hydrogen (H2) and oxygen (O2), and electrochemical methods, photocatalysis has the characteristics of low energy consumption, easy operation and less pollution, and broad application prospects in H2O2 generation. Various photocatalysts, such as titanium dioxide (TiO2), graphitic carbon nitride (g‐C3N4), metal‐organic materials, and nonmetallic materials, have been studied for H2O2 production. Among them, g‐C3N4 materials, which are simple to synthesize and functionalize, have attracted wide attention. The electronic band structure of g‐C3N4 shows a bandgap of 2.77 eV, a valence band maximum of 1.44 V, and a conduction band minimum of −1.33 V, which theoretically meets the requirements for hydrogen peroxide production. In comparison to semiconductor materials like TiO2 (3.2 eV), this material has a smaller bandgap, which results in a more efficient response to visible light. However, the photocatalytic activity of g‐C3N4 and the yield of H2O2 were severely inhibited by the electron‐hole pair with high recombination rate, low utilization rate of visible light, and poor selectivity of products. Although previous reviews also presented various strategies to improve photocatalytic H2O2 production, they did not systematically elaborate the inherent relationship between the control strategies and their energy band structure. From this point of view, this article focuses on energy band engineering and reviews the latest research progress of g‐C3N4 photocatalytic H2O2 production. On this basis, a strategy to improve the H2O2 production by g‐C3N4 photocatalysis is proposed through morphology control, crystallinity and defect, and doping, combined with other materials and other strategies. Finally, the challenges and prospects of industrialization of g‐C3N4 photocatalytic H2O2 production are discussed and envisioned.

How Does Air Pollution Impact Residence Intention of Rural Migrants? Empirical Evidence from the CMDS

Based on data from the China Migrant Dynamic Survey project and urban statistics, this article examines the impact of air pollution on the residence intentions of rural migrants. The research findings indicate that: (1) Air pollution reduces the residence intentions of rural migrants. On average, for every one-unit increase in AQI, the residence intention of rural migrants will reduce by 1.5l%. (2) Mechanism analysis shows that social networks and social integration have a negative moderating effect on the relationship between air pollution and the residence intention of rural migrants. (3) Heterogeneity analysis shows that in cities north of the Qinling Mountains-Huaihe River, cities with low precipitation, and cities with weak environmental regulations, the negative impact of air pollution on residence intention of rural migrants is more significant. Compared with high human capital levels, inter-provincial flow, and the new generation of rural migrants, the residence intention of low human capital levels, intra-provincial flow, and the old generation of rural migrants makes them more vulnerable to the negative impact of air pollution. This article reveals the inherent relationship between air pollution and the residence intention of rural migrants, which has certain practical enlightenment for cities to accelerate the process of citizenization of rural migrants through air pollution control and also provides important empirical evidence for cities to sustainably address air pollution.

Complex system anomaly detection via learnable temporal-spatial graph with degradation tendency segmentation

To guarantee the safety and reliability of equipment operation, such as liquid rocket engine (LRE), carrying out system-level anomaly detection (AD) is crucial. However, current methods ignore the prior knowledge of mechanical system itself, and seldom unite the observations with the inherent relation in data tightly. Meanwhile, they neglect the weakness and nonindependence of system-level anomaly which is different from component fault. To overcome above limitations, we propose a separate reconstruction framework using worsened tendency for system-level AD. To prevent anomalous feature being attenuated, we first propose to divide single sample into two equal-length parts along the temporal dimension. And we maximize the mean maximum discrepancy (MMD) between feature segments to force encoders to learn normal features with different distributions. Then, to fully explore the multivariate time series, we model temporal-spatial dependence by temporal convolution and graph attention. Besides, a joint graph learning strategy is proposed to handle prior knowledge and data characteristics simultaneously. Finally, the proposed method is evaluated on two real multi-sensor datasets from LRE and the results demonstrate the effectiveness and potential of the proposed method on system-level AD.

Research on Public Policies to Promote the Organic Connection Between Rural Revitalization and Poverty Alleviation

Poverty alleviation is foundational to maintaining Chinas comprehensive progress towards a moderately prosperous society. Rural revitalization is a crucial measure to further realize the modernization of agriculture and rural areas. The organic connection between these two is a focal issue in the development process of China's agricultural and rural areas, playing a strategic support role in achieving Chinas second centennial goal. Based on this, this paper focuses on the organic connection between poverty alleviation and rural revitalization in the new era. By summarizing the basic background and overall connotations of both, and employing a research methodology that integrates theory with practice, this study explores the achievements and inherent logical relations of poverty alleviation and rural revitalization at this stage, as well as the strategic significance of their integration in various aspects. With the shift of strategic focus to the "three rural issues," promoting the organic connection between poverty alleviation and rural revitalization may face challenges from group coverage, spatial scope, work objectives, task deadlines, and policy allocation in the future. Therefore, this paper further proposes feasible suggestions for the implementation of governance policies that promote the organic connection between poverty alleviation and rural revitalization from the perspectives of people, society, organizations, institutions, and development.

Effect of LiF and LBSCA glass on the microwave dielectric properties of 0.5BaCuSi4O10–0.5BaCuSi2O6‐based ceramics for LTCC applications

AbstractIn this work, the 0.5BaCuSi4O10–0.5BaCuSi2O6‐based ceramics were synthesized using a standard solid‐phase reaction process, and the inherent relationship between crystal structure and microwave dielectric properties was thoroughly explored. The crystal structure of the 0.5BaCuSi4O10–0.5BaCuSi2O6 ceramic was determined by X‐ray diffractometer, which confirmed the existence of two phases. Microstructure observation of the ceramics was obtained by scanning electron microscope (SEM). Excellent microwave dielectric properties with a low ԑr ∼6.52, a high Q × f ∼46 010 GHz, and the temperature coefficient of resonant frequency (TCF) ∼−14 ppm°C−1 were obtained in the 0.5BaCuSi4O10–0.5BaCuSi2O6 ceramic sintered at 1060°C. By adding sintering additives LiF and Li2O–B2O3–SiO2–CaO–Al2O3 (LBSCA) glass, the sintering temperature was decreased from 1060°C to 870°C. Good microwave dielectric properties with a ԑr ∼6.59, a Q × f ∼18 820 GHz, and TCF ∼−14 ppm°C−1 were achieved in the 0.5BaCuSi4O10–0.5BaCuSi2O6 ‐2 wt.% LiF, 1 wt.% LBSCA (2F1LBSCA) ceramic sintered at 870°C. The low‐temperature firing ceramics could also be well co‐fired with Ag with good chemical compatibility. These results indicated that the 0.5BaCuSi4O10–0.5BaCuSi2O6 ceramic with 2F1LBSCA additions can be utilized in low‐temperature co‐fired ceramics technology to produce high‐frequency communication components.