Network Structure Research Articles

Lightweight Bearing Fault Diagnosis Method Based on Improved Residual Network

A lightweight bearing fault detection approach based on an improved residual network is presented to solve the shortcomings of previous fault diagnostic methods, such as inadequate feature extraction and an excessive computational cost due to high model complexity. First, the raw data are turned into a time–frequency map using the continuous wavelet transform, which captures all of the signal’s time- and frequency-domain properties. Second, an improved residual network model was built, which incorporates the criss-cross attention mechanism and depth-separable convolution into the residual network structure to realize the important distinction of the extracted features and reduce computational resources while ensuring diagnostic accuracy; simultaneously, the Meta-Acon activation function was introduced to improve the network’s self-adaptive characterization ability. The study findings indicate that the suggested approach had a 99.95% accuracy rate and a floating point computational complexity of 0.53 GF. Compared with other networks, it had greater fault detection accuracy and stronger generalization ability, and it could perform high-precision fault diagnostic jobs due to its lower complexity.

Rewiring cattle movements to limit infection spread

Cattle tracing databases have become major resources for representing demographic processes of livestock and assessing potential risk of infections spreading by trade. The herds registered in these databases are nodes of a network of commercial movements, which can be altered to lower the risk of disease transmission. In this study, we develop an algorithm aimed at reducing the number of infected animals and herds, by rewiring specific movements responsible for trade flows from high- to low-prevalence herds. The algorithm is coupled with a generic computational model based on the French cattle movement tracing database (BDNI), and used to describe different scenarios for the spread of infection within and between herds from a recent outbreak (epidemic) or a five-year-old outbreak (endemic). Results show that rewiring successfully contains infections to a limited number of herds, especially if the outbreak is recent and the estimation of disease prevalence frequent, while the respective impact of the parameters of the algorithm depend on the infection parameters. Allowing any animal movement from high to low-prevalence herds reduces the effectiveness of the algorithm in epidemic settings, while frequent and fine-grained prevalence assessments improve the impact of the algorithm in endemic settings. Our approach focusing on a few commercial movements is expected to lead to substantial improvements in the control of a targeted disease, although changes in the network structure should be monitored for potential vulnerabilities to other diseases. This general algorithm could be applied to any network of controlled individual movements liable to spread disease.

Intricate DG and EV Planning Impact Assessment with Seasonal Variation in a Three-Phase Distribution System

Modern power systems present opportunities and challenges when integrating distributed generation and electric vehicle charging stations into unbalanced distribution networks. The performance and efficiency of both Distributed Generation (DG) and Electric Vehicle (EV) infrastructure are significantly affected by global temperature variation characteristics, which are taken into consideration in this study as it investigates the effects of these integrations. This scenario is further complicated by the unbalanced structure of distribution networks, which introduces inequalities that can enhance complexity and adverse effects. This paper analyzes the manner in which temperature changes influence the network operational voltage profile, power quality, energy losses, greenhouse harmful emissions, cost factor, and active and reactive power losses using analytical and heuristic techniques in the IEEE 69 bus network in both three-phase balance and modified unbalanced load conditions. In order to maximize adaptability and efficiency while minimizing the adverse impacts on the unbalanced distribution system, the findings demonstrate significant variables to take into account while locating the optimal location and size of DG and EV charging stations. To figure out the objective, three-phase distribution load flow is utilized by the particle swarm optimization technique. Greenhouse gas emissions dropped by 61.4%, 64.5%, and 60.98% in each of the three temperature case circumstances, while in the modified unbalanced condition, they dropped by 57.55%, 60.39%, and 62.79%. In balanced conditions, energy loss costs are reduced by 95.96%, 96.01%, and 96.05%, but in unbalanced conditions, they are reduced by 91.79%, 92.06%, and 92.46%. The outcomes provide valuable facts that electricity companies, decision-makers, along with other energy sector stakeholders may utilize to formulate strategies that adapt to the fluctuating patterns of electricity distribution during fluctuations in global temperature under balanced and unbalanced conditions of network.

Vulnerability Analysis of a Multilayer Logistics Network against Cascading Failure

One of the most challenging issues in contemporary complex network research is to understand the structure and vulnerability of multilayer networks, even though cascading failures in single networks have been widely studied in recent years. The goal of this work is to compare the similarities and differences between four single layers and understand the implications of interdependencies among cities on the overall vulnerability of a multilayer global logistics network. In this paper, a global logistics network model set as a multilayer network considering cascading failures is proposed in different disruption scenarios. Two types of attack strategies—a highest load attack and a lowest load attack—are used to evaluate the vulnerability of the global logistics network and to further analyze the changes in the topology properties. For a multilayer network, the vulnerability of single layers is compared as well. The results suggest that compared with the results of a single global logistics network, a multilayer network has a higher vulnerability. In addition, the heterogeneity of networks plays an important role in the vulnerability of a multilayer network against targeted attacks. Protecting the most important nodes is critical to safeguard the potential “vulnerability” in the development of the global logistics network. The three-step response strategy of “Prewarning–Response–Postrepair” is the main pathway to improving the adjustment ability and adaptability of logistics hub cities in response to external shocks. These findings supplement and extend the previous attack results on nodes and can thus help us better explain the vulnerability of different networks and provide insight into more tolerant, real, complex system designs.

Thermophysical Investigation of Multiform NiO Nanowalls@carbon Foam/1-Octadecanol Composite Phase Change Materials for Thermal Management.

Multiform NiO nanowalls with a high specific surface area were constructed in situ on carbon foam (CF) to construct NiO@CF/OD composite phase change materials (CPCMs). The synthesis mechanism, microstructures, thermal management capability, and photothermal conversion of NiO@CF/OD CPCMs were systematically studied. Additionally, the collaborative enhancement effects of CF and multiform NiO nanowalls on the thermal properties of OD PCMs were also investigated. NiO@CF not only maintains the porous 3D network structure of CF, but also effectively prevents the aggregation of NiO nanosheets. The chemical structures of NiO@CF/OD CPCMs were analyzed using XRD and FTIR spectroscopy. When combined with CF and NiO nanosheets, OD has high compatibility with NiO@CF. The thermal conductivity of NiO@CF/OD-L CPCMs was 1.12 W/m·K, which is 366.7% higher than that of OD. The improvement in thermal conductivity of CPCMs was theoretically analyzed according to the Debye model. NiO@CF/OD-L CPCMs have a photothermal conversion efficiency up to 77.6%. This article provided a theoretical basis for the optimal design and performance prediction of thermal storage materials and systems.

Three-Dimensional Nanoscale Imaging of SiO2 Nanofiller in Styrene-Butadiene Rubber with High-Resolution and High-Sensitivity Ptychographic X-ray Computed Tomography

Abstract SiO2 aggregates in styrene-butadiene rubber (SBR) were observed using ptychographic X-ray computed tomography (PXCT). The rubber composites were illuminated with X-rays focused by total reflection focusing mirrors, and the ptychographic diffraction patterns were collected using a CITIUS detector in the range of −75° to +75° angle of incidence. The projection images of the rubber composites were reconstructed with a two-dimensional resolution of 76 nm, and no significant structural changes were observed during the PXCT measurements. A three-dimensional image of the rubber composite was reconstructed with an isotropic resolution of 98 nm. Segmentation of SiO2 from the SBR, based on a histogram analysis of the phase shift, revealed a fragmented network structure of interconnected SiO2 aggregates.

Mechanical properties and micro-mechanism of seawater cementitious materials reinforced by in-situ polymerization

Understanding the potential mechanism of in-situ polymerization of acrylamide (AM) for modifying seawater cementitious materials is crucial for designing high-strength and durable marine concrete. Herein, the acrylamide (AM) in-situ polymerization was investigated for its effects on the hydration behavior, micro-morphology, and pore structure of cementitious materials mixed with seawater and freshwater through a series of elaborately designed microscopic characterization methods. The results reveal that the hydration process of cementitious materials proceeds simultaneously with in-situ polymerization. However, compared with freshwater mixtures, seawater provides a large number of metal ions and SO42- ions, which can cross-link with the generated polyacrylamide (PAM) during in-situ polymerization to form a three-dimensional network structure. The synergistic effect of the hydration, in-situ polymerization, and cross-linking processes of cementitious materials can improve the pore structure of seawater-mixed paste, enhance erosion resistance, and improve the stability and toughness of microstructure. These findings were further confirmed by comparing infrared spectroscopy results, hydration products, pore size, and micro-morphology analysis as well as flexural performance tests. This is of great significance to guide the design of novel materials in marine infrastructure.

Digital strategic collaborations in agriculture: a novel asset for local identity enhancement toward Agrifood 5.0

PurposeThe aim of this research is to elucidate the correlation between open innovation, digital strategies and networking in enhancing agricultural enterprises within the new perspective of Agrifood 5.0. As such, it contributes to making businesses more competitive, especially in the Italian agricultural sector, where small and medium-sized enterprises are highly fragmented. Numerous studies have asserted that the competitiveness of actors operating within a specific territory is closely linked to local identity and image enhancement. Agricultural organizations are undergoing a profound transformation, with technological assets emerging as catalysts for new synergies. Advanced technologies such as robotics, the Internet of Things (IoT) and automation (AI) are emerging as differentiating elements capable of further advancing the agricultural sector, transitioning it from Agrifood 4.0 to Agrifood 5.0. The empirical analysis of the research shows a positive correlation between a collaborative attitude and a propensity for innovation. Indeed, the data demonstrated that digital strategies and open innovation positively influence competitiveness in agricultural SMEs.Design/methodology/approachThe methodology employed in this study is mixed, incorporating both qualitative and quantitative approaches. The quantitative aspect involves analysis of the dataset from the Italian Statistical Institute (ISTAT) through logistic regression, while the qualitative component entails analysis of semi-structured interviews conducted with a sample of 174 agricultural cooperatives in southern Italian regions (Campania). This approach allows for a comprehensive understanding of the research topic, capturing both numerical trends and nuanced insights from interviews.FindingsAfter analyzing the data from the 7th General Census of Agriculture conducted by ISTAT, a clear understanding of the sector has emerged, revealing several potential research avenues. It is evident that innovation in the agricultural sector is often driven by the largest and best-capitalized production entities, primarily located in Italy. Conversely, smaller agricultural entities can benefit from networking as new technological assets act as catalysts for new synergies, innovation and competitiveness.Practical implicationsEnhancing the relational contribution within the network and humanizing a fragmented sector are crucial elements for promoting open innovation. Network structuring facilitates the transmission of managerial knowledge, contributing to an overall increase in the intellectual and relational capital of the agricultural sector. These factors, combined with open innovation, enhance the competitiveness of individual firms and elevate the brand of the entire sector, creating a conducive environment for transitioning toward Agrifood 5.0. This transition is characterized by increased interconnection, continuous innovation and overall prosperity. Specific studies on this topic are lacking in Italy, particularly in the southern regions. Therefore, this contribution focuses on investigating the Campania region.Originality/valueThe novelty of this study lies in its investigation of the relationship between agricultural enterprises and innovation in the context of enterprises networking strategies (i.e. associationism and/or cooperation), promoting competitiveness. The limitations of this study are related to the dimension of the sample selected and its relationship with other productive sectors.

Automatic Architecture Design for Distributed Quantum Computing

Abstract In distributed quantum computing (DQC), quantum hardware design mainly focuses on providing as many as possible high-quality inter-chip connections. Meanwhile, quantum software tries best to reduce the required number of remote quantum gates between chips. However, this "hardware first, software follows" methodology may not fully exploit the potential of DQC. Inspired by classical software-hardware co-design, this paper explores the design space of application-specific DQC architectures. More specifically, we propose AutoArch, an automated quantum chip network (QCN) structure design tool. With qubits grouping followed by a customized QCN design, AutoArch can generate a near-optimal DQC architecture suitable for target quantum algorithms. Experimental results show that the DQC architecture generated by AutoArch can outperform other general QCN architectures when executing target quantum algorithms.

Spatiotemporal interactions between jaguars (Panthera onca) and their potential prey in Amazonian islands

AbstractAlthough large carnivores usually prefer large prey, in some situations, they may shift their predation patterns towards smaller but abundant prey. The jaguar (Panthera onca) is a large carnivore capable of changing its diet according to prey and habitat availability. Here, we assessed the temporal and spatiotemporal interactions between jaguars and their prey in the Maracá‐Jipioca Islands (Northeastern Amazon, Brazil) through camera traps. We assessed overlapping activity patterns and tested for spatiotemporal segregation/avoidance between jaguars and nine potential prey species. We used a time‐to‐encounter approach, which consists in calculating the minimum time between prey and jaguar's detections, and vice versa, for each record of preys' species at a specific camera trap station, which translates into aggregation or avoidance behaviors. We found that these insular jaguars are more active in daylight periods when most of their prey are active and in locations used by species that cannot become nocturnal to avoid predators due to morphology constraints. Four prey species (great egret, white‐tailed deer, muscovy duck, and black‐and‐white tegu) presented moderate activity overlapping with jaguars. Agoutis and white‐tailed deer seek to spatiotemporally segregate from jaguars, although jaguars did not show spatiotemporal aggregation with any of the evaluated prey. Understanding the spatiotemporal dynamics is essential to establish the islands' trophic network composition and structure. This is fundamental information to efficiently allocate efforts for reducing costs and maximizing benefits in managing this population aiming to protect and conserve it, and consequently, the related ecosystems.

The Mechanism of Bacterial Endotoxin Invasion Pathways in Porcine Reproductive and Respiratory Syndrome Virus-Positive Porcine Endometrial Epithelial Cells

Porcine reproductive and respiratory syndrome virus (PRRSV) causes abortions, stillbirths, and dummy pregnancies. Previous studies found that PRRSV can promote secondary bacterial infections and elevate bacterial endotoxin levels, further increasing the abortion rate in sows. However, the pathways by which bacterial endotoxins invade the bodies of PRRSV(+) sows and aggravate their clinical symptoms are unknown. In this study, we established a model of PRRSV and lipopolysaccharide (LPS) working together on porcine endometrial epithelial cells (PEECs). We speculate that PRRSV and LPS affect PEECs through viral protein interaction with cytokines and cytokine receptors, natural killer cell-mediated cytotoxicity, and regulation of actin cytoskeleton signaling pathways by analyzing seq-RNA. The PRRSV proteins act on inflammatory factors and their receptors to activate chemokines-5 (CCL5), chemokines-4 (CCL4), and chemokines-8 (CCL8) mRNA expression, causing severe inflammatory reactions. In addition, the elevation of MEK1/2 factors and the integrins acting on NK cells promote the upregulation of VAV1/Tiam1, RAC, and IRSp53, leading to increased expression of Arp2/3 and F-actin in PEECs in the PRRSV + LPS(+) groups. However, the highly expressed cell microfilaments and cytoskeleton disrupt the original network structure, causing changes in the original physiological function of the PEECs. In summary, the PRRSV protein interacts with cytokines and cytokine receptors of PEECs, thereby enhancing virus-mediated chemokine factors and their receptor activity, accelerating bacterial endotoxin entry into the body and the invasion of cells. They destroy the cytoskeletal structure of the cells and increase damage to uterine tissue.

Feature radiance fields (FeRF): A multi-level feature fusion method with deep neural network for image synthesis

Neural Radiance Field (NeRF) has brought revolutionary changes to the field of image synthesis with its unique ability to generate highly realistic multi-view consistent images from a neural scene representation. However, current NeRF-based methods still largely depend on multiple, precisely posed images, especially for complex or dynamic scenes, limiting their versatility. Furthermore, some recent strategies attempt to integrate simple feature extraction networks with volume rendering techniques to reduce multi-view dependence but create blurry outputs, highlighting the need for more sophisticated feature handling to unlock NeRF's full potential. In this paper, we propose an image synthesis method named FeRF, distinguished by its capacity to perform comprehensive feature extraction on individual unposed images and facilitate feature fusion at any stage. Additionally, we present an "elaborated-feature generation network" (EGN) composed of four modules, which is configured with two advanced feature extraction modules aimed at precisely refining and processing subtle, complex visual features from a single image. Given that the core objective of FeRF is the precise capture and processing of intricate features from the input images, we innovatively incorporated precisely designed attention mechanisms into the network architecture to optimize and highlight the importance of key feature attributes, thereby effectively enhancing their contribution to subsequent volume rendering processes. Extensive experimentation validates the outstanding qualitative and quantitative performance of our proposed network structure. In comparison to current image feature-based generalized image synthesis methods, it achieves superior reconstruction quality and level of detail.

Social Network Density Mediates the Association Between Problematic Social Media Use and Depressive Symptoms

Previous studies have indicated that problematic social media use (PSMU) is positively related to depressive symptoms. However, the potential mechanisms underlying this relationship are not well understood. For example, prior research found that depressive symptoms are related to one’s social network structure, such as network size and density. Therefore, we examined whether one’s social network size and density mediate the relationship between PSMU and depressive symptoms. We conducted an in-person survey to collect measures of PSMU, social network size and density, and depressive symptoms. Our analysis showed that there was a positive relationship between PSMU and depressive symptoms, which was mediated by social network density but not size. Specifically, the greater one’s PSMU, the less dense their social network, and the greater their depressive symptoms. Our research suggests that PSMU may affect how individuals maintain their social connections with others, which may affect mental health. Implications were discussed.

Metal-organic framework-derived hierarchical Ag/Ti3C2Tx/Co/C nanocomposite for enhanced electromagnetic wave absorption

The microwave absorption (MA) performance of 2D Ti3C2Tx is significantly restricted due to its lack of magnetic loss capacity and mismatched electromagnetic impedance. The Ag/Ti3C2Tx/Co/C (ATCC) structure, based on a unique 2D layered Ti3C2Tx, has shown exceptional electromagnetic wave absorption (EWA) properties. The addition of a silver nanoparticles enhances the polarization effect and conductance loss of Ti3C2Tx. By incorporating ZIF-67 with varying Co content, nanocomposite heterostructures and interfaces are formed in Ag/Ti3C2Tx. Moreover, the carbon source of organic ligand and the 3D network structure of Ti3C2Tx provide additional electronic transition channels and improve conductive loss. This modification results in non-uniform interfaces between Ti3C2Tx and metal nanoparticles, leading to electron accumulation and the formation of a micro-capacitance structure that generates interface polarization. Eddy current loss and magnetic loss from metal nanoparticles further enhance EWA properties. This study envisions a novel strategy for developing efficient Ti3C2Tx-based microwave absorbers modified with metal nanoparticles.

Advancing low‐light object detection with you only look once models: An empirical study and performance evaluation

AbstractLow‐light object detection is needed for ensuring security, enabling surveillance, and enhancing safety in diverse applications, including autonomous vehicles, surveillance systems, and search and rescue operations. A comprehensive study on low‐light object detection is presented using state‐of‐the‐art you only look once (YOLO) models, including YOLOv3, YOLOv5, YOLOv6, and YOLOv8, aiming to enhance detection performance under challenging low‐light conditions. The ExDark dataset is a dataset that consists of adequate low‐light images, modified to simulate realistic low‐light scenarios, and employed for evaluation. The deep learning algorithm optimises YOLO's architecture for low‐light detection by adapting the network structure and training strategies while preserving the algorithm's integrity. The experimental results show that YOLOv8 consistently outperforms baseline models, achieving significant improvements in accuracy and robustness in low‐light scenarios. The deep learning algorithm that acquired the best score, YOLOv8s, had a mean average precision score of 0.5513. This work contributes to the field of low‐light object detection, offering promising solutions for real‐world applications like nighttime surveillance and autonomous navigation in low‐light conditions, addressing the growing demand for advanced low‐light object detection.

Modulating the Li‐Ion Transport Pathway of Succinonitrile‐Based Plastic Crystalline Electrolytes for Solid‐State Lithium Metal Batteries

AbstractSuccinonitrile (SCN) based plastic crystal electrolytes (SPCEs) have attracted much attention for lithium metal batteries due to their considerable ionic conductivity and thermal stability. Insufficient mechanical properties, weak reductive stability, and the presence of free SCN molecules can result in adverse interfacial reactions. Polymer introduction has been explored to address these challenges. However, the introduction of polymer affects the SCN state, leading to reduced ionic conductivity, potentially due to limited segmental motion of the polymer at room temperature. Herein, a cross‐linked network polymer strategy is proposed to modify the Li‐ion transport pathway in SPCE, aiming to significantly improve the ionic conductivity. The strong interaction between the polymer matrix and SCN enhances their mutual solubility, reduces the crystallinity of SCN, and forms a rapid conduction pathway (polymer—[SCN—Li+]). The ionic conductivity of SPCE increases to 1.28 mS cm−1, with the Li‐ion migration number (tLi+ ) also rising to 0.7. Electrochemical performances in Li symmetrical, Li||LiFePO4 and Li||LiNi0.8Co0.1Mn0.1O2 cells show significant improvement at both room temperature and 0 °C. These findings suggest that designing polymer network structures in SPCEs holds promise for solid‐state lithium metal battery applications.

Bacterial specialists playing crucial roles in maintaining system stability and governing microbial diversity in bioremediation of oil-polluted sediments under typical deep-sea condition

Ecologically, interactions and contributions of microbiota generalists and specialists remain largely unexplored in remediation of deep-sea oil pollution. Herein, ecological and evolutionary characteristics of the two taxa were comprehensively investigated in restoration of oil-polluted sediment at deep-sea microcosm. Niche-specialized taxa exhibited rapid speciation rate, more complex network structure and highly interspecific mutualism. In contrast, generalists possessed higher richness but with poor local performance, as evidenced by higher extinction rate, lower stability, and more interspecific antagonism. Generalists were the primary oil degraders, while specialists acted as auxiliaries promoting degradation via production of biofilm and biosurfactant. Evolutionarily, the continuous transition from specialists to generalists insured the exclusion of generalist at a relatively constant level for ecological trade-offs. Collectively, the findings emphasize the importance of specialists in facilitating oil degradation by elucidating their vital roles in maintaining system stability and regulating microbial diversity during process, and offer valuable guidance for designing remediation plans.

Urban Internal Network Structure and Resilience Characteristics from the Perspective of Population Mobility: A Case Study of Nanjing, China

In the face of diverse chronic pressures and increased factor mobility, the resilience of urban internal network structures has become a cutting-edge research topic. This study utilizes 2019 mobile signaling big data to construct employment and recreational flow networks among 101 townships and streets within Nanjing City. Based on the characteristics of these network structures, the resilience of the network structure is measured from the perspectives of density, symmetry, and transmissibility through interruption simulation techniques. The results show that the intensity of population mobility within Nanjing presents a general decay from the central urban area to the outer layers. In the employment scenario, cross-river population mobility is more frequent, while in the recreational scenario, the natural barrier effect of the Yangtze River is prominent. Due to the concentration of employment centers and high spatial heterogeneity, the employment flow network exhibits greater vulnerability to sudden shocks. Townships and streets with weighted degree values ranking around 60 and 80 are of great importance for maintaining the normal operation of both employment and recreational flow networks. Strengthening the construction of resilient parks and village planning within resilient cities can enhance the risk resistance of employment and recreational flow networks.

Green Development of Chinese Agriculture from the Perspective of Bidirectional Correlation

China’s agricultural green development exhibits spatial imbalances. To promote the coordinated green development of agriculture across various regions, this paper explores the evolution of spatial association networks and driving factors of agricultural green development from a bidirectional correlation perspective, using social network analysis and spatial econometric methods. This research indicates that China’s agricultural green efficiency has steadily improved, with a complex multi-threaded network structure. Although the spatial interaction and spillover effects of the overall network structure have increased, they remain relatively weak. The individual network structure shows significant regional imbalances. The spatial association network of agricultural green efficiency forms four major blocks, with sparse connections within the blocks but close connections between blocks, demonstrating strong spillover effects. Regarding the driving factors, the proportion of the primary industry, labor level, and R&D investment have significant spatial impacts, while the spatial impacts of human capital level, degree of openness, economic development level, and new quality productivity level are not significant. Therefore, we believe that it is necessary to establish the concept of coordinated green development in agriculture, fully leverage regional associations and spillover effects, and formulate differentiated policies to improve agricultural green efficiency.

Sn2+/Pb2+ Doping‐Induced Highly Transparent Boroaluminate Microcrystalline Glass With Deep Traps for Long‐Term Optical Storage and Time‐Lapse X‐ray Imaging

AbstractThe development of microcrystalline glass‐ceramics with high transparency and deep trap energy levels is crucial for the cost‐effective and large‐scale production of scintillators and optical data storage applications. In this study, the incorporation of highly electronegative divalent tin (Sn2+) plays a key role in modulating the network structure of borate glass. This leads to the successful synthesis of SrAl2O4:Eu2+ microcrystalline glass with high transparency, reaching up to 80%, and excellent crystallinity. Additionally, a series of non‐optically active ions with different valence states is co‐doped with Eu2+ to fine‐tune the trap levels of the SrAl2O4 microcrystals. All samples maintain high crystallinity and exhibit good transparency. In particular, the Pb2+ ion co‐doped samples achieve an increased trap energy level of 1.28 eV, significantly enhancing their capacity to capture X‐rays and ultraviolet light. Density functional theory calculations reveal that this enhancement is due to severe lattice distortion caused by Pb2+ ions occupying interstitial sites in SrAl2O4. Utilizing the SrAl2O4:Eu2+, Pb2+ glass‐ceramics materials, X‐ray imaging with a delay of up to 210 s and optical information storage for >60 d is achieved. This study provides valuable insights into the crystal growth and trap modulation of persistent luminescent materials within a three‐dimensional glass network structure.