Vulvovaginal candidiasis (VVC) is a common condition that can lead to significant discomfort, affecting approximately 70–75% of women at least once in their lives. During pregnancy, the prevalence of VVC is estimated to be around 20%, peaking at about 30% in the third trimester, with a number of specific risk factors predisposing to yeast infection being identified and needing elucidation. This review aims to provide updated knowledge on candidiasis during pregnancy, addressing risk factors and maternal and neonatal outcomes, as well as discussing optimal therapeutic strategies to safeguard mothers and newborns. The bibliographic search involved two biomedical databases, PubMed and Embase, without imposing time limits. Among all Candida spp., Candida albicans remains the most frequent causative species. The hyperestrogenic environment of the vaginal mucosa and reduced immune defenses, physiological effects of pregnancy, create conditions favorable for Candida spp. vaginal colonization and hence VVC. Recent evidence shows an association between VVC and adverse obstetric outcomes, including premature membrane rupture (PROM), chorioamnionitis, preterm birth, and puerperal infections. Prompt and effective management of this condition is therefore crucial to prevent adverse obstetric outcomes, maternal–fetal transmission, and neonatal disease. Additional studies are required to confirm the benefits of systemic treatment for maternal candida infection or colonization in preventing premature birth or neonatal systemic candidiasis. Full article

The paper presents an analysis of the low-cycle fatigue (LCF) properties of C45, X20Cr13, and 34CrNiMo6 steels subjected to various heat treatment processes. Strain-controlled LCF tests were carried out with a total cyclic strain amplitude equal to 0.5, 1 and 1.5%. Fatigue life, cyclic stress-strain behavior and hardness were analyzed. Qualitative and quantitative relationships between material LCF properties resulting from the heat treatment processes, were related to the indentation force P*, which was derived experimentally by applying an instrumented indentation procedure with the use of the Vickers indenter. The proposed parameter P* and its changes ΔP* seem to be promising for the identification of the structural stress parameter σ* that is necessary for deriving values of the fatigue strength coefficients σ_f’ corresponding to different tempering temperatures. The common feature of all steels analyzed in this paper is that the elastic parts of the strain-life characteristics remain parallel after being subjected to different tempering temperatures. Full article

Collective movement has emerged as a key area of interest in animal behavior. While individual differences are often viewed as a potential threat to group cohesion, growing evidence suggests that these differences can actually influence an animal’s behavior as an initiator or follower during collective movements, thereby driving the group‘s movement and decision-making processes. To resolve the divergence, we asked how personality can affect the dynamics of collective movements in one group of free-ranging Tibetan macaques (Macaca thibetana) in Huangshan, China. We assessed individual personality using principal component analysis and applied the generalized linear mixed model and linear mixed model to examine the influence of personality on decision making during collective movements. Our findings reveled three distinct personality types among Tibetan macaques: sociability, boldness, and anxiousness. Individuals with higher sociability scores and rank, or those with lower anxiousness scores, were more likely to initiate successful collective movements. Older individuals were less successful in initiating movements compared to young adults. Leaders with lower anxiousness scores or higher rank attracted more followers, with females attracting larger groups than males. As for followers, individuals with higher rank tended to join the collective movement earlier. Additionally, individuals with higher sociability or boldness scores had shorter joining latency in collective movement. Finally, there was a longer joining latency for middle-aged adults compared to young adults. These results suggest that individual differences are a potential driver of collective movements. We provide some insights into the relationships between personality and decision making in Tibetan macaques. Full article

Diabetic kidney disease (DKD) is a major microvascular complication of both type 1 and type 2 diabetes. DKD is characterised by injury to both glomerular and tubular compartments, leading to kidney dysfunction over time. It is one of the most common causes of chronic kidney disease (CKD) and end-stage renal disease (ESRD). Persistent high blood glucose levels can damage the small blood vessels in the kidneys, impairing their ability to filter waste and fluids from the blood effectively. Other factors like high blood pressure (hypertension), genetics, and lifestyle habits can also contribute to the development and progression of DKD. The key features of renal complications of diabetes include morphological and functional alterations to renal glomeruli and tubules leading to mesangial expansion, glomerulosclerosis, homogenous thickening of the glomerular basement membrane (GBM), albuminuria, tubulointerstitial fibrosis and progressive decline in renal function. In advanced stages, DKD may require treatments such as dialysis or kidney transplant to sustain life. Therefore, early detection and proactive management of diabetes and its complications are crucial in preventing DKD and preserving kidney function. Full article

Patient blood samples are invaluable in clinical omics databases, yet current methodologies often fail to fully uncover the molecular mechanisms driving patient pathology. While genome-scale metabolic models (GEMs) show promise in systems medicine by integrating various omics data, having only exometabolomic data remains a limiting factor. To address this gap, we introduce a comprehensive pipeline integrating GEMs with patient plasma metabolome. This pipeline constructs case-specific GEMs using literature-based and patient-specific metabolomic data. Novel computational methods, including adaptive sampling and an in-house developed algorithm for the rational exploration of the sampled space of solutions, enhance integration accuracy while improving computational performance. Model characterization involves task analysis in combination with clustering methods to identify critical cellular functions. The new pipeline was applied to a cohort of trauma patients to investigate shock-induced endotheliopathy using patient plasma metabolome data. By analyzing endothelial cell metabolism comprehensively, the pipeline identified critical therapeutic targets and biomarkers that can potentially contribute to the development of therapeutic strategies. Our study demonstrates the efficacy of integrating patient plasma metabolome data into computational models to analyze endothelial cell metabolism in disease contexts. This approach offers a deeper understanding of metabolic dysregulations and provides insights into diseases with metabolic components and potential treatments. Full article

The traveling wave effect and soil–structure interaction have significant influence on the seismic response of large-span bridges with complex site conditions. In this paper, a 1/10 scaled-down large-span rigid-framed bridge model was designed and fabricated, and a shaking tables test considering the traveling wave effect and soil–structure interaction was carried out on a large-scale continuous rigid bridge model by a real-time substructure hybrid test technique. Influences of the traveling wave effect and soil–structure interaction on the seismic responses of the rigid-framed bridge specimen were systematically analyzed with experimental data. The test results showed that when the apparent wave speed was small, the traveling wave effect increased the seismic responses of the rigid-framed bridge. With the increase in apparent wave speed, the structural response under traveling wave excitation and uniform excitation was basically the same. The SSI effect lead to a great change in the seismic input peaks and spectral characters at the bottom of the pier, and increased the seismic responses of the rigid-framed bridge. When both traveling wave and the SSI effect were considered, there was a phase difference in the seismic excitation. The dynamic responses of a continuous rigid-framed bridge could not be simply obtained by superposition of the separate traveling wave effect or SSI effect. Meanwhile, the real-time substructure test method in this paper solved the problems that the traditional soil box experiment cannot be applied to the test of a large-scale model, the soil and bridge structure find it difficult to meet the unified similarity ratio, and the boundary conditions are difficult to simulate accurately. Full article

Digital twin (DT) technology provides a path for implementing cyber–physical systems (CPS) and developing smart manufacturing because they are essential tools for monitoring and controlling manufacturing processes. It is considered a vital technology in smart manufacturing and is being widely researched in academia and industry. Furthermore, the combination of DTs and immersive environments has shown great potential for integrating novel capabilities into the new generation of CPS. This research presents an architecture for implementing immersive digital twins under ISO 23247 in flexible manufacturing processes. The proposed system is based on the integration of DT technologies in conjunction with augmented reality (AR) and gesture tracking, and validation was performed in the sorting station of the MPS 500 to increase the interaction and flexibility between physical and virtual environments in real time, thus enhancing the capabilities of the DT. The methodology used for the design and implementation of the DT includes (1) general principles and requirements; (2) models with functional views based on domains and entities; (3) attributes of the observable manufacturing elements; and (4) protocols for the exchange of information between entities. The results show that the integration of these technologies improves the monitoring, control, and simulation capabilities of processes using 3D resources and immersive environments, achieving a higher level of interactivity. In addition, error detection tests were carried out, where a reduction of time was observed in the resolution of errors that may be caused by internal or external disturbances of the process, thus avoiding production delays. Full article

Infrared small target detection (ISTD) plays a crucial role in both civilian and military applications. Detecting small targets against dense cluttered backgrounds remains a challenging task, requiring the collaboration of false alarm source elimination and target detection. Existing approaches mainly focus on modeling targets while often overlooking false alarm sources. To address this limitation, we propose a Target and False Alarm Collaborative Detection Network to leverage the information provided by false alarm sources and the background. Firstly, we introduce a False Alarm Source Estimation Block (FEB) that estimates potential interferences present in the background by extracting features at multiple scales and using gradual upsampling for feature fusion. Subsequently, we propose a framework that employs multiple FEBs to eliminate false alarm sources across different scales. Finally, a Target Segmentation Block (TSB) is introduced to accurately segment the targets and produce the final detection result. Experiments conducted on public datasets show that our model achieves the highest and second-highest scores for the IoU, Pd, and AUC and the lowest Fa among the DNN methods. These results demonstrate that our model accurately segments targets while effectively extracting false alarm sources, which can be used for further studies. Full article

The effect of interface dislocation networks on the mechanical properties of new Ni–based single crystal alloys containing Rhenium (Re) is very large. Because the interface dislocations are microscopic in the nano–scale range, this has not been investigated, and it is very difficult to prepare new Ni–based single crystal alloys containing Re. Therefore, six kinds of new Ni–based single crystal alloys containing Re were prepared, and the hardness tests and nonlinear ultrasonic lamb wave tests were performed on the samples. It was found that the density of interface dislocation networks increases with the increase in the content of Re, which improves the blocking ability of matrix phase dislocation cutting into precipitated phase and enhances the inhibition of dislocation movement. The nonlinear ultrasonic lamb wave tests showed that the materials exhibit better mechanical properties when the density of the interface dislocation networks increases. Meanwhile, a new molecular dynamics model which is closer to the real state of an Ni–based single crystal alloy was constructed to reveal the evolution mechanism of interface dislocation networks. The results showed that the potential energy of Re atoms at the interface is the lowest, which affects the reduction of the potential energy of other atoms at the interface, and thus the stability of the model is improved. In addition, according to the change in the total length of dislocation loops in the model system, with the increase in the content of Re atoms, the inhibition of dislocation movement by dislocation networks at the interface is strengthened. Full article

We propose a new vortex lens for producing multiple focused coaxial vortices with approximately equal intensities along the optical axis, termed equal-intensity multi-focus composite spiral zone plates (EMCSZPs). In this typical methodology, two concentric conventional spiral zone plates (SZPs) of different focal lengths were composited together and the alternate transparent and opaque zones were arranged with specific m-bonacci sequence. Based on the Fresnel–Kirchhoff diffraction theory, the focusing properties of the EMCSZPs were calculated in detail and the corresponding demonstration experiment was been carried out to verify our proposal. The investigations indicate that the EMCSZPs indeed exhibit superior performance, which accords well with our physical design. In addition, the topological charges (TCs) of the multi-focus vortices can be flexibly selected and controlled by optimizing the parameters of the zone plates. These findings which were demonstrated by the performed experiment may open new avenues towards improving the performance of biomedical imaging, quantum computation and optical manipulation. Full article

(1) Problem Statement: In graph theory, the weighted Laplacian matrix is the most utilized technique to interpret the local and global properties of a complex graph structure within computer vision applications. However, with increasing graph nodes, the Laplacian matrix’s dimensionality also increases accordingly. Therefore, there is always the “curse of dimensionality”; (2) Methodology: In response to this challenge, this paper introduces a new approach to reducing the dimensionality of the weighted Laplacian matrix by utilizing the Gershgorin circle theorem by transforming the weighted Laplacian matrix into a strictly diagonal domain and then estimating rough eigenvalue inclusion of a matrix. The estimated inclusions are represented as reduced features, termed GC features; (3) Results: The proposed Gershgorin circle feature extraction (GCFE) method was evaluated using three publicly accessible computer vision datasets, varying image patch sizes, and three different graph types. The GCFE method was compared with eight distinct studies. The GCFE demonstrated a notable positive Z-score compared to other feature extraction methods such as I-PCA, kernel PCA, and spectral embedding. Specifically, it achieved an average Z-score of 6.953 with the 2D grid graph type and 4.473 with the pairwise graph type, particularly on the E_Balanced dataset. Furthermore, it was observed that while the accuracy of most major feature extraction methods declined with smaller image patch sizes, the GCFE maintained consistent accuracy across all tested image patch sizes. When the GCFE method was applied to the E_MNSIT dataset using the K-NN graph type, the GCFE method confirmed its consistent accuracy performance, evidenced by a low standard deviation (SD) of 0.305. This performance was notably lower compared to other methods like Isomap, which had an SD of 1.665, and LLE, which had an SD of 1.325; (4) Conclusions: The GCFE outperformed most feature extraction methods in terms of classification accuracy and computational efficiency. The GCFE method also requires fewer training parameters for deep-learning models than the traditional weighted Laplacian method, establishing its potential for more effective and efficient feature extraction in computer vision tasks. Full article

Over the last 20 years, flue gas desulfurization gypsum (FGD gypsum) has become a valuable and widely used substitute for a natural raw material to produce plasters, mortars, and many other construction products. The essential advantages of FGD gypsum include its high purity and stability, which allow for better technical parameters compared to natural gypsum, and, until recently, its low price and easy availability. This FGD gypsum is obtained in the process of desulfurization of flue gases and waste gases in power plants, thermal power plants, refineries, etc., using fossil fuels such as coal or oil. The gradual reduction in energy production from fossil raw materials implemented by European Union countries until its complete cessation in 2049 in favor of renewable energy sources significantly affects the availability of synthetic gypsum, and forces producers of mortars and other construction products to look for new solutions. The gypsum content in commonly used light plaster mortars is usually from 50 to 60% by mass. This work presents the results of tests on mortars wherein the authors reduced the amount of gypsum to 30%, and, to meet the strength requirements specified in the EN 13279-1:2008 standard, added Portland cement in the amount of 6–12% by mass. Such a significant reduction in the content of synthetic gypsum will reduce this raw material’s consumption, thus extending its availability and developing other solutions. The study presented the test results on strength, density, porosity, pore size distribution, and changes in the microstructure of mortars during up to 180 days of maturation in conditions of increased relative humidity. The results show that decreased porosity and increased mechanical strength occur due to the densification of the microstructure caused by the formation of hydration products, such as C-S-H, ettringite, and thaumasite. Full article

Colorectal cancer (CRC) is one of the most prevalent cancers worldwide, ranking as the third most malignant. The incidence of CRC has been increasing with time, and it is reported that Westernized diet and lifestyle play a significant role in its higher incidence and rapid progression. The intake of high amounts of omega-6 (n − 6) PUFAs and low levels of omega-3 (n − 3) PUFAs has an important role in chronic inflammation and cancer progression, which could be associated with the increase in CRC prevalence. Oxylipins generated from PUFAs are bioactive lipid mediators and have various functions, especially in inflammation and proliferation. Carcinogenesis is often a consequence of chronic inflammation, and evidence has shown the particular involvement of n − 6 PUFA arachidonic acid-derived oxylipins in CRC, which is further described in this review. A deeper understanding of the role and metabolism of PUFAs by their modifying enzymes, their pathways, and the corresponding oxylipins may allow us to identify new approaches to employ oxylipin-associated immunomodulation to enhance immunotherapy in cancer. This paper summarizes oxylipins identified in the context of the initiation, development, and metastasis of CRC. We further explore CRC chemo-prevention strategies that involve oxylipins as potential therapeutics. Full article

Modularity and resilience are fundamental properties of brain network organization and function. The interplay of these network characteristics is integral to understanding brain vulnerability, network efficiency, and neurocognitive disorders. One potential methodology to explore brain network modularity and resilience is through percolation theory, a sub-branch of graph theory that simulates lesions across brain networks. In this work, percolation theory is applied to connectivity matrices derived from functional MRI from human, mice, and null networks. Nodes, or regions, with the highest betweenness centrality, a graph theory quantifier that examines shortest paths, were sequentially removed from the network. This attack methodology led to a rapid fracturing of the network, resulting in two terminal modules connected by one transfer module. Additionally, preceding the rapid network fracturing, the average betweenness centrality of the network peaked in value, indicating a critical point in brain network functionality. Thus, this work introduces a methodological perspective to identify hubs within the brain based on critical points that can be used as an architectural framework for a neural network. By applying percolation theory to functional brain networks through a network phase-transition lens, network sub-modules are identified using local spikes in betweenness centrality as an indicator of brain criticality. This modularity phase transition provides supporting evidence of the brain functioning at a near-critical point while showcasing a formalism to understand the computational efficiency of the brain as a neural network. Full article

This study aimed to compare the nanoleakage of retrograde fillings with premixed calcium silicate-based putty and mineral trioxide aggregate (MTA), using two different techniques (traditional and Lid). Sixty-four extracted human teeth were decoronated, then root canals and ends were instrumented for retrograde filling and divided into four groups according to the retrograde filling technique: the traditional and the Lid technique. Each group (n = 15) was filled with Ceraseal + Well-Root putty, Well-Root putty, Ceraseal + ProRoot MTA, and ProRoot MTA. The nanoleakage was evaluated using the Nanoflow device (IB Systems) on days 1, 3, 7, 15 and 30. Data were collected twice per second at the nanoscale (nL/s) and calculated after archiving the stabilization of fluid flow. The Kruskal–Wallis and Mann–Whitney U-tests were used for statistical analysis. All groups showed enhanced sealing ability over time. Regardless of filling materials, the Well-Root putty, Ceraseal+Well-Root putty, and Ceraseal+ProRoot MTA groups indicated less nanoleakage than the ProRoot MTA group in the first week of evaluation (p < 0.05). Although all groups did not show significant differences after 2 weeks, the Ceraseal+ProRoot MTA group leaked less than ProRoot MTA on Days 3 and 7 (p < 0.05). The scanning electron microscopic examined good adaptation to the cavity wall, which was similar to nanoleakage results. Premixed calcium silicate-based putty retrograde filling material alone and using the “lid technique” were shown to be faster and less prone to nanoleakage when compared to MTA. Full article

In this study, we investigated the feasibility of using electroencephalogram (EEG) signals to differentiate between four distinct subject-driven cognitive states: resting state, narrative memory, music, and subtraction tasks. EEG data were collected from seven healthy male participants while performing these cognitive tasks, and the raw EEG signals were transformed into time–frequency maps using continuous wavelet transform. Based on these time–frequency maps, we developed a convolutional neural network model (TF-CNN-CFA) with a channel and frequency attention mechanism to automatically distinguish between these cognitive states. The experimental results demonstrated that the model achieved an average classification accuracy of 76.14% in identifying these four cognitive states, significantly outperforming traditional EEG signal processing methods and other classical image classification algorithms. Furthermore, we investigated the impact of varying lengths of EEG signals on classification performance and found that TF-CNN-CFA demonstrates consistent performance across different window lengths, indicating its strong generalization capability. This study validates the ability of EEG to differentiate higher cognitive states, which could potentially offer a novel BCI paradigm. Full article

This study examined the impact of tea polyphenols (TPs) on the intestinal flora of loaches (Paramisgurnus dabryanus) under chronic ammonia nitrogen stress using high-throughput sequencing. Two groups of 600 loaches were studied over one month, and they were separated into a control group and tea polyphenol group. Alpha and beta diversity analyses showed diverse bacterial communities, with significant differences in the abundance and uniformity observed initially but not between sampling time points. Cluster analyses revealed distinct differences in microbial communities between groups. A predictive function analysis indicated enrichment in pathways related to amino acid and nucleotide biosynthesis. These findings offer initial insights into how tea polyphenols may affect intestinal microbial communities in loaches under ammonia nitrogen stress. Full article

For a micro-indentation hardness test with non-destructivity, the Nix–Gao model is widely used to describe tested hardness or microhardness variation with an indentation depth induced by indentation size effect, in which tested hardness approaches the macrohardness when the indentation depth is large enough. Based on an analysis of hardness measurements on 10 body-centered cubic steels with diverse microstructure, this paper proposes an analytical relation between microhardness to macrohardness ratio and the indentation depth by explicitly linking characteristic indentation depth (a data-fitting parameter) to grain size and ferrite volume fraction using two different methods. In addition, the normal distribution theory is incorporated to consider the inevitable scatter of identical measurements resulting from material heterogeneity and machining/testing errors. Results show that the proposed model, with 96% reliability, can effectively predict microhardness variation with the indentation depth and its scatter. Full article

This study aimed to assess the potential contributions of the Myers–Briggs Type Indicator (MBTI) to the sustainable growth of communities by conducting a comprehensive analysis of social perceptions of the MBTI in South Korea through big data analysis. The investigation encompasses three primary stages: data collection, preprocessing, and analysis, involving text mining, network analysis, CONCOR analysis, and sentiment analysis. A total of 31,308 text data pieces (13.73 MB) from various sources, including news, blogs, and other sections of Naver and Google, over the past three years, were collected and analyzed using the keyword “MBTI.” Tools, such as Textom SV, UCINET, and NetDraw, were employed for data collection and analysis. The study’s key findings include the identification, through term frequency (TF) and TF-inverse document frequency analyses, of top-ranking terms, such as 16Types, 4Indicators, Test, Myself, OthersMBTI, Situation, and Contents. The CONCOR analysis further revealed six clusters, encompassing themes like interest in MBTI personality tests, application of 16 types in daily life, MZ’s MBTI consumption patterns, trending of MBTI characters, extension to K-Test, and professional use of MBTI. Moreover, sentiment analysis indicated that 68.5% of individuals in South Korea expressed a positive sentiment towards MBTI, while 31.5% conveyed a negative sentiment. The specific emotions identified included liking (Good Feeling), disgust, and interest, in order of prominence. In light of these findings, this study delineates a spectrum of perceptions regarding MBTI in South Korea, encompassing both positive interests and negative concerns. To ensure the responsible use of MBTI, it is imperative to implement reliable scientific testing and education, mitigate the potential harm of stereotyping, and reshape social perceptions surrounding MBTI usage. Only through these measures can MBTI genuinely contribute to the sustainable growth of communities without being confined to limiting stereotypes. Full article

Beat-to-beat (B2B) variability in biomedical signals has been shown to have high diagnostic power in the treatment of various cardiovascular and autonomic disorders. In recent years, new techniques and devices have been developed to enable non-invasive blood pressure (BP) measurements. In this work, we aim to establish the concept of two-dimensional signal warping, an approved method from ECG signal processing, for non-invasive continuous BP signals. To this end, we introduce a novel BP-specific beat annotation algorithm and a B2B-BP fluctuation (B2B-BPF) metric novel for BP measurements that considers the entire BP waveform. In addition to careful validation with synthetic data, we applied the generated analysis pipeline to non-invasive continuous BP signals of 44 healthy pregnant women (30.9 ± 5.7 years) between the 21st and 30th week of gestation (WOG). In line with established variability metrics, a significant increase (p < 0.05) in B2B-BPF can be observed with advancing WOGs. Our processing pipeline enables robust extraction of B2B-BPF, demonstrates the influence of various factors such as increasing WOG or exercise on blood pressure during pregnancy, and indicates the potential of novel non-invasive biosignal sensing techniques in diagnostics. The results represent B2B-BP changes in healthy pregnant women and allow for future comparison with those signals acquired from women with hypertensive disorders. Full article

Alzheimer’s disease (AD) is distinguished by the gradual loss of cognitive function, which is associated with neuronal loss and death. Accumulating evidence supports that protein phosphatases (PPs; PP1, PP2A, PP2B, PP4, PP5, PP6, and PP7) are directly linked with amyloid beta (Aβ) as well as the formation of the neurofibrillary tangles (NFTs) causing AD. Published data reported lower PP1 and PP2A activity in both gray and white matters in AD brains than in the controls, which clearly shows that dysfunctional phosphatases play a significant role in AD. Moreover, PP2A is also a major causing factor of AD through the deregulation of the tau protein. Here, we review recent advances on the role of protein phosphatases in the pathology of AD and other neurodegenerative diseases. A better understanding of this problem may lead to the development of phosphatase-targeted therapies for neurodegenerative disorders in the near future. Full article

With the increasing awareness of architectural heritage conservation and the development of digital technology, there is an urgent need in the field of architectural heritage restoration for a novel solution that can enhance data security, collaboration efficiency, and file management capabilities. This study proposes an Architectural Heritage Restoration Distributed Common Data Environment (AHR-DCDE) framework based on blockchain and IPFS technologies to address the above challenges. The AHR-DCDE framework significantly improves data security and collaborative efficiency in architectural heritage restoration projects by creating a decentralized collaborative design process that achieves data immutability, traceability, and efficient large-scale file processing capabilities. The AHR-DCDE framework significantly improves data security and collaborative efficiency in architectural heritage restoration projects by creating a decentralized collaborative design process that achieves data immutability, traceability, and efficient large-scale file processing capabilities. In this study, the practicality and effectiveness of the AHR-DCDE framework is verified by taking the heritage restoration design project of Pinghe Packing Factory in Wuhan, Hubei Province, as an example. Evaluation of the framework’s network latency, throughput, and storage costs indicates that AHR-DCDE can meet the requirements of architectural heritage restoration projects, possessing efficient capabilities for handling and sharing project data. Furthermore, the implementation of the AHR-DCDE framework also facilitates efficient collaboration among interdisciplinary teams, providing robust technical support for the protection and restoration of architectural heritage. Full article