Dynamic Changes Research Articles

High-resolution elevation models of Larsen B glaciers extracted from 1960s imagery

Accelerated warming since the 1950s has caused dramatic change to ice shelves and outlet glaciers on the Antarctic Peninsula. Long observational records of ice loss in Antarctica are rare but essential to accurately inform mass balance estimates of glaciers. Here, we use aerial images from 1968 to reveal glacier configurations in the Larsen B region. We use structure-from-motion photogrammetry to construct high-resolution (3.2 m at best) elevation models covering up to 91% of Jorum, Crane, Mapple, Melville and Flask Glaciers. The historical elevation models provide glacier geometries decades before the Larsen B Ice Shelf collapse in 2002, allowing the determination of pre-collapse and post-collapse elevation differences. Results confirm that these five tributary glaciers of the former Larsen B Ice Shelf were relatively stable between 1968 and 2001. However, the net surface elevation differences over grounded ice between 1968 and 2021 equate to 35.3 ± 1.2 Gt of ice loss related to dynamic changes after the ice shelf removal. Archived imagery is an underutilised resource in Antarctica and was crucial here to observe glacier geometry in high-resolution decades before significant changes to ice dynamics.

Relationship between dynamic changes of microorganisms in Qupi and the quality formation of Fengxiangxing Huairang Daqu.

Fengxiangxing Huairang Daqu (FHD) is one of the major types of Daqu in China. However, the relationship between the microbial community structure at different stages, the changes in the sensory characteristics, fermentation characteristics, volatiles, the most critical process point, and the quality formation of FHD is not clear. Based on microscopic characterization, PacBio SMRT sequencing, and HS-SPME-GC-MS volatile metabolite analysis revealed the relationship between FHD quality formation and the dynamics of Qupi. The results showed that the 12th day of the culture was the most critical process point, highlighting the most significant differences in microbial community structure, sensory characteristics, fermentation characteristics, and flavor substances. Bacillus licheniformis (43.25%), Saccharopolyspora rectivirgula (35.05%), Thermoascus aurantiacus (76.51%), Aspergillus amstelodami (10.81%), and Saccharomycopsis fibuligera (8.88%) were the dominant species in FHD. S. fibuligera, A. amstelodami, and T. aurantiacus were associated with the snow-white color of the FHD epidermis, the yellow color of the interior, and the gray-white color, respectively. The abundance of T. aurantiacus, A. amstelodami, B. licheniformis, and S. rectivirgula was positively associated with the esterifying power and liquefying power of FHD. The abundance of T. aurantiacus and A. amstelodami was positively correlated with the saccharifying power of FHD. The abundance of S. fibuligera was positively related to the fermenting power of FHD. A total of 248 volatiles were detected in Qupi, mainly including alcohols, esters, aldehydes, and ketones. Of them, eleven volatiles had a significant effect on the flavor of Qupi, such as 1-butanol-3-methyl-, hydrazinecarboxamide, ethanol, phenylethyl alcohol, ethyl acetate, 2-octanone, 1-octen-3-ol, formic acid-hexyl ester, (E)-2-octen-1-ol, ethyl hexanoate, and 2(3H)-furanone-dihydro-5-pentyl-. The abundance of B. licheniformis, S. rectivirgula, T. aurantiacus, and S. fibuligera was positively correlated with the alcohols, aromatic compounds, and phenols in FHD. The abundance of S. fibuligera was positively correlated with the acids, esters, and hydrocarbons in FHD. These results indicate important theoretical basis and technical support for controllable adjustment of FHD microbial community structure, stable control of FHD quality, and precise, effective, and large-scale guidance of FHD production.

Longitudinal Relationships Among Child School Engagement, Parental Monitoring, and Child Prosocial Behavior: A Child-Parent Synergistic Mechanism.

Numerous contextual factors have been identified that impact the development of children's prosocial behavior, yet the influence of child-initiated factors on prosocial behavior and its underlying mechanism remains unclear. This study employed three longitudinal models to examine in depth how children's school engagement may promote the development of their own prosocial behavior. Three-wave longitudinal data from 4691 children (M age = 9.480, SD = 0.507; 48.2% female) with 2-year intervals were used. Sequentially, a cross-lagged panel model, a random intercept cross-lagged panel model, and a parallel process latent growth model were constructed. The findings indicated that children's school engagement consistently predicted the future level, dynamic changes at within-person level, and long-term trends in their prosocial behavior, and these longitudinal relationships were partially mediated by parental monitoring. These results reveal a child-parent synergistic mechanism for the development of prosocial behavior, wherein children's school engagement both directly promotes their own prosocial behavior and simultaneously enhances prosocial behavior through eliciting increased parental monitoring.

Combining Prediction Models and Dimensionality Reduction Technology for Water Resources Management Under Incomplete Information and Dynamic Change

Combining Prediction Models and Dimensionality Reduction Technology for Water Resources Management Under Incomplete Information and Dynamic Change

DREAMers’ Perceptions of Relational Impacts After DACA

ABSTRACT DREAMers, undocumented people raised in the US, discussed their perspectives on changes in relational dynamics after access to Deferred Action for Childhood Arrivals (DACA) legislation. Ten participants, who through semi-structured interviews, discussed how DACA legislation impacted their mixed-legal status families. Using Interpretative Phenomenological Analysis with Participatory Action Research and Systems Theory frameworks, themes emerged that represented DREAMers’ collective experiences and the essence of the phenomenon related to how access to DACA legislation affected relationships. Themes included the superordinate theme, Relational Impacts of DACA and subthemes, Shared Wins and Resources and Mending Relationships. Discussion includes implications for clinicians, researchers, and policymakers.

DREAMers’ Perceptions of Relational Impacts After DACA

ABSTRACT DREAMers, undocumented people raised in the US, discussed their perspectives on changes in relational dynamics after access to Deferred Action for Childhood Arrivals (DACA) legislation. Ten participants, who through semi-structured interviews, discussed how DACA legislation impacted their mixed-legal status families. Using Interpretative Phenomenological Analysis with Participatory Action Research and Systems Theory frameworks, themes emerged that represented DREAMers’ collective experiences and the essence of the phenomenon related to how access to DACA legislation affected relationships. Themes included the superordinate theme, Relational Impacts of DACA and subthemes, Shared Wins and Resources and Mending Relationships. Discussion includes implications for clinicians, researchers, and policymakers.

The impact of flooding from the Minjiang River on the succession of harmful algal blooms (HABs) caused by diatoms in China's offshore waters

This study examines diatom assemblages in the Matsu Archipelago, an area influenced by Minjiang River runoff. It focuses on harmful algal blooms (HABs) that occurred between August 2021 and July 2022. Utilizing 18S rRNA metabarcoding and microscopic analysis, we observed a significant diatom bloom during early summer runoff, peaking at 5 × 105 cells L−1. The research reveals dynamic community changes during the runoff season, with dominant genera including Pseudo-nitzschia, Chaetoceros, and Skeletonema. Skeletonema cell density correlated with NO3 levels, Chaetoceros had a slight PO4 affinity, and Pseudo-nitzschia showed a negative correlation with Skeletonema. Pseudo-nitzschia, which prefers high light and pH conditions, had notably high concentrations in the flood season and in the autumn. In both, it was dominated by potential toxin-producing species - P. multistriata and P. pungens during the flooding, and P. cuspidate in the autumn. These findings highlight the intricate relationship between diatom dynamics and environmental factors, providing essential insights for managing HABs, especially Pseudo-nitzschia species, amidst environmental changes.

Trap Depth Engineering from Persistent Luminescence Phosphors Mg2-xZnxSnO4 for Dynamic Optical Information Encryption Application.

Traditional information encryption materials that rely on fluorescent/phosphorescent molecules are facing an increasing risk of counterfeiting or tampering due to their static reading mode and advances in counterfeiting technology. In this study, a series of Mg2-xZnxSnO4 (x = 0.55, 0.6, 0.65, 0.7 0.75, 0.8) that realizes the writing, reading, and erasing of dynamic information is developed. When heated to 90 °C, the materials exhibit a variety of dynamic emission changes with the concentration of Zn2+ ions. As the doping concentration increased, the ratio of the shallow trap to deep trap changed from 7.77 to 20.86. When x = 0.55, the proportion of deep traps is relatively large, resulting in a higher temperature and longer time required to read the information. When x = 0.80, the proportion of shallow traps is larger and the encrypted information is easier to read. Based on the above features, encryption binary codes device was designed, displaying dynamic writing, reading, and erasing of information under daylight and heating conditions. Accordingly, this work provides reliable guidance on advanced dynamic information encryption.

Study on dynamic behavior of atomic diffusion at interface between TiAlN coating and WC matrix

Abstract The molecular dynamics method was used to simulate the dynamic behavior change of atomic diffusion at interface between TiAlN coating and WC matrix in the carbide tool with TiAlN coating. It was determined that the one-way diffusion of C atom to TiAlN coating resulted in serious lattice distortion on the surface. Meanwhile, the whole diffusion process was accompanied by the continuous increase of stress, resulting in gradual deformation of the material. The interface bonding performance also decreases. Then, the surface energy and atomic layer convergence are calculated by the first-principles method. The TiAlN crystal surface is stable at 8 atomic layers, and the WC crystal surface is stable at 6 atomic layers, and then a stable microinterface model can be built. The relaxation processing for the established model was performed to determine the diffusion path of C atom. Then, the energy barrier, adhesion work, interface energy and fracture toughness of different transition state structures in the diffusion path of C atom are calculated, and it is determined that with the deepening of the diffusion of C atom in the coating, the binding ability of the interface of the coating base and the performance of the coating tool are more and more serious damage. Finally, the cutting experiment of TiAlN coated tool is designed. According to the surface topography of TiALN coated tool in different periods, the simulation and theoretical analysis mentioned above are verified, which provides a theoretical basis for the subsequent research on improving the performance of coated tool.

Dynamic Spatio-Temporal Adaptive Graph Convolutional Recurrent Networks for Vacant Parking Space Prediction

The prediction of vacant parking spaces (VPSs) can reduce the time drivers spend searching for parking, thus alleviating traffic congestion. However, previous studies have mostly focused on modeling the temporal features of VPSs using historical data, neglecting the complex and extensive spatial characteristics of different parking lots within the transportation network. This is mainly due to the lack of direct physical connections between parking lots, making it challenging to quantify the spatio-temporal features among them. To address this issue, we propose a dynamic spatio-temporal adaptive graph convolutional recursive network (DSTAGCRN) for VPS prediction. Specifically, DSTAGCRN divides VPS data into seasonal and periodic trend components and combines daily and weekly information with node embeddings using the dynamic parameter-learning module (DPLM) to generate dynamic graphs. Then, by integrating gated recurrent units (GRUs) with the parameter-learning graph convolutional recursive module (PLGCRM) of DPLM, we infer the spatio-temporal dependencies for each time step. Furthermore, we introduce a multihead attention mechanism to effectively capture and fuse the spatio-temporal dependencies and dynamic changes in the VPS data, thereby enhancing the prediction performance. Finally, we evaluate the proposed DSTAGCRN on three real parking datasets. Extensive experiments and analyses demonstrate that the DSTAGCRN model proposed in this study not only improves the prediction accuracy but can also better extract the dynamic spatio-temporal characteristics of available parking space data in multiple parking lots.

Network Traffic Anomaly Detection Based on Spatiotemporal Feature Extraction and Channel Attention

To overcome the challenges of feature selection in traditional machine learning and enhance the accuracy of deep learning methods for anomaly traffic detection, we propose a novel method called DCGCANet. This model integrates dilated convolution, a GRU, and a Channel Attention Network, effectively combining dilated convolutional structures with GRUs to extract both temporal and spatial features for identifying anomalous patterns in network traffic. The one-dimensional dilated convolution (DC-1D) structure is designed to expand the receptive field, allowing for comprehensive traffic feature extraction while minimizing information loss typically caused by pooling operations. The DC structure captures spatial dependencies in the data, while the GRU processes time series data to capture dynamic traffic changes. Furthermore, the channel attention (CA) module assigns importance-based weights to features in different channels, enhancing the model’s representational capacity and improving its ability to detect abnormal traffic. DCGCANet achieved an accuracy rate of 99.6% on the CIC-IDS-2017 dataset, outperforming other algorithms. Additionally, the model attained precision, recall, and F1 score rates of 99%. The generalization capability of DCGCANet was validated on a subset of CIC-IDS-2017, demonstrating superior detection performance and robust generalization potential.

An Approach of BIM-Based Dynamic Adaptive Zoning for Group Piles Construction Multi-Work Areas

In large-scale pile foundation drilling projects, the absence of digital work area management hampers dynamic construction management, affecting efficiency. This article explores multi-work area management during pile foundation drilling using a BIM parameterized model, focusing on informatization. The results indicate the following: (i) A dynamic zoning method for pile foundation construction using BIM models was developed to support information management systems and address resource allocation challenges amid dynamic construction team changes. (ii) Adaptive zoning methods were proposed, incorporating the dynamic adjustment of construction work areas, including the division of virtual work areas and adaptive adjustment of pile foundation partition parameters. (iii) Work area modeling and zoning were applied on site, with pile foundation modeling aligning with engineering design distribution, and work area zoning accurately reflecting the on-site construction status. (iv) This method enables adaptive synchronization between pile foundation model attributes and work area information, integrating zoning management into the information system to enhance the construction unit’s information management system and digital management level.

DLX6-AS1 regulates odonto/osteogenic differentiation in dental pulp cells under the control of BMP9 via the miR-128-3p/MAPK14 axis: A laboratory investigation.

The regenerative capacity of dental pulp relies on the odonto/osteogenic differentiation of dental pulp cells (DPCs), but dynamic microenvironmental changes hinder the process. Bone morphogenetic protein 9 (BMP9) promotes differentiation of DPCs towards an odonto/osteogenic lineage, forming dentinal-like tissue. However, the molecular mechanism underlying its action remains unclear. This study investigates the role of DLX6 antisense RNA 1 (DLX6-AS1) in odonto/osteogenic differentiation induced by BMP9. Custom RT2 profiler PCR array, quantitative Real-Time PCR (qRT-PCR) and western blots were used to investigate the expression pattern of DLX6-AS1 and its potential signal axis. Osteogenic ability was evaluated using alkaline phosphatase and alizarin red S staining. Interactions between lncRNA and miRNA, as well as miRNA and mRNA, were predicted through bioinformatic assays, which were subsequently validated via RNA immunoprecipitation and dual luciferase reporter assays. Student's t-test or one-way ANOVA with post hoc Tukey HSD tests were employed for data analysis, with a p-value of less than .05 considered statistically significant. DLX6-AS1 was upregulated upon BMP9 overexpression in DPCs, thereby promoting odonto/osteogenic differentiation. Additionally, miR-128-3p participated in BMP9-induced odonto/osteogenic differentiation by interacting with the downstream signal MAPK14. Modifying the expression of miR-128-3p and transfecting pcMAPK14/siMAPK14 had a rescue impact on odonto/osteogenic differentiation downstream of DLX6-AS1. Lastly, miR-128-3p directly interacted with both MAPK14 and DLX6-AS1. DLX6-AS1 could regulate the odonto/osteogenic differentiation of DPCs under the control of BMP9 through the miR-128-3p/MAPK14 axis.

Network Traffic Anomaly Detection Based on Spatiotemporal Feature Extraction and Channel Attention

To overcome the challenges of feature selection in traditional machine learning and enhance the accuracy of deep learning methods for anomaly traffic detection, we propose a novel method called DCGCANet. This model integrates dilated convolution, a GRU, and a Channel Attention Network, effectively combining dilated convolutional structures with GRUs to extract both temporal and spatial features for identifying anomalous patterns in network traffic. The one-dimensional dilated convolution (DC-1D) structure is designed to expand the receptive field, allowing for comprehensive traffic feature extraction while minimizing information loss typically caused by pooling operations. The DC structure captures spatial dependencies in the data, while the GRU processes time series data to capture dynamic traffic changes. Furthermore, the channel attention (CA) module assigns importance-based weights to features in different channels, enhancing the model’s representational capacity and improving its ability to detect abnormal traffic. DCGCANet achieved an accuracy rate of 99.6% on the CIC-IDS-2017 dataset, outperforming other algorithms. Additionally, the model attained precision, recall, and F1 score rates of 99%. The generalization capability of DCGCANet was validated on a subset of CIC-IDS-2017, demonstrating superior detection performance and robust generalization potential.

DLX6-AS1 regulates odonto/osteogenic differentiation in dental pulp cells under the control of BMP9 via the miR-128-3p/MAPK14 axis: A laboratory investigation.

The regenerative capacity of dental pulp relies on the odonto/osteogenic differentiation of dental pulp cells (DPCs), but dynamic microenvironmental changes hinder the process. Bone morphogenetic protein 9 (BMP9) promotes differentiation of DPCs towards an odonto/osteogenic lineage, forming dentinal-like tissue. However, the molecular mechanism underlying its action remains unclear. This study investigates the role of DLX6 antisense RNA 1 (DLX6-AS1) in odonto/osteogenic differentiation induced by BMP9. Custom RT2 profiler PCR array, quantitative Real-Time PCR (qRT-PCR) and western blots were used to investigate the expression pattern of DLX6-AS1 and its potential signal axis. Osteogenic ability was evaluated using alkaline phosphatase and alizarin red S staining. Interactions between lncRNA and miRNA, as well as miRNA and mRNA, were predicted through bioinformatic assays, which were subsequently validated via RNA immunoprecipitation and dual luciferase reporter assays. Student's t-test or one-way ANOVA with post hoc Tukey HSD tests were employed for data analysis, with a p-value of less than .05 considered statistically significant. DLX6-AS1 was upregulated upon BMP9 overexpression in DPCs, thereby promoting odonto/osteogenic differentiation. Additionally, miR-128-3p participated in BMP9-induced odonto/osteogenic differentiation by interacting with the downstream signal MAPK14. Modifying the expression of miR-128-3p and transfecting pcMAPK14/siMAPK14 had a rescue impact on odonto/osteogenic differentiation downstream of DLX6-AS1. Lastly, miR-128-3p directly interacted with both MAPK14 and DLX6-AS1. DLX6-AS1 could regulate the odonto/osteogenic differentiation of DPCs under the control of BMP9 through the miR-128-3p/MAPK14 axis.

Current status of surveillance systems for human respiratory syncytial virus

To conduct timely surveillance of the seasonal characteristics and disease burden of Human Respiratory Syncytial Virus (HRSV) in various geographical regions of China, and further develop more precise and effective prevention and intervention strategies, there is an urgent need for China to establish a nationwide, effective, and stable HRSV surveillance system. Through combining the current status of domestic and international HRSV surveillance systems and the existing surveillance framework in China, this study proposed an HRSV surveillance type applicable to China based on different surveillance purposes, and considering the feasibility of implementation. This article aimed to provide solid scientific and technical support to monitor the dynamic changes of HRSV epidemic timely, carry out a risk assessment and early warning, and further understand the disease burden of HRSV in China. It also helps to improve the diagnosis, prevention, and control of the HRSV diseases research and development, use, and evaluation of HRSV vaccines and drugs in China.

Prolonged premature rupture of membranes with increased risk of infection is associated with gut accumulation of Pseudomonas from the environment

BackgroundPreterm premature rupture of membranes (PPROM) contributes to over one-third of preterm births, and PPROM infants are more susceptible to infections. However, the risk factors remain poorly understood. We here aim to investigate the association of duration of premature rupture of membranes (PROM) and environmental microbiota with the gut microbiota and infection in PPROM infants. MethodsForty-six premature infants were recruited from two hospitals, and infant fecal and environmental samples were collected. 16 s rRNA sequencing was performed to analyze the fecal and environmental microbiome. Human inflammatory cytokines in cord vein plasma were measured. ResultsThe gut microbiota composition of PPROM infants was different from that of non-PPROM infants, and the microbiome phenotypes were predicted to be associated with a higher risk of infection, further evidenced by the significantly increased levels of IL-6 and IL-8 in cord vein plasma of PPROM infants. The diversity of the gut microbiota in PPROM infants increased significantly as the duration of PROM excessed 12 h, and Pseudomonas contributed significantly to the dynamic changes. The Pseudomonas species in the gut of PPROM infants were highly homologous to those detected in the ward environment, suggesting that prolonged PROM is associated with horizontal transmission of environmental pathogens, leading to a higher risk of infection. ConclusionsThis study highlights that the duration of PROM is associated with the accumulation of environmental pathogens in the gut of PPROM infants, which is a risk factor for nosocomial infections. Improving environmental hygiene could be effective in optimizing the clinical care of PPROM infants.

Machine learning enhanced rigiflex pillar-membrane triboelectric nanogenerator for universal stereoscopic recognition

The advent of the artificial intelligence (AI) and Internet of Things (IoTs) era has spurred a surge in the analysis of voluminous data gathered from myriad distributed sensors. This endeavor is primarily aimed at executing sophisticated recognition functions, which frequently demand excessive energy consumption. As a result, the development of a streamlined design capable of performing these functions with comparable efficiency continues to pose a significant challenge. Herein, a rigiflex pillar-membrane triboelectric nanogenerator (PM-TENG) is proposed for universal stereoscopic recognition by machine learning. An integral design is adopted to generate dynamic sensing signals in time series, which can obtain abundant and high-resolution information of stereoscopic structures. By combining the advantages of both rigid steel pillars and flexible/elastic membranes, the proposed rigiflex PM-TENG contains information from multiple sensing pillared pixels and focuses on the study of dynamic changes during the whole contact cycle. The proposed rigiflex TENG can effectively recognize objects across nine categories by leveraging machine learning technique, achieving an accuracy rate of 96.39 %. This system offers substantial potential for application in assembly lines for production control management in future smart factories and unattended warehouse workshops.

Reduced dynamic changes in pulmonary artery compliance during isometric handgrip exercise in patients with heart failure

Exercise intolerance is a debilitating symptom in heart failure (HF), adversely affecting both quality of life and long-term prognosis. Emerging evidence suggests that pulmonary artery (PA) compliance may be a contributing factor. This study aims to non-invasively assess PA compliance and its dynamic properties during isometric handgrip (HG) exercise in HF patients and healthy controls, using cardiovascular magnetic resonance (CMR). We prospectively enrolled 36 subjects, comprising 17 HF patients (NYHA class II and III) and 19 healthy controls. Participants performed an HG test, and we assessed changes in PA compliance and hemodynamic flow parameters using advanced CMR techniques. We also explored the relationship between CMR-derived PA compliance metrics and established clinical indicators, ensuring the validity of our findings through intra- and interobserver agreements. HF patients had significantly lower resting PA compliance compared to controls (28.9% vs. 50.1%, p < 0.01). During HG exercise, HF patients exhibited a dampened adaptability in PA compliance. Hemodynamic responses, including heart rate and blood pressure, were not significantly different between the groups. Further analyses revealed a significant correlation between changes in PA compliance and functional capacity, and an inverse relationship with NYHA class. Our study demonstrates a marked difference in PA vascular responses during HG exercise between HF patients and healthy controls. The compromised adaptability in PA compliance in HF patients is correlated with diminished functional capacity. These findings have significant clinical implications and may guide future interventional strategies in HF management.

From teacher to teacher researcher: Identity change in the power dynamics of an EAP reform community

Educational reforms often entail and afford teachers’ identity change. Adopting a community of practice (CoP) framework to examine reform-inflicted teacher identity change and drawing on data from eight rounds of semi-structured interviews over three and a half years, our longitudinal case study traced how three English for Academic Purposes (EAP) teachers negotiate their teacher researcher identity during an educational reform in China. We find that a multi-layered, multi-centered, and multi-functional reform CoP of complex power dynamics can facilitate EAP teachers’ negotiation of teacher-to-teacher researcher identity change during a curriculum reform, which can further enrich the long-term sustainable development of a community. Highlighting the significant role of CoPs in educational reforms and teacher development, we advocate a CoP of dynamic power relations and multiple power positions and call for frontline teachers’ engagement with teaching-based research to potentially strengthen the research-practice nexus. This study bears implications for educational reform participants and teacher educators.