Study Of Dynamic Changes Research Articles

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.

Analysis of depressive EEG signals via symbolic phase transfer entropy with an adaptive template method

Depression is a prevalent mental disorder in contemporary society. Symbolic phase transfer entropy can quantify the dynamic interaction and information flow between electroencephalogram (EEG) signals in depressed patients and healthy groups, which can help diagnose and treat depression. However, the traditional symbolization process of symbolic phase transfer entropy adopts the basic template method, which makes the symbolic phase transfer entropy unable to express the characteristics and changes of time series in different time periods in detail. Therefore, this paper proposes an improved symbolic phase transfer entropy algorithm, which adopts the adaptive template method in the symbolization process of the symbolic phase transfer entropy algorithm so that it can capture the dynamic changes of time series more finely. It was verified on the task EEG signals of 40 depressed patients and 40 healthy people. The experimental results show that the improved symbolic phase transfer entropy can more accurately distinguish depressed patients from healthy people in lead F4 and lead O1, which is helpful for the study of the EEG pathological characteristics of depression. The improved symbolic phase transfer entropy algorithm makes up for the shortcomings of the traditional symbolic phase transfer entropy in capturing the dynamic changes of time series and provides help for the study of dynamic changes in complex systems.

Dynamic Analysis of Street Trees in Vehicle-mounted LiDAR Point Clouds

Street trees, as key elements of urban road scenes, play a significant role in the overall ecological environment and aesthetics of city roads through their dynamic growth and changes. With the continuous development of urban roads, the growth conditions of street trees are also constantly changing. Therefore, studying how to infer the dynamic changes of roads through the variations in street trees is a highly valuable technical issue. This paper delves into several critical parameters of street trees, including diameter at breast height (DBH), tree height, trunk height, crown volume, and crown spread. By updating these parameters, determining transplantation and replanting decisions, and analyzing growth states, we comprehensively analyze the dynamic changes of street trees. To achieve this goal, we integrated multiple parameters into the analysis process of street trees and established a comprehensive analysis system centered on the DBH parameter. Combining this with data visualization technology, we provide an intuitive dynamic analysis method. Experimental results indicate that, compared to single-parameter analysis methods, the multi-parameter comprehensive analysis method demonstrates higher accuracy and stronger persuasive power in the study of dynamic changes in street trees, thereby validating the superiority and practicality of the proposed research algorithm. This research can provide more scientific and reliable references for urban road planning and management.

INFLUENCE OF ANDROGEN STATUS ON THE RISK OF HOSPITALIZATION FOR CARDIOVASCULAR DISEASES IN MEN WITH CORONARY HEART DISEASE

Aim – to create a mathematical model for predicting the risk of hospitalizations within 6 months in men with coronary artery disease (CAD), taking into account androgen status and other clinical and laboratory parameters.Materials and methods. 102 men aged 45-65 with a diagnosis of coronary artery disease were included in the study. Key predictors were identified using ROC analysis, including testosterone level, age, cholesterol, systolic blood pressure, and heart rate. These parameters were integrated into a logistic regression equation to formulate a predictive model for the risk of hospitalization during the next 6 months. The results. The logistic regression model demonstrated reliable predictive capabilities with an area under the ROC curve of 0.790, p<0.05. The coefficients of individual parameters revealed a significant contribution to the overall forecasting accuracy. The obtained formula allows us to estimate the probability of hospitalization during the next six months for men with coronary artery disease.Conclusions: This study created a predictive model for the risk of hospitalization in men with CAD, providing clinicians with a tool for risk assessment and management. Taking androgen status into account along with traditional cardiovascular risk factors increases the accuracy of the received prognosis. The obtained results emphasize the potential of personalized medicine in optimizing patient care. Prospects for further research: Future research should focus on external validation of the developed model in different populations and the study of additional factors that may improve risk prediction. In addition, the study of dynamic changes in the predicted parameters over time can improve the temporal accuracy of the model.

Simulation Evaluation of Transpiration Ratio (T/ET) in the Yellow River Basin

It is of great significance to improve the simulation accuracy of vegetation dynamics in land surface models for the study of dynamic changes of vegetation on land surface water and heat cycling. Parameter optimization is an effective means to improve the simulation accuracy of land surface model. Aiming at the application of Noah-MP land surface model in the Yellow River Basin, this paper improves the simulation performance of regional evAPOtranspiration and transpiration ratio by parameter optimization.

Correlative light and electron microscopy at defined cell cycle stages in a controlled environment.

Cells are dynamic machines that continuously change their architecture to adapt and respond to extracellular and intracellular stimuli. Deciphering dynamic processes with nanometer-scale resolution inside cells is critical for mechanistic understanding. Here, we present a protocol that enables the in situ study of dynamic changes in intracellular structures under close-to-native conditions at high spatiotemporal resolution. Importantly, the cells are grown, transported, and imaged in a chamber in which environmental conditions such as temperature and gas (e.g., carbon dioxide or oxygen) concentration can be controlled. We demonstrate this protocol to quantify ultrastructural changes that occur during the cell cycle of cultured mammalian cells. The environment control system opens up the possibility of applying this method to primary cells, tissues, and organoids by adjusting environmental conditions.

Spatiotemporal characteristics of vegetation cover change in the Central Yunnan urban agglomeration from 2000 to 2020 based on Landsat data and its driving factors

The study of dynamic changes in vegetation coverage has significant implications for urban clusters in terms of ecological environment protection, climate change research, water resource management and urban planning. A thorough understanding of the driving mechanisms behind vegetation coverage helps provide scientific basis and policy recommendations for the ecological environment protection, sustainable development and decision-making of urban clusters. Taking the central Yunnan urban cluster as an example, this study utilizes long-term Landsat remote sensing image data from 2000 to 2020 on the Google Earth Engine platform. The fractional vegetation cover (FVC) is estimated using a pixel-based binary model. The Sen’s slope, Mann–Kendall trend analysis and Hurst index methods are employed to investigate the spatiotemporal dynamic characteristics of FVC. The impacts of climate change and human activities on vegetation coverage are explored through partial correlation analysis and residual analysis. Finally, considering natural factors such as topography and climate, as well as socioeconomic factors, the geographic detector is used to quantitatively analyze the driving factors behind FVC changes. The results indicate that: (1) From 2000 to 2020, the FVC in the central Yunnan urban cluster changed significantly, showing an overall improvement trend. The average FVC is 0.496 with a growth rate of 0.0024 per year, and it exhibits a distribution pattern of higher values in the west and lower values in the east. (2) In terms of percentage distribution, areas with low FVC and high FVC account for a relatively high proportion. The trend of FVC changes is as follows: improvement (49.6%) > degradation (26.4%) > no change (24%). The average Hurst value is 0.45, indicating that future FVC changes in the study area will be opposite to the past. (3) FVC shows a positive correlation with precipitation and a negative correlation with temperature. Human activities have a positive impact on FVC in the study area, accounting for 55.1% of the regional proportion. (4) Slope and nighttime light contribute the most to FVC. The explanatory power of the interaction between slope and precipitation/temperature is the most significant.

Mouse Cardiac Arrest Model for Brain Imaging and Brain Physiology Monitoring During Ischemia and Resuscitation.

Most cardiac arrest (CA) survivors experience varying degrees of neurologic deficits. To understand the mechanisms that underpin CA-induced brain injury and, subsequently, develop effective treatments, experimental CA research is essential. To this end, a few mouse CA models have been established. In most of these models, the mice are placed in the supine position in order to perform chest compression for cardiopulmonary resuscitation (CPR). However, this resuscitation procedure makes the real-time imaging/monitoring of brain physiology during CA and resuscitation challenging. To obtain such critical knowledge, the present protocol presents a mouse asphyxia CA model that does not require the chest compression CPR step. This model allows for the study of dynamic changes in blood flow, vascular structure, electrical potentials, and brain tissue oxygen from the pre-CA baseline to early post-CA reperfusion. Importantly, this model applies to aged mice. Thus, this mouse CA model is expected to be a critical tool for deciphering the impact of CA on brain physiology.

Multilayer brain network modeling and dynamic analysis of juvenile myoclonic epilepsy.

Objective: It is indisputable that the functional connectivity of the brain network in juvenile myoclonic epilepsy (JME) patients is abnormal. As a mathematical extension of the traditional network model, the multilayer network can fully capture the fluctuations of brain imaging data with time, and capture subtle abnormal dynamic changes. This study assumed that the dynamic structure of JME patients is abnormal and used the multilayer network framework to analyze the change brain community structure in JME patients from the perspective of dynamic analysis. Methods: First, functional magnetic resonance imaging (fMRI) data were obtained from 35 JME patients and 34 healthy control subjects. In addition, the communities of the two groups were explored with the help of a multilayer network model and a multilayer community detection algorithm. Finally, differences were described by metrics that are specific to the multilayer network. Results: Compared with healthy controls, JME patients had a significantly lower modularity degree of the brain network. Furthermore, from the level of the functional network, the integration of the default mode network (DMN) and visual network (VN) in JME patients showed a significantly higher trend, and the flexibility of the attention network (AN) also increased significantly. At the node level, the integration of seven nodes of the DMN was significantly increased, the integration of five nodes of the VN was significantly increased, and the flexibility of three nodes of the AN was significantly increased. Moreover, through division of the core-peripheral system, we found that the left insula and left cuneus were core regions specific to the JME group, while most of the peripheral systems specific to the JME group were distributed in the prefrontal cortex and hippocampus. Finally, we found that the flexibility of the opercular part of the inferior frontal gyrus was significantly correlated with the severity of JME symptoms. Conclusion: Our findings indicate that the dynamic community structure of JME patients is indeed abnormal. These results provide a new perspective for the study of dynamic changes in communities in JME patients.

Myanmar's Land Cover Change and Its Driving Factors during 2000-2020.

Land use/cover change (LUCC) research occupies an important place in the study of global change. It is important for the ecological protection and long-term development of a place. Current research is lacking in the study of dynamic changes at the national level in Myanmar over long time periods and sequences. Quantitative research on the driving factors of LUCC is also lacking. This paper uses the GLC_FCS30 (Global Land-Cover product with Fine Classification System) dataset and socio-economic statistical data in Myanmar to conduct the study. The dynamic change process of LUC (land use/cover) was investigated using the land use dynamic degree, land use transfer matrix, and Sankey diagram. Principal component analysis was used to derive the main drivers of LUCC. The drivers were quantified using multiple linear stepwise regression analysis and specific factors were analyzed. The spatial scope of the study is Myanmar, and the temporal scope is 2000-2020. Results: (1) In 2020, the spatial distribution of LUC in Myanmar shows predominantly forests and croplands. Forests account for 56.64% of the country's total area. Agricultural land accounts for 25.59% of the country's total area. (2) Over the time scale of the study, the trend of LUCC in Myanmar showed significant shrinkage of evergreen broad-leaved forest and deciduous broad-leaved forest (a total shrinkage of -3.34 × 104 km2) and expansion of the other land types. (3) Over the time scale of the study, the dynamic changes in LUCC in Myanmar most occurred as an interconversion between two land types, such as between cropland and deciduous broad-leaved forest, evergreen broad-leaved forest and shrubland, deciduous broad-leaved forest and shrubland, evergreen broad-leaved forest and evergreen needle-leaved forest, and evergreen broad-leaved forest and deciduous broad-leaved forest. (4) The dynamics of LUC in Myanmar is mainly influenced by the socio-economic level of the country. Among them, the impact of agricultural level is the most obvious. Specifically, Myanmar's LUCC is mainly driven by urban population, urbanization rate, industrial value added, food production, and total population. Our research will enable the Myanmar government to make more scientific and rational land management and planning and to make more informed decisions. After understanding the basic situation of LUCC in Myanmar, the hydrological effects, biodiversity changes, and ecological service function changes due to land change in the region can be explored. This is the direction of future research.

The application of in situ liquid cell TEM in advanced battery research

The fast development of modern battery research highly relies on advanced characterisation methods to unveil the fundamental mechanisms of their electrochemical processes. The continued development of in situ characterisation techniques allows the study of dynamic changes during battery cycling rather than just the initial and the final phase. Among these, in situ transmission electron microscopy (TEM) is able to provide direct observation of the structural and morphological evolution in batteries at the nanoscale. Using a compact liquid cell configuration, which allows a fluid to be safely imaged in the high vacuum of the TEM, permits the study of a wide range of candidate liquid electrolytes. In this review, the experimental setup is outlined and the important points for reliable operation are summarised, which are critical to the safety and reproducibility of experiments. Furthermore, the application of in situ liquid cell TEM in understanding various aspects, including dendrite growth, the solid electrolyte interface (SEI) formation, and the electrode structural evolution in different battery systems, is systematically presented. Finally, challenges in the current application and perspectives of the future development of the in situ liquid cell TEM technique are briefly addressed.

Historical-evolutionary and Retrograde Approach to the Study of Social Phenomena and Public Administration

Abstract The author of the study presents a new approach to the study of dynamic changes in society. He calls it the historical-evolutionist and retrograde approach. The historical-evolutionist approach is based on the existence of the ontology of the problem. It is based on the reality of evolution. This approach makes it possible to reveal on the historical-evolutionary trajectory the key events (factors) that influenced the historical development and to retrospectively identify their historical significance. On the historical-evolutionary trajectory, we can retrogradely trace various events such as: a node on the trajectory, and a breaking point on an evolutionary trajectory, evolution path branching. disruption, dead end, evolutionary island, embedded history, as well as a form of retrograde revealed possible worlds. These concepts are then demonstrated on the example of the history of public administration in the Czech Republic.

Construction of a Comprehensive Observation Network for Natural-Resource Elements in Heihe River Basin, NW China

The construction of a comprehensive observation platform for natural-resource elements would provide data support for studies of dynamic changes in various natural resources, and could serve the needs of natural-resource management and the construction of ecological civilization during a period of global change. As the second-largest inland river basin in NW China, the Heihe River Basin (HRB) lies in the central part of the Silk Road Economic Belt, consequently, pilot studies of resource management in the basin are urgently needed. This paper describes the construction of a comprehensive natural-resource elements observation network in the HRB to meet requirements for natural-resource management, based on natural-resource and Earth-system science. Based on current observations and research, thirteen observation stations were established in different river basins through integration with existing stations, reconstruction and upgrading, and new construction. The main types of land-surface resources in the HRB (grassland, forests, rivers, lakes, deserts, wetlands, and farmland) were included in the observation network constructed for the monitoring of natural-resource elements. Long-term, continuous, and stable observation can yield key data concerning coupling processes, trends of change, and rates of change in natural resources. This is of great significance in improving cognitive ability, scientific management, and strategic decision-making regarding natural resources in the HRB, and can provide a reference paradigm for the observation of and research into natural resources in other basins.

Dynamic mapping of lake water areas in the Ob-Irtysh drainless interfluve

Dynamic mapping of lake waters shows the movement, development of any phenomena or processes in time and space. Over a certain period of time, the direction of the dynamic process may change, which causes a certain difficulty in its display. Previously performed studies of dynamic changes in the lake waters of the endorheic region of the Ob-Irtysh interfluve showed this. In this paper, on the basis of multi-temporal topographic and remote source data, as well as various cartographic methods of visualization of dynamic processes in the geoinformation environment, an attempt is made to develop an optimal method for integrated display of retrospective data. On its basis, a visual and informative cartographic model was created, which made it possible to justify the order of choice of objects. The obtained result confirms a wide range of possibilities of geoinformation and cartographic modeling and its relevance in the study of dynamic processes.

High-Resolution Single-Molecule Magnetic Tweezers.

Single-molecule magnetic tweezers deliver magnetic force and torque to single target molecules, permitting the study of dynamic changes in biomolecular structures and their interactions. Because the magnetic tweezer setups can generate magnetic fields that vary slowly over tens of millimeters-far larger than the nanometer scale of the single molecule events being observed-this technique can maintain essentially constant force levels during biochemical experiments while generating a biologically meaningful force on the order of 1-100 pN. When using bead-tether constructs to pull on single molecules, smaller magnetic beads and shorter submicrometer tethers improve dynamic response times and measurement precision. In addition, employing high-speed cameras, stronger light sources, and a graphics programming unit permits true high-resolution single-molecule magnetic tweezers that can track nanometer changes in target molecules on a millisecond or even submillisecond time scale. The unique force-clamping capacity of the magnetic tweezer technique provides a way to conduct measurements under near-equilibrium conditions and directly map the energy landscapes underlying various molecular phenomena. High-resolution single-molecule magnetic tweezerscan thus be used to monitor crucial conformational changes in single-protein molecules, including those involved in mechanotransduction and protein folding.

Monitoring Dynamic Changes in Reservoir Production Using Time-Lapse Walkaway Das-Vsp Data

With the rapid development of optical fibre sensing in recent years, Distributed Acoustic Sensing (DAS) is gradually considered as a viable alternative to borehole geophone arrays for the acquisition of borehole seismic or Vertical Seismic Profile (VSP) data. The data collected by an optical cable permanently deployed behind casing have a high degree of consistency and high SRN, which is less affected by receiving factors such as the cable coupling issues and the tube wave. This paper describes the time-lapse Walkaway DAS-VSP data acquisition in Dagang Oilfield of China and the multi-stage time-lapse Walkaway DAS-VSP data consistency processing procedures. Through the fine imaging processing of multi-stage Walkaway DAS-VSP data and detailed geological interpretation, it is possible to identify and map the range of fluid migration around the wellbore due to production, perform cross-validation during the monitoring period using the borehole oil production statistics information, and analyse the dynamic changes of reservoir fluids within the monitoring field. The Walkaway DAS-VSP survey has become an important development of the borehole seismic technique due to its advantages of high density, high efficiency, low cost and good consistency, which can be carried out with repeated observations and also for permanent monitoring. Therefore, the use of time-lapse Walkaway DAS-VSP surveys is proposed for the monitoring of oil and gas production in mature fields. The repeated time-lapse Walkaway DAS-VSP surveys can provide high-quality borehole seismic data for the study of dynamic changes of the reservoir fluid around the wellbore to a certain range. The key points of this paper mainly include: (1) Time-lapse Walkaway DAS-VSP data acquisition; (2) Special time-lapse Walkaway DAS-VSP data processing procedures; (3) Time-lapse Walkaway DAS-VSP data inversion; (4) Map reservoir fluid dynamic changes during the oil and gas production. In the application process of the time-lapse Walkaway DAS-VSP, the high-accuracy velocity fields, high-resolution structure imaging and multiple attribute inversion of the reservoir formation around the wellbore have been obtained. In addition, it also provides reservoir fluid identification and mapping based on high-accuracy Walkaway DAS-VSP and surface seismic data, which provide a basis for the establishment of multi-domain and multi-dimensional geological and reservoir models.

Study of dynamic changes in the parameters of liver function tests in COVID-19 patients: A hospital-based study in Eastern Nepal

Background: COVID-19 has become a focus of healthcare practitioners worldwide after it was declared as a global pandemic. Although SARS-CoV-2 is primarily affecting the respiratory system, numerous studies have documented its impact on other organ systems, including the liver. This study aims to assess liver function in COVID-19 patients in light of SARS-CoV-2's extremely infectious and pathogenic character.
 Material and methods: It was a hospital-based retrospective study conducted between January 1, 2021, and July 31, 2021, using data from the Department of Biochemistry at Birat Medical College Teaching Hospital in Nepal. Age, gender, total protein, albumin, AST, ALT, and the AST/ALT ratio were all measured.
 Results: The median age of COVID-19 patients was 36 years (CI, 25–51), 60 patients (60.0%) were male, 31 patients (31.0%) were obese, and 20 patients (20%) had comorbidities, such as hypertension (14%) and diabetes mellitus (6.0 %). Compared to the non-critical group, the mean values of ALT, AST, ALP, GT, LDH, TBIL, and DBIL were significantly higher in the severe group. On the other hand, total protein and albumin were significantly lower in the severe group than the non-severe group.
 Conclusion: In COVID-19, aberrant liver function, primarily AST increase, appears to be common. Therefore, direct viral hepatotoxicity during a systemic viral infection must be considered, as well as the possibility of sepsis or worsening of existing liver disease.

Click-Coupling to Electrostatically Grafted Polymers Greatly Improves the Stability of a Continuous Monitoring Sensor with Single-Molecule Resolution.

Sensing technologies for the real-time monitoring of biomolecules will allow studies of dynamic changes in biological systems and the development of control strategies based on measured responses. Here, we describe a molecular architecture and coupling process that allow continuous measurements of low-concentration biomolecules over long durations in a sensing technology with single-molecule resolution. The sensor is based on measuring temporal changes of the motion of particles upon binding and unbinding of analyte molecules. The biofunctionalization involves covalent coupling by click chemistry to PLL-g-PEG bottlebrush polymers. The polymer is grafted to a surface by multivalent electrostatic interactions, while the poly(ethylene glycol) suppresses nonspecific binding of biomolecules. With this biofunctionalization strategy, we demonstrate the continuous monitoring of single-stranded DNA and a medically relevant small-molecule analyte (creatinine), in sandwich and competitive assays, in buffer and in filtered blood plasma, with picomolar, nanomolar, and micromolar analyte concentrations, and with continuous sensor operation over 10 h.

Analysis of selected dynamic properties of the composite hydraulic microhose

Composite materials are gaining more and more popularity in mechanical engineering. It is no different in the area of hydrostatic drive, where they are mainly used in high-pressure hydraulic hoses. The paper presents experimental studies of dynamic changes in the length of a microhydraulic hose under the influence of step pressure and flow load. It has been shown that an increase in internal pressure leads to ever greater shortening of the hose, while an increase in the flow rate leads to its elongation. It has also been shown that these two changes do not occur simultaneously. Length changes due to pressure take place virtually immediately, while those due to flow rate are much slower. A mathematical model that describes these changes based on a continuous system was also presented. The Matlab Simulink package was used to solve the model numerically.

Untargeted lipidomics reveals progression of early Alzheimer\u2019s disease in APP/PS1 transgenic mice

Alzheimer’s Disease (AD) is closely connected to aberrant lipid metabolism. However, how early AD-like pathology synchronously influences brain and plasma lipidome in AD mice remains unclear. The study of dynamic change of lipidome in early-stage AD mice could be of great interest for the discovery of lipid biomarkers for diagnosis and monitoring of early-stage AD. For the purpose, an untargeted lipidomic strategy was developed for the characterization of lipids (≤ 1,200 Da) perturbation occurring in plasma and brain in early-stage AD mice (2, 3 and 7 months) by ultra-high performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry. Significant changes were detected in the levels of several lipid species including lysophospholipids, phosphatidylcholines (PCs), phosphatidylethanolamines (PEs) and Ceramides (Cers), as well as other related lipid compounds such as fatty acids (FAs), diacylglycerols (DGs) and triacylglycerols (TGs) in AD mice. In this sense, disorders of lipid metabolism appear to involve in multiple factors including overactivation of phospholipases and diacylglycerol lipases, decreased anabolism of lysophospholipids in plasma and PEs in plasma and brain, and imbalances in the levels of PCs, FAs and glycerides at different ages. We revealed the changing panels of potential lipid biomarkers with the development of early AD. The study raises the possibility of developing lipid biomarkers for diagnosis of early-stage AD.