Dynamic Evaluation Method Research Articles

Correlation Between Osteal Kneading and Carotid Artery Atherosclerotic Plaque.

Adjacent bony structures may directly rub the carotid artery during swallowing or head and/neck movement. Long-term repeated stimulation might be considered to be a potential risk factor for carotid atherosclerotic plaque formation, development, and hazard. we defined the process as "Osteal Kneading". A total of 192 consecutive (384 carotid arteries) neck CT angiography (CTAs) and cerebral MRIs performed within 1 month were analysed. The ipsilateral bones (thyroid cartilage and hyoid bone), carotid arteries, and plaque were characterised using the CTA images. Cerebral ischemic lesions were analysed using the MRI images. According to the relationship between the position of the plaque and the bony structures, the vessels were divided into the osteal kneading positive group (OK+) and the osteal kneading negative group (OK-). The effects of osteal kneading on plaque formation, development, and hazard were then explored. 95.7% of the OK+ plaques were located in the common carotid artery. The thyroid cartilage and hyoid bone were significantly closer to the carotid artery wall (p<0.001) and the vascular radius was significantly larger (p<0.001) in the OK+ group. The distance from the thyroid cartilage and hyoid bone to the ipsilateral carotid arteries was negatively correlated with plaque thickness, carotid artery stenosis rate, and calcified plaque occurrence (p<0.05). This pilot study provides preliminary evidence for the hypothesis of "osteal kneading" of the carotid artery, and also suggests that osteal kneading may play some role in carotid artery plaque formation and development. Further research is required to provide more concrete evidence for the hypothesis, such as the inclusion of samples from multiple centres, precise outcome observation, artificial intelligence assisted image analysis, and dynamic evaluation methods.

Utility of Multiparametric Breast MRI Radiomics to Predict Cyclin D1 and TGF-β1 Expression.

To develop a machine learning model that integrates clinical features and multisequence MRI radiomics for noninvasively predicting the expression status of prognostic-related factors cyclin D1 and TGF-β1 in breast cancer, providing additional information for the clinical development of personalized treatment plans. A total of 123 breast cancer patients confirmed by surgical pathology were retrospectively enrolled in our Hospital from January 2016 to July 2022. The patients were randomly divided into a training group (87 cases) and a validation group (36 cases). Preoperative routine and dynamic contrast-enhanced magnetic resonance imaging scans of the breast were performed for treatment subjects. The region of interest was manually outlined, and texture features were extracted using AK software. Subsequently, the LASSO algorithm was employed for dimensionality reduction and feature selection to establish the MRI radiomics labels. The diagnostic efficacy and clinical value were assessed through receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). In the cyclin D1 cohort, the area under the receiver operating characteristic (ROC) curve in the clinical prediction model training and validation groups was 0.738 and 0.656, respectively. The multisequence MRI radiomics prediction model achieved an AUC of 0.874 and 0.753 in these respective groups, while the combined prediction model yielded an AUC of 0.892 and 0.785. In the TGF-β1 cohort, the ROC AUC for the clinical prediction model was found to be 0.693 and 0.645 in the training and validation groups, respectively. For the multiseries MRI radiomics prediction model, it achieved an AUC of 0.875 and 0.760 in these respective groups; whereas for the combined prediction model, it reached an AUC of 0.904 and 0.833. Decision curve analysis (DCA) demonstrated that both cohorts indicated a higher clinical application value for the combined prediction model compared with both individual models-clinical prediction model alone or radiomics model. The integration of clinical features and multisequence MRI radiomics in a combined modeling approach holds significant predictive value for the expression status of cyclin D1 and TGF-β1. The model provides a noninvasive, dynamic evaluation method that provides effective guidance for clinical treatment.

Innovative dynamic evaluation and classification method for marine atmospheric corrosion based on corrosion sensors and machine learning

Innovative dynamic evaluation and classification method for marine atmospheric corrosion based on corrosion sensors and machine learning

A Two-Stage Model-Based Dynamic Reliability Evaluation Method in Individual Monitoring: A Case Study on Bearing Vibration Data

A Two-Stage Model-Based Dynamic Reliability Evaluation Method in Individual Monitoring: A Case Study on Bearing Vibration Data

Integration of Fuzzy 0/1 Knapsack Dynamic Programming and PROMETHEE Method for Vehicle Exhaust Emission Parametric Optimization and Selection in the Packing Industry

Packaging industries fabricate and transport products in wrapped, sealed, and cushioned containers and boxes on roads, often through fossil-fuelled vehicles that emit carbons. Thus, decarbonization and net zero emission drive are compelling for these vehicles. This paper proposes a robust green logistics interaction model for monitoring and reducing exhaust pipe emissions in an uncertain environment. It uses a hybrid method known as fuzzy-0/1-KDP-PROMETHEE (Fuzzy-0/1 Knapsack dynamic programming-Preference Ranking Organization Method for Enrichment Evaluation) approach to concurrently reduce uncertainty, optimize the capacity of the knapsack and establish the preferred option among the parameters of green logistic. Both PROMETHEE I and II were introduced and tested using logistics data from an Indian environment based on secondary data. The method works by first reducing the effect of uncertainty on the model outcomes. This was achieved by establishing the output space as the fuzzy state, creating fuzzy rules, and mapping degrees to rules. Then, the degrees are used to maximize, ensuring that the weighted sum is not greater than the capacity of the Knapsack. The outcome is then regarded as the element of the green logistics exhaust emission process. The results obtained from the analysis, using the replacement of fuzzy expert (triangular) with fuzzy extent (trapezoidal), fuzzy geometric mean (triangular), and fuzzy geometric mean (trapezoidal) reveal that the fuzzy-0/1-KDP-PROMETHEE method adequately represents the score obtained using the data set from the exhaust emissions.

Automatic evaluation of planners in dynamic scenes during human–robot interaction

AbstractThe evaluation of planners is essential to guarantee the efficiency and performance of trajectories executed by collaborative robots in human–robot interaction (HRI) contexts. The main objective of this research is to develop a method that allows obtaining an automated assessment of the performance of planners widely used in robotic environments. In addition to analyzing specific metrics such as planning time, path length, smoothness, and clearance, this paper introduces novel contributions, including a unique method for dynamic scene evaluation and a new comprehensive and global performance index, named Total Performance Index (TPIx), which encompasses all the aforementioned metrics. The system proposed is composed of a collaborative robot and an RGB-D sensor to monitor the environment using 3D data to perform safe trajectories during multiple tasks. In this quantitative evaluation, two single-query planners (RRT, KPIECE) and two multiple-query planners (SPARS, PRM) were analyzed. The results obtained demonstrate the feasibility and effectiveness of the proposed method to automatically evaluate the planners used by collaborative robots. Therefore, this study contributes to the advancement of collaborative robotics by introducing updated methods for planner evaluation and aims to facilitate the development of more efficient and adaptive systems in light of recent advancements in robotic technologies.

Experimental evaluation of temporary plugging performance and mechanism analysis of degradable preformed particle gel based on NMR technology

Utilizing chemical temporary plugging systems to further increase the stimulated reservoir volume (SRV) and improve oil recovery (IOR) has become a hot topic in the current petroleum industry. Pre-formed gel particles (PPG) possess excellent swelling properties and can form high-strength plugging in high-permeability channels. In addition, degradable pre-formed gel particles (DPPG) further exhibit self-degradation characteristics and excellent temporary plugging and fluid diverting performance. In our previous studies, we examined the temporary plugging mechanisms of DPPG prepared with different molecular weight (MW) of crosslinking agents (PEGDA) through bottle tests and core displacement experiments. However, the microscopic swelling mechanism and temporary plugging mechanism in pore throats of reservoir cores are still unclear. In this paper, comprehensive research was conducted on the microscopic temporary plugging mechanism of DPPG prepared with different MW crosslinking agents using both and method. Results showed that DPPG with different compositions had different micro-swelling performance. NMR-T2 analysis for bottle test showed that DPPG-M1 (with the smallest MW of crosslinking agent) gradually decreased in nuclear signal during the water absorption and swelling process, and had the largest swelling volume and swelling rate. On the other hand, DPPG-M5 (with the largest MW of crosslinking agent) exhibited a slow diffusion process of surface water towards the nucleus, resulting in the smallest swelling volume and swelling rate. However, its complete degradation time was longer. Moreover, the NMR T2 (i.e., spin-spin relaxation time) results of the cores in the dynamic temporary plugging experiment showed that the decrease of the MW of the crosslinking agent enhanced the plugging performance, but with less damage to the formation. The MRI images confirmed that DPPG-M5 had better injection capability in the core and caused greater damage to the core. The research results of this study showed that NMR technology can reveal the temporary plugging mechanism of DPPG from a micro-level perspective.

A dynamic state evaluation method for current sensors based on reference values and time series

Abstract Due to the complex and ever-changing power environment, when designing dynamic state evaluation methods for current sensors, there is often a problem of inaccurate calculation of state evaluation values, leading to poor evaluation accuracy of the methods. A dynamic state evaluation method for current sensors based on reference values and time series is proposed. Firstly, we normalize the time series data of the current sensor, construct a covariance matrix, calculate the degree of correlation between the time series data, and construct a higher quality current sensor state dataset. Then, we select multiple indicators as state evaluation indicators and quantify them. Then, the feature vector of the evaluation indicators and the state evaluation value are calculated. Under the influence of the reference value, we set the threshold of the current sensor in different states to divide the current sensor into different states. Through the above design, the dynamic state evaluation method for current sensors is completed. The experimental results show that the designed dynamic state evaluation method for current sensors based on reference values and time series has an average coefficient of determination of 0.95 in practical applications, with high evaluation accuracy.

Dynamic evaluation method for road resource utilization of expressways and connected urban roads

Dynamic evaluation method for road resource utilization of expressways and connected urban roads

Research on Dynamic Evaluation Method of Tourist Destination Accessibility Based on Interpretive Structure Model

Because of its core role in the process of tourism, tourism destination has always been the focus of discussion among the industry, but the comprehensive evaluation of tourism destination is rarely mentioned. This paper selects tourism destination as the focus of investigation, gives the evaluation method of tourism destination, adapts to the trend of tourism modernization, and discusses the quantifiable model of tourism destination evaluation. This paper first selects several tourism destinations as samples, collects and combs the information related to spatial equity of tourism destinations, and determines the main problem categories of spatial equity of tourism destinations. Then, through the summary of relevant data and case analysis, a framework of tourism target accessibility risk factors is formed. At the same time, considering the interaction between risk factors, we then give a method to determine the weight that can improve granular computing. First, we use this method to obtain the quotient space family with hierarchical characteristics through the similarity relationship between data, and then we can calculate the importance of each feature in the quotient space family, so as to obtain the corresponding weight between different influence indexes. Finally, the paper takes the tourism destination spatial equity problem as the main research goal, and through the dynamic evaluation of the tourism destination spatial equity risk impact structure using the interpretive structure model, and describes the schematic diagram of its impact structure, so as to find out the direct, source, process and other factors that cause the problem.

Brachial and Lumbosacral Plexopathies.

The brachial and lumbosacral plexuses are complex neural structures that transmit sensory, motor, and autonomic information between the spinal cord and the extremities. Plexus disorders can be particularly disabling because lesions in the plexus usually affect large groups of nerve fibers originating from several spinal levels. Electrodiagnostic studies are often required to confirm a plexus lesion and determine the extent of injury and prognosis. Magnetic resonance is the imaging modality of choice for detecting intrinsic nerve abnormalities; recently, high-resolution ultrasound has emerged as an alternative method for dynamic evaluation and visualization of internal nerve architecture. Once a plexopathy is confirmed, the list of possible etiologies is relatively limited and includes traumatic and nontraumatic causes. Treatment relies on symptom management and physical rehabilitation unless a treatable underlying condition is found. Surgical approaches, including nerve grafts or tendon transfers, may improve limb function when spontaneous recovery is suboptimal.

Research on design and control methods of a lightweight upper limb joint isokinetic rehabilitation training equipment.

Isokinetic exercise can improve joint muscle strength and stability, making it suitable for early rehabilitation of stroke patients. However, traditional isokinetic equipment is bulky and costly, and cannot effectively avoid external environmental interference. This paper designed a lightweight upper limb joint isokinetic rehabilitation training equipment, with a control system that includes a speed planning strategy and speed control with disturbance rejection. Based on the established human-machine kinematic closed-loop model between the equipment and the user, a dynamic evaluation method of torque at the joint level was proposed. To validate the effectiveness of the equipment, experiments were conducted by manually applying random disturbances to the equipment operated at an isokinetic speed. The results showed that the root mean square error between the observed torque curve of the second-order linear extended state observer used in this paper and the actual disturbance curve was 0.52, and the maximum speed tracking error of the speed control algorithm was 1.27%. In fast and slow sinusoidal speed curve tracking experiments, the root mean square errors of the speed tracking results for this algorithm were 9.65 and 5.27, respectively, while the tracking errors for the PID speed control algorithm under the same environment were 19.94 and 12.11. The research results indicate that compared with traditional PID control method, the proposed control strategy demonstrates superior performance in achieving isokinetic control and suppressing external disturbances, thereby exhibiting significant potential in promoting upper limb rehabilitation among patients.

Dynamic Evaluation of Road Network Resilience to Traffic Accidents: An Emergency Management Perspective for Sustainable Cities in China

When assessing road network resilience, emergency management behavior should be considered, as this represents the road network’s capacity to adapt to and recover from traffic accidents. Given the timeliness and variability of emergency management behavior, deterministic approaches seem inadequate to represent real road network performance. Thus, this paper innovatively designs an emergency management perspective-based dynamic evaluation method of road network resilience to traffic accidents. Firstly, based on four stages of emergency management, a road network resilience evaluation index system encompassing resilience capabilities, resilience attributes and traffic accident emergency management ability indicators is constructed. Afterwards, the gray relational technique for order preference by similarity to the ideal solution (GRA-TOPSIS) evaluation method based on combination weighting, which integrates factor analysis with hesitant intuitionistic fuzzy expert scoring, is designed to quantify resilience. Finally, the obstacle degree model is utilized for identifying resilience constraints as the input of a long short-term memory (LSTM) model to predict the resilience variation trend. The fast road network of Shanghai in China is adopted as a case study, and the results indicate that road network resilience embodies significant spatial distribution characteristics. Road length, number of tractors, perception and response and disposal time of traffic accidents cast notable effects on resilience. Additionally, some roads are forecast to show descending resilience. The proposed method is valuable for helping policymakers identify current and potential vulnerable roads and to formulate proposals to effectively improve the resilience of urban agglomerations and promote sustainable cities.

A study on the accumulation model of the Santos basin in Brazil utilizing the source–reservoir dynamic evaluation method

The exploration potential within deep-water petroliferous basins holds great promise for oil and gas resources. However, the dearth of geochemical and isotopic data poses a formidable challenge in comprehending the intricate hydrocarbon charging processes, thereby impeding the comprehensive understanding of hydrocarbon accumulation mechanisms and models. Consequently, the establishment of robust source–reservoir relationships in deep-water petroliferous basins represents a pivotal challenge that significantly influences the exploration strategies and the comprehension of hydrocarbon enrichment dynamics within such basins. In this study, we introduce a novel approach, termed the “source–reservoir dynamic evaluation method,” tailored to investigate reservoir accumulation models in deep-water petroliferous basins. This method uses basin simulation technology to recover the thermal evolution history and hydrocarbon generation and expulsion history of source rocks, and on this basis delimits the hydrocarbon kitchen range. At the same time, the maturity of source rocks corresponding to crude oil and natural gas in typical reservoirs is calculated. Then, when the thermal evolution degree of source rocks adjacent to the reservoir reaches this maturity, the corresponding geological period is the main charging period of hydrocarbon. As a typical deep-water petroliferous basin, the Santos Basin in Brazil has abundant oil and gas reservoirs under the thick salt rock, but there are still some fundamental problems such as unclear oil–gas accumulation process and model. Therefore, in this paper, the main charging periods of typical hydrocarbon reservoirs are determined based on the internal relationship between the thermal evolution history of the main source rocks and the maturity of crude oil and natural gas, and then the hydrocarbon accumulation process is analyzed and the dynamic accumulation model is established. Finally, the favorable prospecting direction is pointed out. The results show that the oil and gas in the Barra Velha Formation in the Santos basin are mainly derived from the Itapema Formation lacustrine shale source rock, and the source rock is mainly developed in the Eastern Sag of the Central Depression, and its main hydrocarbon generation period is from the deposition period of Florianopolis Formation to the deposition period of Santos Formation. The main hydrocarbon expulsion period was from the deposition period of the Santos Formation to the Early deposition of the Iguape Formation. The oil and gas in the Barra Velha Formation were mainly charged from the Late deposition period of the Santos Formation to the Early deposition period of the Iguape Formation. During this period, the hydrocarbon migrated vertically along the normal fault formed in the rift period to the trap of the adjacent inheritance structural highs and accumulated in the reservoir, which was dominated by the accumulation model of the “lower generation-upper reservoir-salt cap”. Since the Barra Velha Formation has the characteristics of near-source accumulation, based on the hydrocarbon expulsion center and hydrocarbon expulsion intensity of the source rock of the Itapema Formation, the distribution ranges of 85% and 50% Pre-salt accumulation probability in the Santos basin were calculated by using the quantitative analysis model of the hydrocarbon distribution threshold. It is suggested that the next oil and gas exploration should be carried out in the paleo-structural highs and slope of Class I favorable area (the hydrocarbon accumulation probability is more than 85%) and Class II favorable area (the hydrocarbon accumulation probability is 85–50%).

BSPM: Blockchain-based Security Protection Model for power load management terminals

Power load management terminals are essential components of the smart grid system. However, they encounter numerous security challenges owing to their widespread deployment across large areas. These nodes exist in uncontrollable physical environments and are vulnerable to malicious attacks. Researching security measures for these terminals is vital to enhancing the security of the smart grid. Blockchain technology, with its distributed storage and tamper-proof features, can effectively protect the data security of these terminals. This paper proposes a blockchain-based security protection model for power load management terminals. First, the architecture of the model is designed using blockchain technology. In addition, a dynamic trust evaluation method for terminals is established through the beta reputation system. The blockchain consensus algorithm for power load management terminals is designed based on the trust evaluation value and practical Byzantine fault tolerance. The effectiveness of the trust evaluation model is verified by simulating the terminal operating situation. The message complexity and storage requirements of the security protection model are analyzed by comparing them with results from related studies.

Dynamic Evaluation Method of Electric Power New Energy and River Basin Ecological Restoration

The wide application of renewable energy can reduce the carbon emissions to the environment and the consumption of petrochemical resources, improve the improvement rate of the natural environment, and at the same time, ecological restoration can improve the ecology of the river basin and produce sustainable improvement of the environment together with renewable energy. In this paper, renewable energy is taken as the research object, and the ecological restoration method of the river basin is jointly evaluated to comprehensively evaluate the natural environment. First, the carbon neutrality data of ecological restoration and renewable energy in the river basin were collected, classified, collated and integrated, and the analysis indicators included CO2 emissions, plant coverage, soil and water conservation, and the cost of power generation. Then, the spatial regression model was used for data processing and calculation, and the ecological coefficient and carbon emission coefficient were determined in an iterative manner. Finally, the actual case is used for dynamic evaluation and analysis. The results show that the new power and watershed ecological restoration methods can improve the natural environment, improve the plant coverage rate and soil and water conservation rate, with an increase rate of 25~30%, reduce carbon dioxide emissions and power generation costs, and reduce the reduction rate of 10~30%, and at the same time, a good ecological environment will also react to new energy and expand the development space of new energy, with an expansion rate of 10%. Therefore, the method of electric power new energy and river basin ecological restoration can improve the natural environment, promote the development of electric power new energy, and optimize the overall structure of new energy.

Dynamic reliability evaluation of TBM components in tunnel construction

The complex geological conditions in tunnels pose a huge challenge to the reliability of tunnel boring machine (TBM). However, existing reliability studies typically focus on core structures such as cutters and cutterheads, with less consideration given to the rest of the components that frequently fail. In this study, the reliability analysis and dynamic evaluation of TBM components with high failure rates are carried out relying on the Shanxi Central Yellow River Diversion Project. The life distribution and reliability variation characteristics of TBM components under different rock mass classes are investigated in terms of tunnelling time and tunnelling distance as two types of life data indexes. And the life index which is more suitable for the reliability evaluation of TBM components is identified by comparison. On this basis, a dynamic evaluation method for the reliability of TBM components under the condition of multi‐classes surrounding rock is proposed. This method can quickly evaluate the current reliability of TBM components and serve as the basis for preventive maintenance. The results of this study play a certain role in supplementing the reliability research of TBM and also provide a scientific basis for optimizing the design and maintenance strategy of TBM components.

A Proposed Methodology for the Dynamic Standard Evaluation of Water Quality in Estuaries: A Case Study of the Pearl River Estuary

Currently, China’s river water quality evaluation adopts the “Environmental Quality Standards for Surface Water”, while the seawater quality evaluation uses the “Seawater Quality Standard”. However, estuarine areas, where rivers meet the sea, do not have evaluation standards, and most often, the “Seawater Quality Standard” is applied. At present, the water quality in the estuary area sometimes exceeds the corresponding seawater quality standards, even though the quality of the surface water does not exceed surface water environmental quality standards and the quality of the seawater before mixing also meets the seawater quality standards. This paper proposes a dynamic evaluation standard based on the salinity of estuarine water and uses this standard to evaluate the water quality in estuaries, thus solving the abovementioned issue. The implementation of this method is simple and effective. Taking the Pearl River Estuary as an example, this paper introduces the dynamic standard evaluation method for water quality in the Pearl River Estuary. Compared to the existing seawater quality standards implemented in estuaries, this dynamic evaluation standard can assess the water quality in estuaries more accurately and provide a reference for water quality evaluation methods in estuaries.

Adaptive dynamic fire danger evaluation of logistics warehouses with fusion of evidential reasoning and smart optimization

Considering the background that the current fire risk assessment methods often rely on subjective assessments based on professional knowledge and experience, they are usually limited to the special fire scene. Given the lack of effective tools for assessing fire risks in logistics warehouses across varying scenes, this paper aims to propose a multi-source data-driven, adaptive dynamic fire danger evaluation method. The method is established based on the fusion strategy of evidential reasoning and artificial fish swarm algorithm. According to the method, rules of danger evaluation levels related to each attribute of fire factors can be generated automatically without necessary of professional knowledges and experiences. The lightweight multi-source data at this moment can be fused to evaluate current fire danger level directly, which does not require training on extensive datasets in advance. Finally, multi-source data including temperature and CO concentration in two fire scenes of one logistics warehouse at different times are utilized to support the ability of the method. The results show that the estimated fire source location according to the center of domain with the evaluated highest danger level matches well with the real fire source location. In conclusion, the main contribution of the research is that the reasonable dynamic danger level of the logistics warehouse fire with time variation can be evaluated automatically. The method has an excellent generality, which is not limited to the specific fire scene. The danger zone of the logistics warehouse under sudden fires can be determined adaptively, which can support the fire prevention and fire safety countermeasures of logistics warehouses.

A Dynamic Reserve Evaluation Method for an Ultra-Deep Fractured Tight Sandstone Gas Reservoir

A Dynamic Reserve Evaluation Method for an Ultra-Deep Fractured Tight Sandstone Gas Reservoir