Quantitative Evaluation Model Research Articles

AI-based segmentation of renal enhanced CT images for quantitative evaluate of chronic kidney disease

To quantitatively evaluate chronic kidney disease (CKD), a deep convolutional neural network-based segmentation model was applied to renal enhanced computed tomography (CT) images. A retrospective analysis was conducted on a cohort of 100 individuals diagnosed with CKD and 90 individuals with healthy kidneys, who underwent contrast-enhanced CT scans of the kidneys or abdomen. Demographic and clinical data were collected from all participants. The study consisted of two distinct stages: firstly, the development and validation of a three-dimensional (3D) nnU-Net model for segmenting the arterial phase of renal enhanced CT scans; secondly, the utilization of the 3D nnU-Net model for quantitative evaluation of CKD. The 3D nnU-Net model achieved a mean Dice Similarity Coefficient (DSC) of 93.53% for renal parenchyma and 81.48% for renal cortex. Statistically significant differences were observed among different stages of renal function for renal parenchyma volume (VRP), renal cortex volume (VRC), renal medulla volume (VRM), the CT values of renal parenchyma (HuRP), the CT values of renal cortex (HuRC), and the CT values of renal medulla (HuRM) (F = 93.476, 144.918, 9.637, 170.533, 216.616, and 94.283; p < 0.001). Pearson correlation analysis revealed significant positive associations between glomerular filtration rate (eGFR) and VRP, VRC, VRM, HuRP, HuRC, and HuRM (r = 0.749, 0.818, 0.321, 0.819, 0.820, and 0.747, respectively, all p < 0.001). Similarly, a negative correlation was observed between serum creatinine (Scr) levels and VRP, VRC, VRM, HuRP, HuRC, and HuRM (r = − 0.759, − 0.777, − 0.420, − 0.762, − 0.771, and − 0.726, respectively, all p < 0.001). For predicting CKD in males, VRP had an area under the curve (AUC) of 0.726, p < 0.001; VRC, AUC 0.765, p < 0.001; VRM, AUC 0.578, p = 0.018; HuRP, AUC 0.912, p < 0.001; HuRC, AUC 0.952, p < 0.001; and HuRM, AUC 0.772, p < 0.001 in males. In females, VRP had an AUC of 0.813, p < 0.001; VRC, AUC 0.851, p < 0.001; VRM, AUC 0.623, p = 0.060; HuRP, AUC 0.904, p < 0.001; HuRC, AUC 0.934, p < 0.001; and HuRM, AUC 0.840, p < 0.001. The optimal cutoff values for predicting CKD in HuRP are 99.9 Hu for males and 98.4 Hu for females, while in HuRC are 120.1 Hu for males and 111.8 Hu for females. The kidney was effectively segmented by our AI-based 3D nnU-Net model for enhanced renal CT images. In terms of mild kidney injury, the CT values exhibited higher sensitivity compared to kidney volume. The correlation analysis revealed a stronger association between VRC, HuRP, and HuRC with renal function, while the association between VRP and HuRM was weaker, and the association between VRM was the weakest. Particularly, HuRP and HuRC demonstrated significant potential in predicting renal function. For diagnosing CKD, it is recommended to set the threshold values as follows: HuRP < 99.9 Hu and HuRC < 120.1 Hu in males, and HuRP < 98.4 Hu and HuRC < 111.8 Hu in females.

An international relations quantitative evaluation model (IRQEM) based on an integrated method.

International relations (IR) have great uncertainty and instability. Bad IR or conflicts will bring about heavy economic losses and widespread social unrest domestically and internationally. The accurate prediction for bilateral relations can support decision making for timely responses, which will be used to find ways to maintain development in the complex international situation. An international relations quantitative evaluation model (IRQEM) is proposed by integrating a variety of research models and methods like the interpretative structural modeling method (ISM), Bayesian network (BN) model, the Bayesian search (BS), and the expectation-maximization (EM) algorithm, which is novel for IR research. Factors from several different fields are identified as BN nodes. Each node is assigned different state values. The hierarchical structure of these BN nodes is obtained by ISM. The data collection of 192 cases is used to construct the BN model by GeNIe 4.0. The IRQEM can be used to evaluate the influence of emergencies on IR. The critical factors of IR also can be explored through our proposed model. Results show that the prediction of bilateral relations under emergencies can be realized by updating the indicator set when emergencies occur. The capability to anticipate threats of IR changes is advanced by optimizing the reporting information of IR forecasting through a combination of qualitative and quantitative methods, charts, and texts. Relevant analysis results can provide support for national security decision making.

Damage evolution law of multi-hole blasting igneous rock and quantitative evaluation model of damage degree based on fractal theory and clustering algorithm

The geological phenomenon of igneous rock invading coal seam is widely distributed, which induces mining risk and affects efficient mining. The pre-splitting blasting method of igneous rock is feasible but difficult to implement accurately, resulting in unnecessary safety and environmental pollution risks. In this paper, the blasting model with penetrating structural plane and the multi-hole blasting model with different hole spacing were established based on the Riedel–Hiermaier–Thoma (RHT) damage constitutive to explore the stress wave propagation law under detonation. The damage cloud diagram and damage degree algorithm were used to quantitatively describe the spatio-temporal evolution of blasting damage. The results show that the explosion stress wave presents a significant reflection stretching effect under the action of the structural plane, which can effectively aggravate the presplitting blasting degree of the rock mass inside the structural plane. The damage range of rock mass is synchronously evolved with the change of blasting hole spacing. The blasting in the igneous rock intrusion area of the 21,914 working face is taken as an application example, and the damage degree of rock mass is reasonably evaluated by the box-counting dimension and K-means clustering method, which proves the effectiveness of the blasting scheme and provides reference value for the implementation of related blasting projects.

Evaluation method for health state of highway tunnel structure based on adaptive comprehensive weighting

Accurately evaluating the health state of tunnel structure is of great significance for ensuring the operation safety of tunnel. Based on engineering investigation, fuzzy mathematics and statistical analysis, this paper proposes a new method to evaluate the health state of highway tunnel structure, which considers the multi-disease characteristics and adaptive comprehensive weighting theory. Firstly, by investigating the health state of 20 operation highway tunnels in China, a tunnel disease database with 539 samples was constructed. Considering some new tunnel diseases such as floor heave and lining spalling, a health evaluation index system of highway tunnel was established further. Secondly, utilizing the subjective-objective weighting theory and the principle of variable weight, a new adaptive comprehensive weighting method was proposed to calculate the indexes’ weights. This method overcomes the defect that the constant weight cannot be dynamically updated according to the actual situation of disease development. Then, according to the established four-level classification standard suitable for health evaluation of highway tunnels, the normal distribution membership functions of second-level indexes were determined. Combining with the fuzzy theory, the quantitative evaluation model of tunnel health state was constructed further, which was applied to a case study of the Nanyangshan Tunnel of Kanglin Expressway in China. Furthermore, a comparative analysis with the traditional subjective-objective combination weighting method was conducted, demonstrating the advantages of the proposed evaluation method in terms of engineering applicability and result accuracy. The research findings can provide a decision-making basis for the health maintenance and scientific management of highway tunnels in China.

Tripartite Social Roles of Urban Underground Pipeline Informatization in China

Urban underground pipelines (UUPs) are critical infrastructure, and their safe operation has become a key concern in Chinese society. Currently, the tripartite social roles of the local people’s government, pipeline management departments, and the public are crucial in the informatization of urban underground pipelines. In this study, a survey was conducted among 126 professionals and technical personnel involved in underground pipeline informatization across eight cities in different regions of China. A quantitative weighted evaluation model was established using the Project Quantitative Index (PQI) and principal component analysis (PCA) to investigate the value of the tripartite aforementioned social groups in UUP informatization. The results indicate: (1) There is a significant positive correlation between the tripartite social roles and the promotion of UUP informatization. Moreover, the indicators with the highest PQI value are “Establishment of UUP informatization management departments” and “Support the work of the UUP informatization industry association” under the role of the local people’s government. (2) The informatization work of underground pipelines in different cities is affected differently by the tripartite social roles. This suggests that the local people’s government and professional management departments in different cities can proactively leverage their unique advantages in UUP informatization based on their specific circumstances. (3) PCA results showed that the indicators related to the public carried significant weight, indicating that the public also played an important role in UUP informatization. The degree of UUP informatization in the eight studied cities is ranked as follows: Guangzhou &gt; Beijing &gt; Qingdao &gt; Kunming &gt; Shanghai &gt; Chengdu &gt; Wuhan &gt; Sian. This paper further discusses the unique roles and contributions of the tripartite social groups in UUP informatization, aiming to provide decision support for the future construction, management, and safe operation and maintenance of urban underground pipelines in China.

Method for predicting the injection pressure for horizontal wells in fractured tight sandstone reservoirs

Hydraulic fracturing and horizontal well drilling are the key technologies for increasing the production of continental tight sandstone reservoirs. Taking the typical fractured tight sandstone in the Yuan-287 block of the Ordos Basin as an example, a reservoir geologic model is established based on the stratigraphic correlation of 206 wells. The model and 3D paleostress field are combined to predict fracture parameters such as fracture density and strike. Using reservoir breakdown pressure (RBP) monitoring data and other data such as reservoir physical and mechanical parameters, tectonic fracture characteristics, and in situ stress parameters, a quantitative evaluation model of the RBP, which predicts the 3D distribution of RBP, is established via stepwise regression, and the factors controlling the RBP are analyzed. The rock P-wave velocity, horizontal minimum principal stress, and fracture density are found to be three key parameters that control the breakdown pressure of this tight sandstone reservoir. By comparing the fracture opening pressure of subsurface tectonic fractures with the RBP, a new method for predicting the optimal water injection pressure of this reservoir is developed. This work provides a valuable evaluation model for accelerating the efficient development of continental tight sandstone reservoirs.

Evaluating School Location Based on a Territorial Spatial Planning Knowledge Graph

The reasonable spatial planning of primary and secondary schools is an important factor in education development. In spatial planning, there are many models for the locations of primary and secondary schools; however, few quantitative evaluation models are available. Therefore, based on the many factors affecting the layout planning of primary and secondary schools, a knowledge graph of territorial spatial planning that considers the topological relationship, direction relationship and metric relationship in spatial planning is designed and constructed. A school location evaluation model based on the knowledge graph of territorial spatial planning is proposed. The model combines many factors of the locations of schools, such as the service population, the impact of factories on schools, the adjacency and centrality of school plots, terrain and existing schools in the region, to quantitatively evaluate whether schools are reasonably located within a region. This study focuses on the Guangyang Island area in Chongqing, China, exploring the superiority and rationality of the planned land use for primary and secondary schools within the region. By analyzing the top three and bottom three ranked schools in conjunction with the actual conditions of the site, and comparing them with AHP hierarchical analysis and ArcGIS modelling research, the study concludes that the results of this model are highly reasonable within the scope of China’s territorial spatial planning.

Scraping Effect of Dam-Overtopping Debris Flow—A Case Study of Chutou Gully ‘8.20’ in Miansi Town, Wenchuan County

ABSTRACT After the May 12 earthquake in Wenchuan, numerous debris flow disasters occurred due to heavy rainfall. After the earthquake, disaster prevention and mitigation projects, such as the sand dam, played an essential role in safety. However, when the outflow of solid material exceeds the designed storage capacity of the sand dam, the debris flow overtopping the dam intensifies the scraping effect on the source material of the gully channel. This paper takes the ”8.20” debris flow in the Chutou gully as the research object. A transport mode of the debris flow after dam overtopping is studied through comprehensive field investigation, theoretical analysis, and indoor model tests. A quantitative evaluation model is established for the scraping depth and scraping volume of dam-overtopping debris flow. The study results showed that once the debris flow overtops a dam, the loose source carried by the channel is involved in the entire migration process, enlarging the fluid scale and intensifying the damage along the channel. The results agree with the measurements and observations, with maximum relative errors of 5.81% and 14.70%, respectively. This shows model validity. Furthermore, the test results show that the debris flow scarping phenomenon in the γ=[1.4 t/m3, 1.6 t/m3] interval is the most significant. Overall, this study will help improve future engineering decisions, design strategies, and finding the best design solutions to minimize the risk of dam-overtopping debris flow disasters.

Mining Real-World Big Data to Characterize Adverse Drug Reaction Quantitatively: Mixed Methods Study.

Adverse drug reactions (ADRs), which are the phenotypic manifestations of clinical drug toxicity in humans, are a major concern in precision clinical medicine. A comprehensive evaluation of ADRs is helpful for unbiased supervision of marketed drugs and for discovering new drugs with high success rates. In current practice, drug safety evaluation is often oversimplified to the occurrence or nonoccurrence of ADRs. Given the limitations of current qualitative methods, there is an urgent need for a quantitative evaluation model to improve pharmacovigilance and the accurate assessment of drug safety. In this study, we developed a mathematical model, namely the Adverse Drug Reaction Classification System (ADReCS) severity-grading model, for the quantitative characterization of ADR severity, a crucial feature for evaluating the impact of ADRs on human health. The model was constructed by mining millions of real-world historical adverse drug event reports. A new parameter called Severity_score was introduced to measure the severity of ADRs, and upper and lower score boundaries were determined for 5 severity grades. The ADReCS severity-grading model exhibited excellent consistency (99.22%) with the expert-grading system, the Common Terminology Criteria for Adverse Events. Hence, we graded the severity of 6277 standard ADRs for 129,407 drug-ADR pairs. Moreover, we calculated the occurrence rates of 6272 distinct ADRs for 127,763 drug-ADR pairs in large patient populations by mining real-world medication prescriptions. With the quantitative features, we demonstrated example applications in systematically elucidating ADR mechanisms and thereby discovered a list of drugs with improper dosages. In summary, this study represents the first comprehensive determination of both ADR severity grades and ADR frequencies. This endeavor establishes a strong foundation for future artificial intelligence applications in discovering new drugs with high efficacy and low toxicity. It also heralds a paradigm shift in clinical toxicity research, moving from qualitative description to quantitative evaluation.

Evaluation method of tight sandstone reservoir saturation based on frequency domain electrical dispersion measurement data

The traditional Archie model, which is primarily applicable to medium-high porosity and permeability sandstones, often fails to accurately characterize the the resistivity-saturation relationship in tight sandstones, and it makes the saturation evaluation face great challenges. In this paper, complex resistivity spectrum measurements across 1 kHz to 10 MHz frequencies are performed on tight sandstone reservoir cores. Then, the percentage frequency effect (PFE) values are calculated from complex resistivity modulus at 1 kHz and interfacial polarization frequency under varied saturation states. Furthermore, a new quantitative saturation evaluation model based on the normalized PFE is developed by referring to the normalization method of the resistivity index in the classic Archie formula. The results demonstrate that a good linear correlation between the PFE of the tight sandstone reservoirs and water saturation, and the new saturation evaluation model effectively eliminates electrical differences between samples due to pore structures. Experimental data from tight sandstones shows that higher accuracy of the predicted water saturation by the new saturation evaluation model against measured values compared to the predicted water saturation by the Archie model, and the normalized PFE used in this model can quantitatively evaluate water saturation of tight sandstone reservoirs instead of the traditional Archie formula. The results provide a theoretical model and experimental reference for saturation evaluation in tight sandstone reservoirs.

Risk Coupling Assessment of Vehicle Scheduling for Shipyard in a Complicated Road Environment

Vehicle scheduling at shipyards can involve delays due to numerous risk factors encountered in the complicated shipyard road environment. This paper studies the problems of risk coupling in shipyard vehicle scheduling based on the risk matrix approach, considering the complicated road environment, assessing the degrees of coupling and disorder. Based on safety-engineering theory and comprehensive analysis of the road environment, four key criteria are identified, vehicles, the road environment, the working environment, and humans, including 12 factors and their specific contents. The degree of coupling between various combinations of risk criteria is quantitatively determined utilizing the N-K model. Additionally, the degree of disorder in the risk criteria is assessed based on information entropy theory. The model’s correction coefficients are determined through comparative analysis of experimental data. By integrating the degree of coupling and disorder, delays caused by different combinations of risk criteria in scheduling tasks are computed. The quantitative evaluation model enables accurate appraisal of risk events during shipyard vehicle scheduling. The model provides a valuable managerial tool to analyze delays caused when specific risk criteria are met and to compare these delays to the potential impact on time resulting from adjusting vehicle scheduling plans. This research has significant implications for enhancing vehicle distribution efficiency in shipyards.

Методичні особливості оцінки рівня компетентності експертів у групі при визначенні ефективності ракетно-космічних комплексів

Expert examination methods greatly facilitate the solution of difficult-to-formalize problems. However, in this case the solution is affected by a subjective factor. The decision-making theory has a number of methodological techniques that diminish its effect on the decision made. This paper presents a method of quantitative evaluation of experts’ competence from the results of an expert examination of the efficiency determination of unique, technically complex systems of special and dual purpose, in particular space-rocket complexes. In an expert examination of projects of such systems, it is suggested that the experts’ competence be quantitatively evaluated in two stages: a preliminary evaluation of the experts’ competence from their factual data and a refined evaluation of the experts’ competence just before the calculation of the expected indices of target efficiency using the results of expert examinations made by the procedure developed. The proposed method of quantitative evaluation of experts’ competence is based on evaluating the qualification of the experts involved in the target efficiency determination of a complex engineering system. A rank matrix constructed on the basis of partial criteria of technical efficiency and additional factors of indirect control is proposed as a tool to eliminate cases where at a high level of expert evaluation consistency the most accurate expert evaluations may be considered anomalous in the expert evaluation of the technical and target efficiency of space-rocket systems. The presented mathematical model of quantitative evaluation of experts’ competence includes parameters that adjust the mathematical model to specific conditions of the expert evaluation (expert evaluation methods employed, measurement scales, specific limitations, etc.). The mathematical model is constructed around the axiom that the “true” estimates of the significance of the objects under evaluation lie within the expert evaluation domain. The paper also presents an enlarged algorithm for adjustment parameter calculation from the results of expert estimate preprocessing. The presented mathematical model and algorithm make it possible to develop a computer program for determining experts’ competence from expert evaluation results.

Research on Optimization of Quantitative Evaluation Model of Value for Money for PPP Projects

With the continuous development of China construction industry, in recent years, government departments are actively cooperating with social capital in the PPP model to solve public infrastructure construction problems. This study explores the interaction between the driving factors that affect value for money in the PPP model and their respective importance proportions. It combines the literature analysis method and the Delphi method to construct an index system, uses the analytic hierarchy process to calculate the weight of the driving factors, and adopts decision-making The laboratory method was used to construct a comprehensive influence matrix and calculate centrality. The results show that the comprehensive impact of reasonable risk sharing, recognition of stakeholders, organizational and operational capabilities, long-term contract, full life cycle costs, and government control of prices is relatively high. Government factors are easily affected by other types of factors, while society and the environment have a higher degree of impact on other factors.

Quantitative evaluation model of variable diagnosis for chest X-ray images using deep learning.

The purpose of this study is to demonstrate the use of a deep learning model in quantitatively evaluating clinical findings typically subject to uncertain evaluations by physicians, using binary test results based on routine protocols. A chest X-ray is the most commonly used diagnostic tool for the detection of a wide range of diseases and is generally performed as a part of regular medical checkups. However, when it comes to findings that can be classified as within the normal range but are not considered disease-related, the thresholds of physicians' findings can vary to some extent, therefore it is necessary to define a new evaluation method and quantify it. The implementation of such methods is difficult and expensive in terms of time and labor. In this study, a total of 83,005 chest X-ray images were used to diagnose the common findings of pleural thickening and scoliosis. A novel method for quantitatively evaluating the probability that a physician would judge the images to have these findings was established. The proposed method successfully quantified the variation in physicians' findings using a deep learning model trained only on binary annotation data. It was also demonstrated that the developed method could be applied to both transfer learning using convolutional neural networks for general image analysis and a newly learned deep learning model based on vector quantization variational autoencoders with high correlations ranging from 0.89 to 0.97.

Surface Water Resource Accessibility Assessment of Rural Settlements in the Yellow River Basin

Analyzing the spatial relationship between humans and water is crucial for regional development and water allocation schemes, particularly in the face of extreme water scarcity in the Yellow River Basin. A quantitative evaluation model of surface water resource accessibility (SWRA) has been developed, with rural settlements serving as the research unit. This model is built upon three key dimensions: topography, distance, and surface water resources within the Yellow River Basin. The results show that: (1) The SWRA range spans from 0.13 to 0.88, with an average value of 0.47 and a standard deviation of 0.05. Higher SWRA values are concentrated in the eastern and western regions, while lower values are predominantly found in the central area. (2) The gradient of SWRA across the 12 catchments, from low to high, is as follows: Sanmenxia station, Lanzhou station, Shizuishan station, Longmen station, Tongguan station, Toudaoguai station, Xiaolangdi station, Huayuankou station, Lijin station, Gaocun station, Ai Shan station, and Tangnaihai station. (3) At the city scale, the SWRA values are generally higher in the eastern areas of 10 cities, with one exception being higher in the west. Conversely, in the western areas of nine cities, the SWRA values are lower. The remaining cities exhibit SWRA values at a medium level. The correlation coefficient between primary industry gross domestic product (GDP) and SWRA is 0.271 (N = 56, Sig = 0.043, in 0.05 level, the correlation is significant), which confirms that SWRA serves as a factor influencing GDP and is appropriately designed for assessing water accessibility. Consequently, managers can utilize SWRA results to make informed decisions regarding regional development and water allocation.

The correlation between drivers’ road familiarity and glance behavior using real vehicle experimental data and mathematical models

Objective Road familiarity is an important factor affecting drivers’ visual features. Analyzing the quantitative correlation between drivers’ road familiarity and visual features in complex environment is of great help to improve driving safety. However, there are few relevant studies. This paper takes urban plane intersection as the environmental object to explore the correlation between drivers’ glance behavior and road familiarity, and conducts research on the quantitative evaluation model of road familiarity based on this correlation. Method First, a real vehicle experiment was carried out to record the eye movement data of 24 drivers with different road familiarity. The driver’s visual field plane was divided into 10 areas of interest (AOIs) based on the driver’s perspective. Three measures, including average glance duration, number of glances, and fixation transition probabilities between AOIs at urban plane intersections, were extracted. Finally, based on the experimental results, the driver road familiarity evaluation model was constructed using the factor analysis method. Results There are significant differences between unfamiliar and familiar drivers regarding the average glance duration toward the forward (FW) area, the left window (LW) area, the left rearview mirror (LVM) area and the left forward (LF) area, the number of glances toward the other (OT) area, and the fixation transition probabilities of LW→RF (right forward), LF→LF, LF→FW, FW→LW, FW→FW, FW→RVM (right rearview mirror). The comprehensive evaluation results show that the accuracy rate of the driver road familiarity evaluation model reached 83%. Conclusions This paper revealed that there is a strong correlation between drivers’ road familiarity and drivers’ glance behavior. Based on this correlation, we can include road familiarity as a part of drivers’ working status and establish a high accuracy evaluation model of driver road familiarity. The conclusion of this paper can provide some reference for the humanized design and improvement of advanced driving assistance system, which is of great significance for reducing the driving workload of drivers and improving the driving safety.

Measuring the age-friendliness of streets' walking environment using multi-source big data: A case study in Shanghai, China

The rapidly aging population demands more public and academic attention to both the physical and psychological needs of older adults. Walking is one of the most favored forms of transportation and exercise for older adults. Hence, the conditions of walkways and the built environment are critical for the development of an older adults-friendly society. In this research, considering six indicators of greenness, walkability, safety, imageability, enclosure, and complexity, a novel quantitative evaluation model based on multi-source geospatial big datasets is proposed for assessing the age-friendliness of streets' walking environment from both the older adults' and experts' perspectives and has also been successfully applied in the case study of Xuhui District in Shanghai, China. Based on the survey with both groups of older adults and experts, the age-friendliness of the streets' walking environment from both perspectives was obtained, analyzed, and compared. The results show that among all sub-districts in Xuhui District, Tianping Road Sub-district and Hunan Road Sub-district have the best walking environment; and the roads surrounding expressways or arterial roads typically have lower levels of age-friendliness, while sub-arterial roads and access roads generally exhibit higher levels of age-friendliness according to both older adults and experts. It is also noted that streets with high age-friendliness for older adults tend to be outstanding in walkability and greenness, whereas experts prefer streets with high safety and walkability. In addition, the comprehensive analysis of the impact factors on age-friendliness further reveals a series of hidden significant influencing factors associated with the different preferences between older adults and experts. It is believed that our findings are useful for supporting policy-makers on how to improve the age-friendliness of streets and build age-friendly cities.

Research on quantitative evaluation of digital economy policy in China based on the PMC index model.

The development of digital economy is a strategic choice to grasp the revolution of new science and technology and the new opportunities of industrial reform. The development of digital economy depends on the good support of policy and theoretical system. Therefore, the quantitative evaluation of policy texts provides the basis of decision-making and the suggestions of path optimization for the formulation and improvement of digital economy policy of China. By selecting the text of digital economy policy issued by China government, the paper constructs a quantitative evaluation model of digital economy policy using the methods of content analysis and text mining. The empirical research results show that the overall design evaluation of the selected policy is relatively reasonable. Six policies were evaluated as excellent and two as acceptable. In view of the problems such as lack of predictive policy in the policy type, lack of specific policy in the policy timeliness, imbalance in the use of policy guarantee, and lack of comprehensive coverage in the policy objectives, the paper puts forward corresponding countermeasures and suggestions.

Research on coal mining intensity based on the DPSIR-SPA model.

High-intensity mining has become a major trend in future coal mining. However, it will unavoidably worsen the harm done to the natural environment of mining sites by coal mining, which is already prone to doing so. So, how may coal mining intensity (CMI) be decreased? Minimize the harm that coal mining causes to the environment and offer a theoretical basis for protecting the environment in mining sites. In order to achieve this, based on the existing literature on CMI, we first redefine the concept of CMI, analyze its influencing factors, propose an evaluation index system, and introduce the theory of set pair analysis (SPA) to build a quantitative evaluation model of CMI. We then propose an adjustment strategy for the CMI and conduct a verification analysis using the Halagou Coal Mine and Caojiatan Coal Mine as an example. The results show that the Halagou and Caojiatan Coal Mine belong to the higher-intensity mining stage. It is consistent with existing research. Moreover, the development trend of CMI in the Halagou Coal Mine is analyzed in conjunction with the set pair potential theory, and specific measures to reduce CMI are given, from the perspective of coal mining. It provides the basis for the source protection of the ecological environment in the mining area. Theoretically, this study can help both the quantitative assessment of mining intensity and the source protection of the mining ecological environment. Besides, it offers specific guidelines for building environmentally friendly mines.

Parameter evaluation method of tight carbonate reservoir using electrical imaging pores diameter spectrum

Currently, high-yield gas reservoirs have been discovered by exploring Lower Paleozoic tight carbonate oil and gas reservoirs in the Changqing area. These reservoirs, however, are impacted by subsequent karstification and diagenesis processes, leading to the formation of complex pore spaces with varying pore diameters ranging from microns to millimeters. Conventional well curves, with a resolution at the decimeter level, can only provide an overview of the reservoir porosity, making it challenging to evaluate the pore structure. On the other hand, electrical imaging logging data has a sampling interval of 0.1 inches and a resolution as fine as 5 mm, providing valuable information for pore structure evaluation in tight reservoirs. Since formation resistivity is influenced by factors like porosity, mud filtrate resistivity, and cementation, the cementation index serves as a reflection of the pore structure. Therefore, by calibrating the cementation index using data obtained from experiments at different scales, the pore structure parameters of tight carbonate reservoirs can be evaluated. This study conducted a comparison between numerous core experiments at various scales and both conventional and imaging log data. A method was proposed for extracting pore size distribution characteristics and calculating the pore size spectrum based on electrical imaging logging, ultimately leading to the establishment of a quantitative evaluation model for the pore structure of tight carbonate reservoirs. The results demonstrated improved accuracy and consistency in interpretation, as confirmed by well test data.