Analysis Of Influencing Factors Research Articles

Tackling the modifiable areal unit problem: Enhancing urban sustainability through improved land surface temperature and its influencing factors analysis

Exploring the modifiable areal unit problem (MAUP) in land surface temperature (LST) and its influencing factors is crucial for understanding LST variation patterns and quantifying the factors' impact. Neglecting the MAUP could lead to an incomplete understanding of LST changes and their driving mechanisms. This study used optimal parameters-based geographical detector and gradient boosting regressor models to investigate the MAUP in LST and its influencing factors. The analysis covered 87 cities across seven climate zones in China, examining MAUP in 12 spatial scales to discern the scale and zoning effects on LST and its influencing factors. The research findings were as follows: (1) The sensitivity of LST influencing factors to spatial scales exhibited both spatial and temporal heterogeneity. Significant differences in the q-values of LST influencing factors were observed across various climate zones and periods (daytime and nighttime), with human factors, particularly those related to residents' work, buildings, life, and rest, showing higher spatial scale dependency than natural factors. (2) Zoning effects significantly impacted the q-values of LST influencing factors and were closely linked to the discretization methods and quantities used, which could alter the trends of these q-values. (3) Across the 12 spatial scales, more than 67.34 % of LST influencing factor interaction types were classified as bi-variable enhancement types. The q-values for LST influencing factor interactions were higher and more stable than those of single factors. LST influencing factor interactions in transitional climate zones exhibited high sensitivity to spatial scales. This research enhances our understanding of LST variations, providing valuable insights for urban climate adaptability planning and the development of climate-resilient cities.

A simplified method for evaluating the carrying-soil phenomenon in large cross-section rectangular pipe jacking

During the large cross-section rectangular pipe jacking, the soil within a certain range above the jacking pipe is prone to move along with the pipe jacking machine and jacking pipe under various effects generated by the jacking construction, known as the carrying-soil phenomenon. However, there is currently a lack of evaluation methods to quantitatively analyse this phenomenon. Thus this paper proposes a semi-analytical evaluation method for the carrying-soil phenomenon in large cross-section rectangular pipe jacking. This method considers the soil disturbance caused by the joint effect of the added thrust, the friction force, the grouting pressure, and the over excavation of soil generated during the jacking process through the applications of the Mindlin solution and stochastic medium theory, which allows for a quantitative analysis of the yielding range of the surrounding soil around the pipe jacking and hence a quantitative evaluation of the carrying-soil phenomenon. This method is also well validated by comparing its simulated results with measured ones. Based on the proposed method, the influential factor analysis of carrying-soil phenomenon in large cross-section rectangular pipe jacking is also carried out. It is found that increasing the buried depth, reducing the excavation width, maintaining appropriate grouting pressure, and minimizing ground loss and friction forces can effectively control the carrying-soil phenomenon.

An Influencing Factors Analysis of Road Traffic Accidents Based on the Analytic Hierarchy Process and the Minimum Discrimination Information Principle

Safe traffic is an important part of sustainable transportation. Road traffic accidents lead to a large number of casualties and property losses every year. Current research mainly studies some types of traffic accidents and ignores other types of traffic accidents; therefore, taking various types of road traffic accidents as a whole, an overall study of their influencing factors is urgently needed. To improve road traffic safety, taking various types of road traffic accidents as a whole, this paper analyzes the influencing factors and finds out the causative factors of road traffic accidents. A new index system of road traffic accident influencing factors is constructed based on the existing literature and real traffic data, and their subjective weights and objective weights are obtained by the analytic hierarchy process based on the subjective data and the normalization of the actual traffic data for Yizheng City, Yangzhou, China from January 2020 to December 2020, where the subjective weights are the main weights, and comprehensive weights are obtained by the minimum discrimination information principle correcting the subjective weights with the objective weights. Finally, the global weights, their ranks, and their weight differences are obtained. The main findings are as follows: (1) compared with the real traffic data, experts generally overestimate the impact of road factors on traffic accidents and underestimate the impact of human factors on traffic accidents; (2) in the first-level, human factors and road factors are the causative factors; (3) in the second-level, “motor vehicle drivers’ misconduct”, “road condition”, and “road section” are the causative factors; and (4) in the third-level, “slippery road”, “rain and snow weather”, “intersection”, and “untimely braking” are the causative factors. The research results can provide some scientific basis for improving road traffic safety.

Mitigating development barriers and addressing disparities in border cities of Iran: a comprehensive analysis of border provinces and influential factors

Mitigating development barriers and addressing disparities in border cities of Iran: a comprehensive analysis of border provinces and influential factors

An analytical modelling of the rotation of in-situ principal stresses orientation in the vicinity of an underground opening

ABSTRACT An underground excavation would lead to the redistribution of not only stress magnitude but also stress direction in the host rock mass. In this paper, an equation was derived to quantify the principal stress orientation rotation angle along the roadway radial direction. Besides, the principal stress orientation rotation gradient was analytically modelled followed by a sensitivity analysis of various influential factors on the principal stress orientation rotation. It was found that the principal stress orientation rotation angle tends to decrease as moving further radially into the rock mass from the excavation surface, where the principal stress rotation angle reaches its maximum value of 90°. Four zones have been classified according to the principal stress orientation rotation gradient including high-frequency , medium-frequency, low-frequency and no rotation zone. Finally, the numerical analysis confirmed the redistribution of the principal stress difference and the rotation angle and provided guidance for optimising the bolts setting angle.

Characterization of Rural Spatial Commodification Patterns around Metropolitan Areas and Analysis of Influential Factors: Case Study in Shanghai

Rural areas around developed metropolitan areas continue to attract capital inflows, promoting rural spatial commodification. Taking Shanghai as a case study, this paper analyzes the characteristics of the spatial distribution pattern and the influencing factors of rural spatial commodification (RSC) through kernel density analysis, multiple regression models, and spatial autocorrelation analysis. This study explores four types of RSC at the township scale outside the center of Shanghai: agricultural product-oriented commodification, farm housing commodification, tourism-oriented commodification, and construction land commodification. The results show the following: (1) The four types of RSC show positive spatial correlation, a clear pattern of agglomeration, and an obvious core–edge pattern, with high-density areas distributed in rural areas with specific advantages around metropolitan areas. The distribution of RSC also demonstrates an obvious polarization, forming an uneven distribution pattern. (2) Socio-economic factors, agriculture, transportation location, urbanization, and industrialization are key factors driving RSC. (3) Different types of RSC should be effectively guided at the policy and planning levels according to regional conditions and development stages to enhance the spatial organization of rural regions and achieve the effective revitalization of the countryside surrounding the metropolis.

The efficacy and safety of anlotinib combined with niraparib in treating patients with platinum-resistant recurrent ovarian cancer

Objectives: To investigate the efficacy and safety of anlotinib combined with niraparib in treating patients with platinum-resistant ovarian cancer. Methods: Thirty-five patients with pathological confirmed platinum-resistant ovarian cancer who experienced progression after receiving at least two lines of standard treatment were eligible. All of them were treated with anlotinib combined with niraparib between September 2019 and October 2021. The primary endpoint was progression-free survival (PFS). The second endpoints included overall survival, objective response rate (ORR), disease control rate (DCR) and safety. Survival analysis was performed using the Kaplan-Meier method and Log-rank test, and influence factor analysis was performed using Cox proportional risk regression models. Results: The best overall response showed that partial response was observed in 14 patients, stable disease was noted within 13 patients, and progressive disease was found in 8 patients. Therefore, the ORR and DCR of these 35 patients were 40.0% (95% CI:22.9%-57.1%) and 77.1% (95% CI:62.9%-91.4%), respectively. The median follow-up duration was 18.9 months (6.9-32.2). The median PFS was 6.5 months (95% CI:5.35-7.66). Multivariate Cox regression analysis for PFS indicated that age, Eastern Cooperative Oncology Group performance status (ECOG PS) score, International Federation of Gynecology and Obstetrics (FIGO) stage, and BRCA mutation status were independent factors influencing PFS (P＜0.05). Additionally, the PFS in patients with BRCA mutation who have never received PARP inhibitor treatment was significantly longer than that in patients without BRCA mutation who have been exposed to prior PARPi treatment (15.0 vs 6.0 month, P=0.029). The most common treatment-related adverse reactions were fatigue (85.7%), hematologic toxic (85.7%) and hypertension (74.3%). There were no treatment-related deaths. Conclusion: Anlotinib combined with niraparib shows a promising efficacy and tolerable safety in platinum-resistant ROC patients.

Image-based machine learning and cluster analysis for urban road network: employing Orange for codeless visual programming

ABSTRACT In the pursuit of sustainable urban development, it is imperative to address strategies for optimizing and harmonizing the configuration of urban spatial layouts, especially in response to rapid urbanization. Urban road networks play a pivotal role in shaping intricate urban spatial patterns as they intricately weave throughout the cityscape, significantly influencing land utilization, spatial arrangements, and the mobility of residents. The urban environment is a complex system teeming with multifaceted human activities. With the continuous evolution of advanced data and computational technologies, it is now feasible to construct intricate models for an in-depth exploration of urban spatial structures. In this study, we constructed an analytical framework that leveraged road network image recognition, multi-scale influence factors analysis, and predictions using codeless visual programming. Within this framework, deep learning and unsupervised clustering are applied to complete the hierarchical clustering of road networks and exploit the intrinsic association between cities. Meanwhile, hierarchical clustering and natural and humanistic attributes of cities are connected to investigate the strength of different attributes on specific clusters. The research has the following results: (1) In addition to the alignment of urban road network clustering outcomes, quantitative unevenness and geographical heterogeneity are also observed. (2) Multiple attributes have a complicated impact on urban space, including city size, and socioeconomic and geographic characteristics. Among these factors, socio-economic variables emerge as the principal drivers influencing urban spatial structure. (3) The research enhances the model’s generalization capability and predictive accuracy. As attributes increase and model learning improves, both the Area Under Curve (AUC) and F-1 scores exhibit continuous enhancements, ultimately achieving error-free predictions. The study’s findings offer valuable insights into urban spatial structures and systems, supporting urban decision-makers in addressing challenges associated with urban growth.

Analysis of Influential Factors on Postnatal Care Utilization: A Community-based Cross-sectional Study in Kwadaso Municipality, Ghana

Background: Postnatal care is central to maternal well-being as it averts cognitive complications and illnesses that come from childbirth. Non-utilization of postnatal care hinders initiatives aimed at improving maternal morbidity and mortality because postnatal care is an essential component of those initiatives. This study sought to determine factors associated with the utilization of postnatal care services among mothers in Kwadaso Municipality. Subjects and Method: Community-based cross-sectional study to assess the factors associated with utilization of postnatal care services among mothers in Kwadaso Municipality. A multistage sampling technique was used to recruit 568 women for the study. The dependent variable was mothers' utilization of postnatal care (PNC). The independent variable was demographic characteris­tics of mothers, socio-economic status, educational level, accessibility to healthcare facilities, know­ledge of PNC Services, and awareness of PNC services. The data were collected using a structured questionnaire, the data obtained were analyzed using STATA version 14.0, and the variables were analyzed using the chi-square test and multiple logistic regression and p=0.050 was considered statistically significant. Results: The mean age was 20-25 years 22.4 (SD=3.7 years) and 55.5% were married. The level of knowledge of the mothers on postnatal care services was 71.8% and 82.3% of the subjects knew postnatal period is more dangerous to the lives of mothers and their babies. The unadjusted odds of women who were married (OR= 2.11; 95% CI= 0.86 to 7.46; p=0.042), with two children (OR=4.17; 95% CI= 0.03 to 5.30; p= 0.025), earned GHC 500.00 monthly (OR= 3.47; 95% CI= 0.26 to 5.73; p=0.038), or delivered at the hospital (OR= 3.18= 95%CI= 0.02 to 8.06; p=0.014) were statistically significantly higher in women who were not married, had more than or less than four children, earned less than GHC 500.00 or delivered at other places aside the hospital. Conclusion: This study contributes to the ongoing discourse on maternal health and underscores the importance of evidence-based interventions to improve the health and well-being of mothers and infants.

Analysis of key influencing factors on the financial performance of Jakarta Islamic Index 30 industrial firms

Purpose The purpose of this paper is to investigate the impact of size, asset quality, asset management, financial risk, gross domestic product and inflation rate on the financial performance of companies listed on the Jakarta Islamic Index of 30 industrial firms. Design/methodology/approach Based on the selected criteria, this study analysed an unbalanced panel of data from 30 industrial companies on the Indonesian capital market that are members of the Jakarta Islamic index. Profitability is measured using the dependent variables return on assets (ROA), return on equity (ROE) and stock prices. The influence of explanatory variables of internal factors, namely, size, asset quality, asset management, financial risk, gross domestic product and inflation is investigated using pooled OLS, fixed and random effect estimation. Findings The empirical findings indicate that the scale of a company has a significant impact on its performance, asset quality, asset management and financial risk. GDP has a substantial impact on financial performance, particularly as measured by ROA and ROE. This study’s ramifications have substantial effects on a broad spectrum of stakeholders. The results of this study provide the general public and investors with a greater understanding of the factors that influence a company’s performance on the Jakarta Islamic Index 30. Research limitations/implications The implication of this research is that a deeper comprehension of the factors that influence the financial performance of companies within industrial sectors that follow Islamic finance principles can help design more effective strategies and policies. Practical implications This research has significant practical implications in a number of crucial areas. First, it provides a comprehensive comprehension of the company’s financial performance in the industrial sector in accordance with Islamic finance principles. Second, the research findings provide more precise guidance on how company size, asset quality and macroeconomic variables influence the performance of Indonesia's financial market. Originality/value The study’s authenticity and value hold considerable importance. This study introduces novel perspectives on the assessment of corporate financial performance within industrial sectors through the lens of Islamic finance principles. It offers valuable insights that have not yet been extensively investigated by scholars in the field.

Spatial Prediction and Influencing Factors Analysis of Soil Salinization in Coastal Area Based on MGWR

Quantitative analysis of the spatial non-stationary characteristics of soil salinization influencing factors and the prediction of its spatial distribution are of great significance for the rational use of coastal saline soil resources and the formulation of local prevention and control measures. In this study, the Hekou District of Dongying City, Shandong Province, was used as the study area, and the descriptive statistics of soil salinization status were conducted using classical statistical methods. Spatial autocorrelation theory was used to explore the characteristics of global and local spatial structure of soil salinization in the study area. Influential factors related to soil salinity were selected, and multivariate linear regression （MLR）, geographically weighted regression （GWR）, and multi-scale geographically weighted regression （MGWR） methods were used to model and predict the spatial distribution of soil salinity in the study area and to analyze the spatial heterogeneity of the effects of different influencing factors on soil salinity. The results showed that： ① The mean value of soil salinity in the study area was 5.84 g·kg-1, indicating severe salinization, with a global Moran's I index of 0.19 （P<0.00） and obvious spatial aggregation characteristics. ② Among the three models, the MGWR model had the highest modeling accuracy. Compared with that of the MLR model, the Radj2 of GWR and MGWR improved by 0.05 and 0.07, respectively, and the RSS decreased by 210.13 and 179.95, respectively. ③ The results of MGWR regression showed that the spatial distribution of soil salinity appeared to be mainly affected by the middle soil salinity, soil clay content, and vegetation cover from the mean values of standardized regression coefficients of different influencing factors. Different influencing factors had significant spatial non-stationary characteristics on soil salinization. ④ The results of the spatial distribution prediction of soil salinity in MGWR showed that the areas of high soil salinity （≥6 g·kg-1） were mainly distributed in the northern part of the study area, with an overall spatial trend of decreasing from the coast to the interior. The results of the study can be used as a reference for the analysis and predictive mapping of factors affecting soil salinization in the county and on a larger scale using MGWR.

The Analysis of Influence Factors on Interest In Becoming A Technopreneur Based on Individual Entrepreneurship Orientation

Indonesia’s internet usage is rapidly increasing, especially during Covid-19. Today's most significant number of internet users are students. Students can use the internet for learning, social activities, and business. However, Indonesia still needs a lot of technopreneurs. This study aims to explore factors in internet skills, such as Contextual Efficacy, Internet Self Efficacy, and Computer Self Efficacy, which affect the interest of students in Indonesia to become a technopreneur. The study uses the Structural Equation Model and Partial Lease Square (SEM-PLS) to explore the influence factors of contextual efficacy, internet self-efficacy, and computer self-efficacy that might influence a student's interest in becoming a technopreneur. There were 506 respondents from 11 cities. This study found four relationships that have an influence, such as computer self-efficacy influencing individual entrepreneurship orientation factors. Computer self-efficacy influences the technopreneur intention factor. Contextual element factor influences the technopreneur intention factor, and internet self-efficacy influences the technopreneur intention factor. The study found that three relationships had no effect. The first contextual element factor does not affect Individual Entrepreneurship Orientation. Second, Individual Entrepreneurship Orientation does not affect the Technopreneurship Intention factor; third, Internet Self Efficacy does not affect Individual Entrepreneurship Orientation. The results of this study found that the internet ability factor has a direct influence on asking someone to become a technopreneur. The results of this study are very useful for industry, especially for the education sector, in making technopreneur learning methods. Other factors that were not discussed are being studied further.

Influencing factors analysis of hard limestone reformation and strength weakening under acidic effect

Influencing factors analysis of hard limestone reformation and strength weakening under acidic effect

Influencing Factors Analysis in Railway Engineering Technological Innovation under Complex and Difficult Areas: A System Dynamics Approach

The geological complexity, environmental sensitivity, and ecological fragility inherent in complex and difficult areas (CDAs) present new opportunities and challenges for technological innovation in railway engineering development in China. At the current stage in China, the process of technological innovation in railway engineering within CDAs still faces a series of pressing issues that need addressing. The paper identifies and determines 22 influencing factors for technological innovation in railway engineering within CDAs across five dimensions. Subsequently, a technological innovation model for railway engineering in such areas is constructed based on system dynamics (SD), which is followed by simulation and sensitivity analysis to identify the key influencing factors. The results indicate that key influencing factors for technological innovation in railway engineering within CDAs include technological innovation capability, the adaptability of technology to the environment, R&amp;D funding investment, technological product requirements, technological innovation incentive mechanisms, and the level of technological development. The importance ranking of each dimension is as follows: technological factors &gt; technical factors &gt; management factors &gt; resource factors &gt; environmental factors. The paper provides new insights for promoting technological innovation and management development in complex and challenging railway engineering projects. It offers a fresh perspective to enhance the technological innovation efficiency of railway projects in complex and challenging areas.

Analysis of influential factors on surgical outcomes in pediatric femur neck fractures: a single-institution retrospective study.

This retrospective study aimed to evaluate surgical outcomes and identify influential factors in pediatric femoral neck fractures. A total of 25 hips from 23 pediatric patients who underwent surgical intervention for femoral neck fractures were included. Data encompassing patient demographics, fracture types, surgical techniques, complications, and follow-up outcomes were analyzed retrospectively. Factors such as fracture displacement, timing of surgery, fixation methods, and reduction quality were assessed concerning postoperative complications. Falling from a height accounted for 48% of the trauma mechanisms, and avascular necrosis (AVN) was the most prevalent complication (4 hips). Although fracture displacement, bad reduction quality, and delayed surgery were more common among complicated cases, statistical significance was not attained. The study noted an association between presence of avascular necrosis and worse clinical results(rho: 0.428, p: 0.05, CI: 95%). Surgical treatment yielded favorable clinical outcomes; however, limitations due to the study's retrospective design, limited sample size, and single-center approach underscore the necessity for larger multicenter studies. Our findings emphasize the need for comprehensive investigations to better understand and manage pediatric femoral neck fractures, especially regarding factors influencing AVN and long-term outcomes.

Analysis of Influential Factors of Flipped Classroom to Enhance Students' Independent Learning Ability in Applied Universities

The flipped classroom, as an innovative teaching mode, has been widely used in applied universities, aiming to promote the improvement of students’ independent learning ability. By analyzing the implementation status quo of the flipped classroom in applied universities, this paper explores the influence of teachers’ roles, students’ factors, and instructional design on students’ independent learning ability, and based on this, proposes four strategies, including strengthening teachers’ training and development, optimizing the instructional design and content arrangement, stimulating students’ learning motivation and engagement, and establishing an effective feedback and support system. These strategies can effectively improve students’ independent learning ability, enhance their learning motivation, promote more efficient learning outcomes, and provide more accurate and effective teaching guidance for applied universities.

Dynamic changes in the bauxite trade competition network structure and its influencing factors: Based on temporal exponential random graph model

Global bauxite resource mismatch, limited resources and rising demand have led to intense import competition and export competition. Based on the bauxite trade data from 2005 to 2020, this study uses the complex network method to construct the bauxite import and export competition network, analyzes the evolution of the global bauxite competition pattern and influencing factors. The results show that: First, the density of global bauxite competition network as a whole is on the rise, and competition networks are somewhat resistant to destruction. Second, the import competition network shows a clear trend towards eastward migration, with China's competition strength standing out. Most of the competing economies in the export competition network are large bauxite resource economies. Third, both import and export competition networks show a tendency to spread from core and semi-periphery regions to periphery region. Finally, it can be seen from the influencing factors analysis, the competition network of bauxite has stability. GDP, population, and FTA significantly promote the formation of competition networks, while CO2 and geographic distance play the opposite role. In addition, the effects of industrial structure, common language and common colony on the import and export competition networks are not uniform.

Spatio-temporal variations and influencing factors analysis of coastal wetlands in Tianjin, China

Coastal wetlands are one of the world’s most threatened ecosystems and the influences of climate change and anthropogenic factors on changes in coastal wetland distribution have been the subject of active research in recent years. However, the multi-year intergenerational trends in coastal wetland areas, and the factors influencing them in China have not been fully studied using geographic spatial data during the past 30 years. The relative contributions of climate change and anthropogenic factors to spatio-temporal variations in coastal wetland area also remain to be described. In this study, we discuss changes in the coastal wetland areas and the reasons behind them in Tianjin, which is one of China’s fastest developing regions, using remote sensing and geographic information system technology, Pearson correlation analysis, the relative weight method and the land expansion extraction module, the land expansion analysis strategy (LEAS) model of the Patch-generated Land Use Simulation (PLUS) model. The results showed that the total wetland area declined from 2233.59 km2 in 1984 to 1742.73 km2 in 2020. We conclude that the expansion of non-wetland land types is the main driver of natural wetland area decrease, with 830.86 km2 of natural wetlands having been converted to non-wetlands during this period. The large areas of shallow water and tidal marsh that have been converted to construction and bare land particularly emphasize the primary importance of urbanization and coastal reclamation in driving wetland reduction. The results showed that the temporal variation of natural wetlands area was correlated with population density (PD), total gross domestic product (GDP), annual mean temperature (AMT), average relative humidity (ARH), and primary (PI), secondary (SI), and tertiary industrial development (TI). PD, PI, TI, GDP, SI, AMT, and ARH contributed to 24.47 %, 18.21 %, 15.74 %, 14.16 %, 12.77 %, 7.09 %, and 4.86 % of the wetland area decrease, respectively. PD, distance to settlements (DS), distance to railways (DRW), AMT, distance to rivers (DR), and GDP contributed 17.61 %, 14.27 %, 13.83 %, 13.62 %, 12.07 %, and 10.92 % of the spatial variations of natural wetland area, respectively, which showing the predominant effects of anthropogenic variables on the spatio-temporal natural wetland areas. Our results are valuable in identifying the dynamic spatio-temporal variations in coastal wetlands to improve our understanding of the impacts of climate change and human activities on coastal wetlands to help in the future protection and restoration of wetlands.

Analysis of Exergy Efficiency and Influencing Factors of Tube Heating Furnace in Oilfield Enterprises

Abstract In the energy consumption of surface engineering systems in oilfield enterprises, heating furnaces account for a significant proportion. Therefore, it is necessary to analyze and study their operational performance. This paper conducts an exergy analysis on tubular heating furnaces from the perspective of energy quality. Firstly, an exergy balance model and exergy analysis evaluation criteria are established for the energy system of the heating furnace. Then, the internal and external exergy losses in the heating furnace's energy system are comprehensively considered to identify weak points in the energy system and reflect the utilization of energy quality. The exergy analysis results show that the highest proportion of exergy losses in the heating furnace system is attributed to the heat transfer process. The excess air coefficient, exhaust gas temperature, and external surface temperature of the furnace are inversely proportional to exergy efficiency. Based on the static exergy analysis results, measures to improve the exergy efficiency of the heating furnace are proposed: controlling the excess air coefficient within a reasonable range, reducing the exhaust gas temperature, and lowering the external surface temperature of the furnace. These measures can effectively enhance the energy utilization level of the tubular heating furnace and provide reference and assistance in meeting emission reduction requirements.

HPR83 The Distribution and Influential Factors Analysis of National Price-Negotiated Drugs in Medical Institutions and Pharmacies in China: A Case Study of Hyperkalemia

HPR83 The Distribution and Influential Factors Analysis of National Price-Negotiated Drugs in Medical Institutions and Pharmacies in China: A Case Study of Hyperkalemia