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This research aimed to study the impact of risk factors on the success of irrigation projects in Thailand. Data were collected using questionnaires at irrigation agencies in various projects. A structural equation model (SEM) was then developed, and the risks of various factors affecting the irrigation project were analysed. The study results found that the risk factors affecting the construction process were as follows: The study results found that the risk factors affecting the construction process were as follows: In terms of work control, the presence of multiple chains of command led to delays in decision-making. This was followed by delays caused by the employer’s material approval process, the lack of clear and detailed construction project planning by the employer, and delays resulting from the performance of subcontractors. The factors mentioned above resulted in the highest score of all 56 factors. The findings of this study showed factors that affected the project, causing project managers, related agencies, or stakeholders to know and find a way for problem solution. The results of this study have found that the SEM values of risk management affecting the construction irrigation project have passed the criteria and have caused the relationship. Those values consist of significance chi-square ( -value = 0.799), chi-square relation value at 0.413, normal fit index (NFI) value at 0.999, goodness of fit index (GFI) value at 0.999, CFI value at 0.999, standardised root mean square residual (RMR) value at 0.029, and root mean square error of approximation (RMSEA) value at 0.001.

Aiming at the magnetic seal in an aero-engine accessory, the range of magnetic force required for the normal operation of the magnetic seal is analyzed. The finite element analysis method based on the Maxwell stress tensor method is used to calculate the magnetic force between the dynamic ring and the magnetic static ring in the magnetic sealing system. The magnetic force under different magnetization modes and gaps is tested by the magnetic measuring test bench, which verifies the correctness of the magnetic calculation method. The effects of permanent magnet material, permanent magnet magnetization direction, magnetic seal structural parameters, and working conditions on the magnetic force between the dynamic ring/magnetic static ring in the magnetic seal are analyzed. Under the same structure, the magnetic force of NeFeB35, SmCo28, Alnico5, and Ferrite as magnetic static rings is compared. The results show that NdFeB35 has the largest magnetic force, SmCo28 is second, Alnico5 and Ferrite are relatively small, and their magnetic forces are about 1/4 to 1/5 of the first two materials. When the rotor or the casing is moving axially, NeFeB35 and SmCo28 materials with large magnetic force should be selected to meet the normal working requirements. Under the same material, the magnetic forces of axial, radial, and radiative magnetization modes are compared. the ratio of magnetic force under axial, radiative, and radial magnetization is about 2.3:1.1:1. If NeFeB35 and SmCo28 materials are axially magnetized, their magnetic forces will exceed the allowable range, which will increase the seal wear. The increase in the inner diameter of the dynamic ring and the gap between the dynamic ring and the magnetic static ring causes a decrease in the magnetic force. The increase in temperature affects the magnetic properties of permanent magnetic material, which leads to the decline of magnetic force between the dynamic ring and the magnetic static ring. The increase in rotor speed leads to the acceleration of the fluctuation frequency of the magnetic force. Still, the maximum and minimum values of the magnetic force under different speeds remain unchanged, and the maximum fluctuation rate is less than 3%. According to the analysis results, the fitting formula of magnetic force calculation is presented by the orthogonal test and the least squares method.

Urban agglomeration is a primary source of global energy consumption and CO2 emissions. It is employed as a major means of modern economic and social activities. Analysis of the temporal and spatial characteristics of CO2 emissions in urban agglomerations and prediction of the future trends of CO2 emissions in urban agglomerations will help in the implementation of CO2 reduction policies within region-wide areas. So, based on that, this study contains four aspects. Firstly, it calculates the energy CO2 emissions of China’s Chengdu-Chongqing urban agglomeration. Secondly, it analyzes the time and space changes in the area by using ArcGIS. Then, the STIRPAT model is used to investigate the factors influencing CO2 emissions, and the elasticity coefficient of the influencing factors is estimated using the ridge regression method, and the important influencing factors are screened on the basis of the estimated results, which are then used as input features for prediction. Finally, a combined prediction model based on the improved GM (1, N) and SVR models is constructed, and then the optimal solution is found through the particle swarm optimization algorithm. It sets up different CO2 emission scenarios to predict the energy CO2 emission of the region and its cities. The results show that, first, the CO2 emissions of the Chengdu-Chongqing urban agglomeration have accumulated year by year, but by 2030, as predicted, it will not reach its peak. The spatial layout of CO2 emissions in this region is not expected to undergo major changes by 2030. Second, population, GDP, gas and electricity consumption, and industrial structure have served as important factors affecting energy CO2 emissions in the region. Third, on the basis of the prediction results for different scenarios, the CO2 emissions in the baseline scenario are low in the short term, but the CO2 emissions in the low-carbon scenario are low in the long run. This study also puts forward some policy recommendations on how to reduce CO2 emissions.

In order to identify the sources of indoor PM2.5 and to check which factors influence the concentration of indoor PM2.5 and chemical elements, indoor concentrations of PM2.5 and its related elements in residential houses in Beijing were explored. Indoor and outdoor PM2.5 samples that were monitored continuously for one week were collected. Indoor and outdoor concentrations of PM2.5 and 15 elements (Al, As, Ca, Cd, Cu, Fe, K, Mg, Mn, Na, Pb, Se, Tl, V, Zn) were calculated and compared. The median indoor concentration of PM2.5 was 57.64 μg/m3. For elements in indoor PM2.5, Cd and As may be sensitive to indoor smoking, Zn, Ca and Al may be related to indoor sources other than smoking, Pb, V and Se may mainly come from outdoor. Five factors were extracted for indoor PM2.5 by factor analysis, explained 76.8% of total variance, outdoor sources contributed more than indoor sources. Multiple linear regression analysis for indoor PM2.5, Cd and Pb was performed. Indoor PM2.5 was influenced by factors including outdoor PM2.5, smoking during sampling, outdoor temperature and time of air conditioner use. Indoor Cd was affected by factors including smoking during sampling, outdoor Cd and building age. Indoor Pb concentration was associated with factors including outdoor Pb and time of window open per day, building age and RH. In conclusion, indoor PM2.5 mainly comes from outdoor sources, and the contributions of indoor sources also cannot be ignored. Factors associated indoor and outdoor air exchange can influence the concentrations of indoor PM2.5 and its constituents.

Green buildings have been viewed as one of the most effective solutions to the negative environmental impacts of construction activities. For the sustainable development of the economy and the environment, many governments in the world have launched a variety of policies to encourage the development of green buildings. However, green targets achieved during the operational stage of green buildings are far below the expectations from the design stage. In addition, the development of green buildings is unevenly distributed in different cities. To help resolve these issues, this paper identifies 28 green building influencing factors from two perspectives, the life cycle and stakeholders. Then, a social network analysis is used to analyse their interactions and identify the critical factors. Our results show that government supervision, incremental cost, property management experience, and the awareness of environmental protection in green buildings are the critical influencing factors in promoting green building development. However, some factors related to contractors, designers and suppliers are not as important as perceived. Finally, some policy recommendations are proposed to promote green buildings in China.

Fear of disease progression is a prevalent psychological challenge among breast cancer survivors, often leading to significant psychological dysfunction and serious sequelae, such as post-traumatic stress syndrome and impaired immunity. However, the factors influencing this dysfunction in the early postoperative period remain unclear. Therefore, this study aimed to identify the influencing factors and construct a risk-prediction model for psychological dysfunction in breast cancer patients with postoperative fear of disease. Using convenience sampling, we selected 202 patients who underwent breast cancer surgery in a Class III Grade A hospital in Jiangsu Province, between January and August 2024. All patients completed a general information questionnaire (which collected data on tumor stage), disease-related scales, the Fear of Progression Questionnaire-Short Form, a breast cancer quality-of-life scale, the Posttraumatic Growth Inventory, and the Fear of Cancer Recurrence-Short Form. Of the 202 patients, 75 (37.1%) developed psychological dysfunction. Single-factor analyses revealed that factors such as tumor stage, education level, surgical method, fear of cancer recurrence, and quality of life ( < 0.05) were significantly related to psychological dysfunction. Logistic regression revealed education level, surgical method, fear of cancer recurrence, and quality of life as influencing factors for psychological dysfunction ( < 0.05). The Hosmer-Lemeshow goodness-of-fit test of the model showed a result of χ = 4.179 ( = 0.841). The area under the receiver operating characteristic curve was 0.860 (95% confidence interval: 0.807-0.912). The Youden index was 0.617; the sensitivity and specificity of the optimal cut-off value were 0.853 and 0.764, respectively. Breast cancer patients with postoperative fear of disease have a high risk of psychological dysfunction, which is influenced by factors such as surgical method, education level, postoperative quality of life, and fear of cancer recurrence.

Suaeda salsa (L.) Pall. (S. salsa) acts as a pioneer species in coastal wetlands due to its high salt tolerance. It has significant biodiversity maintenance, socioeconomic values (e.g., tourism) due to its vibrant color, and carbon sequestration (blue carbon). Bohai Bay region, the mainly distributed area of S. salsa, is an economic intensive region with the largest economic aggregate and population in northern China. The coastal wetland is one of the most vulnerable ecosystems with the urbanization and economic developments. S. salsa in Bohai Bay has been changed significantly due to several threats to its habitat in past decades. In this paper, we analyzed all available archived Landsat TM/ETM+/OLI images of the Bohai Bay region by using a decision tree algorithm method based on the Google Earth Engine (GEE) platform to generate annual maps of S. salsa from 1990 to 2020 at a 30-m spatial resolution. The temporal-spatial dynamic changes in S. salsa were studied by landscape metric analysis. The influencing factors of S. salsa changes were analyzed based on principal component analysis (PCA) and a logistic regression model (LRM). The results showed that S. salsa was mainly distributed in three regions: the Liao River Delta (Liaoning Province), Yellow River Delta (Shandong Province), and Hai River Estuary (Hebei Province, Tianjin). During the past 31 years, the total area of S. salsa has dramatically decreased from 692.93 km2 to 51.04 km2, which means that 92.63% of the area of S. salsa in the Bohai Bay region was lost. In the 641.89 km2 area of S. salsa that was lost, 348.80 km2 of this area was converted to other anthropic land use categories, while 293.09 km2 was degraded to bare land. The landscape fragmentation of S. salsa has gradually intensified since 1990. National Nature Reserves have played an important role in the restoration of suitable S. salsa habitats. The analysis results for the natural influencing factors indicated that precipitation, temperature, elevation, and distance to the coastline were considered to be the major influencing factors for S. salsa changes. The results are valuable for monitoring the dynamic changes of S. salsa and can be used as effective factors for the restoration of S. salsa in coastal wetlands.

Multi-fractured horizontal wells (MFHW) is one of the most effective technologies to develop tight gas reservoirs. The gas seepage from tight formations in MFHW can be divided into three stages: early stage with high productivity, transitional stage with declined productivity, and final stage with stable productivity. Considering the characteristics and mechanisms of porous flows in different regions and at different stages, we derive three coupled equations, namely the equations of porous flow from matrix to fracture, from fracture to near wellbore region, and from new wellbore region to wellbore then an unstable productivity prediction model for a MFHW in a tight gas reservoir is well established. Then, the reliability of this new model, which considers the multi-fracture interference, is verified using a commercial simulator (CMG). Finally, using this transient productivity prediction model, the sensitivity of horizontal well’s productivity to several relevant factors is analyzed. The results illustrate that threshold pressure gradient has the most significant influence on well productivity, followed by stress sensitivity, turbulence flow, and slippage flow. To summarize, the proposed model has demonstrated a potential practical usage to predict the productivity of multi-stage fractured horizontal wells and to analyze the effects of certain factors on gas production in tight gas reservoirs.

As logistics carbon emission efficiency is an essential industry linking regions, investigating this issue from a spatial correlation perspective is practically significant. Utilizing data from 282 prefecture-level cities spanning 2006 to 2019, we used a super slacks-based measure model, a modified gravity model, motif analysis, the Infomap algorithm, and an exponential random graph model to analyze the spatial correlation patterns and influencing factors of logistics carbon emission efficiency. The following conclusions were drawn. (1) The spatial correlation of logistics carbon emission efficiency during the study period exhibited a core–edge pattern, with the central region emerging as a high-correlation hub. (2) The scale of the spatial association network community of carbon emission efficiency in the logistics industry changed constantly, and the stability of the network community structure gradually increased. From a microstructural perspective, the dispersed-mode structure was a pivotal element in the formation of the spatial correlation network of logistics carbon emission efficiency. (3) Node interaction tendencies were a critical force driving network formation. Financial investment, government concern, international openness, population density, and innovation ability were conducive to the formation of spatial correlations of logistics carbon emission efficiency.

Background Oral nutritional supplements are one of the preferred methods of nutritional support for postoperative patients. This study aims to investigate the current status of oral nutritional supplements compliance in postoperative patients with digestive tract tumors and its influencing factors. Methods Convenience sampling was employed to select 242 patients who underwent surgery for digestive tract tumors at a tertiary hospital in Shanghai from October 2022 to July 2023 as the study subjects. Data following a normal distribution were analyzed using independent sample t-tests, ANOVA single-factor analysis, Pearson correlation analysis, and multiple linear regression analysis to determine the factors influencing compliance with oral nutritional supplements. Results A total of 252 questionnaires were distributed, with 10 invalid questionnaires excluded, resulting in an effective questionnaire rate of 96.03%. The compliance score for oral nutritional supplements in postoperative patients with digestive tract tumors was (2.40 ± 1.45), General Self-efficacy Scale (GSES) score was (24.72 ± 4.86), Multidimensional Scale of Perceived Social Support Scale (MSPSS) score was (58.67 ± 11.09), and Belief about Medicines Questionnaire Scale (BMQ) score was (0.17 ± 2.78). Multiple linear regression analysis revealed that age, adverse reactions, educational level, self-efficacy, medication beliefs, and social support were factors influencing compliance with oral nutritional supplements in postoperative patients with digestive tract tumors ( P  < 0.05). Conclusion Our study revealed that the compliance to oral nutritional supplements among postoperative patients with digestive tract tumors was at a moderate level and was closely associated with age, educational level, adverse reactions to oral nutritional supplements, medication beliefs, social support, and self-efficacy. Nursing staff should conduct nursing assessments based on the specific circumstances of patients and their families, provide personalized health education management plans based on the patients’ educational level, enhance patients’ nutrition knowledge, improve patient self-efficacy, and enhance social support for patients, while further improving patient nutrition management.