Influence Of Secondary Factors Research Articles

Town gas daily load forecasting based on machine learning combinatorial algorithms: A case study in North China

Town gas daily load forecasting based on machine learning combinatorial algorithms: A case study in North China

Design and Simulation of Intra-Row Obstacle Avoidance Shovel-Type Weeding Machine in Orchard

This paper presents the design of an intra-row obstacle avoidance shovel-type weeding machine. Theoretical analysis of intra-row weeding components guided the determination of the structures and parameters for key parts, including the signal acquisition mechanism, automatic obstacle avoidance mechanism, and weeding shovel. Furthermore, a hydraulic system was designed to support these functions. The design aims to optimize intra-row weeding operations, reduce labor costs, enhance weed control effectiveness, and prevent collisions between weeding equipment and grapevines. Through the construction of a mathematical model, the analysis determined the necessary minimum return speed of the hydraulic cylinder for the intra-row weeding shovel to avoid grapevines. We also established a reasonable range for the extension speed of the hydraulic cylinder to minimize areas missed during weeding. Further analysis showed that using the minimum return speed of the hydraulic cylinder effectively reduced missed weeding areas. A virtual prototype model of the weeding machine was created in ADAMS. Using the coverage rate of weeding operation as the evaluation index, single-factor simulation tests determined that the extension speed of the piston rod in the obstacle avoidance hydraulic cylinder and the forward speed of the weeding machine are the main influencing factors. The preset threshold of the control system, which triggered the automatic obstacle avoidance mechanism when the obstacle avoidance rod reached a specific angle (the “Angle Threshold”), was identified as a secondary influencing factor. Other factors were considered irrelevant. Hydraulic cylinder extension speed, weeding machine forward speed, and angle threshold were chosen as the influencing factors. Following the principles of a Box–Behnken experimental design, a quadratic regression combination experiment was designed using a three-factor, three-level response surface analysis method. The evaluation criterion focused on the coverage rate of weeding operation. A regression model was developed to determine the coverage rate of the weeding operation, identifying the optimal parameters as follows: obstacle avoidance hydraulic cylinder extension speed of 120 mm/s, forward speed of the weeding machine at 0.6 m/s, and an angle threshold of 18°. The optimized coverage rate of the weeding operation achieved 86.1%. This study serves as a reference for further optimization of intra-row weeding machines in vineyards and for other crops.

Development and Experiment of Semi-Physical Simulation Platform for Space Manipulator.

To address the extended development cycle, high costs, and maintenance difficulties associated with existing microgravity simulation methods, this study has developed a semi-physical simulation platform for robotic arms tailored to different gravity environments and loading conditions. The platform represents difficult-to-model joints as physical objects, while the easily modeled components are simulated based on principles of similarity. In response to the strong coupling, nonlinearity, and excess force disturbance issues in the electric variable load loading system, a fractional-order linear active disturbance rejection control algorithm was employed. The controller parameters were tuned using an improved particle swarm algorithm with modified weight coefficients, and experimental results demonstrate that a fractional-order linear active disturbance rejection control improves response speed and disturbance rejection performance compared to linear sliding mode control. The study investigated the differences in the drive force of joint motors in space robotic arms under varying gravity environments and loading conditions. Experimental results indicate that load torque is the primary influencing factor on joint motor drive force, while radial force serves as a secondary influencing factor. Additionally, when the axis of the joint motor is perpendicular to the ground, it can, to some extent, simulate microgravity conditions on the ground.

Trends, turning points, and driving forces of desertification in global arid land based on the segmental trend method and SHAP model

ABSTRACT Desertification is a form of land degradation observed in arid, semiarid, and dry subhumid ecosystems. Assessing the global trends and drivers of desertification in arid land is crucial for developing effective land restoration policies and mitigating desertification. We aimed to evaluate global segmental trends in desertification in arid land using Moderate Resolution Imaging Spectroradiometer images from 2000 to 2022. By constructing a robust MSAVI-Albedo Desertification Distance Index (DDI), we assessed the segmented development characteristics of desertification on Google Earth Engine. Additionally, we employed the SHapley Additive exPlanations (SHAP) model and machine learning methods to analyze the individual and interactive driving mechanisms of desertification. The results indicated an overall reduction in desertification, with approximately 23.21 million km2 (39% of the global arid region area) exhibiting negative trends in DDI. Approximately 31% of the land area showed a DDI of −0.002/y. Precipitation was consistently the primary factor influencing desertification, with an average SHAP value of 11.42. Secondary influencing factors included potential evapotranspiration, soil moisture, and vapor pressure differences. Notably, the coupling between precipitation and soil moisture exhibited the most significant impact on the desertification process, with SHAP coupling values of approximately 3.28 and 5.06 before and after the DDI turning point, respectively. These findings provide new insights into desertification on a global scale and offer valuable scientific support for promoting effective prevention and control measures.

Hydrodynamics and dissolved organic matter components shaped the fate of dissolved heavy metals in an intensely anthropogenically disturbed estuary

Anthropogenic activities and natural erosion caused abundant influx of heavy metals (HMs) and organic matter (OM) into estuaries characterized by the dynamic environments governed by tidal action and river flow. Similarities and differences in the fate of HM and OM as well as the influences of OM on HMs remain incomplete in estuaries with seasonal human activity and hydrodynamic force. To address this gap, dissolved HMs (dHMs) and fluorescence dissolved OM (FDOM) were investigated in the Pearl River Estuary, a highly seasonally anthropogenic and dynamic estuary. It aimed to elucidate the effects of hydrodynamic conditions and DOM on the seasonal fate of dHMs via the multivariate statistical methods. Our findings indicated dHMs and FDOM exhibited consistently higher levels in the upper estuarine and coastal waters in both seasons, predominantly controlled by the terrestrial/anthropogenic discharge. In the wet season, dHMs and humic-like substances (HULIS) were positively correlated, showing that dHMs readily combined with HULIS. This association led to a synchronous decrease offshore along the axis of the estuary and the transport following the river plume in the surface affected by the salt wedge. Contrarily, dHMs were prone to complex with protein-like components impacted by the hydrodynamics during the dry season. Principal component analysis (PCA) results revealed the terrestrial/anthropogenic inputs and the fresh-seawater mixing process were the most crucial factors responsible for the fate of dHM in wet and dry seasons, respectively, with DOM identified as a secondary but significant influencing factor in both seasons. This study holds significance in providing valuable insights into the migration, transformation, the ultimate fate of dHMs in anthropogenically influenced estuaries, as well as the intricate dynamics governing coastal ecosystems.

Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach.

Exploring the local influencing factors and sources of soil arsenic (As) is crucial for reducing As pollution, protecting soil ecology, and ensuring human health. Based on geographically weighted regression (GWR), multiscale GWR (MGWR) considers the different influence ranges of explanatory variables and thus adopts an adaptative bandwidth. It is an effective model in many fields but has not been used in exploring local influencing factors and sources of As. Therefore, using 200 samples collected from the northeastern black soil zone of China, this study examined the effectiveness of MGWR, revealed the spatial non-stationary relationship between As and environmental variables, and determined the local impact factors and pollution sources of As. The results showed that 49% of the samples had arsenic content exceeding the background value, and these samples were mainly distributed in the central and southern parts of the region. MGWR outperformed GWR with the adaptative bandwidth, with a lower Moran's I of residuals and a higher R2 (0.559). The MGWR model revealed the spatially heterogeneous relationship between As and explanatory variables. Specifically, the road density and total nitrogen, clay, and silt contents were the primary or secondary influencing factors at most points. The distance from an industrial enterprise was the secondary influencing factor at only a few points. The main pollution sources of As were thus inferred as traffic and fertilizer, and industrial emissions were also included in the southern region. These findings highlight the importance of considering adaptative bandwidths for independent variables and demonstrate the effectiveness of MGWR in exploring local sources of soil pollutants.

Spatiotemporal Evolution and Influencing Factors of Soybean Production in Heilongjiang Province, China

Heilongjiang Province, as the largest production and supply base for high-quality soybeans in China, plays a vital role in optimizing the layout of soybean production and promoting the revitalization of the soybean industry. Soybean yield is used as a key indicator of soybean production. This study integrated soybean yield data from agricultural reclamation systems and local authorities. A variety of statistical analysis methods, such as barycenter analysis, the Mann–Kendall test, the space–time cube, and grey relational analysis, were used to research the spatiotemporal evolution and influencing factors of soybean production in Heilongjiang Province from 2011 to 2021. This paper revealed the spatiotemporal evolution mechanism and explored the reasons for the differences in the effects of influencing factors. The results were as follows. (1) During the period between 2011 and 2021, the center of gravity of county-level soybean yield in Heilongjiang Province moved towards the northwest over a distance of 16.82 km. The soybean yield in the province experienced a mutation in approximately 2018, from a downward trend to an upward trend. (2) The spatiotemporal hot spots of county-level soybean yield in Heilongjiang Province were concentrated along the line from Hailun to Aihui. The types of hot spots included consecutive hot spots, intensifying hot spots, sporadic hot spots, and new hot spots. (3) The spatiotemporal agglomeration patterns of county-level soybean yield in Heilongjiang Province included only high-high clusters, only low-low clusters, only high-low outliers and multiple types. (4) The temporal changes in soybean yield in various counties of Heilongjiang Province had obvious regional characteristics. (5) Socioeconomic factors had aftereffects on soybean planting decisions. (6) Sunlight hours, the price ratio of local soybeans to local maize, average temperature, the number of soybean patents, the price ratio of imported soybeans to local soybeans, soybean cultivation income, local soybean prices, and the number of newly established soybean enterprises were primary influencing factors. Precipitation and soybean import volume were secondary influencing factors. The income difference between maize and soybeans, crops-hitting disaster area, and maize yield were general influencing factors. This study aims to offer new pathways for alleviating the structural contradiction between soybean supply and demand and to provide a reference for the formulation of national soybean industry policies and food security strategies.

Analysis of Spatial Pattern and Geographical Divergence Characteristics of Traditional Villages Oriented to Digital Landscape Technology

Abstract In order to study the spatial pattern and geographical differentiation characteristics of traditional villages, this paper combines digital landscape technology to analyze the influencing factors of the distribution of traditional villages. Based on multi-source data, this paper reveals the spatial pattern and geographical differentiation characteristics of traditional villages with the help of GIS, RS and mathematical and statistical methods and systematically explores the inner mechanism and laws. It also combines digital landscape technology, which is in line with the trend of the times, and designs an image visual optimization model based on digital landscape technology so as to make the experimental analysis of the distribution of traditional villages more convenient. It is obtained that the 3D modeling errors using digital landscape technology are all lower than 1.12%, the 3D modeling errors of the overhead GIS 3D modeling method are between 3.21% and 4.58%, and the 3D modeling errors of the digital photography 3D modeling method are all higher than 4.82%. The distance from the urban center, population density, ethnic distribution, and average annual temperature was the main influencing factors, all of which exceeded the explanatory power of 0.4. Average annual rainfall, administrative center, and GDP per capita were the secondary influencing factors, with explanatory power over 0.15. Based on this study, the spatial distribution density of traditional socio-economic villages was more influenced by socio-economic factors, followed by physical geographic factors.

Analyzing the Effect of Air Pollutants on Particulate Matter Concentrations of the Tropical coastal city of Thiruvananthapuram, India by Wavelet Coherence

The main environmental risk factor that need regular monitoring and analysis for efficient air quality management is particulate matter (PM). This paper analyzes the effect of gaseous air pollutants (SO2, CO, Ozone, NOx) on PM concentration in the coastal city of Thiruvananthapuram, India using Wavelet coherence (WTC). The study was conducted using the data from Plammoodu station of the city for the period of 2018 to 2021. The teleconnections of air pollutants with PMs are analysed using WTC for the data of different time spells of 2018-2021, 2018 and 2020. Results showed that among the air pollutants, Ozone (AWC of 0.4713 & PoSC of 23.0234 for PM10, AWC of 0.4814 & PoSC of 26.2261 for PM2.5) have most dominant influence on irrespective of the particulate matter type and time spells. The secondary influencing factors were found to be different with respect to the time spells chosen, indicating the dynamics of local variables on PM concentrations of the city.

Analysis of Secondary Influencing Factors for the WIM System Used in Overload Enforcement

Analysis of Secondary Influencing Factors for the WIM System Used in Overload Enforcement

Response surface analysis, tensile properties, and microstructure of Ti–6.5Al–3.5Mo–1.5Zr–0.3Si fabricated by laser powder bed fusion

In this work, Ti–6.5Al–3.5Mo–1.5Zr–0.3Si alloy samples under different laser process parameters were successfully fabricated by laser powder bed fusion technology. The influence of three processing parameters (laser power P, scanning speed V, and hatch spacing H) on the forming quality and tensile properties of Ti–6.5Al–3.5Mo–1.5Zr–0.3Si samples was investigated by response surface analysis. The Non-Dominated Sorting Genetic Algorithm-II was employed to optimize and attain laser process parameters with optimal forming quality and tensile properties. Specifically, the response surface was established to reveal the optimization method of two response values (forming densification and ultimate tensile strength). The results demonstrated that hatch spacing (H) and its secondary influencing factor (H2) exerted significant effects on densification. In addition, the secondary influencing factors of laser power and hatch spacing (P2 and H2) exerted significant effects on the ultimate tensile strength of Ti–6.5Al–3.5Mo–1.5Zr–0.3Si samples. The influence mechanism of laser process parameters on the densification and tensile properties of samples was further illuminated from the perspective of melting instability and the grain growth process. The maximum tensile strength of the Ti–6.5Al–3.5Mo–1.5Zr–0.3Si sample obtained after optimization reached above 1300 MPa. The maximum strain of the Ti–6.5Al–3.5Mo–1.5Zr–0.3Si sample with the optimal plastic performance reached 16.6%. The strength and toughness of Ti–6.5Al–3.5Mo–1.5Zr–0.3Si samples were analyzed from the aspects of the microstructure and phase composition.

Assessment of Water Quality in a Coastal Region of Sea Dike Construction in Korea and the Impact of Low Dissolved Oxygen Concentrations on pH Changes

To investigate the factors affecting water quality in coastal regions with sea dike constructions, surface water outside a sea dike was monitored for six years from 2015 to 2020 in the Saemangeum region of Korea. Statistical analyses of the six years of high-frequency measurements revealed that the water quality in this system was predominantly governed by natural processes followed by pollutant inputs as the secondary influencing factor. Severe dissolved oxygen (DO) depletion was observed in the surface waters during warm periods, probably owing to the advection of DO-depleted water from elsewhere to the surface layer. Based on the apparent oxygen utilization (AOU)–pH relationship (r = 0.52, n = 1837), the maximum AOU (180 µM) led to a pH decrease from 8.04 to 7.50, which was considerably lower than the estimated value of 7.72. This extra pH drop was probably due to a reduction in the buffering capacity associated with increased CO2 in the water column originating from the atmosphere and in situ production, as well as local water column redox reactions associated with benthic inputs of reduced chemical species. Overall, persistent DO depletion with ongoing eutrophication/hypoxia could accelerate ocean acidification in Korean coastal waters, which could be more acute in coastal regions with artificial coastal constructions.

Competitive advantage, relationship, and benefit: Primary and secondary influencing factors of supply chain collaboration in China's advanced manufacturing industry.

The advanced manufacturing industry is located at the top of the manufacturing value chain. Its development is restricted by supply chain collaboration (SCC), the level of which is affected by many factors. Few studies comprehensively summarize what influences SCC and distinguish the impact level of each factor. Practitioners have difficulty isolating the primary factors that affect SCC and managing them effectively. Therefore, based on synergetics and the theory of comparative advantage, this study analyzes what influences SCC in the advanced manufacturing industry and how these influencing factors work, using data from 94 manufacturing enterprises and the Haken model to identify the influencing factors. The results show that China's advanced manufacturing supply chain underwent a phase change and entered a new stage during 2017-2018. In the new stage, the competitive advantages of enterprises are one order parameter (slow variable) and are primary factors affecting SCC. The interest demands of enterprises are a fast variable and are secondary factors affecting SCC. The competitive advantages of enterprises dominate the interests of enterprises in affecting the collaboration level of China's advanced manufacturing supply chain. In addition, in the process of influencing SCC, there is a positive correlation between the competitive advantages of enterprises and the interest demands of enterprises, and the two factors have a positive feedback mechanism. Finally, when the enterprises in the supply chain cooperate based on their differential advantages, the collaboration capability of the supply chain is at the highest level, and the overall operation of the supply chain is orderly. In terms of theoretical contribution, this study is the first to propose a collaborative motivation framework that conforms to the characteristics of sequential parameters, which provides a theoretical reference for subsequent studies on SCC. In addition, the theory of comparative advantage and synergetics are linked for the first time in this study, and both of them are enriched and developed. Equally importantly, this study compares the bidirectional influence between firms' competitive advantages and firms' interest demands and the ability of both to influence SCC, enriching previous validation studies of unidirectional influence. In terms of practical implications, this study guides top managers to focus on the management practice of collaborative innovation in the supply chain and advises purchasing managers and sales managers on selecting supply chain partnerships.

Effects of long‐term mowing on leaf‐ and root‐associated bacterial community structures are linked to functional traits in 11 plant species from a temperate steppe

Abstract Long‐term mowing can cause morphological stuntedness of plants, thus reducing grassland productivity and exacerbating grassland degradation. Although plant microbiomes can enhance plant resistance against disturbance, considerable uncertainty exists regarding how mowing and mowing‐induced plant trait plasticity affect plant microbiomes in natural grasslands. Here we examined the responses of leaf/root‐associated bacterial (LAB/RAB) communities of 11 dominant herbaceous perennials (six replicates per species) to a 17‐year mowing treatment in a temperate grassland. We also measured leaf/root physiological and morphological traits, and analysed the relationships among mowing practice, bacterial community structures and leaf/root trait parameters. We found that both leaf and root functional traits showed interspecific variations (variations across different plant species), while only the leaf traits exhibited intraspecific variation (treatment‐induced variations within plant species) between the treatments. Similarly, the LAB community structure was more sensitive to mowing but less influenced by host species identity, compared to the RAB community. The RAB community structure was primarily shaped by host species identity, while mowing was a secondary influencing factor. The different patterns of LAB and RAB communities in response to mowing could be specifically explained by the inter/intraspecific variations of the related leaf and root traits. The LAB community was strongly correlated with the leaf traits which exhibited mowing‐induced plasticity (intraspecific variation), with the correlations with nitrogen resorption efficiency and above‐ground dry weight being the greatest. The root traits were important indicators of bacterial community structure in the root compartment across the hosts, rather than between the treatments. Root tissue density showed the strongest interspecific variation, and was identified as an overwhelming driver of the RAB community. The shifts in LAB/RAB communities under mowing were largely attributed to the increased proportions of Actinobacteria. The high mowing sensitivity of the LAB community was associated with the enrichment of soil‐derived Actinobacteria in leaves under mowing. Actinobacteria were also the main keystone taxa in the bacterial community networks under mowing. Our results demonstrate that the magnitude of plant‐associated microbial community response to long‐term mowing is plant compartment and trait variation dependent, and advance our understanding of the leaf/root microbiome‐trait relationships in complex plant communities. Read the free Plain Language Summary for this article on the Journal blog.

Dynamic Response Analysis of RC Frame against Progressive Collapse Based on Orthogonal Test

We aimed to investigate the extent to which the initial state generated by unexpected loads, such as explosions and impacts on the remaining structure, affects the dynamic response of the structure. The study used the orthogonal test method to obtain orthogonal table L25 (53) by combining five levels of each of the three factors of column removal time and initial velocity, and the initial displacement of the remaining structure under three failure scenarios of the corner, side, and internal columns at the ground floor of the reinforced concrete frame structure. The degree of influence of different factors on the structural dynamic response, and the result of the unifactorial impact of initial velocity and initial displacement of the remaining structure on the structural dynamic response in the case of failure of the bottom side columns, were obtained by the polar difference method. The results show that the analysis using the orthogonal test polar difference method revealed that the initial displacement has a more significant influence on the dynamic response of the structure, forming the main influencing factor. At the same time, the failure time and initial velocity have a smaller influence on the dynamic response of the structure as secondary influencing factors. In the case of unifactorial influence, the initial upward displacement and initial upward velocity are detrimental to the structure, leading to progressive collapse. In contrast, the initial downward velocity and initial downward displacement are favorable.

Parameter Optimization for Hydrogen-Induced Fluidized Bed Reduction of Magnetite Iron Ore Fines

Hydrogen-based direct reduced iron (HyDRI) produced by fluidized bed has great potential for achieving the target of net-zero carbon in steelmaking. However, when magnetite ores were used as feedstock, several process parameters showed influences on its fluidization and reduction behaviors. To confirm the dominant influencing factors and its optimum process condition, the orthogonal experimental method was conducted in the present study. The result shows that the primary and secondary influencing factors are oxidation temperature, oxidation content, MgO addition amount, and gas velocity. The optimum condition is that the magnetite iron ore is deeply oxidized at 800 °C, mixed with 1.5 wt.% of MgO powder, and reduced in the fluidized bed at a gas velocity of 0.45 m/s.

Method of Geomechanical Parameter Determination and Volumetric Fracturing Factor Simulation under Highly Stochastic Geologic Conditions

In order to accurately predict geomechanical parameters of oil-bearing reservoirs and influencing factors of volumetric fracturing, a new method of geomechanical parameter prediction combining seismic inversion, well logging interpretation and production data is proposed in this paper. Herein, we present a structure model, petrophysical model and geomechanical model. Moreover, a three-dimensional geomechanical model of a typical reservoir was established and corrected using history matching. On this basis, a typical well model was established, 11 influencing factors of volume fracturing including formation parameters and fracturing parameters were analyzed and their impact were ranked, and the oil recovery rate and the accumulated oil production before and after optimal fracturing were compared. The results show that with respect to formation parameters, reservoir thickness is the main influencing factor; interlayer thickness and stress difference are the secondary influencing factors; and formation permeability, Young’s modulus and Poisson’s ratio are the weak influencing factors. For a pilot well of a typical reservoir, the optimized fracture increased production by 7 tons/day relative to traditional fracturing. After one year of production, the method increased production by 4 tons/day relative to traditional fracturing, showing great potential in similar oil reservoirs.

Geodetector based identification of influencing factors on spatial distribution patterns of heavy metals in soil: A case in the upper reaches of the Yangtze River, China

With the acceleration of urbanization and industrialization, soil heavy metal pollution has become a serious problem threatening ecological security. Identification of influencing factors on the variations and distribution of heavy metals in soils becomes a critical problem to be solved. Here, the geographical detector (geodetector) method was applied to study the influencing factors on the spatial distribution pattern of heavy metals (As, Cd, Cr and Hg) in soils in the upper reaches of the Yangtze River, China. Based on the geodetector model, eight influencing factors were selected, including 5 natural factors of pH, precipitation, soil type, rock type and landform, 2 anthropogenic factors of land use and road density, and 1 comprehensive factor of deposit density). The influence of each factor on the spatial distribution of 4 heavy metals shows that 1) for As, the dominant influencing factors were pH and deposit density, and the secondary influencing factors were precipitation and rock type, 2) for Cd, precipitation had the greatest explanatory power, while pH, land uses, road density and deposit density also had significant effects, 3) for Cr, the main influencing factors were rock type and soil type, and the secondary factors were pH and deposit density, 4) for Hg, the deposit density and rock type dominated its distribution. Compared with single factors, the interaction between factors significantly improved the explanatory power of heavy metals. Taking Cd as an example, the single factor contributions of road density and precipitation are 12.4% and 13.2%, respectively, while the explanatory power of their interaction was as high as 49.8%. This finding indicated that the geodetector can be a helpful method for us to understand the factors affecting the distribution patterns of heavy metals in soil, and it can further service for land management and pollution remediation.

Construction and Analysis of Discrete System Dynamic Modeling of Physical Education Teaching Mode Based on Decision Tree Algorithm.

Physical education is not only an important part of national education but also one of the important means to improve the physical quality of students and citizens. Therefore, the reform of physical education is of great significance to the development of physical education. With the application of data mining technology in the field of physical education, the scale of relevant data increases rapidly. The traditional data analysis methods cannot meet the needs of physical education data analysis. Traditional data analysis methods still have many basic problems to be solved. For example, the professionalism of the structural model and standardization of formal expression are dwarfed by the forefront of the world. There are few real valuable data in the database, and the referentiality is not guaranteed. Therefore, this study puts forward the construction and analysis of discrete system dynamic modeling of physical education teaching mode based on the decision tree algorithm. Through the decision tree algorithm, this study analyzes the data related to physical education and constructs the physical education decision tree system according to the analysis structure. The test results show that the primary influencing factor of physical education teaching is the number of students participating in sports competitions, and the secondary influencing factors are students' liking and teachers' skill level. In addition, teachers' adjustment of physical education teaching contents and methods according to the analysis results of decision tree is conducive to improving students' physical education performance.

Seepage and heat transfer characteristics of sandstone thermal reservoir under aquitard damage

Flow and thermal breakthroughs are the main reasons for the reduction in the operational efficiency of a single-well groundwater heat pump (SWGWHP) system. Furthermore, a large initial pumping flow rate, attributed to the large initial heating/cooling load of the building, can consolidate and damage the aquifer. In this study, an aquitard damage test bench was constructed to investigate the formation and development of flow and thermal breakthroughs during the operation of the SWGWHP system by varying the initial recharge rate (Q), aquifer thickness (H), and the time difference between pumping and recharge (Δt); and to investigate the aquifer damage, seepage, and heat transfer patterns. The results indicated that pressure was the main factor that damaged the aquitard. Aquitard damage can cause or aggravate the flow breakthrough phenomenon. The flow breakthrough strength is the main factor affecting the thermal breakthrough strength. The results of the orthogonal test analysis indicate that the order of the primary and secondary influencing factors of the flow breakthrough strength and operating time efficiency is aquitard thickness (H) > initial recharge rate (Q) > pumping and recharging time difference (Δt). The existence of the aquitard reduced the flow breakthrough strength by 15.2–33.2% and prolonged the efficient operation time of the system by 2.5–5 times. Based on quantitative analyses of the flow and thermal breakthrough strengths, and the variation in the system coefficient of performance, this study reveals the reasons for the formation of thermal breakthrough and provides theoretical suggestions to ensure a safe and stable operation of the SWGWHP system. The results of the study can guide practical installations as well as future research on SWGWHP systems.