Primary Control Factor Research Articles

Experimental investigation on wind loads and wind-induced responses of large-span flexible photovoltaic support structure

Flexible photovoltaic (PV) support structure offers benefits such as low construction costs, large span length, high clearance, and high adaptability to complex terrains. However, due to the high flexibility and low damping of the cable system, wind load becomes the primary control factor for structural safety and the key consideration in the design. In this study, a 45 m span flexible PV support structure with 3 spans and 12 rows was designed. The wind loads on PV panels were obtained by wind tunnel tests on a rigid model and the wind-induced responses were investigated by wind tunnel tests on an aeroelastic model. The shielding effects and tilt angle of PV modules on the wind load and wind-induced vibration of the flexible PV support were studied. The experimental results show that in the rigid model wind tunnel test, the wind pressure on the surface of PV modules exhibits a gradient distribution along the direction of wind flow, with symmetric distribution along the mid span. With the increase in the tilt angle of the PV module, the shielding effect becomes significant and the most pronounced shielding effect on the mean wind pressure coefficient occurs in the second row of the windward zone. In aeroelastic model wind tunnel tests, the mean vertical displacement of the flexible PV support structure increases with the increase of wind speed and tilt angle of PV modules. Due to the wind-resistant anchor cables set in both the windward and leeward zones, the vibration amplitude near the edge rows is significantly smaller than that of the middle rows when the structure is subjected to wind suction. When the flexible PV support structure is subjected to wind pressure, the maximum mean vertical displacement occurs in the first rows at high wind speeds. The shielding effect has a noticeable impact on the wind-induced response of the leeward zone at α = 20° under wind pressure, resulting in the decrease of amplitude vibration by approximately 53 %. The wind vibration coefficients in different zones under the wind pressure or wind suction are mostly between 2.0 and 2.15. Compared with the experimental results, the current Chinese national standards are relatively conservative in the equivalent static wind loads of flexible PV support structure.

Multifactorial influences on land surface temperature within local climate zones of typical global cities

The land surface temperature (LST) of most cities is rising steadily due to both human activities and global climate change, affecting the thermal comfort of cities and threatening the physical health of their inhabitants. Investigating surface temperature features and their affecting elements is therefore essential to improve the thermal environment of cities. Due to differences in surface temperature characteristics in different climatic belts. However, there is still a lack of research on how the local climate zones (LCZs) of different climate belts are affected by natural and social causes. According to the Koppen-Geiger climate classification system, we selected three representative cities from each of the four macroclimatic belts and adopted multiple linear stepwise regression and boosted regression trees (BRT) to systematically explore the linear relationships, relative impacts and marginal effects of normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), population density, and road nuclear density within urban LCZs on LST. The results indicate that (1) LCZs with significant differences in LST among the four global climatic belts account for more than 95 % (P < 0.05), demonstrating that LCZs can effectively differentiate LST based on different land surface cover types. This study can delve into the relationships between the four influencing factors and LST based on LCZs. (2) Primary control factors regulating LST differ in different climate belts. The relative effects of NDVI and MNDWI on LST are greater in the arid and temperate belts, with each 0.1 increase in NDVI and MNDWI producing a cooling effect of more than 0.40 °C and 0.92 °C, respectively. Ventilation corridors created by increased road core density produced a cooling effect of 1 °C or more in the cold belt, with the most pronounced cooling effect. (3) When natural factors are higher and social factors are lower, LST cannot be minimized. It was found that LST was minimized when NDVI, MNDWI, population density and road nuclear density were controlled above 0.4, 0.1–0.2, 10,000–50,000 and 1500–2000 respectively. Our study will provide targeted measures for LCZ-based mitigation of urban thermal environments in various macroclimatic belts.

Hazardous High-Energy Seismic Event Discrimination Method Based on Region Division and Identification of Main Impact Factors: A Case Study

An investigation of risk factors has been identified as a crucial aspect of the routine management of rockburst. However, the identification methods for principal impact factors and the examination of the relationship between seismic energy and other source parameters have not been extensively explored to conduct dynamic risk management. This study aims to quantify impact risk factors and discriminate hazardous high-energy seismic events. The analytic hierarchy process (AHP) and entropy weight method (EWM) are utilized to ascertain the primary control factors based on geotechnical data and nearly two months of seismic data from a longwall panel. Furthermore, the distribution law and correlation relationship among seismic source parameters are systematically analyzed. Results show that the effect of coal depth, coal seam thickness, coal dip, and mining speed covers the entire mining process, while the fault is only prominent in localized areas. There are varying degrees of log-positive correlations between seismic energy and other source parameters, and this positive correlation is more pronounced for hazardous high-energy seismic events. Utilizing the linear logarithmic relationship between seismic energy and other source parameters, along with the impact weights of dynamic risks, the comprehensive energy index for evaluating high-energy seismic events is proposed. The comprehensive energy index identification method proves to be more accurate by comparing with the high-energy seismic events based on energy criteria. The limitations and improvements of this method are also synthesized to obtaining a wide range of applications.

Quality control prediction of electrolytic copper using novel hybrid nonlinear analysis algorithm

Traditional linear regression and neural network models demonstrate suboptimal fit and lower predictive accuracy while the quality of electrolytic copper is estimated. A more dependable and accurate model is essential for these challenges. Notably, the maximum information coefficient was employed initially to discern the non-linear correlation between the nineteen factors influencing electrolytic copper quality and the five quality control indicators. Additionally, the random forest algorithm elucidated the primary factors governing electrolytic copper quality. A hybrid model, integrating particle swarm optimization with least square support vector machine, was devised to predict electrolytic copper quality based on the nineteen factors. Concurrently, a hybrid model combining random forest and relevance vector machine was developed, focusing on primary control factors. The outcomes indicate that the random forest algorithm identified five principal factors governing electrolytic copper quality, corroborated by the non-linear correlation analysis via the maximum information coefficient. The predictive accuracy of the relevance vector machine model, when accounting for all nineteen factors, was comparable to the particle swarm optimization—least square support vector machine model, and surpassed both the conventional linear regression and neural network models. The predictive error for the random forest-relevance vector machine hybrid model was notably less than the sole relevance vector machine model, with the error index being under 5%. The intricate non-linear variation pattern of electrolytic copper quality, influenced by numerous factors, was unveiled. The advanced random forest-relevance vector machine hybrid model circumvents the deficiencies seen in conventional models. The findings furnish valuable insights for electrolytic copper quality management.

Types and sedimentary genesis of barriers and interlayers in the composite turbidite sand bodies of a deep-water canyon: A case study of the Central Canyon in the Qiongdongnan Basin

Deep-water oil and gas is currently the hot spot and difficulty of global oil and gas exploration, and the complex flow process inside the turbidite channels of deep-water canyons makes it difficult to characterize reservoir structures, identify barriers and interlayers and clarify the spatial distribution laws of reservoirs, which restricts the development of deep-water oil and gas. Taking the Central Canyon of the Qiongdongnan Basin as an example, this paper identifies and characterizes the barriers and interlayers of composite channel scale by means of seismic sedimentology, based on three-dimensional seismic and core data. Then, based on the turbidite filling process, barriers and interlayers are classified, and their genesis and control factors are analyzed. Finally, a development model of deep-water barriers and interlayers is established based on the quantitative analysis of sediment transportation system parameters. And the following research results are obtained. First, based on sedimentary genesis, barriers and interlayers are classified into four types, namely mudstone interlayers of lateral (aggradational) turbidite channel genesis (type A), mudstone interlayers of fine-grained turbidite channel genesis (type B), barriers of hemipelagic deep-water sediment genesis (type C), and calcareous petrophysical interlayers (type D). Second, based on the filling stage, barriers and interlayers are divided into four combination sequences, i.e., the initial canyon formation stage with strong sediment supply conditions (type A + type C and type B+ type C), the initial canyon formation stage with weak sediment supply conditions (type B + type C), the stable canyon development/late reworking stage with strong sediment supply conditions (types A + type D), and the stable canyon development/late reworking stage with weak sediment supply conditions (type D). Third, the development types and combination sequences of barriers and interlayers are controlled by the change of sediment transportation volume and terrain slope in the canyon. In the initial canyon formation stage, there is sufficient space for the development of turbidite, and the development of thin barriers and interlayers is controlled by sediment dischage volume, while the development of thick barriers and interlayers is controlled by terrain slope change. In the stable canyon development/late reworking stage, turbidite undergoes superimposed development and overbank. The sediment supply is the primary control factor of barrier and interlayer thickness, and the terrain slope change is the secondary factor. In conclusion, the development model of barriers and interlayers can be used to describe and predict the reservoir structure models under the same sedimentary background and provides a technical support for the exploration and development of deep-water oil and gas.

Divergent control and variation in bacterial and fungal necromass carbon respond to the abandonment of rice terraces

The abandonment of rice terraces in hilly agroecosystems in recent decades has caused substantial changes in microbial characteristics and their impact on microbial necromass carbon (MNC) and soil organic carbon (SOC). Nevertheless, the regulatory mechanisms and impact pathways of MNC remain unclear. Here, soil samples were collected from 0 to 120 cm soil profiles in rice terraces, dry land (DL), and forest land (FL) for analysis. After converting rice terraces to DL and FL, MNC decreased significantly by 31.12% and 38.33%, while SOC decreased significantly by 51.26% and 29.87% respectively. These reductions are due to the loss of terrace management practices and associated functions. There were no significant changes in bacterial necromass carbon (BNC), whereas fungal necromass carbon (FNC) experienced a significant decrease. As a result, the decline in SOC may be primarily attributed to the reduction in FNC. BNC and FNC were regulated by bacterial life history strategies and fungal biomass, respectively. However, bacterial copiotrophs experienced a significant reduction after rice terrace abandonment. The regulation of BNC may be influenced by other factors, potentially offsetting the negative impact of abandonment. Dissolved organic carbon and bulk density were the primary control factors for bacterial community composition and fungal biomass, respectively. Additionally, the impact of soil layers on the alterations in MNC and SOC was more significant compared to the abandonment of rice terraces. These findings indicate that short-term abandonment of rice terraces results in a decrease in SOC, potentially compromising the ecological service function of the hilly agroecosystems. In the face of rapid population growth and global warming, it is crucial to minimize terrace abandonment and enhance utilization rates. This approach will effectively support sustainable terrace management and ecological services.

Initial-Productivity Prediction Method of Oil Wells for Low-Permeability Reservoirs Based on PSO-ELM Algorithm

Conventional numerical solutions and empirical formulae for predicting the initial productivity of oil wells in low-permeability reservoirs are limited to specific reservoirs and relatively simple scenarios. Moreover, the few influencing factors are less considered and the application model is more ideal. A productivity prediction method based on machine learning algorithms is established to improve the lack of application performance and incomplete coverage of traditional mathematical modelling for productivity prediction. A comprehensive analysis was conducted on the JY extra-low-permeability oilfield, considering its geological structure and various factors that may impact its extraction and production. The study collected 13 factors that influence the initial productivity of 181 wells. The Spearman correlation coefficient, ReliefF feature selection algorithm, and random forest selection algorithm were used in combination to rank the importance of these factors. The screening of seven main controlling factors was completed. The particle swarm optimization–extreme learning machine algorithm was adopted to construct the initial-productivity model. The primary control factors and the known initial productivity of 127 wells were used to train the model, which was then used to verify the initial productivity of the remaining 54 wells. In the particle swarm optimization–extreme learning machine (PSO-ELM) algorithm model, the root-mean-square error (RMSE) is 0.035 and the correlation factor (R2) is 0.905. Therefore, the PSO-ELM algorithm has a high accuracy and a fast computing speed in predicting the initial productivity. This approach will provide new insights into the development of initial-productivity predictions and contribute to the efficient production of low-permeability reservoirs.

Comparative Study on the Primary Control Factors of Nanopores in Transitional and Deep Marine Shale in the Southeastern Sichuan Basin, China

The deep marine shale from the Silurian Longmaxi Formation and transitional shale from the Permian Longtan Formation in the Sichuan Basin are two promising targets for shale gas exploration in China. In this study, nanopore characteristics for deep marine and transitional shale samples collected from the same well in the southeastern Sichuan Basin are investigated and compared via field emission scanning electron microscopy imaging, low-pressure gas adsorption, total organic carbon measurement, and X-ray diffraction. Results show that clay-related pores are well developed, pores within organic matter (OM) are rare, and shrinkage cracks along the OM-edge are widely prevalent in transitional shales. The deep marine shale is rich in bubble-like OM pores. The transitional shale has a larger pore volume but a lower specific surface area compared to the deep marine shale. The nanoporosity of the transitional shales is primarily controlled by clay and OM content. In deep marine shale, OM is the primary contributor to nanoporosity. Higher nanoporosity in the deep marine shales than in the middle-shallow ones was observed in this study. This occurrence is attributed to the high fluid pressure and the resulting superior preservation conditions for nanopores in deep marine shale formations.

Impacts of River Bank Filtration on Groundwater Hydrogeochemistry in the Upper of Hutuo River Alluvial Plain, North China

River bank filtration (RBF) under human supervision has been applied for groundwater recharge. This study clarified the characteristics of water composition and its origins during the reservoir water recharge RBF. The groundwater samples were collected during four periods: pre-recharge (June 2018), early recharge (November 2018), intermediate recharge (May 2019), and late recharge (October 2019). Hydrogeochemical methods (Piper diagram, chlor-alkali index, and ion correlation) and principal component analysis (PCA) were used to analyze the chemical evolution of groundwater in the aforementioned periods. TDS concentration tended to increase in the later stage due to the aggravation of carbonate rock dissolution and cation exchange adsorption. Results demonstrated a small, temporary influence of reservoir water on groundwater, characterized as the Ca–Mg–HCO3–SO4 and Ca–Mg–SO4–HCO3 types, both before and after the recharge. The research on water chemistry changes under different mixing ratios depicts that the continuation of the recharge process promotes mineral dissolution. Rock dissolution was the primary environmental control factor of groundwater components during the recharge period. This strengthened the water–rock reaction and caused potential risk impacts such as the increase of nitrate.

Sedimentary and crustal structure of eastern North China from joint inversion of Rayleigh wave phase velocity and ellipticity and its implication for magmatism

Understanding the tectonic evolution of the North China Craton and its destruction mechanism requires the knowledge of eastern North China's fine crustal velocity structure. Using the two-year record of 342 ChinArray stations deployed in eastern North China, we obtain the Rayleigh wave phase velocity via ambient noise interferometry and Rayleigh wave ellipticity from ∼1000 teleseismic events. High-resolution sedimentary thickness and crustal 3D S-wave velocity structure are obtained by jointly inverting these observations. The results show that the Bohai Bay Basin is infilled with thick sediments and the Central Bohai Sea is the current depocenter with sedimentary thickness up to about 6 km. The crust and uppermost mantle beneath the Bohai Bay Basin are characterized by low velocity, indicating a high internal temperature of the crust. An upwelling of mantle thermal material under the crust of the Bohai Bay Basin continuously modifies the crust, suggesting that bottom-up thermochemical erosion is likely the primary control factor of crustal modification and thinning in this area. The uplift areas around the basin generally exhibit high velocity with weaker crustal modification, showing the heterogeneity of craton modification. Moreover, localized low-velocity zones observed beneath the Chifeng Volcanos, Dalainor Volcanos, and Kuandian Volcanos indicate the residual magmatism beneath these Quaternary volcanos. Furthermore, the magmatism beneath the Chifeng and Dalainor Volcanos shares a common magmatic source.

Physicochemical properties and salinization characteristics of soils in coastal land reclamation areas: A case study of China-Singapore Tianjin Eco-City

Land salinization is a global environmental problem, and how to manage saline soils and promote healthy ecosystems has become a major challenge. China-Singapore Tianjin Eco-City is located in coastal land reclamation areas, so salinization is severe in this region. In this study, geostatistical methods, the ordinary kriging method, and principal component analysis were used. Vertical sampling was performed over three layers (0–20 cm, 20–40 cm, and 40–60 cm) at 184 locations within the study area to produce a total of 542 soil samples. It was found that areas with soluble salt contents greater than 3000 mg/kg account for over 90% of the study area, and high soluble salt content in surface layer soils is the dominant factor in soil salinization. Na+, Cl−, and SO42- are the primary control factors that determine the coefficient of variation of the soils’ soluble salt content. Total salinity and Na+, Cl−, SO42-, K+, and Mg2+ reflect on the salinization of the soils, while effective phosphorus, available potassium, and soil organic carbon reflect on the state of soil nutrition. Based on our results, we proposed site-specific and scientific soil remediation and greening measures.

Seasonal dynamics of carbon dioxide and water fluxes in a rice-wheat rotation system in the Yangtze-Huaihe region of China

Investigating variations in the characteristics and influencing factors on carbon dioxide and water fluxes in cultivated lands are crucial for improving crop yield and water use efficiency. As a critical cropping system, rice-wheat rotations are widely distributed in South and East Asia. However, few studies have reported on the long-term changes in carbon dioxide and water fluxes in rice-wheat rotation systems using the eddy covariance method. In this study, a typical rice-wheat rotation field located in Anhui province, China, was chosen for our study. We analyzed seasonal dynamics and influencing factors of net ecosystem CO2 exchange (NEE), gross primary production (GPP), ecosystem respiration (Re), and evapotranspiration (ET) based on 5-year observations and boosted regression trees method. The main results were as follows: (1) daily NEE, GPP, Re, and ET displayed a bimodal curve during the year, with two peaks in the rice and wheat growing seasons, respectively. (2) The cumulative values of NEE were negative in the rice and wheat growing seasons, indicating the cultivated land acted as a carbon sink during the study period. Furthermore, cumulative values of NEE, GPP, Re, and ET were slightly higher in the rice-growing seasons than the wheat-growing seasons. (3) The relative importance analysis revealed the leaf area index (LAI) was the primary control factor of NEE and GPP, whereas temperature and LAI essentially controlled Re. In comparison, net radiation was the most crucial controller of ET. However, their contributions varied in different growing seasons. The findings have important implications for parameterizing crop models to improve simulating carbon and water fluxes for rice-wheat rotation systems.

Dramatic thinning of Alaskan river ice and its climatic controls

River ice thickness (RIT) directly influences human activities, such as rural transportation and subsistence activities, in addition to ecosystem and hydrology processes in the Arctic. Knowledge of RIT response to the rapid Arctic warming is very limited or essentially lacking. The scientific objective of this study is to investigate changes and variations in RIT and their response to rapid Arctic warming. We used ground-based measurements of 45 river gauge sites from 1961 through 2015 spanning 12 river basins across Alaska. The results indicate that the long-term mean maximum river ice thickness (MRIT) ranged from 40.3 ± 12.7 cm in the southeast to 187.3 ± 31.9 cm in northwest Alaska. MRIT decreased dramatically from 1961 to 2015, on average, at a rate of −0.26 ± 0.17 cm per year, and RIT declined significantly in all months from October through March, and more rapidly in winter than in autumn and spring. The impacts of air temperature and snowfall on MRIT change were analysed, and their relative influences were 74% and 26%, respectively. Specifically, an increase in air temperature was the primary factor contributing to MRIT decrease, while increasing snowfall, and snow on river ice enhanced MRIT decline. Seasonally, snowfall was the primary regulator for thickness change and higher air temperature resulted in RIT declining in autumn, while ice thickness decrease was mostly driven by warming in spring. However, neither air temperature nor snowfall is the primary control factor for declining RIT in winter, and further work needs to be done to detect the reason.

Suitable stacking sequence of glass fibre reinforcements for specific application of composite using Taguchi design and finite element method

The effect of stacking sequence of the various forms of glass fibre reinforcement on unsaturated polyester resin composite subjected to tensile and flexural loading was investigated. The Taguchi statistical technique had been used to identify a sustainable stacking sequence with unidirectional glass filament structure, glass fibre chopped strand mat, and woven glass roving mesh as reinforcement. The study used top, middle, and bottom layer positions of the reinforcing material stack inside the composite as control factors for tensile strength, and as well as flip (turning the face of the sample about the testing probe) for flexural strength. Response tables and analysis of variance were used to identify the most influential factors, and response graphs indicated the most important level for control factors. It was demonstrated that the location with the highest contribution for tensile strength was the middle layer, and in the instance of flexural loading, the bottom layer rear to the probe of the universal testing machine was the primary control factor. The strongest level was found to be one made of chopped strand mat sandwiched between two cross-oriented unidirectional filament structures. The uniqueness of this research is to establish the optimal way to stack reinforcements in superstructures such as walls, tables, beams that are only partially supported, poles, and boats, considering the different types of loads and the directions in which the forces act. The Finite Element Method (FEM) was used, to simulate the findings.

Improved Understanding of How Catchment Properties Control Hydrological Partitioning Through Machine Learning

AbstractLong‐term hydrological partitioning of catchments can be well described by the Budyko framework with a parameter (e.g., Fu's equations with parameter ω). The Budyko framework considers aridity index as the dominant control on hydrological partitioning, while the parameter represents integrated influences of catchment properties. Our understanding regarding the controls of catchment properties on the parameter is still limited. In this study, two machine learning methods, that is, boosted regression tree (BRT) and CUBIST, were used to model ω. Interpretable machine learning methods were adopted for better physical understanding including feature importance, accumulated local effects (ALE), and local interpretable model‐agnostic explanations. Among the 15 properties of 443 Australian catchments, analysis of feature importance showed that root zone storage capacity (SR), vapor pressure, vegetation coverage (M), precipitation depth, climate seasonality and asynchrony index (SAI), and water use efficiency (WUE) were the six primary control factors on ω. ALE showed that ω varied nonlinearly with all factors, and varied non‐monotonically with M, SAI, and WUE. LIME showed that the importance of the six dominant factors on ω varied between regions. CUBIST was further used to build regionally varying relationships between ω and the primary factors. Continental scale ω and evapotranspiration were further mapped across Australia based on the most robust BRT‐trained parameterization scheme with a resolution of 0.05°. Instead of using the machine learning method as a black box, we employed interpretability approaches to identify the controls. Our findings not only improved the capability of the Budyko method for hydrological partitioning across Australia, but also demonstrated that the controls of catchment properties on hydrological partitioning vary in different regions.

Quality evaluation of glutenite reservoirs of intergranular - secondary dissolution pore type using a quantitative method

Glutenite (coarse-grained clastic) reservoirs of intergranular–secondary dissolution pore type are dominated by residual intergranular pores and secondary dissolution pores, and characterized by low porosity, low permeability, strong heterogeneity, and highly variable physical properties. It is difficult to conduct a quantitative quality assessment of these reservoirs while their primary control factors remain unclear. In this paper, experimental core data and drilling, logging and seismic data are used to assess the effect of sedimentary facies on reservoir quality. Favorable sedimentary facies zones are identified by analyzing the characteristics of glutenite reservoirs, which includes investigating rock components and their effects on reservoir quality. Argillaceous matrix content and rigid particle content are identified as the primary control factors for these reservoirs. Logging curves sensitive to reservoir quality are selected and examined to continuously characterize the physical parameters of the reservoirs. It establishes a calculation model of reservoir assessment parameters through multivariate regression and determines the quantitative assessment parameter Fr. The quality of the glutenite reservoirs is defined using conventional logging curves. This study also predicts the plane distribution of high-quality reservoirs through geostatistical inversion of the reservoir assessment parameters based on conventional wave impedance inversion, thus providing insight and guidance for quantitative assessment and quality prediction of glutenite reservoirs of the intergranular-secondary dissolution pore type. The application of this method to well deployment based on qualitative evaluation of the glutenite reservoirs in oilfields yielded favorable results.

Quantitative assessment of the effects of climate, vegetation, soil and groundwater on soil moisture spatiotemporal variability in the Mongolian Plateau

Soil moisture (SM) is a key parameter regulating the hydrothermal balance of global terrestrial ecosystems and plays an important role in local ecological environment, particularly in arid and semiarid areas. However, current studies have so far obtained insufficient knowledge of SM spatiotemporal variability and its primary control factors, which limits our understanding of the feedback effects of SM on surface vegetation and hydrothermal activity. Here, we chose the ecologically fragile Mongolian Plateau (MP) as the study area to quantitatively reveal the soil moisture spatiotemporal variability (SMSTV) and the influence of control factors (climate, vegetation, soil and groundwater) with the help of empirical orthogonal functions (EOFs) and geographical detector models. The results indicated that a significant trend of decreasing SM and one dominant spatial structure (EOF1) of SM was found in the MP from 1982 to 2019, which explained over 54% of the spatial variability in SM, and as the soil depth increased, the EOF1 interpretation capacity increased. In addition, EOF1 is high in the north and east and low in the south and west of the MP and that vegetation cover is also relatively greater in the high-value areas. Overall, groundwater has the greatest influence on SMSTV in the MP (q = 0.89); however, precipitation and potential evapotranspiration remain the main control factors for SMSTV for different ecological zones, while the influence of vegetation elements (NDVI and GPP) cannot be ignored, and soil textures (clay, sand, silt) have the least influence. Meanwhile, SMSTV is explained to a greater extent by the interaction of the factors rather than by a single factor. However, there are differences in the influence mechanisms of each factor on SMSTV. This study provides strong evidence that meteorological forcing is not the only factor that dominates SMSTV and that the dominant factors may vary considerably between ecological zones.

ECONOMIC TOOLS OF REALIZATION STRATEGIC MANAGEMENT IN UNIVERSITIES

The main purpose of the paper is to analyze the economic instruments for the implementation of strategic management in universities. Methodology. This study combines economic and managerial analysis of higher educational management. In this study, a set of general scientific and special research methods was used to achieve the goal of the study. The method of logical analysis of the literature was used. The functional, structural and economic analysis was used with purpose to research the main economic tools of realization strategic management in universities. Comparative analysis and synthesis methods were used to study different types of economic and strategic instruments. Using the method of generalization, conclusions and recommendations were made to improve the mechanism of strategic management used in the educational sphere. As the information base for the study the works of scholars in the field of strategic and educational management were used. Findings. The article analyzes the process of strategic management based on the concept of balanced scorecard in the university. The authors used an adaptive mechanism to develop and implement a strategy in the management system of the university. A set of indicators assessing the process of implementing strategic management was proposed. Management influence indicators, primary control factors, development indicators and financial outlook were analyzed. The management system of the university on the basis of strategic management was described, in which the correspondence between the economic indicators of the university and its organizational structure is presented. Challenges to the further implementation of strategic management in developing countries are considered. Practical implications. The results of this study form the methodological and practical basis for improving management processes in developing countries. The results of the study can become the basis for the formation of effective educational management, ensuring the continuous development of universities.

Influence of mixed dielectric fluids on material removal performance during electric discharge machining process

The machining performance of hard material using without powder mixed dielectric fluid has less effect on material removal rate. The present investigation enhances the material removal rate (MRR). Chosen for the research work, the mixed dielectric fluids such as deionized water, tap water and hydro carbon oil were used in equal proportions. The electric discharge machining (EDM) experimental investigations were carried out for Rene 41 alloy. The substance properties of Rene 41 were reported. Further increasing of MRR, silicon carbide (SiC) powder was mixed to the dielectric fluid. Gap voltage, powder concentration and size were considered as primary control factors. The effect of process factors and optimization were analyzed through variance analysis and taguchi approach.

Disentangling the independent effects of vegetation cover and pattern on runoff and sediment yield in dryland systems – Uncovering processes through mimicked plant patches

There is strong empirical evidence on the importance of the spatial pattern of vegetation in dryland hydrologic and geomorphologic dynamics. However, changes in vegetation cover and spatial pattern are often linked, making it difficult to disentangle and assess their independent hydro-geomorphologic roles. We used synthetic sponges placed on the soil surface to mimic the aboveground structure of vegetation patches, and manipulated patch cover and pattern as well as the sink capacity of the patches on a set of 24 (2 × 1 m) runoff plots. Combining natural-rainfall and simulated-rainfall experiments, we aimed to test that (1) both vegetation cover and pattern independently control runoff and sediment yield; (2) for any given cover, coarsening the vegetation pattern entails increasing runoff and sediment yield; and (3) pattern effect is mostly exerted by modulating the source-sink dynamics of the system. We found that increasing either patch cover or patch density decreased runoff and sediment yields from natural rainfalls, yet the effect of patch density largely disappeared when the effect of the co-varying patch cover was removed. Simulated-rainfall experiments on plots with equal medium-low patch cover showed however that coarser patterns (lower patch density; higher patch size) increased runoff coefficients and reduced time to runoff as compared with finer patterns. The effect of patch density was particularly clear when the sink function of vegetation patches was also mimicked. Rainfall interception and direct soil protection proved to be critical mechanisms underlying the effects of patch cover, yet they barely contributed to the effects of patch pattern. The control of overland flow by patch pattern was exerted through changes in the level of runoff disruption. However, physical obstructions to runoff hardly reduced runoff unless coupled to mimicked soil sinks. Overall this work demonstrates the independent effects of patch cover and pattern on the hydro-geomorphologic functioning of patchy landscapes, with patch cover being the primary hydrologic control factor and patch pattern exhibiting its full potential for low and medium low patch cover values. Our findings provide useful information for modelling and understanding dryland vegetation dynamics, and for designing management and restoration measures that take into account the critical role played by source-sink dynamics and hydrological connectivity in dryland landscapes.