Moderate Scales Research Articles

Complex multivariate model predictions for coral diversity with climatic change

AbstractModels of the future of coral reefs are potentially sensitive to theoretical assumptions, variable selectivity, interactions, and scales. A number of these aspects were evaluated using boosted regression tree models of numbers of coral taxa trained on ~1000 field surveys and 35 spatially complete influential environmental proxies at moderate scales (~6.25 km2). Models explored influences of climate change, water quality, direct human‐resource extraction, and variable selection processes. We examined the predictions for numbers of coral taxa using all variables and compared them to models based on variables commonly used to predict climate change and human influences (eight and nine variables). Results indicated individual temperature variables alone had lower predictive ability (R2 < 2%–7%) compared to human influence variables (6%–18%) but overall climate had a higher training–testing fit (70%) than the human influence (63%) model. The full variable model had the highest fit to the full data (27 variables; R2 = 85%) and indicated the strongly interactive and complex role of environmental and human influence variables when making moderate‐scale biodiversity predictions. Projecting changes using Coupled Model Intercomparison Project (CMIP) 2050 Representative Concentration Pathways (RCP2.6 and 8.5) water temperature predictions indicated high local variability and fewer negative effects than predictions made by coarse scale threshold and niche models. The persistence of coral reefs over periods of rapid climate change is likely to be caused by smaller scale variability that is poorly simulated with coarse scale modeled predictions.

Genetic and Phenotypic Divergence in a Dung Beetle 50 Years After Its Introduction to Australia.

Species translocations are increasingly being used in conservation and for biological control. The success of a translocation can be strongly influenced by the evolutionary processes occurring during the early phase of the introduction and the subsequent spread to new regions. In this study, morphological variation and population genetic structure were assessed in the African dung beetle Digitonthophagus gazella, a species that was intentionally introduced to Australia for biological control in 1968 and subsequently spread widely across the northern part of the continent. A dataset based on 1594 neutral single nucleotide polymorphism (SNP) loci that were genotyped in 187 individuals from 12 sites revealed significant genetic divergences between sites (global F ST = 0.118) and provides evidence of restricted gene flow among established populations at small to moderate spatial scales (74-500 km). Geometric morphometric analyses revealed significant divergence among populations in the shape of the foretibia, a trait ecologically important for tunnelling in soil and dung. Moreover, phenotypic divergence in this trait for both sexes was significantly higher than genetic differentiation at selectively neutral loci (P ST > F ST), suggesting that directional selection is contributing to the phenotypic divergences among populations. Our study shows how population structure can establish quickly in an introduced species and highlights the importance of considering local adaptation when performing translocations on established populations.

Modeling the spatial distribution of numbers of coral reef fish species and community types in the Western Indian Ocean faunal province

Predicting and mapping coral reef diversity at moderate scales can assist spatial planning and prioritizing conservation activities. We made coarse-scale (6.25 km2) predictive models for numbers of coral reef fish species and community composition starting with a spatially complete database of 70 environmental variables available for 7039 mapped reef cells in the Western Indian Ocean. An ensemble model was created from a process of variable elimination and selectivity to make the best predictions irrespective of human influences. This best model was compared to models using preselected variables commonly used to evaluate climate change and human fishing and water quality influences. Many variables (~27) contributed to the best number of species and community composition models, but local variables of biomass, depth, and retention connectivity were dominant predictors. The key human-influenced variables included fish biomass and distance to human populations, with weaker associations with sediments and nutrients. Climate-influenced variables were generally weaker and included median sea surface temperature (SST) with contributions in declining order from SST kurtosis, bimodality, excess summer heat, SST skewness, SST rate of rise, and coral cover. Community composition variability was best explained by 2 dominant community richness axes of damselfishes-angelfishes and butterflyfishes-parrotfishes. Numbers of damselfish-angelfish species were ecologically separated by depth, and damselfishes declined with increasing depth, median temperature, cumulative excess heat, rate of temperature rise, and chronic temperature stresses. Species of butterflyfish-parrotfish separated by median temperature, and butterflyfish numbers declined with increasing temperature, chronic and acute temperature variability, and the rate of temperature rise. Several fish diversity hotspots were found in the East African Coastal Current Ecoregion centered in Tanzania, followed by Mayotte, southern Kenya, and northern Mozambique. If biomass can be maintained, the broad distributions of species combined with compensatory community responses should maintain high diversity and ecological resilience to climate change and other human stressors.

Lessons to be learned by comparing integrated fisheries stock assessment models (SAMs) with integrated population models (IPMs)

Integrated fisheries stock assessment models (SAMs) and integrated population models (IPMs) are used in biological and ecological systems to estimate abundance and demographic rates. The approaches are fundamentally very similar, but historically have been considered as separate endeavors, resulting in a loss of shared vision, practice and progress. We review the two approaches to identify similarities and differences, with a view to identifying key lessons that would benefit more generally the overarching topic of population ecology. We present a case study for each of SAM (snapper from the west coast of New Zealand) and IPM (woodchat shrikes from Germany) to highlight differences and similarities. The key differences between SAMs and IPMs appear to be the objectives and parameter estimates required to meet these objectives, the size and spatial scale of the populations, and the differing availability of various types of data. In addition, up to now, typical SAMs have been applied in aquatic habitats, while most IPMs stem from terrestrial habitats. SAMs generally aim to assess the level of sustainable exploitation of fish populations, so absolute abundance or biomass must be estimated, although some estimate only relative trends. Relative abundance is often sufficient to understand population dynamics and inform conservation actions, which is the main objective of IPMs. IPMs are often applied to small populations of conservation concern, where demographic uncertainty can be important, which is more conveniently implemented using Bayesian approaches. IPMs are typically applied at small to moderate spatial scales (1 to 104 km2), with the possibility of collecting detailed longitudinal individual data, whereas SAMs are typically applied to large, economically valuable fish stocks at very large spatial scales (104 to 106 km2) with limited possibility of collecting detailed individual data. There is a sense in which a SAM is more data- (or information-) hungry than an IPM because of its goal to estimate absolute biomass or abundance, and data at the individual level to inform demographic rates are more difficult to obtain in the (often marine) systems where most SAMs are applied. SAMs therefore require more 'tuning' or assumptions than IPMs, where the 'data speak for themselves', and consequently techniques such as data weighting and model evaluation are more nuanced for SAMs than for IPMs. SAMs would benefit from being fit to more disaggregated data to quantify spatial and individual variation and allow richer inference on demographic processes. IPMs would benefit from more attempts to estimate absolute abundance, for example by using unconditional models for capture-recapture data.

Diversity-biomass relationships are shaped by tree mycorrhizal associations and stand structural diversity at different spatial scales

Diversity-biomass relationships (DBRs) in terrestrial ecosystems tend to vary across spatial scales, but, particularly in hyperdiverse forests, the mechanisms driving these trends remain uncertain. Until now, few have simultaneously investigated the connections between tree species diversity, stand structural diversity, mycorrhizal associations, and ecosystem functioning. In addition, DBRs have only been studied at limited spatial scales, with limited focus on the direct and indirect effects of environmental factors. We addressed these research gaps using a 30-ha forest dynamics plot located in Pu'er City, Southwest China. Through piecewise structural equation models, we quantified the direct effects of tree species diversity (α, β, γ), stand structural diversity, mycorrhizal associations (AM, EcM), and the environmental factors (soil fertility and topography), as well as the indirect effects of the environmental factors on aboveground tree biomass across spatial scales ranging from 400 to 230,400 ​m2. We hypothesized that complex interactions among these factors underpin the variation in DBRs in natural ecosystems across spatial scales. Our results showed that environmental conditions indirectly affected the tree biomass via changes in tree species diversity, and these effects became stronger as the spatial scale increased. At small to moderate spatial scales, environmental factors were more predictive of tree biomass than tree species diversity (or its components); the effects of stand structural diversity on biomass also gradually increased with spatial scale. Conversely, from the intermediate to the largest spatial scales, mycorrhizal associations gradually became the best predictors of DBR dynamics. Our research offers novel empirical evidence demonstrating the importance of environmental conditions, structural diversity, and mycorrhizal associations in shaping cross-scale DBRs. Future comprehensive studies should consider these factors to assess the mechanisms shaping scale-dependent DBRs in complex natural ecosystems.

Standardized Precipitation Index for Metrological Drought Analysis In Kashmir Valley Of Jammu And Kashmir

Abstract: The term "drought" is multifaceted, with varying interpretations based on personal experiences. It is a natural disaster that is distinguished by a severe and ongoing precipitation deficit. The planning and management of freshwater resources as well as the forecasting of the arrival and intensity of droughts depend heavily on drought monitoring. A number of indices have been put forth over time to track and measure data related to droughts. The Standardized Precipitation index (SPI), which is based on the idea of probability, is the most commonly used index. The current study examines the possibility of analysing the temporal pattern and severity of drought in the Kashmir Valley (Jammu and Kashmir) using precipitation-based SPI. Utilizing the "DrinC" software application, the Indices were calculated. SPI values were calculated using monthly precipitation data for the IMD Srinagar 2003 to 2022. SPI series computations were carried out for short, moderate, and long time scales. Based on the data, the Kashmir region experienced four periods of moderate drought in the years 2007, 2009, 2012, and 2016. Additionally, there was no significant drought from 2003 to 2022. 2007 had the maximum intensity with an SPI value of -1.37 for a moderate drought.

Research on the direction perception of cruising copepods by the fish lateral line using pressure difference matrix and residual neural network regression method

Autonomous Underwater Vehicles (AUVs) are high-end marine equipment. In turbid, dimly lit waters that require high concealment, traditional methods struggle to detect targets effectively. Fish, however, can accurately perceive nearby targets using lateral line systems even in harsh environments. This provides inspiration for intelligent detection in AUVs. In this study, the flow field cloud around copepods and visualization of pressure difference matrix are analyzed. A method combining the pressure difference matrix and residual neural network regression is proposed to study how fish lateral lines perceive the direction of cruising copepods. Furthermore, the effects of three key parameters on direction perception are discussed, including perception time scale, initial vertical distance and training angular intervals number. The results indicate that most predicted directions are close to the true directions, with only a small number of misjudgments occurring in the vicinity of the opposite or perpendicular directions to the true direction. Moderate perception time scales (100 ms), shorter vertical distances (2.5 mm), and reasonable training angular intervals number (24 or 36) all lead to lower perception error. This study provides a quantitative analysis of fish lateral line perception mechanism, offering valuable theoretical guidance and technical support for intelligent detection in AUVs.

Moderate operation scales of agricultural land under the greenhouse and open-field production modes based on DEA model in mountainous areas of southwest China

The identification of the moderate scale of agricultural land was recognized as one of the key measures promoting sustainable agriculture development. However, due to the research gap in mountainous areas, new agricultural business entities (NABE) in these areas usually either refer to the plain area or simply pursue large scale, resulting in low production efficiency and even posing a threat to their sustainable survival. In this study, the Data Envelopment Analysis (DEA) model and Tobit regression model tools were employed to quantitatively reveal the moderate scale and key driving factors of agricultural land under the scale operation modes of greenhouse and open-field types. It was based on 154 NABE questionnaires in the mountainous areas around the Sichuan Basin in China, where NABEs are flourishing. The findings show an approximately "inverted U-shaped" curve relationship between NABE's production efficiency and their planting scale. The primary reason for the failure of NABE to achieve an overall high level of production efficiency is scale inefficiency. The optimal scale intervals for the greenhouse and open-field types of scale operation modes are 3.0–4.3 ha and 3.3–5.0 ha, respectively. Business entities' age, land circulation scale, land rent, and agricultural insurance are common factors that influence the scale efficiencies of both the greenhouse and open-field types. Accordingly, policy interventions regarding the guidance of moderate-scale operation of agricultural land are proposed for achieving the dual goal of cultivating NABE and implementing the Rural Revitalization Strategy in mountainous areas of China. While contributing to the knowledge on scale efficiency of agricultural land, this research also enlightens the practice of policy-making targeted to the sustainable development of agricultural industry led by NABE worldwide.

Statistical analysis of stochastic magnetic fluctuations in space plasma based on theMMSmission

ABSTRACTBased on the Magnetospheric Multiscale (MMS) mission we look at magnetic field fluctuations in the Earth’s magnetosheath. We apply the statistical analysis using a Fokker–Planck equation to investigate processes responsible for stochastic fluctuations in space plasmas. As already known, turbulence in the inertial range of hydromagnetic scales exhibits Markovian features. We have extended the statistical approach to much smaller scales in space, where kinetic theory should be applied. Here we study in detail and compare the characteristics of magnetic fluctuations behind the bow shock, inside the magnetosheath, and near the magnetopause. It appears that the first Kramers–Moyal coefficient is linear and the second term is a quadratic function of magnetic increments, which describe drift and diffusion, correspondingly, in the entire magnetosheath. This should correspond to a generalization of Ornstein–Uhlenbeck process. We demonstrate that the second-order approximation of the Fokker–Planck equation leads to non-Gaussian kappa distributions of the probability density functions. In all cases in the magnetosheath, the approximate power-law distributions are recovered. For some moderate scales, we have the kappa distributions described by various peaked shapes with heavy tails. In particular, for large values of the kappa parameter this shape is reduced to the normal Gaussian distribution. It is worth noting that for smaller kinetic scales the rescaled distributions exhibit a universal global scale invariance, consistently with the stationary solution of the Fokker–Planck equation. These results, especially on kinetic scales, could be important for a better understanding of the physical mechanism governing turbulent systems in space and astrophysical plasmas.

Genetic variation at innate and adaptive immune genes - contrasting patterns of differentiation and local adaptation in a wild gull.

Immunogenetic variation in natural vertebrate populations is expected to respond to spatial and temporal fluctuations in pathogen assemblages. While spatial heterogeneity in pathogen-driven selection enhances local immunogenetic adaptations and population divergence, different immune genes may yield contrasting responses to the environment. Here, we investigated population differentiation at the key pathogen recognition genes of the innate and adaptive immune system in a colonial bird species, the black-headed gull Chroicocephalus ridibundus. We assessed genetic variation at three toll-like receptor (TLR) genes (innate immunity) and the major histocompatibility complex (MHC) class I and II genes (adaptive immunity) in gulls from seven colonies scattered across Poland. As expected, we found much greater polymorphism at the MHC than TLRs. Population differentiation at the MHC class II, but not MHC-I, was significantly stronger than at neutral microsatellite loci, suggesting local adaptation. This could reflect spatial variation in the composition of extracellular parasite communities (e.g., helminths), possibly driven by sharp differences in habitat structure between colonies. Despite contrasting patterns of population differentiation, both MHC classes showed similar regimes of diversifying selection. Some significant population differentiation was also observed at TLRs, suggesting that innate immune receptors may respond to fine-scale spatial variation in pathogen pressure, although this pattern could have been enhanced by drift. Our results suggested that local adaptation at the pathogen recognition immune genes can be maintained at relatively small or moderate spatial scales in species with high dispersal potential and they highlighted the complexity of immunogenetic responses of animals to heterogeneous environments.

Galactic Dynamos

Spiral galaxies, including the Milky Way, have large-scale magnetic fields with significant energy densities. The dominant theory attributes these magnetic fields to a large-scale dynamo. We review the current status of dynamo theory and discuss various numerical simulations designed either to explain particular aspects of the problem or to reproduce galactic magnetic fields globally. Our main conclusions can be summarized as follows: ▪Idealized direct numerical simulations produce mean magnetic fields, whose saturation energy density tends to decline with increasing magnetic Reynolds number. This is still an unsolved problem.▪Large-scale galactic magnetic fields of microgauss strengths can probably be explained only if helical magnetic fields of small or moderate length scales can be rapidly ejected or destroyed.▪Small-scale dynamos are important throughout a galaxy's life and probably provide strong seed fields at early stages.▪The circumgalactic medium (CGM) may play an important role in driving dynamo action at small and large length scales. These interactions between the galactic disk and the CGM may provide important insights into our understanding of galactic dynamos. We expect future research in galactic dynamos to focus on the cosmological history of galaxies and the interaction with the CGM as means of replacing the idealized boundary conditions used in earlier work.

Multi-timescale changes of water temperature due to the Three Gorges Reservoir and climate change in the Yangtze River, China

The water temperature in the Yangtze River has been found altering over the last decades. Although these alterations are expected to occur over multiple timescales and with multiple factors, including both reservoir operation and climate change, few efforts have been made to quantify the alterations and conduct attribution analysis on multiple timescales. It hinders the deep understanding of the thermal regime in the Yangtze River and how it responses to the driving forces. Here, a multiresolution analysis based on the maximal overlap discrete wavelet analysis is applied to quantify the multi-timescale alterations of the water temperature, and to distinguish the impacts caused by reservoirs and climate change. The results indicate that the water temperature displays a significant upward trend in past three decades, which is mostly driven by the climate change. Reservoir operation has reduced the intra-annual variability of the water temperature, especially for the short-term and annual scales, resulting in a smoother and flatter water temperature process. The correlation between the water and air temperature is also investigated on multiple timescales based on the wavelet cross-correlation coefficients. It is found that the correlation between the air and water temperature in the natural state shows an overall increasing trend with the increasing timescale and peaks on annual scale. Due to the reservoir operation, the correlation on the moderate scales (1–4 months) has been notably weakened. While for the longer scales, the correlation between air and water temperature remains high, although their phases difference on some scales may be extended by months due to the reservoir operation.

Magnetospheric Multiscale Observations of Markov Turbulence on Kinetic Scales

In our previous studies we have examined solar wind and magnetospheric plasma turbulence, including Markovian character on large inertial magnetohydrodynamic scales. Here we present the results of the statistical analysis of magnetic field fluctuations in the Earth’s magnetosheath, based on the Magnetospheric Multiscale mission at much smaller kinetic scales. Following our results on spectral analysis with very large slopes of about −16/3, we apply a Markov-process approach to turbulence in this kinetic regime. It is shown that the Chapman–Kolmogorov equation is satisfied and that the lowest-order Kramers–Moyal coefficients describing drift and diffusion with a power-law dependence are consistent with a generalized Ornstein–Uhlenbeck process. The solutions of the Fokker–Planck equation agree with experimental probability density functions, which exhibit a universal global scale invariance through the kinetic domain. In particular, for moderate scales we have the kappa distribution described by various peaked shapes with heavy tails, which, with large values of the kappa parameter, are reduced to the Gaussian distribution for large inertial scales. This shows that the turbulence cascade can be described by the Markov processes also on very small scales. The obtained results on kinetic scales may be useful for a better understanding of the physical mechanisms governing turbulence.

Climate response and sensitivity: time scales and late tipping points

Climate response metrics are used to quantify the Earth’s climate response to anthropogenic changes of atmospheric CO 2 . Equilibrium climate sensitivity (ECS) is one such metric that measures the equilibrium response to CO 2 doubling. However, both in their estimation and their usage, such metrics make assumptions on the linearity of climate response, although it is known that, especially for larger forcing levels, response can be nonlinear. Such nonlinear responses may become visible immediately in response to a larger perturbation, or may only become apparent after a long transient period. In this paper, we illustrate some potential problems and caveats when estimating ECS from transient simulations. We highlight ways that very slow time scales may lead to poor estimation of ECS even if there is seemingly good fit to linear response over moderate time scales. Moreover, such slow processes might lead to late abrupt responses (late tipping points) associated with a system’s nonlinearities. We illustrate these ideas using simulations on a global energy balance model with dynamic albedo. We also discuss the implications for estimating ECS for global climate models, highlighting that it is likely to remain difficult to make definitive statements about the simulation times needed to reach an equilibrium.

PDFchem: A new fast method to determine ISM properties and infer environmental parameters using probability distributions

ABSTRACT Determining the atomic and molecular content of the interstellar medium (ISM) is of fundamental importance to understand the star-formation process. Although there exist various three-dimensional hydro-chemical codes modelling the ISM, they are computationally expensive and inefficient for studies over a large parameter space. Building on our earlier approach, we present PDFchem, a novel algorithm that models the cold ISM at moderate and large scales using functions connecting the quantities of the local (AV, eff) and the observed (AV, obs) visual extinctions, and the local number density, nH, with probability density functions (PDF) of AV, obs on cloud scales typically tens-to-hundreds of pc as an input. For any given AV, obs-PDF, the algorithm instantly computes the average abundances of the most important species (H i, H2, C ii, C i, CO, OH, OH+, H2O+, CH, HCO+) and performs radiative transfer calculations to estimate the average emission of the most commonly observed lines ([C ii] 158$\mu$m, both [C i] fine-structure lines and the first five rotational transitions of 12CO). We examine two AV, obs-PDFs corresponding to a non-star-forming and a star-forming ISM region, under a variety of environmental parameters combinations. These cover far-ultraviolet intensities in the range of χ/χ0 = 10−1 − 103, cosmic ray ionization rates in the range of $\zeta _{\rm CR}=10^{-17}-10^{-13}\, {\rm s}^{-1}$ and metallicities in the range of $Z=0.1-2\, {\rm Z}_{\odot }$. PDFchem is fast, easy to use, reproduces the PDR quantities of the hydrodynamical models, and can be used directly with observed data of the cold ISM.

Agricultural Population Supported in Rural Areas under Traditional Planting Mode Based on Opportunity Cost Analysis

The return of rural migrant workers through increasing agricultural income by expanding farming scale is significant for rural sustainable development without rural population loss. This paper selected six representative counties in Henan Province, China’s major grain-producing province, to conduct a questionnaire survey, investigated the incomes of farmers from farming and migrant workers, calculated moderate farming scale under different opportunity costs, and also estimated the agricultural population that can be supported by arable land resources. Results are as follows: (1) Under the traditional planting mode, annual per capita income of farmers in farming was USD 342.18, which was substantially lower than USD 5255.63 in migrant workers. This huge income gap has led to continuous rural population loss. (2) Under the opportunity cost of farming income equal to migrant workers income, moderate farming scales of the six selected counties were 1.39, 1.17, 1.22, 1.08, 1.34, and 1.01 ha, respectively. Under the 0.8x and 0.6x opportunity cost, corresponding moderate farming scales were 1.11, 0.94, 1.11, 0.86, 1.07, and 1.34 ha; and 0.84, 0.70, 0.73, 0.65, 0.80 and 1.01 ha, respectively. (3) On the basis of the three moderate farming scales and status quo of arable land resources, agricultural populations that can be supported by rural Henan Province were 8.0386 million, 10.0479 million and 13.3942 million, respectively. Findings can guide the formulation of rural revitalization strategic measures and the preparation of village territorial spatial planning.

Dynamical approximations for composite quantum systems: assessment of error estimates for a separable ansatz

Numerical studies are presented to assess error estimates for a separable (Hartree) approximation for dynamically evolving composite quantum systems which exhibit distinct scales defined by their mass and frequency ratios. The relevant error estimates were formally described in our previous work Burghardt et al (2021 J. Phys. A: Math. Theor. 54 414002). Specifically, we consider a representative two-dimensional tunneling system where a double well and a harmonic coordinate are cubically coupled. The time-dependent Hartree approximation is compared with a fully correlated solution, for different parameter regimes. The impact of the coupling and the resulting correlations are quantitatively assessed in terms of a time-dependent reaction probability along the tunneling coordinate. We show that the numerical error is correctly predicted on moderate time scales by a theoretically derived error estimate.

Performance of Coefficient Alpha and Its Alternatives: Effects of Different Types of Non-Normality.

We examined the performance of coefficient alpha and its potential competitors (ordinal alpha, omega total, Revelle's omega total [omega RT], omega hierarchical [omega h], greatest lower bound [GLB], and coefficient H) with continuous and discrete data having different types of non-normality. Results showed the estimation bias was acceptable for continuous data with varying degrees of non-normality when the scales were strong (high loadings). This bias, however, became quite large with moderate strength scales and increased with increasing non-normality. For Likert-type scales, other than omega h, most indices were acceptable with non-normal data having at least four points, and more points were better. For different exponential distributed data, omega RT and GLB were robust, whereas the bias of other indices for binomial-beta distribution was generally large. An examination of an authentic large-scale international survey suggested that its items were at worst moderately non-normal; hence, non-normality was not a big concern. We recommend (a) the demand for continuous and normally distributed data for alpha may not be necessary for less severely non-normal data; (b) for severely non-normal data, we should have at least four scale points, and more points are better; and (c) there is no single golden standard for all data types, other issues such as scale loading, model structure, or scale length are also important.

Numerical analysis on the system instability of the cavitating flow around hydrofoil induced by the non-uniform inflow

One common type of system instabilities of partial cavitation, induced by the non-uniform inflow, was numerically investigated in the present study. Large eddy simulation (LES) method coupled with Zwart-Gerber-Belamri cavitation model and volume of fraction (VOF) are used to capture the unsteady cavitating flow around NACA66. Unlike the intrinsic instabilities caused by the cavity itself, the occurrence of system instabilities is usually accompanied by the intrinsic instabilities. Therefore, it is their nonlinear interaction that makes analyzing the system instabilities separately quite difficult. By comparing the effects of the uniform and non-uniform inflow on the cavitating flow as well as the non-cavitating flow, the influences of the system instability induced by the non-uniform inflow on the partial cavitation are analyzed tentatively. The results show that the non-uniform inflow has little effect on the cavity shape evolution, but makes the dominant frequency caused by the shedding behaviors of the cavities higher. In contrast, the non-uniform inflow significantly changes the mean velocity profile in the cavitation regions near the foil wall especially over the front part of the foil (x/C<0.5). Besides, it also makes the maximum values of the Reynolds shear stress <uv> smaller, and its normal locations closer to the foil wall. By the continuous wavelet analysis, it is observed that the large-scale coherent structures containing relatively high energy are mainly generated during the collapse stage of the cavities. The non-uniform carrier fluid may induce more large-scale coherent structures during this stage. By the discrete wavelet transform, the energy of the non-uniform cavitating flow has a more complex distribution at the expense of the energy for the large and moderate scales.

Innovation for sustainable mining: Integrated planning of underground coal mining and mine reclamation

Coal supplies essential materials for social development. In many cases, however, the separation planning of underground coal mining (UCM) and mine reclamation (MR) and “mining first, reclamation later” lead to the accumulation of land degradation. Thus, the integrated planning of UCM and MR needs to be studied urgently. In this study, concurrent mining and reclamation (CMR) technologies were adopted to divide UCM planning into four key steps from the space-time scale: i) development roadway (macro); ii) preparation roadway (moderate); iii) panel roadway (meso) and iv) working face (micro), and the impacts of mining steps on MR were discussed. In addition, the requirements of four-key links of MR for UCM were discussed: i) reclamation elevation (RE); ii) digging depth (DD); iii) reclamation layout (RL), and iv) reclamation timing (RT). UCM and MR were optimized on macro, moderate, meso, and micro scales. Results show that: (1) RE and DD are moderate and meso scales; (2) RL is macro and moderate scales; (3) RT is meso and micro scales. Integrated planning of UCM and MR can reduce mining damage from the source, which can timely reclaim degraded land, and alleviate the contradictions of the government, colliery and farmer. We hope CMR will be valued to jointly improve application scenarios and guide sustainable mining.