Numerical Index Research Articles

Estimating soil health in urban allotments: Integrated two-way soil quality index and free-living amoebae in nitrogen recycling

Nitrogen deficiency and contamination by potentially toxic elements (PTEs) adversely impact soil health and ecosystem services. Existing tools for assessing contaminated soils, necessary for sustainable management, remain limited. In this study, we introduce an integrated approach using geochemical constituents and biological factors to construct a comprehensive index to evaluate contaminant impacts on soil health. We collected samples in triplicate from six plots within an urban allotment with a history of PTE contamination. Selected biological and chemical characteristics of the samples were quantified to derive impact scores, with a single numerical index representing overall soil quality for each plot. Multivariate, T-statistics and Pearson Correlation analysis were used to identify relationships and differences between selected soil parameters between plots. The role of the free-living amoeba Acanthamoeba in nitrogen recycling was assessed with feeding experiments, enzymatic assays and bioinformatics analysis. The plot with the highest index value, indicative of good health, exhibited higher pH, significantly high microbial load, and a high nitrate to ammonium (NO3−:NH4+) ratio of 5.3. This turnover was associated with Acanthamoeba uptake of exogenous nitrates and secretion of ammonium, through the assimilatory/dissimilatory nitrate reduction pathway. In contrast, the lower index plot with the low nitrogen turnover of 0.69, showed elevated aluminium, low pH activity and a significantly reduced microbial load, dominated by aluminium resistant microorganisms. Our findings highlight the importance of a comprehensive soil quality index by integrating multiple characteristics to assess soil health and contamination. The approach addresses the need for improved tools to identify the direct impact of contaminants on soil biological activity, supporting more sustainable land management.

Class-imbalanced time series anomaly detection method based on cost-sensitive hybrid network

The time series is a structured data form. In practical engineering, most of the time series is in the form of class-imbalance, in which, abnormal data is rare or underrepresented. In this study, we present a cost sensitive hybrid network (CSHN) model to detect data anomaly in class-imbalanced time series. The proposed model adopts a two-flow structure that consist of a Convolutional Neural Network (CNN) and a Gated Recurrent Unit (GRU) network and combine with a cost-sensitive loss function. This model integrates the advantages of CNN and GRU. The former is provided with strong learning ability for local state features and the latter can extract the sequential features and force the decision logic of the network to pay more attention to the long-term dependence of the time series. The cost-sensitive loss function is used to solve the problem that the detection accuracy of the minority class will be inaccuracy caused by skewed data distribution. The simulation experiments are conducted on the speed and temperature datasets that come from real-world measurement and control engineering, as well as the UCR datasets. Compared to using the cross-entropy loss function, the using of the cost-sensitive loss function can improve G-means and F-measure by 3 %∼5% and 5 %∼9%, respectively, on the speed and temperature datasets. Experimental results demonstrate that our method is efficient for the class-imbalanced data sets, and the numerical evaluation indexes are superior to that of the comparison methods.

Linking ecoacoustic indices to psychoacoustic perception of the urban acoustic environment

Research about human perception of the acoustic environment is dominated by the use of sound pressure levels (SPL) or proprietary psychoacoustic indicators, limiting the link between sound and perception to only these two approaches. The aim of this study is to make connections between soundscape perception descriptors and ecoacoustic indices so that a perceptual dimension of ecoacoustic indices is ascertained. The study employs a laboratory experiment where 309 participants were exposed to sound and imagery from nine different urban land uses (n = 2,781) according to the soundscape protocol DIN ISO 12913-2. Spearman’s correlation and ordinal logistic regression are used to analyze the relationship between continuous numeric ecoacoustic indices and Likert scale psychoacoustic perception outcomes. We find that the predictors median amplitude (M), acoustic richness (AR), number of peaks (NP), and acoustic evenness index (AEI) have significant correlations (r > 0.5: p ≤ 0.05) and effect sizes (β ≥ 0.1) for the psychoacoustic outcomes perceived affective quality (PAQ) of calm, pleasant, chaotic, annoying, and soundscape identifiers (SSI) traffic sounds and natural sounds. We find that between 59.4 % and 81.9 % of the variation within dependent SSI and PAQ variables is explained by ecoacoustic indices based on Nagelkerke’s R-Squared values. High M and AR values are indicators for sound environments with high median or consistent amplitude, such as traffic noise, and high NP and AEI values are indicators for high fidelity sound environments with natural sounds or human beings that may be perceived as calm or pleasant. We conclude that combinations of M, AR, NP, and AEI as composite indicators contribute to understanding nine of the 14 psychoacoustic perception outcomes in DIN ISO 12913-2.

Numerical simulation methods for reinforced concrete columns considering parameter stochasticity

Reinforced concrete column specimens with the same parameters often show differences in bearing capacity and deformation under the same loading path. Most of the existing studies attribute the causes to recording instrumentation errors or randomness in material and bond dimensions, but the method to improve the accuracy of numerical simulation of the reinforced concrete components is still unclear. For a given member, the randomness of material and geometry is an important cause of simulation error, which should be taken into account to improve the simulation accuracy. A weighted least squares estimation (WLS) method is used to fuse the test samples of reinforced concrete member section sizes, reinforcement and concrete with confidence factors as weights. The probability distribution functions of reinforced concrete member section sizes, reinforcement and concrete are calculated by a genetic algorithm method. Based on the existing seismic performance test data of reinforced concrete columns, a stochastic finite element model of reinforced concrete columns is developed for seismic performance analysis using Monte Carlo method through a mixture of two software programs, MATLAB and OpenSees. The numerical seismic performance indexes of eight types of reinforced concrete columns were compared with the test results to verify the importance of introducing stochastic parameters into the model. The results demonstrate the simulation accuracy of the finite element model considering parameter randomness is 88% higher than that of the finite element model without considering parameter randomness, which proves the effectiveness of the numerical simulation method for reinforced concrete columns considering parameter randomness.

Composite Indices of Social Determinants of Health: Overview, Measurement Gaps, and Research Priorities for Health Equity.

The goal of health equity is for all people to have opportunities and resources for optimal health outcomes regardless of their social identities, residence in marginalized communities, and/or experience with oppressive systems. Social determinants of health (SDOH)-the conditions in which we are born, grow, live, work, and age-are inextricably tied to health equity. Advancing health equity thus requires reliable measures of SDOH. In the United States, comprehensive individual-level data on SDOH are difficult to collect, may be inaccurate, and do not capture all dimensions of inequitable outcomes. Individual area-based indicators are widely available, but difficult to use in practice. Numerous area-level composite indices are available to describe SDOH, but there is no consensus on which indices are most appropriate to use. This article presents an analytic taxonomy of currently available SDOH composite indices and compares their components and predictive ability, providing insights into gaps and areas for further research.

Deep learning based processing framework for spatio-temporal analysis and classification of laser biospeckle data

Laser biospeckle is an advanced optical technique with the ability to non-destructively visualize various transient phenomenon via spatial and temporal statistics. However, accuracy of the existing image processing techniques used to process biospeckle data is hampered by various experimental and processing dependent factors. Therefore, in this work, a novel 3D convolution neural network (3D CNN) based deep learning (DL) architecture is developed for spatio-temporal analysis of biospeckle data in both qualitative and quantitative domains that effectively reduces errors introduced due to the influence of varying experimental parameters. Firstly, 3D CNN based image processing model is proposed for spatio-temporal analysis and classification of biospeckle data. Furthermore, a novel DL based numerical indexing strategy is developed for identification of level of activity in a sample. Finally, impact of varying experimental parameters on accuracy of the proposed technique is analyzed. In this direction, multiple experiments were performed to examine the effect of variation in input data parameters such as frame dimension, frame rate, number of frames, and background noise on accuracy of the trained model. Performance of the proposed model was analyzed and compared with respect to synthetic data generated by using rotating diffuser based simulation model. Robustness of the proposed strategy was also validated experimentally on practical data associated with identification of disease in seeds. Obtained results demonstrated that the proposed technique is accurate and can perform spatio-temporal classification and numerical indexing of the biospeckle data under varying experimental parameters.

Contrasting impacts of climate change on protection forests of the Italian Alps

Protection forests play a key role in protecting settlements, people, and infrastructures from gravitational hazards such as rockfalls and avalanches in mountain areas. Rapid climate change is challenging the role of protection forests by altering their dynamics, structure, and composition. Information on local- and regional-scale impacts of climate change on protection forests is critical for planning adaptations in forest management. We used a model of forest dynamics (ForClim) to assess the succession of mountain forests in the Eastern Alps and their protective effects under future climate change scenarios. We investigated eleven representative forest sites along an elevational gradient across multiple locations within an administrative region, covering wide differences in tree species structure, composition, altitude, and exposition. We evaluated protective performance against rockfall and avalanches using numerical indices (i.e., linker functions) quantifying the degree of protection from metrics of simulated forest structure and composition. Our findings reveal that climate warming has a contrasting impact on protective effects in mountain forests of the Eastern Alps. Climate change is likely to not affect negatively all protection forest stands but its impact depends on site and stand conditions. Impacts were highly contingent to the magnitude of climate warming, with increasing criticality under the most severe climate projections. Forests in lower-montane elevations and those located in dry continental valleys showed drastic changes in forest structure and composition due to drought-induced mortality while subalpine forests mostly profited from rising temperatures and a longer vegetation period. Overall, avalanche protection will likely be negatively affected by climate change, while the ability of forests to maintain rockfall protection depends on the severity of expected climate change and their vulnerability due to elevation and topography, with most subalpine forests less prone to loosing protective effects. Proactive measures in management should be taken in the near future to avoid losses of protective effects in the case of severe climate change in the Alps. Given the heterogeneous impact of climate warming, such adaptations can be aided by model-based projections and high local resolution studies to identify forest stand types that might require management priority for maintaining protective effects in the future.

Prognostic Value of Admission Neutrophil Count in Asthma Patients with COVID-19: A Comparative Analysis with other Systemic Inflammation Indices for In-Hospital Mortality Prediction.

Despite studies indicating that asthma patients do not exhibit a higher mortality rate or severity compared to the general population when infected with COVID-19, there have been few reports on predictive factors for mortality in this context. This study aimed to assess the predictive value of systemic inflammation indices including neutrophil-to-lymphocyte (NLR), monocyte-to-lymphocyte (MLR), platelet-to-lymphocyte (PLR), systemic inflammation response index (SIR-I), and systemic inflammation index (SII) in determining mortality rate among patients with COVID-19 and asthma. In this prospective study, the laboratory parameters of 1792 COVID-19 patients were examined, with a subgroup consisting of 112 patients with asthma and 1680 patients without asthma. Receiver operating characteristic (ROC) analysis was employed to assess the potential of inflammatory indices in indicating COVID-19 severity, while Kaplan-Meier curves were utilized to analyze the survival probability with death as the outcome. In deceased non-asthma patients, the levels of leukocyte and differential cell counts, and the values of PLR, NLR, MLR, SII, and SIR-I were higher than in survivors. In contrast, all the above values except PLR and MLR were significant in the asthma groups. The Kaplan-Meier survival curves were consistent with the ROC analysis. However, a multivariate Cox regression analysis revealed that neutrophil counts in non-asthma subjects and leukocyte and neutrophil counts in asthma patients remained significant for survival. In conclusion, while numerous inflammatory indices were associated with mortality in COVID-19 patients without asthma, neutrophil counts could independently predict mortality risk in asthma COVID-19 patients.

Several Zagreb indices of power graphs of finite non-abelian groups

Molecular topology can be described by using topological indices. These are quantitative measures of the essential structural features of a proposed molecule calculated from its molecular structure. It is a numerical value obtained from a molecular configuration that reflects the significant physical characteristics of the suggested molecule. Numerous physical properties, chemical reactivity, and biological activity are correlated with the chemical composition using an algebraic number. The power graph P(G) of a finite group G is a graph whose vertex set is G and in which two distinct vertices are connected by an edge when one element is an integral power of the other. This article investigates a wide range of degree-based topological descriptors for power graphs of various finite groups. We find numerous Zagreb indices (given in Table 1) of power graphs of finite non-cyclic and cyclic groups, dihedral, and generalized quaternion groups.

Compression Algorithm for Colored de Bruijn Graphs.

A colored de Bruijn graph (also called a set of k-mer sets), is a set of k-mers with every k-mer assigned a set of colors. Colored de Bruijn graphs are used in a variety of applications, including variant calling, genome assembly, and database search. However, their size has posed a scalability challenge to algorithm developers and users. There have been numerous indexing data structures proposed that allow to store the graph compactly while supporting fast query operations. However, disk compression algorithms, which do not need to support queries on the compressed data and can thus be more space-efficient, have received little attention. The dearth of specialized compression tools has been a detriment to tool developers, tool users, and reproducibility efforts. In this paper, we develop a new tool that compresses colored de Bruijn graphs to disk, building on previous ideas for compression of k-mer sets and indexing colored de Bruijn graphs. We test our tool, called ESS-color, on various datasets, including both sequencing data and whole genomes. ESS-color achieves better compression than all evaluated tools and all datasets, with no other tool able to consistently achieve less than 44% space overhead.

Exploring Asymmetric Fine Motor Impairment Trends in Early Parkinson's Disease via Keystroke Typing.

The nQiMechPD algorithm transforms natural typing data into a numerical index that characterizes motor impairment in people with Parkinson's Disease (PwPD). Use nQiMechPD to compare asymmetrical progression of PD-related impairment in dominant (D-PD) versus non-dominant side onset (ND-PD) de-novo patients. Keystroke data were collected from 53 right-handed participants (15 D-PD, 13 ND-PD, 25 controls). We apply linear mixed effects modeling to evaluate participants' right, left, and both hands nQiMechPD relative change by group. The 6-month nQiMechPD trajectories of right (**P = 0.002) and both (*P = 0.043) hands showed a significant difference in nQiMechPD trends between D-PD and ND-PD participants. Left side trends were not significantly different between these two groups (P = 0.328). Significant differences between D-PD and ND-PD groups were observed, likely driven by contrasting dominant hand trends. Our findings suggest disease onset side may influence motor impairment progression, medication response, and functional outcomes in PwPD.

Tuberculosis detection using a low-loss and highly sensitive photonic crystal fiber technique in the terahertz regime

This research paper introduces a design and numerical investigation of a photonic crystal fiber (PCF) sensor with a hollow core. The sensor is specifically designed to detect Mycobacterium tuberculosis in the frequency range of 1–2 THz. The cladding region of the PCF sensor incorporates six rectangular air holes and an absorbing layer known as the perfectly matched layer (PML), which is utilized to examine various optical properties. Through a thorough investigation, our designed PCF sensor demonstrates enhanced sensitivity of 98.692%, 98.721%, 98.75%, and 98.764% for four different infected samples of tuberculosis at a frequency of 1.6 THz. The sensor exhibits minimal confinement loss and effective material loss. Furthermore, the numerical aperture and effective mode index of the proposed sensor are analyzed. An effective area of 346,550, 345,110, 343,710, and 343,020µm2 for the four different infected samples of tuberculosis is obtained. The fabrication methods employed for this sensor are mentioned. Overall, this PCF sensor can be promising for a wide range of chemical, gas, and biosensing applications.

Effectiveness of rapid sampling assessments for wetland-breeding amphibians

A primary response to the Clean Water Act has been the development of numerous indices of biotic integrity (IBI) using various taxonomic groups to assess the condition of water bodies in the United States. The IBI is a frequently used approach for assessing the ecological integrity of streams; however, it is less commonly applied to wetland systems, despite the scientific and policy needs to assess wetland condition and develop ecological performance goals for wetland monitoring, creation, and restoration. Although a great deal of attention has been given to wetland acreage due to the “no net loss” policy over the past two decades, much less attention has been given to the ecological integrity of these same wetlands. To assess wetland health and function, regulatory agencies and land managers need clear performance standards that are based on the ecology of wetland organisms. To address this need, we sampled 242 wetlands distributed across 45 managed properties in Illinois, U.S.A. over three years (2012–2014) for adult and larval amphibians (12 samples per wetland). Amphibian assemblages make up critical ecological components of many wetland ecosystems and are considered useful bioindicators of environmental change because they are sensitive to various forms of environmental and habitat alteration. We developed an amphibian-based IBI (hereafter aIBI) for the assessment of seasonal wetlands and optimal sampling strategies to best quantify these wetlands (i.e., assessment of time and effort). The aIBI applied a conservation coefficient to all species captured; conservation coefficients were developed through a survey of amphibian experts (n = 16). We captured 25 amphibian species with an average of 7.11 amphibian species (range 1–13) per site. Using the species occurrence data collected from both dipnet and minnow trap surveys over 12 survey periods, we assessed the average percent error in the estimation of aIBIs and species richness. Our results show that a wetland can be accurately evaluated with amphibians in one field season, but that multiple samples during that field season will be necessary. We show that 4–5 surveys using multiple methods distributed throughout the spring and summer are required. This level of effort may be incongruent with existing definitions of ‘rapid assessment’, but it is the necessary level of effort needed to confidently and accurately evaluate wetlands using amphibians as an indicator taxon. Additionally, aIBI provides a more robust framework to assess wetland integrity compared to simple species richness or other similar methodologies.

Proposal of m-Index for Rating Fracture and Damage Models by Their Ability to Represent a Set of Distinctive Experiments

A recent comparative study revealed that the commonly offered experimental validations of peridynamics and phase-field fracture models have been insufficient because they involved only nondistinctive experiments, i.e., experiments that can be closely fitted by different models that at the same time give very different predictions in important practical applications. The comparisons showed that the peridynamic and phase-field models are incapable of simulating a set of 11 distinctive experiments—experiments that are critical for assessing the accuracy of different models and are representative of fracture behavior of engineering structures. Practical applications would be helped by common adoption of a model index that would compare the predictive capability of various fracture models quantitatively. Proposed here for further discussion is an example of a possible numerical index, the m-Index, which attempts to characterize how well the optimal calibration of model parameters can match the experimental evidence, such as the fracture patterns, measured response curves, size effect, and crack-parallel stress effect. Included are only the distinctive experiments. As an example, the m-Index is here calculated for a set of seven fracture models whose performance was previously compared with 11 distinctive experiments. This previous comparison of seven models is here extended to an eighth model, proposed as a fresh improvement of peridynamics. The choice of distinctive experiments is one of the subjects calling for further discussion. Despite inevitable imperfections, a widely adopted index for appraising new material models would mitigate waste of researchers’ effort and grant funds, as well as the space in scientific journals and conference programs.

Transient simulation of laser ablation based on Monte Carlo light transport with dynamic optical properties model

Laser ablation is a minimally invasive therapeutic technique to denature tumors through coagulation and/or vaporization. Computational simulations of laser ablation can evaluate treatment outcomes quantitatively and provide numerical indices to determine treatment conditions, thus accelerating the technique’s clinical application. These simulations involve calculations of light transport, thermal diffusion, and the extent of thermal damage. The optical properties of tissue, which govern light transport through the tissue, vary during heating, and this affects the treatment outcomes. Nevertheless, the optical properties in conventional simulations of coagulation and vaporization remain constant. Here, we propose a laser ablation simulation based on Monte Carlo light transport with a dynamic optical properties (DOP) model. The proposed simulation is validated by performing optical properties measurements and laser irradiation experiments on porcine liver tissue. The DOP model showed the replicability of the changes in tissue optical properties during heating. Furthermore, the proposed simulation estimated coagulation areas that were comparable to experimental results at low-power irradiation settings and provided more than 2.5 times higher accuracy when calculating coagulation and vaporization areas than simulations using static optical properties at high-power irradiation settings. Our results demonstrate the proposed simulation’s applicability to coagulation and vaporization region calculations in tissue for retrospectively evaluating the treatment effects of laser ablation.

Quantifying thresholds for advancing impact-based drought assessment using classification and regression tree (CART) models

Drought events can significantly impact water resources, agriculture, and socioeconomic sectors. Droughts are often quantified using numerous indices, which are widely used for forecasting, risk assessments, and spatiotemporal analysis. Quantifying the appropriate linkage between drought indices and their impact on hydrological and agricultural indicators will improve hazard communication with stakeholders and further advance impact-based forecasting tools. This study aims to quantify the thresholds associated with drought indices that can capture the intricate connection between droughts and impact-specific indicators. We investigated the performance of several drought indices, such as Palmer-based (e.g., PDSI, PMDI, and PHDI) and multiscale drought indices (e.g., SPI and SPEI) with the impact-specific indicators of hydrological (e.g., streamflow and reservoir level) and agricultural (soil moisture and crop yield) indicators. The 'threshold' values associated with drought indices with impact-specific indicators are quantified using the classification and regression tree (CART) algorithm. Our results suggest that the decision tree approach is suitable for identifying critical thresholds of drought indices associated with a range of hydrological and agricultural indicators relevant to the stakeholder's application to different sectors. The drought characteristics derived from selected indices broadly vary during extreme events. The results further indicate that the threshold associated with each drought index and the impact-specific indicator varies drastically within the same climate division. We argue that quantifying the drought indices thresholds can provide valuable information for impact-based monitoring and forecasting.

Study on the seismic response of new staggered story isolated structure under different parameters

The new staggered story isolated structure is developed according to the base-isolated structure and the mid-story isolated structure. Quantitative calculation and evaluation of seismic damage are very important for structural safety. In this paper, the seismic damage evaluation of a new staggered story isolated structure is studied by numerical simulation and damage index calculation. A new staggered story isolated structure is established, and the effects of different layers and different chassis areas on the seismic response of the structure are studied. When the position of the bottom isolated layer stays the same, the upper isolated layer is set at different layers, which is set to the top of the 3rd, 6th and 9th layers. When the upper isolated layer keeps at the top of the 3rd layer, the chassis area is set at a different area, which is 26 m × 26 m, 36 m × 36 m and 46 m × 46 m. The results show that the new staggered story isolated structure has good isolated effects under the ground motion. For the structure set upper isolation layer is lower, the inter-layer shear force, inter-layer acceleration and inter-layer displacement are reduced. The energy dissipation effect of the structure improves. The core tube is less damaged and the plastic hinge is smaller. With the increase of chassis area, the isolated effect of the part above the upper isolated layer is good, while the shear force and acceleration of the part below the upper isolated layer of the structure increase, the damage at the core tube changed little and the appearance of the plastic hinge increased. Under earthquakes, with the change in position of the upper isolated layer and the area of the chassis of the new staggered story isolated structure, the displacement, tensile stress and compressive stress of the isolated bearing still meet the requirements of the standard.

Methods for Evaluating the Effectiveness of Home Oral Hygiene Measures-A Narrative Review of Dental Biofilm Indices.

Dental plaque is a biofilm structured in an extracellular matrix of polymers of host and microbial origin; the microorganisms can coexist in harmony with the host, thus guarantying oral health. Environmental modifications can lead to dysbiosis and onset of oral diseases; in fact, plaque is the etiological agent both of periodontal disease and dental decay. The use of an effective oral hygiene index should be considered as a relevant goal for the clinicians and the researchers, and consequently, numerous plaque indices have been proposed during the years. The present literature review aims primarily to obtain a complete summary of these scores to assess plaque deposits. It is useful because the clinician/researcher will select the right scoring method for the specific situation only if he knows the available options and if he is aware of both their strengths and weaknesses. This review applies a basic classification of plaque indices that distinguishes the ones that use non-quantitative methods from the ones that use quantitative methods. Non-quantitative methods are more subjective because they are based on the ability of the clinician to point out the presence or the entity of deposits, while quantitative methods introduce objectifiable means to measure plaque deposits.

Application of bivariate statistical techniques for landslide susceptibility mapping: A case study in Kaghan Valley, NW Pakistan

Landslides are dangerous events that threaten both human life and property. This research presents a case study of Kaghan valley catchment, which is an area of frequent landslide activity. We aim to present a comparison between the bivariate Landslide Numerical Risk Factor (LNRF), Statistical Index (SI) and Information Value (InfV) statistical models to evaluate landslide susceptibility. A total of 1556 landslides were identified using earlier reports, field surveys, and GOOGLE Earth imagery. The abundance of landslides is primarily controlled by acute deformation caused by a major thrust fault system and proximity to Hazara Kashmir Syntaxis (HKS). A landslide inventory was randomly partitioned into two datasets. 70% (1106) of landslides were used as a training phase of the models, whereas 30% (450) as validation of the three models. A spatial database of 11 conditioning factors was produced consisting of slope, aspect, elevation, lithology, land use, Topographic Wetness Index (TWI), rainfall, Stream Power Index (SPI), distance to faults, rivers and streams. All the landslide susceptibility assessment parameters were obtained from different sources and different landslide susceptibility maps were prepared on the GIS software. Performance of the three models was validated through the Receiver operator Characteristics (ROC) through success and prediction rate curves. Results show that the area under the ROC curve (AUC) for InfV, LNRF and SI models are 70.95, 83.99 and 67.56 for success rate curves and 70.75, 83.99 and 67.85 for prediction rate curves, respectively. LNRF having the highest AUC value proved to be superior for generating regional scale landslide susceptibility maps.

Effect of Grazing Management on Predator Soil Mite Communities (Acari: Mesotigmata) in Some Subalpine Grasslands from the Făgăraş Mountains-Romania.

For the first time in Romania, a complex study was conducted on soil mite communities from two types of managed grasslands: ungrazed and intensively grazed. The study was accomplished in August 2018, in the Făgăraş Mountains. Within the soil mite communities (Mesostigmata), 30 species were identified, from 80 soil samples. The following population parameters were investigated: species richness, numerical abundance, dominance, Shannon index of diversity, evenness and equitability. Eight environmental variables were also measured: soil and air humidity; soil and air temperature; soil pH; resistance of soil to penetration; soil electrical conductivity; and vegetation coverage. The results revealed that species richness, Shannon index of diversity, evenness and equitability indices had higher values in ungrazed grasslands, whereas in intensively grazed areas, the numerical abundance and dominance index had significantly higher values. The species Alliphis halleri was dominant in the ungrazed grasslands. Each type of managed grassland was characterised by specific environmental conditions, which had an important influence, even at the species level.