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Red blood cell (RBC) aggregation and erythrocyte sedimentation rate (ESR) are considered to be promising biomarkers for effectively monitoring blood rheology at extremely low shear rates. In this study, a microfluidic-based measurement technique is suggested to evaluate RBC aggregation under hematocrit variations due to the continuous ESR. After the pipette tip is tightly fitted into an inlet port, a disposable suction pump is connected to the outlet port through a polyethylene tube. After dropping blood (approximately 0.2 mL) into the pipette tip, the blood flow can be started and stopped by periodically operating a pinch valve. To evaluate variations in RBC aggregation due to the continuous ESR, an EAI (Erythrocyte-sedimentation-rate Aggregation Index) is newly suggested, which uses temporal variations of image intensity. To demonstrate the proposed method, the dynamic characterization of the disposable suction pump is first quantitatively measured by varying the hematocrit levels and cavity volume of the suction pump. Next, variations in RBC aggregation and ESR are quantified by varying the hematocrit levels. The conventional aggregation index (AI) is maintained constant, unrelated to the hematocrit values. However, the EAI significantly decreased with respect to the hematocrit values. Thus, the EAI is more effective than the AI for monitoring variations in RBC aggregation due to the ESR. Lastly, the proposed method is employed to detect aggregated blood and thermally-induced blood. The EAI gradually increased as the concentration of a dextran solution increased. In addition, the EAI significantly decreased for thermally-induced blood. From this experimental demonstration, the proposed method is able to effectively measure variations in RBC aggregation due to continuous hematocrit variations, especially by quantifying the EAI.

Coronary artery disease (CAD) remains a significant global health challenge, with percutaneous coronary intervention (PCI) being a primary revascularization method. In-stent restenosis (ISR) post-PCI, although reduced, continues to impact patient outcomes. Inflammation and platelet activation play key roles in ISR development, emphasizing the need for accurate risk assessment tools. The systemic inflammation aggregation index (AISI) has shown promise in predicting adverse outcomes in various conditions but has not been studied in relation to ISR. A retrospective observational study included 1712 patients post-drug-eluting stent (DES) implantation. Data collected encompassed demographics, medical history, medication use, laboratory parameters, and angiographic details. AISI, calculated from specific blood cell counts, was evaluated alongside other variables using machine learning models, including random forest, Xgboost, elastic networks, logistic regression, and multilayer perceptron. The optimal model was selected based on performance metrics and further interpreted using variable importance analysis and the SHAP method. Our study revealed that ISR occurred in 25.8% of patients, with a range of demographic and clinical factors influencing the risk of its development. The random forest model emerged as the most adept in predicting ISR, and AISI featured prominently among the top variables affecting ISR prediction. Notably, higher AISI values were positively correlated with an elevated probability of ISR occurrence. Comparative evaluation and visual analysis of model performance, the random forest model demonstrates high reliability in predicting ISR, with specific metrics including an AUC of 0.9569, accuracy of 0.911, sensitivity of 0.855, PPV of 0.81, and NPV of 0.948. AISI demonstrated itself as a significant independent risk factor for ISR following DES implantation, with an escalation in AISI levels indicating a heightened risk of ISR occurrence.

Studies of the impact of trade restrictiveness on growth, poverty or unemployment are frequent in the academic literature. Few authors, however, provide a precise definition of what they mean by trade restrictiveness. When they do, the definition is unlikely to have tight links with trade theory. The objective of this article is to fill this gap by providing for 78 developing and developed countries clearly defined indicators of trade restrictiveness that are well grounded in trade theory. Results suggest that poor countries tend to have more restrictive trade policies but they also face higher trade barriers on their exports.

Landslides are affected not only by their own environmental factors, but also by the neighborhood environmental factors and the landslide clustering effect, which are represented as the neighborhood characteristics of modelling spatial datasets in landslide susceptibility prediction (LSP). This study aims to innovatively explore the neighborhood characteristics of landslide spatial datasets for reducing the LSP uncertainty. Neighborhood environmental factors were acquired and managed by remote sensing (RS) and the geographic information system (GIS), then used to represent the influence of landslide neighborhood environmental factors. The landslide aggregation index (LAI) was proposed to represent the landslide clustering effect in GIS. Taking Chongyi County, China, as example, and using the hydrological slope unit as the mapping unit, 12 environmental factors including elevation, slope, aspect, profile curvature, plan curvature, topographic relief, lithology, gully density, annual average rainfall, NDVI, NDBI, and road density were selected. Next, the support vector machine (SVM) and random forest (RF) were selected to perform LSP considering the neighborhood characteristics of landslide spatial datasets based on hydrologic slope units. Meanwhile, a grid-based model was also established for comparison. Finally, the LSP uncertainties were analyzed from the prediction accuracy and the distribution patterns of landslide susceptibility indexes (LSIs). Results showed that the improved frequency ratio method using LAI and neighborhood environmental factors can effectively ensure the LSP accuracy, and it was significantly higher than the LSP results without considering the neighborhood conditions. Furthermore, the Wilcoxon rank test in nonparametric test indicates that the neighborhood characteristics of spatial datasets had a great positive influence on the LSP performance.

Field experiments were conducted at the Research Station College of Agriculture, University of Basra at Garmat Ali district. The experiments were carried out during the winter season 2012-2013 in clay loam soil. The purpose of the research was to study the effect of water magnetizing and the quality of irrigation water in mean weight diameter and aggregation index during the plant growth stages (the beginning of the forest and the beginning of flowering and after harvesting) for barley crop (Hordium vulgare L.). The magnetizing of irrigation water treatments Included, non-magnetized water (M0) and water magnetized (M1). The irrigation water quality treatment included five types of water namely, tap water (TW), River water (RW), wastewater (WW),treated sewage water passed through sand filter (WWT) and mixed water (MW) ( 50% RW + 50% WWT).The experiments were conducted using factorial experiments according to randomized complete block design (RCBD). The irrigation water was added on the basis of the shortfall in the level of water of the evaporation pan installed in the field. The amount of water added was 100% of the amount vaporized water plus 20% as leaching requirements. The results showed that : Magnetization of irrigation water resulted in a significant increase in the mean weight diameter and aggregation index for both layers 0-30cm and 30-60 cm compared with non-magnetized water. The order of the effect of treatments on the mean weight diameter and aggregation index is WW >TW >WWT >MW >RW for both layers. The results showed that the values of both parameters increased as growth season progress and layer 0-30 cm surpassed layer of 30-60 cm.

The contribution of recent “multidimensional indices of poverty” may not be as obvious as one thinks. There are two issues in assessing that contribution: whether one believes that a single index can ever be a sufficient statistic of poverty, and whether one aggregates in the space of “attainments,” using prices when appropriate, or “deprivations,” using weights set by the analyst. The paper argues that we should aim for a credible set of multiple indices rather than a single multidimensional index. Partial aggregation will still be necessary, but ideally the weights should be consistent with well-informed choices by poor people.

The wide distribution and dissemination of Meloidogyne exigua in rubber tree plantations ( Hevea brasiliensis ), together with its high reproductive capacity and aggressiveness, make this nematode a limiting factor for the development of rubber tree cultivation. This study evaluated the population density and spatial pattern of M. exigua  race 3 and its relationship to the growth and survival of rubber trees in the Triângulo Mineiro—Minas Gerais. The study was done in an area planted in Jan/2008, with the clone RRIM600, and 7.2 hectares were sampled. The population density of the nematode was estimated, as well as the distribution of plant mortality using the Spatial Analysis based on Distance indices—SADIE method, calculating the basal area and constructing maps representing the variations in M. exigua population and rubber tree mortality. The spatial distribution of the nematode population in the soil and roots revealed the presence of three clusters, with a concentration of nematodes above 5.800 individuals per 150 cm 3 of soil and 50 g of roots. The incidence of dead trees ranged from 0 to 70%, with two foci of high mortality. The greatest population density of M. exigua in the area studied was concentrated in the clusters, coinciding with the greatest number of dead plants in the direction of the planting lines. Nematode spatial distribution was aggregated, according to SADIE, M. exigua   affectd the growth and survival of rubber trees, reducing the development of trees in the productive phase.

As an important part of urban ecology, the planning and construction of ecological landscape gardens is an effective way to improve the quality of urban life. The biodiversity of urban gardens is measured using species diversity indicators such as species richness, Simpson, Shan-Wiener, and Pielou evenness in this paper. The landscape single dynamic attitude, landscape comprehensive dynamic attitude and landscape patch transfer matrix were selected to construct a landscape dynamic change model, and the six urban areas of H city were used as the research object to analyze the landscape pattern of vegetation cover in urban gardens and landscapes. The results showed that among the area indexes on the landscape level in the study area of H city from 2018 to 2022, the maximum patch index, patch density and edge density indexes increased year by year, with an increase of 0.24, 3.42 m*hm , and 0.21 hm , respectively, in five years. Among the aggregation indexes, the effective particle size index showed an increasing trend, with an increase of 4301.3ha in 2022 compared with that in 2018, and the aggregation index showed a gradual decreasing trend, with a decrease of 0.4 in 2022 compared to 2018. By analyzing the landscape pattern, the changes in the landscape pattern can be described, and then the internal mechanism affecting the landscape pattern can be determined, which is of significant significance for the study of urban garden landscape planning and construction.

Based on the soft subspace clustering, this paper constructs an industry clustering index which can be used to test the significance, and then calculates and analyzes the clustering level of China's electronic information industry. The results show that the clustering phenomenon of China's electronic information industry is very obvious, and the clustering index shows an inverted U-shaped trend of rapid rise and slow decline in the whole research range, In terms of the distribution of the gathering areas, they are mainly concentrated in the eastern coastal areas such as Guangdong, Jiangsu and Shanghai, and the concentration degree is constantly increasing.

Ectotherms rely heavily on the spatial variance of environmental conditions to thermoregulate. Theoretically, their fitness is maximized when they can find suitable microhabitats by moving over short distances – this condition is met when spatial variance is high at fine spatial scales. Strikingly, despite the diversity of organisms living in leaf microhabitats, little is known about the impact of warming on the spatial variance of climatic conditions at the scale of individual leaf surfaces. Here, we used experimental manipulation of ambient conditions to quantify the effects of environmental change on the thermal heterogeneity within individual leaf surfaces. We also explored the implications for behavioural thermoregulation by arthropods at a single leaf surface. Using thermography, we characterized the apple leaf microclimate in terms of span and spatial aggregation of surface temperatures across a range of air temperatures and relative humidities. Then, we assessed how thermal heterogeneity within individual leaves affected behavioural thermoregulation by the two‐spotted spider mite (Tetranychus urticae Koch) under both near‐optimal and sublethal conditions in this microhabitat. We measured the upper lethal temperature threshold of the mite to define sublethal exposure. Thermal heterogeneity of individual leaves was driven mainly by ambient air temperature. Higher air temperatures gave both smaller ranges and higher spatial aggregation of temperatures at the leaf surface, such that the leaf microclimate was homogenized. Tetranychus urticae used behavioural thermoregulation at moderate air temperature, when thermal heterogeneity was high at the leaf surface. At higher air temperature, however, heterogeneity declined and spider mites did not perform behavioural thermoregulation. Warming decreases thermal heterogeneity of leaf surfaces with critical implications for arthropods – behavioural thermoregulation alone is not sufficient to escape the heat in the leaf microhabitat. Information on spatial variance of microclimatic conditions is critical for estimating how readily organisms can buffer global warming by moving.