Single Index Research Articles

Temporal-spatial characteristics and sources of heavy metals in bulk deposition across China

With the rapid development of industries, agriculture, and urbanization (including transportation and population growth), there has been a significant alteration in the emission and atmospheric deposition of heavy metal pollutants. This has consequently given rise to a range of ecological and environmental health issues. In this study, we conducted a comprehensive two-year investigation on the temporal and spatial distribution characteristics of heavy metals in atmospheric deposition across China based on the Nationwide Nitrogen Deposition Monitoring Network (NNDMN). The atmospheric bulk deposition of Lead (Pb), Arsenic (As), Nickel (Ni), Selenium (Se), Chromium (Cr) and Cadmium (Cd) were 6.32 ± 1.59, 4.49 ± 0.57, 1.31 ± 0.21, 1.05 ± 0.16, 0.60 ± 0.06 and 0.21 ± 0.03 mg m−2 yr−1, respectively, with a large variation among the different regions of China. The order for atmospheric deposition flux was Southwest China > Southeast China > North China > Northeast China > Qinghai-Tibet Plateau and rural area > urban area > background area. The concentrations of heavy metals in bulk deposition exhibit seasonal variation with higher levels observed during winter compared to summer and spring, which are closely associated with anthropogenic activities. The Positive Matrix Factorization (PMF) results indicated that combustion, industrial emissions and traffic are the primary contributors to atmospheric deposition of heavy metals. The single factor pollution index (Pi) of heavy metals is consistently below 1, and the composite pollution index (Ni) is 0.16 across China, indicating that atmospheric heavy metal deposition is at a pollution-free level. The comprehensive potential ecological risk index of heavy metals is 11.8, with Cd exhibiting the highest single factor potential ecological risk index at 7.09, suggesting that more attention should be paid to Cd deposition in China. The present study reveals the spatial-temporal distribution pattern of atmospheric heavy metals deposition in China, identifying regional source characteristics and providing a theoretical foundation and strategies for reducing emissions of atmospheric pollutants.

Human and ecotoxicological risk assessment of heavy metals in polymer post treatment sludge from Barekese Drinking Water Treatment Plant, Kumasi

The disposal of polymer post-treatment sludge (PTS) from Barekese Water Treatment Plants (WTPs) as organic fertilizer and aquatic feed is a common practice in Ghana, necessitating a thorough evaluation of its ecological and human health risks. This study aims to assess the suitability of PTS samples for soil amendment and fish feed, scrutinizing potential hazards to consumer health and soil. PTS samples were collected from five distinct lateral sections of three clariflocculator tanks. Potentially toxic metals such as Cd, Zn, Pb, Cu, Ni, and Cr were determined using a flame atomic absorption spectrophotometer. The mean concentration of 7.82 ± 2.43, 0.31 ± 0.021, and 0.78 ± 0.042 mg/kg for Mn, Zn, and Pb respectively. The concentrations of Ni, Cr, and Cd were below their detection limits (BDL) in all PTS samples. Upon detailed exposure assessment, ingestion emerged as the primary exposure route for both adults and children, with non-cancer risks (NCR) determined to be below 1 for both age groups. Additionally, an exploration of potential cancer risks (CR) associated with heavy metal exposure in the PTS samples revealed values below the tolerable intake levels ranging from 10−4 to 10−6 for both adults and children (10−8 and 10−9, respectively). This study also employs various ecological indices, such as Nemerow's synthetic pollution index (PN), single factor pollution index (PI), geo-accumulation index (Igeo), contamination factor (CF), potential ecological risk index (PERI), pollution load index (PLI), polymetallic contaminant index (IPD), and ecological risk index (ERI). These indices consistently highlight a low contamination status and ecological sensitivity. Consequently, the study indicates that the presence of metals in the PTS samples does not pose a significant threat to the surrounding environment and human health. Furthermore, this research underscores the inadequacy of relying solely on regulatory limit values in assessing the health risks of waste materials. Such comprehensive assessments are crucial for safeguarding aquatic and human populations.

Forming an Optimal Portfolio with a Single Index Model: Empirical study on banking stocks in the LQ45 Index

The aim of forming an optimal portfolio in this research is to determine the composition of the shares that form the portfolio. Apart from that, this research also aims to determine the expected return and risk of the portfolio being formed. The research sample is 5 banks included in the LQ45 Index. The data used is monthly stock prices. Data analysis uses a single index model, where the reference index is the Composite Stock Price Index on the Indonesian Stock Exchange. The results of the analysis found that there were shares of 4 banks that were included in the optimal portfolio. The proportion of each stock in the portfolio is as follows: Bank Mandiri 60.13%, Bank BRI 26.71%, Bank BCA 8.91%, and Bank BNI 4.25%. The portfolio formed is able to minimize risk compared to the shares that form the portfolio.

Human and Ecological Risk Assessment of Soil and Plants Metal Contamination in a Tropical Municipal Solid Waste Dumping Site

ABSTRACT The growing open waste dumpsite contributes to heavy metal leaching, harming public health and ecosystem services. Hence, the study’s novelty lies in its integrated assessment of heavy metal concentration (Cd, Cr, Cu, Fe, Ni, Pb, and Zn) in soil and plants, incorporating risk assessment using both single and combined indexes in the largest active waste dumpsite in Kelantan, Malaysia. Heavy metal in soil samples Cu (164.896 ± 205.096), Pb (122.177 ± 93.309), and Cr (32.74 ± 26.569) were recorded to exceed the standards, while in the studied plants (castor bean, papaya, pea eggplant, and water spinach), Pb concentrations in all plant parts have surpassed the Malaysia Food Regulation (1985) and FAO/WHO guidelines (2016). Translocation factor (TF) in Cu, Cr, and Pb in water spinach and Cr, Cu, and Cd in pea eggplant have TF > 1. Bioconcentration factor (BCF) was < 1 in pea eggplant and water spinach except for Pb in pea eggplant. Correlation analysis revealed a strong correlation between heavy metals in the soil while contrasts in the plants. The potential ecological risk index (PERI) reveals that the soil waste dumpsite is contaminated with heavy metals. The hazard index (HI) and lifetime cancer risk (LCR) values of heavy metals on the soil surface had negligible non-cancer risks posed to adults and indicate that soil ingestion and dermal absorption were the two primary carcinogenic risk exposure routes. These findings implied that Beris Lalang waste dumpsite is a potential source of toxic pollutants to the environment and the local population. It is recommended to conduct a comprehensive feasibility study and design an implementation plan for leachate treatment plant. Additionally, a comprehensive assessment of the specific contaminants present in the leachate and their potential impacts on the environment should be conducted to inform targeted and effective treatment strategies.

CRITICAL ANALYSIS OF STOCK FROM AN OPTIMAL PORTFOLIO PERSPECTIVE

This study aims to conduct a comprehensive evaluation of the shares of Islamic banking companies listed on the Indonesia Stock Exchange (IDX) during the period 2020 to 2022, focusing on the optimal portfolio perspective. The method used in this research is a descriptive quantitative approach. The population studied was all Islamic banking companies listed on the IDX during the period mentioned. The sample was selected using purposive sampling technique, with certain criteria to select Islamic banking companies listed on the IDX during the period 2020 to 2022. As a result, three stocks were selected as samples of this study. This study uses a single index model to analyze the data. The results of the analysis showed that of the three stocks sampled, none met the criteria to be included in the optimal portfolio. These stocks are considered not meeting the standards set because the expected return value is smaller than the systematic risk value. Nonetheless, these findings provide valuable insights into the performance of stocks of Islamic banking companies on the IDX during the study period. In addition, it also highlights the importance of conducting periodic portfolio evaluations to identify potential changes in stock performance and optimize the allocation of financial resources. This study makes an important contribution to the understanding of optimal investment strategies in the context of Islamic banking companies in the Indonesian capital market.

Efficient Algorithms for Non-gaussian Single Index Models with Generative Priors

In this work, we focus on high-dimensional single index models with non-Gaussian sensing vectors and generative priors. More specifically, our goal is to estimate the underlying signal from i.i.d. realizations of the semi-parameterized single index model, where the underlying signal is contained in (up to a constant scaling) the range of a Lipschitz continuous generative model with bounded low-dimensional inputs, the sensing vector follows a non-Gaussian distribution, the noise is a random variable that is independent of the sensing vector, and the unknown non-linear link function is differentiable. Using the first- and second-order Stein's identity, we introduce efficient algorithms to obtain estimated vectors that achieve the near-optimal statistical rate. Experimental results on image datasets are provided to support our theory.

Improving the prediction performance of leaf water content by coupling multi-source data with machine learning in rice (Oryza sativa L.)

BackgroundLeaf water content (LWC) significantly affects rice growth and development. Real-time monitoring of rice leaf water status is essential to obtain high yield and water use efficiency of rice plants with precise irrigation regimes in rice fields. Hyperspectral remote sensing technology is widely used in monitoring crop water status because of its rapid, nondestructive, and real-time characteristics. Recently, multi-source data have been attempted to integrate into a monitored model of crop water status based on spectral indices. However, there are fewer studies using spectral index model coupled with multi-source data for monitoring LWC in rice plants. Therefore, 2-year field experiments were conducted with three irrigation regimes using four rice cultivars in this study. The multi-source data, including canopy ecological factors and physiological parameters, were incorporated into the vegetation index to accurately predict LWC in rice plants.ResultsThe results presented that the model accuracy of rice LWC estimation after combining data from multiple sources improved by 6–44% compared to the accuracy of a single spectral index normalized difference index (ND). Additionally, the optimal prediction accuracy of rice LWC was produced using a machine algorithm of gradient boosted decision tree (GBDT) based on the combination of ND(1287,1673) and crop water stress index (CWSI) (R2 = 0.86, RMSE = 0.01).ConclusionsThe machine learning estimation model constructed based on multi-source data fully utilizes the spectral information and considers the environmental changes in the crop canopy after introducing multi-source data parameters, thus improving the performance of spectral technology for monitoring rice LWC. The findings may be helpful to the water status diagnosis and accurate irrigation management of rice plants.

Quantifying Abdominal Coloration of Worker Honey Bees.

The main drawback in using coloration to identify honey bee subspecies is the lack of knowledge regarding genetic background, subjectivity of coloration grading, and the effect of the environment. The aim of our study was to evaluate the effect of environmental temperature on the abdominal coloration of honey bee workers and to develop a tool for quantifying abdominal coloration. We obtained four frames of honey bee brood from two colonies and incubated them at two different temperatures (30 and 34 °C). One colony had workers exhibiting yellow marks on the abdomen, while the other did not. We collected hatched workers and photographed abdomens. Images were analyzed using custom-written R script to obtain vectors that summarize the coloration over the abdomen length in a single value-coloration index. We used UMAP to reduce the dimensions of the vectors and to develop a classification procedure with the support vector machine method. We tested the effect of brood origin and temperature on coloration index with ANOVA. UMAP did not distinguish individual abdomens according to experimental group. The trained classifier sufficiently separated abdomens incubated at different temperatures. We improved the performance by preprocessing data with UMAP. The differences among the mean coloration index values were not significant between the gray groups incubated at different temperatures nor between the yellow groups. However, the differences between the gray and yellow groups were significant, permitting options for application of our tool and the newly developed coloration index. Our results indicate that the environmental temperature in the selected range during development does not seem to impact honey bee coloration significantly. The developed color-recording protocol and statistical analysis provide useful tools for quantifying abdominal coloration in honey bees.

Evaluating the Human–Water Relationship over the Past Two Decades Using the SMI-P Method across Nine Provinces along the Yellow River, China

The foundation for ensuring the sustainable utilization of natural resources and human well-being lies in achieving a harmonious balance between nature and humans. In the Yellow River basin (YRB), numerous water crises, including floods, droughts, soil erosion, and water pollution, threaten its crucial role as a significant economic belt and ecological barrier. Unfortunately, less comprehension regarding the complex human–water relationship in this region has impeded watershed water management decision-makers from identifying key priorities for intervention. Here, we selected 29 evaluation indicators, including water resources, environment, ecology, society, economy, and science and technology from three dimensions: healthy water systems, sustainable human systems, and synergy of human–water system. We applied the entropy weight method, hierarchical analysis, and Single index quantification, multiple index synthesis, and poly-criteria integration (SMI-P) methods to quantify the spatial–temporal variation of the human–water harmony degree (HWHD) in nine provinces of the YRB from 2002 to 2021. We observed a consistent increase in the HWHD across all provinces in the YRB in the past two decades. Notably, five provinces have transitioned from Complete disharmony (0 ≤ HWHD ≤ 0.2) to Nearly complete disharmony (0.2 &lt; HWHD ≤ 0.4). Additionally, the average growth rate of the downstream provinces is faster compared to those upstream. By 2021, the HWHD of upstream provinces like Sichuan and Ningxia, constrained by slower growth, became the two lowest provinces of the YRB, at 0.19 and 0.12 respectively. These findings offer valuable guidance for the region and similar areas grappling with the complex challenges of human–water conflicts, providing insights to navigate and address such dilemmas effectively.

Utility of single fibre electromyography (SFEMG)and motor unit number estimation technique (MUNE) in the diagnosis and differentiation of polyneuropathies of different aetiology.

To determine whether single fibre electromyography and motor unit number index can distinguish between axonal and myelin lesions in polyneuropathies. This case-control study was conducted at the Department of Medical Physiology, School of Medicine, University of Duhok, Iraq, and the Neurophysiology Department, Hawler Teaching Hospital, Erbil, Iraq, from January 2021 to March 2022. Group A had patients diagnosed with polyneuropathy regardless of the aetiology, while group B had age-matched healthy controls. Both groups were subjected to single fibre electromyography and motor unit number index as well as conventional nerve conduction study and concentric needle electromyography. Data was analysed using SPSS 26. Of the 140 subjects, 60(43%) were patients in group A; 40(67%) males and 20(33%) females with mean age 55.3±7.2 years. There were 80(57%) controls in group B; 43(54%) females and 37(46%) males with mean age 53.81±7.15. Group A had significantly higher single fibre electromyography jitter, and mean consecutive difference (MCD) values than group B (p<0.05). Group A patients with axonal polyneuropathy had a higher mean jitter (MCD) value (36.476.7ms) than those with demyelinating polyneuropathy (23.262.31 ms) (P <0.05). Patients in group A had a motor unit number index value with a significantly lower mean value (p<0.05) when compared to the controls. Axonal polyneuropathy patients had a lower MUNIX value (99.612.8) than demyelinating polyneuropathy patients (149.845.7) (P< 0.05). Single fibre electromyography and motor unit number index could help differentiate between the pathophysiology of axonal and demyelinating polyneuropathy.

Estimation and convergence rates in the distributional single index model

The distributional single index model is a semiparametric regression model in which the conditional distribution functions of a real‐valued outcome variable depend on ‐dimensional covariates through a univariate, parametric index function , and increase stochastically as increases. We propose least squares approaches for the joint estimation of and in the important case where and obtain convergence rates of , thereby improving an existing result that gives a rate of . A simulation study indicates that the convergence rate for the estimation of might be faster. Furthermore, we illustrate our methods in an application on house price data that demonstrates the advantages of shape restrictions in single index models.

Single-Index Mixed-Effects Model for Asymmetric Bivariate Clustered Data

Studies/trials assessing status and progression of periodontal disease (PD) usually focus on quantifying the relationship between the clustered (tooth within subjects) bivariate endpoints, such as probed pocket depth (PPD), and clinical attachment level (CAL) with the covariates. Although assumptions of multivariate normality can be invoked for the random terms (random effects and errors) under a linear mixed model (LMM) framework, violations of those assumptions may lead to imprecise inference. Furthermore, the response-covariate relationship may not be linear, as assumed under a LMM fit, and the regression estimates obtained therein do not provide an overall summary of the risk of PD, as obtained from the covariates. Motivated by a PD study on Gullah-speaking African-American Type-2 diabetics, we cast the asymmetric clustered bivariate (PPD and CAL) responses into a non-linear mixed model framework, where both random terms follow the multivariate asymmetric Laplace distribution (ALD). In order to provide a one-number risk summary, the possible non-linearity in the relationship is modeled via a single-index model, powered by polynomial spline approximations for index functions, and the normal mixture expression for ALD. To proceed with a maximum-likelihood inferential setup, we devise an elegant EM-type algorithm. Moreover, the large sample theoretical properties are established under some mild conditions. Simulation studies using synthetic data generated under a variety of scenarios were used to study the finite-sample properties of our estimators, and demonstrate that our proposed model and estimation algorithm can efficiently handle asymmetric, heavy-tailed data, with outliers. Finally, we illustrate our proposed methodology via application to the motivating PD study.

Application of BPNN’s intelligent ETQ model in network English teaching

Teaching evaluation is a key initiative to improve the quality of education and teaching. The research significance of this study is rooted in addressing the limitations of the traditional evaluation of teaching quality (ETQ) model, which often relies on a single evaluation index, exhibits a one-sided perspective, and suffers from pronounced subjectivity. In this context, this paper delves into the application of the backpropagation neural network (BPNN) to enhance and refine the ETQ model. The intelligent ETQ model was constructed and utilized in network English teaching to enhance the effect and quality of network English teaching. By analyzing the characteristics and needs of network English teaching, the advantages of BPNN in the ETQ were explored. The intelligent evaluation model was constructed, and its application effect in network English teaching was studied and evaluated. The total number of students satisfied with the BPNN based network English ETQ model was 151, with a total satisfaction rate of 75.5%. The ETQ model on the basis of BPNN was applied to network English teaching, which helped the average final score of Class 2 improve by 5.44 points compared to the division exam. The ETQ model based on BPNN was applied to network English teaching, which can improve the rationality of teaching evaluation and help improve students’ school English proficiency.

A comprehensive risk assessment method for hot work in underground mines based on G1-EWM and unascertained measure theory

A risk assessment method for hot work based on G1-EWM and unascertained measurement theory was proposed to prevent hot work accidents in underground mines. Firstly, based on the risk influencing factors and classification criteria for underground hot work operations in mines, a single indicator measurement matrix was constructed using unascertained measurement theory; Secondly, a risk assessment index system for mine underground hot work operations was established. The combination weight coefficient of each index was determined using the order relationship analysis method (G1) and entropy weight method (EWM) and coupled with the single index measurement evaluation vector to calculate the multi-index comprehensive evaluation vector of the evaluation object; Finally, the model was validated and examined using engineering examples, and the evaluation level was determined using confidence identification criteria. The results showed that the proposed method, when used to evaluate the risk of hot work operations in tunnels and vertical shafts in metal mines, produces risk levels that are in line with reality III (Moderate Risk) for the vertical shaft and IV (High Risk) for the tunnels. The evaluation model results are consistent with the risk evaluation results the whole process of on-site hot work, which verifies the model feasibility. A unique strategy and method for risk management in hot work operations in underground mines is provided by the combination of weighting and unascertained measure models, which has theoretical and practical value. Future research could focus on refineing this model by exploring the applicability in diverse mining environments and integrating advanced analytical techniques to enhance the predictive accuracy and operational efficiency.

Deflation by Expenditure Components: A Harmless Adjustment?

AbstractWe investigate the effect that seemingly minor features of the implementation of cost‐of‐living adjustments have on the distribution of household expenditures, by developing an analytical framework that is consistent with standard consumer theory, and mindful of data limitations faced by practitioners. The main result is at odds with common sense: even when multiple price indices are available (e.g., a food Consumer Price Index and a non‐food one), it turns out that using a single price index (e.g., the total Consumer Price Index), to adjust the consumption aggregate is recommended. The practice of adjusting subcomponents of consumption separately (food with a food index and nonfood with a nonfood index) can lead to a systematic bias in the welfare measure, and consequently in poverty and inequality measures. Using Iran's 2019 Household Expenditures and Income Survey, we find that the bias manifests as a systematic underestimation of urban poverty and overestimation of rural poverty.

SeRF: Segment Graph for Range-Filtering Approximate Nearest Neighbor Search

Effective vector representation models, e.g., word2vec and node2vec, embed real-world objects such as images and documents in high dimensional vector space. In the meanwhile, the objects are often associated with attributes such as timestamps and prices. Many scenarios need to jointly query the vector representations of the objects together with their attributes. These queries can be formalized as range-filtering approximate nearest neighbor search (ANNS) queries. Specifically, given a collection of data vectors, each associated with an attribute value whose domain has a total order. The range-filtering ANNS consists of a query range and a query vector. It finds the approximate nearest neighbors of the query vector among all the data vectors whose attribute values fall in the query range. Existing approaches suffer from a rapidly degrading query performance when the query range width shifts. The query performance can be optimized by a solution that builds an ANNS index for every possible query range; however, the index time and index size become prohibitive -- the number of query ranges is quadratic to the number n of data vectors. To overcome these challenges, for the query range contains all attribute values smaller than a user-provided threshold, we design a structure called the segment graph whose index time and size are the same as a single ANNS index, yet can losslessly compress the n ANNS indexes, reducing the indexing cost by a factor of Ω(n). To handle general range queries, we propose a 2D segment graph with average-case index size O(n log n) to compress n segment graphs, breaking the quadratic barrier. Extensive experiments conducted on real-world datasets show that our proposed structures outperformed existing methods significantly; our index also exhibits superior scalability.

Opposite changes in morphometric similarity of medial reward and lateral non-reward orbitofrontal cortex circuits in obesity

Obesity has a profound impact on metabolic health thereby adversely affecting brain structure and function. However, the majority of previous studies used a single structural index to investigate the link between brain structure and body mass index (BMI), which hinders our understanding of structural covariance between regions in obesity. This study aimed to examine the relationship between macroscale cortical organization and BMI using novel morphometric similarity networks (MSNs). The individual MSNs were first constructed from individual eight multimodal cortical morphometric features between brain regions. Then the relationship between BMI and MSNs within the discovery sample of 434 participants was assessed. The key findings were further validated in an independent sample of 192 participants. We observed that the lateral non-reward orbitofrontal cortex (lOFC) exhibited decoupling (i.e., reduction in integration) in obesity, which was mainly manifested by its decoupling with the cognitive systems (i.e., DMN and FPN) while the medial reward orbitofrontal cortex (mOFC) showed de-differentiation (i.e., decrease in distinctiveness) in obesity, which was mainly represented by its de-differentiation with the cognitive and attention systems (i.e., DMN and VAN). Additionally, the lOFC showed de-differentiation with the visual system in obesity, while the mOFC showed decoupling with the visual system and hyper-coupling with the sensory-motor system in obesity. As an important first step in revealing the role of underlying structural covariance in body mass variability, the present study presents a novel mechanism that underlies the reward-control interaction imbalance in obesity, thus can inform future weight-management approaches.

Dynamic Changes in Vegetation Ecological Quality in the Tarim Basin and Its Response to Extreme Climate during 2000–2022

The Tarim Basin is located in an arid inland area; the ecological environment is fragile, and it is extremely sensitive to climate change. For the purpose of studying dynamic changes in the vegetation response of vegetation in the Tarim Basin to extreme climate, this study used the Vegetation Ecological Quality Index (EQI) as a vegetation indicator and calculated 12 extreme climate indices using Rclimdex. Pearson correlation analysis was used to explore the relationship between EQI values and various extreme climate indices at both inter-annual and intra-annual scales. Additionally, geographic detector analysis was employed to examine the single and interactive effects of extreme climate on the EQI for different vegetation types. The following was found: (1) During 2000–2022, the EQI showed an upward trend in the Tarim Basin, and the increase in agricultural vegetation was the fastest. (2) Since 2000, the extreme warm temperature indices have risen, whereas the extreme cold temperature indices have declined. The warming rate of nighttime temperatures exceeds that of daytime, and the extreme precipitation rises intensively. Simultaneously, continuous dry days have also increased. (3) On an inter-annual scale, the EQI is primarily negatively correlated with the most extreme warm temperature indices, while it is positively correlated with extreme cold temperatures and extreme precipitation indices. On an intra-annual scale, there is an obvious regional concentration in the correlation between the EQI and extreme climate indices. The diurnal temperature range (DTR) and cold daytimes (TX10P) have inhibitory and promoting effects on areas with high and low EQI, respectively. The extremum indices, temperature warm indices, and precipitation intensity indices have a promoting effect on areas with a high EQI and an inhibiting effect on areas with a low EQI. The interaction between extreme climate indices has a greater impact on the EQI than the effect of a single extreme climate index, especially with a significant impact on forests and shrubs. This study provides a reference for the early warning of meteorological disasters, ecosystem protection, and sustainable management in the Tarim Basin.

Spatial-temporal Evolution and Driving Factors Analysis of Ecological Environment Quality in Daihai Basin based on AWRSEI

Watersheds are an important ecological security barrier and social and economic development area. In order to evaluate the ecological environment quality of arid and semi-arid watersheds more objectively and accurately, based on the remote sensing ecological index (RSEI), the salinity index was introduced to construct a remote sensing ecological index (AWRSEI) suitable for arid and semi-arid watersheds, and the Daihai watershed was taken as an example to analyze its applicability. Based on the AWRSEI model, four phases of Landsat TM/OLI composite images were used to quantitatively evaluate the ecological and environmental quality of the Daihai Basin from 2001 to 2020. The spatio-temporal evolution of the ecological and environmental quality of the Daihai Basin was analyzed using the coefficient of variation and spatial autocorrelation, and the cause analysis and driving factor explanation power analysis were carried out using geographic detectors. The results showed that:① the average correlation coefficient between AWRSEI and various ecological factors was 0.860, which was more representative than that of a single index component. The load positive and negative values and ranking of the first principal component were consistent with those of RSEI, the contribution rate of eigenvalues was 3.69% higher than those of RSEI, and the evaluation results were closer to the real surface conditions, which is suitable for the ecological environment quality assessment of arid watersheds. ② The annual average of the AWRSEI index in the Daihai Basin from 2001 to 2020 was 0.427, which indicated a poor ecological environment quality basis. During the study period, the average of AWRSEI showed a fluctuating trend and reached the highest value of 0.502 in 2020. The overall ecological environment quality improved significantly, the deterioration area decreased by 20.51%, and the improvement area increased by 12.71%. In terms of spatial distribution, the ecological environment quality of forest land in the southern and northwestern high-altitude areas of the Daihai Basin was good, whereas that in the northern and southern mid-altitude areas was poor, and that in the northern area was better than that in the southern area. ③ The average variation coefficient of AWRSEI in the Daihai Basin was 0.280, the ecological environment quality was stable, and the overall change fluctuation was small; the high fluctuation was mainly concentrated in the southern part of the lake and the residential area. There was a significant spatial autocorrelation in the ecological environment quality of the Daihai Basin, and the high-high agglomeration area was mainly distributed in the forest area at high altitude and the cultivated land area at low altitude. Low-low concentration areas were scattered in the middle altitude area. ④ The improvement of the ecological environment in Daihai Basin from 2001 to 2020 was mainly due to the increase in NDVI and the decrease in NDBSI and NDSI. NDVI and NDBSI were the combination with the strongest interaction and the strongest interpretation of the ecological environment. Land use was the dominant factor of AWRSEI and had the strongest explanatory power. The combination of land use and meteorological factors was the strongest interaction, and the relationship between each driving factor was enhanced.

Multi-level performance evaluation of BMD surface mixtures with conventional and high RAP contents: a case study in Virginia

ABSTRACT This study investigated one control and five Balanced Mix Design (BMD) optimised asphalt surface mixtures, four of which had high reclaimed asphalt pavement (RAP) contents (HRAP mixtures), using laboratory performance tests characterised with different levels of complexity. The performance of the evaluated mixtures was assessed based on durability, rutting resistance, and cracking resistance as emphasized by BMD. The study explored the ranking of a single index and correlations among various indices. Assisted by 3-Dimensional and ternary plots, this study also proposed a novel composite performance index [CPI] that combines major indices (durability, cracking, and rutting) to evaluate the performance of BMD optimised mixtures. The results revealed discrepancies between basic/intermediate performance test results and advanced performance test results. The comparisons conducted also underscored the beneficial impacts derived from using softer binders and/or recycling agents in HRAP mixtures. Furthermore, the findings indicated that the BMD approach can serve as an effective framework for designing asphalt mixtures that simultaneously enhance both fatigue and rutting performance. Moreover, the study revealed HRAP BMD surface mixtures can exhibit superior overall performance when compared to conventionally designed control mixtures.