Single Index Research Articles

Penerapan Model Indeks Tunggal dalam Menganalisis Portofolio Saham Optimal dan Peramalan Harga Saham Optimal Menggunakan Long Short-Term Memory

Investors in choosing stocks are not only on one type of stock, but can diversify. In this study, Blue Chip stocks were used which was carried out in two stages. The first stage is the Single Index Model to obtain an optimal stock portfolio. The second stage, the optimal share price results from the Single Index Model, namely Bank Mandiri (Persero) Tbk (BMRI), is followed by a forecasting process using Long Short-Term Memory (LSTM). The purpose of this study is to apply the Single Index Model to analyze the portfolio on Blue Chip stocks, forecast the selected optimal stock price in the next day, and evaluate the results of the selected optimal stock price forecasting. In the first stage, the Single Index Model uses data for the period from February 1, 2021 to February 1, 2024. The second stage, forecasting with LSTM uses data for the period from February 1, 2021 to February 29, 2024. The results of the first stage were obtained with optimal shares of 13 shares out of 41 Blue Chip shares. The most optimal stock was obtained by BMRI shares. The second stage is the use of BMRI shares with LSTM forecasting. The results of the second stage were obtained by forecasting the stock price performance with the best performance, so that it shows that the stock price performance on March 1, 2024 was obtained of IDR 6894,574. The accuracy of forecasting stock price performance with MAPE is 4.23%. It can be said that the forecast of stock price performance has very good accuracy.

Outcome Reporting in Prospective Studies Evaluating Neurostimulation for Obstructive Sleep Apnea.

Due to the controversy surrounding the appropriate outcomes in neurostimulation, we sought to systematically describe ways in which polysomnography and apnea-hypopnea index are reported in prospective studies involving unilateral hypoglossal nerve stimulation. MEDLINE (Ovid), Embase (Ovid), Cochrane Library, and Scopus. Following the Preferred Reporting items of Systematic Reviews and Meta-analysis (PRISMA) Statement guidelines, a systematic two-reviewer system was used for study screening and quality assessment. Articles that met inclusion criteria were included. Quality was evaluated with either the Newcastle-Ottawa Quality Assessment Scale or the Covidence risk-of-bias tool. Fifteen studies met the inclusion criteria, which included 14 prospective cohort studies and one randomized controlled trial. Titration polysomnography was the primary sleep study used to acquire data in five of the studies compared to only three studies employing exclusively non-titration polysomnography to report outcomes. Three studies compiled data from two or more sleep studies to report a single apnea-hypopnea index. Within the 15 studies, non-titration apnea-hypopnea index was the most reported type (five studies). Titration apnea-hypopnea index was used to report outcomes in one study. Five studies did not specify what type of apnea-hypopnea index was employed to report treatment effectiveness. The reported sleep studies and corresponding apnea-hypopnea indices were highly variable across the studies. Because of the high degree of heterogeneity, future research would benefit from consistent use of a standardized apnea-hypopnea index to report outcomes related to hypoglossal nerve stimulation. NA Laryngoscope, 134:4873-4881, 2024.

Spatial distribution and migration characteristics of heavy metals at an abandoned industrial site in the Southwest of China

The rapid acceleration of global industrialization has rendered heavy metal contamination at abandoned industrial sites a severe challenge, particularly in geologically complex and fragile karst regions of Southwest China, posing significant threats to ecosystems and public health. However, existing research lacks a comprehensive understanding of the spatial distribution and migration mechanisms of heavy metals in this region. In this study, 523 soil samples and 30 groundwater samples were collected, and the pollution levels were systematically assessed using the Geo-Accumulation Index, Single Pollution Index, and Nemerow Integrated Pollution Index. Horizontal and vertical spatial heterogeneity was explored through Moran's I and Voronoi polygon analysis. Furthermore, 3D geological modeling and groundwater flow simulations were employed to investigate the influence of hydrogeological conditions on contaminant migration. The results indicate elevated concentrations of Cd, Hg, Pb, and As in the surface layer, with concentrations initially decreasing and then increasing with depth, likely due to the presence of discontinuous clay layers. Moran's I revealed significant clustering effects at depths of 0.2 meters and 4 meters, while Voronoi analysis confirmed vertical heterogeneity. This study provides a scientific basis for pollution assessment and targeted remediation in karst regions.

Smoothed Estimation on Optimal Treatment Regime Under Semisupervised Setting in Randomized Trials.

A treatment regime refers to the process of assigning the most suitable treatment to a patient based on their observed information. However, prevailing research on treatment regimes predominantly relies on labeled data, which may lead to the omission of valuable information contained within unlabeled data, such as historical records and healthcare databases. Current semisupervised works for deriving optimal treatment regimes either rely on model assumptions or struggle with high computational burdens for even moderate-dimensional covariates. To address this concern, we propose a semisupervised framework that operates within a model-free context to estimate the optimal treatment regime by leveraging the abundant unlabeled data. Our proposed approach encompasses three key steps. First, we employ a single-index model to achieve dimension reduction, followed by kernel regression to impute the missing outcomes in the unlabeled data. Second, we propose various forms of semisupervised value functions based on the imputed values, incorporating both labeled and unlabeled data components. Lastly, the optimal treatment regimes are derived by maximizing the semisupervised value functions. We establish the consistency and asymptotic normality of the estimators proposed in our framework. Furthermore, we introduce a perturbation resampling procedure to estimate the asymptotic variance. Simulations confirm the advantageous properties of incorporating unlabeled data in the estimation for optimal treatment regimes. A practical data example is also provided to illustrate the application of our methodology. This work is rooted in the framework of randomized trials, with additional discussions extending to observationalstudies.

Evaluating coupled influences of slope class and land use change on water quality using single and composite indices in an agricultural basin

Understanding the critical source areas (CSAs) of pollution and their key relationships with land use is essential for efficient water quality improvement. Despite much efforts to examine their influences on water quality, the role of topographic characteristics in land use processes acting on water quality in lowland regions remains unclear. Therefore, taking a typical rural lowland catchment-the Stör catchment (in northern Germany) as the study area, the influences of different land use types with varying slopes on sediment, total phosphorus (TP), and total nitrogen (TN) loads, as well as on the relevant CSAs of a single or combined index (Ij) were determined using an integrated approach that applies hydrologic model SWAT3s (Soil and Water Assessment Tool, adapted with three groundwater aquifers) and multivariate statistical techniques. The results indicated: (1) sediment and nutrients have markedly declined from 1990 to 1991 over 2009–2011 to 2018–2019, particularly in summer and in comparatively steeper areas with slopes above 5%; (2) the subbasins in the southwest or northeast of the catchment dominated by larger arable fields with slope > 5% and urban areas were identified as CSAs; (3) the spatial heterogeneity of overall water quality was more explained in summer (75% on average) and particularly by croplands (50% on average); (4) water quality indicators were mostly and positively affected by cropland and pasture with relatively steeper slope (>5%). In particular, cropland with slopes > 5% was among those land use indicators producing the highest values of sediment, TP, TN, and Ij. This study demonstrates that the identification of seasonal CSAs could provide insights into the seasonal targeting of conservation practices. Furthermore, communicating model outputs to cause-effect analysis can help formulate slope-adaptability measures for water quality protection.

Practical methodology for quantifying the structural robustness of RC building structures

Several structural robustness requirements have been included in building codes over the past two decades. These mainly include measures to prevent collapse after an initial component failure by ensuring a minimum level of continuity within the structural system so that loads supported by the failed component can be redistributed to the rest of the structural system. However, there is still a general lack of consensus on how to evaluate the effectiveness of these measures. The present study proposes a practical methodology to evaluate the structural robustness of RC building structures by combining an efficient modelling strategy and suitable robustness indexes. The proposed computational modelling strategy for RC buildings accounts for relevant dynamic and nonlinear phenomena, including compressive arching, catenary action, and membrane effects. This strategy can be implemented at a reasonable computational expense in widely used commercial structural analysis software and has been validated by comparisons to selected experimental tests from the literature. The modelling strategy is then employed to compute four different robustness indexes proposed in the literature for three hypothetical building designs, allowing the indexes to be systematically compared. It was found that evaluating a system using multiple robustness indexes can provide a more holistic view of system performance compared to relying solely on a single index. Ultimately, the potential usefulness of the proposed methodology is demonstrated through two practical applications involving the estimation of component damage levels and the evaluation of different design and retrofitting solutions. Such a methodology can be useful for practising engineers to optimise the design of new buildings or to support decisions on the assessment and retrofitting of existing ones.

Uniform in Number of Neighbor Consistency and Weak Convergence of k-Nearest Neighbor Single Index Conditional Processes and k-Nearest Neighbor Single Index Conditional U-Processes Involving Functional Mixing Data

U-statistics are fundamental in modeling statistical measures that involve responses from multiple subjects. They generalize the concept of the empirical mean of a random variable X to include summations over each m-tuple of distinct observations of X. W. Stute introduced conditional U-statistics, extending the Nadaraya–Watson estimates for regression functions. Stute demonstrated their strong pointwise consistency with the conditional expectation r(m)(φ,t), defined as E[φ(Y1,…,Ym)|(X1,…,Xm)=t] for t∈Xm. This paper focuses on estimating functional single index (FSI) conditional U-processes for regular time series data. We propose a novel, automatic, and location-adaptive procedure for estimating these processes based on k-Nearest Neighbor (kNN) principles. Our asymptotic analysis includes data-driven neighbor selection, making the method highly practical. The local nature of the kNN approach improves predictive power compared to traditional kernel estimates. Additionally, we establish new uniform results in bandwidth selection for kernel estimates in FSI conditional U-processes, including almost complete convergence rates and weak convergence under general conditions. These results apply to both bounded and unbounded function classes, satisfying certain moment conditions, and are proven under standard Vapnik–Chervonenkis structural conditions and mild model assumptions. Furthermore, we demonstrate uniform consistency for the nonparametric inverse probability of censoring weighted (I.P.C.W.) estimators of the regression function under random censorship. This result is independently valuable and has potential applications in areas such as set-indexed conditional U-statistics, the Kendall rank correlation coefficient, and discrimination problems.

The joint distribution of years lived in good and poor health

BackgroundIncidence-based multistate models of population health are commonly applied to calculate state expectancies, such as a healthy life expectancy (HLE), or unhealthy life expectancy (UHE). These models also allow the computation of other summary indices, such as the distributions of healthy or unhealthy lifespans.ObjectiveWe aim to show how a multistate health model implies a multistate death distribution, giving joint information on years lived in good and poor health. We also propose three aggregate indices of joint health and mortality inequality.MethodsWe propose a double-accounting approach to increment-decrement life table methods to intuitively derive a multistate health distribution over age and cumulative duration spent in each state. We then define a variety of summary lifespan inequality indices based on different distance metrics, namely Euclidean, Chebyshev, and Manhattan distances.ResultsWe apply the method to multistate transition probabilities between health states based on the activities of daily living index for Italian women from the Survey of Health, Ageing and Retirement in Europe in 2015-2017. We demonstrate the added value of accounting for joint years lived in health states in multistate models for our understanding of the period health and mortality conditions from the perspective of health-specific lifespans of individuals.ConclusionsMultivariate state distributions and summary indices derived from them give a holistic representation of population health inequality. We offer selected summary indices of the multivariate distribution with different demographic interpretations from the measures derived from univariate distributions. Although more theoretical and methodological work is required to motivate a single comprehensive population health inequality index, this direction is a promising path for a better understanding of population health dynamics and relationships between univariate statistics.

Research on the Quality Assessment and Protection of Network Security Body Based on Intelligent Induction and Deep Learning

This mechanism mainly saves the induction information in the distributed cloud storage node, and saves the message summary of the induction information in the block chain node, and then the corresponding relationship between the cloud storage node and the block chain node is saved in the induction information management machine. When the user reads the data, the identity authentication is first completed at the induction information management machine, and the induction information is obtained through the key, and finally the data verification is completed at the block chain node. The main advantages of this mechanism are: using block chain to store intelligent sensing information, and using chain storage to reduce the cost of storage, thus enhancing the scalability of block chain storage. This mechanism uses new hash chains to transmit inductive information, thus improving the security of transmission. Through this study proposes an access control strategy for intelligent sensing information. In this way, the user with the key can quickly complete the work certificate and complete the access, while the illegal intruder who does not hold the key cannot calculate the work certificate of the next block based on the existing block, so he cannot access the intelligence. Under the network topology set in this paper, the model with step 2 is significantly better than step 1 and 3, with 18.30% and 75.01% reduction on MAPE and 15.66% and 87.79% reduction on RMSE. Through hidden Markov, the security situation of the information system under the time series is determined. Through SSIPN, the security event is not used as a single situation assessment index, but the network topology and node vulnerability are included in the assessment scope to enhance the correlation between the security event and each node in the information system. Based on SSIPN, the weight allocation algorithm of the corresponding nodes is proposed, which accurately reflects the impact of the level of the nodes on the network on the overall situation, and realizes the security situation assessment of the overall network.

Current treatment approach and future perspectives in B cell lymphoma.

Diffuse large B cell lymphoma (DLBCL) and follicular lymphoma (FL) represent the two major subtypes of mature B cell lymphoma. A deeper understanding of tumor biology, as well as molecular classification characterized by targetable gene alterations, and the introduction of novel treatment options, including targeted drugs (e.g., antibody-drug conjugates and small molecules [e.g., Bruton tyrosine kinase inhibitor]) and immune therapies (e.g., chimeric antigen receptor [CAR] T cell therapy and bispecific antibody [BsAb]), has changed the treatment paradigms for DLBCL and FL. In clinical practice, however, treatment regimens are determined mainly based on prior treatment history, duration of response after previous treatment, patient age, and patient frailty because there have been few randomized trials to inform treatment selection for patients with relapsed or refractory disease and because there is no single prognostic index that guides suitable treatment for each patient. In this review, we summarize the treatment options for DLBCL and FL and discuss the treatment strategies for these two subtypes. We also discuss future perspectives for the treatment of these subtypes.

Predictive value of urine misfolded protein in preeclampsia in twin pregnancies.

To assess the utility of urinary misfolded proteins (MP) in predicting preeclampsia (PE) in high-risk twin pregnancies. A prospective study was conducted on 600 high-risk twin pregnancies at Shanghai First Maternity and Infant Hospital from March to August 2021. Clinical data were collected, and urinary MP levels were measured. Subsequently, fetal outcomes were monitored. The patients were categorized into three groups based on the presence of PE: unaffected PE group, early-onset PE (ePE) group (gestational age < 34weeks), and late-onset PE (lPE) group (gestational age ≥ 34weeks). The predictive value of MP in PE was evaluated using analysis of variance, Chi-square test, and ROC curve analysis. A total of 464 twin pregnancies were included in the study, among which 66 cases (14.2%) developed PE, including 19 cases of ePE (4.1%) and 47 cases (10.1%) of lPE. Significant differences were found in maternal age, pre-pregnancy body mass index (BMI), BMI ≥ 28km/m2, mean systolic blood pressure, diastolic blood pressure, mean arterial pressure (MAP), MAP ≥ 85mmHg, history of PE, history of chronic hypertension, and positive urine protein. The maternal and fetal complications of twin pregnancies with PE were higher than those without PE (P < 0.05). When maternal factors (MF), MAP, and MP were used to predict ePE and lPE alone, the area under the ROC curve of MF was the largest, at 0.739 (95% CI 0.619-0.860) and 0.692 (95% CI 0.603-0.782), respectively. The area under the ROC curve of the combination of the three factors was 0.770 (95% CI 0.703-0.837), higher than that of a single index. In addition, MP predicted the positive predictive value (PPV) and negative predictive value (NPV) of PE from 12 to 15+6 gestational weeks as 57.9% and 89.2%, respectively; from 16 to 27+6 gestational weeks as 36.2% and 89.9%, respectively; and during the 12-27+6 gestational weeks as 42.4% and 92.2%, respectively. The detection of MP in the urine of women with twin pregnancies is a non-invasive and convenient method for predicting PE. If the test result is positive, enhanced monitoring and timely transfer to a superior hospital are necessary. If the test result is negative, it indicates a low risk of developing PE, reducing the need for excessive clinical examination and intervention.

Groundwater quality assessment and pollution sources identification using statistical analyses at Missan Governorate, Southeast Iraq

The shortage in surface water quantities led to a shift in dependence on the groundwater as an alternative water source in southern parts of Iraq. The groundwater is decreasing in quantity and water quality is degrading due to different factors. Therefore, it is important to assess the groundwater quality of the Missan Governorate of the country by analyzing the physicochemical parameters and distinguishing the probable sources of contaminants in the area. The present study used water quality diagrams and statistical methods such as factor analysis and agglomerative cluster analysis to determine the sources of chemical ions in the forty-four groundwater samples collected from wells in the study area. In addition, the Water Quality Index (WQI) was calculated to evaluate the groundwater quality by combining the parameters into a single index. The WQI results varied from − 66.27 to 1064.13 indicating that the groundwater is unsuitable for drinking at various places and good in quality at most of the wells of the area. The main factors that contribute to the differences in the groundwater quality include the variation in geological materials, influence of climate such as the high rate of evaporation, and anthropogenic factors such as industrial activities in central areas such as oil production in the Bazergan oil field. The most affected wells by the petroleum industry that were clustering in cluster R1which show high levels in most water chemistries. The results have implications for the management of the groundwater resources, which are expected to be under more risk of contamination in near future.

A new variable selection and estimation algorithm for the high-dimensional quantile single-index model

The single-index model (SIM) reveals the intricate relationship between the response variable and covariates, allowing for the consideration of potential heterogeneity and nonlinearity in the data, while also offering interpretability and flexibility. This paper concentrates on estimating the parameters and the unknown link function for the quantile SIM with high-dimensional covariates. We introduce a novel iterative algorithm that strikes a balance between estimation accuracy, computational efficiency, and adaptability to diverse datasets. The initial values for iteration and iteration termination conditions are also discussed. The finite sample performance is illustrated through a simulation study, demonstrating the advantages of our method in terms of accuracy and speed.

Development of Physical Fitness Evaluation Criteria for Women in Compulsory Isolation for Drug Rehabilitationn in Compulsory Isolation for Drug Rehabilitation

Background: The issue of physical and mental rehabilitation for drug users lacks a comprehensive and reliable system for evaluating it difficult to conduct a reasonable assessment of their physical fitness.The establishment of physical fitness evaluation criteria specifically tailored for female drug users serves as the fundamental basis for developing precise and individualized exercise prescriptions.Methods:This study used the national physical fitness test method to assess the physical fitness of female drug addicts undergoing mandatory rehabilitation. The principal component analysis method was employed to calculate the weight of each individual physical fitness test indicator, which was then combined with the indicator T score to obtain a comprehensive evaluation of the physical fitness of these individuals.On this basis, a single physical fitness index T-score scoring equation was determined for two groups of women (with and without diseases) undergoing compulsory isolation and drug rehabilitation, stratified by age (under 30 years old, 31-40 years old, 41-50 years old, 51-60 years old).Results: The weights of various indicators and the combined weights of secondary indicators in the physical fitness evaluation index system for female addicts undergoing compulsory isolation for detoxification have been established. Conclusion:Based on the total score calculated by combining the T-score of each individual physical fitness indicator with its corresponding weight, a comprehensive rating system with four levels - "excellent, good, qualified, and unqualified" - has been set.

Construction of women's empowerment index for Bangladesh.

This study is dedicated to refining and enhancing the measurement model of women's empowerment in Bangladesh. Women's empowerment, a crucial and multifaceted aspect of societal growth, is often hindered by gender disparities. This is particularly evident in societies like Bangladesh, where women face inequalities in education, economic opportunities, and decision-making power. To address these disparities effectively, it is essential to have a comprehensive understanding of women's empowerment. Therefore, this study aims to refine and enhance the measurement model to capture the multifaceted nature of women's empowerment accurately. To gather data for this study, a structured questionnaire was administered to married women of reproductive age (15-49) in eight Mouza/Mohalla in Dhaka, Bangladesh. This unique approach allowed us to capture a diverse range of perspectives. We used thirty-three indicators across economic, socio-cultural, household, and psychological dimensions to measure women's empowerment. The sample data were then randomly divided for exploratory factor analysis (EFA) and Confirmatory Factor Analysis (CFA) to identify and validate a comprehensive multidimensional framework. Out of 625 respondents, only 36% of women worked, and ~39% married before age 18. Employing thirty-three items in EFA led to identifying eight critical factors (economic independence, control over household financial decisions, household decision-making, reproductive decision-making, freedom of movement, media exposure, positive self-esteem, and negative self-esteem). These factors, which explained 72.661% of the total variance in the data, provide a practical framework for understanding and addressing women's empowerment. Each component was then divided into two sub-dimensions to acquire a better understanding. The CFA indicated a good model fit for each dimension, and convergent and discriminant validity assessments were used to establish reliability and validity, further enhancing the practical implications of our findings. The results of our rigorous exploratory and confirmatory factor analyses not only confirmed the sample structures and internal consistency but also provided significant insights. The findings suggested an adequate fit for all CFA models, indicating the robustness of our measurement model. According to the CFA results, each dimension's variables are satisfactory, and all the dimensions can be combined to create a single index measuring women's empowerment. This comprehensive understanding of women's empowerment, with its specific dimensions and factors, equips policymakers and practitioners with the knowledge to develop a wide range of interventions appropriate to particular facets of empowerment, thereby fostering societal growth and gender equality.

Assessment of Vegetation Drought Loss and Recovery in Central Asia Considering a Comprehensive Vegetation Index

In the context of drought events caused by global warming, there is limited understanding of vegetation loss caused by drought and the subsequent recovery of vegetation after drought ends. However, employing a single index representing a specific vegetation characteristic to explore drought’s impact on vegetation may overlook vegetation features and introduce increased uncertainty. We applied the enhanced vegetation index (EVI), fraction of vegetation cover (FVC), gross primary production (GPP), leaf area index (LAI), and our constructed remote sensing vegetation index (RSVI) to assess vegetation drought in Central Asia. We analyzed the differences in drought experiences for different climatic regions and vegetation types and vegetation loss and recovery following drought events. The results indicate that during drought years (2012 and 2019), the differences in vegetation drought across climatic regions were considerable. The vegetation in arid, semiarid, and Mediterranean climate regions was more susceptible to drought. The different indices used to assess vegetation loss exhibited varying degrees of dynamic changes, with vegetation in a state of mild drought experiencing more significantly during drought events. The different vegetation assessment indices exhibited significant variations during the drought recovery periods (with a recovery period of 16 days: EVI of 85%, FVC of 50%, GPP of 84%, LAI of 61%, and RSVI of 44%). Moreover, the required recovery periods tended to decrease from arid to humid climates, influenced by both climate regions and vegetation types. Sensitivity analysis indicated that the primary climatic factors leading to vegetation loss varied depending on the assessment indices used. The proposed RSVI demonstrates high sensitivity, correlation, and interpretability to dry–wet variations and can be used to assess the impact of drought on vegetation. These findings are essential for water resource management and the implementation of measures that mitigate vegetation drought.

A Study on Optimal Equity Portfolio in the Indian Capital Market

This study evaluates optimal equity portfolio construction in the Indian capital market by applying the Sharpe Single Index Model. Data from 20 companies in the NIFTY 50 index are used to analyze risk-return relationships and identify companies for portfolio inclusion. The study concludes that constructing an optimal portfolio with selected stocks, such as Hero MotoCorp and Bharat Petroleum, based on their risk-adjusted returns, offers a strategy for maximizing returns with controlled risk

Pollution and Ecological Risk Assessment of Potentially Toxic Elements in Sediments Along the Fluvial-to-Marine Transition Zone of the Don River

The quality of sediments in the mixing zone of river freshwater and marine saline water as an important geochemical barrier for potentially toxic elements (PTEs) remains poorly understood. This study aims to analyze the current pollution with PTEs and associated ecological risks in sediments of the Don River delta and the surrounding area of the Taganrog Bay of the Sea of Azov (Russia). The PTE content was determined in fifty-four collected samples using the WDXRF and assessed using geochemical and ecotoxicological indicators. The source of Cr, Mn, Ni and Pb is mainly river runoff, and Cu, Zn and Cd are from a variety of anthropogenic sources. As shown by the assessment of the geoaccumulation index (Igeo), single pollution index (PI) and contamination factor (CF), these elements are the priority pollutants. According to these estimates, high and very high contamination of sediments in the estuarine zone of the Don River with Cd and Pb was detected in 72–94% and 2–57% of samples, respectively. However, environmental risks are determined almost exclusively by the level of Cd. Total contamination as assessed by the Nemerow pollution index (NPI), modified degree of contamination (mCd) and metal pollution index (MPI) is of concern in 83–98% of the samples studied. The most heavily polluted sediments are in the vicinity of residential areas of the Taganrog Bay. Despite the lower average pollution levels of deltaic sediments, freshwater biota are exposed to higher potential toxic risks of adverse effects by PTE, particularly from Ni and Pb. Thus, the complex hydrological regime and uneven anthropogenic impact predetermine the geochemical state of the sediments of the estuarine zone of the Don River.

Characterization and source apportionment of heavy metal pollution in soil around red mud disposal sites using absolute principal component scores-multiple linear regression and positive matrix factorization models.

In recent years, industrial waste and agrochemicals have reduced soil fertility and productivity, significantly impacting food security and ecosystems. In China, areas near red mud deposits from the aluminum industry show severe heavy metal contamination. This study examines agricultural soil near a red mud site in Shanxi Province, analyzing Cd, Cr, Hg, Ni, Pb, As, Cu, and Zn levels and distribution. Geostatistical methods and GIS are utilized to assess heavy metal pollution using the single factor index, the Nemerow integrated index, and the Hakanson potential ecological risk index. Absolute Principal Component Scores-Multiple Linear Regression (APCS-MLR) and Positive Matrix Factorization (PMF) models are used for quantitative analysis of pollution sources. Research indicates that the average concentrations of eight heavy metals exceed the natural background values of Shanxi, placing them at a severe pollution level with moderate ecological risk. Specifically, indices for As, Pb, and Cr are 3.79, 3.38, and 3.26, indicating severe pollution; Cd, Cu, and Hg at 2.36, 2.62, and 3.00 suggest moderate pollution; Ni at 1.87 shows mild pollution, while Zn at 0.97 is not polluted. Hg presents the highest ecological risk with a coefficient of 120.00, followed by Cd (70.69) and As (37.92). Spatial analysis shows significant correlations among Pb, Zn, Cu, and Ni, while Cr, Cd, Hg, and As show greater variability and weaker correlations. Both models identify five main sources: industrial activities, agricultural fertilizers, red mud leachate, energy combustion, and natural geological backgrounds, with respective contribution rates in the APCS-MLR model at 27.7%, 24.6%, 18.1%, 15.2%, and 14.4%, and in the PMF model at 29.2%, 21.5%, 16.9%, 16.7%, and 15.7%. This study offers a scientific basis for controlling soil pollution in the region, filling a literature gap.

Pollution risk assessment and spatial distribution of potentially hazardous elements in selenium-rich soils in the Menyuan Basin of the northeastern Tibetan Plateau

The scientific utilization of selenium (Se)-rich soils necessitates the evaluation of the contents of potentially hazardous elements (PHEs). The Menyuan Basin, a representative Se-rich region located on the northern periphery of the Qinghai–Tibet Plateau, was chosen as the research area. Pollution risk and spatial source analysis was conducted for eight PHEs (As, Cr, Cu, Pb, Zn, Cd, Ni and Hg) using 5297 topsoil samples and 480 deep-soil samples. The results show that more than 50% of the total area has a Se content &gt;0.24 mg kg –1 . According to both single and comprehensive pollution indices, there are extremely few sampling sites with PHEs at risk of contamination. Low ecological risk was seen in 93.34% of the topsoil samples. The results show that although the overall level of surface soil pollution in the study area may be deemed negligible, on the surface or in deeper layers, the concentration centres of As, Cr, Ni, Zn, Pb and other elements show consistency, indicating that underlying parent rocks are the primary source of these elements. The findings presented in this study establish a scientific foundation for the secure development and utilization of Se-rich soils.