Evaluating the performance of electrocoagulation system in the removal of polystyrene microplastics from water.

Emerging pollutants, particularly microplastics, present a significant threat to both the environment and human health. Traditional treatment methods lack targeted strategies for their removal. This study thoroughly investigated the efficacy of electrocoagulation as a method for efficiently extracting microplastics from water. Various critical operational parameters, including electrode combinations, pH levels, electrolyte concentrations, electrode geometries, configurations, current intensities, and reaction times, were systematically examined. The study systematically examined the impact of different combinations of aluminium (Al) and stainless steel (SS) electrodes, including Al-Al, SS-SS, Al-SS, and SS-Al. Among these combinations, it was found that the Al-Al pairing exhibited outstanding efficiency in microplastic removal, while simultaneously minimizing energy consumption. Initial pH emerged as a critical parameter, with a neutral pH of 7 demonstrating the highest removal efficiency. In the pursuit of optimizing parameters like electrolyte concentrations, electrode geometry, and configuration, it's noteworthy that consistently achieving removal efficiencies exceeding 90% has been a significant achievement. However, to ascertain economic efficiency, additional factors such as energy consumption, electrode usage, and post-treatment conductivity must be taken into account. To tackle the complexity posed by various parameters and criteria, using multi-criteria decision-making tools like TOPSIS is essential, as it has a track record of effectiveness in practical applications. The electrolyte concentration of 0.5 g L-1 is identified as optimal by TOPSIS analysis Additionally, the TOPSIS highlighted the superiority of cylindrical hollow wire mesh electrodes and established the monopolar parallel configuration as the most effective electrode connection method. The investigation carefully evaluated the effect of reaction time, determining that a 50-min window provides optimal microplastic removal efficiency. This refined system exhibited remarkable proficiency in eliminating microplastics of varying size ranges (0-75 µm, 75-150 µm, and 150-300 µm), achieving removal efficiencies of 90.67%, 93.6%, and 94.6%, respectively, at input concentration of 0.2 g L-1. The present study offers a comprehensive framework for optimizing electrocoagulation parameters, presenting a practical and highly effective strategy to address the critical issue of microplastic contamination in aquatic environments.

Application of hybrid multi-criteria decision-making approach to analyze wastewater microalgae culture systems for bioenergy production.

Bioenergy generation from microalgae can significantly contribute to climate mitigation and renewable energy production. In this regard, several multi-criteria decision-making method were employed to prioritize appropriate microalgae culture system for bioenergy production. Entropy weight, Criteria Importance Through Intercriteria Correlation (CRITIC) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) were the employed MCDA method. Fourteen microalgae culture systems were selected as a case study, which contain teen monoculture and four dual-culture. Initially, through ans in-depth review of the literature and expert views, four categories total eight indicators were selected as the evaluation indices of the study, namely 1) Proliferation: Half growth cycle and Max growth rate,2) Biomass output: Bio-crude yield and Lipid yield, 3) Nutrient utilization: residual concentration of total Nitrogen and total Phosphorus, and, 4) Stability: coefficient of variation of Bio-crude yield and Lipid yield. The result indicated that "Pediastrum sp. & Micractinium sp." was identified as the most bioenergy potential microalgae culture system, and the evaluation results of entropy weight method and CRITIC method are similar. It is pertinent to note that 1)the entropy weight method exhibits lower sample size requirements, 2) the critic method excels when dealing with larger sample sizes, and 3) the TOPSIS method necessitates the incorporation of appropriate weighting methods to ensure credible results. In the application stage, the key indicators related to cost can be further included in the evaluation indices.

Comprehensive evaluation of the implementation of close-type county medical alliance in Shandong Province using entropy weight TOPSIS method and non-integer rank sum ratio method.

BACKGROUND: To improve the capacity of county medical and health services, China encourages all localities to pilot the close-type county medical alliance. In Shandong Province, medical alliances have been piloted in 47 counties, ranking first in the country. The objective of this study is to comprehensively evaluate the implementation of close-type county medical alliance in Shandong Province, identify the differences between different county regions, and analyze the reasons, so as to provide a reference for the construction of a new county medical and health service system with clear goals, powers and responsibilities, and division of labor. METHODS: The implementation of the close-type county medical alliance was comprehensively evaluated in 47 national pilot counties in Shandong Province using entropy weight TOPSIS method and non-integer rank sum ratio method. Variance analysis was used for comparison of the comprehensive evaluation results. RESULTS: The weight coefficient of evaluation indicators was highest for information interconnection, at 18.06%, and lowest for orderly referral of patients, at 3.64%. There was no difference in results of the comprehensive evaluation of entropy weight TOPSIS method and non-integer rank sum ratio method. Comprehensively order the implementation status of each pilot county according to the relative paste progress, 13 counties Y5, Y11, Y14, Y16, Y20, Y25, Y26, Y27, Y28, Y32, Y33, Y40 and Y42 were ranked highest, whereas county Y37 was ranked lowest. Non-integer rank sum ratio method graded counties into three grades: excellent, good and average. Kruskal-Wallis nonparametric test showed that the difference between the grades was statistically significant (H[Formula: see text]37.099, p[Formula: see text]0.001). Variance analysis based on comprehensive evaluation results showed that implementation status was not correlated with the county economic development level, the level of health resources input and the medical service ability of the lead hospital. CONCLUSIONS: Our findings indicated that the implementation of the close-type county medical alliance is significantly different between pilot counties, with a marked differentiation within the same urban area. Therefore, effective measures are recommended to reduce this gap, including promoting informatization empowerment of the county medical community, strengthening government responsibility and improving policy effectiveness.

Selection of data analytic techniques by using fuzzy AHP TOPSIS from a healthcare perspective.

The healthcare industry has been put to test the need to manage enormous amounts of data provided by various sources, which are renowned for providing enormous quantities of heterogeneous information. The data are collected and analyzed with different Data Analytic (DA) and machine learning algorithm approaches. Researchers, scientists, and industrialists must manage or select the best approach associated with DA in healthcare. This scientific study is based on decision analysis between the DA factors and alternatives. The information affects the whole system in a rational manner. This information is very important in healthcare sector for appropriate prediction and analysis. The evaluation discusses its benefits and presents an analytic framework. The Fuzzy Analytic Hierarchy Process (Fuzzy AHP) approach is used to address the weight of the factors. The Fuzzy Techniques for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) address the rank of the data analytic alternatives used in healthcare sector. The models used in the article briefly discuss the challenges of DA and approaches to address those challenges. The assorted factors of DA are capture, cleaning, storage, security, stewardship, reporting, visualization, updating, sharing, and querying. The DA alternatives include descriptive, diagnostic, predictive, prescriptive, discovery, regression, cohort and inferential analyses. The most influential factors of the DA and the most suitable approach for the DA are evaluated. The 'cleaning' factor has the highest weight, and 'updating' is achieved at least by the Fuzzy-AHP approach. The regression approach of data analysis had the highest rank, and the diagnostic analysis had the lowest rank. Decision analyses are necessary for data scientists and medical providers to predict diseases appropriately in the healthcare domain. This analysis also revealed the cost benefits to hospitals.

A Comprehensive Evaluation Algorithm of Multi-Point Relay Based on Link-State Awareness for UANETs.

The Multi-Point Relay (MPR) is one of the core technologies for Optimizing Link State Routing (OLSR) protocols, offering significant advantages in reducing network overhead, enhancing throughput, maintaining network scalability, and adaptability. However, due to the restriction that only MPR nodes can forward control messages in the network, the current evaluation criteria for selecting MPR nodes are relatively limited, making it challenging to flexibly choose MPR nodes based on current link states in dynamic networks. Therefore, the selection of MPR nodes is crucial in dynamic networks. To address issues such as unstable links, poor transmission accuracy, and lack of real-time performance caused by mobility in dynamic networks, we propose a comprehensive evaluation algorithm of MPR based on link-state awareness. This algorithm defines five state evaluation parameters from the perspectives of node mobility and load. Subsequently, we use the entropy weight method to determine weight coefficients and employing the method of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for comprehensive evaluation to select MPR nodes. Finally, the Comprehensive Evaluation based on Link-state awareness of OLSR (CEL-OLSR) protocol is proposed, and simulated experiments are conducted using NS-3. The results indicate that, compared to PM-OLSR, ML-OLSR, LD-OLSR, and OLSR, CEL-OLSR significantly improves network performance in terms of packet delivery rate, average end-to-end delay, network throughput, and control overhead.

Deep Ensemble Learning-Based Sensor for Flotation Froth Image Recognition.

Froth flotation is a widespread and important method for mineral separation, significantly influencing the purity and quality of extracted minerals. Traditionally, workers need to control chemical dosages by observing the visual characteristics of flotation froth, but this requires considerable experience and operational skills. This paper designs a deep ensemble learning-based sensor for flotation froth image recognition to monitor actual flotation froth working conditions, so as to assist operators in facilitating chemical dosage adjustments and achieve the industrial goals of promoting concentrate grade and mineral recovery. In our approach, training and validation data on flotation froth images are partitioned in K-fold cross validation, and deep neural network (DNN) based learners are generated through pre-trained DNN models in image-enhanced training data, in order to improve their generalization and robustness. Then, a membership function utilizing the performance information of the DNN-based learners during the validation is proposed to improve the recognition accuracy of the DNN-based learners. Subsequently, a technique for order preference by similarity to an ideal solution (TOPSIS) based on the F1 score is proposed to select the most probable working condition of flotation froth images through a decision matrix composed of the DNN-based learners' predictions via a membership function, which is adopted to optimize the combination process of deep ensemble learning. The effectiveness and superiority of the designed sensor are verified in a real industrial gold-antimony froth flotation application.

Mitigating Measurement Inaccuracies in Digital Twins of Construction Machinery through Multi-Objective Optimization.

The advent of digital twins facilitates the generation of high-fidelity replicas of actual systems or assets, thereby enhancing the design's performance and feasibility. When developing digital twins, precise measurement data is essential to ensure alignment between the actual and digital models. However, inherent uncertainties in sensors and models lead to disparities between observed and predicted (simulated) behaviors. To mitigate these uncertainties, this study originally proposes a multi-objective optimization strategy utilizing a Gaussian process regression surrogate model, which integrates various uncertain parameters, such as load angle, bucket cylinder stroke, arm cylinder stroke, and boom cylinder stroke. This optimization employs a genetic algorithm to indicate the Pareto frontiers regarding the pressure exerted on the boom, arm, and bucket cylinders. Subsequently, TOPSIS is applied to ascertain the optimal candidate among the identified Pareto optima. The findings reveal a substantial congruence between the experimental and numerical outcomes of the devised virtual model, in conjunction with the TOPSIS-derived optimal parameter configuration.

Green missing spots: Information entropy on greenhouse gas emission disclosure by Brazilian companies.

This study aims to address a critical gap in the literature by examining the incorporation of uncertainty in measuring carbon emissions using the greenhouse gas (GHG) Protocol methodology across all three scopes. By comprehensively considering the various dimensions of CO2 emissions within the context of organizational activities, our research contributes significantly to the existing body of knowledge. We address challenges such as data quality issues and a high prevalence of missing values by using information entropy, techniques for order preference by similarity to ideal solution (TOPSIS), and an artificial neural network (ANN) to analyze the contextual variables. Our findings, derived from the data sample of 56 companies across 18 sectors and 13 Brazilian states between 2017 and 2019, reveal that Scope 3 emissions exhibit the highest levels of information entropy. Additionally, we highlight the pivotal role of public policies in enhancing the availability of GHG emissions data, which, in turn, positively impacts policy-making practices. By demonstrating the potential for a virtuous cycle between improved information availability and enhanced policy outcomes, our research underscores the importance of addressing uncertainty in carbon emissions measurement for advancing effective climate change mitigation strategies.

Land-use simulation for synergistic pollution and carbon reduction: Scenario analysis and policy implications.

Simulations of sustainable land use and management are required to achieve targets to reduce pollution and carbon emissions. Limited research has been conducted on synergistic pollution and carbon reduction (SPCR) in land-use simulations. This study proposed a framework for land-use simulation focused on SPCR. The non-dominated sorting genetic algorithm (NSGA-Ⅱ) and the entropy weight-based technique for order of preference by similarity to an ideal solution (TOPSIS) were used to optimize the land-use structure according to minimum net carbon, nitrogen, and phosphorus emissions. The cellular automata (CA) Markov model was then utilized to simulate the land-use spatial pattern according to the optimal conditions. The proposed framework was applied to the Dongjiang River Basin, South China, and three other scenarios (natural development (ND), carbon minimization (CM), and pollution minimization (PM)) were designed to validate the effectiveness of pollution and carbon emissions reduction under the SPCR scenario. The land-use structure and the pollution and carbon emissions in the scenarios were compared. The results showed the following. (1) The proportions of cultivated land, woodland, grassland, water, and construction land In the SPCR scenario accounted for 14%, 72%, 4%, 3%, and 7% of the total area, respectively. The carbon, nitrogen, and phosphorus emissions were 42.4%, 6.6%, and 7.8% lower, respectively, in the SPCR scenario than in the ND scenario, demonstrating the advantages of simultaneous pollution and carbon reduction. (2) The kappa coefficient of the CA-Markov model was 0.8729, indicating high simulation accuracy. (3) The simulated land-use spatial patterns exhibited low spatial heterogeneity under the CM, PM, and SPCR scenarios. However, there were significant disparities between the ND and SPCR scenarios. The cultivated and construction land areas were significantly smaller in the SPCR scenario than in the ND scenario. In contrast, the woodland and grassland areas were larger, with most differences in the central and southwestern regions of the Dongjiang River Basin. The results of the current study can be used to formulate effective land use policies and strategies in the Dongjiang Basin and similar areas to achieve the Coupling coordination between pollution reduction and carbon reduction. Policy recommendations include increasing the proportion of woodland and grassland, implementing reasonable constraints on expanding cultivated and construction lands, and establishing farmland red lines to promote synergistic pollution and carbon reduction.

Integrating topographic factors for effective urban sponge construction in mountainous regions: A case study.

The construction of sponge cities in mountainous areas is crucial to achieving high-quality development in these regions. Owing to rugged terrain, significant changes in elevation, and uneven distribution of cities, the construction of sponge cities in mountainous areas faces challenges such as difficulties in clearing mountains and roads, high cost, and varying regional development requirements. However, there is currently limited research focusing on the impact of terrain on sponge city construction plans. In this study, we developed an optimal low impact development (LID) system layout method based on the annual runoff control rate. This study suggests implementing LID plans in stages to balance cost-effectiveness and enhance resilience. The optimized case1_100 scheme, which takes regional differences into account, can effectively achieve a runoff control coefficient of less than 0.25 in 98.86% of the area. Remarkably, this achievement comes at a significantly lower total cost of only 1.22 billion RMB compared to the unoptimized case2_100 scheme (which does not consider regional differences) with a cost of 3.03 billion RMB. Interestingly, the optimized case1_100 plan, selected using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, has an LID layout that is closely related to the surface terrain. Structural equation modeling analysis indicates that terrain affects land types, which in turn impacts the surface impermeability and runoff coefficients, ultimately influencing the corresponding LID deployment plan. The coefficients of relative elevation and slope on the final plan are determined as -0.13 and -0.77, respectively, with a high overall explanatory power of 0.84. This indicates that terrain characteristics have a significant impact on the spatial patterns and surface features of typical mountainous cities in China and the optimal LID strategy largely depends on the initial terrain conditions. This study provides valuable insights for optimizing LID construction in sponge cities, particularly in the context of new mountainous urban planning.

Evaluation of the water quality monitoring network layout based on driving-pressure-state-response framework and entropy weight TOPSIS model: A case study of Liao River, China.

The establishment of river water quality monitoring network is crucial for watershed protection. However, the evaluation process of monitoring network layout involves significant subjectivity and has not yet to form a complete indicator system. This study constructed an indicator system based on the DPSR (Driving-Pressure-State-Response) framework in the Liao River Basin, China. SWAT model and ArcGIS were used to quantify the indicators. And the entropy weight-TOPSIS method was employed to rank monitoring points. The results showed that pressure and state indicators had a greater impact on the network layout, with the indicator for proportion of land use in residential areas carrying the largest weight of 0.136. It suggested that the risk of river pollution remained high, and the governance strategies needed to be improved. Priority monitoring points were mainly located in the east and middle of the basin, consistent with the distribution of human activities such as urban areas and farmland. In addition, the redundancy of points should be avoided, and evaluation results should be adjusted based on the actual situation. The study provided an evaluation method for the layout of monitoring points.

District heating system's development decarbonization strategy assessment by system dynamics modeling and multi-criteria analysis.

The study introduces a hybrid model that integrates system dynamics modeling and multi-criteria analysis. Through the system dynamics model, the study examines energy, economic, and environmental indicators of a District Heating (DH) company, assessing its dynamics until 2050. Various decarbonization scenarios are explored, involving the transition of the DH system to a 4th generation DH (4GDH) system based on four strategies: utilizing at least (a) 50% Renewable Energy Sources (RES), (b) 50% waste heat, (c) 75% cogenerated heat, or (d) 50% of the combined aforementioned energy and heat. Additionally, development scenarios incorporate measures to enhance energy efficiency on the consumer side and within the heating networks. The sustainability of each scenario is evaluated using the multi-criteria analysis method TOPSIS. The hybrid model establishes a ranking of the transition pathways based on their sustainability scores and benchmarks the results of the developed scenarios against a carbon-neutral DH system. This model serves as a valuable guide for DH system developers and decision-makers. The study focuses on Riga as a practical case study.

Assessment of site suitability for centralized photovoltaic power stations in Northwest China's six provinces.

Northwest China has abundant solar energy resources and extensive land, making it a pivotal site for solar energy development. However, restrictions on site selection and severe weather conditions have hindered the establishment and operation of photovoltaic (PV) power stations. Previous studies have not considered meteorological factors when evaluating site suitability, leading to research gaps in identifying suitable areas and establishing indicator systems. We aimed to address these gaps by considering seven factors constraining the construction of centralized PV power stations (CPPS) and developing an indicator system based on terrain, climate, soil, and economic factors. Furthermore, we conducted analyses to quantify the solar energy generation potential (SEGP), carbon emissions reduction benefits, and land utilization potential at different sites. The findings indicate that areas rated as very suitable and extremely suitable comprised the largest proportion (62.35%) of site suitability. The correlation between site suitability and electricity consumption was largely non-significant, highlighting the need for enhanced coordination. Additionally, we forecast the electricity consumption in Xinjiang, Gansu, Inner Mongolia, Qinghai, Ningxia, and Shaanxi for 2030 to be 56.62, 19.86, 54.54, 13.59, 15.96, and 33.34 ( × 1011 kWh), respectively, with corresponding carbon emissions reduction potentials of 20.2, 7.1, 19.4, 4.8, 5.7, and 11.9 ( × 109 kg). Consequently, PV carbon reduction and land utilization potential are substantial.

Determining the most appropriate stormwater management strategies within the development of urban landscape infrastructure using the TOPSIS method: applications to Rize city.

In this study, the application of multi-criteria decision-making (MCDM) methods in determining the most appropriate stormwater management strategy is examined using different areas in Rize. The determination of the most appropriate stormwater management practices for the Rize coastal park and Güneysu-Rize connection highway with TOPSIS is presented in detail within this study. In this context, commonly used applications suitable for urban areas are discussed. The criteria and their weights used for the evaluation of the selected applications were determined by consulting expert opinions from leading researchers. The most suitable applications in different scenarios such as changes in the cost or the amount of precipitation for Rize coastal park and Güneysu-Rize connection road were determined by the TOPSIS method. The TOPSIS analyses' ranking of the ideal solutions matches the results of the SWMM simulations one to one. SWMM results confirm that the outcomes of TOPSIS are the alternatives that provide maximum decrease in surface runoff.

Fuzzy logic-based prediction and parametric optimizing using particle swarm optimization for performance improvement in pyramid solar still.

The primary objective of this study is to develop a robust model that employs a fuzzy logic interface (FL) and particle swarm optimization (PSO) to forecast the optimal parameters of a pyramid solar still (PSS). The model considers a range of environmental variables and varying levels of silver nanoparticles (Ag) mixed with paraffin wax, serving as a phase change material (PCM). The study focuses on three key factors: solar intensity ranging from 350 to 950 W/m2, water depth varying between 4 and 8 cm, and silver (Ag) nanoparticle concentration ranging from 0.5 to 1.5% and corresponding output responses are productivity (P), glass temperature (Tg), and basin water temperature (Tw). The experimental design is based on Taguchi's L9 orthogonal array. A technique for ordering preference by similarity to the ideal solution (TOPSIS) is utilized to optimize the process parameters of PSS. Incorporating a fuzzy inference system (FIS) aims to minimize the uncertainty within the system, and the particle swarm optimization algorithm is employed to fine-tune the optimal settings. These methodologies are employed to forecast the optimal conditions required to enhance the productivity of the PSS.

Multivariate Data Analysis Assisted Mining of Nutri-rich Genotypes from North Eastern Himalayan Germplasm Collection of Perilla (Perilla frutescens L.).

Understanding the nutritional diversity in Perilla (Perilla frutescens L.) is essential for selecting and developing superior varieties with enhanced nutritional profiles in the North Eastern Himalayan (NEH) region of India. In this study, we assessed the nutritional composition of 45 diverse perilla germplasm collected from five NEH states using standard protocols and advanced analytical techniques. Significant variability was observed in moisture (0.39-11.67%), ash (2.59-7.13%), oil (28.65-74.20%), protein (11.05-23.15%), total soluble sugars (0.34-3.67%), starch (0.01-0.55%), phenols (0.03-0.87%), ferric reducing antioxidant power (0.45-1.36%), palmitic acid (7.06-10.75%), stearic acid (1.96-2.29%), oleic acid (8.11-13.31%), linoleic acid (15.18-22.74%), and linolenic acid (55.47-67.07%). Similarly, significant variability in mineral content (ppm) was also observed for aluminium, calcium, cobalt, chromium, copper, iron, potassium, magnesium, manganese, molybdenum, sodium, nickel, phosphorus, and zinc. Multivariate analyses, including hierarchical clustering analysis (HCA) and principal component analysis (PCA), revealed the enriched nutritional diversity within the germplasm. Correlation analysis indicated significant positive and negative relationships between nutritional parameters, indicating potential biochemical and metabolic interactions present in the perilla seeds. TOPSIS-based ranking identified promising genotypes for functional foods, pharmaceuticals, and nutritional applications. This study provides a first in-depth report of the nutritional composition and diversity of perilla germplasm in the NEH region, thus aiding in the identification of superior varieties for food and nutritional diversification and security.

A study on the current state and equity level of the health promotion service demands among older adults in China.

BACKGROUND: Meeting the demands of older adults for health promotion services (DOAHPS) is essential for maintaining their health and enhancing their quality of life. The purpose of this study was to construct a model for evaluating DOAHPS to quantitatively evaluate the current state and equity level of DOAHPS in China, as well as to explore the main factors affecting DOAHPS' current state and equity level. METHODS: This study analyzed the DOAHPS data from the "Survey on Chinese Residents' Health Service Demands in the New Era", which included 1542 older adults aged 65 and older. Relationships between evaluation indicators of DOAHPS were explored using Structural Equation Modeling (SEM). The Weighted TOPSIS method and Logistic regression (LR) were used to analyze the current state and factors impacting DOAHPS. The equity level of DOAHPS' allocation among different older adult groups and its influencing factors were determined using the Rank Sum Ratio (RSR) method and T Theil index. RESULTS: The evaluation score for DOAHPS was 42.57 ± 1.51. Health status, health literacy and behavior were positively correlated with DOAHPS (r = 0.40, 0.38; P < 0.05). The LR results revealed that the most significant determinants of DOAHPS were sex, residence, education level and pre-retirement occupation (all P < 0.05). The number of older adults with very poor, poor, general, high and very high level health promotion service demands accounted for 2.27%, 28.60%, 53.05%, 15.43% and 0.65%, respectively. The total T Theil index of DOAHPS was 2.7433*10-4, and the intra-group difference contribution rate exceeded 72%. CONCLUSIONS: Compared to the maximum level, the total DOAHPS level was found to be moderate, although the demands of urban seniors with higher levels of education may be substantially greater. The observed inequities in the allocation of DOAHPS were primarily related to differences in education level and pre-retirement occupation within group. To better address health promotion services for older adults, policymakers could target older males with low education who reside in rural regions.

Spatio-temporal evolution of resources and environmental carrying capacity and its influencing factors: A case study of Shandong Peninsula urban agglomeration.

Promoting ecological conservation and high-quality development in the Yellow River basin is an important objective in China's 14th Five-Year Plan. Understanding the spatio-temporal evolution of and factors affecting the resources and environmental carrying capacity (RECC) of the urban agglomerations is critical for boosting high-quality green-oriented development. We first combined the Driver-Pressure-State-Impact-Response (DPSIR) framework and the improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model to evaluate the RECC of Shandong Peninsula urban agglomeration in 2000, 2010 and 2020; we then used trend analysis and spatial autocorrelation analysis to understand the spatio-temporal evolution and distribution pattern of RECC. Furthermore, we employed Geodetector to detect the influencing factors and classified the urban agglomeration into six zones based on the weighted Voronoi diagram of RECC as well as specific conditions of the study area. The results show that the RECC of Shandong Peninsula urban agglomeration increased consistently over time, from 0.3887 in 2000 to 0.4952 in 2010 and 0.6097 in 2020, respectively. Geographically, RECC decreased gradually from the northeast coast to the southwest inland. Globally, only in 2010 the RECC presented a significant spatial positive correlation, and that in the other years were not significant. The high-high cluster was mainly located in Weifang, while the low-low cluster in Jining. Furthermore, our study reveals three key factors-advancement of industrial structure, resident consumption level, and water consumption per ten thousand yuan of industrial added value-that affected the distribution of RECC. Other factors, including the interactions between residents' consumption level and environmental regulation, residents' consumption level and advancement of industrial structure, as well as between the proportion of R&D expenditure in GDP and resident consumption level also played important roles resulting in the variation of RECC among different cities within the urban agglomeration. Accordingly, we proposed suggestions for achieving high-quality development for different zones.

A comparative evaluation of child health care in China using multicriteria decision analysis methods.

Child health is an important public health issue in China and the Chinese government always attached great importance to child health care. With the implementation of a series of medical and health reforms in China in recent decades, the status of child health improved year by year. Objectives This study aims to comprehensively evaluate if the measures implemented in the medical and health reforms effectively promoted the development of Chinese child health care in recent years and provide theoretical support for future decision-making on the policies of child health care in China. Methods A total of six indicators were selected from the China Health Statistics Yearbook. Based on the multi-criteria decision analysis (MCDA) algorithm, three different evaluation methods were applied in the study, which are the weighted technique for order preference by similarity to an ideal solution (TOPSIS) method, the weighted rank-sum ratio (RSR) method, and the fuzzy comprehensive evaluation (FCE). Each indicator's weight was calculated by the entropy weight methods objectively. The sensitivity analysis was conducted to validate the stability and accuracy of the rank results. Results The results indicated that the rank values of each year's child health care calculated by the different evaluation methods were not exactly the same, but the overall trend is consistent which is that child health care in China improved year by year from 2000 to 2020. The top 5 were ranked from 2016-2020 and the bottom 5 were ranked from 2000-2004. Conclusions The results indicated that the policies and measures implemented in the medical and health reforms, as well as improved sanitation conditions, availability of healthy food and water, etc., have jointly promoted the development of child health care in China in the past 20 years, providing a scientific theoretical basis for future policy-making to promote child health care.

Combination of intercropping maize and soybean with root exudate additions reduces metal mobility in soil-plant system under wastewater irrigation.

The effects of root exudates and irrigation with treated wastewater on heavy metal mobility and soil bacterial composition under intercropping remain poorly understood. We conducted a pot experiment with maize and soybean grown in monocultures or intercultures, irrigated with either groundwater or treated wastewater. In addition, the pre-collected root exudates from hydroponic culture with mono- or inter-cropped maize and soybean were applied to the soil at four levels (0 %, 16 %, 32 % and 64 %). The results showed that application of root exudates increased plant growth and soil nutrient content. The analysis of "Technique for Order of Preference by Similarity to Ideal Solution" for higher plant biomass and lower soil Cd and Pb concentrations indicated that the best performance of soybean under treated wastewater irrigation was recorded under intercropping applied with 64 % of exudates, with a performance score of 0.926 and 0.953 for Cd and Pb, respectively. The second-best performance of maize under treated wastewater irrigation was also observed under intercropping applied with 64 % of exudates. Root exudate application reduced heavy metals migration in the soil-plant system, with a greater impact in intercropping than in monocropping. In addition, certain soil microorganisms were also increased with root exudate application, regardless of irrigation water. This study suggests that appropriate application of root exudates could potentially improve plant growth and soil health, and reduce toxic heavy metal concentrations in soils and plants irrigated with treated wastewater.