Different policies constrained agricultural non-point pollutants emission trading management for water system under interval, fuzzy, and stochastic information.

Formulating suitable policies is essential for resources and environmental management. In this study, an agricultural pollutants emission trading management model driven by water resources and pollutants control is developed to search reasonable policies for agricultural water resources allocation under multiple uncertainties. Random-fuzzy and interval information in water resources system that have directly impact on the effectiveness of management schemes is reflected through interval two-stage stochastic fuzzy-probability programming. The model was root from regional agricultural water resources system in Jining City, China under considering the relationship among effective precipitation, crop water demand, and pollutants emission. Two types policies (water consumption-control and pollutants emission-control) are designed for searching the related interaction on water resources management and water quality improvement. The results indicated that water resources policies would be of water and environmental double benefits, and a large rainfall would reduce irrigation amount from water sources and lead to a larger pollutants emission trading. The results will help for defining scientific and effective water resources protection and management policies and analyzing the related interacted effects on water consumption, pollutants control and system benefit.

Regional bioethanol supply chain optimization with the integration of GIS-MCDM method and quantile-based scenario analysis.

This study presents a novel decision-support framework for the bioethanol supply chain network planning and management under uncertainties. Under the holistic framework, the most suitable sites for biorefineries are first screened out by adopting a GIS-based multi-criteria decision-making approach. Then, a mixed-integer linear programming model combined with quantile-based scenario analysis is developed to determine the strategic planning (i.e. locations and size of biorefineries) and tactical management (i.e. biomass purchasing, feedstock transportation, bioethanol production, and product delivery) under uncertainties. The model can effectively search for reliable solutions under uncertainties and achieve tradeoff solutions with the consideration of decision makers' risk tolerance. The proposed framework is demonstrated through a case study in China. It is suggested to build seven biorefineries with a capacity of 100 million liters in Zhumadian city. Utilizing 41% of local agricultural residues could satisfy the bioethanol requirement in the transportation sector under the E20 policy. However, the estimated production cost of bioethanol in Zhumadian is very high, about 1.11 $/L, which makes it lose cost advantage in the fuel market. Thus, currently, effective subsidies, mandatory energy substitution policies, along other environmental regulatory measures are desired to promote the bioethanol industry development.

A unified deep learning framework for water quality prediction based on time-frequency feature extraction and data feature enhancement.

Deep learning methods exhibited significant advantages in mapping highly nonlinear relationships with acceptable computational speed, and have been widely used to predict water quality. However, various model selection and construction methods resulted in differences in prediction accuracy and performance. Hence, a unified deep learning framework for water quality prediction was established in the paper, including data processing module, feature enhancement module, and data prediction module. In the established model, the data processing module based on wavelet transform method was applied to decomposing complex nonlinear meteorology, hydrology, and water quality data into multiple frequency domain signals for extracting self characteristics of data cyclic and fluctuations. The feature enhancement module based on Informer Encoder was used to enhance feature encoding of time series data in different frequency domains to discover global time dependent features of variables. Finally, the data prediction module based on the stacked bidirectional long and short term memory network (SBiLSTM) method was employed to strengthen the local correlation of feature sequences and predict the water quality. The established model framework was applied in Lijiang River in Guilin, China. The maximum relative errors between the predicted and observed values for dissolved oxygen (DO), chemical oxygen demand (CODMn) were 12.4% and 20.7%, suggesting a satisfactory prediction performance of the established model. The validation results showed that the established model was superior to all other models in terms of prediction accuracy with RMSE values 0.329, 0.121, MAE values 0.217, 0.057, SMAPE values 0.022, 0.063 for DO and CODMn, respectively. Ablation tests confirmed the necessity and rationality of each module for the established model framework. The established method provided a unified deep learning framework for water quality prediction.

Source or sink role of an urban lake for microplastics from Guangdong-Hong Kong-Macao greater bay area, China.

Plastic production and consumption in China are larger than others in the world, and the challenge of microplastic pollution is widespread. With the development of urbanization in the Guangdong-Hong Kong-Macao Greater Bay Area, China, the environmental pollution of microplastics is becoming an increasingly prominent issue. Here, the spatial and temporal distribution characteristics, sources, and ecological risks of microplastics were analyzed in water from an urban lake, Xinghu Lake, as well as the contribution of rivers. Importantly, the roles of urban lakes for microplastics were demonstrated through the investigations of contributions and fluxes for microplastic in rivers. The results showed that the average abundances of microplastics in water of Xinghu Lake were 4.8 ± 2.2 and 10.1 ± 7.6 particles/m3 in wet and dry seasons, and the average contribution degree of the inflow rivers was 75%. The size of microplastics in water from Xinghu Lake and its tributaries was concentrated in the range of 200-1000 µm. In general, the average comprehensive potential ecological risk indexes of microplastics in water were 247 ± 120.6 and 273.1 ± 353.7 in wet and dry seasons, which the high ecological risks of them were found through the adjusted evaluation method. There were also mutual effects among microplastic abundance, the concentrations of total nitrogen and organic carbon. Finally, Xinghu Lake has been a sink for microplastics both in wet and dry seasons, and it would be a source of microplastics under the influence of extreme weather and anthropogenic factors.

Analysis of Hypoxia Inducible Factor-1α Expression and Its Effects on Glycolysis of Esophageal Carcinoma.

We investigated the regulatory effects of hypoxia-inducible factor-1a (HIF-1α) on glycolysis metabolism in esophageal carcinoma (ESCA) cells. A series of bioinformatics databases and tools were used to investigate the expression and role of HIF-1α in ESCA. The expression of HIF-1a in ESCA tissues and adjacent tissues was validated by real-time PCR. Small interfering RNA (siRNA) was used to inhibit HIF-1α-related genes in human ESCA cells (Eca109 and KYSE150). Cell proliferation was detected by the CCK-8 assay. The expression of HIF-1α and glycolytic enzymes were investigated by real-time PCR and Western blot. HIF-1α is highly expressed in ESCA and is involved in many biological processes such as cell hypoxia reaction, glucose metabolic process. Further in vitro experiments showed that expression of HIF-1α in Eca109 and KYSE150 significantly increased under hypoxia compared with normoxia conditions. Also, the glucose uptake and lactate production under hypoxia were higher. The expression levels of hexokinase 2 (HK2) and pyruvate dehydrogenase kinase 1 (PDK1), glycolysis-related genes, were significantly increased under hypoxia. After siRNA knockdown of HIF-1a in Eca109 and KYSE150, the glucose uptake and lactate production, as well as cell proliferation were significantly decreased under hypoxia, and HK2 and PDK1 were significantly downregulated. HIF-1α promotes glycolysis of ESCA cells by upregulating the expression of HK2 and PDK1 under hypoxia.

Potential Life-Cycle Environmental Impacts of the COVID-19 Nucleic Acid Test.

Massive diagnostic testing has been performed for appropriate screening and identification of COVID-19 cases in the ongoing global pandemic. However, the environmental impacts of COVID-19 diagnostics have been least considered. In this paper, the environmental impacts of the COVID-19 nucleic acid diagnostics were assessed by following a full cradle-to-grave life-cycle approach. The corresponding life-cycle anthology was established to provide quantitative analysis. Moreover, three alternative scenarios, i.e., material substitution, improved waste treatment, and electric vehicle (EV)-based transportation, were further proposed to discuss the potential environmental mitigation and conservation strategies. It was estimated that the life cycle of a single COVID-19 nucleic acid diagnostic test in China would lead to the emission of 612.9 g CO2 equiv global warming potential. Waste treatment, as a step of life cycle, worsen the environmental impacts such as global warming potential, eutrophication, and ecotoxicity. Meanwhile, diesel-driven transportation was considered as the major contributor to particulate air. Even though COVID-19 diagnostics are of the greatest importance to end the pandemic, their environmental impacts should not be ignored. It is suggested that improved approaches for waste treatment, low-carbon transportation, and a reliable pool sampling strategy are critical for the achievement of sustainable and green diagnostics.

Multiperspective-driven factorial metabolic network analysis framework for energy-water nexus vulnerability assessment and management-policy simulation.

Energy and water are rapidly consumed as the most basic strategic resources of various nations. It is of vital importance to systematically explore the environmental and economic impacts of energy-water co-management policies. This study is to develop a multiperspective-driven factorial metabolic network analysis framework (MPDF) to (a) investigate the direct/indirect/total resource consumption response mechanisms induced by changes in production and consumption; (b) explore the factor interactions of different policies in diverse energy and water metabolic networks by initiating factorial analysis; (c) quantify the economic effects of co-management policies by proposing multiple vulnerability indicators. A typical energy-dependent region, Shanxi Province, China was selected as a case study. The results indicated that the production- and consumption-oriented policies have various guidelines for reducing direct and indirect energy-water consumption. Significant interactions in simulation results suggest synergistic effects across sectors. Considering that Shanxi's energy-water nexus economic vulnerability is as high as 2.22%, it is recommended to prioritize the allocation of resources to sectors with significant factor effects to avoid economic losses. Implementing corresponding resource conservation policies for light industry, machinery manufacturing, construction can reduce water consumption by 18.8%. The findings are expected to provide a solid scientific basis for formulating co-management strategies to alleviate resource scarcities.

Water-carbon nexus relationship and interaction mechanism analysis within Beijing-Tianjin-Hebei urban agglomeration.

As the total water resources consumption control and carbon mitigation continuous improvement, the weak water-carbon incorporate management is increasingly exposed. In this study, a water-carbon nexus assessment framework is proposed to analyze the nexus relationship between water consumption and carbon emission, and distinguishes the coupled water-carbon transmission intensity and the transfer paths under regional and industrial scales. According to the practical input-output table, water consumption, and carbon emission information, the framework is applied to Beijing-Tianjin-Hebei urban agglomeration (BTHUA), a population, resource, and trade intensive area of China. Inter-regional/intra-regional water consumption and carbon emission transfer fluxes between sectors, the pairwise ecological relationship, and the water-carbon nexus were analyzed. Results indicated that the water-carbon transfer indexes from Hebei to Beijing and Tianjin were 161.85 kg/m3 and 113.88 kg/m3 in the study period, along with the most water consumption and carbon emission, and the worst water-carbon nexus. From the industrial perspective, electricity and gas supplying industry provided 7.8% and 29.1% of the total carbon transfer in Tianjin and Hebei, as the most key node sectors on the water-carbon nexus in the BTHUA. The research provides valuably supporting the adjustment of the existing urban agglomeration water-carbon nexus management schemes.

Changes of vegetational cover and the induced impacts on hydrological processes under climate change for a high-diversity watershed of south China.

Understanding the changes in hydrological process is a key subject for water resource management of a high-diversity watershed. In this paper, through an establishment of a SWAT-based model, the effects of climate change and its induced vegetation change on hydrological process were analyzed in the East River Basin. The model could well simulate the hydrological processes of the basin including surface runoff (SURQ), groundwater (GWQ), lateral flow (LATQ), total water yield (WYLD), actual evapotranspiration (ET), and groundwater recharge (PERC). Under the vegetation change induced by temperature increase, the effects of the vegetation change on hydrological process were larger than that of the temperature change. Under the vegetation change caused by the increase of temperature and precipitation, the vegetation change enhanced the effects of climate change on annual SURQ, LATQ, GWQ, WYLD, and PERC of the basin. Under spatial scale, when the temperature and precipitation changed simultaneously, the increase of precipitation could promote the increase of annual ET in sub-watersheds. Also, the annual SURQ, WYLD, GWQ and ET in western sub-watersheds were more sensitive to the cumulative changes of vegetation and climate. This work can provide useful information to decision makers in water resource management of watersheds.

Towards low-carbon domestic circulation: Insights from the spatiotemporal variations and socioeconomic determinants of emissions embedded within cross-province trade in China.

For a country like China with unbalanced development pattern among provinces, domestic circulation (i.e., cross-province trade) is important for the long-term stability and prosperous development of economic market. However, with the rapid advance of integration of domestic regional economy, while expanding the internal market scale and deepening the provincial division of labor network for promoting the economic growth, the carbon emissions embedded within the cross-province traded products and services cannot be underestimated. Under the background of climate-trade dilemma, it is necessary to exploring the spatiotemporal variations and socioeconomic determinants of provincial "invisible" carbon emissions for a better understanding of trade-induced eco-environmental effects. To that end, this study developed an environmental-economic system model through integrating the environmentally extended multiregional input-output method and weighted average structural decomposition analysis technique to explore the trade-related emissions at the provincial level and generate the mitigation-management strategies for decisionmakers. Overall, more than half the emissions were embedded within cross-province goods and services trade over the whole study period. Furthermore, the distribution of traded emissions showed obvious spatial heterogeneity and great unbalance was existed between provincial imports and exports. Among all provinces, carbon surplus provinces were always more than deficit ones and the trading patterns of approximately 65% regions remained unchanged during 2007-2017. Remarkably, the emissions trading pattern undergone transition from carbon deficit to carbon surplus in provinces like Henan, Hubei, Guizhou, and so on. Conversely, provinces like Jilin, Shanghai, and Xinjiang showed opposite change. With the prevalence of online payment and electronic commerce in the future, the central and sub-national government could consider launching a pilot project for the design and creation of personal carbon consumption account in the carbon surplus provinces such as Guangdong, Henan, and Jiangsu. Meanwhile, for the provinces with larger carbon exports, it is necessary to establish the horizontal high technical transfer channels and vertical compensation mechanisms such as financial subsidies for improving the low-carbon production level. Our findings provided a holistic depict of national traded emissions at the provincial level, highlighting the importance of cross-province emission effect in exploring ways to promote the low-carbon transition of domestic circulation and fulfill the high-quality development of 'dual circulation' new pattern and successful achievement of 'double carbon' solemn commitment.

Dual risk-aversion programming for regional industrial structure adjustment with water-energy nexus: A case study of Tianjin, China.

The water-energy nexus (WEN) system is a large-scale complex system that comes with diverse forms of risks owing to many challenges in the process of maintaining economic-resource-environmental sustainability. First, the rapidly increasing demand for water and energy subjects many regions to the high risk of water and energy shortages. Second, decision makers face difficulties in weighing system benefits and loss risks under a series of stricter water-energy policies. To handle the aforementioned dual risks of WEN, in this study we propose copula-based stochastic downside risk-aversion programming (CSDP) for regional water-energy management. CSDP integrates the superiority of the copula analysis method and downside risk-aversion programming into a framework, which can not only reveal the risk interactions between water resources and energy demand by using copula functions under different probability distributions, even previously unknown correlations, but also control economic risk, tackle systemic uncertainties, and provide an effective linkage between system stability and conflicting economic benefits. The proposed model was applied to a water-energy system case study in Tianjin City, China. Optimal solutions for various water resources and energy demand copulas associated with different scenarios and hierarchical risk levels were examined in the CSDP model. The results showed that water resources have a greater influence than energy on industrial structure adjustment in Tianjin, with consequent effects on system benefits, optimal output value schemes, and environmental protection strategies. In addition, the tertiary industry provides a new opportunity for economic growth based on a large amount of water-energy consumption, and its potential resources and water-air pollution risks also deserve extensive attention.

A multi-sectoral decomposition and decoupling analysis of carbon emissions in Guangdong province, China.

Quantifying the decoupling states of carbon emissions from a multi-sectoral and dual-perspective can guide more detailed emission reduction strategies. Based on the single-regional input-output (SRIO), Tapio decoupling analysis (TDA), and structural decomposition analysis (SDA), this study investigated the dynamic variation feature and decoupling state of multi-sectoral carbon emissions, and revealed their driving factors of consumption-based emissions in Guangdong province from 2002 to 2017. The main discovery can be summarized as follows from results analysis. Firstly, electricity production sector and construction sector were the largest direct and embodied carbon emission sources, and capital formation was the most important factor with the contribution of approximately 100 % that led to embodied carbon emissions of construction. For most of the manufacturing and service sectors, the embodied carbon emissions caused by international export exceed 50 %. Secondly, the consumption structure, consumption per capita, and population effect promoted the embodied emissions during 2002-2012, while the emission intensity effect was the greatest offsetting factor for all sectors. Consumption structure effect was becoming a major driver to the increase of embodied carbon emissions for construction. Thirdly, agriculture, mining, energy transformation, and service sector showed the unsatisfactory decoupling relationship between direct carbon emissions and economic output. According to the decoupling states, the decoupling relationships in some secondary industries were overestimated under the situation of only considering direct carbon emissions. The obtained results and policy implications are expected to provide holistic reference for policymakers to promote the short-term carbon peak and long-term carbon neutrality of Guangdong province from the sectoral perspective.

Assessment of the water-energy-food nexus under spatial and social complexities: A case study of Guangdong-Hong Kong-Macao.

Water, energy, and food resources are indispensable and irreplaceable resources for the survival and development of human society. This study systematically assessed the three resources system in Guangdong, Hong Kong, and Macao based on constructed direct and nexus-oriented, multi-regional input-output, and ecological network analysis models. Various network analysis (e.g., control, utility, hierarchy, and robustness) was adopted to identify the critical factors of inter-regional resources trade from a perspective of supply-demand. The results indicated that Guangdong, Hong Kong, and Macao have complex control linkages in the three resources trade network, and Guangdong is the key to improving the three resources network structure. The three resources network existed highly competition and exploitation in the three regions. Industrial development is unbalanced and competitive for the three resources. The wholeness water-energy-food trade network of the three regions stayed in a positive environment, but the positive effect level was relatively weak. The three resources network robustness in the three regions is at a medium level. Hong Kong and Macao's water-energy-food network systems have a high vulnerability, and the lowest system robustness was food-related energy in Hong Kong. Finally, we provide some measures to help the sustainable development of the water-energy-food resource system in the three regions, such as cross-regional coordinated management, integration industries development, seawater toilets-flushing, sea rice, and renewable energy.

Planning electric power system under carbon-price mechanism considering multiple uncertainties - A case study of Tianjin.

The carbon-price mechanism has been proved to be an effective measure for promoting energy revolution and mitigating climate change. It is of vital importance to develop optimal energy development strategy for electric power-dependent regions by considering the complex interaction among carbon price, carbon emission control, and carbon-responsibility transfer. In order to fill the research gap on the optimal choice of carbon-price mechanism at the urban level, this study is the first attempt to express uncertainties embodied in the carbon price mechanism as interval values, probability distribution and downside risks. The developed risk-aversion-based interval two-stage stochastic programming (RITSP) model is effective in analyzing the effect of internal and electric-transmission related carbon-tax on power system structure. It is discovered that carbon compensation policy for imported electricity is more suitable for Tianjin's power system development. Tianjin would primarily purchase electricity from Inner-Mongolia. With the increase of carbon emission tax, Tianjin would import increasing proportion of electricity from Gansu. Due to the limited endowment of renewable energy in Tianjin, the impact of carbon emission limitations on the renewable energy power generation structure of is trivial, and it has a greater impact on stimulating the development of CCS technology. What's more, Tianjin's future power system planning is more inclined to develop CCS rather than renewable energy.

An optimization model for regional air pollutants mitigation based on the economic structure adjustment and multiple measures: A case study in Urumqi city, China.

A model based on economic structure adjustment and pollutants mitigation was proposed and applied in Urumqi. Best-worst case analysis and scenarios analysis were performed in the model to guarantee the parameters accuracy, and to analyze the effect of changes of emission reduction styles. Results indicated that pollutant-mitigations of electric power industry, iron and steel industry, and traffic relied mainly on technological transformation measures, engineering transformation measures and structure emission reduction measures, respectively; Pollutant-mitigations of cement industry relied mainly on structure emission reduction measures and technological transformation measures; Pollutant-mitigations of thermal industry relied mainly on the four mitigation measures. They also indicated that structure emission reduction was a better measure for pollutants mitigation of Urumqi. Iron and steel industry contributed greatly in SO2, NOx and PM (particulate matters) emission reduction and should be given special attention in pollutants emission reduction. In addition, the scales of iron and steel industry should be reduced with the decrease of SO2 mitigation amounts. The scales of traffic and electric power industry should be reduced with the decrease of NOx mitigation amounts, and the scales of cement industry and iron and steel industry should be reduced with the decrease of PM mitigation amounts. The study can provide references of pollutants mitigation schemes to decision-makers for regional economic and environmental development in the 12th Five-Year Plan on National Economic and Social Development of Urumqi.

Development and validation of a nomogram for predicting heterotopic ossification following spinal cord injury.

OBJECTIVE: Heterotopic ossification (HO) following spinal cord injury (SCI) can severely compromise patient mobility and quality of life. Precise identification of SCI patients at an elevated risk for HO is crucial for implementing early clinical interventions. While the literature presents diverse correlations between HO onset and purported risk factors, the development of a predictive model to quantify these risks is likely to bolster preventive approaches. This study is designed to develop and validate a nomogram-based predictive model that estimates the likelihood of HO in SCI patients, utilizing recognized risk factors to expedite clinical decision-making processes. METHODS: We recruited a total of 145 patients with SCI and presenting with HO who were hospitalized at the China Rehabilitation Research Center, Beijing Boai Hospital, from June 2016 to December 2022. Additionally, 337 patients with SCI without HO were included as controls. Comprehensive data were collected for all study participants, and subsequently, the dataset was randomly partitioned into training and validation groups. Using Least Absolute Shrinkage and Selection Operator regression, variables were meticulously screened during the pretreatment phase to formulate the predictive model. The efficacy of the model was then assessed using metrics including receiver-operating characteristic (ROC) analysis, calibration assessment, and decision curve analysis. RESULTS: The final prediction model incorporated age, sex, complete spinal cord injury status, spasm occurrence, and presence of deep vein thrombosis (DVT). Notably, the model exhibited commendable performance in both the training and validation groups, as evidenced by areas under the ROC curve (AUCs) of 0.756 and 0.738, respectively. These values surpassed the AUCs obtained for single variables, namely age (0.636), sex (0.589), complete spinal cord injury (0.681), spasm occurrence (0.563), and DVT presence (0.590). Furthermore, the calibration curve illustrated a congruence between the predicted and actual outcomes, indicating the high accuracy of the model. The decision curve analysis indicated substantial net benefits associated with the application of the model, thereby underscoring its practical utility. CONCLUSIONS: HO following SCI correlates with several identifiable risk factors, including male gender, youthful age, complete SCI, spasm occurrence and DVT. Our predictive model effectively estimates the likelihood of HO development by leveraging these factors, assisting physicians in identifying patients at high risk. Subsequently, correct positioning to prevent spasm-related deformities and educating healthcare providers on safe lower limb mobilization techniques are crucial to minimize muscle injury risks from rapid iliopsoas muscle extension. Additionally, the importance of early DVT prevention through routine screening and anticoagulation is emphasized to further reduce the incidence of HO.

Preparation of reduced graphene oxide/poly(acrylamide) nanocomposite and its adsorption of Pb(II) and methylene blue.

Carboxyl groups at the periphery of reduced graphene oxide (RGO) sheets are converted to amine groups by reaction with N-hydroxysuccinimide and 1,3-diaminopropane, and a free-radical polymerization initiator is anchored to the RGO sheets. Poly(acrylamide) (PAM) polymer brushes on RGO sheets (RGO/PAM) are synthesized by in situ free-radical polymerization. The heavy metals, Pb(II), and the benzenoid compounds, methylene blue, (MB) were selected and adsorbed by RGO/PAM composites, and the adsorption capacity of RGO/PAM for Pb(II) and MB was measured. The experimental data of RGO/PAM isotherms for Pb(II) and MB followed the Langmuir isotherm model. The RGO/PAM displays adsorption capacities as high as 1000 and 1530 mg/g for Pb(II) and MB, respectively, indicating RGO/PAM is a good adsorbent for the adsorption of Pb(II) and MB. The adsorption kinetics of Pb(II) and MB onto RGO/PAM can be well fitted to the pseudo-second-order model. The adsorption processes of Pb(II) and MB onto RGO/PAM are spontaneous at 298, 308, and 318 K.

Survival nomogram for osteosarcoma patients: SEER data retrospective analysis with external validation.

This study aimed to develop a nomogram based on the clinicopathological factors affecting the prognosis of osteosarcoma patients to help clinicians predict the overall survival of osteosarcoma patients. A total of 1362 patients diagnosed with osteosarcoma were enrolled in this study, among which, 1081 cases were enrolled from the SEER (Surveillance, Epidemiology, and End Results) database as training group, while 281 patients from two Clinical Medicine Center database were used in validation group. Univariate and multivariate Cox analyses were performed to identify the independent prognostic factors for overall survival. Nomogram predicting the 3- and 5-year overall survival probability was constructed and validated. Multiple validation methods, including calibration plots, consistency indices (C-index), and area under the receiver operating characteristic curve (AUC) were used to validate the accuracy and the reliability of the prediction models. Decision curve analysis (DCA) was conducted to validate the clinical application of the prediction model. Furthermore, all patients were divided into low- and high-risk groups based on their nomogram scores. Kaplan-Meier (KM) curves were applied to compare the difference in survival between the two groups. Predictors in the prediction model included age, sex, tumor size, primary site, grade, M stage, and surgery. Our results showed that the model displayed good prediction ability, and the calibration plots demonstrated great power both in the training and the validation groups. In the training group, C-index was 0.80, and the 3- and 5-year AUCs of the nomogram were 0.82 and 0.81, respectively. In the validation group, C-index was 0.79, and the 3- and 5-year AUCs of the nomogram were 0.85 and 0.83, respectively. Furthermore, DCA data indicated the potential clinical application of this model. Therefore, our prediction model could help clinicians evaluate prognoses, identify high-risk individuals, and provide individualized treatment recommendation for patients with osteosarcoma.

Dynamic characteristics of microplastics under tidal influence and potential indirect monitoring methods.

Rivers are an important channel for the transport of microplastics from inland areas to the ocean. It is of great significance to explore the dynamic changes in microplastic pollution characteristics under tidal fluctuations to understand the exchange of microplastics between rivers and oceans. In this study, the occurrence of microplastics in typical tidal channels in the lower reaches of the Dong River was investigated during the wet and dry weather seasons, and high frequency continuous dynamic monitoring of microplastics was carried out in a typical tidal cross section during a tidal cycle. The abundances of microplastics during wet and dry weather seasons were 3.97-102.87 ± 28.63 item/m3 and 5.43-56.43 ± 14.32 item/m3, respectively. The microplastics generally exhibited a fluctuating growth pattern, with low contents in the upstream area and high contents in the downstream area, and the abundance of microplastics differed greatly in the different functional zones. The dynamic monitoring results showed that the abundance of microplastics was clearly affected by the tides, in that it increased during the flood tide and decreased during the ebb tide, with abundances ranging from 11.15 to 95.26 item/m3. In addition, there was a significant linear relationship between the abundance of microplastics and flow in the typical tidal cross section. The relationship between the response of microplastic pollution characteristics and tides combined with local hydrometeorological factors may be a potentially effective real-time monitoring method for assessing microplastic pollution indirectly.

A bi-level multi-objective programming model for water resources management under compound uncertainties in Dongjiang River Basin, Greater Bay Area of China.

To facilitate regional water resources allocation, an integrated bi-level multi-objective programming (IBMP) model with dual random fuzzy variables was developed in this research The proposed model was derived through incorporating dual random fuzzy variables, multi-objective programming, and interval parameter programming within a bi-level optimization framework. This approach improved upon the previous bi-level programming methods and had two advantages. Firstly, it was capable of reflecting tradeoffs among multiple conflict preferences for water related bi-level hierarchical decision-making processes. Secondly, random fuzzy variables were used to tackle the dual uncertainties in both sides of the constraints, which were characterize as probability density functions and discrete intervals. Then, a real-world water resources planning problem was employed for illustrating feasibility of the application of IBMP model in Dongjiang river watershed of south China. Results reflected the alternative decisions for water allocation schemes under a set of probability levels and fuzzy α - cut levels, which can support in-depth analysis of tradeoffs among multiple levels and objective values. Moreover, modeling comparison analysis was undertaken to illustrate the performances of the proposed model.