Delayed treatment of diabetic foot ulcer in patients with type 2 diabetes and its prediction model

BACKGROUND Diabetic foot(DF)is a serious complication of type 2 diabetes.This study aimed to investigate the factors associated with DF occurrence and the role of delayed medical care in a cohort of patients with type 2 diabetes.AIM To reveal the impact of delayed medical treatment on the development of DF in patients with type 2 diabetes and to establish a predictive model for DF.METHODS In this retrospective cohort study,292 patients with type 2 diabetes who underwent examination at our hospital from January 2023 to December 2023 were selected and divided into the DF group(n=82,DF)and nondiabetic foot group(n=210,NDF).Differential and correlation analyses of demographic indicators,laboratory parameters,and delayed medical treatment were conducted for the two groups.Logistic regression was applied to determine influencing factors.Receiver operating characteristic(ROC)analysis was performed,and indicators with good predictive value were selected to establish a combined predictive model.RESULTS The DF group had significantly higher body mass index(BMI)(P<0.001),disease duration(P=0.012),plasma glucose levels(P<0.001),and HbA1c(P<0.001)than the NDF group.The NDF group had significantly higher Acute Thrombosis and Myocardial Infarction Health Service System(ATMHSS)scores(P<0.001)and a significantly lower delayed medical treatment rate(72.38%vs 13.41%,P<0.001).BMI,duration of diabetes,plasma glucose levels,HbA1c,diabetic peripheral neuropathy,and nephropathy were all positively correlated with DF occurrence.ATMHSS scores were negatively correlated with delayed time to seek medical treatment.The logistic regression model revealed that BMI,duration of diabetes,plasma glucose levels,HbA1c,presence of diabetic peripheral neuropathy and nephropathy,ATMHSS scores,and delayed time to seek medical treatment were influencing factors for DF.ROC analysis indicated that plasma glucose levels,HbA1c,and delayed medical treatment had good predictive value with an area under the curve of 0.933 for the combined predictive model.CONCLUSION Delayed medical treatment significantly affects the probability of DF occurrence in patients with diabetes.Plasma glucose levels,HbA1c levels,and the combined predictive model of delayed medical treatment demonstrate good predictive value.

Mathematical Modeling of HIV Investigating the Effect of Inconsistent Treatment

HIV is a retrovirus that infects and impairs the cells and functions of the immune system. It has caused a great challenge to global public health systems and leads to Acquired Immunodeficiency Syndrome (AIDS), if not attended to in good time. Antiretroviral therapy is used for managing the virus in a patient’s lifetime. Some of the symptoms of the disease include lean body mass and many opportunistic infections. This study has developed a SIAT mathematical model to investigate the impact of inconsistency in treatment of the disease. The arising non-linear differential equations have been obtained and analyzed. The DFE and its stability have been obtained and the study found that it is locally asymptotically stable when the basic reproduction number is less than unity. The endemic equilibrium has been obtained and found to be globally asymptotically stable when the basic reproduction number is greater than unity. Numerical solutions have been obtained and analyzed to give the trends in the spread dynamics. The inconsistency in treatment uptake has been analyzed through the numerical solutions. The study found that when the treatment rate of those infected increases, it leads to an increase in treatment population, which slows down the spread of HIV and vice versa. An increase in the rate of treatment of those with AIDS leads to a decrease in the AIDS population, the reverse happens when this rate decreases. The study recommends that the community involvement in advocating for consistent treatment of HIV to curb the spread of the disease.

Mathematical Modeling of the Co-Infection Dynamics of HIV and Tuberculosis Incorporating Inconsistency in HIV Treatment

A non-linear HIV-TB co-infection has been formulated and analyzed. The positivity and invariant region has been established. The disease free equilibrium and its stability has been determined. The local stability was determined and found to be stable under given conditions. The basic reproduction number was obtained and according to findings, co-infection diminishes when this number is less than unity, and persists when the number is greater than unity. The global stability of the endemic equilibrium was calculated. The impact of HIV on TB was established as well as the impact of TB on HIV. Numerical solution was also done and the findings indicate that when the rate of HIV treatment increases the latent TB increases while the co-infected population decreases. When the rate of HIV treatment decreases the latent TB population decreases and the co-infected population increases. Encouraging communities to prioritize the consistent treatment of HIV infected individuals must be emphasized in order to reduce the scourge of HIV-TB co-infection.

Development and validation of a predictive model for the assessment of potassium-lowering treatment among hyperkalemia patients

BACKGROUND:Hyperkalemia is common among patients in emergency department and is associated with mortality.While,there is a lack of good evaluation and prediction methods for the effi cacy of potassium-lowering treatment,making the drug dosage adjustment quite diffi cult.We aimed to develop a predictive model to provide early forecasting of treating eff ects for hyperkalemia patients.METHODS:Around 80%of hyperkalemia patients(n=818)were randomly selected as the training dataset and the remaining 20%(n=196)as the validating dataset.According to the serum potassium(K+)levels after the fi rst round of potassium-lowering treatment,patients were classifi ed into the eff ective and ineff ective groups.Multivariate logistic regression analyses were performed to develop a prediction model.The receiver operating characteristic(ROC)curve and calibration curve analysis were used for model validation.RESULTS:In the training dataset,429 patients had favorable eff ects after treatment(eff ective group),and 389 had poor therapeutic outcomes(ineff ective group).Patients in the ineff ective group had a higher percentage of renal disease(P=0.007),peripheral edema(P<0.001),oliguria(P=0.001),or higher initial serum K+level(P<0.001).The percentage of insulin usage was higher in the effective group than in the ineff ective group(P=0.005).After multivariate logistic regression analysis,we found age,peripheral edema,oliguria,history of kidney transplantation,end-stage renal disease,insulin,and initial serum K+were all independently associated with favorable treatment eff ects.CONCLUSION:The predictive model could provide early forecasting of therapeutic outcomes for hyperkalemia patients after drug treatment,which could help clinicians to identify hyperkalemia patients with high risk and adjust the dosage of medication for potassium-lowering.

Impact of extended nursing model after multi-disciplinary treatment on young patient with post-stroke

BACKGROUND Stroke has become one of the most serious life-threatening diseases due to its high morbidity,disability,recurrence and mortality rates.AIM To explore the intervention effect of multi-disciplinary treatment(MDT)extended nursing model on negative emotions and quality of life of young patients with post-stroke.METHODS A total of 60 young stroke patients who were hospitalized in the neurology department of our hospital from January 2020 to December 2021 were selected and randomly divided into a control group and an experimental group,with 30 patients in each group.The control group used the conventional care model and the experimental group used the MDT extended nursing model.After the inhospital and 3-mo post-discharge interventions,the differences in negative emotions and quality of life scores between the two groups were evaluated and analyzed at the time of admission,at the time of discharge and after discharge,respectively.RESULTS There are no statistically significant differences in the negative emotions scores between the two groups at admission,while there are statistically significant differences in the negative emotions scores within each group at admission and discharge,at discharge and post-discharge,and at discharge and post-discharge.In addition,the negative emotions scores were all statistically significant at discharge and after discharge when compared between the two groups.There was no statistically significant difference in quality of life scores at the time of admission between the two groups,and the difference between quality of life scores at the time of admission and discharge,at the time of discharge and post-discharge,and at the time of admission and post-discharge for each group of patients was statistically significant.CONCLUSION The MDT extended nursing mode can improve the negative emotion of patients and improve their quality of life.Therefore,it can be applied in future clinical practice and is worthy of promotion.

Deterioration of equivalent thermal conductivity of granite subjected to heating-cooling treatment

Understanding the thermal conductivity of granite is critical for many geological and deep engineering applications.The heated granite was subjected to air-,water-,and liquid nitrogen(LN2-)coolings in this context.The transient hot-wire technique was used to determine the equivalent thermal conductivity(ETC)of the granite before and after treatment.The deterioration mechanism of ETC is analyzed from the meso-perspective.Finally,the numerical model is used to quantitatively study the impact of cooling rate on the microcrack propagation and heat conduction characteristics of granite.The results show that the ETC of granite is not only related to the heating temperature,but also affected by the cooling rate.The ETC of granite decreases nonlinearly with increasing heating temperature.A faster cooling rate causes a greater decrease in ETC at the same heating temperature.The higher the heating temperature,the stronger the influence of cooling rate on ETC.The main explanation for the decrease in ETC of granite is the increase in porosity and microcrack density produced by the formation and propagation of pore structure and microcracks during heating and cooling.Further analysis displays that the damage of granite at the heating stage is induced by the difference in thermal expansion and elastic properties of mineral particles.At the cooling stage,the faster cooling rate causes a higher temperature gradient,which in turn produces greater thermal stress.As a result,it not only causes new cracks in the granite,but also aggravates the damage at the heating stage,which induces a further decrease in the heat conduction performance of granite,and this scenario is more obvious at higher temperatures.

Risk Prediction Model for Surgical Treatment of Ruptured Corpus Luteum in the Ovary

Objective: To explore the related factors of surgical treatment of patients with corpus luteum rupture and establish a risk prediction model of surgical treatment of corpus luteum rupture. Methods: 222 patients with corpus luteum rupture treated in Jingzhou First People’s Hospital from January 2015 to March 2022 were analyzed retrospectively, including 45 cases of surgery and 177 cases of conservative treatment. The training set and validation set were randomly assigned according to 7:3. We collected the basic information, laboratory and ultrasonic examination data of 222 patients. Logistic regression analysis was used to determine the independent risk factors and combined predictors of surgical treatment of corpus luteum rupture. The risk prediction model was established and the nomogram was drawn. The discrimination and calibration of the prediction model were verified and evaluated by receiver operating characteristic (ROC) curve, calibration curve and Hosmer-Lemeshow goodness of fit test;Decision curve analysis (DCA) was used to evaluate the clinical effectiveness of the prediction model. Results: Univariate logistic regression showed that whole abdominal pain (OR: 2.314, 95% CI: 1.090 - 4.912), abdominal muscle tension (OR: 2.379, 95% CI: 1.112 - 5.089), adnexal mass ≥ 4 cm (OR: 3.926, 95% CI: 1.771 - 8.266), hemoglobin Conclusion: The nomogram prediction model containing three predictive variables (hemoglobin, depth of pelvic effusion under ultrasound and cervical lifting pain) can be used to predict the risk of surgical treatment in patients with corpus luteum rupture.

Prediction of treatment response to antipsychotic drugs for precision medicine approach to schizophrenia:randomized trials and multiomics analysis

Background:Choosing the appropriate antipsychotic drug(APD)treatment for patients with schizophrenia(SCZ)can be challenging,as the treatment response to APD is highly variable and difficult to predict due to the lack of effective biomarkers.Previous studies have indicated the association between treatment response and genetic and epigenetic factors,but no effective biomarkers have been identified.Hence,further research is imperative to enhance precision medicine in SCZ treatment.Methods:Participants with SCZ were recruited from two randomized trials.The discovery cohort was recruited from the CAPOC trial(n=2307)involved 6 weeks of treatment and equally randomized the participants to the Olanzapine,Risperidone,Quetiapine,Aripiprazole,Ziprasidone,and Haloperidol/Perphenazine(subsequently equally assigned to one or the other)groups.The external validation cohort was recruited from the CAPEC trial(n=1379),which involved 8 weeks of treatment and equally randomized the participants to the Olanzapine,Risperidone,and Aripiprazole groups.Additionally,healthy controls(n=275)from the local community were utilized as a genetic/epigenetic reference.The genetic and epigenetic(DNA methylation)risks of SCZ were assessed using the polygenic risk score(PRS)and polymethylation score,respectively.The study also examined the genetic-epigenetic interactions with treatment response through differential methylation analysis,methylation quantitative trait loci,colocalization,and promoteranchored chromatin interaction.Machine learning was used to develop a prediction model for treatment response,which was evaluated for accuracy and clinical benefit using the area under curve(AUC)for classification,R^(2) for regression,and decision curve analysis.Results:Six risk genes for SCZ(LINC01795,DDHD2,SBNO1,KCNG2,SEMA7A,and RUFY1)involved in cortical morphology were identified as having a genetic-epigenetic interaction associated with treatment response.The developed and externally validated prediction model,which incorporated clinical information,PRS,genetic risk score(GRS),and proxy methylation level(proxyDNAm),demonstrated positive benefits for a wide range of patients receiving different APDs,regardless of sex[discovery cohort:AUC=0.874(95%CI 0.867-0.881),R^(2)=0.478;external validation cohort:AUC=0.851(95%CI 0.841-0.861),R^(2)=0.507].Conclusions:This study presents a promising precision medicine approach to evaluate treatment response,which has the potential to aid clinicians in making informed decisions about APD treatment for patients with SCZ.Trial registration Chinese Clinical Trial Registry(https://www.chictr.org.cn/),18 Aug 2009 retrospectively registered:CAPOC-ChiCTR-RNC-09000521(https://www.chictr.org.cn/showproj.aspx?proj=9014),CAPEC-ChiCTRRNC-09000522(https://www.chictr.org.cn/showproj.aspx?proj=9013).

Animal models of hepatitis E infection: Advances and challenges

Hepatitis E virus(HEV)is one of the leading causes of acute viral hepatitis worldwide.Although most of HEV infections are asymptomatic,some patients will develop the symptoms,especially pregnant women,the elderly,and patients with preexisting liver diseases,who often experience anorexia,nausea,vom-iting,malaise,abdominal pain,and jaundice.HEV infection may become chronic in immunosuppressed individuals.In addition,HEV infection can also cause several extrahepatic manifestations.HEV exists in a wide range of hosts in nature and can be transmitted across species.Hence,animals susceptible to HEV can be used as models.The establishment of animal models is of great significance for studying HEV transmission,clinical symptoms,extrahepatic manifestations,and therapeutic strategies,which will help us understand the pathogenesis,prevention,and treatment of hepatitis E.This review summarized the animal models of HEV,including pigs,monkeys,rabbits,mice,rats,and other animals.For each animal species,we provided a concise summary of the HEV genotypes that they can be infected with,the cross-species transmission pathways,as well as their role in studying extrahepatic manifestations,prevention,and treatment of HEV infection.The advantages and disadvantages of these animal models were also emphasized.This review offers new perspectives to enhance the current understanding of the research landscape surrounding HEV animal models.

Exploring the effects of taurolidine on tumor weight and microvessel density in a murine model of osteosarcoma

Background:Osteosarcoma is the most common malignant primary bone tumor.The prognosis for patients with disseminated disease remains very poor despite recent advancements in chemotherapy.Moreover,current treatment regimens bear a significant risk of serious side effects.Thus,there is an unmet clinical need for effective therapies with improved safety profiles.Taurolidine is an antibacterial agent that has been shown to induce cell death in different types of cancer cell lines.Methods:In this study,we examined both the antineoplastic and antiangiogenic effects of taurolidine in animal models of osteosarcoma.K7M2 murine osteosarcoma cells were injected,both intramuscular and intraperitoneal,into 60 BALB/c mice on day zero.Animals were then randomized to receive treatment with taurolidine 2%(800 mg/kg),taurolidine 1%(400 mg/kg),or NaCl 0.9%control for seven days by intravenous or intraperitoneal administration.Results:After 35 days,mice were euthanized,and the tumors were harvested for analysis.Eighteen mice were excluded from the analysis due to complications.Body weight was significantly lower in the 2%taurolidine intraperitoneal treatment group from day 9 to 21,consistent with elevated mortality in this group.Intraperitoneal tumor weight was significantly lower in the 1%(p=0.003)and 2%(p=0.006)intraperitoneal taurolidine treatment groups compared to the control.No antineoplastic effects were observed on intramuscular tumors or for intravenous administration of taurolidine.There were no significant differences in microvessel density or mitotic rate between treatment groups.Reduced body weight and elevated mortality in the 2%taurolidine intraperitoneal group suggest that the lower 1%dose is preferable.Conclusions:In conclusion,there is no evidence of antiangiogenic activity,and the antitumor effects of taurolidine on osteosarcoma observed in this study are limited.Moreover,its toxic profile grants further evaluation.Given these observations,further research is necessary to refine the use of taurolidine in osteosarcoma treatment.

Evolution mechanism and treatment timing of penetrating fissures

The Inner Mongolia mining area in western China are characterized by the development of numerous penetrating fissures,resulting in severe land damage.It is significant to reveal the underlying evolution mechanism and identify treatment timing for restoring the ecological environment.The Guanbanwusu mining subsidence area in Inner Mongolia,China was selected as the research case for this study.The evolution mechanism of different penetrating fissures was revealed by field measurement,physical simulation and theoretical analysis.The treatment timing prediction model for the mining subsidence area was established based on the enhanced Weibull time function.The results show that the ground fissures are mainly step-type and collapse-type fissures.The breaking form of overlying strata determines their vertical opening and horizontal dislocation.The high mining intensity in the western mining area results in a shortened period of dynamic fissure expansion and reduced closure degree.The damage extent of the overlying strata exhibits zoning characteristics both vertically and horizontally.The relative standard deviation of the prediction model is only 3.7%.Concurrently,the prediction model is employed to determine the optimal timing for treatment in the study area,estimated to be 259 days.Subsequently,once this threshold is reached,the study area undergoes treatment and restoration of its e cological environment.This study addresses the knowledge gap in this field by highlighting the interconnectedness between rock strata structure and evolution mechanism of penetrating fissures,thereby providing a method for determining the treatment timing in mining subsidence areas.

Predicting hepatocellular carcinoma: A new non-invasive model based on shear wave elastography

BACKGROUND Integrating conventional ultrasound features with 2D shear wave elastography(2D-SWE)can potentially enhance preoperative hepatocellular carcinoma(HCC)predictions.AIM To develop a 2D-SWE-based predictive model for preoperative identification of HCC.METHODS A retrospective analysis of 884 patients who underwent liver resection and pathology evaluation from February 2021 to August 2023 was conducted at the Oriental Hepatobiliary Surgery Hospital.The patients were divided into the modeling group(n=720)and the control group(n=164).The study included conventional ultrasound,2D-SWE,and preoperative laboratory tests.Multiple logistic regression was used to identify independent predictive factors for RESULTS In the modeling group analysis,maximal elasticity(Emax)of tumors and their peripheries,platelet count,cirrhosis,and blood flow were independent risk indicators for malignancies.These factors yielded an area under the curve of 0.77(95%confidence interval:0.73-0.81)with 84%sensitivity and 61%specificity.The model demonstrated good calibration in both the construction and validation cohorts,as shown by the calibration graph and Hosmer-Lemeshow test(P=0.683 and P=0.658,respectively).Additionally,the mean elasticity(Emean)of the tumor periphery was identified as a risk factor for microvascular invasion(MVI)in malignant liver tumors(P=0.003).Patients receiving antiviral treatment differed significantly in platelet count(P=0.002),Emax of tumors(P=0.033),Emean of tumors(P=0.042),Emax at tumor periphery(P<0.001),and Emean at tumor periphery(P=0.003).CONCLUSION 2D-SWE’s hardness value serves as a valuable marker for enhancing the preoperative diagnosis of malignant liver lesions,correlating significantly with MVI and antiviral treatment efficacy.

Endoscopic diagnosis and management of gallbladder carcinoma in minimally invasive era:New needs,new models

Gallbladder cancer(GBC)is a rare and lethal malignancy;however,it represents the most common type of biliary tract cancer.Patients with GBC are often diagnosed at an advanced stage,thus,unfortunately,losing the opportunity for curative surgical intervention.This situation leads to lower quality of life and higher mortality rates.In recent years,the rapid development of endoscopic equipment and techniques has provided new avenues and possibilities for the early and minimally invasive diagnosis and treatment of GBC.This editorial comments on the article by Pavlidis et al.Building upon their work,we explore the new needs and corresponding models for managing GBC from the endoscopic diagnosis and treatment perspective.

Classification and Visualization of Surrounding Rock Mass Stability Based on Multi-Dimensional Cloud Model

The classification of the stability of surrounding rock is an uncertain system with multiple indices.The Multidimensional Cloud Model provides an advanced solution through the use of an improved model of One-dimensional Cloud Model.Setting each index as a one-dimensional attribute,the Multi-dimensional Cloud Model can set the digital characteristics of each index according to the cloud theory.The Multi-dimensional cloud generator can calculate the certainty of each grade,and then determine the stability levels of the surrounding rock according to the principle of maximum certainty.Using this model to 5 coal mine roadway surrounding rock examples and comparing the results with those of One-dimensional and Two-dimensional Cloud Models,we find that the Multi-dimensional Cloud Model can provide a more accurate solution.Since the classification results of the Multidimensional Cloud Model are difficult to be presented intuitively and visually,we reduce the Multi-dimensional Cloud Model to One-dimensional and Two-dimensional Cloud Models in order to visualize the results achieved by the Multi-dimensional Cloud Model.This approach provides a more accurate and intuitive method for the classification of the surrounding rock stability,and it can also be applied to other types of classification problems.

Prediction and Analysis of Total Nitrogen in a Sewage Treatment Plant Effluent

Total nitrogen was an important indicator for characterizing eutrophication of polluted water. Although the use of water quality online monitoring instrument can monitor water quality changes in real time, the degree of intelligence was low, so it was urgent to predict the water quality and take precautions in advance. A predictive model for total nitrogen levels in a sewage treatment plant utilizing the Anaerobic-Anoxic-Oxic (AAO) process was investigated in this paper. This model demonstrated significant practical application value. Based on the ARIMA (Autoregressive Integrated Moving Average) model and taking into account the impact of Biochemical Oxygen Demand (BOD), a prediction model for effluent total nitrogen was developed. However, the initial results exhibited significant deviations. To address this issue, seasonal factors were further considered. Then, the dataset was divided into winter and Non-winter sub-samples, leading to a reconstruction of the prediction model. Additionally, in developing the Non-winter prediction model, life cycle considerations were incorporated, and consequently, a SARIMA (Seasonal Autoregressive Integrated Moving Average) model was established. The predicting deviation associated with both the winter and Non-winter forecasting models showed a significant reduction.

The Trends and Tasks of Modeling and Simulation for Reduction of Heat Treatment Distortion

The development of simulation model and benchmark work have expanded in the past decade and many models and soft ware are introduced. The leading software "Hearts’ developed by Prof Inoue[1] and several other have proved the effectiveness as the pre-production simulation work at many part of heat treatment processes[2-10]. Although, numerous other models and simulation studies dealt with many fundamental factors are reported at many conferences except very few models have not completed three dimensional computation methods, or lack of validation work to evaluate their tools exactly. In this paper, several distortion case studies will be introduced and the needs of fundamental study of distortion and internationally collaborative program on model evaluation and construction of materials database are proposed.

Technological Options to Ameliorate Waste Treatment of Intensive Pig Production in China:An Analysis Based on Bio-Economic Model

Ameliorating waste treatment by technological improvements affects the economic and the ecological-environment benefits of intensive pig production. The objective of the research was to develop and test a method to determine the technical optimization to ameliorate waste treatment methods and gain insight into the relationship between technological options and the economic and ecological effects. We developed an integrated bio-economic model which incorporates the farming production and waste disposal systems to simulate the impact of technological improvements in pig manure treatment on economic and environmental benefits for the case of a pilot farm in Beijing, China. Based on different waste treatment technology options, three scenarios are applied for the simulation analysis of the model. The simulation results reveal that the economic-environmental benefits of the livestock farm could be improved by reducing the cropland manure application and increasing the composting production with the current technologies. Nevertheless, the technical efficiency, the waste treatment capacity and the economic benefits could be further improved by the introduction of new technologies. It implies that technological and economic support policies should be implemented comprehensively on waste disposal and resource utilization to promote sustainable development in intensive livestock production in China.

Effect modeling of Cr and Zn on microstructure evolution during homogenization heat treatment of AA3xxx alloys

Both of chromium and zinc could appear as either minor impurities or alloying elements in recycled and commercial aluminum alloys, and they could have detrimental effects on the final product properties if not controlled in an appropriate way. A Kampmann-Wagner numerical modeling approach, built on the basis of computational thermodynamics and diffusion kinetics, is employed to investigate the effect of these two minor impurities on dispersoids precipitation during homogenization heat treatment of AA3xxx alloys. The simulation results obtained from different simulation set-ups were compared. The aim is to demonstrate that the modeling approach has the potential to guide the design or optimization of the chemical compositions and heat treatment parameters of aluminum alloys.

Modelling spatial variation in the treatment costs of nonpoint source pollution in mountainous regions of southwest China

Non-point source(NPS) pollution is considered to be one of the main threats of the aquatic environment. Mountainous regions are particularly important water sources for urban areas. The various driving factors of NPS pollution such as terrain, precipitation, and vegetation type in mountainous regions show clear spatial heterogeneity. Consequently, the management systems required for NPS pollution in mountainous regions are complex. In this study, we developed a framework to estimate and map the treatment costs for NPS pollution in mountainous regions and applied this method in Baoxing County, a typical mountainous county in Sichuan Province of southwest China. The export levels of total nitrogen(TN) and total phosphorus(TP) in Baoxing County were estimated using the water purification model in InVEST(Itegrated Valuation of Ecosystem Services and Tradeoffs) tool. NPS pollutant treatment costs were calculated based on the level of pollutants exports, water yield, water quality targets, and treatment costs of NPS pollutants per unit mass. The results show that at the watershed level the amounts of TN and TP exported in Baoxing County were below threshold limits. However, at the sub-watershed level, TN and TP excesses of 291.64 and 2.96 tons per year were found, respectively, with mean TN and TP treatment costs of 6.58 US$/hm^2 and 0.35 US$/hm^2. Appraising pollution treatment cost intuitively reflects the overall expenditure in NPS pollution reduction from an economic perspective. This study provides a foundation for the implementation of Payment for Ecosystem Service(PES) and the prevention and control of NPS pollution.

Mathematical Model of Decarburization of Ultra Low Carbon Steel during RH Treatment

According to the balance of carbon and oxygen, a decarburization model for the RH treatment has been developed. in which the influence of the mass transfer of carbon and oxygen in the liquid steel and the stirring energy (ε) in the vacuum vessel on decarburization rate has been considered. The conclusion that the volumetric coefficients of the mass transfer of carbon is proportional to ε(1.5) is drawn. Industrical experiment proves this model is reliable. The influence of some factors on decarburization rate has been obtained. which can provide directions for RH treatment The decarburization behavior of steel with RH-OB treatment is also studied. The OB-or-not curve, the optimized OB time and OB amount are discussed.