Connotation and Realization of Ningxia Agricultural Organization:Practice Induction and Model Reference

Through a large number of literature studies and field survey,from the perspective of small farmers’integration into agricultural modernization,this paper innovatively divided the current integration mode of national agricultural organizations into five main types:leading enterprises,leading new agricultural business entities,cooperative financial institutions,embedded scientific research institutes,and financial poverty alleviation and assistance.It found that Ningxia region can gradually carry out horizontal integration and extension in the process of promoting the vertical integration and extension of agricultural industry value chain,and try to explore the possibility of new integration mode such as"modern agricultural cloud service public platform+leading enterprises+farmers"in Ningxia,in order to bridge the gaps in agricultural organization policies through the pilot,because the village plans to plan the development of the industry,adapt to the local conditions to strengthen the village collective economy,and continuously improve the degree of farmers’organization.

Effects of the information–knowledge–attitude–practice nursing model combined with predictability intervention on patients with cerebrovascular disease

BACKGROUND Cerebrovascular disease(CVD)poses a serious threat to human health and safety.Thus,developing a reasonable exercise program plays an important role in the long-term recovery and prognosis for patients with CVD.Studies have shown that predictive nursing can improve the quality of care and that the information–knowledge–attitude–practice(IKAP)nursing model has a positive impact on patients who suffered a stroke.Few studies have combined these two nursing models to treat CVD.AIM To explore the effect of the IKAP nursing model combined with predictive nursing on the Fugl–Meyer motor function(FMA)score,Barthel index score,and disease knowledge mastery rate in patients with CVD.METHODS A total of 140 patients with CVD treated at our hospital between December 2019 and September 2021 were randomly divided into two groups,with 70 patients in each.The control group received routine nursing,while the observation group received the IKAP nursing model combined with predictive nursing.Both groups were observed for self-care ability,motor function,and disease knowledge mastery rate after one month of nursing.RESULTS There was no clear difference between the Barthel index and FMA scores of the two groups before nursing(P>0.05);however,their scores increased after nursing.This increase was more apparent in the observation group,and the difference was statistically significant(P<0.05).The rates of disease knowledge mastery,timely medication,appropriate exercise,and reasonable diet were significantly higher in the observation group than in the control group(P<0.05).The satisfaction rate in the observation group(97.14%)was significantly higher than that in the control group(81.43%;P<0.05).CONCLUSION The IKAP nursing model,combined with predictive nursing,is more effective than routine nursing in the care of patients with CVD,and it can significantly improve the Barthel index and FMA scores with better knowledge acquisition,as well as produce high satisfaction in patients.Moreover,they can be widely used in the clinical setting.

Effect of KAP Intervention Model on Resilience and Cancer-related Fatigue in Patients with Colorectal Cancer Undergoing Chemotherapy

Objective:To explore the effect of KAP intervention mode on resilience and cancer-related fatigue in patients with colorectal cancer undergoing chemotherapy.Methods:A prospective randomized trial was conducted.55 patients with colorectal cancer who received routine nursing from February 2018 to February 2019 were included in the control group,and 55 patients who received routine nursing+KAP intervention from March 2019 to March 2020 were included in the observation group.The scores of Resilience Scale and cancer-related fatigue scale(CFS)before and 6 months after intervention were compared between the two groups.Results:After 6 months of intervention,the score of resilience of the two groups was higher than that before intervention,and that of the observation group was higher than that of the control group,the difference was statistically significant(P<0.05);The CFS score of the two groups was lower than that before intervention,and that of the observation group was lower than that of the control group,the difference was statistically significant(P<0.05).Conclusion:KAP intervention model can improve the resilience of patients with colorectal cancer chemotherapy,reduce cancerrelated fatigue.

Machine learning predicts portal vein thrombosis after splenectomy in patients with portal hypertension: Comparative analysis of three practical models

BACKGROUND For patients with portal hypertension(PH),portal vein thrombosis(PVT)is a fatal complication after splenectomy.Postoperative platelet elevation is considered the foremost reason for PVT.However,the value of postoperative platelet elevation rate(PPER)in predicting PVT has never been studied.AIM To investigate the predictive value of PPER for PVT and establish PPER-based prediction models to early identify individuals at high risk of PVT after splenectomy.METHODS We retrospectively reviewed 483 patients with PH related to hepatitis B virus who underwent splenectomy between July 2011 and September 2018,and they were randomized into either a training(n=338)or a validation(n=145)cohort.The generalized linear(GL)method,least absolute shrinkage and selection operator(LASSO),and random forest(RF)were used to construct models.The receiver operating characteristic curves(ROC),calibration curve,decision curve analysis(DCA),and clinical impact curve(CIC)were used to evaluate the robustness and clinical practicability of the GL model(GLM),LASSO model(LSM),and RF model(RFM).RESULTS Multivariate analysis exhibited that the first and third days for PPER(PPER1,PPER3)were strongly associated with PVT[odds ratio(OR):1.78,95%confidence interval(CI):1.24-2.62,P=0.002;OR:1.43,95%CI:1.16-1.77,P<0.001,respectively].The areas under the ROC curves of the GLM,LSM,and RFM in the training cohort were 0.83(95%CI:0.79-0.88),0.84(95%CI:0.79-0.88),and 0.84(95%CI:0.79-0.88),respectively;and were 0.77(95%CI:0.69-0.85),0.83(95%CI:0.76-0.90),and 0.78(95%CI:0.70-0.85)in the validation cohort,respectively.The calibration curves showed satisfactory agreement between prediction by models and actual observation.DCA and CIC indicated that all models conferred high clinical net benefits.CONCLUSION PPER1 and PPER3 are effective indicators for postoperative prediction of PVT.We have successfully developed PPER-based practical models to accurately predict PVT,which would conveniently help clinicians rapidly differentiate individuals at high risk of PVT,and thus guide the adoption of timely interventions.

PRACTICAL AND PREDICTIVE MODELLING OF ORE DEPOSITS IN HYDROTHERMAL SYSTEMS

Over the pastfive years,we have been making efforts to develop a practical and predic- tive tool to exploreforgiantore deposits in hydrothermal systems. Towards this goal,a sig- nificant progress has been made towards a better understanding of the basic physical and chemical processes behind ore body formation and mineralization in hydrothermal systems. On the scientific developmentside,we have developed analytical solutions to answerthe fol- lowing scientific questions:(1) Can thepore- fluid pressure gradientbemaintained atthe val- ue of the lithostaticpressure gradientin the uppercrustof the Earth?and(2 ) Can convective pore- fluid flow take place in the uppercrustof the Earth ifthere is a fluid/mass leakage from the mantle to the upper crustof the Earth?On the modelling developmentside,we have developed numerical methods to model the following problems:(1) convective pore- fluid flow in two- dimensional hydrothermal systems;(2 ) coupled reactive pore- fluid flow and multiple species transport in porous media;(3) precipitation and dissolution of minerals and rock al- teration in the upper crust of the Earth;(4 ) double diffusion driven reactive flow transport in deformable fluid- saturated porous media with particular consideration of temperature- de- pendentchemical reaction rates;(5 ) pore- fluid flow patterns neargeological lenses in hydro- dynamic and hydrothermal systems;(6 ) dissipative structures for nonequilibrium chemical reactions in fluid- saturated porousmedia;(7) convectivepore- fluid flow and the related min- eralization in three- dimensional hydrothermal systems;(8) fluid- rock interaction problems associated with the rock alteration and metamorphic process in fluid- saturated hydrothermal/ sedimentary basins;and (9) various aspects of the fully coupled problem involving material deformation,pore- fluid flow,heattransferand species transport/ chemical reactionsin pore- fluid saturated porous rock masses. The above- mentioned work has significantly enriched our knowledge about the physical and chemical processes related to ore body formation and mineralization in the upper crustof the

Inclusive Multiple Models(IMM)for predicting groundwater levels and treating heterogeneity

An explicit model management framework is introduced for predictive Groundwater Levels(GWL),particularly suitable to Observation Wells(OWs)with sparse and possibly heterogeneous data.The framework implements Multiple Models(MM)under the architecture of organising them at levels,as follows:(i)Level 0:treat heterogeneity in the data,e.g.Self-Organised Mapping(SOM)to classify the OWs;and decide on model structure,e.g.formulate a grey box model to predict GWLs.(ii)Level 1:construct MMs,e.g.two Fuzzy Logic(FL)and one Neurofuzzy(NF)models.(iii)Level 2:formulate strategies to combine the MM at Level 1,for which the paper uses Artificial Neural Networks(Strategy 1)and simple averaging(Strategy 2).Whilst the above model management strategy is novel,a critical view is presented,according to which modelling practices are:Inclusive Multiple Modelling(IMM)practices contrasted with existing practices,branded by the paper as Exclusionary Multiple Modelling(EMM).Scientific thinking over IMMs is captured as a framework with four dimensions:Model Reuse(MR),Hierarchical Recursion(HR),Elastic Learning Environment(ELE)and Goal Orientation(GO)and these together make the acronym of RHEO.Therefore,IMM-RHEO is piloted in the aquifer of Tabriz Plain with sparse and possibly heterogeneous data.The results provide some evidence that(i)IMM at two levels improves on the accuracy of individual models;and(ii)model combinations in IMM practices bring‘model-learning’into fashion for learning with the goal to explain baseline conditions and impacts of subsequent management changes.