Clinical value of precise rehabilitation nursing in management of cerebral infarction

BACKGROUND Cerebral infarction,previously referred to as cerebral infarction or ischemic stroke,refers to the localized brain tissue experiencing ischemic necrosis or softening due to disorders in brain blood supply,ischemia,and hypoxia.The precision rehabilitation nursing model for chronic disease management is a continuous,fixed,orderly,and efficient nursing model aimed at standardizing the clinical nursing process,reducing the wastage of medical resources,and improving the quality of medical services.AIM To analyze the value of a precise rehabilitation nursing model for chronic disease management in patients with cerebral infarction.METHODS Patients(n=124)admitted to our hospital with cerebral infarction between November 2019 and November 2021 were enrolled as the study subjects.The random number table method was used to divide them into a conventional nursing intervention group(n=61)and a model nursing intervention group(n=63).Changes in the nursing index for the two groups were compared after conventional nursing intervention and precise rehabilitation intervention nursing for chronic disease management.RESULTS Compared with the conventional intervention group,the model intervention group had a shorter time to clinical symptom relief(P<0.05),lower Hamilton Anxiety Scale and Hamilton Depression Scale scores,a lower incidence of total complications(P<0.05),a higher disease knowledge mastery rate,higher safety and quality,and a higher overall nursing satisfaction rate(P<0.05).CONCLUSION The precision rehabilitation nursing model for chronic disease management improves the clinical symptoms of patients with cerebral infarction,reducing the incidence of total complications and improving the clinical outcome of patients,and is worthy of application in clinical practice.

Effects of Continuous Precision Nursing Model on Knowledge, Attitudes, and Practices (KAP) Behavior and Cardiac Function in Patients after Percutaneous Coronary Angiography and Stent Implantation

Objective:To explore the impact of a continuous precision nursing model on patients’Knowledge,Attitudes,and Practices(KAP)and cardiac function during the nursing process of patients undergoing percutaneous coronary angiography and stent implantation.Methods:Ninety patients who underwent percutaneous coronary angiography and stent implantation in our hospital from April 2022 to April 2023 were selected and randomly divided into the control group(45 cases),in which routine nursing support was carried out during the treatment process,and the observation group(45 cases),in which continuous precision nursing model was carried out during the treatment process.Comparisons were made between the two groups of patients on their KAP,cardiac function,and quality of life during recovery.Results:There was no difference in the left ventricular ejection fraction(LVEF),cardiac output(CO),and cardiac index(CI)levels before intervention.After the intervention,the levels of cardiac function in the observation group were higher than those of the control group(P<0.05).There was no difference in the Exercise of Self-Care Agency(ESCA)self-care ability scale scores before the intervention.After the intervention,the observation group had higher ESCA scores than the control group(P<0.05).Conclusion:Implementation of a continuous precision nursing model in the care of patients undergoing percutaneous coronary angiography and stent implantation improved the patient’s cardiac function,and KAP,and promoted recovery.

Infinitesimal dividing modeling method for dual suppliers inventory model with random lead times

As one of the basic inventory cost models, the （Q, τ）inventory cost model of dual suppliers with random procurement lead time is mostly formulated by using the concepts of ＂effective lead time＂ and ＂lead time demand＂, which may lead to an imprecise inventory cost. Through the real-time statistic of the inventory quantities, this paper considers the precise （Q, τ） inventory cost model of dual supplier procurement by using an infinitesimal dividing method. The traditional modeling method of the inventory cost for dual supplier procurement includes complex procedures. To reduce the complexity effectively, the presented method investigates the statistics properties in real-time of the inventory quantities with the application of the infinitesimal dividing method. It is proved that the optimal holding and shortage costs of dual supplier procurement are less than those of single supplier procurement respectively. With the assumption that both suppliers have the same distribution of lead times, the convexity of the cost function per unit time is proved. So the optimal solution can be easily obtained by applying the classical convex optimization methods. The numerical examples are given to verify the main conclusions.

A High Precision Forecasting Model and Its Constructing Method for Vein Type Gold Deposits

A high precision forecasting and prospecting model incorporating the “field theory field structure analysis field simulation”, a temporal and spatial structural framework reflecting local extremely fine structures, is established to make an effective extraction and an integrated analysis of multivariate forecasting information. This model can best show not only the coupling between metallogenic anomalous structure, mineralized structure and information structure, but also the extraction, optimization, matching and summarization of key forecasting information. The technological keys to this model are the fine structural analysis of geological and geophysical and geochemical anomalous fields and metallogenic fields, and the establishment of occurrence patterns for the spatial location of orebodies.

FUZZY ECCENTRICITY AND GROSS ERROR IDENTIFICATION

The dominant and recessive effect made by exceptional interferer is analyzed in measurement system based on responsive character, and the gross error model of fuzzy clustering based on fuzzy relation and fuzzy equipollance relation is built. The concept and calculate formula of fuzzy eccentricity are defined to deduce the evaluation rule and function ofgruss error, on the base of them, a fuzzy clustering method of separating and discriminating the gross error is found, utilized in the dynamic circular division measurement system, the method can identify and eliminate gross error in measured data, and reduce measured data dispersity. Experimental results indicate that the use of the method and model enables repetitive precision of the system to improve 80% higher than the foregoing system, to reach 3.5 s, and angle measurement error is less than 7 s.

Finite element model validation of bridge based on structural health monitoring——Part Ⅱ:Uncertainty propagation and model validation

Because of uncertainties involved in modeling, construction, and measurement systems, the assessment of the FE model validation must be conducted based on stochastic mea- surements to provide designers with confidence for further applications. In this study, based on the updated model using response surface methodology, a practical model vali- dation methodology via uncertainty propagation is presented. Several criteria of testing/ analysis correlation are introduced, and the sources of model and testing uncertainties are also discussed. After that, Monte Carlo stochastic finite element （FE） method is employed to perform the uncertainty quantification and propagation. The proposed methodology is illustrated with the examination of the validity of a large-span prestressed concrete continuous rigid frame bridge monitored under operational conditions. It can be concluded that the calculated frequencies and vibration modes of the updated FE model of Xiabaishi Bridge are consistent with the measured ones. The relative errors of each frequency are all less than 3.7%. Meanwhile, the overlap ratio indexes of each frequency are all more than 75%; The MAC values of each calculated vibration frequency are all more than 90%. The model of Xiabaishi Bridge is valid in the whole operation space including experimental design space, and its confidence level is upper than 95%. The validated FE model of Xia- baishi Bridge can reflect the current condition of Xiabaishi Bridge, and also can be used as basis of bridge health monitoring, damage identification and safety assessment.

Numerical model and experimental demonstration of high precision ablation of pulse CO_2 laser

To reveal the physical mechanism of laser ablation and establish the prediction model for figuring the surface of fused silica, a multi-physical transient numerical model coupled with heat transfer and fluid flow was developed under pulsed CO2laser irradiation. The model employed various heat transfer and hydrodynamic boundary and thermomechanical properties for assisting the understanding of the contributions of Marangoni convention,gravitational force, vaporization recoil pressure, and capillary force in the process of laser ablation and better prediction of laser processing. Simulation results indicated that the vaporization recoil pressure dominated the formation of the final ablation profile. The ablation depth increased exponentially with pulse duration and linearly with laser energy after homogenous evaporation. The model was validated by experimental data of pulse CO2laser ablation of fused silica. To further investigate laser beam figuring, local ablation by varying the overlap rate and laser energy was conducted, achieving down to 4 nm homogenous ablation depth.

Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building

The energy consumption of buildings accounts for approximately 40%of total energy consumption.An accurate energy consumption analysis of buildings can not only promise significant energy savings but also help estimate the demand response potential more accurately,and consequently brings benefits to the upstream power grid.This paper proposes a novel physical-data fusion modeling(PFM)method for modeling smart buildings that can accurately assess energy consumption.First,a thermal process model of buildings and an electrical load model that focus on building heating,ventilation,and air conditioning(HVAC)systems are presented to analyze the thermal-electrical conversion process of energy consumption of buildings.Second,the PFM method is used to improve the accuracy of the energy consumption analysis model for buildings by modifying the parameters that are difficult to measure in the physical model(i.e.,it effectively modifies the electrical load model based on the proposed PFM method).Finally,case studies involving a real-world dataset recorded in a high-tech park in Changzhou,China,demonstrate that the proposed method exhibits superior performance with respect to the traditional physical modeling(TPM)method and data-driven modeling(DDM)method in terms of the achieved accuracy.

Text Classification Using Sentential Frequent Itemsets

Text classification techniques mostly rely on single term analysis of the document data set, while more concepts, especially the specific ones, are usually conveyed by set of terms. To achieve more accurate text classifier, more informative feature including frequent co-occurring words in the same sentence and their weights are particularly important in such scenarios. In this paper, we propose a novel approach using sentential frequent itemset, a concept comes from association rule mining, for text classification, which views a sentence rather than a document as a transaction, and uses a variable precision rough set based method to evaluate each sentential frequent itemset＇s contribution to the classification. Experiments over the Reuters and newsgroup corpus are carried out, which validate the practicability of the proposed system.