一起黄牛瘤胃积食的诊疗体会

牛瘤胃积食,是由于牛贪食大量粗纤维饲料或容易膨账的饲料,致使瘤胃内积滞过多的粗詞料,致使瘤胃容积增大,瘤胃壁扩张,导致牛瘤胃正常的消化和运动机能紊乱的一个疾病.本文笔者对一起黄牛瘤胃积食的发病情况、临床症状及治疗与体会进行了介绍,谨与同行进行共同探讨.

探讨基层兽医诊疗管理现状及解决措施

随着我国畜禽产业快速发展,为基层兽医人员自身素质提出了更加严格的要求。兽医工作对于我们社会的发展,以及畜牧业的良好转变起着十分重要的作用。

一起黄母牛子宫内膜炎的诊疗体会

母牛子宫内膜炎是母牛易患的一种常见生殖系统疾病之一,笔者于2015年8月12日诊治一例,并治愈。本文将其发病情况及治疗过程继续介绍,谨与同行共同商讨。

Treatment of upper gastrointestinal fistula and leakage with personal stage nutrition support

AIM： To investigate the feasibility of treatment for upper gastrointestinal fistula and leakage with personal stage nutrition support. METHODS： Forty-three patients with upper gastrointestinal fistula and leakage were randomly divided into two groups. Patients in group A were treated with personal stage nutrition support and patients in group B were treated with total parental nutrition （TPN） in combination with operation. Nutritional states of the candidates were evaluated by detecting albumin （AIb） and pre-AIb. The balance between nutrition and hepatic function was evaluated by measurement of aspartate aminotransferase （AST）, alanine aminotransferase （ALT） and total bilirubin （Tbill） before and after operation. At the same time their complications and hospitalized time were surveyed. RESULTS： Personal stage nutrition support improved upper gastrointestinal fistula and leakage. The nutrition state and hepatic function were better in patients who received personal stage nutrition support than in those who did not receive TPN. There was no significant difference in the complication and hospitalized time in the two groups of patients. CONCLUSION： Upper gastrointestinal fistula and leakage can be treated with personal stage nutrition support which is more beneficial for the post-operation recovery and more economic than surgical operation.

Fault diagnosis using a probability least squares support vector classification machine

Coal mines require various kinds of machinery. The fault diagnosis of this equipment has a great impact on mine production. The problem of incorrect classification of noisy data by traditional support vector machines is addressed by a proposed Probability Least Squares Support Vector Classification Machine (PLSSVCM). Samples that cannot be definitely determined as belonging to one class will be assigned to a class by the PLSSVCM based on a probability value. This gives the classification results both a qualitative explanation and a quantitative evaluation. Simulation results of a fault diagnosis show that the correct rate of the PLSSVCM is 100%. Even though samples are noisy, the PLSSVCM still can effectively realize multi-class fault diagnosis of a roller bearing. The generalization property of the PLSSVCM is better than that of a neural network and a LSSVCM.

Advances in the diagnosis and treatment of patients with cancer cachexia

Cachexia is a common complication with an incidence rate of 50%–80% in cancer patients. It is also responsible for 20% of mortality among these patients. Cachexia can significantly reduce the efficacy of antitumor therapies and increase treatment-related toxicity and adverse effects in cancer patients. This increases the symptom burden in patients, affects their quality of life, and ultimately shortens their survival time. The mechanism underlying the development of cachexia is complex and diverse and involves various factors and pathways, each playing an important role. Treatment approaches for cachexia are multimodal, including nutrition support therapy, appetite stimulants, and therapeutic drugs that specifically target the mechanism behind the disease. In recent years, we have gradually gained a better understanding of cachexia, and significant progress has been made in delineating molecular mechanisms, staging and diagnosis, and therapeutic drug treatment of cancer cachexia. This article reviews the research progress of cancer cachexia based on these contexts.

A method combining refined composite multiscale fuzzy entropy with PSO-SVM for roller bearing fault diagnosis

Combining refined composite multiscale fuzzy entropy(RCMFE)and support vector machine(SVM)with particle swarm optimization(PSO)for diagnosing roller bearing faults is proposed in this paper.Compared with refined composite multiscale sample entropy(RCMSE)and multiscale fuzzy entropy(MFE),the smoothness of RCMFE is superior to that of those models.The corresponding comparison of smoothness and analysis of validity through decomposition accuracy are considered in the numerical experiments by considering the white and 1/f noise signals.Then RCMFE,RCMSE and MFE are developed to affect extraction by using different roller bearing vibration signals.Then the extracted RCMFE,RCMSE and MFE eigenvectors are regarded as the input of the PSO-SVM to diagnose the roller bearing fault.Finally,the results show that the smoothness of RCMFE is superior to that of RCMSE and MFE.Meanwhile,the fault classification accuracy is higher than that of RCMSE and MFE.

Fault diagnosis method for switch control circuit based on SVM-AdaBoost

In order to realize the fault diagnosis of the control circuit of all-electronic computer interlocking system(ACIS)for railway signals,taking a five-wire switch electronic control module as an research object,we propose a method of selecting the sample set of the basic classifier by roulette method and realizing fault diagnosis by using SVM-AdaBoost.The experimental results show that the proportion of basic classifier samples affects classification accuracy,which reaches the highest when the proportion is 85%.When selecting the sample set of basic classifier by roulette method,the fault diagnosis accuracy is generally higher than that of the maximum weight priority method.When the optimal proportion 85%is taken,the accuracy is highest up to 96.3%.More importantly,this way can better adapt to the critical data and improve the anti-interference ability of the algorithm,and therefore it provides a basis for fault diagnosis of ACIS.

An Improved Sensor Fault Diagnosis Scheme Based on TA-LSSVM and ECOC-SVM

Monitoring the operational state of sensors promptly and the accurate diagnosis of faults are essential. This paper proposes an improved fault diagnosis scheme for sensors, which includes both fault detection and fault identification. Firstly, trend analysis combined with least squares support vector machine（TA-LSSVM） method is proposed and implemented to detect faults. Secondly, an improved error correcting output coding-support vector machine（ECOC-SVM） based fault identification method is proposed to distinguish different sensor failure modes. To demonstrate the effectiveness of the proposed scheme, experiments are conducted with an MTi-series sensor, and some comparisons are made with other fault identification methods. The experimental results demonstrate that the proposed fault diagnosis scheme offers an essential improvement with detection real-time property and better identification accuracy.

Agro-pedological Assessment of the Traditional Yuanyang Rice Terraces of Yunnan Province, China

As a contribution to a long-term evaluation of the sustainability of agro-ecosystems in the Yuanyang rice terraces, this paper reviews the bio-physical environment of the terraced landscape in a small catchment around Qingkou village and assesses soil fertility in the village crop growing area. A soil- landscape organization pattern was developed using a geomorphopedological approach. Anthropogenic, climatic and topographic factors were identified as the main soil forming factors. Soil capability and associated edaphic constraints were assessed and the main soil types were identified and classified using FAO protocols. Spatial patterns of soil fertility appear quite homogeneous. Soils are acid and cation exchange capacity and nutrient reserves are generally low. The agro-ecosystem has largely been developed by the Hani population. This agro-ecosystem has succeeded in maintaining a relatively dynamic nutrient circulation system. Current research is focusing on： （i） the evaluation of the representativeness of the studied area compared to the whole terraced system in the Yuanyang terraced landscape; （ii） measurements of nutrients flows at the field scale; （iii） the effects of soil properties on crop yields; and （iv） up-scaling field levels results to larger spatial units.