Better or worse food: Nutrition value of the prey fishes and the potential health implications for Indo-Pacific humpback dolphins

IntroductionOverfishing and climate change have combined to cause fishery stocks to decline and fish community composition to change, further threatening the predation and nutritional health of marine mammals.MethodsIn this study, we collected potential prey fishes catched by fishermen in six habitats of Indo-Pacific humpback dolphins and analyzed their proximate composition (moisture, water, fat and protein), the fatty acid composition and the amino acid composition to evaluate the possible health effect on humpback dolphins.ResultsThe results showed that the nutritional composition varied significantly with species and locations. Fishes in the families Sciaenidae and Engraulidae displayed richer fatty acid composition, while those in the family Clupeidae had the highest value of amino acid quality index. In Zhuhai, home to the largest Indo-Pacific humpback dolphin population, pelagic/neritic prey fishes possessed lower energy density, PUFA content, PUFA/SFA ratio, DHA content, and EAA content compared to demersal fish, suggesting nutritional stress when there is a dietary switch from demersal to pelagic/neritic fishes in Zhuhai population.DiscussionOur study provided a framework, with energy density and fatty acid composition as its most important indicator, for assessment of the marine top predators based on the nutritional composition of their prey fishes and revealed the potential threats. Data here is expected to facilitate the development of scientific programs for successful conservation of not only the Indo-Pacific humpback dolphins, but also other marine top predators, possibly through reconstructing their prey fish’s quantity and quality

Research on fuzzy control charts for fuzzy multilevel quality characteristics

Fuzzy control charts are proposed to solve the problem that traditional control charts cannot be applied to fuzzy quality characteristics. First, fuzzy quality characteristics are converted to representative statistics, which are fuzzy mode transformation, fuzzy level midrange transformation and fuzzy level median transformation. Control charts are designed based on the Poisson distribution. Second, the effects of the different statistics are analysed. Direct Fuzzy Control Charts are designed to avoid some information omission when translating fuzzy quality characteristics into representative statistics. The area ratio that falls within the control limits is used to conclude whether the process is relative out of control or in control. The performance of the control charts is analysed by MATLAB simulation. Finally, an example of an energy metre assembly is given to prove the proposed method

Research on fuzzy control charts for fuzzy multilevel quality characteristics

Fuzzy control charts are proposed to solve the problem that traditional control charts cannot be applied to fuzzy quality characteristics. First, fuzzy quality characteristics are converted to representative statistics, which are fuzzy mode transformation, fuzzy level midrange transformation and fuzzy level median transformation. Control charts are designed based on the Poisson distribution. Second, the effects of the different statistics are analysed. Direct Fuzzy Control Charts are designed to avoid some information omission when translating fuzzy quality characteristics into representative statistics. The area ratio that falls within the control limits is used to conclude whether the process is relative out of control or in control. The performance of the control charts is analysed by MATLAB simulation. Finally, an example of an energy metre assembly is given to prove the proposed method

Assembly process optimization of electromechanical meters based on robust design

The assembly precision of the magnetic system of an electromechanical meter is an important guarantee for the measurement accuracy of the meter. The large gap between the two poles of the magnetic system is the key point that affects the assembly precision and is also the difficulty that electromechanical meter manufacturers face. In this paper, the controllable factors that affect the gap of a magnetic system are selected by performing an orthogonal experiment, the assembly process is analyzed, and the noise factors are selected; the orthogonal design achieved by using inner and outer array is used to design the experiment scheme, and the optimal combination of process parameters with a large signal-noise ratio (SNR) and good robustness is found through the experiment. Through experimental verification, the improved process capability index reaches 1.34, and the optimization effect is remarkable

Research on the Reliability Allocation Method of Smart Meters Based on DEA and DBN

Reliability allocation can reasonably determine the reliability index of each unit in the system to ensure product quality in design, manufacturing, testing and acceptance. In the design process of the smart meter, the preliminary reliability allocation results may be unreasonable, so in the middle and later stages of the design stage, the reliability needs to be reallocated. The traditional allocation method has some limitations, such as strong subjectivity, large amount of calculation and too much reliance on expert judgment. In order to solve these problems, this paper presents a multi-method fusion method of reliability allocation. First, this paper uses the goal-oriented methodology (GO methodology) to integrate dynamic Bayesian networks (DBNs) to predict the reliability of smart meters. Second, a data envelopment analysis (DEA) reliability allocation model is established, the posterior probability obtained by DBN reasoning together with the failure rate and structural complexity of each unit are used as the output indicators of this model. Finally, the reliability allocation weight is calculated by using the efficiency value obtained from the DEA reliability allocation model. The validity and accuracy of this method is verified by an accelerated life test. This provides a new idea for reliability reallocation of smart meters

Towards a Highly-Scalable and Effective Metasearch Engine

A metasearch engine is a system that supports unified access to multiple local search engines. Database selection is one of the main challenges in building a large-scale metasearch engine. The problem is to efficiently and accurately determine a small number of potentially useful local search engines to invoke for each user query. In order to enable accurate selection, metadata that reect the contents of each search engine need to be collected and used. In this paper, we propose a highly scalable and accurate database selection method. This method has several novel features. First, the metadata for representing the contents of all search engines are organized into a single integrated representative. Such a representative yields both computation efficiency and storage efficiency. Second, our selection method is based on a theory for ranking search engines optimally. Experimental results indicate that this new method is very effective. An operational prototype system has been built based on the proposed approach

Fault Diagnosis Method of Smart Meters Based on DBN-CapsNet

Rapid and accurate fault diagnosis of smart meters can greatly improve the operational and maintenance ability of power systems. Focusing on the historical fault data information of smart meters, a fault diagnosis model of smart meters based on an improved capsule network (CapsNet) is proposed. First, we count the sample size of each fault type, and a mixed sampling method combining undersampling and oversampling is used to solve the problem of distribution imbalance of sample size. The one-hot encoding method is adopted to solve the problem of the fault samples containing more discrete and disordered data. Then, the strong adaptive feature extraction capability and nonlinear mapping capability of the deep belief network (DBN) are utilized to improve the single convolution layer feature extraction part of a traditional capsule network; DBN can also address the problem of high data dimensions and sparse data due to one-hot encoding. The important features and key information of the input sample are extracted and used as the input of the primary capsule layer, and the dynamic routing algorithm is used to construct the digital capsule. Finally, the results of experiments show that the improved capsule network model can effectively improve the accuracy of diagnosis and shorten the training time