Short communication: Identification of variation in the ovine prolactin gene of Chios sheep with a cost-effective sequence-based typing assay

The present study identified single nucleotide polymorphisms (SNP) in the coding and untranslated regions of the ovine prolactin gene of Chios sheep. By developing a cost-effective direct sequence-based typing assay, around 600 bp of reliable sequencing data and clear identification of heterozygous positions was achieved. Five SNP were found, located in exons 2 (KC764410:g.567G>A, g.625C>T, g.683C>A) and 3 (KC764410:g.2015C>A, g.2101G>A), whereas the remaining exons were monomorphic. The identified SNP were synonymous, with the exception of the g.567G>A SNP, which results in an Arg to His amino acid change. As the sequencing cost of the sequence-based typing assay was 20 orders of magnitude lower compared with a standard Sanger method, the assay was also used as a genotyping tool. The identified polymorphism was genotyped for 247 ewes and was subsequently used in mixed model association analyses of milk yield, milk fat content, and litter size at birth. The association analysis revealed a significant dominance effect of 0.17 ± 0.07 of the g.2015C>A SNP on milk fat percentage, whereas a dominance effect of ?21.33 ± 10.51 of the same SNP on total lactation milk yield was also estimated. The g.2015C>A SNP explained 2.47 and 3.68% of the total phenotypic variance of milk yield and milk fat percentage, respectively, whereas the corresponding values for the animal variance were 7.14 and 11.75%. A suggestive association of the nonsynonymous g.567G>A SNP with litter size at birth was also detected.     

Extended Process to Product Modeling (xPPM) for integrated and seamless IDM and MVD development

This paper proposes a new extended Process to Product Modeling (xPPM) method for integrated and seamless information delivery manual (IDM) and model view definition (MVD) development. Current IDM development typically uses Business Process Modeling Notation (BPMN) to represent a process map (PM). Exchange requirements (ERs) and functional parts (FPs) specify the information required when information is exchanged between different activities. A set of information requirements, specifically defined as a subset of Industry Foundation Classes (IFC), is called an MVD. Currently however, PMs, ERs, FPs, and MVDs are developed as separate documents through independent development steps. Moreover, even though ERs and FPs are designed to be reused, tracking and reusing the ERs and FPs developed by others is practically impossible. The xPPM method is proposed to provide a tight connection between PMs, ERs, FPs, and MVDs and to improve the reusability of predefined ERs and FPs. The theoretical framework is based on the approach of the Georgia Tech Process to Product Modeling (GTPPM) to suit the IDM development process. An xPPM tool is developed, and the validity of xPPM is analyzed through the reproduction of existing IDMs and MVDs. The benefits and limitations of xPPM and lessons from the applicability tests are discussed.     

Development of a Method of Selecting Important Alarms for Nuclear Power Plants

Presenting important alarms selected from a large number of activated alarms provides useful operational support under a transient status in a nuclear power plant. We have developed an alarm processing method which selects and presents important alarms depending on plant status.

In this method, important alarms are selected, first, based on physical relationships between alarms and component status including alarms themselves and second, even more important alarms are selected from the previously selected alarms according to the identified initial event causing the transient. Identification of the initial event is implemented by a neural network. The identified initial event and selected important alarms are presented to show the cause and influence of the transient.

A prototype based on the above alarm processing method was validated during the start-up test at Kashiwazaki Kariwa Nuclear Power Plant Unit Number 4 of Tokyo Electric Power Co. The initial events, which were load rejection, turbine trip and main steam isolation valve closure, were correctly identified and about 30% of all activated alarms were selected as important. It was verified by an operating expert that the presentation of the identified initial event and the selected important alarms were effective to understand rapidly and correctly the transient status of the plant.     

ATR-FTIR结合判别分析法快速鉴别天然柠檬风味精油种类

以5种山苍子精油、2种柠檬草精油为研究对象,采用ATR-FTIR结合判别分析法建立了几类天然柠檬风味精油的识别模型。模型清晰地将样品分成了3大类:低浓度柠檬醛精油、高浓度柠檬醛精油、烯烃类(柠檬烯及长叶烯)精油。利用模型可以简便、快速地鉴别不同类型的天然柠檬风味精油,为天然柠檬风味精油的快速鉴别提供了一种新的方法。     

Determinants of the intention to use Buy-Online,Pickup In-Store (BOPS): The moderating effects of situational factors and product type

Despite the wider introduction of the buy online and pick up in-store (BOPS) service by retailers, research on BOPS is still sparse, especially those from the consumer perspective. This paper employs the scenario-based factorial survey method to investigate how the perceived characteristics of innovation and the perceived risk of online shopping influence the consumers’ intention to use BOPS while also considering the moderating effects of situational factors (location convenience) and product type (involvement). Our findings indicated that the consumer perceptions of relative advantage, complexity, compatibility, and risks involved in online shopping are important antecedents to intention to use BOPS, and that these relationships were significantly moderated by locational convenience and product involvement. The implications of the findings and suggestions for future research are discussed in detail.     

An unsupervised artificial neural network versus a rule-based approach for fault detection and identification in an automated assembly machine

Artificial neural networks (ANNs) are suitable for fault detection and identification (FDI) applications because of their pattern recognition abilities. In this study, an unsupervised ANN based on Adaptive Resonance Theory (ART) is tested for FDI on an automated O-ring assembly machine testbed, and its performance and practicality are compared to a conventional rule-based method. Three greyscale sensors and two redundant limit switches are used as cost-effective sensors to monitor the machine’s assembly process. Sensor data are collected while the machine is operated under normal condition, as well as 10 fault conditions. Features are selected from the raw sensor data, and data sets are created for training and testing the ANN. The performance of the ANN for detecting and identifying known, unknown and multiple faults is evaluated; the performance is compared to a conventional rule-based method using the same data sets. Results show that the ART ANN is able to achieve excellent fault detection performance with minimal modeling requirements; however, the performance depends on careful tuning of its vigilance parameter. Although the rule-based system requires more effort to set up, it is judged to be more useful when unknown or multiple faults are present. The ART network creates new outputs for unknown and multiple fault conditions, but it does not give any more information as to what the new fault is. By contrast, the rule-based method is able to generate symptoms that clearly identify the unknown and multiple fault conditions. Thus, the rule-based method is judged to be the most feasible method for FDI applications.     

混凝土坝基防渗帷幕体老化的基本模式及识别指标

采用综合分析方法,对混凝土坝基防渗帷幕体的老化模式以及识别指标进行了系统的探讨。基于相对防渗效率,把帷幕体老化分为相对稳定型、缓慢变化型以及阶段性变化型3种基本模式;在分析时段内,不同的模式具有不同的防渗效率。用于识别帷幕体老化的指标有3类:第一类为渗流的微观动态要素,包括析钙量、TDS值、pH值、无机质和有机质等,可用于揭示帷幕体的老化机制;第二类为渗流的宏观动态要素,包括扬压力、排水量等,可用于判断帷幕体的老化程度及其基本模式;第三类为数值分析指标,包括饱和指数和渗漏水源比例系数等,可用于量化分析帷幕体的老化过程。3类指标的识别有助于构建混凝土坝基防渗帷幕体老化评估体系。     

Review of lithium-ion battery state of charge estimation

The technology deployed for lithium-ion battery state of charge (SOC) estimation is an important part of the design of electric vehicle battery management systems. Accurate SOC estimation can forestall excessive charging and discharging of lithium-ion batteries, thereby improving discharge efficiency and extending cycle life. In this study, the key lithium-ion battery SOC estimation technologies are summarized. First, the research status of lithium-ion battery modeling is introduced. Second, the main technologies and difficulties in model parameter identification for lithium-ion batteries are discussed. Third, the development status and advantages and disadvantages of SOC estimation methods are summarized. Finally, the current research problems and prospects for development trends are summarized.     

Isolation and identification of two major acylated anthocyanins from purple sweet potato (Ipomoea batatas L. cultivar Eshu No. 8) by UPLC‐QTOF‐MS/MS and NMR

In recent years, researches on the isolation and preparation of monomeric anthocyanins have intensified because of the requirements of quantitative and structure–bioactivity relationship analyses. However, simple and effective methods about the scale of monomeric anthocyanins from the natural purple sweet potato powder are rarely reported. In this study, high molecular weight acylated monomeric anthocyanins were isolated from purple sweet potato (Ipomoea batatas L. cultivar Eshu No. 8) via the combination of column chromatography and semi‐preparative HPLC technology and identified mainly by ultra‐high‐performance liquid chromatography quadrupole time‐of‐flight tandem mass spectrometry/mass spectrometry (UPLC‐QTOF‐MS/MS) and ¹H and ¹³C nuclear magnetic resonance (NMR). Two major acylated anthocyanins were unambiguously determined as peonidin 3‐O‐(6‐O‐(E)‐caffeoyl‐(2‐O‐(6‐O‐p‐hydroxybenzoyl)‐β‐D‐glucopyranosyl)‐β‐D‐glucopyranoside)‐5‐O‐(β‐D‐glucopyranoside) and peonidin 3‐O‐(6‐O‐(E)‐caffeoyl‐(2‐O‐(6‐O‐(E)‐feruloyl)‐β‐D‐ glucopyranosyl)‐β‐D‐glucopyranoside)‐5‐O‐(β‐D‐glucopyranoside). The results of this study may help promote the purification of high molecular weight acylated anthocyanins from purple sweet potato as well as from other plant materials in nature.     

Automatic monitoring system for individual dairy cows based on a deep learning framework that provides identification via body parts and estimation of body condition score

Body condition score (BCS) is a common tool for indirectly estimating the mobilization of energy reserves in the fat and muscle of cattle that meets the requirements of animal welfare and precision livestock farming for the effective monitoring of individual animals. However, previous studies on automatic BCS systems have used manual scoring for data collection, and traditional image extraction methods have limited model performance accuracy. In addition, the radio frequency identification device system commonly used in ranching has the disadvantages of misreadings and damage to bovine bodies. Therefore, the aim of this research was to develop and validate an automatic system for identifying individuals and assessing BCS using a deep learning framework. This work developed a linear regression model of BCS using ultrasound backfat thickness to determine BCS for training sets and tested a system based on convolutional neural networks with 3 channels, including depth, gray, and phase congruency, to analyze the back images of 686 cows. After we performed an analysis of image model performance, online verification was used to evaluate the accuracy and precision of the system. The results showed that the selected linear regression model had a high coefficient of determination value (0.976), and the correlation coefficient between manual BCS and ultrasonic BCS was 0.94. Although the overall accuracy of the BCS estimations was high (0.45, 0.77, and 0.98 within 0, 0.25, and 0.5 unit, respectively), the validation for actual BCS ranging from 3.25 to 3.5 was weak (the F1 scores were only 0.6 and 0.57, respectively, within the 0.25-unit range). Overall, individual identification and BCS assessment performed well in the online measurement, with accuracies of 0.937 and 0.409, respectively. A system for individual identification and BCS assessment was developed, and a convolutional neural network using depth, gray, and phase congruency channels to interpret image features exhibited advantages for monitoring thin cows.