Coagulation process in Manchega sheep milk from Spain: A path analysis approach

Characteristics of sheep milk are of great interest for the dairy industry, as almost the totality of production is intended for cheesemaking. However, the existing relationships between these variables are complex. This study assessed composition, hygienic quality, coagulation properties, and curd yield of 1,200 individual Manchega sheep milk samples. The aim was to compare the effect of composition and hygienic quality on coagulation and curdling, and to evaluate the relationship between curd yields and the coagulation process and the effect of other features by using path analysis methodologies. Outcomes proved path analysis to be a useful and effective tool to assess these relationships through direct and indirect paths within the same model. Results showed that the factors that had a direct influence on milk coagulation were lactose concentration, casein content, and initial pH of milk. Contrastingly, somatic cells did not seem to have any effect (direct or indirect) on the coagulation process. Factors that directly affected curd yield were fat content, lactose concentration, casein content, and curd moisture. However, technological parameters showed little effect over curd yield.     

基于k-shell的复杂网络最短路径近似算法

复杂网络最短路径经典算法的处理效率较低，不适用于大规模复杂网络，而现有近似算法通用性有限，且计算准确率不理想，不能满足规模日益扩大的复杂网络中的最短路径计算需求。针对于此，提出基于[k]-shell的复杂网络最短路径近似算法。算法利用节点的[k]-shell值进行网络划分并引导搜索路径，利用超点聚合处理[k]-shell子网来降低路径搜索中节点和连边的规模，通过在路径搜索过程使用双向搜索树方法提高算法的计算效率和准确率。实验结果表明，算法通用性较好，在现实与仿真大规模复杂网络中均具有较高的计算效率和准确率。     

A gradient boosting decision tree approach for insider trading identification: An empirical model evaluation of China stock market

Insider trading is a kind of criminal behavior in stock market by using nonpublic information. In recent years, it has become the major illegal activity in China’s stock market. In this study, a combination approach of GBDT (Gradient Boosting Decision Tree) and DE (Differential Evolution) is proposed to identify insider trading activities by using data of relevant indicators. First, insider trading samples occurred from year 2007 to 2017 and corresponding non-insider trading samples are collected. Next, the proposed method is trained by the GBDT, and initial parameters of the GBDT are optimized by the DE. Finally, out-of-samples are classified by the trained GBDT–DE model and its performances are evaluated. The experiment results show that our proposed method performed the best for insider trading identification under time window length of ninety days, indicating the relevant indicators under 90-days time window length are relatively more useful. Additionally, under all three time window lengths, relative importance result shows that several indicators are consistently crucial for insider trading identification. Furthermore, the proposed approach significantly outperforms other benchmark methods, demonstrating that it could be applied as an intelligent system to improve identification accuracy and efficiency for insider trading regulation in China stock market.     

Bioecology of macrobenthic communities in the microtidal monsoonal Kodungallur–Azhikode Estuary,southwest coast of India

A comprehensive study of the macrobenthic communities in the Kodungallur–Azhikode Estuary (KAE) was conducted during 2009–2011 period. A total of 18,846 organisms were collected, with 60% being malacostracans, followed by polychaetes (20%), molluscs (9%) and ‘others’ group (11%). A total of 79 species in 71 genera belonging to 49 families were identified, with 33 spp. being polychaetes, 26 spp. being malacostracans, 11 spp. being molluscs and 9 spp. being in the ‘others’ group. A single species of opportunistic amphipod (Americorophium triaeonyx) comprised more than 62.05% of the total numerical abundance of macrobenthos. The other dominant species were Obelia bidentata, Arcuatula senhousia, Cirolana fluviatilis, Prionospio cirrifera and Capitella sp. Canonical correspondence analysis (CCA) results indicated environmental parameters such as water column salinity, turbidity, sediment Eh, substratum type, chlorophyll‐a concentration, depth and organic matter in sediment were the significant factors influencing the distribution of macrobenthic species in the KAE. The present study provides baseline information for future KAE benthic studies.     

2.5 GHz城市环境电磁态势的计算与补偿

复杂城市空间的电磁态势分布可以通过电磁场数值计算的方法进行预测。文章使用矩量法(MOM)结合一致性几何绕射理论(UTD)的方法对2.5 GHz频率下某小区环境中的电磁态势进行了仿真计算以及实际测量。场景中的树木结构在此频率下显示出比较强烈的波动性,对电磁仿真计算结果产生了不可忽略的影响,文中将其对仿真计算结果的影响进行了单独建模仿真,给出其产生影响的经验值,对仿真结果进行修正。修正结果与测量结果对比证明该经验值在该频率下可以有效补偿计算结果的缺失。     

Design structure matrix (DSM) method application to issue of modeling and analyzing the fault tree of a wind energy asset

The perpetual energy production of a wind farm could be accomplished (under proper weather conditions) if no failures occurred. But even the best possible design, manufacturing, and maintenance of a system cannot eliminate the failure possibility. In order to understand and minimize the system failures, the most crucial components of the wind turbines, which are prone to failures, should be identified. Moreover, it is essential to determine and classify the criticality of the system failures according to the impact of these failure events on wind turbine safety. The present study is processing the failure data from a wind farm and uses the Fault Tree Analysis as a baseline for applying the Design Structure Matrix technique to reveal the failure and risk interactions between wind turbine subsystems. Based on the analysis performed and by introducing new importance measures, the “readiness to fail” of a subsystem in conjunction with the “failure riskiness” can determine the “failure criticality.” The value of the failure criticality can define the frame within which interventions could be done. The arising interventions could be applied either to the whole system or could be focused in specified pairs of wind turbine subsystems. In conclusion, the method analyzed in the present research can be effectively applied by the wind turbine manufacturers and the wind farm operators as an operation framework, which can lead to a limited (as possible) design‐out maintenance cost, failures' minimization, and safety maximization for the whole wind turbine system.     

Correlations of sensory parameters with physicochemical characteristics of Argentinean honeys by multivariate statistical techniques

Honey acceptability is mainly determined by its colour, crystallisation degree and aroma. In the present work, the sensory characteristics and physicochemical parameters of Argentinean honeys from different ecoregions were analysed. Moisture content, Pfund colour, diastase activity, hydroxymethylfurfural content, electrical conductivity, sugar profile and volatile compounds were analytically determined in honey samples, while sensory characteristics (crystal size, fluency score, sweetness, persistence, granularity, crystallisation, colour intensity and aroma) were evaluated by a trained panel. Significant correlations were found between honey crystallisation degree and hydroxymethylfurfural content and diastase activity (P ≤ 0.05). It could be confirmed that honey crystallisation interferes with the visual perception of colour. Floral, fresh fruit, ripe fruit, balsamic and wood aromas could be successfully linked to honey volatile profile (P ≤ 0.05). These results demonstrate that the parameters that could best guarantee the consumers’ preference can be successfully associated with the chemical composition of honey by multivariate statistical analysis.     

Predicting Prostephanus truncatus (Horn) (Coleoptera: Bostrichidae) populations and associated grain damage in smallholder farmers’ maize stores: A machine learning approach

Prostephanus truncatus is a notorious pest of stored-maize grain and its spread throughout sub-Saharan Africa has led to increased levels of grain storage losses. The current study developed models to predict the level of P. truncatus infestation and associated damage of maize grain in smallholder farmer stores. Data were gathered from grain storage trials conducted in Hwedza and Mbire districts of Zimbabwe and correlated with weather data for each site. Insect counts of P. truncatus and other common stored grain insect pests had a strong correlation with time of year with highest recorded numbers from January to May. Correlation analysis showed insect-generated grain dust from boring and feeding activity to be the best indicator of P. truncatus presence in stores (r = 0.70), while a moderate correlation (r = 0.48) was found between P. truncatus numbers and storage insect parasitic wasps, and grain damage levels significantly correlated with the presence of Tribolium castaneum (r = 0.60). Models were developed for predicting P. truncatus infestation and grain damage using parameter selection algorithms and decision-tree machine learning algorithms with 10-fold cross-validation. The P. truncatus population size prediction model performance was weak (r = 0.43) due to the complicated sampling and detection of the pest and eight-week long period between sampling events. The grain damage prediction model had a stronger correlation coefficient (r = 0.93) and is a good estimator for in situ stored grain insect damage. The models were developed for use under southern African climatic conditions and can be improved with more input data to create more precise models for building decision-support tools for smallholder maize-based production systems.