A novel peptide with a specific calcium-binding capacity was isolated from whey protein hydrolysates. The isolation procedures included diethylaminoethyl (DEAE) anion-exchange chromatography, Sephadex G-25 gel filtration, and reversed-phase high-performance liquid chromatography (HPLC). A peptide with a molecular mass of 237.99 Da was identified by liquid chromatography-electrospray ionization/mass spectrometry (LC-ESI/MS), and its amino acid sequence was confirmed to be Gly-Tyr. The calcium-binding capacity of Gly-Tyr reached 75.38 μg/mg, increasing by 122% when compared to the hydrolysate complex. The chelating interaction mode between the Gly-Tyr and calcium ion was investigated, indicating that the major binding sites included the oxygen atom of the carbonyl group and nitrogen of the amino or imino group. The folding and structural modification of the peptide arose along with the addition of the calcium ion. The profile of (1)H nuclear magnetic resonance (NMR) spectroscopy demonstrated that the electron cloud density around the hydrogen nucleus in the peptide changed was caused by the calcium ion. The results of ζ potential showed that the Gly-Tyr-Ca chelate was a neutral molecule in which the calcium ion was surrounded by the specific binding sites of the peptide. Moreover, thermogravimetry-differential scanning calorimetry (TG-DSC) and calcium-releasing assay revealed that the Gly-Tyr-Ca chelate exerted excellent thermal stability and solubility in both acidic and basic conditions, which were beneficial to calcium absorption in the gastrointestinal tract of the human body and, therefore, improved its bioavailability. These findings further the progress in the research of whey protein, suggesting the potential in making peptide-calcium chelate as a dietary supplement.

Human and organizational factors (HOFs) play an important role in electric misoperation accidents (EMAs), but research into the reliability of human factors is still in its infancy in the field of EMAs, and further investment in research is urgently required. To analyze the HOFs in EMAs, a hybrid method including the Human Factors Analysis and Classification System (HFACS) and fuzzy fault tree analysis (FFTA) was applied to EMAs for the first time in the paper. HFACS is used to identify and classify the HOFs with 135 accidents, reorganized as basic events (BEs), intermediate events (IEs), and top event (TE), and develop the architecture of fault tree (FT). Fuzzy aggregation is employed to address experts’ expressions and obtain the failure probabilities of the BEs and the minimal cut sets (MCSs) of the FT. The approach generates BEs failure probabilities without reliance on quantitative historical failure statistics of EMAs via qualitative records processing. The FFTA–HFACS model is applied for quantitative analysis of the probability of failure of electrical mishaps and the interaction between accident risk factors. It can assist professionals in deciding whether and where to take preventive or corrective actions and assist in knowledgeable decision-making around the electric operation and maintenance process. Finally, applying this hybrid method to EMAs, the results show that the probability of an EMAs is 1.0410 × 10−2, which is a risk level that is likely to occur and must be controlled. Two of the most important risk factors are habitual violations and supervisory violation; a combination of risk factors of inadequate work preparation and paralysis, and irresponsibility on the part of employees are also frequent errors.

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The proportion of electric maloperation accidents (EMAs) in substations caused by human and organizational factors (HOFs) has gradually increased. Although there has been some research into the factors affecting EMAs in substations, the available results are insufficient to support the interpretation of HOFs in EMAs. This article explores the relationships between the HOFs and EMAs using Human Factors Analysis and Classification System‐gradient boosting with categorical features support (HFACS–CatBoost) and Shapley Additive exPlanation (SHAP) methods. First, the HFACS framework was introduced to identify 135 EMAs in the Southern Power Grid risk causation. CatBoost was used to construct an accident classification model to analyze the important relationship between accidents and HOFs and to compare and analyze with the extreme gradient boosting (XGBoost) and the binary logistic regression (BLR) to verify the superiority of CatBoost. Finally, to solve the problem of inadequate interpretation of the CatBoost black‐box model, the SHAP value plot was applied to express the contribution degree relationship between accidents and HOFs. The results show that the above method can explore and explain the importance and contribution of HOFs in EMAs. And from this, it is concluded that poor psychological state, poor communication and coordination, inadequate supervision, and inadequate training and education are highly correlated with the occurrence of EMAs. The findings will help substation operations and maintenance staff to develop safety measures to address the confusion of HOFs in substations and prevent the occurrence of EMAs.