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Abstract In this study, complexes formed by sodium caseinate (CS) and different concentrations of (−)‐epigallocatechin‐3‐gallate (EGCG) were successfully prepared. The structures of the CS–EGCG complexes were then analyzed by fluorescence spectroscopy, circular dichroism, and Fourier transform infrared spectroscopy (FTIR). Their thermodynamic features were also analyzed by Van't Hoff equation before evaluating antioxidant properties as well as emulsion stability. The results showed that EGCG grafting significantly altered the CS structure, primarily due to their interactions. However, besides the covalent bonding within CS–EGCG, thermodynamic analysis further highlighted the presence of slight interactions driven by hydrophobic and entropic changes. The EGCG grafting also decreased the surface hydrophobicity of CS while increasing its solubility, thus improving the stability of the protein in water. Finally, the grafting process significantly improved the thermal and emulsifying stability of CS as well as its antioxidant properties, especially 3‐ethylbenzothiazoline‐6‐sulfonic acid and 2,2‐diphenyl‐1‐picrylhydrazyl scavenging properties and ferric ion reducing power. These findings can contribute to the study of protein and polyphenol interactions in food fields.

To reduce the consumption of oxidant and catalyst in Fenton-like reaction and to realize the reuse of catalyst, yeast supported iron nanoparticles (nZVI@SCM) was synthesized by tobacco leaf extract and applied in the heterogeneous Fenton-like degradation of aqueous methylene blue (MB) at ambient conditions. The performance of the composite was exploited in terms of catalytic activity and factors influencing MB degradation. The surface changes of nZVI@SCM before and after reaction were characterized by XPS, SEM, FT-IR and XRD. Iron leaching, primary reactive oxidizing species, and the storage stability and reusability of catalyst were also investigated. Typically, 99.7% removal of 50 mg/L MB, with a TOC removal of 97.2%, could be achieved within 10 h by 0.1 g/L nZVI@SCM coupled with 1.0 mM H2O2. The MB degradation is in good agreement with the pseudo-first-order model, and hydroxyl radicals in the bulk solution is the main reactive oxidizing species responsible for MB degradation. Based on the identified intermediates by liquid chromatography/mass spectrometry, the possible MB degradation mechanism in the nZVI@SCM/H2O2 system is discussed. The developed high-performance nZVI@SCM catalyst strategy can provide a new route in enhancing the Fenton-like degradation of organic contaminants with less consumption of catalyst and oxidant.

According to the characteristics of electromagnetic thermal energy storage, the full-bridge inverter and resonant circuit with simple structure, high voltage utilization and high output power are used in this paper. By establishing and simplifying the electromagnetic thermal conversion circuit model of composite resonance, the control characteristics of resonant circuit are analyzed. Through the maximum thermal efficiency tracking control of constant power, the constant power frequency conversion control of full-bridge inverter thermal conversion is realized based on voltage, current, temperature rise and time, so as to reduce the impact on the power grid and achieve better waveform output. In view of the phenomenon that the constant power control of high-power thermal energy storage equipment may be out of control, the load multi-signal detection closed-loop control is added. The mathematical model of electromagnetic thermal conversion coupling system is established, and the relationship between electromagnetic thermal conversion and temperature coefficient, impedance is derived. By selecting the appropriate resonant capacitor capacity, the thermal conversion efficiency of the system is effectively improved. Finally, the resonant circuit is simulated by Matlab/ Simulink and the 100 kW thermal energy storage prototype experiment is carried out. The effectiveness and practicability of the method are verified, and the better output waveform is obtained.

The dynamic characteristics of a multi-span double-curved brick arch thin-shell factory of Changleyuan in Baoji City and the dynamic response to train vibration load were studied using field dynamic tests and finite-element numerical simulations, and a vibration evaluation of the thin-shell factory was carried out. The results showed that the first-order frequency of the thin-shell factory was 6.24 Hz in the horizontal direction (east–west) and 9.31 Hz in the vertical direction. Moreover, it was established that the horizontal vibration is the overall vibration of the factory, while the vertical vibration is the individual vibration of the double-curved brick arch. In addition, the self-oscillation frequency obtained from the numerical simulation results was greater compared with the field measurements, with a maximum error rate of 7.14%. Both in acceleration and velocity, the vertical vibration for each measurement point was larger than the horizontal vibration, and the farther away from the railroad, the smaller the vibration. The vibration of the velocity at the bottom of the arch was almost the same as that at the top of the arch, while the acceleration vibration at the bottom of the arch was significantly larger than that at the top of the arch, with an average amplitude of 40.64%. For every 20 km/h increase in train running speed, the average increase in vertical acceleration amplitude, vertical velocity amplitude, horizontal acceleration amplitude, and horizontal velocity amplitude for each measurement point of the thin-shell factory was 35.4%, 29.8%, 23.7%, and 12.5%, respectively. When v = 150 km/h, the maximum velocity amplitude for each measurement point of the thin-shell factory was 1.163 mm/s, which is less than the security specification limit of 2.5 mm/s, such that the security of the thin-shell factory meets the requirement, and the maximum horizontal velocity amplitude was 0.272 mm/s, which is close to the integrity specification limit of 0.27 mm/s, such that the integrity of the thin-shell factory just exceeds the requirement; so it is suggested that train running speeds should not exceed 150 km/h and that the thin-shell factory needs to strengthen the monitoring and protection of its integrity.

In this paper, defatted Antarctic krill powder was used as raw material to prepare hydrolysate with delicate taste by compound enzyme method. By means of sensory evaluation, peptide yield and degree of hydrolysis, the screening of exogenous enzymes and single factor experiments were carried out. In order to further optimize the taste of enzymatic hydrolysate, the total amount of amino acids with delicate flavor was used as the index to optimize the enzymatic hydrolysis condition. The results showed that the optimal hydrolysis conditions were as follows: The ratio of trypsin and pepsin 1:100, the total amount of enzyme 2800 U/g, the liquid and material ratio 1:4 g·mL−1, the pH of enzymatic hydrolysis 7.0, the temperature of enzymatic hydrolysis 40 ℃, and the time of enzymatic hydrolysis 3 h. Under these conditions, the content of free amino acids with delicate taste in the hydrolysate was 331.79 mg/100 mL. Except tryptophan, the remaining eight essential amino acids accounted for 63.66% of the total amino acids. Free bitter amino acids accounted for 27.83% of the total amino acids, and free sweet amino acids accounted for 26.22% of the total amino acids. Free glutamic acid and free aspartic acid, which played a key role in the formation of umami taste, accounted for 10.26% of the total free amino acids. This study could provide theoretical basis and technical guidance for the development of Antarctic krill flavoring products.

Stochastic configuration network (SCN) is a mathematical model of incremental generation under a supervision mechanism, which has universal approximation property and advantages in data modeling. However, the efficiency of SCN is affected by some network parameters. An optimized searching algorithm for the input weights and biases is proposed in this paper. An optimization model with constraints is first established based on the convergence theory and inequality supervision mechanism of SCN; Then, a hybrid bat-particle swarm optimization algorithm (G-BAPSO) based on gradient information is proposed under the framework of PSO algorithm, which mainly uses gradient information and local adaptive adjustment mechanism characterized by pulse emission frequency to improve the searching ability. The algorithm optimizes the input weights and biases to improve the convergence rate of the network. Simulation results over some datasets demonstrate the feasibility and validity of the proposed algorithm. The training RMSE of G-BAPSO-SCN increased by 5.57×10−5 and 3.2×10−3 compared with that of SCN in the two regression experiments, and the recognition accuracy of G-BAPSO-SCN increased by 0.07% on average in the classification experiments.

Oil-in-water emulsions are widely encountered in the food and health product industries. However, the unsaturated fatty acids in emulsions are easily affected by light, oxygen, and heat, which leads to oxidation, bringing forward difficulties in controlling emulsion quality during transportation, storage, and retail. Proteins are commonly used as emulsifiers that can enhance the shelf, thermal and oxidation stability of emulsions. Polyphenols are commonly found in plants and members of the family have been reported to possess antioxidant, anticancer, and antimicrobial activities. Numerous studies have shown that binding of polyphenols to proteins can change the structure and function of the latter. In this paper, the formation of protein–polyphenol complexes (PPCs) is reviewed in relation to the latters' use as emulsifiers, using the (covalent or non-covalent) interactions between the two as a starting point. In addition, the effects polyphenol binding on the structure and function of proteins are discussed. The effects of proteins from different sources interacting with polyphenols on the emulsification, antioxidation, nutrient delivery and digestibility of oil-in-water emulsion are also summarized. In conclusion, the interaction between proteins and polyphenols in emulsions is complicated and still understudied, thereby requiring further investigation. The present review results in a critical appraisal of the relevant state-of-the-art with a focus on complexes' application potential in the food industry, including digestion and bioavailability studies.

The tendon-to-bone interface is a special structure connecting the tendon and bone and is crucial for mechanical load transfer between dissimilar tissues. After an injury, fibrous scar tissues replace the native tendon-to-bone interface, creating a weak spot that needs to endure extra loading, significantly decreasing the mechanical properties of the motor system. Macrophages play a critical role in tendon-bone healing and can be divided into various phenotypes, according to their inducing stimuli and function. During the early stages of tendon-bone healing, M1 macrophages are predominant, while during the later stages, M2 macrophages replace the M1 macrophages. The two macrophage phenotypes play a significant, yet distinct, role in tendon-bone healing. Growing evidence shows that regulating the macrophage phenotypes is able to promote tendon-bone healing. This review aims to summarize the impact of different macrophages on tendon-bone healing and the current immunomodulatory biomaterials for regulating macrophages, which are used to promote tendon-bone healing. Although macrophages are a promising target for tendon-bone healing, the challenges and limitations of macrophages in tendon-bone healing research are discussed, along with directions for further research.

Since the rolling bearing is complex during the signal acquisition process, there is a certain loss during the process of collecting the vibration signal. This has led to the weakness of the early fault characteristics of the rolling bearing, affecting the accuracy of the rolling bearing fault feature extraction. In response to the above problems, an early fault detection method based on the Improved Deep Principal Component Analysis (ID-PCA) is proposed. The proposed method uses the time-series characteristic information of the vibration signal to establish a model, which solves the problem that the principal component analysis method cannot detect the vibration signal directly. Through the deep decomposition theorem, a multi-layer data processing model is established to fully mine the weak fault features in the vibration signal. It can solve the problem of inaccurate early fault detection results due to weak fault feature information. The reliability of this method is proved theoretically through sensitivity analysis. Finally, through experimental simulation, the accuracy and feasibility of this method are proved from the perspective of practice.

In this paper, we propose a decentralised production planning system which is designed to release all production planning through a distributed manufacturing network. This system emphasises the requirements of make-to-order and is able to produce satisfactory delivery dates, super quality and competitively priced products. The system architecture is designed and implemented using distributed object-oriented and powerful component technology, satisfying all the function requirements of information systems in an environment of distributed manufacturing. The application result shows its effectiveness in reducing customer order tardiness and improving the devices utilisation rate of the key processes.