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The comprehensive process of laser peening (LP) for forming and fatigue strengthening of 6005A-T6 integral stiffened structure (ISS) was investigated. The formation characteristics of ISS under the accumulation of multiple pulse lasers were emphasized. The stress distribution, microstructure response, and phase evolution of the LPed region and deformation region were analyzed. The fatigue crack propagation mode and the mechanism for extending fatigue life were revealed. The results demonstrate that a scanning method perpendicular to the target forming direction achieves better forming effects. For specimens scanned in unfold direction scanning, chord direction scanning, and unfold-chord directions, compressive stresses at the bottom of stiffeners are −87 MPa, −134 MPa, and −169 MPa, respectively. The overall bending elongation forming combined with local impact plastic deformation induced by LP refines surface grains, while slightly elongating those in the deformation region through compression. LP enhances the solid solubility of alloy elements, while residual compressive stress (RCS) does not significantly influence the dissolution of the second phase. The fatigue extension percentages for three specimens are 33.9%, 17.3%, and 43.5%, respectively. The increase in grain boundary density of refined grains impedes crack propagation, while RCS that brings materials on both sides of the crack tip closer together induces deviation from the original crack path. Under RCS, micropores and microcracks are closed to prevent further propagation. Fatigue striations propagate simultaneously parallel and perpendicular to the surface, and the multi-level dimple structure consisting of “large dimples containing small dimples” suppresses crack propagation.

To optimize the extraction process of blackberry polysaccharide and investigate its hypoglycemic activity in vitro, this study used enzymatic extraction to extract blackberry polysaccharide. The effects of pectinase addition, material-liquid ratio, extraction temperature, and extraction time on the yield of blackberry polysaccharides were analyzed. Based on the results of the one-way experiments, the extraction of the blackberry polysaccharide enzyme was optimized using response surface design. The structure of blackberry polysaccharide was characterized by Fourier transform infrared spectroscopy and monosaccharide composition analysis, and its inhibitory capacity on α-glucosidase and α-amylase in vitro were also studied. The results showed that, the optimal conditions for extracting blackberry polysaccharide were 0.42% pectinase, material-liquid ratio of 1:6 g/mL, extraction temperature of 55 ℃, and extraction time of 84 min. Under this condition, the yield of blackberry polysaccharide was 10.68%±0.03%. The extracted blackberry polysaccharide was an acidic polysaccharide with a β-pyranose configuration and exhibited good inhibitory activity against α-glucosidase and α-amylase. The semi-inhibitory concentrations were 0.061 and 0.36 mg/mL, respectively. The enzymatic extraction process optimized in this study can effectively extract blackberry polysaccharides. The extracted blackberry polysaccharides exhibit good in vitro hypoglycemic activity, providing a basis for the potential application of blackberry polysaccharides in the development of hypoglycemic functional foods.

Abstract Soybean protein is of plant origin and is commonly appropriate for improving the processing characteristics of foods. This study aimed to explore a novel functional ingredient that contained soybean protein isolate (SPI) and blueberry anthocyanins (BANs). The spatial conformation and secondary structure of SPI-BANs complexes were analyzed using circular dichroism and fluorescence spectroscopy, the processing properties were investigated as well as the retention of antioxidant activity during thermal treatments. Results showed that the contents of free sulfhydryl and free amino groups in complexes increased to 3.50 and 1.19 folds than those of SPI, respectively, while the surface hydrophobicity decreased by 74.23%. Compared with SPI, the BANs-modified SPI had a smaller particle size of 29.12 nm and a lower zeta-potential of -8.73 mV and on the other hand, the complexes possessed higher solubility (83.08%) and foaming and emulsifying properties (115.08% and 54.03 m2/g). After fortification with SPI-BANs, the baking loss rate and adhesiveness of chiffon cake were reduced by 10.82% and improved to 0.24 N.mm, respectively. The high antioxidant activities of SPI-BANs under heat led to the cake’s bioactivities largely enhanced by 1.99 ~ 12.71 folds, being 345.19 µg Trolox/g for the DPPH radical scavenging activity. This study developed the functional food ingredients as antioxidants and a substitute for animal-based proteins in bakery products, which was safe and sustainable by using the dietary components from plant resources. Graphical Abstract

This study aimed to optimize the formula of plant-based almond yogurt to improve its quality and storage stability. Using sweet almonds as the main ingredient and adding conventional yogurt stabilizers such as emulsifiers and thickeners, plant-based almond yogurt was produced through lactic acid fermentation. The optimal stabilizer formula for yogurt was determined using principal component synthesis evaluation analysis and response surface optimization methodology, and the physical properties were characterized. The results showed that the optimal formula for plant-based almond yogurt was 0.4% tartaric acid mono-diglyceride, 2.0% hydroxypropyl distarch phosphate, and 0.1% glutamine transaminase, with a comprehensive score of 0.990. The predicted and verified comprehensive scores of plant-based yogurt were close. The quality and acceptability of the plant-based yogurt significantly improved after adjustment, and the micro-particles of the mixed plant-based almond yogurt (MPBAY) were fuller, the distribution of milk fat was more uniform, and the texture was richer. This study provides a certain research idea and scientific theoretical value reference for the development of plant-based yogurt and the study of plant protein gel aggregation systems.

Laser powder bed fusion (LPBF) Al–Li alloy technology demonstrates adaptability to the development requirements of structural complexity and functional integration, making it a promising area of research in additive manufacturing for the aerospace, defense industry, and other fields. However, challenges such as microstructure anisotropy and metallurgical defects inevitably arise during the LPBF process of lightweight alloys, significantly impeding further enhancements in their formability properties. Some improvement measures offer a viable solution to enhance the microstructure, mitigate metallurgical defects, and optimize residual stress distribution in additive structures, thereby enabling high–performance Al–Li alloy production by LPBF. This paper first introduces the fundamental principle of LPBF and discusses the process of parameter optimization for LPBF Al–Li alloys. It then describes the typical microstructures and common metallurgical defects of LPBF Al–Li alloys, along with examining the evolution process of microstructures and formation mechanism of metallurgical defects. Specifically, it focuses on discussing the influence of Li element on solidification structure. On this basis, it presents the mechanical properties and anisotropy of mechanical properties for LPBF Al–Li alloys. Afterward, a comprehensive review is provided of the current research status regarding LPBF improvement measures, including powder alloying, heat treatment, and other technologies. Emphasis is placed on improvements in microstructure anisotropy and reductions in metallurgical defects through these improvement measures. Finally, this article summarizes the research progress made in LPBF Al–Li alloys and its improvement measures while also prospecting future research work.

To explore the preparation, enrichment, and structural analysis of bitter almond antimicrobial peptides, in this experiment, the protein was extracted from the defatted bitter almonds, and the protein was hydrolyzed by enzymes to prepare bitter almond protein antibacterial peptides. The preparation process was optimized, and the resulting peptides' antibacterial activity was detected. The peptides were further purified and enriched. The components with the strongest antibacterial activity were isolated, and their structure was analyzed. The results showed that the papain hydrolysate had the strongest bacteriostatic effect on Gram-positive bacteria, especially Staphylococcus aureus. The optimal conditions for the preparation of bitter almond antibacterial peptides were as follows: The enzymatic hydrolysis temperature was 74℃, the enzyme-to-substrate ratio was 2.5%, and the pH was 7. The protein concentration of the antibacterial peptides was 19.21%, and the minimum inhibitory concentration was 3.13 mg/mL. The antibacterial peptides were isolated and purified by ultrafiltration and gel filtration chromatography (Sephadex G-25). Peptide component A-II-b was screened out to have the strongest inhibitory activity against Staphylococcus aureus. The gel filtration conditions were optimized with a sample concentration of 9.38 mg/mL, an elution rate of 0.68 mL/min, and pure water as the eluent. Under these conditions, the peak area ratio of peptide component A-II-b was 13.15%. The structure of peptide component A-II-b was analyzed by liquid chromatography-mass spectrometry, and the results were imported into the UniProt database and the APD3 database for matching. Seven kinds of bitter almond peptides with potential antibacterial activity with molecular weights of 1500~2500 Da were screened out, including ALPDEVLQNAFRIS, ESWNPRDPQFQWAGVA, VAYWSYNNGEQPLVA, FLDLSNDQNQLDQVPR, GENDNRNQIIRVR, RNLQGENDNRNQIIRVR, and RALPDEVLQNAFRIS. This study promoted the in-depth development and resource reuse of almond active peptides and also provided a theoretical basis for their research and further development and utilization as an antibacterial peptide.

Abstract Background Periodontitis is a chronic inflammatory condition triggered by immune system malfunction. Mitochondrial extracellular vesicles (MitoEVs) are a group of highly heterogeneous extracellular vesicles (EVs) enriched in mitochondrial fractions. The objective of this research was to examine the correlation between MitoEVs and the immune microenvironment of periodontitis. Methods Data from MitoCarta 3.0, GeneCards, and GEO databases were utilized to identify differentially expressed MitoEV-related genes (MERGs) and conduct functional enrichment and pathway analyses. The random forest and LASSO algorithms were employed to identify hub MERGs. Infiltration levels of immune cells in periodontitis and healthy groups were estimated using the CIBERSORT algorithm, and phenotypic subgroups of periodontitis based on hub MERG expression levels were explored using a consensus clustering method. Results A total of 44 differentially expressed MERGs were identified. The random forest and LASSO algorithms identified 9 hub MERGs (BCL2L11, GLDC, CYP24A1, COQ2, MTPAP, NIPSNAP3A, FAM162A, MYO19, and NDUFS1). ROC curve analysis showed that the hub gene and logistic regression model presented excellent diagnostic and discriminating abilities. Immune infiltration and consensus clustering analysis indicated that hub MERGs were highly correlated with various types of immune cells, and there were significant differences in immune cells and hub MERGs among different periodontitis subtypes. Conclusion The periodontitis classification model based on MERGs shows excellent performance and can offer novel perspectives into the pathogenesis of periodontitis. The high correlation between MERGs and various immune cells and the significant differences between immune cells and MERGs in different periodontitis subtypes can clarify the regulatory roles of MitoEVs in the immune microenvironment of periodontitis. Future research should focus on elucidating the functional mechanisms of hub MERGs and exploring potential therapeutic interventions based on these findings.

Surface polishing is a critical factor in optimizing the practical utility of laser additive manufacturing (LAM) parts. This study explores the use of nanosecond pulsed lasers for polishing the surface of laser-directed energy deposition (LDED) TA15 titanium alloy. Various polished surfaces are achieved by adjusting laser processing parameters. Analytical techniques, including scanning electron microscopy (SEM), X-ray diffraction (XRD), laser confocal microscopy, and ultra-depth of field microscopy, are employed to assess phase composition, oxidation levels, and surface roughness under different laser polishing conditions. The study also investigates changes in surface properties and the underlying mechanisms that occur during laser polishing. Microhardness tests and room temperature tensile tests are conducted to evaluate the compressive strength and tensile strength of the TA15 alloy's surface after laser polishing. The results demonstrate that different laser polishing methods yield varying surface quality and mechanical properties. Notably, ultrasonic assisted underwater laser polishing (UWP) exhibits significantly lower surface roughness Ra (3.035 μm) and Sa (3.172 μm) compared to air laser polishing (AP) and underwater laser polishing (WP), indicating improved surface quality and enhanced surface strength and plasticity.

To improve the content of extracellular polysaccharide (EPS) in fermented blueberry juice, three strains of lactic acid bacteria with high EPS production were selected in this study, and the fermentation conditions of blueberry juice were optimized by single-factor method and response surface methodology (RSM), and the four most influential factors were screened out, namely, initial pH, inoculum amount, fermentation temperature and fermentation time. Based on this, the optimal fermentation process conditions were obtained by artificial neural network (ANN) and genetic algorithm (GA). The optimized process conditions were 2:1:1 ratio of Lactobacillus plantarum 9sh, Lactobacillus fermentum SR2-6 and Citrobacter cepacia GM11, lactose was 6%, soybean peptide was 0.6%, the initial pH of blueberry juice was 4.5, the inoculum amount was 8%, fermentation temperature was 30 ℃, and fermentation time was 60 h. Under these conditions, the EPS content was 3.537 g/L. This study shows that RSM and ANN can be used to optimize the EPS production process of blueberry juice by lactic acid bacteria fermentation.

Abstract Background Pheochromocytoma (PCC) crisis is a rare life-threatening endocrine emergency. The diagnosis and treatment of PCC crisis, with acute respiratory distress syndrome (ARDS) as the first manifestation, is highly challenging, and traditional PCC management strategies are no longer suitable for these patients. Case presentation A 46-year-old female patient was admitted to the Intensive Care Unit (ICU) following sudden-onset acute respiratory distress and subsequent initiation of mechanical ventilation via endotracheal intubation. She was initially suspected of having a PCC crisis through the bedside critical care ultrasonic examination protocol. The computed tomography examination revealed a left adrenal neoplasm of 6.5cm × 5.9cm. The plasma-free metanephrine level was 100 times higher than the reference value. These findings were compatible with her PCC diagnosis. Alpha-blockers and fluid intake were started immediately. The endotracheal intubation was removed on the 11th day after admission to the ICU. The patient progressed to severe ARDS again, and invasive ventilation and continuous renal replacement therapy were needed. Despite aggressive therapy, her condition deteriorated. Therefore, she underwent veno-arterial extracorporeal membrane oxygenation (VA-ECMO)-assisted emergency adrenalectomy after multidisciplinary discussion. Postoperatively, the patient was supported by VA-ECMO for 7days. She was discharged from the hospital on day 30 after tumor resection. Conclusions This case highlighted the challenges in diagnosing and managing ARDS associated with PCC crisis. The traditional preoperative preparation protocol and optimal operation timing for patients with PCC are not suitable for patients with PCC crisis. Patients with life-threatening PCC crisis may benefit from early tumor removal, and VA-ECMO could maintain hemodynamic stability during and after surgery.

Abstract Metastable ε-Fe2O3 was discovered on a ceramic ware of Deqing kiln of the Tang Dynasty, which coexists with α-Fe2O3 in the brown area between adjacent black glaze areas, and it is the earliest known artificially synthesized ε-Fe2O3. Based on the composition analysis of α-Fe2O3, ε-Fe2O3 and the glass phase around them, it is speculated that some Fe3+ ions were replaced by Al3+ ions during the formation of ε-Fe2O3, and a relatively low CaO content may be required for its crystallization. This work may promote researchers' understanding of ε-Fe2O3 crystals, and provide clues for the artificial synthesis of large ε-Fe2O3 crystals.

TA15 titanium alloy is sensitive to temperature, and it is one of the main materials used in aircraft bearing parts that operate for long periods of time at 500 °C. The thermomechanical processing of this alloy is difficult to control because of its high activity and oxidation. In the present research, nanosecond pulse lasers of various powers were used to polish the TA15 surface. The surface morphology, microstructure evolution, oxidation depth and mechanical properties were investigated. The results revealed that the initial sample has a rough surface, and both high-power and low-power lasers can significantly decrease the surface roughness. Three methods, SEM morphology analysis, EDS line scanning and EDS mapping, are used to detect the oxidation depth of the polished zone. The TEM images and XRD patterns show typical Al2O3, Ti2O3 and TiO2 constituents, which affect the tensile strength of the alloy. A low-power laser has a better polishing effect than a high-power laser; the oxidation depth with a high power is approximately 100 μm, while the oxidation depth with a low power is less than 10 μm. The polished surface roughness parameters Ra (arithmetical mean deviation of the profile) and Sa (arithmetical mean height of the scale limited surface) obtained with a low-power laser are reduced from approximately 6 μm–3.197 μm and 4.298 μm, respectively.

In this study, to select special pepper varieties for processing, 12 dry pepper varieties mainly grown in Xinjiang were used as materials to compare the differences of quality indexes among different varieties. Next, the comprehensive quality evaluation and processing suitability analysis of the 12 pepper resources were performed through principal component analysis and cluster analysis. The results showed that differences were present in various indexes among the different pepper varieties. Yuhong 113 had the largest single fruit weight (6.166 g). Changxian 1 had the largest fruit shape index (14.322) and the highest soluble reducing sugar content (354.311 mg/g). And large leaf pod pepper had the highest edible rate (96.973%). By comparing the coefficient of variation of 16 indicators, including color difference, crude fat and capsaicin, it was observed that the coefficient of variation of capsaicin content was the greatest, as high as 76.104%. The difference in the average membership degree of the 12 pepper varieties was 0.203, thus indicating that there were significant differences among the varieties. Four principal components were extracted by principal component analysis, and the cumulative contribution rate reached 88.369%. The results showed that the varieties possessing superior quality were Hongsu 3, Changxian 1, Hong'an 6, Juyuan 5 and Honglong 23. The core indexes of pepper quality were selected as single fruit weight, capsanthin, fat, vitamin C content and a*, according to the R-type clustering. The 12 varieties of pepper were grouped into four categories by Q-cluster. Through comprehensive analysis, it was found that the line pepper was suitable for use as the raw material of fermented pepper sauce, pepper shreds and oil pepper. Pod pepper was suitable for extracting capsaicin, and could also be used as the raw material for making powder, pepper oil and other seasonings. And horn pepper was suitable for extracting capsanthin. Hongsu 3 was the best pepper variety with the best quality and was the most suitable one for drying among the 12 pepper varieties.

High-precision load forecasting is crucial for the power system planning and electricity market transactions. Recently, deep learning models have been widely used due to their powerful data mining capabilities. However, the existing research mainly focus on model structure adjustment and input feature selection, ignoring the influence of model ensemble on prediction. A single deep learning model is not yet able to address the various complex challenges that arise in short-term load forecasting. To improve the accuracy of short-term load forecasting, this paper proposes a novel multi-scale ensemble method and multi-scale ensemble neural network. This neural network uses long short-term memory, gate recurrent units, and temporal convolutional network as the basic model. By coupling the stochastic weight averaging ensemble method and differential evolution ensemble method, these deep learning networks were assembled from single-model scale and multi-model scale, respectively, thereby effectively improving the model prediction accuracy. For predicting the power load of Hubei Province in China, meteorological features and time features were in consideration. The proposed model was trained and compared with eleven intelligent short-term load forecasting models, including machine learning, deep learning and ensemble deep learning models. Simulations show that the proposed model has the best comprehensive prediction performance. This study highlights the power of ensemble deep learning models coupled with multiple ensemble techniques and the promising prospect of our proposed model in short-term load forecasting.

Exposure to particulate matter (PM) from agricultural environments has been extensively reported to cause respiratory health concerns in both animals and agricultural workers. Furthermore, PM from agricultural environments, containing fungal spores, has emerged as a significant threat to public health and the environment. Despite its potential toxicity, the impact of fungal spores present in PM from agricultural environments on the lung microbiome and metabolic profile is not well understood. To address this gap in knowledge, we developed a mice model of immunodeficiency using cyclophosphamide and subsequently exposed the mice to fungal spores via the trachea. By utilizing metabolomics techniques and 16 S rRNA sequencing, we conducted a comprehensive investigation into the alterations in the lung microbiome and metabolic profile of mice exposed to fungal spores. Our study uncovered significant modifications in both the lung microbiome and metabolic profile post-exposure to fungal spores. Additionally, fungal spore exposure elicited noticeable changes in α and β diversity, with these microorganisms being closely associated with inflammatory factors. Employing non-targeted metabolomics analysis via GC-TOF-MS, a total of 215 metabolites were identified, among which 42 exhibited significant differences. These metabolites are linked to various metabolic pathways, with amino sugar and nucleotide sugar metabolism, as well as galactose metabolism, standing out as the most notable pathways. Cysteine and methionine metabolism, along with glycine, serine and threonine metabolism, emerged as particularly crucial pathways. Moreover, these metabolites demonstrated a strong correlation with inflammatory factors and exhibited significant associations with microbial production. Overall, our findings suggest that disruptions to the microbiome and metabolome may hold substantial relevance in the mechanism underlying fungal spore-induced lung damage in mice.

Background Dietary magnesium and serum magnesium play an important part in cardiovascular disease (CVD). However, the association between magnesium depletion score (MDS) and CVD development and prognosis remains unclear. This analysis examines the cross‐sectional relationship between MDS and CVD, and the longitudinal association between MDS and all‐cause and CVD mortality in individuals with CVD. Methods and Results In all, 42 711 individuals were selected from the National Health and Nutrition Examination Survey, including 5015 subjects with CVD. The association between MDS and total and individual CVDs was examined using the survey‐weighted multiple logistic regression analysis. Among 5011 patients with CVD, 2285 and 927 participants were recorded with all‐cause and CVD deaths, respectively. We applied survey‐weighted Cox proportional hazards regression analyses to investigate the impact of MDS on the mortality of individuals with CVD. The CVD group had higher MDS levels than the non‐CVD groups. After controlling all confounding factors, individuals with MDS of 2 and ≥3 had higher odds of total CVD and specific CVD than those with MDS of 0. Besides, each 1‐unit increase in MDS was strongly related to the risk of total CVD and specific CVD. The relationship between MDS and total CVD was stable and significant in all subgroups. The fully adjusted Cox regression model indicated that high MDS, irrespective of MDS as a categorical or continuous variable, was significantly associated with an elevated risk of all‐cause and CVD deaths. Conclusions MDS is a vital risk factor for the prevalence and mortality of individuals with CVD.

Summary: Salmonella, a foodborne pathogen, has become a major public health concern because of its widespread drug resistance, including resistance to multiple drugs such as third-generation cephalosporin, ceftriaxone (CRO). However, the metabolic profile changes and associated mechanisms engendered by cephalosporin-resistant mutations remain uncharted. In this study, we have employed the LC-MS/MS metabolomics platform to determine the metabolic profiles of 138 strains of Salmonella. Our results show that metabolic profiles correspond to specific serotypes, sources, processing stages, and antibiotic resistance patterns. Notably, we observed that Salmonella Derby (S. Derby) with drug resistance to CRO has a different metabolic status with changes in glutathione biosynthesis. Specifically, glutathione oxidized (GSSG) and citrulline abundances are greatly suppressed in CRO-resistant S. Derby. Furthermore, exogenous GSSG or citrulline, but not glutathione reduced (GSH), restored the susceptibility of multidrug-resistant S. Derby to CRO. This study establishes a strategy based on functional metabolomics to manage the survival of antibiotic-resistant bacteria.

Abstract This paper aimed to investigate the multi‐objective optimization and decision‐making of the combined control law of guide vane (CCLGV) and pressure regulating valve (PRV) for hydroelectric unit under load rejection. Firstly, the multi‐objective optimization problem of CCLGV‐PRV is formulated. Then, a new multi‐objective gravitation search algorithm with non‐dominated screening and chaos mutation (NCMOGSA) is adopted to solve it. Moreover, an improved entropy weight method and relative objective proximity method are applied to select suitable solution. Finally, a comprehensive comparative study is carried out to reveal the mechanism between the CCLGV‐PRV and several basic parameters. The results indicate that replacing surge tank with PRV can effectively solve the contradictory between water pressure at the bottom of volute and rotational speed under load rejection. The NCMOGSA shows excellent performance in solving the multi‐objective optimization problem of CCLGV‐PRV and get a group of Pareto set. The improved entropy weight and relative objective proximity method make the final scheme clear. The rise rate of rotational speed is completely determined by the guide vane (GV), and the water pressure is jointly determined by the control law of the GV and PRV. The two‐stage closing and two‐stage opening (TCTO) is the final recommendation for CCLGV‐PRV.

This paper investigates the synergetic control strategy for water hammer wave and mass wave in hydropower station with downstream channel. Firstly, the mathematical model of hydropower station with downstream channel (HSDC) and hydropower station with downstream channel and surge tank (HSDCST) are established. Then, the controller formula of HSDC and HSDCST are derived using the synergetic theory. Furthermore, it is parsed that the dynamic characteristics of HSDC and HSDCST to various disturbances under synergetic control strategy. Finally, the robustness and applicability of synergetic control strategy for HSDC and HSDCST are analyzed. The results indicate that the hydropower station under the synergetic control strategy has excellent dynamic characteristics with employing sensitive response, small overshoot, fast attenuation speed and no static error. The synergetic control strategy is superior to the PID control strategy under various disturbances in controlling the amplitude of water hammer wave in HSDC, which is down 12.5%. The attenuation of mass wave in HSDCST under synergetic control strategy is faster, and the settling time is 6.16s less than that of PID control strategy. Compared with PID control strategy, it is the robustness of synergetic control strategy that are much more advantageous for HSDC and HSDCST. Specifically, RI value is about 1.65, 0.18 for HSDC and 2.21, 0.25 for HSDCST under synergetic control strategy, while it is about 3.05, 1.20 and 3.32, 1.30 under PID control strategy. Moreover, the synergetic control strategy has a wide applicability in controlling the water hammer wave in HSDC and mass wave in HSDCST.

ObjectiveBased on pretherapy dual-energy computed tomography (DECT) images, we developed and validated a nomogram combined with clinical parameters and radiomic features to predict the pathologic subtypes of non-small cell lung cancer (NSCLC) — adenocarcinoma (ADC) and squamous cell carcinoma (SCC).MethodsA total of 129 pathologically confirmed NSCLC patients treated at the Second Affiliated Hospital of Nanchang University from October 2017 to October 2021 were retrospectively analyzed. Patients were randomly divided in a ratio of 7:3 (n=90) into training and validation cohorts (n=39). Patients’ pretherapy clinical parameters were recorded. Radiomics features of the primary lesion were extracted from two sets of monoenergetic images (40 keV and 100 keV) in arterial phases (AP) and venous phases (VP). Features were selected successively through the intra-class correlation coefficient (ICC) and the least absolute shrinkage and selection operator (LASSO). Multivariate logistic regression analysis was then performed to establish predictive models. The prediction performance between models was evaluated and compared using the receiver operating characteristic (ROC) curve, DeLong test, and Akaike information criterion (AIC). A nomogram was developed based on the model with the best predictive performance to evaluate its calibration and clinical utility.ResultsA total of 87 ADC and 42 SCC patients were enrolled in this study. Among the five constructed models, the integrative model (AUC: Model 4 = 0.92, Model 5 = 0.93) combining clinical parameters and radiomic features had a higher AUC than the individual clinical models or radiomic models (AUC: Model 1 = 0.84, Model 2 = 0.79, Model 3 = 0.84). The combined clinical-venous phase radiomics model had the best predictive performance, goodness of fit, and parsimony; the area under the ROC curve (AUC) of the training and validation cohorts was 0.93 and 0.90, respectively, and the AIC value was 60.16. Then, this model was visualized as a nomogram. The calibration curves demonstrated it’s good calibration, and decision curve analysis (DCA) proved its clinical utility.ConclusionThe combined clinical-radiomics model based on pretherapy DECT showed good performance in distinguishing ADC and SCC of the lung. The nomogram constructed based on the best-performing combined clinical-venous phase radiomics model provides a relatively accurate, convenient and noninvasive method for predicting the pathological subtypes of ADC and SCC in NSCLC.

Abstract Soy whey, a liquid nutritional by-product of soybean manufacture, is rich in proteins, oligosaccharides and isoflavones. Soy whey can be used to produce functional beverages, instead of discarding it as a waste. In this study, unfermented soy whey (USW) and Cordyceps militaris SN-18-fermented soy whey (FSW) were investigated and compared for their physicochemical and functional properties by high performance liquid chromatography (HPLC) and DNA damage assay. Results show that C. militaris SN-18 fermentation could increase the contents of essential amino acids, total phenolic and flavonoid and isoflavone aglycones and eliminate the oligosaccharides in soy whey. Furthermore, C. militaris SN-18 could significantly enhance the ABTS radical scavenging ability, reducing power and ferric reducing power of soy whey, and its fermented products could prominently attenuate Fenton reaction-induced DNA damage. These findings indicate that soy whey can potentially be converted into a novel soy functional beverage by C. militaris SN-18 fermentation. Graphical abstract

Abstract Pumped storage units (PSUs) are now widely used for energy storage. However, the uncertainty of the identification results of the pump‐turbine governing system (PTGS) caused by the random observation noises and the lack of prior knowledge remains an unaddressed issue. In recent years, the differential evolution adaptive Metropolis algorithm (DREAM) based on the Bayesian theory has been extensively used for parameter estimation and uncertainty analysis, but its application to the uncertainty analysis of PTGS has been rare. This study systematically evaluates the applicability of DREAM in the parameter identification and uncertainty quantification of PTGS. A real PSU in China has been employed as a case study for numerical experiments. Four groups of control experiments with different proportions of observation noises and different prior search spaces have been constructed in this study. It can be concluded from this study that: (a) accurate point identification results and effective uncertainty quantification can be obtained simultaneously using DREAM, (b) a lower proportion of observation noises can enhance the efficiency and effectiveness of the DREAM algorithm when applying to PTGS simulation. The DREAM method can narrow the prior search space of PTGS parameters effectively and thus help the hydropower engineers to make decisions.

Based on the problem of abandoned water and insufficient output caused by runoff randomness, the short-term runoff inflow interval prediction method combined with nonparametric kernel density estimation (NKDE)and Monte Carlo (MC)sampling is proposed. The method analyzes the impact of hydropower station abandonment risk and insufficient output risk caused by inflow uncertainty on hydropower station output and thermal power output in the power grid, and the corresponding output adjustment strategy is given. This paper mainly carries out these studies: real-time load distribution, short-term runoff interval prediction and real-time output adjustment of hydro-thermal power plant under uncertain incoming water: (1) Risk factors of water and electricity may be involved in the scheduling process, the process of considering runoff uncertainty on hydropower unit output, analyze the impact of hydropower abandoned water risk, estimate based on short-term nonparametric kernel density estimation (NKDE) runoff forecasting methods, given the range of the forecast runoff, reducing forecast error runoff, combine Monte Carlo (MC) sampling and propose a new real-time power control adjustment strategy. (2) Taking Qingjiang cascade power station as a case study, considering the increase of incoming water and the decrease of incoming water, the impact of water abandonment risk and insufficient output risk on short-term power generation plan is verified. The results show that the proposed hydropower and thermal power output adjustment can effectively deal with the risk of abandoned water and the risk of insufficient output. It is of positive significance to make full use of hydropower resources. It is helpful for the safe and stable operation of Hubei Power Grid.

Large hydropower stations often undertake peak regulation tasks during the non-abandoning water season. This requires a reasonable arrangement of unit commitment and load distribution (UCLD). In this study, a load distribution model considering constraints of substations was established, and a novel refined and practical method (RPM) was proposed by considering a whole plant-substation module, substation-unit module, and adjustment module along with practical strategies. The Three Gorges hydropower station in China was selected to demonstrate the effects of the RPM. The results showed that the RPM with high calculation timeliness can obtain UCLD schemes with high rationality and practicability under the constraints of the whole plant, substations, and units. The relative mean absolute error of the simulation outflow could be controlled within 1%, thereby providing a valuable reference for forecasting the outflow process which involving the unit level.

The high-head pumped storage power station (PSPS) has complex working conditions and severe transient processes. Under load rejection conditions, the turbine speed and the flow channel pressure will threaten the unit. By adjusting the wicket gate closing law (WGCL), we can effectively alleviate the adverse effects of the transient process. In this study, a full flow channel refined model of a high-head PSPS was constructed, and the influence mechanism of load rejection conditions on key parameters was analyzed by tracking the operating trajectory on the S characteristic curves and simulating the transient flow field inside the pumped turbine by three-dimensional computational fluid dynamics (3D-CFD). Given the contradiction between different parameters in the wicket gate closing process, a third-generation non-dominated genetic algorithm based on reference point selection (NSGA-III) was introduced, and a high-dimensional multi-objective WGCL optimization model was constructed. We compared parameter changes in the two-phase and three-phase WGCL optimized elite solutions and explained the rationality of adopting the three-phase WGCL in high-head PSPSs. By proposing an improved three-phase WGCL, a new scheme—the most suitable WGCL under load rejection—was obtained through precise optimization. The research embodied the excellent effect of the heuristic multi-objective (MO) optimization algorithm based on the Pareto strategy in solving the operation problem of the large fluctuation transient process of hydraulic machinery. It revealed the theoretical basis of engineering adjustment for the safe production and operation of hydropower.

Superspreaders are critical infectious resources in multiple infectious diseases. They can be asymptomatic or present mild symptoms but can transmit pathogens to susceptible populations, leading to severe symptoms, and even death. Early identification of this population is extremely important to inhibit the spread of infectious diseases. Right now, the whole global world is suffering from a devastating infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this article, a superspreader cluster event in coronavirus disease 2019 (COVID-19) was identified by tracking contacting histories of infected patients. This cluster was found to be originated from an asymptomatic SARS-CoV-2 carrier, which resulted in 13 secondary cases getting infected. All the secondary patients presented with non-typical symptoms of COVID-19, such as fever, dry cough, and myalgia, one of which died of respiratory failure at the end. From this cluster, we learn that people with older ages, low immunity, multiple underlying diseases, especially pulmonary diseases, can contribute to a poor prognosis. Thus, asymptomatic superspreaders of COVID-19 can be extremely dangerous and must be handled time-efficiently.

Soybean whey, as a byproduct of soybean industry, has caused considerable concern recently because of its abundant nutrients. To further utilize soybean whey, it was fermented with Weissella hellenica D1501, and the neuroprotective potency of this beverage was studied in the present work. The phenolic profile and antioxidant capacity of fermented soybean whey (FSBW) were analyzed. The neuroprotective effects were evaluated based on the hydrogen peroxide-stimulated oxidative damage model in a neural-like cell (PC12). Results demonstrated that soybean whey's phenolic contents and antioxidant activities were markedly improved after fermentation. Glycoside isoflavones were efficiently converted into aglycones by W. hellenica D1501. FSBW extract apparently increased cell viability, decreased reactive oxide species levels, and protected antioxidant enzymes in oxidative damage. Furthermore, FSBW effectively reduced apoptosis rate by inhibiting Bax protein and improving Bcl-2 and Bcl-xL proteins. FSBW ameliorated the cell cycle through the decrease of p21 protein and an increase of cyclin A protein. The findings of this study thus suggested that W. hellenica D1501-fermented soybean whey could potentially protect nerve cells against oxidative damage.

Abstract Multireservoir joint flood control operation is an important nonstructural measure for flood control in some basins. Owing to the existence of uncertainties in flood control operation, various degrees of risk can be shown in flood control operation systems. Examining these uncertainties and the resulting risk of failure in multireservoir operation systems is extremely beneficial and indispensable for basin flood mitigation. In this study, different stochastic simulation methods were employed to generate streamflow series at multiple stations and other uncertainties such as the flood forecast errors, discharge capacity, and reservoir storage. Then, the Monte Carlo framework was established based on the simulation sequence to estimate the risk of a failure of each flood control unit in the flood control system. Furthermore, a comprehensive risk assessment indicator was proposed to consider the failure probability and failure consequences at the same time. The Xiluodu‐Xiangjiaba‐Three Gorges cascade reservoirs were selected as a case study. Results show that the applied probabilistic simulation methods could simulate various uncertainties in a multireservoir flood control system effectively, and the proposed risk analysis method could comprehensively evaluate the risks of reservoir overtopping, flood flows exceeding a standard, and flood diversion from the perspective of overall watershed security.

To advance the industrialization production of steamed buns, the current study explored the freeze-stability of unfermented, pre-fermented and par-steamed frozen dough. The results showed that the steamed bun made from unfermented dough with 2.0% yeast, the pre-fermented dough with a pre-fermented time of 30 min and the par-steamed dough with a pre-steamed time of 15 min showed the best sensory properties quality upon frozen storage. The gassing power of un- and pre-fermented dough gradually decreased, and dough with longer pre-fermented time exhibited more evident loss of gassing power. Freeze-induced depolymerization of gluten protein was the least distinct in the par-steamed dough, followed by the pre- and un-fermented dough, which was probably related to the superior freeze stability of glutenin-gliadin macro-crosslinks upon the pre-steaming stage. The surface hydrophobicity of gluten proteins of frozen dough decreased during the initial storage and was enhanced subsequently, which was related with the combined effects of the unfolding and synchronous aggregation induced by freezing and steaming, respectively. Moreover, the surface hydrophobicity of gluten in par-steamed frozen dough and steamed buns was more resistant to frozen storage, which was probably attributed to the established stable structure during the pre-steaming process.

Hydropower, as a kind of clean energy, has the characteristics of rapid response to load changes, and is often used to undertake peak regulation and frequency regulation tasks. However, due to the uncertainties of reservoir inflow and runoff, the relative lag of power grid outgoing channels, and the interconnection of intermittent new energy, such as wind power and solar energy, the hydropower system is facing enormous peak regulation pressure and the continuous prominent problem of discarding water. In the study, aiming at the problem of risk control of discarding water in power generation process of hydropower station, a practical calculation method of risk of discarding water in power generation is proposed based on the analysis of frequency of runoff. Taking the Ertan hydropower station as an example, the corresponding water level processes of different discarding water risks under the control of pre-flood falling water level, flood limit water level and storage water level after flood season are deduced, and in the routine dispatching process, the water level operation control guidance corresponding to different discarding risk is introduced. The simulation results show that the proposed discarding water risk control method is practical and effective, and can provide guidance for discarding water risk control in the actual dispatching process of hydropower station.

Accurate health evolution trend forecasting of aero-engine is essential for operation reliability and maintenance costs of aeronautical equipment. In this study, an intelligent deep learning method, systematically blending the dispersion entropy-based multi-scale series aggregation scheme and long short term memory (LSTM) neural network, is proposed for forecasting the health evolution trend of aero-engine. Firstly, a comprehensive measurement of health levels, namely, integrated health state index (IHSI), is developed with high-dimensional dataset. Secondly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is exploited to decompose the IHSI sequence into several multi-scale series to further capture the internal characteristics of original sequence. Subsequently, multi-scale series aggregation assisted with dispersion entropy analysis theory is conducted for obtaining the aggregated sub-series (ASS). Finally, the ASS are served as the inputs of LSTM network to complete the health evolution trend forecasting of aero-engine. To demonstrate the effectiveness of the proposed method, six approaches are present for the comparisons of forecasting performance. The experimental results indicate that the proposed method can effectively measure the health evolution process of aero-engine and further obtain more accurate trend forecasting results.

Wind speed and streamflow series always are nonlinear and unstable because the effects of chaotic weather systems. These inherent features make them difficult to forecast, especially in a changing environment. To improve forecasting accuracy, an innovation uncertainty forecasting architecture is developed by coupling data decomposition method, feature selection, multiple artificial intelligence (AI) techniques and composite strategy to do unstable time series forecasting. In the designed architecture, the AVMD (adaptive variational mode decomposition) is first applied to excavate implicit information from the original time series. Then, the random forest is utilized to select the suitable inputs for each mode. After that, the GPR (Gaussian Process Regression), a very famous probabilistic AI technique, is driven by various neural networks (ELM (Extreme Learning Machine), BP (Back Propagation Neural Networks), GRNN(Generalized Regression Neural Networks) and RBF (Radial Basis Function Neural Networks)) to produce both deterministic and probabilistic forecasting results in a nonlinear manner to play strengths of each other. The effectiveness and applicability of the proposed approach is verified by unstable wind speed data and streamflow data, and also compared with eleven related models. Results indicate that the proposed model not only improves the forecasting accuracy for deterministic predictions, but also provides more probabilistic information for decision making. The proposed method achieves significantly better performance than the traditional forecasting models both on wind speed forecasting and streamflow forecasting with at least 50% average performance promotion over all the eleven competitors. Comprehensive comparisons demonstrate the superior performance of the proposed method than the involved models as a powerful tool for unstable series forecasting.

The characterization of the mapping relationship (MR) between outflow of the upstream reservoir (OUR) and inflow of the downstream reservoir (IDR) in the short-term generation dispatching of cascade reservoirs (SGDCR) greatly impacts the safe and economic operation of hydropower plants. If this MR is not characterized properly, the operation process of hydropower stations will deviate from the planning dispatching schemes. Especially when the upstream reservoir (UR) undertakes peak load regulation tasks frequently and the downstream reservoir (DR) owns weak re-regulation capacity, the safety of cascade reservoirs will be seriously threatened. In this extreme system, the commonest characterizing models in SGDCR, such as the lag time (LT) model and the Muskingum model, may cause a huge deviation when simulating or forecasting the IDR. Given this dilemma, the Gaussian process regression (GPR) model, which is a representative for Bayesian regression methods, is firstly introduced to handle this MR and compared with the LT and the Muskingum model in this paper. The Three Gorges-Gezhouba cascade reservoirs (TGGCR) in China are selected as a typical case study. The performance indicators of four categories are adopted to evaluate the simulated IDR in a single period and a set of pivotal indicators are proposed to estimate the simulation dispatching in multiple periods. The results show that (1) The GPR model reduces the mean absolute deviation (MAD) about inflow of Gezhouba by 197m3/s and 263m3/s than the LT and the Muskingum model respectively; (2) The distribution characteristics of inflow deviation produced by the GPR model are most competitive. Meanwhile, the GPR model has the strongest ability when conducting the multi-period simulation dispatching and owns best applicability on the whole range of streamflow. (3) The Muskingum model is not recommended to characterize the hour-scale hydraulic connection in the extreme system of SGDCR.

The Takagi-Sugeno (T-S) fuzzy model identification is a very powerful tool for modelling of complicated nonlinear system. However, the traditional T-S fuzzy model typically uses the L2-norm loss function, which is very sensitive to outliers or noises. So an unreliable model may be obtained due to the presence of outliers or noises. In this paper, the outliers and noises robust T-S fuzzy model identification method based on the fuzzy c-regression model (FCRM) clustering and the L1-norm loss function is proposed. The hyper-plane-shaped clustering algorithm has been proved to be more effective than hyper-sphere-shaped clustering algorithm in T-S fuzzy model identification. Therefore the FCRM clustering algorithm is used in T-S fuzzy model identification for structure identification in the antecedent part. A mass of relevant researches have pointed out that the L1-norm loss function is more robust to outliers and noises than L2-norm loss function. In order to reduce the negative influence of outliers and noises, the L1-norm loss function is employed to enhance the robustness of T-S fuzzy model instead of the L2-norm loss function in the consequent part. Regression and classification applications have been used to demonstrate the validity of the proposed method. The experimental results show that the proposed method has significantly improved the modelling accuracy in dealing with data contaminated by outliers and noises compared with other algorithms.

The preparation of n-N homotypic heterojunction by growing n-type VO2 film on N-type Al-doped ZnO substrate using DC magnetron sputtering and post-annealing was reported in this paper. The composition and microstructure of the VO2/AZO heterojunction were measured by XRD and SEM. The photoelectrical properties and the phase transition properties were tested by adjusting voltage at different temperature. The forward current was perfectly fitted on the thermionic emission model and the breakdown voltage was about 5 V at 20 °C. Meanwhile, the threshold voltage of current mutation was 3 V at 20 °C and decreased with increasing temperature until it completely disappeared at 60 °C. The voltage dependence transmittance of VO2/AZO heterojunction at 1310 nm was obtained. It indicated that the decrease of transmittance is the double effect of thermally induced phase transition and electrically induced phase transition. Keywords: VO2/AZO, Homotypic heterojunction, Metal-insulator transition, Forward current, Threshold voltage of current mutation, Transmittance

Satellite-retrieved and model-based reanalysis precipitation products with high resolution have received increasing attention in recent decades. Their hydrological performance has been widely evaluated. However, whether they can be applied in characterizing the novel category of extreme events, such as compound moist heat-flood (CMHF) events, has not been fully investigated to date. The CMHF refers to the rapid transition from moist heat stress to devastating floods and has occurred increasingly frequently under the current warming climate. This study focuses on the applicability of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and the fifth generation of European Reanalysis (ERA5-Land) in simulating CMHF events over 120 catchments in China. Firstly, the precipitation accuracy of IMERG and ERA5-Land products is appraised for each catchment, using the gridded in situ meteorological dataset (CN05.1) as a baseline. Then, the ability of IMERG and ERA5-Land datasets in simulating the fraction, magnitude, and decade change of floods and CMHFs is comprehensively evaluated by forcing the XAJ and GR4J hydrological models. The results show that: (a) the IMERG and ERA5-Land perform similarly in terms of precipitation occurrences and intensity; (b) the IMERG yields discernably better performance than the ERA5-Land in streamflow simulation, with 71.7% and 50.8% of catchments showing the Kling–Gupta efficiency (KGE) higher than 0.5, respectively; (c) both datasets can roughly capture the frequency, magnitude, and their changes of floods and CMHFs in recent decades, with the IMERG exhibiting more satisfactory accuracy. Our results indicate that satellite remote sensing and atmospheric reanalysis precipitation can not only simulate individual hydrological extremes in most regions, but monitor compound events such as CMHF episodes, and especially, the IMERG satellite can yield better performance than the ERA5-Land reanalysis.

The shafting systems of hydropower units work as the core component for the conversion of water energy to electric energy and have been running for a long time in the hostile hydraulic–mechanical–electrical-coupled environment—their vibration faults are frequent. How to quickly and accurately identify vibration faults to improve the reliability of the unit is a key issue. This study proposes a novel shafting vibration fault identification framework, which is divided into three coordinated stages: nonlinear modeling, signal denoising, and holographic identification. A nonlinear dynamical model of bending–torsion coupling vibration induced by multiple excitation vibration sources of the shafting system is established in the first stage. The multi-stage signal denoising method combines Savitzky–Golay (SG) smoothing filtering, singular value decomposition (SVD), and variational mode decomposition (VMD). SG-SVD-VMD is used for the guide bearing the vibration signals in the second stage. Further, the holospectrum theory is innovatively introduced to obtain the holospectra of the simulated and measured signals, and the shafting vibration faults of the real unit are identified by comparing the holospectrum of the measured signal with the simulated signal. These results show that the shafting nonlinear model can effectively reflect the vibration characteristics of the coupled vibration source and reveal the influence and fault characteristics of each external excitation on the shafting vibration. The shafting vibration faults of operating units can be identified by analyzing the holospectra of the shafting simulation signals and measuring the noise reduction signals. Thus, this framework can guide the safe and stable operation of hydropower units.

The stable operation of the Francis turbine unit (FTU) determines the safety of the hydropower plant and the energy grid. The traditional FTU performance evaluation methods with a fixed threshold cannot avoid the influence of variable operating conditions. Meanwhile, anomaly samples and missing values in the low-quality on-site data distort the monitoring signals, which greatly affects the evaluation and prediction accuracy of the FTU. Therefore, an approach to the performance evaluation and prediction of the FTU considering low-quality data and variable operating conditions is proposed in this study. First, taking the variable operating conditions into consideration, a FTU on-site data-cleaning method based on DBSCAN is constructed to adaptively identify the anomaly samples. Second, the gate recurrent unit with decay mechanism (GRUD) and the Wasserstein generative adversarial network (WGAN) are combined to propose the GRUD–WGAN model for missing data imputation. Third, to reduce the impact of data randomness, the healthy-state probability model of the FTU is established based on the GPR. Fourth, the prediction model based on the temporal pattern attention–long short-term memory (TPA–LSTM) is constructed for accurate degradation trend forecasting. Ultimately, validity experiments were conducted with the on-site data set of a large FTU in production. The comparison experiments indicate that the proposed GRUD–WGAN has the highest accuracy at each data missing rate. In addition, since the cleaning and imputation improve the data quality, the TPA–LSTM-based performance indicator prediction model has great accuracy and generalization performance.

Water quality is a significant issue, and its assessment plays an important role in environmental management and pollution control. In this paper, we proposed a comprehensive water quality assessment method which takes into account both absolute and temporal trends in water quality. As the first step, we derived and applied a comprehensive pollution index (CPI) to characterize water pollution in 16 major tributaries to the Danjiangkou Reservoir, located in the upper reaches of the Hanjiang River in China. Next, we used Spearman’s rank correlation analysis to quantify temporal CPI trends in each tributary. As the final step, we conducted principal component analysis (PCA) using data on 8 water quality parameters and the temporal CPI trend from each of the 16 tributaries. The resultant comprehensive water quality assessment method identified tributaries, which stand to improve and threaten water quality in the Danjiangkou Reservoir from both immediate and future perspectives.

Abstract The Clausius‐Clapeyron relationship holds that the atmospheric water vapor content enhances with warming temperatures, suggesting intensifications of precipitable water and also altering runoff generation. Drought conditions are determined by variations in water fluxes such as precipitation and runoff, which tightly connect with temperature scaling characteristics. However, whether and how water fluxes' scaling with temperatures may affect the evolution of droughts under climate change has not yet been systematically investigated. This study develops a cascade modeling chain consisting of the climate model ensemble, bias correction technique, and hydrological models to investigate the precipitation and runoff scaling relationships with warming temperatures under the current (1961–2005) and future periods (2011–2055 and 2056–2100), as well as their implications on future drought changes across 151 catchments in China. The results show that (1) precipitation (runoff) scaling relationships with temperatures are stable during different time periods; (2) return level analysis indicates drought risks are projected to become (1–10 times) more severe across central and southern catchments, where the precipitation (runoff) strengthens with rising temperatures up to a peak point and then decline in a hotter environment. The northeastern and western catchments, where a monotonic increasing scaling type dominated, are accompanied by drought mitigations for two future periods; (3) future changes in hydrological droughts relative to the baseline are (1–5 times) larger than those in meteorological droughts. These results imply that changes in future drought risks are highly dependent on the present precipitation (runoff)‐temperature relationships, suggesting a meaningful implication of scaling types for future drought prediction.

The prebiotic potential of exopolysaccharide (r-EPS) structures released from Lactobacillus delbrueckii ssp. bulgaricus SRFM-1 was assessed using in vitro fermentations. Fermentations with pure cultures of Lactobacillus (nine strains) and a Streptococcus strain as well as two food-borne pathogenic Escherichia coli were performed. Scanning electron microscopy and Congo red testing revealed that the three r-EPS fractions had different individual structures. All r-EPS fractions were found to stimulate the growth of the lactobacilli and the Streptococcus strain and inhibit the growth of the E. coli strains. Additionally, the primary structure including molecular weight, monosaccharide composition and glycosidic linkages and the individual structures of three r-EPS fractions were found to influence the μmax and cell density of these strains. The results strengthen previous studies on the r-EPS from L. delbrueckii ssp. bulgaricus SRFM-1 and provide additional validation of r-EPS as a potentially novel prebiotic for incorporation in functional foods.

Reservoir optimal operation (ROO) needs to coordinate various profit-making objectives, which is a typical multiobjective optimization problem (MOP) with complex constraints. With the development of multiobjective evolutionary algorithms (MOEAs) in the past decades, more and more research has focused on MOEAs to solve MOP. Considering that multiobjective ROO is also a typical multi-stage Markov decision-making problem, this paper introduces the application of multiobjective dynamic programming (MODP) for multiobjective ROO in detail. On this basis, an improved MODP with selection mechanism of non-dominated solutions based on reference lines (MODP-BRL) is proposed to improve the convergence efficiency of MODP. The experimental results show that the proposed MODP-BRL is a reliable and effective tool in solving multiobjective ROO. In addition, MODP-BRL has better performance in convergence effect and efficiency in comparison experiments with NSGAII, NSGAIII, and SPEA2. It is noteworthy that MODP and MODP-BRL are very sensitive to the discrete step. With the decrease of the discrete step (the higher the discrete precision), the computing time increases nonlinearly. The appropriate discrete step of the state variable is key presets to balance the superiority and computational efficiency of non-dominated solutions with the application of MODP and MODP-BRL.

In order to improve the control performance and suppress the “S” characteristics area instability of the pumped-storage unit (PSU), this paper proposes an adaptive Takagi-Sugeno fuzzy model-based generalized predictive controller (ATS-GPC) for the PSU. First, the T-S fuzzy model is used to obtain the controlled autoregressive integrated moving average (CARIMA) model, in which the fuzzy C-means (FCM) clustering algorithm is used for the identification of antecedent parameters and the least square method (LSM) is used to obtain the consequent parameters. Meanwhile, the T-S fuzzy model can be online adjusted according to the real-time tracking error feedback to decrease the influence of the initial offline trained fuzzy model. Then, the generalized predictive controller is designed for the PSU based on the CARIMA. Finally, some numerical simulation experiments including the start-up process, frequency disturbance process, frequency tracking experiments, and robustness analyses have been conducted to verify the proposed method. The experiments results have shown that the proposed ATS-GPC can significantly improve the control performance of the PSU and effectively suppress the unstable operation in “S” characteristics area. In addition, the strong robustness of the proposed controller is verified.

Operating rules have been widely used to decide reservoir operations because they can help operators make an approximately optimal decision with limited runoff forecast information. As an effective alternative to explicit stochastic optimization (ESO) for considering hydrologic uncertainty, the implicit stochastic optimization (ISO) has been widely used to derive operating rules for the long-term operation of hydropower reservoirs. Within an ISO framework, operating rules extraction is a typical regression problem. In the past decades, various regression methods have been applied to derive operating rules, including artificial neural network (ANN), support vector regression (SVR) and so on, but these methods almost all are parametric regression method and there are few publications applying Bayesian regression method to derive operating rules. Therefore, Gaussian process regression (GPR), which is the representative Bayesian regression method, is introduced to derive operating rules for the first time in this paper and compared with ANN, SVR and conventional scheduling graph (CSG). China's Three Gorges Reservoir (TGR) is selected as a case study, and four performance indexes are defined to evaluate different methods. The results show that (1) GPR, ANN and SVR can provide better performance than CSG method and are more practical than deterministic optimization operation; (2) GPR method can provide greater power generation benefits and higher reliability than ANN, SVR and CSG methods, and the average annual power generation increases from 88.481 billion kWh, 88.559 billion kWh and 87.563 billion kWh to 88.586 billion kWh, while the generation guarantee rate increase from 86.88%, 87.07% and 81.99% to 87.45%.

The prognostic is the key to the state-based maintenance of Francis turbine units (FTUs), which consists of performance state evaluation and degradation trend prediction. In practical engineering environments, there are three significant difficulties: low data quality, complex variable operation conditions, and prediction model parameter optimization. In order to effectively solve the above three problems, an ensemble prognostic method of FTUs using low-quality data under variable operation conditions is proposed in this study. Firstly, to consider the operation condition parameters, the running data set of the FTU is constructed by the water head, active power, and vibration amplitude of the top cover. Then, to improve the robustness of the proposed model against anomaly data, the density-based spatial clustering of applications with noise (DBSCAN) is introduced to clean outliers and singularities in the raw running data set. Next, considering the randomness of the monitoring data, the healthy state model based on the Gaussian mixture model is constructed, and the negative log-likelihood probability is calculated as the performance degradation indicator (PDI). Furthermore, to predict the trend of PDIs with confidence interval and automatically optimize the prediction model on both accuracy and certainty, the multiobjective prediction model is proposed based on the non-dominated sorting genetic algorithm and Gaussian process regression. Finally, monitoring data from an actual large FTU was used for effectiveness verification. The stability and smoothness of the PDI curve are improved by 3.2 times and 1.9 times, respectively, by DBSCAN compared with 3-sigma. The root-mean-squared error, the prediction interval normalized average, the prediction interval coverage probability, the mean absolute percentage error, and the R2 score of the proposed method achieved 0.223, 0.289, 1.000, 0.641%, and 0.974, respectively. The comparison experiments demonstrate that the proposed method is more robust to low-quality data and has better accuracy, certainty, and reliability for the prognostic of the FTU under complex operating conditions.

Abstract Staphylokinase (Sak) holds promise for use in thrombolytic therapy for acute myocardial infarction. However, its immunogenicity is a major disadvantage under clinical conditions. PEGylation has become a sophisticated method to decrease that immunogenicity. In this report, according predicted epitope from the active center, five residues, including Gly79, Leu82, Lys84, Ala97, and Arg104 have been mutant as cysteine for mono PEGylation, respectively. According to the relative immunogenicity of Sak or its PEGylation derivatives, the amount of specific anti-Sak IgG antibodies elicited by PEGylation proteins, including C79G, C82L, C84K, C97A, and C104R in BALB/c mice decreased by approximately 15–75% each. PEGylated Sak derivatives showed a decrease of up to 75% in the immune reactivity in PEG-Sak-C104R. Thrombelastography experiments showed that two PEG-conjugated derivatives, PEG-Sak-C97A (Ly30, 68.14 ± 2.51%) and PEG-Sak-C104R (Ly30, 66.49 ± 5.97%), the LY30 of PEG-Sak-C97A, and PEG-Sak-C104R produced values very similar to those of wild-type Sak. The fibrin plate assays showed the bioactivity of PEG-Sak-C104R to exhibit the most activity approximately as much as urokinase (diameter of halo pattern, 18.6 ± 1.06 mm) and tPA (diameter of halo pattern, 17.2 ± 0.49 mm). The Sak PEGylation derivative PEG-Sak-C104R was also selected for further in vivo activity experimentation. The thrombolytic ability of PEG-Sak-C104R is a little lower than wild-type Sak, whereas, this PEGylated protein retained high activity suitable for thrombolytic therapy. Collectively, with the in vivo and in vitro experiments, the present study suggests that site mutant PEGylation, PEG-Sak-C104R, is a suitable type of PEGylation for clinical applications. Further optimization would help maintain the bioactivity and decrease the immunogenicity of staphylokinase.

Glycosidases enhance flavor during wine-making by mediating the enzymatic release of aroma molecules. In order to better understand the aroma enhancement potential of Oenococcus oeni SD-2a, β-D-glucosidase (βG) activities in the culture supernatant, whole cells, and disrupted cell lysate were assessed at mid log, late log and stationary growth phase. The enzymatic activity was also compared further from cell cultures with 5 different carbon sources (glucose, cellobiose, arbutin, glucose and cellobiose, glucose and arbutin) at late log phase. Correspondingly, expression levels of 3 bgl genes, OEOE-0224, OEOE-1210, and OEOE-1569 were investigated from cell cultures of the 3 growth phases, and the 5 cell cultures with different carbon sources. Finally, the volatile aroma compounds released by O. oeni SD-2a in synthetic wines with natural glycosides were evaluated by GC-MS. Results showed βG of O. oeni SD-2a was not extracellular enzyme, and the location of it didn't change with the change of growth phase and carbon source studied. βG activities in the whole cells and disrupted cell lysate were similar and constant at the 3 growth phases. As for the carbon sources, βG activities of whole cells and disrupted lysate were positively affected by cellobiose. While arbutin displayed positive and negative effect on βG activity of whole cells and disrupted lysate, respectively. It is probably that bgl genes OEOE-0224 and OEOE-1210 were related to βG activity of SD-2a whole cells, while OEOE-1569 was responsible for βG activity of disrupted lysate. More kinds of volatile compounds and higher total concentration were released by SD-2a in synthetic wine compared with control. Thus, SD-2a showed a great potential for flavor enhancement under wine-like conditions. This study provides more information for further study of βG activity from O. oeni SD-2a.

Fault identification under variable operating conditions is a task of great importance and challenge for equipment health management. However, when dealing with this kind of issue, traditional fault diagnosis methods based on the assumption of the distribution coherence of the training and testing set are no longer applicable. In this paper, a novel state identification method integrated by time-frequency decomposition, multi-information entropies, and joint distribution adaptation is proposed for rolling element bearings. At first, fast ensemble empirical mode decomposition was employed to decompose the vibration signals into a collection of intrinsic mode functions, aiming at obtaining the multiscale description of the original signals. Then, hybrid entropy features that can characterize the dynamic and complexity of time series in the local space, global space, and frequency domain were extracted from each intrinsic mode function. As for the training and testing set under different load conditions, all data was mapped into a reproducing space by joint distribution adaptation to reduce the distribution discrepancies between datasets, where the pseudolabels of the testing set and the final diagnostic results were obtained by the k-nearest neighbor algorithm. Finally, five cases with the training and testing set under variable load conditions were used to demonstrate the performance of the proposed method, and comparisons with some other diagnosis models combined with the same features and other dimensionality reduction methods were also discussed. The analysis results show that the proposed method can effectively recognize the multifaults of rolling element bearings under variable load conditions with higher accuracies and has sound practicability.

Soymilk fermentation with kombucha was perfomed at 28 °C and 37 °C, and changes in the number of microorganisms, bioactivity and chemical compositions were analyzed during fermentation. Yeast, acetic acid bacteria (AAB), and lactic acid bacteria (LAB) showed substantial growth in the soymilk, and fermentation at 37 °C and 28 °C promoted the growth of LAB and AAB, respectively. The flatulence factors of raffinose and stachyose were entirely and mostly consumed during fermentation, respectively. Nearly all the glucoside isoflavones were transformed into aglycone isoflavones by β-glucosidase hydrolysis. Total phenolic, ferulic, chlorogenic and ascorbic acid (VC) contents significantly increased after fermentation. The antioxidant and inhibition activities to α-glucosidase and α-amylase of kombucha-fermented soymilk significantly increased, and were higher at 37 °C than at 28 °C. In conclusion, fermentation with kombucha can enhance the health-promoting properties of soymilk.

Fermented soybean products have attracted great attention due to their health benefits. In the present study, the hypoxia-injured PC12 cells induced by cobalt chloride (CoCl2) were used to evaluate the neuroprotective potency of tofu fermented by Actinomucor elegans (FT). Results indicated that FT exhibited higher phenolic content and antioxidant activity than tofu. Moreover, most soybean isoflavone glycosides were hydrolyzed into their corresponding aglycones during fermentation. FT demonstrated a significant protective effect on PC12 cells against hypoxic injury by maintaining cell viability, reducing lactic dehydrogenase leakage, and inhibiting oxidative stress. The cell apoptosis was significantly attenuated by the FT through down-regulation of caspase-3, caspases-8, caspase-9, and Bax, and up-regulation of Bcl-2 and Bcl-xL. S-phase cell arrest was significantly inhibited by the FT through increasing cyclin A and decreasing the p21 protein level. Furthermore, treatment with the FT activated autophagy, indicating that autophagy possibly acted as a survival mechanism against CoCl2-induced injury. Overall, FT offered a potential protective effect on nerve cells in vitro against hypoxic damage.