Search

Objective: In order to improve the mushroom drying efficiency and reduce the power consumption, a new type of co-heating and dehumidification drying machine was designed and built. Methods: The drying machine was a closed system, which took the semiconductor refrigeration piece as the main component, with its hot end as heat source for air pre-heating and its cold end as the cold source for water vapor condensation. On the device, the mushroom drying rate at different drying temperatures （55 and 65 ℃） and sample mass （200 and 400 g） were tested. Results: Nine thin layer mathematical drying models were used to simulate the drying rate, which revealed that the Midilli model had a better predict effect than the others. In addition, it was found that the higher drying temperature resulted in a greater color change and a higher shrinkage rate. The comprehensive energy efficiency ratio of the drying machine was 1.96, and the power consumption of drying mushroom was 81.21 MJ/kg. Conclusion: In the experiment, the indexes such as moisture content and color change of dried mushroom met the national standards, and the energy-saving effect was remarkable compared with the traditional electric heating drying machine.

Abstract.In the history of researching to estimate the unknown parameters that were in the uncertain differential equation (UDE), the problem of parameter estimation with known functional forms is often studied. However, in practical situations, its functional form is often unknown. In order to deal with this problem, this article proposes the cubic spline method to approximate the autonomous UDE, and perform non parametric estimation on it. The cross validation is introduced to determine the number of term (J), which is in the approximate cubic spline. In addition, the uncertain hypothesis testing is given to verify the rationality of this method. Finally, some numerical examples are given. Then this method is applied to a case study of the Beijing Air Quality Index, and a comparative analysis is given to verify the practicability and superiority of the cubic spline method. [ABSTRACT FROM AUTHOR]

Objective: To obtain the optimal enzyme and preparation process for Schisandra chinensis protein peptides and investigate its in vitro antioxidant activity. Methods: Seven proteases were used to hydrolyze Schisandra chinensis protein. Based on the degree of hydrolysis, free radical scavenging activity, polypeptide yield and content of different hydrolysates of Schisandra chinensis protein and the comprehensive evaluation of molecular weight in SDS-PAGE, the optimal protease was screened. The DPPH free radical scavenging rate was used as the index, and the optimal enzymatic hydrolysis process was determined by single factor test combined with response surface analysis. The scavenging ability of O2-*, *OH, DPPH*, ABTS+*, Fe2+ chelating ability and Fe3+ reducing ability of Schisandrae chinensis protein peptides were analyzed and compared with Schisandra chinensis protein. Results: The optimum enzyme for the preparation of Schisandra chinensis protein peptides was alkaline protease. The optimum enzymatic hydrolysis parameters were as follows: substrate concentration 5%, enzyme-to-substrate ratio 1%, enzymatic hydrolysis time 3 h, enzymatic hydrolysis temperature 55 °C, pH9.0. Under these conditions, the polypeptide content was 88.61%, the degree of hydrolysis was 24.21%, and the DPPH* scavenging rate was 86.96%. The free radical scavenging ability and reducing ability of Schisandrae chinensis protein peptides were better than those of Schisandrae chinensis protein. Conclusion: This study determined the optimum enzyme and hydrolysis process of Schisandrae chinensis protein peptides, and pointed out that Schisandrae chinensis protein peptides had better antioxidant activity in vitro and could be used as a natural antioxidant. [ABSTRACT FROM AUTHOR]

Although power conversion efficiency (PCE) of solar cells (SCs) continues to improve, they are still far from practical application because of their complex synthesis process, high cost and inferior operational stability. Carbon quantum dots with high material stability and remarkable photoluminescence are successfully used in light‐emitting diodes. A good light emitter should also be an efficient SC according to the photon balance in Shockley–Quieisser formulation, in which all excitons are ultimately separated. However, the finite quantum‐sized sp2 domain leads to tight exciton bonding, and highly delocalized electron clouds in irregular molecular stacks form disordered charge transfer, resulting in severe energy loss. Herein, an axially growing carbon quantum ribbon (AG‐CQR) with a wide optical absorption range of 440–850 nm is reported. Structural and computational studies reveal that AG‐CQRs (aspect ratio ≈2:1) with carbonyl groups at both ends regulate energy level and efficiently separate excitons. The stacking‐controlled two‐dimensional AG‐CQR film further directionally transfers electrons and holes, particularly in AB stacking mode. Using this film as active layer alone, the SCs yield a maximum PCE of 1.22%, impressive long‐term operational stability of 380 h, and repeatability. This study opens the door for the development of new‐generation carbon‐nanomaterial‐based SCs for practical applications. [ABSTRACT FROM AUTHOR]

We present generalizing statistical surrogate models to select an optimized photosensitizer, TPP, which is subsequently loaded into autologous exosomes by ultrasonic mixing to achieve tumor targeting with enhanced therapeutic efficacy by photodynamic therapy (PDT). This work contributes to the exploration of PDT enhancement strategies, and provides new prospects for using autologous exosomes as a delivery system. [ABSTRACT FROM AUTHOR]

Accurate jamming detection is a prerequisite and important support for efficient anti‐jamming communication in integrated sensing and communications. In recent years, the reactive follower jamming, characterized by the high jamming efficiency and the difficulty of detection, is seen as the main opponent for frequency hopping spread spectrum communication, which poses a threat to the reliability of wireless communication. Based on the background, a multiple‐nodes based cooperative jamming detection scheme is proposed for accurately detecting the reactive follower jamming. Specifically, the secondary nodes send the average energy of the suspected attacked symbols to the primary user as the uplink data in each detection period, and then the detection result is provided by the detection threshold. The optimal detection threshold is derived for the primary user according to the uplink data and channel conditions. In addition, the theoretical probability of detection under the optimal threshold is derived, and the complexity of the scheme is analysed. Simulation results show that the proposed multiple‐nodes based cooperative jamming detection scheme for reactive follower jamming can effectively improve the probability of detection. [ABSTRACT FROM AUTHOR]

Acute pancreatitis (AP) is an inflammatory disease of the pancreas. Despite of a steadily increasing in morbidity and mortality, there is still no effective therapy. Gut microbial dysbiosis and its derived-metabolites disorder have been shown to play an important role in the development of AP, however, little is known regarding the crosstalk between gut microbiota and metabolites. In this study, we assessed the alterations in gut microbiota and metabolites by constructing three AP mouse models by means of metagenomic and metabolomic sequencing, and further clarified their relationship by correlation analysis. The results revealed that each model exhibited unique flora and metabolite profiles. KEGG analysis showed that the differential flora and metabolite-enriched pathway functions were correlated with lipid metabolism and amino acid metabolism. Moreover, two core differential bacterial species on Burkholderiales bacterium YL45 and Bifidobacterium pseudolongum along with eleven differential metabolites appeared to exert certain effects during the course of AP. In conclusion, further exploration of the crosstalk between microbiota and derived metabolites may provide novel insights and strategies into the diagnosis and treatment of AP. [ABSTRACT FROM AUTHOR]

Purpose: Sarcopenia is considered to be an important predictor of adverse outcomes following spinal surgery, but the specific relationship between the two is not clear. The purpose of this meta-analysis is to systematically review all relevant studies to evaluate the impact of sarcopenia on spinal surgery outcomes. Methods: We systematically searched PubMed, Embase and the Cochrane Library for relevant articles published on or before January 9, 2023. The pooled odds ratio (OR) with 95% confidence intervals (CIs) was calculated in a random effects meta-analysis. The main outcome was the risk of adverse outcomes after spinal surgery, including adverse events and mortality. This systematic review and meta-analysis was conducted following the PRISMA guidelines to evaluate the impact of sarcopenia on spinal surgery outcomes. In addition, we also conducted a subgroup analysis and leave-one-out sensitivity analyses to explore the main sources of heterogeneity and the stability of the results. Results: Twenty-four cohort studies, with a total of 243,453 participants, met the inclusion criteria. The meta-analysis showed that sarcopenia was significantly associated with adverse events (OR 1.63, 95% CI 1.17–2.27, P < 0.001) but was no significantly associated with mortality (OR 1.17, 95% CI 0.93–1.46, P = 0.180), infection (OR 2.24, 95% CI 0.95–5.26, P < 0.001), 30-day reoperation (OR 1.47, 95% CI 0.92–2.36, P = 0.413), deep vein thrombosis (OR 1.78, 95% CI 0.69–4.61, P = 0.234), postoperative home discharge (OR 0.60, 95% CI 0.26–1.37, P = 0.002) and blood transfusion (OR 3.28, 95% CI 0.74–14.64, P = 0.015). Conclusion: The current meta-analysis showed that patients with sarcopenia have an increased risk of adverse events and mortality after spinal surgery. However, these results must be carefully interpreted because the number of studies included is small and the studies are significantly different. These findings may help to increase the clinicians' awareness of the risks concerning patients with sarcopenia to improve their prognosis. [ABSTRACT FROM AUTHOR]

To tackle the issue of the traditional intelligent diagnostic algorithm's insufficient utilization of correlation characteristics within the time series of fault signals and to meet the challenges of accuracy and computational complexity in rotating machinery fault diagnosis, a novel approach based on a recurrence binary plot (RBP) and a lightweight, deep, separable, dilated convolutional neural network (DSD-CNN) is proposed. Firstly, a recursive encoding method is used to convert the fault vibration signals of rotating machinery into two-dimensional texture images, extracting feature information from the internal structure of the fault signals as the input for the model. Subsequently, leveraging the excellent feature extraction capabilities of a lightweight convolutional neural network embedded with attention modules, the fault diagnosis of rotating machinery is carried out. The experimental results using different datasets demonstrate that the proposed model achieves excellent diagnostic accuracy and computational efficiency. Additionally, compared with other representative fault diagnosis methods, this model shows better anti-noise performance under different noise test data, and it provides a reliable and efficient reference solution for rotating machinery fault-classification tasks. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the adhesion of municipal solid waste incinerator bottom ash (MSWIBA) and limestone to asphalt using the principles of surface energy theory. MSWIBA asphalt mixes are known for their satisfactory water stability, with adhesion being a crucial factor, as demonstrated in previous freeze-thaw splitting tests. Contact angles of ground MSWIBA and limestone were measured using a capillary rise test, eliminating the influence of material surface structure. Subsequently, adhesion work and spalling work were calculated to correlate with water-induced damage. The results showed that the sequence of adhesion work was fine MSWIBA > limestone > coarse MSWIBA, while the sequence of spalling work was coarse MSWIBA > limestone > fine MSWIBA. The adhesion of MSWIBA asphalt mixes was evaluated using the water boiling test image method to validate the findings of surface energy theory. In both asphalt tests, the spalling rate followed this sequence: coarse MSWIBA > limestone > fine MSWIBA. The Adhesion index ER ranked as follows: ER Fine MSWIBA > ER limestone > ER coarse MSWIBA. These results suggest that the water stability of MSWIBA asphalt mixes is influenced by the surface energy of the components. Consequently, indices derived from surface energy theory can be applied in formulating and predicting the properties of MSWIBA asphalt mixes. [ABSTRACT FROM AUTHOR]

Uncertain regression model with autoregressive time series errors studies the relations between response variables and explanatory variables when the errors are dependence. The estimations of the unknown parameters are worth exploring in further research. The least square (LS) estimations were proposed to estimate the unknown parameters. However, when the outliers appear, we find that the LS is not effective. So, in this article, the least absolute deviations (LAD) and the maximum likelihood estimations (MLE) are proposed to estimate the unknown parameters. Moreover, some examples are given to verify the applicability and practicability of these two methods. The comparative analysis is given to verify that the proposed methods are more advantageous and effective than the least square method when the observed data are affected by outliers. Finally, the example of exploring the relationships between GDP and related influencing factors for China, from 2004 to 2020, is given to testify the practicability of the methods. [ABSTRACT FROM AUTHOR]

In order to prevent and reduce the disaster losses caused by compound disasters to cities, it was of great theoretical and practical significance to study the exposure degree of chain compound disaster system. Through the research and analysis of the exposure degree of chain compound disaster system, considering the existing research and defining the connotation of the exposure degree of the compound disaster system, the evaluation model and calculation method of the exposure degree of the chain compound disaster system were deduced and established. Taking the rainstorm-landslide disaster chain in Guangdong-Hong Kong-Macao Greater Bay Area as an example for empirical analysis, the evaluation index system of regional disaster chain exposure was screened and constructed from the aspects of population, economy and society. The order relationship analysis and TOPSIS method were used to calculate the single disaster exposure index of rainstorm and landslide in 52 districts and counties in the bay area. Then, the theoretical model of exposure degree of chain compound disaster system and ArcGIS image processing technology were used to obtain the zoning map of rainstorm-landslide disaster chain exposure in the Guangdong- Hong Kong-Macao Greater Bay Area, and the corresponding disaster prevention and mitigation measures were put forward according to the exposure degree attribute of each regional disaster chain. The results showed that the exposure index corresponding to the economically developed areas was significantly higher than that of other regions, and the low exposure were mainly in the relatively backward areas. Among the indicators, population density and GDP per land area account for the main position, which had the highest impact on regional exposure. By the exposure of regional compound disaster systems, reasonable scientific basis and decision-making support could serve for the government and relevant departments, helping the government, urban planners, and the public better understand and respond to disaster risks, thereby reduce the losses and impacts of disasters. [ABSTRACT FROM AUTHOR]

Considering that different regions and races have different skeletal development stages, it is imperative to adopt appropriate standards for the age assessment of specific populations. The study aimed to identify a suitable method for knee epiphysis grading and evaluate the feasibility of age estimation based on magnetic resonance imaging (MRI) and X-ray in the Chinese population. MRI images and X-ray images of the knee were collected from 1018 volunteers and 833 volunteers aged 6–20 years old, respectively. A modified Kramer's classification method was used for the experiment. The minimum, maximum, mean, and standard deviation of radiography and MRI were defined for males and females in each age group. The ossification process in knee joints of the female volunteers occurred earlier compared to the male, and the X-ray grading was higher than that of the MRI during the same period. In the regression analysis, the R2 value with age of MRI was generally higher than the X-ray in males. MRI can be used as an effective, reliable, non-invasive, and non-radiation method for evaluating bone age. This study verified the feasibility of applying the knee epiphysis grading for forensic investigations in Chinese population. [ABSTRACT FROM AUTHOR]