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Soil salinization and water scarcity are main restrictive factors for irrigated agriculture development in arid regions. Knowing dynamics of soil water and salt content is an important antecedent in remediating salinized soils and optimizing irrigation management. Previous studies mostly used remote sensing technologies to individually monitor water or salt content dynamics in agricultural areas. Their ability to asses different levels of crop water and salt management has been less explored. Therefore, how to extract effective diagnostic features from remote sensing images derived spectral information is crucial for accurately estimating soil water and salt content. In this study, Linear spectral unmixing method (LSU) was used to obtain the contribution of soil water and salt to each band spectrum (abundance), and endmember spectra from Sentinel-2 images. Calculating spectral indices and selecting optimal spectal combination were individually based on soil water and salt endmember spectra. The estimation models were constructed using six machine learning algorithms: BP Neural Network (BPNN), Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), Gradient Boost Regression Tree (GBRT), and eXtreme Gradient Boosting tree (XGBoost). The results showed that the spectral indices calculated from endmember spectra were able to effectively characterize the response of crop spectral properties to soil water and salt, which circumvent spectral ambiguity induced by water-salt mixing. NDRE spectral index was a reliable indicator for estimating water and salt content, with determination coefficients (R2) being 0.55 and 0.57, respectively. Compared to other models, LSU-XGBoost model achieved the best performance. This model properly reflected the process of soil water-salt dynamics in farmland during crop growth period. This study provided new methods and ideas for soil water-salt estimation in dry irrigated agricultural areas, and provided decision support for governance of salinized land and optimal management of irrigation.

To investigate the changes in the quality of yak meat at different dry aging times (7, 14, 21, 28 and 35 d), in this study, sensory evaluation, texture analysis, and GC-IMS were employed to investigate the changes in texture and flavor of yak meat during different dry aging periods. Additionally, PLS-DA was utilized to identify the characteristic flavor compounds. The results showed that the dry aged 35 days sample exhibited the highest comprehensive score, with intact soft tissue structure and a favorable overall acceptability of aroma. The dry aging time had significant effects on the hardness, chewability, and adhesivity of yak meat (P

To investigate the effects of Angel yeast, Meyerozyma guilliermondii, Saccharomyces cerevisiae, Zygosaccharomyces rouxii and Candida parapsilosis fermentation on volatile flavour compounds in bean paste, the volatile flavour substances of fermented bean soup paste were analyzed by gas chromatography-ion mobility spectrometry (GC-IMS) technique using unfermented bean soup paste as a control group. The relative odour activity value (ROAV) method was used to determine the key flavour components. Principal component analysis (PCA) and partial least squares-discrimination analysis (PLS-DA) were used to comprehensively evaluate the aroma of fermented bean soup paste. A total of 50 volatile compounds were identified, which were primarily esters and alcohols in six fermented bean sauces. These compounds contribute to the distinct mellow, fruity, and malty flavour profiles characteristic of these sauces. The types and concentrations of volatile compounds in different fermented bean sauces exhibited statistically significant differences (P

Cooking methods could have a great influence on the flavour of food, to investigate the effect of cooking methods on the aroma of pork, this study used electronic nose combined with gas chromatography-ion mobility spectrometry (GC-IMS) for multivariate statistical analyses of the volatile compounds in pork under different cooking methods. GC-IMS was used to characterise 100 volatile compounds (including monomers and dimers) of pork under different cooking methods. Finally, a stable model was established using orthogonal partial least squares discriminant analysis. Results showed that, the cooking method is crucial for the formation of pork flavour compounds. Pork cooked by oven roasting and air fryer methods contained characteristic volatile compounds such as ethyl (E)-2-hexenoate, 1-octen-3-ol, (Z)-4-decenal, 3-nonen-2-one, (E)-2-hexen-1-ol, propyl butyrate, furazanone, and dipentenyl-M, as compared to the two cooking methods of steaming and boiling. The air fryer method pork contained higher levels of (E)-ethyl 2-hexenoate, 1-octen-3-ol, (Z)-4-decenal, and 3-nonen-2-one than the oven-roasted pork. Valeraldehyde and ethyl acetate were labelled as characteristic volatile substance compounds for both steamed and boiled cooking styles, and the results from the electronic nose sensor showed that the RPCA model was effective in identifying volatile substances in pork for each cooking style. The results of the study revealed the effects of different cooking methods on the volatile components of pork and found that the aromas of air fryer preparation and oven roasting were more similar, providing some reference for the cooking and processing of pork.

Background Tennis, the second-largest ball game in the world, has a particularly wide audience. To date, little research has been conducted on the biomechanics of female serves. Purpose The purpose of this study was to capture the key moments by using 1,000 frames/s high-speed video analysis, to analyze the kinematics of the serving techniques of national athletes and provincial athletes, to determine the differences between the two levels of athletes, and to provide theoretical references for the improvement of scientific training level. Methods Ten female athletes were selected as participants for this study, five of whom are national athletes, and the other are provincial athletes. Three-dimensional filming techniques were employed to capture videos of the first and second serve techniques. Simi Motion was applied to obtain the 3D kinematic data. Statistical analyses were performed using IBM SPSS Statistics 27.0, and Mann–Whitney U tests were conducted to determine differences between groups. Results Significant differences in kinematics were found between national and provincial athletes. At the moment of the left knee’s minimum flexion (T2), in the first serve, national athletes had a smaller shoulder-hip vertical plane angle (−18.281 ± 6.142° vs. −25.631 ± 3.497°; p = 0.047) and a larger hip vertical plane rotation angle (−9.378 ± 4.263° vs. −0.470 ± 4.724°; p = 0.047). In the second serve, national athletes had a smaller hip horizontal plane rotation angle (−1.720 ± 4.683° vs. 24.146 ± 24.014°; p = 0.047) but a larger hip vertical plane rotation angle (−11.553 ± 1.949° vs. −0.422 ± 4.958°; p = 0.009). At the moment of impact (T4), in the second serve, national athletes’ batting position (0.296 ± 0.088 m vs. 0.446 ± 0.094 m; p = 0.047) was further back. Additionally, in the second serve, national athletes consistently had their body center of gravity further back at T2 (−0.106 ± 0.052 m vs. −0.018 ± 0.048 m; p = 0.028), T3 (0.002 ± 0.038 m vs. 0.132 ± 0.039 m; p = 0.009), and T4 (0.073 ± 0.050 m vs. 0.217 ± 0.034 m; p = 0.009). Conclusions The results of this study indicated several significant kinematic differences between national and provincial athletes, these variations were noted in the shoulder, hip, and body center of gravity. In summary, for the overall first and second serves, it is recommended that national athletes increase the horizontal plane angle of the shoulders and hips at T2, whereas provincial athletes decrease the horizontal plane angle of the shoulder–hip. In addition, provincial athletes need to increase the vertical plane angle of the hip joint, so that the top of the hip can be increased more, and provincial athletes need to be careful not to have the center of gravity too far in front of the body at T2, T3, and T4, so that it can hit the ball at a higher position to increase the swing speed.

The vitrinite-rich concentrates were extracted respectively from the Datong bituminous coal and the Xinjiang bituminous coal by the density gradient centrifugation method. The activated carbons were prepared by physical activation with carbon dioxide using the above raw coals and vitrinite. The structural properties of vitrinite and the corresponding raw coals were compared, and the characteristics of crystallite and pore structure were analyzed after the carbonization and CO2 activation process. The differences of aromatic and aliphatic hydrocarbon structures of coal and its vitrinite samples were analyzed by Fourier transform infrared spectroscopy. Microcrystalline structure parameters and aromaticity index of coals, chars and activated carbons were analyzed by X-ray diffractometer. The pore structure parameters of chars and activated carbons were studied by the low-temperature nitrogen adsorption method. Compared with raw coals, the crystallite unit sizes of vitrinite were smaller, the length of aliphatic hydrocarbon chain was shorter, and the number of branch chains was larger than those of the corresponding coals, which was beneficial to enhance the fluidity in the carbonization process. Moreover, the weight loss rates of vitrinite were higher, and the weight loss peaks were wider than that of the coals during the carbonization process by thermogravimetric analyzer. The above differences of structural properties and carbonization reactivity of coal and its vitrinite samples could affect the structure characteristics of chars. The results showed that the crystallite lamella size and stacking thickness of vitrinite chars were both increased after carbonization at 850 ℃, while the stacking thickness of crystallite in coal chars was decreased, which indicated that the differences of maceral mainly affected the vertical stacking of crystallite lamella during carbonization. Meanwhile, it led to higher aromaticity and larger BET specific surface area of both vitrinite chars. After the CO2 activation process, the stacking thickness and lamella size of crystallite in activated carbons were lower than that of the corresponding chars, indicating that the crystallite unit got burnt by CO2 activation. The dominant micropore structures were obtained after activation process. The BET specific surface areas of activated carbons from vitrinite reached 744.1 and 797.4 m2/g, respectively, which were significantly higher than that of activated carbons from coals. The micropore and mesopore volumes of vitrinite-based activated carbons were both higher than that of coal-based activated carbons, and the micropore volumes were 1.7−2.2 times more than that of coal-based activated carbons. The above results indicated that the chars with higher aromaticity and more porosity were beneficial to generate micropore structure during activation process.

Background: Although still the significance of the lung-gut axis for COPD is increasingly highlighted, it’s urgent to ulteriorly comprehend the sophisticated disturbance of the core material system along the lung-gut axis, which is of great importance for the accurate precaution and prognosis of COPD efficiently. Aim of the study: The purpose of this study was to analyze the information connections of the lung-gut axis, thus supporting the effective treatment of COPD. Materials and methods: An integrated multi-omics approach was applied to explore the lung-gut axis in COPD rats. Firstly, based on transcriptomics, the ssGSEA algorithm was used to evaluate changes in pulmonary inflammatory cells. Then, the disturbances of metabolic pathways in lung and feces were revealed by the Lilikoi algorithm using LC-MS and 1H NMR metabolomics. Next, the composition and function of microbial communities in lung and feces were analyzed by 16 s rRNA sequencing. Finally, the association analysis was employed to explore the possible crosstalk between the lung and gut. Furthermore, the core material system in the lung-gut axis was described based on network topology analysis. Result: Firstly, 1652 differential expression genes (involving in immune response-regulating signaling pathway, etc.) and 15 types of inflammatory cells (including neutrophil, etc.) were identified related to COPD. 135 pulmonary differential metabolites (involving in arachidonic acid metabolism, etc.) and 105 fecal differential metabolites (involving in alanine metabolism, etc.) were revealed by metabolomics. The f_Pasteurellaceae, etc. and g_Ruminococcus_2, etc. were identified associated with COPD in lung and gut. Finally, disturbances of the core material system, composed of macrophage, neutrophil, activated dendritic cell, myeloid derived suppressor cell, arachidonic acid metabolism, alpha linolenic acid & linoleic acid metabolism, g_Psychrobacter in lung and bile secretion, p_Proteobacteria in gut, were obtained to analyze the possible information flow of the lung-gut axis. Conclusion: The core material system for the lung-gut axis have been revealed, which might contribute to the illustration of the pathogenesis of COPD. In the future, more researches are required on the impact of the core material system in the lung-gut axis on the onset and recovery process of COPD, suggesting more precise identifying effective treatments for the disease.

The intelligent senses (Electronic nose and tongue), were combined with headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) and free amino acid were used in combination to determine the aroma and taste components during the processing of Chinese traditional dish Steamed beef with rice flour (SBD). The findings revealed that E-nose and E-tongue, could clearly distinguish and identify the aroma and taste of SBD. A total of 66 volatile substances and 19 free amino acids were identified by HS-GC-IMS and amino acid analyzer, respectively. The highest contribution to aroma in the production of SBD was alcohols, esters and aldehydes. Further analysis of relative odor activity showed that 3-Methylbutanol-D, 3-Methylbutanol-M and 3-Methylthio propanal is the marinating stage (T2) main aroma components. Ethyl 3-methylbutanoate-M and Ethyl 3-methylbutanoate-D were the main aroma components in the seasoning stage (T3). Additionally, the calculation of the taste activity value showed that Glutamic contributed significantly to the umami of SBD. Alanine was a representative taste component in the marinating stage (T2), while Proline, Aspartic, Lysine, Glutamic, Valine, Arginine, and Histidine were characteristic amino acids of the seasoning stage (T3). Consequently, this study offers valuable insights into the industrial-scale production and flavor regulation of SBD products.

Abstract Quantum oscillation (QO), a physical phenomenon that reflects the characteristics of the Fermi surface and transport fermions, has been extensively observed in metals and semimetals through various approaches, like magnetostriction, magnetization, resistivity, and thermoelectric power. However, only limited oscillation frequencies can be revealed by the aforementioned methods, particularly in semimetals with intricate Fermi pockets and associated magnetic breakdown phenomena. In this paper, we present the application of an ac composite magnetoelectric (ME) technique to measure the QOs of a topological nodal-line semimetal, ZrSiS, which possesses six fundamental QO frequencies. By employing the ME technique with a maximum magnetic field of 13 T and a minimum temperature of 2 K, we are able to capture all the fundamental frequencies and most of the permissible magnetic breakdown frequencies. Remarkably, a series of magnetic breakdown frequencies around 8000 T were revealed even in a magnetic field as low as 7.5 T. These findings highlight the ME technique as an ultrahigh-sensitive tool for studying Dirac Fermions and other topological semimetals with complex Fermi surfaces.

The traditionally produced pea paste (PP) suffers from suboptimal flavor and inferior quality. Based on the study of single-strain fermentation, we further selected S. cerevisiae, Z. rouxii, and L. paracasei for PP production by dual-strain fermentation (SL, ZL). By combining intelligent sensory technology, gas chromatography–mass spectrometry (GC-MS), and ultra-high-performance liquid chromatography (UPLC) technology, the aroma and taste characteristics of SL- and ZL-fermented PP were compared. The electronic nose and tongue revealed the differences in the aroma and taste characteristics between the two fermentation methods for fermenting PP. In total, 74 volatile compounds (VOCs) in PP were identified through GC-MS analysis. In contrast, the number of VOCs and the concentrations of alcohols and acids compounds in SL were higher than in ZL. Among the 15 VOCs that were common to both and had significant differences, the concentrations of ethanol, 1-pentanol, and ethyl acetate were higher in SL. For taste characteristics, SL demonstrated significantly higher levels of sweet and bitter amino acids, as well as tartaric acid, compared with ZL. These results elucidate the flavor differences of dual-strain fermented PP, providing a theoretical basis for selecting suitable strains for fermenting PP.

Accurate, timely, and continuous soil moisture information is helpful for crop stress diagnosis and irrigation management decision. OPtical TRApezoid Model (OPTRAM) based on optical satellite data has been proven to be an effective method for assessing soil moisture status. However, the applicability of OPTRAM to small-scale field soil moisture assessment remains to be explored. In this study, we propose a strategy for the genetically parameterized OPTRAM and evaluate its applicability on Unmanned Aerial Vehicle (UAV) high-resolution hyspectral data. The results showed that: (1) When OPTRAM was used to genetically parameterized with PROSAIL generated dataset, 46 characteristic narrowband bands (|R|= 0.52–0.78) were determined in the spectral region of near infrared (NIR) (750–850 nm) and SWIR (1060–1080 and 1450–1500 nm); (2) By fine-tuned soil moisture estimation model using transfer learning strategy, the reliable soil moisture estimation was achieved in three crops (R2=0.57–0.64; RMSE=0.008–0.022 m3m−3);(3) Compared to soil moisture estimation model using a single spectral region (NIR or SWIR), the DSWC model that combine NIR and SWIR was more effective for tracking soil moisture; (4) The scale effect was observed when the fine-tuned soil moisture estimation model was applied on the high-resolution UAV images. The model performance was stable in pixel size of 1–7 cm and began to drop at pixel size of 11 cm. The above results advance the application of OPTRAM on small farmland soil moisture assessment and demonstrate the application potential of OPTRAM on narrow-band hyperspectral data. This study provides a new candidate for the use of hyperspectral data to estimate soil moisture, and scientific support for precision agriculture and irrigation scheduling.

High oxidative stress and inflammatory cell infiltration are major causes of the persistent bone erosion and difficult tissue regeneration in rheumatoid arthritis (RA). Triptolide (TPL) has become a highly anticipated anti-rheumatic drug due to its excellent immunomodulatory and anti-inflammatory effects. However, the sudden drug accumulation caused by the binding of “stimulus-response” and “drug release” in a general smart delivery system is difficult to meet the shortcoming of extreme toxicity and the demand for long-term administration of TPL. Herein, we developed a dual dynamically cross-linked hydrogel (SPT@TPL), which demonstrated sensitive RA microenvironment regulation and microenvironment modulation-independent TPL release for 30 days. The abundant borate ester/tea polyphenol units in SPT@TPL possessed the capability to respond and regulate high reactive oxygen species (ROS) levels on-demand. Meanwhile, based on its dense dual crosslinked structure as well as the spontaneous healing behavior of numerous intermolecular hydrogen bonds formed after the breakage of borate ester, TPL could remain stable and slowly release under high ROS environments of RA, which dramatically reduced the risk of TPL exerting toxicity while maximized its long-term efficacy. Through the dual effects of ROS regulation and TPL sustained-release, SPT@TPL alleviated oxidative stress and reprogrammed macrophages into M2 phenotype, showing marked inhibition of inflammation and optimal regeneration of articular cartilage in RA rat model. In conclusion, this hydrogel platform with both microenvironment initiative regulation and TPL long-term sustained release provides a potential scheme for rheumatoid arthritis.

Objective The purpose of this study was to investigate whether a virtual reality (VR) program designed and developed based on the hallucinogenic harm reduction and integration (PHRI) clinical model could be more effective in guiding positive thinking training, improving positive thinking awareness and ability, and, to some extent, facilitating personal efficacy and emotional state compared to a traditional VR program that places users in a virtual natural ecological environment to guide positive thinking training. We also sought to understand the factors that may influence the effectiveness of VR interventions and user experience. Method Seventy-six randomly recruited participants were divided into a control group and an experimental group of 38 participants, each according to a random number table, and were trained in VR meditation for eight weeks. The experimental group used a PHRI-based mindfulness program, while the control group used a traditional mindfulness meditation program. We used The Mindful Attention Awareness Scale and the PAD emotional three-dimensional scale to assess the level of state mindfulness and changes in the emotional state before and at the end of the experiment. The Immersive Tendencies Questionnaire measured the user's sense of presence and immersion in the virtual environment. The Five Facet Mindfulness Questionnaires and the Depression Anxiety and Stress Scale (DASS-21) were used at the baseline assessment stage before and at the 4-week follow-up after the experiment to assess the change in trait mindfulness levels due to the mindfulness training. The Five Facet Mindfulness Questionnaires and the DASS-21 were used to assess changes in mindfulness and mental health trait levels. Results At the end of the experiment, the MMSQ score was significantly lower in the control group than in the experimental group, while the ITQ score was significantly higher than in the experimental group, and both scores were statistically significant ( p

Abstract Inflammation plays a crucial role in the progression of Subacute Ruminal Acidosis (SARA). The experiment was designed to investigate anti-inflammatory effects of glycyrrhizin on goats ruminal epithelial cells (GREC) which were induced SARA by Lipopolysaccharide (LPS) in vitro. The GREC were induced SARA by adding LPS at the concentration of 5 μm and glycyrrhizin was added at different concentration of 0, 60, 90, 120, 150 μm. The structural integrity of LPS-induced GREC with the treatment of glycyrrhizin were observed by electron microscope; The levels of inflammatory factors TNF-α, IL-1β, IL-6, IL-8 and IL-12 were measured by ELISA; The number of Zo-1 and Occludin were measured, the expression of tight junction protein Occludin were measured by Western blot, and the mRNA expression of NF-κB, TNF-α, IL-1β, IL-6, IL-8 and IL-12 were measured in vitro. The results showed that higher concentration treatment of glycyrrhizin led to better morphology in LPS-induced GREC. Glycyrrhizin inhibited the growth of inflammatory factors TNF-α, IL-1β, IL-6, IL-8 and IL-12 in a dose-dependent manner. The number of ZO-1 and Occludin increased with the increase of adding of glycyrrhizin. Western blot analysis showed that the expression of tight junction protein Occludin in LPS-induced GREC increased with the adding of glycyrrhizin in a dose-dependent manner. Furthermore, the mRNA expression of NF-κB, TNF-α, IL-1β, IL-6, IL-8 and IL-12 decreased significantly with the increase treatment of glycyrrhizin. Glycyrrhizin significantly inhibits LPS-induced inflammatory mediators in GREC and the effects are better with the increase treatment of glycyrrhizin in vitro.

Acute lung injury (ALI) is a clinically prevalent inflammatory disorder that still needs to be developed with more accurate diagnostic biomarkers and more satisfactory therapies. This study intended to explore the therapeutic material basis and molecular mechanisms of a classical traditional Chinese medicinal prescription, Sang Ju Yin (SJY), against ALI, focusing on the chemical components of formula composition, the disturbance of host genes/metabolites and the dysbiosis of intestinal flora. First, based on liquid chromatography-mass spectrometry (LC-MS) component analysis, 212 in vitro and 44 in vivo compounds were identified respectively in SJY. Then, network pharmacology was adopted to calculate potential anti-ALI compounds in SJY and predict that CXCR2, PI3K-Akt signaling and arachidonic acid (AA) metabolism could be potential targets. Subsequently, integrative multi-omics techniques were employed to elaborate deeper systematic molecular mechanisms. Metabolomic data combining both 1H NMR and LC-MS techniques illustrated that 146 pulmonary and 75 fecal biomarkers, associated with AA and other metabolisms, were recuperated by SJY’s intervention. Transcriptomic analyses suggested that SJY could significantly regulate genes and signaling pathways involved with inflammation and apoptosis, such as PI3K-Akt. Further the obtained key targets (IL-10, LCAT, CXCR2 and C5) were verified by qRT-PCR and their relative compound-target interaction were validated by molecular docking. Notably, the disturbance of intestinal microbial community (such as the abundance of Lactobacillus, etc.) was detected through 16S rRNA gene sequencing, that could be effectively reshaped by SJY. Collectively, our integrated work showed SJY could regulate the crosstalk of metabolite-target-pathway-microflora along the gut-microbiota-lung axis on the whole. Noteworthy, our study provided fundamental and new insights into how molecular networks connected different types of components, genes, metabolites, microbes and potential pathways to map an endogenous functional landscape for clinical ALI diagnosis and SJY application.

The escalating challenge of municipal solid waste (MSW) critically tests the sustainable development capacities of urban centers. In response, China initiated pilot policies in 2017 aimed at bolstering MSW management. The effectiveness of these initiatives, however, necessitates empirical scrutiny. This study leverages panel data spanning 95 cities at the prefectural level or higher, covering the period from 2006 to 2020, to assess the impact of the MSW sorting pilot policy on urban sustainable development using a difference-in-differences approach. The research found that the MSW sorting pilot policy has significantly increased the processing volume of MSW, thereby enhancing the sustainable development capabilities of cities. Further, the study identifies augmented fixed asset investments as a key mechanism through which pilot cities have enhanced their MSW management capabilities. Notably, the policy's stimulative effects are more pronounced in less densely populated and economically lagging regions. These findings provide critical insights for developing nations in shaping MSW sorting strategies and advancing urban sustainability.

As one of the most vital energy conversation systems, the safe operation of wind turbines is very important; however, weak fault and time-varying speed may challenge the conventional monitoring strategies. Thus, an entropy-aided meshing-order modulation method is proposed for detecting the optimal frequency band, which contains the weak fault-related information. Specifically, the variable rotational frequency trend is first identified and extracted based on the time–frequency representation of the raw signal by constructing a novel scaling-basis local reassigning chirplet transform (SLRCT). A new entropy-aided meshing-order modulation (EMOM) indicator is then constructed to locate the most sensitive modulation frequency area according to the extracted fine speed trend with the help of order tracking technique. Finally, the raw vibration signal is bandpass filtered via the corresponding optimal frequency band with the highest EMOM indicator. The order components resulting from the weak fault can be highlighted to accomplish weak fault detection. The effectiveness of the proposed EMOM analysis-based method has been tested using the experimental data of three different gear fault types of different fault levels from a planetary test rig.

The study focuses on the four-strand tundish as the research object, aiming at the phenomenon of fewer strand casting (stable blockage) and sudden blockage of the tundish in industrial production. Numerical simulation methods are employed to compare the velocity vectors, flow fields, residence time distribution (RTD) curves, and outflow percentage curves under stable blockage and sudden blockage of the tundishes with a double-weir structure, U-shaped weir structure, and U-shaped weir structure with holes in the front. The results indicate that, after sudden blockage of the tundish strands, the flow field transitions from an unstable four-strand flow field to a stable three-strand flow field. Both the double-weir tundish and the U-shaped weir tundish reach a stable state after 200 s, while the U-shaped weir tundish with holes in the front reaches stability after 150 s. Additionally, compared to other structures, the tundish strands of the U-shaped weir with holes in the front are less affected by blockage, showing better consistency among strands and better adaptability under non-standard casting conditions.

Salt is a key ingredient that can both enhance the taste and extend the shelf life of fermented vegetables. However, it is important to note that excessive salt levels can have adverse effects on consumer health. This study aimed to investigate the impact of various salt additions (2%, 4%, 6%, 8%, and 10% wt/wt) on the flavor profile of fermented ciba pepper, a traditional Chinese fermented chili sauce, using gas chromatography–ion mobility spectrometry (GC-IMS) in combination with an electronic nose (E-nose). Fermented ciba pepper samples were prepared with different salt additions: 2% (LJA), 4% (LJB), 6% (LJC), 8% (LJD), and 10% (LJE) (wt/wt). The physicochemical and sensory properties of the fermented ciba pepper samples were evaluated. Sensory evaluation indicated that LJC and LJD received higher scores compared to the other groups. The total acid and amino acid nitrogen contents displayed contrasting trends with the salt additions (p < 0.05). The E-nose analysis successfully differentiated the flavor profiles of the ciba pepper samples fermented with varying salt additions. Additionally, the GC-IMS analysis identified a total of 72 volatile compounds, including 14 alcohols, 21 esters, nine aldehydes, four acids, eight ketones, three terpenes, and eight other substances. Notably, the ciba pepper samples with lower salt additions exhibited higher levels of alcohols, aldehydes, and esters. In conclusion, the addition of salt during the fermentation process significantly influenced the formation of flavor compounds in ciba pepper. This study provides valuable insights into ciba pepper fermentation with different salt additions and offers prospects for the development of low-salt fermented ciba pepper products.

Workers in deep coal mines are often exposed to hyperthermal conditions, which is a health and safety hazard. Although many novel approaches have been proposed in recent years, air distribution optimization combined with thermal analysis based on existing cooling systems has rarely been conducted. Here, heat dissipation in the deep mine environment was estimated based on field measurements and numerical simulations. A pilot study was conducted to validate the performance of the multistage cooling system and optimize the distribution ventilation. The results show that the “2 + 3” layout (Two air cooler outlets are in the machine lane, and three air cooler outlets are in the open-off cut) was superior to other schemes in terms of the temperature field and energy consumption. In addition, the outlet air temperature should be set to ≤20 °C, and the air volumetric flowrate of the longest outlet should be > 400 m3/min to improve the thermal environment of the wind lane. This effectively reduces the average temperature of the entire working face by 4.6–5.1 °C compared to the situation without cooling measures, thereby increasing the comfortable zone of the working face (

Abstract Endometritis is the failure of embryo implantation and an important cause of infertility in dairy cows. IFN-τ is a type I interferon unique to ruminants. In regulating the process of inflammatory response, IFN-τ can be expressed through MicroRNAs (miRNAs) to regulate the process of inflammation. However, IFN-τ regulates lipopolysaccharide (LPS)-induced inflammatory injury of bEECs through the highly conserved miR-26a in mammals, and the mechanism remains unclear. Bovine endometrial epithelial cells (bEECs)were isolated and cultured to establish an inflammatory injury model. RT–qPCR and ELISA were used to detect the secretion of inflammatory factors. Dual-luciferase assays and target gene silencing assays determine the regulatory role of miRNAs. The target protein was detected by immunofluorescence and western blotting. This study showed that the expression of miR-26a was significantly down-regulated in mouse endometrium inflammatory injury tissue and LPS stimulated bEECs; and IFN-τ reversed the expression of miR-26a. The study also showed that the overexpression of miR-26a significantly inhibited the secretion of pro-inflammatory cytokines IL-1β, IL-6 and TNF-α. In addition, studies have shown that miR-26a inhibits its translation by targeting PTEN 3′-UTR, which in turn activates the Phosphatidylinositide 3-kinases/protein kinase B (PI3K/AKT) pathway, so that nuclear factor kappa-B (NF-κB) signaling is inhibited. In summary, the results of this study further confirm that IFN-τ as an anti-inflammatory agent can up-regulate the expression of miR-26a and target the PTEN gene to inhibit the inflammatory damage of bEECs.

Using Meyerozyma guilliermondii YB4, which was isolated and screened from southern Sichuan pickles in the laboratory, as the experimental group, we investigated the changes in growth, total ester content, and volatile flavor substances of M. guilliermondii YB4 under different NaCl concentrations. The growth of M. guilliermondii YB4 was found to be inhibited by NaCl, and the degree of inhibition increased at higher NaCl concentrations. Additionally, the total ester content of the control group (CK) was significantly lower compared to the other groups (p < 0.05). The application of NaCl also resulted in distinct changes in the volatile profile of YB4, as evidenced by E-nose results. Gas chromatography-mass spectrometry (GC-MS) and gas chromatography-ion mobility spectrometry (GC-IMS) were employed to analyze the volatile compounds. A total of 148 and 86 volatiles were detected and identified using GC-MS and GC-IMS, respectively. Differential volatiles among the various NaCl concentrations in YB4 were determined by a variable importance in projection (VIP) analysis in partial least squares-discriminant analysis (PLS-DA). These differentially expressed volatiles were further confirmed by their relative odor activity value (ROAV) and odor description. Ten key contributing volatiles were identified, including ethanol, 1-pentanol, nonanal, octanal, isoamyl acetate, palmitic acid ethyl ester, acrolein, ethyl isobutanoate, prop-1-ene-3,3’-thiobis, and 2-acetylpyrazine. This study provides insights into the specificities and contributions of volatiles in YB4 under different NaCl concentrations. These findings offer valuable information for the development of aroma-producing yeast agents and the subsequent enhancement in the flavor of southern Sichuan pickles.

Childlessness or infertility among couples has become a global health concern. Due to the rise in infertility, couples are looking for medical supports to attain reproduction. This paper deals with diagnosing infertility among men and the major factor in diagnosing infertility among men is the Sperm Morphology Analysis (SMA). In this manuscript, we explore establishing deep learning frameworks to automate the classification problem in the fertilization of sperm cells. We investigate the performance of multiple state-of-the-art deep neural networks on the MHSMA dataset. The experimental results demonstrate that the deep learning-based framework outperforms human experts on sperm classification in terms of accuracy, throughput and reliability. We further analyse the sperm cell data by visualizing the feature activations of the deep learning models, providing a new perspective to understand the data. Finally, a comprehensive analysis is also demonstrated on the experimental results obtained and attributing them to pertinent reasons.

Deep learning-based Computer-Aided Diagnosis has gained immense attention in recent years due to its capability to enhance diagnostic performance and elucidate complex clinical tasks. However, conventional supervised deep learning models are incapable of recognizing novel diseases that do not exist in the training dataset. Automated early-stage detection of novel infectious diseases can be vital in controlling their rapid spread. Moreover, the development of a conventional CAD model is only possible after disease outbreaks and datasets become available for training (viz. COVID-19 outbreak). Since novel diseases are unknown and cannot be included in training data, it is challenging to recognize them through existing supervised deep learning models. Even after data becomes available, recognizing new classes with conventional models requires a complete extensive re-training. The present study is the first to report this problem and propose a novel solution to it. In this study, we propose a new class of CAD models, i.e., Deep-Precognitive Diagnosis, wherein artificial agents are enabled to identify unknown diseases that have the potential to cause a pandemic in the future. A de novo biologically-inspired Conv-Fuzzy network is developed. Experimental results show that the model trained to classify Chest X-Ray (CXR) scans into normal and bacterial pneumonia detected a novel disease during testing, unseen by it in the training sample and confirmed to be COVID-19 later. The model is also tested on SARS-CoV-1 and MERS-CoV samples as unseen diseases and achieved state-of-the-art accuracy. The proposed model eliminates the need for model re-training by creating a new class in real-time for the detected novel disease, thus classifying it on all subsequent occurrences. Second, the model addresses the challenge of limited labeled data availability, which renders most supervised learning techniques ineffective and establishes that modified fuzzy classifiers can achieve high accuracy on image classification tasks.

Precipitation nowcasting is mainly achieved by the radar echo extrapolation method. Due to the timing characteristics of radar echo extrapolation, convolutional recurrent neural networks (ConvRNNs) have been used to solve the task. Most ConvRNNs have been proven to perform far better than traditional optical flow methods, but they still have fatal problems. These models lack differentiation in the prediction of echoes of different intensities, which leads to the omission of responses from regions with high intensities. Moreover, because it is difficult for these models to capture long-term feature dependencies among multiple echo maps, the extrapolation effect declines sharply over time. This paper proposes an embedded multi-layer attention module (MLAM) to address the shortcomings of ConvRNNs. Specifically, an MLAM mainly enhances attention to critical regions in echo images and the processing of long-term spatiotemporal features through the interaction between input and memory features in the current moment. Comprehensive experiments were conducted on the radar dataset HKO-7 provided by the Hong Kong Observatory and the radar dataset HMB provided by the Hunan Meteorological Bureau. Experiments show that ConvRNNs embedded with MLAMs achieve more advanced results than standard ConvRNNs.

Abstract The remaining useful life (RUL) estimation of bearings is critical for ensuring the reliability of mechanical systems. Owing to the rapid development of deep learning methods, a multitude of data-driven RUL estimation approaches have been proposed recently. However, the following problems remain in existing methods: 1) Most network models use raw data or statistical features as input, which renders it difficult to extract complex fault-related information hidden in signals; 2) for current observations, the dependence between current states is emphasized, but their complex dependence on previous states is often disregarded; 3) the output of neural networks is directly used as the estimated RUL in most studies, resulting in extremely volatile prediction results that lack robustness. Hence, a novel prognostics approach is proposed based on a time–frequency representation (TFR) subsequence, three-dimensional convolutional neural network (3DCNN), and Gaussian process regression (GPR). The approach primarily comprises two aspects: construction of a health indicator (HI) using the TFR-subsequence–3DCNN model, and RUL estimation based on the GPR model. The raw signals of the bearings are converted into TFR-subsequences by continuous wavelet transform and a dislocated overlapping strategy. Subsequently, the 3DCNN is applied to extract the hidden spatiotemporal features from the TFR-subsequences and construct HIs. Finally, the RUL of the bearings is estimated using the GPR model, which can also define the probability distribution of the potential function and prediction confidence. Experiments on the PRONOSTIA platform demonstrate the superiority of the proposed TFR-subsequence–3DCNN–GPR approach. The use of degradation-related spatiotemporal features in signals is proposed herein to achieve a highly accurate bearing RUL prediction with uncertainty quantification.

The accuracy of hotel demand forecasting is affected by factors such as the completeness of historical data and the maturity of models. Most of the existing methods are based on rich data, without considering that single hotels may only obtain sparse data. Therefore, a K-means group division and Long Short-Term Memory (LSTM) based method is proposed in this paper. Guest types are introduced into the forecasting to provide reference for hotel's further decision-making. Using an example of 1493 hotels in Europe, we divide hotel groups and forecast the flow of leisure and business guests. The experimental results show that, compared with the benchmark models, LSTM can improve the forecasting performance of hotel group; compared with single hotels, the forecasting of hotel groups can effectively avoid inaccuracy caused by sparse data. The results can provide necessary reference for hospitality to make decisions based on guest types.

Fault diagnosis technique is the core of Prognostics and Health Management (PHM) system, which plays a crucial role in the intelligent operation and maintenance of various rotating machineries. In this paper, we present a novel sparse representation classification framework with structured dictionary design strategy (SRC-SDD) for intelligent fault diagnosis of rotating machineries. The proposed SRC-SDD method consists of two stages, i.e., the structured dictionary design stage and the sparsity-based intelligent diagnosis stage. In the first stage, the novelty of SRC-SDD lies in the overlapping segmentation strategy for structured dictionary design, which leverages the structured prior knowledge of rotating machinery vibration signals, namely, the periodic self-similarity and shift-invariance properties. In the second stage, SRC-SDD achieves fault recognitions of testing samples using a sparsity-based diagnosis strategy based on the minimum sparse reconstruction error. The proposed structured dictionary design strategy can enhance the representation power of dictionaries and thus promote the recognition performance of the sparsity-based diagnosis strategy. Finally, the effectiveness of SRC-SDD has been validated on the gearbox fault dataset from IEEE PHM society. The diagnosis results show that SRC-SDD achieves the excellent recognition accuracy of 100% for predicting six different gearbox health states. Further, the comparative studies with three conventional SRC methods prove the superiority of SRC-SDD in terms of both the recognition performance and computation efficiency.

Engineering organic small molecule materials capable of generating Wannier excitons is a pinnacle of research for organic optoelectronic devices. In this work, we used a coherently degenerate state strategy to design and synthesize an organic small molecule dimer compound consisting of two triphenylamine based monomers. A technique was developed to identify the coherently degenerate states in the dimer compound from the results of density functional theory calculations of. The time-resolved fluorescence measurements showed that the lifetime of the Wannier excitons in the newly designed dimer compound is less solvent dependent than that of the Frenkel excitons in the monomer. The power conversion efficiency of the photovoltaics constructed using this new material increases by 8-fold in comparison to that using the monomer. The method developed here offers extraordinary opportunities for rational design of organic materials to generate Wannier excitons.

Abstract Background Until recently, synthetic fiber conjunctival granuloma (SFCG) is rarely reported and has been poorly understood. Our study was to explore the clinical features, histopathologic characteristics, surgical outcomes, and prognosis of SFCG after surgical excision. Methods Retrospective review of clinical findings, histopathological and immunohistochemical studies identified 18 cases of SFCG. Specimens were routinely processed and stained with H&E. Immunohistochemical stains for GMS, PAS, CD68 and CK-pan were also performed. Results Eighteen patients with an average age of 9.3 ± 6.6 years had a tender white to red mass on the conjunctiva. All the lesions were completely removed, and none of the patients relapsed. Histologically, all of the specimens revealed inflammatory granulation tissues with a large number of inflammatory cells infiltration and the presence of synthetic fibers. Immunohistochemical stains were positive for CD68, CK, GMS and PAS. Conclusions Synthetic fiber conjunctival granuloma is an uncommon lesion with foreign body sensation caused by inoculation of synthetic exogenous materials. These lesions are mostly unilateral and occur in the inferior conjunctival fornix. SFCGs are characterized by a large number of inflammatory cells infiltration and the presence of synthetic fibers. Surgical excision followed by topical corticosteroids has been clinically proven to be effective.

Abstract Background The Guangxi Zhuang Autonomous Region bears a relatively high burden of HIV/AIDS infection. The number of accumulatively reported HIV/AIDS cases in Guangxi is the third highest among 31 provinces or Autonomous Region from 2004 to 2007, changed to the second highest between 2011 and 2013, then returned to the third highest again after 2014. We aim to estimate the new infections and evaluate the real-time HIV epidemic in Guangxi, China, in order to reveal the rule of HIV transmission. Methods Firstly, the number of annually reported HIV and AIDS cases, as well as the number of cases linked with CD4 data are extracted from the HIV/AIDS information system in China. Secondly, two CD4-staged models are formulated by linking the with-host information on CD4 level to between-host transmission and surveillance data. Thirdly, new HIV infections, diagnosis rates and undiagnosed infections over time are estimated by using Bayesian method and Maximum Likelihood Estimation method. Results The data reveal that the newly reported cases have been decreasing since 2011, while lots of cases are identified at late CD4 stage. The data fitted results indicate that both models can describe the trend of the epidemic well. The estimation results show that the new and undiagnosed infections began to decrease from the period2006 - 2008. However, the diagnosis probabilities/rates keep at a very low level, and there are still a large number of infections undiagnosed, most of which have a large probability to be identified at late CD4 stage. Conclusions Our findings suggest that HIV/AIDS epidemic in Guangxi has been controlled to a certain extent, while the diagnosis rate still needs to be improved. More attentions should be paid to identify infections at their early CD4 stages. Meanwhile, comprehensive intervention measures should be continually strengthened in avoid of the rebound of new infections.

Coal dust endangers the health and safety of workers in underground coal mines. Therefore, developing coal dust suppressants with dust prevention and explosion-proof properties is critical. The influence of molasses on the explosion and decomposition of the coal dust deposited in underground mines was investigated using 20 L explosion experiments and thermogravimetric and differential thermal analysis (TG-DTA). Findings reveal that, first, molasses can weakly promote the explosion of coal dust at low coal dust concentrations (3) but has no significant effect on the explosion at high coal dust concentrations (≥400 g/m3). Second, the decomposition process of the coal dust mixed with molasses has three stages: the moisture evaporation stage (0–150 °C), the molasses decomposition stage (150–300 °C), and the coal dust decomposition stage (300–500 °C). Molasses oxidation consumes oxygen and releases heat; at low coal dust concentrations, the released heat can promote coal dust decomposition to produce combustible gas, enhancing the coal dust explosion; at high coal dust concentrations, under the co-influence of the heat generation and oxygen consumption, molasses has no effect on the coal dust explosion. This is the mechanism of which molasses influences coal dust explosions.

Hospitality industry plays a crucial role in the development of tourism. Predicting the future demand of a hotel is a key step in the process of hotel revenue management. Hotel passenger flow prediction plays an important role in guiding the formulation of hotel pricing and operating strategies. On the one hand, hotel passenger flow prediction can provide decision support for hotel managers and effectively avoid the waste of hotel resources and loss of revenue caused by the loss of customers. On the other hand, it is the guarantee of the priority occupation of business opportunities by hotel enterprises, which can help hotel enterprises adjust their operation strategies reasonably to better adapt to the market situation. In addition, hotel passenger flow prediction is helpful to judge the overall operating condition of the hotel industry and assess the risk level of the hotel project to be built. Hotel passenger flow is affected by many factors, such as weather, environment, season, holidays, economy, and emergencies, and has the characteristics of complex nonlinear fluctuation. The existing demand predicting methods include linear methods and nonlinear methods. The linear prediction methods rely on the stability of environment and time series, so they cannot completely simulate the complex nonlinear fluctuations characteristics of hotel passenger flow. Traditional nonlinear prediction methods need to improve the prediction accuracy, and they are difficult to deal with the increasing data of hotel passenger flow. Based on the above analysis, this paper constructs a deep learning prediction model based on Long Short-Term Memory (LSTM) to predict the number of actual monthly arrival bookings. The number of actual monthly arrival bookings can reflect the actual monthly passenger flow of a hotel. The prediction model can effectively reduce the loss caused by cancellation or nonarrival of bookings due to various reasons and improve the hotel revenue. The experimental part of this paper is based on the booking demand dataset of a resort hotel in Portugal from July 1, 2015, to August 31, 2017. Artificial neural network (ANN) and support vector regression (SVR) are built as benchmark models to predict the number of actual monthly arrival bookings of this hotel. The experimental results show that, compared with the benchmark models, the LSTM model can effectively improve the prediction ability and provide necessary reference for the hotel's future pricing decision and operation mode arrangement.

The flow field, tracer dispersion and uniformity of strands in two designs of four-strand tun-dishes under normal conditions and single-strand blockage conditions are studied by numerical simulation. The casting speed (flow rate) of strands are increasing uniformly or non-uniformly to improve the strand blockage condition. The uniformity of strands of the cases are evaluated by a novel outflow percentage analysis method. The results show that the flow field in the tundish does not change significantly when the single-strand is blocked or the casting flow rate is increased. After blockage of one strand, the consistency of each strand of u-shaped weir tundish is better than that of double-weir tundish. With the uniform increasing of the casting flow rate, the response time of each strand decreases and the outflow percentage increases. However, the uniformity of strands improved slightly in double-weir tundish but decreased in u-shaped tundish. For the double-weir tundish, significantly increasing the casting flow rate of the strand located in the blocked part by a factor of 1.5 and slightly increasing the casting flow rate of the other strands by a factor of 1.25, the consistency of each strand is the best. For the u-shaped weir tundish, the consistency of each strand is improved by non-uniform increasing of the casting flow rate of the strands. The flow rate of the strand located in the blocked part and the other strands is increased by a factor of 1.25, and 1.375 or 1.2 and 1.4 are the optimized cases.

A biodegradable poly(lactic-co-glycolic acid) loading atorvastatin calcium (AC) nanoparticles (AC-PLGA-NPs) were prepared by probe ultrasonication and evaporation method aiming at improving the oral bioavailability of AC. The effects of experimental parameters, including stabilizer species, stabilizer concentration and pH of aqueous phase, on particle size were also evaluated. The resultant nanoparticles were in spherical shape with an average diameter of 174.7 nm and a narrow particle size distribution. And the drug loading and encapsulation efficiency were about 8% and 71%, respectively. The particle size and polydispersion were almost unchanged in 10 days. The release curves of AC-PLGA-NPs in vitro displaying sustained release characteristics indicated that its release mechanisms were matrix erosion and diffusion. The pharmacokinetic study in vivo revealed that the Cmax and AUC0-∞ of AC-PLGA-NPs in rats were nearly 3.7-fold and 4.7-fold higher than that of pure atorvastatin calcium suspension. Our results demonstrated that the delivery of AC-PLGA-NPs could be a promising approach for the oral delivery of AC for enhanced bioavailability.

The aim of this study was to enhance oral bioavailability of itraconazole (ITZ) by developing Liposome containing sodium deoxycholate (ITZ-Lip-NaDC). The liposome, consisting of egg yolk lecithin and sodium deoxycholate, was prepared by thin-film dispersion method. Differential Scanning Calorimetry (DSC) results indicated an amorphous state in the liposome. The physicochemical characteristics including particle size, morphology, entrapment efficiency, dissolution properties were also investigated. The performance of single-pass intestinal infusion exhibited that the transport order of intestinal segment was jejunum, duodenum, colon and ileum, and that all the segments participated in the absorption of ITZ in intestinal tract. The bioavailability study in rats showed that the AUC0-72 of the liposome was nearly 1.67-fold higher than that of commercial capsules (SPORANOX) in terms of oral administration, and the RSD of AUC0-72 of ITZ-Lip-NaDC was also decreased. Our results indicated that ITZ-Lip-NaDC liposome was facilitated to improve dissolution efficiency, augment transmembrane absorption, and then enhance the oral bioavailability of ITZ, successfully.

Limited research has been conducted on the contributions of local and nonlocal emission sources to ambient fine particulate matter (PM2.5) and ozone (O3) and their associated mortality. In this study, we estimated the total mortality resulting from long-term PM2.5 and O3 exposures in California in 2012 using multiple concentration response functions (CRFs) and attributed the estimated mortality to different emission groups. The point estimates of PM2.5-associated mortality in California ranged from 12,700 to 26,700, of which 53% were attributable to in-state anthropogenic emissions. Based on new epidemiological evidence, we estimated that O3 could be associated with up to 13,700 deaths from diseases of both the respiratory and cardiovascular systems in California. In addition, 75% of the ambient O3 in California was due to distant emissions outside the western United States, leading to 92% of the O3-associated mortality. Overall, distant emissions lead to greater mortality burdens of air pollution in California than local anthropogenic emissions. Keywords: Emissions, Fine particulate matter, Ozone, Mortality

Chrysanthemum indicum Flower is usually consumed as functional food. This paper described an improved total quality assessment method for Chrysanthemum indicum Flower by simultaneous quantitation using a single standard to determine multi-components method combined with high performance liquid chromatography fingerprint analysis. Six main components of Chrysanthemum indicum Flower including two flavonoids and four phenolic acids were simultaneously quantified using linarin as the internal reference standard. The method was fully validated with respect to linearity, precision, accuracy, robustness and stability. The validated method was successfully applied to the analysis of thirty three batches of Chrysanthemum indicum Flower samples. Under the same chromatographic conditions, fingerprint analysis in combination with Similarity analysis and principal component analysis was performed to identify the samples from different regions. In general, an effective assessment using a single standard to determinate multi-components method combined with fingerprint analysis make the reliable qualitation and quantitation analysis of Chrysanthemum indicum Flower available.