Search

The precise wake model is crucial for accurately estimating wind farm loads and power, playing a key role in wake control within wind farms. This study proposes a segmented dual-Gaussian wake model, which is built upon existing dual-Gaussian wake models but places greater emphasis on the influence of initial wake generation and evolution processes on the wind speed profile in the near-wake region. The enhanced model optimizes the wake speed profile in the near-wake region and improves the accuracy of wake diffusion throughout the entire flow field. Furthermore, the optimized dual-Gaussian wake model is utilized to estimate the power output and blade root vibration loads in offshore wind farms. Through comparative analysis of high-fidelity simulation results and actual measurement data, the accuracy of the optimized dual-Gaussian wake model is validated. This approach offers high computational efficiency and provides valuable insights for load fluctuations and power estimation, thereby advancing the development of wake control strategies rapidly.

In this study, an ensemble forecasting model for in situ wind speed and wave height was developed using the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model. This model utilized four bias correction algorithms—Model Output Statistics (MOS), Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM) neural network, and Convolutional Neural Network (CNN)—to construct ensemble forecasts. The training data were derived from the COAWST simulations of one year and observations from three buoy stations (Laohutan, Zhifudao, and Lianyungang) in the Yellow Sea and Bohai Sea. After the optimization of the bias correction model training, the subsequent evaluations on the ensemble forecasts showed that the in situ forecasting accuracy of wind speed and wave height was significantly improved. Although there were some uncertainties on bias correction performance levels for individual algorithms, the uncertainties were greatly reduced by the ensemble forecasts. Depending on the dynamic weight assignment, the ensemble forecasts presented a stable performance even when the corrected forecasts by three algorithms had an obvious negative bias. Specifically, the ensemble forecasting bias was found with a mean reduction of about 96%~99% and 91%~95% for wind speed and wave height, and a reduction of about 91%~98% and 16%~54% during the period of Typhoon “Muifa”. For the four correction algorithms, the performance of bias correction was not directly related to the algorithm complexity. However, the strategies with more complex algorithms (i.e., CNN) were more conservative, and simple algorithms (i.e., MOS) might have induced unstable performance levels despite their lower bias in some cases.

The Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model serves as the foundation for creating a forecast model to detect lower atmospheric ducts in this study. A set of prediction tests with different forecasting times focusing on the South China Sea domain was conducted to evaluate the short-term forecasting effectiveness of lower atmospheric ducts. The assessment of sounding observation data revealed that the prediction model performed well in predicting the characteristics of all types of ducts. The mean values of the forecasting errors were slightly lower than the reanalysis data but had lower levels of correlation coefficients. At an altitude of about 2000 m, the forecasted error of modified atmospheric refractivity reached peak values and then decreased gradually with increasing altitude. The accuracy of forecasted surface ducts was higher than that of elevated ducts. Noticeable land–sea differences were identified for the spatial distributions of duct characteristics, and the occurrence rates of both the surface and elevated ducts were high at sea. As for the differences among the forecasts of 24, 48, and 72 h ahead, the differences primarily occurred at altitude levels below 20 m and 500 m~1500 m, which are consistent with the differences in the duct height.

The sea surface roughness parameterization and the universal stability function are key components of the evaporation duct prediction model based on the Monin–Obukhov similarity theory. They determine the model’s performance, which in turn affects the efficiency and accuracy of electromagnetic applications at sea. In this study, we collected layered meteorological and hydrological observation data and preprocessed them to obtain near-surface reference modified refractivity profiles. We then optimized the sea surface roughness parameterization and the universal stability function using particle swarm optimization and simulated annealing algorithms. The results show that the particle swarm optimization algorithm outperforms the simulated annealing algorithm. Compared to the original model, the particle swarm optimization algorithm improved the prediction accuracy of the model by 5.09% under stable conditions and by 9.97% under unstable conditions, demonstrating the feasibility of the proposed method for optimizing the evaporation duct prediction model. Subsequently, we compared the electromagnetic wave propagation path losses under two different evaporation duct heights and modified refractivity profile states, confirming that the modified refractivity profile is more suitable as the accuracy criterion for the evaporation duct prediction model.

In this study, a forecasting model was developed based on the COAWST and atmospheric 3D EnVar module to investigate the effects of assimilation of the sounding and COSMIC–2 data on the forecasting of the revised atmospheric refraction. Three groups of 72 h forecasting tests, with assimilation of different data obtained for a period of one month, were constructed over the Yellow Sea. The results revealed that the bias of the revised atmospheric refraction was the lowest if both the sounding and COSMIC–2 data were assimilated. As a result of the assimilation of the hybrid data, the mean bias reduced by 6.09–6.28% within an altitude of 10 km, and the greatest reduction occurred below the altitude of 3000 m. In contrast, the test that assimilated only the sounding data led to an increase in bias at several levels. This increased bias was corrected after the introduction of the COSMIC–2 data, with the mean correction of 1.6 M within the middle and lower troposphere. During the typhoon period, the improvements in the assimilation were more significant than usual. The improved forecasts of the revised atmospheric refraction were mainly due to the moisture changes within the middle and lower troposphere, while the changes in the upper troposphere were influenced by multiple factors.

Evaporation ducts are abnormal states of the atmosphere in the air–sea boundary layer that directly affect the propagation trajectory of electromagnetic (EM) waves. Therefore, an accurate diagnosis of the evaporation duct height (EDH) is important for studying the propagation trajectory of EM waves in evaporation ducts. Most evaporation duct models (EDMs) based on the Monin–Obukhov similarity theory are empirical methods. Different EDMs have different levels of environmental adaptability. Evaporation duct diagnosis methods based on machine learning methods only consider the mathematical relationship between data and do not explore the physical mechanism of evaporation ducts. To solve the above problems, this study observed the meteorological and hydrological parameters of the five layers of the low-altitude atmosphere in the East China Sea on board the research vessel Xiangyanghong 18 in April 2021 and obtained the atmospheric refractivity profile. An evaporation duct multimodel fusion diagnosis method (MMF) based on a library for support vector machines (LIBSVM) is proposed. First, based on the observed meteorological and hydrological data, the differences between the EDH diagnosis results of different EDMs and MMF were analyzed. When ASTD ≥ 0, the average errors of the diagnostic results of BYC, NPS, NWA, NRL, LKB, and MMF are 2.57 m, 2.92 m, 2.67 m, 3.27 m, 2.57 m, and 0.24 m, respectively. When ASTD < 0, the average errors are 2.95 m, 2.94 m, 2.98 m, 2.99 m, 2.97 m, and 0.41 m, respectively. Then, the EM wave path loss accuracy analysis was performed on the EDH diagnosis results of the NPS model and the MMF. When ASTD ≥ 0, the average path loss errors of the NPS model and MMF are 5.44 dB and 2.74 dB, respectively. When ASTD < 0, the average errors are 5.21 dB and 3.46 dB, respectively. The results show that the MMF is suitable for EDH diagnosis, and the diagnosis accuracy is higher than other models.

The plant growth regulator ethylene influences rachis browning in grape (Vitis vinifera L.). Although the ethylene signaling pathway is well defined, there is limited knowledge on its mode of action during rachis browning. Here, we show that an ethylene response factor (VvERF111) positively regulates chlorophyll degradation in rachis by binding to a DRE motif in the promoter of VvCLH1. The expression of VvERF111 and VvCLH1 in rachis was induced by ethylene and inhibited by 1-methylcyclopropene (1-MCP). VvERF111 belongs to the ERF IX subfamily of the APETALA2/ethylene responsive factor (AP2/ERF) superfamily, shows transcriptional activity in yeast, and is localized in the nucleus and membrane. The transient overexpression of VvERF111 or chlorophyllase (VvCLH1) in grape leaves accelerated chlorophyll degradation. In VvERF111-overexpressing leaves, transcript levels of VvCLH1 were also increased. Our findings offer a deeper understanding of the transcriptional regulation of chlorophyll degradation during the rachis browning of grape.

Fruit ripening includes several metabolic changes that lead to sweeter and softer fruit. Pectin depolymerization is one of major factors that softens developing grape berries. Pectin lyases (PLs) play important roles in pectin degradation in the grape berry. However, little is known about the temporal and spatial expression of grapevine (Vitis spp.) pectin lyase genes (VvPLs) or their function during fruit ripening and softening. In this study, 18 individual VvPL genes were identified in the grape genome. All VvPL genes were sorted into group I and group II, except VvPL12 which demonstrated higher and similar expression trends in different tissues and organs. In grape berry, VvPL1, 5, 7, 11 and 16 were highly expressed, whereas VvPL18, 15, 2, 13, 10, 14, 17, 6 and 8 showed lower expression levels at different berry developmental stages. Expression of VvPL11 firstly increased and then decreased, and the highest expression was shown at 6 weeks after full bloom (WAFB) during berry development. Over-expression of the VvPL11 gene in tomato caused higher ethylene production and lower firmness compared to wild-type fruit. Moreover, decreased propectin and increased water-soluble pectin (WSP) levels were observed in VvPL11 transgenic tomato fruit. Consistent with this result, the expression levels of SlPG2, SlEXP, and SlPME1, all of which are genes involved in fruit softening, were up-regulated in VvPL11-OE tomato fruit, which supported the idea that VvPL11 plays an important role in fruit ripening and softening. This study provided a comprehensive analysis of the grapevine PL family and advanced our knowledge of the functions of VvPLs during fruit softening.

In order to diagnose an incipient fault in rotating machinery under complicated conditions, a fast sparse decomposition based on the Teager energy operator (TEO) is proposed in this paper. In this proposed method, firstly, the TEO is employed to enhance the envelope of the impulses, which is more sensitive to frequency and can eliminate the low-frequency harmonic component and noise; secondly, a smoothing filtering algorithm was adopted to suppress the noise in the TEO envelope; thirdly, the fault signal was reconstructed by multiplication of the filtered TEO envelope and the original fault signal; finally, sparse decomposition was used based on a generalized S-transform (GST) to obtain the sparse representation of the signal. The proposed preprocessing method using the filtered TEO can overcome the interference of high-frequency noise while maintaining the structure of fault impulses, which helps the processed signal perform better on sparse decomposition; sparse decomposition based on GST was used to represent the fault signal more quickly and more accurately. Simulation and application prove that the proposed method has good accuracy and efficiency, especially in conditions of very low SNR, such as impulses with anSNR of −8.75 dB that are submerged by noise of the same amplitude.

The wake management of offshore wind farms (OWFs) mainly considers the wake effect. Wake effects commonly occur in offshore wind farms, which cause a 5–10% reduction in power production. Although there have been many studies on wake management, many methods are not accurate enough; for instance, look-up table and static wake model control methods do not consider the time-varying wake state. Dynamic wake management is based on the real-time dynamic wake, so it can increase the energy of the OWFs effectively. For OWFs, dynamic wake control is the main method of dynamic wake management. In this paper, the existing wake model and control progress are discussed, mainly emphasizing the dynamic wake model and the dynamic wake control method, solving the gap of the review for dynamic wake management. This paper presents a digital twins (DT) framework for power and fatigue damage for the first time.. The structure of this paper is as follows: (1) the mechanism of wind farm wake interference is described and then the dynamic wake model is reviewed and summarized; (2) different control methods are analyzed and the dynamic wake management strategies for different control methods are reviewed; (3) in order to solve the problems of dynamic wake detection and real-time effective control, the technology of DT is applied to the dynamic wake control of OWFs. This new DT frame has a promising application prospect in improving power and reducing fatigue damage.

Aiming at the problems of early weak fault feature extraction of bearings in rotating machinery, an improved stochastic resonance (SR) is proposed combined with the advantage of SR to enhance weak characteristic signals with noise energy. Firstly, according to the characteristics of the large parameters of the actual fault signal, the amplitude transform coefficient and frequency transform coefficient are introduced to convert the large parameter signal into small parameter signal which can be processed by SR, and the relationship of second-order parameters are introduced. Secondly, a comprehensive evaluation index (CEI) consisted of power spectrum kurtosis, correlation coefficient, structural similarity, root mean square error, and approximate entropy, is constructed through BP neural network. Moreover, this CEI is adopted as fitness function to search the optimal damping coefficient and amplitude transform coefficient with adaptive weight particle swarm optimization (PSO). Finally, according to the improved optimal SR system, the weak fault feature can be extracted. The simulation and experiment verify the effectiveness of the proposed method compared with traditional second-order general scale transform adaptive SR.

Aiming at the parachute recovery of fixed-wing unmanned aerial vehicles, a method of parachute deployment by tractor rocket is proposed. First, the tensile tests were carried out on high-strength polyethylene and brocade silk-weaved composite materials. The dynamic property parameters of the materials were obtained, which was the input for the dynamic model of the parachute deployment phase. Second, the model was verified by the experiment results. Finally, parachute weight and rocket launch temperature during the deployment phase were studied. The results showed that the dynamic model has good accuracy; as the parachute weight increases, the maximum snatch force of the extraction line and the sling decreases as the force on the suspension lines increases and the deployment effect worsens. With the temperature rise, the maximum snatch force on the extraction line, sling, and suspension lines increases and the deployment length changes slightly.

Atmospheric reactive oxygen species (ROS) play a key role in the process of air pollution and oxidative damage to organisms. The analysis of ROS was carried out by the capture-derivative method. Therefore, it is necessary to prepare an effective molecular membrane to trap and detect ROS. Electrospinning membranes were prepared by combining the electrospinning technique with chrysin, baicalein, scutellarin, genistein, quercetin, and baicalin. By comparing the structures of the membranes before and after the reaction, the fluorescence enhancement characteristics of the reactive molecular membranes and the atmospheric radicals were studied. The ability of the active molecular membranes to trap atmospheric radicals was also studied. It was found that the genistein active molecular membrane had good trapping ability in four environments. The fluorescence enhancement rates in ROS, OH radical and O3 simulated environments were 39.32%, 7.99% and 11.92%, respectively. The fluorescence enhancement rate in atmospheric environment was 16.16%. Indeed, the sites where the atmospheric radicals react with the active molecular membranes are discussed. It is found that it is mainly related to the 5,7 phenolic hydroxyl of ring A, catechol structure and the coexistence structure of 4′ phenolic hydroxyl of ring B and 7 phenolic hydroxyl of ring A. Therefore, the genistein molecular membrane has shown great potential in its trapping ability and it is also environmentally friendly.

It is of prime importance to develop anode electrocatalysts for direct methanol fuel cells (DMFCs) with good performance, which is critical for their commercial applications. Metal-organic framework (MOF)-derived carbon materials are extensively developed as supports of catalysts. Herein, Co embedded nitrogen-doped carbon nanotube hollow porous carbon (Co-NCNT-HPC) derived from MOFs have been fabricated, which were synthesized by pyrolyzing at an optimized temperature of 800 °C using ZIF-8@ZIF-67 as a precursor. The presence of ZIF-8@ZIF-67 ensures the doping of nitrogen and the large specific surface area of the support materials at high temperatures. A Pt/Co-NCNT-HPC800 sample, which was synthesized using Co-NCNT-HPC800 as a support, showed an enhanced mass activity of 416.2 mA mg−1Pt for methanol oxidation reaction (MOR), and the onset potential of COad oxidation of 0.51 V, which shifted negatively about 0.13 V compared with Pt/C (20%). Moreover, the Pt/Co-NCNT-HPC800 sample exhibits high stability. This work provides a facile strategy for MOF-derived carbon materials to construct advanced electrocatalysts for MOR.

Phenacoccus solenopsis Tinsley is a pest that poses a significant threat to agricultural crops, especially cotton, and is now widely distributed across many regions worldwide. In this study, we performed a meta-analysis on the collected experimental data and found that within the suitable temperature range, the survival rate of P. solenopsis increases with rising temperatures, indicating that climate plays a decisive role in its distribution. Using the MaxEnt model this study predicted that under three future climate scenarios (SSP1–2.6, SSP3–7.0, and SSP5–8.5), the distribution of P. solenopsis will expand and move towards higher latitudes. Climate change is the primary factor influencing changes in pest distribution. We conducted a meta-analysis of P. solenopsis, including seven independent studies covering 221 observation results, and examined the impact of temperature ranging from 18 °C to 39 °C on the developmental cycle of P. solenopsis. As the temperature rises, the development cycle of P. solenopsis gradually decreases. Additionally, by combining the MaxEnt model, we predicted the current and potential future distribution range of P. solenopsis. The results show that under future climate warming, the distribution area of P. solenopsis in China will expand. This research provides a theoretical basis for early monitoring and control of this pest’s occurrence and spread. Therefore, the predictive results of this study will provide important information for managers in monitoring P. solenopsis and help them formulate relevant control strategies.

Spodoptera frugiperda (J.E. Smith) is a significant economic pest that has recently invaded Africa and Asia. However, much of the information regarding its ecological capabilities in these newly invaded environments remains largely unknown. In this study, the life history traits of the fall armyworm under conditions of increased temperature, different photoperiods, and varying humidity levels were systematically evaluated. Among 43 studies, a total of 20 studies were included in the analysis by passing the screening criteria, and random-effects meta-analysis, fixed-effects meta-analysis, and meta-regression were conducted. It has been found that with the increase in temperature above 20 °C, various physiological indicators of the fall armyworm are significantly enhanced. When the temperature reaches 32 °C, the physiological activities of S. frugiperda are at their highest point. As the temperature increases, the duration of each developmental stage of the fall armyworm decreases significantly, accompanied by an increase in oviposition quantity and period in females. Additionally, the pupal development time is shortened, which leads to an increase in the lifespan of the adult moth. Using temperature and relative humidity as environmental variables, the optimal survival conditions for each insect state of the fall armyworm were calculated. These findings can assist in predicting the population dynamics of the fall armyworm and in formulating appropriate and practical management strategies.

Short-chain fatty acids (SCFA) and lactate in ruminal fluid are products resulting from the microbial fermentation of substrates and can be used to reflect the composition and activity of the ruminal microbiome. Determination of SCFA and D-/L-lactate in ruminal fluid currently requires two separate protocols, which is time-consuming and costly. In this study, we have optimised and validated a simple and unified 3-nitrophenylhydrazine (3-NPH) derivatisation protocol and a 20 min chiral-LC-MS method for the simultaneous quantification of all SCFA and D- and L-lactate in ruminal fluid. This method, which requires no sample pretreatment or purification shows adequate sensitivity (limit of detection (LOD): 0.01 µg/mL), satisfactory accuracy (recovery: 88–103%), and excellent reproducibility (relative standard deviation (RSD) for repeated analyses < 3% for most analytes). The application of this method to a cohort of 24 animals allowed us to reveal a large inter-cow variation in ruminal SCFA and lactate level, the concentration range for each species, the widespread correlation between different SCFA, and the strong correlation between D- and L-lactate.

Lubricant additive plays an important role in reducing the friction and wear for base oil. MAX phase ceramics may have superior advantages for additive application due to their unique nanolayered structure. In this paper, Ti2SnC ceramic is prepared by sintering the elemental mixtures at 1250 °C. In addition, Cu-coated Ti2SnC ceramic is successfully prepared using a chemical plating method for the first time. It is confirmed that the Ti2SnC ceramic has good self-catalytic activity, and a layer of stacking Cu nano-particles can be deposited on the Ti2SnC surface without pretreatment. When the Cu-coated Ti2SnC ceramic powder is doped into PAO10 base oil, the oil can exhibit excellent lubrication properties, where the friction coefficient is as low as 0.095. A layer of tribo-film can be formed during the sliding process when the Cu-coated Ti2SnC ceramic is incorporated into PAO10 base oil, which can reduce the friction coefficient. The superior lubrication properties can be attributed to the synergistic lubrication effect of Ti2SnC ceramic and Cu nano-particles.

Batocera horsfieldi (Hope) (Coleoptera: Cerambycidae) is an important forest pest in China that mainly infests timber and economic forests. This pest primarily causes plant tissue to necrotize, rot, and eventually die by feeding on the woody parts of tree trunks. To gain a deeper understanding of the genetic mechanism of B. horsfieldi, this study employed single-molecule real-time sequencing (SMRT) and Illumina RNA-seq technologies to conduct full-length transcriptome sequencing of the insect. Total RNA extracted from male and female adults was mixed and subjected to SMRT sequencing, generating a complete transcriptome. Transcriptome analysis, prediction of long non-coding RNA (lncRNA), coding sequences (CDs), analysis of simple sequence repeats (SSR), prediction of transcription factors, and functional annotation of transcripts were performed in this study. The collective 20,356,793 subreads (38.26 G, clean reads) were generated, including 432,091 circular consensus sequences and 395,851 full-length non-chimera reads. The full-length non-chimera reads (FLNC) were clustered and redundancies were removed, resulting in 39,912 consensus reads. SSR and ANGEL software v3.0 were used for predicting SSR and CDs. In addition, four tools were used for annotating 6058 lncRNAs, identifying 636 transcription factors. Furthermore, a total of 84,650 transcripts were functionally annotated in seven different databases. This is the first time that the full-length transcriptome of B. horsfieldi has been obtained using SMRT sequencing. This provides an important foundation for investigating the gene regulation underlying the interaction between B. horsfieldi and its host plants through gene editing in the future and provides a scientific basis for the prevention and control of B. horsfieldi.

Titanium alloy is an important material for the manufacture of key components of deep-sea submersibles. High-power vacuum laser welding is an important method for welding TC4 thick plate (40–120 mm) structures. However, due to the low melting point of aluminum, its uneven distribution in the weld caused by evaporation during welding affects the quality of joints. This paper conducted experimental and simulation studies to investigate the effect of process parameters on multi-component evaporation and uneven aluminum distribution. Based on a three-dimensional model of vacuum laser welding, the mechanism of the uneven distribution of aluminum in the weld is explained. The results show that the uneven distribution of aluminum in the weld is mainly related to the metal vapor behavior and keyhole morphology. As the welding speed rises from 1 m/min to 3 m/min, the proportion of aluminum in the metal vapor and the degree of compositional unevenness increase. When the laser power increases from 6 kW to 18 kW, the proportion of aluminum in the metal vapor and degree of unevenness increase, peak at 12 kW, and then decrease. This work facilitates the selection of suitable process parameters to reduce aluminum evaporation during the high-power vacuum welding of Ti-6Al-4V alloys. Joints with a more stable performance can be obtained by avoiding the uneven distribution of aluminum.

Milk is a rich source of lipids, with the major components being triglycerides (TAG) and phospholipids (mainly phosphatidylcholine (PC), sphingomyelin (SM), phosphatidylethanolamine (PE), phosphatidylserine (PS) and phosphatidylinositol (PI)). Liquid chromatography-mass spectrometry (LC-MS) is the predominant technique for lipid identification and quantification across all biological samples. While fatty acid (FA) composition of the major lipid classes of milk can be readily determined using tandem MS, elucidating the regio-distribution and double bond position of the FA remains difficult. Various workflows have been reported on the quantification of lipid species in biological samples in the past 20 years, but no standard or consensus methods are currently available for the quantification of milk phospholipids. This study will examine the influence of several common factors in lipid analysis workflow (including lipid extraction protocols, LC stationary phases, mobile phase buffers, gradient elution programmes, mass analyser resolution and isotope correction) on the quantification outcome of bovine milk phospholipids. The pros and cons of the current LC-MS methods as well as the critical problems to be solved will also be discussed.

An accurate dry matter intake (DMI) measurement method is of great importance for determining the performance of dairy cows. While n-alkane technique is commonly used to estimate the DMI of ruminants, the protocols used to measure alkanes are complex, time consuming and low throughput. Herein we present a simplified and miniaturised protocol that combines a smaller sample matrix (60 mg of faeces or herbage), a shorter (3 h) saponification step and a direct purification of the crude extract by filtration through silica powder or mixing with silica powder prior to GC-FID analysis. In addition to a much higher throughput compared with the existing methods, by skipping the commercial SPE columns and reducing the organic solvent volume, our miniaturised protocol should significantly reduce the sample processing cost and is thus suitable for alkane determination for large-scale experiments.

Short-chain fatty acids (SCFA, C2-C5) in milk and serum are derived from rumen bacterial fermentation and, thus, have the potential to be used as biomarkers for the health status of dairy cows. Currently, there is no comprehensive and validated method that can be used to analyse all SCFAs in both bovine serum and milk. This paper reports an optimised protocol, combining 3-nitrophenylhydrazine (3-NPH) derivatisation and liquid chromatography-mass spectrometry (LC-MS) analysis for quantification of SCFA and β-hydroxybutyric acid (BHBA) in both bovine milk and bovine serum. This method is sensitive (limit of detection (LOD) ≤ 0.1 µmol/L of bovine milk and serum), accurate (recovery 84–115% for most analytes) and reproducible (relative standard deviation (RSD) for repeated analyses below 7% for most measurements) with a short sample preparation step. The application of this method to samples collected from a small cohort of animals allowed us to reveal a large variation in SCFA concentration between serum and milk and across different animals as well as the strong correlation of some SCFAs between milk and serum samples.

Regioisomers (or positional isomers) of triacylglycerols (TAGs) of milk are known to show differential outcome in relation to human absorption. Quantitation of TAG regioisomers remains a big challenge due to the lack of facile chromatographic separation technique. The feasibility of using fragment ion intensity ratio to determine the ratio of co-eluting AAB/ABA-type regioisomer pairs was confirmed in this study. The ability of C30 stationary phase in resolving interfering TAG isomers was demonstrated for the first time. This allowed us to reveal the complexity of using fragment ion intensity to quantify 1,2-olein-3-palmitin (OOP), 1,3-olein-2-palmitin (OPO), 1,2-olein-3-stearin (OOS), and 1,3-olein-2-stearin (OSO) regioisomers in milk samples. A novel algorithm was proposed to consider the contribution of OPO/OOP and OSO/OOS double bond (DB)-isomers and to eliminate the interference of isobaric ions from other isomers, an aspect overlooked in previous studies. This liquid chromatography-mass spectrometry method that requires no pre-fractioning and a moderate chromatographic separation time of 36 min is simple and, thus, suitable for screening a large number of samples for genetic analysis of this trait. Preliminary results using a small cohort of animals showed that OPO/OOP ratio differs significantly between Jersey and Holstein cows, and a large variation was also observed across individual Holstein cows.

Volatile phenols (VPs) derived from smoke-exposed grapes are known to confer a smoky flavor to wine. Current methods for determination of VPs in grape berries either involve complex sample purification/derivatization steps or employ two analytical platforms for free and bound VP fractions. We report here a simple gas chromatography-tandem mass spectrometry (GC-MS/MS) method for quantification of both free and bound VPs in grapes, based on optimized (1) GC-MS/MS parameters, (2) an analyte extraction procedure, and (3) phenol glycoside hydrolysis conditions. Requiring neither sample cleanup nor a derivatization step, this method is sensitive (LOD ≤ 1 ng/g berries) and reproducible (RSD < 12% for repeated analyses) and is expected to significantly reduce the sample turnover time for smoke taint detection in vineyards.

A series of caffeic acid amides were designed, synthesized and evaluated for anti-inflammatory activity. Most of them exhibited promising anti-inflammatory activity against nitric oxide (NO) generation in murine macrophage RAW264.7 cells. A 3D pharmacophore model was created based on the biological results for further structural optimization. Moreover, predication of the potential targets was also carried out by the PharmMapper server. These amide analogues represent a promising class of anti-inflammatory scaffold for further exploration and target identification.

Ensuring the large signal stability of the DC microgrid is the premise of the safe operation of the DC microgrid, but the research on the large-signal stability of microgrids with multiple droop control micro-sources is still scarce. In this paper, a DC microgrid system model with multiple droop control micro-sources was established by appropriate simplification. Addressing the problem that most stability research methods cannot be quantitatively analyzed, the mixed potential function method was used to analyze the large signal stability of the system. However, the criterion obtained by the conventional mixed potential function method is complicated and contains multiple time-varying parameters, which is not convenient for analysis. Therefore, the simple form of the criterion was obtained through simplification and the analysis proved the rationality of the simplification. On this basis, a nonlinear droop control method was proposed to improve the anti-interference ability of the system. Finally, the accuracy of the large signal stability criterion and the effectiveness of nonlinear droop control on the system’s large signal stability were verified by simulation.