Search

Fatty acid elongases play crucial roles in synthesizing long-chain polyunsaturated fatty acids. Identifying more efficient elongases is essential for enhancing oleaginous microorganisms to produce high yields of target products. We characterized three elongases that were identified with distinct specificities: McELO from Mucor circinelloides, PrELO from Phytophthora ramorum, and PsELO from Phytophthora sojae. Heterologous expression in Saccharomyces cerevisiae showed that McELO preferentially elongates C16 to C18 fatty acids, PrELO targets Δ6 polyunsaturated fatty acids, and PsELO uses long chain saturated fatty acids as substrates. McELO and PrELO exhibited more homology, potentially enabling fatty acid composition remodeling and enhanced LC-PUFAs production in oleaginous microorganisms. Site-directed mutagenesis of conserved amino acids across elongase types identified residues essential for activity, supported by molecular docking. Alanine substitution of conserved polar residues led to enzyme inactivation, underscoring their importance in the condensation reaction. Our findings offer promising elongase candidates for polyunsaturated fatty acid production, contributing to the bioindustry’s sustainable development.

The highly conserved Sal1 encodes a bifunctional enzyme with inositol polyphosphate-1-phosphatase and 3′ (2′), 5′-bisphosphate nucleotidase activity and has been shown to alter abiotic stress tolerance in plants when disrupted. Precise gene editing techniques were used to generate Sal1 mutants in hexaploid bread wheat. The CRISPR (Clustered Regulatory Interspaced Short Palindromic Repeats) Cas9 system with three guide RNAs (gRNAs) was used to inactivate six Sal1 homologous genes within the Bobwhite wheat genome. The resulting mutant wheat plants with all their Sal1 genes disabled had slimmer stems, had a modest reduction in biomass and senesced more slowly in water limiting conditions, but did not exhibit improved yield under drought conditions. Our results show that multiplexed gRNAs enabled effective targeted gene editing of the Sal1 gene family in hexaploid wheat. These Sal1 mutant wheat plants will be a resource for further research studying the function of this gene family in wheat.

One of the grand challenges of new generation Condensed Matter physicists is the development of novel devices enabling the control of sound propagation at terahertz frequency. Indeed, phonon excitations in this frequency window are the leading conveyor of heat transfer in insulators. Their manipulation is thus critical to implementing heat management based on the structural design. To explore the possibility of controlling the damping of sound waves, we used high spectral contrast Inelastic X-ray Scattering (IXS) to comparatively study terahertz acoustic damping in a dilute suspension of 50 nm nanospheres in glycerol and on pure glycerol. Bayesian inference-based modeling of measured spectra indicates that, at sufficiently large distances, the spectral contribution of collective modes in the glycerol suspension becomes barely detectable due to the enhanced damping, the weakening, and the slight softening of the dominant acoustic mode.

High-molecular-weight glutenin subunits (HMW-GS) account for only 10% of total wheat storage proteins, but play an important role in the processing quality of wheat flour. Therefore, identifying HMW-GS alleles associated with good end-use quality provides important information for wheat breeders. To rapidly, accurately and reproducibly identify HMW-GS, we established an optimized reversed-phase ultra-performance liquid chromatography (RP-UPLC) method. Separation parameters were optimized using an ACQUITY UPLC Protein BEH C4 column and stepwise ACN gradient, and the separation patterns and retention times (RTs) of 22 subunits were comparatively analyzed in 16 standard wheat cultivars. All HMW-GS proteins were well separated within about 5.5 min, and all analyses were complete within 12 min. We distinguished the 16 subunits based on RT, although three subunits in 1Bx (1Bx7/1Bx7OE and 1Bx17) and three subunits in 1By (1By8*, 1By9 and 1By15) had overlapping RTs; these were differentiated by SDS-PAGE. To distinguish 1Bx7 and 1Bx7OE, which differ in protein abundance, RP-UPLC was combined with PCR analysis of DNA junction markers. The optimized method was successfully applied to determine HMW-GS alleles in a large collection of bread wheat germplasm (1787 lines). This protocol is an appropriate option for selecting lines harboring favorable HMW-GS alleles in wheat breeding.

We used the high-resolution Inelastic X-ray Scattering beamline of the Advanced Photon Source at Argonne National Laboratory to measure the terahertz spectrum of pure water and a dilute aqueous suspension of 15 nm diameter spherical Au nanoparticles (Au-NPs). We observe that, despite their sparse volume concentration of about 0.5%, the immersed NPs strongly influence the collective molecular dynamics of the hosting liquid. We investigate this effect through a Bayesian inference analysis of the spectral lineshape, which elucidates how terahertz transport properties of water change upon Au-NP immersion. In particular, we observe a nearly complete disappearance of the longitudinal acoustic mode and a mildly decreased ability to support shear wave propagation.

Warming of the Arctic leads to a decrease in sea ice, and the decrease of sea ice, in turn, results in warming of the Arctic again. Several microwave sensors have provided continuously updated sea ice data for over 30 years. Many studies have been conducted to investigate the relationships between the satellite-derived sea ice concentration (SIC) of the Arctic and climatic factors associated with the accelerated warming. However, linear equations using the general circulation model (GCM) data, with low spatial resolution, cannot sufficiently cope with the problem of complexity or non-linearity. Time-series techniques are effective for one-step-ahead forecasting, but are not appropriate for future prediction for about ten or twenty years because of increasing uncertainty when forecasting multiple steps ahead. This paper describes a new approach to near-future prediction of Arctic SIC by employing a deep learning method with multi-model ensemble. We used the regional climate model (RCM) data provided in higher resolution, instead of GCM. The RCM ensemble was produced by Bayesian model averaging (BMA) to minimize the uncertainty which can arise from a single RCM. The accuracies of RCM variables were much improved by the BMA2 method, which took into consideration temporal and spatial variations to minimize the uncertainty of individual RCMs. A deep neural network (DNN) method was used to deal with the non-linear relationships between SIC and climate variables, and to provide a near-future prediction for the forthcoming 10 to 20 years. We adjusted the DNN model for optimized SIC prediction by adopting best-fitted layer structure, loss function, optimizer algorithm, and activation function. The accuracy was much improved when the DNN model was combined with BMA2 ensemble, showing the correlation coefficient of 0.888. This study provides a viable option for monitoring Arctic sea ice change of the near future.

This paper investigates the state estimation problem for nonlinear cyber–physical systems (CPSs). To conserve system resources, we propose a novel hybrid dynamic event-triggered mechanism (ETM) that prevents the occurrence of Zeno behavior. This work is based on designing an interval observer under the hybrid dynamic ETM to solve the state reconstruction problem of Lipschitz nonlinear CPSs subject to disturbances. That is, the designed triggering mechanism is integrated into the design of the Interval Observer (IO), resulting in a hybrid dynamic event-triggered interval observer (HDETIO), and the system stability and robustness are proved using a Lyapunov function, demonstrating that the observer can effectively provide interval estimation for CPSs with nonlinearity and disturbances. Compared to existing work, the primary contribution of this work is its ability to pre-specify the minimum inter-event time (MIET) and apply it to interval state estimation, enhancing its practicality for real-world physical systems. Finally, the correctness and effectiveness of the designed hybrid dynamic ETM and IO framework are validated with an example.

The effective lifetime of ultra-cold atoms in specific quantum states plays a crucial role in studying interaction parameters within quantum systems. Measuring the effective lifetime of various quantum states within ultra-cold atoms is a fundamental task in quantum operations. In this paper, the effective lifetimes of the excited electronic states F=2,mF=−2, F=2,mF=−1, and F=2,mF=0 for a sodium atomic Bose–Einstein condensate (BEC) are investigated in both the optical dipole trap (ODT) and one-dimensional optical lattice. Through the analysis of experimental data, we demonstrate the significant advantage of lattice loading over the optical dipole trap in terms of atomic lifetimes. The results provide crucial insights into the temporal scales relevant for investigating the evolution of boson gases in optical lattices, facilitating the realization of quantum simulations pertaining to unique quantum phases, and providing an important experimental basis for the research of non-equilibrium dynamics between different spin states.

Point cloud data, known for their accuracy and ease of acquisition, are commonly used for reconstructing level of detail 2 (LoD-2) building models. However, factors like object occlusion can cause incompleteness, negatively impacting the reconstruction process. To address this challenge, this paper proposes a method for reconstructing LoD-2 building models from incomplete point clouds. We design a generative adversarial network model that incorporates geometric constraints. The generator utilizes a multilayer perceptron with a curvature attention mechanism to extract multi-resolution features from the input data and then generates the missing portions of the point cloud through fully connected layers. The discriminator iteratively refines the generator’s predictions using a loss function that is combined with plane-aware Chamfer distance. For model reconstruction, the proposed method extracts a set of candidate polygons from the point cloud and computes weights for each candidate polygon based on a weighted energy term tailored to building characteristics. The most suitable planes are retained to construct the LoD-2 building model. The performance of this method is validated through extensive comparisons with existing state-of-the-art methods, showing a 10.9% reduction in the fitting error of the reconstructed models, and real-world data are tested to evaluate the effectiveness of the method.

With the continuous advancement of urbanization, urban renewal has become a vital means of enhancing urban functionality and improving living environments. Traditional urban renewal research primarily focuses on the macro level, analyzing regions or units, with limited studies targeting individual buildings. Consequently, the unique characteristics and specific requirements of individual buildings during urban renewal have often been overlooked. This study first identified individual buildings undergoing urban renewal in the Longgang and Longhua Districts of Shenzhen, China, from 2018 to 2023 using multisource data such as the 2018 Shenzhen Building Census. A regression analysis based on building characteristics and locational factors was conducted using a stacking ensemble machine learning model. In addition, buildings were categorized into residential, industrial, and commercial types based on their usage, enabling both overall- and category-specific predictions of building renewal. The results show the following: (1) Using the prediction results of multilayer perceptron (MLP) and eXtreme Gradient Boosting (XGBoost) base models as inputs and fusing them with an AdaBoost classifier as the final metamodel, the goodness of fit of the overall building renewal regression model increased by 2.19%. (2) The regression model achieved an overall urban renewal prediction accuracy of 89.41%. Categorizing urban renewal projects improved the goodness of fit for residential and industrial building renewal by 0.14% and 6.13%, respectively. (3) Compared with traditional macro-level evaluation methods, the experimental results of this study improved by 8.41%, and compared with single-model approaches based on planning permit data, the accuracy improved by 29.11%.

As a crucial biomarker for the early warning and prognosis of liver cancer diseases, elevated levels of alpha-fetoprotein (AFP) are associated with hepatocellular carcinoma and germ cell tumors. Herein, we present a novel signal-on electrochemical aptamer sensor, utilizing AuNPs–MXene composite materials, for sensitive AFP quantitation. The AuNPs–MXene composite was synthesized through a simple one-step method and modified on portable microelectrodes. As signal molecules, AFP aptamers were conjugated with methylene blue (MB) and immobilized on the electrode surface. When interacting with AFP, conformational changes in the aptamer–target complex caused MB to approach the electrode, and the electrochemical signal was enhanced through signal-on mechanisms. The developed sensor demonstrated high sensitivity and selectivity for AFP, with a log-linear relationship defined as 1–300 ng/mL, and the LOD was 0.05 ng/mL (S/N = 3). The method was applied to laboratorial and real clinical samples and presented satisfactory selectivity, reproducibility, and long-term stability. The proposed high-performance sensor highlights the potential of electrochemical aptamer sensors in improving the warning capabilities in disease management.

This study aims to develop an emotion evaluation method for second language learners, utilizing multimodal information to comprehensively evaluate students’ emotional expressions. Addressing the limitations of existing emotion evaluation methods, which primarily focus on the acoustic features of speech (e.g., pronunciation, frequency, and rhythm) and often neglect the emotional expressions conveyed through voice and facial videos, this paper proposes an emotion evaluation method based on multimodal information. The method includes the following three main parts: (1) generating virtual data using a Large Language Model (LLM) and audio-driven facial video synthesis, as well as integrating the IEMOCAP dataset with self-recorded student videos and audios containing teacher ratings to construct a multimodal emotion evaluation dataset; (2) a graph convolution-based emotion feature encoding network to extract emotion features from multimodal information; and (3) an emotion evaluation network based on Kolmogorov–Arnold Networks (KAN) to compare students’ emotion features with standard synthetic data for precise evaluation. The emotion recognition method achieves an unweighted accuracy (UA) of 68.02% and an F1 score of 67.11% in experiments with the IEMOCAP dataset and TTS data. The emotion evaluation model, using the KAN network, outperforms the MLP network, with a mean squared error (MSE) of 0.811 compared to 0.943, providing a reliable tool for evaluating language learners’ emotional expressions.

Objectives: To investigate the acceptance and factors influencing acceptance of rubella-containing vaccine (RCV) among women of reproductive age in Guangdong, Henan, Hubei, Liaoning, Shanxi, Sichuan and Zhejiang provinces of China. Methods: Using a stratified sampling method, we selected one urban and one rural community health services center in each of two cities in seven provinces. From these centers, we recruited women 15–49 years of age to complete a questionnaire on their willingness to receive RCV and factors influencing willingness. The survey instrument was adapted from the behavioral and social drivers (BeSD) of vaccination survey tool developed by the World Health Organization. Results: Among 1286 participants, 981 (76.3%) were willing to receive RCV. Awareness of rubella ranged from 12.4% to 70.6%. Willingness to receive RCV differed significantly by region, occupation, vaccination history, and awareness. All latent variables of the structural equation model (SEM) were positively correlated with willingness, and all standardized paths were statistically significant (p < 0.001). Thinking and Feeling had direct positive effects on Social Processes (β = 0.789) and Practical Issues (β = 0.542), thereby indirectly affecting motivation. Conclusions: Women of reproductive age had high willingness to receive the rubella vaccination, but their general awareness of rubella was relatively low. It is necessary to strengthen the health education of women of reproductive age regarding rubella to improve RCV coverage.

Aiming at the problems of large capacity, narrow transverse width, large excitation, high safety level, and difficulty in accurately grasping the working state of the cable-stayed bridge for the long-span track, this research obtains the structural response data in real time by establishing a health monitoring system. The adaptive filtering method was employed to separate the train load response and the temperature load response. Then, a train load effect analysis method based on the influence line and a temperature load effect analysis method based on the correlation were proposed to assess the operational status of the bridge in real time and objectively. The Chongqing Nanjimen Railway Track Bridge (hereinafter Chongqing Nanjimen track bridge) project was utilized as a case study to demonstrate the application of these methods. The results show that the adaptive filtering method can effectively separate the response of train and temperature loads. The normalized cross-correlation (NCC) results of the measured train load response and the influence line’s finite element calculation show a high degree of fit between the measured values and the theory, proving that no significant anomalies are found in the bridge. There is a strong correlation between the ambient temperature difference and the Pearson correlation coefficient of structural response, which indicates that the Chongqing Nanjimen track bridge is currently in normal working condition.

There are still great uncertainties about effects of climate warming and no-tillage on soil carbon, nitrogen, phosphorus and potassium contents and pH in alpine farmlands. A warming (control; daytime warming, DW; nighttime warming, NW; all-day warming, DW + NW) and no-tillage (no-tillage vs. tillage) experiment was conducted in an alpine farmland of the Lhasa, Xizang since 2015. Soil organic carbon, total nitrogen, total phosphorus, total potassium (TK), available nitrogen, available phosphorus, available potassium, dissolved organic carbon (DOC), active organic carbon, particulate organic carbon (POC), light fraction organic carbon, and heavy fraction organic carbon contents and pH at four depths (0–5, 5–15, 15–25, and 25–35 cm) were measured. Warming effects on concerned soil variables differed with warming time, soil depth, and no-tillage. No-tillage effects on concerned soil variables differed with warming-time (daytime, nighttime, and all-day warming) and soil depths. Therefore, daytime warming and nighttime warming have different effects on soil variables, although the effects of nighttime warming on soil variables are not always greater than those of daytime warming. Effects of daytime warming and nighttime warming on soil variables are not simple addition or subtraction effects. There are interactions between diurnal asymmetrical warming and no-tillage on soil variables.

A lime–sand pile is a three-phase particle composite material composed of a lime matrix, sand, and a loess aggregate at the meso level. Establishing a random aggregate model that can reflect the actual aggregate gradation, content, and morphology is the premise of numerical simulations of the meso-mechanics of lime–sand piles. In this paper, the secondary development of Abaqus 2022 is realized by writing Python 3.12 scripts, and a parameterized three-phase mixed lime–sand pile random meso-spherical aggregate model is obtained. A meso-modeling idea of lime–sand piles is creatively proposed, the integrated-generation algorithm of a spherical aggregate of different materials is written, and the material’s properties and mesh generation are given by the algorithm. Finally, a numerical simulation of temperature–expansion statics is carried out using an established mesoscopic model. Under different mixing ratios, the expansion force increases with an increase in the lime proportion. The increase in the expansion force is approximately 45% of that without an increased lime proportion. The simulated expansion forces are 8.81 kN, 12.61 kN, and 18.89 kN, respectively, which are similar to the experimental results for laboratory specimens, and the relative error is less than 3%. In the case of different height ratios, with increases in height, the change in the expansion force is very small, and the relative error between the simulated value and the experimental value is less than 2%, which further verifies the reliability of the mesoscopic random model of lime–sand piles. The model can be used to guide the practical application of engineering.

Scoliosis, characterized by spine deformity, is most common in adolescent idiopathic scoliosis (AIS). Manual Cobb angle measurement limitations underscore the need for automated tools. This study employed a vertebral landmark extraction method and Feedforward Neural Network (FNN) to predict scoliosis progression in 79 AIS patients. The novel intervertebral angles matrix format showcased results. The mean absolute error for the intervertebral angle progression was 1.5 degrees, while the Pearson correlation of the predicted Cobb angles was 0.86. The accuracy in classifying Cobb angles (45°) was 0.85, with 0.65 sensitivity and 0.91 specificity. The FNN demonstrated superior accuracy, sensitivity, and specificity, aiding in tailored treatments for potential scoliosis progression. Addressing FNNs’ over-fitting issue through strategies like “dropout” or regularization could further enhance their performance. This study presents a promising step towards automated scoliosis diagnosis and prognosis.

Preclinical evidence indicates the potential anti-tumor capabilities of cannabinoids in prostate cancer (PC). We undertook a cross-sectional study using National Survey on Drug Use and Health data from 2002 to 2020, involving 2503 participants in the USA. The independent variable was marijuana use status (current, former, never), while the dependent variable was self-reported PC (yes, no). Eleven other demographic variables were assessed as covariates. PC prevalence was lower among current marijuana users (46/145, 31.7%) and former users (323/1021, 31.6%) compared to non-users (534/1337, 39.9%, p < 0.001). PC prevalence was lower among users versus non-users in the elderly (≥65) (36.4% vs. 42.4%, p = 0.016) and non-Hispanic white subgroups (28.9% vs. 38.3%, p < 0.001). There were no significant PC prevalence differences between users and non-users in the younger population (50–64) or other race/ethnicity. In the multivariable analyses, former marijuana use was associated with lower PC compared to never using (odd ratio = 0.74, 95% CI 0.62–0.90, p = 0.001). Current use was also suggestive of reduced prevalence but was not statistically significant (odd ratio = 0.77, 95% CI 0.52–1.14, p = 0.198), possibly due to low sample size. Our findings from a large national survey provide additional data to link marijuana use with lower PC prevalence.

Lettuce is susceptible to high-temperature stress during cultivation, leading to bolting and affecting yield. Plant-specific transcription factors, known as GRAS proteins, play a crucial role in regulating plant growth, development, and abiotic stress responses. In this study, the entire lettuce LsGRAS gene family was identified. The results show that 59 LsGRAS genes are unevenly distributed across the nine chromosomes. Additionally, all LsGRAS proteins showed 100% nuclear localization based on the predicted subcellular localization and were phylogenetically classified into nine conserved subfamilies. To investigate the expression profiles of these genes in lettuce, we analyzed the transcription levels of all 59 LsGRAS genes in the publicly available RNA-seq data under the high-temperature treatment conducted in the presence of exogenous melatonin. The findings indicate that the transcript levels of the LsGRAS13 gene were higher on days 6, 9, 15, 18, and 27 under the high-temperature (35/30 °C) treatment with melatonin than on the same treatment days without melatonin. The functional studies demonstrate that silencing LsGRAS13 accelerated bolting in lettuce. Furthermore, the paraffin sectioning results showed that flower bud differentiation in LsGRAS13-silenced plants occurred significantly faster than in control plants. In this study, the LsGRAS genes were annotated and analyzed, and the expression pattern of the LsGRAS gene following melatonin treatment under high-temperature conditions was explored. This exploration provides valuable information and identifies candidate genes associated with the response mechanism of lettuce plants high-temperature stress.

This study is dedicated to developing an innovative method for evaluating spoken English by integrating large language models (LLMs) with effective space learning, focusing on the analysis and evaluation of emotional features in spoken language. Addressing the limitation of current spoken English evaluation software that primarily focuses on acoustic features of speech (such as pronunciation, frequency, and prosody) while neglecting emotional expression, this paper proposes a method capable of deeply recognizing and evaluating emotional features in speech. The core of the method comprises three main parts: (1) the creation of a comprehensive spoken English emotion evaluation dataset combining emotionally rich speech data synthesized using LLMs with the IEMOCAP dataset and student spoken audio; (2) an emotion feature encoding network based on transformer architecture, dedicated to extracting effective spatial features from audio; (3) an emotion evaluation network for the spoken English language that accurately identifies emotions expressed by Chinese students by analyzing different audio characteristics. By decoupling emotional features from other sound characteristics in spoken English, this study achieves automated emotional evaluation. This method not only provides Chinese students with the opportunity to improve their ability to express emotions in spoken English but also opens new research directions in the fields of spoken English teaching and emotional expression evaluation.

Aiming at the problems that it is difficult to control the expansion force of lime-sand pile in the foundation correcting and how the upper existing structural pressure affects the expansion force of lime-sand pile, this paper adopts a new type of lime-sand pile reaction tester to carry out the experimental research on the expansion force of lime-sand pile under the influences of different pre-pressure, different mixing ratios, different heights, and different granular sizes. The purpose of this paper is to explore the influence of the pre-pressure brought by the existing building structure on the expansion force of the lime-sand pile and the coupling effect of the pre-pressure and other influencing factors on the expansion force of the lime-sand pile through the multi-parameter test. The results show that with the increase in pre-pressure, the expansion force of the lime-sand pile shows the trend of increasing first and then decreasing when the pre-pressure is 0.88 KN, the effect is most obvious and its expansion force can be increased by 14.9%. Within a certain range, the larger the proportion of quicklime is, the lower the expansion reaction force is. The expansion force of the lime-sand pile with the ratio of lime/sand/soil of 5:4:1 is the most ideal, and its value reaches 10.2 KN, which is 10% and 15.3% higher than the rest of the two groups, respectively. Within a certain range, the effect of specimen height change on the expansion force of the lime-sand pile is almost negligible. The smaller particle size of quicklime makes its reaction speed faster, the reaction degree is more full, and the expansion force is greater. The expansion force of the small particle size group was 12.1% and 34.2% higher than that of the medium and large particle size groups, respectively.

Spatiotemporal information on individual trajectories in urban rail transit is important for operational strategy adjustment, personalized recommendation, and emergency command decision-making. However, due to the lack of journey observations, it is difficult to accurately infer unknown information from trajectories based only on AFC and AVL data. To address the problem, this paper proposes a spatiotemporal probabilistic graphical model based on adaptive expectation maximization attention (STPGM-AEMA) to achieve the reconstruction of individual trajectories. The approach consists of three steps: first, the potential train alternative set and the egress time alternative set of individuals are obtained through data mining and combinatorial enumeration. Then, global and local potential variables are introduced to construct a spatiotemporal probabilistic graphical model, provide the inference process for unknown events, and state information about individual trajectories. Further, considering the effect of missing data, an attention mechanism-enhanced expectation-maximization algorithm is proposed to achieve maximum likelihood estimation of individual trajectories. Finally, typical datasets of origin-destination pairs and actual individual trajectory tracking data are used to validate the effectiveness of the proposed method. The results show that the STPGM-AEMA method is more than 95% accurate in recovering missing information in the observed data, which is at least 15% more accurate than the traditional methods (i.e., PTAM-MLE and MPTAM-EM).

Spatial truss structures (STSs), serving as the bottom support structure of a cooling tower, effectively harness the superior load-bearing capacity offered by lattice-type truss structures. STSs are composed of main bars, diagonal bars, and horizontal bars, with horizontal bars serving as vital components of the truss structure. They play a pivotal role in maintaining the overall integrity and stability of the structure. The proportional relationship between the stiffness of each bar in STSs has a profound impact on the mechanical characteristics of the overall structure. This relationship directly influences the ultimate load-bearing capacity of the structure. Therefore, conducting research on the influence patterns of this relationship is of utmost importance. This paper explores the study of triangular truss structures (TTSs) and quadrilateral truss structures (QTSs). Firstly, through theoretical analysis, considering structural elements such as the stiffness of the horizontal bars, the number of layers in the truss, and the angle between the diagonal bars and the horizontal bars, theoretical expressions for the calculation of the ultimate load capacity of TTSs and QTSs are derived. Furthermore, a parametric finite element (FE) model was established for the TTSs and QTSs. Through numerical simulations, the validity of the theoretical calculation expressions was verified. Finally, this paper discusses the influence of factors such as the stiffness of the horizontal bars, the number of layers in the truss, and the angle between the diagonal and horizontal bars on the TTSs and QTSs. It analyzes the patterns and trends of these influences. The research results indicate that the theoretical and numerical simulation results for the TTSs have an error ranging from 0.40% to 4.93%, while the relative error for the QTSs ranges from 1.59% to 4.88%. These errors are within an acceptable range for engineering calculations. As the stiffness of the horizontal bars increases, the proportionality coefficient of the truss’s ultimate load capacity shows an initial increase followed by a stable trend. It reaches an equilibrium state when the stiffness of the horizontal bars reaches a certain threshold. As the number of layers in the truss and the angle between the diagonal and horizontal bars increase, the proportionality coefficient of the load capacity gradually decreases. The research findings provide a theoretical basis for the application of TTSs and QTSs in cooling towers.

In recent years, short-term composting techniques have been widely applied in oyster mushroom cultivation, but there is still a lack of systematic research on their impact on the nutritional and functional properties of fruiting bodies. In this study, the microbial inoculant Streptomyces thermoviolaceus BUA-FM01 (ST) was applied in the short-term composting process for oyster mushroom cultivation. The agronomic traits, nutritional composition, flavor compounds, and antioxidant activity of fruiting bodies from the first three flushes were evaluated. The results show that microbial inoculation significantly (p < 0.05) reduced the total carbon content and C/N ratio of the composted substrates and, furthermore, increased the total yield of the fruiting bodies. Moreover, microbial inoculation significantly (p < 0.05) increased the crude protein, crude polysaccharide, total amino acid, and essential amino acid contents of the fruiting bodies. The fruiting bodies of the first flush of ST treatment possessed the highest umami amino acid content and equivalent umami concentration value. Furthermore, microbial inoculation significantly (p < 0.05) enhanced the scavenging ability of crude polysaccharides toward free radicals. The results indicate that microbial inoculation has many benefits for the composting cultivating process of oyster mushrooms and good application prospects.

Garlic, an important economic crop, provides nutrient-rich straw. When appropriately balanced with silage corn stalks, it is a high-quality forage resource. However, studies on the impact of garlic straw with silage corn stalks on Hu sheep’s digestive metabolism and rumen microbiota are scarce. In this study, different addition ratios of garlic straw and silage corn stalks were utilized for in vitro experiments. We designed six experimental groups (CON, G0, G20, G40, G60, G80, and G100) based on varying ratios of garlic straw to silage corn stalks. Rumen microbiota was analyzed through 16S rRNA sequencing. Nutrient composition analysis indicated that garlic straw’s relative feeding value (RFV) closely resembled that of silage corn stalks. After 24 h of fermentation, dry matter digestibility and in vitro gas production significantly increased, reaching peak values at a 60% addition ratio. Furthermore, volatile fatty acids (VFAs) such as acetic, propionic, and butyric acid exhibited elevated contents, with the highest yields observed at 60% inclusion. At the genus level, Prevotella, Rikenellaceae RC9 gut group, and Succiniclasticum were identified as the dominant bacterial groups. The gas production test showed a significant decrease in the G80 group compared to others. Microbial analysis revealed a higher abundance of Prevotella in G80 compared to G20, offering valuable insights for reducing greenhouse gas emissions from ruminant animals. Finally, this study predicted the impact of garlic straw with silage corn stalks’ addition on Hu sheep’s metabolic pathways and biological functions of the rumen microbiota. This research highlights the potential for effectively utilizing garlic straw as a feed resource for Hu sheep and proposes a rational proportion for combining garlic straw with silage corn stalks.

This paper investigates the problem of interval estimation for cyber–physical systems with unknown disturbance. In order to realize the interval estimation of cyber–physical systems, two technical methods are adopted. The first one requires the observer dynamic error system to be non-negative, and the second one relaxes this limitation by coordinate transformation. The sufficient conditions are established using both Lyapunov stability and positive system theory. Furthermore, according to the Schur complement, the linear matrix inequality is solved to determine the observer gains. Finally, the effectiveness and feasibility of the designed interval observer are verified by one numerical simulation.

Calcium ions participate in the regulation of almost all biological functions of the body, especially in cardiac excitation–contraction coupling, acting as vital signaling through ion channels. Various cardiovascular drugs exert their effects via affecting the ion channels on the cell membrane. The current strategies for calcium ion monitoring are mainly based on fluorescent probes, which are commonly used for intracellular calcium ion detection (calcium imaging) and cannot achieve long-term monitoring. In this work, an all-solid-state silicone–rubber ion-sensitive membrane was fabricated on light-addressable potentiometric sensors to establish a program-controlled field-effect-based ion-sensitive light-addressable potentiometric sensor (LAPS) platform for extracellular calcium ion detection. L-type calcium channels blocker verapamil and calcium channel agonist BayK8644 were chosen to explore the effect of ion channel drugs on extracellular calcium ion concentration in HL-1 cell lines. Simultaneously, microelectrode array (MEA) chips were employed to probe the HL-1 extracellular field potential (EFP) signals. The Ca2+ concentration and EFP parameters were studied to comprehensively evaluate the efficacy of cardiovascular drugs. This platform provides more dimensional information on cardiovascular drug efficacy that can be utilized for accurate drug screening.

Using carbon dioxide as a gasification agent for underground coal gasification (UCG) can not only reduce carbon dioxide emissions but is also expected to lead to a new natural gas technology revolution and ensure national energy security. To explore the effect of the oxygen content in oxygen-enriched carbon dioxide gasification agents on the results of gasification experiments, underground gasification experiments under different oxygen-enrichment conditions were designed, and quantitative parameters were used to analyze and evaluate the gas produced in the gasification experiments. The results showed that as the oxygen content in the oxygen-enriched carbon dioxide gasification agent increased, the CO and H2 in the combustible gas gradually increased, and the calorific value of the combustible gas also slowly increased, reaching a peak value under the gasification condition of 60% oxygen concentration, and then decreased slightly; the product formation rate and the gas production per unit mass of coal fluctuated. The coal consumption rate increased with time and was relatively stable. According to theoretical calculations for the gasification energy recovery evaluation system, the overall energy recovery rate was 56.34%, and the energy utilization rate was relatively high. Research on quantitative indicators based on gas production data has good practical significance for evaluating the gasification efficiency of UCG, which can be used to better evaluate and control the reaction process of UCG.

This review focuses on electroencephalogram (EEG) acquisition and feedback technology and its core elements, including the composition and principles of the acquisition devices, a wide range of applications, and commonly used EEG signal classification algorithms. First, we describe the construction of EEG acquisition and feedback devices encompassing EEG electrodes, signal processing, and control and feedback systems, which collaborate to measure faint EEG signals from the scalp, convert them into interpretable data, and accomplish practical applications using control feedback systems. Subsequently, we examine the diverse applications of EEG acquisition and feedback across various domains. In the medical field, EEG signals are employed for epilepsy diagnosis, brain injury monitoring, and sleep disorder research. EEG acquisition has revealed associations between brain functionality, cognition, and emotions, providing essential insights for psychologists and neuroscientists. Brain–computer interface technology utilizes EEG signals for human–computer interaction, driving innovation in the medical, engineering, and rehabilitation domains. Finally, we introduce commonly used EEG signal classification algorithms. These classification tasks can identify different cognitive states, emotional states, brain disorders, and brain–computer interface control and promote further development and application of EEG technology. In conclusion, EEG acquisition technology can deepen the understanding of EEG signals while simultaneously promoting developments across multiple domains, such as medicine, science, and engineering.

Carbon nanotube (CNT)/metal composites have attracted much attention due to their enhanced electrical and thermal performance. How to achieve the scalable fabrication of composites with efficient dispersion of CNTs to boost their performance remains a challenge for their wide realistic applications. Herein, the nanoscale dispersion of CNTs in the Stannum (Sn) matrix to boost thermal and electrical conductivity via facile ball milling techniques was demonstrated. The results revealed that CNTs were tightly attached to metal Sn, resulting in a much lower resistivity than that of bare Sn. The resistivity of Sn with 1 wt.% and 2 wt.% CNTs was 0.087 mΩ·cm and 0.056 mΩ·cm, respectively. The theoretical calculation showed that there was an electronic state near the Fermi level, suggesting its electrical conductivity had been improved to a certain extent. In addition, the thermal conductivity of Sn with 2 wt.% CNTs was 1.255 W·m−1·K−1. Moreover, Young’s modulus of the composites with CNTs mass fraction of 10 wt.% had low values (0.933 MPa) under low strain conditions, indicating the composite shows good potential for various applications with different flexible requirements. The good electrical and thermal conductive CNT networks were formed in the metal matrix via facile ball milling techniques. This strategy can provide guidance for designing high-performance metal samples and holds a broad application potential in electronic packaging and other fields.

Proanthocyanidin (PRO) has been implicated in a variety of biological functions, such as antibacterial, antioxidant, and anti-obesity effects. However, little is known about its safety dose for animals. To explore its safety and effect on growth performance and intestinal health, thirty weaned pigs were divided into five groups and fed with basal diet containing 0, 30, 300, 600, and 1200 mg/kg PRO for 42 days. Results showed that PRO supplementation at 30 and 300 mg/kg significantly decreased the feed/gain ratio (F:G) and diarrhea rate (p < 0.05). PRO also increased the digestibilities of dry matter, ether extract, gross energy, and ash (p < 0.05). Interestingly, PRO not only elevated the villus height and the ratio of villus height to crypt depth (V/C) in duodenum and jejunum (p < 0.01), but also decreased the crypt depth in the duodenum (p < 0.01). Moreover, PRO supplementation at 30, 300, and 600 mg/kg elevated the expression levels of mucin 1 (MUC1), MUC2, and fatty acid transport protein 1 (FATP-1) in the duodenum (p < 0.05). The expression levels of FATP-4 in jejunum and ileum were also elevated by PRO (p < 0.05). Importantly, histopathological findings of tissues (e.g., heart, liver, kidney, spleen, lungs, pancreas, thymus, mesenteric lymph nodes, stomach, small intestine), serum clinical chemistry, and major hematological parameters were not affected by PRO supplementation. These results suggest that PRO may act as a safe and effective supplement to decrease F:G and improve intestinal health in weaned pigs.

ZnO is a commonly used material for hydrogen gas sensors. In this study, a ZnO nanofiber film with a diameter of approximately 60 nm was synthesized by the electrospinning method. Compared to previously reported ZnO hydrogen gas sensors, an abnormal phenomenon was observed here, where the resistance of the ZnO nanofiber film increased upon exposure to hydrogen gas in the temperature range from 210 °C to 330 °C. The physical mechanism of this phenomenon was explored through microstructure analysis and DFT simulation calculations that showed a total charge transfer of 0.65 e for the hydrogen molecule. This study can push forward the understanding of ZnO hydrogen sensing.

Biofilm is considered as the hotspot of antibiotic resistance gene (ARG) dissemination. Bacterial growth substrates are important factors for biofilm formation, but its spatial–temporal effects on ARG spread in biofilm is still unclear. In this study, microfluidics combined with microscopic observation were used to reveal spatial–temporal effects of bacterial growth substrates on ARG transfer at real time. The initial horizontal gene transfer events were found to be independent of substrate levels. However, subsequent transfer processes varied greatly depending on the availability of growth substrates. The proportion of transconjugants was much higher (~12%) when observed in substrate-rich regions (under the channel) at 24 h, followed by an exponential decline, with the distance far from the channel. Furthermore, three-dimensional observation revealed that vertical gene transfer influenced by the concentrations of bacterial growth substrates was important for ARG spread in biofilm. The transfer frequency was 8.2 times higher in the high substrate concentration (50×) compared to low concentration (0.5×) in simulated sewage, underscoring the substantial impact of bacterial growth substrate variability on ARG dissemination. This study is helpful for in-depth understanding of ARG dissemination through biofilms and indicates that reducing pollutant emission is important for ARG control in the environment.

A train traction system is an important part of an urban rail transit system. However, a train traction system has many components and a high risk of internal faults. How to systematically evaluate the fault coverage and diagnosis ability of testing equipment is a fundamental problem in the technical field of train operation. In response to this problem, this study attempts to apply testability technology to the test capability analysis of train traction systems for rail transit. In view of the uncertainty in actual tests, a method for constructing a fault diagnosis strategy for a traction system under unreliable testing is proposed. The concept of test credibility is introduced for the first time, and the quantitative evaluation of test credibility is realized using a cloud model, so as to construct a new “fault-test” credibility correlation matrix. On this basis, a single-fault diagnosis strategy of the traction system is constructed and compared based on information theory. The results show that a using a fault diagnosis strategy under the condition of unreliable testing is more similar to actual maintenance work, proving the significance of the diagnosis strategy constructed using this method for the practical application of the project.

Wind and photovoltaic (PV) power forecasting are crucial for improving the operational efficiency of power systems and building smart power systems. However, the uncertainty and instability of factors affecting renewable power generation pose challenges to power system operations. To address this, this paper proposes a digital twin-based method for predicting wind and PV power. By utilizing digital twin technology, this approach provides a highly realistic simulation environment that enables accurate monitoring, optimal control, and decision support for power system operations. Furthermore, a digital twin platform for the AI (Artificial Intelligence) Grid is established, allowing real-time monitoring, and ensuring the safe, reliable, and stable operation of the grid. Additionally, a deep learning-based model WPNet is developed to predict wind and PV power at specific future time points. Four datasets are constructed based on weather conditions and historical wind and PV power data from the Flanders and Wallonia regions. The prediction models presented in this paper demonstrate excellent performance on these datasets, achieving mean square error (MSE) values of 0.001399, 0.001833, 0.000704, and 0.002708; mean absolute error (MAE) values of 0.025164, 0.027854, 0.018592, and 0.033501; and root mean square error (RMSE) values of 0.037409, 0.042808, 0.026541, and 0.052042, respectively.

Breast cancer treatment can be improved with biomarkers for early detection and individualized therapy. A set of 86 microRNAs (miRNAs) were identified to separate breast cancer tumors from normal breast tissues (n = 52) with an overall accuracy of 90.4%. Six miRNAs had concordant expression in both tumors and breast cancer patient blood samples compared with the normal control samples. Twelve miRNAs showed concordant expression in tumors vs. normal breast tissues and patient survival (n = 1093), with seven as potential tumor suppressors and five as potential oncomiRs. From experimentally validated target genes of these 86 miRNAs, pan-sensitive and pan-resistant genes with concordant mRNA and protein expression associated with in-vitro drug response to 19 NCCN-recommended breast cancer drugs were selected. Combined with in-vitro proliferation assays using CRISPR-Cas9/RNAi and patient survival analysis, MEK inhibitors PD19830 and BRD-K12244279, pilocarpine, and tremorine were discovered as potential new drug options for treating breast cancer. Multi-omics biomarkers of response to the discovered drugs were identified using human breast cancer cell lines. This study presented an artificial intelligence pipeline of miRNA-based discovery of biomarkers, therapeutic targets, and repositioning drugs that can be applied to many cancer types.

Objective: The objective of this study is to develop a novel automatic convolutional neural network (CNN) that aids in the diagnosis of meniscus injury, while enabling the visualization of lesion characteristics. This will improve the accuracy and reduce diagnosis times. Methods: We presented a cascaded-progressive convolutional neural network (C-PCNN) method for diagnosing meniscus injuries using magnetic resonance imaging (MRI). A total of 1396 images collected in the hospital were used for training and testing. The method used for training and testing was 5-fold cross validation. Using intraoperative arthroscopic diagnosis and MRI diagnosis as criteria, the C-PCNN was evaluated based on accuracy, sensitivity, specificity, receiver operating characteristic (ROC), and evaluation performance. At the same time, the diagnostic accuracy of doctors with the assistance of cascade- progressive convolutional neural networks was evaluated. The diagnostic accuracy of a C-PCNN assistant with an attending doctor and chief doctor was compared to evaluate the clinical significance. Results: C-PCNN showed 85.6% accuracy in diagnosing and identifying anterior horn injury, and 92% accuracy in diagnosing and identifying posterior horn injury. The average accuracy of C-PCNN was 89.8%, AUC = 0.86. The diagnosis accuracy of the attending physician with the aid of the C-PCNN was comparable to that of the chief physician. Conclusion: The C-PCNN-based MRI technique for diagnosing knee meniscus injuries has significant practical value in clinical practice. With a high rate of accuracy, clinical auxiliary physicians can increase the speed and accuracy of diagnosis and decrease the number of incorrect diagnoses.

This paper considers the regional consensus problem for multi-agent systems with actuator saturation. By utilizing the theory of convex set, a novel multiple nonlinear feedback control protocol is presented, which can effectively reduce the conservatism in dealing with saturated nonlinear input. In order to obtain a larger estimate on the domain of consensus, the composite Laplacian quadratics function is constructed to derive sufficient conditions for the consensus of multi-agent systems. In addition, an alternative convex hull representation is employed to further enlarge the above-mentioned domain of consensus. Finally, a numerical simulation case study illustrates the validity as well as the superiority of the proposed approaches.

Focusing aerosol particles efficiently is of great significance for high-precision aerosol jet printing and detection of the airborne target. A new method was proposed herein to achieve the efficient focusing of aerosol particles in the microchannel by using a reverse external force. Considering the slip at the interface between the gas and the aerosol particle, a numerical model of the particle movement in the microchannel was established and simulations were conducted on the gas–particle two-phase flow in the microchannel under the effect of the reverse external force. The results showed that a suitable reverse external force in a similar order of magnitude to the Stokes force can dramatically increase the velocity difference between the particle and the gas, which significantly enhances the Saffman lift force exerted on the aerosol particle. Eventually, the aerosol particle can be efficiently focused at the center of the microchannel in a short channel length. In addition, the influence of the channel geometry, the magnitude, and the direction of the external force on the particle focusing was also studied. This work is of great significance for the precise detection of aerosol particles and the design of nozzles for aerosol jet printing.

The environmentally friendly gas C4F7N has been considered a potential replacement gas for SF6 due to its excellent insulation. At present, research on C4F7N and its mixture mainly focuses on its insulation performance, but few people study its surface discharge mechanism and variation law. In order to study the surface discharge characteristics of the C4F7N/CO2 mixture, a fluid model of surface discharge was established and simulated by COMSOL, which was used to study the mechanism of streamers during surface discharge, fit the expression of the maximum photon flux, analyze the influence of voltage and the dielectric constant of the insulator on discharge, and compare the surface discharge of C4F7N/CO2 with that of SF6, providing the theoretical basis for the optical detection and reliable diagnosis of partial discharge. The results show that, under the same discharge conditions, the optical phenomenon of C4F7N/CO2 discharge along the surface is more obvious than that of SF6, and as the applied voltage or the dielectric constant of the insulator increases, the streamer develops more quickly, the electric field becomes stronger, and the photon flux increases.

The origin of the high content of wet gases in coalbed seams is very important geologically, especially in the Enhong syncline in China. The present study focuses on the role of the material that generates the hydrocarbons. The effect of the pore structure on the generation of wet gases has not been thoroughly examined. The present paper characterizes the coal pore structure in the “wet gas area” and “dry gas area”. The pore structures in the two areas are shown to have different features, which affect the distribution of the wet gases. With respect to the pore structure parameters, coals in the wet gas area have a greater total specific surface area and pore volume in micropores. The pore structure types also differ between the two areas: the pore structures in the dry gas areas are mainly of the parallel type and reverse S type, which is favorable for the migration and dissipation of coalbed gases. The pore structure in the wet gas area is relatively closed, with poor connectivity and susceptibility to blockage. The micropore volume, total specific surface area, and the connectivity of the pore structure significantly affect the reserve of wet gases. The adsorption capacity of the micropores and the closed pore structure contribute to the preservation of wet gases.