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Liver fibrosis is an important step in the transformation of chronic liver disease into cirrhosis and liver cancer, and structural changes and functional disorders of liver sinusoidal endothelial cells (LSECs) are early events in the occurrence of liver fibrosis. Therefore, it is necessary to identify the key regulatory genes of endothelial dysfunction in the process of liver fibrosis to provide a reference for the diagnosis and treatment of liver fibrosis. In this study, we identified 230 common differentially expressed genes (Co-DEGs) by analyzing transcriptomic data of primary LSECs from three different liver fibrosis mouse models (carbon tetrachloride; choline-deficient, l-amino acid-defined diet; and nonalcoholic steatohepatitis). Enrichment analysis revealed that the Co-DEGs were mainly involved in regulating the inflammatory response, immune response, angiogenesis, formation and degradation of the extracellular matrix, and mediating chemokine-related pathways. A Venn diagram analysis was used to identify 17 key genes related to the progression of liver cirrhosis. Regression analysis using the Lasso–Cox method identified genes related to prognosis among these key genes: SOX4, LGALS3, SERPINE2, CD52, and LPXN. In mouse models of liver fibrosis (bile duct ligation and carbon tetrachloride), all five key genes were upregulated in fibrotic livers. This study identified key regulatory genes for endothelial dysfunction in liver fibrosis, namely SOX4, LGALS3, SERPINE2, CD52, and LPXN, which will provide new targets for the development of therapeutic strategies targeting endothelial dysfunction in LSECs and liver fibrosis.

Computer systems and applications generate large amounts of logs to measure and record information, which is vital to protect the systems from malicious attacks and useful for repairing faults, especially with the rapid development of distributed computing. Among various logs, the anomaly log is beneficial for operations and maintenance (O&M) personnel to locate faults and improve efficiency. In this paper, we utilize a large language model, ChatGPT, for the log parser task. We choose the BERT model, a self-supervised framework for log anomaly detection. BERT, an embedded transformer encoder, with a self-attention mechanism can better handle context-dependent tasks such as anomaly log detection. Meanwhile, it is based on the masked language model task and next sentence prediction task in the pretraining period to capture the normal log sequence pattern. The experimental results on two log datasets show that the BERT model combined with an LLM performed better than other classical models such as Deelog and Loganomaly.

Sound source identification technology based on a microphone array has many application scenarios. The compressive beamforming method has attracted much attention due to its high accuracy and high-resolution performance. However, for the far-field measurement problem of large microphone arrays, existing methods based on fixed grids have the defect of basis mismatch. Due to the large number of grid points representing potential sound source locations, the identification accuracy of traditional grid adjustment methods also needs to be improved. To solve this problem, this paper proposes a sound source identification method based on adaptive grid splitting and refinement. First, the initial source locations are obtained through a sparse Bayesian learning framework. Then, higher-weight candidate grids are retained, and local regions near them are split and updated. During the iteration process, Green’s function and the source strength obtained in the previous iteration are multiplied to get the sound pressure matrix. The robust principal component analysis model of the Gaussian mixture separates and replaces the sound pressure matrix with a low-rank matrix. The actual sound source locations are gradually approximated through the dynamically adjusted sound pressure low-rank matrix and optimized grid transfer matrix. The performance of the method is verified through numerical simulations. In addition, experiments on a standard aircraft model are conducted in a wind tunnel and speakers are installed on the model, proving that the proposed method can achieve fast, high-precision imaging of low-frequency sound sources in an extensive dynamic range at long distances.

The advancement of artificial intelligence (AI) demands significant data and computational resources that have an adverse impact on the environment. To address this issue, a novel computing architecture that is both energy efficient and eco-friendly is urgently required. Edge computing has emerged as an increasingly popular solution to this problem. In this study, we explore the development history of edge computing and AI and analyze the potential of model quantization to link AI and edge computing. Our comparative analysis demonstrates that the quantization approach can effectively reduce the model’s size and accelerate model inference while maintaining its functionality, thereby enabling its deployment on edge devices. This research serves as a valuable guide and reference for future advancements in edge AI.

In this paper, we propose a novel approach for solving two-dimensional time-fractional advection–diffusion equations, where the fractional derivative is described in the Caputo sense. The discrete scheme is constructed based on the barycentric rational interpolation collocation method and the Gauss–Legendre quadrature rule. We employ the barycentric rational interpolation collocation method to approximate the unknown function involved in the equation. Through theoretical analysis, we establish the convergence rate of the discrete scheme and show its remarkable accuracy. In addition, we give some numerical examples, to illustrate the proposed method. All the numerical results show the flexible application ability and reliability of the present method.

Mastitis typically arises from bacterial invasion, where host cell apoptosis significantly contributes to the inflammatory response. Gram-positive bacteria predominantly utilize the virulence factor lipoteichoic acid (LTA), which frequently leads to chronic breast infections, thereby impacting dairy production and animal husbandry adversely. This study employed LTA to develop models of mastitis in cow mammary gland cells and mice. Transcriptomic analysis identified 120 mRNAs associated with endocytosis and apoptosis pathways that were enriched in the LTA-induced inflammation of the Mammary Alveolar Cells-large T antigen (MAC-T), with numerous differential proteins also concentrated in the endocytosis pathway. Notably, actin-related protein 2/3 complex subunit 3 (ARPC3), actin-related protein 2/3 complex subunit 4 (ARPC4), and the heat shock protein 70 (HSP70) are closely related. STRING analysis revealed interactions among ARPC3, ARPC4, and HSP70 with components of the apoptosis pathway. Histological and molecular biological assessments confirmed that ARPC3, ARPC4, and HSP70 were mainly localized to the cell membrane of mammary epithelial cells. ARPC3 and ARPC4 are implicated in the mechanisms of bacterial invasion and the initiation of inflammation. Compared to the control group, the expression levels of these proteins were markedly increased, alongside the significant upregulation of apoptosis-related factors. While HSP70 appears to inhibit apoptosis and alleviate inflammation, its upregulation presents novel research opportunities. In conclusion, we deduced the development mechanism of ARPC3, ARPC4, and HSP70 in breast inflammation, laying the foundation for further exploring the interaction mechanism between the actin-related protein 2/3 (ARP2/3) complex and HSP70.

Path planning is one of the core issues in the autonomous navigation of an Unmanned Surface Vehicle (USV), as the accuracy of the results directly affects the safety of the USV. Hence, this paper proposes a USV path planning algorithm that integrates an improved Particle Swarm Optimisation (PSO) algorithm with a Dynamic Window Approach (DWA). Firstly, in order to advance the solution accuracy and convergence speed of the PSO algorithm, a nonlinear decreasing inertia weight and adaptive learning factors are introduced. Secondly, in order to solve the problem of long path and path non-smoothness, the fitness function of PSO is modified to consider both path length and path smoothness. Finally, the International Regulations for Preventing Collisions at Sea (COLREGS) are utilised to achieve dynamic obstacle avoidance while complying with maritime practices. Numerical cases verify that the path planned via the proposed algorithm is shorter and smoother, guaranteeing the safety of USV navigation while complying with the COLREGS.

This study aims to unveil correlations between wastewater microbiota and the catchment-specific population health risk, specifically alcohol abuse, with smoking and obesity as confounding factors. Our study highlights the importance of extracting human-associated microbial communities from wastewater metagenomes by excluding environmental microorganisms, due to their irrelevance to human health. After excluding environmental microbes, we observed strong associations of all three health risk factors, including alcohol abuse, smoking and obesity, with the human gut microbiome in wastewater. The linear discriminant analysis effect size (LEfSe) analysis showed Lactococcus_A, Leuconostoc, Aeromicrobium, Akkermansia, Weissella, Limosilactobacillus, Klebsiella_A, Desulfovibrio and Cloacibacillus as potential microbial biomarkers for alcoholism, after accounting for the confounding effects of smoking and obesity. Functional annotations of microorganisms linked with lower alcoholism rates are primarily related to energy metabolism and intercellular communication. Microorganisms associated with higher alcoholism rates are predominantly involved in immune regulation and cellular DNA architecture. This study highlights the need for a comprehensive exploration of different health risk factors together to identify potential associations between the wastewater microbiome and population lifestyle.

Parking path optimization is the principal problem of automatic vertical parking (AVP); however, it is difficult to determine a collision avoiding, smooth, and accurate optimized parking path using traditional parking reference trajectory optimization methods. In order to implement high-performance automatic parking reference trajectory optimization, we establish an automatic parking reference trajectory optimization model using cubic spline interpolation, and we propose an improved immune shark smell optimization (IISSO) to solve it. Firstly, we take the length of the parking reference trajectory as the optimization objective, and we introduce an intelligent automatic parking path optimization model using cubic spline interpolation. Secondly, the improved immune shark optimization algorithm combines the immune, refraction, and Gaussian variation mechanisms, thus effectively improving its global optimization ability. The simulation results for the parking path optimization experiments indicate that the proposed IISSO has a higher optimization accuracy and faster calculation speed; hence, it can obtain a parking path with higher optimization performance.

Neutron diffraction instruments offer a platform for materials science and engineering studies at extended temperature ranges far from ambient. As one of the widely used neutron sample environment types, cryogenic furnaces are usually bulky and complex, and they may need hours of beamtime overhead for installation, configuration, cooling, and sample change, etc. To reduce the overhead time and expedite experiments at the state-of-the-art high-flux neutron source, we developed a low-cost, miniature, and easy-to-use cryogenic environment (77–473 K) for in situ neutron diffraction. A travel-size mug serves for the environment where the samples sit inside. Immediate cooling and an isothermal dwell at 77 K are realized on the sample by direct contact with liquid N2 in the mug. The designed Al inserts serve as the holder of samples and heating elements, alleviate the thermal gradient, and clear neutron pathways. Both a single-sample continuous measurement and multi-sample high-throughput measurements are demonstrated in this environment. High-quality and refinable in situ neutron diffraction patterns are acquired on model materials. The results quantify the orthorhombic-to-cubic phase transformation process in LiMn2O4 and differentiate the anisotropic lattice thermal expansions and bond length evolutions between rhombohedral perovskite oxides with composition variation.

In addressing the challenges of non-standardization and limited annotation resources in Chinese marine domain texts, particularly with complex entities like long and nested entities in coral reef ecosystem-related texts, existing Named Entity Recognition (NER) methods often fail to capture deep semantic features, leading to inefficiencies and inaccuracies. This study introduces a deep learning model that integrates Bidirectional Encoder Representations from Transformers (BERT), Bidirectional Gated Recurrent Units (BiGRU), and Conditional Random Fields (CRF), enhanced by an attention mechanism, to improve the recognition of complex entity structures. The model utilizes BERT to capture context-relevant character vectors, employs BiGRU to extract global semantic features, incorporates an attention mechanism to focus on key information, and uses CRF to produce optimized label sequences. We constructed a specialized coral reef ecosystem corpus to evaluate the model’s performance through a series of experiments. The results demonstrated that our model achieved an F1 score of 86.54%, significantly outperforming existing methods. The contributions of this research are threefold: (1) We designed an efficient named entity recognition framework for marine domain texts, improving the recognition of long and nested entities. (2) By introducing the attention mechanism, we enhanced the model’s ability to recognize complex entity structures in coral reef ecosystem texts. (3) This work offers new tools and perspectives for marine domain knowledge graph construction and study, laying a foundation for future research. These advancements propel the development of marine domain text analysis technology and provide valuable references for related research fields.

To improve probiotics’ survivability during gastrointestinal digestion and heat treatment, Lactobacillus plantarum was microencapsulated by spray-drying using Laminaria japonica polysaccharide/sodium caseinate/gelatin (LJP/SC/GE) composites. Thermogravimetry and differential scanning calorimetry results revealed that the denaturation of LJP/SC/GE microcapsules requires higher thermal energy than that of SC/GE microcapsules, and the addition of LJP may improve thermal stability. Zeta potential measurements indicated that, at low pH of the gastric fluid, the negatively charged LJP attracted the positively charged SC/GE, helping to maintain an intact microstructure without disintegration. The encapsulation efficiency of L. plantarum-loaded LJP/SC/GE microcapsules reached about 93.4%, and the survival rate was 46.9% in simulated gastric fluid (SGF) for 2 h and 96.0% in simulated intestinal fluid (SIF) for 2 h. In vitro release experiments showed that the LJP/SC/GE microcapsules could protect the viability of L. plantarum in SGF and release probiotics slowly in SIF. The cell survival of LJP/SC/GE microcapsules was significantly improved during the heat treatment compared to SC/GE microcapsules and free cells. LJP/SC/GE microcapsules can increase the survival of L. plantarum by maintaining the lactate dehydrogenase and Na+-K+-ATPase activity. Overall, this study demonstrates the great potential of LJP/SC/GE microcapsules to protect and deliver probiotics in food and pharmaceutical systems.

Research studies on the conservation genetics of endangered plants play a crucial role in establishing management plans for biodiversity conservation. Phoebe chekiangensis is a precious and scarce tree species resource in the East China region. To comprehend the origin, evolutionary history, geographical, and historical factors that has contributed to the current distribution pattern of Phoebe chekiangensis in the East China region, we conducted a phylogeographic analysis that utilized intergenic spacers of chloroplast DNA (cpDNA). We amplified and sequenced three spacer regions of cpDNA (psbC-trnS, trnL-Intro, and Ycf3) intergenic spacer regions of 306 individuals from 11 populations, encompassing the majority of its geographical range in China. Our analysis revealed a total of 11 haplotypes. The research findings show that the spacer regions of the cpDNA genetic diversity of Phoebe chekiangensis was Hd = 0.423, and the nucleotide diversity was Pi × 10−3 = 0.400. At the species level, the population differentiation index Fst = 0.25610 (p < 0.05), and the gene flow Nm = 0.73. The genetic variation between populations was 29.14%, while within populations, it was 70.86%, with the inter-population genetic variation much lower than the within-population variation. The divergence time between the genera Phoebe and Machilus was estimated to be approximately 37.87 mya (PP = 1; 95%HPD: 25.63–44.54 mya), and the crown group time of the genus Phoebe was estimated to be 21.30 mya (PP = 1; 95%HPD: 9.76–34.94 mya). The common ancestor of the 11 Phoebe chekiangensis haplotypes was 7.85 mya, while the H7, H8, and H10 haplotypes of Phoebe chekiangensis (northern region) differentiated relatively late, with a divergence time of 1.90 mya. Neutrality tests (NTs) and mismatch distribution analysis (MDA) suggest that the time frame for Phoebe chekiangensis to expand southwestward along Wuyishan was relatively short and its adaptability to the environment was low, thereby limiting the formation of new haplotypes. These results suggest that Phoebe chekiangensis exhibited greater adaptation to the northern subtropics than to the central subtropics, offering valuable insights for the conservation and utilization of germplasm resources.

The main active component of Polygonatum sibiricum (P. sibiricum) rhizome is Polygonatum sibiricum Polysaccharide (PsP) with antioxidant function. At present, only the mature rhizome of P. sibiricum is used to extract PsP, while the young rhizome of by-product is discarded directly as waste, resulting in significant wastage of P. sibiricum resources. We used ultrasound-assisted extraction-deep eutectic solvents (UAE-DESs) method to extract PsP of young and mature rhizomes, respectively. The extraction rate, structure composition and antioxidant ability of PsP between young and mature rhizomes were compared, so as to provide references for comprehensive utilization of P. sibiricum resources. The PsP extraction rate (33.88 ± 1.95%) of young rhizome was close to that (45.08 ± 1.92%) of mature rhizomes. The main component (PsP-2) of the PsP in young rhizome contained six kinds of monosaccharides, which belonged to acidic polysaccharides. The above characteristics of the PsP of young rhizome were similar to those of mature rhizome. The PsP of young rhizome also exhibited similar biological activity to that of the mature rhizome, which indicated even more advantages in DPPH free radical scavenging ability. The results of this study support the utility of the young rhizome, consequently helping to avoid unnecessary waste and provide reference for comprehensive utilization of P. sibiricum.

Cisplatin is a widely used antineoplastic drug for treating various types of cancers. However, it can cause severe side effects, such as bilateral and irreversible hearing loss, which significantly impacts quality of life. Ferroptosis, an iron-dependent form of programmed cell death, has been implicated in the pathogenesis of cisplatin-induced ototoxicity. Here, we investigated the effects of nuciferine, a natural active ingredient isolated from lotus species, on the ferroptosis of cochlear hair cells. Firstly, our results demonstrated that nuciferine can protect hair cells against RSL3-induced and cisplatin-induced damage. Secondly, nuciferine treatment reduced ferrous iron (Fe2+) overload in cochlear hair cells via inhibiting NCOA4-mediated ferritinophagy. Inhibition of ferritinophagy by knocking down Ncoa4 alleviated cisplatin-induced ototoxicity. Importantly, nuciferine treatment mitigated cochlear hair cell loss and damage to ribbon synapse, and improved mouse hearing function in an acute cisplatin-induced hearing loss model. Our findings highlight the role of NCOA4-mediated ferritinophagy in the pathogenesis of cisplatin-induced ototoxicity and provide evidence for nuciferine as a promising protective agent for treating cisplatin-induced hearing loss.

TiO2-based catalysts with various surface heterostructures (0D, 1D, 2D, and 3D) have been widely researched owing to their cost-effectiveness, high stability, and environmentally friendly nature, and can be used for many applications in various fields, including hydrogen production and pollutant degradation. However, there are also many existing problems limiting their practical application, such as their large band gap and rapid electron–hole recombination rate. Owing to the abundance of recent achievements in materials science, we will summarize the recent structural engineering strategies which provide favorable photocatalytic activity enhancements, such as enhanced visible light absorption, stability, an increased charge–carrier separation rate and improved specific surface area. Among the various structural engineering methods in this review, we will introduce TiO2-based materials with different dimensional structures. Meanwhile, we also discuss recent achievements in synthesis methods and application of TiO2-based catalysts in various fields. We aim to display a comprehensive overview which can be a guide for the development of a new generation of TiO2-based catalysts according to their structural design for enhanced solar energy conversion.

Fly ash was used as raw material to prepare zeolites through silicate gels, assisted by the hydrothermal method. The silicate gels could be effectively formed in a few minutes in a molten alkali environment. The zeolites could be prepared by using these silicate gels through the hydrothermal method, which realizes the transformation from useless materials to highly valuable materials. The obtained zeolites were applied to the removal of ammonium in water, achieving the highvalue utilization of fly ash. The synthesized zeolites were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectrum (EDS), thermogravimetric (TG), and Fourier transform infrared (FTIR) spectroscopy. The study on the adsorption and removal of ammonium in water shows that the adsorption of ammonium is more in line with pseudo first-order kinetics, and the adsorption mainly occurs in the first 20 min. The adsorption can reach equilibrium in 30 min, and the maximum adsorption capacity can reach 49.1 mg/g. The adsorption capacity of ammonium has the best performance at pH = 5. Furthermore, within a certain range, an increase in temperature is beneficial for the removal of ammonium.

NAC (NAM, ATAF1/2, and CUC2) transcription factors regulate plant growth and development and response to various stresses. However, there is still limited insight into the NAC family in Chimonanthus praecox. This study performed a genome-wide characterization of the NAC transcription factor family members in C. praecox. A total of 105 NAC family members were identified from the C. praecox genome. The phylogenetic tree categorized the CpNACs into nine groups and the accuracy of this classification was confirmed by the analysis results of conserved motifs, conserved domain, and gene structure. Cis-acting element analysis revealed that the promoters of CpNACs were abundant in elements responsive to various hormones and stresses, implying the functional diversity and complexity of CpNACs. Furthermore, we investigated the function of the CpNAC30. The expression level of CpNAC30 could be significantly induced by abiotic stress and the CpNAC30 was the highest expressed in mature leaves of C. praecox. Overexpression of CpNAC30 reduced salt stress tolerance of transgenic Arabidopsis. Nevertheless, the drought stress tolerance of transgenic plants was enhanced. This study lays a foundation for further understanding the function of CpNACs genes and provides insights for abiotic stress tolerance breeding of C. praecox and other woody plants.

Arc (also known as Arg3.1) is an activity-dependent immediate early gene product enriched in neuronal dendrites. Arc plays essential roles in long-term potentiation, long-term depression, and synaptic scaling. Although its mechanisms of action in these forms of synaptic plasticity are not completely well established, the activities of Arc include the remodeling of the actin cytoskeleton, the facilitation of AMPA receptor (AMPAR) endocytosis, and the regulation of the transcription of AMPAR subunits. In addition, Arc has sequence and structural similarity to retroviral Gag proteins and self-associates into virus-like particles that encapsulate mRNA and perhaps other cargo for intercellular transport. Each of these activities is likely to be influenced by Arc’s reversible self-association into multiple oligomeric species. Here, we used mass photometry to show that Arc exists predominantly as monomers, dimers, and trimers at approximately 20 nM concentration in vitro. Fluorescence fluctuation spectroscopy revealed that Arc is almost exclusively present as low-order (monomer to tetramer) oligomers in the cytoplasm of living cells, over a 200 nM to 5 μM concentration range. We also confirmed that an α-helical segment in the N-terminal domain contains essential determinants of Arc’s self-association.

Five new diterpenes including four diterpenes with 1,2,3,4,4a,5,6,8a-octalin skeleton talaroacids A–D (1–4) and an isopimarane diterpenoid talaromarane A (5) were isolated from the mangrove endophytic fungus Talaromyces sp. JNQQJ-4. Their structures and absolute configurations were determined by analysis of high-resolution electrospray ionization mass spectroscopy (HRESIMS), 1D/2D Nuclear Magnetic Resonance (NMR) spectra, single-crystal X-ray diffraction, quantum chemical calculation, and electronic circular dichroism (ECD). Talaromarane A (5) contains a rare 2-oxabicyclo [3.2.1] octan moiety in isopimarane diterpenoids. In bioassays, compounds 1, 2, 4, and 5 displayed significant anti-inflammatory activities with the IC50 value from 4.59 to 21.60 μM.

The objective of the pansharpening task is to integrate multispectral (MS) images with low spatial resolution (LR) and to integrate panchromatic (PAN) images with high spatial resolution (HR) to generate HRMS images. Recently, deep learning-based pansharpening methods have been widely studied. However, traditional deep learning methods lack transparency while deep unrolling methods have limited performance when using one implicit prior for HRMS images. To address this issue, we incorporate one implicit prior with a semi-implicit prior and propose a double prior deep unrolling network (DPDU-Net) for pansharpening. Specifically, we first formulate the objective function based on observation models of PAN and LRMS images and two priors of an HRMS image. In addition to the implicit prior in the image domain, we enforce the sparsity of the HRMS image in a certain multi-scale implicit space; thereby, the feature map can obtain better sparse representation ability. We optimize the proposed objective function via alternating iteration. Then, the iterative process is unrolled into an elaborate network, with each iteration corresponding to a stage of the network. We conduct both reduced-resolution and full-resolution experiments on two satellite datasets. Both visual comparisons and metric-based evaluations consistently demonstrate the superiority of the proposed DPDU-Net.

High diffraction signal-to-background ratios (SBRs), the ratio of diffraction peak integrated intensity over its background intensity, are desirable for a neutron diffractometer to acquire good statistics for diffraction pattern measurements and subsequent data analysis. For a given detector, while the diffraction peak signals primarily depend on the characteristics of the neutron beam and sample coherent scattering, the background largely originates from the sample incoherent scattering and the scattering from the instrument space. In this work, we investigated the effect of collimation on neutron diffraction SBRs of Si powder measurements using one high-angle area detector bank coupled with six different collimation configurations in a large and complex instrument space at the engineering materials diffractometer VULCAN, SNS, ORNL. The results revealed that the diffraction SBRs can be significantly improved by a proper coarse collimator that leaves no gap between the detector and the collimator, and the improvement of SBRs by a fine radial collimator was remarkable with a proper coarse collimator in place but not distinguishable without one. It was also found that the diffraction SBRs were not effectively improved by adding the neutron-absorbing element boron to the fine radial collimator body, which indicates that either the absorption of secondary scattered neutrons by the added boron is insignificant or the collimator base material (resin and ABS) alone attenuates background scattering sufficiently. These findings could serve as a useful reference for diffractometer developers and/or operators to optimize their collimation to achieve higher diffraction SBRs.

Ambient particulate matter (PM) pollution is a leading environmental health threat worldwide. PM with an aerodynamic diameter ≤ 1.0 μm, also known as PM1, has been implicated in the morbidity and mortality of several cardiorespiratory and cerebrovascular diseases. However, previous studies have mostly focused on analyzing fine PM (PM2.5) associated with disease metrics, such as emergency department visits and mortality, rather than ultrafine PM, including PM1. This study aimed to evaluate the association between short-term PM1 exposure and hospital admissions (HAs) for all-cause diseases, chronic obstructive pulmonary disease (COPD), and respiratory infections (RIs), as well as the associated expenditures, using Beijing as a case study. Here, based on air pollution and hospital admission data in Beijing from 2015 to 2017, we performed a time-series analysis and meta-analysis. It was found that a 10 μg/m3 increase in the PM1 concentration significantly increased all-cause disease HAs by 0.07% (95% Confidence Interval (CI): [0, 0.14%]) in Beijing between 2015 and 2017, while the COPD and RI-related HAs were not significantly associated with short-term PM1 exposure. Meanwhile, we estimated the attributable number of HAs and hospital expenditures related to all-cause diseases. This study revealed that an average of 6644 (95% CI: [351, 12,917]) cases of HAs were attributable to ambient PM1, which was estimated to be associated with a 106 million CNY increase in hospital expenditure annually (95% CI: [5.6, 207]), accounting for 0.32% (95% CI: [0.02, 0.62%]) of the annual total expenses. The findings reported here highlight the underlying impact of ambient PM pollution on health risks and economic burden to society and indicate the need for further policy actions on public health.

Copy number variation (CNV) serves as a significant source of genetic diversity in mammals and exerts substantial effects on various complex traits. Pingliang red cattle, an outstanding indigenous resource in China, possess remarkable breeding value attributed to their tender meat and superior marbling quality. However, the genetic mechanisms influencing carcass and meat quality traits in Pingliang red cattle are not well understood. We generated a comprehensive genome-wide CNV map for Pingliang red cattle using the GGP Bovine 100K SNP chip. A total of 755 copy number variable regions (CNVRs) spanning 81.03 Mb were identified, accounting for approximately 3.24% of the bovine autosomal genome. Among these, we discovered 270 potentially breed-specific CNVRs in Pingliang red cattle, including 143 gains, 73 losses, and 54 mixed events. Functional annotation analysis revealed significant associations between these specific CNVRs and important traits such as carcass and meat quality, reproduction, exterior traits, growth traits, and health traits. Additionally, our network and transcriptome analysis highlighted CACNA2D1, CYLD, UBXN2B, TG, NADK, and ITGA9 as promising candidate genes associated with carcass weight and intramuscular fat deposition. The current study presents a genome-wide CNV map in Pingliang red cattle, highlighting breed-specific CNVRs, and transcriptome findings provide valuable insights into the underlying genetic characteristics of Pingliang red cattle. These results offer potential avenues for enhancing meat quality through a targeted breeding program.

This paper presents an acoustic emission (AE) detection method for refined oil storage tanks which is aimed towards specialized places such as oil storage tanks with high explosion-proof requirements, such as cave oil tanks and buried oil tanks. The method utilizes an explosion-proof acoustic emission instrument to detect the floor of a refined oil storage tank. By calculating the time difference between the defective acoustic signal and the speed of acoustic wave transmission, a mathematical model is constructed to analyze the detected signals. An independent channel AE detection system is designed, which can store the collected data in a piece of independent explosion-proof equipment, and can analyze and process the data in a safe area after the detection, solving the problems of a short signal acquisition distance and the weak safety protection applied to traditional AE instruments. A location analysis of the AE sources is conducted on the bottom plate of the tank, evaluating its corrosion condition accurately. The consistency between the evaluation and subsequent open-tank tests confirms that using AE technology effectively captures corrosion signals from oil storage tanks’ bottoms. The feasibility of carrying out online inspection under the condition of oil storage in vertical steel oil tanks was verified through a comparison with open inspections, which provided a guide for determining the inspection target and opening order of large-scale oil tanks.

Rayleigh damping is proportional to the combination of the structural mass matrix and stiffness matrix and is widely used in structural seismic analysis. The accuracy of seismic analysis of nuclear power structures directly depends on the value of the Rayleigh damping parameters. However, the stiffness component of Rayleigh damping is not included in the explicit integral, so the Rayleigh damping in the explicit and implicit integrals needs to be handled differently. LS-DYNA R11.1.0 software provides various calculation methods for the value of the Rayleigh damping parameter in the explicit integral. To investigate the influence of the value of the Rayleigh damping parameter in the explicit and implicit integrals on the results of the dynamic analysis of a nuclear power plant, the AP1000 nuclear island plant is taken as an example, and the explicit and implicit dynamic calculation are carried out respectively for the nuclear power plant, considering the soil–structure interaction. The results show that the Rayleigh damping parameter calculated by different methods in the explicit integral has a large influence on the results of seismic analysis of nuclear power plants. The mass component of Rayleigh damping in the explicit and implicit integrals takes the same value, and the stiffness component of Rayleigh damping in the explicit integrals is taken as the negative of the stiffness component in the implicit integrals. Thus, the results of the two dynamic analyses can be in good agreement. The results provide a reference for the application of Rayleigh damping in the explicit integral for the seismic analysis of nuclear power structures.

The protection of ecological systems is currently a trending topic. Numerous countries have implemented various measures to safeguard ecosystems. Evaluating the effectiveness of regional ecological protection and cooperative conservation is of paramount importance. In this paper, Hainan Island, China, was taken as an example to construct an evaluation framework of the ecosystem change characteristics and conservation effects of different types of protected areas. In this way, the study evaluated the ecological status of important protected areas and the effects of collaborative management and control, and it discussed the changes in ecological status and protection effects. The results revealed the following. (1) Important protected areas occupy a large proportion of natural ecological space, reaching 76.33%, more than twice the proportion of ecological space on Hainan Island. In the past 20 years, the ecological space retention rate has increased by 0.57%. (2) The contribution of excellent-quality and good-quality ecosystems is the greatest, accounting for 82.65% of the whole island. In the past 20 years, the ec7osystem quality has improved in more than 80% of the areas. The proportion of excellent-quality and good-quality ecosystems is as high as 96.75% in these conservation areas, and their contribution to Hainan Island is 44.29%, while conservation areas only account for about 38%. (3) The ecosystem services of Hainan Island have improved and become stable in the past 20 years. The contribution of all types of protected areas to the ecosystem services of Hainan Island is more than 53.22%. In general, the ecological quality and service function has increased in Hainan Island, and the ecological space tends to be stable. Different types of protected areas have effectively protected various ecosystems on Hainan Island with different protection and management mechanisms.

This study was conducted to investigate the effects of dietary curcumin supplementation on growth performance, anticoccidial index, antioxidant capacity, intestinal inflammation, and cecum microbiota in broilers infected with Eimeria tenella. A total of 234 one-day-old broilers were categorized into three treatments, with six replicates per treatment containing 13 broilers each. The three treatments included the control group, Eimeria tenella group, and Eimeria tenella + curcumin (200 mg/kg) group. The feeding trial lasted for 42 days, during which the broilers were orally administered with 0.9% saline or 5 × 104 Eimeria tenella oocysts on day 14 of the study. On day 17 and day 21, one bird per replicate was selected for slaughtering. Results indicated an increased survival rate and anticoccidial index and improved productive performance in coccidia-infected broilers with curcumin supplementation. Furthermore, curcumin enhanced the serum antioxidant capacity in Eimeria tenella-infected broilers, evidenced by increased serum catalase activity (3d, 7d), as well as decreased malondialdehyde level (3d, 7d) and nitric oxide synthase activity (7d) (p < 0.05). Curcumin also improved intestinal inflammation and barrier function, evidenced by the downregulation of interleukin (IL)-1β (3d, 7d), TNF-alpha (TNF-α) (3d, 7d), and IL-2 (7d) and the up-regulated mRNA levels of claudin-1 (7d), zonula occludens (ZO-1; 3d, 7d), and occludin (3d, 7d) in the ceca of infected broilers (p < 0.05). Eimeria tenella infection significantly disrupted cecum microbial balance, but curcumin did not alleviate cecum microbial disorder in broilers infected with Eimeria tenella. Collectively, curcumin supplementation enhanced growth performance and anticoccidial index in Eimeria tenella-infected broilers via improving antioxidant ability and cecum inflammation without affecting cecum microbiota.

Lushan Yunwu tea (LSYWT) is a famous green tea in China. However, the effects of intercropping tea with flowering cherry on the overall aroma of tea have not been well understood. In this study, headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography–mass spectrometry (GC–MS) was used for analysis. A total of 54 volatile compounds from eight chemical classes were identified in tea samples from both the intercropping and pure-tea-plantation groups. Principal component analysis (PCA), orthogonal partial least-squares discriminant analysis (OPLS-DA), and odor activity value (OAV) methods combined with sensory evaluation identified cis-jasmone, nonanal, and linalool as the key aroma compounds in the intercropping group. Benzaldehyde, α-farnesene, and methyl benzene were identified as the main volatile compounds in the flowering cherry using headspace solid-phase microextraction/gas chromatography–mass spectrometry (HS-SPME/GC–MS). These findings will enrich the research on tea aroma chemistry and offer new insights into the product development and quality improvement of LSYWT.

This study aimed to investigate the preventive effects of the total polyphenols from Nymphaea candida (NCTP) on LPS-induced septic acute lung injury (ALI) in mice and its mechanisms. NCTP could significantly ameliorate LPS-induced lung tissue pathological injury in mice as well as lung wet/dry ratio and MPO activities (p < 0.05). NCTP could significantly decrease the blood leukocyte, neutrophil, monocyte, basophil, and eosinophil amounts and LPS contents in ALI mice compared with the model group (p < 0.05), improving lymphocyte amounts (p < 0.05). Moreover, compared with the model group, NCTP could decrease lung tissue TNF-α, IL-6, and IL-1β levels (p < 0.05) and downregulate the protein expression of TLR4, MyD88, TRAF6, IKKβ, IκB-α, p-IκB-α, NF-κB p65, p-NF-κB p65, NLRP3, ASC, and Caspase1 in lung tissues (p < 0.05). Furthermore, NCTP could inhibit ileum histopathological injuries, restoring the ileum tight junctions by increasing the expression of ZO-1 and occludin. Simultaneously, NCTP could reverse the gut microbiota disorder, restore the diversity of gut microbiota, increase the relative abundance of Clostridiales and Lachnospiraceae, and enhance the content of SCFAs (acetic acid, propionic acid, and butyric acid) in feces. These results suggested that NCTP has preventive effects on septic ALI, and its mechanism is related to the regulation of gut microbiota, SCFA metabolism, and the TLR-4/NF-κB and NLRP3 pathways.

With the rapid growth and development of urban areas, the economy has often been prioritized at the expense of the environment and ecological systems. However, it is essential to delve deeper into the relationship between the economy and the ecological environment. Therefore, this study introduces a comprehensive evaluation system that encompasses economic, ecological, and environmental factors in the Beijing–Tianjin–Hebei urban agglomeration in China. The Criteria Importance Through Inter-criteria Correlation (CRITIC) method is utilized to determine the weights of various indicators, and coupling as well as coupling coordination models are employed to investigate the spatiotemporal trends and interrelationships of the three factors. The results indicate that the economic development index has displayed a consistent uptrend since 2000, with the economic development index from 2015–2020 increasing by approximately four times compared to 2000–2005. The ecological status index has also increased in the last five years, with a rise of about 0.05 from 2015–2020 compared to 2000–2005. The environmental status index has fluctuated but generally increased, with a rise of approximately 5.6 times from 2015–2020 compared to 2000–2005. The coupling degree of the Beijing–Tianjin–Hebei urban agglomeration is relatively high, with intense coupling from 2005–2020, and moderate coupling from 2000–2005. Furthermore, the coupling coordination has continuously improved from mild maladjustment to barely coordinated from 2000 to 2020, indicating the enhancement of the coupling coordination of the three factors. It is crucial to acknowledge that there exists spatial heterogeneity in both the coupling degree and coupling coordination degree. This heterogeneity stems from the uneven progress in economic development, ecological conditions, and environmental status across various cities. Additional endeavors are required to foster the harmonized advancement of these factors across the entire region.

Hepatocellular carcinoma (HCC) is the second-most-common cause of cancer death. In recent years, studies have suggested that intestinal microbiota dysregulation is closely related to HCC and can affect the therapeutic efficacy of immune checkpoint inhibitors. However, there are few data on the relationship between altered gut microbiota composition and its potential association in patients with advanced hepatocellular carcinoma. Hence, in this study, we aimed to investigate the gut microbiota profile associated with advanced hepatocarcinoma. In total, 20 patients with advanced hepatocarcinoma and 20 matched healthy participants were recruited. Stool samples were collected for 16S rRNA sequencing to confirm intestinal microbiota dysbiosis. The results showed that the Nseqs index in advanced hepatocarcinoma patients was significantly different compared with that in healthy individuals, while the butyrate-producing bacteria decreased and LPS-producing bacteria increased. Meanwhile, Lactobacillus, Anaerostipes, Fusicatenibacter, Bifidobacterium, and Faecalibacterium were significantly correlated with AFP, ALT, AST, and PIVKA. Our findings characterized the gut microbiota composition of advanced hepatocarcinoma, providing an experimental basis and theoretical support for using microbiota to regulate immunotherapy, achieve potential biomarkers for diagnosis, and improve the effect of clinical treatment for patients with advanced hepatocarcinoma.

In an unmanned orchard, various tasks such as seeding, irrigation, health monitoring, and harvesting of crops are carried out by unmanned vehicles. These vehicles need to be able to distinguish which objects are fruit trees and which are not, rather than relying on human guidance. To address this need, this study proposes an efficient and robust method for fruit tree detection in orchard point cloud maps. Feature extraction is performed on the 3D point cloud to form a two-dimensional feature vector containing three-dimensional information of the point cloud and the tree target is detected through the customized deep learning network. The impact of various feature extraction methods such as average height, density, PCA, VFH, and CVFH on the detection accuracy of the network is compared in this study. The most effective feature extraction method for the detection of tree point cloud objects is determined. The ECA attention module and the EVC feature pyramid structure are introduced into the YOLOv8 network. The experimental results show that the deep learning network improves the precision, recall, and mean average precision by 1.5%, 0.9%, and 1.2%, respectively. The proposed framework is deployed in unmanned orchards for field testing. The experimental results demonstrate that the framework can accurately identify tree targets in orchard point cloud maps, meeting the requirements for constructing semantic orchard maps.

Recent advancements in computational performance and medical simulation technology have made significant strides, particularly in predictive diagnosis. This study focuses on the blood flow simulation reduced-order models, which provide swift and cost-effective solutions for complex vascular systems, positioning them as practical alternatives to 3D simulations in resource-limited medical settings. The paper introduces a visualization platform for patient-specific and image-based 1D–0D simulations. This platform covers the entire workflow, from modeling to dynamic 3D visualization of simulation results. Two case studies on, respectively, carotid stenosis and arterial remodeling demonstrate its utility in blood flow simulation applications.

The aim of this study was to investigate targets through which Gualou Xiebai Banxia decoction aids in treating myocardial infarction (MI) using network pharmacology in combination with molecular docking. The principal active ingredients of Gualou Xiebai Banxia decoction were identified from the TCMSP database using the criteria of drug-likeness ≥30% and oral bioavailability ≥0.18. Interactions and pathway enrichment were investigated using protein–protein interaction (PPI) networks and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, respectively. Active component structures were docked with those of potential protein targets using AutoDock molecular docking relative softwares. HIF1A was of particular interest as it was identified by the PPI network, GO and KEGG pathway enrichment analyses. In conclusion, the use of network pharmacology prediction and molecular docking assessments provides further information on the active components and mechanisms of action Gualou Xiebai Banxia decoction.

In this work, a fluorescent complex [Zn(NTD)2(DTP)2(H2O)2]·(H2O)0.8 (Complex Zn), (H2NTD = 1,4-naphthalenedicarboxylic acid and DTP = 3,5-di(1,2,4-triazol-1-yl)pyridine) was synthesized. The fluorescent complex was characterized by single-crystal X-ray diffraction, powder X-ray diffraction, and thermogravimetric, elemental, infrared spectroscopy, and fluorescence analyses. In the fluorescence sensing tests, Complex Zn exhibited excellent fluorescence quenching efficiency towards Fe3+, MnO4−, Cr2O72−, nitrofurantoin, and imidacloprid in aqueous media. A mechanism investigation suggested that the fluorescence quenching caused by the quenchers toward the sensor was due to the inner filter effect and the fluorescence resonance energy transfer effect in the fluorescent sensing process.

This study was performed to investigate the effects of structural nonlinearity and large deformations on the aeroelastic loads of flexible wind turbine blades. First, a blade structural analysis model was established using the geometrically exact beam (GEB) theory. Subsequently, the blade element momentum (BEM) theory was corrected using the geometrically exact method leading to the development of a geometrically exact blade element momentum (GE-BEM) model. The results from the GE-BEM model indicated that flapwise deformations always reduce blade fatigue loads, while torsional deformations decrease fatigue loads under low wind speeds but increase them under high wind speeds. Finally, the linear Euler–Bernoulli beam and the GEB were compared to explore the influence of geometric nonlinearity on the blade aeroelastic loads, which revealed that the Euler beam model underestimates the blade loads. The simulations that used the GEB model produced torsional root twist fatigue loads that were 57.49% greater than those generated when the Euler beam model was used. Furthermore, the flapwise bending moment fatigue loads at the root were 8.24% greater than those obtained by the Euler beam model. The smallest discrepancy between the results of the two models was 7.26%, and it corresponded to the edgewise fatigue load.

Hemophilia A (HA) is a common X-linked recessive hereditary bleeding disorder. Coagulation factor VIII (FVIII) is insufficient in patients with HA due to the mutations in the F8 gene. The restoration of plasma levels of FVIII via both recombinant B-domain-deleted FVIII (BDD-FVIII) and B-domain-deleted F8 (BDDF8) transgenes was proven to be helpful. FVIII-Padua is a 23.4 kb tandem repeat mutation in the F8 associated with a high F8 gene expression and thrombogenesis. Here we screened a core enhancer element in FVIII-Padua for improving the F8 expression. In detail, we identified a 400 bp efficient enhancer element, C400, in FVIII-Padua for the first time. The core enhancer C400 extensively improved the transcription of BDDF8 driven by human elongation factor-1 alpha in HepG2, HeLa, HEK-293T and induced pluripotent stem cells (iPSCs) with different genetic backgrounds, as well as iPSCs-derived endothelial progenitor cells (iEPCs) and iPSCs-derived mesenchymal stem cells (iMSCs). The expression of FVIII protein was increased by C400, especially in iEPCs. Our research provides a novel molecular target to enhance expression of FVIII protein, which has scientific value and application prospects in both viral and nonviral HA gene therapy strategies.

The advancement of exosome studies has positioned engineered exosomes as crucial biomaterials for the development of advanced drug delivery systems. This study focuses on developing a hybrid exosome system by fusing mesenchymal stem cells (MSCs) exosomes with folate-targeted liposomes. The aim was to improve the drug loading capacity and target modification of exosome nanocarriers for delivering the first-line chemotherapy drug paclitaxel (PTX) and its effectiveness was assessed through cellular uptake studies to evaluate its ability to deliver drugs to tumor cells in vitro. Additionally, in vivo experiments were conducted using a CT26 tumor-bearing mouse model to assess the therapeutic efficacy of hybrid exosomes loaded with PTX (ELP). Cellular uptake studies demonstrated that ELP exhibited superior drug delivery capabilities to tumor cells in vitro. Moreover, in vivo experiments revealed that ELP significantly suppressed tumor growth in the CT26 tumor-bearing mouse model. Notably, for the first time, we examined the tumor microenvironment following intratumoral administration of ELP. We observed that ELP treatment activated CD4+ and CD8+ T cells, reduced the expression of M2 type tumor-associated macrophages (TAMs), polarized TAMs towards the M1 type, and decreased regulatory T cells (Tregs). Our research highlights the considerable therapeutic efficacy of ELP and its promising potential for future application in cancer therapy. The development of hybrid exosomes presents an innovative approach to enhance drug delivery and modulate the tumor microenvironment, offering exciting prospects for effective cancer treatment strategies.

This work discusses the electric vehicle (EV) ordered charging planning (OCP) optimization problem. To address this issue, an improved dual-population genetic moth–flame optimization (IDPGMFO) is proposed. Specifically, to obtain an appreciative solution of EV OCP, the design for a dual-population genetic mechanism integrated into moth–flame optimization is provided. To enhance the global optimization performance, the adaptive nonlinear decreasing strategies with selection, crossover and mutation probability, as well as the weight coefficient, are also designed. Additionally, opposition-based learning (OBL) is also introduced simultaneously. The simulation results show that the proposed improvement strategies can effectively improve the global optimization performance. Obviously, more ideal optimization solution of the EV OCP optimization problem can be obtained by using IDPGMFO.

Accurate prediction of fine particulate matter (PM2.5) concentration is crucial for improving environmental conditions and effectively controlling air pollution. However, some existing studies could ignore the nonlinearity and spatial correlation of time series data observed from stations, and it is difficult to avoid the redundancy between features during feature selection. To further improve the accuracy, this study proposes a hybrid model based on empirical mode decomposition (EMD), minimal-redundancy-maximal-relevance (mRMR), and geographically weighted neural network (GWNN) for hourly PM2.5 concentration prediction, named EMD-mRMR-GWNN. Firstly, the original PM2.5 concentration sequence with distinct nonlinearity and non-stationarity is decomposed into multiple intrinsic mode functions (IMFs) and a residual component using EMD. IMFs are further classified and reconstructed into high-frequency and low-frequency components using the one-sample t-test. Secondly, the optimal feature subset is selected from high-frequency and low-frequency components with mRMR for the prediction model, thus holding the correlation between features and the target variable and reducing the redundancy among features. Thirdly, the residual component is predicted with the simple moving average (SMA) due to its strong trend and autocorrelation, and GWNN is used to predict the high-frequency and low-frequency components. The final prediction of the PM2.5 concentration value is calculated by an artificial neural network (ANN) composed of the predictive values of each component. PM2.5 concentration prediction experiments in three representational cities, such as Beijing, Wuhan, and Kunming were carried out. The proposed model achieved high accuracy with a coefficient of determination greater than 0.92 in forecasting PM2.5 concentration for the next 1 h. We compared this model with four baseline models in forecasting PM2.5 concentration for the next few hours and found it performed the best in PM2.5 concentration prediction. The experimental results indicated the proposed model can improve prediction accuracy.

Severe and fatal crashes involving large trucks result in significant social and economic losses for human society. Unfortunately, the notably low proportion of severe and fatal injury crashes involving large trucks creates an imbalance in crash data. Models trained on imbalanced crash data are likely to produce erroneous results. Therefore, there is a need to explore novel sampling approaches for imbalanced crash data, and it is crucial to determine the appropriate combination of a machine learning model, sampling approach, and ratio. This study introduces a novel cluster-based under-sampling technique, utilizing the k-prototypes clustering algorithm. After initial cluster-based under-sampling, the consolidated cluster-based under-sampled data set was further resampled using three different sampling approaches (i.e., adaptive synthetic sampling (ADASYN), NearMiss-2, and the synthetic minority oversampling technique + Tomek links (SMOTETomek)). Later, four machine learning models (logistic regression (LR), random forest (RF), gradient-boosted decision trees (GBDT), and the multi-layer perceptron (MLP) neural network) were trained and evaluated using the geometric mean (G-Mean) and area under the receiver operating characteristic curve (AUC) scores. The findings suggest that cluster-based under-sampling coupled with the investigated sampling approaches improve the performance of the machine learning models developed on crash data significantly. In addition, the GBDT model combined with ADASYN or SMOTETomek is likely to yield better predictions than any model combined with NearMiss-2. Regarding changes in sampling ratios, increasing the sampling ratio with ADASYN and SMOTETomek is likely to improve the performance of models up to a certain level, whereas with NearMiss-2, performance is likely to drop significantly beyond a specific point. These findings provide valuable insights for selecting optimal strategies for treating the class imbalance issue in crash data.

The characterization of the PYL/RCAR ABA receptors in a great deal of plant species has dramatically advanced the study of ABA functions involved in key physiological processes. However, the genes in this family are still unclear in Lycium (Goji) plants, one of the well–known economically, medicinally, and ecologically valuable fruit crops. In the present work, 12 homologs of Arabidopsis PYL/RCAR ABA receptors were first identified and characterized from Lycium (L.) barbarum (LbPYLs). The quantitative real–time PCR (qRT–PCR) analysis showed that these genes had clear tissue–specific expression patterns, and most of them were transcribed in the root with the largest amount. Among the three subfamilies, while the Group I and Group III members were down–regulated by extraneous ABA, the Group II members were up–regulated. At 42 °C, most transcripts showed a rapid and violent up–regulation response to higher temperature, especially members of Group II. One of the genes in the Group II members, LbPYL10, was further functionally validated by virus–induced gene silencing (VIGS) technology. LbPYL10 positively regulates heat stress tolerance in L. barbarum by alleviating chlorophyll degradation, thus maintaining chlorophyll stability. Integrating the endogenous ABA level increase following heat stress, it may be concluded that LbPYL–mediated ABA signaling plays a vital role in the thermotolerance of L. barbarum plants. Our results highlight the strong potential of LbPYL genes in breeding genetically modified L. barbarum crops that acclimate to climate change.

Dual quaternions have wide applications in automatic differentiation, computer graphics, mechanics, and others. Due to its application in control theory, matrix equation AXB=C has been extensively studied. However, there is currently limited information on matrix equation AXB=C regarding the dual quaternion algebra. In this paper, we provide the necessary and sufficient conditions for the solvability of dual quaternion matrix equation AXB=C, and present the expression for the general solution when it is solvable. As an application, we derive the ϕ-Hermitian solutions for dual quaternion matrix equation AXAϕ=C, where the ϕ-Hermitian extends the concepts of Hermiticity and η-Hermiticity. Lastly, we present a numerical example to verify the main research results of this paper.

Water market development in the Yellow River Basin (YRB) unfolds new opportunities for alleviating water scarcity and improving water productivity. However, the further development of an effective water market in the basin faces challenges such as unclear water rights, regulatory deficiencies, market deficiencies, and insufficient compensation to third-parties, among others. Studying water market development in Western countries provides useful insights for addressing similar challenges, thus providing useful case studies despite the different cultural, economic, institutional, and political settings. This paper investigates water markets in the Murray–Darling Basin in Australia, the western United States, and Chile to synthesize cases of water market development that could potentially contribute to overcoming the challenges encountered in the YRB. After analyzing these cases, recommendations are made for enhancing the YRB’s water market development from the perspectives of water rights systems, as well as the roles of the government and market, legal system, and third-party effects.

The hardness of passion fruit is a critical feature to consider when determining maturity during post-harvest storage. The capacity of near-infrared diffuse reflectance spectroscopy (NIRS) for non-destructive detection of outer and inner hardness of passion fruit epicarp was investigated in this work. The passion fruits’ spectra were obtained using a near-infrared spectrometer with a wavelength range of 10,000–4000 cm−1. The hardness of passion fruit’s outer epicarp (F1) and inner epicarp (F2) was then measured using a texture analyzer. Moving average (MA) and mean-centering (MC) techniques were used to preprocess the collected spectral data. Competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA), and uninformative variable elimination (UVE) were used to pick feature wavelengths. Grid-search-optimized random forest (Grids-RF) models and genetic-algorithm-optimized support vector regression (GA-SVR) models were created as part of the modeling process. After MC preprocessing and CARS selection, MC-CARS-Grids-RF model with 7 feature wavelengths had the greatest prediction ability for F1. The mean square error of prediction set (RMSEP) was 0.166 gN. Similarly, following MA preprocessing, the MA-Grids-RF model displayed the greatest predictive performance for F2, with an RMSEP of 0.101 gN. When compared to models produced using the original spectra, the R2P for models formed after preprocessing and wavelength selection improved. The findings showed that near-infrared spectroscopy may predict the hardness of passion fruit epicarp, which can be used to identify quality during post-harvest storage.

To elucidate the variation characteristics of nitrogen (N), phosphorus (P), and potassium (K) content and accumulation in cotton at different yield levels, a two-year experiment was conducted using cotton variety Lumianyan 24 under four N treatments (506, 402.5, 299, and 195.5 kg ha−1, designated as N1, N2, N3, and N4, respectively). The four P and K fertilization ratios were (PK-M1, 25% P and K applied at squaring stage (SS) and 75% at the bloom-bolling stages (BS)), 50%:50% (PK-M2, 50% P and K applied at each stage), 75%:25% (PK-M3, 75% P and K applied at SS and 25% at the BS) and 100%:0% (PK-M4, total P and K applied at SS). The results showed that the N content (Nc), P content (Pc), and K content (Kc) of cotton plants at high yield levels were 23.3%, 44.2%, and 31.6% higher than those at low yield levels. In addition, the reproductive organs maintained higher Pc and Kc at high yield levels, and the Nc, Pc, and Kc exhibited positive linear correlations with yield, while Nc/Pc, Nc/Kc, and Kc/Pc had significant negative correlations with yield. In conclusion, seed cotton yield was mainly limited by Nc at low yield levels and affected by Pc and Kc at high yield levels. Then, when 0.85 < Nc/Kc < 1.0 at the full squaring stage (FS), 0.8 < Nc/Kc < 1.0 at the full flowering stage (FF), 4.3 < Nc/Pc < 6.7 at the early-full bolling stage (EFB) and 4.9 < Nc/Pc < 7.1 at the late-full bolling stage (LFB), there is a high yield potential of 7000–9000 kg ha−1. Furthermore, yields can be improved by reducing the application of N during the growing period and increasing the application of P fertilizer during the later growth period.

A series of NH2-functionalized nano-sized magnetic metal–organic frameworks (MOFs) were prepared in this study for Cr(VI) removal from wastewater. It was observed that not only the morphological, i.e., orientation growth of N-doped and iron-based metal–organic frameworks, but also the adsorption of magnetic MOFs is largely related to the used amount of ammonium hydroxide in preparation. For example, with increasing amounts of ammonium hydroxide used in preparation, the morphology of magnetic MOFs changed from spherical to cube and triangular cone. Moreover, the maximum adsorption capacity of spherical-magnetic MOFs, cubic-magnetic MOFs and triangular cone-magnetic MOFs could be up to 204.08 mg/g, 232.56 mg/g and 270.27 mg/g, respectively. Under optimal conditions, the adsorption process of magnetic MOFs for Cr(VI) was consistent with the pseudo-second-order rate equation (R2 = 1) and Langmuir isotherm model (R2 > 0.99). Therefore, magnetic MOFs developed in this work offered a viable option for the removal of Cr(VI) from wastewater.

It is well demonstrated that the complex chemical fluctuations on high/medium-entropy alloys (H/MEAs) play critical roles in their deformation process, but there are few reports related to the effect of such complex chemical fluctuations on the crack behavior. In this paper, the effects of chemical short-range order (CSRO) and lattice distortion (LD) on the crack-tip behavior of CrCoNi MEAs under mode I loading at room temperature are investigated by carrying out molecular dynamics (MD) simulation, hybrid MD/Monte-Carlo (MC) simulation and the J-integral method. The results reveal that CSRO can improve the J-integral value without significant changes in the localized deformation zone size. On the contrary, LD can lower the J-integral value with an increase in the localized deformation zone size. The energetic analysis shows that CSRO improves the activation energy barrier of Shockley partial dislocation from the crack-tip while LD reduces the activation energy barrier. Our work is a step forward in understanding the effects of CSRO and LD on the crack-tip behavior and deformation mechanisms of CrCoNi MEAs.

Multifunctional road marking coatings with the functions of high-temperature stability, degradation of exhaust gas, and self-cleaning are of great significance for the safe operation and environmental protection of tunnels. This article uses active acrylic resin and an organosilicon hydrophobic agent as the base material, selects expanded vermiculite and glass microspheres as insulation fillers, and uses ammonium polyphosphate, pentaerythritol, melamine, and aluminum hydroxide as high-thermal-stability systems to prepare a two-component road marking coating base material. Then, nano SiO2 and modified nano TiO2 are added as modifiers to prepare a multifunctional road marking coating for tunnels. The physical and chemical properties of multifunctional road marking coatings are evaluating based on laboratory tests including thermogravimetry and derivative thermogravimetry, differential scanning calorimetry, infrared spectroscopy, scanning electron microscopy, exhaust degradation, and contact angle tests. The results indicate that the developed multifunctional road marking coating effectively reduces the thermal conductivity of the carbon layer through physical changes in the flame retardant system and the heat resistance formed by the high breaking bond energy of nano SiO2 during the combustion process. It forms a ceramic-like structure of titanium pyrophosphate with nano TiO2 that is beneficial for improving flame retardancy without generating harmful volatile gases and has good flame retardant properties. N–V co-doping reduces the bandgap of TiO2, broadens the absorption range of visible light by nano TiO2, improves the catalytic efficiency of visible light, and achieves the degradation efficiency of the four harmful components NOx, HC, CO, and CO2 in automotive exhaust by 23.4%, 8.3%, 2.5%, and 2.9%, respectively. The solid–liquid phase separation in the multifunctional road marking coating in the tunnel causes the formation and accumulation of nano SiO2 and TiO2 particles on the coating surface, resulting in a microstructure similar to the “micro–nano micro-convex” on the lotus leaf surface and making a water droplet contact angle of 134.2° on the coating surface.