Search

Clostridium perfringens (C. perfringens) is an important veterinary pathogen and a noteworthy threat to human and animal health. Recently, there has been a significant rise in the number of moose fatalities caused by this rare, endemic species in China. Currently, there is an increasing trend in conducting whole-genome analysis of C. perfringens strains originating from pigs and chickens, whereas fewer studies have been undertaken on Elaphurus davidianus-originating strains at the whole-genome level. Our laboratory has identified and isolated five C. perfringens type A from affected Elaphurus davidianus. The current study identified the most potent strain of C. perfringens, which originated from Elaphurus davidianus, and sequenced its genome to reveal virulence genes and pathogenicity. Our findings show that strain CX1-4 exhibits the highest levels of phospholipase activity, hemolytic activity, and mouse toxicity compared to the other four isolated C. perfringens type A strains. The chromosome sequence length of the CX1-4 strain was found to be 3,355,389 bp by complete genome sequencing. The current study unveils the genomic characteristics of C. perfringens type A originating from Elaphurus davidianus. It provides a core foundation for further investigation regarding the prevention and treatment of such infectious diseases in Elaphurus davidianus.

The emerging upward shield method (USM) for constructing vertical shafts has been used in various projects, including the Midosuji utility tunnel in Japan. A scaled-down model testing system, featuring a geometric similarity ratio of 1:30, was developed specifically for studying the USM. This system incorporates sand inflow control, propulsion control, data acquisition, and water level control. It facilitates detailed observation and recording of parameters such as vertical displacement of surface soil layers, support force at the excavation face, and earth pressure within the model box. Consequently, it enables investigation into the excavation face instability process, modes, and the formation and evolution of the soil arch zone above the excavation face during upward shield tunneling. Additionally, through the application of particle image velocimetry (PIV) technology and GeoPIV-RG software v1.1, quantitative analysis of soil displacement fields during excavation face instability is conducted, capturing microscopic displacements and deformations of soil planes. This approach more accurately elucidates the accuracy of understanding the dynamic response of soil. Pre-test research using the model testing system explores the variation patterns of excavation face load displacement, vertical earth pressure within the failure zone, surface displacement, and internal soil displacement during the instability process. Analysis reveals that excavation face load variation typically progresses through three stages: rapid growth, slow growth, and descent. Moreover, vertical earth pressure shifts upward in tandem with excavation face displacement, while overall surface displacement initially shows slight settlement followed by accelerated uplift.

Consumer perception of beef is heavily influenced by overall meat quality, a critical factor in the cattle industry. Genomics has the potential to improve important beef quality traits and identify genetic markers and causal variants associated with these traits through genomic selection (GS) and genome-wide association studies (GWAS) approaches. Transcriptomics, proteomics, and metabolomics provide insights into underlying genetic mechanisms by identifying differentially expressed genes, proteins, and metabolic pathways linked to quality traits, complementing GWAS data. Leveraging these functional genomics techniques can optimize beef cattle breeding for enhanced quality traits to meet high-quality beef demand. This paper provides a comprehensive overview of the current state of applications of omics technologies in uncovering functional variants underlying beef quality complexities. By highlighting the latest findings from GWAS, GS, transcriptomics, proteomics, and metabolomics studies, this work seeks to serve as a valuable resource for fostering a deeper understanding of the complex relationships between genetics, gene expression, protein dynamics, and metabolic pathways in shaping beef quality.

In response to the issue of weak laser connection strength between 304 stainless steel and the carbon fiber-reinforced polymer PCCF30, this study proposes the addition of a polyethylene terephthalate (PET) intermediate layer between 304 stainless steel and PCCF30, along with surface texture pretreatment of the stainless steel surface, to enhance the joint connection strength. First, three types of surface textures (vertical, wavy, and vertical plus wave) were investigated for their effect on joint quality. Once the optimal texture type was determined, the effect of texture scanning repetition on the surface morphology and groove cross-sectional geometry of 304 stainless steel was examined. The joint strengths achieved by adding intermediate layers and employing different surface texture treatment methods were compared, followed by a comparison of the joint strengths obtained from different pretreatment methods. Finally, the connection mechanism of the joint was analyzed. The results indicated that the maximum joint strength achieved through the vertical and wavy texture design was 4.39 MPa, which is 3.3 times greater than that achieved without reinforcement. Moreover, the maximum joint strength achieved with the addition of a PET intermediate layer and surface texture composite treatment was 11.85 MPa, which is approximately nine times greater than that achieved via direct connection. The inclusion of a PET intermediate layer enhances the fluidity of the molten polymer at the joint, facilitating better filling of the grooves with surface texture. This strengthens the mechanical anchoring effect of the joint and contributes to the improvement in the joint’s connection strength. An XPS analysis revealed that after multiple etchings of the joint interface with an added intermediate layer, the Cr in the stainless steel chemically bonded with the PET, resulting in the formation of Cr–O–C chemical bonds, which is advantageous for enhancing joint quality.

The ecological stoichiometric characteristics of soil elements have greatly enhanced our understanding of the circulation of soil nutrients. However, there is limited knowledge regarding the alteration of carbon, nitrogen, and phosphorus stoichiometric ratios in deep soil after afforestation. To examine the variations in stoichiometric ratios of soil elements with different vegetation types, restoration times, and soil depths, we collected soil samples from grassland, Caragana korshinskii shrubland, and Picea asperata forestland at different stand ages (10a, 25a, and 40a) in Xining City, which is located on the Loess Plateau. Our results showed that, at 25a, the carbon-to-nitrogen (C:N) and carbon-to-phosphorus (C:P) ratios were significantly higher in the grassland soil than under other vegetation types, whereas the nitrogen-to-phosphorus (N:P) ratio had no significant difference among the three vegetation types. At 40a, the ratios of soil C:N, C:P, and N:P in the shrubland were the highest. With the increasing of the restoration time, the ratios of soil C:N, C:P, and N:P in grassland with 25a became higher than for 40a or 10a. The ratios in the shrubland were highest at 40a, followed by 25a and then 10a, while the ratios in the forestland showed no significant difference. At 40a, the soil C:N, C:P, and N:P ratios of shrubland were highest at the soil depth of 40–100 cm. The soil C:N, C:P, and N:P ratios showed positive correlations with soil ammonium nitrogen and nitrate nitrogen, and the soil N:P ratios showed a negative correlation with soil available phosphorus. Plant diversity significantly influenced the soil stoichiometric ratio of the upper soil layer. In the upper soil layer (0–40 cm), species richness showed a positive correlation with soil C:N, C:P, and N:P ratios, and the Margalef index exhibited a positive correlation with soil C:N and C:P ratios. The results of this study indicate that the stoichiometric ratio and nutrient availability of Caragana korshinskii shrubland were the highest over time. Therefore, these findings can be served as a valuable reference for local revegetation and ecological restoration.

Solar energy, as a clean and renewable energy source, holds significant promise for addressing water shortages. Utilizing solar energy for water evaporation is seen as an effective solution in this regard. While many existing interfacial photothermal water evaporation systems rely on nanoparticles or graphene as photothermal or support materials, this study introduced polydopamine (PDA) as a photothermal material due to its environmental friendliness and excellent photon absorption characteristics that closely match the solar spectrum. Polystyrene (PS) was also introduced as a support material for its porous structure and density similar to water, enabling it to float on water. The resulting PS-PDA composite porous structure solar evaporator exhibited a photothermal conversion efficiency comparable to nanoparticles (over 75%), yet with lower production costs and minimal environmental impact. This innovative approach offers a scalable solution for water-scarce regions, providing a cost-effective and efficient means to address water scarcity. The use of PDA and PS in this context highlights the potential for utilizing common materials in novel ways to meet pressing environmental challenges.

Sixteen novel antioxidant peptides from Asian swamp eel (ASE) were identified in previous studies. However, their chemical and cellular antioxidant mechanisms remain unclear. Molecular docking of these peptides with ABTS and DPPH radicals revealed the critical role of hydrogen bonding and Pi–Pi stacking hydrophobic interactions between hydrophobic amino acid residues and free radicals. Residues, such as tryptophan, proline, leucine, and valine, played significant roles in these interactions. All these peptides exhibited notable erythrocyte morphoprotective effects in a model of AAPH-induced oxidative damage of human erythrocytes. Erythrocyte hemolysis was reduced primarily through the modulation of both non-enzymatic (GSH/GSSG) and enzymatic antioxidant systems (SOD, CAT, and GSH-Px) by these peptides. A decrease in levels of MDA, LDH release, and hemoglobin oxidation was observed. Among the peptides, VLYPW demonstrated superior chemical and cellular antioxidant activities, which may be attributed to its higher levels of tyrosine and tryptophan, as well as to its increased hydrophobic amino acid content.

To refine the grain size and improve the mechanical properties of ultrahigh-strength aluminum alloy (Al-10Zn-1.9Mg-1.6Cu-0.12Zr), the Al-Ti-B-Er grain refiner was prepared by the melt reaction method using the aluminum melt and Al + Ti + B precursor. The results exhibit that the Al-Ti-B-Er grain refiner is mainly composed of a block TiAl3 phase, and loose agglomerated nano-sized TiB2 and Al3Er phases. The microstructure of ultrahigh-strength aluminum is significantly affected by the Al-Ti-B-Er refiner, which changes from dendrite to equiaxial grain with increasing Al-Ti-B-Er content, and the size of the eutectic phase is significantly refined. The high-efficiency refinement of Al-Ti-B-Er is due to Er promoting the uniform distribution of TiAl3 particles and the formation of loose agglomerated nano-sized TiB2 particles. The optimal addition content of Al-Ti-B-Er into ultrahigh-strength aluminum alloys is 1 wt%, whose grain size is approximately 40 µm. Additionally, the strength and ductility of ultrahigh-strength aluminum alloys are simultaneously improved by adding 1wt% Al-Ti-B-Er after the T6 treatment, reaching 756 MPa and 20%, respectively. This enhancement in strength and ductility is mainly attributed to grain refinement and the eutectic phase refinement.

Ore is a crucial component in the process of industrialization, and its crushing is a practice that is inextricably linked to our society. This study aims to simulate the crushing process of minerals in a comminution device using the Discrete Element Method (DEM) by characterizing the bonding parameters of mineral particles. Utilizing the EDEM software (2018) for discrete element simulations, the study investigated the influence of bonding parameters on the compressive strength and other performance indicators of the particle bonding model. The study was executed through the application of a Box–Behnken Design (BBD) and Response Surface Methodology (RSM), which facilitated the construction of a second-order response surface regression model. The optimal values for normal stiffness per unit area, shear stiffness per unit area, critical normal stress, and critical shear stress were meticulously determined. The subsequent simulation experiments strongly verify the feasibility of the proposed characterization method for key parameters.

The accurate evaluation of aerodynamic characteristics is a prerequisite and foundation for the design of high-performance aerodynamic shapes, navigation guidance, and strength of projectiles. The nonlinearity of aerodynamic calculations for a projectile is high, and the modeling and simulation are difficult, especially under the high-angle of attack flight conditions. Small variations in flight conditions, and structural parameters, etc., may cause large deviations in aerodynamic responses. Taking a small cylindrical projectile as an example, and to realize its attitude control, it is necessary to conduct aerodynamic characteristics analysis on it and analyze the main influencing factors of its aerodynamic characteristics parameters. In this paper, the finite volume method is used to solve the three-dimensional unsteady N-S equation, combined with the SST k-ω turbulence model, the overlapping grid technology, and the forced pitching vibration method, and the aerodynamic characteristics analysis model of the projectile is established, which realizes the accurate simulation of the surrounding flow field, aerodynamic coefficients, and dynamic derivative of the projectile under different flight conditions. On this basis, the Sobol global sensitivity analysis method based on the augmented radial basis function surrogate model of aerodynamics characteristics and Latin hypercube sampling is used to efficiently analyze and obtain the main influence parameters of cylindrical projectile aerodynamic characteristics. This paper provides a basic theory and fast algorithm for subsequent engineering system design, which has important theoretical and engineering value.

This paper proposes an enhanced multi-strategy sparrow search algorithm to optimize the trajectory of a six-axis industrial robot, addressing issues of low efficiency and high vibration impact on joints during operation. Initially, the improved D-H parametric method is employed to establish both forward and inverse kinematic models of the robot. Subsequently, a 3-5-3 mixed polynomial interpolation trajectory planning approach is applied to the robot. Building upon the conventional sparrow algorithm, a two-dimensional Logistic chaotic system initializes the population. Additionally, a Levy flight strategy and nonlinear adaptive weighting are introduced to refine the discoverer position update operator, while an inverse learning strategy enhances the vigilante position update operator. These modifications boost both the local and global search capabilities of the algorithm. The improved sparrow algorithm, based on 3-5-3 hybrid polynomial trajectory planning, is then used for the time-optimal trajectory planning of the robot. This is compared with traditional sparrow search algorithm and particle swarm algorithm optimization results. The findings indicate that the proposed enhanced sparrow search algorithm outperforms both the standard sparrow algorithm and the particle swarm algorithm in terms of convergence speed and accuracy for robot trajectory optimization. This can lead to the increased work efficiency and performance of the robot.

The rational design of magnetic carbon composites, encompassing both their composition and microstructure, holds significant potential for achieving exceptional electromagnetic wave-absorbing materials (EAMs). In this study, FeCo@CM composites were efficiently fabricated through an advanced microwave plasma-assisted reduction chemical vapor deposition (MPARCVD) technique, offering high efficiency, low cost, and energy-saving benefits. By depositing graphitized carbon microspheres, the dielectric properties were significantly enhanced, resulting in improved electromagnetic wave absorption performances through optimized impedance matching and a synergistic effect with magnetic loss. A systematic investigation revealed that the laminar-stacked structure of FeCo exhibited superior properties compared to its spherical counterpart, supplying a higher number of exposed edges and enhanced catalytic activity, which facilitated the deposition of uniform and low-defect graphitized carbon microspheres. Consequently, the dielectric loss performance of the FeCo@CM composites was dramatically improved due to increased electrical conductivity and the formation of abundant heterogeneous interfaces. At a 40 wt% filling amount and a frequency of 7.84 GHz, the FeCo@CM composites achieved a minimum reflection loss value of −58.2 dB with an effective absorption bandwidth (fE) of 5.13 GHz. This study presents an effective strategy for developing high-performance EAMs.

Background: Eurotium cristatum (E. cristatum) is the probiotic fungus in Fu-brick tea, with which fermentation brings a unique flavor and taste and health-promoting effects. Tartary buckwheat leaves are rich in functional active substances such as flavonoids and phenolic compounds, yet are not effectively utilized. Methods: Tartary buckwheat leaves were processed into raw green tea first and subsequently fermented with E. cristatum to develop a novel fermented leaf tea. The tea quality was evaluated by the aspects of the sensory scores by E-tongue, the volatile compounds by HS-SPME-GC-MS, the non-volatile profile by biochemical and UPLC-MS/MS methods and the antioxidant activity by the colorimetric assay. Results: Fermented leaf tea displayed a golden yellow color, a unique “flower” aroma and a dark-tea taste, with an improved sensory acceptability. Fermentation raised the content of volatile heterocyclic and aromatic compounds, alkenes and other aromatic components, which produced a unique floral flavor. The proportion of sour, bitter and astringency accounting non-volatile compounds such as phenolic acids and amino acids decreased, while the proportion of umami and sweet accounting substances such as responsible amino acids increased. Fermented leaf tea displayed a relative stronger total antioxidant activity against ABTS. Conclusion: E. cristatum fermentation exerted positive effects on Tartary buckwheat leaf tea quality.

In order to supplement the research gap concerning Salvia miltiorrhiza polysaccharide extracted from Danshen in NMR analysis, and to clarify its immune enhancement effect as an adjuvant, we isolated and purified SMPD–2, which is composed of nine monosaccharides such as Ara, Gal, and Glc from Danshen. Its weight average molecular weight was 37.30 ± 0.096 KDa. The main chain was mainly composed of →4)-α-D-Galp-(1→, →3,6)-β-D-Glcp-(1→ and a small amount of α-L-Araf-(1→. After the subcutaneous injection of SMPD–2 as an adjuvant to OVA in mice, we found that it enhanced the immune response by activating DCs from lymph nodes, increasing OVA-specific antibody secretion, stimulating spleen lymphocyte activation, and showing good biosafety. In conclusion, SMPD–2 could be a promising candidate for an adjuvant.

For conducting scientific research at depths in the ocean, deep-sea probes are essential pieces of equipment. The cylindrical shell is the most sensible and rational packaging structure for these detectors. New technical challenges for enhancing the pressure resistance and lightweight design of the pressure-resistant cylindrical shell arise from the need to ensure that the detector packaging structure can withstand the immense water pressure at tens of thousands of meters in the underwater environment, while simultaneously reducing the detector packaging structure’s self-weight. This article examines the detection system’s deep-sea pressure-resistant cylindrical shell. To address the issue of the pressure-resistant shell’s insufficient ability to counteract the overall instability caused by the inability to form unstable half-waves in the radial direction when the ring rib pressure-resistant shell experiences it, a design method for the ribs inside the unequal-stiffness pressure-resistant cylindrical shell is suggested. The shell’s instability pressure increases by 9.65 MPa following the stiffness adjustment. Simultaneously, in order to attain even more lightweight optimization, the optimal inner rib section was obtained by applying the orthogonal topology optimization method, which also reduced the weight by 106.8 g and effectively improved the compression stability of the high-pressure cylindrical shell structure. Based on this, key optimization variables were found by performing sensitivity analysis on the cylindrical shell structure’s parameters. Then, with lightweighting as the primary objective, the high-pressure-resistant cylindrical shell’s optimal structural parameters were found using a multi-objective optimization process using the second-generation fast non-dominated genetic algorithm (NSGA-2). This resulted in a weight reduction of 1.2492 kg, or 17.26% of the original pressure-resistant shell. This has led to the development of a lightweight, highly pressure-resistant method for packaging marine exploration equipment structures.

Seagrass serves as a quintessential reservoir for obligate marine Lulworthiaceae fungi. Our current knowledge of the mycological diversity associated with seagrass in Hong Kong remains poor. We analyzed the diversity of fungi associated with the most widely distributed seagrass species in Hong Kong Halophila ovalis (Hydrocharitaceae), using a combination of culture-based methods and high-throughput amplicon sequencing. Halophilomyces hongkongensis, a novel fungal species in a newly proposed genus within the Lulworthiaceae family, was isolated from H. ovalis roots and rhizomes. The novel fungus showed distinct morphological characteristics, while both combined 18S-28S and internal transcribed spacer (ITS) phylogenetic trees based on maximum likelihood and Bayesian methods supported its discrimination from other existing Lulworthiaceae members. The ITS2 region in the Illumina sequencing results of multiple H. ovalis compartments, water, and adjacent non-seagrass sediments revealed continuous recruitment of H. hongkongensis by H. ovalis throughout the year despite dramatically fluctuating environmental conditions, with remarkably high proportions of this taxon found in root and rhizome internal tissues, possibly indicating a strong and specialized relationship established between the Lulworthiaceae fungal partner and its seagrass host. The inhibitory abilities exhibited by H. hongkongensis against Staphylococcus aureus SA29213 and ATCC 43300 (methicillin-resistant) may imply its capacity in producing (novel) antibacterial compounds. The discovery of H. hongkongensis as the first novel Lulworthiaceae taxon in Hong Kong, along with its distributional pattern in the seagrass meadow, provides valuable insights into the systematics and ecology of this strictly marine fungal family.

The subcellular localization of messenger RNAs (mRNAs) is a pivotal aspect of biomolecules, tightly linked to gene regulation and protein synthesis, and offers innovative insights into disease diagnosis and drug development in the field of biomedicine. Several computational methods have been proposed to predict the subcellular localization of mRNAs within cells. However, there remains a deficiency in the accuracy of these predictions. In this study, we propose an mRCat predictor based on the gradient boosting tree algorithm specifically to predict whether mRNAs are localized in the nucleus or in the cytoplasm. This predictor firstly uses large language models to thoroughly explore hidden information within sequences and then integrates traditional sequence features to collectively characterize mRNA gene sequences. Finally, it employs CatBoost as the base classifier for predicting the subcellular localization of mRNAs. The experimental validation on an independent test set demonstrates that mRCat obtained accuracy of 0.761, F1 score of 0.710, MCC of 0.511, and AUROC of 0.751. The results indicate that our method has higher accuracy and robustness compared to other state-of-the-art methods. It is anticipated to offer deep insights for biomolecular research.

Each application of neurostimulators requires unique stimulation parameter specifications to achieve effective stimulation. Balancing the current magnitude with stimulation resolution, waveform, size, and channel count is challenging, leading to a loss of generalizability across broad neural interfaces. To address this, this paper proposes a highly scalable, programmable neurostimulator with a System-on-Chip (SOC) capable of 32 channels of independent stimulation. The compliance voltage reaches up to ±22.5 V. A pair of 8-bit current-mode DACs support independent waveforms for source and sink operations and feature a user-selectable dual range for low-current intraparenchymal microstimulation with a resolution of 4.31 μA/bit, as well as high current stimulation for spinal cord and DBS applications with a resolution of 48.00 μA/bit, achieving a wide stimulation range of 12.24 mA while maintaining high-resolution biological stimulation. A dedicated communication protocol enables full programmable control of stimulation waveforms, effectively improving the range of stimulation parameters. In vivo electrophysiological experiments successfully validate the functionality of the proposed stimulator. This flexible stimulator architecture aims to enhance its generality across a wide range of neural interfaces and will provide more diverse and refined stimulation strategies.

To investigate the mechanical properties and energy evolution laws of rocks under true triaxial unloading conditions, a study was conducted using a true triaxial rock testing system on three different types of rocks: coal, sandy mudstone, and siltstone. The study examined the mechanical behavior, failure patterns, and fractal dimensions of these rocks under true triaxial unloading conditions. The tests revealed significant variations in stress–strain curves and failure patterns among the different rock types. Observation indicated that rocks with lower peak strength exhibited higher fractal dimensions and increased fragmentation upon failure. Subsequently, based on the experimental data of siltstone, the impact of the unloading rate and particle size distribution on the energy evolution under true triaxial single-sided unloading paths was further investigated using the three-dimensional particle flow software PFC3D 6.0, revealing the micro-mechanisms of rock energy evolution. The study revealed that when the initial stress unloading level was low, the total energy and strain energy at the peak strength exhibited a strong linear relationship with the unloading rate. Before the stress peak, the dissipative energy was mainly composed of frictional energy. After the stress peak, the dissipative energy consisted of frictional energy, damping energy, and kinetic energy. The heterogeneity of rock significantly affected the distribution of dissipative energy, with an increase in rock heterogeneity leading to a decrease in frictional energy and an increase in kinetic energy.

Deep geothermal energy is a sustainable and renewable spacing heating source. Although many studies have discussed the design optimization of deep borehole systems, few have accomplished optimization and in-depth analysis of system operation control. In this study, an analytical model of the U-type deep borehole heat exchanger is proposed, and the average relative error between the simulated outlet temperatures and experimental data is −3.2%. Then, this paper presents an integrated model for the operation optimization study of the U-type deep-borehole ground source heat pump system. The optimal control of flow rate is adopted to match the variation in heating load. Compared with the constant-flow rate (110 m3/h) operation mode, the variable flow rate method reduces the power consumption of the heat pump and circulating pump by 22.1%, from 288,423 kW·h to 224,592 kW·h, during 2112 h of operation. In addition, the system has a larger RHS and COP when the thermal conductivity of the backfill material increases. When the borehole depth increases by 200 m from 2300 m, the energy consumption of the circulating pump will drop from 85,844 kW·h to 56,548 kW·h. The COP of the heat pump unit will decrease approximately linearly as the heating load increases, and the total power consumption will increase accordingly. This work can provide guidance for the design and optimization of U-shaped GSHP systems.

Currently, China has entered the development stage of a “low birth rate” demographically. There is a huge contradiction between the unbalanced and inadequate distribution of preschool education facilities (PEFs) and the people’s expectations for “full and good education for children”. This study took the Lingui New District, a typical new urban area in West China as the research area, and through the introduction of POI big data and GIS analysis methods, supplemented by GeoDA Bivariate Moran index analyses, established a kindergarten spatial database. The study found that preschool education facilities have problems such as insufficient quantity, uneven quality, low service coverage, poor accessibility, etc. Therefore, it is suggested to increase the proportion of public affordable preschool education facilities including kindergartens and nurseries, optimize the spatial distribution of preschool education, and improve the accessibility of preschool services to promote affordable, safe, and high-quality development of preschool education and to provide reference suggestions for the revision of relevant standards and the adjustment of the layout of preschool education in undeveloped regions of China.

Landslide susceptibility prediction usually involves the comprehensive analysis of terrain and other factors that may be distributed with spatial patterns. Without considering the spatial correlation and mutual influence between pixels, conventional prediction methods often focus only on information from individual pixels. To address this issue, the present study proposes a new strategy for neighboring pixel collaboration based on the Unified Perceptual Parsing Network (UPerNet), the Vision Transformer (ViT), and Vision Graph Neural Networks (ViG). This strategy efficiently utilizes the strengths of deep learning in feature extraction, sequence modeling, and graph data processing. By considering the information from neighboring pixels, this strategy can more accurately identify susceptible areas and reduce misidentification and omissions. The experimental results suggest that the proposed strategy can predict landslide susceptibility zoning more accurately. These predictions can identify flat areas such as rivers and distinguish between areas with high and very high landslide susceptibility. Such refined zoning outcomes are significant for landslide prevention and mitigation and can help decision-makers formulate targeted response measures.

To further improve the manufacturing process and product performance of decorated bamboo filament board, the Box–Behnken response surface analysis method was used to analyze the correlation between the hot-pressing parameters and surface bonding strength, and the optimal process optimization parameters were obtained. In addition, the wettability and color of each group of samples were tested. The results show that the optimum process parameters of decorated bamboo filament boards were 130 °C, 165 s and 2.00 MPa, and the surface bonding strength was 1.58 MPa. The relative error between the measured value and the predicted value was less than 5%. The contact angle of the bamboo filament after hot pressing was higher than without hot pressing. However, there was no correlation between wettability and the hot-pressing parameters. There was no effect on the change in bamboo surface color. This indicates that the temperature range selected in this study meets the requirements of surface color control in production.

Soil desertification is an important challenge in global soil management, and effectively and stably restoring soil function is an urgent problem. Using synthetic microbial communities (SynComs) is a burgeoning microbial strategy aimed at enhancing soil nutrients through functional synergies among diverse microorganisms; nevertheless, their effectiveness in restoring desertified soils remains unknown. In this study, we conducted a two-year field experiment using a SynCom constructed by in situ probiotic bacteria and set up control, chemical fertilizer, and combined SynCom–chemical fertilizer (combined fertilizer) treatments to investigate the linkage between microbial communities and soil multifunctionality in the soil surface layer (0–10 cm). Both the bacterial and fungal communities differed the most under the combined fertilizer treatment compared to the control. The bacterial communities differed more under treatments of the SynCom than the chemical fertilizer, while the fungal communities differed more under the chemical fertilizer treatment than the SynCom treatment. Regarding soil function, the SynCom strengthened the correlation between enzyme activities and both bacterial communities and functional properties. pH and available potassium were the main influencing factors under the chemical fertilizer and combined fertilizer treatments. The beta-diversity of the bacterial communities was significantly correlated with soil multifunctionality. Random forest analyses showed that the SynCom significantly enhanced the bacterial communities, driving soil multifunctionality, and that some potential microbial taxa drove multiple nutrient cycles simultaneously. In summary, the SynCom effectively increased the abundance of most carbon, nitrogen, and phosphorus functional genes as well as soil enzyme activities. The bacterial community composition contributed significantly to soil multifunctionality. Hence, the development of novel microbial agents holds significant potential for improving soil functionality and managing desertification.

The mathematical model of a flexible multistate switch (FMSS) exhibits nonlinear and strong coupling characteristics, whereas traditional power decoupling control makes it difficult to completely decouple the output power. The traditional proportional–integral control parameters are difficult to adjust, and their robustness and dynamic performance are poor, which affects the stability of the voltage of the power distribution network and feeder power. To address these problems, this study first converted the original system into a linear system via coordinate transformation using feedback-accurate linearization to decouple active and reactive currents. Thereafter, a super-twisting sliding mode control (ST-SMC) algorithm was introduced, and an adaptive quasi-super-twisting sliding mode control (AQST-SMC) algorithm comprising the quasi-sliding mode function and adaptive proportional term was proposed. An FMSS double closed-loop controller was designed to achieve improved vibration suppression and convergence speed. A three-port FMSS simulation model was developed using MATLAB/Simulink, and the simulation results show that the proposed control strategy enhances the robustness and dynamic performance of the system.

Developing clinically meaningful nanomedicines for cancer therapy requires the drugs to be effective, safe, simple, cheap, and easy to store. In the present work, we report that a simple cationic Fe(III)-rich salt of [FeIIICl(TMPPH2)][FeIIICl4]2 (Fe-TMPP) exhibits a superior anticancer performance on a broad spectrum of cancer cell lines, including breast, colorectal cancer, liver, pancreatic, prostate, and gastric cancers, with half maximal inhibitory concentration (IC50) values in the range of 0.098–3.97 μM (0.066–2.68 μg mL−1), comparable to the best-reported medicines. Fe-TMPP can form stand-alone nanoparticles in water without the need for extra surface modification or organic-solvent-assisted antisolvent precipitation. Critically, Fe-TMPP is TME-responsive (TME = tumor microenvironment), and can only elicit its function in the TME with overexpressed H2O2, converting H2O2 to the cytotoxic •OH to oxidize the phospholipid of the cancer cell membrane, causing ferroptosis, a programmed cell death process of cancer cells.

Temperature has a great influence on the stability of bearing performance. For the study of bearing steady-state temperature, this paper proposes a test method to quickly predict the steady-state temperature of the outer ring of a bearing, which solves the problems in traditional theoretical calculations and simulation analysis methods such as the large number of calculations, complex models, and large errors. Firstly, a mathematical prediction model is established according to the bearing temperature-rise law; then, a bearing steady-state temperature detection device is designed; and finally, the prediction model parameters are solved according to the experimental results, and experimental verification is carried out. It is shown that the prediction model has high accuracy under different load and speed conditions, and the error between the predicted steady-state temperature and the tested steady-state temperature is less than 0.7 °C. This prediction method reduces the single test time of the same speed to 60 min, which greatly improves the efficiency of the temperature detection test. The steady-state temperature model has important theoretical significance in guiding the study of the limiting speed of bearings.

As the prime candidate for plasma-facing materials (PFM), the response of tungsten (W) to thermal shock loads is an important research topic for future fusion devices. Under heat loads, the surface of tungsten plasma-facing materials (W-PFM) can experience thermal damage, including brittle cracking and fatigue cracks. Therefore, exploring solutions for thermal damage of W-PFM remains one of the current research focuses. We propose a novel approach to mitigate thermal radiation damage in PFM, namely, the stacked structure W-PFM. The surface thermal stress distribution of the stacked structure W-PFM under heat loads was simulated and analyzed by the finite element method. As the foil thickness decreases, both the peak thermal stresses in the normal direction (ND) and rolling direction (RD) decrease. When the thickness decreases to a certain value, the peak thermal stress in the RD decreases to about 1384 MPa and no longer decreases; while the peak thermal stress in the ND approaches 0 MPa and can be neglected. In the range of approximately 5–100 mm, the accumulated equivalent plastic strain decreases sharply as the thickness decreases; in other thickness ranges, it decreases slowly. Thermal fatigue experiments were conducted on the stacked structure W composed of W foils with different thicknesses and bulk W using an electron beam facility. The samples were applied with a power density of 30 MW/m2 for 10,000 and 20,000 pulses. The cracks on the surface of the stacked structure W extended along the ND direction, while on the surface of bulk W, besides the main crack in the ND direction, a crack network also formed. The experimental results were consistent with finite element simulations. When the pulse number was 10,000, as the thickness of the W foil decreased, the number and width of the cracks on the surface of the stacked structure W decreased. Only four small cracks were present on the surface of stacked structure W (0.05 mm). When the pulse number increased to 20,000, the plastic deformation and number of cracks on the surface of all samples increased. However, the stacked structure W (0.05 mm) only added one small crack and had the smallest surface roughness (Ra = 1.536 μm). Quantitative analysis of the fatigue cracks showed that the stacked structure W-PFM (0.05 mm) exhibited superior thermal fatigue performance.

This paper presents an ultra-wideband, low insertion loss, and high accuracy 6-bit digital step attenuator (DSA). To improve the accuracy of amplitude and phase shift of the attenuator, two innovative compensation structures are proposed in this paper: a series inductive compensation structure (SICS) designed to compensate for high frequency attenuation values and a small bit compensation structure (SBCS) intended for large attenuation bits. Additionally, we propose insertion loss reduction techniques (ILRTs) to reduce insertion loss. The fabricated 6-bit DSA core area is only 0.51 mm2, and it exhibits an attenuation range of 31.5 dB in 0.5 dB steps. Measurements reveal that the root-mean-square (RMS) attenuation and phase errors for the 64 attenuation states are within 0.18 dB and 7°, respectively. The insertion loss is better than 2.54 dB; the return loss is better than −17 dB; and the input 1 dB compression point (IP1 dB) is 29 dBm at IF 12 GHz. To the best of our knowledge, this chip presents the highest attenuation accuracy, the lowest insertion loss, the best IP1dB, and a good matching performance in the range of 2–22 GHz using the 0.25 μm GaAs p-HEMT process.

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are associated with adverse health effects. This study examined the trend of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS) levels in individuals with and without pre-existing comorbidities. We analyzed the characteristics of 13,887 participants across nine U.S. NHANES cycles (1999–2000 to 2017–2018) and calculated the geometric mean (GM) of PFOA and PFOS levels, standardized by sex and age. A joinpoint regression model was used to analyze the temporal trends of serum PFOA and PFOS levels. We observed declining PFOA and PFOS serum levels among adults in NHANES from 1999–2000 to 2017–2018. Serum PFOA and PFOS concentrations were higher in men, smokers, and individuals with pre-existing CKD, hyperlipidemia, CVD, and cancer. We observed faster decline rates in PFOA levels among individuals with diabetes and CKD and faster decline rates in PFOS levels among individuals with diabetes and those without CKD. This study provided evidence of varying levels and changing trends of PFOA and PFOS between groups with and without established chronic disease, highlighting the role of environmental chemicals in the onset and development of chronic diseases.

Structural health monitoring is an effective method to evaluate the safety status of dams. Measurement error is an important factor which affects the accuracy of monitoring data modeling. Processing the abnormal monitoring data before data analysis is a necessary step to ensure the reliability of the analysis. In this paper, we proposed a method to process the abnormal dam displacement monitoring data on the basis of matrix manipulation and Cuckoo Search algorithm. We first generate a scatter plot of the monitoring data and exported the matrix of the image. The scatter plot of monitoring data includes isolate outliers, clusters of outliers, and clusters of normal points. The gray scales of isolated outliers are reduced using Gaussian blur. Then, the isolated outliers are eliminated using Ostu binarization. We then use the Cuckoo Search algorithm to distinguish the clusters of outliers and clusters of normal points to identify the process line. To evaluate the performance of the proposed data processing method, we also fitted the data processed by the proposed method and by the commonly used 3-σ method using a regression model, respectively. Results indicate that the proposed method has a better performance in abnormal detection compared with the 3-σ method.

Antimicrobial peptides (AMPs) are vital components of innate immunotherapy. Existing approaches mainly rely on either deep learning for the automatic extraction of sequence features or traditional manual amino acid features combined with machine learning. The peptide sequence contains symmetrical sequence motifs or repetitive amino acid patterns, which may be related to the function and structure of the peptide. Recently, the advent of large language models has significantly boosted the representational power of sequence pattern features. In light of this, we present a novel AMP predictor called UniproLcad, which integrates three prominent protein language models—ESM-2, ProtBert, and UniRep—to obtain a more comprehensive representation of protein features. UniproLcad utilizes deep learning networks, encompassing the bidirectional long and short memory network (Bi-LSTM) and one-dimensional convolutional neural networks (1D-CNN), while also integrating an attention mechanism to enhance its capabilities. These deep learning frameworks, coupled with pre-trained language models, efficiently extract multi-view features from antimicrobial peptide sequences and assign attention weights to them. Through ten-fold cross-validation and independent testing, UniproLcad demonstrates competitive performance in the field of antimicrobial peptide identification. This integration of diverse language models and deep learning architectures enhances the accuracy and reliability of predicting antimicrobial peptides, contributing to the advancement of computational methods in this field.

RNA pseudouridine modification exists in different RNA types of many species, and it has a significant role in regulating the expression of biological processes. To understand the functional mechanisms for RNA pseudouridine sites, the accurate identification of pseudouridine sites in RNA sequences is essential. Although several fast and inexpensive computational methods have been proposed, the challenge of improving recognition accuracy and generalization still exists. This study proposed a novel ensemble predictor called PseUpred-ELPSO for improved RNA pseudouridine site prediction. After analyzing the nucleotide composition preferences between RNA pseudouridine site sequences, two feature representations were determined and fed into the stacking ensemble framework. Then, using five tree-based machine learning classifiers as base classifiers, 30-dimensional RNA profiles are constructed to represent RNA sequences, and using the PSO algorithm, the weights of the RNA profiles were searched to further enhance the representation. A logistic regression classifier was used as a meta-classifier to complete the final predictions. Compared to the most advanced predictors, the performance of PseUpred-ELPSO is superior in both cross-validation and the independent test. Based on the PseUpred-ELPSO predictor, a free and easy-to-operate web server has been established, which will be a powerful tool for pseudouridine site identification.

Ocean waves are one of the leading environmental factors that cause motion of the ocean’s structure. Wave prediction is of great significance for the safety of marine structures. The deterministic sea wave prediction (DSWP) has been focused on because it provided an accurate temporal wave surface. The propagation velocity of wave components is one of the critical problems in DSWP. In this paper, the research of propagation velocity is focused on. The Taylor expansion to wave number is used to prove that the group velocity is the propagation velocity of wave components. The simulated irregular long crest wave data is generated. Utilizing the simulated data, the calculated wave surfaces based on group velocity are consistent with the simulated results. Meanwhile, the comparisons of calculated results based on the group velocity and phase velocity are given. Then, a tank experiment is set to verify the prediction results. To further investigate the prediction performance under different conditions, the influences of environmental factors, including the wind speed, water depth and sea state are analyzed in this paper.

Herein, the morphological characteristics of submarine archipelagic aprons were presented for five guyots, Suda, Arnold, Lamont, Niulang, and Zhinyv, which are over 80 Ma years old and are located in the Marcus–Wake seamount group, northwestern Pacific Ocean. Nearly 28 landslide deposits were recognized using the bathymetry and backscatter intensity data collected from the studied guyots. Landslides and their deposits that surround seamounts are mostly related to the morphology of debris avalanches, scarps, gullies/channels, and bedforms. The morphology of the archipelagic aprons of the studied guyots indicates mutual landslide processes, including slump and distinct debris avalanches arising from a cohesive or cohesionless landslide material flow. The superimposition of debris flows and sedimentation dominates the recent stages of the studied guyots. The archipelagic aprons corresponding to convex-arc-shaped scarps exhibit larger domains compared to the invagination-arc-shaped scarps with similar lateral lengths. The scarp morphologies of the studied guyots are predominantly of the complex-arc shape, indicating multiple landslide events. Parallel and convergent gullies and channels are mostly found on the elongated landslide deposits, whereas divergent and radial gullies and channels are mostly distributed on the fan-shaped aprons. Ubiquitous sediment waves occurred on the bedforms of the distal archipelagic apron across the studied guyots because of sediment creep. Small-scale sediment waves were only observed in the channels on the aprons of the Suda guyot.

Introduction: Benign prostatic hyperplasia (BPH) is a common pathologic process in aging men, and the contraction of the prostatic smooth muscles (SMs) in the stroma plays a vital role in this pathogenesis, leading to lower urinary tract symptoms (LUTSs). The isoforms of both the SM myosin (SMM) and non-muscle myosin (NMM) are associated with the contraction type of the prostatic SMs, but the mechanism has not been fully elucidated. Methods: We collected prostate tissues from 30 BPH patients receiving surgical treatments, and normal human prostate samples were obtained from 12 brain-dead men. A testosterone-induced (T-induced) rat model was built, and the epithelial hyperplastic prostates were harvested. Competitive RT-PCR was used to detect the expression of SMM isoforms. We investigated the contractility of human prostate strips in vitro in an organ bath. Results: The results regarding the comparisons of SMM isoforms varied between rat models and human samples. In comparison with T-induced rats and controls, competitive RT-PCR failed to show any statistically significant difference regarding the compositions of SMM isoforms. For human prostates samples, BPH patients expressed more SM-1 isoforms (66.8% vs. 60.0%, p < 0.001) and myosin light chain-17b (MLC17b) (35.9% vs. 28.5%, p < 0.05) when compared to young donors. There was a significant decrease in prostate myosin heavy chain (MHC) expression in BPH patients, with a 66.4% decrease in MHC at the mRNA level and a 51.2% decrease at the protein level. The upregulated expression of non-muscle myosin heavy chain-B (NMMHC-B) was 1.6-fold at the mRNA level and 2.1-fold at the protein level. The organ bath study showed that isolated prostate strips from BPH patients produced slower tonic contraction compared to normal humans. Conclusion: In this study, we claim that in the enlarged prostates of patients undergoing surgeries, MHC expression significantly decreased compared to normal tissues, with elevated levels of SM-1, MLC17b, and NMMHC-B isoforms. Modifications in SMM and NMM might play a role in the tonic contractile properties of prostatic SMs and the development of LUTS/BPH. Understanding this mechanism might provide insights into the origins of LUTS/BPH and facilitate the identification of novel therapeutic targets.

Mycobacterium tuberculosis (Mtb) is one of the major causes of human death. In its battle with humans, Mtb has fully adapted to its host and developed ways to evade the immune system. At the same time, the human immune system has developed ways to respond to Mtb. The immune system responds to viral and bacterial infections through a variety of mechanisms, one of which is alternative splicing. In this study, we summarized the overall changes in alternative splicing of the transcriptome after macrophages were infected with Mtb. We found that after infection with Mtb, cells undergo changes, including (1) directly reducing the expression of splicing factors, which affects the regulation of gene expression, (2) altering the original function of proteins through splicing, which can involve gene truncation or changes in protein domains, and (3) expressing unique isoforms that may contribute to the identification and development of tuberculosis biomarkers. Moreover, alternative splicing regulation of immune-related genes, such as IL-4, IL-7, IL-7R, and IL-12R, may be an important factor affecting the activation or dormancy state of Mtb. These will help to fully understand the immune response to Mtb infection, which is crucial for the development of tuberculosis biomarkers and new drug targets.

The natural flavonoid compound chrysin has promising anti-tumor effects. In this study, we aimed to investigate the mechanism by which chrysin inhibits the growth of non-small cell lung cancer (NSCLC). Through in vitro cell culture and animal models, we explored the impact of chrysin on the growth of NSCLC cells and the pro-cancer effects of tumor-associated macrophages (TAMs) and their mechanisms. We observed that M2-TAMs significantly promoted the growth and migration of NSCLC cells, while also markedly activating the autophagy level of these cells. Chrysin displayed a significant inhibitory effect on the growth of NSCLC cells, and it could also suppress the pro-cancer effects of M2-TAMs and inhibit their mediated autophagy. Furthermore, combining network pharmacology, we found that chrysin inhibited TAMs-mediated autophagy activation in NSCLC cells through the regulation of the CDK1/ULK1 signaling pathway, rather than the classical mTOR/ULK1 signaling pathway. Our study reveals a novel mechanism by which chrysin inhibits TAMs-mediated autophagy activation in NSCLC cells through the regulation of the CDK1/ULK1 pathway, thereby suppressing NSCLC growth. This discovery not only provides new therapeutic strategies for NSCLC but also opens up new avenues for further research on chrysin.

Background: As one of the important components of immunotherapies, mRNA vaccines have displayed promising clinical outcomes in solid tumors. Nonetheless, their efficacy remains unclear in pancreatic adenocarcinoma (PAAD). Given the interaction of pyroptosis with anticancer immunity, our study aims to identify pyroptosis-related antigens for mRNA vaccine development and discern eligible candidates for vaccination. Methods: Utilizing gene expression data from TCGA and ICGC, we integrated RNA-seq data and compared genetic alterations through cBioPortal. Differential gene expressions were integrated using GEPIA. Relationships between immune cell abundance and tumor antigens were analyzed and visualized via TIMER. WGCNA facilitated the clustering of pyroptosis-related genes, identification of hub genes, and pathway enrichment analyses. Pyroptosis landscape was depicted through graph learning-based dimensional reduction. Results: Four overexpressed and mutant pyroptosis-related genes associated with poor prognosis were identified as potential antigens for mRNA vaccines in PAAD, including ANO6, PAK2, CHMP2B, and RAB5A. These genes displayed positive associations with antigen-presenting cells. PAAD patients were stratified into three pyroptosis subtypes. Notably, the PS3 subtype, characterized by a lower mutation count and TMB, exhibited “cold” immunological traits and superior survival compared to other subtypes. The pyroptosis landscape exhibited considerable heterogeneity among individuals. Furthermore, the turquoise module emerged as an independent prognostic indicator and patients with high expressions of hub genes might not be suitable candidates for mRNA vaccination. Conclusions: In PAAD, ANO6, PAK2, CHMP2B, and RAB5A are prospective pyroptosis-related antigens for mRNA vaccine development, which holds potential benefits for patients classified as PS3 and those with diminished hub gene expressions, providing insights into personalized mRNA vaccine strategies.

In the rapidly urbanizing world, as one of the distinct anthropogenic alterations of global climate change, global warming has attracted rising concerns due to its negative effects on human well-being and biodiversity. However, existing studies mostly focused on the difference in temperature elevation among urbanized areas and non-urbanized areas, i.e., rural or suburban areas. The allometric urban warming at intra-urban scales was overlooked. This research aimed to expand our understanding of urbanization–temperature relationships by applying a concept of a “previous-new” dichotomy of urbanized areas. To quantify the land surface temperature (LST) dynamics of 340 cities in China, we analyzed the LST of different land use types through trend analysis and absolute change calculation models. The urban heat island (UHI) effect of two spatial units, i.e., newly expanded urbanized area (“new UA” hereinafter) during 2000–2015 and previously existing urbanized area (“previous UA” hereinafter) in 2000, were compared and discussed. Our findings reveal that urban growth in China coincided with an LST increase of approximately 0.68 °C across the entire administrative boundary, with higher increases observed in regions between the Yellow River and Yangtze River and lower increases in other areas. Moreover, the new UA exhibited significantly greater LST increases and urban heat island intensity (HUII) compared to the previous UA. The dynamics of LST corresponded to the speed and scale of urban growth, with cities experiencing higher growth rates and percentages exhibiting more pronounced LST increases. This study reveals the impact of the underlying surface on human settlements on a large scale.

Vitis vinifera L. possesses high economic value, but its growth and yield are seriously affected by salt stress. Though melatonin (MT) has been widely reported to enhance tolerance towards abiotic stresses in plants, the regulatory role melatonin plays in resisting salt tolerance in grapevines has scarcely been studied. Here, we observed the phenotypes under the treatment of different melatonin concentrations, and then transcriptome and metabolome analyses were performed. A total of 457 metabolites were detected in CK- and MT-treated cell cultures at 1 WAT (week after treatment) and 4 WATs. Exogenous melatonin treatment significantly increased the endogenous melatonin content while down-regulating the flavonoid content. To be specific, the melatonin content was obviously up-regulated, while the contents of more than a dozen flavonoids were down-regulated. Auxin response genes and melatonin synthesis-related genes were regulated by the exogenous melatonin treatment. WGCNA (weighted gene coexpression network analysis) identified key salt-responsive genes; they were directly or indirectly involved in melatonin synthesis and auxin response. The synergistic effect of salt and melatonin treatment was investigated by transcriptome analysis, providing additional evidence for the stress-alleviating properties of melatonin through auxin-related pathways. The present study explored the impact of exogenous melatonin on grapevines’ ability to adapt to salt stress and provided novel insights into enhancing their tolerance to salt stress.

Supersaturated designs (SSDs) refer to those designs in which the run size is much smaller than the main effects to be estimated. They are commonly used to identify a few, but critical, active factors from a large set of potentially active ones, keeping the cost as low as possible. In this regard, the development of new construction and analysis methods has recently seen a rapid increase. In this paper, we provide some methods to construct equi- and mixed-level E(f NOD) optimal SSDs with a large number of inert factors using the substitution method. The proposed methods are easy to implement, and many new SSDs can then be constructed from them. We also study a variable selection method based on the screening-selection network (SSnet) method for regression problems. A real example is analyzed to illustrate that it is able to effectively identify active factors. Eight different analysis methods are used to analyze the data generated from the proposed designs. Three scenarios with different setups of parameters are designed, and the performances of each method are illustrated by extensive simulation studies. Among all these methods, SSnet produced the most satisfactory results according to power.

The nonantimicrobial properties and relatively poor mechanical properties of hydroxyethyl cellulose (HEC) limit its use in packaging. Sulfated rice bran polysaccharides (SRBP) possess significant antioxidant and antimicrobial activities. The purpose of this study was to investigate the effect of different concentrations of SRBP on the physical and mechanical properties and the functional characteristics of HEC/SRBP films. The physical properties of the HEC/20% SRBP films, such as water resistance, water vapor barrier, light barrier, and tensile strength, improved significantly (p < 0.05) compared with those of the HEC films. Scanning electron microscopy and Fourier transform infrared spectrometry showed that HEC formed hydrogen bonds with SRBP and exhibited better compatibility. Thermogravimetric analysis revealed that the addition of SRBP was beneficial to the thermal stability of the films. In addition, the antioxidant and bacteriostatic properties of the films were enhanced by the addition of SRBP to HEC, with the 20% SRBP films showing the most significant enhancement in activity. Therefore, the HEC/20% SRBP films show potential for development for use as active food packaging.

The mapping and dynamic monitoring of large-scale cropland erosion rates are critical for agricultural planning but extremely challenging. In this study, using field investigation data collected from 20,155 land parcels in 2817 sample units in the National Soil Erosion Survey, as well as land use change data for two decades from the National Land Use/Cover Database of China (NLUD-C), we proposed a new point-to-surface approach to quantitatively assess long-term cropland erosion based on the CSLE model and non-homologous data voting. The results show that cropland in Yunnan suffers from serious problems, with an unsustainable mean soil erosion rate of 40.47 t/(ha·a) and an erosion ratio of 70.11%, which are significantly higher than those of other land types. Engineering control measures (ECMS) have a profound impact on reducing soil erosion; the soil erosion rates of cropland with and without ECMs differ more than five-fold. Over the past two decades, the cropland area in Yunnan has continued to decrease, with a net reduction of 7461.83 km2 and a ratio of −10.55%, causing a corresponding 0.32 × 108 t (12.12%) reduction in cropland soil loss. We also quantified the impact of different LUCC scenarios on cropland erosion, and extraordinarily high variability was found in soil loss in different basins and periods. Conversion from cropland to forest contributes the most to cropland erosion reduction, while conversion from grassland to cropland contributes 56.18% of the increase in soil erosion. Considering the current speed of cropland regulation, it is the sharp reduction in land area that leads to cropland erosion reduction rather than treatments. The choice between the Grain for Green Policy and Cropland Protecting Strategy in mountainous areas should be made carefully, with understanding and collaboration between different roles.

Artificial scent screening systems, inspired by the mammalian olfactory system, hold promise for fruit ripeness detection, but their commercialization is limited by low sensitivity or pattern recognition inaccuracy. This study presents a portable fruit ripeness prediction system based on colorimetric sensing combinatorics and deep convolutional neural networks (DCNN) to accurately identify fruit ripeness. Using the gas chromatography–mass spectrometry (GC-MS) method, the study discerned the distinctive gases emitted by mango, peach, and banana across various ripening stages. The colorimetric sensing combinatorics utilized 25 dyes sensitive to fruit volatile gases, generating a distinct scent fingerprint through cross-reactivity to diverse concentrations and varieties of gases. The unique scent fingerprints can be identified using DCNN. After capturing colorimetric sensor image data, the densely connected convolutional network (DenseNet) was employed, achieving an impressive accuracy rate of 97.39% on the validation set and 82.20% on the test set in assessing fruit ripeness. This fruit ripeness prediction system, coupled with a DCNN, successfully addresses the issues of complex pattern recognition and low identification accuracy. Overall, this innovative tool exhibits high accuracy, non-destructiveness, practical applicability, convenience, and low cost, making it worth considering and developing for fruit ripeness detection.

Oil-immersed transformers play a pivotal role owing to their environmentally friendly characteristics, compact footprint, and cost-effectiveness. Ensuring the online monitoring of oil-immersed transformers is a fundamental measure to ensure the secure and stable operation of modern power systems. In this paper, metal particle cluster-doped SnS is firstly used in the adsorption and sensing of decomposition components (CO, C2H2) under fault conditions in oil-immersed transformers. The study comprehensively analyzed band structure, differential charge density, density of states, and molecular orbital theory to unveil the adsorption and sensing mechanisms of target gases. The findings suggest that the modification of metal particle clusters can enhance the surface electronic properties of single-layer SnS. In the regions of metal particle clusters and the gas–surface reaction area, electronic activity is significantly heightened, primarily attributed to the contribution of d-orbital electrons of the metal cluster structures. The modified SnS exhibits adsorption capacity in the following order: Ru3-SnS > Mo3-SnS > Au3-SnS. Additionally, the modified material demonstrates increased competitiveness for C2H2, with adsorption types falling under physical chemistry adsorption. Different metal elements exert diverse effects on the electronic distribution of the entire system, providing a theoretical foundation for the preparation of corresponding sensors. The findings in this work offer numerical insights for the further preparation and development of SnS nanosensors, concurrently shedding light on the online monitoring of faults in oil-immersed transformers.

The path planning strategy of deep-sea mining vehicles is an important factor affecting the efficiency of deep-sea mining missions. However, the current traditional path planning algorithms suffer from hose entanglement problems and small coverage in the path planning of mining vehicle cluster. To improve the security and coverage of deep-sea mining systems, this paper proposes a cluster-coverage path planning strategy based on a traditional algorithm and Deep Q Network (DQN). First, we designed a deep-sea mining environment modeling and map decomposition method. Subsequently, the path planning strategy design is based on traditional algorithms and DQN. Considering the actual needs of deep-sea mining missions, the mining vehicle cluster path planning algorithm is optimized in several aspects, such as loss function, neural network structure, sample selection mechanism, constraints, and reward function. Finally, we conducted simulation experiments and analysis of the algorithm on the simulation platform. The experimental results show that the deep-sea mining cluster path planning strategy proposed in this paper performs better in terms of security, coverage, and coverage rate.

In this study, a deep learning network for extracting spatial-temporal features is proposed to estimate significant wave height (Hs) and wave period (Ts) from X-band marine radar images. Since the shore-based radar image in this study is interfered with by other radar radial noise lines and solid target objects, to ensure that the proposed convolutional neural network (CNN) extracts the image features accurately, it is necessary to pre-process the radar image to eliminate interference. Firstly, a pre-trained GoogLeNet is used to extract multi-scale depth space features from the radar images to estimate Hs and Ts. Since CNN-based models cannot analyze the temporal behavior of wave features in radar image sequences, self-attention is connected after the deep convolutional layer of the CNN to construct a convolutional self-attention (CNNSA)-based model that generates spatial-temporal features for Hs and Ts estimation. Simultaneously, Hs and Ts measured by nearby buoys are used for model training and reference. The experimental results show that the proposed CNNSA model reduces the RMSD by 0.24 m and 0.11 m, respectively, in Hs estimation compared to the traditional SNR-based and CNN-based methods. In Ts estimation, the RMSD is reduced by 0.3 s and 0.08 s, respectively.

In this paper, a finite-time, active fault-tolerant control (AFTC) scheme is proposed for a class of autonomous surface vehicles (ASVs) with component faults. The designed AFTC framework is based on an integrated design of fault detection (FD), fault estimation (FE), and controller reconfiguration. First, a nominal controller based on the Barrier Lyapunov function is presented, which guarantees that the tracking error converges to the predefined performance constraints within a settling time. Then, a performance-based monitoring function with low complexity is designed to supervise the tracking behaviors and detect the fault. Different from existing results where the fault is bounded by a known scalar, the FE in this study is implemented by a finite-time estimator without requiring any prioir information of fault. Furthermore, under the proposed finite-time AFTC scheme, both the transient and steady-state performance of the ASV can be guaranteed regardless of the occurrence of faults. Finally, a simulation example on CyberShip II is given to confirm the effectiveness of the proposed AFTC method.

2’-Hydroxychalcone is a hydroxyl derivative of chalcones, which are biosynthetic precursors of flavonoids and rich in the human diet. The anticancer activity of 2’-hydroxychalcone has been reported in several cancers but remains to be investigated in breast cancer. In the current study, 2’-hydroxychalcone showed significant cytotoxicity against breast cancer cell lines MCF-7 and CMT-1211. It could inhibit breast cancer cell proliferation, migration, and invasion in vitro and suppress tumor growth and metastasis in vivo. Mechanistic investigation revealed that the NF-κB pathway was significantly inhibited by 2’-hydroxychalcone treatment accompanied by an excessive intracellular accumulation of reactive oxygen species, induction of endoplasmic reticulum stress, and activation of JNK/MAPK. In addition, 2’-hydroxychalcone elevated the autophagic levels in breast cancer cells equipped with increasing numbers of autophagy vesicles and complete autophagic flux. Finally, autophagy-dependent apoptosis was observed in 2’-hydroxychalcone-induced cell death. In conclusion, 2’-hydroxychalcone enhances the autophagic levels and induces apoptosis in breast cancer cells, which could be contributed to the inhibition of the pro-survival NF-κB signaling, indicating a promising potential for 2’-hydroxychalcone in future anticancer drug development.