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Optical fiber sensors have emerged as a critical sensing technology across various fields due to their advantages, including high potential bandwidth, electrical isolation that is safe for utilization in electrically hazardous environments, high reliability, and ease of maintenance. However, conventional optical fiber sensors face limitations in achieving high sensitivity and precision. The integration of nanostructures with advanced coating technology is one of the critical solutions to enhancing sensor functionality. This review examined nanostructure coating techniques that are compatible with optical fiber sensors and evaluated etching techniques for the improvement of optical fiber sensing technology. Techniques such as vapor deposition, laser deposition, and sputtering to coat the nanostructure of novel materials on the optical fiber sensors are analyzed. The ability of optical fiber sensors to interact with the environment via etching techniques is highlighted by comparing the sensing parameters between etched and bare optical fibers. This comprehensive overview aims to provide a detailed understanding of nanostructure coating and etching for optical fiber sensing and offer insights into the current state and future prospects of optical fiber sensor technology for sensing performance advancement, emphasizing its potential in future sensing applications and research directions.

Non-native fish species introduced into new areas, especially when they develop into large populations, pose a threat to native fauna. Understanding the current status of the fish community and invasion risks of non-native fish are essential for invasive species control and diversity conservation. The community structure of fish and a risk assessment on 10 non-native fish species were systematically assessed in the Jiulong River Basin, China, in January, April, and July of 2024. The species richness, with 105 species, showed a notable decrease compared to the 124 species recorded in 1975, while the number of non-native species has increased from zero to ten. Furthermore, the non-native fish species, Coptodon zillii and Sarotherodon galilaeus, have become dominant species, with IRI values of 4038.43 and 1180.30, respectively. The AS-ISK established risk thresholds for BRA and BRA + CCA as 29.5 and 35.5, respectively, identifying 70% of the non-native fish species as high-risk species, including C. zillii, S. galilaeus, Oreochromis niloticus, Clarias batrachus, Hypostomus plecostomus, and Oreochromis aureus. This study indicates that the fish species richness in the Jiulong River Basin has declined, with C. zillii and S. galilaeus becoming dominant and posing high ecological risks to the native fish community. In addition, targeted fishing during the breeding season should be used to control the population of tilapia and restore fish diversity.

For a retaining wall adjacent to rock or rigid structures, existing model test results indicate that the slip soil in the limit state can be approximated as a trapezoidal slip wedge. Based on the static equilibrium condition of the slip wedge, a calculation method for active and passive earth pressures is proposed that considers the effect of backfill width through extreme value analysis. As the backfill width increases, the trapezoidal slip wedge transitions to a triangular slip wedge, introducing a critical width to distinguish between finite and semi-infinite soil conditions. For cohesionless soils, the proposed method converges to Coulomb theory at the critical width; when the backfill is clay, the critical width exceeds the width of Coulomb’s triangular slip wedge due to the stabilizing contribution of cohesion. Parameter analysis reveals that with increasing backfill width, the active earth pressure of cohesionless soil follows a non-linear upward trend, whereas the passive earth pressure decays exponentially. For clay, the active earth pressure initially increases with backfill width and then decreases, whereas the passive earth pressure first decays exponentially and then exhibits a slight increase. Variations in the friction angle significantly affect both active and passive earth pressures, while cohesion mainly influences active earth pressure, and wall-soil friction angle exerts a stronger impact on passive earth pressure. The effectiveness of the proposed method is verified by comparison with results from model tests and numerical simulations.

Accurate deformation prediction is crucial for ensuring the safety and longevity of bridges. However, the complex fluctuations of deformation pose a challenge to achieving this goal. To improve the prediction accuracy, a bridge deformation prediction method based on a bidirectional gated recurrent unit (BiGRU) neural network and error correction is proposed. Firstly, the BiGRU model is employed to predict deformation data, which aims to enhance the modeling capability of the GRU network for time-series data through its bidirectional structure. Then, to extract the valuable information concealed in the error, a transformer model is introduced to rectify the error sequence. Finally, the preliminary and error prediction results are integrated to yield high-precision deformation prediction results. Two deformation datasets collected from an actual bridge health monitoring system are utilized as examples to verify the effectiveness of the proposed method. The results show that the proposed method outperforms the comparison model in terms of prediction accuracy, robustness, and generalization ability, with the predicted deformation results being closer to the actual results. Notably, the error-corrected model exhibits significantly improved evaluation metrics compared to the single model. The research findings herein offer a scientific foundation for bridges’ early safety warning and health monitoring. Additionally, they hold significant relevance for developing time-series prediction models based on deep learning.

High-performance fiber-reinforced concrete (HPFRC) offers exceptional strength, ductility, and durability, making it highly promising for electric power pipe jacking applications. However, limited research exists on the mechanical properties of HPFRC pipes, especially regarding reinforcement schemes. This study bridges this gap by using a combination of three-point testing, analytical calculations, and numerical simulations to investigate the mechanical behavior and performance of HPFRC pipes under various reinforcement configurations. The results show that the load–displacement curve of HPFRC pipes initially follows a linear elastic relationship, but as the load exceeds 200 kN/m, displacement increases and cracks form, with failure occurring at 410 kN/m. HPFRC pipes demonstrate significantly enhanced load-bearing and crack resistance capabilities, with reduced reinforcement and wall thickness compared to traditional materials, maintaining high load-bearing capacity even after damage. The three analysis methods generally align in terms of load-bearing and failure processes, though the analytical method reveals limitations in accurately predicting crack widths. The study also reveals that reinforcement schemes significantly affect the pipes’ structural performance, with double layer and inner layer reinforcement providing superior damage resistance. This study contributes new insights into HPFRC pipe performance and provides a basis for optimizing reinforcement designs in pipe jacking projects.

Turbodrills are extensively utilized within the oilfield development industry. In order to enhance the performance output of turbodrills, a novel stator and rotor structure has been conceptualized. Fluent 19.0 numerical simulation software was employed to ascertain the output characteristics of the rotor within the flow field. The drilling engine output performance was the subject of a qualitative study, which was conducted using the drilling mud pump test equipment during drilling. Furthermore, a speed torque meter measurement system was designed to quantitatively analyze the output torque and speed performance of the prototype. The results show that: ① Through the numerical simulation method, it is verified that the new structural rotor has the output performance of a conventional rotor. ② Comparing the numerical solution and the test value, it can be seen that the relative error value is within 4.3%, indicating that the numerical simulation has a certain accuracy. ③ When the inlet displacement is 30 L/s and the speed is 1400 r/min, the maximum value of the numerical solution of the output torque of a single rotor group is 12.45 N/m. When the inlet displacement increases from 18 L/s to 30 L/s, the maximum torque numerical solution increases from 10.78 N/m to 27.44 N/m.

Streptococcus suis (S. suis) is a major swine pathogen throughout the world as well as an emerging zoonotic agent. Among the symptoms caused by S. suis, including septicemia, pneumonia, endo-carditis, arthritis, and meningitis, the latter is the most overlooked. In the present study, we explored the mechanism of action of berberine against S. suis meningitis by obtaining berberine-related action targets, porcine S. suis meningitis targets, and human S. suis meningitis targets from open databases. We constructed a protein–protein interaction (PPI) network by using the STRING database and employed Cytoscape 3.8.0 to screen for core targets. We performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses through DAVID. We identified 31 potential targets of berberine, of which Toll-like receptor 4 (TLR4), fibronectin 1 (FN1), superoxide dismutase (SOD1), and catalase (CAT) were the four most critical targets. GO analysis revealed the enrichment of terms related to the response to oxidative stress and the inflammatory response. KEGG analysis revealed the enrichment of the interleukin 17 (IL-17), phosphoinositide 3-kinase (PI3K)-Akt, TLR, tumor necrosis factor (TNF), and mitogen-activated protein kinase (MAPK) signaling pathways. In addition, the admetSAR results showed that berberine can cross the blood–brain barrier. The molecular docking results indicated key binding activity between TLR4–berberine and FN1–berberine. In summary, berberine protects against Streptococcus suis meningitis by regulating inflammatory response and oxidative stress in humans and pigs. Our study updates the current knowledge of the targets of S. suis meningitis to exploit new drugs in humans and pigs, to develop environmentally friendly and antibiotic-free animal-derived food products, and to improve the farming industry and economic development.

Aboveground biomass (AGB) is a sensitive indicator of grassland resource quality and ecological degradation. However, accurately estimating AGB at large scales to reveal long-term trends remains challenging. Here, single-factor parametric models, multi-factor parametric models, and multi-factor non-parametric models (Random Forest) were developed for three grassland types (alpine meadow, alpine grassland, and swampy meadow) in the Bayanbuluk Grassland using MODIS satellite data and environmental factors, including climate and topography. A 10-fold cross-validation method was employed to assess the accuracy and stability of these models, and an AGB remote sensing inversion model was established to estimate the AGB of the Bayanbuluk Grassland from 2005 to 2024. Moreover, the BEAST mutation test, Theil–Sen median trend analysis, and Mann–Kendall test were used to analyse the temporal trends of AGB, identify the years of mutation points, and explore the changes in AGB across the entire study period (2005–2024) and at 5-year intervals, considering the influence of climatic factors. The results indicated that the machine learning (RF) model outperformed both multi-factor parametric and single-factor parametric models, with specific improvements in R2 and RMSE across all grassland types. For instance, the RF model achieved an R2 of 0.802 in alpine grasslands, outperforming the multi-factor parametric model with an R2 of 0.531. The overall spatial distribution of AGB exhibited heterogeneity, with a gradual increase from northwest to southeast over the study period. Interannual AGB changes fluctuated significantly, with an overall increasing trend. Notably, from 2015 to 2019, 78% of the Bayanbuluk Grassland area showed a nonsignificant increase in AGB. Specifically, 46.7% of the alpine meadow AGB, 23% of the alpine grassland AGB, and 8.3% of the swampy meadow AGB showed non-significant increases. Further, temperature was found to be the dominant driver of AGB, with a stronger effect on alpine meadows and alpine grasslands than on swampy meadows. This is likely due to the relatively constant moisture levels in the swampy meadows, where precipitation plays a more prominent role. This study provides a comprehensive assessment of AGB trends, including both spatial and temporal analyses, which will inform future grassland resource management.

Existing methods for detecting road damage mainly depend on manual inspections or sensor-equipped vehicles, which are inefficient, have limited coverage, and are susceptible to errors and delays. These traditional methods also struggle with detecting minor damage, such as small cracks and initial potholes, making real-time road monitoring challenging. To address these issues and improve the performance for real-time road damage detection using Street View Image Data (SVRDD), this study propose DAPONet, a new deep learning model. DAPONet proposes three main innovations: (1) a dual attention mechanism that combines global context and local attention, (2) a multi-scale partial overparameterization module (CPDA), and (3) an efficient downsampling module (MCD). Experimental results on the SVRDD public dataset show that DAPONet reaches a mAP50 of 70.1%, surpassing YOLOv10n (an optimized version of YOLO) by 10.4%, while reducing the model’s size to 1.6 M parameters and cutting FLOPs to 1.7 G, resulting in a 41% and 80% decrease, respectively. Furthermore, the model’s mAP50-95 of 33.4% on the MS COCO2017 dataset demonstrates its superior performance, with a 0.8% improvement over EfficientDet-D1, while reducing parameters and FLOPs by 74%.

The TiB2 film exhibits exceptional hardness and chemical stability due to its unique crystal structure and robust covalent bonds, but it also demonstrates high brittleness and poor toughness, which restricts its practical applications in engineering. By appropriately incorporating metal dopants, the toughness of the ceramic matrix can be enhanced without compromising its inherent hardness. In this study, TiB2 films with different nickel contents (0–32.22 at.%) were fabricated through radio frequency magnetron sputtering. The microstructure, chemical composition, phase structure, and mechanical properties were analyzed using scanning electron microscopy, transmission electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy and nanoindentation tester. The pure TiB2 film exhibited (0001) and (0002) peaks; however, the addition of nickel resulted in broadening of the (0001) peak and disappearance of the (0002) peak, and no crystalline nickel or other nickel-containing phases could be identified. It was found that the incorporation of nickel refines the grain structure of titanium diboride, with nickel present in an amorphous form at the boundaries of titanium diboride, thereby forming a wrapped structure. The enrichment of nickel at the grain boundary becomes more pronounced as the nickel content is further increased, which hinders the growth of TiB2 grains, resulting in the thinning of columnar crystals and formation of nanocrystalline in the film, and the coating hardness remains above 20 GPa, when the nickel content is less than 10.83 at.%. With the increase in nickel content, titanium diboride exhibited a tendency to form an amorphous structure, while nickel became increasingly enriched at the boundaries, and the coating hardness and elastic modulus decreased. The wrapped microstructure could absorb the energy generated by compressive shear stress through plastic deformation, which should be beneficial to improve the toughness of the coatings. The addition of nickel enhanced the adhesion between the film and substrate while reducing the friction coefficient of the film. Specifically, when the nickel content reached 4.26 at.%, a notable enhancement in both nanohardness and toughness was observed for nanocomposite films.

This study aims to establish an equivalent parallel capacitance model for a copper/polydimethylsiloxane (Cu/PDMS) capacitive flexible pressure sensor and modulate its relative permittivity to optimize pressure sensing performance. The Cu/PDMS composite material is an ideal dielectric layer for sensors due to its high dielectric constant and tunable elasticity. By adjusting the different mixing ratios of PDMS and copper particles in micro size, the components and structure properties of the composite material can be modified, thereby affecting the electrical and mechanical performance of the sensor. We used finite element analysis (FEA) to model the sensor structure and studied the capacitance changes under various normal loading conditions to assess its sensitivity and distribution characteristics. Experimental results show that the sensor has good sensitivity and repeatability in the pressure range of 0 to 50 kPa. Additionally, we explored the effect of the addition of carbon black particles. It could be inferred that the added carbon black can enhance electrical properties due to its conductivity, which would be consequenced by the distribution optimization of Cu particles for carbon black’s low density, and it can mechanically restore some flexibility up to nearly 20%. Through these studies, our work can provide theoretical support for the design and application of flexible pressure sensors.

Behavioral decision-making is an important part of the high-level intelligent driving system of intelligent vehicles, and efficient and safe behavioral decision-making plays an important role in the deployment of intelligent transportation system, which is a hot topic of current research. This paper proposes a deep reinforcement learning (DRL) method based on mixed-state space and driving risk for autonomous vehicle behavior decision-making, which enables autonomous vehicles to make behavioral decisions with minimal instantaneous risk through deep reinforcement learning training. Firstly, based on the various behaviors that may be taken by autonomous vehicles during high-speed driving, a calculation method for autonomous vehicle driving risk is proposed. Then, deep reinforcement learning methods are used to improve the safety and efficiency of behavioral decision-making from the interaction between the vehicle and the driving environment. Finally, the effectiveness of the proposed method is proved by training verification in different simulation scenarios, and the results show that the proposed method can enable autonomous vehicles to make safe and efficient behavior decisions in complex driving environments. Compared with advanced algorithms, the method proposed in this paper improves the driving distance of autonomous vehicle by 3.3%, the safety by 2.1%, and the calculation time by 43% in the experiment.

This study aimed to enhance the efficiency and surface quality of shear-thickening polishing (STP) for optical glass through optimizing the polishing parameters. Sixteen orthogonal experiments were conducted to assess the effects of polishing speed (V), angle (θ), and slurry concentration (C) on the material removal rate (MRR) and surface roughness (Ra). Grey relational analysis simplified the multi-objective optimization problem, and a regression model was formulated to determine the optimal combination of polishing parameters. The results indicate that slurry concentration has the most significant impact on the optimization objective, followed by polishing angle, whereas polishing speed has the least effect. Under the optimal combination of polishing parameters—polishing speed of 70 rpm, polishing angle of 70°, and slurry concentration of 12%—the surface roughness (Ra) of optical glass was significantly reduced to 8.23 nm during a 20 min polishing process, while the material removal rate (MRR) reached 813.63 nm/min. Shear-thickening polishing under the optimized process parameters can effectively remove scratches from the workpiece surface and significantly enhance surface quality.

The adhesion between dibenzofuran (DF) and degrading bacteria is the first step of DF biodegradation and affects the efficient degradation of DF. However, their efficient adhesion mechanism at the molecular level remains unclear. Therefore, this study first examined the adhesive behaviors and molecular mechanisms of Rhodococcus sp. strain p52 upon exposure to DF. The results showed that the adhesion between strain p52 and DF is mediated by extracellular polymeric substances (EPSs). Compared with sodium acetate as a carbon source, the percentages of glucose and proteins related to electron transfer, toxin–antitoxin, and stress responses were elevated, which were analyzed by polysaccharide composition and proteomics, and the contents of extracellular polysaccharides and proteins were increased. Moreover, biofilm analysis suggested an increase in EPS content, and the change in components increased biofilm yield and promoted loose and porous aggregation between the bacteria; this aggregation caused an increase in the specific surface area in contact with DF. The surface characteristics analysis indicated that the production of EPS reduced the absolute value of the zeta potential and increased the hydrophobicity of strain p52, which was beneficial for the adhesion of strain p52 and DF. These findings help us to enhance the understanding of the adhesion mechanisms and bioremediation of polycyclic aromatic hydrocarbons by degrading bacteria.

The dissolution mechanism of YbOF in a fluoride-containing (LiF-CaF2)eut. molten salt is the basis for analyzing the structure of the resulting medium and optimizing the electrolytic preparation of rare-earth Yb alloys. In this study, isothermal saturation was used to analyze solubility changes of YbOF in the (LiF-CaF2)eut. system. Quantum chemical and molecular dynamics ab initio methods were used to study the basic properties of the components of the (LiF-CaF2)eut.-YbOF system and the microscopic structural changes during the dissolution process. In addition, structural changes in the YbOF-saturated (LiF-CaF2)eut. system were analyzed by combining cryogenic-temperature Raman spectroscopy with experimental methods. The results show the solubility of YbOF increased linearly in the temperature range of 1073–1323 K. As the melting temperature exceeded 1073 K, LiF and CaF2 gradually dissociated into Li+, Ca2+, and F−. In the initial stages of YbOF dissolution (1073–1173 K), the Yb–F bond was less stable than the Yb–O bond; YbOF dissociated into YbO+ and F− in this temperature range. When the temperature was increased above 1173 K, YbO+ further dissociated into Yb3+ and O2−. Overall, the dissolution of YbOF did not affect the main structure of the (LiF-CaF2)eut. system.

The degradation of concrete caused by sulfate attack poses a significant challenge to its durability. Using nanomaterials to enhance the mechanical and durability properties of concrete is a promising solution. A study of the durability of nano-alumina (NA)-modified concrete by sulfate erosion was carried out. The results showed that the compressive strength, quality, and permeability of concrete to chloride ions decreased during its long-term erosion by a sodium sulfate solution. The NA-modified concrete exhibited higher resistance to erosion by the sodium sulfate than ordinary concrete (OPC), the rate of reduction in its tensile strength was low, and the resistance of sample NA1 to penetration by chloride ions decreased only by one-fifth compared with that of OPC. The results of mercury intrusion porosimetry (MIP) and scanning electron microscopy (SEM) tests showed that erosion had severely damaged the pore characteristics and micromorphology of concrete. The total porosities of the OPC and NA1 samples increased from 12.68% and 10.29% to 16.03% and 12.71%, respectively. Their microscopic morphology revealed loose particles and poor compactness. The leading causes of damage to concrete due to erosion by the sodium sulfate were its crystallization pressure and the swelling-induced stress caused by the deposition of crystals in its pores. This study demonstrates that NA can significantly enhance the durability of concrete against sulfate attack, offering valuable insights for strategic applications of NA in concrete materials.

In highly charged ions, significant nuclear hyperfine mixing (NHM) effects can arise when the electromagnetic field generated by the electrons interacts strongly with the nucleus, leading to mixing of nuclear states. While previous studies have primarily attributed the NHM effect to unpaired valence electrons, we present a reformulation of the theoretical framework using dressed hyperfine states and investigate the NHM effect in 205Pb76+, 205Pb75+, 205Pb74+, and 205Pb73+ ions. Our numerical results show that significant NHM effects occurred in all of the studied ions, even in the absence of unpaired valence electrons in 205Pb76+ and 205Pb74+. We found that the lifetime of the isomeric state was reduced by 2–4 orders of magnitude compared with the bare 205Pb nucleus, depending on the charge state of the ion. These results indicate that it is the active valence electrons rather than unpaired electrons which play a key role in the NHM effect, thereby deepening our understanding of this phenomenon.

In recent years, environmental and food safety have garnered substantial focus due to their intimate connection with human health. Numerous biosensors have been developed for identifying deleterious compounds; however, these biosensors reveal certain limitations. Nanopore sensors, featuring nano-scaled pore size, have demonstrated outstanding performance in terms of rapidity, sensitivity, and selectivity as a single-molecule technique for environmental and food surveillance. In this review, we present a comprehensive overview of nanopore applications in these two fields. To elucidate the pioneering roles of nanopores, analytes are categorized into three distinct groups, including metal ions, synthetic contaminants, and biotoxins. Moreover, a variety of strategies are involved, such as the coalescence with ligand probes, the implementation of chemical reactions, the functionalization of nanopores, etc. These scientific studies showcase the versatility and diversity of the nanopore technique, paving the way for further developments of nanopore technology in environmental and food safety.

A reliable prediction model can greatly contribute to the research of car seating system vibration control. The novelty of this paper lies in the development of a hybrid method of an artificial neural network (ANN) and response surface methodology (RSM) to predict the peak seat-to-head transmissibility ratio of a seating suspension system and to evaluate its ride comfort for different seat design parameters. Additionally, this method can remove the experimental design of the RSM model. In this paper, four seat design parameters are selected as input parameters and arranged using the central composite design method. The peak transmissibility ratio from seat to head at 4 Hz is chosen as the response target output value. To illustrate this hybrid method, the response target output value of the peak transmissibility ratio is calculated from the frequency response of a five-degrees-of-freedom (5-DOF) lumped-parameter biodynamic seating suspension model. The input design parameters and the response target output values are used to train an ANN to establish the relationship between the seat design parameters and the peak transmissibility ratio. At the same time, the input design parameters and the response target output values predicted by the ANN are used to develop the relationship between the seat design parameters and the peak transmissibility ratio using the response surface method and linear regression models. The hybrid of the ANN and response surface methods makes the planning or design of experiments not essential. The hybrid model of the ANN and response surface method is more accurate and convenient than a linear regression model for the study of seating system vibration isolation.

Keeping the epidermis of apple rootstock cuttings moist is important for maintaining physiological activities. It is necessary to monitor the epidermis moisture in real time during the growth process of apple rootstock cuttings. A machine vision-based discrimination model for the moisture degree of cuttings’ epidermis was designed. This model optimizes the structure of the semantic segmentation model U-Net. The model takes the Saturation channel and Value channel information of the cutting images in the HSV color space as the characteristics of the cuttings’ moisture, so that the model has good performance in the blue-purple supplementary light environment. The average accuracy of the improved model is 95.07% for dry and wet cuttings without supplementary light, and 84.83% with supplementary light. The humidification system implanted in the model can control the atomizer to complete the task of moisturizing the cuttings’ epidermis. The average moisture retention rate of the humidification system for cuttings was 92.5%. Compared with the original model, the moisturizing effect of the humidification system increased by 26.87%. The experimental results show that the improved U-Net model has good generalization and high accuracy, which provides a method for the design of an accurate humidification system.

Multipath transmission in ICN provides high transmission efficiency and stability. In an IP-ICN compatible network environment, unmodified IP terminal devices can access ICN through gateways, benefiting from these performance enhancements. This paper proposes a gateway framework for hybrid IP-ICN multipath transmission systems, enabling protocol conversion and quality of service management. A packet reordering module is integrated at the egress gateway to address complex packet disorder issues caused by ICN multipath transmission, thereby enhancing the service quality provided to IP terminals. A Reordering Buffer Management Method (RBMM) is introduced, consisting of two key components. First, RBMM employs an improved dynamic threshold scheme for reserved buffer partitioning, efficiently identifying congestion and optimizing buffer resource utilization. Second, a flow-priority-based replacement strategy is designed to enhance fairness in resource allocation by evicting packets with lower delivery probability during congestion. Experimental results demonstrate that RBMM dynamically adapts to varying traffic conditions, maintaining high transmission performance while reducing buffer resource consumption. In comparison to existing methods, RBMM significantly reduces queuing delay and flow completion time, providing more balanced resource allocation when multiple flows compete for limited buffer capacity.

The recognition and classification of building shapes are the prerequisites and foundation for building simplification, matching, and change detection, which have always been important research problems in the field of cartographic generalization. Due to the ambiguity and uncertainty of building shape outlines, it is difficult to describe them using unified rules, which has always limited the quality and automation level of building shape recognition. In response to the above issues, by introducing object detection technology in computer vision, this article proposes a building shape recognition and classification method based on the YOLO object detection model. Firstly, for different types of buildings, four levels of building training data samples are constructed, and YOLOv5, YOLOv8, YOLOv9, and YOLOv9 integrating attention modules are selected for training. The trained models are used to test the shape judgment of buildings in the dataset and verify the learning effectiveness of the models. The experimental results show that the YOLO model can accurately classify and locate the shape of buildings, and its recognition and detection effect have the ability to simulate advanced human visual cognition, which provides a new solution for the fuzzy shape recognition of buildings with complex outlines and local deformation.

The widespread utilization of antibiotic growth promoters (AGPs) boosts the growth rate of food animals and enhances human living standards. Nevertheless, it is accompanied by escalating antibiotic resistance. Consequently, there is an urgent demand to develop novel alternatives to growth promoters. The objective of this study was to develop a non-antibiotic growth promoter (NAGP) for augmenting the growth rate of food animals. The growth-promoting effect of plant-derived NAGPs was assessed in mice and broiler chickens, and its growth-promoting mechanism was initially investigated. The results reveal that a combination of hawthorn (also known as shanzha) and astragalus (also known as huangqi) extracts (SQ) enhanced the growth rate of mice both in vivo and in vitro, attributed to their significant capacity to promote muscle growth and improve immunity (p < 0.05). The composite super energy extract M (CSEE-M), further optimized on the basis of SQ, significantly improved growth performance and feed conversion ratio, and elevated the activity of intestinal digestive enzymes (p < 0.05) in both mice and broilers and reshaped the gut microbiota of broilers. The addition of 0.5% CSEE-M to broiler drinking water significantly increased muscle content and improved carcass quality (p < 0.05). In conclusion, both SQ and CSEE-M hold great promise as NAGPs and serve as effective substitutes to AGPs. This research not only furnishes new solutions for the misuse of antibiotics but presents a fresh perspective for the development of growth promoters.

Lüliang black goat (LBG) is a unique livestock genetic resource of great significance for the local agriculture and economic development of Shanxi, China. However, the kidding rate of LBG is relatively low, which limits efforts to improve the reproductive performance and economic benefits of this breed. Therefore, improving the kidding characteristics of LBG is crucial for increasing its economic benefits. In this study, 20 LBG does were selected for whole-genome resequencing and divided into two groups: 10 in the T group (does with three consecutive kiddings of twin kids) and 10 in the S group (does with three consecutive kiddings of single kids). Based on whole-genome resequencing data, this study comprehensively assessed the population structure and genetic diversity of LBG and explored the related genes that affect reproductive performance. Three selection signal analysis methods—Fst (Fixation Index), π (nucleotide diversity), and XP-CLR (Cross Population Composite Likelihood Ratio)—were applied to screen a total of 838 genes, and enrichment analysis was performed to identify genes closely related to the reproductive performance of LBG, including ENPP3, APC, and GLI2. A generalized linear model was used to conduct a correlation analysis between non-synonymous mutations in the three genes and the number of kids produced. Two loci that were significantly correlated with kidding number were identified (p < 0.05): GLI2 g.63400363 C>T and GLI2 g.63417538 C>T. In general, the LBG population has high genetic diversity and good prospects for genetic improvement. The findings revealed that mining high-fecundity selection characteristics provides a basis for research on goat reproductive mechanisms.

The vast seabed holds tremendous resource potential that can provide necessary materials for future human societal development. This study focuses on the mineralogy of seafloor manganese nodules off the coast of China in the Western Pacific and the primary techniques for extracting valuable metal elements from manganese nodules. The research indicates that the main valuable metal elements in the manganese nodules from this region include Cu, Co, Ni, Mn, Fe, etc. The key to extracting these valuable metals lies in reducing Mn(IV) to Mn(II) to disrupt the structure of the nodules, thereby releasing the valuable elements. The extraction processes for the main valuable metal elements of manganese nodules are mainly divided into two categories: pyrometallurgical–hydrometallurgical and solely hydrometallurgical. In order to cope with the challenges of environmental change and improve utilization efficiency, bioleaching, hydrogen metallurgy, and co-extraction are gaining increasing attention. For promoting commercialization, the future development of manganese nodule resources can refer to the technical route of efficient short-process extraction technology, the comprehensive recovery of associated resources, and tail-free utilization.

T-2 toxin, a highly toxic type A trichothecene, is a secondary fungal metabolite produced by various Fusarium species. The consumption of food and feed contaminated with T-2 toxin is a major factor contributing to growth retardation, posing significant risks to both human and animal health. However, the specific targets and mechanisms that mitigate T-2 toxin-induced growth retardation remain unclear. In this study, transcriptomic analysis was employed to identify key differentially expressed genes associated with the alleviation of T-2 toxin-induced growth retardation. Peroxiredoxin 4 (PRDX4), a gene linked to oxidative stress and apoptosis, was found to be one of the most downregulated in T-2 toxin-treated GH3 cells, an in vitro model of growth retardation. The experiments demonstrated that T-2 toxin significantly increased reactive oxygen species’ production, apoptosis, and cell cycle arrest while reducing the activity of antioxidant enzymes (superoxide dismutase and glutathione peroxidase) and PRDX4 expression in GH3 cells. Furthermore, PRDX4 silencing exacerbated T-2 toxin-induced oxidative stress and apoptosis, whereas PRDX4 overexpression effectively mitigated these effects. These findings highlight the protective role of PRDX4 in counteracting T-2 toxin-induced oxidative stress and apoptosis, suggesting that PRDX4 can serve as a therapeutic target for the treatment of T-2 toxin-induced growth retardation.

With the decrease in fish diversity, the monitoring of fish has become a current research hotspot. Here, the fish diversity and spatial distribution along a salinity gradient (0.28‰–3.32‰) in Xinglinwan Reservoir, China, were investigated using eDNA and the TSM. With the combination of both methods, 12 species from 6 orders, 9 families, and 11 genera were observed. The invasive species Oreochromis niloticus had the highest relative abundance, accounting for 56.91% in the TSM and 89.80% in eDNA, respectively. eDNA detected 83.33% of the species detected using the TSM, and an additional six species (50.0% of the total species) were detected via only eDNA. Moreover, eDNA supplemented the unavailable abundance data from the TSM. The TSM revealed that the diversity and relative abundance of fish inhabiting seawater and brackish water increased with an increase in salinity, but this was not found using eDNA. Thus, our study highlights that Xinglinwan Reservoir has a low species richness and is suffering from the great harm of ecological invasion. Our findings also show that eDNA could be reliable for providing a comprehensive picture of fish, but discriminating the spatial heterogeneity of fish communities is a challenge for its widespread adoption in lentic brackish systems.

Nosema is a diverse fungal genus of unicellular, obligate symbionts infecting various arthropods. We performed comparative genomic analyses of seven Nosema species that infect bees, wasps, moths, butterflies, and amphipods. As intracellular parasites, these species exhibit significant genome reduction, retaining only about half of the genes found in free-living yeast genomes. Notably, genes related to oxidative phosphorylation are entirely absent (p < 0.001), and those associated with endocytosis are significantly diminished compared to other pathways (p < 0.05). All seven Nosema genomes display significantly lower G-C content compared to their microsporidian outgroup. Species-specific 5~12 bp motifs were identified immediately upstream of start codons for coding genes in all species (p ≤ 1.6 × 10−72). Our RNA-seq data from Nosema muscidifuracis showed that this motif is enriched in highly expressed genes but depleted in lowly expressed ones (p < 0.05), suggesting it functions as a cis-regulatory element in gene expression. We also discovered diverse telomeric repeats within the genus. Phylogenomic analyses revealed two major Nosema clades and incongruency between the Nosema species tree and their hosts’ phylogeny, indicating potential host switch events (100% bootstrap values). This study advances the understanding of genomic architecture, gene regulation, and evolution of Nosema, offering valuable insights for developing strategies to control these microbial pathogens.

Mitochondria are important organelles in eukaryotes and are involved in various metabolic processes. Mitochondrial proteotoxic stress triggers the mitochondrial unfolded protein response (UPRmt) to restore mitochondrial protein homeostasis and maintain normal life activities. However, the regulatory mechanism of plant UPRmt remains to be revealed in Arabidopsis. Based on the fact that UPRmt activates heat shock protein (HSP) genes, we identified the heat shock transcription factor HSFA6b as a key regulator mediating UPRmt through reverse genetics. HSFA6b responded to mitochondrial proteotoxic stress and regulated mitochondrial heat shock proteins’ genes’ (mtHSPs) expression. HSFA6b translocated to the nuclear after treatment with doxycycline (Dox)—a mitochondrial ribosome translation inhibitor. HSFA6b binds to the mtHSPs promoters and activates mtHSPs expression. The HSFA6b mutation blocked the UPRmt signals to promote root growth under mitochondrial proteotoxic stress and accelerated leaf senescence during development. Our study reveals a novel signal-regulating mechanism in the UPRmt pathways and provides new insights regarding the regulation of plant growth and development and stress resistance by the UPRmt pathways.

Lvliang black goat (LBG) is an excellent local breed resource in China that is known for its black fur, excellent meat quality, and strong adaptability. Studying the genetic mechanism and germplasm characteristics of LBG can provide theoretical and practical basis for the protection of the genetic resources of this breed and help implement conservation and breeding. In this study, the genetic diversity of the LBG population was evaluated using whole-genome SNP data. It was found that the LBG population had a high genetic diversity and a low degree of inbreeding. According to the clustering results of male goats and the relationship between individuals, the LBG population was divided into 13 families. Then, through population structure analysis, it was found that LBG had a close genetic relationship with the Nanjiang goat and Qinggoda goat populations, and they may have the same ancestors. The LBG population has retained some ancient genetic characteristics and is a special population that integrates local genetic characteristics and foreign gene flow. Through four selection signal analyses, we detected multiple candidate genes related to economic traits (CFL2, SCD, NLRP14, etc.) and adaptability (C4BPA, FUT8, PRNP, etc.) in the LBG population. In addition, in a comparative analysis with three commercial breeds (Saanen goat, Boer goat and Angora goat) we also found multiple genes related to physical characteristics (ERG, NRG3, EDN3, etc.). Finally, we performed functional enrichment analysis on these genes and explored their genetic mechanisms. This study provides important data support for the protection and breeding of LBG and provides a new perspective for enriching the genetic diversity of goat populations.

Path planning and tracking is the most basic technology for mowing robots, among which the performance of algorithms has a great impact on their intelligence and efficiency. Based on the research of relevant references on mower robots, it mainly focuses on complete coverage path planning, path tracking control, and obstacle avoidance path planning. In complete coverage path planning, three methods were introduced, including simple complete coverage planning, optimal complete coverage planning, and hybrid complete coverage planning. In the path tracking control section, the control methods are divided into three types based on whether the control method depends on the robot model and the type of model, namely model free control method, kinematic model-based control method, and dynamic model-based control method. In obstacle avoidance path planning, we introduce the environment detection device and obstacle avoidance planning algorithm. Then the relevant research papers are analyzed in classification, comparing the research and validation methods adopted by the researchers in the form of charts. Finally, we pointed out the limitations of path planning technology in the application of mower robots. Meanwhile, future development trends are predicted.

The coupling effects of clamping pressure and catalyst distribution are comprehensively considered to improve proton exchange membrane fuel cell (PEMFC) performance. Numerical models were constructed to study the performance changes and the corresponding internal states of PEMFC under different clamping pressures. Since the increased clamping pressure reduces the uniformity of current density, non-uniform designs with decreased catalyst loading under channel and increased catalyst loading under rib are proposed for performance improvement. A weighted objective function considering current density magnitude and uniformity was constructed, and the performances of different catalyst loading distributions were analyzed. Compared to the uniform distribution, the optimized distribution with a variation of −15% and 15% under channel and rib had the maximum objective function value of 17.24%. The deformation analysis of the gas diffusion layer and optimization of catalyst loading distribution based on deformation analysis provided a reference for the assembly of PEMFC and the production of MEA.

Solid wood is renowned as a superior material for construction and furniture applications. However, characteristics such as dead knots, live knots, piths, and cracks are easily formed during timber’s growth and processing stages. These features and defects significantly undermine the mechanical characteristics of sawn timber, rendering it unsuitable for specific applications. This study introduces BDCS-YOLO (Bilateral Defect Cutting Strategy based on You Only Look Once), an artificial intelligence bilateral sawing strategy to advance the automation of timber processing. Grounded on a dual-sided image acquisition platform, BDCS-YOLO achieves a commendable mean average feature detection precision of 0.94 when evaluated on a meticulously curated dataset comprising 450 images. Furthermore, a dual-side processing optimization module is deployed to enhance the accuracy of defect detection bounding boxes and establish refined processing coordinates. This innovative approach yields a notable 12.3% increase in the volume yield of sawn timber compared to present production, signifying a substantial leap toward efficiently utilizing solid wood resources in the lumber processing industry.

Macrophages are an important component of the first-line defense against pathogens, including viruses [...]

Author Correction: We apologize for unintentionally using the wrong figures (Figure 5b and Figure 6e) in the original article [...]

In this study, nine concrete short columns and nine UHPC (ultra-high-performance concrete) short columns were produced, and an accelerated corrosion test method was used to compare and analyze the cracking phenomena and reinforcement corrosion after different durations of electrical current application. The analysis revealed that the corrosion rate of the UHPC specimens was approximately half of that of the concrete specimens, demonstrating excellent corrosion resistance. Although the corrosion rate of the UHPC specimen was lower, the length of the internal steel reinforcement decreased more significantly under prolonged electrification. For example, after 38 days of accelerated corrosion, the internal steel reinforcement in the ordinary concrete specimen measured 48 mm in length, while in the UHPC specimen, it measured only 43 mm. It was also found that the corrosion rates of both the concrete and UHPC specimens were significantly lower than the theoretical corrosion rate. This discrepancy is attributed to the fact that Faraday’s law, used to calculate the theoretical corrosion rate, does not fully account for factors such as the thickness of the protective layer and chloride ion concentration. The actual corrosion rate of the concrete specimens was generally only 70% of the theoretical value, while the UHPC specimens showed a corrosion rate which was only 40% of the theoretical value.

Integrating electric vehicles (EVs) into the coupled power distribution network (PDN) and transportation network (TN) presents substantial challenges. This paper explores three key areas in EV integration: charging/discharging scheduling, charging navigation, and charging station planning. First, the paper discusses the features and importance of EV integrated traffic–power networks. Then, it examines key factors influencing EV strategy, such as user behavior, charging preferences, and battery performance. Next, the study establishes an EV charging and discharging model, with particular emphasis on the complexities introduced by factors such as pricing mechanisms and integration approaches. Furthermore, the charging navigation model and the role of real-time traffic information are discussed. Additionally, the paper highlights the importance of multi-type charging stations and the impact of uncertainty on charging station planning. The paper concludes by identifying significant challenges and potential opportunities for EV integration. Future research should focus on enhancing coupled network modeling, refining user behavior models, developing incentive pricing mechanisms, and advancing autonomous driving and automated charging technologies. Such efforts will be essential for achieving a sustainable and efficient EV ecosystem.

Microorganisms harbor catabolic plasmids to tackle refractory organic pollutants, which is crucial for bioremediation and ecosystem health. Understanding the impacts of plasmids on hosts provides insights into the behavior and adaptation of degrading bacteria in the environment. Here, we examined alterations in the physiological properties and gene expression profiles of Rhodococcus sp. strain p52 after losing two conjugative dioxin-catabolic megaplasmids (pDF01 and pDF02). The growth of strain p52 accelerated after pDF01 loss, while it decelerated after pDF02 loss. During dibenzofuran degradation, the expression levels of dibenzofuran catabolic genes on pDF01 were higher compared to those on pDF02; accordingly, pDF01 loss markedly slowed dibenzofuran degradation. It was suggested that pDF01 is more beneficial to strain p52 under dibenzofuran exposure. Moreover, plasmid loss decreased biofilm formation, especially after pDF02 loss. Transcriptome profiling revealed different pathways enriched in upregulated and downregulated genes after pDF01 and pDF02 loss, indicating different adaptation mechanisms. Based on the transcriptional activity variation, pDF01 played roles in transcription and anabolic processes, while pDF02 profoundly influenced energy production and cellular defense. This study enhances our knowledge of the impacts of degradative plasmids on native hosts and the adaptation mechanisms of hosts, contributing to the application of plasmid-mediated bioremediation in contaminated environments.

Identifying seismic CH4 anomalies via remote sensing has been verified as a legitimate method. However, there are still some problems, such as unknown reliability due to the complex characteristics of seismic anomalies. In this study, a multi-dimensional and multi-scale methane seismic anomaly extraction process for remote sensing was constructed with the Robust Satellite Technique (RST) based on the Atmospheric Infrared Sounder (AIRS) CH4 data and then applied to the 2023 Türkiye–Syria earthquake. This study obtained the two-dimensional temporal–spatial distribution of methane anomalies and temporal variation in the anomaly index. Based on this, the three-dimensional profile structure of the 8-day methane anomaly was extracted to determine the reliability of the anomaly. Finally, based on the daily methane anomaly, combined with atmospheric circulation and backward trajectory analysis as auxiliary tools, the influence of air mass migration was excluded to enhance the accuracy of CH4 anomaly determination. The results show that the three-dimensional anomalous structure is consistent with the geological characteristics of tectonic activities, and it appears as a “pyramid” or “inverted pyramid” type in a three-dimensional space. The anomalies caused by air mass migration can be eliminated by combining them with synoptic-scale circulation motion. The time series calculated at the epicenter or a certain point in a region may not accurately reflect the influence of regional or specific tectonic activity in the atmosphere. Thus, the optimal determination of the range and magnitude of atmospheric anomalies caused by tectonic activities is a difficult task for future research.

To comprehensively understand the explosion risk in underground energy transportation tunnels, this study employed computational fluid dynamics technology and finite element simulation to numerically analyze the potential impact of an accidental explosion for a specific oil and gas pipeline in China and the potential damage risk to nearby buildings. Furthermore, the study investigated the effects of tunnel inner diameter (d = 4.25 m, 6.5 m), tunnel length (L = 4 km, 8 km, 16 km), and soil depth (primarily Lsoil = 20 m, 30 m, 40 m) on explosion dynamics and on structural response characteristics. The findings indicated that as the tunnel length and inner diameter increased, the maximum explosion overpressure gradually rose and the peak arrival time was delayed, especially when d = 4.25 m; with the increase in L, the maximum explosion overpressure rapidly increased from 1.03 MPa to 2.12 MPa. However, when d = 6.5 m, the maximum explosion overpressure increased significantly by 72.8% from 1.25 MPa. Evidently, compared to the change in tunnel inner diameter, tunnel length has a more significant effect on the increase in explosion risk. According to the principle of maximum explosion risk, based on the peak explosion overpressure of 2.16 MPa under various conditions and the TNT equivalent calculation formula, the TNT explosion equivalent of a single section of the tunnel was determined to be 1.52 kg. This theoretical result is further supported by the AUTODYN 15.0 software simulation result of 2.39 MPa (error < 10%). As the soil depth increased, the distance between the building and the explosion source also increased. Consequently, the vibration peak acceleration and velocity gradually decreased, and the peak arrival time was delayed. In comparison to a soil depth of 10 m, the vibration acceleration at soil depths of 20 m and 30 m decreased by 81.3% and 91.7%, respectively. When the soil depth was 10 m, the building was at critical risk of vibration damage.

We aimed to investigate the species composition of a small mammal community and the prevalence of Echinococcus spp. in a typical endemic area of the Tibetan Plateau. One pika and five rodent species were identified based on the morphological characteristics of 1278 small mammal specimens collected during 2014–2019. Detection of Echinococcus DNA in tissue samples from small mammal specimens revealed that Ochotona curzoniae (pika, total prevalence: 6.02%, 26/432), Neodon fuscus (5.91%, 38/643), N. leucurus (2.50%, 3/120), and Alexandromys limnophilus (21.74%, 10/46) were infected by both E. multilocularis and E. shiquicus; Cricetulus longicaudatus (16.67%, 1/6) was infected by E. shiquicus; and no infection was detected in N. irene (0/15). Neodon fuscus and O. curzoniae were the two most abundant small mammal species. There was no significant difference in the prevalence of pika and the overall rodent species assemblage (6.26%, 53/846); however, the larger rodent populations suggested that more attention should be paid to their role in the transmission of echinococcosis in the wildlife reservoir, which has long been underestimated. Moreover, although DNA barcoding provides a more efficient method than traditional morphological methods for identifying large numbers of small mammal samples, commonly used barcodes failed to distinguish the three Neodon species in this study. The close genetic relationships between these species suggest the need to develop more powerful molecular taxonomic tools.

In the digital age, China’s economic development is transitioning from high speed to high quality. Through the application of digital technology, China’s manufacturing industry is moving toward more environmentally friendly and sustainable innovation, which makes it of great significance to study the effect of green investment and big data on innovation. Grounded in strategic management theory, this paper examines the interplay between CEO big data orientation, environmental investment, and their joint impact on technological innovation in manufacturing enterprises. Data are extracted from annual reports of listed Chinese manufacturing companies using computer-assisted text analysis methods and evaluated with negative binomial regression. The results indicate an inverted U-shaped relationship between CEO big data orientation and technological innovation. The results further explain that as CEO big data orientation increases, enterprises with higher levels of green investment will reach the peak of technological innovation performance earlier. According to China’s intelligent manufacturing in 2035, we have proposed some methods and suggestions for green investment and big data applications.

There is limited evidence about the gender- and obesity-specific effects of personal care product and plasticizing chemicals (PCPPCs) on short sleep duration in adults. We evaluated the gender- and obesity-specific association of co-exposure to PCPPCs and short sleep duration among adults aged 20–60 years using the National Health and Nutrition Examination Survey (NHANES) 2011–2016, a secondary data source from the United States. Seventeen PCPPCs, including five phenols, two parabens, and ten phthalates, were detected, and sleep duration was assessed among 3012 adults. Logistic regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) were employed. We found that bisphenol A (BPA), mono (caboxy-isooctyl) phthalate (MCOP), and mono (3-carboxypropyl) phthalate (MCPP) were consistently positively associated with short sleep duration in both females and males regardless of obesity status, except for BPA with general obesity. In particular, mono benzyl phthalate (MBzP) revealed a positive association in females, mono (2-ethyl-5-oxohexyl) phthalate (MEOHP) revealed a positive association in males, and MiBP revealed a positive association in abdominal obesity. Similar associations were observed in the mixture. Our study highlights that PCPPCs are independently associated with an increasing risk of short sleep duration in adults both individually and as a mixture; however, gender- and obesity-specific differences may have little effect on certain individual PCPPCs on short sleep duration.

This article presents a coordinated control method used for wireless power transfer (WPT) systems. This method can improve WPT system transmission efficiency while maintaining the constant output voltage. First, the topology of the DC–DC converter is selected and the equivalent circuit model of the WPT system is established. Then, the WPT system characteristics are discussed and the mutual inductance estimation process is presented. Furthermore, the coordinated control method is proposed, where the constant voltage output is achieved by connecting the Buck–Boost converter after the diode rectifier. Meanwhile, the optimal phase shift angle is calculated and sent to the controller to achieve maximum transmission efficiency tracking control, according to the measured load voltage and current. Finally, simulations and experiments are adopted to verify the proposed coordinated control method. The experimental results indicate that the average system transmission efficiency is increased by 1.80% and the efficiency fluctuation is decreased by 2.67% when the system load resistance varies, while the average system transmission efficiency is increased by 1.80%, and the efficiency fluctuation is decreased by 3.14% when the mutual inductance changes. This means the proposed coordinated control method is effective under the conditions of the WPT load and mutual inductance variations.

The China–Russia crude oil pipeline (CRCOP) operates at a temperature that continuously thaws the surrounding permafrost, leading to secondary periglacial phenomena along the route. However, the evolution and formation mechanisms of these phenomena are still largely unknown. We used multi-temporal airborne light detection and ranging (LiDAR), geophysical, and field observation data to quantify the scale of ponding and icing, capture their dynamic development process, and reveal their development mechanisms. The results show that the average depth of ponding within 5 m on both sides of the pipeline was about 31 cm. The volumes of three icings (A–C) above the pipeline were 133 m3, 440 m3, and 186 m3, respectively. Icing development can be divided into six stages: pipe trench settlement, water accumulation in the pipe trench, ponding pressure caused by water surface freezing, the formation of ice cracks, water overflow, and icing. This study revealed the advantages of airborne LiDAR in monitoring the evolution of periglacial phenomena and provided a new insight on the development mechanisms of the phenomena by combining LiDAR with geophysics and field observation. The results of our study are of great significance for developing disaster countermeasures and ensuring the safe operation of buried pipelines.

Background: Pancreatic ductal adenocarcinoma (PDAC) is the most prevalent subtype of pancreatic cancer and one of the most malignant tumors worldwide. Due to the heterogeneity of its genomics and proteomics, the prognosis of PDAC remains disappointing despite advances in surgery and medicines. Recently, a novel form of programmed cell death, cuproptosis, was proposed, although its role in PDAC has not been investigated. This study aimed to quantify the expression of cuproptosis-related genes and characterize the novel subtypes of PDAC. Methods: To evaluate the pattern of cuproptosis in PDAC, the gene expression data and clinical information of 372 samples were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A consensus cluster analysis was performed using the transcriptional levels, genetic alterations, and individual prognostic values of seven pre-selected cuproptosis-related genes (DLAT, LIPT1, FDX1, DLD, PDHB, PDHA1, and LIAS) to identify the novel subtypes associated with cuproptosis in PDAC. A univariate Cox regression analysis was used to determine the significant prognostic indicators and cuproptosis scores among the differentially expressed genes (DEGs) between the dividing subclusters, followed by a principal component analysis. The prognostic values, immune profiles, treatment sensitivities, and cuproptosis scores were evaluated between the different subgroups. Results: Seven cuproptosis-related genes showed aberrant expression levels and genetic alterations in the PDAC tumor microenvironment. Among them, LIPT1, LIAS, DLAT, PDHA1, and DLD were significantly correlated with overall survival. Based on the expression profiles of the seven cuproptosis-related genes, three cuproptosis clusters (Clusters A, B, and C) were identified, which were represented by different clinicopathologic features, gene expression levels, and biological processes. A total of 686 DEGs were identified among the three cuproptosis clusters, of which 35 prognosis-related DEGs were selected to further classify the PDAC samples into two subgroups with different survival rates, clinicopathologic features, immune infiltration levels, and drug sensitivities. Higher cuproptosis scores were associated with a significantly poorer prognosis. Conclusion: The cuproptosis subtypes, scores, and relevant genes represent valuable information for assessing the heterogeneity, treatment, and prognosis of PDAC.

The accurate characterization of typhoon wind profile properties is of great importance in the field of wind engineering and wind design of high-rise structures. In this paper, the average typhoon wind profile characteristics are investigated using the 930 m height measurement data of Typhoon Lekima 2019 obtained from the observations of the mobile acoustic profiling radar deployed in the coastal area. Specifically, this paper adopts a cohesive hierarchical cluster analysis method to classify the mean wind profiles of Super Typhoon Lekima 2019, and the optimal number of clusters is obtained as two classes by the profile coefficient with the sum of squares of clustering errors, the Calinski–Harabasz index, and the Davies–Bouldin index, and the two classes of typical wind profiles are named as cluster 1 type and cluster 2 type. The model fitting analysis of the two types of typical wind profiles was carried out in the height range of 0~300 m after classification, and the effects of fitting the cluster 1-type mean wind profiles with the Vickery model and the Snaiki and Wu model and the cluster 2-type mean wind profiles with the Power-law model, the Log-law model, and the Deaves–Harris and Gryning models were discussed. The results show that the cohesive hierarchical cluster analysis method used in this paper can effectively categorize the mean typhoon profiles. In addition, this paper has some reference significance for future research on the characteristics of measured typhoon wind profiles and engineering applications such as the wind-resistant design of high-rise structures.

The development of childhood obesity is a complex process influenced by a combination of genetic predisposition and environmental factors, such as sleep, diet, physical activity, and socioeconomic status. Long-term solutions for decreasing the risk of childhood obesity remain elusive, despite significant advancements in promoting health and well-being in school and at home. Challenges persist in areas such as adherence to interventions, addressing underlying social determinants, and individual differences in response to treatment. Over the last decade, there has been significant progress in epigenetics, along with increased curiosity in gaining insights into how sleep and lifestyle decisions impact an individual’s health. Epigenetic modifications affect the expression of genes without causing changes to the fundamental DNA sequence. In recent years, numerous research studies have explored the correlation between sleep and the epigenome, giving a better understanding of DNA methylation, histone modification, and non-coding RNAs. Although significant findings have been made about the influence of sleep on epigenetics, a notable gap exists in the literature concerning sleep-related genes specifically associated with childhood obesity. Consequently, it is crucial to delve deeper into this area to enhance our understanding. Therefore, this review primarily focuses on the connection between sleep patterns and epigenetic modifications in genes related to childhood obesity. Exploring the interplay between sleep, epigenetics, and childhood obesity can potentially contribute to improved overall health outcomes. This comprehensive review encompasses studies focusing on sleep-related genes linked to obesity.

The reversible data hiding scheme in the encrypted domain is a potential solution to the concerns regarding user privacy in cloud applications. The 3D mesh model is an emerging file format and is widely used in engineering modeling, special effects, and video games. However, studies on reversible data hiding in encrypted 3D mesh models are still in the preliminary stage. In this paper, two novel techniques, multi-group partition (MGP) and closest pair prediction (CPP), are proposed to improve performance. The MGP technique adaptively classifies vertices into reference and embeddable vertices, while the CPP technique efficiently predicts embeddable vertices and generates shorter recovery information to vacate more redundancy for additional data embedding. Experimental results indicate that the proposed scheme significantly improves the embedding rate compared to state-of-the-art schemes and can be used in real-time applications.

As the demand for sensing and monitoring the marine environment increases, the Ocean Mobile Internet of Things (OM-IoT) has gradually attracted the interest of researchers. However, the unreliability of communication links represents a significant challenge to data transmission in the OM-IoT, given the complex and dynamic nature of the marine environment, the mobility of nodes, and other factors. Consequently, it is necessary to enhance the reliability of underwater data transmission. To address this issue, this paper proposes a reinforcement learning-based adaptive network coding (RL-ANC) approach. Firstly, the channel conditions are estimated based on the reception acknowledgment, and a feedback-independent decoding state estimation method is proposed. Secondly, the sliding coding window is dynamically adjusted based on the estimates of the channel erasure probability and decoding probability, and the sliding rule is adaptively determined using a reinforcement learning algorithm and an enhanced greedy strategy. Subsequently, an adaptive optimization method for coding coefficients based on reinforcement learning is proposed to enhance the reliability of the underwater data transmission and underwater network coding while reducing the redundancy in the coding. Finally, the sampling period and time slot table are updated using the enhanced simulated annealing algorithm to optimize the accuracy and timeliness of the channel estimation. Simulation experiments demonstrate that the proposed method effectively enhances the data transmission reliability in unreliable communication links, improves the performance of underwater network coding in terms of the packet delivery rate, retransmission, and redundancy transmission ratios, and accelerates the convergence speed of the decoding probability.