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The natural product Desmosdumotin D (hereafter referred to as Des-D), isolated from the plant Desmos dumosus, showed potent anti-HIV activity. However, the subsequent pharmacological activity and clinical studies are limited due to the low content of Des-D in the plant. Therefore, the total synthesis path of Des-D was optimized in this paper, and the total yield was increased from 4.4% to 11.9%. Additionally, twelve analogues were obtained following the synthesis route of Des-D. The anti-HIV activity evaluation results in vitro showed that Des-D had the highest activity, with an IC50 value of 13.6 μM, and compounds 17 and 11 had the lowest anti-HIV activity, with IC50 values of 101.3 μM and 161.0 μM, respectively. Through the molecular docking of compounds Des-D and 17 with HIV-IN, the results show that phenolic hydroxyl groups and two benzene rings interact with HIV-IN and are possible pharmacodynamic groups.

Understanding the driving mechanisms behind surface urban heat island (SUHI) effects is essential for mitigating the degradation of urban thermal environments and enhancing urban livability. However, previous studies have primarily concentrated on central urban areas, lacking a comprehensive analysis of the entire metropolitan area over distinct time periods. Additionally, most studies have relied on regression analysis models such as ordinary least squares (OLS) or logistic regression, without adequately analyzing the spatial heterogeneity of factors influencing the surface urban heat island (SUHI) effects. Therefore, this study aims to explore the spatial heterogeneity and driving mechanisms of surface urban heat island (SUHI) effects in the Guangzhou-Foshan metropolitan area across different time periods. The Local Climate Zones (LCZs) method was employed to analyze the landscape characteristics and spatial structure of the Guangzhou-Foshan metropolis for the years 2013, 2018, and 2023. Furthermore, Geographically Weighted Regression (GWR), Multi-scale Geographically Weighted Regression (MGWR), and Geographical Detector (GD) models were utilized to investigate the interactions between influencing factors (land cover factors, urban environmental factors, socio-economic factors) and Surface Urban Heat Island Intensity (SUHII), maximizing the explanation of SUHII across all time periods. Three main findings emerged: First, the Local Climate Zones (LCZs) in the Guangzhou-Foshan metropolitan area exhibited significant spatial heterogeneity, with a non-linear relationship to SUHII. Second, the SUHI effects displayed a distinct core-periphery pattern, with Large lowrise (LCZ 8) and compact lowrise (LCZ 3) areas showing the highest SUHII levels in urban core zones. Third, land cover factors emerged as the most influential factors on SUHI effects in the Guangzhou-Foshan metropolis. These results indicate that SUHI effects exhibit notable spatial heterogeneity, and varying negative influencing factors can be leveraged to mitigate SUHI effects in different metropolitan locations. Such findings offer crucial insights for future urban policy-making.

Saturated sand foundations are susceptible to liquefaction under dynamic loads. This can result in roadbed subsidence, flotation of underground structures, and other engineering failures. Compared with the traditional foundation reinforcement technology, enzyme-induced calcium carbonate precipitation technology (EICP) is a green environmental protection reinforcement technology. The EICP technology can use enzymes to induce calcium carbonate to cement soil particles and fill soil pores, thus effectively improving soil strength and inhibiting sand liquefaction damage. The study takes EICP-solidified standard sand as the research object and, through the dynamic triaxial test, analyzes the influence of different confining pressure (σ3) cementation times (CT), cyclic stress ratio (CSR), dry density (ρd), and vibration frequency (f) on dynamic strength characteristics. Then, a modified dynamic strength model of EICP-solidified standard sand was established. The results show that, under the same confining pressure, the required vibration number for failure decreases with the increase in dynamic strength, and the dynamic strength increases with the rise in dry density. At the same number of cyclic vibrations, the greater the confining pressure and cementation times, the greater the dynamic strength. When the cementation times are constant, the dynamic strength of EICP-solidified sand decreases with the increase in the vibration number. When cementation times are 6, the dynamic strength of the specimens with CSR of 0.35 is 25.9% and 32.4% higher than those with CSR of 0.25 and 0.30, respectively. The predicted results show that the model can predict the measured values well, which fully verifies the applicability of the model. The research results can provide a reference for liquefaction prevention in sand foundations.

To improve the potential of lupeol against cancer cells, a privileged structure, thiazolidinedione, was introduced into its C-3 hydroxy group with ester, piperazine-carbamate, or ethylenediamine as a linker, and three series of thiazolidinedione-conjugated compounds (6a–i, 9a–i, and 12a–i) were prepared. The target compounds were evaluated for their cytotoxic activities against human lung cancer A549, human breast cancer MCF-7, human hepatocarcinoma HepG2, and human hepatic LO2 cell lines, and the results revealed that most of the compounds displayed improved potency over lupeol. Compound 12i exhibited significant activity against the HepG2 cell line, with an IC50 value of 4.40 μM, which is 9.9-fold more potent than lupeol (IC50 = 43.62 μM). Mechanistic studies suggested that 12i could induce HepG2 cell apoptosis, as evidenced by AO/EB staining and annexin V-FITC/propidium iodide dual staining assays. Western blot analysis suggested that compound 12i can upregulate Bax expression, downregulate Bcl-2 expression, and activate the mitochondria-mediated apoptotic pathway. Collectively, compound 12i is worthy of further investigation to support the discovery of effective agents against cancer.

Aiming at the problem of system controller performance failure caused by improperly setting the value of each weighting coefficient of the model predictive control (MPC), a fractional-order MPC strategy with Takagi–Sugeno fuzzy optimization (T–SFO MPC) is proposed for a vehicle active suspension system. Firstly, the fractional-order model predictive control framework for active suspension systems is designed based on a 1/4 vehicle model. Then, we analyze the influence of different weighting coefficients on the suspension performance and introduce the Takagi–Sugeno fuzzy optimization theory to adaptively adjust the weighting coefficients of the fractional-order MPC controller. Finally, the system responses of the T–SFO MPC, traditional MPC, linear quadratic regulator (LQR), and passive suspension control are numerically analyzed under various road conditions. Simulation results show that suspension response with the T–SFO MPC is significantly improved compared with passive suspension control, traditional MPC control, and LQR control, and the weight coefficients of the T–SFO MPC can be adaptively adjusted according to the dynamic changes of suspension response. Compared with passive suspension, the root mean square (RMS) value of the vertical acceleration of the T–SFO MPC under various roads decreased by a maximum of 37.97%, and the RMS value of suspension dynamic deflection and tire dynamic load decreased by a maximum of 32.94% and 37.8%, respectively. These results validate that the proposed control method can achieve coordinated optimization of vehicle comfort and handling stability.

Preeclampsia is a pregnancy syndrome characterized by complex symptoms which cause maternal and fetal problems and deaths. The aim of this study is to achieve preeclampsia risk prediction and early risk prediction in Xinjiang, China, based on the placental growth factor measured using the SiMoA or Elecsys platform. A novel reliable calibration modeling method and missing data imputing method are proposed, in which different strategies are used to adapt to small samples, training data, test data, independent features, and dependent feature pairs. Multiple machine learning algorithms were applied to train models using various datasets, such as single-platform versus bi-platform data, early pregnancy versus early plus non-early pregnancy data, and real versus real plus augmented data. It was found that a combination of two types of mono-platform data could improve risk prediction performance, and non-early pregnancy data could enhance early risk prediction performance when limited early pregnancy data were available. Additionally, the inclusion of augmented data resulted in achieving a high but unstable performance. The models in this study significantly reduced the incidence of preeclampsia in the region from 7.2% to 2.0%, and the mortality rate was reduced to 0%.

In this study, we conducted a single-factor experiment where fine aggregates in each mixture were replaced with coal-fired slag at replacement rates in the range of 0–100%. We investigated the effect of slag substitution rate on the cubic compressive strength, splitting tensile strength, axial compressive strength, and static modulus of elasticity of slag concrete. Based on the experimental data, the stress–strain curve of the coal-fired slag concrete was divided into four phases: elastic, elasto-plastic, peak, and decline phases. A stress–strain constitutive equation was established to describe the coal-fired slag concrete. A replacement rate of 50% of the formulated coal-fired slag concrete meets the strength requirements of C60 structural applications, and the cubic compressive strength is the same as that of ordinary concrete. Coal-fired slag can be utilized in large quantities, improving the economic value of coal-fired slag and expanding the scope of application of slag concrete.

Under traffic load, earthquake load, and wave load, saturated sand foundation is prone to liquefaction, and foundation reinforcement is the key measure to improve its stability and liquefaction resistance. Traditional foundation treatment methods have many problems, such as high cost, long construction period, and environmental pollution. As a new solidification method, enzyme-induced calcium carbonate precipitation (EICP) technology has the advantages of economy, environmental protection, and durability. Through a triaxial consolidated undrained shear test under cyclic loading, the impacts of confining pressure (σ3), cementation number (Pc), cyclic stress ratio (CSR), initial dry density (ρd), and vibration frequency (f) on the development law of pore water pressure of EICP-solidified sand are analyzed and then a pore water pressure model suitable for EICP-solidified sand is established. The result shows that as σ3 and CSR increase, the rise rate of pore water pressure of solidified sand gradually accelerates, and with a lower vibration number required for liquefaction, the anti-liquefaction ability of solidified sand gradually weakens. However, as Pc, ρd, and f rise, the increase rate of pore water pressure of solidified sand gradually lowers, the vibration number required for liquefaction increases correspondingly, and its liquefaction resistance gradually increases. The test results are highly consistent with the predictive results, which show that the three-parameter unified pore water pressure model is suitable for describing the development law of A-type and B-type pore water pressure of EICP-solidified sand at the same time. The study results provide essential reference value and scientific significance in guidance for preventing sand foundations from liquefying.

This paper presents the design and validation of a high-precision angular vibration calibration system based on a laser vibrometer, aimed at meeting the high-precision requirements for measuring small angular vibrations. The system primarily consists of a self-driving angular vibration platform and a laser vibrometer. The platform is isolated from ground interference via an air-floating platform and uses a split-type motor to control the platform, generating specific angular vibrations. Detailed simulations of the platform’s modal characteristics and the stability of the spring plates were conducted using the finite element analysis software ANSYS 11. Moreover, fundamental frequency testing and measurement accuracy testing were conducted on the system. Experimental results demonstrate that the system has a fundamental frequency of 2.69 Hz and a maximum measurement error of 0.00172″, confirming the system’s effectiveness in dynamic characteristics, stability, and measurement accuracy. This research provides essential technical support for high-precision angular vibration control in spacecraft.

Chrysanthemum morifolium is a perennial herbaceous plant in the Asteraceae family that is used as a medicine and food owing to its superior pharmacological properties. Irrespective of its application, C. morifolium must be dried before use. Shade drying (YG) and heat drying (HG) are the two drying methods used in most origins. Given the abundance of flavonoids, phenolic acids, and terpenoids, the primary medicinal active constituents of C. morifolium, it is important to determine whether the composition and content of these compounds are altered during the drying processes. To test this, the changes in the chemical composition of C. morifolium flowers after YG and HG using full-spectrum, non-targeted LC/GC–MS−based metabolomics and, subsequently, the three indicator components of C. morifolium—chlorogenic acid, 3,5−dicaffeoylquinic acid, and luteolin−7−O−glucoside—were accurately quantified by HPLC. The results of the non-targeted metabolomics analysis revealed that YG- and HG-processed C. morifolium differed significantly with respect to chemical contents, especially flavonoids, phenolic acids, and terpenoids. The levels of the indicator components and their precursors also differed significantly between the YG and HG treatments. The contents of most of the flavonoids and key phenolic acids, terpenoids, and carbohydrates were higher with YG than with HG pre-treatment. These results revealed the changes in the chemical composition of C. morifolium during the YG and HG processes, thus providing a reference for the further optimization of the production and processing of chrysanthemums.

The model network based on YOLOv8 for detecting race cones and buckets in the Formula Unmanned Competition for Chinese university students needs help with problems with complex structure, redundant number of parameters, and computation, significantly affecting detection efficiency. A lightweight detection model based on YOLOv8 is proposed to address these problems. The model includes improving the backbone network, neck network, and detection head, as well as introducing knowledge distillation and other techniques to construct a lightweight model. The specific improvements are as follows: firstly, the backbone network for extracting features is improved by introducing the ADown module in YOLOv9 to replace the convolution module used for downsampling in the YOLOv8 network, and secondly, the FasterBlock in FasterNet network was introduced to replace the fusion module in YOLOv8 C2f, and then the self-developed lightweight detection head was introduced to improve the detection performance while achieving lightweight. Finally, the detection performance was further improved by knowledge distillation. The experimental results on the public dataset FSACOCO show that the improved model’s accuracy, recall, and average precision are 92.7%, 84.6%, and 91%, respectively. Compared with the original YOLOv8n detection model, the recall and average precision increase by 2.7 and 1.2 percentage points, the memory is half the original, and the model computation is 51%. The model significantly reduces the misdetection and leakage of conical buckets in real-vehicle tests and, at the same time, ensures the detection speed to satisfy the deployment requirements on tiny devices. Satisfies all the requirements for deployment of tiny devices in the race car of the China University Student Driverless Formula Competition. The improved method in this paper can be applied to conebucket detection in complex scenarios, and the improved idea can be carried over to the detection of other small targets.

This paper proposes a mixed-mode (combining shear and squeeze working modes) vibration isolator using magnetorheological elastomer (MRE), which enables the isolator to have a larger working area and better isolation performance by combining the working modes of the MRE. Firstly, based on the magnetorheological effect working principle of the MRE, the material selection and dimensional parameters of each component are determined through structural design and magnetic circuit calculation. On this basis, magnetic field simulation is conducted using Maxwell 16.0 software to analyze the distribution of magnetic field lines and magnetic induction in the working area. Simultaneously, equivalent stiffness and equivalent damping models are established to explore the variation of vibration response with external current and excitation frequency conditions. Finally, a vibration isolation experimental platform is built to test the mixed-mode MRE isolator. The experimental results are basically consistent with the simulation modeling results. The experimental results showed that when the external excitation is in the frequency range of 16 Hz, effective semi-active vibration isolation control could be achieved by applying different current inputs. The isolation effect of the system is difficult to effectively control using current input when the external excitation is at high frequency. These results validate the rationality and feasibility of the mixed-mode MRE isolator structure, which provides a good reference for the design of MRE isolators.

Objectives: During deep learning model training, it is essential to consider the balance among the effects of sample size, actual resources, and time constraints. Single-arm objective performance criteria (OPC) was proposed to determine the optimal positive sample size for training deep learning models in caries recognition. Methods: An expected sensitivity (PT) of 0.6 and a clinically acceptable sensitivity (P0) of 0.5 were applied to the single-arm OPC calculation formula, yielding an optimal training set comprising 263 carious teeth. U-Net, YOLOv5n, and CariesDetectNet were trained and validated using clinically self-collected cone-beam computed tomography (CBCT) images that included varying quantities of carious teeth. To assess performance, an additional dataset was utilized to evaluate the accuracy of caries detection by both the models and two dental radiologists. Results: When the number of carious teeth reached approximately 250, the models reached the optimal performance levels. U-Net demonstrated superior performance, achieving accuracy, sensitivity, specificity, F1-Score, and Dice similarity coefficients of 0.9929, 0.9307, 0.9989, 0.9590, and 0.9435, respectively. The three models exhibited greater accuracy in caries recognition compared to dental radiologists. Conclusions: This study demonstrated that the positive sample size of CBCT images containing caries was predictable and could be calculated using single-arm OPC.

Electric motors play a crucial role in self-driving vehicles. Therefore, fault diagnosis in motors is important for ensuring the safety and reliability of vehicles. In order to improve fault detection performance, this paper proposes a motor fault diagnosis method based on vibration signals. Firstly, the vibration signals of each operating state of the motor at different frequencies are measured with vibration sensors. Secondly, the characteristic of Gram image coding is used to realize the coding of time domain information, and the one-dimensional vibration signals are transformed into grayscale diagrams to highlight their features. Finally, the lightweight neural network Xception is chosen as the main tool, and the attention mechanism Convolutional Block Attention Module (CBAM) is introduced into the model to enforce the importance of the characteristic information of the motor faults and realize their accurate identification. Xception is a type of convolutional neural network; its lightweight design maintains excellent performance while significantly reducing the model’s order of magnitude. Without affecting the computational complexity and accuracy of the network, the CBAM attention mechanism is added, and Gram’s corner field is combined with the improved lightweight neural network. The experimental results show that this model achieves a better recognition effect and faster iteration speed compared with the traditional Convolutional Neural Network (CNN), ResNet, and Xception networks.

DnaJ proteins, also known as HSP40s, play a key role in plant growth and development, and response to environmental stress. However, little comprehensive research has been conducted on the DnaJ gene family in maize. Here, we identify 91 ZmDnaJ genes from maize, which are likely distributed in the chloroplast, nucleus, and cytoplasm. Our analysis revealed that ZmDnaJs were classified into three types, with conserved protein motifs and gene structures within the same type, particularly among members of the same subfamily. Gene duplication events have likely contributed to the expansion of the ZmDnaJ family in maize. Analysis of cis-regulatory elements in ZmDnaJ promoters suggested involvement in stress responses, growth and development, and phytohormone sensitivity in maize. Specifically, four cis-acting regulatory elements associated with stress responses and phytohormone regulation indicated a role in adaptation. RNA-seq analysis showed constitutive expression of most ZmDnaJ genes, some specifically in pollen and endosperm. More importantly, certain genes also responded to salt, heat, and cold stresses, indicating potential interaction between stress regulatory networks. Furthermore, early responses to heat stress varied among five inbred lines, with upregulation of almost tested ZmDnaJ genes in B73 and B104 after 6 h, and fewer genes upregulated in QB1314, MD108, and Zheng58. After 72 h, most ZmDnaJ genes in the heat-sensitive inbred lines (B73 and B104) returned to normal levels, while many genes, including ZmDnaJ55, 79, 88, 90, and 91, remained upregulated in the heat-tolerant inbred lines (QB1314, MD108, and Zheng58) suggesting a synergistic function for prolonged protection against heat stress. In conclusion, our study provides a comprehensive analysis of the ZmDnaJ family in maize and demonstrates a correlation between heat stress tolerance and the regulation of gene expression within this family. These offer a theoretical basis for future functional validation of these genes.

This paper addresses the challenge of trajectory planning for autonomous vehicles operating in complex, constrained environments. The proposed method enhances the hybrid A-star algorithm through back-end optimization. An adaptive node expansion strategy is introduced to handle varying environmental complexities. By integrating Dijkstra’s shortest path search, the method improves direction selection and refines the estimated cost function. Utilizing the characteristics of hybrid A-star path planning, a quadratic programming approach with designed constraints smooths discrete path points. This results in a smoothed trajectory that supports speed planning using S-curve profiles. Both simulation and experimental results demonstrate that the improved hybrid A-star search significantly boosts efficiency. The trajectory shows continuous and smooth transitions in heading angle and speed, leading to notable improvements in trajectory planning efficiency and overall comfort for autonomous vehicles in challenging environments.

In this article, we propose a new path-conservative discontinuous Galerkin (DG) method to solve non-conservative hyperbolic partial differential equations (PDEs). In particular, the method here applies the one-stage ADER (Arbitrary DERivatives in space and time) approach to fulfill the temporal discretization. In addition, this method uses the differential transformation (DT) procedure rather than the traditional Cauchy–Kowalewski (CK) procedure to achieve the local temporal evolution. Compared with the classical ADER methods, the current method is free of solving generalized Riemann problems at inter-cells. In comparison with the Runge–Kutta DG (RKDG) methods, the proposed method needs less computer storage, thanks to the absence of intermediate stages. In brief, this current method is one-step, one-stage, and fully-discrete. Moreover, this method can easily obtain arbitrary high-order accuracy both in space and in time. Numerical results for one- and two-dimensional shallow water equations (SWEs) show that the method enjoys high-order accuracy and keeps good resolution for discontinuous solutions.

Flowering in plants is pivotal for initiating and advancing reproductive processes, impacting regional adaptation and crop yield. Despite numerous cloned and identified flowering time genes, research in cotton remains sparse. This study identified GhSWEET42 as a key determinant of the flowering time in cotton, demonstrating that its heterologous expression in Arabidopsis accelerated flowering under LD conditions compared to WT. Transgenic plants exhibited upregulated expression of the flowering inducers AtFT, AtSOC1, AtGI, and AtFKF1, alongside downregulated expression of the repressors AtTSF, AtFLC, and AtRGL2, correlating with the earlier flowering phenotype. GhSWEET42 showed a constitutive expression pattern, with elevated levels in the leaves, petals, and flower buds, and was notably higher in early-maturing cotton varieties. Subcellular localization assays confirmed GhSWEET42’s presence on the cell membrane. Transcriptome analysis between WT and GhSWEET42-overexpressing Arabidopsis plants revealed 2393 differentially expressed genes (DEGs), spanning 221 biological processes, 93 molecular functions, and 37 cellular components according to Gene Ontology (GO) enrichment analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis categorized the DEGs into metabolism and environmental information processing. These findings enhance the understanding of GhSWEET42’s function and provide a foundation for elucidating the molecular mechanisms governing flowering time regulation in cotton.

In cross-country skiing, ski poles play a crucial role in technique, propulsion, and overall performance. The kinematic parameters of ski poles can provide valuable information about the skier’s technique, which is of great significance for coaches and athletes seeking to improve their skiing performance. In this work, a new smart ski pole is proposed, which combines the uniaxial load cell and the inertial measurement unit (IMU), aiming to provide comprehensive data measurement functions more easily and to play an auxiliary role in training. The ski pole can collect data directly related to skiing technical actions, such as the skier’s pole force, pole angle, inertia data, etc., and the system’s design, based on wireless transmission, makes the system more convenient to provide comprehensive data acquisition functions, in order to achieve a more simple and efficient use experience. In this experiment, the characteristic data obtained from the ski poles during the Double Poling of three skiers were extracted and the sample t-test was conducted. The results showed that the three skiers had significant differences in pole force, pole angle, and pole time. Spearman correlation analysis was used to analyze the sports data of the people with good performance, and the results showed that the pole force and speed (r = 0.71) and pole support angle (r = 0.76) were significantly correlated. In addition, this study adopted the commonly used inertial sensor data for action recognition, combined with the load cell data as the input of the ski technical action recognition algorithm, and the recognition accuracy of five kinds of cross-country skiing technical actions (Diagonal Stride (DS), Double Poling (DP), Kick Double Poling (KDP), Two-stroke Glide (G2) and Five-stroke Glide (G5)) reached 99.5%, and the accuracy was significantly improved compared with similar recognition systems. Therefore, the equipment is expected to be a valuable training tool for coaches and athletes, helping them to better understand and improve their ski maneuver technique.

A liquid–liquid extraction pretreatment method using n-hexane as the extractant was developed for the analysis of volatile aroma substances in three flavors (six cola samples, six lemon samples, and six orange samples) of carbonated beverages by gas chromatography–mass spectrometry (GC-MS). Quantitative analysis was conducted using the external standard method. The spiked recovery rate of α-terpineol was used as the evaluation criterion. Single-factor and response surface experiments were conducted to investigate the effects of extraction temperature, extraction time, and solvent-to-sample ratio. The results indicated that the maximum spiked recovery rate of α-terpineol, 81.00%, was achieved at an extraction temperature of 45 °C, extraction time of 30 min, and a solvent-to-sample ratio of 1 mL:15 mL. Thirty-four components were identified by GC-MS on the pretreated samples via the internal standard method. 1,4-Cineole, fenchyl alcohol, borneol, and α-terpineol are covered aroma substances in cola beverages. Two aromatic substances, D-limonene and α-terpineol, were detected in orange juices. α-Terpineol was detected in each lemon-flavor carbonated beverage sample. Going a step further, α-terpineol was detected in all 18 carbonated beverage samples and had high response values. The principal component analysis by functional group classification led to the conclusion that acids, phenols, hydrocarbons, alcohols, and ethers played a major contribution to the aroma of these 18 beverages. Increased separation of target compounds was found using the new pre-treatment methods, resulting in improved analytical resolution and selectivity.

To further improve the braking energy recovery efficiency of battery electric vehicles and increase the range of the cars, this paper proposes a multi-mode switching braking energy recovery control strategy based on fuzzy control. The control strategy is divided into three modes: single-pedal energy recovery, coasting energy recovery, and conventional braking energy recovery. It takes the accelerator pedal and brake pedal opening as the switching conditions. It calculates the front and rear wheel braking ratio allocation coefficients and the motor braking ratio through fuzzy control to recover braking energy. The genetic algorithm (GA) is used to update the optimized affiliation function to optimize the motor braking allocation ratio through fuzzy control, and joint simulation is carried out based on the NEDC (New European Driving Cycle) and CLTC-P (China Light-duty Vehicle Test Cycle for Passenger vehicles) cycle conditions. The results show that the multi-mode braking energy recovery control strategy proposed in this paper improves the energy recovery rate and range contribution rate by 4% and 9.6%, respectively, and increases the range by 22.5 km under NEDC cycle conditions. It also improves the energy recovery rate and range contribution rate by 8.7% and 5.5%, respectively, and increases the range by 13 km under CLTC-P cycle conditions, which can effectively improve the energy recovery efficiency of the vehicle and increase the range of battery electric vehicles.

Sand solidification of earth-rock dams is the key to flood discharge capacity and collapse prevention of earth-rock dams. It is urgent to find an economical, environmentally friendly, and durable sand solidification technology. However, the traditional grouting reinforcement method has some problems, such as high costs, complex operations, and environmental pollution. Enzyme-induced calcium carbonate precipitation (EICP) is an anti-seepage reinforcement technology emerging in recent years with the characteristics of economy, environmental protection, and durability. The erosion resistance and shear strength of earth-rock dams solidified by EICP need further verification. In this paper, EICP-solidified standard sand is taken as the research object, and EICP-cemented standard sand is carried out by a consolidated undrained triaxial test. A two-stage pouring method is adopted to pour samples, and the effects of dry density, cementation times, standing time, and confining pressure on the shear strength of cemented standard sand are emphatically analyzed. The relationship between cohesion, internal friction angle, and CaCO3 formation was analyzed. After the optimal curing conditions are obtained through the triaxial shear strength test, the erosion resistance model test is carried out. The effects of erosion angle, erosion flow rate, and erosion time on the erosion resistance of EICP-solidified sand were analyzed through an erosion model test. The results of triaxial tests show that the standard sand solidified by EICP exhibits strain softening, and the peak strength increases with the increase in initial dry density, cementation times, standing time, and confining pressure. When the content of CaCO3 increases from 2.84 g to 12.61 g, the cohesive force and internal friction angle change to 23.13 times and 1.18 times, and the determination coefficients reach 0.93 and 0.94, respectively. Erosion model test results indicate that the EICP-solidified sand dam has good erosion resistance. As the increase in erosion angle, erosion flow rate, and erosion time, the breach of solidified samples gradually becomes larger. Due to the deep solidification of sand by EICP, the development of breaches is relatively slow. Under different erosion conditions, the solidified samples did not collapse and the dam broke. The research results have important reference value and scientific significance for the practice of sand consolidation engineering in earth-rock dams.

To integrate the sponge city concept into urban development, we propose an analytical approach for runoff volume control, considering urban expansion. Using Changchun City as a case study and historical land-use data, we simulated the prediction of Changchun City’s land-use structure for 2035 change with the GeoSOS-FLUS platform. We calculated storage volumes for Low Impact Development (LID) designs using 2019 and 2035 land surface data. The objective is an 80% runoff volume control rate by 2035. Through Monte Carlo simulation and sensitivity analysis, we assessed the impact of various land-use types on LID storage volume calculations. Findings show that industrial land significantly influences LID storage volumes. This highlights the need for precise surveys of industrial land properties and surface composition in sponge city planning for more accurate runoff volume control analysis in Changchun City. The results indicate that LID storage volumes based on current data may not meet long-term sponge city goals due to increased impervious surfaces and runoff coefficients during urbanization.

The performance of critical components in a sensor testing system may be compromised in a thermal vacuum environment as a result of the impact of extreme temperatures. Moreover, the precision of the angle measurement may be influenced by the thermal deformation effect. This paper presents a simulated analysis of the temperature regulation impact of the thermoelectric cooler (TEC) and outlines the design and optimization process of a sensor test chamber that can function within a consistent temperature range. The mathematical model of TEC is utilized to suggest a design choice, taking into account the aforementioned model, in a temperature-controlled environment with thermal vacuum circumstances. Moreover, the orthogonal test method is employed in combination with the FloEFD finite element analysis to validate the effectiveness of temperature control. In addition, the parameters of the radiation radiator are tuned and designed. Therefore, the temperature range difference inside the test system decreased by 20%. The thermoelectric temperature control system’s steady-state model is investigated using the PSpice simulation, based on the equivalent circuit theory. The discovered conclusions establish a theoretical foundation for improving the efficiency of temperature regulation. The design concepts presented in this work, particularly the optimization technique for radiation radiators in aerospace test equipment using thermoelectric cooling temperature control research and development, hold promise for practical implementation.

Automated detection of cervical lesion cell/clumps in cervical cytological images is essential for computer-aided diagnosis. In this task, the shape and size of the lesion cell/clumps appeared to vary considerably, reducing the detection performance of cervical lesion cell/clumps. To address the issue, we propose an adaptive feature extraction network for cervical lesion cell/clumps detection, called AFE-Net. Specifically, we propose the adaptive module to acquire the features of cervical lesion cell/clumps, while introducing the global bias mechanism to acquire the global average information, aiming at combining the adaptive features with the global information to improve the representation of the target features in the model, and thus enhance the detection performance of the model. Furthermore, we analyze the results of the popular bounding box loss on the model and propose the new bounding box loss tendency-IoU (TIoU). Finally, the network achieves the mean Average Precision (mAP) of 64.8% on the CDetector dataset, with 30.7 million parameters. Compared with YOLOv7 of 62.6% and 34.8M, the model improved mAP by 2.2% and reduced the number of parameters by 11.8%.

A low-carbon and environmentally friendly EICP method for repairing concrete cracks is presented to prolong the service life of concrete. In this study, we took concrete as the research object and quartz sand as the filling medium and employed the EICP injection method to repair concrete cracks. The internal repair effect of EICP on concrete cracks was evaluated with a combination of ultrasonic and compressive strength tests. The concrete repair mechanism of EICP was identified with a combination of EDS, XRD, and SEM tests. The results indicate that with an increase in the fracture depth, the ultrasonic sound time of the crack specimen increased gradually, and the ultrasonic wave transit time value of the crack specimen decreased significantly after EICP repair. After repair, the compressive strength rose. The highest compressive-strength recovery rate of a 0.3 mm wide specimen is 98.41%. The calcium carbonate crystal formed using EICP is vaterite. The probability density function model of the Laplace distribution was constructed, which showed good applicability and consistency in the ultrasonic sound time and compressive strength measured via experiments. The formed calcium carbonate crystals can be tightly and evenly attached to the cracks with the EICP injection repair method, resulting in a better repair effect.

Aiming at the problem that the traditional rapidly exploring random tree (RRT) algorithm only considers the global path of unmanned vehicles in a static environment, which has the limitation of not being able to avoid unknown dynamic obstacles in real time, and that the traditional dynamic window approach (DWA) algorithm is prone to fall into a local optimum during local path planning, this paper proposes a path planning method for unmanned vehicles that integrates improved RRT and DWA algorithms. The RRT algorithm is improved by introducing strategies such as target-biased random sampling, adaptive step size, and adaptive radius node screening, which enhance the efficiency and safety of path planning. The global path key points generated by the improved RRT algorithm are used as the subtarget points of the DWA algorithm, and the DWA algorithm is optimized through the design of an adaptive evaluation function weighting method based on real-time obstacle distances to achieve more reasonable local path planning. Through simulation experiments, the fusion algorithm shows promising results in a variety of typical static and dynamic mixed driving scenarios, can effectively plan a path that meets the driving requirements of an unmanned vehicle, avoids unknown dynamic obstacles, and shows higher path optimization efficiency and driving stability in complex environments, which provides strong support for an unmanned vehicle’s path planning in complex environments.

A multi-strain yeast-based paraprobiotic (MsYbP) comprising inactive cells and polysaccharides (β-glucan, mannan oligosaccharides, and oligosaccharides) derived from Saccharomyces cerevisiae and Cyberlindnera jadinii could ensure optimal growth and health in farmed fish. This study assessed the impact of an MsYbP on the growth, immune responses, antioxidant capacities, and liver health of largemouth bass (Micropterus salmoides) through lab-scale (65 days) and pilot-scale (15 weeks) experiments. Two groups of fish were monitored: one fed a control diet without the MsYbP and another fed 0.08% and 0.1% MsYbP in the lab-scale and pilot-scale studies, respectively (referred to as YANG). In the lab-scale study, four replicates were conducted, with 20 fish per replicate (average initial body weight = 31.0 ± 0.8 g), while the pilot-scale study involved three replicates with approximately 1500 fish per replicate (average initial body weight = 80.0 ± 2.2 g). The results indicate that the MsYbP-fed fish exhibited a significant increase in growth in both studies (p < 0.05). Additionally, the dietary MsYbP led to a noteworthy reduction in the liver function parameters (p < 0.05), such as alanine aminotransferase (ALT), aspartate aminotransferase (AST) and alkaline phosphatase (AKP), and hepatic nuclear density, indicating improved liver health. Furthermore, the dietary MsYbP elevated the antioxidative capacity of the fish by reducing their malondialdehyde levels and increasing their levels and gene expressions related to antioxidative markers, such as total antioxidant ca-pacity (T-AOC), total superoxide dismutase (T-SOD), glutathione peroxidase (GSH-Px), catalase (CAT), nuclear factor erythroid 2-related factor 2 (nrf2) and kelch-1ike ech-associated protein (keap1) in both studies (p < 0.05). In terms of hepatic immune responses, the lab-scale study showed an increase in inflammation-related gene expressions, such as interleukin-1β (il-1β) and transforming growth factor β1 (tgf-β1), while the pilot-scale study significantly suppressed the expressions of genes related to inflammatory responses, such as tumor necrosis factor α (tnfα) and interleukin-10 (il-10) (p < 0.05). In summary, our findings underscore the role of dietary multi-strain yeast-based paraprobiotics in enhancing the growth and liver health of largemouth bass, potentially through increased antioxidative capacity and the modulation of immune responses, emphasizing the significance of employing yeast-based paraprobiotics in commercial conditions.

Earth–rock dams are widely distributed in China and play an important role in flood control, water storage, water-level regulation, and water quality improvement. As an emerging seepage control and reinforcement technology in the past few years, enzyme (urease)-induced calcium carbonate precipitation (EICP) has the qualities of durability, environmental friendliness, and great economic efficiency. For EICP-solidified standard sand, this study analyzes the effect of dry density, amount of cementation, standing time, perfusion method, and other factors on the permeability and strength characteristics of solidified sandy soil by conducting a permeability test and an unconfined compression test and then working out the optimal solidification conditions of EICP. Furthermore, a quantitative relationship is established between the permeability coefficient (PC), unconfined compressive strength (UCS), and CaCO3 generation (CG). The test findings indicate that the PC of the solidified sandy soil decreases and the UCS rises as the starting dry density, amount of cementation, and standing time rise. With the increase of CG, the PC of the solidified sandy soil decreases while the UCS increases, indicating a good correlation among PC, UCS, and CG. The optimal condition of solidification by EICP is achieved by the two-stage grouting method with an initial dry density of 1.65 g/cm3, cementation time of 6 d, and standing time of 5 d. Under such conditions, the permeability of the solidified sandy soil is 6.25 × 10−4 cm/s, and the UCS is 1646.94 kPa. The findings of this study are of great theoretical value and scientific significance for guiding the reinforcement of earth–rock dams.

Loess is widely distributed in the northwest and other regions, and its unique structural forms such as large pores and strong water sensitivity lead to its collapsibility and collapse, which can easily induce slope instability. Guar gum and basalt fiber are natural green materials. For these reasons, this study investigated the solidification of loess by combining guar gum and basalt fiber and analyzed the impact of the guar gum content, fiber length, and fiber content on the soil shearing strength. Using scanning electron microscopy (SEM), the microstructure of loess was examined, revealing the synergistic solidification mechanism of guar gum and basalt fibers. On this basis, a shear strength model was established through regression analysis with fiber length, guar gum content, and fiber content. The results indicate that adding guar gum and basalt fiber increases soil cohesion, as do fiber length, guar gum content, and fiber content. When the fiber length was 12 mm, the fiber content was 1.00%, and the guar gum content was equal to 0.50%, 0.75%, or 1.00%, the peak strength of the solidified loess increased by 82.80%, 85.90%, and 90.40%, respectively. According to the shear strength model, the predicted and test data of the shear strength of solidified loess are evenly distributed on both sides of parallel lines, indicating a good fit. These findings are theoretically significant and provide practical guidance for loess solidification engineering.

Loess has the characteristics of loose, large pore ratio, and strong water sensitivity. Once it encounters water, its structure is damaged easily and its strength is degraded, causing a degree of subgrade settlement. The water sensitivity of loess can be evaluated by permeability and disintegration tests. This study analyzes the effects of guar gum content, basalt fiber content, and basalt fiber length on the permeability and disintegration characteristics of solidified loess. The microstructure of loess was studied through scanning electron microscopy (SEM) testing, revealing the synergistic solidification mechanism of guar gum and basalt fibers. A permeability model was established through regression analysis with guar gum content, confining pressure, basalt fiber content, and length. The research results indicate that the addition of guar gum reduces the permeability of solidified loess, the addition of fiber improves the overall strength, and the addition of guar gum and basalt fiber improves the disintegration resistance. When the guar gum content is 1.00%, the permeability coefficient and disintegration rate of solidified soil are reduced by 50.50% and 94.10%, respectively. When the guar gum content is 1.00%, the basalt fiber length is 12 mm, and the fiber content is 1.00%, the permeability of the solidified soil decreases by 31.9%, and the disintegration rate is 4.80%. The permeability model has a good fitting effect and is suitable for predicting the permeability of loess reinforced with guar gum and basalt fiber composite. This research is of vital theoretical worth and great scientific significance for guidelines on practicing loess solidification engineering.

Soil degradation threatens agricultural productivity and food supply, leading to hunger issues in some developing regions. To address this challenge, we developed a low-cost, highly efficient, and long-term stable soil improvement method. We chose polyvinyl alcohol (PVA), a commercially available polymer that is safe and non-degradable, to serve as a soil adhesive. We mixed PVA solution into the soil and applied a drying treatment to enhance the bonding between PVA and the soil, achieving highly water-stable soil. This PVA-stabilized soil exhibits low bulk density, high porosity, and high permeability, making it an ideal substrate for planting. In a germination test, the PVA-stabilized soil revealed a higher germination rate and growth rate compared to those of the non-treated soil. We believe this simple and efficient soil improvement method can restore degraded soil and contribute to sustainable agriculture.

In view of the fact that the global planning algorithm cannot avoid unknown dynamic and static obstacles and the local planning algorithm easily falls into local optimization in large-scale environments, an improved path planning algorithm based on the integration of A* and DWA is proposed and applied to driverless ferry vehicles. Aiming at the traditional A* algorithm, the vector angle cosine value is introduced to improve the heuristic function to enhance the search direction; the search neighborhood is expanded and optimized to improve the search efficiency; aiming at the problem that there are many turning points in the A* algorithm, a cubic quasi-uniform B-spline curve is used to smooth the path. At the same time, fuzzy control theory is introduced to improve the traditional DWA so that the weight coefficient of the evaluation function can be dynamically adjusted in different environments, effectively avoiding the problem of a local optimal solution. Through the fusion of the improved DWA and the improved A* algorithm, the key nodes in global planning are used as sub-target punctuation to guide the DWA for local planning, so as to ensure that the ferry vehicle avoids obstacles in real time. Simulation results show that the fusion algorithm can avoid unknown dynamic and static obstacles efficiently and in real time on the basis of obtaining the global optimal path. In different environment maps, the effectiveness and adaptability of the fusion algorithm are verified.

One of the primary challenges in cable-stayed bridges is to assess the service performance of stay cables in response to applied loads and ensure that they meet safety requirements. This paper proposes a new strategy to analyze the time-varying reliability of the ultimate load-carrying capacity of stay cables under resistance and stress uncertainty conditions. Initially, we employ the frequency-squeezing processing (FSP) technique within the vibration frequency method (VFM) to enhance the accuracy and effectiveness of cable force identification through field measurement. Subsequently, we thoroughly discuss and establish the statistical characteristics and probabilistic models of stress, including both slow-varying trend and fast-varying trend components, as well as resistance considering the strengthening deterioration effect. The slow-varying trends of the cable forces are extracted using the moving average method (MAM), and both the extracted slow variation and the fast-varying trend components are analyzed in detail. Finally, we introduce a Gaussian process-based surrogate model to assess the time-varying structural reliability by analyzing the associated limit-state function for the ultimate load capacity of the stay cables. In this study, the proposed strategy is applied to quantify the ultimate load-carrying reliability of a stay cable under the uncertainty of the coupled action of corrosion and fatigue. Compared with conventional reliability analysis, the failure probability interval estimation shows the uncertainty boundaries and provides specific years of reliability failure, which can serve as an important reference for bridge maintenance and strengthening.

Our study aimed to investigate the role of ferroptosis in sevoflurane-induced hearing impairment and explore the mechanism of the microRNA-182-5p (miR-182-5p)/Glutathione Peroxidase 4 (GPX4) pathway in sevoflurane-induced ototoxicity. Immunofluorescence staining was performed using myosin 7a and CtBP2. Cell viability was assessed using the CCK-8 kit. Fe2+ concentration was measured using FerroOrange and Mi-to-FerroGreen fluorescent probes. The lipid peroxide level was assessed using BODIPY 581/591 C11 and MitoSOX fluorescent probes. The auditory brainstem response (ABR) test was conducted to evaluate the hearing status. Bioinformatics tools and dual luciferase gene reporter analysis were used to confirm the direct targeting of miR-182-5p on GPX4 mRNA. GPX4 and miR-182-5p expression in cells was assessed by qRT-PCR and Western blot. Ferrostatin-1 (Fer-1) pretreatment significantly improved hearing impairment and damage to ribbon synapses in mice caused by sevoflurane exposure. Immunofluorescence staining revealed that Fer-1 pretreatment reduced intracellular and mitochondrial iron overload, as well as lipid peroxide accumulation. Our findings indicated that miR-182-5p was upregulated in sevoflurane-exposed HEI-OC1 cells, and miR-182-5p regulated GPX4 expression by binding to the 3′UTR of GPX4 mRNA. The inhibition of miR-182-5p attenuated sevoflurane-induced iron overload and lipid peroxide accumulation. Our study elucidated that the miR-182-5p/GPX4 pathway was implicated in sevoflurane-induced ototoxicity by promoting ferroptosis.

As an emerging repair method, the enzyme-induced calcium carbonate precipitation (EICP) technique has the advantages of being highly economical, eco-friendly, and durable. The optimal repair conditions were obtained by taking cement mortar as the research object, adding two types of filling medium, using three EICP-based repair methods to repair the cement mortar with different crack widths, and combining ultrasonic testing and strength testing to evaluate the mechanical properties and repair effects of the repair mortar. The microscopic structure of the mortar was established using mesoscopic and microscopic tests (XRD, SEM, and EDS), thereby revealing the mechanism of repair based on EICP. The test results show that, when quartz sand is used as the repair medium, more calcium carbonate adheres to the cross-section of test samples, and it has a better repair effect. Moreover, the repair effect of the injection method is significantly higher than those of the perfusion and immersion methods, and the ultrasonic wave transit time decreases by 1.22% on average. Based on the combination of quartz sand and EICP repair methods, the calcium carbonate precipitated among the sand granules contributes to a binding effect that strengthens the cohesive force among the sand granules.

For the RRT* algorithm, there are problems such as greater randomness, longer time consumption, more redundant nodes, and inability to perform local obstacle avoidance when encountering unknown obstacles in the path planning process of autonomous vehicles. And the artificial potential field method (APF) applied to autonomous vehicles is prone to problems such as local optimality, unreachable targets, and inapplicability to global scenarios. A fusion algorithm combining the improved RRT* algorithm and the improved artificial potential field method is proposed. First of all, for the RRT* algorithm, the concept of the artificial potential field and probability sampling optimization strategy are introduced, and the adaptive step size is designed according to the road curvature. The path post-processing of the planned global path is carried out to reduce the redundant nodes of the generated path, enhance the purpose of sampling, solve the problem where oscillation may occur when expanding near the target point, reduce the randomness of RRT* node sampling, and improve the efficiency of path generation. Secondly, for the artificial potential field method, by designing obstacle avoidance constraints, adding a road boundary repulsion potential field, and optimizing the repulsion function and safety ellipse, the problem of unreachable targets can be solved, unnecessary steering in the path can be reduced, and the safety of the planned path can be improved. In the face of U-shaped obstacles, virtual gravity points are generated to solve the local minimum problem and improve the passing performance of the obstacles. Finally, the fusion algorithm, which combines the improved RRT* algorithm and the improved artificial potential field method, is designed. The former first plans the global path, extracts the path node as the temporary target point of the latter, guides the vehicle to drive, and avoids local obstacles through the improved artificial potential field method when encountered with unknown obstacles, and then smooths the path planned by the fusion algorithm, making the path satisfy the vehicle kinematic constraints. The simulation results in the different road scenes show that the method proposed in this paper can quickly plan a smooth path that is more stable, more accurate, and suitable for vehicle driving.

Vehicle running state parameters and road surface state are crucial to the stability of four-wheel independent drive and steering electric vehicle control. Therefore, this study explores the estimation of vehicle driving state parameters and road surface adhesion coefficients using a combination of federal Kalman filtering and an intelligent bionic antlion optimization algorithm. Firstly, according to the research purpose of the paper and the focus on the accuracy of the establishment of the three degrees of freedom dynamics model, fully considering the road conditions, the paper adopts the Dugoff tire model and finally completes the establishment of the vehicle state estimation model. Secondly, the drive state estimation algorithm is developed utilizing the principles of federal Kalman filtering and volume Kalman filtering. At the same time, robust estimation theory is introduced into the sub-filter, and the antlion optimization module is designed at the lower layer of the main filter to enhance the accuracy of estimates. It is easy to see that the design of the Antlion federal Kalman travel state estimation algorithm has noticeably enhanced accuracy and traceability, according to the result. Thirdly, a joint estimation algorithm of state estimation and road surface adhesion coefficient has been devised to enhance the stability and precision of the estimation process. Finally, the results showed that the joint estimation algorithm has high accuracy in estimating vehicle driving state parameters such as the center of mass lateral deflection angle and road surface adhesion coefficient by simulation.

Solid electrolyte materials with high structural stability and excellent proton conductivity (σ) have long been a popular and challenging research topic in the fuel cell field. This problem can be addressed because of the crystalline metal–organic frameworks’ (MOFs’) high structural stability, adjustable framework composition, and dense H-bonded networks. Herein, one highly stable Sr(II) MOF, {[Sr(H2tmidc)2(H2O)3]·4H2O}n (1) (H3tmidc = 2-(1H-tetrazolium-1-methylene)-1H-imidazole-4,5-dicarboxylic acid) was successfully fabricated, which was structurally characterized by single-crystal X-ray diffraction and electrochemically examined by the AC impedance determination. The results demonstrated that the σ of the compound manifested a positive dependence on temperature and humidity, and the optimal proton conductivity is as high as 1.22 × 10−2 S/cm under 100 °C and 98% relative humidity, which is at the forefront of reported MOFs with ultrahigh σ. The analysis of the proton conduction mechanism reveals that numerous tetrazolium groups, carboxyl groups, coordination, and crystallization water molecules in the framework are responsible for the high efficiency of proton transport. This work offers a fresh perspective on how to create novel crystalline proton conductive materials.

The 20th century saw notable fluctuations in global temperatures, which significantly impacted agricultural climate zones across the Earth. Focusing on Xinjiang, China, a leading region in machine-picked cotton production, we identified several key thermal indicators influencing the yield, including the sum of active temperatures ≥ 10 °C, the mean temperature in July, the climatological growing season length, the April–May sum of active temperatures, the last frost day, and the defoliant spray time. Using meteorological data from 58 weather stations in Xinjiang, we examined the spatiotemporal trends of these indicators during the 1981–2020 period. Additionally, we attempted to determine the effects of plastic mulching on the sowing area and the zoning area of machine-picked cotton in different suitable zones based on these indicators. In conclusion, the overall thermal resources in Xinjiang are exhibiting an upward trend and show a distribution pattern of “more in the south of Xinjiang than in the north of Xinjiang, and more in the plains and basins than in the mountains”. Under the plastic-mulching mechanism, the zoning area of the suitable zone has increased by 15.7% (2.15 × 103 km2), suggesting that climate warming and the widespread application of mulching technology provide unexplored potential for the most suitable regions for machine-picked cotton in Xinjiang, while the 14.5% (0.26 × 103 km2) and 7.8% (0.17 × 103 km2) reductions in the unsuitable and less suitable zones, respectively, suggest that the planting areas of machine-picked cotton in both the less suitable and unsuitable zones, particularly with the existing regional planning, continue to demonstrate an irrational expansion. Therefore, to sustain Xinjiang’s cotton industry’s resilience and productivity, policymakers need to prioritize proactive land management and sustainable land allocation practices in response to changing climate patterns to optimize cotton production.

In driverless formula car racing, cone detection faces two significant challenges: one is recognizing cones at long distances accurately, and the other is being prone to leakage under bright light conditions. These challenges directly affect the detection accuracy and response speed. In order to cope with these problems, the thesis is based on YOLOv8s to improve the cone bucket detection algorithm. Firstly, a P2 detection layer for detecting tiny objects is added on top of YOLOv8s to detect small targets with 160 × 160 pixels, which improves the detection of small conical buckets in the distant view. At the same time, to reduce the network’s complexity to achieve lightweightness, the original 20 × 20 pixel detection header is deleted. Second, the head of the original YOLOv8 is replaced with a multi-scale fusion Dynamic Head, designed to improve the head’s ability in scale, space, and task perception to enhance the detection performance of the model in complex scenes. Again, a novel loss function, MPDIoU, is introduced, which has advantages in simplifying the bounding box similarity comparison, and it can adapt to the overlapping or non-overlapping situation of the bounding box more effectively. It reduces the phenomenon of missed detection caused by overlapping conical buckets. Finally, the LAMP pruning method is used to trim the model to make the model lightweight. By adding and modifying the above modules, the improved algorithm improves the detection accuracy from 92.2% to 95.2%, the recall rate from 84.2% to 91.8%, and the average accuracy from 91.3% to 96%, while the number of parameters is reduced from 28.7 M to 26.6 M. The detection speed still meets the real-time requirement in real-vehicle testing compared to the original algorithm. In the real car test, compared with the original algorithm, the improved algorithm shows apparent advantages in reducing the missed detection of cones and barrels, which meets the demand for high accuracy of cones and barrel detection in the complex race environment and also meets the conditions for deployment on small devices with limited resources.

With the increasing use of composite materials in aircraft structures and the extension of their service life, the selection of repair patch shapes for composite material damage repair has become a significant engineering concern. The ultimate strength of the repaired structure is among the main evaluation criteria for selecting optimal patch shapes. In this study, strength analysis is conducted along with the use of the Digital Image Correlation (DIC) method to assess the quality of the repaired components, making the evaluation method more rational. Early studies often focused on simplified models, which diverged significantly from the practicalities of maintenance engineering in civil aviation. Therefore, it is essential to research full-scale composite material repair patches, as this will provide a more reliable basis for the optimal selection of patch shapes in composite material maintenance engineering for civil aircraft.

Current assessments for generalized anxiety disorder (GAD) are often subjective and do not rely on a standardized measure to evaluate the GAD across its severity levels. The lack of objective and multi-level quantitative diagnostic criteria poses as a significant challenge for individualized treatment strategies. To address this need, this study aims to establish a GAD grading and quantification diagnostic model by integrating an electroencephalogram (EEG) and ensemble learning. In this context, a total of 39 normal subjects and 80 GAD patients were recruited and divided into four groups: normal control, mild GAD, moderate GAD, and severe GAD. Ten minutes resting state EEG data were collected for every subject. Functional connectivity features were extracted from each EEG segment with different time windows. Then, ensemble learning was employed for GAD classification studies and brain mechanism analysis. Hence, the results showed that the Catboost model with a 10 s time window achieved an impressive 98.1% accuracy for four-level classification. Particularly, it was found that those functional connections situated between the frontal and temporal lobes were significantly more abundant than in other regions, with the beta rhythm being the most prominent. The analysis framework and findings of this study provide substantial evidence for the applications of artificial intelligence in the clinical diagnosis of GAD.

Heavy oil accounts for a considerable proportion of the world’s petroleum resources, and its exploitation helps to mitigate reliance on conventional oil resources and diversify energy supply. However, due to the high viscosity and high adhesion characteristics of heavy oil, conventional methods such as thermal recovery, emulsification, and dilution have significant limitations and cannot meet the growing demands for heavy oil production. In this study, 3-propyltrimethoxysilane (MPS) was used to modify and graft amphiphilic surfactants (AS) onto nanosilica to prepare a salt-resistant (total mineralization > 8000 mg/L, Ca2+ + Mg2+ > 1000 mg/L) and temperature-resistant (250 °C) nanosilicon viscosity reducer (NSD). This article compares amphiphilic surfactants (AS) as conventional viscosity-reducing agents with NSD. FTIR and TEM measurements indicated successful bonding of 3-propyltrimethoxysilane to the surface of silica. Experimental results show that at a concentration of 0.2 wt% and a mineralization of 8829 mg/L, the viscosity reduction rates of thick oil (LD-1) before and after aging were 85.29% and 81.36%, respectively, from an initial viscosity of 38,700 mPa·s. Contact angle experiments demonstrated that 0.2 wt% concentration of NSD could change the surface of reservoir rock from oil-wet to water-wet. Interfacial tension experiments showed that the interfacial tension between 0.2 wt% NSD and heavy oil was 0.076 mN/m. Additionally, when the liquid-to-solid ratio was 10:1, the dynamic and static adsorption amounts of 0.2 wt% NSD were 1.328 mg/g-sand and 0.745 mg/g-sand, respectively. Furthermore, one-dimensional displacement experiments verified the oil recovery performance of NSD at different concentrations (0.1 wt%, 0.15 wt%, 0.2 wt%, 0.25 wt%) at 250 °C and compared the oil recovery efficiency of 0.2 wt% NSD with different types of demulsifiers. Experimental results indicate that the recovery rate increased with the increase in NSD concentration, and 0.2 wt% NSD could improve the recovery rate of heavy oil by 22.8% at 250 °C. The study of nano-demulsification oil recovery systems can effectively improve the development efficiency of heavy oil.

Bladder cancer (BC) is a common genitourinary malignancy that exhibits silent morbidity and high mortality rates because of a lack of diagnostic markers and limited effective treatments. Here, we evaluated the role of the lncRNA brain cytoplasmic RNA 1 (BCYRN1) in BC. We performed loss-of-function assays to examine the effects of BCYRN1 downregulation in T24 and BOY BC cells. We found that BCYRN1 downregulation significantly inhibited the proliferation, migration, invasion, and three-dimensional spheroid formation ability and induced apoptosis in BC cells. Additionally, gene set enrichment analysis (GSEA) using RNA sequences from tumor fractions showed that BCYRN1 downregulation decreased the expression of mRNAs associated with the cell cycle. These findings were supported by observations of G2/M arrest in flow cytometry assays. Finally, we examined the expression of serum exosomal BCYRN1 as a biomarker. Clinically, BCYRN1 expression in serum exosomes from patients with BC (n = 31) was significantly higher than that in healthy donors (n = 19; mean difference: 4.1-fold higher, p < 0.01). Moreover, in patients who had undergone complete resection of BC, serum exosomal BCYRN1 levels were significantly decreased (n = 8). Thus, serum exosomal BCYRN1 may be a promising diagnostic marker and therapeutic target in patients with BC.

In this study, we develop an Atmospheric Motion Vector (AMV)-based method for retrieving wind vectors using 183.31 GHz water-vapor absorption channels. The method involves tracking water-vapor features from image triplets and subsequently deriving wind fields from motion vectors. The height of the derived wind for each channel is determined by calculating the weighing function peak using monthly averaged ERA5 reanalysis data. By utilizing Microwave Humidity Sounder-II (MWHS-II) brightness temperatures from the five channels centered around 183.31 GHz, wind vectors are retrieved within the Arctic region for the entire year of 2022. The retrieval quality is evaluated through comparative analysis with ERA5 reanalysis data and the Visible Infrared Imaging Radiometer Suite (VIIRS) wind product. The resultant vector root mean square errors (RMSEs) are approximately 4.5 m/s for the three lower-height channels and 5.5 m/s for the two upper-height channels. These findings demonstrate a wind retrieval performance comparable to the existing methods, highlighting its potential for augmenting wind availability at lower height levels.

Tubular joints are important connecting parts of a welded steel tube structure. The S-N curves based on the hot spot stress (HSS) method are often used to evaluate the fatigue life of tubular joints in practical engineering. The stress concentration factor (SCF) is a key parameter to calculate HSS. In this paper, stress concentration tests of hollow-section and concrete-filled double-skin tubular (CFDST) K-joints were carried out, respectively, and then finite element models of K-joints considering the weld were established. The developed models were validated with the experimental results. The influence of key geometrical parameters, such as the diameter ratio of brace to chord β, the diameter to thickness ratio of chord γ, the wall thickness ratio of brace to chord τ, brace angle θ, and hollow section ratio ζ on the distribution and key position of SCFs along the weld toe, was discussed. Parametric studies were conducted to obtain the calculating equations for the SCF values of CFDST K-joints. The results demonstrate that infill concrete can effectively reduce SCFs along the weld on the chord. When the hollow section ratio was reduced to 0.317, the SCF was reduced by 77.2%. Notably, the SCF reduction rate was sensitive to γ and θ, with a decrease observed as γ increased. The hollow section ratio ζ had a less pronounced effect on SCF distribution patterns, but as ζ decreased, the chord’s stiffness improved, suggesting a potential approach to enhance joint performance. The distribution of SCFs is similar for joints of the same type but different geometric configurations. The innovatively integrated hollow section ratio in the CFDST design equation significantly simplifies and enhances the precision of SCF calculations for CFDST K-joints.

Background: This study aims to explore the relationship of the low-density lipoprotein cholesterol (LDL-C)/high-density lipoprotein (HDL-C) ratio with glycated hemoglobin (HbA1c), renal dysfunction, coronary heart disease (CHD) and cardiac structure and function in elderly patients with hypercholesterolemia. Methods: A total of 1129 hospitalized Chinese elderly (aged ≥ 65 years) with hypercholesterolemia were collected retrospectively. The patients were divided into low (Results: Regression analysis of the LDL-C/HDL-C ratio with metabolic and echocardiographic parameters revealed that a high LDL-C/HDL-C ratio (≥3.33) was associated independently with male gender, elevated HbA1c, decreased estimated glomerular filtration rate (eGFR), prevalent CHD and left ventricular dilatation (all p < 0.05). Conclusions: A high LDL-C/HDL-C ratio was associated with male gender, increased HbA1c, decreased eGFR, CHD and enlarged left ventricle in elderly with hypercholesterolemia.

A long-term time series of 319 Sentinel-1 SAR Imagery with Interferometric Wide Swath (IW) mode was used to study the characteristics of submesoscale eddies over Japanese coastal regions from 2015 to 2021, including spatiotemporal eddy properties and possible mechanisms of their formation. The results showed that around 98% of the 1499 eddies identified from the SAR snapshots were submesoscale eddies (horizontal scales of O1–20 km) with a ratio of around 78% cyclones to around 22% anticyclones. Around 8% of the submesoscale eddies were found in these SAR images in winter since the submesoscale current-induced signals are masked by the stronger wind speed, compared with other seasons. Typical features of submesoscale eddies are summarized, providing a preliminary qualitative analysis of potential generation mechanisms specific to the eddy characteristics in this region. This study suggests that Sentinel-1 images are capable of providing insights into the observed submesoscale eddies near the coastal regions of eastern Japan, thereby contributing to the improved understanding of the generation of submesoscale eddies.

In order to improve the yaw stability of a front-wheel dual-motor-driven driverless vehicle, a yaw stability control strategy is proposed for a front-wheel dual-motor-driven formula student driverless racing car. A hierarchical control structure is adopted to design the upper torque distributor based on the integral sliding mode theory, which establishes a linear two-degree-of-freedom model of the racing car to calculate the expected yaw angular velocity and the expected side slip angle and calculates the additional yaw moments of the two front wheels. The lower layer is the torque distributor, which optimally distributes the additional moments to the motors of the two front wheels based on torque optimization objectives and torque distribution rules. Two typical test conditions were selected to carry out simulation experiments. The results show that the driverless formula racing car can track the expected yaw angular velocity and the expected side slip angle better after adding the yaw stability controller designed in this paper, effectively improving driving stability.