Sea ice concentration (SIC) is a core parameter for quantifying polar sea ice coverage and plays a crucial role in climate research, oceanographic studies, maritime safety, and remote sensing monitoring. The all-weather and large-scale observations of this parameter primarily rely on passive microwave remote sensing technology. In this study, atmospheric correction techniques are applied to the dual-polarized ratio (DPR) algorithm to retrieve SIC from vertically and horizontally polarized brightness temperatures observed at 89.0 GHz and a retrieval resolution of 6.25 km. Compared to existing SIC retrieval algorithms, the DPR algorithm is a theoretical method based on the passive microwave radiative transfer equation. The key parameter in the DPR algorithm is the ratio (α) of microwave emissivities of sea ice for horizontal (H) and vertical (V) polarizations, which can be determined from the brightness temperatures at HV 89.0 GHz with 100% sea ice coverage. The atmospheric effects are implicitly treated as a smooth function in the DPR algorithm. The results show that atmospheric compensation is unnecessary when SIC exceeds 0.82, whereas this compensation is required when SIC is below 0.82. The SIC error induced by atmospheric compensation is closely related to air temperature: the lower the temperature, the smaller the atmospheric impact on the DPR algorithm. When the temperature drops below -5 ℃, the error falls below 10%. The evaluation results indicate that when SIC exceeds 0.25, the DPR algorithm generally outperforms the ARTIST Sea Ice algorithm. Thus, the DPR algorithm can provide more reliable SIC data for Arctic sea ice monitoring, climate modeling, polar navigation safety, and global energy balance research.

With the increasing influence of global climate change on sea levels, accurate prediction of sea-surface-height variations is essential for marine environmental monitoring, disaster early warning, and coastal resource management. Using sea-surface-height observations from 28 tide gauge stations along the coast of Japan from 2004 to 2023, this study proposes a prediction framework that integrates Singular Spectrum Analysis (SSA), a Long Short-Term Memory (LSTM) model, and an Autoregressive Moving Average (ARMA) model. The proposed method is applied to predict sea surface height along the Japanese coast and is compared with an SSA+ARMA hybrid approach. The results indicate that the SSA+LSTM+ARMA model outperforms the conventional SSA+ARMA approach in predicting sea surface height for 2019 to 2023. Specifically, the annual Mean Absolute Error (MAE) is reduced by approximately 6%~8% relative to SSA+ARMA. Projections for 2024 to 2025 suggest that sea-surface-height variations will continue to exhibit historical periodicity without significant anomalies, with an overall stable trend. These findings demonstrate that the SSA+LSTM+ARMA model more effectively captures nonlinear dynamics and long-term trends in sea surface height, providing a useful reference for marine management and disaster early warning.

This study evaluated the antibacterial activity of Compound Shiyacha Antibacterial Liniment and its safety on animal skin to provide evidence for its safe clinical use. The agar diffusion method was used to measure inhibition zone diameters against Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Candida albicans. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were determined using the double dilution method. Skin safety and sensitization tests were conducted on rats. The results showed that Compound Shiyacha Antibacterial Liniment exhibited strong inhibitory effects against Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans, with the strongest antibacterial activity observed against Staphylococcus aureus (inhibition zone diameter: 29.1 mm; MIC: 1.562 5 mg/mL; MBC: 3.125 mg/ml). The liniment caused no skin irritation or sensitization reactions in rats, demonstrating good safety. It may serve as a potential alternative to traditional antibacterial drugs and may be suitable for preventing and treating skin infections in sensitive populations.

This study investigated the anti-thrombotic activity and underlying mechanisms of action of Pheretima extract (Phe) based on a zebrafish model. A Ponatinib (PON)-induced zebrafish thrombosis model was used to evaluate the anti-thrombotic activity of Phe by measuring heart rate and blood flow velocity. The transgenic zebrafish line Tg(CD41:eGFP), with fluorescently labeled platelets, was used to quantify circulating platelets in vivo, while the macrophage-labeled transgenic line Tg(coro1a:EGFP) was used to assess the effects of Phe on inflammatory responses. In addition, quantitative real-time PCR (qRT-PCR) was used to examine the impact of the Phe on the expression of related target genes. The results showed that Phe significantly improved multiple pathological indicators in the PON-induced zebrafish thrombosis model: it effectively restored drug-induced reductions in heart rate, improved tail blood flow velocity, alleviated inflammatory responses, and reduced the consumption of circulating platelets. Further quantitative PCR (qPCR) analysis indicated that Phe, as the primary active component of Qilong capsules, effectively inhibited the upregulation of multiple coagulation- (VWF, f2, fga, fgb, and tbxas1) and inflammation-related genes (NF-κB and IL-1β) induced by PON. These findings suggest that Phe might exert anti-thrombotic effects by regulating the coagulation cascade, inhibiting platelet activation and aggregation, and suppressing inflammatory response.

In this study, a multiscale computational approach combining density functional theory calculations with all-atom molecular dynamics (MD) simulations was employed to systematically evaluate the corrosion inhibition performance of 20 amino acids at oil-gas pipeline interfaces. A three-dimensional evaluation system integrating “electronic structure-adsorption behavior-practical inhibition” was established to reveal the highly efficient inhibition mechanism of tryptophan in acidic and asphaltene-containing environments. Quantum chemical calculations show that tryptophan has the lowest molecular orbital energy gap (3.42 eV), with its frontier molecular orbitals highly localized on the aromatic rings and nitrogen-containing heterocyclic rings, endowing it with notable electronic activity.MD simulations reveal a strong negative correlation (R²=0.91) between adsorption energy and energy gap. Tryptophan exhibits the most favorable interfacial binding characteristics under acidic conditions (-944.88 kcal/mol) and during co-adsorption with oleic acid (-1 608.25 kcal/mol). Strong electrostatic interactions between the carboxyl oxygen atoms and the Fe(110) surface (electrostatic potential is -32.06 kcal/mol) form the core adsorption sites. Experimental validation shows that tryptophan achieves a corrosion inhibition efficiency of 92.3% at a concentration of 140 mg/L, with a deviation of less than 5% from theoretical predictions. Overall, the cross-scale computational method developed in this study establishes an accurate linkage between electronic structure and macroscopic performance, providing a theoretical paradigm for molecular design and engineering application of green corrosion inhibitors.

Compressed-air energy storage in aquifers (CAESA) has garnered considerable attention as a crucial large-scale energy storage technology in recent years, under the context of global energy transition and the “dual-carbon” goals. This study proposes an underground CAESA system and presents a transient simulation of the cyclic injection and extraction of compressed air within an anticlinal aquifer structure, conducted using CMG software. Data interaction and calculations were performed using a Matlab/Simulink platform. Under the design operating conditions, the evolution of bottom-hole pressure throughout the cyclic injection and extraction processes was simulated, effectively achieving coupling between dynamic operation conditions of the surface thermal system and the real-time evolution of underground aquifer injection and production. The simulation analyzed the response characteristics of key state parameters, adiabatic efficiency, and input power at each stage of the compressors and turbines during compression and expansion. The results indicate that, under the design conditions, the outlet density, temperature, pressure, power consumption, and adiabatic efficiency of each compressor stage during the compression process, as well as the outlet temperature and mass flow rate of the high-temperature heat transfer oil, exhibit an upward trend. After three-stage expansion, the air pressure decreases from 2.087 MPa to atmospheric pressure and the air density drops from 19.09 to 0.904 kg/m³. Owing to the progressively decreasing expansion ratio, the work output of the turbine stages diminishes, resulting in sequentially increasing outlet temperatures. When the adiabatic efficiency remains constant at 0.8, the power consumption follows the order W_HT>W_MT>W_LT; the mass flow rate and temperature of the heat transfer oil increase first and then decrease as heat exchange occurs during the expansion process. The above-ground and underground components of the entire system operate smoothly, and the energy storage efficiency under the design conditions reaches 62.77%.

According to the actual geological structure of the Kendong oil reservoir, a geometric model of the reservoir aquifer and caprock was established. Using the COMSOL simulation platform, a large-scale subsurface physical model of the compressed air energy storage in aquifers (CAESA) system in oil reservoirs was constructed by integrating interfaces such as porous media two-phase flow, Darcy’s law, and solid heat transfer to enable dynamic simulation of the full cycle from gas injection for reservoir construction to cyclic injection production. Under baseline conditions, the temporal variations of gas saturation, temperature field, and pressure field within the reservoir during the full cycle were simulated. A sensitivity analysis was conducted for the steady injection-production stage to investigate the impacts of permeability, injection-production rate, horizontal well length, and well location on the average wellbore temperature and pressure. The results reveal that increased permeability, reduced injection-production rates, and extended horizontal wells decrease the thermal and pressure differences between the wellbore and reservoir during cyclic injection production. The impact of horizontal well location depends on its average distance from the caprock: a greater distance results in a larger temperature difference but a smaller pressure difference during injection production. Comparative analysis reveals that permeability and the injection-production rate have more substantial impacts on wellbore pressure. In particular, when permeability increased from 600 to 1 400 mD and the injection rate from 12 to 20 kg/s, the pressure amplitude decreased by approximately 28.9% and 24.1%, respectively, substantially exceeding those of equivalent variations in horizontal well length and location. This study provides theoretical guidance for the design and energy efficiency evaluation of the CAESA systems in oil reservoirs, specifically regarding well configuration and operational parameter optimization.

Due to the issues of large-scale central-air-conditioning water humidification systems, such as operational difficulty under high loads, uneven humidification, and easy fogging, numerical simulations were performed using the FLUENT software. Models were established for vertical and horizontal supply pipes connected to the multioutlet pipes for studying flow discharge along the route (five-hole configuration vs. symmetrically distributed six-hole configuration) in large central-air-conditioning water humidification systems. Based on the volume of fluid model, the transport characteristics of wet steam in these configurations were investigated. The pressure, flow velocity, and density distributions of the steam along the pipe at different volume fractions were analyzed, and the uniformity of steam outflow under different operating conditions was studied. The results showed that in the vertical supply pipe, pressure gradually increased from the inlet to the end, whereas in the horizontal supply pipe, pressure was high at the inlet and low on both sides of pipe; moreover, as the volumetric fraction of the steam decreased, the pressure became more uniform on both sides. The six-hole horizontal supply configuration achieved the most uniform flow distribution under the current model settings. Density analysis revealed that decreasing the steam volumetric fraction significantly increased the outlet moisture content in the horizontal supply system. This study provides a reference for the design of water humidification equipment and research on along-the-route steam outflow in a multioutlet pipe, offering a theoretical basis for related multiphase outflow scenarios paths.

To address the optimization challenges associated with two-dimensional sparse arrays, this study proposes an improved dynamic genetic algorithm.This algorithm focuses on reducing the number of array elements while enhancing peak sidelobe suppression. This proposed algorithm introduces an evolutionary completion index that integrates the number of iterations and population fitness to accurately characterize the evolutionary progress of the population. In addition, according to this index, dynamically adjustable crossover and mutation operators are designed, which enable the algorithm to flexibly adjust crossover and mutation probabilities to satisfy the needs of different optimization stages, thereby improving the overall optimization performance.The experimental results revealed that compared with traditional genetic algorithms, the proposed algorithmcan effectively suppress the peak sidelobe ratio by more than 3.9 dB in sparse array optimization, thereby exhibiting robust adaptability and stability across various sparsity levels.

To investigate the adaptability of double-trailer trains on typical roads in China, two simulation models for double-trailer trains were developed based on vehicle dynamics. Typical road scenarios, such as freeway ramps, freeways, U-turns, interchanges, and intersections, were constructed on the Trucksim platform. The road adaptability of double-trailer trains was simulated and evaluated based on maximum swept road width and rearward amplification factor. Simulation results reveal that both models travel normally along the target trajectory and exhibit high adaptability to the constructed scenarios, except for scenarios with excessively small turning radii or low road adhesion coefficients. A comparative analysis reveals that the rear unit of the C-double model achieves a higher degree of trajectory coincidence between the rear unit and tractor unit, thereby demonstrating better pass ability, whereas the B-double model, with wheel connections between all units, is more stable during driving.

Using jar tests, a novel bimetallic fluoride-removing coagulant (BFrC) composed of iron and aluminum was prepared by a two-stage progressive orthogonal test in treating a synthesized water with low-temperature and low-turbidity containing fluoride, and the impact of dilution degree on its particle size and charge characteristics were investigated, respectively. Compared with poly-Si-Fe coagulant, the fluoride and turbidity removal by both liquid BFrC and its solid sample were evaluated in an integrated coagulation-adsorption process, along with the comparison of their mesoscopic morphology. The results indicated that the optimal iron/aluminum, iron/polyaluminum and copolymerization temperature for BFrC preparation were 0.5, 0.8, and 20 ℃, respectively. When BFrC was diluted to 0.012 5 mg/L (as “iron+ aluminum”), moderate primary hydrolysis was achieved, forming flocs with a reasonable size distribution and strong adsorption capacity. When BFrC dosage was 18.5 mg/L, fluoride concentration was reduced to<1 mg/L, corresponding to the removal rate of 58.4%, while the greatest fluoride removal by poly-Si-Fe coagulant was only 11.3%, though giving a higher turbidity removal than BFrC. Liquid BFrC gave similar fluoride removal to solid BFrC, but the former posed a higher turbidity removal due to its larger flocs having better settling performance. The larger flocs formed by liquid BFrC exhibited strong aggregative properties, but the size of smaller flocs in liquid BFrC was significantly larger than that in solid BFrC, but having lower number. The filtration almost gave no further removal of fluoride, but showed a higher turbidity removal.

Current approaches to assessing the innovativeness of scientific and technological papers rely predominantly on expert peer review, a process that is often inefficient and subject to bias. Quantitative metrics offer greater objectivity but are largely retrospective and provide limited foresight or explanatory insight. This study proposes a novel framework for evaluating the innovativeness of stem cell scientific and technological papers using the DeepSeek model. Focusing on a corpus of stem cell research articles, the titles and abstracts of representative papers were vectorized using the bge-large-en-v1.5 model to construct a semantic vector database. Subsequently, the deepseek-reasoner model was applied to extract innovation-related features, which were organized into a vectorized innovation feature database. The two databases were subsequently integrated using a weighted fusion strategy. Target papers were then evaluated through FAISS-based vector retrieval and Top-k similarity matching within the unified database, resulting in a final innovativeness score and ranking. The results were rigorously validated against scores generated by the unassisted DeepSeek model to assess the framework’s effectiveness in evaluating innovativeness in biomedical scientific and technological papers. Empirical results indicate that the DeepSeek model tends to overestimate innovation when used without calibration. However, after targeted training, the model exhibits substantially improved stability and validity in innovation assessment, highlighting its strong potential for identifying innovative dimensions and distinguishing features in scientific literature.

Chenopodium album L. is a common traditional Chinese medicinal herb widely distributed throughout China and possesses abundant medicinal properties. It is recorded in many ancient books that it can treat diarrhea, itching, and vitiligo, and has rich medicinal effects. Chenopodium album L. not only contains abundant nutrients but also active ingredients such as phenols, alkaloids, glycosides, flavonoids, terpenes, and sterols, which have high potential for scientific research and pharmaceutical development. In this study, CiteSpace was used to visually analyze relevant literature published worldwide from 1997 to 2025. The bioactivities of Chenopodium album L. were summarized, and its active components were systematically reviewed to provide references and guidance for the further utilization of its resources and the development of related products.

Current statistical work in the field of educational development faces challenges such as low knowledge-retrieval efficiency and high technical barriers to data utilization. This study focuses on the knowledge related to the indicator system that underpins educational statistical work and proposes a large language model (LLM)-based method for constructing a knowledge graph for the educational statistical indicator system so as to provide knowledge support for addressing the above issues. Specifically, a research framework comprising the following layers is established: data processing, ontology construction, graph construction, and application prospect. In the data processing layer, original documents are converted into Markdown format and cleaned to enhance structural parsing. In the ontology construction layer, a seven-step method is used to construct the ontology of the educational statistical indicator system. In the graph construction layer, an enhanced prompting strategy integrating chain-of-thought prompting with the ontology structure guides the LLM in knowledge extraction, with Neo4j used for storage and visualization. Experimental results show that this strategy achieved an F₁ score of 96.22% for entity-attribute extraction and 92.23% for entity-relation extraction, significantly outperforming the basic prompting strategy, thereby verifying the effectiveness of chain-of-thought prompting in complex information extraction. The constructed knowledge graph effectively represents the complex knowledge in the educational statistical indicator system, providing a knowledge foundation for intelligent information retrieval and natural language data querying and offering insights for knowledge organization in other statistical fields.