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Reservoir porosity is a crucial indicator of the physical properties of reservoirs, forming the foundation for oil and gas exploration, development design, and decision-making. Currently, it is primarily obtained through core testing or logging interpretation, but the lack of quantitative evaluation methods during drilling limits the timeliness and efficiency of porosity acquisition. Based on this, this study focuses on the tight sandstone reservoir in the East China Sea shelf basin, conducting modeling and rock-breaking simulations of 5 blade and 6 blade polycrystalline diamond compact (PDC) bits commonly used in the region. It investigates the relationships between rate of penetration (ROP), torque, mechanical specific energy (MSE), physical index, and other parameters for rocks with varying physical characteristics. A real-time quantitative prediction method for reservoir porosity, based on drilling and logging engineering parameters, is proposed. The results indicate that: (1) Significant differences in the response characteristics of rate of penetration, torque, and MSE are observed when drilling formations with identical mechanical characteristics, due to the influence of bit type. Therefore, these engineering parameters are not suitable for directly predicting reservoir porosity. (2) The relationship between the physical index and elastic modulus for 5 blade and 6 blade PDC bits is highly consistent, with both increasing logarithmically as elastic modulus increases. This suggests that the physical index can eliminate the influence of bit type and more accurately reflect changes in formation characteristics during drilling. (3) Using elastic modulus as an intermediary parameter, a model is established that relates porosity to the physical index, showing that porosity decreases as a power function of the physical index. The research findings were cross-verified in well NB13-4-A, with a 91.57% agreement between the porosity predicted by engineering parameters and the logging-derived porosity. The prediction method was applied to 20 exploration wells in the NB13-4 working area, yielding an average porosity consistency rate of 85.74%. This demonstrates that the method can provide timely, efficient, and accurate support for decision-making in exploration operations, such as intermediate testing and well completion.

The pore degree prediction method based on well logging interpretation for tight sandstone reservoirs cannot meet the requirements of timeliness and rapidity for well exploration decisions. This paper utilizes the logging parameters during drilling, combined with acoustic time difference experiments, dynamic and static parameter experiments, and full-scale drill bit rock-breaking simulation, to reveal the response characteristics of reservoir properties to the feedback information from logging and rock-breaking and establishes a pore degree prediction method. The results show that as the pore degree decreases, the dynamic and static elastic modulus increases, the rate of penetration decreases, the torque increases, the mechanical specific energy increases, and a mathematical relationship model between pore degree and mechanical specific energy is established, achieving real-time drilling prediction of pore degree. The new method has been applied in the NB block, and the coincidence rate with the well-logging interpretation results reaches over 83%. The research results have provided real-time predictions of reservoir pore degrees and improved the efficiency of exploration decisions.

Mesoscale eddies play an important role in regulating the global ocean ecosystem and climate variability. However, few studies have been found to focus on the survey of the underwater gliders (UGs) motion performance inside mesoscale eddies. The dynamic model of an UG considering the eddy density is established to predict its motion performance inside an eddy. Ignoring the effect of vertical velocity inside the eddy on the motion of UG, the simulation results and experimental data are compared to verify the derived model. From the analysis of the motion performance, the vertical velocity is larger at 400∼940 m depth than that at a depth of 0∼400 m in the ascent. Considering the vertical structures of parameters within eddies, the climbing profiles are chosen as the available samples to capture an eddy better. The larger error caused by the eddy density mainly occurs near the depth of the thermocline. Moreover, there is a stronger influence of eddy density on the motion performance of the UG in the ascent than that in the descent. The results show the differences in the effect of the mesoscale eddy density on the motion performance of “Petrel II” UG in the descent and ascent, and they provide a sampling suggestion for the application of UGs in the mesoscale eddy observation.

Combination prediction models have gained great development in the area of information science, and are widely applied in engineering fields. The underwater glider (UG) is a new type of unmanned vehicle used in ocean observation for the advantages of long endurance, low noise, etc. However, due to its lower speed relative to the ocean current, the surfacing positioning point (SPP) of an UG often drifts greatly away from the preset waypoint. Therefore, this paper proposes a new combination model for predicting the SPP at different time scales. First, the kinematic model and working flow of the Petrel-L glider is analyzed. Then, this paper introduces the principles of a newly proposed combination model which integrates single prediction models with optimal weight. Afterwards, to make an accurate prediction, ocean current data are interpolated and averaged according to the diving depth of UGs as an external influencing factor. Meanwhile, with sea trial data collected in the northern South China Sea by Petrel-L, which had a total range of 4230.5 km, SPPs are predicted using single prediction models at different time scales, and the combination weights are derived with a novel simulated annealing optimized Frank–Wolfe method. Finally, the evaluated results demonstrate that the MAE and MSE are 966 m and 969 m, which proves that the single models achieved good performance under specified situations, and the combination model performed better at full scale because it integrates the advantages of the single models. Furthermore, the predicted SPPs will be helpful in the dead reckoning of the UG, and the proposed new combination method could extend into other fields for prediction.

The physical and chemical stability of perovskite films has always been a key issue for their industrialization, which has been extensively studied in terms of materials, environment, and encapsulation. Spin coating is one of the most commonly used methods for the preparation of perovskite films in research. However, little attention has been paid to the deformation state of the substrate when it is fixed by means of adsorption and its impact. In this work, the three-dimensional digital image correlation (3D-DIC) method and hyperspectral technology are used to acquire and analyze the adsorption deformation characteristics of the substrate during spin coating, as well as the resulting inhomogeneity. Plastic and four different thicknesses of float glass (0.2, 0.5, 0.7, 1.1 mm) were selected as substrates, and they were tested separately on two suction cups with different structures. The results show that the plastic and 0.2 mm specimens exhibit obvious strain localization behavior. The distribution and magnitude of the strain are affected by the size of the sucker structure, especially the width of the groove. For glass specimens, this effect shows a nonlinear decrease with increasing substrate thickness. Compared to the strain value, the irregularity of local deformation has a greater impact on the non-uniform distribution of materials. Finally, inhomogeneities in the perovskite films were observed through optical lens and hyperspectral data. Obviously, the deformation of the substrate caused by adsorption should attract the attention of researchers, especially for flexible or rigid substrates with low thickness. This may affect the centrifugal diffusion path of the precursor, causing microstructure inhomogeneity and residual stress, etc.

In this work, density functional theory (DFT) calculations and x-ray photoelectron spectroscopy (XPS) were carried out to investigate the hydroxyl groups on a wurtzite GaN (0001) surface. Surface treatments of GaN with piranha and HCl-based solutions were studied via XPS, and peak shifts in the Ga 2p and O 1s XPS spectra were caused by the signal change resulting from surface hydroxyl groups. Further DFT study revealed that the adsorption of hydroxyl groups is more favourable near the centre location than near gallium atoms. To investigate the thermodynamic stability of hydroxyl groups under different coverages, a surface phase diagram of hydroxyl group adsorption on the GaN (0001) surface was constructed over a coverage range of 1/6–1 monolayer (ML). The results showed that a high hydroxyl group coverage is more likely to be present on the GaN surface. The energy barrier for split hydroxyl groups is 1.41 eV. Therefore, the hydroxyl groups can be stable at room temperature. These results provide a systematic explanation of the adsorption between the hydroxyl groups and the GaN (0001) surface.

Underwater vehicles (UVs) are pivotal for ocean exploration, yet their effectiveness is fundamentally constrained by acoustic performance in noisy and dynamic seas. Self-noise, non-stationary interference, and extreme conditions not only degrade sensing, navigation, and stealth but also cascade into losses in propulsion efficiency, actuation reliability, and control precision. This review provides a system-performance-oriented synthesis of advances across four key areas: bioinspired and intelligent noise reduction materials/structures, active noise control and adaptive signal processing, noise-robust navigation and collaborative localization, and deep learning-enhanced acoustic perception. Key findings indicate that bioinspired surfaces reduce flow noise by ≈5 dB, adaptive filtering improves SNR by up to 20 dB, and distributed robust filtering ensures multi-AUV consistency under uncertainty. These developments collectively establish acoustic performance not as a parallel metric, but as a fundamental enabler and critical bottleneck for the integrated propulsion-actuation-control stack of next-generation UVs. Consequently, this review outlines viable pathways toward high-performance acoustic–mechanical integration.

Due to the increasing demand for eco-friendly, cost-effective and safe technologies, biosynthetic metal nanoparticles have attracted worldwide attention. In this study, silver nanoparticles (AgNPs) were extracellularly biosynthesized using the culture supernatants of Aspergillus sydowii . During synthesis, color change was preliminarily judge of the generation of AgNPs, and the UV absorption peak at 420 nm further confirms the production of AgNPs. Transmission electron microscopy and X-ray diffraction were also used to identify the AgNPs. The results shows that AgNPs has crystalline cubic feature and is a polydisperse spherical particle with size between 1 and 24 nm. Three main synthesis factors (temperature, pH and substrate concentration) were optimized, the best synthesis conditions were as follows 50 °C, 8.0 and 1.5 mM. In the biological application of AgNPs, it shows effective antifungal activity against many clinical pathogenic fungi and antiproliferative activity to HeLa cells and MCF-7 cells in vitro. Our research finds a new path to biosynthesis of AgNPs in an eco-friendly manner, and bring opportunity for biomedical applications in clinic.

The objective of this study was to find one or more fungal strains that could be utilized to biosynthesize antifungal silver nanoparticles (AgNPs). Using morphological and molecular methods, Arthroderma fulvum was identified as the most effective fungal strain for synthesizing AgNPs. The UV-visible range showed a single peak at 420 nm, which corresponded to the surface plasmon absorbance of AgNPs. X-ray diffraction and transmission electron microscopy demonstrated that the biosynthesized AgNPs were crystalline in nature with an average diameter of 15.5±2.5 nm. Numerous factors could potentially affect the process of biosynthesis, and the main factors are discussed here. Optimization results showed that substrate concentration of 1.5 mM, alkaline pH, reaction temperature of 55°C, and reaction time of 10 hours were the optimum conditions for AgNP biosynthesis. Biosynthesized AgNPs showed considerable activity against the tested fungal strains, including Candida spp., Aspergillus spp., and Fusarium spp., especially Candida spp.

Traditional sentiment analysis methods mainly focus on textual data, while human emotions are multidimensional, usually related to sound, body language, etc. The use of multi-modal data can provide a deeper and broader understanding of human sentiment conveyance. To address the above issues, a method for extracting and analyzing emotional information features based on improved nonverbal representation learning networks and multi-modal data (Improved Nonverbal Representation Learning Networks and MD, INPNRLN-MD) is proposed. On this basis, an improved multi-modal data fusion method based on Gated Hierarchical Recurrent Neural Network and Cross-Modal Attention (GHRNN-CMA) is designed for the MD fusion part. Compared with traditional baselines, INPNRLN-MD extracts text features through the BERT model and utilizes ELN to process audio and video data, which can more effectively capture emotional information in multi-modal data. The cross-modal attention mechanism of GHRNN-CMA can enhance the interaction between modalities and improve the accuracy of emotion information recognition. Finally, the performance of the model is validated on the CMU-OSI and CMU-MCSEI datasets using indicators such as F1 value, Pearson correlation, mean absolute error, and second-order accuracy. During the training process, the study used a single NVIDIA GTX TITAN X GPU for testing, with 12 VRAM and a batch size of only 32 to converge. The inference stage has a relatively light computational load and can be deployed on ordinary cloud servers or edge devices. The research results show that compared with mainstream algorithms, the emotion information feature extraction and analysis method based on improved nonverbal representation learning network and multi-modal data performs the best, with F1 score, Pearson correlation, average absolute error, and second-order accuracy reaching 83.06/85.12, 0.803, 0.696, and 83.17/85.23, respectively. The average absolute error, Pearson correlation, F1 score, and second-order accuracy of the improved multi-modal data fusion method have been improved by 1.0%, 14.67%, 3.1%, and 3.3% respectively compared to the latest method. The above results indicate that research methods are helpful in perceiving and analyzing human emotions, which is beneficial for understanding and predicting human behavior in the future, and is of great significance for maintaining social relationships and improving social governance.