Layer Depth Research Articles

Inversion of Fracture Weakness Parameters Based on the 3CVSP Data—Part II: ORT Media Composed of A Single Fracture Set in the VTI Background Media

The inversion of fracture parameters from seismic data is a key issue in predicting fractured reservoirs. When a set of vertical fractures developed in a background medium of laminated shales or thin interbedded sandstone and mudstone with VTI (transversely isotropic media with a vertical symmetry axis) characteristics, this medium is a typical ORT (orthotropic) medium. In such a medium, due to the presence of fractures, the PS wave in the original VTI medium splits into two types of waves: PS1 and PS2 waves (fast and slow shear waves). To use the travel times of these two waveforms to invert the fracture weakness parameters, we derive approximate formulas for the travel times of PS1 and PS2 waves recorded in a Vertical Seismic Profile (VSP) for horizontally layered media. These approximate formulas are obtained using rational form approximation methods for short offsets with respect to the shot position. This approximation transforms the calculation of travel time from the complex phase velocity domain (the function of the two horizontal slownesses) to the group velocity domain (the function of shot point coordinates). The results from numerical errors analysis indicate that even with a near offset assumption, the approximated formula has high accuracy for offsets close to the depth of the target layer. By utilizing the travel times of PS1 and PS2 waves picked up by velocity under isotropic conditions, combined with undetermined anisotropic parameters and rational form approximations, we establish an objective function for inversion. The inversion results from different azimuths indicate that the accuracy of the inverted anisotropic parameters has different azimuthal sensitivities. The inversion method is applied to field multi-directional 3CVSP data and the results confirm the effectiveness of the method.

Dynamics of Soil N and P Nutrient Heterogeneity in Mixed Forest of Populus × Euramercana ‘Neva’ and Robinia pseucdoacacia in Coastal Saline–Alkali Land

The mixing of poplar and robinia in coastal saline land is a useful attempt at difficult site afforestation. Investigating the long–term mixing effects of nitrogen–fixing and non–nitrogen–fixing tree species on the spatial heterogeneity of N and P nutrients and their ecological stoichiometric characteristics in the coastal saline–alkali soil can provide a scientific basis for soil improvement and plantation management in the coastal saline–alkali soil. By replacing time with space, poplar and robinia mixed forests and corresponding pure forests with the ages of 3, 7 and 18 years were selected, and soil profiles of 0–20 cm, 20–40 cm and 40–60 cm were dug up to determine the contents of total nitrogen, hydrolyzed nitrogen, total phosphorus and available phosphorus, the activities of soil urease and phosphatase and the number of soil bacteria, fungi and actinomycetes in rhizosphere soil. The mixture of poplar and robinia and the increase in planting years led to the heterogeneity of soil N and P in a coastal saline–alkali forest, which could significantly increase the contents of soil total nitrogen, hydrolyzed nitrogen, total phosphorus and available phosphorus between soil layers. Compared with the pure forest of poplar and robinia at the same age, the soil urease activity in the 0–20 cm soil layer of an 18a poplar and robinia mixed forest increased by 94.75% and 73.36%, and the soil phosphatase activity increased by 30.36% and 70.27%. The mix of poplar and robinia significantly increased the abundance of soil microorganisms in saline–alkali soil. The number of bacteria, fungi and actinomycetes in the 0–20 cm soil layer of the 18a poplar and robinia mixed forest was the highest, which were 703,200, 31,297 and 1903, respectively. Redundancy analysis showed that there was a significant positive correlation between soil N and P nutrient contents, soil enzyme activities and microbial abundance. The soil depth of N and P nutrient decomposition and transformation in the mixed poplar and robinia plantation was expanded. The soil N and P nutrient contents, enzyme activities and microbial abundance in the 40–60 cm soil layer of the mixed forest were higher than those of the pure forest. With the increase in plantation years, the depth of soil that can be used in the forest land is increasing. The mixture of poplar and robinia plantation is an excellent choice for the construction of coastal saline–alkali land plantation, which has a significant mixed gain for the decomposition and transformation of N and P nutrients and increases the depth of the available soil layer in the forest land in coastal saline–alkali land. However, the coastal saline–alkali land soil N/P is < 14 and is still restricted by nitrogen, so the application of nitrogen fertilizer can be increased during the afforestation process.

Geothermal Condition Investigation and Resource Potential Evaluation of Shallow Geothermal Energy in the Yinchuan Area, Ningxia, China

Shallow geothermal energy (SGE) is a promising green and sustainable energy source, gaining prominence in light of the dual-carbon target. This study investigated the SGE resources in the Yinchuan area. Suitability zones and the potential of SGE resources were determined based on the comprehensive analysis about thermophysical parameters, hydrogeological conditions, and geological environment. Our findings revealed that the effective thermal conductivity in the Yinchuan area surpasses those of other cities, indicating significant potential for SGE. The thermostat layer depth ranges from 40 to 60 m, with a geothermal gradient between 0.81 and 6.19 °C/100 m. Regions with poor adaptability for a borehole heat exchanger (BHE) are mainly distributed in the western and southern parts of the Yinchuan area, whereas moderately and highly adaptable areas are primarily located in the central and eastern areas, respectively. The total geothermal resource of the BHE in the Yinchuan area amounts to 1.07 × 108 GJ/a, generating significant economic benefits of 1.07 × 109 CNY/a and saving 1.09 × 106 t/a of standard coal annually. This initiative leads to significant reductions in CO2, SO2, and NOx emissions by 2.61 × 106 t/a, 1.86 × 104 t/a, and 6.57 × 103 t/a, respectively. Additionally, it results in potential savings of 0.309 × 109 CNY/a in environmental treatment costs. The methods and models used in this study have potential for similar geothermal surveys in arid and cold regions. The results also contribute essential insights for policy formulation and sustainable development strategies related to shallow geothermal resources in the Yinchuan area.

A seesaw in the South Pacific western and eastern subtropical mode waters

Abstract The South Pacific Western Subtropical Mode Water (WSTMW) and Eastern Subtropical Mode Water (ESTMW) are important components of the subtropical meridional overturning cells. By synthesizing observations, reanalysis products, and eddy-resolving ocean model results, we find an interannual volume seesaw in the WSTMW and ESTMW during 1980-2020. Further analysis of the data and model outputs indicates that this volume seesaw is closely related to the El Niño–Southern Oscillation (ENSO) through its modulation of the Ekman pumping velocity, buoyancy fluxes, and mixed layer depth over the formation regions of the two water masses. This negative correlation between the WSTMW and ESTMW modulates the depth of the permanent pycnocline and directly contributes to the east-west contrast between the two water masses in their core layer properties, especially, during negative phase of the Interdecadal Pacific Oscillation (IPO) since 2000.

ENHANCING DEEP LEARNING PERFORMANCE THROUGH A GENETIC ALGORITHM-ENHANCED APPROACH: FOCUSING ON LSTM

Deep learning has shown remarkable success in various applications, such as image classification, natural language processing, and speech recognition. However, training deep neural networks is challenging due to their complex architecture and the number of parameters required. Genetic algorithms have been proposed as an alternative optimization technique for deep learning, offering an efficient alternative way to find an optimal set of network parameters that minimize the objective function. In this paper, we propose a novel approach integrating genetic algorithms with deep learning, specifically LSTM models, to enhance performance. Our method optimizes crucial hyper-parameters including learning rate, batch size, neuron count per layer, and layer depth through genetic algorithms. Additionally, we conduct a comprehensive analysis of how genetic algorithm parameters influence the optimization process and illustrate their significant impact on improving LSTM model performance. Overall, the presented method provides a powerful mechanism for improving the performance of deep neural networks, and; thus, we believe that it has significant potential for future applications in the artificial intelligence discipline.

Laboratory study of the breaking and energy distribution of internal solitary waves over a ridge

The breaking and energy distribution of mode-1 depression internal solitary wave interactions with Gaussian ridges are examined through laboratory experiments. A series of processes, such as shoaling, breaking, transmission and reflection, are captured completely by measuring the velocity field in a large region. It is found that the maximum interface descent ( $a_{max}$ ) during wave shoaling is an important parameter for diagnosing the type of wave–ridge interaction and energy distribution. The wave breaking on the ridge depends on the modified blockage parameter $\zeta _m$ , the ratio of the sum of the upper layer depth and $a_{max}$ to the water depth at the top of the ridge. As $\zeta _m$ increases, the interaction type transitions from no breaking to plunging and mixed plunging–collapsing breaking. Within the scope of this experiment, the energy distribution can be characterized solely by $\zeta _m$ . The transmission energy decreases monotonically with increasing $\zeta _m$ , and there is a linear relationship between $\zeta _m^2$ and the reflection coefficient. The value of $a_{max}$ can be determined from the basic initial parameters of the experiment. Based on the incident wave parameters, the depth of the upper and lower layers, and the topographic parameters, two new simple methods for predicting $a_{max}$ on the ridge are proposed.

Структура та властивості поверхневих шарів деформованого цирк¬нію, легованого Nb, Мо та В шляхом лазерної обробки поверхні

The phase composition and structure of the near-surface layers of zirconium ribbons subjected to laser treatment in the initial state and after alloying with B, Mo, Nb were studied. The distribution of microhardness values on the surface of the coating and changes in microhardness at different distances from the surface were analyzed. X-ray structural analysis of the surface layers was performed, and their phase composition was determined. The influence of β-stabilizing elements on changes in the phase composition was demonstrated. In the Zr—Mo coating, a phase transformation of Zr with bcc modification takes place. It is observed that small intermetallic particles of the ZrMo2 compound are released, which help to increase the hardness in the coating to the values of μ = 1200—1300. The content of niobium in the zirconium matrix in the Zr—Nb coating, calculated according to X-ray analysis, is 29,4% (at.) which is sufficient to stabilize the BCC lattice. The value of microhardness Hμ = 600—700 on the surface of the coating is typical for hardened zirconium with a bcc lattice. The hardness of the Zr—B coating on the surface reaches values of Hμ = 1600—1400, gradually decreases in the depth of the molten layer to Hμ = 1300 and sharply decreases outside the molten zone to Hμ = 300. Significant surface hardening of the coating is associated with a large amount of the strengthening phase ZrB2. As a result of the experiments, it was established that doping with niobium and molybdenum causes fundamental phase changes in the surface layer of coatings due to the stabilization of the β phase. This is important for protecting the surfaces of nuclear power and medical devices. The addition of B significantly increases the hardness due to the mechanism of dispersed strengthening, which contributes to the improvement of the wear resistance of the coatings. Keywords: laser processing, coating, phase composition, dispersed particles, hardness.

Contribution of High-mode Near-inertial Waves to Enhanced Typhoon-induced Sea Surface Temperature Cooling in the South China Sea

Sea surface temperature cooling (SSTC) is an important indicator of the ocean response to typhoons and is a factor in the evolution of typhoons. Understanding the intricate mechanisms underlying the SSTC induced by different typhoons is important. Based on the numerical simulation, we investigated the SSTC induced by typhoons Megi (2010), Linfa (2015), and Sarika (2011), which had relatively similar tracks in the South China Sea. As the strongest (weakest) typhoon, Megi (Sarika) induced the largest (smallest) SSTC, which is consistent with the traditional understanding that stronger typhoons usually induce larger SSTC than weaker typhoons. However, the SSTC induced by the moderate typhoon Linfa was nearly comparable to that induced by Megi, while Linfa had a wind power input an order of magnitude smaller. A comparison of near-inertial waves (NIWs) induced by Linfa and Megi showed that the former contained a larger proportion of high modes, substantially contributing to vertical shear. Consequently, the vertical mixing coefficient during Linfa reached one third of that during Megi. Because the SSTC is primarily influenced by vertical mixing, which is dominated by vertical diffusion at the mixed layer depth, the relatively strong vertical mixing coefficient and large temperature gradient during Linfa ultimately resulted in the SSTC nearly comparable to that induced by Megi. The results of this study enhance the understanding of typhoon-induced SSTC.

Tundra fires and surface subsidence increase spectral diversity on the Yukon–Kuskokwim Delta, Alaska

Abstract Tundra fires can dramatically influence plant species cover and abundance, organic layer depth, and the magnitude of seasonal permafrost thaw. However, knowledge of the impact of wildfire on short and long-term interactions between vegetation and permafrost thaw remains limited. Here, we evaluate the spatial and temporal interactions between wildfire disturbance and surface subsidence on a remotely derived proxy for species diversity (i.e. spectral diversity (SD)) of 16 fire scars within the Izaviknek and Kingaglia uplands of southwestern Alaska’s Yukon–Kuskokwim Delta with burn dates between 1971 and 2015. SD was calculated as the sum of squared spectral variance of pixel spectra from the mean spectra, within a plant community (analogous to alpha diversity), between plant communities (beta diversity), and across terrain composed of a mosaic of communities (gamma diversity). Surface subsidence was calculated from spaceborne interferometric synthetic aperture radar data from Sentinel-1. Results indicate the burn scars had consistently lower total gamma diversity and greater rates of subsidence than paired unburned reference areas, where both gamma diversity (R 2 = 0.74, p < 0.001) and relative subsidence (R 2 = 0.86, p < 0.001) decreased with the time since burn. Compared to older burn scars, young scars had higher gamma spectral diversities (0.013 and 0.005) and greater subsidence rates (−0.097 cm day−1 and −0.053 cm day−1). Communities subsiding at higher rates had higher gamma diversities (R 2 = 0.81, p < 0.001). Results indicate that rates of post-fire vegetation succession are amplified by the thickening of active layers and surface subsidence that increases both spectral and species diversity over 15 years following fire. These results support the idea that SD may be used as a remotely sensed analog of species diversity, used to advance knowledge of the trajectories of plant community change in response to interacting arctic disturbance regimes.

Surface mechanical property and residual stress stability of nanostructured CNT/Al-Cu-Mg composites induced by shot peening

Shot peening (SP) is recognized for its capacity to enhance surface strength and induce compressive residual stresses (CRS). Nevertheless, quantifying the mechanical property improvements in thin shot peened layers remains challenging with traditional methods. In this study, CNT/Al-Cu-Mg composites were subjected to shot peening. The microstructure evolution of the peened layers along the layer depth direction was investigated by XRD and TEM. Meanwhile, the changes in the mechanical properties of the shot peened layers and the release rules of CRS during tensile cycling at different numbers loads were measured by an in-situ X-ray stress analyzer and a micro-tensile device in conjunction with the Von Mises stress criterion. The results showed that SP generated the nano-gradient deformation layer with nanograins size of 50–100 nm near the surface. The yield strength of the surface layer was increased from 352 MPa before SP to 435 MPa, an increase of 23.6 %. Moreover, the observation of fracture morphology indicated that SP reduced the material plasticit moderately and the agglomeration of the Al2Cu. In addition, in the case of a certain CRS on the initial surface, the relaxation of CRS was correlated with the magnitude of applied loads and the number of cycles and couldn't occur when the applied loads is below a specific value.

Modelling global mesozooplankton biomass using machine learning

Mesozooplankton are a crucial link between primary producers and higher trophic levels and play a vital role in marine food webs, biological carbon pumps, and sustaining fishery resources. However, the global distribution of mesozooplankton biomass and the relevant controlling mechanisms remain elusive. We compared four machine learning algorithms (Boosted Regression Trees, Random Forest, Artificial Neural Network, and Support Vector Machine) to model the spatiotemporal distributions of global mesozooplankton biomass. These algorithms were trained on a compiled dataset of published mesozooplankton biomass observations with corresponding environmental predictors from contemporaneous satellite observations (temperature, chlorophyll, salinity, and mixed layer depth). We found that Random Forest achieved the best predictive accuracy with R2 and RMSE (Root Mean Standard Error) of 0.57 and 0.39, respectively. Also, the global distribution of mesozooplankton biomass predicted by the Random Forest model was more consistent with the observational data than other models. We used the Random Forest model to create a global map of mesozooplankton biomass which serves as a reference for validating process-based ecosystem models. The model outputs confirm that environmental factors, especially surface Chl a, a proxy for prey availability, significantly correlate with the spatiotemporal distribution of mesozooplankton biomass. The scaling relationship between the mesozooplankton biomass and Chl a can be used as an emergent constraint for model validation and development. Moreover, our model predicts that the global mesozooplankton biomass will decrease by 3% by the end of this century under the “business-as-usual” scenarios, potentially reducing fishery production and carbon sequestration. Our study contributes to predicting global mesozooplankton biomass and provides deep insights into the underlying environmental impacts on the distribution of mesozooplankton biomass.

Assessing Tidal Current Velocity Across Different Water Depth Layers in Khenifiss Lagoon (Southern Atlantic Coast of Morocco) – Implications for Potential Tidal Energy Extraction

Assessing Tidal Current Velocity Across Different Water Depth Layers in Khenifiss Lagoon (Southern Atlantic Coast of Morocco) – Implications for Potential Tidal Energy Extraction

Advanced multimodal ultrasound for pre-operative assessment of skin tumours: A case series.

Pre-operative assessment of non-melanoma skin cancers via advanced ultrasound techniques may potentially provide additional information to tumour margins and morphology compared to current assessment via dermoscopy or optical coherence tomography. In this case series, the findings of multimodal ultrasonography of non-melanoma skin cancer are described, with histological correlation. Consecutive patients with clinical suspicion of malignant skin lesions underwent multimodal ultrasonography, comprising B-mode, colour Doppler imaging, superb microvascular imaging, strain elastography, and shear-wave elastography, followed by surgical excision. Images were reviewed by two radiologists. There were 8 female and 3 male patients, whose age ranged from 66 to 98 years. A total of 11 malignant skin tumours (basal cell carcinoma, n = 6; squamous cell carcinoma, n = 5) were reviewed. Tumour depth measured via ultrasound assessment ranged from 1.20 to 7.00 mm (mean ± standard deviation: 4.35 ± 1.99 mm). Tumours were located within dermis (n = 1) and abutting subcutaneous layer (n = 10) sonographically. Where corresponding histological reports for tumour depth were available, ultrasound correlated well with histology. Ultrasound also correctly predicted the deepest layer of involvement for all lesions. The sonographic features observed in all malignant skin tumours (11/11:100%) are solid, hypoechoic, with abundant neovascularity on colour Doppler imaging and superb microvascular imaging, and appeared stiff on strain elastography and shear-wave elastography. In some cases, the microvascular network of intra-tumoural flow were better demonstrated on superb microvascular imaging. Multimodal ultrasound can potentially be a useful and quick adjunctive tool for the pre-operative assessment of skin cancer by delineating the tumour depth and deepest skin layer involvement, ensuring complete excision of tumour.

A comprehensive investigation involving numerous HTL and ETL layers to design and simulate high-efficiency Ca3AsI3-based perovskite solar cells

A lot of people are interested in inorganic cubic Calcium-Arsenic-Iodide perovskites because of their unique electronic, structural, and visual properties. This research explores new hybrid perovskite solar cells (HPSCs) based on Calcium-Arsenic-Iodide (Ca3AsI3) with various wide-energy-gap chalcogenide electron transport layers (ETLs) (IGZO, ZnO, SnO2, and TiO2) and several hole transport layers (HTLs) (Sb2S3, P3HT, and CuO). Initially, the optimal ETL was selected, and its thickness was varied in the SCAPS-1D simulator to determine its impact on device performance. A detailed study was carried out on three devices (device-I (Al/FTO/ZnO/Ca3AsI3/Sb2S3/Ni), device-II (Al/FTO/ZnO/Ca3AsI3/P3HT/Ni), and device III (Al/FTO/ZnO/Ca3AsI3/CuO/Ni)) using the most promising ETL, which was ZnO. The effects of key parameters like doping concentration, layer depth, defect density, operating thermal level, and interface defect density were thoroughly investigated. Finally, a comprehensive investigation of several presented composite structures was conducted to establish the benchmark for the latest effective Ca3AsI3-based photocell. The top power conversion efficiency (PCE) achieved 29.12 % for the device −I, having a value of VOC 1.281 V, a value of JSC 25.387 mA/cm2, and a value of fill factor (FF) 89.48 %. In evaluation, PCEs of 26.39 % and 21.25 % were acquired for devices II and III, respectively. Furthermore, quantum efficiency (QE%), the electron hole, the generation, recombination rates, and series-shunt resistance characteristics were examined in detail. Therefore, the device-I exhibits great promise for integration into Ca3AsI3-based hybrid perovskite high-performance photovoltaic devices.

Global climatological dataset of undersea acoustic parameters derived from the NCEI World Ocean Atlas 2023

Acoustics is most effective in undersea detection, localization, and communication. Establishment of a global climatological dataset of undersea acoustic parameters becomes urgent. In building such a dataset, first we use the Thermodynamic Equation of Seawater-2010 (TEOS-10) to calculate the sound speed (SS) from the gridded temperature and salinity fields of the NOAA/NCEI World Ocean Atlas 2023. Second, we determine the depth of overall minimum from SS profile as the deep sound channel (DSC) axis depth, the depth of overall maximum between the surface and DSC axis as the sonic layer depth (SLD), the depth of the local minimum between SLD and DSC axis as the second sound channel (SSC) depth, and the depth with the SS equalling the maximum SS as the critical depth. Third, we obtain the SS at the surface, SLD, DSC axis, and SSC axis. Fourth, we determine the other acoustic parameters such as In-layer gradient, below layer gradient, DSC strength, SSC strength, depth excess, and surface duct cut-off frequency. The dataset is publicly available.

Unravelling depth layers of microbial communities, nitrogen transformation rate, and extracellular polymeric substances in anammox granules

Anammox granules harbor a variety of bacteria, with each granule layer providing a niche for bacteria with specific metabolic functions. To investigate microbial distribution, nitrogen transformation rates, and extracellular polymeric substance (EPS) content across the depth layers of anammox granules, granules sized 0.9–2.0 mm were sheared into seven layers. The outer layers exhibited higher anammox bacterial abundance (9.9 %) than the inner layers (8.1 %). In contrast, the abundance of denitrifying bacteria increased from 25.0 % in outer layers to 34.9 % in inner layers. Nitrifying bacteria, along with heterotrophic nitrifying and aerobic denitrifying bacteria, were predominantly found in outer layers. Despite the higher EPS content in inner layers, denitrification rates remained low across all layers, likely due to limited carbon availability from microbial lysis or EPS. These findings offer valuable insights into the niche distribution of microbial communities within anammox granules.

Plume discharge strategies for artificial downwelling in stagnant linear stratified environments

Artificial downwelling (AD) potentially mitigates oceanic hypoxia by utilizing pipelines or other artificial structures to transport oxygen-rich seawater from the surface to greater depths. However, limited research has focused on determining the discharge parameters for surface waters, which can often rise back to the upper layer or scour the seafloor sediments if discharged at inappropriate parameters. To address this issue, this study proposes a theory for determining the optimal discharge height of oxygen-rich plumes based on their hydrodynamic characteristics. Laboratory experiments were conducted in a linearly stratified flume to validate the theoretical model. The results show that the proposed theory can effectively calculate the important parameters of the AD jet, including the maximum depth, spread depth, and thickness of the intrusion layer. Furthermore, based on the theory of optimal discharge height, this paper provides plume discharge strategies for a typical AD device in field applications to regulate the operating parameters of the AD equipment, which can effectively keep the oxygen-enriched seawater completely in the hypoxic layer, avoid sediment-suspended secondary pollution, and maximize the oxygenation efficiency in different sea conditions.

Habitat suitability modeling of loggerhead sea turtles in the Central-Eastern Mediterranean Sea: a machine learning approach using satellite tracking data

Understanding how sea turtle species move through the environment and respond to environmental features is fundamental for sustainable ecosystem management and effective conservation. This study investigates the habitat suitability of the loggerhead sea turtle (Caretta caretta) in the Adriatic and Northern Ionian Seas (Central-Eastern Mediterranean) by developing and validating a multidisciplinary framework that leverages machine learning to investigate movement patterns collected by satellite tags Argos satellite tags. Satellite tracking data, enriched with sixteen environmental variables from the Copernicus Marine Service and EMODnet-bathymetry, were analyzed using Random Forest models, obtaining an accuracy of 80.9% when classifying presence versus pseudo-absence of loggerhead sea turtles. As main findings, sea bottom depth, surface chlorophyll (chl-a), and mixed layer depth (MLD) were identified as the most influential features in the habitat suitability of these specimens. Moreover, statistically significant differences, evaluated using t-test statistics, were found between coastal and pelagic locations, for the different seasons, in mixed layer depth, chl-a, 3D-clorophyll, salinity and phosphate. Although based on a limited sample of tagged animals, this study demonstrates that the distribution patterns of loggerhead sea turtles in Mediterranean coastal and pelagic areas are primarily influenced by sea water features linked to productivity and, consequently, to potential prey abundance. Additionally, this multidisciplinary framework presents a replicable approach that can be adapted for various species and regions.

Global distribution prediction and ecological conservation of basking shark (Cetorhinus maximus) under integrated impacts

Global climate change presents substantial threats to marine ecosystems, with particularly profound impacts on widely distributed migratory species. The basking shark (Cetorhinus maximus), the second-largest fish species, faces significant conservation challenges due to overfishing, habitat degradation, and climate change, necessitating urgent research to address knowledge gaps in its spatial distribution and interactions with changing marine environments. This study employs various environmental variables and distribution data to construct a global species distribution model for basking sharks, predicting their distribution patterns under current and future climate scenarios. The results indicate that chlorophyll, sea surface temperature, silicate and mixed layer depth are the primary factors determining habitat suitability for basking sharks. Under high-emission scenarios (Shared Socioeconomic Pathway, SSP5–8.5), our model predicts a shift toward the higher latitudes of the northern hemisphere in basking shark habitats, including temperate and sub-Arctic waters. Ecological corridor analysis identifies critical migratory pathways and pinch points, emphasizing the importance of incorporating the effects of climate change and human activities in the formulation of conservation strategies. The finding underscores the importance of integrated conservation strategies, highlighting how positive human interventions can aid in accurately identifying critical ecological corridors to ensure the long-term survival of the basking shark. Adaptive, science-based conservation measures are crucial to mitigating the effects of climate change and human activities, supporting the resilience of marine ecosystems.

Axial Load Test Procedure of Short Bored Pile Foundation Model in the Field for Tip Support Bearing Study

Axial load testing on short drilled pile foundation models in the field requires several criteria to be met, including: depth of the tip support layer, adjustments to the capacity and arrangement of the test equipment so that it can still refer to the testing standards. In order for the axial working load on the foundation model to be carried only by the toe bearing resistance, the frictional resistance of the blanket is eliminated through lubrication with liquid silicone. The toe bearing resistance is mobilized at large deformations from 18%d to 30%d. The load test tip bearing resistance value is only about half of the empirically calculated tip bearing resistance.