Main Layer Research Articles

Land subsidence along the Beijing-Tianjin high-speed railway before and after the South-to-North water diversion project with multi-source monitoring datasets

The development of land subsidence has seriously affected the safe operation of Beijing-Tianjin high-speed railway. The South-to-North Water Diversion Project Central Route (SNWDP-CR) was officially put into operation in December 2014. It has changed the water supply pattern in Beijing and provided conditions for reducing groundwater exploitation and controlling land subsidence. In this paper, the time-series interferometric data, in situ monitoring data of recent 20 years and the basic geological datasets are combined to compare and analyze the changes of groundwater level, land subsidence and the main subsidence layers along the Beijing-Tianjin high-speed railway before and after the SNWDP-CR. The effects of the environment of Quaternary sedimentary, groundwater exploitation and soil deformation of different lithology on land subsidence along the high-speed railway under the background of new water conditions are revealed. The main conclusions are as follows: 1) The serious land subsidence area along the Beijing-Tianjin high-speed railway always concentrated in the section of DK11-DK23. After the operation of SNWDP-CR, the land subsidence along the railway generally showed a slowing trend. The maximum subsidence rate was reduced from 80 mm/yr to 49 mm/yr. The length of subsidence rate that more than 50 mm/yr of the section was reduced from 8.0 km to 0 km. 2) The groundwater level of different aquifer groups along the Beijing-Tianjin high-speed railway rose and declined before and after the SNWDP-CR. in eastern part of the plain, the groundwater level of each aquifer group has changed from a continuous decline (range 0.13–1.82 m) to a gradual rise (range 0.45–1.87 m) since 2017. However, in the southeast of the plain, the groundwater level still showed a continuous decline trend, with an average annual decline of 1.2–1.8 m. 3) From 2006 to 2019, the subsidence of the first, second and third compression layer group along the railway accounted for 2.71%, 28.29% and 69%, respectively. The third compression layer group (monitoring layer 94–182 m) had the largest subsidence proportion and was the main subsidence layer. 4) The land subsidence along the Beijing-Tianjin high-speed railway is controlled by the basement structure. The difference of groundwater exploitation intensity led to differences in the spatial distribution of land subsidence along the railway. The subsidence of the soil layer below the bearing layer (about 50 m) of the high-speed railway pile foundation exhibited the characteristics of viscoplastic or viscoelastic plasticity deformation. This section of strata is a key layer that needs to be considered for land subsidence control along the Beijing-Tianjin high-speed railway in the future.

A new shared energy storage business model for data center clusters considering energy storage degradation

In recent years, the energy consumption of data centers (DCs) has shown a sharp upward trend. Given the high investment cost of energy storage, this study introduces the concept of energy sharing within a data center cluster (DCC) and proposes a novel shared energy storage (SES) business model. The model realizes the co-optimization for DCC and SES and is divided into three main layers. Firstly, considering the SES's economy and the DCC's cleanliness, the upper layer establishes a novel multi-objective optimization configuration scheme for SES with lifetime degradation. In addition, a two-stage robust optimization technique for DCC is proposed in the middle layer, considering the demand response mechanism for data loads. Subsequently, the concept of contribution degree (CD) is defined, and a novel benefit distribution mechanism based on cooperative game and CD is presented in the lower layer. The simulation results demonstrate that the proposed business model positively enhances the economic benefits of the SES and DCC. The constructed uncertainty optimization model exhibits strong robustness, and the proposed benefit allocation scheme is fairer and more reasonable.

Molecular and Heterojunction Device Engineering of Solution-Processed Conjugated Reticular Oligomers: Enhanced Photoelectrochemical Hydrogen Evolution through High-Effective Exciton Separation.

Covalent organic frameworks (COFs) face limited processability challenges as photoelectrodes in photoelectrochemical water reduction. Herein, sub-10nm benzothiazole-based colloidal conjugated reticular oligomers (CROs) are synthesized using an aqueous nanoreactor approach, and the end-capping molecular strategy to engineer electron-deficient units onto the periphery of a CRO nanocrystalline lattices (named CROs-Cg). This results in stable and processable "electronic inks" for flexible photoelectrodes. CRO-BtzTp-Cg and CRO-TtzTp-Cg expand the absorption spectrum into the infrared region and improve fluorescence lifetimes. Heterojunction device engineering is used to develop interlayer heterojunction and bulk heterojunction (BHJ) photoelectrodes with a hole transport layer, electron transport layer, and the main active layers, using a CROs/CROs-Cg or one-dimensional (1D) electron-donating polymer HP18 mixed solution via spinning coating. The ITO/CuI/CRO-TtzTp-Cg-HP18/SnO2/Pt photoelectrode shows a photocurrent of 94.9µA cm‒2 at 0.4V versus reversible hydrogen electrode (RHE),which is 47.5 times higher than that of ITO/Bulk-TtzTp. Density functional theory calculations show reduced energy barriers for generating adsorbed H* intermediates and increased electron affinity in CROs-Cg. Mott-Schottky and charge density difference analyses indicate enhanced charge carrier densities and accelerated charge transfer kinetics in BHJ devices. This study lays the groundwork for large-scale production of COF nanomembranes and heterojunction structures, offering the potential for cost-effective, printable energy systems.

Revealing the Spatial-Temporal Evolution and Obstacles of Ecological Security in the Xiamen-Zhangzhou-Quanzhou Region, China

Climate change and human activities have caused various ecological risks to coastal urban agglomerations. Ecological security refers to the state of health of an ecosystem and its integrity. An objective and comprehensive evaluation of ecological security is significant for protecting the structure and function of coastal ecosystems. The driving force–pressure–state–impact–response (DPSIR) model was used to construct a dynamic simulation model of ecological security in the Xiamen–Zhangzhou–Quanzhou region (XZQR), located on the eastern coast of China. The ecological security level (ESL) characteristics of the spatial and temporal patterns were evaluated by calculating the ecological security index (ESI). Obstacle factors were analyzed as well. The results show the following: (1) From 2011 to 2021, the average ESI rose from 0.238 to 0.686 and went through a relatively insecure stage (2011–2015), a critical stage (2016–2019), and a relatively secure stage (2020–2021). (2) The ESI level in Quanzhou was higher in the early stage, and the level of ecological security in Zhangzhou showed a significant rising trend, increasing by 0.541. Its increase depended on increases in the impact layer. (3) The impact layer is the main obstacle layer affecting the ESL, and the main obstacles include CO2 emissions (0.117), annual rainfall (0.091), general public budget expenditures (0.082), GDP growth rates (0.082), and green coverage in built-up areas (0.075). Therefore, we recommend promoting the complementary advantages of the XZQR and implementing ecological restoration projects.

Long-term impact of low-energy femtosecond laser and manual cataract surgery on macular layer thickness: A prospective randomized study.

To evaluate change in retinal layers 18 months after femtosecond laser-assisted cataract surgery (LCS) and manual cataract surgery (MCS) in a representative age-related cataract population using artificial intelligence (AI)-based automated retinal layer segmentation. This was a prospective, randomized and intraindividual-controlled study including 60 patients at the Medical University of Vienna, Austria. Bilateral same-day LCS and MCS were performed in a randomized sequence. To provide insight into the development of cystoid macular oedema (CME), retinal layer thickness was measured pre-operatively and up to 18 months post-operatively in the central 1 mm, 3 mm and 6 mm. Fifty-six patients completed all follow-up visits. LCS compared to MCS did not impact any of the investigated retinal layers at any follow-up visit (p > 0.05). For the central 1 mm, a significant increase in total retinal thickness (TRT) was seen after 1 week followed by an elevated plateau thereafter. For the 3 mm and 6 mm, TRT increased only after 3 weeks and 6 weeks and decreased again until 18 months. TRT remained significantly increased compared to pre-operative thickness (p < 0.001). Visual acuity remained unaffected by the macular thickening and no case of CME was observed. Inner nuclear layer (INL) and outer nuclear layer (ONL) were the main causative layers for the total TRT increase. Photoreceptors (PR) declined 1 week after surgery but regained pre-operative values 18 months after surgery. Low-energy femtosecond laser pre-treatment did not influence thickness of the retinal layers in any topographic zone compared to manual high fluidic phacoemulsification. TRT did not return to pre-operative values 18 months after surgery. The causative layers for subclinical development of CME were successfully identified.

Testing validity of 1D models for impurity fraction scaling for divertor detachment with EDGE2D-EIRENE

Predictions of the Huber–Chankin (HC) scaling for the upstream impurity fraction were verified in a series of EDGE2D-EIRENE (‘code’) runs for highly radiating plasmas with nitrogen injection. The main quantity extracted from the code was poloidally averaged, from X-point to X-point, separatrix impurity fraction cZ in the main scrape-off layer (SOL). Variation of the main working gas (H, D and T) revealed a qualitative agreement between the model and code results owing to the very large isotope difference in the predicted cZ values caused primarily by the inverse isotope mass dependence of the H-mode power threshold assumed in the HC model and implemented in the code. At the same time, the variation of the toroidal field and safety factor in deuterium cases yielded no correlation between the model predictions and code results. The code showed much higher local impurity fractions (fZ ) in the divertor compared to the main SOL, as well as large case-to-case variations in the divertor to the main SOL ratio of impurity fractions. The analysis of code results has wide-ranging consequences not only for the HC model, but also for other similar 1D models which use simple geometry ignoring strong neutral recycling in the divertor/ Different topology makes plasma parameters in the divertor and main SOL very different, resulting in different impurity charge state composition. Missing mechanisms in 1D codes (e.g. friction and thermo-forces exerted on impurity ions by main working gas ions) lead to impurity density redistribution. Neglecting all above factors, 1D models assume a constant impurity fraction along field lines.

LiDAR point clouds analysis computer tools for teaching autonomous vehicles perception algorithms

AbstractThe technological developments behind autonomous vehicles cover several areas and engineers training in this field represents a challenge. The main layers include perception, decision making, and acting. In the first one, different technologies can be used. The processing of the information provided by the sensors must allow successive modules to understand the environment and Laser imaging Detection and Ranging (LiDAR) technology is one of the most promising ones nowadays for this task. It offers great robustness in detection, but the extraction of information from the point cloud involves the development of complex algorithms that could be very time‐consuming if an experimental teaching is intended. This article presents two educational solutions for deepening in perception algorithms using LiDAR for autonomous driving: a closed ad‐hoc computer application for two‐dimensional (2D) LiDAR point cloud processing and an oriented set of commands for three‐dimensional (3D) LiDARs in Matlab. Their use allows main concept exploration in practical sessions with little time consumption and provides students a general overview of the tasks that must be performed by the perception layer in the autonomous vehicles. Furthermore, these tools provide the possibility of organizing different activities in the classroom related to theoretical and experimental issues, and understanding of results because the most tedious tasks are eased.

Investigation of the driving voltage on the high performance flexible ATF-ECDs based on PET/ITO/NiOX/LiTaO3/WO3/ITO

High performance flexible all-thin-film electrochromic devices (ATF-ECDs) have been fabricated and systematically investigated by operating with different driving voltages during the electrochromic processes. The device structure (cross-section) and material properties of some main functional layers were presented and analysed. The electrochromic properties including kinetic and spectral tests were systematically investigated through combining chronoamperometry, cyclic voltammetry measurements and optical measurements. In addition, the open circuit memory measurement was also carried out. A much higher driving voltage might lead to a current leakage inside the device during coloring process. A proper driving voltage is needed for achieving high device performances. More details were widely described and deeply discussed.

Ambient Monitoring Portable Sensor Node for Robot-Based Applications.

The leakage of gases and chemical vapors is a common accident in laboratory processes that requires a rapid response to avoid harmful effects if humans and instruments are exposed to this leakage. In this paper, the performance of a portable sensor node designed for integration with mobile and stationary robots used to transport chemical samples in automated laboratories was tested and evaluated. The sensor node has four main layers for executing several functions, such as power management, control and data preprocessing, sensing gases and environmental parameters, and communication and data transmission. The responses of three metal oxide semiconductor sensors, BME680, ENS160, and SGP41, integrated into the sensing layer have been recorded for various volumes of selected chemicals and volatile organic compounds, including ammonia, pentane, tetrahydrofuran, butanol, phenol, xylene, benzene, ethanol, methanol, acetone, toluene, and isopropanol. For mobile applications, the sensor node was attached to a sample holder on a mobile robot (ASTI ProBOT L). In addition, the sensor nodes were positioned close to automation systems, including stationary robots. The experimental results revealed that the tested sensors have a different response to the tested volumes and can be used efficiently for hazardous gas leakage detection and monitoring.

Exploring the differences of moisture traceability methods based on MixSIAR model under different nitrogen applications of wheat in the Arid Region of Northwest China

The Bayesian stable isotope mixing (MixSIAR) model was widely used in water source tracing. However, double isotope and single isotope calculated by the MixSIAR model led to different results in terms of previous studies. The effect of different nitrogen treatments on the water traceability results of wheat crops is still unclear. This study investigated the wheat root water uptake patterns at different nitrogen application gradients, as well as at different isotopes in the MixSIAR model. The results showed the main soil water uptake layer was relative constant at the treatment of 15% nitrogen reduction, and the water source was mainly from 0–10 cm soil layer from jointing to harvest. The main soil water uptake layer significantly varied at the treatment of 30% nitrogen reduction, which yielded the highest. 45% reduction of nitrogen treatment showed the deepest soil water uptake absorption on average with the whole wheat growth period. The consistency of the results between the two single isotope methods was better in the early stage of wheat and worse in the later stage. The inconsistency of root water uptake distribution results between the two single isotope methods might be influenced by the water transport mechanism of wheat stem and sampling errors. The double isotope method had the lowest uncertainty, but it might amplify the error of the sampling process. The uncertainty of the single hydrogen isotope method was less than single oxygen isotope method. This study provided a new evaluation of hydrogen-oxygen stable isotope traceability methods for wheat under different nitrogen treatments, and gave more ideas and insights for subsequent crop water traceability by the MixSIAR method.

Electrical Properties of Vertical Dominant Charge Structures Observed in Corsican Thunderstorms With a LMA

AbstractLightning characteristics of Corsican storms with different charge structures are investigated in this study. Observations of an LMA network are used to document the total lightning activity. Complementary lightning observations of the lightning detection network Météorage are also used. A clustering algorithm is used to build a database of electrical cells from June to October 2018. A method is also applied to infer the vertical charge structure, as dominant dipoles, per 10‐min period for each electrical cell. As an example, one cell recorded in July 2018 is discussed. The cell database is then presented as well as the main electrical properties according to the dominant charge structures. For instance, the higher in altitude the dominant dipole, the higher the flash rate. Overall, dominant negative dipole are observed for 25% of the 10‐min periods and can be separated into two categories: (a) low altitude negative dipole class dominated by negative cloud‐to‐ground (CG) flashes with a main positive layer located between 2 and 4 km height and (b) high altitude negative dipole class, dominated by negative intracloud (IC) with a main positive layer at 5 km height. Dominant positive dipole can also be separated into two categories with (a) a dominant positive dipole located between 4.5 and 10 km high, −CG dominance, weak flash rate and (b) higher altitude dominant positive dipole, +IC dominance and a larger +CG fraction. The synergistic use of LMA and Météorage observations independently gives a rational type and polarity classification with regard to the vertical charge structure.

Influence of energy density on the microstructure and property regulation of additive manufactured pure nickel

In this work, laser powder bed fusion (LPBF) technology was used to fabricate pure nickel components, and the densification behavior and microstructure of pure nickel with different energy densities were investigated. The results indicate that for LPBF-fabricated pure nickel components, the relative density reaches a peak of 98.76% at an energy density of 101 J/mm3. With the increase of energy density, a large number of pores appear inside the grains, and the grains grow epitaxially along the building direction within multiple molten pools, pores gradually disappear after undergoing remelting at the edges of the melting tracks. Among these, competitive inward growth of columnar crystals may be the main cause of dislocations and new grain generation. The grains are primarily distributed along the Ni (111) or Ni (110) orientations, and with the increase of energy density, the grains with these two orientations increase. The surface energy follows the sequence of Ni (220) &gt; Ni (200) &gt; Ni (111). Due to the stacking of the &lt;101&gt; oriented main layer and the &lt;001&gt; oriented sub-layer in the building direction, the sample with higher energy density exhibits a strong Ni {110} texture, accompanied by increased tensile properties.

Research on Measurement Technology of Coefficient of Thermal Expansion of Honeycomb Structure

Honeycomb structure is widely used as the main sandwich layer of load-bearing structure in aviation, aerospace and other fields because of its light weight and high strength. Under some service conditions, the deformation of the honeycomb structure affected by high and low temperature will affect the overall mechanical performance of the structure. Therefore, accurate evaluation of the thermal expansion performance of the honeycomb structure becomes the key to structural design. However, the existing measurement methods of thermal expansion coefficient mainly have problems such as the hollow structure cannot be effectively contacted, the area of the grid optical target is limited and difficult to detect. In view of the above conditions, in this paper, a method for measuring the thermal expansion coefficient of honeycomb structure is proposed, and the measurement system is built, and the thermal expansion performance of honeycomb structure is effectively obtained by testing the honeycomb structure. At the same time, the measurement accuracy of the system is verified by using standard test pieces. The experimental results show that the method has high measurement accuracy.

Ultrastructural diversity in the shell of Artemia resting eggs.

The shell of Artemia resting egg, which is a delicate multilayered envelope surrounding the inside diapause embryo, plays an important role in the survival strategy of Artemia. To date, the ultrastructure of resting eggshell has been studied for only handful populations, and knowledge about the diversity of shell structure is still limited. In this paper, resting eggs from 13 Artemia populations were studied by transmission electron microscopy. Results show that the basic configuration of resting eggshell is quite conservative, but variations are not uncommon in the fine ultrastructure of each main layer of the shell (e.g., the shape and distribution of the radially oriented pores in the cortical layer; the size, number and arrangement of chambers in the alveolar layer; and the development state of outer cuticular membrane [OCM]). The ultrastructural variation of eggshell seems not to be linked with species and reproductive mode of Artemia. Resting eggs from very high habitats (4300+ m above sea level [a.s.l.]) on Qinghai-Tibet Plateau and certain tropical salterns have a hypoplastic OCM, which may be related to the adaptation to habitat conditions such as low oxygen concentration. RESEARCH HIGHLIGHTS: Comparative study on resting eggs from 13 Artemia populations reveals high diversity in the fine structure of eggshell. Resting eggs from very high (4300+ m a.s.l.) habitats commonly have a hypoplastic OCM.

Prediction of Heavy Metal Pollution in Soil Based on SSA-XGBoost Model and 3D Geological Model

ABSTRACT The problem of soil heavy metal pollution in decommissioned sites has become an environmental threat and challenge faced by countries around the world. Establishing a high-precision 3D model of contaminants is essential for pollution risk assessment and accurate monitoring of contaminated sites. In this study, 3D geological model and SSA-XGBoost model are proposed to predict heavy metal concentration in a site. These models can effectively improve the prediction accuracy of soil heavy metals, and the RMSE of the XGBoost model optimized by the SSA algorithm is reduced by 24.3%-34.3%. Compared with other machine learning models, the SSA-XGBoost model has optimal performance in improving the prediction accuracy of soil heavy metals. It is suitable for the areas with significant spatial heterogeneity of soil heavy metals. Using the SSA-XGBoost model and 3D geological model, the 3D spatial distribution characteristics of heavy metals in contaminated soil are determined. The concentration of soil pollutants ranked as As>Pb>Mo, and the overall pollution degree decreases gradually from top to bottom. The pollutants are mainly distributed in the production workshop area in the southwest of the site, and the miscellaneous fill layer is the main soil layer that needs to be remediated.

Evaluation of biophysical alterations in the epithelial and endothelial layer of patients with Bullous Keratopathy

The cornea is a fundamental ocular tissue for the sense of sight. Thanks to it, the refraction of two-thirds of light manages to participate in the visual process and protect against mechanical damage. Because it is transparent, avascular, and innervated, the cornea comprises five main layers: Epithelium, Bowman's layer, stroma, Descemet's membrane, and endothelium. Each layer plays a key role in the functionality and maintenance of ocular tissue, providing unique ultrastructural and biomechanical properties. Bullous Keratopathy (BK) is an endothelial dysfunction that leads to corneal edema, loss of visual acuity, epithelial blisters, and severe pain, among other symptoms. The corneal layers are subject to changes in their biophysical properties promoted by Keratopathy. In this context, the Atomic Force Microscopy (AFM) technique in air was used to investigate the anterior epithelial surface and the posterior endothelial surface, healthy and with BK, using a triangular silicone tip with a nominal spring constant of 0.4 N/m. Six human corneas (n = 6) samples were used for each analyzed group. Roughness data, calculated by third-order polynomial adjustment, adhesion, and Young's modulus, were obtained to serve as a comparison and identification of morphological and biomechanical changes possibly associated with the pathology, such as craters and in the epithelial layer and exposure of a fibrotic layer due to loss of the endothelial cell wall. Endothelial cell membrane area and volume data were calculated, obtaining a relevant comparison between the control and patient. Such results may provide new data on the physical properties of the ocular tissue to understand the physiology of the cornea when it has pathology.

Periodontitis and Outer Retinal Thickness: a Cross-Sectional Analysis of the United Kingdom Biobank Cohort

PurposePeriodontitis, a ubiquitous severe gum disease affecting the teeth and surrounding alveolar bone can heighten systemic inflammation. We investigated the association between very severe periodontitis and early biomarkers of age-related macular degeneration, in individuals with no eye disease. DesignCross-sectional analysis of the prospective community-based cohort United Kingdom (UK) Biobank. ParticipantsSixty-seven thousand three hundred eleven UK residents aged 40-70 years recruited between 2006-2010 underwent retinal imaging. MethodsMacular-centered optical coherence tomography images acquired at the baseline visit were segmented for retinal sublayer thicknesses. Very severe periodontitis was ascertained through a touchscreen questionnaire. Linear mixed effects regression modeled the association between very severe periodontitis and retinal sublayer thicknesses adjusting for age, sex, ethnicity, socioeconomic status, alcohol consumption, smoking status, diabetes mellitus, hypertension, refractive error, and previous cataract surgery. Main Outcome MeasuresPhotoreceptor layer (PRL) and retinal pigment epithelium-Bruch’s membrane (RPE-BM) thicknesses. ResultsAmong 36,897 participants included in the analysis, 1,571 (4.3%) reported very severe periodontitis. Affected individuals were older, lived in areas of greater socioeconomic deprivation and were more likely to be hypertensive, diabetic and current smokers (all p<0.001). On average, those with very severe periodontitis were myopic (-0.29 ± 2.40 diopters) while those unaffected were hyperopic (0.05 ± 2.27 diopters, p<0.001). Following adjusted analysis, very severe periodontitis was associated with thinner PRL (-0.55 μm, 95% CI: -0.97, -0.12, p=0.022) but there was no difference in RPE-BM thickness (0.00 μm, 95% CI: -0.12, 0.13, p=0.97). The association between PRL thickness and very severe periodontitis was modified by age (p<0.001). Stratifying individuals by age, thinner PRL was seen among those aged 60-69 years with disease (-1.19 μm, 95% CI: -1.85, -0.53, p<0.001) but not among those under 60 years. ConclusionsAmong those with no known eye disease, very severe periodontitis is statistically associated with a thinner PRL, consistent with incipient age-related macular degeneration. Optimizing oral hygiene may hold additional relevance for people at risk of degenerative retinal disease.

A Deep Learning Approach for the Automated Classification of Geomagnetically Induced Current Scalograms

During geomagnetic storms, which are a result of solar wind’s interaction with the Earth’s magnetosphere, geomagnetically induced currents (GICs) begin to flow in the long, high-voltage electrical networks on the Earth’s surface. It causes a number of negative phenomena that affect the normal operation of the entire electric power system. To investigate the nature of the phenomenon and its effects on transformers, a GIC monitoring system was created in 2011. The system consists of devices that are installed in the neutrals of autotransformers at five substations of the Kola–Karelian power transit in northwestern Russia. Considering the significant amount of data accumulated over 12 years of operating the GIC monitoring system, manual analysis becomes impractical. To analyze the constantly growing volume of recorded data effectively, a method for the automatic classification of GICs in autotransformer neutrals was proposed. The method is based on a continuous wavelet transform of the neutral current data combined with a convolutional neural network (CNN) to classify the obtained scalogram images. The classifier’s performance is evaluated using accuracy and binary cross-entropy loss metrics. As the result of comparing four CNN architectures, a model that showed high GIC classification performance on the validation set was chosen as the final model. The proposed CNN model, in addition to the main layers, includes pre-processing layers and a dropout layer.

Geochemical Behavior of Shallow Buried Nodules from Clarion–Clipperton Fracture Zone in the East Pacific: A LA-ICP-MS Mapping Analysis Perspective

The Clarion–Clipperton Fracture Zone of the east Pacific contains numerous shallow buried nodules that are in direct contact with pore water in sediment, providing a direct reflection of the interaction between nodules and sediment. However, research on the geochemical behavior of these shallow-buried nodules is limited. This study used laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), high-resolution transmission electron microscopy (HRTEM), and X-ray diffraction (XRD) to compare mineral and element distribution in shallow buried nodules with surface nodules. The shallow buried nodules are products of nodules entering the burial stage. In comparison to surface nodules, shallowly buried nodules develop a fourth oxidized-suboxic diagenetic growth layer after entering the burial stage, in addition to the three main growth inner layers (L1, L2, L3). We suggest that L4 is not influenced by the bottom water source and that the presence of todorokite and the high flux of Mn2+ in the sediment pore water compete with other metal elements to enter the lattice of manganate, resulting in significantly higher Mn, W, and Li contents in L4 compared to L2. However, the content of Ni, Mg, and other hydrogenetic elements is much lower in L4 compared to L2. We suggest that the instantaneous change in surface primary productivity results in a sudden shift in the redox environment of the upper sediment layer. This reaction leads to the reduction of solid-phase Mn, providing growth opportunities for the buried nodules. Simultaneously, this may also be the reason why the growth layer of the nodules is jointly controlled by the sedimentary processes of hydrogenetic, oxic diagenetic, and suboxic diagenetic processes.

A deep learning approach based on multi-view consensus for SQL injection detection

SQL injection (SQLi) attacks are one of the oldest and most serious security threats, consistently ranking among the top ten critical web security risks. Traditional defense mechanisms against SQL injection predominantly use blacklists to disallow common injection characters or terms. However, the major challenge for these systems is to create a comprehensive list of potential SQLi characters, terms, and multi-terms that encompass various types of SQLi attacks (time-based, error-based, etc.), taking into account various SQL datasets (such as MySQL, Oracle, and NoSQL). Recently, some research studies have concentrated on feature learning from SQL queries by applying some well-known deep architectures to detect SQLi attacks. Motivated by a similar objective, this research introduces a novel deep learning-based SQLi detection system named “Bidirectional LSTM-CNN based on Multi-View Consensus” (MVC-BiCNN). The proposed method implements a pre-processing step that generates multiple views from SQL data by semantically encoding SQL statements into their corresponding SQL tags. By utilizing two different main layers, which are bidirectional long short-term memory (LSTM) and convolutional neural network (CNN), the proposed method learns a joint latent space from multi-view representations. In the detection phase, the proposed method yields separate predictions for each representation and assesses whether the query constitutes an SQLi attack based on a consensus function’s output. Moreover, Interpretable Model-Agnostic Annotations (LIME), one of the methods of Explainable Artificial Intelligence (XAI), is employed for the purpose of interpreting the model’s results and analyzing the SQL injection (SQLi) inputs. The experimental results demonstrate that MVC-BiCNN outperforms the baseline methods, yielding 99.96% detection rate.