Search

The age and dolomitization processes in the Paleo-oil reservoir zone, which is composed of massive dolostones found in the Qiangtang Basin (SW China), are still debated. In this research, the Long'eni-Geluguanna Area was selected. Macroscopic information, thin sections, and geochemical methods were used to investigate the dolomitization characteristics and the processes that controlled dolomitization. Five types of replacive dolomites and two types of dolomite cement were observed. Some of the dolomites displayed ghosts of primary sedimentary structures. Saddle dolomites were prevalent, occurring in the interparticle and moldic pores of the limestone which should have been filled at an early diagenetic stage. Ten microfacies types were identified. The foraminifera assemblage provides evidence that the studied interval is of Early Jurassic age. The δ13C values are similar to the contemporaneous seawater signature. The REE+Y patterns of limestones and dolostones exhibit similarities to that of seawater. The mean Na and Sr values are comparable to those of other near-normal seawater dolomites. The δ18O values of all lithologies are markedly depleted. The dolomitization started penecontemporaneously, with deposition. A general sand shoal setting with patch reefs developed. The dolomitizing fluids, near-normal seawater, was probably formed by slight evaporation on top of the shoal. Saddle dolomites in the interparticle and moldic pores might indicate hydrothermal activity, which also caused the recrystallization of some pre-existing dolomites. The recrystallization might have slightly increased the crystal size, demolished the ghost structures, formed saddle dolomites, and altered the REE+Y patterns. The recrystallization extent diminished with increasing distance from the fluids-providing fracture. Furthermore, the existence of protected areas within the sand shoal settings could enhance the vertical and horizontal heterogeneity of dolostone reservoirs. [ABSTRACT FROM AUTHOR]

Porosity assessment is a vital component for reservoir evaluation in the oil and gas sector, and with technological advancement, reliance on conventional methods has decreased. In this regard, this research aims to reduce reliance on well logging, purposing successive machine learning (ML) techniques for precise porosity measurement. So, this research examines the prediction of the porosity curves in the Sui main and Sui upper limestone reservoir, utilizing ML approaches such as an artificial neural networks (ANN) and fuzzy logic (FL). Thus, the input dataset of this research includes gamma ray (GR), neutron porosity (NPHI), density (RHOB), and sonic (DT) logs amongst five drilled wells located in the Qadirpur gas field. The ANN model was trained using the backpropagation algorithm. For the FL model, ten bins were utilized, and Gaussian-shaped membership functions were chosen for ideal correspondence with the geophysical log dataset. The closeness of fit (C-fit) values for the ANN ranged from 91% to 98%, while the FL model exhibited variability from 90% to 95% throughout the wells. In addition, a similar dataset was used to evaluate multiple linear regression (MLR) for comparative analysis. The ANN and FL models achieved robust performance as compared to MLR, with R2 values of 0.955 (FL) and 0.988 (ANN) compared to 0.94 (MLR). The outcomes indicate that FL and ANN exceed MLR in predicting the porosity curve. Moreover, the significant R2 values and lowest root mean square error (RMSE) values support the potency of these advanced approaches. This research emphasizes the authenticity of FL and ANN in predicting the porosity curve. Thus, these techniques not only enhance natural resource exploitation within the region but also hold broader potential for worldwide applications in reservoir assessment. [ABSTRACT FROM AUTHOR]

In constructing rapid rock identification models for measurement while drilling (MWD) via neural network methods, collecting actual drilling data to train the model is extremely time-consuming and labor-intensive. This requires extensive drilling experiments in various rock types, resulting in limited neural network training data for rock identification that covers a limited range of rock types. To suitably address this issue, a dynamic numerical simulation model for rock drilling is established that generates extensive drilling data. The input parameters for the simulations include torque, drill bit rotation speed, and drilling speed. A neural network model is then developed for rock classification using large datasets from dynamic numerical simulations, specifically those of granite, limestone, and sandstone. Building upon this model, transfer learning is appropriately applied to store the knowledge obtained in the rock identification based on the neural network model. Further training through transfer learning is conducted with smaller datasets obtained during actual drilling, making the model suitable for practical rock identification and prediction in the drilling processes. The neural network rock classification model, incorporating dynamic numerical simulation and transfer learning, achieves a prediction accuracy of 99.36% for granite, 99.53% for sandstone, and 99.82% for limestone. This reveals an enhancement in prediction accuracy of up to 22.94% compared to the models without transfer learning. [ABSTRACT FROM AUTHOR]

Natural disasters, notably landslides, pose significant threats to communities and infrastructure. Landslide susceptibility mapping (LSM) has been globally deemed as an effective tool to mitigate such threats. In this regard, this study considers the northern region of Pakistan, which is primarily susceptible to landslides amid rugged topography, frequent seismic events, and seasonal rainfall, to carry out LSM. To achieve this goal, this study pioneered the fusion of baseline models (logistic regression (LR), K-nearest neighbors (KNN), and support vector machine (SVM)) with ensembled algorithms (Cascade Generalization (CG), random forest (RF), Light Gradient-Boosting Machine (LightGBM), AdaBoost, Dagging, and XGBoost). With a dataset comprising 228 landslide inventory maps, this study employed a random forest classifier and a correlation-based feature selection (CFS) approach to identify the twelve most significant parameters instigating landslides. The evaluated parameters included slope angle, elevation, aspect, geological features, and proximity to faults, roads, and streams, and slope was revealed as the primary factor influencing landslide distribution, followed by aspect and rainfall with a minute margin. The models, validated with an AUC of 0.784, ACC of 0.912, and K of 0.394 for logistic regression (LR), as well as an AUC of 0.907, ACC of 0.927, and K of 0.620 for XGBoost, highlight the practical effectiveness and potency of LSM. The results revealed the superior performance of LR among the baseline models and XGBoost among the ensembles, which contributed to the development of precise LSM for the study area. LSM may serve as a valuable tool for guiding precise risk-mitigation strategies and policies in geohazard-prone regions at national and global scales. [ABSTRACT FROM AUTHOR]

Traffic congestion prediction has become an indispensable component of an intelligent transport system. However, one limitation of the existing methods is that they treat the effects of spatio-temporal correlations on traffic prediction as invariable during modeling spatio-temporal features, which results in inadequate modeling. In this paper, we propose an attention-based spatio-temporal 3D residual neural network, named AST3DRNet, to directly forecast the congestion levels of road networks in a city. AST3DRNet combines a 3D residual network and a self-attention mechanism together to efficiently model the spatial and temporal information of traffic congestion data. Specifically, by stacking 3D residual units and 3D convolution, we proposed a 3D convolution module that can simultaneously capture various spatio-temporal correlations. Furthermore, a novel spatio-temporal attention module is proposed to explicitly model the different contributions of spatio-temporal correlations in both spatial and temporal dimensions through the self-attention mechanism. Extensive experiments are conducted on a real-world traffic congestion dataset in Kunming, and the results demonstrate that AST3DRNet outperforms the baselines in short-term (5/10/15 min) traffic congestion predictions with an average accuracy improvement of 59.05%, 64.69%, and 48.22%, respectively. [ABSTRACT FROM AUTHOR]

The muddy limestone in the Lei32 sub-member of the Middle Triassic Leikoupo Formation in the well Chongtan 1 (CT1) of the Sichuan Basin has yielded promising industrial oil and gas production. This discovery has the potential to become a significant strategic reservoir in the future for oil and gas exploration in the Sichuan Basin. However, the understanding of hydrocarbon accumulation in the muddy limestone of the Lei32 sub-member remains insufficient, which poses limitations on further exploration selection and deployment strategies. This study focuses on the analysis of core samples and laboratory data in the wells CT1 and Jianyang 1 (JY1), aiming to investigate the source rock and reservoir characteristics of the muddy limestone in the Lei32 sub-member, as well as the primary controlling factors influencing the development of the source rock and reservoir. The results show that the Lei32 sub-member in the Central Sichuan Basin is an evaporated lagoon deposition; the tight argillaceous limestone and limy mudstone exhibit the characteristic of source–reservoir integration, belonging to a new type of unconventional oil and gas reservoir. The reservoir space of the argillaceous limestone and limy mudstone in the Lei32 sub-member primarily consists of inorganic and organic micro–nanopores and microfractures. The average porosity and permeability are 2.7% and 0.19 mD, indicating a low-porosity and low-permeability unconventional reservoir. The clay minerals and gypsum content are the important factors influencing reservoir porosity, and the fractures are key factors influencing permeability. This study will elucidate the specific features of hydrocarbon accumulation in the muddy limestone reservoirs of the Lei32 sub-member and provide insights into its exploration potential. [ABSTRACT FROM AUTHOR]

In the field of shield tunnels, the occurrence of uneven longitudinal settlement in segment linings has presented persistent challenges, including heightened risks of localized damage and water leakage. While the adoption of a secondary lining has been proposed as a viable solution to these issues, the question of how to select an appropriate construction time for the secondary lining, one that enables it to fully harness its load-bearing capacity while optimizing the tunnel's overall stress and deformation characteristics, continues to be a pressing concern. To address this issue, this study established a three-dimensional longitudinal refined numerical model of double-layer-lined shield tunnel. In addition, the deformation degree of the segment lining was used as a time indicator to define the construction time for the secondary lining. Subsequently, an analysis of the impact of the construction time of the secondary lining on the longitudinal mechanical properties of the double-layer-lined shield tunnel is conducted through an assessment of tunnel longitudinal deformation and structural stress. The research findings indicated that the construction of the secondary lining improved the longitudinal deformation resistance of shield tunnels. Simultaneously, it led to a significant increase in the longitudinal shear forces within the segment lining and a notable reduction in longitudinal bending moments. Moreover, the construction time of the secondary lining played a pivotal role in these alterations. Considering the longitudinal force situations and load-bearing characteristics of the double-layer lining structure, it was determined that the optimal construction time for the secondary lining fell within the range of 20% to 40% of the total construction duration. In this scenario, the deformation and internal forces within the segment lining remained within permissible limits. Additionally, both the segment lining and the secondary lining were able to fully utilize their load-bearing capacities, ensuring the economic and safety aspects of the tunnel. [ABSTRACT FROM AUTHOR]

The joint roughness coefficient JRC of rock joints is an important parameter for measuring the geometric morphology of rock joints. However, the parameter obtained from traditional calculation methods has certain limitations in reflecting the differences in shear strength of rock joints in different shear directions with the same orientation. Firstly, native joint surface test blocks were cast using three-dimensional reverse reconstruction technology. Subsequently, direct shear tests were conducted under different normal stress and shear direction conditions, followed by numerical simulations using the finite difference software Flac3d. The JRC coefficient calculation method proposed by Yuan was modified and extended by considering the percentage of climbing sections and the effective contact area during the ramping process. This study indicates that the numerical simulation results are in good agreement with the shear test results. The introduction of the shear climbing rate SCR and the curvature coefficient of profile CCP is used to reflect the geometric differences of the joint surfaces in different shear directions. These two parameters are nonlinearly fitted with the experimental results, leading to a calculation formula that characterizes the shear strength characteristics of the joint surfaces in different shear directions. This novel formula is an extension of the JRC–JCS model. [ABSTRACT FROM AUTHOR]

As the typical multi-phase geotechnical material, the particle size distribution of the natural soil–rock mixture (S–RM) has a significant impact on the structural and mechanical properties. The coarse grain content used in the laboratory and simulation tests falls short of accurately describing the particle size distribution feature of the entire material. The main subject of this article is the influence of the fractal dimension on mechanical behaviors based on the fractal theory. The double fractal characteristics were principally discussed along with the typical particle size distribution characteristics of the S–RM in the Three Gorges Reservoir and southwest China. The influence of the various fractal dimensions on the mechanical behaviors of S–RM was then investigated using three groups of large–scale triaxial tests, and the responses of the linear and nonlinear strength indexes were analyzed. The results show that the stress–strain curves of S–RM in the hyperbolic shape are visible under various confining pressure, and the nonlinear strength characteristics can be observed. The coarse grain content exhibits a negative correlation to the average fraction dimension. The difference between the coarse and fine grain fraction dimensions becomes considerably more obvious as the coarse grain content increases, which also increases the error when using the average fractal dimension. The voids between the coarse grains cannot be filled with the fine grains as the grain coarseness grows, resulting in a loose structure and a contact frictional effect, which lowers cohesion and raises the peak friction angle. [ABSTRACT FROM AUTHOR]

Wild Camptotheca acuminata Decne (C. acuminata) resources are becoming endangered and face poor growth. Preliminary investigation results found that the growth of C. acuminata in an artificial mixed forest of Taxus chinensis var. mairei (Lemee et Levl.), Cheng et L. K. Fu (T. chinensis var. mairei) and C. acuminata was significantly higher than that in pure forests. Understanding the reasons for the above differences can help create a mixed forest of T. chinensis var. mairei and C. acuminata to solve the problem of depleting C. acuminata resources. In this study, the growth and soil indexes under two different modes (C. acuminata/T. chinensis var. mairei interplanted and monocultured C. acuminata seedlings) were compared. The results showed that plant height, basal diameter, photosynthesis rate and chlorophyll content of C. acuminata under the interplanting mode were higher than those under monoculture. The growth rates of plant height and basal diameter that were calculated from interplanted specimens increased by 25% and 40%, respectively, compared with those from specimens that were monocultured. Photosynthetic rates from different light intensities under interplanting were higher than those in seedlings under monoculture. The contents of chlorophylls a and b and total chlorophyll under interplanting were 1.50, 1.59, and 1.47 times higher than those under monoculture, respectively. The numbers of bacteria and fungi in the interplanted culture were higher than those in the monoculture. Furthermore, the differences in microbial diversity under different planting modes were analyzed via the amplicon sequencing method. Soil enzyme activities increased under interplanting compared with that in the monoculture. Taxane allelochemicals were detected in the range of 0.01–0.67 μg/g in the interplanting mode from April to September. T. chinensis var. mairei may increase the establishment and productivity of C. acuminata seedlings under interplanting mode through improvements in enzyme activity, changes in microorganism population structure, and release of allelochemicals. [ABSTRACT FROM AUTHOR]

To solve the problem of poor seismic resistance due to the disadvantages of traditional concrete composite beams, such as heavy self-weight in prefabricated buildings, prestressed specified-density concrete composite beams are proposed herein. First, a mix ratio test of specified-density concrete was performed. Second, five prestressed specified-density composite beams, a prestressed ordinary concrete composite beam, and a prestressed semi lightweight concrete cast-in-situ beam were tested. The influence of the precast concrete height, reinforcement ratio, and concrete materials on the failure mechanism, flexural bearing capacity, and short-term stiffness of the composite beams were analysed. From the results, the specified-density concrete composite beams and the ordinary composite beam had similar ultimate bearing capacities, but the average distance between crack spacings of the former was smaller. The precast concrete height affected the bending performance of the prestressed specified density concrete composite beam insignificantly, but the maximum ultimate bearing capacity of the composite beam could be increased by 35.6% by increasing the reinforcement ratio. The composite beam and the cast-in-place beam exhibited similar load-carrying capabilities and deformation properties. The average crack spacing, cracking load, and ultimate load value of the specified density concrete composite beams calculated according to the China national standard "Code for design of concrete structures" were consistent with the measured values. [ABSTRACT FROM AUTHOR]

The decentralized and tamper-proof features of blockchain technology can solve the problems of low compatibility, poor flexibility, and single point of failure in the traditional Industrial Internet of Things (IIoT). However, the transparency of the blockchain ledger makes the privacy disclosure of users a huge security risk. Given the privacy leakage problem exposed in the existing energy trading scheme based on the blockchain, this paper creatively proposes a privacy protection scheme for IIoT energy trading based on an identity-based signature (IBS) and homomorphic encryption. On the premise of satisfying the transaction traceability and verifiability, this scheme uses IBS technology to provide an anonymous mechanism for energy trading nodes and utilizes Paillier homomorphic encryption to prevent the disclosure of transaction amounts. To meet the high-concurrency and high-throughput energy trading requirements in IIoT, moreover, the proposed scheme combines the off-chain storage with cloud assistance and the off-chain transaction based on PCN to reduce redundant data written into the blockchain and to improve the concurrent trading efficiency, respectively. The security analysis and performance evaluation results show that the proposed scheme can realize the dual privacy protection of identities and transaction amounts in the trading process at the cost of reasonable calculation. [ABSTRACT FROM AUTHOR]

The expansion and exploitation of mining resources are essential for social and economic growth. Remote sensing provides vital tools for surface-mining monitoring operations as well as for reclamation efforts in the central Salt Range of the Indus River Basin, Pakistan. This research demonstrates the applicability of remote sensing techniques to the coal mining monitoring scheme to allow for effective and efficient monitoring and to offset the adverse consequences of coal mining activities. Landsat 8 OLI images from June 2019 and 2020, and a Landsat 7 ETM+ image from June 2002, were used for this study. A three-phase methodology including Normalized Difference Vegetation Index (NDVI) analysis, land cover mapping, and change detection approaches was adopted. Image classification based on Tasseled Cap Transformation and the brightness temperature At-satellite using the K-means algorithm was implemented in a GIS program to identify seven land cover classes within the study area. The results show some level of surface disturbance to the landscape due to the coal mining reclamation activities that had taken place over the 18-year time period. From 2019 to 2020, about 3.622 km2 of coal mines or barren land were converted into bare agricultural land. Over the years, it was also observed that reclamation areas exhibited higher values of NDVI than coal mining areas. The mean NDVI for coal mining areas was 0.252 km2, and for areas of reclamation, it was 0.292 km2 in 2020, while in 2019, the value for coal mining sites was 0.133 km2, and 0.163 km2 for reclamation sites. This trend suggests that coal-mining operations can be monitored using satellite data, and the progress of reclamation efforts can be assessed using satellite NDVI data from the target locations. This study is beneficial to agencies responsible for monitoring land cover changes in a coal mine because it provides a cost-effective, efficient, and robust scientific tool for making mine site allocation decisions and for monitoring the progress of reclamation efforts. [ABSTRACT FROM AUTHOR]

The damage of a long tunnel is found in parts with an adverse geological structure zone under an earthquake. The phenomenon is normally the consequence of a non-uniform seismic load. Thus, to reveal the mechanism of the phenomenon, the dynamic response of the lining structure in a long tunnel passing through an adverse geological structure zone subjected to a non-uniform seismic load is mainly studied in this paper. Firstly, based on the random ground motion synthesis theory, the non-uniform ground motion acceleration–time history curves that reflect local site effects, such as traveling wave effects and attenuation effects, are generated. Secondly, the behavior of the tunnel with a different adverse geological structure zone (including different inclinations, thicknesses, and lithologies) under non-uniform seismic input is studied. Then, the impact of the different adverse geological structure zone on the internal force and safety factor of the tunnel lining is analyzed. Finally, the failure characteristics of the lining structure in the tunnel crossing through the adverse geological structure zone subjected to a non-uniform seismic load are revealed. The results show that the seismic dynamic responses significantly increase under non-uniform seismic input compared with the results under uniform seismic input, and the dynamic responses distribution along the tunnel axial is distinctly different under non-uniform seismic input. The inclination and thickness of the adverse geological structure zone have a significant influence on the internal force and safety factor of the tunnel lining, while the lithology mainly acts around the adverse geological structure zone. When the inclination angle of the adverse geological structure zone is 45°, a large number of compression-bending cracks appear in the entrance and exit sections of the tunnel, and the tunnel is in the most dangerous state. [ABSTRACT FROM AUTHOR]

Since the basic theory of the discontinue deformation analysis (DDA) method was proposed, the DDA open source has gone through a long development process. At present, different kinds of programs have been widely applied in rock mass engineering such as slope, dam, and tunnel. This paper introduces the solution principle of DDA motion equations in detail, as well as the development status of the 2D open-source program. Numerical simulation of shaking table test of rock mass engineering using 2D DDA program is highlighted, and investigations of seismic wave pre-processing and seismic input method are carried out. First, based on the Newmark integration scheme, the integration algorithms of synthetic or measured seismic wave time history, correction function of seismic wave, and DDA simulation are unified. Then, three seismic input methods are implanted in the DDA program, and the applicability of various seismic input methods is discussed. On this basis, using the improved seismic 2D DDA program, a shaking table test of typical rock mass engineering is simulated. Through the comparison between the theoretical/test data and simulation results, the reliability of the improved DDA program in seismic response analysis is verified; the large mass method and the large stiffness method are more suitable for rigid foundation, such as shaking table test; the propagation of the seismic wave presents a significant amplification effect due to the reflection, refraction, and diffraction in the tunnel. The research results provide DDA theory and an open-source program for analyzing the seismic response of rock mass engineering. [ABSTRACT FROM AUTHOR]

The dynamic modeling, motion control and flexible vibration active suppression of space robot under the influence of flexible base, flexible link and flexible joint are explored, and motion and vibration integrated fixed-time sliding mode control of fully flexible system is designed. The flexibility of the base and joints are equivalent to the vibration effect of linear springs and torsion springs. The flexible links are regarded as Euler–Bernoulli simply supported beams, which are analyzed by the hypothetical mode method, and the dynamic model of the fully flexible space robot is established by using the Lagrange equation. Then, the singular perturbation theory is used to decompose the model into slow subsystem including rigid motion and the link flexible vibrations, and fast subsystems including the base and the joint flexible vibrations. A fixed time sliding mode control based on hybrid trajectory is designed for the slow subsystem to ensure that the base and joints track the desired trajectory in a limited time while achieving vibration suppression on the flexible links. For the fast subsystem, linear quadratic optimal control is used to suppress the flexible vibration of the base and joints. The simulation results show that the controller proposed in the paper can make the system state converge within a fixed time, is robust to model uncertainty and external interference, and can effectively suppress the flexible vibration of the base, links, and joints. [ABSTRACT FROM AUTHOR]

During the process of satellite capture by a flexible base–link–joint space robot, the base, joints, and links vibrate easily and also rotate in a disorderly manner owing to the impact torque. To address this problem, a repetitive learning sliding mode stabilization control is proposed to stabilize the system. First, the dynamic models of the fully flexible space robot and the captured satellite are established, respectively, and the impact effect is calculated according to the motion and force transfer relationships. Based on this, a dynamic model of the system after capturing is established. Subsequently, the system is decomposed into slow and fast subsystems using the singular perturbation theory. To ensure that the base attitude and the joints of the slow subsystem reach the desired trajectories, link vibrations are suppressed simultaneously, and a repetitive learning sliding mode controller based on the concept of the virtual force is designed. Moreover, a multilinear optimal controller is proposed for the fast subsystem to suppress the vibration of the base and joints. Two sub-controllers constitute the repetitive learning sliding mode stabilization control for the system. This ensures that the base attitude and joints of the system reach the desired trajectories in a limited time after capturing, obtain better control quality, and suppress the multiple flexible vibrations of the base, links and joints. Finally, the simulation results verify the effectiveness of the designed control strategy. [ABSTRACT FROM AUTHOR]

A soil–rock mixture (SRM) is a type of heterogeneous geomaterial, and the particle distribution of SRM can be described by fractal theory. At present, it is difficult to quantify the fractal dimension of a particle size distribution and understand the scale effect in SRMs. In this study, the fractal theory and discrete element method (DEM) were introduced to solve this problem. First, the particle gradation of SRM was dealt with by using fractal theory. The fractal structure of particle distribution was studied, and a method of calculation of the fractal dimension is presented in this paper. Second, based on the fractal dimension and relative threshold, the particle gradations of SRMs at different scales were predicted. Third, numerical direct shear tests of SRM at different scales were simulated by using the DEM. The scale effects of shear displacement, shear zone, and shear strength parameters were revealed. Last, taking the maximum particle size of 60 mm as the standard value, the piece-wise functional relationship between shear strength parameters and particle size was established. The results are as follows: for SRM in a representative engineering area, by plotting the relationship between particle cumulative mass percentage and particle size, we can judge whether the SRM has a fractal structure; in Southwest China, the frequency of the fractal dimension of the SRM is in the normal distribution, and the median fractal dimension is 2.62; the particle gradations of SRMs at different scales calculated by fractal dimension and relative threshold can expand the study scope of particle size analysis; when the particle size is less than 70 mm, the strength parameters show a parabolic trend with the particle size increases, and if not, a nearly linear trend is found. The proposed method can describe the fractal characteristics of SRM in a representative engineering area and provides a quantitative estimation of shear strength parameters of SRM at different scales. [ABSTRACT FROM AUTHOR]

Cancer subtype classification helps us to understand the pathogenesis of cancer and develop new cancer drugs, treatment from which patients would benefit most. Most previous studies detect cancer subtypes by extracting features from individual samples, ignoring their associations with others. We believe that the interactions of cancer samples can help identify cancer subtypes. This work proposes a cancer subtype classification method based on a residual graph convolutional network and a sample similarity network. First, we constructed a sample similarity network regarding cancer gene co-expression patterns. Then, the gene expression profiles of cancer samples as initial features and the sample similarity network were passed into a two-layer graph convolutional network (GCN) model. We introduced the initial features to the GCN model to avoid over-smoothing during the training process. Finally, the classification of cancer subtypes was obtained through a softmax activation function. Our model was applied to breast invasive carcinoma (BRCA), glioblastoma multiforme (GBM) and lung cancer (LUNG) datasets. The accuracy values of our model reached 82.58%, 85.13% and 79.18% for BRCA, GBM and LUNG, respectively, which outperformed the existing methods. The survival analysis of our results proves the significant clinical features of the cancer subtypes identified by our model. Moreover, we can leverage our model to detect the essential genes enriched in gene ontology (GO) terms and the biological pathways related to a cancer subtype. [ABSTRACT FROM AUTHOR]

The quality of hydrocarbon source rocks is affected by the sedimentary paleoenvironment. A paleoenvironment with anoxia and a high paleoproductivity is beneficial to source rocks. The paleoenvironment of the Lower Ordovician Meitan Formation of the Sichuan Basin and its adjacent areas is lacking, restricting the oil and gas exploration of the Ordovician in the Sichuan Basin and its adjacent areas. In this paper, the content of major and trace elements of 50 samples was tested to clarify the paleoenvironment of the Meitan Formation. The paleoclimate, paleosalinity, paleoredox, and paleoproductivity during the deposition of the Meitan Formation were analyzed. The control effect of the paleoenvironment on the development of source rocks was clarified, and the favorable paleoenvironment for source rock development was pointed out. The results show that the paleoenvironment of the Meitan Formation has the following characteristics: humidity, brackish water, oxygen depletion, anoxia environment, and high paleoproductivity. These characteristics are conducive to the development of poor and moderate source rocks. The source rocks of the Meitan Formation were developed in the north, west, and south of the Sichuan Basin and its adjacent areas. The organic matter of the source rocks is mainly typed II1 kerogen, and the quality is evaluated as poor-medium source rocks having the potential of generating oil and gas. This study can provide fundamental parameters for the further exploration of Ordovician petroleum. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) is an innovative architecture that designs a logical controller to manage and program the network based on the global view, providing more efficient management, better performance, and higher flexibility for the network. Therefore, applying the SDN concept in a multi-hop wireless network (MWN) has been proposed and extensively studied to overcome the challenges of MWN. In this paper, we propose an energy-efficient global routing algorithm for a software-defined multi-hop wireless network (SDMWN), which is able to get transmission paths for several users at the same time to minimize the global energy consumption with the premise of satisfying the QoS required by users. To this end, we firstly propose a Lagrange relaxation-based aggregated cost (LARAC) and K-Dijkstra combined algorithm to get the top K energy-minimum paths that satisfy the QoS in polynomial time. Then, we combine the alternative paths of each user obtained by K-LARAC and propose an improved genetic algorithm to solve the global routing strategy. The simulation results show that the proposed K-LARAC and genetic algorithm combined method has the ability to obtain an approximate optimal solution with lower time cost. [ABSTRACT FROM AUTHOR]

Cancer subtype classification helps us to understand the pathogenesis of cancer and develop new cancer drugs, treatment from which patients would benefit most. Most previous studies detect cancer subtypes by extracting features from individual samples, ignoring their associations with others. We believe that the interactions of cancer samples can help identify cancer subtypes. This work proposes a cancer subtype classification method based on a residual graph convolutional network and a sample similarity network. First, we constructed a sample similarity network regarding cancer gene co-expression patterns. Then, the gene expression profiles of cancer samples as initial features and the sample similarity network were passed into a two-layer graph convolutional network (GCN) model. We introduced the initial features to the GCN model to avoid over-smoothing during the training process. Finally, the classification of cancer subtypes was obtained through a softmax activation function. Our model was applied to breast invasive carcinoma (BRCA), glioblastoma multiforme (GBM) and lung cancer (LUNG) datasets. The accuracy values of our model reached 82.58%, 85.13% and 79.18% for BRCA, GBM and LUNG, respectively, which outperformed the existing methods. The survival analysis of our results proves the significant clinical features of the cancer subtypes identified by our model. Moreover, we can leverage our model to detect the essential genes enriched in gene ontology (GO) terms and the biological pathways related to a cancer subtype.