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Sparse matrix-vector multiplication (SpMV) plays a critical role in a wide range of linear algebra computations, particularly in scientific and engineering disciplines. However, the irregular memory access patterns, extensive memory usage, high bandwidth requirements, and underutilization of parallelism hinder the computational efficiency of SpMV on GPUs. In this paper, we propose a novel approach called block-wise dynamic mixed-precision (BDMP) to address these challenges. Our methodology involves partitioning the original matrix into uniformly sized blocks, with each block's size determined by considering architectural characteristics and accuracy requirements. Additionally, we dynamically assign precision to each block using a precision selection method that takes into account the value distribution of the original sparse matrix. We develop two distinct SpMV computation algorithms for BDMP: BDMP-PBP (Precision-based partitioning) and BDMP-TCKI (Tailored compression and kernel implementation). BDMP-PBP partitions the matrix into two independent matrices for separate computations based on block precision, offering flexibility for integration with other optimization techniques. Meanwhile, BDMP-TCKI focuses on achieving significant thread-level parallelism and memory utilization by tailoring an appropriate compressed storage format and kernel implementation for each block. We compare BDMP with NVIDIA's cuSPARSE library and three state-of-the-art SpMV methods, including SELLP, MergeBase, and BalanceCSR, using matrices from the University of Florida's SuiteSparse dataset collection. BDMP-PBP and BDMP-TCKI show average speedups up to 2.64 × and 2.91 × on Turing RTX 2080Ti, and up to 2.99 × and 3.22 × on Ampere A100. The results demonstrate that BDMP enables the optimization of computation speed without compromising the precision necessary for reliable results. [ABSTRACT FROM AUTHOR]

Deep neural networks have made great achievements in remote sensing image analyses; however, previous studies have shown that deep neural networks exhibit incredible vulnerability to adversarial examples, which raises concerns about regional safety and production safety. In this paper, we propose an adversarial denoising method based on latent representation guidance for remote sensing image scene classification. In the training phase, we train a variational autoencoder to reconstruct the data using only the clean dataset. At test time, we first calculate the normalized mutual information between the reconstructed image using the variational autoencoder and the reference image as denoised by a discrete cosine transform. The reconstructed image is selectively utilized according to the result of the image quality assessment. Then, the latent representation of the current image is iteratively updated according to the reconstruction loss so as to gradually eliminate the influence of adversarial noise. Because the training of the denoiser only involves clean data, the proposed method is more robust against unknown adversarial noise. Experimental results on the scene classification dataset show the effectiveness of the proposed method. Furthermore, the method achieves better robust accuracy compared with state-of-the-art adversarial defense methods in image classification tasks. [ABSTRACT FROM AUTHOR]

Thermoelectric technology plays a crucial role in harnessing waste heat from automobile exhaust, and developing low-resistance and high-efficiency thermoelectric generator is currently a focal point of research. The intermediate fluid thermoelectric generator (IFTEG) introduces a structural modification to conventional designs by utilizing boiling-condensation heat transfer of the intermediate fluid to enhance power generation performance. In this study, we constructed an experimental system to investigate the thermoelectric characteristics of IFTEG. The results demonstrate that the IFTEG outperforms traditional thermoelectric generators, exhibiting a remarkable increase in total output power ranging from 171% to 283%, a reduction in pressure drop ranging from 23% to 42%, and a significant improvement in voltage distribution uniformity across the modules. Increasing the exhaust temperature and flow rate significantly enhances the net output power while minimally affecting the voltage distribution uniformity. The net output power of 10.17 W and the net thermoelectric conversion efficiency of 1.39% are achieved at 200 °C and 30 m3/h. Overall, this novel generator offers advantages such as low pressure loss, high efficiency, and high reliability. The findings of this study provide valuable guidance for the development of exhaust thermoelectric generators. • The thermoelectric performance of IFTEG is experimentally investigated. • The power output of IFTEG can be enhanced by 171%–283%. • The pressure drop of IFTEG be reduced by 23%–42%. • The distribution of module voltage becomes more uniform. [ABSTRACT FROM AUTHOR]

• New method for determining waveguide axial stress using guided waves. • Analytical insight into how axial stress affects guided wave dispersion properties. • In-depth investigation on selecting optimum modes for guided wave testing. • Detailed parametric study for predicting longitudinal rail stress. The determination of the longitudinal stress in continuous welded rails is essential for the safe operation of high-speed railways. Guided wave inspections have many advantages, and can provide an effective tool for non-destructive measurements of the longitudinal stress in rails. This paper provides a new method for determining longitudinal rail stress using ultrasonic guided wave dispersion properties. First, the characteristic equations of guided waves propagating in a waveguide with existing longitudinal stress are constructed, generating the dispersion properties of the waveguide such as the wavenumber, phase velocity and group velocity. The sensitivity of the dispersion properties of propagative modes to the longitudinal rail stress is investigated, which is then used for selecting appropriate modes for guided wave inspections. On the basis of the perturbation method, the relationship between the change in longitudinal rail stress and the change in dispersion properties is developed, which can be used for determining the longitudinal rail stress from dispersion properties. Furthermore, a mode selection index considering both the stress sensitivity and mode shape characteristics is employed for selecting appropriate propagative modes for guide wave inspections. Finally, a numerical example for CHN60 rail is adopted to demonstrate the effectiveness of the proposed method. From the results, the proposed method can accurately determine the longitudinal stress in the rail by using the dispersion properties available. [ABSTRACT FROM AUTHOR]

In recent years, mobile medical services have gradually become the common focus of the communications and healthcare industries. People can get medical information and services efficiently and conveniently anytime, anywhere. However, due to the limitations of the mobile terminal's own computing and storage capabilities, mobile therapy is greatly challenged. Therefore, we propose a mobile medical health system based on cloud computing. Firstly, principal component analysis was used to obtain representative features. Then, a simplified feature subset was applied to support vector machine (SVM) based on Sigmoid kernel function. The data set was categorized by SVM as cancer patient and normal object experimental results show that the method is improved in accuracy, sensitivity, and specificity. [ABSTRACT FROM AUTHOR]

• Extensive dolomitic rocks are the favorable lithologies for tight oil. • Microscale dissolution pores and fractures are the major reservoir spaces. • We summarize two different types of tight oil accumulation plays. The Permian Fengcheng Formation in the western slope of the Mahu Sag, which is located in the Junggar Basin, NW China, has long been considered to be a high-quality source rock in this area. The lacustrine dolomitic rocks within the Fengcheng Formation has recently been reported to have a good tight oil potential as a special type of tight oil reservoir. However, the characteristics of the tight reservoir and tight oil accumulation, which are key factors for commercial development, are not well documented. We conducted a comprehensive reservoir characterization using petrological (core, thin section, scanning electron microscope, and nanoscale computed tomography) and geophysical (seismic and well logging) data. The Fengcheng Formation contains extensive dolomitic rocks deposited around the evaporite under a saline lacustrine environment accompanied by volcanic activities. The western slope of the Mahu Sag can be divided into two zones: the Wufeng Fault Zone and Fengnan Slope Zone. Dolomitic rock reservoirs and tight oil accumulation exist in both zones. Salt minerals, feldspar, calcite, and dolomite in the dolomitic rocks are susceptible to dissolution, and secondary intergranular or intragranular dissolution pores are the major reservoir spaces. Abundant nanoscale pores exist in the dolomitic rocks, but microscale pores, which dominate in volume, play a more important role. Brittle dolomitic rocks commonly develop fractures, but the Wufeng Fault Zone develops far more fault-related fractures than the Fengnan Slope Zone. The oil properties of the Wufeng Fault Zone and the Fengnan Slope Zone are also different. We propose that these two zones are two different types of tight oil accumulation plays: a fracture controlled accumulation play and a pore controlled accumulation play. [ABSTRACT FROM AUTHOR]

Automatic waveform classification and arrival picking methods are widely studied to reduce or replace the manual works. Machine learning based methods, especially neural networks, and clustering based methods have shown great potentials in previous studies. However, most of the existing methods are sensitive to noise. The convolution neural networks (CNNs), developed from the traditional neural networks, have been successfully applied in many different fields, but are rarely studied in seismic waveform classification. In this paper, we propose a novel antinoise CNN architecture for waveform classification and also propose to combine k‐means clustering (KC) with CNN classification to pick arrivals (CNN‐KC). Seismic data are sampled to 1‐D vectors using a specific time window. Using the trained CNN classifier, these 1‐D vectors are classified into two categories: waveform and nonwaveform. With the constraint of the first waveform, CNN‐KC can pick the arrival more accurately. We also apply the proposed methods to the synthetic microseismic data with different noise levels and the actual field microseismic data to test their robustness. CNNs perform much better than the traditional multilayer perceptron on the waveform classification of the noisy microseismic data. Based on the analysis of the CNN internal architecture, we also conclude that the main reason that CNN is insensitive to noise is the convolution and pooling layers which behave like smooth operation in some ways. The final results show that the CNN and CNN‐KC are effective and robust methods for waveform classification and arrival picking. Key Points: We introduce the concept of waveform classification, by which we can automatically pick the arrivals and potentially reject noiseWe propose a novel CNN architecture for waveform classification and also combine k‐means clustering with CNN to pick arrivalsCNNs perform much better than the traditional multilayer perceptron (MLP) on the waveform classification of the noisy microseismic data [ABSTRACT FROM AUTHOR]

With the rapid development of the mobile Internet and the Internet of Things, the number of personal wireless devices and wireless services have grown exponentially resulting in the increase in massive data which requires massive spectrum to transmission. To improve the utilization of scarce spectrum resource, cognitive radio (CR) is considered as the most promising technology. Spectrum sensing is the premise and key technology in CR. However, current spectrum sensing strategies have some issues to sense multiple heterogeneous channels in heterogeneous network, such as neglect of the different performances of each secondary user on heterogeneous channels and sensing task assignment without consideration of the selection of sensing time. To address these issues, two parallel cooperative spectrum sensing strategies based on heuristic method are proposed, which are heuristic method with sensing time exhaustion and heuristic method with sensing time heuristic. These strategies consider both the sensing task assignment and sensing time selection to maximize the available throughput under the restrictions of detection probability and false alarm probability. Monte Carlo simulations show that the proposed strategies can obtain higher available throughputs than iterative Hungarian-based strategy, improved iterative Hungarian-based strategy and greedy-based strategy. [ABSTRACT FROM AUTHOR]

To address the issue of spectrum scarcity in future communication for smart world, cognitive radio (CR) has emerged as a promising solution. In CR, spectrum sensing is the premise and key technology. To defense against the PUE attack in spectrum sensing, two spectrum sensing methods against PUE attack based on fractal dimension are proposed, which are spectrum sensing defending against PUE attack based on normalized Sevcik fractal dimension in frequency domain (SSMS) and spectrum sensing defending against PUE attack based on normalized Sevcik fractal dimension in frequency domain and normalized Petrosian fractal dimension (SSMSP). They identify the modulation type of the received signal by SVM classifier according to the fractal dimension of the received signal to detect PUE attack. The simulation results show that the two proposed methods can effectively identify the modulation type and detect the PUE attack. When SNR is larger than 10 dB, the PUE detection probabilities of both SSMS and SSMSP can reach 100%. Even when SNR is - 20 dB, the PUE detection probabilities are still larger than 97%. Therefore, SSMS and SSMSP can effectively defend against PUE attack. [ABSTRACT FROM AUTHOR]

To address the problem of spectrum scarcity in future communication for smart world, cognitive radio (CR) is viewed as an effective way and has been widely studied in recent years. Spectrum sensing is the key to deploy Cognitive radio (CR) network. Parallel cooperative spectrum sensing provides an effective solution in multi-channel scenario. In this paper, joint optimal sensing duration selection and sensing task allocation is studied in parallel cooperative spectrum sensing for heterogeneous multi-channel CR systems. Two cross-layer parallel cooperative spectrum sensing methods based on iterative KM algorithm are proposed. Sensing duration selection and sensing task allocation are designed jointly to maximize the available throughput. A two-step method is implemented. Firstly it determines the optimal sensing task allocation for fixed sensing duration by iterative KM algorithm, and then selects the optimal sensing duration by exhaustion method and quartering method, respectively. The simulation results show that the proposed method can optimally select the sensing duration and obtain higher available throughput than other compared methods. [ABSTRACT FROM AUTHOR]

Abstract Permeability prediction has long been one of the most important and difficult tasks of reservoir characterization, especially for tight sands with strong heterogeneity. A unified model is important for the un-cored wells and can reduce a lot of work in practice. In this case, we are going to establish a field-scale unified permeability model for the tight gas sands of the Middle Jurassic Shaximiao Formation in the Western Sichuan Basin, China. The tight gas sands are isolated channel sands with strong heterogeneity. In addition to the porosity, well logs are also selected as the input feature to help to derive the non-linear relations. We choose simple linear regression (SLR) using porosity, multiple linear regression (MLR), multiple-layer perceptron (MLR) and support vector regression (SVR) using multiple input features as our machine learning methods for this task. For the non-linear methods, MLP and SVR, we present how to decide their key parameters based on the theoretical analysis and experiments to keep their generalization capability. Finally, the results show that the non-linear MLP and SVR outperform the linear SLR and MLR for the unified permeability model. The blind well average performance of MLP and SVR improves about 50% on R 2 and decrease about 30% on MARE than MLR. Highlights • Field-scale unified permeability models are established for all the isolated channel sands with strong heterogeneity. • Proper key parameter values of MLP and SVR are chosen for permeability prediction. • The non-linear MLP and SVR outperform the linear SLR and MLR. • Multiple input features help the derivation of the non-linear relations. [ABSTRACT FROM AUTHOR]

Seismic prediction has been a huge challenge because of the great uncertainties contained in the seismic data. Deep learning (DL) has been successfully applied in many fields and brought revolutionary changes, such as computer vision and natural language processing. The traditional artificial neural networks have been studied to improve the accuracy and resolution of seismic prediction for years, but not DL. In this paper, we develop a new architecture for seismic reservoir characterization based on the DL technique. We apply the convolutional neural network (CNN), which is a DL framework, to predict lithology and have achieved better results compared with traditional methods. We also propose to use continuous wavelet transforms (CWTs) to get a time–frequency spectrum for neural networks. CWTs help to make full use of the frequency content of the post-stack seismic data. According to the difference in the convolution layers and the organization of the input data, we propose four DL architectures for seismic lithology prediction, namely the deep neural networks (DNNs), the CNNs, the CWT-DNNs and the CWT-CNNs. All of these four architectures are applied in the case study. The final results on blind wells, profile and horizontal slice show that CWT-CNN models have the best performance on post-stack seismic lithology prediction. CWT maps contain more information about thinner layers and convolution layers are better at feature extraction from CWT maps. The CWT-CNN model has higher accuracy and resolution, especially on medium and thin layer prediction. [ABSTRACT FROM AUTHOR]

Location sharing service has become an indispensable part in mobile social networks. However, location sharing may introduce a new class of privacy threats ranging from localizing an individual to profiling and identifying him based on the places he shared. Although users may avoid releasing geocontent in sensitive locations, it does not necessarily prevent the adversary from inferring users’ privacy through space-temporal correlations and historical information. In this paper, we design a Prophet framework, which provides an effective security scheme for users sharing their location information. First, we define fingerprint identification based on Markov chain and state classification to describe the users’ behavior patterns. Then, we propose a novel location anonymization mechanism, which adopts a ε-indistinguishability strategy to protect user’s sensitive location information published. Finally, experimental results are given to illustrate good performance and effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Polyvinylidene fluoride (PVDF) and hydrophobic silica sol (Si-R) hybrid hollow fiber membranes were fabricated by dry-jet wet spinning. The morphology and surface roughness of the PVDF/Si-R hybrid membranes were examined by scanning electron microscope (SEM) and atomic force microscopy (AFM), respectively. FTIR and contact angle analysis were used to characterize hydrophobic Si-R particles in the hybrid membrane. The membrane adsorption efficiency for dissolved organics including BETX (benzene, toluene, ethyl benzene and xylene) and two fatty acids: hexanoic acid and octanoic acid, as well as the membrane regeneration ability, were investigated by membrane adsorption experiments under different pressures, feed rates and temperatures. The results revealed that the hybrid hollow fiber membranes showed a microcellular structure rather than the typical asymmetrical structure of neat PVDF hollow fiber membranes. As the membrane collecting velocity increased, the hybrid hollow fiber membranes possessed enhanced surface roughness on both inner and outer surface. The adsorption efficiency of the PVDF/Si-R hybrid hollow fiber membrane was three times higher than that of the neat PVDF membrane. The adsorption efficiency of hybrid hollow fiber membranes increased with the increase of testing pressure; however, it decreased with the increase in feed rate, and was not influenced by temperature in the range of 0–80 °C. With membrane collecting velocity of 424 cm/min, the PVDF/Si-R hybrid membrane exhibited adsorption efficiency of 94.5% and 96.7% for benzene and octanoic acid, with membrane adsorption capacities of 208.1 mg/g and 173.9 mg/g. respectively. The hybrid membrane also showed high regeneration ability with 500 ppm benzene as feed solution. The PVDF/Si-R hollow fiber membrane offers an attractive possibility for the removal of dissolved organics in oilfield produced water. [ABSTRACT FROM AUTHOR]

Because of the mobility, computing power and changeable topology of dynamic networks, it is difficult for random linear network coding (RLNC) in static networks to satisfy the requirements of dynamic networks. To alleviate this problem, a minimal increase network coding (MINC) algorithm is proposed. By identifying the nonzero elements of an encoding vector, it selects blocks to be encoded on the basis of relationship between the nonzero elements that the controls changes in the degrees of the blocks; then, the encoding time is shortened in a dynamic network. The results of simulations show that, compared with existing encoding algorithms, the MINC algorithm provides reduced computational complexity of encoding and an increased probability of delivery. [ABSTRACT FROM AUTHOR]

Due to different reasons, the seismic reflections are not continuous even when no faults or no discontinuities exist. We propose a novel approach for enhancing the amplitude of seismic reflections and making the seismic reflections continuous. We use plane-wave flattening technique to provide horizontal events for the following empirical mode decomposition (EMD) based smoothing in the flattened domain. The inverse plane-wave flattening can be used to obtain original curved events. The plane-wave flattening process requires a precise local slope estimation, which is provided by the plane-wave destruction (PWD) algorithm. The EMD based smoothing filter is a non-parametric and adaptive filter, thus can be conveniently used. Both pre-stack and post-stack field data examples show tremendous improvement for the data quality, which makes the following interpretation easier and more reliable. [ABSTRACT FROM AUTHOR]

Let R be any ring with identity. Let N(R) (resp. J(R)) denote the prime radical (resp. Jacobson radical) of R, and let Specr(R) (resp. Specl(R), Maxr(R), Primr(R)) denote the set of all right prime ideals (resp. all left prime ideals, all maximal right ideals, all right primitive ideals) of R. In this article, we study the relationships among various ring-theoretic properties and topological conditions on Specr(R) (with weak Zariski topology). The following results are obtained: (1) R/N(R) is a Gelfand ring if and only if Specr(R) is a normal space if and only if Specl(R) is a normal space; (2) R/J(R) is a Gelfand ring if and only if every right prime ideal containing J(R) is contained in a unique maximal right ideal. [ABSTRACT FROM AUTHOR]

R is any ring with identity. Let Spec r ( R ) (resp. Spec( R )) be the set of all prime right ideals (resp. all prime ideals) of R and let U r ( eR ) = { P ∈ Spec r ( R ) | e ∉ P }. In this article, we study the relationships among various ring-theoretic properties and topological conditions on Spec r ( R ) (with weak Zariski topology). A ring R is called Abelian if all idempotents in R are central (see Goodearl, 1991). A ring R is called 2- primal if every nilpotent element is in the prime radical of R (see Lam, 2001). It will be shown that for an Abelian ring R there is a bijection between the set of all idempotents in R and the clopen (i.e., closed and open) sets in Spec r ( R ). And the following results are obtained for any ring R : (1) For any clopen set U in Spec r ( R ), there is an idempotent e in R such that U = U r ( eR ). (2) If R is an Abelian ring or a 2-primal ring, then, for any idempotent e in R , U r ( eR ) is a clopen set in Spec r ( R ). (3) Spec r ( R ) is connected if and only if Spec( R ) is connected. [ABSTRACT FROM AUTHOR]

R is any ring with identity. Let Spec r ( R ) (resp. Max r ( R ), Prim r ( R )) denote the set of all right prime ideals (resp. all maximal right ideals, all right primitive ideals) of R and let U r ( eR ) = { P ∈ Spec r ( R )| e ∉ P }. Let 𝒜 = ∪ P∈Prim r (R) Spec r P (R), where Spec r P (R) = {Q ∈Spec r P (R)|P is the largest ideal contained in Q }. A ring is called right quasi-duo if every maximal right ideal is 2-sided. In this article, we study the properties of the weak Zariski topology on 𝒜 and the relationships among various ring-theoretic properties and topological conditions on it. Then the following results are obtained for any ring R : (1) R is right quasi-duo ring if and only if 𝒜 is a space with Zariski topology if and only if, for any Q ∈ 𝒜 , Q is irreducible as a right ideal in R . (2) For any clopen (i.e., closed and open) set U in ℬ = Max r ( R ) ∪ Prim r ( R ) (resp. 𝒞 = Prim r ( R )) there is an element e in R with e 2 - e ∈ J ( R ) such that U = U r ( eR ) ∩ ℬ (resp. U = U r ( eR ) ∩ 𝒞 ), where J ( R ) is the Jacobson of R . (3) Max r ( R ) ∪ Prim r ( R ) is connected if and only if Max l ( R ) ∪ Prim l ( R ) is connected if and only if Prim r ( R ) is connected. [ABSTRACT FROM AUTHOR]

Let R be a commutative ring. An R-module M is called a multiplication module if for each submodule N of M, N = IM for some ideal I of R. An R-module M is called a pm-module, i.e., M is pm, if every prime submodule of M is contained in a unique maximal submodule of M. In this paper the following results are obtained. (1) If R is pm, then any multiplication R-module M is pm. (2) If M is finitely generated, then M is a multiplication module if and only if Spec(M) is a spectral space if and only if Spec(M) = {PMP ∈ Spec(R) and P ⊇ M⊥}. (3) If M is a finitely generated multiplication R-module, then: (i) M is pm if and only if Max(M) is a retract of Spec(M) if and only if Spec(M) is normal if and only if M is a weakly Gelfand module; (ii) M is a Gelfand module if and only if Mod(M) is normal. (4) If M is a multiplication R-module, then Spec(M) is normal if and only if Mod(M) is weakly normal. [ABSTRACT FROM AUTHOR]

Permeability prediction is a key and difficult task in hydrocarbon reservoir characterization. Machine learning has long been studied for permeability prediction using porosity and wireline logs but has not been widely accepted and applied in real cases because it is always treated as a Black Box and hard to interpret. In this paper, we propose novel approaches called univariate predictive pattern (UPP) and bivariate predictive pattern (BPP) to visualize predictive patterns learned by machine learning models. Machine learning models for permeability prediction are built in advance, including support vector regression (SVR), random forest (RF), and deep residual neural network (ResNet). The visualized predictive patterns show that ResNet learns the best-fitted nonlinear porosity-permeability relationships from the training dataset, which explains why ResNet achieves the best performance in permeability prediction in the scenario with porosity and wireline logs as input features. Wireline logs provide information about pore structure and contribute differently in machine learning models according to the visualized predictive patterns and quantitative sensitivity analysis. The proposed approach provides us a way to get a glimpse of the Black Box of machine learning models, and is also helpful in sensitivity analysis, feature selection, and model optimization for permeability prediction. • UPP and BPP are proposed to visualize predictive patterns learned by machine learning models. • ResNet performs the best in permeability prediction with porosity and wireline logs as input features. • Predictive patterns reveal that ResNet model contains the best-fitted nonlinear porosity-permeability relationships. • Machine learning models learn the changing porosity-permeability relationships from wireline logs. [ABSTRACT FROM AUTHOR]

High molecular weight polymers are required for the purpose of mobility and conformance control during surfactant chemical flooding. However, they are usually not incorporated in the process of phase behavior tests to screen surfactant formulations. Literature review shows some particular polymers have a boosting effect on surfactant solubilization efficiency. To gain more insight into this subject, we performed phase behavior tests in the presence of four commonly used EOR polymers for low concentration surfactant systems (0.3%). They were two partially hydrolyzed polyacrylamides (HPAM) with different molecular weights, one xanthan gum, and one hydrophobically associating polymer (HAP). Their influence on the optimal salinity, solubilization ratio, quickness of phase equilibration, and middle phase rheological behavior was assessed. The results showed: (1) a decrease of surfactant solubilization efficiency and the optimal salinity was observed with HPAM polymers. This trend became more pronounced as increase of polymer concentration and molecular weight; (2) xanthan seemed to slightly increase the optimal salinity without significantly affecting middle phase volume; (3) both HPAM and xanthan did not delay or quicken the equilibration of various phases compared to the zero polymer case. The analysis of the oil/water solubilization ratios showed HPAM tended to drag water out of the middle phase, while the presence of xanthan increased the water solubilization in the middle phase; (4) the viscosity and rheology of the middle phase microemulsions were very similar for systems containing HPAM, xanthan, and zero polymer. This confirmed the partitioning of polymer molecules in the middle phase was negligible; (5) the association of HAP with surfactants thickened the surfactant-rich phase, which made the phase separation dramatically slow. These phenomena could be explained well by the nature of their molecular structures. For both water-soluble HPAM and xanthan gum, unlike the reported diblock copolymers, they lack the amphiphilic features, which prevents them from adsorbing in the surfactant film. Conversely, the introduction of hydrophobic group into the backbone renders associating polymers some surfactant-like attributes, enabling them to associate with surfactant molecules and affect the surfactant-rich phase behaviors. [ABSTRACT FROM AUTHOR]

Scaling problems and complex produced fluid treatment are amongst the issues attributed to the use of alkali in Daqing chemical enhanced oil recovery (EOR) operations. These problems have incurred additional operating cost due to damage suffered by production/injection wells, surface facilities, and possibly the reservoir. This study presents the formulation of surfactant mixtures for chemical EOR in the absence of alkali by using combination of zwitterionic and anionic surfactants. The zwitterionic-anionic surfactant mixtures were formulated and screened based on aqueous stability, interfacial tension (IFT) measurement, and microemulsion phase behavior. Coreflood experiments were conducted to investigate recovery performance of the selected surfactant pair. Mixture of cocamidopropyl hydroxysultaine and alcohol propoxy sulfate outperformed other mixtures of zwitterionic and anionic surfactants investigated. This binary surfactant mixture appeared as a clear and transparent aqueous solution, achieved ultralow IFT in spinning-drop IFT measurement, and formed substantial middle-phase microemulsion in phase behavior test, indicating a synergistic behavior between the surfactant pair. The ultralow IFT achieved by the binary surfactant mixture led to displacement 63–75% of residual oil in place during coreflood experiments, for which the surfactant retention was around 0.08 mg/g rock. This result shows that zwitterionic-anionic surfactant mixture can provide an alternative approach toward optimizing chemical EOR processes without alkali. [ABSTRACT FROM AUTHOR]

As the location trajectory contains more spatial-temporal information about the user, it will be even dangerous for jeopardizing the privacy of the user. In order to cope with the correlation, an algorithm that utilizes the query division had been proposed. In this algorithm, random blocks of query context was used, so as the adversary was obfuscated and difficult to correlate the real result. However, this algorithm fails to dispose the size of each query block, as once same size blocks were obtained by the adversary continuously, so the adversary can regard them as blocks from the same query context, and then obtains the query context to correlate the discrete locations. In view of above conditions, in this paper we propose a fine granularity block division algorithm based on the conception of granularity measurement as well as granularity layer division, so with the help of collaborative users the location privacy of the user will be protected. In this algorithm, the query context will be divided into fine granularity size of information blocks that difficult to be distinguished with others, and then these blocks will be exchanged with other collaborative users to eliminate the difference in block size. In addition, as each block is divided into fine granularity size, the adversary will be difficult to correlate the discrete locations into location trajectory, so the location privacy will be protected. At last, through security analysis and experimental verification, this granularity indistinguishable algorithm is analyzed and verified at both theoretical and practical levels, which further demonstrate the superiority of the proposed algorithm compared with other similar algorithms. [ABSTRACT FROM AUTHOR]

Social networks are ubiquitous in contemporary society, where one's opinions and behaviors are easily influenced by those around them. The effectiveness of social influence analysis has become significant for numerous application fields such as online recommendation, advertising, and viral marketing. Graph-based deep learning has achieved significant success in influence prediction. However, the existing methods ignore the privacy issue and are incapable of cross-organizational collaboration. Inspired by the recent success of federated learning in privacy protection and breaking the data barrier. In this paper, a novel federated learning framework, FedInf, is proposed to tackle the problem of social influence prediction. Specially, to further protect user privacy and achieve secure aggregation for multiple local models, we introduce differential privacy into the local parameters, adding artificial noise before model aggregation. In order to trade-off utility and privacy, we first freeze the embedding layers to reduce the number of upload parameters, and then project model parameters into low-dimensional space. Therefore, less noise is required to provide the same level of privacy protection. Extensive experiments on four real-world datasets demonstrate that the proposed FedInf is effective while providing privacy protection. [ABSTRACT FROM AUTHOR]

As is well known, the target recognition algorithm of hybrid information system has intrinsic disadvantages, such as high time complexity, high performance requirements of hardware and complex operations, in this paper, a fast golf gesture recognition algorithm of static image and video sequence is proposed for the field of sports auxiliary training. In static image recognition, a fast multi-scale aggregation channel feature is utilized to extract hybrid information, and the extraction speed can be improved through an approximate calculation method. An improved AdaBoost classifier is adopted to classify the information. On this basis, the aggregation of channel feature detector locates the prominence region of static image, and then scans the generated fractional sequence through the gesture detector as the feature data of golf gesture in the video sequence. Finally, the real-time judgment of feature data is carried out with a linear support vector machine, the rapid identification of golf swing gesture can therefore be obtained. The experimental results show that the recognition speed is over 30 fps and the accuracy is 97% on iPhone5s and later versions, which suggest the validity of algorithm in practical application. [ABSTRACT FROM AUTHOR]

Underwater images intuitively reflect the underwater environment information. However, underwater images have defects such as colour distortion and low contrast, which seriously affect the processing of complex underwater visual tasks. Here, a novel mixed model called mixed underwater image generative adversarial network (MUGAN) is presented, consisting of a generator and corresponding discriminator. The generator is built following the U‐shaped architecture, where a mixed block of convolution and self‐attention is developed. It effectively exploits the complementarity between the two paradigms. In addition, a dual discriminator is employed to induce the generator to produce realistic images at both the global semantic and local detail levels, which is only discriminates based on the patch‐level information. Meanwhile, a multi‐term loss function is formulated to supervise adversarial training by evaluating the perceptual quality of an image based on its global content, local texture and illumination smoothness. To validate the proposed approach, extensive experiments are conducted on the public underwater datasets. MUGAN achieves promising performance in terms of colour, contrast and naturalness, showing a significant improvement over other competitive models in visual quality and quantitative metrics. [ABSTRACT FROM AUTHOR]

In the present study, members of the interleukin (IL)-10 family of cytokines, including IL-10 (TOIL-10) and IL-22 (TOIL-22) of golden pompano ( Trachinotus ovatus ), were cloned for the first time, and their expression patterns and 3D structures analyzed. The full-length cDNA sequences of TOIL-10 and TOIL-22 contained open reading frames of 564 and 567 bp, respectively. TOIL-10 and TOIL-22 shared higher homology (78%–89%) with the corresponding genes from various fish relative to other species (25%–34%) and contained the IL-10 family signature and four cysteine residues that are well conserved in other vertebrate IL-10 members. Phylogenetic tree analysis of our sequences alongside other IL-10 family proteins revealed that TOIL-10 and TOIL-22 cluster together with other teleost IL-10 and IL-22 molecules. Expression of TOIL-10 and TOIL-22 genes was ubiquitous in all tissues examined. The TOIL-10 gene was also highly expressed in skin, heart, gill, spleen, kidney, brain and liver, and lower levels were detected in intestine and muscle. High expression of the TOIL-22 gene was observed in gill, intestine, kidney, spleen, with the lowest levels in liver. TOIL-10 and TOIL-22 were rapidly activated after SAΔ phoB immunization and significantly increased to peak levels at 12 h and 4 d in golden pompano kidney and spleen respectively following challenge. Expression in the brain reached peak levels at 4 d and 3 d respectively after post-immunization. Our results collectively indicate that TOIL-10 and TOIL-22 participate in the host immune response to bacterial infection. Moreover, TOIL-22 plays a potentially important role in mucosal immunity. [ABSTRACT FROM AUTHOR]

Highly accuracy classification and mapping of seafloor hydrothermal fields provides an important addition to research into the geological background and exploration of seafloor massive sulfides (SMS). Currently, acoustic remote sensing using multibeam sounding systems (MBES) is the primary means of achieving large-scale seafloor classification. However, the characteristics of complex topography, complex seafloor distribution, and deep water depth of the hydrothermal fields make it difficult to obtain accurate high-resolution seafloor classification maps using shipboard MBES surveys. Here, a seafloor classification strategy combining shipboard MBES and autonomous underwater vehicle (AUV) sidescan sonar data is proposed. First, a downscaling model is established, which downscales the MBES backscatter mosaic (12-kHz and 10-m resolution) to a resolution of 2 m. The second step performs feature extraction of the downscaled MBES backscatter mosaic (12-kHz and 2-m resolution), the AUV sidescan sonar backscatter mosaic (150-kHz and 2-m resolution), and a seafloor digital elevation model (2-m resolution). Finally, a deep neural network model was built for training and classification. To evaluate the classification performance of the model, the method was applied to the survey of the Duanqiao hydrothermal field. Results were verified using field data (deep-tow video). The overall root mean square error and coefficient of determination ($R^{2}$) for the classification were 0.032 and 0.840, respectively. The experimental results show that the method can effectively meet the challenges to seafloor classification presented by complex seafloor distribution, and can obtain an accurate high-resolution seafloor classification map. [ABSTRACT FROM AUTHOR]

The recently explored Tianzuo hydrothermal field in serpentinized ultramafic rocks of the amagmatic segment of the ultraslow-spreading Southwest Indian Ridge displays high-temperature sulfide mineralization (isocubanite, sphalerite, and minor pyrrhotite) and low-temperature (pyrite and covellite) phases. Pyrite can be subdivided into pyrite-I and -II, with the former generally having a pseudomorphic texture after pyrrhotite and the latter typically growing around isocubanite, sphalerite, and pyrite-I or occurring as individual grains in quartz veinlets. The sulfide minerals have the greatest range of δ34S values (− 23.8 to 14.1‰), found so far among modern sediment-starved ridges, with distinct δ34S values for low- and high-temperature mineral phases. The high δ34S values of isocubanite (9.6 to 12.2‰) and sphalerite (9.1 to 14.1‰) suggest that sulfate, which precipitated from seawater during an early low-temperature phase of hydrothermal circulation, was the main sulfur source for these sulfides. Pyrite-II has the lowest and most variable δ34S values (− 23.8 to − 3.6‰), suggesting microbial sulfate reduction. Pyrite-I has variable and generally positive δ34S values (− 0.1 to 12.0‰), with sulfur being inherited from pyrrhotite from the original thermochemical reduction of sulfate, mixed with volcanogenic sulfur. Intermittent magmatism represented by gabbroic intrusions, and high permeability caused by well-developed fractures associated with detachment faults, contributed to the formation of sulfides in the Tianzuo hydrothermal field. These factors possibly control sulfide mineralization in amagmatic segments of ultraslow-spreading ridges. [ABSTRACT FROM AUTHOR]

Seafloor classification based on multibeam data is an attractive approach due to its wide-coverage capability and relatively low cost. It has been successfully applied in shallow water, but may not work well in deep sea, particularly that with complex seafloor topography. In this paper, a method for deep-sea seafloor classification based on multibeam data is proposed. Firstly, bathymetry data are processed to obtain local slopes. Secondly, backscattering strength (BS) data from depth datagrams are corrected by calculated incident angles and insonified areas on the seafloor. Then BS data from several consecutive pings along the track are grouped into a set of small units in the across-track direction and the angular response curve of BS in each unit is extracted. Moreover, inversions of angular response curves are carried out to obtain the empirical parameter BS O based on a functional model. BS O values related to seafloor types and the standard deviations of inversion results are acquired at the same time. Finally, with the help of the prior information gained by analyzing the relationship between BS O values and ground truth, BS O values of all the units can be statistically analyzed and classified to generate a seafloor classification map. This method has been applied in a complex terrain area around the Duanqiao hydrothermal field on the Southwest Indian Ridge (SWIR) at 50.47°E, and six seafloor types have been classified according to the statistical analysis on BS O values and the visual ground truth. • We propose a method for deep-sea seafloor classification based on multibeam data. • BS data from several consecutive pings are grouped into small homogeneous units. • Inversions of angular response curves are carried out by the functional model. • We draw a seafloor classification map of six types in the survey area. [ABSTRACT FROM AUTHOR]

• Phase change chambers for heat flux enhancement were experimentally investigated. • Effects and mechanisms of heat flux, filling ratio, and area ratio on heat transfer in the chamber were obtained. • The lowest thermal resistance of the chamber was achieved at the smallest area ratio of 1/5 when the boiling heat flux is lower than 7.5 kW/m2. • Visualization of bubble and droplet behaviors was performed. One promising way to increase the output power and the thermoelectric conversion efficiency of an automotive exhaust thermoelectric generator (TEG) is to add a phase change chamber between the exhaust channel and the thermoelectric (TE) modules with the chamber surface contacting with the exhaust channel where boiling occurs being larger than that with the module surfaces where the condensation occurs. The power is increased because a phase change chamber significantly increases the heat flux through the modules; thus, increasing the temperature difference between the module hot and cold sides. Although there is a significant body of literature on phase change chambers, most prior research has focused on chambers with larger condensation areas than boiling areas since they act as heat spreaders, while little is on chambers with condensation areas smaller than boiling areas for heat flux enhancement. These chambers have great potential to improve TEG power generation by enhancing the heat flux, therefore, more studies are needed to understand the chamber heat transfer characteristics. This research experimentally investigated the heat transfer characteristics of three chambers with condensation to boiling area ratios of 1/5, 2/5, and 3/5 for boiling heat fluxes from 4.50 to 22.70 kW/m2 and deionized water filling ratios from 33 to 50%. The results show that the boiling heat transfer coefficient increases with increasing heat flux or decreasing area ratio and is weakly influenced by the filling ratio. The condensation heat transfer characteristics depend on the distribution of droplets which is strongly affected by both the heat flux and the area ratio. The influence of the filling ratio on the condensation heat transfer coefficient increases with increasing area ratio. The thermal resistance of the chamber decreases with increasing heat flux and the lowest thermal resistance is obtained at 1/5 area ratio at lower heat fluxes and at 2/5 or 3/5 area ratio at higher heat fluxes. [ABSTRACT FROM AUTHOR]

Dual-layer hydrophilic-hydrophobic hollow fiber membrane is a promising membrane that simultaneously shows enhanced vapor transfer coefficient and inhibited conductive heat loss in direct contact membrane distillation (DCMD). The presence of an effective water-intrudable hydrophilic layer enables to reduce the vapor transportation distance and improve the water flux in DCMD. The aim of this study is to estimate the water-intrudable hydrophilic layer thickness in a dual-layer hydrophilic-hydrophobic hollow fiber membrane comprising a PVDF/PEG inner layer and a PVDF outer layer. The water-intrudable hydrophilic layer thickness was precisely measured by a dye solution permeation method under simulated DCMD conditions. The results showed that the water-intrudable hydrophilic layer thickness was highly determined by the pore size and effective porosity of the PVDF/PEG inner layer. The applied hydraulic pressure contributed significantly to the difference in the thickness of the water-intrudable hydrophilic layer. The membranes with higher thickness percentages of the water-intrudable hydrophilic layer exhibited simultaneously enhanced permeate water flux and energy efficiency in DCMD. This study offered a simple and reliable approach for the estimation of the effective thickness of the water-intrudable hydrophilic layer for the dual-layer hydrophilic-hydrophobic hollow fiber membrane that contributes substantially to the desalination performance enhancement in DCMD. Image 1 • Dual-layer hydrophilic-hydrophobic HF membrane was fabricated for DCMD. • The thickness of the water-intrudable hydrophilic layer (WIHL) was measured. • Both spinning and operating parameters affected the WIHL thickness. • The WIHL thickness highly determined DCMD performance. [ABSTRACT FROM AUTHOR]

In this study, a high-performance surfactant-polymer (SP) formulation was developed using commercially available surfactants instead of lab-synthesized ones with limited sources. Alcohol propoxysulfates (AES-1 and AES-2), cocamidopropyl hydroxysultaine (CAS), alkyl benzene sulfonate (ABS), and alpha olefin sulfonate (AOS) were mixed in pairs for the formulation screening. The AES-1/ABS formulation with a mixing ratio of 6/4 was able to achieve ultralow interfacial tension (IFT) and possessed excellent aqueous stability at additional NaCl concentration greater than 1.25 wt%. Phase behavior tests illustrated that the optimum salinity (1.6 wt%) and salinity range (1.5 ~ 1.8 wt%) for type III microemulsions were nearly unchanged in spite of the water-oil ratio variation as no in situ soap was generated in the absence of alkali. To investigate the effect of different salinity profiles on the post-polymer SP flooding, 3 corefloods were conducted at 0.3 wt% surfactant concentration. Highest incremental oil recovery (34.8% OOIP) by SP flooding was obtained in coreflood No.2 in which 1 PV NaCl preflush, 0.3 PV NaCl-SP slug, and 0.2 PV polymer drive with additional NaCl of 2.0 wt%, 1.6 wt% and 0.5 wt% were successively injected. Reducing preflush volume in coreflood No.1 and No.3 resulted in lower incremental oil recovery. Effluent TDS analysis indicated that only in coreflood No.2 the maximum effluent salinity (22,249 ppm) exceeded optimum salinity (about 21,000 ppm) and negative salinity gradient concept was realized in the whole SP flooding process. With sufficient salinity preflush, the AES-1/ABS formulation achieved a cumulative oil recovery as high as 93.9% OOIP after polymer flooding. [ABSTRACT FROM AUTHOR]

• A novel bio-inspired soybean-based solvent (SBB solvent) was formulated for CO 2 capture. • The performance of the SBB solvent was thoroughly investigated. • The SBB solvent can be easily regenerated at the low temperature of 80 °C. • The thermal stability of the SBB solvent remained unchanged up to 200 °C. • The SBB solvent possessed high oxidative stability with up to 17% O 2 in the flue gas. A novel bio-inspired soybean-based solvent (SBB solvent) was formulated for the capture of CO 2 with the partial pressure less than 15 kPa in the flue gas. The performance of the SBB solvent was thoroughly characterized in terms of equilibrium solubility of CO 2 , the heat of CO 2 absorption, vapor pressure of the SBB solvent, and the CO 2 absorption flux under different operating conditions including CO 2 partial pressure in the flue gas, initial CO 2 loading in the SBB solvent, SBB solvent concentration, and temperature. The results showed that the equilibrium solubility of CO 2 in the SBB solvent was much lower than that in the conventional amine-based solvent at the regeneration condition. The heat of CO 2 absorption is only at half of the monoethanolamine (MEA). The near-zero vapor pressure made the SBB solvent as a promising and environmentally friendly solvent for CO 2 capture. The optimized concentration of the SBB solvent was 1.0 M for the effective absorption of CO 2 from the flue gas. The CO 2 absorption performance can be further improved by integrating the use of high concentration SBB solvent with reduced CO 2 -gas mass transfer resistance. Both thermal stability and oxidative stability of the SBB solvent were evaluated through a 120 h CO 2 absorption-desorption experiment. The SBB solvent showed high thermal stability at the regeneration temperature up to 200 °C, and also excellent oxidative stability with the O 2 concentration up to 17% in the flue gas. [ABSTRACT FROM AUTHOR]

In this study, an innovative Janus composite hollow fiber membrane-based direct contact membrane distillation (DCMD) process was boosted for desalination of actual produced water with elevated total dissolved solids (TDS) of 154,220 mg/L. The Janus composite hollow fiber membrane (Janus-HFM) was characterized with a thin and hydrophobic polyvinylidene difluoride (PVDF) and superhydrophobic silica nanoparticles (Si-R) outer layer, and a thick porous and hydrophilic PVDF and polyethylene glycol (PEG) inner layer. Compared to the neat PVDF hollow fiber membrane, the Janus-HFM showed both increased permeate water flux and improved energy efficiency with more than 99.99% salt rejection. The long-term desalination performance of the Janus-HFM was investigated via 200 h of continuous DCMD experiment. The results showed that the permeate water flux declined from 25.41 kg/m2h to 15.21 kg/m2h, and the salt rejection was 98.4% at the end of the operation. It was found that both scales and dissolved organic matters induced foulants accumulated at the feed side of the membrane due to the complex composition of the produced water. Nonetheless, the desalination performance decline can be effectively inhibited from 40% to 9.7% with a simple physical cleaning process by using the permeate water that recovered in the DCMD process. • A novel Janus composite hollow fiber membrane (J-HFM) was fabricated. • High-salinity actual produced water was used as the feed solution in the J-HFM DCMD. • 200 h of continuous J-HFM based DCMD process was performed. • The desalination performance of the J-HFM was thoroughly investigated. • Both membrane fouling and scaling were effectively mitigated through a physical cleaning process. [ABSTRACT FROM AUTHOR]

To give full play to the high efficiency and parallelism of multi-processor systems, the fireworks algorithm (FWA) is improved, and a multi-processor task scheduling algorithm based on improved FWA, named IMFWA, is proposed. IMFWA maps continuous space to discrete space by designing the fireworks location coding method, improves the Gaussian mutation process, and sets adaptive dimensions to accelerate the convergence speed of the algorithm. At the same time, in order to reduce the time complexity of the algorithm and shorten the time finding the optimal task scheduling sequence, the fitness-based tournament selection strategy is used instead of the rule based on Euclidean distance. Finally, IMFWA is compared with the basic fireworks algorithm and the genetic algorithms on the Matlab platform for performance analysis. The results show that the IMFWA has advantages in the convergence speed, and the negative impact of the number of tasks is also lower than the fireworks algorithm and genetic algorithm. [ABSTRACT FROM AUTHOR]

The superficial marine sediment is an important boundary of ocean acoustic propagation. So, the acoustic property of seafloor surface is always research hotspot. The acoustic property of sediment is affected by temperature and pressure which is not considered by conventional lab acoustic measurement. A new type of system, called "Small-scale Geo-acoustic Physical Model Pilot System" (SGPMP) has been developed. The system measures geo-acoustic property of sediment under specific temperature, pressure and frequency conditions which can be controlled conveniently and accurately. The components, structure, measurement principle, error analysis and application example of this system are introduced in this article. As a laboratory platform, the system makes it convenient for us to study the relationship between the temperature, pressure, frequency and acoustic properties of marine sediment. [ABSTRACT FROM AUTHOR]

A high performance surfactant formulation was developed using more universally accepted Winsor type phase behavior tests rather than relying primarily on interfacial tension (IFT) measurement for the first time for Daqing oilfield. A surfactant mixture of alkylbenzene sulfonate and fatty alcohol propoxylated ether sulfate was identified which was able to form middle-phase microemulsion with Daqing less reactive crude oil (acid number below 0.1 mg KOH/g crude), lower oil water IFT to ultralow level, and hold desired aqueous stability. Winsor type phase behavior tests at various water oil ratios (WORs) showed the optimum salinity changed slightly from 1.95% to 2% when WOR was increased from 5:5 to 9:1 due to the low reactivity of the Daqing crude oil. To investigate the significance of salinity on Daqing alkaline-surfactant-polymer (ASP) flooding, 4 salinity profiles were designed during coreflooding that corresponded to different types of microemulsions including III–III–I, I–III–I, II–III–I, and III–III–III. For each coreflooding, 0.3 pore volume (PV) of ASP slug with 0.3% total surfactant concentration and 0.2 PV polymer drive was injected. The results demonstrated when negative salinity gradient (3.0%–2.0%–0.5% Na 2 CO 3 in waterflood, ASP, and polymer slugs) was applied, the highest incremental oil recovery of 45.9% original oil in place (OOIP) was achieved. If no Na 2 CO 3 was added in water preflush prior to the injection of ASP slug, the lowest incremental recovery of 32.8% OOIP was observed. For a constant salinity profile at optimum salinity, an intermediate oil recovery (37.4%) was encountered. The measurement of surfactant and Na 2 CO 3 concentrations in the effluents indicated that when negative salinity gradient (II–III–I) was applied, the surfactant loss in the porous media and the oil phase was moderate compared to the two corefloods with III–III–III (highest surfactant loss) emulsion and I–III–I emulsion (lowest surfactant loss). This implied that at optimized salinity profile, the surfactants were more close to hydrophobic-hydrophilic balance during the propagation in the reservoir, which showed the highest potential of increasing oil recovery for the Daqing Oilfield without significantly increasing chemical consumption. [ABSTRACT FROM AUTHOR]

The distribution of hydrothermal vents and the biogeography of associated faunal communities in the Indian Ocean are still not well studied, particularly the ultraslow-spreading Southwest Indian Ridge (SWIR). Herein, we present the geological and morphological data for the first reported active hydrothermal field on the ultraslow-spreading center. In this context, we created a detailed seafloor map based on visual data obtained from human-occupied vehicle (HOV) dives. Longqi-1 vent field (LVF) is an off-axis, mafic-hosted but detachment fault-controlled, high-temperature hydrothermal area with at least 28 hydrothermal structures, and their contacting host rocks were identified and mapped. The morphologies of hydrothermal structures in the LVF exhibit unique characteristics, with Zone M characterized by chimneys grown on the relatively flat-lying sulfide mound and Zone S characterized by large sulfide mounds and steep-sided structures hosting large flanges, and isolated beehive structures occurred more often at the boundary between hydrothermal deposits and basalts outcrops in both M and S zone. Microscopic examination showed that anhydrite is the dominant sulfate phase and chalcopyrite, pyrite, pyrrhotite, and sphalerite are the dominant sulfide minerals. Subseafloor hydrothermal circulation controlled by the detachment fault and local fracture zone may have directly determined the morphology of the sulfide structure in the LVF. Our results provide a picture of the LVF as well as complement and expand on previous studies on metal resource evaluation along the SWIR and Indian Ocean ridges, a detailed scenario for studying the surface distribution characteristics of hydrothermal circulation and its relationship with the distribution of hydrothermal fauna, and further enhance our understanding of the formation of modern seafloor hydrothermal systems. • This study report geological and morphological data for the Longqi-1vent field along the ultraslow-spreading SWIR. • In this context, we created a detailed seafloor map based on visual data obtained from human-occupied vehicle dives. • Zones M and S are characterized by relatively flat-lying sulfide mound and large sulfide edifices with steep-sided flanges, respectively. • The Longqi-1 hydrothermal field is the first documented high-temperature venting field along the ultraslow-spreading SWIR. [ABSTRACT FROM AUTHOR]

Explicit structural inference is one key point to improve the accuracy of scene parsing. Meanwhile, adversarial training method is able to reinforce spatial contiguity in output segmentations. To take both advantages of the structural learning and adversarial training simultaneously, we propose a novel deep learning network architecture called Structural Inference Embedded Adversarial Networks (SIEANs) for pixel-wise scene labeling. The generator of our SIEANs, a novel designed scene parsing network, makes full use of convolutional neural networks and long short-term memory networks to learn the global contextual information of objects in four different directions from RGB-(D) images, which is able to describe the (three-dimensional) spatial distributions of objects in a more comprehensive and accurate way. To further improve the performance, we explore the adversarial training method to optimize the generator along with a discriminator, which can not only detect and correct higher-order inconsistencies between the predicted segmentations and corresponding ground truths, but also exploit full advantages of the generator by fine-tuning its parameters so as to obtain higher consistencies. The experimental results demonstrate that our proposed SIEANs is able to achieve a better performance on PASCAL VOC 2012, SIFT FLOW, PASCAL Person-Part, Cityscapes, Stanford Background, NYUDv2, and SUN-RGBD datasets compared to the most of state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The fluids found in sulfide-producing hydrothermal vents are rich in ore-forming elements. These elements usually precipitate as solid particles that are dispersed by the plumes and deposited as sediments around the hydrothermal field. To assess how the geochemical features of such sediments may be used in seafloor sulfide exploration, this study analyzed the surface sediment geochemistry in the Dragon Horn area on the Southwest Indian Ridge. The results indicate that the sediments are mainly composed of pelagic material, basalt and ultramafic debris, (Mn,Fe) oxyhydroxides and Mn oxides, hydrothermal components. The debris content in this area is lower than that in sediments found at fast- and medium-spreading mid-ocean ridges, with relatively high amounts of ultramafic components, indicating that ultramafic rock outcrops are present in the study area. Precipitated hydrothermal elements in the sediments exhibit zonation in the spatial distributions of Zn, Cu, Fe, and Mn. The sediments proximal to the hydrothermal field display high Cu, Zn, and Fe contents and low Mn contents. Hydrothermal Cu and Zn phases occurred within a limited range of the hydrothermal field, and Zn was found to precipitate earlier than Cu during dispersal from the plume. Hydrothermal Fe distributed similarly with Cu spatially but with a larger dispersion distance. Hydrothermal Mn was able to migrate beyond the ridge valley to precipitate in the ridge flank sediments. Two hydrothermal Cu and Zn anomalies were identified on the south ridge flanks, which suggest the presence of undiscovered hydrothermal field. The sediments in the study area are additionally characterized by relatively low Mn contents and high Fe/Mn molar ratios. The relatively reducing environment of the plume may facilitate the migration of hydrothermal Mn over large distances, which probably accounts for the lower Mn contents in the sediments. The Cu-Cu/Fe molar ratio model was proposed to identify mafic- and ultramafic-related hydrothermal field. The low Cu/Fe molar ratios and REE characteristics of the sediments suggest that ultramafic rocks are likely not the only source of the ore-forming element for hydrothermal mineralization. Low-temperature diffuse flow on the ridge flanks of the study area probably results from serpentinization of ultramafic rocks. [ABSTRACT FROM AUTHOR]

Soybean and moringa based green biosolvents were employed for low-concentration CO 2 capture via a super-hydrophobic PVDF/Si-R hollow fiber membrane contactor from flue gas. The performance of the hollow fiber membrane contactor was examined in terms of CO 2 absorption flux, CO 2 absorption efficiency, and CO 2 absorption capacity with varied biosolvent concentration, biosolvent flow rate, and feed gas flow rate. In the presence of soybean based CO 2 biosolvent, the CO 2 absorption flux and CO 2 absorption efficiency reached 0.52 mol.m −2 .h −1 and 97% for the hollow fiber membrane contactor, respectively. The results showed that the CO 2 absorption flux and CO 2 absorption efficiency were affected more by concentration and flow rate of the soybean based biosolvent, but less by the feed gas flow rate. The biosolvent concentration exhibited the most significant effect on the CO 2 absorption capacity. It was also revealed that the components interactions in the soybean based biosolvent affect the performance of the membrane contactor. The optimal biosolvent concentration, biosolvent flow rate and gas feed rate was 1.0 M, 0.5 ml/min, and 15 ml/min, respectively. Both soybean and moringa based biosolvents exhibited comparable CO 2 absorption flux to the conventional solvent of monoethanolamine (MEA). [ABSTRACT FROM AUTHOR]

Large-scale tridiagonal matrix solvers based on heterogeneous systems currently cannot balance computational efficiency and numerical stability when solving a non-diagonally dominant matrix. A tridiagonal solver combined central processing unit with graphics processing unit is proposed, based on SPIKE 2 as a solver framework, a simplified SPIKE algorithm as a central processing unit component, and a diagonal pivot algorithm as a graphics processing unit component. The solver performance is further improved by using a data-layout-transformation mechanism to obtain continuous addresses, reducing memory communication using constant memory to store unchanged data in the calculation process, and employing a kernel-fusion mechanism to reduce power consumption of graphics processing unit. For a diagonally dominant matrix, extended Thomas algorithms and cycle reduction to replace the graphics processing unit component are proposed in the solver. Experimental results show that the tridiagonal matrix solver in this paper can effectively consider both numerical stability and computational efficiency, and reduce total power consumption while improving memory efficiency. [ABSTRACT FROM AUTHOR]

Seafloor hydrothermal polymetallic sulfide deposits are a new type of resource, with great potential economic value and good prospect development. This paper discusses turbidity, oxidation-reduction potential, and temperature anomalies of hydrothermal plumes from the Zouyu-1 and Zouyu-2 hydrothermal fields on the southern Mid-Atlantic Ridge. We use the known location of these vent fields and plume data collected in multiple years (2009, 2011, 2013) to demonstrate how real-time plume exploration can be used to locate active vent fields, and thus associated sulfide deposits. Turbidity anomalies can be detected 10 s of km from an active source, but the location precision is no better than a few kilometers because fine-grained particles are quasi-conservative over periods of many days. Temperature and oxidation-reduction potential anomalies provide location precision of a few hundred meters. Temperature anomalies are generally weak and difficult to reliably detect, except by chance encounters of a buoyant plume. Oxidation-reduction potential is highly sensitive (nmol concentrations of reduced hydrothermal chemicals) to discharges of all temperatures and responds immediately to a plume encounter. Real-time surveys using continuous tows of turbidity and oxidation-reduction potential sensors offer the most efficient and precise surface ship exploration presently possible. [ABSTRACT FROM AUTHOR]

A novel two-stage anti-fouling hollow fiber membrane (TS-af-HFM) nanofiltration (NF) process was integrated with a super hydrophobic organic silica modified polyvinylidene fluoride (PVDF/Si-R) hollow fiber membrane and a super hydrophilic silica modified polyethersulfone (PES/SiO 2 ) based TFC hollow fiber membrane for portable treatment of oilfield produced water. The performance of the TS-af-HFM NF system was investigated in terms of organics removal efficiency, permeate water flux and salt rejection. The result showed that the PVDF/Si-R membrane can dramatically remove dissolved organics from the produced water in the first stage, with the organics rejection efficiency up to 98.7%. Meanwhile, the PES/SiO 2 TFC membrane showed encouraging desalination ability with a relatively low operating pressure in the second stage. The high-efficient removal of dissolved organics in the first stage effectively eliminated the organics fouling occurred during the desalination process in the second stage. The variation of feed rate (0.05–0.2 ml/min) and operating pressure (0–30 psi) in the first stage has no significant effect on the performances of the TS-af-HFM NF system. However, with the increase of operating pressure from 50 psi to 80 psi in the second stage, the permeate water flux and salt rejection efficiency linearly increased. The system showed attractive regeneration ability for oilfield produced water treatment application. [ABSTRACT FROM AUTHOR]

An extremely magmatic accretion could exist along an ultraslow-spreading ridge that has been approved and surveyed, but less attention has been paid to its associated hydrothermal system, as it is supposed to be similar to those of fast/medium-spreading ridges. Here using an autonomous underwater vehicle and a human-occupied vehicle, a systematic investigation was conducted on the hydrothermal system of an extremely magmatic accretion segment along the Southwest Indian Ridge (50°28′ E). A collapsed chimney, microbial mat, and diffused fluid were observed in the old Duanqiao hydrothermal field (DQF), which was earlier a weakly active hydrothermal area. Notably, evident hydrothermal anomalies, including oxidation–reduction potential (ORP), turbidity, and methane, were detected above the new DQF, which lies southwest of the old DQF. Based on this investigation, we concluded that ORP is one of the most effective parameters available to identify vent location by analyzing each type of detected hydrothermal anomaly and the near-bottom magnetic inversion can image the suspected hydrothermal fluid circulation pathways. In general, we believe that the new DQF is controlled by a magmatic chamber different from those found on fast/medium-spreading ridges, and could be a large hydrothermal system. • Obvious hydrothermal anomalies observed were earlier described as inactive by AUV. • Indicators of hydrothermal activities have their own recognition features. • Duanqiao is a large magmatic-hosted hydrothermal system compared with Lucky Strike. [ABSTRACT FROM AUTHOR]

• Fresh Southwestern Indian Ridge basalt glasses are found to have high water contents and H 2 O/Ce ratios (>600). • High water contents and H 2 O/Ce ratios are not caused by assimilation or diffusion. • The water enrichment is associated with the recycling of arc-mantle residue. Trace amounts of water in the sub-oceanic mantle play crucial role in the vigor of mantle convection and the production of oceanic crust, and other many geodynamic processes. Consequently, the cycling of H 2 O between the mantle and the exosphere in the mantle is one of the critical processes governing Earth's geodynamical and geochemical evolution. While the deep cycling of altered oceanic lithosphere was considered as the main way to replenish the water in oceanic upper mantle, the significance of the arc mantle wedge after the genesis of arc magmatism dragged down by the subducting slab concomitantly has been not well constrained. Here, we report that fresh depleted basaltic glasses from the ultraslow-spreading Southwestern Indian Ridge (SWIR), located far from any recent subduction zones, show unusually high H 2 O/Ce ratios (>600), water contents and heavy hydrogen isotopic compositions. These results could be best explained by recycling of water through melting of a residual hydrous mantle wedge after early melt extraction. Considering that such mantle wedges residue dragged down to the deeper mantle could occupy a volume one order of magnitude larger than that of the subducted lithosphere in the earth history, we suggest that the potential role of such shallow recycling should be considered in studies of global water recycling and the origin of water in the upper asthenosphere. [ABSTRACT FROM AUTHOR]

Polymetallic sulfides have attracted more attentions, which are regarded as one of the potential seabed mineral resources for their high grade of precious metal elements such as Cu, Zn, Pb, Au, and Ag. Since 2007, there are four China dayang cruises (CDCs) with eight legs totally, which have been carried out to investigate polymetallic sulfides on the southwest Indian ridge (SWIR), and eight hydrothermal vents have been found. In 2011, the China Ocean Mineral Resources Research and Development Association (COMRA) and International Seabed Authority (ISA) signed a 15-year contract for exploration for polymetallic sulfides located on the SWIR between 46°S and 53°S. The area allocated to the COMRA is approximately 10,000 km. In this paper, we review the recent research advances about hydrothermal activity of the SWIR by the CDCs. As for the sulfides exploration in the future, some aspects should be emphasized, such as the controlling factors of sulfide mineralization on the SWIR, prospecting methods of inactive and buried sulfide deposits, sulfides resources assessment method, and the near-bottom sulfides exploration technology systems. [ABSTRACT FROM AUTHOR]