Storage Resources Research Articles

AutoGMap: Learning to Map Large-Scale Sparse Graphs on Memristive Crossbars.

The sparse representation of graphs has shown great potential for accelerating the computation of graph applications (e.g., social networks and knowledge graphs) on traditional computing architectures (CPU, GPU, or TPU). But, the exploration of large-scale sparse graph computing on processing-in-memory (PIM) platforms (typically with memristive crossbars) is still in its infancy. To implement the computation or storage of large-scale or batch graphs on memristive crossbars, a natural assumption is that a large-scale crossbar is demanded, but with low utilization. Some recent works question this assumption; to avoid the waste of storage and computational resource, the fixed-size or progressively scheduled "block partition" schemes are proposed. However, these methods are coarse-grained or static and are not effectively sparsity-aware. This work proposes the dynamic sparsity-aware mapping scheme generating method that models the problem with a sequential decision-making model, and optimizes it by reinforcement learning (RL) algorithm (REINFORCE). Our generating model [long short-term memory (LSTM), combined with the dynamic-fill scheme] generates remarkable mapping performance on the small-scale graph/matrix data (complete mapping costs 43% area of the original matrix) and two large-scale matrix data (costing 22.5% area on qh882 and 17.1% area on qh1484). Our method may be extended to sparse graph computing on other PIM architectures, not limited to the memristive device-based platforms.

JCDC: A blockchain-based framework for secure data storage and circulation in JointCloud

JointCloud computing represents a new generation cloud computing paradigm, which deeply integrates the cloud resources of multiple Cloud Service Providers (CSPs) to offer tailored cloud services to users. In contrast to traditional multi-cloud environment, JointCloud environment involve data circulation among multiple CSPs. However, in JointCloud environment, CSPs are not always fully trustworthy and they may illegally infringe upon users’ data privacy and security for their own benefit. Additionally, the heterogeneity arising from different data storage formats, structures, access control, and permission management mechanisms adopted by various CSPs makes achieving unified data management in JointCloud challenging. Therefore, to ensure secure storage and efficient circulation of data within JointCloud, it is essential to prevent violations for user privacy and data ownership, shield the heterogeneity of underlying data management mechanisms across different CSPs, and establish trusted transactions between CSPs. In this paper, we propose a framework called JointCloud Data Chain (JCDC) based on JointCloud computing and blockchain for data storage and circulation, aiming to ensure secure data storage and trustworthy transactions. JCDC utilizes blockchain to record data ownership and control data circulation, while integrating storage resources from various CSPs to construct a distributed off-chain Personal Data Storage (PDS) for expanding system storage capacity. Additionally, JCDC employs Certificateless Public Key Cryptography (CL-PKC) and Proxy Re-encryption technologies for user identity management and secure data transactions. Furthermore, smart contracts are designed to enable automated data storage and sharing. We conduct a security analysis of JCDC and develop a prototype system to validate its performance and practicality. Finally, extensive experimentation and analysis demonstrate that JCDC exhibits low time latency and cost, which makes it practical.

Global analysis of geological CO2 storage by pressure-limited injection sites

Limiting global warming to a 2 °C rise may require large-scale deployment of carbon capture and storage (CCS). Due to the key role CCS plays in integrated assessment models of climate change mitigation, it is important that fundamental physical constraints are accounted for. We produce a global estimate of CO2 storage resource that accounts for pressure-limits within basin-scale reservoir systems. We use a dynamic physics model of reservoir pressurisation that is sufficiently simple to be incorporated into energy systems models. Our estimates address regionally inconsistent methodologies and the general lack of consideration for pressure limitations in global storage resource estimates. We estimate a maximum pressure-limited resource base and explore scenarios with different injection patterns, and scenarios where the extent of CCS deployment is limited by the history of regional hydrocarbon exploration and the readiness of countries for deployment. The maximum pressure-limited global storage achievable after thirty years of injection is 3640GtCO2 (121GtCO2yr-1), increasing to 5630GtCO2 (70 GtCO2yr-1) at the end of the century. These represent an update to volumetric-based estimates that suggest in excess of 10,000Gt of storage resource available. When CCS deployment is limited to the top ten countries ranked by the GCCSI Storage Readiness Index, our maximum storage estimate decreases to 780GtCO2 (26GtCO2yr-1) at the mid-century and 1177GtCO2 (15GtCO2yr-1) at the end of the century. These latter results fall within the range of projected deployment by the IPCC and IEA and suggest that reservoir pressurisation will limit CCS deployment if development does not rapidly expand beyond the current implementation.

Gigaton commercial-scale carbon storage and mineralization potential in stacked Columbia River basalt reservoirs

This work presents a detailed supercritical CO2 storage resource estimation for the stacked basalt reservoirs in the Grande Ronde Basalt of the Columbia River Basalt Group in eastern Washington and Oregon. The assessment aims to derisk the commercialization potential of geologic carbon storage in basalt by leveraging both structural and mineralization trapping of CO2 in basalt. The structural closures formed by anticlinal ridges and synclinal valleys in Yakima Fold Belt are excellent physical traps to accommodate injected supercritical CO2. Rigorous hydraulic testing, well logs and simulation results from the Wallula Basalt Pilot #1 well showed the occurrence of 17 suitable permeable injection zones (up to 2,496 mD) intercalated with dense seals (∼2.6E-10 mD) in the Grand Ronde Basalt. In addition, geochemical studies showed fast reactions between supercritical CO2 and dissolved basalt minerals to form stable carbonates. Our calculation indicates up to 40 gigatons (P90) of mineralization storage resources exist in the Grande Ronde Basalt reservoirs.

Basin-scale study of CO2 storage in stacked sequence of geological formations

Commercial scale decarbonization through carbon capture and storage may likely involve many CO2 storage projects located in close proximity. The close proximity could raise concerns over caprock integrity associated with reservoir pressure buildup and interference among adjacent projects. Commercial-scale injection will also require large prospective CO2 storage resource and high injectivity in the targeted storage formations. To accommodate the need for both large resource and high injectivity, project operators could consider injecting CO2 into a stacked sequence of formations. This analysis investigates the benefits of injecting CO2 into a vertically stacked sequence of saline formations, over injecting the same amount of CO2 into a single saline formation, in addressing these challenges. Our analysis shows that injecting into the stacked sequence mitigates the extent of pressure buildup among the stacked formations, while still achieving the same or greater target CO2 storage volumes. Among cases modeled, the resulting pressure buildup front is most reduced when each storage site distributes injection volumes over several wells, each of which injects a portion of the total CO2 mass across the stacked sequence. This favorable case not only results in the smallest CO2 aerial footprint, but also shows the largest reduction in the pressure buildup at the top of perforation at the injection wells (upwards of approximately 46% compared to the single-formation storage), the result of which is crucial to maintain caprock integrity. This analysis provides insights into required decision-making when considering multi-project deployment in a shared basin.

Bayesian Inversion with Neural Operator (BINO) for modeling subdiffusion: Forward and inverse problems

Fractional diffusion equations have been an effective tool for modeling anomalous diffusion in complicated systems. However, traditional numerical methods require expensive computation cost and storage resources because of the memory effect brought by the convolution integral of time fractional derivative. We propose a Bayesian Inversion with Neural Operator (BINO) to overcome the difficulty in traditional methods as follows. We employ a deep operator network to learn the solution operators for the fractional diffusion equations, allowing us to swiftly and precisely solve a forward problem for given inputs (including fractional order, diffusion coefficient, source terms, etc.). In addition, we integrate the deep operator network with a Bayesian inversion method for modeling a problem by subdiffusion process and solving inverse subdiffusion problems, which reduces the time costs (without suffering from overwhelm storage resources) significantly. A large number of numerical experiments demonstrate that the operator learning method proposed in this work can efficiently solve the forward problems and Bayesian inverse problems of the subdiffusion equation.

The Impact of Cloud Computing on System Lifecycle Methodologies: an Innovative Approach to Scalability Flexibility

This research analyzes the impact of cloud computing on traditional system lifecycle methodologies and offers an innovative approach that leverages the advantages of cloud computing. Qualitative research was conducted with a literature study on conventional system lifecycle methodologies and cloud computing. The results show that cloud computing enables increased scalability and flexibility through a service-based approach, automation, and elastic management of resources. This research provides recommendations for implementing a hybrid lifecycle methodology that leverages the advantages of both cloud and traditional systems.This paper discusses the impact of cloud computing on system lifecycle methodologies. Cloud computing offers an innovative approach to system scalability and flexibility by allowing computing and storage resources to scale dynamically according to demand. Traditional system lifecycle methodologies are becoming less relevant for rapidly changing cloud environments, so they need to be adapted to respond to the scalability and flexibility capabilities offered by cloud computing.This paper evaluates the impact of the shift towards cloud computing on the methodologies used in the system development lifecycle. Cloud computing offers the ability to scale elastically according to demand, which opens up opportunities to adopt a more adaptable and flexible approach to system development. This paper discusses how traditional system lifecycle methodologies can be adapted to accommodate key cloud computing characteristics such as scalability, flexibility, and virtualization of computing resources. This paper reviews the implications of the move towards a cloud computing paradigm on the methodologies used in developing and maintaining software systems. The shift towards cloud-based environments offers the advantages of scalability and flexibility in dynamically managing system resources. However, these key characteristics of cloud computing pose challenges to traditional system lifecycle methodologies designed for on-premise environments. This paper discusses how system lifecycle methodologies can be adapted to better support system development in cloud computing environments.

A new data-driven distributed power grid load storage resource-wide area voltage self-optimization control method for distribution system

Abstract Different from the conventional energy distribution network, which solely relies on energy distribution, the novel distribution system gradually exhibits a complex and interconnected form of various resources, such as source network load storage. These resources possess characteristics of “multi-point, wide area, and small amount” distribution. Investigating the potential for wide-area voltage regulation by using distributed resources like renewable energy power generation, distributed energy storage, and flexible loads is crucial for constructing a highly integrated source network load storage in the new type of distribution system. This paper proposes a novel data-driven method for optimizing control of wide-area voltage through distributed power network load storage resources. By comprehensively and cooperatively controlling renewable energy power generation, energy storage systems, and flexible loads, this approach enhances the quality of wide-area voltage in the distribution network while ensuring optimal utilization of energy storage capacity and minimizing network losses. The effectiveness and advanced nature of this method are demonstrated by applying it to an urban distribution network example. Compared to traditional voltage regulation methods, this approach aligns better with the requirements for voltage regulation in the new type of distribution network.

Primary School English Teaching Strategy with Information Technology as the Carrier

With the rapid development of information technology, its application in the field of education is increasingly extensive, which has brought new opportunities and challenges for primary school English teaching. This paper aims to explore the primary school English teaching strategy with information technology as the carrier, aiming to improve the quality and efficiency of English teaching in primary schools by making rational use of information technology. This paper first analyzes the characteristics of pupils' thinking and learning, and the advantages of information technology in primary school English teaching, including resource richness, strong interaction, and convenient situation reproduction. These advantages provide a more vivid and intuitive teaching environment for English teaching, and help to stimulate students' interest and enthusiasm in learning. Then, the paper elaborated the primary school English teaching strategy with information technology as the carrier, namely, interest stimulation, new lesson introduction; micro course teaching, focus on the difficult points, create the situation, create atmosphere, enrich resources, expand horizons, store resources, accumulate precipitation. In short, information technology can not only enrich the teaching content, improve the teaching effect, but also stimulate students 'interest in learning and cultivate students' information support. Therefore, combining information technology with primary school English teaching is an effective way to improve the quality of English teaching in primary school.

Nationwide cost and capacity estimates for sedimentary basin geothermal power and implications for geologic CO2 storage

Introduction: Sedimentary basins are naturally porous and permeable subsurface formations that underlie approximately half of the United States. In addition to being targets for geologic CO2 storage, these resources could supply geothermal power: sedimentary basin geothermal heat can be extracted with water or CO2 and used to generate electricity. The geothermal power potential of these basins and the accompanying implication for geologic CO2 storage are, however, understudied.Methods: Here, we use the Sequestration of CO2 Tool (SCO2TPRO) and the generalizable GEOthermal techno-economic simulator (genGEO) to address this gap by a) estimating the cost and capacity of sedimentary basin geothermal power plants across the United States and b) comparing those results to nationwide CO2 sequestration cost and storage potential estimates.Results and discussion: We find that across the United States, using CO2 as a geothermal heat extraction fluid reduces the cost of sedimentary basin power compared to using water, and some of the lowest cost capacity occurs in locations not typically considered for their geothermal resources (e.g., Louisiana, South Dakota). Additionally, using CO2 effectively doubles the sedimentary basin geothermal resource base, equating to hundreds of gigawatts of new capacity, by enabling electricity generation in geologies that are otherwise (with water) too impermeable, too thin, too cold, or not deep enough. We find there is competition for the best sedimentary basin resources between water- and CO2-based power, but no overlap between the lowest-cost resources for CO2 storage and CO2-based power. In this way, our results suggest that deploying CO2-based power may increase the cost of water based systems (by using the best resources) and the cost of CO2 storage (by storing CO2 in locations that otherwise may not be targeted). As such, our findings demonstrate that determining the best role for sedimentary basins within the energy transition may require balancing tradeoffs between competing priorities.

LIBRARIANS’ EMOTIONAL INTELLIGENCE AND PROFICIENCY AS CORRELATES OF ELECTRONIC COLLECTION DEVELOPMENT IN UNIVERSITIES IN NORTH-CENTRAL, NIGERIA

The study examined librarians’ emotional intelligence and proficiency as correlate of electronic resources collection development (ERCD) in universities in North-central, Nigeria. The objectives considered were to: find out the relationship between librarians’ proficiency and ERCD in university libraries in North-central, Nigeria; and determine librarians’ emotional intelligence and ERCD in these university libraries. Correlational research design was used. The total population for study was 400 librarians. Multi-stage sampling procedure was adopted to stratify the university into Federal, State and Private Universities. Questionnaire was used to collect data. SPSS was used for the quantitative analysis and result were presented using frequency, mean, and standard deviation. The result of the study showed that proficiency skills in database, social media, online public access catalogue, e-document and e-book delivery, data management, storage of electronic resources, e-processing, and e-ordering by librarians has a relationship on electronic resources collection development in university libraries. The findings also revealed that librarians possessed self-awareness skills, self-regulatory skills, motivational skills, empathy skills, and social skills, and it has influenced the ERCD in the studied university libraries. The study concluded that addressing the identified deficiencies in proficiency, and emotional intelligence can lead to a more effective and efficient electronic resources collection development, thereby improving the overall academic and research experience for library users. Therefore, the study recommended that training that will boost the emotional intelligent of librarians by the management should be done regularly.

A new model for comprehensively evaluating the economic and environmental effects of vehicle-to-grid(V2G) towards carbon neutrality

Vehicle-to-grid (V2G) technology enables electric vehicles (EVs) to serve as flexible load storage resources, which is expected to play a pivotal role in pursuing carbon neutrality. However, existing studies on the effect of V2G at different stages of carbon neutrality is not sufficient, and there is a lack of discussion on the optimal adoption period for V2G in the context of electricity marketization trading. To fill this gap, a new methodology is proposed in this study to analyze multi-dimension effects of V2G towards carbon neutrality. The model is a novel attempt of applying partial market equilibrium model to depict the interaction between electricity suppliers and V2G adopters. By applying in a China's case, the results demonstrate that: (1) EVs with V2G can substitute 22.2 %–30.1 % energy storage and accelerate the phase-out of coal-fired power. (2) V2G can effectively mitigate electricity price fluctuations, moreover, more fast charging infrastructure will strengthen such effect. (3) V2G only become attractive when renewable energy penetration rate reaches 80 %, otherwise, it cannot effectively reduce the total social cost and carbon emission. (4) In the carbon neutrality scenario with limited emission, the emission reduction effect of V2G is weakened, however, its economic benefit keeps increasing.

Knowledge mapping and evolutionary analysis of energy storage resource management under renewable energy uncertainty: a bibliometric analysis

The research on energy storage resource management is an important measure to cope with the present problem of uncertainty in the use of renewable energy, in order to explore the evolution of the research focus and future trend of energy storage resource management under the uncertainty of renewable energy, this paper adopts the method of bibliometric analysis, and conducts a visual analysis of the relevant 800 documents in the core collection of the Web of Science by using the CiteSpace to draw co-occurrence maps and analyze the collaborative networks of different countries/regions, institutions and authors in this field, and further conducted journal co-citation analysis, literature co-citation analysis and author co-citation analysis. The keyword co-occurrence, emergent analysis, and cluster co-occurrence analysis reveal the current research focus and trend in this field, and summarize and propose four future key focus directions: energy storage technology improvement, energy storage system integration, expansion of business models for energy storage resource management, and intelligent control of energy storage system, which provide new research paths for solving the problem of renewable energy uncertainty in the future.

A Non-Metallic pipeline leak size recognition method based on CWT acoustic image transformation and CNN

Accurately identifying the pipeline leak size is crucial for risk assessment and timely rescue. In this study, a Convolutional Neural Network (CNN) based on Continuous Wavelet Transform (CWT) acoustic image transformation is proposed to identify small-sized leak in non-metallic pipes. Firstly, one-dimensional acoustic signals are filtered using the Piecewise Aggregate Approximation (PAA) algorithm to reduce noise and storage resource consumption. Then, the filtered signals are transformed into two-dimensional images by CWT to enrich signal feature information, serving as the input for the CNN. Further, a leak size recognition model based on CWT-CNN is established. The effectiveness of this model is verified using experimental data from a non-metallic pipeline leak test. A comparative analysis is conducted on diverse acoustic image transformation methods, including CWT, Gramian Angular Summation Field (GASF), and Relative Position Matrix (RPM). The results demonstrate the superiority of the CWT-CNN model in pipeline leak size recognition. Finally, the impact of the signal length in an acoustic image on recognition accuracy is also examined. The results demonstrate that when the signal length in an acoustic image is 0.75 s, the accuracy obtained by CWT-CNN can reach 95 %.

Multi-Stage Coordinated Planning for Transmission and Energy Storage Considering Large-Scale Renewable Energy Integration

Due to the large-scale integration of renewable energy and the rapid growth of peak load demand, it is necessary to comprehensively consider the construction of various resources to increase the acceptance capacity of renewable energy and meet power balance conditions. However, traditional grid planning methods can only plan transmission lines, often resulting in low utilization rates of newly constructed lines. Additionally, static planning methods can only address single-target scenarios and cannot cope with dynamic growth in load and renewable energy. To address these issues, this paper proposes a multi-stage collaborative planning method for transmission networks and energy storage. This method considers the non-line substitution effect of energy storage resources and their characterization methods. It establishes the coupling relationship between resources across different planning stages to achieve coordinated multi-stage planning for transmission networks and energy storage. Based on the IEEE-24 node system and a case study in a northern province of China, the results show that the proposed method reduces investment costs by approximately 30% compared to static planning methods and by about 7.79% compared to conventional grid planning methods. Furthermore, this method can accommodate more renewable energy.

A resilience-oriented pre-positioning approach for electric vehicle routing and scheduling in coupled energy and transport sectors

High-impact low-probability (HILP) events have occurred more frequently than before and caused severe damage to conventional power systems. Energy hubs (EHs) consist of various distributed energy resources (DERs) for energy generation, conversion and storage across different sectors. The high energy integration of EH systems makes them more robust under extreme events than traditional power systems. In addition, the large penetration of electric vehicles (EVs) has been witnessed in modern energy systems due to their significant benefits in accelerating transport electrification and reducing carbon emissions. Due to the characteristics of mobility and flexibility, EVs can be utilised as mobile energy resources in coupled energy and transport sectors and shift energy between different regions via effective routing and scheduling behaviours. In this context, this paper proposes a resilient-oriented pre-positioning approach for the routing and scheduling of multiple EVs in a coupled energy-transport system, where the energy system includes multiple EHs across both electricity and heat sectors. To simulate real-world scenarios, uncertainties associated with renewable generation, load profiles, and EV commuting time as well as contingencies related to component failures are incorporated into the proposed pre-positioning approach via stochastic programming. Extensive case studies are carried out based on an EH system including three EHs and five EVs, which illustrate that EVs can coordinate with static DERs in the system and perform energy shifting between different EHs via appropriate routing and scheduling behaviours. Additionally, results demonstrate that the proposed pre-positioning approach can achieve higher resilience level and ensure the supply continuity of critical loads.

An Overview of Postprocessing in Quantum Key Distribution

Quantum key distribution (QKD) technology is a frontier in the field of secure communication, leveraging the principles of quantum mechanics to offer information-theoretically secure keys. Postprocessing is an important part of a whole QKD system because it directly impacts the secure key rate and the security of the system. In particular, with the fast increase in the photon transmission frequency in a QKD system, the processing speed of postprocessing becomes an essential issue. Our study embarks on a comprehensive review of the development of postprocessing of QKD, including five subprotocols, namely, parameter estimation, sifting, information reconciliation, privacy amplification, and channel authentication. Furthermore, we emphasize the issues raised in the implementation of these subprotocols under practical scenarios, such as limited computation or storage resources and fluctuations in channel environments. Based on the composable security theory, we demonstrate how enhancements in each subprotocol influence the secure key rate and security parameters, which can provide meaningful insights for future advancements in QKD.

Mechanical Properties of Rock Salt from the Kłodawa Salt Dome-A Statistical Analysis of Geomechanical Data.

Rock salt is a potential medium for underground storage of energy resources and radioactive substances due to its physical and mechanical properties, distinguishing it from other rock media. Designing storage facilities that ensure stability, tightness, and safety requires understanding the geomechanical properties of rock salt. Despite numerous research efforts on the behaviour of rock salt mass, many cases still show unfavourable phenomena occurring within it. Therefore, the formulation of strength criteria in a three-dimensional stress state and the prediction of deformation processes significantly impact the functionality of storage in salt caverns. This article presents rock salt's mechanical properties from the Kłodawa salt dome and a statistical analysis of the determined geomechanical data. The analysis is divided into individual mining fields (Fields 1-6). The analysis of numerical parameter values obtained in uniaxial compression tests for rock salt from mining Fields 1-6 indicates an average variation in their strength and deformation properties. Upon comparing the results of Young's modulus (E) with uniaxial compressive strength (UCS), its value was observed with a decrease in uniaxial compressive strength (E = 4.19968·UCS2, R-square = -0.61). The tensile strength of rock salt from mining Fields 1-6 also exhibits moderate variability. An increasing trend in tensile strength was observed with increased bulk density (σt = 0.0027697·ρ - 4.5892, r = 0.60). However, the results of triaxial tests indicated that within the entire range of normal stresses, the process of increasing maximum shear stresses occurs linearly ((σ1 - σ3)/2 = ((σ1 + σ3)/2)·0.610676 + 2.28335, r = 0.92). A linear relationship was also obtained for failure stresses as a function of radial stresses (σ1 = σ3·2.51861 + 32.9488, r = 0.73). Based on the results, the most homogeneous rock salt was from Field 2 and Field 6, while the most variable rock salt was from Field 3.

Unreliability tracing of power systems with reservoir hydropower based on a temporal recursive model

AbstractPower system unreliability tracing model allocates the system's reliability index to individual components, identifying potential weaknesses. This study expands its scope by considering the impact of storage resources. Unreliable factors leading to load shedding are categorized into two groups: objective factors inherent to the component and insufficient storage resources. The latter requires a retrospective analysis of other components that caused unreliability previously. When allocating responsibility for load shedding at a certain time, it begins by allocating it among components based on differences between fixed expected output and actual supply. Expected output insufficiency is considered as the unreliable factor. This insufficiency due to insufficient storage resources is then decomposed into segments, each caused by excessive output in earlier instances of the same component. The expected output excess is attributed to the expected output insufficiency of other components in previous times, for which responsibility has been allocated to each component. Consequently, the expected output insufficiency at a particular time can be traced back based on a temporal recursive model, with the load shedding further allocated to components before that time. Case studies based on several systems demonstrate that the proposed model's allocation results are reasonable and more accurate than the traditional model.

Deep underwater image compression for enhanced machine vision applications

Underwater image compression is fundamental in underwater visual applications. The storage resources of autonomous underwater vehicles (AUVs) and underwater cameras are limited. By employing effective image compression methods, it is possible to optimize the resource utilization of these devices, thereby extending the operational time underwater. Current image compression methods neglect the unique characteristics of the underwater environment, thus failing to support downstream underwater visual tasks efficiently. We propose a novel underwater image compression framework that integrates frequency priors and feature decomposition fusion in response to these challenges. Our framework incorporates a task-driven feature decomposition fusion module (FDFM). This module enables the network to understand and preserve machine-friendly information during the compression process, prioritizing task relevance over human visual perception. Additionally, we propose a frequency-guided underwater image correction module (UICM) to address noise issues and accurately identify redundant information, enhancing the overall compression process. Our framework effectively preserves machine-friendly features at a low bit rate. Extensive experiments across various downstream visual tasks, including object detection, semantic segmentation, and saliency detection, consistently demonstrated significant improvements achieved by our approach.