Intelligent Search Research Articles

Node Importance Estimation for Knowledge Graphs Based on Multi-Perspectives Attention Fusion Mechanism

The advent of the Internet has led to the emergence of multi-source heterogeneous knowledge graphs, which have become a crucial means of storing and disseminating knowledge. Node importance estimation is a technique that is employed extensively in a number of fields, including recommender systems, intelligent search and resource allocation. This study introduces a Multi-perspective attention mechanism Fusion Algorithm for the mapping of multi-perspective features of knowledge graphs to Node Importance Estimation (MFA-NIE). First, structural embedding features, relational predicate features, and attribute features (both textual and quantitative) are established for the nodes. Subsequently, an enhanced attention mechanism is employed to extract, compress, and fuse these features. The fused hidden layer vector is employed in the design of a key-based attention mechanism, which enables the propagation of messages from neighboring nodes to the source node. This process results in the iterative updating of the source node’s hidden features. Finally, TOPSIS centrality, based on the topology of the source node, is employed to dynamically adjust the mapping between the fused features and node importance. Experiments were conducted on real-world, large-scale, multi-source, heterogeneous knowledge graphs, comparing the MFA-NIE algorithm with traditional and advanced baseline algorithms, including PR, PPR, GENI, RGTN, CLINE, MCRL, and others. The results demonstrate that the MFA-NIE algorithm significantly improves effectiveness and accuracy, showcasing its superiority, versatility, and practical application value.

Development of an Evaluation Instrument on Artificial Intelligence Search Tools for Evidence Synthesis

What Was the Question? There are conflicting calls for evidence synthesis producers to adopt or avoid recent Artificial Intelligence (AI) technologies. Our question was: How do we evaluate rapidly evolving AI tools to enhance the production of evidence syntheses and maintain quality standards? What Did We Do? To advance information retrieval science for producing evidence syntheses at Canada’s Drug Agency (CDA-AMC), the Research Information Services team developed a process to evaluate promising AI search tools. We inventoried 51 tools in the fall of 2023, established selection criteria, assessed specific attributes, and built a standalone instrument to support continuous monitoring and evaluation of new tools. What Did We Find? Rapid development of AI search tools requires a flexible evaluation instrument to inform adoption decisions and enable comparison between tools. We identified mandatory and desirable characteristics for suitable AI tools to assist with information retrieval tasks conducted by CDA-AMC. This work enabled the development of a flexible instrument to evaluate novel AI search tools for evidence synthesis. What Does This Mean? CDA-AMC operationalized a replicable process to monitor and evaluate AI search tools. Our approach to evaluating AI technologies will advance information retrieval methods by CDA-AMC, and our evaluation instrument will assist any evidence synthesis producer interested in adopting AI search tools.

Development of an Evaluation Instrument on Artificial Intelligence Search Tools for Evidence Synthesis

Canada’s Drug Agency has developed an instrument to monitor and evaluate artificial intelligence tools for information retrieval so that, as these tools evolve, we can leverage them appropriately. We are sharing our instrument to help other evidence synthesis producers evaluate and make use of artificial intelligence tools for information retrieval.

Prediction of stable structure and unique charge transfer in Li–Pt intermetallic compounds under pressure

The exploration of new intermetallic compounds is of great significance for basic research and practical application. There is a huge electronegativity difference between Li and Pt, and the metals exhibit interesting and diverse properties; however, the structural behavior of Li–Pt intermetallics with different ratios under high pressure has not been systematically studied. In this study, an intelligent structure search method based on a particle swarm optimization algorithm combined with first-principles calculations was used to extensively explore the stable structures and unique conditions governing charge transfer in Li–Pt intermetallic compounds under different pressures. In addition to reproducing the known LiPt (P6‾m2) and Li2Pt (P6/mmm), the simulation was consistent with the experimental results. New phases were also found by calculation: LiPt3(Cmmm), Li2Pt (P3‾m1), and Li4Pt (I4/m) at ambient pressure; Li3Pt (Fm3‾m), Li4Pt (R3‾m), and Li5Pt (P6/mmm) at 10 GPa; and Li5Pt (P3‾m1) at 20 GPa. Bader charge analysis and electron localization function (ELF) mapping showed that the transition metal (Pt) atoms exhibit unusual oxidation states both under ambient and high pressure, and more electrons were localized on Pt as the Li content increased. The highest negative valence state was approximately −4. Intermetallic compounds LiPt3 (Cmmm) and Li2Pt (P6/mmm) were prepared successfully by arc melting furnace. The reliability of structure prediction method and pseudo potential selection was verified. This work demonstrates that tuning the pressure and stoichiometry is an effective means of forming novel, stable intermetallic compounds.

Intelligent controllable ultrafast fiber laser via deep learning and adaptive optimization algorithm

Ultrafast fiber lasers based on nonlinear polarization rotation can generate femtosecond pulses with different pulse durations and high peak powers, which are powerful tools for engineering applications and scientific research. However, achieving a precise and repeatable polarization state for generating the ultrashort pulses with the shortest pulse duration remains a significant challenge. In this paper, we extend the use of recurrent neural networks and adaptive optimization algorithms, specifically designed to optimize repetitive processes in optical systems, to facilitate intelligent search and control aimed at achieving the minimum pulse duration within a mode-locked fiber laser cavity. Our multi-algorithm-based intelligent system can fully simulate and optimize the processes involved in hands-on experiments. Our intelligent system identified a mode-locked fiber laser with the shortest pulse duration of 465 fs, which was experimentally verified. The proposed intelligent algorithm not only identifies the shortest pulse but also holds significant potential for selecting related laser characteristic parameters. We believe this work opens up a novel avenue for exploration and optimization in mode-locked lasers and the intelligent laser can find practical applications in engineering and scientific research.

B-146 Unlocking the potential of large-cohort proteomics studies with the Orbitrap Astral mass platform

Abstract Background Large-cohort proteomics analysis using mass spectrometry is a powerful approach to discover and validate new biomarkers. In combination with clinical data and computational analysis, large-cohort proteomics study brings opportunities in improving early diagnosis, refining patient stratification, and predicting/monitoring treatment response. Yet, to achieve meaningful biological insights in large-cohort studies, robust, reproducible, and comprehensive proteome profiling in a high-throughput manner still remains challenging. Here, we use a high-resolution accurate mass (HRAM) Oribtrap Astral platform to enable high-quality and robust protein quantification across thousands of LC-MS/MS analyses. With a throughput of 100 samples/day, we can reproducibly profile ∼9000 proteins from human cell line and ∼800 proteins from undepleted plasma across multiple instruments and more than 10 consecutive days in a 24/7 operation mode. Methods Experiments were performed on multiple Orbitrap Astral mass spectrometers with and without FAIMS Pro interface. Chromatographic separations were performed using a trap/elute method on Vanquish Neo system at a 11-minute gradient and a flow rate of 1ul/min, resulting in a throughput of 100 samples/day. Undepleted plasma digest was analyzed in a 24/7 mode for > 1000 injections on each LC-MS/MS setup. In addition, HeLa digest as quality control (QC) was analyzed every 12hours. The Oribtrap Astral platform was operated in data-independent acquisition (DIA) mode using a full scan with m/z 380-980, and DIA MS/MS scans with an isolation window of 2Th. Resulting DIA raw files were automatically transferred and processed using Chimerys in a beta version of Proteome Discoverer software. Results Multiple LC-MS/MS systems were operated in DIA mode either with or without FAIMS Pro device in a 24/7 operation mode at a throughput of 100SPD. Undepleted plasma digest was analyzed with >1000 injections on each LC-MS/MS setup. To monitor the baseline performance, HeLa digest served as QC were analyzed periodically every 12 hours with 3 technical replicates each time. To effectively manage and analyze these thousands of data files generated, we developed an automated data transfer and analysis pipeline. Automatically, the resulting DIA raw data files were immediately transferred to a server, then processed by state-of-the-art intelligent search algorithm Chimerys. Benefiting from the ultra-high scan speed of up to 200Hz on the Orbitrap Astral mass spectrometer, a much narrower isolation window width of 2Th was applied in the DIA method, comparing to 10-20Th on the classic DIA method. As a result, ∼9000 proteins from HeLa digest and ∼800 proteins from undepleted plasma digest were identified within only a 11-minutes gradient, respectively. More than 80% of the plasma proteins were reproducibly identified and quantified from all the runs on each LC-MS/MS setup, indicating a great reproducibility from run-to-run longitudinally. Stable and robust peptide quantitation was observed by extracting peptides with high, medium, and low abundant across the runs. Importantly, QC showed no performance degradation throughout the entire study, indicating high robustness of the entire LC-MS/MS setup. Conclusions Orbitrap Astral mass platform can comprehensively analyze the proteome of >1000s of sample robustly and reproducibly in a high-throughput manner, addressing the needs in large-cohort studies.

Enhancing Target Tracking: A Novel Grid-Based Beetle Antennae Search Algorithm and Confusion-Aware Detection.

Unmanned aerial vehicle target tracking is a complex task that encounters challenges in scenarios involving limited computing resources, real-time requirements, and target confusion. This research builds on previous work and addresses challenges by proposing a grid-based beetle antennae search algorithm and designing a lightweight multi-target detection and positioning method, which integrates interference-sensing mechanisms and depth information. First, the grid-based beetle antennae search algorithm's rapid convergence advantage is combined with a secondary search and rollback mechanism, enhancing its search efficiency and ability to escape local extreme areas. Then, the You Only Look Once (version 8) model is employed for target detection, while corner detection, feature point extraction, and dictionary matching introduce a confusion-aware mechanism. This mechanism effectively distinguishes potentially confusing targets within the field of view, enhancing the system's robustness. Finally, the depth-based localization of the target is performed. To verify the performance of the proposed approach, a series of experiments were conducted on the grid-based beetle antennae search algorithm. Comparisons with four mainstream intelligent search algorithms are provided, with the results showing that the grid-based beetle antennae search algorithm excels in the number of iterations to convergence, path length, and convergence speed. When the algorithm faces non-local extreme-value-area environments, the speed is increased by more than 89%. In comparison with previous work, the algorithm speed is increased by more than 233%. Performance tests on the confusion-aware mechanism by using a self-made interference dataset demonstrate the model's high discriminative ability. The results also indicate that the model meets the real-time requirements.

Intelligent Tourism Information Search Behavior based on Data Mining

Intelligent Tourism Information Search Behavior based on Data Mining

Resilient water infrastructure partnerships in institutionally complex systems face challenging supply and financial risk tradeoffs

As regions around the world invest billions in new infrastructure to overcome increasing water scarcity, better guidance is needed to facilitate cooperative planning and investment in institutionally complex and interconnected water supply systems. This work combines detailed water resource system ensemble modeling with multiobjective intelligent search to explore infrastructure investment partnership design in the context of ongoing canal rehabilitation and groundwater banking in California. Here we demonstrate that severe tradeoffs can emerge between conflicting goals related to water supply deliveries, partnership size, and the underlying financial risks associated with cooperative infrastructure investments. We show how hydroclimatic variability and institutional complexity can create significant uncertainty in realized water supply benefits and heterogeneity in partners’ financial risks that threaten infrastructure investment partnership viability. We demonstrate how multiobjective intelligent search can design partnerships with substantially higher water supply benefits and a fraction of the financial risk compared to status quo planning processes. This work has important implications globally for efforts to use cooperative infrastructure investments to enhance the climate resilience and financial stability of water supply systems.

Augmented multi-agent algorithm utilizing intelligent search range detection heuristic to solve assembly line sequencing problem: A case study in the truck industry

Car sequencing is a widely adopted approach to implicitly enhance the short-term balancing of mixed-model assembly lines in practical applications. In this research, we investigate this problem in the context of a real-world case taken from a truck final assembly line. Our study considers cross-ratio constraints that address limitations related to dependent features of the products. Additionally, to manage the distribution of violation errors in cases where predefined congestion level thresholds (soft rules) exist, we propose a piecewise linear penalty function. To tackle these considerations, we formulate a mathematical programming model aimed at minimizing the total weighted penalty incurred from violating both independent and dependent sequencing rules. As the problem falls into the NP-hard category, we develop a multi-start parallel local search algorithm based on multi-agent approach to efficiently solve it. The algorithm is augmented by incorporating a self-adaptive diversification mechanism and a novel intelligent search range detection heuristic. The proposed heuristic enables the searching agents to intelligently explore the solution space rather than relying on a purely random search. The numerical experiments are conducted using 30 real-world instances from three truck assembly lines to validate the quality of the proposed algorithm. The comparison of the results indicates that our algorithm outperforms state-of-the-art algorithms. Furthermore, computational experiments demonstrate the effectiveness of each proposed mechanism in enhancing the algorithm's performance.

Applications of knowledge graph in medical and financial fields: Data integration and intelligent decision-making from an interdisciplinary perspective

With the rapid advancement of AI and deep learning technologies, knowledge graphs have emerged as a key technology for improving the performance of intelligent decision-making systems and driving interdisciplinary innovation. This article outlines the core principles and structure of knowledge graphs, including how they construct knowledge networks to support complex queries and intelligent reasoning. It reviews their innovative applications in the healthcare and financial industries, emphasizing their significant roles in data integration, decision support, and risk assessment. In the healthcare domain, knowledge graphs contribute to improving the accuracy of medical diagnoses, accelerating drug discovery, and enabling intelligent semantic searches. In the financial sector, they optimize risk management and aid in fraud prevention. The article also looks ahead to the future potential of knowledge graphs, stressing the importance of interdisciplinary collaboration and technological innovation in their development. It aims to provide valuable references for further research and application of knowledge graphs.

Multi‐feature fusion and memory‐based mobile robot target tracking system

AbstractIn crowded settings, mobile robots face challenges like target disappearance and occlusion, impacting tracking performance. Despite existing optimisations, tracking in complex environments remains insufficient. To address this issue, the authors propose a tailored visual navigation tracking system for crowded scenes. For target disappearance, an autonomous navigation strategy based on target coordinates, utilising a path memory bank for intelligent search and re‐tracking is introduced. This significantly enhances tracking success. To handle target occlusion, the system relies on appearance features extracted by a target detection network and a feature memory bank for enhanced sensitivity. Servo control technology ensures robust target tracking by fully utilising appearance information and motion characteristics, even in occluded scenarios. Comprehensive testing on the OTB100 dataset validates the system's effectiveness in addressing target tracking challenges in diverse crowded environments, affirming algorithm robustness.

Automatic construction and optimization method of enterprise data asset knowledge graph based on graph attention network

Traditional knowledge graph construction methods often rely on a large amount of human intervention and specialized knowledge, which seems inefficient and error-prone when dealing with massive, multi-source, and heterogeneous data assets of enterprises. To address this issue, a Multi Neighborhood Awareness Network (MNAN) model was proposed, which combines a multi language pre training model based on Bidirectional Encoder Representations from Transformers (BERT) and a Graph Convolutional Neural Network (GCN). By introducing an improved attention mechanism to deeply explore the neighborhood characteristics of entities, the accuracy and efficiency of entity matching are enhanced. In addition, to improve the integrity of the knowledge graph, a dynamic graph attention network (RDGAT) model based on graph attention network fusion relationship features is proposed. Through dynamic attention mechanism and relationship fusion strategy, deep learning of relationship features is carried out to optimize the completion performance of the knowledge graph spectrum. The performance test results show that the Hits@1 metric of the multi-neighborhood perceptual network model is close to 90% as the proportion of aligned entity seeds increases to 50%; the average inverse rank of the dynamic graph attention network on the test dataset improves by 6.3%. The experimental results show that the research-proposed knowledge graph fusion and complementation model exhibits significant effectiveness in automatically identifying and integrating large-scale, multi-source heterogeneous data at the enterprise level, and can significantly improve the automation level and accuracy of knowledge graph construction. The research method has achieved significant results in the data asset knowledge graph of petroleum enterprises and other fields, providing strong support for applications such as intelligent search, recommendation systems, and semantic analysis.

Chaotic Artificial Algae Algorithm for Solving Global Optimization With Real-World Space Trajectory Design Problems

AbstractThe artificial algae algorithm (AAA) is a recently introduced metaheuristic algorithm inspired by the behavior and characteristics of microalgae. Like other metaheuristic algorithms, AAA faces challenges such as local optima and premature convergence. Various strategies to address these issues and enhance the performance of the algorithm have been proposed in the literature. These include levy flight, local search, variable search, intelligent search, multi-agent systems, and quantum behaviors. This paper introduces chaos theory as a strategy to improve AAA's performance. Chaotic maps are utilized to effectively balance exploration and exploitation, prevent premature convergence, and avoid local minima. Ten popular chaotic maps are employed to enhance AAA's performance, resulting in the chaotic artificial algae algorithm (CAAA). CAAA's performance is evaluated on thirty benchmark test functions, including unimodal, multimodal, and fixed dimension problems. The algorithm is also tested on three classical engineering problems and eight space trajectory design problems at the European Space Agency. A statistical analysis using the Friedman and Wilcoxon tests confirms that CAA demonstrates successful performance in optimization problems.

Tác động của chiến lược tìm việc đến kết quả tìm việc của thanh niên trên địa bàn Bình Dương

This study explores the impact of exploratory, haphazard, and focused strategies on three aspects of re-employment outcomes, including income satisfaction, compatibility between abilities, and organizational commitment. Data were collected from 384 young people who were unemployed and found work in the past three months in Binh Duong. The results of hypothesis testing show that the exploratory strategy has a positive impact on organizational commitment and a negative effect on the level of income satisfaction. A focused strategy positively impacts all three aspects of re-employment outcomes. The haphazard strategy only hurt organizational commitment. The results suggest implications for policymakers and unemployed individuals in building intelligent job search strategies that are ready to adapt to changes in the labor market.

AUV path planning in a three-dimensional marine environment based on a novel multiple swarm co-evolutionary algorithm

Autonomous underwater vehicles (AUVs) are rapidly advancing in ocean exploration. High-performance path planning techniques are essential for AUVs. Path planning for AUVs is a multi-faceted challenge, necessitating careful consideration of safety, energy consumption and the influence of sea currents to ensure the development of a high-quality trajectory that satisfies all operational criteria. Existing path planning algorithms have problems such as incomplete consideration of influencing factors, computational complexity and weak applicability. Therefore, we propose a swarm intelligence optimisation algorithm based on multiple swarm co-evolution (MCO) to address the issue of AUV path planning in a three-dimensional marine environment. First, a three-dimensional marine environment model and the corresponding path evaluation mechanism are established. Second, the MCO rule is established to ensure that the MCO has balanced exploration and exploitation capabilities. A shared dynamic optimal particle between populations is introduced to ensure information exchange between populations. In addition, the cross-integration mutation strategy has been proposed for promoting the fusion of two populations of superior genes to ensure the inheritance of superior paternal genes to the offspring. Finally, four comparison experiments are designed, and the experiments compare eight commonly used intelligent search algorithms and improved versions. The results of the experiments proved that the MCO has excellent three-dimensional marine environment path planning capability, with robustness and search capabilities superior to other swarm intelligence optimisation algorithms.

Butterfly valve erosion prediction based on LSTM network

When valves and pipelines are used, erosion wear is a major concern. Erosion wear can lead to equipment downtime, material replacement, and other issues, as well as seal surface failure. The research delves into the phenomenon of erosion in butterfly valves, crucial components utilized across diverse industrial sectors. Erosion primarily affects the valve's disc and seat regions, leading to premature wear and compromised performance. To address this issue, the research employs computational fluid dynamics (CFD) and machine learning techniques, including long short-term memory (LSTM) and BP neural networks, to predict erosion rates under various conditions (different valve openings and particle diameters). Results indicate that erosion escalates with time and larger valve openings, highlighting the need for proactive maintenance strategies. The LSTM model demonstrates superior predictive capabilities compared to the BP neural network, offering valuable insights for improving butterfly valve design and operational efficiency in industrial settings. Furthermore, to seek a more efficient network configuration, the intelligent search capabilities of the particle swarm optimization (PSO) algorithm have been utilized to systematically explore the optimal network structure parameters. The prediction results highlight the advantages of LSTM in handling time series data, particularly in predicting erosion rates with complex dynamic characteristics.

Quasi-random Fractal Search (QRFS): A dynamic metaheuristic with sigmoid population decrement for global optimization

Global optimization of complex and high-dimensional functions remains a central challenge with broad applications in science and engineering. This study introduces a new optimization approach called quasi-random metaheuristic based on fractal search (QRFS), which harnesses the power of fractal geometry, low discrepancy sequences, and intelligent search space partitioning techniques. The QRFS uses fractals’ inherent self-similarity and intricate structure to guide the solution space exploration. For the proposal, a deterministic but quasi-random element is used in the search process using low discrepancy sequences, such as Sobol, Halton, Hammersley, and Latin Hypercube. This integration allows the algorithm to systematically cover the search space while maintaining the level of diversity necessary for efficient exploration. The QRFS employs a dynamic strategy of partitioning the search space and reducing the population of solutions to optimize the use of function accesses, which causes it to adapt well to the characteristics of the problem. The algorithm intelligently identifies and prioritizes promising regions within the fractal-based representation, allocating computational resources where they are most likely to yield optimal solutions. Experimental evaluations on several benchmark problems demonstrate that QRFS consistently outperforms modern, canonical metaheuristics and variants of algorithms such as differential evolution (DE), particle swarm optimization (PSO), covariance matrix adaptive evolution strategy (CMA-ES), regarding solution quality. Besides, the algorithm shows remarkable scalability, which makes it suitable for high-dimensional optimization tasks. Overall, QRFS offers a robust and efficient approach to solving complex optimization problems in various domains, paving the way for improved decision-making in real-world applications.

Research Paper on Geek-Grid

Geek-Grid is a transformative web application designed to bridge the gap between traditional education and the dynamic needs of modern industry. Powered by React.js and Firebase, Geek-Grid offers personalized learning, industry integrated content and intelligent job search algorithms to prepare learners for the competitive job market. This article explores the motivation behind Geek-Grid, its features, system requirements, and its impact on education and career readiness. The traditional education system often fails to keep pace with the rapidly changing demands of the modern workforce, creating a significant gap between what students learn and what employers need. Geek-Grid is a web application specifically designed to bridge this gap by integrating educational content with real-world industry requirements. Utilizing modern technologies such as React.js and Firebase, Geek-Grid offers a comprehensive learning platform that includes personalized learning paths, video lectures, informative articles, and live job alerts. It also provides recruiters with tools to search for and connect with suitable candidates, making it a dual-ended platform benefiting both students and employers. By leveraging the SERP API, Geek-Grid ensures that job listings are current and relevant, thereby enhancing the job search experience for users. This paper explores the motivation behind Geek-Grid, its innovative features, the technologies used in its development, and its potential impact on education and career readiness. Geek-Grid aims to not only enhance the learning experience but also to make students industry-ready, ultimately contributing to a more competent and prepared workforce.

Intelligent Search and Matching Method for Innovation and Entrepreneurship Teaching Cases in Universities Based on the Twin Bert Model

Intelligent Search and Matching Method for Innovation and Entrepreneurship Teaching Cases in Universities Based on the Twin Bert Model