High Complexity Research Articles

Research on improving the ranging accuracy of ships with stereo vision through Kalman filter optimization.

The real-time and accurate detection and ranging of ships play a pivotal role in ensuring navigation safety, this study aims to enhance the navigation safety and environmental perception capabilities of inland waterway vessels. In the ship detection stage, addressing challenges such as large parameters, high computational complexity, and poor real-time performance in existing ship detection models, this paper proposes the MS-YOLOv5s ship target detection algorithm. This algorithm, based on YOLOv5s, utilizes the lightweight MobileNetV3-Small network to replace the original YOLOv5s feature extraction backbone network, thereby improving the detection speed. The results indicate that the parameter size of the MS-YOLOv5s model is 3.55M, only 50.49% of YOLOv5s. Achieving a detection rate of 50.28 FPS, the precision is 96.80%, and the mAP is 98.40%, striking a balance between high accuracy and low computational demand. In the depth estimation stage, influenced by the environment, leading to unstable measurement data, this paper proposes a binocular Kalman filter fusion ranging algorithm. The standard deviation of the ranging results is minimized to 6.032μm, which is one order of magnitude smaller than traditional ranging algorithms, significantly enhancing the robustness of the measurement results. Within a distance of 20m from the ship target, the error can be controlled within 3%, showcasing the applicability of the method proposed in this paper in complex inland waterway environments contributes to the enhancement of ships' environmental perception capabilities and navigation safety, holding positive implications for the development of intelligent vessels.

Quantifying the fractal complexity of nutrient transport channels in Escherichia coli biofilms under varying cell shape and growth environment.

Recent mesoscopic characterization of nutrient-transporting channels in Escherichia coli has allowed the identification and measurement of individual channels in whole mature colony biofilms. However, their complexity under different physiological and environmental conditions remains unknown. Analysis of confocal micrographs of colony biofilms formed by cell shape mutants of E. coli shows that channels have high fractal complexity, regardless of cell phenotype or growth medium. In particular, colony biofilms formed by the mutant strain ΔompR, which has a wide-cell phenotype, have a higher fractal dimension when grown on rich medium than when grown on minimal medium, with channel complexity affected by glucose and agar concentrations in the medium. Osmotic stress leads to a dramatic reduction in the ΔompR cell size but has a limited effect on channel morphology. This work shows that fractal image analysis is a powerful tool to quantify the effect of phenotypic mutations and growth environment on the morphological complexity of internal E. coli biofilm structures. If applied to a wider range of mutant strains, this approach could help elucidate the genetic determinants of channel formation in E. coli colony biofilms.

Adaptive Robust Control of Nonlinear Constrained Stirred‐Tank Reactors via Self‐Organizing Fuzzy Neural Network

ABSTRACTThis paper presents an adaptive robust constraint controller for a continuous stirred tank reactor (CSTR) system based on a self‐organizing fuzzy neural network (SOFNN). Due to the high complexity of the chemical reactions, the CSTR system contains many strong nonlinearities and uncertainties. This is the first time to introduce the SOFNN into the adaptive controller design of the CSTR system, which improves the adaptability to dynamic system changes through the adjustment of the fuzzy network structure. Meanwhile, the time‐varying integral barrier Lyapunov functions (TVIBLFs) are employed to ensure the dimensionless reactant concentration and mixture temperature within a reasonable scope, which can improve the stability and safety of the CSTR system. Based on Lyapunov stability analysis, all the signals in a closed‐loop system are ultimately bounded. Simulation results substantiate the efficacy of the proposed control scheme.

Development of a Load‐Bearing, Terrain‐Adaptive Hexapod Robot With Chebyshev‐Linkage Legs

ABSTRACTThis study introduces an exploratory design for a hexapod load‐carrying robot, aiming to address challenges commonly associated with quadruped robots, including limited load capacity and high control complexity. By leveraging a Chebyshev linkage‐based overconstrained leg‐foot architecture that combines high rigidity and low friction, along with a multi‐drive system for lateral movement, we propose a robot with improved adaptability to diverse terrains, such as snow, sand, puddles, ice, and deserts. This versatility suggests potential applications in areas such as factory inspection, field reconnaissance and transport, and desert exploration. Using static and dynamic analyses, we evaluated the leg structure's stress distribution and motion requirements, employing simulations in Workbench and Adams to cross‐validate our theoretical calculations. The development of a prototype and its subsequent testing under various environmental conditions aimed to demonstrate the design's practicality and the robot's operational capabilities in real‐world settings. Although the findings indicate progress toward enhancing load‐bearing capabilities and terrain adaptability with reduced control complexity, further research and development are necessary to fully realize the potential of such robotic systems in practical applications.

Semantic Guided Matting Net

Abstract Human matting refers to extracting human parts from natural images with high quality, including human detail information such as hair, glasses, hats, etc. This technology plays an essential role in image synthesis and visual effects in the film industry. When the green screen is not available, the existing human matting methods need the help of additional inputs (such as trimap, background image, etc.), or the model with high computational cost and complex network structure, which brings great difficulties to the application of human matting in practice. To alleviate such problems, we use a segmentation network as the foundation and use multiple branches to achieve human segmentation, contour detail extraction, and information fusion. We also propose a foreground probability map module, which uses the feature maps in the segmentation network to pre-estimate the foreground probabilities of each pixel and obtain Semantic Guided Matting Net. Under the condition that only a single image is needed as the input, the human matting task can be realized by making full use of the semantic information in the image. We validate our method on the P3M-10k dataset. Compared with the benchmark, our method has made significant improvements in various evaluation indicators.

Detecting muscle fatigue among community-dwelling senior adults with shape features of the probability density function of sEMG

BackgroundPhysical exercise is an important method for both the physical and mental health of the senior population. However, excessive exertion can lead to increased risks of falls, severe injuries, and diminished quality of life. Therefore, simple and effective methods for fatigue monitoring during exercise are highly desirable, particularly in community settings. The purpose of this study was to explore the possibility of real-time detection of exercise-induced fatigue using surface Electromyogram (sEMG) features, including the kurtosis and skewness of the Probability Density Function (PDF) in the community settings to solve the issues of low sensitivity and high computational complexity of commonly used sEMG features.MethodssEMG signals from six forearm muscles were recorded during hand grip tasks at 20% maximal voluntary contraction (MVC) task-to-failure contractions from 30 healthy community-dwelling elders at their respective community centers. PDF shape features of the sEMG, namely kurtosis and skewness, were computed from 25 s of non-fatigue stable phase and 25 s of fatigue data for comparison. Statistical tests were conducted to compare and test for the significance of these features. We further proposed a novel fatigue indicator, Temporal-Mean-Kurtosis (TMK) of channel-averaged kurtosis, to detect fatigue with relatively low computational complexity and adequate sensitivity in community settings. ANOVA and post-hoc analyses were performed to examine the performance of TMK.ResultsStatistically significant differences were found between the non-fatigue period and the fatigue period for both kurtosis and skewness, with increasing values when approaching fatigue. TMK was shown to be sensitive in detecting fatigue with respect to time with lower computational complexity than the Sample Entropy.ConclusionThis study investigated PDF shape features of sEMG signals during a handgrip exercise to identify muscle fatigue in older adults in community experiments. Results revealed significant changes in kurtosis upon fatigue, indicating that PDF shape features were suitable convenient detectors of muscle fatigue in community experiments. The proposed indicator, TMK, showed potential sensitivity in tracking muscle fatigue over time in community-based settings with limited computational complexity, highlighting the promise of sEMG’s PDF features in detecting muscle fatigue among the elderly.

Evaluating process parameters of SLS part of polyamide-12 recycled powder using RSM

Abstract Selective laser sintering (SLS) is a highly flexible rapid prototyping (RP) technology which can use a wide range of powdered materials to generate high quality complex geometries directly from digital models. The SLS process uses a laser to sinter selected areas of powdered materials to produce solid objects layer by layer, according to the 2D data obtained from CT scan or MRI imaging techniques. This paper presents research work for evaluating the effect of process parameters on impact toughness and surface roughness of the part manufactured on Farsoon SS402P SLS from recycled ageing FS 3300PA which is PA-12 based powder and standard operating parameters using Response Surface Methodology (RSM) design of experiments. Impact tests have been performed as per the ASTM D6110 standard using recommendations from ISO 179-2. According to the results, effect of process parameters are successfully analyzed by employing impact toughness and surface roughness tests. Based on the results of the ANOVA analysis, impact toughness increases with increasing laser power (4.467 j/cm2 at 72.5 watts). Similarly, it is also clear from the results that the region with the lowest laser power, high layer thickness, and 90-degree orientation had the lowest surface roughness (3.46 μm).

Experimental investigation on fracture propagation in shale fractured by high-temperature carbon dioxide

The productivity of shale reservoirs was significantly enhanced by the high-temperature CO2 fracturing technique. The injection of high-temperature CO2 into the formation induced rock fracture propagation, creating advantageous pathways for fluid flow. In this research, a self-developed in situ high-temperature convective heat simulation experimental apparatus was employed to systematically conduct simulated experiments on high-temperature CO2 fractured shale under different influencing factors. The experimental results demonstrated that the permeability of CO2 increased as the injection temperature increased. The rock fracture pressure was effectively reduced by high-temperature CO2 fractured shale. Higher complexity was observed in fracture propagation, accompanied by a substantial increase in microcracks and branching fractures. The shale fracture pressure increased with increasing triaxial stress and CO2 injection rate. The confining pressure restricted the further propagation of fractures under relatively high stress conditions, thereby reducing the width and density of fractures, lowering the fracture complexity. Nevertheless, the thermal shock effect of the fluid was exacerbated as the injection rate of high-temperature CO2 increased. The initiation of microcracks was facilitated by the intensification of local thermal stress in shale, inducing multiple curved fractures and forming a more complex fracture network. Compared to horizontal bedding shale, the fracture pressure of vertical bedding shale was relatively higher during high-temperature CO2 fracturing. In addition, the geometric morphology of fracture propagation was more complex, characterized by rougher fracture surfaces, leading to a greater improvement in reservoir reconstruction volume. This research contributed to the optimization of CO2 resource utilization, provided experimental evidence for the application of high-temperature convection fracturing technology in in situ shale conversion projects.

An entropy measure-based study on flow pattern of gas-liquid two-phase flow in a U-Tube

U-tubes are widely applied in gas-liquid two-phase transportation in chemical engineering. The diverse flow patterns within these tubes significantly affect the pressure loss, heat transfer efficiency, and even the fluid-induced vibration amplitude of the tubes. This study explores the complex flow pattern features in a U-tube in a vertical plane and focuses on recognizing them. For the acquisition and classification of flow patterns, a Computational Fluid Dynamics (CFD) model for gas-liquid two-phase flow is first established, and its quantitative calculation error is ensured to be less than 5%. Then, the spatiotemporal evolution characteristics of flow patterns is analyzed. The real-time pressure drop response is chosen as the representation signal, and its nonlinear features in the time and frequency domain under different flow patterns are explored. A nonlinear time series is constructed by extracting a segment from the real-time pressure drop data, and six entropy measures are applied to analyze and identify them. Finally, the sensitivity of entropy measures to both the time series lengths and the tested sections are evaluated. Results show that there are six typical flow patterns in a U-tube. According to most entropy measures, the bubble flow has the highest complexity; however, the plug flow presents the lowest complexity. In the U-bend, pressure drop signals for the bubble and annular flows show random fluctuations within a specific range, in contrast to the marked periodicity in plug flow signals, while wavy and slug flows exhibit intermittent peak values. Including the upstream and downstream straight pipes in the analysis, rather than focusing solely on the U-bend, significantly increases the complexity of the stratified, plug, and slug flows. Fuzzy entropy is an effective tool for identifying the six flow patterns, demonstrating good resilience to variations in the length of the data series. This characteristic makes it highly useful for real-time identification of flow patterns in the U-bend sections of non-transparent U-tubes, offering considerable potential in chemical equipment.

Three-dimensional honeycomb structured BaTiO3-based piezoelectric ceramics via texturing and vat photopolymerization

Textured piezoelectric ceramics have attracted significant attention due to their ability to achieve ultra-high piezoelectric properties comparable to single crystals at a lower cost. Traditional processing techniques, such as tape casting, can efficiently produce textured piezoelectric ceramics with simple structures but are inadequate for fabricating three-dimensional structures with high complexity, thereby limiting their applications in specific fields. Vat photopolymerization (VPP), an advanced additive manufacturing technology, can rapidly and accurately create intricate three-dimensional structures. Crucially, VPP can provide the necessary shear force to align the templates, resulting in the highly-textured piezoelectric ceramics. In this study, BaTiO3-based piezoelectric ceramics with a high degree of texture (97.2 %) were produced using VPP technology. These ceramics exhibited a large piezoelectric coefficient (d33 = 511 pC/N), which was 66 % higher than that of non-textured ceramics. Furthermore, textured ceramics with a honeycomb structure were fabricated, demonstrating their potential in sensing applications. This work confirms the feasibility of using VPP technology to prepare high-performance, complex-structured textured ceramics, thereby promoting the development and application of textured piezoelectric ceramics.

A Deep Reinforcement Learning‐Based Physical Layer Security Enhancement Design for RIS‐Aided mmWave Communications With Practical Constraints

ABSTRACTReconfigurable intelligent surfaces (RIS) offer new opportunities for enhancing security in millimeter‐wave (mmWave) communications. However, some significant practical challenges still need to be addressed before their extensive implementation in future wireless networks. This article considers the practical constraints in a secure mmWave system aided by distributed RISs, including imperfect channel state information (CSI) in dynamic channel conditions and high complexity of non‐convex optimization in complex environments. To address these challenges, we propose a robust and efficient physical layer security (PLS) enhancement algorithm based on the deep reinforcement learning (DRL) framework to effectively tackle the issues of limited dynamic adaptation and high computational complexity encountered with conventional optimization methods. This algorithm, utilizing an actor‐critic architecture, can dynamically track channel variations and optimize strategies for improved system secrecy rate. Numerical simulations demonstrate that the proposed DRL‐based PLS enhancement algorithm outperforms non‐convex optimization benchmarks in robustness and efficiency for secure mmWave communication systems aided by distributed RISs and affected by imperfect CSI.

Insights into degradation and targeting of the photoreceptor channelrhodopsin-1.

In Chlamydomonas, the directly light-gated, plasma membrane-localized cation channels channelrhodopsins ChR1 and ChR2 are the primary photoreceptors for phototaxis. Their targeting and abundance is essential for optimal movement responses. However, our knowledge how Chlamydomonas achieves this is still at its infancy. Here we show that ChR1 internalization occurs via light-stimulated endocytosis. Prior or during endocytosis ChR1 is modified and forms high molecular mass complexes. These are the solely detectable ChR1 forms in extracellular vesicles and their abundance therein dynamically changes upon illumination. The ChR1-containing extracellular vesicles are secreted via the plasma membrane and/or the ciliary base. In line with this, ciliogenesis mutants exhibit increased ChR1 degradation rates. Further, we establish involvement of the cysteine protease CEP1, a member of the papain-type C1A subfamily. ΔCEP1-knockout strains lack light-induced ChR1 degradation, whereas ChR2 degradation was unaffected. Low light stimulates CEP1 expression, which is regulated via phototropin, a SPA1 E3 ubiquitin ligase and cyclic AMP. Further, mutant and inhibitor analyses revealed involvement of the small GTPase ARL11 and SUMOylation in ChR1 targeting to the eyespot and cilia. Our study thus defines the degradation pathway of this central photoreceptor of Chlamydomonas and identifies novel elements involved in its homoeostasis and targeting.

Physics-informed line graph neural network for power flow calculation

Power flow calculation plays a significant role in the operation and planning of modern power systems. Traditional numerical calculation methods have good interpretability but high time complexity. They are unable to cope with increasing amounts of data in power systems; therefore, many machine learning based methods have been proposed for more efficient power flow calculation. Despite the good performance of these methods in terms of computation speed, they often overlook the importance of transmission lines and do not fully consider the physical mechanisms in the power systems, thereby weakening the prediction accuracy of power flow. Given the importance of the transmission lines as well as to comprehensively consider their mutual influence, we shift our focus from bus adjacency relationships to transmission line adjacency relationships and propose a physics-informed line graph neural network framework. This framework propagates information between buses and transmission lines by introducing the concepts of the incidence matrix and the line graph matrix. Based on the mechanics of the power flow equations, we further design a loss function by integrating physical information to ensure that the output results of the model satisfy the laws of physics and have better interpretability. Experimental results on different power grid datasets and different scenarios demonstrate the accuracy of our proposed model.

Seesaw: A 4096-bit vector processor for accelerating Kyber based on RISC-V ISA extensions

The ML-KEM standard based on Kyber algorithm is one of the post-quantum cryptography (PQC) standards released by the National Institute of Standards and Technology (NIST) to withstand quantum attacks. To increase throughput and reduce the execution time that is limited by the high computational complexity of the Kyber algorithm, an RISC-V-based processor Seesaw is designed to accelerate the Kyber algorithm. The 32 specialized extension instructions are mainly designed to enhance the parallel computing ability of the processor and accelerate all the processes of the Kyber algorithm by thoroughly analyzing its characteristics. Subsequently, by carefully designing hardware such as poly vector registers and algorithm execution units on the RISC-V processor, the support of microarchitecture for extension instructions was achieved. Seesaw supports 4096-bit vector calculations through its poly vector registers and execution unit to meet high-throughput requirements and is implemented on the field-programmable gate array (FPGA). In addition, we modify the compiler simultaneously to adapt to the instruction extension and execution of Seesaw. Experimental results indicate that the processor achieves a speed-up of 432× and 18864× for hash and NTT, respectively, compared with that without extension instructions and a speed-up of 5.6× for the execution of the Kyber algorithm compared with the advanced hardware design.

A beginner's guide to supervised analysis for mass cytometry data in cancer biology.

Mass cytometry enables deep profiling of biological samples at single-cell resolution. This technology is more than relevant in cancer research due to high cellular heterogeneity and complexity. Downstream analysis of high-dimensional datasets increasingly relies on machine learning (ML) to extract clinically relevant information, including supervised algorithms for classification and regression purposes. In cancer research, they are used to develop predictive models that will guide clinical decision making. However, the development of supervised algorithms faces major challenges, such as sufficient validation, before being translated into the clinics. In this work, we provide a framework for the analysis of mass cytometry data with a specific focus on supervised algorithms and practical examples of their applications. We also raise awareness on key issues regarding good practices for researchers curious to implement supervised ML on their mass cytometry data. Finally, we discuss the challenges of supervised ML application to cancer research.

MATEdb2, a Collection of High-Quality Metazoan Proteomes across the Animal Tree of Life to Speed Up Phylogenomic Studies

Abstract Recent advances in high-throughput sequencing have exponentially increased the number of genomic data available for animals (Metazoa) in the last decades, with high-quality chromosome-level genomes being published almost daily. Nevertheless, generating a new genome is not an easy task due to the high cost of genome sequencing, the high complexity of assembly, and the lack of standardized protocols for genome annotation. The lack of consensus in the annotation and publication of genome files hinders research by making researchers lose time in reformatting the files for their purposes but can also reduce the quality of the genetic repertoire for an evolutionary study. Thus, the use of transcriptomes obtained using the same pipeline as a proxy for the genetic content of species remains a valuable resource that is easier to obtain, cheaper, and more comparable than genomes. In a previous study, we presented the Metazoan Assemblies from Transcriptomic Ensembles database (MATEdb), a repository of high-quality transcriptomic and genomic data for the two most diverse animal phyla, Arthropoda and Mollusca. Here, we present the newest version of MATEdb (MATEdb2) that overcomes some of the previous limitations of our database: (i) we include data from all animal phyla where public data are available, and (ii) we provide gene annotations extracted from the original GFF genome files using the same pipeline. In total, we provide proteomes inferred from high-quality transcriptomic or genomic data for almost 1,000 animal species, including the longest isoforms, all isoforms, and functional annotation based on sequence homology and protein language models, as well as the embedding representations of the sequences. We believe this new version of MATEdb will accelerate research on animal phylogenomics while saving thousands of hours of computational work in a plea for open, greener, and collaborative science.

Rearing laying hens: Early environmental complexity and genetic strain have life-long effects on keel bone size and fractures

Keel bone damage (KBD) is a major welfare concern for laying hens. Environmental complexity during rearing is suggested to promote skeletal development and reduce KB fractures (KBF). We investigated the effect of rearing environment and genetic strain on KB development and health. Three consecutive replicate flocks of brown (B) and white-feathered (W) layer chicks (3000/ flock) were raised in three styles of rearing aviaries of Low,Mid, or High complexity and in conventional cages (Conv). Pullets from flocks 1 and 3 (1080/ flock) were subsequently housed in 24 furnished cages of two sizes (small: 30 vs large: 60 birds) for lay. Samples of pullets and hens were assessed as follows: Dissections at six, 11, and 16 WoA in flocks 1 and 2 for KB size, radiographs at 30, 50, and 70 WoA in flocks 1 and 3, for KBD and dissections at 70 WoA in flock 3 for KBD.Rearing complexity affected KB size of pullets (Conv < High < Mid, Low; P< 0.0001). W pullets had larger keels than B when adjusted for body weight throughout rearing (strain x age P= 0.005) and at 70 WoA (P< 0.001). KBF prevalence at end-of-lay was affected by rearing complexity (High < Low, Conv, P= 0.002). During lay, KBF severity was affected by rearing complexity (High < Low, Mid; P= 0.007), strain (W < B; P< 0.0001) and age (30 < 50 < 70 WoA; P< 0.0001). At 70 WoA, an interaction of rearing by strain affected KBF severity (most severe: B-Conv, least severe: B-Mid and W-High, P= 0.003). KBF severity scores from radiographs and dissections corresponded moderately (r= 0.547, P< 0.0001). The severity of KB deviations was affected by the size of the furnished cages (large > small, P= 0.049).In conclusion, rearing complexity and strain affected KB size and fracture severity and prevalence. These data confirm that complex rearing aviaries that are known to promote exercise reduce KBF severity and prevalence throughout lay.

Kinematic matrix: One-shot human action recognition using kinematic data structure

One-shot action recognition, which refers to recognizing human-performed actions using only a single training example, holds significant promise in advancing video analysis, particularly in domains requiring rapid adaptation to new actions. However, existing algorithms for one-shot action recognition face multiple challenges, including high computational complexity, limited accuracy, and difficulties in generalization to unseen actions. To address these issues, we propose a novel kinematic-based skeleton representation that effectively reduces computational demands while enhancing recognition performance. This representation leverages skeleton locations, velocities, and accelerations to formulate the one-shot action recognition task as a metric learning problem, where a model projects kinematic data into an embedding space. In this space, actions are distinguished based on Euclidean distances, facilitating efficient nearest-neighbour searches among activity reference samples. Our approach not only reduces computational complexity but also achieves higher accuracy and better generalization compared to existing methods. Specifically, our model achieved a validation accuracy of 78.5%, outperforming state-of-the-art methods by 8.66% under comparable training conditions. These findings underscore the potential of our method for practical applications in real-time action recognition systems.

Online Direct Infusion Mass Spectrometry of Liquid–Liquid Extraction Phases for Metabolite and Lipid Profiling with the Direct Infusion Probe

Background/Objectives: Profiling of metabolites and lipids in biological samples can provide invaluable insights into life-sustaining chemical processes. The ability to detect both metabolites and lipids in the same sample can enhance these understandings and connect cellular dynamics. However, simultaneous detection of metabolites and lipids is generally hampered by chromatographic systems tailored to one molecular type. This void can be filled by direct infusion mass spectrometry (MS), where all ionizable molecules can be detected simultaneously. However, in direct infusion MS, the high chemical complexity of biological samples can introduce limitations in detectability due to matrix effects causing ionization suppression. Methods: Decreased sample complexity and increased detectability and molecular coverage was provided by combining our direct infusion probe (DIP) with liquid–liquid extraction (LLE) and directly sampling the different phases for direct infusion. Three commonly used LLE methods for separating lipids and metabolites were evaluated. Results: The butanol–methanol (BUME) method was found to be preferred since it provides high molecular coverage and have low solvent toxicity. The established BUME DIP-MS method was used as a fast and sensitive analysis tool to study chemical changes in insulin-secreting cells upon glucose stimulation. By analyzing the metabolome at distinct time points, down to 1-min apart, we found high dynamics of the intracellular metabolome. Conclusions: The rapid workflow with LLE DIP-MS enables higher sensitivity of phase separated metabolites and lipids. The application of BUME DIP-MS provides novel information on the dynamics of the intracellular metabolome of INS-1 during the two phases of insulin release for both metabolite and lipid classes.

Information fusion for large-scale multi-source data based on the Dempster-Shafer evidence theory

There exists many large-scale multi-source data, ranging from genetic information to medical records, and military intelligence. The inherent intricacies and uncertainties embedded within these data sources pose significant challenges to the process of information fusion. Owing to its exceptional capacity to represent data uncertainty, Dempster-Shafer (D-S) evidence theory has emerged as a widely utilized approach in information fusion. However, the evidence theory encounters three significant issues when applied to multi-source data information fusion: (1) the conversion of sample information into evidence and the construction of the basic probability assignment (BPA) function; (2) the resolution of conflicting evidence; and (3) the mitigation of exponential explosion in computation. Addressing the aforementioned challenges, this paper delves into the information fusion strategies for large-scale multi-source data based on Dempster-Shafer evidence theory. Initially, the concept of support matrix is introduced and the data matrix is transformed into a support matrix to address the construction challenges associated with BPA. Next, a method for addressing evidence conflicts is introduced by incorporating an additional data source composed of average values. Furthermore, a solution for mitigating high computational complexity is presented through the utilization of a hierarchical fusion approach. Finally, experimental results show that compared with other five advanced information fusion methods, our information method has improved the classification accuracy by 4.66% on average and reduced the time by 66.35% on average. Hence, our method is both efficient and effective, demonstrating exceptional performance in information fusion.