Loading Process Research Articles

A Gaussian statistics-based unit-cell modeling method for the mechanical behavior prediction of 2.5D shallow straight-link-shaped woven composites

The shallow straight-link-shaped structure of 2.5D woven composites (2.5D-SSLS-WC) is a novel material with a wide range of promising applications, but the randomness of its internal structure has brought challenges to performance prediction and engineering design. The gap of existing unit-cell models in capturing the internal randomness of the 2.5D-SSLS-WC material limits the accurate prediction of properties and the optimization of engineering design. In this work, the modeling method for random unit-cell model based on Gaussian distribution is proposed for 2.5D-SSLS-WC structure considering random distribution property of weft yarns and extrusion effects between structural surfaces and internal yarns during the molding process, which can more effectively reflect the practical mesoscopic structure of the material, reveal the micro mechanical behavior inside materials and provide theoretical basis for material design and performance optimization. The mechanical property and damage evolution of the proposed model is predicted and analyzed using finite element (FE) analysis and progressive damage method. The maximum errors of equivalent stiffness and strength between prediction and experimental values are 6.7% and 2.3%, respectively. The results show that the proposed model has high prediction accuracy and can reasonably reflect the damage extension and ultimate failure mode of the material during the loading process, which verifies the rationality of the proposed unit-cell modeling strategy. The modeling method proposed in this paper provides a new perspective for the in-depth understanding and description of the microstructure and mechanical behavior of 2.5D-SSLS-WC materials, which is of significant theoretical and engineering value and can be extended to the modeling of other woven composites.

A comparative analysis of face and object perception in 2D laboratory and virtual reality settings: insights from induced oscillatory responses.

In psychophysiological research, the use of Virtual Reality (VR) for stimulus presentation allows for the investigation of how perceptual processing adapts to varying degrees of realism. Previous time-domain studies have shown that perceptual processing involves modality-specific neural mechanisms, as evidenced by distinct stimulus-locked components. Analyzing induced oscillations across different frequency bands can provide further insights into neural processes that are not strictly phase-locked to stimulus onset. This study uses a simple perceptual paradigm presenting images of faces and cars on both a standard 2D monitor and in an immersive VR environment. To investigate potential modality-dependent differences in attention, cognitive load, and task-related post-movement processing, the induced alpha, theta and beta band responses are compared between the two modalities. No evidence was found for differences in stimulus-dependent attention or task-related post-movement processing between the 2D conditions and the realistic virtual conditions in electrode space, as posterior alpha suppression and re-synchronization of centro-parietal beta did not differ between conditions. However, source analysis revealed differences in the attention networks engaged during 2D and 3D perception. Midfrontal theta was significantly stronger in laboratory conditions, indicating higher cognitive load than in the VR environment. Exploratory analysis of posterior theta showed stronger responses in VR, possibly reflecting the processing of depth information provided only by the 3D material. In addition, the theta response seems to be generated by distinct neuronal sources under realistic virtual conditions indicating enhanced involvement of semantic information processing and social cognition.

Evaluating the connection between diet quality, EpiNutrient intake and epigenetic age: an observational study

BackgroundDNA methylation (DNAm) has unique properties which makes it a potential biomarker for lifestyle-related exposures. Epigenetic clocks, particularly DNAm-based biological age predictors [epigenetic age (EA)], represent an exciting new area of clinical research and deviations of EA from chronological age [epigenetic age acceleration (EAA)] have been linked to overall health, age-related diseases, and environmental exposures. ObjectivesThis observational study investigates the relationships between biological aging and various dietary factors within the LifeLines-DEEP Cohort. These factors include diet quality, processed food consumption, dietary glycemic load, and intake of vitamins involved in maintaining the epigenetic homeostasis (vitamins B-9, B-12, B-6, B-2, and C). MethodsDietary records collected using food-frequency questionnaires were used to estimate diet quality [LifeLines Diet Score (LLDS)], measure the intake of unprocessed/ultraprocessed food according to the NOVA food classification system, and the adequacy of the dietary intake of vitamins B-9, B-12, B-2, B-6, and C. EA using Horvath, Hannum, Levine, and Horvath2 epigenetic clock models and DNAm-predicted telomere length (DNAm-TL) were calculated from DNAm data in 760 subjects. Associations between dietary factors and EAA were tested, adjusting for sex, energy intake, and body composition. ResultsLLDS was associated with EAA (EAA_Horvath: β: −0.148; P = 1 × 10−4; EAA_Hannum: β: −0.148; P = 9 × 10−5; EAA_Levine: β: −0.174; P = 1 × 10−5; and EAA_Horvath2: β: −0.176; P = 4 × 10−6) and DNAm-TL (β: 0.116; P = 0.003). Particularly, EAA was associated with dietary glycemic load (EAA_Horvath: β: 0.476; P = 9 × 10−10; EAA_Hannum: β: 0.565; P = 1 × 10−13; EAA_Levine: β: 0.469; P = 5 × 10−9; EAA_Horvath2: β: 0.569; P = 1 × 10−13; and DNAmTL adjusted for age: β: −0.340; P = 2 × 10−5) and different measures of food processing (NOVA classes 1 and 4). Positive EAA was also associated with inadequate intake of vitamin B-12 (EAA_Horvath: β: −0.167; P = 0.002; EAA_Hannum: β: −0.144; P = 0.007; and EAA_Horvath2: β: −0.126; P = 0.019) and C (EAA_Hannum: β: −0.136; P = 0.010 and EAA_Horvath2: β: −0.151; P = 0.005). ConclusionsOur findings corroborate the hypothesis that nutrition plays a pivotal role in influencing epigenetic homeostasis, especially DNAm, thereby contributing to individual health trajectories and the pace of aging.

Mesoporous Silica Microparticle-Protein Complexes: Effects of Protein Size and Solvent Properties on Diffusion and Loading Efficiency.

Oral administration of protein-based therapeutics is highly desirable due to lower cost, enhanced patient compliance, and convenience. However, the harsh pH environment of the gastrointestinal tract poses significant challenges. Silica-based carriers have emerged as potential candidates for the delivery of protein molecules, owing to their tuneable surface area and pore volume. We explored the use of a commercial mesoporous silica carrier, SYLOID, for the delivery of octreotide and bovine serum albumin (BSA) using a solvent evaporation method in three different solvents. The loading of proteins into SYLOID was driven by diffusion, as described by the Stokes-Einstein equation. Various parameters were investigated, such as protein size, diffusion, and solubility. Additionally, 3D fluorescence confocal imaging was employed to identify fluorescence intensity and protein diffusion within the carrier. Our results indicated that the loading process was influenced by the molecular size of the protein as octreotide exhibited a higher recovery rate (71%) compared to BSA (32%). The methanol-based loading of octreotide showed uniform diffusion into the silica carrier, whereas water and ethanol loading resulted in the drug being concentrated on the surface, as shown by confocal imaging, and further confirmed by scanning electron microscopy (SEM). Pore volume assessment supported these findings, showing that octreotide loaded with methanol had a low pore volume (1.2cc/g). On the other hand, BSA loading was affected by its solubility in the three solvents, its tendency to aggregate, and its low solubility in ethanol and methanol, which resulted in dispersed particle sizes of 223 and 231μm, respectively. This reduced diffusion into the carrier, as confirmed by fluorescence intensity and diffusivity values. This study underscores the importance of protein size, solvent properties, and diffusion characteristics when using porous carriers for protein delivery. Understanding these factors allows for the development of more effective oral protein-based therapeutics by enhancing loading efficiency. This, in turn, will lead to advances in targeted drug delivery and improved patient outcomes.

Stability of slender GFRP tube-confined UHPC-filled steel-encased column under axial compression: Experiment and mesoscale modeling

The stability of GFRP tube-confined UHPC-filled steel-encased columns (FUSCs) is critical for optimal utilization of its superior cross-sectional load-bearing capacity that guarantees overall structural safety. This study conducted an experimental investigation on 14 slender FUSC specimens under uniaxial compression for different aspect ratios and aggregate contents. The pressure-sensing films were applied to measure the contact pressure between the GFRP tube and UHPC, and a mesoscale finite element model was developed to uncover the role of randomly distributed fibers and aggregates in the UHPC during the failure process. Accordingly, the stability performance of slender FUSC was analyzed, and specific discussions were held on the mutual interaction mechanism between GFRP and UHPC during the loading process. The results showed that the stability of FUSCs decreased faster with an aspect ratio of l0/D > 8 (λ>30.77), resulting in the decrease of load-carrying capacity for up to 25 %. By including coarse aggregate, the distribution of steel fibers was densified, and Young's Modulus of UHPC was increased, considerably improving the stability of FUSCs. Moreover, the GFRP tube with a fixed winding angle of 50° remarkably enhanced the stability of the specimen due to the sharing of bending moment instead of providing confinement. This research outcome offers a basis for the stability control of slender FUSCs.

Crack characteristic and fractal analysis of SFRC shear wall with CFST columns under repeated low cycle load

In this study, we conducted a comprehensive analysis of the crack characteristics and fractal behavior in steel fiber reinforced concrete shear walls (SFRCSW) reinforced with concrete-filled steel tube columns (CFSTCs) under repeated low-cycle loading conditions after failure. Notably, the incorporation of steel fibers and CFSTCs did not alter the fundamental shear failure mode but effectively constrained crack widths to 2.0 mm and 1.6 mm, respectively, from an initial width of 2.4 mm. The use of steel fibers or CFSTCs led to significant improvements in the surface crack fractal dimension (SCFD) of shear walls, with increase ratios of 9.7 % and 7.88 %, respectively. Conversely, an increase in SCFD was associated with improvements in yield load, peak load, failure load, and cumulative hysteretic energy consumption in SFRCSWs. SCFD is suitable for comparative evaluation of the bearing capacity and energy dissipation capacity of different shear walls, but it is not suitable for comparative evaluation of displacement and ductility. For shear walls, it seems more reasonable to evaluate the damage degree in loading process by displacement - fractal dimension relation, rather than load - fractal dimension relation, with the critical values of fractal dimension are about 1.12 and 1.17.

AI-Driven Resource Provisioning: Enhancing Elasticity and Efficiency with Hybrid RNN-LSTM Models

Automatic resource provisioning techniques that dynamically modify resources in response to service demand are essential to implementing elasticity services. For systems with strict latency or reaction time requirements, such as Enterprise Resource Planning (ERP) systems under high traffic loads, this flexibility is crucial for lowering power consumption and guaranteeing quality of service (QoS). Determining the best point at which to scale resources is still a difficult task. In this research, we provide an AI-integrated system that adjusts resources according to anticipated demand. To predict load requests, the system uses an LSTM model and a Hybrid Recurrent Neural Network (RNN). This strategy seeks to minimize overprovisioning, which will lower the cost of infrastructure and energy usage. The deep learning model aims to estimate the resources required to improve service response time and satisfy customer requests, as well as to anticipate with high accuracy the processing load of distributed servers. Proactive provisioning decisions are made for the servers based on the anticipated load. Our tests on a common server request dataset show that the suggested RNN+LSTM model performs better than traditional deep learning models in terms of efficient resource management and prediction accuracy.

Smart-KG: Partition-Based Linked Data Fragments for querying knowledge graphs

RDF and SPARQL provide a uniform way to publish and query billions of triples in open knowledge graphs (KGs) on the Web. Yet, provisioning of a fast, reliable, and responsive live querying solution for open KGs is still hardly possible through SPARQL endpoints alone: while such endpoints provide a remarkable performance for single queries, they typically can not cope with highly concurrent query workloads by multiple clients. To mitigate this, the Linked Data Fragments (LDF) framework sparked the design of different alternative low-cost interfaces such as Triple Pattern Fragments (TPF), that partially offload the query processing workload to the client side. On the downside, such interfaces still come with the expense of unnecessarily high network load due to the necessary transfer of intermediate results to the client, leading to query performance degradation compared with endpoints. To address this problem, in the present work, we investigate alternative interfaces, refining and extending the original TPF idea, which also aims at reducing server-resource consumption, by shipping query-relevant partitions of KGs from the server to the client. To this end, first, we align formal definitions and notations of the original LDF framework to uniformly present existing LDF implements and such “partition-based” LDF approaches. These novel LDF interfaces retrieve, instead of the exact triples matching a particular query pattern, a subset of pre-materialized, compressed, partitions of the original graph, containing all answers to a query pattern, to be further evaluated on the client side. As a concrete representative of partition-based LDF, we present smart-KG+, extending and refining our prior work (In WWW ’20: The Web Conference 2020 (2020) 984–994 ACM / IW3C2) in several respects. Our proposed approach is a step forward towards a better-balanced share of the query processing load between clients and servers by shipping graph partitions driven by the structure of RDF graphs to group entities described with the same sets of properties and classes, resulting in significant data transfer reduction. Our experiments demonstrate that the smart-KG+ significantly outperforms existing Web SPARQL interfaces on both pre-existing benchmarks for highly concurrent query execution as well as an accustomed query workload inspired by query logs of existing SPARQL endpoints.

A Study on the Damage Evolution Law of Layered Rocks Based on Ultrasonic Waves Considering Initial Damage

The damage evolution process of layered rock is influenced by its fine structure, lamination direction, and confining pressure, exhibiting significant anisotropic characteristics. This study focuses on shale as the research object, employing indoor tests and theoretical analysis to define damage variables and initial damage based on ultrasonic wave velocity. This research investigates the damage evolution law of layered rock under varying confining pressures and dip angles. The findings reveal that damage variables defined using transverse wave velocity effectively reflect the damage evolution process. Additionally, confining pressure significantly affects damage evolution, with increasing pressure causing a rightward shift in the damage variable–strain curve and an increase in initial damage. The slab inclination angle also influences damage evolution; samples with 45° and 60° inclinations are more susceptible to damage, with initial damage showing a trend of increasing and then decreasing. To accurately describe the relationship between damage variables and strain during the loading process, this paper establishes a segmented damage evolution equation characterized by wave velocity. Initially, an inverse proportional function is employed to characterize the strain before crack closure. Subsequently, a logistic function represents the curve from crack strain to peak strain. This combined approach provides a comprehensive depiction of the damage evolution. This study underscores the importance of considering confining pressure and laminar inclination in the analysis of rock stability and integrity. These results provide critical insights into the damage evolution characteristics of layered rocks, offering valuable references for engineering safety assessments.

Thermodynamically consistent model of dislocation-mediated plasticity

ABSTRACT We present a thermodynamically consistent damage theory of deformation where the damage mechanism is determined by the presence of dislocations and their motion is the origin of plasticity. The damage parameter is proportional to the square root of the dislocation density of a network. For the evolution of damage, a gradient-flow equation, which describes the relaxation process of plastic deformation, is derived. Analysis shows that the dislocation-mediated plasticity is an activated process with upper and lower yield points, as opposed to a barrierless plasticity with a single yield point, which may be realised by other mechanisms of deformation. Dislocation nucleation is not a limiting factor of the dislocation-mediated plasticity because the free energy barrier for the nucleation is rather low. Application of the theory to the description of uniaxial deformation revealed many features of the experimentally observed deformation processes of metals, e.g. three stages of strain hardening with various characteristics dependent on the material and rate of loading. Applications of the theory to processes of dynamic loading of materials, which simultaneously undergo phase transformations, can bring deeper understanding of the material behaviour.

Advanced monitoring of damage behavior and 3D visualization of fiber distribution in assessing crack resistance mechanisms in steel fiber-reinforced concrete

This study presents a novel approach to understanding the reinforcing mechanisms of steel fiber-reinforced concrete, particularly its crack resistance and toughening effects. By integrating dynamic surface strain monitoring with advanced three-dimensional visualization techniques, new insights are provided into how steel fibers enhance concrete's structural performance. X-ray computed tomography (CT) technology enabled detailed 3D visualization and analysis of steel fibers embedded within the concrete matrix, facilitating an in-depth examination of their distribution patterns, orientation, and positioning. Additionally, the Digital Image Correlation (DIC) three-dimensional full-field strain measurement system was utilized to dynamically monitor the loading process during three-point bending tests on semicircular concrete specimens. The findings indicate that the uniform distribution of steel fibers significantly reduces stress concentration and effectively delays crack initiation and propagation. Steel fibers bridge cracks, mitigate local stress concentrations, and substitute for the tensile components of concrete after macro-crack formation, thereby inhibiting further crack propagation. Quantitative analysis of fiber distribution through 3D image reconstruction and coordinate extraction confirmed a strong correlation between fiber orientation and crack resistance. This study contributes to the field by offering a comprehensive analysis of key parameters such as fracture process strain monitoring, 3D visualization, and quantification of fiber distribution, advancing the understanding of crack propagation mechanisms and failure processes in fiber-reinforced concrete.

Program Edukasi untuk Peningkatan Keterampilan Administrasi Logistik Unit Food di PT Sekar Bumi Tbk

This service outlines the implementation of an educational program aimed at improving the logistics administration skills of the food unit at PT Sekar Bumi Tbk. Through training needs analysis, comprehensive counseling, and training methods that include theory and practice, this program has succeeded in significantly improving employees' technical and soft skills. The evaluation results show a reduction in errors in managing logistics administration, goods release and goods entry transactions, receiving finished products from the packing team, and following the loading process for local and export shipments. In addition, establishing a culture of collaboration and continuous learning among employees creates an innovative work environment. Thus, this educational program not only strengthens human resource capacity, but also increases the competitiveness of PT Sekar Bumi Tbk in the increasingly competitive frozen food industry, preparing employees to face future challenges more prepared and responsive.

Adaptive boundary control for a friction hoisting system during the loading process

This paper aims at the rope dynamic tension fluctuation (DTF) control in the friction hoisting system (FHS) during the loading process. The exact dynamic modelling of the tail and head ropes connected to the cage is considered. A disturbance observer is proposed to track the actual impact disturbance. The actuator acts at the friction wheel to pretension the system. The use of an adaptive neural network allows for the handling of system uncertainty. An appropriate control law is developed to stabilize the cage and suppress the rope DTF in FHS during the loading process. Numerical simulations and theoretical analysis are presented to prove the feasibility and effectiveness of the proposed control law. Numerical analysis results show that the head rope dynamic displacement fluctuation (DDF) and DTF amplitudes can be suppressed to at least 1/125 of their initial fluctuation amplitudes under the proposed control law.

Wideband beam domain sparse Bayesian learning passive focusing localisation algorithm

AbstractTo address the challenges of large‐aperture sonar systems passive localisation, this paper proposes the application of sparse Bayesian learning (SBL) for passive target localisation in the wideband beam domain. The proposed algorithm aims to overcome the issues of massive computational requirements for two‐dimensional SBL scanning and increased localisation errors due to interference energy leakage. The wideband beam domain SBL focusing localisation algorithm is developed by constructing an azimuth‐range two‐dimensional transformation matrix to preprocess array data, which effectively reduces the computational load of SBL processing while suppressing strong interference energy leakage in passive sonar operating environments, thus improving the range resolution and parameter estimation accuracy of focusing localisation. Simulation and sea trial data analyses demonstrate the feasibility of the proposed algorithm, with results indicating its superior performance compared to existing algorithms.

Experiment and restoring force model study of precast shear walls with new rabbet-unbonded horizontal connection

The 'rabbet-unbonded horizontal connection' (RUHC) is a new connection form of precast shear wall. This study conducted a low-cycle repeated loading test on three RUHC walls to investigate the hysteretic performance of the walls with the 'axial compression ratio' and 'unbonded level' as the main variables and the results showed that the above two parameters remarkably affected the seismic performance of RUHC walls. Based on the analysis of test skeleton and hysteresis curve characteristic, the influence of parameters such as axial compression ratio and unbonded level on the load and displacement during the loading process was analyzed. The theoretical values of yield, peak and limit points were calculated, and the skeleton curve model was proposed. Moreover, the unloading stiffness formula was proposed by the non-linear relation of dimensionless parameters and Matlab software fitting, and the hysteresis rule model was established. Finally, a three-fold-linear restoring force model of RUHC walls composed of the theoretical skeleton curve and hysteresis rule was established and verified. This model provides an effective prediction for the hysteresis characteristics and a theoretical basis for conducting elastic-plastic analysis of RUHC structures.

3D mesoscale model of steel fiber reinforced concrete based on equivalent failure of steel fibers

Steel fiber reinforced concrete (SFRC) is widely used in civil engineering. Investigating the mechanical properties and failure mechanisms of SFRC materials using mesoscopic models holds significant theoretical and practical importance. The bond-slip interaction between steel fibers and the cementitious matrix is crucial, yet current computational capabilities struggle to simulate the interactions among the numerous components within concrete containing a large number of steel fibers. This paper establishes an equivalent failure model to replace the bond-slip interaction. Based on this model, a multiphase mesoscopic model (including aggregates, the interfacial transition zone (ITZ), mortar, and steel fibers) is developed to predict the mechanical properties and failure modes of SFRC. The accuracy and validity of the model are verified by comparing the simulation results with the experimental results from four-point bending tests on SFRC beams, focusing on load-displacement curves, failure modes, and crack propagation. The results indicate that under load, the ITZ at the sharp edges of large aggregates in the SFRC beam is the first to be damaged, forming microcracks. Subsequently, these cracks bypass the large particles and propagate towards weaker regions with fewer steel fibers, forming macrocracks. At this stage, the steel fibers take over the load from the cementitious matrix and limit crack propagation. The simulated crack propagation trend and crack width during the loading process of the SFRC multiphase mesoscale model match well with the experimental observations. A higher fiber content can mitigate stress concentration within SFRC beams and enhance the fiber bridging effect, significantly improving the flexural load-bearing capacity and toughness of the beams. A smaller steel fiber inclination angle can effectively inhibit the propagation of vertical cracks, thereby increasing the load-bearing capacity and toughness of SFRC beams and extending the service life of the structure.

Research on concrete-filled square CFRP steel tube under compressive-torsional hysteresis loading

Concrete-filled CFRP steel tube structures are presently employed on extensively in various engineering practices. This study examines the mechanical performance of concrete-filled square CFRP steel tubes under compressive-torsional hysteresis loads. Through the analysis of nine specimens, the research focuses on failure modes, torque–angle (T–θ) behavior, triaxial strain, and the synergistic effects of the steel tube and CFRP during cyclic loading. A numerical simulation method is initially introduced based on the test data to predict the actions of concrete-filled CFRP steel tube compressive-torsional specimens under hysteresis loading. This method is subsequently verified by contrasting it with the outcomes from representative experiments. In addition, the stress distribution of each material during the entire loading process of the sample is examined. Finally, using data from experiments and finite elements, combined with the trilinear model, a hysteretic model under compressive-torsional hysteresis stresses has been established for square CFRP steel tube filled with concrete. The calculated outcomes of the developed finite element model closely align with those of the hysteretic model.

Are cleft sentence structures more difficult to process?

This study compares the processing of cleft structures against that of monoclausal sentences using event-related potential (ERP). We aim to understand how syntactic complexity is processed by comparing the neural response to cleft and single-clause sentences with identical verb phrases, controlling for verb bias frequency effects. 60 participants were tested, and we presented 100 cleft and 100 monoclausal sentences, balanced for active and passive verb usage. The P600 component, which is associated with syntactic complexity, was examined to assess the processing load of cleft structures. Results showed that cleft structures incur a greater processing load, as indicated by a P600, compared to monoclausal sentences. This indexed the increased difficulty of processing the additional syntactic operation required to parse cleft sentences. These findings echo the results of behavioral studies of sentence comprehension on clinical populations.

Energy-efficient optimization design of bio-butanol fermentation broth purification process

To address the downstream processing challenges of the IBE (isopropanol-butanol-ethanol) system and obtain biobutanol products with a mass fraction of 0.9999, as well as obtain a mass fraction of 0.99975 for the IE (isopropanol-Ethanol) mixture product as a gasoline additive, this study proposes a four-column distillation process termed "Dehydration-Butanol-Extractive Four Column Distillation" (DBE-4CD). With the heat load as the optimization target, the DBE-4CD process was optimized to determine the optimal operating parameters. Based on the optimized process and considering the energy-saving potential of the dividing wall column, an “Azeotropic Dividing Wall-Extractive Three Column Distillation” (ADE-3CD) process was subsequently proposed to further enhance energy efficiency and reduce consumption. Compared to both conventional literature process and the DBE-4CD process, the total load of the ADE-3CD process decreased to 7433.5 kW, representing reductions of 20.35% and 10.11%, respectively. Additionally, the mass recovery rates of butanol and the IE mixture reached 99.90% and 99.64%, respectively, exceeding those of the conventional literature process, which were 99.11% and 99.18%.

Heterogeneous system list scheduling algorithm based on improved optimistic cost matrix

In heterogeneous computing systems, efficient task-scheduling methods are paramount for enhancing computational performance. However, the existing algorithm exhibits certain deficiencies, notably its oversight of load balancing concerns and inadequate emphasis on the out-degree property of tasks. To address these issues, a novel list scheduling algorithm is proposed, Average Earliest Finish Time (AEFT), which proficiently allocates task flows onto heterogeneous processors. The AEFT algorithm primarily consists of two key stages: (1) prioritizing tasks to determine the distribution of task priorities and (2) assigning optimal processors for tasks with given priorities. By leveraging its specific topology, the AEFT algorithm minimizes the scheduling length of task flows. Simultaneously, a prediction mechanism in determining task prioritization and selecting processors stages is proposed to reduce the scheduling time of task flows. In addition, in the processor selection stage, AEFT algorithm considers the out-degree characteristics of tasks, ameliorating situations of processor load imbalance. The AEFT algorithm demonstrates superior performance compared to prior list scheduling algorithms concerning makespan, speedup, and the percentage of occurrences of better solutions, as evidenced by experiments conducted on randomly generated and real-application graphs. Specifically, for t tasks and p processors, the AEFT algorithm achieves a time complexity of O(t2p).