Average Contour Research Articles

GSMS: a goaf scanning and modeling system for underground mines based on a quadruped robot

Abstract Underground mining creates goafs, impacting resource extraction and safety. To address the issues of low efficiency and high reliance on manual operations in traditional techniques, this paper proposes a goaf scanning and modeling system (GSMS) for underground mine based on a quadruped robot. This system performs three-dimensional modeling of goafs through two main modules: frontend mapping and backend optimization, and evaluates the modeling accuracy using a multi-dimensional precision evaluation module. To validate the effectiveness of this system, both simulation and field experiments were conducted. The experimental results indicate that the goaf models established using GSMS achieved a level of accuracy comparable to traditional methods: the volume error of the constructed goaf models compared to the reference models was less than 3%, and the average profile contour similarity exceeded 98%.

The Concept of Ideal Lips of Caucasian Female: An Anthropometric Analysis of the Lower Third of the Face.

The demand for lip-redefining procedures has been rising in recent years, thus creating the need for reliable and detailed reference sources on aesthetic female lips. This study investigates the morphology of the lower third of the face, including the lips and jawline, of attractive young Caucasian females. A semi-automatic photogrammetric analysis of the faces of professional female photograph models (n=400) of the Caucasian race aged 18-39 was performed. Angular, linear, and surface area parameters were evaluated. A graphical summarization of the average facial shape of all analyzed attractive females was generated as an average body contours (ABC) image. The height of the lower third of the face equaled 0.32 ± 0.02 of the total facial height. The average lip width was 48.06 ± 3.34mm. The upper vermilion height was found to be significantly lower than the lower vermilion height (6.47 ± 1.36 vs. 11.64 ± 1.46mm, p < 0.01). The lip obliquity angle was found to be 1.05° ± 0.79°. The area surface of the upper lip vermilion was considerably smaller than the vermilion of the lower lip (p < 0.01). This is one of the largest studies on attractive Caucasian females' lips and lower face morphology. The data it provides, including the graphical presentation of the aesthetic lower face as an ABC image, may provide physicians with valuable guidelines for lips rejuvenation and reconstruction procedures. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

CT- and MR-based image-based data mining are consistent in the brain

PurposeImage-based data mining (IBDM) is a voxel-based analysis technique to investigate dose–response. Most often, IBDM uses radiotherapy planning CTs because of their broad accessibility, however, it was unknown whether CT provided sufficient soft tissue contrast for brain IBDM. This study evaluates whether MR-based IBDM improves upon CT-based IBDM for studies of children with brain tumours. MethodsWe compared IBDM pipelines using either CT- or MRI-based spatial normalisation in 128 children (ages 3.3–19.7 years) who received photon radiotherapy for primary brain tumours at a single institution. We quantified spatial-normalisation accuracy using contour comparison measures (centre-of-mass separation, average contour distance-to-agreement (DTavg), and Hausdorff distance) at multiple anatomic loci. We performed an end-to-end test of CT- and MRI-IBDM using modified clinical dose distributions and simulated effect labels to detect associations in pre-defined anatomic loci. Accuracy was assessed via sensitivity and specificity. ResultsSpatial normalisation accuracy was comparable for both modalities, with a significant but small improvement for MRI compared to CT in all structures except the brainstem. The median (range) difference between the DTavg for the two pipelines was 0.37 (0.00–2.91) mm. The end-to-end test revealed no significant difference in sensitivity of the IBDM-identified regions for the two pipelines. Specificity slightly improved for MR-IBDM at the 99% significance level. ConclusionCT-based IBDM was comparable to MR-based IBDM, despite a small advantage in spatial normalisation accuracy with MRI. The use of CT-IBDM over MR-IBDM is useful for multi-institutional retrospective IBDM studies, where the availability of standardised MRI data can be limited.

Learning carotid vessel wall segmentation in black-blood MRI using sparsely sampled cross-sections from 3D data.

Atherosclerosis of the carotid artery is a major risk factor for stroke. Quantitative assessment of the carotid vessel wall can be based on cross-sections of three-dimensional (3D) black-blood magnetic resonance imaging (MRI). To increase reproducibility, a reliable automatic segmentation in these cross-sections is essential. We propose an automatic segmentation of the carotid artery in cross-sections perpendicular to the centerline to make the segmentation invariant to the image plane orientation and allow a correct assessment of the vessel wall thickness (VWT). We trained a residual U-Net on eight sparsely sampled cross-sections per carotid artery and evaluated if the model can segment areas that are not represented in the training data. We used 218 MRI datasets of 121 subjects that show hypertension and plaque in the ICA or CCA measuring in ultrasound. The model achieves a high mean Dice coefficient of 0.948/0.859 for the vessel's lumen/wall, a low mean Hausdorff distance of , and a low mean average contour distance of on the test set. The model reaches similar results for regions of the carotid artery that are not incorporated in the training set and on MRI of young, healthy subjects. The model also achieves a low median Hausdorff distance of on the 2021 Carotid Artery Vessel Wall Segmentation Challenge test set. The proposed method can reduce the effort for carotid artery vessel wall assessment. Together with human supervision, it can be used for clinical applications, as it allows a reliable measurement of the VWT for different patient demographics and MRI acquisition settings.

Research on the classification of ancient silicate glass artifacts based on machine learning

AbstractClassifying cultural relics has always been a major challenge for archaeologists. Using glass artifacts as the research object, a classification model for glass artifacts was constructed using decision trees, support vector machines, and logistic regression methods based on their patterns, colors, surface weathering conditions, types, and composition ratios. Three models were used to identify the types of unknown glass artifacts. A subclassification model for high‐potassium glass and lead barium glass was established using the K‐means clustering method. The elbow method and average contour method were used to determine the optimal number of clusters, and the decision tree model was named based on the characteristics of the cluster center components. The research results indicate that the three models yield consistent identification results for unknown types of glass relics, and the classification results are good. Lead barium glass and high‐potassium glass can be divided into three and six subclasses, respectively, and the naming of the subclass decision tree is reasonable. The identification method for ancient glass relics in this article is highly practical and can provide a reference for the classification and identification of other component data.

Fibrillation of soy protein isolate in the presence of metal ions: Structure and gelation behavior

The structure and functional properties of protein fibrils are closely related to environmental factors in fibrillation. Herein, soy protein isolate fibrils (SPIFs, 22 mg/mL) were prepared under acid-heating conditions in the presence of 100 mM metal ions (K+, Na+, Ca2+, Mg2+, and Fe3+). Except for Fe3+, fibrillation and subsequent larger fibril aggregates were promoted, ultimately leading to gel formation. Compared with K+ or Na+, the addition of Ca2+ or Mg2+ resulted in more organized SPIF structures with increased β-sheet contents and higher ThT fluorescence intensities. Furthermore, both of them resulted in longer fibrils with an average contour length of 700–800 nm, which significantly enhanced the storage modulus. However, the presence of Fe3+ accelerated protein hydrolysis and inhibited SPIF formation, resulting in samples consistently exhibited liquid behavior. These findings provide a foundation for understanding the influence of metal ions on regulating the fibrillation and gelling properties of SPIFs.

MHD mixed convection of nanofluid flow Ag- Mgo/water in a channel contain a rotational cylinder

This investigation explores the mixed convection phenomenon of a hybrid nanofluid flowing within a Channel oriented horizontally featuring a triangular cavity attached to the lower channel wall. The cavity houses a rotating circular cylinder with a radius of R = 0.12. The fluid within the enclosure is a water-based nanofluid containing Ag-MgO nanoparticles. Two scenarios of combined natural and forced convection are examined: in Case 1, all channel walls, the left wall of the cavity, and the surface of the rotating cylinder are adiabatic, while the inclined wall of the cavity is isothermal. In Case 2, all channel walls, the ready wall of the cavity, and the surface of the rotating cylinder are adiabatic, except for the left wall of the cavity, which is isothermal. The dimensionless governing equations are solved under steady flow conditions for various physical parameters using a higher-order and stable Galerkin-based finite element method implemented through the software package FlexPDE. Key governing parameters such as Re number (falling within the amplitude of 10 to 100), Ri number (ranging from Ri = 0.1 to 30), and the location of cylinder rotation (Xo = 0.65 to 0.9) are simulated. The study presents results in terms of average Nusselt numbers and contour maps of streamlines and isotherms. It is observed that the average Nusselt number increases with higher Reynolds number, angular rotation speed, cylinder location, and Richardson number. Specifically, heating the inclined surface suggests that the optimal location for the rotating cylinder is Xo = 0.9, whereas if the left wall of the cavity is heated, the preferred location for the cylinder is Xo = 0.75. These findings are corroborated by comparing them with previous research, demonstrating significant agreement.

Von-Hippel Lindau protein amyloid formation. The role of GST-tag

In the last decade, much attention was given to the study of physiological amyloid fibrils. These structures include A-bodies, which are the nucleolar fibrillar formations that appear in the response to acidosis and heat shock, and disassemble after the end of stress. One of the proteins involved in the biogenesis of A-bodies, regardless of the type of stress, is Von-Hippel Lindau protein (VHL). Known also as a tumor suppressor, VHL is capable to form amyloid fibrils both in vitro and in vivo in response to the environment acidification. As with most amyloidogenic proteins fusion with various tags is used to increase the solubility of VHL. Here, we first performed AFM-study of fibrils formed by VHL protein and by VHL fused with GST-tag (GST-VHL) at acidic conditions. It was shown that formed by full-length VHL fibrils are short heterogenic structures with persistent length of 2400 nm and average contour length of 409 nm. GST-tag catalyzes VHL amyloid fibril formation, superimpose chirality, increases length and level of hierarchy, but decreases rigidity of amyloid fibrils. The obtained data indicate that tagging can significantly affect the fibrillogenesis of the target protein.

Dynamic Multiple Object Segmentation with Spatio-Temporal Filtering.

This article primarily focuses on the localization and extraction of multiple moving objects in images taken from a moving camera platform, such as image sequences captured by drones. The positions of moving objects in the images are influenced by both the camera's motion and the movement of the objects themselves, while the background position in the images is related to the camera's motion. The main objective of this article was to extract all moving objects from the background in an image. We first constructed a motion feature space containing motion distance and direction, to map the trajectories of feature points. Subsequently, we employed a clustering algorithm based on trajectory distinctiveness to differentiate between moving objects and the background, as well as feature points corresponding to different moving objects. The pixels between the feature points were then designated as source points. Within local regions, complete moving objects were segmented by identifying these pixels. We validated the algorithm on some sequences in the Video Verification of Identity (VIVID) program database and compared it with relevant algorithms. The experimental results demonstrated that, in the test sequences when the feature point trajectories exceed 10 frames, there was a significant difference in the feature space between the feature points on the moving objects and those on the background. Correctly classified frames with feature points accounted for 67% of the total frames.The positions of the moving objects in the images were accurately localized, with an average IOU value of 0.76 and an average contour accuracy of 0.57. This indicated that our algorithm effectively localized and segmented the moving objects in images captured by moving cameras.

Estimation of footprints of the canine stifle ligaments using deformable shape templates of bones

Knowledge regarding the ligament footprints in the canine stifle is essential for biomechanical modeling of the joint and patient-specific surgical planning for anatomical ligament reconstruction. The present study aimed to establish and evaluate deformable shape templates (DSTs) of the femur and tibia with footprints of the cruciate and collateral ligaments embedded for the noninvasive estimation of ligament footprint positions. To this end, a data set of computed tomography (CT)-derived surface models of the femur and tibia were established and used to build statistical shape models (SSMs). The contours of the stifle ligaments were obtained from CT scans of 27 hindlimb specimens with radio-opaque markings on the ligament footprints. The DST, constructed by embedding averaged footprint contours into the SSM, was used to estimate subject-specific ligament footprints in a leave-one-out cross-validation framework. The DST predictions were compared with those derived from radio-opaque-marked footprints. The results showed that the averaged Euclidean distances between the estimated and reference footprint centroids were less than 1.2 mm for the cruciate ligaments and 2.0 mm for the collateral ligaments. The DST appeared to provide a feasible alternative approach for noninvasively estimating the footprints of the stifle ligaments in vivo.

Enhancing the Contouring Efficiency for Head and Neck Cancer Radiotherapy Using Atlas-based Auto-segmentation and Scripting.

Intensity-modulated radiation therapy can deliver a highly conformal dose to a target while minimizing the dose to the organs at risk (OARs). Delineating the contours of OARs is time-consuming, and various automatic contouring software programs have been employed to reduce the delineation time. However, some software operations are manual, and further reduction in time is possible. This study aimed to automate running atlas-based auto-segmentation (ABAS) and software operations using a scripting function, thereby reducing work time. Dice coefficient and Hausdorff distance were used to determine geometric accuracy. The manual delineation, automatic delineation, and modification times were measured. While modifying the contours, the degree of subjective correction was rated on a four-point scale. The model exhibited generally good geometric accuracy. However, some OARs, such as the chiasm, optic nerve, retina, lens, and brain require improvement. The average contour delineation time was reduced from 57 to 29 min (p<0.05). The subjective revision degree results indicated that all OARs required minor modifications; only the submandibular gland, thyroid, and esophagus were rated as modified from scratch. The ABAS model and scripted automation in head and neck cancer reduced the work time and software operations. The time can be further reduced by improving contour accuracy.

Clinical validation of commercial deep-learning based auto-segmentation models for organs at risk in the head and neck region: a single institution study.

To evaluate organ at risk (OAR) auto-segmentation in the head and neck region of computed tomography images using two different commercially available deep-learning-based auto-segmentation (DLAS) tools in a single institutional clinical applications. Twenty-two OARs were manually contoured by clinicians according to published guidelines on planning computed tomography (pCT) images for 40 clinical head and neck cancer (HNC) cases. Automatic contours were generated for each patient using two deep-learning-based auto-segmentation models-Manteia AccuContour and MIM ProtégéAI. The accuracy and integrity of autocontours (ACs) were then compared to expert contours (ECs) using the Sørensen-Dice similarity coefficient (DSC) and Mean Distance (MD) metrics. ACs were generated for 22 OARs using AccuContour and 17 OARs using ProtégéAI with average contour generation time of 1 min/patient and 5 min/patient respectively. EC and AC agreement was highest for the mandible (DSC 0.90 ± 0.16) and (DSC 0.91 ± 0.03), and lowest for the chiasm (DSC 0.28 ± 0.14) and (DSC 0.30 ± 0.14) for AccuContour and ProtégéAI respectively. Using AccuContour, the average MD was<1mm for 10 of the 22 OARs contoured, 1-2mm for 6 OARs, and 2-3mm for 6 OARs. For ProtégéAI, the average mean distance was<1mm for 8 out of 17 OARs, 1-2mm for 6 OARs, and 2-3mm for 3 OARs. Both DLAS programs were proven to be valuable tools to significantly reduce the time required to generate large amounts of OAR contours in the head and neck region, even though manual editing of ACs is likely needed prior to implementation into treatment planning. The DSCs and MDs achieved were similar to those reported in other studies that evaluated various other DLAS solutions. Still, small volume structures with nonideal contrast in CT images, such as nerves, are very challenging and will require additional solutions to achieve sufficient results.

Quantitative characterization of microstructures and elastic properties in plasma sprayed coatings based on “Ultrasonic – Elasticity – Microstructure” interaction mechanism

In plasma sprayed coatings, pores are the primary microstructure, and their features such as porosity, shape, and orientation significantly influence the thermal insulation and elastic properties of the coatings. Therefore, the ultrasonic characterization of pore feature parameters plays a crucial role in guiding the processing and predicting the lifespan of coatings. However, the coupling of multiple pore feature parameters leads to ill-posed problem and nonlinearity between these parameters and the ultrasonic feature signals, making it challenging to identify multiple pore feature parameters accurately. In this study, based on the “ultrasonic - elasticity - structure” interaction mechanism, a novel strategy for the ultrasonic quantitative identification of pore feature parameters and elastic constants in plasma sprayed coatings is proposed.Firstly, a random pore model (RPM) was developed to reveal the complex morphology of pores in plasma sprayed coatings. The porosity, average aspect ratio, orientation factor, and contour roughness factor were statistically analyzed for 15 sets of constructed RPMs, followed by ultrasonic finite element simulations. Then, a sensitivity matrix (SM) inversion method is developed for the “ultrasonic - elasticity” relationship by accurately measuring the elastic constants of the coatings through ultrasonic wave velocity inversion at multiple angles, yielding a relative error of only 0.57%. Lastly, a Multi-output Support Vector Regression machine learning model optimized by genetic algorithm (GA-MSVR) was proposed to establish the “elasticity - microstructure” relationship and identify multiple pore feature parameters based on elastic constants. The maximum relative errors for the four pore feature parameters are found to be 12.28%, 6.35%, 18.87%, and 7.62%, respectively, demonstrating the feasibility of the proposed strategy. Furthermore, the sensitivity matrix was employed to explain the cause for the larger relative error of the orientation factor.In summary, a challenging task of quantitatively identifying pore feature parameters in plasma sprayed coatings is addressed by proposing a novel solving strategy based on the “ultrasonic - elasticity - microstructure” interaction mechanism. The developed RPM, SM inversion method, and GA-MSVR model contribute to the accurate and comprehensive identification of pore feature parameters. The results obtained validate the feasibility of the proposed strategy.

Structure and Conformation of Hydroxypropylmethyl Cellulose with a Wide Range of Molar Masses in Aqueous Solution─Effects of Hydroxypropyl Group Addition.

The structure of hydroxypropylmethyl cellulose (HpMC) samples with a wide range of weight average molar masses (Mw) from 23 to 5000 kg mol-1, a controlled degree of substitution (DS) of 1.9 by methyl groups, and a molar substitution number (MS) of 0.25 by hydroxypropyl groups dissolved in aqueous solution was examined using static light scattering (SLS), dynamic light scattering (DLS), small-to-wide angle neutron scattering (S-WANS) techniques, and intrinsic viscosity ([η]) measurements. The determined Mw and the radius of gyration (Rg) showed the relationships Rg ∝ Mw1.0 and [η] ∝ Mw1.7 in a range of Mw < 100 kg mol-1, similar to rigid rod molecules in solution. However, exponents in the relationships decreased gradually with increasing Mw and reached ∼0.5 in a high Mw region, which is a typical value of flexible chain molecules for both Rg and [η]. These observations suggest that the HpMC samples behave as semiflexible rods with a certain persistence length (lp). The ratios of the hydrodynamic radius via DLS measurements to Rg also supported semiflexible rod behavior. Particle form factors and the average lengths (L) resulting from SLS and S-WANS experiments are well described with rigid rod particles in the range of Mw < 100 kg mol-1 and semiflexible rods with lp ∼ 100 nm in Mw > 100 kg mol-1. Because the average contour length (lc) calculated from Mw is approximately twice as long as L in the Mw range < 100 kg mol-1, the formed HpMC particles possess a folded hairpin-like elongated rigid rod structure. However, the lc/L value increases gradually in the range Mw > 200 kg mol-1, where the formed HpMC particles behave as semiflexible rods. The formed particle structure was substantially different from that found in methyl cellulose samples with a similar DS value, which showed rod-like behavior over a wide Mw range. The addition of hydroxypropyl groups only at MS = 0.25 effectively changed the formed particle structure.

Effect of fin length and angle on the melting process of phase change materials in vertical latent heat storage unit

The installation of fins offers an effective solution for addressing the poor thermal conductivity of phase change materials (PCMs) in latent heat thermal energy storage (LHTES) systems. This paper aims to investigate the combined effects of unequal fin length arrangements and the inclination angle of fins on the performance of an LHTES system during the charging process. The LHTES system consists of a cylindrical container filled with paraffin (RT55) serving as the PCM. Numerical models are established and validated, followed by simulating various fin structures to analyze the complete melting time of the PCM. The characteristics of the melting process are further examined in four representative cases, including the full melting time, evolution of the melt fraction, average maximum velocity, streamline contours, temperature uniformity, and average Nusselt number. The results reveal that the optimal case, featuring unequal length and downward annular fins, achieves a significant 49.8% reduction in the complete melting time of the PCM compared to the benchmark case with ordinary annular fins. Moreover, the optimal configuration increases the largest average maximum velocity of the PCM by 35.36%, resulting in a more uniform temperature distribution and a higher Nusselt number.

A Computational Model of Cn2 Profile Inversion for Atmospheric Laser Communication in the Vertical Path.

In this paper, an atmospheric structure constant Cn2 model is proposed for evaluating the channel turbulence degree of atmospheric laser communication. First, we derive a mathematical model for the correlation between the atmospheric coherence length r0, the isoplanatic angle θ0 and Cn2 using the Hufnagel-Valley (HV) turbulence model. Then, we calculate the seven parameters of the HV model with the actual measured r0 and θ0 data as input quantities, so as to draw the Cn2 profile and the θ0 profile. The experimental results show that the fitted average Cn2 contours and single-day Cn2 contours have superior fitting performance compared with our historical data, and the daily correlation coefficient between the single-day computed θ0 contours and the measured θ0 contours is up to 87%. This result verifies the feasibility of the proposed method. The results validate the feasibility of the proposed method and provide a new technical tool for the inversion of turbulence Cn2 profiles.

K-Means Module Division Method of FDM3D Printer-Based Function–Behavior–Structure Mapping

Product performance, function, cost, and the level of module generalization are all significantly influenced by product modular design, but different goods require different division indicators and techniques. The purpose of this study is to provide a set of appropriate modular division techniques for FDM 3D printers. This research offers an ecologically friendly module division index and uses module clustering as the module division principle in accordance with the current industrial development trend and the fundamental requirements of FDM 3D printer consumers in the current market. The K-means algorithm is used to use the Jaccard similarity coefficient as the metric of similarity of the DSM clustering process to realize the module division of the FDM 3D printer after studying the function–behavior–structure mapping model of the 3D printer. Additionally, the elbow method–cluster error variance and average contour coefficient evaluation systems were built, respectively, in order to verify the viability of the FDM 3D printer module division method and obtain the best module division results. By analyzing these two systems, it was discovered that when the FDM 3D printer was divided into three modules, the in-cluster error variance diagram obviously had an inflection point, and the average profile coefficient and other modular approaches that need to be adjusted to their respective goods can use this division method as a theoretical foundation and point of reference.

Area average heat transfer from a vertical flat plate impinged by circular inclined jet

ABSTRACT The inclined impinging jets owing to higher efficiency are widely used to disperse concentrated and transient heat loads. The temperature and heat transfer characteristics of jet cooling vertical hot plate at different inclinations are experimentally investigated. Experiments are performed to investigate the area average heat transfer over a flat surface of 600 × 300 mm2 placed at different location (z) of 3−20 d from the jet. The investigation is employed for Reynolds number based on hydraulic diameter of 11.5 mm ranging from 10,000 to 75,000. The orientation of the jet varies from 15° to 75°. The study aims to investigate the influence of jet orientation at different area locations on the surface from the point of impingement. The effect in stagnation region with different jet tilt is investigated. The area average temperature variation and characteristic temperature contours with increasing surface area and inclination is delineated. The characteristics of heat transfer on the surface’s periphery are studied. Heat transfer increases by 40% at lower tilt angle as the jet is displaced from 20 to 6 d, whereas the maximum average Nu occurs at higher inclination. The available Nusselt numbers in inclined jet are compared with present experimental results. A new empirical correlation is developed for a complete range of z/d, Re and large radial span.

Data-Driven Analysis of Risk-Assessment Methods for Cold Food Chains.

The problem of cold-chain food safety is becoming increasingly prominent. Cold food chain risk assessment is an important way to ensure cold-chain food safety. Using CiteSpace, this study analyzes the knowledge map of research hotspots in the field of cold-chain food safety over the past 18 years, identifies the research keywords, presents the centrality statistics, and calculates the cluster values and average cluster contour values. Adopting a data-driven perspective, risk-assessment methods for cold food chains are summarized based on qualitative risk assessment, quantitative risk assessment, and comprehensive qualitative and quantitative risk assessment. The advantages and disadvantages of each are summarized. Finally, the problems and challenges in current cold food chain risk-assessment research are summarized in three aspects: the data credibility of cold food chain traceability systems, cold-chain food safety audit methods, and nontraditional cold food chain risk assessment. Suggestions are given for strengthening the cold food chain risk-assessment system to provide a decision-making reference to help regulatory authorities take risk prevention and control measures.

Language dependent cue-weighting of tone cues in speech and non-speech stimuli

Previous research on tone perception has identified several important pitch related cues including average pitch height (AH), contour, onset and offset, and the weighting of these cues has shown to be language dependent. However, since multiple pitch cues are covarying with each other, few studies have directly compared relative importance of these cues. It is also not clear whether there is a same ranking of cues in speech and non-speech stimuli. The current study aims to tease apart the relative role of each cue using AX discrimination. Four pairs of tone contrasts with minimal pitch differences were created. Tone contrasts within contour condition are two level tones with 7 Hz differences. Tone contrasts within AH, onset and offset conditions have one rising tone and one falling tone sharing the same AH, onset and offset respectively. If one cue is important, then when this cue is kept constant, variation in other cues should be hard to perceive. 48 Mandarin speakers and 48 Cantonese speakers were recruited. Results showed highest importance of AH for both Mandarin and Cantonese listeners and higher importance of contour (offset) than onset for Mandarin (Cantonese) listeners in speech stimuli. This ranking was not held for nonspeech stimuli.