Acoustic Characteristics Research Articles

The contribution of prosody to machine classification of schizophrenia.

We show how acoustic prosodic features, such as pitch and gaps, can be used computationally for detecting symptoms of schizophrenia from a single spoken response. We compare the individual contributions of acoustic and previously-employed text modalities to the algorithmic determination whether the speaker has schizophrenia. Our classification results clearly show that we can extract relevant acoustic features better than those textual ones. We find that, when combined with those acoustic features, textual features improve classification only slightly.

Do Acoustic Characteristics of Dysarthria in People With Parkinson's Disease Differ Across Languages?

Cross-language studies suggest more similarities than differences in how dysarthria affects the speech of people with Parkinson's disease (PwPD) who speak different languages. In this study, we aimed to identify the relative contribution of acoustic variables to distinguish PwPD from controls who spoke varieties of two Romance languages, French and Portuguese. This bi-national, cross-sectional, and case-controlled study included 129 PwPD and 124 healthy controls who spoke French or Portuguese. All participants underwent the same clinical examinations, voice/speech recordings, and self-assessment questionnaires. PwPD were evaluated off and on optimal medication. Inferential analyses included Disease (controls vs. PwPD) and Language (French vs. Portuguese) as factors, and random decision forest algorithms identified relevant acoustic variables able to distinguish participants: (a) by language (French vs. Portuguese) and (b) by clinical status (PwPD on and off medication vs. controls). French-speaking and Portuguese-speaking individuals were distinguished from each other with over 90% accuracy by five acoustic variables (the mean fundamental frequency and the shimmer of the sustained vowel /a/ production, the oral diadochokinesis performance index, the relative sound level pressure and the relative sound pressure level standard deviation of the text reading). A distinct set of parameters discriminated between controls and PwPD: for men, maximum phonation time and the oral diadochokinesis speech proportion were the most significant variables; for women, variables calculated from the oral diadochokinesis were the most discriminative. Acoustic variables related to phonation and voice quality distinguished between speakers of the two languages. Variables related to pneumophonic coordination and articulation rate were the more effective in distinguishing PwPD from controls. Thus, our research findings support that respiration and diadochokinesis tasks appear to be the most appropriate to pinpoint signs of dysarthria, which are largely homogeneous and language-universal. In contrast, identifying language-specific variables with the speech tasks and acoustic variables studied was less conclusive.

Validity of Acoustic Measures Obtained Using Various Recording Methods Including Smartphones With and Without Headset Microphones.

The goal of this study was to assess various recording methods, including combinations of high- versus low-cost microphones, recording interfaces, and smartphones in terms of their ability to produce commonly used time- and spectral-based voice measurements. Twenty-four vowel samples representing a diversity of voice quality deviations and severities from a wide age range of male and female speakers were played via a head-and-thorax model and recorded using a high-cost, research standard GRAS 40AF (GRAS Sound & Vibration) microphone and amplification system. Additional recordings were made using various combinations of headset microphones (AKG C555 L [AKG Acoustics GmbH], Shure SM35-XLR [Shure Incorporated], AVID AE-36 [AVID Products, Inc.]) and audio interfaces (Focusrite Scarlett 2i2 [Focusrite Audio Engineering Ltd.] and PC, Focusrite and smartphone, smartphone via a TRRS adapter), as well as smartphones direct (Apple iPhone 13 Pro, Google Pixel 6) using their built-in microphones. The effect of background noise from four different room conditions was also evaluated. Vowel samples were analyzed for measures of fundamental frequency, perturbation, cepstral peak prominence, and spectral tilt (low vs. high spectral ratio). Results show that a wide variety of recording methods, including smartphones with and without a low-cost headset microphone, can effectively track the wide range of acoustic characteristics in a diverse set of typical and disordered voice samples. Although significant differences in acoustic measures of voice may be observed, the presence of extremely strong correlations (rs > .90) with the recording standard implies a strong linear relationship between the results of different methods that may be used to predict and adjust any observed differences in measurement results. Because handheld smartphone distance and positioning may be highly variable when used in actual clinical recording situations, smartphone + a low-cost headset microphone is recommended as an affordable recording method that controls mouth-to-microphone distance and positioning and allows both hands to be available for manipulation of the smartphone device.

Vocal complexity in the long calls of Bornean orangutans.

Vocal complexity is central to many evolutionary hypotheses about animal communication. Yet, quantifying and comparing complexity remains a challenge, particularly when vocal types are highly graded. Male Bornean orangutans (Pongo pygmaeus wurmbii) produce complex and variable "long call" vocalizations comprising multiple sound types that vary within and among individuals. Previous studies described six distinct call (or pulse) types within these complex vocalizations, but none quantified their discreteness or the ability of human observers to reliably classify them. We studied the long calls of 13 individuals to: (1) evaluate and quantify the reliability of audio-visual classification by three well-trained observers, (2) distinguish among call types using supervised classification and unsupervised clustering, and (3) compare the performance of different feature sets. Using 46 acoustic features, we used machine learning (i.e., support vector machines, affinity propagation, and fuzzy c-means) to identify call types and assess their discreteness. We additionally used Uniform Manifold Approximation and Projection (UMAP) to visualize the separation of pulses using both extracted features and spectrogram representations. Supervised approaches showed low inter-observer reliability and poor classification accuracy, indicating that pulse types were not discrete. We propose an updated pulse classification approach that is highly reproducible across observers and exhibits strong classification accuracy using support vector machines. Although the low number of call types suggests long calls are fairly simple, the continuous gradation of sounds seems to greatly boost the complexity of this system. This work responds to calls for more quantitative research to define call types and quantify gradedness in animal vocal systems and highlights the need for a more comprehensive framework for studying vocal complexity vis-à-vis graded repertoires.

EarSleep: In-ear Acoustic-based Physical and Physiological Activity Recognition for Sleep Stage Detection

Since sleep plays an important role in people's daily lives, sleep monitoring has attracted the attention of many researchers. Physical and physiological activities occurring in sleep exhibit unique patterns in different sleep stages. It indicates that recognizing a wide range of sleep activities (events) can provide more fine-grained information for sleep stage detection. However, most of the prior works are designed to capture limited sleep events and coarse-grained information, which cannot meet the needs of fine-grained sleep monitoring. In our work, we leverage ubiquitous in-ear microphones on sleep earbuds to design a sleep monitoring system, named EarSleep1, which interprets in-ear body sounds induced by various representative sleep events into sleep stages. Based on differences among physical occurrence mechanisms of sleep activities, EarSleep extracts unique acoustic response patterns from in-ear body sounds to recognize a wide range of sleep events, including body movements, sound activities, heartbeat, and respiration. With the help of sleep medicine knowledge, interpretable acoustic features are derived from these representative sleep activities. EarSleep leverages a carefully designed deep learning model to establish the complex correlation between acoustic features and sleep stages. We conduct extensive experiments with 48 nights of 18 participants over three months to validate the performance of our system. The experimental results show that our system can accurately detect a rich set of sleep activities. Furthermore, in terms of sleep stage detection, EarSleep outperforms state-of-the-art solutions by 7.12% and 9.32% in average precision and average recall, respectively.

Self-Adaptive Incremental PCA-Based DBSCAN of Acoustic Features for Anomalous Sound Detection

In modern industry, maintaining continuous machine operations is important for improving production efficiency and reducing costs. Therefore, the smart technology of acoustic monitoring to detect anomalous machine conditions earlier before breakdowns works as part of predictive maintenance and is applied not only in industry fault detection but also in safety monitoring and surveillance systems. This paper proposes a self-adaptive unsupervised machine learning algorithm with dimension-reduction technology to detect anomalous sounds after extracting acoustic machine features. Technically, the automatic EPS calculation algorithm-based genetic algorithm optimizes the automatic clustering algorithm’s configuration for incremental principal component analysis and density-based spatial clustering algorithms with noise. IPCA is enhanced by the sequential Karhunen–Loeve (SKL) algorithm, and the condensation algorithm works as the second layer of the algorithm to reduce the number of effective components. This architecture could select an optimized set of parameters based on different test environments and keeps performance quality with fewer computational requirements. In the experiments, 228 sets of normal sounds and 100 sets of anomaly sounds are used. The sound files are collected from the same machine type (stepper motors) at a real plant site. We compare the proposed algorithm with K-means++, one-class SVM, agglomerative clustering, DCGAN and DCNN-Autoencoder, and this new algorithm performs best, with an AUC of 0.84 and the shortest execution time. The algorithm is generic and can be applied to detect anomalies in machines to provide early warning to people to avoid serious accidents or disasters.

Research on Noise Reduction of Water Hydraulic Throttle Valve Based on RBF Neural Network and Multi-Island Genetic Algorithm

Excessive pressure drop within the internal flow channel of the water hydraulic throttle valve will generate severe noise. In order to reduce the noise of the throttle valve, in this paper, the model of the throttle valve was established, and the flow characteristics and acoustic characteristics of the valve were simulated. The simulation results showed that the parameters of the throat structure, such as the half angle, throat inlet angle and throat length, have a significant effect on the noise of the valve. Then, the three main structural parameters were used as optimization variables, and radial basis function (RBF) neural networks and multi-island genetic algorithms (MIGA) were used to reduce the noise of the valve. The approximate model of the relationship between the structural parameters of the valve and noise was established by RBF neural networks, and MIGA was used to optimize the approximate model. Finally, the optimal valve model was established based on the obtained optimal parameters, and its noise was analyzed through simulation and experiment. The research results indicated that the optimization method, which combines RBF Neural Network and MIGA, can effectively reduce the noise of hydraulic throttle valves.

Cyclic compressive behavior of high-performance hybrid fiber reinforced recycled aggregate concrete: Degradation law and acoustic emission features

To tackle the issues of inadequate mechanical properties of recycled aggregate concrete and its limited application in load-bearing structural components, a high-performance hybrid fiber reinforced recycled aggregate concrete (HP-HFRAC) was developed in this study. Sixteen groups of HP-HFRAC specimens fabricated with varying replacement rates of recycled coarse aggregate and different fiber parameters were subjected to uniaxial compression, as well as acoustic emission (AE) tests, to investigate the cyclic behavior and uncover the underlying mechanisms. The findings demonstrated a strong correlation between the AE signals and the macroscopic phenomenon throughout the whole compression process. The AE amplitude distribution was found to be capable of identifying damage mechanisms, including matrix cracking (40–50 dB), fiber sliding (50–80 dB), and fiber pull-out (more than 80 dB). Additionally, the inclusion of hybrid fibers contributed to mitigating the degradation of stiffness and the accumulation of plastic strain, resulting in the ductile failure of the specimens and significantly enhancing the energy dissipation capacity. Specifically, the peak stress of specimens with 1.5% steel fibers increased by 19% compared to that of specimens without steel fibers, compensating for the strength loss due to the presence of recycled coarse aggregate. Finally, constitutive equations governing the skeleton curves and the unloading and reloading curves of HP-HFRAC subjected to cyclic compression were formulated, which were validated using both the test results and the predicted results of the physics-based data-driven model previously published in our companion paper. The findings of this study might contribute to the development of sustainable high performance recycled aggregate concrete for reliable application in load-bearing components.

Novel Approach Combining Shallow Learning and Ensemble Learning for the Automated Detection of Swallowing Sounds in a Clinical Database

Cervical auscultation is a simple, noninvasive method for diagnosing dysphagia, although the reliability of the method largely depends on the subjectivity and experience of the evaluator. Recently developed methods for the automatic detection of swallowing sounds facilitate a rough automatic diagnosis of dysphagia, although a reliable method of detection specialized in the peculiar feature patterns of swallowing sounds in actual clinical conditions has not been established. We investigated a novel approach for automatically detecting swallowing sounds by a method wherein basic statistics and dynamic features were extracted based on acoustic features: Mel Frequency Cepstral Coefficients and Mel Frequency Magnitude Coefficients, and an ensemble learning model combining Support Vector Machine and Multi-Layer Perceptron were applied. The evaluation of the effectiveness of the proposed method, based on a swallowing-sounds database synchronized to a video fluorographic swallowing study compiled from 74 advanced-age patients with dysphagia, demonstrated an outstanding performance. It achieved an F1-micro average of approximately 0.92 and an accuracy of 95.20%. The method, proven effective in the current clinical recording database, suggests a significant advancement in the objectivity of cervical auscultation. However, validating its efficacy in other databases is crucial for confirming its broad applicability and potential impact.

Stimulus–Response Plots as a Novel Bowel-Sound-Based Method for Evaluating Motor Response to Drinking in Healthy People

Constipation is a common gastrointestinal disorder that impairs quality of life. Evaluating bowel motility via traditional methods, such as MRI and radiography, is expensive and inconvenient. Bowel sound (BS) analysis has been proposed as an alternative, with BS-time-domain acoustic features (BSTDAFs) being effective for evaluating bowel motility via several food and drink consumption tests. However, the effect of BSTDAFs before drink consumption on those after drink consumption is yet to be investigated. This study used BS-based stimulus–response plots (BSSRPs) to investigate this effect on 20 participants who underwent drinking tests. A strong negative correlation was observed between the number of BSs per minute before carbonated water consumption and the ratio of that before and after carbonated water consumption. However, a similar trend was not observed when the participants drank cold water. These findings suggest that when carbonated water is drunk, bowel motility before ingestion affects motor response to ingestion. This study provides a non-invasive BS-based approach for evaluating motor response to food and drink, offering a new research window for investigators in this field.

Scleroglucan-Based Foam Incorporating Recycled Rigid Polyurethane Waste for Novel Insulation Material Production

This study details the synthesis and performance evaluation of a novel lightweight thermal and acoustic insulation material, resulting from the combination of a scleroglucan-based hydrogel and recycled rigid polyurethane waste powder. Through a sublimation-driven water-removal process, a porous three-dimensional network structure is formed, showcasing notable thermal and acoustic insulation properties. Experimental data are presented to highlight the material’s performance, including comparisons with commercially available mineral wool and polymeric foams. This material versatility is demonstrated through tunable mechanical, thermal and acoustic characteristics, achieved by strategically adjusting the concentration of the biopolymer and additives. This adaptability positions the material as a promising candidate for different insulation applications. Addressing environmental concerns related to rigid polyurethane waste disposal, the study contributes to the circular economy.

Experimental Study on the Effects of High- and Low-Temperature Cyclic Impact on Pore and Fracture Distributions and Acoustic Emission Characteristics of Coal Measure Sandstone

Experimental Study on the Effects of High- and Low-Temperature Cyclic Impact on Pore and Fracture Distributions and Acoustic Emission Characteristics of Coal Measure Sandstone

Characteristics of jet noise: A synthesis

It is a distinct privilege to produce this article to honor Professor Tam, the foremost authority on jet aeroacoustics. The fundamental characteristics of jet noise have been studied for 70 years, since the pioneering work of Lighthill in the 1950s. The acoustic analogy, with many variants, has served as the leading theory for nearly 50 years. Many leading researchers in the 1970s formulated theories to interpret the measured trends from subsonic and supersonic jets, using acoustic analogy and flow features as the framework. Quadrupoles, dipoles and monopoles were believed to constitute the sources of noise. The discovery of large-scale organized structures in free shear layers and jets sparked a different avenue of thinking about their importance for noise generation. Tam was the first to clearly demonstrate that these structures are efficient generators of noise and constitute the dominant noise sources, especially in the downstream direction. Now, two schools of thought emerged on the sources of jet noise. Experimental measurements showed that the mean flow as well as the turbulence statistics exhibit a self-similarity in the mixing layer and another similarity in the fully developed jet. Based on these observations, Tam proposed that since noise is generated by the turbulence of the jet, the noise spectra generated by fine-scale and large-scale turbulence should also exhibit self-similarity. By examining a large set of supersonic jet noise data acquired at NASA Langley, Tam offered evidence that the turbulent mixing noise of high-speed jets does consist of two independent self-similar components. In this paper, experimental evidence is compiled from an extensive database that quantifies the effect of several parameters that affect jet spectra. A new scaling method is developed and extended to noise predictions for realistic dual-stream nozzle geometries. The objectives of this paper are to serve as a synthesis of noise characteristics and to focus on application to real-world problems. Results from five different experimental measurements are examined: (1) farfield spectral characteristics; (2) azimuthal and polar correlations in the farfield; (3) correlations of jet turbulence fluctuations and farfield sound; (4) measurement of source distributions with an elliptic mirror; and (5) space-time correlation measurements in the nearfield with a cage array, and nearfield-farfield correlations. Two distinctly different trends are observed in the angular ranges of 50° – ∼120° and ∼120° – 165° for all the parameters investigated with the above five approaches. The salient observations are mutually supporting, and the cumulative weight lends credence to the proposition that there are two distinct sources of turbulent mixing noise.

Anthropogenic noise effects on Risso's dolphin vocalizations in the Gulf of Taranto (Northern Ionian sea, central Mediterranean sea)

Anthropogenic noise may significantly affect marine mammals' vocalizations. The aim of this work is to describe for the first time the acoustic features of the click trains, buzzes, burst pulses and whistles produced by the Risso's dolphins in the Gulf of Taranto (Northern Ionian Sea, Central Mediterranean), the concurrent levels of noise to which dolphins are exposed, and the potential reactive changes in the acoustic structure of the species vocalizations. About 6 h of recordings were collected from April 2019 to September 2022 during daytime boat surveys. Elevated ambient noise levels were mainly present at the frequencies lower than 63 Hz, at about 1 kHz and 50 kHz likely attributable to anthropogenic activity, such as shipping and sonars. Even if the acoustic features of all the vocalizations can be disrupted, especially with increasing noise below 1 kHz and between 20 and 63 kHz, the click trains were the most affected vocalizations, showing changes in their Inter-Click-Interval and in the Peak Amplitude. Since these vocalizations are used for navigation and searching for prey, their alteration raises concerns on the potential negative effects on the well-being of the Risso's dolphin population in the Gulf of Taranto.

Experimental study on the damage characteristics of cyclic disturbance and acoustic emission characteristics of different types of sandstones under high stress in deep mines

AbstractThree sandstone specimens common in rock engineering were selected to study the differences in the mechanical properties of rocks with different lithologies. The development and expansion of the internal cracks in the specimens were observed by combining the simulation system with the acoustic emission system. Through the combination of dynamic and static stresses, the deformation and damage of rocks under deep rock excavation and blasting were simulated. As the results show, the acoustic emission events of specimens with different lithologies under combined static and dynamic cyclic loading can be roughly divided into three phases: weakening, stabilizing, and surging periods. In addition, the acoustic emission characteristics of specimens with different lithologies show general consistency in different compression phases. The degree of fragmentation of specimens increases with the applied stress level; therefore, the stress level is one of the important factors influencing the damage pattern of specimens. The acoustic emission system was used to simulate the deformation and damage of rocks subjected to deep rock body excavation and engineering blasting. Cyclic dynamic perturbations under sinusoidal waves with a frequency of 5 Hz, a loading rate of 0.1 mm/min, a cyclic amplitude of 5 MPa, and a loading rate of 0.1 mm/min were applied to the three rock samples during the experiments. Among them, the fine‐grained sandstones are the most sensitive to the sinusoidal cyclic perturbation, followed by the muddy siltstone and the medium‐grained sandstones. On this basis, the acoustic emission energy release characteristics were analyzed, and the waveform characteristics in the damage evolution of the specimen under dynamic perturbation were studied by extracting the key points and searching for the main frequency eigenvalues.

AI-Empowered Multimodal Hierarchical Graph-Based Learning for Situation Awareness on Enhancing Disaster Responses

Situational awareness (SA) is crucial in disaster response, enhancing the understanding of the environment. Social media, with its extensive user base, offers valuable real-time information for such scenarios. Although SA systems excel in extracting disaster-related details from user-generated content, a common limitation in prior approaches is their emphasis on single-modal extraction rather than embracing multi-modalities. This paper proposed a multimodal hierarchical graph-based situational awareness (MHGSA) system for comprehensive disaster event classification. Specifically, the proposed multimodal hierarchical graph contains nodes representing different disaster events and the features of the event nodes are extracted from the corresponding images and acoustic features. The proposed feature extraction modules with multi-branches for vision and audio features provide hierarchical node features for disaster events of different granularities, aiming to build a coarse-granularity classification task to constrain the model and enhance fine-granularity classification. The relationships between different disaster events in multi-modalities are learned by graph convolutional neural networks to enhance the system’s ability to recognize disaster events, thus enabling the system to fuse complex features of vision and audio. Experimental results illustrate the effectiveness of the proposed visual and audio feature extraction modules in single-modal scenarios. Furthermore, the MHGSA successfully fuses visual and audio features, yielding promising results in disaster event classification tasks.

Multimodal Embodiment Research of Oral Music Traditions: Electromyography in Oud Performance and Education Research of Persian Art Music

With the recent advent of research focusing on the body’s significance in music, the integration of physiological sensors in the context of empirical methodologies for music has also gained momentum. Given the recognition of covert muscular activity as a strong indicator of musical intentionality and the previously ascertained link between physical effort and various musical aspects, electromyography (EMG)—signals representing muscle activity—has also experienced a noticeable surge. While EMG technologies appear to hold good promise for sensing, capturing, and interpreting the dynamic properties of movement in music, which are considered innately linked to artistic expressive power, they also come with certain challenges, misconceptions, and predispositions. The paper engages in a critical examination regarding the utilisation of muscle force values from EMG sensors as indicators of physical effort and musical activity, particularly focusing on (the intuitively expected link to) sound levels. For this, it resides upon empirical work, namely practical insights drawn from a case study of music performance (Persian instrumental music) in the context of a music class. The findings indicate that muscle force can be explained by a small set of (six) statistically significant acoustic and movement features, the latter captured by a state-of-the-art (full-body inertial) motion capture system. However, no straightforward link to sound levels is evident.

Improving Short-Duration Speaker Recognition by Joint Bark-Wavelet Acoustic Feature Coupling and Triplet Dual-Attention Mechanism Network

Improving Short-Duration Speaker Recognition by Joint Bark-Wavelet Acoustic Feature Coupling and Triplet Dual-Attention Mechanism Network

Performance evaluation of deep learning approaches for fault diagnosis of rotational mechanical systems using vibration, sound, and acoustic emission signals

The present study emphasizes an optimized deep learning algorithm for gearbox fault detection using vibration, sound, and acoustic emission signals. Statistical and acoustic features are extracted from these signals, and various neural network algorithms are explored. The supervised deep feed forward neural network (DFFNN) demonstrates excellent performance with vibration signals but limited accuracy with sound and acoustic emission signals. To address this, unsupervised algorithms are optimized and compared with vibration-based classification. The findings show that unsupervised neural networks, particularly the auto-encoder and stacked auto-encoder architectures, achieve improved classification accuracy by leveraging the unique characteristics of acoustic emission signals. The unsupervised models also effectively overcome the vanishing gradient problem via regularization, enhancing their training efficiency. The stacked auto-encoder, with multiple layers of encoders and decoders, reduces computation time by 40% and memory consumption. These optimized algorithms hold promise for automated fault detection systems. The auto-encoder and stacked auto-encoder, utilizing vibration, sound, and acoustic emission signals, offer enhanced classification accuracy and can facilitate real-time monitoring of rotating mechanical systems. However, further optimization is needed to maximize their performance. In a nutshell, the supervised DFFNN excels in utilizing vibration signals for fault detection, while the unsupervised models exploit the distinctive characteristics of acoustic emission signals. Future research will focus on refining these algorithms to enhance their effectiveness. Implementing these optimized deep learning approaches can lead to autonomous fault detection systems, eliminating the need for continuous human supervision.

Statistical relationships between surface form and sensory meanings of English words influence lexical processing.

Across spoken languages, there are some words whose acoustic features resemble the meanings of their referents by evoking perceptual imagery, i.e., they are iconic (e.g., in English, "splash" imitates the sound of an object hitting water). While these sound symbolic form-meaning relationships are well-studied, relatively little work has explored whether the sensory properties of English words also involve systematic (i.e., statistical) form-meaning mappings. We first test the prediction that surface form properties can predict sensory experience ratings for over 5,000 monosyllabic and disyllabic words (Juhasz & Yap, 2013), confirming they explain a significant proportion of variance. Next, we show that iconicity and sensory form typicality, a statistical measure of how well a word's form aligns with its sensory experience rating, are only weakly related to each other, indicating they are likely to be distinct constructs. To determine whether form typicality influences processing of sensory words, we conducted regression analyses using lexical decision, word recognition, naming and semantic decision tasks from behavioral megastudy data sets. Across the data sets, sensory form typicality was able to predict more variance in performance than sensory experience or iconicity ratings. Further, the effects of typicality were consistently inhibitory in comprehension (i.e., more typical forms were responded to more slowly and less accurately), whereas for production the effect was facilitatory. These findings are the first evidence that systematic form-meaning mappings in English sensory words influence their processing. We discuss how language processing models incorporating Bayesian prediction mechanisms might be able to account for form typicality in the lexicon. (PsycInfo Database Record (c) 2024 APA, all rights reserved).