Magnitude Estimation Research Articles

Psychological responses to amplitude-modulated low-frequency sound

Amplitude-modulated low-frequency sound is a sound in which a pure tone with a carrier frequency lower than 100 Hz is a carrier wave, and the sound pressure level continuously fluctuates over time. This study measured psychological responses to amplitude-modulated low-frequency sounds using psychological questionnaires. We measured three sensations peculiar to low-frequency sounds, annoyance, vibratory sensation, and oppressive sensation caused by amplitude-modulated low-frequency sounds using a seven-grade rating scale. In addition, we measured the loudness and fluctuating sensation caused by amplitude-modulated low-frequency sounds using a magnitude estimation method. This paper discusses the relationship between the psychological response quantities and the physical quantity that constitutes the amplitude-modulated low-frequency sound, the dose-response relationship when exposed to amplitude-modulated low-frequency sound above the sensation threshold.

Detection of suspended macroplastics using acoustic doppler current profiler (ADCP) echo

Plastic pollution has become an enormous environmental problem, endangering ecosystems, livelihoods, safety and human health. Large quantities of plastics are trapped in or transported by rivers. Monitoring methods mostly focus on plastics floating at the surface or deposited on riverbanks, while a substantial part of plastics may be transported below the water surface. Available underwater monitoring methods rely on nets and large equipment, making them labour-intensive, expensive and invasive. The measurements are, therefore, limited to occasional point measurements. In this paper, we explore the potential of echo sounding for the monitoring of suspended macroplastic (plastic items bigger than 5 mm). We performed tests in a controlled (basin), a semi-controlled (harbour) and an uncontrolled (river) environment using the high-end Acoustic Doppler Current Profiler (ADCP). This device is already in use for the estimation of flow velocity and suspended sediment concentrations using the wide network of ADCPs in the Netherlands and other countries. In the undisturbed controlled environment, 25 items varying in size, material, and orientation could be detected up to at least 4.6 m from the ADCP. The semi-controlled experiments showed that most of these items can also be detected among other naturally occurring scatterers, such as aquatic life, organic material and air bubbles. The field tests under natural conditions, combining ADCP and net measurements, showed that ADCP data can be calibrated towards a correct order of magnitude estimate of plastic transport. The coupling of the ADCP data to item characteristics such as size, material and orientation is still challenging, but more research into, for example, the signature of items may enable distinguishing item characteristics. This fundamental knowledge, combined with repetitions of validated field measurements under different flow conditions, is needed for the development of a robust monitoring method. Such a method may enable continuous or cross-sectional monitoring of suspended plastics and give insight into historic and plastic transport through 30-year long datasets. These insights can help improve and determine the effect of current mitigation and cleaning efforts.

Integrated Earthquake Catalog III: Gakkel Ridge, Knipovich Ridge, and Svalbard Archipelago

This paper represents the final part of a series of studies aimed at creating the most reliable and representative earthquake catalog covering the Russian and European Arctic. The earthquake catalog of the Gakkel and Knipovich ridges, as well as the Svalbard Archipelago with a unified magnitude scale, was formed based on the combination of four regional Russian catalogs and the ISC catalog. The merging of catalogs was carried out using the modification of the author’s methodology, which allowed for the identification of records in different datasets related to the same seismic event. The modification was introduced due to significant changes over time in the source catalogs. The unified proxy moment magnitude scale was formed based on regression analysis of the different magnitude estimates provided by various agencies. The integrated catalog included 17,922 earthquakes that occurred during the period 1962–2022. Analysis of the integrated catalog showed that the level of registration in the studied area significantly varies over space and time. Before 1995, the catalog contained only strong and moderate earthquakes, and the magnitude of complete registration Mc was 5.0 in the Gakkel Ridge, 4.7 in the Knipovich Ridge, and 4.5 in the Svalbard Archipelago. The number of recorded events increased in the period 1995–2011, and Mc decreased to 4.0 in the Gakkel and Knipovich ridges and to 2.8 in the Svalbard Archipelago. The best level of registration in the Svalbard Archipelago and the Knipovich Ridge was achieved after 2012, when Mc reached 1.7 and 2.8, respectively. In the Gakkel Ridge, despite a noticeable increase in the number of reported events from 2012, the magnitude of complete registration did not improve and was 4.0. The presented integrated earthquake catalog is intended for a wide range of studies of the seismic regime of the Arctic.

Road Users Fail to Appreciate the Special Optical Properties of Retroreflective Materials.

To determine whether typical road users appreciate the special optical properties of retroreflective materials. Retroreflective surfaces reflect light back towards the source of the illumination. All drivers benefit from retroreflective materials, as they are required on road signs, on large trailers, in lane delineation, and other traffic control devices. Retroreflective markings can also greatly enhance the conspicuity of pedestrians at night, but pedestrians typically underuse retroreflective markings. One possible reason is that pedestrians may not appreciate the special optical properties of retroreflective materials. Two experiments tested whether observers could correctly predict that retroreflective materials appear remarkably bright when illuminated by a source that is aligned with the observers' eyes. Observers used a magnitude estimation procedure to predict how bright retroreflective and non-retroreflective stimuli would appear during a demonstration designed to highlight retroreflectivity. They then judged the brightness again during the demonstration. In general, observers underestimated how bright retroreflective stimuli would be and overestimated how bright diffuse reflective and fluorescent stimuli would be. The underestimates for retroreflective stimuli were particularly striking when the observers had not closely examined the stimuli in advance. The fact that road users do not appreciate retroreflectivity may help explain why pedestrians underuse retroreflective markings at night. Educational interventions could prove useful in this domain.

Full-waveform simulation of DAS records, response and cable-ground coupling

SUMMARY Over the past several years, the use of optical fibre cables as ground motion sensors has become a central topic for seismologists, with successful applications of distributed acoustic sensing (DAS) in various key fields such as seismic monitoring, structural imaging and source characterization. DAS response is a combination of both instrument response and cable-ground coupling, with the latter having a strong, spatially variable, but yet largely unquantified effect. This limits the application of a large number of staple seismological techniques (e.g. earthquake magnitude estimation, waveform tomography) that can require accurate knowledge of a signal's amplitude and frequency content. Here we present a method for accurately simulating a DAS cable and its ground coupling. The scheme is based on molecular dynamic-like particle-based numerical modelling, allowing the investigation of the effect of varying DAS-ground coupling scenarios. We start by computing the full strain field directly, for each pair of neighbouring particles in the model. We then define a virtual DAS cable, embedded within the model and formed by a single string of interconnected particles. This allows us to control all aspects of the cable-ground coupling and their properties at an effective granular level through changing the bond stiffness and bond types (e.g. non-linearity) for both the cable and the surrounding medium. Arbitrary cable geometries and heterogeneous materials can be accommodated at the desired scale of investigation. We observe that at the meter scale, the cable-ground coupling and local site effects can substantially alter the recorded signal. We find that the stiffness of the thin layer of material to which the cable is coupled has the strongest effects, selectively amplifying portions of the wave train and contributing to substantial phase delays. These differences show that cable coupling and local site effects should be considered both when designing a DAS deployment and analysing its data when either true or along-cable relative amplitudes and/or frequencies are considered. The codes developed herein for calculating full waveform DAS responses and coupling are made publicly available.

A Global Map for Selecting Stationary and Nonstationary Methods to Estimate Extreme Floods

Comprehending the changing patterns of flood magnitudes globally, particularly in the context of nonstationary conditions, is crucial for effective flood risk management. This study introduces a unique approach that employs simulated discharge data to unravel these intricate variations. Through a comprehensive analysis of a substantial ensemble of General Circulation Models (GCMs) runoff datasets, we examine the dynamics of nonstationary flood magnitudes on a global scale. A pivotal aspect of our investigation is the development of a reference map, which helps delineate suitable scenarios for applying stationary or nonstationary methods in estimating extreme floods. This map is then employed to compare estimations of 100-year flood magnitudes using both methodologies across specific geographical areas. Our findings distinctly highlight the disparities arising from the use of stationary versus nonstationary approaches for estimating extreme floods. These insights underscore the significance of considering nonstationary for accurate flood risk assessment and mitigation strategies. The practical utility of our reference map in aiding informed decision making for stakeholders and practitioners further underscores its importance. This study contributes to the scholarly understanding of the evolving nature of flood phenomena and provides valuable insights for crafting adaptive measures in response to changing climatic conditions.

Earthquake Early Warning Starting From 3 s of Records on a Single Station With Machine Learning

AbstractWe introduce the Ensemble Earthquake Early Warning System (E3WS), a set of Machine Learning (ML) algorithms designed to detect, locate, and estimate the magnitude of an earthquake starting from 3 s of P‐waves recorded by a single station. The system is made of six Ensemble ML algorithms trained on attributes computed from ground acceleration time series in the temporal, spectral, and cepstral domains. The training set comprises data sets from Peru, Chile, Japan, and the STEAD global data set. E3WS consists of three sequential stages: detection, P‐phase picking, and source characterization. The latter involves magnitude, epicentral distance, depth, and back azimuth estimation. E3WS achieves an overall success rate in the discrimination between earthquakes and noise of 99.9%, with no false positive (noise mis‐classified as earthquakes) and very few false negatives (earthquakes mis‐classified as noise). All false negatives correspond to M ≤ 4.3 earthquakes, which are unlikely to cause any damage. For P‐phase picking, the Mean Absolute Error is 0.14 s, small enough for earthquake early warning purposes. For source characterization, the E3WS estimates are virtually unbiased, have better accuracy for magnitude estimation than existing single‐station algorithms, and slightly better accuracy for earthquake location. By updating estimates every second, the approach gives time‐dependent magnitude estimates that follow the earthquake source time function. E3WS gives faster estimates than present alert systems relying on multiple stations, providing additional valuable seconds for potential protective actions.

Application of New Statistical Methods to Estimation of the Seismicity Field Parameters by an Example of the Japan Region

Abstract—This study is devoted to application of some new statistical methods to analysis of the spatial struc-ture of the seismic field in the seismically active region in the Japan region bounded by the following coordinates: 28°–50° north latitude, 130°–150° east longitude. The estimates of the seismic flux by using the k nearest neighbors method for the magnitude interval m ≥ 5.2. The highest values of intensity of about 10–4 \(\frac{1}{{{\text{year}}{\kern 1pt} - {\kern 1pt} {\text{k}}{{{\text{m}}}^{{\text{2}}}}}}\) are located at depths of down to 100 km and manifest themselves in the neighborhood of the Tohoku megathrust earthquake. The spatial resolution of the intensity estimates is ranging from 33–50 km in the regions with a high intensity to 100 км and larger in the zones of weak intensity. It has been shown that the seismic filed parameters – intensity λ, slope of the graph of repetition β, maximum possible magnitude m1 – have different scales of their spatial variability and, thus, it is necessary to apply different scales of spatial averaging to them. Based on the Gutenberg—Richter truncated distribution model, the estimates are obtained for the slope of the graph of repetition (b-value) and the upper boundary of the distribution m1. An original method is proposed for determining the optimal averaging radius for an arbitrary cell of the space grid. The method is based on the use of the statistical coefficient of variation of the corresponding parameter. For the considered region, the estimate of the maximum possible magnitude Мmax = 9.60 ± 0.41 was obtained with consideration of the correction for bias.

Evaluating the Effect of Voice Quality Covariance on Auditory-Perceptual Evaluation Using a Novel Two-Dimensional Magnitude Estimation Task.

Most people with dysphonia present with voices that vary along more than one voice quality (VQ) dimension. This study sought to examine the effect of covariance between breathy and rough VQ in natural voices. A two-dimensional matrix of 16 /a/ vowels was selected such that two VQ dimensions (breathiness and roughness) were sampled on a 4-point severity scale (none, mild, moderate, and severe). Ten listeners evaluated 480 stimuli (16 stimuli × 10 repetitions × 3 blocks) on one-dimensional magnitude estimation (1DME) tasks and a novel two-dimensional magnitude estimation (2DME) task that allowed for simultaneous measurement of breathiness and roughness. Data indicated high intra- and interrater reliabilities for both breathiness and roughness in the 2DME and 1DME tasks. Correlation analyses revealed a strong correlation between 2DME and 1DME judgments for breathiness and roughness (r > .95). There was also a minimal correlation between breathy and rough VQ in the 2DME task (r < .10). Covarying roughness or breathiness had less impact on the perception of the other VQ in natural dysphonic voices in 2DME compared to 1DME. An understanding and quantification of the perceptual interactions among the dimensions will aid in the refinement of computational models and in the establishment of the validity of clinical scales for VQ perception.

Combining Satellite Optical and Radar Image Data for Streamflow Estimation Using a Machine Learning Method

River water surface extent can be extracted from optical and radar satellite images; this is useful for estimating streamflow from space. The radiation characteristics of open water from the visible and microwave bands are different and provide independent information. In this study, for the purpose of improving streamflow estimation from space for data-sparse regions, a method that combines satellite optical and radar images data for streamflow estimation using a machine learning technique was proposed. The method was demonstratedthrough a case study in the river segment upstream of the Ganzi gauging station on the Yalong River, China. Utilizing the support vector regression (SVR) model, the feasibility of different combinations of water surface area derived from Sentinel-1 synthetic aperture radar images (AREA_SAR), modified normalized difference water index derived from Landsat 8 images (MNDWI), and reflectance ratios between NIR and SWIR channels derived from MODIS images (RNIR/RSWIR) for streamflow estimation were evaluated through three experiments. In Experiment I, three models using AREA_SAR (Model 1), MNDWI (Model 2), and a combination of AREA_SAR and MNDWI (Model 3) were built; the mean relative error (MRE) and mean absolute error (MAE) of streamflow estimates corresponding to the SVR model using both AREA_SAR and MNDWI (Model 3) were 0.19 and 31.6 m3/s for the testing dataset, respectively, and were lower than two models using AREA_SAR (Model 1) or MNDWI (Model 2) solely as inputs. In Experiment II, three models with AREA_SAR (Model 4), RNIR/RSWIR (Model 5), and a combination of AREA_SAR and RNIR/RSWIR (Model 6) as inputs were developed; the MRE and MAE for the model using AREA_SAR and RNIR/RSWIR (Model 6) were 0.25 and 56.5 m3/s, respectively, which outperformed the two models treating AREA_SAR (Model 4) or MNDWI (Model 5) as single types of inputs. In Experiment III, three models using AREA_SAR (Model 7), MNDWI, and RNIR/RSWIR (Model 8) and the combination of AREA_SAR, MNDWI and RNIR/RSWIR (Model 9) were built; combining all three types of satellite observations (Model 9) exhibited the highest accuracy, for which the MRE and MAE were 0.18 and 18.4 m3/s, respectively. The results of all three experiments demonstrated that integrating optical and microwave observations could improve the accuracy of streamflow estimates using a data-driven model; the proposed method has great potential for near-real-time estimations of flood magnitude or to reconstruct past variations in streamflow using historical satellite images in data-sparse regions.

Associations Between Physiological Correlates of Cochlear Synaptopathy and Tinnitus in a Veteran Population.

Animal models and human temporal bones indicate that noise exposure is a risk factor for cochlear synaptopathy, a possible etiology of tinnitus. Veterans are exposed to high levels of noise during military service. Therefore, synaptopathy may explain the high rates of noise-induced tinnitus among Veterans. Although synaptopathy cannot be directly evaluated in living humans, animal models indicate that several physiological measures are sensitive to synapse loss, including the auditory brainstem response (ABR), the middle ear muscle reflex (MEMR), and the envelope following response (EFR). The purpose of this study was to determine whether tinnitus is associated with reductions in physiological correlates of synaptopathy that parallel animal studies. Participants with normal audiograms were grouped according to Veteran status and tinnitus report (Veterans with tinnitus, Veterans without tinnitus, and non-Veteran controls). The effects of being a Veteran with tinnitus on ABR, MEMR, and EFR measurements were independently modeled using Bayesian regression analysis. Modeled point estimates of MEMR and EFR magnitude showed reductions for Veterans with tinnitus compared with non-Veterans, with the most evident reduction observed for the EFR. Two different approaches were used to provide context for the Veteran tinnitus effect on the EFR by comparing to age-related reductions in EFR magnitude and synapse numbers observed in previous studies. These analyses suggested that EFR magnitude/synapse counts were reduced in Veterans with tinnitus by roughly the same amount as over 20 years of aging. These findings suggest that cochlear synaptopathy may contribute to tinnitus perception in noise-exposed Veterans. https://doi.org/10.23641/asha.24347761.

Estimation of Magnitude of Heterosis for Grain Yield and Related Traits in Sesame (Sesamum indicum L.)

Sesame (Sesamum indicum L.) is an important oilseed crop which is native to India and cultivated in around 2.3 million ha across its several states. India stands way below in terms of productivity compared to several other countries across the globe. Being an often-cross pollinated crop, heterosis even in a marginal magnitude could be exploited in sesame in varietal development. Hence, the present study was performed with the objective of estimating the extent of relative heterosis, heterobeltiosis and standard heterosis in eight cross combinations for important yield attributing traits in sesame in order to select promising cross combinations for utilization in heterosis breeding and/or selection of superior segregants in segregating generations. Eight cross combinations derived out of crossing high yielding parents viz., TMV (Sv) 7 and VRI (Sv) 2 as female and E8, DSS 9, DS 5 and Prachi as male in L × T fashion were utilized for the analysis considering TMV (Sv) 7 as the Standard parent. The analysis showed significant differences between the genotypes for all the characters studied. The hybrids showed Relative/ Mid parental heterosis ranging from 2.02 to 26.05 per cent; heterobeltiosis from -4.55 to 15.85 per cent and standard heterosis from -11.27 to 15.85 per cent for grain yield /ha. Mid Parental Heterosis (RH) was found to be positive and significant for the characters no. of branches/plant, no. of capsules / plant, grain yield (kg/ha) in all the hybrid combinations except VRI (Sv) 2 / Prachi. Based on heterobeltiosis, the hybrids TMV (Sv) 7 / E 8, TMV (Sv) 7 / DSS 9, TMV (Sv) 7 / DS 5, VRI (Sv) 2 / DS 5 could be selected since they showed significant heterobeltiosis for important yield contributing characters viz., no. of branches / plant, no. of capsules /plant and grain yield. With respect to the Standard heterosis for grain yield and contributing characters like no. of branches/plant, no. of capsules/plant and grain yield/ha the hybrids viz., TMV (Sv) 7 / E 8, TMV (Sv) 7 / DS 5 and TMV (Sv) 7 / DSS 9 showed positive and significant heterosis. Considering all the three types of heterosis, the hybrids TMV (Sv) 7 / E 8, TMV (Sv) 7 / DS 5 and TMV (Sv) 7 / DSS 9 may be selected for yield enhancement in Sesame.

Humans can sense large numbers of objects in a box by touch alone.

Everyday experiences suggest that a container, such as a box of cereal, can convey pertinent information about the nature and quantity of its content. This study investigated how well people can judge large quantities of objects in a container through haptic perception. Stimuli consisted of plastic drinking straws cut to "small" (1.5 cm) or "big" (4.5 cm) pieces contained in plastic food containers. Participants performed both a magnitude estimation of the number of objects and a direct estimation of the proportion of the container perceived to be filled with objects. Overall, participants demonstrated considerable accuracy for both tasks and irrespective of the size of the content. Post-experiment interviews revealed three potential strategies. Participants either focused on the container's contents, the excess space in the container, or the perceived weight of the container (content).

Intelligent state estimation for fault tolerant integrated frequent RTO and adaptive nonlinear MPC

Combinations of real-time optimization (RTO) and model predictive control (MPC) have been widely employed in the process industry for tracking the economic optimum in the face of drifting disturbances and parameters. Online update of model parameters is a critical step in the implementation of RTO. In this work, an intelligent state and parameter estimation approach is developed by combining a fault diagnosis approach with a simultaneous state and parameter estimator. Since faults are more likely to develop as slow drifts, a nonlinear generalized likelihood ratio (GLR) approach available in the literature is modified by considering ramp models for the progression of faults with time. When a fault is isolated by the fault diagnosis and identification (FDI) component, the magnitude of the isolated fault is refined using a moving window state and parameter estimator until the fault magnitude continues to change. The estimation of fault magnitudes is carried out only when required and triggered by the fault identification scheme. Thus, the subset of parameters/faults that are being estimated online can change with time. The intelligent state and parameter estimator is further combined with an online optimizing control scheme consisting of integrated frequent RTO and adaptive MPC. The integrated optimizing control scheme has embedded intelligence to auto-correct models used for estimation, control, and optimization and decide whether the detected changes require the invocation of RTO. The proposed approach employs a single model to carry out four different tasks: process monitoring, state and parameter estimation, nonlinear predictive control, and real-time optimization. This eliminates difficulties that can arise due to model mismatch between different components of the online optimizing control scheme. The efficacy of the proposed scheme is investigated using the benchmark Williams Otto reactor and a continuously operated fermenter. The economic optimum operating point of these systems is sensitive to mean shifts in unmeasured disturbances or system parameters. The proposed ramp model based approach successfully isolates the parameter/ unmeasured disturbance/ sensor bias/ actuator bias that has undergone slow drift and tracks the shifting economic optimum without significant delays. Thus, the proposed integrated approach has the ability to handle normal and abnormal operating envelopes of the system.

A comparative study of automatic vowel articulation index and auditory-perceptual assessments of speech intelligibility in Parkinson’s disease

Purpose The purpose of this study was to analyse the relationship between automatic vowel articulation index (aVAI) and direct magnitude estimation (DME) among speakers with Parkinson’s disease (PD) and healthy controls. We further analysed the potential of aVAI to serve as an objective measure of speech impairment in the clinical setting. Method Speech samples from native Finnish speakers were utilised. Expert raters utilised DME to scale the intelligibility of speech samples. aVAI scores for PD speakers and healthy control speakers were analysed in relationship to DME speech intelligibility ratings and, among PD speakers, disease stage utilising nonparametric statistical analysis. Result Mean DME intelligibility ratings were lower among PD speakers compared to healthy controls. Mean aVAI scores were nearly the same between speaker groups. DME intelligibility ratings and aVAI were strongly correlated within the PD speaker group. aVAI and DME intelligibility ratings were moderately correlated with disease stage as measured by the Hoehn and Yahr scale. Conclusion aVAI was observed to be a promising tool for analysing vowel articulation in PD speakers. Further research is warranted on the application of aVAI as an objective measure of severity of speech impairment in the clinical setting, with varying patient populations and speech samples.

Quantitative Assessment of Systematic Bias: A Guide for Researchers.

Observational research provides valuable opportunities to advance oral health science but is limited by vulnerabilities to systematic bias, including unmeasured confounding, errors in variable measurement, or bias in the creation of study populations and/or analytic samples. The potential influence of systematic biases on observed results is often only briefly mentioned among the discussion of limitations of a given study, despite existing methods that support detailed assessments of their potential effects. Quantitative bias analysis is a set of methodological techniques that, when applied to observational data, can provide important context to aid in the interpretation and integration of observational research findings into the broader body of oral health research. Specifically, these methods were developed to provide quantitative estimates of the potential magnitude and direction of the influence of systematic biases on observed results. We aim to encourage and facilitate the broad adoption of quantitative bias analyses into observational oral health research. To this end, we provide an overview of quantitative bias analysis techniques, including a step-by-step implementation guide. We also provide a detailed appendix that guides readers through an applied example using real data obtained from a prospective observational cohort study of preconception periodontitis in relation to time to pregnancy. Quantitative bias analysis methods are available to all investigators. When appropriately applied to observational studies, findings from such studies can have a greater impact in the broader research context.

Estimation of historical earthquake-induced liquefaction in Fraser River delta using NBCC 2020 GMPEs in deterministic and probabilistic frameworks

Paleo-liquefaction features of sand dykes and sand blows were identified in the 1990s at multiple host sediments in the Fraser River delta in southern British Columbia all younger than 3500 BP. These paleo-liquefaction sites could be linked to Cascadia subduction earthquakes. Empirical magnitude-bound relationships are often used to estimate paleo-earthquake magnitudes. To determine the lower bound magnitude of Cascadia interface earthquakes that could have generated the paleo-liquefaction features, we use ground motion prediction equations for interface earthquakes from the sixth Canadian seismic hazard model of the 2020 National Building Code of Canada. We estimate the minimum M and its peak ground acceleration ( amax) of an interface earthquake required to initiate paleo-liquefaction in the study region. Starting with three full-rupture deterministic scenarios of varying source-to-site distance, we determine the minimum M required from Cascadia subduction zone interface mega-thrust earthquakes to induce liquefaction in the Fraser River delta spans 8.0–8.9 with a corresponding amax range of 0.09–0.13 g. We also perform a back-calculation paleo-liquefaction analysis in a probabilistic framework to incorporate aleatory uncertainties (cone penetration resistance, groundwater table, and amax) and epistemic uncertainties (liquefaction simplified model) via the Monte Carlo simulation. The developed probabilistic methodology is also applicable to a forward liquefaction assessment and other liquefaction sites globally. The median M from this probabilistic paleo-liquefaction for the four investigated sites lies between 8.8 and 9.0. Our probabilistic results also reveal that Cascadia interface earthquakes with M &gt; 8.9 lead to a 31%–57% probability of liquefaction triggering in the Fraser River delta. In addition, we developed deterministic and probabilistic magnitude-bound curves specific to Cascadia interface earthquakes and representative site class E conditions. These curves provide more accurate magnitude estimations for predicting seismic-induced liquefaction from Cascadia interface earthquakes for sites in the Pacific Northwest than empirical bound curves.

The Estill Voice Model: A paradigm for voice training and treatment

For over 40 years, the Estill Voice Model (EVM) has defined voice quality according to movement of anatomy and physiology. EVM addresses the daunting degrees of freedom issue in voice motor control by isolating Craft of voice production from Artistry and Performance Metaphysics. The EVM proposes an integrated implicit-explicit approach for voice motor learning that flows through all training and therapy protocols. Implicit instructions include auditory-perceptual prompts (e.g., quack like a duck to produce “twang”) and explicit prompts train physiologic conditions of the vocal anatomy correlated with the voice quality (e.g., narrow your aryepiglottic sphincter to produce “twang”). Estill exercises address power, source, and filter properties of voice production, and include narrowing the aryepiglottic sphincter for “ring” in opera and belt and for increased power in hypofunctional voices, and varying vocal fold mass for register shifts and optimizing contact for hyperfunctional voices. Patients learn to feel, see, and hear the voice via multiple feedback modes including hand gestures, magnitude estimation of bodily kinesthetic effort, visual acoustic cues in real-time spectral analysis programs. This presentation will highlight objective measurement science and clinical evidence for using Estill exercises in treatment for all voices, from the novice speaker to the expert performer.

Discomfort estimation for aircraft cabin noise using linear regression and modified psychoacoustic annoyance approaches.

Appropriate sound quality models for noise-induced discomfort are necessary for a better acoustic comfort design in the aircraft cabin. This study investigates the acoustic discomfort in two large passenger aeroplanes (i.e., planes A and B). We recorded the noise at 21 positions in each aircraft cabin and selected 42 stimuli ranging from 72 to 81 dB(A) during the cruising flights. Twenty-four participants rated the noise discomfort by the absolute magnitude estimation method. The discomfort values in the middle section of the aircraft cabin are 10% points higher than in the front or rear section. The discomfort magnitude was dominated by loudness and influenced by roughness and sharpness. A multiple linear (MA) discomfort model was established, accounting for the relationship between the discomfort and sound quality metrics (i.e., loudness, sharpness, and roughness). The MA model estimated noise discomfort better than the Zwicker and other (i.e., More and Di) psychoacoustic annoyance (PA) models. We modified the coefficients of independent variables in the formulations of Zwicker, Di, and More PA models, respectively, according to the present experimental results. The correlation coefficients between the estimated and measured values of the modified models were at least 20% points higher than the original ones.

Estimation and management of shrimp processing waste in organised shrimp processing sector in India: Opportunities and challenges

This study was conducted to estimate the magnitude of processing waste produced in theorganised shrimp processing sector of India in order to assess the opportunities availablefor its utilisation. The estimation of magnitude of shrimp processing waste was based onsecondary data and its management was based on primary data collected through surveysat processing plants. The organised shrimp processing sector in India generated a largevolume of shrimp processing waste at a growth rate of 12% during 2000-2018. The quantityof shrimp processing waste generated ranged from 1.2 to 4.6 lakh t, which can be utilisedto produce 6712 to 27453 t of chitin alone. However, our study indicated that 75% of thetotal waste generated remained either unused or managed unscientifically, even when thereis huge demand for chitin and its derivatives in the domestic as well as in the internationalmarket. The case studies from chitin producing plants revealed that some costs wereincurred in the procurement of shrimp processing waste as a secondary raw material for theproduction of chitin. Thus forming a link between shrimp processing plants and chitin and itsderivatives producing plants would improve the supply of quality raw material and improveutilisation of processing wastes. The number of chitin and its derivatives producing plantsare too few in India to fully accommodate the large volume of shrimp processing wastegenerated every year. Therefore, infrastructure development in the chitin and associatedindustry with active support from government organisations to overcome the burden of initialinvestment is needed urgently, which can in turn encourage entrepreneurship developmentin the sector for diversified applications. Keywords:Chitin, Chitosan, Glucosamine hydrochloride,Growth rate, Waste utilisation