Multidimensional Measurements Research Articles

Unsupervised learning for feature projection: Extracting patterns from multidimensional building measurements

Data visualization is an important resource for decision makers to obtain information from large datasets. Based on the data obtained from either predictions or measurements, different strategies are combined and tested to reduce the energy demand, whilst keeping the indoor comfort at suitable level. Although the information expressed from data representation can significantly influence the decisions, little research has focused on extracting features from building measurements. This paper provides an in-depth view into representation of building data, and applies three dimensionality reduction algorithms Principle Component Analysis (PCA), autoencoder and t-Distributed Stochastic Neighbour Embedding (t-SNE) on measurements from a teaching building. Results show that whilst PCA returns linear representations, it also has the least data compression, which can be useful for obtaining more general features. On the other hand, t-SNE returns the most compressed data, which is suitable for seeking large margins within a dataset. However, t-SNE may be unsuitable for datasets with recurring step-like temporal profiles. Autoencoder is the best overall option, as they capture the nonlinearities within a dataset whilst avoiding excessive data compression. Fine-tuning the hyperparameters of studied the algorithms, and the perils of relying on poorly tuned models is discussed at the end of the study.

The Aitken counter: Revisiting its design and performance characteristics

Advances in aerosol science have often been driven by innovations in aerosol instrumentation. Our ability to make real-time, multi-dimensional measurements of particle properties can be attributed ...

Multi-Scale and Multi-Dimensional Time Series InSAR Characterizing of Surface Deformation over Shandong Peninsula, China

Shandong peninsula, the largest peninsula of China, is prone to severe land subsidence hazards along the coastline. In this paper, we provide, for the first time, multi-scale and multi-dimensional time series deformation measurements of the entire Shandong peninsula with advanced time series Interferometric Synthetic Aperture Radar (InSAR) techniques. We derive the spatiotemporal evolutions of the land subsidence by integrating multi-track Sentinel-1A/B and RADARSAT-2 satellite images. InSAR measurements are cross validated by the independent deformation rate results generated from different SAR tracks, reaching a precision of less than 1.3 cm/a. Two-dimensional time series over the Yellow River Delta (YRD) from 2017 to 2019 are revealed by integrating time series InSAR measurements from both descending and ascending tracks. Land subsidence zones are mainly concentrated on the YRD. In total, twelve typical localized subsidence zones are identified in the cities of Dongying (up to 290 mm/a; brine and groundwater exploitation for industrial usage), Weifang (up to 170 mm/a; brine exploitation for industrial usage), Qingdao (up to 70 mm/a; aquaculture and land reclamation), Yantai (up to 50 mm/a; land reclamation) and Rizhao (up to 60 mm/a; land reclamation). The causal factors of localized ground deformation are discussed, encompassing groundwater and brine exploitation, aquaculture and land reclamation. Multi-scale surveys of spatiotemporal deformation evolution and mechanism analysis are critical to make decisions on underground fluid exploitation and land reclamation.

Single Chemical Sensor for Multi-Analyte Mixture Detection and Measurement: A Review

Multi-analyte chemical sensor aims to transform subtle variations in multiple analytes’ physical or chemical properties into distinct output signals. Chemically responsive nanostructure array (nanoarray) promises as a competitive sensor platform due to its robust physical properties, tunable chemical composition, and high surface area for analyte interaction. Specifically, the well-defined size, shape, and tunable surface structure and properties make it feasible to develop either new sensing modes on single device or integrated multi-modular sensors. In conjunction with the well-developed resistor-type sensors and sensor arrays, the complementary utilization of and intercorrelation with the electrochemical, optical, voltammetry modes in the multi-modular sensing strategies could provide multi-dimensional measurements to different analytes in a complex mixture form, where species information could be accurately and robustly separated from spatially collective responses. This review intends to provide a survey of the recent progress on multi-analyte sensing strategies and their unique structure design, as well as the related sensing mechanics in interaction of analytes and sensitizer and the behind mechanism for analytes’ differentiation.

Critical appraisal of multidimensional CT measurements following acute open repair of type A aortic dissection.

IntroductionTo identify patients with aneurysmal degeneration of the native aorta following type A aortic dissection (TAAD), reproducible serial measurements of aortic dimensions are critical. We used a systematic workflow for measuring aortic geometry following TAAD, using computed tomography angiography data, and test its reproducibility.MethodsThe workflow for aortic measurements included centerline generation, luminal diameter, and area measurement at six anatomically defined locations along the aorta and luminal volumetric measurements in the descending aorta. Two independent observers measured the aortic geometry in 20 surgically repaired TAAD patients, preoperatively and at 3 months follow‐up. To test reproducibility, intraobserver and interobserver agreement scores were analyzed using a concordance correlation coefficient (CCC).ResultsThe interobserver agreement scores of the diameter, area, and volumetric measurements in the descending aorta were acceptable. The agreement scores of the area measurements were highest, with CCCs ranging from 0.909 to 0.984. Luminal diameter measurements scored lower than luminal area measurements and were least reproducible at the mid aortic arch (CCC < 0.886). Overall, intraobserver agreement scores were better than interobserver agreement scores (SD of mean difference was 1.89 vs 1.94 for intraobserver vs interobserver diameter measurements, and 0.61 vs 0.66 for area measurements).ConclusionAlthough overall reproducibility was acceptable in descending aortic measurements, our results show that it remains challenging to reliably measure luminal diameters, compared with areas. To aid identification of early adverse remodeling following acute TAAD, novel two‐ and three‐dimensional measurement techniques are needed that capture locoregional changes in the false lumen and true lumen morphology more accurately.

A time-resolved multi-omic atlas of the developing mouse liver.

Liver organogenesis and development are composed of a series of complex, well-orchestrated events. Identifying key factors and pathways governing liver development will help elucidate the physiological and pathological processes including those of cancer. We conducted multidimensional omics measurements including protein, mRNA, and transcription factor (TF) DNA-binding activity for mouse liver tissues collected from embryonic day 12.5 (E12.5) to postnatal week 8 (W8), encompassing major developmental stages. These data sets reveal dynamic changes of core liver functions and canonical signaling pathways governing development at both mRNA and protein levels. The TF DNA-binding activity data set highlights the importance of TF activity in early embryonic development. A comparison between mouse liver development and human hepatocellular carcinoma (HCC) proteomic profiles reveal that more aggressive tumors are characterized with the activation of early embryonic development pathways, whereas less aggressive ones maintain liver function–related pathways that are elevated in the mature liver. This work offers a panoramic view of mouse liver development and provides a rich resource to explore in-depth functional characterization.

Constructing a Consciousness Meter Based on the Combination of Non-Linear Measurements and Genetic Algorithm-Based Support Vector Machine.

Constructing a framework to evaluate consciousness is an important issue in neuroscience research and clinical practice. However, there is still no systematic framework for quantifying altered consciousness along the dimensions of both level and content. This study builds a framework to differentiate the following states: coma, general anesthesia, minimally conscious state (MCS), and normal wakefulness. This study analyzed electroencephalography (EEG) recorded from frontal channels in patients with disorders of consciousness (either coma or MCS), patients under general anesthesia, and healthy participants in normal waking consciousness (NWC). Four non-linear methods-permutation entropy (PE), sample entropy (SampEn), permutation Lempel-Ziv complexity (PLZC), and detrended fluctuation analysis (DFA)-as well as relative power (RP), extracted features from the EEG recordings. A genetic algorithm-based support vector machine (GA-SVM) classified the states of consciousness based on the extracted features. A multivariable linear regression model then built EEG indices for level and content of consciousness. The PE differentiated all four states of consciousness (p<0.001). Altered contents of consciousness for NWC, MCS, coma, and general anesthesia were best differentiated by the SampEn, and PLZC. In contrast, the levels of consciousness for these four states were best differentiated by RP of Gamma and PE. A multi-dimensional index, combined with the GA-SVM, showed that the integration of PE, PLZC, SampEn, and DFA had the highest classification accuracy (92.3%). The GA-SVM was better than random forest and neural networks at differentiating these four states. The 'coordinate value' in the dimensions of level and content were constructed by the multivariable linear regression model and the non-linear measures PE, PLZC, SampEn, and DFA. Multi-dimensional measurements, especially the PE, SampEn, PLZC, and DFA, when combined with GA-SVM, are promising methods for constructing a framework to quantify consciousness.

Multidimensional Information Fusion in Active Sonar via the Generalized Labeled Multi-Bernoulli Filter

Under the conditions of low detection probabilities, high clutter rates, low data-sampling rates, large measurement errors, and unknown prior information of the target position, multi-object tracking is difficult. This paper proposes a multidimensional information fusion method in active sonar via the generalized labeled multi-Bernoulli (GLMB) filter. After modeling the position measurement, radial velocity measurement and amplitude measurement of the target and clutter, new target births are adaptively generated by the measurement-driven model, predictions are made by the target motion model and updates are performed via multidimensional measurements with the generalized likelihood in the GLMB filter, which enables the measurement information of different dimensions to be elegantly applied to information fusion and significantly improves the filter performance. The contribution of specific dimension measurement to fusion can be evaluated by the Kullback-Leibler (KL) divergence. In the efficient implementation, we propose flat Gibbs sampling to realize multiple hypothesis optimization. Moreover, the filtering recursion is derived from Gaussian mixtures. Simulations are presented to verify the proposed method.

Assessing radiation dose limits for X-ray fluorescence microscopy analysis of plant specimens.

X-ray fluorescence microscopy (XFM) is a powerful technique to elucidate the distribution of elements within plants. However, accumulated radiation exposure during analysis can lead to structural damage and experimental artefacts including elemental redistribution. To date, acceptable dose limits have not been systematically established for hydrated plant specimens. Here we systematically explore acceptable dose rate limits for investigating fresh sunflower (Helianthus annuus) leaf and root samples and investigate the time-dose damage in leaves attached to live plants. We find that dose limits in fresh roots and leaves are comparatively low (4.1 kGy), based on localized disintegration of structures and element-specific redistribution. In contrast, frozen-hydrated samples did not incur any apparent damage even at doses as high as 587 kGy. Furthermore, we find that for living plants subjected to XFM measurement in vivo and grown for a further 9 d before being reimaged with XFM, the leaves display elemental redistribution at doses as low as 0.9 kGy and they continue to develop bleaching and necrosis in the days after exposure. The suggested radiation dose limits for studies using XFM to examine plants are important for the increasing number of plant scientists undertaking multidimensional measurements such as tomography and repeated imaging using XFM.

Dimension Reduction Using Quantum Wavelet Transform on a High-Performance Reconfigurable Computer

The high resolution of multidimensional space-time measurements and enormity of data readout counts in applications such as particle tracking in high-energy physics (HEP) is becoming nowadays a major challenge. In this work, we propose combining dimension reduction techniques with quantum information processing for application in domains that generate large volumes of data such as HEP. More specifically, we propose using quantum wavelet transform (QWT) to reduce the dimensionality of high spatial resolution data. The quantum wavelet transform takes advantage of the principles of quantum mechanics to achieve reductions in computation time while processing exponentially larger amount of information. We develop simpler and optimized emulation architectures than what has been previously reported, to perform quantum wavelet transform on high-resolution data. We also implement the inverse quantum wavelet transform (IQWT) to accurately reconstruct the data without any losses. The algorithms are prototyped on an FPGA-based quantum emulator that supports double-precision floating-point computations. Experimental work has been performed using high-resolution image data on a state-of-the-art multinode high-performance reconfigurable computer. The experimental results show that the proposed concepts represent a feasible approach to reducing dimensionality of high spatial resolution data generated by applications such as particle tracking in high-energy physics.

Extreme Nonuniform Sampling for Protein NMR Dynamics Studies in Minimal Time.

NMR spectroscopy is an extraordinarily rich source of quantitative dynamics of proteins in solution using spin relaxation or chemical exchange saturation transfer (CEST) experiments. However, 15N-CEST measurements require prolonged multidimensional, so-called pseudo-3D HSQC experiments where the pseudo dimension is a radio frequency offset Δω of a weak 15N saturation field. Nonuniform sampling (NUS) approaches have the potential to significantly speed up these measurements, but they also carry the risk of introducing serious artifacts and the systematic optimization of nonuniform sampling schedules has remained elusive. It is demonstrated here how this challenge can be addressed by using fitted cross-peaks of a reference 2D HSQC experiment as footprints, which are subsequently used to reconstruct cross-peak amplitudes of a pseudo-3D data set as a function of Δω by a linear least-squares fit. It is shown for protein Im7 how the approach can yield highly accurate CEST profiles based on an absolutely minimally sampled (AMS) data set allowing a speed-up of a factor 20-30. Spectrum-specific optimized nonuniform sampling (SONUS) schemes based on the Cramer-Rao lower bound metric were critical to achieve such a performance, revealing also more general properties of optimal sampling schedules. This is the first systematic exploration and optimization of NUS schedules for the dramatic speed-up of quantitative multidimensional NMR measurements that minimize unwanted errors.

AFM Modes for in-Situ Hyperspectral Mapping of Nanomechanics, Conductivity, and Local Electrochemical Activity

The local differences in structural, mechanical, electrical, and/or electrochemical properties all together account for the reactivity heterogeneity of materials on the nanoscale. This requires approaches capable of simultaneously capturing correlated multidimensional information at this level. Additionally, in situ, in vivo, non-invasive methodologies are also highly desired. Atomic force microscopy (AFM) is a well-adopted method with its capability of measuring surface topography, mechanics, electricity, chemistry, optical properties, and electrochemistry in different environments. Traditional AFM modes operate in either ‘imaging’ or ‘spectroscopic’ modes, in situ or ex situ. The traditional imaging modes performed at discrete operation conditions captured only snap-shot-wise information in an inefficient way. In conventional spectroscopic mode, however, only a single or few sample point locations are selected. Our DataCube modes combine the ‘imaging’ and ‘spectroscopic’ modes into a single approach providing a spectrum of a property or multiple properties in every pixel of the image. This new approach probing in situ, in operando, continuous, complete, and structured information about the system performance when critical properties are varying by control. We have recently also developed batch-fabricated, high-quality and robust nanoelectrode probes. These probes have the exposed Pt-coated tip apex of ~200 nm height and the end tip radius of ~25 nm. Modes enabled by these probes include include PeakForce scanning electrochemical microscopy (SECM) for the simultaneous acquisition of local EC, conductivity and nanomechanical information. This has also been extended to DataCube SECM, which provides highly-dimensional, big-data results allowing us to perform in-depth data mining for improved electrochemical kinetic quantification, 3-D nano-EC and nanomechanical characterization. In addition, many applications require performing nanoelectrical studies in electrolyte solutions, e.g. battery, bioelectricity, and bioelectromechanics. However, these are technically challenging. The general implementation requires avoiding electrical shorting, liquid spilling and chemical corrosion. Minimal parasitic electrochemistry and stray capacitance are also needed. With the use of these nanoelectrode AFM tips, we have developed a suite of solutions to conduct multi-dimensional nanoelectrical measurements in liquid, where the DataCube method has been also integrated with these implementations. This work introduces our recent developments with a variety of applications examples in different research areas. All these cases demonstrate the versatile capabilities of these solutions for today’s highly-multidisciplinary research. Figure 1. Left: SEM images of the probe. Middle: PeakForce SECM images of a mesh pattern with 1 µm wide platinum strands separated by 2 µm x 2 µm square silicon nitride islands. Right: DataCube conductive AFM mode in aqueous solution. Reference: [1] Cui et al., Nanotechnology, 2019, 30, 235701 Nellist et al., Nanotechnology, 2017, 28, 095711. [2] Jiang et al., ChemSusChem 2017, 10, 4657 Figure 1

Rapid Control of the Octane Number of Gasolines Using an IR Octanometer

The problem of using octanometers for the rapid control of the octane number of compounds in the production of commercial gasoline is considered, and the factors forming it are indicated. Error spectra of a multidimensional absorption spectral analytic signal of commercial gasolines and compounds of their individual fractions (components) were studied in measurements in the mid-IR region. The effect of a change of the proportion of the main constituents (aromatic hydrocarbons, benzene catalysate, isomerizate, methyl tert-butyl ether, and isopentane) in the compound on the spectra of commercial gasolines was evaluated. Multidimensional measurements of light absorption by gasolines in a wide spectral region and difference spectra were used. It was shown that spectral noise does not form limitations in spectroscopic measurements, while uncontrollable variations in the process conditions can form significant deviations in the absorption spectra, leading to problems in using calibration characteristics based on commercial gasoline samples.

Nonlinear sampling in ultrafast Laplace NMR.

Ultrafast Laplace NMR (UF-LNMR) reduces the experiment time of multidimensional relaxation and diffusion measurements to a fraction. Here, we demonstrate a method for nonlinear (in this case logarithmic) sampling of the indirect dimension in UF-LNMR measurements. The method is based on the use of frequency-swept pulses with the frequency nonlinearly increasing with time. This leads to an optimized detection of exponential experimental data and significantly improved resolution of LNMR parameters.

The Validity of Skin Conductance For Assessing Acute Pain in Infants: A Scoping Review.

Measuring pain in infants is important but challenging, as there is no "gold standard." The measurement of skin conductance (SC) is considered to be a measure of stress and as a surrogate indicator of pain. The objectives of this study were to identify the extent of research conducted and to synthesize the validity evidence of SC for assessing acute pain in infants. The Arksey and O'Malley framework for scoping reviews was followed, and 9 electronic databases were searched. Data were analyzed thematically and presented descriptively including the following main categories: study information/details, sampling information, characteristics of participants and settings, SC outcome measures, and validity evidence. Twenty-eight studies with 1061 infants were included, including 23 cross-sectional observation studies and 5 interventional studies. The most studied infants were those with mild severity of illness (n=13) or healthy infants (n=12). The validity evidence of SC was tested in relation to referent pain measures (13 variables), stimuli (13 variables), age (2 variables), and other contextual variables (11 variables). SC was not significantly correlated with vital signs, except for heart rate in 2 of the 8 studies. SC was significantly correlated with the unidimensional behavioral pain assessment scales and crying time rather than with multidimensional measurements. Fourteen of 15 studies (93.3%) showed that SC increased significantly during painful procedures. Inconsistent findings on validity of SC exist. Future research should aim to identify the diagnostic test accuracy of SC compared with well-accepted referent pain measures in infants, study the validity evidence of SC in critically ill infants, and utilize rigorous research design and transparent reporting.

Bayesian analysis of longitudinal quality of life measures with informative missing data using a selection model.

Health-related quality of life consists of multi-dimensional measurements of physical and mental health domains. Health-related quality of life is often followed up to evaluate efficacy of treatments in clinical studies. During the follow-up period, a missing data problem inevitably arises. When missing data occur for reasons related to poor health-related quality of life, a complete-case only analysis can lead to invalid inferences. We propose a Bayesian approach to analyze longitudinal moderate to high-dimensional multivariate outcome data in the presence of non-ignorable missing data. To account for non-ignorable missing data, we employ a selection model for the joint likelihood factorization where we apply Bayesian spike and slab variable selection in the missing data mechanism to detect informative factors among multiple outcomes. We model the relationship between multiple outcomes and covariates using linear mixed effects models where multiple outcome correlations are captured by a hierarchical structure. We conduct simulation studies to evaluate the performance of the proposed method compared with the conventional last observation carried forward approach. We use a motivating example that originates from a longitudinal study of quality of life in gastric cancer patients who underwent distal gastrectomy. In this application, we demonstrate that our proposed method can offer efficiency gain in the marginal associations and provide the associations between outcomes and the absence of patients' information.

Multi-agent learning neural network and Bayesian model for real-time IoT skin detectors: a new evaluation and benchmarking methodology

This study aimed to develop a new methodology for evaluating and benchmarking a multi-agent learning neural network and Bayesian model for real-time skin detectors based on Internet of things (IoT) by using multi-criteria decision-making (MCDM). The novelty of this work is in the use of an evaluation matrix for the performance evaluation of real-time skin detectors that are based on IoT. Nevertheless, an issue with the performance evaluation of real-time skin detector approaches is the determination of sensible criteria for performance metrics and the trade-off amongst them on the basis of different colour spaces. An experiment was conducted on the basis of three phases. In the first phase, a real-time camera based on cloud IoT was used to gather different caption images. The second phase could be divided into two stages. In the first stage, a skin detection approach was developed by applying multi-agent learning based on different colour spaces. This stage aimed to create a decision matrix of various colour spaces and three groups of criteria (i.e. reliability, time complexity and error rate within a dataset) for testing and evaluating the developed skin detection approaches. In the second stage, Pearson rules were utilised to calculate the correlation between the criteria in order to make sure, either needs to use all of the criteria in decision matrix and the criteria facts that affect the behaviour of each criterion, in order to make sure that use all the criteria in evaluation as multidimensional measurements or not. In the third phase, the MCDM method was used by integrating between a technique in order of preference by similarity to the ideal solution and multi-layer analytic hierarchy process to benchmark numerous real-time IoT skin detection approaches based on the performed decision matrix from the second phase. Three groups of findings were obtained. Firstly, (1) statistically significant differences were found between the criteria that emphasise the need to use all of the criteria in evaluation. (2) The behaviour of the criteria in all scenarios was affected by the distribution of threshold values for each criterion based on the different colour spaces used. Therefore, the differences in the behaviour of criteria that highlight the use of the criteria in evaluation were included as multidimensional measurements. Secondly, an overall comparison of external and internal aggregation values in selecting the best colour space, namely the normalised RGB at the sixth threshold, was discussed. Thirdly, (1) the YIQ colour space had the lowest value and was the worst case, whereas the normalised RGB had the highest value and was the most recommended of all spaces. (2) The lowest threshold was obtained at 0.5, whereas the best value was 0.9.

Quantum coherences reveal excited-state dynamics in biophysical systems

Ultrafast, multi-dimensional spectroscopic measurements of photosynthetic light-harvesting complexes have revealed quantum coherences with timescales comparable to those of energy-transfer processes. These observations have led to a debate regarding the states that give rise to the coherences and whether the presence of the coherences has implications for photosynthetic light harvesting. In these experiments, laser pulses create a coherent superposition of quantum states with a defined phase relationship across an ensemble, which gives rise to the quantum coherence and associated quantum beating signal. Dephasing of these quantum coherences, seen as a decay of the beating signal, is among the most sensitive probes of the interactions between a system and its surrounding environment. In this Review, we discuss the proposed origin and assignment of the observed quantum coherences in photosynthetic systems as electronic, vibronic or vibrational. We describe the latest experimental efforts towards unravelling the nature of the coherences, in particular ultrafast, two-dimensional electronic spectroscopy, as well as the accompanying theoretical and computational results. We discuss how measuring coherences can inform us about the excited-state dynamics of biophysical and chemical systems relevant to natural light harvesting and how these measurements reveal electronic structure beyond that captured by simplistic models. Femtosecond spectroscopy of photosynthetic systems reveals long-lived quantum coherences. This Review focuses on efforts to understand the microscopic origins of these signals and discusses how such coherences may be exploited to probe design principles of photosynthetic light harvesting.

The three-dimensional impact of the absorption effects of European funds on the competitiveness of the SMEs from the Danube Delta

The Danube Delta, declared a Biosphere Reserve, has been a permanent challenge in terms of harmonizing the economic activities with landscape conservation and development interests. Through public and environmental policies, but also through the development plans of the area, it was attempted to optimize the relationship between the functioning of the biosphere (whose natural integrity should be preserved), on the one hand, and the human activity exploiting the resources, on the one hand. The Structural Funds have made a major contribution to achieving this goal, being aimed mainly at meeting the European standards on the environmental protection and economic development of the area, while respecting its biodiversity and the inhabitants’ general interest in improving the life quality in the Danube Delta. This paper aims at determining the economic, social or environmental effects of the SMEs from the Danube Delta, as a result of the absorption of European funds, and their impact on competitiveness. By formulating three hypotheses, we determined the impact of the identified effects on competitiveness. The research method consisted in a quantitative investigation based on an assessment questionnaire, as the main tool, addressed to the SMEs from the Danube Delta that benefited from structural funds. The hypotheses were validated by calculating the correlations between the identified variables and competitiveness. The results showed which effects contributed more to the competitiveness of these companies. The comparison of the relationship between the economic, social and environmental effects, on the one hand, and the internal factors that determine competitiveness, on the other hand, represented one of its multidimensional measurements. In conclusion, the results revealed a strong connection between the economic, social and environmental effects and the competitiveness of the SMEs from the Danube Delta, emphasizing that the access of structural funds should be enhanced in the next period.

Intervention Outcomes of Two Treatments for Muscle Tension Dysphonia: A Randomized Controlled Trial.

Purpose The aim of this study was to test the hypothesis that stretch-and-flow voice therapy (SnF) is noninferior to resonant voice therapy (RVT) for speakers with muscle tension dysphonia. Method Participants with primary muscle tension dysphonia were randomly assigned to 1 of 2 treatment groups. Participants received 6 sessions of either SnF or RVT for 6 weeks (1 session per week). Pretreatment and posttreatment audio recordings of sustained vowels and connected speech were acquired. Response to treatment was assessed using the voice handicap index (VHI) as the primary outcome measure. Secondary outcome measures included the acoustic voice quality index, the smoothed cepstral peak prominence, and scales from the Consensus Auditory-Perceptual Evaluation of Voice instrument. Data were analyzed for 21 participants who completed the study (12 in the SnF group, 9 in the RVT group). Results Direction of change for the primary outcome measure and all 3 secondary outcome measures at posttreatment was in the direction of improvement for both SnF and RVT. Confidence intervals for VHI measures did not cross the null effect line on forest plots, suggesting significant effects for both treatments on the primary outcome measure. The effect sizes for pretreatment to posttreatment changes in VHI were large for both treatment groups. Similar results were found for the secondary acoustic outcome measures. There were statistically significant pretreatment to posttreatment changes in the primary and secondary outcome measures for patients receiving both treatments, indicating significant improvement in response to both RVT and SnF. There were no statistically significant differences in pretreatment to posttreatment changes in the primary outcome measure or any secondary outcome measure between the two groups. The within-group pretreatment to posttreatment changes in Consensus Auditory-Perceptual Evaluation of Voice scales did not reach statistical significance for either RVT or SnF. Conclusions Both SnF and RVT produced positive treatment response in speakers with muscle tension dysphonia, with no statistically significant difference in the outcome measures between the two treatments. This suggests that SnF is noninferior to RVT and that both are effective options for treating vocal hyperfunction. Results from this study also support previous findings documenting the sensitivity of multidimensional acoustic measurements to treatment response.