Discrete Measurements Research Articles

Development of a sex-specific visceral fat area estimation using discrete multi-wavelength near-infrared spectroscopy measurements in Korean individuals

Development of a sex-specific visceral fat area estimation using discrete multi-wavelength near-infrared spectroscopy measurements in Korean individuals

Indoor Radon Monitoring and Associated Diffuse Radon Emissions in the Flanks of Mt. Etna (Italy)

Between October 2021 and July 2024, radon measurements in air and soil were carried out in the South and East flanks of Etna volcano to check the possible correlation between radon emissions and active faults/eruptive fissures and to obtain preliminary data on any negative impacts on human health. Fifteen continuous indoor radon monitors were installed in homes, some of which are inhabited by patients suffering from Multiple Sclerosis and Amyotrophic Lateral Sclerosis. In all sites, the limit of 300 Bq/m3 indicated by the Euratom Directive 2013/59 was exceeded, even if slightly and for short periods. The highest values were recorded closest to active fault zones and during winters. Furthermore, 27 discrete indoor radon measurements were carried out using a passive method by means of activated charcoal canisters that were exposed for 48 h. Most of the values (>70%) were <100 Bq/m3; six canisters gave values >100 Bq/m3 and one >200 Bq/m3. Measurements of radon in soils were carried out using a Durridge RAD7 in the gardens of the homes in which the indoor radon measurements were made. The background radon values in soils were <5000 Bq/m3; the highest values (12,500 Bq/m3) were measured near the Aci Catena fault. The role of Etna’s faults in draining the deeper radon towards the surface and, therefore, into nearby homes is evident, with a consequent increase in the health risk caused by indoor radon pollution.

Reconstructing discrete measures from projections. Consequences on the empirical Sliced Wasserstein Distance

Reconstructing discrete measures from projections. Consequences on the empirical Sliced Wasserstein Distance

A case study of canal seepage quantification using gain/loss method and electrical resistivity tomography in an intensively managed water resource system in the Treasure Valley, Idaho, United States

Monitoring groundwater-surface water (GW-SW) interactions is essential for effectively managing the available water resources in semi-arid and arid environments. The focus of this study was to quantify how much SW is being exchanged with the shallow GW aquifer in the Treasure Valley (TV), Idaho, USA. Previous water budgets estimated regional canal seepage without incorporating canal variability and flow measurement uncertainty. To address this, we applied both direct (gain/loss) and indirect (electrical resistivity tomography (ERT)) techniques. Direct seepage measurements were taken on canals capturing a range of different characteristics in the TV. The discrete measurements were then used to estimate the total seepage anticipated to be lost from these canals during the irrigation season. Our findings showed high seepage variability across the canals. The ERT inversion approach was utilized before and after the irrigation season by applying an advanced inversion scheme to better constrain the canal seepage spatial variability and uncertainty by quantifying changes in the saturated zone with 2D-ERT results. Temporal changes in the subsurface resistivity were observed due to the lateral flow from the nearby surface canal during the irrigation season. The combination of these approaches improves our understanding of SW-GW interactions in intensively managed irrigation systems.

Development of an Experimental Acoustic Noise Characterization Setup for Electric Motor Drive Applications

This paper presents the development of an experimental setup for acoustic noise characterization of electric motors. It describes the sound measurement microphones utilized in the setup and discusses the application of octave bands and A-weighting in noise measurement. Various methods for acoustic noise measurement and sound power calculation, including those based on sound pressure and sound intensity, are also covered. Given the relatively noisy test environment and restricted access around the test setup, discrete point sound intensity measurement is selected for sound power calculation. Initially, a stationary probe-holding fixture is designed and fabricated for sound intensity measurements. To enhance the fixture’s flexibility and the accuracy of the measurements, a transportable fixture is subsequently designed and fabricated. The necessary hardware and software settings for acoustic noise characterization are then developed. Finally, the setup is used to conduct acoustic noise characterization of an IPM motor, validating the application of the transportable probe-holding fixture.

Dean–Kawasaki equation with initial condition in the space of positive distributions

We show that the Dean–Kawasaki equation does not admit non-trivial solutions in the space of tempered measures. More specifically, we consider martingale solutions taking values, and with initial conditions, in the subspace of measures admitting infinite mass and satisfying some integrability conditions. Following work by the first author, Lehmann and von Renesse (Electron Commun Probab 24:3916340, 2019), we show that the equation only admits solutions if the initial measure is a discrete measure. Our result extends the previously mentioned works by allowing measures with infinite mass.

A 3D Statistical Shape Model of the Right Ventricular Outflow Tract in Pulmonary Valve Replacement Patients Post-Surgical Repair.

Assessment of the right ventricular outflow tract and pulmonary arteries (RVOT) for percutaneous pulmonary valve implantation (PPVI) uses discrete measurements (diameters and lengths) from medical images. This multi-centre study identified the 3D RVOT shape features prevalent in patients late after surgical repair of congenital heart disease (CHD). A 3D RVOT statistical shape model (SSM) was computed from 81 retrospectively selected CHD patients (14.7 ± 6.8 years) who required pulmonary valve replacement late after surgical repair. A principal component analysis identified prevalent shape features (modes) within the population which were compared with standard geometric measurements (diameter, length and surface area) and between sub-groups of diagnosis, RVOT type and dysfunction. Shape mode 1 and 2 represented RVOT size and curvature and tapering and length, respectively. Shape modes 3-5 related to branch pulmonary artery calibre, conical vs. bulbous RVOTs and RVOT curvature, respectively. Tetralogy of Fallot, transannular patch type and regurgitant RVOTs were larger and straighter while conduit and stenotic types were longer and more cylindrical than other subgroups. This SSM analysed the main 3D shape features present in a population of RVOTs, exploiting the wide 3D anatomical information provided by routine imaging. This morphological information may have implications for PPVI patient selection and device design.

Route Planning Method of UAV Patrol Based on High Precision Tower Model

The high-precision positioning technology of industrial Unmanned Aerial Vehicle (UAV) plays a vital role in intelligent trajectory planning in power inspection. First, the information on power facilities is collected and processed to form a planning grid to optimize the flight path and correct the trajectory of UAV. However, the preparatory work is more complex, and the algorithm model which needs more detailed and complex terrain database data is not universal enough. To improve the safety of UAV and the efficiency of path planning, we establish a three-dimensional model of the power tower, calculate the coordinate position of the inspection work according to the information of the high-voltage transmission tower, and then determine the corresponding relationship between the inspection point and the flight trajectory combined with the safe distance. Based on the set of random interference semaphores and model rasterization, the discrete error strategy and Euler method are introduced to improve the performance. In each iteration, the best solution is first updated using the execution strategy. We continue to calculate the spatial coordinates of all reference points and provide the coordinate position distribution for the flight mission. The solution of equipment inspection trajectory optimization is realized by using dynamic obstacle perception. The proposed discrete measurement error self-correction algorithm is deployed on the flight platform to guide UAV inspection tasks according to the point cloud coordinate model of transmission equipment in the power system and to correct the flight route in time. To verify the research results, the Algorithm Artemisinin optimization and Whale Optimization Algorithm test verify that the ability of the proposed algorithm to get rid of local optimization is improved by 8.94% and, 12.42% respectively compared with other optimization algorithms. The convergence accuracy is 12.4% and 7.9% higher than other schemes, and it has a better effect in solving numerical problems. The research results using the embedded system to complete the task deployment and unloading provide a reference for trajectory planning and task design in different application scenarios.

Acoustic near field effects in distribution transformer noise measurement

This paper examines the near field effect, including the active and reactive acoustic field around a liquid-filled distribution transformer with heat-dissipating corrugated fins on the tank walls. The analysis was carried out on the basis of measurements of sound pressure and sound intensity levels along with acoustic maps including the phase correlation of acoustic waves. The acoustic field around the transformer was evaluated in the measurement planes showing normal direction radiation of up to 1,0 m distance. Discrete point measurements made with the PU (pressure - velocity) probe was used to obtain parameters such as particle velocity, active and reactive sound intensity as well as the transfer function associated with the reference microphone. The last parameter, the transfer function, allows to show the phase correlation between acoustic wave propagation, which contributes to a better understanding of acoustic near-field effects where energy circulates, creating a sound field that can have overestimated sound pressure and intensity levels.

Quantum Fluctuation Theorem for Arbitrary Measurement and Feedback Schemes.

Fluctuation theorems and the second law of thermodynamics are powerful relations constraining the behavior of out-of-equilibrium systems. While there exist generalizations of these relations to feedback controlled quantum systems, their applicability is limited, in particular when considering strong and continuous measurements. In this Letter, we overcome this shortcoming by deriving a novel fluctuation theorem, and the associated second law of information thermodynamics, which remain applicable in arbitrary feedback control scenarios. In our second law, the entropy production is bounded by the coarse-grained entropy production that is inferrable from the measurement outcomes, an experimentally accessible quantity that does not diverge even under strong continuous measurements. We illustrate our results by a qubit undergoing discrete and continuous measurement, where our approach provides a useful bound on the entropy production for all measurement strengths.

The first satellite measurements of HFC-125 by the ACE-FTS: Long-term trends and distribution in the Earth’s upper troposphere and lower stratosphere

HFC-125 (CF3CHF2, pentafluoroethane) volume mixing ratios (VMRs) have been determined for the first time using infrared absorption spectra from the Atmospheric Chemistry Experiment Fourier transform spectrometer (ACE-FTS) from 2004 to 2024. These VMRs provide global altitude-latitude VMR distributions. A VMR time series for HFC-125 has also been calculated and compared to values from in situ discrete flask measurements conducted by the National Oceanic and Atmospheric Administration Earth System Research Laboratory. The abundance of HFC-125 is currently experiencing exponential growth. ACE data shows a growth rate of 3.47 ± 0.05 ppt/year in the past six years.

Capturing the Dynamics of Dissolved Organic Carbon (DOC) in Tidal Saltmarsh Estuaries Using Remote‐Sensing‐Informed Models

AbstractThe fluxes of dissolved organic carbon (DOC) through tidal marsh‐influenced estuaries remain poorly quantified and have been identified as a missing component in carbon‐cycle models. The extreme variability inherent to these ecosystems of the land‐ocean interface challenge our ability to capture DOC‐concentration dynamics and to calculate accurate DOC fluxes. In situ discrete and continuous measurements provide high‐quality estimates of DOC concentration, but these strategies are constrained spatially and temporally and can be costly to operate. Here, field measurements and high‐spatial‐resolution remote sensing were used to train and validate a predictive model of DOC‐concentration distributions in the Plum Island Estuary (PIE), a mesotidal saltmarsh‐influenced estuary in Massachusetts. A large set of field measurements collected between 2017 and 2023 was used to develop and validate an empirical algorithm to retrieve DOC concentration with a ±15% uncertainty from Sentinel‐2 imagery. Implementation on 141 useable images produced a 6‐year time series (2017–2023) of DOC distributions along the thalweg. Analysis of the time series helped identify river discharge, tidal water level (WL), and a marsh enhanced vegetation index 2 as predictors of DOC distribution in the estuary, and facilitated the training and validation of a simple model estimating the distribution. This simple model was able to predict DOC along the PIE thalweg within ±16% of the in situ measurements. Implementation for three years (2020–2022) illustrated how this type of remote‐sensing‐informed models can be coupled with the outputs hydrodynamic models to calculate DOC fluxes in tidal marsh‐influenced estuaries and estimate DOC export to the coastal ocean.

Optimization based data enrichment using stochastic dynamical system models

We develop a general framework for state estimation in systems modeled with noise-polluted continuous time dynamics and discrete time noisy measurements. Our approach is based on maximum likelihood estimation and employs the calculus of variations to derive optimality conditions for continuous time functions. We make no prior assumptions on the form of the mapping from measurements to state-estimate or on the distributions of the noise terms, making the framework more general than Kalman filtering/smoothing where this mapping is assumed to be linear and the noises Gaussian. The optimal solution that arises is interpreted as a continuous time spline, the structure and temporal dependency of which is determined by the system dynamics and the distributions of the process and measurement noise. Similar to Kalman smoothing, the optimal spline yields increased data accuracy at instants when measurements are taken, in addition to providing continuous time estimates outside the measurement instances. We demonstrate the utility and generality of our approach via illustrative examples that render both linear and nonlinear data filters depending on the particular system. Application of the proposed approach to a Monte Carlo simulation exhibits significant performance improvement in comparison to a common existing method.

A novel shape sensing approach based on the coupling of Modal Virtual Sensor Expansion and iFEM: Numerical and experimental assessment on composite stiffened structures

Shape sensing, i.e. the reconstruction of the displacement field of a structure from discrete strain measurements, is becoming crucial for the development of a modern Structural Health Monitoring framework. Nevertheless, an obstacle to the affirmation of shape sensing as an efficient monitoring system for existing structures is represented by its requirement for a significant amount of sensors. Two shape sensing methods have proven to exhibit complementary characteristics in terms of accuracy and required sensors that make them suitable for different applications, the inverse Finite Element Method (iFEM) and the Modal Method (MM). In this work, the formulations of these two methods are coupled to obtain an accurate shape sensing approach that only requires a few strain sensors. In the proposed procedure, the MM is used to virtually expand the strains coming from a reduced number of strain measurement locations. The expanded set of strains is then used to perform the shape sensing with the iFEM. The proposed approach is numerically and experimentally tested on the displacement reconstruction of composite stiffened structures. The results of these analyses show that the formulation is able to strongly reduce the number of required sensors for the iFEM and achieve an extremely accurate displacement reconstruction.

Breast-torso movement coordination during running in different breast support

To reduce breast motion with a bra, we need to understand what drives the motion of the breasts, and what variables change as support increases. Quantifying breast-torso coordination and movement complexity across the gait cycle may offer deeper insights than previously reported discrete time lag. We aimed to compare breast-torso coordination and mutual influence across breast support conditions during running. Twelve female participants ran on a treadmill at 10 km h−1 with an encapsulation and compression sports bra, and in no bra. Nipple and torso position was recorded. Vector coding, granger causality and transfer entropy were calculated within gait cycles. In both bra conditions, a greater percentage of gait cycles was spent with the breast and torso in-phase (> 90%) compared to no bra running (~ 66%, p < 0.001), with most time spent in-phase in the encapsulation versus compression bra (p = 0.006). There was a main effect of breast support condition on Granger causality (p < 0.001), both from breast to torso and torso to breast. Transfer of information was highest from torso to breast, compared to breast to torso in all conditions. Overall, these results provide novel insight into the mutual and complex interaction between the breast and the torso while running in different bra conditions. The approaches presented allow for a greater understanding of bra support conditions than existing discrete measures, which may relate to comfort and performance. Therefore, measures of coupling, predictability and transfer of complexity should be employed in future work examining these features.

A note on statistical distances for discrete log-concave measures

In this note we explore how standard statistical distances are equivalent for discrete log-concave distributions. Distances include total variation distance, Wasserstein distance, and f-divergences.

ASSESSING SATELLITE RAINFALL ACCURACY IN DENSE TROPICAL SABAH EAST COAST FOREST, MALAYSIA: A CROSS-VALIDATION OF DOWNSCALING TECHNIQUE

Rainfall is one of pivotal elements in the hydrological cycle that essential for ensuring the water balance in present and in the future. Traditional in-situ observations have been the conventional method for obtaining rainfall data, but their limitations arise from discrete point measurements that fail to represent the entire area. To overcome these limitations, this study utilizes satellite based TRMM data products for rainfall estimation. The research aims to cross-validate the TRMM 3B43-v7 product against corresponding in-situ measured rainfall, focusing on error localization in Sabah's east coast. The derivation of the rainfall rate from TRMM data is adequate with additional data from three manual gauges within the plots. The correlation between TRMM and ground was good (R2 = 0.78, RMSE = 65.65 mm). The Nash and Sutcliffe Error results closed to value 1 indicate that the accuracy of the TRMM data compared to the rain-gauge data in Danum has a good agreement. This is due to the IDW downscaling method for satellite rainfall data using additional data from three manual-gauges within the plots to increase the accuracy of the TRMM data. In summary, the downscaling method proves capable of providing fine spatial resolution and increasing the number of pixels in the study area. Future research endeavours may benefit from incorporating more station data to further improve the interpolation of satellite data and derive more precise results.

Discrete measurements of RNA polymerase and reverse transcriptase fidelity reveal evolutionary tuning.

Direct methods for determining the fidelity of DNA polymerases are robust, with relatively little sample manipulation before sequencing. In contrast, methods for measuring RNA polymerase and reverse transcriptase fidelities are complicated by additional preparation steps that introduce ambiguity and error. Here, we describe a sequencing method, termed Roll-Seq, for simultaneously determining the individual fidelities of RNA polymerases and reverse transcriptases (RT) using Pacific Biosciences single molecule real-time sequencing. By using reverse transcriptases with high rolling-circle activity, Roll-Seq generates long concatemeric cDNA from a circular RNA template. To discern the origin of a mutation, errors are recorded and determined to occur within a single concatemer (reverse transcriptase error) or all concatemers (RNA polymerase error) over the cDNA strand. We used Roll-Seq to measure the fidelities of T7 RNA polymerases, a Group II intron-encoded RT (Induro), and two LINE RTs (Fasciolopsis buski R2-RT and human LINE-1). Substitution rates for Induro and R2-RT are the same for cDNA and second-strand synthesis while LINE-1 has 2.5-fold lower fidelity when performing second-strand synthesis. Deletion and insertion rates increase for all RTs during second-strand synthesis. In addition, we find that a structured RNA template impacts fidelity for both RNA polymerase and RT. The accuracy and precision of Roll-Seq enable this method to be applied as a complementary analysis to structural and mechanistic characterization of RNA polymerases and reverse transcriptases or as a screening method for RNAP and RT fidelity.

Emergence of Internet of Things technology in food and agricultural sector: A review

AbstractFood processing is an indispensable sector crucial for controlling the food losses. Discrete measures were introduced and implemented to enhance the food shelf life and preservation techniques. However, poor handling and preservation methods usher to the food deprivation. In such milieu, amalgamation of futuristic technologies like sophisticated sensors tether with Internet of Things (IoT) could shoot up the food safety and minimize the deprivation. Research across the globe have proved that integration of IoT‐smart sensors in scrutinizing ecological factors such as temperature, radiation, gaseous composition, relative humidity, and moisture content that are critical for food processing and preservation. IoT has the prospects to ameliorate nationwide explicable execution, slash energy depletion, slash manufacturing expenses, inflate worker health and safety during food processing unit. Smart agricultural techniques also enable measurement of temperature, relative humidity, soil moisture, and nitrogen contents in smart farming and helps the user to determine the status of crops and commodity. This article aims to focus a few aspects and budding areas of IoT in the food and agricultural sectors. With this outlook, advancement and smartness in agriculture and food processing can be created by collaborating with IoT technology.

Extremal values-based aggregation functions

We introduce and study aggregation functions based on extremal values, namely extended (l,u)-aggregation functions whose outputs only depend on a fixed number l of extremal lower input values and a fixed number u of extremal upper input values, independently of the arity of the input n-tuples (n≥l+u). We discuss several general properties of (l,u)-aggregation functions and we study special (l,u)-aggregation functions with neutral element, including t-conorms, t-norms and uninorms. We also study (l,u)-aggregation functions defined by means of integrals with respect to discrete fuzzy measures, as well as (l,u)-ordered weighted quasi-arithmetic means based on appropriate weighting vectors. We also stress some generalizations based on recently introduced new types of monotonicity. Some possible applications are sketched, too.