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Escalating transportation demands have mandated extensive construction of highways in proximity to residential zones, culminating in heightened vehicular noise levels. This investigation centered on the assessment of noise pollution across diverse sites in Lahore, Pakistan, with a specific focus on the Lahore Ring Road (LRR) and surrounding regions. Variables including land utilization, road categorization, arboreal presence, physical impediments, and distances between noise sources and receivers were meticulously analyzed. Utilizing a UNI-T-MS6700 sound level meter, measurements spanning LA99, LA90, LA50, LA10, and LA1 exposed concerning findings, notably recording 80.5 dB from motorcycles in Township (Ali Road) and peaking at 86.5 dB from trucks in Model Town (Ferozepur Road) and Township (Ali Road). While Sui Gas Society demonstrated an 8 dB(A) noise reduction via purposeful tree planting (15 feet), DHA Phase V exhibited a marginal 3 dB(A) decrease due to the absence of such interventions (20 feet). Strikingly, Ashiana-E-Quaid displayed a substantial 19 dB(A) reduction attributed to a considerable distance between noise sources and receivers (300 feet), notwithstanding the absence of trees and barriers. All surveyed locations surpassed the Punjab EPD-prescribed limit of 45-55 dBA, resulting in stress (62.5%) and sleep disruptions (43.3%) among residents. This study underscores the critical roles of tree plantation and source-receiver distance in noise abatement, underscoring the necessity for urban architecture designed with noise barriers to efficaciously manage traffic-related noise. [ABSTRACT FROM AUTHOR]

The huge integration of photovoltaic (PV) systems into power systems due to fossil fuel scarcity and environmental considerations leads to serious issues in the operation of electrical grids. The reliability and accuracy of PV module models allow the optimization of PV systems. The PV cells are described by a nonlinear current–voltage characteristic, and the parameters not specified by the manufacturer's sheet are generally estimated. Despite the significant effort provided by the PV parameter extraction problem, the latter is still an arduous task. This work suggests a new modified coot optimization algorithm with local search (MCOOT-LS) for effectively extracting the unknown parameters of PV modules. More to the point, the conventional COOT algorithm profits from chaotic dynamic-opposite learning, a new exploration strategy, and a local search to reach a high balance between exploitation and exploration for complex problems. In this work, we employed two PV modules composed of silicon monocrystalline and polycrystalline cells, like the LSM 20 and STE 4/100 photovoltaic panels. Previous works in the domain of PV parameter estimation overlooked the importance of anti-noise ability. However, with a view to revealing the efficacy of the proposed MCOOT-LS, this manuscript examined the solution accuracy, convergence speed, and anti-noise properties of all the algorithms used. To assess the effect of noise on the obtained results, two levels of noise (5% and 10%) have been considered, and the objective function called a Root Mean Square Error (RMSE) is adopted. The results prove that the proposed MCOOT-LS algorithm outperformed all the comparative algorithms regarding convergence feature and solution reliability for both noise-free and noisy results. • A modified COOT optimization algorithm for PV parameter estimation is proposed. • A simulation model of PV modules is developed. • Experimental validation for the used PV modules is implemented. • Statistical analysis is conducted for all used algorithms. • The effects of noise measurement on all used algorithms is investigated. [ABSTRACT FROM AUTHOR]

In this paper, an assessment of a topography measurement method for fracture surfaces of 10HNAP steel after bending-torsion fatigue tests was performed. Surface roughness was measured by using a non-contact Focus Variation Microscopy (FVM) technique in which the non-measured points (NMPs) and outliers (spikes) were removed by the application of general methods. The results revealed, that the optical measurement method introduced variations in the high-frequency errors, considered as noise within the selected bandwidth. Therefore, the minimization of the high-frequency noise (HFN) was proposed based on an extensive examination of ISO 25178 roughness parameters. Additionally, a general S -filter was applied, as recommended by international standards and commercial software. It was used to identify and remove noise from the measured data after pre-processing. Consequently, levelling and eliminating of NMPs and spikes was successfully performed. Subsequently, the results obtained by using various filters were compared to further assess the impact of different filtration bandwidths. Finally, the proposed procedure was validated by implementing different general functions, such as autocorrelation (ACF), power spectral densities (PSD), and texture direction (TD). It was concluded, that coupled characteristics, including profile and areal measurements, should be studied simultaneously since they are necessary to analyze the fracture surfaces comprehensively. • Fatigue fracture surfaces were characterized after bending, torsion and bending-torsion. • Surface topography measurements were performed by using the FVM technique. • Approaches for noise compensation measurement and error data analysis were presented. • The error reduction was applied after the ACF, PSD, TD and thresholding operations. • The accuracy of ISO 25178 roughness parameter evaluation was validated and improved. [ABSTRACT FROM AUTHOR]

In order to solve the problems of poor automaticity and difficult identification and elimination of spurious modes by covariance-driven stochastic subspace method, a new modal parameter identification method is proposed. Firstly, the covariance - driven stochastic subspace method is used to identify the modal parameters of the system. Secondly, according to the soft and hard criteria, the spurious modes are preliminarily eliminated and the 3D stabilization diagram is drawn. Then, the density-based spatial clustering algorithm with noise (DBSCAN) is improved, the sensitive parameter ε is automatically determined, and the candidate modes are clustered and analyzed. For each cluster of modalities, the modal quality assessment criterion (MQAC) is calculated, and a screening standard is formulated to determine the true modes of the system. Finally, the proposed method is used to verify the modal parameter identification of truss structure, Guangzhou Tower and Z24 bridge examples, The results indicate that this method can achieve autonomous modal analysis of typical engineering structures and effectively eliminate false modes caused by non - white noise excitation and measurement noise. [ABSTRACT FROM AUTHOR]

This paper addresses the state estimation problem for a class of two‐dimensional shift‐varying systems with time‐correlated multiplicative measurement noises and additive noises. The time‐correlated multiplicative noises can be described as a linear system model corrupted by white noise. The main objective of this paper is to propose a recursive iterative coupled new‐type estimators and design the estimated gain reasonably to guarantee the minimum error covariance at each step. First, two new‐type coupling estimators are designed by introducing a new term. The unbiasedness of the two estimators are guaranteed by resorting to the mathematical inductive approach. Subsequently, an effective estimation is proposed given initial conditions. Moreover, the boundedness performance is investigated of the error covariance, and an upper bound is given based on the spectral norm. Finally, two numerical example are presented as an example to show the effectiveness of the estimation algorithm. [ABSTRACT FROM AUTHOR]

This work addresses the development of diagnostics for core plasma radiated power and soft x-ray intensity measurements, which will be useful in future fusion reactors to ensure reliable plasma control (by monitoring the power loss across the separatrix) in accordance with the DEMO control requirements. For this purpose, we look into the development of such a detection system that will provide the required information via soft x-ray diagnostics. The target photon range for such a detecting system is considered to be 3–50 keV. The aspects of the development have included detailed diagnostics design, physics, engineering and integration studies, as well as an investigation into the feasibility and performance of the diagnostics and its components. The development is currently in the design phase. Nevertheless, the answer is already needed regarding the fulfillment of system requirements. In order to monitor the power crossing the separatrix, a precise estimation of the plasma radiation is needed to maintain the high-efficiency plasma. This requires strict measurement accuracy criteria, with 3% accuracy margin for the core plasma radiated power estimate and 5% noise for a single measurement of a single detector within the detector array. Here, an initial estimation of the detecting system's accuracy was provided based on an analysis of both the tomography reconstruction and detector measurement capabilities. The optimal number of lines of sight for tomography reconstruction was found for the considered plasma field of view. Additionally, the initial concept for a photon-sensitive chamber of the detecting system was developed. This allowed for the assessment of the predicted measurement accuracy of the detector for horizontal and vertical lines of sight. [ABSTRACT FROM AUTHOR]

The existence of dynamic outliers poses a significant challenge to the Kalman filter (KF). In addressing this challenge, this paper presents an innovative solution: Firstly, by analyzing a period of measurement information to more accurately identify state and measurement dynamic outliers, the system's capacity to adapt to dynamic changes is significantly improved. Next, noise is modeled as a Gaussian-Student's t mixture distribution (GSTM), with mixed model parameters inferred using the variational Bayesian (VB) method based on measurement information, cleverly integrated into the Moving Horizon Estimation (MHE) framework, thus enhancing the flexibility and accuracy of the noise model. Lastly, the optimal window size was identified through simulation experiment analysis to further increase the estimation accuracy. Simulation results demonstrate that the proposed filter exhibits stronger robustness in resisting dynamic outliers compared to existing filters. • Noise is modeled as a GSTM distribution. • Sliding window uses multiple measurements to resist dynamic outliers. • VB method and MHE framework combine for joint inference of unknown parameters. • The superiority of the proposed filter has been confirmed through simulations. [ABSTRACT FROM AUTHOR]

The characteristics of a mechanical structure under operating conditions may differ from those in a static state. It is often more desirable to obtain the frequency response function (FRF) of the operating structure in engineering applications. While operational modal analysis (OMA) can estimate modal parameters during operation, it fails to provide mass-normalized mode shapes for FRF synthesis. This paper presents a new method using experimental modal analysis (EMA) to compensate for the absent information in OMA. It categorizes operational mode shapes into changed ones and those that remain the same compared to the static state, applying different scaling techniques accordingly. This method adapts to changes in dynamic characteristics without altering the operating conditions. Stability is emphasized throughout the process. Two examples are provided to verify the method, considering noise and incompleteness in measurement, and disturbances in dynamic properties. The proposed method is proven to be feasible and reliable to capture the changes in operational FRFs. [ABSTRACT FROM AUTHOR]

Distributed state estimation is one of the critical technologies in the field of target tracking, where the process noise and measurement noise may have a heavy-tailed distribution. Traditionally, heavy-tailed distributions like the student-t distribution are employed, but our observation reveals that Gaussian noise predominates in many instances, with occasional outliers. This sporadic reliance on heavy-tailed distributions can degrade performances or necessitate frequent parameter adjustments. To overcome this, we introduce a novel distributed consensus multi-distribution state estimation method that combines Gaussian and student-t filters. Our approach establishes a system model using both Gaussian and student-t distributions. We derive a multi-distribution filter for a single sensor, assigning probabilities to Gaussian and student-t noise models. Parallel estimation under both distributions, utilizing Gaussian and student-t filters, allows us to calculate the likelihood of each distribution. The fusion of these results yields a mixed-state estimation and corresponding error matrix. Recognizing the increasing degrees of freedom in the student-t distribution over time, we provide an effective approximation. An information consensus strategy for multi-distribution filters is introduced, achieving global estimation through consensus on fused local filter results via interaction with neighboring nodes. This methodology is extended to the distributed case, and the recursive process of the distributed multi-distribution consensus state estimation method is presented. Simulation results demonstrate that the estimation accuracy of the proposed algorithm improved by at least 20% compared to that of the traditional algorithm in scenarios involving both Gaussian and heavy-tailed distributions. [ABSTRACT FROM AUTHOR]

The impact of urban traffic on human health is significant. This research conducts field measurements in Guangzhou, China, focusing on a building situated near subgrade roads and viaducts to investigate the characteristics of airborne and structure-borne noise generated by these infrastructures. The analysis involves the use of both sound pressure level and overall sound pressure level, as well as an examination of the transfer function between outdoor and indoor noise levels. The findings indicate that traffic-related airborne noise demonstrates a characteristic frequency at 1000 Hz in this scenario, while viaduct- and building-generated structure-borne noise is predominantly distributed at lower frequencies. Additionally, it is worth noting that structural vibrations generate significantly less energy compared to airborne traffic noise sources. The variation in outdoor road noise across different floors over the entire frequency range demonstrates an initial increase followed by a decrease with rising floor height due to air damping effects as well as sound barriers' attenuation properties. These results enhance engineers' understanding of urban traffic-induced airborne or structure-borne noise while establishing foundational data for designing layouts integrating urban buildings with roads. [ABSTRACT FROM AUTHOR]

The measurement of the abundance of galaxy clusters in the Universe is a sensitive probe of cosmology, which depends on both the expansion history of the Universe and the growth of structure. Density fluctuations across the finite survey volume add noise to this measurement, this is often referred to as super-sample covariance (SSC). For an unbinned cluster analysis, such noise has not been included in the cluster likelihood, since the effect of SSC was small compared to the Poisson shot-noise for samples of a few hundred clusters. For upcoming large cluster surveys such as the Rubin LSST, which will deliver catalogues of tens of thousands of clusters, this effect will no longer be negligible. In this paper, we propose a new hybrid likelihood based on the Gauss-Poisson Compound model (GPC), by using infinitesimal mass bins and standard redshift bins. This likelihood has the advantages of an unbinned Poisson likelihood while successfully incorporating the effects of SSC. Using a simulated dark matter halo catalogue, we find that the hybrid likelihood, accounting for both Poisson noise and SSC, increases the dispersion of the parameter posteriors by 20 per cent when using 100 000 clusters compared to the standard unbinned likelihood, based on Poisson statistics only. [ABSTRACT FROM AUTHOR]

Silicon spin qubits are promising candidates for scalable quantum computers, due to their coherence and compatibility with CMOS technology. Advanced industrial processes ensure wafer-scale uniformity and high device yield, but traditional transistor processes cannot be directly transferred to qubit structures. To leverage the micro-electronics industry expertise, we customize a 300 mm wafer fabrication line for silicon MOS qubit integration. With careful optimization of the gate stack, we report uniform quantum dot operation at the Si/SiO2 interface at mK temperature. We measure a record-low average noise with a value of 0.61 μ eVH z − 0.5 at 1 Hz and even below 0.1 μ eVH z − 0.5 for some operating conditions. Statistical analysis of the charge noise measurements show that the noise source can be described by a two-level fluctuator model. This reproducible low noise level, in combination with uniform operation of our quantum dots, marks CMOS manufactured spin qubits as a mature platform towards scalable high-fidelity qubits. [ABSTRACT FROM AUTHOR]

This study investigates the impact of different passenger rolling stock structures and train configurations on vibrations and noise generated during operation. Vibroacoustic measurements were performed during acceleration, constant speed, and braking phases to analyze the effects using statistical analysis according to the relevant standards, revealing a statistically significant relationship between the equivalent noise level and vibration dose. In the context of the expanding Warsaw metro network, which is adding new lines and modern rolling stock, trends in the development of metro rolling stock were analyzed using five different types of metro vehicles, from the oldest to the newest designs. Vehicle performance was ranked in the context of ride comfort using standards and combined vibration and noise measures. The research results allowed for a collective comparative assessment of the construction of individual types of passenger rolling stock in terms of vibroacoustic phenomena, thanks to which it is possible to assess the impact of modern solutions and the justification for investing in modern rolling stock. Newer trains generally record significantly lower vibration and noise levels. The difference between the oldest and the most modern vehicle types is 57% for the vibration acceleration level (0.08 ms−2 RMS) and 66% for the noise level (2.2 dB LAeq). [ABSTRACT FROM AUTHOR]

Background: Magnetic resonance imaging (MRI) scans are known to suffer from a variety of acquisition artifacts as well as equipment‐based variations that impact image appearance and segmentation performance. It is still unclear whether a direct relationship exists between magnetic resonance (MR) image quality metrics (IQMs) (e.g., signal‐to‐noise, contrast‐to‐noise) and segmentation accuracy. Purpose: Deep learning (DL) approaches have shown significant promise for automated segmentation of brain tumors on MRI but depend on the quality of input training images. We sought to evaluate the relationship between IQMs of input training images and DL‐based brain tumor segmentation accuracy toward developing more generalizable models for multi‐institutional data. Methods: We trained a 3D DenseNet model on the BraTS 2020 cohorts for segmentation of tumor subregions enhancing tumor (ET), peritumoral edematous, and necrotic and non‐ET on MRI; with performance quantified via a 5‐fold cross‐validated Dice coefficient. MRI scans were evaluated through the open‐source quality control tool MRQy, to yield 13 IQMs per scan. The Pearson correlation coefficient was computed between whole tumor (WT) dice values and IQM measures in the training cohorts to identify quality measures most correlated with segmentation performance. Each selected IQM was used to group MRI scans as "better" quality (BQ) or "worse" quality (WQ), via relative thresholding. Segmentation performance was re‐evaluated for the DenseNet model when (i) training on BQ MRI images with validation on WQ images, as well as (ii) training on WQ images, and validation on BQ images. Trends were further validated on independent test sets derived from the BraTS 2021 training cohorts. Results: For this study, multimodal MRI scans from the BraTS 2020 training cohorts were used to train the segmentation model and validated on independent test sets derived from the BraTS 2021 cohort. Among the selected IQMs, models trained on BQ images based on inhomogeneity measurements (coefficient of variance, coefficient of joint variation, coefficient of variation of the foreground patch) and the models trained on WQ images based on noise measurement peak signal‐to‐noise ratio (SNR) yielded significantly improved tumor segmentation accuracy compared to their inverse models. Conclusions: Our results suggest that a significant correlation may exist between specific MR IQMs and DenseNet‐based brain tumor segmentation performance. The selection of MRI scans for model training based on IQMs may yield more accurate and generalizable models in unseen validation. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive maximum correntropy cubature Kalman filter based on multiple fading factors (MAMCKF) is proposed to address the problem of inaccurate process noise covariance and unknown measurement noise covariance together with outliers in target tracking. Although there are many adaptive filters and robust filters have been proposed to handle unknown measurement noise covariance or measurement outliers, most filters cannot deal with both unknown noise covariance and outliers simultaneously. In this article, we propose an adaptive and robust cubature Kalman filter. The modified measurement noise covariance matrix (MNCM) and innovation covariance matrix are used to construct multiple fading factors for correcting the prediction error covariance matrix (PECM), which can achieve adaptability. Then, the maximum correntropy criterion (MCC) is introduced to suppress outliers, which further enhances the robustness. Compared with the existing approaches, the proposed approach improves the performance by at least 5% in unknown time-varying noise, unknown time-varying heavy-tailed noise, and non-Gaussian heavy-tailed noise scenarios. The simulation results show that the proposed approach can effectively suppress inaccurate process noise covariance and unknown time-varying measurement noise together with outliers. Compared with the existing filtering approaches, the proposed approach exhibits both adaptability and robustness. [ABSTRACT FROM AUTHOR]

ARIADNE (Axion Resonant InterAction Detection Experiment) is a table-top experiment that intends to search for QCD axions from exotic spin-dependent interactions mediated by axion between nuclei at sub-mm range. This experiment includes a non-magnetic mass to source the axion field, and a dense ensemble of hyper-polarized 3He nuclei to detect the axion field with nuclear-magnetic-resonance (NMR)-based method. The expected NMR signal from the interaction could be easily buried in the noise spectrum of the magnetometer, especially in a frequency range (~ 100 Hz) where the interaction signal is supposed to exist. In this work, we report optimization of SQUID gradiometer for ARIADNE including noise spectrum measurement. [ABSTRACT FROM AUTHOR]

One potential way to enhance the performance of facial expression recognition (FER) is to augment the training set by increasing the number of samples. By incorporating multiple FER datasets, deep learning models can extract more discriminative features. However, the inconsistent labeling criteria and subjective biases found in annotated FER datasets can significantly hinder the recognition accuracy of deep learning models when handling mixed datasets. Effectively perform joint training on multiple datasets remains a challenging task. In this study, we propose a joint training method for training an FER model using multiple FER datasets. Our method consists of four steps: (1) selecting a subset from the additional dataset, (2) generating pseudo-continuous labels for the target dataset, (3) refining the labels of different datasets using continuous label mapping and discrete label relabeling according to the labeling criteria of the target dataset, and (4) jointly training the model using multi-task learning. We conduct joint training experiments on two popular in-the-wild FER benchmark databases, RAF-DB and CAER-S, while utilizing the AffectNet dataset as an additional dataset. The experimental results demonstrate that our proposed method outperforms the direct merging of different FER datasets into a single training set and achieves state-of-the-art performance on RAF-DB and CAER-S with accuracies of 92.24% and 94.57%, respectively. [ABSTRACT FROM AUTHOR]

Water distribution network (WDN) models are a common decision support tool for understanding the behavior and performance of WDNs, aiding in the planning and management of WDN systems. The increasing availability of real-time data has recently promoted the exploration of Data Assimilation (DA) techniques to improve these models. However, flow, pressure and demand data are uncertain, particularly due to sensor characteristics such as precision and noise. An open question is to what extent DA can still improve hydraulic models when the data used to this end is uncertain. This paper proposes a three-step Ensemble Kalman Filter based DA approach for WDNs (3-EnKF-WDN), building on previous approaches, and advancing in two main fronts: the use of extended period simulation, and the use of pressure-dependent demand (PDD) analysis. Different scenarios considering uncertain sensor data, with varied precision and noise, are applied to two networks of different sizes, representative of real-world WDNs. The computational demand of the 3-EnKF-WDN method is also assessed. Results show that increasing sensor's precision and decreasing the noise in state measurements reduce model error, as expected. However, we also found that model errors: 1) are reduced more effectively by using 3-EnKF-WDN than by increasing sensors' precision; 2) are not reduced if certain noise thresholds are surpassed; 3) can be reduced without assimilating demand data if the WDNs are fully monitored with head sensors in all the nodes and flow sensors in all the links. [ABSTRACT FROM AUTHOR]

• We analyze the impact of plasma noise on Taiji using Wind's electron number density data. • Lomb–Scargle method is used to estimate the spectrum of plasma noise in 10−6 to 10−2 Hz. • The spectrum of plasma noise is fitted by power law and its effect on TDI is estimated. • It provides a basis for estimating plasma noises in similar gravitational wave observatories. In space-based laser interferometric gravitational wave antennas, plasma from the solar wind is an important noise floor source for laser interferometric measurements between two distant spacecraft. In this study, we aim to analyze the impact of solar wind on the inter-satellite laser interferometric measurements of the Taiji mission, a space-based laser interferometric gravitational wave antenna proposed by the University of Chinese Academy of Sciences. Based on the 6.5-year electron number density data from the National Aeronautics and Space Administration Wind spacecraft and considering the uneven sampling characteristics of the data, we employed the Lomb–Scargle spectral analysis method to study the impact of solar wind plasma on intersatellite laser interferometric measurements within the frequency range of 1 × 10 - 6 Hz to 0.01 Hz. The study shows that the effect of the plasma on the intersatellite laser interferometric measurement is approximately 1 pm/ Hz at 3.3 mHz, which is one order of magnitude smaller than the requirements on the displacement noise of the interferometric measurement of the Taiji mission, indicating that the noise due to plasma will not affect the aim of the Taiji mission to detect gravitational waves. The research can be applied to Taiji, Laser Interferometer Space Antenna, and future gravitational wave detection missions. [ABSTRACT FROM AUTHOR]

A complete kinematic error model that contains all potential error sources, is one of the fundamentals to ensure the best result of kinematic calibration. In the case of parallel manipulators, the complete error model includes items of passive joint motions. These motions usually cannot be accurately observed during the identification process but can be derived through kinematic analysis. However, due to kinematic errors, the obtained passive joint motions deviate from their actual values, causing motion errors of passive joints. In other words, the passive joints introduce input errors to the error model, which are of the same order of magnitude as kinematic errors and much larger than other measurement and random noises. This issue can significantly affect the stability and accuracy of parameter identification, but it has not been clearly recognized in the literature. To address this problem, this paper systematically analyzes the influence of passive joints' motion errors on the kinematic calibration of parallel manipulators. It is found that when the complete error model including the passive joints' motion errors is used for kinematic calibration of parallel manipulators, the variance of kinematic error parameters solved by the least squares (LS) method greatly increases, leading to iterative divergence even when the identification matrix is column full rank. To improve the stability and accuracy of parameter identification, this study employs the total least squares (TLS) method, which is a dedicated approach for handling input errors, in the identification process. Numerical simulations and experiments of kinematic calibration are conducted on several parallel manipulators. The results validate the correctness and effectiveness of the analysis of the influence of passive joints' motion errors on the kinematic calibration of parallel manipulators. Furthermore, the results indicate that the TLS method can efficiently and accurately accomplish the identification of kinematic parameters. • Input errors which drawn little attention in kinematic calibration, are analyzed. • Influence of passive joints' motion errors on kinematic calibration is revealed. • TLS method is employed, ensuring the stability of parameter identification. • Results of numerical simulations and experiments verify the analysis in this paper. [ABSTRACT FROM AUTHOR]

The present study aims to determine submarine structural elements by analysing high-resolution magnetic data sets and to examine the impact of geological surface structures characterizing Lake Sapanca. Lake Sapanca is located on the northern branch of the North Anatolian Fault Zone. The study area and its immediate surroundings are located within the most active tectonic structure of Turkey. A magnetic study was carried out in Sapanca Lake to reveal the structural elements that caused the formation of the lake. Diurnal corrections were applied to the magnetic data. During the analysis of the marine magnetic data set, there are variations in the mean field level of the magnetic field caused by the routing effects of ship noise on the measurements. To eliminate this noise effect, directional correction was performed. Then, de-stripping correction was also applied. It is essential to estimate the boundaries and depths of the structural features of the region. In order to elucidate the structural features of the region, different techniques such as tilt angle derivative, the total horizontal derivative of tilt angle derivative, Euler 2D deconvolution, horizontal gradient magnitude, and source edge detection were applied to magnetic data. 2-D modelling using GM-SYS in Oasis Montaj were performed. These techniques identified and mapped the edges of anomalous sources related to magnetic data corresponding to geological elements in the study area. The results allow us to image the submarine lineaments for the survey area. These structural lineaments are in E–W, NE–SW and NW–SE trends. These lineaments in our results are quite compatible with the faults in previous geophysical results. The E–W trending lineaments are related to the E–W trending faults in the region. [ABSTRACT FROM AUTHOR]

Today, one of the main challenges in the quantum infrared detection field is to increase the operating temperature in order to reduce size, weight and power-cost (SWAP). However, this leads to two major issues: an increase in the number of random telegraph signal (RTS) pixels and a residual fixed pattern noise (RFPN) instability. To identify these noise sources and assess the performance of a component, numerous measurements at the operating temperature are necessary, which can be laborious. Nevertheless, there exists a noise quality indicator for detectors at high temperatures known as the Tobin factor. In this paper, our objective is to investigate and understand the relationship between random telegraph signal (RTS) pixels and the Tobin factor. The aim of this study is to evaluate the relevance of the Tobin factor extracted at high temperatures to forecast noise performance at operating temperatures. [ABSTRACT FROM AUTHOR]

Indoor air quality has become a more prominent concern since the arrival of the COVID-19 pandemic. Manufacturing industries have always been prone to occupational health risks, which depend on the dynamics of the production shop floors. The furniture industry is one of these sectors with a unique work environment. Although a typical furniture manufacturing facility involves physical, chemical, and noise pollution-producing elements, this industry has been studied relatively less for indoor air quality and noise-related risks. This study investigated nine furniture manufacturing organizations’ indoor air quality and noise pollution levels through comprehensive quantitative techniques. The results of the measurements were compared against reference values set by specific guidelines to explore the degree of occupational health risk associated with the World Health Organization's (WHO) suggested levels. Repetitive measurements from five pre-designated workstations were taken at each facility. The study’s results indicated that organization size and department were significant factors for PM 2.5 and HCHO parameters, while only department type was substantial for noise exposure levels. However, across all departments and organization sizes, LAeq noise levels were below the safety threshold of 85 dB(A). Most organizations presented a lack of proper use of personal protective equipment and poor ventilation across shop floors.

Exposure and damage caused by noise have been reported in many countries around the world. However, few nationwide studies explored the association of residential environmental noise with depressive symptoms, this study aims to examine this association. The Korean Community Health Survey at the individual-level and the Korean Environmental Noise Measurement Database at the regional-level were used. A total of 30,630 individuals were eligible for the analysis. Multilevel model framework was applied to account for the clustered structure of the regional-level data in which individual-level data containing demographic characteristics and health information were nested. As a result of the analysis, Individuals living in the highest environmental noise area had a 1.55 times higher likelihood of experiencing depressive symptoms than those living in the lowest environmental noise area (95% CI, 1.04–2.31). After stratified analysis according to depressive symptom severity, individuals residing in areas with the highest environmental noise exposure had significantly higher odds of mild (aOR, 1.46; 95% CI, 1.02–2.07) and moderate symptoms (aOR, 1.70; 95% CI, 1.00–2.91). In conclusion, the higher the residential environmental noise, the higher the possibility of mild-to-moderate depressive symptoms. Our findings suggest the need for continued attention to and management of noise pollution, which has the potential to adversely affect individual's mental health. [ABSTRACT FROM AUTHOR]

The Kalman filter is an important technique for system state estimation. It requires the exact knowledge of system noise statistics to achieve optimal state estimation. However, in practice, this knowledge is often unknown or inaccurate due to uncertainties and disturbances involved in the dynamic environment, leading to degraded or even divergent filtering solutions. To address this issue, this paper presents a new method by combining the random weighting concept with the limited memory technique to accurately estimate system noise statistics. To avoid the influence of excessive historical information on state estimation, random weighting theories are established based on the limited memory technique to estimate both process noise and measurement noise statistics within a limited memory. Subsequently, the estimated system noise statistics are fed back into the Kalman filtering process for system state estimation. The proposed method improves the Kalman filtering accuracy by adaptively adjusting the weights of system noise statistics within a limited memory to suppress the interference of system noise on system state estimation. Simulations and experiments as well as comparison analysis were conducted, demonstrating that the proposed method can overcome the disadvantage of the traditional limited memory filter, leading to im-proved accuracy for system state estimation. [ABSTRACT FROM AUTHOR]

The paper presents research on measuring and the possibility of prediction of noise levels on the left and right sides of the operator within the cabin of an agricultural tractor when moving across various agrotechnical surfaces, considering movement velocity and tire pressures while employing machine learning techniques. Noise level measurements were conducted on a LANDINI POWERFARM 100 type tractor, and aligned with standards (HRN ISO 5008, HRN ISO 6396 and HRN ISO 5131). The obtained noise values were divided into two data sets (left and right set) and processed using multiple linear regression (mlr) and three machine learning methods (gradient boosting machine (gbm); support vector machine using radial basis function kernel (svmRadial); monotone multi-layer perceptron neural network (monmlp)). The most accurate method, considering surfaces, from the left side data set—(R2 0.515–0.955); (RMSE 0.302–0.704); (MAE 0.225–0.488)—and the right side—(R2 0.555–0.955); (RMSE 0.180–0.969); (MAE 0.139–0.644)—was monmlp predominantly, and to a lesser extent svmRadial. On analyzing the total data sets from the left and right sides regarding surfaces, gbm emerged as the most accurate method. The application of machine learning methods demonstrated data accuracy, yet in future research, measurements on certain surfaces may need to be repeated multiple times potentially to improve accuracy further. [ABSTRACT FROM AUTHOR]

Palatine and pharyngeal tonsil hypertrophy may lead to dysfunction of the auditory tube due to a propensity for infection, potentially giving rise to otitis media. This is a quantitative and longitudinal study, developed from 2019 to 2021, at the State University of Campinas (UNICAMP). The studied sample comprised 15 participants aged 5 to 12 years (mean 7.9 years), 12 male and 3 female, arranged into two groups: children diagnosed with pharyngeal and/or palatine tonsil hypertrophy who were candidates for surgery (G1), and children who were later evaluated after surgery (G2). As part of the test, an otoscopy and measurements of logoaudiometry, pure-tone threshold audiometry, wideband tympanometry (ambient and peak pressure), and otoacoustic emissions (TEOAEs and DPOAEs, both at ambient and peak pressure) were all performed. There were statistically significant differences between phases in pure-tone audiometry, in terms of 226 Hz tympanometry, wideband tympanometry in peak pressure conditions, in the amplitude measurement TEOAEs in both pressure conditions, in DPOAEs in ambient pressure conditions, and in the signal/noise measurement in both pressures in DPOAEs. Overall, it was found that hearing tests were different for subjects with palatine and pharyngeal tonsil hypertrophy compared to the post-surgical group. [ABSTRACT FROM AUTHOR]

Background: Accurate noise power spectra (NPS) measurement in clinical X‐ray CT exams is challenging due to the need for repeated scans, which expose patients to high radiation risks. A reliable method for single CT acquisition NPS estimation is thus highly desirable. Purpose: To develop a method for estimating local NPS from a single photon counting detector‐CT (PCD‐CT) acquisition. Methods: A novel nearly statistical bias‐free estimator was constructed from the raw counts data of PCD‐CT scan to estimate the variance of sinogram projection data. An analytical algorithm is employed to reconstruct point‐wise covariance cov(xi,xj)$\text{cov}({\bf x}_i,{\bf x}_j)$ between any two image pixel/voxel locations xi${\bf x}_i$ and xj${\bf x_j}$. A Fourier transform is applied to obtain the desired point‐wise NPS for any chosen location xi${\bf x}_i$. The method was validated using experimental data acquired from a benchtop PCD‐CT system with various physical phantoms, and the results were compared with the conventional local NPS measurement method using repeated scans and statistical ensemble averaging. Results: The experimental results demonstrate that (1) the proposed method can achieve pointwise/local NPS measurement for a region of interest (ROI) located at any chosen position, accurately characterizing the NPS with spatial structures resulting from image content heterogeneity; (2) the local NPS measured using the proposed method show a higher precision in the measured NPS compared to the conventional measurement method; (3) spatial averaging of the local NPS yields the conventional NPS for a given local ROI. Conclusion: A new method was developed to enable local NPS from a single PCD‐CT acquisition. [ABSTRACT FROM AUTHOR]

Quantum error mitigation is becoming increasingly crucial. We have reformulated the expression of the Pauli channel, termed as the Z-mixed-state expression of the Pauli channel (ZMSEPC). Based on this expression, we have studied the changes of measurement expectation values after composing multiple Pauli channels and proposed related theorems. Afterward, we proposed a method called quantum error mitigation based on the Z-mixed-state expression of the Pauli channel (QEM-ZMSEPC) that can mitigate both quantum gate noise and quantum measurement noise, which offers a lower complexity compared to traditional measurement error mitigation methods. We have conducted experiments for the QEM-ZMSEPC method on classical computers and real quantum computers. The results demonstrate that compared to zero noise extrapolation method, QEM-ZMSEPC has superior error mitigation effects. Furthermore, our experimental results demonstrate the potential of the QEM-ZMSEPC combining other error mitigation techniques such as Pauli twirling. These positive results imply the significance of QEM-ZMSEPC. [ABSTRACT FROM AUTHOR]

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

Jet noise generated by turbofan engines which are designed for supersonic aircraft operation is expected to remain a challenge for the aerospace industry. For civil supersonic transport, low bypass ratio engines with internal mixing and external plugs are currently seen as promising exhaust architecture to meet aircraft mission and certification requirements. Measurements of the noise from a representative low-bypass ratio long duct mixed flow with external plug exhaust configuration were made, along with corresponding steady-RANS and LES analyses. The measured data showed excess high frequency noise across a wide range of operating conditions as compared to an equivalent single stream jet, or an equivalent uniform jet with internal plug. The absolute levels of the excess noise were relatively insensitive to forward flight, and the excess noise was most dominant when the primary and secondary streams had large velocity and temperature mismatch just upstream of the internal mixer. Preliminary indications from the LES analysis also indicated that significant pressure fluctuations, at the same frequencies which are insensitive to forward flight, develop inside the nozzle as the internal shear layer from the mixer grows and gets accelerated by the external plug. Parametric study test measurements on the fan and core operating conditions were performed, providing support for the hypothesis that the excess noise is generated as the internal shear layer, starting inside the nozzle, gets accelerated by the plug and passes through the weak shock at the nozzle exit where the flow accelerates more and turns over the external plug. [ABSTRACT FROM AUTHOR]

We propose a broadband noise test method (10 Hz–1 MHz) using a direct power method to measure the excess noise of InGaAs/InP avalanche photodiodes (APDs) with a separate absorption, grading, charge, and multiplication structure. By this means, the impact ionization coefficient ratio (the k value) can be accurately determined. Compared with previous studies, this method can distinguish between low-frequency noise and excess noise, so for an appropriate high frequency spectral range, the excess noise can be analyzed without mixing in other noise information. Three different APDs fabricated in-house with different diameters (12, 25, and 60 µm) were investigated using the above-mentioned method under temperatures varying from −10 to −40 °C. Our findings showed that the k value of the APDs decreased as the temperature decreased. When the temperature decreased to −40 °C, the k value of the 12, 25, and 60 µm diameter diodes was as low as 0.1, 0.3, and 0.2, respectively. The measurement results are consistent with our device design and not only provide useful information for the future selection of device materials and structures but also demonstrate the feasibility of the broadband noise measurement method in the accurate determination of the k value. It indicates that low excess noise can be achieved by utilizing the temperature dependence of k. [ABSTRACT FROM AUTHOR]

To mitigate vortex shedding for flow and noise control of a circular cylinder, an experimental approach combining air blowing and porous coating was implemented simultaneously as a hybrid method. Localized air blowing was symmetrically applied through structured porous media at four angles, corresponding to different regions of the flow field: boundary layers, shear layers on the cylinder, separated shear layers, and the cylinder's base. The study involved synchronizing near-field pressure fluctuation and far-field noise measurements with flow field measurements obtained via particle image velocimetry. Near-field pressure measurements were taken around the cylinder's circumference using a remote sensing method. This comprehensive investigation revealed that vortex shedding primarily induced pressure fluctuations at the cylinder's shoulders, resulting in the propagation of acoustic waves to the far field. The hybrid method, alongside the separate application of porous coating and local blowing, showcased substantial efficacy in mitigating near-field pressure, consequently leading to a reduction in far-field noise. These techniques achieved this by strategically shifting the vortex formation region further downstream and expanding the wake region compared to the baseline. Notably, the hybrid method, particularly when local blowing was applied at the base of the porous coated cylinder, exhibited a significantly enhanced impact in this regard, resembling the behavior observed with the individual application of porous coating. [ABSTRACT FROM AUTHOR]

The low-temperature properties of amorphous solids are usually explained in terms of atomic-scale tunneling two level systems (TLS). For almost 20 years, individual TLS have been probed in insulating layers of superconducting quantum circuits. Detecting individual TLS in mechanical systems has been proposed but not definitively demonstrated. We describe an optomechanical system that is appropriate for this goal and describe our progress toward achieving it. In particular, we show that the expected coupling between the mechanical mode and a resonant TLS is strong enough for high visibility of a TLS given the linewidth of the mechanical mode. Furthermore, the electronic noise level of our measurement system is low enough, and the anomalous force noise observed in other nanomechanical devices is absent. [ABSTRACT FROM AUTHOR]

Image processing systems can be used to measure the accuracy of 3D-printed objects. These systems must compare images of the CAD model of the object to be printed with its 3D-printed counterparts to identify any discrepancies. Consequently, the integrity of the accuracy measurement process is heavily dependent on the image processing settings chosen. This study focuses on this issue by developing a customized image processing system. The system generates binary images of a given CAD model and its 3D-printed counterparts and then compares them pixel by pixel to determine the accuracy. Users can experiment with various image processing settings, such as grayscale to binary image conversion threshold, noise reduction parameters, masking parameters, and pixel-fineness adjustment parameters, to see how they affect accuracy. The study concludes that the grayscale to binary image conversion threshold has the most significant impact on accuracy and that the optimal threshold varies depending on the color of the 3D-printed object. The system can also effectively eliminate noise (filament marks) during image processing, ensuring accurate measurements. Additionally, the system can measure the accuracy of highly complex porous structures where the pore size, depth, and distribution are random. The insights gained from this study can be used to develop intelligent systems for the metrology of additive manufacturing. [ABSTRACT FROM AUTHOR]

In India, railway is the major transportation mode for carrying goods and people. The tracks for the movement of the rail were initially constructed in the city for the pre-eminence and expediency of the vantage of the people. Rapid modernization and increasing population in the city crammed the area around the railway tracks. Moving rail on the tracks passing through the city is not compatible, which is creating problems for the nearby residents. In the urban and suburban regions, the railway noise has become a major problem. This study was conducted to examine the perception of the physiological and psychological effects of railway noise in the nearby areas of railway stations in Delhi, India. For this purpose, 10 sites near the railway station were selected for the study. To assess the impact of railway noise pollution on the health of humans, a questionnaire survey was conducted. The data of 344 individuals were collected through the questionnaire survey and analyzed to get the perception towards railway noise. Noise level was monitored by a Sound Level Meter (SLM) and the equivalent noise level (Leq) in dB(A) was used to compute the noise pollution in three shifts, i.e., morning, noon, and evening time. Results showed that 57.65% of female and 86.11% of male respondents in the survey reported the disturbance due to railway noise. The level of noise pollution was found higher in the evening time as compared to the noon and morning period, which exceeds the limit set by the Central Pollution Control Board (CPCB) at all the monitored locations. Findings of the study show that the primary cause of the health problems is railroad noise, which is negatively impacting the health of the residents, who are living in the proximity of the rail track region. The perception survey reported that headache, sleep disturbance, irritation, and stress are common health issues among the locals residing around the railway track proximity in Delhi. [ABSTRACT FROM AUTHOR]

A new afterburner installation is proposed, fueled with pure hydrogen (100%H2) or hydrogen–methane mixtures (60% H2 + 40% CH4, 80% H2 + 20% CH4) for use in cogeneration applications. Two prototypes (P1 and P2) with the same expansion angle (45 degrees) were developed and tested. P1 was manufactured by the classic method and P2 by additive manufacturing. Both prototypes were manufactured from Inconel 625. During the tests, analysis of flue gas (CO2, CO, and NO concentration), PIV measurements, and noise measurements were conducted. The flue gas analysis emphasizes that the behavior of the two tested prototypes was very similar. For all three fuels used, the CO2 concentration levels were slightly lower in the case of the additive-manufactured prototype P2. The CO concentration levels were significantly higher in the case of the additive-manufactured prototype P2 when 60% H2/40% CH4 and 80% H2/20% CH4 mixtures were used as fuel. When pure H2 was used as fuel, the measured data suggest that no additional CO was produced during the combustion process, and the level of CO was similar to that from the Garrett micro gas turbine in all five measuring points. The NO emissions gradually decreased as the percentage of H2 in the fuel mixture increased. The NO concentration was significantly lower in the case of the additive-manufactured prototype (P2) in comparison with the classic manufactured prototype (P1). Examining the data obtained from the PIV measurements of the flow within the mixing region shows that the highest axial velocity component value on the centerline was measured for the P1 prototype. The acoustic measurements showed that a higher H2 concentration led to a reduction in noise of approximately 1.5 dB for both afterburner prototypes. The outcomes reveal that the examined V-gutter flame holder prototype flow was smooth, without any perpendicular oscillations, without chaotic motions or turbulent oscillations to the flow direction, across all tested conditions, keeping constant thermal power. [ABSTRACT FROM AUTHOR]

In this article, we consider the numerical prediction of the noise emission from a wheelset in laboratory conditions. We focus on the fluid–structure interaction leading to sound emission in the fluid domain by analyzing three different methods to account for acoustic sources. These are a discretized baffled piston using the discrete calculation method (DCM), a closed cylindrical volume using the boundary element method (BEM) and radiating elastic disks in a cubic enclosure solved with the finite element method (FEM). We provide the validation of the baffled piston and the BEM using measurements of the noise emission of a railway wheel by considering ground reflections in the numerical models. Selected space-resolved waveforms are compared with experimental results as well as with a fluid–structure interaction finite element model. The computational advantage of a discretized disk mounted on a baffle and BEM compared to FEM is highlighted, and the baffled pistons limitations caused by a lack of edge radiation effects are investigated. [ABSTRACT FROM AUTHOR]

Indoor positioning is a key technology in today's intelligent environments, and it plays a crucial role in many application areas. This paper proposed an unscented Kalman filter (UKF) based on the maximum correntropy criterion (MCC) instead of the minimummean square error criterion (MMSE). This innovative approach is applied to the loose coupling of the Inertial Navigation System (INS) and Ultra-Wideband (UWB). By introducing the maximum correntropy criterion, the MCCUKF algorithm dynamically adjusts the covariance matrices of the system noise and the measurement noise, thus enhancing its adaptability to diverse environmental localization requirements. Particularly in the presence of non-Gaussian noise, especially heavy-tailed noise, the MCCUKF exhibits superior accuracy and robustness compared to the traditional UKF. The method initially generates an estimate of the predicted state and covariance matrix through the unscented transform (UT) and then recharacterizes the measurement information using a nonlinear regression method at the cost of theMCC. Subsequently, the state and covariance matrices of the filter are updated by employing the unscented transformation on the measurement equations. Moreover, to mitigate the influence of non-line-of-sight (NLOS) errors positioning accuracy, this paper proposes a k-medoid clustering algorithm based on bisection k-means (Bikmeans). This algorithm preprocesses the UWB distance measurements to yield a more precise position estimation. Simulation results demonstrate that MCCUKF is robust to the uncertainty of UWB and realizes stable integration of INS and UWB systems. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicles (AUVs) are susceptible to non-line-of-sight (NLOS) errors and noise bias at receiving stations during the application of hydroacoustic localization systems, leading to a degradation in positioning accuracy. To address this problem, this paper optimizes the Chan-Taylor algorithm. Initially, we propose the Weighted Modified Chan-Taylor (WMChan-Talor) algorithm, which introduces dynamic weights into the Chan algorithm to correct noise variance at measurement stations, thereby improving the accuracy of AUV positioning. Computer simulations validate the effectiveness of the WMChan-Taylor algorithm in enhancing positioning accuracy. To further address the accuracy degradation caused by noise deviations across different receiving stations, we introduce an error-corrected WMChan-Taylor algorithm. This algorithm utilizes a standard residual function to eliminate significant delays caused by large errors at receiving stations and applies standard residual weighting to improve the combined positioning solution. The performance of the error-corrected WMChan-Taylor algorithm is demonstrated through both computer and semi-physical simulation experiments, confirming its capability to isolate noisier stations and thus enhance overall positioning accuracy. [ABSTRACT FROM AUTHOR]

This paper introduces a novel and comprehensive model for the analysis of dam deformation trends, integrating the variational mode decomposition (VMD) method, fractal theory, and the whale optimization algorithm (WOA) to re6ne the deep extreme learning machine (DELM) model. This integration allows for a meticulous denoising process through VMD, e7ectively isolating pertinent signal characteristics from noise and measurement interference. Following this, fractal theory is utilized to conduct an in-depth qualitative analysis of the denoised data, capturing intricate patterns within the deformation trends. The model further evolves with the application of WOA to optimize the DELM model, thereby facilitating an integrated approach that merges qualitative insights with quantitative analysis. The efficacy of this advanced model is demonstrated through a case study, highlighting its capability to deliver accurate and reliable predictions that are in harmony with practical engineering scenarios. This research not only o7ers a robust framework for analyzing dam deformation trends but also sets a new standard in the 6eld, providing a new solution for assessing structural integrity in hydrological engineering. [ABSTRACT FROM AUTHOR]

The laser interferometer space antenna (LISA) uses laser interferometry to measure gravitational wave-induced distance changes between freely falling test masses on separate spacecraft. In practice, the space-borne gravitational wave detector operates in a plasma medium, and subsequently, the variations in electron density affect the refractive index and add displacement noise to measurements. Geometric time-delay interferometry (TDI) is the TDI combinations searched by geometric method, which is employed to mitigate overwhelming laser phase noise. This study explores all forty-five second-generation geometric TDI combinations up to sixteen links, analyzing plasma noise effects via power spectral density and cross-spectral density calculations for different optical links. Our findings reveal that plasma noise can exceed the noise floor, which comprises optical metrology system and test mass acceleration noise, in certain low-frequency regions due to different noise transfer behaviors. Further, we establish electron density requirements for various TDI combinations, showing that densities below 100 cm − 3 / Hz @ 10 mHz satisfy LISA's criteria across all forty-five combinations. This allows for optimized TDI selection based on specific plasma densities. [ABSTRACT FROM AUTHOR]

Silicon–germanium heterostructures are a promising quantum circuit platform, but crucial aspects, such as the long-term charge dynamics and cooldown-to-cooldown variations, are still widely unexplored quantitatively. In this Letter, we present the results of an extensive bias cooling study performed on gated silicon–germanium quantum dots with an Al2O3 dielectric. Over 80 cooldowns were performed in the course of our investigations. The performance of the devices is assessed by low-frequency charge noise measurements in the band of 200 μHz to 10 mHz. We measure the total noise power as a function of the applied voltage during cooldown in four different devices and find a minimum in noise at 0.7 V bias cooling voltage for all observed samples. We manage to decrease the total noise power median by a factor of 6 and compute a reduced tunneling current density using Schrödinger–Poisson simulations. Furthermore, we show the variation in noise from the same device in the course of eleven different cooldowns performed under the nominally same conditions. [ABSTRACT FROM AUTHOR]

This article proposes a two‐step interval estimation method for continuous‐time linear time‐invariant systems with unknown but bounded disturbances and measurement noise. In the first step, H∞$$ {H}_{\infty } $$ method is used to design a robust reduced‐order observer that attenuates the impact of the system uncertainties on the estimation error. In the second step, a non‐conservative interval prediction of the estimation error is achieved by applying a symmetric set‐integration approach. The resulting tight set‐valued state estimation is obtained by combining the point estimation from the reduced‐order observer with the predicted estimation error set provided by the set‐integration method. Compared to the conventional methods, the proposed method is built on less‐restrictive design conditions, that allow one to deal with a large range of applications. On the other hand, numerical simulations show that the proposed method offers more precise interval estimation compared to an optimal reduced‐order interval observer selected from recent literature. [ABSTRACT FROM AUTHOR]

Increased use of recreational areas after the lifting of COVID-19 pandemic restrictions has led to increased noise levels. This study aims to determine the level of noise pollution experienced in recreational areas with the increasing domestic and international tourism activities after the lifting of pandemic lockdowns, to produce spatial distribution maps of noise pollution, and to develop strategic planning suggestions for reducing noise pollution in line with the results obtained. Antalya-Konyaaltı Beach Recreation Area, the most important international tourism destination of Turkey, is determined as the study area. To determine the existing noise pollution, 31 measurement points were marked at 100 m intervals within the study area. Noise measurements were taken during the daytime (07:00–19:00), evening (19:00–23:00), and nighttime (23:00–07:00) on weekdays (Monday, Wednesday, Friday) and weekends (Sunday) over 2 months in the summer when the lockdown was lifted. In addition, the sound level at each measurement point was recorded for 15 min, while the number of vehicles passing through the area during the same period was determined. The database created as a result of measurements and observations was analyzed using statistical and geostatistical methods. After the analysis of the data, it was found that the co-kriging-stable model showed superior performance in noise mapping. Additionally, it was revealed that there is a high correlation between traffic density and noise intensity, with the highest equivalent noise level (Leq) on weekdays and weekend evenings due to traffic and user density. In conclusion, regions exposed to intense noise pollution were identified and strategic planning recommendations were developed to prevent/reduce noise sources in these identified regions. [ABSTRACT FROM AUTHOR]

The object of this study is the process of determining and managing the risks of noise exposure (NE) for employees at the mechanical department when machining metals on a drilling machine (DM). The problem relates to the increase in the risk of an employee receiving an industrial injury because of NE at the workplace of a machine tool. The impact of noise on the human body depends on the duration of exposure, the level of sound pressure, and its intensity. An important characteristic of noise is its frequency composition, as it can affect the perception of sounds and the human body. Prolonged exposure to noise can damage various systems in the human body or cause pain. Therefore, it is important to understand the impact of NE on workers and devise measures to reduce the negative impact on their health and work productivity. The research is based on real-time noise measurements followed by their decomposition into octave frequencies and modeling of noise propagation in the room with and without the use of various types and designs of soundproof barriers (SB). In the course of the study of NE propagation during drilling on the machine, an excess of noise levels at medium and high frequencies near the noise source was found. An employee who works on DM is exposed to high-frequency noise that exceeds the established normative indicators. Therefore, it is necessary to use personal protective equipment for the machine operator and install safety equipment to protect other workplaces. The use of the latter makes it possible to significantly reduce the impact of NE on workers, in particular, at low frequencies by 20.8 %, at medium frequencies by 15.6 %, and at high frequencies by 17.3 % [ABSTRACT FROM AUTHOR]