Spatial Distribution Of Noise Research Articles

Study on the Impact of Drainage Noise in Residential Bathrooms Based on Finite Element Simulation

Residential bathroom drainage noise is a primary source of indoor noise that directly affects quality of life and physical and mental health. Therefore, based on the acoustic theory and the finite element simulation technology, this paper proposes a method to simulate the drainage noise characteristics and its impact range jointly using the flow and acoustic fields. The pressure at the pipe wall caused by the internal flow field of the bathroom drainage pipe is calculated by the Fluent software. Simulations are carried out with the Virtual Lab software to predict the drainage noise characteristics and spatial distribution and to analyse the influence of factors such as the door position, riser position, and the partition wall material on the noise distribution. The results show that drainage noise has prominent high-frequency characteristics, the position of the bathroom drainage pipes and doors affects the spatial noise distribution, and the sound insulation performance of a par- tition wall with ordinary fired bricks in the bathroom is slightly better than that of ordinary concrete bricks, lightweight aggregate concrete blocks or fly ash blocks. This paper provides a theoretical basis and practical reference for reducing the impact of residential drainage noise and creating a healthy and comfortable indoor acoustic environment.

Analysis of traffic noise spatial distribution characteristics and influencing factors in high-density cities

Urban spatial structure factors can have considerable effects on traffic noise distribution. This study aimed to describe the spatial distribution characteristics of traffic noise and influencing factors in high-density cities. Fifteen indexes were selected from the three aspects of building layout, road organization and land use to analyse the influence of urban spatial structure factors on the noise spatial distribution. This paper selected the central area of Tianjin as the study area. The noise level was generated by the noise simulation software CadnaA, and the numerical statistics were determined by ArcGIS. The correlation between various urban indexes and the spatial distribution of traffic noise were determined with correlation analysis, and multiscale geographically weighted regression (MGWR) analysis was used to determine the degree and scope of influence of various urban indexes on the spatial distribution of traffic noise. The results indicate that there are significant differences in the spatial agglomeration forms of high, medium, and low noise areas among research units, and the spatial agglomeration of low noise areas in each unit is stronger. The building layout indexes, except for the average building height (ABH), are significantly negatively correlated with the noise levels in low noise areas (L70–L90). The road length index (RLI), road area density (RAD), road landscape shape index (RLSI), trunk road length index (TRLI), proportion of traffic land (PT), and degree of land mixing (DLM) are significantly positively correlated with the noise levels of all areas within the unit (L10–L90). Among the three aspects of building layout, road organization, and land use, the architectural landscape shape index (ALSI), RAD, and DLM have the greatest influence on the average spatial sound pressure level (Lavg), with mean regression coefficients of −0.254, 0.520, and 0.174, respectively. The ground space index (GSI), ALSI, proportion of residential land (PR) and PT are the global factors influencing Lavg.

Signal Photon Extraction Method for ICESat-2 Data Using Slope and Elevation Information Provided by Stereo Images

Laser altimetry data from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) contain a lot of noise, which necessitates the requirement for a signal photon extraction method. In this study, we propose a density clustering method, which combines slope and elevation information from optical stereo images and adaptively adjusts the neighborhood search direction in the along-track direction. The local classification density threshold was calculated adaptively according to the uneven spatial distribution of noise and signal density, and reliable surface signal points were extracted. The performance of the algorithm was validated for strong and weak beam laser altimetry data using optical stereo images with different resolutions and positioning accuracies. The results were compared qualitatively and quantitatively with those obtained using the ATL08 algorithm. The signal extraction quality was better than that of the ATL08 algorithm for steep slope and low signal-to-noise ratio (SNR) regions. The proposed method can better balance the relationship between recall and precision, and its F1-score was higher than that of the ATL08 algorithm. The method can accurately extract continuous and reliable surface signals for both strong and weak beams among different terrains and land cover types.

Whitening method for passive water pipeline assessment using ambient noise

Previous studies have verified the feasibility of using ambient noise in water pipeline networks as natural signals to assess pipeline condition passively. This passive technology uses correlation peaks of the ambient noise as characteristics to identify pipe faults and deterioration, and it requires the noise power spectrum density (PSD) to be adequately whitened. However, conventional noise whitening methods cannot provide sufficient whitening for the ambient noise in water pipelines due to the significant noise PSD variations that are unevenly distributed across the wide relative bandwidth of the noise signals. This paper proposes a tailored whitening process for water pipeline ambient noise by applying logarithmic transforms on both the magnitude and frequency axes to compresses the large dynamic range of the PSD magnitude and bandwidth. Numerical simulations and field experiment results have verified that the proposed method can effectively whiten the noise signals in water pipelines, and accurately restore the amplitude of correlation peaks to be consistent with the theoretical derivation using white noise assumptions. The theoretical derivation also reveals the relation between the amplitudes of correlation peaks and the noise spatial distribution, fault distance and its reflection/transmission coefficients. The feasibility of estimating the reflection coefficient of pipe faults from the correlation results with appropriately whitened noise is also investigated in this paper.

Spatiotemporal evaluation of Rayleigh surface wave estimated from roadside dark fiber DAS array and traffic noise

Seismic imaging and monitoring of the near-surface structure are crucial for the sustainable development of urban areas. However, standard seismic surveys based on cabled or autonomous geophone arrays are expensive and hard to adapt to noisy metropolitan environments. Distributed acoustic sensing (DAS) with pre-existing telecom fiber optic cables, together with seismic ambient noise interferometry, have the potential to fulfill this gap. However, a detailed noise wavefield characterization is needed before retrievingcoherent waves from chaotic noise sources. We analyze local seismic ambient noise by tracking five-month changes in signal-to-noise ratio (SNR) of Rayleigh surface wave estimated from traffic noise recorded by DAS along the straight university campus busy road. We apply the seismic interferometry method to the 800 m long part of the Penn State Fiber-Optic For Environment Sensing (FORESEE) array. We evaluate the 160 virtual shot gathers (VSGs) by determining the SNR using the slant-stack technique. We observe strong SNR variations in time and space. We notice higher SNR for virtual source points close to road obstacles. The spatial noise distribution confirms that noise energy focuses mainly on bumps and utility holes. We also see the destructive impact of precipitation, pedestrian traffic, and traffic along main intersections on VSGs. A similar processing workflow can be applied to various straight roadside fiber optic arrays in metropolitan areas.

Spatial Distribution of Noise from Religious Houses in Rumu-Okwachi Community, Rivers State, Nigeria

Noise from 12 religious structures was mapped in the Rumu-Okwachi community. Religious noise in residential settings is a growing concern. Noise levels for daytime religious activity were monitored before, during, and after religious sessions at 15 sample sites defined by superimposing 150-by-150-meter gridlines. The findings showed that during religious activities, the highest equivalent noise level (Leq) was 75.5 (dBA) at sampling point 13, whereas during religious non-activities, it was 63.3 (dBA) at point 7. A two-way analysis of variance (ANOVA) showed p<0.05 that the average daily noise level differed across sample sites. The highest peak noise level (L10), intermediate noise level (L50), and background noise level (L90) were recorded on Sundays during religious events. The geographical distribution of Leq values at all sample sites verified the noise map's prediction of higher Leq values during religious occasions, with Sunday having the highest Leq values of 69.2054 to 75.544. (dBA). The noise indices were compared to the WHO's recommended noise exposure limit, which showed that during religious events, the Leq values were higher than the WHO's recommended noise standard, with the maximum noise pollution level (NPL) being 96.17 (dBA). This suggests that residents of this neighborhood may experience bothersome noise levels.

Auxiliary Segmentation Method of Osteosarcoma MRI Image Based on Transformer and U-Net.

One of the most prevalent malignant bone tumors is osteosarcoma. The diagnosis and treatment cycle are long and the prognosis is poor. It takes a lot of time to manually identify osteosarcoma from osteosarcoma magnetic resonance imaging (MRI). Medical image processing technology has greatly alleviated the problems faced by medical diagnoses. However, MRI images of osteosarcoma are characterized by high noise and blurred edges. The complex features increase the difficulty of lesion area identification. Therefore, this study proposes an osteosarcoma MRI image segmentation method (OSTransnet) based on Transformer and U-net. This technique primarily addresses the issues of fuzzy tumor edge segmentation and overfitting brought on by data noise. First, we optimize the dataset by changing the precise spatial distribution of noise and the data-increment image rotation process. The tumor is then segmented based on the model of U-Net and Transformer with edge improvement. It compensates for the limitations of U-semantic Net by using channel-based transformers. Finally, we also add an edge enhancement module (BAB) and a combined loss function to improve the performance of edge segmentation. The method's accuracy and stability are demonstrated by the detection and training results based on more than 4,000 MRI images of osteosarcoma, which also demonstrate how well the method works as an adjunct to clinical diagnosis and treatment.

Traffic Noise as a Factor of Health Risks and Restrictions on the Use of Land Plots of Urban and Rural Settlements

Introduction: Noise is one of the priority risk factors for human health and the environment. Urban noise exposure demonstrates a steady increase. New instruments of government regulation are necessary to prevent sanitary and epidemiological ill-being of the population. The existing regulatory framework and methodological approaches facilitate management of the current situation while maintaining health of citizens. Objective: To zone the territory of a large city by noise levels for the tasks of urban planning and assessment of health risks posed by road traffic noise. Materials and methods: Data on 4,200 sections of the road network as sources of urban noise with the average daily traffic of ca. 300,000 vehicles were used for acoustic modeling. Complex acoustic calculations were performed using the “Ecolog-Noise” software incorporating current regulations and guidelines on spatial distribution of noise for the entire area of the city within the boundaries of the computational rectangle sized 50 × 35 km and a grid step of 200 meters. The mapping of zoning results was made using geographic information systems. Potential risks of health disorders were assessed in accordance with the current methodology for assessing human health risk posed by traffic noise. Results: Based on simulation and situational acoustic modeling of spatial distribution, we zoned the territory of the city by noise levels for the tasks of urban development and assessed potential health risks for its citizens. We established zones of acoustic discomfort with the noise levels above 55 dBA and 45 dBA, taken as safety criteria for daytime and nighttime, respectively. Three major zones with different levels of noise exposure were identified: zone 1 (< 45 dBA) with the area of 255.44 km2 ; zone 2 (45–55 dBA), 263.72 km2 ; and zone 3 (> 55 dBA), 289.88 km2 . We estimated that most city inhabitants (893,306 people) lived in the third zone and were at risk of noise-related health disorders. The results of health risk assessment showed that long-term exposure to urban noise generated moderate risks by 14 years of age and high and very high risks by 45 and 55 years of age, respectively. Conclusion: Under current circumstances, the use of certain land plots in the city is limited. Such areas require effective noise protection and minimal residential development to eliminate human exposure to harmful noise levels. Our findings have substantiated managerial decisions on the spatial development of urban territories.

Hierarchies of localizable entanglement due to spatial distribution of local noise

Complete characterization of a noisy multipartite quantum state in terms of entanglement requires full knowledge of how the entanglement content in the state is affected by the spatial distribution of noise in the state. Specifically, we find that if the measurement basis in the protocol of computing localizable entanglement and the basis of the Kraus operator representing the local noisy channel do not commute, the information regarding the noise is retained in the system even after the qubit is traced out after measurement. Using this result and the basic properties of entanglement under noise, we present a set of hierarchies that localizable entanglement over a specific subsystem in a multiqubit state can obey when local noise acts on the subparts or on all the qubits of the whole system. In particular, we propose two types of hierarchies---one tailored according to the number of noisy unmeasured qubits, and the other one that depends additionally on the cardinality of the set of noisy measured qubits, leading to the classification of quantum states. We report the percentage of states satisfying the proposed hierarchies in the case of random three- and four-qubit systems and show, using both analytical methods and numerical simulations, that in almost all the cases, anticipated hierarchies tend to hold with the variation of the strength of noise.

Mapping and distribution of noise using IDW interpolation algorithm in Thuan An city, Binh Duong province

The study is conducted to assess the level of noise pollution from traffic activities in urban areas (in the case of Thuan An city, Binh Duong province) with the specific goal of applying geographic information systems. (GIS) in building noise pollution distribution maps in the study area. The research team collected noise data at peak hours and normal hours on weekdays and weekends using noise meters at 61 survey points. Noise measurement data was then interpolated using IDW (Inverse Distance Weighting) method to assess the spatial distribution of noise in Thuan An city. In addition, the study digitizes traffic routes and special areas (hospitals, schools, churches - pagodas) in the study area to identify areas affected by sound noisy. The results show that the areas near the main road are most affected by noise during rush hour, while areas in the small lane are noise level within the permitted range. In addition, the results also show the difference in noise level and noise time between weekdays and weekends.

Reconstruction of sparse ocean noise fields with generative neural networks

Modeling the underwater noise field offers important insights for underwater navigation, assessing noise impacts, and establishing noise budgets. In situ measurements of the noise field are often sparse with limited information about spatially distributed underwater acoustic fields. In this work, we demonstrate the use of a generative neural network for estimating noise fields over regions not directly measured. An ARiA developed ambient noise model is used to generate noise fields for various ocean noise-source scenarios. The model output includes vertical directionality, depth dependence, and incorporates multiple sources of sound, including anthropogenic, biologic, and environmental. We train generative models on these simulated spatial noise distributions a priori. These generative models are able to take sparsely sampled noise measurements and construct the spatial distribution of the noise field. The generative model learns the spatial distribution by minimizing the point-to-point difference between the generated fields and the true fields at each step while also maximizing the fidelity of the general distribution of the generated noise fields to the true fields. Through a nested generative model approach, along with auxiliary sound-speed-profile information, the local field is used to extrapolate the noise field to larger spatial scales.

Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm.

To characterize the noise and spatial resolution properties of a commercially available deep learning-based computed tomography (CT) reconstruction algorithm. Two phantom experiments were performed. The first used a multisized image quality phantom (Mercury v3.0, Duke University) imaged at five radiation dose levels (CTDIvol : 0.9, 1.2, 3.6, 7.0, and 22.3mGy) with a fixed tube current technique on a commercial CT scanner (GE Revolution CT). Images were reconstructed with conventional (FBP), iterative (GE ASiR-V), and deep learning-based (GE True Fidelity) reconstruction algorithms. Noise power spectrum (NPS), high-contrast (air-polyethylene interface), and intermediate-contrast (water-polyethylene interface) task transfer functions (TTF) were measured for each dose level and phantom size and summarized in terms of average noise frequency (fav ) and frequency at which the TTF was reduced to 50% (f50% ), respectively. The second experiment used a custom phantom with low-contrast rods and lung texture sections for the assessment of low-contrast TTF and noise spatial distribution. The phantom was imaged at five dose levels (CTDIvol : 1.0, 2.1, 3.0, 6.0, and 10.0mGy) with 20 repeated scans at each dose, and images reconstructed with the same reconstruction algorithms. The local noise stationarity was assessed by generating spatial noise maps from the ensemble of repeated images and computing a noise inhomogeneity index, , following AAPM TG233 methods. All measurements were compared among the algorithms. Compared to FBP, noise magnitude was reduced on average (± one standard deviation) by 74±6% and 68±4% for ASiR-V (at "100%" setting) and True Fidelity (at "High" setting), respectively. The noise texture from ASiR-V had substantially lower noise frequency content with 55±4% lower NPS fav compared to FBP while True Fidelity had only marginally different noise frequency content with 9±5% lower NPS fav compared to FBP. Both ASiR-V and True Fidelity demonstrated locally nonstationary noise in a lung texture background at all radiation dose levels, with higher noise near high-contrast edges of vessels and lower noise in uniform regions. At the 1.0mGy dose level values were 314% and 271% higher in ASiR-V and True Fidelity compared to FBP, respectively. High-contrast spatial resolution was similar between all algorithms for all dose levels and phantom sizes (<3% difference in TTF f50% ). Compared to FBP, low-contrast spatial resolution was lower for ASiR-V and True Fidelity with a reduction of TTF f50% of up to 42% and 36%, respectively. The deep learning-based CT reconstruction demonstrated a strong noise magnitude reduction compared to FBP while maintaining similar noise texture and high-contrast spatial resolution. However, the algorithm resulted in images with a locally nonstationary noise in lung textured backgrounds and had somewhat degraded low-contrast spatial resolution similar to what has been observed in currently available iterative reconstruction techniques.

Assessment of engineering equipment noise impact of agricultural enterprise on the adjacent residential building

A significant source of increased noise in cities is the engineering and technological equipment of enterprises located in immediate vicinity of standardized objects. The expected noise levels from technological and ventilation equipment of an agricultural enterprise located in the city are estimated. A computer model of the territory under consideration is built using the software package “ARM Acoustics”. As the initial data, the results of field measurements were used. The calculation of the expected noise levels near the facades of residential buildings showed the excess of regulatory levels. It is shown the necessity to install noise screens not only near equipment located on the ground, but also near equipment installed on the roof of the industrial building. The calculation was carried out taking into account the location of the U - shaped screens, which provided effective protection against noise. To illustrate the effect obtained, maps of the spatial distribution of noise are constructed.

Observational and Critical State Physics Descriptions of Long-Range Flow Structures

Using Fracture Seismic methods to map fluid-conducting fracture zones makes it important to understand fracture connectivity over distances greater 10–20 m in the Earth’s upper crust. The principles required for this understanding are developed here from the observations that (1) the spatial variations in crustal porosity are commonly associated with spatial variations in the magnitude of the natural logarithm of crustal permeability, and (2) many parameters, including permeability have a scale-invariant power law distribution in the crust. The first observation means that crustal permeability has a lognormal distribution that can be described as κ ≈ κ 0 exp ( α ( φ − φ 0 ) ) , where α is the ratio of the standard deviation of ln permeability from its mean to the standard deviation of porosity from its mean. The scale invariance of permeability indicates that αϕο = 3 to 4 and that the natural log of permeability has a 1/k pink noise spatial distribution. Combined, these conclusions mean that channelized flow in the upper crust is expected as the distance traversed by flow increases. Locating the most permeable channels using Seismic Fracture methods, while filling in the less permeable parts of the modeled volume with the correct pink noise spatial distribution of permeability, will produce much more realistic models of subsurface flow.

Assessment of Noise Level and Distribution from Socotherm Nigeria Limited: Its Health Implications on the Workers

Exposure to noise for a long period of time has been an issue of concern to occupational health workers. This study investigated noise pollution level (NPL) and its spatial distribution in Socotherm Nigeria Limited coating plants and its effects on the health of the workers. Six sampling points were selected on the grounds of intensity of activities. A digital sound level Metre-Extech 407730 was used to measure noise level at the sampling points namely, application area, welding spot, aggregate separator, generator/powerhouse, impingement plant, bushing area. A global positioning system (GPS)-NAVA 300 was used to record the GPS coordinates of the sampling noise hotspots in the factory. The Arc GIS software was used to interpolate the spatial distribution of noise within the factory for the working periods which include morning, afternoon and evening. With a wellconstructed questionnaire, results of the health implication of workers exposure to noise from the factory were recorded. Results revealed that afternoon has the highest noise level of 99.20 dB(A) at welding point and application area conveyor of the factory while the lowest noise was observed at 85.20 dB(A) which was at cold spot of chemical feed area of the factory. The study observed that majority of the machines (noise hotspots) produce noise that was above Occupational Safety and Health Association (OSHA) permissible noise level. This study therefore recommends that workers be encouraged to use personal protective equipment and be educated regarding noise induced hearing loss and other non-auditory effects of noise exposure. The factories should adopt and implement hearing conservation programs under the direction of an occupational and environmental health professional.

On the Use of Linear-Modelling-based Algorithms for Physiological Noise Correction in fMRI Studies of the Central Breathing Control.

A full characterization of the physiological behavior of human central chemoreceptors through fMRI is crucial to understand the pathophysiology of central abnormal breathing patterns. In this scenario, physiological noise and activity of interest may be naturally correlated. Here, we examined the adequacy of linear-modelling-based retrospective physiological noise correction for studies of the central breathing control. We focused on the relationship between a nonlinear model of BOLD response, hypothesized to describe neuronal specific activity, and noise modelled by correction algorithms. Analyses were performed on fMRI acquisitions from healthy subjects during a breath hold task. A general linear model including static nonlinearities in the response to end-tidal CO2 was applied to data preprocessed both with and without physiological noise correction. Relations between physiological noise and PETCO2 were explored both with linear and nonlinear measures. Lastly, parametric maps of noise spatial distribution were extracted. Our results evidenced that correction algorithms based on linear modelling remove components that are both linearly and nonlinearly related to end-tidal CO2, whereas uncorrected data showed spurious activations in regions outside gray matter. Thus, despite a correction step is fundamental, these algorithms are shown to be over-conservative approaches to noise correction and need to be adapted to the specific purpose.

Estimation of shipping noise from sparse measurements via generative adversarial networks

There is growing interest in prediction of anthropogenic noise levels in the ocean. Evidence suggests that sources of ambient noise such as shipping traffic may be destructive to marine organisms that rely on acoustics for communication. Characterizing and predicting ambient noise are also critical to effective naval operations. Understanding ocean noise is constrained by the limited ability to directly measure the spatial distribution of noise levels. In this work, we present a deep-learning method to estimate the spatial distribution of ambient noise due to shipping from a small number of measurements. Noise levels are typically estimated using forward models based on statistical information about shipping routes and source levels and predictions or measurements of environmental variables including bathymetry and sound-speed profile. Inverse methods are sometimes used to estimate input parameters from in situ data. In contrast, we demonstrate the robust estimation of shipping noise using a pretrained generative adversarial network (GAN) as a prior. By using a context and prior loss, our algorithm is able to accurately predict the entire spatial distribution of noise from sparsely sampled measurements. This can yield greater accuracy than a forward model based on environmental parameters taken from archival databases or inferred in situ.

Spatial mapping of underwater noise radiated from passing vessels using automatic identification system data

Information regarding the spatial distribution of noise radiated by ships in specific areas is used as basic data for the protection of marine organisms and establishment of marine spatial planning. This study derived the spatial distribution of the noise radiated from ships navigating the southern region of Korea by acoustic modeling using Automatic Identification System data. The acoustic energy propagated from a ship to all spatial points in the experimental area was modeled using a ray-based model, and the noise level at a particular point was incoherently summed over all spatial points and averaged over time. By applying the acoustic modeling technique, we derived the average noise level at frequencies below 1 kHz radiated from ships in the ocean, and compared it with the acoustic data measured during the same period. Discrepancy between both results was found at frequencies below 250 Hz, and the possible cause of this discrepancy is related to the power-law model used to calculate the source level of the ship based on an empirical formula. The difference in current can be reliably reduced by applying a more accurate ship source model.

Forecasting increases in recreational value that would result from restoration of natural soundscapes in National Parks

U. S. National Parks are justly celebrated for the superlative quality of their scenic and cultural resources. The Grand Canyon Enlargement Act of 1975 identified natural quiet as an important resource and value. These considerations are two motivations for extensive monitoring of acoustic environments in national park units, and development of models that predict sound levels throughout the system. These models also estimate the spatial distribution of noise of noise exposure. Related social science research has documented the effects of noise on the quality of visitor experience in parks, including the accessibility of wildlife for public viewing. Surveys of park visitors and the American public also document broad support of park efforts to reduce noise. Collectively, this research provides a quantitative framework for estimating the increases in the quality of visitor experience that could be realized through reductions in noise.U. S. National Parks are justly celebrated for the superlative quality of their scenic and cultural resources. The Grand Canyon Enlargement Act of 1975 identified natural quiet as an important resource and value. These considerations are two motivations for extensive monitoring of acoustic environments in national park units, and development of models that predict sound levels throughout the system. These models also estimate the spatial distribution of noise of noise exposure. Related social science research has documented the effects of noise on the quality of visitor experience in parks, including the accessibility of wildlife for public viewing. Surveys of park visitors and the American public also document broad support of park efforts to reduce noise. Collectively, this research provides a quantitative framework for estimating the increases in the quality of visitor experience that could be realized through reductions in noise.

Nonspecific synaptic plasticity improves the recognition of sparse patterns degraded by local noise

Many forms of synaptic plasticity require the local production of volatile or rapidly diffusing substances such as nitric oxide. The nonspecific plasticity these neuromodulators may induce at neighboring non-active synapses is thought to be detrimental for the specificity of memory storage. We show here that memory retrieval may benefit from this non-specific plasticity when the applied sparse binary input patterns are degraded by local noise. Simulations of a biophysically realistic model of a cerebellar Purkinje cell in a pattern recognition task show that, in the absence of noise, leakage of plasticity to adjacent synapses degrades the recognition of sparse static patterns. However, above a local noise level of 20%, the model with nonspecific plasticity outperforms the standard, specific model. The gain in performance is greatest when the spatial distribution of noise in the input matches the range of diffusion-induced plasticity. Hence non-specific plasticity may offer a benefit in noisy environments or when the pressure to generalize is strong.