Mixed Distribution Research Articles

Region-Aware Exposure Consistency Network for Mixed Exposure Correction

Exposure correction aims to enhance images suffering from improper exposure to achieve satisfactory visual effects. Despite recent progress, existing methods generally mitigate either overexposure or underexposure in input images, and they still struggle to handle images with mixed exposure, i.e., one image incorporates both overexposed and underexposed regions. The mixed exposure distribution is non-uniform and leads to varying representation, which makes it challenging to address in a unified process. In this paper, we introduce an effective Region-aware Exposure Correction Network (RECNet) that can handle mixed exposure by adaptively learning and bridging different regional exposure representations. Specifically, to address the challenge posed by mixed exposure disparities, we develop a region-aware de-exposure module that effectively translates regional features of mixed exposure scenarios into an exposure-invariant feature space. Simultaneously, as de-exposure operation inevitably reduces discriminative information, we introduce a mixed-scale restoration unit that integrates exposure-invariant features and unprocessed features to recover local information. To further achieve a uniform exposure distribution in the global image, we propose an exposure contrastive regularization strategy under the constraints of intra-regional exposure consistency and inter-regional exposure continuity. Extensive experiments are conducted on various datasets, and the experimental results demonstrate the superiority and generalization of our proposed method. The code is released at: https://github.com/kravrolens/RECNet.

New method to identify optimal discontinuity set number of rock tunnel excavation face orientation based on Fisher mixed evaluation

Discontinuity is critical for strength, deformability, and permeability of rock mass. Set information is one of the essential discontinuity characteristics and is usually accessed by orientation grouping. Traditional methods of identifying optimal discontinuity set numbers are usually achieved by clustering validity indexes, which mainly relies on the aggregation and dispersion of clusters and leads to the inaccuracy and instability of evaluation. This paper proposes a new method of Fisher mixed evaluation (FME) to identify optimal group numbers of rock mass discontinuity orientation. In FME, orientation distribution is regarded as the superposition of Fisher mixed distributions. Optimal grouping results are identified by considering the fitting accuracy of Fisher mixed distributions, the probability monopoly and central location significance of independent Fisher centers. A Halley-Expectation-Maximization (EM) algorithm is derived to achieve an automatic fitting of Fisher mixed distribution. Three real rock discontinuity models combined with three orientation clustering algorithms are adopted for discontinuity grouping. Four clustering validity indexes are used to automatically identify optimal group numbers for comparison. The results show that FME is more accurate and robust than the other clustering validity indexes in optimal discontinuity group number identification for different rock models and orientation clustering algorithms.

A P–S–N curve fitting method based on mixed Weibull distribution and expectation-maximization algorithm

In practical engineering, there are many factors affecting the fatigue life of actual components, and a single Weibull distribution is insufficient to accurately describe the impact of various factors on fatigue life. In order to solve the above problems, 316L stainless steel is taken as the experimental object to study the effects of phase difference and stress levels on fatigue life based on the mixed Weibull distribution model. Firstly, the mixed Weibull distribution model is converted into the mixed extreme value distribution model, and used Euler's constant to calculate relevant parameters of extreme value distribution and Expectation-Maximization (EM) algorithm to determine the probability density function of the mixed Weibull distribution. Secondly, the probability density function is integrated to obtain the distribution function and the fatigue life under different failure probabilities is calculated. Finally, the influence of non-proportional additional reinforcement and stress amplitude on fatigue life is studied by taking them as hidden variables of mixed Weibull distribution, which is used to obtain P-S-N curves for various failure probabilities. Through the proposed method, the influence of different factors on fatigue life can be considered from the perspective of statistics, and the accuracy of fatigue life prediction can be promoted.

Joint equalization of RSOP and PMD fused probability-aware with square-root cubature Kalman filter for the PDM PCS-64QAM system under extreme scenarios.

Extreme scenario of lightning strikes would generate ultra-fast rotation of state-of-polarization (RSOP) up to 5.1 Mrad/s and large polarization mode dispersion (PMD) in optical ground wire (OPGW). Unfortunately, the conventional multiple modulus algorithm (MMA) cannot equalize these polarization impairments in polarization division multiplexing (PDM) probabilistic constellation shaping (PCS)-64QAM system. Moreover, due to unavoidable linearization errors and higher modulation order, the extended Kalman filter based on measurement equations of concatenated multiplication (EKF-CM) is highly unstable and fails under such scenarios. To address the above issues, we have proposed a joint equalization scheme of PMD and RSOP, which fuses probability-aware with square-root cubature Kalman filter (PA-SCKF). Firstly, according to the characteristic that the amplitude of PCS signals obeys mixed Rician distribution, the scheme combines maximum a posteriori criterion to obtain the optimal radius of constellation ring which the received symbol belongs to, for the sake of calculating the innovations of SCKF. Secondly, it performs joint equalization of PMD and RSOP impairments based on SCKF and time-frequency conversion architecture. 28GBaud PDM PCS-64QAM simulation results demonstrate that our scheme can jointly equalize maximum impairments of 8.34 Mrad/s RSOP and 90ps DGD under entropy of 4.5bits/symbol. Additionally, only 0.9 dB OSNR penalty is obtained after joint equalization of 6 Mrad/s RSOP with 70ps DGD impairments. Even under entropy of 5.5bit/symbol, it can still jointly equalize impairments of 6.05 Mrad/s RSOP with 60ps DGD. Furthermore, 16GBaud PCS-64QAM experimental results indicate that the maximum joint equalization performances of PA-SCKF scheme under entropy of 4.5bit/symbol and 5bit/symbol are 17 Mrad/s RSOP with 52ps DGD, and 9 Mrad/s RSOP with 52ps DGD, respectively. These results manifest that our PA-SCKF scheme outperforms both MMA and EKF-CM schemes. Importantly, its complexity is on an order of O(Llog2 L), which is comparable to that of EKF-CM scheme.

Peeling back the layers of extinction of dusty galaxies in the era of JWST: modelling joint NIRSpec + MIRI spectra at rest-frame 1.5–28 μm

ABSTRACT We present an analysis of the combined NIRSpec and MIRI spectra of dusty galaxies between 1.5 and 28 $\mu$m rest-frame by implementing a differential extinction model, where the strength of extinction varies across the spectrum as different layers of the obscuring dust are probed. Our model is able to recover a 2D distribution of dust temperature and extinction, which allows inference of the physical nature of the dust in these environments. We show that differential extinction is necessary to reproduce the spectra of four highly obscured Luminous Infrared Galaxies observed with NIRSpec IFU and MIRI MRS, where simple screen or uniformly mixed dust distributions fail to fit the data. We additionally compare the extinction of H ii regions in these galaxies via hydrogen recombination lines, the extinction of molecular gas via the H2 lines, polycyclic aromatic hydrocarbons via the 12.7/11.3 PAH ratio and the stellar continuum. We find that the molecular gas is deeply buried with the H ii regions in star-forming regions, with a similar extinction to the hottest dust components. However, we find the cooler dust to be less obscured, at a similar extinction to the stellar continuum and PAHs. The nuclei show a complex dust distribution with VV114 NE, NGC 3256 S, and IIZw96 SW, showing a deeply buried continuum source relative to the molecular gas/H ii regions. Additionally, NGC 3256 S, NGC 7469, and VV114 SW show an isolated hot dust component, indicative of AGN heating, where NGC 3256 S and NGC 7469 are previously known as AGN.

Anaerobic co-digestion of food waste and bio flocculated sewage sludge towards bio-methane production

The existing sewage treatment plants are energy consumers rather than producers and integration of anaerobic reactor can be a viable approach for enhancing their commercial viability. Therefore, anaerobic co-digestion of food waste and bio-flocculated sewage sludge was studied in a continuous stirred tank reactors (R1 and R2) having a working volume of 6.5 Litre at organic loading rates of 2.5 and 2.4 g VS L−1D−1. At mesophilic temperature, the stabilisation of inoculum was performed in batch mode for 60 days followed by continuous mode feeding for 120 days. The feeding ratio was decided based on availability of food waste and bio-flocculated sewage sludge from a typical domestic household for analysing the effect of volatile fatty acids accumulation on reactor performance. The R1 was fed with equal proportion of bio-flocculated sewage sludge and food waste while R2 was fed in 98:2 (v/v), respectively. To analyse the mixing and temperature distribution within the digester, the velocity and temperature profiles were obtained using Ansys Fluent. In R1, more biogas yield were observed than R2 due to 80% average VS reduction as compared to 55% in case of R2. The highest volatile fatty acids accumulation of 10951 mg L−1 was observed in R1 whereas R2 showed 2800 mg L−1, respectively. The more fluctuations in pH were observed in R1 as compared to R2 due to more food waste concentration and maximum bio-methane production was 133 and 233 mL CH4 g−1 VSadded in R1 and R2, respectively.

The impact of autonomous vehicles on the performance of steel bridge deck pavement

The widespread application of autonomous driving technology has become a prominent trend in the transportation industry. The purpose of this study was to investigate the impact of the lateral distribution pattern of autonomous vehicles (AVs) on steel bridge deck pavements (SBDP), with the aim of providing reliable theoretical support for future bridge deck pavement management and operation. A mixed traffic flow lateral distribution model was established and the Nanjing Yangtze Fourth Bridge was used as a case study. A corresponding finite-element model (FE) was constructed and Abaqus FE software was used to analyse rutting depths under different scenarios. The simulation results demonstrated that, under the zero distribution mode, a rational layout of AVs can significantly alleviate rut formation. Conversely, an unreasonable layout will reduce the service life of SBDP. Comparing the lateral distribution patterns of different vehicles, both the normal distribution and uniform distribution patterns exhibited positive effects on the SBDP. However, there was no significant difference in the magnitude of this positive impact between the two modes. This research not only fills the gap in the study of AV impact on SBDP but also proposes management methods for AVs.

Short-term Safety Performance Functions by Random Parameters Negative Binomial-Lindley model for Part-time Shoulder Use

Part-time Shoulder Use (PTSU) is a traffic management and operation strategy that allows the use of the left or right shoulder as a travel lane, typically during the peak hours of the day. Though PTSU is an effective strategy for increasing roadway capacity in congested traffic conditions, there is very limited quantitative information about PTSU design elements and operational strategy in the existing literature, which could impact the occurrence of crashes on freeways. This study contributes to the safety literature by analyzing various potential crash contributing factors related to PTSU operation and design elements through the development of short-term Safety Performance Functions (SPFs). A comparison of the estimated models demonstrated that by utilizing the mixed distribution and allowing the posterior parameter estimates of explanatory variables to vary from one observation to another, the Random Parameters Negative Binomial-Lindley (RPNB-L) model outperformed the traditional NB and fixed coefficient NB-L models. The results of the proposed RPNB-L model indicated that the PTSU implemented sections experienced a lower number of traffic crashes compared to the non-PTSU freeway sections. Among the attributes related to PTSU operation and design elements, the usage of the leftmost shoulder lane as PTSU, the presence of emergency rest areas for damaged vehicles, and adequate shoulder width would significantly reduce crash frequency for the PTSU implemented freeways. Moreover, investigation of the identified hotspots revealed that the transition areas (start/end locations of PTSU) are the most critical sections. The findings from this research could assist transportation agencies to take appropriate countermeasures for preventing and reducing crash occurrences on PTSU implemented freeways.

A nonparametric random effects model for the valuation of forest recreation services: An application to forest sites in Tuscany, Italy

AbstractThis study assesses individuals' preferences for the use of forest sites for recreational purposes by means of the logit‐mixed logit (LML) model. The appeal of the LML is that the analyst does not need to assume any specific functional form for the mixing distributions of random preferences. The empirical analysis generates a data‐driven nonparametric representation of individuals' preference heterogeneity. We apply this approach to data collected using an unlabelled discrete choice experiment (DCE), consisting of three recreational options, two of which are in two hypothetical forest sites. Forest destinations are described by means of six attributes: forest type, signposting, hiking time, access to rivers or lakes, wildlife watch hides for visitors and cost of access. The empirical findings reveal that the signpost for each trail is the attribute for which respondents are on average willing to pay the most (6.565€). Further evidence suggests that respondents have strong positive preferences for those forest sites that offer amenities such as wildlife watching hides and access to rivers or lakes. Finally, the histograms derived from the semi‐parametric LML estimation reveal multimodality of random taste amongst respondents for different hypothetical forest sites.

Comparison of Generalized Linear Model between Gamma and Tweedie Compound Response for Rainfall Prediction in Lampung Province

Rainfall plays a pivotal role in influencing agricultural production in Lampung province. The precision of rainfall predictions holds significant importance for enhancing agricultural yields in the region. One effective approach for modeling rainfall is Statistical Downscaling (SD), which employs statistical models to examine the correlation between large-scale (global) climatological data and small-scale (local) data. SD addresses the limitation of global scale data, such as the General Circulation Model (GCM), which lacks the resolution to directly forecast localized climate conditions like rainfall. Rainfall can be broadly categorized into continuous and discrete components. The continuous component delineates the intensity of rainfall, while the discrete component describes the occurrence of rain. Both components are integral to accurate rainfall predictions. The mixed Tweedie distribution, combining Gamma and Poisson distributions, is proficient in handling both continuous and discrete components of rainfall. GCMs commonly encounter multicollinearity issues in SD modeling, which can be mitigated through Principal Component Analysis. This study seeks to compare two regression models: the generalized linear model with a gamma response and the Tweedie compound response. Rainfall data from three distinct regions in Lampung province, representing high, medium, and lowlands, is utilized. The research findings indicate that, for high and lowlands, the Tweedie compound exhibits a smaller Root Mean Square Error of Prediction (RMSEP) compared to gamma. Conversely, in medium lands, gamma-GLM demonstrates a smaller RMSEP value than the Tweedie compound. Thus, the distribution of the Tweedie compound is better suited for use than Gamma-GLM, especially for high and lowland areas.

Influence of Geometric Characteristics onWaterFlow and Solute Transport atFracture Intersections.

Laboratory experiments and numerical simulations were performed to explore the influence of intersection geometry on fluid flow and solute transport in fractures. Fractures were engraved and sealed into an acrylic plate and two orthogonal intersections with different geometry were constructed. The effects of curvature and relative shear displacement at intersections on preferential flow and solute transport were investigated. By solving the Navier-Stokes (NS) equation, the fluid mixing and solute distribution were predicted. The results showed that the geometric characteristics at the intersection have a significant effect on the preferential flow and solute distribution. The results agreed well with the experimental results, in terms of flow direction, preferential flow rate, and heterogeneous solute distribution. With an increase in curvature, the flow difference between the two outlets increases gradually. Increasing curvature can reduce the preferential flow and weaken the inhomogeneity of solute distribution. An increase of relative shear displacement decreases the pressure gradient and flow rate at the entrance of the two branch fractures, and thereby increases preferential flow and inhomogeneity of solute distribution. The results provide a basis and reference for further exploring the relationship between the geometric characteristics of fracture intersections and flow behaviors.

Machine learning assisted bayesian inference of mix and hot-spot conditions in NIF implosions

Experiments on the National Ignition Facility (NIF) have provided clear evidence of ablator material mixing into the Hot-Spot, leading to degraded performance. However, inferring the amount of mix and Hot-Spot conditions from typical experimental observations (e.g. x-ray spectra and images) is highly challenging. We have developed an analysis method that utilizes machine learning assisted Bayesian inference to find the probability distributions of the Hot-Spot and mix conditions. This approach uses a neural network, trained on an idealized 2-dimensional representation of the Hot-Spot and mix distribution, and Bayesian inference to find the statistical distributions of Hot-Spot conditions that provide a match with observations. We have tested this method with synthetic data from simulations.

Estimating galvanic corrosion rate of copper/steel couple by using remote earth mixed potential

AbstractCopper and steel are often electrically connected in industrial devices, which causes galvanic corrosion in environments. To ensure the safety of the devices, it is needed to assess the corrosion rate of galvanic couples. The galvanic current is often used to calculate the galvanic corrosion rate in theory, however, in the actual devices, the copper parts are generally coupled with the steel parts and it is difficult to separate them to measure the galvanic current between them, as well as to determine the galvanic corrosion rates for actual devices. In this paper, the galvanic couple mixed potential distribution and influencing factors including area ratios, polarization characteristics, and medium resistivity are studied by experiments and calculation. The mixed potential becomes stable with a certain distance from the surface of copper/steel couple and acts as remote earth mixed potential. Remote earth mixed potential is positively associated with galvanic corrosion rate. Based on the results of experiments and calculation, a method to estimate the galvanic corrosion rate of steel in copper/steel couple by using the remote earth mixed potential is proposed.

Improved estimation of extreme floods with data pooling and mixed probability distribution

The accurate estimation of flood probability is crucial for designing water storage and flood retention structures. However, the assumption of identical distribution in flood samples is unrealistic, given the influence of various flood mechanisms. To address this challenge, we proposed a novel framework based on flood clustering and data pooling that encompasses the key steps such as 1) flood event separation based on a peak-detection flood separation algorithm, 2) grouping flood events using the k-prototypes algorithm, 3) application of the UNprecedented Simulated Extreme ENsemble (UNSEEN) approach to pool reforecast ensemble datasets, and 4) statistical mixing approach to derive common quantiles from all the flood groups. We applied the framework to the Dresden gauge in the Elbe River for a detailed case study. Various tests have been performed to assess the applicability of the UNSEEN approach and the reforecast dataset consistently shows the potential for data pooling. The proposed methodology outperformed the classical approach in terms of goodness-of-fit. The relative difference between the classical and the proposed approach ((classical-proposed)/proposed) for the 100-year return level is 0.16, with a reduction in root mean square error (RMSE) value from 163 to 98 m3/s. Further, replication of the approach to the gauges in North Germany exhibited a relative difference ranging from −0.3 to +0.15 and produced better estimates in terms of RMSE compared with the traditional model. In summary, the proposed framework offers a better estimation of flood probability by addressing the inherent sample inhomogeneity along with the inclusion of unprecedented flood samples.

The effects of the velocity distribution of impact generated water ice ejecta on exospheric escape from airless bodies

We explore the velocity distribution of impact generated water ice ejecta from planetary surface. We show that the ejecta velocity distribution should be modeled by a mixed Maxwellian distribution, rather than the commonly used single Maxwellian distribution, in order to better capture the outcome of the different emission processes and the dynamics of the clustered water molecules in the ejecta. We find that, comparing to the mixed Maxwellian distribution, the single Maxwellian distribution may lead to a significant underestimation of the impact-ejected water molecules that stay gravitationally bound for exosphere models. While the relative difference is within 10% for celestial bodies with a large escape velocity such as Mercury, it can be about 1000% for celestial bodies with a small escape velocity such as Enceladus.

Effects of sub-distribution zone structure of bipolar plate on the mass transport of large-area proton exchange membrane fuel cells

Sub-distribution zone of the flow field plays an important role in distributing gas reactants and removing liquid products in large-active area proton exchange membrane (PEM) fuel cells. This study evaluates the effects of sub-distribution zone of PEM fuel cells with the active area of 300 cm2 based on a three-plus-one-dimensional (3D+1D) model. A new structural design of sub-distribution zone, namely, mixed structure distribution zone (MSDZ) is proposed and compared with the classic dot matrix distribution zone (DMDZ) and forced shunt distribution zone (FSDZ) design regarding pressure drop, gas distribution uniformity and cell performance. It is found that FSDZ and MSDZ design distribution uniformities are improved by 90.4 % and 55.5 %, compared to DMDZ design. The cell with the MSDZ design reaches an optimal value of 0.647 V, attributed to the reduction in mass transport voltage loss. Subsequently, based on structure characteristics of FSDZ and MSDZ designs, the effects of the rib/channel width ratio in sub-distribution zone are explored. It is found that primary shunt affects the overall distribution and secondary shunt regulates the localization. Finally, the cell performance of the three types of sub-distribution zones is compared under different humidification conditions. Results show that the cell with MSDZ design demonstrates a power density increased by 4.0 % (0.05 W cm−2) and 7.6 % (0.09 W cm−2) compared with that with DMDZ design at high and low humidification conditions, respectively.

Investigation of the Mixing Time Distribution and Connected Flow Fields in Two-Stage Stirred Vessels

In this study, laser-induced fluorescence is used to investigate the homogenization in stirred vessels equipped with single- and two-stage stirrers. The acquired local mixing times across the reactor cross-section are plotted as mixing time distribution (MTD) and then compared with the previously measured flow fields of the identical systems. With the help of a novel evaluation method, the mixing times are characterized with a normal distribution fit. With mean value and standard deviation as determined parameters, the mixing results of different installation heights and stirrer combinations are quantitatively evaluated and lead to clear recommendations for installations that enable efficient mixing.

Statistical inference for scale mixture models via Mellin transform approach

This paper deals with statistical inference for the scale mixture models. We study an estimation approach based on the Mellin–Stieltjes transform that can be applied to both discrete and absolute continuous mixing distributions. The accuracy of the corresponding estimate is analysed in terms of its expected pointwise error. As an important technical result, we prove the analogue of the Berry–Esseen inequality for the Mellin transforms. The proposed statistical approach is illustrated by numerical examples.

Granulometric Analyses of Stream Sediments from Kolar River Sub-Basin, Central India

This paper focuses on a granulometric study conducted to investigate the sedimentary characteristics of channel sediments in the upper, middle, and lower reaches of the Kolar River Sub-basin. The study's primary objective is to gain a detailed understanding of sediment transport by seamlessly integrating field surveys and laboratory analyses. The study investigates grain size distribution and the deposition environment in the Kolar River Sub-basin. From sediment data, statistical measures were prepared and utilized to construct bivariate diagrams and examine the inter-relationship of various parameters. The results indicate that the upper reach exhibits poor sediment sorting, while the middle and lower reaches show more moderate sorting. Skewness values indicate dominance of coarser sediments in the upper reach, finer grain sizes in the middle reach, and a mixed distribution in the lower reach. Kurtosis values suggest concentrated distributions in specific areas of the upper and middle reaches, while the lower reach displays a broader distribution. The CM plot analysis reveals distinct sediment transport types in each reach, reflecting the energy level of water transport and sediment load. This study provides valuable insights into spatial variations in sediment dynamics, contributing to a comprehensive understanding of the overall sedimentary regime of the river. With this understanding, effective river basin management techniques, such as vegetative stabilization, check dam construction and prevention of excessive deposition can be implemented to ensure the sustainable management of the Kolar River Sub-basin. Keywords: Granulometry, Stream Sediments, Depositional Environment, Sustainable Management, Kolar River Sub-Basin, Central India

Quantitative comparison of CSVD imaging markers between patients with possible amyloid small vessel disease and with non-amyloid small vessel disease

The spatial distribution patterns of cerebral microbleeds are associated with different types of cerebral small vessel disease (CSVD). This study aims to examine the disparities in brain imaging markers of CSVD among patients diagnosed with possible amyloid and non-amyloid small vessel disease. The head MR scans including susceptibility-weighted imaging (SWI) sequences from 351 patients at our institute were collected for analysis. CSVD imaging markers were quantified or graded across various CSVD dimensions in the patient images. Patients were categorized into the cerebral amyloid angiopathy group (CAA), hypertensive arteriopathy group (HA), or mixed small vessel disease group (Mixed), based on the spatial distribution of microbleeds. White matter lesions (WML) were segmented using an artificial neural network and assessed via a voxel-wise approach. Significant differences were observed among the three groups in several indices: microbleed count, lacune count at the centrum semiovale and basal ganglia levels, grade of enlarged perivascular space (EPVS) at the basal ganglia, and white matter lesion volume. These indices were substantially higher in the Mixed group compared to the other groups. Additionally, the incidences of cerebral hemorrhages (χ2 = 7.659, P = 0.006) and recent small subcortical infarcts (χ2 = 4.660, P = 0.031) were significantly more frequent in the HA group than in the CAA group. These results indicate that mixed spatial distribution patterns of microbleeds demonstrated the highest burden of cerebral small vessel disease. Microbleeds located in the deep brain regions were associated with a higher incidence of recent small subcortical infarcts and cerebral hemorrhages compared to those in the cortical areas.