Local Quality Measures Research Articles

Application of a new multi-elevation method for determining the elemental composition of atmospheric precipitation in coastal marine zones.

Assessing local air quality using traditional methods, such as analyzing precipitation composition, is often difficult due to the complex data, which is influenced by a variety of chemical elements from distant atmospheric sources and cyclone formation areas. This study presents a new approach to overcome these challenges: a multi-elevation sampling method that improves the accuracy of local air quality measurements. By collecting precipitation samples at different ground elevations, the technique takes advantage of the natural process where raindrops and snowflakes collect more elements as they fall through the air. This method helps to distinguish local air quality from background levels found at higher, non-industrial elevations. The primary goal was to isolate and identify the elemental fingerprints of marine influences, while excluding contributions from cyclones. In February 2023, 51 meltwater samples were collected from 17 observation points in the city of Sochi under southeast wind conditions. These samples revealed a spectrum of chemical elements, predominantly of marine origin (Mg > Na > Sr > Ca > K > Ce > Mn). The results showed significant differences in elemental concentrations between upland and coastal areas. The Geochemical Indicator of Marine Origin (GIM) ranged from 22 to 3235, confirming the strong influence of marine aerosols in the area. These findings demonstrate the effectiveness of the multi-elevation sampling method in providing robust environmental assessments. This approach, combined with the GIM index, offers valuable insights for improving environmental policies and public health, particularly in coastal regions affected by marine aerosols.

A Visual Environment for Data Driven Protein Modeling and Validation.

In structural biology, validation and verification of new atomic models are crucial and necessary steps which limit the production of reliable molecular models for publications and databases. An atomic model is the result of meticulous modeling and matching and is evaluated using a variety of metrics that provide clues to improve and refine the model so it fits our understanding of molecules and physical constraints. In cryo electron microscopy (cryo-EM) the validation is also part of an iterative modeling process in which there is a need to judge the quality of the model during the creation phase. A shortcoming is that the process and results of the validation are rarely communicated using visual metaphors. This work presents a visual framework for molecular validation. The framework was developed in close collaboration with domain experts in a participatory design process. Its core is a novel visual representation based on 2D heatmaps that shows all available validation metrics in a linear fashion, presenting a global overview of the atomic model and provide domain experts with interactive analysis tools. Additional information stemming from the underlying data, such as a variety of local quality measures, is used to guide the user's attention toward regions of higher relevance. Linked with the heatmap is a three-dimensional molecular visualization providing the spatial context of the structures and chosen metrics. Additional views of statistical properties of the structure are included in the visual framework. We demonstrate the utility of the framework and its visual guidance with examples from cryo-EM.

A Reference Process for Assessing the Reliability of Predictive Analytics Results

Organizations employ data mining to discover patterns in historic data in order to learn predictive models. Depending on the predictive model the predictions may be more or less accurate, raising the question about the reliability of individual predictions. This paper proposes a reference process aligned with the CRISP-DM to enable the assessment of reliability of individual predictions obtained from a predictive model. The reference process describes activities along the different stages of the development process required to establish a reliability assessment approach for a predictive model. The paper then presents in more detail two specific approaches for reliability assessment: perturbation of input cases and local quality measures. Furthermore, this paper describes elements of a knowledge graph to capture important metadata about the development process and training data. The knowledge graph serves to properly configure and employ the reliability assessment approaches.

Robustness investigation of cross-validation based quality measures for model assessment

In this paper the accuracy and robustness of quality measures for the assessment of machine learning models are investigated. The prediction quality of a machine learning model is evaluated model-independent based on a cross-validation approach, where the approximation error is estimated for unknown data. The presented measures quantify the amount of explained variation in the model prediction. The reliability of these measures is assessed by means of several numerical examples, where an additional data set for the verification of the estimated prediction error is available. Furthermore, the confidence bounds of the presented quality measures are estimated and local quality measures are derived from the prediction residuals obtained by the cross-validation approach.

Realistic simulation of air pollution in an urban area to promote environmental policies

Visualizing tools are now capable of synthesizing satellite imagery with Computational Fluid Dynamics (CFD) results. The new method is applied in Augsburg, Germany, and consists of two main phases: pre-processing and post-processing. The pre-processing phase involves creating geometry, mesh, and extracting results from simulations of traffic pollutant dispersion. In the post-processing phase, the results are combined with satellite images to produce visually optimized results. The demonstration of road traffic air pollution is based on real data from local air quality measurements and specific scenarios. The results indicate that using these visualization tools produce understandable and impressive images and videos. This enhances the public's comprehension of scientific results and raises awareness of environmental issues. An increased understanding of scientific results can reinforce the implementation of environmental policies by pressuring responsible authorities to take action. This paper provides a valuable tool for visualizing air pollution and facilitating public engagement with environmental issues.

Assessing Lifetime Cancer Risk Associated with Population Exposure to PM-Bound PAHs and Carcinogenic Metals in Three Mid-Latitude Metropolitan Cities.

Lifetime cancer risk characterization of ambient PM-bound carcinogenic metals and polycyclic aromatic hydrocarbons (PAHs) were examined in the cities of Los Angeles (USA), Thessaloniki (Greece) and Milan (Italy), which share similar Mediterranean climates but are different in their urban emission sources and governing air quality regulations. The samples in Milan and Thessaloniki were mostly dominated by biomass burning activities whereas the particles collected in Los Angeles were primary impacted by traffic emissions. We analyzed the ambient PM2.5 mass concentration of Cadmium (Cd), Hexavalent Chromium (Cr(VI)), Nickel (Ni), Lead (Pb), as well as 13 PAH compounds in the PM samples, collected during both cold and warm periods at each location. Pb exhibited the highest annual average concentration in all three cities, followed by Ni, As, Cr(VI), Cd and PAHs, respectively. The cancer risk assessment based on outdoor pollutants was performed based on three different scenarios, with each scenario corresponding to a different level of infiltration of outdoor pollutants into the indoor environment. Thessaloniki exhibited a high risk associated with lifetime inhalation of As, Cr(VI), and PAHs, with values in the range of (0.97-1.57) × 10-6, (1.80-2.91) × 10-6, and (0.77-1.25) × 10-6, respectively. The highest cancer risk values were calculated in Milan, exceeding the US EPA standard by a considerable margin, where the lifetime risk values of exposure to As, Cr(VI), and PAHs were in the range of (1.29-2.08) × 10-6, (6.08-9.82) × 10-6, and (1.10-1.77) × 10-6, respectively. In contrast, the estimated risks associated with PAHs and metals, except Cr(VI), in Los Angeles were extremely lower than the guideline value, even when the infiltration factor was assumed to be at peak. The lifetime cancer risk values associated with As, Cd, Ni, Pb, and PAHs in Los Angeles were in the range of (0.04-0.33) × 10-6. This observation highlights the impact of local air quality measures in improving the air quality and lowering the cancer risks in Los Angeles compared to the other two cities.

Cyclists as Intelligent Carriers of Space-Time Environmental Information: Crowd-Sourced Sensor Data for Local Air Quality Measurement and Mobility Analysis in the Netherlands

ABSTRACT In recent years, slow travel modes (walking, cycling) have gained much interest in the context of urban air quality management. This article presents the findings from a novel air quality measurement experiment in the Netherlands, by regarding cyclists as carriers and transmitters of real-world information on fine-grained air quality conditions. Using individual sensors on bicycles—connected to a GPS positioning system—online local pollution information originating from cyclists’ detailed spatial mobility patterns is obtained. Such air quality surface maps and cyclists’ mobility maps are then used to identify whether there are significant differences between the actual route choice and the cyclists’ shortest route choice, so as to identify the implications of poor air quality conditions for their mobility choices. Thus, the article seeks to present both a detailed pollution surface map and the complex space-time mobility patterns of cyclists in a region, on the basis of online quantitative data—at any point in time and space—from bicycle users in a given locality. In addition, the article estimates their response—in terms of route choice—to detailed air-quality information through the use of a novel geoscience-inspired analysis of space-time “big data.” The empirical test of our quantitative modeling approach was carried out for the Greater Utrecht area in the Netherlands. Our findings confirm that spatial concentration of air pollutants have great consequences for bike users’ route choice patterns, especially in the case of non-commuting trips. We also find that cyclists make longer trips on weekends and in the evenings, especially towards parks and natural amenities.

New evidence on geographic disparities in United States hospital capacity.

To characterize the quantity and quality of hospital capacity across the United States. We combine a 2017 near-census of US hospital inpatient discharges from the Healthcare Cost and Utilization Project (HCUP) with American Hospital Association Survey, Hospital Compare, and American Community Survey data. This study produces local hospital capacity quantity and care quality measures by allocating capacity to zip codes using market shares and population totals. Disparities in these measures are examined by race and ethnicity, income, age, and urbanicity. All data are derived from pre-existing sources. All hospitals and zip codes in states, including the District of Columbia, contributing complete data to HCUP in 2017 are included. Non-Hispanic Black individuals living in zip codes supplied, on average, 0.11 more beds per 1000 population (SE=0.01) than places where non-Hispanic White individuals live. However, the hospitals supplying this capacity have 0.36 fewer staff per bed (SE=0.03) and perform worse on many care quality measures. Zip codes in the most urban parts of America have the least hospital capacity (2.11 beds per 1000 persons; SEM=0.01) from across the rural-urban continuum. While more rural areas have markedly higher capacity levels, urban areas have advantages in staff and capital per bed. We do not find systematic differences in care quality between rural and urban areas. This study highlights the importance of lower hospital care quality and resource intensity in driving racial and ethnic, as well as income, disparities in hospital care-related outcomes. This study also contributes an alternative approach for measuring local hospital capacity that accounts for cross-hospital service area flows. Adjusting for these flows is necessary to avoid underestimating the supply of capacity in rural areas and overestimating it in places where non-Hispanic Black individuals tend to live.

On the Use of Local Fixations and Quality Measures for Deep Face Recognition

Automatic face recognition systems have achieved impressive performance in the last decade, thanks to the advances of deep learning techniques. However, these methods are highly dependent on the quality of the acquired face data, which is influenced by several factors. Changes in the face appearance due to variations in illumination, head pose, and visual occlusions, do not affect all regions of the face in the same way. Therefore, a novel approach to extract the quality information from facial regions for building a local deep representation is proposed. In order to determine the local image quality, face images are divided into regions and different quality measures are computed. The good quality regions are used to obtain the final deep face representation. The same strategy is applied for representing face videos by processing every frame of a video sequence as an individual image. Four deep face models are used to evaluate the proposal on five challenging databases, containing both still face images and videos. The performance obtained from the experiments carried out, demonstrates that the proposed approach outperforms the original models on all the tested datasets. The results from the performed longitudinal experimental testing also demonstrates the capability of the proposed deep model to retain robust and highly discriminant textural information from the face image while discarding the data segments corrupted by the considered noise sources.

Air Quality Measurements in Kitchener, Ontario, Canada Using Multisensor Mini Monitoring Stations

The Region of Waterloo is the third fastest growing region in Southern Ontario in Canada with a population of 619,000 as of 2019. However, only one air quality monitoring station, located in a city park in Kitchener, Ontario, is currently being used to assess the air quality of the region. In September 2020, a network of AQMesh Multisensor Mini Monitoring Stations (pods) were installed near elementary schools in Kitchener located near different types of emission source. Data analysis using a custom-made long-distance scaling software showed that the levels of nitrogen oxides (NO and NO2), ground level ozone (O3), and fine particulate matter (PM2.5) were traffic related. These pollutants were used to calculate the Air Quality Health Index-Plus (AQHI+) at each location, highlighting the inability of the provincial air quality monitoring station to detect hotspot areas in the city. The case study presented here quantified the impact of the 2021 summer wildfires on the local air quality at a high time resolution (15-min). The findings in this article show that these multisensor pods are a viable alternative to expensive research-grade equipment. The results highlight the need for networks of local scale air quality measurements, particularly in fast-growing cities in Canada.

Restraint use as a quality indicator for the hospital setting: a secondary data analysis.

A reduction in restraint use is recommended for all health care settings. For this purpose, local or national quality measurement and improvement initiatives have been implemented in various countries, primarily in the mental health and long-term care settings. However, restraints are also frequently used in the somatic acute care hospital setting, and strong variations in their prevalence rates have been reported. Therefore, the aim of this study was to reanalyse existing data on restraint use in Swiss hospitals in order to assess the potential of restraint use as a national quality indicator for the hospital setting. Using a cross-sectional, multicentre design, data were collected between 2016 and 2018 as part of the ANQ"s (Swiss National Association for Quality Development in Hospitals and Clinics) "falls and pressure ulcers" national prevalence measurement in acute care hospitals in Switzerland. The hospitals measured restraint use on a voluntary basis in addition to falls and pressure ulcers. All medical specialities and patients aged 18 and over who gave their informed consent were included in the measurement. Descriptive and multilevel regression analyses were performed using institutional, ward and patient-level data relating to restraint use. The sample consisted of 18,938 inpatients from 55 hospitals. The 30-day prevalence rate of patients with at least one restraint was 10.2% (n = 1933). The risk-adjusted hospital comparison revealed that hospitals in Switzerland differ significantly in their restraint use, even after adjusting for patient characteristics. In total, 10 hospitals used restraints significantly less and 12 used them significantly more than the national average. Restraint use varies significantly between Swiss hospitals: 40% of all hospitals used restraints either significantly more or significantly less often than the average. In comparison to the other quality indicators, this is a very high value, indicating potential for improvements in the quality of care. Since restraint use is associated not only with quality of care, but also with human rights, these large differences seem questionable from a professional, ethical and legal point of view. Clearer and binding regulations in combination with monitoring and benchmarking of restraint use in hospitals, such as with a national quality indicator, seem necessary. These would help to ensure that restraint use is in alignment with professional values, as well as ethical and legal requirements.

Preventing Increased Air Pollution in Pennsylvania’s Environmental Justice Communities

Air pollutants are known to cause serious health impacts, and historically marginalized groups are disproportionately exposed to these risks. Other hazardous pollutants often accompany carbon dioxide emissions during fossil fuel combustion, and therefore reductions in greenhouse gas emissions from climate policy can also improve air quality. However, although these policies may reduce pollution overall, existing programs have often increased local emissions levels – particularly in the most overburdened neighborhoods. The adverse health effects caused by a redistribution of emissions must be considered as Pennsylvania plans to join the Regional Greenhouse Gas Initiative. We recommend the Department of Environmental Protection include an annual impact assessment of their cap-and-trade program on vulnerable communities using both the available carbon dioxide emissions data and additional local air quality measurements.

Local bone quality measure and construct failure prediction: a biomechanical study on distal femur fractures

IntroductionThe aim of this investigation was to better understand the differences in local bone quality at the distal femur and their correlation with biomechanical construct failure, with the intention to identify regions of importance to optimize implant anchorage.Materials and methodsSeven fresh–frozen female femurs underwent high-resolution peripheral quantitative computed tomography (HR-pQCT) to determine bone mineral density (BMD) within three different regions of interest (distal, intermedium, and proximal) at the distal femur. In addition, local bone quality was assessed by measuring the peak torque necessary to break out the trabecular bone along each separate hole of a locking compression plate (LCP) during its instrumentation. Finally, biomechanical testing was performed using cyclic axial loading until failure in an AO/OTA 33 A3 fracture model.ResultsLocal BMD was highest in the distal region. This was confirmed by the measurement of local bone quality using DensiProbe™. The most distal holes represented locations with the highest breakaway torque resistance, with the holes on the posterior side of the plate indicating higher values than those on its anterior side. We demonstrated strong correlation between the cycles to failure and local bone strength (measured with DensiProbe™) in the most distal posterior screw hole, having the highest peak torque.ConclusionThe local bone quality at the distal femur indicates that in plated distal femur fractures the distal posterior screw holes seem to be the key ones and should be occupied. Measurement of the local bone strength with DensiProbe™ is one possibility to determine the risk of construct failure, therefore, thresholds need to be defined.

Asymmetrically distorted 3D video quality assessment: From the motion variation to perceived quality

The two-stage framework has been widely used in objective image/video quality assessment (IQA/VQA) methods, which includes local quality measurement and global quality pooling. Most of the existing IQA/VQA methods dedicate to local quality degradation modeling. Nevertheless, the significant pooling strategies are often neglected, especially in the quality assessment methods for asymmetrically distorted 3D video sequences. In this paper, motivated by the fact that the perceptual visual information might be affected by the visual masking due to motion suppression, a novel pooling strategy is proposed for visual quality assessment of asymmetrically distorted 3D video sequences by considering the motion variation-based perceptual visual information (MVPVI). In specific, to extract valuable visual information of video sequences, the proposed method computes the motion energy information (MEI) and edge energy information (EEI) by quantifying the frame difference map and the gradient map, respectively. Furthermore, in order to simulate the phenomenon of motion suppression in local motion scenes, where the perceptual information of low-attentional regions might be neglected by the human visual system (HVS), the proposed method first employs the coefficient of variation (CV) to determine the type of motion (local or global) in consecutive frames. Then, the perceptual visual information of the two cases is quantified. Finally, to simulate the role of HVS as an optimal information extractor, the estimated perceptual visual information of each single-view video sequence is used as the weighting factor for the quality pooling of left and right-view video sequences. Extensive comparison experiments conducted on Waterloo-IVC 3D video quality databases demonstrate that the proposed pooling strategy outperforms other relevant pooling strategies, and it achieves better performance than state-of-the-art 3D-IQA/VQA methods when combined with 2D-IQA/VQA methods.

Modeling Catchment-Scale Nitrogen Losses Across a Land-Use Gradient in the Subtropics

Changing land use in subtropical and tropical catchments to farmland can result in higher nitrogen (N) loss to aquatic ecosystems. Here, we developed a lumped water and N balance model to estimate regional N losses to creeks at catchment scale within understudied subtropical catchments in Australia. The conceptual water balance model CoCa-RFSGD was extended by the nitrogen mass balance in top and sub soil by adding nitrogen cycle transformation estimates depending on meteorological, soil and land use properties. The model estimates the impact of pristine and agricultural land use on catchment-wide water quality using only loworder creek samples as water quality measurements of nitrate and nitrite (NOx), with increased model performance with increased agricultural coverage. The model revealed that an agricultural proportion of 3% in the study site drove a 3.5-fold increase of N losses to creeks and a 6.7-fold increase of N losses to the atmosphere compared to catchments without agriculture. Agricultural land use lost 92 kg-N ha−1, of which 85% evaded to the atmosphere and 15% discharged via surface waters. A change from forest to cleared land may increase the total denitrification potential of a catchment. Overall, our lumped model provides a simple but effective tool to upscale local aquatic water quality measurements to the catchment scale, allowing assessment of changing land use on aquatic N loads in areas with limited data availability.

The locality dilemma of Sankoff-like RNA alignments.

MotivationElucidating the functions of non-coding RNAs by homology has been strongly limited due to fundamental computational and modeling issues. While existing simultaneous alignment and folding (SA&F) algorithms successfully align homologous RNAs with precisely known boundaries (global SA&F), the more pressing problem of identifying new classes of homologous RNAs in the genome (local SA&F) is intrinsically more difficult and much less understood. Typically, the length of local alignments is strongly overestimated and alignment boundaries are dramatically mispredicted. We hypothesize that local SA&F approaches are compromised this way due to a score bias, which is caused by the contribution of RNA structure similarity to their overall alignment score.ResultsIn the light of this hypothesis, we study pairwise local SA&F for the first time systematically—based on a novel local RNA alignment benchmark set and quality measure. First, we vary the relative influence of structure similarity compared to sequence similarity. Putting more emphasis on the structure component leads to overestimating the length of local alignments. This clearly shows the bias of current scores and strongly hints at the structure component as its origin. Second, we study the interplay of several important scoring parameters by learning parameters for local and global SA&F. The divergence of these optimized parameter sets underlines the fundamental obstacles for local SA&F. Third, by introducing a position-wise correction term in local SA&F, we constructively solve its principal issues.Availability and implementationThe benchmark data, detailed results and scripts are available at https://github.com/BackofenLab/local_alignment. The RNA alignment tool LocARNA, including the modifications proposed in this work, is available at https://github.com/s-will/LocARNA/releases/tag/v2.0.0RC6.Supplementary information Supplementary data are available at Bioinformatics online.

A statistics-guided approach to dimensional quality characterization of free-form surfaces with an application to 3D printing

Free-from surfaces such as three-dimensional (3D) printing products play an important role in customized production of manufacturing. Despite its popularity, 3D printing products often suffer dimensional quality issues due to geometric deformation during the layer-by-layer printing process. Different from traditional production, 3D printing products are often customized and not for mass production, thus it is often difficult to designate tolerance profiles to assess the production quality. To address this challenge, we propose a statistics-guided approach to characterize the dimensional quality for free-form surfaces. The proposed approach can provide local quality measures based on the original customized design and the scanned profile of a printed product. It gives a unified scale of the quality assessment measurements to conveniently compare the quality of products from different customized designs. Case studies and simulation experiments show that, the proposed approach can provide effective quality assessment and characterization for 3D printing.

Local image quality measurement for multi-scale forensic palmprints

Numerous studies show that palmprint image quality has a significant effect on every stage of a palmprint recognition system. Although some palmprint image quality measurement(PIQM) methods are proposed, some insufficiency in classification accuracy occurs and attention to detail in measuring local area image quality of multi-scale palmprint images is lacking. On the one hand, the classification accuracy is not very high for 2-class classification and it degrades significantly as the number of classes increases. On the other hand, local area image quality measurement of multi-scale palmprint images has not yet been resolved since the handcrafted features designed through domain knowledge usually works for certain scale image blocks. Meanwhile, the intricate domain knowledge used in the previous methods is difficult for some common users to acquire. In this paper, we propose an end-to-end deep-learning method of strengthening representation ability that learns more abstract, essential, and reliable features to measure the local image quality for multi-scale forensic palmprints. Popular convolutional neural networks (CNNs) are considered because of their powerful representation ability in learning complex features. However, the powerful existing CNNs usually have complex architectures with a large amount of parameters, which need the support of high-performance computers. They are not suitable to be used directly for palmprint image quality assignment and the follow-up palmprint recognition work, which prefers real-time response on commonly available personal computers or even mobile devices. Hence, a new lightweight CNN must be designed to achieve a trade-off between high classification accuracy and practical usability. Considering the attributes of under-processed input images, we reduce the weight of the CNN architecture by reducing the amount of some parameters, and finally a lightweight CNN is designed. As a result, a raw rectangular palmprint image of variable size can be put into the trained model directly and a quality label quickly predicted with high accuracy. After comparison with previous methods, results show that the proposed method can deal with un-pre-processed raw images of a multi-scale input size. Furthermore, it can acquire a richer amount of quality classes with a higher accuracy, which are stable on many different datasets. It also leads to finer and more precise full palmprint image quality maps when compared to previous methods.

DeepRes: a new deep-learning- and aspect-based local resolution method for electron-microscopy maps.

In this article, a method is presented to estimate a new local quality measure for 3D cryoEM maps that adopts the form of a 'local resolution' type of information. The algorithm (DeepRes) is based on deep-learning 3D feature detection. DeepRes is fully automatic and parameter-free, and avoids the issues of most current methods, such as their insensitivity to enhancements owing to B-factor sharpening (unless the 3D mask is changed), among others, which is an issue that has been virtually neglected in the cryoEM field until now. In this way, DeepRes can be applied to any map, detecting subtle changes in local quality after applying enhancement processes such as isotropic filters or substantially more complex procedures, such as model-based local sharpening, non-model-based methods or denoising, that may be very difficult to follow using current methods. It performs as a human observer expects. The comparison with traditional local resolution indicators is also addressed.

Short term sediment accumulation rates reveal seasonal time lags between sediment delivery and deposition in an oxbow lake

Recent sedimentation rates are useful for quantifying how changes in a watershed affect soil erosion; however, typical geochronological methods for dating sediments are limited in temporal resolution, particularly for newly deposited sediments. We used sediment traps to measure short-term sediment accumulation rates in a natural oxbow lake whose watershed has a mix of agricultural and forested land. Precipitation data from a local Soil Climate Analysis Network (SCAN) site and water quality measurements from Beasley Lake were used to explain intra-annual patterns in sediment deposition. We found that sediment accumulation in the traps was highest in July and August, while the highest rate of runoff, indicated by increased precipitation and total suspended solids, occurred in late winter and early spring (February-April). Our results indicate that the trapping efficiency of Beasley Lake may be lower than expected due to timing of particle settling predominantly in the summer months. The rate and timing of sediment accumulation in Beasley lake was highly seasonal and occurred when water temperature and pH increased, potentially through interactions with algal blooms. Our findings contribute new understanding of the interactions between suspended sediments, algal biomass, and water chemistry in a natural oxbow lake and provide support for using sediment traps to measure intra-annual variability in sedimentation rates in oxbow lakes.