Coverage Information Research Articles

Unitig level assembly graph based metagenome-assembled genome refiner (UGMAGrefiner): A tool to increase completeness and resolution of metagenome-assembled genomes

De novo assembly of next generation metagenomic reads is widely used to provide taxonomic and functional information of genomes in a microbial community. As strains are functionally specific, recovery of strain-resolved genomes is important but still a challenge. Unitigs and assembly graphs are mid-products generated during the assembly of reads into contigs, and they provide higher resolution for sequences connection information. In this study, we propose a new approach UGMAGrefiner (a unitig level assembly graph-based metagenome-assembled Genome refiner), which uses the connection and coverage information from unitig level assembly graphs to recruit unbinned unitigs to MAGs, adjust binning result, and infer unitigs shared by multiple MAGs. In two simulated datasets (Simdata and CAMI data) and one real dataset (GD02), it outperforms two state-of-the-art assembly graph-based binning refine tools in the refinement of MAGs’ quality by stably increasing the completeness of genomes. UGMAGrefiner can identify genome specific clusters of genomes with below 99% average nucleotide identity for homologous sequences. For MAGs mixed with 99% similarity genome clusters, it could distinguish 8 out of 9 genomes in Simdata and 8 out of 12 genomes in CAMI data. In GD02 data, it could identify 16 new unitig clusters representing genome specific regions of mixed genomes and 4 unitig clusters representing new genomes from total 135 MAGs for further functional analysis. UGMAGrefiner provides an efficient way to obtain more complete MAGs and study genome specific functions. It will be useful to improve taxonomic and functional information of genomes after de novo assembly.

An Experimental Assessment of Using Theoretical Defect Predictors to Guide Search-Based Software Testing

Automated test generators, such as search-based software testing (SBST) techniques are primarily guided by coverage information. As a result, they are very effective at achieving high code coverage. However, is high code coverage alone sufficient to detect bugs effectively? In this paper, we propose a new SBST technique, predictive many objective sorting algorithm (PreMOSA), which augments coverage information with defect prediction information to decide where to increase the test coverage in the class under test (CUT). Through an experimental evaluation using 420 labelled bugs on the Defects4J benchmark and using theoretical defect predictors, we demonstrate the improved effectiveness and efficiency of PreMOSA in detecting bugs when using any acceptable defect predictor, i.e., a defect predictor with recall and precision <inline-formula><tex-math notation="LaTeX">$\geq$</tex-math></inline-formula> 75%, compared to the state-of-the-art dynamic many objective sorting algorithm (DynaMOSA). PreMOSA detects up to 8.3% more labelled bugs on average than DynaMOSA when given a time budget of 2 minutes for test generation per CUT.

Estimating Hourly Land Surface Temperature From FY-4A AGRI Using an Explicitly Emissivity-Dependent Split-Window Algorithm

Land surface emissivity (LSE) has been roughly treated in the current split-window (SW) LST retrieval algorithms. This paper extended the National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS) Enterprise algorithm to Feng Yun-4A (FY-4A)/Advanced Geostationary Radiation Imager (AGRI) thermal infrared (TIR) data by incorporating a daily LSE database for high-temporal resolution LST retrieval. To improve the retrieval accuracy, the day/night split-window (SW) algorithm coefficients were calculated for different total water vapor content and view zenith angle conditions using the simulation database constructed by MODTRAN 5.2 and SeeBor V5.0 atmospheric profiles. The validation results show that the daily AGRI LSE has better accuracy than the LSE retrieved from the vegetation cover method (VCM), with average biases of -1.1×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> and -6×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> for channels 12 and 13. The accuracy of the AGRI LST retrieved using the daily AGRI LSE is slightly better than that retrieved using the VCM-retrieved LSE. The overall bias, MAE, and RMSE of the retrieved AGRI LST using the daily AGRI LSE at fourteen in situ sites are 0.11, 2.55, and 2.55 K, whereas these values are -0.11, 2.70 and 2.70 K for the LST using the VCM-retrieved LSE. This study demonstrates the daily LSE constructed from physically retrieved LSE can improve the accuracy of LST retrieved with the SW algorithm. The constructed daily LSE has high spatial coverage and dynamic emissivity information and can provide nearly complete spatial coverage if supplemented by the constructed 8-day or monthly AGRI LSE. It can also be applied to other LST retrieval algorithms that need LSE as a priori.

KMCP: accurate metagenomic profiling of both prokaryotic and viral populations by pseudo-mapping.

The growing number of microbial reference genomes enables the improvement of metagenomic profiling accuracy but also imposes greater requirements on the indexing efficiency, database size and runtime of taxonomic profilers. Additionally, most profilers focus mainly on bacterial, archaeal and fungal populations, while less attention is paid to viral communities. We present KMCP (K-mer-based Metagenomic Classification and Profiling), a novel k-mer-based metagenomic profiling tool that utilizes genome coverage information by splitting the reference genomes into chunks and stores k-mers in a modified and optimized Compact Bit-Sliced Signature Index for fast alignment-free sequence searching. KMCP combines k-mer similarity and genome coverage information to reduce the false positive rate of k-mer-based taxonomic classification and profiling methods. Benchmarking results based on simulated and real data demonstrate that KMCP, despite a longer running time than all other methods, not only allows the accurate taxonomic profiling of prokaryotic and viral populations but also provides more confident pathogen detection in clinical samples of low depth. The software is open-source under the MIT license and available at https://github.com/shenwei356/kmcp. Supplementary data are available at Bioinformatics online.

CRMF: A fault localization approach based on class reduction and method call frequency

AbstractIdentifying the location of faults in real‐world programs is one of the costly processes during software debugging. To reduce the debugging effort, various fault localization techniques have been proposed in recent years. Spectrum‐based fault localization (SBFL) is one kind of widely investigated fault localization technique. Most SBFL techniques first calculate the suspiciousness of program elements (such as statements, methods) to be faulty using the coverage information and execution results of tests. Then a rank list of program elements is generated according to their suspiciousness. However, some SBFL techniques only consider the binary coverage information (i.e., whether the program element is covered) but ignore some of the tests' running behaviors, such as the execution frequency when faults occur in the iteration entities or loop bodies, which are more likely to be faulty followed the propagation‐infection‐execution model. The execution frequency based techniques only replace the feature items of the existing formula limiting their effectiveness in fault localization. In this article, we propose a fault localization technique, class reduction and method call frequency (CRMF), which utilizes mutation analysis and information retrieval techniques. In particular, CRMF first uses mutation analysis to identify and reduce the classes, in which the program elements with a low probability of being faulty. Then we propose a new suspiciousness formula that applies information retrieval and considers method call frequency. To evaluate the effectiveness of CRMF, we conduct empirical studies on 264 real‐world programs from the Defects4J benchmark. Final results show that CRMF outperforms the statement frequency based technique FLSF and SBFL techniques (i.e., Ochiai, OP2, Tarantula, and Dstar) in both single‐fault programs and multiple‐fault programs. Specifically, CRMF can rank 29, 74, and 112 faults at the top 1, 3, 5 ranks and achieve a higher mean reciprocal rank for single‐fault programs and multiple‐fault programs. Finally, we discuss the essence of CRMF and analyze its effectiveness on multi‐fault programs in detail.

A Semi-Empirical Retrieval Method of Above-Ground Live Forest Fuel Loads by Combining SAR and Optical Data

Forest fuel load is the key factor for fire risk assessment, firefighting, and carbon emissions estimation. Remote sensing technology has distinct advantages in fuel load estimation due to its sensitivity to biomass and adequate spatiotemporal observations for large scales. Many related works applied empirical methods with individual satellite observation data to estimate fuel load, which is highly conditioned on local data and limited by saturation problems. Here, we combined optical data (i.e., Landsat 7 ETM+) and spaceborne Synthetic Aperture Radar (SAR) data (i.e., ALOS PALSAR) in a proposed semi-empirical retrieval model to estimate above-ground live forest fuel loads (FLAGL). Specifically, optical data was introduced into water cloud model (WCM) to compensate for vegetation coverage information. For comparison, we also evaluated the performance of single spaceborne L-band SAR data (i.e., ALOS PALSAR) in fuel load estimation with common WCM. The above two comparison experiments were both validated by field measurements (i.e., BioSAR-2008) and leave-one-out cross-validation (LOOCV) method. WCM with single SAR data could achieve reasonable performance (R2 = 0.64 or higher and RMSEr = 35.3% or lower) but occurred an underestimation problem especially in dense forests. The proposed method performed better with R2 increased by 0.05–0.13 and RMSEr decreased by 5.8–12.9%. We also found that the underestimation problem (i.e., saturation problem) was alleviated even when vegetation coverage reached 65% or the total FLAGL reached about 183 Tons/ha. We demonstrated our FLAGL estimation method by validation in an open-access dataset in Sweden.

Frontier-led swarming: Robust multi-robot coverage of unknown environments

This paper proposes a novel swarm-based control algorithm for exploration and coverage of unknown environments, while maintaining a formation that permits short-range communication. The algorithm combines two elements: swarm rules for maintaining a close-knit formation and frontier search for driving exploration and coverage. Inspired by natural systems in which large numbers of simple agents (e.g., schooling fish, flocking birds, swarming insects) perform complicated collective behaviors for efficiency and safety, the first element uses three simple rules to maintain a swarm formation. The second element provides a means to select promising regions to explore (and cover) by minimising a cost function involving robots’ relative distance to frontier cells and the frontier’s size. We tested the performance of our approach on heterogeneous and homogeneous groups of mobile robots in different environments. We measure both coverage performance and swarm formation statistics as indicators of the robots’ ability to explore effectively while maintaining a formation conducive to short-range communication. Through a series of comparison experiments, we demonstrate that our proposed strategy has superior performance to recently presented map coverage methodologies and conventional swarming methods. • A novel, distributed algorithm leveraging the strengths of frontier-based area coverage and swarm behaviors is proposed for large-scale multi-robot systems. • Each robot can directly exchange the individual coverage and obstacle information with a set of neighbors without passing through a central station. • An improved artificial potential field obstacle avoidance method is designed based on Lidar information. • System robustness and fault tolerance of the proposed strategy is validated and analysed through a series of real experiments. • This paper demonstrates our space coverage controller’s relative merits with respect to the frontierbased map coverage strategy without swarming, and the three different map coverage control laws through a rigorous comparative study. • Algorithms are examined on both homogeneous and heterogeneous robot teams within a shortranged communication. Hence, the proposed system can normally work in unknown and uncertain environments.

The CoveRT Approach for Coverage Management in Analog and Mixed-Signal Integrated Circuits

Coverage is a key indicator for verification progress, verification closure, and verification sign-off in an integrated circuit design. The notion of coverage management, namely, the use of coverage information across the design hierarchy to identify verification loopholes, is well understood in the digital context, but requires considerable disambiguation in the analog/mixed-signal (AMS) context. This article develops the core artifacts of AMS coverage and presents a comprehensive coverage management approach based on our tool, CoveRT. Our results, gleaned from live industrial designs, demonstrate the benefits of AMS coverage management across the design hierarchy, both in terms of identifying verification gaps, as well as in finding design bugs.

Hierarchical Bayesian geostatistics for C stock prediction in disturbed plantation forest in Zimbabwe

We develop and present a novel Bayesian hierarchical geostatistical model for the prediction of plantation forest carbon stock (C stock) in the eastern highlands of Zimbabwe using multispectral Landsat-8 and Sentinel-2 remotely sensed data. Specifically, we adopt a Bayesian hierarchical methodology encompassing a model-based inferential framework making use of efficient Markov Chain Monte Carlo (MCMC) techniques for assessing model input parameters. Our proposed hierarchical modelling framework evaluates the influence of two but related covariate information sources in C stock prediction in order to build sustainable capacity on carbon reporting and monitoring. The perceived improvements in the spectral and spatial properties of Landsat-8 and Sentinel-2 data and their potential to predict C stock with shorter uncertainty bounds is tested in the developed hierarchical Bayesian models. We utilized the Mean Squared Shortest Distance (MSSD) as the objective function for optimization of sampling locations for equal area coverage. Specifically, we evaluated the models using four selected remotely sensed vegetation indices namely, the normalised difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), enhanced vegetation index (EVI) and an additional distance to settlements anthropogenic variable that justifies from the history of the studied plantation forest in the eastern highlands of Zimbabwe. We evaluated two models making use of Landsat-8 and Sentinel-2 derived predictors using the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Coverage (CVG) and Deviance Information Criteria (DIC). The Sentinel-2 based C stock model resulted in RMSE of 1.16 MgCha−1, MAE of 1.11 MgCha−1, CVG of 94.7% and a DIC of −554.7 whilst its Landsat-8 based C stock counterpart yielded a RMSE, MAE, CVG and DIC of 2.69 MgCha−1, 1.77 MgCha−1, 85.4% and 43.1 respectively. Although predictive models from both sensors show great improvement in predictive accuracy when modelling the spatial random effects, the Sentinel-2 based C stock predictive model substantially outperforms its Landsat-8 based C stock counterpart. The Sentinel-2 based C stock predictive hierarchical model therefore adequately addresses multiple sources of uncertainty inherent in the spatial prediction of C stock in disturbed plantation ecosystems. It is evident from the results of this study that carbon reporting and monitoring can always be improved by scouting for improved and easily accessible remote sensing data and allow forest practitioners to keep track of error across space in resource environments of interest.

Accurate Binning of Metagenomic Contigs Using Composition, Coverage, and Assembly Graphs.

Metagenomics enables the recovery of various genetic materials from different species, thus providing valuable insights into microbial communities. Metagenomic binning group sequences belong to different organisms, which is an important step in the early stages of metagenomic analysis pipelines. The classic pipeline followed in metagenomic binning is to assemble short reads into longer contigs and then bin these resulting contigs into groups representing different taxonomic groups in the metagenomic sample. Most of the currently available binning tools are designed to bin metagenomic contigs, but they do not make use of the assembly graphs that produce such assemblies. In this study, we propose MetaCoAG, a metagenomic binning tool that uses assembly graphs with the composition and coverage information of contigs. MetaCoAG estimates the number of initial bins using single-copy marker genes, assigns contigs into bins iteratively, and adjusts the number of bins dynamically throughout the binning process. We show that MetaCoAG significantly outperforms state-of-the-art binning tools by producing similar or more high-quality bins than the second-best binning tool on both simulated and real datasets. To the best of our knowledge, MetaCoAG is the first stand-alone contig-binning tool that directly makes use of the assembly graph information along with other features of the contigs.

On the K-Means Clustering Model for Performance Enhancement of Port State Control

Nowadays, the concept of port state control is viewed as a safety net to safeguard maritime security, protect the marine environment, and ensure decent working and living circumstances for seafarers on board to a large extent. The ship can be detained for further checking if significant deficiencies are discovered during a port state control inspection. There is much research on this topic, but there have been few studies on the relationship between ship deficiencies and ship detention decisions using unsupervised machine learning artificial intelligence techniques. Although the previous methods or models are feasible for ship detention decisions, they all have shortcomings to some extent, such as large training model errors caused by the imbalance of class labels in the dataset and the fact that the training model cannot comprehensively consider all factors influencing ship detention decision due to the complexity and diversity of the problem. Unsupervised algorithms do not need to label all data in advance, and we can incorporate some fields related to port state control inspection data that can be collected into the model to allow the computer to automatically classify the ships at different risk levels according to relative criteria, e.g., the Tokyo memorandum of understanding, which may result in more objective results, thus eliminating the influence of subjective domain knowledge. It may also have more comprehensive coverage and more information on port state control inspection and decision models. Therefore, this research explores and develops an unsupervised algorithm based on k-means to improve port state control inspection decision-making models using the six-years inspection data from the Tokyo memorandum of understanding. The results show that the accuracy rate is around 50%.

Flu and Covid-19: is there adherence to vaccination in target groups?

Abstract Background In Italy, the flu vaccine is recommended and free for target groups (adults≥60 years old, fragile people, healthcare workers, pregnant women). During the 2020/2021 flu season, an increased vaccination coverage (+6,9%) was observed compared to the previous season, also due to the Covid-19 pandemic. We aimed to investigate how strong the adherence to the flu vaccine was by the vulnerable groups and assess if the Covid-19 vaccination campaign may have influenced the rate of flu vaccines. Methods At the beginning of autumn 2021 we conducted an online survey among the population of Tuscany. We collected data on demographics, health status (pregnancy, vulnerable), flu and COVID-19 vaccinations coverage and health information sources. In addition, we performed a descriptive and a risk factors analysis to assess correlation between our variables with R v 4.0.0. Significance level was set at p &amp;lt; 0.05. Results Among 408 participants, 248 (61%) belong to a vulnerable group and are recommended to receive the flu vaccine, 229 (56%) usually get the flu vaccine, 386 (95%) got the Covid-19 vaccine, 267 (65%) choose and trust the general practitioner (GP) as their health information source. There is a statistically significant association between being part of a vulnerable group and getting the seasonal flu vaccine (OR 6.63 95% CI 4.26-10.3 p &amp;lt; 0.001). In addition, getting the Covid-19 vaccine increases the likelihood of receiving the flu vaccine (2.90 95% CI 1.16-7.28 p = 0.018). Moreover, participants who trust their GP as their health information source (OR 1.63 CI 1.08-2.46 p = 0.019) are more likely to receive the flu vaccine; other information sources (TV, newspaper, social media) are not associated with the flu vaccine. Conclusions Our research shows that vulnerable groups get vaccinated against the flu. The increase in flu vaccine coverage may be due to the COVID-19 vaccines campaign. GPs play a crucial role in the health promotion, prevention and health literacy of patients. Key messages

Distributed Supervision Model for Enterprise Data Asset Trading Based on Blockchain Multi-Channel in Industry Alliance.

Compared with traditional physical commodities, data are intangible and easy to leak, and the related trading process has problems, such as complex participating roles, lengthy information flow, poor supervisory coverage and difficult information traceability. To handle these problems, we construct a distributed supervision model for data trading based on blockchain, and conduct multi-party hierarchical and multi-dimensional supervision of the whole process of data trading through collaborative supervision before the event, at present and after the event. First, the characteristics of information flow in the data trading process are analyzed, and the main subject and key supervision information in the data trading process are sorted out and refined. Secondly, combined with the actual business process of data trading supervision, a multi-channel structure of distributed supervision is proposed by adopting an access–verification–traceability strategy. Finally, under the logical framework of the supervision model, the on-chain hierarchical structure and the data hybrid storage method of “on-chain + off-chain” are designed, and multi-supervisor-oriented hierarchical supervision and post-event traceability are realized through smart contracts. The results show that the constructed blockchain-based distributed supervision model of data trading can effectively isolate and protect sensitive and private information between data trading, so as to realize the whole process, multi-subject and differentiated supervision of key information of data trading, and provide an effective and feasible method for the controllable and safe supervision of data trading.

Vegetation Coverage in the Desert Area of the Junggar Basin of Xinjiang, China, Based on Unmanned Aerial Vehicle Technology and Multisource Data

Vegetation coverage information is an important indicator of desert ecological environments. Accurately grasping vegetation coverage changes in desert areas can help in assessing the quality of ecosystems and maintaining their functions. Improving remote sensing methods to detect the vegetation coverage in areas of low vegetation coverage is an important challenge for the remote sensing of vegetation in deserts. In this study, based on the fusion of MOD09GA and MOD09GQ data, 2019–2021 low-altitude unmanned aerial vehicle (UAV) remote sensing data, and other factors (such as geographical, topographic, and meteorological factors), three types of inversion models for vegetation coverage were constructed: a multivariate parametric regression model, a support vector machine (SVM) regression model, and a back-propagation neural network (BPNN) regression model. The optimal model was then used to map the spatial distribution of vegetation coverage and its dynamic change in the Junggar Basin of Xinjiang, China, over 22 years (from 2000 to 2021). The results show that: (1) The correlation between enhanced vegetation index (EVI) obtained from image fusion and vegetation coverage in desert areas is the highest (r = 0.72). (2) Among the geographical and topographic factors, only longitude and latitude were significantly correlated with vegetation coverage (p &lt; 0.05). The average monthly temperature and precipitation from the previous six months were correlated with the vegetation coverage (p &lt; 0.05), but the vegetation coverage of the current month had the highest correlation with the average temperature (r = −0.27) and precipitation (r = 0.33) of the previous month. (3) Among the multivariate parametric models established by selecting the five aforementioned factors, the multiple linear regression model performed the best (R2 = 0.64). (4) The SVM regression model was superior to the other regression models (R2 = 0.80, mean squared error = 8.35%). (5) The average vegetation coverage in the desert area of the Junggar Basin was 7.36%, and from 2000–2021, the vegetation coverage in 54.59% of the desert area increased.

“The Most Wonderful Specimen of Man”

Abstract In the early twentieth century, Eugen Sandow popularized bodybuilding by selling it as a scientific discipline to a wide and international clientele. Linking bodybuilding to medical science, Sandow marketed it as a therapeutic system that improved the whole body, internally and externally. While the field of Sandow studies has grown lately, with a couple of essays discussing his marketing techniques, no studies have closely considered his persistent use of popular science in the widescale selling of bodybuilding. This article aims to fill this gap. Drawing on Sandow's many publications, media coverage, and biographical information, it argues that Sandow's finesse in linking bodybuilding to popular science—practices in anatomical science, ideas from evolutionary biology, and rhetoric regarding energy efficiency, machine technology, and factory systems—helped him conceptualize a curative physical culture. With the new medical-scientific framework that Sandow promoted internationally through print media and publicity campaigns, bodybuilding became a mainstream activity for both men and women across class and cultural divides, and the health and fitness business grew into a lucrative global industry. Along the way, Sandow taught people about the human body and changed our ways of viewing and knowing it.

Static test flakiness prediction: How Far Can We Go?

Test flakiness is a phenomenon occurring when a test case is non-deterministic and exhibits both a passing and failing behavior when run against the same code. Over the last years, the problem has been closely investigated by researchers and practitioners, who all have shown its relevance in practice. The software engineering research community has been working toward defining approaches for detecting and addressing test flakiness. Despite being quite accurate, most of these approaches rely on expensive dynamic steps, e.g., the computation of code coverage information. Consequently, they might suffer from scalability issues that possibly preclude their practical use. This limitation has been recently targeted through machine learning solutions that could predict the flakiness of tests using various features, like source code vocabulary or a mixture of static and dynamic metrics computed on individual snapshots of the system. In this paper, we aim to perform a step forward and predict test flakiness only using static metrics. We propose a large-scale experiment on 70 Java projects coming from the iDFlakies and FlakeFlagger datasets. First, we statistically assess the differences between flaky and non-flaky tests in terms of 25 test and production code metrics and smells, analyzing both their individual and combined effects. Based on the results achieved, we experiment with a machine learning approach that predicts test flakiness solely based on static features, comparing it with two state-of-the-art approaches. The key results of the study show that the static approach has performance comparable to those of the baselines. In addition, we found that the characteristics of the production code might impact the performance of the flaky test prediction models.

Population data for 94 identity SNPs in four U.S. population groups

The U.S. National Institute of Standards and Technology (NIST) sequenced 1036 human DNA samples from four United States population groups (African American, Asian, Hispanic, and Caucasian) using the ForenSeq DNA Signature Prep Kit with Primer Mix B (DPMB) on a MiSeq FGx instrument. In addition to STR markers, DPMB includes amplification primers for single nucleotide polymorphisms (SNPs) used for individual identification (iiSNPs, n = 94), ancestry inference (aiSNPs, n = 56), and phenotype prediction (piSNPs, n = 22). Resulting sequencing coverage information was interpreted for the 94 iiSNP markers. Here we present performance characteristics of the ForenSeq DNA Signature Prep Kit in the population studied.

Injuries That Happen at Work Lead to More Opioid Prescriptions and Higher Opioid Costs.

This study aimed to compare opioid prescription incidence, supply days, and cost associated with occupational injury and other injury-caused conditions. We used Medical Expenditure Panel Survey (MEPS) data for 2010-2019. The MEPS provides information on medical conditions and associated medical encounters, treatments, and treatment costs, as well as demographic, education, health, working status, income, and insurance coverage information. We used descriptive statistics and logistic and 2-part regressions. Controlling for covariates and compared with other injury-caused conditions, occupational injury-caused conditions resulted in 33% higher odds of opioid prescribing, 32.8 more opioid prescription supply days, and $134 higher average cost. Occupational injuries were associated with higher opioid incidence and costs, and more opioid supply days. These findings point to the need to focus on making work safer and the role employers may play in supporting worker recovery from injury and opioid use disorders.

Improving Analysis and Prediction of Tropical Cyclones by Assimilating Radar and GNSS-R Wind Observations: Ensemble Data Assimilation and Observing System Simulation Experiments Using a Coupled Atmosphere–Ocean Model

Abstract This study investigates the impact of assimilating ground-based radar reflectivity and wind data on tropical cyclone (TC) intensity prediction. The effect on a high-impact TC in the western North Pacific region that penetrated the Bashi Channel is examined. A multiscale correction based on the successive covariance localization (SCL) method is adopted to improve the analysis and forecast performance. In addition, GNSS-R wind speed is assimilated jointly in the rapid update assimilation framework to complement the TC boundary layer where radar data are limited. Model experiments are conducted and evaluated using the observing system simulation experiments (OSSEs) framework with a coupled atmosphere–ocean model nature run. Taking the experiment without data assimilation as the baseline, assimilating the radar data with the standard localization and SCL methods reduces the wind speed analysis error by 12% and 44%, respectively. The SCL method dominates the improvement in TC intensity prediction with a lead time longer than 2 days and the TC’s peak intensity forecast is improved by 18 hPa. The additional assimilation of the GNSS-R wind speed observation further reduces the wind speed error in the low-level analysis by 12% and 5% under the standard and SCL radar assimilation framework, respectively. GNSS-R wind assimilation leads to a further 6-hPa improvement in TC’s peak intensity. However, the sampling error introduced by the SCL method restrains the effect of GNSS-R assimilation. Sensitivity experiments with different GNSS-R data arrangements show that better GNSS-R wind coverage and additional wind direction information can further improve the TC analysis. Significance Statement Tropical cyclone (TC) intensity prediction over the western North Pacific (WNP) region remains a significant challenge due to limited observations. This study aims to improve the TC intensity prediction in WNP by assimilating the ground-based radar data using a multiscale correction framework and incorporating with the satellite ocean surface wind speed observation. We particularly focus on a high-impact TC like Typhoon Hato (2017), which penetrated the Bashi Channel and later made landfall in China, causing great damage. Our results showed that the assimilation strategy improved the TC intensity prediction for a lead time longer than 2 days. These results demonstrate the great potential of these observations and can provide guidance for future applications in operation centers.

Using contextual knowledge in interactive fault localization

Tool support for automated fault localization in program debugging is limited because state-of-the-art algorithms often fail to provide efficient help to the user. They usually offer a ranked list of suspicious code elements, but the fault is not guaranteed to be found among the highest ranks. In Spectrum-Based Fault Localization (SBFL) – which uses code coverage information of test cases and their execution outcomes to calculate the ranks –, the developer has to investigate several locations before finding the faulty code element. Yet, all the knowledge she a priori has or acquires during this process is not reused by the SBFL tool. There are existing approaches in which the developer interacts with the SBFL algorithm by giving feedback on the elements of the prioritized list. We propose a new approach called iFL which extends interactive approaches by exploiting contextual knowledge of the user about the next item in the ranked list (e. g., a statement), with which larger code entities (e. g., a whole function) can be repositioned in their suspiciousness. We implemented a closely related algorithm proposed by Gong et al., called Talk. First, we evaluated iFL using simulated users, and compared the results to SBFL and Talk. Next, we introduced two types of imperfections in the simulation: user’s knowledge and confidence levels. On SIR and Defects4J, results showed notable improvements in fault localization efficiency, even with strong user imperfections. We then empirically evaluated the effectiveness of the approach with real users in two sets of experiments: a quantitative evaluation of the successfulness of using iFL, and a qualitative evaluation of practical uses of the approach with experienced developers in think-aloud sessions.