Mosaic Plots Research Articles

Undesirable Levels of Practice Behaviours and Associated Knowledge amongst Community Health Workers in Rural South India Responsible for Type 2 Diabetes Screening and Management.

Type 2 diabetes (T2DM) poses an enormous global health care challenge, especially among rural communities. Healthcare in these areas can be inadequate and inaccessible due to socio-demographic barriers. To overcome this situation, many low- and middle-income countries are resorting to task shifting, using community health workers (CHWs) for diabetes management. However, its successful implementation depends on the practice behaviours and knowledge of these workers. This cross-sectional study aimed to evaluate the proficiency of CHWs involved in diabetes screening and management in rural South India by identifying the existing practice behaviours and knowledge gaps. Employing a customised questionnaire, developed through inputs from experts and government officials, we assessed practice behaviours and the corresponding knowledge base of 275 CHWs. Analytical methodologies consisted of descriptive statistics, logistic regression, and mosaic plots for comprehensive data interpretation. The study showcased significant deficiencies in both practice behaviours (97%) and knowledge (95%) with current mean levels ranging from 48 to 50%, respectively, among the participants. The identified areas of insufficiency were broadly representative of the core competencies required for effective diabetes management, encompassing diabetes diagnosis and referral, HbA1c testing, diabetes diet, diabetes type and self-management, microvascular complications and their screening, peripheral neuropathy management, and diabetes risk assessment. In several areas, correct practice behaviour was reported by a relatively large number of CHWs despite incorrect answers to the related knowledge questions such as referral to the health centres, self-management, and calculation of diabetes risk assessment. This study highlights widespread deficiencies (97% CHWs) in diabetes management practices and knowledge (95% CHWs). To overcome these deficiencies, a thorough needs assessments is vital for effective CHW training. Training of CHWs should not only identify prior knowledge and/or behaviour but also their interrelationship to help create a robust and flexible set of practice behaviours.

Discourse Task Type-Specific Linguistic Characteristics in Anomic Aphasia and Healthy Controls: Evidence From Mandarin-Chinese AphasiaBank.

This study was designed to examine the hypothesis that discourse task types influence language performance in Mandarin Chinese-speaking people and to reveal the discourse task-specific linguistic properties of persons with anomic aphasia compared to neurotypical controls. Language samples from persons with aphasia (n = 31) and age- and education-matched controls (n = 31) across four discourse tasks (sequential-picture description, single-picture description, story narrative, and procedural discourse) were collected from Mandarin AphasiaBank. Task-specific distributions of parts of speech were analyzed using mosaic plots. The main effects of tasks in each group and the between-group differences within each task for several typical linguistic variables were evaluated, including the mean length of utterance, tokens, moving-average type-token ratio, words per minute, propositional density, noun-verb ratio, noun percentage, and verb percentage. The results revealed an impact of discourse tasks on most language variables in both groups. In the healthy controls, story narratives yielded the highest total words and lowest verb percentage. In the aphasia group, procedural discourse elicited the fewest total words and densest expressions, whereas their single-picture descriptions had the highest noun-verb ratio. For all tasks, the aphasia group performed worse than the control group in the mean length of utterance, tokens, moving-average type-token ratio, and words per minute. For noun-verb ratio, noun percentage, and verb percentage, only one task (i.e., single-picture description) showed significant between-group differences. The selection of discourse tasks should be addressed in assessments and interventions for Mandarin Chinese-speaking individuals with aphasia to obtain more accurate and feasible outcomes.

Injury Types and Training Habits among Soccer (Football) Athletes

Background For soccer athletes, injuries are frequent and pose a considerable health and financial burden for individuals and families. While studies have previously assessed the incidence of soccer injuries and preventive strategies male athletes use to reduce these occurrences, few have included women and players of varying skill levels. Objective To report the frequency of injuries in a cohort of male and female soccer athletes and describe the training habits that have helped prevent injury. Methods Two hundred (n=200) United States participants completed a questionnaire on soccer practicing frequency, habits, injuries, and treatments. A screening question ensured all respondents had played soccer for at least one year and determined eligibility for the study. Participant information related to age, sex, education, income, and race was also collected. JMP statistical software was used to analyze collected data and build multivariate regressions, mosaic plots, and histograms. Results The mean number of practice sessions per week was 3.60 +/- 1.64, and the median experience playing soccer was 2-4 years. Older participants were more likely to practice once (p = 0.0001) or twice (p= 0.0008) per week. Women were less likely to include warmups before playing soccer (p = 0.022). This was problematic as participants who did not include a proper warmup routine were more likely to have been absent from play for longer amounts of time following injury (p = 0.032). The four most common injury sites were knees (n = 35, 17.5%), ankles (n = 31, 15.5%), shoulders (n = 25, 12.5%), and head/neck (n = 24, 12%). 140 (47.62%) patients used pain medication as their main remedy, 128 (43.54%) went to physical therapy, and 26 (10.78%) underwent surgery. Conclusion In any sample of soccer athletes involving variations in sex, race, and competitive play, injuries are highly common. Few studies before this one have included female athletes, and our findings highlight an important discrepancy in training habits between sexes. Women are less likely to follow a warmup regimen and are thus injured for longer. Incorporating dynamic stretching and plyometrics are particularly helpful to stay healthy.

Biodiversity in mosaic communities: Soil microbial diversity associates with plant functional groups relating to soil available phosphorus in Tibetan alpine meadow

Natural landscape of alpine grassland is characterized by nested occupations of distinct communities, i.e., mosaic communities that differ not only in dominant plants but also in soil microbiome. Yet, we know little about the decoupled or coupled relationship between plant and soil microbial diversity, involving plant-soil feedback and confounding effects of environmental factors, synergistically maintaining biodiversity and function in mosaic communities. Here, we measured plant and soil microbial diversity in mosaic plots dominated by different plant functional groups (i.e., typical, sedges-, grasses-, legumes- or forbs-dominated communities) in the same Tibetan alpine meadow. We examined the difference in plant and soil microbial diversity between distinct communities using generalized linear models and assessed the relationships between plants, soil microbes, and edaphic factors using canonical discriminant analysis and generalized partial regression. Compared to the typical community: i) plant diversity increased and decreased in forbs- and sedges-dominated plots, respectively; ii) soil bacterial diversity increased in plots dominated by sedges and grasses; and iii) soil fungal diversity increased in plots dominated by sedges and legumes. The increase in soil bacterial diversity is associated with increasing standing biomass, plant diversity and soil available phosphorus but decreasing soil pH and soil available nitrogen, accounting for 52–66% of the variation. Despite being less pronounced, soil fungal diversity was positively associated with plant diversity and soil available phosphorus but tended to be negatively associated with standing biomass. In short, the results revealed that under complex interaction of soil-plant-microbes, the biodiversity of soil microbes is strongly coupled with plant functional groups relating to the availability of soil phosphorus, maintaining ecosystem function and service in soil phosphorus-limited mosaic meadows.

Generalized Mosaic Plots in the \pkg{ggplot2} Framework

Graphical methods for categorical variables are not well developed when compared with visualizations for numeric data. One method available for multidimensional categorical data visualizations is mosaic plots. Mosaic plots are an easy and powerful option for identifying relationships between multiple categorical variables. Although various packages have implemented mosaic plots, no implementation within the grammar of graphics supports mosaic plots. We present a new implementation of mosaic plots in R, ggmosaic, that implements a custom ggplot2 geom designed for generalized mosaic plots. Equipped with the functionality and flexibility of ggplot2, ggmosaic introduces new features not previously available for mosaic plots, including a novel method of incorporating a rendering of the underlying density via jittering. This paper provides an overview of the implementation and examples that highlight the versatility and ease of use of ggmosaic while demonstrating the practicality of mosaic plots.

Collecting Patient Feedback as a Means of Monitoring Patient Experience and Hospital Service Quality - Learning from a Government-led Initiative.

Patient feedback plays a significant role in hospital service improvement. However, how to encourage patient feedback that can guide hospital service improvement is still being explored. By examining patient feedback data related to a tertiary hospital in China that was collected from the "12345" Government Service Convenience Hotline (GSCH), the paper discusses the learnings from GSCH in encouraging patient feedback and how quality improvement initiatives have effected the number and types of complaints made by patients and their families via GSCH. The study retrospectively collected and analyzed complaints on a Tertiary General University-affiliated hospital made via GSCH between 2016 and 2020. Patient care process-related complaints were coded using the health care complaint analysis tool (HCAT) and other complaint data were categorized based on the nature of the complaints. The autoregressive integrated moving average (ARIMA) models and mosaic plots were used to observe complaints trends and different complaint variables, respectively. The relationship between various quality improvement initiatives introduced since 2018 and patient complaints was also tested. Close to 67% (n=2688) of calls made to the GSCH hotlines about the hospital were classified as a complaint including 60.6% vs 39.4% related to patient care process and nonpatient care process, respectively. For patient care process-related complaints, specifically against departments and personnel, 57.72% (n=961) were on clinical departments and 55.87% (n=471) were on doctors. Comparing the proportion of the complaint data in different categories in the two-year period of 2017-2018 and 2019-2020, an increase in management problems (47.73% vs 58.50%, P<0.001) and decrease in relationship problems (33.65% vs 25.69%, P=0.002) were recorded. A unified, transparent, and impartial GSCH platform greatly encourages feedback from patients and families. Feedbacks provide evidence to guide health care organizations in improving the overall experience of patients and the quality of services that they provide.

OPTIMIZATION OF QUANTITATIVE RESEARCH METHODS IN SOCIAL SCIENCES IN THE ERA OF BIG DATA

As information technology continues to advance, the complexity of data is ever-increasing. Traditional quantitative research methods in the social sciences, such as basic visualization and traditional statistical models, are gradually becoming inadequate in meeting the demands of modern data analysis. Despite the challenges that big data presents, it also brings new opportunities – through its usage, the optimization of traditional methods can be achieved. More intricate graphing techniques such as mosaic plots, alluvial plots, slope charts, and area charts, alongside machine learning algorithms that are better adapted for big data analysis such as decision trees, random forests, and K-Means algorithm, are opening new avenues for quantitative analysis in social sciences. This will ultimately foster further development of the field by allowing new methods and ideas to emerge.

Statistical approaches for assessing the effectiveness of safety devices use in preventing head injuries from motorcycle crashes

This study investigates the effectiveness of motorcyclist’s helmet use in preventing head injuries using the binary regression model and the mosaic plots. The association between head injuries and helmet use was modeled by applying binary logistic regression. The fitted model to the Changhua city accidents dataset has revealed that wearing a helmet had a positive impact in preventing the motorcyclist from sustaining head injuries in road traffic crashes. In addition to the association between head injuries and non-wearing of helmet, we found that alcohol impairment risk factor was increasing the likelihood of head injuries during crashes. Besides, the results were supported by using the mosaic plots which provide a visualization of the joint effect of Alcohol, helmet and heading injury for motorcyclists involved in crashes.

Evaluation of factors that influenced the length of hospital stay using data mining techniques

Backgroundlength of stay (LOS) is the time between hospital admission and discharge. LOS has an impact on hospital management and hospital care functions.MethodsA descriptive, retrospective study was designed on about 27,500 inpatients between March 2019 and 2020. Required data were collected from six wards (CCU, ICU, NICU, General, Maternity, and Women) in a teaching hospital. Clinical data such as demographic characteristics (age, sex), type of ward, and duration of hospital stay were analyzed by the R-studio program. Violin plots, bar charts, mosaic plots, and tree-based models were used to demonstrate the results.ResultsThe mean age of the population was 40.8 ± 19.2 years. The LOS of the study population was 2.43 ± 4.13 days. About 60% of patients were discharged after staying one day in the hospital. After staying one day in the hospital, 67% of women were discharged. However, 23% of men were discharged within this time frame. The majority of LOS in the CCU, ICU, and NICU ranged from 5 to 9 days.; In contrast, LOS was one day in General, Maternity, and Woman wards. Due to the tree plot, there was a different LOS pattern between Maternity-Women and the CCU-General-ICU-NICU wards group.ConclusionWe observed that patients with more severe diseases hospitalized in critical care wards had a longer LOS than those not admitted to critical care wards. The older patient had longer hospital LOS than the younger. By excluding Maternity and Woman wards, LOS in the hospital was comparable between males and females and demonstrated a similar pattern.

Review Policy Moderates the Correlation Between CiteScore and JIF For BMC Journals

This article examines the relationship between four review policies (RP) run by different BioMed Central (BMC) journals, namely single-blind peer review (SBPR), double-blind peer review (DBPR), open peer review (OPR), and transparent peer review (TPR), and CiteScore and Journal impact factor (JIF). As of December 12, 2021, BMC publishes 306 journals, of which 14 were discontinued. The final data set was 213 journals with data on CiteScore and JIF. Descriptive statistics and the use of violin and mosaic plots were used. Normality tests were conducted and non-parametric correlation and analysis of variance and Mann Whitney tests were used. Moderation analysis was used to assess the extent of the relationship between CiteScore and JIF. 14 (4.8%) are run with DBPR, and 22 (7.5%), 211 (72.3%) and 45 (15.4%) are run with OPR, SBPR and TPR respectively. Analysis of the final dataset shows that the mean, median, and standard deviation of the CiteScore of the 213 journals are 5.64, 4.80, and 3.65, respectively, while the mean, median, and standard deviation of the JIF of the 213 journals are 4.27, 3.36 and 2.90. In descending order, the CiteScore and JIF across the four RP are highest in DBPR, SBPR, OPR, and TPR. There is a strong positive correlation (Spearman rho = 0.87318, p-value &lt; 8.7e-68) between the CiteScore and JIF of the 213 BMC journals. Mann Whitney test (U = 14771.5, Wilcoxon W = 37562.5, Z = −6.228, p-value &lt; 0.000) shows that there is an evidence of significance median differences between the CiteScore and JIF of the 213 journals. There are significant median differences in the CiteScore in JIF across the four review policies. Finally, the RP moderates the relationship between CiteScore and JIF and between JIF and CiteScore, respectively. This article reveals that the review policies adopted by BMC journals are somewhat related to the journal metrics that measure the impact, prestige, relevance, and acceptability of the respective journals.

Positive-Bias-Temperature-Instability Induced Random-Trap-Fluctuation Enhanced Physical Unclonable Functions on 14-nm nFinFETs

We report one sort of weak physical unclonable functions (PUFs) composed of 14-nm nFinFETs with entropy of the random-trap-fluctuation (RTF). After the positive-bias-temperature-instability (PBTI) stress at high temperatures (85 °C ~ 150 °C), the generation of abundant random traps at the interface of gate-dielectric layers and channel efficiently improves cryptographic parameters of nFinFET-PUFs. Results show that bit-error-rates of the MOSAIC plots reduce to 1.4%; average values/standard-deviation of the inter- and intra- Hamming-distance reach 50.28%/1.7% and 0.38%/0.42%, respectively. This work provides an implacable technique to boost characteristics of weak PUFs through combinations of device-reliability and cryptography.

Exploring the trajectory curve of long-term musculoskeletal post-COVID pain symptoms in hospitalized COVID-19 survivors: a multicenter study.

This multicenter cohort study investigated the prevalence of musculoskeletal post-COVID pain during the first year after the infection with mosaic plots and an exponential bar plot model and its associated risk factors. Patients hospitalized because of COVID-19 in 5 hospitals of Madrid (Spain) were scheduled for a telephone interview at 2 follow-up periods after hospitalization for collecting data about musculoskeletal post-COVID pain. Hospitalization and clinical data were collected from hospital medical records. From 2000 patients initially recruited, 1593 (44.6% women, age: 61 ± 15 years) were assessed at T0 (hospital admission), T1 (mean: 8.0 ± 1.5 months after discharge), and T2 (mean: 13.2 ± 1.5 months after discharge). The prevalence of musculoskeletal pain (myalgia) was 30.3% (n = 483) at T0, increased to 43.4% (n = 692) at T1, and decreased to 37.8% (n = 603) at T2. The trajectory curve revealed a decreasing prevalence trend of musculoskeletal post-COVID pain the following years after hospitalization. According to the presence of pre-existing pain symptoms, the prevalence of new-onset post-COVID pain was 75.9%. Female sex (odds ratio [OR] 1.593, 95% confidence interval [CI] 1.148-2.211), history of musculoskeletal pain (OR 1.591, 95% CI 1.211-2.07), the presence of myalgia (OR 1.371, 95% CI 1.032-1.821) or headache (OR 2.278, 95% CI 1.622-3.199) at hospitalization, the days of hospitalization (OR 1.013, 95% CI 1.000-1.025), and the presence of post-COVID pain at T1 (OR 11.02, 95% CI 8.493-14.305) were factors associated with musculoskeletal post-COVID pain 1 year after hospitalization. In conclusion, musculoskeletal post-COVID pain remains highly prevalent 1 year after hospitalization. Female sex, previous history of pain symptoms, pain symptoms at onset, and days at hospital were factors associated with musculoskeletal post-COVID pain 1 year after hospitalization.

Exploring the trajectory recovery curve of the number of post-COVID Symptoms: The LONG-COVID-EXP-CM Multicenter Study

ObjectivesThis multicenter study investigated the recovery curve of the number of post-COVID-19 symptoms in previously hospitalized patients using an exponential decay model and mosaic plots. MethodsPatients hospitalized during the first wave of the pandemic (from March 10, 2010–May 31, 2020) due to COVID-19 from 5 hospitals in Madrid, Spain were scheduled for 2 telephone interviews at 2 follow-ups with a 5-month period in between and were asked about the presence of post-COVID-19 symptoms. The total number of post-COVID-19 symptoms was monitored. Clinical features, symptoms at hospital admission, and hospitalization data were collected from medical records. ResultsA total of 1593 patients who had COVID-19 were assessed 8.4 (T1) and 13.2 (T2) months after hospitalization. The mean number of post-COVID-19 symptoms was 2.6 (SD 2.0) at T1 and 1.5 (SD 1.4) at T2. The trajectory curve showed a decrease in prevalence trend. The analysis also revealed that 985 (61.8%) subjects reported more (T1>T2), 549 (34.5%) equal (T1 = T2), and 59 (3.7%) fewer (T1<T2) post-COVID-19 symptoms in the first tertile (T1: 8.4 months) compared with the second tertile (T2: 13.2 months) assessment. ConclusionsCurrent trajectory analysis revealed an overall decrease in the tendency in the number of post-COVID-19 symptoms throughout the 2 years after the infection.

Investigating association between factors fostering attention to a stock and rationales to buy it: an empirical analysis

PurposeCan something that drives our initial attention toward a stock have any implications on final decision to buy it? This paper empirically and statistically tests association, if any, between factors fostering attention toward a stock and rationales to buy it.Design/methodology/approachThis paper uses survey responses of individual investors involving multiple response categorical data. Association between attention fostering factors and rationales is tested using a modified first-order corrected Rao-Scott chi-square test statistic (to adjust for within-participant dependence among responses in case of multiple response categorical variables). Further, odds ratios and mosaic plots are used to determine the effect size of association.FindingsStrong association is seen between attention fostering factors and rationales to buy a stock. Further, strongest associations are seen in cases where origin is the same underlying influencing factor. Some of the most cited attention fostering factors and rationales in this research stem from familiarity bias and expert bias.Practical implicationsWhat starts as a trivial attention fostering factor, which may not even be recognized by majority investors, can go on to become one of the rationales for buying a stock. This can result in substantial financial implications for an individual investor. Investor education agencies and regulatory authorities can make investors cognizant of such association, which can help investors to improve and adjust their decision making accordingly.Originality/valueThe extant literature discusses factors/biases influencing buying decisions of individual investors. This research takes a step ahead by distinguishing these factors in terms of whether they play role of (1) fostering attention toward a stock or (2) of reasons for ultimately buying it. Such dissection of factors/biases, to the best of authors' knowledge, has not been done previously in any empirical and statistical analysis. The paper uses multiple response categorical data and applies a modified first-order corrected Rao-Scott chi-square statistic to test association. Application of the above-mentioned test statistic has not been done previously in context of individual investor decision-making.

Mosaics for Visualizing Knowledge Structures

Mosaic plots are state-of-the-art graphics for multivariate categor ical data in statistical visualization. Knowledge structures are mathematical models that belong to the theory of knowledge spaces in psychometrics. This paper presents an application of mosaic plots to psychometric data arising from underlying knowledge structure models. In simulation trials and with empirical data, the scope of this graphing method in knowledge space theory is investigated.

An intermodal approach to cohesion in constrained and unconstrained language

AbstractThis article investigates cohesion in the spoken and written registers of constrained language varieties to highlight the similarities and differences in the cohesion patterns of mediated (i.e., interpreted and translated) and non-native texts with respect to original texts produced by native speakers. In particular, it examines how different types of cohesive devices are distributed across spoken and written native, non-native, and mediated speeches originally delivered impromptu and read out at the plenary sessions of the European Parliament. The dataset comes from the European Translation and Interpreting Corpus (EPTIC) (Ferraresi and Bernardini 2019). The context provides a rare opportunity to examine the spoken and written registers of professional communication, both mono- and multilingual, in a relatively homogenous setting. First, in the exploratory analysis, I investigate the distribution of different types of cohesive devices across the investigated varieties drawing on mosaic plots and correspondence analysis. Thereafter, I make use of regression modelling of the overall frequency of cohesive devices across the examined varieties to evaluate the effect of constrainedness, mode of delivery, and individual variation. The results indicate that non-native and mediated texts do diverge from native production in the use of cohesive devices, but in different ways.

Contemporaneous trajectories of physical activity and screen time in adolescents.

Adolescents often report low moderate-to-vigorous physical activity (MVPA) and high screen time. We modeled sex-specific MVPA and screen time trajectories during adolescence and identified contemporaneous patterns of evolution. Data were drawn from 2 longitudinal investigations. The Nicotine Dependence in Teens (NDIT) study included 1294 adolescents recruited at age 12-13 years who completed questionnaires every 3 months for 5 years. The Monitoring Activities of Teenagers to Comprehend their Habits (MATCH) study included 937 participants recruited at age 9-12 years who completed questionnaires every 4 months for 7 years. MVPA was measured as the number of days per week of being active for at least 5 min (NDIT) or 60 min (MATCH). In both studies, screen time was measured as the number of hours spent weekly in screen activities. In each study, sex-specific group-based trajectories were modeled separately for MVPA and screen time from grade 7 to 11. Contemporaneous patterns of evolution were examined in mosaic plots. In both studies, 5 MVPA trajectories were identified in both sexes, and 4 and 5 screen time trajectories were identified in boys and girls, respectively. All combinations of MVPA and screen time trajectories were observed. However, the contemporaneous patterns of evolution were favourable in 14%-31% of participants (i.e., they were members of the stable high MVPA and the lower screen time trajectories). Novelty: MVPA and screen time trajectories during adolescence and their combinations showed wide variability in 2 Canadian studies. Up to 31% of participants showed favourable contemporaneous patterns of evolution in MVPA and screen time. Using uniform methods for trajectory modeling may increase the potential for replication across studies.

COVID-19 Pandemic and Financial Contagion

The original contribution of this paper is to empirically document the contagion of the Covid-19 on financial markets. We merge databases from Johns Hopkins Coronavirus Center, Oxford-Man Institute Realized Library, NYU Volatility Lab, and St-Louis Federal Reserve Board. We deploy three types of models throughout our experiments: (i) the Susceptible-Infective-Removed (SIR) that predicts the infections’ peak on 2020-03-27; (ii) volatility (GARCH), correlation (DCC), and risk-management (Value-at-Risk (VaR)) models that relate how bears painted Wall Street red; and, (iii) data-science trees algorithms with forward prunning, mosaic plots, and Pythagorean forests that crunch the data on confirmed, deaths, and recovered Covid-19 cases and then tie them to high-frequency data for 31 stock markets.

Defining clinical subgroups and genotype–phenotype correlations in NBAS-associated disease across 110 patients

PurposePathogenic variants in neuroblastoma-amplified sequence (NBAS) cause an autosomal recessive disorder with a wide range of symptoms affecting liver, skeletal system, and brain, among others. There is a continuously growing number of patients but a lack of systematic and quantitative analysis. MethodsIndividuals with biallelic variants in NBAS were recruited within an international, multicenter study, including novel and previously published patients. Clinical variables were analyzed with log-linear models and visualized by mosaic plots; facial profiles were investigated via DeepGestalt. The structure of the NBAS protein was predicted using computational methods. ResultsOne hundred ten individuals from 97 families with biallelic pathogenic NBAS variants were identified, including 26 novel patients with 19 previously unreported variants, giving a total number of 86 variants. Protein modeling redefined the β-propeller domain of NBAS. Based on the localization of missense variants and in-frame deletions, three clinical subgroups arise that differ significantly regarding main clinical features and are directly related to the affected region of the NBAS protein: β-propeller (combined phenotype), Sec39 (infantile liver failure syndrome type 2/ILFS2), and C-terminal (short stature, optic atrophy, and Pelger–Huët anomaly/SOPH). ConclusionWe define clinical subgroups of NBAS-associated disease that can guide patient management and point to domain-specific functions of NBAS.

Data Mining Effective for Casing-Failure Prediction and Prevention

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 19311, “Data-Mining Approaches for Casing-Failure Prediction and Prevention,” by Christine Noshi, SPE, Samuel Noynaert, SPE, and Jerome Schubert, SPE, Texas A&amp;M University, prepared for the 2019 International Petroleum Technology Conference, Beijing, 26–28 March. The paper has not been peer reviewed. Copyright 2019 International Petroleum Technology Conference. Reproduced by permission. Recent casing failures in the Granite Wash play in the western Anadarko Basin have sparked deep concerns for operators in North Texas and Oklahoma. Hydrostatic tests made in the field show that current API standards do not assure adequate joint and bursting strength to meet deep-well requirements. This paper is part of an ongoing effort to minimize the likelihood of failure using data-mining and machine-learning algorithms. Introduction Casing failure has long presented a challenge to the industry. The combined effects of design, dynamic borehole conditions, metallurgy, and handling have been challenging to quantify and predict accurately. Additionally, most casing-string challenges have been handled reactively instead of proactively; the total number of failures have been underreported and overlooked. The authors focus on the effects of poor cement as a primary factor; this translates into the absence of cement in a case study presented in the complete paper. Additional factors are the pumping of corrosive acids and poor standardized casing design that does not account for varied formations along with cyclical temperatures. Casing with partial cementation and sheaths with voids can contribute to excessive buckling-related collapse and tensile failures. Large pressure loadings, along with significant change in temperature, contribute to significant stresses in the intercasing annuli. In fragmentally cemented casing, tensile loading can show a great discrepancy between compression and high tension, with instances of failures in both the outer and inner strings. Additionally, cement thickening by downward flow could lack uniformity and could be prone to channeling. Air entrapment might occur, establishing bridges that hinder the process. Some authors in the literature related cementing failures with hole enlargements and washouts in long cement depths. The lack of cement support in those significant intervals exposed the casing to movement during drillpipe rotation, which triggered wear and ultimate buckling. Methodology The data were descriptively visualized using methods such as box plots, mosaic plots, and trellis charts, while predictive techniques included artificial neural networks (ANNs) and boosted-ensemble trees. A statistical software package was used along with Python coding to implement the models and choose the most-significant factors contributing to failure. Data-preprocessing techniques were implemented. The process began with data cleaning to account for missing data, re-move the bias incurred by noise, and remove outliers. For missing values, multivariate normal imputation on the basis of all samples belonging to the same class was used. Then, several parameters from different databases were integrated. Data transformation involved standardizing the data by the subtraction of the mean value and the subsequent division by standard deviation from each feature. Categorical variables were converted to numerical values because models such as neural nets, regression, and nearest-neighbor involve only numeric inputs. The compiled data set comprised 78 wells. Caution should be taken when assessing its statistical significance.