Binary Scales Research Articles

The use of practice evidence by Australian occupational therapists: Perspectives and actions.

Evidence-based practice supports clinical decision-making by using multiple sources of evidence arising from research and practice. Research evidence develops through empirical study while practice evidence arises through clinical experience, client preferences, and the practice context. Although occupational therapists have embraced the paradigm of evidence-based practice, some studies have identified limits in the availability and use of research, which can lead to reliance on other forms of evidence. This study aimed to understand how Australian occupational therapists use practice evidence, manage potential bias, and enhance trustworthiness. Potential use of a critical appraisal tool for practice evidence was also explored. A 42-item questionnaire was developed to address the study aims. It consisted of a 7-point Likert scale, ordinal and free text questions. Likert scales were collapsed into binary scales and analysed using SPSS. Ordinal data were graphed and free text responses were analysed using manifest content analysis. Most respondents (82%) indicated that practice evidence was an important informant of practice and is used alongside research evidence. Almost all respondents (98%) expressed confusion when reconciling discrepancies between research and practice evidence. There was general acknowledgement that practice evidence is prone to bias (82%), yet 92% were confident in trusting their own practice evidence. Most respondents (74.5%) undertook some measures to appraise practice evidence, and almost all respondents (90%) agreed they would refer to a critical appraisal tool that helped them evaluate practice evidence. Occupational therapists in this study routinely use practice evidence arising from their own experience, client perspectives, and their practice context to inform clinical decision-making. While they agreed that practice evidence was prone to bias and misinterpretation, they generally trusted their own practice evidence. Participants indicated they needed guidance to critically appraise their practice evidence and supported the development of a critical appraisal tool for this purpose.

Building bridges: evaluating policymakers' research capacities, engagement, and utilization in health policymaking within the Kuwaiti context: a cross-sectional study

BackgroundHealth policymaking is a critical aspect of governmental decision-making that shapes the well-being of populations. In the Middle East and North Africa, particularly in Kuwait, limited attention has been given to exploring the research capacities, engagement, and utilization among health policymakers. This study aims to bridge this gap by investigating how Kuwaiti health policymakers incorporate evidence-based research into the formulation of health-related policies.MethodsThis cross-sectional study targeted health policymakers in leadership positions within the Kuwait Ministry of Health (MOH). Using the Seeking, Engaging with and Evaluating Research (SEER) questionnaire, participants' capacities, engagement, and use of research were assessed. The targeted sample was all health policymakers in leadership positions, starting from the head of departments and above. The questionnaire comprises four domains, 14 sections, and 50 questions and utilizes Likert and binary scales, with aggregate scores predicting engagement actions and research use. The data were collected between March and July 2023. All the statistical analyses were performed using SPSS v27, and the numerical and categorical variables were analyzed using appropriate statistical tests, including t-tests, ANOVA, and Pearson's correlation.ResultsOut of 205 policymakers, 88 participated (42.9% response rate): predominantly male (51.1%) and married (78.4%). The mean age was 49.84 ± 7.28 years, with a mean MOH tenure of 24.39 ± 6.80 years. Participants demonstrated high value for research (mean score 4.29 ± 0.55) and expressed confidence in the research utilization. Organizational emphasis on research use exhibited nuanced perceptions, identifying areas where MOH support may be lacking. Access to research resources and processes for policy development guidance were highlighted as challenges.ConclusionsThis study provides crucial insights into the research capacities and engagement of Kuwaiti health policymakers. It emphasizes the need for targeted interventions to align individual perceptions with organizational expectations, address confidence disparities, and enhance collaborative efforts. Organizational investments are crucial for fostering a dynamic research ecosystem to improve evidence-based policy development in Kuwait's healthcare landscape.

Patients’ voices matter: Translating pretreatment patient-reported outcomes into high and low health-related quality of life provides a new clinical aspect on prognosis and survival in patients with previously untreated diffuse large B‐cell lymphoma.

e23165 Background: Patient-reported health-related quality of life (HRQoL) measures collected prior to treatment initiation have been shown to be prognostic of disease outcomes and survival in patients with previously untreated diffuse large B-cell lymphoma (DLBCL), including progression-free survival (PFS) and overall survival (OS), However, these HRQoL measures are usually expressed as numbers from a scale (e.g. from 0 to 100), which limit their ease of clinical utility compared to binary scales (i.e. high vs. low). Using HRQoL measures collected from the phase 3 study GOYA, we aim to convert these measures into binary scales by identifying optimal cutoffs and demonstrate their clinical utility in differentiating patient risks in DLBCL. Methods: HRQoL data from the physical functioning (PF2), global health status (QL2), and fatigue (FA) scales from the EORTC QLQ-C30 questionnaire and the lymphoma subscale (LYMS) from the FACT-LYM questionnaire were used in this analysis. The optimal cutoff points were identified using statistical metrics that evaluate the prognostic value and goodness-of-fit for each assessed cutoff point. The top performing binary HRQoL scale was further assessed in its clinical utility as an addition to the Internal Prognostic Index (IPI), a standard risk classification tool in DLBCL based on 5 binary measures of patient baseline characteristics, to evaluate whether adding the HRQoL scale would potentially improve the risk classification. Results: As shown in the table, while all binary scales can clearly differentiate patient overall survival (demonstrated by the hazard ratios), PF2 numerically exhibited the best performance, and was further assessed as an addition to IPI. After adding PF2 to the base OS model containing only IPI, we observed improvements in model performance metrics, including the concordance index and the goodness-of-fit. In addition, among patients classified as high risk by IPI (n = 185, 3-yr OS: 65% [95% CI 68-72%]), those with low HRQoL (n = 102, 3-yr OS: 56% [95% CI 47-67%]) had a substantially worse OS, suggesting that further refinement of patient-risk defined by IPI may be feasible with HRQoL measures. Conclusions: Our work highlights the potential of the binary HRQoL scales to provide additional prognostic value to IPI, and suggests that patient reported outcomes may be useful in further refining patient risk classification in addition to IPI in untreated DLBCL. [Table: see text]

The Italian Bella Figura – a challenge for politeness theories

Abstract This paper examines the Italian folk concept of bella figura in the framework of politeness-theories and the various binary conceptions it provides for explaining the nature of human communication. BF is a culture-specific understanding of self-projection in everyday discourse: It is both, a cognitive concept concerning the “beautiful” embodiment of self, and a performative concept concerning the active “figuring out” of a bella figura in order to avoid its contrary, the brutta figura. Thus, the figura-concept represents a pragmatic principle for acting in line with socially accepted norms setting out a reference frame for judging social endeavours according to a culture-inherent value system. Accordingly, affinities can be assumed between the figura-concept and the sociopragmatic core notions ‘face’ and ‘politeness’. A comparison of the figura-concept with the respective first- and second-order conceptions of the two, reflects a cultural version of ‘impression management’ conceiving an image of self which coincides with Goffman’s, but not at all with Brown and Levinson’s face-concept. This makes it difficult to identify figura in relation to politeness. Connections and disconnections are discussed alongside the paradigmatic binary scales and the positive/negative value-attributes. Finally, the findings are verified by comparing the use of the respective expressions as evaluation tools.

Statistical inference for agreement between multiple raters on a binary scale.

Agreement studies often involve more than two raters or repeated measurements. In the presence of two raters, the proportion of agreement and of positive agreement are simple and popular agreement measures for binary scales. These measures were generalized to agreement studies involving more than two raters with statistical inference procedures proposed on an empirical basis. We present two alternatives. The first is a Wald confidence interval using standard errors obtained by the delta method. The second involves Bayesian statistical inference not requiring any specific Bayesian software. These new procedures show better statistical behaviour than the confidence intervals initially proposed. In addition, we provide analytical formulas to determine the minimum number of persons needed for a given number of raters when planning an agreement study. All methods are implemented in the R package simpleagree and the Shiny app simpleagree.

Modeling hypertension risk factors: continuous versus binary blood pressure scales

Modeling hypertension risk factors: continuous versus binary blood pressure scales

Evaluating the agreement between different substance use recall periods in multiple HIV cohorts

BackgroundThis study aims to evaluate the agreement in substance use on both binary and ordinal scales between 3-month and 6-month recall periods with samples from different communities, demographic backgrounds, and HIV status. MethodsWe administered the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) to 799 participants from three different North American cohorts focused on substance use and HIV. We conducted a within-person agreement analysis by calculating the agreement levels and Kappa statistic between data collected using the 3-month recall ASSIST and 6-month custom substance use surveys as well as different terminology for each substance in multiple cohorts. ResultsFor all drugs studied, the agreement on the binary use or ordinal frequency of use metrics showed a high agreement level between 80.4% and 97.9% and an adequate adjusted kappa value between 0.61 and 0.96, suggesting substantial agreement. According to the agreement criteria we proposed, substance use data collected using different recall periods and with variation in drug names can be harmonized across cohorts. ConclusionsThis study is the first to evaluate the feasibility of data harmonization of substance use by demonstrating high level of agreement between different recall periods in different cohorts. The results can inform data harmonization efforts in consortia where data are collected from cohorts using different questions and recall periods.

Первые 10000 маммографических исследований, выполненных в рамках услуги «Описание и интерпретация данных маммографического исследования с использованием искусственного интеллекта».

Artificial intelligence technologies have great potential in improving the effectiveness of screening programs in the detection of malignant neoplasms of the breast. Given the high social, demographic and economic importance of mass preventive research, there is no doubt that the diagnostic accuracy of artificial intelligence must match or even exceed the accuracy of radiologists. In this regard, studies are needed to compare the accuracy of software based on artificial intelligence technology and radiologists during the mammography examinations in a clinical environment. P u r p o s e : to assess the quality of the medical service “Description and interpretation of mammography data using artificial intelligence” as part of screening. M a t e r i a l s a n d m e t h o d s . The sample for analysis consisted of 9684 digital mammograms. For each study, the BI-RADS category was determined by a radiologist and using software based on artificial intelligence technologies (AI based software) registered in the Russian Federation as a medical device. Forty-five studies from this sample with significant discrepancies in physician and software assessments were subject to peer review, which resulted in a BI-RADS category according to the physician expert. R e s u l t s . When evaluating weighted averages, there were no statistically significant differences between physician results and AI based software for 9684 digital mammography exams. Evaluation of physician and software consistency showed that matches are observed in 43,89% of cases for the BI-RADS scale and in 80,69% – 84,10% for binary scales. The presence of a case in which the pathology identified with the help of software and confirmed during the review of the results by the expert was missed by the doctor indicates the promise of using AI based software for evaluating mammography studies and requires further research. F i n d i n g s . When evaluating mammography studies, the agreement between the AI based decision and the radiologist reaches 84,10%, with the software assigning a higher BI-RADS category more often. Expert review of part of these discrepancies showed a potential reduction in the number of missed breast malignancies with the help of software.

Role of sleep-disordered breathing in age-related macular degeneration

AimsTo examine the association between obstructive sleep apnoea (OSA) and age-related macular degeneration (AMD), and the subphenotype of AMD with reticular pseudodrusen (RPD).MethodsCase–control study with 351 participants (211 AMD and...

Hydrodynamical evolution of black hole binaries embedded in AGN discs: II. dependence on equation of state, binary mass, and separation scales

ABSTRACT Stellar-mass binary black holes (BBHs) embedded in active galactic nucleus (AGN) discs offer a promising dynamical channel to produce black hole mergers that are detectable by LIGO/Virgo. Modelling the interactions between the disc gas and the embedded BBHs is crucial to understand their orbital evolution. Using a suite of 2D high-resolution simulations of prograde equal-mass circular binaries in local disc models, we systematically study how their hydrodynamical evolution depends on the equation of state (EOS; including the γ-law and isothermal EOS) and on the binary mass and separation scales (relative to the supermassive black hole mass and the Hill radius, respectively). We find that binaries accrete slower and contract in orbit if the EOS is far from isothermal such that the surrounding gas is diffuse, hot, and turbulent. The typical orbital decay rate is of the order of a few times the mass doubling rate. For a fixed EOS, the accretion flows are denser, hotter, and more turbulent around more massive or tighter binaries. The torque associated with accretion is often comparable to the gravitational torque, so both torques are essential in determining the long-term binary orbital evolution. We carry out additional simulations with non-accreting binaries and find that their orbital evolution can be stochastic and is sensitive to the gravitational softening length, and the secular orbital evolution can be very different from those of accreting binaries. Our results indicate that stellar-mass BBHs may be hardened efficiently under ideal conditions, namely less massive and wider binaries embedded in discs with a non-isothermal EOS.

Clustering heterogeneous categorical data using enhanced mini batch K-means with entropy distance measure

<span lang="EN-US">Clustering methods in data mining aim to group a set of patterns based on their similarity. In a data survey, heterogeneous information is established with various types of data scales like nominal, ordinal, binary, and Likert scales. A lack of treatment of heterogeneous data and information leads to loss of information and scanty decision-making. Although many similarity measures have been established, solutions for heterogeneous data in clustering are still lacking. The recent entropy distance measure seems to provide good results for the heterogeneous categorical data. However, it requires many experiments and evaluations. This article presents a proposed framework for heterogeneous categorical data solution using a mini batch k-means with entropy measure (MBKEM) which is to investigate the effectiveness of similarity measure in clustering method using heterogeneous categorical data. Secondary data from a public survey was used. The findings demonstrate the proposed framework has improved the clustering’s quality. MBKEM outperformed other clustering algorithms with the accuracy at 0.88, v-measure (VM) at 0.82, adjusted rand index (ARI) at 0.87, and Fowlkes-Mallow’s index (FMI) at 0.94. It is observed that the average minimum elapsed time-varying for cluster generation, </span><em><span lang="EN-US">k</span></em><span lang="EN-US"> at 0.26 s. In the future, the proposed solution would be beneficial for improving the quality of clustering for heterogeneous categorical data problems in many domains.</span>

Modeling the effects of clumpy winds in the high-energy light curves of γ-ray binaries

Context. High-mass gamma-ray binaries are powerful nonthermal galactic sources, some of them hosting a pulsar whose relativistic wind interacts with a likely inhomogeneous stellar wind. So far, modeling these sources including stellar wind inhomogeneities has been done using either simple analytical approaches or heavy numerical simulations, none of which allow for an exploration of the parameter space that is both reasonably realistic and general. Aims. Applying different semi-analytical tools together, we study the dynamics and high-energy radiation of a pulsar wind colliding with a stellar wind with different degrees of inhomogeneity to assess the related observable effects. Methods. We computed the arrival of clumps to the pulsar wind-stellar wind interaction structure using a Monte Carlo method and a phenomenological clumpy-wind model. The dynamics of the clumps that reach deep into the pulsar wind zone was computed using a semi-analytical approach. This approach allows for the characterization of the evolution of the shocked pulsar wind region in times much shorter than the orbital period. With this three-dimensional information about the emitter, we applied analytical adiabatic and radiative models to compute the variable high-energy emission produced on binary scales. Results. An inhomogeneous stellar wind induces stochastic hour-timescale variations in the geometry of the two-wind interaction structure on binary scales. Depending on the degree of stellar wind inhomogeneity, 10–100% level hour-scale variability in the X-rays and gamma rays is predicted, with the largest variations occurring roughly once per orbit. Conclusions. Our results, based on a comprehensive approach, show that present X-ray and future very-high-energy instrumentation can allow us to trace the impact of a clumpy stellar wind on the shocked pulsar wind emission in a gamma-ray binary.

Building an Initial Validity Argument for Binary and Analytic Rating Scales for an EFL Classroom Writing Assessment: Evidence from Many-Facets Rasch Measurement

Although much research has compared the functioning between analytic and holistic rating scales, little research has compared the functioning of binary rating scales with other types of rating scales. This quantitative study set out to preliminarily and comparatively validate binary and analytic rating scales intended for use in formative assessment and for paragraph writing assessment in a Thai EFL university classroom context. Specifically, this study applied an argument-based validation approach to build an initial validity argument for the rating scales with emphasis on the evaluation, generalization, and explanation inferences, and employed a many-facets Rasch measurement (MFRM) approach to investigate the psychometric functionalities of the rating scales which served as the initial validity evidence for the rating scales. Three trained teacher raters applied the rating scales to rate the same set of 51 opinion paragraphs written by English-major students. The rating scores were analysed following the MFRM psychometrics. Overall, the MFRM results revealed that (1) the rating scales largely generated accurate writing scores, supporting the valuation inference, (2) the raters were self-consistent in applying the rating scales, contributing to the generalization inference, (3) the rating scales sufficiently captured the defined writing construct, substantiating the explanation inference, and (4) the binary rating scale showed more desirable psychometric properties than the analytic rating scale. The present findings confirm the appropriate functioning and reasonable validity argument of the rating scales and highlight the greater potential of the binary rating scale to mitigate rater inconsistency and cognitive load in a formative classroom assessment.

Agency Theory: Its Discourse and Practice in the Selected Public-Sectors

This research investigates the discourse and practice of Agency Theory (AT) in the selected public-sectors of Ethiopia. The Agency Theory is assumed to bring about efficiency and accountability of the public-sector by adopting a contract and Pay-for-Performance (PFP) scheme; however, performance and citizens-cum-customers satiety was not improved as envisioned. Thus, to investigate this problem, the researcher has employed the mixed methods approach and, hence the cross-sectional design, where data has been collected at a point in time from selected public-sector employees to respond to the research problem and questions. To gather data, measurement scales like Binary and Likert Scales and research instruments like Self-Completion Questionnaires (SCQ), Semi-Structured Interviews (SSI), and Secondary Sources (SS) have been carried out at different tiers of government. The results indicated that the complex contexts like “Diverse Objectives, Public Values, and Diffuse Power” in the selected public-sectors required the co-existence of different initiatives and approaches based on what works and what does not work standard. Therefore, this study recommended adopting the home-grown reform system: The needed competencies that include the Traditional Public Administration (TPA), New Public Management (NPM), or Public Service Motivation (PSM) in the selected public-sectors based on the fitting contexts could increase the likelihood of performance enhancement.

Examining Sentiment in Complex Texts. A Comparison of Different Computational Approaches.

Can we rely on computational methods to accurately analyze complex texts? To answer this question, we compared different dictionary and scaling methods used in predicting the sentiment of German literature reviews to the “gold standard” of human-coded sentiments. Literature reviews constitute a challenging text corpus for computational analysis as they not only contain different text levels—for example, a summary of the work and the reviewer's appraisal—but are also characterized by subtle and ambiguous language elements. To take the nuanced sentiments of literature reviews into account, we worked with a metric rather than a dichotomous scale for sentiment analysis. The results of our analyses show that the predicted sentiments of prefabricated dictionaries, which are computationally efficient and require minimal adaption, have a low to medium correlation with the human-coded sentiments (r between 0.32 and 0.39). The accuracy of self-created dictionaries using word embeddings (both pre-trained and self-trained) was considerably lower (r between 0.10 and 0.28). Given the high coding intensity and contingency on seed selection as well as the degree of data pre-processing of word embeddings that we found with our data, we would not recommend them for complex texts without further adaptation. While fully automated approaches appear not to work in accurately predicting text sentiments with complex texts such as ours, we found relatively high correlations with a semiautomated approach (r of around 0.6)—which, however, requires intensive human coding efforts for the training dataset. In addition to illustrating the benefits and limits of computational approaches in analyzing complex text corpora and the potential of metric rather than binary scales of text sentiment, we also provide a practical guide for researchers to select an appropriate method and degree of pre-processing when working with complex texts.

Susceptibility to misinformation is consistent across question framings and response modes and better explained by myside bias and partisanship than analytical thinking

AbstractMisinformation presents a significant societal problem. To measure individuals’ susceptibility to misinformation and study its predictors, researchers have used a broad variety of ad-hoc item sets, scales, question framings, and response modes. Because of this variety, it remains unknown whether results from different studies can be compared (e.g., in meta-analyses). In this preregistered study (US sample;N= 2,622), we compare five commonly used question framings (eliciting perceived headline accuracy, manipulativeness, reliability, trustworthiness, and whether a headline is real or fake) and three response modes (binary, 6-point and 7-point scales), using the psychometrically validated Misinformation Susceptibility Test (MIST). We test 1) whether different question framings and response modes yield similar responses for the same item set, 2) whether people’s confidence in their primary judgments is affected by question framings and response modes, and 3) which key psychological factors (myside bias, political partisanship, cognitive reflection, and numeracy skills) best predict misinformation susceptibility across assessment methods. Different response modes and question framings yield similar (but not identical) responses for both primary ratings and confidence judgments. We also find a similar nomological net across conditions, suggesting cross-study comparability. Finally, myside bias and political conservatism were strongly positively correlated with misinformation susceptibility, whereas numeracy skills and especially cognitive reflection were less important (although we note potential ceiling effects for numeracy). We thus find more support for an “integrative” account than a “classical reasoning” account of misinformation belief.

Individual symptom visual analogue scale severity scores for determining EPOS guideline-based chronic rhinosinusitis disease control.

The goal of this study was to determine how to translate visual analogue scale (VAS) symptom scores to the binary, descriptive symptom scales used in the European Position Paper on Rhinosinusitis and Nasal Polyps (EPOS) criteria for chronic rhinosinusitis (CRS) disease control. 309 CRS patients were recruited. All patients rated their burden of 5 symptoms (nasal blockage, rhinorrhea/postnasal drip, facial pain/pressure, smell loss, sleep disturbance or fatigue) using the binary EPOS descriptive symptom scales and a VAS (on a scale of 0 to 10). In addition, participants completed a 22-item Sinonasal Outcome Test (SNOT-22) and rated their overall CRS disease control as "controlled", "partly controlled" or "uncontrolled" Results: Symptom burdens measured by VAS, binary descriptive EPOS scale and SNOT-22 were associated with worsening CRS disease control reported by participants. Each symptom had a distinct VAS score cut-off that strongly predicted the uncontrolled option on the corresponding binary descriptive EPOS symptom scale. However, the predictive ability of VAS for rhinorrhea/ postnasal drip was disparately worse than the other 4 symptoms. When considering all symptom data simultaneously, a VAS score higher than 3.5 strongly predicted the uncontrolled option on the corresponding binary descriptive EPOS symptom scale for all 5 symptoms. A VAS symptom score of higher than 3.5 translates to the uncontrolled option in the binary, descriptive symptom scale of the EPOS control criteria. The rhinorrhea/postnasal drip descriptive symptom scale translates disparately worse to VAS scores and may be considered for revision in future criteria.

Firefly Algorithm with Mini Batch K-Means Entropy Measure for Clustering Heterogeneous Categorical Timber Data

Clustering analysis is the process of identifying similar patterns in various types of data. Heterogeneous categorical data consists of data on ordinal, nominal, binary, and Likert scales. The clustering solution for heterogeneous data clustering remains difficult due to partitioning complex and dissimilarity features. It is necessary to find a solution to high-quality clustering techniques to efficiently determine the significant features of the data. This paper emphasizes using the firefly algorithm to reduce the distance gap between features and improve clustering performance. To obtain an optimal global solution for clustering, we proposed a hybrid of mini-batch k-means (MBK) clustering-based entropy distance measures (EM) with a firefly optimization algorithm (FA). This study compares the performance of hybrid K-Means, Agglomerative, DBSCAN, and Affinity clustering models with EM and FA. The evaluation uses a variety of data from the timber perception survey dataset. In terms of performance, the proposed MBK+EM+FA has superior and most effective clustering. It achieves a higher accuracy of 96.3 percent, a 97 percent F-measure, a 98 percent precision, and a 97 percent recall. Other external assessments revealed that the Homogeneity (HOMO) is 79.14 percent, the Fowlkes-Mallows Index (FMI) is 93.07 percent, the Completeness (COMP) is 78.04 percent, and the V-Measure (VM) is 78.58 percent. Both proposed MBK+EM+FA and MBK+EM took about 0.45s and 0.35s to compute, respectively. The excellent quality of the clustering results does not justify such time constraints. Surprisingly, the proposed model reduced the distance measure of all heterogeneous features. The future model could put heterogeneous categorical data from a different domain to the test.

Relativistic hydrodynamical simulations of the effects of the stellar wind and the orbit on high-mass microquasar jets

ABSTRACT High-mass microquasar jets, produced in an accreting compact object in orbit around a massive star, must cross a region filled with stellar wind. The combined effects of the wind and orbital motion can strongly affect the jet properties on binary scales and beyond. The study of such effects can shed light on how high-mass microquasar jets propagate and terminate in the interstellar medium. We study for the first time, using relativistic hydrodynamical simulations, the combined impact of the stellar wind and orbital motion on the properties of high-mass microquasar jets on binary scales and beyond. We have performed 3-dimensional relativistic hydrodynamic simulations, using the PLUTO code, of a microquasar scenario in which a strong weakly relativistic wind from a star interacts with a relativistic jet under the effect of the binary orbital motion. The parameters of the orbit are chosen such that the results can provide insight on the jet-wind interaction in compact systems like for instance Cyg X-1 or Cyg X-3. The wind and jet momentum rates are set to values that may be realistic for these sources and lead to moderate jet bending, which together with the close orbit and jet instabilities could trigger significant jet precession and disruption. For high-mass microquasars with orbit size a ∼ 0.1 AU, and (relativistic) jet power $L_j\sim 10^{37}(\dot{M}_w/10^{-6}\, {\rm M}_\odot \, {\rm yr}^{-1})$ erg s−1, where $\dot{M}_w$ is the stellar wind mass rate, the combined effects of the stellar wind and orbital motion can induce relativistic jet disruption on scales ∼1 AU.

Cluster analysis of coffee blends for some sensory properties: a comparative approach to the ABIC’s classification criteria

In Brazil, coffee beverage quality is classified according to technical recommendations of the Associação Brasileira da Indústria de Café (ABIC), which determines cutoff points to discriminate from non-recommended to gourmet coffees. Accordingly, this study aimed to propose the use of cluster analysis to evaluate coffee blends composed of coffees with different qualities and of different varieties regarding a few sensory properties, using continuous and binary scales obtained by a cutoff, which defines whether the coffee is recommendable or not according to the ABIC criteria. It is believed, therefore, that this technique can be used to analyze coffee beverage quality as it is easily accessible and implemented by researchers. In conclusion, a qualitative cluster analysis using the minimum cutoff value of the ABIC had more promising results. This is because blends whose composition contained high and moderate proportions of specialty coffees were more homogeneous in the clustering.