Taxonomic status of Bambusaspis miliaris : B. robusta, and B. pseudomiliaris (Hemiptera: Coccoidea: Asterolecaniidae)

Based on an assessment of 50 morphological characters from 110 specimens of Bambusaspis miliaris (BoisduvalI869), B. robusta (Green 1908), andB. pseudomiliaris (Green 1922) from different geographic regions around the world, we conclude that these specimens represent the same species. Therefore, the taxa B. robusta and B. pseudomiliaris are considered junior subjective synonyms of B. miliaris

Taxonomic Status of \u3ci\u3eBambusaspis miliaris\u3c/i\u3e, \u3ci\u3eB. robusta\u3c/i\u3e, and \u3ci\u3eB. pseudomiliaris\u3c/i\u3e (Hemiptera: Coccoidea: Asterolecaniidae)

Based on an assessment of 50 morphological characters from 110 specimens of Bambusaspis miliaris (Boisduval 1869), B. robusta (Green 1908), and B. pseudomiliaris (Green 1922) from different geographic regions around the world, we conclude that these specimens represent the same species. Therefore, the taxa B. robusta and B. pseudomiliaris are considered junior subjective synonyms of B. miliaris

Taxonomic Status of \u3ci\u3eBambusaspis miliaris\u3c/i\u3e, \u3ci\u3eB. robusta\u3c/i\u3e, and \u3ci\u3eB. pseudomiliaris\u3c/i\u3e (Hemiptera: Coccoidea: Asterolecaniidae)

Based on an assessment of 50 morphological characters from 110 specimens of Bambusaspis miliaris (Boisduval 1869), B. robusta (Green 1908), and B. pseudomiliaris (Green 1922) from different geographic regions around the world, we conclude that these specimens represent the same species. Therefore, the taxa B. robusta and B. pseudomiliaris are considered junior subjective synonyms of B. miliaris

The effect of weld porosity on the cryogenic fatigue strength of ELI grade Ti-5Al-2.5Sn

The effect of weld porosity on the fatigue strength of ELI grade Ti-5Al-2.5Sn at cryogenic temperature was determined. A series of high cycle fatigue (HCF) and tensile tests were performed at -320 F on specimens made from welded sheets of the material. All specimens were tested with weld beads intact and some amount of weld offset. Specimens containing porosity and control specimens containing no porosity were tested. Results indicate that for the weld configuration tested, the fatigue life of the material is not affected by the presence of spherical embedded pores

Risk, ambiguity and quantum decision theory

In the present article we use the quantum formalism to describe the effects of risk and ambiguity in decision theory. The main idea is that the probabilities in the classic theory of expected utility are estimated probabilities, and thus do not follow the classic laws of probability theory. In particular, we show that it is possible to use consistently the classic expected utility formula, where the probability associated to the events are computed with the equation of quantum interference. Thus we show that the correct utility of a lottery can be simply computed by adding to the classic expected utility a new corrective term, the uncertainty utility, directly connected with the quantum interference term.Comment: 1 figur

The analysis of survey data with framing effects

A well-known difficulty in survey research is that respondents’ answers to questions can depend on arbitrary features of a survey’s design, such as the wording of questions or the ordering of answer choices. In this paper, we describe a novel set of tools for analyzing survey data characterized by such framing effects. We show that the conventional approach to analyzing data with framing effects—randomizing survey-takers across frames and pooling the responses—generally does not identify a useful parameter. In its place, we propose an alternative approach and provide conditions under which it identifies the responses that are unaffected by framing. We also present several results for shedding light on the population distribution of the individual characteristic the survey is designed to measure

Introducing Quantum-Like Influence Diagrams for Violations of the Sure Thing Principle

It is the focus of this work to extend and study the previously proposed quantum-like Bayesian networks to deal with decision-making scenarios by incorporating the notion of maximum expected utility in influence diagrams. The general idea is to take advantage of the quantum interference terms produced in the quantum-like Bayesian Network to influence the probabilities used to compute the expected utility of some action. This way, we are not proposing a new type of expected utility hypothesis. On the contrary, we are keeping it under its classical definition. We are only incorporating it as an extension of a probabilistic graphical model in a compact graphical representation called an influence diagram in which the utility function depends on the probabilistic influences of the quantum-like Bayesian network. Our findings suggest that the proposed quantum-like influence digram can indeed take advantage of the quantum interference effects of quantum-like Bayesian Networks to maximise the utility of a cooperative behaviour in detriment of a fully rational defect behaviour under the prisoner's dilemma game

Reviewing progress: 7 Year Trends in Characteristics of Adults and Children Enrolled at HIV Care and Treatment Clinics in the United Republic of Tanzania.

To evaluate the on-going scale-up of HIV programs, we assessed trends in patient characteristics at enrolment and ART initiation over 7 years of implementation. Data were from Optimal Models, a prospective open cohort study of HIV-infected (HIV+) adults (>=15 years) and children (<15 years) enrolled from January 2005 to December 2011 at 44 HIV clinics in 3 regions of mainland Tanzania (Kagera, Kigoma, Pwani) and Zanzibar. Comparative statistics for trends in characteristics of patients enrolled in 2005--2007, 2008--2009 and 2010--2011 were examined. Overall 62,801 HIV+ patients were enrolled: 58,102(92.5%) adults, (66.5% female); 4,699(7.5%) children.Among adults, pregnant women enrolment increased: 6.8%, 2005--2007; 12.1%, 2008--2009; 17.2%, 2010--2011; as did entry into care from prevention of mother-to-child HIV transmission (PMTCT) programs: 6.6%, 2005--2007; 9.5%, 2008--2009; 12.6%, 2010--2011. WHO stage IV at enrolment declined: 27.1%, 2005--2007; 20.2%, 2008--2009; 11.1% 2010--2011. Of the 42.5% and 29.5% with CD4+ data at enrolment and ART initiation respectively, median CD4+ count increased: 210cells/muL, 2005--2007; 262cells/muL, 2008--2009; 266cells/muL 2010--2011; but median CD4+ at ART initiation did not change (148cells/muL overall). Stavudine initiation declined: 84.9%, 2005--2007; 43.1%, 2008--2009; 19.7%, 2010--2011.Among children, median age (years) at enrolment decreased from 6.1(IQR:2.7-10.0) in 2005--2007 to 4.8(IQR:1.9-8.6) in 2008--2009, and 4.1(IQR:1.5-8.1) in 2010--2011 and children <24 months increased from 18.5% to 26.1% and 31.5% respectively. Entry from PMTCT was 7.0%, 2005--2007; 10.7%, 2008--2009; 15.0%, 2010--2011. WHO stage IV at enrolment declined from 22.9%, 2005--2007, to 18.3%, 2008--2009 to 13.9%, 2010--2011. Proportion initiating stavudine was 39.8% 2005--2007; 39.5%, 2008--2009; 26.1%, 2010--2011. Median age at ART initiation also declined significantly. Over time, the proportion of pregnant women and of adults and children enrolled from PMTCT programs increased. There was a decline in adults and children with advanced HIV disease at enrolment and initiation of stavudine. Pediatric age at enrolment and ART initiation declined. Results suggest HIV program maturation from an emergency response

Quantum Experimental Data in Psychology and Economics

We prove a theorem which shows that a collection of experimental data of probabilistic weights related to decisions with respect to situations and their disjunction cannot be modeled within a classical probabilistic weight structure in case the experimental data contain the effect referred to as the 'disjunction effect' in psychology. We identify different experimental situations in psychology, more specifically in concept theory and in decision theory, and in economics (namely situations where Savage's Sure-Thing Principle is violated) where the disjunction effect appears and we point out the common nature of the effect. We analyze how our theorem constitutes a no-go theorem for classical probabilistic weight structures for common experimental data when the disjunction effect is affecting the values of these data. We put forward a simple geometric criterion that reveals the non classicality of the considered probabilistic weights and we illustrate our geometrical criterion by means of experimentally measured membership weights of items with respect to pairs of concepts and their disjunctions. The violation of the classical probabilistic weight structure is very analogous to the violation of the well-known Bell inequalities studied in quantum mechanics. The no-go theorem we prove in the present article with respect to the collection of experimental data we consider has a status analogous to the well known no-go theorems for hidden variable theories in quantum mechanics with respect to experimental data obtained in quantum laboratories. For this reason our analysis puts forward a strong argument in favor of the validity of using a quantum formalism for modeling the considered psychological experimental data as considered in this paper.Comment: 15 pages, 4 figure

The Systems Analysis and Improvement Approach: specifying core components of an implementation strategy to optimize care cascades in public health.

This work was supported from grants from the National Institutes of Health, including R01MH113435 (SAIA-SCALE), F32HD088204 and R34AI129900 (SAIA-PEDS), R21AI124399 (mPCAT), K24HD088229 (SAIA-FP), R21MH113691 (SAIA-MH), P30AI027757 (CFAR), R21DA046703 (SAIA-Naloxone), R01HL142412 (SAIA-HTN), 1UG3HL156390-01 (SCALE SAIA-HTN) R01HD0757 and R01HD0757-02S1 (SAIA), K08CA228761 (CCS SAIA) and T32AI070114 (UNC TIDE), Support was provided by the Implementation Science Core of the University of Washington/Fred Hutch Center for AIDS Research, an NIH-funded program under award number AI027757 which is supported by the following NIH Institutes and Centers: NIAID, NCI, NIMH, NIDA, NICHD, NHLBI, NIA, NIGMS, and NIDDK. This work was also supported by the Doris Duke Charitable Foundation and the Rita and Alex Hillman Foundation (SAIA-JUV), and the Thrasher Foundation (SAIA-MAL). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the Doris Duke Charitable Foundation, the Rita and Alex Hillman Foundation, or the Thrasher Foundation. © 2023. The Author(s). Publisher Copyright: © 2023, The Author(s). © 2023. The Author(s).BACKGROUND: Healthcare systems in low-resource settings need simple, low-cost interventions to improve services and address gaps in care. Though routine data provide opportunities to guide these efforts, frontline providers are rarely engaged in analyzing them for facility-level decision making. The Systems Analysis and Improvement Approach (SAIA) is an evidence-based, multi-component implementation strategy that engages providers in use of facility-level data to promote systems-level thinking and quality improvement (QI) efforts within multi-step care cascades. SAIA was originally developed to address HIV care in resource-limited settings but has since been adapted to a variety of clinical care systems including cervical cancer screening, mental health treatment, and hypertension management, among others; and across a variety of settings in sub-Saharan Africa and the USA. We aimed to extend the growing body of SAIA research by defining the core elements of SAIA using established specification approaches and thus improve reproducibility, guide future adaptations, and lay the groundwork to define its mechanisms of action. METHODS: Specification of the SAIA strategy was undertaken over 12 months by an expert panel of SAIA-researchers, implementing agents and stakeholders using a three-round, modified nominal group technique approach to match core SAIA components to the Expert Recommendations for Implementing Change (ERIC) list of distinct implementation strategies. Core implementation strategies were then specified according to Proctor's recommendations for specifying and reporting, followed by synthesis of data on related implementation outcomes linked to the SAIA strategy across projects. RESULTS: Based on this review and clarification of the operational definitions of the components of the SAIA, the four components of SAIA were mapped to 13 ERIC strategies. SAIA strategy meetings encompassed external facilitation, organization of provider implementation meetings, and provision of ongoing consultation. Cascade analysis mapped to three ERIC strategies: facilitating relay of clinical data to providers, use of audit and feedback of routine data with healthcare teams, and modeling and simulation of change. Process mapping matched to local needs assessment, local consensus discussions and assessment of readiness and identification of barriers and facilitators. Finally, continuous quality improvement encompassed tailoring strategies, developing a formal implementation blueprint, cyclical tests of change, and purposefully re-examining the implementation process. CONCLUSIONS: Specifying the components of SAIA provides improved conceptual clarity to enhance reproducibility for other researchers and practitioners interested in applying the SAIA across novel settings.Peer reviewe