Critical Choices Research Articles

Dilemma Motives in Contemporary Ghanaian Hilife Songs: Paapa Yankson’s ‘Woyer Anaa WoMaame’in Perspective

In Ghana and Africa, dilemma motives and concepts constitute a large number of the folklore. Ideas related to dilemma motives and concepts like the dilemma tales leave listeners and audiences analyzing whilst making informed and critical choices presented among dicey alternatives. Given that these choices are quite difficult and may question the ethical, moral and legal thoughts of the individual; dilemma motives leave the audiences to judge the comparative skills and abilities of characters presented in the dilemma situation. The richness of dilemma concepts cannot be ignored when blended in the lyrics of African beats. An idea of African Rhetoric is indicating the critical thinking skills of the forbearers before the inception of written literature. Oral Ghanaian literature made use of dilemma motives to develop individual analytical skills and in effect arrive at the resolution of conflicting ideas embedded in woven choices.For instance, Ghanaian oral literature use dilemma motives to develop individual analytical skills, and in effect arrive at the resolution of conflicting ideas embedded in dicey choices. These choices are difficult such that it tends to question the ethical, moral and legal thoughts of the individual. Thus, leaving the audience to judge uses comparative skills and abilities of characters presented in the situation. In PaapaYankson’s song, the audience is presented with a dilemma, a husband is given a choice to save either the wife or mother who are both drowning. Using a qualitative approach, ethnographic research methodologies and elements of rhetoric, I thematically analyse the lyrics of the title and uncover how individuals familiar with the song solve this dilemma.

Considerations regarding choice of primary outcome in clinical trials in deprescribing.

Considerations regarding choice of primary outcome in clinical trials in deprescribing.

Mean absorption estimation from room impulse responses using virtually supervised learning.

In the context of building acoustics and the acoustic diagnosis of an existing room, it introduces and investigates a new approach to estimate the mean absorption coefficients solely from a room impulse response (RIR). This inverse problem is tackled via virtually supervised learning, namely, the RIR-to-absorption mapping is implicitly learned by regression on a simulated dataset using artificial neural networks. Simple models based on well-understood architectures are the focus of this work. The critical choices of geometric, acoustic, and simulation parameters, which are used to train the models, are extensively discussed and studied while keeping in mind the conditions that are representative of the field of building acoustics. Estimation errors from the learned neural models are compared to those obtained with classical formulas that require knowledge of the room's geometry and reverberation times. Extensive comparisons made on a variety of simulated test sets highlight different conditions under which the learned models can overcome the well-known limitations of the diffuse sound field hypothesis underlying these formulas. Results obtained on real RIRs measured in an acoustically configurable room show that at 1 kHz and above, the proposed approach performs comparably to classical models when reverberation times can be reliably estimated and continues to work even when they cannot.

Market entry of over-the-counter nutritional supplement – a case study from Thailand

Learning Outcomes Teaching objectives are as follows: students need to understand the critical choices involved in introducing a product into a new market, including but not limited to the macroeconomic context, the target consumer segment, the positioning of the product, distribution channels, pricing and promotion strategy. Students must learn to appreciate the importance of anticipating the reaction of incumbents, and how such reactions may determine the success or failure of a new product entry into the market. Students develop skills to analyze complementarities between different distribution channels and understand how investments in developing one channel can result in positive or negative consequences in other channels. Case Overview/Synopsis How can health products such as multivitamins and other nutritional supplements make headway into emerging markets that are moving up the ranks of middle-income economies? This case study investigates the case of Thailand, a country that in the early 1990s registered a per capita income comparable to Vietnam and Laos and Cambodia today. It illustrates, through the real experience of Pat – an executive of a local subsidiary of an American multinational pharmaceutical company – how a new entrant exploited the rapidly changing economic and retailing environment to become a successful player in an important and growing segment of consumer products. Complexity Academic Level This case is suitable for master’s degree students or short-course executives. Supplementary materials Teaching Notes are available for educators only. Subject code CSS 11: Strategy.

Tweeting #RamNavami: A Comparison of Approaches to Analyzing Bipartite Networks

Bipartite networks, also known as two-mode networks or affiliation networks, are a class of networks in which actors or objects are partitioned into two sets, with interactions taking place across but not within sets. These networks are omnipresent in society, encompassing phenomena such as student-teacher interactions, coalition structures and international treaty participation. With growing data availability and proliferation in statistical estimators and software, scholars have increasingly sought to understand the methods available to model the data-generating processes in these networks. This article compares three methods for doing so: (a) Logit (b) the bipartite Exponential Random Graph Model (ERGM) and (c) the Relational Event Model (REM). This comparison demonstrates the relevance of choices with respect to dependence structures, temporality, parameter specification and data structure. Considering the example of Ram Navami, a Hindu festival celebrating the birth of Lord Ram, the ego network of tweets using #RamNavami on 21April 2021 is examined. The results of the analysis illustrate that critical modelling choices make a difference in the estimated parameters and the conclusions to be drawn from them.

An interactive approach to multi-objective mean semi-variance model with transaction cost for portfolio optimization

Portfolio selection may be considered as a complicated decision making under uncertain conditions. Regardless of an unknown future, a fund manager has to make critical financial choices based on the investors’ preferences towards risk and return and perceptions. Since the pioneer work of Markowitz, a lot of studies have been published considering his Mean-Variance (MV) model as a basis. In our proposed model we had considered Semivariance as the risk measure in our proposed portfolio optimization model. We also extend our model to include the effect of transaction cost in portfolio optimization and then the concept of entropy is incorporated as an objective function in our model to obtain a well diversified portfolio within optimal asset allocation. An interactive approach is used to solve the model. A numerical example is used to illustrate that the model can be efficiently used in practice. Finally the result obtained in interactive method has been compared with the result obtained by Fuzzy Non-Linear Programming (FNLP) and Fuzzy Additive Goal Programming (FAGP) techniques.

Using a Secure, Continually Updating, Web Source Processing Pipeline to Support the Real-Time Data Synthesis and Analysis of Scientific Literature: Development and Validation Study.

BackgroundThe scale and quality of the global scientific response to the COVID-19 pandemic have unquestionably saved lives. However, the COVID-19 pandemic has also triggered an unprecedented “infodemic”; the velocity and volume of data production have overwhelmed many key stakeholders such as clinicians and policy makers, as they have been unable to process structured and unstructured data for evidence-based decision making. Solutions that aim to alleviate this data synthesis–related challenge are unable to capture heterogeneous web data in real time for the production of concomitant answers and are not based on the high-quality information in responses to a free-text query.ObjectiveThe main objective of this project is to build a generic, real-time, continuously updating curation platform that can support the data synthesis and analysis of a scientific literature framework. Our secondary objective is to validate this platform and the curation methodology for COVID-19–related medical literature by expanding the COVID-19 Open Research Dataset via the addition of new, unstructured data.MethodsTo create an infrastructure that addresses our objectives, the PanSurg Collaborative at Imperial College London has developed a unique data pipeline based on a web crawler extraction methodology. This data pipeline uses a novel curation methodology that adopts a human-in-the-loop approach for the characterization of quality, relevance, and key evidence across a range of scientific literature sources.ResultsREDASA (Realtime Data Synthesis and Analysis) is now one of the world’s largest and most up-to-date sources of COVID-19–related evidence; it consists of 104,000 documents. By capturing curators’ critical appraisal methodologies through the discrete labeling and rating of information, REDASA rapidly developed a foundational, pooled, data science data set of over 1400 articles in under 2 weeks. These articles provide COVID-19–related information and represent around 10% of all papers about COVID-19.ConclusionsThis data set can act as ground truth for the future implementation of a live, automated systematic review. The three benefits of REDASA’s design are as follows: (1) it adopts a user-friendly, human-in-the-loop methodology by embedding an efficient, user-friendly curation platform into a natural language processing search engine; (2) it provides a curated data set in the JavaScript Object Notation format for experienced academic reviewers’ critical appraisal choices and decision-making methodologies; and (3) due to the wide scope and depth of its web crawling method, REDASA has already captured one of the world’s largest COVID-19–related data corpora for searches and curation.

Systematic Evaluation of Design Choices for Deep Facial Action Coding Across Pose

The performance of automated facial expression coding is improving steadily. Advances in deep learning techniques have been key to this success. While the advantage of modern deep learning techniques is clear, the contribution of critical design choices remains largely unknown, especially for facial action unit occurrence and intensity across pose. Using the The Facial Expression Recognition and Analysis 2017 (FERA 2017) database, which provides a common protocol to evaluate robustness to pose variation, we systematically evaluated design choices in pre-training, feature alignment, model size selection, and optimizer details. Informed by the findings, we developed an architecture that exceeds state-of-the-art on FERA 2017. The architecture achieved a 3.5% increase in F1 score for occurrence detection and a 5.8% increase in Intraclass Correlation (ICC) for intensity estimation. To evaluate the generalizability of the architecture to unseen poses and new dataset domains, we performed experiments across pose in FERA 2017 and across domains in Denver Intensity of Spontaneous Facial Action (DISFA) and the UNBC Pain Archive.

A new methodological framework coupling computational fluid dynamics and fingerprinting for assessment of aeolian sediment transport processes

A systematic understanding of the transport processes of aeolian sand from source to sedimentary areas is important for both hazard control and fundamental mechanism research. This study proposes a methodological framework consisting of wind data standardisation, computational fluid dynamics (CFD) simulation and fingerprinting technology to clarify aeolian sediment transport processes. The methodological framework was applied to the Cuona Lake section of the Qinghai-Tibetan Railway as a case study. One main finding was that the mountain passes located on the west of the lake act as outlets that converge and accelerate the predominant wind. The Basuoqu river valley and Amdo-Cuona down-faulted valley act as a ventilation tunnel in the sedimentary area. The CFD simulation clearly showed the influence of the complex terrain on the wind flow field. Second, the area southwest of the lake was predicted to be the main sediment source; a large portion of this area has an annual wind power density exceeding 1000 W/m2. Third, the fingerprinting estimation results revealed that the predicted main source area contributed more than 80% to the target sediment, which was in good agreement with the CFD simulation result. A decision-tree is provided to guide interested users in critical choices for different situations and research objectives. This CFD- and fingerprinting- based framework has the potential to contribute significantly to aeolian research and facilitate the development of a more detailed methodology.

Practical choices for space‐filling designs

AbstractSpace‐filling designs are now commonly used as a flexible model‐free strategy for providing good coverage throughout an input space of interest for a variety of computer and physical experiment design scenarios. Some of the preliminary choices about how to frame the problem and which type of design to use can have a substantial impact on the success or failure of the experiment, and yet how to make these critical choices is often under‐emphasized in the literature. In this paper, we explore several of the practical choices required by the experimenter and describe a sequence of steps to help create an ideal design that matches the goals and constraints of the experiment. These choices include the specification of the input space, the scaling of the variables, the degree of uniformity of the design points across the input space, and the space‐filling characteristics. In addition, some new tools for defining the weights to implement non‐uniform space‐filling designs are provided. The methods are demonstrated with several illustrative examples and a real‐world chemical engineering experiment for carbon capture.

Rational Vaccine Design in Times of Emerging Diseases: The Critical Choices of Immunological Correlates of Protection, Vaccine Antigen and Immunomodulation.

Vaccines are the most effective medical intervention due to their continual success in preventing infections and improving mortality worldwide. Early vaccines were developed empirically however, rational design of vaccines can allow us to optimise their efficacy, by tailoring the immune response. Establishing the immune correlates of protection greatly informs the rational design of vaccines. This facilitates the selection of the best vaccine antigens and the most appropriate vaccine adjuvant to generate optimal memory immune T cell and B cell responses. This review outlines the range of vaccine types that are currently authorised and those under development. We outline the optimal immunological correlates of protection that can be targeted. Finally we review approaches to rational antigen selection and rational vaccine adjuvant design. Harnessing current knowledge on protective immune responses in combination with critical vaccine components is imperative to the prevention of future life-threatening diseases.

Review of Waterborne Organic Semiconductor Colloids for Photovoltaics.

Development of carbon neutral and sustainable energy sources should be considered as a top priority solution for the growing worldwide energy demand. Photovoltaics are a strong candidate, more specifically, organic photovoltaics (OPV), enabling the design of flexible, lightweight, semitransparent, and low-cost solar cells. However, the active layer of OPV is, for now, mainly deposited from chlorinated solvents, harmful for the environment and for human health. Active layers processed from health and environmentally friendly solvents have over recent years formed a key focus topic of research, with the creation of aqueous dispersions of conjugated polymer nanoparticles arising. These nanoparticles are formed from organic semiconductors (molecules and macromolecules) initially designed for organic solvents. The topic of nanoparticle OPV has gradually garnered more attention, up to a point where in 2018 it was identified as a "trendsetting strategy" by leaders in the international OPV research community. Hence, this review has been prepared to provide a timely roadmap of the formation and application of aqueous nanoparticle dispersions of active layer components for OPV. We provide a thorough synopsis of recent developments in both nanoprecipitation and miniemulsion for preparing photovoltaic inks, facilitating readers in acquiring a deep understanding of the crucial synthesis parameters affecting particle size, colloidal concentration, ink stability, and more. This review also showcases the experimental levers for identifying and optimizing the internal donor-acceptor morphology of the nanoparticles, featuring cutting-edge X-ray spectromicroscopy measurements reported over the past decade. The different strategies to improve the incorporation of these inks into OPV devices and to increase their efficiency (to the current record of 7.5%) are reported, in addition to critical design choices of surfactant type and the advantages of single-component vs binary nanoparticle populations. The review naturally culminates by presenting the upscaling strategies in practice for this environmentally friendly and safer production of solar cells.

Six ways to build circular business models

PurposeCircular business models can improve resource use in a financially and environmentally feasible way. However, companies struggle to choose among the vast variety of ways to achieve circularity within a business model. The purpose of this paper is to offer a pragmatic guide for making strategic decisions on circular business models.Design/methodology/approachThis paper develops a conceptual model of six different strategic approaches to circular business models and provides examples to business cases and practice to illustrate these.FindingsThis study identifies two critical strategy choices companies should make. First, an innovation strategy addresses the extent to which circularity is achieved with internal or external stakeholders. Second, a resource strategy addresses how companies achieve circularity by narrowing, slowing or closing resource loops. Using examples from business practice, this study illustrates how the combinations of these two strategies can be used to design competitive circular business models. Key managerial questions are also identified to help decide upon a feasible strategy for circular business model innovation.Originality/valueWhile different types of circular business models have been described, it is less clear what the strategic choices are that companies need to make to find feasible business cases for circularity in terms of value proposition, value creation and delivery and value capture. This study outlines these through a “circular business model strategy framework”.

Sample Selection in Systematic Literature Reviews of Management Research

Systematic review techniques are about to become the “new normal” in reviews of management research. However, there is not yet much advice on how to organize the sample selection process as part of such reviews. This article addresses this void and analyzes this vital part of systematic reviews in more detail. In particular, it offers a critical review of systematic literature reviews published in the Academy of Management Annals and the International Journal of Management Reviews between 2004 and 2018. Based on this methodological literature review, the article presents issues to consider in the most critical choices during the sample selection process. Furthermore, this review identifies several descriptive features such as the mean number of research items included in systematic reviews, the mean number of databases used, and the mean coverage period of such reviews. These numbers may be used as benchmark figures in future reviews.

Preservice teachers\u2019 expressed awarenesses: emerging threads of retro-spection of learning and pro-spection of teaching

In this paper, we report an enquiry into elementary preservice teachers’ learning, as they engage in doing mathematics for themselves. As a group of researchers working in elementary Initial Teacher Education in English universities, we co-planned and taught sessions on growing pattern generalisation. Following the sessions, interviews of fifteen preservice teachers at two universities focused on their expressed awareness of their approach to the mathematical activity. Preservice teachers’ prospective planning and post-teaching evaluations of similar activities in their classrooms were also examined. We draw on aspects of enactivism and the notion of reflective “spection” in the context of teacher learning, tracing threads between preservice teachers’ retro-spection of learning and pro-spection of teaching. Our analysis indicates that increasing sensitivity to their own embodied processes of generalisation offers opportunities for novice teachers to respond deliberately, rather than to react impulsively, to different pedagogical possibilities. The paper contributes a new dimension to the discussion about the focus of novice elementary school teachers’ retrospective reflection by examining how deliberate retrospective analysis of doing mathematics, and not only of teaching actions, can develop awarenesses that underlie the growth of expertise in mathematics teaching. We argue that engaging preservice teachers in mathematics to support deliberate retrospective analysis of their mathematics learning and prospective consideration of the implications for teaching can enable more critical pedagogical choices.

A Practical Finite Element Modeling Strategy to Capture Cracking and Crushing Behavior of Reinforced Concrete Structures.

Nonlinear finite element (FE) analysis of reinforced concrete (RC) structures is characterized by numerous modeling options and input parameters. To accurately model the nonlinear RC behavior involving concrete cracking in tension and crushing in compression, practitioners make different choices regarding the critical modeling issues, e.g., defining the concrete constitutive relations, assigning the bond between the concrete and the steel reinforcement, and solving problems related to convergence difficulties and mesh sensitivities. Thus, it is imperative to review the common modeling choices critically and develop a robust modeling strategy with consistency, reliability, and comparability. This paper proposes a modeling strategy and practical recommendations for the nonlinear FE analysis of RC structures based on parametric studies of critical modeling choices. The proposed modeling strategy aims at providing reliable predictions of flexural responses of RC members with a focus on concrete cracking behavior and crushing failure, which serve as the foundation for more complex modeling cases, e.g., RC beams bonded with fiber reinforced polymer (FRP) laminates. Additionally, herein, the implementation procedure for the proposed modeling strategy is comprehensively described with a focus on the critical modeling issues for RC structures. The proposed strategy is demonstrated through FE analyses of RC beams tested in four-point bending—one RC beam as reference and one beam externally bonded with a carbon-FRP (CFRP) laminate in its soffit. The simulated results agree well with experimental measurements regarding load-deformation relationship, cracking, flexural failure due to concrete crushing, and CFRP debonding initiated by intermediate cracks. The modeling strategy and recommendations presented herein are applicable to the nonlinear FE analysis of RC structures in general.

Outliers and Robust Inference in Archival Accounting Research

This study examines the nature of outliers in archival accounting research, and evaluates the merits and limitations of robust estimators in identifying and downweighting their influence. Using simulated and actual data, we demonstrate how outliers arise non-randomly from the data generating process, research design choices such as scaling, and model misspecification. We find that robust estimators lead to more precise inferences than OLS in common archival data, but these estimators can also bias inferences due their downweighting of substantial and non-random proportions of the data. In addition, we demonstrate that a failure to account for nonlinear relations can induce biases in robust estimators that are more severe than with OLS. We advise researchers to carefully evaluate the source and nonrandom nature of outliers in their samples, to be cautious in implementing and interpreting robust estimators, and to evaluate and report the sensitivity of these methods to critical design choices. We also highlight the usefulness of combining data visualizations with robust estimators to assess both the nature of outliers and their impact on inference.

Influential Factors on Risk-reduction Mastectomy in a High-risk Breast Cancer Population With Genetic Predispositions

BackgroundCarriers of deleterious mutations in breast cancer predisposition genes are presented with critical choices regarding cancer risk management. Risk-reduction mastectomy is a major preventative strategy in this population. Understanding the decision-making process for prophylactic mastectomy is essential in patient-centered care for high-risk carriers and patients with breast cancer. We sought to provide insight into influential factors underlying preventative surgery decisions among individuals with high breast cancer risk. Materials and MethodsWe conducted a retrospective chart review of pathogenic carriers of high-risk breast cancer genes who presented to the Moffitt GeneHome clinic between March 2017 and June 2020. Associations between preventative mastectomy choice and influence variables were analyzed via unadjusted and adjusted logistic regression models. ResultsOf 258 high-risk mutation carriers, 104 (40.3%) underwent risk-reduction mastectomy. A significantly higher proportion of mastectomy patients reported prior history of breast cancer (68.9% vs. 16.5%; P < .001) and history of other risk-reduction or noncancer-related surgeries (61.7% vs. 25.8%; P < .001). Significant predictors affecting surgery decision included previous breast cancer history (adjusted odds ratio [aOR], 10.48; 95% confidence interval [CI], 5.59-19.63; P < .0001), other risk-reduction or noncancer-related surgical history (aOR, 4.65; 95% CI, 2.28-9.47; P < .0001), and age at presentation to the genetics clinic (< 35 years old: aOR, 2.77; 95% CI, 1.04-7.4; P = .042; 35-55 years old: aOR, 2.48; 95% CI, 1.19-5.18; P = .016). ConclusionsPreventive mastectomy decisions are highly personal and complex. In our sample, we observed prior history or concurrent breast cancer, history of other risk-reduction surgery or noncancer-related surgery, and younger age at presentation to the GeneHome clinic to be predictive of mastectomy uptake.

Critical Choices in Predicting Stone Wool Biodurability: Lysosomal Fluid Compositions and Binder Effects.

The hazard potential, including carcinogenicity, of inhaled man-made vitreous fibers (MMVFs) is correlated with their biodurability in the lung, as prerequisite for biopersistence. Abiotic dissolution testing serves to predict biodurability. We re-analyzed the International Agency for Research on Cancer Monograph on MMVFs and found that the correlation between in vivo biopersistence and abiotic dissolution presented therein confounded different simulant fluids and further confounded evaluation of leaching vs structural elements. These are critical choices for abiotic dissolution testing, as are binder removal and the rate of the flow that removes ions during testing. Therefore, we experimentally demonstrated how fluid composition and binder affect abiotic dissolution of a representative stone wool MMVF. We compared six simulant fluids (all pH 4.5, reflecting the environment of alveolar macrophage lysosomes) that differed in organic acids, which have a critical role in their ability to modulate the formation of Si-rich gels on the fiber surfaces. Removing the binder accelerates the average dissolution rate by +104% (max. + 273%) across the fluids by suppression of gel formation. Apart from the high-citrate fluid that predicted a 10-fold faster dissolution than is observed in vivo, none of the five other fluids resulted in dissolution rates above 400 ng/cm2/h, the limit associated with the exoneration from classification for carcinogenicity in the literature. These findings were confirmed with and without binder. For corroboration, five more stone wool MMVFs were assessed with and without binder in one specific fluid. Again, the presence of the binder caused gel formation and reduced dissolution rates. To enhance the reliability and robustness of abiotic predictions of biodurability, we recommend replacing the critically influential citric acid in pH 4.5 fluids with other organic acids. Also, future studies should consider structural transformations of the fibers, including changes in fiber length, fiber composition, and reprecipitation of gel layers.

Grounds for surrogate decision-making in Japanese clinical practice: a qualitative survey

BackgroundIn the coming years, surrogate decision-making is expected to become highly prevalent in Japanese clinical practice. Further, there has been a recent increase in activities promoting advance care planning, which potentially affects the manner in which judgements are made by surrogate decision-makers. This study aims to clarify the grounds on which surrogate decision-makers in Japan base their judgements.MethodsIn this qualitative study, semi-structured interviews were conducted to examine the judgement grounds in surrogate decision-making for critical life-sustaining treatment choices in acute care hospitals.ResultsA total of 228 participants satisfied the inclusion criteria, and 15 were selected for interviews. We qualitatively analysed the content of 14 interview transcripts, excluding one that did not meet the inclusion criteria. Based on this analysis, we extracted 4 core categories, 17 categories, 35 subcategories, and 55 codes regarding judgement grounds in surrogate decision-making. The four core categories were as follows: patient preference-oriented factor (Type 1), patient interest-oriented factor (Type 2), family preference-oriented factor (Type 3), and balanced patient/family preference-oriented factor (Type 4). The Type 4 core category represented attempts to balance the preferences of the patient with those of the surrogate decision-maker.ConclusionsSurrogate decision-makers based their decisions on important aspects related to a patient’s life, and they considered not only the patient’s preferences and best interests but also their own preferences. As the need for surrogate decisions will increase in the future, decision-makers will need to consider judgement grounds from a more diverse perspective.