Empirical Tools Research Articles

Knowledge, boundaries, and bodies: Social construction between medical sociology and science and technology studies

AbstractMedical sociology and science and technology studies (STS) emerged from different positions, but often closely related concerns, within the broad discipline of sociology. Their interface and areas of overlap have mostly been shaped by theoretical positions broadly considered “social constructionist.” Taken together, these perspectives provide empirical and theoretical tools to analyze important questions about how social inequalities, forms of scientific knowledge, and patterns of human health come to be produced and feedback into one another. Examining their intersection enables sociological questions such as: How is medical and public health scientific knowledge produced, stabilized, and taken as fact? How are scientific facts about health and illness used, experienced, and challenged? What is the relationship between health inequalities and public health or medical knowledge? This article seeks to briefly trace the important contributions that social constructionist research has made at the interstices of medical sociology and STS, further clarifying the history, points of intersection, and areas of diversion between them. The current COVID‐19 pandemic has unveiled the political struggles that constitute public health scientific knowledge and circulation. The interface between STS and medical sociology can help us to make sense of the interrelationships between politics, power inequalities, and public health scientific knowledge.

Implementation of a parallel high-order WENO-type Euler equation solver using a CUDA PTX paradigm

ABSTRACT This study proposes the optimization of a low-level assembly code to reconstruct the flux for a splitting flux Harten–Lax–van Leer (SHLL) scheme on high-end graphic processing units. The proposed solver is implemented using the weighted essentially non-oscillatory reconstruction method to simulate compressible gas flows that are derived using an unsteady Euler equation. Instructions in the low-level assembly code, i.e. parallel thread execution and instruction set architecture in compute unified device architecture (CUDA), are used to optimize the CUDA kernel for the flux reconstruction method. The flux reconstruction method is a fifth-order one that is used to process the high-resolution intercell flux for achieving a highly localized scheme, such as the high-order implementation of SHLL scheme. Many benchmarking test cases including shock-tube and four-shock problems are demonstrated and compared. The results show that the reconstruction method is computationally very intensive and can achieve excellent performance up to 5183 GFLOP/s, ∼66% of peak performance of NVIDIA V100, using the low-level CUDA assembly code. The computational efficiency is twice the value as compared with the previous studies. The CUDA assembly code reduces 26.7% calculation and increases 37.5% bandwidth. The results show that the optimal kernel reaches up to 990 GB/s for the bandwidth. The overall efficiency of bandwidth and computation performance achieves 127% of the predicted performance based on the HBM2-memory roofline model estimated by Empirical Roofline Tool.

Non-empirical problems in fair machine learning

The problem of fair machine learning has drawn much attention over the last few years and the bulk of offered solutions are, in principle, empirical. However, algorithmic fairness also raises important conceptual issues that would fail to be addressed if one relies entirely on empirical considerations. Herein, I will argue that the current debate has developed an empirical framework that has brought important contributions to the development of algorithmic decision-making, such as new techniques to discover and prevent discrimination, additional assessment criteria, and analyses of the interaction between fairness and predictive accuracy. However, the same framework has also suggested higher-order issues regarding the translation of fairness into metrics and quantifiable trade-offs. Although the (empirical) tools which have been developed so far are essential to address discrimination encoded in data and algorithms, their integration into society elicits key (conceptual) questions such as: What kind of assumptions and decisions underlies the empirical framework? How do the results of the empirical approach penetrate public debate? What kind of reflection and deliberation should stakeholders have over available fairness metrics? I will outline the empirical approach to fair machine learning, i.e. how the problem is framed and addressed, and suggest that there are important non-empirical issues that should be tackled. While this work will focus on the problem of algorithmic fairness, the lesson can extend to other conceptual problems in the analysis of algorithmic decision-making such as privacy and explainability.

Market volatility and illiquidity during the COVID-19 outbreak: Evidence from the Saudi stock exchange through the wavelet coherence approaches

The aim of this paper is to examine the explanatory power of realized volatility on the illiquidity in Saudi stock market during the COVID-19outbreak. To achieve this objective, we consider the Wavelet Coherence approaches as empirical tools to investigate the combined effect of realized volatility and COVID-19 counts on the market illiquidity across frequencies and over time space by taking in account the number of infected cases in Saudi Arabia and over the World, and the number of death cases in Saudi Arabia as well as over the World. Our study reaches two main findings. First, the preliminary results reported by the ARDL bound test as a benchmark model showed significant long-run and short-run effects of the market volatility on illiquidity in contemporaneous and lagged manner. Second, the wavelet coherence analysis tools exhibited important results: (i) the wavelet coherency between illiquidity ratio and realized volatility in Saudi Arabia appear highly pronounced over all time horizons. (ii) PWC plots showed a significant mutual effect between liquidity risk and realized volatility when eliminating the effect of local COVID-19 cases. (iii) MWC plots highlighted that the response of the market illiquidity index to both the amplification in confirmed local cases (resp. international confirmed cases) and the stock market volatility appear significant in the short and middle horizons.

Uniqueness in the life sciences: how did the elephant get its trunk?

Researchers in the life sciences often make uniqueness attributions; about branching events generating new species, the developmental processes generating novel traits and the distinctive cultural selection pressures faced by hominins. Yet since uniqueness implies non-recurrence, such attributions come freighted with epistemic consequences. Drawing on the work of Aviezer Tucker, we show that a common reaction to uniqueness attributions is pessimism: both about the strength of candidate explanations as well as the ability to even generate such explanations. Looking at two case studies—elephant trunks and human teaching—we develop a more optimistic account. As we argue, uniqueness attributions are revisable claims about the availability of several different kinds of comparators. Yet even as researchers investigate the availability of such comparators, they are able to mobilize complex sets of empirical and theoretical tools. Rather than hindering scientific investigation, then, we argue that uniqueness attributions often spur the generation of a range of epistemic goods.

Teaching CAPM for a Pre-Finance Graduate Program at the STEM Undergraduate Level: Linear Algebra Perspective

Students considering a masters in Finance Engineering or Artificial Intelligence in Finance are usually required to have an undergraduate background in science, technology, engineering, or mathematics (STEM). STEM students have a good capacity in mathematics and science, but they may not have studied financial theory. To facilitate the classroom teaching of the Capital Asset Pricing Model (CAPM) for STEM students, this paper seeks to expound on the essence of the theory starting at a two-asset framework. Adopting the concepts proposed by Merton (1972), this paper accomplishes the derivation by virtue of basic mathematical tools such as linear algebra, geometry, and statistics except for calculus. We show that the major aspects of Merton’s derivation of the CAPM for a universe of N assets may also be obtained in a two-asset world. Through the methods of this article, students will learn the in-depth theory of CAPM and its hands-on empirical tool. For example, students will realize that even if investors specify different threshold rewards, their different CAPMs will yield identical pricing for assets and portfolios.

Dyconnmap: Dynamic connectome mapping-A neuroimaging python module.

Despite recent progress in the analysis of neuroimaging data sets, our comprehension of the main mechanisms and principles which govern human brain cognition and function remains incomplete. Network neuroscience makes substantial efforts to manipulate these challenges and provide real answers. For the last decade, researchers have been modelling brain structure and function via a graph or network that comprises brain regions that are either anatomically connected via tracts or functionally via a more extensive repertoire of functional associations. Network neuroscience is a relatively new multidisciplinary scientific avenue of the study of complex systems by pursuing novel ways to analyze, map, store and model the essential elements and their interactions in complex neurobiological systems, particularly the human brain, the most complex system in nature. Due to a rapid expansion of neuroimaging data sets' size and complexity, it is essential to propose and adopt new empirical tools to track dynamic patterns between neurons and brain areas and create comprehensive maps. In recent years, there is a rapid growth of scientific interest in moving functional neuroimaging analysis beyond simplified group or time‐averaged approaches and sophisticated algorithms that can capture the time‐varying properties of functional connectivity. We describe algorithms and network metrics that can capture the dynamic evolution of functional connectivity under this perspective. We adopt the word ‘chronnectome’ (integration of the Greek word ‘Chronos’, which means time, and connectome) to describe this specific branch of network neuroscience that explores how mutually informed brain activity correlates across time and brain space in a functional way. We also describe how good temporal mining of temporally evolved dynamic functional networks could give rise to the detection of specific brain states over which our brain evolved. This characteristic supports our complex human mind. The temporal evolution of these brain states and well‐known network metrics could give rise to new analytic trends. Functional brain networks could also increase the multi‐faced nature of the dynamic networks revealing complementary information. Finally, we describe a python module (https://github.com/makism/dyconnmap) which accompanies this article and contains a collection of dynamic complex network analytics and measures and demonstrates its great promise for the study of a healthy subject's repeated fMRI scans.

A Feasibility Study on The Implementation of Neural Network Classifiers for Open Stope Design

Assessing the stability of stopes is essential in open stope mine design as unstable hangingwalls and footwalls lead to sloughing, unplanned stope dilution, and safety concerns compromising the profitability of the mine. Over the past few decades, numerous empirical tools have been developed to dimension open stope in connection with its stability, using the stability graph method. However, one of the principal limitations of the stability graph method is to objectively determine the boundary of the stability zones, and gain a clear probabilistic interpretation of the graph. To overcome this issue, this paper aims to explore the feasibility of artificial neural network (ANN) based classifiers for the design of open stopes. A stope stability database was compiled and included the stope dimensions, rock mass properties, and the stope stability conditions. The main parameters included the modified stability number (N’), and the stope stability conditions (stable, unstable, and failed), and hydraulic radius (HR). A feed-forward neural network (FFNN) classifier containing two hidden layers (110 neurons each) was employed to identify the stope stability conditions. Overall, the outcome of the analysis showed good agreement with the field data; most stope surfaces were correctly predicted with an average accuracy of 91%. This shows an improvement over using the existing stability graph method. In addition, for a better interpretation of the results, the associated probability of occurrence of stable, unstable, or caved stope was determined and shown in iso-probability contour charts which were compared with the stability graph. The proposed FFNN-based classifier outperformed the conventional stability graph method in terms of accuracy and better prabablistic interpretation. It is suggested that the classifier could be a reliable tool that can complement the conventional stability graph for the design of open stopes.

Instance-Based Learning Following Physician Reasoning for Assistance during Medical Consultation

This article presents an automatic system for modeling clinical knowledge to follow a physician’s reasoning in medical consultation. Instance-based learning is applied to provide suggestions when recording electronic medical records. The system was validated on a real case study involving advanced medical students. The proposed system is accurate and efficient: 2.5× more efficient than a baseline empirical tool for suggestions and two orders of magnitude faster than a Bayesian learning method, when processing a testbed of 250 clinical case types. The research provides a framework to implement a real-time system to assist physicians during medical consultations.

Snow pollution management in urban areas: an idea whose time has come?

ABSTRACT Needs for extending the principles of a sustainable urban drainage approach to the winter season with snow and pollutant accumulations have been so far unanswered. One of the first steps to remedy this situation is to make available a prototype snow management tool (PSMT) described herein. It is a simple empirical spreadsheet tool, as yet untested, which uses generally available input data to simulate accumulation of snow (as snow water equivalent) and pollutants in an urban catchment, intermittent snowmelt episodes, and snow management options including in situ melting, removal from the catchment, treatment of snowmelt by settling, and snow disposal in the receiving waters. The tool output comprises snow and the selected pollutant mass balances, and pollutant concentrations in snowmelt from individual snow deposits. Such data provide decision support for choosing the methods of disposal for individual snow deposits. The testing and refinement of the tool is planned.

Sustainability of Urbanization, Non-Agricultural Output and Air Pollution in the World’s Top 20 Polluting Countries

Rapid urbanization is being increasingly recognized as a significant factor of environmental pollution across the world. However, the significance of sustainable urbanization in controlling both pollution and population remains either limited in scope, in the case of developed countries, or less researched, in the case of developing nations. To fill this gap, the present study employed both theoretical and empirical tools to investigate the significant link between sustainable urbanization, pollution and non-agricultural output. In order to empirically examine the supposed link among the key variables mentioned above, the present study considered a panel of the world’s top 20 polluting countries for the 1991–2018 period, which significantly includes both developed and developing nations. Panel vector error correction model and panel co-integration techniques were employed to derive the possible correlation between the variables through sustainable urbanization. Empirical findings show an absence of equilibrium relations among the three variables in the panel of developed countries. However, the study clearly finds that all the three indicators maintain long-run associations for the panel of developing countries. Furthermore, in the short run, the results determine unambiguously that there are significant causal interplays between any two sets of variables and the remaining one variable for both the panel data of developed and developing countries. On the other hand, short-run interplays among the variables we considered exist for both developed and developing economies. From the perspective of policy formulation, the present study shows that policy makers from both the developed and developing nations should be cautious before encouraging urbanization, at least in the short term. However, the combined effects in the short and long term suggest policy makers should be more careful before encouraging urbanization in developing economies.

Crystal Engineering of Binary Organic Eutectics: Significant Improvement in the Physicochemical Properties of Polycyclic Aromatic Hydrocarbons via the Computational and Mechanochemical Discovery of Composite Materials

Derivatives of polycyclic aromatic hydrocarbons (PAHs) are widely used in optoelectronic materials. However, the poor solubility of unfunctionalized PAHs represents a challenge for the continued application of these compounds in emerging technologies. For organic compounds bearing one or more functional groups capable of engaging in directional hydrogen or halogen-bonding interactions, the crystal engineering toolkit currently offers many routes for optimizing the solid-state properties of these compounds. Such efforts typically lead to the discovery of periodic crystal forms that display strong adhesive intermolecular forces between the molecular fragments. By contrast, the crystal engineering of organic eutectic composites is relatively unexplored and poorly understood. Here, we report the mechanosynthesis and experimental characterization of the properties of three eutectic composites of pyrene (PYR) and anthracene (ANTH) that were discovered using the coformer bisphenol A (BPA) or phenothiazine (PTZ). The resulting eutectic composites (PYR-BPA, PYR-PTZ, and ANTH-PTZ) display significant melting point depressions ranging from 19 to 51 °C relative to the melting point of the PAH. The equilibrium solubilities of the composite materials were also observed to be 2–5 times greater than that of the PAH. The crystal engineering of eutectic solid forms is currently hampered by the lack of reliable empirical or theoretical tools for predicting their formation. A weighted Monte Carlo simulation was used to estimate the mixing energies and binding modes of a limited set of molecular pairs, leading to temperature-dependent interaction parameters that show promise in the selection of coformers with a high likelihood of forming eutectic composites. Complementary dispersion-corrected density functional theory (DFT-D) calculations on a set of PYR and ANTH composite models reveal that organic eutectic composites are not driven to form on the basis of favorable thermodynamics as evidenced by an average interaction energy of 2.60 kJ mol–1 across the series. Synthon incompatibility and molecular shape mismatch appear to be important factors to consider in targeting eutectic solid forms. This work paves the way for the systematic crystal engineering of organic eutectic solid forms with tunable physicochemical properties using a synergistic computational modeling and mechanosynthesis approach.

A hybrid evaluation method for human error probability by using extended DEMATEL with Z-numbers: A case of cargo loading operation

Human error probability (HEP) evaluation and prediction is one of the most significant tasks to enhance human reliability in marine industry. Among various kinds of HEP evaluation techniques, the Human Error Assessment and Reduction Technique (HEART) technique is regarded as an effective and empirical tool that has been widely adopted in various fields. However, current HEART techniques are insufficient to address HEP evaluation problem in which the self-assurance of expert's judgment and inter-dependencies between Error-producing conditions (EPCs) are considered. Therefore, the purpose of this paper is to develop a hybrid HEART framework (H-HEART-F) to address this problem by integrating Z-numbers and the decision-making trial and evaluation laboratory (DEMATEL) method. First, the Z-numbers are introduced to model the uncertainty and self-assurance of evaluation information from various experts. Then, the Z-number power weighted average (Z-PWA) operator is proposed to aggregate the individual evaluation information into a group direct influenced matrix. Next, an extended DEMATEL method based on possibility degree measure is constructed to determine the proportion of effect of each EPC by considering the self-assurance of expert's judgment and inter-dependencies between EPCs. Finally, the HEP estimation for the cargo loading operation in oil or chemical tanker ship is presented to demonstrate the availability and feasibility of the H-HEART-F. After that, the sensitivity analysis and comparison study are conducted to further illustrate the reasonableness and effectiveness of the proposed method.

Inequality and water pollution in India

AbstractIndia is notorious for high inequality and high water pollution. There is a growing body of literature that says inequality is harmful to the environment, but it does not receive strong empirical support. We discuss some econometric problems that may have caused mixed findings in the empirical literature and use appropriate tools to overcome the problems. Our empirical results using Indian time-series data show (i) that inequality leads to an increase in water pollution, (ii) that the magnitude of inequality is nearly as large as that of corruption, suggesting that reducing inequality is almost as important as curbing corruption in addressing water pollution challenges in India, and (iii) that increases in water pollution, in turn, widen inequality in India. Our results are robust to various sensitivity checks. We also find no evidence of the environmental Kuznets curve hypothesis for water pollution in India.

Edusat: remote sensing as an educational resource. The use of data from the Copernicus program as an innovative teaching method for students, teachers, and researchers

Abstract. The intensification in recent decades of scientific evidence on climate change and on the degradation of natural systems has led to increasing public awareness about the environment. In recent times, this commitment to respecting the natural environment has emerged strongly among young people. Through various platforms, entities, and slogans, students from all over the world, and belonging to different disciplines, are coming together to defend their right to have a planet that enjoys good environmental health.The platform Edusat presented in this article aims to provide young people with empirical and quantitative learning tools to strengthen their ecology message. By means of remote sensing and through the data generated by the Copernicus program, an educational resource that analyzes the consequences of global environmental change is presented. In this context, remote sensing is a technological and transdisciplinary resource that provides young people with scientific arguments to censure the current relationship between human societies and nature.

A distress-continuum, disorder-threshold model of depression: a mixed-methods, latent class analysis study of slum-dwelling young men in Bangladesh

BackgroundBinary categorical approaches to diagnosing depression have been widely criticized due to clinical limitations and potential negative consequences. In place of such categorical models of depression, a ‘staged model’ has recently been proposed to classify populations into four tiers according to severity of symptoms: ‘Wellness;’ ‘Distress;’ ‘Disorder;’ and ‘Refractory.’ However, empirical approaches to deriving this model are limited, especially with populations in low- and middle-income countries.MethodsA mixed-methods study using latent class analysis (LCA) was conducted to empirically test non-binary models to determine the application of LCA to derive the ‘staged model’ of depression. The study population was 18 to 29-year-old men (n = 824) from an urban slum of Bangladesh, a low resource country in South Asia. Subsequently, qualitative interviews (n = 60) were conducted with members of each latent class to understand experiential differences among class members.ResultsThe LCA derived 3 latent classes: (1) Severely distressed (n = 211), (2) Distressed (n = 329), and (3) Wellness (n = 284). Across the classes, some symptoms followed a continuum of severity: ‘levels of strain’, ‘difficulty making decisions’, and ‘inability to overcome difficulties.’ However, more severe symptoms such as ‘anhedonia’, ‘concentration issues’, and ‘inability to face problems’ only emerged in the severely distressed class. Qualitatively, groups were distinguished by severity of tension, a local idiom of distress.ConclusionsThe results indicate that LCA can be a useful empirical tool to inform the ‘staged model’ of depression. In the findings, a subset of distress symptoms was continuously distributed, but other acute symptoms were only present in the class with the highest distress severity. This suggests a distress-continuum, disorder-threshold model of depression, wherein a constellation of impairing symptoms emerge together after exceeding a high level of distress, i.e., a tipping point of tension heralds a host of depression symptoms.

Risk to investment and renewables production in the United States: An inference for environmental sustainability

With the increasing drive toward cleaner environment, accessing lower risk investment with financing opportunities has remained a pertinent hurdle to achieving a paradigm shift from the business-as-usual approaches to a responsible climate actions and environmental awareness. On this note, the current study examined for the first time the impact of the risk to investment on environmental quality over the period of 1984–2017 for the case of the United States. Considering that the burning of fuels constitutes the largest source of Greenhouse gas in the United States, this study employed energy carbon emissions both as a proxy for environmental quality and dependent variable. The real Gross Domestic Product and renewable energy production were both incorporated in the carbon function model as an additional explanatory variable that was examined by a handful of empirical tools. Importantly, the study found that an associated lower risk to investment is a suitable and significant approach toward improving the quality of the environment in both the short and long run. Similarly, the production and utilization of renewable energy exhibits a statistically significant and desirable impact on environmental quality i.e renewable energy production and utilization improves environmental quality. However, the study found that economic expansion significantly spur hindrance to environmental sustainability. This study justifies that risk associated with all investment aspects is fundamental to environmental quality, and thus posing an indirect health and socioeconomic concerns. Additionally, the result proved that the circumstance of renewable energy waste and energy loss to transmission arising production-consumption disparity is negligible. Moreover, the robustness and diagnostic test affirms the validity of the investigation. Thus, this study proffers a relevant policy mechanism toward the attainment of the country's sustainable development goals via cleaner productivity, especially from the perspective of associated risks in public and private low-carbon and clean technologies financing.

Negotiation complexity: a review and an integrative model

PurposeThis paper aims to review and synthesize the existing literature related to negotiation complexity and provides an integrative model to systematically identify and examine factors contributing to negotiation complexity and how they affect negotiating parties’ behaviors and economic and subjective outcomes.Design/methodology/approachThe approach was to combine relevant literature from negotiation in general and from negotiation complexity in particular and to develop and support an integrative model of complexity in real-world negotiations.FindingsThe literature on negotiation complexity and previous analytical frameworks are reviewed from a cross-disciplinary perspective. Based on the integrative review, an integrative model of negotiation complexity is proposed for identifying important complexity contributory factors. Six contributory factors are distinguished based on the three negotiation components – negotiation task, dynamic variables of negotiators and negotiation context. Their effects on negotiation complexity are examined and discussed with respect to four complexity dimensions (i.e. informational and computational, procedural, social and strategic dimensions). Finally, the effects of negotiation complexity on parties’ behaviors and outcomes are examined based on previous theoretical and empirical research and practical tools for managing negotiation complexity are delineated.Originality/valueThe integrative review and conceptualization of negotiation complexity are helpful for gaining a better understanding of negotiation complexity and its management in various real-world domains.

Visualizing the potential for transit-oriented development: Insights from an open and interactive planning support tool in Flanders, Belgium

This paper reports on the development trajectory of an empirical tool for transit-oriented development planning in Flanders, Belgium. The tool, StationsRadar, draws on a branch of empirical railway station assessment tools that aim to support transit-oriented development planning processes by visualizing the performance of station locations for a range of transport (‘node’) and land use (‘place’) accessibility indicators. At the root of this paper lies the observation that, while the vast majority of reviewed studies highlight the relevance of the developed tools for planning practice, little work is undertaken to systematically verify that claim. Against this backdrop, we invoke an experiential research strategy as recently proposed in the field of planning research; we organize a series of experiential workshops in which we probe the tool’s added value for regional planning in Flanders. In the process, we specifically work towards a qualitative appraisal of tool ‘usability’ and discuss how our findings bear relevance to the well-rehearsed practice of developing empirical transit-oriented development support tools. Additionally, we elaborate on and illustrate the ramifications of our findings in terms of the subsequent/iterative technical revision of the tool. We conclude this paper by putting forward three major usability recommendations pertaining to: interactive and diversified data visualizations, actor-mobilizing momentum in light of data transparency, and the integration of ‘hard’ and ‘soft’ data in light of crowdsourcing aspirations. We reflect on the broader technical and methodological challenges that come with implementing these in practice.

‘In any case, you believe him one hundred percent, everything he says.’ Trustworthiness in Holocaust survivor talks with high school students in Germany

ABSTRACT Our study will turn to one Jewish male Holocuast survivor who visited ten high schools in Germany from January to March 2020. Based on the results of empirical and theoretical considerations of testifying, a communicative model was considered appropriate for understanding the encounter. Of special concern was to what extent trustworthiness was constructed during these talks by students. A mixed methods approach was chosen, combining testimony analysis with empirical tools in social science. The result of the study points to the students’ trust in the survivor story, which is supported by the school institution and by the survivor’s narration, his rhetorical strategies, and his personality.