Consequential Problems Research Articles

Can ethnic disparities in sentencing be taken as evidence of judicial discrimination?

Large research efforts have been directed at the exploration of ethnic disparities in the criminal justice system, documenting harsher treatment of minority ethnic defendants, across offence types, criminal justice decisions, and jurisdictions. However, most studies on the topic have relied on observational data, which can only approximate ‘like with like’ comparisons. We use causal diagrams to lay out explicitly the different ways estimates of ethnic disparities in sentencing derived from observational data could be biased. Beyond the commonly acknowledged problem of unobserved case characteristics, we also discuss other less well-known, yet likely more consequential problems: measurement error in the form of racially-determined case characteristics or as a result of disparities within the ‘Whites’ reference group, and selection bias from non-response and missing offenders’ ethnicity data. We apply such causal framework to review findings from two recent studies showing ethnic disparities in custodial sentences imposed at the Crown Court (England and Wales). We also use simulations to recreate the most comprehensive of those studies, and demonstrate how the reported ethnic disparities appear robust to a problem of unobserved case characteristics. We conclude that ethnic disparities observed in the Crown Court are likely reflecting evidence of direct discrimination in sentencing.

A hybrid deep neural operator/finite element method for ice-sheet modeling

One of the most challenging and consequential problems in climate modeling is to provide probabilistic projections of sea level rise. A large part of the uncertainty of sea level projections is due to uncertainty in ice sheet dynamics. At the moment, accurate quantification of the uncertainty is hindered by the cost of ice sheet computational models. In this work, we develop a hybrid approach to approximate existing ice sheet computational models at a fraction of their cost. Our approach consists of replacing the finite element model for the momentum equations for the ice velocity, the most expensive part of an ice sheet model, with a Deep Operator Network, while retaining a classic finite element discretization for the evolution of the ice thickness. We show that the resulting hybrid model is very accurate and it is an order of magnitude faster than the traditional finite element model. Further, a distinctive feature of the proposed model compared to other neural network approaches, is that it can handle high-dimensional parameter spaces (parameter fields) such as the basal friction at the bed of the glacier, and can therefore be used for generating samples for uncertainty quantification. We study the impact of hyper-parameters, number of unknowns and correlation length of the parameter distribution on the training and accuracy of the Deep Operator Network on a synthetic ice sheet model. We then target the evolution of the Humboldt glacier in Greenland and show that our hybrid model can provide accurate statistics of the glacier mass loss and can be effectively used to accelerate the quantification of uncertainty.

The Strengths and Weaknesses of Crowds to Address Global Problems

Global climate change, the COVID-19 pandemic, and the spread of misinformation on social media are just a handful of highly consequential problems affecting society. We argue that the rough contours of many societal problems can be framed within a “wisdom of crowds” perspective. Such a framing allows researchers to recast complex problems within a simple conceptual framework and leverage known results on crowd wisdom. To this end, we present a simple “toy” model of the strengths and weaknesses of crowd wisdom that easily maps to many societal problems. Our model treats the judgments of individuals as random draws from a distribution intended to represent a heterogeneous population. We use a weighted mean of these individuals to represent the crowd’s collective judgment. Using this setup, we show that subgroups have the potential to produce substantively different judgments and we investigate their effect on a crowd’s ability to generate accurate judgments about societal problems. We argue that future work on societal problems can benefit from more sophisticated, domain-specific theory and models based on the wisdom of crowds.

Which Virus Will Cause the Next Pandemic?

One of the most pressing and consequential problems in infectious disease research is to better understand the potential of viruses to cause a pandemic, or, in simple terms, determine which virus will cause the next pandemic. We here define pandemics as WHO-declared pandemics, or disease outbreaks commonly referred to as pandemics that predate the WHO pandemic framework. Despite extensive research in the field of infectious diseases in recent decades, all pandemics have found us unprepared, with enormous losses of human lives, tremendous costs for public health, and vast and potentially long-lasting economic losses. Here, we discuss viruses that may cause a pandemic in the future.

Machine learning-aided engineering services' cost overruns prediction in high-rise residential building projects: Application of random forest regression

Current approaches to automating cost estimation mainly focus on construction costs. Yet, the two main services provided by design firms, namely ‘designing the project’, and ‘supervision of construction operations’ labelled as engineering services, despite their comparatively low cost, can significantly affect the total cost of construction projects as they can engender reworks, changes and disputes on project participants during the subsequent stages of the project. Continuous evaluation of engineering services' cost overruns (ESCO) is quintessentially important in order to prevent consequential problems later on in the project's development and use. Consequently, this research proposes a robust random forest (RF) regression model to predict ESCOs considering both project-related and organizational-related variables. A database consisting of 95 high-rise residential building projects designed during the past eight years in Iran, along with 12 related variables, were collected to develop and validate the model. The results were also compared with those of support vector regression (SVR) and multiple linear regression (MLR), which indicated that with an R2 value of 0.8680 and mean-absolute-error (MAE) of 3.88, the RF regression model performs better than those baseline models, namely SVR and MLR. This research presents two main contributions to the existing body of knowledge. From the practical point of view, it provides an efficient tool for design firms enabling them to screen and prioritize their projects from the cost overrun standpoint and to devise a contingency plan for them. From the theoretical point of view, it revealed that to mitigate ESCOs, three key factors should be given thorough consideration, namely: ‘the level of computer-aided design technologies adoption’; ‘level of communication among the project team’; and scope definition adequacy’ – cumulatively, these three factors contribute to 52.35% of ESCO variations.

Neoliberalism

The criticism of neoliberalism is omnipresent. The term is seemingly self-explanatory, but its original use in public has been forgotten. Its form originated in the international movement of ordoliberalism in the 1930s, which used neoliberalism to describe its distinction from laissez-faire capitalism. This conceptual confusion has created considerable consequential problems that overlay today’s debate on the future of the market economy. The fact that the neoliberalism of the ordoliberals is today equated by its critics with the capitalism of the libertarians raises questions. Since the economic dogma of Milton Friedman, who was the inspiration for the so-called Chicago Boys in Chile, whose economic policy was first captured by today’s negatively connoted term neoliberalism, this approach needs to be looked at more closely. Should the ideas of the Chicago school of thought substantially distinguish themselves from the other currents of economic liberalism, a solution to the confusion of terms could be possible, giving a new twist to the debate on the market economy. Such a clarifiation would be of fundamental importance for the ethical question of the social orientation of the economic order, since ordoliberalism was in turn the godfather of the social market economy. Furthermore, this would have consequences for applied political ethics in the context of political theories of justice, whose theoretical constructs reflect the existing economic order as a fact of experience.

Ecological impacts of an invasive mesopredator do not differ from those of a native mesopredator: lionfish in Caribbean Panama

The impacts of invasive lionfish (Pterois volitans/miles) on native coral reef populations in the Western Atlantic Ocean and Caribbean Sea can be enormous. However, how much lionfish differ from native predators and whether their effects outweigh the abundant mesopredators that occupy many reefs invite continued examination. Here, we present empirical evidence from Caribbean Panama and beyond suggesting that lionfish are less abundant than native mesopredators. Furthermore, we show that their direct impacts on survivorship and size distributions of one native prey species are similar to those of a native mesopredator. These results support calls for lionfish management that considers evolving local ecological and social dynamics, including prey community composition, the roles of native mesopredators, and regional goals for conservation and fisheries. Recognition of regional context creates the potential for synergies between conservation actions aimed both at the invasion and other consequential problems such as overexploitation and climate change.

Evolution of Technology in Replacement of Heart Valves: Transcatheter Aortic Valves, a Revolution for Management of Valvular Heart Diseases

Objectives: The goal of this study is to analyze the evolution of technology in artificial heart valves for solving the problem of aortic stenosis (a narrowing of the aortic valve opening) that is one of the most common valvular heart diseases in society. In particular, this study explains the technological evolution of the new technology of transcatheter aortic valve implantation (TAVI) compared to surgical aortic valve replacement (SAVR). Methods: Data of Scopus (2020) based scientific products and patents that have in title, abstract or keywords the following terms: “transcatheter aortic valve implantation (TAVI)” or “surgical aortic valve replacement (SAVR)”. The evolution of technology is analyzed with a model, which shows how TAVI technique is substituting established technique of SAVR in cardiology. The relationships are investigated with loglinear models using ordinary least squares method for estimating the unknown parameters in relationships under study. Statistical analyses are performed with the Statistics Software SPSS 26. Results: Statistical analyses reveal that TAVI, with its growing scientific and technological production, is expected to be a dominant technology in the not too distant future for the treatment of aortic stenosis in society. In fact, the scientific production of TAVI has a coefficient of growth in function of time equal to 0.40(P<.001) compared to a coefficient for SAVR that is 0.10(P<.001). Statistical analyses with patent production confirm these results. Findings hint at general properties of the evolutionary behavior of this medical technology: 1) TAVI has a disproportionate growth in scientific products and patents compared to SAVR; 2) TAVI has a growing scientific and technological production driven by major and minor technological advances to solve consequential problems to treat valvular heart diseases; 3) learning via diffusion and diffusion by learning are driving forces underlying the development and adoption of TAVI in cardiology. Conclusions: TAVI, introduced in 2002, is growing in terms of scientific production and innovative activity to revolutionize the management of aortic stenosis in cardiology with important implications of health management based on improved cost-effectiveness of this new technology and better efficiency in healthcare of people.

Evolution of technology in replacement of heart valves: Transcatheter aortic valves, a revolution for management of valvular heart diseases

ObjectivesThe goal of this study is to analyze the evolution of technology in artificial heart valves for solving the problem of aortic stenosis (a narrowing of the aortic valve opening) that is one of the most common valvular heart diseases in society. In particular, this study explains the evolution of new technology of transcatheter aortic valve implantation (TAVI) compared to surgical aortic valve replacement (SAVR). MethodsData of Scopus (2020) based scientific products and patents that have in title, abstract or keywords the following terms: “transcatheter aortic valve implantation (TAVI)” or “surgical aortic valve replacement (SAVR)”. The evolution of technology is analyzed with a model, which shows how TAVI technique is substituting established technique of SAVR in cardiology. The relationships are investigated with loglinear models using ordinary least squares method for estimating the unknown parameters. Statistical analyses are performed with the Statistics Software SPSS® 26. ResultsStatistical analyses reveal that TAVI, with its growing scientific and technological production, is expected to be a dominant technology in future for the treatment of aortic stenosis in society. In fact, the scientific production of TAVI has a coefficient of growth in function of time equal to 0.39(P<.001) compared to a coefficient for SAVR that is 0.10(P<.001). Statistical analyses with patent production confirm these results. Findings hint at general properties of the evolutionary behavior of this medical technology: 1) TAVI has a disproportionate growth in scientific products and patents compared to SAVR; 2) TAVI has a growing scientific and technological production driven by major and minor technological advances to solve consequential problems to treat valvular heart diseases; 3) learning via diffusion and diffusion by learning are driving forces underlying the development and adoption of TAVI in cardiology. ConclusionsTAVI, introduced in 2002, is growing in terms of scientific production and innovative activity to revolutionize the management of aortic stenosis in cardiology with important implications of health management based on improved cost-effectiveness of this new technology and better efficiency in healthcare of people.

Neonatal NIRS monitoring: recommendations for data capture and review of analytics.

Brain injury is one of the most consequential problems facing neonates, with many preterm and term infants at risk for cerebral hypoxia and ischemia. To develop effective neuroprotective strategies, the mechanistic basis for brain injury must be understood. The fragile state of neonates presents unique research challenges; invasive measures of cerebral blood flow and oxygenation assessment exceed tolerable risk profiles. Near-infrared spectroscopy (NIRS) can safely and non-invasively estimate cerebral oxygenation, a correlate of cerebral perfusion, offering insight into brain injury-related mechanisms. Unfortunately, lack of standardization in device application, recording methods, and error/artifact correction have left the field fractured. In this article, we provide a framework for neonatal NIRS research. Our goal is to provide a rational basis for NIRS data capture and processing that may result in better comparability between studies. It is also intended to serve as a primer for new NIRS researchers and assist with investigation initiation.

A Medico-legal Perspective on the Termination of Pregnancies Resulting from Wartime-Rape

The study is designed to investigate the issue of abortion for rape-victims to draw the attentions of Islamic clerics in order to appraise consciousness and outlook of legal edicts within constrain of Islamic bio-medical and bio-ethical norms. For this purpose, a number of Islamic treatises are systematically examined, and a cross-cultural along with a cross-country assessment of 47 countries abortion laws particularly ‘Muslim-majority’ (OIC member) countries are conducted to assess the momentous impacts on rape-victims. Additionally, contemporary data i.e. legal edicts and various reported rape/adultery cases in different Islamic countries are analyzed with special reference to the circumstances in which the verdicts are issued. The study asserts that mostly the classical and contemporary literature silent on the cited issue except few of the legal edicts. Although, Islamic lit and scripts including bio-medical and bio-ethical has discussed it in general way. As a result, the rape victims appeared that they may be allowed termination of unwanted fetus due to coerced sexual relation. But, whether in preview of, some of the legal edicts which causes intricacy, the state laws, rules and more particularly the religious and cultural aspects may allow this kind of deliberation? A predominantly conventional approach was found, since 18 out of 47 countries do not allow abortion except for necessity. Less than 50% of OIC members legally permit abortion on medical grounds. Nevertheless, there was a substantial diversity between Muslim countries. Albeit, a multidimensional viewpoint is very important due to the consequential psychiatric and social problems, since there is an immense need to provide real solutions to such cases that would not contradict Islamic bioethical principles. Therefore, termination of rape resulting pregnancies may be declared valid, provided with specific conditions and guidelines as per Shariah keeping in view bio-medical and bio-ethical norms.&#x0D; Bangladesh Journal of Medical Science Vol.19(3) 2020 p.372-385

A theory of the evolution of technology: Technological parasitism and the implications for innovation magement

How do technologies develop? This study proposes the theory of technological parasitism that is based on the idea that parasite-host relationships between technologies and technologies with a high number of technological parasites have an accelerated evolution driven by long-run mutualistic symbioses, providing the basis for extensive macroevolution and adaptive behavior of systems of interactive technologies in markets. This theory may be useful for bringing a new perspective to explain and generalize the evolution of technology directed to sustain competitive advantage of firms and nations. In particular, technological parasitism explains the relationship of mutualistic symbiosis between a host (or master) technology and inter-related technologies to satisfy needs and/or to solve consequential problems of socioeconomic subjects over time. To explore the potential of adopting a theory of technological parasitism and to predict which technologies are likeliest to evolve rapidly, this study implements a theoretical test based on a computational agent-based model developed from these concepts and a empirical test based on historical data on the evolution of four example technologies (aircraft, tractor, locomotive and bicycle technology). Computational and empirical evidence are broadly consistent with the theoretical expectation that host (or master) technologies with many associated parasitic technologies advance rapidly, whereas master technologies with fewer parasitic technologies improve slowly. The proposed theory has a demonstrated capacity to explain relationships between technologies in complex systems that clarify the driving forces of the evolution of technology in markets. The finding of this study could aid management of firms and innovation strategy of nations to implement best practices of product/process design and development for supporting R&D of technologies that are likely to evolve rapidly, sustaining and safeguarding competitive advantage. Overall, then, this study, for the first time to our knowledge, begins the process of clarifying and generalizing, as far as possible, the role of long-run coevolution between host and parasitic technologies in complex systems, suggesting fruitful implications for innovation management. markets. This conceptual scheme here is especially relevant in Schumpeterian markets with innovation-based competition to explain a major source of technological evolution and success, the parasite-host relationships between technologies. Finally, this theory here suggests a new direction for the development of more sophisticated concepts and theoretical frameworks to explain technological and industrial change in economic systems.

Deep learning technology for improving cancer care in society: New directions in cancer imaging driven by artificial intelligence

The goal of this study is to show emerging applications of deep learning technology in cancer imaging. Deep learning technology is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior. Applications of deep learning technology to cancer imaging can assist pathologists in the detection and classification of cancer in the early stages of its development to allow patients to have appropriate treatments that can increase their survival. Statistical analyses and other analytical approaches, based on data of ScienceDirect (a source for scientific research), suggest that the sharp increase of the studies of deep learning technology in cancer imaging seems to be driven by high rates of mortality of some types of cancer (e.g., lung and breast) in order to solve consequential problems of a more accurate detection and characterization of cancer types to apply efficient anti-cancer therapies. Moreover, this study also shows sources of the trajectories of deep learning technology in cancer imaging at level of scientific subject areas, universities and countries with the highest scientific production in these research fields. This new technology, in accordance with Amara's law, can generate a shift of technological paradigm for diagnostic assessment of any cancer type and disease. This new technology can also generate socioeconomic benefits for poor regions because they can send digital images to labs of other developed regions to have diagnosis of cancer types, reducing as far as possible current gap in healthcare sector among different regions.

Optimal sitting and sizing of shunt capacitor for real power loss reduction on radial distribution system using firefly algorithm: A case study of Nigerian system

ABSTRACT Geometrical increase in power demand and high load density at the distribution ends of modern power systems have key consequential problems of high power loss and poor voltage profile, as a result of which the integrity of radial distribution networks to faithfully account for the energy received from transmission subsystems has been seriously undermined. This challenge is, however, being overcome by placement of shunt capacitors to supply the reactive power required for compensation; hence, optimal sitting and sizing of compensators has been intensively researched. As efficient as the use of meta-heuristic algorithms for joint optimal placement and sizing of the shunt capacitors are, employment of the approach on the Nigerian radial distribution system (RDS) is not yet popular as most of the earlier works reported rather used analytical and numerical programming approaches. In this study, therefore, the use of firefly algorithm (FA) on a Nigerian 11-kV feeder is presented as an approach to optimally site and size shunt capacitor for real power loss reduction on such network. Backward-forward sweep load flow technique, with voltage stability index (VSI), is employed to find the candidate buses where the shunt capacitors would be installed, then FA is employed to determine the optimal size required. This approach is implemented on a 34-bus 11-kV feeder and it is found out that the system’s real power loss reduced from 762.6419 to 597.7486 kW, while the minimum bus voltage magnitude was raised from 0.8295 to 0.8456 p.u. and the minimum system VSI was improved from 0.4741 to 0.5121 p.u. Based on these results, the proposed approach is, therefore, considered a promising technique for sitting and sizing shunt capacitor optimally in real practical RDS.

A New Chromogenic Spray Reagent for the Detection and Identification of 2,4-Dichlorophenol, an Intermediate of 2,4-D Herbicide in Biological Material by High-Performance Thin-Layer Chromatography

Summary 2,4-Dichlorophenoxyacetic acid (2,4-D) is a phenoxy group of herbicide used worldwide. As it is extensively used, it has consequential problems on living beings. 2,4-D is degraded into the ...

Coarsening in nanocrystalline thin films: A 4D problem

Materials properties of polycrystalline solids depend strongly on the microstructure. Hence, investigating grain microstructures as they change during grain growth, e.g., by thermal annealing, is of utmost importance. Historically, this has been done by optical microscopy yielding only a two-dimensional characterization. Consequential problems have long been recognized. In particular, a two-dimensional section through a three-dimensional polycrystal yields a rather poor idea about the true size and form of the individual grains, and therefore of the microstructure, and in addition, such a section represents only a snapshot in time and does not give any clues about structural changes during grain growth. In the present study, we investigate coarsening in nanocrystalline thin films using a modified three-dimensional Potts model. By taking the increased importance of higher order grain boundary junctions, i.e. triple lines, at smaller grain sizes into account, it is shown that the microstructure, as well as the associated growth kinetics, undergo distinct changes during coarsening.

Radical and Incremental Innovation Problem-Driven to Support Competitive Advantage of Firms

A fundamental problem in the field of management of technology is how to explain the sources of radical and incremental innovations that sustain the competitive advantage of firms. Current frameworks have trouble explaining some facts about the source of technical change and dynamics of innovation of firms in markets. The current study confronts this problem by developing a conceptual framework of problem-driven innovation. The evidence of a case study research in medicine (ground-breaking drugs for lung cancer treatment) seems to support that consequential problems and their solutions induce the evolution of technology by radical and incremental innovations and paradigms shift as well. In fact, firms have a strong incentive to find innovative solutions to unsolved problems in order to achieve the prospect of a (temporary) profit monopoly and competitive advantage in markets characterized by competitive intensity, technological and market dynamisms. The theoretical framework of this study can be generalized to explain the source and evolution of technology of different industries and, as a consequence, one of driving forces that supports technological and industrial change in a Schumpeterian world of innovation-based competition.

Disruptive Technologies and Competitive Advantage of Firms in Dynamic Markets

A fundamental problem in the field of management of technology is how firms develop and sustain disruptive technologies for competitive advantage in markets. The vast literature has analyzed several characteristics of disruptive innovations. However, the determinants are hardly known. The study here seems to show, in a market with high intensity of R&D investments (anticancer drugs), that the emergence of disruptive technologies can be driven by the coevolution of consequential problems and their solution in R&D labs of firms. In general, incumbent and entrant firms have a strong incentive to find innovative solutions to unsolved, consequential and new problems in order to achieve and sustain the prospect of a (temporary) profit monopoly and competitive advantage in markets with technological dynamisms. Overall, then this study shows one of the general sources of disruptive technologies that seems to support industrial and corporate change in a Schumpeterian world of innovation-based competition.

Sources of technological innovation: Radical and incremental innovation problem-driven to support competitive advantage of firms

ABSTRACTA fundamental problem in the field of management of technology is how firms develop radical and incremental innovations that sustain the competitive advantage in markets. Current frameworks provide some explanations but the general sources of major and minor technological breakthroughs are hardly known. The study here confronts this problem by developing a conceptual framework of problem-driven innovation. The inductive study of the pharmaceutical industry (focusing on ground-breaking drugs for lung cancer treatment) seems to show that the co-evolution of consequential problems and their solutions induce the emergence and development of radical innovations. In fact, firms have a strong incentive to find innovative solutions to unsolved problems in order to achieve the prospect of a (temporary) profit monopoly and competitive advantage in markets characterised by technological dynamisms. The theoretical framework of this study can be generalised to explain one of the sources of innovation that supports technological and industrial change in a Schumpeterian world of innovation-based competition.

‘Let your data tell a story:’ climate change experts and students navigating disciplinary argumentation in the classroom

ABSTRACTScience education trends promote student engagement in authentic knowledge in practice to tackle personally consequential problems. This study explored how partnering scientists and students on a social media platform supported students’ development of disciplinary practice knowledge through practice-based learning with experts during two pilot enactments of a project-based curriculum focusing on the ecological impacts of climate change. Through the online platform, scientists provided feedback on students' infographics, visual argumentation artifacts that use data to communicate about climate change science. We conceptualize the infographics and professional data sets as boundary objects that supported authentic argumentation practices across classroom and professional contexts, but found that student generated data was not robust enough to cross these boundaries. Analysis of the structure and content of the scientists’ feedback revealed that when critiquing argumentation, scientists initiated engagement in multiple scientific practices, supporting a holistic rather than discrete model of practice-based learning. While traditional classroom inquiry has emphasized student experimentation, we found that engagement with existing professional data sets provided students with a platform for developing expertise in systemic scientific practices during argument construction. We further found that many students increased the complexity and improved the visual presentation of their arguments after feedback.