Specific Version Research Articles

The Epistemic Cost of Opacity: How the Use of Artificial Intelligence Undermines the Knowledge of Medical Doctors in High-Stakes Contexts

Artificial intelligent (AI) systems used in medicine are often very reliable and accurate, but at the price of their being increasingly opaque. This raises the question whether a system’s opacity undermines the ability of medical doctors to acquire knowledge on the basis of its outputs. We investigate this question by focusing on a case in which a patient’s risk of recurring breast cancer is predicted by an opaque AI system. We argue that, given the system’s opacity, as well as the possibility of malfunctioning AI systems, practitioners’ inability to check the correctness of their outputs, and the high stakes of such cases, the knowledge of medical practitioners is indeed undermined. They are lucky to form true beliefs based on the AI systems’ outputs, and knowledge is incompatible with luck. We supplement this claim with a specific version of the safety condition on knowledge, Safety*. We argue that, relative to the perspective of the medical doctor in our example case, his relevant beliefs could easily be false, and this despite his evidence that the AI system functions reliably. Assuming that Safety* is necessary for knowledge, the practitioner therefore doesn’t know. We address three objections to our proposal before turning to practical suggestions for improving the epistemic situation of medical doctors.

Application of Western models of posttraumatic stress disorder in Nepal: Confirmatory factor analysis in earthquake survivors and in spinal cord injury patients

Posttraumatic stress disorder (PTSD) is often diagnosed by counting the number of symptoms experienced in specified symptom clusters, as outlined by the American Psychiatric Association's Diagnostic and Statistical Manual (DSM) of mental illness. However, PTSD measurement tools and this method of diagnosing and treating PTSD, even in non-Western countries, is based on Western concepts of the underlying factor structure. While some research has suggested that PTSD is a disorder found in Western, or “WEIRD” (White, Educated, Industrialized, Rich, and Democratic) countries and it may not translate to non-Western cultural contexts, other research has documented PTSD in a variety of non-Western countries including India, China, and the Philippines. We examined if the underlying factor structure of PTSD was evident in another non-Western context—in Nepal. We translated and back-translated the PTSD Checklist Specific Stress Version (PCL-S), adapting it for use with populations with low literacy affected by two life changing traumas. We then tested a series of theoretical models of PTSD across two different trauma samples—in survivors of the 2015 earthquake ( N = 392) and in people who experienced a life-changing spinal cord injury ( N = 163). Using confirmatory factor analysis, we found some evidence that PTSD symptoms appear to map onto Western factors, with evidence of the four-factor Dysphoria model (Re-experiencing, Avoidance, Dysphoria, and Hyperarousal symptoms) in both populations, and demonstrating scalar invariance across samples. However, exploratory factor analyses suggested a different underlying structure. Further, measurement invariance testing suggested gendered responses to PTSD items. The results demonstrate PTSD symptoms in this non-Western context somewhat fit hypothesized Western concepts of PTSD but point to a holistic approach to diagnosing PTSD in such contexts, as opposed to using clusters or PTSD cut-offs.

Utilizing Unique Device Identifiers to Enhance Adverse Event Analysis

On September 24, 2023, the US Food and Drug Administration (FDA) and other stakeholders marked the 10-year anniversary of the publication of the FDA Unique Device Identifier (UDI) Final Rule. The infrastructure for the UDI system is in place—UDI is on the label of medical devices for which UDI requirements apply, unless an exception or alternative applies, and key device identification information, including the device identifier portion of the UDI, is available in the Global Unique Device Identification Database. The focus now is on adoption and implementation of UDI throughout the healthcare ecosystem to realize the intent of the UDI Rule, which is to enhance patient safety through accurate device identification. To that end, this article provides an example of the utility of the UDI for evaluating device performance as a device evolves over time. The example demonstrates that UDI can facilitate identification of the specific version or model of a device that has malfunctioned and/or contributed to an adverse event by enabling more accurate data aggregation compared with other identifying information, potentially leading to key safety insights.

Supervaluationism about Vague Names Cannot Account for Statements about Those Names

Vague names, like “Everest” and “Belle Epoque” seem to refer to objects without clear boundaries. Supervaluationism claims that this vagueness is a feature of language, not of the objects referred to; vagueness in names is just ambiguity between many possible referents. This general idea admits of two more specific versions. Both give similar treatments of standard uses of vague names, but have very different results for other cases, such as reference achieved by descriptions including mentioned names. Considering two examples, I show neither variant of supervaluationism can account for the truth of all types of sentences about those names themselves. If I am right that these types exhaust supervaluationism, the theory is shown to be false. This problem closely resembles others in the super-valuationist literature about disquotation failure for truth. Treatments of vague truth and vague reference come apart though, and I show that the two problems are different enough that none of the popular solutions will succeed for reference. I consider—and reject—several specific objections, and two more general ways to recover supervaluationism following my arguments. I conclude that supervaluationism is at best a useful formalism for some kinds of vagueness reference, but fails as a general account.

Creating a Psychological Test in a Few Seconds

Abstract: The development of Situational Judgment Tests (SJTs) is a multi-step procedure that often requires substantial resources and time. ChatGPT as a large language model has already proven helpful for many complex tasks, including the development of psychological questionnaires. However, ChatGPT has so far not been tested for its ability to develop SJT items, which could speed up SJT development and thus contribute to the dissemination of psychometrically well-designed SJTs in practice and also support research requiring specific SJTs or SJT versions. Thus, the current study ( N = 419) examined whether ChatGPT (2023, Feb 13 version using ChatGPT-3.5) can create SJT items, assessing a facet of personality (i.e., gregariousness), that show similar psychometric properties (examined through confirmatory factor, reliability, and correlational analyses) as human-created SJT items. Results revealed that the measurement model for all eleven SJT items that were created by ChatGPT showed a poor model fit, while a reduced set of eight items yielded an acceptable fit. Reliability estimates as well as convergent and discriminant validity evidence were similar for the human-created and the ChatGPT-created SJT. Implications as well as potential boundary conditions of SJT development through ChatGPT are discussed.

Multicenter Study on the Impact of the Masker Babble Spectrum on the Acceptable Noise Level (ANL) Test.

Acceptable Noise Level (ANL) is defined as the most comfortable level (MCL) intensity for speech and is calculated by subtracting the maximum noise tolerable by an individual. The ANL test has been used over time to predict hearing aid use and the impact of digital noise reduction. This study analyzes this impact by using different masker babble spectra when performing the ANL test in both hearing-impaired and healthy subjects in three different languages (Dutch, French, and Italian). A total of 198 patients underwent the ANL test in their native language using a standardized protocol. The babble file was speech-weighted to match the long-term spectrum of the specific ANL language version. ANL was proposed in three different masking conditions: with multitalker Matched babble speech noise, with the same masking signal with the spectrum reduced from 2 kHz onwards (High Cut), and with the spectrum increased from 2 kHz onwards (High Boost). In all of the comparisons among the three languages, ANL with High Boost noise gave significantly higher (worse) scores than ANL with Matched noise (p-value S1: <0.0001, S2: <0.0001, S3: 0.0003) and ANL with High Cut noise (p-value S1: 0.0002, S2: <0.0001, S3: <0.0001). The ANL values did not show any significant correlation with age and gender. In French, a weak correlation was found between ANL with High Cut noise and the Fletcher index of the worst ear. In Italian, a weak correlation was found between both ANL with Matched and High Boost noise and the Fletcher index of the best ear. ANL with High Boost added to noise stimuli was less acceptable for all patients in all of the languages. The ANL results did not vary in relation to the patients' characteristics. This study confirms that the ANL test has potential application for clinical use regardless of the native language spoken.

Design conception of a food dehydrator

Nowadays, the household is unimaginable without the three main pillars of the technical field, which are mechanical engineering, electrical engineering and IT. The paper deals with the design conception of a food dehydrator unit. These types of devices are capable of drying fruits and mushrooms. The article covers the design conception of a specific version, and then by building a test system, it becomes possible to check the correctness of the control program code belonging to the system.

Social Cohesion and COVID-19: Integrative Review.

Nations of considerable wealth and sophisticated health care infrastructures have experienced high rates of illness and death from COVID-19. Others with limited economic means and less developed health systems have achieved much lower burdens. To build a full understanding, an appraisal of the contribution of social relationships is necessary. Social cohesion represents a promising conceptual tool. This study aimed to examine scholarship on social cohesion during the COVID-19 pandemic: specifically, the constructions of social cohesion being deployed, the variables chosen for representation, and the effects of and on social cohesion being reported. The PubMed, Scopus, and JSTOR databases were searched for relevant journal articles and gray literature. A total of 100 studies met the inclusion criteria. Data were extracted and analyzed from these using spreadsheet software. Several constructions of social cohesion were found. These concerned interpersonal relationships, sameness and difference, collective action, perceptions or emotions of group members, structures and institutions of governance, locally or culturally specific versions, and hybrid or multidimensional models. Social cohesion was reported to be influential on health outcomes, health behaviors, resilience, and emotional well-being, but there was some potential for it to drive undesirable outcomes. Scholarship reported increases or decreases in quantitative measures of social cohesion, a temporary "rally round the flag" effect early in the pandemic, the variable impacts of policy on social cohesion, and changing interpersonal relationships due to the pandemic conditions. There are numerous issues with the literature that reflect the well-documented limitations of popular versions of the concept. Social cohesion has been used to express a range of different aspects of relationships during the pandemic. It is claimed to promote better health outcomes, more engagement with positive health behaviors, and greater resilience and emotional well-being. The literature presents a range of ways in which it has been altered by the pandemic conditions. There are significant weaknesses to this body of knowledge that greatly impede its overall quality.

Exploring the utilization and deficiencies of Generative Artificial Intelligence in students' cognitive and emotional needs: a systematic mini-review.

Despite advances in educational technology, the specific ways in which Generative Artificial Intelligence (GAI) and Large Language Models cater to learners' nuanced cognitive and emotional needs are not fully understood. This mini-review methodically describes GAI's practical implementations and limitations in meeting these needs. It included journal and conference papers from 2019 to 2024, focusing on empirical studies that employ GAI tools in educational contexts while addressing their practical utility and ethical considerations. The selection criteria excluded non-English studies, non-empirical research, and works published before 2019. From the dataset obtained from Scopus and Web of Science as of June 18, 2024, four significant studies were reviewed. These studies involved tools like ChatGPT and emphasized their effectiveness in boosting student engagement and emotional regulation through interactive learning environments with instant feedback. Nonetheless, the review reveals substantial deficiencies in GAI's capacity to promote critical thinking and maintain response accuracy, potentially leading to learner confusion. Moreover, the ability of these tools to tailor learning experiences and offer emotional support remains limited, often not satisfying individual learner requirements. The findings from the included studies suggest limited generalizability beyond specific GAI versions, with studies being cross-sectional and involving small participant pools. Practical implications underscore the need to develop teaching strategies leveraging GAI to enhance critical thinking. There is also a need to improve the accuracy of GAI tools' responses. Lastly, deep analysis of intervention approval is needed in cases where GAI does not meet acceptable error margins to mitigate potential negative impacts on learning experiences.

Effects of metaphorical communication on vaccine confidence in the adolescent population

Abstract Vaccine hesitancy, defined as delay or refusal of vaccination despite the availability of vaccination services, has been included by WHO among the top 10 global health threats. Individual decision-making process can be strongly affected by communication. The use of metaphors is common in public health communication and increased during the COVID19 pandemic. Although some evidence has supported the persuasive potential of metaphorical language over scientific language, further research is needed. Our study aimed at comparing the effects of neutral scientific talk with war and protective metaphors on health communication. An online survey was conducted on a convenience sample of Italian teenagers randomly allocated to three study arms. Each participant was invited to watch a specific version of an informative video on infections and vaccines (group 1: neutral-scientific approach, group 2: metaphorical-war, and group 3: metaphorical-protective approach). After watching the video, each participant responded to an ad-hoc questionnaire. A total of 2681 subjects were randomly allocated to the three groups. No significant differences regarding age, sex and geographical area were detected between groups. Median age of the sample was 17 years (mean 16,8) and 56.8% were girls. The general attitude towards vaccination in group 2 and 3 was largely positive, with the majority agreeing (strongly or tend to agree) on the fact that vaccination is important (group 2: 89%; group 3: 92%), safe (group 2: 91%; group 3: 92,5%), effective (group 2: 85%; group 3: 88%), and does not generate anxiety (group 2: 80%; group 3: 83%). Higher level of distrust against vaccines was found in group 1, where 32% of participating adolescents are strongly or tend to be anxious about getting vaccinated. The results of our survey suggest that emotional involvement evoked through the use of metaphorical language may have, at least in the short run, some positive effect on vaccine confidence. Key messages • Use of metaphors can be successfully used in public health communication. • Messages evoking either war or protecting concepts are more efficacious than plain scientific language.

Remixing Britishness: Affect and National Pride in the London 2012 Olympic Opening Ceremony

During the London 2012 Opening Olympic Ceremony, emotional moments such as the torch relay and the celebration of the National Health Service affected many viewers. Such a spectacle of national history and culture was wide-sweeping and is still remembered today as a moment of triumph for the country. With the Paris 2024 Olympics only months away, it becomes timely to observe how the sensorial spectacle of the Olympics works to create a certain emotional effect in its viewers, and how they leave with a specific understanding of what the nation stands for. Crucially, it is imperative to survey the ways in which national histories and cultures have been edited for an "Olympic affect" during the Opening Ceremony and comprehend why they play such a crucial role in contemporary political discourse. The London 2012 Opening Olympic Ceremony revealed a particular perspective on British history, used emotion and spectacle to recount a specific version of events, and attempted to garner a certain sense of national pride during a time of national crisis. In examining specific visual and auditory cues of this Opening Olympic Ceremony, this study will define the "Olympic affect" and examine the idealised Britishness that is performed, so that we may understand what kind of affectual and political power the Olympics hold.

Designing a Positive Health Dialogue Tool for Adolescents and Young Adults: A Mixed Methods Study.

Being able to express and address strengths and needs related to health is an important asset to enhance resilience during emerging adulthood. Towards this end, we aimed to develop a specific version of the My Positive Health (MPH) dialogue tool for this developmental period (ages 16-25). By adopting a broad perspective on health and striving for applicability in various settings, this tool ultimately seeks to promote integrated collaboration across various domains (healthcare, social care, school). The tool was co-designed with end users, using mixed methods: a descriptive cross-sectional survey questionnaire (N = 118) followed by qualitative focus groups and interview sessions (N = 36). Participants were Dutch citizens (mean age 21.6; 79% female) who were stratified by educational level and chronic disease status (yes/no). The final selection of the content of the tool was made during expert sessions. A preference appeared for the following dimensions to be part of the dialogue tool: My body, My feelings and thoughts, Meaningfulness, Quality of life, Participation and Daily life. Each dimension was operationalised by aspects (43 in total) to facilitate reflection and dialogue. The following new aspects that are typical for emerging adulthood were formulated and included in the dialogue tool: 'Confidence in yourself', 'Being in control', 'Having confidence in the future', 'Self-expression', 'Meaningful relationships', 'Being able to work or study' and 'Ability to plan'. Other aspects derived from the adult and children's versions of the MPH dialogue tool. A dialogue tool was designed for individuals aged 16-25 in various health- and vulnerability-related conditions and with applicability in various domains. The development of the dialogue tool was specifically driven by the needs expressed by the users themselves. Planned public and patient contribution comprised consultation and collaboration in (i) design, (ii) recruitment, (iii) focus group sessions, (iv) analysis and discussion of the data and (v) dissemination. Not applicable.

A comprehensive analysis of observational cosmology in [formula omitted] gravity with deep learning and MCMC method

Our goal in this study is to build FRW cosmological models inside the f(Q) theory of gravity framework by using Bayesian statistics and deep learning method. We investigate the universe’s accelerating behaviour for a specific version of the f(Q) gravity model using a novel, straightforward parameterization of the Hubble parameter in the form H=H0(1+z)1+q0−q1exp(q1z). The corresponding free parameters in H(z) are limited between 1σ and 2σ confidence bounds using the χ2-minimization procedure. The results show that all the numbers we got are in the ballpark of what cosmological observations would predict. In our model, we examined the physical behaviour of the cosmos using characteristics such as energy density, pressure, and equation of state. We analysed kinematic factors including Hubble parameter, acceleration parameter, and universe age in our model. In our concept, the deceleration parameter q(z) represents the universe’s transition from deceleration to acceleration. We employ a novel approach for parameter estimation by utilizing a mixed neural network (MNN) that combines artificial neural networks (ANN) and mixture density networks (MDN). This new methodology leverages the strengths of ANN, MDN, and MNN to enhance the accuracy of parameter estimation.

Two-Higgs-doublet model effective field theory

We construct the general two-Higgs doublet model effective field theory where the effects of additional new physics are parametrized by operators up to mass dimension-six. We further transform this effective theory to the Higgs basis and provide matching of the Wilson coefficients between the two descriptions. We illustrate the advantages of the Higgs basis which include the separation of operators that modify standard model couplings and masses from operators that contribute to scattering processes only, transparent correlations between scattering processes resulting from the same operator, and derivation of correlations between different operators in specific UV completions. For completeness, we also construct specific versions corresponding to four types of two-Higgs-doublet models: type-I, -II, -X, and -Y, distinguished by Z2 symmetries which restrict the couplings of the Higgs doublets to standard model fermions. Furthermore, we derive general vacuum and stability conditions of the scalar potential in the presence of higher-dimensional terms. Published by the American Physical Society 2024

Improvement of decadal predictions of monthly extreme Mei-yu rainfall via a causality guided approach

Abstract While the improved performance of climate prediction systems has allowed better predictions of the East Asian Summer Monsoon rainfall to be made, the ability to predict extreme Mei-yu rainfall (MYR) remains a challenge. Given that large scale climate modes (LSCMs) tend to be better predicted by climate prediction systems than local extremes, one useful approach is to employ causality-guided statistical models (CGSMs), which link known LSCMs to improve MYR prediction. However, previous work suggests that CGSMs trained with data from 1979–2018 might struggle to model MYR in the pre-1978 period. One hypothesis is that this is due to potential changes in causal processes, which modulate MYR in different phases of the multidecadal variability, such as the Pacific decadal oscillation (PDO). In this study, we explore this hypothesis by constructing CGSMs for different PDO phases, which reflect the different phases of specific causal process, and examine the difference in quality as well as with respect to difference drivers and thus causal links between CGSMs of different PDO phases as well as the non-PDO phase specific CGSMs. Our results show that the set of predictors of CGSMs is PDO phase specific. Furthermore, the performance of PDO phase specific CGSMs are better than the non-PDO phase specific CGSMs. To demonstrate the added value of CGSMs, the PDO phase specific versions are applied to the latest UK Met Office decadal prediction system, DePreSys4, and it is shown that the root-mean squared errors of MYR prediction based on PDO phase specific CGSMs is consistently smaller than the MYR predicted based on the direct DePreSys4 extreme rainfall simulations. We conclude that the use of a causality approach improves the prediction of extreme precipitation based solely on known LSCMs because of the change in the main drivers of extreme rainfall during different PDO-phases.

Introduction to the Special Issue: Hindutva and the Rule(s) of Law

India is increasingly described as an ‘ethnic democracy’, ‘populist majoritarian autocracy’ or ‘ethnocracy’: a form of rule supported by an electoral majority rooted in ethnic affiliation, with limited and eroding checks and balances that would protect minorities. Over the past decade, constitutional arrangements shifted, ‘dog-whistle’ laws were passed and legal institutions starved of resources. In studying these developments, we want to ground generic studies of populist/majoritarian/autocratic law by unpacking the specific Indian version of it: how does Hindutva as a political ideology and the current dispensation as political agents conceive of the rule of law, its purpose and function? Which rules do they want the law to follow? We combine papers that trace Hindutva's own ideological commitments with those tracking material changes in legislation or jurisprudence and map out their differential consequences for India's minorities, culminating in a wider reflection on the rule(s) of law under autocratic circumstances.

A multilevel analysis of data quality for formal software citation

Abstract Software is a central part of modern science, and knowledge of its use is crucial for the scientific community with respect to reproducibility and attribution of its developers. Several studies have investigated in-text mentions of software and its quality, while the quality of formal software citations has only been analyzed superficially. This study performs an in-depth evaluation of formal software citation based on a set of manually annotated software references. It examines which resources are cited for software usage, to what extent they allow proper identification of software and its specific version, how this information is made available by scientific publishers, and how well it is represented in large-scale bibliographic databases. The results show that software articles are the most cited resource for software, while direct software citations are better suited for identification of software versions. Moreover, we found current practices by both publishers and bibliographic databases to be unsuited to represent these direct software citations, hindering large-scale analyses such as assessing software impact. We argue that current practices for representing software citations—the recommended way to cite software by current citation standards—stand in the way of their adoption by the scientific community, and urge providers of bibliographic data to explicitly model scientific software.

Portability and scalability evaluation of large-scale statistical modeling and prediction software through HPC-ready containers

HPC-based applications often have complex workflows with many software dependencies that hinder their portability on contemporary HPC architectures. In addition, these applications often require extraordinary efforts to deploy and execute at performance potential on new HPC systems, while the users expert in these applications generally have less expertise in HPC and related technologies. This paper provides a dynamic solution that facilitates containerization for transferring HPC software onto diverse parallel systems. The study relies on the HPC Workflow as a Service (HPCWaaS) paradigm proposed by the EuroHPC eFlows4HPC project. It offers to deploy workflows through containers tailored for any of a number of specific HPC systems. Traditional container image creation tools rely on OS system packages compiled for generic architecture families (x86_64, amd64, ppc64, …) and specific MPI or GPU runtime library versions. The containerization solution proposed in this paper leverages HPC Builders such as Spack or Easybuild and multi-platform builders such as buildx to create a service for automating the creation of container images for the software specific to each hardware architecture, aiming to sustain the overall performance of the software. We assess the efficiency of our proposed solution for porting the geostatistics ExaGeoStat software on various parallel systems while preserving the computational performance. The results show that the performance of the generated images is comparable with the native execution of the software on the same architectures. On the distributed-memory system, the containerized version can scale up to 256 nodes without impacting performance.

On the Mathieu conjecture for Sp(N) and G2

As a direct continuation of Zwart [J. Math. Phys. 64(10), 101701 (2023)], which is built on the work of Müger and Tuset [Indagationes Math. 35(1), 114 (2024)], we reduce the Mathieu conjecture, formulated by Mathieu [Algèbra Non Commutative, Groupes Quantiques et Invariants, edited by Alex, J. and Cauchon, G. (Société Mathématique de France, Reims, 1997), Vol. 2, pp. 263–279], for Sp(N) and G2 to a conjecture involving functions over Rn×(S1)m with n,m∈N0. The proofs rely on Euler-style parametrizations of these groups, a specific version of the KAK decomposition, which we discuss and prove.

Filter generators with variable transition functions over finite fields of characteristic 2

Filter generators are a traditional basis for creating synchronous stream ciphers. They are built with the help of linear shift registers (usually over a field of two elements) and nonlinear complexity functions, which are subject to a number of requirements in terms of the generators security against known attacks. Intensive researches of filter generators during the last decades show that meeting these requirements without degrading the performance of the generators is a very difficult task. Despite a large number of publications devoted to the construction of complexity functions with known “good cryptographic properties”, the usage of such functions in practice often becomes unacceptable due to the bulkiness of their constructions, which slows down the functioning of the corresponding generators, especially during software implementation. The way to overcome the noted difficulties by using an additional secret parameter that determines the appearance of the generator transitions’ function is proposed. Such a modification makes it possible to increase the security of generator (compared to traditional filter generators) against known attacks without increasing the length of its initial state. In particular, a specific version of a generator construction with a complexity function, which is determined with the help of substitutions used in the “Kalyna” encryption scheme, is considered. A lower estimate of the output sequences periods of the proposed generators was obtained. A research of their security to known attacks, in particular, Babbage-Golic balancing attack; an attack associated with a small number of terms in the polynomial representation of the complexity function (which negatively affects the value of the equivalent linear complexity of the output sequences of the generator); a natural correlation attack associated with the specifics of the proposed generator construction scheme; algebraic attacks of the Courtois-Mayer type were also conducted. At the end of the article, it is indicated how to choose the components of the proposed generators to ensure their security at a predetermined level.