Naive Aufklärung und resignative Entmündigung:das Subjekt zwischen Erkenntnis- und Gesellschaftstheorie

Die Geschichte des neuzeitlichen Subjekts ist eine wechselvolle Geschichte. Galt es einst als autonome Gestalt, wurde es zwischenzeitlich für tot erklärt. Die Theorie des Subjekts pendelt zwischen Erkenntnis- und Gesellschaftstheorie. Der Beitrag fragt nach dem Zusammenspiel von Subjekt und Umwelt: Kann das Subjekt seine Umwelt gestalten oder wird es selbst durch diese gestaltet

Analysis and Estimation of Human Errors From Major Accident Investigation Reports

Abstract Risk analyses require proper consideration and quantification of the interaction between humans, organization, and technology in high-hazard industries. Quantitative human reliability analysis approaches require the estimation of human error probabilities (HEPs), often obtained from human performance data on different tasks in specific contexts (also known as performance shaping factors (PSFs)). Data on human errors are often collected from simulated scenarios, near-misses report systems, and experts with operational knowledge. However, these techniques usually miss the realistic context where human errors occur. The present research proposes a realistic and innovative approach for estimating HEPs using data from major accident investigation reports. The approach is based on Bayesian Networks used to model the relationship between performance shaping factors and human errors. The proposed methodology allows minimizing the expert judgment of HEPs, by using a strategy that is able to accommodate the possibility of having no information to represent some conditional dependencies within some variables. Therefore, the approach increases the transparency about the uncertainties of the human error probability estimations. The approach also allows identifying the most influential performance shaping factors, supporting assessors to recommend improvements or extra controls in risk assessments. Formal verification and validation processes are also presented.</jats:p

Learning from major accidents: Graphical representation and analysis of multi-attribute events to enhance risk communication

Major accidents are complex, multi-attribute events, originated from the interactions between intricate systems, cutting-edge technologies and human factors. Usually, these interactions trigger very particular accident sequences, which are hard to predict but capable of producing exacerbated societal reactions and impair communication channels among stakeholders. Thus, the purpose of this work is to convert high-dimensional accident data into a convenient graphical alternative, in order to overcome barriers to communicate risk and enable stakeholders to fully understand and learn from major accidents. This paper first discusses contemporary views and biases related to human errors in major accidents. The second part applies an artificial neural network approach to a major accident dataset, to disclose common patterns and significant features. The complex data will be then translated into 2-D maps, generating graphical interfaces which will produce further insight into the conditions leading to accidents and support a novel and comprehensive “learning from accidents” experience

Learning from accidents: Interactions between human factors, technology and organisations as a central element to validate risk studies

Many industries are subjected to major hazards, which are of great concern to stakeholders groups. Accordingly, efforts to control these hazards and manage risks are increasingly made, supported by improved computational capabilities and the application of sophisticated safety and reliability models. Recent events, however, have revealed that apparently rare or seemingly unforeseen scenarios, involving complex interactions between human factors, technologies and organisations, are capable of triggering major catastrophes. The purpose of this work is to enhance stakeholders’ trust in risk management by developing a framework to verify if tendencies and patterns observed in major accidents were appropriately contemplated by risk studies. This paper first discusses the main accident theories underpinning major catastrophes. Then, an accident dataset containing contributing factors from major events occurred in high-technology industrial domains serves as basis for the application of a clustering and data mining technique (self-organising maps – SOM), allowing the exploration of accident information gathered from in-depth investigations. Results enabled the disclosure of common patterns in major accidents, leading to the development of an attribute list to validate risk assessment studies to ensure that the influence of human factors, technological issues and organisational aspects was properly taken into account

Special section on decommissioning and life extension of complex industrial assets

The final phase of the operational life of complex industrial systems leads to a very significant business decision: to engage in life extension, repurpose its use, or decommission the asset. Decommissioning is the ultimate stage of the lifecycle of an industrial system. It comprises the removal and disposal of equipment, structures and residues, environmental recovery and/or remediation, and postdisposal monitoring. Due to the timescale and complexity of the decommissioning activities, it involves many challenges in order to make safe and cost-effective decommissioning an achievable objective

Observation of the Stimulated Quantum Cherenkov Effect

As charged particles surpass the speed of light in an optical medium they produce radiation - analogously to the way jet planes surpass the speed of sound and produce a sonic boom. This radiation emission, known as the Cherenkov effect, is among the most fundamental processes in electrodynamics. As such, it is used in numerous applications of particle detectors, particle accelerators, light sources, and medical imaging. Surprisingly, all Cherenkov-based applications and experiments thus far were fully described by classical electrodynamics even though theoretical work predicts new Cherenkov phenomena coming from quantum electrodynamics. The quantum description could provide new possibilities for the design of highly controllable light sources and more efficient accelerators and detectors. Here, we provide a direct evidence of the quantum nature of the Cherenkov effect and reveal its intrinsic quantum features. By satisfying the Cherenkov condition for relativistic electron wavefunctions and maintaining it over hundreds of microns, each electron simultaneously accelerates and decelerates by absorbing and emitting hundreds of photons in a coherent manner. We observe this strong interaction in an ultrafast transmission electron microscope, achieving for the first time a phase-matching between a relativistic electron wavefunction and a propagating light wave. Consequently, the quantum wavefunction of each electron evolves into a coherent plateau, analogous to a frequency comb in ultrashort laser pulses, containing hundreds of quantized energy peaks. Our findings prove that the delocalized wave nature of electrons can become dominant in stimulated interactions. In addition to prospects for known applications of the Cherenkov effect, our work provides a platform for utilizing quantum electrodynamics for applications in electron microscopy and in free-electron pump-probe spectroscopy.Comment: 15 pages, 4 figure

Impact of sex and gender on post-COVID-19 syndrome, Switzerland, 2020

Background: Women are overrepresented among individuals with post-acute sequelae of SARS-CoV-2 infection (PASC). Biological (sex) as well as sociocultural (gender) differences between women and men might account for this imbalance, yet their impact on PASC is unknown. Aim: We assessed the impact of sex and gender on PASC in a Swiss population. Method: Our multicentre prospective cohort study included 2,856 (46% women, mean age 44.2 ± 16.8 years) outpatients and hospitalised patients with PCR-confirmed SARS-CoV-2 infection.ResultsAmong those who remained outpatients during their first infection, women reported persisting symptoms more often than men (40.5% vs 25.5% of men; p < 0.001). This sex difference was absent in hospitalised patients. In a crude analysis, both female biological sex (RR = 1.59; 95% CI: 1.41-1.79; p < 0.001) and a score summarising gendered sociocultural variables (RR = 1.05; 95% CI: 1.03-1.07; p < 0.001) were significantly associated with PASC. Following multivariable adjustment, biological female sex (RR = 0.96; 95% CI: 0.74-1.25; p = 0.763) was outperformed by feminine gender-related factors such as a higher stress level (RR = 1.04; 95% CI: 1.01-1.06; p = 0.003), lower education (RR = 1.16; 95% CI: 1.03-1.30; p = 0.011), being female and living alone (RR = 1.91; 95% CI: 1.29-2.83; p = 0.001) or being male and earning the highest income in the household (RR = 0.76; 95% CI: 0.60-0.97; p = 0.030). Conclusion: Specific sociocultural parameters that differ in prevalence between women and men, or imply a unique risk for women, are predictors of PASC and may explain, at least in part, the higher incidence of PASC in women. Once patients are hospitalised during acute infection, sex differences in PASC are no longer evident

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN