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'The eyes and ears of the railway’: How frontline workers uphold safety through their occupational expertise and embodied epistemic authority
Get PDFFrontline workers who occupy public facing, non-managerial roles are critical to the ongoing sociotechnical accomplishment of safety in complex systems, yet their role is often overlooked in relation to organizational safety programs, protocols, and training. In this paper we examine how frontline workers make judgements about potential hazards during routine work and how they respond to organizational directives that contravene their own expertise. Drawing on interviews with train drivers working for private franchises in the United Kingdom, our findings show how frontline workers’ safety culture and unique embodied knowledge constitutes their epistemic authority which ultimately supports robust safety voice and listening in a complex sociotechnical system. We show how train drivers are subject to extensive organizational controls that are meant to realize safety, but that in practice these controls are insufficient for responding to the incidents that occur on the tracks. These findings offer insight into how frontline workers draw on occupational authority to uphold a societal mandate for safety
The canonical deep neural network as a model for human symmetry processing
Get PDFA key property of our environment is the mirror symmetry of many objects, although symmetry is an abstract global property with no definable shape template, making symmetry identification a challenge for standard template-matching algorithms. We therefore ask whether Deep Neural Networks (DNNs) trained on typical natural environmental images develop a selectivity for symmetry similar to that of the human brain. We tested a DNN trained on such typical natural images with object-free random-dot images of 1, 2, and 4 symmetry axes. Symmetry coding was negligible in the earliest DNN layers. The strongest discriminability occurred in the first fully connected layer, FC6, plausibly analogous to the human lateral occipital complex (LOC), matching many structural properties of human symmetry processing. These results support the homology between the feedforward DNN trained on natural images and the global processing of the extended visual hierarchy as it has evolved in the human brain
Optimal training of finitely sampled quantum reservoir computers for forecasting of chaotic dynamics
Get PDFIn the current Noisy Intermediate Scale Quantum (NISQ) era, the presence of noise deteriorates the performance of quantum computing algorithms. Quantum reservoir computing (QRC) is a type of quantum machine learning algorithm, which, however, can benefit from different types of tuned noise. In this paper, we analyze how finite sampling noise affects the chaotic time series prediction of the gate-based QRC and recurrence-free quantum reservoir computing (RF-QRC) models. First, we examine RF-QRC and show that, even without a recurrent loop, it contains temporal information about previous reservoir states using leaky integrated neurons. This makes RF-QRC different from quantum extreme learning machines (QELM). Second, we show that finite sampling noise degrades the prediction capabilities of both QRC and RF-QRC while affecting QRC more due to the propagation of noise. Third, we optimize the training of the finite-sampled quantum reservoir computing framework using two methods: (a) singular value decomposition (SVD) applied to the data matrix containing noisy reservoir activation states and (b) data-filtering techniques to remove the high frequencies from the noisy reservoir activation states. We show that denoising reservoir activation states improves the signal-to-noise ratios with smaller training loss. Finally, we demonstrate that the training and denoising of the noisy reservoir activation signals in RF-QRC are highly parallelizable on multiple quantum processing units (QPUs) as compared to the QRC architecture with recurrent connections. The analyses are numerically showcased on prototypical chaotic dynamical systems with relevance to turbulence. This work opens opportunities for using quantum reservoir computing with finite samples for time series forecasting on near-term quantum hardware
A mixed methods study investigating alexithymia, experiential avoidance, and psychological distress: Insights into men with high externally oriented thinking
Get PDFPrevious research suggests that experiential avoidance mediates the relationship between alexithymia and psychological distress. However, concerns persist regarding the validity of: 1) mediation analyses in cross-sectional samples, 2) common measures of alexithymia and experiential avoidance, and 3) solely quantitative approaches to studying alexithymic individuals. This study addresses these gaps using a sequential explanatory methodology comprising of: 1) a quantitative phase employing improved psychometric questionnaires to examine relationships between alexithymia, experiential avoidance, and distress, and 2) a qualitative phase exploring the lived experiences of individuals with alexithymia. A sample of 211 UK adults replicated prior quantitative findings, showing strong positive correlations between experiential avoidance, alexithymia, and psychological distress. However, no link was found between the Externally Oriented Thinking (EOT) facet of alexithymia and psychological distress. This led to a qualitative investigation of men with EOT, analysed using template analysis, a codebook approach to Thematic Analysis. The combined results suggest that life experiences may drive avoidance of unwanted private experiences. Moreover, the qualitative findings indicate two mechanisms explaining EOT's lack of association with psychological distress. First, EOT may serve as a protective factor against positive and negative emotional affect. Second, patriarchal norms may encourage emotional suppression and avoidant coping, leading to underreporting distress in mood questionnaires. Important theoretical and clinical implications are discussed through a Counselling Psychology lens, leading to a critique of assumptions underlying modern therapeutic techniques that may contribute to social injustice
A semantic framework for neurosymbolic computation
Get PDFThe field of neurosymbolic AI aims to benefit from the combination of neural networks and symbolic systems. A cornerstone of the field is the translation or encoding of symbolic knowledge into neural networks. Although many neurosymbolic methods and approaches have been proposed, and with a large increase in recent years, no common definition of encoding exists that can enable a precise, theoretical comparison of neurosymbolic methods. This paper addresses this problem by introducing a semantic framework for neurosymbolic AI. We start by providing a formal definition of semantic encoding, specifying the components and conditions under which a knowledge-base can be encoded correctly by a neural network. We then show that many neurosymbolic approaches are accounted for by this definition. We provide a number of examples and correspondence proofs applying the proposed framework to the neural encoding of various forms of knowledge representation. Many, at first sight disparate, neurosymbolic methods, are shown to fall within the proposed formalization. This is expected to provide guidance to future neurosymbolic encodings by placing them in the broader context of semantic encodings of entire families of existing neurosymbolic systems. The paper hopes to help initiate a discussion around the provision of a theory for neurosymbolic AI and a semantics for deep learning
How to create a mindful community of practice: Exploring the social functions of group-based mindfulness practices facilitated via Zoom during Covid-19
Get PDFThis exploratory qualitative study was conducted to investigate the experiences of individuals who have been participating in online mindfulness sessions with an online mindfulness community since the beginning of Covid-19, i.e. during a period of heightened uncertainty and social isolation. The study’s purpose was to better understand the social functions of regularly practicing mindfulness in this online community of practice. Analyses from semi-structured interviews reveal how shared mindfulness practice may foster several pillars of connection and interbeing in this community of practice. These include improved mind-body awareness, coupled with a unique sense of trust and connection, which may have helped cultivate collective alignment and a sense of common humanity among research participants. Findings are discussed through the lens of interdependence theory, resulting in several exploratory propositions on how to create a mindful community of practice. The study concludes with a call for more research in this understudied research domain and invites mindfulness researchers and practitioners to test these propositions further. Its overall aim is to stimulate debate among individuals and groups intent on creating a mindful community in their workplace, educational setting, or neighborhood
Identification of positive childhood experiences with the potential to mitigate childhood unhealthy weight status in children within the context of adverse childhood experiences: a prospective cohort study
Get PDFBackground
Despite potential protective and mitigating effects of positive childhood experiences (PCEs) on poor health outcomes, limited research has identified relevant PCEs and examined their individual and cumulative associations with weight status, or their mitigating effects on the associations between adverse childhood experiences (ACEs) and obesity in children. This study aims to develop an exploratory PCEs Index with the potential to protect against or mitigate the association between ACEs and unhealthy weight status.
Methods
Data came from the Growing Up in New Zealand study. The analytic sample was restricted to those who provided obesity data at age 8 and one child per mother, resulting in a sample of 4,895 children. Nine individual ACEs and their cumulative scores, a newly developed PCEs index consisting of six individual PCEs and (their) cumulative scores, and an overweight/obesity variable were included in the analyses.
Results
By age eight, experience of at least 3 PCEs was reported by 72.1% of the sample. However, the experience of the highest number of PCEs (5–6) was only reported by 23% of the sample. Four out of six assessed PCEs were associated with decreased likelihood of overweight/obesity. A dose-response effect was observed where experience of three or more PCEs was associated with decreased odds for obesity (AORs decreased from 0.77 for 3 PCEs to 0.54 for 5–6 PCEs). No consistent mitigating effects were found for individual PCEs; however interactions were found between reporting at least four of the six PCEs, experience of cumulative ACEs, and reduced odds for overweight/obesity at age 8.
Conclusions
A critical number of PCEs may be required to mitigate the detrimental impacts of ACEs on weight status among children. These findings reinforce the need to consider a constellation of strength-focused ecological domains to alleviate the burden of childhood obesity, particularly for children exposed to multiple adversities
Stackelberg Evolutionary Games of Cancer Treatment: What Treatment Strategy to Choose if Cancer Can be Stabilized?
Get PDFWe present a game-theoretic model of a polymorphic cancer cell population where the treatment-induced resistance is a quantitative evolving trait. When stabilization of the tumor burden is possible, we expand the model into a Stackelberg evolutionary game, where the physician is the leader and the cancer cells are followers. The physician chooses a treatment dose to maximize an objective function that is a proxy of the patient’s quality of life. In response, the cancer cells evolve a resistance level that maximizes their proliferation and survival. Assuming that cancer is in its ecological equilibrium, we compare the outcomes of three different treatment strategies: giving the maximum tolerable dose throughout, corresponding to the standard of care for most metastatic cancers, an ecologically enlightened therapy, where the physician anticipates the short-run, ecological response of cancer cells to their treatment, but not the evolution of resistance to treatment, and an evolutionarily enlightened therapy, where the physician anticipates both ecological and evolutionary consequences of the treatment. Of the three therapeutic strategies, the evolutionarily enlightened therapy leads to the highest values of the objective function, the lowest treatment dose, and the lowest treatment-induced resistance. Conversely, in our model, the maximum tolerable dose leads to the worst values of the objective function, the highest treatment dose, and the highest treatment-induced resistance
Modelling low-cycle fatigue behaviour of structural aluminium alloys
Get PDFRecently, use of 6000 series aluminium alloys in braced frame structures has been increased due to their superior structural properties. Fracturing of braces as a result of low-cycle fatigue has a major impact on nonlinear behaviour of structures under earthquake loading. Therefore, modelling low-cycle fatigue life, i.e., number of reversals to failure, is important to understanding braced-frame structural performance. To date, there are no readily available methods for predicting the low-cycle fatigue behaviour of 6000 series aluminium alloys. This research study aims to provide structural engineers with a computationally efficient approach to assess aluminium alloy structures in the context of potential low cycle fatigue. For this purpose, 18 low-cycle high amplitude fatigue tests (up to +-6% strain amplitude) were conducted to establish strain−life relationships for 6082-T6, 6063-T6 and 6060-T5 aluminium alloys. The obtained experimental results were then used to calibrate a low-cycle fatigue life model to capture the fracture behaviour of the studied materials. The comparison of experimental results and predicted fatigue behaviour shows the capability of the proposed model to predict to a high degree of precision the onset of fracture and the overall low-cycle fatigue behaviour of material
Identification and analysis of key factors limiting the performance of electrical soil sensors: A review
Get PDFCurrent agricultural practices are increasingly adopting sustainable methods to achieve high crop yields and meet market demands. However, the excessive use of water and fertilisers has led to issues such as food insecurity and climate change. The over-application of plant nutrients increases food prices and results in unused fertilisers contributing to harmful greenhouse gas emissions, which affect the ozone layer. This raises the question: why are excessive amounts of water and fertiliser wasted despite the availability of agricultural sensors and technologies that aim to improve sustainability? This paper critically examines the underlying theory and technology behind these practices to identify their challenges and limitations. The review focuses on the shortcomings of current soil theories, covering soil physics, electrical properties, and factors influencing soil characteristics. Additionally, this paper discusses various techniques used to measure the electrical properties of soil, including traditional methods, capacitive sensors, time-domain and frequency-domain reflectometry, amplitude-domain reflectometry, and broadband dielectric spectroscopy. The challenges and limitations of these techniques are also explored. Furthermore, the paper addresses the theory and challenges of electrical property measurement techniques at the system level, analysing the injection, load, and output stages to identify the difficulties in each part
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