General Process Research Articles

A prospective multi-center study quantifying visual inattention in delirium using generative models of the visual processing stream.

The visual attentional deficits in delirium are poorly characterized. Studies have highlighted neuro-anatomical abnormalities in the visual processing stream but fail at quantifying these abnormalities at a functional level. To identify these deficits, we undertook a multi-center eye-tracking study where we recorded 210 sessions from 42 patients using a novel eye-tracking system that was made specifically for free-viewing in the (ICU); each session lasted 10min and was labeled with the delirium status of the patient using the Confusion Assessment Method in ICU (CAM-ICU). To analyze this data, we formulate the task of visual attention as a hierarchical generative process that yields a probabilistic distribution of the location of the next fixation. This distribution can then be compared to the measured patient fixation producing a correctness score which is tallied compared across delirium status. This analysis demonstrated that the visual processing system of patients suffering from delirium is functionally restricted to a statistically significant degree. This is the first study to explore the potential mechanisms underpinning visual inattention in delirium and suggests a new target of future research into a disease process that affects one in four hospitalized patients with severe short and long-term consequences.

Adding a scholarly analysis of teaching and learning to SoTL: the development of the hands-on Utrecht Roadmap for SoTL

ABSTRACT This article outlines the development of a tool designed to enhance Scholarship of Teaching and Learning (SoTL) endeavours: the Utrecht Roadmap for Scholarship of Teaching and Learning (UR-SoTL). Integral to this tool is the incorporation of the Context-Intervention-Mechanism-Outcome (CIMO) logic model, offering a nuanced analysis of the teaching and learning context within the conventional SoTL steps. The CIMO-logic model aims to guide both novice and experienced SoTL practitioners in scrutinizing their teaching activities within the framework of generative learning processes described in relevant literature. This approach enables the scholarly analysis that directs SoTL-projects towards formulating well-grounded and focused research questions and plan-of-approaches.

Evaluating the Geometry of Objects in Cartographic Generalization with Hu's Invariants

ABSTRACT This paper presents a novel approach to assessing the geometry of objects using Hu's invariants in the context of cartographic generalization. The primary focus is to improve the generalization process and produce more readable and informative maps. The study demonstrates the applicability and effectiveness of the modified invariant moment M1* in evaluating regular shape similarity. Experiments, based on 24 shapes, exhibit greater stability in the results and reveal the high suitability of this moment in the investigation and classification of buildings, among other generalization processes. The efficiency of the proposed method is compared to previous generalization techniques, showing a significant improvement in the generalization process. In conclusion, this research contributes to the development of cartographic generalization methods by introducing the use of Hu's invariants for evaluating object geometry. This approach can improve the automation of map generalization processes and more effective communication of geographic information.

Beyond alpha band: prestimulus local oscillation and interregional synchrony of the beta band shape the temporal perception of the audiovisual beep-flash stimulus.

Objective.Multisensory integration is more likely to occur if the multimodal inputs are within a narrow temporal window called temporal binding window (TBW). Prestimulus local neural oscillations and interregional synchrony within sensory areas can modulate cross-modal integration. Previous work has examined the role of ongoing neural oscillations in audiovisual temporal integration, but there is no unified conclusion. This study aimed to explore whether local ongoing neural oscillations and interregional audiovisual synchrony modulate audiovisual temporal integration.Approach.The human participants performed a simultaneity judgment (SJ) task with the beep-flash stimuli while recording electroencephalography. We focused on two stimulus onset asynchrony (SOA) conditions where subjects report ∼50% proportion of synchronous responses in auditory- and visual-leading SOA (A50V and V50A).Main results.We found that the alpha band power is larger in synchronous response in the central-right posterior and posterior sensors in A50V and V50A conditions, respectively. The results suggested that the alpha band power reflects neuronal excitability in the auditory or visual cortex, which can modulate audiovisual temporal perception depending on the leading sense. Additionally, the SJs were modulated by the opposite phases of alpha (5-10 Hz) and low beta (14-20 Hz) bands in the A50V condition while the low beta band (14-18 Hz) in the V50A condition. One cycle of alpha or two cycles of beta oscillations matched an auditory-leading TBW of ∼86 ms, while two cycles of beta oscillations matched a visual-leading TBW of ∼105 ms. This result indicated the opposite phases in the alpha and beta bands reflect opposite cortical excitability, which modulated the audiovisual SJs. Finally, we found stronger high beta (21-28 Hz) audiovisual phase synchronization for synchronous response in the A50V condition. The phase synchrony of the beta band might be related to maintaining information flow between visual and auditory regions in a top-down manner.Significance.These results clarified whether and how the prestimulus brain state, including local neural oscillations and functional connectivity between brain regions, affects audiovisual temporal integration.

‘Finding the play’ – exploring with occupational therapists practice possibilities in the context of Irish schoolyards

Background Research has identified diverse constraints to the adoption of school-based occupational therapy approaches and a lack of attention to addressing the barriers to children’s play opportunities. Critical contextualised research is advocated to inform practice possibilities. Aims/Objectives This inquiry aimed to explore with occupational therapists their existing practices in Irish schoolyards to generate practice possibilities concerned with play, as an issue of occupational justice. Materials and Methods Using the theory of practice architectures, six occupational therapists from diverse sites of practice participated in the first phase of a critical action research process using dialogical focus group and occupational mapping methods. Results Three themes were generated (1) Existing practices as situated (2) (Re)mattering play and practices as occupations and (3) Practice possibilities – ‘Finding the play’ between responsiveness and responsibilities. A further interrelated dimension was how the research methods provided mechanisms of raising consciousness. Conclusions, and Significance Alongside constructing knowledges on existing practices in an Irish context, this inquiry contributes to understandings of practices as socially embedded generative processes of ‘finding the play’, highlighting ethical responsibilities to make visible inequities reproduced in habitual practices and engage in relationships of solidarity to (re)construct alternative shared practices.

Novel titanium-based sulfur-containing BMG for PBF-LB/M

This paper investigates the additive manufacturing route for a novel glass-forming titanium-based sulfur-containing alloy of the composition Ti60Zr15Cu17S8\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{Ti}}_{60} {\ ext{Zr}}_{15} {\ ext{Cu}}_{17} {\ ext{S}}_{8}$$\\end{document}. This system is a promising candidate for medical devices since the lack of toxic components is combined with a high corrosion resistance and strength. Preliminary experiments and simulations show a general processability of bulk material by powder bed fusion technologies. TEM and XRD reveal an amorphous microstructure of laser-treated surfaces. Ultrasonic atomization is used to fabricate a flowable powder feed stock with spherical morphology and crystalline microstructure, which is suitable for processing in powder bed fusion. Correlations between detected crystalline phase formations and melt pool dynamics are revealed by SEM and EDX. It is shown that bulk samples with a high relative density and partially crystalline microstructure can be manufactured.

Mapping cell diversity in human sporadic cerebral cavernous malformations

BackgroundCerebral cavernous malformation (CCM) is a low-flow, bleeding-prone vascular disease that can cause cerebral hemorrhage, seizure and neurological deficits. Its inheritance mode includes sporadic or autosomal dominant inheritance with incomplete penetrance, namely sporadic CCM (SCCM) and familial CCM. SCCM is featured by single lesion and single affection in a family. Among CCM patients especially SCCM, the pathogenesis of the corresponding phenotypes and pathological features or candidate genes have not been fully elucidated yet. MethodsHere, we performed in-depth single-cell RNA sequencing (scRNA-Seq) and bulk assay for transposase-accessible chromatin sequencing (ATAC-Seq) in SCCM and control patients. Further validation was conducted for the gene of interest using qPCR and RNA in situ hybridization (RNA FISH) techniques to provide further atlas and evidence for SCCM generative process. ResultsWe identified six cell types in the SCCM and control vessels and found that the expression of NEK1, RNPC3, FBRSL1, IQGAP2, MCUB, AP3B1, ESCO1, MYO9B and PVT1 were up-regulated in SCCM tissues. Among the six cell types, we found that compared with control conditions, PVT1 showed a rising peak which followed the pseudo-time axis in endothelial cell clusters of SCCM samples, while showed an increasing trend in smooth muscle cell clusters of SCCM samples. Further experiments indicated that, compared with the control vessels, PVT1 exhibited significantly elevated expression in SCCM samples. ConclusionIn SCCM conditions, We found that in the process of development from control to lesion conditions, PVT1 showed a rising peak in endothelial cells and showed an increasing trend in smooth muscle cells at the same time. Overall, there was a significantly elevated expression of NEK1, RNPC3, FBRSL1, IQGAP2, MCUB, AP3B1, ESCO1, MYO9B and PVT1 in SCCM specimens compared to control samples.

A robust multi-scale feature extraction framework with dual memory module for multivariate time series anomaly detection

Although existing reconstruction-based multivariate time series anomaly detection (MTSAD) methods have shown advanced performance, most assume the training data is clean. When faced with noise or contamination in training data, they can also reconstruct the anomaly well, weakening the distinction between normal and anomaly. Some probabilistic generation-based methods have been used to address this issue because of their implicit robust structure to noise, but the training process and suppression of anomalous generalization are not stable. The recently proposed explicit method based on the memory module would also sacrifice the reconstruction effect of normal patterns, resulting in limited performance improvement. Moreover, most existing MTSAD methods use a single fixed-length window for input, which weakens their ability to extract long-term dependency. This paper proposes a robust multi-scale feature extraction framework with the dual memory module to comprehensively extract features fusing different levels of semantic information and lengths of temporal dependency. First, this paper designs consecutive neighboring windows as inputs to allow the model to extract local and long-term dependency information. Secondly, a dual memory-augmented encoder is proposed to extract global typical patterns and local common features. It ensures the reconstruction ability of normal data while suppressing the generalization of the anomaly. Finally, this paper proposes a multi-scale fusion module to fuse latent variables representing different levels of semantic information and uses the reconstructed latent variables to reconstruct samples for anomaly detection. Experimental results on five datasets from diverse domains show that the proposed method outperforms 16 typical baseline methods.

Does Targeted Memory Reactivation during SWS and REM sleep have differential effects on mnemonic discrimination and generalization?

Targeted memory reactivation (TMR), or the presentation of learning-related cues during sleep, has been shown to benefit memory consolidation for specific memory traces when applied during non-rapid eye movement (NREM) sleep. Prior studies suggest that TMR during REM sleep may play a role in memory generalization processes, but evidence remains scarce. We tested the hypothesis that TMR exerts a differential effect on distinct mnemonic processes as a function of the sleep state (REM vs. NREM) in which TMR is delivered. Mnemonic discrimination and generalization of semantic categories were investigated using an adapted version of the Mnemonic Similarity Task, before and after sleep. Forty-eight participants encoded pictures from eight semantic categories, each associated with a sound. In the pre-sleep immediate test, they had to discriminate "old" (targets) from "similar" (lures) or "new" (foils) pictures. During sleep, half of the sounds were replayed in slow wave sleep (SWS) or REM sleep. Recognition, discrimination, and generalization memory indices were tested in the morning. These indices did not differ between SWS and REM TMR groups or reactivated and non-reactivated item categories. Additional results suggest a positive effect of TMR on performance for highly similar items mostly relying on mnemonic discrimination processes. During sleep, EEG activity after cue presentation increased in the delta-theta and sigma band in the SWS group, and in the beta band in the REM TMR group. These results do not support the hypothesis of a differential processing of novel memory traces when TMR is administered in distinctive physiological sleep states.

A prediction framework for pharmaceutical drug consumption using short time-series

The increasing pharmaceutical expenditure in many countries has raised concerns regarding the sustainability of healthcare services. To address this issue, accurate forecasting of pharmaceutical demand is crucial for healthcare planning and policy development. This paper proposes a novel prediction framework that integrates different types of historical data and simulates a part of the generative process that produces pharmaceutical consumption, considering both exogenous and endogenous factors, such as per capita consumption trends and population dynamics. The output of the framework is a distribution of likely values, enabling the use not only of the central value for making a prediction but also of the explicitly stated uncertainty, which is crucial for decision-makers in such a critical and complex context. The reliability and consistency of the framework are ensured through backtesting and comparing the predicted results with actual data.

Promoting emergent literacy skills among children at risk for specific learning disorder: general vs. Specific vs. Combined metacognitive interventions

ABSTRACT First graders with Developmental Language Disorder are considered at risk for exhibiting Specific Learning Disorder during school years. They also have deficiencies in their metacognitive skills, which leads to less effective learning processes. The current study examined, for the first time, the added value of various types of metacognitive interventions prior to an activity with an e-book for promoting language and literacy skills. Three experimental groups, each comprised of 30 first graders at risk for Specific Learning Disorder, were provided metacognitive intervention (specific, general, or combined) prior to an individual learning process with an e-book. The metacognitive interventions were designed according to the Triple A Model. Two control groups were also assigned: one control group engaged with an e-book without receiving any prior metacognitive intervention, and another followed its regular school activities. All groups were measured pre- and post-individual learning processes for language (vocabulary) and literacy (story comprehension) achievements. Results demonstrated an advantage to prior specific metacognitive intervention for promoting vocabulary as well as story comprehension measures. Although children at risk for Specific Learning Disorder tend to exhibit difficulties with generalisation processes, the combined metacognitive intervention proved to be as effective as the specific one.

Thermodynamics of Computations with Absolute Irreversibility, Unidirectional Transitions, and Stochastic Computation Times

Developing a thermodynamic theory of computation is a challenging task at the interface of nonequilibrium thermodynamics and computer science. In particular, this task requires dealing with difficulties such as stochastic halting times, unidirectional (possibly deterministic) transitions, and restricted initial conditions, features common in real-world computers. Here, we present a framework which tackles all such difficulties by extending the martingale theory of nonequilibrium thermodynamics to generic nonstationary Markovian processes, including those with broken detailed balance and/or absolute irreversibility. We derive several universal fluctuation relations and second-law-like inequalities that provide both lower and upper bounds on the intrinsic dissipation (mismatch cost) associated with any periodic process—in particular, the periodic processes underlying all current digital computation. Crucially, these bounds apply even if the process has stochastic stopping times, as it does in many computational machines. We illustrate our results with exhaustive numerical simulations of deterministic finite automata processing bit strings, one of the fundamental models of computation from theoretical computer science. We also provide universal equalities and inequalities for the acceptance probability of words of a given length by a deterministic finite automaton in terms of thermodynamic quantities, and outline connections between computer science and stochastic resetting. Our results, while motivated from the computational context, are applicable far more broadly. Published by the American Physical Society 2024

Assessing the Copyright Infringement Risk of Generative AI Created Works

The rapid evolution of the Internet and big data technology has ushered in significant advancements in data production and utilization. However, this progress has brought to the forefront the intricate issue of copyright protection. This is particularly evident with the widespread adoption of generative artificial intelligence (AI), where concerns regarding data source protection, data processing standards, and the boundaries of data application have garnered considerable attention from both academia and industry. Big data mining techniques coupled with generative AI algorithms offer robust support for data collection and processing, but they also introduce risks of data infringement and pose challenges regarding algorithmic compliance and ownership rights over generated works. Addressing these issues is imperative. This paper recommends enhancing legal compliance standards throughout each stage of the generative AI process, recalibrating the scope of acceptable copyright use, and establishing a regulatory framework for generative AI works. These measures are crucial for fostering the sustainable and orderly advancement of the generative AI industry.

An exploration of generativity in faculty group processes in post-secondary.

Like most organizations, a university needs to plan for its success. The environment in which universities operate continues to shift and change consequent to economic realities, changing demographics, changes in technology, and most recently a global pandemic. Planning in higher education must be creative and responsive to address multifaceted demands. To sustain post-secondary education, institutional leaders need to develop skill sets that promote effective dialogue, group work, and generativity within internal organizations. Concepts of leadership for the 21st century shift focus away from the previous approaches of making incremental improvements to already existing processes toward discovering possibilities, exploring potential innovations, and generating actions (Burgess & Newton, 2015; Webber, 2016). Building on existing frameworks for understanding generativity in group work and planning, this study sought to understand generative processes and conversations that compel people to act upon thoughts and feelings arising from social interactions. A descriptive study design was utilized to explore and summarize the experiences of faculty involved in three different group planning processes: brainstorming (Osborn 1953, 1957, 1963), a force field analysis (Lewin, 1947), and a variation of an appreciative inquiry process (Cooperrider & Srivastva, 1987). The development of a generative conversations survey tool focused on how the faculty participants perceived the qualities of their experiences. A key outcome of the research was the creation of a set of recommendations for thinking about the design of group sessions and meetings that can transmethodologically enhance chances for generative results.

Students’ perceptions on different sources of self-feedback

ABSTRACT Feedback is crucial for improving student learning. In this regard, overcoming the transmissive conception of feedback in favour of its dialogic function introduces new reflections concerning the internal generative feedback process. In this regard, Nicol [(2020). The power of internal feedback: Exploiting natural comparator processes. Assessment & Evaluation in Higher Education, 46(5), 756–778. https://doi.org/10.1080/02602938.2020.1823314; (2021). Guiding learning by activating students’ inner feedback. Times Higher Education] outlines the concept of self-feedback, i.e., the continuous comparative process that students produce from various external information and sources triggering internal feedback. In this context, the present research aims to explore students’ perceptions concerning the activation of internal/self-feedback, investigating which sources are most effective from their point of view, and researching the relationships between different individual characteristics. The results show that students’ individual differences and contextual cultural factors might play a crucial role in creating ideal environments to enable the generation of self-feedback. Finally, students consider the teacher to be most effective in activating self-feedback, and many have never experienced self-feedback or automated feedback situations.

Generative AI and process systems engineering: The next frontier

This review article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE). These cutting-edge GenAI models, particularly foundation models (FMs), which are pre-trained on extensive, general-purpose datasets, offer versatile adaptability for a broad range of tasks, including responding to queries, image generation, and complex decision-making. Given the close relationship between advancements in PSE and developments in computing and systems technologies, exploring the synergy between GenAI and PSE is essential. We begin our discussion with a compact overview of both classic and emerging GenAI models, including FMs, and then dive into their applications within key PSE domains: synthesis and design, optimization and integration, and process monitoring and control. In each domain, we explore how GenAI models could potentially advance PSE methodologies, providing insights and prospects for each area. Furthermore, the article identifies and discusses potential challenges in fully leveraging GenAI within PSE, including multiscale modeling, data requirements, evaluation metrics and benchmarks, and trust and safety, thereby deepening the discourse on effective GenAI integration into systems analysis, design, optimization, operations, monitoring, and control. This paper provides a guide for future research focused on the applications of emerging GenAI in PSE.

A Residential Building Extension Prototype to face Post-Pandemic Needs: Foreseen Challenges and Impacts

The COVID-19 pandemic highlighted the importance of outdoor spaces for maintaining physical and mental well-being. However, many urban residents lack private outdoor areas, which led to harmful health consequences during lockdowns. The research project aims to address and solve this issue by developing a physical/digital prototype called “X-TEND” that extends living space outdoors by attaching it to existing multi-story residential building facades. This prototype intends to promote healthier lifestyles, improving the well-being of individuals and positively impacts the urban surroundings aesthetics. X-TEND prototype uses a light, modular, and prefabricated approach for cost-effectiveness and environmental sustainability. It incorporates generative processes for structural and bioclimatic optimisation complemented by green building strategies to reduce environmental impact while maximising thermal and energy performance. Ultimately, X-TEND prototype contributes to a more sustainable built environment, fostering healthier and more liveable cities for current and future generations.

Smooth-Guided Implicit Data Augmentation for Domain Generalization.

The training process of a domain generalization (DG) model involves utilizing one or more interrelated source domains to attain optimal performance on an unseen target domain. Existing DG methods often use auxiliary networks or require high computational costs to improve the model's generalization ability by incorporating a diverse set of source domains. In contrast, this work proposes a method called Smooth-Guided Implicit Data Augmentation (SGIDA) that operates in the feature space to capture the diversity of source domains. To amplify the model's generalization capacity, a distance metric learning (DML) loss function is incorporated. Additionally, rather than depending on deep features, the suggested approach employs logits produced from cross entropy (CE) losses with infinite augmentations. A theoretical analysis shows that logits are effective in estimating distances defined on original features, and the proposed approach is thoroughly analyzed to provide a better understanding of why logits are beneficial for DG. Moreover, to increase the diversity of the source domain, a sampling-based method called smooth is introduced to obtain semantic directions from interclass relations. The effectiveness of the proposed approach is demonstrated through extensive experiments on widely used DG, object detection, and remote sensing datasets, where it achieves significant improvements over existing state-of-the-art methods across various backbone networks.

COVID-19 transmission between the community and meat processing plants in Ireland: A retrospective modelling study

Outbreaks of COVID-19 in meat processing plants (MPPs) were recorded globally throughout the pandemic. There was speculation these outbreaks resulted in dissemination of COVID-19 throughout the surrounding county leading to high incidence rates. We aimed to investigate the dynamics of spread between MPPs and their surrounding counties. In this retrospective longitudinal study, data were collected on the number and size of outbreaks in 33 MPPs and county infections in Ireland between March 2020 and May 2021. These data were used to investigate the relationship between outbreaks in MPPs and county infection rates through statistical analysis, and the development of a novel SEIR model. We found an association between the number of MPPs present in a county and county incidence rates, however, incidence rates in the counties did not increase as a consequence of an outbreak in an MPP. The model results indicate that county incidence rates in the weeks prior to an MPP outbreak could reliably predict the size of that outbreak in a plant, r(49) = 0·62, p < 0·0001, RMSD = 5·6. In Ireland, outbreaks in MPPs were strongly correlated with high levels of infection in the surrounding county, rather than being a driver of infection in the county. The modified SEIR model described here can provide an explanation of the generative process required to cause outbreaks of the size and scale that occur in MPPs.

Neural Networks in Legal Theory

Abstract This article explores the domain of legal analysis and its methodologies, emphasising the significance of generalisation in legal systems. It discusses the process of generalisation in relation to legal concepts and the development of ideal concepts that form the foundation of law. The article examines the role of logical induction and its similarities with semantic generalisation, highlighting their importance in legal decision-making. It also critiques the formal-deductive approach in legal practice and advocates for more adaptable models, incorporating fuzzy logic, non-monotonic defeasible reasoning, and artificial intelligence. The potential application of neural networks, specifically deep learning algorithms, in legal theory is also discussed. The article discusses how neural networks encode legal knowledge in their synaptic connections, while the syllogistic model condenses legal information into axioms. The article also highlights how neural networks assimilate novel experiences and exhibit evolutionary progression, unlike the deductive model of law. Additionally, the article examines the historical and theoretical foundations of jurisprudence that align with the basic principles of neural networks. It delves into the statistical analysis of legal phenomena and theories that view legal development as an evolutionary process. The article then explores Friedrich Hayek’s theory of law as an autonomous self-organising system and its compatibility with neural network models. It concludes by discussing the implications of Hayek’s theory on the role of a lawyer and the precision of neural networks.