Temporal Elements Research Articles

The stratigraphic biography of an archaeological site. Timing depositional events

AbstractThe stratigraphic sequence of an archaeological site constitutes its biography, which details what was deposited, when it was deposited, and how the deposition occurred. However, many current methods of recording archaeological stratigraphy do not allow for a full reconstruction of a site's biography. This paper introduces a theoretical framework for analysing a site's formation and deformation processes, emphasizing the integration of different temporal, spatial, geological, and archaeological information elements. This approach offers a way to study change and dynamics by deciphering successive episodes, depositional events, their temporal and causal ordering, and, when possible, the speed and rhythm of such depositions. The integration of micromorphological data and absolute dating can increase the chronological resolution of a site's biography by formally defining the temporal boundaries of depositional events. Additionally, the use of multidimensional geometric analysis of occupational floors and sedimentary volumes enhances the understanding of the complex relationships between stratigraphic depth and temporality. These tools enable archaeologists to create multidimensional visualizations, make inferences, and enhance interpretations of archaeological sites.

Temporal second-order two-grid finite element method for semilinear time-fractional Rayleigh–Stokes equations

In this paper, we have developed a temporal second-order two-grid FEM to solve the semilinear time-fractional Rayleigh–Stokes equations. The proposed two-grid FEM uses the L2-1σ scheme and second order scheme to approximate the Caputo fractional derivative and the time first-order derivative in temporal direction and the standard FEM in spatial direction. The L2-norm and H1-norm stability and error estimates for the standard finite element solution and the two-grid solution are derived. The results shown that as long as the mesh sizes satisfy H=h12 and H=hr2r+2 respectively, the two-grid algorithm can achieve asymptotically optimal approximation. Furthermore, the non-uniform L2-1σ scheme was applied for temporal discretization to handle the weak singularity of the solution. Finally, the theoretical findings were confirmed by numerical results, and the effectiveness of the two-grid algorithm was demonstrated.

Investigating spatial and temporal characteristics and elements influencing running among residents of Nanchang city in green open areas

Understanding the exercise behaviors of urban residents in green open spaces is crucial for optimizing future urban spatial planning and improving residents’ quality of life. This study provides an in-depth analysis of residents’ running behaviors across 87 green open spaces in Nanchang City. Running data collected from the exercise platform Keep, coupled with urban geographic data and field research, reveals the spatial distribution and temporal variations of running activities, including running flow, distance, time, and pace. A set of indicator systems, such as total area, landscape form index, surrounding residential land area, and road conditions, is used to analyze its relationship with the environmental elements of green open spaces. The research results show that the running flow, running distance, and running time in green open spaces in Nanchang City are significantly influenced by different objective elements, while running pace shows little correlation with these environmental elements. Specifically, the study highlights the significant impact of elements such as total area and road length, as well as the number of nearby bus stops and population density, on residents’ running behaviors in Nanchang City’s green open spaces.

지역문화콘텐츠로서 ‘문희연(聞喜宴)’ 공연예술 재현(再現)을 위한 기초 연구

This study presents foundational research on utilizing 'Munheeyeon' performance culture as cultural content. It explains the significance of 'Munheeyeon' and analyzes the performance forms before and after the rise of pansori, proposing desirable directions for leveraging its original form. The key points are as follows: Definition and Historical Background of 'Munheeyeon': 'Munheeyeon' (聞喜宴) refers to a celebratory performance hosted by parents for a successful candidate in the Goryeo Dynasty's civil service examination. This event, lasting approximately 1000 years during the examination system’s operation, featured various performances. 'Munheeyeon' was the most elaborate event for celebrating successful candidates and served as a foundation for the growth of performers (gwangdae) in the performing arts. Performance Forms Before and After the Rise of Pansori: During the Goryeo Dynasty, performances such as ‘Uhui’ (a type of drama), ‘Yeongsan’ (a traditional genre of song), tightrope walking, mask dances, sword dances, and acrobatics were popular. With the rise of pansori, ''Munheeyeon' performances expanded to include pansori along with traditional performances like Yeongsan, acrobatics, mask dances, tightrope walking, and more. Historical Transformation and Utilization Potential of 'Munhyeon': The performance form of 'Munheeyeon' transformed from drama and storytelling to pansori, incorporating regional and temporal elements. Based on its originality and potential, the study emphasizes the need for various cultural reconfigurations and innovations to enhance ''Munheeyeon' as a contemporary regional cultural content. This can contribute to popularization, regional activation, and economic value creation. The research aims to reframe 'Munheeyeon' performance culture as modern cultural content and explore its potential for contributing to regional cultural development and economic value.

Collocation Finite Element Method for the Fractional Fokker–Planck Equation

ABSTRACTIn this study, the approximate results of the fractional Fokker–Planck equations have been investigated. First, finite element schemes have been obtained using collocation finite element method based on the trigonometric quintic B‐spline basis functions. Then, the present method is tested on two fundamental problems having appropriate initial conditions. The newly obtained numerical results contained the error norms and for various temporal and spatial steps are compared with the exact ones and other solutions. More accurate results have been obtained for large numbers of spatial and temporal elements.

Chronicles of Involuntary Career Changes: A Qualitative Longitudinal Analysis

Although extensive research on career transitions exists, little is known about the challenges and processes of involuntary career change. Building on Savickas’s theoretical framework of objective and subjective careers, we investigated the subjective experiences of involuntary career changes while apprehending their objective unfolding. Following a longitudinal qualitative design, we conducted two waves of semistructured interviews with 18 participants who had been forced to change careers due to health issues, migration, or saturated labor market in Switzerland. A four-step temporal thematic analysis covering case description, case comparison, case processes analysis, and processes comparison highlighted a spectrum of career change processes. At one end, individuals experienced synchronous progressions and regained meaning and control over their career combined with a rather linear status sequence. Conversely, some individuals underwent asynchronous developments consisting of either modest objective steps that instigated meaningful subjective experiences or status stagnation, leading to a loss of control and meaning. These findings underscore the variety of involuntary career change processes and unveil synchronicity as a key temporal element in involuntary career change processes. Implications for research and practice are discussed.

Unraveling Time: Reading Temporal Shifts as Intergenerational Narrative Weaving in Wanda John-Kehewin’s Visions of the Crow

ABSTRACT: Wanda John-Kehewin’s Visions of the Crow ( Dreams Trilogy: Volume One ) centers on protagonist Daemon Quinn, a Cree teenager facing challenges at home and school. As the narrative interweaves different temporalities, Daemon’s quest to overcome his present-day challenges and comprehend his ancestral identity illuminates the direct correlations between the effects of colonialism and his contemporary struggles. This paper functions as a conversation. It is interspersed with visual and narrative analysis of page layouts as we explore the distinctive capabilities of comics to weave temporal elements within Indigenous-centered storytelling, supporting a critical and expanded reading of the text. By situating the discussion and analysis in moments of temporal shifts, we investigate how Visions of the Crow facilitates the intertwining of timescales and broader narrative devices within its structure. Through this process, we aim to illustrate how comics are particularly well-suited to examining the enduring legacy of intergenerational trauma and offer the potential for intergenerational healing. Aligning with Wanda John-Kehewin’s intentions as the author of Visions of the Crow —to critically reflect on Canada’s history with Indigenous Peoples and to demonstrate that Indigenous Peoples can address social issues and history in a way that fosters critical thinking and growth—this paper also examines the profound and lasting impact of colonialism on Indigenous communities and family systems. Through our discussion, we aim to acknowledge how the past continues to shape the present while exploring spaces of hope and healing for the future within the storytelling of comics.

Putting tendencies and trajectories to work: useful tools for engaging with accounts of change and recovery?

Dominant understandings of recovery emphasise personal responsibility for initiating and sustaining changes in people's subjectivities and relationships to alcohol and other drugs. However, this potentially obscures the complexities and temporalities of change processes and the range of socio-material elements involved. Addressing this gap, critical drug studies scholars have productively employed the concepts of tendencies and trajectories to analyse how past events of drug consumption flow into current and future consumption events. Critiquing notions of personal responsibility within recovery processes, we apply the concepts of tendencies and trajectories to help explain recovery's emergence and continuities. Doing so helps decentre the individual as the agent responsible for improved capacity by broadening the perspective of developing health and wellbeing. In this paper, we provide a qualitative analysis of interviews with fourteen people with lived recovery experiences within an urban-rural setting in Melbourne, Australia. This analysis illustrates how recovery tendencies and trajectories are cultivated through repeated actions, habits, and practices over time. Applying the concept of trajectories to change narratives reveals how accumulated moments precede and follow turning points, supporting shifts in consumption patterns. These moments are not necessarily connected but, when considered collectively, contribute to a recovery trajectory and assemblage of health. In reflecting on the affordances of thinking, researching and doing with recovery tendencies and trajectories, we argue that analysing tendencies and trajectories illuminate opportunities where change lies within an endless combination of human and non-human forces. Applying these concepts to recovery research, practice, and policy engages with temporal and socio-material elements of recovery, offering a more emancipatory approach than is currently provided by common recovery theories and approaches that assume individuals are personally responsible for change.

Machine learning-based retrieval of total column water vapor over land using GMI-sensed passive microwave measurements

ABSTRACT The Global Precipitation Measurement (GPM) Microwave Imager (GMI) is a microwave (MW) radiometer that has near-global coverage and frequent revisit time. To date, operational total column water vapor (TCWV) data records from the GPM GMI sensor have been exclusively offered over oceanic regions. It is challenging to retrieve TCWV over land from satellite MW measurements because of varying land surface characteristics. In this paper, a novel Light Gradient Boosting Machine-based retrieval algorithm is proposed to derive TCWV over land from GMI-sensed MW brightness temperature (BT) observations. The GMI-observed MW BT at 18.7 GHz and 23.8 GHz, differential BT between 18.7 GHz and 23.8 GHz, latitude, longitude, and month are selected and utilized as the input variables of the retrieval approach, because of their strong correlation with satellite-sensed MW TCWV retrievals. Instead of surface emissivity data or radiative transfer model, we take into account the spatial and temporal elements, namely latitude, longitude, and month. The training of the retrieval method is performed based on ground-based TCWV estimates from worldwide 4,471 Global Navigation Satellite System (GNSS) stations in 2017. The performance of the newly proposed retrieval algorithm is independently validated in a worldwide coverage using reference TCWV from additional 4,341 GNSS stations in 2018–2020 and 605 radiosonde stations in 2017–2020. The newly retrieved TCWV estimates over land have a correlation coefficient of 0.76 and 0.83, a root-mean-square error (RMSE) of 5.82 mm and 6.02 mm, a relative RMSE of 34.91% and 34.36%, and a mean bias of 0.02 mm and −0.42 mm compared to reference TCWV from GNSS and radiosonde, respectively. The performance of the retrieval algorithm is satisfactory when compared to that of land-purpose TCWV of other satellite missions, though we have not used either surface emissivity data or radiative transfer model. This result increases confidence in retrieving TCWV over land from satellite-sensed MW BT measurements based on machine learning using ground-based TCWV observations. The newly developed retrieval algorithm has the potential for integration into operational satellite missions or meteorological services, thereby enhancing weather forecasting, climate modeling, and other relevant applications.

The relationship between space and time perception: A registered replication of Casasanto and Boroditsky (2008).

Everything in our environment moves through both space and time, and to effectively act we must be aware of both spatial and temporal elements in relation to our own bodies. Thus, perceptions of space and time have an intimate relationship. Walsh's a theory of magnitude (ATOM) suggests that space and time perception rely on a general magnitude system and their relationship should be roughly symmetrical. Alternatively, metaphor theory, which is based on the philosophical work of Lakoff and Johnson, argues that we represent time using a spatial metaphor and thus the relationship should be asymmetrical (with space influencing time more than time influences space). A compelling line of evidence for metaphor theory comes from the work of Casasanto & Boroditsky. Cognition, 106(2), 579-593. (2008) who experimentally demonstrated this asymmetric effect. However, in our previous unpublished online replication attempt of this work, we found a roughly symmetrical relationship between space and time, more in line with the theoretical predictions of ATOM. Given this, we performed a registered replication of Casasanto & Boroditsky. Cognition, 106(2), 579-593. (2008) in both an online and laboratory environment.

A hybrid shape functions based temporal finite element method to solve transient heat conduction problems

A novel temporal finite element method is presented to solve Fourier and non-Fourier heat conduction problems. Two kinds of temporal finite element models are developed using Gurtin variational principle and weighted residual technique, respectively. A kind of hybrid shape function with polynomial and trigonometric basis is stressed to give more flexible and appropriate description of temporal behavior of temperature. A recursive algorithm is developed by which the temperature solution at a specific time can be obtained only via a matrix power product with the initial condition, and a criterion of stability analysis is derived, which is can numerically be conducted when the constitution of shape function and time step size are prescribed. The proposed approach is available for Fourier and non-Fourier heat transfer problems, and can conveniently be combined with well-developed numerical algorithms for the boundary value problem, such as FE, SBFEM, etc. Various numerical examples, including those with the nonlinear thermal conductivity, radiative boundary condition, etc. are provided to illustrate the efficiency of proposed approaches, and impacts of the temporal FE model, constitution of shape functions, and step size, etc. are taken into account. Satisfactory results are achieved in comparison with the analytical and the ABAQUS based numerical solutions.

“The Horror of It Made Me Mad”: Hysterical Narration in Richard Marsh’s The Beetle (1897)

This article analyzes the hysterical narration styles of two major characters in Richard Marsh’s The Beetle (1897) to reveal the ways late-Victorian discourses attempted (and often failed) to distance particular social anxieties from their modern origins. Attending to previous literary criticism regarding socially Othered groups of this period—racialized foreigners, New Women, and the urban poor—as well as (pseudo)scientific studies from the 1870s–80s, this reading notes the ways that Victorian cultural biases surrounding race, gender, and class could be projected onto Gothicized, Orientalized figures in literary texts. Pairing a postcolonial examination of the novel’s spatial and temporal elements with a psychoanalytic reading of this text, I argue that the slowing pace in Robert Holt’s narrative and the compulsive repetition of Marjorie Lindon’s both reflect the novel’s disruption of space and time and structurally parallel the symptoms of a “hallucinatory hysterical attack,” as conceived by Josef Breuer and Sigmund Freud. Together, these hysterical narratives reveal the failure of particular cultural and scientific discourses to completely bury Victorian anxieties about modernity into different, explicitly Othered spaces and times by collapsing both space and time in the narration of psychic trauma.

Demystifying oral history with natural language processing and data analytics: a case study of the Densho digital collection

PurposeThis study aims to explore the applications of natural language processing (NLP) and data analytics in understanding large-scale digital collections in oral history archives.Design/methodology/approachNLP and data analytics were used to analyse the oral interview transcripts of 904 survivors of the Japanese American incarceration camps collected from Densho Digital Repository, relying specifically on descriptive analysis, keyword extraction, topic modelling and sentiment analysis (SA).FindingsThe researchers found multiple geographic areas of large residential communities of ethnic Japanese people and the place names of the concentration camps. The keywords and topics extracted reflect the deplorable conditions and militaristic nature of the camps and the forced labour of the internees. When remembering history, the main focus for the narrators remains the redress and reparation movement to obtain the restitution of their civil rights. SA further found that the forcible removal and incarceration of Japanese Americans during Second World War negatively impacted and brought deep trauma to the narrators.Originality/valueThis case study demonstrated how NLP and data analytics could be applied to analyse oral history archives and open avenues for discovery. Archival researchers and the general public may benefit from this type of analysis in making connections between temporal, spatial and emotional elements, which will contribute to a holistic understanding of individuals and communities in terms of their collective memory.

A second-order in time, BGN-based parametric finite element method for geometric flows of curves

Over the last two decades, the field of geometric curve evolutions has attracted significant attention from scientific computing. One of the most popular numerical methods for solving geometric flows is the so-called BGN scheme, which was proposed by Barrett et al. (2007) [8], due to its favorable properties (e.g., its computational efficiency and the good mesh property). However, the BGN scheme is limited to first-order accuracy in time, and how to develop a higher-order numerical scheme is challenging. In this paper, we propose a fully discrete, temporal second-order parametric finite element method, which integrates with two different mesh regularization techniques, for solving geometric flows of curves. The scheme is constructed based on the BGN formulation and a semi-implicit Crank-Nicolson leap-frog time stepping discretization as well as a linear finite element approximation in space. More importantly, we point out that the shape metrics, such as manifold distance and Hausdorff distance, instead of function norms, should be employed to measure numerical errors. Extensive numerical experiments demonstrate that the proposed BGN-based scheme is second-order accurate in time in terms of shape metrics. Moreover, by employing the classical BGN scheme as mesh regularization techniques, our proposed second-order schemes exhibit good properties with respect to the mesh distribution. In addition, an unconditional interlaced energy stability property is obtained for one of the mesh regularization techniques.

Hidden schemes and suspicious constructions. Inversive moments of occult infrastructures in Madagascar

ABSTRACT This contribution explores rumours of dubious activities at coastal sites in Madagascar, investigating occult formations through their underlying infrastructures. By introducing the concept of ‘occult infrastructure’, it illuminates the socio-material arrangements enabling the flow of occult forces. Suspicious constructions, marked by technological innovations and symbolic displays of power and social status, embody tangible expressions of occult infrastructures. The contribution contends that the interplay between the enduring nature of these structures and the transient quality of occult rumours creates moments of inversion – a temporary overlap between the occult and the ordinary, resulting in brief revelations before receding into concealed realms. This alignment of temporal, discursive, and material elements within occult phenomena aims not to uncover a hidden scheme but to provide an additional layer of understanding to an ongoing narrative.

Automated Liver Tumor Segmentation in DCE-MRI with 4D Deep Learning Integrating 3D CNNS and ConvLSTM

A state-of-the-art method for automatically segmenting liver tumours using Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is shown in this study. This study is significant because it uses a 4D information deep learning model to tackle the hard problem of liver tumor segmentation. A combination of 3D CNNs and ConvLSTM networks, specifically built to capture spatial and temporal information inside the dynamic imaging sequence of DCE-MRI, is what the suggested model is all about. Utilizing diffusion-computed tomography (DCE-MRI) gives a lot of information vital for precise tumor segmentation by providing a complete picture of the vascular dynamics in the liver. The model makes use of spatial and temporal elements by combining 3D Convolutional Neural Networks (CNNs) with ConvLSTM networks; this allows for a more detailed comprehension of the changes that are happening over time. To overcome the difficulties caused by the constantly changing nature of DCE-MRI data, this integration of 4D information greatly improves the accuracy and consistency of liver tumor segmentation. Implementing and optimizing the suggested deep learning model are the main goals of this work. The training and calibration of the model to accurately capture liver tumor characteristics in dynamic imaging sequences is of utmost importance.

Leveraging CNNs-RNNs and Adam Optimization: A Revolutionary Deep Learning approach to landslide prediction in district of Than Uyen, Lao Cai province, Vietnam

This paper presents an innovative deep learning framework for predicting landslides in the District of Than Uyen, Lao Cai Province, Vietnam, harnessing the power of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and the Adam Optimization algorithm. Given the multifaceted nature of landslide phenomena, influenced by a myriad of geographical and meteorological factors, there is a growing need for advanced computational strategies that can decipher complex patterns and temporal correlations. Our cutting-edge model employs CNNs to analyze and extract significant spatial attributes from topographical and geological data. Concurrently, RNNs -specifically Long Short-Term Memory (LSTM) networks - are deployed to manage time-series data, such as weather conditions and other temporal elements. The Adam Optimization algorithm, renowned for its superior efficiency and effective performance, is used to optimize the model parameters. The model was trained and validated using a comprehensive dataset from the Than Uyen district, comprising 114 landslide and 114 non-landslide locations, along with ten key influential factors: elevation, slope, curvature, aspect, relief amplitude, soil type, geology, and proximity to faults, roads, and rivers. The results demonstrate a noteworthy predictive accuracy, sensitivity, and specificity, with the model surpassing benchmarks in prediction power (PPV=93.3%, NPV=83.2%, Sen=82.3%, Spe=94.1%, Acc=88.2%, F-score=0.875, Kappa=0.765, and AUC=0.968). The study advances deep learning in landslide prediction, aiding proactive disaster mitigation and showing potential for application in global regions prone to geographical hazards.

Energy Expenditure Prediction from Accelerometry Data Using Long Short-Term Memory Recurrent Neural Networks.

The accurate estimation of energy expenditure from simple objective accelerometry measurements provides a valuable method for investigating the effect of physical activity (PA) interventions or population surveillance. Methods have been evaluated previously, but none utilize the temporal aspects of the accelerometry data. In this study, we investigated the energy expenditure prediction from acceleration measured at the subjects' hip, wrist, thigh, and back using recurrent neural networks utilizing temporal elements of the data. The acceleration was measured in children (N = 33) performing a standardized activity protocol in their natural environment. The energy expenditure was modelled using Multiple Linear Regression (MLR), stacked long short-term memory (LSTM) networks, and combined convolutional neural networks (CNN) and LSTM. The correlation and mean absolute percentage error (MAPE) were 0.76 and 19.9% for the MLR, 0.882 and 0.879 and 14.22% for the LSTM, and, with the combined LSTM-CNN, the best performance of 0.883 and 13.9% was achieved. The prediction error for vigorous intensities was significantly different (p < 0.01) from those of the other intensity domains: sedentary, light, and moderate. Utilizing the temporal elements of movement significantly improves energy expenditure prediction accuracy compared to other conventional approaches, but the prediction error for vigorous intensities requires further investigation.

Temporal repertoires in contemporary activism: The cases of Fridays for Future, 16 Days of Activism Against Gender-Based Violence and ‘It’s Thursday Again!’

ABSTRACT This paper proposes the novel concept of ‘temporal activist repertoires’ (TAR) defined as the intentional and meaningful selection and use of temporal elements, such as time frame, timing, tempo, duration, and temporality in collective action’s tactics and frames. Collectives thereby draw on culturally resonant temporal references to interweave the symbolic and strategic choices in making their demands, organising political action and/or sustaining commitment to the political cause. To answer the question of how social movements use TAR and how these are positioned alongside other repertoires of contention, the paper analyses: the global Fridays for the Future and 16 Days of Activism Against Gender-Based Violence, and the Austrian ‘It’s Thursday Again!’. The paper shows that the innovative potential of TAR is linked to the use of technologically advanced media tools for the purposes of coordination, pressure, recruitment, mobilisation and visibility, as well as for the adaptations that these movements require, which include strategies to build on past traditions and/or to connect with currently existing allied movements.

Implementation on Large-Scale Mobile Fitness App Usage Analysis for Smart Health

Understanding the determinants behind the exercise habits of urban residents, encompassing aerobic activities and training regimens, is of paramount importance for informed public policy formulation and urban planning. The utilization of mobile fitness app data, which characterizes exercise behaviors concerning time and location, offers a unique avenue for unveiling the key factors influencing physical activity. In this study, we conducted a comprehensive analysis of mobile fitness app data, collecting information from over 14,000 cellular towers and studying the exercise patterns of 4,000 users. Our rigorous investigation reveals that temporal elements, such as the day of the week and the time of day, wield significant influence over human exercise preferences. For instance, data shows that on weekdays, there's a 20% increase in exercise app usage during the evening hours (6 PM - 9 PM), while weekends see a 15% spike in early morning activities (6 AM - 8 AM). Geographical location also emerges as a prominent influencer. Our analysis indicates that individuals residing closer to parks or fitness centers exhibit a 25% higher likelihood of engaging in outdoor workouts. Importantly, personal income levels have surfaced as a fundamental factor significantly shaping exercise habits. Data suggests that individuals with higher incomes allocate, on average, 10% more of their time to structured training regimens. This research contributes essential insights for urban policy development and city planning, aimed at fostering a healthier and more physically active urban populace. Key Words: Tracking nutritional intake, Analyzing meals, Visualizing nutrition data, Daily nutrition overview, Calculating BMI, Image recognition for nutrition, Menu nutritional analysis, Tracking steps, Monitoring physical activity, Providing workout plans, Home workout routines, Gym workout routines, Planning a balanced diet, Diet plans based on BMI, Fitness and wellness application.