Temporal Context Research Articles

Hair-ing and haring: post (auto) theoretical more-than-human entanglement with hair/her/stories of Muslim schoolgirls

ABSTRACT In this paper, I use posthumanism and autotheory to materialize the gendered, racialized and sexualized hair/her/stories of British-Muslim schoolgirls in my study. As an act of feminist indiscipline and a feminist transdisciplinary innovative practice, I entangle with those affective, material, embodied and embedded encounters and stories that grow, escape, flow and glow with the vital materiality of hair and her stories. Hair, as one of the transgressive data of my PhD research, emerged in various temporal, spatial and material contexts throughout our research encounters. Considering hair as having posthuman agency, I argue how it can enable different material relations in everyday lived experiences of Muslim schoolgirls with hijab, body, educational environments and practices. Inspired by the notion that theory can do more the closer it gets to the skin what I call Posthumanist autotheory, has emerged from the body-theory encounters. I use posthumanist autotheory to materialize how hair as the ‘auto’ and the more-than-human agency in autotheory grows and becomes matter, method and theory. In using posthumanist autotheory as a feminist transdisciplinary innovative and speculative affirmative approach, this paper hopes to enable knowledge shifts in participatory researching on gendered and racializing assemblages to those everyday ordinary economic, institutional, biological and medical matters and assemblages that constitute racism.

Software Refactoring Network: An Improved Software Refactoring Prediction Framework Using Hybrid Networking‐Based Deep Learning Approach

ABSTRACTSoftware refactoring plays a vital role in maintaining and improving the quality of software systems. The software refactoring network aims to connect developers, researchers, and practitioners to share knowledge, best practices, and tools related to refactoring. However, the network faces various challenges, such as the complexity of software systems, the diversity of refactoring techniques, and the need for automated and intelligent solutions to assist developers in making refactoring decisions. By leveraging deep learning techniques, the software refactoring network can enhance the speed, accuracy, and relevance of refactoring suggestions, ultimately improving the overall quality and maintainability of software systems. So, in this paper, an advanced deep learning–based software refactoring framework is proposed. The suggested model performs three phases as (a) data collection, (b) feature extraction, and (c) prediction of software refactoring. Initially, the data is collected from ordinary datasets. Then, the collected data is fed to the feature extraction stage, where the source code, process, and ownership metrics of all refactored and non‐refactored data are retrieved for further processing. After that, the extracted features are predicted using Adaptive and Attentive Dilation Adopted Hybrid Network (AADHN) techniques, in which it is performed using Deep Temporal Context Networks (DTCN) with a Bidirectional Long‐Short Term Memory (Bi‐LSTM) model. Here, the parameters in the hybrid networking model are optimized with the help of Constant Integer Updated Golden Tortoise Beetle Optimizer (CIU‐GTBO) for improving the prediction process. Therefore, the accuracy of the developed algorithm has achieved for different datasets, whereas it shows the value of 96.41, 96.38, 96.38, 96.38, 96.41, 96.38, and 96.39 for antlr4, junit, mapdb, mcMMO, mct, oryx, and titan. Also, the precision of the developed model has shown the better performance of 96.38, 96.32, 96.37, 96.33, 96.35, 96.37, and 96.31 for the datasets like antlr4, junit, mapdb, mcMMO, mct, oryx, and titan.

Taking critique of psychiatry to the streets. Mad Pride parades and the Blue Caravan as work on multiplicity

This article focuses on performative articulations of critiques of psychiatry, with two forms of demonstration in particular: First, the Mad Pride Parades, which have been held in various German cities since 2013, and second actions by the "Blaue Karawane" in Bremen, amovement that emerged in the 1980s in the wake of the dissolution of apsychiatric clinic. Although they are situated in different temporal and local contexts, both rely on forms of street protest to question the demarcation between 'normal' and 'mad' and to promote the equal recognition of mental alterity. Adetailed examination of the forms of action highlights the importance of carnivalesque celebration, provocation and spectacle for both forms of psychiatric critique. We argue that these forms allow experiencing this critique and, at the same time, make drafts of adifferent-better-society tangible. To elaborate on these aspects, we draw on queer and gender theoretical considerations as well as on approaches from performance studies. Looking at Mad Pride parades and the Blaue Karawane in their respective contexts reveals similarities in the way in which critique of the exclusion of mental alterity is articulated, but also highlights differences in terms of affectedness, participation and argumentation.

LIDeepDet: Deepfake Detection via Image Decomposition and Advanced Lighting Information Analysis

The proliferation of AI-generated content (AIGC) has empowered non-experts to create highly realistic Deepfake images and videos using user-friendly software, posing significant challenges to the legal system, particularly in criminal investigations, court proceedings, and accident analyses. The absence of reliable Deepfake verification methods threatens the integrity of legal processes. In response, researchers have explored deep forgery detection, proposing various forensic techniques. However, the swift evolution of deep forgery creation and the limited generalizability of current detection methods impede practical application. We introduce a new deep forgery detection method that utilizes image decomposition and lighting inconsistency. By exploiting inherent discrepancies in imaging environments between genuine and fabricated images, this method extracts robust lighting cues and mitigates disturbances from environmental factors, revealing deeper-level alterations. A crucial element is the lighting information feature extractor, designed according to color constancy principles, to identify inconsistencies in lighting conditions. To address lighting variations, we employ a face material feature extractor using Pattern of Local Gravitational Force (PLGF), which selectively processes image patterns with defined convolutional masks to isolate and focus on reflectance coefficients, rich in textural details essential for forgery detection. Utilizing the Lambertian lighting model, we generate lighting direction vectors across frames to provide temporal context for detection. This framework processes RGB images, face reflectance maps, lighting features, and lighting direction vectors as multi-channel inputs, applying a cross-attention mechanism at the feature level to enhance detection accuracy and adaptability. Experimental results show that our proposed method performs exceptionally well and is widely applicable across multiple datasets, underscoring its importance in advancing deep forgery detection.

Perturbation context in paced finger tapping tunes the error-correction mechanism

Sensorimotor synchronization (SMS) is the mainly specifically human ability to move in sync with a periodic external stimulus, as in keeping pace with music. The most common experimental paradigm to study its largely unknown underlying mechanism is the paced finger-tapping task, where a participant taps to a periodic sequence of brief stimuli. Contrary to reaction time, this task involves temporal prediction because the participant needs to trigger the motor action in advance for the tap and the stimulus to occur simultaneously, then an error-correction mechanism takes past performance as input to adjust the following prediction. In a different, simpler task, it has been shown that exposure to a distribution of individual temporal intervals creates a “temporal context” that can bias the estimation/production of a single target interval. As temporal estimation and production are also involved in SMS, we asked whether a paced finger-tapping task with period perturbations would show any time-related context effect. In this work we show that a perturbation context can indeed be generated by exposure to period perturbations during paced finger tapping, affecting the shape and size of the resynchronization curve. Response asymmetry is also affected, thus evidencing an interplay between context and intrinsic nonlinearities of the correction mechanism. We conclude that perturbation context calibrates the underlying error-correction mechanism in SMS.

Context-Aware Recommender Systems: Exploring the Role of Time, Location, and Social Influence in Personalized Recommendations

Recommender systems have become a cornerstone of personalized user experiences across various domains, from e-commerce to entertainment. While traditional models focus on user-item interactions, they often overlook critical contextual information that could significantly enhance prediction accuracy. In this paper, we explore the concept of context-aware recommender systems, which integrate temporal, geospatial, and social context to improve recommendation quality. Temporal context considers factors such as time of day, seasonality, or the user's activity history, while geospatial context leverages the user's location or proximity to items, enhancing the relevance of recommendations. Additionally, social context incorporates user interactions with social networks or communities, enriching the understanding of preferences through peer influence and shared interests. We examine various techniques for incorporating these contexts into recommendation algorithms, including hybrid models, deep learning approaches, and context-sensitive matrix factorization. Furthermore, we address the challenges in balancing the complexity of these models with the need for real-time recommendations and scalability. Finally, we present empirical evaluations on real-world datasets, demonstrating that context-aware models significantly outperform traditional recommender systems in terms of prediction accuracy, diversity, and user satisfaction. This paper aims to provide a comprehensive framework for developing context-aware recommender systems and outline key areas for future research, such as integrating emerging contextual dimensions like sentiment and emotional state.

Exploring the Impact of Artificial Intelligence on Healthcare Management: A Combined Systematic Review and Machine-Learning Approach

The integration of artificial intelligence (AI) in healthcare management marks a significant advance in technological innovation, promising transformative effects on healthcare processes, patient care, and the efficacy of emergency responses. The scientific novelty of the study lies in its integrated approach, combining systematic review and predictive algorithms to provide a comprehensive understanding of AI’s role in improving healthcare management across different contexts. Covering the period between 2019 and 2023, which includes the global challenges posed by the COVID-19 pandemic, this research investigates the operational, strategic, and emergency response implications of AI adoption in the healthcare sector. It further examines how the impact of AI varies across temporal and geographical contexts. The study addresses two main research objectives: to explore how AI influences healthcare management in operational, strategic, and emergency response domains, and to identify variations in the impact of AI on healthcare management based on temporal and geographical contexts. Utilizing an integrated approach, we compared various prediction algorithms, including logistic regression, and interpreted the results through SHAP (SHapley Additive exPlanations) analysis. The findings reveal five key thematic areas: AI’s role in enhancing quality assurance, resource management, technological innovation, security, and the healthcare response to the COVID-19 pandemic. The study highlights AI’s positive influence on operational efficiency and strategic decision making, while also identifying challenges related to data privacy, ethical considerations, and the need for ongoing technological integration. These insights provide opportunities for targeted interventions to optimize AI’s impact in current and future healthcare landscapes. In conclusion, this work contributes to a deeper understanding of the role of AI in healthcare management and provides insights for policymakers, healthcare professionals, and researchers, offering a roadmap for addressing both the opportunities and challenges posed by AI integration in the healthcare sector.

Short-Term Cryptocurrency Price Prediction Using Bi-LSTM Method with Interactive Web

Short-term Bitcoin price prediction is a crucial aspect of transaction decision-making, especially for investors. In this study, a Bidirectional Long Short-Term Memory (Bi-LSTM) model was developed for short-term Bitcoin price prediction. The Bidirectional LSTM is designed to capture temporal context in both directions, allowing the model to process information from past and future time steps simultaneously. The model was validated using real-world data, including Bitcoin stock price datasets. The results show that the model achieved high accuracy, with a Root Mean Square Error (RMSE) of 56.90 on the training data and 157.35 on the test data, along with a Mean Absolute Error (MAE) of 366.40 and 486.63, respectively. The Bidirectional Least Square Memory model accurately predicted Bitcoin prices over a specific time period. This application integrates the model into a web application, enabling users to obtain real-time Bitcoin price predictions through a user-friendly interface.

Time after Time: Preserving Temporal Memories When Experiences Repeat.

Remembering when events occur in time is fundamental to episodic memory. Yet, many experiences repeat over time creating the potential for interference when attempting to recall temporally specific memories. Here, we argue that temporal memories are protected, in part, by reinstatement of temporal context information that is triggered by stimulus repetitions. We motivate this argument by integrating seminal findings across several distinct literatures and methodologies. Specifically, we consider key insights from foundational behavioral studies of temporal memory, recent electrophysiological and neuroimaging approaches to measuring memory reinstatement, and computational models that describe how temporal context representations shape memory processes. We also note several open questions concerning how temporal context reinstatement might influence subsequent temporal memory, including potential mediating effects of event spacing and event boundaries. These ideas and questions have the potential to guide future research and, ultimately, to advance theoretical accounts of how we preserve temporal memories.

Ideas behind transformative innovation policy: Economists confronting missions and sustainability transition in Finland

The ideas that experts share and draw on shape policies across various spatial and temporal contexts. In this study, we use document analysis and expert interviews to examine the views and beliefs of one important group of experts, economists, on mission-oriented innovation policy and sustainability transition in Finland. As a result, we outline the epistemic landscape that for its part conditions the progress of transformative innovation policy in Finland. While sustainability scholars have been intrigued by the promises of mission-oriented thinking, our findings suggest that they should acknowledge the epistemic limits of communities of economists regarding missions and transformative innovation policy as political vehicles to advance sustainability transition.

LACOSTE: Exploiting stereo and temporal contexts for surgical instrument segmentation

Surgical instrument segmentation is instrumental to minimally invasive surgeries and related applications. Most previous methods formulate this task as single-frame-based instance segmentation while ignoring the natural temporal and stereo attributes of a surgical video. As a result, these methods are less robust against the appearance variation through temporal motion and view change. In this work, we propose a novel LACOSTE model that exploits Location-Agnostic COntexts in Stereo and TEmporal images for improved surgical instrument segmentation. Leveraging a query-based segmentation model as core, we design three performance-enhancing modules. Firstly, we design a disparity-guided feature propagation module to enhance depth-aware features explicitly. To generalize well for even only a monocular video, we apply a pseudo stereo scheme to generate complementary right images. Secondly, we propose a stereo-temporal set classifier, which aggregates stereo-temporal contexts in a universal way for making a consolidated prediction and mitigates transient failures. Finally, we propose a location-agnostic classifier to decouple the location bias from mask prediction and enhance the feature semantics. We extensively validate our approach on three public surgical video datasets, including two benchmarks from EndoVis Challenges and one real radical prostatectomy surgery dataset GraSP. Experimental results demonstrate the promising performances of our method, which consistently achieves comparable or favorable results with previous state-of-the-art approaches.

Rhythms in insect olfactory systems: underlying mechanisms and outstanding questions.

Olfaction is a critical sensory modality for invertebrates, and it mediates a wide range of behaviors and physiological processes. Like most living organisms, insects live in rhythmic environments: the succession of nights and days is accompanied by cyclic variations in light intensity and temperature, as well as in the availability of resources and the activity of predators. Responding to olfactory cues in the proper temporal context is thus highly adaptive and allows for the efficient allocation of energy resources. Given the agricultural or epidemiological importance of some insect species, understanding olfactory rhythms is critical for the development of effective control strategies. Although the vinegar fly Drosophila melanogaster has been a classical model for the study of olfaction and circadian rhythms, recent studies focusing on non-model species have expanded our understanding of insect olfactory rhythms. Additionally, recent evidence revealing receptor co-expression by sensory neurons has brought about an ongoing paradigm shift in our understanding of insect olfaction, making it timely to review the state of our knowledge on olfactory rhythms and identify critical future directions for the field. In this Review, we discuss the multiple biological scales at which insect olfactory rhythms are being analyzed, and identify outstanding questions.

Artistic Production in a Necropolis in Motion

The present article studies aspects of the artistic production at New Kingdom Saqqara, a necropolis of the ancient Egyptian royal residence city Memphis. Following a brief review of the functions of ancient Egyptian tombs, this article will first set out to scrutinize the tomb-making section of society (e.g., size, membership). Second, the corpus of tombs will be reviewed to uncover the diverse nature of the tomb owners and to investigate access to resources required for tomb making. Third, the article will proceed to place the tombs in spatial and temporal context and reflect on the artistic production in a necropolis in motion.

Real-Time Policy Optimization for UAV Swarms Based on Evolution Strategies

Multi-agent decision-making faces many challenges such as non-stationarity and sparse rewards, while the complexity and randomness of the real environment further complicate policy development. This paper addresses the high-dimensional policy optimization problems of unmanned aerial vehicle (UAV) swarms. By modeling the problem scenario as a Markov decision process, a real-time policy optimization algorithm based on evolution strategy (ES) pre-training is proposed. This approach combines decision-time planning with background planning to evaluate and integrate different sets of policy parameters in a temporal context. In the experimental phase, the policy network is trained using both ES and REINFORCE algorithms on a constructed simulation platform. Comparative experiments demonstrate the effectiveness of using ES for policy pre-training. Finally, the proposed real-time policy optimization algorithm further improves the performance of the swarm by approximately 10% in simulations, offering a feasible solution for adversarial games between swarms and extending the research scope of evolutionary algorithms.

Urban Exposome and Health

Abstract Background The environment in which we live is thought to explain 70% of the non-communicable disease burden. The aim of EXPANSE project (funded from the European Union Horizon 2020 research and innovation programme under grant agreement No 874627) is to evaluate the optimal strategies for maintaining health in the context of contemporary urban environments. As a component of the project, Urban Labs allow deep phenotyping of the urban exposome and assessing its use and perception. The Urban Labs aim to improve exposure assessment, identify mediating and modifying lifestyle factors, and link the urban exposome to intermediate endpoints relevant for cardiometabolic and pulmonary health. Methods By monitoring the activities of 4,000 individuals in five distinct urban areas, Urban Labs provide a comprehensive understanding of the urban exposome, which can be compared with residential address-based model estimates. The participants complete a baseline questionnaire, repeated bi-monthly questionnaires and exposure-specific questionnaires over a period of 2 years using mobile phone application (ExpanSeeker). Two-week personalized measurements including spatial-temporal activity patterns, sensor-based environmental and behavioral measurements, and minimally invasive biological sample collection are performed covering different seasons. Results The Baseline Questionnaire (BQ) and two-weeks measurement campaigns (MC) were completed in European cities as follows: Athens BQ = 379, MC = 232; Barcelona BQ = 1040, MC = 303; Basel BQ = 731, MC = 655; Lodz BQ = 634, MC = 100; The Netherlands (multicity) BQ = 1033, MC = 326. Data collection process is ongoing. Conclusions The collection of novel data on individuals activities and perceptions within temporal and geographical contexts in Urban Labs will contribute to a more comprehensive understanding of how the urban dwellers are exposed to and interact with their environment, thereby supporting healthier urban engineering and policy making. Key messages • Urban Labs will provide a more comprehensive understanding of the impact of the urban environment on individual’s behavior, their exposome, enabling more accurate prediction of non-communicable risk. • Urban Labs will provide a base to support healthier urban engineering and policy making.

Proposal Semantic Relationship Graph Network for Temporal Action Detection

Temporal action detection, a critical task in video activity understanding, is typically divided into two stages: proposal generation and classification. However, most existing methods overlook the importance of information transfer among proposals during classification, often treating each proposal in isolation, which hampers accurate label prediction. In this paper, we propose a novel method for inferring semantic relationships both within and between action proposals, guiding the fusion of action proposal features accordingly. Building on this approach, we introduce the Proposal Semantic Relationship Graph Network (PSRGN), an end-to-end model that leverages intra-proposal semantic relationship graphs to extract cross-scale temporal context and an inter-proposal semantic relationship graph to incorporate complementary neighboring information, significantly improving proposal feature quality and overall detection performance. This is the first method to apply graph structure learning in temporal action detection, adaptively constructing the inter-proposal semantic graph. Extensive experiments on two datasets demonstrate the effectiveness of our approach, achieving state-of-the-art results. Code and results are available at http://github.com/Riiick2011/PSRGN .

From 'cooking wife' to 'cooking mother': Exploring generational shifts in perceptions regarding domestic cooking practices.

Utilising the life course perspective as the theoretical framework, we investigate how women from different generations perceive their cooking experiences and articulate their feelings regarding this activity. Following a review of the literature and four exploratory focus groups, an interview guide was developed and piloted. Interviews were conducted with 22 Brazilian women spanning three distinct generations. Participants were prompted to recall their life stories, spanning from childhood to the present day and focused on cooking-related situations. Interviews were transcripted and analysed using reflexive thematic analysis. There were three major themes developed. The first theme outlines variations in the food context across generations. The second theme encompasses perceptions of the responsibility for cooking as being linked to the female gender, indicating a generational shift from the role of the 'cooking wife' to that of the 'cooking mother'. The third theme explores thoughts and reflections on feelings of obligation, oppression, and liberation in cooking, tied to changes in the social and temporal context in which these women operate. Strategies promoting cooking should consider the meanings attributed to it, contribute to the deconstruction of gender-specific family dynamics, and encourage the pursuit for pleasure, satisfaction and autonomy in this practice. This study contributes novel insights into cooking, including reflections on obligation or oppression versus liberation and the evolving social roles of women as primary cooks.

Constellation of the Five Constant Virtues in English Translations of <i>The Analects</i>

The five constant virtues (<i>rén</i>, <i>yì</i>, <i>lǐ</i>, <i>zhì</i>, and <i>xìn</i>) are central to Confucian philosophy and have grown in influence over time, shaping not only traditional moral teachings but also contemporary practices in business management and personal development. However, limited research has explored how these virtues are translated and conceptualized in English versions of <i>The Analects</i>. This study aims to address this gap by examining how the five constant virtues are formed, related, and legitimized across four major English translations, spanning different historical periods. To achieve this, a mixed-methods approach is employed, integrating both qualitative and quantitative analyses. First, a multi-parallel corpus is established, enabling a detailed quantitative examination of how the five constant virtues are expressed across different translations. This analysis highlights variations in the rendering of these key concepts in each text. In parallel, Legitimation Code Theory’s (LCT) constellation analysis is applied for the qualitative component of the study. This approach explores the relationships among the virtues and how translators have conceptualized and structured these interrelations. Results indicate a reduction in variation over time, suggesting a stabilization in how the virtues are conceptualized. While translations consistently represent simpler pairings of virtues, differences emerge when more complex constellations involving all five elements are considered. These findings reveal how philosophical perspectives and temporal contexts shape translation, impacting the way Confucian virtues are legitimized and structured.

Next-Generation Cancer Phenomics: A Transformative Approach to Unraveling Lung Cancer Complexity and Advancing Precision Medicine.

Lung cancer remains one of the leading causes of cancer-related deaths globally, with its complexity driven by intricate and intertwined genetic, epigenetic, and environmental factors. Despite advances in genomics, transcriptomics, and proteomics, understanding the phenotypic diversity of lung cancer has lagged behind. Next-generation phenomics, which integrates high-throughput phenotypic data with multiomics approaches and digital technologies such as artificial intelligence (AI), offers a transformative strategy for unraveling the complexity of lung cancer. This approach leverages advanced imaging, single-cell technologies, and AI to capture dynamic phenotypic variations at cellular, tissue, and whole organism levels and in ways resolved in temporal and spatial contexts. By mapping the high-throughput and spatially and temporally resolved phenotypic profiles onto molecular alterations, next-generation phenomics provides deeper insights into the tumor microenvironment, cancer heterogeneity, and drug efficacy, safety, and resistance mechanisms. Furthermore, integrating phenotypic data with genomic and proteomic networks allows for the identification of novel biomarkers and therapeutic targets in ways informed by biological structure and function, fostering precision medicine in lung cancer treatment. This expert review examines and places into context the current advances in next-generation phenomics and its potential to redefine lung cancer diagnosis, prognosis, and therapy. It highlights the emerging role of AI and machine learning in analyzing complex phenotypic datasets, enabling personalized therapeutic interventions. Ultimately, next-generation phenomics holds the promise of bridging the gap between molecular alterations and clinical and population health outcomes, providing a holistic understanding of lung cancer biology that could revolutionize its management and improve patient survival rates.

Aspirasi Pemuda di Kabupaten Klaten untuk Pendidikan Tinggi sebagai Kerja Generasi dan Konstruksi Identitas

This article qualitatively explores the production processes of higher education aspirations among students and recent graduates of upper-secondary schools in Klaten Regency. Considering the socio-spatial and temporal context, this study examines the dynamics of aspirations in the ‘desakota’ region during the digital era, an area that has been relatively underresearched. Data generation in this study primarily relies on in-depth interviews with eleven participants. Employing the concepts of identity and generations, this study reveals that higher education aspirations are refinements of individual narratives constructed through interactions with adults and peers in educational, familial, and digital settings. Situated in an in-between area, the production process of aspirations among Klaten’s youth reflects an ambiguity as traditional factors collide with values brought by modernity. I propose that aspirations for higher education in this context are both social and individual projects, encompassing not only future, but also present matters through which youth fashion themselves. This mirrors the critics towards developmental approach that frames educational aspirations predominantly as rational decision aimed at achieving upward social mobility.