Temporal Flow Research Articles

Introduction: Form and Medium

Introduction: Form and Medium

회피애착형 상담전공 대학원생의 대인관계에서 겪은 자기 성찰 경험에 대한 내러티브 탐구

The purpose of this study is to examine the experiences of self-reflection experienced through interpersonal relationship of avoidant attachment type counseling graduate students. The narrative method suggested by Clandinin (2015) was used, and the experience was examined by applying it to the temporal flow of the past, present, and future. The participants of this study were master’s students in the avoidance attachment type counseling major. The results of this study are as follows. <Filling me through others>, <It is myself that gnaw at me>, <Finding a pattern in a relationship>, and <Power to overcome>. This study is meaningful in that it aims to help graduate students of the avoidant attachment type counseling major develop their professionalism as counseling experts through interpersonal reflection. This study was to explore the meaning of refelctions of avoidant attachment type counseling graduate students and to deal with their importance for proper use as a therapeutic tool.

Flow Dynamics and Acoustics from Glottal Vibrations at Different Frequencies

Glottal vibration is fundamental to breathing-related disorders and respiratory sound generation. However, responses of the flow and acoustics to glottal vibrations of different frequencies are unclear. The objective of this study is to numerically evaluate the influences of glottal vibration frequencies on inspiratory airflow dynamics and flow-induced sound signals; this is different from normal phonation that is driven by controlled expiratory flows. A computational model was developed that comprised an image-based mouth–throat–lung model and a dynamic glottis expanding/contracting following a sinusoidal waveform. Large Eddy simulations were used to solve the temporal and spatial flow evolutions, and pressure signals were analyzed using different transform algorithms (wavelet, Hilbert, Fourier, etc.). Results show that glottal vibrations significantly altered the flows in the glottis and trachea, especially at high frequencies. With increasing vibration frequencies, the vortices decreased in scale and moved from the main flow to the walls. Phase shifts occurred between the glottis motion and glottal flow rates for all frequencies considered. Due to this phase shift, the pressure forces resisted the glottal motion in the first half of contraction/expansion and assisted the glottal motion in the second half of contraction/expansion. The magnitude of the glottal flow fluctuation was approximately linear with the vibration frequency (~f0), while the normal pressure force increased nonlinearly with the frequency (~f01.85). Instantaneous pressure signals were irregular at low vibration frequencies (10 and 20 Hz) but became more regular with increasing frequencies in the pressure profile, periodicity, and wavelet-transformed parameters. The acoustic characteristics specific to the glottal vibration frequency were explored in temporal and frequency domains, which may be used individually or as a combination in diagnosing vocal fold dysfunction, snoring, sleep apnea, or other breathing-related diseases.

Potential Strategies in the Biopesticide Formulations: A Bibliometric Analysis

Biopesticides are pest and pathogen management agents based on living microorganisms or natural products (botanical origin). Due to their natural origins, they stand out as an environmentally friendly tool, since they quickly decompose and minimize pollution problems produced by synthetic pesticides. However, these products present significant challenges that affect the bioactivities of the active components, due to the degradation of the biomass or bioactive metabolite by factors such as air, light, and temperature. Therefore, in this study, a systematic search of the Scopus database was conducted and scientometric tools were used to evaluate formulation techniques and approaches that seek to improve the bioactivities of natural preparations. The results showed that published research on biopesticides has significantly increased by 71.24% in the last decade (2011–2021). Likewise, the bibliometrics showed, through temporal flow analysis, and in the period from 2010 to 2021, investigations evolved have toward the use of nanotechnology, with the purpose of improving and potentiating the formulations of biopesticides. Consequently, nanotechnology tools can be classified as current strategies of interest that allow the increase and protection of bioefficacy to a greater extent than traditional biopesticide preparations. This review constitutes an important contribution to future research and expands the panorama in relation to biopesticide formulations for the control of agricultural pests.

범죄 현상에 대한 사회구조요인의 영향 연구

As crime began to be viewed as a social structural problem, away from the perspective of limited in individual propensity, criminology of perspective began to develop to prevent crime at the social level. In the case of these social structural characteristics, they change according to time and environment, and the influencing factors disappear or are newly revealed, so research needs to be continuously conducted according to the temporal and spatial flow. Despite the characteristics of social phenomena that require simultaneous consideration of spatio-temporal characteristics, domestic crime research tends to focus on spatial characteristics. Of course, in recent years, a method of explaining a crime phenomenon by simultaneously considering spatio-temporal characteristics has been attempted, but it is still insufficient. Therefore, this study aims to examine the effect of social disintegration factors on crime occurrence by securing data related to the social structure of the KOSIS and data on crime statistics of the National Police Agency from 2015 to 2020. Furthermore, juvenile crimes often show different influencing factors from adult crimes due to their timing characteristics, so we tried to examine their characteristics compared to all crimes.
 As a result of the study, it was confirmed that housing instability, ethnic heterogeneity, and female householder ratio in Korea affect crime, and in the case of juvenile crime, there was no effect of social dissolution factors, and the ratio of row/multi-family houses had a significant effect.

Traffic Flow Prediction with Attention Mechanism Based on TS-NAS

The prediction of traffic flow is of great significance in the traffic field. However, because of the high uncertainty and complexity of traffic data, it is challenging that doing traffic flow prediction. Most of the existing methods have achieved good results in traffic flow prediction, but are not accurate enough to capture the dynamic temporal and spatial relationship of data by using the structural information of traffic flow. In this study, we propose a traffic flow prediction method with temporal attention mechanism and spatial attention mechanism based on neural architecture search (TS-NAS). Firstly, based on temporal and spatial attention mechanisms, we design a new attention mechanism. Secondly, we define a novel model to learn temporal flow and space flow in traffic network. Finally, the proposed method uses different modules about time, space and convolution and neural architecture search to be used for optimizing the model. We use two datasets to test the method. Experimental results show that the performance of the method is better than that of the existing method.

High-speed measurements and conditional analysis of boundary-layer flows at engine speeds up to 2500 rpm in a motored IC engine

The importance of near-wall processes in internal combustion engines, such as near-wall heat transfer, will increase with the current trend toward engine downsizing. Engine boundary layers have been shown to differ from canonical steady-state turbulent boundary layers. A comprehensive understanding of the structure and behaviour of engine boundary layers is required, especially for predictive wall models used in numerical simulations.In the present study, the boundary layer flow over the piston surface in an optically accessible spark ignition engine was investigated using high-resolution, high-speed particle tracking velocimetry. To resolve the phase-averaged viscous sublayer for technically relevant engine speeds up to 2500 rpm, a vector resolution of up to 5 µm perpendicular to the wall was implemented. During compression, the conventionally phase-averaged flow relative to the piston surface resembles canonical flows over a flat plate. However, the results show that no logarithmic layer is present even at higher engine speeds and Reynolds numbers, extending the findings of previous studies. At 2500 rpm and 0.95 bar intake load, the viscous sublayer (y+=5) gets as thin as 30 µm. Additionally, the outer layer is small, as the bulk flow already starts at a wall-scaled distance of y+=30−90, which has to be considered when applying wall models in numerical simulations.A conditional analysis revealed flow modes that differ substantially from the conventional phase-average. Furthermore, a strong impact of temporal flow evolution and horizontal position on the boundary layer was found. Even in the case of wall-parallel flows, a strong acceleration and deceleration of the flow can be observed, which affects the adherence of boundary layer profiles to the law-of-the-wall. Another dominant flow mode is the impingement on the piston, where the degree of boundary layer development increases with distance from the stagnation point. These results show a striking similarity to classical channel flows with impinging wall jets.

Intentionality's Breaking Point: A Lesson from Grief

This paper develops elements of a phenomenological account of how interpersonal care contributes to the structure of intentionality. It does so by reflecting on a first-person account of parental grief by the poet and thinker Denise Riley. Her autobiographical notes on the aftermath of the death of her adult son revolve around a marked experience of altered temporal flow. By relating what she considers to be an almost unspeakable alteration in her experience of time, Riley unearths a level of nuanced phenomenological reflection that can inform philosophical studies of lived time and intentionality. The paper will build on Riley's observations on altered temporal experience in grief to explore the entanglement of temporality and intersubjectivity in the constitution of intentionality. By doing so, the paper demonstrates that and how a phenomenological study of grief can directly speak to key concerns of the philosophy of mind and of interdisciplinary consciousness studies.

Genetic sizing optimization of residential multi-carrier energy systems: The aim of energy autarky and its cost

The energy transition process fosters decentralized renewable energy generation and is characterized by an increased effort to achieve energy autarky. In this context, residential-size, photovoltaic-based multi-carrier energy systems using hydrogen as seasonal storage are analyzed as a possible solution to gain energy autarky. A high temporal (15 min) and long-term (10 a) power flow simulation approach is applied to a multi-objective optimization algorithm to minimize costs and grid energy feed-out. Approximate pareto-optimal system configurations are analyzed regarding system sizing, energy autarky, and economic performance for three residential building types and four European locations. Building type and location strongly influence the techno-economic feasibility of approximate pareto-optimal system configurations. Low-Energy-Houses are technically and economically most feasible to reach autarky, while Single-Family and Multi-Family-Houses show available PV energy as main restricting factor, which becomes especially problematic at high latitude locations. Under current economic constraints, no cost-competitive autarky system could be identified. Low Energy Houses show the lowest additional cost of 172% compared to a Base system at the low-seasonality location, but cost-competitiveness is possible under certain cost projections within a time horizon of 2030–2035. The developed energy system model is open-source and can be used for future research in this context.

Information flow estimation: A study of news on Twitter

News media has long been an ecosystem of creation, reproduction, and critique, where news outlets report on current events and add commentary to ongoing stories. Understanding the dynamics of news information creation and dispersion is important to accurately ascribe credit to influential work and understand how societal narratives develop. These dynamics can be modelled through a combination of information-theoretic natural language processing and networks; and can be parameterised using large quantities of textual data. However, it is challenging to see “the wood for the trees”, i.e., to detect small but important flows of information in a sea of noise. Here we develop new comparative techniques to estimate temporal information flow between pairs of text producers. Using both simulated and real text data we compare the reliability and sensitivity of methods for estimating textual information flow, showing that a metric that normalises by local neighbourhood structure provides a robust estimate of information flow in large networks. We apply this metric to a large corpus of news organisations on Twitter and demonstrate its usefulness in identifying influence within an information ecosystem, finding that average information contribution to the network is not correlated with the number of followers or the number of tweets. This suggests that small local organisations and right-wing organisations which have lower average follower counts still contribute significant information to the ecosystem. Further, the methods are applied to smaller full-text datasets of specific news events across news sites and Russian troll accounts on Twitter. The information flow estimation reveals and quantifies features of how these events develop and the role of groups of trolls in setting disinformation narratives. In summary, this work provides a new methodology for examining the information transmitted between content producers in any connected system of natural language, a toolkit with applications to the many networked discourses of our online world.

Spatial–temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism

Short-term traffic flow prediction is a core branch of intelligent traffic systems (ITS) and plays an important role in traffic management. The graph convolution network (GCN) is widely used in traffic prediction models to efficiently handle the graphical structural data of road networks. However, the influence weights among different road sections are usually distinct in real life and are difficult to analyze manually. The traditional GCN mechanism, which relies on a manually set adjacency matrix, is unable to dynamically learn such spatial patterns during training. To address this drawback, this study proposes a novel location graph convolutional network (location-GCN). The location-GCN solves this problem by adding a new learnable matrix to the GCN mechanism, using the absolute value of this matrix to represent the distinct influence levels among different nodes. Subsequently, long short-term memory (LSTM) is employed in the proposed traffic prediction model. Moreover, trigonometric function encoding was used in this study to enable the short-term input sequence to convey long-term periodic information. Finally, the proposed model was compared with the baseline models and evaluated on two real-world traffic flow datasets. The results show that our model is more accurate and robust than the other representative traffic prediction models.

Comparison of various temporal air traffic flow management models in critical scenarios

Overloads (i.e., critical imbalances between traffic demand and capacity) are currently resolved in the European air transportation network by activating air traffic flow management regulations. Flights subject to regulations are delayed on the ground by the computer assisted slot allocation system, which uses a first-planned, first-served principle to allocate the delays. In the last decades, many researchers have proposed alternative models based on more or less complex optimisation techniques, which could effectively resolve overloads with less delay. Despite the fact that these models were very promising, changing the current operational system and procedures - which trust is supported by a successful working history dating back to 1995–may not be realistic in the short term. This paper examines the gradual transition from current air traffic flow management to a futuristic model in which flights are assigned delays with massive cherry-picking measures that minimise the total delay in the network, without necessarily adhering to the first-planned, first-served rule. A reference model with greedy regulations representative of current operations is compared to a model where the Network Manager optimises regulations using hyper-heuristics, a model based on massive cherry-picking measures, and hybrid models that consider regulations to resolve major overloads and cherry-picking measures only for residual overloads. These models are compared in critical scenarios with major capacity issues caused by adverse weather conditions. Results suggest that hybrid models could be a viable option for reducing delays with minor changes in the current air traffic flow management paradigm.

Study of trailing vortices and impeller jet instabilities of a flat blade impeller in small‐scale reactors

AbstractThe design of economically feasible and profitable production processes has driven companies toward integrated continuous manufacturing by reducing working volumes and increasing operating frequencies. Thus, the development of robust small‐scale devices capable of multivariate process optimization is essential. The aim of this work is to characterize the flow developed in a ml‐scale stirred tank operating at intermediate 𝑅𝑒 ~3732, and assess trailing vortex stability with respect to baffle presence and size. Velocity characteristics are computed via computational fluid dynamics (CFD) and validated experimentally for an unbaffled (UB) and two baffled configurations. Proper orthogonal decomposition (POD) is used to extract dominant spatial–temporal flow features affecting the underlying flow patterns. Results show very good agreement between simulations and experiments, while POD analysis revealed the existence of highly energetic and periodic modes, linked to interactions between impeller jet and reactor walls. These modes are responsible for an impeller jet instability, which is amplified by the presence and size of baffles.

Valve Vibration Induced Intake Air Flow Dynamics Analysis Using Near Valve Particle Image Velocimetry

Abstract The transient dynamics of air flow running through the moving intake valve gaps in combustion cylinders is crucial to the performance of spark-ignition direct-injection engines. However, research on the air flow behavior in the vicinity of valve exits is still limited. In this work, transient air flow characteristics of a custom-designed dual-valve system under the operating conditions of a fixed valve lift and a vibrating valve lift at two frequencies are experimentally investigated. The velocity vector field measured using planar particle image velocimetry (PIV) is first analyzed in time domain based on temporal mean and root-mean-square (RMS) values. Comparison of temporal mean flow fields reveals the difference in flow pattern while RMS represents the variation of intake air jet velocity along the inlet path of the vibration-affected jet. Instantaneous snapshots provide direct analysis of the valve vibration-induced intake air jet behavior. Furthermore, investigation in frequency domain extends insights into the dominant spectral components of flow structures. Fast Fourier transform (FFT) applied to every vector on the velocity field yields a vibration frequency affected zone (VFAZ), indicating the regions where the effect of valve vibration is significant. By employing dynamic mode decomposition (DMD) method, the spatio-temporal PIV results are decomposed into modes with specific frequencies. Reconstructed flow field using modes with the valve operating frequency visually unveils a cyclic vortex structure near the valve exit. In summary, this study elucidates the mechanism of near-valve intake air flow impingement and interaction behavior induced by the valve vibrating motion.

Quantitatively Monitoring Bubble-Flow at a Seep Site Offshore Oregon: Field Trials and Methodological Advances for Parallel Optical and Hydroacoustical Measurements

Two lander-based devices, the Bubble-Box and GasQuant-II, were used to investigate the spatial and temporal variability and total gas flow rates of a seep area offshore Oregon, United States. The Bubble-Box is a stereo camera–equipped lander that records bubbles inside a rising corridor with 80 Hz, allowing for automated image analyses of bubble size distributions and rising speeds. GasQuant is a hydroacoustic lander using a horizontally oriented multibeam swath to record the backscatter intensity of bubble streams passing the swath plain. The experimental set up at the Astoria Canyon site at a water depth of about 500 m aimed at calibrating the hydroacoustic GasQuant data with the visual Bubble-Box data for a spatial and temporal flow rate quantification of the site. For about 90 h in total, both systems were deployed simultaneously and pressure and temperature data were recorded using a CTD as well. Detailed image analyses show a Gaussian-like bubble size distribution of bubbles with a radius of 0.6–6 mm (mean 2.5 mm, std. dev. 0.25 mm); this is very similar to other measurements reported in the literature. Rising speeds ranged from 15 to 37 cm/s between 1- and 5-mm bubble sizes and are thus, in parts, slightly faster than reported elsewhere. Bubble sizes and calculated flow rates are rather constant over time at the two monitored bubble streams. Flow rates of these individual bubble streams are in the range of 544–1,278 mm3/s. One Bubble-Box data set was used to calibrate the acoustic backscatter response of the GasQuant data, enabling us to calculate a flow rate of the ensonified seep area (∼1,700 m2) that ranged from 4.98 to 8.33 L/min (5.38 × 106 to 9.01 × 106 CH4 mol/year). Such flow rates are common for seep areas of similar size, and as such, this location is classified as a normally active seep area. For deriving these acoustically based flow rates, the detailed data pre-processing considered echogram gridding methods of the swath data and bubble responses at the respective water depth. The described method uses the inverse gas flow quantification approach and gives an in-depth example of the benefits of using acoustic and optical methods in tandem.

Idiopathic intracranial hypertension imaging approaches and the implications in patient management.

Idiopathic intracranial hypertension (IIH) represents a clinical disease entity without a clear etiology, that if left untreated, can result in severe outcomes, including permanent vision loss. For this reason, early diagnosis and treatment is necessary. Historically, the role of cross-sectional imaging has been to rule out secondary or emergent causes of increased intracranial pressure, including tumor, infection, hydrocephalus, or venous thrombosis. MRI and MRV, however, can serve as valuable imaging tools to not only rule out causes for secondary intracranial hypertension but can also detect indirect signs of IIH resultant from increased intracranial pressure, and demonstrate potentially treatable sinus venous stenosis. Digital subtraction venographic imaging also plays a central role in both diagnosis and treatment, providing enhanced anatomic delineation and temporal flow evaluation, quantitative assessment of the pressure gradient across a venous stenosis, treatment guidance, and immediate opportunity for endovascular therapy. In this review, we discuss the multiple modalities for imaging IIH, their limitations, and their contributions to the management of IIH.

Characterizing purely elastic turbulent flow of a semi-dilute entangled polymer solution in a serpentine channel

Polymer solutions in the semi-dilute regime are of considerable industrial importance. The complex rheological properties of such highly viscoelastic fluids and the complexity of their flow characteristics, especially in curved geometries, necessitate a thorough experimental characterization of the dynamics of such fluid flows. We apply statistical, spectral, and structural analyses to the experimentally obtained velocity fields of a semi-dilute entangled polymer solution in a serpentine channel to fully characterize the corresponding flow. Our results show that at high Weissenberg numbers, yet vanishing Reynolds numbers, the flow resistance is significantly increased, which indicates the emergence of a purely elastic turbulent flow. Spatial flow observations and statistical analysis of temporal flow features show that this purely elastic turbulent flow is non-homogeneous, non-Gaussian, and anisotropic at all scales. Moreover, spectral analysis indicates that compared to elastic turbulence in the dilute regime, the range of present scales of the excited fluctuations is narrower. This is partly due to the entanglement of the polymers in this concentration regime, which restricts their movement, and partly due to the mixed flow type inherent in the serpentine geometry, which can reduce the extent of polymer stretching and, thus, reduce the intensity of the fluctuations in the flow. Furthermore, proper orthogonal decomposition analysis is applied to directly extract the turbulent flow structure and reveals the activity of the counter-rotating vortices associated with secondary flow, which significantly contribute to the total kinetic energy of the flow.

Efficient temporal flow Transformer accompanied with multi-head probsparse self-attention mechanism for remaining useful life prognostics

Predictive maintenance, such as remaining useful life (RUL) prognostics, requires precise long time-series forecasting, which demands a higher predictive capability of data-driven models. Nevertheless, the typical convolution and recurrent frameworks are still inadequate in the feature extraction and temporal complexity analysis, which makes them difficult to efficiently capture the precise long-term dependency coupling. Recent research has demonstrated the potential of Transformer-based framework to improve the prediction capability by the massive success in sequence processing. Inspired by the above, this paper proposes an efficient end-to-end Temporal Flow Transformer (TFT) for RUL prognostics of rolling bearings. Its main framework is composed of multi-layer encoders, which can directly extract effective degradation features from the time-frequency representations of raw signals, with two distinctive characteristics: (1) Specially designed multi-head probsparse self-attention mechanism can effectively highlight the dominant attention, which makes the TFT have considerable performance in reducing the computational complexity of extremely long time-series; (2) The TFT trained by knowledge-induced distillation strategy can significantly improve its domain adaptability, making it possible to achieve accurate RUL prediction under cross-operating conditions. Extensive experiments on two life-cycle bearing datasets indicate that the TFT greatly outperforms the existing state-of-the-art methods and provides a new solution for RUL prognostics.

Sexual recombination and temporal gene flow maintain host resistance and genetic diversity

Sexual recombination and temporal gene flow maintain host resistance and genetic diversity

Resource Management in 5G Networks Assisted by UAV Base Stations: Machine Learning for Overloaded Macrocell Prediction Based on Users’ Temporal and Spatial Flow

The rapid growth of data traffic due to the demands of new services and applications poses new challenges to the wireless network. Unmanned aerial vehicles (UAVs) can be a solution to support wireless networks during congestion, especially in scenarios where the region has high traffic peaks due to the temporal and spatial flow of users. In this paper, an intelligent machine-learning-based system is proposed to deploy UAV base stations (UAV-BS) to temporarily support the mobile network in regions suffering from the congestion effect caused by the high density of users. The system includes two main steps, the load prediction algorithm (LPA) and the UAV-BSs clustering and positioning algorithm (UCPA). In LPA, the load history generated by the mobile network is used to predict which macrocells are congested. In UCPA, planning is performed to calculate the number of UAV BSs needed based on two strategies: naïve and optimized, in addition to calculating the optimal positioning for each device requested to support the overloaded macrocells. For prediction, we used two models, generalized regression neural networks (GRNN) and random forest, and the results showed that both models were able to make accurate predictions, and the random forest model was better with an accuracy of over 85%. The results showed that the intelligent system significantly reduced the overhead of the affected macrocells, improved the quality of service (QoS), and reduced the probability of blocking users, as well as defined the preventive scheduling for the UAV BSs, which benefited the scheduling and energy efficiency.