Multiple Spaces Research Articles

Pore-Scale Modeling of Pressure-Driven Flow and Spontaneous Imbibition in Fracturing-Shut-In-Flowback Process of Tight Oil Reservoirs

Tight oil reservoirs are characterized by multiple pore spaces where nano-micropores and multiscale fractures coexist, and each type of medium varies in scale, implying a tight coupling of multiscale fractures with matrix and giving rise to extremely complicated flow patterns. To further investigate its flow mechanism, we first construct three two-dimensional (2-D) fracture-pore geometry models based on microfocus computed tomography (CT) imaging of a typical tight rock. A pore-scale modeling workflow is thereafter developed using the Shan-Chen lattice Boltzmann model (SC-LBM) to simulate the pressure-driven flow and spontaneous imbibition. The influence of fracture-pore geometry on the pore-scale fluid exchange dynamics in the fracturing-shut-in-flowback process is clearly clarified. Results show that, for the porous medium model without fracture, the fracturing fluid can displace and replace some crude oil by spontaneous imbibition while a large amount of crude oil droplets remains unexploited away from the oil/water contact line, resulting in a low oil imbibition recovery. The injected fracturing fluid migrates into the deep position along the fractures, only a few entering the matrix pore space near the fractures. Small pores are the main channel for fracturing fluid to imbibe into the matrix pores, and the replaced crude oil droplets flow into fractures through large pores as intermittent or continuous pipe flow. The complexity of fracture network typically exerts a significant impact on the fluid exchange dynamics during pressure-driven flow and spontaneous imbibition. The more complex the fracture network, the larger the volume of fracturing fluid injected, the easier for oil droplets replaced from the matrix pores, and the more difficult for the flowback of fracturing fluid in the fracture-pore geometry model. Our understanding will provide a basis for explaining the underlying mechanisms of oil replacement by pressure-driven flow and spontaneous imbibition in the fracturing-shut-in-flowback process.

Risk factors for vascular injuries in anterior lumbar interbody fusion: a single-institution retrospective study.

Vascular injuries in anterior lumbar interbody fusion (ALIF) occur due to the vascular manipulation needed to achieve optimal disc space exposure. In this study, the authors aimed to evaluate intraoperative vascular injuries in patients undergoing single- and multilevel ALIF at a large tertiary academic center. Prospectively collected data specifically addressing postoperative complications in patients who underwent ALIF by neurosurgery spine faculty working with a specialized vascular surgeon were retrospectively reviewed. Demographic characteristics and intraoperative data were collected. Patients were split into vascular injury and non-vascular injury groups. Outcome comparisons were conducted using the chi-square exact and Wilcoxon rank-sum tests for categorical and continuous data, respectively. Logistic regression was used to investigate associations with vascular injury, with univariate analysis first conducted to identify candidate associations. Based on these results, variables demonstrating a between-groups test statistic of p < 0.10 were included in the multivariate analysis to determine the independent predictors of vascular injuries. In total, 323 patients who underwent ALIF procedures were identified: 51% were male (n = 166) and 49% were female (n = 157). The mean age was 57.1 years, with 56% (n = 182) having undergone prior lumbar surgery. Vascular injuries were encountered intraoperatively in 7.1% (n = 23) of patients, and the majority (91% [n = 21]) involved the left common iliac vein. Patients with vascular injuries were statistically older (mean 63.6 vs 56.6 years), had greater incidence rates of coronary artery disease, opioid use, multilevel ALIF surgery involving the L2-3 level, and surgery involving multiple disc spaces, had more multilevel anterior instrumentation, and experienced greater blood loss during surgery and longer length of stay (all p < 0.05). Overall, there was 1 death (0.3%) secondary to aortic artery injury. Multivariate analysis identified opioid use, multilevel ALIF involving L2-3, and multiple interbodies as statistically independent predictors of vascular injury (all p < 0.05). Identifying patient risk factors can reduce the risk of vascular injuries in ALIF. Opioid use, multilevel ALIF involving L2-3, and multiple interbodies were independent predictors of vascular injuries in ALIF.

Multi-view contrastive clustering via integrating graph aggregation and confidence enhancement

Multi-view clustering endeavors to effectively uncover consistent clustering patterns across multiple data sources or feature spaces. This field grapples with two key challenges: (1) the effective integration and utilization of consistency and complementarity information from diverse view spaces, and (2) the capturing of structural correlations between data samples in the multi-view context. To address these challenges, this paper proposes the Multi-view contrAstive clustering with Graph Aggregation and confidence enhancement (MAGA) algorithm. Specifically, we employ a deep autoencoder network to learn embedded features for each independent view. To harness consistency and complementarity information, we introduce the Simple Cross-view Spectral Graph Aggregation module. This module utilizes graph convolutional layers to generate view-specific graph embeddings and subsequently aggregates these embeddings from different views into a unified feature space using a cross-view self-attention mechanism. To capture both inter-view and intra-view structural correlations among different samples, we propose a dual representation contrastive learning mechanism, which operates concurrently at both the instance and feature levels. Additionally, we introduce the maximizing cluster assignment confidence mechanism to obtain more compact clustering assignments. As a result, MAGA outperforms 20 competitive methods across nine benchmark datasets, showcasing its superior performance. Code: https://github.com/BJT-bjt/MAGA.

A note on Köthe–Toeplitz duals and multiplier spaces of sequence spaces involving bicomplex numbers

Abstract In this paper, we generalize the notions of the Köthe–Toeplitz duals of sequence spaces by introducing the concepts of bicomplex α-dual, bicomplex β-dual and bicomplex γ-dual, and also we compute them for some bicomplex sequence spaces l p ⁢ ( 𝔹 ⁢ ℂ ) {l_{p}(\mathbb{BC})} for 1 ≤ p ≤ ∞ {1\leq p\leq\infty} , c 0 ⁢ ( 𝔹 ⁢ ℂ ) {c_{0}(\mathbb{BC})} and c ⁢ ( 𝔹 ⁢ ℂ ) {c(\mathbb{BC})} . Furthermore, we define a concept of bicomplex multiplier space as the bicomplex version of multiplier space of two sequence spaces and support this definition with examples.

Some Outcomes using Conditionally Sequential Absorbing and Pseudo Reciprocally Continuous in Multiplicative Metric Space

Objective: The purpose of this paper is to establish two common fixed point theorems in Multiplicative Metric Space (MMS). Method: By utilizing the conditions Conditionally Sequentially Absorbing (CSA) and Pseudo Reciprocally Continuous (PRC) mappings. Findings: Two unique common fixed point theorems are generated and supported it with relevant examples. Novelty: In the first theorem, we use the concept CSA and PRC mappings. In the second theorem, we use the notion of CSA, non-compatible and PRC mappings. These conditions are weaker than the existing conditions like weakly compatible mappings which generalizes the theorem of Monika Verma, P Kumar and Andnawneet Hooda. Keywords Multiplicative Metric Space (MMS), fixed point, Psudo Reciprocally Continuous (PRC), Conditionally Sequential Absorbing (CSA), Occasionally Weakly Compatible (OWC) mappings

M3SOT: Multi-Frame, Multi-Field, Multi-Space 3D Single Object Tracking

3D Single Object Tracking (SOT) stands a forefront task of computer vision, proving essential for applications like autonomous driving. Sparse and occluded data in scene point clouds introduce variations in the appearance of tracked objects, adding complexity to the task. In this research, we unveil M3SOT, a novel 3D SOT framework, which synergizes multiple input frames (template sets), multiple receptive fields (continuous contexts), and multiple solution spaces (distinct tasks) in ONE model. Remarkably, M3SOT pioneers in modeling temporality, contexts, and tasks directly from point clouds, revisiting a perspective on the key factors influencing SOT. To this end, we design a transformer-based network centered on point cloud targets in the search area, aggregating diverse contextual representations and propagating target cues by employing historical frames. As M3SOT spans varied processing perspectives, we've streamlined the network—trimming its depth and optimizing its structure—to ensure a lightweight and efficient deployment for SOT applications. We posit that, backed by practical construction, M3SOT sidesteps the need for complex frameworks and auxiliary components to deliver sterling results. Extensive experiments on benchmarks such as KITTI, nuScenes, and Waymo Open Dataset demonstrate that M3SOT achieves state-of-the-art performance at 38 FPS. Our code and models are available at https://github.com/ywu0912/TeamCode.git.

Approximating common fixed points on multiplicative b-metricspaces using the weakly commuting mappings

The aim of this paper is to prove some common fixed point theorems for four pair of weakly commuting mappings using multiplicative b-metric spaces. Our results obtained in this paper improve, extend and unify some related results in the literature.

A non-Newtonian magnetic curves in multiplicative Riemann manifolds

The aim of this study is to rearrange magnetic curves and their main properties with the help of multiplicative calculi. Magnetic curves have been examined in many spaces with the tools of traditional (Newtonian) analysis and their characterizations have been obtained. The innovation brought by this study; magnetic curves and many other getometric and physical expressions were studied for the first time with non-Newtonian arguments in multiplicative space. In the study, the advantages of purely multiplicative operations and multiplicative calculation are used. Moreover, it unveils the distinctions (angle, norm, distance, line vb.) between the multiplicative Euclidean space and the conventional Euclidean space, offering a novel perspective on geometrically magnetic curves. As a result, the concept of multiplicative magnetic curves (t − magnetic, n − magnetic and b − magnetic) are introduced to the academic discourse, and the essential characterizations are established. The study also provides illustrative examples to facilitate a better is understood of the subject matter and employs Geogebra to generate visual representations of new concepts.

Multiple SSO Space Debris Flyby Trajectory Design Based on Cislunar Orbit

This paper investigates the trajectory design problem in the scenario of a multiple Sun-synchronous Orbit (SSO) space debris flyby mission from a DRO space station. At first, the characteristics of non-planar transfer from DRO to SSO in the Earth–Moon system are analyzed. The methods of large-scale ergodicity and pruning are utilized to investigate single-impulse and two-impulse DRO–Earth transfers. Using a powered lunar flyby, the two-impulse DRO–Earth transfer is able to fly by SSO debris while satisfying the requirements of the mission. After the local optimization, the optimal result of two-impulse DRO–Earth transfer and flyby is obtained. A multi-objective evolutionary algorithm is used to design the Pareto-optimal trajectories of multiple flybys. The semi-analytical optimization method is developed to provide the estimations of the transfer parameters in order to reduce the computations caused by the evolutionary algorithm. Simulations show that transferring from the 3:2 resonant DRO to a near-coplanar flyby of a SSO target debris using a powered lunar gravity assist needs a 0.47 km/s velocity increment. The mission’s total velocity increment is 1.39 km/s, and the total transfer time is 2.23 years.

An Interface-Preserving Moving Mesh in Multiple Space Dimensions

An interface-preserving moving mesh algorithm in two or higher dimensions is presented. It resolves a moving ( d -1)-dimensional manifold directly within the d -dimensional mesh, which means that the interface is represented by a subset of moving mesh cell-surfaces. The underlying mesh is a conforming simplicial partition that fulfills the Delaunay property. The local remeshing algorithms allow for strong interface deformations. We give a proof that the given algorithms preserve the interface after interface deformation and remeshing steps. Originating from various numerical methods, data is attached cell-wise to the mesh. After each remeshing operation, the interface-preserving moving mesh retains valid data by projecting the data to the new mesh cells. An open source implementation of the moving mesh algorithm is available at Reference [ 1 ].

Resampling approaches for the quantitative analysis of spatially distributed cells

ABSTRACT Image segmentation is a crucial step in various image analysis pipelines and constitutes one of the cutting-edge areas of digital pathology. The advent of quantitative analysis has enabled the evaluation of millions of individual cells in tissues, allowing for the combined assessment of morphological features, biomarker expression, and spatial context. The recorded cells can be described as a point pattern process. However, the classical statistical approaches to point pattern processes prove unreliable in this context due to the presence of multiple irregularly-shaped interstitial cell-devoid spaces in the domain, which correspond to anatomical features (e.g. vessels, lipid vacuoles, glandular lumina) or tissue artefacts (e.g. tissue fractures), and whose coordinates are unknown. These interstitial spaces impede the accurate calculation of the domain area, resulting in biased clustering measurements. Moreover, the mistaken inclusion of empty regions of the domain can directly impact the results of hypothesis testing. The literature currently lacks any introduced bias correction method to address interstitial cell-devoid spaces. To address this gap, we propose novel resampling methods for testing spatial randomness and evaluating relationships among different cell populations. Our methods obviate the need for domain area estimation and provide non-biased clustering measurements. We created the SpaceR software (https://github.com/GBertolazzi/SpaceR) to enhance the accessibility of our methodologies.

Optimizing launch window opportunities for ESA's comet Interceptor mission using primer vector theory

Comet Interceptor (Comet-I) was selected in June 2019 as the first ESA F-Class mission and aims to explore a pristine comet, which will visit the inner Solar System for the first time. Comet-I will hitch a ride to a Sun-Earth L2 quasi-halo orbit, as a co-passenger in ESA's M4 ARIEL's launch. At the time of writing, this is scheduled for 2029. It will then remain there awaiting the right departure conditions to leave definitively and intercept a newly discovered comet. Comet-I will be the first mission to be designed and launched without an already identified target. Given the latter, the Comet-I Target Identification Working Group is tasked with the continuous follow-up of newly discovered comets, to analyse their characteristics as virtual targets for Comet-I. The goal of the paper is to inform the decision-making process that will eventually be necessary to commit Comet-I spacecraft to its final target.Within this context, this paper presents a trajectory optimization process based on Primer Vector theory that considers intercept trajectories with multiple revolutions and multiple deep space manoeuvres (DSM). The process is applied to 21 long period comets, which were discovered since July 1, 2020 and whose characteristics are of interest for the Comet-I mission. In fact, one of the latest of these recently discovered comets, C/2023H2, is the most accessible virtual target discovered at the time of writing, with delta-V intercept costs well within Comet-I capabilities. A complete launch window analysis is thus presented for C/2023H2, which accounts not only for the most optimal delta-V intercept transfer, but also considers all launch options that satisfy all Comet-I's current boundary conditions and flyby constraints. A systematic launch window analysis is made possible by a grid search approach on which departure and fly-by dates are fixed and the remaining design variables, including the times for manoeuvres and their Δv impulse vector, are optimized via Primer Vector theory. This allows to understand the latest possible departure date, but also the sensitivity to departure and arrival dates of key encounter characteristics such as relative fly-by speed, the solar aspect angle and the Earth distance.

Electroencephalography (EEG) based epilepsy diagnosis via multiple feature space fusion using shared hidden space-driven multi-view learning.

Epilepsy is a chronic, non-communicable disease caused by paroxysmal abnormal synchronized electrical activity of brain neurons, and is one of the most common neurological diseases worldwide. Electroencephalography (EEG) is currently a crucial tool for epilepsy diagnosis. With the development of artificial intelligence, multi-view learning-based EEG analysis has become an important method for automatic epilepsy recognition because EEG contains difficult types of features such as time-frequency features, frequency-domain features and time-domain features. However, current multi-view learning still faces some challenges, such as the difference between samples of the same class from different views is greater than the difference between samples of different classes from the same view. In view of this, in this study, we propose a shared hidden space-driven multi-view learning algorithm. The algorithm uses kernel density estimation to construct a shared hidden space and combines the shared hidden space with the original space to obtain an expanded space for multi-view learning. By constructing the expanded space and utilizing the information of both the shared hidden space and the original space for learning, the relevant information of samples within and across views can thereby be fully utilized. Experimental results on a dataset of epilepsy provided by the University of Bonn show that the proposed algorithm has promising performance, with an average classification accuracy value of 0.9787, which achieves at least 4% improvement compared to single-view methods.

Policy instruments for governing water in cross-border metropolitan areas, the case of Greater Geneva

ABSTRACT Governing transboundary waters is inherently a complex policy issue. However, when waters flow through multiple urban spaces (multiple cities in different countries), it presents a unique challenge to access and control. Policy actors employ a variety of policy instruments to govern such spaces and users’ water demands. Drawing from the case of Geneva Metropolis (shared between Switzerland and France), we explain how policy instruments are formulated to manage a combination of water sources. We answer two questions: (1) what type of policy instruments contribute to reaching transboundary water governance in Greater Geneva? and (2) What are the underlying drivers that led to the success or failures of implementing policy instruments at the transboundary level? Based on policy documents and closed-door interviews, we present various policy instruments that have been developed and implemented in the Geneva metropolis. We further argue that the underlying drivers, such as the existence of communities of practice, political willingness, the influence of civil society actors and the evolution of an anticipatory water governance system, have influenced the policy instruments’ success and failure. To conclude, this article calls for further research on comparing different cross-border metropolitan areas, focusing on processual aspects (development and implementation) of policy instruments.

Direction of arrival estimation in reverberant environments using a single vibration sensor on an elastic panel

The vibrational response of an elastic panel to an incoming acoustic pressure wave is dependent on the coupling between the incident angle and the panel’s bending modes. By examining the relative modal excitations recorded by a single structural vibration sensor affixed to the panel, the direction of arrival (DOA) of the incident wave may be inferred. In reverberant environments, determination of the DOA of a sound source is complicated by acoustic reflections. Panel microphones may be particularly susceptible to this effect due to their large surface areas and finite modal decay times. A study on the impact of reverberation on DOA estimation using panel microphones was conducted. The panel’s response to wake word utterances was recorded in eight spaces, with reverberation times (RT60s) ranging from 0.27 to 3.00 s. These responses were used to train neural networks for DOA estimation. Results indicate an inverse relationship between RT60 and DOA estimation reliability. Within ±5°, DOA estimation reliability was measured at 95% in the least reverberant space and decreased to 78% in the most reverberant space. Results also suggest that DOA estimation using panel microphones can adapt to diverse acoustic environments by training the system with data from multiple spaces with different RT60s.

Machine Learning Classification of Event-Related Brain Potentials during a Visual Go/NoGo Task.

Machine learning (ML) methods are increasingly being applied to analyze biological signals. For example, ML methods have been successfully applied to the human electroencephalogram (EEG) to classify neural signals as pathological or non-pathological and to predict working memory performance in healthy and psychiatric patients. ML approaches can quickly process large volumes of data to reveal patterns that may be missed by humans. This study investigated the accuracy of ML methods at classifying the brain's electrical activity to cognitive events, i.e., event-related brain potentials (ERPs). ERPs are extracted from the ongoing EEG and represent electrical potentials in response to specific events. ERPs were evoked during a visual Go/NoGo task. The Go/NoGo task requires a button press on Go trials and response withholding on NoGo trials. NoGo trials elicit neural activity associated with inhibitory control processes. We compared the accuracy of six ML algorithms at classifying the ERPs associated with each trial type. The raw electrical signals were fed to all ML algorithms to build predictive models. The same raw data were then truncated in length and fitted to multiple dynamic state space models of order nx using a continuous-time subspace-based system identification algorithm. The 4nx numerator and denominator parameters of the transfer function of the state space model were then used as substitutes for the data. Dimensionality reduction simplifies classification, reduces noise, and may ultimately improve the predictive power of ML models. Our findings revealed that all ML methods correctly classified the electrical signal associated with each trial type with a high degree of accuracy, and accuracy remained high after parameterization was applied. We discuss the models and the usefulness of the parameterization.

A Cultural Geopolitics of Hosting: Domesticity, Violence and Hospitality in the Home

ABSTRACT This paper aims to enhance geopolitical understandings of hosting by exploring the practice of bringing guests – known or unknown – into a home. It starts with a recognition that whilst a growing body of geographical literature has explored the geopolitics of home, this has yet to be substantially brought into conversation with the practice of hosting. Grounded within our research on refugee hosting in the UK, and being hosted in Palestinian homestays we contend that an attention to a cultural geopolitics of hosting enriches geographical accounts of hosting. Our case studies muddy the cultural and political borders of both home and hosting and question the multiscalar and relational understandings of hospitality. A cultural geopolitics of hosting then, demonstrates how hosting intersects with dynamics of geopolitics and culture through and in the multiple spaces of home. This paper, shows how a braiding of literatures on home and hosting is productive for understandings of the geopolitics of home, and hospitality at the different scales of the political.

Corporate interest groups and their implications for global food governance: mapping and analysing the global corporate influence network of the transnational ultra-processed food industry.

A major challenge to transforming food systems to promote human health and sustainable development is the global rise in the manufacture and consumption of ultra-processed foods (UPFs). A key driver of this dietary transition is the globalization of UPF corporations, and their organized corporate political activity (CPA) intended to counter opposition and block government regulation. UPF industry CPA and the corporate interest groups who lobby on their behalf have been well described at the national level, however, at the global level, this network has not been systematically characterized. This study aims to map, analyse, and describe this network, and discuss the implications for global food policy action on UPFs, global food governance (GFG), and food systems transformation. We conducted a network analysis of the declared interest group memberships of the world's leading UPF corporations, extracted from web sources, company reports, and relevant academic and grey literature. Data on the characteristics of these interest groups were further extracted for analysis, including year founded, level, type, and headquarter location. We identified 268 interest groups affiliated with the UPF industry. The UPF manufacturers Nestlé (n = 171), The Coca-Cola Company (n = 147), Unilever (n = 142), PepsiCo (n = 138), and Danone (n = 113) had the greatest number of memberships, indicating strong centrality in coordinating the network. We found that this network operates at all levels, yet key actors now predominantly coordinate globally through multistakeholder channels in GFG. The most common interest group types were sustainability/corporate social responsibility/multistakeholder initiatives, followed by branding and advertising, and food manufacturing and retail. Most corporate interest groups are headquartered where they can access powerful government and GFG decision-makers, nearly one-third in Washington DC and Brussels, and the rest in capital cities of major national markets for UPFs. The UPF industry, and especially its leading corporations, coordinate a global network of interest groups spanning multiple levels, jurisdictions, and governance spaces. This represents a major structural feature of global food and health governance systems, which arguably poses major challenges for actions to attenuate the harms of UPFs, and to realising of healthy and sustainable food systems.

General single/multiple integral inequalities and their applications to stability of time-delay systems

This paper is concerned with the stability of time delay systems. A new type of multiple integral inner product and a new class of multiple integral inner product space are firstly introduced. Based on them, general single/multiple integral inequalities are developed, and some previous integral inequalities including single and multiple integral inequalities can be regarded as their special cases. Secondly, suitable Lyapunov–Krasovskii functionals (LKF) are constructed. On the basis of the proposed integral inequalities, the derivative of the LKF can be gauged more accurate than those in the literature. As a result, some improved stability criteria of time-delay systems are formulated in terms of linear matrix inequalities. Through two numerical examples, it is shown that these criteria can provide larger delay upper bounds than some existing ones, showing the effectiveness and superiority of the proposed method.

Fallopian Tube Hemangioma Discovered on Follow-up for Uterine Leiomyoma.

Hemangioma in female reproductive organs, particularly in the fallopian tube (FT), is a sporadic disease. In this report, we describe a case of hidden capillary hemangioma in FT in a 39-year-old woman who suffered from uterine leiomyoma. During the preoperative stage, pelvic sonography, computed tomography, and diagnostic laparoscopy revealed a subserous leiomyomatous nodule located along the posterior wall of the uterus. Despite this, intraoperatively, a benign vascular neoplasm was diagnosed. Histologically, it is characterized by multiple thin-walled vascular spaces lined with a single layer of endothelial cells, in which single mitoses were observed. The diagnosis was then confirmed immunohistochemically by CD31 and CD34 expression in the endothelial cells lining the inner surface of the spaces and the low mitotic activity of the tumor cells. It is virtually impossible to diagnose this asymptomatic neoplasm before and during surgery, which can result in an inadequate number of surgeries. Incorrect interpretation of a benign tumor at a young age can lead to unnecessary radical surgery with a resulting loss of fertility, and an unrevealed malignant process can threaten life.