Physical Boundaries Research Articles

Boundaries and Connections. Producing, organizing and sharing knowledge on medieval Iberian art

To what extent and how are today's increasingly interconnected and porous real and virtual worlds conditioning and even determining research in Art History? Several art historians have pointed out that, as the globe becomes hyper-connected, the discipline is shifting its focus from local and national questions to global or worldwide issues. This paper aims to analyze this shift in the historiography of medieval Iberian art. I argue that research is indeed being reshaped by the removal or the crossing of physical, epistemic and virtual boundaries as well as by pushing forth an increasing interest in connectivity. Additionally, I propose to cross or remove surviving rigid boundaries still further, to construct more flexible and fluid epistemic limits, and to establish additional connections at several levels.

Modern rodenticides and non-target species

It is generally recognized that environmental pollution with agrotechnical substances is the major problem for the conservation of biological diversity and human food security. However, their use cannot be completely excluded. Of particular importance are modern rodenticides, i.e. superwarfarins, designed to reduce the numbers of rodents damaging agriculture, i.e. to destroy animals. However, these poisons are highly toxic and are passed up the food chains. Besides, it is impossible to draw a line between agrocenoses in their classical sense and the natural or slightly modified environment. The absence of clear physical boundaries of agrocenoses means that the processes occurring therein affect the biological diversity of adjacent territories, which are considered natural. Animals use both fields and the spaces adjacent to them. In addition, there is a water exchange between cultivated fields and the adjacent natural areas and reservoirs. Modern changes in the structure of agrocenoses increase the segment of fields which rodenticides are used on. The plowing of ravines, riversides and other inconvenient lands (due to an improved quality of equipment) facilitates the ingress of rodenticides into water bodies. Data on the consequences of the use of superwarfarins in some anthropogenic biocenoses of the southern regions of the Russian Federation are presented. Facts of mass destruction of consumers of the 1st and 2nd order (the so-called non-target species), directly related to rodenticid use, are given. Complex pathomorphological and toxicological studies of animals died in nature were carried out for the first time in the Russian Federation. The presence of bromadiolone in tissue samples was confirmed by chemical analysis, and the descriptions of autopsies of dead birds have been processed. Recommendations on the specific features for the collecting and storage of materials sent for research have been prepared.

A Self-Supervised Learning Approach for Extracting China Physical Urban Boundaries Based on Multi-Source Data

Physical urban boundaries (PUBs) are basic geographic information data for defining the spatial extent of urban landscapes with non-agricultural land and non-agricultural economic activities. Accurately mapping PUBs provides a spatiotemporal database for urban dynamic monitoring, territorial spatial planning, and ecological environment protection. However, traditional extraction methods often have problems, such as subjective parameter settings and inconsistent cartographic scales, making it difficult to identify PUBs objectively and accurately. To address these problems, we proposed a self-supervised learning approach for PUB extraction. First, we used nighttime light and OpenStreetMap road data to map the initial urban boundary for data preparation. Then, we designed a pretext task of self-supervised learning based on an unsupervised mutation detection algorithm to automatically mine supervised information in unlabeled data, which can avoid subjective human interference. Finally, a downstream task was designed as a supervised learning task in Google Earth Engine to classify urban and non-urban areas using impervious surface density and nighttime light data, which can solve the scale inconsistency problem. Based on the proposed method, we produced a 30 m resolution China PUB dataset containing six years (i.e., 1995, 2000, 2005, 2010, 2015, and 2020). Our PUBs show good agreement with existing products and accurately describe the spatial extent of urban areas, effectively distinguishing urban and non-urban areas. Moreover, we found that the gap between the national per capita GDP and the urban per capita GDP is gradually decreasing, but regional coordinated development and intensive development still need to be strengthened.

Applications of finite difference‐based physics‐informed neural networks to steady incompressible isothermal and thermal flows

AbstractPhysics‐informed neural networks (PINNs) have aroused an upsurge in scientific machine/deep learning community, especially in research areas involving solving partial differential equations. In this work, based on the multilayer perceptron neural network and finite difference (FD) method, we apply the finite difference‐based PINNs (FD‐PINNs) to simulate the steady incompressible flows. The spatial derivatives in nonlinear governing equations are discretized by the central FD scheme, and the physical boundary conditions can be exactly satisfied in a similar way to conventional fluid flow solvers. Compared to original PINNs using automatic differentiation (AD) to discretize the governing equations, FD‐PINNs hold three‐fold potential advantages in decoupling the predictive accuracy of derivatives from the network's approximating accuracy, fulfilling the physical boundary conditions, and saving computational costs when large‐size networks or meshes are used. We first attempt to make relatively fair comparisons between AD‐PINNs, FD‐PINNs and conventional flow solvers using a benchmark flow problem—steady incompressible laminar isothermal lid‐driven cavity flow across a wide range of Reynolds numbers (Re). Our results indicate that FD‐PINNs are superior to AD‐PINNs in both predictive precision, computational efficiency and training convergence. Moreover, FD‐PINNs exhibit higher accuracy than flow solvers when insufficient mesh points are used to solve high‐Re cavity flow. Even at Re = 10,000, with only 10 internal samples, FD‐PINNs can still precisely recover the flow details on a uniform mesh of size 51 × 51. Besides, the time consumption of FD‐PINNs grows much slower than that of conventional flow solvers. We also investigate the robustness of FD‐PINNs w.r.t. the noise level of samples and the effects of pressure boundary condition imposed on solid cavity walls. Furthermore, FD‐PINNs are extensively applied to predict the incompressible thermal flows: natural convective flows in a square cavity and in a concentric annulus, where the effects of governing equations on the predictive accuracy are discovered and discussed for the first time.

FDM data driven U-Net as a 2D Laplace PINN solver

Efficient solution of partial differential equations (PDEs) of physical laws is of interest for manifold applications in computer science and image analysis. However, conventional domain discretization techniques for numerical solving PDEs such as Finite Difference (FDM), Finite Element (FEM) methods are unsuitable for real-time applications and are also quite laborious in adaptation to new applications, especially for non-experts in numerical mathematics and computational modeling. More recently, alternative approaches to solving PDEs using the so-called Physically Informed Neural Networks (PINNs) received increasing attention because of their straightforward application to new data and potentially more efficient performance. In this work, we present a novel data-driven approach to solve 2D Laplace PDE with arbitrary boundary conditions using deep learning models trained on a large set of reference FDM solutions. Our experimental results show that both forward and inverse 2D Laplace problems can efficiently be solved using the proposed PINN approach with nearly real-time performance and average accuracy of 94% for different types of boundary value problems compared to FDM. In summary, our deep learning based PINN PDE solver provides an efficient tool with various applications in image analysis and computational simulation of image-based physical boundary value problems.

Physical origin and boundary of scalable imaging through scattering media: a deep learning-based exploration

Imaging through scattering media is valuable for many areas, such as biomedicine and communication. Recent progress enabled by deep learning (DL) has shown superiority especially in the model generalization. However, there is a lack of research to physically reveal the origin or define the boundary for such model scalability, which is important for utilizing DL approaches for scalable imaging despite scattering with high confidence. In this paper, we find the amount of the ballistic light component in the output field is the prerequisite for endowing a DL model with generalization capability by using a “one-to-all” training strategy, which offers a physical meaning invariance among the multisource data. The findings are supported by both experimental and simulated tests in which the roles of scattered and ballistic components are revealed in contributing to the origin and physical boundary of the model scalability. Experimentally, the generalization performance of the network is enhanced by increasing the portion of ballistic photons in detection. The mechanism understanding and practical guidance by our research are beneficial for developing DL methods for descattering with high adaptivity.

Lung lobe segmentation in computed tomography images based on multi‐feature fusion and ensemble learning framework

AbstractLung lobe segmentation in computed tomography (CT) images can be regarded as essential supporting information for the diagnosis and treatment of lung diseases, yet it is a challenging uncertainty for the complex segmentation task due to the diverse structures like indistinguishable pulmonary arteries and veins, unpredictable pathological deformation and blurring pulmonary fissures. To circumvent these challenges, we present a productive deep learning network based on multi‐feature fusion and ensemble learning approach to highlight pulmonary fissure representation and suppress other structures for lung lobe segmentation in CT images. Considering that pulmonary fissures are the physical boundaries of lung lobes, a multi‐feature fusion approach is presented to integrate original images, enhanced fissure magnitude images, and enhanced fissure orientation images to highlight shape representation for semantic segmentation. In addition, a deep‐supervised ensemble learning network is employed to combine multiple inducers for lung lobe segmentation improvement. The performance of the proposed deep learning model is validated in experiments with an available dataset, it acquired a high IoU value and a low Hausdorff distance compared with manual references. Experimental results demonstrate that the proposed computational model based on multi‐feature fusion and deep‐supervised ensemble learning framework has an improved predictive performance than many state‐of‐the‐art deep neural networks in lung lobe segmentation.

Chondroitinase ABC Treatment Improves the Organization and Mechanics of 3D Bioprinted Meniscal Tissue.

The meniscus is a fibrocartilage tissue that is integral to the correct functioning of the knee joint. The tissue possesses a unique collagen fiber architecture that is integral to its biomechanical functionality. In particular, a network of circumferentially aligned collagen fibers function to bear the high tensile forces generated in the tissue during normal daily activities. The limited regenerative capacity of the meniscus has motivated increased interest in meniscus tissue engineering; however, the in vitro generation of structurally organized meniscal grafts with a collagen architecture mimetic of the native meniscus remains a significant challenge. Here we used melt electrowriting (MEW) to produce scaffolds with defined pore architectures to impose physical boundaries upon cell growth and extracellular matrix production. This enabled the bioprinting of anisotropic tissues with collagen fibers preferentially oriented parallel to the long axis of the scaffold pores. Furthermore, temporally removing glycosaminoglycans (sGAGs) during the early stages of in vitro tissue development using chondroitinase ABC (cABC) was found to positively impact collagen network maturation. Specially we found that temporal depletion of sGAGs is associated with an increase in collagen fiber diameter without any detrimental effect on the development of a meniscal tissue phenotype or subsequent extracellular matrix production. Moreover, temporal cABC treatment supported the development of engineered tissues with superior tensile mechanical properties compared to empty MEW scaffolds. These findings demonstrate the benefit of temporal enzymatic treatments when engineering structurally anisotropic tissues using emerging biofabrication technologies such as MEW and inkjet bioprinting.

Advances in planetary sustainability

In the 21st century, existing human societies and biodiversity on the Earth are under threat because human resource consumption is exceeding or projected to exceed some of the physical and chemical boundaries of our planet (Rockström et al., 2009). Space research and space exploration are an integral part of a sustainable development that mitigates these threats: Space science and exploration allow us to monitor environmental threats and they open up access to global communication and participation for all human societies. In addition, space exploration also promises to expand the existing limitations and planetary boundaries imposed on human development. On the other hand space exploration can also cause additional environmental problems. The best known example for the latter is the anthropogenic space debris orbiting Earth, but similar problems are likely to occur in other places, for instance on the Moon, due to scientific and commercial space exploration in the near future.Planetary sustainability is a helpful concept to address the promises and challenges posed by space exploration with respect to sustainability. This concept can be understood as a sustainable development that considers the Earth as a planet in its space environment and considers the space environment as an integral part of sustainable development, with scientific, ethical, economic, and legal ramifications.In this article we review the recent advancements in planetary sustainability. This includes the proposal that the space environment of Earth should be added as an independent goal to the existing 17 Sustainable Development Goals defined by the United Nations, considerations of the planned return of humans to the Moon in 2024, and the implications of the increase of commercial satellite networks in low Earth orbit.

In vitro-in vivo relationship for amorphous solid dispersions using a double membrane dissolution-permeation setup

The use of amorphous solid dispersions (ASDs) is one commonly applied formulation strategy to improve the oral bioavailability of poorly water-soluble drugs by overcoming dissolution rate and/or solubility limitations. While bioavailability enhancement of ASDs is well documented, it has often been a challenge to establish a predictive model describing in vitro-in vivo relationship (IVIVR). In this study, it is hypothesized that drug absorption might be overestimated by in vitro dissolution-permeation (D/P)-setups, when drug in suspension has the possibility of directly interacting with the permeation barrier. This is supported by the overprediction of drug absorption from neat crystalline efavirenz compared to four ASDs in a D/P-setup based on the parallel artificial membrane permeability assay (PAMPA). However, linear IVIVR (R2 = 0.97) is established in a modified D/P-setup in which the addition of a hydrophilic PVDF-filter acts as a physical boundary between the donor compartment and the PAMPA-membrane. Based on microscopic visualization, the improved predictability of the modified D/P-setup is due to the avoidance of direct dissolution of drug particles in the lipid components of the PAMPA-membrane. In general, this principle might aid in providing a more reliable evaluation of formulations of poorly water-soluble drugs before initiating animal models.

Mean flow and fluctuations in the three-dimensional turbulent cellular flow

Theoretical and numerical turbulent flow studies either consider turbulence as generated by fluid-solid interactions or focus on intrinsic, bulk properties. There is another group of flows for which homogeneity and isotropy are broken by the body force that sustains the flow, not by physical boundaries. In this class we consider the cellular flow produced by a two-dimensional spatially periodic forcing whose laminar state corresponds to a regular array of counter-rotating vortices. We find that, despite the developed turbulence recovering isotropic features at small scales, the signature of forcing remains in the mean flow, allowing us to quantify the efficiency of the work done by the force.

Prof. Amod Gupta - The leader and the legacy.

Prof. Amod Gupta - The leader and the legacy.

Oh No, The Aunts Are Here by Adam Rex

Reviewed by: Oh No, The Aunts Are Here by Adam Rex Kate Quealy-Gainer Rex, Adam Oh No, The Aunts Are Here; illus. by Lian Cho. Chronicle, 2023 [40p] Trade ed. ISBN 9781797207940 $16.99 Reviewed from digital galleys R 5-8 yrs Our young protagonist's quiet breakfast becomes decidedly less so with the arrival of her four aunts, who are armed with fanny packs, hand sanitizers, and an overwhelming lack of physical boundaries. After enduring a predictable (but not at all enjoyable) round of hugging, pinching, and poking, the bewildered young girl is then grilled on nearly every life topic as they drive to the (not at all) exciting Quilt Museum. That evening, the aunts take over the girl's bedroom, and the next morning, they've co-opted bathrooms, showers, and the breakfast tables—but wait, there's one more aunt than last night, and she's particularly hairy and fearsome looking. "It's a wolf. A wolf 's come to eat you," but the aunts quickly dispatch of the fearsome canine and then promptly turn into colorful birds, chatting away as they fly off. The dip into the absurd wolf storyline is an odd but wise authorial choice, rescuing the aunts' image as much as it (literally) rescues the girl: the four relentlessly annoying women come to her aid when needed, making a sweet allegory for finding support among family, even when that family is especially chaotic. The narrative's direct address enhances the potential for relatability here, and plenty of kids will find kinship with the girl beleaguered by ruthlessly interfering relatives. Loose linework calls up Patricia Polacco's styling with an added dash of vibrant cartoonishness, and the aunts move through the pages with vim and vigor, dominating the spreads as much as they dominate their niece's current situation. While she appreciates the aunts' dealing with the wolf, the little girl clearly is happy to see them gone . . . until more relatives show up in the last page. Copyright © 2023 The Board of Trustees of the University of Illinois

Rerouting planning of suborbital debris hazard zone based on reinforcement learning DDPG

Aiming at the debris hazard area caused by the explosion and disintegration of the suborbital vehicle, the plane convex hull divide and conquer method is used to process the physical boundary which is in accordance with the air traffic control rules. On this basis, a new method of civil aircraft rerouting planning based on reinforcement learning DDPG algorithm is proposed. According to the state-action space DDPG algorithm, the reward function is optimized to achieve the effect of reasonable routing. With the increase in training times, the reward curve gradually becomes stable and converges in a certain time step, which verifies the effectiveness of the route change planning method.

Development of a Moving-Least-Square-based Immersed Boundary Method for the simulation of viscous Compressible Flows

In this paper, a Moving-Least-Squares (MLS)-based immersed boundary method (IBM) for simulating viscous compressible flows is proposed. For this method, the compressible lattice Boltzmann flux solver (LBFS) is used to solve the flow field on the Eulerian mesh and the MLS-based IBM introduced in present paper is used to implement boundary condition. The discrete Lagrangian points are used to represent the Solid boundaries. A second-order MLS interpolation is used for numerical exchange between two grids of IBM. The forced implicit boundary condition is adopted to ensure that the physical boundary condition can be accurately satisfied. The present method is able to achieve a higher numerical accuracy than traditional IBM, which use the Delta function for interpolation. Some numerical validation tests are used for the validation of proposed method. The order and accuracy of the MLS approximation are tested by using a classical example of flow past a cylinder. Test of flow past the NACA0012 airfoil at different Mach numbers, angles of attack and Reynolds numbers is applied to verified the accuracy.

The Role and Significance of Hepatic Environmental Cells in Tumor Metastatic Colonization to Liver

Metastasis, a main cause of death in tumor patients, is a complicated process that involves multiple steps, presenting a major clinical challenge. Tumor cells break the physical boundaries of a primary tumor, intravasate into the lumina of blood vessels, travel around through blood circulation, extravasate into distant organs, colonize the host organs, and eventually develop into the foci of metastatic cancer. The metastasis of tumor cells exhibits organ-tropism, i.e., tumor cells preferentially spread to specific organs. Liver is a common site for metastasis. The pattern of metastasis in uveal melanoma, colorectal carcinoma, and pancreatic ductal adenocarcinoma shows organ-tropism for liver. The anatomical structure of liver determines its hemodynamic characteristics, e.g., low pressure and slow blood flow, which tend to facilitate the stasis and colonization of tumor cells in the liver. Besides the hemodynamic features, the metastatic colonization of liver depends largely on the interaction between tumor cells and the hepatic microenvironment (especially liver-resident cellular components). Resident cells of the hepatic microenvironment include hepatocytes, liver sinusoidal endothelial cells (LSECs), hepatic stellate cells (HSCs), Kupffer cells (KCs), etc. Herein, we discussed the role and significance of liver-resident cells in the metastatic colonization of tumor in the liver.

Optimization of a Virtual Synchronous Control Parameter for a Wind Turbine Generator Considering the Physical Constraint Boundary of Primary Frequency Regulation

The wind turbine generator participates in the primary frequency regulation of the power system by releasing kinetic energy from the rotor. It is necessary to ensure that the rotor speed and converter capacity are within the safe range during the frequency regulation process; otherwise, it will have serious negative effects on the frequency stability of the power system. As an important primary frequency regulation parameter, the dead zone affects the evaluation of the frequency regulation ability of WTG. Therefore, the influence of the dead zone should also be further considered. In order to evaluate the frequency regulation capability of wind turbine generators more comprehensively and accurately, this paper proposes an optimized method for the parameter of virtual synchronous control for wind turbine generators by considering the dead zone and physical constraint boundary of primary frequency regulation. After establishing the time domain expression by considering the frequency regulation dead zone, the real-time frequency regulation capacity of the wind turbine generator is quantified by considering the speed limit of the rotor and the capacity limit of the converter. Furthermore, the optimal value of the frequency regulation coefficient can be derived. Simulation results show that the proposed method can effectively reduce the frequency deviation and frequency change rate of the power system, which can also keep the response within the physical constraint boundary. Consequently, the proposed method can fully utilize the ability for frequency regulation of the wind power generation system and effectively improve the frequency stability of the power system.

Narrating History, Home and Dyaspora: Critical Essays on Edwidge Danticat

Narrating History, Home and Dyaspora, edited by Maia L. Butler, Joanna Davis-McElligatt, and Megan Feifer—the founders and members of the Edwidge Danticat Society—is a thought-provoking collection of cutting-edge research articles on Danticat’s works by both emerging and established scholars. Alongside recent publications, Approaches to Teaching the Works of Edwidge Danticat (2020) and The Bloomsbury Handbook to Edwidge Danticat (2021), it ushers in a new stage of scholarship on Danticat. Danticat, with her bicultural background and transnational vision, has created many works set in both Haiti and America, which—as the various authors in this collection demonstrate—have earnt her reputation as the “voice of Caribbean dyaspora.” The first three parts of this collection, with “dyaspora” always as the underlying context, address the title’s keywords, namely: home and nation, memory and history, and storytelling and narration. The first part investigates Danticat’s engagement with the idea of nation, and her navigation between Haiti and the USA as a diasporic writer, discussing the strategies she adopts to transcend both physical and psychological boundaries. Part two delves into the prominent themes of “history” and “memory,” as well as the underlying trauma and trauma-healing in Danticat’s works. Part three focuses on “narrating,” that is, narrative form and narrative structure, investigating the role of the “active and responsible reader” in imparting meaning to the text (142); it also examines the essay as a space for Danticat’s self-reflection, the paratextual elements at work in her infusion of personal experience into her literary creation, as well as Danticat’s “intertextual Global South thinking” in influencing the teaching of literary works from the Global South. The final section, part four, explores Danticat’s advocacy of activism and community building as a form of resistance, with the goal of defending citizenship rights, mitigating the effects of colonialism, giving voice to the silenced, and emphasizing the resilience of the Haitian people.

An embedded strategy for large scale incompressible flow simulations in moving domains

In this work we describe a methodology to approximate the incompressible Navier-Stokes equations in time dependent domains. To deal with the motion of the domain, we employ a fixed mesh method that we call fixed-mesh ALE. It consists of writing the equations in a moving ALE reference system but then projecting them onto a fixed background mesh. This implies that the boundaries of the elements do not necessarily coincide with the physical boundaries, and thus there is the possibility of badly cut elements. We use a Nitsche's type formulation to prescribe the boundary conditions and stabilise the bad cuts by introducing a term that penalises the gradient of the unknown orthogonal to the finite element space in a patch that contains the badly cut element. The flow formulation is a stabilised finite element method that allows one to treat convection dominated flows and to use equal velocity–pressure interpolation. Furthermore, this formulation can be shown to behave as an implicit large eddy simulation approach. A key issue is that the sub-grid scales on which the formulation depends are allowed to be time dependent; this fact has proved to be crucial for the robustness of the approach. The calculation of the velocity and the pressure is segregated by using a fractional step scheme designed at the pure algebraic level, and the conditioning of the pressure equation is improved by using an artificial compressibility technique. Finally, an adaptive mesh refinement strategy is described. All the algorithms are implemented in a parallel environment. The strategy described can be applied to complex flow problems, and in particular here we show the simulation of the air flow generated by a train moving inside a tunnel.

Honest bookkeeping for source-to-sink sediment mass-balance analysis with examples from the Angoche margin of Mozambique and the Corsica trough of France

If sediments in a basin share a similar genetic history (derived from a common place, from a common suite of raw materials, under a common set of environmental conditions, and subsequently experienced a common burial and thermal history), then physical characteristics of those sediments should be internally consistent and inferences about one physical property in one part of the basin can be drawn reliably from other physical properties in other parts of the basin.Balanced mass between a prospective source and an observed sink can indicate shared genetic history. If the sediments in the sink don't balance with the source, then additional history must be accounted: perhaps other sources, or perhaps leaks. Balance can only be evaluated with deliberate analysis that precisely defines and accurately measures the relevant physical boundaries and conditions of the sink and the potential sources. The analysis must start with the sink and work backward to potential sources.An example from sediments of the last 190 Ma on the Angoche margin of Mozambique demonstrates that the immediately adjacent hinterland of a basin may not supply all the sediment, so that exotic sources must be considered.An example from sediments of the last 190 ka in the Corsican Trough illustrates the requirements for strict sedimentary accounting, including precise definition of the sediment body in 3 spatial and 1 temporal dimension; segmentation and characterization of the body according to physical characteristics and data quality; identification of sediment entry points; association of sediment sources with sediment entry points; segmentation and characterization of sources according to geology, environment, and volume and character of expected sediment.Constraining the range of possible explanations, and quantifying uncertainty around relevant parameters of both sink and source(s) provides valuable insight into the genetic history of sedimentary systems, even if the mass does not balance precisely.