Soft Boundary Research Articles

Observation of Brownian elastohydrodynamic forces acting on confined soft colloids

Confined motions in complex environments are ubiquitous in microbiology. These situations invariably involve the intricate coupling between fluid flow, soft boundaries, surface forces, and fluctuations. In the present study, such a coupling is investigated using a method combining holographic microscopy and advanced statistical inference. Specifically, the Brownian motion of soft micrometric oil droplets near rigid walls is quantitatively analyzed. All the key statistical observables are reconstructed with high precision, allowing for nanoscale resolution of local mobilities and femtonewton inference of conservative or nonconservative forces. Strikingly, the analysis reveals the existence of a novel, transient, but large, soft Brownian force. The latter might be of crucial importance for microbiological and nanophysical transport, target finding, or chemical reactions in crowded environments, and hence the whole life machinery.

You Shall Not Pass: The Pacific Oxygen Minimum Zone Creates a Boundary to Shortfin Mako Shark Distribution in the Eastern North Pacific Ocean

ABSTRACTAimShoaling of large oxygen minimum zones (OMZs) that form along eastern margins of the world's oceans can reduce habitat availability for some pelagic fishes. Our aim was to test the hypothesis that habitat compression caused by shoaling of the Pacific OMZ in tropical regions creates a boundary to the southern distribution of shortfin mako sharks (Isurus oxyrinchus) in the Eastern North Pacific Ocean.LocationEastern North Pacific and Western North Atlantic oceans.MethodsWe compared environmental conditions between areas used by satellite‐tagged mako sharks in the Eastern North Pacific, encompassing the world's largest OMZ, to those used in the Western North Atlantic where no OMZ is present. In the Pacific we quantified the effects of temperature and dissolved oxygen (DO) on depth use and tested if sharks spent less time in areas with strong habitat compression over the OMZ than expected by chance.ResultsThe southern distribution of sharks in the Pacific corresponded with the apex of OMZ shoaling in the North Equatorial Current. Sharks in the Atlantic occupied areas with warm surface temperatures (≥ 26°C) more often than the Pacific, and waters with these temperatures in the Atlantic had greater DO at depth. Sharks in the Pacific reduced time near the surface in warm temperatures and consistently avoided depths with low DO and spent less time in areas with strong habitat compression than expected by chance.Main ConclusionsThe combination of warm surface temperatures and shoaling of the OMZ creates a soft boundary to mako shark movements in the Eastern North Pacific Ocean. The expected expansion of OMZs due to climate change could have considerable impact on future distribution of mako sharks and other pelagic fish. As such, development of species distribution models to predict the effects of climate change on pelagic fish distributions should incorporate oxygen availability.

Ultralow-frequency absorption mechanism of a hybrid membrane resonator with acoustic soft boundary condition

Hybrid membrane resonator (HMR) as a typical metamaterial-based absorber with acoustic hard boundary condition (AHBC) has demonstrated excellent noise absorption abilities, but the challenge lies in achieving broadband absorption of ultralow-frequency noise in the tens of Hz to 150 Hz regime. In this research, we investigate systematically the absorption characteristics, absorption mechanisms, and casual optimality of an HMR with three openings in lower surface of the cavity, which functioned as an acoustic soft boundary condition (ASBC). Compared to the well-studied HMR with AHBC, a new absorption mechanism, which states that most energy dissipates occur in the opening region rather than in the membrane, has been found first. As a result, the HMR with ASBC can demonstrate outstanding ultralow-frequency sound absorption performance with a very small thickness, and the full width at half maximum of the HMR can be enlarged over 7 times. Furthermore, the causal inequalities of the absorbers with ideal AHBC and ASBC are derived based on the Cauchy integral and causality principle. The causal optimality of the proposed absorber is also achieved. This research provides valuable guidelines for the design of excellent ultralow-frequency sound absorbers which could contribute to solving the major issue of noise reduction.

Uniqueness, regularity, continuity, and stability of solutions of integral equations in irregular domains

This study explores the uniqueness, regularity, continuity, and stability of solutions to integral equations related to elliptical boundary value problems in irregular domains. Traditional methods usually consider soft boundaries, but this work extends these results to include domains with rough boundaries. By employing Sobolev spaces, especially fractional Sobolev spaces, we develop a comprehensive theoretical framework. Under appropriate conditions, the studied integral equation has a unique solution in fractional Sobolev spaces, maintaining the regularity properties of the input function. Two new theorems are introduced to strengthen the findings. The first theorem establishes that solutions are continuous with respect to perturbations in the input data. This means small changes in the input do not cause significant deviations in the solution, ensuring robustness in practical applications. The second theorem demonstrates the stability of the solutions when the domain geometry undergoes small changes. This stability is crucial for real-world scenarios where the exact shapes of domains may not be perfectly known or may vary slightly. These results significantly improve the mathematical understanding of boundary value problems in non-smooth domains. The proposed extended framework allows for more generalized applications, addressing challenges in various fields of physics and engineering. The ability to handle irregular domains with rough boundaries opens up new possibilities for modeling and solving complex problems where traditional soft boundary assumptions are not valid. This research provides a robust basis for future studies and applications, highlighting the importance of considering fractional Sobolev spaces in the analysis of integral equations.

Radiological segmentation of knee meniscus ultrasound images based on boundary constraints and multi-scale fusion network

Knee osteoarthritis (KOA) is one of the most popular joint diseases endangering human health because of its high incidence, disability, and younger onset. However, there is no suitable method for early diagnosis, evaluation, and treatment of KOA. In recent years, some clinical studies have found that ultrasound can detect early changes in KOA in advance, and the automatic segmentation of ultrasound images can achieve rapid and effective quantitative research on KOA. However, in ultrasound images, the soft tissue boundaries of the lesion are blurred, making lesion segmentation difficult. Although the U-Net family is one of the best networks for image segmentation, they still have defects such as blurred segmentation boundaries, distorted morphology, and insufficient accuracy when segmenting ultrasound images. To address this issue, we added attention, atrous spatial pyramid pooling (ASSP), and edge loss function terms into the Unet3+ network, which improved the contour clarity and accuracy of output images (the improved Unet3+ Dice acc = 78.74%. Then, we extract the key features of the improved Unet3+ for outputting meniscus images: calculating meniscus area and distance, where the average accuracy of the area is: area_ avg_ acc = 91.12%, with an average distance accuracy of distance_ avg_ acc = 91.14%. This thesis creates a new dataset collection from West China Hospital, Sichuan University, and automated measurement of knee meniscus protrusion area has been achieved for the first time. This article is the first to apply deep learning to ultrasound image segmentation of the knee meniscus, helping doctors conduct qualitative and quantitative analysis of the diagnosis and treatment of early KOA. The results indicate that the improved Unet3+ can assist doctors in automatically diagnosing and evaluating KOA based on meniscus ultrasound images, which is beneficial for guiding early clinical intervention.

Motility-Induced Clustering of Active Particles under Soft Confinement.

We investigate the structural and dynamic properties of active Brownian particles (APs) confined within a soft annulus-shaped channel. Depending on the strength of the confinement and the Péclet number, we observe a novel reentrant behavior that is not present in unconfined systems. Our findings are substantiated by numerical simulations and analytical considerations, revealing that this behavior arises from the strong coupling between the Péclet number and the effective confining dimensionality of the APs. Our work highlights the peculiarities of soft boundaries for APs and how clogging can be avoided under such conditions.

Interaction of ultrasonically driven bubble with a soft tissue-like boundary

This study investigates the size-dependent dynamics of bubbles and their interaction with soft boundaries under various ultrasound (US) conditions. We found that bubble behavior is influenced by size, with smaller bubbles displaying reduced inertial motion in similar ultrasound environments. Detailed analyses of three bubble sizes (1.5 µm, 15 µm, and 150 µm) next to a soft 1 kPa boundary revealed distinct patterns in radial oscillation, bubble center displacement, and boundary deflection for different ultrasound frequencies (5 kHz − 4 MHz). The smallest bubble maintained a spherical shape, while the largest experienced significant shape changes, indicative of impending jet formation. Investigating interactions at various frequencies highlighted the collapse tendency of the larger bubbles, showcasing maximum radial amplitude, displacement, and bubble wall velocity around its natural frequency. The presence of a soft boundary minimally affected radial amplitude and velocity, while the bubble displacement was contingent on the soft boundary modulus. Furthermore, boundary responses demonstrated that softer boundaries experienced less stress during bubble oscillations, exhibiting sharper peaks at resonance frequencies for larger bubbles. These findings provide valuable insights into optimizing ultrasound conditions for a variety of applications, highlighting the influence of bubble size and boundary properties on outcomes.

Tentacle Microelectrode Arrays Uncover Soft Boundary Neurons in Hippocampal CA1.

Hippocampal CA1 neurons show intense firing at specific spatial locations, modulated by isolated landmarks. However, the impact of real-world scene transitions on neuronal activity remains unclear. Moreover, long-term neural recording during movement challenges device stability. Conventional rigid-based electrodes cause inflammatory responses, restricting recording durations. Inspired by the jellyfish tentacles, the multi-conductive layer ultra-flexible microelectrode arrays (MEAs) are developed. The tentacle MEAs ensure stable recordings during movement, thereby enabling the discovery of soft boundary neurons. The soft boundary neurons demonstrate high-frequency firing that aligns with the boundaries of scene transitions. Furthermore, the localization ability of soft boundary neurons improves with more scene transition boundaries, and their activity decreases when these boundaries are removed. The innovation of ultra-flexible, high-biocompatible tentacle MEAs improves the understanding of neural encoding in spatial cognition. They offer the potential for long-term in vivo recording of neural information, facilitating breakthroughs in the understanding and application of brain spatial navigation mehanisms.

A Stepwise Cosimulation Framework for Modeling Critical Elements in Copper Porphyry Deposits

The increased attention given to batteries has given rise to apprehensions regarding their availability; they have thus been categorized as essential commodities. Cobalt (Co), copper (Cu), lithium (Li), nickel (Ni), and molybdenum (Mo) are frequently selected as the primary metallic elements in lithium-ion batteries. The principal aim of this study was to develop a computational algorithm that integrates geostatistical methods and machine learning techniques to assess the resources of critical battery elements within a copper porphyry deposit. By employing a hierarchical/stepwise cosimulation methodology, the algorithm detailed in this research paper successfully represents both soft and hard boundaries in the simulation results. The methodology is evaluated using several global and local statistical studies. The findings indicate that the proposed algorithm outperforms the conventional approach in estimating these five elements, specifically when utilizing a stepwise estimation strategy known as cascade modeling. The proposed algorithm is also validated against true values by using a jackknife method, and it is shown that the method is precise and unbiased in the prediction of critical battery elements.

Simultaneously enhancing strength–ductility synergy in refractory high entropy alloys by a heterogeneous structure design

Improvement in the strength of metallic materials is often achieved at the expense of material ductility; this long-standing conflict is referred to as the strength–ductility trade-off. In this study, the composition design of high-entropy alloys was combined with the design of multiscale microstructures, and heterogeneous refractory high-entropy alloys (RHEA) with multi-sized grains were fabricated via a simple rolling-annealing treatment process, which significantly alleviates the strength–ductility trade-off. The RHEA annealed at 970 ℃ was composed of fully-recrystallized grains of size 25 μm surrounded by nanosized grains. Owing to the heterogeneity in the structure, dislocations preferentially accumulated at soft recrystallization grain boundaries, thus inducing dislocation interactions such as cross-slip and jog, strengthening the soft region grains, and resulting in Heterogeneous deformation-induced (HDI) strengthening. This mechanism resulted in a significant increase in the work hardening rate during tensile deformation and ensured a high tensile strength of 905 MPa, while improving its uniform elongation to 10.6%. Thus, the proposed heterogeneous design accomplished by simple thermal treatments presents a novel strategy to develop a new generation of high-strength and high-ductility high-entropy alloys.

The Global Track Concept for Assessment of Engaging Hill-Sachs Defects in Anterior Shoulder Instability

Background: The glenoid track concept is used to determine preoperatively whether a Hill-Sachs defect is engaging or not. Currently, the glenoid track concept relies on measurements of bony structures as well as on the confines and elasticity of the rotator cuff as a reference point, which varies extensively among individuals and therefore limits the reliability and accuracy of this concept. Purpose: To evaluate the reliability of the global track concept, which determines the angular distance of the Hill-Sachs defect from the center of the articular surface of the humeral head as a new reference point with the help of an automated image analysis software and 3-dimensional analysis of the humeral head. Study Design: Controlled laboratory study. Methods: Computed tomography scans of 100 patients treated for anterior shoulder instability with different sizes of Hill-Sachs defects were evaluated manually by 2 orthopaedic surgeons independently using the software OsiriX as well as automatically by using a dedicated prototype software (ImFusion). Obtained manual and automated measurements included the Hill-Sachs length, Hill-Sachs width, and Hill-Sachs depth of the defect; the Hill-Sachs interval (HSI); and the glenoid width for the glenoid track concept, as well as the angular distance of the Hill-Sachs defect from the center of the articular surface of the humeral head (global track concept). The reliability of the different measurement techniques was compared by calculating intraclass correlation coefficients (ICCs). Results: There was a significant difference for all obtained parameters comparing manual and automatic measurements. For manually obtained parameters, measurements referring to bony boundaries (glenoid width, Hill-Sachs length, and Hill-Sachs width) showed good to excellent agreement (ICC, 0.86, 0.82, and 0.62, respectively), while measurements referring to soft tissue boundaries (HSI and glenoid track; ICC, 0.56 and 0.53, respectively) or not directly identifiable reference points (center of articular surface and global track) only showed fair reliability (ICC middle excursion, 0.42). When the same parameters were measured with the help of an automated software, good reliability for the glenoid track concept and excellent reliability for the global track concept in the middle excursion were achieved. Conclusion: The present study showed that the more complex global track measurements of humeral defects are more reliable than the current standard HSI and glenoid track measurements. However, this is only true when automated software is used to perform the measurements. Clinical Relevance: Future studies using the new proposed method in combination with an automated software need to be conducted to determine critical threshold values for defects prone to engagement.

The Intimacy of Held Solidarity: A Joint Memoir of Activism

This work reflects the slow intimacy of solidarity over time. We uncover some of what we know and have known with examples from anti-apartheid activism and the women’s health movement in South Africa. Solidarity is an expression of knowing trust. Knowing that you are often on each other’s side and leaning towards justice, demanding a relationship of knowing in instances of vulnerability. Being held in solidarity is a gesture of engaging in a shared humanity that at times gives the comfort of familiarity. In spaces where there are shared feminist and political goals and objectives, we assume safety and belonging only to find this is not to be true. Acting in solidarity demands consideration and risk. In moving towards solidarity, one turns one’s body and face towards the other. Solidarity at times demands resistance and speaking truths to power. An engagement of solidarity is conscious and intentional. We reflect engaging with each other over four decades, lessons on listening deeply, hearing seasons of life’s celebrations, joys, questions, and struggles. In this process, questions surface, asking for more on consideration. Our reflection is of collective experiences of slow intimacy, of soft and hard boundaries, remembering and longing as we hold each other in solidarity. We use our individual voices to show specific angles to these collective experiences.

Physical informed neural networks with soft and hard boundary constraints for solving advection-diffusion equations using Fourier expansions

Deep learning methods have gained considerable interest in the numerical solution of various partial differential equations (PDEs). One particular focus is physics-informed neural networks (PINN), which integrate physical principles into neural networks. This transforms the process of solving PDEs into optimization problems for neural networks. To address a collection of advection-diffusion equations (ADE) in a range of difficult circumstances, this paper proposes a novel network structure. This architecture integrates the solver, a multi-scale deep neural networks (MscaleDNN) utilized in the PINN method, with a hard constraint technique known as HCPINN. This method introduces a revised formulation of the desired solution for ADE by utilizing a loss function that incorporates the residuals of the governing equation and penalizes any deviations from the specified boundary and initial constraints. By surpassing the boundary constraints automatically, this method improves the accuracy and efficiency of the PINN technique. To address the “spectral bias” phenomenon in neural networks, a subnetwork structure of MscaleDNN and a Fourier-induced activation function are incorporated into the HCPINN, resulting in a hybrid approach called SFHCPINN. The effectiveness of SFHCPINN is demonstrated through various numerical experiments involving ADE in different dimensions. The numerical results indicate that SFHCPINN outperforms both standard PINN and its subnetwork version with Fourier feature embedding. It achieves remarkable accuracy and efficiency while effectively handling complex boundary conditions and high-frequency scenarios in ADE.

Machine Learning-Based Automated Fault Detection and Diagnostics in Building Systems

Automated fault detection and diagnostics analysis in commercial building systems using machine learning (ML) can improve the building’s efficiency and conserve energy costs from inefficient equipment operation. However, ML can be challenging to implement in existing systems due to a lack of common data standards and because of a lack of building operators trained in ML techniques. Additionally, results from ML procedures can be complicated for untrained users to interpret. Boolean rule-based analysis is standard in current automated fault detection and diagnostics (AFDD) solutions but limits analysis to the rules defined and calibrated by energy engineers. Boolean rule-based analysis and ML can be combined to create an effective fault detection and diagnostics (FDD) tool. Three examples of ML’s advantages over rule-based analysis are explored by analyzing functional building equipment. ML can detect long-term faults in the system caused by a lack of system maintenance. It can also detect faults in system components with incomplete sets of sensors by modeling expected system operations and by making comparisons to actual system operations. An example of ML detecting a failure in a building is shown along with a demonstration of the soft decision boundaries of ML-based FDD compared to Boolean rule-based FDD analysis. The results from the three examples are used to demonstrate the strengths and weaknesses of using ML for AFDD analysis.

The Image Data and Backbone in Weakly Supervised Fine-Grained Visual Categorization: A Revisit and Further Thinking

Weakly-supervised fine-grained visual categorization (FGVC) aims to achieve subclass classification within the same large class using only label information. Compared to general images, fine-grained images have similar appearances and features, and are often affected by disturbances such as viewpoint, lighting, and occlusion during data collection, resulting in significant intra-class variance and small inter-class variance. To achieve FGVC, carefully designed models are often needed to explore the locally discriminative regions of the image. This paper revisits high-quality FGVC publications based on deep learning and analyzes from two new perspective: fine-grained image data and backbone. We address two ignored but interesting problems in FGVC. First, we argue that the reasons for exacerbating intra-class variance are not the same in data of animal, plant, and commodity types, and it is necessary to consider the effects of posture, covariate shift, and structural changes. Additionally, the “soft boundary” between subclasses intensifies the difficulty of classification. Second, we highlight that convolutional networks and self-attention networks have different receptive fields and shape biases, leading to performance differences when processing different types of fine-grained data. Overall, our analysis provides new insights into recent advances, challenges, and future directions for FGVC based on deep learning, which can help researchers develop more effective models for FGVC.

Fuzzy C Means Clustering Coupled with Firefly Optimization Algorithm for the Segmentation of Neurodisorder Magnetic Resonance Images

Medical image segmentation is a critical task in medical image analysis, and clustering algorithms can be utilized to achieve this goal. This research work focuses on the segmentation of neuro disorder magnetic resonance images using Otsu, K-means, and FCM coupled with the firefly optimization algorithm. Otsu is a classical thresholding algorithm that relies on a single threshold value to segment the images. K-Means is one of the simplest and most widely used clustering algorithms. It aims to partition data into K clusters, where each data point belongs to the cluster with the nearest mean. Fuzzy C-Means is an extension of K-Means, allowing data points to belong to multiple clusters with varying degrees of membership. In medical image segmentation, FCM was used to classify pixels or voxels into different tissue classes with soft boundaries, accounting for partial volume effects. Firefly Optimization helps in improving the convergence speed of the FCM algorithm. Firefly optimization is good at exploring the solution space and finding global optima. The combination of FCM and Firefly Optimization leads to more accurate clustering results. The performance evaluation was done by Renyi entropy and Shannon entropy, FCM coupled with the firefly optimization was found to exhibit superior results when compared with the Otsu and K-means clustering algorithms.

Intelligent X-ray waste detection and classification via X-ray characteristic enhancement and deep learning

Waste management plays an important role in environmental protection and resource recycling. In fact, traditional waste recycling requires a large number of workers to sort manually, which is not only labor-intensive and time-consuming, but also increases the health risk for workers. To further improve the efficiency of waste detection and classification, related scholars have used computer vision technology to achieve automatic waste management. However, existing methods tend to use RGB images as input, requiring workers to open waste bags and disperse waste items in advance. To avoid comprehensive preparation procedures and identify occluded waste items, we use deep learning models to detect and recognize waste items in X-ray images. Compared to RGB images, X-ray images can effectively preserve boundary information and reflect material types. To take advantage of the inherent characteristics of X-ray images, we propose a novel X-ray Characteristic Enhancement Module (XCEM) to improve the performance of X-ray waste detection. XCEM consists of three sub-modules, namely Hybrid Interaction Module (HIM), Multi-scale Material Perception Module (MMPM), and Boundary Guidance Module (BGM). Specifically, HIM not only generates soft and hard boundary maps, but also achieves information interaction and feature enhancement of different boundary maps. Meanwhile, MMPM can effectively capture different multi-scale material features. In addition, BGM uses auxiliary boundary information and attention mechanisms to effectively enhance critical material features. As a plug-and-play module, XCEM can be readily inserted into the most popular detection pipelines. We select several representative one-stage and two-stage methods and perform related experiments on the WIXRay dataset. Experimental results show that our method can achieve consistent performance gains on diverse detection paradigms. This study promotes the development of intelligent waste inspection and recycling.

New soft rough approximations via ideals and its applications

<abstract><p>Theories of soft sets and rough sets are two different approaches to analyzing vagueness. A possible fusion of rough sets and soft sets was proposed in 2011. At this time the concept of soft rough sets was introduced, where parametrized subsets of a universal set are basic building blocks for lower and upper approximations of a subset. The main purpose of soft rough sets is to reduce the soft boundary region by increasing the lower approximation and decreasing the upper approximation. In this paper, we present two new approaches for soft rough sets that is related to the notion of ideals. The main characteristics of these recent approaches are explained and interpreted through the use of suitable propositions and examples. These recent approaches satisfy most of the conditions of well known properties of Pawlak's model. Comparisons between our methods and previous ones are introduced. In addition, we prove that our approaches produce a smaller boundary region and greater value of accuracy than the corresponding defined definitions. Furthermore, two new styles of approximation spaces related to two distinct ideals, called soft bi-ideal approximation spaces, are introduced and studied. Analysis of the fulfilled and the non-fulfilled properties is presented, and many examples to ensure and explain the advantages and the disadvantages between our styles and the previous ones are provided.</p></abstract>

Binary Bipolar Soft Points and Topology on Binary Bipolar Soft Sets with Their Symmetric Properties

The aim of this paper is to give an interesting connection between two mathematical approaches to vagueness: binary bipolar soft sets and binary bipolar soft topology. The binary bipolar soft points are defined using binary bipolar soft sets. The binary bipolar soft set will be the binary bipolar soft union of its binary bipolar soft points. Moreover, the notion of binary bipolar soft topological spaces over two universal sets and a parameter set is proposed. Some topological properties of binary bipolar soft sets, such as binary bipolar soft open, binary bipolar soft closed, binary bipolar soft closure, binary bipolar soft interior, and binary bipolar soft boundary, are introduced. Some important properties of these classes of binary bipolar soft sets are investigated. Furthermore, the symmetry relation is compared between binary bipolar soft topology and binary soft topology on a common universe set. Finally, some results and counterexamples are demonstrated to explain this work.

Investigation on acoustical performances of micro-perforated panel with soft boundary backing cavity

Low-frequency noise control is still an issue due to its characteristic of having a long wavelength. Therefore, attenuation requires a bulky noise control component, including an acoustic absorber. Conventional absorber materials, such as micro-perforated panels (MPP), are a candidate to circumvent such a problem. However, the requirement of backing cavity depth causes the MPP to work under a quarter wavelength constraint. Eventually, the bulky characteristic of the absorber is persistent. Therefore, this study introduces a soft boundary condition to the MPP to enable the system to work at a lower frequency while the associated dimensions reduce. Open gaps between the backing cavity and rigid surface realize the soft boundary conditions and double-layer MPP is considered to test such a system. The experimental result shows that the soft boundary can enhance at low frequencies (<250 Hz) by 0.3-0.6 points. At the same time, the open gap can slightly increase the resulting absorption performance deficiency at mid-frequency.